def test_source_space():
"Test SourceSpace dimension"
data_dir = mne.datasets.sample.data_path()
subjects_dir = os.path.join(data_dir, 'subjects')
annot_path = os.path.join(subjects_dir, '%s', 'label', '%s.%s.annot')
for subject in ['fsaverage', 'sample']:
mne_src = datasets._mne_source_space(subject, 'ico-4', subjects_dir)
vertices = [mne_src[0]['vertno'], mne_src[1]['vertno']]
ss = SourceSpace(vertices, subject, 'ico-4', subjects_dir)
# labels
for hemi_vertices, hemi in zip(ss.vertices, ('lh', 'rh')):
labels, _, names = read_annot(annot_path %
(subject, hemi, 'aparc'))
start = 0 if hemi == 'lh' else len(ss.lh_vertices)
hemi_tag = '-' + hemi
for i, v in enumerate(hemi_vertices, start):
label = labels[v]
if label == -1:
assert ss.parc[i] == 'unknown' + hemi_tag
else:
assert ss.parc[i] == names[label].decode() + hemi_tag
# connectivity
conn = ss.connectivity()
mne_conn = mne.spatial_src_connectivity(mne_src)
assert_array_equal(conn, _matrix_graph(mne_conn))
# sub-space connectivity
sssub = ss[ss._array_index('superiortemporal-rh')]
ss2 = SourceSpace(vertices, subject, 'ico-4', subjects_dir, 'aparc')
ss2sub = ss2[ss2._array_index('superiortemporal-rh')]
assert_array_equal(sssub.connectivity(), ss2sub.connectivity())
def test_source_space():
"Test SourceSpace dimension"
data_dir = mne.datasets.sample.data_path()
subjects_dir = os.path.join(data_dir, 'subjects')
annot_path = os.path.join(subjects_dir, '%s', 'label', '%s.%s.annot')
for subject in ['fsaverage', 'sample']:
mne_src = datasets._mne_source_space(subject, 'ico-4', subjects_dir)
vertno = [mne_src[0]['vertno'], mne_src[1]['vertno']]
ss = SourceSpace(vertno, subject, 'ico-4', subjects_dir)
# labels
for hemi_vertices, hemi in izip(ss.vertno, ('lh', 'rh')):
labels, _, names = read_annot(annot_path % (subject, hemi, 'aparc'))
start = 0 if hemi == 'lh' else len(ss.lh_vertno)
hemi_tag = '-' + hemi
for i, v in enumerate(hemi_vertices, start):
label = labels[v]
if label == -1:
eq_(ss.parc[i], 'unknown' + hemi_tag)
else:
eq_(ss.parc[i], names[label] + hemi_tag)
# connectivity
conn = ss.connectivity()
mne_conn = mne.spatial_src_connectivity(mne_src)
assert_array_equal(conn, _matrix_graph(mne_conn))
# sub-space connectivity
sssub = ss[ss.dimindex('superiortemporal-rh')]
ss2 = SourceSpace(vertno, subject, 'ico-4', subjects_dir, 'aparc')
ss2sub = ss2[ss2.dimindex('superiortemporal-rh')]
assert_array_equal(sssub.connectivity(), ss2sub.connectivity())
def test_morphing():
mne.set_log_level('warning')
data_dir = mne.datasets.sample.data_path()
subjects_dir = os.path.join(data_dir, 'subjects')
sss = datasets._mne_source_space('fsaverage', 'ico-4', subjects_dir)
vertices_to = [sss[0]['vertno'], sss[1]['vertno']]
ds = datasets.get_mne_sample(-0.1,
0.1,
src='ico',
sub='index==0',
stc=True)
stc = ds['stc', 0]
morph_mat = mne.compute_morph_matrix('sample', 'fsaverage', stc.vertices,
vertices_to, None, subjects_dir)
ndvar = ds['src']
morphed_ndvar = morph_source_space(ndvar, 'fsaverage')
morphed_stc = mne.morph_data_precomputed('sample', 'fsaverage', stc,
vertices_to, morph_mat)
assert_array_equal(morphed_ndvar.x[0], morphed_stc.data)
morphed_stc_ndvar = load.fiff.stc_ndvar([morphed_stc],
'fsaverage',
'ico-4',
subjects_dir,
'dSPM',
False,
'src',
parc=None)
assert_dataobj_equal(morphed_ndvar, morphed_stc_ndvar)
def test_source_space():
"Test SourceSpace Dimension"
subject = 'fsaverage'
data_path = mne.datasets.sample.data_path()
mri_sdir = os.path.join(data_path, 'subjects')
mri_dir = os.path.join(mri_sdir, subject)
label_dir = os.path.join(mri_dir, 'label')
label_ba1 = mne.read_label(os.path.join(label_dir, 'lh.BA1.label'))
label_v1 = mne.read_label(os.path.join(label_dir, 'lh.V1.label'))
label_mt = mne.read_label(os.path.join(label_dir, 'lh.MT.label'))
label_ba1_v1 = label_ba1 + label_v1
label_v1_mt = label_v1 + label_mt
src = datasets._mne_source_space(subject, 'ico-5', mri_sdir)
source = SourceSpace.from_mne_source_spaces(src, 'ico-5', mri_sdir)
source_v1 = source[source.dimindex(label_v1)]
eq_(source_v1, SourceSpace.from_mne_source_spaces(src, 'ico-5', mri_sdir,
label=label_v1))
source_ba1_v1 = source[source.dimindex(label_ba1_v1)]
source_v1_mt = source[source.dimindex(label_v1_mt)]
source_v1_intersection = source_ba1_v1.intersect(source_v1_mt)
assert_source_space_equal(source_v1, source_v1_intersection)
# index from label
index = source.index_for_label(label_v1)
assert_array_equal(index.source[index.x].vertno[0],
np.intersect1d(source.lh_vertno, label_v1.vertices, 1))
# parcellation and cluster localization
parc = mne.read_labels_from_annot(subject, parc='aparc', subjects_dir=mri_sdir)
indexes = [source.index_for_label(label) for label in parc
if len(label) > 10]
x = np.vstack([index.x for index in indexes])
ds = source._cluster_properties(x)
for i in range(ds.n_cases):
eq_(ds[i, 'location'], parc[i].name)
# multiple labels
lingual_index = source.dimindex('lingual-lh')
cuneus_index = source.dimindex('cuneus-lh')
assert_array_equal(source.dimindex(('cuneus-lh', 'lingual-lh')),
np.logical_or(cuneus_index, lingual_index))
lingual_source = source[lingual_index]
cuneus_source = source[cuneus_index]
assert_raises(IndexError, lingual_source.dimindex, cuneus_source)
sub_source = source[source.dimindex(('cuneus-lh', 'lingual-lh'))]
eq_(sub_source[sub_source.dimindex('lingual-lh')], lingual_source)
eq_(sub_source[sub_source.dimindex('cuneus-lh')], cuneus_source)
eq_(len(sub_source), len(lingual_source) + len(cuneus_source))
# indexing
tgt = np.hstack(sub_source.vertno)
assert_array_equal([i for i in sub_source], tgt)
assert_array_equal([sub_source[i] for i in range(len(sub_source))], tgt)
# hemisphere indexing
lh = source.dimindex('lh')
source_lh = source[lh]
eq_(source_lh.dimindex('rh'), slice(0, 0))
eq_(source_lh.dimindex('lh'), slice(len(source_lh)))
def test_source_space():
"Test SourceSpace Dimension"
subject = 'fsaverage'
data_path = mne.datasets.sample.data_path()
mri_sdir = os.path.join(data_path, 'subjects')
mri_dir = os.path.join(mri_sdir, subject)
label_dir = os.path.join(mri_dir, 'label')
label_ba1 = mne.read_label(os.path.join(label_dir, 'lh.BA1.label'))
label_v1 = mne.read_label(os.path.join(label_dir, 'lh.V1.label'))
label_mt = mne.read_label(os.path.join(label_dir, 'lh.MT.label'))
label_ba1_v1 = label_ba1 + label_v1
label_v1_mt = label_v1 + label_mt
src = datasets._mne_source_space(subject, 'ico-5', mri_sdir)
source = SourceSpace.from_mne_source_spaces(src, 'ico-5', mri_sdir)
source_v1 = source[source.dimindex(label_v1)]
eq_(source_v1, SourceSpace.from_mne_source_spaces(src, 'ico-5', mri_sdir,
label=label_v1))
source_ba1_v1 = source[source.dimindex(label_ba1_v1)]
source_v1_mt = source[source.dimindex(label_v1_mt)]
source_v1_intersection = source_ba1_v1.intersect(source_v1_mt)
assert_source_space_equal(source_v1, source_v1_intersection)
# index from label
index = source.index_for_label(label_v1)
assert_array_equal(index.source[index.x].vertno[0],
np.intersect1d(source.lh_vertno, label_v1.vertices, 1))
# parcellation and cluster localization
parc = mne.read_labels_from_annot(subject, parc='aparc', subjects_dir=mri_sdir)
indexes = [source.index_for_label(label) for label in parc
if len(label) > 10]
x = np.vstack([index.x for index in indexes])
ds = source._cluster_properties(x)
for i in xrange(ds.n_cases):
eq_(ds[i, 'location'], parc[i].name)
# multiple labels
lingual_index = source.dimindex('lingual-lh')
cuneus_index = source.dimindex('cuneus-lh')
assert_array_equal(source.dimindex(('cuneus-lh', 'lingual-lh')),
np.logical_or(cuneus_index, lingual_index))
lingual_source = source[lingual_index]
cuneus_source = source[cuneus_index]
assert_raises(IndexError, lingual_source.dimindex, cuneus_source)
sub_source = source[source.dimindex(('cuneus-lh', 'lingual-lh'))]
eq_(sub_source[sub_source.dimindex('lingual-lh')], lingual_source)
eq_(sub_source[sub_source.dimindex('cuneus-lh')], cuneus_source)
eq_(len(sub_source), len(lingual_source) + len(cuneus_source))
# indexing
tgt = np.hstack(sub_source.vertno)
assert_array_equal([i for i in sub_source], tgt)
assert_array_equal([sub_source[i] for i in xrange(len(sub_source))], tgt)
# hemisphere indexing
lh = source.dimindex('lh')
source_lh = source[lh]
eq_(source_lh.dimindex('rh'), slice(0, 0))
eq_(source_lh.dimindex('lh'), slice(0, len(source_lh)))
def test_source_space():
"Test SourceSpace dimension"
for subject in ['fsaverage', 'sample']:
mne_src = datasets._mne_source_space(subject, 'ico-4', subjects_dir)
vertno = [mne_src[0]['vertno'], mne_src[1]['vertno']]
ss = SourceSpace(vertno, subject, 'ico-4', subjects_dir, 'aparc')
# connectivity
conn = ss.connectivity()
mne_conn = mne.spatial_src_connectivity(mne_src)
assert_array_equal(conn, _matrix_graph(mne_conn))
# sub-space connectivity
sssub = ss[ss.dimindex('superiortemporal-rh')]
ss2 = SourceSpace(vertno, subject, 'ico-4', subjects_dir, 'aparc')
ss2sub = ss2[ss2.dimindex('superiortemporal-rh')]
assert_array_equal(sssub.connectivity(), ss2sub.connectivity())
def test_source_space():
"Test SourceSpace dimension"
for subject in ['fsaverage', 'sample']:
mne_src = datasets._mne_source_space(subject, 'ico-4', subjects_dir)
vertno = [mne_src[0]['vertno'], mne_src[1]['vertno']]
ss = SourceSpace(vertno, subject, 'ico-4', subjects_dir, 'aparc')
# connectivity
conn = ss.connectivity()
mne_conn = mne.spatial_src_connectivity(mne_src)
assert_array_equal(conn, connectivity_from_coo(mne_conn))
# sub-space connectivity
sssub = ss[ss.dimindex('superiortemporal-rh')]
ss2 = SourceSpace(vertno, subject, 'ico-4', subjects_dir, 'aparc')
ss2sub = ss2[ss2.dimindex('superiortemporal-rh')]
assert_array_equal(sssub.connectivity(), ss2sub.connectivity())
def test_morphing():
mne.set_log_level('warning')
sss = datasets._mne_source_space('fsaverage', 'ico-4', subjects_dir)
vertices_to = [sss[0]['vertno'], sss[1]['vertno']]
ds = datasets.get_mne_sample(-0.1, 0.1, src='ico', sub='index==0', stc=True)
stc = ds['stc', 0]
morph_mat = mne.compute_morph_matrix('sample', 'fsaverage', stc.vertno,
vertices_to, None, subjects_dir)
ndvar = ds['src']
morphed_ndvar = morph_source_space(ndvar, 'fsaverage')
morphed_stc = mne.morph_data_precomputed('sample', 'fsaverage', stc,
vertices_to, morph_mat)
assert_array_equal(morphed_ndvar.x[0], morphed_stc.data)
morphed_stc_ndvar = load.fiff.stc_ndvar([morphed_stc], 'fsaverage', 'ico-4',
subjects_dir, 'src', parc=None)
assert_dataobj_equal(morphed_ndvar, morphed_stc_ndvar)
