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/pull/2650#issuecomment-505059812,https://api.github.com/repos/pydata/xarray/issues/2650,505059812,MDEyOklzc3VlQ29tbWVudDUwNTA1OTgxMg==,5635139,2019-06-24T15:26:55Z,2019-06-24T15:26:55Z,MEMBER,Thanks for all the help!,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,396084551
https://github.com/pydata/xarray/pull/2650#issuecomment-505056728,https://api.github.com/repos/pydata/xarray/issues/2650,505056728,MDEyOklzc3VlQ29tbWVudDUwNTA1NjcyOA==,5635139,2019-06-24T15:19:26Z,2019-06-24T15:19:26Z,MEMBER,Updated! Let me know any final changes!,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,396084551
https://github.com/pydata/xarray/pull/2650#issuecomment-504490536,https://api.github.com/repos/pydata/xarray/issues/2650,504490536,MDEyOklzc3VlQ29tbWVudDUwNDQ5MDUzNg==,5635139,2019-06-21T16:37:31Z,2019-06-21T16:37:31Z,MEMBER,Great - updated! Let me know any final comments!,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,396084551
https://github.com/pydata/xarray/pull/2650#issuecomment-502934591,https://api.github.com/repos/pydata/xarray/issues/2650,502934591,MDEyOklzc3VlQ29tbWVudDUwMjkzNDU5MQ==,5635139,2019-06-18T03:48:17Z,2019-06-18T03:48:17Z,MEMBER,The gentlest of reminders that I think this is ready to merge (mea culpa for leaving it at 90% for so long),"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,396084551
https://github.com/pydata/xarray/pull/2650#issuecomment-500608059,https://api.github.com/repos/pydata/xarray/issues/2650,500608059,MDEyOklzc3VlQ29tbWVudDUwMDYwODA1OQ==,5635139,2019-06-10T21:49:13Z,2019-06-10T21:49:13Z,MEMBER,"This is updated! Could put an ""Experimental"" label on if we want (or maybe that's implicit).

Let me know any final changes. Will be good to get this merged at last.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,396084551
https://github.com/pydata/xarray/pull/2650#issuecomment-457372793,https://api.github.com/repos/pydata/xarray/issues/2650,457372793,MDEyOklzc3VlQ29tbWVudDQ1NzM3Mjc5Mw==,5635139,2019-01-24T21:56:18Z,2019-01-24T21:56:18Z,MEMBER,"Any thoughts on the API?

@shoyer is making some updates to `numbagg` so as soon as those are complete, we could point the dependencies at that release and merge this","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,396084551
https://github.com/pydata/xarray/pull/2650#issuecomment-454213540,https://api.github.com/repos/pydata/xarray/issues/2650,454213540,MDEyOklzc3VlQ29tbWVudDQ1NDIxMzU0MA==,5635139,2019-01-15T00:15:28Z,2019-01-15T00:15:28Z,MEMBER,"I made an attempt to add the ""skip variables without the dimension"" to `apply_ufunc`, but it's much harder than I expected - there are more cases than I expected (e.g. multiple datasets).

I may be missing something - let me know if there's an reasonable approach

Otherwise I'll do the close thing for this PR, and potentially we can have a look at the general solution later","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,396084551
https://github.com/pydata/xarray/pull/2650#issuecomment-454161008,https://api.github.com/repos/pydata/xarray/issues/2650,454161008,MDEyOklzc3VlQ29tbWVudDQ1NDE2MTAwOA==,5635139,2019-01-14T21:05:17Z,2019-01-14T21:05:17Z,MEMBER,"> That said, if EWM preserves the dimension size/labels you probably don't nee any special logic for coordinates 

The error is when applying over a dimension on a dataset where only some of the variables have the dimension; e.g. applying over `time` on this:

```python
<xarray.Dataset>
Dimensions:  (time: 10, x: 8, y: 2)
Coordinates:
  * x        (x) float64 0.0 0.1429 0.2857 0.4286 0.5714 0.7143 0.8571 1.0
  * time     (time) float64 0.0 0.1111 0.2222 0.3333 ... 0.7778 0.8889 1.0
    c        (y) <U1 'a' 'b'
  * y        (y) int64 0 1
Data variables:
    z1       (y, x) float64 -0.1035 -0.8153 -1.583 ... 1.447 0.7768 -0.2699
    z2       (time, y) float64 0.968 0.7156 -1.64 ... 0.1889 -1.142 0.7172
```

...rather than any issues applying on `coords`. I think the solution is easy - only apply on variables where the dimension exists.

If it's helpful to add that functionality directly to `apply_ufunc`, lmk. I'll focus on getting this working, though","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,396084551
https://github.com/pydata/xarray/pull/2650#issuecomment-453896690,https://api.github.com/repos/pydata/xarray/issues/2650,453896690,MDEyOklzc3VlQ29tbWVudDQ1Mzg5NjY5MA==,5635139,2019-01-14T03:50:09Z,2019-01-14T03:50:45Z,MEMBER,"I think this is in a reasonable state for DataArray, excluding docs. Let me know any feedback on the APi

Does anyone have a view on the canonical way to implement these for Dataset, given potentially only a subset of the variables will have the dimension? 
Tests fail when naively using `apply_ufunc`; `.reduce` looks like it has some functionality for skipping those variables. Or I could do it manually in a couple of lines.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,396084551
https://github.com/pydata/xarray/pull/2650#issuecomment-453842477,https://api.github.com/repos/pydata/xarray/issues/2650,453842477,MDEyOklzc3VlQ29tbWVudDQ1Mzg0MjQ3Nw==,5635139,2019-01-13T16:12:26Z,2019-01-13T16:12:26Z,MEMBER,Tests seem to be failing on a different issue? https://travis-ci.org/pydata/xarray/jobs/479042667#L7759,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,396084551
https://github.com/pydata/xarray/pull/2650#issuecomment-452343420,https://api.github.com/repos/pydata/xarray/issues/2650,452343420,MDEyOklzc3VlQ29tbWVudDQ1MjM0MzQyMA==,5635139,2019-01-08T15:39:43Z,2019-01-08T15:39:43Z,MEMBER,"> BTW, does ewm computes the window mean based on index or coordinate value?

Index, currently. 

Would be great to have an algo that dealt with coord value, and I think not too difficult","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,396084551
https://github.com/pydata/xarray/pull/2650#issuecomment-451837195,https://api.github.com/repos/pydata/xarray/issues/2650,451837195,MDEyOklzc3VlQ29tbWVudDQ1MTgzNzE5NQ==,5635139,2019-01-07T06:44:25Z,2019-01-07T06:44:25Z,MEMBER,"> I know the name ewm matches pandas, but I find it rather inscrutable if you don't already know the acronym. 

👍 

> What about something a little longer, maybe exp_rolling()?

Yes, that works.
One alternative is to add a `window_type` / `weighting` argument to `Rolling` - Exponentially weighted is one of many alternatives

I'm fairly balanced between them - others' thoughts?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,396084551