"""Utility functions to baseline-correct data."""

# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause

import numpy as np

from .utils import logger, verbose, _check_option


def _log_rescale(baseline, mode='mean'):
    """Log the rescaling method."""
    if baseline is not None:
        _check_option('mode', mode, ['logratio', 'ratio', 'zscore', 'mean',
                                     'percent', 'zlogratio'])
        msg = 'Applying baseline correction (mode: %s)' % mode
    else:
        msg = 'No baseline correction applied'
    return msg


@verbose
def rescale(data, times, baseline, mode='mean', copy=True, picks=None,
            verbose=None):
    """Rescale (baseline correct) data.

    Parameters
    ----------
    data : array
        It can be of any shape. The only constraint is that the last
        dimension should be time.
    times : 1D array
        Time instants is seconds.
    %(baseline_rescale)s
    mode : 'mean' | 'ratio' | 'logratio' | 'percent' | 'zscore' | 'zlogratio'
        Perform baseline correction by

        - subtracting the mean of baseline values ('mean')
        - dividing by the mean of baseline values ('ratio')
        - dividing by the mean of baseline values and taking the log
          ('logratio')
        - subtracting the mean of baseline values followed by dividing by
          the mean of baseline values ('percent')
        - subtracting the mean of baseline values and dividing by the
          standard deviation of baseline values ('zscore')
        - dividing by the mean of baseline values, taking the log, and
          dividing by the standard deviation of log baseline values
          ('zlogratio')

    copy : bool
        Whether to return a new instance or modify in place.
    picks : list of int | None
        Data to process along the axis=-2 (None, default, processes all).
    %(verbose)s

    Returns
    -------
    data_scaled: array
        Array of same shape as data after rescaling.
    """
    if copy:
        data = data.copy()
    if verbose is not False:
        msg = _log_rescale(baseline, mode)
        logger.info(msg)
    if baseline is None or data.shape[-1] == 0:
        return data

    bmin, bmax = baseline
    if bmin is None:
        imin = 0
    else:
        imin = np.where(times >= bmin)[0]
        if len(imin) == 0:
            raise ValueError('bmin is too large (%s), it exceeds the largest '
                             'time value' % (bmin,))
        imin = int(imin[0])
    if bmax is None:
        imax = len(times)
    else:
        imax = np.where(times <= bmax)[0]
        if len(imax) == 0:
            raise ValueError('bmax is too small (%s), it is smaller than the '
                             'smallest time value' % (bmax,))
        imax = int(imax[-1]) + 1
    if imin >= imax:
        raise ValueError('Bad rescaling slice (%s:%s) from time values %s, %s'
                         % (imin, imax, bmin, bmax))

    # technically this is inefficient when `picks` is given, but assuming
    # that we generally pick most channels for rescaling, it's not so bad
    mean = np.mean(data[..., imin:imax], axis=-1, keepdims=True)

    if mode == 'mean':
        def fun(d, m):
            d -= m
    elif mode == 'ratio':
        def fun(d, m):
            d /= m
    elif mode == 'logratio':
        def fun(d, m):
            d /= m
            np.log10(d, out=d)
    elif mode == 'percent':
        def fun(d, m):
            d -= m
            d /= m
    elif mode == 'zscore':
        def fun(d, m):
            d -= m
            d /= np.std(d[..., imin:imax], axis=-1, keepdims=True)
    elif mode == 'zlogratio':
        def fun(d, m):
            d /= m
            np.log10(d, out=d)
            d /= np.std(d[..., imin:imax], axis=-1, keepdims=True)

    if picks is None:
        fun(data, mean)
    else:
        for pi in picks:
            fun(data[..., pi, :], mean[..., pi, :])
    return data


def _check_baseline(baseline, times, sfreq, on_baseline_outside_data='raise'):
    """Check if the baseline is valid, and adjust it if requested.

    ``None`` values inside the baseline parameter will be replaced with
    ``times[0]`` and ``times[-1]``.

    Parameters
    ----------
    baseline : tuple | None
        Beginning and end of the baseline period, in seconds. If ``None``,
        assume no baseline and return immediately.
    times : array
        The time points.
    sfreq : float
        The sampling rate.
    on_baseline_outside_data : 'raise' | 'info' | 'adjust'
        What do do if the baseline period exceeds the data.
        If ``'raise'``, raise an exception (default).
        If ``'info'``, log an info message.
        If ``'adjust'``, adjust the baseline such that it's within the data
        range again.

    Returns
    -------
    (baseline_tmin, baseline_tmax) | None
        The baseline with ``None`` values replaced with times, and with
        adjusted times if ``on_baseline_outside_data='adjust'``; or ``None``
        if the ``baseline`` parameter is ``None``.

    """
    if baseline is None:
        return None

    if not isinstance(baseline, tuple) or len(baseline) != 2:
        raise ValueError(f'`baseline={baseline}` is an invalid argument, must '
                         f'be a tuple of length 2 or None')

    tmin, tmax = times[0], times[-1]
    tstep = 1. / float(sfreq)

    # check default value of baseline and `tmin=0`
    if baseline == (None, 0) and tmin == 0:
        raise ValueError('Baseline interval is only one sample. Use '
                         '`baseline=(0, 0)` if this is desired.')

    baseline_tmin, baseline_tmax = baseline

    if baseline_tmin is None:
        baseline_tmin = tmin
    baseline_tmin = float(baseline_tmin)

    if baseline_tmax is None:
        baseline_tmax = tmax
    baseline_tmax = float(baseline_tmax)

    if baseline_tmin > baseline_tmax:
        raise ValueError(
            "Baseline min (%s) must be less than baseline max (%s)"
            % (baseline_tmin, baseline_tmax))

    if (baseline_tmin < tmin - tstep) or (baseline_tmax > tmax + tstep):
        msg = (f"Baseline interval [{baseline_tmin}, {baseline_tmax}] sec "
               f"is outside of epochs data [{tmin}, {tmax}] sec. Epochs were "
               f"probably cropped.")
        if on_baseline_outside_data == 'raise':
            raise ValueError(msg)
        elif on_baseline_outside_data == 'info':
            logger.info(msg)
        elif on_baseline_outside_data == 'adjust':
            if baseline_tmin < tmin - tstep:
                baseline_tmin = tmin
            if baseline_tmax > tmax + tstep:
                baseline_tmax = tmax

    return baseline_tmin, baseline_tmax