pull_requests: 1016457019

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id node_id number state locked title user body created_at updated_at closed_at merged_at merge_commit_sha assignee milestone draft head base author_association auto_merge repo url merged_by
1016457019 PR_kwDOAMm_X848lec7 6874 closed 0 Avoid calling np.asarray on lazy indexing classes 2448579 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 2022-08-03T15:13:00Z 2023-03-31T15:15:31Z 2023-03-31T15:15:29Z 2023-03-31T15:15:29Z aa4361dafbf69e5872d8870ce73d082ac9400db0     0 cbd030e236048be1541627a8436c7b86a46cc43f 0ac5541f12894616f35f63cbc884d8d84bcc07d4 MEMBER   13221727 https://github.com/pydata/xarray/pull/6874  

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