# idx='amplitude')
###############################################################################
# Show the evoked response and the residual for gradiometers
ylim = dict(grad=[-120, 120])
evoked.pick_types(meg='grad', exclude='bads')
evoked.plot(titles=dict(grad='Evoked Response: Gradiometers'), ylim=ylim,
proj=True, time_unit='s')
residual.pick_types(meg='grad', exclude='bads')
residual.plot(titles=dict(grad='Residuals: Gradiometers'), ylim=ylim,
proj=True, time_unit='s')
###############################################################################
# Generate stc from dipoles
stc = make_stc_from_dipoles(dipoles, forward['src'])
###############################################################################
# View in 2D and 3D ("glass" brain like 3D plot)
plot_sparse_source_estimates(forward['src'], stc, bgcolor=(1, 1, 1),
opacity=0.1, fig_name="TF-MxNE (cond %s)"
% condition, modes=['sphere'], scale_factors=[1.])
time_label = 'TF-MxNE time=%0.2f ms'
clim = dict(kind='value', lims=[10e-9, 15e-9, 20e-9])
brain = stc.plot('sample', 'inflated', 'rh', views='medial',
clim=clim, time_label=time_label, smoothing_steps=5,
subjects_dir=subjects_dir, initial_time=150, time_unit='ms')
brain.add_label("V1", color="yellow", scalar_thresh=.5, borders=True)
brain.add_label("V2", color="red", scalar_thresh=.5, borders=True)
# for dip in dipoles:
# plot_dipole_locations(dip, forward['mri_head_t'], 'sample',
# subjects_dir=subjects_dir, mode='orthoview',
# idx='amplitude')
###############################################################################
# Plot residual
ylim = dict(eeg=[-10, 10], grad=[-400, 400], mag=[-600, 600])
evoked.pick_types(meg=True, eeg=True, exclude='bads')
evoked.plot(ylim=ylim, proj=True, time_unit='s')
residual.pick_types(meg=True, eeg=True, exclude='bads')
residual.plot(ylim=ylim, proj=True, time_unit='s')
###############################################################################
# Generate stc from dipoles
stc = make_stc_from_dipoles(dipoles, forward['src'])
###############################################################################
# View in 2D and 3D ("glass" brain like 3D plot)
solver = "MxNE" if n_mxne_iter == 1 else "irMxNE"
plot_sparse_source_estimates(forward['src'], stc, bgcolor=(1, 1, 1),
fig_name="%s (cond %s)" % (solver, condition),
opacity=0.1)
###############################################################################
# Morph onto fsaverage brain and view
stc_fsaverage = stc.morph(subject_from='sample', subject_to='fsaverage',
grade=None, sparse=True, subjects_dir=subjects_dir)
src_fsaverage_fname = subjects_dir + '/fsaverage/bem/fsaverage-ico-5-src.fif'
src_fsaverage = mne.read_source_spaces(src_fsaverage_fname)
