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  • Wrap bottleneck for fast moving window aggregations · 4 ✖

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186492043 https://github.com/pydata/xarray/issues/130#issuecomment-186492043 https://api.github.com/repos/pydata/xarray/issues/130 MDEyOklzc3VlQ29tbWVudDE4NjQ5MjA0Mw== jhamman 2443309 2016-02-20T02:35:09Z 2016-02-20T02:35:09Z MEMBER

closed via #668

 
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   Wrap bottleneck for fast moving window aggregations 33559045
132776633 https://github.com/pydata/xarray/issues/130#issuecomment-132776633 https://api.github.com/repos/pydata/xarray/issues/130 MDEyOklzc3VlQ29tbWVudDEzMjc3NjYzMw== shoyer 1217238 2015-08-19T20:41:28Z 2015-08-19T20:41:28Z MEMBER

There's been some related discussion about a better API for the rolling aggregation functions in pandas recently: https://github.com/pydata/pandas/pull/10702

 
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   Wrap bottleneck for fast moving window aggregations 33559045
116411021 https://github.com/pydata/xarray/issues/130#issuecomment-116411021 https://api.github.com/repos/pydata/xarray/issues/130 MDEyOklzc3VlQ29tbWVudDExNjQxMTAyMQ== shoyer 1217238 2015-06-29T03:22:13Z 2015-08-19T20:01:41Z MEMBER

Some further thoughts: - I would probably implement these as methods on Dataset/DataArray (e.g., ds.rolling_mean(dim='time', window=5) instead of functions. This is different from pandas, but seems slightly more in line with how everything else in pandas/xray operates. - I don't have a strong opinion whether we use the name rolling (like pandas) or moving/move (like bottleneck). Maybe rolling is slightly more obvious? - These functions from bottleneck could be easily extended to work on dask arrays by using using ghost: http://dask.pydata.org/en/latest/ghost.html

 
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   Wrap bottleneck for fast moving window aggregations 33559045
74933558 https://github.com/pydata/xarray/issues/130#issuecomment-74933558 https://api.github.com/repos/pydata/xarray/issues/130 MDEyOklzc3VlQ29tbWVudDc0OTMzNTU4 shoyer 1217238 2015-02-18T19:44:13Z 2015-02-18T19:44:13Z MEMBER

Wrapping bottleneck for operations like nanmean was fixed in #304.

 
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   Wrap bottleneck for fast moving window aggregations 33559045

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