def run(self, args, extra_args):
batch_profile = batch_profile_factory(
args.batch_profile, os.path.realpath(args.data_folder))
subjects_selection = None
if args.subjects_index or args.subjects_id:
indices = args.subjects_index if args.subjects_index else []
subject_ids = args.subjects_id if args.subjects_id else []
subjects_selection = SelectedSubjects(indices=indices,
subject_ids=subject_ids)
tmp_results_dir = args.tmp_results_dir
for match, to_set in [('true', True), ('false', False),
('none', None)]:
if tmp_results_dir.lower() == match:
tmp_results_dir = to_set
break
mdt.batch_fit(os.path.realpath(args.data_folder),
args.models_to_fit,
output_folder=args.output_folder,
subjects_selection=subjects_selection,
batch_profile=batch_profile,
recalculate=args.recalculate,
cl_device_ind=args.cl_device_ind,
double_precision=args.double_precision,
dry_run=args.dry_run,
tmp_results_dir=tmp_results_dir,
use_gradient_deviations=args.use_gradient_deviations)
subjects.append(
SimpleSubjectInfo(data_folder, subject_pjoin(),
directory + '_' + resolution,
dwi_fname, protocol_loader,
mask_fname))
return subjects
def __str__(self):
return 'Rheinland'
'''batch fit all subjects'''
mdt.batch_fit(
'/home/robbert/phd-data/rheinland/',
model_names,
batch_profile=RheinLandBatchProfile(resolutions_to_use=['data_ms20']))
mdt.batch_fit('/home/robbert/phd-data/hcp_mgh/',
model_names,
batch_profile='HCP_MGH')
def func(subject_info):
subject_id = subject_info.subject_id
wm_mask = generate_simple_wm_mask(os.path.join(
subject_info.data_folder[:-1] + '_output', subject_id, 'Tensor',
'Tensor.FA.nii.gz'),
subject_info.get_input_data().mask,
threshold=0.3,