issue_comments

@dcherian No problem! Thanks for all your help!

LGTM. The reason for requires_pint_0_15 would be nice but I don't feel strongly about it so it's up to you.

Thanks for being patient, @rpmanser.

No worries, thanks for the reviews!

I meant is_duck_array_or_ndarray, not is_duck_dask_array_or_ndarray. I don't suppose there's a way to fix that at this point?

Tests are failing in test_formatting.py for test_diff_attrs_repr_with_array. This looks very similar to the previous problem that was occurring for duck array equality due to NEP18. Is a similar xfail appropriate here?

I'm not sure why FormattingBlack failed. It's claiming that 45 files would be reformatted. I used the pre-commit hook, which passed.

I have erred on the side of leaving the isinstance checks in place where @jthielen reviewed them. If it is decided that no further tests are needed, then this should be ready for review.

On another note, if it is appropriate to apply these changes:

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.

I can do that as well. If not, should a new issue be opened for this?

I'm getting a bit lost within the testing suite while trying to figure out the implications of this change and what kinds of tests are necessary for it. Would a test module for this - Individually test all functions that include the newly implemented is_duck_dask_array()? - Test that a duck dask Array has attributes of a dask Array and redirects the appropriate methods to those defined by Variable, DataArray, and Dataset? - Be similar to test_dask.py? - All, some, or none of the above?

Any suggestions would be greatly appreciated.

I can go ahead with putting together a PR for this. Before I do so, I'd like to clarify what is expected.

• Implement the is_duck_dask_array() function
• In that implementation, use dask.base.is_dask_collection() and the existing duck array check(s)
• Replace isinstance(x, dask.array.Array) checks with the new is_dask_duck_array() function

I searched for existing duck array checks in xarray and nothing immediately obvious to me is showing up. It looks like a check for __array_function__ is inappropriate based on discussion in #3917. Could someone point out the proper duck array check?