def test_lcmv_raw():
"""Test LCMV with raw data."""
raw, _, _, _, noise_cov, label, forward, _, _, _ =\
_get_data(all_forward=False, epochs=False, data_cov=False)
tmin, tmax = 0, 20
start, stop = raw.time_as_index([tmin, tmax])
# use only the left-temporal MEG channels for LCMV
data_cov = mne.compute_raw_covariance(raw, tmin=tmin, tmax=tmax)
filters = make_lcmv(raw.info,
forward,
data_cov,
reg=0.01,
noise_cov=noise_cov,
label=label)
stc = apply_lcmv_raw(raw,
filters,
start=start,
stop=stop,
max_ori_out='signed')
assert_array_almost_equal(np.array([tmin, tmax]),
np.array([stc.times[0], stc.times[-1]]),
decimal=2)
# make sure we get an stc with vertices only in the lh
vertno = [forward['src'][0]['vertno'], forward['src'][1]['vertno']]
assert len(stc.vertices[0]) == len(
np.intersect1d(vertno[0], label.vertices))
assert len(stc.vertices[1]) == 0
def _update(self):
t1 = time.time()
input_array = self.input_node.output
raw_array = mne.io.RawArray(input_array,
self._mne_info,
verbose='ERROR')
raw_array.pick_types(eeg=True, meg=False, stim=False, exclude='bads')
raw_array.set_eeg_reference(ref_channels='average', projection=True)
t2 = time.time()
self.logger.debug('Prepare arrays in {:.1f} ms'.format(
(t2 - t1) * 1000))
if self.is_adaptive:
self._update_covariance_matrix(input_array)
t1 = time.time()
self._filters = make_lcmv(info=self._mne_info,
forward=self.fwd_surf,
data_cov=self._Rxx,
reg=0.5,
pick_ori='max-power',
weight_norm='unit-noise-gain',
reduce_rank=False)
t2 = time.time()
self.logger.debug('Assembled lcmv instance in {:.1f} ms'.format(
(t2 - t1) * 1000))
self._filters['source_nn'] = []
t1 = time.time()
stc = apply_lcmv_raw(raw=raw_array,
filters=self._filters,
max_ori_out='signed')
t2 = time.time()
self.logger.debug('Applied lcmv inverse in {:.1f} ms'.format(
(t2 - t1) * 1000))
output = stc.data
t1 = time.time()
if self.fixed_orientation is True:
if self.output_type == 'power':
output = output**2
else:
vertex_count = self.fwd_surf['nsource']
output = np.sum(np.power(output, 2).reshape((vertex_count, 3, -1)),
axis=1)
if self.output_type == 'activation':
output = np.sqrt(output)
self.output = output
t2 = time.time()
self.logger.debug('Finalized in {:.1f} ms'.format((t2 - t1) * 1000))
def test_lcmv_raw():
"""Test LCMV with raw data."""
raw, _, _, _, noise_cov, label, forward, _, _, _ =\
_get_data(all_forward=False, epochs=False, data_cov=False)
tmin, tmax = 0, 20
start, stop = raw.time_as_index([tmin, tmax])
# use only the left-temporal MEG channels for LCMV
data_cov = mne.compute_raw_covariance(raw, tmin=tmin, tmax=tmax)
filters = make_lcmv(raw.info, forward, data_cov, reg=0.01,
noise_cov=noise_cov, label=label)
stc = apply_lcmv_raw(raw, filters, start=start, stop=stop,
max_ori_out='signed')
assert_array_almost_equal(np.array([tmin, tmax]),
np.array([stc.times[0], stc.times[-1]]),
decimal=2)
# make sure we get an stc with vertices only in the lh
vertno = [forward['src'][0]['vertno'], forward['src'][1]['vertno']]
assert len(stc.vertices[0]) == len(np.intersect1d(vertno[0],
label.vertices))
assert len(stc.vertices[1]) == 0
def test_make_lcmv(tmpdir, reg, proj):
"""Test LCMV with evoked data and single trials."""
raw, epochs, evoked, data_cov, noise_cov, label, forward,\
forward_surf_ori, forward_fixed, forward_vol = _get_data(proj=proj)
for fwd in [forward, forward_vol]:
filters = make_lcmv(evoked.info, fwd, data_cov, reg=reg,
noise_cov=noise_cov)
stc = apply_lcmv(evoked, filters, max_ori_out='signed')
stc.crop(0.02, None)
stc_pow = np.sum(np.abs(stc.data), axis=1)
idx = np.argmax(stc_pow)
max_stc = stc.data[idx]
tmax = stc.times[np.argmax(max_stc)]
assert 0.08 < tmax < 0.15, tmax
assert 0.9 < np.max(max_stc) < 3.5, np.max(max_stc)
if fwd is forward:
# Test picking normal orientation (surface source space only).
filters = make_lcmv(evoked.info, forward_surf_ori, data_cov,
reg=reg, noise_cov=noise_cov,
pick_ori='normal', weight_norm=None)
stc_normal = apply_lcmv(evoked, filters, max_ori_out='signed')
stc_normal.crop(0.02, None)
stc_pow = np.sum(np.abs(stc_normal.data), axis=1)
idx = np.argmax(stc_pow)
max_stc = stc_normal.data[idx]
tmax = stc_normal.times[np.argmax(max_stc)]
lower = 0.04 if proj else 0.025
assert lower < tmax < 0.14, tmax
lower = 3e-7 if proj else 2e-7
assert lower < np.max(max_stc) < 3e-6, np.max(max_stc)
# No weight normalization was applied, so the amplitude of normal
# orientation results should always be smaller than free
# orientation results.
assert (np.abs(stc_normal.data) <= stc.data).all()
# Test picking source orientation maximizing output source power
filters = make_lcmv(evoked.info, fwd, data_cov, reg=reg,
noise_cov=noise_cov, pick_ori='max-power')
stc_max_power = apply_lcmv(evoked, filters, max_ori_out='signed')
stc_max_power.crop(0.02, None)
stc_pow = np.sum(np.abs(stc_max_power.data), axis=1)
idx = np.argmax(stc_pow)
max_stc = np.abs(stc_max_power.data[idx])
tmax = stc.times[np.argmax(max_stc)]
lower = 0.08 if proj else 0.04
assert lower < tmax < 0.15, tmax
assert 0.8 < np.max(max_stc) < 3., np.max(max_stc)
stc_max_power.data[:, :] = np.abs(stc_max_power.data)
if fwd is forward:
# Maximum output source power orientation results should be
# similar to free orientation results in areas with channel
# coverage
label = mne.read_label(fname_label)
mean_stc = stc.extract_label_time_course(label, fwd['src'],
mode='mean')
mean_stc_max_pow = \
stc_max_power.extract_label_time_course(label, fwd['src'],
mode='mean')
assert_array_less(np.abs(mean_stc - mean_stc_max_pow), 1.0)
# Test NAI weight normalization:
filters = make_lcmv(evoked.info, fwd, data_cov, reg=reg,
noise_cov=noise_cov, pick_ori='max-power',
weight_norm='nai')
stc_nai = apply_lcmv(evoked, filters, max_ori_out='signed')
stc_nai.crop(0.02, None)
# Test whether unit-noise-gain solution is a scaled version of NAI
pearsoncorr = np.corrcoef(np.concatenate(np.abs(stc_nai.data)),
np.concatenate(stc_max_power.data))
assert_almost_equal(pearsoncorr[0, 1], 1.)
# Test if spatial filter contains src_type
assert 'src_type' in filters
# __repr__
assert len(evoked.ch_names) == 22
assert len(evoked.info['projs']) == (3 if proj else 0)
assert len(evoked.info['bads']) == 2
rank = 17 if proj else 20
assert 'LCMV' in repr(filters)
assert 'unknown subject' not in repr(filters)
assert '4157 vert' in repr(filters)
assert '20 ch' in repr(filters)
assert 'rank %s' % rank in repr(filters)
# I/O
fname = op.join(str(tmpdir), 'filters.h5')
with pytest.warns(RuntimeWarning, match='-lcmv.h5'):
filters.save(fname)
filters_read = read_beamformer(fname)
assert isinstance(filters, Beamformer)
assert isinstance(filters_read, Beamformer)
# deal with object_diff strictness
filters_read['rank'] = int(filters_read['rank'])
filters['rank'] = int(filters['rank'])
assert object_diff(filters, filters_read) == ''
# Test if fixed forward operator is detected when picking normal or
# max-power orientation
pytest.raises(ValueError, make_lcmv, evoked.info, forward_fixed, data_cov,
reg=0.01, noise_cov=noise_cov, pick_ori='normal')
pytest.raises(ValueError, make_lcmv, evoked.info, forward_fixed, data_cov,
reg=0.01, noise_cov=noise_cov, pick_ori='max-power')
# Test if non-surface oriented forward operator is detected when picking
# normal orientation
pytest.raises(ValueError, make_lcmv, evoked.info, forward, data_cov,
reg=0.01, noise_cov=noise_cov, pick_ori='normal')
# Test if volume forward operator is detected when picking normal
# orientation
pytest.raises(ValueError, make_lcmv, evoked.info, forward_vol, data_cov,
reg=0.01, noise_cov=noise_cov, pick_ori='normal')
# Test if missing of noise covariance matrix is detected when more than
# one channel type is present in the data
pytest.raises(ValueError, make_lcmv, evoked.info, forward_vol,
data_cov=data_cov, reg=0.01, noise_cov=None,
pick_ori='max-power')
# Test if wrong channel selection is detected in application of filter
evoked_ch = deepcopy(evoked)
evoked_ch.pick_channels(evoked_ch.ch_names[1:])
filters = make_lcmv(evoked.info, forward_vol, data_cov, reg=0.01,
noise_cov=noise_cov)
pytest.raises(ValueError, apply_lcmv, evoked_ch, filters,
max_ori_out='signed')
# Test if discrepancies in channel selection of data and fwd model are
# handled correctly in apply_lcmv
# make filter with data where first channel was removed
filters = make_lcmv(evoked_ch.info, forward_vol, data_cov, reg=0.01,
noise_cov=noise_cov)
# applying that filter to the full data set should automatically exclude
# this channel from the data
# also test here that no warnings are thrown - implemented to check whether
# src should not be None warning occurs
with pytest.warns(None) as w:
stc = apply_lcmv(evoked, filters, max_ori_out='signed')
assert len(w) == 0
# the result should be equal to applying this filter to a dataset without
# this channel:
stc_ch = apply_lcmv(evoked_ch, filters, max_ori_out='signed')
assert_array_almost_equal(stc.data, stc_ch.data)
# Test if non-matching SSP projection is detected in application of filter
if proj:
raw_proj = deepcopy(raw)
raw_proj.del_proj()
with pytest.raises(ValueError, match='do not match the projections'):
apply_lcmv_raw(raw_proj, filters, max_ori_out='signed')
# Test if spatial filter contains src_type
assert 'src_type' in filters
# check whether a filters object without src_type throws expected warning
del filters['src_type'] # emulate 0.16 behaviour to cause warning
with pytest.warns(RuntimeWarning, match='spatial filter does not contain '
'src_type'):
apply_lcmv(evoked, filters, max_ori_out='signed')
# Now test single trial using fixed orientation forward solution
# so we can compare it to the evoked solution
filters = make_lcmv(epochs.info, forward_fixed, data_cov, reg=0.01,
noise_cov=noise_cov)
stcs = apply_lcmv_epochs(epochs, filters, max_ori_out='signed')
stcs_ = apply_lcmv_epochs(epochs, filters, return_generator=True,
max_ori_out='signed')
assert_array_equal(stcs[0].data, next(stcs_).data)
epochs.drop_bad()
assert (len(epochs.events) == len(stcs))
# average the single trial estimates
stc_avg = np.zeros_like(stcs[0].data)
for this_stc in stcs:
stc_avg += this_stc.data
stc_avg /= len(stcs)
# compare it to the solution using evoked with fixed orientation
filters = make_lcmv(evoked.info, forward_fixed, data_cov, reg=0.01,
noise_cov=noise_cov)
stc_fixed = apply_lcmv(evoked, filters, max_ori_out='signed')
assert_array_almost_equal(stc_avg, stc_fixed.data)
# use a label so we have few source vertices and delayed computation is
# not used
filters = make_lcmv(epochs.info, forward_fixed, data_cov, reg=0.01,
noise_cov=noise_cov, label=label)
stcs_label = apply_lcmv_epochs(epochs, filters, max_ori_out='signed')
assert_array_almost_equal(stcs_label[0].data, stcs[0].in_label(label).data)
# Test condition where the filters weights are zero. There should not be
# any divide-by-zero errors
zero_cov = data_cov.copy()
zero_cov['data'][:] = 0
filters = make_lcmv(epochs.info, forward_fixed, zero_cov, reg=0.01,
noise_cov=noise_cov)
assert_array_equal(filters['weights'], 0)
# Test condition where one channel type is picked
# (avoid "grad data rank (13) did not match the noise rank (None)")
data_cov_grad = pick_channels_cov(
data_cov, [ch_name for ch_name in epochs.info['ch_names']
if ch_name.endswith(('2', '3'))])
assert len(data_cov_grad['names']) > 4
make_lcmv(epochs.info, forward_fixed, data_cov_grad, reg=0.01,
noise_cov=noise_cov)
def _compute_LCMV_inverse_solution(raw_filename, sbj_id, subjects_dir,
fwd_filename, cov_fname, parc='aparc',
all_src_space=False, ROIs_mean=True,
is_fixed=False):
"""
Compute the inverse solution on raw data by LCMV and return the average
time series computed in the N_r regions of the source space defined by
the specified cortical parcellation
Inputs
raw_filename : str
filename of the raw data
sbj_id : str
subject name
subjects_dir : str
Freesurfer directory
fwd_filename : str
filename of the forward operator
cov_filename : str
filename of the noise covariance matrix
parc: str
the parcellation defining the ROIs atlas in the source space
all_src_space: bool
if True we compute the inverse for all points of the s0urce space
ROIs_mean: bool
if True we compute the mean of estimated time series on ROIs
Outputs
ts_file : str
filename of the file where are saved the estimated time series
labels_file : str
filename of the file where are saved the ROIs of the parcellation
label_names_file : str
filename of the file where are saved the name of the ROIs of the
parcellation
label_coords_file : str
filename of the file where are saved the coordinates of the
centroid of the ROIs of the parcellation
"""
print(('\n*** READ raw filename %s ***\n' % raw_filename))
raw = read_raw_fif(raw_filename, preload=True)
subj_path, basename, ext = split_f(raw_filename)
print(('\n*** READ noise covariance %s ***\n' % cov_fname))
noise_cov = mne.read_cov(cov_fname)
print(('\n*** READ FWD SOL %s ***\n' % fwd_filename))
forward = mne.read_forward_solution(fwd_filename)
forward = mne.convert_forward_solution(forward, surf_ori=True)
# compute data covariance matrix
# reject = _create_reject_dict(raw.info)
picks = pick_types(raw.info, meg=True, ref_meg=False, exclude='bads')
data_cov = mne.compute_raw_covariance(raw, picks=picks)
# compute LCMV filters
filters = make_lcmv(raw.info, forward, data_cov, reg=0.05,
noise_cov=noise_cov, pick_ori='normal',
weight_norm='nai', depth=0.8)
# apply spatial filter
stc = apply_lcmv_raw(raw, filters, max_ori_out='signed')
ts_file, label_ts, labels_file, label_names_file, label_coords_file = \
_process_stc(stc, basename, sbj_id, subjects_dir, parc, forward,
False, is_fixed, all_src_space=False, ROIs_mean=True)
return ts_file, labels_file, label_names_file, \
label_coords_file
def run_correlation(subjects_dir, subject, volume_spacing, freq, ortho_bool):
num_threads(8)
ortho_flag = str(ortho_bool)
frequency = str(freq)
DATA_DIR = Path(f'{subjects_dir}', f'{subject}', 'mne_files')
eye_proj1 = f'{DATA_DIR}/{subject}_eyes1-proj.fif.gz'
eye_proj2 = f'{DATA_DIR}/{subject}_eyes2-proj.fif.gz'
fname_meg = f'{DATA_DIR}/{subject}_ses-rest_task-rest.fif'
t1_fname = os.path.join(subjects_dir, subject, 'mri', 'T1.mgz')
heartbeat_proj = f'{DATA_DIR}/{subject}_heartbeat-proj.fif.gz'
fwd_fname = f'{DATA_DIR}/{subject}_{volume_spacing}-fwd.fif.gz'
src_fname = f'{DATA_DIR}/{subject}_{volume_spacing}-src.fif.gz'
cov_fname = f'{DATA_DIR}/{subject}-cov_{volume_spacing}.fif.gz'
raw_cov_fname = f'{DATA_DIR}/{subject}-rawcov_{volume_spacing}.fif.gz'
raw_proj = f'{DATA_DIR}/{subject}_ses-rest_task-rest_proj.fif.gz'
source_voxel_coords = f'{DATA_DIR}/{subject}_coords_{volume_spacing}.pkl'
corr_file_acLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_acLeft.npy'
corr_file_scLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_scLeft.npy'
corr_file_vcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_vcLeft.npy'
corr_file_mtLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mtLeft.npy'
corr_file_mtlLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mtlLeft.npy'
corr_file_smcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_smcLeft.npy'
corr_file_lpcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_lpcLeft.npy'
corr_file_dpfcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_dpfcLeft.npy'
corr_file_tmpcLeft = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_tmpcLeft.npy'
corr_file_acRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_acRight.npy'
corr_file_scRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_scRight.npy'
corr_file_vcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_vcRight.npy'
corr_file_mtRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mtRight.npy'
corr_file_mtlRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mtlRight.npy'
corr_file_smcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_smcRight.npy'
corr_file_lpcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_lpcRight.npy'
corr_file_dpfcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_dpfcRight.npy'
corr_file_tmpcRight = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_tmpcRight.npy'
corr_file_mpfc = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_mpfc.npy'
corr_file_sma = f'{DATA_DIR}/{subject}_{ortho_flag}_{volume_spacing}_{frequency}_sma.npy'
check_for_files = []
check_for_files.append(corr_file_acLeft)
check_for_files.append(corr_file_scLeft)
check_for_files.append(corr_file_vcLeft)
check_for_files.append(corr_file_mtLeft)
check_for_files.append(corr_file_mtlLeft)
check_for_files.append(corr_file_smcLeft)
check_for_files.append(corr_file_lpcLeft)
check_for_files.append(corr_file_dpfcLeft)
check_for_files.append(corr_file_tmpcLeft)
check_for_files.append(corr_file_acRight)
check_for_files.append(corr_file_scRight)
check_for_files.append(corr_file_vcRight)
check_for_files.append(corr_file_mtRight)
check_for_files.append(corr_file_mtlRight)
check_for_files.append(corr_file_smcRight)
check_for_files.append(corr_file_lpcRight)
check_for_files.append(corr_file_dpfcRight)
check_for_files.append(corr_file_tmpcRight)
check_for_files.append(corr_file_mpfc)
check_for_files.append(corr_file_sma)
file_exist = [f for f in check_for_files if os.path.isfile(f)]
file_not_exist = list(set(file_exist) ^ set(check_for_files))
if not file_not_exist:
print('SC, AC, VC correlation files exists...')
else:
trans = f'/home/senthilp/caesar/camcan/cc700/camcan_coreg-master/trans/{subject}-trans.fif' # The transformation file obtained by coregistration
file_trans = pathlib.Path(trans)
file_ss = pathlib.Path(src_fname)
file_fm = pathlib.Path(fwd_fname)
file_proj = pathlib.Path(raw_proj)
file_cov = pathlib.Path(cov_fname)
file_rawcov = pathlib.Path(raw_cov_fname)
t1 = nib.load(t1_fname)
if not file_trans.exists():
print (f'{trans} File doesnt exist...')
sys.exit(0)
#info = mne.io.read_info(fname_meg)
# plot_registration(info, trans, subject, subjects_dir)
if not file_ss.exists():
src = compute_SourceSpace(subject, subjects_dir, src_fname, source_voxel_coords, plot=True, ss='volume',
volume_spacing=volume_spacing)
seed_l_sc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SSC_Left'])
seed_r_sc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SSC_Right'])
seed_l_ac = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['AC_Left'])
seed_r_ac = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['AC_Right'])
seed_l_vc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['VC_Left'])
seed_r_vc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['VC_Right'])
seed_l_mt = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MT+_Left'])
seed_r_mt = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MT+_Right'])
seed_l_mtl = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MTL_Left'])
seed_r_mtl = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MTL_Right'])
seed_l_smc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SMC_Left'])
seed_r_smc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SMC_Right'])
seed_l_lpc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['LPC_Left'])
seed_r_lpc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['LPC_Right'])
seed_l_dpfc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['DPFC_Left'])
seed_r_dpfc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['DPFC_Right'])
seed_l_tmpc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['TMPC_Left'])
seed_r_tmpc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['TMPC_Right'])
seed_mpfc = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['MPFC_MidBrain'])
seed_sma = MNI_to_MRI(subject, subjects_dir, t1, ROI_mni['SMA_MidBrain'])
src_inuse = np.where(src[0]['inuse'] == 1)
loc_l_sc = src_inuse[0][0]
loc_r_sc = src_inuse[0][1]
loc_l_ac = src_inuse[0][2]
loc_r_ac = src_inuse[0][3]
loc_l_vc = src_inuse[0][4]
loc_r_vc = src_inuse[0][5]
loc_l_mt = src_inuse[0][6]
loc_r_mt = src_inuse[0][7]
loc_l_mtl = src_inuse[0][8]
loc_r_mtl = src_inuse[0][9]
loc_l_smc = src_inuse[0][10]
loc_r_smc = src_inuse[0][11]
loc_l_lpc = src_inuse[0][12]
loc_r_lpc = src_inuse[0][13]
loc_l_dpfc = src_inuse[0][14]
loc_r_dpfc = src_inuse[0][15]
loc_l_tmpc = src_inuse[0][16]
loc_r_tmpc = src_inuse[0][17]
loc_mpfc = src_inuse[0][18]
loc_sma = src_inuse[0][19]
src[0]['rr'][loc_l_sc] = seed_l_sc
src[0]['rr'][loc_r_sc] = seed_r_sc
src[0]['rr'][loc_l_ac] = seed_l_ac
src[0]['rr'][loc_r_ac] = seed_r_ac
src[0]['rr'][loc_l_vc] = seed_l_vc
src[0]['rr'][loc_r_vc] = seed_r_vc
src[0]['rr'][loc_l_mt] = seed_l_mt
src[0]['rr'][loc_r_mt] = seed_r_mt
src[0]['rr'][loc_l_mtl] = seed_l_mtl
src[0]['rr'][loc_r_mtl] = seed_r_mtl
src[0]['rr'][loc_l_smc] = seed_l_smc
src[0]['rr'][loc_r_smc] = seed_r_smc
src[0]['rr'][loc_l_lpc] = seed_l_lpc
src[0]['rr'][loc_r_lpc] = seed_r_lpc
src[0]['rr'][loc_l_dpfc] = seed_l_dpfc
src[0]['rr'][loc_r_dpfc] = seed_r_dpfc
src[0]['rr'][loc_l_tmpc] = seed_l_tmpc
src[0]['rr'][loc_r_tmpc] = seed_r_tmpc
src[0]['rr'][loc_mpfc] = seed_mpfc
src[0]['rr'][loc_sma] = seed_sma
src.save(src_fname, overwrite=True)
src = mne.read_source_spaces(src_fname)
#view_SS_brain(subject, subjects_dir, src)
if not file_fm.exists():
forward_model(subject, subjects_dir, fname_meg, trans, src, fwd_fname)
fwd = mne.read_forward_solution(fwd_fname)
# sensitivty_plot(subject, subjects_dir, fwd)
raw = mne.io.read_raw_fif(fname_meg, verbose='error', preload=True)
srate = raw.info['sfreq']
n_time_samps = raw.n_times
time_secs = raw.times
ch_names = raw.ch_names
n_chan = len(ch_names)
freq_res = srate/n_time_samps
print('\n')
print('-------------------------- Data summary-------------------------------')
print(f'Subject {subject}')
print(f"Frequency resolution {freq_res} Hz")
print(f"The first few channel names are {ch_names[:3]}")
print(f"The last time sample at {time_secs[-1]} seconds.")
print(f"Sampling Frequency (No of time points/sec) {srate} Hz")
print(f"Miscellaneous acquisition info {raw.info['description']}")
print(f"Bad channels marked during data acquisition {raw.info['bads']}")
print(f"Convert time in sec ( 60s ) to ingeter index {raw.time_as_index(60)}") # Convert time to indices
print(f"The raw data object has {n_time_samps} time samples and {n_chan} channels.")
print('------------------------------------------------------------------------')
print('\n')
# raw.plot(n_channels=10, scalings='auto', title='Data from arrays', show=True, block=True)
if not file_proj.exists():
projs_ecg, _ = compute_proj_ecg(raw, n_grad=1, n_mag=2, ch_name='ECG063')
projs_eog1, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='EOG061')
projs_eog2, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='EOG062')
if projs_ecg is not None:
mne.write_proj(heartbeat_proj, projs_ecg) # Saving projectors
raw.info['projs'] += projs_ecg
if projs_eog1 is not None:
mne.write_proj(eye_proj1, projs_eog1)
raw.info['projs'] += projs_eog1
if projs_eog2 is not None:
mne.write_proj(eye_proj2, projs_eog2)
raw.info['projs'] += projs_eog2
raw.apply_proj()
raw.save(raw_proj, proj=True, overwrite=True)
print(raw_proj)
raw_proj_applied = mne.io.read_raw_fif(raw_proj, verbose='error', preload=True)
print(f'High-pass filtering data at 0.5 Hz')
raw_proj_applied.filter(l_freq=0.5, h_freq=None, method='iir')
if not file_cov.exists():
cov = mne.compute_raw_covariance(raw_proj_applied) # compute before band-pass of interest
mne.write_cov(cov_fname, cov)
cov = mne.read_cov(cov_fname)
# cov.plot(raw.info, proj=True, exclude='bads', show_svd=False
# raw_proj_applied.crop(tmax=10)
do_epochs = False
l_freq = freq-2.0
h_freq = freq+2.0
print(f'Band pass filter data [{l_freq}, {h_freq}]')
raw_proj_filtered = raw_proj_applied.filter(l_freq=l_freq, h_freq=h_freq)
if do_epochs:
print('Segmenting raw data...')
events = mne.make_fixed_length_events(raw_proj_filtered, duration=5.)
raw_proj_filtered = mne.Epochs(raw_proj_filtered, events=events, tmin=0, tmax=5.,
baseline=None, preload=True)
data_cov = mne.compute_covariance(raw_proj_filtered)
else:
if not file_rawcov.exists():
data_cov = mne.compute_raw_covariance(raw_proj_filtered)
mne.write_cov(raw_cov_fname, data_cov)
else:
data_cov = mne.read_cov(file_rawcov)
filters = make_lcmv(raw_proj_filtered.info, fwd, data_cov, 0.05, cov,
pick_ori='max-power', weight_norm='nai')
raw_proj_filtered_comp = raw_proj_filtered.apply_hilbert()
if do_epochs:
stcs = apply_lcmv_epochs(raw_proj_filtered_comp, filters, return_generator=False)
else:
stcs = apply_lcmv_raw(raw_proj_filtered_comp, filters, verbose=True)
stcs = [stcs]
# Power Envelope Correlation
print(f'Computing Power Envelope Correlation for {subject}....Orthogonalize {ortho_flag}')
all_corr = envelope_correlation(stcs, combine=None, orthogonalize=False,
log=False, absolute=True, verbose=None)
np.save(corr_file_scLeft, all_corr[seed_left_sc])
np.save(corr_file_acLeft, all_corr[seed_left_ac])
np.save(corr_file_vcLeft, all_corr[seed_left_vc])
np.save(corr_file_mtLeft, all_corr[seed_left_mt])
np.save(corr_file_mtlLeft, all_corr[seed_left_mtl])
np.save(corr_file_smcLeft, all_corr[seed_left_smc])
np.save(corr_file_lpcLeft, all_corr[seed_left_lpc])
np.save(corr_file_dpfcLeft, all_corr[seed_left_dpfc])
np.save(corr_file_tmpcLeft, all_corr[seed_left_tmpc])
np.save(corr_file_scRight, all_corr[seed_right_sc])
np.save(corr_file_acRight, all_corr[seed_right_ac])
np.save(corr_file_vcRight, all_corr[seed_right_vc])
np.save(corr_file_mtRight, all_corr[seed_right_mt])
np.save(corr_file_mtlRight, all_corr[seed_right_mtl])
np.save(corr_file_smcRight, all_corr[seed_right_smc])
np.save(corr_file_lpcRight, all_corr[seed_right_lpc])
np.save(corr_file_dpfcRight, all_corr[seed_right_dpfc])
np.save(corr_file_tmpcRight, all_corr[seed_right_tmpc])
np.save(corr_file_mpfc, all_corr[seed_mpfc_index])
np.save(corr_file_sma, all_corr[seed_sma_index])
del stcs
ch_names.append('UPPT001')
raw.pick_types(meg=True, eeg=False)
if source_method == 'beamformer':
for label in labels:
print label.name
filters = make_lcmv(raw.info,
fwd,
data_cov,
reg=0.05,
noise_cov=noise_cov,
label=label,
pick_ori=None,
weight_norm='unit-noise-gain')
# Apply lcmv on evoked and raw data
evoked_stc = apply_lcmv(evoked, filters)
raw_stc = apply_lcmv_raw(raw, filters)
ch_names.append(label.name)
evoked_stcs.append(evoked_stc.data.mean(0))
raw_stcs.append(raw_stc.data.mean(0))
evoked_stcs = np.array(evoked_stcs)
raw_stcs = np.array(raw_stcs)
else:
# Setup inverse model ----------------------------------------------------
inv = make_inverse_operator(epochs.info,
fwd,
noise_cov,
loose=0.2,
depth=0.8)
snr_evoked = 3.0
snr_raw = 1.0
for label in labels:
# beamformer requirements
bem = op.join(subjects_dir, subject, 'bem', 'genz501_17a-5120-bem-sol.fif')
sphere = mne.make_sphere_model(r0='auto', head_radius='auto',
info=raw_filt.info)
src = mne.setup_volume_source_space(subject='fsaverage', bem=bem,
mri=op.join(subjects_dir, 'fsaverage',
'mri', 'T1.mgz')
subjects_dir=subjects_dir)
fwd = mne.make_forward_solution(raw_filt.info, trans=None, src=src,
bem=bem, n_jobs=n_jobs)
filters = make_lcmv(raw_filt.info, fwd, data_cov, reg=0.05,
pick_ori='max-power', weight_norm='nai',
reduce_rank=True)
t0 = time.time()
stc = apply_lcmv_raw(raw_filt, filters)
print(' Time: %s mns' % round((time.time() - t0) / 60, 2))
# Save result in stc files
stc.save(op.join(datapath, subject, 'lcmv-vol'))
stc.crop(0.0, 1.0)
# plot dSPM time course in src space
kwargs = dict(color='c', linestyle='--', linewidth=.5)
f, ax = plt.subplots(1, 1, figsize=(8, 11))
mx = np.argmax(stc.data, axis=0)
ax.plot(stc.times, stc.data[mx[:100], :].T)
ax.autoscale(enable=True, axis='x', tight=True)
ax.grid(True, which='major', axis='y', **kwargs)
ax.set_xlabel('time (s)')
ax.set_ylabel('strength')
def run_correlation(subjects_dir, subject, volume_spacing, freq):
num_threads(8)
frequency = str(freq)
DATA_DIR = Path(f'{subjects_dir}', f'{subject}', 'mne_files')
eye_proj1 = f'{DATA_DIR}/{subject}_eyes1-proj.fif.gz'
eye_proj2 = f'{DATA_DIR}/{subject}_eyes2-proj.fif.gz'
fname_meg = f'{DATA_DIR}/{subject}_ses-rest_task-rest.fif'
t1_fname = os.path.join(subjects_dir, subject, 'mri', 'T1.mgz')
heartbeat_proj = f'{DATA_DIR}/{subject}_heartbeat-proj.fif.gz'
fwd_fname = f'{DATA_DIR}/{subject}_{volume_spacing}-fwd-label.fif.gz'
src_fname = f'{DATA_DIR}/{subject}_{volume_spacing}-src-label.fif.gz'
cov_fname = f'{DATA_DIR}/{subject}-cov_{volume_spacing}-label.fif.gz'
raw_cov_fname = f'{DATA_DIR}/{subject}-rawcov_{volume_spacing}-label.fif.gz'
raw_proj = f'{DATA_DIR}/{subject}_ses-rest_task-rest_proj-label.fif.gz'
source_voxel_coords = f'{DATA_DIR}/{subject}_coords_{volume_spacing}.pkl'
freesurfer_label = f'{DATA_DIR}/{subject}_freesurferlabel_{volume_spacing}-label.pkl'
corr_true_file_label = f'{DATA_DIR}/{subject}_corr_ortho_true_{volume_spacing}_{frequency}_label.npy'
check_for_files = []
check_for_files.append(corr_true_file_label)
file_exist = [f for f in check_for_files if os.path.isfile(f)]
file_not_exist = list(set(file_exist) ^ set(check_for_files))
if not file_not_exist:
print('correlation files exists...')
else:
trans = f'/home/senthilp/caesar/camcan/cc700/camcan_coreg-master/trans/{subject}-trans.fif' # The transformation file obtained by coregistration
file_trans = pathlib.Path(trans)
file_ss = pathlib.Path(src_fname)
file_fm = pathlib.Path(fwd_fname)
file_proj = pathlib.Path(raw_proj)
file_cov = pathlib.Path(cov_fname)
file_rawcov = pathlib.Path(raw_cov_fname)
t1 = nib.load(t1_fname)
if not file_trans.exists():
print (f'{trans} File doesnt exist...')
sys.exit(0)
info = mne.io.read_info(fname_meg)
# plot_registration(info, trans, subject, subjects_dir)
print(file_ss)
if not file_ss.exists():
src = compute_SourceSpace(subject, subjects_dir, src_fname, source_voxel_coords, plot=True, ss='volume',
volume_spacing=volume_spacing)
src.save(src_fname, overwrite=True)
src = mne.read_source_spaces(src_fname)
#view_SS_brain(subject, subjects_dir, src)
if not file_fm.exists():
forward_model(subject, subjects_dir, fname_meg, trans, src, fwd_fname)
fwd = mne.read_forward_solution(fwd_fname)
# sensitivty_plot(subject, subjects_dir, fwd)
raw = mne.io.read_raw_fif(fname_meg, verbose='error', preload=True)
srate = raw.info['sfreq']
n_time_samps = raw.n_times
time_secs = raw.times
ch_names = raw.ch_names
n_chan = len(ch_names)
freq_res = srate/n_time_samps
print('\n')
print('-------------------------- Data summary-------------------------------')
print(f'Subject {subject}')
print(f"Frequency resolution {freq_res} Hz")
print(f"The first few channel names are {ch_names[:3]}")
print(f"The last time sample at {time_secs[-1]} seconds.")
print(f"Sampling Frequency (No of time points/sec) {srate} Hz")
print(f"Miscellaneous acquisition info {raw.info['description']}")
print(f"Bad channels marked during data acquisition {raw.info['bads']}")
print(f"Convert time in sec ( 60s ) to ingeter index {raw.time_as_index(60)}") # Convert time to indices
print(f"The raw data object has {n_time_samps} time samples and {n_chan} channels.")
print('------------------------------------------------------------------------')
print('\n')
# raw.plot(n_channels=10, scalings='auto', title='Data from arrays', show=True, block=True)
if not file_proj.exists():
projs_ecg, _ = compute_proj_ecg(raw, n_grad=1, n_mag=2, ch_name='ECG063')
projs_eog1, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='EOG061')
projs_eog2, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='EOG062')
if projs_ecg is not None:
mne.write_proj(heartbeat_proj, projs_ecg) # Saving projectors
raw.info['projs'] += projs_ecg
if projs_eog1 is not None:
mne.write_proj(eye_proj1, projs_eog1)
raw.info['projs'] += projs_eog1
if projs_eog2 is not None:
mne.write_proj(eye_proj2, projs_eog2)
raw.info['projs'] += projs_eog2
raw.apply_proj()
raw.save(raw_proj, proj=True, overwrite=True)
print(raw_proj)
raw_proj_applied = mne.io.read_raw_fif(raw_proj, verbose='error', preload=True)
print(f'High-pass filtering data at 0.5 Hz')
raw_proj_applied.filter(l_freq=0.5, h_freq=None, method='iir')
if not file_cov.exists():
cov = mne.compute_raw_covariance(raw_proj_applied) # compute before band-pass of interest
mne.write_cov(cov_fname, cov)
cov = mne.read_cov(cov_fname)
# cov.plot(raw.info, proj=True, exclude='bads', show_svd=False
# raw_proj_applied.crop(tmax=10)
do_epochs = False
l_freq = freq-2.0
h_freq = freq+2.0
print(f'Band pass filter data [{l_freq}, {h_freq}]')
raw_proj_filtered = raw_proj_applied.filter(l_freq=l_freq, h_freq=h_freq)
if do_epochs:
print('Segmenting raw data...')
events = mne.make_fixed_length_events(raw_proj_filtered, duration=5.)
raw_proj_filtered = mne.Epochs(raw_proj_filtered, events=events, tmin=0, tmax=5.,
baseline=None, preload=True)
data_cov = mne.compute_covariance(raw_proj_filtered)
else:
if not file_rawcov.exists():
data_cov = mne.compute_raw_covariance(raw_proj_filtered)
mne.write_cov(raw_cov_fname, data_cov)
else:
data_cov = mne.read_cov(file_rawcov)
filters = make_lcmv(raw_proj_filtered.info, fwd, data_cov, 0.05, cov,
pick_ori='max-power', weight_norm='nai')
raw_proj_filtered_comp = raw_proj_filtered.apply_hilbert()
if do_epochs:
stcs = apply_lcmv_epochs(raw_proj_filtered_comp, filters, return_generator=False)
else:
stcs = apply_lcmv_raw(raw_proj_filtered_comp, filters, verbose=True)
print('Extracting label time course...')
atlas = f'{subjects_dir}/{subject}/mri/aparc.a2009s+aseg.mgz'
label_ts = mne.extract_label_time_course(stcs, atlas, fwd['src'], return_generator=False)
label_ts = [label_ts]
# Power Envelope Correlation
print(f'Computing Power Envelope Correlation for {subject}....Orthogonalize True')
all_corr = envelope_correlation(label_ts, combine=None, orthogonalize="pairwise",
log=True, absolute=True, verbose=None)
print(f'Correlation saved to {corr_true_file_label}')
np.save(corr_true_file_label, all_corr)
del stcs
def test_make_lcmv(tmpdir, reg, proj):
"""Test LCMV with evoked data and single trials."""
raw, epochs, evoked, data_cov, noise_cov, label, forward,\
forward_surf_ori, forward_fixed, forward_vol = _get_data(proj=proj)
for fwd in [forward, forward_vol]:
filters = make_lcmv(evoked.info, fwd, data_cov, reg=reg,
noise_cov=noise_cov)
stc = apply_lcmv(evoked, filters, max_ori_out='signed')
stc.crop(0.02, None)
stc_pow = np.sum(np.abs(stc.data), axis=1)
idx = np.argmax(stc_pow)
max_stc = stc.data[idx]
tmax = stc.times[np.argmax(max_stc)]
assert 0.09 < tmax < 0.12, tmax
assert 0.9 < np.max(max_stc) < 3., np.max(max_stc)
if fwd is forward:
# Test picking normal orientation (surface source space only).
filters = make_lcmv(evoked.info, forward_surf_ori, data_cov,
reg=reg, noise_cov=noise_cov,
pick_ori='normal', weight_norm=None)
stc_normal = apply_lcmv(evoked, filters, max_ori_out='signed')
stc_normal.crop(0.02, None)
stc_pow = np.sum(np.abs(stc_normal.data), axis=1)
idx = np.argmax(stc_pow)
max_stc = stc_normal.data[idx]
tmax = stc_normal.times[np.argmax(max_stc)]
lower = 0.04 if proj else 0.025
assert lower < tmax < 0.13, tmax
lower = 3e-7 if proj else 2e-7
assert lower < np.max(max_stc) < 5e-7, np.max(max_stc)
# No weight normalization was applied, so the amplitude of normal
# orientation results should always be smaller than free
# orientation results.
assert (np.abs(stc_normal.data) <= stc.data).all()
# Test picking source orientation maximizing output source power
filters = make_lcmv(evoked.info, fwd, data_cov, reg=reg,
noise_cov=noise_cov, pick_ori='max-power')
stc_max_power = apply_lcmv(evoked, filters, max_ori_out='signed')
stc_max_power.crop(0.02, None)
stc_pow = np.sum(np.abs(stc_max_power.data), axis=1)
idx = np.argmax(stc_pow)
max_stc = np.abs(stc_max_power.data[idx])
tmax = stc.times[np.argmax(max_stc)]
lower = 0.08 if proj else 0.04
assert lower < tmax < 0.12, tmax
assert 0.8 < np.max(max_stc) < 3., np.max(max_stc)
stc_max_power.data[:, :] = np.abs(stc_max_power.data)
if fwd is forward:
# Maximum output source power orientation results should be
# similar to free orientation results in areas with channel
# coverage
label = mne.read_label(fname_label)
mean_stc = stc.extract_label_time_course(label, fwd['src'],
mode='mean')
mean_stc_max_pow = \
stc_max_power.extract_label_time_course(label, fwd['src'],
mode='mean')
assert_array_less(np.abs(mean_stc - mean_stc_max_pow), 0.6)
# Test NAI weight normalization:
filters = make_lcmv(evoked.info, fwd, data_cov, reg=reg,
noise_cov=noise_cov, pick_ori='max-power',
weight_norm='nai')
stc_nai = apply_lcmv(evoked, filters, max_ori_out='signed')
stc_nai.crop(0.02, None)
# Test whether unit-noise-gain solution is a scaled version of NAI
pearsoncorr = np.corrcoef(np.concatenate(np.abs(stc_nai.data)),
np.concatenate(stc_max_power.data))
assert_almost_equal(pearsoncorr[0, 1], 1.)
# Test sphere head model with unit-noise gain beamformer and orientation
# selection and rank reduction of the leadfield
sphere = mne.make_sphere_model(r0=(0., 0., 0.), head_radius=0.080)
src = mne.setup_volume_source_space(subject=None, pos=15., mri=None,
sphere=(0.0, 0.0, 0.0, 80.0),
bem=None, mindist=5.0, exclude=2.0)
fwd_sphere = mne.make_forward_solution(evoked.info, trans=None, src=src,
bem=sphere, eeg=False, meg=True)
# Test that we get an error if not reducing rank
with pytest.raises(ValueError): # Singular matrix or complex spectrum
make_lcmv(
evoked.info, fwd_sphere, data_cov, reg=0.1,
noise_cov=noise_cov, weight_norm='unit-noise-gain',
pick_ori='max-power', reduce_rank=False, rank='full')
# Now let's reduce it
filters = make_lcmv(evoked.info, fwd_sphere, data_cov, reg=0.1,
noise_cov=noise_cov, weight_norm='unit-noise-gain',
pick_ori='max-power', reduce_rank=True)
stc_sphere = apply_lcmv(evoked, filters, max_ori_out='signed')
stc_sphere = np.abs(stc_sphere)
stc_sphere.crop(0.02, None)
stc_pow = np.sum(stc_sphere.data, axis=1)
idx = np.argmax(stc_pow)
max_stc = stc_sphere.data[idx]
tmax = stc_sphere.times[np.argmax(max_stc)]
lower = 0.08 if proj else 0.04
assert lower < tmax < 0.15, tmax
assert 0.4 < np.max(max_stc) < 2., np.max(max_stc)
# Test if spatial filter contains src_type
assert 'src_type' in filters
# __repr__
assert len(evoked.ch_names) == 22
assert len(evoked.info['projs']) == (4 if proj else 0)
assert len(evoked.info['bads']) == 2
rank = 17 if proj else 20
assert 'LCMV' in repr(filters)
assert 'unknown subject' not in repr(filters)
assert '484' in repr(filters)
assert '20' in repr(filters)
assert 'rank %s' % rank in repr(filters)
# I/O
fname = op.join(str(tmpdir), 'filters.h5')
with pytest.warns(RuntimeWarning, match='-lcmv.h5'):
filters.save(fname)
filters_read = read_beamformer(fname)
assert isinstance(filters, Beamformer)
assert isinstance(filters_read, Beamformer)
# deal with object_diff strictness
filters_read['rank'] = int(filters_read['rank'])
filters['rank'] = int(filters['rank'])
assert object_diff(filters, filters_read) == ''
# Test if fixed forward operator is detected when picking normal or
# max-power orientation
pytest.raises(ValueError, make_lcmv, evoked.info, forward_fixed, data_cov,
reg=0.01, noise_cov=noise_cov, pick_ori='normal')
pytest.raises(ValueError, make_lcmv, evoked.info, forward_fixed, data_cov,
reg=0.01, noise_cov=noise_cov, pick_ori='max-power')
# Test if non-surface oriented forward operator is detected when picking
# normal orientation
pytest.raises(ValueError, make_lcmv, evoked.info, forward, data_cov,
reg=0.01, noise_cov=noise_cov, pick_ori='normal')
# Test if volume forward operator is detected when picking normal
# orientation
pytest.raises(ValueError, make_lcmv, evoked.info, forward_vol, data_cov,
reg=0.01, noise_cov=noise_cov, pick_ori='normal')
# Test if missing of noise covariance matrix is detected when more than
# one channel type is present in the data
pytest.raises(ValueError, make_lcmv, evoked.info, forward_vol,
data_cov=data_cov, reg=0.01, noise_cov=None,
pick_ori='max-power')
# Test if wrong channel selection is detected in application of filter
evoked_ch = deepcopy(evoked)
evoked_ch.pick_channels(evoked_ch.ch_names[1:])
filters = make_lcmv(evoked.info, forward_vol, data_cov, reg=0.01,
noise_cov=noise_cov)
pytest.raises(ValueError, apply_lcmv, evoked_ch, filters,
max_ori_out='signed')
# Test if discrepancies in channel selection of data and fwd model are
# handled correctly in apply_lcmv
# make filter with data where first channel was removed
filters = make_lcmv(evoked_ch.info, forward_vol, data_cov, reg=0.01,
noise_cov=noise_cov)
# applying that filter to the full data set should automatically exclude
# this channel from the data
# also test here that no warnings are thrown - implemented to check whether
# src should not be None warning occurs
with pytest.warns(None) as w:
stc = apply_lcmv(evoked, filters, max_ori_out='signed')
assert len(w) == 0
# the result should be equal to applying this filter to a dataset without
# this channel:
stc_ch = apply_lcmv(evoked_ch, filters, max_ori_out='signed')
assert_array_almost_equal(stc.data, stc_ch.data)
# Test if non-matching SSP projection is detected in application of filter
if proj:
raw_proj = deepcopy(raw)
raw_proj.del_proj()
with pytest.raises(ValueError, match='do not match the projections'):
apply_lcmv_raw(raw_proj, filters, max_ori_out='signed')
# Test if setting reduce_rank to True returns a NotImplementedError
# when no orientation selection is done or pick_ori='normal'
pytest.raises(NotImplementedError, make_lcmv, evoked.info, forward_vol,
data_cov, noise_cov=noise_cov, pick_ori=None,
weight_norm='nai', reduce_rank=True)
pytest.raises(NotImplementedError, make_lcmv, evoked.info,
forward_surf_ori, data_cov, noise_cov=noise_cov,
pick_ori='normal', weight_norm='nai', reduce_rank=True)
# Test if spatial filter contains src_type
assert 'src_type' in filters
# check whether a filters object without src_type throws expected warning
del filters['src_type'] # emulate 0.16 behaviour to cause warning
with pytest.warns(RuntimeWarning, match='spatial filter does not contain '
'src_type'):
apply_lcmv(evoked, filters, max_ori_out='signed')
# Now test single trial using fixed orientation forward solution
# so we can compare it to the evoked solution
filters = make_lcmv(epochs.info, forward_fixed, data_cov, reg=0.01,
noise_cov=noise_cov)
stcs = apply_lcmv_epochs(epochs, filters, max_ori_out='signed')
stcs_ = apply_lcmv_epochs(epochs, filters, return_generator=True,
max_ori_out='signed')
assert_array_equal(stcs[0].data, next(stcs_).data)
epochs.drop_bad()
assert (len(epochs.events) == len(stcs))
# average the single trial estimates
stc_avg = np.zeros_like(stcs[0].data)
for this_stc in stcs:
stc_avg += this_stc.data
stc_avg /= len(stcs)
# compare it to the solution using evoked with fixed orientation
filters = make_lcmv(evoked.info, forward_fixed, data_cov, reg=0.01,
noise_cov=noise_cov)
stc_fixed = apply_lcmv(evoked, filters, max_ori_out='signed')
assert_array_almost_equal(stc_avg, stc_fixed.data)
# use a label so we have few source vertices and delayed computation is
# not used
filters = make_lcmv(epochs.info, forward_fixed, data_cov, reg=0.01,
noise_cov=noise_cov, label=label)
stcs_label = apply_lcmv_epochs(epochs, filters, max_ori_out='signed')
assert_array_almost_equal(stcs_label[0].data, stcs[0].in_label(label).data)
# Test condition where the filters weights are zero. There should not be
# any divide-by-zero errors
zero_cov = data_cov.copy()
zero_cov['data'][:] = 0
filters = make_lcmv(epochs.info, forward_fixed, zero_cov, reg=0.01,
noise_cov=noise_cov)
assert_array_equal(filters['weights'], 0)
