-
How? |
Beta Was this translation helpful? Give feedback.
Answered by
mankoff
Jan 28, 2023
Replies: 1 comment
-
Do this using dask. Optionally, add a import xarray as xr
ds = xr.open_dataset('/home/kdm/data/Mankoff_2020/water/ice/MAR.nc',
chunks={'time':365, 'station':100})
ds = ds[['M2019_region','discharge']]
ds = ds.groupby('M2019_region')\
.sum()
delayed_obj = ds.to_netcdf('tmp/MAR.nc', compute=False)
from dask.diagnostics import ProgressBar
with ProgressBar():
results = delayed_obj.compute() |
Beta Was this translation helpful? Give feedback.
0 replies
Answer selected by
mankoff
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Do this using dask. Optionally, add a
where
clause to limit to discharge near the region (e.g., to exclude Disko Island from the CW region)