# -*- coding: utf-8 -*-
from warnings import warn

import numpy as np
import pandas as pd

from ..misc import NeuroKitWarning
from ..misc.check_type import is_string
from .mad import mad


def standardize(data, robust=False, window=None, **kwargs):
    """**Standardization of data**

    Performs a standardization of data (Z-scoring), i.e., centering and scaling, so that the data is
    expressed in terms of standard deviation (i.e., mean = 0, SD = 1) or Median Absolute Deviance
    (median = 0, MAD = 1).

    Parameters
    ----------
    data : Union[list, np.array, pd.Series]
        Raw data.
    robust : bool
        If ``True``, centering is done by substracting the median from the variables and dividing
        it by the median absolute deviation (MAD). If ``False``, variables are standardized by
        substracting the mean and dividing it by the standard deviation (SD).
    window : int
        Perform a rolling window standardization, i.e., apply a standardization on a window of the
        specified number of samples that rolls along the main axis of the signal. Can be used for
        complex detrending.
    **kwargs : optional
        Other arguments to be passed to :func:`.pandas.rolling`.

    Returns
    ----------
    list
        The standardized values.


    Examples
    ----------
    .. ipython:: python

      import neurokit2 as nk
      import pandas as pd

      # Simple example
      nk.standardize([3, 1, 2, 4, 6, np.nan])

      nk.standardize([3, 1, 2, 4, 6, np.nan], robust=True)

      nk.standardize(np.array([[1, 2, 3, 4], [5, 6, 7, 8]]).T)

      nk.standardize(pd.DataFrame({"A": [3, 1, 2, 4, 6, np.nan],
                                   "B": [3, 1, 2, 4, 6, 5]}))

      # Rolling standardization of a signal
      signal = nk.signal_simulate(frequency=[0.1, 2], sampling_rate=200)
      z = nk.standardize(signal, window=200)
      @savefig p_standardize1.png scale=100%
      nk.signal_plot([signal, z], standardize=True)
      @suppress
      plt.close()

    """
    # Return appropriate type
    if isinstance(data, list):
        if any(is_string(data)):
            out = data
            warn(
                "The data is not standardized."
                "Some elements in the list is of string type.",
                category=NeuroKitWarning
            )
        else:
            out = list(_standardize(np.array(data), robust=robust, window=window, **kwargs))

    elif isinstance(data, pd.DataFrame):
        # only standardize columns that are not string and are not nan
        _data = data.loc[:, ~is_string(data) & ~np.array(data.isnull().all())]
        to_append = data.loc[:, is_string(data) | np.array(data.isnull().all())]
        out = pd.DataFrame(_standardize(_data, robust=robust, window=window, **kwargs))
        out = pd.concat([to_append, out], axis=1)

    elif isinstance(data, pd.Series):
        if is_string(data):
            out = data
            warn(
                "The data is not standardized as it is of string type.",
                category=NeuroKitWarning)
        else:
            out = pd.Series(_standardize(data, robust=robust, window=window, **kwargs))

    else:
        if is_string(data):
            out = data
            warn(
                "The data is not standardized as it is of string type.",
                category=NeuroKitWarning)
        else:
            out = _standardize(data, robust=robust, window=window, **kwargs)

    return out


# =============================================================================
# Internals
# =============================================================================
def _standardize(data, robust=False, window=None, **kwargs):

    # Compute standardized on whole data
    if window is None:
        if robust is False:
            z = (data - np.nanmean(data, axis=0)) / np.nanstd(data, axis=0, ddof=1)
        else:
            z = (data - np.nanmedian(data, axis=0)) / mad(data)

    # Rolling standardization on windows
    else:
        df = pd.DataFrame(data)  # Force dataframe

        if robust is False:
            z = (df - df.rolling(window, min_periods=0, **kwargs).mean()) / df.rolling(
                window, min_periods=0, **kwargs
            ).std(ddof=1)
        else:
            z = (df - df.rolling(window, min_periods=0, **kwargs).median()) / df.rolling(
                window, min_periods=0, **kwargs
            ).apply(mad)

        # Fill the created nans
        z = z.fillna(method="bfill")

        # Restore to vector or array
        if z.shape[1] == 1:
            z = z[0].values
        else:
            z = z.values

    return z
