stylish improvements

propaganda_plot.py

@@ -50,15 +50,15 @@ z0_guess       = 3.3    # z0 in beta inversion (referene point with assumed mol.
 
 # initialize the axis ranges
 x_min = 0    # in km 
-x_max = 6.5  # in km 
+x_max = 6.3  # in km 
 y_min = 11.8
-y_max = 21.5
+y_max = 22.5
 
 # molecular fit ranges
-x_mol0 = 0.6  # in km
-x_mol1 = 1.3   # in km
-x_mol2 = 1.75  # in km
-x_mol3 = 2.25   # in km
+x_mol0 = 0.65 * 0.94   # in km
+x_mol1 = 1.3  * 0.94  # in km
+x_mol2 = 1.75 * 0.94  # in km
+x_mol3 = 2.25 * 0.94  # in km
 
 max_ranges_visualized = [ 8. , 0.5,     # 355 nm       in km  
                           4. , 4.,      # 387 nm       in km
@@ -76,14 +76,14 @@ min_ranges_beta = [ 0.5 ]
 max_ranges_beta = [ 4.  ]
 
 sigma_min = -3e-4 *1e3
-sigma_max =  0.0025*1e3
+sigma_max =  0.003*1e3
 
 
 # standard channel configuration 
-labels = [ '355 nm (elastic)', '355 nm (photon counting)',
-           '387 nm (analog)', '387 nm (Raman)',
-           '532 nm (elastic)', '532 nm (photon  counting)',
-           'near range (analog)', 'near range (532nm, elastic)' ]
+labels = [ 'BRL 355 nm (elastic)', 'BRL 355 nm (photon counting)',
+           'BRL  387 nm (analog)', 'BRL 387 nm (Raman)',
+           'BRL 532 nm (elastic)', 'BRL 532 nm (photon  counting)',
+           'BRL near range (analog)', 'BRL near range (532nm)' ]
 colors = [ 'purple' , 'purple', 'mediumslateblue', 'mediumslateblue', 'green', 'green', 'limegreen', 'limegreen']
 uv_id    = 0
 raman_id = 2
@@ -341,7 +341,7 @@ def show_file(filename):
                  ray        = Rayleigh(channel.wavelength_nm * u.nm)    # initialize Rayleigh backscatter coefficient for used wavelength
                  beta_mol_0 = ray.dbeta_domega((180 * u.deg).to(u.rad)) # mol. backscatter coefficient at altitude 0 from LIDAR
                  
-                 ids2       = (x_height > x_mol0*costheta) & (x_height < x_mol1*costheta) 
+                 ids2       = (x_height > x_mol0) & (x_height < x_mol1) 
                  
                  # fit  molecular  profile to data and obtain the LIDAR constant c0 
                  ans1, pcov1 = curve_fit(lambda xl, c0: eval_molecular_profile(xl, c0, integrate_mol_from, costheta, beta_mol_0), x_height[ids2] + H0.to(u.km).value, sr[ids2],p0=[ 15. ])
@@ -353,7 +353,7 @@ def show_file(filename):
                  #ax[0].plot(y_fit, x_height[ids2], label = r"molecular fit {:.0f} nm".format(channel.wavelength_nm), color=colors[channel_id])
                  ax[0].plot(y_fit, x_height[ids2], color=colors[channel_id])
                  
-                 ids2       = (x_height > x_mol2*costheta) & (x_height < x_mol3*costheta) 
+                 ids2       = (x_height > x_mol2) & (x_height < x_mol3) 
                  
                  # fit  molecular  profile to data and obtain the LIDAR constant c0 
                  ans2, pcov2 = curve_fit(lambda xl, c0: eval_molecular_profile(xl, c0, integrate_mol_from, costheta, beta_mol_0), x_height[ids2] + H0.to(u.km).value, sr[ids2],p0=[ 15. ])
@@ -418,10 +418,10 @@ def show_file(filename):
 
 
         if (vertical_axis):
-             ax[0].text(y_min+(y_max-y_min)*0.65,x_min+(x_max_used-x_min)*0.12, 
+             ax[0].text(y_min+(y_max-y_min)*0.65,x_min+(x_max_used-x_min)*0.94, 
                       u'zenith={:.0f}\u00B0'.format(licel.zenith_ang),
                       fontsize='x-large')
-             ax[0].text(y_min+(y_max-y_min)*0.65,x_min+(x_max_used-x_min)*0.05, 
+             ax[0].text(y_min+(y_max-y_min)*0.65,x_min+(x_max_used-x_min)*0.87, 
                       '{:.0f} shots'.format(licel.laser1['shots']),
                       fontsize='x-large')
              ax[0].set_ylim(x_min,x_max_used)
@@ -537,7 +537,7 @@ 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_green,mean_h,std_h,std_green, color=colors[channel_id])
-    plt.title('Barcelona Raman LIDAR - CTAO Pathfinder {:s}, {:s} UTC'.format(ddate,dtime))
+    plt.title('Barcelona Raman LIDAR (BRL) - CTAO Pathfinder {:s}, {:s} UTC -  commissioning mode only (reduced HV)'.format(ddate,dtime))
 
     ax3  = ax[2].twiny()
     angstrom = np.log(alpha_fitted_green/alpha_fitted_uv) / np.log(532./355.)
@@ -559,11 +559,14 @@ def show_file(filename):
     ids_smagic    = np.where(height_smagic > min_magic)
     ax[1].plot(sigma_magic[ids_smagic],height_smagic[ids_smagic], label=r"$\alpha_{aer}$ (MAGIC)",color='gray', linestyle='dashed')
     
-    ax[0].legend(loc='best')
-    ax[1].legend(loc='best')
-    ax2.legend(loc='center right')
-    ax3.legend(loc='center right')
-    ax[2].legend(loc='best')
+    #ax[0].legend(loc='lower right')
+    ax[0].legend(loc='lower left', bbox_to_anchor=(0.465,0.03))
+    #ax[1].legend(loc='lower right')
+    ax[1].legend(loc='lower left', bbox_to_anchor=(0.465,0.03))
+    ax2.legend(loc='upper right', bbox_to_anchor=(0.95,0.95))
+    ax3.legend(loc='upper right', bbox_to_anchor=(0.95,0.95))
+    #ax[2].legend(loc='lower right')
+    ax[2].legend(loc='lower left', bbox_to_anchor=(0.465,0.03))    
     ax[0].grid(axis='y')
     ax[1].grid(axis='y')
     ax[2].grid(axis='y')
@@ -583,7 +586,7 @@ def show_file(filename):
          ax3.set_xlim(0.,1.)
     else:
          ax[0].set_ylabel(r'Logarithm range-corrected signal  (log(p.e. $\cdot m) )$')
-    plt.savefig('propaganda_plot.pdf')
+    plt.savefig('BRL_RamanLIDAR_{:s}_{:s}.pdf'.format(ddate,dtime))
     plt.show()
 
 if __name__ == "__main__":