def anatomy_preproc(args):
args = anat.read_cmd_args(dict(
subject=args.subject,
remote_subject_dir='/autofs/space/lilli_001/users/DARPA-Recons/{subject}',
high_level_atlas_name='darpa_atlas'
))
anat.call_main(args)
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 init_anatomy(args):
args = anat.read_cmd_args(
dict(subject=args.subject,
remote_subject_dir=args.remote_subject_dir,
exclude='create_new_subject_blend_file',
ignore_missing=True))
anat.call_main(args)
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 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 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 anatomy_preproc(args, subject=''):
args = anat.read_cmd_args(
dict(subject=args.subject if subject == '' else subject,
remote_subject_dir=
'/autofs/space/lilli_001/users/DARPA-Recons/{subject}',
high_level_atlas_name='darpa_atlas',
function='create_annotation,create_high_level_atlas',
ignore_missing=True))
anat.call_main(args)
def calc_anatomy(subject, atlas, remote_subject_dir, n_jobs):
from src.preproc import anatomy as anat
args = anat.read_cmd_args(
dict(subject=subject,
atlas=atlas,
function='all,check_bem',
remote_subject_dir=remote_subject_dir,
exclude='create_new_subject_blend_file',
n_jobs=n_jobs))
anat.call_main(args)
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_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 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_local_subjects_folder'
anat.run_on_subjects(args)
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_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 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 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 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 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 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 darpa_prep_angelique(args):
import glob
for subject in args.subject:
darpa_subject = subject[:2].upper() + subject[2:]
root = op.join('/homes/5/npeled/space1/Angelique/recon-alls', darpa_subject)
recon_all_dirs = glob.glob(op.join(root, '**', '*SurferOutput*'), recursive=True)
if len(recon_all_dirs) == 0:
print("Can't find the recon-all folder for {}!".format(subject))
continue
args = anat.read_cmd_args(utils.Bag(
subject=subject,
function='prepare_subject_folder',
remote_subject_dir=recon_all_dirs[0]
))
pu.run_on_subjects(args, anat.main)
def get_subject_files_from_mad(subjects, atlas):
for subject in subjects:
root_fol = '/mnt/cashlab/Original Data/{}'.format(subject[:2].upper())
args = anat.read_cmd_args(
dict(
subject=subject,
atlas=atlas,
remote_subject_dir=op.join(
root_fol,
'{subject}/{subject}_Notes_and_Images/{subject}_SurferOutput'
),
function='prepare_subject_folder',
ignore_missing=1,
))
pu.run_on_subjects(args, anat.main)
def recon_all_clin(args):
# python -m src.preproc.examples.anatomy -s nmr01426 -f recon_all_clin --clin_fol clin_6966926 --dicoms_fol Prisma_fit-67026-20200618-141203-000586
import os
for subject, clin_fol, dicoms_fol in zip(args.subject, args.clin_fol,
args.dicoms_fol):
clin_full_fol = utils.make_dir(
op.join(args.clin_root, clin_fol, 'mne_dicom'))
memprage_fols = glob.glob(op.join(clin_full_fol, '*MEMPRAGE*'))
print('mne_organize_dicom output fol: {}'.format(clin_full_fol))
if len(memprage_fols) > 0:
ret = au.is_true(
input(
'It seems like you already have memprage folders, are you sure you want to rerun?'
))
if not ret:
continue
utils.delete_folder_files(clin_full_fol)
fs_dir = utils.make_dir(op.join(args.fs_root, subject))
print('FreeSurfer output fol: {}'.format(fs_dir))
dicoms_full_path = op.join(args.dicoms_root, dicoms_fol)
if not op.isdir(dicoms_full_path):
print('{} does not exist!'.format(dicoms_full_path))
continue
rs = utils.partial_run_script(locals(), print_only=args.print_only)
os.chdir(clin_full_fol)
rs('mne_organize_dicom {dicoms_full_path}')
anat.recon_all(subject,
clin_full_fol,
overwrite=True,
subjects_dir=args.fs_root,
print_only=False,
n_jobs=args.n_jobs)
args = anat.read_cmd_args(
dict(
subject=subject,
function='all,create_skull_surfaces',
remote_subject_dir=op.join(args.fs_root, subject),
n_jobs=args.n_jobs,
))
pu.run_on_subjects(args, anat.main)
def get_subject_files_from_server(subject, args):
args = anat.read_cmd_args(['-s', subject])
args.remote_subject_dir = op.join('/autofs/cluster/neuromind/npeled/subjects', subject)
anat.run_on_subjects(args)
def get_subject_files_using_sftp(subject, args):
args = anat.read_cmd_args(['-s', subject, '--sftp_username', args.sftp_username, '--sftp_domain', args.sftp_domain])
args.sftp = True
anat.run_on_subjects(args)
def add_parcellation(subject, args):
args = anat.read_cmd_args(['-s', subject, '-a', args.atlas])
args.function = 'create_annotation_from_template,parcelate_cortex,calc_faces_verts_dic,' + \
'save_labels_vertices,save_hemis_curv,calc_labels_center_of_mass,save_labels_coloring'
anat.run_on_subjects(args)
def darpa(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))
anat.run_on_subjects(args)
def prepare_subject_folder_from_huygens(subject, args):
args = anat.read_cmd_args(['-s', subject])
subject = subject[:2].upper() + subject[2:]
args.remote_subject_dir = op.join('/space/huygens/1/users/kara/{}_SurferOutput/'.format(subject))
args.function = 'prepare_local_subjects_folder'
anat.run_on_subjects(args)
def prepare_subject_folder_from_franklin(subject, args):
args = anat.read_cmd_args(['-s', subject])
args.remote_subject_dir = op.join('/autofs/space/franklin_003/users/npeled/subjects_old/{}'.format(subject))
args.function = 'prepare_local_subjects_folder'
anat.run_on_subjects(args)
def add_parcellation(subject, args):
args = anat.read_cmd_args(['-s', subject, '-a', args.atlas])
args.function = 'create_annotation_from_template,parcelate_cortex,calc_faces_verts_dic,' + \
'save_labels_vertices,save_hemis_curv,calc_labels_center_of_mass,save_labels_coloring'
pu.run_on_subjects(args, anat.main)
def language(args):
# -f language -s nmr01361 --clinical_dir clin_4090354
# -s nmr01353 -f clean_4d_data --fsd sycabs --remote_fmri_dir "/space/megraid/clinical/MEG-MRI/seder/freesurfer" --nconditions 4
if args.clinical_dir == '':
print('You should set the clinical_dir first. Example: clin_4090354')
return
clinical_root_dir = op.join(args.remote_fmri_dir, args.clinical_dir)
if not op.isdir(clinical_root_dir):
print('{} does not exist!'.format(clinical_root_dir))
task = 'sycabs'
fwhm = 6
subject = args.subject[0]
remote_mri_dir = args.remote_clinical_subjects_dir
subject_mri_dir = op.join(remote_mri_dir, subject)
mri_subject_task_dir = utils.make_dir(op.join(subject_mri_dir, task))
clinical_dirs = glob.glob(op.join(clinical_root_dir, '*'))
clinical_dirs = [
d for d in clinical_dirs if utils.namebase(d) != 'mne_dcm'
]
remote_fmri_dir = utils.select_one_file(clinical_dirs)
fmri_fols = sorted(glob.glob(op.join(remote_fmri_dir, '*_SyCAbs')))
par_fol = utils.make_dir(op.join(remote_mri_dir, subject, 'par'))
par_files = glob.glob(op.join(par_fol, '*.par'))
sessions = sorted(
[utils.find_num_in_str(utils.namebase(d))[0] for d in fmri_fols])
# Warning: You first need to put the original ones in the following folder:
if len(par_files) == 0:
print('\n *** Please put the original par files in {} and rerun ***'.
format(op.join(remote_mri_dir, subject, 'par')))
return
par_files.sort(key=lambda x: int(utils.namebase(x).split('_')[-1]))
ret = input('''
Patient: {}
MRI folder: {}
fMRI root folder: {}
fMRI sessions: {}
Session and pars: {}
Do you want to continue (y/n)? '''.format(
subject, subject_mri_dir, remote_fmri_dir,
[utils.namebase(d) for d in fmri_fols],
list(zip([utils.namebase(f) for f in par_files], sessions))))
if not au.is_true(ret):
return
# You need first to run src.preproc.anatomy
if not op.isfile(anat.get_blend_fname(subject, args.atlas)):
args = anat.read_cmd_args(
dict(
subject=subject,
remote_subject_dir=subject_mri_dir,
ignore_missing=True,
))
pu.run_on_subjects(args, anat.main)
# convert the fMRI dicom files to nii
for fmri_fol in fmri_fols:
ses_num = utils.find_num_in_str(utils.namebase(fmri_fol))[0]
ses_files = glob.glob(op.join(fmri_fol, '**', '*.*'), recursive=True)
output_fname = op.join(
utils.make_dir(op.join(mri_subject_task_dir, ses_num)), 'f.nii.gz')
if not op.isfile(output_fname):
fu.mri_convert(ses_files[0], output_fname)
# Convert and arrange the par file
from src.misc.fmri_scripts import convert_par
for par_file, session in zip(par_files, sessions):
fs_par_fname = op.join(mri_subject_task_dir, session,
'{}.par'.format(task))
# if not op.isfile(fs_par_fname):
warnings = convert_par.sycabs(par_file, fs_par_fname)
if warnings != '':
print(
'\n *** Please fix the problems with the par convertion ({}) and rerun ***\n'
.format(par_file))
return
for hemi in utils.HEMIS:
utils.delete_folder_files(
op.join(remote_mri_dir, '{}_sm{}_{}'.format(task, fwhm, hemi)))
# Run the FreeSurfer analysis
args = fmri.read_cmd_args(
dict(subject=subject,
atlas=args.atlas,
function='clean_4d_data',
fsd=task,
fwhm=fwhm,
remote_fmri_dir=remote_mri_dir,
nconditions=4,
ignore_missing=True,
print_only=False,
overwrite_4d_preproc=False))
pu.run_on_subjects(args, fmri.main)
# Load the fMRI results
args = fmri.read_cmd_args(
dict(
subject=subject,
atlas=args.atlas,
function='load_surf_files',
fmri_file_template=op.join(MMVT_DIR, subject, 'fmri',
'words_v_symbols_{hemi}.mgz'),
))
pu.run_on_subjects(args, fmri.main)