def transfer_electrodes_to_template_system(electrodes,
template_system,
use_mri_robust_lta=False,
vox2vox=False):
import time
teamplte_electrodes = defaultdict(list)
for subject in electrodes.keys():
# if subject != 'mg101':
# continue
now, N = time.time(), len(electrodes[subject])
for run, (elc_name, coords) in enumerate(electrodes[subject]):
utils.time_to_go(now, run, N, runs_num_to_print=5)
if use_mri_robust_lta:
if vox2vox:
template_cords = lta_transfer_vox2vox(subject, coords)
else:
template_cords = lta_transfer_ras2ras(subject, coords)
else:
if template_system == 'ras':
template_cords = fu.transform_subject_to_ras_coordinates(
subject, coords, SUBJECTS_DIR)
elif template_system == 'mni':
template_cords = fu.transform_subject_to_mni_coordinates(
subject, coords, SUBJECTS_DIR)
else:
template_cords = fu.transform_subject_to_subject_coordinates(
subject, template_system, coords, SUBJECTS_DIR)
if template_cords is not None:
teamplte_electrodes[subject].append((elc_name, template_cords))
return teamplte_electrodes
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 transform_electrodes_to_mni(subject, args):
from src.utils import freesurfer_utils as fu
elecs_names, elecs_coords = read_electrodes_file(subject, args.bipolar)
elecs_coords_mni = fu.transform_subject_to_mni_coordinates(subject, elecs_coords, SUBJECTS_DIR)
save_electrodes_coords(elecs_names, elecs_coords_mni, args.good_channels, args.bad_channels)
