id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
110890919,MDU6SXNzdWUxMTA4OTA5MTk=,621,An iris cube converter?,2062210,closed,0,,,1,2015-10-12T00:11:40Z,2017-12-20T15:14:17Z,2017-12-20T15:14:17Z,NONE,,,,"Would it be possible to include a function to convert from an `xray.DataArray` to an `iris.cube`? 

For weather/climate/ocean science, xray is by far the best library for general data processing, while [iris and cartopy](http://scitools.org.uk/) are the best for geospatial plotting. At the moment I usually write the results of my xray data processing out to a netCDF file, then read that file in with iris for plotting. I'm assuming many other people do the same, so a `.to_cube()` function might allow people to skip that extra input/output step.
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51518779,MDU6SXNzdWU1MTUxODc3OQ==,290,Journal of Open Research Software,2062210,closed,0,,,2,2014-12-10T04:58:59Z,2016-08-04T21:17:53Z,2016-08-04T21:17:53Z,NONE,,,,"I'm envisaging that people (including myself in the future) might like to cite xray in their journal papers (along with the other software packages and libraries that they used). I was therefore wondering if you have thought about writing a paper about xray for the [Journal of Open Research Software](http://openresearchsoftware.metajnl.com/)? This would be a great way for you to get academic credit for the hard work you've put into it, and would also make it very easy for authors to cite.
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91665446,MDU6SXNzdWU5MTY2NTQ0Ng==,447,Add rolling mean function,2062210,closed,0,,,2,2015-06-29T01:37:23Z,2015-06-29T03:17:53Z,2015-06-29T03:17:53Z,NONE,,,,"A nice addition to the [time-series functionality](http://xray.readthedocs.org/en/stable/time-series.html) that is built into xray would be the ability to calculate a rolling mean (e.g. for those cases where you've got daily data but you want to work on the 10-day running mean or something).

As far as I'm aware (apologies if I'm wrong), to do a rolling mean at the moment you have to convert to a pandas DataFrame, used the pandas [`rolling_mean()`](http://pandas.pydata.org/pandas-docs/stable/computation.html#moving-rolling-statistics-moments) function and then convert back to an xray data array. This is fine for one dimensional data, but not so nice when you want to do it over a large (time, lat, lon) array.
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32763132,MDU6SXNzdWUzMjc2MzEzMg==,112,Why not CDAT?,2062210,closed,0,,,3,2014-05-04T07:03:45Z,2014-09-27T01:20:43Z,2014-09-02T03:41:41Z,NONE,,,,"In your main README file you ask the questions ""Why not Pandas?"" and ""Why not Iris?"" Another question you might want to ask is ""Why not [CDAT](http://www2-pcmdi.llnl.gov/cdat)?"" It was written quite a long time ago now but is still used extensively by [UV-CDAT](http://uvcdat.llnl.gov/) and thus by the [ESGF](http://esgf.org/). In particular, the cdms2, cdutil, genutil and MV2 libraries within CDAT do some of what xray does.  
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