# Author: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk>
#
# License: BSD (3-clause)

import mne
from mne.datasets import sample
from mne.minimum_norm.resolution_matrix import make_inverse_resolution_matrix
from mne.minimum_norm.spatial_resolution import resolution_metrics

print(__doc__)

data_path = sample.data_path()
subjects_dir = data_path + '/subjects/'
fname_fwd_emeg = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif'
fname_evo = data_path + '/MEG/sample/sample_audvis-ave.fif'

# read forward solution with EEG and MEG
forward_emeg = mne.read_forward_solution(fname_fwd_emeg)
# forward operator with fixed source orientations
forward_emeg = mne.convert_forward_solution(forward_emeg, surf_ori=True,
                                            force_fixed=True)

# create a forward solution with MEG only
forward_meg = mne.pick_types_forward(forward_emeg, meg=True, eeg=False)

# noise covariance matrix
noise_cov = mne.read_cov(fname_cov)

# evoked data for info
evoked = mne.read_evokeds(fname_evo, 0)

# make inverse operator from forward solution for MEG and EEGMEG
inv_emeg = mne.minimum_norm.make_inverse_operator(
    info=evoked.info, forward=forward_emeg, noise_cov=noise_cov, loose=0.,
    depth=None)

inv_meg = mne.minimum_norm.make_inverse_operator(
    info=evoked.info, forward=forward_meg, noise_cov=noise_cov, loose=0.,
    depth=None)

# regularisation parameter
snr = 3.0
lambda2 = 1.0 / snr ** 2

"""
Explanation: Compute spatial resolution metrics to compare MEG with EEG+MEG
Compute peak localisation error and spatial deviation for the point-spread
functions of dSPM and MNE. Plot their distributions and difference of
distributions.
This example mimics some results from [1]_, namely Figure 3 (peak localisation
error for PSFs, L2-MNE vs dSPM) and Figure 4 (spatial deviation for PSFs,
L2-MNE vs dSPM). It shows that combining MEG with EEG reduces the
point-spread function and increases the spatial resolution of source imaging,
especially for deeper sources.
End of explanation
"""


rm_emeg = make_inverse_resolution_matrix(forward_emeg, inv_emeg,
                                         method='MNE', lambda2=lambda2)
ple_psf_emeg = resolution_metrics(rm_emeg, inv_emeg['src'],
                                  function='psf', metric='peak_err')
sd_psf_emeg = resolution_metrics(rm_emeg, inv_emeg['src'],
                                 function='psf', metric='sd_ext')
del rm_emeg

"""
Explanation: EEGMEG
Compute resolution matrices, localization error, and spatial deviations
for MNE:
End of explanation
"""


rm_meg = make_inverse_resolution_matrix(forward_meg, inv_meg,
                                        method='MNE', lambda2=lambda2)
ple_psf_meg = resolution_metrics(rm_meg, inv_meg['src'],
                                 function='psf', metric='peak_err')
sd_psf_meg = resolution_metrics(rm_meg, inv_meg['src'],
                                function='psf', metric='sd_ext')
del rm_meg

"""
Explanation: MEG
Do the same for MEG:
End of explanation
"""


brain_ple_emeg = ple_psf_emeg.plot('sample', 'inflated', 'lh',
                                   subjects_dir=subjects_dir, figure=1,
                                   clim=dict(kind='value', lims=(0, 2, 4)))

brain_ple_emeg.add_text(0.1, 0.9, 'PLE PSF EMEG', 'title', font_size=16)

brain_ple_meg = ple_psf_meg.plot('sample', 'inflated', 'lh',
                                 subjects_dir=subjects_dir, figure=2,
                                 clim=dict(kind='value', lims=(0, 2, 4)))

brain_ple_meg.add_text(0.1, 0.9, 'PLE PSF MEG', 'title', font_size=16)

# Subtract the two distributions and plot this difference
diff_ple = ple_psf_emeg - ple_psf_meg

brain_ple_diff = diff_ple.plot('sample', 'inflated', 'lh',
                               subjects_dir=subjects_dir, figure=3,
                               clim=dict(kind='value', pos_lims=(0., .5, 1.)),
                               smoothing_steps=20)

brain_ple_diff.add_text(0.1, 0.9, 'PLE EMEG-MEG', 'title', font_size=16)

"""
Explanation: Visualization
Look at peak localisation error (PLE) across the whole cortex for PSF:
End of explanation
"""


brain_sd_emeg = sd_psf_emeg.plot('sample', 'inflated', 'lh',
                                 subjects_dir=subjects_dir, figure=4,
                                 clim=dict(kind='value', lims=(0, 2, 4)))

brain_sd_emeg.add_text(0.1, 0.9, 'SD PSF EMEG', 'title', font_size=16)

brain_sd_meg = sd_psf_meg.plot('sample', 'inflated', 'lh',
                               subjects_dir=subjects_dir, figure=5,
                               clim=dict(kind='value', lims=(0, 2, 4)))

brain_sd_meg.add_text(0.1, 0.9, 'SD PSF MEG', 'title', font_size=16)

# Subtract the two distributions and plot this difference
diff_sd = sd_psf_emeg - sd_psf_meg

brain_sd_diff = diff_sd.plot('sample', 'inflated', 'lh',
                             subjects_dir=subjects_dir, figure=6,
                             clim=dict(kind='value', pos_lims=(0., .5, 1.)),
                             smoothing_steps=20)

brain_sd_diff.add_text(0.1, 0.9, 'SD EMEG-MEG', 'title', font_size=16)

"""
Explanation: These plots show that with respect to peak localization error, adding EEG to
MEG does not bring much benefit. Next let's visualise spatial deviation (SD)
across the whole cortex for PSF:
End of explanation
"""


