# Author: Denis Engemann <denis.engemann@gmail.com>
#         Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#         Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)

from collections import Counter

import numpy as np

from .mixin import TransformerMixin, EstimatorMixin
from .base import _set_cv
from ..utils import logger, verbose
from ..parallel import parallel_func
from .. import pick_types, pick_info


class EMS(TransformerMixin, EstimatorMixin):
    """Transformer to compute event-matched spatial filters.

    This version operates on the entire time course. The result is a spatial
    filter at each time point and a corresponding time course. Intuitively,
    the result gives the similarity between the filter at each time point and
    the data vector (sensors) at that time point.

    .. note : EMS only works for binary classification.

    References
    ----------
    [1] Aaron Schurger, Sebastien Marti, and Stanislas Dehaene, "Reducing
        multi-sensor data to a single time course that reveals experimental
        effects", BMC Neuroscience 2013, 14:122

    Attributes
    ----------
    filters_ : ndarray, shape (n_channels, n_times)
        The set of spatial filters.
    classes_ : ndarray, shape (n_classes,)
        The target classes.
    """

    def __repr__(self):
        if hasattr(self, 'filters_'):
            return '<EMS: fitted with %i filters on %i classes.>' % (
                len(self.filters_), len(self.classes_))
        else:
            return '<EMS: not fitted.>'

    def fit(self, X, y):
        """Fit the spatial filters.

        .. note : EMS is fitted on data normalized by channel type before the
                  fitting of the spatial filters.

        Parameters
        ----------
        X : array, shape (n_epochs, n_channels, n_times)
            The training data.
        y : array of int, shape (n_epochs)
            The target classes.

        Returns
        -------
        self : returns and instance of self.
        """
        classes = np.unique(y)
        if len(classes) != 2:
            raise ValueError('EMS only works for binary classification.')
        self.classes_ = classes
        filters = X[y == classes[0]].mean(0) - X[y == classes[1]].mean(0)
        filters /= np.linalg.norm(filters, axis=0)[None, :]
        self.filters_ = filters
        return self

    def transform(self, X):
        """Transform the data by the spatial filters.

        Parameters
        ----------
        X : array, shape (n_epochs, n_channels, n_times)
            The input data.

        Returns
        -------
        X : array, shape (n_epochs, n_times)
            The input data transformed by the spatial filters.
        """
        Xt = np.sum(X * self.filters_, axis=1)
        return Xt


@verbose
def compute_ems(epochs, conditions=None, picks=None, n_jobs=1, verbose=None,
                cv=None):
    """Compute event-matched spatial filter on epochs.

    This version operates on the entire time course. No time window needs to
    be specified. The result is a spatial filter at each time point and a
    corresponding time course. Intuitively, the result gives the similarity
    between the filter at each time point and the data vector (sensors) at
    that time point.

    .. note : EMS only works for binary classification.
    .. note : The present function applies a leave-one-out cross-validation,
              following Schurger et al's paper. However, we recommend using
              a stratified k-fold cross-validation. Indeed, leave-one-out tends
              to overfit and cannot be used to estimate the variance of the
              prediction within a given fold.
    .. note : Because of the leave-one-out, thise function needs an equal
              number of epochs in each of the two conditions.

    References
    ----------
    [1] Aaron Schurger, Sebastien Marti, and Stanislas Dehaene, "Reducing
        multi-sensor data to a single time course that reveals experimental
        effects", BMC Neuroscience 2013, 14:122

    Parameters
    ----------
    epochs : instance of mne.Epochs
        The epochs.
    conditions : list of str | None, defaults to None
        If a list of strings, strings must match the epochs.event_id's key as
        well as the number of conditions supported by the objective_function.
        If None keys in epochs.event_id are used.
    picks : array-like of int | None, defaults to None
        Channels to be included. If None only good data channels are used.
    n_jobs : int, defaults to 1
        Number of jobs to run in parallel.
    verbose : bool, str, int, or None, defaults to self.verbose
        If not None, override default verbose level (see mne.verbose).
    cv : cross-validation object | str | None, defaults to LeaveOneOut
        The cross-validation scheme.

    Returns
    -------
    surrogate_trials : ndarray, shape (n_trials // 2, n_times)
        The trial surrogates.
    mean_spatial_filter : ndarray, shape (n_channels, n_times)
        The set of spatial filters.
    conditions : ndarray, shape (n_classes,)
        The conditions used. Values correspond to original event ids.
    """
    logger.info('...computing surrogate time series. This can take some time')

    # Default to leave-one-out cv
    cv = 'LeaveOneOut' if cv is None else cv

    if picks is None:
        picks = pick_types(epochs.info, meg=True, eeg=True)

    if not len(set(Counter(epochs.events[:, 2]).values())) == 1:
        raise ValueError('The same number of epochs is required by '
                         'this function. Please consider '
                         '`epochs.equalize_event_counts`')

    if conditions is None:
        conditions = epochs.event_id.keys()
        epochs = epochs.copy()
    else:
        epochs = epochs[conditions]

    epochs.drop_bad()

    if len(conditions) != 2:
        raise ValueError('Currently this function expects exactly 2 '
                         'conditions but you gave me %i' %
                         len(conditions))

    ev = epochs.events[:, 2]
    # Special care to avoid path dependent mappings and orders
    conditions = list(sorted(conditions))
    cond_idx = [np.where(ev == epochs.event_id[k])[0] for k in conditions]

    info = pick_info(epochs.info, picks)
    data = epochs.get_data()[:, picks]

    # Scale (z-score) the data by channel type
    # XXX the z-scoring is applied outside the CV, which is not standard.
    for ch_type in ['mag', 'grad', 'eeg']:
        if ch_type in epochs:
            # FIXME should be applied to all sort of data channels
            if ch_type == 'eeg':
                this_picks = pick_types(info, meg=False, eeg=True)
            else:
                this_picks = pick_types(info, meg=ch_type, eeg=False)
            data[:, this_picks] /= np.std(data[:, this_picks])

    # Setup cross-validation. Need to use _set_cv to deal with sklearn
    # deprecation of cv objects.
    y = epochs.events[:, 2]
    _, cv_splits = _set_cv(cv, 'classifier', X=y, y=y)

    parallel, p_func, _ = parallel_func(_run_ems, n_jobs=n_jobs)
    # FIXME this parallization should be removed.
    #   1) it's numpy computation so it's already efficient,
    #   2) it duplicates the data in RAM,
    #   3) the computation is already super fast.
    out = parallel(p_func(_ems_diff, data, cond_idx, train, test)
                   for train, test in cv_splits)

    surrogate_trials, spatial_filter = zip(*out)
    surrogate_trials = np.array(surrogate_trials)
    spatial_filter = np.mean(spatial_filter, axis=0)

    return surrogate_trials, spatial_filter, epochs.events[:, 2]


def _ems_diff(data0, data1):
    """Aux. function to compute_ems that computes the default diff
    objective function."""
    return np.mean(data0, axis=0) - np.mean(data1, axis=0)


def _run_ems(objective_function, data, cond_idx, train, test):
    d = objective_function(*(data[np.intersect1d(c, train)] for c in cond_idx))
    d /= np.sqrt(np.sum(d ** 2, axis=0))[None, :]
    # compute surrogates
    return np.sum(data[test[0]] * d, axis=0), d
