use numpy dtype exposed by zarr array instead of metadata.data_type #10348
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* main: Fix performance regression in interp from pydata#9881 (pydata#10370) html repr: improve style for dropdown sections (pydata#10354) Grouper tweaks. (pydata#10362) Docs: Add links to getting help mermaid diagram (pydata#10324) Enforce ruff/flynt rules (FLY) (pydata#10375) Add missing AbstractWritableDataStore base methods and arguments (pydata#10343) Improve html repr in dark mode (Jupyterlab + Xarray docs) (pydata#10353) Pin Mypy to 1.15 (pydata#10378) use numpy dtype exposed by zarr array instead of metadata.data_type (pydata#10348) Fix doc typo for caption "Interoperability" (pydata#10374) Implement cftime vectorization as discussed in PR pydata#8322 (pydata#8324) Enforce ruff/flake8-pyi rules (PYI) (pydata#10359) Apply assorted ruff/Pylint rules (PL) / Enforce PLE rules (pydata#10366) (fix): pandas extension array repr for int64[pyarrow] (pydata#10317) Enforce ruff/flake8-implicit-str-concat rules (ISC) (pydata#10368) Enforce ruff/refurb rules (FURB) (pydata#10367) Ignore ruff/Pyflakes rule F401 more precisely (pydata#10369) Apply assorted ruff/flake8-simplify rules (SIM) (pydata#10364) Apply assorted ruff/flake8-pytest-style rules (PT) (pydata#10363) Fix "a array" misspelling (pydata#10365)
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in
mainthe zarr backend uses thezarr_array.metadata.data_typeattribute to get the dtype of the zarr array. If the goal is to ultimately get a numpy data type, it's simpler and safer to retrieve the data type from from thedtypeattribute of the zarr array. This PR makes that change.This addresses on of the incompatibilities noted in #10329