Compute proportion of information used for theta plots

swp2.py

             # divide by N0
             y[i] = y[i]/N0
             x[i] = x[i]/N0
-        sum_theta_i = 0
-        print(epoch, x, y)
-        for i in range(2, len(y)-1):
-            sum_theta_i=y[i] / (i-1)
-        prop = []
-        for k in range(2, len(y)-1):
-            prop.append(y[k+1] / (k - 1) / sum_theta_i)
-        #print(epoch, prop)
         plt.plot(x, y, 'o', linestyle = "-", alpha=0.75, lw=2, label = str(epoch)+' BrkPt | Lik='+greatest_likelihood)
         if theta_scale:
             plt.xlabel("Coal. time")
     # number of monomorphic sites
     L = L_stored
     S0 = L-S
-    print("SFS", SFS_stored)
-    print("S", S, "L", L, "S0=", S0)
+    # print("SFS", SFS_stored)
+    # print("S", S, "L", L, "S0=", S0)
     # compute Ln
     Ln = log_facto(S+S0) - log_facto(S0) + np.log(float(S0)/(S+S0)) * S0
     for xi in range(0, len(SFS_stored)):
         p_i = SFS_stored[xi] / float(S+S0)
         Ln += np.log(p_i) * SFS_stored[xi] - log_facto(SFS_stored[xi])
     res = Ln
-    print(res)
+    # print(res)
     # basic plot likelihood
     plt.figure(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
     plt.rcParams['font.size'] = '18'
                 N0 = y[0]
         for i in range(len(y)):
             y[i] = y[i]/N0
+        # compute the proportion of information used at each bin of the SFS
+        sum_theta_i = 0
+        for i in range(2, len(y)-1):
+            sum_theta_i+=y[i] / (i-1)
+        prop = []
+        for k in range(2, len(y)-1):
+            prop.append(y[k] / (k - 1) / sum_theta_i)
+        # plot 
         plt.plot(x, y, 'o', linestyle="dotted", alpha=0.75, lw=2, label = str(epoch)+' brks')
         plt.xlabel("# breaks")
         plt.ylabel("theta")