def run_subject_preproc(jobfile, subject, session=None):
""" Create jobfile and run it on """
output_name = os.path.join(
'/tmp', os.path.basename(jobfile)[:-4] + '_%s.ini' % subject)
_adapt_jobfile(jobfile, subject, output_name, session)
# Read the jobfile
list_subjects, params = _generate_preproc_pipeline(output_name)
# Preproc and Dump data
subject_data = do_subjects_preproc(output_name, report=True)
return subject_data
def generate_glm_input(jobfile):
""" retrun a list of dictionaries that represent the data available
for GLM analysis"""
list_subjects, params = _generate_preproc_pipeline(jobfile)
output = []
for subject in list_subjects:
output_dir = subject.output_dir
reports_output_dir = os.path.join(output_dir, 'reports')
basenames = [
'wr' + os.path.basename(func_)[:-3] for func_ in subject.func
]
gii_basenames = [
'r' + os.path.basename(func_).split('.')[0] + '_fsaverage_lh.gii'
for func_ in subject.func
]
gii_basenames += [
'r' + os.path.basename(func_).split('.')[0] + '_fsaverage_rh.gii'
for func_ in subject.func
]
func = [
os.path.join(output_dir, 'freesurfer', basename)
for basename in gii_basenames
]
realignment_parameters = [
os.path.join(session_output_dir, 'rp_' + basename[2:-4] + '.txt')
for (session_output_dir,
basename) in zip(subject.session_output_dirs, basenames)
] * 2
session_ids = [
session_id
for (session_id, onset) in zip(subject.session_id, subject.onset)
if onset is not None
]
onsets = [onset for onset in subject.onset if onset is not None]
subject_ = {
'output_dir': output_dir,
'session_output_dirs': subject.session_output_dirs,
'subject_id': subject.subject_id,
'session_id': session_ids * 2,
'TR': subject.TR,
'drift_model': subject.drift_model,
'hfcut': subject.hfcut,
'time_units': subject.time_units,
'hrf_model': subject.hrf_model,
'onset': onsets * 2,
'report': True,
'reports_output_dir': reports_output_dir,
'basenames': gii_basenames,
'func': func,
'realignment_parameters': realignment_parameters,
}
output.append(subject_)
return output
def generate_glm_input(jobfile, smooth=None, lowres=False):
""" retrun a list of dictionaries that represent the data available
for GLM analysis"""
list_subjects, params = _generate_preproc_pipeline(jobfile)
output = []
for subject in list_subjects:
if lowres:
output_dir = subject.output_dir.replace('derivatives', '3mm')
elif smooth is not None:
output_dir = subject.output_dir.replace('derivatives',
'smooth_derivatives')
else:
output_dir = subject.output_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
anat = glob.glob(
os.path.join(subject.anat_output_dir, 'wsub*_T1w.nii.gz'))[0]
reports_output_dir = os.path.join(output_dir, 'reports')
report_log_filename = os.path.join(reports_output_dir,
'report_log.html')
report_preproc_filename = os.path.join(reports_output_dir,
'report_preproc.html')
report_filename = os.path.join(reports_output_dir, 'report.html')
tmp_output_dir = os.path.join(output_dir, 'tmp')
basenames = [
'wr' + os.path.basename(func_)[:-3] for func_ in subject.func
]
func = [
os.path.join(session_output_dir, basename + '.gz')
for (session_output_dir,
basename) in zip(subject.session_output_dirs, basenames)
]
if lowres:
func = [f.replace('derivatives', '3mm') for f in func]
realignment_parameters = [
os.path.join(session_output_dir, 'rp_' + basename[2:-4] + '.txt')
for (session_output_dir,
basename) in zip(subject.session_output_dirs, basenames)
]
hrf_model = subject.hrf_model
if 'retino' in jobfile:
hrf_model = 'spm'
subject_ = {
'scratch': output_dir,
'output_dir': output_dir,
'session_output_dirs': subject.session_output_dirs,
'anat_output_dir': subject.anat_output_dir,
'tmp_output_dir': tmp_output_dir,
'data_dir': subject.data_dir,
'subject_id': subject.subject_id,
'session_id': subject.session_id,
'TR': subject.TR,
'drift_model': subject.drift_model,
'high_pass': 1. / 128,
'time_units': subject.time_units,
'hrf_model': hrf_model,
'anat': anat,
'onset': subject.onset,
'report': True,
'reports_output_dir': reports_output_dir,
'report_log_filename': report_log_filename,
'report_preproc_filename': report_preproc_filename,
'report_filename': report_filename,
'basenames': basenames,
'func': func,
'n_sessions': len(func),
'realignment_parameters': realignment_parameters,
}
output.append(subject_)
return output
# CONFIGURATION
protocols = [
'WM', 'MOTOR', 'LANGUAGE', 'EMOTION', 'GAMBLING', 'RELATIONAL',
'SOCIAL'
]
slicer = 'ortho' # slicer of activation maps QA
cut_coords = None
threshold = 3.
cluster_th = 15 # minimum number of voxels in reported clusters
####################################
# read input configuration
conf_file = os.path.join(os.path.dirname(sys.argv[0]), "HCP.ini")
for protocol in protocols:
subjects, preproc_params = _generate_preproc_pipeline(
conf_file, protocol=protocol)
fwhm = preproc_params.get("fwhm")
task_output_dir = os.path.join(os.path.dirname(subjects[0].output_dir))
kwargs = {
"regress_motion": True,
"slicer": slicer,
"threshold": threshold,
"cluster_th": cluster_th,
"protocol": protocol,
"dc":
not preproc_params.get("disable_distortion_correction", False),
"realign": preproc_params["realign"],
"coregister": preproc_params["coregister"],
"segment": preproc_params["segment"],
"normalize": preproc_params["normalize"],
def generate_glm_input(jobfile, smooth=None, lowres=False):
""" retrun a list of dictionaries that represent the data available
for GLM analysis"""
list_subjects, params = _generate_preproc_pipeline(jobfile)
output = []
for subject in list_subjects:
output_dir = subject.output_dir
print(output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# use normalized anat
anat = os.path.join(os.path.dirname(subject.anat),
'w' + os.path.basename(subject.anat))
# use normalized fMRI
basenames = [
'wr' + os.path.basename(func_)[:-3] for func_ in subject.func
]
dirnames = [os.path.dirname(func_) for func_ in subject.func]
func = [
os.path.join(dirname, basename + '.gz')
for (dirname, basename) in zip(dirnames, basenames)
]
if lowres:
func = [f.replace('derivatives', '3mm') for f in func]
realignment_parameters = [
os.path.join(dirname, 'rp_' + basename[2:-4] + '.txt')
for (dirname, basename) in zip(dirnames, basenames)
]
# misc report directories
reports_output_dir = os.path.join(output_dir, 'reports')
report_log_filename = os.path.join(reports_output_dir,
'report_log.html')
report_preproc_filename = os.path.join(reports_output_dir,
'report_preproc.html')
report_filename = os.path.join(reports_output_dir, 'report.html')
tmp_output_dir = os.path.join(output_dir, 'tmp')
subject_ = {
'scratch': output_dir,
'output_dir': output_dir,
'session_output_dirs': subject.session_output_dirs,
'anat_output_dir': subject.anat_output_dir,
'tmp_output_dir': tmp_output_dir,
'data_dir': subject.data_dir,
'subject_id': subject.subject_id,
'session_id': subject.session_id,
'TR': subject.TR,
'drift_model': subject.drift_model,
'hfcut': subject.hfcut,
'time_units': subject.time_units,
'hrf_model': subject.hrf_model,
'anat': anat,
'onset': subject.onset,
'report': True,
'reports_output_dir': reports_output_dir,
'report_log_filename': report_log_filename,
'report_preproc_filename': report_preproc_filename,
'report_filename': report_filename,
'basenames': basenames,
'func': func,
'n_sessions': len(func),
'realignment_parameters': realignment_parameters,
}
output.append(subject_)
return output
# additional ``kwargs`` for more informative report
paradigm=paradigm.__dict__,
TR=tr,
n_scans=n_scans,
hfcut=hfcut,
frametimes=frametimes,
drift_model=drift_model,
hrf_model=hrf_model,
)
ProgressReport().finish_dir(subject_session_output_dir)
print "Statistic report written to %s\r\n" % stats_report_filename
return z_maps
if __name__ == '__main__':
#File containing configuration for preprocessing the data
jobfile = (
'/neurospin/unicog/resources/git_depot/unicog/unicogfmri/localizer'
'/volume_glm/Step1_config.ini')
list_subjects, params = _generate_preproc_pipeline(jobfile)
#Preproc
subject_data = preproc(jobfile)
#first_level
for dict_subject in subject_data:
dict_subject = dict_subject.__dict__
z_maps = first_level(dict_subject)
'EMOTION',
'GAMBLING',
'RELATIONAL',
'SOCIAL'
]
slicer = 'ortho' # slicer of activation maps QA
cut_coords = None
threshold = 3.
cluster_th = 15 # minimum number of voxels in reported clusters
####################################
# read input configuration
conf_file = os.path.join(os.path.dirname(sys.argv[0]), "HCP.ini")
for protocol in protocols:
subjects, preproc_params = _generate_preproc_pipeline(
conf_file, protocol=protocol)
fwhm = preproc_params.get("fwhm")
task_output_dir = os.path.join(os.path.dirname(subjects[0].output_dir))
kwargs = {"regress_motion": True,
"slicer": slicer,
"threshold": threshold,
"cluster_th": cluster_th,
"protocol": protocol,
"dc": not preproc_params.get(
"disable_distortion_correction", False),
"realign": preproc_params["realign"],
"coregister": preproc_params["coregister"],
"segment": preproc_params["segment"],
"normalize": preproc_params["normalize"],
'func_write_voxel_sizes': preproc_params[
subject_id="sub001",
start_time=stats_start_time,
title="GLM for subject %s" % subject_dic['session_id'],
# additional ``kwargs`` for more informative report
paradigm=paradigm.__dict__,
TR=tr,
n_scans=n_scans,
hfcut=hfcut,
frametimes=frametimes,
drift_model=drift_model,
hrf_model=hrf_model,
)
ProgressReport().finish_dir(subject_session_output_dir)
print "Statistic report written to %s\r\n" % stats_report_filename
return z_maps
if __name__ == '__main__':
#File containing configuration for preprocessing the data
jobfile=sys.argv[1]
list_subjects, params = _generate_preproc_pipeline(jobfile)
#Preproc
subject_data = preproc(jobfile)
#first_level
for dict_subject in subject_data:
dict_subject = dict_subject.__dict__
z_maps = first_level(dict_subject)