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"
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)
src_path = os.path.join(mri_dir, 'bem', subject + '-ico-5-src.fif')
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 = mne.read_source_spaces(src_path)
source = SourceSpace((src[0]['vertno'], src[1]['vertno']), subject,
'ico-5', mri_sdir)
index = source.dimindex(label_v1)
source_v1 = source[index]
index = source.dimindex(label_ba1_v1)
source_ba1_v1 = source[index]
index = source.dimindex(label_v1_mt)
source_v1_mt = source[index]
index = source_ba1_v1.dimindex(source_v1_mt)
source_v1_intersection = source_ba1_v1[index]
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]
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))
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)
src_path = os.path.join(mri_dir, 'bem', subject + '-ico-5-src.fif')
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 = mne.read_source_spaces(src_path)
source = SourceSpace((src[0]['vertno'], src[1]['vertno']), subject,
'ico-5', mri_sdir)
index = source.dimindex(label_v1)
source_v1 = source[index]
index = source.dimindex(label_ba1_v1)
source_ba1_v1 = source[index]
index = source.dimindex(label_v1_mt)
source_v1_mt = source[index]
index = source_ba1_v1.dimindex(source_v1_mt)
source_v1_intersection = source_ba1_v1[index]
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
if mne.__version__ < '0.8':
return
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)
