diff --git a/455-Codage_Sources/Cours/chapA.tex b/455-Codage_Sources/Cours/chapA.tex
index cc07ac4..46f75f5 100644
--- a/455-Codage_Sources/Cours/chapA.tex
+++ b/455-Codage_Sources/Cours/chapA.tex
@@ -7,12 +7,15 @@
 \subsection{version objet}
 
 \inputminted{python}{../algo_code/huffman2.py}
-\section{Codage arithétique}
+\section{Codage arithmétique}
+\inputminted{python}{../algio_code/code_arithmetique.py}
 \section{Codage LZW}
 \inputminted{python}{../algo_code/LZW.py}
 \section{Quantification}
 \subsection{Quantification uniforme}
+\inputminted{python}{../algo_code/quantif.py}
 \subsection{Algorithme de Llyod-max}
+\inputminted{python}{../algo_code/llyod_max.py}
 \subsection{Algorithme LBG}
 en 2D , ne pas essayer de tracer les cellule de voronoi
 \section{Codeur prédictif}
diff --git a/455-Codage_Sources/algo_code/code_arithmetique.py b/455-Codage_Sources/algo_code/code_arithmetique.py
index f05f0f8..90f325c 100755
--- a/455-Codage_Sources/algo_code/code_arithmetique.py
+++ b/455-Codage_Sources/algo_code/code_arithmetique.py
@@ -74,6 +74,5 @@ def arithm_pratique(X,p):
                 l[-1] = 2*l[-1]-0.5
                 h[-1] = 2*h[-1]-0.5
     return c
-
 #print(arithm(X,p))
 print(arithm_pratique(X,p))
diff --git a/455-Codage_Sources/algo_code/llyod_max.py b/455-Codage_Sources/algo_code/llyod_max.py
new file mode 100755
index 0000000..f34eb3d
--- /dev/null
+++ b/455-Codage_Sources/algo_code/llyod_max.py
@@ -0,0 +1,58 @@
+#!/usr/bin/env python3
+import numpy as np
+from sipy import integrate
+from scipy import norm
+
+M = 8
+X = np.random.normal(0,1,1000)
+
+def ddp(x):
+    mean = 0,
+    sigma = 1
+    return norm.pdf(x,mean,sigma)
+
+def init_thres_vec(M,X):
+    step = (np.max(X)-np.min(X))/M
+    thres_intervals = np.array([])
+    mid = np.mean(X)
+    for i in range(int(M/2)):
+        thres_intervals = np.append(thres_vec,mid+(i+1)*step)
+        thres_intervals = np.insert(thtres_vec,0,mid-(1+1)*step)
+    return thres_intervals
+
+def quant(x,thres,intervals):
+    thres= np.append(thres, np.inf)
+    thres= np.insert(thres, 0, -np.inf)
+    x_hat_q = np.zeros(np.shape(x))
+    for i in range(len(thres)-1):
+        if i == 0:
+            x_hat_q = np.where(np.logical_and(x > thres[i], x <= thres[i+1]),
+                                  np.full(np.size(x_hat_q), intervals[i]), x_hat_q)
+        elif i == range(len(thres))[-1]-1:
+            x_hat_q = np.where(np.logical_and(x > thres[i], x <= thres[i+1]),
+                               np.full(np.size(x_hat_q), intervals[i]), x_hat_q)
+        else:
+            x_hat_q = np.where(np.logical_and(x > thres[i], x < thres[i+1]),
+                               np.full(np.size(x_hat_q), intervals[i]), x_hat_q)
+    return x_hat_q
+
+
+def LlyodMax(X,intervals, max_iter=1000,eps=1e-5):
+    err_min = np.inf
+    for i in range(max_iter):
+        for j in range(len(x_hat_q)):
+            centroids[i] = integrate.quad(lambda x : x*ddp(x),
+                                        intervals[j],intervals[j+1])[0]/
+                         integrate.quad(lambda x : ddp(x),
+                                        intervals[j],intervals[j+1])[0]
+            intervals = 0.5*(centroids[1:]+centroids[:-1])
+            x_hat = quant(X,centroids,intervals)
+            err = np.linalg.norm(X-x_hat)
+            if err < err_min:
+                err_min =err
+                intervals_min = intervals
+                centroids_min = centroids
+        if err_min< 1e-5:
+            break
+    best_x_hat = quant(X,centroids_min,intervals_min)
+    return best_x_hat
diff --git a/455-Codage_Sources/algo_code/quantif.py b/455-Codage_Sources/algo_code/quantif.py
new file mode 100755
index 0000000..ce8ae35
--- /dev/null
+++ b/455-Codage_Sources/algo_code/quantif.py
@@ -0,0 +1,46 @@
+#!/usr/bin/env python3
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+N = 1000
+X = np.random.rand(N)
+X_c = (X - 0.5)*10
+
+def quantif_uniforme(M,X,xmin=-1,xmax=1,d=0):
+    """
+    réalise la quantification uniforme d'un vecteur sur M niveau
+    """
+    delta = 2 * xmax/M # pas de quantification
+    Q = np.zeros(len(X))
+    for k in range(len(X)):
+       q =  (X[k]/ delta)
+       if abs(q)<d: #seuil
+           Q[k] = 0
+           continue
+       elif abs(q)<2*delta:
+           if q <0:
+               Q[k] =-1
+           else:
+               Q[k] = 1
+           continue
+       else:
+           Q[k] = int(q)
+
+    return Q,delta
+
+def reverse_quantif(Q,delta):
+    return Q*delta
+
+Q,delta = quantif_uniforme(4,X_c)
+Q_2,delta = quantif_uniforme(4,X_c,d=0.5):
+
+
+print(len(Q),len(X_c))
+plt.figure()
+plt.grid()
+plt.plot(X_c,Q,'.')
+plt.plot(X_c,Q_2,'.')
+plt.show()
+
+