🍾 Array-API fallback for sigma_func (MAD) when the namespace is not numpy

[mwcraig]

Part of #929 (the sigma_func half). Companion PR: #1001 (the Combiner.sigma_clipping half), built off the same main.

sigma_func — the default uncertainty_func of median_combine, also used by background_deviation_box/background_deviation_filter — wrapped astropy.stats.median_absolute_deviation, which is numpy-only: on any other array namespace the data was converted to numpy, and on array-api-strict the call failed outright (8 of the 13 remaining strict failures).

Policy (numpy is untouched): when the namespace is numpy, sigma_func still calls astropy exactly as before — same call, same masked-CCDData handling, no new work before the array_api_compat.is_numpy_namespace check. Every other namespace goes to a new private core._mad_fallback that computes the median absolute deviation purely in terms of the array API, on the input's device, using the sort-based medians in _nanfuncs (nanmedian for ignore_nan=True, median otherwise). The fallback promotes int/bool input to the namespace default float, flattens for axis=None, reduces over tuples of axes (permute last + merge), validates axes, and excludes the masked pixels of a CCDData. sigma_func stays the same function object because median_combine tests uncertainty_func is sigma_func.

Also: test_combiner_with_scaling built its reference with xp.asarray((a, b, c)) on a tuple of arrays, which array-api-strict rejects; it now uses xp.stack. The test never got that far before because it failed first in sigma_func.

Tests: the fallback is exercised directly on every backend (the strict job uploads no coverage) against astropy over axis ∈ {None, int, numpy int, tuples incl. negative} × ignore_nan, with int/bool/float32 input and all-NaN slices; the public entry point is checked against astropy.stats.mad_std, for namespace/device of the result, for the fallback branch on numpy (patched is_numpy_namespace), and for the CCDData mask. Design was prototype-verified with zero mismatches vs astropy on numpy, strict (device1), jax and dask.

Docs: changelog entry; the "What limitations should I be aware of?" list in docs/array_api.rst is refreshed (it only mentioned the nanmedian fallback, but nansum/nanmean/nanstd, median and now the MAD also fall back). The core.py sigma_func line leaves the escape baseline (confirmed by a full-suite dask regeneration; the diff is that one line).

Verified locally

• numpy: 616 passed
• array-api-strict: 5 failed (down from 13: the three sigma_clipping tests for the companion PR, Array API: units/Quantity handling with non-numpy arrays #936, Consider marray as a uniform masked-array representation across array backends #983), 0 xpassed
• jax (X64): 615 passed
• dask: 609 passed; escape-baseline enforce run clean

🤖 Generated with Claude Code

https://claude.ai/code/session_01XGArZMnLPC1G7YP62vN18V

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 97.84%. Comparing base (57fd985) to head (b8b0e9f).
⚠️ Report is 1 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1000      +/-   ##
==========================================
+ Coverage   97.78%   97.84%   +0.05%     
==========================================
  Files           9        9              
  Lines        1808     1852      +44     
==========================================
+ Hits         1768     1812      +44     
  Misses         40       40              

Flag	Coverage Δ
dask	96.85% <93.61%> (-0.09%)	⬇️
jax	97.02% <93.61%> (-0.10%)	⬇️
numpy	97.73% <100.00%> (+0.05%)	⬆️

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