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feat(metrics): add smooth and baseline-corrected spectral weights#34

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feat(metrics): add smooth and baseline-corrected spectral weights#34
alxndrkalinin wants to merge 6 commits intomainfrom
feat/smooth-spectral-weights

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@alxndrkalinin alxndrkalinin commented Mar 12, 2026 •
edited by sourcery-ai bot
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  • smooth_spectral_weights: SG-smoothed Wiener weights for spectral PCC
  • estimate_noise_baseline: frequency-dependent noise floor from running low-quantile of SG-smoothed log-power spectrum
  • baseline_snr2_weights: SNR²-based weights against freq-dependent baseline
  • spectral_pcc_baseline: new spectral PCC variant using baseline approach
  • spectral_pcc: add smooth/sg_window/sg_polyorder params for optional SG-smoothed weights

Summary by Sourcery

Introduce smoothed and baseline-corrected spectral weighting options for spectral PCC metrics.

New Features:

  • Add smooth_spectral_weights to compute Savitzky–Golay–smoothed Wiener-style spectral weights.
  • Add estimate_noise_baseline to derive a frequency-dependent noise floor from the smoothed power spectrum.
  • Add baseline_snr2_weights to compute SNR²-based spectral weights using the noise baseline.
  • Add spectral_pcc_baseline metric that applies baseline-corrected SNR² weights in Fourier-domain PCC.
  • Extend spectral_pcc to optionally use SG-smoothed spectral weights via new smoothing parameters.

Enhancements:

  • Improve flexibility of spectral PCC weighting by supporting optional smoothing parameters and alternative weight computation paths.

@alxndrkalinin @claude
feat(metrics): add smooth and baseline-corrected spectral weights
e44e383 
- smooth_spectral_weights: SG-smoothed Wiener weights for spectral PCC
- estimate_noise_baseline: frequency-dependent noise floor from
  running low-quantile of SG-smoothed log-power spectrum
- baseline_snr2_weights: SNR²-based weights against freq-dependent baseline
- spectral_pcc_baseline: new spectral PCC variant using baseline approach
- spectral_pcc: add smooth/sg_window/sg_polyorder params for optional
  SG-smoothed weights

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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sourcery-ai bot commented Mar 12, 2026 •
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Reviewer's Guide

Adds Savitzky–Golay-smoothed spectral weights and a new baseline-corrected spectral PCC metric, plus an option for spectral_pcc to use the smoothed weights instead of the existing ones.

File-Level Changes

Change Details Files
Introduce SG-smoothed Wiener-style spectral weights and optional use in spectral_pcc.
  • Add smooth_spectral_weights to compute Wiener weights from an SG-smoothed log-power spectrum with window-length clamping and optional high-frequency cutoff
  • Extend spectral_pcc signature with smooth, sg_window, and sg_polyorder parameters and branch between existing spectral_weights and the new smooth_spectral_weights based on the smooth flag
 cubic/metrics/bandlimited.py
Add frequency-dependent noise-baseline estimation and SNR²-based baseline weights, and use them to implement a new spectral_pcc_baseline variant.
  • Implement _running_quantile_1d to compute a sliding-window quantile used for baseline estimation
  • Add estimate_noise_baseline to derive a smoothed power spectrum and monotone non-increasing noise baseline from the log-power spectrum using Savitzky–Golay smoothing and a running low quantile
  • Add baseline_snr2_weights to compute SNR²-based radial weights with low-frequency exclusion, hard cutoff, and soft quantile-based capping
  • Implement spectral_pcc_baseline, mirroring spectral_pcc’s pipeline but sourcing weights from estimate_noise_baseline and baseline_snr2_weights, supporting optional frozen radial weights, and reusing existing radial binning and apodization utilities
 cubic/metrics/bandlimited.py
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Getting Help

alxndrkalinin and others added 3 commits March 12, 2026 17:35
@alxndrkalinin @claude
feat(metrics): add binomial splitting for single-image FRC/FSC
0b99383 
Implement random binomial splitting (Rieger et al., Optics Express 2024)
as an alternative to checkerboard splitting for single-image FRC/FSC.
Each pixel's photon count is split into n1 ~ Binomial(n, 0.5) and
n2 = n - n1, producing two noise-independent images with preserved
Poisson statistics and no spatial subsampling.

New `split_type="binomial"` parameter threads through calculate_frc,
frc_resolution, fsc_resolution, and calculate_sectioned_fsc. Supports
counts mode (raw camera data with gain/offset/readout noise correction)
and poisson_thinning mode (fallback for float/deconvolved images).
Multi-repeat averaging via `n_repeats` produces correlation-std and
resolution-std for uncertainty quantification.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
@alxndrkalinin @claude
feat(metrics): add low-k bin exclusion to spectral_pcc
a43839b 
Add nbins_low parameter to zero out the lowest radial frequency bins
before computing spectrally-weighted PCC, suppressing DC/illumination/
background contributions that can dominate the correlation.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
@alxndrkalinin @claude
feat(metrics): add FRC-weighted spectral PCC
ceef746 
Add frc_weights() to derive per-frequency reliability weights from
single-image FRC, and spectral_pcc_frcw() which uses these weights
for spectral PCC computation. Export both from cubic.metrics.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
@alxndrkalinin alxndrkalinin requested a review from Copilot March 13, 2026 00:48
Copilot AI reviewed Mar 13, 2026
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Pull request overview

Adds new spectral weighting options and a binomial (photon-statistics-preserving) splitting path to improve robustness/interpretability of Fourier-domain correlation metrics (Spectral PCC, FRC/FSC), including uncertainty estimation via repeated splits.

Changes:

  • Introduces SG-smoothed and baseline-corrected spectral weighting utilities, plus new spectral PCC variants (baseline-based and FRC-weight-based).
  • Extends FRC/FSC preprocessing to support split_type="binomial" with optional read-noise correction and n_repeats aggregation (std estimates).
  • Adds binomial split implementation + unit tests, and updates spectral correlation data structures/docs accordingly.

Reviewed changes

Copilot reviewed 8 out of 8 changed files in this pull request and generated 9 comments.

Show a summary per file
File Description
cubic/metrics/bandlimited.py Adds smoothed/baseline/FRC-derived spectral weighting functions and extends spectral_pcc with optional smoothing + low-k exclusion.
cubic/metrics/spectral/frc.py Adds binomial split support to FRC/FSC workflows, including repeat RNG handling and optional std outputs.
cubic/image_utils.py Implements binomial_split (counts + poisson thinning modes) used by single-image FRC/FSC.
cubic/metrics/spectral/analysis.py Extends result containers to include correlation-std and resolution-std fields.
cubic/metrics/spectral/README.md Documents checkerboard vs binomial splitting and recommended usage patterns.
cubic/metrics/__init__.py Exposes new bandlimited metrics helpers (frc_weights, spectral_pcc_frcw).
tests/test_image_utils.py Adds unit tests covering statistical/behavioral properties of binomial_split.
tests/metrics/spectral/test_frc.py Adds integration tests for binomial-split FRC/FSC behavior and repeat/stability expectations.

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alxndrkalinin and others added 2 commits March 12, 2026 20:57
@alxndrkalinin @claude
fix(metrics): address code review issues on spectral weights PR
a651ef1 
- Use cubic.cuda abstraction (to_same_device/asnumpy) in binomial_split (C1)
- Derive frequency scaling factor from image dimensions in frc_weights (C2)
- Use nanmean/nanstd for multi-repeat FRC averaging (S3)
- Add xy_std/z_std keys to single-pass binomial fsc_resolution (S4)
- Make internal baseline functions private (_estimate_noise_baseline, etc) (S5)
- Document resolution averaging asymmetry in FRC (S7)
- Add ndim, gain, readout_noise_rms validation to binomial_split (N1)
- Fix "1-bit" → "1/7" in frc_weights docstring (N2)
- Replace assert with raise ValueError in frc_weights (N3)
- Extract duplicate binomial log message to module constant (N4)
- Fix checkerboard uncertainty in README table (N6)
- Fix bin_delta type annotation to int in spectral_pcc_frcw (N7)
- Add __all__ to bandlimited.py (N8)
- Fix SG window fallback for tiny arrays in smooth_spectral_weights (Copilot)
- Add frozen_weights length validation in spectral PCC functions (Copilot)
- Fix gain convention docstring in binomial_split (Copilot)
- Add nbins_low to spectral_pcc docstring (Copilot)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
@alxndrkalinin @claude
fix(metrics): address code review findings scored >50
98a2091 
- Replace xp/get_array_module pattern with to_same_device in all three
  spectral PCC functions (CLAUDE.md: prefer cubic.cuda abstractions)
- Use cubic.scipy proxy for savgol_filter and median_filter instead of
  direct scipy imports (CLAUDE.md: device-agnostic wrappers)
- Add xy_std/z_std keys to fsc_resolution mask backend return path to
  match the documented return contract
- Warn when n_repeats>1 is silently ignored (checkerboard or two-image)
- Clarify n_repeats docstring: requires single-image + binomial mode
- Add tests for spectral_pcc(smooth=True) and nbins_low parameter

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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