First try with jupyter

module2/exo1/toy_notebook_fr.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titre du document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2+4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## sous titre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1., 0., 0., 0., 0., 1., 0., 0., 0., 1.]),\n",
+       " array([1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6, 2.8, 3. ]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "x=(1,2,3)\n",
+    "y=2*x\n",
+    "plt.hist(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +95,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-