def calc_labels_data(elecs_lookup, stim_data, stim_labels, args, hemi=None):
stim_labels = np.array(stim_labels)
labels_names = list(elecs_lookup.keys())
labels_data = np.zeros(
(len(labels_names), stim_data.shape[1], stim_data.shape[2]))
# colors = np.zeros((*labels_data.shape, 3))
labels_data_names = []
label_ind = 0
for label_name, electordes_data in elecs_lookup.items():
if not hemi is None:
if lu.get_hemi_from_name(label_name) != hemi:
continue
labels_data_names.append(label_name)
for elec_name, elec_prob in electordes_data:
elec_inds = np.where(stim_labels == elec_name)[0]
if len(elec_inds) > 0:
elec_data = stim_data[elec_inds[0], :, :] * elec_prob
labels_data[label_ind, :, :] += elec_data
else:
print('calc_labels_data: No data found for {}!'.format(
elec_name))
label_ind += 1
# Calc colors for each freq
# for freq_id in range(labels_data.shape[2]):
# data_min, data_max = utils.calc_min_max(labels_data[:, :, freq_id], norm_percs=args.norm_percs)
# colors[:, :, freq_id] = utils.mat_to_colors(
# labels_data[:, :, freq_id], data_min, data_max, colorsMap=args.colors_map)
return labels_data, labels_data_names
def save_sz_pick_values(subject, files_names, func_name, atlas):
bands = dict(theta=[4, 8],
alpha=[8, 15],
beta=[15, 30],
gamma=[30, 55],
high_gamma=[65, 120])
output_fol = utils.make_dir(
op.join(MMVT_DIR, subject, 'labels', 'labels_data'))
names = np.load(
op.join(op.join(MMVT_DIR, subject, 'connectivity',
'labels_names.npy')))
labels_indices = np.load(
op.join(
op.join(MMVT_DIR, subject, 'connectivity', 'labels_indices.npy')))
names = names[labels_indices]
for band_name in bands.keys():
output_fname = op.join(output_fol,
'{}_{}.npz'.format(func_name, band_name))
# if op.isfile(output_fname):
# continue
fname = [
fname for fname in files_names
if utils.namebase(fname).endswith('SZ')
][0]
file_name = utils.namebase(fname)
con_name = 'meg_{}_mi'.format(band_name)
input_fname = op.join(MMVT_DIR, subject, 'connectivity', file_name,
'{}_{}.npy'.format(con_name, func_name))
vals = np.load(input_fname)
data_min, data_max = np.min(vals), np.max(vals)
t_axis = np.linspace(-2, 5, vals.shape[1])
print('onset index: {}'.format(np.where(t_axis > 0)[0][0]))
# Find pick in time
t_peak = np.argmax(np.max(vals, axis=0))
max_vals = vals[:, t_peak]
# all_vals = np.zeros((len(names)))
# all_vals = [max_vals[list(names).index(l)] if l in names else 0 for l in labels]
print('Saving {}'.format(output_fname))
np.savez(output_fname,
data=vals,
names=names,
atlas=atlas,
data_min=data_min,
data_max=data_max,
title='{}-{}'.format(func_name, band_name),
cmap='YlOrRd')
for hemi in utils.HEMIS:
labels_output_fname = op.join(
MMVT_DIR, subject, 'meg',
'labels_data_epilepsy_laus125_{}_{}_{}.npz'.format(
band_name, func_name, hemi))
hemis = np.array([lu.get_hemi_from_name(l) == hemi for l in names])
np.savez(labels_output_fname,
data=vals[hemis],
names=names[hemis],
conditions=['sz'])
def create_labels_electordes_lookup(subject, atlas, elecs_labeling, n_jobs):
# delim, pos = lu.get_hemi_delim_and_pos(elecs_labeling[0]['cortical_rois'][0])
# atlas_labels = lu.get_atlas_labels_names(subject, atlas, delim, pos, n_jobs)
labels_elecs_lookup = dict(rh=defaultdict(list), lh=defaultdict(list))
subcortical_elecs_lookup = defaultdict(list)
# for label_name in atlas_labels['rh'] + atlas_labels['lh']:
for elec_labeling in elecs_labeling:
for label_name, label_prob in zip(elec_labeling['cortical_rois'], elec_labeling['cortical_probs']):
# label_ind = elec_labeling['cortical_rois'].index(label_name)
# label_prob = elec_labeling['cortical_probs'][label_ind]
hemi = lu.get_hemi_from_name(label_name)
labels_elecs_lookup[hemi][label_name].append((elec_labeling['name'], label_prob))
for label_name, label_prob in zip(elec_labeling['subcortical_rois'], elec_labeling['subcortical_probs']):
subcortical_elecs_lookup[label_name].append((elec_labeling['name'], label_prob))
return labels_elecs_lookup, subcortical_elecs_lookup
def create_labels_electordes_lookup(elecs_labeling):
# delim, pos = lu.get_hemi_delim_and_pos(elecs_labeling[0]['cortical_rois'][0])
# atlas_labels = lu.get_atlas_labels_names(subject, atlas, delim, pos, n_jobs)
labels_elecs_lookup = dict(rh=defaultdict(list), lh=defaultdict(list))
subcortical_elecs_lookup = defaultdict(list)
# for label_name in atlas_labels['rh'] + atlas_labels['lh']:
for elec_labeling in elecs_labeling:
for label_name, label_prob in zip(elec_labeling['cortical_rois'],
elec_labeling['cortical_probs']):
# label_ind = elec_labeling['cortical_rois'].index(label_name)
# label_prob = elec_labeling['cortical_probs'][label_ind]
hemi = lu.get_hemi_from_name(label_name)
labels_elecs_lookup[hemi][label_name].append(
(elec_labeling['name'], label_prob))
for label_name, label_prob in zip(elec_labeling['subcortical_rois'],
elec_labeling['subcortical_probs']):
subcortical_elecs_lookup[label_name].append(
(elec_labeling['name'], label_prob))
return labels_elecs_lookup, subcortical_elecs_lookup
def calc_labels_data(elecs_lookup, stim_data, stim_labels, hemi=None):
labels_names = list(elecs_lookup.keys())
labels_data = np.zeros((len(labels_names), stim_data.shape[1], stim_data.shape[2]))
colors = np.zeros((*labels_data.shape, 3))
labels_data_names = []
label_ind = 0
for label_name, electordes_data in elecs_lookup.items():
if not hemi is None:
if lu.get_hemi_from_name(label_name) != hemi:
continue
labels_data_names.append(label_name)
for elec_name, elec_prob in electordes_data:
elec_inds = np.where(stim_labels == elec_name)[0]
if len(elec_inds) > 0:
elec_data = stim_data[elec_inds[0], :, :] * elec_prob
labels_data[label_ind, :, :] += elec_data
label_ind += 1
# Calc colors for each freq
for freq_id in range(labels_data.shape[2]):
data_min, data_max = utils.calc_min_max(labels_data[:, :, freq_id], norm_percs=args.norm_percs)
colors[:, :, freq_id] = utils.mat_to_colors(
labels_data[:, :, freq_id], data_min, data_max, colorsMap=args.colors_map)
return labels_data, colors, labels_data_names
def filter_meg_labels_ts(subject,
inv_method='dSPM',
em='mean_flip',
atlas='electrodes_labels',
low_freq_cut_off=10,
fs=1000,
do_plot=False,
n_jobs=4):
folders = glob.glob(
op.join(MMVT_DIR, subject, 'meg',
'{}_*_*_Resting_eeg_meg_Demi_ar-epo'.format(subject)))
now = time.time()
for fol_ind, fol in enumerate(folders):
utils.time_to_go(now, fol_ind, len(folders), runs_num_to_print=1)
files = glob.glob(
op.join(
fol, 'labels_data_rest_{}_{}_{}_?h.npz'.format(
atlas, inv_method, em)))
for fname in files:
hemi = lu.get_hemi_from_name(utils.namebase(fname))
d = utils.Bag(np.load(fname))
L = d.data.shape[0] # labels * time * epochs
filter_data = np.empty(d.data.shape)
params = [(d.data[label_ind], label_ind, fs, low_freq_cut_off,
do_plot) for label_ind in range(L)]
results = utils.run_parallel(_filter_meg_label_ts_parallel, params,
n_jobs)
for filter_label_data, label_ind in results:
filter_data[label_ind] = filter_label_data
new_fname = op.join(
fol, 'labels_data_rest_{}_{}_{}_filter_{}.npz'.format(
atlas, inv_method, em, hemi))
np.savez(new_fname,
data=filter_data,
names=d.names,
conditions=d.conditions)
