[antonsoo]

correlate_windows passes nfftz to the forward FFT but omits the original shape when calling irfftn. For an odd depth, the inverse assumes an even output length, corrupting the correlation and sometimes losing its final plane. Pass the same FFT lengths to the inverse, as required by SciPy's shape convention.

For example, identical synthetic frames currently produce false motion:

import numpy as np
from openpiv.pyprocess3D import extended_search_area_piv3D

frame = np.random.default_rng(0).integers(0, 256, size=(16, 16, 16))
u, v, w = extended_search_area_piv3D(
    frame, frame, window_size=(8, 8, 8), search_area_size=(8, 8, 8), nfftz=17,
)
print(float(np.max(np.abs(w))))
# Before: about 0.808; after: 0.0.

Regression tests compare complete correlations with direct spatial correlation for float32, float64 and integer inputs, unequal windows and odd/even padding. They also check stationary PIV and unchanged inputs. Eighteen cases fail before the change; six controls already pass. Separate particle-volume/tracking runs repair 72 odd-depth configurations while preserving all 72 default/even configurations exactly on Python 3.10/3.12. Recorded-image workflows and the original tutorial retain identical results.

The new test module passes Ruff; the full run retains the same existing findings as master. This is separate from #379; both fixes retain their complete workload results together. Local Linux test suites:

Environment	Standalone	With #379
Python 3.12, Rust installed	278 passed	296 passed
Python 3.10, no Rust	243 passed, 4 optional skips	261 passed, 4 optional skips

Summary by Sourcery

Preserve FFT dimensions during inverse 3D correlation to prevent incorrect motion results for odd-depth transforms.

Bug Fixes:

• Preserve explicitly requested odd FFT depths during 3D correlation so inverse transforms retain the correct output shape and correlation results.

Tests:

• Add regression coverage comparing 3D FFT correlations with direct spatial correlations across numeric types, window shapes, and odd/even padding.
• Verify stationary 3D PIV produces zero motion for odd FFT depths without modifying input frames.

[sourcery-ai]

Reviewer's Guide

The PR fixes 3D FFT-based correlation by supplying irfftn with the explicit (nfftx, nffty, nfftz) shape used for the forward transform, preventing odd-depth outputs from being inferred as even. New regression tests validate correlation values, shape behavior, stationary PIV results, dtype coverage, padding variants, and input immutability.

Sequence diagram for explicit-shape 3D FFT correlation

sequenceDiagram
    participant Correlate as correlate_windows
    participant FFT as scipy.fft
    Correlate->>FFT: rfftn(normalize_intensity(window_a), s=(nfftx,nffty,nfftz))
    FFT-->>Correlate: f2a
    Correlate->>FFT: rfftn(normalize_intensity(window_b), s=(nfftx,nffty,nfftz))
    FFT-->>Correlate: f2b
    Correlate->>FFT: irfftn(f2a * f2b, s=(nfftx,nffty,nfftz))
    FFT-->>Correlate: corr with explicit odd-depth shape
    Correlate->>Correlate: crop correlation to window dimensions

Loading

Flow diagram for preserving odd FFT depth

flowchart LR
    A["Two normalized 3D windows"] --> B["rfftn with nfftx, nffty, nfftz"]
    B --> C["Multiply frequency-domain arrays"]
    C --> D["irfftn with the same FFT shape"]
    D --> E["Crop complete correlation"]

Loading

File-Level Changes

Change	Details	Files
Preserve the requested real-FFT output depth during inverse transforms.	Pass the same three-dimensional FFT lengths to irfftn that are used by rfftn. Retain the existing correlation cropping and real-valued output handling.	openpiv/pyprocess3D.py
Add regression coverage for odd-depth FFT correlation and stationary 3D PIV.	Compare correlations across numeric dtypes, unequal shapes, and varied odd/even padding against direct spatial correlation. Verify odd-depth stationary volumes produce zero motion and that inputs remain unchanged.	openpiv/test/test_pyprocess3d_fft.py

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[sourcery-ai]

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

Sourcery assessment

Approved.

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