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nipy / nipype

Workflows and interfaces for neuroimaging packages
https://nipype.readthedocs.org/en/latest/
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RF: Simplify high-pass filtering in algorithms.confounds #3651

Open effigies opened 6 months ago

effigies commented 6 months ago

Legendre and cosine detrending are implemented almost identically, although with several minor variations. Here I separate regressor creation from detrending to unify the implementations.

This now uses np.linalg.pinv(X) to estimate the betas in both cases, rather than using np.linalg.lstsq in the cosine filter. lstsq uses SVD and can thus fail to converge in rare cases. Under no circumstances should (X.T @ X) be singular, so the pseudoinverse is unique and precisely what we want.

Issue raised in https://neurostars.org/t/fmriprep-numpy-linalg-linalg-linalgerror-svd-did-not-converge/29525.

effigies commented 6 months ago

@jhlegarreta I wonder if I could bug you for a review. I suspect this would be a quick one for you, but let me know if it's not.

codecov[bot] commented 6 months ago

Codecov Report

Attention: Patch coverage is 85.71429% with 2 lines in your changes are missing coverage. Please review.

Project coverage is 70.47%. Comparing base (4d1352a) to head (4dde564).

:exclamation: Current head 4dde564 differs from pull request most recent head 17bac08

Please upload reports for the commit 17bac08 to get more accurate results.

Files Patch % Lines
nipype/algorithms/confounds.py 85.71% 2 Missing :warning:
Additional details and impacted files ```diff @@ Coverage Diff @@ ## master #3651 +/- ## ========================================== - Coverage 70.83% 70.47% -0.36% ========================================== Files 1276 1276 Lines 59314 59305 -9 Branches 9824 9822 -2 ========================================== - Hits 42013 41797 -216 - Misses 16125 16353 +228 + Partials 1176 1155 -21 ```

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