def _run_interface(self, runtime):
if isdefined(self.inputs.spec):
spec = self.inputs.spec
else:
spec = pkgr.resource_filename('fmriprep', 'data/bids.json')
bids_dict = collect_bids_data(self.inputs.bids_root,
self.inputs.subject_id,
spec=spec)
self._results['out_dict'] = bids_dict
self._results['t1w'] = bids_dict['t1w']
if not bids_dict['t1w']:
raise FileNotFoundError(
'No T1w images found for subject sub-{}, bids_root={}'.format(
self.inputs.subject_id, self.inputs.bids_root))
self._results['func'] = bids_dict['func']
if not bids_dict['func']:
raise FileNotFoundError(
'No functional images found for subject sub-{}, bids_root={}'.
format(self.inputs.subject_id, self.inputs.bids_root))
for imtype in ['fmap', 'sbref']:
self._results[imtype] = bids_dict[imtype]
if not bids_dict[imtype]:
LOGGER.warn(
'No \'{}\' images found for sub-{} in BIDS dataset ({})'.
format(imtype, self.inputs.subject_id,
self.inputs.bids_root))
return runtime
def setUp(self):
try:
all_subjects = misc.collect_bids_data(c.DATASET)
self.imaging_data = {
self.subject_id: all_subjects[self.subject_id]
}
except Exception as e:
url = "http://googledrive.com/host/0BxI12kyv2olZbl9GN3BIOVVoelE"
raise_from(Exception("Couldn't find data at " + c.DATASET +
". Download from " + url), e)
def setUp(cls):
if not os.path.exists(cls.fake_data_root_location):
print("Downloading test data")
url = "http://github.com/chrisfilo/BIDS-examples-1/archive/enh/ds054.zip"
with urlopen(url) as zipresp:
with ZipFile(BytesIO(zipresp.read())) as zfile:
zfile.extractall(cls.fake_data_root_location)
cls.imaging_data = {
cls.subject_id: misc.collect_bids_data(os.path.join(cls.fake_ds_location),
cls.subject_id)
}
def setUp(cls):
if not os.path.exists(cls.fake_data_root_location):
print("Downloading test data")
url = "http://github.com/chrisfilo/BIDS-examples-1/archive/enh/ds054.zip"
with urlopen(url) as zipresp:
with ZipFile(BytesIO(zipresp.read())) as zfile:
zfile.extractall(cls.fake_data_root_location)
cls.imaging_data = {
cls.subject_id:
misc.collect_bids_data(os.path.join(cls.fake_ds_location),
cls.subject_id)
}
def base_workflow_generator(subject_id, task_id, settings):
subject_data = collect_bids_data(settings['bids_root'], subject_id, task_id)
settings["biggest_epi_file_size_gb"] = get_biggest_epi_file_size_gb(subject_data['func'])
if subject_data['t1w'] == []:
raise Exception("No T1w images found for participant %s. All workflows require T1w images."%subject_id)
if (subject_data['sbref'] != [] and settings['workflow_type'] == "auto") or settings['workflow_type'] == "ds054":
return wf_ds054_type(subject_data, settings, name=subject_id)
elif (subject_data['sbref'] == [] and settings['workflow_type'] == "auto") or settings['workflow_type'] == "ds005":
return wf_ds005_type(subject_data, settings, name=subject_id)
else:
raise Exception("Could not figure out what kind of workflow to run for this dataset.")
def init_single_subject_wf(subject_id, task_id, name, ignore, debug, anat_only,
omp_nthreads, skull_strip_ants, reportlets_dir,
output_dir, bids_dir, freesurfer, output_spaces,
template, hires, bold2t1w_dof, fmap_bspline,
fmap_demean, use_syn, force_syn, output_grid_ref,
use_aroma, ignore_aroma_err):
"""
The adaptable fMRI preprocessing workflow
"""
if name == 'single_subject_wf':
# for documentation purposes
subject_data = {
'func': ['/completely/made/up/path/sub-01_task-nback_bold.nii.gz']
}
layout = None
else:
layout = BIDSLayout(bids_dir)
subject_data = collect_bids_data(bids_dir, subject_id, task_id)
if not anat_only and subject_data['func'] == []:
raise Exception(
"No BOLD images found for participant {} and task {}. "
"All workflows require BOLD images.".format(
subject_id, task_id if task_id else '<all>'))
if subject_data['t1w'] == []:
raise Exception(
"No T1w images found for participant {}. "
"All workflows require T1w images.".format(subject_id))
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['subjects_dir']),
name='inputnode')
bidssrc = pe.Node(BIDSDataGrabber(subject_data=subject_data,
anat_only=anat_only),
name='bidssrc')
# Preprocessing of T1w (includes registration to MNI)
anat_preproc_wf = init_anat_preproc_wf(name="anat_preproc_wf",
skull_strip_ants=skull_strip_ants,
output_spaces=output_spaces,
template=template,
debug=debug,
omp_nthreads=omp_nthreads,
freesurfer=freesurfer,
hires=hires,
reportlets_dir=reportlets_dir,
output_dir=output_dir)
workflow.connect([
(inputnode, anat_preproc_wf, [('subjects_dir',
'inputnode.subjects_dir')]),
(bidssrc, anat_preproc_wf, [('t1w', 'inputnode.t1w'),
('t2w', 'inputnode.t2w')]),
])
if anat_only:
return workflow
for bold_file in subject_data['func']:
func_preproc_wf = init_func_preproc_wf(
bold_file=bold_file,
layout=layout,
ignore=ignore,
freesurfer=freesurfer,
bold2t1w_dof=bold2t1w_dof,
reportlets_dir=reportlets_dir,
output_spaces=output_spaces,
template=template,
output_dir=output_dir,
omp_nthreads=omp_nthreads,
fmap_bspline=fmap_bspline,
fmap_demean=fmap_demean,
use_syn=use_syn,
force_syn=force_syn,
debug=debug,
output_grid_ref=output_grid_ref,
use_aroma=use_aroma,
ignore_aroma_err=ignore_aroma_err)
workflow.connect([(anat_preproc_wf, func_preproc_wf,
[('outputnode.t1_preproc', 'inputnode.t1_preproc'),
('outputnode.t1_brain', 'inputnode.t1_brain'),
('outputnode.t1_mask', 'inputnode.t1_mask'),
('outputnode.t1_seg', 'inputnode.t1_seg'),
('outputnode.t1_tpms', 'inputnode.t1_tpms'),
('outputnode.t1_2_mni_forward_transform',
'inputnode.t1_2_mni_forward_transform'),
('outputnode.t1_2_mni_reverse_transform',
'inputnode.t1_2_mni_reverse_transform')])])
if freesurfer:
workflow.connect([
(anat_preproc_wf, func_preproc_wf,
[('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.fs_2_t1_transform',
'inputnode.fs_2_t1_transform')]),
])
return workflow
def init_single_subject_wf(subject_id, task_id, name,
ignore, debug, anat_only, omp_nthreads,
skull_strip_ants, reportlets_dir, output_dir, bids_dir,
freesurfer, output_spaces, template, hires,
bold2t1w_dof, fmap_bspline, fmap_demean, use_syn, force_syn,
output_grid_ref, use_aroma, ignore_aroma_err):
"""
The adaptable fMRI preprocessing workflow
"""
if name == 'single_subject_wf':
# for documentation purposes
subject_data = {'func': ['/completely/made/up/path/sub-01_task-nback_bold.nii.gz']}
layout = None
else:
layout = BIDSLayout(bids_dir)
subject_data = collect_bids_data(bids_dir, subject_id, task_id)
if not anat_only and subject_data['func'] == []:
raise Exception("No BOLD images found for participant {} and task {}. "
"All workflows require BOLD images.".format(
subject_id, task_id if task_id else '<all>'))
if not subject_data['t1w']:
raise Exception("No T1w images found for participant {}. "
"All workflows require T1w images.".format(subject_id))
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['subjects_dir']),
name='inputnode')
bidssrc = pe.Node(BIDSDataGrabber(subject_data=subject_data, anat_only=anat_only),
name='bidssrc')
# Preprocessing of T1w (includes registration to MNI)
anat_preproc_wf = init_anat_preproc_wf(name="anat_preproc_wf",
skull_strip_ants=skull_strip_ants,
output_spaces=output_spaces,
template=template,
debug=debug,
omp_nthreads=omp_nthreads,
freesurfer=freesurfer,
hires=hires,
reportlets_dir=reportlets_dir,
output_dir=output_dir)
workflow.connect([
(inputnode, anat_preproc_wf, [('subjects_dir', 'inputnode.subjects_dir')]),
(bidssrc, anat_preproc_wf, [('t1w', 'inputnode.t1w'),
('t2w', 'inputnode.t2w')]),
])
if anat_only:
return workflow
for bold_file in subject_data['func']:
func_preproc_wf = init_func_preproc_wf(bold_file=bold_file,
layout=layout,
ignore=ignore,
freesurfer=freesurfer,
bold2t1w_dof=bold2t1w_dof,
reportlets_dir=reportlets_dir,
output_spaces=output_spaces,
template=template,
output_dir=output_dir,
omp_nthreads=omp_nthreads,
fmap_bspline=fmap_bspline,
fmap_demean=fmap_demean,
use_syn=use_syn,
force_syn=force_syn,
debug=debug,
output_grid_ref=output_grid_ref,
use_aroma=use_aroma,
ignore_aroma_err=ignore_aroma_err)
workflow.connect([
(anat_preproc_wf, func_preproc_wf,
[('outputnode.t1_preproc', 'inputnode.t1_preproc'),
('outputnode.t1_brain', 'inputnode.t1_brain'),
('outputnode.t1_mask', 'inputnode.t1_mask'),
('outputnode.t1_seg', 'inputnode.t1_seg'),
('outputnode.t1_tpms', 'inputnode.t1_tpms'),
('outputnode.t1_2_mni_forward_transform', 'inputnode.t1_2_mni_forward_transform'),
('outputnode.t1_2_mni_reverse_transform', 'inputnode.t1_2_mni_reverse_transform')])
])
if freesurfer:
workflow.connect([
(anat_preproc_wf, func_preproc_wf,
[('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.fs_2_t1_transform', 'inputnode.fs_2_t1_transform')]),
])
return workflow