Using flox for daily climatology (getting 'kernel died')

[tlsw231]

I am trying to use flox to compute the climatology. I am guessing the way to do this is to just reduce along the appropriate label (day, month, etc.) using the sum function and then take the mean. The example below appears to almost go through on the dashboard but always ends up giving me a 'kernel died' message (My original dataset is about 21 times larger in size). Any ideas on what I might be doing wrong?

import xarray as xr

import flox
import flox.xarray
from flox.xarray import xarray_reduce

import dask
from dask.distributed import Client
from dask import delayed # to allow parallel computation
from distributed import Client
from dask_jobqueue import SLURMCluster

# Request cores
ncores=8; nprocs=ncores; totalcores= ncores * 1
cluster = SLURMCluster(cores=ncores, processes=nprocs, memory="264GB",queue="medium",
                       walltime="00:30:00")
cluster.scale(totalcores)
client = Client(cluster)

# Create dataarray of shape (1095, 28, 489, 655)
times = xr.cftime_range(start = '2011-01-01', end = '2013-12-31', freq='1D')
(nt,nz,ny,nx) =(len(times),28,489,655)
da = xr.DataArray(data=dask.array.random.random([nt,nz,ny,nx]),
                  dims=['time','z','y','x'],coords=dict(time=times)).rename('dummy')

# Create additional label to be used with xarray_reduce
da['time_dayofyear']=da.time.dt.dayofyear

# Reduce by computing sum along 'dayofyear' labels
da_clim = xarray_reduce(da, da['time_dayofyear'], method="map-reduce",func="sum").compute()

[tlsw231]

I am marking this as answered because I ended up asking the wrong question (!) but am unable to delete the discussion. Will start another thread later which is about flox and resampling.

[dcherian]

For the groupby problem, there's some discussion here:https://flox.readthedocs.io/en/latest/user-stories/climatology.html