"""Time Series Operators"""

from typing import cast

from scipy import stats
import polars as pl
import numpy as np

from .utility import DataProxy


def ts_delay(feature: DataProxy, window: int) -> DataProxy:
    """Get the value from a fixed time in the past"""
    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").shift(window).over("vt_symbol")
    )
    return DataProxy(df)


def ts_min(feature: DataProxy, window: int) -> DataProxy:
    """Calculate the minimum value over a rolling window"""
    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").rolling_min(window, min_samples=1).over("vt_symbol")
    )
    return DataProxy(df)


def ts_max(feature: DataProxy, window: int) -> DataProxy:
    """Calculate the maximum value over a rolling window"""
    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").rolling_max(window, min_samples=1).over("vt_symbol")
    )
    return DataProxy(df)


def ts_argmax(feature: DataProxy, window: int) -> DataProxy:
    """Return the index of the maximum value over a rolling window"""
    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").rolling_map(lambda s: cast(int, s.arg_max()) + 1, window).over("vt_symbol")
    )
    return DataProxy(df)


def ts_argmin(feature: DataProxy, window: int) -> DataProxy:
    """Return the index of the minimum value over a rolling window"""
    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").rolling_map(lambda s: cast(int, s.arg_min()) + 1, window).over("vt_symbol")
    )
    return DataProxy(df)


def ts_rank(feature: DataProxy, window: int) -> DataProxy:
    """Calculate the percentile rank of the current value within the window"""
    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").rolling_map(lambda s: stats.percentileofscore(s, s[-1]) / 100, window).over("vt_symbol")
    )
    return DataProxy(df)


def ts_sum(feature: DataProxy, window: int) -> DataProxy:
    """Calculate the sum over a rolling window"""
    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").rolling_sum(window).over("vt_symbol")
    )
    return DataProxy(df)


def ts_mean(feature: DataProxy, window: int) -> DataProxy:
    """Calculate the mean over a rolling window"""
    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").rolling_map(lambda s: np.nanmean(s), window, min_samples=1).over("vt_symbol")
    )
    return DataProxy(df)


def ts_std(feature: DataProxy, window: int) -> DataProxy:
    """Calculate the standard deviation over a rolling window"""
    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").rolling_map(lambda s: np.nanstd(s, ddof=0), window, min_samples=1).over("vt_symbol")
    )
    return DataProxy(df)


def ts_slope(feature: DataProxy, window: int) -> DataProxy:
    """Calculate the slope of linear regression over a rolling window (optimized)"""
    # 预计算 x 相关的常数 (x = 0, 1, 2, ..., window-1)
    n = window
    sum_x = n * (n - 1) / 2  # 等差数列求和
    sum_x2 = (n - 1) * n * (2 * n - 1) / 6  # 平方和公式
    denominator = n * sum_x2 - sum_x * sum_x

    # 计算 sum(i * y[t-window+1+i]) for i in 0..window-1
    # 等价于 sum((window-1-j) * y[t-j]) for j in 0..window-1
    sum_xy_expr: pl.Expr = pl.sum_horizontal([
        (window - 1 - j) * pl.col("data").shift(j)
        for j in range(window)
    ])

    df: pl.DataFrame = feature.df.with_columns([
        pl.col("data").rolling_sum(window, min_samples=window).over("vt_symbol").alias("sum_y"),
        sum_xy_expr.over("vt_symbol").alias("sum_xy")
    ])

    df = df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        ((n * pl.col("sum_xy") - sum_x * pl.col("sum_y")) / denominator).alias("data")
    )
    return DataProxy(df)


def ts_quantile(feature: DataProxy, window: int, quantile: float) -> DataProxy:
    """Calculate the quantile value over a rolling window"""
    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").rolling_map(lambda s: s.quantile(quantile=quantile, interpolation="linear"), window).over("vt_symbol")
    )
    return DataProxy(df)


def ts_rsquare(feature: DataProxy, window: int) -> DataProxy:
    """Calculate the R-squared value of linear regression over a rolling window (optimized)"""
    # 预计算 x 相关的常数 (x = 0, 1, 2, ..., window-1)
    n = window
    sum_x2 = (n - 1) * n * (2 * n - 1) / 6  # 平方和公式
    mean_x = (n - 1) / 2
    var_x = sum_x2 / n - mean_x * mean_x  # 总体方差

    # 计算 sum(i * y[t-window+1+i]) for i in 0..window-1
    sum_xy_expr: pl.Expr = pl.sum_horizontal([
        (window - 1 - j) * pl.col("data").shift(j)
        for j in range(window)
    ])

    df: pl.DataFrame = feature.df.with_columns([
        pl.col("data").rolling_sum(window, min_samples=window).over("vt_symbol").alias("sum_y"),
        pl.col("data").rolling_var(window, min_samples=window, ddof=0).over("vt_symbol").alias("var_y"),
        sum_xy_expr.over("vt_symbol").alias("sum_xy")
    ])

    # mean_y 和 cov(x, y) = E(xy) - E(x)E(y)
    df = df.with_columns([
        (pl.col("sum_y") / n).alias("mean_y"),
    ])

    df = df.with_columns([
        (pl.col("sum_xy") / n - mean_x * pl.col("mean_y")).alias("cov_xy")
    ])

    # r = cov(x,y) / (std_x * std_y), r^2 = cov(x,y)^2 / (var_x * var_y)
    df = df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        (pl.col("cov_xy").pow(2) / (var_x * pl.col("var_y"))).alias("data")
    )

    df = df.with_columns(
        pl.when(pl.col("data").is_infinite() | pl.col("data").is_nan())
        .then(None)
        .otherwise(pl.col("data"))
        .alias("data")
    )

    return DataProxy(df)


def ts_resi(feature: DataProxy, window: int) -> DataProxy:
    """Calculate the residual of linear regression over a rolling window (optimized)"""
    # 预计算 x 相关的常数 (x = 0, 1, 2, ..., window-1)
    n = window
    sum_x = n * (n - 1) / 2  # 等差数列求和
    sum_x2 = (n - 1) * n * (2 * n - 1) / 6  # 平方和公式
    mean_x = (n - 1) / 2
    denominator = n * sum_x2 - sum_x * sum_x

    # 计算 sum(i * y[t-window+1+i]) for i in 0..window-1
    sum_xy_expr: pl.Expr = pl.sum_horizontal([
        (window - 1 - j) * pl.col("data").shift(j)
        for j in range(window)
    ])

    df: pl.DataFrame = feature.df.with_columns([
        pl.col("data").rolling_sum(window, min_samples=window).over("vt_symbol").alias("sum_y"),
        sum_xy_expr.over("vt_symbol").alias("sum_xy")
    ])

    # 计算 slope 和 intercept
    df = df.with_columns([
        ((n * pl.col("sum_xy") - sum_x * pl.col("sum_y")) / denominator).alias("slope"),
        (pl.col("sum_y") / n).alias("mean_y"),
    ])

    df = df.with_columns([
        (pl.col("mean_y") - pl.col("slope") * mean_x).alias("intercept")
    ])

    # residual = y - (slope * (n-1) + intercept)，最后一个点的 x = n-1
    df = df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        (pl.col("data") - (pl.col("slope") * (n - 1) + pl.col("intercept"))).alias("data")
    )

    return DataProxy(df)


def ts_corr(feature1: DataProxy, feature2: DataProxy, window: int) -> DataProxy:
    """Calculate the correlation between two features over a rolling window"""
    df_merged: pl.DataFrame = feature1.df.join(feature2.df, on=["datetime", "vt_symbol"])

    df: pl.DataFrame = df_merged.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.rolling_corr("data", "data_right", window_size=window, min_samples=1).over("vt_symbol").alias("data")
    )

    df = df.with_columns(
        pl.when(pl.col("data").is_infinite()).then(None).otherwise(pl.col("data")).alias("data")
    )

    return DataProxy(df)


def ts_less(feature1: DataProxy, feature2: DataProxy | float) -> DataProxy:
    """Return the minimum value between two features"""
    if isinstance(feature2, DataProxy):
        df_merged: pl.DataFrame = feature1.df.join(feature2.df, on=["datetime", "vt_symbol"])
    else:
        df_merged = feature1.df.with_columns(pl.lit(feature2).alias("data_right"))

    df: pl.DataFrame = df_merged.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.min_horizontal("data", "data_right").over("vt_symbol").alias("data")
    )

    return DataProxy(df)


def ts_greater(feature1: DataProxy, feature2: DataProxy | float) -> DataProxy:
    """Return the maximum value between two features"""
    if isinstance(feature2, DataProxy):
        df_merged: pl.DataFrame = feature1.df.join(feature2.df, on=["datetime", "vt_symbol"])

    else:
        df_merged = feature1.df.with_columns(pl.lit(feature2).alias("data_right"))

    df: pl.DataFrame = df_merged.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.max_horizontal("data", "data_right").over("vt_symbol").alias("data")
    )

    return DataProxy(df)


def ts_log(feature: DataProxy) -> DataProxy:
    """Calculate the natural logarithm of the feature"""
    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").log().over("vt_symbol")
    )
    return DataProxy(df)


def ts_abs(feature: DataProxy) -> DataProxy:
    """Calculate the absolute value of the feature"""
    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").abs().over("vt_symbol")
    )
    return DataProxy(df)


def ts_delta(feature: DataProxy, window: int) -> DataProxy:
    """Calculate difference between current value and value from window periods ago"""
    return feature - ts_delay(feature, window)


def ts_cov(feature1: DataProxy, feature2: DataProxy, window: int) -> DataProxy:
    """Calculate covariance between two features over a rolling window"""
    return ts_corr(feature1, feature2, window) * ts_std(feature1, window) * ts_std(feature2, window)


def ts_decay_linear(feature: DataProxy, window: int) -> DataProxy:
    """Calculate linear decay weighted average"""
    def decay_func(s: pl.Series) -> float:
        """Calculate linear decay weighted average for a series"""
        weights = pl.Series(range(window, 0, -1))
        denominator: int = window * (window + 1) // 2
        return float((s * weights).sum() / denominator)

    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").rolling_map(lambda s: decay_func(s), window).over("vt_symbol")
    )
    return DataProxy(df)


def ts_product(feature: DataProxy, window: int) -> DataProxy:
    """Calculate the product over a rolling window"""
    df: pl.DataFrame = feature.df.select(
        pl.col("datetime"),
        pl.col("vt_symbol"),
        pl.col("data").rolling_map(lambda s: s.product(), window).over("vt_symbol")
    )
    return DataProxy(df)