fix: accept numpy integers as chunk sizes - #4257
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d-v-b merged 3 commits intoAug 12, 2026
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`normalize_chunks_nd` dispatches the scalar convenience form on `numbers.Integral`, but `normalize_chunks_1d` narrowed on `int`. Numpy integer scalars satisfy the former and not the latter, so a per-dimension numpy integer passed the outer dispatch and then fell into the branch meant for explicit per-dimension chunk sequences, where `list(chunks)` raised `TypeError: 'numpy.int64' object is not iterable`. Numpy integers arise naturally whenever a chunk shape is computed rather than written as a literal, since numpy reductions and elementwise ops yield numpy scalars. Narrow on `numbers.Integral` and coerce with `int()`, matching the caller and the sequence branch, which already accepted `Integral` elements. Move the `-1` sentinel check inside that branch. It previously ran on the raw input, so a numpy array chunk specification made `chunks == -1` return an array and raise an ambiguous-truth-value error; rectilinear specs given as numpy arrays now work. A chunk specification that is neither an integer nor iterable now names the offending value and its type instead of surfacing an opaque "object is not iterable" from `list(chunks)`. Fixes zarr-developers#4255 Assisted-by: ClaudeCode:claude-opus-5
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marked this pull request as ready for review
August 12, 2026 19:41
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #4257 +/- ##
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Coverage 94.00% 94.00%
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Files 91 91
Lines 12791 12795 +4
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+ Hits 12024 12028 +4
Misses 767 767
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this is an internal bugfix so I'm going to self-merge. |
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Summary
Fixes #4255 by handling numpy ints correctly in chunk input normalization. written by claude. see the original PR here: d-v-b#288
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TODO
docs/user-guide/*.mdchanges/