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- How to broadcast along dayofyear · 6 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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1161471072 | https://github.com/pydata/xarray/issues/1844#issuecomment-1161471072 | https://api.github.com/repos/pydata/xarray/issues/1844 | IC_kwDOAMm_X85FOqRg | aasdelat 43267076 | 2022-06-21T09:05:35Z | 2022-06-21T09:05:53Z | NONE | I also suggest that, for some applications, it can be useful to simply drop all the 29th of February. This is accomplished by means of: dataset = dataset.convert_calendar('365_day') |
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How to broadcast along dayofyear 290023410 | |
441034802 | https://github.com/pydata/xarray/issues/1844#issuecomment-441034802 | https://api.github.com/repos/pydata/xarray/issues/1844 | MDEyOklzc3VlQ29tbWVudDQ0MTAzNDgwMg== | avatar101 33062222 | 2018-11-22T13:43:23Z | 2018-11-22T13:44:48Z | NONE | For anyone stumbling upon this thread in the future, I would like to mention that I used the above grouping approach suggested by @spencerkclark for my dataset to calculate climatology with calendar day and it works smoothly. The only thing one should be careful is that you can't directly plot the data using
To get around it, either use group by the
Or convert back the grouped coordinate month_day_str to numeric. However, after doing all this I found out that the CDO function also calculates climatology by the ordinal day of the year. So, to be consistent I would stick to that method but it's anyway good to know that there is a way around to group by day and month if required in Xarray. |
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How to broadcast along dayofyear 290023410 | |
359406359 | https://github.com/pydata/xarray/issues/1844#issuecomment-359406359 | https://api.github.com/repos/pydata/xarray/issues/1844 | MDEyOklzc3VlQ29tbWVudDM1OTQwNjM1OQ== | botev 1889878 | 2018-01-22T12:12:57Z | 2018-01-22T12:12:57Z | NONE | Thanks a lot for the help! |
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How to broadcast along dayofyear 290023410 | |
359066317 | https://github.com/pydata/xarray/issues/1844#issuecomment-359066317 | https://api.github.com/repos/pydata/xarray/issues/1844 | MDEyOklzc3VlQ29tbWVudDM1OTA2NjMxNw== | botev 1889878 | 2018-01-19T19:31:43Z | 2018-01-19T19:43:38Z | NONE | I end up doing the following: ``` dset, mean, std - all XArray objects as explained abovetime_index = dset.time.dt.dayofyear
dset_mean = mean.sel(dayofyear=time_index)
dset_std = std.sel(dayofyear=time_index)
new_dset = ((dset - dset_mean) / dset_std).drop("dayofyear")
|
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How to broadcast along dayofyear 290023410 | |
359061384 | https://github.com/pydata/xarray/issues/1844#issuecomment-359061384 | https://api.github.com/repos/pydata/xarray/issues/1844 | MDEyOklzc3VlQ29tbWVudDM1OTA2MTM4NA== | botev 1889878 | 2018-01-19T19:12:23Z | 2018-01-19T19:12:23Z | NONE | Thanks for the suggestion. However, option 2 and 3 are not really options, as after this, I need to provide the standardized field with the original time index. I'm using Xarray for the first time but will try to do the reindexing. |
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359025678 | https://github.com/pydata/xarray/issues/1844#issuecomment-359025678 | https://api.github.com/repos/pydata/xarray/issues/1844 | MDEyOklzc3VlQ29tbWVudDM1OTAyNTY3OA== | fischcheng 7747527 | 2018-01-19T16:55:25Z | 2018-01-19T16:55:25Z | NONE | So you got a two-year temperature field with dimension [730, 1, 481, 781], and another mean, and std data arrays of [366, 1, 481, 781] and you want to normalize the temperature field. Sorry I'm not familiar with the Xarray's groupby functions, I'll try several things before some experts jumping in.
I'm also interested in the right way to do it using built-in Xarray functions. I'm pretty sure there are some more clever ways to do this. |
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