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  • shoyer · 4 ✖

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  • add average function · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
218520080 https://github.com/pydata/xarray/issues/422#issuecomment-218520080 https://api.github.com/repos/pydata/xarray/issues/422 MDEyOklzc3VlQ29tbWVudDIxODUyMDA4MA== shoyer 1217238 2016-05-11T16:51:10Z 2016-05-11T16:51:10Z MEMBER

Yes, +1 for da.weighted(weight).mean(dim='time'). The mean method on weighted should have the same arguments as the mean method on DataArray -- it's just changed due to the context.

We may still end up implementing all required methods separately in weighted.

This is a fair point, I haven't looked in to the details of these implementations yet. But I expect there are still at least a few picks of logic that we will be able to share.

 
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   add average function 84127296
218360875 https://github.com/pydata/xarray/issues/422#issuecomment-218360875 https://api.github.com/repos/pydata/xarray/issues/422 MDEyOklzc3VlQ29tbWVudDIxODM2MDg3NQ== shoyer 1217238 2016-05-11T04:47:46Z 2016-05-11T04:47:46Z MEMBER

I would suggest not using keyword arguments for weighted. Instead, just align based on the labels of the argument like regular xarray operations. So we'd write da.weighted(days_per_month(da.time)).mean()

 
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   add average function 84127296
140797623 https://github.com/pydata/xarray/issues/422#issuecomment-140797623 https://api.github.com/repos/pydata/xarray/issues/422 MDEyOklzc3VlQ29tbWVudDE0MDc5NzYyMw== shoyer 1217238 2015-09-16T16:40:20Z 2015-09-16T16:40:20Z MEMBER

Possibly using where, e.g., weights.where(self.notnull()).sum(dim).

 
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   add average function 84127296
108118570 https://github.com/pydata/xarray/issues/422#issuecomment-108118570 https://api.github.com/repos/pydata/xarray/issues/422 MDEyOklzc3VlQ29tbWVudDEwODExODU3MA== shoyer 1217238 2015-06-02T22:41:22Z 2015-06-02T22:41:22Z MEMBER

Module error checking, etc., this would look something like:

python def average(self, dim=None, weights=None): if weights is None: return self.mean(dim) else: return (self * weights).sum(dim) / weights.sum(dim)

This is pretty easy to do manually, but I can see the value in having the standard method around, so I'm definitely open to PRs to add this functionality.

 
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   add average function 84127296

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