RF: Use scipy.interpolate.BSpline to construct spline basis

[effigies]

This builds on and replaces #388. The additional padding broke our ability to reconstruct the field, as we were estimating more parameters but not actually saving them.

This PR maintains the current functionality and paves the way for implementing the Jacobian (#391) by using BSpline() objects directly instead of through BSpline.design_matrix(). I also restore the use of a within_support mask, which significantly decreases the runtime of unwarp_wf.resample nodes. There is little effect on bs_filter nodes, as we downsample to a low enough resolution to make the fit process very quick already.

[codecov]

Codecov Report

Attention: 1 lines in your changes are missing coverage. Please review.

Comparison is base (c90c4ed) 80.53% compared to head (1d715a9) 80.56%.

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #393      +/-   ##
==========================================
+ Coverage   80.53%   80.56%   +0.03%     
==========================================
  Files          26       26              
  Lines        2296     2290       -6     
  Branches      286      287       +1     
==========================================
- Hits         1849     1845       -4     
+ Misses        402      401       -1     
+ Partials       45       44       -1     

Flag	Coverage Δ
unittests	∅ <ø> (∅)

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Files	Coverage Δ
sdcflows/transform.py	77.65% <100.00%> (+0.35%)	⬆️
sdcflows/interfaces/bspline.py	68.92% <66.66%> (ø)

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[effigies]

@oesteban This should be a relatively quick review. Apart from the specific B-spline invocation change, the main change is that our design matrices are transposed from before. The effect is that we can stop transposing them after generation.

The tests verify that the kron() products are unchanged apart from the transposition.

[effigies]

Read through with fresh eyes. I'm going to go ahead and merge.