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/236#issuecomment-56758849,https://api.github.com/repos/pydata/xarray/issues/236,56758849,MDEyOklzc3VlQ29tbWVudDU2NzU4ODQ5,2062210,2014-09-25T00:28:11Z,2014-09-25T00:28:11Z,NONE,"I'm not sure exactly how it handles dates under the hood, however as a user before using a convenience function from the `cdutil` library to calculate a seasonal climatology or something, you have to use `cdutil.setTimeBounds()` to specify the time bounds of your data (i.e. whether it is daily, monthly, etc). 

To view the time axis of a cdms2 transient variable in a convenient YYYY-MM-DD HH:MM:SS format (e.g. let's say that variable's name is `data`), you can use `data.getTime().asComponentTime()`.

Does that help? 
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,43442970
https://github.com/pydata/xarray/pull/236#issuecomment-56451763,https://api.github.com/repos/pydata/xarray/issues/236,56451763,MDEyOklzc3VlQ29tbWVudDU2NDUxNzYz,2062210,2014-09-22T22:13:11Z,2014-09-22T22:13:11Z,NONE,"Your approach looks good to me. All people need to be able to do is move to and from cdms2 transient variables and then they'll have access to all the convenience functions defined in cdat-lite libraries like `cdutil` and `genutil`. 
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,43442970
https://github.com/pydata/xarray/issues/133#issuecomment-56313445,https://api.github.com/repos/pydata/xarray/issues/133,56313445,MDEyOklzc3VlQ29tbWVudDU2MzEzNDQ1,2062210,2014-09-21T21:29:11Z,2014-09-21T21:29:11Z,NONE,"Happy to test it out for you.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,33639540
https://github.com/pydata/xarray/issues/133#issuecomment-56258103,https://api.github.com/repos/pydata/xarray/issues/133,56258103,MDEyOklzc3VlQ29tbWVudDU2MjU4MTAz,2062210,2014-09-20T06:05:19Z,2014-09-20T06:05:19Z,NONE,"@shoyer I love this suggested enhancement. If I could use xray and CDAT interchangeably, then I'd add xray into my workflow immediately (I'd image many other people would too, as CDAT has a fairly large user base).

The first thing I'd say is that you don't need to install all of [UV-CDAT](http://uvcdat.llnl.gov/) to get the useful modules. Instead, people have developed cdat-lite, which strips away all the visualisation stuff associated with UV-CDAT and just leaves the core convenience functions for calculating climatologies etc (i.e. it strips away the UV bit and just leaves the CDAT).

With the emergence of conda and binstar, it's now very easy to install cdat-lite with Anaconda. This page should be all you need: https://binstar.org/ajdawson/cdat-lite
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,33639540