moyenne_couleur_bloc

Projet/src/bloc.py

@@ -12,40 +12,35 @@
 
 from PIL import Image, ImageDraw
 
+def moyenne_couleur_bloc(liste: list[tuple]) -> tuple:
+    """
+    Renvoie la moyenne de la couleur d'un bloc
+    Précondition : aucune
+    Exemple(s) :
+    $$$ moyenne_couleur_bloc([(1,2,3),(4,5,6),(7,8,9)])
+    (4,5,6)
+    """
+    total_r = 0
+    total_g = 0
+    total_b = 0
+    for couleur in liste:
+        total_r = total_r + couleur[0]
+        total_g = total_r + couleur[1]
+        total_b = total_r + couleur[2]
+    nombre_blocs = len(liste)
+    moyenne_r = total_r / nombre_blocs
+    moyenne_g = total_g / nombre_blocs
+    moyenne_b = total_b / nombre_blocs
+    return (moyenne_r, moyenne_g, moyenne_b)
+    
 class Bloc:
     """Classe d'un bloc d'une image en pixel
     """
 
     def __init__(self, image: Image):
         """
-        $$$ 
+        Initialise le type bloc à la valeur de l'image 
         """
         self.image = image
-
-    def __repr__(self) -> str:
-        """
-        $$$ repr(DicoTrie([Association('a', 1)]))
-        "DicoTrie([Association('a', 1)])"
-        $$$ repr(DicoTrie([Association('a', 1), Association('b', 2)]))
-        "DicoTrie([Association('a', 1), Association('b', 2)])"
-        $$$ repr(DicoTrie([Association('c', 3), Association('a', 2), Association('b', 1)]))
-        "DicoTrie([Association('a', 2), Association('b', 1), Association('c', 3)])"
-        """
-        return f"DicoTrie({repr(self.liste_assos)})"
-
-    def __eq__(self, autre) -> bool:
-        """
-        $$$ d1 = DicoTrie([Association("a", 1), Association("b", 2)])
-        $$$ d2 = DicoTrie([Association("b", 2), Association("a", 1)])
-        $$$ d3 = DicoTrie([Association("a", 1), Association("b", 2), Association("c", 3)])
-        $$$ d1 == d2
-        True
-        $$$ d1 == d3
-        False
-        $$$ d1 == {"a": 1, "b": 2}
-        False
-        """
-        if isinstance(autre, DicoTrie):
-            return self.liste_assos == autre.liste_assos
-        else:
-            return False
\ No newline at end of file
+        
+    
\ No newline at end of file

Tp07/analyse_tris.csv

@@ -3,100 +3,100 @@ taille;"tri séléction";"tri insertion"
   1;    0.00;    0.00
   2;    1.00;    1.00
   3;    3.00;    2.64
-  4;    6.00;    5.00
-  5;   10.00;    7.86
-  6;   15.00;   11.46
-  7;   21.00;   14.70
+  4;    6.00;    4.92
+  5;   10.00;    7.54
+  6;   15.00;   10.94
+  7;   21.00;   15.32
   8;   28.00;   19.64
-  9;   36.00;   25.42
- 10;   45.00;   29.94
- 11;   55.00;   35.96
- 12;   66.00;   43.34
- 13;   78.00;   48.08
- 14;   91.00;   53.64
- 15;  105.00;   63.70
- 16;  120.00;   71.38
- 17;  136.00;   83.42
- 18;  153.00;   91.68
- 19;  171.00;  101.96
- 20;  190.00;  115.44
- 21;  210.00;  123.02
- 22;  231.00;  132.92
- 23;  253.00;  142.60
- 24;  276.00;  153.86
- 25;  300.00;  172.64
- 26;  325.00;  184.34
- 27;  351.00;  198.60
- 28;  378.00;  217.44
- 29;  406.00;  225.00
- 30;  435.00;  239.34
- 31;  465.00;  259.34
- 32;  496.00;  280.90
- 33;  528.00;  284.24
- 34;  561.00;  310.76
- 35;  595.00;  325.26
- 36;  630.00;  343.40
- 37;  666.00;  359.78
- 38;  703.00;  375.08
- 39;  741.00;  400.10
- 40;  780.00;  430.74
- 41;  820.00;  452.26
- 42;  861.00;  468.32
- 43;  903.00;  495.84
- 44;  946.00;  507.40
- 45;  990.00;  533.92
- 46; 1035.00;  555.38
- 47; 1081.00;  598.14
- 48; 1128.00;  599.04
- 49; 1176.00;  635.12
- 50; 1225.00;  658.20
- 51; 1275.00;  676.26
- 52; 1326.00;  709.66
- 53; 1378.00;  732.12
- 54; 1431.00;  760.52
- 55; 1485.00;  802.84
- 56; 1540.00;  807.68
- 57; 1596.00;  846.38
- 58; 1653.00;  861.46
- 59; 1711.00;  890.74
- 60; 1770.00;  946.72
- 61; 1830.00;  992.92
- 62; 1891.00; 1023.54
- 63; 1953.00; 1019.50
- 64; 2016.00; 1057.94
- 65; 2080.00; 1086.24
- 66; 2145.00; 1128.60
- 67; 2211.00; 1165.44
- 68; 2278.00; 1204.84
- 69; 2346.00; 1219.90
- 70; 2415.00; 1281.70
- 71; 2485.00; 1316.64
- 72; 2556.00; 1342.46
- 73; 2628.00; 1418.52
- 74; 2701.00; 1405.10
+  9;   36.00;   25.24
+ 10;   45.00;   29.92
+ 11;   55.00;   36.68
+ 12;   66.00;   43.12
+ 13;   78.00;   47.28
+ 14;   91.00;   55.54
+ 15;  105.00;   63.20
+ 16;  120.00;   72.32
+ 17;  136.00;   82.30
+ 18;  153.00;   89.66
+ 19;  171.00;  101.32
+ 20;  190.00;  112.30
+ 21;  210.00;  123.76
+ 22;  231.00;  132.08
+ 23;  253.00;  148.94
+ 24;  276.00;  158.76
+ 25;  300.00;  172.68
+ 26;  325.00;  181.14
+ 27;  351.00;  199.98
+ 28;  378.00;  212.36
+ 29;  406.00;  227.14
+ 30;  435.00;  249.68
+ 31;  465.00;  263.34
+ 32;  496.00;  277.68
+ 33;  528.00;  274.10
+ 34;  561.00;  313.86
+ 35;  595.00;  330.42
+ 36;  630.00;  354.54
+ 37;  666.00;  364.18
+ 38;  703.00;  390.42
+ 39;  741.00;  401.12
+ 40;  780.00;  430.32
+ 41;  820.00;  446.28
+ 42;  861.00;  478.54
+ 43;  903.00;  497.62
+ 44;  946.00;  510.08
+ 45;  990.00;  539.00
+ 46; 1035.00;  564.02
+ 47; 1081.00;  599.94
+ 48; 1128.00;  606.68
+ 49; 1176.00;  632.84
+ 50; 1225.00;  653.74
+ 51; 1275.00;  674.24
+ 52; 1326.00;  704.72
+ 53; 1378.00;  734.54
+ 54; 1431.00;  760.22
+ 55; 1485.00;  788.30
+ 56; 1540.00;  819.02
+ 57; 1596.00;  857.40
+ 58; 1653.00;  890.08
+ 59; 1711.00;  912.04
+ 60; 1770.00;  938.86
+ 61; 1830.00;  971.26
+ 62; 1891.00; 1025.26
+ 63; 1953.00; 1046.14
+ 64; 2016.00; 1090.40
+ 65; 2080.00; 1096.62
+ 66; 2145.00; 1145.48
+ 67; 2211.00; 1173.32
+ 68; 2278.00; 1189.14
+ 69; 2346.00; 1251.80
+ 70; 2415.00; 1261.46
+ 71; 2485.00; 1306.84
+ 72; 2556.00; 1332.00
+ 73; 2628.00; 1376.90
+ 74; 2701.00; 1417.70
  75; 2775.00; 1451.02
- 76; 2850.00; 1518.22
- 77; 2926.00; 1518.80
- 78; 3003.00; 1564.58
- 79; 3081.00; 1623.58
- 80; 3160.00; 1642.04
- 81; 3240.00; 1710.88
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- 83; 3403.00; 1764.28
- 84; 3486.00; 1816.94
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+100; 4950.00; 2606.24