def test_expand(): """Test stc expansion """ stc = read_source_estimate(fname, 'sample') assert_true('sample' in repr(stc)) labels_lh, _ = labels_from_parc('sample', hemi='lh', subjects_dir=subjects_dir) stc_limited = stc.in_label(labels_lh[0] + labels_lh[1]) stc_new = stc_limited.copy() stc_new.data.fill(0) for label in labels_lh[:2]: stc_new += stc.in_label(label).expand(stc_limited.vertno) # make sure we can't add unless vertno agree assert_raises(ValueError, stc.__add__, stc.in_label(labels_lh[0]))
def test_extract_label_time_course(): """Test extraction of label time courses from stc """ n_stcs = 3 n_times = 50 src = read_inverse_operator(fname_inv)['src'] vertices = [src[0]['vertno'], src[1]['vertno']] n_verts = len(vertices[0]) + len(vertices[1]) # get some labels labels_lh, _ = labels_from_parc('sample', hemi='lh', subjects_dir=subjects_dir) labels_rh, _ = labels_from_parc('sample', hemi='rh', subjects_dir=subjects_dir) labels = list() labels.extend(labels_lh[:5]) labels.extend(labels_rh[:4]) n_labels = len(labels) label_means = np.arange(n_labels)[:, None] * np.ones((n_labels, n_times)) # compute the mean with sign flip label_means_flipped = np.zeros_like(label_means) for i, label in enumerate(labels): label_means_flipped[i] = i * np.mean(label_sign_flip(label, src)) # generate some stc's with known data stcs = list() for i in range(n_stcs): data = np.zeros((n_verts, n_times)) # set the value of the stc within each label for j, label in enumerate(labels): if label.hemi == 'lh': idx = np.intersect1d(vertices[0], label.vertices) idx = np.searchsorted(vertices[0], idx) elif label.hemi == 'rh': idx = np.intersect1d(vertices[1], label.vertices) idx = len(vertices[0]) + np.searchsorted(vertices[1], idx) data[idx] = label_means[j] this_stc = SourceEstimate(data, vertices, 0, 1) stcs.append(this_stc) # test some invalid inputs assert_raises(ValueError, extract_label_time_course, stcs, labels, src, mode='notamode') # have an empty label empty_label = labels[0].copy() empty_label.vertices += 1000000 assert_raises(ValueError, extract_label_time_course, stcs, empty_label, src, mode='mean') # but this works: tc = extract_label_time_course(stcs, empty_label, src, mode='mean', allow_empty=True) for arr in tc: assert_true(arr.shape == (1, n_times)) assert_array_equal(arr, np.zeros((1, n_times))) # test the different modes modes = ['mean', 'mean_flip', 'pca_flip'] for mode in modes: label_tc = extract_label_time_course(stcs, labels, src, mode=mode) label_tc_method = [ stc.extract_label_time_course(labels, src, mode=mode) for stc in stcs ] assert_true(len(label_tc) == n_stcs) assert_true(len(label_tc_method) == n_stcs) for tc1, tc2 in zip(label_tc, label_tc_method): assert_true(tc1.shape == (n_labels, n_times)) assert_true(tc2.shape == (n_labels, n_times)) assert_true(np.allclose(tc1, tc2, rtol=1e-8, atol=1e-16)) if mode == 'mean': assert_array_almost_equal(tc1, label_means) if mode == 'mean_flip': assert_array_almost_equal(tc1, label_means_flipped) # test label with very few vertices (check SVD conditionals) label = Label(vertices=src[0]['vertno'][:2], hemi='lh') x = label_sign_flip(label, src) assert_true(len(x) == 2) label = Label(vertices=[], hemi='lh') x = label_sign_flip(label, src) assert_true(x.size == 0)
def test_extract_label_time_course(): """Test extraction of label time courses from stc """ n_stcs = 3 n_times = 50 src = read_inverse_operator(fname_inv)['src'] vertices = [src[0]['vertno'], src[1]['vertno']] n_verts = len(vertices[0]) + len(vertices[1]) # get some labels labels_lh, _ = labels_from_parc('sample', hemi='lh', subjects_dir=subjects_dir) labels_rh, _ = labels_from_parc('sample', hemi='rh', subjects_dir=subjects_dir) labels = list() labels.extend(labels_lh[:5]) labels.extend(labels_rh[:4]) n_labels = len(labels) label_means = np.arange(n_labels)[:, None] * np.ones((n_labels, n_times)) # compute the mean with sign flip label_means_flipped = np.zeros_like(label_means) for i, label in enumerate(labels): label_means_flipped[i] = i * np.mean(label_sign_flip(label, src)) # generate some stc's with known data stcs = list() for i in range(n_stcs): data = np.zeros((n_verts, n_times)) # set the value of the stc within each label for j, label in enumerate(labels): if label.hemi == 'lh': idx = np.intersect1d(vertices[0], label.vertices) idx = np.searchsorted(vertices[0], idx) elif label.hemi == 'rh': idx = np.intersect1d(vertices[1], label.vertices) idx = len(vertices[0]) + np.searchsorted(vertices[1], idx) data[idx] = label_means[j] this_stc = SourceEstimate(data, vertices, 0, 1) stcs.append(this_stc) # test some invalid inputs assert_raises(ValueError, extract_label_time_course, stcs, labels, src, mode='notamode') # have an empty label empty_label = labels[0].copy() empty_label.vertices += 1000000 assert_raises(ValueError, extract_label_time_course, stcs, empty_label, src, mode='mean') # but this works: tc = extract_label_time_course(stcs, empty_label, src, mode='mean', allow_empty=True) for arr in tc: assert_true(arr.shape == (1, n_times)) assert_array_equal(arr, np.zeros((1, n_times))) # test the different modes modes = ['mean', 'mean_flip', 'pca_flip'] for mode in modes: label_tc = extract_label_time_course(stcs, labels, src, mode=mode) label_tc_method = [stc.extract_label_time_course(labels, src, mode=mode) for stc in stcs] assert_true(len(label_tc) == n_stcs) assert_true(len(label_tc_method) == n_stcs) for tc1, tc2 in zip(label_tc, label_tc_method): assert_true(tc1.shape == (n_labels, n_times)) assert_true(tc2.shape == (n_labels, n_times)) assert_true(np.allclose(tc1, tc2, rtol=1e-8, atol=1e-16)) if mode == 'mean': assert_array_almost_equal(tc1, label_means) if mode == 'mean_flip': assert_array_almost_equal(tc1, label_means_flipped)