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module2/exo1/Tutorial.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titre du document\n"
+   ]
+  },
+  {
+   "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": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 100, 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 93.45729681, 104.24685967, 105.61563295, ..., 104.24157181,\n",
+       "        94.97280394,  80.28000038])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.random.normal(loc = mu, scale =  sigma, size = 10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "    \n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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
+}