id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
290023410,MDU6SXNzdWUyOTAwMjM0MTA=,1844,How to broadcast along dayofyear,1889878,closed,0,,,16,2018-01-19T16:01:16Z,2022-06-22T15:30:45Z,2018-01-23T17:07:17Z,NONE,,,,"Ok so here is the problem I'm trying to solve, for which I did not find any solution:
I have a spatial dataset with the following format (tempeature over time on a spatial grid):
```
Dimensions:    (depth: 1, latitude: 481, longitude: 781, time: 730)
Coordinates:
  * time       (time) datetime64[ns] 2016-01-01T11:59:37.961193472 ...
  * longitude  (longitude) float32 -75.0 -74.9167 -74.8333 -74.75 -74.6667 ...
  * latitude   (latitude) float32 25.0 25.0833 25.1667 25.25 25.3333 25.4167 ...
  * depth      (depth) float32 0.494025
Data variables:
    thetao     (time, depth, latitude, longitude) float64 ...
Attributes:
    title:            daily mean fields from Global Ocean Physics Analysis an...
    institution:      MERCATOR OCEAN
    references:       http://www.mercator-ocean.fr
    source:           MERCATOR PSY4QV3R1
    Conventions:      CF-1.0
    history:          Data extracted from dataset http://opendap-glo.mercator...
    time_min:         578556.0
    time_max:         596052.0
    julian_day_unit:  hours since 1950-01-01 00:00:00
    z_min:            0.494024991989
    z_max:            0.494024991989
    latitude_min:     25.0
    latitude_max:     65.0
    longitude_min:    -75.0
    longitude_max:    -10.0
```
These all contain temperture values. From another source I receive specially calibrated mean and standard deviation of temperatures for every day of the year. The mean dataset (std is the same) looks like this:
```
Dimensions:    (dayofyear: 366, depth: 1, latitude: 481, longitude: 781)
Coordinates:
  * longitude  (longitude) float32 -75.0 -74.9167 -74.8333 -74.75 -74.6667 ...
  * latitude   (latitude) float32 25.0 25.0833 25.1667 25.25 25.3333 25.4167 ...
  * depth      (depth) float32 0.494025
  * dayofyear  (dayofyear) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
Data variables:
    thetao     (dayofyear, depth, latitude, longitude) float64 25.06 25.16 ...
```
What I want to achieve is to construct from the temperatures dataset a new one with standardized temperatures. The issue is I do not know how. The first thing I thought is to do something like:
```
new_dset = (dset.groupby(""time.dateofyear"") - mean) / std
```
However, the issue now is that I can't undo the ""groupby"". At this point I did not manage to figure out of how to either undo this, or in general the right approach for performing the operation I want. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1844/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue