Array-API-native block_reduce, block_average and block_replicate off numpy

[mwcraig]

ccdproc.core.block_reduce, block_average and block_replicate are thin
wrappers around astropy.nddata, which starts by calling numpy.asanyarray
on its input. On a non-numpy array library that silently hands back a numpy
array (dask, jax) or fails outright when the data live on a device numpy
cannot reach (array-api-strict, and by extension cupy on a GPU). These were
three of the remaining array-API escapes in the escape baseline.

This adds ccdproc/_blocks.py, which does the same work using only array API
operations (reshape, permute_dims, repeat and slicing), and dispatches
to it for every namespace that is not numpy.

This is deliberately a stopgap: it exists so ccdproc's array-API work does not
have to wait for an astropy release. _blocks.block_reduce and
_blocks.block_replicate, minus the ccdproc dispatch, are the intended
starting point for astropy/astropy#15073; once astropy's own blocks.py is
array-API aware and that version is ccdproc's floor, this module and the
dispatch both go away. (block_average has no astropy counterpart -- it is
ccdproc's own thin wrapper -- so only the other two are candidates for the
lift.)

Dispatch policy

The numpy path is unchanged: if array_api_compat.is_numpy_namespace(xp) the
functions call astropy.nddata exactly as before, with the same arguments.
Everything else goes to _blocks. The CCDData rebuild and the
"following attributes were set ... will be ignored" warning behaviour are
untouched -- the native helpers are decorated with
astropy.nddata.support_nddata, as astropy's own are, so a CCDData
argument is unpacked and warned about identically on every backend.

block_size validation is done in pure Python and reproduces astropy's three
checks in astropy's order with astropy's messages verbatim; the tests assert
against astropy's live output rather than hard-coded strings so the two cannot
drift apart.

Behaviour differences

Results are identical to astropy's except for two dtype promotions, both of
integer/boolean input to the namespace's default real floating dtype:

• block_replicate(..., conserve_sum=True) promotes before dividing, because
  array-api-strict rejects integer true division rather than promoting.
• block_average promotes before averaging, because array-api-strict rejects
  a non-floating mean. numpy promotes on its own here, and jax and dask
  follow it, so without this an integer image averaged fine on three backends
  and raised on the fourth.

numpy returns float64 in both cases anyway, so these differ only for a
library whose default real dtype is not float64. Documented in
docs/array_api.rst and CHANGES.rst.

Verified backends

Run with the dev environment directly, CCDPROC_ARRAY_LIBRARY=<lib> (plus
JAX_ENABLE_X64=1 for jax); array-api-strict runs on its non-default
device1, which is where np.asarray raises.

pytest ccdproc/tests/test_blocks.py ccdproc/tests/test_ccdproc.py:

backend	result
numpy	168 passed
jax	168 passed
dask	168 passed
array-api-strict (device1)	159 passed, 9 xfailed

Full suite, this branch vs. the merge base:

backend	branch	merge base
numpy	1045 passed, 5 skipped	975 passed, 5 skipped
array-api-strict	997 passed, 10 skipped, 43 xfailed	924 passed, 10 skipped, 46 xfailed
dask (escape enforcement)	1034 passed, 16 skipped	not run
jax	1040 passed, 10 skipped	not run

The deltas are fully accounted for: the 70 new tests in test_blocks.py, plus
the three formerly-xfailed array-api-strict tests now passing.

The three block_* lines are removed from
ccdproc/tests/array_escape_baseline.txt. Verified with a full dask run under
CCDPROC_LOG_ARRAY_ESCAPES=1 CCDPROC_ENFORCE_ESCAPE_BASELINE=1: no escapes
outside the baseline, no block_* sites in the escape log, and temporarily
restoring the three lines makes the ratchet report exactly those three as
"not hit this run" and nothing else.

Things worth a reviewer's eye

• _nanfuncs._fill_doc gained a positional-only template argument so
  _blocks.py can reuse the docstring templating with its own parameter
  block. Backwards compatible; no _nanfuncs docstring changed.
• core._block_namespace resolves the namespace as
  ccd.data if isinstance(ccd, CCDData) else ccd, matching sigma_func's
  convention. The old code used ccd.data unconditionally, which crashed for
  a bare array (np.ndarray.data is a memoryview) -- bare arrays now work.
  The narrowing is that a plain list passed with an explicit func used to
  reach astropy and now raises TypeError from array_namespace; nothing in
  the test suite or docs does that.
• The promotion guard is xp.isdtype(dtype, ("integral", "bool")) rather than
  "not real floating", so complex input is not silently truncated to real.
• With the array-api-strict xfail markers removed, the three existing block
  tests failed one step later on their own xp.zeros((4, 4), dtype=bool),
  which array-api-strict rejects; they now ask for dtype=xp.bool, as the
  rest of that file already does. The ccd._mask = ... TODO is untouched.
• The first commit is green only on numpy: test_blocks.py ships with it and
  includes two tests of the core dispatch that need the second commit on
  non-numpy backends. Squashing the first two commits would fix that if you
  care about per-commit bisection on the backend jobs.

Part of #971.

🤖 Generated with Claude Code

https://claude.ai/code/session_01F6f9L1GyMHrKnvLrrgWbWN

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 98.05%. Comparing base (9d18599) to head (fdaad17).
⚠️ Report is 5 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1009      +/-   ##
==========================================
+ Coverage   97.97%   98.05%   +0.07%     
==========================================
  Files           9       10       +1     
  Lines        1927     2005      +78     
==========================================
+ Hits         1888     1966      +78     
  Misses         39       39              

Flag	Coverage Δ
dask	97.05% <94.31%> (-0.15%)	⬇️
jax	97.20% <94.31%> (-0.15%)	⬇️
numpy	97.80% <96.59%> (-0.07%)	⬇️

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