18h59

module2/exo1/toy_notebook_fr.ipynb

@@ -42,7 +42,7 @@
    },
    "source": [
     "##  En utilisant la méthode des aiguilles de Buffon  \n",
-    "Mais calculé avec la **méthode** des <span style=\"color: #0002e1\">aiguilles de Buffon</span>, on obtiendrait comme **approximation** :  "
+    "Mais calculé avec la **méthode** des [Aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon), on obtiendrait comme **approximation** :  "
    ]
   },
   {
@@ -67,9 +67,9 @@
    "source": [
     "import numpy as np\n",
     "np.random.seed(seed=42)\n",
-    "N=10000\n",
-    "x=np.random.uniform(size=N, low=0, high=1)\n",
-    "theta=np.random.uniform(size=N, low=0, high=pi/2)\n",
+    "N = 10000\n",
+    "x = np.random.uniform(size=N, low=0, high=1)\n",
+    "theta = np.random.uniform(size=N, low=0, high=pi/2)\n",
     "2/(sum((x+np.sin(theta))>1)/N)"
    ]
   },
@@ -110,12 +110,12 @@
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "np.random.seed(seed=42)\n",
-    "N=1000\n",
-    "x=np.random.uniform(size=N, low=0, high=1)\n",
-    "y=np.random.uniform(size=N, low=0, high=1)\n",
-    "accept=(x*x+y*y)<=1\n",
-    "reject=np.logical_not(accept)\n",
-    "fig, ax=plt.subplots(1)\n",
+    "N = 1000\n",
+    "x = np.random.uniform(size=N, low=0, high=1)\n",
+    "y = np.random.uniform(size=N, low=0, high=1)\n",
+    "accept = (x*x+y*y)<=1\n",
+    "reject = np.logical_not(accept)\n",
+    "fig, ax = plt.subplots(1)\n",
     "ax.scatter(x[accept], y[accept], c='b', alpha=0.2, edgecolor=None)\n",
     "ax.scatter(x[reject], y[reject], c='r', alpha=0.2, edgecolor=None)\n",
     "ax.set_aspect('equal')"