import mne
from mne.preprocessing import maxwell_filter

data_path = mne.datasets.sample.data_path()

"""
Explanation: Artifact correction with Maxwell filter
This tutorial shows how to clean MEG data with Maxwell filtering.
Maxwell filtering in MNE can be used to suppress sources of external
intereference and compensate for subject head movements.
See maxwell for more details.
End of explanation
"""


raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
ctc_fname = data_path + '/SSS/ct_sparse_mgh.fif'
fine_cal_fname = data_path + '/SSS/sss_cal_mgh.dat'

"""
Explanation: Set parameters
End of explanation
"""


raw = mne.io.read_raw_fif(raw_fname)
raw.info['bads'] = ['MEG 2443', 'EEG 053', 'MEG 1032', 'MEG 2313']  # set bads
# Here we don't use tSSS (set st_duration) because MGH data is very clean
raw_sss = maxwell_filter(raw, cross_talk=ctc_fname, calibration=fine_cal_fname)

"""
Explanation: Preprocess with Maxwell filtering
End of explanation
"""


tmin, tmax = -0.2, 0.5
event_id = {'Auditory/Left': 1}
events = mne.find_events(raw, 'STI 014')
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,
                       include=[], exclude='bads')
for r, kind in zip((raw, raw_sss), ('Raw data', 'Maxwell filtered data')):
    epochs = mne.Epochs(r, events, event_id, tmin, tmax, picks=picks,
                        baseline=(None, 0), reject=dict(eog=150e-6))
    evoked = epochs.average()
    evoked.plot(window_title=kind, ylim=dict(grad=(-200, 250),
                                             mag=(-600, 700)), time_unit='s')

"""
Explanation: Select events to extract epochs from, pick M/EEG channels, and plot evoked
End of explanation
"""


