fix marker size in benchmarks/dis/analysis/truth_reconstruction.py

benchmarks/dis/analysis/truth_reconstruction.py

@@ -146,7 +146,7 @@ for i in range(len(MC_list)): #Repeat the following steps for each variable (the
         Y_plot[j] = ratio
         if i == 0:  #Theta
             boolean_tilt_x = np.logical_and(X_list[j] > 0 , X_list[j] < 0.5)
-            boolean_tilt_y = np.logical_or(Y_plot[j] < -0.02 , Y_plot[j] > 0.02)
+            boolean_tilt_y = np.logical_or(Y_plot[j] < -0.002 , Y_plot[j] > 0.002)
             boolean_tilt[j] = np.logical_and(boolean_tilt_x, boolean_tilt_y)
 
 
@@ -168,12 +168,12 @@ for i in range(len(MC_list)): #Repeat the following steps for each variable (the
         ax4.scatter(X_plot[3], Y_plot[3], s = ssize)
         ax5.scatter(X_plot[4], Y_plot[4], s = ssize)
         ax6.scatter(X_plot[5], Y_plot[5], s = ssize)
-        ax1.scatter(X_plot[0][boolean_tilt[0]], Y_plot[0][boolean_tilt[0]], s = ssize, c = 'red')
-        ax2.scatter(X_plot[1][boolean_tilt[1]], Y_plot[1][boolean_tilt[1]], s = ssize, c = 'red')
-        ax3.scatter(X_plot[2][boolean_tilt[2]], Y_plot[2][boolean_tilt[2]], s = ssize, c = 'red')
-        ax4.scatter(X_plot[3][boolean_tilt[3]], Y_plot[3][boolean_tilt[3]], s = ssize, c = 'red')
-        ax5.scatter(X_plot[4][boolean_tilt[4]], Y_plot[4][boolean_tilt[4]], s = ssize, c = 'red')
-        ax6.scatter(X_plot[5][boolean_tilt[5]], Y_plot[5][boolean_tilt[5]], s = ssize, c = 'red')
+        ax1.scatter(X_plot[0][boolean_tilt[0]], Y_plot[0][boolean_tilt[0]], s = ssize+1, c = 'red')
+        ax2.scatter(X_plot[1][boolean_tilt[1]], Y_plot[1][boolean_tilt[1]], s = ssize+1, c = 'red')
+        ax3.scatter(X_plot[2][boolean_tilt[2]], Y_plot[2][boolean_tilt[2]], s = ssize+1, c = 'red')
+        ax4.scatter(X_plot[3][boolean_tilt[3]], Y_plot[3][boolean_tilt[3]], s = ssize+1, c = 'red')
+        ax5.scatter(X_plot[4][boolean_tilt[4]], Y_plot[4][boolean_tilt[4]], s = ssize+1, c = 'red')
+        ax6.scatter(X_plot[5][boolean_tilt[5]], Y_plot[5][boolean_tilt[5]], s = ssize+1, c = 'red')
         
         if i == 1:  # for momentum
             ax1.set_ylabel('rc/mc') #ratio
@@ -255,12 +255,12 @@ for i in range(len(MC_list)): #Repeat the following steps for each variable (the
             ax4.scatter(X_plot[3], Y_plot[3], s = ssize)
             ax5.scatter(X_plot[4], Y_plot[4], s = ssize)
             ax6.scatter(X_plot[5], Y_plot[5], s = ssize)
-            ax1.scatter(X_plot[0][boolean_tilt[0]], Y_plot[0][boolean_tilt[0]], s = ssize, c = 'red')
-            ax2.scatter(X_plot[1][boolean_tilt[1]], Y_plot[1][boolean_tilt[1]], s = ssize, c = 'red')
-            ax3.scatter(X_plot[2][boolean_tilt[2]], Y_plot[2][boolean_tilt[2]], s = ssize, c = 'red')
-            ax4.scatter(X_plot[3][boolean_tilt[3]], Y_plot[3][boolean_tilt[3]], s = ssize, c = 'red')
-            ax5.scatter(X_plot[4][boolean_tilt[4]], Y_plot[4][boolean_tilt[4]], s = ssize, c = 'red')
-            ax6.scatter(X_plot[5][boolean_tilt[5]], Y_plot[5][boolean_tilt[5]], s = ssize, c = 'red')
+            ax1.scatter(X_plot[0][boolean_tilt[0]], Y_plot[0][boolean_tilt[0]], s = ssize+1, c = 'red')
+            ax2.scatter(X_plot[1][boolean_tilt[1]], Y_plot[1][boolean_tilt[1]], s = ssize+1, c = 'red')
+            ax3.scatter(X_plot[2][boolean_tilt[2]], Y_plot[2][boolean_tilt[2]], s = ssize+1, c = 'red')
+            ax4.scatter(X_plot[3][boolean_tilt[3]], Y_plot[3][boolean_tilt[3]], s = ssize+1, c = 'red')
+            ax5.scatter(X_plot[4][boolean_tilt[4]], Y_plot[4][boolean_tilt[4]], s = ssize+1, c = 'red')
+            ax6.scatter(X_plot[5][boolean_tilt[5]], Y_plot[5][boolean_tilt[5]], s = ssize+1, c = 'red')
             ax1.set_xscale('log')
             ax1.set_ylabel('rc-mc')
             ax3.set_ylabel('rc-mc')
@@ -362,12 +362,12 @@ def particle_plots(boolean_particle):
     ax4.scatter(-theta_rc_filtered, ratio, s = ssize)
     ax5.scatter(-theta_mc_filtered, phi_mc_filtered, s = ssize)
     ax6.scatter(-theta_rc_filtered, phi_rc_filtered, s = ssize)
-    ax1.scatter(-theta_mc_filtered_with_tilt, ratio_tilt, s = ssize, c = 'red')
-    ax2.scatter(-theta_rc_filtered_with_tilt, ratio_tilt, s = ssize, c = 'red')
-    ax3.scatter(-theta_mc_filtered_with_tilt, ratio_tilt, s = ssize, c = 'red')
-    ax4.scatter(-theta_rc_filtered_with_tilt, ratio_tilt, s = ssize, c = 'red')
-    ax5.scatter(-theta_mc_filtered_with_tilt, phi_mc_filtered_with_tilt, s = ssize, c = 'red')
-    ax6.scatter(-theta_rc_filtered_with_tilt, phi_rc_filtered_with_tilt, s = ssize, c = 'red')
+    ax1.scatter(-theta_mc_filtered_with_tilt, ratio_tilt, s = ssize+1, c = 'red')
+    ax2.scatter(-theta_rc_filtered_with_tilt, ratio_tilt, s = ssize+1, c = 'red')
+    ax3.scatter(-theta_mc_filtered_with_tilt, ratio_tilt, s = ssize+1, c = 'red')
+    ax4.scatter(-theta_rc_filtered_with_tilt, ratio_tilt, s = ssize+1, c = 'red')
+    ax5.scatter(-theta_mc_filtered_with_tilt, phi_mc_filtered_with_tilt, s = ssize+1, c = 'red')
+    ax6.scatter(-theta_rc_filtered_with_tilt, phi_rc_filtered_with_tilt, s = ssize+1, c = 'red')
     ax1.set_ylabel('Theta rc-mc')
     ax2.set_ylabel('Theta rc-mc')
     ax3.set_ylabel('Theta rc-mc')