# Authors: Jean-Remi King <jeanremi.king@gmail.com>
#          Asish Panda <asishrocks95@gmail.com>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt

import mne
from mne.datasets import sample
from mne.decoding import UnsupervisedSpatialFilter

from sklearn.decomposition import PCA, FastICA

print(__doc__)

# Preprocess data
data_path = sample.data_path()

# Load and filter data, set up epochs
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
tmin, tmax = -0.1, 0.3
event_id = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4)

raw = mne.io.read_raw_fif(raw_fname, preload=True)
raw.filter(1, 20, fir_design='firwin')
events = mne.read_events(event_fname)

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

epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False,
                    picks=picks, baseline=None, preload=True,
                    verbose=False)

X = epochs.get_data()

"""
Explanation: Analysis of evoked response using ICA and PCA reduction techniques
This example computes PCA and ICA of evoked or epochs data. Then the
PCA / ICA components, a.k.a. spatial filters, are used to transform
the channel data to new sources / virtual channels. The output is
visualized on the average of all the epochs.
End of explanation
"""


pca = UnsupervisedSpatialFilter(PCA(30), average=False)
pca_data = pca.fit_transform(X)
ev = mne.EvokedArray(np.mean(pca_data, axis=0),
                     mne.create_info(30, epochs.info['sfreq'],
                                     ch_types='eeg'), tmin=tmin)
ev.plot(show=False, window_title="PCA", time_unit='s')

"""
Explanation: Transform data with PCA computed on the average ie evoked response
End of explanation
"""


ica = UnsupervisedSpatialFilter(FastICA(30), average=False)
ica_data = ica.fit_transform(X)
ev1 = mne.EvokedArray(np.mean(ica_data, axis=0),
                      mne.create_info(30, epochs.info['sfreq'],
                                      ch_types='eeg'), tmin=tmin)
ev1.plot(show=False, window_title='ICA', time_unit='s')

plt.show()

"""
Explanation: Transform data with ICA computed on the raw epochs (no averaging)
End of explanation
"""


