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Chunk specifications were parsed by `normalize_chunks_nd`, but a separate duck-typed classifier, `_is_rectilinear_chunks`, ran on the raw input first at three sites to decide whether the spec was rectilinear. Two opinions on the same input is the shape of the bug in zarr-developers#4374, and they could disagree: a 0-d numpy array counted as rectilinear because it has `__iter__`. The classifier is gone. Each site normalizes first and asks the resulting `ChunkGrid` (`is_regular`); stored rectilinear metadata passed as `chunks=` counts as rectilinear even when its edges are uniform. The shard resolver sends regular and rectilinear shard specs through the same normalizer. Also fixed on the way: - The legacy v2 branch of `AsyncArray.create` tested `chunks or chunk_shape`, so `zarr.create(chunks=np.array([...]), zarr_format=2)` failed with "truth value of an array is ambiguous". It now uses the `is not None` form the v3 branch already had. - 0-d numpy arrays unwrap to their scalar in both normalizers instead of failing with "len() of unsized object". - A non-integer scalar spec (`2.0`, `np.float64`) raises the normalizer's own TypeError instead of "object has no len()". Assisted-by: ClaudeCode:claude-fable-5-1
Assisted-by: ClaudeCode:claude-fable-5-1
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #4376 +/- ##
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- Coverage 94.34% 94.22% -0.12%
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Files 92 92
Lines 12935 12932 -3
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- Hits 12203 12185 -18
- Misses 732 747 +15
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Follow-up to #4374 / #4375, asking whether there is a single, correct function from user input (
chunks=) to a chunk grid configuration.What was there
The parser is
normalize_chunks_nd→normalize_chunks_1d, and it already handled numpy scalars (numbers.Integral) and numpy arrays (iterated, elements coerced withint()) correctly at every entry point. But it was not the only logic on the path: a separate duck-typed classifier,_is_rectilinear_chunks, ran on the raw input at three sites (AsyncArray.create,init_array,resolve_outer_and_inner_chunks) to decide whether the spec was rectilinear, so the v2 and sharding policy errors could fire. Two opinions on the same input is the shape of the bug in #4374, and they could disagree (a 0-d numpy array counted as rectilinear because it has__iter__).Changes
_is_rectilinear_chunksis deleted. Each site normalizes first and asks theChunkGrid(is_regular); storedRectilinearChunkGridMetadatapassed aschunks=still counts as rectilinear even when its edges are uniform. The shard resolver sends regular and rectilinear shard specs through the same normalizer.AsyncArray.createdidchunks or chunk_shape, sozarr.create(chunks=np.array([5, 3]), zarr_format=2)failed with "truth value of an array is ambiguous". It now uses theis not Noneform the v3 branch already had.2.0,np.float64(2.0)) raises the normalizer's ownTypeError(was "object has no len()").Tests
-1) × three creation paths (create_arrayv3,create_arrayv2, legacycreatev2), asserting the stored chunks are plain ints.(2, np.array([3, 3])), 2-D arrays, edges of numpy ints) produce the expectedRectilinearChunkGridMetadata.shards=.test_mixed_chunks_gates_are_order_independent,test_pipeline_sharding_rejects_rectilinear_chunks_with_shards) still pass against the normalized-grid check.Not changed:
Trueis still accepted as chunk size 1 (boolisIntegral); flagging rather than deciding that here.🤖 Generated with Claude Code