Plot the MNE brain and helmet

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

Out:

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...
Computing dot products for 304 surface locations...
Field mapping data ready
    Preparing the mapping matrix...
    Truncating at 210/305 components to omit less than 0.0001 (9.9e-05)

import os.path as op
import mne

sample_path = mne.datasets.sample.data_path()
subjects_dir = op.join(sample_path, 'subjects')
fname_evoked = op.join(sample_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
fname_inv = op.join(sample_path, 'MEG', 'sample',
                    'sample_audvis-meg-oct-6-meg-inv.fif')
fname_trans = op.join(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: 103 MB

Plotting sensor layouts of MEG systems Plotting sensor layouts of EEG systems