from __future__ import print_function import os.path as op from nose.tools import assert_true, assert_raises import warnings from copy import deepcopy import numpy as np from numpy.testing import (assert_array_almost_equal, assert_array_equal, assert_allclose, assert_equal) from scipy.fftpack import fft from mne.datasets import testing from mne import (stats, SourceEstimate, VolSourceEstimate, Label, read_source_spaces, MixedSourceEstimate, read_source_estimate, morph_data, extract_label_time_course, spatio_temporal_tris_connectivity, spatio_temporal_src_connectivity, spatial_inter_hemi_connectivity) from mne.source_estimate import (compute_morph_matrix, grade_to_vertices, grade_to_tris) from mne.minimum_norm import read_inverse_operator from mne.label import read_labels_from_annot, label_sign_flip from mne.utils import (_TempDir, requires_pandas, requires_sklearn, requires_h5py, run_tests_if_main, slow_test) warnings.simplefilter('always') # enable b/c these tests throw warnings data_path = testing.data_path(download=False) subjects_dir = op.join(data_path, 'subjects') fname_inv = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-meg-eeg-oct-6-meg-inv.fif') fname_t1 = op.join(data_path, 'subjects', 'sample', 'mri', 'T1.mgz') fname_src = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-meg-eeg-oct-6-fwd.fif') fname_src_3 = op.join(data_path, 'subjects', 'sample', 'bem', 'sample-oct-4-src.fif') fname_stc = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-meg') fname_smorph = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-meg') fname_fmorph = op.join(data_path, 'MEG', 'sample', 'fsaverage_audvis_trunc-meg') fname_vol = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-grad-vol-7-fwd-sensmap-vol.w') fname_vsrc = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-meg-vol-7-fwd.fif') rng = np.random.RandomState(0) @testing.requires_testing_data def test_spatial_inter_hemi_connectivity(): """Test spatial connectivity between hemispheres""" # trivial cases conn = spatial_inter_hemi_connectivity(fname_src_3, 5e-6) assert_equal(conn.data.size, 0) conn = spatial_inter_hemi_connectivity(fname_src_3, 5e6) assert_equal(conn.data.size, np.prod(conn.shape) // 2) # actually interesting case (1cm), should be between 2 and 10% of verts src = read_source_spaces(fname_src_3) conn = spatial_inter_hemi_connectivity(src, 10e-3) conn = conn.tocsr() n_src = conn.shape[0] assert_true(n_src * 0.02 < conn.data.size < n_src * 0.10) assert_equal(conn[:src[0]['nuse'], :src[0]['nuse']].data.size, 0) assert_equal(conn[-src[1]['nuse']:, -src[1]['nuse']:].data.size, 0) c = (conn.T + conn) / 2. - conn c.eliminate_zeros() assert_equal(c.data.size, 0) # check locations upper_right = conn[:src[0]['nuse'], src[0]['nuse']:].toarray() assert_equal(upper_right.sum(), conn.sum() // 2) good_labels = ['S_pericallosal', 'Unknown', 'G_and_S_cingul-Mid-Post', 'G_cuneus'] for hi, hemi in enumerate(('lh', 'rh')): has_neighbors = src[hi]['vertno'][np.where(np.any(upper_right, axis=1 - hi))[0]] labels = read_labels_from_annot('sample', 'aparc.a2009s', hemi, subjects_dir=subjects_dir) use_labels = [l.name[:-3] for l in labels if np.in1d(l.vertices, has_neighbors).any()] assert_true(set(use_labels) - set(good_labels) == set()) @slow_test @testing.requires_testing_data def test_volume_stc(): """Test volume STCs """ tempdir = _TempDir() N = 100 data = np.arange(N)[:, np.newaxis] datas = [data, data, np.arange(2)[:, np.newaxis]] vertno = np.arange(N) vertnos = [vertno, vertno[:, np.newaxis], np.arange(2)[:, np.newaxis]] vertno_reads = [vertno, vertno, np.arange(2)] for data, vertno, vertno_read in zip(datas, vertnos, vertno_reads): stc = VolSourceEstimate(data, vertno, 0, 1) fname_temp = op.join(tempdir, 'temp-vl.stc') stc_new = stc for _ in range(2): stc_new.save(fname_temp) stc_new = read_source_estimate(fname_temp) assert_true(isinstance(stc_new, VolSourceEstimate)) assert_array_equal(vertno_read, stc_new.vertices) assert_array_almost_equal(stc.data, stc_new.data) # now let's actually read a MNE-C processed file stc = read_source_estimate(fname_vol, 'sample') assert_true(isinstance(stc, VolSourceEstimate)) assert_true('sample' in repr(stc)) stc_new = stc assert_raises(ValueError, stc.save, fname_vol, ftype='whatever') for _ in range(2): fname_temp = op.join(tempdir, 'temp-vol.w') stc_new.save(fname_temp, ftype='w') stc_new = read_source_estimate(fname_temp) assert_true(isinstance(stc_new, VolSourceEstimate)) assert_array_equal(stc.vertices, stc_new.vertices) assert_array_almost_equal(stc.data, stc_new.data) # save the stc as a nifti file and export try: import nibabel as nib with warnings.catch_warnings(record=True): warnings.simplefilter('always') src = read_source_spaces(fname_vsrc) vol_fname = op.join(tempdir, 'stc.nii.gz') stc.save_as_volume(vol_fname, src, dest='surf', mri_resolution=False) with warnings.catch_warnings(record=True): # nib<->numpy img = nib.load(vol_fname) assert_true(img.shape == src[0]['shape'] + (len(stc.times),)) with warnings.catch_warnings(record=True): # nib<->numpy t1_img = nib.load(fname_t1) stc.save_as_volume(op.join(tempdir, 'stc.nii.gz'), src, dest='mri', mri_resolution=True) with warnings.catch_warnings(record=True): # nib<->numpy img = nib.load(vol_fname) assert_true(img.shape == t1_img.shape + (len(stc.times),)) assert_array_almost_equal(img.affine, t1_img.affine, decimal=5) # export without saving img = stc.as_volume(src, dest='mri', mri_resolution=True) assert_true(img.shape == t1_img.shape + (len(stc.times),)) assert_array_almost_equal(img.affine, t1_img.affine, decimal=5) except ImportError: print('Save as nifti test skipped, needs NiBabel') @testing.requires_testing_data def test_expand(): """Test stc expansion """ stc = read_source_estimate(fname_stc, 'sample') assert_true('sample' in repr(stc)) labels_lh = read_labels_from_annot('sample', 'aparc', 'lh', subjects_dir=subjects_dir) new_label = labels_lh[0] + labels_lh[1] stc_limited = stc.in_label(new_label) 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.vertices) assert_raises(TypeError, stc_new.expand, stc_limited.vertices[0]) assert_raises(ValueError, stc_new.expand, [stc_limited.vertices[0]]) # make sure we can't add unless vertno agree assert_raises(ValueError, stc.__add__, stc.in_label(labels_lh[0])) def _fake_stc(n_time=10): verts = [np.arange(10), np.arange(90)] return SourceEstimate(np.random.rand(100, n_time), verts, 0, 1e-1, 'foo') def test_io_stc(): """Test IO for STC files """ tempdir = _TempDir() stc = _fake_stc() stc.save(op.join(tempdir, "tmp.stc")) stc2 = read_source_estimate(op.join(tempdir, "tmp.stc")) assert_array_almost_equal(stc.data, stc2.data) assert_array_almost_equal(stc.tmin, stc2.tmin) assert_equal(len(stc.vertices), len(stc2.vertices)) for v1, v2 in zip(stc.vertices, stc2.vertices): assert_array_almost_equal(v1, v2) assert_array_almost_equal(stc.tstep, stc2.tstep) @requires_h5py def test_io_stc_h5(): """Test IO for STC files using HDF5 """ tempdir = _TempDir() stc = _fake_stc() assert_raises(ValueError, stc.save, op.join(tempdir, 'tmp'), ftype='foo') out_name = op.join(tempdir, 'tmp') stc.save(out_name, ftype='h5') stc3 = read_source_estimate(out_name) stc4 = read_source_estimate(out_name + '-stc.h5') assert_raises(RuntimeError, read_source_estimate, out_name, subject='bar') for stc_new in stc3, stc4: assert_equal(stc_new.subject, stc.subject) assert_array_equal(stc_new.data, stc.data) assert_array_equal(stc_new.tmin, stc.tmin) assert_array_equal(stc_new.tstep, stc.tstep) assert_equal(len(stc_new.vertices), len(stc.vertices)) for v1, v2 in zip(stc_new.vertices, stc.vertices): assert_array_equal(v1, v2) def test_io_w(): """Test IO for w files """ tempdir = _TempDir() stc = _fake_stc(n_time=1) w_fname = op.join(tempdir, 'fake') stc.save(w_fname, ftype='w') src = read_source_estimate(w_fname) src.save(op.join(tempdir, 'tmp'), ftype='w') src2 = read_source_estimate(op.join(tempdir, 'tmp-lh.w')) assert_array_almost_equal(src.data, src2.data) assert_array_almost_equal(src.lh_vertno, src2.lh_vertno) assert_array_almost_equal(src.rh_vertno, src2.rh_vertno) def test_stc_arithmetic(): """Test arithmetic for STC files """ stc = _fake_stc() data = stc.data.copy() out = list() for a in [data, stc]: a = a + a * 3 + 3 * a - a ** 2 / 2 a += a a -= a with warnings.catch_warnings(record=True): warnings.simplefilter('always') a /= 2 * a a *= -a a += 2 a -= 1 a *= -1 a /= 2 b = 2 + a b = 2 - a b = +a assert_array_equal(b.data, a.data) with warnings.catch_warnings(record=True): warnings.simplefilter('always') a **= 3 out.append(a) assert_array_equal(out[0], out[1].data) assert_array_equal(stc.sqrt().data, np.sqrt(stc.data)) stc_mean = stc.mean() assert_array_equal(stc_mean.data, np.mean(stc.data, 1)[:, None]) @slow_test @testing.requires_testing_data def test_stc_methods(): """Test stc methods lh_data, rh_data, bin, center_of_mass, resample""" stc = read_source_estimate(fname_stc) # lh_data / rh_data assert_array_equal(stc.lh_data, stc.data[:len(stc.lh_vertno)]) assert_array_equal(stc.rh_data, stc.data[len(stc.lh_vertno):]) # bin bin = stc.bin(.12) a = np.array((1,), dtype=stc.data.dtype) a[0] = np.mean(stc.data[0, stc.times < .12]) assert a[0] == bin.data[0, 0] assert_raises(ValueError, stc.center_of_mass, 'sample') assert_raises(TypeError, stc.center_of_mass, 'sample', subjects_dir=subjects_dir, surf=1) stc.lh_data[:] = 0 vertex, hemi, t = stc.center_of_mass('sample', subjects_dir=subjects_dir) assert_true(hemi == 1) # XXX Should design a fool-proof test case, but here were the results: assert_equal(vertex, 124791) assert_equal(np.round(t, 2), 0.12) stc = read_source_estimate(fname_stc) stc.subject = 'sample' label_lh = read_labels_from_annot('sample', 'aparc', 'lh', subjects_dir=subjects_dir)[0] label_rh = read_labels_from_annot('sample', 'aparc', 'rh', subjects_dir=subjects_dir)[0] label_both = label_lh + label_rh for label in (label_lh, label_rh, label_both): assert_true(isinstance(stc.shape, tuple) and len(stc.shape) == 2) stc_label = stc.in_label(label) if label.hemi != 'both': if label.hemi == 'lh': verts = stc_label.vertices[0] else: # label.hemi == 'rh': verts = stc_label.vertices[1] n_vertices_used = len(label.get_vertices_used(verts)) assert_equal(len(stc_label.data), n_vertices_used) stc_lh = stc.in_label(label_lh) assert_raises(ValueError, stc_lh.in_label, label_rh) label_lh.subject = 'foo' assert_raises(RuntimeError, stc.in_label, label_lh) stc_new = deepcopy(stc) o_sfreq = 1.0 / stc.tstep # note that using no padding for this STC reduces edge ringing... stc_new.resample(2 * o_sfreq, npad=0, n_jobs=2) assert_true(stc_new.data.shape[1] == 2 * stc.data.shape[1]) assert_true(stc_new.tstep == stc.tstep / 2) stc_new.resample(o_sfreq, npad=0) assert_true(stc_new.data.shape[1] == stc.data.shape[1]) assert_true(stc_new.tstep == stc.tstep) assert_array_almost_equal(stc_new.data, stc.data, 5) @testing.requires_testing_data 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 = read_labels_from_annot('sample', hemi='lh', subjects_dir=subjects_dir) labels_rh = read_labels_from_annot('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)) label_maxs = 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: with warnings.catch_warnings(record=True): # empty label 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', 'max'] 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) if mode == 'max': assert_array_almost_equal(tc1, label_maxs) # 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) @slow_test @testing.requires_testing_data def test_morph_data(): """Test morphing of data """ tempdir = _TempDir() subject_from = 'sample' subject_to = 'fsaverage' stc_from = read_source_estimate(fname_smorph, subject='sample') stc_to = read_source_estimate(fname_fmorph) # make sure we can specify grade stc_from.crop(0.09, 0.1) # for faster computation stc_to.crop(0.09, 0.1) # for faster computation assert_array_equal(stc_to.time_as_index([0.09, 0.1], use_rounding=True), [0, len(stc_to.times) - 1]) assert_raises(ValueError, stc_from.morph, subject_to, grade=3, smooth=-1, subjects_dir=subjects_dir) stc_to1 = stc_from.morph(subject_to, grade=3, smooth=12, buffer_size=1000, subjects_dir=subjects_dir) stc_to1.save(op.join(tempdir, '%s_audvis-meg' % subject_to)) # Morphing to a density that is too high should raise an informative error # (here we need to push to grade=6, but for some subjects even grade=5 # will break) assert_raises(ValueError, stc_to1.morph, subject_from, grade=6, subjects_dir=subjects_dir) # make sure we can specify vertices vertices_to = grade_to_vertices(subject_to, grade=3, subjects_dir=subjects_dir) stc_to2 = morph_data(subject_from, subject_to, stc_from, grade=vertices_to, smooth=12, buffer_size=1000, subjects_dir=subjects_dir) # make sure we can use different buffer_size stc_to3 = morph_data(subject_from, subject_to, stc_from, grade=vertices_to, smooth=12, buffer_size=3, subjects_dir=subjects_dir) # make sure we get a warning about # of steps with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') morph_data(subject_from, subject_to, stc_from, grade=vertices_to, smooth=1, buffer_size=3, subjects_dir=subjects_dir) assert_equal(len(w), 2) assert_array_almost_equal(stc_to.data, stc_to1.data, 5) assert_array_almost_equal(stc_to1.data, stc_to2.data) assert_array_almost_equal(stc_to1.data, stc_to3.data) # make sure precomputed morph matrices work morph_mat = compute_morph_matrix(subject_from, subject_to, stc_from.vertices, vertices_to, smooth=12, subjects_dir=subjects_dir) stc_to3 = stc_from.morph_precomputed(subject_to, vertices_to, morph_mat) assert_array_almost_equal(stc_to1.data, stc_to3.data) assert_raises(ValueError, stc_from.morph_precomputed, subject_to, vertices_to, 'foo') assert_raises(ValueError, stc_from.morph_precomputed, subject_to, [vertices_to[0]], morph_mat) assert_raises(ValueError, stc_from.morph_precomputed, subject_to, [vertices_to[0][:-1], vertices_to[1]], morph_mat) assert_raises(ValueError, stc_from.morph_precomputed, subject_to, vertices_to, morph_mat, subject_from='foo') # steps warning with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') compute_morph_matrix(subject_from, subject_to, stc_from.vertices, vertices_to, smooth=1, subjects_dir=subjects_dir) assert_equal(len(w), 2) mean_from = stc_from.data.mean(axis=0) mean_to = stc_to1.data.mean(axis=0) assert_true(np.corrcoef(mean_to, mean_from).min() > 0.999) # make sure we can fill by morphing stc_to5 = morph_data(subject_from, subject_to, stc_from, grade=None, smooth=12, buffer_size=3, subjects_dir=subjects_dir) assert_true(stc_to5.data.shape[0] == 163842 + 163842) # Morph sparse data # Make a sparse stc stc_from.vertices[0] = stc_from.vertices[0][[100, 500]] stc_from.vertices[1] = stc_from.vertices[1][[200]] stc_from._data = stc_from._data[:3] assert_raises(RuntimeError, stc_from.morph, subject_to, sparse=True, grade=5, subjects_dir=subjects_dir) stc_to_sparse = stc_from.morph(subject_to, grade=None, sparse=True, subjects_dir=subjects_dir) assert_array_almost_equal(np.sort(stc_from.data.sum(axis=1)), np.sort(stc_to_sparse.data.sum(axis=1))) assert_equal(len(stc_from.rh_vertno), len(stc_to_sparse.rh_vertno)) assert_equal(len(stc_from.lh_vertno), len(stc_to_sparse.lh_vertno)) assert_equal(stc_to_sparse.subject, subject_to) assert_equal(stc_from.tmin, stc_from.tmin) assert_equal(stc_from.tstep, stc_from.tstep) stc_from.vertices[0] = np.array([], dtype=np.int64) stc_from._data = stc_from._data[:1] stc_to_sparse = stc_from.morph(subject_to, grade=None, sparse=True, subjects_dir=subjects_dir) assert_array_almost_equal(np.sort(stc_from.data.sum(axis=1)), np.sort(stc_to_sparse.data.sum(axis=1))) assert_equal(len(stc_from.rh_vertno), len(stc_to_sparse.rh_vertno)) assert_equal(len(stc_from.lh_vertno), len(stc_to_sparse.lh_vertno)) assert_equal(stc_to_sparse.subject, subject_to) assert_equal(stc_from.tmin, stc_from.tmin) assert_equal(stc_from.tstep, stc_from.tstep) def _my_trans(data): """FFT that adds an additional dimension by repeating result""" data_t = fft(data) data_t = np.concatenate([data_t[:, :, None], data_t[:, :, None]], axis=2) return data_t, None def test_transform_data(): """Test applying linear (time) transform to data""" # make up some data n_sensors, n_vertices, n_times = 10, 20, 4 kernel = rng.randn(n_vertices, n_sensors) sens_data = rng.randn(n_sensors, n_times) vertices = np.arange(n_vertices) data = np.dot(kernel, sens_data) for idx, tmin_idx, tmax_idx in\ zip([None, np.arange(n_vertices // 2, n_vertices)], [None, 1], [None, 3]): if idx is None: idx_use = slice(None, None) else: idx_use = idx data_f, _ = _my_trans(data[idx_use, tmin_idx:tmax_idx]) for stc_data in (data, (kernel, sens_data)): stc = VolSourceEstimate(stc_data, vertices=vertices, tmin=0., tstep=1.) stc_data_t = stc.transform_data(_my_trans, idx=idx, tmin_idx=tmin_idx, tmax_idx=tmax_idx) assert_allclose(data_f, stc_data_t) def test_transform(): """Test applying linear (time) transform to data""" # make up some data n_verts_lh, n_verts_rh, n_times = 10, 10, 10 vertices = [np.arange(n_verts_lh), n_verts_lh + np.arange(n_verts_rh)] data = rng.randn(n_verts_lh + n_verts_rh, n_times) stc = SourceEstimate(data, vertices=vertices, tmin=-0.1, tstep=0.1) # data_t.ndim > 2 & copy is True stcs_t = stc.transform(_my_trans, copy=True) assert_true(isinstance(stcs_t, list)) assert_array_equal(stc.times, stcs_t[0].times) assert_equal(stc.vertices, stcs_t[0].vertices) data = np.concatenate((stcs_t[0].data[:, :, None], stcs_t[1].data[:, :, None]), axis=2) data_t = stc.transform_data(_my_trans) assert_array_equal(data, data_t) # check against stc.transform_data() # data_t.ndim > 2 & copy is False assert_raises(ValueError, stc.transform, _my_trans, copy=False) # data_t.ndim = 2 & copy is True tmp = deepcopy(stc) stc_t = stc.transform(np.abs, copy=True) assert_true(isinstance(stc_t, SourceEstimate)) assert_array_equal(stc.data, tmp.data) # xfrm doesn't modify original? # data_t.ndim = 2 & copy is False times = np.round(1000 * stc.times) verts = np.arange(len(stc.lh_vertno), len(stc.lh_vertno) + len(stc.rh_vertno), 1) verts_rh = stc.rh_vertno t_idx = [np.where(times >= -50)[0][0], np.where(times <= 500)[0][-1]] data_t = stc.transform_data(np.abs, idx=verts, tmin_idx=t_idx[0], tmax_idx=t_idx[-1]) stc.transform(np.abs, idx=verts, tmin=-50, tmax=500, copy=False) assert_true(isinstance(stc, SourceEstimate)) assert_true((stc.tmin == 0.) & (stc.times[-1] == 0.5)) assert_true(len(stc.vertices[0]) == 0) assert_equal(stc.vertices[1], verts_rh) assert_array_equal(stc.data, data_t) times = np.round(1000 * stc.times) t_idx = [np.where(times >= 0)[0][0], np.where(times <= 250)[0][-1]] data_t = stc.transform_data(np.abs, tmin_idx=t_idx[0], tmax_idx=t_idx[-1]) stc.transform(np.abs, tmin=0, tmax=250, copy=False) assert_true((stc.tmin == 0.) & (stc.times[-1] == 0.2)) assert_array_equal(stc.data, data_t) @requires_sklearn def test_spatio_temporal_tris_connectivity(): """Test spatio-temporal connectivity from triangles""" tris = np.array([[0, 1, 2], [3, 4, 5]]) connectivity = spatio_temporal_tris_connectivity(tris, 2) x = [1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1] components = stats.cluster_level._get_components(np.array(x), connectivity) # _get_components works differently now... old_fmt = [0, 0, -2, -2, -2, -2, 0, -2, -2, -2, -2, 1] new_fmt = np.array(old_fmt) new_fmt = [np.nonzero(new_fmt == v)[0] for v in np.unique(new_fmt[new_fmt >= 0])] assert_true(len(new_fmt), len(components)) for c, n in zip(components, new_fmt): assert_array_equal(c, n) @testing.requires_testing_data def test_spatio_temporal_src_connectivity(): """Test spatio-temporal connectivity from source spaces""" tris = np.array([[0, 1, 2], [3, 4, 5]]) src = [dict(), dict()] connectivity = spatio_temporal_tris_connectivity(tris, 2) src[0]['use_tris'] = np.array([[0, 1, 2]]) src[1]['use_tris'] = np.array([[0, 1, 2]]) src[0]['vertno'] = np.array([0, 1, 2]) src[1]['vertno'] = np.array([0, 1, 2]) connectivity2 = spatio_temporal_src_connectivity(src, 2) assert_array_equal(connectivity.todense(), connectivity2.todense()) # add test for dist connectivity src[0]['dist'] = np.ones((3, 3)) - np.eye(3) src[1]['dist'] = np.ones((3, 3)) - np.eye(3) src[0]['vertno'] = [0, 1, 2] src[1]['vertno'] = [0, 1, 2] connectivity3 = spatio_temporal_src_connectivity(src, 2, dist=2) assert_array_equal(connectivity.todense(), connectivity3.todense()) # add test for source space connectivity with omitted vertices inverse_operator = read_inverse_operator(fname_inv) with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') src_ = inverse_operator['src'] connectivity = spatio_temporal_src_connectivity(src_, n_times=2) assert len(w) == 1 a = connectivity.shape[0] / 2 b = sum([s['nuse'] for s in inverse_operator['src']]) assert_true(a == b) assert_equal(grade_to_tris(5).shape, [40960, 3]) @requires_pandas def test_to_data_frame(): """Test stc Pandas exporter""" n_vert, n_times = 10, 5 vertices = [np.arange(n_vert, dtype=np.int), np.empty(0, dtype=np.int)] data = rng.randn(n_vert, n_times) stc_surf = SourceEstimate(data, vertices=vertices, tmin=0, tstep=1, subject='sample') stc_vol = VolSourceEstimate(data, vertices=vertices[0], tmin=0, tstep=1, subject='sample') for stc in [stc_surf, stc_vol]: assert_raises(ValueError, stc.to_data_frame, index=['foo', 'bar']) for ncat, ind in zip([1, 0], ['time', ['subject', 'time']]): df = stc.to_data_frame(index=ind) assert_true(df.index.names == ind if isinstance(ind, list) else [ind]) assert_array_equal(df.values.T[ncat:], stc.data) # test that non-indexed data were present as categorial variables assert_true(all([c in ['time', 'subject'] for c in df.reset_index().columns][:2])) def test_get_peak(): """Test peak getter """ n_vert, n_times = 10, 5 vertices = [np.arange(n_vert, dtype=np.int), np.empty(0, dtype=np.int)] data = rng.randn(n_vert, n_times) stc_surf = SourceEstimate(data, vertices=vertices, tmin=0, tstep=1, subject='sample') stc_vol = VolSourceEstimate(data, vertices=vertices[0], tmin=0, tstep=1, subject='sample') for ii, stc in enumerate([stc_surf, stc_vol]): assert_raises(ValueError, stc.get_peak, tmin=-100) assert_raises(ValueError, stc.get_peak, tmax=90) assert_raises(ValueError, stc.get_peak, tmin=0.002, tmax=0.001) vert_idx, time_idx = stc.get_peak() vertno = np.concatenate(stc.vertices) if ii == 0 else stc.vertices assert_true(vert_idx in vertno) assert_true(time_idx in stc.times) ch_idx, time_idx = stc.get_peak(vert_as_index=True, time_as_index=True) assert_true(vert_idx < stc.data.shape[0]) assert_true(time_idx < len(stc.times)) @testing.requires_testing_data def test_mixed_stc(): """Test source estimate from mixed source space """ N = 90 # number of sources T = 2 # number of time points S = 3 # number of source spaces data = rng.randn(N, T) vertno = S * [np.arange(N // S)] # make sure error is raised if vertices are not a list of length >= 2 assert_raises(ValueError, MixedSourceEstimate, data=data, vertices=[np.arange(N)]) stc = MixedSourceEstimate(data, vertno, 0, 1) vol = read_source_spaces(fname_vsrc) # make sure error is raised for plotting surface with volume source assert_raises(ValueError, stc.plot_surface, src=vol) run_tests_if_main()