hw_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Necessary Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "#    IMPORTS\n",
    "# \n",
    "\n",
    "#    Import some standard python modules\n",
    "import os, json, time\n",
    "#\n",
    "#    The module pyproj is a standard module that can be installed using pip or your other favorite\n",
    "#    installation tool. This module provides tools to convert between different geodesic coordinate systems\n",
    "#    and for calculating distances between points (coordinates) in a specific geodesic system.\n",
    "#\n",
    "from pyproj import Transformer, Geod\n",
    "#\n",
    "#    The 'wildfire' module is a user module. This module is available from the course website. The module\n",
    "#    includes one object, a Reader, that can be used to read the GeoJSON files associated with the\n",
    "#    wildefire dataset. The module also contains a sample datafile that is GeoJSON compliant and that\n",
    "#    contains a small number of wildfires extracted from the main wildfire dataset.\n",
    "#    \n",
    "from wildfire.Reader import Reader as WFReader\n",
    "#\n",
    "#    There is a GeoJSON reader that you might try if you wanted to read the data. It has its own quirks.\n",
    "#    There will be an example below that reads the sample file \"Wildfire_short_sample.json\"\n",
    "#    \n",
    "import geojson\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "# following libraries are used for predictive models and visualization later throughout the notebook\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "import warnings\n",
    "import statsmodels.api as sm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I used the example code provided by Dr. David McDonald in `wildfire_geo_proximity_example.ipynb`, particularly example #2 to load in the wildfire json data by batches "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# city specification dictionary \n",
    "CITY_LOCATIONS = {\n",
    "    'dayton' :       {'city'   : 'Dayton',\n",
    "                       'county' : 'Montgomery',\n",
    "                       'state'  : 'Ohio',\n",
    "                       'fips'   : '39113',\n",
    "                       'latlon' : [39.759444, -84.191667] }} \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attempting to open './GeoJSON Exports/USGS_Wildland_Fire_Combined_Dataset.json'\n",
      "File './GeoJSON Exports/USGS_Wildland_Fire_Combined_Dataset.json' opened successfully\n",
      "processing final batch of 135061 features...\n",
      "Finishing reading 135061 features from the file\n"
     ]
    }
   ],
   "source": [
    "# wildfire data reader from given files\n",
    "from wildfire.Reader import Reader as WFReader\n",
    "\n",
    "#filepath to get data from\n",
    "DATA_FILENAME = './GeoJSON Exports/USGS_Wildland_Fire_Combined_Dataset.json'\n",
    "\n",
    "#initalize reader object\n",
    "reader = WFReader()\n",
    "\n",
    "# check file exists\n",
    "try:\n",
    "    print(f\"attempting to open '{DATA_FILENAME}'\")\n",
    "    reader.open(DATA_FILENAME)\n",
    "    print(f\"File '{DATA_FILENAME}' opened successfully\")\n",
    "except Exception as e:\n",
    "    print(f\"Failed to open file: {str(e)}\")\n",
    "    raise\n",
    "\n",
    "# initalize empty list to store json objects to\n",
    "batch = []\n",
    "count = 0\n",
    "\n",
    "while True:\n",
    "    try:\n",
    "\n",
    "        # get next feature\n",
    "        feature = reader.next()\n",
    "\n",
    "        # if feature exists \n",
    "        if feature:\n",
    "            batch.append(feature)\n",
    "            count += 1\n",
    "\n",
    "        # reached end of file \n",
    "        else:\n",
    "            break\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"Error reading feature {str(e)}\")\n",
    "        break\n",
    "\n",
    "if len(batch) > 0:\n",
    "    print(f\"processing final batch of {len(batch)} features...\")\n",
    "\n",
    "reader.close()\n",
    "\n",
    "# display number of features (wildfires) read \n",
    "print(f\"Finishing reading {count} features from the file\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geodetic Distance Computations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I used functions from the `wildfire_geo_proximity_example.ipynb` notebook to calculate the geodetic distance between my city and wildfires within the US meeting the time criteria. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "#    Transform feature geometry data\n",
    "#\n",
    "#    The function takes one parameter, a list of ESRI:102008 coordinates that will be transformed to EPSG:4326\n",
    "#    The function returns a list of coordinates in EPSG:4326\n",
    "def convert_ring_to_epsg4326(ring_data=None):\n",
    "    converted_ring = list()\n",
    "    #\n",
    "    # We use a pyproj transformer that converts from ESRI:102008 to EPSG:4326 to transform the list of coordinates\n",
    "    to_epsg4326 = Transformer.from_crs(\"ESRI:102008\",\"EPSG:4326\")\n",
    "    # We'll run through the list transforming each ESRI:102008 x,y coordinate into a decimal degree lat,lon\n",
    "    for coord in ring_data:\n",
    "        lat,lon = to_epsg4326.transform(coord[0],coord[1])\n",
    "        new_coord = lat,lon\n",
    "        converted_ring.append(new_coord)\n",
    "    return converted_ring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#    \n",
    "#    The function takes two parameters\n",
    "#        A place - which is coordinate point (list or tuple with two items, (lat,lon) in decimal degrees EPSG:4326\n",
    "#        Ring_data - a list of decimal degree coordinates for the fire boundary\n",
    "#\n",
    "#    The function returns a list containing the shortest distance to the perimeter and the point where that is\n",
    "#\n",
    "def shortest_distance_from_place_to_fire_perimeter(place=None,ring_data=None):\n",
    "    # convert the ring data to the right coordinate system\n",
    "    ring = convert_ring_to_epsg4326(ring_data)    \n",
    "    # create a epsg4326 compliant object - which is what the WGS84 ellipsoid is\n",
    "    geodcalc = Geod(ellps='WGS84')\n",
    "    closest_point = list()\n",
    "    # run through each point in the converted ring data\n",
    "    for point in ring:\n",
    "        # calculate the distance\n",
    "        d = geodcalc.inv(place[1],place[0],point[1],point[0])\n",
    "        # convert the distance to miles\n",
    "        distance_in_miles = d[2]*0.00062137\n",
    "        # if it's closer to the city than the point we have, save it\n",
    "        if not closest_point:\n",
    "            closest_point.append(distance_in_miles)\n",
    "            closest_point.append(point)\n",
    "        elif closest_point and closest_point[0]>distance_in_miles:\n",
    "            closest_point = list()\n",
    "            closest_point.append(distance_in_miles)\n",
    "            closest_point.append(point)\n",
    "    return closest_point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `filter_wildfire_data` function below takes in the loaded wildfire json file and filters out any wildfires that did not occur within wildfire season (May 1 - Oct 31), and those that did not occuur within (1961 to 2021) and that are not within 1800 miles of Dayton, Ohio. I filtered this fitlered batch to get a separate set of fires within 650 miles of Dayton, as this is the threshold used for predictive modeling and some graphs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import re\n",
    "\n",
    "def filter_wildfire_data(batch, city_coordinates):\n",
    "    '''\n",
    "    batch: the wildfire json data\n",
    "    city_coordinates: latitude and longitude of Dayton, Ohio\n",
    "    '''\n",
    "\n",
    "    #initalize list store filtered wildfire data\n",
    "    filtered_batch = []\n",
    "    \n",
    "    # iterate through batches \n",
    "    for feature in batch:\n",
    "\n",
    "        # get attribute dict\n",
    "        attributes = feature.get('attributes', {})\n",
    "\n",
    "        # get the year and the fire dates\n",
    "        fire_year = attributes.get('Fire_Year')\n",
    "        fire_dates = attributes.get('Listed_Fire_Dates', '')\n",
    "\n",
    "        # if fire dates exist \n",
    "        if fire_dates: \n",
    "            match = re.search(r'\\d{4}-\\d{2}-\\d{2}', fire_dates)\n",
    "            if match:\n",
    "                fire_dates = match.group(0)  # Return the matched date\n",
    "        else:\n",
    "            fire_dates = None\n",
    "        \n",
    "        # to get ring data \n",
    "        if 'geometry' not in feature.keys():\n",
    "            print(\"no geometry key\")\n",
    "            continue\n",
    "        elif 'rings' not in feature['geometry'].keys():\n",
    "            print(\"no rings key\")\n",
    "            continue\n",
    "        else:\n",
    "            ring_data = feature['geometry']['rings'][0]\n",
    "\n",
    "\n",
    "        \n",
    "        # if the fire year is within the last 60 years\n",
    "        if (fire_year >= 1961) and (fire_year <= 2021):\n",
    "            \n",
    "            # check there are fire dates \n",
    "            if fire_dates:  \n",
    "\n",
    "                # get all fire dates \n",
    "                fire_date_parts = fire_dates.split(':')[-1].strip().split(' - ')\n",
    "                for date_str in fire_date_parts:\n",
    "                    \n",
    "                    try:\n",
    "                        # Convert date strings to datetime objects\n",
    "                        fire_date = datetime.strptime(date_str, \"%Y-%m-%d\")\n",
    "\n",
    "                        # check if fire date within may 1 and oct 31\n",
    "                        start_date = datetime(fire_date.year, 5, 1)  \n",
    "                        end_date = datetime(fire_date.year, 10, 31) \n",
    "\n",
    "                        if start_date <= fire_date <= end_date:\n",
    "\n",
    "                            # check there is ring data\n",
    "                            if ring_data:  \n",
    "                                distance_info = shortest_distance_from_place_to_fire_perimeter(city_coordinates, ring_data)\n",
    "                                \n",
    "                                # Check if within 1800 miles\n",
    "                                if distance_info and distance_info[0] <= 1800:  \n",
    "                                    feature['distance'] = distance_info[0]\n",
    "                                    filtered_batch.append(feature)\n",
    "                                    \n",
    "                    except ValueError:\n",
    "\n",
    "                        # skip incorrect date format \n",
    "                        print(\"date format unrecognized\")\n",
    "                        continue\n",
    "\n",
    "    return filtered_batch\n",
    "\n",
    "latitude = CITY_LOCATIONS['dayton']['latlon'][0]\n",
    "longitude = CITY_LOCATIONS['dayton']['latlon'][1]\n",
    "\n",
    "# define tuple of city coordinates\n",
    "city_coordinates = (latitude, longitude)  \n",
    "\n",
    "# call function to get filtered batch\n",
    "filtered_batch = filter_wildfire_data(batch, city_coordinates)\n",
    "\n",
    "print(f\"Filtered {len(filtered_batch)} features that meet the criteria.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 40,853 wildfires within 1800 miles of Dayton, Ohio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40853"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(filtered_batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then I filtered my existing fitlered dataframe to now include wildfires only within 650 miles of Dayton, Ohio. As this data is used for the rest of the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# intialize empty list to store data\n",
    "filtered_batch_650 = []\n",
    "\n",
    "# iterate through fires \n",
    "for feature in filtered_batch:\n",
    "\n",
    "    #check fires are within 650 miles \n",
    "    if (feature['distance']) and (feature['distance'] <= 650):\n",
    "        filtered_batch_650.append(feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the fires within 650 miles, I calculates the smoke estimate using the following equation: *GIS_Acres / (distance)^2*. I calculated the smoke estimate for each fire and then added the estimates for a given year to get each year's final smoke estimate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "# intialize empty dictionary to store year and estimate\n",
    "estimate = {}\n",
    "\n",
    "# iterate through wildfires \n",
    "for feature in filtered_batch_650:\n",
    "\n",
    "    # calcualte estimate \n",
    "    est = feature['attributes']['GIS_Acres'] / (feature['distance']**2)\n",
    "\n",
    "    # if we have seen year already \n",
    "    if feature['attributes']['Fire_Year'] in estimate:\n",
    "\n",
    "        # add estimate to existing year estimate\n",
    "        estimate[feature['attributes']['Fire_Year']] += est \n",
    "    else:\n",
    "        estimate[feature['attributes']['Fire_Year']] = est \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I saves the filtered_batch (within 1800 miles) to a json file as reading it in might be faster than having to run the code to filter the original wildfire data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write filtered data (within 1800 miles to json file)\n",
    "with open('filtered_batch.json', 'w') as json_file:\n",
    "    json.dump(filtered_batch, json_file, indent=4)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in json file \n",
    "with open('filtered_batch.json', 'r') as json_file:\n",
    "    filtered_data = json.load(json_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I used code below from the `epa_air_quality_history_example.ipynb` to set up the API request template and make the necessary calls to access the AQI data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import json, time, urllib.parse\n",
    "import json, time\n",
    "#\n",
    "#    The 'requests' module is a distribution module for making web requests. If you do not have it already, you'll need to install it\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#########\n",
    "#\n",
    "#    CONSTANTS\n",
    "#\n",
    "\n",
    "#\n",
    "#    This is the root of all AQS API URLs\n",
    "#\n",
    "API_REQUEST_URL = 'https://aqs.epa.gov/data/api'\n",
    "\n",
    "#\n",
    "#    These are some of the 'actions' we can ask the API to take or requests that we can make of the API\n",
    "#\n",
    "#    Sign-up request - generally only performed once - unless you lose your key\n",
    "API_ACTION_SIGNUP = '/signup?email={email}'\n",
    "#\n",
    "#    List actions provide information on API parameter values that are required by some other actions/requests\n",
    "API_ACTION_LIST_CLASSES = '/list/classes?email={email}&key={key}'\n",
    "API_ACTION_LIST_PARAMS = '/list/parametersByClass?email={email}&key={key}&pc={pclass}'\n",
    "API_ACTION_LIST_SITES = '/list/sitesByCounty?email={email}&key={key}&state={state}&county={county}'\n",
    "#\n",
    "#    Monitor actions are requests for monitoring stations that meet specific criteria\n",
    "API_ACTION_MONITORS_COUNTY = '/monitors/byCounty?email={email}&key={key}&param={param}&bdate={begin_date}&edate={end_date}&state={state}&county={county}'\n",
    "API_ACTION_MONITORS_BOX = '/monitors/byBox?email={email}&key={key}&param={param}&bdate={begin_date}&edate={end_date}&minlat={minlat}&maxlat={maxlat}&minlon={minlon}&maxlon={maxlon}'\n",
    "#\n",
    "#    Summary actions are requests for summary data. These are for daily summaries\n",
    "API_ACTION_DAILY_SUMMARY_COUNTY = '/dailyData/byCounty?email={email}&key={key}&param={param}&bdate={begin_date}&edate={end_date}&state={state}&county={county}'\n",
    "API_ACTION_DAILY_SUMMARY_BOX = '/dailyData/byBox?email={email}&key={key}&param={param}&bdate={begin_date}&edate={end_date}&minlat={minlat}&maxlat={maxlat}&minlon={minlon}&maxlon={maxlon}'\n",
    "#\n",
    "#    It is always nice to be respectful of a free data resource.\n",
    "#    We're going to observe a 100 requests per minute limit - which is fairly nice\n",
    "API_LATENCY_ASSUMED = 0.002       # Assuming roughly 2ms latency on the API and network\n",
    "API_THROTTLE_WAIT = (1.0/100.0)-API_LATENCY_ASSUMED\n",
    "#\n",
    "#\n",
    "#    This is a template that covers most of the parameters for the actions we might take, from the set of actions\n",
    "#    above. In the examples below, most of the time parameters can either be supplied as individual values to a\n",
    "#    function - or they can be set in a copy of the template and passed in with the template.\n",
    "# \n",
    "AQS_REQUEST_TEMPLATE = {\n",
    "    \"email\":      \"\",     \n",
    "    \"key\":        \"\",      \n",
    "    \"state\":      \"\",     # the two digit state FIPS # as a string\n",
    "    \"county\":     \"\",     # the three digit county FIPS # as a string\n",
    "    \"begin_date\": \"\",     # the start of a time window in YYYYMMDD format\n",
    "    \"end_date\":   \"\",     # the end of a time window in YYYYMMDD format, begin_date and end_date must be in the same year\n",
    "    \"minlat\":    0.0,\n",
    "    \"maxlat\":    0.0,\n",
    "    \"minlon\":    0.0,\n",
    "    \"maxlon\":    0.0,\n",
    "    \"param\":     \"\",     # a list of comma separated 5 digit codes, max 5 codes requested\n",
    "    \"pclass\":    \"\"      # parameter class is only used by the List calls\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "#    This implements the sign-up request. The parameters are standardized so that this function definition matches\n",
    "#    all of the others. However, the easiest way to call this is to simply call this function with your preferred\n",
    "#    email address.\n",
    "#\n",
    "def request_signup(email_address = None,\n",
    "                   endpoint_url = API_REQUEST_URL, \n",
    "                   endpoint_action = API_ACTION_SIGNUP, \n",
    "                   request_template = AQS_REQUEST_TEMPLATE,\n",
    "                   headers = None):\n",
    "    \n",
    "    # Make sure we have a string - if you don't have access to this email addres, things might go badly for you\n",
    "    if email_address:\n",
    "        request_template['email'] = email_address        \n",
    "    \n",
    "    if not request_template['email']: \n",
    "        raise Exception(\"Must supply an email address to call 'request_signup()'\")\n",
    "\n",
    "    if '@' not in request_template['email']: \n",
    "        raise Exception(f\"Must supply an email address to call 'request_signup()'. The string '{request_template['email']}' does not look like an email address.\")\n",
    "\n",
    "    # Compose the signup url - create a request URL by combining the endpoint_url with the parameters for the request\n",
    "    request_url = endpoint_url+endpoint_action.format(**request_template)\n",
    "        \n",
    "    # make the request\n",
    "    try:\n",
    "        # Wait first, to make sure we don't exceed a rate limit in the situation where an exception occurs\n",
    "        # during the request processing - throttling is always a good practice with a free data source\n",
    "        if API_THROTTLE_WAIT > 0.0:\n",
    "            time.sleep(API_THROTTLE_WAIT)\n",
    "        response = requests.get(request_url, headers=headers)\n",
    "        json_response = response.json()\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        json_response = None\n",
    "    return json_response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requesting SIGNUP ...\n",
      "{\n",
      "    \"Header\": [\n",
      "        {\n",
      "            \"status\": \"Success\",\n",
      "            \"request_time\": \"2024-10-28T16:30:00-04:00\",\n",
      "            \"url\": \"https://aqs.epa.gov/data/api/signup?email=igokhale101@gmail.com\"\n",
      "        }\n",
      "    ],\n",
      "    \"Data\": [\n",
      "        \"You should receive a registration confirmation email with a link for confirming your email shortly.\"\n",
      "    ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "#print(\"Requesting SIGNUP ...\")\n",
    "#USERNAME = \"igokhale101@gmail.com\"\n",
    "#response = request_signup(USERNAME)\n",
    "#print(json.dumps(response,indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#    This implements the list request. There are several versions of the list request that only require email and key.\n",
    "#    This code sets the default action/requests to list the groups or parameter class descriptors. Having those descriptors \n",
    "#    allows one to request the individual (proprietary) 5 digit codes for individual air quality measures by using the\n",
    "#    param request. Some code in later cells will illustrate those requests.\n",
    "#\n",
    "def request_list_info(email_address = None, key = None,\n",
    "                      endpoint_url = API_REQUEST_URL, \n",
    "                      endpoint_action = API_ACTION_LIST_CLASSES, \n",
    "                      request_template = AQS_REQUEST_TEMPLATE,\n",
    "                      headers = None):\n",
    "    \n",
    "    #  Make sure we have email and key - at least\n",
    "    #  This prioritizes the info from the call parameters - not what's already in the template\n",
    "    if email_address:\n",
    "        request_template['email'] = email_address\n",
    "    if key:\n",
    "        request_template['key'] = key\n",
    "    \n",
    "    # For the basic request we need an email address and a key\n",
    "    if not request_template['email']:\n",
    "        raise Exception(\"Must supply an email address to call 'request_list_info()'\")\n",
    "    if not request_template['key']: \n",
    "        raise Exception(\"Must supply a key to call 'request_list_info()'\")\n",
    "\n",
    "    # compose the request\n",
    "    request_url = endpoint_url+endpoint_action.format(**request_template)\n",
    "        \n",
    "    # make the request\n",
    "    try:\n",
    "        # Wait first, to make sure we don't exceed a rate limit in the situation where an exception occurs\n",
    "        # during the request processing - throttling is always a good practice with a free data source\n",
    "        if API_THROTTLE_WAIT > 0.0:\n",
    "            time.sleep(API_THROTTLE_WAIT)\n",
    "        response = requests.get(request_url, headers=headers)\n",
    "        json_response = response.json()\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        json_response = None\n",
    "    return json_response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below I test out the `request_list_info` function call to see how it works. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "    {\n",
      "        \"code\": \"AIRNOW MAPS\",\n",
      "        \"value_represented\": \"The parameters represented on AirNow maps (88101, 88502, and 44201)\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"ALL\",\n",
      "        \"value_represented\": \"Select all Parameters Available\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"AQI POLLUTANTS\",\n",
      "        \"value_represented\": \"Pollutants that have an AQI Defined\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"CORE_HAPS\",\n",
      "        \"value_represented\": \"Urban Air Toxic Pollutants\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"CRITERIA\",\n",
      "        \"value_represented\": \"Criteria Pollutants\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"CSN DART\",\n",
      "        \"value_represented\": \"List of CSN speciation parameters to populate the STI DART tool\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"FORECAST\",\n",
      "        \"value_represented\": \"Parameters routinely extracted by AirNow (STI)\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"HAPS\",\n",
      "        \"value_represented\": \"Hazardous Air Pollutants\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"IMPROVE CARBON\",\n",
      "        \"value_represented\": \"IMPROVE Carbon Parameters\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"IMPROVE_SPECIATION\",\n",
      "        \"value_represented\": \"PM2.5 Speciated Parameters Measured at IMPROVE sites\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"MET\",\n",
      "        \"value_represented\": \"Meteorological Parameters\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"NATTS CORE HAPS\",\n",
      "        \"value_represented\": \"The core list of toxics of interest to the NATTS program.\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"NATTS REQUIRED\",\n",
      "        \"value_represented\": \"Required compounds to be collected in the National Air Toxics Network\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"PAMS\",\n",
      "        \"value_represented\": \"Photochemical Assessment Monitoring System\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"PAMS_VOC\",\n",
      "        \"value_represented\": \"Volatile Organic Compound subset of the PAMS Parameters\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"PM COARSE\",\n",
      "        \"value_represented\": \"PM between 2.5 and 10 micrometers\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"PM10 SPECIATION\",\n",
      "        \"value_represented\": \"PM10 Speciated Parameters\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"PM2.5 CONT NONREF\",\n",
      "        \"value_represented\": \"PM2.5 Continuous, Nonreference Methods\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"PM2.5 MASS/QA\",\n",
      "        \"value_represented\": \"PM2.5 Mass and QA Parameters\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"SCHOOL AIR TOXICS\",\n",
      "        \"value_represented\": \"School Air Toxics Program Parameters\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"SPECIATION\",\n",
      "        \"value_represented\": \"PM2.5 Speciated Parameters\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"SPECIATION CARBON\",\n",
      "        \"value_represented\": \"PM2.5 Speciation Carbon Parameters\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"SPECIATION CATION/ANION\",\n",
      "        \"value_represented\": \"PM2.5 Speciation Cation/Anion Parameters\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"SPECIATION METALS\",\n",
      "        \"value_represented\": \"PM2.5 Speciation Metal Parameters\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"UATMP CARBONYL\",\n",
      "        \"value_represented\": \"Urban Air Toxics Monitoring Program Carbonyls\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"UATMP VOC\",\n",
      "        \"value_represented\": \"Urban Air Toxics Monitoring Program VOCs\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"VOC\",\n",
      "        \"value_represented\": \"Volatile organic compounds\"\n",
      "    }\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "#   The default should get us a list of the various groups or classes of sensors. These classes are user defined names for clustors of\n",
    "#   sensors that might be part of a package or default air quality sensing station. We need a class name to start getting down to the\n",
    "#   a sensor ID. Each sensor type has an ID number. We'll eventually need those ID numbers to be able to request values that come from\n",
    "#   that specific sensor.\n",
    "#\n",
    "request_data = AQS_REQUEST_TEMPLATE.copy()\n",
    "request_data['email'] = USERNAME\n",
    "request_data['key'] = APIKEY\n",
    "\n",
    "response = request_list_info(request_template=request_data)\n",
    "\n",
    "if response[\"Header\"][0]['status'] == \"Success\":\n",
    "    print(json.dumps(response['Data'],indent=4))\n",
    "else:\n",
    "    print(json.dumps(response,indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "    {\n",
      "        \"code\": \"42101\",\n",
      "        \"value_represented\": \"Carbon monoxide\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"42401\",\n",
      "        \"value_represented\": \"Sulfur dioxide\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"42602\",\n",
      "        \"value_represented\": \"Nitrogen dioxide (NO2)\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"44201\",\n",
      "        \"value_represented\": \"Ozone\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"81102\",\n",
      "        \"value_represented\": \"PM10 Total 0-10um STP\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"88101\",\n",
      "        \"value_represented\": \"PM2.5 - Local Conditions\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"88502\",\n",
      "        \"value_represented\": \"Acceptable PM2.5 AQI & Speciation Mass\"\n",
      "    }\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "AQI_PARAM_CLASS = \"AQI POLLUTANTS\"\n",
    "#   Structure a request to get the sensor IDs associated with the AQI\n",
    "#\n",
    "request_data = AQS_REQUEST_TEMPLATE.copy()\n",
    "request_data['email'] = USERNAME\n",
    "request_data['key'] = APIKEY\n",
    "request_data['pclass'] = AQI_PARAM_CLASS  # here we specify that we want this 'pclass' or parameter classs\n",
    "\n",
    "response = request_list_info(request_template=request_data, endpoint_action=API_ACTION_LIST_PARAMS)\n",
    "\n",
    "if response[\"Header\"][0]['status'] == \"Success\":\n",
    "    print(json.dumps(response['Data'],indent=4))\n",
    "else:\n",
    "    print(json.dumps(response,indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "#   Gaseous AQI pollutants CO, SO2, NO2, and O2\n",
    "AQI_PARAMS_GASEOUS = \"42101,42401,42602,44201\"\n",
    "#\n",
    "#   Particulate AQI pollutants PM10, PM2.5, and Acceptable PM2.5\n",
    "AQI_PARAMS_PARTICULATES = \"81102,88101,88502\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, I set my city fips code into the reqest data dictionary and see the monitoring stations in my county."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "    {\n",
      "        \"code\": \"0001\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0002\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0003\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0004\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0005\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0006\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0007\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0008\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0014\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0015\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0016\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0017\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0018\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0019\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0021\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0022\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0025\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0026\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0027\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0028\",\n",
      "        \"value_represented\": \"FROC\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0029\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0030\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0031\",\n",
      "        \"value_represented\": \"ROOF OF WILBUR WRIGHT SCHOOL\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0032\",\n",
      "        \"value_represented\": \"LIBRARY (DAYTON PUBLIC\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0033\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0034\",\n",
      "        \"value_represented\": \"Reibold\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0035\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0036\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0037\",\n",
      "        \"value_represented\": \"Eastwood\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0038\",\n",
      "        \"value_represented\": \"Sinclair\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"0999\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"1001\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"1002\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"1003\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"1004\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"1101\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"1201\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"2001\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"2002\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"2003\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"3001\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"3002\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"3003\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"4001\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"4002\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"4003\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"5001\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"6001\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"7001\",\n",
      "        \"value_represented\": \"Moraine Fire St28\"\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"8001\",\n",
      "        \"value_represented\": null\n",
      "    },\n",
      "    {\n",
      "        \"code\": \"9001\",\n",
      "        \"value_represented\": null\n",
      "    }\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "#  This list request should give us a list of all the monitoring stations in the county specified by the\n",
    "#  given city selected from the CITY_LOCATIONS dictionary\n",
    "#\n",
    "request_data = AQS_REQUEST_TEMPLATE.copy()\n",
    "request_data['email'] = USERNAME\n",
    "request_data['key'] = APIKEY\n",
    "request_data['state'] = CITY_LOCATIONS['dayton']['fips'][:2]   # the first two digits (characters) of FIPS is the state code\n",
    "request_data['county'] = CITY_LOCATIONS['dayton']['fips'][2:]  # the last three digits (characters) of FIPS is the county code\n",
    "\n",
    "response = request_list_info(request_template=request_data, endpoint_action=API_ACTION_LIST_SITES)\n",
    "\n",
    "if response[\"Header\"][0]['status'] == \"Success\":\n",
    "    print(json.dumps(response['Data'],indent=4))\n",
    "else:\n",
    "    print(json.dumps(response,indent=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below I check the non null values to determe the existing monitoring stations: I have a total of 7 monitoring stations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'code': '0028', 'value_represented': 'FROC'},\n",
       " {'code': '0031', 'value_represented': 'ROOF OF WILBUR WRIGHT SCHOOL'},\n",
       " {'code': '0032', 'value_represented': 'LIBRARY (DAYTON PUBLIC'},\n",
       " {'code': '0034', 'value_represented': 'Reibold'},\n",
       " {'code': '0037', 'value_represented': 'Eastwood'},\n",
       " {'code': '0038', 'value_represented': 'Sinclair'},\n",
       " {'code': '7001', 'value_represented': 'Moraine Fire St28'}]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list comprehension to filter our nulls \n",
    "stations = [key for key in response['Data'] if key['value_represented'] != None]\n",
    "\n",
    "#display\n",
    "stations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I use the `request_daily_summary` function to get pollutant data from my 8 existing monitoring stations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "#    This implements the daily summary request. Daily summary provides a daily summary value for each sensor being requested\n",
    "#    from the start date to the end date. \n",
    "#\n",
    "#    Like the two other functions, this can be called with a mixture of a defined parameter dictionary, or with function\n",
    "#    parameters. If function parameters are provided, those take precedence over any parameters from the request template.\n",
    "#\n",
    "def request_daily_summary(email_address = None, key = None, param=None,\n",
    "                          begin_date = None, end_date = None, fips = None,\n",
    "                          endpoint_url = API_REQUEST_URL, \n",
    "                          endpoint_action = API_ACTION_DAILY_SUMMARY_COUNTY, \n",
    "                          request_template = AQS_REQUEST_TEMPLATE,\n",
    "                          headers = None):\n",
    "    \n",
    "    #  This prioritizes the info from the call parameters - not what's already in the template\n",
    "    if email_address:\n",
    "        request_template['email'] = email_address\n",
    "    if key:\n",
    "        request_template['key'] = key\n",
    "    if param:\n",
    "        request_template['param'] = param\n",
    "    if begin_date:\n",
    "        request_template['begin_date'] = begin_date\n",
    "    if end_date:\n",
    "        request_template['end_date'] = end_date\n",
    "    if fips and len(fips)==5:\n",
    "        request_template['state'] = fips[:2]\n",
    "        request_template['county'] = fips[2:]            \n",
    "\n",
    "    # Make sure there are values that allow us to make a call - these are always required\n",
    "    if not request_template['email']:\n",
    "        raise Exception(\"Must supply an email address to call 'request_daily_summary()'\")\n",
    "    if not request_template['key']: \n",
    "        raise Exception(\"Must supply a key to call 'request_daily_summary()'\")\n",
    "    if not request_template['param']: \n",
    "        raise Exception(\"Must supply param values to call 'request_daily_summary()'\")\n",
    "    if not request_template['begin_date']: \n",
    "        raise Exception(\"Must supply a begin_date to call 'request_daily_summary()'\")\n",
    "    if not request_template['end_date']: \n",
    "        raise Exception(\"Must supply an end_date to call 'request_daily_summary()'\")\n",
    "    # Note we're not validating FIPS fields because not all of the daily summary actions require the FIPS numbers\n",
    "        \n",
    "    # compose the request\n",
    "    request_url = endpoint_url+endpoint_action.format(**request_template)\n",
    "        \n",
    "    # make the request\n",
    "    try:\n",
    "        # Wait first, to make sure we don't exceed a rate limit in the situation where an exception occurs\n",
    "        # during the request processing - throttling is always a good practice with a free data source\n",
    "        if API_THROTTLE_WAIT > 0.0:\n",
    "            time.sleep(API_THROTTLE_WAIT)\n",
    "        response = requests.get(request_url, headers=headers)\n",
    "        json_response = response.json()\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        json_response = None\n",
    "    return json_response\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I iterate from 1961 to 2021 and request_daily_summary for those years during wildfire season. I make two separate calls per year to `request_daily_summary`: one for gaseous pollutants, and one for particulate pollutants. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "request_data = AQS_REQUEST_TEMPLATE.copy()\n",
    "request_data['email'] = USERNAME\n",
    "request_data['key'] = APIKEY\n",
    "request_data['state'] = CITY_LOCATIONS['dayton']['fips'][:2]\n",
    "request_data['county'] = CITY_LOCATIONS['dayton']['fips'][2:]\n",
    "\n",
    "#initalize empty lists to pollutant data\n",
    "gaseous_aqi = []\n",
    "particulate_aqi = []\n",
    "\n",
    "# loop through each year from 1961 to 2021\n",
    "for year in range(1961, 2022):\n",
    "\n",
    "    # start and end dates for the fire season\n",
    "    begin_date = f\"{year}0501\"  # May 1st of the current year\n",
    "    end_date = f\"{year}1031\"     # October 31st of the current year\n",
    "    \n",
    "    # function call for gaseous pollutants \n",
    "    request_data['param'] = AQI_PARAMS_GASEOUS\n",
    "    year_gaseous_aqi = request_daily_summary(request_template=request_data, begin_date=begin_date, end_date=end_date)\n",
    "\n",
    "    # function call for partiuclate pollutants \n",
    "    request_data['param'] = AQI_PARAMS_PARTICULATES\n",
    "    year_particulate_aqi = request_daily_summary(request_template=request_data, begin_date=begin_date, end_date=end_date)\n",
    "\n",
    "    # append to respective list \n",
    "    gaseous_aqi.append(year_gaseous_aqi)\n",
    "    particulate_aqi.append(year_particulate_aqi)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below I find how many actual aqi values I have for each pollutant type (gaseous and particulate). I had 59 years worth of data for particulate pollutants but only 37 for the gaseous pollutants. Thus, I decided to use the AQI value from the particulate pollutants only. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gaseous 37\n",
      "particulate 59\n"
     ]
    }
   ],
   "source": [
    "# list comprehension to check non null values for AQI pollutants \n",
    "not_empty = [item for item in particulate_aqi if len(item['Data']) > 0]\n",
    "print(\"gaseous\", len(not_empty))\n",
    "\n",
    "not_empty_gas = [item for item in gaseous_aqi if len(item['Data']) > 0]\n",
    "print(\"particulate\", len(not_empty_gas))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gathering Data for Graphs and Predictive Model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then I added the AQI values from the particulate data dictionaries for the available years into a list. I also made a list of the year at which it was gathered. Due to the fact that I had limited data, I did not multiple AQI values for a given year. I also double check that the date the value was determined was within wildfire season. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "# intialize empty lists to store year and AQI value for that year \n",
    "part_aqi = []\n",
    "year_aqi  = []\n",
    "\n",
    "# iterate through aqi dictionaries to extract aqi value\n",
    "for item in particulate_aqi:\n",
    "\n",
    "    # elements exist in the list \n",
    "    if len(item['Data']) > 0:\n",
    "\n",
    "        # aqi key exists \n",
    "        if ('aqi' in list(item['Data'][0].keys())):\n",
    "\n",
    "            #convert to datetime\n",
    "            date = datetime.strptime(item['Data'][0]['date_local'], \"%Y-%m-%d\")\n",
    "            \n",
    "            year = date.year\n",
    "\n",
    "            #check AQI val was gathered during wildfire season\n",
    "            start_date = datetime(year, 5, 1)\n",
    "            end_date = datetime(year, 10, 31)\n",
    "\n",
    "            # add to lists \n",
    "            if start_date <= date <= end_date:\n",
    "                part_aqi.append(item['Data'][0]['aqi'])\n",
    "                year_aqi.append(item['Data'][0]['date_local'].split('-')[0])\n",
    "\n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, I put these two lists into a dataframe so that it is easier to plot. I also use the estimate dictionary from earlier and convert it to a dataframe. Lastly I add a column to each dataframe with scaled AQI and scaled smoke estimate for a future plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "# put lists into dataframe\n",
    "df = pd.DataFrame({\n",
    "    'AQI': part_aqi,\n",
    "    'Year': year_aqi\n",
    "})\n",
    "\n",
    "# convert dict to dataframe \n",
    "df_estimate = pd.DataFrame(list(estimate.items()), columns=['Year', 'Estimate'])\n",
    "\n",
    "# drop outlier for predictive model\n",
    "df_estimate = df_estimate.drop(27)\n",
    "\n",
    "# scale AQI and smoke estimate for future graph \n",
    "df['AQI_scaled'] = (df['AQI'] - df['AQI'].min()) / (df['AQI'].max() - df['AQI'].min())\n",
    "df_estimate['estimate_scaled'] = (df_estimate['Estimate'] - df_estimate['Estimate'].min()) / (df_estimate['Estimate'].max() - df_estimate['Estimate'].min())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictive Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I used an ARIMA time series model to get my future predictions with my explanatory variable as the smoke estimate from 1961 to 2021. I wanted to keep my model simple and interpretiable which is why I used just one variable. Also, my smoke estimate is a function of Acres burned and distance from fire so it encapsulates multiple inportant variables. I added some random noise to my model due to the fact that randomness exists in the real world. Also, I checked that my data met the assumption of stationatity through the Augmented Dickey-Fuller test, and the p value was below 0.05 confirming that my yearly smoke estimate data is stationary. I also removed an outlier from my data so that it would not skew my predictions. Also if a Confidence interval bound is below 0, I set it to 0. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -3.7566832081455814\n",
      "p-value: 0.0033829297425266016\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Estimate</th>\n",
       "      <th>estimate_scaled</th>\n",
       "      <th>Predicted Estimate</th>\n",
       "      <th>Lower Bound</th>\n",
       "      <th>Upper Bound</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1962-01-01</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1963-01-01</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1965-01-01</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1967-01-01</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2046-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.252768</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.173623</td>\n",
       "      <td>0.252768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2047-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.552793</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.481516</td>\n",
       "      <td>0.552793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2048-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.506205</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.442730</td>\n",
       "      <td>0.506205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2049-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.850197</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.794461</td>\n",
       "      <td>0.850197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2050-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.837513</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.789453</td>\n",
       "      <td>0.837513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year  Estimate  estimate_scaled  Predicted Estimate  Lower Bound  \\\n",
       "0  1962-01-01  0.000413         0.000247                 NaN          NaN   \n",
       "1  1963-01-01  0.000217         0.000129                 NaN          NaN   \n",
       "2  1965-01-01  0.000082         0.000048                 NaN          NaN   \n",
       "3  1966-01-01  0.000003         0.000000                 NaN          NaN   \n",
       "4  1967-01-01  0.000128         0.000075                 NaN          NaN   \n",
       "..        ...       ...              ...                 ...          ...   \n",
       "81 2046-12-31       NaN              NaN            0.252768          0.0   \n",
       "82 2047-12-31       NaN              NaN            0.552793          0.0   \n",
       "83 2048-12-31       NaN              NaN            0.506205          0.0   \n",
       "84 2049-12-31       NaN              NaN            0.850197          0.0   \n",
       "85 2050-12-31       NaN              NaN            0.837513          0.0   \n",
       "\n",
       "    Upper Bound         A  \n",
       "0           NaN       NaN  \n",
       "1           NaN       NaN  \n",
       "2           NaN       NaN  \n",
       "3           NaN       NaN  \n",
       "4           NaN       NaN  \n",
       "..          ...       ...  \n",
       "81     1.173623  0.252768  \n",
       "82     1.481516  0.552793  \n",
       "83     1.442730  0.506205  \n",
       "84     1.794461  0.850197  \n",
       "85     1.789453  0.837513  \n",
       "\n",
       "[86 rows x 7 columns]"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# suppress warnings\n",
    "warnings.simplefilter(action='ignore')\n",
    "\n",
    "# year column to datetime format\n",
    "df_estimate['Year'] = pd.to_datetime(df_estimate['Year'], format='%Y')\n",
    "\n",
    "# year is index \n",
    "df_estimate = df_estimate.set_index('Year')\n",
    "\n",
    "# check for stationarity using the Augmented Dickey-Fuller test\n",
    "result = adfuller(df_estimate['Estimate'])\n",
    "print('ADF Statistic:', result[0])\n",
    "print('p-value:', result[1])\n",
    "\n",
    "# fit arima model \n",
    "model = ARIMA(df_estimate['Estimate'], order=(1, 1, 1))\n",
    "model_fit = model.fit()\n",
    "\n",
    "# Forecast for the next 20 years\n",
    "forecast = model_fit.get_forecast(steps=30)\n",
    "predicted_mean = forecast.predicted_mean\n",
    "\n",
    "\n",
    "predictions = predicted_mean \n",
    "\n",
    "# add some noise to the predictions\n",
    "np.random.seed(0)  \n",
    "predictions += np.random.normal(0, 0.2, size= predictions.shape)  # adjust the noise level\n",
    "\n",
    "confidence_intervals = forecast.conf_int()\n",
    "\n",
    "# Create a DataFrame for predictions\n",
    "future_years = pd.date_range(start='2021', periods=30, freq='Y')\n",
    "pred_df = pd.DataFrame({\n",
    "    'Year': future_years,\n",
    "    'Predicted Estimate': predictions,\n",
    "    'Lower Bound': confidence_intervals.iloc[:, 0], #* 1.5,  # Scale lower bound\n",
    "    'Upper Bound': confidence_intervals.iloc[:, 1] #* 1.5   # Scale upper bound\n",
    "})\n",
    "\n",
    "pred_df['A'] = pred_df['Predicted Estimate'].where(pred_df['Predicted Estimate'] >= 0, 0)\n",
    "pred_df['Lower Bound'] = pred_df['Lower Bound'].where(pred_df['Lower Bound'] >= 0, 0)\n",
    "pred_df['Upper Bound'] = pred_df['Upper Bound'].where(pred_df['Upper Bound'] >= 0, 0)\n",
    "\n",
    "\n",
    "\n",
    "# combine historical data with preds\n",
    "combined_df = pd.concat([df_estimate.reset_index(), pred_df], ignore_index=True)\n",
    "\n",
    "# plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(df_estimate.index, df_estimate['Estimate'], marker='o', color='b', label='Historical Estimates')\n",
    "plt.plot(pred_df['Year'], pred_df['Predicted Estimate'], marker='s', color='orange', linestyle='--', label='Predicted Estimates')\n",
    "plt.fill_between(pred_df['Year'], \n",
    "                 pred_df['Lower Bound'], \n",
    "                 pred_df['Upper Bound'], \n",
    "                 color='lightgray', alpha=0.5, label='Confidence Interval')\n",
    "plt.title('Smoke Estimate Predictions (2021-2040) using ARIMA with Confidence Intervals', fontsize=16)\n",
    "plt.xlabel('Year', fontsize=14)\n",
    "plt.ylabel('Smoke Estimate', fontsize=14)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "combined_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph 1: Histogram showing the number of fires occurring every 50 mile distance from your assigned city for all fires ranging up to 1800 miles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the number of fires occuring every 50 miles ranging to 1800 miles, I iterated through each fire in my filtered_batch (which contains data for fires up 1800 miles away from Dayton, Ohio). Then I stored the distance from Dayton and year for each wildfire. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "# intialize list to store wildfire data \n",
    "num_fires = []\n",
    "\n",
    "# iterate through fires within 1800 miles\n",
    "for feature in filtered_batch:\n",
    "\n",
    "    # get fire year \n",
    "    year = feature['attributes']['Fire_Year']\n",
    "\n",
    "    # get distance from Dayton\n",
    "    distance = feature['distance']\n",
    "    count = 1\n",
    "\n",
    "    # add year and distance to list \n",
    "    num_fires.append({'Year': year, 'Distance': distance, 'Num_Fires': count})\n",
    "\n",
    "# list of dict to dataframe\n",
    "fires = pd.DataFrame(num_fires)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set up bins 50 units long bins till 1800 \n",
    "bins = range(0, 1850, 50)\n",
    "\n",
    "# get ditances from fires dataframe \n",
    "fire_distances = fires[\"Distance\"]\n",
    "\n",
    "# plot histogram\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.hist(fire_distances, bins=bins, edgecolor='black', color = 'skyblue')\n",
    "\n",
    "# line for distance used for analysis\n",
    "plt.axvline(x=650, color='red', linestyle='--', label=\"Modeling Distance Cutoff (650 miles)\")\n",
    "plt.xlabel(\"Distance to Dayton, Ohio (miles)\")\n",
    "plt.ylabel(\"Number of Fires\")\n",
    "plt.title(\"Histogram of Number of Fires Every 50 Miles up to 1800 Miles from Dayton, Ohio\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph 2: Time series graph of total acres burned per year for fires within 650 miles of Dayton, Ohio in the last 60 years. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I calculated the total acres burned per year by iterating through my filtered batch of fires within 650 miles of Dayton, Ohio. I saved the the distance, year, and GIS acres data to a dataframe. I then aggregated number of acres burned each year by summing those values. There is a sharp spike in acres burned in 1991, where the acres burned reaches almost 3500. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "acres = []\n",
    "for feature in filtered_batch_650:\n",
    "    year = feature['attributes']['Fire_Year']\n",
    "    distance = feature.get('distance', None)  # Include distance if needed\n",
    "    gis_acres = feature['attributes'].get('GIS_Acres', 0)\n",
    "    acres.append({'Year': year, 'Distance': distance, 'GIS_Acres': gis_acres})\n",
    "\n",
    "# Create a DataFrame\n",
    "acres_df = pd.DataFrame(acres)\n",
    "\n",
    "# Group by year and sum the GIS acres\n",
    "acres_df_final = acres_df.groupby('Year').agg({'GIS_Acres': 'mean'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(acres_df_final['Year'], acres_df_final['GIS_Acres'], marker='o', color='b', label = 'Acres Burned')\n",
    "plt.title('Total Acres Burned Per Year for Wildfires Within 650 Miles of Dayton, Ohio', fontsize=16)\n",
    "plt.xlabel('Year', fontsize=14)\n",
    "plt.ylabel('Total Acres Burned', fontsize=14)\n",
    "#plt.xticks(acres_df_final['Year'], rotation=45)\n",
    "plt.xticks(range(int(acres_df_final['Year'].min()), int(acres_df_final['Year'].max()) + 1, 5), rotation=45)\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph 3: Time series graph containing fire smoke estimates and the AQI estimates for Dayton, Ohio.\n",
    "\n",
    "Since my smoke estimates were very small (on the order of around 0.0001) and my AQI estimates were in the 10s, I decided to scale both those columns to be between 0 and 1. For example to scale a value x I followed the following equation: *(x - min) / (max - min)* to get my scaled value. Now with the scaled values we can see that the trend between smoke estimate and AQI follow each other pretty closely. We have to keep in mind there is no AQI data before 1985 which is why there is missing AQI data for that time on the graph. We can see a spike in both AQI and smoke estimate around 2004 and then both drop off around 2020, conveying the smoke estimate follows the AQI estimate decently. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convert year col to integer \n",
    "df_estimate.Year =df_estimate['Year'].astype(int)\n",
    "df.Year =df['Year'].astype(int)\n",
    "df_plt = pd.merge(df, df_estimate, how = 'right', on ='Year')\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# plot\n",
    "plt.plot('Year', 'AQI_scaled', data=df_plt, marker='o', color='b', linestyle='-', linewidth=2, markersize=8, label = 'AQI')\n",
    "plt.plot('Year', 'estimate_scaled', data=df_plt, marker='o', color='orange', linestyle='-', linewidth=2, markersize=8, label = 'Smoke Estimate')\n",
    "\n",
    "# title and labels \n",
    "plt.title('Air Quality Index (AQI) and Smoke Estimate Over Years', fontsize=16)\n",
    "plt.xlabel('Year', fontsize=14)\n",
    "plt.ylabel('Scaled AQI and Smoke Estimate', fontsize=14)\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# show \n",
    "plt.tight_layout()\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": "3.11.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}