Plot the MNE brain and helmet

This tutorial shows how to make the MNE helmet + brain image.

Reading inverse operator decomposition from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif...
    Reading inverse operator info...
    [done]
    Reading inverse operator decomposition...
    [done]
    305 x 305 full covariance (kind = 1) found.
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Noise covariance matrix read.
    22494 x 22494 diagonal covariance (kind = 2) found.
    Source covariance matrix read.
    22494 x 22494 diagonal covariance (kind = 6) found.
    Orientation priors read.
    22494 x 22494 diagonal covariance (kind = 5) found.
    Depth priors read.
    Did not find the desired covariance matrix (kind = 3)
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    2 source spaces read
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Source spaces transformed to the inverse solution coordinate frame
Getting helmet for system 306m
Prepare MEG mapping...
Computing dot products for 305 coils...
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.3s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.3s finished
Computing dot products for 304 surface locations...
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.2s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.2s finished
Field mapping data ready
    Preparing the mapping matrix...
    Truncating at 210/305 components to omit less than 0.0001 (9.9e-05)
Channel types:: grad: 203, mag: 102

import mne

sample_path = mne.datasets.sample.data_path()
subjects_dir = sample_path / 'subjects'
fname_evoked = sample_path / 'MEG' / 'sample' / 'sample_audvis-ave.fif'
fname_inv = (sample_path / 'MEG' / 'sample' /
             'sample_audvis-meg-oct-6-meg-inv.fif')
fname_trans = sample_path / 'MEG' / 'sample' / 'sample_audvis_raw-trans.fif'
inv = mne.minimum_norm.read_inverse_operator(fname_inv)
evoked = mne.read_evokeds(fname_evoked, baseline=(None, 0),
                          proj=True, verbose=False, condition='Left Auditory')
maps = mne.make_field_map(evoked, trans=fname_trans, ch_type='meg',
                          subject='sample', subjects_dir=subjects_dir)
time = 0.083
fig = mne.viz.create_3d_figure((256, 256))
mne.viz.plot_alignment(
    evoked.info, subject='sample', subjects_dir=subjects_dir, fig=fig,
    trans=fname_trans, meg='sensors', eeg=False, surfaces='pial',
    coord_frame='mri')
evoked.plot_field(maps, time=time, fig=fig, time_label=None, vmax=5e-13)
mne.viz.set_3d_view(
    fig, azimuth=40, elevation=87, focalpoint=(0., -0.01, 0.04), roll=-25,
    distance=0.55)

Estimated memory usage: 56 MB