/
base.py
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/
base.py
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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
fMRIPrep base processing workflows
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autofunction:: init_fmriprep_wf
.. autofunction:: init_single_subject_wf
"""
import sys
import os
from copy import deepcopy
from nipype.pipeline import engine as pe
from nipype.interfaces import utility as niu
from .. import config
from ..interfaces import SubjectSummary, AboutSummary, DerivativesDataSink
from .bold import init_func_preproc_wf
def init_fmriprep_wf():
"""
Build *fMRIPrep*'s pipeline.
This workflow organizes the execution of FMRIPREP, with a sub-workflow for
each subject.
If FreeSurfer's ``recon-all`` is to be run, a corresponding folder is created
and populated with any needed template subjects under the derivatives folder.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.tests import mock_config
from fmriprep.workflows.base import init_fmriprep_wf
with mock_config():
wf = init_fmriprep_wf()
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.bids import BIDSFreeSurferDir
fmriprep_wf = Workflow(name='fmriprep_wf')
fmriprep_wf.base_dir = config.execution.work_dir
freesurfer = config.workflow.run_reconall
if freesurfer:
fsdir = pe.Node(
BIDSFreeSurferDir(
derivatives=config.execution.output_dir,
freesurfer_home=os.getenv('FREESURFER_HOME'),
spaces=config.workflow.spaces.get_fs_spaces()),
name='fsdir_run_%s' % config.execution.run_uuid.replace('-', '_'),
run_without_submitting=True)
if config.execution.fs_subjects_dir is not None:
fsdir.inputs.subjects_dir = str(config.execution.fs_subjects_dir.absolute())
for subject_id in config.execution.participant_label:
single_subject_wf = init_single_subject_wf(subject_id)
single_subject_wf.config['execution']['crashdump_dir'] = str(
config.execution.output_dir / "fmriprep" / "-".join(("sub", subject_id))
/ "log" / config.execution.run_uuid
)
for node in single_subject_wf._get_all_nodes():
node.config = deepcopy(single_subject_wf.config)
if freesurfer:
fmriprep_wf.connect(fsdir, 'subjects_dir',
single_subject_wf, 'inputnode.subjects_dir')
else:
fmriprep_wf.add_nodes([single_subject_wf])
# Dump a copy of the config file into the log directory
log_dir = config.execution.output_dir / 'fmriprep' / 'sub-{}'.format(subject_id) \
/ 'log' / config.execution.run_uuid
log_dir.mkdir(exist_ok=True, parents=True)
config.to_filename(log_dir / 'fmriprep.toml')
return fmriprep_wf
def init_single_subject_wf(subject_id):
"""
Organize the preprocessing pipeline for a single subject.
It collects and reports information about the subject, and prepares
sub-workflows to perform anatomical and functional preprocessing.
Anatomical preprocessing is performed in a single workflow, regardless of
the number of sessions.
Functional preprocessing is performed using a separate workflow for each
individual BOLD series.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.tests import mock_config
from fmriprep.workflows.base import init_single_subject_wf
with mock_config():
wf = init_single_subject_wf('01')
Parameters
----------
subject_id : :obj:`str`
Subject label for this single-subject workflow.
Inputs
------
subjects_dir : :obj:`str`
FreeSurfer's ``$SUBJECTS_DIR``.
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.bids import BIDSInfo, BIDSDataGrabber
from niworkflows.interfaces.nilearn import NILEARN_VERSION
from niworkflows.utils.bids import collect_data
from niworkflows.utils.misc import fix_multi_T1w_source_name
from niworkflows.utils.spaces import Reference
from smriprep.workflows.anatomical import init_anat_preproc_wf
name = "single_subject_%s_wf" % subject_id
subject_data = collect_data(
config.execution.layout,
subject_id,
config.execution.task_id,
config.execution.echo_idx,
bids_filters=config.execution.bids_filters)[0]
if 'flair' in config.workflow.ignore:
subject_data['flair'] = []
if 't2w' in config.workflow.ignore:
subject_data['t2w'] = []
anat_only = config.workflow.anat_only
anat_derivatives = config.execution.anat_derivatives
spaces = config.workflow.spaces
# Make sure we always go through these two checks
if not anat_only and not subject_data['bold']:
task_id = config.execution.task_id
raise RuntimeError(
"No BOLD images found for participant {} and task {}. "
"All workflows require BOLD images.".format(
subject_id, task_id if task_id else '<all>')
)
if anat_derivatives:
from smriprep.utils.bids import collect_derivatives
std_spaces = spaces.get_spaces(nonstandard=False, dim=(3,))
anat_derivatives = collect_derivatives(
anat_derivatives.absolute(),
subject_id,
std_spaces,
config.workflow.run_reconall,
)
if anat_derivatives is None:
config.loggers.workflow.warning(f"""\
Attempted to access pre-existing anatomical derivatives at \
<{config.execution.anat_derivatives}>, however not all expectations of fMRIPrep \
were met (for participant <{subject_id}>, spaces <{', '.join(std_spaces)}>, \
reconall <{config.workflow.run_reconall}>).""")
if not anat_derivatives and not subject_data['t1w']:
raise Exception("No T1w images found for participant {}. "
"All workflows require T1w images.".format(subject_id))
workflow = Workflow(name=name)
workflow.__desc__ = """
Results included in this manuscript come from preprocessing
performed using *fMRIPrep* {fmriprep_ver}
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* {nipype_ver}
(@nipype1; @nipype2; RRID:SCR_002502).
""".format(fmriprep_ver=config.environment.version,
nipype_ver=config.environment.nipype_version)
workflow.__postdesc__ = """
Many internal operations of *fMRIPrep* use
*Nilearn* {nilearn_ver} [@nilearn, RRID:SCR_001362],
mostly within the functional processing workflow.
For more details of the pipeline, see [the section corresponding
to workflows in *fMRIPrep*'s documentation]\
(https://fmriprep.readthedocs.io/en/latest/workflows.html \
"FMRIPrep's documentation").
### 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)
output_dir = str(config.execution.output_dir)
inputnode = pe.Node(niu.IdentityInterface(fields=['subjects_dir']),
name='inputnode')
bidssrc = pe.Node(BIDSDataGrabber(subject_data=subject_data,
anat_only=anat_only,
anat_derivatives=anat_derivatives,
subject_id=subject_id),
name='bidssrc')
bids_info = pe.Node(BIDSInfo(
bids_dir=config.execution.bids_dir, bids_validate=False), name='bids_info')
summary = pe.Node(SubjectSummary(std_spaces=spaces.get_spaces(nonstandard=False),
nstd_spaces=spaces.get_spaces(standard=False)),
name='summary', run_without_submitting=True)
about = pe.Node(AboutSummary(version=config.environment.version,
command=' '.join(sys.argv)),
name='about', run_without_submitting=True)
ds_report_summary = pe.Node(
DerivativesDataSink(base_directory=output_dir, desc='summary', datatype="figures",
dismiss_entities=("echo",)),
name='ds_report_summary', run_without_submitting=True)
ds_report_about = pe.Node(
DerivativesDataSink(base_directory=output_dir, desc='about', datatype="figures",
dismiss_entities=("echo",)),
name='ds_report_about', run_without_submitting=True)
# Preprocessing of T1w (includes registration to MNI)
anat_preproc_wf = init_anat_preproc_wf(
bids_root=str(config.execution.bids_dir),
debug=config.execution.sloppy,
existing_derivatives=anat_derivatives,
freesurfer=config.workflow.run_reconall,
hires=config.workflow.hires,
longitudinal=config.workflow.longitudinal,
omp_nthreads=config.nipype.omp_nthreads,
output_dir=output_dir,
skull_strip_fixed_seed=config.workflow.skull_strip_fixed_seed,
skull_strip_mode=config.workflow.skull_strip_t1w,
skull_strip_template=Reference.from_string(
config.workflow.skull_strip_template)[0],
spaces=spaces,
t1w=subject_data['t1w'],
)
workflow.connect([
(inputnode, anat_preproc_wf, [('subjects_dir', 'inputnode.subjects_dir')]),
(inputnode, summary, [('subjects_dir', 'subjects_dir')]),
(bidssrc, summary, [('bold', 'bold')]),
(bids_info, summary, [('subject', 'subject_id')]),
(bids_info, anat_preproc_wf, [(('subject', _prefix), 'inputnode.subject_id')]),
(bidssrc, anat_preproc_wf, [('t1w', 'inputnode.t1w'),
('t2w', 'inputnode.t2w'),
('roi', 'inputnode.roi'),
('flair', 'inputnode.flair')]),
(summary, ds_report_summary, [('out_report', 'in_file')]),
(about, ds_report_about, [('out_report', 'in_file')]),
])
if not anat_derivatives:
workflow.connect([
(bidssrc, bids_info, [(('t1w', fix_multi_T1w_source_name), 'in_file')]),
(bidssrc, summary, [('t1w', 't1w'),
('t2w', 't2w')]),
(bidssrc, ds_report_summary, [(('t1w', fix_multi_T1w_source_name), 'source_file')]),
(bidssrc, ds_report_about, [(('t1w', fix_multi_T1w_source_name), 'source_file')]),
])
else:
workflow.connect([
(bidssrc, bids_info, [(('bold', fix_multi_T1w_source_name), 'in_file')]),
(anat_preproc_wf, summary, [('outputnode.t1w_preproc', 't1w')]),
(anat_preproc_wf, ds_report_summary, [('outputnode.t1w_preproc', 'source_file')]),
(anat_preproc_wf, ds_report_about, [('outputnode.t1w_preproc', 'source_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 = 'fmriprep'
if anat_only:
return workflow
# Append the functional section to the existing anatomical exerpt
# That way we do not need to stream down the number of bold datasets
anat_preproc_wf.__postdesc__ = (anat_preproc_wf.__postdesc__ or '') + """
Functional data preprocessing
: For each of the {num_bold} BOLD runs found per subject (across all
tasks and sessions), the following preprocessing was performed.
""".format(num_bold=len(subject_data['bold']))
for bold_file in subject_data['bold']:
func_preproc_wf = init_func_preproc_wf(bold_file)
workflow.connect([
(anat_preproc_wf, func_preproc_wf,
[('outputnode.t1w_preproc', 'inputnode.t1w_preproc'),
('outputnode.t1w_mask', 'inputnode.t1w_mask'),
('outputnode.t1w_dseg', 'inputnode.t1w_dseg'),
('outputnode.t1w_aseg', 'inputnode.t1w_aseg'),
('outputnode.t1w_aparc', 'inputnode.t1w_aparc'),
('outputnode.t1w_tpms', 'inputnode.t1w_tpms'),
('outputnode.template', 'inputnode.template'),
('outputnode.anat2std_xfm', 'inputnode.anat2std_xfm'),
('outputnode.std2anat_xfm', 'inputnode.std2anat_xfm'),
# Undefined if --fs-no-reconall, but this is safe
('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.t1w2fsnative_xfm', 'inputnode.t1w2fsnative_xfm'),
('outputnode.fsnative2t1w_xfm', 'inputnode.fsnative2t1w_xfm')]),
])
return workflow
def _prefix(subid):
return subid if subid.startswith('sub-') else f'sub-{subid}'
def _pop(inlist):
if isinstance(inlist, (list, tuple)):
return inlist[0]
return inlist