issue_comments

Things seem to have died down here so let's test it out.

Thanks @rpmanser This is an amazing first PR. Thanks for your efforts and welcome to xarray!

you mean the commit message? If so, you can use git commit --amend to change it (make sure you don't have anything staged because that would be added to the commit) and force-push. It's not that important, though, I doubt anyone really reads the commit messages.

we went with https://github.com/pydata/xarray/blob/edd5c1e96204d59d30496e3f85e2f805a911ac9b/xarray/core/variable.py#L938 in #4379, you could use something like that here: https://github.com/pydata/xarray/blob/3337b6fc4024dcaa2c11831e6d34551adaee4a8b/xarray/core/formatting.py#L554 too, e.g. with: python def is_duck_array_or_ndarray(array): return is_duck_array(array) or (not IS_NEP18_ACTIVE and isinstance(data, np.ndarray))

yes, the failing test is definitely due to NEP18: there's a numpy array in an attribute, but since numpy 1.15 doesn't define __array_function__, is_duck_array returns False. We could extend is_duck_array with an isinstance check for numpy.ndarray, or we could take your suggestion and xfail the test. Not sure which is better.

I would suggest adding an explicit check for NumPy arrays. Otherwise we would need to bump our minimum supported NumPy version.

yes, the failing test is definitely due to NEP18: there's a numpy array in an attribute, but since numpy 1.15 doesn't define __array_function__, is_duck_array returns False. We could extend is_duck_array with an isinstance check for numpy.ndarray, or we could take your suggestion and xfail the test. Not sure which is better.

black recently released a new version, which changed the way it treats a trailing comma, and it also reformats docstrings (at least as much as it can without breaking formats), but neither conda nor the pre-commit hook install the new version (yet). To make the diff of this PR as small as possible, I'm opening a new PR with just the automatic changes, once that is merged, it should be fixed by merging in master.

Edit: see #4381

Thanks for working on this @rpmanser.

Since you've changed every isinstance check, if test_dask passes, you're doing fine in general.

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?

Seems like this is covered by test_dask already but I could be wrong.

We could add specific pint-as-dask tests to test_units with appropriate xfails (they should run on the upstream-dev test config).

This could be a discussion topic for our next dev meeting on wednesday.

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.