un premier essai avec Jupyter

module2/exo1/Tutorial.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titre du document "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2+2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=x+10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Petit exemple de completion  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np \n",
+    "mu , sigma = 100 , 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.normal(loc=mu, scale=sigma, size=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "%matplotlib inline\n",
+    "plt.hist(x)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

module2/exo1/toy_notebook_fr.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +24,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-