# Author: Eric Larson # # License: BSD-3-Clause from contextlib import nullcontext import glob from itertools import product import os import os.path as op import pickle import shutil import numpy as np from scipy import sparse from numpy.testing import (assert_array_equal, assert_array_almost_equal, assert_equal, assert_allclose, assert_array_less) import pytest from mne.datasets import testing from mne import (read_label, stc_to_label, read_source_estimate, read_source_spaces, grow_labels, read_labels_from_annot, write_labels_to_annot, split_label, spatial_tris_adjacency, read_surface, random_parcellation, morph_labels, labels_to_stc) from mne.label import (Label, _blend_colors, label_sign_flip, _load_vert_pos, select_sources, _n_colors, _read_annot, _read_annot_cands) from mne.source_space import SourceSpaces from mne.source_estimate import mesh_edges from mne.surface import _mesh_borders from mne.utils import (requires_sklearn, get_subjects_dir, check_version, _record_warnings) data_path = testing.data_path(download=False) subjects_dir = op.join(data_path, 'subjects') src_fname = op.join(subjects_dir, 'sample', 'bem', 'sample-oct-6-src.fif') stc_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-meg-lh.stc') real_label_fname = op.join(data_path, 'MEG', 'sample', 'labels', 'Aud-lh.label') real_label_rh_fname = op.join(data_path, 'MEG', 'sample', 'labels', 'Aud-rh.label') v1_label_fname = op.join(subjects_dir, 'sample', 'label', 'lh.V1.label') fwd_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-meg-eeg-oct-6-fwd.fif') src_bad_fname = op.join(data_path, 'subjects', 'fsaverage', 'bem', 'fsaverage-ico-5-src.fif') label_dir = op.join(subjects_dir, 'sample', 'label', 'aparc') test_path = op.join(op.split(__file__)[0], '..', 'io', 'tests', 'data') label_fname = op.join(test_path, 'test-lh.label') label_rh_fname = op.join(test_path, 'test-rh.label') # This code was used to generate the "fake" test labels: # for hemi in ['lh', 'rh']: # label = Label(np.unique((np.random.rand(100) * 10242).astype(int)), # hemi=hemi, comment='Test ' + hemi, subject='fsaverage') # label.save(op.join(test_path, 'test-%s.label' % hemi)) # XXX : this was added for backward compat and keep the old test_label_in_src def _stc_to_label(stc, src, smooth, subjects_dir=None): """Compute a label from the non-zero sources in an stc object. Parameters ---------- stc : SourceEstimate The source estimates. src : SourceSpaces | str | None The source space over which the source estimates are defined. If it's a string it should the subject name (e.g. fsaverage). Can be None if stc.subject is not None. smooth : int Number of smoothing iterations. subjects_dir : str | None Path to SUBJECTS_DIR if it is not set in the environment. Returns ------- labels : list of Labels | list of list of Labels The generated labels. If connected is False, it returns a list of Labels (one per hemisphere). If no Label is available in a hemisphere, None is returned. If connected is True, it returns for each hemisphere a list of connected labels ordered in decreasing order depending of the maximum value in the stc. If no Label is available in an hemisphere, an empty list is returned. """ src = stc.subject if src is None else src if isinstance(src, str): subject = src else: subject = stc.subject if isinstance(src, str): subjects_dir = get_subjects_dir(subjects_dir) surf_path_from = op.join(subjects_dir, src, 'surf') rr_lh, tris_lh = read_surface(op.join(surf_path_from, 'lh.white')) rr_rh, tris_rh = read_surface(op.join(surf_path_from, 'rh.white')) rr = [rr_lh, rr_rh] tris = [tris_lh, tris_rh] else: if not isinstance(src, SourceSpaces): raise TypeError('src must be a string or a set of source spaces') if len(src) != 2: raise ValueError('source space should contain the 2 hemispheres') rr = [1e3 * src[0]['rr'], 1e3 * src[1]['rr']] tris = [src[0]['tris'], src[1]['tris']] labels = [] cnt = 0 for hemi_idx, (hemi, this_vertno, this_tris, this_rr) in enumerate( zip(['lh', 'rh'], stc.vertices, tris, rr)): this_data = stc.data[cnt:cnt + len(this_vertno)] e = mesh_edges(this_tris) e.data[e.data == 2] = 1 n_vertices = e.shape[0] e = e + sparse.eye(n_vertices, n_vertices) clusters = [this_vertno[np.any(this_data, axis=1)]] cnt += len(this_vertno) clusters = [c for c in clusters if len(c) > 0] if len(clusters) == 0: this_labels = None else: this_labels = [] colors = _n_colors(len(clusters)) for c, color in zip(clusters, colors): idx_use = c for k in range(smooth): e_use = e[:, idx_use] data1 = e_use * np.ones(len(idx_use)) idx_use = np.where(data1)[0] label = Label(idx_use, this_rr[idx_use], None, hemi, 'Label from stc', subject=subject, color=color) this_labels.append(label) this_labels = this_labels[0] labels.append(this_labels) return labels def assert_labels_equal(l0, l1, decimal=5, comment=True, color=True): """Assert two labels are equal.""" if comment: assert_equal(l0.comment, l1.comment) if color: assert_equal(l0.color, l1.color) for attr in ['hemi', 'subject']: attr0 = getattr(l0, attr) attr1 = getattr(l1, attr) msg = "label.%s: %r != %r" % (attr, attr0, attr1) assert_equal(attr0, attr1, msg) for attr in ['vertices', 'pos', 'values']: a0 = getattr(l0, attr) a1 = getattr(l1, attr) assert_array_almost_equal(a0, a1, decimal) def test_copy(): """Test label copying.""" label = read_label(label_fname) label_2 = label.copy() label_2.pos += 1 assert_array_equal(label.pos, label_2.pos - 1) def test_label_subject(): """Test label subject name extraction.""" label = read_label(label_fname) assert label.subject is None assert ('unknown' in repr(label)) label = read_label(label_fname, subject='fsaverage') assert (label.subject == 'fsaverage') assert ('fsaverage' in repr(label)) def test_label_addition(): """Test label addition.""" pos = np.random.RandomState(0).rand(10, 3) values = np.arange(10.) / 10 idx0 = list(range(7)) idx1 = list(range(7, 10)) # non-overlapping idx2 = list(range(5, 10)) # overlapping l0 = Label(idx0, pos[idx0], values[idx0], 'lh', color='red') l1 = Label(idx1, pos[idx1], values[idx1], 'lh') l2 = Label(idx2, pos[idx2], values[idx2], 'lh', color=(0, 1, 0, .5)) assert_equal(len(l0), len(idx0)) l_good = l0.copy() l_good.subject = 'sample' l_bad = l1.copy() l_bad.subject = 'foo' pytest.raises(ValueError, l_good.__add__, l_bad) pytest.raises(TypeError, l_good.__add__, 'foo') pytest.raises(ValueError, l_good.__sub__, l_bad) pytest.raises(TypeError, l_good.__sub__, 'foo') # adding non-overlapping labels l01 = l0 + l1 assert_equal(len(l01), len(l0) + len(l1)) assert_array_equal(l01.values[:len(l0)], l0.values) assert_equal(l01.color, l0.color) # subtraction assert_labels_equal(l01 - l0, l1, comment=False, color=False) assert_labels_equal(l01 - l1, l0, comment=False, color=False) # adding overlapping labels l02 = l0 + l2 i0 = np.where(l0.vertices == 6)[0][0] i2 = np.where(l2.vertices == 6)[0][0] i = np.where(l02.vertices == 6)[0][0] assert_equal(l02.values[i], l0.values[i0] + l2.values[i2]) assert_equal(l02.values[0], l0.values[0]) assert_array_equal(np.unique(l02.vertices), np.unique(idx0 + idx2)) assert_equal(l02.color, _blend_colors(l0.color, l2.color)) # adding lh and rh l2.hemi = 'rh' bhl = l0 + l2 assert_equal(bhl.hemi, 'both') assert_equal(len(bhl), len(l0) + len(l2)) assert_equal(bhl.color, l02.color) assert ('BiHemiLabel' in repr(bhl)) # subtraction assert_labels_equal(bhl - l0, l2) assert_labels_equal(bhl - l2, l0) bhl2 = l1 + bhl assert_labels_equal(bhl2.lh, l01) assert_equal(bhl2.color, _blend_colors(l1.color, bhl.color)) assert_array_equal((l2 + bhl).rh.vertices, bhl.rh.vertices) # rh label assert_array_equal((bhl + bhl).lh.vertices, bhl.lh.vertices) pytest.raises(TypeError, bhl.__add__, 5) # subtraction bhl_ = bhl2 - l1 assert_labels_equal(bhl_.lh, bhl.lh, comment=False, color=False) assert_labels_equal(bhl_.rh, bhl.rh) assert_labels_equal(bhl2 - l2, l0 + l1) assert_labels_equal(bhl2 - l1 - l0, l2) bhl_ = bhl2 - bhl2 assert_array_equal(bhl_.vertices, []) @testing.requires_testing_data @pytest.mark.parametrize('fname', (real_label_fname, v1_label_fname)) def test_label_fill_restrict(fname): """Test label in fill and restrict.""" src = read_source_spaces(src_fname) label = read_label(fname) # construct label from source space vertices label_src = label.restrict(src) vert_in_src = label_src.vertices values_in_src = label_src.values if check_version('scipy', '1.3') and fname == real_label_fname: # Check that we can auto-fill patch info quickly for one condition for s in src: s['nearest'] = None with _record_warnings(): label_src = label_src.fill(src) else: label_src = label_src.fill(src) assert src[0]['nearest'] is not None # check label vertices vertices_status = np.in1d(src[0]['nearest'], label.vertices) vertices_in = np.nonzero(vertices_status)[0] vertices_out = np.nonzero(np.logical_not(vertices_status))[0] assert_array_equal(label_src.vertices, vertices_in) assert_array_equal(np.in1d(vertices_out, label_src.vertices), False) # check values value_idx = np.digitize(src[0]['nearest'][vertices_in], vert_in_src, True) assert_array_equal(label_src.values, values_in_src[value_idx]) # test exception vertices = np.append([-1], vert_in_src) with pytest.raises(ValueError, match='does not contain all of the label'): Label(vertices, hemi='lh').fill(src) # test filling empty label label = Label([], hemi='lh') label.fill(src) assert_array_equal(label.vertices, np.array([], int)) @testing.requires_testing_data def test_label_io_and_time_course_estimates(): """Test IO for label + stc files.""" stc = read_source_estimate(stc_fname) label = read_label(real_label_fname) stc_label = stc.in_label(label) assert (len(stc_label.times) == stc_label.data.shape[1]) assert (len(stc_label.vertices[0]) == stc_label.data.shape[0]) @testing.requires_testing_data def test_label_io(tmp_path): """Test IO of label files.""" tempdir = str(tmp_path) label = read_label(label_fname) # label attributes assert_equal(label.name, 'test-lh') assert label.subject is None assert label.color is None # save and reload label.save(op.join(tempdir, 'foo')) label2 = read_label(op.join(tempdir, 'foo-lh.label')) assert_labels_equal(label, label2) # pickling dest = op.join(tempdir, 'foo.pickled') with open(dest, 'wb') as fid: pickle.dump(label, fid, pickle.HIGHEST_PROTOCOL) with open(dest, 'rb') as fid: label2 = pickle.load(fid) assert_labels_equal(label, label2) def _assert_labels_equal(labels_a, labels_b, ignore_pos=False): """Ensure two sets of labels are equal.""" for label_a, label_b in zip(labels_a, labels_b): assert_array_equal(label_a.vertices, label_b.vertices) assert (label_a.name == label_b.name) assert (label_a.hemi == label_b.hemi) if not ignore_pos: assert_array_equal(label_a.pos, label_b.pos) @testing.requires_testing_data def test_annot_io(tmp_path): """Test I/O from and to *.annot files.""" # copy necessary files from fsaverage to tempdir tempdir = str(tmp_path) subject = 'fsaverage' label_src = os.path.join(subjects_dir, 'fsaverage', 'label') surf_src = os.path.join(subjects_dir, 'fsaverage', 'surf') label_dir = os.path.join(tempdir, subject, 'label') surf_dir = os.path.join(tempdir, subject, 'surf') os.makedirs(label_dir) os.mkdir(surf_dir) shutil.copy(os.path.join(label_src, 'lh.PALS_B12_Lobes.annot'), label_dir) shutil.copy(os.path.join(label_src, 'rh.PALS_B12_Lobes.annot'), label_dir) shutil.copy(os.path.join(surf_src, 'lh.white'), surf_dir) shutil.copy(os.path.join(surf_src, 'rh.white'), surf_dir) # read original labels with pytest.raises(IOError, match='\nPALS_B12_Lobes$'): read_labels_from_annot(subject, 'PALS_B12_Lobesey', subjects_dir=tempdir) labels = read_labels_from_annot(subject, 'PALS_B12_Lobes', subjects_dir=tempdir) # test saving parcellation only covering one hemisphere parc = [label for label in labels if label.name == 'LOBE.TEMPORAL-lh'] write_labels_to_annot(parc, subject, 'myparc', subjects_dir=tempdir) parc1 = read_labels_from_annot(subject, 'myparc', subjects_dir=tempdir) parc1 = [label for label in parc1 if not label.name.startswith('unknown')] assert_equal(len(parc1), len(parc)) for lt, rt in zip(parc1, parc): assert_labels_equal(lt, rt) # test saving only one hemisphere parc = [label for label in labels if label.name.startswith('LOBE')] write_labels_to_annot(parc, subject, 'myparc2', hemi='lh', subjects_dir=tempdir) annot_fname = os.path.join(tempdir, subject, 'label', '%sh.myparc2.annot') assert os.path.isfile(annot_fname % 'l') assert not os.path.isfile(annot_fname % 'r') parc1 = read_labels_from_annot(subject, 'myparc2', annot_fname=annot_fname % 'l', subjects_dir=tempdir) parc_lh = [label for label in parc if label.name.endswith('lh')] for lt, rt in zip(parc1, parc_lh): assert_labels_equal(lt, rt) # test that the annotation is complete (test Label() support) rr = read_surface(op.join(surf_dir, 'lh.white'))[0] label = sum(labels, Label(hemi='lh', subject='fsaverage')).lh assert_array_equal(label.vertices, np.arange(len(rr))) @testing.requires_testing_data def test_morph_labels(): """Test morph_labels.""" # Just process the first 5 labels for speed parc_fsaverage = read_labels_from_annot( 'fsaverage', 'aparc', subjects_dir=subjects_dir)[:5] parc_sample = read_labels_from_annot( 'sample', 'aparc', subjects_dir=subjects_dir)[:5] parc_fssamp = morph_labels( parc_fsaverage, 'sample', subjects_dir=subjects_dir) for lf, ls, lfs in zip(parc_fsaverage, parc_sample, parc_fssamp): assert lf.hemi == ls.hemi == lfs.hemi assert lf.name == ls.name == lfs.name perc_1 = np.in1d(lfs.vertices, ls.vertices).mean() * 100 perc_2 = np.in1d(ls.vertices, lfs.vertices).mean() * 100 # Ideally this would be 100%, but we do not use the same algorithm # as FreeSurfer ... assert perc_1 > 92 assert perc_2 > 88 with pytest.raises(ValueError, match='wrong and fsaverage'): morph_labels(parc_fsaverage, 'sample', subjects_dir=subjects_dir, subject_from='wrong') with pytest.raises(RuntimeError, match='Number of surface vertices'): _load_vert_pos('sample', subjects_dir, 'white', 'lh', 1) for label in parc_fsaverage: label.subject = None with pytest.raises(ValueError, match='subject_from must be provided'): morph_labels(parc_fsaverage, 'sample', subjects_dir=subjects_dir) @testing.requires_testing_data def test_labels_to_stc(): """Test labels_to_stc.""" labels = read_labels_from_annot( 'sample', 'aparc', subjects_dir=subjects_dir) values = np.random.RandomState(0).randn(len(labels)) with pytest.raises(ValueError, match='1 or 2 dim'): labels_to_stc(labels, values[:, np.newaxis, np.newaxis]) with pytest.raises(ValueError, match=r'values\.shape'): labels_to_stc(labels, values[np.newaxis]) with pytest.raises(ValueError, match='multiple values of subject'): labels_to_stc(labels, values, subject='foo') stc = labels_to_stc(labels, values) assert stc.subject == 'sample' for value, label in zip(values, labels): stc_label = stc.in_label(label) assert (stc_label.data == value).all() stc = read_source_estimate(stc_fname, 'sample') @testing.requires_testing_data def test_read_labels_from_annot(tmp_path): """Test reading labels from FreeSurfer parcellation.""" # test some invalid inputs pytest.raises(ValueError, read_labels_from_annot, 'sample', hemi='bla', subjects_dir=subjects_dir) pytest.raises(ValueError, read_labels_from_annot, 'sample', annot_fname='bla.annot', subjects_dir=subjects_dir) with pytest.raises(IOError, match='does not exist'): _read_annot_cands('foo') with pytest.raises(IOError, match='no candidate'): _read_annot(str(tmp_path)) # read labels using hemi specification labels_lh = read_labels_from_annot('sample', hemi='lh', subjects_dir=subjects_dir) for label in labels_lh: assert label.name.endswith('-lh') assert label.hemi == 'lh' assert label.color is not None # read labels using annot_fname annot_fname = op.join(subjects_dir, 'sample', 'label', 'rh.aparc.annot') labels_rh = read_labels_from_annot('sample', annot_fname=annot_fname, subjects_dir=subjects_dir) for label in labels_rh: assert label.name.endswith('-rh') assert label.hemi == 'rh' assert label.color is not None # combine the lh, rh, labels and sort them labels_lhrh = list() labels_lhrh.extend(labels_lh) labels_lhrh.extend(labels_rh) names = [label.name for label in labels_lhrh] labels_lhrh = [label for (name, label) in sorted(zip(names, labels_lhrh))] # read all labels at once labels_both = read_labels_from_annot('sample', subjects_dir=subjects_dir) # we have the same result _assert_labels_equal(labels_lhrh, labels_both) # aparc has 68 cortical labels assert (len(labels_both) == 68) # test regexp label = read_labels_from_annot('sample', parc='aparc.a2009s', regexp='Angu', subjects_dir=subjects_dir)[0] assert (label.name == 'G_pariet_inf-Angular-lh') # silly, but real regexp: label = read_labels_from_annot('sample', 'aparc.a2009s', regexp='.*-.{4,}_.{3,3}-L', subjects_dir=subjects_dir)[0] assert (label.name == 'G_oc-temp_med-Lingual-lh') with pytest.raises(RuntimeError, match='did not match any of'): read_labels_from_annot( 'sample', parc='aparc', annot_fname=annot_fname, regexp='foo', subjects_dir=subjects_dir) @testing.requires_testing_data def test_read_labels_from_annot_annot2labels(): """Test reading labels from parc. by comparing with mne_annot2labels.""" label_fnames = glob.glob(label_dir + '/*.label') label_fnames.sort() labels_mne = [read_label(fname) for fname in label_fnames] labels = read_labels_from_annot('sample', subjects_dir=subjects_dir) # we have the same result, mne does not fill pos, so ignore it _assert_labels_equal(labels, labels_mne, ignore_pos=True) @testing.requires_testing_data def test_write_labels_to_annot(tmp_path): """Test writing FreeSurfer parcellation from labels.""" tempdir = str(tmp_path) labels = read_labels_from_annot('sample', subjects_dir=subjects_dir) # create temporary subjects-dir skeleton surf_dir = op.join(subjects_dir, 'sample', 'surf') temp_surf_dir = op.join(tempdir, 'sample', 'surf') os.makedirs(temp_surf_dir) shutil.copy(op.join(surf_dir, 'lh.white'), temp_surf_dir) shutil.copy(op.join(surf_dir, 'rh.white'), temp_surf_dir) os.makedirs(op.join(tempdir, 'sample', 'label')) # test automatic filenames dst = op.join(tempdir, 'sample', 'label', '%s.%s.annot') write_labels_to_annot(labels, 'sample', 'test1', subjects_dir=tempdir) assert (op.exists(dst % ('lh', 'test1'))) assert (op.exists(dst % ('rh', 'test1'))) # lh only for label in labels: if label.hemi == 'lh': break write_labels_to_annot([label], 'sample', 'test2', subjects_dir=tempdir) assert (op.exists(dst % ('lh', 'test2'))) assert (op.exists(dst % ('rh', 'test2'))) # rh only for label in labels: if label.hemi == 'rh': break write_labels_to_annot([label], 'sample', 'test3', subjects_dir=tempdir) assert (op.exists(dst % ('lh', 'test3'))) assert (op.exists(dst % ('rh', 'test3'))) # label alone pytest.raises(TypeError, write_labels_to_annot, labels[0], 'sample', 'test4', subjects_dir=tempdir) # write left and right hemi labels with filenames: fnames = [op.join(tempdir, hemi + '-myparc') for hemi in ['lh', 'rh']] for fname in fnames: with pytest.warns(RuntimeWarning, match='subjects_dir'): write_labels_to_annot(labels, annot_fname=fname) # read it back labels2 = read_labels_from_annot('sample', subjects_dir=subjects_dir, annot_fname=fnames[0]) labels22 = read_labels_from_annot('sample', subjects_dir=subjects_dir, annot_fname=fnames[1]) labels2.extend(labels22) names = [label.name for label in labels2] for label in labels: idx = names.index(label.name) assert_labels_equal(label, labels2[idx]) # same with label-internal colors for fname in fnames: write_labels_to_annot(labels, 'sample', annot_fname=fname, overwrite=True, subjects_dir=subjects_dir) labels3 = read_labels_from_annot('sample', subjects_dir=subjects_dir, annot_fname=fnames[0]) labels33 = read_labels_from_annot('sample', subjects_dir=subjects_dir, annot_fname=fnames[1]) labels3.extend(labels33) names3 = [label.name for label in labels3] for label in labels: idx = names3.index(label.name) assert_labels_equal(label, labels3[idx]) # make sure we can't overwrite things pytest.raises(ValueError, write_labels_to_annot, labels, 'sample', annot_fname=fnames[0], subjects_dir=subjects_dir) # however, this works write_labels_to_annot(labels, 'sample', annot_fname=fnames[0], overwrite=True, subjects_dir=subjects_dir) # label without color labels_ = labels[:] labels_[0] = labels_[0].copy() labels_[0].color = None write_labels_to_annot(labels_, 'sample', annot_fname=fnames[0], overwrite=True, subjects_dir=subjects_dir) # duplicate color labels_[0].color = labels_[2].color pytest.raises(ValueError, write_labels_to_annot, labels_, 'sample', annot_fname=fnames[0], overwrite=True, subjects_dir=subjects_dir) # invalid color inputs labels_[0].color = (1.1, 1., 1., 1.) pytest.raises(ValueError, write_labels_to_annot, labels_, 'sample', annot_fname=fnames[0], overwrite=True, subjects_dir=subjects_dir) # overlapping labels labels_ = labels[:] cuneus_lh = labels[6] precuneus_lh = labels[50] labels_.append(precuneus_lh + cuneus_lh) pytest.raises(ValueError, write_labels_to_annot, labels_, 'sample', annot_fname=fnames[0], overwrite=True, subjects_dir=subjects_dir) # unlabeled vertices labels_lh = [label for label in labels if label.name.endswith('lh')] write_labels_to_annot(labels_lh[1:], 'sample', annot_fname=fnames[0], overwrite=True, subjects_dir=subjects_dir) labels_reloaded = read_labels_from_annot('sample', annot_fname=fnames[0], subjects_dir=subjects_dir) assert_equal(len(labels_lh), len(labels_reloaded)) label0 = labels_lh[0] label1 = labels_reloaded[-1] assert_equal(label1.name, "unknown-lh") assert (np.all(np.in1d(label0.vertices, label1.vertices))) # unnamed labels labels4 = labels[:] labels4[0].name = None pytest.raises(ValueError, write_labels_to_annot, labels4, annot_fname=fnames[0]) @requires_sklearn @testing.requires_testing_data def test_split_label(): """Test splitting labels.""" aparc = read_labels_from_annot('fsaverage', 'aparc', 'lh', regexp='lingual', subjects_dir=subjects_dir) lingual = aparc[0] # Test input error pytest.raises(ValueError, lingual.split, 'bad_input_string') # split with names parts = ('lingual_post', 'lingual_ant') post, ant = split_label(lingual, parts, subjects_dir=subjects_dir) # check output names assert_equal(post.name, parts[0]) assert_equal(ant.name, parts[1]) # check vertices add up lingual_reconst = post + ant lingual_reconst.name = lingual.name lingual_reconst.comment = lingual.comment lingual_reconst.color = lingual.color assert_labels_equal(lingual_reconst, lingual) # compare output of Label.split() method post1, ant1 = lingual.split(parts, subjects_dir=subjects_dir) assert_labels_equal(post1, post) assert_labels_equal(ant1, ant) # compare fs_like split with freesurfer split antmost = split_label(lingual, 40, None, subjects_dir, True)[-1] fs_vert = [210, 4401, 7405, 12079, 16276, 18956, 26356, 32713, 32716, 32719, 36047, 36050, 42797, 42798, 42799, 59281, 59282, 59283, 71864, 71865, 71866, 71874, 71883, 79901, 79903, 79910, 103024, 107849, 107850, 122928, 139356, 139357, 139373, 139374, 139375, 139376, 139377, 139378, 139381, 149117, 149118, 149120, 149127] assert_array_equal(antmost.vertices, fs_vert) # check default label name assert_equal(antmost.name, "lingual_div40-lh") # Apply contiguous splitting to DMN label from parcellation in Yeo, 2011 label_default_mode = read_label(op.join(subjects_dir, 'fsaverage', 'label', 'lh.7Networks_7.label')) DMN_sublabels = label_default_mode.split(parts='contiguous', subject='fsaverage', subjects_dir=subjects_dir) assert_equal([len(label.vertices) for label in DMN_sublabels], [16181, 7022, 5965, 5300, 823] + [1] * 23) @pytest.mark.slowtest @testing.requires_testing_data @requires_sklearn def test_stc_to_label(): """Test stc_to_label.""" src = read_source_spaces(fwd_fname) src_bad = read_source_spaces(src_bad_fname) stc = read_source_estimate(stc_fname, 'sample') os.environ['SUBJECTS_DIR'] = op.join(data_path, 'subjects') labels1 = _stc_to_label(stc, src='sample', smooth=3) labels2 = _stc_to_label(stc, src=src, smooth=3) assert_equal(len(labels1), len(labels2)) for l1, l2 in zip(labels1, labels2): assert_labels_equal(l1, l2, decimal=4) with pytest.warns(RuntimeWarning, match='have holes'): labels_lh, labels_rh = stc_to_label(stc, src=src, smooth=True, connected=True) pytest.raises(ValueError, stc_to_label, stc, 'sample', smooth=True, connected=True) pytest.raises(RuntimeError, stc_to_label, stc, smooth=True, src=src_bad, connected=True) assert_equal(len(labels_lh), 1) assert_equal(len(labels_rh), 1) # test getting tris tris = labels_lh[0].get_tris(src[0]['use_tris'], vertices=stc.vertices[0]) pytest.raises(ValueError, spatial_tris_adjacency, tris, remap_vertices=False) adjacency = spatial_tris_adjacency(tris, remap_vertices=True) assert (adjacency.shape[0] == len(stc.vertices[0])) # "src" as a subject name pytest.raises(TypeError, stc_to_label, stc, src=1, smooth=False, connected=False, subjects_dir=subjects_dir) pytest.raises(ValueError, stc_to_label, stc, src=SourceSpaces([src[0]]), smooth=False, connected=False, subjects_dir=subjects_dir) pytest.raises(ValueError, stc_to_label, stc, src='sample', smooth=False, connected=True, subjects_dir=subjects_dir) pytest.raises(ValueError, stc_to_label, stc, src='sample', smooth=True, connected=False, subjects_dir=subjects_dir) labels_lh, labels_rh = stc_to_label(stc, src='sample', smooth=False, connected=False, subjects_dir=subjects_dir) assert (len(labels_lh) > 1) assert (len(labels_rh) > 1) # with smooth='patch' with pytest.warns(RuntimeWarning, match='have holes'): labels_patch = stc_to_label(stc, src=src, smooth=True) assert len(labels_patch) == len(labels1) for l1, l2 in zip(labels1, labels2): assert_labels_equal(l1, l2, decimal=4) @pytest.mark.slowtest @testing.requires_testing_data def test_morph(): """Test inter-subject label morphing.""" label_orig = read_label(real_label_fname) label_orig.subject = 'sample' # should work for specifying vertices for both hemis, or just the # hemi of the given label vals = list() for grade in [5, [np.arange(10242), np.arange(10242)], np.arange(10242)]: label = label_orig.copy() # this should throw an error because the label has all zero values pytest.raises(ValueError, label.morph, 'sample', 'fsaverage') label.values.fill(1) label = label.morph(None, 'fsaverage', 5, grade, subjects_dir, 1) label = label.morph('fsaverage', 'sample', 5, None, subjects_dir, 2) assert (np.in1d(label_orig.vertices, label.vertices).all()) assert (len(label.vertices) < 3 * len(label_orig.vertices)) vals.append(label.vertices) assert_array_equal(vals[0], vals[1]) # make sure label smoothing can run assert_equal(label.subject, 'sample') verts = [np.arange(10242), np.arange(10242)] for hemi in ['lh', 'rh']: label.hemi = hemi with _record_warnings(): # morph map maybe missing label.morph(None, 'fsaverage', 5, verts, subjects_dir, 2) pytest.raises(TypeError, label.morph, None, 1, 5, verts, subjects_dir, 2) pytest.raises(TypeError, label.morph, None, 'fsaverage', 5.5, verts, subjects_dir, 2) with _record_warnings(): # morph map maybe missing label.smooth(subjects_dir=subjects_dir) # make sure this runs @testing.requires_testing_data def test_grow_labels(): """Test generation of circular source labels.""" seeds = [0, 50000] # these were chosen manually in mne_analyze should_be_in = [[49, 227], [51207, 48794]] hemis = [0, 1] names = ['aneurism', 'tumor'] labels = grow_labels('sample', seeds, 3, hemis, subjects_dir, names=names) tgt_names = ['aneurism-lh', 'tumor-rh'] tgt_hemis = ['lh', 'rh'] for label, seed, hemi, sh, name in zip(labels, seeds, tgt_hemis, should_be_in, tgt_names): assert (np.any(label.vertices == seed)) assert (np.all(np.in1d(sh, label.vertices))) assert_equal(label.hemi, hemi) assert_equal(label.name, name) # grow labels with and without overlap seeds = [57532, [58887, 6304]] l01, l02 = grow_labels('fsaverage', seeds, 20, [0, 0], subjects_dir) seeds = [57532, [58887, 6304]] l11, l12 = grow_labels('fsaverage', seeds, 20, [0, 0], subjects_dir, overlap=False) # test label naming assert_equal(l01.name, 'Label_0-lh') assert_equal(l02.name, 'Label_1-lh') assert_equal(l11.name, 'Label_0-lh') assert_equal(l12.name, 'Label_1-lh') # test color assignment l11_c, l12_c = grow_labels('fsaverage', seeds, 20, [0, 0], subjects_dir, overlap=False, colors=None) assert_equal(l11_c.color, _n_colors(2)[0]) assert_equal(l12_c.color, _n_colors(2)[1]) lab_colors = np.array([[0, 0, 1, 1], [1, 0, 0, 1]]) l11_c, l12_c = grow_labels('fsaverage', seeds, 20, [0, 0], subjects_dir, overlap=False, colors=lab_colors) assert_array_equal(l11_c.color, lab_colors[0, :]) assert_array_equal(l12_c.color, lab_colors[1, :]) lab_colors = np.array([.1, .2, .3, .9]) l11_c, l12_c = grow_labels('fsaverage', seeds, 20, [0, 0], subjects_dir, overlap=False, colors=lab_colors) assert_array_equal(l11_c.color, lab_colors) assert_array_equal(l12_c.color, lab_colors) # make sure set 1 does not overlap overlap = np.intersect1d(l11.vertices, l12.vertices, True) assert_array_equal(overlap, []) # make sure both sets cover the same vertices l0 = l01 + l02 l1 = l11 + l12 assert_array_equal(l1.vertices, l0.vertices) # non-overlapping (gh-8848) for overlap in (False, True): grow_labels('fsaverage', [0], 1, 1, subjects_dir, overlap=overlap) @testing.requires_testing_data 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))) @testing.requires_testing_data def test_label_sign_flip(): """Test label sign flip computation.""" src = read_source_spaces(src_fname) label = Label(vertices=src[0]['vertno'][:5], hemi='lh') src[0]['nn'][label.vertices] = np.array( [[1., 0., 0.], [0., 1., 0.], [0, 0, 1.], [1. / np.sqrt(2), 1. / np.sqrt(2), 0.], [1. / np.sqrt(2), 1. / np.sqrt(2), 0.]]) known_flips = np.array([1, 1, np.nan, 1, 1]) idx = [0, 1, 3, 4] # indices that are usable (third row is orthognoal) flip = label_sign_flip(label, src) assert_array_almost_equal(np.dot(flip[idx], known_flips[idx]), len(idx)) bi_label = label + Label(vertices=src[1]['vertno'][:5], hemi='rh') src[1]['nn'][src[1]['vertno'][:5]] = -src[0]['nn'][label.vertices] flip = label_sign_flip(bi_label, src) known_flips = np.array([1, 1, np.nan, 1, 1, 1, 1, np.nan, 1, 1]) idx = [0, 1, 3, 4, 5, 6, 8, 9] assert_array_almost_equal(np.dot(flip[idx], known_flips[idx]), 0.) src[1]['nn'][src[1]['vertno'][:5]] *= -1 flip = label_sign_flip(bi_label, src) assert_array_almost_equal(np.dot(flip[idx], known_flips[idx]), len(idx)) @testing.requires_testing_data def test_label_center_of_mass(): """Test computing the center of mass of a label.""" stc = read_source_estimate(stc_fname) stc.lh_data[:] = 0 vertex_stc = stc.center_of_mass('sample', subjects_dir=subjects_dir)[0] assert_equal(vertex_stc, 124791) label = Label(stc.vertices[1], pos=None, values=stc.rh_data.mean(axis=1), hemi='rh', subject='sample') vertex_label = label.center_of_mass(subjects_dir=subjects_dir) assert_equal(vertex_label, vertex_stc) labels = read_labels_from_annot('sample', parc='aparc.a2009s', subjects_dir=subjects_dir) src = read_source_spaces(src_fname) # Try a couple of random ones, one from left and one from right # Visually verified in about the right place using mne_analyze for label, expected in zip([labels[2], labels[3], labels[-5]], [141162, 145221, 55979]): label.values[:] = -1 pytest.raises(ValueError, label.center_of_mass, subjects_dir=subjects_dir) label.values[:] = 0 pytest.raises(ValueError, label.center_of_mass, subjects_dir=subjects_dir) label.values[:] = 1 assert_equal(label.center_of_mass(subjects_dir=subjects_dir), expected) assert_equal(label.center_of_mass(subjects_dir=subjects_dir, restrict_vertices=label.vertices), expected) # restrict to source space idx = 0 if label.hemi == 'lh' else 1 # this simple nearest version is not equivalent, but is probably # close enough for many labels (including the test ones): pos = label.pos[np.where(label.vertices == expected)[0][0]] pos = (src[idx]['rr'][src[idx]['vertno']] - pos) pos = np.argmin(np.sum(pos * pos, axis=1)) src_expected = src[idx]['vertno'][pos] # see if we actually get the same one src_restrict = np.intersect1d(label.vertices, src[idx]['vertno']) assert_equal(label.center_of_mass(subjects_dir=subjects_dir, restrict_vertices=src_restrict), src_expected) assert_equal(label.center_of_mass(subjects_dir=subjects_dir, restrict_vertices=src), src_expected) # degenerate cases pytest.raises(ValueError, label.center_of_mass, subjects_dir=subjects_dir, restrict_vertices='foo') pytest.raises(TypeError, label.center_of_mass, subjects_dir=subjects_dir, surf=1) pytest.raises(IOError, label.center_of_mass, subjects_dir=subjects_dir, surf='foo') @testing.requires_testing_data def test_select_sources(): """Test the selection of sources for simulation.""" subject = 'sample' label_file = op.join(subjects_dir, subject, 'label', 'aparc', 'temporalpole-rh.label') # Regardless of other parameters, using extent 0 should always yield a # a single source. tp_label = read_label(label_file) tp_label.values[:] = 1 labels = ['lh', tp_label] locations = ['random', 'center'] for label, location in product(labels, locations): label = select_sources( subject, label, location, extent=0, subjects_dir=subjects_dir) assert (len(label.vertices) == 1) # As we increase the extent, the new region should contain the previous # one. label = select_sources(subject, 'lh', 0, extent=0, subjects_dir=subjects_dir) for extent in range(1, 3): new_label = select_sources(subject, 'lh', 0, extent=extent * 2, subjects_dir=subjects_dir) assert (set(new_label.vertices) > set(label.vertices)) assert (new_label.hemi == 'lh') label = new_label # With a large enough extent and not allowing growing outside the label, # every vertex of the label should be in the region. label = select_sources(subject, tp_label, 0, extent=30, grow_outside=False, subjects_dir=subjects_dir) assert (set(label.vertices) == set(tp_label.vertices)) # Without this restriction, we should get new vertices. label = select_sources(subject, tp_label, 0, extent=30, grow_outside=True, subjects_dir=subjects_dir) assert (set(label.vertices) > set(tp_label.vertices)) # Other parameters are taken into account. label = select_sources(subject, tp_label, 0, extent=10, grow_outside=False, subjects_dir=subjects_dir, name='mne') assert (label.name == 'mne') assert (label.hemi == 'rh') @testing.requires_testing_data @pytest.mark.parametrize('fname, area', [ (real_label_fname, 0.657e-3), (v1_label_fname, 3.245e-3), ]) def test_label_geometry(fname, area): """Test label geometric computations.""" label = read_label(fname, subject='sample') got_area = label.compute_area(subjects_dir=subjects_dir) assert_allclose(got_area, area, rtol=1e-3) # using a sparse label emits a warning label_sparse = label.restrict(src_fname) assert 0 < len(label_sparse.vertices) < len(label.vertices) with pytest.warns(RuntimeWarning, match='No complete triangles'): assert label_sparse.compute_area(subjects_dir=subjects_dir) == 0. if not check_version('scipy', '1.3'): ctx = pytest.raises(RuntimeError, match='required to calculate') stop = True else: ctx = nullcontext() stop = False with ctx: dist, outside = label.distances_to_outside(subjects_dir=subjects_dir) if stop: return rr, tris = read_surface( op.join(subjects_dir, 'sample', 'surf', 'lh.white')) mask = np.zeros(len(rr), bool) mask[label.vertices] = 1 border_mask = np.in1d(label.vertices, _mesh_borders(tris, mask)) # The distances of the border vertices is smaller than that of non-border lo, mi, hi = np.percentile(dist[border_mask], (0, 50, 100)) assert 0.1e-3 < lo < 0.5e-3 < mi < 1.0e-3 < hi < 2.0e-3 lo, mi, hi = np.percentile(dist[~border_mask], (0, 50, 100)) assert 0.5e-3 < lo < 1.0e-3 < mi < 9.0e-3 < hi < 25e-3 # check that the distances are close but uniformly <= than euclidean assert not np.in1d(outside, label.vertices).any() border_dist = dist[border_mask] border_euc = 1e-3 * np.linalg.norm( rr[label.vertices[border_mask]] - rr[outside[border_mask]], axis=1) assert_allclose(border_dist, border_euc, atol=1e-4) inside_dist = dist[~border_mask] inside_euc = 1e-3 * np.linalg.norm( rr[label.vertices[~border_mask]] - rr[outside[~border_mask]], axis=1) assert_array_less(inside_euc, inside_dist) assert_array_less(0.25 * inside_dist, inside_euc)