issue_comments: 1510169910

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https://github.com/pydata/xarray/issues/7721#issuecomment-1510169910 https://api.github.com/repos/pydata/xarray/issues/7721 1510169910 IC_kwDOAMm_X85aA102 14808389 2023-04-16T08:09:09Z 2023-04-16T08:09:09Z MEMBER

The issue is that here: https://github.com/pydata/xarray/blob/d4db16699f30ad1dc3e6861601247abf4ac96567/xarray/core/duck_array_ops.py#L193-L206 we try to convert everything to the same dtype, casting numpy and python scalars to an array. The latter is important, because e.g. numpy.array_api.where only accepts arrays as input.

However, detecting cupy beneath (multiple) layers of duckarrays is not easy, which means that for example passing a pint(dask(cupy)) array together with scalars will currently cast the scalars to 0-d numpy arrays, while passing a cupy array instead will result in 0-d cupy arrays.

My naive suggestion was to treat np.int64(0) and np.array(0, dtype="int64") the same, where at the moment the latter would fail for the same reason as np.array([0], dtype="int64").

 
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