def test_plot_sensors_connectivity(renderer):
"""Test plotting of sensors connectivity."""
from mne import io, pick_types
data_path = data_dir
raw_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_raw.fif')
raw = io.read_raw_fif(raw_fname)
picks = pick_types(raw.info,
meg='grad',
eeg=False,
stim=False,
eog=True,
exclude='bads')
n_channels = len(picks)
con = np.random.RandomState(42).randn(n_channels, n_channels)
info = raw.info
with pytest.raises(TypeError, match='must be an instance of Info'):
plot_sensors_connectivity(info='foo', con=con, picks=picks)
with pytest.raises(ValueError, match='does not correspond to the size'):
plot_sensors_connectivity(info=info, con=con[::2, ::2], picks=picks)
fig = plot_sensors_connectivity(info=info, con=con, picks=picks)
if renderer._get_3d_backend() == 'pyvistaqt':
title = list(fig.plotter.scalar_bars.values())[0].GetTitle()
else:
assert renderer._get_3d_backend() == 'mayavi'
# the last thing we add is the Tube, so we need to go
# vtkDataSource->Stripper->Tube->ModuleManager
mod_man = fig.children[-1].children[0].children[0].children[0]
title = mod_man.scalar_lut_manager.scalar_bar.title
assert title == 'Connectivity'
def test_plot_sensors_connectivity(renderer):
"""Test plotting of sensors connectivity."""
from mne import io, pick_types
data_path = data_dir
raw_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_raw.fif')
raw = io.read_raw_fif(raw_fname)
picks = pick_types(raw.info, meg='grad', eeg=False, stim=False,
eog=True, exclude='bads')
n_channels = len(picks)
con = np.random.RandomState(42).randn(n_channels, n_channels)
info = raw.info
with pytest.raises(TypeError):
plot_sensors_connectivity(info='foo', con=con,
picks=picks)
with pytest.raises(ValueError):
plot_sensors_connectivity(info=info, con=con[::2, ::2],
picks=picks)
plot_sensors_connectivity(info=info, con=con, picks=picks)
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
events = mne.read_events(event_fname)
# Add a bad channel
raw.info['bads'] += ['MEG 2443']
# Pick MEG gradiometers
picks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=False, eog=True,
exclude='bads')
# Create epochs for the visual condition
event_id, tmin, tmax = 3, -0.2, 1.5 # need a long enough epoch for 5 cycles
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6))
# Compute connectivity for band containing the evoked response.
# We exclude the baseline period
fmin, fmax = 3., 9.
sfreq = raw.info['sfreq'] # the sampling frequency
tmin = 0.0 # exclude the baseline period
epochs.load_data().pick_types(meg='grad') # just keep MEG and no EOG now
con, freqs, times, n_epochs, n_tapers = spectral_connectivity(
epochs, method='pli', mode='multitaper', sfreq=sfreq, fmin=fmin, fmax=fmax,
faverage=True, tmin=tmin, mt_adaptive=False, n_jobs=1)
# Now, visualize the connectivity in 3D
plot_sensors_connectivity(epochs.info, con[:, :, 0])
