cpf.figconf['colorbar'] = False
cpf.figconf['shadedcolormap'] = 'Greys'
cpf.figconf['shadedmin'] = 0.0
cpf.figconf['shadedmax'] = 1.0
color_mask = 0.4 * np.ones(np.shape(radar_mask))
color_mask[radar_mask] = np.nan
cpf.plot_x_y_cartopy(lon, lat, color_mask, max_dbz[:, :],
color_mask)
#========================================================================================
#Generate regional averages of the moment.
#========================================================================================
bimodality_regional_mean[var][it, :], bimodality_regional_max[var][
it, :], bimodality_regional_min[var][
it, :] = cmf.get_regional_average_grid(
my_bimodality.data, xi, xe, yi, ye, 0, 0, undef)
#=========================================================================================
#Acumulate the moment statistics in time.
#=========================================================================================
bimodality_time_mean[var] = bimodality_time_mean[
var] + my_bimodality[:, :] #Accumulate the mean.
bimodality_time_std[var] = bimodality_time_std[var] + np.power(
my_bimodality[:, :], 2) #Accumulate the standard deviation.
time_mean_max_dbz = time_mean_max_dbz + max_dbz #To accumulate integrated liquid.
#=========================================================================================
#Advance time
#=========================================================================================
else:
cpf.figconf[
'title'] = 'KLD G.R. for variable ' + var + ' at ' + date
cpf.figconf[
'figname'] = '/Figure_KLDGR_' + var + '_' + date
cpf.plot_x_y_cartopy(lon, lat, my_kldgr[:, :],
max_dbz[:, :, 0], my_kldgr[:, :])
#========================================================================================
#Generate regional averages of the moment.
#========================================================================================
kldgr_regional_mean[var][it, :], kldgr_regional_max[var][
it, :], kldgr_regional_min[var][
it, :] = cmf.get_regional_average_grid(
my_kldgr, xi, xe, yi, ye, zi, ze, undef)
#=========================================================================================
#Acumulate the moment statistics in time.
#=========================================================================================
kldgr_time_mean[var] = kldgr_time_mean[
var] + my_kldgr[:, :, :, 0] #Accumulate the mean.
kldgr_time_std[var] = kldgr_time_std[var] + np.power(
my_kldgr[:, :, :, 0], 2) #Accumulate the standard deviation.
time_mean_max_dbz = time_mean_max_dbz + max_dbz #To accumulate integrated liquid.
#=========================================================================================
#Advance time
#=========================================================================================
cpf.figconf['colorbar'] = False
cpf.figconf['shadedcolormap'] = 'Greys'
cpf.figconf['shadedmin'] = 0.0
cpf.figconf['shadedmax'] = 1.0
color_mask = 0.4 * np.ones(np.shape(radar_mask))
color_mask[radar_mask] = np.nan
cpf.plot_x_y_cartopy(lon, lat, color_mask, max_dbz[:, :],
color_mask)
#========================================================================================
#Generate regional averages of the moment.
#========================================================================================
kld_regional_mean[var][it, :], kld_regional_max[var][
it, :], kld_regional_min[var][
it, :] = cmf.get_regional_average_grid(
my_kld.data, xi, xe, yi, ye, 0, 0, undef)
#=========================================================================================
#Acumulate the moment statistics in time.
#=========================================================================================
kld_time_mean[
var] = kld_time_mean[var] + my_kld[:, :] #Accumulate the mean.
kld_time_std[var] = kld_time_std[var] + np.power(
my_kld[:, :], 2) #Accumulate the standard deviation.
time_mean_max_dbz = time_mean_max_dbz + max_dbz #To accumulate integrated liquid.
#=========================================================================================
#Advance time
#=========================================================================================
cpf.figconf['title']='KLD G.R. for variable ' + var + ' at level ' + str( int(ctl_dict['vlevels'][np.asscalar(il)])) + ' at ' + date
cpf.figconf['figname']='/Figure_KLDGR_' + var + '_' + date + '_' + str(int(ctl_dict['vlevels'][np.asscalar(il)]))
cpf.plot_x_y_cartopy( lon , lat , my_kldgr[:,:,il] , max_dbz[:,:,il] , my_kldgr[:,:,il] )
else :
cpf.figconf['title']='KLD G.R. for variable ' + var + ' at ' + date
cpf.figconf['figname']='/Figure_KLDGR_' + var + '_' + date
cpf.plot_x_y_cartopy( lon , lat , my_kldgr[:,:] , max_dbz[:,:,0] , my_kldgr[:,:] )
#========================================================================================
#Generate regional averages of the moment.
#========================================================================================
kldgr_regional_mean[var][it,:],kldgr_regional_max[var][it,:],kldgr_regional_min[var][it,:]=cmf.get_regional_average_grid(my_kldgr,xi,xe,yi,ye,zi,ze,undef)
#=========================================================================================
#Acumulate the moment statistics in time.
#=========================================================================================
kldgr_time_mean[var]=kldgr_time_mean[var] + my_kldgr[:,:,:,0] #Accumulate the mean.
kldgr_time_std[var]=kldgr_time_std[var] + np.power( my_kldgr[:,:,:,0] , 2 ) #Accumulate the standard deviation.
time_mean_max_dbz=time_mean_max_dbz + max_dbz #To accumulate integrated liquid.
#=========================================================================================
#Advance time
#=========================================================================================
ctime = ctime + delta
my_file=basedir + expname + ctime.strftime("%Y%m%d%H%M%S") + '/guesgp/bimodality_index.grd'
bim=ctlr.read_data_grads(my_file,ctl_dict,masked=False,undef2nan=False)
my_file=basedir + expname + ctime.strftime("%Y%m%d%H%M%S") + '/guesgp/moment0001.grd'
m01=ctlr.read_data_grads(my_file,ctl_dict,masked=False,undef2nan=False)
my_file=basedir + expname + ctime.strftime("%Y%m%d%H%M%S") + '/guesgp/moment0002.grd'
m02=ctlr.read_data_grads(my_file,ctl_dict,masked=False,undef2nan=False)
print( kld.keys() )
for ireg,my_reg in enumerate( reg_names ) :
for ivar,my_var in enumerate( plot_variables ) :
kld_mean[my_reg][my_var][it] , _ , _ = cmf.get_regional_average_grid(kld[my_var],xi[ireg],xe[ireg],yi[ireg],ye[ireg],zi[ireg],ze[ireg],undef)
bim_mean[my_reg][my_var][it] , _ , _ = cmf.get_regional_average_grid(bim[my_var],xi[ireg],xe[ireg],yi[ireg],ye[ireg],zi[ireg],ze[ireg],undef)
m01_mean[my_reg][my_var][it] , _ , _ = cmf.get_regional_average_grid(m01[my_var],xi[ireg],xe[ireg],yi[ireg],ye[ireg],zi[ireg],ze[ireg],undef)
m02_mean[my_reg][my_var][it] , _ , _ = cmf.get_regional_average_grid(m02[my_var],xi[ireg],xe[ireg],yi[ireg],ye[ireg],zi[ireg],ze[ireg],undef)
#time_index[it] = np.floor( it/2 )
time_index[it] = it * 30.0 / 60.0
it = it + 1
##Read the analysis
#my_file=basedir + expname + ctime.strftime("%Y%m%d%H%M%S") + '/analgp/kldistance.grd'
#kld=ctlr.read_data_grads(my_file,ctl_dict,masked=False,undef2nan=False)
#=======================================================================================
#Plot kld
#=======================================================================================
# for key in kld :
for var in plot_variables :
my_kld = mean_kld[var]
#========================================================================================
#Generate regional averages of the moment.
#========================================================================================
kld_regional_mean[var][it,:],kld_regional_max[var][it,:],kld_regional_min[var][it,:]=cmf.get_regional_average_grid(my_kld.data,xi,xe,yi,ye,0,0,undef)
#=========================================================================================
#Acumulate the moment statistics in time.
#=========================================================================================
kld_time_mean[var]=kld_time_mean[var] + my_kld[:,:] #Accumulate the mean.
kld_time_std[var]=kld_time_std[var] + np.power( my_kld[:,:] , 2 ) #Accumulate the standard deviation.
time_mean_max_dbz=time_mean_max_dbz + max_dbz #To accumulate integrated liquid.
#=========================================================================================
#Advance time
#=========================================================================================
ctime = ctime + delta
