def _create_testing_brain(hemi, surf='inflated', src='surface',
size=300, n_time=5, diverging=False, **kwargs):
assert src in ('surface', 'vector', 'mixed', 'volume')
meth = 'plot'
if src in ('surface', 'mixed'):
sample_src = read_source_spaces(src_fname)
klass = MixedSourceEstimate if src == 'mixed' else SourceEstimate
if src == 'vector':
fwd = read_forward_solution(fname_fwd)
fwd = pick_types_forward(fwd, meg=True, eeg=False)
evoked = read_evokeds(fname_evoked, baseline=(None, 0))[0]
noise_cov = read_cov(fname_cov)
free = make_inverse_operator(
evoked.info, fwd, noise_cov, loose=1.)
stc = apply_inverse(evoked, free, pick_ori='vector')
return stc.plot(
subject=subject_id, hemi=hemi, size=size,
subjects_dir=subjects_dir, colormap='auto',
**kwargs)
if src in ('volume', 'mixed'):
vol_src = setup_volume_source_space(
subject_id, 7., mri='aseg.mgz',
volume_label='Left-Cerebellum-Cortex',
subjects_dir=subjects_dir, add_interpolator=False)
assert len(vol_src) == 1
assert vol_src[0]['nuse'] == 150
if src == 'mixed':
sample_src = sample_src + vol_src
else:
sample_src = vol_src
klass = VolSourceEstimate
meth = 'plot_3d'
assert sample_src.kind == src
# dense version
rng = np.random.RandomState(0)
vertices = [s['vertno'] for s in sample_src]
n_verts = sum(len(v) for v in vertices)
stc_data = np.zeros((n_verts * n_time))
stc_size = stc_data.size
stc_data[(rng.rand(stc_size // 20) * stc_size).astype(int)] = \
rng.rand(stc_data.size // 20)
stc_data.shape = (n_verts, n_time)
if diverging:
stc_data -= 0.5
stc = klass(stc_data, vertices, 1, 1)
clim = dict(kind='value', lims=[0.1, 0.2, 0.3])
if diverging:
clim['pos_lims'] = clim.pop('lims')
brain_data = getattr(stc, meth)(
subject=subject_id, hemi=hemi, surface=surf, size=size,
subjects_dir=subjects_dir, colormap='auto',
clim=clim, src=sample_src,
**kwargs)
return brain_data
def _create_testing_brain(hemi,
surf='inflated',
src='surface',
size=300,
n_time=5,
diverging=False,
**kwargs):
assert src in ('surface', 'mixed', 'volume')
meth = 'plot'
if src in ('surface', 'mixed'):
sample_src = read_source_spaces(src_fname)
klass = MixedSourceEstimate if src == 'mixed' else SourceEstimate
if src in ('volume', 'mixed'):
vol_src = setup_volume_source_space(
subject_id,
7.,
mri='aseg.mgz',
volume_label='Left-Cerebellum-Cortex',
subjects_dir=subjects_dir,
add_interpolator=False)
assert len(vol_src) == 1
assert vol_src[0]['nuse'] == 150
if src == 'mixed':
sample_src = sample_src + vol_src
else:
sample_src = vol_src
klass = VolSourceEstimate
meth = 'plot_3d'
assert sample_src.kind == src
# dense version
rng = np.random.RandomState(0)
vertices = [s['vertno'] for s in sample_src]
n_verts = sum(len(v) for v in vertices)
stc_data = np.zeros((n_verts * n_time))
stc_size = stc_data.size
stc_data[(rng.rand(stc_size // 20) * stc_size).astype(int)] = \
rng.rand(stc_data.size // 20)
stc_data.shape = (n_verts, n_time)
if diverging:
stc_data -= 0.5
stc = klass(stc_data, vertices, 1, 1)
clim = dict(kind='value', lims=[0.1, 0.2, 0.3])
if diverging:
clim['pos_lims'] = clim.pop('lims')
brain_data = getattr(stc, meth)(subject=subject_id,
hemi=hemi,
surface=surf,
size=size,
subjects_dir=subjects_dir,
colormap='auto',
clim=clim,
src=sample_src,
**kwargs)
return brain_data
def run():
args = sys.argv
if len(args) <= 1:
print 'Usage: run_anatomy_tutorial.sh <sample data directory>'
return
sample_dir = args[1]
subjects_dir = join(sample_dir, 'subjects')
meg_dir = join(sample_dir, 'MEG', 'sample')
os.environ['SUBJECTS_DIR'] = subjects_dir
os.environ['MEG_DIR'] = meg_dir
subject = 'sample'
bem = join(subjects_dir, subject, 'bem', 'sample-5120-bem-sol.fif')
mri = join(subjects_dir, subject, 'mri', 'T1.mgz')
fname = join(subjects_dir, subject, 'bem', 'volume-7mm-src.fif')
src = setup_volume_source_space(subject,
fname=fname,
pos=7,
mri=mri,
bem=bem,
overwrite=True,
subjects_dir=subjects_dir)
###############################################################################
# Compute forward solution a.k.a. lead field
raw = mne.io.Raw(join(meg_dir, 'sample_audvis_raw.fif'))
fwd_fname = join(meg_dir, 'sample_audvis-meg-vol-7-fwd.fif')
trans = join(meg_dir, 'sample_audvis_raw-trans.fif')
# for MEG only
fwd = make_forward_solution(raw.info,
trans=trans,
src=src,
bem=bem,
fname=fwd_fname,
meg=True,
eeg=False,
overwrite=True)
# Make a sensitivity map
smap = mne.sensitivity_map(fwd, ch_type='grad', mode='free')
smap.save(join(meg_dir, 'sample_audvis-grad-vol-7-fwd-sensmap'), ftype='w')
###############################################################################
# Compute MNE inverse operators
#
# Note: The MEG/EEG forward solution could be used for all
#
noise_cov = mne.read_cov(join(meg_dir, 'sample_audvis-cov.fif'))
inv = make_inverse_operator(raw.info, fwd, noise_cov)
fname = join(meg_dir, 'sample_audvis-meg-vol-7-meg-inv.fif')
write_inverse_operator(fname, inv)
def run():
args = sys.argv
if len(args) <= 1:
print 'Usage: run_anatomy_tutorial.sh <sample data directory>'
return
sample_dir = args[1]
subjects_dir = join(sample_dir, 'subjects')
meg_dir = join(sample_dir, 'MEG', 'sample')
os.environ['SUBJECTS_DIR'] = subjects_dir
os.environ['MEG_DIR'] = meg_dir
subject = 'sample'
bem = join(subjects_dir, subject, 'bem', 'sample-5120-bem-sol.fif')
mri = join(subjects_dir, subject, 'mri', 'T1.mgz')
fname = join(subjects_dir, subject, 'bem', 'volume-7mm-src.fif')
src = setup_volume_source_space(subject, fname=fname, pos=7, mri=mri,
bem=bem, overwrite=True,
subjects_dir=subjects_dir)
###############################################################################
# Compute forward solution a.k.a. lead field
raw = mne.io.Raw(join(meg_dir, 'sample_audvis_raw.fif'))
fwd_fname = join(meg_dir, 'sample_audvis-meg-vol-7-fwd.fif')
trans = join(meg_dir, 'sample_audvis_raw-trans.fif')
# for MEG only
fwd = make_forward_solution(raw.info, trans=trans, src=src, bem=bem,
fname=fwd_fname, meg=True, eeg=False,
overwrite=True)
# Make a sensitivity map
smap = mne.sensitivity_map(fwd, ch_type='grad', mode='free')
smap.save(join(meg_dir, 'sample_audvis-grad-vol-7-fwd-sensmap'), ftype='w')
###############################################################################
# Compute MNE inverse operators
#
# Note: The MEG/EEG forward solution could be used for all
#
noise_cov = mne.read_cov(join(meg_dir, 'sample_audvis-cov.fif'))
inv = make_inverse_operator(raw.info, fwd, noise_cov)
fname = join(meg_dir, 'sample_audvis-meg-vol-7-meg-inv.fif')
write_inverse_operator(fname, inv)
