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  • CFTimeIndex · 5 ✖

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388649761 https://github.com/pydata/xarray/pull/1252#issuecomment-388649761 https://api.github.com/repos/pydata/xarray/issues/1252 MDEyOklzc3VlQ29tbWVudDM4ODY0OTc2MQ== spencerahill 6200806 2018-05-13T19:21:41Z 2018-05-13T19:21:41Z CONTRIBUTOR

Credit also due to @rabernat for organizing the workshop in late 2016 where this effort got off the ground, and to @shoyer who sketched out an initial roadmap for the implementation at that meeting.

So excited to have this in! In aospy alone, we'll be able to get rid of 100s (1000+?) of lines of code now that CFTime is in place.

 
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   CFTimeIndex 205473898
380580598 https://github.com/pydata/xarray/pull/1252#issuecomment-380580598 https://api.github.com/repos/pydata/xarray/issues/1252 MDEyOklzc3VlQ29tbWVudDM4MDU4MDU5OA== agoodm 5179430 2018-04-11T20:11:44Z 2018-04-11T20:14:43Z CONTRIBUTOR

Hi all, any updates on the current status for this? This will be a big help for me as well in particular for processing daily CMIP5 netcdf files. I have been following this thread as well as the original issue and really appreciate this work. One other question: This PR doesn't allow for resampling on non-standard calendars as is, but I remember @shoyer mentioning that a workaround using pandas Grouper objects will exist. Would someone be able to explain to me how this would work? Thanks!

 
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374091871 https://github.com/pydata/xarray/pull/1252#issuecomment-374091871 https://api.github.com/repos/pydata/xarray/issues/1252 MDEyOklzc3VlQ29tbWVudDM3NDA5MTg3MQ== spencerahill 6200806 2018-03-19T03:35:05Z 2018-03-19T03:35:05Z CONTRIBUTOR

Just seeing this. I'm tied up the next couple days but would be happy to review it on Wednesday. Although I doubt I'll find anything you or @shoyer wouldn't, so feel free to merge if you'd rather not hold it up another few days.

 
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307813862 https://github.com/pydata/xarray/pull/1252#issuecomment-307813862 https://api.github.com/repos/pydata/xarray/issues/1252 MDEyOklzc3VlQ29tbWVudDMwNzgxMzg2Mg== spencerahill 6200806 2017-06-12T14:53:26Z 2017-06-12T14:53:26Z CONTRIBUTOR

Pinging folks on this. Summer is upon us and, generally speaking for the academics among us, is a good time for projects like this.

@spencerkclark looks like you're still looking for guidance w/r/t the last batch of comments from May 10.

From the CI it also looks like I need to accommodate some updates in pandas in NetCDFTimeIndex. I will try and look into that tomorrow.

Did your subsequent commits resolve this?

 
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280726382 https://github.com/pydata/xarray/pull/1252#issuecomment-280726382 https://api.github.com/repos/pydata/xarray/issues/1252 MDEyOklzc3VlQ29tbWVudDI4MDcyNjM4Mg== spencerahill 6200806 2017-02-17T18:18:48Z 2017-02-17T18:18:48Z CONTRIBUTOR

@spencerkclark @shoyer I got some more concrete information c.f. on my previous comment on negative and/or 5 digit dates. The TRACE simulation outputs netCDF files uses units of thousands of years relative to 1950, and therefore doesn't use 5 integers. But it does use negative and positive floats...negative for <1950, positive for >1950.

Re: 5 digits, there is growing research interest in very long climate model integrations, e.g. http://www.longrunmip.org/, but even those for now appear <10k yr in duration.

But there are also so called EMICs (Earth Models of Intermediate Complexity) that are cheap to run for 1000s of years. Although I couldn't immediately find any published results using them for >10k yr duration...

So ultimately I'd say there definitely exists a use-case for negative times (albeit with odd format) and there likely exists a use-case for 5 digit years. IMHO these are not must-haves for the initial netcdftime implementation but should at least be kept in mind, i.e. code design that makes them not-overly-difficult to introduce eventually.

I suspect there are xarray users with more direct experience with these cases...feel free to chime in

 
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   CFTimeIndex 205473898

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