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)
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
