# plot the results.
# Get Landmarks from MEG file, 0, 1, and 2 correspond to LPA, NAS, RPA
# and the 'r' key will provide us with the xyz coordinates
pos = np.asarray((raw.info['dig'][0]['r'],
raw.info['dig'][1]['r'],
raw.info['dig'][2]['r']))
# We use a function from MNE-Python to convert MEG coordinates to MRI space
# for the conversion we use our estimated transformation matrix and the
# MEG coordinates extracted from the raw file. `subjects` and `subjects_dir`
# are used internally, to point to the T1-weighted MRI file: `t1_mgh_fname`
mri_pos = head_to_mri(pos=pos,
subject='sample',
mri_head_t=estim_trans,
subjects_dir=op.join(data_path, 'subjects')
)
# Our MRI written to BIDS, we got `anat_dir` from our `write_anat` function
t1_nii_fname = op.join(anat_dir, 'sub-01_ses-01_T1w.nii.gz')
# Plot it
fig, axs = plt.subplots(3, 1)
for point_idx, label in enumerate(('LPA', 'NAS', 'RPA')):
plot_anat(t1_nii_fname, axes=axs[point_idx],
cut_coords=mri_pos[point_idx, :],
title=label)
plt.show()
###############################################################################
pos = np.asarray(
(raw.info['dig'][0]['r'], raw.info['dig'][1]['r'],
raw.info['dig'][2]['r']))
bids_fname = bids_basename + '_meg.fif'
estim_trans = get_head_mri_trans(
bids_fname=bids_fname, # name of the MEG file
bids_root=bids_root # root of our BIDS dir
)
# We use a function from MNE-Python to convert MEG coordinates to MRI space
# for the conversion we use our estimated transformation matrix and the
# MEG coordinates extracted from the raw file. `subjects` and `subjects_dir`
# are used internally, to point to the T1-weighted MRI file: `t1_mgh_fname`
mri_pos = head_to_mri(pos=pos,
subject=subject,
mri_head_t=estim_trans,
subjects_dir=subjects_dir)
# Our MRI written to BIDS, we got `anat_dir` from our `write_anat` function
t1_nii_fname = op.join(
anat_dir, 'sub-' + subject + '_acq-t1w_T1w.nii.gz')
# sub-SB01_acq-t1w_T1w
# Plot it
fig, axs = plt.subplots(3, 1)
for point_idx, label in enumerate(('LPA', 'NAS', 'RPA')):
plot_anat(t1_nii_fname,
axes=axs[point_idx],
cut_coords=mri_pos[point_idx, :],
title=label)
plt.show()