diff --git a/Probas.py b/Probas.py
index 2fd03ce..14baec0 100755
--- a/Probas.py
+++ b/Probas.py
@@ -9,7 +9,7 @@ import matplotlib.pyplot as pt
 
 def simulate_NFBP(N):
     """
-    Tries to simulate T_i, V_i and H_n for N packages of random size.
+    Tries to simulate T_i, V_i and H_n for N items of random size.
     """
     i = 0  # Nombre de boites
     R = [0]  # Remplissage de la i-eme boite
@@ -43,9 +43,9 @@ def simulate_NFBP(N):
 
 def stats_NFBP(R, N):
     """
-    Runs R runs of NFBP (for N packages) and studies distribution, variance, mean...
+    Runs R runs of NFBP (for N items) and studies distribution, variance, mean...
     """
-    print("Running {} NFBP simulations with {} packages".format(R, N))
+    print("Running {} NFBP simulations with {} items".format(R, N))
     I = []
     H = [[] for _ in range(N)]  # List of empty lists
     
@@ -62,11 +62,11 @@ def stats_NFBP(R, N):
 
 def stats_NFBP_iter(R, N):
     """
-    Runs R runs of NFBP (for N packages) and studies distribution, variance, mean...
+    Runs R runs of NFBP (for N items) and studies distribution, variance, mean...
     Calculates stats during runtime instead of after to avoid excessive memory usage.
     """
     P=R*N
-    print("Running {} NFBP simulations with {} packages".format(R, N))
+    print("Running {} NFBP simulations with {} items".format(R, N))
     ISum = 0
     IVarianceSum = 0
     HSum = [0 for _ in range(N)]
@@ -100,7 +100,7 @@ def stats_NFBP_iter(R, N):
     for n in range(N):
         Hn = HSum[n]/R # moyenne
         HVariance = sqrt(HSumVariance[n]/(R-1) - Hn**2) # Variance
-        print("Index of bin containing the {}th package (H_{}) : {} (variance {})".format(n, n, Hn, HVariance))
+        print("Index of bin containing the {}th item (H_{}) : {} (variance {})".format(n, n, Hn, HVariance))
     HSum=[x/R for x in HSum]
     print(HSum)
 #Plotting
@@ -113,7 +113,7 @@ def stats_NFBP_iter(R, N):
     ax.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,3), yticks=np.linspace(0, 3, 5))
     ax.set_ylabel('Items')
     ax.set_xlabel('Bins (1-{})'.format(N))
-    ax.set_title('T histogram for {} packages (Number of packages in each bin)'.format(P))
+    ax.set_title('T histogram for {} items (Number of items in each bin)'.format(P))
     ax.legend(loc='upper left',title='Legend')
     #V plot
     bx = fig.add_subplot(222) 
@@ -121,7 +121,7 @@ def stats_NFBP_iter(R, N):
     bx.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0, 1), yticks=np.linspace(0, 1, 10))
     bx.set_ylabel('First item size')
     bx.set_xlabel('Bins (1-{})'.format(N))
-    bx.set_title('V histogram for {} packages (first package size of each bin)'.format(P))
+    bx.set_title('V histogram for {} items (first item size of each bin)'.format(P))
     bx.legend(loc='upper left',title='Legend')
     #H plot
     #We will simulate this part for a asymptotic study
@@ -130,7 +130,7 @@ def stats_NFBP_iter(R, N):
     cx.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0, 10), yticks=np.linspace(0, N, 5))
     cx.set_ylabel('Bin ranking of n-item')
     cx.set_xlabel('n-item (1-{})'.format(N))
-    cx.set_title('H histogram for {} packages'.format(P))
+    cx.set_title('H histogram for {} items'.format(P))
     xb=linspace(0,N,10)
     yb=Hn*xb/10
     wb=HVariance*xb/10
@@ -141,7 +141,7 @@ def stats_NFBP_iter(R, N):
 
 def simulate_NFDBP(N):
     """
-    Tries to simulate T_i, V_i and H_n for N packages of random size.
+    Tries to simulate T_i, V_i and H_n for N items of random size.
     """
     i = 0  # Nombre de boites
     R = [0]  # Remplissage de la i-eme boite
@@ -176,9 +176,9 @@ def simulate_NFDBP(N):
 
 def stats_NFDBP(R, N,t_i):
     """
-    Runs R runs of NFDBP (for N packages) and studies distribution, variance, mean...
+    Runs R runs of NFDBP (for N items) and studies distribution, variance, mean...
     """
-    print("Running {} NFDBP simulations with {} packages".format(R, N))
+    print("Running {} NFDBP simulations with {} items".format(R, N))
     P=N*R
     I = []
     H = [[] for _ in range(N)]  # List of empty lists
@@ -232,7 +232,7 @@ def stats_NFDBP(R, N,t_i):
     ax.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,20), yticks=np.linspace(0, 20, 2))
     ax.set_ylabel('Items(n) in %')
     ax.set_xlabel('Bins (1-{})'.format(N))
-    ax.set_title('Items percentage for each bin and {} packages (Number of packages in each bin)'.format(P))
+    ax.set_title('Items percentage for each bin and {} items (Number of items in each bin)'.format(P))
     ax.legend(loc='upper left',title='Legend')
 
     #Mathematical P(Ti=k) plot. It shows the Ti(t_i) law with the probability of each number of items.
@@ -242,7 +242,7 @@ def stats_NFDBP(R, N,t_i):
     bx.set(xlim=(0, N), xticks=np.arange(0, N),ylim=(0,len(Tk[t_i])), yticks=np.linspace(0, 1, 1))
     bx.set_ylabel('P(T{}=i)'.format(t_i))
     bx.set_xlabel('Bins i=(1-{}) in %'.format(N))
-    bx.set_title('T{} histogram for {} packages (Number of packages in each bin)'.format(t_i,P))
+    bx.set_title('T{} histogram for {} items (Number of items in each bin)'.format(t_i,P))
     bx.legend(loc='upper left',title='Legend')
 
     #Loi mathematique