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1327380960 PR_kwDOAMm_X848lec7 6874 Avoid calling np.asarray on lazy indexing classes dcherian 2448579 closed 0     6 2022-08-03T15:13:00Z 2023-03-31T15:15:31Z 2023-03-31T15:15:29Z MEMBER   0 pydata/xarray/pulls/6874

This is motivated by https://docs.rapids.ai/api/kvikio/stable/api.html#kvikio.zarr.GDSStore which on read loads the data directly into GPU memory.

Currently we rely on np.asarray to convert a BackendArray wrapped with a number of lazy indexing classes to a real array but this breaks for GDSStore because the underlying array is a cupy array, so using np.asarray raises an error.

np.asarray will raise if a non-numpy array is returned so we need to use something else.

Here I added get_array which like np.array recurses down until it receives a duck array.

Quite a few things are broken I think , but I'd like feedback on the approach.

I considered np.asanyarray(..., like=...) but that would require the lazy indexing classes to know what they're wrapping which doesn't seem right.

Ref: https://github.com/xarray-contrib/cupy-xarray/pull/10 which adds a kvikio backend entrypoint

 
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     xarray 13221727 pull

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