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Jun 27, 2025
Merged

updated auto-generated code with function tasks #7

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8 changes: 4 additions & 4 deletions pydra/tasks/mriqc/interfaces/bids/iqm_file_sink.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@ def function(
fields = list(set(fields) - set(self_dict["inputs"].copyable_trait_names()))
self_dict["_input_names"] = fields
undefined_traits = {
key: _add_field(key, add_trait=add_trait, _outputs=_outputs)
key: _add_field(key, _outputs=_outputs, add_trait=add_trait)
for key in fields
}
self_dict["inputs"].trait_set(trait_change_notify=False, **undefined_traits)
Expand All @@ -82,7 +82,7 @@ def function(
self_dict["_always_run"] = True
self_dict = {}
out_file = _gen_outfile(
in_file=in_file, out_dir=out_dir, dismiss_entities=dismiss_entities
dismiss_entities=dismiss_entities, out_dir=out_dir, in_file=in_file
)

if root is not attrs.NOTHING:
Expand Down Expand Up @@ -151,14 +151,14 @@ def function(
return out_file


def _add_field(name, value=attrs.NOTHING, add_trait=None, _outputs=None):
def _add_field(name, value=attrs.NOTHING, _outputs=None, add_trait=None):
self_dict = {}
self_dict["inputs"].add_trait(name, traits.Any)
_outputs[name] = value
return value


def _gen_outfile(in_file=None, out_dir=None, dismiss_entities=None):
def _gen_outfile(dismiss_entities=None, out_dir=None, in_file=None):
out_dir = Path()
if out_dir is not attrs.NOTHING:
out_dir = Path(out_dir)
Expand Down
109 changes: 36 additions & 73 deletions pydra/tasks/mriqc/workflows/anatomical/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,17 +51,7 @@ def anat_qc_workflow(
wf_inputs_metadata=None,
wf_species="human",
wf_template_id="MNI152NLin2009cAsym",
) -> [
"ty.Any",
"ty.Any",
"ty.Any",
"ty.Any",
"ty.Any",
"ty.Any",
"ty.Any",
"ty.Any",
"ty.Any",
]:
) -> tuple[ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any]:
"""
One-subject-one-session-one-run pipeline to extract the NR-IQMs from
anatomical images
Expand Down Expand Up @@ -151,10 +141,10 @@ def anat_qc_workflow(
norm = workflow.add(
spatial_normalization(
wf_species=wf_species,
nipype_omp_nthreads=nipype_omp_nthreads,
wf_template_id=wf_template_id,
exec_ants_float=exec_ants_float,
exec_debug=exec_debug,
nipype_omp_nthreads=nipype_omp_nthreads,
modality=modality,
name="norm",
)
Expand Down Expand Up @@ -196,9 +186,9 @@ def anat_qc_workflow(
# Reports
anat_report_wf = workflow.add(
init_anat_report_wf(
wf_species=wf_species,
exec_verbose_reports=exec_verbose_reports,
exec_work_dir=exec_work_dir,
wf_species=wf_species,
in_ras=to_ras.out_file,
headmask=hmsk.out_file,
airmask=amw.air_mask,
Expand Down Expand Up @@ -235,12 +225,12 @@ def anat_qc_workflow(
# fmt: on
outputs_["norm_report"] = norm.report
outputs_["iqmswf_noise_report"] = iqmswf.noise_report
outputs_["anat_report_wf_airmask_report"] = anat_report_wf.airmask_report
outputs_["anat_report_wf_bg_report"] = anat_report_wf.bg_report
outputs_["anat_report_wf_bmask_report"] = anat_report_wf.bmask_report
outputs_["anat_report_wf_artmask_report"] = anat_report_wf.artmask_report
outputs_["anat_report_wf_headmask_report"] = anat_report_wf.headmask_report
outputs_["anat_report_wf_bg_report"] = anat_report_wf.bg_report
outputs_["anat_report_wf_airmask_report"] = anat_report_wf.airmask_report
outputs_["anat_report_wf_zoom_report"] = anat_report_wf.zoom_report
outputs_["anat_report_wf_headmask_report"] = anat_report_wf.headmask_report
outputs_["anat_report_wf_segm_report"] = anat_report_wf.segm_report

return tuple(outputs_)
Expand All @@ -252,7 +242,7 @@ def airmsk_wf(
in_file: ty.Any = attrs.NOTHING,
ind2std_xfm: ty.Any = attrs.NOTHING,
name="AirMaskWorkflow",
) -> ["ty.Any", "ty.Any", "ty.Any", "ty.Any"]:
) -> tuple[ty.Any, ty.Any, ty.Any, ty.Any]:
"""
Calculate air, artifacts and "hat" masks to evaluate noise in the background.

Expand Down Expand Up @@ -314,7 +304,7 @@ def headmsk_wf(
name="HeadMaskWorkflow",
omp_nthreads=1,
wf_species="human",
) -> ["ty.Any", "ty.Any"]:
) -> tuple[ty.Any, ty.Any]:
"""
Computes a head mask as in [Mortamet2009]_.

Expand All @@ -337,56 +327,32 @@ def _select_wm(inlist):
return [f for f in inlist if "WM" in f][0]

enhance = workflow.add(
FunctionTask(
func=_enhance,
input_spec=SpecInfo(
name="FunctionIn",
bases=(BaseSpec,),
fields=[("in_file", ty.Any), ("wm_tpm", ty.Any)],
),
output_spec=SpecInfo(
name="FunctionOut", bases=(BaseSpec,), fields=[("out_file", ty.Any)]
),
in_file=in_file,
wm_tpm=in_tpms,
),
python.define(
_enhance,
inputs={"in_file": ty.Any, "wm_tpm": ty.Any},
outputs={"out_file": ty.Any},
)(in_file=in_file, wm_tpm=in_tpms),
name="enhance",
)
gradient = workflow.add(
FunctionTask(
func=image_gradient,
input_spec=SpecInfo(
name="FunctionIn",
bases=(BaseSpec,),
fields=[("in_file", ty.Any), ("brainmask", ty.Any), ("sigma", ty.Any)],
),
output_spec=SpecInfo(
name="FunctionOut", bases=(BaseSpec,), fields=[("out_file", ty.Any)]
),
brainmask=brainmask,
in_file=enhance.out_file,
),
python.define(
image_gradient,
inputs={"in_file": ty.Any, "brainmask": ty.Any, "sigma": ty.Any},
outputs={"out_file": ty.Any},
)(brainmask=brainmask, in_file=enhance.out_file),
name="gradient",
)
thresh = workflow.add(
FunctionTask(
func=gradient_threshold,
input_spec=SpecInfo(
name="FunctionIn",
bases=(BaseSpec,),
fields=[
("in_file", ty.Any),
("brainmask", ty.Any),
("aniso", ty.Any),
("thresh", ty.Any),
],
),
output_spec=SpecInfo(
name="FunctionOut", bases=(BaseSpec,), fields=[("out_file", ty.Any)]
),
brainmask=brainmask,
in_file=gradient.out_file,
),
python.define(
gradient_threshold,
inputs={
"in_file": ty.Any,
"brainmask": ty.Any,
"aniso": ty.Any,
"thresh": ty.Any,
},
outputs={"out_file": ty.Any},
)(brainmask=brainmask, in_file=gradient.out_file),
name="thresh",
)
if wf_species != "human":
Expand All @@ -412,7 +378,7 @@ def init_brain_tissue_segmentation(
name="brain_tissue_segmentation",
nipype_omp_nthreads=12,
std_tpms: ty.Any = attrs.NOTHING,
) -> ["ty.Any", "ty.Any"]:
) -> tuple[ty.Any, ty.Any]:
"""
Setup a workflow for brain tissue segmentation.

Expand Down Expand Up @@ -454,15 +420,12 @@ def _format_tpm_names(in_files, fname_string=None):
return file_format, out_files

format_tpm_names = workflow.add(
FunctionTask(
python.define(
_format_tpm_names,
inputs={"in_files": ty.Any},
outputs={"file_format": ty.Any},
)(
execution={"keep_inputs": True, "remove_unnecessary_outputs": False},
func=_format_tpm_names,
input_spec=SpecInfo(
name="FunctionIn", bases=(BaseSpec,), fields=[("in_files", ty.Any)]
),
output_spec=SpecInfo(
name="FunctionOut", bases=(BaseSpec,), fields=[("file_format", ty.Any)]
),
in_files=std_tpms,
),
name="format_tpm_names",
Expand Down Expand Up @@ -510,7 +473,7 @@ def spatial_normalization(
nipype_omp_nthreads=12,
wf_species="human",
wf_template_id="MNI152NLin2009cAsym",
) -> ["ty.Any", "ty.Any", "ty.Any"]:
) -> tuple[ty.Any, ty.Any, ty.Any]:
"""Create a simplified workflow to perform fast spatial normalization."""
from pydra.tasks.niworkflows.interfaces.reportlets.registration import (
SpatialNormalizationRPT as RobustMNINormalization,
Expand Down Expand Up @@ -595,7 +558,7 @@ def compute_iqms(
segmentation: ty.Any = attrs.NOTHING,
std_tpms: ty.Any = attrs.NOTHING,
wf_species="human",
) -> ["ty.Any", "ty.Any"]:
) -> tuple[ty.Any, ty.Any]:
"""
Setup the workflow that actually computes the IQMs.

Expand Down
2 changes: 1 addition & 1 deletion pydra/tasks/mriqc/workflows/anatomical/output.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ def init_anat_report_wf(
name: str = "anat_report_wf",
segmentation: ty.Any = attrs.NOTHING,
wf_species="human",
) -> ["ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any"]:
) -> tuple[ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any]:
"""
Generate the components of the individual report.

Expand Down
49 changes: 19 additions & 30 deletions pydra/tasks/mriqc/workflows/diffusion/base.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -44,17 +44,8 @@ def dmri_qc_workflow(
wf_inputs_metadata=None,
wf_species="human",
wf_template_id="MNI152NLin2009cAsym",
) -> [
"ty.Any",
"ty.Any",
"ty.Any",
"ty.Any",
"ty.Any",
"ty.Any",
"ty.Any",
"ty.Any",
"ty.Any",
"ty.Any",
) -> tuple[
ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any
]:
"""
Initialize the dMRI-QC workflow.
Expand Down Expand Up @@ -209,12 +200,12 @@ def dmri_qc_workflow(
# EPI to MNI registration
spatial_norm = workflow.add(
epi_mni_align(
nipype_omp_nthreads=nipype_omp_nthreads,
wf_species=wf_species,
wf_template_id=wf_template_id,
nipype_omp_nthreads=nipype_omp_nthreads,
nipype_nprocs=nipype_nprocs,
exec_debug=exec_debug,
wf_template_id=wf_template_id,
exec_ants_float=exec_ants_float,
exec_debug=exec_debug,
epi_mean=dwi_ref.out_file,
epi_mask=dmri_bmsk.out_mask,
name="spatial_norm",
Expand Down Expand Up @@ -249,11 +240,11 @@ def dmri_qc_workflow(
# Generate outputs
dwi_report_wf = workflow.add(
init_dwi_report_wf(
wf_species=wf_species,
wf_biggest_file_gb=wf_biggest_file_gb,
exec_verbose_reports=exec_verbose_reports,
wf_fd_thres=wf_fd_thres,
exec_work_dir=exec_work_dir,
wf_species=wf_species,
wf_fd_thres=wf_fd_thres,
wf_biggest_file_gb=wf_biggest_file_gb,
in_bdict=shells.b_dict,
brain_mask=dmri_bmsk.out_mask,
in_avgmap=averages.out_file,
Expand Down Expand Up @@ -313,14 +304,14 @@ def averages_out_file_to_iqms_wf_in_b0_callable(in_: ty.Any) -> ty.Any:
# fmt: on
outputs_["iqms_wf_out_file"] = iqms_wf.out_file
outputs_["iqms_wf_noise_floor"] = iqms_wf.noise_floor
outputs_["dwi_report_wf_noise_report"] = dwi_report_wf.noise_report
outputs_["dwi_report_wf_heatmap_report"] = dwi_report_wf.heatmap_report
outputs_["dwi_report_wf_spikes_report"] = dwi_report_wf.spikes_report
outputs_["dwi_report_wf_fa_report"] = dwi_report_wf.fa_report
outputs_["dwi_report_wf_carpet_report"] = dwi_report_wf.carpet_report
outputs_["dwi_report_wf_md_report"] = dwi_report_wf.md_report
outputs_["dwi_report_wf_bmask_report"] = dwi_report_wf.bmask_report
outputs_["dwi_report_wf_noise_report"] = dwi_report_wf.noise_report
outputs_["dwi_report_wf_snr_report"] = dwi_report_wf.snr_report
outputs_["dwi_report_wf_carpet_report"] = dwi_report_wf.carpet_report
outputs_["dwi_report_wf_fa_report"] = dwi_report_wf.fa_report
outputs_["dwi_report_wf_spikes_report"] = dwi_report_wf.spikes_report
outputs_["dwi_report_wf_heatmap_report"] = dwi_report_wf.heatmap_report
outputs_["dwi_report_wf_bmask_report"] = dwi_report_wf.bmask_report

return tuple(outputs_)

Expand All @@ -332,7 +323,7 @@ def hmc_workflow(
name="dMRI_HMC",
reference: ty.Any = attrs.NOTHING,
wf_fd_radius=50,
) -> ["ty.Any", "ty.Any", "ty.Any", "ty.Any"]:
) -> tuple[ty.Any, ty.Any, ty.Any, ty.Any]:
"""
Create a :abbr:`HMC (head motion correction)` workflow for dMRI.

Expand Down Expand Up @@ -406,7 +397,7 @@ def epi_mni_align(
nipype_omp_nthreads=12,
wf_species="human",
wf_template_id="MNI152NLin2009cAsym",
) -> ["ty.Any", "ty.Any", "ty.Any"]:
) -> tuple[ty.Any, ty.Any, ty.Any]:
"""
Estimate the transform that maps the EPI space into MNI152NLin2009cAsym.

Expand Down Expand Up @@ -487,9 +478,7 @@ def epi_mni_align(
suffix="mask",
)[0]
)
bspline_grid = workflow.add(
FunctionTask(func=_bspline_grid), name="bspline_grid"
)
bspline_grid = workflow.add(python.define(_bspline_grid)(), name="bspline_grid")
# fmt: off
bspline_grid.inputs.in_file = epi_mean
n4itk.inputs.args = bspline_grid.out
Expand Down Expand Up @@ -557,7 +546,7 @@ def compute_iqms(
qspace_neighbors: ty.Any = attrs.NOTHING,
spikes_mask: ty.Any = attrs.NOTHING,
wm_mask: ty.Any = attrs.NOTHING,
) -> ["ty.Any", "ty.Any"]:
) -> tuple[ty.Any, ty.Any]:
"""
Initialize the workflow that actually computes the IQMs.

Expand All @@ -582,7 +571,7 @@ def compute_iqms(
# from mriqc.workflows.utils import _tofloat, get_fwhmx

estimate_sigma = workflow.add(
FunctionTask(func=_estimate_sigma, in_file=in_noise, mask=brain_mask),
python.define(_estimate_sigma)(in_file=in_noise, mask=brain_mask),
name="estimate_sigma",
)
measures = workflow.add(
Expand Down
6 changes: 3 additions & 3 deletions pydra/tasks/mriqc/workflows/diffusion/output.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
import attrs
import logging
from pathlib import Path
from pydra.compose import workflow
from pydra.compose import python, workflow
from pydra.tasks.mriqc.workflows.diffusion.output import init_dwi_report_wf
from pydra.tasks.nireports.interfaces.dmri import DWIHeatmap
from pydra.tasks.nireports.interfaces.reporting.base import (
Expand Down Expand Up @@ -41,7 +41,7 @@ def init_dwi_report_wf(
wf_biggest_file_gb=1,
wf_fd_thres=0.2,
wf_species="human",
) -> ["ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any"]:
) -> tuple[ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any, ty.Any]:
"""
Write out individual reportlets.

Expand Down Expand Up @@ -118,7 +118,7 @@ def _gen_entity(inlist):
outputs_['md_report'] = mosaic_md.out_file
# fmt: on
get_wm = workflow.add(
FunctionTask(func=_get_wm, in_file=in_parcellation), name="get_wm"
python.define(_get_wm)(in_file=in_parcellation), name="get_wm"
)
plot_heatmap = workflow.add(
DWIHeatmap(
Expand Down
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