issue_comments: 300654851

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https://github.com/pydata/xarray/pull/1252#issuecomment-300654851 https://api.github.com/repos/pydata/xarray/issues/1252 300654851 MDEyOklzc3VlQ29tbWVudDMwMDY1NDg1MQ== 6628425 2017-05-11T01:12:54Z 2017-05-11T01:12:54Z MEMBER
  1. How is this going? Could you use some help with anything?

@jhamman sure thing -- I just pushed some updates to show where I'm at.

I've made a first pass at the following: - Resetting the logic for decoding datetimes such that np.datetime64 objects are never used for non-standard calendars - Adding logic to use a NetCDFTimeIndex whenever netcdftime.datetime objects are used in an array being cast as an index (so if one reads in a Dataset from a netCDF file or creates one in Python, which is indexed by a time coordinate that uses netcdftime.datetime objects a NetCDFTimeIndex will be used rather than a generic object-based index) - Adding logic to encode netcdftime.datetime objects when saving out to netCDF files

I've added and updated some tests, but it's possible some may be redundant, and others could still be needed.

Hopefully with some guidance from you and @shoyer I can clean things up. I'll add a couple comments to the latest diff to highlight a few questions that came up as I was making these changes.

  1. Pandas provides a convenience function to create arrays of datetime objects (pd.date_range). This function just passes its arguments directly on to DatetimeIndex. I'm wondering if we can provide an xr.date_range function that mimics the pandas version with an additional calendar argument?

I agree this would be quite useful. pandas' implementation is pretty complicated, but perhaps we could do something, possibly more limited, but simpler.

 
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