figures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams as mrc\n",
    "import matplotlib.ticker as mt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fsize_lims = 1e3, 1e17\n",
    "ttime_lims = 1, 1e6\n",
    "\n",
    "datasets = {\"Library of Congress\": (7e15, 3600*24*7),\n",
    "            \"Paper (pdf)\": (1e6, 60),\n",
    "            \"microCT\": (350e9, 3600)}\n",
    "\n",
    "labels = list(datasets.keys())\n",
    "sizes, times = zip(*datasets.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aobR3ThkqH374IZxOJzZt2oQ77rgD+/fvRzweR39/PwCgWq3i8OHD8Hq9OOec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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xkcd()\n",
    "mrc.update({'font.family': ['xkcd script'],\n",
    "            'text.usetex': False})\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.scatter(sizes, times, color='k', marker='.')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "\n",
    "ax.set_xlim(*fsize_lims)\n",
    "ax.set_ylim(*ttime_lims)\n",
    "\n",
    "ax.xaxis.set_major_locator(mt.FixedLocator((1e3,1e6,1e9,1e12,1e15)))\n",
    "ax.yaxis.set_major_locator(mt.FixedLocator((10,3600,3600*24,3600*24*7)))\n",
    "\n",
    "ax.xaxis.set_major_formatter(mt.FixedFormatter(('1kB','1MB','1GB','1TB', '1PB')))\n",
    "ax.yaxis.set_major_formatter(mt.FixedFormatter(('seconds','hours','days','weeks')))\n",
    "\n",
    "display(fig)\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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