html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/3413#issuecomment-545140066,https://api.github.com/repos/pydata/xarray/issues/3413,545140066,MDEyOklzc3VlQ29tbWVudDU0NTE0MDA2Ng==,6213168,2019-10-22T20:29:58Z,2019-10-22T20:29:58Z,MEMBER,@jhamman are you happy to close the ticket?,"{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,508743579
https://github.com/pydata/xarray/issues/3413#issuecomment-544210342,https://api.github.com/repos/pydata/xarray/issues/3413,544210342,MDEyOklzc3VlQ29tbWVudDU0NDIxMDM0Mg==,6213168,2019-10-20T01:06:27Z,2019-10-20T01:06:27Z,MEMBER,"It's working as intended.

apply_ufunc verifies that indices are aligned. Note the optional parameter ``join='exact'``. You have two implicit pd.RangeIndex on dimension t that have a different number of elements - which means they are not aligned. Hence, when apply_ufunc internally calls ``xarray.align(Xda, yda, join=""exact"")``, it falls over.

You have two options:
1. add ``join='outer'`` to the apply_ufunc call, which will cause the shorter of the two variables to be padded with NaNs. You'll also need to replace ``mean()`` with ``nanmean()`` in your kernel. This however is horribly inefficient.
2. rename one of the two dimensions to tell apply_ufunc that they aren't meant to be aligned:

```python
out = xr.apply_ufunc(
    diff_mean,
    Xda,
    yda.rename({""t"": ""t2""}),
    dask=""parallelized"",
    input_core_dims=[['t'], ['t2']],
    output_core_dims=[[]],
    output_dtypes=[np.float],
)
```

While on the topic, note that ``vectorize=True`` is asking xarray to slice the numpy array, do a for loop in pure python applying your kernel multiple times, and then concatenate the output back together - that is, horribly slow. If you can avoid it, you should, and your kernel definitely can be changed to process arbitrary unknown dimensions:

```python
def diff_mean(X, y):
    assert X.shape[-1] != y.shape[-1]
    return X.mean(axis=-1) - y.mean(axis=-1)
```","{""total_count"": 1, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 1, ""rocket"": 0, ""eyes"": 0}",,508743579