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  • toddrjen · 2 ✖

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  • Allow for All-NaN in argmax, argmin · 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
604775165 https://github.com/pydata/xarray/issues/3884#issuecomment-604775165 https://api.github.com/repos/pydata/xarray/issues/3884 MDEyOklzc3VlQ29tbWVudDYwNDc3NTE2NQ== toddrjen 2272878 2020-03-27T01:57:44Z 2020-03-27T01:57:44Z CONTRIBUTOR

@shoyer xarray uses bottleneck for that if it can in xarray.nputils, so copying the numpy method would result in a performance hit. However, xarray maintains a wrapper around the numpy/bottleneck version in xarray.nanops where this could perhaps be implemented.

 
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   Allow for All-NaN in argmax, argmin 587062505
604220730 https://github.com/pydata/xarray/issues/3884#issuecomment-604220730 https://api.github.com/repos/pydata/xarray/issues/3884 MDEyOklzc3VlQ29tbWVudDYwNDIyMDczMA== toddrjen 2272878 2020-03-26T04:22:18Z 2020-03-26T04:22:18Z CONTRIBUTOR

The problem I had when implementing idxmin and idxmax is that this behavior is defined by numpy, not by xarray, and bottleneck follows the same behavior, with xarray generally delegating the computation to one of these. So you would need to somehow work around the behavior of numpy in xarray or get a fix implemented both in numpy and bottleneck.

 
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   Allow for All-NaN in argmax, argmin 587062505

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