🚧 [WIP] 🚑️ Hotfix 1.8.7: Restore longitudinal functionality

[shnizzedy]

Fixes

Fixes #2159 by @Shinwon Park
Related to Longitudinal project spec by @e-kenneally

Description

• Adds T1w-brain-template to warp_longitudinal_T1w_to_template's inputs and updates

  C-PAC/CPAC/longitudinal_pipeline/longitudinal_workflow.py

  Line 314 in 96db8b0

  node, out = strat_pool.get_data("T1w_brain_template")

  calls to

  C-PAC/CPAC/longitudinal_pipeline/longitudinal_workflow.py

  Line 316 in 16cebc1

  node, out = strat_pool.get_data("T1w-brain-template")

  calls.
• Generates from-longitudinal_to-T1w_mode-image_desc-linear_xfm on the fly with

  C-PAC/CPAC/longitudinal_pipeline/longitudinal_workflow.py

  Lines 366 to 380 in 16cebc1

  	if strat_pool.check_rpool("from-longitudinal_to-T1w_mode-image_desc-linear_xfm"):
  	xfm_prov = strat_pool.get_cpac_provenance(
  	"from-longitudinal_to-T1w_mode-image_desc-linear_xfm")
  	reg_tool = check_prov_for_regtool(xfm_prov)
  	xfm: tuple[pe.Node, str] = strat_pool.get_data("from-longitudinal_to-T1w_mode-image_desc-linear_xfm")
  	else:
  	xfm_prov = strat_pool.get_cpac_provenance(
  	"from-T1w_to-longitudinal_mode-image_desc-linear_xfm")
  	reg_tool = check_prov_for_regtool(xfm_prov)
  	# create inverse xfm if we don't have it
  	invt = pe.Node(interface=fsl.ConvertXFM(), name='convert_xfm')
  	invt.inputs.invert_xfm = True
  	wf.connect(
  	*strat_pool.get_data("from-T1w_to-longitudinal_mode-image_desc-linear_xfm"), invt, "in_file")
  	xfm = (invt, "out_file")

  if it doesn't already exist (and it never does).
• Updates each

  C-PAC/CPAC/longitudinal_pipeline/longitudinal_workflow.py

  Line 469 in 96db8b0

  workflow.run()

  to

  C-PAC/CPAC/longitudinal_pipeline/longitudinal_workflow.py

  Lines 491 to 492 in 16cebc1

  	if not dry_run:
  	workflow.run()

  to allow for test_config to run without running the graphs.
• Changes

  C-PAC/CPAC/longitudinal_pipeline/longitudinal_workflow.py

  Lines 475 to 493 in 96db8b0

  	# Now we have all the anat_preproc set up for every session
  	# loop over the different anat preproc strategies
  	strats_brain_dct = {}
  	strats_head_dct = {}
  	for cpac_dir in cpac_dirs:
  	if os.path.isdir(cpac_dir):
  	for filename in os.listdir(cpac_dir):
  	if 'T1w.nii' in filename:
  	for tag in filename.split('_'):
  	if 'desc-' in tag and 'brain' in tag:
  	if tag not in strats_brain_dct:
  	strats_brain_dct[tag] = []
  	strats_brain_dct[tag].append(os.path.join(cpac_dir,
  	filename))
  	if tag not in strats_head_dct:
  	strats_head_dct[tag] = []
  	head_file = filename.replace(tag, 'desc-reorient')
  	strats_head_dct[tag].append(os.path.join(cpac_dir,
  	head_file))

  to

  C-PAC/CPAC/longitudinal_pipeline/longitudinal_workflow.py

  Lines 495 to 502 in 16cebc1

  	# Now we have all the anat_preproc set up for every session
  	# loop over the different anat preproc strategies
  	strats_dct: dict[str, list] = {key: [pool.get_data(key) for pool in pools]
  	for key in ["desc-brain_T1w", "desc-head_T1w"]}
  	# Rename nodes to include session name to avoid duplicates
  	for key in strats_dct:
  	for i, resource in enumerate(strats_dct[key]):
  	resource[0] = resource[0].clone(f"{resource[0].name}_{session_id_list[i]}")

  to get the resources from the ResourcePool instead of the output directory (affording graph-building without running).
• Pulls

  C-PAC/CPAC/longitudinal_pipeline/longitudinal_workflow.py

  Lines 504 to 644 in 16cebc1

  	wf = initialize_nipype_wf(config, sub_list[0],
  	# just grab the first one for the name
  	name=f"template_node_brain")
  	config.pipeline_setup[
  	'pipeline_name'] = f'longitudinal_{orig_pipe_name}'
  	template_node_name = 'longitudinal_anat_template_brain'
  	# This node will generate the longitudinal template (the functions are
  	# in longitudinal_preproc)
  	# Later other algorithms could be added to calculate it, like the
  	# multivariate template from ANTS
  	# It would just require to change it here.
  	template_node = subject_specific_template(
  	workflow_name=template_node_name
  	)
  	template_node.inputs.set(
  	avg_method=config.longitudinal_template_generation[
  	'average_method'],
  	dof=config.longitudinal_template_generation['dof'],
  	interp=config.longitudinal_template_generation['interp'],
  	cost=config.longitudinal_template_generation['cost'],
  	convergence_threshold=config.longitudinal_template_generation[
  	'convergence_threshold'],
  	thread_pool=config.longitudinal_template_generation[
  	'thread_pool'],
  	unique_id_list=list(session_wfs.keys())
  	)
  	num_sessions = len(strats_dct["desc-brain_T1w"])
  	merge_brains = pe.Node(Merge(num_sessions), name="merge_brains")
  	merge_skulls = pe.Node(Merge(num_sessions), name="merge_skulls")
  	for i in list(range(0, num_sessions)):
  	wf.connect(*strats_dct["desc-brain_T1w"][i], merge_brains, f"in{i + 1}")
  	wf.connect(*strats_dct["desc-head_T1w"][i], merge_skulls, f"in{i + 1}")
  	wf.connect(merge_brains, "out", template_node, "input_brain_list")
  	wf.connect(merge_skulls, "out", template_node, "input_skull_list")
  	long_id = f'longitudinal_{subject_id}_strat-desc-brain_T1w'
  	wf, rpool = initiate_rpool(wf, config, part_id=long_id)
  	rpool.set_data("space-longitudinal_desc-brain_T1w",
  	template_node, 'brain_template', {},
  	"", template_node_name)
  	rpool.set_data("space-longitudinal_desc-brain_T1w-template",
  	template_node, 'brain_template', {},
  	"", template_node_name)
  	rpool.set_data("space-longitudinal_desc-reorient_T1w",
  	template_node, 'skull_template', {},
  	"", template_node_name)
  	rpool.set_data("space-longitudinal_desc-reorient_T1w-template",
  	template_node, 'skull_template', {},
  	"", template_node_name)
  	pipeline_blocks = [mask_longitudinal_T1w_brain]
  	pipeline_blocks = build_T1w_registration_stack(rpool, config,
  	pipeline_blocks, space="longitudinal")
  	pipeline_blocks = build_segmentation_stack(rpool, config,
  	pipeline_blocks)
  	wf = connect_pipeline(wf, config, rpool, pipeline_blocks)
  	excl = ['space-longitudinal_desc-brain_T1w',
  	'space-longitudinal_desc-reorient_T1w',
  	'space-longitudinal_desc-brain_mask']
  	rpool.gather_pipes(wf, config, add_excl=excl)
  	if not dry_run:
  	wf.run()
  	# now, just write out a copy of the above to each session
  	config.pipeline_setup['pipeline_name'] = orig_pipe_name
  	for session in sub_list:
  	unique_id = session['unique_id']
  	try:
  	creds_path = session['creds_path']
  	if creds_path and 'none' not in creds_path.lower():
  	if os.path.exists(creds_path):
  	input_creds_path = os.path.abspath(creds_path)
  	else:
  	err_msg = 'Credentials path: "%s" for subject "%s" ' \
  	'session "%s" was not found. Check this path ' \
  	'and try again.' % (creds_path, subject_id,
  	unique_id)
  	raise Exception(err_msg)
  	else:
  	input_creds_path = None
  	except KeyError:
  	input_creds_path = None
  	wf = initialize_nipype_wf(config, sub_list[0])
  	wf, rpool = initiate_rpool(wf, config, session, rpool=rpool)
  	config.pipeline_setup[
  	'pipeline_name'] = f'longitudinal_{orig_pipe_name}'
  	if "derivatives_dir" in session:
  	rpool = ingress_output_dir(
  	wf, config, rpool, long_id, data_paths=session, part_id=subject_id,
  	ses_id=unique_id, creds_path=input_creds_path)
  	select_node_name = f'FSL_select_{unique_id}'
  	select_sess = pe.Node(Function(input_names=['session',
  	'output_brains',
  	'warps'],
  	output_names=['brain_path', 'warp_path'],
  	function=select_session),
  	name=select_node_name)
  	select_sess.inputs.session = unique_id
  	wf.connect(template_node, 'output_brain_list', select_sess,
  	'output_brains')
  	wf.connect(template_node, 'warp_list', select_sess, 'warps')
  	rpool.set_data("space-longitudinal_desc-brain_T1w",
  	select_sess, 'brain_path', {}, "",
  	select_node_name)
  	rpool.set_data("from-T1w_to-longitudinal_mode-image_"
  	"desc-linear_xfm",
  	select_sess, 'warp_path', {}, "",
  	select_node_name)
  	config.pipeline_setup['pipeline_name'] = orig_pipe_name
  	excl = ['space-template_desc-brain_T1w',
  	'space-T1w_desc-brain_mask']
  	rpool.gather_pipes(wf, config, add_excl=excl)
  	if not dry_run:
  	wf.run()

  out of a

  C-PAC/CPAC/longitudinal_pipeline/longitudinal_workflow.py

  Line 495 in 96db8b0

  for strat in strats_brain_dct.keys():

  loop; I don't think we need the loop when we don't have to look in the output directory.
• Updated some node names to include session to allow those nodes to exist in a single graph, e.g.

  flowchart LR
  subgraph separate_graphs ["separate graphs"]
   direction TB
   subgraph ses_1 ["session 1"]
     direction TB
     node_1["node_1"] --> 1_inner["…"]
     1_inner --> 1_out["outputs"]
   end
   subgraph ses_2 ["session 2"]
     direction TB
     node_2["node_1"] --> 2_inner["…"]
     2_inner --> 2_out["outputs"]
   end
   1_out --> subsequent
   2_out --> subsequent
   subgraph subsequent ["…"]
   end
  end
  subgraph combined
    node_3["node_1_ses-1"] --> combined_subsequent["…"]
    node_4["node_1_ses-2"] --> combined_subsequent
  end
  separate_graphs --> combined
  node_1 --> node_3
  node_2 --> node_4
  1_inner --> combined_subsequent
  2_inner --> combined_subsequent
  subsequent --> combined_subsequent
  

  Loading
• Refactors

  C-PAC/CPAC/registration/registration.py

  Lines 3515 to 3529 in 96db8b0

  	@nodeblock(
  	name="transform_whole_head_T1w_to_T1template",
  	config=["registration_workflows", "anatomical_registration"],
  	switch=["run"],
  	inputs=[
  	(
  	"desc-head_T1w",
  	"from-T1w_to-template_mode-image_xfm",
  	"space-template_desc-head_T1w",
  	),
  	"T1w-template",
  	],
  	outputs={"space-template_desc-head_T1w": {"Template": "T1w-template"}},
  	)
  	def warp_wholeheadT1_to_template(wf, cfg, strat_pool, pipe_num, opt=None):

  and

  C-PAC/CPAC/registration/registration.py

  Lines 3570 to 3583 in 96db8b0

  	@nodeblock(
  	name="transform_T1mask_to_T1template",
  	switch=[
  	["registration_workflows", "anatomical_registration", "run"],
  	["anatomical_preproc", "run"],
  	["anatomical_preproc", "brain_extraction", "run"],
  	],
  	inputs=[
  	("space-T1w_desc-brain_mask", "from-T1w_to-template_mode-image_xfm"),
  	"T1w-template",
  	],
  	outputs={"space-template_desc-brain_mask": {"Template": "T1w-template"}},
  	)
  	def warp_T1mask_to_template(wf, cfg, strat_pool, pipe_num, opt=None):

  into

  C-PAC/CPAC/registration/registration.py

  Lines 3516 to 3558 in e7e82b7

  	def warp_to_template(warp_what: Literal["mask", "wholehead"],
  	space_from: Literal["longitudinal", "T1w"]) -> NodeBlockFunction:
  	"""Get a NodeBlockFunction to transform a resource from ``space`` to template.
  	The resource being warped needs to be the first list or string in the tuple
  	in the first position of the decorator's "inputs".
  	"""
  	_decorators = {"mask": {
  	"name": f"transform_{space_from}-mask_to_T1-template",
  	"switch": [
  	["registration_workflows", "anatomical_registration", "run"],
  	["anatomical_preproc", "run"],
  	["anatomical_preproc", "brain_extraction", "run"],
  	],
  	"inputs": [
  	(f"space-{space_from}_desc-brain_mask",
  	f"from-{space_from}_to-template_mode-image_xfm"),
  	"T1w-template",
  	],
  	"outputs": {"space-template_desc-brain_mask": {"Template": "T1w-template"}},
  	}, "wholehead": {
  	"name": f"transform_wholehead_{space_from}_to_T1template",
  	"config": ["registration_workflows", "anatomical_registration"],
  	"switch": ["run"],
  	"inputs": [
  	(
  	["desc-head_T1w", "desc-reorient_T1w"],
  	[f"from-{space_from}_to-template_mode-image_xfm",
  	f"from-{space_from}_to-template_mode-image_xfm"],
  	"space-template_desc-head_T1w",
  	),
  	"T1w-template",
  	],
  	"outputs": {"space-template_desc-head_T1w": {"Template": "T1w-template"}},
  	}}
  	if space_from != "T1w":
  	_decorators[warp_what]["inputs"][0] = tuple((prepend_space(
  	_decorators[warp_what]["inputs"][0][0], space_from),
  	*_decorators[warp_what]["inputs"][0][1:]
  	))
  	@nodeblock(**_decorators[warp_what])
  	def warp_to_template_fxn(wf, cfg, strat_pool, pipe_num, opt=None):

  so we can just have one definition for the four nearly identical functions for [mask, wholehead] × [T1w, longitudinal]
• Updates

  C-PAC/CPAC/pipeline/engine.py

  Lines 1095 to 1097 in 96db8b0

  	# del all_forks
  	for pipe_idx in self.rpool[resource]:
  	pipe_x = self.get_pipe_number(pipe_idx)

  to

  C-PAC/CPAC/pipeline/engine.py

  Lines 1116 to 1121 in 16cb70c

  	# del all_forks
  	for pipe_idx in self.rpool[resource]:
  	try:
  	pipe_x = self.get_pipe_number(pipe_idx)
  	except ValueError:
  	continue

  so already-deleted forks don't cause any issues.
• Adds keyword-only parameter rpool to

  C-PAC/CPAC/pipeline/engine.py

  Lines 2362 to 2364 in 16cb70c

  	def initiate_rpool(
  	wf, cfg, data_paths=None, part_id=None, *, rpool: Optional[ResourcePool] = None
  	):

  so we can initiate a ResourcePool from an existing pool instead of only from {}

  C-PAC/CPAC/pipeline/engine.py

  Line 2403 in 16cb70c

  rpool = ResourcePool(rpool=rpool.rpool if rpool else None, name=unique_id, cfg=cfg)

• Removes https://github.com/FCP-INDI/C-PAC/blob/main/CPAC/registration/registration.py#L2374 from

  C-PAC/CPAC/registration/registration.py

  Lines 2367 to 2374 in 96db8b0

  	if 'space-longitudinal' in brain:
  	for key in outputs:
  	for direction in ['from', 'to']:
  	if f'{direction}-T1w' in key:
  	new_key = key.replace(f'{direction}-T1w',
  	f'{direction}-longitudinal')
  	outputs[new_key] = outputs[key]
  	del outputs[key]

  to keep both T1w and longitudinal xfms in rpool
• Changes

  C-PAC/CPAC/seg_preproc/seg_preproc.py

  Lines 437 to 459 in 96db8b0

  	outputs=[
  	"label-CSF_mask",
  	"label-GM_mask",
  	"label-WM_mask",
  	"label-CSF_desc-preproc_mask",
  	"label-GM_desc-preproc_mask",
  	"label-WM_desc-preproc_mask",
  	"label-CSF_probseg",
  	"label-GM_probseg",
  	"label-WM_probseg",
  	"label-CSF_pveseg",
  	"label-GM_pveseg",
  	"label-WM_pveseg",
  	"space-longitudinal_label-CSF_mask",
  	"space-longitudinal_label-GM_mask",
  	"space-longitudinal_label-WM_mask",
  	"space-longitudinal_label-CSF_desc-preproc_mask",
  	"space-longitudinal_label-GM_desc-preproc_mask",
  	"space-longitudinal_label-WM_desc-preproc_mask",
  	"space-longitudinal_label-CSF_probseg",
  	"space-longitudinal_label-GM_probseg",
  	"space-longitudinal_label-WM_probseg",
  	],

  to

  C-PAC/CPAC/seg_preproc/seg_preproc.py

  Lines 437 to 440 in e7e82b7

  	outputs=[f"{long}label-{tissue}_{entity}" for
  	long in ["", "space-longitudinal_"] for
  	tissue in ["CSF", "GM", "WM"] for
  	entity in ["mask", "desc-preproc_mask", "probseg", "pveseg"]],

  in order to include some missing longitudinal resources and to make the list pithier.

Technical details

• Disables building and testing variant images (ABCD-HCP and fMRIPrep-LTS). These were included in v1.8.7‒proper but include some dependencies that have been deprecated since their release which was complicating building, packaging, and the codebase itself (see ⚰️ Remove stale variants #2115).
• Includes backmerge of 🐛 Set subject_id attribute on Configuration object in anatomical longitudinal workflow #2158 from 1.8.8 development.
• Includes backmerge of 🐛 Dynamically capture 1D header #2153 from 1.8.8 development.
• Includes some other CI updates from 1.8.8:
  • Bugfixes in .github/scripts/autoversioning.sh
  • Skip update-yaml-comments job in pre-commit CI

♻️ refactor warp_T1mask_to_template to warp_mask_to_template

C-PAC/CPAC/registration/registration.py

Lines 3570 to 3623 in 96db8b0

	@nodeblock(
	name="transform_T1mask_to_T1template",
	switch=[
	["registration_workflows", "anatomical_registration", "run"],
	["anatomical_preproc", "run"],
	["anatomical_preproc", "brain_extraction", "run"],
	],
	inputs=[
	("space-T1w_desc-brain_mask", "from-T1w_to-template_mode-image_xfm"),
	"T1w-template",
	],
	outputs={"space-template_desc-brain_mask": {"Template": "T1w-template"}},
	)
	def warp_T1mask_to_template(wf, cfg, strat_pool, pipe_num, opt=None):
	xfm_prov = strat_pool.get_cpac_provenance(
	'from-T1w_to-template_mode-image_xfm')
	reg_tool = check_prov_for_regtool(xfm_prov)
	num_cpus = cfg.pipeline_setup['system_config'][
	'max_cores_per_participant']
	num_ants_cores = cfg.pipeline_setup['system_config']['num_ants_threads']
	apply_xfm = apply_transform(f'warp_T1mask_to_T1template_{pipe_num}',
	reg_tool, time_series=False, num_cpus=num_cpus,
	num_ants_cores=num_ants_cores)
	apply_xfm.inputs.inputspec.interpolation = "NearestNeighbor"
	'''
	if reg_tool == 'ants':
	apply_xfm.inputs.inputspec.interpolation = cfg.registration_workflows[
	'functional_registration']['func_registration_to_template'][
	'ANTs_pipelines']['interpolation']
	elif reg_tool == 'fsl':
	apply_xfm.inputs.inputspec.interpolation = cfg.registration_workflows[
	'functional_registration']['func_registration_to_template'][
	'FNIRT_pipelines']['interpolation']
	'''
	connect = strat_pool.get_data("space-T1w_desc-brain_mask")
	node, out = connect
	wf.connect(node, out, apply_xfm, 'inputspec.input_image')
	node, out = strat_pool.get_data("T1w-template")
	wf.connect(node, out, apply_xfm, 'inputspec.reference')
	node, out = strat_pool.get_data("from-T1w_to-template_mode-image_xfm")
	wf.connect(node, out, apply_xfm, 'inputspec.transform')
	outputs = {
	'space-template_desc-brain_mask': (apply_xfm, 'outputspec.output_image')
	}
	return (wf, outputs)

becomes

C-PAC/CPAC/registration/registration.py

Lines 3575 to 3633 in e916d33

	def warp_mask_to_template(space: Literal["longitudinal", "T1w"]) -> NodeBlockFunction:
	"""Get a NodeBlockFunction to transform a mask from ``space`` to template."""
	@nodeblock(
	name=f"transform_{space}-mask_to_T1-template",
	switch=[
	["registration_workflows", "anatomical_registration", "run"],
	["anatomical_preproc", "run"],
	["anatomical_preproc", "brain_extraction", "run"],
	],
	inputs=[
	(f"space-{space}_desc-brain_mask",
	f"from-{space}_to-template_mode-image_xfm"),
	"T1w-template",
	],
	outputs={"space-template_desc-brain_mask": {"Template": "T1w-template"}},
	)
	def warp_mask_to_template_fxn(wf, cfg, strat_pool, pipe_num, opt=None):
	"""Transform a mask to template space."""
	xfm_prov = strat_pool.get_cpac_provenance(
	f'from-{space}_to-template_mode-image_xfm')
	reg_tool = check_prov_for_regtool(xfm_prov)
	num_cpus = cfg.pipeline_setup['system_config'][
	'max_cores_per_participant']
	num_ants_cores = cfg.pipeline_setup['system_config']['num_ants_threads']
	apply_xfm = apply_transform(f'warp_T1mask_to_T1template_{pipe_num}',
	reg_tool, time_series=False, num_cpus=num_cpus,
	num_ants_cores=num_ants_cores)
	apply_xfm.inputs.inputspec.interpolation = "NearestNeighbor"
	'''
	if reg_tool == 'ants':
	apply_xfm.inputs.inputspec.interpolation = cfg.registration_workflows[
	'functional_registration']['func_registration_to_template'][
	'ANTs_pipelines']['interpolation']
	elif reg_tool == 'fsl':
	apply_xfm.inputs.inputspec.interpolation = cfg.registration_workflows[
	'functional_registration']['func_registration_to_template'][
	'FNIRT_pipelines']['interpolation']
	'''
	connect = strat_pool.get_data(f"space-{space}_desc-brain_mask")
	node, out = connect
	wf.connect(node, out, apply_xfm, 'inputspec.input_image')
	node, out = strat_pool.get_data("T1w-template")
	wf.connect(node, out, apply_xfm, 'inputspec.reference')
	node, out = strat_pool.get_data(f"from-{space}_to-template_mode-image_xfm")
	wf.connect(node, out, apply_xfm, 'inputspec.transform')
	outputs = {
	'space-template_desc-brain_mask': (apply_xfm, 'outputspec.output_image')
	}
	return wf, outputs
	return warp_mask_to_template_fxn

which just uses f-strings to plug in the space and otherwise use identical code.

Tests

Screenshots

Checklist

• My pull request has a descriptive title (not a vague title like Update index.md).
• My pull request targets the main branch of the repository.
• My commit messages follow best practices.
• My code follows the established code style of the repository.
• I added tests for the changes I made (if applicable).
• I updated the changelog.
• I added or updated documentation (if applicable).
• I tried running the project locally and verified that there are no visible errors.

Developer Certificate of Origin

Developer Certificate of Origin

Developer Certificate of Origin
Version 1.1

Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
1 Letterman Drive
Suite D4700
San Francisco, CA, 94129

Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.


Developer's Certificate of Origin 1.1

By making a contribution to this project, I certify that:

(a) The contribution was created in whole or in part by me and I
    have the right to submit it under the open source license
    indicated in the file; or

(b) The contribution is based upon previous work that, to the best
    of my knowledge, is covered under an appropriate open source
    license and I have the right under that license to submit that
    work with modifications, whether created in whole or in part
    by me, under the same open source license (unless I am
    permitted to submit under a different license), as indicated
    in the file; or

(c) The contribution was provided directly to me by some other
    person who certified (a), (b) or (c) and I have not modified
    it.

(d) I understand and agree that this project and the contribution
    are public and that a record of the contribution (including all
    personal information I submit with it, including my sign-off) is
    maintained indefinitely and may be redistributed consistent with
    this project or the open source license(s) involved.

[shnizzedy]

I don't understand why we do this twice:

C-PAC/CPAC/longitudinal_pipeline/longitudinal_workflow.py

Lines 520 to 535 in e7e82b7

	template_node = subject_specific_template(
	workflow_name=template_node_name
	)
	template_node.inputs.set(
	avg_method=config.longitudinal_template_generation[
	'average_method'],
	dof=config.longitudinal_template_generation['dof'],
	interp=config.longitudinal_template_generation['interp'],
	cost=config.longitudinal_template_generation['cost'],
	convergence_threshold=config.longitudinal_template_generation[
	'convergence_threshold'],
	thread_pool=config.longitudinal_template_generation[
	'thread_pool'],
	unique_id_list=list(session_wfs.keys())
	)

C-PAC/CPAC/longitudinal_pipeline/longitudinal_workflow.py

Lines 1178 to 1189 in e7e82b7

	template_node = subject_specific_template(
	workflow_name='subject_specific_func_template_' + subject_id
	)
	template_node.inputs.set(
	avg_method=config.longitudinal_template_average_method,
	dof=config.longitudinal_template_dof,
	interp=config.longitudinal_template_interp,
	cost=config.longitudinal_template_cost,
	convergence_threshold=config.longitudinal_template_convergence_threshold,
	thread_pool=config.longitudinal_template_thread_pool,
	)

but it might just be a gap in my understanding