TypeError for time_bnds variable when calling Dataset.to_netcdf #7794
Description
What happened?
In the workflow I load the dataset (clisops), remap it (xesmf), chunk it according to the available memory, then write it to disk (clisops). I encounter however a TypeError when writing the Dataset to disk, since xarray>=2023.3.0.
What did you expect to happen?
The Dataset to be written to disk.
Minimal Complete Verifiable Example
import xarray as xr
import os
ds_url="https://github.com/roocs/mini-esgf-data/raw/master/test_data/badc/cmip5/data/cmip5/output1/MOHC/HadGEM2-ES/rcp85/day/land/day/r1i1p1/latest/mrsos/mrsos_day_HadGEM2-ES_rcp85_r1i1p1_20051201.nc"
ds_path="mrsos_day_HadGEM2-ES_rcp85_r1i1p1_20051201.nc"
if not os.path.isfile(ds_path): os.system(f"wget {ds_url}")
ds=xr.open_dataset(ds_path)
# With printing the values of the time_bnds variable, the to_netcdf-call later fails
# This is basically simulating the processing of the data
print(ds["time_bnds"].values)
chunked_ds_in = ds.chunk({"time":1})
chunked_ds_in.to_netcdf(path="input.nc", compute=True)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
> TypeError: Invalid array type: <class 'cftime._cftime.Datetime360Day'>
I think the problem comes mainly from the change in
> xarray/core/common.py", line 1811, in _contains_cftime_datetimes
with xarray 2023.3.0
At least the problems disappears when I use the old implementation of `_contains_cftime_datetimes`.Anything else we need to know?
No response
Environment
INSTALLED VERSIONS
commit: None
python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0]
python-bits: 64
OS: Linux
OS-release: 5.4.0-147-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.14.0
libnetcdf: 4.9.2
xarray: 2023.4.2
pandas: 1.5.3
numpy: 1.23.5
scipy: 1.10.1
netCDF4: 1.6.3
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.6.2
nc_time_axis: None
PseudoNetCDF: None
iris: None
bottleneck: 1.3.7
dask: 2023.3.2
distributed: 2023.3.2
matplotlib: 3.7.1
cartopy: None
seaborn: None
numbagg: None
fsspec: 2023.3.0
cupy: None
pint: None
sparse: 0.14.0
flox: None
numpy_groupies: None
setuptools: 67.6.1
pip: 23.0.1
conda: None
pytest: 7.2.2
mypy: None
IPython: 8.12.0
sphinx: 6.1.3