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/214#issuecomment-235742423,https://api.github.com/repos/pydata/xarray/issues/214,235742423,MDEyOklzc3VlQ29tbWVudDIzNTc0MjQyMw==,1217238,2016-07-27T22:34:12Z,2016-07-27T22:34:12Z,MEMBER,"Fixed by https://github.com/pydata/xarray/pull/507
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,40395257
https://github.com/pydata/xarray/issues/214#issuecomment-58566084,https://api.github.com/repos/pydata/xarray/issues/214,58566084,MDEyOklzc3VlQ29tbWVudDU4NTY2MDg0,1217238,2014-10-09T19:44:25Z,2014-10-09T19:44:25Z,MEMBER,"What do you mean by ""dependent coordinates""?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,40395257
https://github.com/pydata/xarray/issues/214#issuecomment-58554847,https://api.github.com/repos/pydata/xarray/issues/214,58554847,MDEyOklzc3VlQ29tbWVudDU4NTU0ODQ3,1217238,2014-10-09T18:27:11Z,2014-10-09T18:27:18Z,MEMBER,"The main logic there -- it looks like this is a routine for broadcasting data arrays? I have something similar, but not exactly the same, in `xray.core.variable.broadcast_variables`. It's also very similar to the logic in `xray.Dataset.to_dataframe`, e.g., right now I think you could do the broadcasting by doing `xray.Dataset.from_dataframe(spat_only.to_dataframe())`.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,40395257
https://github.com/pydata/xarray/issues/214#issuecomment-58553960,https://api.github.com/repos/pydata/xarray/issues/214,58553960,MDEyOklzc3VlQ29tbWVudDU4NTUzOTYw,1217238,2014-10-09T18:21:24Z,2014-10-09T18:21:24Z,MEMBER,"The only part that wouldn't work for a Dataset is `spat_only.shape`. On a dataset, you can get that information from the values of the `dims` dictionary (the difference between dims on a dataset and dataarray is definitely an ugly corner of the API).

Also, you probably want to use `c.ravel()` instead of `c.flatten()`, because the later always makes a copy.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,40395257
https://github.com/pydata/xarray/issues/214#issuecomment-57863711,https://api.github.com/repos/pydata/xarray/issues/214,57863711,MDEyOklzc3VlQ29tbWVudDU3ODYzNzEx,1217238,2014-10-03T21:19:57Z,2014-10-03T21:19:57Z,MEMBER,"@WeatherGod Very nice! I'm not entirely sure why you have to reverse y and x at the end, either -- what is the order of the dimensions on `mod['longitude']`?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,40395257
https://github.com/pydata/xarray/issues/214#issuecomment-57849594,https://api.github.com/repos/pydata/xarray/issues/214,57849594,MDEyOklzc3VlQ29tbWVudDU3ODQ5NTk0,1217238,2014-10-03T20:03:53Z,2014-10-03T20:03:53Z,MEMBER,"@WeatherGod You are totally correct. The last dataset on which I have needed to do this was an unprojected grid with a constant increment of 0.5 degrees between points, so finding nearest neighbors was easy.

If you have a lot of points to select at, finding nearest neighbor points could be done efficiently with a tree, e.g., `scipy.spatial.cKDTree`: http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.query.html#scipy.spatial.KDTree.query
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,40395257
https://github.com/pydata/xarray/issues/214#issuecomment-52872417,https://api.github.com/repos/pydata/xarray/issues/214,52872417,MDEyOklzc3VlQ29tbWVudDUyODcyNDE3,1217238,2014-08-21T02:48:07Z,2014-08-21T02:48:07Z,MEMBER,"This operation is actually sort of like reindexing. So perhaps this should be spelled `ds.reindex_like(other)` or `ds.reindex(other.coords)`. With labeled dimensions and variables there is enough metadata to make the reshaping unambiguous.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,40395257