RF: Simplify high-pass filtering in algorithms.confounds

[effigies]

trafficstars

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]

@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]]

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

@@            Coverage Diff             @@
##           master    #3651      +/-   ##
==========================================
- Coverage   70.83%   70.47%   -0.36%     
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  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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