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src/kepost/workflows/diffusion/procedures/parcellations/parcellations copy.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,121 @@ | ||
| from nipype.interfaces import utility as niu | ||
| from nipype.pipeline import engine as pe | ||
|
|
||
| from kepost.interfaces.bids import DerivativesDataSink | ||
| from kepost.workflows.diffusion.procedures.utils.derivatives import ( | ||
| DIFFUSION_WF_OUTPUT_ENTITIES, | ||
| ) | ||
|
|
||
|
|
||
| def parcellate_all_measures(in_file: str, atlas_nifti: str): | ||
| """ | ||
| Parcellate the brain using a specific atlas | ||
| Parameters | ||
| ---------- | ||
| in_file : str | ||
| The input file | ||
| atlas_name : str | ||
| The atlas name | ||
| """ | ||
| import os | ||
|
|
||
| import pandas as pd | ||
| from bids.layout import parse_file_entities | ||
|
|
||
| from kepost.atlases.utils import get_atlas_properties, parcellate | ||
| from kepost.workflows.diffusion.procedures.parcellations.available_measures import ( | ||
| AVAILABLE_MEASURES, | ||
| ) | ||
|
|
||
| atlas_name = parse_file_entities(atlas_nifti)["atlas"] | ||
| _, description, region_col, index_col = get_atlas_properties(atlas_name) | ||
| df = pd.read_csv(description, index_col=index_col).copy() | ||
| for measure_name, measure_func in AVAILABLE_MEASURES.items(): | ||
| df[measure_name] = parcellate( | ||
| atlas_description=description, | ||
| index_col=index_col, | ||
| atlas_nifti=atlas_nifti, | ||
| region_col=region_col, | ||
| metric_image=in_file, | ||
| measure=measure_func, # type: ignore[arg-type] | ||
| )["value"] | ||
| out_file = f"{os.getcwd()}/parcellations.pkl" | ||
| df.to_pickle(out_file) | ||
| return out_file, atlas_name | ||
|
|
||
|
|
||
| def init_parcellations_wf( | ||
| inputs: list, software: str, name: str = "parcellations_wf" | ||
| ) -> pe.Workflow: | ||
| """ | ||
| Workflow to parcellate the brain | ||
| """ | ||
| workflow = pe.Workflow(name=f"{software}_{name}") | ||
| inputnode = pe.Node( | ||
| niu.IdentityInterface( | ||
| fields=[ | ||
| "base_directory", | ||
| "acq_label", | ||
| "source_file", | ||
| "atlas_name", | ||
| "atlas_nifti", | ||
| ] | ||
| + inputs | ||
| ), | ||
| name="inputnode", | ||
| ) | ||
|
|
||
| parcellate_node = pe.MapNode( | ||
| niu.Function( | ||
| input_names=["in_file", "atlas_nifti"], | ||
| output_names=["out_file", "atlas_name"], | ||
| function=parcellate_all_measures, | ||
| ), | ||
| name="parcellate_node", | ||
| iterfield=["in_file"], | ||
| ) | ||
| listify_inputs_node = pe.Node( | ||
| niu.Merge(len(inputs)), | ||
| name="listify_inputs_node", | ||
| ) | ||
| ds_parcellation_node = pe.MapNode( | ||
| DerivativesDataSink( # type: ignore[arg-type] | ||
| **DIFFUSION_WF_OUTPUT_ENTITIES.get("parcellations"), | ||
| reconstruction_software=software, | ||
| desc=inputs, | ||
| ), | ||
| iterfield=["desc", "in_file"], | ||
| name="ds_parcellation_node", | ||
| ) | ||
| workflow.connect( | ||
| [ | ||
| ( | ||
| inputnode, | ||
| listify_inputs_node, | ||
| [(p, f"in{i+1}") for i, p in enumerate(inputs)], | ||
| ), | ||
| (listify_inputs_node, parcellate_node, [("out", "in_file")]), | ||
| ( | ||
| inputnode, | ||
| parcellate_node, | ||
| [("atlas_nifti", "atlas_nifti")], | ||
| ), | ||
| ( | ||
| parcellate_node, | ||
| ds_parcellation_node, | ||
| [("out_file", "in_file"), ("atlas_name", "atlas")], | ||
| ), | ||
| ( | ||
| inputnode, | ||
| ds_parcellation_node, | ||
| [ | ||
| ("acq_label", "acquisition"), | ||
| ("source_file", "source_file"), | ||
| ("base_directory", "base_directory"), | ||
| ], | ||
| ), | ||
| ] | ||
| ) | ||
|
|
||
| return workflow |
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