cleaned a bit the main code

algogenetique.py

@@ -10,38 +10,50 @@ from random import random
 import matplotlib.pyplot as plt
 import time
 
-# Debut du decompte du temps
+# Start of the time counting 
 start_time = time.time()
 
 
 def main(N,tmax,pmutation, proportion):
 
+    #Setting up initial variables
     L=[]
     lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
     brin = ''.join(lineList[1:])
+
+    #Creation of the initial population
     People=Population(N)
+
+    #Avaliating the initial population
     S1=[]
     for individu in People.indiv:
         individu.evaluate()
         S1.append(int(individu.score))
     maximum=int(max(S1))
+
+    #The main loop of new generations
     for i in range(tmax):
         mini=People.indiv[0].score
         best=People.indiv[0]
         People.reproduction(p = proportion, proba_mutation= pmutation)
+
+        #Searching for the best individual in each generation
         for individu in People.indiv:
             if individu.score<mini:
                 best=individu
                 mini=individu.score
         
+        #Printing usefull data (generation average and their best score)
         S2=[individu.score for individu in People.indiv]
         avg = sum(S2)/len(S2)
         L.append(mini)
         print(i,"avg:",avg,"best score:", mini)
 
+
+
+    #Plotting all the graphs
     plt.subplot(221)
     plt.plot([i for i in range(tmax)], L)
-    
 
     plt.subplot(223)
     plt.hist(S1, range = (0, maximum+10), bins = 20, color = 'red')
@@ -58,13 +70,12 @@ def main(N,tmax,pmutation, proportion):
 
     return(best,People)
 
+
+#Testing our solution and printing the result in 3D
 lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
 brin = ''.join(lineList[1:])
 best,People = main(60,60,0.05,30)
 test = Traj3D()
 test.compute(brin, best.table)
 test.draw("first_plot")
-
-
-# Affichage du temps d execution
 print("Temps d'execution : %s secondes " % (time.time() - start_time))