print out LR and A

propaganda_plot.py

@@ -383,8 +383,8 @@ def show_file(filename):
                       aod_green          = aod_fitted
                       offset          = 0
                  
-                 print ('AOD fitted: ', aod_fitted, ' ans2: ', ans2[0], ' ans1: ', ans1[0], 'alpha_fitted:', alpha_fitted)
                  alpha_err = np.sqrt(aod_err**2/(x_mol2 - x_mol1)**2 + aod_fitted**2/(x_mol2 - x_mol1)**2 /12)
+                 print ('AOD fitted: ', aod_fitted, ' ans2: ', ans2[0], ' ans1: ', ans1[0], 'alpha_fitted:', alpha_fitted, ' alpha_err:', alpha_err)
                  
                  ax[1].errorbar(alpha_fitted, 0.5*(x_mol1+x_mol2)+offset, (x_mol2-x_mol1)/np.sqrt(12), alpha_err, label=r'$\alpha_{aer}$'+' ({:.0f} nm)'.format(channel.wavelength_nm))
             
@@ -482,6 +482,10 @@ def show_file(filename):
         std_h  = np.std(x_raman_filt[ids_lr]/1000.*costheta)
         #ax[2].plot(alphaaer_filt[ids_lr]/beta_aerosol[ids_lr],x_raman_filt[ids_lr]/1000.*costheta,label=r'Lidar Ratio (355 nm)', color=colors[channel_id])
         ax[2].errorbar(mean_uv,mean_h,std_h,std_uv,label=r'Lidar Ratio (355 nm)', color=colors[channel_id])
+
+        print ('LR: ', mean_uv, ' LR_err:', std_uv)
+
+        
         ids_lr = np.where((x_raman_filt/1000.*costheta > 2.65)  &  (x_raman_filt/1000.*costheta < 2.85))
         mean_uv = np.mean(alphaaer_filt[ids_lr]/beta_aerosol_uv[ids_lr])
         std_uv  = np.std(alphaaer_filt[ids_lr]/beta_aerosol_uv[ids_lr])*10
@@ -489,6 +493,8 @@ def show_file(filename):
         std_h  = np.std(x_raman_filt[ids_lr]/1000.*costheta)
         #ax[2].plot(alphaaer_filt[ids_lr]/beta_aerosol[ids_lr],x_raman_filt[ids_lr]/1000.*costheta, color=colors[channel_id])
         ax[2].errorbar(mean_uv,mean_h,std_h,std_uv, color=colors[channel_id])
+
+        print ('LR: ', mean_uv, ' LR_err:', std_uv)        
                 
     for (channel_id,channel) in enumerate(licel.channels):   # loop over all channels
 
@@ -541,10 +547,11 @@ def show_file(filename):
 
     ax3  = ax[2].twiny()
     angstrom = np.log(alpha_fitted_green/alpha_fitted_uv) / np.log(532./355.)
-    print ('angstrom:',angstrom)
     angstrom_err = 1./(alpha_fitted_green/alpha_fitted_uv) * np.sqrt((aod_err_uv / aod_uv)**2 + (aod_err_green/aod_green)**2) / np.log(532./355.) 
     ax3.errorbar(angstrom, hh,  hh_err,  angstrom_err, label=r'$\AA$ngstr$\"o$m exponent (355 nm - 532 nm)')
-    
+
+    print ('angstrom:',angstrom,' angstrom_err', angstrom_err )
+
     height_magic = np.array(magic_tab.x/1000.)
     sr_magic     = np.array(magic_tab.y)
     ids_magic    = np.where(height_magic > min_magic)