From bc434ee253b5589a1fc1d981395cd78ccdbbdb4a Mon Sep 17 00:00:00 2001
From: e357093c53bd5903b5ce129c36733f78
 <e357093c53bd5903b5ce129c36733f78@app-learninglab.inria.fr>
Date: Mon, 8 Jul 2024 21:28:05 +0000
Subject: [PATCH] Update module2/exo1/toy_notebook_fr.ipynb

---
 module2/exo1/toy_notebook_fr.ipynb | 26 ++++++++++++++------------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb
index d893a3b..c76448d 100644
--- a/module2/exo1/toy_notebook_fr.ipynb
+++ b/module2/exo1/toy_notebook_fr.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# À propos du calcul de π"
+    "# À propos du calcul de $\\pi$"
    ]
   },
   {
@@ -12,7 +12,7 @@
    "metadata": {},
    "source": [
     "## En demandant à la lib maths\n",
-    "Mon ordinateur m’indique que π vaut approximativement"
+    "Mon ordinateur m'indique que $\\pi$ vaut *approximativement*"
    ]
   },
   {
@@ -38,21 +38,21 @@
    "metadata": {},
    "source": [
     "## En utilisant la méthode des aiguilles de Buffon\n",
-    "Mais calculé avec la __méthode__ des [aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon), 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__ :\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3.128911138923655"
+       "3.1289111389236548"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -93,15 +93,17 @@
     }
    ],
    "source": [
-    "%matplotlib inline\n",
+    "%matplotlib inline  \n",
     "import matplotlib.pyplot as plt\n",
+    "\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",
-    "1\n",
+    "\n",
     "accept = (x*x+y*y) <= 1\n",
     "reject = np.logical_not(accept)\n",
+    "\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",
@@ -117,16 +119,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3.112"
+       "3.1120000000000001"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
-- 
2.18.1