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  • shoyer · 4 ✖
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352485702 https://github.com/pydata/xarray/pull/1733#issuecomment-352485702 https://api.github.com/repos/pydata/xarray/issues/1733 MDEyOklzc3VlQ29tbWVudDM1MjQ4NTcwMg== shoyer 1217238 2017-12-18T16:51:05Z 2017-12-18T16:51:05Z MEMBER

Thank you!

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349106643 https://github.com/pydata/xarray/pull/1733#issuecomment-349106643 https://api.github.com/repos/pydata/xarray/issues/1733 MDEyOklzc3VlQ29tbWVudDM0OTEwNjY0Mw== shoyer 1217238 2017-12-04T21:10:40Z 2017-12-04T21:10:40Z MEMBER

Should i move the implementation on Variable and use temp datasets like quantile does ?

I would be happy either way here. In some ways, yes, that would be the cleanest solution.

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349091429 https://github.com/pydata/xarray/pull/1733#issuecomment-349091429 https://api.github.com/repos/pydata/xarray/issues/1733 MDEyOklzc3VlQ29tbWVudDM0OTA5MTQyOQ== shoyer 1217238 2017-12-04T20:15:49Z 2017-12-04T20:15:49Z MEMBER

when the ranking dimension is missing from an array in the dataset, should we do nothing, or put 1s everywhere ?

Good point, maybe it needs to be slightly more complicated than apply.

Two other options are raise an error (safest) or to drop all such variables from the result. The later is similar to what we do in aggregations like sum() if an argument is non-numeric. I think this is probably the best choice: it strikes a good balance between not returning an invalid result and not raising errors so often as to be annoying.

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346510205 https://github.com/pydata/xarray/pull/1733#issuecomment-346510205 https://api.github.com/repos/pydata/xarray/issues/1733 MDEyOklzc3VlQ29tbWVudDM0NjUxMDIwNQ== shoyer 1217238 2017-11-23T00:46:21Z 2017-11-23T00:46:21Z MEMBER

Thanks for putting this together!

Rather than using the injection stuff in ops.py (which we would like to eliminate), I would prefer to simply implement this as a method on Dataset and DataArray.

Also take a look at the use of apply_ufunc in https://github.com/pydata/xarray/pull/1640 for a straightforward example of wrapping external functions. I think that would make things easier here.

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