The inverse operator’s source space is shown in 3D.

# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#

from mne.datasets import sample

print(__doc__)

data_path = sample.data_path()
fname = data_path
fname += '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'

print("Method: %s" % inv['methods'])
print("fMRI prior: %s" % inv['fmri_prior'])
print("Number of sources: %s" % inv['nsource'])
print("Number of channels: %s" % inv['nchan'])


Out:

Reading inverse operator decomposition from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif...
[done]
[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
22494 x 22494 diagonal covariance (kind = 2) found.
22494 x 22494 diagonal covariance (kind = 6) found.
22494 x 22494 diagonal covariance (kind = 5) found.
Did not find the desired covariance matrix (kind = 3)
Computing patch statistics...
[done]
Computing patch statistics...
[done]
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
Method: 1
fMRI prior: None
Number of sources: 7498
Number of channels: 305


Show result on 3D source space

lh_points = inv['src'][0]['rr']
lh_faces = inv['src'][0]['use_tris']
rh_points = inv['src'][1]['rr']
rh_faces = inv['src'][1]['use_tris']
from mayavi import mlab  # noqa

mlab.figure(size=(600, 600), bgcolor=(0, 0, 0))
mesh = mlab.triangular_mesh(lh_points[:, 0], lh_points[:, 1], lh_points[:, 2],
lh_faces, colormap='RdBu')
mesh.module_manager.scalar_lut_manager.reverse_lut = True

mesh = mlab.triangular_mesh(rh_points[:, 0], rh_points[:, 1], rh_points[:, 2],
rh_faces, colormap='RdBu')
mesh.module_manager.scalar_lut_manager.reverse_lut = True


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