aled paul

Probas.py

@@ -70,7 +70,7 @@ def stats_NFBP_iter(R, N):
     IVarianceSum = 0
     HSum = [0 for _ in range(N)]
     HSumVariance = [0 for _ in range(N)]
-    Sum_T=[]
+    Sum_T=[0 for _ in range(10)]
     Sum_V=[]
     Sum_H=[]
     for i in range(R):
@@ -80,34 +80,36 @@ def stats_NFBP_iter(R, N):
         for n in range(N):
             HSum[n] += sim["H"][n]
             HSumVariance[n] += sim["H"][n]**2
-        Sum_T=Sum_T+sim['T']
+        T=sim['T']
+        for i in range(5):
+            T.append(0)
+        Sum_T=[x+y for x,y in zip(Sum_T,T)]
         Sum_H=Sum_H+sim['H']
         for k in range(sim['i']):
             #we use round to approximate variations of continuous variable V
             Sum_V.append(round(sim['V'][k],2))
+    Sum_T=[x/R for x in Sum_T]
+    print(Sum_T)
     I = ISum/R
     IVariance = sqrt(IVarianceSum/(R-1) - I**2)
     print("Mean number of boxes : {} (variance {})".format(I, IVariance),'\n')
     print(" {} * {} iterations of T".format(R,N),'\n')
    
 #Plotting
-    #fig = plt.figure()
-    #ax = fig.add_subplot(111)
     #matplotlib.stairs(Sum_T,bins=[0,1,2,3,4])
     #ax.hist(Sum_T, bins=8, edgecolor='k', density=True, label='Valeurs empiriques')
     #ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
     #ylim=(0, 500), yticks=np.linspace(0, 56, 9))
-    #ax.legend()
-    #plt.show()
-    #plt.style.use('_mpl-gallery')
-    #make data
     #plot:
     #fig = plt.subplots()
     fig = plt.figure()
     #T plot
+    x =  np.arange(7)
+    print(x)
     ax = fig.add_subplot(221)
-    ax.hist(Sum_T, bins=6, linewidth=0.5, edgecolor="white", label='Empirical values')
+    ax.bar(x,Sum_T, width=1, edgecolor="white", linewidth=0.7)
-    ax.set(xlim=(0, 6), xticks=np.arange(0, 6),ylim=(0, 6000), yticks=np.linspace(0, 6000, 9))
+  # ax.hist(Sum_T, bins=6, linewidth=0.5, edgecolor="white", label='Empirical values')
+    ax.set(xlim=(0, 10), xticks=np.arange(0, 10),ylim=(0,10), yticks=np.linspace(0, 10, 1))
     ax.set_title('T histogram for {} packages (Number of packages in each box)'.format(P))
     ax.legend()
     #V plot
@@ -168,28 +170,51 @@ def stats_NFDBP(R, N):
     Runs R runs of NFDBP (for N packages) and studies distribution, variance, mean...
     """
     print("Running {} NFDBP simulations with {} packages".format(R, N))
+    P=N*R
     I = []
     H = [[] for _ in range(N)]  # List of empty lists
     Tmean=[]
     T=[]
+    Sum_T=[]
+    #First iteration to use zip after
+    sim=simulate_NFDBP(N)
+    Sum_T=sim["T"]
     for i in range(R):
         sim = simulate_NFDBP(N)
         I.append(sim["i"])
         for n in range(N):
             H[n].append(sim["H"][n])
         T=sim["T"]
+        for k in range(10):
+            Sum_T.append(0)
         for k in range(sim["i"]):
-            # for o in range(sim["i"]):
-                #Tmean+=sim["T"]
                 Tmean.append(T[k])
-
+        Sum_T=[x+y for x,y in zip(Sum_T,sim["T"])]
+    print(Sum_T)
+    print(sum(Sum_T))
+    print(P)
+    Sum_T=[x*100/(sum(Sum_T)) for x in Sum_T]
+    print(Sum_T)
     print("Mean number of boxes : {} (variance {})".format(mean(I), variance(I)))
 
     for n in range(N):
         print("Mean H_{} : {} (variance {})".format(n, mean(H[n]), variance(H[n])))
-    for k in range(int(sim["i"])):
     print("Mean T_{} : {} (variance {})".format(k, mean(Tmean), variance(Tmean)))
 
+#Plotting
+    fig, ax = plt.subplots()
+    #T plot
+    x = 0.5 + np.arange(8)
+    x=x.tolist()
+    print(type(x))
+    print(x)
+    ax.bar(x, Sum_T, width=1, edgecolor="white", linewidth=0.5)
+    ax.set(xlim=(0, 10), xticks=np.arange(0, 10),ylim=(0, 25), yticks=np.linspace(0, 25, 9))
+    ax.set_title('Repartition of packets in each box percents for {} packages '.format(P))
+    ax.legend()
+    plt.show()
+
+
 N = 10 ** 1
 sim = simulate_NFBP(N)
 
@@ -213,7 +238,7 @@ for j in range(sim["i"] + 1):
 
 print()
 stats_NFBP_iter(10**3, 10)
-stats_NFDBP(10 ** 3, 10)
+#stats_NFDBP(10 ** 3, 10)
 #
 #pyplot.plot([1, 2, 4, 4, 2, 1], color = 'red', linestyle = 'dashed', linewidth = 2,
 #markerfacecolor = 'blue', markersize = 5)