plotting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "targets = ['alpine','fastconda','official','revsys','miniconda', 'intel']\n",
    "django1_result = np.array([0.6692, 0.528033333333333, 0.638883333333333, 0.607616666666667, 0.5707, 0.6723])\n",
    "django2_result = np.array([0.735166666666667, 0.584866666666667, 0.695633333333333, 0.636516666666667, 0.599416666666667, 0.722033333333333])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "relative_performance1 = django1_result[2] / django1_result\n",
    "relative_performance2 = django2_result[2] / django2_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af25da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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l5MHMrAFKO4vmcccdx/z585k8eTKXXHIJGzZsyFXotgOoK5dqmplZNUo7i2aTJk2ATGLR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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a337128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = len(targets)\n",
    "ind = np.arange(N)  # the x locations for the groups\n",
    "width = 0.35 # the width of the bars\n",
    "\n",
    "plt.figure(figsize=(20, 6))\n",
    "fig, ax = plt.subplots(figsize=(8, 4.5))\n",
    "rects1 = ax.barh(ind, relative_performance1, width, color='#3498db')\n",
    "rects2 = ax.barh(ind + width, relative_performance2, width, color='#2ecc71')\n",
    "\n",
    "ax.set_title('Relative Performance (higher is better)')\n",
    "ax.set_yticks(ind + width / 2)\n",
    "ax.set_yticklabels(targets)\n",
    "ax.set_xlim((0., 1.4))\n",
    "\n",
    "ax.legend((rects2[0], rects1[0]), ('Django 2.0', 'Django 1.11'), loc=4)\n",
    "\n",
    "def autolabel(rects):\n",
    "    \"\"\"\n",
    "    Attach a text label above each bar displaying its height\n",
    "    \"\"\"\n",
    "    for rect in rects:\n",
    "        ax.text(rect.get_x() + rect.get_width() + 0.2 * rect.get_height()  , rect.get_y() + 0.05 * rect.get_height(),\n",
    "                '%.2f' % rect.get_width(),\n",
    "                ha='center', va='bottom', fontdict={'size': 8})\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "\n",
    "plt.savefig('plot.png', dpi=300)"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}