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  • Having trouble with time dim of CMIP5 dataset · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
134827757 https://github.com/pydata/xarray/issues/531#issuecomment-134827757 https://api.github.com/repos/pydata/xarray/issues/531 MDEyOklzc3VlQ29tbWVudDEzNDgyNzc1Nw== jhamman 2443309 2015-08-26T04:40:03Z 2015-08-26T04:40:03Z MEMBER

@jsbj - glad to hear you were able to work around this. Extended calendar support in numpy, pandas, and xray is something we're all rooting for.

I'm going to close this as a wontfix since we are working on this via the numpy and pandas channels.

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  Having trouble with time dim of CMIP5 dataset 100980878
134757704 https://github.com/pydata/xarray/issues/531#issuecomment-134757704 https://api.github.com/repos/pydata/xarray/issues/531 MDEyOklzc3VlQ29tbWVudDEzNDc1NzcwNA== jsbj 1151830 2015-08-25T22:15:22Z 2015-08-25T22:15:22Z NONE

Thanks Daniel, that workaround totally works.

-j

2015-08-14 15:26 GMT-04:00 Daniel Rothenberg notifications@github.com:

Hi @jsbj https://github.com/jsbj,

The fancy indexing notation you're trying to use only works when xray successfully decodes the time dimension. As discussed in the documentation here http://xray.readthedocs.org/en/stable/time-series.html#creating-datetime64-data, this only works when the year of record falls between 1678 and 2262. Since you have years 2262-2300 in your dataset, this is a feature - xray is failing gracefully.

There are a few current open discussions on this behavior, which is an issue higher up the python chain with numpy: 1. time decoding error with "days since" https://github.com/xray/xray/issues/521 2. Fix datetime decoding when time units are 'days since 0000-01-01 00:00:00' https://github.com/xray/xray/pull/522 3. ocefpaf - Loading non-standard dates with cf_units https://ocefpaf.github.io/python4oceanographers/blog/2015/08/10/cf_units_and_time/ 4. numpy - Non-standard Calendar Support https://github.com/numpy/numpy/issues/6207

For now, a very simple hack would be to re-compute your time units so that they're re-based, say, with units 'days since 1700-01-01 00:00:00'. That way all of them would fit within the permissible range to use the decoding routine built into xray. You could simply pass the decode_cf=False flag when you open the dataset, modify the non-decoded time array and units, then run xray.decode_cf() on the modified dataset.

— Reply to this email directly or view it on GitHub https://github.com/xray/xray/issues/531#issuecomment-131214583.

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  Having trouble with time dim of CMIP5 dataset 100980878
131214583 https://github.com/pydata/xarray/issues/531#issuecomment-131214583 https://api.github.com/repos/pydata/xarray/issues/531 MDEyOklzc3VlQ29tbWVudDEzMTIxNDU4Mw== darothen 4992424 2015-08-14T19:26:18Z 2015-08-14T19:26:18Z NONE

Hi @jsbj,

The fancy indexing notation you're trying to use only works when xray successfully decodes the time dimension. As discussed in the documentation here, this only works when the year of record falls between 1678 and 2262. Since you have years 2262-2300 in your dataset, this is a feature - xray is failing gracefully.

There are a few current open discussions on this behavior, which is an issue higher up the python chain with numpy: 1. time decoding error with "days since" 2. Fix datetime decoding when time units are 'days since 0000-01-01 00:00:00' 3. ocefpaf - Loading non-standard dates with cf_units 4. numpy - Non-standard Calendar Support

For now, a very simple hack would be to re-compute your time units so that they're re-based, say, with units 'days since 1700-01-01 00:00:00'. That way all of them would fit within the permissible range to use the decoding routine built into xray. You could simply pass the decode_cf=False flag when you open the dataset, modify the non-decoded time array and units, then run xray.decode_cf() on the modified dataset.

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  Having trouble with time dim of CMIP5 dataset 100980878

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