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/121#issuecomment-42893119,https://api.github.com/repos/pydata/xarray/issues/121,42893119,MDEyOklzc3VlQ29tbWVudDQyODkzMTE5,1217238,2014-05-12T21:51:14Z,2014-05-12T21:51:33Z,MEMBER,"Those precision issues are unfortunate but perhaps unavoidable in this case because you are representing dates as floating point numbers -- the units are in ""days"" but the frequency between time points is measured in ""hours"".
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,33272937
https://github.com/pydata/xarray/issues/121#issuecomment-42798808,https://api.github.com/repos/pydata/xarray/issues/121,42798808,MDEyOklzc3VlQ29tbWVudDQyNzk4ODA4,1217238,2014-05-12T06:09:09Z,2014-05-12T06:09:21Z,MEMBER,"Timedelta operations are used in exactly one place in xray: speeding up decoding of dates from netCDF if a standard calendar is being used. Otherwise, that sort of stuff is left up to the user.

If dates with non-standard calendars can generally be most usefully expressed as a pandas.DatetimeIndex, then let's go ahead and default to decoding them into datetime64 arrays. The relevant function to modify is [here](https://github.com/xray-pydata/xray/blob/master/xray/conventions.py#L60) (see also [here](https://github.com/xray-pydata/xray/blob/master/xray/conventions.py#L249)) if you'd like to make a pull request!
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,33272937
https://github.com/pydata/xarray/issues/121#issuecomment-42789906,https://api.github.com/repos/pydata/xarray/issues/121,42789906,MDEyOklzc3VlQ29tbWVudDQyNzg5OTA2,1217238,2014-05-12T01:31:11Z,2014-05-12T01:31:11Z,MEMBER,"Yes, this is certainly related to #118. Virtual variables work by using pandas.DatetimeIndex methods, but if you're not using a standard calendar, you end up with an object array of netCDF4.datetime objects instead of an array of numpy.datetime64 objects (which can be turned into a DatetimeIndex).

Unfortunately, we do need to be able to make a DatetimeIndex to be able to use its (very quick) calculations for properties like year. The alternative is to write our own implementation, which would likely mean far slower pure-python code. We could also write a function to cast an array into a DatetimeIndex from datetime objects, which I'm guessing would be your preferred solution, even though there are issues like the difference between dates, as DatetimeIndex objects and numpy's datetime64 always assume a standard gregorian calendar.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,33272937