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  • aulemahal · 2 ✖

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  • xr.infer_freq · 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
634215961 https://github.com/pydata/xarray/pull/4033#issuecomment-634215961 https://api.github.com/repos/pydata/xarray/issues/4033 MDEyOklzc3VlQ29tbWVudDYzNDIxNTk2MQ== aulemahal 20629530 2020-05-26T19:01:36Z 2020-05-26T19:01:36Z CONTRIBUTOR

@spencerkclark Here you go! Tests added for all cases you mentioned.

 
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   xr.infer_freq 612846594
628857894 https://github.com/pydata/xarray/pull/4033#issuecomment-628857894 https://api.github.com/repos/pydata/xarray/issues/4033 MDEyOklzc3VlQ29tbWVudDYyODg1Nzg5NA== aulemahal 20629530 2020-05-14T20:02:34Z 2020-05-14T20:02:34Z CONTRIBUTOR

@spencerkclark Thanks for the feedback!

I rewrote the _CFTimeFrequencyInferer class independently of pandas private objects. I removed outputs that wouldn't match offsets defined in xarray: weekly frequencies, week-of-month (WOM) and all business-related anchors. It wouldn't be difficult to add them later on if need be and if cftime_offsets.py defines them. Note that infer_freq is quite independent of the rest of xarray, this choice was purely so that the behavior matches the rest of xr. I did leave the sub-seconds frequencies (L for ms and U for µs).

I expect the performance of xr.infer_freq to be slightly worst than pandas' since they use optimized and compiled code whereas I only used common numpy and xarray stuff. However, I assume that this part of the workflow is far from being the bottleneck of most xarray usecases...

Will write up some docs soon.

 
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   xr.infer_freq 612846594

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