Update.

src/fmripost_rapidtide/workflows/base.py

@@ -134,16 +134,16 @@
 
     """
     from bids.utils import listify
     from nipype.interfaces import utility as niu
     from niworkflows.engine.workflows import LiterateWorkflow as Workflow
     from niworkflows.interfaces.bids import BIDSInfo
     from niworkflows.interfaces.nilearn import NILEARN_VERSION
 
     from fmripost_rapidtide.interfaces.bids import DerivativesDataSink
     from fmripost_rapidtide.interfaces.nilearn import MeanImage
     from fmripost_rapidtide.interfaces.reportlets import AboutSummary, SubjectSummary
     from fmripost_rapidtide.utils.bids import collect_derivatives
     from fmripost_rapidtide.workflows.rapidtide import init_denoise_single_run_wf
 
     spaces = config.workflow.spaces
 
@@ -201,7 +201,7 @@
             spaces=None,
         )
         # Patch standard-space BOLD files into 'bold' key
         subject_data['bold'] = listify(subject_data['bold_boldref'])
 
     if not subject_data['bold_boldref']:
         task_id = config.execution.task_id
@@ -282,38 +282,38 @@
 """
     workflow.__desc__ += func_pre_desc
 
     denoise_within_run = (len(subject_data['bold']) > 1) and not config.workflow.average_over_runs
     if not denoise_within_run:
         # Average the lag map across runs before denoising
         merge_lag_maps = pe.Node(
             niu.Merge(len(subject_data['bold'])),
             name='merge_lag_maps',
         )
         average_lag_map = pe.Node(
             MeanImage(),
             name='average_lag_map',
         )
 
     for i_run, bold_file in enumerate(subject_data['bold']):
         single_run_wf = init_single_run_wf(bold_file, denoise=denoise_within_run)
         workflow.add_nodes([single_run_wf])
 
         if not denoise_within_run:
             # Denoise the BOLD data using the mean lag map
             workflow.connect([
                 (single_run_wf, merge_lag_maps, [('outputnode.delay_map', f'in{i_run + 1}')]),
                 (merge_lag_maps, average_lag_map, [('out', 'in_file')]),
             ])  # fmt:skip
 
             denoise_single_run_wf = init_denoise_single_run_wf(bold_file)
             workflow.connect([
                 (single_run_wf, denoise_single_run_wf, [
                     ('outputnode.regressor', 'inputnode.regressor'),
                 ]),
                 (average_lag_map, denoise_single_run_wf, [('out_file', 'inputnode.delay_map')]),
             ])  # fmt:skip
 
     return workflow
 
 
 def init_single_run_wf(bold_file, denoise=True):
@@ -322,11 +322,11 @@
     from nipype.interfaces import utility as niu
     from niworkflows.engine.workflows import LiterateWorkflow as Workflow
 
     from fmripost_rapidtide.interfaces.misc import ApplyTransforms
     from fmripost_rapidtide.utils.bids import collect_derivatives, extract_entities
     from fmripost_rapidtide.workflows.confounds import init_confounds_wf
     from fmripost_rapidtide.workflows.outputs import init_func_fit_reports_wf
     from fmripost_rapidtide.workflows.rapidtide import (
         init_denoise_single_run_wf,
         init_rapidtide_wf,
     )
@@ -414,13 +414,13 @@
     rapidtide_wf.inputs.inputnode.confounds = functional_cache['confounds']
     rapidtide_wf.inputs.inputnode.skip_vols = skip_vols
 
     boldref_buffer = pe.Node(
         niu.IdentityInterface(fields=['bold', 'bold_mask']),
         name='boldref_buffer',
     )
 
     # Warp the dseg from anatomical space to boldref space
     dseg_to_boldref = pe.Node(
         ApplyTransforms(
             interpolation='GenericLabel',
             input_image=functional_cache['anat_dseg'],
@@ -465,7 +465,7 @@
         else:
             workflow.connect([(validate_bold, stc_buffer, [('out_file', 'bold_file')])])
 
         bold_boldref_wf = init_bold_volumetric_resample_wf(
             metadata=bold_metadata,
             fieldmap_id=None,  # XXX: Ignoring the field map for now
             omp_nthreads=omp_nthreads,
@@ -473,13 +473,13 @@
             jacobian='fmap-jacobian' not in config.workflow.ignore,
             name='bold_MNI6_wf',
         )
         bold_boldref_wf.inputs.inputnode.motion_xfm = functional_cache['hmc']
         bold_boldref_wf.inputs.inputnode.boldref2fmap_xfm = functional_cache['boldref2fmap']
         bold_boldref_wf.inputs.inputnode.resolution = 'native'
         # use mask as boldref?
         bold_boldref_wf.inputs.inputnode.bold_ref_file = functional_cache['boldref']
         bold_boldref_wf.inputs.inputnode.target_mask = functional_cache['bold_mask_boldref']
         bold_boldref_wf.inputs.inputnode.target_ref_file = functional_cache['boldref']
 
         workflow.connect([
             # Resample BOLD to MNI152NLin6Asym, may duplicate bold_std_wf above
@@ -497,8 +497,8 @@
         workflow.__desc__ += """\
 Preprocessed BOLD series in boldref:res-native space were collected for rapidtide denoising.
 """
         boldref_buffer.inputs.bold = functional_cache['bold_boldref']
         boldref_buffer.inputs.bold_mask = functional_cache['bold_mask_boldref']
 
     else:
         raise ValueError('No valid BOLD series found for rapidtide denoising.')
@@ -513,8 +513,8 @@
 
     # Denoising workflow
     if denoise:
         denoise_single_run_wf = init_denoise_single_run_wf(bold_file)
         workflow.connect([
             (rapidtide_wf, denoise_single_run_wf, [
                 ('outputnode.regressor', 'inputnode.regressor'),
                 ('outputnode.delay_map', 'inputnode.delay_map'),
@@ -526,7 +526,7 @@
     func_fit_reports_wf.inputs.inputnode.source_file = bold_file
     func_fit_reports_wf.inputs.inputnode.anat2std_xfm = functional_cache['anat2mni152nlin6asym']
     func_fit_reports_wf.inputs.inputnode.anat_dseg = functional_cache['anat_dseg']
     workflow.connect([(boldref_buffer, func_fit_reports_wf, [('bold', 'inputnode.bold_mni6')])])
 
     # Generate confounds
     confounds_wf = init_confounds_wf(
@@ -544,24 +544,20 @@
         ]),
     ])  # fmt:skip
 
     return clean_datasinks(workflow, bold_file=bold_file)
 
 
-def _prefix(subid):
-    return subid if subid.startswith('sub-') else f'sub-{subid}'
-
-
 def clean_datasinks(workflow: pe.Workflow, bold_file: str) -> pe.Workflow:
     """Overwrite attributes of DataSinks."""
     for node in workflow.list_node_names():
         node_name = node.split('.')[-1]
         if node_name.startswith('ds_'):
             workflow.get_node(node).interface.out_path_base = ''
             workflow.get_node(node).inputs.base_directory = str(config.execution.output_dir)
             workflow.get_node(node).inputs.source_file = bold_file
 
         if node_name.startswith('ds_report_'):
             workflow.get_node(node).inputs.datatype = 'figures'
 
     return workflow
 

src/fmripost_rapidtide/workflows/rapidtide.py

@@ -117,8 +117,6 @@
             fields=[
                 'delay_map',
                 'regressor_file',
-                'confound_regressed',
-                'denoised',
             ],
         ),
         name='outputnode',
@@ -191,7 +189,7 @@
         ]),
     ])  # fmt:skip
 
     ds_delay_map = pe.Node(
         DerivativesDataSink(
             compress=True,
             desc='delay',
@@ -200,7 +198,7 @@
         name='ds_delay_map',
         run_without_submitting=True,
     )
     workflow.connect([
         (rapidtide, ds_delay_map, [
             ('maxtimemap', 'in_file'),
             ('maxtimemap_json', 'meta_dict'),
@@ -208,7 +206,7 @@
         (ds_delay_map, outputnode, [('out_file', 'delay_map')]),
     ])  # fmt:skip
 
     ds_regressor = pe.Node(
         DerivativesDataSink(
             desc='regressor',
             suffix='timeseries',
@@ -217,7 +215,7 @@
         name='ds_regressor',
         run_without_submitting=True,
     )
     workflow.connect([
         (rapidtide, ds_regressor, [
             ('lagtcgenerator', 'in_file'),
             ('lagtcgenerator_json', 'meta_dict'),
@@ -225,7 +223,7 @@
         (ds_regressor, outputnode, [('out_file', 'regressor_file')]),
     ])  # fmt:skip
 
     return workflow
 
 
 def init_denoise_single_run_wf(
@@ -236,17 +234,17 @@
 ):
     """Denoise a single run using rapidtide."""
 
     from niworkflows.engine.workflows import LiterateWorkflow as Workflow
 
     from fmripost_rapidtide.interfaces.bids import DerivativesDataSink
     from fmripost_rapidtide.interfaces.rapidtide import RetroGLM
 
     workflow = Workflow(name=_get_wf_name(bold_file, 'rapidtide_denoise'))
     workflow.__postdesc__ = """\
 Identification and removal of traveling wave artifacts was performed using rapidtide.
 """
 
     inputnode = pe.Node(
         niu.IdentityInterface(
             fields=[
                 'bold',
@@ -259,11 +257,11 @@
         ),
         name='inputnode',
     )
     denoise_bold = pe.Node(
         RetroGLM(),
         name='denoise_bold',
     )
     workflow.connect([
         (inputnode, denoise_bold, [
             ('bold', 'in_file'),
             ('bold_mask', 'brainmask'),
@@ -274,7 +272,7 @@
         ]),
     ])  # fmt:skip
 
     ds_denoised_bold = pe.Node(
         DerivativesDataSink(
             compress=True,
             desc='denoised',
@@ -283,7 +281,7 @@
         name='ds_denoised_bold',
         run_without_submitting=True,
     )
     workflow.connect([
         (denoise_bold, ds_denoised_bold, [
             ('denoised', 'in_file'),
             ('denoised_json', 'meta_dict'),