Build(deps): update visqol-python requirement from >=3.5.0 to >=3.6.0

[dependabot]

Updates the requirements on visqol-python to permit the latest version.

Release notes

Sourced from visqol-python's releases.

Release v3.6.0

Full Changelog: talker93/visqol-python@v3.5.0...v3.6.0

Changelog

Sourced from visqol-python's changelog.

[3.6.0] - 2026-05-27

Added

• Optional pyFFTW backend (pip install visqol-python[fftw]):
  • Routes scipy.fft.fft / ifft / rfft / irfft through FFTW3 via pyfftw.interfaces.scipy_fft for the alignment and cross-correlation FFTs. Detected at module load time in signal_utils and applied transparently via a thin _fft_backend() context manager.
  • Plan cache enabled with 60 s keep-alive so consecutive measurements on equal-length signals reuse the FFTW plan.

Improved

• Fused NSIM kernel: _measure_patch_similarity_numba merged its 5 separate 2-D convolutions (μ_r, μ_d, ref², deg², ref·deg) and the intensity/structure recombination into one (r, c) double loop. Each patch element is read from L1 once per visit instead of five times, and the four intermediate (rows × cols) matrices are no longer materialised between convs. Bit-exact with the previous split-conv path (ULP-level FP rounding only).
• nsim.measure_patch_similarity now dispatches to the fused JIT kernel when Numba is available — the same code path the DP patch matcher uses, so finely_align_and_recreate_patches shares the speedup. The pure-NumPy implementation is preserved as a fallback.
• signal_utils._hilbert is a drop-in scipy.signal.hilbert replacement built on rfft for the real-valued input (~2× less forward-transform work than the original full complex fft).
• signal_utils.find_best_lag now uses rfft + irfft for the cross-correlation, again exploiting the real input. Net effect: alignment FFTs see roughly 2× less work overall.
• Removed dead helper _conv2d_boundary_valid from numba_accel.py (subsumed by the fused kernel).

Fixed

• find_best_lag interpreter hot loop: the previous implementation ran xcorr_full[-max_lag:].tolist() + xcorr_full[:max_lag+1].tolist() and then builtin argmax(list) over a ~1.2 M-element Python list, costing ~33 ms per call in pure interpreter overhead. Now uses np.concatenate + np.argmax entirely in C.

Performance

Apple M-series, Python 3.13, audio mode, the guitar48_stereo 12.5 s conformance case, average of 3 runs with Numba + pyFFTW both installed:

Stage	v3.5.0	v3.6.0	Speedup
DP Patch matching	0.397 s	0.131 s	3.0×
Global align / FFT	0.173 s	0.091 s	1.9×
Fine align + NSIM	0.093 s	0.043 s	2.2×
Gammatone	0.173 s	0.179 s	~

... (truncated)

Commits

• 388c68a v3.6.0: fused NSIM kernel + pyFFTW alignment backend
• 23501db perf: rfft/irfft for real-input alignment FFTs
• e6f2f57 perf: fused NSIM kernel + pyFFTW alignment backend
• See full diff in compare view

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[dependabot]

Superseded by #18.