# Authors: Denis A. Engemann <denis.engemann@gmail.com>
#          Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
data_path = mne.datasets.somato.data_path()
raw_fname = data_path + '/MEG/somato/sef_raw_sss.fif'
event_id, tmin, tmax = 1, -1., 3.

# Setup for reading the raw data
raw = mne.io.Raw(raw_fname, preload=True)
raw.filter(1, 30, method='iir')

baseline = None
events = mne.find_events(raw, stim_channel='STI 014')

# picks MEG gradiometers
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True, stim=False)

epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=baseline, reject=dict(grad=4000e-13, eog=350e-6),
                    preload=True)
###############################################################################
# Compute covariance using automated regularization and show whitening
noise_covs = mne.cov.compute_covariance(epochs[:30], tmax=0, method='auto',
                                        return_estimators=True)

evoked = epochs.average()
evoked.plot()  # plot evoked response
evoked.plot_white(noise_covs)  # compare estimators