def test_dig_mri_distances(dig_kinds, exclude, count, bounds, outliers):
"""Test the trans obtained by coregistration."""
info = read_info(fname_raw)
dists = dig_mri_distances(info, fname_trans, 'sample', subjects_dir,
dig_kinds=dig_kinds, exclude_frontal=exclude)
assert dists.shape == (count,)
assert bounds[0] < np.mean(dists) < bounds[1]
assert np.sum(dists > 0.03) == outliers
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces='head-dense',
show_axes=True,
dig=True,
eeg=[],
meg='sensors',
coord_frame='meg')
mne.viz.set_3d_view(fig, 45, 90, distance=0.6, focalpoint=(0., 0., 0.))
print('Distance from head origin to MEG origin: %0.1f mm' %
(1000 * np.linalg.norm(raw.info['dev_head_t']['trans'][:3, 3])))
print('Distance from head origin to MRI origin: %0.1f mm' %
(1000 * np.linalg.norm(trans['trans'][:3, 3])))
dists = mne.dig_mri_distances(raw.info,
trans,
'sample',
subjects_dir=subjects_dir)
print('Distance from %s digitized points to head surface: %0.1f mm' %
(len(dists), 1000 * np.mean(dists)))
###############################################################################
# Coordinate frame definitions
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# .. raw:: html
#
# <style>
# .pink {color:DarkSalmon; font-weight:bold}
# .blue {color:DeepSkyBlue; font-weight:bold}
# .gray {color:Gray; font-weight:bold}
# .magenta {color:Magenta; font-weight:bold}
# .purple {color:Indigo; font-weight:bold}
def gen_forwards(p, subjects, structurals, run_indices):
"""Generate forward solutions
Can only complete successfully once coregistration is performed
(usually in mne_analyze).
Parameters
----------
p : instance of Parameters
Analysis parameters.
subjects : list of str
Subject names to analyze (e.g., ['Eric_SoP_001', ...]).
structurals : list (of str or None)
The structural data names for each subject (e.g., ['AKCLEE_101', ...]).
If None, a spherical BEM and volume grid space will be used.
run_indices : array-like | None
Run indices to include.
"""
for si, subj in enumerate(subjects):
struc = structurals[si]
fwd_dir = op.join(p.work_dir, subj, p.forward_dir)
if not op.isdir(fwd_dir):
os.mkdir(fwd_dir)
raw_fname = get_raw_fnames(p, subj, 'sss', False, False,
run_indices[si])[0]
info = read_info(raw_fname)
bem, src, trans, bem_type = _get_bem_src_trans(p, info, subj, struc)
if not getattr(p, 'translate_positions', True):
raise RuntimeError('Not translating positions is no longer '
'supported')
print(' Creating forward solution(s) using a %s for %s...' %
(bem_type, subj),
end='')
# XXX Don't actually need to generate a different fwd for each inv
# anymore, since all runs are included, but changing the filename
# would break a lot of existing pipelines :(
try:
subjects_dir = get_subjects_dir(p.subjects_dir, raise_error=True)
subject = src[0]['subject_his_id']
dist = dig_mri_distances(info,
trans,
subject,
subjects_dir=subjects_dir)
except Exception as exp:
# old MNE or bad args
print(' (dig<->MRI unknown: %s)' % (str(exp)[:20] + '...', ))
else:
dist = np.median(dist)
print(' (dig<->MRI %0.1f mm)' % (1000 * dist, ))
if dist > 5:
warnings.warn(
'%s dig<->MRI distance %0.1f mm could indicate a problem '
'with coregistration, check coreg' %
(subject, 1000 * dist))
fwd_name = get_cov_fwd_inv_fnames(p, subj, run_indices[si])[1][0]
fwd = make_forward_solution(info,
trans,
src,
bem,
n_jobs=p.n_jobs,
mindist=p.fwd_mindist)
write_forward_solution(fwd_name, fwd, overwrite=True)
