def func_data_consistency(ds_single):
# print(ds_single)
# mdo = CARDAMOMlib.load_mdo_greg(ds_single)
# abs_err, rel_err = mdo.check_data_consistency()
abs_err, rel_err = CARDAMOMlib.check_data_consistency(
ds_single, params["time_step_in_days"])
data_vars = dict()
data_vars['abs_err'] = xr.DataArray(
data=abs_err.data.filled(fill_value=np.nan),
attrs={'units': abs_err.unit} # ignored by map_blocks
)
data_vars['rel_err'] = xr.DataArray(
data=rel_err.data.filled(fill_value=np.nan),
attrs={'units': rel_err.unit} # ignored by map_blocks
)
ds_res = xr.Dataset(data_vars=data_vars)
ds_single.close()
return ds_res
yearly_flux_variable = variable.isel(
time=slice(delay_in_months, None, 1)
).shift(time=-1).coarsen( # first u is useless in CARDAMOM and ELM
time=nr_months,
boundary="pad",
coord_func="min",
keep_attrs=True).mean().shift(
time=1) # unit is gC/m2/d, create useless first u again
yearly_flux_variable.coords["time"] = yearly_coords_time
data_vars[variable_name] = yearly_flux_variable
yearly_ds = xr.Dataset(data_vars=data_vars,
attrs={"delay_in_months": delay_in_months})
yearly_ds
# +
# %%time
print(target_path)
yearly_ds.to_netcdf(target_path, compute=True)
# -
check_ds = xr.open_dataset(target_path)
check_ds
#sub_ds = check_ds.isel(lat=9, lon=26, prob=0).compute()
sub_ds = check_ds.isel(lat=5, lon=23, prob=0).compute() # bad reconstruction
sub_ds
CARDAMOMlib.check_data_consistency(sub_ds.compute(), time_step_in_days=31 * 12)