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  • DamienIrving · 4 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
56758849 https://github.com/pydata/xarray/pull/236#issuecomment-56758849 https://api.github.com/repos/pydata/xarray/issues/236 MDEyOklzc3VlQ29tbWVudDU2NzU4ODQ5 DamienIrving 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? 

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   WIP: convert to/from cdms2 variables 43442970
56451763 https://github.com/pydata/xarray/pull/236#issuecomment-56451763 https://api.github.com/repos/pydata/xarray/issues/236 MDEyOklzc3VlQ29tbWVudDU2NDUxNzYz DamienIrving 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. 

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   WIP: convert to/from cdms2 variables 43442970
56313445 https://github.com/pydata/xarray/issues/133#issuecomment-56313445 https://api.github.com/repos/pydata/xarray/issues/133 MDEyOklzc3VlQ29tbWVudDU2MzEzNDQ1 DamienIrving 2062210 2014-09-21T21:29:11Z 2014-09-21T21:29:11Z NONE

Happy to test it out for you.

 
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   Functions for converting to and from CDAT cdms2 variables 33639540
56258103 https://github.com/pydata/xarray/issues/133#issuecomment-56258103 https://api.github.com/repos/pydata/xarray/issues/133 MDEyOklzc3VlQ29tbWVudDU2MjU4MTAz DamienIrving 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 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

 
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   Functions for converting to and from CDAT cdms2 variables 33639540

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