Validate hotspots() kernel like apply()/focal_stats()#2799
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hotspots() never ran its kernel through custom_kernel(), so kernel=None and a list-of-list kernel raised AttributeError on kernel.shape, an even-dimensioned kernel silently succeeded, and a zero-sum kernel divided by zero during normalization. Run custom_kernel(kernel) before the memory check and add a zero-sum guard (hotspots is the only focal function that normalizes by kernel.sum()). Add regression tests for the None, list-of-list, even-dim, and zero-sum cases plus a valid-kernel happy path.
brendancol
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Jun 1, 2026
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PR Review: Validate hotspots() kernel like apply()/focal_stats()
Blockers (must fix before merge)
None.
Suggestions (should fix, not blocking)
None.
Nits (optional improvements)
- focal.py:1509 -- the guard catches exactly-zero sums. A mixed-sign kernel that sums to roughly zero but not exactly zero (floating-point near-cancellation) still slips through and produces a near-infinite normalization. This matches focal_stats() and is within the issue scope, so leaving it is fine.
What looks good
- Validation runs before backend dispatch, so numpy, cupy, dask+numpy, and dask+cupy all reject bad kernels the same way. The 3D path recurses through hotspots() per band, so each band validates too.
- custom_kernel() runs before kernel.sum(), so the sum call always has an ndarray. No AttributeError risk.
- Error messages name the function and explain the failure.
- Tests cover None, list-of-list, even-dim, and zero-sum kernels plus a valid happy path. Full test_focal.py passes (155).
Checklist
- Algorithm unchanged; only input validation added
- All backends get consistent validation (runs before dispatch)
- NaN handling unchanged
- Edge cases covered by tests
- Dask path unaffected (validation is pre-dispatch)
- No premature materialization
- Benchmark not needed (validation-only change)
- README feature matrix not applicable (no new function)
- Docstrings present (existing hotspots docstring unchanged)
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Summary
hotspots() skipped the kernel validation that apply() and focal_stats() run via custom_kernel(), so bad kernels produced confusing errors or wrong behavior:
Backend coverage
The validation runs before backend dispatch, so it applies to numpy, cupy, dask+numpy, and dask+cupy.
Test plan
Closes #2771