From e538db6c3f7e996700fd140f982b2f2c09901747 Mon Sep 17 00:00:00 2001
From: fa5d65b9c887dadf0383a678f20d94ad
 <fa5d65b9c887dadf0383a678f20d94ad@app-learninglab.inria.fr>
Date: Fri, 25 Aug 2023 21:11:33 +0000
Subject: [PATCH] no commit message

---
 module2/exo1/toy_notebook_fr.ipynb | 62 +++++++++++++++---------------
 1 file changed, 31 insertions(+), 31 deletions(-)

diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb
index 884d4b2..9d1e082 100644
--- a/module2/exo1/toy_notebook_fr.ipynb
+++ b/module2/exo1/toy_notebook_fr.ipynb
@@ -4,14 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# A propos du calcul de $\\pi$"
+    "# 1. A propos du calcul de $\\pi$"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## En demandant à la lib maths"
+    "## 1.1 En demandant à la lib maths"
    ]
   },
   {
@@ -43,7 +43,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## En utilisant la méthode des aiguilles de Buffon"
+    "## 1.2 En utilisant la méthode des aiguilles de Buffon"
    ]
   },
   {
@@ -82,7 +82,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Avec un argument \"fréquentiel\" de surface"
+    "## 1.3 Avec un argument \"fréquentiel\" de surface"
    ]
   },
   {
@@ -96,30 +96,6 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "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)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -135,6 +111,16 @@
     }
    ],
    "source": [
+    "%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",
+    "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",
@@ -150,8 +136,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -159,7 +147,7 @@
        "3.112"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -168,6 +156,18 @@
     "4*np.mean(accept)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-- 
2.18.1