def run_subject_glm(jobfile, protocol, subject, session=None, smooth=None,
lowres=False):
""" Create jobfile and run it """
if protocol == 'preferences' and subject in ['sub-11']:
jobfile = 'ini_files/IBC_preproc_preferences_sub-11.ini'
output_name = os.path.join(
'/tmp', os.path.basename(jobfile)[:-4] + '_%s.ini' % subject)
_adapt_jobfile(jobfile, subject, output_name, session)
list_subjects_update = generate_glm_input(output_name, smooth, lowres)
clean_anatomical_images(IBC)
if lowres:
mask_img = '../ibc_data/gm_mask_3mm.nii.gz'
else:
mask_img = '../ibc_data/gm_mask_1_5mm.nii.gz'
for subject in list_subjects_update:
subject['onset'] = [onset for onset in subject['onset']
if onset is not None]
clean_subject(subject)
if len(subject['session_id']) > 0:
if protocol == 'clips4':
first_level(subject, compcorr=True,
additional_regressors=RETINO_REG,
smooth=smooth, mask_img=mask_img)
else:
first_level(subject, compcorr=True, smooth=smooth,
mask_img=mask_img)
fixed_effects_analysis(subject, mask_img=mask_img)
def run_subject_surface_glm(jobfile, subject, session, protocol, lowres=False):
""" Create jobfile and run it """
output_name = os.path.join(
'/tmp',
os.path.basename(jobfile)[:-4] + '_%s.ini' % subject)
_adapt_jobfile(jobfile, subject, output_name, session)
list_subjects_update = generate_glm_input(output_name, lowres)
clean_anatomical_images(IBC)
for subject in list_subjects_update:
clean_subject(subject)
if len(subject['session_id']) > 0:
print(len(subject['session_id']))
if len(subject['session_id']) > 0:
#if protocol == 'retino':
# first_level(subject, compcorr=True,
# additional_regressors=RETINO_REG,
# smooth=None, surface=True)
#else:
first_level(subject, compcorr=True, smooth=None, surface=True)
fixed_effects_analysis(subject, surface=True, lowres=True)
for subject, session in subject_session)
subject_data = subject_data + subject_data_[0]
list_subject_update = []
list_files_parameter = []
for dict_subject in subject_data:
dict_subject = dict_subject.__dict__
update_dict_subject_data = {
k: v for (k, v) in dict_subject.items()
if v.__class__.__module__ == 'builtins'}
update_dict_subject_data.pop('nipype_results')
list_subject_update.append(update_dict_subject_data)
# FileName for the dumped dictionnary from the preproc
json_file_name = 'subjects_data.json'
json_file = open(json_file_name, "w")
json.dump(list_subject_update, json_file)
json_file.flush()
# resampling toward pre-defined shape
resample_func_and_anat()
# Load the dump data
list_subjects_update = json.load(open(json_file_name))
for subject in list_subjects_update:
subject = clean_subject(subject)
print(subject['subject_id'], subject['onset'])
if len(subject['session_id']) > 0:
first_level(subject, compcorr=True)
fixed_effects_analysis(subject)