# X = X.where(X>=0, other=0)
# # Apply model mask
# X = X.where(X.imask)
# Calculate regridding matrix
regridder = xe.Regridder(ds,
obs_grid,
method,
periodic=False,
reuse_weights=weights_flag)
weights_flag = True # Set true for following loops
# Add NaNs to empty rows of matrix (forces any target cell with ANY source cells containing NaN to be NaN)
if method == 'conservative':
regridder = import_data.add_matrix_NaNs(regridder)
# Regrid variable
var_out = regridder(ds[cvar])
# Expand dims
var_out = import_data.expand_to_sipn_dims(var_out)
# # Save regridded to netcdf file
var_out.to_netcdf(f_out)
var_out = None # Memory clean up
print('Saved ', f_out)
# In[ ]:
# Calculate regridding matrix
regridder_top = xe.Regridder(ds_top,
obs_grid,
method,
periodic=False,
reuse_weights=weights_flag)
regridder_bottom = xe.Regridder(ds_bottom,
obs_grid,
method,
periodic=False,
reuse_weights=weights_flag)
weights_flag = True # Set true for following loops
# Add NaNs to empty rows of matrix (forces any target cell with ANY source cells containing NaN to be NaN)
if method == 'conservative':
regridder_top = import_data.add_matrix_NaNs(regridder_top)
regridder_bottom = import_data.add_matrix_NaNs(regridder_bottom)
# Average over all sea ice categories
native_top = ds_top.sum(dim='ct', skipna=False)
native_bottom = ds_bottom.sum(dim='ct', skipna=False)
# Loop through each variable (xemsf not dataset enabled yet)
da_out_all_list = []
for cvar in native_top.data_vars:
da_out_all_list.append(
import_data.regrid_gfdl_split_domain(ds_all, native_top[cvar],
native_bottom[cvar],
regridder_top,
regridder_bottom))
ds_out_all = xr.merge(da_out_all_list)