def compute_noise_cov(subject, hcp_path, noise_cov_fname=''):
if noise_cov_fname == '':
noise_cov_fname = meg.NOISE_COV.format(cond='empty_room')
if op.isfile(noise_cov_fname):
noise_cov = mne.read_cov(noise_cov_fname)
return noise_cov
utils.make_dir(utils.get_parent_fol(noise_cov_fname))
raw_noise = hcp.read_raw(subject=subject,
hcp_path=hcp_path,
data_type='noise_empty_room')
raw_noise.load_data()
# apply ref channel correction and drop ref channels
preproc.apply_ref_correction(raw_noise)
raw_noise.filter(0.50,
None,
method='iir',
iir_params=dict(order=4, ftype='butter'),
n_jobs=1)
raw_noise.filter(None,
60,
method='iir',
iir_params=dict(order=4, ftype='butter'),
n_jobs=1)
##############################################################################
# Note that using the empty room noise covariance will inflate the SNR of the
# evkoked and renders comparisons to `baseline` rather uninformative.
noise_cov = mne.compute_raw_covariance(raw_noise, method='empirical')
noise_cov.save(noise_cov_fname)
return noise_cov
def save_evoked_to_blender(mri_subject,
events,
evoked=None,
norm_by_percentile=True,
norm_percs=(1, 99)):
fol = op.join(MMVT_DIR, mri_subject, 'eeg')
utils.make_dir(fol)
if '{cond}' in meg.EVO:
for event_ind, event_id in enumerate(events.keys()):
if evoked is None:
evo = mne.read_evokeds(meg.get_cond_fname(meg.EVO, event_id))
else:
evo = evoked[event_id]
if event_ind == 0:
ch_names = evo[0].ch_names
data = np.zeros(
(evo[0].data.shape[0], evo[0].data.shape[1], 2))
data[:, :, event_ind] = evo[0].data
else:
if evoked is None:
evoked = mne.read_evokeds(meg.EVO)
data = evoked.data
data_max, data_min = utils.get_data_max_min(data, norm_by_percentile,
norm_percs)
max_abs = utils.get_max_abs(data_max, data_min)
data = data / max_abs
np.save(op.join(fol, 'eeg_data.npy'), data)
np.savez(op.join(fol, 'eeg_data_meta.npz'),
names=ch_names,
conditions=list(events.keys()))
return True
def create_annotation_from_fsaverage(subject, aparc_name='aparc250', fsaverage='fsaverage',
overwrite_annotation=False, overwrite_morphing=False, do_solve_labels_collisions=False,
morph_labels_from_fsaverage=True, fs_labels_fol='', n_jobs=6):
annotations_exist = np.all([op.isfile(op.join(SUBJECTS_DIR, subject, 'label', '{}.{}.annot'.format(hemi,
aparc_name))) for hemi in HEMIS])
existing_freesurfer_annotations = ['aparc.DKTatlas40.annot', 'aparc.annot', 'aparc.a2009s.annot']
if '{}.annot'.format(aparc_name) in existing_freesurfer_annotations:
morph_labels_from_fsaverage = False
do_solve_labels_collisions = False
if not annotations_exist:
utils.make_dir(op.join(SUBJECTS_DIR, subject, 'label'))
annotations_exist = fu.create_annotation_file(subject, aparc_name, subjects_dir=SUBJECTS_DIR, freesurfer_home=FREE_SURFER_HOME)
if morph_labels_from_fsaverage:
utils.morph_labels_from_fsaverage(subject, SUBJECTS_DIR, aparc_name, n_jobs=n_jobs,
fsaverage=fsaverage, overwrite=overwrite_morphing, fs_labels_fol=fs_labels_fol)
if do_solve_labels_collisions:
solve_labels_collisions(subject, aparc_name, fsaverage, n_jobs)
# Note that using the current mne version this code won't work, because of collissions between hemis
# You need to change the mne.write_labels_to_annot code for that.
if overwrite_annotation or not annotations_exist:
try:
utils.labels_to_annot(subject, SUBJECTS_DIR, aparc_name, overwrite=overwrite_annotation)
except:
print("Can't write labels to annotation! Trying to solve labels collision")
solve_labels_collisions(subject, aparc_name, fsaverage, n_jobs)
try:
utils.labels_to_annot(subject, SUBJECTS_DIR, aparc_name, overwrite=overwrite_annotation)
except:
print("Can't write labels to annotation! Solving the labels collision didn't help...")
print(traceback.format_exc())
return utils.both_hemi_files_exist(op.join(
SUBJECTS_DIR, subject, 'label', '{}.{}.annot'.format('{hemi}', aparc_name)))
def freesurfer_surface_to_blender_surface(subject, hemi='both', overwrite=False):
for hemi in utils.get_hemis(hemi):
utils.make_dir(op.join(MMVT_DIR, subject, 'surf'))
for surf_type in ['inflated', 'pial']:
surf_name = op.join(SUBJECTS_DIR, subject, 'surf', '{}.{}'.format(hemi, surf_type))
surf_wavefront_name = '{}.asc'.format(surf_name)
surf_new_name = '{}.srf'.format(surf_name)
hemi_ply_fname = '{}.ply'.format(surf_name)
mmvt_hemi_ply_fname = op.join(MMVT_DIR, subject, 'surf', '{}.{}.ply'.format(hemi, surf_type))
mmvt_hemi_npz_fname = op.join(MMVT_DIR, subject, 'surf', '{}.{}.npz'.format(hemi, surf_type))
if overwrite or not op.isfile(mmvt_hemi_ply_fname) and not op.isfile(mmvt_hemi_npz_fname):
print('{} {}: convert srf to asc'.format(hemi, surf_type))
utils.run_script('mris_convert {} {}'.format(surf_name, surf_wavefront_name))
os.rename(surf_wavefront_name, surf_new_name)
print('{} {}: convert asc to ply'.format(hemi, surf_type))
convert_hemis_srf_to_ply(subject, hemi, surf_type)
if op.isfile(mmvt_hemi_ply_fname):
os.remove(mmvt_hemi_ply_fname)
shutil.copy(hemi_ply_fname, mmvt_hemi_ply_fname)
ply_fname = op.join(MMVT_DIR, subject, 'surf', '{}.{}.ply'.format(hemi, surf_type))
if not op.isfile(mmvt_hemi_npz_fname):
verts, faces = utils.read_ply_file(ply_fname)
np.savez(mmvt_hemi_npz_fname, verts=verts, faces=faces)
return utils.both_hemi_files_exist(op.join(MMVT_DIR, subject, 'surf', '{hemi}.pial.ply')) and \
utils.both_hemi_files_exist(op.join(MMVT_DIR, subject, 'surf', '{hemi}.pial.npz')) and \
utils.both_hemi_files_exist(op.join(MMVT_DIR, subject, 'surf', '{hemi}.inflated.ply')) and \
utils.both_hemi_files_exist(op.join(MMVT_DIR, subject, 'surf', '{hemi}.inflated.npz'))
def get_output_using_sftp(subjects, subject_to):
sftp_domain = 'door.nmr.mgh.harvard.edu'
sftp_username = '******'
remote_subject_dir = '/space/thibault/1/users/npeled/subjects/{subject}'
necessary_files = {
'mri_cvs_register_to_{}'.format(subject_to):
['combined_to{}_elreg_afteraseg-norm.tm3d'.format(subject_to)]
}
password = ''
for subject in subjects:
print('Getting tm3d file for {}'.format(subject))
utils.make_dir(op.join(SUBJECTS_DIR, subject, 'electrodes'))
ret, password_temp = utils.prepare_subject_folder(
necessary_files,
subject,
remote_subject_dir.format(subject=subject.lower()),
SUBJECTS_DIR,
True,
sftp_username,
sftp_domain,
sftp_password=password)
if not ret:
print('Error in copying the file!')
if password_temp != '':
password = password_temp
def split_baseline(fif_fnames, clips_length=6, shift=6, overwrite=False):
output_fol = op.join(utils.get_parent_fol(fif_fnames[0]), 'new_baselines')
if not overwrite and op.isdir(output_fol) and len(
glob.glob(op.join(output_fol, '*.fif'))) > 0:
return glob.glob(op.join(output_fol, '*.fif'))
utils.make_dir(output_fol)
data = []
for fif_fname in fif_fnames:
clip = mne.read_evokeds(fif_fname)[0]
freq = clip.info['sfreq']
step = int(freq * clips_length)
start_t, end_t = 0, len(clip.times)
while start_t + clips_length * freq < end_t:
new_clip = mne.EvokedArray(clip.data[:, start_t:start_t + step],
clip.info,
comment='baseline')
data.append(new_clip.data[0, :10])
mne.write_evokeds(
op.join(
output_fol, '{}_{}.fif'.format(utils.namebase(fif_fname),
int(start_t / freq))),
new_clip)
start_t += int(freq * shift)
data = np.array(data)
return glob.glob(op.join(output_fol, '*.fif'))
def create_lut_file_for_atlas(subject, atlas):
# Read the subcortical segmentation from the freesurfer lut
lut = utils.read_freesurfer_lookup_table(FREE_SURFER_HOME, get_colors=True)
lut_new = [list(l) for l in lut if l[0] < 1000]
for hemi, offset in zip(['lh', 'rh'], [1000, 2000]):
if hemi == 'lh':
lut_new.append([1000, 'ctx-lh-unknown', 25, 5, 25, 0])
else:
lut_new.append([2000, 'ctx-rh-unknown', 25, 5, 25, 0])
_, ctab, names = _read_annot(op.join(SUBJECTS_DIR, subject, 'label', '{}.{}.annot'.format(hemi, atlas)))
names = [name.astype(str) for name in names]
for index, (label, cval) in enumerate(zip(names, ctab)):
r,g,b,a, _ = cval
lut_new.append([index + offset + 1, label, r, g, b, a])
lut_new.sort(key=lambda x:x[0])
# Add the values above 3000
for l in [l for l in lut if l[0] >= 3000]:
lut_new.append(l)
new_lut_fname = op.join(SUBJECTS_DIR, subject, 'label', '{}ColorLUT.txt'.format(atlas))
with open(new_lut_fname, 'w') as fp:
csv_writer = csv.writer(fp, delimiter='\t')
csv_writer.writerows(lut_new)
# np.savetxt(new_lut_fname, lut_new, delimiter='\t', fmt="%s")
utils.make_dir(op.join(MMVT_DIR, subject, 'freeview'))
shutil.copyfile(new_lut_fname, op.join(MMVT_DIR, subject, 'freeview', '{}ColorLUT.txt'.format(atlas)))
def save_labels_coloring(subject, atlas, n_jobs=2):
ret = False
coloring_dir = op.join(MMVT_DIR, subject, 'coloring')
utils.make_dir(coloring_dir)
coloring_fname = op.join(coloring_dir, 'labels_{}_coloring.csv'.format(atlas))
coloring_names_fname = op.join(coloring_dir, 'labels_{}_colors_names.csv'.format(atlas))
try:
labels = lu.read_labels(subject, SUBJECTS_DIR, atlas, n_jobs=n_jobs)
colors_rgb_and_names = cu.get_distinct_colors_and_names()
labels_colors_rgb, labels_colors_names = {}, {}
for label in labels:
label_inv_name = lu.get_label_hemi_invariant_name(label.name)
if label_inv_name not in labels_colors_rgb:
labels_colors_rgb[label_inv_name], labels_colors_names[label_inv_name] = next(colors_rgb_and_names)
with open(coloring_fname, 'w') as colors_file, open(coloring_names_fname, 'w') as col_names_file:
for label in labels:
label_inv_name = lu.get_label_hemi_invariant_name(label.name)
color_rgb = labels_colors_rgb[label_inv_name]
color_name = labels_colors_names[label_inv_name]
colors_file.write('{},{},{},{}\n'.format(label.name, *color_rgb))
col_names_file.write('{},{}\n'.format(label.name, color_name))
ret = op.isfile(coloring_fname)
except:
print('Error in save_labels_coloring!')
print(traceback.format_exc())
return ret
def save_cerebellum_coloring(subject):
ret = False
coloring_dir = op.join(MMVT_DIR, subject, 'coloring')
utils.make_dir(coloring_dir)
coloring_fname = op.join(coloring_dir, 'cerebellum_coloring.csv')
lut_name = 'Buckner2011_17Networks_ColorLUT_new.txt'
lut_fname = op.join(MMVT_DIR, 'templates', lut_name)
if not op.isfile(lut_fname):
lut_resources_fname = op.join(utils.get_resources_fol(), lut_name)
if op.isfile(lut_resources_fname):
shutil.copy(lut_resources_fname, lut_fname)
else:
print("The Buckner2011 17Networks Color LUT is missing! ({})".format(lut_fname))
return False
try:
with open(coloring_fname, 'w') as colors_file, open(lut_fname, 'r') as lut_file:
lut = lut_file.readlines()
for ind, lut_line in zip(range(1, 34), lut[1:]):
color_rgb = [float(x) / 255 for x in ' '.join(lut_line.split()).split(' ')[2:-1]]
colors_file.write('{},{},{},{}\n'.format('cerebellum_{}'.format(ind), *color_rgb))
ret = op.isfile(coloring_fname)
except:
print('Error in save_cerebellum_coloring!')
print(traceback.format_exc())
return ret
def create_lut_file_for_atlas(subject, atlas):
# Read the subcortical segmentation from the freesurfer lut
lut = utils.read_freesurfer_lookup_table(FREE_SURFER_HOME, get_colors=True)
lut_new = [list(l) for l in lut if l[0] < 1000]
for hemi, offset in zip(['lh', 'rh'], [1000, 2000]):
if hemi == 'lh':
lut_new.append([1000, 'ctx-lh-unknown', 25, 5, 25, 0])
else:
lut_new.append([2000, 'ctx-rh-unknown', 25, 5, 25, 0])
_, ctab, names = _read_annot(
op.join(SUBJECTS_DIR, subject, 'label',
'{}.{}.annot'.format(hemi, atlas)))
names = [name.astype(str) for name in names]
for index, (label, cval) in enumerate(zip(names, ctab)):
r, g, b, a, _ = cval
lut_new.append([index + offset + 1, label, r, g, b, a])
lut_new.sort(key=lambda x: x[0])
# Add the values above 3000
for l in [l for l in lut if l[0] >= 3000]:
lut_new.append(l)
new_lut_fname = op.join(SUBJECTS_DIR, subject, 'label',
'{}ColorLUT.txt'.format(atlas))
with open(new_lut_fname, 'w') as fp:
csv_writer = csv.writer(fp, delimiter='\t')
csv_writer.writerows(lut_new)
# np.savetxt(new_lut_fname, lut_new, delimiter='\t', fmt="%s")
utils.make_dir(op.join(BLENDER_ROOT_DIR, subject, 'freeview'))
shutil.copyfile(
new_lut_fname,
op.join(BLENDER_ROOT_DIR, subject, 'freeview',
'{}ColorLUT.txt'.format(atlas)))
def export_into_csv(template_system,
mmvt_dir,
bipolar=False,
prefix='',
input_fname=''):
template = 'fsaverage' if template_system == 'ras' else 'colin27' if template_system == 'mni' else template_system
if input_fname == '':
input_name = '{}electrodes{}_positions.npz'.format(
prefix, '_bipolar' if bipolar else '')
input_fname = op.join(mmvt_dir, template, 'electrodes', input_name)
electrodes_dict = utils.Bag(np.load(input_fname))
fol = utils.make_dir(op.join(MMVT_DIR, template, 'electrodes'))
csv_fname = op.join(
fol, '{}{}_{}RAS.csv'.format(prefix, template,
'bipolar_' if bipolar else ''))
print('Writing csv file to {}'.format(csv_fname))
with open(csv_fname, 'w') as csv_file:
wr = csv.writer(csv_file, quoting=csv.QUOTE_NONE)
wr.writerow(['Electrode Name', 'R', 'A', 'S'])
for elc_name, elc_coords in zip(electrodes_dict.names,
electrodes_dict.pos):
wr.writerow([
elc_name, *['{:.2f}'.format(x) for x in elc_coords.squeeze()]
])
fol = utils.make_dir(op.join(MMVT_DIR, template, 'electrodes'))
csv_fname2 = op.join(fol, utils.namebase_with_ext(csv_fname))
if csv_fname != csv_fname2:
utils.copy_file(csv_fname, csv_fname2)
print('export_into_csv: {}'.format(
op.isfile(csv_fname) and op.isfile(csv_fname2)))
return csv_fname
def save_subject_ct_trans(subject,
ct_name='ct_reg_to_mr.mgz',
overwrite=False):
output_fname = op.join(MMVT_DIR, subject, 'ct', 'ct_trans.npz')
if op.isfile(output_fname) and not overwrite:
return True
ct_fname, ct_exist = utils.locating_file(ct_name,
['*.mgz', '*.nii', '*.nii.gz'],
op.join(MMVT_DIR, subject, 'ct'))
# ct_fname = op.join(MMVT_DIR, subject, 'ct', ct_name)
if not ct_exist: # op.isfile(ct_fname):
# subjects_ct_fname = op.join(SUBJECTS_DIR, subject, 'mri', ct_name)
ct_fname, ct_exist = utils.locating_file(
ct_name, ['*.mgz', '*.nii', '*.nii.gz'],
op.join(SUBJECTS_DIR, subject, 'mri'))
if ct_exist: #op.isfile(subjects_ct_fname):
utils.make_dir(op.join(MMVT_DIR, subject, 'ct'))
ct_fname = utils.copy(ct_fname, op.join(MMVT_DIR, subject, 'ct'))
else:
print("Can't find subject's CT! ({})".format(ct_fname))
return False
if ct_fname != op.join(MMVT_DIR, subject, 'ct', ct_name):
utils.make_link(ct_fname, op.join(MMVT_DIR, subject, 'ct', ct_name))
print('save_subject_ct_trans: loading {}'.format(ct_fname))
header = nib.load(ct_fname).header
ras_tkr2vox, vox2ras_tkr, vox2ras, ras2vox = anat.get_trans_functions(
header)
print('save_subject_ct_trans: Saving {}'.format(output_fname))
np.savez(output_fname,
ras_tkr2vox=ras_tkr2vox,
vox2ras_tkr=vox2ras_tkr,
vox2ras=vox2ras,
ras2vox=ras2vox)
return op.isfile(output_fname)
def save_evoked_to_blender(mri_subject, events, args, evoked=None):
fol = op.join(MMVT_DIR, mri_subject, 'eeg')
utils.make_dir(fol)
if '{cond}' in meg.EVO:
for event_ind, event_id in enumerate(events.keys()):
if evoked is None:
evo = mne.read_evokeds(meg.get_cond_fname(meg.EVO, event_id))
else:
evo = evoked[event_id]
if event_ind == 0:
ch_names = np.array(evo[0].ch_names)
dt = np.diff(evo[0].times[:2])[0]
data = np.zeros((evo[0].data.shape[0], evo[0].data.shape[1], 2))
data[:, :, event_ind] = evo[0].data
else:
if evoked is None:
evoked = mne.read_evokeds(meg.EVO)
data = evoked[0].data
data = data[..., np.newaxis]
ch_names = np.array(evoked[0].ch_names)
dt = np.diff(evoked[0].times[:2])[0]
if 'Event' in ch_names:
event_ind = np.where(ch_names == 'Event')[0]
ch_names = np.delete(ch_names, event_ind)
data = np.delete(data, event_ind, 0)
if args.normalize_evoked:
data_max, data_min = utils.get_data_max_min(data, args.norm_by_percentile, args.norm_percs)
max_abs = utils.get_max_abs(data_max, data_min)
data = data / max_abs
np.save(op.join(fol, 'eeg_data.npy'), data)
np.savez(op.join(fol, 'eeg_data_meta.npz'), names=ch_names, conditions=list(events.keys()), dt=dt)
return True
def project_on_surface(subject, volume_file, surf_output_fname, target_subject=None, overwrite_surf_data=False,
modality='fmri', subjects_dir='', mmvt_dir='', **kargs):
if target_subject is None:
target_subject = subject
if subjects_dir == '':
subjects_dir = utils.get_link_dir(utils.get_links_dir(), 'subjects', 'SUBJECTS_DIR')
if mmvt_dir == '':
mmvt_dir = utils.get_link_dir(utils.get_links_dir(), 'mmvt')
utils.make_dir(op.join(mmvt_dir, subject, 'fmri'))
os.environ['SUBJECTS_DIR'] = subjects_dir
os.environ['SUBJECT'] = subject
for hemi in utils.HEMIS:
if not op.isfile(surf_output_fname.format(hemi=hemi)) or overwrite_surf_data:
print('project {} to {}'.format(volume_file, hemi))
if modality != 'pet':
surf_data = project_volume_data(volume_file, hemi, subject_id=subject, surf="pial", smooth_fwhm=3,
target_subject=target_subject, output_fname=surf_output_fname.format(hemi=hemi))
else:
surf_data = project_pet_volume_data(subject, volume_file, hemi, surf_output_fname.format(hemi=hemi))
nans = np.sum(np.isnan(surf_data))
if nans > 0:
print('there are {} nans in {} surf data!'.format(nans, hemi))
surf_data = np.squeeze(nib.load(surf_output_fname.format(hemi=hemi)).get_data())
output_fname = op.join(mmvt_dir, subject, modality, '{}_{}'.format(modality, op.basename(
surf_output_fname.format(hemi=hemi))))
npy_output_fname = op.splitext(output_fname)[0]
if not op.isfile('{}.npy'.format(npy_output_fname)) or overwrite_surf_data:
print('Saving surf data in {}.npy'.format(npy_output_fname))
utils.make_dir(utils.get_parent_fol(npy_output_fname))
np.save(npy_output_fname, surf_data)
def aseg_to_srf(subject, subjects_dir, output_fol, region_id, mask_fname, norm_fname,
overwrite_subcortical_objs=False):
ret = True
tmp_fol = op.join(subjects_dir, subject, 'tmp', utils.rand_letters(6))
utils.make_dir(tmp_fol)
rs = utils.partial_run_script(locals())
output_fname = op.join(output_fol, '{}.srf'.format(region_id))
tmp_output_fname = op.join(tmp_fol, '{}.asc'.format(region_id))
if overwrite_subcortical_objs:
utils.remove_file(output_fname)
try:
rs(mri_pretess)
rs(mri_tessellate)
rs(mris_smooth)
rs(mris_convert)
if op.isfile(tmp_output_fname):
shutil.move(tmp_output_fname, output_fname)
shutil.rmtree(tmp_fol)
else:
ret = False
except:
print('Error in aseg_to_srf! subject: {}'.format(subject))
print(traceback.format_exc())
ret = False
return ret
def create_unknown_labels(subject, atlas):
labels_fol = op.join(SUBJECTS_DIR, subject, 'label', atlas)
utils.make_dir(labels_fol)
unknown_labels_fname_template = op.join(
labels_fol, 'unknown-{}.label'.format('{hemi}'))
if utils.both_hemi_files_exist(unknown_labels_fname_template):
unknown_labels = {
hemi:
mne.read_label(unknown_labels_fname_template.format(hemi=hemi),
subject)
for hemi in utils.HEMIS
}
return unknown_labels
unknown_labels = {}
for hemi in utils.HEMIS:
labels = read_labels(subject, SUBJECTS_DIR, atlas, hemi=hemi)
unknown_label_name = 'unknown-{}'.format(hemi)
labels_names = [l.name for l in labels]
if unknown_label_name not in labels_names:
verts, _ = utils.read_pial(subject, MMVT_DIR, hemi)
unknown_verts = set(range(verts.shape[0]))
for label in labels:
unknown_verts -= set(label.vertices)
unknown_verts = np.array(sorted(list(unknown_verts)))
unknown_label = mne.Label(unknown_verts,
hemi=hemi,
name=unknown_label_name,
subject=subject)
else:
unknown_label = labels[labels_names.index(unknown_label_name)]
unknown_labels[hemi] = unknown_label
if not op.isfile(unknown_labels_fname_template.format(hemi=hemi)):
unknown_label.save(unknown_labels_fname_template.format(hemi=hemi))
return unknown_labels
def run_on_subjects(args):
subjects_flags, subjects_errors = {}, {}
args.sftp_password = utils.get_sftp_password(
args.subject, SUBJECTS_DIR, args.necessary_files, args.sftp_username, args.overwrite_fs_files) \
if args.sftp else ''
for subject in args.subject:
utils.make_dir(op.join(MMVT_DIR, subject, 'mmvt'))
# os.chdir(op.join(SUBJECTS_DIR, subject, 'mmvt'))
try:
print('*******************************************')
print('subject: {}, atlas: {}'.format(subject, args.atlas))
print('*******************************************')
flags = main(subject, args)
subjects_flags[subject] = flags
except:
subjects_errors[subject] = traceback.format_exc()
print('Error in subject {}'.format(subject))
print(traceback.format_exc())
errors = defaultdict(list)
for subject, flags in subjects_flags.items():
print('subject {}:'.format(subject))
for flag_type, val in flags.items():
print('{}: {}'.format(flag_type, val))
if not val:
errors[subject].append(flag_type)
print('Errors:')
for subject, error in errors.items():
print('{}: {}'.format(subject, error))
def solve_labels_collision(subject, subjects_dir, atlas, backup_atlas, n_jobs=1):
now = time.time()
print('Read labels')
# utils.read_labels_parallel(subject, subjects_dir, atlas, labels_fol='', n_jobs=n_jobs)
labels = read_labels(subject, subjects_dir, atlas, n_jobs=n_jobs)
backup_labels_fol = op.join(subjects_dir, subject, 'label', backup_atlas)
labels_fol = op.join(subjects_dir, subject, 'label', atlas)
if op.isdir(backup_labels_fol):
shutil.rmtree(backup_labels_fol)
os.rename(labels_fol, backup_labels_fol)
utils.make_dir(labels_fol)
hemis_verts, labels_hemi, pia_verts = {}, {}, {}
print('Read surface ({:.2f}s)'.format(time.time() - now))
for hemi in HEMIS:
surf_fname = op.join(subjects_dir, subject, 'surf', '{}.pial'.format(hemi))
hemis_verts[hemi], _ = mne.surface.read_surface(surf_fname)
labels_hemi[hemi] = [l for l in labels if l.hemi == hemi]
print('Calc centroids ({:.2f}s)'.format(time.time() - now))
centroids = calc_labels_centroids(labels_hemi, hemis_verts)
for hemi in HEMIS:
print('Calc vertices labeling for {} ({:.2f}s)'.format(hemi, time.time() - now))
hemi_centroids_dist = cdist(hemis_verts[hemi], centroids[hemi])
vertices_labels_indices = np.argmin(hemi_centroids_dist, axis=1)
labels_hemi_chunks = utils.chunks(list(enumerate(labels_hemi[hemi])), len(labels_hemi[hemi]) / n_jobs)
params = [(labels_hemi_chunk, atlas, vertices_labels_indices, hemis_verts, labels_fol) for labels_hemi_chunk in labels_hemi_chunks]
print('Save labels for {} ({:.2f}s)'.format(hemi, time.time() - now))
utils.run_parallel(_save_new_labels_parallel, params, n_jobs)
def get_empty_fnames(subject, tasks, args):
utils.make_dir(op.join(MEG_DIR, subject))
utils.make_link(op.join(args.remote_subject_dir.format(subject=subject), 'bem'),
op.join(MEG_DIR, subject, 'bem'))
for task in tasks:
utils.make_dir(op.join(MEG_DIR, task, subject))
utils.make_link(op.join(MEG_DIR, subject, 'bem'), op.join(MEG_DIR, task, subject, 'bem'))
utils.make_link(op.join(MEG_DIR, subject, 'bem'), op.join(SUBJECTS_DIR, subject, 'bem'))
remote_meg_fol = '/autofs/space/lilli_003/users/DARPA-TRANSFER/meg/{}'.format(subject)
csv_fname = op.join(remote_meg_fol, 'cfg.txt')
if not op.isfile(csv_fname):
print('No cfg file!')
return {task:'' for task in tasks}
days, empty_fnames = {}, {}
for line in utils.csv_file_reader(csv_fname, ' '):
for task in tasks:
if line[4].lower() == task.lower():
days[task] = line[2]
print(days)
for line in utils.csv_file_reader(csv_fname, ' '):
if line[4] == 'empty':
for task in tasks:
empty_fnames[task] = op.join(MEG_DIR, task, subject, '{}_empty_raw.fif'.format(subject))
if op.isfile(empty_fnames[task]):
continue
task_day = days[task]
if line[2] == task_day:
empty_fname = op.join(remote_meg_fol, line[0].zfill(3), line[-1])
if not op.isfile(empty_fname):
raise Exception('empty file does not exist! {}'.format(empty_fname[task]))
utils.make_link(empty_fname, empty_fnames[task])
return empty_fnames
def create_labels_coloring(subject,
labels_names,
labels_values,
coloring_name,
norm_percs=(3, 99),
norm_by_percentile=True,
colors_map='jet'):
coloring_dir = op.join(MMVT_DIR, subject, 'coloring')
utils.make_dir(coloring_dir)
coloring_fname = op.join(coloring_dir, '{}.csv'.format(coloring_name))
ret = False
try:
labels_colors = utils.arr_to_colors(
labels_values, norm_percs=norm_percs,
colors_map=colors_map) # norm_by_percentile=norm_by_percentile
print('Saving coloring to {}'.format(coloring_fname))
with open(coloring_fname, 'w') as colors_file:
for label_name, label_color, label_value in zip(
labels_names, labels_colors, labels_values):
colors_file.write('{},{},{},{},{}\n'.format(
label_name, *label_color[:3], label_value))
ret = op.isfile(coloring_fname)
except:
print('Error in create_labels_coloring!')
print(traceback.format_exc())
return ret
def save_labels_coloring(subject, atlas, n_jobs=2):
ret = False
coloring_dir = op.join(MMVT_DIR, subject, 'coloring')
utils.make_dir(coloring_dir)
coloring_fname = op.join(coloring_dir, 'labels_{}_coloring.csv'.format(atlas))
coloring_names_fname = op.join(coloring_dir, 'labels_{}_colors_names.csv'.format(atlas))
try:
labels = lu.read_labels(subject, SUBJECTS_DIR, atlas, n_jobs=n_jobs)
colors_rgb_and_names = cu.get_distinct_colors_and_names()
labels_colors_rgb, labels_colors_names = {}, {}
for label in labels:
label_inv_name = lu.get_label_hemi_invariant_name(label.name)
if label_inv_name not in labels_colors_rgb:
labels_colors_rgb[label_inv_name], labels_colors_names[label_inv_name] = next(colors_rgb_and_names)
with open(coloring_fname, 'w') as colors_file, open(coloring_names_fname, 'w') as col_names_file:
for label in labels:
label_inv_name = lu.get_label_hemi_invariant_name(label.name)
color_rgb = labels_colors_rgb[label_inv_name]
color_name = labels_colors_names[label_inv_name]
colors_file.write('{},{},{},{}\n'.format(label.name, *color_rgb))
col_names_file.write('{},{}\n'.format(label.name, color_name))
ret = op.isfile(coloring_fname)
except:
print('Error in save_labels_coloring!')
print(traceback.format_exc())
return ret
def run_on_subjects(args):
subjects_flags, subjects_errors = {}, {}
args.sftp_password = utils.get_sftp_password(
args.subject, SUBJECTS_DIR, args.necessary_files, args.sftp_username, args.overwrite_fs_files) \
if args.sftp else ''
for subject in args.subject:
utils.make_dir(op.join(MMVT_DIR, subject, 'mmvt'))
# os.chdir(op.join(SUBJECTS_DIR, subject, 'mmvt'))
try:
print('*******************************************')
print('subject: {}, atlas: {}'.format(subject, args.atlas))
print('*******************************************')
flags = main(subject, args)
subjects_flags[subject] = flags
except:
subjects_errors[subject] = traceback.format_exc()
print('Error in subject {}'.format(subject))
print(traceback.format_exc())
errors = defaultdict(list)
for subject, flags in subjects_flags.items():
print('subject {}:'.format(subject))
for flag_type, val in flags.items():
print('{}: {}'.format(flag_type, val))
if not val:
errors[subject].append(flag_type)
print('Errors:')
for subject, error in errors.items():
print('{}: {}'.format(subject, error))
def init_meg(subject):
utils.make_dir(op.join(MEG_DIR, subject))
utils.make_link(
op.join(args.remote_subject_dir.format(subject=subject), 'bem'),
op.join(MEG_DIR, subject, 'bem'))
utils.make_link(op.join(MEG_DIR, subject, 'bem'),
op.join(SUBJECTS_DIR, subject, 'bem'))
def change_frames_names(fol,
images_prefix,
images_type,
images_format_len,
new_fol_name='new_images'):
import shutil
images = glob.glob(
op.join(fol, '{}*.{}'.format(images_prefix, images_type)))
images.sort(key=lambda x: int(utils.namebase(x)[len(images_prefix):]))
images_formats = {
1: '{0:0>1}',
2: '{0:0>2}',
3: '{0:0>3}',
4: '{0:0>4}',
5: '{0:0>5}'
}
root = op.join(op.sep.join(images[0].split(op.sep)[:-1]))
new_fol = op.join(root, new_fol_name)
utils.delete_folder_files(new_fol)
utils.make_dir(new_fol)
for num, image_fname in enumerate(images):
num_str = images_formats[images_format_len].format(num + 1)
new_image_fname = op.join(
new_fol, '{}{}.{}'.format(images_prefix, num_str, images_type))
print('{} -> {}'.format(image_fname, new_image_fname))
utils.copy_file(image_fname, new_image_fname)
return new_fol
def save_evoked_to_blender(mri_subject, events, args, evoked=None):
fol = op.join(MMVT_DIR, mri_subject, 'eeg')
utils.make_dir(fol)
if '{cond}' in meg.EVO:
for event_ind, event_id in enumerate(events.keys()):
if evoked is None:
evo = mne.read_evokeds(meg.get_cond_fname(meg.EVO, event_id))
else:
evo = evoked[event_id]
if event_ind == 0:
ch_names = np.array(evo[0].ch_names)
dt = np.diff(evo[0].times[:2])[0]
data = np.zeros((evo[0].data.shape[0], evo[0].data.shape[1], 2))
data[:, :, event_ind] = evo[0].data
else:
if evoked is None:
evoked = mne.read_evokeds(meg.EVO)
data = evoked[0].data
data = data[..., np.newaxis]
ch_names = np.array(evoked[0].ch_names)
dt = np.diff(evoked[0].times[:2])[0]
if 'Event' in ch_names:
event_ind = np.where(ch_names == 'Event')[0]
ch_names = np.delete(ch_names, event_ind)
data = np.delete(data, event_ind, 0)
data_max, data_min = utils.get_data_max_min(data, args.norm_by_percentile, args.norm_percs)
max_abs = utils.get_max_abs(data_max, data_min)
if args.normalize_evoked:
data = data / max_abs
np.save(op.join(fol, 'eeg_data.npy'), data)
np.savez(op.join(fol, 'eeg_data_meta.npz'), names=ch_names, conditions=list(events.keys()), dt=dt,
minmax=(-max_abs, max_abs))
return True
def create_aparc_aseg_file(subject, atlas, overwrite_aseg_file=False, print_only=False, args={}):
if not utils.both_hemi_files_exist(op.join(SUBJECTS_DIR, subject, 'label', '{}.{}.annot'.format('{hemi}', atlas))):
print('No annot file was found for {}!'.format(atlas))
print('Run python -m src.preproc.anatomy -s {} -a {} -f create_surfaces,create_annotation'.format(subject, atlas))
return False
# aparc_aseg_fname
ret = fu.create_aparc_aseg_file(
subject, atlas, SUBJECTS_DIR, overwrite_aseg_file, print_only, mmvt_args=args)
if isinstance(ret, Iterable):
ret, aparc_aseg_fname = ret
if not ret:
return False
aparc_aseg_file = utils.namebase_with_ext(aparc_aseg_fname)
utils.make_dir(op.join(MMVT_DIR, subject, 'freeview'))
blender_file = op.join(MMVT_DIR, subject, 'freeview', aparc_aseg_file)
utils.remove_file(blender_file)
shutil.copyfile(aparc_aseg_fname, blender_file)
atlas_mat_fname = utils.change_fname_extension(blender_file, 'npy')
if not op.isfile(atlas_mat_fname) or overwrite_aseg_file:
d = nib.load(blender_file)
x = d.get_data()
np.save(atlas_mat_fname, x)
return op.isfile(blender_file) and op.isfile(atlas_mat_fname)
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 solve_labels_collision(subject, subjects_dir, atlas, backup_atlas, n_jobs=1):
now = time.time()
print('Read labels')
labels = utils.read_labels_parallel(subject, subjects_dir, atlas, n_jobs)
backup_labels_fol = op.join(subjects_dir, subject, 'label', backup_atlas)
labels_fol = op.join(subjects_dir, subject, 'label', atlas)
if op.isdir(backup_labels_fol):
shutil.rmtree(backup_labels_fol)
os.rename(labels_fol, backup_labels_fol)
utils.make_dir(labels_fol)
hemis_verts, labels_hemi, pia_verts = {}, {}, {}
print('Read surface ({:.2f}s)'.format(time.time() - now))
for hemi in HEMIS:
surf_fname = op.join(subjects_dir, subject, 'surf', '{}.pial'.format(hemi))
hemis_verts[hemi], _ = mne.surface.read_surface(surf_fname)
labels_hemi[hemi] = [l for l in labels if l.hemi == hemi]
print('Calc centroids ({:.2f}s)'.format(time.time() - now))
centroids = calc_labels_centroids(labels_hemi, hemis_verts)
for hemi in HEMIS:
print('Calc vertices labeling for {} ({:.2f}s)'.format(hemi, time.time() - now))
hemi_centroids_dist = cdist(hemis_verts[hemi], centroids[hemi])
vertices_labels_indices = np.argmin(hemi_centroids_dist, axis=1)
labels_hemi_chunks = utils.chunks(list(enumerate(labels_hemi[hemi])), len(labels_hemi[hemi]) / n_jobs)
params = [(labels_hemi_chunk, atlas, vertices_labels_indices, hemis_verts, labels_fol) for labels_hemi_chunk in labels_hemi_chunks]
print('Save labels for {} ({:.2f}s)'.format(hemi, time.time() - now))
utils.run_parallel(_save_new_labels_parallel, params, n_jobs)
def calc_time_series_per_label(x,
labels,
measure,
excludes=(),
figures_dir='',
do_plot=False,
do_plot_all_vertices=False):
import sklearn.decomposition as deco
import matplotlib.pyplot as plt
labels, _ = remove_exclude_labels(labels, excludes)
if measure.startswith('pca'):
comps_num = 1 if '_' not in measure else int(measure.split('_')[1])
labels_data = np.zeros((len(labels), x.shape[-1], comps_num))
else:
labels_data = np.zeros((len(labels), x.shape[-1]))
labels_names = []
if do_plot_all_vertices:
all_vertices_plots_dir = op.join(figures_dir, 'all_vertices')
utils.make_dir(all_vertices_plots_dir)
if do_plot:
measure_plots_dir = op.join(figures_dir, measure)
utils.make_dir(measure_plots_dir)
for ind, label in enumerate(labels):
if measure == 'mean':
labels_data[ind, :] = np.mean(x[label.vertices, 0, 0, :], 0)
elif measure.startswith('pca'):
print(label)
_x = x[label.vertices, 0, 0, :].T
remove_cols = np.where(np.all(_x == np.mean(_x, 0), 0))[0]
_x = np.delete(_x, remove_cols, 1)
_x = (_x - np.mean(_x, 0)) / np.std(_x, 0)
comps = 1 if '_' not in measure else int(measure.split('_')[1])
pca = deco.PCA(comps)
x_r = pca.fit(_x).transform(_x)
# if x_r.shape[1] == 1:
labels_data[ind, :] = x_r
# else:
# labels_data[ind, :] = x_r
elif measure == 'cv': #''coef_of_variation':
label_mean = np.mean(x[label.vertices, 0, 0, :], 0)
label_std = np.std(x[label.vertices, 0, 0, :], 0)
labels_data[ind, :] = label_std / label_mean
labels_names.append(label.name)
if do_plot_all_vertices:
plt.figure()
plt.plot(x[label.vertices, 0, 0, :].T)
plt.savefig(
op.join(all_vertices_plots_dir, '{}.jpg'.format(label.name)))
plt.close()
if do_plot:
plt.figure()
plt.plot(labels_data[ind, :])
plt.savefig(
op.join(measure_plots_dir,
'{}_{}.jpg'.format(measure, label.name)))
plt.close()
return labels_data, labels_names
def subcortical_segmentation(subject, overwrite_subcorticals=False, model='subcortical', lookup=None,
mask_name='aseg.mgz', mmvt_subcorticals_fol_name='subcortical',
template_subject='', norm_name='norm.mgz', overwrite=True):
# 1) mri_pretess: Changes region segmentation so that the neighbors of all voxels have a face in common
# 2) mri_tessellate: Creates surface by tessellating
# 3) mris_smooth: Smooth the new surface
# 4) mris_convert: Convert the new surface into srf format
template_subject = subject if template_subject == '' else template_subject
norm_fname = op.join(SUBJECTS_DIR, template_subject, 'mri', norm_name)
if not op.isfile(norm_fname):
print('norm file does not exist! {}'.format(norm_fname))
return False
mask_fname = op.join(SUBJECTS_DIR, template_subject, 'mri', mask_name)
if not op.isfile(mask_fname):
print('mask file does not exist! {}'.format(mask_fname))
return False
codes_file = op.join(MMVT_DIR, 'sub_cortical_codes.txt')
if not op.isfile(codes_file):
print('subcortical codes file does not exist! {}'.format(codes_file))
return False
# subcortical_lookup = np.genfromtxt(codes_file, dtype=str, delimiter=',')
function_output_fol = op.join(SUBJECTS_DIR, subject, 'mmvt', '{}_objs'.format(model))
utils.make_dir(function_output_fol)
renamed_output_fol = op.join(SUBJECTS_DIR, subject, 'mmvt', model)
utils.make_dir(renamed_output_fol)
if lookup is None:
lookup = load_subcortical_lookup_table()
obj_files = glob.glob(op.join(function_output_fol, '*.srf'))
errors = []
if len(obj_files) < len(lookup) or overwrite_subcorticals:
if overwrite:
utils.delete_folder_files(function_output_fol)
utils.delete_folder_files(renamed_output_fol)
print('Trying to write into {}'.format(function_output_fol))
for region_id in lookup.keys():
if op.isfile(op.join(function_output_fol, '{}.srf'.format(region_id))):
continue
ret = fu.aseg_to_srf(subject, SUBJECTS_DIR, function_output_fol, region_id, mask_fname, norm_fname,
overwrite_subcorticals)
if not ret:
errors.append(lookup[region_id])
if len(errors) > 0:
print('Errors: {}'.format(','.join(errors)))
ply_files = glob.glob(op.join(renamed_output_fol, '*.ply'))
if len(ply_files) < len(lookup) or overwrite_subcorticals:
convert_and_rename_subcortical_files(subject, function_output_fol, renamed_output_fol, lookup,
mmvt_subcorticals_fol_name)
blender_dir = op.join(MMVT_DIR, subject, mmvt_subcorticals_fol_name)
if not op.isdir(blender_dir) or len(glob.glob(op.join(blender_dir, '*.ply'))) < len(ply_files) or overwrite_subcorticals:
utils.delete_folder_files(blender_dir)
copy_subcorticals_to_mmvt(renamed_output_fol, subject, mmvt_subcorticals_fol_name)
flag_ok = len(glob.glob(op.join(blender_dir, '*.ply'))) >= len(lookup) and \
len(glob.glob(op.join(blender_dir, '*.npz'))) >= len(lookup)
return flag_ok
def create_real_folder(real_fol):
try:
if real_fol == '':
real_fol = utils.get_resources_fol()
utils.make_dir(real_fol)
except:
print('Error with creating the folder "{}"'.format(real_fol))
print(traceback.format_exc())
def plot_all_windows(edf_raw, live_channels, T, hz, window, edf_fname, ylim):
pics_fol = op.join(op.split(edf_fname)[0], 'pics')
utils.make_dir(pics_fol)
for t_start in np.arange(0, T-window, window):
plot_window(edf_raw, live_channels, t_start, window, hz, ylim)
print('plotting {}-{}'.format(t_start, t_start+window))
plt.savefig(op.join(pics_fol, '{}-{}.jpg'.format(t_start, t_start+window)))
plt.close()
def create_real_folder(real_fol):
try:
if real_fol == '':
real_fol = utils.get_resources_fol()
utils.make_dir(real_fol)
except:
print('Error with creating the folder "{}"'.format(real_fol))
print(traceback.format_exc())
def robust_register(subject, subjects_dir, source_fname, target_fname, output_fname, lta_name,
cost_function='nmi', print_only=False, **kargs):
xfms_dir = op.join(subjects_dir, subject, 'mri', 'transforms')
utils.make_dir(xfms_dir)
lta_fname = op.join(xfms_dir, lta_name)
rs = utils.partial_run_script(locals(), print_only=print_only)
rs(mri_robust_register)
return True if print_only else op.isfile(lta_fname)
def create_dup_frames_links(subject, dup_frames, fol):
fol = op.join(MMVT_DIR, subject, 'figures', fol)
utils.delete_folder_files(fol)
utils.make_dir(fol)
for ind, frame in enumerate(dup_frames):
utils.make_link(
frame, op.join(fol, 'dup_{}.{}'.format(ind,
utils.file_type(frame))))
return fol
def morph_fmri(args):
morph_from, morph_to = 'mg78', 'colin27'
nii_template = 'non-interference-v-interference_{hemi}.mgz'
from src.utils import freesurfer_utils as fu
utils.make_dir(op.join(MMVT_DIR, morph_to, 'fmri'))
for hemi in utils.HEMIS:
fu.surf2surf(
morph_from, morph_to, hemi, op.join(MMVT_DIR, morph_from, 'fmri', nii_template.format(hemi=hemi)),
op.join(MMVT_DIR, morph_to, 'fmri', nii_template.format(hemi=hemi)))
def morph_fmri(morph_from, morph_to, nii_template):
utils.make_dir(op.join(MMVT_DIR, morph_to, 'fmri'))
for hemi in utils.HEMIS:
fu.surf2surf(
morph_from, morph_to, hemi,
op.join(MMVT_DIR, morph_from, 'fmri',
nii_template.format(hemi=hemi)),
op.join(MMVT_DIR, morph_to, 'fmri',
nii_template.format(hemi=hemi)))
def get_empty_fnames(subject, tasks, args, overwrite=False):
utils.make_dir(op.join(MEG_DIR, subject))
utils.make_link(op.join(args.remote_subject_dir.format(subject=subject),
'bem'),
op.join(MEG_DIR, subject, 'bem'),
overwrite=overwrite)
for task in tasks:
utils.make_dir(op.join(MEG_DIR, task, subject))
utils.make_link(op.join(MEG_DIR, subject, 'bem'),
op.join(MEG_DIR, task, subject, 'bem'),
overwrite=overwrite)
utils.make_link(op.join(MEG_DIR, subject, 'bem'),
op.join(SUBJECTS_DIR, subject, 'bem'),
overwrite=overwrite)
remote_meg_fol = op.join(args.remote_meg_dir, subject)
csv_fname = op.join(remote_meg_fol, 'cfg.txt')
empty_fnames, cors, days = '', '', ''
if not op.isfile(csv_fname):
print('No cfg file!')
return '', '', ''
days, empty_fnames, cors = {}, {}, {}
for line in utils.csv_file_reader(csv_fname, ' '):
for task in tasks:
if line[4].lower() == task.lower():
days[task] = line[2]
# print(days)
for line in utils.csv_file_reader(csv_fname, ' '):
if line[4] == 'empty':
for task in tasks:
empty_fnames[task] = op.join(
MEG_DIR, task, subject, '{}_empty_raw.fif'.format(subject))
if op.isfile(empty_fnames[task]):
continue
task_day = days[task]
if line[2] == task_day:
empty_fname = op.join(remote_meg_fol, line[0].zfill(3),
line[-1])
if not op.isfile(empty_fname):
raise Exception('empty file does not exist! {}'.format(
empty_fname[task]))
utils.make_link(empty_fname, empty_fnames[task])
cor_dir = op.join(args.remote_subject_dir.format(subject=subject), 'mri',
'T1-neuromag', 'sets')
for task in tasks:
if op.isfile(
op.join(cor_dir, 'COR-{}-{}.fif'.format(subject,
task.lower()))):
cors[task] = op.join(
cor_dir, 'COR-{}-{}.fif'.format('{subject}', task.lower()))
elif op.isfile(
op.join(cor_dir,
'COR-{}-day{}.fif'.format(subject, days[task]))):
cors[task] = op.join(
cor_dir, 'COR-{}-day{}.fif'.format('{subject}', days[task]))
return empty_fnames, cors, days
def main(subject, args):
utils.make_dir(op.join(BLENDER_ROOT_DIR, subject, 'electrodes'))
stim_data = None
if 'all' in args.function or 'load_stim_file' in args.function:
stim_data = load_stim_file(subject, args)
if 'all' in args.function or 'create_stim_electrodes_positions' in args.function:
labels = stim_data['labels'] if stim_data else None
create_stim_electrodes_positions(subject, args, labels)
if 'all' in args.function or 'set_labels_colors' in args.function:
set_labels_colors(subject, args, stim_data)
def main(subject, args):
utils.make_dir(op.join(BLENDER_ROOT_DIR, subject, 'electrodes'))
stim_data = None
if 'all' in args.function or 'load_stim_file' in args.function:
stim_data = load_stim_file(subject, args)
if 'all' in args.function or 'create_stim_electrodes_positions' in args.function:
labels = stim_data['labels'] if stim_data else None
create_stim_electrodes_positions(subject, args, labels)
if 'all' in args.function or 'set_labels_colors' in args.function:
set_labels_colors(subject, args, stim_data)
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 save_fmri_colors(subject, hemi, contrast_name, fmri_file, surf_name='pial', threshold=2, output_fol=''):
if not op.isfile(fmri_file.format(hemi)):
print('No such file {}!'.format(fmri_file.format(hemi)))
return
fmri = nib.load(fmri_file.format(hemi))
x = fmri.get_data().ravel()
if output_fol == '':
output_fol = op.join(BLENDER_ROOT_DIR, subject, 'fmri')
utils.make_dir(output_fol)
output_name = op.join(output_fol, 'fmri_{}_{}'.format(contrast_name, hemi))
_save_fmri_colors(subject, hemi, x, threshold, output_name, surf_name=surf_name)
def plot_all_files_graph_max(subject,
baseline_fnames,
event_fname,
func_name,
bands_names,
modality,
input_template,
sz_name='',
sfreq=None,
do_plot=False,
overwrite=False):
if sz_name == '':
sz_name = utils.namebase(event_fname)
output_fol = utils.make_dir(
op.join(MMVT_DIR, subject, 'connectivity',
'{}_{}'.format(modality, func_name), 'runs', sz_name))
if modality == 'ieeg':
# clip = np.load(event_fname)
t_start, t_end = -5, 5
else:
clip = mne.read_evokeds(event_fname)[0]
sfreq = clip.info['sfreq']
t_start, t_end = clip.times[0], clip.times[-1]
windows_length = 500
half_window = (1 / sfreq) * (windows_length / 2) # In seconds
scores = {}
for band_name in bands_names:
band_fol = utils.make_dir(
op.join(MMVT_DIR, subject, 'connectivity',
'{}_{}'.format(modality, func_name), band_name))
con_name = '{}_{}_mi'.format(modality, band_name)
figure_name = '{}_{}_{}.jpg'.format(con_name, func_name, sz_name)
output_fname = op.join(output_fol, figure_name)
# if op.isfile(output_fname) and not overwrite:
# print('{} already exist'.format(figure_name))
# continue
all_files_found = True
for fname in baseline_fnames + [event_fname]:
if input_template != '':
file_name = utils.namebase(fname)
input_fname = input_template.format(file_name=file_name,
band_name=band_name)
if not op.isfile(input_fname):
print('{} does not exist!!!'.format(input_fname))
all_files_found = False
break
if not all_files_found:
continue
scores[band_name] = calc_score(event_fname, baseline_fnames,
input_template, band_name, t_start,
t_end, half_window, output_fname,
band_fol, figure_name, do_plot)
return scores
def transform_mni_to_subject(subject, subjects_dir, volue_fol, volume_fname='sig.mgz',
subject_contrast_file_name='sig_subject.mgz', print_only=False):
mni305_sig_file = os.path.join(volue_fol, volume_fname)
subject_sig_file = os.path.join(volue_fol, subject_contrast_file_name)
rs = utils.partial_run_script(locals(), print_only=print_only)
rs(mni305_to_subject_reg)
rs(mni305_to_subject)
subject_fol = op.join(subjects_dir, subject, 'mmvt')
utils.make_dir(subject_fol)
shutil.move(op.join(utils.get_parent_fol(), 'mn305_to_{}.dat'.format(subject)),
op.join(subject_fol, 'mn305_to_{}.dat'.format(subject)))
def save_electrodes_coords(elecs_names, elecs_coords_mni, good_channels=None, bad_channels=None):
good_elecs_names, good_elecs_coords_mni = [], []
for elec_name, elec_coord_min in zip(elecs_names, elecs_coords_mni):
if (not good_channels or elec_name in good_channels) and (not bad_channels or elec_name not in bad_channels):
good_elecs_names.append(elec_name)
good_elecs_coords_mni.append(elec_coord_min)
good_elecs_coords_mni = np.array(good_elecs_coords_mni)
electrodes_mni_fname = save_electrodes_file(subject, args.bipolar, good_elecs_names, good_elecs_coords_mni, '_mni')
output_file_name = op.split(electrodes_mni_fname)[1]
utils.make_dir(op.join(MMVT_DIR, 'colin27', 'electrodes'))
blender_file = op.join(MMVT_DIR, 'colin27', 'electrodes', output_file_name.replace('_mni', ''))
shutil.copyfile(electrodes_mni_fname, blender_file)
def post_meg_preproc(args):
inv_method, em = 'MNE', 'mean_flip'
atlas = 'darpa_atlas'
bands = dict(theta=[4, 8],
alpha=[8, 15],
beta=[15, 30],
gamma=[30, 55],
high_gamma=[65, 200])
times = (-2, 4)
subjects = args.subject
res_fol = utils.make_dir(
op.join(utils.get_parent_fol(MMVT_DIR), 'msit-ecr'))
for subject in subjects:
args.subject = subject
for task in args.tasks:
task = task.lower()
if not utils.both_hemi_files_exist(
op.join(
MMVT_DIR, subject, 'meg',
'labels_data_{}_{}_{}_{}_lh.npz'.format(
task, atlas, inv_method, em, '{hemi}'))):
print('label data can\'t be found for {} {}'.format(
subject, task))
continue
utils.make_dir(op.join(res_fol, subject))
meg.calc_labels_func(subject,
task,
atlas,
inv_method,
em,
tmin=0,
tmax=0.5,
times=times,
norm_data=False)
meg.calc_labels_mean_power_bands(subject,
task,
atlas,
inv_method,
em,
tmin=times[0],
tmax=times[1],
overwrite=True)
for fname in [
f for f in glob.glob(
op.join(MMVT_DIR, subject, 'labels', 'labels_data', '*'))
if op.isfile(f)
]:
shutil.copyfile(
fname, op.join(res_fol, subject,
utils.namebase_with_ext(fname)))
def get_digitization_points(subject, raw_fname):
raw = mne.io.read_raw_fif(raw_fname)
info = raw.info
pos = np.array([p['r'] for p in info['dig']])
kind = np.array([p['kind'] for p in info['dig']])
ident = np.array([p['ident'] for p in info['dig']])
coord_frame = np.array([p['coord_frame'] for p in info['dig']])
utils.make_dir(op.join(MMVT_DIR, subject, 'meg'))
np.savez(op.join(MMVT_DIR, subject, 'meg', 'digitization_points.npz'),
pos=pos,
kind=kind,
ident=ident,
coord_frame=coord_frame)
def run_on_subjects(args, main_func, subjects_itr=None, subject_func=None):
if subjects_itr is None:
subjects_itr = args.subject
subjects_flags, subjects_errors = {}, {}
args.n_jobs = utils.get_n_jobs(args.n_jobs)
args.sftp_password = utils.get_sftp_password(
args.subject, SUBJECTS_DIR, args.necessary_files, args.sftp_username, args.overwrite_fs_files) \
if args.sftp else ''
if '*' in args.subject:
args.subject = [utils.namebase(fol) for fol in glob.glob(op.join(SUBJECTS_DIR, args.subject))]
os.environ['SUBJECTS_DIR'] = SUBJECTS_DIR
for tup in subjects_itr:
subject = get_subject(tup, subject_func)
utils.make_dir(op.join(MMVT_DIR, subject, 'mmvt'))
remote_subject_dir = utils.build_remote_subject_dir(args.remote_subject_dir, subject)
print('****************************************************************')
print('subject: {}, atlas: {}'.format(subject, args.atlas))
print('remote dir: {}'.format(remote_subject_dir))
print('****************************************************************')
os.environ['SUBJECT'] = subject
flags = dict()
if args.necessary_files == '':
args.necessary_files = dict()
try:
if utils.should_run(args, 'prepare_local_subjects_folder'):
# *) Prepare the local subject's folder
flags['prepare_local_subjects_folder'] = prepare_local_subjects_folder(
subject, remote_subject_dir, args)
if not flags['prepare_local_subjects_folder'] and not args.ignore_missing:
ans = input('Do you which to continue (y/n)? ')
if not au.is_true(ans):
continue
flags = main_func(tup, remote_subject_dir, args, flags)
subjects_flags[subject] = flags
except:
subjects_errors[subject] = traceback.format_exc()
print('Error in subject {}'.format(subject))
print(traceback.format_exc())
errors = defaultdict(list)
for subject, flags in subjects_flags.items():
print('subject {}:'.format(subject))
for flag_type, val in flags.items():
print('{}: {}'.format(flag_type, val))
if not val:
errors[subject].append(flag_type)
if len(errors) > 0:
print('Errors:')
for subject, error in errors.items():
print('{}: {}'.format(subject, error))
def create_freeview_cmd(subject, args):#, atlas, bipolar, create_points_files=True, way_points=False):
blender_freeview_fol = op.join(MMVT_DIR, subject, 'freeview')
freeview_command = 'freeview -v T1.mgz:opacity=0.3 ' + \
'{0}+aseg.mgz:opacity=0.05:colormap=lut:lut={0}ColorLUT.txt '.format(args.atlas)
if args.elecs_names:
groups = set([utils.elec_group(name, args.bipolar) for name in args.elecs_names])
freeview_command += '-w ' if args.way_points else '-c '
postfix = '.label' if args.way_points else '.dat'
for group in groups:
freeview_command += group + postfix + ' '
utils.make_dir(blender_freeview_fol)
with open(op.join(blender_freeview_fol, 'run_freeview.sh'), 'w') as sh_file:
sh_file.write(freeview_command)
print(freeview_command)
def read_psd_mat_file(subject, psd_fname, stim_channel):
x = utils.read_mat_file_into_bag(psd_fname)
labels = get_labels(x)
data = x.psd if 'psd' in x else x.Psd
if 'time' in x:
time = x.time.reshape((x.time.shape[1]))
else:
time = None
freqs = [(0, 4), (4, 8), (8, 15), (15, 30), (30, 55), (65, 100)]
# plt.plot(time, psd[0, 0, :])
# plt.show()
out_fol = op.join(BLENDER_ROOT_DIR, subject, 'electrodes')
utils.make_dir(out_fol)
np.savez(op.join(out_fol, 'psd_{}'.format(stim_channel)), labels=labels, psd=data, time=time, freqs=freqs)
def load_tracula_trk(subject):
tracks_fols = utils.get_subfolders(op.join(DTI_DIR, subject, 'dpath'))
output_fol = op.join(BLENDER_ROOT_DIR, subject, 'dti', 'tracula')
utils.make_dir(output_fol)
for track_fol in tracks_fols:
track_fol_name = os.path.basename(track_fol)
print('Reading {}'.format(track_fol_name))
track_gen, hdr = nib.trackvis.read(op.join(track_fol, 'path.pd.trk'), as_generator=True, points_space='rasmm')
hdr = convert_header(hdr)
vox2ras_trans = get_vox2ras_trans(subject)
tracks = read_tracks(track_gen, hdr, vox2ras_trans)
output_fname = op.join(output_fol, '{}.pkl'.format(track_fol_name))
utils.save(tracks, output_fname)
print('Save in {}'.format(output_fname))
def main(subject, remote_subject_dir, args, flags):
utils.make_dir(op.join(MMVT_DIR, subject, 'electrodes'))
stim_data = None
if utils.should_run(args, 'load_stim_file'):
stim_data = load_stim_file(subject, args)
flags['load_stim_file'] = not stim_data is None
if utils.should_run(args, 'create_stim_electrodes_positions'):
labels = stim_data['labels'] if stim_data else None
flags['create_stim_electrodes_positions'] = create_stim_electrodes_positions(subject, args, labels)
if utils.should_run(args, 'set_labels_colors'):
flags['set_labels_colors'] = set_labels_colors(subject, args, stim_data)
return flags
def create_links(links_fol_name='links', gui=True):
#todo: Work only on linux (maybe mac also)
if gui:
from tkinter.filedialog import askdirectory
links_fol = utils.get_links_dir(links_fol_name)
utils.make_dir(links_fol)
links_names = ['mmvt', 'subjects', 'blender', 'meg', 'fMRI', 'electrodes', 'freesurfer']
all_links_exist = np.all([op.islink(op.join(links_fol, link_name)) for link_name in links_names])
if all_links_exist:
return True
if os.environ.get('FREESURFER_HOME', '') == '':
print('Please source FreeSurfer and rerun')
return
print('Where do you want to put the blend files? ')
mmvt_fol = askdirectory() if gui else input()
create_real_folder(mmvt_fol)
print('Where do you want to store the FreeSurfer recon-all files neccessary for MMVT?\n' +
'It prefered to create a local folder, because MMVT is going to save files to this directory: ')
subjects_fol = askdirectory() if gui else input()
create_real_folder(subjects_fol)
freesurfer_fol = os.environ['FREESURFER_HOME']
create_real_folder(freesurfer_fol)
print('Where did you install Blender? ')
blender_fol = askdirectory() if gui else input()
create_real_folder(blender_fol)
print('Where do you want to put the MEG files (Enter/Cancel if you are not going to use MEG data): ')
meg_fol = askdirectory() if gui else input()
create_real_folder(meg_fol)
print('Where do you want to put the fMRI files (Enter/Cancel if you are not going to use fMRI data): ')
fmri_fol = askdirectory() if gui else input()
create_real_folder(fmri_fol)
print('Where do you want to put the electrodes files (Enter/Cancel if you are not going to use electrodes data): ')
electrodes_fol = askdirectory() if gui else input()
create_real_folder(electrodes_fol)
for real_fol, link_name in zip([mmvt_fol, subjects_fol, blender_fol, meg_fol, fmri_fol, electrodes_fol, freesurfer_fol],
links_names):
try:
if not op.islink(op.join(links_fol, link_name)):
os.symlink(real_fol, op.join(links_fol, link_name))
# Add the default task in meg folder
if link_name == 'meg' and real_fol != utils.get_resources_fol():
utils.make_dir(op.join(real_fol, 'default'))
except:
print('Error with folder {} and link {}'.format(real_fol, link_name))
print(traceback.format_exc())
return np.all([op.islink(op.join(links_fol, link_name)) for link_name in links_names])
def compare_coh_windows(subject, task, conditions, electrodes, freqs=((8, 12), (12, 25), (25,55), (55,110)), do_plot=False):
electrodes_coh = np.load(op.join(ELECTRODES_DIR, subject, task, 'electrodes_coh_windows.npy'))
meg_electrodes_coh = np.load(op.join(ELECTRODES_DIR, subject, task, 'meg_electrodes_ts_coh_windows.npy'))
figs_fol = op.join(MMVT_DIR, subject, 'figs', 'coh_windows')
utils.make_dir(figs_fol)
results = []
for cond_id, cond in enumerate(conditions):
now = time.time()
for freq_id, freq in enumerate(freqs):
freq = '{}-{}'.format(*freq)
indices = list(utils.lower_rec_indices(electrodes_coh.shape[0]))
for ind, (i, j) in enumerate(indices):
utils.time_to_go(now, ind, len(indices))
meg = meg_electrodes_coh[i, j, :, freq_id, cond_id][:22]
elc = electrodes_coh[i, j, :, freq_id, cond_id][:22]
elc_diff = np.max(elc) - np.min(elc)
meg *= elc_diff / (np.max(meg) - np.min(meg))
meg += np.mean(elc) - np.mean(meg)
if sum(meg) > len(meg) * 0.99:
continue
data_diff = meg - elc
# data_diff = data_diff / max(data_diff)
rms = np.sqrt(np.mean(np.power(data_diff, 2)))
corr = np.corrcoef(meg, elc)[0, 1]
results.append(dict(elc1=electrodes[i], elc2=electrodes[j], cond=cond, freq=freq, rms=rms, corr=corr))
if False: #do_plot and electrodes[i]=='RPT7' and electrodes[j] == 'RPT5': #corr > 10 and rms < 3:
plt.figure()
plt.plot(meg, label='pred')
plt.plot(elc, label='elec')
plt.legend()
# plt.title('{}-{} {} {}'.format(electrodes[i], electrodes[j], freq, cond)) # (rms:{:.2f})
plt.savefig(op.join(figs_fol, '{:.2f}-{}-{}-{}-{}.jpg'.format(rms, electrodes[i], electrodes[j], freq, cond)))
plt.close()
results_fname = op.join(figs_fol, 'results{}.csv'.format('_bipolar' if bipolar else ''))
rmss, corrs = [], []
with open(results_fname, 'w') as output_file:
for res in results:
output_file.write('{},{},{},{},{},{}\n'.format(
res['elc1'], res['elc2'], res['cond'], res['freq'], res['rms'], res['corr']))
rmss.append(res['rms'])
corrs.append(res['corr'])
rmss = np.array(rmss)
corrs = np.array(corrs)
pass
def read_new_psd_mat_file(subject, psd_fname, stim_channel, labels):
x = utils.read_mat_file_into_bag(psd_fname)
psd = None
freqs = [(0, 4), (4, 8), (8, 15), (15, 30), (30, 55), (65, 100)]
F = len(freqs)
for ind, field in enumerate(['P{}'.format(ind) for ind in range(1, F + 1)]):
if psd is None:
T, L = x[field].shape
psd = np.zeros((F, T, L))
psd[ind, :, :] = x[field]
del x
time = range(psd.shape[1])
# plt.plot(time, psd[0, 0, :])
# plt.show()
out_fol = op.join(BLENDER_ROOT_DIR, subject, 'electrodes')
utils.make_dir(out_fol)
np.savez(op.join(out_fol, 'psd_{}'.format(stim_channel)), labels=labels, psd=psd, time=time, freqs=freqs)
def main(subject, remote_subject_dir, args, flags):
utils.make_dir(op.join(MMVT_DIR, subject, 'freeview'))
args.elecs_pos, args.elecs_names = read_electrodes_pos(subject, args)
if utils.should_run(args, 'copy_T1'):
flags['copy_T1'] = copy_T1(subject)
if utils.should_run(args, 'create_freeview_cmd'):
flags['create_freeview_cmd'] = create_freeview_cmd(subject, args)
if utils.should_run(args, 'create_electrodes_points'):
flags['create_electrodes_points'] = create_electrodes_points(subject, args)
if utils.should_run(args, 'create_aparc_aseg_file'):
flags['create_aparc_aseg_file'] = create_aparc_aseg_file(subject, args)
if utils.should_run(args, 'create_lut_file_for_atlas'):
flags['create_lut_file_for_atlas'] = create_lut_file_for_atlas(subject, args.atlas)
return flags
def run_on_subjects(subject, args):
args.sftp_password = utils.get_sftp_password(
args.subject, SUBJECTS_DIR, args.necessary_files, args.sftp_username, args.overwrite_fs_files) \
if args.sftp else ''
utils.prepare_local_subjects_folder(
args.necessary_files, subject, args.remote_subject_dir, SUBJECTS_DIR,
args.sftp, args.sftp_username, args.sftp_domain, args.sftp_password,
args.overwrite_fs_files, args.print_traceback)
# Create the files for freeview bridge
utils.make_dir(op.join(BLENDER_ROOT_DIR, subject, 'freeview'))
args.elecs_pos, args.elecs_names = read_electrodes_pos(subject, args)
if 'all' in args.function or 'copy_T1' in args.function:
copy_T1(subject)
if 'all' in args.function or 'create_freeview_cmd' in args.function:
create_freeview_cmd(subject, args)
if 'all' in args.function or 'create_electrodes_points' in args.function:
create_electrodes_points(subject, args)
if 'all' in args.function or 'create_aparc_aseg_file' in args.function:
create_aparc_aseg_file(subject, args)
if 'all' in args.function or 'create_lut_file_for_atlas' in args.function:
create_lut_file_for_atlas(subject, args.atlas)
def save_hemis_curv(subject, atlas):
out_curv_file = op.join(MMVT_DIR, subject, 'surf', '{hemi}.curv.npy')
# out_border_file = op.join(MMVT_DIR, subject, 'surf', '{hemi}.curv.borders.npy')
# if utils.both_hemi_files_exist(out_file):
# return True
for hemi in utils.HEMIS:
# Load in curvature values from the ?h.curv file.
if not op.isfile(out_curv_file.format(hemi=hemi)):
curv_path = op.join(SUBJECTS_DIR, subject, 'surf', '{}.curv'.format(hemi))
curv = nib.freesurfer.read_morph_data(curv_path)
bin_curv = np.array(curv > 0, np.int)
np.save(out_curv_file.format(hemi=hemi), bin_curv)
else:
bin_curv = np.load(out_curv_file.format(hemi=hemi))
labels_fol = op.join(MMVT_DIR, subject, 'surf', '{}_{}_curves'.format(atlas, hemi))
utils.make_dir(labels_fol)
labels = lu.read_labels(subject, SUBJECTS_DIR, atlas, hemi=hemi)
for label in labels:
labels_curv = bin_curv[label.vertices]
np.save(op.join(labels_fol, '{}_curv.npy'.format(label.name)), labels_curv)
return utils.both_hemi_files_exist(out_curv_file) # and utils.both_hemi_files_exist(out_border_file)
def project_on_surface(subject, volume_file, colors_output_fname, surf_output_fname,
target_subject=None, threshold=2, overwrite_surf_data=False, overwrite_colors_file=True):
if target_subject is None:
target_subject = subject
utils.make_dir(op.join(BLENDER_ROOT_DIR, subject, 'fmri'))
for hemi in ['rh', 'lh']:
print('project {} to {}'.format(volume_file, hemi))
if not op.isfile(surf_output_fname.format(hemi=hemi)) or overwrite_surf_data:
surf_data = fu.project_volume_data(volume_file, hemi, subject_id=subject, surf="pial", smooth_fwhm=3,
target_subject=target_subject, output_fname=surf_output_fname.format(hemi=hemi))
nans = np.sum(np.isnan(surf_data))
if nans > 0:
print('there are {} nans in {} surf data!'.format(nans, hemi))
# np.save(surf_output_fname.format(hemi=hemi), surf_data)
else:
surf_data = np.load(surf_output_fname.format(hemi=hemi))
if not op.isfile(colors_output_fname.format(hemi=hemi)) or overwrite_colors_file:
print('Calulating the activaton colors for {}'.format(surf_output_fname))
_save_fmri_colors(target_subject, hemi, surf_data, threshold, colors_output_fname.format(hemi=hemi))
shutil.copyfile(colors_output_fname.format(hemi=hemi), op.join(BLENDER_ROOT_DIR, subject, 'fmri',
op.basename(colors_output_fname.format(hemi=hemi))))
def calc_subject_evoked_response(subject, root_fol, task, atlas, events_id, fname_format, fwd_fol, neccesary_files,
remote_subjects_dir, fsaverage, raw_cleaning_method, inverse_method, indices=None,
overwrite_epochs=False, overwrite_evoked=False, positive=True, moving_average_win_size=100):
meg.init_globals(subject, fname_format=fname_format, raw_cleaning_method=raw_cleaning_method,
subjects_meg_dir=SUBJECTS_MEG_DIR, task=task, subjects_mri_dir=SUBJECTS_DIR,
BLENDER_ROOT_DIR=BLENDER_ROOT_DIR, files_includes_cond=True, fwd_no_cond=True)
epochs_fname = '{}_arc_rer_{}-epo.fif'.format(subject, raw_cleaning_method)
events_fname = '{}_arc_rer_{}-epo.csv'.format(subject, raw_cleaning_method)
if indices is None:
indices = find_events_indices(op.join(root_fol, events_fname))
if not indices is None:
utils.make_dir(op.join(SUBJECTS_MEG_DIR, task, subject))
utils.make_dir(op.join(SUBJECTS_DIR, subject, 'mmvt'))
utils.make_dir(op.join(BLENDER_ROOT_DIR, subject))
# utils.prepare_local_subjects_folder(
# neccesary_files, subject, remote_subjects_dir, SUBJECTS_DIR, print_traceback=False)
# anatomy_preproc.freesurfer_surface_to_blender_surface(subject, overwrite=False)
# anatomy_preproc.create_annotation_file_from_fsaverage(subject, atlas, fsaverage, False, False, False, True)
# calc_evoked(indices, op.join(root_fol, epochs_fname), overwrite_epochs, overwrite_evoked)
fwd_fname = '{}_arc_rer_tsss-fwd.fif'.format(subject)
if not op.isfile(op.join(SUBJECTS_MEG_DIR, task, subject, fwd_fname)):
shutil.copy(op.join(fwd_fol, fwd_fname), op.join(SUBJECTS_MEG_DIR, task, subject, fwd_fname))
# meg_preproc.calc_inverse_operator(events_id, calc_for_cortical_fwd=True, calc_for_sub_cortical_fwd=False)
# stcs = meg_preproc.calc_stc_per_condition(events_id, inverse_method)
stcs = None
for hemi in utils.HEMIS:
meg.calc_labels_avg_per_condition(
atlas, hemi, 'pial', events_id, labels_from_annot=False, labels_fol='', stcs=stcs,
inverse_method=inverse_method, positive=positive, moving_average_win_size=moving_average_win_size,
do_plot=True)
