Analysis of truth/reconstruction associations

Correlation and ratio plots to study truth (mc) reconstruction.

benchmarks/dis/analysis/truth_reconstruction.py

@@ -311,6 +311,47 @@ for i in range(1,len(MC_list),1):
     plt.savefig(os.path.join(args.outdir, '%gon%g/minQ2=%g/truth_reconstruction/%s_ratio_vs_momentum_%gx%g_minQ2=%g.png' %  (k,p,minq2,title_list[i],k,p,minq2)))
 
 
+################
+
+theta_mc_fil = ak.Array(theta_mc[simID][booll])[boolean_photon]
+theta_rc_fil = ak.Array(theta_rc[recID][booll])[boolean_photon]
+phi_mc_fil = ak.Array(phi_mc[simID][booll])[boolean_photon]
+phi_rc_fil = ak.Array(phi_rc[recID][booll])[boolean_photon]
+
+theta_mc_fil_len = ak.count(theta_mc_fil,axis=None)
+theta_rc_fil_len = ak.count(theta_rc_fil,axis=None)
+
+if theta_mc_fil_len > theta_rc_fil_len:
+    F_boolean = np.ones_like(theta_rc_fil) == 1
+else: 
+    F_boolean = np.ones_like(theta_mc_fil) == 1
+theta_mc_F = np.array(ak.flatten(theta_mc_fil[F_boolean]))
+theta_rc_F = np.array(ak.flatten(theta_rc_fil[F_boolean]))
+phi_mc_F = np.array(ak.flatten(phi_mc_fil[F_boolean]))
+phi_rc_F = np.array(ak.flatten(phi_rc_fil[F_boolean]))
+ratio = np.array((ak.Array(theta_rc_F)-(ak.Array(theta_mc_F))))
+
+fig = plt.figure()
+gs = fig.add_gridspec(2, 2, wspace=0.03)
+(ax1, ax2), (ax3, ax4) = gs.subplots(sharex=True, sharey=True)
+fig.suptitle('Photons in (%g x %g)GeV  %gGeV minQ2  %s events'%(k,p,minq2,Nevents))
+
+ax1.scatter(-theta_mc_F, ratio, s = 0.01)
+ax2.scatter(-theta_rc_F, ratio, s = 0.01)
+ax3.scatter(-theta_mc_F, phi_mc_F, s = 0.01)
+ax4.scatter(-theta_rc_F, phi_rc_F, s = 0.01)
+
+ax1.set_ylabel('rc-mc')
+ax2.set_ylabel('rc-mc')
+ax3.set_ylabel('Phi mc')
+ax4.set_ylabel('Phi rc')
+ax3.set_xlabel('Theta mc')
+ax4.set_xlabel('Theta rc')
+fig.set_figwidth(20)
+fig.set_figheight(20)
+    
+plt.savefig(os.path.join(args.outdir, '%gon%g/minQ2=%g/truth_reconstruction/photons_%gx%g_minQ2=%g.png' %  (k,p,minq2,title_list[i],k,p,minq2)))
+