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  • shoyer · 2 ✖

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  • Xarray test suite failing with dask-master · 2 ✖

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502731388 https://github.com/pydata/xarray/issues/3009#issuecomment-502731388 https://api.github.com/repos/pydata/xarray/issues/3009 MDEyOklzc3VlQ29tbWVudDUwMjczMTM4OA== shoyer 1217238 2019-06-17T15:27:25Z 2019-06-17T15:27:25Z MEMBER

We're down to 1 failing test!

```python # Invalid cases if not use_dask or LooseVersion(dask.version) > LooseVersion('0.17.4'): with pytest.raises(TypeError):

          xr.dot(da_a, dims='a', invalid=None)

E Failed: DID NOT RAISE <class 'TypeError'> ```

I think this was an intentional change -- see https://github.com/dask/dask/pull/4914.

 
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   Xarray test suite failing with dask-master 454168102
502721965 https://github.com/pydata/xarray/issues/3009#issuecomment-502721965 https://api.github.com/repos/pydata/xarray/issues/3009 MDEyOklzc3VlQ29tbWVudDUwMjcyMTk2NQ== shoyer 1217238 2019-06-17T15:04:40Z 2019-06-17T15:04:40Z MEMBER

Xarray definitely had a bug with ImplicitToExplicitIndexingAdapter. See https://github.com/pydata/xarray/pull/3027 for the fix.

Unfortunately, this triggers new failures from inside dask's blockwise_meta helper: ```

  return meta.astype(dtype)

E AttributeError: 'ImplicitToExplicitIndexingAdapter' object has no attribute 'astype' ```

The bigger issue this surfaces is that I'm not sure whether ImplicitToExplicitIndexingAdapter (and xarray's other custom array types used for data loading) should be in meta or not. These array types overload indexing, but they don't implement the rest of NumPy's ndarray API. So it's a little strange to see them in meta (and of course methods like astype don't work on them).

On the other hand, they do need to be preserved rather than always cast to NumPy arrays. Otherwise repeated indexing like x[i][j] doesn't get optimized by dask, which turns out to be a really important optimization.

 
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   Xarray test suite failing with dask-master 454168102

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