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  • shoyer · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
460019498 https://github.com/pydata/xarray/pull/2458#issuecomment-460019498 https://api.github.com/repos/pydata/xarray/issues/2458 MDEyOklzc3VlQ29tbWVudDQ2MDAxOTQ5OA== shoyer 1217238 2019-02-03T03:21:52Z 2019-02-03T03:21:52Z MEMBER 
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   WIP: sketch of resample support for CFTimeIndex 365961291
427056750 https://github.com/pydata/xarray/pull/2458#issuecomment-427056750 https://api.github.com/repos/pydata/xarray/issues/2458 MDEyOklzc3VlQ29tbWVudDQyNzA1Njc1MA== shoyer 1217238 2018-10-04T15:12:07Z 2018-10-04T15:12:07Z MEMBER

I never saw clear use-cases for TimeGrouper but I could be convinced. On Thu, Oct 4, 2018 at 2:51 PM David Huard notifications@github.com wrote:

Do you think there would be a benefit to implementing a TimeGrouper class based on panda's ?

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/pydata/xarray/pull/2458#issuecomment-427006309, or mute the thread https://github.com/notifications/unsubscribe-auth/ABKS1rj2dc2Fk1el8e5kjU5VQGs5759jks5uhgRKgaJpZM4XEUir . 

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   WIP: sketch of resample support for CFTimeIndex 365961291
426681357 https://github.com/pydata/xarray/pull/2458#issuecomment-426681357 https://api.github.com/repos/pydata/xarray/issues/2458 MDEyOklzc3VlQ29tbWVudDQyNjY4MTM1Nw== shoyer 1217238 2018-10-03T15:30:45Z 2018-10-03T15:30:45Z MEMBER

Inspired by @spencerkclark's suggestion, I tried another version based on cftime_range and reindex with method='pad'. This one seems to be working in more cases: ```python In [7]: times = xarray.cftime_range('2000', periods=30, freq='MS')

In [8]: da = xarray.DataArray(range(30), [('time', times)])

In [9]: times Out[9]: CFTimeIndex([2000-01-01 00:00:00, 2000-02-01 00:00:00, 2000-03-01 00:00:00, 2000-04-01 00:00:00, 2000-05-01 00:00:00, 2000-06-01 00:00:00, 2000-07-01 00:00:00, 2000-08-01 00:00:00, 2000-09-01 00:00:00, 2000-10-01 00:00:00, 2000-11-01 00:00:00, 2000-12-01 00:00:00, 2001-01-01 00:00:00, 2001-02-01 00:00:00, 2001-03-01 00:00:00, 2001-04-01 00:00:00, 2001-05-01 00:00:00, 2001-06-01 00:00:00, 2001-07-01 00:00:00, 2001-08-01 00:00:00, 2001-09-01 00:00:00, 2001-10-01 00:00:00, 2001-11-01 00:00:00, 2001-12-01 00:00:00, 2002-01-01 00:00:00, 2002-02-01 00:00:00, 2002-03-01 00:00:00, 2002-04-01 00:00:00, 2002-05-01 00:00:00, 2002-06-01 00:00:00], dtype='object')

In [10]: da.resample(time='12MS').mean() Out[10]: <xarray.DataArray (time: 3)> array([ 5.5, 17.5, 26.5]) Coordinates: * time (time) object 2000-01-01 00:00:00 ... 2002-01-01 00:00:00

In [11]: da.resample(time='6MS').mean() Out[11]: <xarray.DataArray (time: 5)> array([ 2.5, 8.5, 14.5, 20.5, 26.5]) Coordinates: * time (time) object 2000-01-01 00:00:00 ... 2002-01-01 00:00:00

In [12]: da.resample(time='3MS').mean() Out[12]: <xarray.DataArray (time: 10)> array([ 1., 4., 7., 10., 13., 16., 19., 22., 25., 28.]) Coordinates: * time (time) object 2000-01-01 00:00:00 ... 2002-04-01 00:00:00 ```

 
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   WIP: sketch of resample support for CFTimeIndex 365961291

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