"""
Time Series Statistics
----------------------
"""

import math
from typing import List, Optional, Tuple, Union

import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import argrelmax
from scipy.stats import norm
from statsmodels.tsa.seasonal import STL, seasonal_decompose
from statsmodels.tsa.stattools import acf, adfuller, grangercausalitytests, kpss, pacf

from darts import TimeSeries
from darts.logging import get_logger, raise_if, raise_if_not, raise_log

from .missing_values import fill_missing_values
from .utils import ModelMode, SeasonalityMode

logger = get_logger(__name__)


def check_seasonality(
    ts: TimeSeries, m: Optional[int] = None, max_lag: int = 24, alpha: float = 0.05
):
    """
    Checks whether the TimeSeries `ts` is seasonal with period `m` or not.

    If `m` is None, we work under the assumption that there is a unique seasonality period, which is inferred
    from the Auto-correlation Function (ACF).

    Parameters
    ----------
    ts
        The time series to check for seasonality.
    m
        The seasonality period to check.
    max_lag
        The maximal lag allowed in the ACF.
    alpha
        The desired confidence level (default 5%).

    Returns
    -------
    Tuple[bool, int]
        A tuple `(season, m)`, where season is a boolean indicating whether the series has seasonality or not
        and `m` is the seasonality period.
    """

    ts._assert_univariate()

    if m is not None and (m < 2 or not isinstance(m, int)):
        raise_log(ValueError("m must be an integer greater than 1."), logger)

    if m is not None and m > max_lag:
        raise_log(ValueError("max_lag must be greater than or equal to m."), logger)

    n_unique = np.unique(ts.values()).shape[0]

    if n_unique == 1:  # Check for non-constant TimeSeries
        return False, 0

    r = acf(
        ts.values(), nlags=max_lag, fft=False
    )  # In case user wants to check for seasonality higher than 24 steps.

    # Finds local maxima of Auto-Correlation Function
    candidates = argrelmax(r)[0]

    if len(candidates) == 0:
        return False, 0

    if m is not None:
        # Check for local maximum when m is user defined.
        test = m not in candidates

        if test:
            return False, m

        candidates = [m]

    # Remove r[0], the auto-correlation at lag order 0, that introduces bias.
    r = r[1:]

    # The non-adjusted upper limit of the significance interval.
    band_upper = r.mean() + norm.ppf(1 - alpha / 2) * r.var()

    # Significance test, stops at first admissible value. The two '-1' below
    # compensate for the index change due to the restriction of the original r to r[1:].
    for candidate in candidates:
        stat = _bartlett_formula(r, candidate - 1, len(ts))
        if r[candidate - 1] > stat * band_upper:
            return True, candidate
    return False, 0


def _bartlett_formula(r: np.ndarray, m: int, length: int) -> float:
    """
    Computes the standard error of `r` at order `m` with respect to `length` according to Bartlett's formula.

    Parameters
    ----------
    r
        The array whose standard error is to be computed.
    m
        The order of the standard error.
    length
        The size of the underlying sample to be used.

    Returns
    -------
    float
        The standard error of `r` with order `m`.
    """

    if m == 1:
        return math.sqrt(1 / length)
    else:
        return math.sqrt((1 + 2 * sum(map(lambda x: x**2, r[: m - 1]))) / length)


def extract_trend_and_seasonality(
    ts: TimeSeries,
    freq: int = None,
    model: Union[SeasonalityMode, ModelMode] = ModelMode.MULTIPLICATIVE,
    method: str = "naive",
    **kwargs,
) -> Tuple[TimeSeries, TimeSeries]:
    """
    Extracts trend and seasonality from a TimeSeries instance using `statsmodels.tsa`.

    Parameters
    ----------
    ts
        The series to decompose
    freq
        The seasonality period to use.
    model
        The type of decomposition to use.
        Must be ``from darts import ModelMode, SeasonalityMode`` Enum member.
        Either ``MULTIPLICATIVE`` or ``ADDITIVE``.
        Defaults ``ModelMode.MULTIPLICATIVE``.
    method
        The method to be used to decompose the series.
        - "naive" : Seasonal decomposition using moving averages [1]_.
        - "STL" : Season-Trend decomposition using LOESS [2]_. Only compatible with ``ADDITIVE`` model type.
    kwargs
        Other keyword arguments are passed down to the decomposition method.
    Returns
    -------
    Tuple[TimeSeries, TimeSeries]
        A tuple of (trend, seasonal) time series.

    References
    -------
    .. [1] https://www.statsmodels.org/devel/generated/statsmodels.tsa.seasonal.seasonal_decompose.html
    .. [2] https://www.statsmodels.org/devel/generated/statsmodels.tsa.seasonal.STL.html
    """

    ts._assert_univariate()
    raise_if_not(
        model in ModelMode or model in SeasonalityMode,
        f"Unknown value for model_mode: {model}.",
        logger,
    )
    raise_if_not(
        model is not SeasonalityMode.NONE,
        "The model must be either MULTIPLICATIVE or ADDITIVE.",
    )

    if method == "naive":

        decomp = seasonal_decompose(
            ts.pd_series(), period=freq, model=model.value, extrapolate_trend="freq"
        )

    elif method == "STL":
        raise_if_not(
            model in [SeasonalityMode.ADDITIVE, ModelMode.ADDITIVE],
            f"Only ADDITIVE model is compatible with the STL method. Current model is {model}.",
            logger,
        )

        decomp = STL(
            endog=ts.pd_series(),
            period=freq,
            **kwargs,
        ).fit()

    else:
        raise_log(ValueError(f"Unknown value for method: {method}"), logger)

    season = TimeSeries.from_times_and_values(
        ts.time_index,
        decomp.seasonal,
        static_covariates=ts.static_covariates,
        hierarchy=ts.hierarchy,
    )
    trend = TimeSeries.from_times_and_values(
        ts.time_index,
        decomp.trend,
        static_covariates=ts.static_covariates,
        hierarchy=ts.hierarchy,
    )

    return trend, season


def remove_from_series(
    ts: TimeSeries, other: TimeSeries, model: Union[SeasonalityMode, ModelMode]
) -> TimeSeries:
    """
    Removes the TimeSeries `other` from the TimeSeries `ts` as specified by `model`.
    Use e.g. to remove an additive or multiplicative trend from a series.

    Parameters
    ----------
    ts
        The TimeSeries to be modified.
    other
        The TimeSeries to remove.
    model
        The type of model considered.
        Must be `from darts import ModelMode, SeasonalityMode` Enums member.
        Either MULTIPLICATIVE or ADDITIVE.
    Returns
    -------
    TimeSeries
        A TimeSeries defined by removing `other` from `ts`.
    """

    ts._assert_univariate()
    raise_if_not(
        model in ModelMode or model in SeasonalityMode,
        f"Unknown value for model_mode: {model}.",
        logger,
    )

    if model.value == "multiplicative":
        new_ts = ts / other
    elif model.value == "additive":
        new_ts = ts - other
    else:
        raise_log(
            ValueError(
                "Invalid parameter; must be either ADDITIVE or MULTIPLICATIVE. Was: {}".format(
                    model
                )
            )
        )
    return new_ts


def remove_seasonality(
    ts: TimeSeries,
    freq: int = None,
    model: SeasonalityMode = SeasonalityMode.MULTIPLICATIVE,
    method: str = "naive",
    **kwargs,
) -> TimeSeries:
    """
    Adjusts the TimeSeries `ts` for a seasonality of order `frequency` using the `model` decomposition.

    Parameters
    ----------
    ts
        The TimeSeries to adjust.
    freq
        The seasonality period to use.
    model
        The type of decomposition to use.
        Must be a `from darts import SeasonalityMode` Enum member.
        Either SeasonalityMode.MULTIPLICATIVE or SeasonalityMode.ADDITIVE.
        Defaults SeasonalityMode.MULTIPLICATIVE.
    method
        The method to be used to decompose the series.
        - "naive" : Seasonal decomposition using moving averages [1]_.
        - "STL" : Season-Trend decomposition using LOESS [2]_. Only compatible with ``ADDITIVE`` model type.
        Defaults to "naive"
    kwargs
        Other keyword arguments are passed down to the decomposition method.
     Returns
    -------
    TimeSeries
        A new TimeSeries instance that corresponds to the seasonality-adjusted 'ts'.
    References
    -------
    .. [1] https://www.statsmodels.org/devel/generated/statsmodels.tsa.seasonal.seasonal_decompose.html
    .. [2] https://www.statsmodels.org/devel/generated/statsmodels.tsa.seasonal.STL.html
    """
    ts._assert_univariate()
    raise_if_not(
        model is not SeasonalityMode.NONE,
        "The model must be either MULTIPLICATIVE or ADDITIVE.",
    )
    raise_if(
        model not in [SeasonalityMode.ADDITIVE, ModelMode.ADDITIVE] and method == "STL",
        f"Only ADDITIVE seasonality is compatible with the STL method. Current model is {model}.",
        logger,
    )

    _, seasonality = extract_trend_and_seasonality(ts, freq, model, method, **kwargs)
    new_ts = remove_from_series(ts, seasonality, model)
    return new_ts


def remove_trend(
    ts: TimeSeries,
    model: ModelMode = ModelMode.MULTIPLICATIVE,
    method: str = "naive",
    **kwargs,
) -> TimeSeries:
    """
    Adjusts the TimeSeries `ts` for a trend using the `model` decomposition.

    Parameters
    ----------
    ts
        The TimeSeries to adjust.
    model
        The type of decomposition to use.
        Must be a `from darts import ModelMode` Enum member.
        Either ModelMode.MULTIPLICATIVE or ModelMode.ADDITIVE.
        Defaults ModelMode.MULTIPLICATIVE.
    method
        The method to be used to decompose the series.
        - "naive" : Seasonal decomposition using moving averages [1]_.
        - "STL" : Season-Trend decomposition using LOESS [2]_. Only compatible with ``ADDITIVE`` model type.
        Defaults to "naive"
    kwargs
        Other keyword arguments are passed down to the decomposition method.
    Returns
    -------
    TimeSeries
        A new TimeSeries instance that corresponds to the trend-adjusted 'ts'.
    """

    ts._assert_univariate()

    raise_if(
        model not in [SeasonalityMode.ADDITIVE, ModelMode.ADDITIVE] and method == "STL",
        f"Only ADDITIVE seasonality is compatible with the STL method. Current model is {model}.",
        logger,
    )
    trend, _ = extract_trend_and_seasonality(ts, model=model, method=method, **kwargs)
    new_ts = remove_from_series(ts, trend, model)
    return new_ts


def stationarity_tests(
    ts: TimeSeries,
    p_value_threshold_adfuller: float = 0.05,
    p_value_threshold_kpss: float = 0.05,
) -> bool:

    """
    Double test on stationarity using both Kwiatkowski-Phillips-Schmidt-Shin and Augmented
    Dickey-Fuller statistical tests.

    WARNING
    Because Augmented Dickey-Fuller is testing null hypothesis that ts IS NOT stationary and
    Kwiatkowski-Phillips-Schmidt-Shin that
    ts IS stationary, we can't really decide on the same p_value threshold for both tests in general.
    It seems reasonable to keep them both at 0.05.
    If other threshold has to be tested, they have to go in opposite direction (for example,
    p_value_threshold_adfuller = 0.01 and p_value_threshold_kpss = 0.1).

    Parameters
    ----------
    ts
        The TimeSeries to test.
    p_value_threshold_adfuller
        p_value threshold to reject stationarity for Augmented Dickey-Fuller test.
    p_value_threshold_kpss
        p_value threshold to reject non-stationarity for Kwiatkowski-Phillips-Schmidt-Shin test.

    Returns
    -------
    bool
        If ts is stationary or not.
    """
    adf_res = stationarity_test_adf(ts)
    kpss_res = stationarity_test_kpss(ts)

    return (adf_res[1] < p_value_threshold_adfuller) and (
        kpss_res[1] > p_value_threshold_kpss
    )


def stationarity_test_kpss(
    ts: TimeSeries, regression: str = "c", nlags: Union[str, int] = "auto"
) -> set:
    """
    Provides Kwiatkowski-Phillips-Schmidt-Shin test for stationarity for a time series,
    using :func:`statsmodels.tsa.stattools.kpss`. See [1]_.


    Parameters
    ----------
    ts
        The time series to test.
    regression
        The null hypothesis for the KPSS test.
        'c' : The data is stationary around a constant (default).
        'ct' : The data is stationary around a trend.
    nlags
       Indicates the number of lags to be used. If 'auto' (default), lags is calculated using the data-dependent method
       of Hobijn et al. (1998). See also Andrews (1991), Newey & West (1994), and Schwert (1989). If set to 'legacy',
       uses int(12 * (n / 100)**(1 / 4)) , as outlined in Schwert (1989).

    Returns
    -------
    set
        | kpss_stat: The test statistic.
        | pvalue: The p-value of the test. The p-value is interpolated from Table 1 in [2]_,
        | and a boundary point is returned if the test statistic is outside the table of critical values,
        | that is, if the p-value is outside the interval (0.01, 0.1).
        | lags: The truncation lag parameter.
        | crit: The critical values at 10%, 5%, 2.5% and 1%. Based on [2]_.

    References
    ----------
    .. [1] https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.kpss.html
    .. [2] Kwiatkowski et al. (1992)
    """
    ts._assert_univariate()
    ts._assert_deterministic()

    return kpss(ts.values(copy=False), regression, nlags)


def stationarity_test_adf(
    ts: TimeSeries,
    maxlag: Union[None, int] = None,
    regression: str = "c",
    autolag: Union[None, str] = "AIC",
) -> set:
    """
    Provides Augmented Dickey-Fuller unit root test for a time series,
    using :func:`statsmodels.tsa.stattools.adfuller`. See [1]_.


    Parameters
    ----------
    ts
        The time series to test.
    maxlag
        Maximum lag which is included in test, default value of 12*(nobs/100)^{1/4} is used when None.
    regression
        Constant and trend order to include in regression.
        "c" : constant only (default).
        "ct" : constant and trend.
        "ctt" : constant, and linear and quadratic trend.
        "n" : no constant, no trend.
    autolag
        Method to use when automatically determining the lag length among the values 0, 1, …, maxlag.
        If "AIC" (default) or "BIC", then the number of lags is chosen to minimize the corresponding
        information criterion. "t-stat" based choice of maxlag. Starts with maxlag and drops a lag
        until the t-statistic on the last lag length is significant using a 5%-sized test.
        If None, then the number of included lags is set to maxlag.

    Returns
    -------
    set
        | adf: The test statistic.
        | pvalue: MacKinnon's approximate p-value based on [2]_.
        | usedlag: The number of lags used.
        | nobs: The number of observations used for the ADF regression and calculation of the critical values.
        | critical: Critical values for the test statistic at the 1 %, 5 %, and 10 % levels. Based on [2]_.
        | icbest: The maximized information criterion if autolag is not None.

    References
    ----------
    .. [1] https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.adfuller.html
    .. [2] MacKinnon (1994, 2010)
    """

    ts._assert_univariate()
    ts._assert_deterministic()

    return adfuller(ts.values(copy=False), maxlag, regression, autolag)


def granger_causality_tests(
    ts_cause: TimeSeries,
    ts_effect: TimeSeries,
    maxlag: int,
    addconst: bool = True,
    verbose: bool = True,
) -> None:
    """
    Provides four tests for granger non causality of 2 time series using
    :func:`statsmodels.tsa.stattools.grangercausalitytests`.
    See [1]_.


    Parameters
    ----------
    ts_cause
        A univariate deterministic time series. The statistical test determines if this time series
        'Granger causes' the time series ts_effect (second parameter). Missing values are not supported.
        if H_0 (non causality) is rejected (p near 0), then there is a 'granger causality'.
    ts_effect
        Univariate time series 'Granger caused' by ts_cause.
    maxlag
        If an integer, computes the test for all lags up to maxlag.
        If an iterable, computes the tests only for the lags in maxlag.
    addconst
        Include a constant in the model.
    verbose
        Print results.

    Returns
    -------
    Dict
        All test results, dictionary keys are the number of lags. For each lag the values are a tuple,
        with the first element a dictionary with test statistic, pvalues, degrees of freedom, the second element are
        the OLS estimation results for the restricted model, the unrestricted model and the restriction (contrast)
        matrix for the parameter f_test.

    References
    ----------
    .. [1] https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.grangercausalitytests.html
    """

    ts_cause._assert_univariate()
    ts_effect._assert_univariate()

    ts_cause._assert_deterministic()
    ts_effect._assert_deterministic()

    raise_if_not(
        ts_cause.freq == ts_effect.freq,
        "ts_cause and ts_effect must have the same frequency.",
    )

    if not ts_cause.has_same_time_as(ts_effect):
        logger.warning(
            "ts_cause and ts_effect time series have different time index. "
            "We will slice-intersect ts_cause with ts_effect."
        )

    ts_cause = ts_cause.slice_intersect(ts_effect)
    ts_effect = ts_effect.slice_intersect(ts_cause)

    if not stationarity_tests(ts_cause):
        logger.warning(
            "ts_cause doesn't seem to be stationary. Please review granger causality validity in your problem context."
        )
    if not stationarity_tests(ts_effect):
        logger.warning(
            "ts_effect doesn't seem to be stationary. Please review granger causality validity in your problem context."
        )

    return grangercausalitytests(
        np.concatenate(
            (ts_effect.values(copy=False), ts_cause.values(copy=False)), axis=1
        ),
        maxlag,
        addconst,
        verbose,
    )


def plot_acf(
    ts: TimeSeries,
    m: Optional[int] = None,
    max_lag: int = 24,
    alpha: float = 0.05,
    bartlett_confint: bool = True,
    fig_size: Tuple[int, int] = (10, 5),
    axis: Optional[plt.axis] = None,
) -> None:
    """
    Plots the ACF of `ts`, highlighting it at lag `m`, with corresponding significance interval.
    Uses :func:`statsmodels.tsa.stattools.acf` [1]_

    Parameters
    ----------
    ts
        The TimeSeries whose ACF should be plotted.
    m
        Optionally, a time lag to highlight on the plot.
    max_lag
        The maximal lag order to consider.
    alpha
        The confidence interval to display.
    bartlett_confint
        The boolean value indicating whether the confidence interval should be
        calculated using Bartlett's formula. If set to True, the confidence interval
        can be used in the model identification stage for fitting ARIMA models.
        If set to False, the confidence interval can be used to test for randomness
        (i.e. there is no time dependence in the data) of the data.
    fig_size
        The size of the figure to be displayed.
    axis
        Optionally, an axis object to plot the ACF on.

    References
    ----------
    .. [1] https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.acf.html
    """

    ts._assert_univariate()
    raise_if(
        max_lag is None or not (1 <= max_lag < len(ts)),
        "max_lag must be greater than or equal to 1 and less than len(ts).",
    )
    raise_if(
        m is not None and not (0 <= m <= max_lag),
        "m must be greater than or equal to 0 and less than or equal to max_lag.",
    )
    raise_if(
        alpha is None or not (0 < alpha < 1),
        "alpha must be greater than 0 and less than 1.",
    )

    r, confint = acf(
        ts.values(),
        nlags=max_lag,
        fft=False,
        alpha=alpha,
        bartlett_confint=bartlett_confint,
    )

    if axis is None:
        plt.figure(figsize=fig_size)
        axis = plt

    for i in range(len(r)):
        axis.plot(
            (i, i),
            (0, r[i]),
            color=("#b512b8" if m is not None and i == m else "black"),
            lw=(1 if m is not None and i == m else 0.5),
        )

    # Adjusts the upper band of the confidence interval to center it on the x axis.
    upp_band = [confint[lag][1] - r[lag] for lag in range(1, max_lag + 1)]

    axis.fill_between(
        np.arange(1, max_lag + 1),
        upp_band,
        [-x for x in upp_band],
        color="#003DFD",
        alpha=0.25,
    )
    axis.plot((0, max_lag + 1), (0, 0), color="black")


def plot_pacf(
    ts: TimeSeries,
    m: Optional[int] = None,
    max_lag: int = 24,
    method: str = "ywadjusted",
    alpha: float = 0.05,
    fig_size: Tuple[int, int] = (10, 5),
    axis: Optional[plt.axis] = None,
) -> None:
    """
    Plots the Partial ACF of `ts`, highlighting it at lag `m`, with corresponding significance interval.
    Uses :func:`statsmodels.tsa.stattools.pacf` [1]_

    Parameters
    ----------
    ts
        The TimeSeries whose ACF should be plotted.
    m
        Optionally, a time lag to highlight on the plot.
    max_lag
        The maximal lag order to consider.
    method
        The method to be used for the PACF calculation.
        - | "yw" or "ywadjusted" : Yule-Walker with sample-size adjustment in
          | denominator for acovf. Default.
        - "ywm" or "ywmle" : Yule-Walker without adjustment.
        - "ols" : regression of time series on lags of it and on constant.
        - "ols-inefficient" : regression of time series on lags using a single
          common sample to estimate all pacf coefficients.
        - "ols-adjusted" : regression of time series on lags with a bias
          adjustment.
        - "ld" or "ldadjusted" : Levinson-Durbin recursion with bias
          correction.
        - "ldb" or "ldbiased" : Levinson-Durbin recursion without bias
          correction.
    alpha
        The confidence interval to display.
    fig_size
        The size of the figure to be displayed.
    axis
        Optionally, an axis object to plot the ACF on.

    References
    ----------
    .. [1] https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.pacf.html
    """

    ts._assert_univariate()
    raise_if(
        max_lag is None or not (1 <= max_lag < len(ts) // 2),
        "max_lag must be greater than or equal to 1 and less than len(ts)//2.",
    )
    raise_if(
        m is not None and not (0 <= m <= max_lag),
        "m must be greater than or equal to 0 and less than or equal to max_lag.",
    )
    raise_if(
        alpha is None or not (0 < alpha < 1),
        "alpha must be greater than 0 and less than 1.",
    )

    r, confint = pacf(ts.values(), nlags=max_lag, method=method, alpha=alpha)

    if axis is None:
        plt.figure(figsize=fig_size)
        axis = plt

    for i in range(len(r)):
        axis.plot(
            (i, i),
            (0, r[i]),
            color=("#b512b8" if m is not None and i == m else "black"),
            lw=(1 if m is not None and i == m else 0.5),
        )

    # Adjusts the upper band of the confidence interval to center it on the x axis.
    upp_band = [confint[lag][1] - r[lag] for lag in range(1, max_lag + 1)]

    axis.fill_between(
        np.arange(1, max_lag + 1),
        upp_band,
        [-x for x in upp_band],
        color="#003DFD",
        alpha=0.25,
    )
    axis.plot((0, max_lag + 1), (0, 0), color="black")


def plot_hist(
    data: Union[TimeSeries, List[float], np.ndarray],
    bins: Optional[Union[int, np.ndarray, List[float]]] = None,
    density: bool = False,
    title: Optional[str] = None,
    fig_size: Optional[Tuple[int, int]] = None,
    ax: Optional[plt.axis] = None,
) -> None:
    """This function plots the histogram of values in a TimeSeries instance or an array-like.

    All types of TimeSeries are supported (uni-, multivariate, deterministic, stochastic).
    Depending on the number of components in `data`, up to four histograms can be plotted on one figure.
    All stochastic samples will be displayed with the corresponding component.

    If `data` is an array-like, ALL values will be displayed in the same histogram.

    Parameters
    ----------
    data
        TimeSeries instance or an array-like from which to plot the histogram.
    bins
        Optionally, either an integer value for the number of bins to be displayed
        or an array-like of floats determining the position of bins.
    density
        bool, if `density` is set to True, the bin counts will be converted to probability density
    title
        The title of the figure to be displayed
    fig_size
        The size of the figure to be displayed.
    ax
        Optionally, an axis object to plot the histogram on.
    """

    n_plots_max = 4

    if isinstance(data, TimeSeries):
        if len(data.components) > n_plots_max:
            logger.warning(
                "TimeSeries contains more than 4 components. Only the first 4 components will be displayed."
            )

        components = list(data.components[:n_plots_max])
        values = data[components].all_values(copy=False).flatten(order="F")
    else:
        values = data if isinstance(data, np.ndarray) else np.array(data)
        if len(values.shape) > 1:
            logger.warning(
                "Input array-like data with `dim>1d` will be flattened and displayed in one histogram."
            )

        components = ["Data"]
        values = values.flatten(order="F")

    # compute the number of columns and rows for subplots depending on shape of input data
    n_components = len(components)
    n_cols = 1 if n_components == 1 else 2
    n_rows = math.ceil(n_components / n_cols)

    title = "Histogram" if title is None else title
    if ax is None:
        fig = plt.figure(constrained_layout=True, figsize=fig_size)
        gs = fig.add_gridspec(n_rows, n_cols)
        fig.suptitle(title)
        ax_all = [fig.add_subplot(gs[i]) for i in range(n_components)]
    else:
        if n_components > 1:
            logger.warning(
                "Only the first component is plotted when calling plot_hist() with a given `ax`"
            )
        ax.set_title(title)
        ax_all = [ax]

    n_entries = len(values) // n_components
    for i, label, ax_i in zip(range(n_components), components, ax_all):
        ax_i.hist(
            values[i * n_entries : (i + 1) * n_entries],
            bins=bins,
            density=density,
            label=label,
        )
        ax_i.set_xlabel("value")
        ax_i.set_ylabel("count" if not density else "probability density")
        ax_i.legend()


def plot_residuals_analysis(
    residuals: TimeSeries, num_bins: int = 20, fill_nan: bool = True
) -> None:
    """Plots data relevant to residuals.

    This function takes a univariate TimeSeries instance of residuals and plots their values,
    their distribution and their ACF.
    Please note that if the residual TimeSeries instance contains NaN values, the plots
    might be displayed incorrectly. If `fill_nan` is set to True, the missing values will
    be interpolated.

    Parameters
    ----------
    residuals
        Univariate TimeSeries instance representing residuals.
    num_bins
        Optionally, an integer value determining the number of bins in the histogram.
    fill_nan
        A boolean value indicating whether NaN values should be filled in the residuals.
    """

    residuals._assert_univariate()

    fig = plt.figure(constrained_layout=True, figsize=(8, 6))
    gs = fig.add_gridspec(2, 2)

    if fill_nan:
        residuals = fill_missing_values(residuals)

    # plot values
    ax1 = fig.add_subplot(gs[:1, :])
    residuals.plot(ax=ax1)
    ax1.set_ylabel("value")
    ax1.set_title("Residual values")

    # plot histogram and distribution
    res_mean, res_std = np.mean(residuals.univariate_values()), np.std(
        residuals.univariate_values()
    )
    res_min, res_max = min(residuals.univariate_values()), max(
        residuals.univariate_values()
    )
    x = np.linspace(res_min, res_max, 100)
    ax2 = fig.add_subplot(gs[1:, 1:])
    plot_hist(residuals, bins=num_bins, ax=ax2)
    ax2.plot(
        x,
        norm(res_mean, res_std).pdf(x)
        * len(residuals)
        * (res_max - res_min)
        / num_bins,
    )
    ax2.yaxis.set_major_locator(plt.MaxNLocator(integer=True))
    ax2.set_title("Distribution")
    ax2.set_ylabel("count")
    ax2.set_xlabel("value")

    # plot ACF
    ax3 = fig.add_subplot(gs[1:, :1])
    plot_acf(residuals, axis=ax3)
    ax3.set_ylabel("ACF value")
    ax3.set_xlabel("lag")
    ax3.set_title("ACF")
