def test_io_bem(tmpdir, ext):
"""Test reading and writing of bem surfaces and solutions."""
import h5py
temp_bem = op.join(str(tmpdir), f'temp-bem.{ext}')
# model
with pytest.raises(ValueError, match='BEM data not found'):
read_bem_surfaces(fname_raw)
with pytest.raises(ValueError, match='surface with id 10'):
read_bem_surfaces(fname_bem_3, s_id=10)
surf = read_bem_surfaces(fname_bem_3, patch_stats=True)
surf = read_bem_surfaces(fname_bem_3, patch_stats=False)
write_bem_surfaces(temp_bem, surf[0])
with pytest.raises(IOError, match='exists'):
write_bem_surfaces(temp_bem, surf[0])
write_bem_surfaces(temp_bem, surf[0], overwrite=True)
if ext == 'h5':
with h5py.File(temp_bem, 'r'): # make sure it's valid
pass
surf_read = read_bem_surfaces(temp_bem, patch_stats=False)
_compare_bem_surfaces(surf, surf_read)
# solution
with pytest.raises(RuntimeError, match='No BEM solution found'):
read_bem_solution(fname_bem_3)
temp_sol = op.join(str(tmpdir), f'temp-sol.{ext}')
sol = read_bem_solution(fname_bem_sol_3)
assert 'BEM' in repr(sol)
write_bem_solution(temp_sol, sol)
sol_read = read_bem_solution(temp_sol)
_compare_bem_solutions(sol, sol_read)
sol = read_bem_solution(fname_bem_sol_1)
with pytest.raises(RuntimeError, match='BEM does not have.*triangulation'):
_bem_find_surface(sol, 3)
def _compute_depth(dip, fname_bem, fname_trans, subject, subjects_dir):
"""Compute dipole depth."""
trans = _get_trans(fname_trans)[0]
bem = read_bem_solution(fname_bem)
surf = _bem_find_surface(bem, 'inner_skull')
points = surf['rr']
points = apply_trans(trans['trans'], points)
depth = _compute_nearest(points, dip.pos, return_dists=True)[1][0]
return np.ravel(depth)
def _compute_depth(dip, fname_bem, fname_trans, subject, subjects_dir):
"""Compute dipole depth."""
trans = _get_trans(fname_trans)[0]
bem = read_bem_solution(fname_bem)
surf = _bem_find_surface(bem, 'inner_skull')
points = surf['rr']
points = apply_trans(trans['trans'], points)
depth = _compute_nearest(points, dip.pos, return_dists=True)[1][0]
return np.ravel(depth)
def _prepare_for_forward(src,
mri_head_t,
info,
bem,
mindist,
n_jobs,
perts,
bem_extra='',
trans='',
info_extra='',
meg=True,
eeg=True,
ignore_ref=False,
verbose=None):
"""Prepare for forward computation."""
# Read the source locations
logger.info('')
# let's make a copy in case we modify something
src = _ensure_src(src).copy()
nsource = sum(s['nuse'] for s in src)
if nsource == 0:
raise RuntimeError('No sources are active in these source spaces. '
'"do_all" option should be used.')
logger.info('Read %d source spaces a total of %d active source locations' %
(len(src), nsource))
# Delete some keys to clean up the source space:
for key in ['working_dir', 'command_line']:
if key in src.info:
del src.info[key]
# Read the MRI -> head coordinate transformation
logger.info('')
_print_coord_trans(mri_head_t)
# make a new dict with the relevant information
arg_list = [
info_extra, trans, src, bem_extra, meg, eeg, mindist, n_jobs, verbose
]
cmd = 'make_forward_solution(%s)' % (', '.join([str(a) for a in arg_list]))
mri_id = dict(machid=np.zeros(2, np.int32), version=0, secs=0, usecs=0)
info = Info(chs=info['chs'],
comps=info['comps'],
dev_head_t=info['dev_head_t'],
mri_file=trans,
mri_id=mri_id,
meas_file=info_extra,
meas_id=None,
working_dir=os.getcwd(),
command_line=cmd,
bads=info['bads'],
mri_head_t=mri_head_t)
info._update_redundant()
info._check_consistency()
logger.info('')
megcoils, compcoils, megnames, meg_info = [], [], [], []
eegels, eegnames = [], []
if meg and len(pick_types(info, ref_meg=False, exclude=[])) > 0:
megcoils, compcoils, megnames, meg_info = \
_prep_meg_channels(info, perts, ignore_ref=ignore_ref)
if eeg and len(
pick_types(info, meg=False, eeg=True, ref_meg=False,
exclude=[])) > 0:
eegels, eegnames = _prep_eeg_channels(info)
# Check that some channels were found
if len(megcoils + eegels) == 0:
raise RuntimeError('No MEG or EEG channels found.')
# pick out final info
info = pick_info(
info, pick_types(info, meg=meg, eeg=eeg, ref_meg=False, exclude=[]))
# Transform the source spaces into the appropriate coordinates
# (will either be HEAD or MRI)
for s in src:
transform_surface_to(s, 'head', mri_head_t)
logger.info('Source spaces are now in %s coordinates.' %
_coord_frame_name(s['coord_frame']))
# Prepare the BEM model
bem = _setup_bem(bem, bem_extra, len(eegnames), mri_head_t)
# Circumvent numerical problems by excluding points too close to the skull
if not bem['is_sphere']:
inner_skull = _bem_find_surface(bem, 'inner_skull')
_filter_source_spaces(inner_skull, mindist, mri_head_t, src, n_jobs)
logger.info('')
rr = np.concatenate([s['rr'][s['vertno']] for s in src])
if len(rr) < 1:
raise RuntimeError('No points left in source space after excluding '
'points close to inner skull.')
# deal with free orientations:
source_nn = np.tile(np.eye(3), (len(rr), 1))
update_kwargs = dict(nchan=len(info['ch_names']),
nsource=len(rr),
info=info,
src=src,
source_nn=source_nn,
source_rr=rr,
surf_ori=False,
mri_head_t=mri_head_t)
return megcoils, meg_info, compcoils, megnames, eegels, eegnames, rr, \
info, update_kwargs, bem
