affected_idx = raw.ch_names.index('MEG 1531')
# plot the component that correlates most with the ECG
pl.figure()
pl.plot(times, data[affected_idx])
pl.title('Affected channel MEG 1531 before cleaning.')
y0, y1 = pl.ylim()
# plot the component that correlates most with the ECG
pl.figure()
pl.plot(times, ica_data[affected_idx])
pl.title('Affected channel MEG 1531 after cleaning.')
pl.ylim(y0, y1)
pl.show()
###############################################################################
# Export ICA as raw for subsequent processing steps in ICA space.
from mne.layouts import make_grid_layout
ica_raw = ica.export_sources(raw, start=start, stop=stop, picks=None)
print ica_raw.ch_names
ica_lout = make_grid_layout(ica_raw.info)
# Uncomment the following two lines to save sources and layout.
# ica_raw.save('ica_raw.fif')
# ica_lout.save(os.path.join(os.environ['HOME'], '.mne/lout/ica.lout'))
