def init_single_subject_wf(
opts: ArgumentParser,
layout: BIDSLayout,
run_uuid: str,
work_dir: str,
output_dir: str,
name: str,
subject_id: str,
reportlets_dir: str,
):
import nilearn
if name in ('single_subject_wf', 'single_subject_test_wf'):
# for documentation purposes
subject_data = {
'bold': ['/completely/made/up/path/sub-01_task-nback_bold.nii.gz']
}
else:
subject_data = collect_data(layout, subject_id, None)[0]
workflow = Workflow(name=name)
workflow.base_dir = work_dir
workflow.__desc__ = """
Results included in this manuscript come from processing
performed using *atlasTransform* {atlasTransform_ver}
(@atlasTransform1; @atlasTransform; RRID:some.id),
which is based on *Nipype* {nipype_ver}
(@nipype1; @nipype2; RRID:SCR_002502).
""".format(atlasTransform_ver=__version__, nipype_ver=nipype_ver)
workflow.__postdesc__ = """
Many internal operations of *atlasTransform* use
*Nilearn* {nilearn_ver} [@nilearn, RRID:SCR_001362].
### Copyright Waiver
The above boilerplate text was automatically generated by fMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0]\
(https://creativecommons.org/publicdomain/zero/1.0/) license.
### References
""".format(nilearn_ver=nilearn.version.__version__)
inputnode = pe.Node(niu.IdentityInterface(fields=['subjects_dir']),
name='inputnode')
#
# # require_masks = opts.source_format == 'bold'
# bidssrc = pe.Node(BIDSPlusDataGrabber(subject_data=subject_data, require_masks=False),
# name='bidssrc')
#
# # bids_info = pe.Node(BIDSInfo(
# # bids_dir=layout.root, bids_validate=False), name='bids_info')
#
# summary = pe.Node(SubjectSummary(),
# name='summary', run_without_submitting=True)
#
# about = pe.Node(AboutSummary(version=__version__,
# command=' '.join(sys.argv)),
# name='about', run_without_submitting=True)
#
# ds_report_summary = pe.Node(
# DerivativesDataSink(base_directory=reportlets_dir,
# desc='summary', keep_dtype=True),
# name='ds_report_summary', run_without_submitting=True)
#
# ds_report_about = pe.Node(
# DerivativesDataSink(base_directory=reportlets_dir,
# desc='about', keep_dtype=True),
# name='ds_report_about', run_without_submitting=True)
# Preprocessing of T1w (includes registration to MNI)
# workflow.connect([
# # (bidssrc, bids_info, [('bold', 'in_file')]),
# (inputnode, summary, [('subjects_dir', 'subjects_dir')]),
# (bidssrc, summary, [('t1w', 't1w'),
# ('t2w', 't2w'),
# ('bold', 'bold')]),
# # (bids_info, summary, [('subject', 'subject_id')]),
# (bidssrc, ds_report_summary, [('bold', 'source_file')]),
# # (summary, ds_report_summary, [('out_report', 'in_file')]),
# (bidssrc, ds_report_about, [('bold', 'source_file')]),
# (about, ds_report_about, [('out_report', 'in_file')]),
# ])
# Overwrite ``out_path_base`` of smriprep's DataSinks
for node in workflow.list_node_names():
if node.split('.')[-1].startswith('ds_'):
workflow.get_node(node).interface.out_path_base = 'atlasTransform'
for i in range(len(subject_data[opts.source])):
transform_wf = init_atlas_transform_workflow(
nifti=subject_data[opts.source][i],
atlas_name=opts.atlas_name,
options=opts,
bids_dir=str(layout.root),
name='atlas_transform_%d_wf' % i)
workflow.connect([
(inputnode, transform_wf, [('subjects_dir',
'inputnode.subjects_dir')]),
])
# outputs_wf = init_datasink_wf(bids_root=str(layout.root), output_dir=str(opts.output_dir), atlas_name=opts.atlas_name, name='ds_%d_wf' % i)
#
# workflow.connect([(transform_wf,outputs_wf,[('outputnode.transformed','inputnode.source_file')])])
# # outputs_wf.inputs.source_file = subject_data['csv'][i] if subject_data.__contains__('mask') else subject_data['bold'][i]
#
# workflow.connect([(transform_wf, outputs_wf, [
# ('outputnode.transformed', 'inputnode.transformed')
# ])])
return workflow
