Whitening filter rework#359
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- Support 1D shapes in radial_reduced_grid/create_gaussian_*_pass so the bandpass filter chain can be applied directly to a 1D radial profile. - Replace radial_average/power_spectrum_profile with estimate_whitening_filter, which estimates the noise power spectrum on overlapping, windowed, mean- subtracted patches, masked to only the Fourier voxels actually sampled by the tilt series (via create_wedge with CTF/dose stripped from the metadata), rejecting the highest-variance patches and returning an already-transformed whitening filter (1/sqrt, DC zeroed, high-frequency tapered, normalized). - Wire TMJob to call estimate_whitening_filter directly instead of manually transforming a raw power spectrum profile. - Fix an off-by-one in profile_to_weighting's map_coordinates interpolation that rolled the near-Nyquist weights off toward 0, and zero the DC component of the returned weighting. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Use max(template_shape[0], 64) instead of a fixed 64 so larger template boxes get a correspondingly larger estimation patch, still clamped to the smallest search region dimension. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Replace the hand-rolled fftfreq/rfftfreq radial-distance computation with radial_reduced_grid, matching the normalized-frequency convention already used by the bandpass filters, CTF, and wedge in this module. This also fixes a small inconsistency for odd patch sizes, where the fftfreq-based Nyquist normalization diverged from radial_reduced_grid's convention. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Match the numpydoc formatting used elsewhere in weights.py: single-line opening summary, single spaces after periods, Optional[type] for None defaults, and no trailing periods on single-sentence parameter entries. No behavior change. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Minimum allowed coverage is Generated by 🐒 cobertura-action against a226ded |
sroet
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Jul 15, 2026
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I think this is a nice improvement
I do have quite some comments and questions, and you're missing some coverage.
Co-authored-by: Sander Roet <sanderroet@hotmail.com>
- Reject non-cubic templates with a clear error (tmjob.py) - Use keyword arguments for estimate_whitening_filter call - Use int steps instead of float in radial_reduced_grid's np.arange calls - Drop the unused exclude/focus-mask parameter from estimate_whitening_filter - Fix step rounding to avoid Python's banker's rounding - Simplify patch rejection: drop the silent auto-turnoff fallback, raise RuntimeError only when zero patches survive - Correct _patch_slices axis naming to match pytom's internal xyz convention - Raise instead of silently leaving NaNs when too few radial shells are valid - Add/extend tests for the above, including previously uncovered branches (_masked_radial's new error path and the statistic="mean" code path) Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Addressed all the comments! |
# Conflicts: # src/pytom_tm/weights.py # tests/test_weights.py
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Merged the radial_grid rework into this now! |
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Jul 16, 2026
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One small comment left (assuming all changed lines are hit by tests now). Will not need another round of review
Co-authored-by: Sander Roet <sanderroet@hotmail.com>
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Main things happening here is a rework of whitening filter estimation:
Side things:
This is an example of the old and new whitening filter. Main things to note is that its a lot smoother due to the small patch size and averaging over patches. The new filter also more strongly up weights high frequencies, owing to the better estimate from the tilt mask: