var_list = []
for cvar in ds.data_vars:
# 0 to NaN hack
#offset = 10
#da_coarse = regridder(ds[cvar]+10)
#da_coarse = da_coarse.where(da_coarse>(offset)) - offset
#var_list.append(da_coarse)
# When doing nearest neighbor
da_coarse = regridder(ds[cvar])
var_list.append(da_coarse)
ds_out = xr.merge(var_list)
# Expand dims
ds_out = import_data.expand_to_sipn_dims(ds_out)
# # Save regridded to netcdf file
ds_out.to_netcdf(f_out)
ds_out = None # Memory clean up
print('Saved ', f_out)
# In[ ]:
# Clean up
if weights_flag:
regridder.clean_weight_file() # clean-up
# # Plotting
fore_sic = fore_sic.where(fore_sic <= 1, other=1).where(ocnmask)
# Add cords
fore_sic.coords['fore_time'] = np.timedelta64(int(fore_days), 'D')
da_l.append(fore_sic)
# xr.exit()
da_sic = xr.concat(da_l, dim='fore_time')
da_sic.coords['init_time'] = c_sic.time
da_sic = da_sic.drop(['xm', 'ym', 'lag', 'doy', 'hole_mask', 'time'])
da_sic.name = 'sic'
da_sic = import_data.expand_to_sipn_dims(da_sic)
da_sic = da_sic.rename({'x': 'nj', 'y': 'ni'})
da_sic.to_netcdf(file_out)
print("Saved file:", file_out)
# In[ ]:
# Test plots for presentations
# In[ ]:
if test_plots:
fig_dir = '/home/disk/sipn/nicway/Nic/figures/pres/A'
# 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)
# Rename to common sipn variable names
ds_out_all = ds_out_all.rename({'CN': 'sic', 'HI': 'hi'})
# Expand dims
ds_out_all = import_data.expand_to_sipn_dims(ds_out_all)
# Save regridded to netcdf file
ds_out_all.to_netcdf(f_out)
# Save regridded to multiple netcdf files by month
# months, datasets = zip(*ds_out_all.groupby('init_time.month'))
# paths = [os.path.join(data_out, 'GFDL_FLOR_'+str(year)+'_'+str(m)+'_Stereo.nc') for m in months]
# xr.save_mfdataset(datasets, paths)
ds_out_all = None # Memory clean up
print('Saved file', f_out)
# In[ ]:
# now add the damped anomly to the climo
predict.load()
fore_climo.load()
predict = predict + fore_climo
predict.coords['init_time'] = time_hold.drop('doy')
predict = predict.drop(['xm', 'ym', 'time'])
predict = predict.rename({'fore_doy': 'fore_time'})
predict['fore_time'] = pd.to_timedelta(predict.fore_time.values, unit='d')
# print('predict ', predict)
# must limit to [0,1]
predict = predict.where(predict > 0, other=0).where(oceanmask)
predict = predict.where(predict < 1, other=1).where(oceanmask)
predict = import_data.expand_to_sipn_dims(predict)
predict = predict.rename({'x': 'nj', 'y': 'ni'})
# print('predict for saving ',predict)
predict.to_netcdf(file_out)
print("Saved file:", file_out)
# In[13]:
# set up colors and print some stuff
cmap_diff_2 = matplotlib.colors.LinearSegmentedColormap.from_list(
"", ["purple", "white", "green"])
cmap_diff_2.set_bad(color='lightgrey')
cmap_sic = matplotlib.colors.ListedColormap(sns.color_palette("Blues_r", 10))