def test_plot_vector_source_estimates(renderer_interactive): """Test plotting of vector source estimates.""" sample_src = read_source_spaces(src_fname) vertices = [s['vertno'] for s in sample_src] n_verts = sum(len(v) for v in vertices) n_time = 5 data = np.random.RandomState(0).rand(n_verts, 3, n_time) stc = VectorSourceEstimate(data, vertices, 1, 1) brain = stc.plot('sample', subjects_dir=subjects_dir, hemi='both', smoothing_steps=1, verbose='error') brain.close() del brain with pytest.raises(ValueError, match='use "pos_lims"'): stc.plot('sample', subjects_dir=subjects_dir, clim=dict(pos_lims=[1, 2, 3])) with pytest.raises(ValueError, match='cannot be used'): stc.plot('sample', subjects_dir=subjects_dir, show_traces=True, time_viewer=False)
def test_plot_vector_source_estimates(): """Test plotting of vector source estimates.""" sample_src = read_source_spaces(src_fname) vertices = [s['vertno'] for s in sample_src] n_verts = sum(len(v) for v in vertices) n_time = 5 data = np.random.RandomState(0).rand(n_verts, 3, n_time) stc = VectorSourceEstimate(data, vertices, 1, 1) brain = stc.plot('sample', subjects_dir=subjects_dir) brain.close() del brain gc.collect() with pytest.raises(ValueError, match='use "pos_lims"'): stc.plot('sample', subjects_dir=subjects_dir, clim=dict(pos_lims=[1, 2, 3])) gc.collect() brain = stc.plot('sample', subjects_dir=subjects_dir, hemi='both') brain.close() del brain gc.collect()
def test_plot_vec_source_estimates(): """Test plotting of vector source estimates.""" sample_src = read_source_spaces(src_fname) vertices = [s['vertno'] for s in sample_src] n_verts = sum(len(v) for v in vertices) n_time = 5 data = np.random.RandomState(0).rand(n_verts, 3, n_time) stc = VectorSourceEstimate(data, vertices, 1, 1) with warnings.catch_warnings(record=True): warnings.simplefilter('always') stc.plot('sample', subjects_dir=subjects_dir)
def test_plot_vec_source_estimates(): """Test plotting of vector source estimates.""" sample_src = read_source_spaces(src_fname) vertices = [s['vertno'] for s in sample_src] n_verts = sum(len(v) for v in vertices) n_time = 5 data = np.random.RandomState(0).rand(n_verts, 3, n_time) stc = VectorSourceEstimate(data, vertices, 1, 1) stc.plot('sample', subjects_dir=subjects_dir) with pytest.raises(ValueError, match='use "pos_lims"'): stc.plot('sample', subjects_dir=subjects_dir, clim=dict(pos_lims=[1, 2, 3]))
def test_plot_vec_source_estimates(): """Test plotting of vector source estimates.""" sample_src = read_source_spaces(src_fname) vertices = [s['vertno'] for s in sample_src] n_verts = sum(len(v) for v in vertices) n_time = 5 data = np.random.RandomState(0).rand(n_verts, 3, n_time) stc = VectorSourceEstimate(data, vertices, 1, 1) with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') stc.plot('sample', subjects_dir=subjects_dir) assert len(w) == 0 # not using deprecated params with pytest.raises(ValueError, match='use "pos_lims"'): stc.plot('sample', subjects_dir=subjects_dir, clim=dict(pos_lims=[1, 2, 3])) assert len(w) == 0