late policy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.poly1d([-1/48**2, 0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x216 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "x = np.linspace(0, 48, 100)\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.plot(x, y(x))\n",
    "plt.plot([12, 12], [0, y(12)], color='black')\n",
    "plt.plot([24, 24], [0, y(24)], color='black')\n",
    "plt.plot([36, 36], [0, y(36)], color='black')\n",
    "plt.plot([12, 24, 36], [y(12), y(24), y(36)], 'o')\n",
    "plt.annotate('  {0:.2f}'.format(y(12)), xy=(12, y(12)))\n",
    "plt.annotate('  {0:.2f}'.format(y(24)), xy=(24, y(24)))\n",
    "plt.annotate('  {0:.2f}'.format(y(36)), xy=(36, y(36)))\n",
    "plt.ylim(0, plt.ylim()[1])\n",
    "plt.xticks([0, 6, 12, 18, 24, 30, 36, 42, 48])\n",
    "plt.xlim(0, plt.xlim()[1])\n",
    "plt.ylabel('Percent of total points')\n",
    "plt.xlabel('Hours late')\n",
    "plt.tight_layout()\n",
    "plt.savefig('img/late_policy.png', bbox_inches='tight')"
   ]
  },
  {
   "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": "3.6.12-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}