football_field_v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
    "\n",
    "%matplotlib inline\n",
    "# %load_ext nb_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function to draw field parameters\n",
    "def draw_field(ax):\n",
    "    # Set field outline\n",
    "    plt.plot([0, 0], [0, 53.33], c=\"white\", lw=2)\n",
    "    plt.plot([0, 120], [53.33, 53.33], c=\"white\", lw=2)\n",
    "    plt.plot([120, 120], [53.33, 0], c=\"white\", lw=2)\n",
    "    plt.plot([120, 0], [0, 0], c=\"white\", lw=2)\n",
    "\n",
    "    # Set end zones\n",
    "    plt.plot([10, 10], [0, 53.33], c=\"white\", lw=2)\n",
    "    plt.plot([110, 110], [0, 53.33], c=\"white\", lw=2)\n",
    "\n",
    "    # Set 10-yard lines\n",
    "    yard_line = 15\n",
    "    for i in range(19):\n",
    "        plt.plot([yard_line, yard_line], [0, 53], c=\"white\")\n",
    "        yard_line += 5\n",
    "\n",
    "    # Set emphasis on 20-yard lines\n",
    "    plt.plot([29.8, 29.8], [0.3, 53], c=\"blue\")\n",
    "    plt.plot([30.3, 30.3], [0.3, 53], c=\"blue\")\n",
    "    plt.plot([89.8, 89.8], [0.3, 53], c=\"blue\")\n",
    "    plt.plot([90.3, 90.3], [0.3, 53], c=\"blue\")\n",
    "\n",
    "    # Set hash marks\n",
    "    x_pos = 11\n",
    "    for i in range(100):\n",
    "        plt.plot([x_pos, x_pos], [19.5, 20.5], c=\"white\")\n",
    "        x_pos += 1\n",
    "\n",
    "    x_pos = 11\n",
    "    for i in range(100):\n",
    "        plt.plot([x_pos, x_pos], [32.83, 33.83], c=\"white\")\n",
    "        x_pos += 1\n",
    "\n",
    "    x_pos = 11\n",
    "    for i in range(100):\n",
    "        plt.plot([x_pos, x_pos], [0, 1], c=\"white\")\n",
    "        x_pos += 1\n",
    "\n",
    "    x_pos = 11\n",
    "    for i in range(100):\n",
    "        plt.plot([x_pos, x_pos], [53.33, 52.33], c=\"white\")\n",
    "        x_pos += 1\n",
    "\n",
    "    # Set PAT snap mark\n",
    "    plt.plot([13, 13], [26.34, 27], c=\"white\")\n",
    "    plt.plot([107, 107], [26.34, 27], c=\"white\")\n",
    "\n",
    "    # Set kickoff 'x'\n",
    "    plt.plot([44.6, 45.4], [26.34, 27], c=\"white\")\n",
    "    plt.plot([44.6, 45.4], [27, 26.34], c=\"white\")\n",
    "    plt.plot([74.6, 75.4], [26.34, 27], c=\"white\")\n",
    "    plt.plot([74.6, 75.4], [27, 26.34], c=\"white\")\n",
    "\n",
    "    # Add goalposts\n",
    "    plt.plot([0.3, 0.3], [23.47, 29.7], c=\"yellow\", lw=3)\n",
    "    plt.plot([119.6, 119.6], [23.47, 29.7], c=\"yellow\", lw=3)\n",
    "\n",
    "    # Add padding to surround the field\n",
    "    plt.plot([-0.5, -0.5], [0, 53.33], c=\"white\", lw=10)\n",
    "    plt.plot([120.5, 120.5], [0, 53.33], c=\"white\", lw=10)\n",
    "    plt.plot([-0.5, 120.5], [54, 54], c=\"white\", lw=10)\n",
    "    plt.plot([-0.5, 120.5], [-0.72, -0.72], c=\"white\", lw=10)\n",
    "\n",
    "    # Add coaches box (padding)\n",
    "    plt.plot([36.25, 83.75], [56, 56], c=\"white\", lw=25)\n",
    "    plt.plot([36.25, 83.75], [-2.5, -2.5], c=\"white\", lw=25)\n",
    "\n",
    "    # Add yardage markers\n",
    "    yard_line = 17.5\n",
    "    yard_number = 10\n",
    "    for i in range(9):\n",
    "        plt.text(\n",
    "            yard_line, 7.5, yard_number, fontsize=36, fontweight=\"heavy\", color=\"white\"\n",
    "        )\n",
    "        if i > 3:\n",
    "            yard_number -= 10\n",
    "        else:\n",
    "            yard_number += 10\n",
    "        yard_line += 10\n",
    "\n",
    "    yard_line = 16.5\n",
    "    yard_number = 10\n",
    "    for i in range(9):\n",
    "        plt.text(\n",
    "            yard_line,\n",
    "            43.83,\n",
    "            yard_number,\n",
    "            fontsize=36,\n",
    "            fontweight=\"heavy\",\n",
    "            color=\"white\",\n",
    "            rotation=180,\n",
    "        )\n",
    "        if i > 3:\n",
    "            yard_number -= 10\n",
    "        else:\n",
    "            yard_number += 10\n",
    "        yard_line += 10\n",
    "\n",
    "    # Add end zone abbreviations\n",
    "    plt.text(\n",
    "        0.5,\n",
    "        18.5,\n",
    "        \"BYU\",\n",
    "        fontsize=90,\n",
    "        fontweight=\"extra bold\",\n",
    "        color=\"white\",\n",
    "        rotation=90,\n",
    "    )\n",
    "    plt.text(\n",
    "        109.7,\n",
    "        3.5,\n",
    "        \"COUGARS\",\n",
    "        fontsize=76,\n",
    "        fontweight=\"extra bold\",\n",
    "        color=\"white\",\n",
    "        rotation=270,\n",
    "    )\n",
    "\n",
    "    # Set background color\n",
    "    ax.patch.set_facecolor(\"#348C31\")\n",
    "\n",
    "    # Set center BYU logo\n",
    "    byu_logo = mpimg.imread(\"byu_logo_oval.png\")\n",
    "    image_box = OffsetImage(byu_logo, zoom=0.15)\n",
    "    ab = AnnotationBbox(image_box, (60, 26.5), frameon=False)\n",
    "    ax.add_artist(ab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1620x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up field viz\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches(22.5, 10)\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.tick_params(bottom=False)\n",
    "plt.tick_params(left=False)\n",
    "draw_field(ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}