{
 "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": [
    "r=10\n",
    "print(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "r = r + 10 \n",
    "print(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## test de complétion"
   ]
  },
  {
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 7,
     "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": [
    "%matplotlib inline\n",
    "plt.hist(x)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## utilisation d'autre language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": {
  "celltoolbar": "Hide code",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}