Closed
Description
What happened?
There is still something wrong with the groupby indexing changes from 2023.04.0 onward. Here are two typical ways of doing a seasonal anomaly calculation, which return the same result on 2023.03.0 but are different on 2023.04.2:
import numpy as np
import xarray as xr
# monthly timeseries that should return "zero anomalies" everywhere
time = xr.date_range("2023-01-01", "2023-12-31", freq="MS")
data = np.linspace(-1, 1, 12)
x = xr.DataArray(data, coords={"time": time})
clim = xr.DataArray(data, coords={"month": np.arange(1, 13, 1)})
# seems to give the correct result if we use the full x, but not with a slice
x_slice = x.sel(time=["2023-04-01"])
# two typical ways of computing anomalies
anom_gb = x_slice.groupby("time.month") - clim
anom_sel = x_slice - clim.sel(month=x_slice.time.dt.month)
# passes on 2023.3.0, fails on 2023.4.2
# the groupby version is aligning the indexes wrong, giving us something other than 0
assert anom_sel.equals(anom_gb)Related: #7759 #7766
cc @dcherian
What did you expect to happen?
No response
Minimal Complete Verifiable Example
No response
MVCE confirmation
- Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
- Complete example — the example is self-contained, including all data and the text of any traceback.
- Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
- New issue — a search of GitHub Issues suggests this is not a duplicate.
Relevant log output
No response
Anything else we need to know?
No response