un premier essai JupyterSG

module2/exo1/Tutorial SG.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)\n"
+   ]
+  },
+  {
+   "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\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma= 100, 15\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=np.random.normal(loc=mu, 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": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD8CAYAAACRkhiPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAEi1JREFUeJzt3X+s3fdd3/HnC7uEtBDVwTeZsd3Zq9yxJKIuufO8VZtKwxZDUB3+qOQKiKVFMorSrUzshw3SgD8sZRvQLdKSKdAQZ3SNLGgXq21YPQ9WTQoxN1kax0m9eMRLbu3FhooRNslg970/zifKwTn2Pfde+557+Twf0tH3e97fz/d73l/J1697Pt/vOTdVhSSpT9826QYkSZNjCEhSxwwBSeqYISBJHTMEJKljhoAkdcwQkKSOGQKS1DFDQJI6tnrSDcxl7dq1tWnTpkm3IUkryrPPPvsHVTU117hlHwKbNm1iZmZm0m1I0oqS5H+NM87pIEnqmCEgSR0zBCSpY4aAJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6tiy/8SwtFxt2vulib32qQfumthr6y8W3wlIUscMAUnqmCEgSR0zBCSpY4aAJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6ticIZDkO5IcTfK1JMeT/EKr35jkcJJX2nLN0D77kpxMciLJnUP125Mca9seTJJrc1qSpHGM807gPPDRqvogsBXYkWQ7sBc4UlVbgCPtOUluAXYBtwI7gIeSrGrHehjYA2xpjx1X8VwkSfM0ZwjUwJ+0p+9qjwJ2Agda/QBwd1vfCTxRVeer6lXgJLAtyTrghqp6uqoKeHxoH0nSBIx1TSDJqiTPA2eBw1X1DHBzVZ0BaMub2vD1wOtDu8+22vq2fmldkjQhY4VAVV2sqq3ABga/1d92heGj5vnrCvV3HiDZk2Qmycy5c+fGaVGStADzujuoqv4I+B0Gc/lvtCke2vJsGzYLbBzabQNwutU3jKiPep1Hqmq6qqanpqbm06IkaR7GuTtoKsl72/r1wA8CXwcOAbvbsN3Ak239ELAryXVJNjO4AHy0TRm9mWR7uyvonqF9JEkTMM5fFlsHHGh3+HwbcLCqvpjkaeBgknuB14CPA1TV8SQHgZeAC8D9VXWxHes+4DHgeuCp9pAkTcicIVBVLwAfGlH/Q+COy+yzH9g/oj4DXOl6giRpCfmJYUnqmCEgSR0zBCSpY4aAJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6pghIEkdMwQkqWOGgCR1zBCQpI4ZApLUMUNAkjpmCEhSxwwBSeqYISBJHTMEJKljhoAkdcwQkKSOGQKS1DFDQJI6NmcIJNmY5LeTvJzkeJJPtfrPJ/lGkufb44eH9tmX5GSSE0nuHKrfnuRY2/Zgklyb05IkjWP1GGMuAD9dVc8l+S7g2SSH27ZPV9UvDg9OcguwC7gV+B7gPyf5QFVdBB4G9gC/C3wZ2AE8dXVORZI0X3O+E6iqM1X1XFt/E3gZWH+FXXYCT1TV+ap6FTgJbEuyDrihqp6uqgIeB+5e9BlIkhZsXtcEkmwCPgQ800qfTPJCkkeTrGm19cDrQ7vNttr6tn5pXZI0IWOHQJLvBH4T+Kmq+mMGUzvvB7YCZ4BfemvoiN3rCvVRr7UnyUySmXPnzo3boiRpnsYKgSTvYhAAn62qzwNU1RtVdbGqvgX8CrCtDZ8FNg7tvgE43eobRtTfoaoeqarpqpqempqaz/lIkuZhnLuDAnwGeLmqfnmovm5o2I8CL7b1Q8CuJNcl2QxsAY5W1RngzSTb2zHvAZ68SuchSVqAce4O+jDwE8CxJM+32s8An0iylcGUzingJwGq6niSg8BLDO4sur/dGQRwH/AYcD2Du4K8M0iSJiiDG3WWr+np6ZqZmZl0G9I7bNr7pUm3sOROPXDXpFvQmJI8W1XTc43zE8OS1DFDQJI6ZghIUscMAUnqmCEgSR0zBCSpY+N8TkBa1nq8VVO6WnwnIEkdMwQkqWOGgCR1zBCQpI4ZApLUMUNAkjpmCEhSxwwBSeqYISBJHTMEJKljhoAkdcwQkKSOGQKS1DFDQJI6ZghIUscMAUnq2JwhkGRjkt9O8nKS40k+1eo3Jjmc5JW2XDO0z74kJ5OcSHLnUP32JMfatgeT5NqcliRpHOO8E7gA/HRV/TVgO3B/kluAvcCRqtoCHGnPadt2AbcCO4CHkqxqx3oY2ANsaY8dV/FcJEnzNGcIVNWZqnqurb8JvAysB3YCB9qwA8DdbX0n8ERVna+qV4GTwLYk64Abqurpqirg8aF9JEkTMK9rAkk2AR8CngFurqozMAgK4KY2bD3w+tBus622vq1fWpckTcjYIZDkO4HfBH6qqv74SkNH1OoK9VGvtSfJTJKZc+fOjduiJGmexgqBJO9iEACfrarPt/IbbYqHtjzb6rPAxqHdNwCnW33DiPo7VNUjVTVdVdNTU1PjnoskaZ7GuTsowGeAl6vql4c2HQJ2t/XdwJND9V1JrkuymcEF4KNtyujNJNvbMe8Z2keSNAGrxxjzYeAngGNJnm+1nwEeAA4muRd4Dfg4QFUdT3IQeInBnUX3V9XFtt99wGPA9cBT7SFJmpA5Q6Cq/huj5/MB7rjMPvuB/SPqM8Bt82lQknTt+IlhSeqYISBJHTMEJKljhoAkdcwQkKSOGQKS1DFDQJI6ZghIUscMAUnqmCEgSR0zBCSpY4aAJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6pghIEkdMwQkqWOGgCR1zBCQpI4ZApLUMUNAkjo2ZwgkeTTJ2SQvDtV+Psk3kjzfHj88tG1fkpNJTiS5c6h+e5JjbduDSXL1T0eSNB/jvBN4DNgxov7pqtraHl8GSHILsAu4te3zUJJVbfzDwB5gS3uMOqYkaQnNGQJV9VXgm2MebyfwRFWdr6pXgZPAtiTrgBuq6umqKuBx4O6FNi1JujoWc03gk0leaNNFa1ptPfD60JjZVlvf1i+tS5ImaKEh8DDwfmArcAb4pVYfNc9fV6iPlGRPkpkkM+fOnVtgi5KkuSwoBKrqjaq6WFXfAn4F2NY2zQIbh4ZuAE63+oYR9csd/5Gqmq6q6ampqYW0KEkaw4JCoM3xv+VHgbfuHDoE7EpyXZLNDC4AH62qM8CbSba3u4LuAZ5cRN+SpKtg9VwDknwO+AiwNsks8HPAR5JsZTClcwr4SYCqOp7kIPAScAG4v6outkPdx+BOo+uBp9pDkjRBc4ZAVX1iRPkzVxi/H9g/oj4D3Dav7iRJ15SfGJakjhkCktQxQ0CSOmYISFLHDAFJ6pghIEkdMwQkqWOGgCR1zBCQpI4ZApLUMUNAkjpmCEhSxwwBSeqYISBJHTMEJKljhoAkdcwQkKSOGQKS1DFDQJI6ZghIUscMAUnqmCEgSR0zBCSpY3OGQJJHk5xN8uJQ7cYkh5O80pZrhrbtS3IyyYkkdw7Vb09yrG17MEmu/ulIkuZjnHcCjwE7LqntBY5U1RbgSHtOkluAXcCtbZ+Hkqxq+zwM7AG2tMelx5QkLbE5Q6Cqvgp885LyTuBAWz8A3D1Uf6KqzlfVq8BJYFuSdcANVfV0VRXw+NA+kqQJWeg1gZur6gxAW97U6uuB14fGzbba+rZ+aV2SNEFX+8LwqHn+ukJ99EGSPUlmksycO3fuqjUnSfrzFhoCb7QpHtrybKvPAhuHxm0ATrf6hhH1karqkaqarqrpqampBbYoSZrLQkPgELC7re8Gnhyq70pyXZLNDC4AH21TRm8m2d7uCrpnaB9J0oSsnmtAks8BHwHWJpkFfg54ADiY5F7gNeDjAFV1PMlB4CXgAnB/VV1sh7qPwZ1G1wNPtYckaYLmDIGq+sRlNt1xmfH7gf0j6jPAbfPqTpJ0TfmJYUnqmCEgSR0zBCSpY4aAJHXMEJCkjhkCktSxOW8Rlcaxae+XJt2CpAUwBCSNbVJhf+qBuybyuj1wOkiSOmYISFLHDAFJ6pghIEkdMwQkqWOGgCR1zBCQpI4ZApLUMUNAkjpmCEhSxwwBSeqYISBJHTMEJKljhoAkdcwQkKSOLSoEkpxKcizJ80lmWu3GJIeTvNKWa4bG70tyMsmJJHcutnlJ0uJcjXcCP1BVW6tquj3fCxypqi3AkfacJLcAu4BbgR3AQ0lWXYXXlyQt0LWYDtoJHGjrB4C7h+pPVNX5qnoVOAlsuwavL0ka02JDoICvJHk2yZ5Wu7mqzgC05U2tvh54fWjf2VaTJE3IYv/G8Ier6nSSm4DDSb5+hbEZUauRAweBsgfgfe973yJblCRdzqLeCVTV6bY8C3yBwfTOG0nWAbTl2TZ8Ftg4tPsG4PRljvtIVU1X1fTU1NRiWpQkXcGCQyDJe5J811vrwN8DXgQOAbvbsN3Ak239ELAryXVJNgNbgKMLfX1J0uItZjroZuALSd46zn+oqt9K8nvAwST3Aq8BHweoquNJDgIvAReA+6vq4qK6lyQtyoJDoKp+H/jgiPofAndcZp/9wP6FvqYk6eryE8OS1DFDQJI6ZghIUscMAUnqmCEgSR0zBCSpY4aAJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6pghIEkdMwQkqWOGgCR1zBCQpI4t9g/Na5nZtPdLk25B0gpiCEha9ib5y82pB+6a2GsvBaeDJKljhoAkdcwQkKSOGQKS1DFDQJI6tuQhkGRHkhNJTibZu9SvL0l625KGQJJVwL8Ffgi4BfhEkluWsgdJ0tuW+p3ANuBkVf1+Vf0p8ASwc4l7kCQ1S/1hsfXA60PPZ4G/scQ9XHN+alf6i2NSP89L9SG1pQ6BjKjVOwYle4A97emfJDlxTbtamLXAH0y6iUWw/8la6f3Dyj+HZd1//sWcQ+bq/y+P8zpLHQKzwMah5xuA05cOqqpHgEeWqqmFSDJTVdOT7mOh7H+yVnr/sPLPwf4HlvqawO8BW5JsTvLtwC7g0BL3IElqlvSdQFVdSPJJ4D8Bq4BHq+r4UvYgSXrbkn+LaFV9GfjyUr/uNbCsp6vGYP+TtdL7h5V/DvYPpOod12UlSZ3wayMkqWOGwJiSrEry35N8sT2/McnhJK+05ZpJ93g5Sd6b5DeSfD3Jy0n+5grr/x8lOZ7kxSSfS/Idy73/JI8mOZvkxaHaZXtOsq99lcqJJHdOpuu3Xab/f9X+Db2Q5AtJ3ju0bdn3P7TtHyepJGuHaiui/yT/oPV4PMm/HKovuH9DYHyfAl4eer4XOFJVW4Aj7fly9W+A36qq7wU+yOA8VkT/SdYD/xCYrqrbGNxQsIvl3/9jwI5LaiN7bl+dsgu4te3zUPuKlUl6jHf2fxi4raq+D/gfwD5YUf2TZCPwd4HXhmorov8kP8DgGxa+r6puBX6x1RfVvyEwhiQbgLuAXx0q7wQOtPUDwN1L3dc4ktwA/B3gMwBV9adV9UeskP6b1cD1SVYD72bw2ZJl3X9VfRX45iXly/W8E3iiqs5X1avASQZfsTIxo/qvqq9U1YX29HcZfM4HVkj/zaeBf8qf/5DqSun/PuCBqjrfxpxt9UX1bwiM518z+IfzraHazVV1BqAtb5pEY2P4K8A54NfadNavJnkPK6T/qvoGg994XgPOAP+nqr7CCun/EpfredTXqaxf4t7m6+8DT7X1FdF/ko8B36iqr12yaUX0D3wA+NtJnknyX5P89VZfVP+GwByS/AhwtqqenXQvC7Qa+H7g4ar6EPB/WX5TJ5fV5s13ApuB7wHek+THJ9vVVTfW16ksF0l+FrgAfPat0ohhy6r/JO8Gfhb456M2j6gtq/6b1cAaYDvwT4CDScIi+zcE5vZh4GNJTjH41tOPJvl14I0k6wDa8uzlDzFRs8BsVT3Tnv8Gg1BYKf3/IPBqVZ2rqj8DPg/8LVZO/8Mu1/NYX6eyHCTZDfwI8GP19v3lK6H/9zP4ReJr7Wd5A/Bckr/EyugfBn1+vgaOMpiZWMsi+zcE5lBV+6pqQ1VtYnDx5b9U1Y8z+LqL3W3YbuDJCbV4RVX1v4HXk/zVVroDeIkV0j+DaaDtSd7dfuu5g8GF7ZXS/7DL9XwI2JXkuiSbgS3A0Qn0d0VJdgD/DPhYVf2/oU3Lvv+qOlZVN1XVpvazPAt8f/v5WPb9N/8R+ChAkg8A387gC+QW139V+RjzAXwE+GJb/24Gd3i80pY3Trq/K/S9FZgBXmj/kNassP5/Afg68CLw74Hrlnv/wOcYXMP4Mwb/4dx7pZ4ZTFX8T+AE8EPLtP+TDOaen2+Pf7eS+r9k+ylg7Urqn8F/+r/efg6eAz56Nfr3E8OS1DGngySpY4aAJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6pghIEkd+/+5YQt381p7vQAAAABJRU5ErkJggg==\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": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
+      "  %reload_ext rpy2.ipython\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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
+}