# -*- coding: utf-8 -*-
"""
.. _ex-decoding-csp-eeg:

===========================================================================
Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
===========================================================================

Decoding of motor imagery applied to EEG data decomposed using CSP. A
classifier is then applied to features extracted on CSP-filtered signals.

See https://en.wikipedia.org/wiki/Common_spatial_pattern and
:footcite:`Koles1991`. The EEGBCI dataset is documented in
:footcite:`SchalkEtAl2004` and is available at PhysioNet
:footcite:`GoldbergerEtAl2000`.
"""
# Authors: Martin Billinger <martin.billinger@tugraz.at>
#
# License: BSD-3-Clause

# %%


import numpy as np
import matplotlib.pyplot as plt

from sklearn.pipeline import Pipeline
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import ShuffleSplit, cross_val_score

from mne import Epochs, pick_types, events_from_annotations
from mne.channels import make_standard_montage
from mne.io import concatenate_raws, read_raw_edf
from mne.datasets import eegbci
from mne.decoding import CSP

print(__doc__)

# #############################################################################
# # Set parameters and read data

# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = -1., 4.
event_id = dict(hands=2, feet=3)
subject = 1
runs = [6, 10, 14]  # motor imagery: hands vs feet

raw_fnames = eegbci.load_data(subject, runs)
raw = concatenate_raws([read_raw_edf(f, preload=True) for f in raw_fnames])
eegbci.standardize(raw)  # set channel names
montage = make_standard_montage('standard_1005')
raw.set_montage(montage)

# Apply band-pass filter
raw.filter(7., 30., fir_design='firwin', skip_by_annotation='edge')

events, _ = events_from_annotations(raw, event_id=dict(T1=2, T2=3))

picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
                   exclude='bads')

# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
epochs = Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
                baseline=None, preload=True)
epochs_train = epochs.copy().crop(tmin=1., tmax=2.)
labels = epochs.events[:, -1] - 2

# %%
# Classification with linear discrimant analysis

# Define a monte-carlo cross-validation generator (reduce variance):
scores = []
epochs_data = epochs.get_data()
epochs_data_train = epochs_train.get_data()
cv = ShuffleSplit(10, test_size=0.2, random_state=42)
cv_split = cv.split(epochs_data_train)

# Assemble a classifier
lda = LinearDiscriminantAnalysis()
csp = CSP(n_components=4, reg=None, log=True, norm_trace=False)

# Use scikit-learn Pipeline with cross_val_score function
clf = Pipeline([('CSP', csp), ('LDA', lda)])
scores = cross_val_score(clf, epochs_data_train, labels, cv=cv, n_jobs=None)

# Printing the results
class_balance = np.mean(labels == labels[0])
class_balance = max(class_balance, 1. - class_balance)
print("Classification accuracy: %f / Chance level: %f" % (np.mean(scores),
                                                          class_balance))

# plot CSP patterns estimated on full data for visualization
csp.fit_transform(epochs_data, labels)

csp.plot_patterns(epochs.info, ch_type='eeg', units='Patterns (AU)', size=1.5)

# %%
# Look at performance over time

sfreq = raw.info['sfreq']
w_length = int(sfreq * 0.5)   # running classifier: window length
w_step = int(sfreq * 0.1)  # running classifier: window step size
w_start = np.arange(0, epochs_data.shape[2] - w_length, w_step)

scores_windows = []

for train_idx, test_idx in cv_split:
    y_train, y_test = labels[train_idx], labels[test_idx]

    X_train = csp.fit_transform(epochs_data_train[train_idx], y_train)
    X_test = csp.transform(epochs_data_train[test_idx])

    # fit classifier
    lda.fit(X_train, y_train)

    # running classifier: test classifier on sliding window
    score_this_window = []
    for n in w_start:
        X_test = csp.transform(epochs_data[test_idx][:, :, n:(n + w_length)])
        score_this_window.append(lda.score(X_test, y_test))
    scores_windows.append(score_this_window)

# Plot scores over time
w_times = (w_start + w_length / 2.) / sfreq + epochs.tmin

plt.figure()
plt.plot(w_times, np.mean(scores_windows, 0), label='Score')
plt.axvline(0, linestyle='--', color='k', label='Onset')
plt.axhline(0.5, linestyle='-', color='k', label='Chance')
plt.xlabel('time (s)')
plt.ylabel('classification accuracy')
plt.title('Classification score over time')
plt.legend(loc='lower right')
plt.show()

##############################################################################
# References
# ----------
# .. footbibliography::
