def test_morphed_source_space_return(): """Test returning a morphed source space to the original subject""" # let's create some random data on fsaverage data = rng.randn(20484, 1) tmin, tstep = 0, 1. src_fs = read_source_spaces(fname_fs) stc_fs = SourceEstimate(data, [s['vertno'] for s in src_fs], tmin, tstep, 'fsaverage') # Create our morph source space src_morph = morph_source_spaces(src_fs, 'sample', subjects_dir=subjects_dir) # Morph the data over using standard methods stc_morph = stc_fs.morph('sample', [s['vertno'] for s in src_morph], smooth=1, subjects_dir=subjects_dir) # We can now pretend like this was real data we got e.g. from an inverse. # To be complete, let's remove some vertices keeps = [np.sort(rng.permutation(np.arange(len(v)))[:len(v) - 10]) for v in stc_morph.vertices] stc_morph = SourceEstimate( np.concatenate([stc_morph.lh_data[keeps[0]], stc_morph.rh_data[keeps[1]]]), [v[k] for v, k in zip(stc_morph.vertices, keeps)], tmin, tstep, 'sample') # Return it to the original subject stc_morph_return = stc_morph.to_original_src( src_fs, subjects_dir=subjects_dir) # Compare to the original data stc_morph_morph = stc_morph.morph('fsaverage', stc_morph_return.vertices, smooth=1, subjects_dir=subjects_dir) assert_equal(stc_morph_return.subject, stc_morph_morph.subject) for ii in range(2): assert_array_equal(stc_morph_return.vertices[ii], stc_morph_morph.vertices[ii]) # These will not match perfectly because morphing pushes data around corr = np.corrcoef(stc_morph_return.data[:, 0], stc_morph_morph.data[:, 0])[0, 1] assert_true(corr > 0.99, corr) # Degenerate cases stc_morph.subject = None # no .subject provided assert_raises(ValueError, stc_morph.to_original_src, src_fs, subject_orig='fsaverage', subjects_dir=subjects_dir) stc_morph.subject = 'sample' del src_fs[0]['subject_his_id'] # no name in src_fsaverage assert_raises(ValueError, stc_morph.to_original_src, src_fs, subjects_dir=subjects_dir) src_fs[0]['subject_his_id'] = 'fsaverage' # name mismatch assert_raises(ValueError, stc_morph.to_original_src, src_fs, subject_orig='foo', subjects_dir=subjects_dir) src_fs[0]['subject_his_id'] = 'sample' src = read_source_spaces(fname) # wrong source space assert_raises(RuntimeError, stc_morph.to_original_src, src, subjects_dir=subjects_dir)
def test_morphed_source_space_return(): """Test returning a morphed source space to the original subject.""" # let's create some random data on fsaverage data = rng.randn(20484, 1) tmin, tstep = 0, 1. src_fs = read_source_spaces(fname_fs) stc_fs = SourceEstimate(data, [s['vertno'] for s in src_fs], tmin, tstep, 'fsaverage') n_verts_fs = sum(len(s['vertno']) for s in src_fs) # Create our morph source space src_morph = morph_source_spaces(src_fs, 'sample', subjects_dir=subjects_dir) n_verts_sample = sum(len(s['vertno']) for s in src_morph) assert n_verts_fs == n_verts_sample # Morph the data over using standard methods stc_morph = compute_source_morph(src_fs, 'fsaverage', 'sample', spacing=[s['vertno'] for s in src_morph], smooth=1, subjects_dir=subjects_dir, warn=False).apply(stc_fs) assert stc_morph.data.shape[0] == n_verts_sample # We can now pretend like this was real data we got e.g. from an inverse. # To be complete, let's remove some vertices keeps = [ np.sort(rng.permutation(np.arange(len(v)))[:len(v) - 10]) for v in stc_morph.vertices ] stc_morph = SourceEstimate( np.concatenate([ stc_morph.lh_data[keeps[0]], stc_morph.rh_data[keeps[1]] ]), [v[k] for v, k in zip(stc_morph.vertices, keeps)], tmin, tstep, 'sample') # Return it to the original subject stc_morph_return = stc_morph.to_original_src(src_fs, subjects_dir=subjects_dir) # This should fail (has too many verts in SourceMorph) with pytest.warns(RuntimeWarning, match='vertices not included'): morph = compute_source_morph(src_morph, subject_from='sample', spacing=stc_morph_return.vertices, smooth=1, subjects_dir=subjects_dir) with pytest.raises(ValueError, match='vertices do not match'): morph.apply(stc_morph) # Compare to the original data with pytest.warns(RuntimeWarning, match='vertices not included'): stc_morph_morph = compute_source_morph( src=stc_morph, subject_from='sample', spacing=stc_morph_return.vertices, smooth=1, subjects_dir=subjects_dir).apply(stc_morph) assert_equal(stc_morph_return.subject, stc_morph_morph.subject) for ii in range(2): assert_array_equal(stc_morph_return.vertices[ii], stc_morph_morph.vertices[ii]) # These will not match perfectly because morphing pushes data around corr = np.corrcoef(stc_morph_return.data[:, 0], stc_morph_morph.data[:, 0])[0, 1] assert corr > 0.99, corr # Explicitly test having two vertices map to the same target vertex. We # simulate this by having two vertices be at the same position. src_fs2 = src_fs.copy() vert1, vert2 = src_fs2[0]['vertno'][:2] src_fs2[0]['rr'][vert1] = src_fs2[0]['rr'][vert2] stc_morph_return = stc_morph.to_original_src(src_fs2, subjects_dir=subjects_dir) # test to_original_src method result equality for ii in range(2): assert_array_equal(stc_morph_return.vertices[ii], stc_morph_morph.vertices[ii]) # These will not match perfectly because morphing pushes data around corr = np.corrcoef(stc_morph_return.data[:, 0], stc_morph_morph.data[:, 0])[0, 1] assert corr > 0.99, corr # Degenerate cases stc_morph.subject = None # no .subject provided pytest.raises(ValueError, stc_morph.to_original_src, src_fs, subject_orig='fsaverage', subjects_dir=subjects_dir) stc_morph.subject = 'sample' del src_fs[0]['subject_his_id'] # no name in src_fsaverage pytest.raises(ValueError, stc_morph.to_original_src, src_fs, subjects_dir=subjects_dir) src_fs[0]['subject_his_id'] = 'fsaverage' # name mismatch pytest.raises(ValueError, stc_morph.to_original_src, src_fs, subject_orig='foo', subjects_dir=subjects_dir) src_fs[0]['subject_his_id'] = 'sample' src = read_source_spaces(fname) # wrong source space pytest.raises(RuntimeError, stc_morph.to_original_src, src, subjects_dir=subjects_dir)
def test_morphed_source_space_return(): """Test returning a morphed source space to the original subject.""" # let's create some random data on fsaverage data = rng.randn(20484, 1) tmin, tstep = 0, 1. src_fs = read_source_spaces(fname_fs) stc_fs = SourceEstimate(data, [s['vertno'] for s in src_fs], tmin, tstep, 'fsaverage') n_verts_fs = sum(len(s['vertno']) for s in src_fs) # Create our morph source space src_morph = morph_source_spaces(src_fs, 'sample', subjects_dir=subjects_dir) n_verts_sample = sum(len(s['vertno']) for s in src_morph) assert n_verts_fs == n_verts_sample # Morph the data over using standard methods stc_morph = compute_source_morph( src_fs, 'fsaverage', 'sample', spacing=[s['vertno'] for s in src_morph], smooth=1, subjects_dir=subjects_dir, warn=False).apply(stc_fs) assert stc_morph.data.shape[0] == n_verts_sample # We can now pretend like this was real data we got e.g. from an inverse. # To be complete, let's remove some vertices keeps = [np.sort(rng.permutation(np.arange(len(v)))[:len(v) - 10]) for v in stc_morph.vertices] stc_morph = SourceEstimate( np.concatenate([stc_morph.lh_data[keeps[0]], stc_morph.rh_data[keeps[1]]]), [v[k] for v, k in zip(stc_morph.vertices, keeps)], tmin, tstep, 'sample') # Return it to the original subject stc_morph_return = stc_morph.to_original_src( src_fs, subjects_dir=subjects_dir) # This should fail (has too many verts in SourceMorph) with pytest.warns(RuntimeWarning, match='vertices not included'): morph = compute_source_morph( src_morph, subject_from='sample', spacing=stc_morph_return.vertices, smooth=1, subjects_dir=subjects_dir) with pytest.raises(ValueError, match='vertices do not match'): morph.apply(stc_morph) # Compare to the original data with pytest.warns(RuntimeWarning, match='vertices not included'): stc_morph_morph = compute_source_morph( src=stc_morph, subject_from='sample', spacing=stc_morph_return.vertices, smooth=1, subjects_dir=subjects_dir).apply(stc_morph) assert_equal(stc_morph_return.subject, stc_morph_morph.subject) for ii in range(2): assert_array_equal(stc_morph_return.vertices[ii], stc_morph_morph.vertices[ii]) # These will not match perfectly because morphing pushes data around corr = np.corrcoef(stc_morph_return.data[:, 0], stc_morph_morph.data[:, 0])[0, 1] assert corr > 0.99, corr # Explicitly test having two vertices map to the same target vertex. We # simulate this by having two vertices be at the same position. src_fs2 = src_fs.copy() vert1, vert2 = src_fs2[0]['vertno'][:2] src_fs2[0]['rr'][vert1] = src_fs2[0]['rr'][vert2] stc_morph_return = stc_morph.to_original_src( src_fs2, subjects_dir=subjects_dir) # test to_original_src method result equality for ii in range(2): assert_array_equal(stc_morph_return.vertices[ii], stc_morph_morph.vertices[ii]) # These will not match perfectly because morphing pushes data around corr = np.corrcoef(stc_morph_return.data[:, 0], stc_morph_morph.data[:, 0])[0, 1] assert corr > 0.99, corr # Degenerate cases stc_morph.subject = None # no .subject provided pytest.raises(ValueError, stc_morph.to_original_src, src_fs, subject_orig='fsaverage', subjects_dir=subjects_dir) stc_morph.subject = 'sample' del src_fs[0]['subject_his_id'] # no name in src_fsaverage pytest.raises(ValueError, stc_morph.to_original_src, src_fs, subjects_dir=subjects_dir) src_fs[0]['subject_his_id'] = 'fsaverage' # name mismatch pytest.raises(ValueError, stc_morph.to_original_src, src_fs, subject_orig='foo', subjects_dir=subjects_dir) src_fs[0]['subject_his_id'] = 'sample' src = read_source_spaces(fname) # wrong source space pytest.raises(RuntimeError, stc_morph.to_original_src, src, subjects_dir=subjects_dir)