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  • fujiisoup · 2 ✖

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  • interpolate_na with limit argument changes size of chunks · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
433528586 https://github.com/pydata/xarray/issues/2514#issuecomment-433528586 https://api.github.com/repos/pydata/xarray/issues/2514 MDEyOklzc3VlQ29tbWVudDQzMzUyODU4Ng== fujiisoup 6815844 2018-10-26T20:08:33Z 2018-10-27T20:50:47Z MEMBER

Nice catch!

For some historical reasons, mean and some reduction method uses bottleneck as default, while count does not.

mean goes through this function https://github.com/pydata/xarray/blob/b622c5e7da928524ef949d9e389f6c7f38644494/xarray/core/dask_array_ops.py#L23

It looks there is another but for this function.

 
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   interpolate_na with limit argument changes size of chunks 374279704
433353091 https://github.com/pydata/xarray/issues/2514#issuecomment-433353091 https://api.github.com/repos/pydata/xarray/issues/2514 MDEyOklzc3VlQ29tbWVudDQzMzM1MzA5MQ== fujiisoup 6815844 2018-10-26T09:49:04Z 2018-10-26T09:49:04Z MEMBER

Thanks, @cchwala, for reporting the issue.

It looks that the actual chunks size is ((10, 735), ) not all 10. python In [16]: ds_test.interpolate_na(dim='time', limit=20)['foo'].chunks Out[16]: ((10, 735),) (why does our __repr__ only show the first chunk size?) But it should be ((745, ), ) as you suggested.

The problem would be in https://github.com/pydata/xarray/blob/5940100761478604080523ebb1291ecff90e779e/xarray/core/dask_array_ops.py#L74-L85

This method is desinged to be used for multiplly chunked array, so I didn't care to add a small chunk on the head. Do you mind to look the inside?

 
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   interpolate_na with limit argument changes size of chunks 374279704

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