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  • jbusecke · 3 ✖

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  • Multi-dimensional binning/resampling/coarsening · 3 ✖

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  • CONTRIBUTOR · 3 ✖
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439892007 https://github.com/pydata/xarray/issues/2525#issuecomment-439892007 https://api.github.com/repos/pydata/xarray/issues/2525 MDEyOklzc3VlQ29tbWVudDQzOTg5MjAwNw== jbusecke 14314623 2018-11-19T13:26:45Z 2018-11-19T13:26:45Z CONTRIBUTOR

I think mean would be a good default (thinking about cell center dimensions like longitude and latitude) but I would very much like it if other functions could be specified e. g. for grid face dimensions (where min and max would be more appropriate) and other coordinates like surface area (where sum would be the most appropriate function).

On Nov 18, 2018, at 11:13 PM, Ryan Abernathey notifications@github.com wrote:

What would the coordinates look like?

apply func also for coordinate always apply mean to coordinate If I think about my applications, I would probably always want to apply mean to dimension coordinates, but would like to be able to choose for non-dimension coordinates.

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   Multi-dimensional binning/resampling/coarsening 375126758
435201618 https://github.com/pydata/xarray/issues/2525#issuecomment-435201618 https://api.github.com/repos/pydata/xarray/issues/2525 MDEyOklzc3VlQ29tbWVudDQzNTIwMTYxOA== jbusecke 14314623 2018-11-01T21:59:19Z 2018-11-01T21:59:19Z CONTRIBUTOR

My favorite would be da.coarsen({'lat': 2, 'lon': 2}).mean(), but all the others sound reasonable to me. Also +1 for consistency with resample/rolling/groupby.

 
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   Multi-dimensional binning/resampling/coarsening 375126758
434531970 https://github.com/pydata/xarray/issues/2525#issuecomment-434531970 https://api.github.com/repos/pydata/xarray/issues/2525 MDEyOklzc3VlQ29tbWVudDQzNDUzMTk3MA== jbusecke 14314623 2018-10-31T01:46:19Z 2018-10-31T01:46:19Z CONTRIBUTOR

I agree with @rabernat, and favor the index based approach. For regular lon-lat grids its easy enough to implement a weighted mean, and for irregular spaced grids and other exotic grids the coordinate based approach might lead to errors. To me the resample API above might suggest to some users that some proper regridding (a la xESMF) onto a regular lat/lon grid is performed.

‚block_reduce‘ sounds good to me and sounds appropriate for non-dask arrays. Does anyone have experience how ‚dask.coarsen‘ compares performance wise? 

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   Multi-dimensional binning/resampling/coarsening 375126758

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