"""
==============================================
Maxwell filter data with movement compensation
==============================================

Demonstrate movement compensation on simulated data. The simulated data
contains bilateral activation of auditory cortices, repeated over 14
different head rotations (head center held fixed). See the following for
details:

    https://github.com/mne-tools/mne-misc-data/blob/master/movement/simulate.py

"""
# Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)

from os import path as op

import mne
from mne.preprocessing import maxwell_filter

print(__doc__)

data_path = op.join(mne.datasets.misc.data_path(verbose=True), 'movement')

head_pos = mne.chpi.read_head_pos(op.join(data_path, 'simulated_quats.pos'))
raw = mne.io.read_raw_fif(op.join(data_path, 'simulated_movement_raw.fif'))
raw_stat = mne.io.read_raw_fif(op.join(data_path,
                                       'simulated_stationary_raw.fif'))

###############################################################################
# Visualize the "subject" head movements. By providing the measurement
# information, the distance to the nearest sensor in each direction
# (e.g., left/right for the X direction, forward/backward for Y) can
# be shown in blue, and the destination (if given) shown in red.

mne.viz.plot_head_positions(
    head_pos, mode='traces', destination=raw.info['dev_head_t'], info=raw.info)

###############################################################################
# This can also be visualized using a quiver.

mne.viz.plot_head_positions(
    head_pos, mode='field', destination=raw.info['dev_head_t'], info=raw.info)

###############################################################################
# Process our simulated raw data (taking into account head movements).

# extract our resulting events
events = mne.find_events(raw, stim_channel='STI 014')
events[:, 2] = 1
raw.plot(events=events)

topo_kwargs = dict(times=[0, 0.1, 0.2], ch_type='mag', vmin=-500, vmax=500,
                   time_unit='s')

###############################################################################
# First, take the average of stationary data (bilateral auditory patterns).
evoked_stat = mne.Epochs(raw_stat, events, 1, -0.2, 0.8).average()
evoked_stat.plot_topomap(title='Stationary', **topo_kwargs)

###############################################################################
# Second, take a naive average, which averages across epochs that have been
# simulated to have different head positions and orientations, thereby
# spatially smearing the activity.
evoked = mne.Epochs(raw, events, 1, -0.2, 0.8).average()
evoked.plot_topomap(title='Moving: naive average', **topo_kwargs)

###############################################################################
# Third, use raw movement compensation (restores pattern).
raw_sss = maxwell_filter(raw, head_pos=head_pos)
evoked_raw_mc = mne.Epochs(raw_sss, events, 1, -0.2, 0.8).average()
evoked_raw_mc.plot_topomap(title='Moving: movement compensated', **topo_kwargs)
