diff --git a/plot/main.py b/plot/main.py
index df36a68..0c0c740 100644
--- a/plot/main.py
+++ b/plot/main.py
@@ -26,18 +26,43 @@ def mean_mkn() -> np.ndarray:
     return averages_mkn
 
 
+def rb_available() -> np.ndarray:
+    available = np.zeros((size, 2))
+    nb = 0
+    for i in nb_files:
+        data = pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy()
+        nb_users = i.split(".")[0]
+        available[nb, 0] = int(nb_users)
+        available[nb, 1] = data.shape[0] / (200 * 10000)
+        nb += 1
+
+        """for j in range(0, 2):
+            for k in range(0, 10000):
+                nb_users = i.split(".")[0]
+                available[nb, 0] = int(nb_users)
+                if j == data[nb, 1] and k == data[:, 2]:
+                    available[nb, 1] += 1
+        """
+    return available
+
+
 averages = mean_mkn()
+available = rb_available()
 # Data for plotting
 averages.sort(axis=0)
-x = averages[:, 0]
-y = averages[:, 1]
 
 print(averages)
 fig, ax = plt.subplots()
-ax.scatter(x, y)
+ax.scatter(averages[:, 0], averages[:, 1])
 
-ax.set(xlabel='users', ylabel='ressources (RB)', title='MaxSNR')
+ax.set(xlabel='number of users', ylabel='Efficacité spectrale', title='Efficacité spectrale')
 ax.grid()
 
 # fig.savefig("test.png")
 plt.show()
+
+fig, ax = plt.subplots()
+ax.scatter(available[:, 0], available[:, 1])
+ax.set(xlabel='number of users', ylabel='RB utilisés', title='Pourcentage de RB utilisés')
+ax.grid()
+plt.show()