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  • How to broadcast along dayofyear · 6 ✖

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  • NONE · 6 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
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

In[1]: da.groupby(month_day_str).mean('time').plot() Out[1]: TypeError: Plotting requires coordinates to be numeric or dates of type np.datetime64 or datetime.datetime.

To get around it, either use group by the

modified_ordinal _day

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 above

time_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") `` One issue though is that this quite bad on memory as it constructs 3 arrays in memmory as large as the original one. If anoyne has any suggestion on how to improve this I would be very grateful. Also is it possible to compute and storenew_dset` simutlanously so I don't create it in memory?

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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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  How to broadcast along dayofyear 290023410
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.

  • Concat two std/mean fields along dayofyear, and reindex to the time index from the temperature data. Then you can do the (dset-mean)/std
  • Separate the temperature fields into two one-year chunks, reindex time to dayofyear, then do the calculation.
  • Flatten the spatial grid then use numpy to do the trick.

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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  How to broadcast along dayofyear 290023410

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