Precompute unit-pair spatial overlap once in find_collisions

[JESUSROYETH]

Description

find_collisions (used by compute("amplitude_scalings") when handle_collisions=True, the default) calls _are_units_spatially_overlapping(sparsity_mask, i, j) once per temporally-overlapping spike-pair candidate. That helper recomputes np.any(sparsity_mask[i] & sparsity_mask[j]) from scratch every time, but the answer only depends on the pair of unit indices — a fixed fact of sparsity_mask with at most num_units**2 distinct values. On a realistic recording this ends up being recomputed millions of times over the course of a run.

This replaces it with _unit_pair_overlap_matrix(sparsity_mask), a single integer matrix multiplication that returns the full unit-pair overlap matrix. Since sparsity_mask is fixed for the whole node's lifetime, it's precomputed once in AmplitudeScalingNode.__init__ (not once per chunk inside compute()/find_collisions, which is where the matrix multiplication originally landed) and looked up with vectorized fancy indexing instead of a per-candidate Python loop. Same boolean logic, so results are unchanged.

Performance

Representative public call, sorting_analyzer.compute("amplitude_scalings", n_jobs=1), on a 384-channel/150 s/200-unit ground-truth recording (450,920 spikes, materialized to binary before timing), with waveforms/templates/noise_levels precomputed outside the timed region. Measured on a GCP c3-standard-8 (Intel Sapphire Rapids), numerical threads pinned to 1, 5 repeats with baseline/patched order alternated, 1 discarded warmup:

	median	range
Before	142.351 s	141.847 – 144.392 s
After	125.011 s	124.554 – 126.046 s

Per-repeat speedup: 12.18%, 13.74%, 12.20%, 11.14%, 12.29% (median 12.20%, same sign every time).

Validation

• Bit-identical: 300 randomized trials (2–40 units, 2–64 channels, both random and explicitly non-contiguous per-unit channel subsets, an empty-sparsity-row edge case) comparing find_collisions before/after (precomputed-matrix call included) — 0 mismatches.
• New test test_unit_pair_overlap_matrix_matches_naive_reference: checks the overlap matrix against a direct np.any(mask[i] & mask[j]) reference for every unit pair, including the empty-row case. Fails with ImportError on unpatched main (function doesn't exist yet), passes on this patch.
• A discriminating mutant (requiring full rather than any channel overlap) is caught by both the new test and the existing test_find_collisions_with_margin_indices (added in Speed up collision-aware amplitude scaling #4786).
• Also ran the full public path end to end (compute("amplitude_scalings", handle_collisions=True), real 18,027-scaling output) before/after — bit-identical.
• Full postprocessing suite: 123 passed, 44 skipped, no regressions.
• black/style clean; no collision with the open draft PR touching this file ([DRAFT] Extract waveforms to zarr dataset #4708, unrelated import only).

Tested on Linux only (no macOS/Windows access here); the change has no platform-dependent code path, and this module is already covered by the ubuntu/macOS/Windows × py3.10/3.13 CI matrix. The overlap matrix is num_units x num_units, so memory grows quadratically with unit count — negligible here (~500 KB at 200 units) but worth knowing about at extreme unit counts (~500 MB at 10,000 units).