HuskyHunt2019.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# <https://joshspicer.com/husky-hunt-2019>\n",
    "\n",
    "# TODO:\n",
    "# - Table with \"solves per hour\"\n",
    "# - Map with Draggable Timeline rendering pins of solved/unsolved\n",
    "# - Average solve time. (check drive for list upload times.)\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os, json\n",
    "from datetime import datetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parse data files into `clue_data`, `clue_status`, and `hunters`\n",
    "\n",
    "path_to_json = './data'\n",
    "json_files = [pos_json for pos_json in os.listdir(path_to_json) if pos_json.endswith('.json')]\n",
    "json_files.sort()\n",
    "\n",
    "\n",
    "clue_data = {}\n",
    "clue_status = {}\n",
    "hunters = {}\n",
    "for f in json_files:\n",
    "    with open(\"./data/\"+f) as json_file:\n",
    "        tmp_json = json.load(json_file)\n",
    "        if \"clues\" in tmp_json:\n",
    "            date = f.replace(\"-04:00.json\", \"\")\n",
    "            # Clues\n",
    "            for identifer, clue in tmp_json['clues'].items():\n",
    "                # Grab Relevant Fields\n",
    "                clueList = clue['clueListId']\n",
    "                clueNum = clue['clueNum'].zfill(3)\n",
    "                clueName = clueList + clueNum\n",
    "                \n",
    "                # Record General Clue Data (location, title)\n",
    "                if clueName not in clue_data:\n",
    "                    clue_data[clueName] = {\"inCrawl\": clue['inCrawl'],\n",
    "                                           \"lat\": clue[\"latitude\"],\n",
    "                                           \"long\": clue['longitude'],\n",
    "                                           \"title\": clue['title']}\n",
    "                    \n",
    "                # Record Clue's status at this moment in time\n",
    "                if clueName not in clue_status:\n",
    "                    clue_status[clueName] = {}\n",
    "                clue_status[clueName][date] =  clue[\"completed\"]\n",
    "                                            \n",
    "        # Hunters\n",
    "        if \"hunters\" in tmp_json:\n",
    "            hunters[f.replace(\"-04:00.json\", \"\")] = tmp_json[\"hunters\"]\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Clues Completion Times\n",
    "\n",
    "clueSolvedAt = {}\n",
    "cluesSolvedAtCount = {}\n",
    "\n",
    "for clueNum, clue in clue_status.items():\n",
    "    for time, isSolved in clue.items():\n",
    "        # Init Data Structure\n",
    "        if time not in clueSolvedAt:\n",
    "            clueSolvedAt[time] = []\n",
    "            cluesSolvedAtCount[time] = 0\n",
    "        \n",
    "        # Catches first time the clue swaps from False --> True\n",
    "        if isSolved == True:\n",
    "            clueSolvedAt[time].append(clueNum)\n",
    "            cluesSolvedAtCount[time] += 1\n",
    "            break\n",
    "            \n",
    "            \n",
    "clueSolvedAtDF = pd.DataFrame.from_dict(cluesSolvedAtCount, orient=\"index\")\n",
    "ax = clueSolvedAtDF.plot(kind=\"bar\")\n",
    "\n",
    "# Reduce amount of x-axis labels rendered\n",
    "for i, t in enumerate(ax.get_xticklabels()):\n",
    "    if (i % 20) != 0:\n",
    "        t.set_visible(False)\n",
    "    "
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}