From 6df2878ab7d7aa33a2a917578f3a105bb4b1a6b6 Mon Sep 17 00:00:00 2001
From: fe77b5323a938dbf30e79610829bed48
 <fe77b5323a938dbf30e79610829bed48@app-learninglab.inria.fr>
Date: Thu, 26 Nov 2020 16:57:37 +0000
Subject: [PATCH] =?UTF-8?q?Avance=20jusqu'=C3=A0=201.3=20non=20inclut?=
MIME-Version: 1.0
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---
 module2/exo1/toy_notebook_fr.ipynb | 48 +++++++++++++++++++++++++-----
 1 file changed, 40 insertions(+), 8 deletions(-)

diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb
index 01b105d..73dcd38 100644
--- a/module2/exo1/toy_notebook_fr.ipynb
+++ b/module2/exo1/toy_notebook_fr.ipynb
@@ -4,23 +4,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# toy_notebook_fr"
+    "# A propos du calcul de $\\pi$"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## March 28,2019\n"
+    "## En demandant à la lib maths"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "1.A propos du calcul de :pi:\n",
-    "1.1 En demandant à la lib maths\n",
-    "Mon ordinateur m'indique que :pi: vaut *approximativement*"
+    "Mon ordinateur m'indique que $\\pi$ vaut *approximativement*"
    ]
   },
   {
@@ -41,12 +39,46 @@
     "print(pi)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideOutput": true
+   },
+   "source": [
+    "## En utilisant la méthode des aiguilles de Buffon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mais calculé avec la **méthode** des aiguilles de Buffon, on obtiendrait comme **approximation** :"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.128911138923655"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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",
+    "2/(sum((x+np.sin(theta))>1)/N)"
+   ]
   }
  ],
  "metadata": {
-- 
2.18.1