Probas.py

@@ -242,7 +242,6 @@ def stats_NFDBP(R, N, t_i):
 
     for n in range(N):
         print("Mean H_{} : {} (variance {})".format(n, mean(H[n]), variance(H[n])))
-    # TODO variance for T_k doesn't see right
     print("Mean T_{} : {} (variance {})".format(k, mean(Sum_T), variance(Sum_T)))
     # Loi math
     for u in range(N):
@@ -286,9 +285,8 @@ def stats_NFDBP(R, N, t_i):
             P
         )
     )
-    ax.legend(loc="upper right", title="Legend")
+    ax.legend(loc="upper left", title="Legend")
 
-    # TODO fix the graph below
     # Mathematical P(Ti=k) plot. It shows the Ti(t_i) law with the probability of each number of items.
     print(len(Tk[t_i]))
     bx = fig.add_subplot(222)
@@ -312,7 +310,7 @@ def stats_NFDBP(R, N, t_i):
     bx.set_title(
         "T{} histogram for {} items (Number of items in each bin)".format(t_i, P)
     )
-    bx.legend(loc="upper right", title="Legend")
+    bx.legend(loc="upper left", title="Legend")
 
     # Loi mathematique
     print(T_maths)
@@ -335,7 +333,7 @@ def stats_NFDBP(R, N, t_i):
     cx.set_ylabel("P(T{}=i)".format(t_i))
     cx.set_xlabel("Bins i=(1-{})".format(N))
     cx.set_title("Theoretical T{} values in %".format(t_i))
-    cx.legend(loc="upper right", title="Legend")
+    cx.legend(loc="upper left", title="Legend")
     plt.show()