# Authors: Eric Larson # Alexandre Gramfort # # License: BSD-3-Clause from functools import partial import os import numpy as np from scipy import sparse, linalg, stats from numpy.testing import (assert_equal, assert_array_equal, assert_array_almost_equal, assert_allclose) import pytest from mne import (SourceEstimate, VolSourceEstimate, MixedSourceEstimate, SourceSpaces) from mne.stats import ttest_ind_no_p, combine_adjacency from mne.stats.cluster_level import (permutation_cluster_test, f_oneway, permutation_cluster_1samp_test, spatio_temporal_cluster_test, spatio_temporal_cluster_1samp_test, ttest_1samp_no_p, summarize_clusters_stc) from mne.utils import (catch_logging, check_version, requires_sklearn, _record_warnings) n_space = 50 def _get_conditions(): noise_level = 20 n_time_1 = 20 n_time_2 = 13 normfactor = np.hanning(20).sum() rng = np.random.RandomState(42) condition1_1d = rng.randn(n_time_1, n_space) * noise_level for c in condition1_1d: c[:] = np.convolve(c, np.hanning(20), mode="same") / normfactor condition2_1d = rng.randn(n_time_2, n_space) * noise_level for c in condition2_1d: c[:] = np.convolve(c, np.hanning(20), mode="same") / normfactor pseudoekp = 10 * np.hanning(25)[None, :] condition1_1d[:, 25:] += pseudoekp condition2_1d[:, 25:] -= pseudoekp condition1_2d = condition1_1d[:, :, np.newaxis] condition2_2d = condition2_1d[:, :, np.newaxis] return condition1_1d, condition2_1d, condition1_2d, condition2_2d def test_thresholds(numba_conditional): """Test automatic threshold calculations.""" # within subjects rng = np.random.RandomState(0) X = rng.randn(10, 1, 1) + 0.08 want_thresh = -stats.t.ppf(0.025, len(X) - 1) assert 0.03 < stats.ttest_1samp(X[:, 0, 0], 0)[1] < 0.05 my_fun = partial(ttest_1samp_no_p) with catch_logging() as log: with pytest.warns(RuntimeWarning, match='threshold is only valid'): out = permutation_cluster_1samp_test( X, stat_fun=my_fun, seed=0, verbose=True, out_type='mask') log = log.getvalue() assert str(want_thresh)[:6] in log assert len(out[1]) == 1 # 1 cluster assert_allclose(out[2], 0.033203, atol=1e-6) # between subjects Y = rng.randn(10, 1, 1) Z = rng.randn(10, 1, 1) - 0.7 X = [X, Y, Z] want_thresh = stats.f.ppf(1. - 0.05, 2, sum(len(a) for a in X) - len(X)) p = stats.f_oneway(*X)[1] assert 0.03 < p < 0.05 my_fun = partial(f_oneway) # just to make the check fail with catch_logging() as log: with pytest.warns(RuntimeWarning, match='threshold is only valid'): out = permutation_cluster_test(X, tail=1, stat_fun=my_fun, seed=0, verbose=True, out_type='mask') log = log.getvalue() assert str(want_thresh)[:6] in log assert len(out[1]) == 1 # 1 cluster assert_allclose(out[2], 0.041992, atol=1e-6) with pytest.warns(RuntimeWarning, match='Ignoring argument "tail"'): permutation_cluster_test(X, tail=0, out_type='mask') # nan handling in TFCE X = np.repeat(X[0], 2, axis=1) X[:, 1] = 0 with pytest.warns(RuntimeWarning, match='invalid value'): # NumPy out = permutation_cluster_1samp_test( X, seed=0, threshold=dict(start=0, step=0.1), out_type='mask') assert (out[2] < 0.05).any() assert not (out[2] < 0.05).all() X[:, 0] = 0 with pytest.raises(RuntimeError, match='finite'): with np.errstate(invalid='ignore'): permutation_cluster_1samp_test( X, seed=0, threshold=dict(start=0, step=0.1), buffer_size=None, out_type='mask') def test_cache_dir(tmp_path, numba_conditional): """Test use of cache dir.""" tempdir = str(tmp_path) orig_dir = os.getenv('MNE_CACHE_DIR', None) orig_size = os.getenv('MNE_MEMMAP_MIN_SIZE', None) rng = np.random.RandomState(0) X = rng.randn(9, 2, 10) try: os.environ['MNE_MEMMAP_MIN_SIZE'] = '1K' os.environ['MNE_CACHE_DIR'] = tempdir # Fix error for #1507: in-place when memmapping with catch_logging() as log_file: permutation_cluster_1samp_test( X, buffer_size=None, n_jobs=2, n_permutations=1, seed=0, stat_fun=ttest_1samp_no_p, verbose=False, out_type='mask') assert 'independently' not in log_file.getvalue() # ensure that non-independence yields warning stat_fun = partial(ttest_1samp_no_p, sigma=1e-3) if check_version('numpy', '1.17'): random_state = np.random.default_rng(0) else: random_state = 0 with pytest.warns(RuntimeWarning, match='independently'): permutation_cluster_1samp_test( X, buffer_size=10, n_jobs=2, n_permutations=1, seed=random_state, stat_fun=stat_fun, verbose=False, out_type='mask') finally: if orig_dir is not None: os.environ['MNE_CACHE_DIR'] = orig_dir else: del os.environ['MNE_CACHE_DIR'] if orig_size is not None: os.environ['MNE_MEMMAP_MIN_SIZE'] = orig_size else: del os.environ['MNE_MEMMAP_MIN_SIZE'] def test_permutation_large_n_samples(numba_conditional): """Test that non-replacement works with large N.""" X = np.random.RandomState(0).randn(72, 1) + 1 for n_samples in (11, 72): tails = (0, 1) if n_samples <= 20 else (0,) for tail in tails: H0 = permutation_cluster_1samp_test( X[:n_samples], threshold=1e-4, tail=tail, out_type='mask')[-1] assert H0.shape == (1024,) assert len(np.unique(H0)) >= 1024 - (H0 == 0).sum() def test_permutation_step_down_p(numba_conditional): """Test cluster level permutations with step_down_p.""" rng = np.random.RandomState(0) # subjects, time points, spatial points X = rng.randn(9, 2, 10) # add some significant points X[:, 0:2, 0:2] += 2 # span two time points and two spatial points X[:, 1, 5:9] += 0.5 # span four time points with 4x smaller amplitude thresh = 2 # make sure it works when we use ALL points in step-down t, clusters, p, H0 = \ permutation_cluster_1samp_test(X, threshold=thresh, step_down_p=1.0, out_type='mask') # make sure using step-down will actually yield improvements sometimes t, clusters, p_old, H0 = \ permutation_cluster_1samp_test(X, threshold=thresh, step_down_p=0.0, out_type='mask') assert_equal(np.sum(p_old < 0.05), 1) # just spatial cluster p_min = np.min(p_old) assert_allclose(p_min, 0.003906, atol=1e-6) t, clusters, p_new, H0 = \ permutation_cluster_1samp_test(X, threshold=thresh, step_down_p=0.05, out_type='mask') assert_equal(np.sum(p_new < 0.05), 2) # time one rescued assert np.all(p_old >= p_new) p_next = p_new[(p_new > 0.004) & (p_new < 0.05)][0] assert_allclose(p_next, 0.015625, atol=1e-6) def test_cluster_permutation_test(numba_conditional): """Test cluster level permutations tests.""" condition1_1d, condition2_1d, condition1_2d, condition2_2d = \ _get_conditions() for condition1, condition2 in zip((condition1_1d, condition1_2d), (condition2_1d, condition2_2d)): T_obs, clusters, cluster_p_values, hist = permutation_cluster_test( [condition1, condition2], n_permutations=100, tail=1, seed=1, buffer_size=None, out_type='mask') p_min = np.min(cluster_p_values) assert_equal(np.sum(cluster_p_values < 0.05), 1) assert_allclose(p_min, 0.01, atol=1e-6) # test with 2 jobs and buffer_size enabled buffer_size = condition1.shape[1] // 10 T_obs, clusters, cluster_p_values_buff, hist =\ permutation_cluster_test([condition1, condition2], n_permutations=100, tail=1, seed=1, n_jobs=2, buffer_size=buffer_size, out_type='mask') assert_array_equal(cluster_p_values, cluster_p_values_buff) def stat_fun(X, Y): return stats.f_oneway(X, Y)[0] with pytest.warns(RuntimeWarning, match='is only valid'): permutation_cluster_test([condition1, condition2], n_permutations=1, stat_fun=stat_fun, out_type='mask') @pytest.mark.parametrize('stat_fun', [ ttest_1samp_no_p, partial(ttest_1samp_no_p, sigma=1e-1) ]) def test_cluster_permutation_t_test(numba_conditional, stat_fun): """Test cluster level permutations T-test.""" condition1_1d, condition2_1d, condition1_2d, condition2_2d = \ _get_conditions() # use a very large sigma to make sure Ts are not independent for condition1, p in ((condition1_1d, 0.01), (condition1_2d, 0.01)): # these are so significant we can get away with fewer perms T_obs, clusters, cluster_p_values, hist =\ permutation_cluster_1samp_test(condition1, n_permutations=100, tail=0, seed=1, out_type='mask', buffer_size=None) assert_equal(np.sum(cluster_p_values < 0.05), 1) p_min = np.min(cluster_p_values) assert_allclose(p_min, p, atol=1e-6) T_obs_pos, c_1, cluster_p_values_pos, _ =\ permutation_cluster_1samp_test(condition1, n_permutations=100, tail=1, threshold=1.67, seed=1, stat_fun=stat_fun, out_type='mask', buffer_size=None) T_obs_neg, _, cluster_p_values_neg, _ =\ permutation_cluster_1samp_test(-condition1, n_permutations=100, tail=-1, threshold=-1.67, seed=1, stat_fun=stat_fun, buffer_size=None, out_type='mask') assert_array_equal(T_obs_pos, -T_obs_neg) assert_array_equal(cluster_p_values_pos < 0.05, cluster_p_values_neg < 0.05) # test with 2 jobs and buffer_size enabled buffer_size = condition1.shape[1] // 10 with _record_warnings(): # sometimes "independently" T_obs_neg_buff, _, cluster_p_values_neg_buff, _ = \ permutation_cluster_1samp_test( -condition1, n_permutations=100, tail=-1, out_type='mask', threshold=-1.67, seed=1, n_jobs=2, stat_fun=stat_fun, buffer_size=buffer_size) assert_array_equal(T_obs_neg, T_obs_neg_buff) assert_array_equal(cluster_p_values_neg, cluster_p_values_neg_buff) # Bad stat_fun with pytest.raises(TypeError, match='must be .* ndarray'): permutation_cluster_1samp_test( condition1, threshold=1, stat_fun=lambda x: None, out_type='mask') with pytest.raises(ValueError, match='not compatible'): permutation_cluster_1samp_test( condition1, threshold=1, stat_fun=lambda x: stat_fun(x)[:-1], out_type='mask') @requires_sklearn def test_cluster_permutation_with_adjacency(numba_conditional, monkeypatch): """Test cluster level permutations with adjacency matrix.""" from sklearn.feature_extraction.image import grid_to_graph condition1_1d, condition2_1d, condition1_2d, condition2_2d = \ _get_conditions() n_pts = condition1_1d.shape[1] # we don't care about p-values in any of these, so do fewer permutations args = dict(seed=None, max_step=1, exclude=None, out_type='mask', step_down_p=0, t_power=1, threshold=1.67, check_disjoint=False, n_permutations=50) did_warn = False for X1d, X2d, func, spatio_temporal_func in \ [(condition1_1d, condition1_2d, permutation_cluster_1samp_test, spatio_temporal_cluster_1samp_test), ([condition1_1d, condition2_1d], [condition1_2d, condition2_2d], permutation_cluster_test, spatio_temporal_cluster_test)]: out = func(X1d, **args) adjacency = grid_to_graph(1, n_pts) out_adjacency = func(X1d, adjacency=adjacency, **args) assert_array_equal(out[0], out_adjacency[0]) for a, b in zip(out_adjacency[1], out[1]): assert_array_equal(out[0][a], out[0][b]) assert np.all(a[b]) # test spatio-temporal w/o time adjacency (repeat spatial pattern) adjacency_2 = sparse.coo_matrix( linalg.block_diag(adjacency.asfptype().todense(), adjacency.asfptype().todense())) # nesting here is time then space: adjacency_2a = combine_adjacency(np.eye(2), adjacency) assert_array_equal(adjacency_2.toarray().astype(bool), adjacency_2a.toarray().astype(bool)) if isinstance(X1d, list): X1d_2 = [np.concatenate((x, x), axis=1) for x in X1d] else: X1d_2 = np.concatenate((X1d, X1d), axis=1) out_adjacency_2 = func(X1d_2, adjacency=adjacency_2, **args) # make sure we were operating on the same values split = len(out[0]) assert_array_equal(out[0], out_adjacency_2[0][:split]) assert_array_equal(out[0], out_adjacency_2[0][split:]) # make sure we really got 2x the number of original clusters n_clust_orig = len(out[1]) assert len(out_adjacency_2[1]) == 2 * n_clust_orig # Make sure that we got the old ones back data_1 = {np.sum(out[0][b[:n_pts]]) for b in out[1]} data_2 = {np.sum(out_adjacency_2[0][a]) for a in out_adjacency_2[1][:]} assert len(data_1.intersection(data_2)) == len(data_1) # now use the other algorithm if isinstance(X1d, list): X1d_3 = [np.reshape(x, (-1, 2, n_space)) for x in X1d_2] else: X1d_3 = np.reshape(X1d_2, (-1, 2, n_space)) out_adjacency_3 = spatio_temporal_func( X1d_3, n_permutations=50, adjacency=adjacency, max_step=0, threshold=1.67, check_disjoint=True) # make sure we were operating on the same values split = len(out[0]) assert_array_equal(out[0], out_adjacency_3[0][0]) assert_array_equal(out[0], out_adjacency_3[0][1]) # make sure we really got 2x the number of original clusters assert len(out_adjacency_3[1]) == 2 * n_clust_orig # Make sure that we got the old ones back data_1 = {np.sum(out[0][b[:n_pts]]) for b in out[1]} data_2 = {np.sum(out_adjacency_3[0][a[0], a[1]]) for a in out_adjacency_3[1]} assert len(data_1.intersection(data_2)) == len(data_1) # test new versus old method out_adjacency_4 = spatio_temporal_func( X1d_3, n_permutations=50, adjacency=adjacency, max_step=2, threshold=1.67) out_adjacency_5 = spatio_temporal_func( X1d_3, n_permutations=50, adjacency=adjacency, max_step=1, threshold=1.67) # clusters could be in a different order sums_4 = [np.sum(out_adjacency_4[0][a]) for a in out_adjacency_4[1]] sums_5 = [np.sum(out_adjacency_4[0][a]) for a in out_adjacency_5[1]] sums_4 = np.sort(sums_4) sums_5 = np.sort(sums_5) assert_array_almost_equal(sums_4, sums_5) monkeypatch.delenv('MNE_FORCE_SERIAL', raising=False) with pytest.raises(ValueError, match='must not be less'): spatio_temporal_func( X1d_3, n_permutations=1, adjacency=adjacency, max_step=1, threshold=1.67, n_jobs=-1000) # not enough TFCE params with pytest.raises(KeyError, match='threshold, if dict, must have'): spatio_temporal_func( X1d_3, adjacency=adjacency, threshold=dict(me='hello')) # too extreme a start threshold with _record_warnings() as w: spatio_temporal_func(X1d_3, adjacency=adjacency, threshold=dict(start=10, step=1)) if not did_warn: assert len(w) == 1 did_warn = True with pytest.raises(ValueError, match='threshold.*<= 0 for tail == -1'): spatio_temporal_func( X1d_3, adjacency=adjacency, tail=-1, threshold=dict(start=1, step=-1)) with pytest.warns(RuntimeWarning, match='threshold.* is more extreme'): spatio_temporal_func( X1d_3, adjacency=adjacency, tail=1, threshold=dict(start=100, step=1)) bad_con = adjacency.todense() with pytest.raises(ValueError, match='must be a SciPy sparse matrix'): spatio_temporal_func( X1d_3, n_permutations=50, adjacency=bad_con, max_step=1, threshold=1.67) bad_con = adjacency.tocsr()[:-1, :-1].tocoo() with pytest.raises(ValueError, match='adjacency.*the correct size'): spatio_temporal_func( X1d_3, n_permutations=50, adjacency=bad_con, max_step=1, threshold=1.67) with pytest.raises(TypeError, match='must be a'): spatio_temporal_func( X1d_3, adjacency=adjacency, threshold=[]) with pytest.raises(ValueError, match='Invalid value for the \'tail\''): # sometimes ignoring tail with _record_warnings(): spatio_temporal_func( X1d_3, adjacency=adjacency, tail=2) # make sure it actually found a significant point out_adjacency_6 = spatio_temporal_func( X1d_3, n_permutations=50, adjacency=adjacency, max_step=1, threshold=dict(start=1, step=1)) assert np.min(out_adjacency_6[2]) < 0.05 with pytest.raises(ValueError, match='not compatible'): with pytest.warns(RuntimeWarning, match='No clusters'): spatio_temporal_func( X1d_3, n_permutations=50, adjacency=adjacency, threshold=1e-3, stat_fun=lambda *x: f_oneway(*x)[:-1], buffer_size=None) @pytest.mark.parametrize('threshold', [ 0.1, pytest.param(dict(start=0., step=0.5), id='TFCE'), ]) @pytest.mark.parametrize('kind', ('1samp', 'ind')) def test_permutation_cluster_signs(threshold, kind): """Test cluster signs.""" # difference between two conditions for 3 subjects x 2 vertices x 2 times X = np.array([[[-10, 5], [-2, -7]], [[-4, 5], [-8, -0]], [[-6, 3], [-4, -2]]], float) want_signs = np.sign(np.mean(X, axis=0)) n_permutations = 1 if kind == '1samp': func = permutation_cluster_1samp_test stat_fun = ttest_1samp_no_p use_X = X else: assert kind == 'ind' func = permutation_cluster_test stat_fun = ttest_ind_no_p use_X = [X, np.random.RandomState(0).randn(*X.shape) * 0.1] tobs, clu, clu_pvalues, _ = func( use_X, n_permutations=n_permutations, threshold=threshold, tail=0, stat_fun=stat_fun, out_type='mask') clu_signs = np.zeros(X.shape[1:]) used = np.zeros(X.shape[1:]) assert len(clu) == len(clu_pvalues) for c, p in zip(clu, clu_pvalues): assert not used[c].any() assert len(np.unique(np.sign(tobs[c]))) == 1 clu_signs[c] = np.sign(tobs[c])[0] used[c] = True assert used.all() assert clu_signs.all() assert_array_equal(np.sign(tobs), want_signs) assert_array_equal(clu_signs, want_signs) @requires_sklearn def test_permutation_adjacency_equiv(numba_conditional): """Test cluster level permutations with and without adjacency.""" from sklearn.feature_extraction.image import grid_to_graph rng = np.random.RandomState(0) # subjects, time points, spatial points n_time = 2 n_space = 4 X = rng.randn(6, n_time, n_space) # add some significant points X[:, :, 0:2] += 10 # span two time points and two spatial points X[:, 1, 3] += 20 # span one time point max_steps = [1, 1, 1, 2, 1] # This will run full algorithm in two ways, then the ST-algorithm in 2 ways # All of these should give the same results adjs = [None, grid_to_graph(n_time, n_space), grid_to_graph(1, n_space), grid_to_graph(1, n_space), None] stat_map = None thresholds = [2, 2, 2, 2, dict(start=0.01, step=1.0)] sig_counts = [2, 2, 2, 2, 5] stat_fun = partial(ttest_1samp_no_p, sigma=1e-3) cs = None ps = None for thresh, count, max_step, adj in zip(thresholds, sig_counts, max_steps, adjs): t, clusters, p, H0 = \ permutation_cluster_1samp_test( X, threshold=thresh, adjacency=adj, n_jobs=2, max_step=max_step, stat_fun=stat_fun, seed=0, out_type='mask') # make sure our output datatype is correct assert isinstance(clusters[0], np.ndarray) assert clusters[0].dtype == bool assert_array_equal(clusters[0].shape, X.shape[1:]) # make sure all comparisons were done; for TFCE, no perm # should come up empty inds = np.where(p < 0.05)[0] assert_equal(len(inds), count) assert_allclose(p[inds], 0.03125, atol=1e-6) if isinstance(thresh, dict): assert_equal(len(clusters), n_time * n_space) assert np.all(H0 != 0) continue this_cs = [clusters[ii] for ii in inds] this_ps = p[inds] this_stat_map = np.zeros((n_time, n_space), dtype=bool) for ci, c in enumerate(this_cs): if isinstance(c, tuple): this_c = np.zeros((n_time, n_space), bool) for x, y in zip(c[0], c[1]): this_stat_map[x, y] = True this_c[x, y] = True this_cs[ci] = this_c c = this_c this_stat_map[c] = True if cs is None: ps = this_ps cs = this_cs if stat_map is None: stat_map = this_stat_map assert_array_equal(ps, this_ps) assert len(cs) == len(this_cs) for c1, c2 in zip(cs, this_cs): assert_array_equal(c1, c2) assert_array_equal(stat_map, this_stat_map) @requires_sklearn def test_spatio_temporal_cluster_adjacency(numba_conditional): """Test spatio-temporal cluster permutations.""" from sklearn.feature_extraction.image import grid_to_graph condition1_1d, condition2_1d, condition1_2d, condition2_2d = \ _get_conditions() rng = np.random.RandomState(0) noise1_2d = rng.randn(condition1_2d.shape[0], condition1_2d.shape[1], 10) data1_2d = np.transpose(np.dstack((condition1_2d, noise1_2d)), [0, 2, 1]) noise2_d2 = rng.randn(condition2_2d.shape[0], condition2_2d.shape[1], 10) data2_2d = np.transpose(np.dstack((condition2_2d, noise2_d2)), [0, 2, 1]) adj = grid_to_graph(data1_2d.shape[-1], 1) threshold = dict(start=4.0, step=2) T_obs, clusters, p_values_adj, hist = \ spatio_temporal_cluster_test([data1_2d, data2_2d], adjacency=adj, n_permutations=50, tail=1, seed=1, threshold=threshold, buffer_size=None) buffer_size = data1_2d.size // 10 T_obs, clusters, p_values_no_adj, hist = \ spatio_temporal_cluster_test([data1_2d, data2_2d], n_permutations=50, tail=1, seed=1, threshold=threshold, n_jobs=2, buffer_size=buffer_size) assert_equal(np.sum(p_values_adj < 0.05), np.sum(p_values_no_adj < 0.05)) # make sure results are the same without buffer_size T_obs, clusters, p_values2, hist2 = \ spatio_temporal_cluster_test([data1_2d, data2_2d], n_permutations=50, tail=1, seed=1, threshold=threshold, n_jobs=2, buffer_size=None) assert_array_equal(p_values_no_adj, p_values2) pytest.raises(ValueError, spatio_temporal_cluster_test, [data1_2d, data2_2d], tail=1, threshold=-2.) pytest.raises(ValueError, spatio_temporal_cluster_test, [data1_2d, data2_2d], tail=-1, threshold=2.) pytest.raises(ValueError, spatio_temporal_cluster_test, [data1_2d, data2_2d], tail=0, threshold=-1) def ttest_1samp(X): """Return T-values.""" return stats.ttest_1samp(X, 0)[0] @pytest.mark.parametrize('kind', ('surface', 'volume', 'mixed')) def test_summarize_clusters(kind): """Test cluster summary stcs.""" src_surf = SourceSpaces( [dict(vertno=np.arange(10242), type='surf') for _ in range(2)]) assert src_surf.kind == 'surface' src_vol = SourceSpaces( [dict(vertno=np.arange(10), type='vol')]) assert src_vol.kind == 'volume' if kind == 'surface': src = src_surf klass = SourceEstimate elif kind == 'volume': src = src_vol klass = VolSourceEstimate else: assert kind == 'mixed' src = src_surf + src_vol klass = MixedSourceEstimate n_vertices = sum(len(s['vertno']) for s in src) clu = (np.random.random([1, n_vertices]), [(np.array([0]), np.array([0, 2, 4]))], np.array([0.02, 0.1]), np.array([12, -14, 30])) kwargs = dict() if kind == 'volume': with pytest.raises(ValueError, match='did not match'): summarize_clusters_stc(clu) assert len(src) == 1 kwargs['vertices'] = [src[0]['vertno']] elif kind == 'mixed': kwargs['vertices'] = src stc_sum = summarize_clusters_stc(clu, **kwargs) assert isinstance(stc_sum, klass) assert stc_sum.data.shape[1] == 2 clu[2][0] = 0.3 with pytest.raises(RuntimeError, match='No significant'): summarize_clusters_stc(clu, **kwargs) def test_permutation_test_H0(numba_conditional): """Test that H0 is populated properly during testing.""" rng = np.random.RandomState(0) data = rng.rand(7, 10, 1) - 0.5 with pytest.warns(RuntimeWarning, match='No clusters found'): t, clust, p, h0 = spatio_temporal_cluster_1samp_test( data, threshold=100, n_permutations=1024, seed=rng) assert_equal(len(h0), 0) for n_permutations in (1024, 65, 64, 63): t, clust, p, h0 = spatio_temporal_cluster_1samp_test( data, threshold=0.1, n_permutations=n_permutations, seed=rng) assert_equal(len(h0), min(n_permutations, 64)) assert isinstance(clust[0], tuple) # sets of indices for tail, thresh in zip((-1, 0, 1), (-0.1, 0.1, 0.1)): t, clust, p, h0 = spatio_temporal_cluster_1samp_test( data, threshold=thresh, seed=rng, tail=tail, out_type='mask') assert isinstance(clust[0], np.ndarray) # bool mask # same as "128 if tail else 64" assert_equal(len(h0), 2 ** (7 - (tail == 0))) # exact test def test_tfce_thresholds(numba_conditional): """Test TFCE thresholds.""" rng = np.random.RandomState(0) data = rng.randn(7, 10, 1) - 0.5 # if tail==-1, step must also be negative with pytest.raises(ValueError, match='must be < 0 for tail == -1'): permutation_cluster_1samp_test( data, tail=-1, out_type='mask', threshold=dict(start=0, step=0.1)) # this works (smoke test) permutation_cluster_1samp_test(data, tail=-1, out_type='mask', threshold=dict(start=0, step=-0.1)) # thresholds must be monotonically increasing with pytest.raises(ValueError, match='must be monotonically increasing'): permutation_cluster_1samp_test( data, tail=1, out_type='mask', threshold=dict(start=1, step=-0.5)) # Should work with 2D data too permutation_cluster_1samp_test(X=data[..., 0], threshold=dict(start=0, step=0.2)) # 1D gives slices, 2D+ gives boolean masks @pytest.mark.parametrize('shape', ((11,), (11, 3), (11, 1, 2))) @pytest.mark.parametrize('out_type', ('mask', 'indices')) @pytest.mark.parametrize('adjacency', (None, 'sparse')) def test_output_equiv(shape, out_type, adjacency): """Test equivalence of output types.""" rng = np.random.RandomState(0) n_subjects = 10 data = rng.randn(n_subjects, *shape) data -= data.mean(axis=0, keepdims=True) data[:, 2:4] += 2 data[:, 6:9] += 2 want_mask = np.zeros(shape, int) want_mask[2:4] = 1 want_mask[6:9] = 2 if adjacency is not None: assert adjacency == 'sparse' adjacency = combine_adjacency(*shape) clusters = permutation_cluster_1samp_test( X=data, n_permutations=1, adjacency=adjacency, out_type=out_type)[1] got_mask = np.zeros_like(want_mask) for n, clu in enumerate(clusters, 1): if out_type == 'mask': if len(shape) == 1 and adjacency is None: assert isinstance(clu, tuple) assert len(clu) == 1 assert isinstance(clu[0], slice) else: assert isinstance(clu, np.ndarray) assert clu.dtype == bool assert clu.shape == shape got_mask[clu] = n else: assert isinstance(clu, tuple) for c in clu: assert isinstance(c, np.ndarray) assert c.dtype.kind == 'i' assert out_type == 'indices' got_mask[np.ix_(*clu)] = n assert_array_equal(got_mask, want_mask)