[antonsoo]

Fixes #152.

find_second_peak_3D clips the depth of its exclusion region to corr.shape[1]. For a correlation volume deeper than its width, this can leave the first peak unmasked and return it as the second peak. The resulting peak-to-peak signal-to-noise ratio is one, so subsequent validation rejects the vector.

Use corr.shape[2] for that bound. For example, a (7, 8, 20) volume with peaks of 10 at (3, 4, 15) and 4 at (3, 4, 5) now selects the second location and returns a ratio of 2.5, rather than selecting the first location again.

The tests cover supplied/inferred first peaks, exclusion widths, rectangular and cubic volumes, depth-edge clipping and unchanged inputs. Ten cases fail before the fix; eight existing-behavior controls also pass. Separate synthetic 3D particle-volume runs restore 192 falsely rejected vectors across 24 pipelines, while 24 complete volume controls and all velocity fields remain unchanged. Recorded 2D image workflows, saved/reloaded fields and the original tutorial retain identical results on Python 3.10/3.12.

Full suites: 272 passed with the Rust extension on Python 3.12; 237 passed and 4 optional skips without Rust on Python 3.10. All original test outcomes are retained. Full Ruff has 561 existing findings versus 562 on unchanged master; this change adds none. The 3D function uses the SciPy path; the Rust sources are unchanged.

Summary by Sourcery

Fix 3D peak selection to use the depth dimension when excluding the primary correlation peak.

Bug Fixes:

• Correct the 3D second-peak exclusion bounds so correlation volumes deeper than their width no longer return the primary peak as the secondary peak.

Tests:

• Add coverage for inferred and explicit first peaks across rectangular and cubic volumes, exclusion widths, depth-edge clipping, signal-to-noise results, and input immutability.

[sourcery-ai]

Reviewer's Guide

Fixes 3D second-peak exclusion by clipping the masked depth region against corr.shape[2] instead of the width axis, preventing the first peak from being selected again in deeper-than-wide volumes. Adds parametrized regression tests covering peak inference, dimensions, widths, edge clipping, input immutability, and the resulting signal-to-noise ratio.

Flow diagram for 3D second-peak exclusion

flowchart TD
    A[3D correlation volume] --> B[find_second_peak_3D]
    B --> C[Infer or use first peak]
    C --> D[Build exclusion region]
    D --> E["Clip depth bound to corr.shape[2]"]
    E --> F[Mask first peak region]
    F --> G[find_first_peak on masked volume]
    G --> H[Return second peak and peak-to-peak ratio]

Loading

File-Level Changes

Change	Details	Files
Correct the depth-axis boundary used when masking the first correlation peak.	Bound the exclusion region with the correlation volume’s depth dimension. Preserve existing peak-search behavior for other axes and input arrays.	openpiv/pyprocess3D.py
Add focused regression coverage for second-peak selection and signal-to-noise behavior.	Test explicit and inferred first peaks across rectangular and cubic volumes. Cover zero and nonzero exclusion widths, depth-edge clipping, and unchanged inputs. Verify deep-volume peak-to-peak ratios no longer reuse the first peak.	openpiv/test/test_pyprocess3D.py

Assessment against linked issues

Issue	Objective	Addressed	Explanation
#152	In find_second_peak_3D, bound the peak-exclusion region along the depth axis using the correlation volume’s depth dimension, so the first peak is excluded even when depth exceeds width.	✅
#152	Add regression coverage showing second-peak selection and peak-to-peak signal-to-noise behavior work for deep, rectangular, and other 3D correlation volumes without modifying the input.	✅

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[sourcery-ai]

Hey - I've reviewed your changes and they look great!

Sourcery assessment

Approved.

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