Reuse saved ICA component maps when generating component figures

[tsalo]

Closes none.

Changes proposed in this pull request:

• Reuse saved ICA component maps when generating component figures instead of recalculating weights.
• Load the 4D component NIfTI once and iterate over components linearly.
• Refactor single-component figure generation into a dedicated helper.
• Preserve non-orthogonalized mixing matrices for standard component maps, reports, and external-regressor correlations when TEDORT is enabled.
• Save orthogonalized component maps separately as desc-ICAOrth_components.nii.gz in verbose TEDORT runs.
• Apply consistent component-map handling in tedana and ica_reclassify.
• Correct mixing-matrix shapes and stale parameters in writeresults documentation.
• Add regression tests for component-map selection and single-load figure generation.

[codecov]

Codecov Report

❌ Patch coverage is 94.28571% with 2 lines in your changes missing coverage. Please review.
✅ Project coverage is 90.52%. Comparing base (fd18a66) to head (74229f9).
⚠️ Report is 2 commits behind head on main.

Files with missing lines	Patch %	Lines
tedana/reporting/static_figures.py	91.30%	1 Missing and 1 partial ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1356      +/-   ##
==========================================
+ Coverage   90.43%   90.52%   +0.09%     
==========================================
  Files          31       31              
  Lines        5100     5107       +7     
  Branches      937      937              
==========================================
+ Hits         4612     4623      +11     
+ Misses        326      322       -4     
  Partials      162      162              

☔ View full report in Codecov by Harness.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[Copilot]

Pull request overview

This PR updates tedana’s static component figure generation to reuse already-saved ICA component beta maps (instead of recomputing them) and adds joblib-based parallelization for per-component figure creation.

Changes:

• Update comp_figures to load "ICA components img" from the output registry rather than recomputing beta maps.
• Refactor per-component plotting into a helper and run it in parallel via joblib.Parallel.
• Update workflow call sites to match the new comp_figures signature (and pass n_threads from tedana_workflow).

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 4 comments.

File	Description
tedana/workflows/tedana.py	Updates comp_figures call signature and passes n_threads.
tedana/workflows/ica_reclassify.py	Updates comp_figures call signature after removing ts/mask parameters.
tedana/reporting/static_figures.py	Implements loading cached component beta maps and parallel per-component figure generation.

Comments suppressed due to low confidence (2)

tedana/workflows/ica_reclassify.py:596

• comp_figures now reads spatial maps from "ICA components img" written by io.writeresults using mixing (after any TEDORT orthogonalization), but this call passes mixing_orig. That can make the displayed time series inconsistent with the displayed spatial maps when tedort is used.

        reporting.static_figures.comp_figures(
            component_table=component_table,
            mixing=mixing_orig,
            io_generator=io_generator,
            png_cmap=png_cmap,

tedana/reporting/static_figures.py:402

• comp_figures behavior changed significantly (now depends on an on-disk "ICA components img" and adds parallel execution), but there’s no test coverage for this function while tedana.reporting does have tests. Consider adding a unit test that mocks nb.load/plot_component to verify it uses the expected file key and iterates over all components (and optionally exercises n_threads>1).

def comp_figures(component_table, mixing, io_generator, png_cmap, n_threads=1):
    """Create static figures that highlight certain aspects of tedana processing.

    This includes a figure for each component showing the component time course,
    the spatial weight map and a fast Fourier transform of the time course.

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[Copilot]

Pull request overview

Copilot reviewed 6 out of 6 changed files in this pull request and generated 7 comments.

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.