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failed attempt: fields removes sources
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| from mayavi import mlab | |
| import numpy as np | |
| import mne | |
| from mne.datasets import sample | |
| from mne.minimum_norm import make_inverse_operator, apply_inverse | |
| mne.set_log_level(False) | |
| data_path = sample.data_path() | |
| subjects_dir = data_path + '/subjects' | |
| raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' | |
| raw = mne.io.read_raw_fif(raw_fname) # already has an average reference | |
| events = mne.find_events(raw, stim_channel='STI 014') | |
| event_id = dict(visual_left=3, visual_right=4) | |
| raw.info['bads'] = ['MEG 2443', 'EEG 053'] | |
| picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True, exclude='bads') | |
| epochs = mne.Epochs(raw, events, event_id, tmin=-.2, tmax=.5, proj=True, | |
| picks=picks, baseline=(None, 0), | |
| reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6)) | |
| evoked = epochs.average() | |
| trans_fname = data_path + ('/MEG/sample/sample_audvis_raw-trans.fif') | |
| maps = mne.make_field_map(evoked, trans_fname, subject='sample', | |
| subjects_dir=subjects_dir, n_jobs=1) | |
| # source | |
| noise_cov = mne.compute_covariance(epochs, tmax=0., method='empirical') | |
| del epochs | |
| fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-oct-6-fwd.fif' | |
| fwd = mne.read_forward_solution(fname_fwd) | |
| fwd = mne.convert_forward_solution(fwd, surf_ori=True) | |
| # Restrict forward solution as necessary for MEG | |
| fwd = mne.pick_types_forward(fwd, meg=True, eeg=False) | |
| # make an MEG inverse operator | |
| inverse_operator = make_inverse_operator(evoked.info, fwd, noise_cov, | |
| loose=0.2, depth=0.8) | |
| stc = apply_inverse(evoked, inverse_operator, lambda2=1. / 3. ** 2, | |
| method="dSPM", pick_ori=None) | |
| # Plot source estimates | |
| fig = mlab.figure(bgcolor=(0., 0., 0.), fgcolor=(1., 1., 1.), size=(800, 800)) | |
| brain = stc.plot(surface='white', hemi='both', subjects_dir=subjects_dir, | |
| clim=dict(kind='value', lims=[2, 4, 15]), background='w', | |
| initial_time=.100, time_unit='s', colormap='rocket', | |
| foreground='k', figure=fig) | |
| # convert sources estimates into meters | |
| for child in fig.children[:2]: # next two are shared, skip | |
| loc = np.array(child.data.points.data) | |
| child.data.points.data = loc / 1e3 | |
| child.update() | |
| # plot sensors | |
| mne.viz.plot_alignment(evoked.info, trans_fname, fig=fig, | |
| coord_frame='mri', surfaces=['head-dense'], | |
| subject='sample', subjects_dir=subjects_dir, | |
| meg='sensors') | |
| fig.children[-2].children[0].children[0].actor.property.opacity = 0.33 | |
| # attempt to plot fields | |
| surf = maps[0]['surf'] | |
| map_data = maps[0]['data'] | |
| time_idx = np.where(evoked.times > .170)[0][0] | |
| pick = mne.pick_types(evoked.info, meg=True, eeg=False, ref_meg=False) | |
| data = np.dot(map_data, evoked.data[pick, time_idx]) | |
| mesh = mne.viz._3d._create_mesh_surf(surf, fig, scalars=data) | |
| cont = mlab.pipeline.contour_surface(mesh) | |
| mlab.view(19, 82, 0.6) # m |
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