Standardize one-dimensional fancy indices

[crusaderky]

The current situation when an index is an array of ints or bools is very messy:

• numpy blindly passes every unexpected object it finds to np.asarray, which makes it accept np.ndarray, lists, tuples, but also memoryviews and anything with an __array__ interface
• Sparse accepts lists, tuples, or numpy arrays; other sparse arrays don't work
• PyTorch won't accept numpy arrays of unsigned integers
• PyTorch won't accept PyTorch arrays of integers with dtype other than the native int
• JAX won't accept lists or tuples
• dask won't accept tuples

I think the array API standard should define a reasonable common surface.

xref data-apis/array-api-compat#205 (comment)

NOTE: the much more complicated case of multiple fancy indices, e.g. a[[0, 1], [1, 2]] is out of scope for this issue.

[rgommers]

Just a quick note that take is the standardized/uniform way of doing indexing with an integer array (xref gh-416 and the issue that PR links to).

You're completely right that it's very messy across libraries - it's just very hard, since we can't mandate that libraries stop doing something and break backwards compatible (or technically we can, but it'll probably just be ignored rather than implemented).

[crusaderky]

Just a quick note that take is the standardized/uniform way of doing indexing with an integer array (xref gh-416 and the issue that PR links to).

I couldn't find put or anything similar though? How do you update along a fancy index?