issue_comments

Confirmed that NUMPY_EXPERIMENTAL_ARRAY_FUNCTION=0 fixes this issue for me.

Have submitted a PR with your suggested fix https://github.com/pydata/xarray/pull/3220

Confirmed the submitted code fixes my issue.

Thanks for the explanation.

I still don't understand the different code paths between decode_cf=True and decode_cf=False + explicit call to xarray.decode_cf()

In the first case, CF conventions decoding is done on xarray's internal lazy BackendArray objects.

In the second case, CF conventions decoding is done on dask arrays. There are a few cases where this can be slower / require loading more data to look at a small slice of array data.

Thanks @shoyer.

I still don't understand the different code paths between decode_cf=True and decode_cf=False + explicit call to xarray.decode_cf()

A short term work around is to set the environment variable NUMPY_EXPERIMENTAL_ARRAY_FUNCTION=0 before importing NumPy.

This is definitely due to NumPy 1.17 / __array_function__, which means that np.ravel now calls out to a dask function rather than coercing to NumPy.

I think the right fix is to add explicit casting to NumPy, e.g., np.ravel(np.asarray(array[indexer]))

Seems that this is by design, from here

Dask Array doesn’t implement operations like tolist that would be very inefficient for larger datasets. Likewise, it is very inefficient to iterate over a Dask array with for loops

So dask has never had a tolist method, so in one case the object is a dask array, but not in the other case.

I still don't understand why it fails when decode_cf is called separately. Suggests there is a different code path as all underlying packages are identical.

I think this is being thrown by dask, here is an even more minimal example: ```python

import dask as da da.array.from_array([]).tolist() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'Array' object has no attribute 'tolist' ```

I'm having the same issue with xarray 0.12.3, numpy 1.17.0, python 3.7.3.