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"""
==============================================
Estimate covariance matrix from a raw FIF file
==============================================
"""
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
print(__doc__)
import mne
from mne import io
from mne.datasets import sample
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
raw = io.Raw(fname)
include = [] # or stim channels ['STI 014']
raw.info['bads'] += ['EEG 053'] # bads + 1 more
# pick EEG channels
picks = mne.pick_types(raw.info, meg=True, eeg=True, stim=False, eog=True,
include=include, exclude='bads')
# setup rejection
reject = dict(eeg=80e-6, eog=150e-6)
# Compute the covariance from the raw data
cov = mne.compute_raw_data_covariance(raw, picks=picks, reject=reject)
print(cov)
###############################################################################
# Show covariance
fig_cov, fig_svd = mne.viz.plot_cov(cov, raw.info, colorbar=True, proj=True)
# try setting proj to False to see the effect
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