Refactor workflows with shared module architecture #1281
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Summary
This PR refactors the tedana workflow files to use a new shared module architecture (
tedana/workflows/shared/). The goal is to improve code organization, reduce duplication, and make the codebase more maintainable.Key Changes
New
workflows/shared/submodule with focused, reusable components:containers.py- Typed dataclasses (MultiEchoData,MaskData,DecayMaps,OptcomData)setup.py- Workflow setup/teardown utilities (logging, output directory, command saving)data_loading.py- Multi-echo data loading and TR validationmasking.py- Adaptive mask creation for denoising and classificationfitting.py- T2*/S0 decay model fittingcombination.py- Optimal echo combinationoutput.py- Result writing, tedort, MIR, metadata generationreporting.py- Static figures and dynamic report generationRefactored all three workflows to use the shared modules:
tedana.py- Now uses shared functions and returns a results dictionaryt2smap.py- Simplified using shared data loading, masking, and fittingica_reclassify.py- Simplified using shared output and reporting functionsBenefits
Changes Summary
workflows/shared/*.pyworkflows/tedana.pyworkflows/t2smap.pyworkflows/ica_reclassify.pyTest plan
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