def set_labels_colors(subject, args, stim_dict=None):
stim_labels = stim_dict['labels'] if stim_dict else None
if stim_dict is None:
stim_labels, bipolar = get_stim_labels(subject, args, stim_labels)
stim_data_fname = op.join(
MMVT_DIR, subject, 'electrodes',
'stim_electrodes_{}{}_{}.npz'.format(args.file_frefix,
'bipolar' if bipolar else '',
args.stim_channel))
stim_dict = np.load(stim_data_fname)
stim_data = stim_dict['data']
electrode_labeling_fname = op.join(
MMVT_DIR, subject, 'electrodes',
'{}_{}_electrodes_cigar_r_{}_l_{}{}_stim_{}.pkl'.format(
subject, args.atlas, args.error_radius, args.elec_length,
'_bipolar' if args.bipolar else '', args.stim_channel))
elecs_labeling, electrode_labeling_fname = utils.get_electrodes_labeling(
subject,
MMVT_DIR,
args.atlas,
args.bipolar,
args.error_radius,
args.elec_length,
other_fname=electrode_labeling_fname)
if elecs_labeling is None:
print('No electrodes labeling file!')
return
electrodes = [e['name'] for e in elecs_labeling]
if len(set(stim_labels) - set(electrodes)) > 0:
print(
"The electrodes labeling isn't calculated for all the stim electrodes!"
)
print(set(stim_labels) - set(electrodes))
return
labels_elecs_lookup, subcortical_elecs_lookup = create_labels_electordes_lookup(
elecs_labeling)
labels_data, data_labels_names = {}, {}
for hemi in utils.HEMIS:
labels_data[hemi], data_labels_names[hemi] = \
calc_labels_data(labels_elecs_lookup[hemi], stim_data, stim_labels, args, hemi)
output_fname = op.join(
MMVT_DIR, subject, 'electrodes',
'stim_labels_{}{}_{}-{}.npz'.format(
args.file_frefix, 'bipolar' if args.bipolar else '',
args.stim_channel, hemi))
np.savez(output_fname,
data=labels_data[hemi],
names=data_labels_names[hemi],
conditions=stim_dict['conditions'])
subcortical_data, data_subcortical_names = calc_labels_data(
subcortical_elecs_lookup, stim_data, stim_labels, args)
output_fname = op.join(
MMVT_DIR, subject, 'electrodes', 'stim_subcortical_{}{}_{}.npz'.format(
args.file_frefix, 'bipolar' if args.bipolar else '',
args.stim_channel))
np.savez(output_fname,
data=subcortical_data,
names=data_subcortical_names,
conditions=stim_dict['conditions'])
def prepare_darpa_csv(subject, bipolar, atlas, good_channels=None, groups_ordering=None, error_radius=3, elec_length=4, p_threshold=0.05):
elecs_names, elecs_coords = elec_pre.read_electrodes_file(subject, bipolar)
elecs_coords_mni = fu.transform_subject_to_mni_coordinates(subject, elecs_coords, SUBJECTS_DIR)
elec_pre.save_electrodes_coords(elecs_names, elecs_coords_mni, good_channels)
elecs_coords_mni_dic = {elec_name:elec_coord for (elec_name,elec_coord) in zip(elecs_names, elecs_coords_mni)}
elecs_probs, _ = utils.get_electrodes_labeling(subject, BLENDER_ROOT_DIR, atlas, bipolar, error_radius, elec_length)
assert(len(elecs_names) == len(elecs_coords_mni) == len(elecs_probs))
most_probable_rois = elec_pre.get_most_probable_rois(elecs_probs, p_threshold, good_channels)
rois_colors = elec_pre.get_rois_colors(most_probable_rois)
elec_pre.save_rois_colors_legend(subject, rois_colors, bipolar)
utils.make_dir(op.join(BLENDER_ROOT_DIR, 'colin27', 'coloring'))
results = defaultdict(list)
for elec_name, elec_probs in zip(elecs_names, elecs_probs):
assert(elec_name == elec_probs['name'])
if not good_channels is None and elec_name not in good_channels:
continue
group = get_elec_group(elec_name, bipolar)
roi = elec_pre.get_most_probable_roi([*elec_probs['cortical_probs'], *elec_probs['subcortical_probs']],
[*elec_probs['cortical_rois'], *elec_probs['subcortical_rois']], p_threshold)
color = rois_colors[utils.get_hemi_indifferent_roi(roi)]
results[group].append(dict(name=elec_name, roi=roi, color=color))
coloring_fname = 'electrodes{}_coloring.csv'.format('_bipolar' if bipolar else '')
with open(op.join(OUTPUT_DIR, '{}_electrodes_info.csv'.format(subject)), 'w') as csv_file, \
open(op.join(BLENDER_ROOT_DIR, 'colin27', 'coloring', coloring_fname), 'w') as colors_csv_file:
csv_writer = csv.writer(csv_file, delimiter=',')
colors_csv_writer = csv.writer(colors_csv_file, delimiter=',')
elec_ind = 0
for group in groups_ordering:
group_res = sorted(results[group], key=lambda x:natural_keys(x['name']))
for res in group_res:
csv_writer.writerow([elec_ind, res['name'], *elecs_coords_mni_dic[res['name']], res['roi'], *res['color']])
colors_csv_writer.writerow([res['name'], *res['color']])
elec_ind += 1
def prepare_darpa_csv(subject,
bipolar,
atlas,
good_channels=None,
groups_ordering=None,
error_radius=3,
elec_length=4,
p_threshold=0.05):
elecs_names, elecs_coords = elec_pre.read_electrodes_file(subject, bipolar)
elecs_coords_mni = fu.transform_subject_to_mni_coordinates(
subject, elecs_coords, SUBJECTS_DIR)
elec_pre.save_electrodes_coords(elecs_names, elecs_coords_mni,
good_channels)
elecs_coords_mni_dic = {
elec_name: elec_coord
for (elec_name, elec_coord) in zip(elecs_names, elecs_coords_mni)
}
elecs_probs, _ = utils.get_electrodes_labeling(subject, BLENDER_ROOT_DIR,
atlas, bipolar,
error_radius, elec_length)
assert (len(elecs_names) == len(elecs_coords_mni) == len(elecs_probs))
most_probable_rois = elec_pre.get_most_probable_rois(
elecs_probs, p_threshold, good_channels)
rois_colors = elec_pre.get_rois_colors(most_probable_rois)
elec_pre.save_rois_colors_legend(subject, rois_colors, bipolar)
utils.make_dir(op.join(BLENDER_ROOT_DIR, 'colin27', 'coloring'))
results = defaultdict(list)
for elec_name, elec_probs in zip(elecs_names, elecs_probs):
assert (elec_name == elec_probs['name'])
if not good_channels is None and elec_name not in good_channels:
continue
group = get_elec_group(elec_name, bipolar)
roi = elec_pre.get_most_probable_roi(
[*elec_probs['cortical_probs'], *elec_probs['subcortical_probs']],
[*elec_probs['cortical_rois'], *elec_probs['subcortical_rois']],
p_threshold)
color = rois_colors[utils.get_hemi_indifferent_roi(roi)]
results[group].append(dict(name=elec_name, roi=roi, color=color))
coloring_fname = 'electrodes{}_coloring.csv'.format(
'_bipolar' if bipolar else '')
with open(op.join(OUTPUT_DIR, '{}_electrodes_info.csv'.format(subject)), 'w') as csv_file, \
open(op.join(BLENDER_ROOT_DIR, 'colin27', 'coloring', coloring_fname), 'w') as colors_csv_file:
csv_writer = csv.writer(csv_file, delimiter=',')
colors_csv_writer = csv.writer(colors_csv_file, delimiter=',')
elec_ind = 0
for group in groups_ordering:
group_res = sorted(results[group],
key=lambda x: natural_keys(x['name']))
for res in group_res:
csv_writer.writerow([
elec_ind, res['name'], *elecs_coords_mni_dic[res['name']],
res['roi'], *res['color']
])
colors_csv_writer.writerow([res['name'], *res['color']])
elec_ind += 1
def get_electrodes_with_cortical_vertices(mri_subject,
atlas,
bipolar,
error_radius=3,
elec_length=4):
elecs_probs, _ = utils.get_electrodes_labeling(mri_subject, MMVT_DIR,
atlas, bipolar,
error_radius, elec_length)
return [
elec_probs for elec_probs in elecs_probs
if len(elec_probs['cortical_indices_dists']) > 0
and len(elec_probs['cortical_rois']) > 0
]
def set_labels_colors(subject, args, stim_dict=None):
stim_labels = stim_dict['labels'] if stim_dict else None
stim_labels, bipolar = get_stim_labels(subject, args, stim_labels)
if stim_dict is None:
stim_data_fname = op.join(MMVT_DIR, subject, 'electrodes', 'stim_electrodes_{}{}_{}.npz'.format(
args.file_frefix, 'bipolar' if bipolar else '', args.stim_channel))
stim_dict = np.load(stim_data_fname)
stim_data = stim_dict['data']
electrode_labeling_fname = op.join(MMVT_DIR, subject, 'electrodes',
'{}_{}_electrodes_cigar_r_{}_l_{}{}_stim_{}.pkl'.format(subject, args.atlas, args.error_radius,
args.elec_length, '_bipolar' if bipolar else '', args.stim_channel))
elecs_labeling, electrode_labeling_fname = utils.get_electrodes_labeling(
subject, MMVT_DIR, args.atlas, bipolar, args.error_radius, args.elec_length,
other_fname=electrode_labeling_fname)
if elecs_labeling is None:
print('No electrodes labeling file!')
return
electrodes = [e['name'] for e in elecs_labeling]
if len(set(stim_labels) - set(electrodes)) > 0:
print("The electrodes labeling isn't calculated for all the stim electrodes!")
print(set(stim_labels) - set(electrodes))
return
labels_elecs_lookup, subcortical_elecs_lookup = create_labels_electordes_lookup(
subject, args.atlas, elecs_labeling, args.n_jobs)
labels_data, labels_colors, data_labels_names = {}, {}, {}
for hemi in utils.HEMIS:
labels_data[hemi], labels_colors[hemi], data_labels_names[hemi] = \
calc_labels_data(labels_elecs_lookup[hemi], stim_data, stim_labels, hemi)
output_fname = op.join(MMVT_DIR, subject, 'electrodes', 'stim_labels_{}{}_{}-{}.npz'.format(
args.file_frefix, 'bipolar' if bipolar else '', args.stim_channel, hemi))
np.savez(output_fname, data=labels_data[hemi], names=data_labels_names[hemi],
conditions=stim_dict['conditions'], colors=labels_colors[hemi])
subcortical_data, subcortical_colors, data_subcortical_names = calc_labels_data(
subcortical_elecs_lookup, stim_data, stim_labels)
output_fname = op.join(MMVT_DIR, subject, 'electrodes', 'stim_subcortical_{}{}_{}.npz'.format(
args.file_frefix, 'bipolar' if bipolar else '', args.stim_channel))
np.savez(output_fname, data=subcortical_data, names=data_subcortical_names,
conditions=stim_dict['conditions'], colors=subcortical_colors)
def create_electrodes_labeling_coloring(subject, bipolar, atlas, good_channels=None, error_radius=3, elec_length=4,
p_threshold=0.05, legend_name='', coloring_fname=''):
elecs_names, elecs_coords = read_electrodes_file(subject, bipolar)
elecs_probs, electrode_labeling_fname = utils.get_electrodes_labeling(
subject, MMVT_DIR, atlas, bipolar, error_radius, elec_length)
if elecs_probs is None:
print('No electrodes labeling file!')
return
if electrode_labeling_fname != op.join(MMVT_DIR, subject, 'electrodes',
op.basename(electrode_labeling_fname)):
shutil.copy(electrode_labeling_fname, op.join(MMVT_DIR, subject, 'electrodes',
op.basename(electrode_labeling_fname)))
most_probable_rois = get_most_probable_rois(elecs_probs, p_threshold, good_channels)
rois_colors_rgbs, rois_colors_names = get_rois_colors(subject, atlas, most_probable_rois)
save_rois_colors_legend(subject, rois_colors_rgbs, bipolar, legend_name)
utils.make_dir(op.join(MMVT_DIR, subject, 'coloring'))
if coloring_fname == '':
coloring_fname = 'electrodes{}_coloring.csv'.format('_bipolar' if bipolar else '')
coloring_fol = op.join(MMVT_DIR, subject, 'coloring')
coloring_fname = op.join(coloring_fol, coloring_fname)
colors_names_fname = op.join(coloring_fol, 'electrodes{}_colors_names.txt'.format('_bipolar' if bipolar else ''))
elec_names_rois_colors = defaultdict(list)
with open(coloring_fname, 'w') as colors_rgbs_file, open(colors_names_fname, 'w') as colors_names_file:
# colors_csv_writer = csv.writer(colors_csv_file, delimiter=',')
for elec_name, elec_probs in zip(elecs_names, elecs_probs):
assert(elec_name == elec_probs['name'])
if not good_channels is None and elec_name not in good_channels:
continue
roi = get_most_probable_roi([*elec_probs['cortical_probs'], *elec_probs['subcortical_probs']],
[*elec_probs['cortical_rois'], *elec_probs['subcortical_rois']], p_threshold)
if roi != '':
inv_roi = utils.get_hemi_indifferent_roi(roi)
colors_rgbs_file.write('{},{},{},{}\n'.format(elec_name, *rois_colors_rgbs[inv_roi]))
colors_names_file.write('{},{},{}\n'.format(elec_name, inv_roi, rois_colors_names[inv_roi]))
elec_names_rois_colors[inv_roi].append(elec_name)
# colors_csv_writer.writerow([elec_name, *color_rgb])
with open(op.join(coloring_fol, 'electrodes_report.txt'), 'w') as elecs_report_file:
for inv_roi, electrodes_names in elec_names_rois_colors.items():
elecs_report_file.write('{},{},{}\n'.format(inv_roi, rois_colors_names[inv_roi], ','.join(electrodes_names)))
def get_electrodes_with_cortical_vertices(mri_subject, atlas, bipolar, error_radius=3, elec_length=4):
elecs_probs, _ = utils.get_electrodes_labeling(
mri_subject, MMVT_DIR, atlas, bipolar, error_radius, elec_length)
return [elec_probs for elec_probs in elecs_probs if
len(elec_probs['cortical_indices_dists']) > 0 and len(elec_probs['cortical_rois']) > 0]
