def test_random_parcellation():
"""Test generation of random cortical parcellation."""
hemi = 'both'
n_parcel = 50
surface = 'sphere.reg'
subject = 'sample_ds'
rng = np.random.RandomState(0)
# Parcellation
labels = random_parcellation(subject,
n_parcel,
hemi,
subjects_dir,
surface=surface,
random_state=rng)
# test number of labels
assert_equal(len(labels), n_parcel)
if hemi == 'both':
hemi = ['lh', 'rh']
hemis = np.atleast_1d(hemi)
for hemi in set(hemis):
vertices_total = []
for label in labels:
if label.hemi == hemi:
# test that labels are not empty
assert (len(label.vertices) > 0)
# vertices of hemi covered by labels
vertices_total = np.append(vertices_total, label.vertices)
# test that labels don't intersect
assert_equal(len(np.unique(vertices_total)), len(vertices_total))
surf_fname = op.join(subjects_dir, subject, 'surf',
hemi + '.' + surface)
vert, _ = read_surface(surf_fname)
# Test that labels cover whole surface
assert_array_equal(np.sort(vertices_total), np.arange(len(vert)))
def test_random_parcellation():
"""Test generation of random cortical parcellation."""
hemi = 'both'
n_parcel = 50
surface = 'sphere.reg'
subject = 'sample_ds'
rng = np.random.RandomState(0)
# Parcellation
labels = random_parcellation(subject, n_parcel, hemi, subjects_dir,
surface=surface, random_state=rng)
# test number of labels
assert_equal(len(labels), n_parcel)
if hemi == 'both':
hemi = ['lh', 'rh']
hemis = np.atleast_1d(hemi)
for hemi in set(hemis):
vertices_total = []
for label in labels:
if label.hemi == hemi:
# test that labels are not empty
assert (len(label.vertices) > 0)
# vertices of hemi covered by labels
vertices_total = np.append(vertices_total, label.vertices)
# test that labels don't intersect
assert_equal(len(np.unique(vertices_total)), len(vertices_total))
surf_fname = op.join(subjects_dir, subject, 'surf', hemi + '.' +
surface)
vert, _ = read_surface(surf_fname)
# Test that labels cover whole surface
assert_array_equal(np.sort(vertices_total), np.arange(len(vert)))
def make_random_parcellation(path_annot,
n,
hemi,
subjects_dir,
random_state,
subject,
remove_corpus_callosum=False):
parcels = random_parcellation(subject,
n,
hemi,
subjects_dir=subjects_dir,
surface='white',
random_state=random_state)
if remove_corpus_callosum:
corpus_callosum = find_corpus_callosum(subject,
subjects_dir,
hemi=hemi)
# instead of removing all the overlapping parcels we will remove only
# the vertices which belong to corpus callosum
to_remove = []
for idx, parcel in enumerate(parcels):
cc_free = set(parcel.vertices) - set(corpus_callosum.vertices)
parcel.vertices = np.array(list(cc_free))
if len(parcel.vertices) == 0:
to_remove.append(idx)
# remove all the parcels which after removing corpus callosum now
# have 0 indices
[parcels.pop(idc) for idc in to_remove[::-1]]
write_labels_to_annot(parcels,
subjects_dir=subjects_dir,
subject=subject,
annot_fname=path_annot,
overwrite=True)