muneebalam/scrapenhl2

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/muneebalam/anaconda/lib/python3.6/site-packages/ggplot/utils.py:81: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n",
      "You can access Timestamp as pandas.Timestamp\n",
      "  pd.tslib.Timestamp,\n",
      "/Users/muneebalam/anaconda/lib/python3.6/site-packages/ggplot/stats/smoothers.py:4: FutureWarning: The pandas.lib module is deprecated and will be removed in a future version. These are private functions and can be accessed from pandas._libs.lib instead\n",
      "  from pandas.lib import Timestamp\n",
      "/Users/muneebalam/anaconda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.patches import Circle, Polygon\n",
    "from matplotlib.collections import PatchCollection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_rink_boundaries(ax):\n",
    "    # Center ice and bluelines\n",
    "    for x, c in zip((-25, 0, 25), ('b', 'r', 'b')):\n",
    "        ax.plot([x, x], [-42.5, 42.5], c=c)\n",
    "    \n",
    "    # Center circle\n",
    "    p = Circle((0, 0), radius=15)\n",
    "    p = PatchCollection([p])\n",
    "    p.set_edgecolor('deepskyblue')\n",
    "    p.set_facecolor('w')\n",
    "    ax.add_collection(p)\n",
    "    \n",
    "    # Four corners\n",
    "    rad_x = 13\n",
    "    rad_y = 15\n",
    "    center_x = 87\n",
    "    center_y = 27.5\n",
    "    xs = []\n",
    "    ys = []\n",
    "    for quadrant in range(4):\n",
    "        xmult = 1\n",
    "        ymult = 1\n",
    "        if quadrant == 1 or quadrant == 2:\n",
    "            xmult = -1\n",
    "        if quadrant == 2 or quadrant == 3:\n",
    "            ymult = -1\n",
    "        \n",
    "        start = quadrant * np.pi / 2\n",
    "        end = start + np.pi / 2\n",
    "        angles = np.linspace(start, end, num=20)\n",
    "        \n",
    "        # Add start\n",
    "        xs.append(np.cos(start) * rad_x + center_x * xmult)\n",
    "        ys.append(np.sin(start) * rad_y + center_y * ymult)\n",
    "        \n",
    "        for angle in angles:\n",
    "            xs.append(np.cos(angle) * rad_x + center_x * xmult)\n",
    "            ys.append(np.sin(angle) * rad_y + center_y * ymult)\n",
    "        \n",
    "        # Add end\n",
    "        xs.append(np.cos(end) * rad_x + center_x * xmult)\n",
    "        ys.append(np.sin(end) * rad_y + center_y * ymult)\n",
    "    \n",
    "    # Add in starting point again\n",
    "    xs.append(np.cos(0) * rad_x + center_x)\n",
    "    ys.append(np.sin(0) * rad_y + center_y)\n",
    "        \n",
    "    ax.plot(xs, ys, color='k')        \n",
    "            \n",
    "        \n",
    "def foxy(reduced=False):\n",
    "    for y in [-22, 22]:\n",
    "        for x in [-69, 69]:\n",
    "            yield x, y\n",
    "        if not reduced:\n",
    "            for x in [-20, 20]:\n",
    "                yield x, y\n",
    "            \n",
    "            \n",
    "def add_faceoff_dots(ax):\n",
    "    patches = []\n",
    "    for x, y in foxy(): \n",
    "        patches.append(Circle((x, y), radius=1))\n",
    "    p = PatchCollection(patches)\n",
    "    p.set_color('r')\n",
    "    \n",
    "    ax.add_collection(p)\n",
    "    \n",
    "    \n",
    "def add_faceoff_dashes(ax):\n",
    "    # Around the inner circle, four lines    \n",
    "    for x, y in foxy(reduced=True):\n",
    "        for dx1, dx2 in ((-2, -6), (2, 6)):\n",
    "            for dy1, dy2 in ((-0.75, -0.75), (0.75, 0.75)):\n",
    "                ax.plot([x-dx1, x-dx2], [y-dy1, y-dy2], color='r')\n",
    "                \n",
    "    for x, y in foxy(reduced=True):\n",
    "        for dx1, dx2 in ((2, 2), (-2, -2)):\n",
    "            for dy1, dy2 in ((0.75, 3.75), (-0.75, -3.75)):\n",
    "                ax.plot([x-dx1, x-dx2], [y-dy1, y-dy2], color='r')\n",
    "    \n",
    "    # Around the outer circles, short lines\n",
    "    for x, y in foxy(reduced=True):\n",
    "        for dx1, dx2 in ((-6, -6), (6, 6)):\n",
    "            for dy1, dy2 in ((-15, -17), (15, 17)):\n",
    "                ax.plot([x-dx1, x-dx2], [y-dy1, y-dy2], color='r')\n",
    "                \n",
    "def add_faceoff_circles(ax):\n",
    "    patches = []\n",
    "    for x, y in foxy(reduced=True): \n",
    "        patches.append(Circle((x, y), radius=15))\n",
    "    p = PatchCollection(patches)\n",
    "    p.set_edgecolor('r')\n",
    "    p.set_facecolor('w')\n",
    "    \n",
    "    ax.add_collection(p)\n",
    "\n",
    "def add_goal_lines(ax):\n",
    "    xs = [89, 89, -89, -89]\n",
    "    ys = [42.5 - 15 + np.sqrt(209), -1 * (42.5 - 15 + np.sqrt(209))]\n",
    "    ax.plot(xs[:2], ys, color='r')\n",
    "    ax.plot(xs[2:], ys, color='r')\n",
    "    \n",
    "def add_nets(ax):\n",
    "    # Black background\n",
    "    for mult in (-1, 1):\n",
    "        ax.plot(np.array([90, 92, 92, 90]) * mult, [-3, -3, 3, 3], color='k')\n",
    "        \n",
    "    # Creases \n",
    "    patches = []\n",
    "    yseq = np.linspace(-4, 4, num=100)\n",
    "    for xmult in (-1, 1):\n",
    "        x1 = (83 + yseq**2 / 16 * 1.5) * xmult\n",
    "        xy = np.array(list(zip(x1, yseq)))\n",
    "        xy = np.vstack([np.array([[89 * xmult, -4]]), xy, np.array([[89 * xmult, 4]])])\n",
    "        patches.append(Polygon(xy))\n",
    "    p = PatchCollection(patches)\n",
    "    p.set_edgecolor('r')\n",
    "    p.set_facecolor('deepskyblue')\n",
    "    ax.add_collection(p)\n",
    "    \n",
    "        \n",
    "def add_restricted_areas(ax):\n",
    "    for xmult in (-1, 1):\n",
    "        for ymult in (-1, 1):\n",
    "            ax.plot([89 * xmult, 100 * xmult], [11 * ymult, 14 * ymult], color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-100.0, 100.0, -45.0, 45.0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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3diW/0MszZsGYHCKymWj0BFauvIRTTvme7t2jPPecdcUvk2pqYOJE6NMnyjHH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\nckmSGmbIJUlqmCGXJKlhhlySpIb9DYxelwdiInZ3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119bf6048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=[8, 4])\n",
    "ax = plt.gca()\n",
    "add_rink_boundaries(ax)\n",
    "\n",
    "add_faceoff_dashes(ax)\n",
    "add_faceoff_circles(ax)\n",
    "add_faceoff_dots(ax) # put this later to get zorder right\n",
    "\n",
    "add_goal_lines(ax)\n",
    "add_nets(ax)\n",
    "add_restricted_areas(ax)\n",
    "\n",
    "ax.set_xlim(-100, 100)\n",
    "ax.set_ylim(-45, 45)\n",
    "plt.axis('off')"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}