def test_fetch_parcellations(tmpdir):
"""Test fetching parcellations."""
this_subjects_dir = str(tmpdir)
os.mkdir(op.join(this_subjects_dir, 'fsaverage'))
os.mkdir(op.join(this_subjects_dir, 'fsaverage', 'label'))
os.mkdir(op.join(this_subjects_dir, 'fsaverage', 'surf'))
for hemi in ('lh', 'rh'):
shutil.copyfile(
op.join(subjects_dir, 'fsaverage', 'surf', '%s.white' % hemi),
op.join(this_subjects_dir, 'fsaverage', 'surf', '%s.white' % hemi))
# speed up by prenteding we have one of them
with open(op.join(this_subjects_dir, 'fsaverage', 'label',
'lh.aparc_sub.annot'), 'wb'):
pass
datasets.fetch_aparc_sub_parcellation(subjects_dir=this_subjects_dir)
with ArgvSetter(('--accept-hcpmmp-license',)):
datasets.fetch_hcp_mmp_parcellation(subjects_dir=this_subjects_dir)
for hemi in ('lh', 'rh'):
assert op.isfile(op.join(this_subjects_dir, 'fsaverage', 'label',
'%s.aparc_sub.annot' % hemi))
# test our annot round-trips here
kwargs = dict(subject='fsaverage', hemi='both', sort=False,
subjects_dir=this_subjects_dir)
labels = read_labels_from_annot(parc='HCPMMP1', **kwargs)
write_labels_to_annot(
labels, parc='HCPMMP1_round',
table_name='./left.fsaverage164.label.gii', **kwargs)
orig = op.join(this_subjects_dir, 'fsaverage', 'label', 'lh.HCPMMP1.annot')
first = hashfunc(orig)
new = orig[:-6] + '_round.annot'
second = hashfunc(new)
assert first == second
def test_fetch_parcellations(tmpdir):
"""Test fetching parcellations."""
this_subjects_dir = str(tmpdir)
os.mkdir(op.join(this_subjects_dir, 'fsaverage'))
os.mkdir(op.join(this_subjects_dir, 'fsaverage', 'label'))
os.mkdir(op.join(this_subjects_dir, 'fsaverage', 'surf'))
for hemi in ('lh', 'rh'):
shutil.copyfile(
op.join(subjects_dir, 'fsaverage', 'surf', '%s.white' % hemi),
op.join(this_subjects_dir, 'fsaverage', 'surf', '%s.white' % hemi))
# speed up by prenteding we have one of them
with open(
op.join(this_subjects_dir, 'fsaverage', 'label',
'lh.aparc_sub.annot'), 'wb'):
pass
datasets.fetch_aparc_sub_parcellation(subjects_dir=this_subjects_dir)
try:
sys.argv.append('--accept-hcpmmp-license')
datasets.fetch_hcp_mmp_parcellation(subjects_dir=this_subjects_dir)
finally:
sys.argv.pop(-1)
for hemi in ('lh', 'rh'):
assert op.isfile(
op.join(this_subjects_dir, 'fsaverage', 'label',
'%s.aparc_sub.annot' % hemi))
def test_fetch_parcellations(tmpdir):
"""Test fetching parcellations."""
this_subjects_dir = str(tmpdir)
os.mkdir(op.join(this_subjects_dir, 'fsaverage'))
os.mkdir(op.join(this_subjects_dir, 'fsaverage', 'label'))
os.mkdir(op.join(this_subjects_dir, 'fsaverage', 'surf'))
for hemi in ('lh', 'rh'):
shutil.copyfile(
op.join(subjects_dir, 'fsaverage', 'surf', '%s.white' % hemi),
op.join(this_subjects_dir, 'fsaverage', 'surf', '%s.white' % hemi))
# speed up by prenteding we have one of them
with open(op.join(this_subjects_dir, 'fsaverage', 'label',
'lh.aparc_sub.annot'), 'wb'):
pass
datasets.fetch_aparc_sub_parcellation(subjects_dir=this_subjects_dir)
with ArgvSetter(('--accept-hcpmmp-license',)):
datasets.fetch_hcp_mmp_parcellation(subjects_dir=this_subjects_dir)
for hemi in ('lh', 'rh'):
assert op.isfile(op.join(this_subjects_dir, 'fsaverage', 'label',
'%s.aparc_sub.annot' % hemi))
import numpy as np import matplotlib.pyplot as plt import mne from mne.datasets import sample, fetch_hcp_mmp_parcellation from mne.minimum_norm import apply_inverse, read_inverse_operator from mne import read_evokeds data_path = sample.data_path() sample_dir = op.join(data_path, 'MEG', 'sample') subjects_dir = op.join(data_path, 'subjects') fname_evoked = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif') fname_stc = op.join(sample_dir, 'sample_audvis-meg') fetch_hcp_mmp_parcellation(subjects_dir) # %% # Then, we read the stc from file. stc = mne.read_source_estimate(fname_stc, subject='sample') # %% # This is a :class:`SourceEstimate <mne.SourceEstimate>` object. print(stc) # %% # The SourceEstimate object is in fact a *surface* source estimate. MNE also # supports volume-based source estimates but more on that later. # # We can plot the source estimate using the # :func:`stc.plot <mne.SourceEstimate.plot>` just as in other MNE