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  • jmichel-otb · 2 ✖

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  • Allow grouping by dask variables · 2 ✖

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  • CONTRIBUTOR · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
478624700 https://github.com/pydata/xarray/issues/2852#issuecomment-478624700 https://api.github.com/repos/pydata/xarray/issues/2852 MDEyOklzc3VlQ29tbWVudDQ3ODYyNDcwMA== jmichel-otb 10595679 2019-04-01T15:23:35Z 2019-04-01T15:23:35Z CONTRIBUTOR

That's a tough question ;) In the current dataset I have 950 unique labels, but in my use cases it can be be a lot more (e.g. agricultaral crops) or a lot less (adminstrative boundaries or regions). 

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   Allow grouping by dask variables 425320466
478488200 https://github.com/pydata/xarray/issues/2852#issuecomment-478488200 https://api.github.com/repos/pydata/xarray/issues/2852 MDEyOklzc3VlQ29tbWVudDQ3ODQ4ODIwMA== jmichel-otb 10595679 2019-04-01T08:37:42Z 2019-04-01T08:37:42Z CONTRIBUTOR

Many thanks for your answers @shoyer and @rabernat .

I am relatively new to xarray and dask, I am trying to determine if it can fit our need for analysis of large stacks of Sentinel data on our cluster.

I will give a try to dask.array.histogram ass @rabernat suggested.

I also had the following idea. Given that: * I know exactly beforehand which labels (or groups) I want to analyse, * .where(label=xxx).mean('variable') does the job perfectly for one label,

I do not actually need the discovery of unique labels that groupby() performs, what I really need is an efficient way to perform multiple where() aggregate operations at once, to avoid traversing the data multiple time.

Maybe there is already something like that in xarray, or maybe this is something I can derive from the implementation of where() ?

 
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   Allow grouping by dask variables 425320466

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