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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
29008494 MDU6SXNzdWUyOTAwODQ5NA== 55 Allow datetime.timedelta coordinates. akleeman 514053 closed 0     4 2014-03-07T23:56:39Z 2014-12-12T09:41:01Z 2014-12-12T09:41:01Z CONTRIBUTOR      

This would allow you to have coordinates which are offsets from a time coordinates which comes in handy when dealing with forecast data where the 'time' coordinate might be the forecast run time and you then want a 'lead' coordinate which is an offset from the run time.

 
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   completed xarray 13221727 issue
28600785 MDU6SXNzdWUyODYwMDc4NQ== 39 OpenDAP loaded Dataset has lon/lats with type 'object'. akleeman 514053 closed 0     4 2014-03-03T06:07:17Z 2014-03-24T07:21:02Z 2014-03-24T07:21:02Z CONTRIBUTOR      

ds = xray.open_dataset('http://motherlode.ucar.edu/thredds/dodsC/grib/NCEP/GFS/Global_0p5deg/files/GFS_Global_0p5deg_20140303_0000.grib2', decode_cf=False) In [4]: ds['lat'].dtype Out[4]: dtype('O')

This makes serialization fail.

 
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   completed xarray 13221727 issue
28445412 MDU6SXNzdWUyODQ0NTQxMg== 26 Allow the ability to add/persist details of how a dataset is stored. akleeman 514053 closed 0     4 2014-02-27T19:10:38Z 2014-03-03T02:54:16Z 2014-03-03T02:54:16Z CONTRIBUTOR      

Both Issues https://github.com/akleeman/xray/pull/20 and https://github.com/akleeman/xray/pull/21 are dealing with similar conceptual issues. Namely sometimes the user may want fine control over how a dataset is stored (integer packing, time units and calendars ...). Taking time as an example, the current model interprets the units and calendar in order to create a DatetimeIndex, but then throws out those attributes so that if the dataset were re-serialized the units may not be preserved.

One proposed solution to this issue is to include a distinct set of encoding attributes that would hold things like 'scale_factor', and 'add_offset' allowing something like this

``` ds['time'] = ('time', pd.date_range('1999-01-05', periods=10)) ds['time'].encoding['units'] = 'days since 1989-08-19' ds.dump('netcdf.nc')

ncdump -h ... int time(time) ; time:units = "days since 1989-08-19" ; ... ```

The encoding attributes could also handle masking, scaling, compression etc ...

 
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   completed xarray 13221727 issue

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