lon, lat)
#=========================================================
# READ THE DATA
#=========================================================
my_file = basedir + '/' + my_exp + '/time_mean/' + filetype + '/update_comp_meandiff_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(
nbv) + '_obs_inc_' + str(obs_increment) + '_rmse' + '.grd'
rmse[my_exp] = np.nanmean(
np.delete(ctlr.read_data(my_file, ctl_dict, undef2nan=True), 4, 2), 2)
rmse[my_exp][np.isnan(rmse[my_exp])] = undef
rmse[my_exp] = cf.gaussian_filter(field0=rmse[my_exp],
dx=1.0,
sigma=sigma_smooth,
nx=nx,
ny=ny,
undef=undef)
rmse[my_exp][rmse[my_exp] == undef] = np.nan
print('RMSE values')
print(my_exp)
print(np.min(rmse[my_exp]), np.max(rmse[my_exp]))
my_file = basedir + '/' + my_exp + '/time_mean/' + filetype + '/' + '/moment0001_mean.grd'
ensemble_mean[my_exp] = np.delete(
ctlr.read_data_grads(my_file, ctl_dict_2)['dbz'], 4, 2)
ensemble_mean[my_exp] = np.max(ensemble_mean[my_exp], 2)
ensemble_mean[my_exp] = cf.gaussian_filter(field0=ensemble_mean[my_exp],
dx=1.0,
my_file = basedir + '/LE_D1_1km_5min/time_mean/guesgp/update_comp_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(
nbv) + '_obs_inc_' + str(obs_increment[iv]) + '_kld' + '.grd'
parameter_5MIN.append(
np.nanmean(
np.delete(ctlr.read_data(my_file, ctl_dict, undef2nan=False), 4,
2), 2))
my_file = basedir + '/LE_D1_1km_30sec/time_mean/guesgp/update_comp_vobs_' + var_obs + '_upd_obs_' + var_upd + '_ens_size_' + str(
nbv) + '_obs_inc_' + str(obs_increment[iv]) + '_kld' + '.grd'
parameter_30SEC.append(
np.nanmean(
np.delete(ctlr.read_data(my_file, ctl_dict, undef2nan=False), 4,
2), 2))
parameter_5MIN[iv] = cf.gaussian_filter(field0=parameter_5MIN[iv],
dx=1.0,
sigma=sigma_smooth,
nx=nx,
ny=ny,
undef=undef)
parameter_30SEC[iv] = cf.gaussian_filter(field0=parameter_30SEC[iv],
dx=1.0,
sigma=sigma_smooth,
nx=nx,
ny=ny,
undef=undef)
parameter_5MIN[iv][parameter_5MIN[iv] == undef] = np.nan
parameter_30SEC[iv][parameter_30SEC[iv] == undef] = np.nan
print(my_var)
print(np.nanmin(parameter_5MIN[iv]), np.nanmax(parameter_5MIN[iv]))
print(np.nanmin(parameter_30SEC[iv]), np.nanmax(parameter_30SEC[iv]))
#for key in bim_time_mean :
for var in plot_variables :
for iexp,my_exp in enumerate(exps) :
#Prepare the data for plotting.
#Smooth output
parameter[my_exp][var][ parameter[my_exp][var] == undef ] = np.nan
plot_bim_mean[:,:,iexp] = np.squeeze( np.nanmean( np.delete( parameter[my_exp][var], 4,2) , 2) )
plot_dbz_mean[:,:,iexp] = np.squeeze( np.nanmax( ensemble_mean[my_exp]['dbz'] , 2) )
print('Smoothing parameter for var=',var)
plot_bim_mean[:,:,iexp]=cf.gaussian_filter(field0=plot_bim_mean[:,:,iexp],dx=1.0,sigma=sigma_smooth,nx=nx,ny=ny,undef=undef)
if iexp > 0 :
plot_bim_mean[:,:,iexp] = 100 * ( ( plot_bim_mean[:,:,iexp] - plot_bim_mean[:,:,0] ) / plot_bim_mean[:,:,0] )
tmp = np.copy(plot_bim_mean[:,:,iexp])
tmp[ np.logical_not( radar_mask ) ] = np.nan
plot_bim_mean[:,:,iexp] = tmp[:]
print('Parameter Min=',np.nanmin( tmp ),' Parameter Max=',np.nanmax( tmp ) )
tmp = np.copy( plot_dbz_mean[:,:,iexp] )
tmp[ np.logical_not( radar_mask ) ] = np.nan
plot_dbz_mean[:,:,iexp] = tmp[:]
print('Ref Min=',np.nanmin( tmp ),' Ref Max=',np.nanmax( tmp ) )
#Plot time mean of the moments.
for ivar, var in enumerate(plot_variables):
print(var)
ifig = icol + ncols * (irow)
plot_bim_5min = np.squeeze(
np.nanmean(np.delete(parameter['LE_D1_1km_5min'][var], 4, 2), 2))
plot_bim_30sec = np.squeeze(
np.nanmean(np.delete(parameter['LE_D1_1km_30sec'][var], 4, 2), 2))
print('Smoothing parameter for var=', var)
plot_bim_5min = cf.gaussian_filter(field0=plot_bim_5min,
dx=1.0,
sigma=sigma_smooth,
nx=nx,
ny=ny,
undef=undef)
plot_bim_30sec = cf.gaussian_filter(field0=plot_bim_30sec,
dx=1.0,
sigma=sigma_smooth,
nx=nx,
ny=ny,
undef=undef)
plot_bim_diff = 100 * ((plot_bim_30sec - plot_bim_5min) / plot_bim_5min)
plot_bim_5min[np.logical_not(radar_mask)] = np.nan
plot_bim_diff[np.logical_not(radar_mask)] = np.nan
#Axes limits