From 7564326c951ca5114f81f3fab9271954e3b9afc1 Mon Sep 17 00:00:00 2001
From: 8d8f65243a3005308e219351e328782f
 <8d8f65243a3005308e219351e328782f@app-learninglab.inria.fr>
Date: Wed, 15 Apr 2020 14:40:35 +0000
Subject: [PATCH] Un premier essai avec Jupyter

---
 module2/exo1/Tutoriel.ipynb | 188 ++++++++++++++++++++++++++++++++++++
 1 file changed, 188 insertions(+)
 create mode 100644 module2/exo1/Tutoriel.ipynb

diff --git a/module2/exo1/Tutoriel.ipynb b/module2/exo1/Tutoriel.ipynb
new file mode 100644
index 0000000..211a91b
--- /dev/null
+++ b/module2/exo1/Tutoriel.ipynb
@@ -0,0 +1,188 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titre du document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2+2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "nu, sigma = 100, 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.normal(loc=nu, scale=sigma, size=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "## Utilisation d'autres langages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%R\n",
+    "plot(cars)"
+   ]
+  },
+  {
+   "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
+}
-- 
2.18.1