From fbc52ef55127b1a8a13c0020781aedaa4470b6e5 Mon Sep 17 00:00:00 2001 From: wehub-resource-sync Date: Mon, 13 Jul 2026 10:20:47 +0000 Subject: [PATCH] docs: preserve upstream English README --- README.en.md | 203 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 203 insertions(+) create mode 100644 README.en.md diff --git a/README.en.md b/README.en.md new file mode 100644 index 0000000..98069ce --- /dev/null +++ b/README.en.md @@ -0,0 +1,203 @@ +====================================================== +NumExpr: Fast numerical expression evaluator for NumPy +====================================================== + +:Author: David M. Cooke, Francesc Alted, and others. +:Maintainer: Francesc Alted +:Contact: faltet@gmail.com +:URL: https://github.com/pydata/numexpr +:Documentation: http://numexpr.readthedocs.io/en/latest/ +:GitHub Actions: |actions| +:PyPi: |version| +:DOI: |doi| +:readthedocs: |docs| + +.. |actions| image:: https://github.com/pydata/numexpr/workflows/Build/badge.svg + :target: https://github.com/pydata/numexpr/actions +.. |travis| image:: https://travis-ci.org/pydata/numexpr.png?branch=master + :target: https://travis-ci.org/pydata/numexpr +.. |docs| image:: https://readthedocs.org/projects/numexpr/badge/?version=latest + :target: http://numexpr.readthedocs.io/en/latest +.. |doi| image:: https://zenodo.org/badge/doi/10.5281/zenodo.2483274.svg + :target: https://doi.org/10.5281/zenodo.2483274 +.. |version| image:: https://img.shields.io/pypi/v/numexpr + :target: https://pypi.python.org/pypi/numexpr + + +What is NumExpr? +---------------- + +NumExpr is a fast numerical expression evaluator for NumPy. With it, +expressions that operate on arrays (like :code:`'3*a+4*b'`) are accelerated +and use less memory than doing the same calculation in Python. + +In addition, its multi-threaded capabilities can make use of all your +cores -- which generally results in substantial performance scaling compared +to NumPy. + +Last but not least, numexpr can make use of Intel's VML (Vector Math +Library, normally integrated in its Math Kernel Library, or MKL). +This allows further acceleration of transcendent expressions. + + +How NumExpr achieves high performance +------------------------------------- + +The main reason why NumExpr achieves better performance than NumPy is +that it avoids allocating memory for intermediate results. This +results in better cache utilization and reduces memory access in +general. Due to this, NumExpr works best with large arrays. + +NumExpr parses expressions into its own op-codes that are then used by +an integrated computing virtual machine. The array operands are split +into small chunks that easily fit in the cache of the CPU and passed +to the virtual machine. The virtual machine then applies the +operations on each chunk. It's worth noting that all temporaries and +constants in the expression are also chunked. Chunks are distributed among +the available cores of the CPU, resulting in highly parallelized code +execution. + +The result is that NumExpr can get the most of your machine computing +capabilities for array-wise computations. Common speed-ups with regard +to NumPy are usually between 0.95x (for very simple expressions like +:code:`'a + 1'`) and 4x (for relatively complex ones like :code:`'a*b-4.1*a > 2.5*b'`), +although much higher speed-ups can be achieved for some functions and complex +math operations (up to 15x in some cases). + +NumExpr performs best on matrices that are too large to fit in L1 CPU cache. +In order to get a better idea on the different speed-ups that can be achieved +on your platform, run the provided benchmarks. + +Installation +------------ + +From wheels +^^^^^^^^^^^ + +NumExpr is available for install via `pip` for a wide range of platforms and +Python versions (which may be browsed at: https://pypi.org/project/numexpr/#files). +Installation can be performed as:: + + pip install numexpr + +If you are using the Anaconda or Miniconda distribution of Python you may prefer +to use the `conda` package manager in this case:: + + conda install numexpr + +From Source +^^^^^^^^^^^ + +On most \*nix systems your compilers will already be present. However if you +are using a virtual environment with a substantially newer version of Python than +your system Python you may be prompted to install a new version of `gcc` or `clang`. + +For Windows, you will need to install the Microsoft Visual C++ Build Tools +(which are free) first. The version depends on which version of Python you have +installed: + +https://wiki.python.org/moin/WindowsCompilers + +For Python 3.6+ simply installing the latest version of MSVC build tools should +be sufficient. Note that wheels found via pip do not include MKL support. Wheels +available via `conda` will have MKL, if the MKL backend is used for NumPy. + +See `requirements.txt` for the required version of NumPy. + +NumExpr is built in the standard Python way:: + + pip install [-e] . + +You can test `numexpr` with:: + + python -c "import numexpr; numexpr.test()" + +Do not test NumExpr in the source directory or you will generate import errors. + +Enable Intel® MKL support +^^^^^^^^^^^^^^^^^^^^^^^^^ + +NumExpr includes support for Intel's MKL library. This may provide better +performance on Intel architectures, mainly when evaluating transcendental +functions (trigonometrical, exponential, ...). + +If you have Intel's MKL, copy the `site.cfg.example` that comes with the +distribution to `site.cfg` and edit the latter file to provide correct paths to +the MKL libraries in your system. After doing this, you can proceed with the +usual building instructions listed above. + +Pay attention to the messages during the building process in order to know +whether MKL has been detected or not. Finally, you can check the speed-ups on +your machine by running the `bench/vml_timing.py` script (you can play with +different parameters to the `set_vml_accuracy_mode()` and `set_vml_num_threads()` +functions in the script so as to see how it would affect performance). + +Usage +----- + +:: + + >>> import numpy as np + >>> import numexpr as ne + + >>> a = np.arange(1e6) # Choose large arrays for better speedups + >>> b = np.arange(1e6) + + >>> ne.evaluate("a + 1") # a simple expression + array([ 1.00000000e+00, 2.00000000e+00, 3.00000000e+00, ..., + 9.99998000e+05, 9.99999000e+05, 1.00000000e+06]) + + >>> ne.evaluate("a * b - 4.1 * a > 2.5 * b") # a more complex one + array([False, False, False, ..., True, True, True], dtype=bool) + + >>> ne.evaluate("sin(a) + arcsinh(a/b)") # you can also use functions + array([ NaN, 1.72284457, 1.79067101, ..., 1.09567006, + 0.17523598, -0.09597844]) + + >>> s = np.array([b'abba', b'abbb', b'abbcdef']) + >>> ne.evaluate("b'abba' == s") # string arrays are supported too + array([ True, False, False], dtype=bool) + + +Free-threading support +---------------------- +Starting on CPython 3.13 onwards there is a new distribution that disables the +Global Interpreter Lock (GIL) altogether, thus increasing the performance yields +under multi-threaded conditions on a single interpreter, as opposed to having to use +multiprocessing. + +Whilst numexpr has been demonstrated to work under free-threaded +CPython, considerations need to be taken when using numexpr native parallel +implementation vs using Python threads directly in order to prevent oversubscription, +we recommend either using the main CPython interpreter thread to spawn multiple C threads +using the parallel numexpr API, or spawning multiple CPython threads that do not use +the parallel API. + +For more information about free-threaded CPython, we recommend visiting the following +`community Wiki ` + + +Documentation +------------- + +Please see the official documentation at `numexpr.readthedocs.io `_. +Included is a user guide, benchmark results, and the reference API. + + +Authors +------- + +Please see `AUTHORS.txt `_. + + +License +------- + +NumExpr is distributed under the `MIT `_ license. + + +.. 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