douglasgoodwin/getinfo.ipynb

## getinfo.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import strftime, localtime\n",
    "import json\n",
    "import requests\n",
    "import pytablewriter, dominate\n",
    "import matplotlib.pyplot as plt\n",
    "from django import template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import APIKEY\n",
    "url_locations = 'https://api.parkingsense.com/parking/api/1.0/locations'\n",
    "headers = {'apikey': APIKEY}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'LA Metro Citrus Garage', 'id': '5b81ee9500000021f0ce8a8b'},\n",
       " {'name': 'LA Metro Irwindale Garage', 'id': '5ba05e7e00000029464d31c1'},\n",
       " {'name': 'LA Metro Monrovia Garage', 'id': '5ba190ba0000002cf6f0a7f7'}]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = requests.get(url_locations, headers=headers)\n",
    "json_data = json.loads(response.text)\n",
    "\n",
    "json_data['locations']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table id=\"GoldLineParkingstatusatFri16Aug20190327PM\">\n",
       "    <caption>Gold Line Parking status at Fri, 16 Aug 2019 03:27 PM</caption>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Station</th>\n",
       "            <th>Total</th>\n",
       "            <th>Available</th>\n",
       "            <th>Disabled Total</th>\n",
       "            <th>Disabled Available</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td align=\"left\">LA Metro Citrus Garage</td>\n",
       "            <td align=\"right\">206</td>\n",
       "            <td align=\"right\">19</td>\n",
       "            <td align=\"right\">7</td>\n",
       "            <td align=\"right\">1</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td align=\"left\">LA Metro Irwindale Garage</td>\n",
       "            <td align=\"right\">349</td>\n",
       "            <td align=\"right\">158</td>\n",
       "            <td align=\"right\">7</td>\n",
       "            <td align=\"right\">2</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td align=\"left\">LA Metro Monrovia Garage</td>\n",
       "            <td align=\"right\">343</td>\n",
       "            <td align=\"right\">241</td>\n",
       "            <td align=\"right\">8</td>\n",
       "            <td align=\"right\">8</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pytablewriter.writer.text._markdown.MarkdownTableWriter at 0x10a36a710>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curstatus = []\n",
    "\n",
    "for loc in json_data['locations']:\n",
    "    url_status = 'https://api.parkingsense.com/parking/api/1.0/locations/%s' %(loc['id'])\n",
    "    response = requests.get(url_status, headers=headers)\n",
    "    curstatus.append(json.loads(response.text))\n",
    "\n",
    "# for loc in curstatus:\n",
    "#     print(loc['name'])\n",
    "#     print('FREE/TOTAL:',loc['free'],'/',loc['total'])\n",
    "#     print('DISABLED FREE/TOTAL:',loc['bays']['disabled']['free'],'/',loc['bays']['disabled']['total'])\n",
    "#     print()\n",
    "\n",
    "mtrx = []\n",
    "for loc in curstatus:\n",
    "    disabled = loc['bays']['disabled']\n",
    "    l = [loc['name'],loc['total'],loc['free'],disabled['total'],disabled['free']]\n",
    "    mtrx.append(l)\n",
    "    \n",
    "writer = pytablewriter.MarkdownTableWriter()\n",
    "curtime = strftime(\"%a, %d %b %Y %I:%M %p\",localtime())\n",
    "writer.table_name = \"Gold Line Parking status at %s\" %(curtime)\n",
    "writer.headers = [\"Station\", \"Total\", \"Available\", \"Disabled Total\", \"Disabled Available\"]\n",
    "writer.value_matrix = mtrx\n",
    "\n",
    "\n",
    "# All formatted for Jupyter\n",
    "writer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table id=\"GoldLineParkingstatusatFri16Aug20190327PM\">\n",
       "    <caption>Gold Line Parking status at Fri, 16 Aug 2019 03:27 PM</caption>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Station</th>\n",
       "            <th>Total</th>\n",
       "            <th>Available</th>\n",
       "            <th>Disabled Total</th>\n",
       "            <th>Disabled Available</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td align=\"left\">LA Metro Citrus Garage</td>\n",
       "            <td align=\"right\">206</td>\n",
       "            <td align=\"right\">19</td>\n",
       "            <td align=\"right\">7</td>\n",
       "            <td align=\"right\">1</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td align=\"left\">LA Metro Irwindale Garage</td>\n",
       "            <td align=\"right\">349</td>\n",
       "            <td align=\"right\">158</td>\n",
       "            <td align=\"right\">7</td>\n",
       "            <td align=\"right\">2</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td align=\"left\">LA Metro Monrovia Garage</td>\n",
       "            <td align=\"right\">343</td>\n",
       "            <td align=\"right\">241</td>\n",
       "            <td align=\"right\">8</td>\n",
       "            <td align=\"right\">8</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pytablewriter.writer.text._markdown.MarkdownTableWriter at 0x10a36a710>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from django import template\n",
    "register = template.Library()\n",
    "\n",
    "\n",
    "@register.simple_tag(takes_context=True)\n",
    "def parking_table(context):\n",
    "    # we will want to return a blob of HTML in production\n",
    "    # instead of this fanciness!\n",
    "    return writer\n",
    "\n",
    "\n",
    "parking_table(context=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for loc in curstatus:\n",
    "    # create data\n",
    "    occupied = loc['total']-loc['free']\n",
    "    name_available = \"%s Available\" %(loc['free'])\n",
    "    name_occupied = \"%s Occupied\" %(occupied)\n",
    "    names=name_available,name_occupied,\n",
    "    size=[loc['free'],occupied]\n",
    "\n",
    "    # Create a circle for the center of the plot\n",
    "    my_circle=plt.Circle( (0,0), 0.5, color='ghostwhite')\n",
    "\n",
    "    # Give color names\n",
    "    plt.pie(size, labels=names, colors=['lightcyan','lightsteelblue'])\n",
    "    p=plt.gcf()\n",
    "    p.gca().add_artist(my_circle)\n",
    "    plt.title(loc['name'])\n",
    "    plt.show()"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}