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 zip(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_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_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_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)
def test_source_space(): "Test SourceSpace dimension" for subject in ['fsaverage', 'sample']: path = os.path.join(subjects_dir, subject, 'bem', subject + '-ico-4-src.fif') mne_src = mne.read_source_spaces(path) 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_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): assert_equal(ds[i, 'location'], parc[i].name)