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  • jthielen · 3 ✖

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  • Change isinstance checks to duck Dask Array checks #4208 · 3 ✖

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660157829 https://github.com/pydata/xarray/pull/4221#issuecomment-660157829 https://api.github.com/repos/pydata/xarray/issues/4221 MDEyOklzc3VlQ29tbWVudDY2MDE1NzgyOQ== jthielen 3460034 2020-07-17T15:04:50Z 2020-07-17T15:04:50Z CONTRIBUTOR

@rpmanser For what it's worth, I'd think doing tests like test_dask.py but with a post-https://github.com/hgrecco/pint/pull/1129 Pint Quantity or suitably mocked wrapper class between xarray and Dask would be best. But, I think this is more of a maintainer-level question.

 
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   Change isinstance checks to duck Dask Array checks #4208 656163384
658140708 https://github.com/pydata/xarray/pull/4221#issuecomment-658140708 https://api.github.com/repos/pydata/xarray/issues/4221 MDEyOklzc3VlQ29tbWVudDY1ODE0MDcwOA== jthielen 3460034 2020-07-14T12:03:05Z 2020-07-14T12:03:19Z CONTRIBUTOR

Revisiting it I now realize that is_array_like is actually not a good name for that function: officially, "array_like" means that it can be casted using np.asarray. Maybe we should rename that to is_duck_array? But then we'd also need to check for __array_ufunc__ and __array_function__ because we also need those to properly wrap duck arrays.

As long as that doesn't break any of the current uses, I think that would be the best way forwards. This would require xarray to be on NumPy 1.16+ (in order to ensure hasattr(np.ndarray, "__array_function__"), but that's only 9 days away by NEP 29, so hopefully that isn't too much of an issue. Then, most of the checks for "can this be wrapped by xarray" should be able to become something like

python is_duck_array(x) and not isinstance(x, (DataArray, Variable)) with is_duck_dask_array only being needed for checking Dask Array-like support.

Although, while we're at it, do we also need more careful handling of upcast types? I'm not sure if there even are any out there right now (not sure if a HoloViews Dataset counts here), but that doesn't necessarily mean there never will be any.

 
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   Change isinstance checks to duck Dask Array checks #4208 656163384
657895835 https://github.com/pydata/xarray/pull/4221#issuecomment-657895835 https://api.github.com/repos/pydata/xarray/issues/4221 MDEyOklzc3VlQ29tbWVudDY1Nzg5NTgzNQ== jthielen 3460034 2020-07-14T00:22:09Z 2020-07-14T00:22:09Z CONTRIBUTOR

Also, a broader discussion that's I've seen hinted to in the past, but is brought to the forefront by this PR: how should xarray be checking for general duck array types it can wrap?

Right now, it looks like there is a mix of

python is_array_like(data) and python hasattr(data, "__array_function__") or isinstance(data, dask_array_type)

and it would be nice to bring consistency. However, both these checks seem like they'd let too many types through. For example, is_array_like as currently implemented would still let through too much, such as MemoryCachedArray and xarray Variables/DataArrays, and __array_function__ doesn't truly indicate a NumPy duck array on its own (and it really only works to only capture downcast types right now since xarray does not yet implement __array_function__ itself, #3917).

 
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   Change isinstance checks to duck Dask Array checks #4208 656163384

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