def calc_diff(subject, fmri_file_template='*linda_{hemi}*,*hesheng_{hemi}'):
# Calc diff
args = fmri.read_cmd_args(
dict(subject=subject,
function='calc_files_diff',
fmri_file_template=fmri_file_template))
pu.run_on_subjects(args, fmri.main)
def calc_linda_surf(subject, atlas):
# Check for Linda's output fname
if not utils.both_hemi_files_exist(op.join(fmri.MMVT_DIR, subject, 'fmri', 'fmri_linda_{}.npy'.format('{hemi}'))) \
and not op.isfile(op.join(fmri.MMVT_DIR, subject, 'fmri', 'linda_minmax.pkl')):
# Find Linda's files
linda_volume_fnames = glob.glob(op.join(
linda_fol.format(subject=subject), linda_vol_template.format(subject=subject)))
linda_volume_folder = utils.get_parent_fol(linda_volume_fnames[0])
# project linda files on the surface
args = fmri.read_cmd_args(dict(
subject=subject, function='project_volume_to_surface', remote_fmri_dir=linda_volume_folder,
fmri_file_template=linda_vol_template.format(subject=subject), overwrite_surf_data=True))
pu.run_on_subjects(args, fmri.main)
# rename Linda's files
linda_fnames = glob.glob(op.join(fmri.MMVT_DIR, subject, 'fmri', 'fmri_{}'.format(
linda_template_npy.format(subject=subject))))
for fname in linda_fnames:
hemi = lu.get_label_hemi(utils.namebase(fname))
target_file = op.join(fmri.MMVT_DIR, subject, 'fmri', 'fmri_linda_{}.npy'.format(hemi))
if not op.isfile(target_file):
os.rename(fname, target_file)
# rename minmax file
linda_minmax_name = '{}.pkl'.format(utils.namebase(glob.glob(op.join(
fmri.MMVT_DIR, subject, 'fmri', '{}_minmax.pkl'.format(
utils.namebase(linda_vol_template.format(subject=subject)))))[0]))
os.rename(op.join(fmri.MMVT_DIR, subject, 'fmri', linda_minmax_name),
op.join(fmri.MMVT_DIR, subject, 'fmri', 'linda_minmax.pkl'))
# delete mgz files
mgz_files = glob.glob(op.join(fmri.MMVT_DIR, subject, 'fmri', 'fmri_{}_?h.mgz'.format(
utils.namebase(linda_vol_template.format(subject=subject)))))
for mgz_file in mgz_files:
os.remove(mgz_file)
def calc_freesurfer_surf(subject, atlas):
# Clean
args = fmri.read_cmd_args(
dict(subject=subject,
atlas=atlas,
function='clean_4d_data',
fmri_file_template='rest.nii*',
fsd='rest_linda',
overwrite_4d_preproc=False))
pu.run_on_subjects(args, fmri.main)
# save the surf files
args = fmri.read_cmd_args(
dict(subject=subject,
atlas=atlas,
function='load_surf_files',
overwrite_surf_data=True,
fmri_file_template=fs_surf_template.format(subject=subject,
hemi='{hemi}')))
pu.run_on_subjects(args, fmri.main)
# Renaming the files
root_fol = op.join(fmri.MMVT_DIR, subject, 'fmri')
for hemi in utils.HEMIS:
os.rename(
op.join(root_fol,
'fmri_rest_linda.sm6.{}.{}.npy'.format(subject, hemi)),
op.join(root_fol, 'fmri_freesurfer_{}.npy'.format(hemi)))
os.rename(
op.join(root_fol, 'rest_linda.sm6.{}_minmax.pkl'.format(subject)),
op.join(root_fol, 'freesurfer_minmax.pkl'))
def create_annot_from_mad(args):
remote_subject_dir_template = '/mnt/cashlab/Original Data/MG/{subject}/{subject}_Notes_and_Images/{subject}_SurferOutput'
for subject in args.subject:
remote_subject_dir = remote_subject_dir_template.format(subject=subject)
if utils.both_hemi_files_exist(op.join(remote_subject_dir, 'label', '{hemi}.aparc.DKTatlas.annot')):
print('{} has already both annot files!'.format(subject))
continue
args = anat.read_cmd_args(dict(
subject=subject.lower(),
atlas=args.atlas,
remote_subject_dir=remote_subject_dir_template,
function='create_annotation',
ignore_missing=True,
))
pu.run_on_subjects(args, anat.main)
if not utils.both_hemi_files_exist(op.join(SUBJECTS_DIR, subject.lower(), 'label', '{hemi}.aparc.DKTatlas.annot')):
print('Couldn\'t create annot files for {}!'.format(subject))
continue
local_annot_fol = utils.make_dir(op.join(SUBJECTS_DIR, 'annot_files', subject.lower()))
for hemi in utils.HEMIS:
local_annot_fname = op.join(SUBJECTS_DIR, subject.lower(), 'label', '{}.aparc.DKTatlas.annot'.format(hemi))
remote_annot_fname = op.join(remote_subject_dir, 'label', '{}.aparc.DKTatlas.annot'.format(hemi))
local_temp_annot_fname = op.join(local_annot_fol, '{}.aparc.DKTatlas.annot'.format(hemi))
if not op.isfile(remote_annot_fname):
if op.isfile(local_annot_fname):
utils.copy_file(local_annot_fname, local_temp_annot_fname)
else:
print('Can\'t copy {} for {}, it doesn\'t exist!'.format(local_annot_fname, subject))
def compare_connectivity(subject, atlas, n_jobs=6):
for name, fol, template in zip(['hesheng', 'linda', 'freesurfer'],
[hesheng_surf_fol, linda_surf_fol, fs_surf_fol],
[hesheng_template, linda_hemi_template, fs_surf_template]):
output_fname_template = op.join(
fmri.MMVT_DIR, subject, 'fmri', '{}_labels_data_laus125_mean_{}.npz'.format(name, '{hemi}'))
if not utils.both_hemi_files_exist(output_fname_template):
args = fmri.read_cmd_args(dict(
subject=subject, atlas=atlas, function='analyze_4d_data', fmri_file_template=template, remote_fmri_dir=fol,
labels_extract_mode='mean', overwrite_labels_data=False))
pu.run_on_subjects(args, fmri.main)
for hemi in utils.HEMIS:
os.rename(op.join(fmri.MMVT_DIR, subject, 'fmri', 'labels_data_laus125_mean_{}.npz'.format(hemi)),
output_fname_template.format(hemi=hemi))
args = con.read_cmd_args(dict(
subject=subject, atlas='laus125', function='calc_lables_connectivity',
connectivity_modality='fmri', connectivity_method='corr,cv', labels_extract_mode='mean',
windows_length=34, windows_shift=4, save_mmvt_connectivity=False, calc_subs_connectivity=False,
labels_name=name, recalc_connectivity=True, n_jobs=n_jobs))
pu.run_on_subjects(args, con.main)
conn_fol = op.join(con.MMVT_DIR, subject, 'connectivity')
coloring_fol = op.join(con.MMVT_DIR, subject, 'coloring')
os.rename(op.join(conn_fol, 'fmri_corr.npy'), op.join(conn_fol, 'fmri_corr_{}.npy'.format(name)))
os.rename(op.join(conn_fol, 'fmri_corr_cv_mean.npz'), op.join(conn_fol, 'mri_corr_cv_mean_{}.npz'.format(name)))
os.rename(op.join(conn_fol, 'fmri_corr_cv_mean_mean.npz'), op.join(conn_fol, 'fmri_corr_cv_mean_mean_{}.npz'.format(name)))
os.rename(op.join(coloring_fol, 'fmri_corr_cv_mean.csv'), op.join(coloring_fol, 'fmri_corr_cv_mean_{}.csv'.format(name)))
def prepare_subject_folder_from_franklin(subject, args):
args = anat.read_cmd_args(['-s', subject, '-a', args.atlas])
args.remote_subject_dir = op.join(
'/autofs/space/franklin_003/users/npeled/subjects_old/{}'.format(
subject))
args.function = 'prepare_subject_folder'
pu.run_on_subjects(args, anat.main)
def load_edf_data_seizure_2(args):
args = elecs.read_cmd_args(
utils.Bag(
subject=args.subject,
atlas='laus125',
function='create_raw_data_for_blender',
task='seizure',
bipolar=args.bipolar,
raw_fname=op.join(
ELECTRODES_DIR, args.subject[0], 'DMphaseIISz_TG.edf'
), # '/cluster/neuromind/npeled/taha/dm04002705/edf/DMphaseIISz_TG.edf',
start_time='00:00:00',
seizure_onset='00:01:20',
seizure_end='00:02:00',
baseline_onset='00:00:00',
baseline_end='00:01:00',
lower_freq_filter=2,
upper_freq_filter=70,
power_line_notch_widths=5,
ref_elec='PST1',
normalize_data=False,
calc_zscore=False,
factor=1000,
channels_names_mismatches='LFO=LIF'))
pu.run_on_subjects(args, elecs.main)
def darpa_prep_huygens(subject, args):
args = anat.read_cmd_args(['-s', subject, '-a', args.atlas])
subject = subject[:2].upper() + subject[2:]
args.remote_subject_dir = op.join(
'/space/huygens/1/users/kara/{}_SurferOutput/'.format(subject))
args.function = 'prepare_subject_folder'
pu.run_on_subjects(args, anat.main)
def load_edf_data_seizure(args):
args = elecs.read_cmd_args(
utils.Bag(
subject=args.subject,
atlas='laus125',
function='create_raw_data_from_edf',
task='seizure',
bipolar=args.bipolar,
raw_fname=op.join(ELECTRODES_DIR, args.subject[0],
'{}.edf'.format(args.edf)),
start_time='00:00:00',
seizure_onset='00:01:20',
seizure_end='00:02:00',
baseline_onset='00:00:00',
baseline_end='00:01:00',
lower_freq_filter=1,
upper_freq_filter=150,
power_line_notch_widths=5,
ref_elec='PST1',
normalize_data=False,
calc_zscore=False,
factor=1000,
# channels_names_mismatches='LFO=LIF'
))
pu.run_on_subjects(args, elecs.main)
def calc_hesheng_surf(subject, atlas):
subject_fol = op.join(fmri.MMVT_DIR, subject, 'fmri')
if not (utils.both_hemi_files_exist(
op.join(subject_fol, 'fmri_hesheng_{hemi}.npy'))
and op.isfile(op.join(subject_fol, 'hesheng_minmax.pkl'))):
# Copy and rename Hesheng's files
hesheng_fnames = glob.glob(
op.join(hesheng_surf_fol.format(subject=subject),
hesheng_template.format(subject=subject)))
for fname in hesheng_fnames:
hemi = lu.get_label_hemi_invariant_name(utils.namebase(fname))
target_file = op.join(fmri.FMRI_DIR, subject,
'hesheng_{}.nii.gz'.format(hemi))
mgz_target_file = utils.change_fname_extension(target_file, 'mgz')
if not op.isfile(mgz_target_file):
shutil.copy(fname, target_file)
fu.nii_gz_to_mgz(target_file)
os.remove(target_file)
# Load Hesheng's files
args = fmri.read_cmd_args(
dict(subject=subject,
atlas=atlas,
function='load_surf_files',
overwrite_surf_data=True,
fmri_file_template='hesheng_{hemi}.mgz'))
pu.run_on_subjects(args, fmri.main)
def project_and_calc_clusters(args):
if not op.isdir(args.root_fol):
print('You should first set args.root_fol!')
return False
img_files = [
f for f in glob.glob(op.join(args.root_fol, '*.img')) if op.isfile(f)
]
for img_fname in img_files:
mgz_fname = fu.mri_convert_to(img_fname, 'mgz', overwrite=False)
if ' ' in utils.namebase(mgz_fname):
mgz_new_fname = op.join(
utils.get_parent_fol(mgz_fname),
utils.namebase_with_ext(mgz_fname).replace(' ', '_').replace(
',', '').lower())
os.rename(mgz_fname, mgz_new_fname)
nii_files = [
f for f in glob.glob(op.join(args.root_fol, '*'))
if op.isfile(f) and utils.file_type(f) in ('nii', 'nii.gz', 'mgz')
]
for fname in nii_files:
fmri_args = fmri.read_cmd_args(
dict(subject=args.subject,
function='project_volume_to_surface,find_clusters',
fmri_file_template=fname,
threshold=args.cluster_threshold))
pu.run_on_subjects(fmri_args, fmri.main)
def darpa(args):
for subject in args.subject:
darpa_subject = subject[:2].upper() + subject[2:]
args = anat.read_cmd_args(utils.Bag(
subject=subject,
remote_subject_dir=op.join('/space/huygens/1/users/kara/{}_SurferOutput/'.format(darpa_subject))
))
pu.run_on_subjects(args, anat.main)
def save_dynamic_activity_map(args):
args = fmri.read_cmd_args(
dict(subject=args.subject,
atlas=args.atlas,
function='save_dynamic_activity_map',
fmri_file_template='fmcpr.up.sm6.{subject}.{hemi}.*',
overwrite_activity_data=True))
pu.run_on_subjects(args, fmri.main)
def calc_electrodes_con(args):
# -s mg78 -a laus250 -f save_electrodes_coh --threshold_percentile 95 -c interference,non-interference
args = con.read_cmd_args(
utils.Bag(subject=args.subject,
atlas='laus250',
function='save_electrodes_coh',
threshold_percentile=95,
conditions='interference,non-interference'))
pu.run_on_subjects(args, con.main)
def get_ras_from_mad(args):
args = elecs.read_cmd_args(
utils.Bag(
subject=args.subject,
function='get_ras_file',
remote_ras_fol=
'/mnt/cashlab/Original Data/MG/{subject}/{subject}_Notes_and_Images/{subject}_SurferOutput'
))
pu.run_on_subjects(args, elecs.main)
def get_subject_files_from_server(args):
args = anat.read_cmd_args(dict(
subject=args.subject,
atlas=args.atlas,
function='prepare_subject_folder',
sftp=True,
sftp_username='******',
sftp_domain='door.nmr.mgh.harvard.edu',
remote_subject_dir='/space/thibault/1/users/npeled/subjects/{subject}'))
pu.run_on_subjects(args, anat.main)
def get_subject_files_using_sftp_from_ohad(subject, args):
args = anat.read_cmd_args(['-s', subject,'-a', args.atlas])
args.sftp = True
args.sftp_username = '******'
args.sftp_domain = '127.0.0.1'
args.sftp_port = 4444
args.sftp_subject_dir = '/media/ohadfel/New_Volume/subs/{}'.format(subject)
args.remote_subject_dir = '/media/ohadfel/New_Volume/subs/{}'.format(subject)
args.function = 'prepare_subject_folder'
pu.run_on_subjects(args, anat.main)
def darpa_prep_lili(subject, args):
args = anat.read_cmd_args([
'-s', subject, '-a', args.atlas, '--sftp_username', args.sftp_username,
'--sftp_domain', args.sftp_domain
])
args.remote_subject_dir = op.join(
'/autofs/space/lilli_001/users/DARPA-Recons', subject)
args.sftp = True
args.function = 'prepare_subject_folder'
pu.run_on_subjects(args, anat.main)
def get_subject_files_from_mad(args):
for subject in args.subject:
args = anat.read_cmd_args(
dict(
subject=subject,
atlas=args.atlas,
remote_subject_dir=
'/mnt/cashlab/Original Data/MG/{subject}/{subject}_Notes_and_Images/{subject}_SurferOutput',
function='prepare_subject_folder'))
pu.run_on_subjects(args, anat.main)
def fmri_msit_pipeline(args):
'-s pp009 -a laus250 -f fmri_pipeline -t MSIT --contrast_template "*Interference*"'
for subject in args.subject:
args = fmri.read_cmd_args(
dict(subject=subject,
atlas=args.atlas,
function='fmri_pipeline',
task='MSIT',
contrast_template='*Interference*'))
pu.run_on_subjects(args, fmri.main)
def calc_subcorticals_activity(args):
args = fmri.read_cmd_args(
dict(
subject=args.subject,
function='calc_subcorticals_activity',
# fmri_file_template='rest*',
fmri_file_template='fmcpr.sm6.mni305.2mm.*',
labels_extract_mode='mean', #,pca,pca_2,pca_4,pca_8',
overwrite_subs_data=True))
pu.run_on_subjects(args, fmri.main)
def anat_preproc_clin(args):
# python -m src.preproc.examples.anatomy -s nmr01426 -f anat_preproc_clin
for subject in args.subject:
args = anat.read_cmd_args(
dict(
subject=subject,
function='all,create_outer_skin_surface,check_bem',
remote_subject_dir=op.join(args.fs_root, subject),
n_jobs=args.n_jobs,
))
pu.run_on_subjects(args, anat.main)
def recon_all(args):
# python -m src.preoroc.anatomy -f recon-all --ignore_missing 1 --n_jobs 1
# --nifti_fname "/autofs/space/thibault_001/users/npeled/T1/{subject}/mprage.nii" -s "wake5,wake6,wake7,wake8"
args = anat.read_cmd_args(dict(
subject=args.subject,
function='recon-all',
nifti_fname='/autofs/space/thibault_001/users/npeled/T1/{subject}/mprage.nii',
ignore_missing=True,
n_jobs=1,
))
pu.run_on_subjects(args, anat.main)
def darpa_sftp(args):
for subject in args.subject:
darpa_subject = subject[:2].upper() + subject[2:]
args = anat.read_cmd_args(utils.Bag(
subject=subject,
remote_subject_dir=op.join('/space/huygens/1/users/kara/{}_SurferOutput/'.format(darpa_subject)),
sftp=True,
sftp_username='******',
sftp_domain='door.nmr.mgh.harvard.edu',
))
pu.run_on_subjects(args, anat.main)
def get_subject_files_using_sftp(args):
for subject in args.subject:
args = anat.read_cmd_args(
dict(subject=subject,
atlas=args.atlas,
sftp_username=args.sftp_username,
sftp_domain=args.sftp_domain,
sftp=True,
remote_subject_dir=args.remote_subject_dir,
function='prepare_subject_folder'))
pu.run_on_subjects(args, anat.main)
def project_volume_to_surface(args):
args = fmri.read_cmd_args(dict(
subject=args.subject,
function='project_volume_to_surface',
fmri_file_template='f*.gz',
sftp_username=args.sftp_username,
sftp_domain=args.sftp_domain,
sftp=True,
remote_subject_dir=args.remote_subject_dir,
))
pu.run_on_subjects(args, fmri.main)
def get_subject_files_from_mad(args=None, subjects=None, necessary_files=None):
subjects = args.subject if args is not None else subjects
for subject in subjects:
args = anat.read_cmd_args(dict(
subject=subject,
atlas=args.atlas,
remote_subject_dir='/mnt/cashlab/Original Data/MG/{subject}/{subject}_Notes_and_Images/{subject}_SurferOutput',
function='prepare_subject_folder'
))
if necessary_files is not None:
args.necessary_files = necessary_files
pu.run_on_subjects(args, anat.main)
def get_subject_files_from_mad(org_args=None, subjects=None, necessary_files=None):
subjects = org_args.subject if org_args is not None else subjects
for subject in subjects:
root_fol = '/mnt/cashlab/Original Data/{}'.format(subject[:2].upper())
args = anat.read_cmd_args(dict(
subject=subject,
atlas=org_args.atlas,
remote_subject_dir=op.join(root_fol, '{subject}/{subject}_Notes_and_Images/{subject}_SurferOutput'),
function='prepare_subject_folder'
))
if necessary_files is not None:
args.necessary_files = necessary_files
pu.run_on_subjects(args, anat.main)
def load_edf_data_rest(args):
args = elecs.read_cmd_args(
utils.Bag(
subject=args.subject,
function='create_raw_data_for_blender',
task='rest',
bipolar=False,
remove_power_line_noise=True,
raw_fname='MG102_d3_Fri.edf',
# rest_onset_time='6:50:00',
# end_time='7:05:00',
normalize_data=False,
preload=False))
pu.run_on_subjects(args, elecs.main)
def project_and_calc_clusters(args):
if not op.isdir(args.root_fol):
print('You should first set args.root_fol!')
return False
nii_files = [
f for f in glob.glob(op.join(args.root_fol, '*'))
if op.isfile(f) and utils.file_type(f) in ('nii', 'nii.gz', 'mgz')
]
for fname in nii_files:
args = fmri.read_cmd_args(
dict(subject=args.subject,
function='project_volume_to_surface,find_clusters',
fmri_file_template=fname))
pu.run_on_subjects(args, fmri.main)
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 read_cmd_args(argv):
import argparse
from src.utils import args_utils as au
parser = argparse.ArgumentParser(description='MMVT freeview preprocessing')
parser.add_argument('-b', '--bipolar', help='bipolar', required=False, default=0, type=au.is_true)
parser.add_argument('--overwrite_aseg_file', help='overwrite_aseg_file', required=False, default=0, type=au.is_true)
parser.add_argument('--create_volume_file', help='create_volume_file', required=False, default=1, type=au.is_true)
parser.add_argument('--electrodes_pos_fname', help='electrodes_pos_fname', required=False, default='')
parser.add_argument('--way_points', help='way_points', required=False, default=0, type=au.is_true)
pu.add_common_args(parser)
args = utils.Bag(au.parse_parser(parser, argv))
args.necessary_files = {'mri': ['T1.mgz', 'orig.mgz']}
print(args)
return args
if __name__ == '__main__':
args = read_cmd_args(None)
pu.check_freesurfer()
pu.run_on_subjects(args, main)
print('finish!')
flags['save_evoked_to_blender'] = save_evoked_to_blender(mri_subject, conditions, args, evoked)
if not op.isfile(meg.COR):
eeg_cor = op.join(meg.SUBJECT_MEG_FOLDER, '{}-cor-trans.fif'.format(subject))
if not op.isfile(eeg_cor):
raise Exception("Can't find head-MRI transformation matrix. Should be in {} or in {}".format(meg.COR, eeg_cor))
meg.COR = eeg_cor
flags = meg.calc_fwd_inv_wrapper(subject, mri_subject, conditions, args, flags)
flags = meg.calc_stc_per_condition_wrapper(subject, conditions, inverse_method, args, flags)
return flags
def read_cmd_args(argv=None):
args = meg.read_cmd_args(argv)
args.pick_meg = False
args.pick_eeg = True
args.reject = False
args.fwd_usingMEG = False
args.fwd_usingEEG = True
return args
if __name__ == '__main__':
from src.utils import preproc_utils as pu
from itertools import product
args = read_cmd_args()
subjects_itr = product(zip(args.subject, args.mri_subject), args.inverse_method)
subject_func = lambda x:x[0][1]
pu.run_on_subjects(args, main, subjects_itr, subject_func)
print('finish!')
