FinalProject_group010.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis and Prediction on California Wildfire and Climate Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In California, wildfire has been a thorny issue over the years. In 2018, the state went through the deadliest and most destructive wildfire season in California’s history upon that time. In the past year, the record was refreshed. The issue is becoming more and more severe. More importantly, those wildfire incidents have caused lots of problems, including burning out the wild forests, soil erosion, and human casualties, impairing both environmental condition and public security. Connecting them to global warming, wildfires require our urgent attention. **With this analysis, we want to focus on measuring the relationship between climate variables of humidity, temperature, windspeed, and precipitation, and the scale of wildfire in California based on the wildfire incidents in 2013-2019, as well as predicting the scale of wildfire in California by using these climate variables.** As a result, we have found that only the humidity has significant relationship with the scale of the wildfire, and we have provided a predictive model for the scale of the wildfire in California."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Jiayi Zhao\n",
    "- Wenbo Hu\n",
    "- Yunyi Huang\n",
    "- Xiaotong Zeng"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there a statistically significant relationship between the scale (burning area) of wildfire and climate variables in California that are associated with global warming such as air relative humidity level, temperature, wind speed, and precipitation? Additionally, how can we utilize these climate variables to predict the wildfire events in California and the scale of wildfire?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "# Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The continuing growth of severity of wildfire in recent years has led to tremendous ecological damage and economic downfall to the society. The 2020 California wildfire season, the largest wildfire season recorded in California’s modern history, suggests that the recent wildfire cases tend to become more intense and affect greater scales. Although the direct cause for wildfire, such as lighting campfires and discarding lit cigarettes, is not always associated with climate change, other climate variables including temperature and humudity level are key factors that would contribute to the increase of the risk and extent of wildfire. Warmer weather could lead to a longer fire season and proliferate insects that weaken trees. Meanwhile, low humidity would make forests drier and more susceptible to burning.  \n",
    "Prior works performed on this topic suggest that the average wildfire seasons have increased by three and a half months than a few decades ago. The number of annual large fires and the burning areas also increased significantly over the last decade. These phenomena are tied with rising temperatures which is the direct result of global warming __[1]__ . At the same time, global warming also causes the decline in relative humidity around the globe which leads to drier conditions making the spread of wildfire more quickly and readily __[2]__. Lastly, we have found many exploratory analysis on wildfire cases in California discovering a positive trend in the burning areas as the year progresses __[3]__.\n",
    "Although climate change does not have a direct relationship with what starts the wildfire, we are interested in discovering how climate factors such as temperature, wind speed, precipitation, and humidity help spread the wildfires expanding the burning areas.  \n",
    "\n",
    "**References:**\n",
    "1. “Here's How Climate Change Affects Wildfires.” *Environmental Defense Fund*, www.edf.org/climate/heres-how-climate-change-affects-wildfires. \n",
    "2. Willett, Kate. “Investigating Climate Change's 'Humidity Paradox'.” *World Economic Forum*, www.weforum.org/agenda/2020/12/climate-change-humidity-paradox/#:~:text=The%20atmosphere%2C%20land%20and%20oceans,evaporated%20from%20the%20Earth's%20surface.&amp;text=According%20to%20the%20Clausius%2DClapeyron,every%201C%20of%20temperature%20rise.  \n",
    "3. Patel, Sourav. “Wildfires in California - An Exploratory Data Analysis.” *Kaggle*, 3 Oct. 2020, www.kaggle.com/skpatel12/california-wildfire-analysis. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We believe that there is a relationship between the climate variables and the scale of wildfires. The scale of wildfires will have a negative correlation with air relative humidity and precipation and a positive correlation with temperature, wind speed. As air gets drier and warmer and wind and precipation get larger, we expect the scale of wildfire to increase. \n",
    "\n",
    "We believe that global warming may also increase the scale of wildfires. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dataset Name:** California Wildfire Incidents (2013-2020) --- List of wildfires in California between 2013 and 2020<br>\n",
    "**Link to the dataset:** https://www.kaggle.com/ananthu017/california-wildfire-incidents-20132020 <br>\n",
    "**Number of observations:** 1636 x 40 <br>\n",
    "**Description of the dataset:** This dataset contains the list of wildfires that have occurred in California between 2013 and 2020, and it also includes other variables such as location and injuries. \n",
    "\n",
    "\n",
    "**Dataset Name:** NOAA Daily global surface summary 2013-2019<br>\n",
    "**Link to the dataset:** https://www.ncei.noaa.gov/data/global-summary-of-the-day/archive/ <br>\n",
    "**Number of observations:** We are going to use the sub datasets of the year 2013-2019, as well as the dataset for stations. The number of observations for each datasets are below:\n",
    "    - 2013: 4.01m\n",
    "    - 2014: 4.12m\n",
    "    - 2015: 4.20m\n",
    "    - 2016: 4.29m\n",
    "    - 2017: 4.29m\n",
    "    - 2018: 4.01m\n",
    "    - 2019: 3.29m\n",
    "**Description of the dataset:** These datasets contain the weather records, including temperature, precipitation, and wind speed, etc., from different weather stations around the world. There is no direct information of humidity from this dataset, so we use the temperature and dew point to calculate the relative humidity percentage.\n",
    "\n",
    "\n",
    "\n",
    "**Dataset Name:** The Integrated Surface Data (ISD) Station List<br>\n",
    "**Link to the dataset:**  https://www1.ncdc.noaa.gov/pub/data/noaa/isd-history.csv <br>\n",
    "**Number of observations:** 29700 <br>\n",
    "**Description of the dataset:** This dataset contains identification numbers and information for weather stations in the Federal Climate Complex ISD.\n",
    "\n",
    "**Data Combination:** In order to perform our analysis on these dataset, we firstly clean and wrangle each dataset separately. For the second dataset, which is the NOAA GSOD Daily global surface summary, we will need to combine the sub datasets together since the datasets were divided into different years. After we finished cleaning each dataset, we will merge them together by weather station id and date of the event."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pandas to read csv file and manage heterogenous data\n",
    "import pandas as pd\n",
    "\n",
    "# Import numpy to store numeric information and perform numerical analysis\n",
    "import numpy as np\n",
    "\n",
    "# Import seaborn and matplotlib to visualize data\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import numpy to store numeric information and perform numerical analysis\n",
    "import numpy as np\n",
    "\n",
    "# Import seaborn and matplotlib to visualize data\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Import scipy to gather statistics\n",
    "from scipy import stats\n",
    "\n",
    "# Import patsy and statsmodels for regression analysis\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Import math for using some math function\n",
    "import math\n",
    "\n",
    "import warnings\n",
    "\n",
    "import os\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report, precision_recall_fscore_support\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the California wildfire incidents data set in data frame\n",
    "# We get this data set from Kaggle (https://www.kaggle.com/ananthu017/california-wildfire-incidents-20132020)\n",
    "wildfire = pd.read_csv(\"California_Fire_Incidents.csv\")\n",
    "\n",
    "\n",
    "# Load the US weather station ID data set in data frame\n",
    "# We get the Integrated Surface Data (ISD) station list from ncdc.noaa.gov\n",
    "station = pd.read_csv(\"https://www1.ncdc.noaa.gov/pub/data/noaa/isd-history.csv\")\n",
    "\n",
    "\n",
    "\n",
    "# Load the US weather daily data set from 2013 to 2019 in data frame\n",
    "# We get this data from ncei.noaa.gov and download to the local.\n",
    "# (https://www.ncei.noaa.gov/data/global-summary-of-the-day/archive/)\n",
    "for dirname, _, filenames in os.walk('/Users/wenbohu/Desktop/Weather'):\n",
    "    for filename in filenames:\n",
    "        print((os.path.join(dirname, filename)))\n",
    "        \n",
    "# get all subdiretory of all tables\n",
    "file_dict ={}\n",
    "for path, dirs, files in os.walk('/Users/wenbohu/Desktop/Weather', topdown=False):\n",
    "    file_dict[path]=files\n",
    "    \n",
    "paths = list(file_dict.keys())\n",
    "\n",
    "events = []\n",
    "for path in paths:\n",
    "    events += [os.path.join(path,file) for file in file_dict[path]]\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have three data sets, we deicide to clean them seperatly and merge them by locations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First, we clean the California wildfire incidents data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we only need the dates, acres burned (scale), and county name for the following analysis, we update these information back to `wildfire`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete the irrelevant columns\n",
    "wildfire = wildfire[['AcresBurned','Started','Counties', 'Latitude', 'Longitude']]\n",
    "\n",
    "# change the started time into date\n",
    "wildfire['Started'] = [x[0:10] for x in wildfire['Started']]\n",
    "\n",
    "# change the 'Started' column name into 'Date'\n",
    "wildfire = wildfire.rename({'Started':'Date'}, axis='columns')\n",
    "\n",
    "#drop the null values \n",
    "wildfire['Latitude'] = wildfire['Latitude'].apply(lambda x: np.nan if x == 0 else x)\n",
    "wildfire = wildfire.dropna().reset_index(drop=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now take a look on the `wildfire` dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AcresBurned</th>\n",
       "      <th>Date</th>\n",
       "      <th>Counties</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>257314.0</td>\n",
       "      <td>2013-08-17</td>\n",
       "      <td>Tuolumne</td>\n",
       "      <td>37.857000</td>\n",
       "      <td>-120.086000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30274.0</td>\n",
       "      <td>2013-05-30</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>34.585595</td>\n",
       "      <td>-118.423176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27531.0</td>\n",
       "      <td>2013-07-15</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>33.709500</td>\n",
       "      <td>-116.728850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27440.0</td>\n",
       "      <td>2013-08-10</td>\n",
       "      <td>Placer</td>\n",
       "      <td>39.120000</td>\n",
       "      <td>-120.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22992.0</td>\n",
       "      <td>2013-07-22</td>\n",
       "      <td>Fresno</td>\n",
       "      <td>37.279000</td>\n",
       "      <td>-119.318000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AcresBurned        Date     Counties   Latitude   Longitude\n",
       "0     257314.0  2013-08-17     Tuolumne  37.857000 -120.086000\n",
       "1      30274.0  2013-05-30  Los Angeles  34.585595 -118.423176\n",
       "2      27531.0  2013-07-15    Riverside  33.709500 -116.728850\n",
       "3      27440.0  2013-08-10       Placer  39.120000 -120.650000\n",
       "4      22992.0  2013-07-22       Fresno  37.279000 -119.318000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wildfire.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second, clean the Integrated Surface Data (ISD) station list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the weather station ID is a combination of column 'USAF' and 'WBAN',\n",
    "# we combine these two columns into a new column called 'ID'\n",
    "station['ID']= station['USAF'].astype(str) + station['WBAN'].astype(str)\n",
    "\n",
    "# we only analyze California weather\n",
    "station = station[(station['STATE']=='CA') & (station['CTRY']=='US')].reset_index(drop=True)\n",
    "\n",
    "# station only need to include the ID and the nameof the station\n",
    "pd.set_option(\"max_rows\", None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now take a look on the `station` dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USAF</th>\n",
       "      <th>WBAN</th>\n",
       "      <th>STATION NAME</th>\n",
       "      <th>CTRY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ICAO</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>ELEV(M)</th>\n",
       "      <th>BEGIN</th>\n",
       "      <th>END</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>690020</td>\n",
       "      <td>93218</td>\n",
       "      <td>JOLON HUNTER LIGGETT MIL RES</td>\n",
       "      <td>US</td>\n",
       "      <td>CA</td>\n",
       "      <td>KHGT</td>\n",
       "      <td>36.000</td>\n",
       "      <td>-121.233</td>\n",
       "      <td>317.0</td>\n",
       "      <td>19640715</td>\n",
       "      <td>19970401</td>\n",
       "      <td>69002093218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>690020</td>\n",
       "      <td>99999</td>\n",
       "      <td>JOLON HUNTER LIGGETT MIL RES</td>\n",
       "      <td>US</td>\n",
       "      <td>CA</td>\n",
       "      <td>KHGT</td>\n",
       "      <td>36.000</td>\n",
       "      <td>-121.233</td>\n",
       "      <td>317.0</td>\n",
       "      <td>20030702</td>\n",
       "      <td>20030801</td>\n",
       "      <td>69002099999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>690070</td>\n",
       "      <td>93217</td>\n",
       "      <td>FRITZSCHE AAF</td>\n",
       "      <td>US</td>\n",
       "      <td>CA</td>\n",
       "      <td>KOAR</td>\n",
       "      <td>36.683</td>\n",
       "      <td>-121.767</td>\n",
       "      <td>43.0</td>\n",
       "      <td>19600404</td>\n",
       "      <td>19930831</td>\n",
       "      <td>69007093217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>690140</td>\n",
       "      <td>93101</td>\n",
       "      <td>EL TORO MCAS</td>\n",
       "      <td>US</td>\n",
       "      <td>CA</td>\n",
       "      <td>KNZJ</td>\n",
       "      <td>33.667</td>\n",
       "      <td>-117.733</td>\n",
       "      <td>116.7</td>\n",
       "      <td>19890101</td>\n",
       "      <td>19990630</td>\n",
       "      <td>69014093101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>690150</td>\n",
       "      <td>93121</td>\n",
       "      <td>TWENTY NINE PALMS</td>\n",
       "      <td>US</td>\n",
       "      <td>CA</td>\n",
       "      <td>KNXP</td>\n",
       "      <td>34.300</td>\n",
       "      <td>-116.167</td>\n",
       "      <td>625.1</td>\n",
       "      <td>19900102</td>\n",
       "      <td>20210312</td>\n",
       "      <td>69015093121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     USAF   WBAN                  STATION NAME CTRY STATE  ICAO     LAT  \\\n",
       "0  690020  93218  JOLON HUNTER LIGGETT MIL RES   US    CA  KHGT  36.000   \n",
       "1  690020  99999  JOLON HUNTER LIGGETT MIL RES   US    CA  KHGT  36.000   \n",
       "2  690070  93217                 FRITZSCHE AAF   US    CA  KOAR  36.683   \n",
       "3  690140  93101                  EL TORO MCAS   US    CA  KNZJ  33.667   \n",
       "4  690150  93121             TWENTY NINE PALMS   US    CA  KNXP  34.300   \n",
       "\n",
       "       LON  ELEV(M)     BEGIN       END           ID  \n",
       "0 -121.233    317.0  19640715  19970401  69002093218  \n",
       "1 -121.233    317.0  20030702  20030801  69002099999  \n",
       "2 -121.767     43.0  19600404  19930831  69007093217  \n",
       "3 -117.733    116.7  19890101  19990630  69014093101  \n",
       "4 -116.167    625.1  19900102  20210312  69015093121  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thrid, we merge the`wildfire` and `station` dataframes by matching the LATITUDE and LONGTITUDE of the wildfire incident locations and weather stations. \n",
    "We compare each error index (0.1, 0.2, 0.3, 0.5) in order to find which diameter we should choose for more unique stations is determined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "IDlist = []\n",
    "for i,j in wildfire.iterrows():\n",
    "    before = len(IDlist)\n",
    "    for a,b in station.iterrows():\n",
    "        #about 50km * 40km when 0.5 then tried 0.3, 0.1, and 0.2\n",
    "        if (((b['LAT'] <= j['Latitude'] + 0.2) and (b['LAT'] >= j['Latitude'] - 0.2)) \n",
    "        and (( b['LON'] <= j['Longitude'] + 0.2) and ( b['LON'] >= j['Longitude'] - 0.2))):\n",
    "            IDlist.append(b['ID'])\n",
    "            break\n",
    "    after = len(IDlist)\n",
    "    if (before == after):\n",
    "        IDlist.append(\"Not_Found\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "127\n"
     ]
    }
   ],
   "source": [
    "# 0.5-95 0.3-119 0.1-119 0.2-127(THIS IS THE BEST!!!!)\n",
    "# when 0.1 it's also 119 but lots of not found values\n",
    "unique = []\n",
    "for x in IDlist:\n",
    "    if x not in unique:\n",
    "        unique.append(x)\n",
    "print(len(unique))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we create a dataframe called `matched_wildfire` that consists the scale, date, county name, latitude, and longitude of the wildfire incidents and the weather station ID in that incident area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "IDlist = []\n",
    "row_fire = []\n",
    "join_id =[]\n",
    "num = 1\n",
    "# create a new dataframe to store these matched data\n",
    "matched_wildfire = pd.DataFrame(columns=wildfire.columns)\n",
    "\n",
    "# iterate the rows in wildfire and station to find the matched data\n",
    "for i,j in wildfire.iterrows():\n",
    "    for a,b in station.iterrows():\n",
    "        #about 22.2km * 20km area from fire accidents   \n",
    "        if (((b['LAT'] <= j['Latitude'] + 0.2) and (b['LAT'] >= j['Latitude'] - 0.2)) \n",
    "        and (( b['LON'] <= j['Longitude'] + 0.2) and ( b['LON'] >= j['Longitude'] - 0.2))):\n",
    "            IDlist.append(b['ID'])\n",
    "            join_id.append(num)\n",
    "            num += 1\n",
    "            row_fire.append(list(j))\n",
    "            break\n",
    "        \n",
    "matched_wildfire = matched_wildfire.append(pd.DataFrame(row_fire,columns=wildfire.columns))\n",
    "matched_wildfire = matched_wildfire.assign(Station_ID = IDlist)\n",
    "matched_wildfire = matched_wildfire.assign(Join_ID=join_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, take a look on the `matched_wildfire`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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>AcresBurned</th>\n",
       "      <th>Date</th>\n",
       "      <th>Counties</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Station_ID</th>\n",
       "      <th>Join_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30274.0</td>\n",
       "      <td>2013-05-30</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>34.585595</td>\n",
       "      <td>-118.423176</td>\n",
       "      <td>99999923180</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27440.0</td>\n",
       "      <td>2013-08-10</td>\n",
       "      <td>Placer</td>\n",
       "      <td>39.120000</td>\n",
       "      <td>-120.650000</td>\n",
       "      <td>72584523225</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20292.0</td>\n",
       "      <td>2013-08-07</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>33.861570</td>\n",
       "      <td>-116.904270</td>\n",
       "      <td>72068799999</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8073.0</td>\n",
       "      <td>2013-09-09</td>\n",
       "      <td>Shasta</td>\n",
       "      <td>40.498332</td>\n",
       "      <td>-122.535496</td>\n",
       "      <td>9999994222</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7055.0</td>\n",
       "      <td>2013-07-06</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>32.954350</td>\n",
       "      <td>-116.473810</td>\n",
       "      <td>99999923178</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AcresBurned        Date     Counties   Latitude   Longitude   Station_ID  \\\n",
       "0      30274.0  2013-05-30  Los Angeles  34.585595 -118.423176  99999923180   \n",
       "1      27440.0  2013-08-10       Placer  39.120000 -120.650000  72584523225   \n",
       "2      20292.0  2013-08-07    Riverside  33.861570 -116.904270  72068799999   \n",
       "3       8073.0  2013-09-09       Shasta  40.498332 -122.535496   9999994222   \n",
       "4       7055.0  2013-07-06    San Diego  32.954350 -116.473810  99999923178   \n",
       "\n",
       "   Join_ID  \n",
       "0        1  \n",
       "1        2  \n",
       "2        3  \n",
       "3        4  \n",
       "4        5  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matched_wildfire.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fourth, using the `matched_wildfire` data frame merge with the weather dataset from 2013 to 2019 so that every wildfire incident has the weather data of that day. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a brief look on how the dataset of one station in the weather dataset in 2013 looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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>STATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>TEMP_ATTRIBUTES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>DEWP_ATTRIBUTES</th>\n",
       "      <th>...</th>\n",
       "      <th>MXSPD</th>\n",
       "      <th>GUST</th>\n",
       "      <th>MAX</th>\n",
       "      <th>MAX_ATTRIBUTES</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MIN_ATTRIBUTES</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>PRCP_ATTRIBUTES</th>\n",
       "      <th>SNDP</th>\n",
       "      <th>FRSHTT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40854099999</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>29.083925</td>\n",
       "      <td>58.450397</td>\n",
       "      <td>940.0</td>\n",
       "      <td>BAM, IR</td>\n",
       "      <td>44.1</td>\n",
       "      <td>8</td>\n",
       "      <td>23.9</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>999.9</td>\n",
       "      <td>56.1</td>\n",
       "      <td></td>\n",
       "      <td>36.3</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>G</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40854099999</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>29.083925</td>\n",
       "      <td>58.450397</td>\n",
       "      <td>940.0</td>\n",
       "      <td>BAM, IR</td>\n",
       "      <td>43.4</td>\n",
       "      <td>10</td>\n",
       "      <td>24.7</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>999.9</td>\n",
       "      <td>56.1</td>\n",
       "      <td></td>\n",
       "      <td>33.8</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>G</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40854099999</td>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>29.083925</td>\n",
       "      <td>58.450397</td>\n",
       "      <td>940.0</td>\n",
       "      <td>BAM, IR</td>\n",
       "      <td>43.9</td>\n",
       "      <td>7</td>\n",
       "      <td>21.5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>999.9</td>\n",
       "      <td>56.1</td>\n",
       "      <td></td>\n",
       "      <td>36.3</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>G</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40854099999</td>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>29.083925</td>\n",
       "      <td>58.450397</td>\n",
       "      <td>940.0</td>\n",
       "      <td>BAM, IR</td>\n",
       "      <td>44.4</td>\n",
       "      <td>8</td>\n",
       "      <td>23.3</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>999.9</td>\n",
       "      <td>57.2</td>\n",
       "      <td></td>\n",
       "      <td>36.3</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>G</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40854099999</td>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>29.083925</td>\n",
       "      <td>58.450397</td>\n",
       "      <td>940.0</td>\n",
       "      <td>BAM, IR</td>\n",
       "      <td>45.2</td>\n",
       "      <td>10</td>\n",
       "      <td>24.5</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>999.9</td>\n",
       "      <td>57.2</td>\n",
       "      <td></td>\n",
       "      <td>35.6</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>G</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       STATION        DATE   LATITUDE  LONGITUDE  ELEVATION     NAME  TEMP  \\\n",
       "0  40854099999  2013-01-01  29.083925  58.450397      940.0  BAM, IR  44.1   \n",
       "1  40854099999  2013-01-02  29.083925  58.450397      940.0  BAM, IR  43.4   \n",
       "2  40854099999  2013-01-03  29.083925  58.450397      940.0  BAM, IR  43.9   \n",
       "3  40854099999  2013-01-04  29.083925  58.450397      940.0  BAM, IR  44.4   \n",
       "4  40854099999  2013-01-05  29.083925  58.450397      940.0  BAM, IR  45.2   \n",
       "\n",
       "   TEMP_ATTRIBUTES  DEWP  DEWP_ATTRIBUTES  ...  MXSPD   GUST   MAX  \\\n",
       "0                8  23.9                8  ...    7.8  999.9  56.1   \n",
       "1               10  24.7               10  ...   12.0  999.9  56.1   \n",
       "2                7  21.5                7  ...    7.8  999.9  56.1   \n",
       "3                8  23.3                8  ...    5.8  999.9  57.2   \n",
       "4               10  24.5               10  ...   12.0  999.9  57.2   \n",
       "\n",
       "   MAX_ATTRIBUTES   MIN  MIN_ATTRIBUTES  PRCP  PRCP_ATTRIBUTES   SNDP  FRSHTT  \n",
       "0                  36.3                   0.0                G  999.9       0  \n",
       "1                  33.8                   0.0                G  999.9       0  \n",
       "2                  36.3                   0.0                G  999.9       0  \n",
       "3                  36.3                   0.0                G  999.9       0  \n",
       "4                  35.6                   0.0                G  999.9       0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example = pd.read_csv(\"/Users/wenbohu/Desktop/Weather/2013/40854099999.csv\")\n",
    "example.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loop the `matched_wildfire` and find the weather data with the matched date and station ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the weather information of wildfire start date \n",
    "row_weather = []\n",
    "join_id = []\n",
    "num = 1\n",
    "\n",
    "# create a data frame to store the weather data\n",
    "weather = pd.DataFrame(columns = example.columns)\n",
    "\n",
    "for i, j in matched_wildfire.iterrows():\n",
    "    for file in events:\n",
    "        if num in join_id:\n",
    "            break\n",
    "        if (file[-15:-4] == j['Station_ID']):\n",
    "            temp = pd.read_csv(file)\n",
    "            for a, b in temp.iterrows():\n",
    "                if (b[\"DATE\"] == j['Date']):\n",
    "                    row_weather.append(list(b))\n",
    "                    join_id.append(num)\n",
    "                    break \n",
    "    num += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Append these matched weather data to `weather`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather = pd.DataFrame(columns = example.columns)\n",
    "weather = weather.append(pd.DataFrame(row_weather, columns = example.columns))\n",
    "weather = weather.assign(Join_ID = join_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, take a look on the `weather`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>TEMP_ATTRIBUTES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>DEWP_ATTRIBUTES</th>\n",
       "      <th>...</th>\n",
       "      <th>GUST</th>\n",
       "      <th>MAX</th>\n",
       "      <th>MAX_ATTRIBUTES</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MIN_ATTRIBUTES</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>PRCP_ATTRIBUTES</th>\n",
       "      <th>SNDP</th>\n",
       "      <th>FRSHTT</th>\n",
       "      <th>Join_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99999953150</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>37.7592</td>\n",
       "      <td>-119.82080</td>\n",
       "      <td>2017.8</td>\n",
       "      <td>YOSEMITE VILLAGE 12 W, CA US</td>\n",
       "      <td>59.3</td>\n",
       "      <td>24</td>\n",
       "      <td>9999.9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>999.9</td>\n",
       "      <td>67.8</td>\n",
       "      <td></td>\n",
       "      <td>52.9</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>G</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72383023187</td>\n",
       "      <td>2013-07-19</td>\n",
       "      <td>34.7436</td>\n",
       "      <td>-118.72420</td>\n",
       "      <td>1374.6</td>\n",
       "      <td>SANDBERG, CA US</td>\n",
       "      <td>80.5</td>\n",
       "      <td>24</td>\n",
       "      <td>33.8</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>96.1</td>\n",
       "      <td></td>\n",
       "      <td>66.0</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>G</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99999993243</td>\n",
       "      <td>2013-05-03</td>\n",
       "      <td>37.2381</td>\n",
       "      <td>-120.88250</td>\n",
       "      <td>23.8</td>\n",
       "      <td>MERCED 23 WSW, CA US</td>\n",
       "      <td>71.3</td>\n",
       "      <td>24</td>\n",
       "      <td>9999.9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>999.9</td>\n",
       "      <td>93.6</td>\n",
       "      <td></td>\n",
       "      <td>46.2</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>G</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74504893210</td>\n",
       "      <td>2013-10-04</td>\n",
       "      <td>39.4900</td>\n",
       "      <td>-121.61833</td>\n",
       "      <td>57.9</td>\n",
       "      <td>OROVILLE MUNICIPAL AIRPORT, CA US</td>\n",
       "      <td>66.2</td>\n",
       "      <td>24</td>\n",
       "      <td>27.4</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>82.9</td>\n",
       "      <td></td>\n",
       "      <td>51.1</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>G</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74504893210</td>\n",
       "      <td>2013-06-03</td>\n",
       "      <td>39.4900</td>\n",
       "      <td>-121.61833</td>\n",
       "      <td>57.9</td>\n",
       "      <td>OROVILLE MUNICIPAL AIRPORT, CA US</td>\n",
       "      <td>80.8</td>\n",
       "      <td>24</td>\n",
       "      <td>51.1</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>999.9</td>\n",
       "      <td>96.1</td>\n",
       "      <td></td>\n",
       "      <td>61.0</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>G</td>\n",
       "      <td>999.9</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       STATION        DATE  LATITUDE  LONGITUDE  ELEVATION  \\\n",
       "0  99999953150  2013-06-16   37.7592 -119.82080     2017.8   \n",
       "1  72383023187  2013-07-19   34.7436 -118.72420     1374.6   \n",
       "2  99999993243  2013-05-03   37.2381 -120.88250       23.8   \n",
       "3  74504893210  2013-10-04   39.4900 -121.61833       57.9   \n",
       "4  74504893210  2013-06-03   39.4900 -121.61833       57.9   \n",
       "\n",
       "                                NAME  TEMP TEMP_ATTRIBUTES    DEWP  \\\n",
       "0       YOSEMITE VILLAGE 12 W, CA US  59.3              24  9999.9   \n",
       "1                    SANDBERG, CA US  80.5              24    33.8   \n",
       "2               MERCED 23 WSW, CA US  71.3              24  9999.9   \n",
       "3  OROVILLE MUNICIPAL AIRPORT, CA US  66.2              24    27.4   \n",
       "4  OROVILLE MUNICIPAL AIRPORT, CA US  80.8              24    51.1   \n",
       "\n",
       "  DEWP_ATTRIBUTES  ...   GUST   MAX  MAX_ATTRIBUTES   MIN  MIN_ATTRIBUTES  \\\n",
       "0               0  ...  999.9  67.8                  52.9                   \n",
       "1              24  ...   27.0  96.1                  66.0                   \n",
       "2               0  ...  999.9  93.6                  46.2                   \n",
       "3              24  ...   27.0  82.9                  51.1                   \n",
       "4              24  ...  999.9  96.1                  61.0                   \n",
       "\n",
       "  PRCP  PRCP_ATTRIBUTES   SNDP  FRSHTT  Join_ID  \n",
       "0  0.0                G  999.9       0       10  \n",
       "1  0.0                G  999.9       0       12  \n",
       "2  0.0                G  999.9       0       18  \n",
       "3  0.0                G  999.9       0       19  \n",
       "4  0.0                G  999.9       0       20  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge `matched_wildfire` and `weather`, and store this merge dataset in local as a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = matched_wildfire.merge(weather, on = 'Join_ID')\n",
    "dataframe.to_csv('/Users/wenbohu/Desktop/df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lastly, import and clean this final data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The weather variable columns descriptions:\n",
    " - TMP: Mean temperature for the day in degrees Fahrenheit to tenths. Missing = 9999.9\n",
    " - DEWP: Mean dew point for the day in degrees Fahrenheit to tenths. Missing = 9999.9\n",
    " - WDSD: Mean wind speed for the day in knots to tenths. Missing = 999.9\n",
    " - PRCP: Total precipitation (rain and/or melted snow) reported during the day in inches and hundredths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('df.csv')\n",
    "\n",
    "df = df[['AcresBurned', 'Date', 'Counties','TEMP','DEWP','WDSP','PRCP']] \n",
    "\n",
    "# By the column description, we replace the missing value (9999.9 or 999.9) with np.nan\n",
    "df['TEMP'] = df['TEMP'].replace(9999.9, np.nan, regex=True) \n",
    "df['DEWP'] = df['DEWP'].replace(9999.9, np.nan, regex=True) \n",
    "df['WDSP'] = df['WDSP'].replace(999.9, np.nan, regex=True) \n",
    "# Drop the NAN value\n",
    "df = df.dropna(subset=['TEMP','DEWP','WDSP'])\n",
    "\n",
    "# convert the temperature and the dewpoint from Fahrenheit to Celsius\n",
    "df['TEMP'] = 5.0 / 9.0 * (df['TEMP'] - 32.0)\n",
    "df['DEWP'] = 5.0 / 9.0 * (df['DEWP'] - 32.0)\n",
    "\n",
    "# calculate saturation vapor pressure(Es) and actual vapor pressure(E) in millibars.\n",
    "df['Es'] = 6.11*10.0**(7.5*df['TEMP']/(237.7+df['TEMP']))\n",
    "df['E'] = 6.11*10.0**(7.5*df['DEWP']/(237.7+df['DEWP']))\n",
    "\n",
    "# Once you have the saturation vapor pressure and the actual vapor pressure, \n",
    "# relative humidity(RH) can be computed by dividing the actual vapor pressure by the saturation vapor pressure \n",
    "# and then multiplying by 100 to convert the quantity to a percent.\n",
    "df ['RelaHumPct'] = (df['E']/df['Es'])*100\n",
    "\n",
    "# Rename the columns\n",
    "df = df.rename(columns={'Counties':'County','TEMP':'Temp','DEWP':'DewPt','WDSP': 'WindSpd','PRCP':'Precipitation'})\n",
    "df = df[['AcresBurned','Date','County','Temp','WindSpd','Precipitation','RelaHumPct']]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Final Dataframe\n",
    "\n",
    "Description on the columns of the final dataframe:\n",
    " - AcresBurned: The area of the wildfire incident burns in degree acre\n",
    " - Date: The date of the wildfire incident\n",
    " - County: The county where the wildfire incident belongs\n",
    " - Temp: Mean temperature for the day in degrees Celsius\n",
    " - DewPt: Mean dew point for the day in degrees Celsius\n",
    " - WindSpd: Mean wind speed for the day in knots to tenths.\n",
    " - Precipitation: Total precipitation (rain and/or melted snow) reported during the day in inches and hundredths\n",
    " - RelaHumPct: Relative humidity for the day in percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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>AcresBurned</th>\n",
       "      <th>Date</th>\n",
       "      <th>County</th>\n",
       "      <th>Temp</th>\n",
       "      <th>WindSpd</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>RelaHumPct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>712.0</td>\n",
       "      <td>2013-07-19</td>\n",
       "      <td>Kern</td>\n",
       "      <td>26.944444</td>\n",
       "      <td>13.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.527509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>305.0</td>\n",
       "      <td>2013-10-04</td>\n",
       "      <td>Butte</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.086979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>298.0</td>\n",
       "      <td>2013-06-03</td>\n",
       "      <td>Butte</td>\n",
       "      <td>27.111111</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.698961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>240.0</td>\n",
       "      <td>2013-06-08</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>28.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.230183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>200.0</td>\n",
       "      <td>2013-07-04</td>\n",
       "      <td>Tehama</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.131822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AcresBurned        Date   County       Temp  WindSpd  Precipitation  \\\n",
       "1        712.0  2013-07-19     Kern  26.944444     13.2            0.0   \n",
       "3        305.0  2013-10-04    Butte  19.000000     10.1            0.0   \n",
       "4        298.0  2013-06-03    Butte  27.111111      3.5            0.0   \n",
       "5        240.0  2013-06-08  Alameda  28.666667      4.0            0.0   \n",
       "6        200.0  2013-07-04   Tehama  36.500000     10.0            0.0   \n",
       "\n",
       "   RelaHumPct  \n",
       "1   18.527509  \n",
       "3   23.086979  \n",
       "4   35.698961  \n",
       "5   35.230183  \n",
       "6   19.131822  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll get a sense of what information we have in the df dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(372, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# determine the shape of the data\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>AcresBurned</th>\n",
       "      <th>Temp</th>\n",
       "      <th>WindSpd</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>RelaHumPct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>372.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>372.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6147.862903</td>\n",
       "      <td>22.626045</td>\n",
       "      <td>6.004301</td>\n",
       "      <td>0.005376</td>\n",
       "      <td>41.945580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>42796.625726</td>\n",
       "      <td>5.260780</td>\n",
       "      <td>2.880510</td>\n",
       "      <td>0.074306</td>\n",
       "      <td>17.166863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.611111</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.578408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>19.041667</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.354164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>22.972222</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.853739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>241.750000</td>\n",
       "      <td>26.236111</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.300922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>410203.000000</td>\n",
       "      <td>41.222222</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>91.355476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AcresBurned        Temp     WindSpd  Precipitation  RelaHumPct\n",
       "count     372.000000  372.000000  372.000000     372.000000  372.000000\n",
       "mean     6147.862903   22.626045    6.004301       0.005376   41.945580\n",
       "std     42796.625726    5.260780    2.880510       0.074306   17.166863\n",
       "min         0.000000    2.611111    0.300000       0.000000    6.578408\n",
       "25%        33.000000   19.041667    4.300000       0.000000   29.354164\n",
       "50%        78.000000   22.972222    5.400000       0.000000   38.853739\n",
       "75%       241.750000   26.236111    6.900000       0.000000   53.300922\n",
       "max    410203.000000   41.222222   22.000000       1.360000   91.355476"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get descriptive statistics for quantitative variables\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the results above, we can see that there are 372 observations in total for seven variables, and those five of the numerical variables are the ones we are going to focus here. \n",
    "Among these five variables, AcresBurned has the largest standard deviation around 42796.626, which means that the data points are far from mean. Precipitation has the smallest standard deviation around 0.005, which means that the data points are clustered closely around the mean. \n",
    "The table also shows the minimum and maximum value, and the value at 25%, 50%, 75% for each variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AcresBurned      float64\n",
       "Date              object\n",
       "County            object\n",
       "Temp             float64\n",
       "WindSpd          float64\n",
       "Precipitation    float64\n",
       "RelaHumPct       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the data type\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/opt/conda/lib/python3.7/site-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/opt/conda/lib/python3.7/site-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/opt/conda/lib/python3.7/site-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first look at the scatter matrix\n",
    "scatter = pd.plotting.scatter_matrix(df, figsize=(15, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "# look at burning areas (in acres) distributions\n",
    "sns.boxplot(x=df['AcresBurned']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you should see a distribution skewed to the right, with most burning area are in few acreas and a few getting many more.\n",
    "We note that there exists an outlier that had 410203 acres burnning area. This is an interesting data point and something we want to investigate further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>AcresBurned</th>\n",
       "      <th>Date</th>\n",
       "      <th>County</th>\n",
       "      <th>Temp</th>\n",
       "      <th>WindSpd</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>RelaHumPct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>410203.0</td>\n",
       "      <td>2018-07-27</td>\n",
       "      <td>Colusa</td>\n",
       "      <td>27.611111</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.269169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>410203.0</td>\n",
       "      <td>2018-07-27</td>\n",
       "      <td>Glenn</td>\n",
       "      <td>27.611111</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.269169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>410203.0</td>\n",
       "      <td>2018-07-27</td>\n",
       "      <td>Lake</td>\n",
       "      <td>27.611111</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.269169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>410203.0</td>\n",
       "      <td>2018-07-27</td>\n",
       "      <td>Mendocino</td>\n",
       "      <td>27.611111</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.269169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     AcresBurned        Date     County       Temp  WindSpd  Precipitation  \\\n",
       "221     410203.0  2018-07-27     Colusa  27.611111      4.5            0.0   \n",
       "222     410203.0  2018-07-27      Glenn  27.611111      4.5            0.0   \n",
       "223     410203.0  2018-07-27       Lake  27.611111      4.5            0.0   \n",
       "224     410203.0  2018-07-27  Mendocino  27.611111      4.5            0.0   \n",
       "\n",
       "     RelaHumPct  \n",
       "221   27.269169  \n",
       "222   27.269169  \n",
       "223   27.269169  \n",
       "224   27.269169  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier = df[df['AcresBurned'] == df['AcresBurned'].max()]\n",
    "outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If we Google this date, we see that [Mendocino complex fire affects four counties](https://en.wikipedia.org/wiki/Mendocino_Complex_Fire). It makes sense that the readership increased by 410203 there. \n",
    "Before we carry out our analysis, we're going to **remove the repeated three rows and leave with Mendocino. Store this output in `df` again.**\n",
    "\n",
    "At this point, we have noticed that there are many wildfire incidents that spreaded into multiple counties, meaning that there are duplicates in the current dataset. To counter this problem, we decided to drop all the duplicates. Also, we decided not to drop the outlier of the acres burned since it is an actual data point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates(subset=['Date','AcresBurned'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the change in acreas burned after removing the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "# AcresBurned\n",
    "ax = sns.distplot(df['AcresBurned'], hist=True, kde=True, \n",
    "                  bins=int(len(df['AcresBurned'])/10),\n",
    "                  color='red',hist_kws={'edgecolor':'black'}, \n",
    "                  kde_kws={'linewidth':4})\n",
    "\n",
    "ax.set(xlabel='Acres Burned', ylabel='Frequency', title='Acres Burned Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see the plot of **acres burned**, and the data is strongly skewed-right distributed because of the Mendocino complex fire. We will perform a log10-transformation to shift the values away from being centered near zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HH51eTK5l80tMzuVo7VpqLADUq9cTtMmjv6LMZLBgQTpxuNYhj361nctv06bFh44ecggceOCc9ALKYsiQeHnFCti6dd90gnEtnicI53Kwfn3Ns4ezzwYpv3qB99sP+veP1737bt90gnEtXqIJQtJYSUsklUm6Jst2Sbop3L5A0ojItq6SHpT0tqS3JB2Xub9zzeXZZ+NnDwcfHKzqlo8yLzOVlfVJJxDX4iWWICQVAZOBccBg4EJJgzOajQNKwsdE4JbItv8F/mlmg4ChwFtJxepcXXbubMeLL8brPv958qrvISozQSxb1pOdO9OJxbVsSf6KHwuUmdlSM9sN3A+Mz2gzHrjLAq8CXSUdKqkLcDLwVwAz221mmxKM1blazZ8/KDYhX5cuNWdQzSc9e8ZXsKuoaMtLL6UXj2u5kkwQPYHoKOzysC6XNocB64DbJc2T9BdJHbO9iaSJkkolla5bt67poncO+OQTmD07vurPqacGQ0rzlQSDM87Vn3oqnVhcy5Zkgsg2K01mj15tbdoCI4BbzGw4sB2o0YcBYGZTzGykmY3snuZcB65VeuAB2Lq106fl4mI45ZQUA8pRZoKYMSOdOFzLlmSCKAd6R8q9gMzFEGtrUw6Um1nVuJEHCRKGc83qj3+Ml48/Hjp1yt42nwwaFC/Pmwd+gu32VpIJYjZQIqm/pHbABGBqRpupwGXhaKYxwGYzW21ma4CVkgaG7U4H3kwwVudqmDMHXn01Xnf66enEsrc6d4Y+GYOXnn46nVhcy5XYVBtmViHpamA6UATcZmaLJV0Vbr8VmAacBZQBO4ArIi/xH8A9YXJZmrHNucRNnhwvDx4cDG9tKQYPDm6Uq3LjjUtYtOj5WJsePToxadJFzRyZaykSnYvJzKYRJIFo3a2R5wZMqmXf+UCejjR3rd2GDXDfffG6U09NJZQGO+II+Oc/q8srVgykT5+BsTUrli+f0vyBuRYjT0dyO5eu224jdu9At241p7HId4cfHh9ttWkTrF6dXjyu5fEE4VyGykq45ZZ43ckn5++NcbUpLoYBA+J1vsqc2xst7FfeueQ9/TS8/351uaioghNPTC+exhg4MF5+55104nAtkycI5zJMybgsf8QR77eIoa3ZZCaId9/1VeZc7jxBOBexZg383//F64YPb7kjrHv3hqKi6vVRt271fgiXO19RzrUq0eVBM+UypPP226Giorp8xBHQq9eHTRlisyoqgs6d32PTpurpQt59F3r0SDEo12J4gnCtSnR50Ez1DemsrIQ//zleN3Fiy78Deb/93okliCVLWsZ0IS59fonJudAzz8Q7p9u3h0svTS+eptKlS7xn+p13vB/C5cYThHOhzM7pCy4I7n9o6Tp2XEH79tXlrVvhw5Z71cw1I08QzhH8w3z00XjdxOxXqlqcNm0qOfzweJ3fD+Fy4QnCOeCOO+Kd0wMHwkknpRZOk8s23NW5+niCcAWvts5pZVutpIUqKYmXlyzxfghXP08QruA99xy89151uV07uOyy9OJJQr9+wXFV2bIF1q5NLRzXQniCcAUvs3P6/PPhwAPTiSUpRUXwmc/E67wfwtXHE4QraGvXwiOPxOtaS+d0pszLTN4P4erjCcIVtDvugE8+qS6XlLTem8iyTdzn/RCuLokmCEljJS2RVCbpmizbJemmcPsCSSMi25ZJWihpvqTSJON0hakQOqej+vatuT7Exo1d0gvI5b3EptqQVARMBs4AyoHZkqaaWXTms3FASfgYDdwSfq3yWTNbn1SMrrDNnAllZdXloqI9fPDB37j22l2xdqWli+jbt3ljS0LbtkE/xFtvVdetWHFoegG5vJdTgpD0EHAb8KSZVeb42scCZWa2NHyN+4HxQDRBjAfuCpcefVVSV0mHmpnPN+kSl9k5PWJEEUcc8dUa7WbOvKqZIkpeSUk8QSxf7rP2udrleonpFuAi4F1JN0oalMM+PYGVkXJ5WJdrGwNmSJojqdZuQ0kTJZVKKl3X0mdVc81m3Tp4+OF4XWu6Ma42mf0QK1Yc6v0QrlY5JQgze9rMLgZGAMuApyS9LOkKScW17JbtSm7mr2JdbU4wsxEEl6EmSTq5ltimmNlIMxvZvXv3eo/FOYB77ol3Tu+//+Yay3O2Rpn9EFu3dmLp0vTicfkt505qSd2Ay4ErgXnA/xIkjKdq2aUc6B0p9wJW5drGzKq+rgUeIbhk5VyjmQXrPkQNHfp2q+2cjiouhsMOi9fNmpVOLC7/5ZQgJD0MvADsC5xrZueZ2d/N7D+A2hZjnA2USOovqR0wAZia0WYqcFk4mmkMsNnMVkvqKKlz+N4dgTOBRXt9dM5lMW8eLFhQXW7TBoYMKZybAjLvh/AE4WqT6yimv5jZtGiFpPZmtsvMRmbbwcwqJF0NTAeKgNvMbLGkq8LttwLTgLOAMmAHcEW4+8HAIwo+0rUF7jWzf+7doTmXXebZw+c/D50770gnmBRkXkp7/vl04nD5L9cE8VOCf+ZRrxBcYqpVmFSmZdTdGnluwKQs+y0FhuYYm3M527UL7r03Xvev/xqcVRSK/v2DIa9Vs9e+/z6sXBmsX+1cVJ2XmCQdIukYoIOk4ZJGhI9TCS43OdeiTJ0KH31UXT7gADj33PTiSUO7dsHkfVF+mcllU98ZxOcJOqZ7Ab+L1G8FfpRQTM4lJvPy0sUXE1ttrVCUlMRvEpw1K/heOBdVZ4IwszuBOyWdb2YPNVNMziVi69Z9mT49XnfFFdnbtnYDBsCTT1aXvR/CZVNngpB0iZn9Degn6TuZ283sd1l2cy4vLVw4gMrIPABDh8Lw4enFk6bDDgtGb1V9P5YsCZZdPfjgdONy+aW+Ya4dw6+dgM5ZHs61CGawYEF8+M6//mtKweSBffaBPn3idd4P4TLVd4npT+HXG5onHOeS8d578NFHXT8tFxfDRRcl+56lpfO59topWerzY/K/khJYtqy6PGsWfOlLqYXj8lCuk/X9imCo68fAPwmGoH47vPzkXN57+eV4+bzzkl81btu2Svr2rTmNWL5M/ldSAk9F5kHwfgiXKdepNs40sy3AOQTTYwwA/iuxqJxrQrt3w5w58bpC7ZyOCpYgrZ4ebeHC+BBg53JNEFXTe50F3Gdm/mvkWoyFC2HnzuryIYcEd08Xuo4d4aCDNsTqXnghpWBcXso1QTwm6W1gJPCMpO7Aznr2cS4vvPZavHzhhcGdxA769IkvveId1S4q1+m+rwGOA0aa2SfAdoLFfpzLa9u3w6KMaR6T7pxuSfr0WRMrez+Ei9qbz1FHENwPEd3nriaOx7kmNW8e7NlTXR4wAI45Jr148k3v3vEziHnzYMsW6OJLVTtyn+77buA3wInAqPCRdRZX5/JJ5uWliy6iINZ9yFXHjjs54ojqcmUlvPRSevG4/JLrGcRIYHA4+6pzLcLGjfBuxjIPfnmpplNOia9TPWsWjBuXXjwuf+TaSb0IOCTJQJxranPmEFtv+dBD19ZYLMfByRmL+Xo/hKuS6xnEgcCbkl4HdlVVmtl5iUTlXBPIvPfhyCPfAw5KJZZ8dsop8fLs2bBtG3Sqba1IVzByTRA/bsiLSxpLsHZ1EcGqdDdmbFe4/SyCFeUuN7O5ke1FQCnwgZmd05AYXGH66CNYujReN2jQUoLBeC6qR4+g8/6dd4JyRQW8+CKMHZtuXC59uQ5zfR5YBhSHz2cDc+vaJ/znPhkYBwwGLpQ0OKPZOKAkfEwEbsnY/i3gLZzbS3Mzfjv794cuXbanE0weq5ovqmPHN2P1N9zwBpMn31vLXq5Q5DqK6d+BB4E/hVU9gUfr2e1YoMzMlprZbuB+at47MR64ywKvAl0lHRq+Zy/gbOAvOR2JcxGZCcKHtmZXNV/UyJHxz26rVw9l1aptKUXl8kWundSTgBOALQBm9i71X8ztCayMlMvDulzb/AH4PlBJHSRNlFQqqXTdunX1hOQKwcaNweytUZ4g6jYgPhM6K1bAxx+3SycYlzdyTRC7wrMAAMKb5eob8ppttHnmPlnbSDoHWGtmc7Jsjzc2m2JmI81sZPfu3etr7gpA5tlDv37B2tOudl26BH0RVcxg5cpD0wvI5YVcE8Tzkn4EdJB0BvAA8Fg9+5QDvSPlXsCqHNucAJwnaRnBpanTJPnU4i4nfnmpYQYNipeXLeuRvaErGLkmiGuAdcBC4GvANOC6evaZDZRI6i+pHTABmJrRZipwmQJjgM1mttrMfmhmvcysX7jfs2Z2SY6xugK2ffs+NS4vjRiRTiwtzcCB8fLy5Z4gCl1Ow1zNrFLSo8CjZpbThX4zq5B0NTCdYJjrbWa2WNJV4fZbCRLNWUAZwTBXn6XfNUpZWd/YzXG9eye/MFBrUVISTENS9f1bt64ba9fCQX7rSMGqM0GE9ylcD1xN0F8gSXuAP5rZ/9T34mY2jSAJROtujTw3gg7wul5jJjCzvvdyDuCdd/rFykOHVj+vbQnQYFt+LAOapo4dg4S6YkV13bPPwoQJ6cXk0lXfGcS3CfoDRpnZ+wCSDgNukfSfZvb7pAN0Llfbt8P778cHyg0bVv28tiVAIX+WAU3boEHxBPHUU54gCll9fRCXARdWJQcAM1sKXBJucy5vzJgBFRXVn3m6dYNevVIMqAU68sh4efr0+HxWrrDUlyCKzWx9ZmXYD1Gcpb1zqXk049bNYcN8au+9dfjhUBz5y/7gg/hMr66w1Jcgdjdwm3PNqqICHn88Xhftf3C5KS6uOZppxox0YnHpqy9BDJW0JctjKzCkOQJ0LhcvvRRM0FelY0f4zGfSi6cliy4gBJ4gClmdndRmVtRcgTjXGE88ES8PGQJF/tvbIIMzptR86qkKfvCDO2jbtnrWmx49OjFpkq++1NrtzZrUzuWtzARx1FHpxNEaHHoodO0KmzYF5YqKtnzyyZUcfnh1m+XLsw8Xdq1LrndSO5e3li2DNyOzVbdpU3M0jsudVPMsYvHidGJx6fIE4Vq8zLOHww+HffdNJ5bWwhOEA08QrhXI1v/gGifoqK7uc/jgg/ggAFcYPEG4Fm3HDnjuuXidJ4jG69QJOneOz3q4cGFKwbjUeIJwLdqzz8LOndXl/fbbyqG+jEGT2H//eEZYsCClQFxqPEG4Fq1m/8MKv3u6iRxwQDxBvP027NqVUjAuFZ4gXItlVjNBfOYzK7I3dnutQ4fVdOtWXa6oCJKEKxyeIFyLtWgRrIysaN6hA/Ttm7looWsoqWZ/jvdDFJZEE4SksZKWSCqTdE2W7ZJ0U7h9gaQRYf0+kl6X9IakxZJuSDJO1zJlnj2cdhoUF+9JJ5hW6uij4+WFC31210KSWIKQVARMBsYBg4ELJWWMrmYcUBI+JgK3hPW7gNPMbCgwDBgbLknq3KcyE8TZZ6cTR2s2YAC0b19d3rQpuDHRFYYkzyCOBcrMbKmZ7QbuB8ZntBkP3GWBV4Gukg4Ny9vCNsXhwz+3uE999BG8/HK8zhNE0ysurnlX+ty56cTiml+SCaInELlCTHlYl1MbSUWS5gNrgafM7LVsbyJpoqRSSaXr1uW0XLZrBaZPh8rq+7gYMgT69EkvntZs+PB4ed48v8xUKJJMENkGG2b+WtXaxsz2mNkwoBdwrKSs06+Z2RQzG2lmI7t3796ogF3L4ZeXms+QIdA2Mq3nunWwdm232ndwrUaSCaIc6B0p9wIyh5jU28bMNgEzgbFNH6JrifbsgX/+M17nCSI5HTrUXCPi7bf7pxOMa1ZJJojZQImk/pLaAROAqRltpgKXhaOZxgCbzWy1pO6SugJI6gB8DvAR2A6A116DDRuqy/vvD2N8CEOijjkmXvYEURgSWw/CzCokXQ1MB4qA28xssaSrwu23AtOAs4AyYAdwRbj7ocCd4UioNsA/zOzxzPdwhSnz8tLYsfFLIK7pHX10MI16Vb/Phg378+abNWd9da1Lon9WZjaNIAlE626NPDdgUpb9FgDDM+udA+9/SEPHjjBoUHzdjQcegOuvTy8mlzy/k9q1KOXl8MYb1eU2bYIzCJe8zMtM99zjo5laO08QrkWZNi1eHjOG2HxBLjnDh8cv5b37LsyenV48LnmeIFyL8nhGT5RfXmo+HTvWXOv7nnvSicU1D+sZSr4AABTPSURBVE8QrsXYuROeeSZe5wmieY0eHS/ff38wy6trnTxBuBZj5sxgBbkqvXrVnEzOJWvIkOC+iCpr18LTT6cXj0uWJwjXYmSOXjrrLHxxoGZWXAwjRsTr7r47nVhc8nz0uGsRsi0O5JeX0jF6NLz0UnX5kUeCWV67dg3Kkyffy6pV22rs16NHJyZNuqiZonRNwROEaxHefhvef7+63L49nH56evEUspIS6NJlK1u2dAbg44/hvvvg618Ptq9atY2+fSfW2G/58inNGaZrAn6JybUImWcPp54ajKpxza9NGxg6dEms7i9/SSkYlyhPEK5F8MtL+WXo0CWx/p+5c32diNbIE4TLe5s2wYsvxus8QaSrS5ftNe5g97OI1scThMt7M2bEx9oPGgSHHZZePC5w5ZXx8j33xIchu5bPO6ldXoqOhHnssVOBAZ9uq6x8jmuvfTfrfqWli+jbtxkCdJxzDhx0UHAvBMCWLUFntWs9PEG4vFQ1EmbPHli6NL6tuHgefft+J+t+M2de1QzROYB27eCKK+CXv6yuu/lmGDcuvZhc0/JLTC6vlZXB9u3V5X33hc6dy9ILyMVcdVX8ZsX586G8/OD0AnJNyhOEy2vz5sXLwcI1lekE42ro1w/OPTdeN2fOkanE4ppeoglC0lhJSySVSbomy3ZJuincvkDSiLC+t6TnJL0labGkbyUZp8tPZsEn0qjhvoxU3rn66nj57bcPY/PmdGJxTSuxBBEuFzoZGAcMBi6UlLlA4TigJHxMBG4J6yuA75rZEcAYYFKWfV0rt2IFbNxYXS4u9iUu89Hpp8PAgdXlyso2zJqVXjyu6SR5BnEsUGZmS81sN3A/MD6jzXjgLgu8CnSVdKiZrTazuQBmthV4C+iZYKwuD2VeXjryyKBj1OWXNm1gUsbCwc8/D7t3pxOPazpJJoiewMpIuZya/+TrbSOpH8H61K9lexNJEyWVSipdt25dI0N2+SS6tCjAsGHpxOHq99WvQpcu1eWtW+HVV9OLxzWNJBNEtomYM1ewrbONpE7AQ8C3zWxLtjcxsylmNtLMRnbv3r3Bwbr8smHDfqxaVV1u08bXfshnXbrA174Wr3v6aaj08QQtWpIJohzoHSn3Albl2kZSMUFyuMfMHk4wTpeH3nzz8Fh5wACfnC/fffOb8TWrP/wQFi5MLx7XeEkmiNlAiaT+ktoBE4CpGW2mApeFo5nGAJvNbLUkAX8F3jKz3yUYo8tDZjUTxKhRKQXjctarF1x4Ybxuxox0YnFNI7EEYWYVwNXAdIJO5n+Y2WJJV0mqut11GrAUKAP+DHwjrD8BuBQ4TdL88HFWUrG6/LJgAWzYsP+n5TZtfHhrS/Hd78bLZWXxdTxcy5LoVBtmNo0gCUTrbo08N2BSlv1eJHv/hCsA998fLx95pF9eaimGDoX+/ct5//1en9Y99RRMrLl+kGsB/E5ql1fMaiaIkSPTicU1zOjR8eFnc+eCDzBsmTxBuLzy+uuwbFl1ubg4+FTqWo7+/T+gZ2Swulkwosm1PJ4gXF65++54ecgQ6NAhnVhcw0hwxhnxupdfhh072qcTkGswTxAub3z8cbDoTJSPXmqZRo2Crl2ry7t3+yR+LZEnCJc3HnkkWF60SqdOfnNcS9W2LZx2WryutPQotm1LJx7XMJ4gXN7461/j5dGj4zdeuZbl5JOD9TuqfPzxPtx6a+3tXf7xBOHywnvvwbPPxutOPDGdWFzT6NABPvvZeN1vfws7d6YTj9t7niBcXrj99ni5Z88P6dEjnVhc0zntNGgf6Ztes6bmz9rlL08QLnW7d8Ntt8Xrhg59O51gXJPq1Cm41BT1y1/CJ5+kE4/bO54gXOruuw9Wr64ud+wIRxyxNL2AXJM644x4X9Ly5XDvvenF43LnCcKlygx+85t43b/9G7Rv7x8xW4v99oPjj4/X/eIXsGdPOvG43HmCcKmaMQMWLaout2kD3/52evG4ZHz+8yBVLw6xZAk87JP45z1PEC5Vv/1tvHz++dC/fzqxuOQceCAcdVRZrO5nPwvOIF3+8gThUjNnTjDTZ1TmdNGu9TjuuHkoMkfzG2/AY4+lF4+rnycIlwoz+P7343UnnRTcHOdapwMP3Mz558frrr/ezyLymScIl4rp02veGHfttenE4prPf/93vDx/Pjz6aDqxuPolmiAkjZW0RFKZpGuybJekm8LtCySNiGy7TdJaSYsy93Mt2549Nc8ePvc5OPPMdOJxzWfIEPjyl+N1118PlZXZ27t0JZYgJBUBk4FxwGDgQkmDM5qNA0rCx0Tglsi2O4CxScXn0nPHHfHF7CX41a+IXZ92rdf118d/1gsXwkMPpRePq12SZxDHAmVmttTMdgP3A+Mz2owH7rLAq0BXSYcCmNks4KME43MpWLmyZkf0JZf4mtOFZPBgmDAhXnfddX53dT5Kcq7MnsDKSLkcyOyCzNamJ7CaHEmaSHD2QZ8+fRoUqMvN5Mn3smpV9vmae/ToxKRJF9W5f2UlXH45bN5cXde2bQVduvyDa6+Nv25p6SL69m1sxC6flJbO59prpwDQpct+SF/CLPiM+s47wXQrX/tamhG6TEkmiGwXDDLHK+TSpk5mNgWYAjBy5EgfD5GgVau20bdv9tXnly+fUu/+f/xjzY7p889vy9FH10wsM2de1aAYXf7atq3y09+fvn3hhBPgxRert//4x8HZZMeO6cTnakryElM50DtS7gWsakAb1wo8+SR873vxun79yjn11FTCcXngnHOCNcerrFkDv/99evG4mpJMELOBEkn9JbUDJgBTM9pMBS4LRzONATabWc6Xl1zL8MorwR3SFRXVdfvtB+ec8zxtfKB1wdp/fzj99HjdL38Jq/wjYt5I7M/TzCqAq4HpwFvAP8xssaSrJFVdP5gGLAXKgD8D36jaX9J9wCvAQEnlkv4tqVhdcp59Fs4+O1hvuooUrB7Xpcv29AJzeWHs2PglpW3b4L/+K714XFyiCzqa2TSCJBCtuzXy3IBJtex7YZKxuWSZBfMs/eAHNce433xzcEYxd246sbn80aEDnHsu3H9/dd2998LEiXDKKenF5QK+4q9rci+/HPQ3vPJKzW3XXw/f+EbNeteyREck1dy2dyPQTjkFnntuPR9+eOCndVdfHXyAiPZRuObnCcI1ia1b9+Xmm+GBB2DWrJrbJfif//HpNFqL6IikTHs7Aq1NGzjzzJe4++7q26QWLQpunvTfl3R5gnAN9tFHwae8uXPhvfcuqbVd167BZYNx45oxONei9O79IZddBnfdVV13ww1B/9WwYenFVeg8Qbi9sn59dVJ4//362593HvzhD77Gg6vfr38N06YFv2MQ3Fl96aVQWgrt26cbW6HyBOHqtX59sG707bd/IbZ2dF2OPx5+/nPvaHS5O+ggmDIF/uVfqusWLYIf/hB+97v04ipkPgrdZWUWdDJfeCH06AHf/CasXn1Qnfsce2xw3fi99+Cllzw5uL33xS8GZw1Rv/893H13OvEUOj+DcDE333wvs2YdwEsvDWfVqoPrbX/YYXDMMbBmzXWcdFIfNm0K7nGIymWeJueq3HQTzJwZTOxY5d//HQYODD6EuObjCcIBwRnD44/Dz352BmvWdK+1nQSHHx4kheHDg7thAe68cz19+/406z65zNPkXJWuXYPpv086CXbtCup27Qr6s2bOhEGDUg2voHiCKHBmMHVqMAQ1uHEte3I46CDYZ59H+PrXv8gBBzRriK4AjRoVnIleEhkc9+GHwWXLZ5+FI49ML7ZC4n0QBaqyMljqccQI+MIXar+r+aijgv6HG26AXr2me3Jwzebii2uuPLh2LZx6anwWWJccP4MoMLt3B/ck/PrX8OabtbcbNiyYbbN379rbOJe0X/wiWD/kT3+qrlu/PkgSN94YLD7lKxEmxxNEgdi6Ff7852BESHl57e0GDlzKl798GL16NV9sztWmTRu45RZo2xYmT66u37MnmNTvsceC+2x8RcJk+CWmVswsuMno61+HXr2CT1vZkoMEX/oSLFgA55//tCcHl1ekYLGpbNNuzJoVDJi4+GJ4/fXmj6218zOIPNDYpTyjdu2C116Dn/zkTebM6cnGjfvV2raoCL7yleBGpKOOCuqis2o611zqmvyv6m/gpz+FMWPgsstg48bq7WbBZdN774VDDlnHoEHvU1KyjKOPruDqq5t2eHVtf6utdSi3J4g80JClPHfuDDrsli+HpUuDT//z5sGrr1atvTC41vdr27aCq65qy3e/C/36NT5+5xqrrsn/on8D55wTDKj4xjeCVQozrVnTnTVrujNz5rF07LiD554LLj8NHAgDBkBJCey7b8PjrO1vtbUO5fYE0cTMgk61devij7Vr4+UNG4JP+7t2wdq1wdIX0RXXqlRWXhbroDODHTuCBLG3unQJxpZ/5jP38oc/XNbAI3QuXf36BXM2PfkkXHrpRjZs2D9ru+3b9+Xhh+Hhh+P1vXrBwQfDAQcEj27dgr8NqbrDWwr+Hqv+RqsepaWfo127YFtFRTBfVEUFfPzxl3j00WDxo06dqr/ut1/w+tkeBx4Y3PORz6sqJpogJI0F/hcoAv5iZjdmbFe4/SxgB3C5mc3NZd+msHUrvPVW9Q87+gPPVt61K/jnn+2xaVPwj3/9+mCfvdO5jm37xFZj21tt28LgwcEC8UOGBJeVli9vQHZxLs+MGwcTJz7A1q0TmTkzmLcpF+XldQ/UqNthtdTvz4YNe/9qbdoEN5vWlkQOOAD22QfatQvWxog+2rUL9m/Tpjq5DRvWuDOkTIklCElFwGTgDKAcmC1pqplFB1eOA0rCx2jgFmB0jvs22oIFcOKJTfmK+aFHDzjggHcZPbqEIUOCVbuca42k4IPPkCFBv8SCBTB/fjAfWNVd2PmssjK4mtCQ5JLN4sXBB8KmkuQZxLFAmZktBZB0PzAeiP6THw/cFS49+qqkrpIOBfrlsG+jtW3BF9jatg0+XfTpE5xyDxgQfHoYPjyYCuO6656jb9+StMN0rtnsv39wp/UppwTDYGfPfphRo/6FJUvgnXdgyRJYtqzmEritSVPfE6Lgf3PTk3QBMNbMrgzLlwKjzezqSJvHgRvN7MWw/AzwA4IEUee+kdeYCFT1Gg0EliRyQHU7EFifwvvmg0I9dj/uwtNaj72vmWWdYyfJz9DZcllmNqqtTS77BpVmU4BUhxBIKjWzkWnGkJZCPXY/7sJTiMeeZIIoB6ITNfQCVuXYpl0O+zrnnEtQkgOsZgMlkvpLagdMAKZmtJkKXKbAGGCzma3OcV/nnHMJSuwMwswqJF0NTCcYqnqbmS2WdFW4/VZgGsEQ1zKCYa5X1LVvUrE2gdZ5l0xuCvXY/bgLT8Ede2Kd1M4551q2PL6HzznnXJo8QTjnnMvKE0QjSRoraYmkMknXpB1PUiTdJmmtpEWRugMkPSXp3fBr9klxWjBJvSU9J+ktSYslfSusL4Rj30fS65LeCI/9hrC+1R87BLNBSJoX3q9VMMcd5QmiESJTgowjmD71QklNeKN7XrkDGJtRdw3wjJmVAM+E5damAviumR0BjAEmhT/jQjj2XcBpZjYUGAaMDUcbFsKxA3wLeCtSLpTj/pQniMb5dDoRM9sNVE0J0uqY2Szgo4zq8cCd4fM7gS80a1DNwMxWV00gaWZbCf5h9KQwjt3MrGrxg+LwYRTAsUvqBZwN/CVS3eqPO5MniMbpCayMlMvDukJxcHjfCuHXg1KOJ1GS+gHDgdcokGMPL7PMB9YCT5lZoRz7H4DvA9GZmwrhuGM8QTROzlOCuJZNUifgIeDbZrYl7Xiai5ntMbNhBLMZHCvpqLRjSpqkc4C1ZjYn7VjS5gmicXKZTqQ1+zCcfZfw69qU40mEpGKC5HCPmVUtP1MQx17FzDYBMwn6oVr7sZ8AnCdpGcFl49Mk/Y3Wf9w1eIJonEKfEmQq8NXw+VeB/0sxlkSEi1r9FXjLzH4X2VQIx95dUtfweQfgc8DbtPJjN7MfmlkvM+tH8Df9rJldQis/7mz8TupGknQWwfXKqilBfpZySImQdB9wKsGUxx8C1wOPAv8A+gArgC+ZWWZHdosm6UTgBWAh1dejf0TQD9Haj/1ogs7YIoIPk/8ws/+R1I1WfuxVJJ0KfM/Mzimk467iCcI551xWfonJOedcVp4gnHPOZeUJwjnnXFaeIJxzzmXlCcI551xWSa5J7VzeCIcoPhMWDwH2AOvC8rHhXFp5IRxaudvMXk47FlfYPEG4gmBmGwhmJEXSj4FtZvabtOKR1NbMKmrZfCqwDcg5QUgqMrM9TRGbc1X8EpMrWJKOkfS8pDmSpkemUZgp6feSZoXrQIyS9HC4DsBPwzb9JL0t6U5JCyQ9KGnfHF7355KeB74l6VxJr4VrDjwt6eBwQsCrgP+UNF/SSZLukHRBJO5t4ddTw7Uq7gUWhhPr/VrS7DCmrzXn99O1Pp4gXKES8EfgAjM7BrgNiN4Fv9vMTgZuJZhSYRJwFHB5eLkKYCAwxcyOBrYA3wjnbarrdbua2Slm9lvgRWCMmQ0nmPPn+2a2LHzP35vZMDN7oZ7jOBa41swGA/8GbDazUcAo4N8l9d/7b41zAb/E5ApVe4J/+E8F0y1RBKyObK+aU2shsLhqmmdJSwkmaNwErDSzl8J2fwO+Cfyzntf9e+R5L+Dv4RlGO+D9BhzH62ZWtd+ZwNGRs439gJIGvq5zniBcwRLBP/7jatm+K/xaGXleVa76u8mcp8ZyeN3tked/BH5nZlPDjukf17JPBeHZfjh5YLtaXk/Af5jZ9Fpex7m94peYXKHaBXSXdBwEU3pLOnIvX6NP1f7AhQSXjJbsxevuB3wQPv9qpH4r0DlSXgYcEz4fT7CyWzbTga+Hl7mQNEBSx9wPx7k4TxCuUFUCFwC/lPQGMB84fi9f4y3gq5IWAAcAt4TDZXN93R8DD0h6AVgfqX8M+GJVJzXwZ+AUSa8Do4mfNUT9BXgTmCtpEfAn/CqBawSfzdW5BghHGz1uZq1+hTVXuPwMwjnnXFZ+BuGccy4rP4NwzjmXlScI55xzWXmCcM45l5UnCOecc1l5gnDOOZfV/weIPjmpuY4xTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at temperature distributions\n",
    "ax = sns.distplot(df['Temp'], hist=True, kde=True, \n",
    "                  bins=int(len(df['Temp'])/10),\n",
    "                  color='blue',hist_kws={'edgecolor':'black'}, \n",
    "                  kde_kws={'linewidth':4})\n",
    "\n",
    "ax.set(xlabel='Temperature', ylabel='Density', title='Temperature Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see the plot of **temperature**, and the data is normally distributed with the median approximately at 24. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "# look at wind speed distributions\n",
    "ax = sns.distplot(df['WindSpd'], hist=True, kde=True, \n",
    "                  bins=int(len(df['WindSpd'])/10),\n",
    "                  color='brown',hist_kws={'edgecolor':'black'}, \n",
    "                  kde_kws={'linewidth':4})\n",
    "\n",
    "ax.set(xlabel='Wind Speed', ylabel='Density', title='Wind Speed Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For **Wind Speed**, the data seems to follow a skewed-right distribution with the median approximately at 5 and a few outliers that are greater than 15."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n6tY75tkPs1Fa5/JK1gKHpGLgDuAcoAyYImmsmc2JyTYcGBQtJwB3Rn8rgOuiINIOeEfS+Jh9bzGzP2Sr7C5/NdcOWPIIrJsMldv2pH/qEGDpY2FpdzB0Px06HZuzcjqXz7J5xnE8sNDMFgNIehQYCcQGjpHAA2ZmwERJHSX1NLOVwEoAM9siaS7QK2Ff5+KteIELu9wNa7bUnm/L+2HpeDStij7RMGVzroBk8+J4L2BZzHpZlJZRHkn9gWOASTHJ10qaKekeSZ2SPbmk0ZKmSpq6du3aZFlcoTCDmT+HCcNpW1xH0Ii1cUYINCvHZ69szhWgbAYOJUmzTPJIags8CXzHzDZHyXcCA4EhhLOSPyZ7cjMbY2ZDzWxoaWndbdouj836Fcz6Zc30Zm2h68nQ57M8N783tB1YI0urou3w2ggoe7YBCupcYchmU1UZ0CdmvTeQ2E0lZR5JJYSg8ZCZPVWdwcxWVz+WdDfwXP0W2+WVuX+A936WkCjoeS70GgFFJQA8N38VIy67AratgCUPQvnivdmrdsEbn4VTHoM+n224sjuXp7J5xjEFGCRpgKTmwChgbEKescDlCk4ENpnZyqi31T+AuWb2p9gdJPWMWf0MMCt7VXCN1fRpU3n+jotg2g1x6dt3N4PDroc+n9kTNOK0PiBs7/Xp+HSrgDcvgtUTsldo5wpE1gKHmVUA1wIvAnOBx81stqSrJV0dZRsHLAYWAncD10TpJwOXAWdKmh4t50XbfifpPUkzgTOA72arDq7xaqv1DC9N6IVd1IK/TDwU2h1U+84qhl6fgv6XYrGNp1YBb14I5Uvqu7jOFZSs3scR3V8xLiHtrpjHBtS4m8rM3iT59Q/M7LJ6LqbLN1bFFccshIrymMQiOPgaFj89KeVuNXQ7lQkzNnJGh5jWzp3r4PWRNOOT9VZc5wqNDzni8s/qCRzebWN8Wq8R0P7QjA+1YMdRcGTChfWNMzm5/Yv7UUDnCpsHDpdfdm+G5c/Ep7U7CA4Yvu/HPOLH0OdzcUmHtHoP1k/b92M6V8A8cLj8UvYMVO7Yu17cEg78Mmg/PsoqghPvgw6D49OX/DMEKudcHB/k0OWPrUth7Vvxab3Ohxad9/mQ06dNZcwtPwagS7MTuaDzPIpVFTZWlMMH/4RBXwepRv5YPviha0o8cLj8YBbGmYq9h7Rld+h2+n4dtmpXecxghv1gxbpwVlNt4wzYMA06H5sk/14++KFrSrypyuWHTbNhy8L4tL5fgKJ6/u3T81xoMyA+benj8c1jzjVxHjhc42cGy+MHCJi1uiN0PKL+n0vFMOByKqtieoPv2gDL/1P/z+VcnvLA4Rq/zfNg6wdxSWPn9c3e87U+gJcX9YxPW/0ybFueved0Lo944HCNXM2zDTocydJNbbP6rOPe7wPNYwZetipY+kRWn9O5fOGBwzVqPUuWQnnCtY1en8r68+6sLIa+F8Unbp7D4NINWX9u5xo7DxyuUTuqTcIQIh0GQ9sByTPXt05DoN0hcUmfO3wJWGXDPL9zjZQHDtd4bV5AvxYJZxsHZP9sYw8J+l5I7LBpvdpvr3kviXNNTFqBQ9KTkj4l7c/tuc5l6P3b4tfb9E86GVNWtekLXU6IT1v+LFTuathyONeIpBsI7gQuARZIullS5qPJOZeJXRth8b3xad3P3HMHd4PqcwEoZm6P3ZthzYSGL4dzjURagcPMXjazLwLHAkuA8ZL+J+mqaKY+5+rXonugYuve9ZIO0Pm43JSleSfocUZ82soX/aZA12Sl3fQkqQtwJfAVYBpwKyGQjM9KyVzTZVXw/u3xad1Pr/+7xDPR41woarl3vaIcVr+au/I4l0PpXuN4CngDaA2cb2afNrPHzOybQHY71LumZ+X4+Bv+1AxKT81deQBK2kKPs+LTVo6Him25KY9zOZTuGcffzWywmf3WzFYCSGoBYGZDs1Y61zQtGhO/3nkolLTLTVli9TibrbuK965XboNVL+euPM7lSLqB41dJ0t6uz4I4B8D2VVA2Nj6tW47PNqo1a83Li3rFp616BXaXJ8/vXIGqNXBI6iHpOKCVpGMkHRstpxOarZyrX4vvA6vYs7qhokvDd8GtxauLe0KzmNbZqh2wyqeZdU1LXWcc5wJ/AHoDfwL+GC3fA35U18ElDZM0X9JCSTcm2S5Jt0XbZ0o6NkrvI+m/kuZKmi3p2zH7dJY0XtKC6G+nxOO6PGVVsOjuuKS524/JTRfcFHZWFoeh12Ot/i+tivyswzUdtQYOM7vfzM4ArjSzM2KWT5vZU7XtK6kYuAMYDgwGLpaUMDcnw4FB0TKacL8IQAVwnZkdBpwIfCNm3xuBV8xsEPBKtO4KwepXoXzx3vWiFizYnoWh0/dXt9OhpP3e9ardDGntLbeu6airqerS6GF/Sd9LXOo49vHAQjNbbGa7gEeBkQl5RgIPWDAR6Cipp5mtNLN3AcxsCzAX6BWzz/3R4/uBC9KpqMsDi++LX+/zOXZaI2wRLW4OPYfHJR3WehpsX5mjAjnXsOpqqmoT/W0LtEuy1KYXsCxmvYy9X/5p55HUHzgGqB7trnt1z67ob7dkTy5ptKSpkqauXbu2jqK6nNu9GZYlnMQO/HJuypKObqfGDbveTBUw5/c5LJBzDafWO6rM7G/R31/sw7GTNUxbJnkktQWeBL5jZpszeXIzGwOMARg6dGji87rGZum/oHL73vXWfcNNf7ySqxLVrqgkXOv48NG9aQvvgsE/gFbdc1cu5xpAujcA/k5Se0klkl6R9FFMM1YqZUCfmPXewIp080RDmTwJPJRwPWW1pJ5Rnp7AmnTq4Bq5xGaqA6+Axj6mZukpUNJx73rldpjrZx2u8KX7n/nJ6Bf/CMKX/cHADXXsMwUYJGmApObAKCChgz5jgcuj3lUnApvMbKUkAf8A5prZn5Lsc0X0+ArgmTTr4BqrLQth7ZvxaQOuSJ63Mak+64i14K+wfXVuyuNcA0k3cFQPZHge8IiZra9rBzOrAK4FXiRc3H7czGZLulrS1VG2ccBiYCFwN3BNlH4ycBlwpqTp0XJetO1m4BxJC4BzonWXzz54IH699BRo13ju3ahVt1PDAIzVKrfDvD/krjzONYB0R417VtI8YDtwjaRSoM6hQc1sHCE4xKbdFfPYgG8k2e9Nkl//wMzWAWcl2+bykBl88M/4tAOvzElR9kn1WcfSx/emvf9XOOwGaJm034ZzeS/dYdVvBE4ChprZbmArNbvWOpe5dZPiBzQsbgl9P5+78uyLbqeyrbLN3vXKbTD3j7krj3NZlsnVx8OAiyRdDlwIfDI7RXJNypKH4td7nR9/c10+KGrO9G0nxactuAN2fJSb8jiXZen2qnqQMPTIKcDHosVHxXX7p6oCPnwsPq3fJbkpy36au+0YaBnTDbdiK8zzsw5XmNK9xjEUGBxdk3Cufqx6BXbG3JxZ0gEOGJ46fyNWSQkc9n2Ydt3exPdvh0Ovg5Zdc1cw57Ig3aaqWUCPbBbENUEfPhy/3vdCKG6Rm7LUh0FXx18QryiHeYm9yZ3Lf+kGjq7AHEkvShpbvWSzYK7AVWyvOcRInjZT7dGsdehNFev9v8DOdbkpj3NZkm5T1U3ZLIRrglY8F36RV2vVE7p9InflqS+Dvg5zfre3Ca6iHObdAkcnmwvNufyUbnfc14AlQEn0eArwbhbL5QpdYm+qvqOgqDh53nzSrA0cdn182vxbvYeVKyhpnXFI+iphvozOwEDCCLZ34TfiuX2xawOsiLsvlBfea8bS139cI+usGVPh/H4NVbL6MeiaMGbVzihYVJQz48ERTCoP/y5tOx7AJVfVuO/VubyRblPVNwjza0wCMLMFkvy2WLdvlj4JVbv3rrc7mKULmzE6SYC4etKEhitXfSlpC4NvhGl7zzyObvsuR5/8WWjekTHPfpjDwjm3/9K9OL4zmowJAEnNqDlEunPpSexN1f8SUowwk78GXcPWypi5yW13jbMs5/JVuoHjNUk/AlpJOgf4F/Bs9orlCta25bB6Qnxav4tzUpSsataKd7eeEp+29g3Y4ZOKufyXbuC4EVgLvAd8jTBw4U+yVShXwD58jLiT1c5Dof3BOStONs3ffjS0iLn5z6qg7OncFci5epLWNQ4zq5L0NPC0mflPJrfvkjZTFaYqisPYW4vv3Zu4fiqlzY7IXaGcqwe1Bo5oQqWfE+bVUJRUCfzFzH7ZAOVzhWTzfFj/TkyCoO9FOStOg+hyPKx6GbYt25N0YrtXwnDySu+6zsP33kH5xsTJM713lsudus44vkOYVOljZvYBgKQDgTslfdfMbsl2AV0BWZJwttH9DGh9QG7K0lBUBH0+B/P/vCepZ/NlsHws9E5vZoLyjSuS9jjz3lkuV+q6xnE5cHF10AAws8XApdE259JjVjNwFHAzVZwOh0GHhOapd6+DyjrnQnOuUaorcJSYWY1bXqPrHCVJ8juX3PqpUL5w73pR8/BLvKno81niuhyXL/IBEF3eqitw7NrHbc7FSzzbOOBT0LxjbsqSC617QbfT4tNm/Rq2Lkue37lGrK7AcbSkzUmWLcCRdR1c0jBJ8yUtlHRjku2SdFu0faakY2O23SNpjaRZCfvcJGm5pOnRcl66lXU5UlUJHz4an9ZUmqli9R4ZxrKqVrktfv4O5/JErYHDzIrNrH2SpZ2Z1dpUJakYuAMYDgwGLpY0OCHbcGBQtIwG7ozZdh8wLMXhbzGzIdHit+M2dmv+CztW7V1v1i6ccTQ1zdpA7wvi05b+C5b/JzflcW4fpTtW1b44HlgYXUxH0qPASGBOTJ6RwAPRzIITJXWU1NPMVprZ65L6Z7F8rp7U2V00sZmqz2ehWasGKl0jU3oKaxe8QmlJTCCd/DUYMSdnc617d1+XqWwGjl5AbANuGXBCGnl6ASvrOPa1ki4HpgLXmdmGxAySRhPOYujbt29mJXcZqbW7aOUOWPZk/Iam2ExVTUW8vvk8Ptf1frDKkLZ9OUz7Phx/V06K5N19XabSHXJkXyS7uylxYMR08iS6kzC0+xBCgPljskxmNsbMhprZ0NLS0rrK6rJlxTjYvXnvestu0P3M3JWnEVhX0aPmnB0L/xbmYHcuD2QzcJQBfWLWewOJ58Pp5IljZqvNrNLMqoC7CU1irrFKbKbqexEUZfNEN08c8XNolzBG19uX+SCILi9kM3BMAQZJGiCpOTAKSJynfCxwedS76kRgk5nV2kwlqWfM6meAWanyutwq0Q5Y/lx8YlNuporVrBWc8HfiTrq3r4SJV4WbJZ1rxLIWOMysgjDG1YvAXOBxM5st6WpJV0fZxgGLgYWEs4drqveX9AjwNnCIpDJJX442/U7Se5JmAmcA381WHdz+GdBiPlTt3JvQ9kDokniZqwnrdioc/qP4tBX/CXOUO9eIZbXNIOoqOy4h7a6Yx0aYXTDZvkknaTCzy+qzjC57BrVMOBnsd3HaA/s1GUf+HFa/Ch+9vTdt+g3Q4XA44Nzclcu5Wnhjs8uOXRs4oHlCr5z+l+amLA1g+rSpjLklfs70VPOlJ+ZtW3QMn+vyLi2KorMzq4K3vgCfnBjGuXKukfHA4bJj3eT4k4vOQ6HDoTkrTrZV7Sqv0aU11XzpNfP2gw1fwd6/Y+9rtnszvDYCzn4jK+V1bn9k8+K4a8o+mhS/XsBnG/Wi01FMLD8rPq18Mbx6Jq2KynNTJudS8MDh6t+2snBTWzUVQ79RuStPnnhv2/Ew8MvxiZvnM6LTQ7BrU24K5VwSHjhc/Us82+hxDrTqnpuy5BXB0L/WmOCpU7N1MOe3cbMIOpdLHjhc/bIqWDc5Pm2Ad4RLW3FzOPmxmoNA7toAc36fMPWuc7nhF8dd/do8H3Zv3LO6u6qEB/89jQpmAz5wXlqKW8CpT8Abn4cVMTdQVu2EhWPCvTD9sj9Xe7KeYuDvofPA4erbuvhmqpLS4/jSiYP2rPvAeWkqbgmn/TtMMfv+bfHb1k2CzXM5rNXHoWo3FGVnMs5kPcXA30PnTVWuPlXugvXT4tO6+p3i+6yoGQy9lTc2nxs6GMTavZlT278A/zkcFtwFFVtzU0bXJHngcPVn4wyo2rFnddOOEmh/SA4LVBjmbj8ODrToK+YAABvcSURBVP8htOpdc+OWBTDl6/DvXjBpNKx4IQRw57LIA4erPwnNVFOWd635S9ntm9Z94PAbw0XzZE1TuzfBorthwnB4qjv873JY9jRUbG/4srqC54HD1Y/dm2HT7LikSWU+D0q9KiqB3p+Go/4PSk+hylKM+7V7Iyx5EN74DDzZFd64MAxv7/eCuHrigcPVj48mha641Vr2ZNmmNrkrTyFr3gkGXMYjH30jjK7bomvqvJXbwgyM//siPFUKE0ZA2bNQVdlw5XUFx3tVuXpg8NFb8UmlJwGrauRM1cUz1YCALrWtVe3h6F+HSaFW/zcEiLKnYWeKyaCqdodh21f8h82VHXhv6wnM3X4Ms2ZM89feZcQDh9tv3UpWhEmI9iiCricB/66RN1UXz1QDAro0FDcPQ7AfcC587K88+9crOf/Q5bBhetw9NbHaF2/i5PYvcXLpu9xT1ilMHuVD3rs0eeBw++2QljPiEzoeCSXtc1OYpq6oGSt394P+p4WbBLd+CBumhW7SO9fUzL/zI7503EfhXpH+l0GLzg1fZpd3/BqH2z8VWxnYck58WunJuSmLi6ciaDsA+nwWjvolv3ntqHAmqCS/FzfNgVm/gI8m19zmXAIPHG7/LH2C5kUx9w2UtIeOR+SuPC45iaWb2sKBV8LRv4Fup4fAEqtyByz+Byx9Mr6jg3MJvKnK7Z9F/4hf73qS37vR2DXvAP0vhh5nwQcPwpb347evegm2r6B5sfeKc8n5GYfbd5vfh7UJM9R1/XhuyuIy17IbHPpdHp05oGbz1aZZfOvEOVDpNxC6mrIaOCQNkzRf0kJJNybZLkm3RdtnSjo2Zts9ktZImpWwT2dJ4yUtiP52ymYdXC0W3xu/3vYgaNUjN2Vx+0ZFTFjSEw67Hko6xG06qMsWmPdnHwfL1ZC1wCGpGLgDGA4MBi6WNDgh23BgULSMBu6M2XYfMCzJoW8EXjGzQcAr0bpraFUV8MH98WmlfraRt9oOCDcTtu4Tn751Ccz/S7j+4Vwkm2ccxwMLzWyxme0CHgVGJuQZCTxgwUSgo6SeAGb2OrA+yXFHAtXfWPcDF2Sl9K52K1+Iv3ejqAV0Pi535XH7r3lHOPR70GZAfPrWD2Dh38KPBefIbuDoBcTOdVkWpWWaJ1F3M1sJEP3tliyTpNGSpkqaunZtijtp3b5bdE/8epehYQ4Jl9+atYZDvwPtBsWnb5oTzjDNclMu16hkM3Akuw018VOXTp59YmZjzGyomQ0tLfXB9urVtuWwfGx8Wle/d6NgFLeEg7/BhxsTelWtmxyGLHFNXjYDRxkQ22DaG1ixD3kSra5uzor+Jrkd1mXVor+D7R0kb31FV2h7YA4L5OpdcStun3QYtEg4oV/+LANazMtNmVyjkc3AMQUYJGmApObAKCDhZypjgcuj3lUnApuqm6FqMRa4Inp8BfBMfRba1aFqd5j3Osacbcf6OEcFaMvO5nDIt6BZ27j00zs8G8bBck1W1gKHmVUA1wIvAnOBx81stqSrJV0dZRsHLAYWAncD11TvL+kR4G3gEEllkr4cbboZOEfSAuCcaN01lLKxsD3mpLBZGxbsODJ35XHZ1bIUDvpa3F3mJdod5vjYlXwARVf4snrnuJmNIwSH2LS7Yh4b8I0U+16cIn0dcFY9FtNlYsGd8ev9L2X38ha5KYtrGO0Phn5fDJNDVStfBBOvglOf8rPNJsjvHHfp2zQPVr8Snzbo67kpi2tY3U6BbqfFp5U9DfP+mJvyuJzywOHS9/5t8eulJ0Ono3NTFtfw+n4B2iTMpTL9RljzRvL8rmB54HDp2bkeFifcKT4oaSujK1RFJXDQaHZUxdyvY5Xw1kWwveZsj65weeBw6Vl0d5i/ulqrXtD3wtyVx+VGi65M2HR+fNr2lfDWxX5neRPiw6q7ulXthvdvj087+NrwC9TlTK7mb1+6axAM/iHM+e3exDUT4L2fhznQXcHzwOHqtuwp2Fa2d724FRw0OnflcUCO528/6pewbiKs/u/etNm/CcPq9/pU9p/f5ZQ3VbnamcHc38enDbjC56Zu6oqawccfgVY949PfvizMc+4KmgcOV7vVr8D6d+LTDvlWbsriGpdW3eHkR+NnfNy1Ad74PFTuzF25XNZ54HC1m/3b+PXeI6HDYbkpi2t8up0W5jCPtX4KvHtdbsrjGoQHDpfaR5Nh9avxaYN93iyX4LDroden49MW3AFLHs1NeVzWeeBwqc1JGAas2+nQ9cScFMU1YiqCk+6DNv3j0yd/BTa+l4sSuSzzXlUuuY3vhSElYvnZRlblqnttplKVs0+nLzC85Z+haldIqNgKr42EYVOgRZcGLqXLJg8cLrmZPyduTq1Ox0DPT+asOE1BTrvXZiBVOcc8+yGMuA2mXL03cesH8OYX4IwX/L6fAuJNVa6m9e9C2b/j0468yUdBdXU7aHTNe3xWvwpTr/VpZwuIBw5X08yfxa93Hgq9zk+e17lYEhz3Fyg9JT594RiY8/9yUyZX7zxwuHgfTaw5r/RRv/SzDZe+4uZw6pPQum98+owfwgcP5aZMrl554HB7mcG06+PTup4EPYflpjwuf7XsBmc8DyUd4tMnXhlmkXR5zS+OF5CH772D8o0raqS37XgAl1yVxhDoy56EtW/FJT33/gBWzP5JZsdxDqDDYF7eegVnlNxOsapCmlVQ+dpneW331Zx55e217+8aLQ8cBaR844rUvV3qUrkTpv8gPq3DkYw4/hOZHce5GIs3tOXsj38JFv2D6l56xarktJK/wYoRcICfzeajrDZVSRomab6khZJq3ASg4LZo+0xJx9a1r6SbJC2XND1azstmHZqM92+H8sUxCUXQ93M5K44rIF0+BgMujUtqpgp4/dOw9F85KpTbH1kLHJKKgTuA4cBg4GJJgxOyDQcGRcto4M40973FzIZEy7hs1aHJ2FYG7/0iPq3baTVHPnVuX5WeAn0vik+r2g1vjYIFd+amTG6fZfOM43hgoZktNrNdwKPAyIQ8I4EHLJgIdJTUM819XX1559tQsWXP6q6qFtBrRA4L5ApSjzOh36j4NKuCKdfA1G/5DIJ5JJuBoxewLGa9LEpLJ09d+14bNW3dI6lTsieXNFrSVElT165du691KHxlY8NETTEml38CStrlqECuoHU/Aw68iipL6N79/l9gwnDYsSY35XIZyWbgSNbxP/HW0VR5atv3TmAgMARYCfwx2ZOb2RgzG2pmQ0tLS9MrcVOze0u4ozdWl+OZu/3Y5Pmdqw9dT+SljRdCszbx6atehnFHx88q6BqlbPaqKgP6xKz3BhL7iqbK0zzVvma2ujpR0t3Ac/VX5CbmnW/DtpgTOxXD8X/D5iS/YJkvg/C53Ej2+Uj12Vi6axCc8xa8dn78Z3DHKuzlM5m57QSmlp9GJWF8q7nzFnDYoYNqHMe7iOdGNgPHFGCQpAHAcmAUcElCnrGEZqdHgROATWa2UtLaVPtK6mlmK6P9PwPMymIdCtfSJ2HxvfFph34XOg0BkgeOfBmEz+VGss9HrZ+NTkfDuZPDIIhr39iTLMHRbSZxdJcl0P+L0P4Qrp40gdHnn13jEN5FPDeyFjjMrELStcCLQDFwj5nNlnR1tP0uYBxwHrAQ2AZcVdu+0aF/J2kIoelqCfC1bNWhYG1bAZMTBqLrMBiO/GVuyuOarlY94KxX4b2bYPZviGvN3rEa5v0JOh1Ll9a5KqBLJqs3AEZdZcclpN0V89iApOeZyfaN0i+r52I2LdVdIHet35tWVAIffwiatcpduVzTVdQMjv4VdD+DLeM/S7vizfHbN7zLTWcIlj0dbhgsbpmbcro9fKyqpubd6+KaBQA46ldRE5VzOdTjLJ5Y91UoPbXGppJig5XPh5GbV726d7IolxM+5EhTsviB0O0xVo+z4dDrclMe5xLsthbhLvOuJ8HSx2BrwjWM3ZtC+srnoee5FNM/J+Vs6jxwNBWrXglzQMdq0w9OfhSKinNTJudSaTcwTFX80SQoewp2JzRf7d4MS//FxV1bw5yuYfKo5h1zU9YmyANHE9Cl2Sp4/TPh+ka14pZw6r99LmjXeKkISk+Czsfw/CO/Y/iha+I/w0Dr4m1hcM5Zv4QDvwSHfBvaDdz/kaIj9XWcQuOBo9BtX8Xwjo9Bxdb49BP+AZ2PyU2ZnMtEcUuemdeP4V/4NqwaD2teq3mNo2JraIZ9/3boPZL2W1tzyYjTakxAlmn33f0acbqAeeAoZNuWw7xbaF2cEDSO+QP0T7ylxrlGrnkH6Hsh9Dw33GW++r9QtTMhk0HZ04zoDMx8GbqdEq6XlLTPRYkLlgeOQrX1Q5h/a80zjUOvg8P8YrjLYyXtoM9noMc5TH59LMd3ex+2r6yZb+eaMA5b2TOh12DpKYDfEFIfvDtuIdowA+b+oWbQOGg0HPO73JTJufpW0pbp206GTy+Bkx5I3aXcKmH9OzD/VkZ1uRNm/RrKlzRkSQuOB46CYrByfJjfILEN+OBr4WN3hQuOzhWS4uYw4DIY9i6cNQH6jaLSkvcUbN9sI8z8CYwdAC9/AhbeDbs2Nmx5C4A3VRWKXZs4u8NTsGx+jU3Tt57IkONuq3Gh0Llcqa8BM5Mf50CWzDmV31zeNdzsumN10n1Z83pYpn6TpbsHM3fLQJbtHEgVe4OOD+CZnAeOQrD2f/D25RzYclHChiLoN4rJk/szxIOGa0Tqa8DMWo/T85xwg+uWBSGArH8XLMlkUVU76Vs8jb4dp0FxG+gyFLqcAG0P9AE8U/DAkc8qd8DMn8LcP1JjqpPiljBwNHQ8HGjaXQddEyZB+4PD0vci3nztJU4ZsAnWTUyev3Jr6O675jVoUcqIQ1qEyaVadmvYcjdyHjjy1fJx8M43oXxxzW2t+8BBX4OWPoGVc3uUtGXO9uM45dxfw5aF8ME/Yck/oTzxTD2ycy0jDiH8OGszALqeAJ2H+uyYeODIPxtnw/QbYUWK+au6fQL6fj6MeOucS67dQXDUTXDkz+Gjicx+5loObzc/nHEks/WDsHz4OHQ4nIEtDoSKbdCsaXbv9S42+WLLIpj4JXj+qORBo3Uf/rPh4nBjnwcN59IjQelJvLVlWOiqPujr0OlYUKrf1FWw6T3O6vgMPNUd3r4i9GSsqmzQYuean3E0dhumh3syPnwErKrmdhXBQV+Ho3/N8jv8Hg3n9llRs3AvSKch4Wxi/Tu8P3UsB3fdnDx/RTl88EBYWvaAfheHkX07HVPwPRg9cDRGFdtg2b9h4Z2w9q3U+bp+HIbeXueYU6m6Pjb1gdqcS6lZa+h2Kn/632Lu+ukIWDclXFBPdoc6wI5VMP+WsLQbBL0vgN6fCddFCvDeKQ8cjUXljjBBzbInYOkTULEldd72h8LRv4XeI9P6ZZOqy2JTH6jNubS06BJmHux5Lmwrg3WT2Lr8bdoUlyfPv2UBzP19WFr2CP+nPYdB909A804NW/Ys8cCRK1WVsHluGKht9SthvoyKFB/Eah0Gw+AfQb+Lwmm1c67hSNCmD7Tpw8MzjuOrF58demUtfTL1D70dq2Dh38KCQjNY9zOg2+nQ+Tho1TMvm7X82yfbrCr8Stk8P1rmweY5sG5q7WcVsbqfBYd8E3qdX5Cnvc7lG6MIepwVlqF/heXPhiCy8oUac4bE7sWGaWGZ96eQ1KIUOh0dAkqHw8Pkam36QaveYSiVRiqrgUPSMOBWoBj4u5ndnLBd0fbzgG3AlWb2bm37SuoMPAb0B5YAXzCzDfVe+HVTwxmAVUBVRfxfqwyPq3aGgQT3LOWwa0O4YWjH6rDsrDn5TFpa9oD+X4SBXwpnGs65xqlZK+j3hbDs2gQrnoeyf8OKcXW3IuxcG4aIX/VywgaFs5GWPUJTWfPO0KJzaOoqbh1u8C1qEf5WL0UtopaIovADs3pB0KYvtD2w/qpcb0dKIKkYuAM4BygDpkgaa2ZzYrINBwZFywnAncAJdex7I/CKmd0s6cZo/Qf1XoG3LobyhfV+2Fq16gm9RoYho7uf6c1RzuWb5h2g/6iwVO4MTdCrXgpN0htnZnAgg+0rwlIfDruhXkfGzuY30/HAQjNbDCDpUWAkEBs4RgIPmJkBEyV1lNSTcDaRat+RwOnR/vcDE8hG4GiIL+3mnaHriaHNs8fZ0PEob4pyrlAUt4Be54UFYMdHYSiT1f+F9VNDIKnc3kCFqd/rKArf2fVP0oXAMDP7SrR+GXCCmV0bk+c54GYzezNaf4UQBPqn2lfSRjPrGHOMDWZWo6uCpNHA6Gj1EKDmsLGNS1fgo1wXoh4VUn0KqS5QWPUppLpA46tPPzOrMXZRNn9WJwtxiVEqVZ509q2VmY0BxmSyTy5JmmpmQ3NdjvpSSPUppLpAYdWnkOoC+VOfbLaLlAF9YtZ7A4kNdqny1Lbv6qg5i+jvmnoss3POuTpkM3BMAQZJGiCpOTAKGJuQZyxwuYITgU1mtrKOfccCV0SPrwCeyWIdnHPOJchaU5WZVUi6FniR0KX2HjObLenqaPtdwDhCV9yFhO64V9W2b3Tom4HHJX0ZWAp8Plt1aGB506yWpkKqTyHVBQqrPoVUF8iT+mTt4rhzzrnC5H0/nXPOZcQDh3POuYx44GgEJA2TNF/Swuhu+LwhqY+k/0qaK2m2pG9H6Z0ljZe0IPqbN8OCSiqWNC26zyjf69JR0hOS5kXv0Ul5Xp/vRp+zWZIekdQyX+oj6R5JayTNiklLWXZJP4y+E+ZLOjc3pU7OA0eOxQyvMhwYDFwsKZ8Gp6oArjOzw4ATgW9E5a8eGmYQ8Eq0ni++DcyNWc/nutwKvGBmhwJHE+qVl/WR1Av4FjDUzI4gdJwZRf7U5z5gWEJa0rJH/0OjgMOjff4afVc0Ch44cm/P0CxmtguoHl4lL5jZyuqBKc1sC+GLqRehDvdH2e4HLshNCTMjqTfwKeDvMcn5Wpf2wGnAPwDMbJeZbSRP6xNpBrSS1AxoTbi/Ky/qY2avA+sTklOVfSTwqJntNLMPCD1Pj2+QgqbBA0fu9QKWxayXRWl5R1J/4BhgEtA9uieH6G+33JUsI38Gvg/EztObr3U5EFgL3Bs1vf1dUhvytD5mthz4A6Eb/krCfV8vkaf1iaQqe6P+XvDAkXv7PbxKYyCpLfAk8B0zSzFJc+MmaQSwxszeyXVZ6kkz4FjgTjM7BthK423GqVPU/j8SGAAcALSRdGluS5U1jfp7wQNH7qUzNEujJqmEEDQeMrOnouR8HBrmZODTkpYQmgzPlPRP8rMuED5bZWY2KVp/ghBI8rU+ZwMfmNlaM9sNPAV8nPytD6Que6P+XvDAkXvpDM3SaEkSoQ19rpn9KWZT3g0NY2Y/NLPeZtaf8D68amaXkod1ATCzVcAySYdESWcRpibIy/oQmqhOlNQ6+tydRbimlq/1gdRlHwuMktRC0gDCnEWTc1C+pPzO8UZA0nmEtvXq4VV+neMipU3SKcAbwHvsvS7wI8J1jseBvkRDw5hZ4oXBRkvS6cD1ZjZCUhfytC6ShhAu9DcHFhOG9Skif+vzC+AiQm++acBXgLbkQX0kPUKYS6grsBr4OfA0Kcou6cfAlwh1/Y6ZPZ+DYiflgcM551xGvKnKOedcRjxwOOecy4gHDueccxnxwOGccy4jHjicc85lxAOHyxpJlZKmRyOZPiupYx35b5J0fR15LogdBFLSLyWdXQ9lvU/ShQlp5ft73Og4B0h6IsW2CZKGRo/HRaPZdpR0TYbP0V/S9uj1niPpLkkN/v+d+P64wuSBw2XTdjMbEo1kuh74Rj0c8wLCKMIAmNnPzOzlejhu1pjZCjO7MI1850WDEHYEMgockUVmNgQ4ivAapTXYXzRgYH2Je39cYfLA4RrK20SDtEkaKOkFSe9IekPSoYmZJX1V0hRJMyQ9Gd0t/HHg08Dvo1/WA6vPFCQNl/R4zP6nS3o2evxJSW9LelfSv6JxtdIWHeu5mPXbJV0ZPV4i6TfR8adKOlbSi5IWSbo6ytO/eg4GSa0kPSpppqTHgFYxx10iqStwMzAwquPvJT0oaWRMvockfTpVec2sAvgfcJCk0uj1mxItJ0fHuEnSGEkvAQ9I6i7p39HrPSN6rZF0qaTJUVn+pmhob0nlkn4d5Z0Y7Z/s/anxPkb7D4z2mxKdNe45u5N0Q5Q+M7rhzzUyHjhc1kVfNmexdyiVMcA3zew44Hrgr0l2e8rMPmZm1XNIfNnM/hcd44boTGZRTP7xhOEo2kTrFwGPRV/EPwHONrNjganA91IUtfoLb7qk6RlUcZmZnUS4g/4+4ELC3CS/TJL368A2MzsK+DVwXJI8NxKdPZjZDYQ7v68CkNSBMD7TuFSFib6czyLczX8rcIuZfQz4HPHDxR8HjDSzS4DbgNei1/tYYLakwwiv48nRmUwl8MVo3zbAxCj/68BXU7w/Nd7HaP9bgVujcu0Zg0nSJwnDaxwPDAGOk3Raqrq63KjPU1TnErWKvoD7A+8A46Nf+x8H/iXtGQC0RZJ9j5D0K0KzTVvgxdqeyMwqJL0AnB9dT/gUYXj0TxCaTt6Knq854ewnmRvMbM+1iAyucVQHxPeAttG8JFsk7VDN6zqnEb6kMbOZkmbWdXAze03SHZK6AZ8FnozOKhINjF5vA54xs+cl3Q8Mjnmt20tqV11uM9sePT4TuDx6vkpgk6TLCMFlSrR/K/YOwrcLqD4Lewc4J0XxU72PJ7G3Ke1hwnDpAJ+MlmnReltCIHk9xfFdDnjgcNm03cyGRL+SnyNc47gP2Bj9gq3NfcAFZjYjahY6PY3neyx6jvXAFDPbovCNN97MLt63KgBhrKDYs/OWCdt3Rn+rYh5Xryf7H9uXcX4eJPzaH0UYvyiZRUle1yLgpJgAAUAUCLbW8ZwC7jezHybZttv2jldUServkvvI7H0U8Fsz+1sd+VwOeVOVyzoz20SY8vN6YDvwgaTPQxhdV9LRSXZrB6xUGLL9izHpW6JtyUwgNLN8lRBEACYCJ0s6KHq+1pIOzrAKHxJ+tbeIguBZGe4f63Wi+kg6gnAhO1GyOt4HfAfAzGZn8HwvAddWrygMepjMK4RmtOo519tHaRdGZzrV82P3q+P5Esue6n2cSGg6gxAMq70IfKn6OpSkXtXP7xoPDxyuQZjZNGAG4Uvii8CXJc0AZpN8qtyfEkbYHQ/Mi0l/FLhBYUa7gQnPUUk4sxke/cXM1gJXAo9EzUITgRoX4+so+zLCCKYzgYfY24yyL+4E2kZl+T5Jhso2s3WEprVZkn4fpa0mXCO4N8Pn+xYwNLrQPAe4OkW+bwNnSHqP0PR0uJnNIVwfeikq73igZx3Pl/j+pHofvwN8T9Lk6Jibonq+RGi6ejsqyxOk/qHgcsRHx3UuD0QXvN8Djo3O4PJaVJ/tZmaSRgEXm1myHxCuEfJrHM41cgo3ON4D/KkQgkbkOOD26BrURlJft3GNkJ9xOOecy4hf43DOOZcRDxzOOecy4oHDOedcRjxwOOecy4gHDueccxn5/zF6zggNt4gFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at air relative humidity (in percentage) distributions\n",
    "ax = sns.distplot(df['RelaHumPct'], hist=True, kde=True, \n",
    "                  bins=int(len(df['RelaHumPct'])/10),\n",
    "                  color='orange',hist_kws={'edgecolor':'black'}, \n",
    "                  kde_kws={'linewidth':4})\n",
    "\n",
    "ax.set(xlabel='Relative Humidity Percentage', ylabel='Density', title='Relative Humidity Percent Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " For **Relative Humidity Percentage**, the data seems to follow a weak normal distribution with a light skew to the right, having the median approximately at 35 and a few outliers that are greater than 85."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "sns.boxplot(x=df['Precipitation']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For **Precipitation**, the median is 0 and there are a few outliers since the lack of precipitation in this region. We decided to not to examine the relationship between precipitation and the acres burned, because most of time there was no precitation, making the process unnecessary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inferential Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acres burned V.S. Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df['Month'] = df['Date'].dt.month\n",
    "sns.lineplot(x = 'Month', y = 'AcresBurned', data = df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lineplot above shows us the changing of the scale of the wildfire incidents seasonally. We can see that the acre burned are high in July and October, implying that the wildfire incidents are likely to be severe in summer and fall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acres burned V.S. Temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the relationship between the acres burned and temperatures of the wildfire incidents across the years in this dataset to get a sense of what's going on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 734.4x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationship between the arces burned and temperatures of wildfire incidents\n",
    "sns.lmplot(x = 'Temp',\n",
    "           y = 'AcresBurned',\n",
    "           data = df, \n",
    "           fit_reg = True, \n",
    "           height = 6,\n",
    "           aspect = 1.7, \n",
    "           line_kws={'color': 'purple'},\n",
    "           scatter_kws={'color': 'purple'})\n",
    "plt.ylabel('Acres of Land Affected By Wildfires', size=18)\n",
    "plt.xlabel('The Mean Temperature of The Day (in Celsius)', size=18)\n",
    "plt.title('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows no relationship between these two variables but note that there are outliers in acres burned, where acres burned is much higher than number of temperature degree. Most of the acres burned data are clustered around zero Celsius. This is because the distributions of acres burned is skewed right. This suggests that we may need to transform these data to use linear regression to ensure that the large outlier values are not driving our relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, as discussed in class, we **apply a log10-transformation the AcresBurned count data, with an offset of 0.1.** This will shift the values away from being centered near zero when put on the log scale. Then, we store this in a new column `AcresBurned_log10`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "df['AcresBurned_log10'] = np.log10(df['AcresBurned'] + 0.1)\n",
    "\n",
    "sns.distplot(df['AcresBurned_log10'], \n",
    "             kde=True, bins=40, color=\"red\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution appears to be less skewed, but there is a value around -1. This is because there were zeroes in the original dataset (see below). Due to this we used an offset of 0.1 in the log transformation above. These zeroes all show up at -1 as the $log_{10}(0+0.1) = -1$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>AcresBurned</th>\n",
       "      <th>Date</th>\n",
       "      <th>County</th>\n",
       "      <th>Temp</th>\n",
       "      <th>WindSpd</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>RelaHumPct</th>\n",
       "      <th>AcresBurned_log10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-04-19</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>20.111111</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.252206</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-05-19</td>\n",
       "      <td>San Luis Obispo</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>32.720699</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-02-07</td>\n",
       "      <td>Butte</td>\n",
       "      <td>13.722222</td>\n",
       "      <td>19.7</td>\n",
       "      <td>1.36</td>\n",
       "      <td>88.698477</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     AcresBurned        Date           County       Temp  WindSpd  \\\n",
       "101          0.0  2016-04-19      Los Angeles  20.111111      9.6   \n",
       "219          0.0  2017-05-19  San Luis Obispo  20.000000      6.1   \n",
       "220          0.0  2017-02-07            Butte  13.722222     19.7   \n",
       "\n",
       "     Precipitation  RelaHumPct  AcresBurned_log10  \n",
       "101           0.00   20.252206               -1.0  \n",
       "219           0.00   32.720699               -1.0  \n",
       "220           1.36   88.698477               -1.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this should show you all of the papers with zero pulitzer prizes\n",
    "df[df['AcresBurned_log10'] < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression with single climate feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acres burned V.S. Temperature\n",
    "\n",
    "Now it's time to begin the analysis. First, we will plot the relationship between our two variables of interest, then, we will use linear regression to determine the effect of temperature on log 10 acres burned and take a look at the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 734.4x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot relationship between the burning area and temperature \n",
    "sns.lmplot(y = 'AcresBurned_log10',\n",
    "           x = 'Temp',\n",
    "           data = df, \n",
    "           fit_reg = True, \n",
    "           height = 6,\n",
    "           aspect = 1.7, \n",
    "           line_kws={'color': 'blue'},\n",
    "           scatter_kws={'color': 'blue'})\n",
    "plt.ylabel('Acres of Land Affected By Wildfires', size=18)\n",
    "plt.xlabel('The Mean Temperature of The Day (in Celsius)', size=18)\n",
    "plt.title('');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      AcresBurned_log10   R-squared:                       0.002\n",
      "Model:                            OLS   Adj. R-squared:                 -0.000\n",
      "Method:                 Least Squares   F-statistic:                    0.9026\n",
      "Date:                Mon, 15 Mar 2021   Prob (F-statistic):              0.343\n",
      "Time:                        21:37:39   Log-Likelihood:                -499.33\n",
      "No. Observations:                 372   AIC:                             1003.\n",
      "Df Residuals:                     370   BIC:                             1011.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.8873      0.213      8.865      0.000       1.469       2.306\n",
      "Temp           0.0087      0.009      0.950      0.343      -0.009       0.027\n",
      "==============================================================================\n",
      "Omnibus:                       84.292   Durbin-Watson:                   0.392\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              194.747\n",
      "Skew:                           1.131   Prob(JB):                     5.14e-43\n",
      "Kurtosis:                       5.729   Cond. No.                         103.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# carry out linear regression\n",
    "acres_temp = df[['Temp', 'AcresBurned_log10']]\n",
    "acres_temp.columns = ['Temp', 'AcresBurned_log10']\n",
    "\n",
    "outcome, predictors = patsy.dmatrices('AcresBurned_log10 ~ Temp', acres_temp)\n",
    "\n",
    "# Now use statsmodels to intialize an OLS linear model\n",
    "# This step initializes the model, and provides the data (but does not actually compute the model)\n",
    "mod_log = sm.OLS(outcome, predictors)\n",
    "\n",
    "# fit the model\n",
    "res_log = mod_log.fit()\n",
    "\n",
    "# Check out the results\n",
    "print(res_log.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are not able to interpret $\\beta_1$ estimate (`coef`) on the original scale above since we log-transformed the data. However, we could determine that for every one unit increase in the temperature, the acres land affected by wildfires increases by (0.0005/100) = 0.000005, which means that the effect is really small. The p-value is 0.950, suggesting that these results happens due to random chance alone approximately 95% of the time. Therefore, we conclude that **there is not a significant relationship between the number of acres burned and temperature**. We also know that this analysis is likely failing to take into account the effect of confounders we discussed in class, limiting our confidence in this result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acres burned V.S. Wind Speed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the relationship between the acres burned and wind speed of the wildfire incidents across the years in this dataset to get a sense of what's going on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 734.4x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationship between the arces burned and temperatures of wildfire incidents\n",
    "sns.lmplot(y = 'AcresBurned',\n",
    "           x = 'WindSpd',\n",
    "           data = df, \n",
    "           fit_reg = True, \n",
    "           height = 6,\n",
    "           aspect = 1.7, \n",
    "           line_kws={'color': 'brown'},\n",
    "           scatter_kws={'color': 'brown'})\n",
    "plt.ylabel('Acres of Land Affected By Wildfires', size=18)\n",
    "plt.xlabel('The Mean Wind Speed of The Day (in Knots)', size=18)\n",
    "plt.title('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows no relationship between these two variables but note that there are outliers in acres burned. Overall, most of the data are clustered around zero acres. We will apply the transformed acres burned data from previous part. \n",
    "\n",
    "Use linear regression to determine the effect of wind speed on log 10 acres burned and take a look at the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 734.4x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationship between the arces burned and temperatures of wildfire incidents\n",
    "sns.lmplot(y = 'AcresBurned_log10',\n",
    "           x = 'WindSpd',\n",
    "           data = df, \n",
    "           fit_reg = True, \n",
    "           height = 6,\n",
    "           aspect = 1.7, \n",
    "           line_kws={'color': 'brown'},\n",
    "           scatter_kws={'color': 'brown'})\n",
    "plt.ylabel('Acres of Land Affected By Wildfires', size=18)\n",
    "plt.xlabel('The Mean Wind Speed of The Day (in knots)', size=18)\n",
    "plt.title('');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      AcresBurned_log10   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                 -0.003\n",
      "Method:                 Least Squares   F-statistic:                   0.04285\n",
      "Date:                Mon, 15 Mar 2021   Prob (F-statistic):              0.836\n",
      "Time:                        21:38:53   Log-Likelihood:                -499.76\n",
      "No. Observations:                 372   AIC:                             1004.\n",
      "Df Residuals:                     370   BIC:                             1011.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.0635      0.112     18.494      0.000       1.844       2.283\n",
      "WindSpd        0.0035      0.017      0.207      0.836      -0.029       0.036\n",
      "==============================================================================\n",
      "Omnibus:                       84.386   Durbin-Watson:                   0.393\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              202.619\n",
      "Skew:                           1.116   Prob(JB):                     1.00e-44\n",
      "Kurtosis:                       5.845   Cond. No.                         15.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# carry out linear regression\n",
    "acres_windspd = df[['WindSpd', 'AcresBurned_log10']]\n",
    "acres_windspd.columns = ['WindSpd', 'AcresBurned_log10']\n",
    "\n",
    "outcome, predictors = patsy.dmatrices('AcresBurned_log10 ~ WindSpd', acres_windspd)\n",
    "\n",
    "# Now use statsmodels to intialize an OLS linear model\n",
    "# This step initializes the model, and provides the data (but does not actually compute the model)\n",
    "mod_log = sm.OLS(outcome, predictors)\n",
    "\n",
    "# fit the model\n",
    "res_log = mod_log.fit()\n",
    "\n",
    "# Check out the results\n",
    "print(res_log.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could determine that for every one unit increase in the wind speed, the acres land affected by wildfires increases by (0.0163/100) = 0.000163, which means that the effect is really small. The p-value is 0.293, suggesting that these results happens due to random chance alone approximately 29.3% of the time. Therefore, we conclude that **there is not a significant relationship between the number of acres burned and wind speed**. We also know that this analysis is likely failing to take into account the effect of confounders we discussed in class, limiting our confidence in this result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acres burned V.S. Relative Humidity Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 734.4x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y = 'AcresBurned',\n",
    "           x = 'RelaHumPct',\n",
    "           data = df, \n",
    "           fit_reg = True, \n",
    "           height = 6,\n",
    "           aspect = 1.7, \n",
    "           line_kws={'color': 'orange'},\n",
    "           scatter_kws={'color': 'orange'})\n",
    "plt.ylabel('Acres of Land Affected By Wildfires', size=18)\n",
    "plt.xlabel('The Relative Humidity Percentage of The Day', size=18)\n",
    "plt.title('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows a slight negative relationship between these two variables but note that there are outliers in acres burned. Overall, most of the data are clustered around zero acres. We will apply the transformed acres burned data from previous part. \n",
    "\n",
    "\n",
    "Use linear regression to determine the effect of relative humidity percentage on log 10 acres burned and take a look at the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 734.4x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y = 'AcresBurned_log10',\n",
    "           x = 'RelaHumPct',\n",
    "           data = df, \n",
    "           fit_reg = True, \n",
    "           height = 6,\n",
    "           aspect = 1.7, \n",
    "           line_kws={'color': 'orange'},\n",
    "           scatter_kws={'color': 'orange'})\n",
    "plt.ylabel('Acres of Land Affected By Wildfires', size=18)\n",
    "plt.xlabel('The Relative Humidity Percentage of The Day', size=18)\n",
    "plt.title('');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      AcresBurned_log10   R-squared:                       0.031\n",
      "Model:                            OLS   Adj. R-squared:                  0.028\n",
      "Method:                 Least Squares   F-statistic:                     11.80\n",
      "Date:                Mon, 15 Mar 2021   Prob (F-statistic):           0.000661\n",
      "Time:                        21:39:25   Log-Likelihood:                -493.95\n",
      "No. Observations:                 372   AIC:                             991.9\n",
      "Df Residuals:                     370   BIC:                             999.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.4831      0.125     19.795      0.000       2.236       2.730\n",
      "RelaHumPct    -0.0095      0.003     -3.435      0.001      -0.015      -0.004\n",
      "==============================================================================\n",
      "Omnibus:                       80.570   Durbin-Watson:                   0.455\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              190.830\n",
      "Skew:                           1.072   Prob(JB):                     3.65e-42\n",
      "Kurtosis:                       5.778   Cond. No.                         120.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# carry out linear regression\n",
    "acres_rhp = df[['RelaHumPct', 'AcresBurned_log10']]\n",
    "acres_rhp.columns = ['RelaHumPct', 'AcresBurned_log10']\n",
    "\n",
    "outcome, predictors = patsy.dmatrices('AcresBurned_log10 ~ RelaHumPct', acres_rhp)\n",
    "\n",
    "# Now use statsmodels to intialize an OLS linear model\n",
    "# This step initializes the model, and provides the data (but does not actually compute the model)\n",
    "mod_log = sm.OLS(outcome, predictors)\n",
    "\n",
    "# fit the model\n",
    "res_log = mod_log.fit()\n",
    "\n",
    "# Check out the results\n",
    "print(res_log.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could determine that for every one unit increase in the relative humidity percent, the acres land affected by wildfires decreases by (0.0069/100) = 0.000069, which means that the effect is really small. However, the p-value is 0.007, suggesting that these results happen due to random chance alone approximately 0.7% of the time. Therefore, we conclude that **there is a significant relationship between the number of acres burned and relative humidity percent**. We also know that this analysis is likely failing to take into account the effect of confounders we discussed in class, limiting our confidence in this result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression with combined climate features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acres burned V.S. Temperature, Wind Speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Combine Temperature, Wind Speed\n",
    "df['Temp_Wsp'] = df['Temp'] + df['WindSpd'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 734.4x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y = 'AcresBurned_log10',\n",
    "           x = 'Temp_Wsp',\n",
    "           data = df, \n",
    "           fit_reg = True, \n",
    "           height = 6,\n",
    "           aspect = 1.7, \n",
    "           line_kws={'color': 'green'},\n",
    "           scatter_kws={'color': 'green'})\n",
    "plt.ylabel('Acres of Land Affected By Wildfires', size=18)\n",
    "plt.xlabel('Combined Temperature, Wind Speed', size=18)\n",
    "plt.title('');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      AcresBurned_log10   R-squared:                       0.003\n",
      "Model:                            OLS   Adj. R-squared:                 -0.000\n",
      "Method:                 Least Squares   F-statistic:                    0.9749\n",
      "Date:                Mon, 15 Mar 2021   Prob (F-statistic):              0.324\n",
      "Time:                        21:40:06   Log-Likelihood:                -499.30\n",
      "No. Observations:                 372   AIC:                             1003.\n",
      "Df Residuals:                     370   BIC:                             1010.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.8438      0.248      7.427      0.000       1.356       2.332\n",
      "Temp_Wsp       0.0084      0.009      0.987      0.324      -0.008       0.025\n",
      "==============================================================================\n",
      "Omnibus:                       84.435   Durbin-Watson:                   0.398\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              200.153\n",
      "Skew:                           1.122   Prob(JB):                     3.45e-44\n",
      "Kurtosis:                       5.807   Cond. No.                         151.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# carry out linear regression\n",
    "acres_rhp = df[['Temp_Wsp', 'AcresBurned_log10']]\n",
    "acres_rhp.columns = ['Temp_Wsp', 'AcresBurned_log10']\n",
    "\n",
    "outcome, predictors = patsy.dmatrices('AcresBurned_log10 ~ Temp_Wsp', acres_rhp)\n",
    "\n",
    "# Now use statsmodels to intialize an OLS linear model\n",
    "# This step initializes the model, and provides the data (but does not actually compute the model)\n",
    "mod_log = sm.OLS(outcome, predictors)\n",
    "\n",
    "# fit the model\n",
    "res_log = mod_log.fit()\n",
    "\n",
    "# Check out the results\n",
    "print(res_log.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could determine that for every one unit increase in temperature and wind speed, the acres land affected by wildfires increases by (0.0045/100) = 0.000045, which means that the effect is really small. However, the p-value is 0.559, suggesting that these results happen due to random chance alone approximately 55.9% of the time. Therefore, we conclude that **there is not a significant relationship between the number of acres burned and temperature + wind speed**. We also know that this analysis is likely failing to take into account the effect of confounders we discussed in class, limiting our confidence in this result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acres burned V.S. Relative Humidity Percentage, Temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Combine Relative Humidity Percentage, Temperature\n",
    "df['Humid_Temp'] = df['RelaHumPct'] + df['Temp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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mizHm08aYVxtjKjGhEmvtZ6y1p1pr+4lP4Nxqrb2yEreliQDS7GqdOS414673poiINJOCJ1dO9fO+kHi99yriQXknME48IN9OvN77F64P0pjVwCcqNblSpNml1ni3t7QzHhkn7ISrFsQO3DrA3Na5GHNyvom1lsHJQZ7/yPMVv30REZEqyjm5suB2gqltBK21ryfez/si4H8CbcAXiC+i4zpr7baZgm4Rya3WmeNaZ9ylcOqbLiJSOUW1E0xeKN5W8DWc7HbyCqAFsNZar5sDLIYy3iL1qdYZdymMnicREVeUl/E2xrQaYy41xmw0xvySeLeT+4DPEF8+/pfEa8EvdmGwItJkap1xl8JUsvuNMukiIjNkvI0xXyCe0b6QeEbbAGHgV8TLSh4AHrLWhis/1Jkp4y0iUrpK1eIrky4is0xpC+gQ72QSBR7hZKD9X9baSffGJiIi9WCgd2DaSqFu1OKnZtKB+O9wfLsCbxGZTWYqNVkLzLHW/om19m+ttfcr6BaRerFh+wbm3DwH3wYfc26ew4btG2o9pIZWqb7p5faRV5mKiDSLvIG3tfYn1tqxag1GRKRQG7ZvYOOOjYyHx/F7/IyHx9m4Y6OC7zJUqha/nK42pS6+JCJSj0rqalKvVOMtMnvMuXkO4+FxfN6TFXPRWJR2fzuDnxqs4cgkUzk13ms2r5lW/jIWHmNx12K2Xr210kMXESlF+X28RUTqSTAUxGvSu5d6jZfR0GiNRiS5lJNJL7dMRUSknlRkuXcRqbwtu7aw6cFN7B7azUDvAOsuWjerJqp1BbriGe+Uj7GYjdEZ6KzhqCSXtcvWlvT6rNSETxGRWlDGW6QBqe4Vblh5Aw4O0VgU61iisSgODjesvAHQhLxmUakJn/VKr1uR5qbAW6QBVXKhk0axftV6brz4Rtr97UScCO3+dm68+EbWr1pf8x0TBU/umU2LL9X6dSsilVfw5EpjzGeBO6y1Byo7pNJpcqXMFpVa6KRZ1HJCnhaLkVJpIqlI03BlcuVGYK8x5kfGmDcbkzGrSUSqppz2bLNBLSfk6WiElEoTSUWaXzGB90rgW8CfAt8HXjTG3GSMWV6RkYlITvVW91pvpRW13DGZzcFTvb0OGo12qEWaX8GBt7X219baDwKLgWuAncAngT8YY3YYY95tjGmr0DhFJEU91b3WY11qLXdMZmvwVI+vg0ZTbzvUIuK+shbQMcacCbwPuApYBASBu4BvWGsfc2WERVCNt0j11WtdaqLd4p6hPfT39let3eJsrfGu19dBo6nV61ZEXJWzxrvcwNsHvBn4ILAGcABLPJN+H/BX1tqDJd9AkRR4S6bZ3uu6GjTRc7rZGDzpdSAikpQz8C5pAR1jzLnEM91XAvOAA8D/BL4JhIEPAZ8Abgea+9tG6lZq5jH10PdtNHfmsdq0wMl0pS4W08j0OhARmVnBNd7GmE5jzPuNMQ8DTwAfBn5FPON9urV2vbV2n7X2kLV2PfB54OJKDFqkEOouUR2qSxXQ60BEpBDFdDU5BHyd+OTKDcCAtfb/sdb+yFrrZDn/XkCTLaVmZnN3iWqqp4meUjt6HYiIzKyYBXTuBb4BbMkRaNecarwllSZ7iYiISA2Uv4COtfZN1tr/W69Bt0gmHfoWERGRelJMqUkaY8wcY8zrjTFvM8ac4eagRNygQ98iIiJST2YsNTHGfAR4D/FuJV+31t5hjLka+P+AjpSzfsNae12lBloIlZqIiIhMp9aqIlVVWjtBY8w7gFuACDAJfNMY4yU+yfIp4H6ghXjLwA8YY/7bWvsNt0YtIiIi5VFrVZH6MVOpyXXAM8Q7mfQCdwK3AjuAl1trb7DWXg+cCzxJvLe3iDShLbu2sGbzGgZuHWDN5jVaCrxG6uV5qJdxyMzUWlWkfswUeP8RsNlae8LGa1L+nniLwDustbHEmay1k8B3gHMqNlIRqZlExuxg8GBaxkzBVnW59TyUGzTr9dBY1FpVpH7MFHj3AftT/k8s/34gy3n3A+1Ztjc1ZX1kNlDGrD648Ty4ETTr9dBYBnoHGI+Mp23TqqIitTFT4G2AWMr/ib+zzcgsrCF4E1HWR2YLZczqgxvPgxtBs14PjUWtVUXqRyHtBBVk56Csj8wWzZ4xa5QjV248D24Ezc3+emg2aq0qUj8KCbxvMsY8YYx5gvikSku8u8kTqT/A/6roSOuQsj4yW+TLmG3YvoE5N8/Bt8HHnJvnsGH7hloPtyiNdOTKjcylG0GzMqiNZ+2ytWy9eivPf+R5tl69VUG3SI3MFHjvAxyga+qnfWqbJ2Vb4seZOm3WUNZHZotcGbNHDjzCxh0bGQ+P4/f4GQ+Ps3HHxoYKvhvpyJUbmUs3gmZlUEVESjPjAjqNpNoL6KT2Rm1vaWc8Mk7YCesLSGaNOTfPYTw8js97ckmAaCxKu7+dwU8N1nBkhRu4dYC5rXMx5uR6B9ZaBicHef4jz9dwZJWzZdcWPn3/p9l5bCcYWD53OTe99iZ9bomIuCPnAjolLxkvyvqIBENBvMabts1rvIyGRms0ouLN1iNXo+FR+nv7OWf+OYRj4botr5HZrVHmX4gUSoF3mWpZN6cPJKm1rkAXMRtL2xazMToDnTUaUfFmY71yoeU1+oyRWmqk+RcihVLg3aD0gST14IaVN+DgEI1FsY4lGovi4HDDyhtqPbSCzcYjV4VMDNdnjNRaI82/ECmUb+azSD1K/UAC4r/D8e3NHDBIfVm/aj0Atzx8C6OhUToDndyw8obk9kaxdtnaWfW+Gegd4GDwYPLzA6aX1+gzRmpt99Bu5rbOTdumzmHS6JTxblC1amWoQ8+Saf2q9Qx+apDI+giDnxpsuKB7NiqkvEbtUqXWZuv8C2luCrwbVC0+kHToWaQ5FFJeU8pnjHbMxU2zcf6FND+1E2xQtWhluGbzmmmHp8fCYyzuWszWq7dW5DZFpDaK/YxRe1WphC27trDpwU3sGdpDf28/6y5ap9eTNIKc7QQVeDewan8gzcZ+xyKzWTGfMdoxFxFJyhl455xcaYxxiC8PXwxrrdWEzSqp9oSwQiZkNbpEoLF7aDcDvQPKrsisVsxnjCbCiYjMLF+N951Zfp4kHsXvBO4Ffjj1t5k67V8rOViprWavt2vGGnbV3Eq1aCJcafQeFZldcgbe1tr3WGuvSfwAdwEDwJuttWdba6+w1l5urT0buGLqtLvcGpgxptUY82tjzOPGmCeNMV9w67qlNM3e77jZesY2445EI2v2AKvZd8wrQe9Rkdmn4BpvY8zDwC+ttZ/IcfpXgNdYa1/lysDihcQd1tpRY0wL8EvgI9bah3NdZrbVeIu7mq2GXTW39WO2TDzURLji6D0q0rSKr/HO4nxgc57TnwU+WMT15WXjewSjU/+2TP00z0xQqTvNVsOumtv6MVsWo5ltCxGVS+9RkdmnmD7eg8Dr8pz+emC4vOGkM8Z4jTGPAUeAn1lrf5XlPB8wxjxqjHn06NGjbt68zDLNdqi8WWtuG7FkQ4vRSDbN+h4VkdyKCbzvAt5kjPmWMebsqaDYO/X37cAbge+4OThrbcxa+zLgVOBCY8x5Wc7zDWvtCmvtigULFrh58zLLNFsNe7PtSEDj1sQqwCpMI+5UlaMZ36P5zLbnVySbYmq8A8B3gTcRL/lwpk7yEK9l+RHwdmttqALjxBjzOWDMWvvlXOdRjbdIumaruW3UmtjZUuNdjtn6GDXbezSX2fr8yqzl3gI6xpjXAW8m3sXEAM8B91prf1rOCLPczgIgYq0dMsa0AT8FbrbW/meuyyjwFmlujTwBdrYEWKUqdadKvfcbQ6PuNIuUyJXJlQBMBdiuBtk5LAY2G2O8xLPq/54v6BaRxlBOoNTIE2CbZeJhpQLdUiYapmZRU0uPbkNZ1HqjiaQiccXUeCcZY840xvyJMabH7QElWGufsNa+3Fp7vrX2PGvthkrdljSWZqgTbIb7UIpya7RnW01svalkjX0pdfDN1nu/mWmeg0hcUYG3MeaNxpjngGeAHcArprb3GWOeNca8tQJjFElq1Ml1qZrhPpSq3ECp2SbANppKBrql7FSpW0zj0E6zSFzBgbcxZjVwD3AC+AIp9SvW2iPEa73f6fL4RNI0Q4arGe5DqdwIlNYuW8vWq7fy/EeeZ+vVWxV0V1ElA91SdqqURW0c2mkWiSumxns98DjwKmAO8PmM0x8CrnJnWCLZNUOdYC3uQ71MQGvkGm2p/PNXbB38uovWcf2W6yFMWqcMZVHrU7PMcxApRzGlJiuA71hrnRynvwgsKn9IIrk1Q4ar2vehnkpbdLi5sdXb86csqog0mmICby+Qr0f3fCBc3nBE8qu3L/5SVPs+1FNpiwKlxlaPz59Kj0SkkRSzgM6jwC5r7V8aY+YBR4HXWmu3Tp3+SyBmrV1VsdHOQH28Z4dm6IdczftQ7d7X9VLWIiIiUiPlL6BjjLkO+Afgg8APgcPApcCvgJuAvwausta6umx8MV6x4hX2kUcewWNK6pIo0pSquXDFbFmdTjsXIiKShzsrVxpj/g14FzACdBHPes8jXoZyh7X2feWNszznvew8e/f9d2Mw+Dw+fB4fXo83+Xdym/EmTxMFEc0uWzA8ODnIws6FjIRGXH3OZ8PqdLNl50JERErm6pLxlwNXAmdNXfEu4E5r7ffLGaEbEoF3oVID9HxBejMH6AoiZofU0pYufxdHxo7Q29rr+nPeyEu6F2o27FyIiEhZXF0y/h7i/bwbnsUScSJEnEje8zVzBj114h0Q/x2Ob1fg3TxS23it2byGcCxcked8NrQLbIaWlqAjXSIitVDMAjpbjTGX5jn9EmNMU6Z7EgH6RHSC0fAoQ5NDHBs/xqHRQ7w48iJ7h/fy3OBz7Dq+i92Du3lh+AUOBA9wZOwIJyZOMBIaYTwyTigaIubEan130mjlt/pUySXlK/mcN0PXmZk0Q0vLemoxKSLNp5LfYZVSrTEXMwtxNbAwz+l9QM06mtSDYgL05wefZ9/wPvaP7Ofw6GGOjx9naHKI0fAok9FJIrEIxZYBlWKmIKIR3zyNrtJBUSUDx3psN+e2Zti5yNdispz3vD4vRKQRd+yrOeZiupo4wJXW2rtynH4tcIu1tj3b6dVQbI13I/AYT1o5S2q5S+a2UuSr8QZU/10Dla4hVl1/+Rq9pWWuWvz9I/vpCHSU9NrQ60qakUqyiteI82AqMObSJlcaY84HXjb177eBfya+NHymucCHgBFr7YpSRuiGZgy8C2UwafXnXuOlxduSVove4mlJ+6JNyBVENOKbpxlUY4JiIwWO+uJzX6739qGxQyzqWFTSe16fF9JstDNZmkacZF+BMZc8ufJy4HOJMQDXTv1kEwT+puihiSsslqgTJepE854vW+Z85akrufed9+L1eJOnQ/NMIms01ZigmDrZsp6lfvGlHv67DX3xlWPdReu4fsv1ECYtoAh4AyXX/5fyeaGdqsrQ4+oONR8oTSNOsq/mmGeq8f42cAmwhnj0/r+m/k/9WQ2sABZaa+9zfYTiqpiNEYqFGIuMMRIa4cTECY6OH+Xg6MG0OvSdx3eyqGMRw5PDhGNhIrEIkViEYCjIqd2nxr+oY2Ec69T6LjWdZqghdks9LXdfrnqqf85Vi3/OgnNKrv8vdu5AI9aBlqNaz/9se1wrSc0HStOI32HVHHMxNd5XAzustbtdH4VLZnOpSSVs37OdDTs24Pf4afW1MhmdJOyEWX/xelb1n5xHm6hDz2yvmFmP3ojtFmulkUpBClVKFq4RD1lm0yiHrMsZZ7GXnU2lKdV8/uvpcW30zHs9PZaNphG/w1wesytLxvuAdmvtSI7Tu4Fxa23+WocK8p/mt3/8uT/mfS9/X1pgKKXbvmc73/rvb7F/ZD9LupeU9dgm6tBT+54nAvLU4Dzx22OKaboj9azUwKNZvvga6X6U8+VTzGWbZaeqENV8/uvlcW2Unc18EvchHAkzFB4iFA3h9Xj5zGs+w/pV62s9PKlvrgTetwJrrbXLc5z+DPCf1tqPlzREF7QtbbOnfPyUrFlZaTyJhYsyJ41mW220XI2emal3pQYezfDlDfUTDNWTRtoZKVc1n/96eVzrZRzl2rB9A1/65ckgS1QAACAASURBVJeIOlFava30tvXi9/ob7jNIqi5n4F1MSvHPgHzLwn8fqOmrMGZjxGwM61i+9sjXGJocqrsFa6Rwib7ok9HJZF/04xPHOTx2mP3B/ewd3svzg8+n9UU/EDyQ7Is+PDlcUF901URWXqm1ks3SF7xSvdPrqW68WI1YB1qqai66VC+Pa7PUR2/bs43+nn4uWHgBfzT/j1jYsbBh55lIfSgm4z0GfMRa+80cp/8V8T7eXS6OryjmFGOz9Vzp9HfSHeimy99FT6CHrsDJ392B7uRPV6CLbn/6/x0tHVlb8Elj8hhPWqmLz+PjLd97C4dHDycn8BkMY+ExTuk+paEyM/WsnrNfG7Zv4JaHbyEYCtIV6OKGlTe4fhi5Epn7ZjgaUExpSiMflar2c1UP9bX1/J4vho5WSYlcKTUZBL5qrd2Y4/S/BdZZa3tKGqILzBJj+YC71+k13mkBenegm25/d1oAny2Q7w504/f63R2QuG7N5jX0BHqmfbCOhEb4xXt/MS1Qz/b/bNg5KyfwqdcgccP2DWzcsREP8R2ymI3h4HDjxTdWJPh2MxhqlsCmEPX6+ilGPQTD1dQMzxnMrveZuMqVwHsrsAB4hbU2nHGaH/gNMGSt/dMyBlqWtqVtdvHHFhNyQnz4lR/mnL5zGJ4cZiQ0QjAcZCQ0kvwJhoIMh6aflnk4sFytvtZ4pr21Jy3jPi2Qz8i89wR66PR3aoJhFVx1z1UcHTtKW0tbcttEZIIFHQu48/I7C7qO1ImhqfXnmRNJfR5fQwbpbnyJ1mPgMefmOYyHx/F5T84TiMaitPvbGfzUYA1HNrPZlInLDH6GJ4c5EDyAYx1efdqr6+K1JNPV43u+WI20A9HIR4WakCuB91uA/wDuBz4DPDF10vnE+3tfCrzLWvu9soZaBje6mkRiEYLhYDIwT/09Eh5hZHIk/jsjgA+G4sF7xIm4dn8MJlkmky0wT8u0+7vpbu1OK5Vp9bU2ZJBXbYW2TXRLZvvFbD/1lkXPlvU5PHaY0fAova29Dfsh79vgw+/xYzwpwasTn1sQWe/ee7kS6jUTl/jyf+roU4RiIfweP+f2nVvW6yN1J2N4cph9I/swGBzrcMacM+o2EJLm0Ag7EI20gzBLlB94Axhjvkg86LYpP56pG7jZWvuZ8sZZnlr38bbWMhmdPJlZTwnUswbyKRn4kdAIo+FRLIU/HzNp8bScrF0vslSmy99Fi7fFtbHUOzfbJrols9Vitm4uPo+vKkdFMrOrw6Fh9g7tBeClfS9t2A/5Rs54F/pFW80sWLL9WizM4dHDGAwY6Gvrw99SeieI1J2Mncd3Eo6FMRhavC0sn7e8LnY4ak3ZztmtXnfEZzF3Am8AY8wrgSuBM6eu+BngLmvtI+WM0A21DrzLFXNijEXGpgXm2QL11FKZRLnMZHTS1fG0t7SnBelZs+4pZTSpp3f4O1QmUyWJtouJgHz7nu384yP/yL6RffT39POxV3+MP1/+52Vl0TM/1Hce30koGiLgC7B8XrzDqJsf8plBxOr+1Wzbs831oKKaNd6VkJgYOhoapTPQOW1iaLWzYInXyf7gfiKxCB6Ph5gTw+/1s6RrScmvj9T78fzg8xgM1liW9iylJ9BT9RKbegtyle2U2VR61iDcC7zrWaMH3uUKx8JpwflwaJjR0Oi0zHtmpj3xE7PutV70GA9d/q6Cy2Iys+6tvlbXxjKbzFQ2k23BoswJo9lq0TO/2J848gQe42Fp91J6WuPzqd36kM+8rSPjRzg0eohF7Yvo6+xzPaiYKXitV4UEW/myYOsuWudK8JgahB4aPcSSziW8EHwBn/GBib8uYjbGeQvOK+v1kbidh158CA8eTuk+hZ5AT9p9qkZmrx6DXGU7a6/WO2N6DdQddwNvY8yZwELg99ba4TIG5qrZHniXw1rLeGQ8LYOeGZgnAvm0kpmp842GR10dj9/rz9kCcqZOMl3+rlm7PL0bE0UTMlsvbtu9jX969J/YN7yP4dAwHb4O2lraODx2mLATxmd8nDn3TB6/7vGy7kO1s+uNqpAv2lxZsP0j+8FAMBQk6kTxeXx0Bbq44013FBUsZAahTx97mogTwWvi7z+3Mt75btOtwLfQwKkeAxxlO2urHnbG6mEMkiZn4F3Ukn/GmDcCtwL9U5suA7YaY/qAB4FPW2v/T4mDlCwStccvjrzIqd2nVqz22BhDh7+DDn8Hi7sWF335qBNlNDyaddJp1smpKZNUhyeHp01KDcfCHBs/xrHxYyXdn46WjoI6yWQrmWlvac9bklGt56QUL468mMwCJrT6WuOBVpEc6+BYJ/ncrFiygm8t+RYQfww+c/9nODJ8JP5YWQgR4kDwAHc+dievPeO1ebPr+XaMdg/tZm7r3OT/oVgIr/ESioWS23ItxJEInp488iRhJ0zAG+CcBefUvBSgEjIfJ5j+uAz0DkwLEscj4wQjwXgpiPHg9cRLbE5MnODTP/90UY/Tpgc3EY6FOTp2NPk8OdbBYzzx10/MAQM9/h7XFnFZu2wtt3Gbq5PdUoOW1EW0bmN60FLI415tuZ5ntxfocSOrW+vMcCVsenATfo8/+fh3+DsgHN9erftWifeFVEbBgbcxZjVwD/AYsBn4fOI0a+0RY8xzwDsBBd4uSS0b6An0cHTsKBt2bGA9lem2UQ6fx0dvay+9rb0lXT4UDaUF6ongPd8k1dRsfOak1LHIGGORsZLvS6JmPbNEZjg0zEMvPoTf4yfgC7B3aC+f3fpZPnzhh7n0JZfWvHf7qd2nTst4T0YnWdK9xNXbWdW/ivnt8xkLjxGzMfw+P/Pa5+EzPv7x0X9k5Wkr817eYNImiKZm1k/rPo3Do4fp9HcCEPAGkhnvhGxBRXJiXyTMUGgILIwxxq4Tu3IGUY2skGBr3UXruH7L9RAmLQuWGnRDfCIvDuw8sbOoMTx19ClOTJyIP3/Gh2MdIL7TNrdtLqFYiIAnwLL5y1wNAtYuW1tSwPfpn386fh8tLJ+/nJsuvYm1y9YWFThVK8gtRq7n2c3VKovZOankddSjetkZK+V9IdVXTMZ7PfA48CpgDimB95SHgKvcGZYAfOu/v4Xf408GUW0tbRCJb6+3wLtcAV+APl8ffR19RV/WsQ5j4bH0yafhIMOTw2klM6kBezIDn6V3e9SJMjg5yOBk4V0t1m9bz/pt8brgXL3bZ1o11Y3e7e97+fvYsGMDREir8X7fy99X8nXmMhoe5SVzXpK9jGEGFkvUiRJ1otNOe/f572bDjg1EnSitvtZkINHh6yAUDTEZnSTiRLhuxXWMhkeTQfvf/dff4ff4ORo+Gg/svV4cx2F4cpglXUuqmn2qhkKCrVxZsCu+dwXTygxLmO4TioUwGDye+GvWYzx4rZd2fzuHPnGonLvnqi27tnDNvddwYuJE/P1l4zsN7733vdz+ptuLCpyqEeQWq5RsZ7GZZzeyuvWQGa6EetwZk/pVTOC9AvictdbJcRj+RWCRK6MSwN2ygWbmMR66AvGJnEsoPrObr3d7WplMaIT7n78fY+L9g2NOLOuE1MnoJJPRSY6OHy16LJm922ecnJoxSfXi0y9m/cXrK9IaMbPEptPfyWR0Mmt2vZxynFX9q1jPyfvQ39vP2899O7/e/+u0+3TBogs4EDyQvNyuE7voCfQwGZ1MljoYDJPRSQLeAM8PPs9kdLKhFzJKVWiwlS0Ltnz+cp46+hTGMcnHKkaMc+afU9QY/B4/42acmBNLXg8GAp7AzBeuok0PbiIYCqZl+Y1jGAmNsOnBTUUFTvV6SL+YbGcpmedSs7ppk2+Dh6Ydfat1mY4b6nFnTOpXMYG3FwjlOX0+EM5zuhSpWmUDs12Lt4W5bXOZ2zZ3xvNmTl5MTEqd0zaHjZdszNmzPVcZTTAcTLt+i43vBISD7A8Wv4OV2rt9bvtcWrwt/OCZH7B1z9ZpvduzTUrN1bs9W9nTcGg43qeZ9Oz6hUsuLLtEalX/qqJ3FhLvF7/XTzgWTrYH9Hv9BMNBFnUuYt/wvuT5EwsZpZa5pNage42Xnz33M255+Bb2DO+py5rWUg8t33TpTbz33vcmF/1q8bQwv3U+N116U1HXc27fuew6tovh8HC8rMQboMffw7L5y4oeUyXtHtpN1ImmzS3wGA8RJ8KeoT187Q1fKypwavRD+qVknkvJ6mYG+EdGj7B3eC/GmGRSqRkyw/W6Myb1qZjA+w/AnwL/mOP0NxIvRRGXVLNsQAqT7TmJ2ijXrbiOZfOKDzYye7cX2rM9UUaT2bs94kQ4PnGc4xPHS7p/qb3bu1tPdpV56MWHCEVD8fsci2eN/V4/fk+8+8yRsSOc2n0qf/XHf5VWIhUMBTk+cZxQNMTHf/pxvvK6r1SsTCrx3HS2dHIidoJYLIbF0unvzPq+caxDOJY7V3Dbr2/jn3/zz8ScGAFvgMnoJNf+57V8cc0XueyMy9ImjP78uZ9z669uZe/wXvp7+/nkRZ/kDcvfULc1rWuXreX2N91edqCQyPQtCSzJG7DWekLdQO8AR0aPELOxZMcVxzq0eFro7+2v2ITNep1EWEr2upSsbmaAf0rXKewd3suBkQN0z+9uqsxwo++MSfUUs2T8dcA/AB8EfggcJr5M/K+Am4C/Bq6y1n6nMkOdWTO2E6zHFRVnu3p6ThK927O1eky2hcwzMbUSvdtHw6O0eFqw2GRg68GDg0NPaw9vOestvOrUV00ro0mdPFmoxHPx7IlnCcfCWGsxxiR/+71+zpx7ZtHP0fY927l+y/VYx+L1xDt1WCxz2+bS39vPnZffmXbbwXCQOYE5zGufl9xB/vyqz/PN336To2NHafe3J48OTEQmWNS1iB//vz9OtmxMZNiNMa4GbNUI/mZaTrse2pxl1njHnBhRG8VrvJy74Fxuem080+/m417r+5xPqS0Ri106PVubw6GJIfaP7mdx52JlhqWZubZk/L8B7wJGgC7gKDCPeBnKHdbamqZiV6xYYR/61UNYLNZaLDb+hVnE39ba5Jdsru2JbSKNLLN3e2oHmeHQcFp7yG17tjERmUi+L2I2luxg4ZZE7/bU1VLzTUh99vizfOuxbxFzYpwYn5o0Z2Bu61xafC3JRYNKcdU9V/GbA7/B6zm52qfjOPg8Pua2zWX9qvXJcppDo4eIxCJgYHHnYroCXcne6Yl5GpkTUEdCI9x/9f3TbnfHnh3J621raUtOJL35tTfz+jNfnwzQPcaTrFfONRm3XoI/t/tel7ozkehq8odjfyDiRAh4A5zadSot3haGJoewWOa0znHlsarHXt+pqvXaqPfHQaSCXF0y/nLiS8afNXXFu4A7rbXfL2eEblixYoV99NFHq3Z72YLxbL8LOU+2QL+Y3yKVlG1FzFAsxCde/QleuvClaZn2e5++l+17tyez6YlMb6J+PLFgS74yj3J48OD3xgPX1f2rc09U9Z8M5DN7t6/ZvIbByUEiscjJumAbL+VZccoKgGSt/87jO9Pqyft7+5PB9ZLuJUUtaFTqAkiZmXOvx8tb//2tHBk9kgx6jDGMh8dZ3LWYB97zQFmPcTHcXNzFjYAxWzD4h6N/AODsBWcnt5UTIDbCgjbFZq9LvY162PkTqYHiF9AxxqwH7rbW/n7q/6XAUWvtPcT7ec96xphkvWCtFRroF7oTkO28iR2DbEcEpLlldhrJLLE5jdOAeIC+88RO5rfP5+jYURzir5W+9j4WdCxICyJD0dC01VGTWfeMGvfM8wTDwZwZdweHydgkk7FJ7nm6sI+qzN7twXAw3rVm6ie1fGXlkpXc9fu76PZ3E3WitHhaiDpRPMaT3JmYjE7S0dLB0OQQe4f30uJpYUH7Alq8LXnnaZTayShzsSOAPUN76An0pG3zerw8e+JZdh7fOS1YT2TQM/9OZNVnWvgoFzdbrbnRji5bfXPEiSR3EBPK6bbRCO3lqlGTrEmHItPlm1z5eeBZ4PdT/+8G3g3cVeExSQmMMfEvDgNeqrszkK9MJ1ugD+QN+n/+3M/52iNfY9/IPpZ2L+WDKz7I6v7VyvzXWCGdRhITK3sCPQS8AQ6OHgQb7/mdOckx4AuwwLeABR0Lih6LYx2uvPtKjowd4ej4UaKxaLLNo9d4aW9pJ+ALsPLUlewe2s3Tx55OvkZiTmzaa2XG3u325O9bf30rQNZ2kQbDsyeeTa7eGPAG6GjpYDwyzovBF1ncuZh3nvdO5rfP54WRF5IlNYlykVydjDr8HVx1z1VFtWecqStStmB9JgYzLSif6Xe+SXmZZSOr+1ezbc+2nGUkbixUki0obvFM7+ZTTqCs9nInadKhSLqcpSbGmBPA31pr/3Hqfwe40lpbt4F3tUtNxH3lHposJGNfyrZijhbMZms2r0mraQ6GghybOEYoEmLFkhWuTkRNlL9EYhGOjx9P7nhm1nhnK98YD8dbQH7ptV/KnnUPjfDMsWf4/dHfMxYew+fx0envJOJEGAmNZF34p1Spvds9xsORsSN4PV78Hj8Wm6wf72jpoNXXmuwf/7GVH+PPzvwzWn2tWXuSZysPCjvhsmrfS71/v9j7C/7lt//C/uB+Tus+jQ+98kN4jIfP3P+Z5Hv96NhRDo8fZlHnIvra+xiPjBOxkbT3vhs1w6mfMZFYhIOjB5Odeha2L6Svs8+VkohqlHK4TUvCi7im+BpvY8z9wBnALcAg8G3gn4mvUJmTtTZ3IWKFKfBufM0wGaeYWv58k3wh95GBfDsVmSUY5SxmU6xSapTLGV9mVxO/x8+Z89K7mGTuDED+CY4zsdYyGZ3kp8/9lH994l85FDzEnPY5vOa017CkewnDoWFu/+3t+Dy+5CTUmI2XrCQCdjd30Fo8LTn7sg9PDvPk0ScJhoLMb5/Pny//c16z9DUF9W6vtMzXyp6hPYSjYfw+fzLTPBGZoK+jj+++9bt4PV627d7GZx/4bDJYn4hMELERvvq6r7J22dqTk06NN+vOSEJiouVTx57C7/GzuGsxoViIY+PH6PH3cE7fObMuYHSjHls13SJJJQXeFwB3AwNTm2y+K0qcx1pbs6JnBd6NrxEmJTWCRBD+450/5m/u+5tkl4zEF+FXLvsKl51xWbLcIPHzs+d+lizzOa37NK59xbWs7l9dcF1/sVnWamRlS52wWKnbvONNd6T1bs+VcU/Utz/4woMYY5LBu9tHVbL1bs8M4nP9dLR0lLwCaOYO0c7jO5NtJ5fPWw5k30Eqpp1nageY1C4wHuPhiu9dwZHRI2mtHscj4yzqXMR9V96Xdt5cnWOaiRtJj3pNnCgLLzVQ/ORKa+3jxpjlwEuAxcA24IvAz90enUhCI0xKagSJmv+vPvzVeJ3x1OPZFehiLDzGbY/cxhXnXJF2mS27tnDjthvxe/z0tfcxODHIhh0b6OvoY+2ytdO+vD7+6o/z+jNfnxaQv/uCdzO/fT5///Dfs3d4L0t7lvLhCz/Ma1/y2qz1/99+7NsEPAHa/e1Ya2lvaYdIvFbcrcC7FgtR5brNC5dcyDX3XlNUdj81iA+GghwIHgALPm+8tWEwHAQbr5n3GA+hWIioE+Ws+WcR8AWmBfKZvdvHI+OMR8Y5xKGi76fHeJJ16tMy7qltIKeC+tTfp3SdwvHx48mdE7/Xn8x4J2RbqbeYVU1jNpazV31i8mlq2ZDP4+P5wed5YeSFrPc1kUk3GIyJ17sn/k7Uv+f7SVwmcbnEtnrgRv28G9fhtnpdxEpmr3xdTS4G/mCt3QXsMsZsB7ZZa7dXY2DGmNOAO4FFgAN8w1p7azVuW2qnESYlNVL2pJgvwnwdI4BpX15/c9/fZD2EfPnZl3P52ZcXNL4DowemHeHwe/3sGdrDtT+6lt1Du+nv7U/WMxdSY5/5+81nvZlOfye3/fo2Xhh5gdO6T0tO2E1k+hM7BG71Js/WBebCJRdyz9P3JCefHh07yoYdG1hP/ux+ahB/bOJYPDvrgb7OPrr8XZyYOAHA0s6lyctMRCbweXzc8aY70q7LWstYZCwt057MvGcsspRchCnlvGORsbTrc6zDUGiIodBQyY+Vx3jSj6LE4kHxZHQSxzqMhcf46oNfZeVpK9NKZLoD3SV1WUmYafJpJjdfH6kS2fZEVj0RnCdOyxbgFxrop2btZ1Js0iPb5+BM1+HWZ2cx1+NGJxwRN+UrNYkB705MpjTG7AY+Yq39YVUGZsxiYLG19rfGmC7gN8CbrbVP5bqMSk3cUevAsp4nJTVaDWMxh37zlfn09/ZX5BBytvEdHjvMsfFj9Pf01+QxzuzOkwzOrc0aqBfawvMv/89fcmTsSEklL4nyikf3P0rAF2B+x3y6/F0A7Dy2E4vlj+b/Udp9KLWGPZ+oEyUYChIMBxmeHI6vjBrKWBV1ahGmbIG8273bO1o66GntyVkek693+yP7H2HjLzbWfPJptSQy9rmC9K27t/Kpn38qWZaWqJ//8mVf5vVnvj4t2P/Jsz/hoz/5aNrnYMRGuPqCq9n8+Oasn4+AK5+dxX4Gq3xRaqSkGu9x4K+ttXdM/V/TribGmHuB26y1P8t1HgXe5Wu0wLLa6rWGMZdins989y2ROXf7yyvb+PYM72F+63wWdi2cNo56fIwLlRoADE8Oc2j0EKFoCI/Hw3ff8l0uO+OyZB13ZoCfCODf9h9v48joyeDdYnnuxHMAnDH3jORtVbqGvRTb92znX377L7ww/EKyfOkHT/+AwYlBvB5vslvL0OQQ1lraWtqSj0clVkr1eXy0+lqJxCI41qGtpY1lc5dxxpwzppXFZFtF1e/1z3wjDabQ+vl8cxje/8fv55u//WaynOraV1zLJQOX8Jff/8tkTX3qZRZ2LuTud9ydVnqTOAqQ7e8/v+vPOTx6OD2rPrUw1Nart04r3Wm0z+xMtU6ESclKCrwfA4aAjxDvarJn6u97892StXZfqaPMxRjTD+wAzrPWjuQ6nwLv8jX6h1SluZE9qfYHaaFHEPIF6Zse3FSx10Xm+J488iSndp9akSC/nMe9lMunXmZocohOfyet3lb2jeyLBxQ2vqjNoq5FBe3cZnuOTkycwGDobe1Nm0D75cu+zKUvuZSfPvtT/uHX/8C+4X1Zy2we2P0A3/jtN2asOy+3+0y2SbQTkQkWdixMe65zZfCHJ4f54bt+mJZpHwmNTPs/6yTVcJDxyHhBYy1Um68tvbY9R4CeLRPf6e9s6AmbpXQKcqu7UCHXk5rJ375nO+u3rafFtMQ74UQniDgRbrr0Ji4747K0uvtEDX/mkYFa1eErEdbQSgq830p8sZyiiujc7mpijOkEtgNftNbeneX0DwAfAFi6dOkr9u7d6+bNzzo6LJdfuTsmbn+Quh3E5wrSZxq3m+OoxM5fuY97KZfPvMyR0SMcGj+UXO3WYHBwWNqzFJ/xFfUaynyOgJKft8Tp4WiYg6MHCTthzl5wNhtWb0h2vrnv2fv45M8+SYunhTZfW7K04AurvsCfnv6nyex8LrkypEfHj7KgfUHa9kpl8COxSN4AfVpde8YKqm73bu8KdNHl70orlekKdBEMBfnD0T8QDAeZ1z6PN5z5Bl592qvpCfQkz9Pma5sWDNZ721C3uguV07K0kE442aTW2qfV0Gcp3Umtw09cFpj2f/K6U57HzNr+1/3r6zgUPJT8LDTGKBHWOIoPvAGMMcuA1cS7mnwO+AHwRL5bstZ+oaQhZr/9FuA/gZ9Ya7860/lXrFhhN/7vjTosUwZlvPMrN4Bz8/Gtdjak1KC8lNtx+34lHveoE+Xw2GFCsRA+4+PMuWfy+HWPF3z5Yp63rPXrwcPsH92fLHNY2LmQnkBPxXZuZxp36uOSLwtfyP1PZNATpTLJHuZOjAu+fgG9gV6MMckA3XEcDo0eos3flpYJHw4NYzB0B7rrpvbaWstEdGJaPftMde2JbHswFKxo7/aoE2X34G58Hh9+rz9emoTD2895O6869VXT+ruX27u9lDagbrUOrZeFoaohX3Z/+zXbs064nek3UPK21J2HQrbNcqUF3mlnrHKNt4k/c5uBE9bajxZymWUvXWad9zs6LFOGejq0Va+1beVM/nTziEK97CRVKkNd6mOc7XXzoR9/iMnwJIfHD2OxGAw+jw+L5Yfv/OGM113K85brMk8de4r+nv6qPG8zjTtx+q4TuwjHwvEuIRaiNkpfWx+j0VF6W3s5NHqIJZ1L6G3rLfj+p8r3Gll30Tr+7r/+jj1De1jas5SPrvwojnX4+4f/Pl4e03Ma1624jlX9q9LqvVNrvt1YhKmSmWLHOoyGR/OWwzx97Gm2792etnhWpSS6pPi9fpZ0L+G07tNm7NmeyM4nereXkkXevmc7X3nwK+we3g0WBubE25IW+3iXm8FuFLVYg8AtM+0EeIyHB3Y/wNcf/Tr7RvaxtHspH3rlh7j0JZcWFNiXuxORua0iD0GuEwoNvPNeuzELgauB91hrzyn7CuPX+RrgF8DviLcTBPgf1tof57pM90C3XfLxJTUPRBpdPXQVqacdADe5GaRmTtY7PHY4OVnv7rffXbXHqZydiUqUymR73TiOw97hvcmgO8Hv9XPRaRfN+Ni7lfEeC4/h9/oZjYy60t1hpseu0Iz380PPJ+tbHSf+cesQD27P7zufp489TcSJcHrP6fS09hR0/1Nt2L6BL/3yS8ScGAFfgF5/L/4Wf1nvZ2stMRvjxzt/zEd+8pF4Nw7fyRr3L136JS4ZuCQZrGebpFlP2dOr7rmKRw88SounJfmVHXNitHhbmBOYw3+8/T9yZtq/8dtv4DM+HJyT99NxiNooBpOzl3kpZurdnlrznpykOrXt8UOP86X/+lJdPN6NoJ5en26rx/tWanY/WyDf19HnfuBtjPEAbwTeB6wl3hM8aK3tKekKXRBYGrDnffY81Sc3gXrJ5rrNzR2KQssEKq3U56qSJSWZY3lh5IVkD2oPvtUEvAAAIABJREFUHqaa/QHQ6m3l7nfk31Fxo8Y7s7VaOTu3hY6n0BrvQ8FDxGwMgyFqo8mMstd4GZgTX7x4z9Ae/B4/Z80/q6jnKnEb4ViYoYkhJmOT+Dw+PvOaz7B+1fqC73Mupbz+EsHpZXdexqHRQ7S3tCfbP45HxpPL1KeWy1QyA524H4MTg0SdKB5PvNbXWkvMifGKU16RN8OZKzPa4m2hJ9DDCyMvsLBjIYOTg4RjYXweX/J+hWIhAt4AK09dmbUGPrN3e7kMJtlf3Gu8ycc44A3QE+jhotMu4mWLXjZtgmqXv4uuQBc+T87lR5pSs2b3GzmbX4jl85a7F3gbY/4IeC9wFdBHvOPJD4HvAz+z1rrbqLUIyng3j2ae5OnWEYVsQVMpk/XKVWoAXWzAVEiGN9fr5onDT+Dz+ghFQ9MCqHZfe0E7KqU8b6mX6fJ3gYGR0EhVJ6DONO4tu7bw6Z9/mqeOPYXXeIk60WSGtMXTgjGGpT1Lsdayf2Q/i7sWT5vUWU7WPXOcxR79KPWzYsuuLVzxvSuI2VhB9faJnZGoE52WRc/cFnWiRQfqV91zFXuG9iS71HiMh5gTw3gMt629LW+wlS17OBIawWLpCfQkt+0d3sspXafQHehOe6zydRbJ1bs9bfGlLLXvifNEnEhRj8NMOlo6CuvZnlgxNeU87S3tqj+uE251ualXZQfexpgO4B3EA+5XA1Hgv4BVwNuydRupBdV4N49yMt71WhteCcUGD5UcR7FBaTEBU6HBfa7XzaGxQ3T6OjkycYRILJIMigyGM+acgc9T2R2VQsaf+rrt9ndnDdIT59mxdwdtLW0s6liULP0o9znfsmsL77r7XYyFx3Csg8d4aPG24DgOLd4WlnQtSXuMCn1OCnmeyzn6UcpnRTV2WhMZ5dRgPN/fieA5EosQDAUJxUJ4PV6ufcW1XH/h9TPeXmZmdGhiiIgTqUrHmHxC0VAyIP/ETz/BiYkTeD1eHOtwbPwYMSeGx3hob2knZmNEnAgGQ5uvjZHwiKv9273GmztAn6W922tFGe9cJxpzEfFSkrcBncB/E5/w+B1gDrATeGu9BN6pXU3qcdVDKVypX8TNWhueT6OW5RQz7kLPm6uWOLGiXjgSZv/o/mTgncjo+r1+OnwdHFp3qCb3NfV1G4lF2DeyD4vl9J7TafG0EHbCaasCvjjyYnwVSANLu5fS09rjynOeCJJHQiPJ8iUPHiI2wtKepWnvo0Kfk0LOV8jjk2tnupT3fL2UaSUk6tX/787/y1ce+kqy5/p1K67jkoFLpnWJmal9I2TPKI5MjnBg9ACn95zuel1tIZNUMzPzzxx/Bo/xsLhzMV2BruRjkch6OtZhPDKezLQX1LM9pRWk273bW32t6Zn2WdS73W31WOPtpnICbwc4TDzQ3myt/V3KaWcAu6izwFsL6DSPUrKojRqElqNSOxuVPnKQa9xXX3A12/Zsm9aVpNCsaa5a4sT9eeiFh+JlAzjJPrwxJ1bRSakzZX1TX7c7j+8kHAtjMLR4W1g+b3kya7+oYxFRJ8r+4H4mohPJzhQDvQOuPOep40hM2J2MTtLh7+CuK+4qOpMN8eflmnuvIRgKxie6WgcsnNd3Hje99ibWLlub97q+9oavFXy0oNDPinqZmFys1Pdkf08/N6y8gcvOuIyoE01mzhNlL2/7j7dxePRwsn4dpuq+PS30tvW6WjNcTBCVmpkfDg3T7mtnfsf85OluZj1z9W7PLIfJFcxXo3f7jCUzU+dr9bU2XZlMs9avQ/mB9wjwH8C3rbX/lXKaAm+pO81cG56P251oqnXkIHPcq/tXJ7O6qbfb6e8kHA2XlTVNvc0r/v0KHMdJTjJzcOjr6GPZ3GUV2UGbaWypr9vfHfldvMPI1ETHl/a9FGstjx9+nP6efvaO7J022e/8vvOTQWwuhexIFfO85+vaMq99XvJ2Vvev5p8e+ScGJwcJxULJyXWL2hclO5vkWxkVcH1nulI76JXcWS32PZl5/rHwGGEnzFdf91UuO+OytBr1RBY9tQSmmE4opZYN1HPWM9G7PVHDnlm/HgwHc/ZuT5zuphZPy/TOMYk2j3ky7W71bpfilBN4nwX8FXAlsADYTbzU5E7iXUwUeEtdmY0Z72zKDQBq9Thm3u7B4EEOjR7CwcGDh7ltc1naszRr0FHMTteiLy9KBiIBb4CFnQvp9ne7uoOWVrMd6Obw6GHmtM7JGjQVmvGeiEwkg1eDSXZnOX/h+Tz+wdwLARUTtBW6E5ftOgcnBzHEl69PbNszvIf57fMZnhwmEovg8cSPMPi9/mTd+LqL1uUcX+rRjuHQMIdH41l4r/HO2I3GjcejlteZqtRa9nJ2yFMnj+b6iTkxLtl8SckT5dzq7V1vYk4s3rs9ZXGl1EB92mqpGVn3yeikq+Npb2lPK4+ZqW97tt7tUjg3Jlf6gL8gXu/9OuJdRp8ALgDebq39vjtDLY8Cb5mNNd6Z3HgManXkIPV2DwYPcmD0AECy1tjBoae1h5cvevm0IKISNeOlyvYcDE0O0dfRRzAcnBYEFVrj/bltn0s+Hgle48Xn8THxtxM5x1PpDG8isDs+cXzakYknDj9BwBsg7ITxGR+YkzXN5y04L/mayhUkJuuxbZR9w/uS7SC9prx6bLePElX6NVXPR/NWf3s1B4MH08paxsJjLOhYwF1vuStvBr2es96VUkg9fDgWzppJzxXIZ57P7d7t2UpjpgXw/uwB/MMvPlzxRarqjdvtBBcD1wDvAc4EJoGfEG8n+CNr7XDJIy2TAm+B+lgAqJbcCAAu+PoFPHv8WaI2mswIV6JFYWZmPjVwe+zQY8mOBsYYWn2tRGNR2v3tDH5qMOt1FZPVLTYDXMzRg3Kzk4nWg4dH48vb+z1+zu07lx17d6Q9Ji2eluTfE5/NHXhXK2jLdjvPHH+Gyegkbb62tIx3YvVExzq8+rRXz5hZPxQ8RMyJxQN3LEu7l7rSjcat8pDUian7g/uTGUufx8e977w3a/eaYm6vno/mFfJ+stam1aEnfi6+/WJ2D+0mZuNHQea1zcPn8dW0u0UlVzOtxo6GtZaxyNjJzHo4o9XjZPa69sQEVrd7t0M8QeD1eJMLzJzfdz5nzjszLVjPNlG1y98VX1G3wbgaeKdd2JhVxLPgbwHagLC1trXkKyyTAm+R8oOsxGS4ExMn4rPwLcSIMb9tPre/6faK1qymlio8ffxpIJ7dbfG04PP6sI4l4kSIrM/eG7iYna4tu7bw6fs/zc5jO8HA8rnLp9VJl3r0wI1AN9ttPzf4HI6N16Z7jCfZW/rs+Wfz+HW5S02qFbRlu53DwcMcmzzG/Pb5HB49nGzd5zgOHo8nLaufr145UZcf8AVY2LGQntby22a6vaDVrmO7ODR+KG1SnsHQ19HHHW+6A6Dk26v3o3ml9rn/i+/+RXKSc+L1fFrXaTg4PPnXT+bslV5KLXohKh0Y13MbvcQOxwvDL7CocxFXnH0FZy84u6Ce7YlAfnhy2PXe7Z3+zpydZDID9sxAvlZlMvkC77KWgLLWbge2G2OuB95FvM+3NKDZ1Pu6WSWew0PBQxwZPcIpXackezyPR8bp7+0v6Ho2PbiJOa1z6PJ3xbs9xEL4jZ++jj5XXxObHtyE3+NPBmmJ34nJec8cfwYgGXQDxGyMzkBn8jqyvW6LCSRHw6P09/YnA5nrt1zPbZwMZLKOMfz/t3fvcXLV9f3HX5/ZezabDWFzgYRckIBGEEGUghZCQDSWitLWC60i4g+r0qqtUazKzRsWrbXGGyoQtWitRUFrVBAiUpWKIuVqAuRCIORCSLLZJHubz++P75nN7GRmds7Mmctu3s885rHZM2fP+Zzv+Z4z3/me7yUsL1rwnrrggAJonHNQaN9TWqawvX97GEqQkDbTOqZx9VlXj/rb3HTJdFplgFGFtswkOEnJtNXO3k9rSysfPOmDrFq3isHhQfqH++kb6KO9uZ3DpxxOd1s02XGRdF26cCmnzDml4jTNVe75LXTs5333vDDpTVZToOZUM739vVzzq2sAyt7f0oVLWc7yqj/NK/ezYOnCpbFnXz3/pvNDx07Cl8nmpmZIw9N9T3PynJOZ1DKppG3ljo1eqKA+lB4ac1zwr9/7dVpTrSMF446WDhgMyzPvV1ITvnHXxv15PtLe3M6Tu56MtZ2kZX/hmNo+lR37dvDl3325rC8cmQmcevt7efMP3kxHc8f+ITHTaYZ8iH1D+3jZES8b1eY9U4DPPUe7B3aze2A3T/U+Ffu4csduH9UspsiY7Zn1qjF2e0U13o1GNd7lafSaFBnbqDbC6UHW71yPYcydMpeWpuK1ibnq2Swhez9X/eIqPnrnR0mRNbU0aT5y2kdGhgesJN+WUgtcyayISbez37lvJxt2bWA4PUxHc0fBqdfHGqax2k2wSqn5LCddq3GfSjqvz/r0LDb3bR55nN6SahkZrnL2lNk43rDttKG2oxldsvKSkTb7A+nwRbI11YphDDPMLW+4pSr5M9O/oFDH0ZO/djLdrd1kfXfC3dm8ezMdrR0V14Q3ao13teKKu93csdvvXH8nN/zhBlKkSKVS9A/1M+RDHDv9WDpaOkaNLlPtsduzR5DJN5JM9vIzF5xZnRpvmRiSrPWR0Wr1JCH3HJoZT+16iid3P8kpcwq3n80nidraJPaTKUx+9jefZXf/bia3Tea9f/LekeWV5tu1O9YyrX3aqGWTWiaxbse6kmMsJInaydx9b+7bDB5inDl5Jpt3b2bv0F4++5vP8uLDXzxmLf2qdatq0ha4lJrPOOmaOzoMDs/uezaRLw9J5/VF0xfR2x9q7FKpMFnKcHqY5lTzyDZrcW2Vq1afBZn9ZNr9tzW1MTA8EGbabO7gmEOPqdpnj5nRbM00p/IXfxZOWxjOUUsn7mHUoL6BPoZ8iI6mDia1TsLd6WztxAaM6+69jsXzF485oVHGRSdcxFV3XgWDjCrAX3TCRUkeZiy/WPcL7t10L2kPTbkOnXQoXa1didTExz3elKWY3DqZya3hyeYnfvkJutu6Dyi4O86Xz/nyAX9faOz23DHcR62TVXDPHbt939A+9g3tY0vflljH7ZcXzg8qeEtJBRCJL7v2aFr7NDb1buKtN7+VGZ0z2DWwa8yCeJxCe+457G7rZkpPGB4vbmErX3OBWjVLyN3PZadfNmrym+v/cD2r1q1i2anLKs63pRS6KkmLuI/ec+XuO9NZr6u1a6SmsMVa6BvoG9VEpt7Xcyn5dvH8xXzyrk8ylB6ivamdqR1TaW1qPSBdc6+hTPp/4VVfSGRyqIe2PsTO/p30tPcwY/KMks9voWNcduoy3nrzW9m2dxtNw01goQZvavvUkW3W4toqV63yTmY/MyfPHMnL7U3tDPkQs7pmcfWZxZtOVbMpZO51t3dwL0M+RHtzO11tXaOeVrS0t7C5bzMLD104Mqtodi36T9b8hOW/Xc76nes5YsoRvO2Et3H6/NO5jMsaZuKYTBOTlIXRggaHB9nUuwm6oNmamT1ldkXbr/R44zbNaWkKze+mdUzL+34x7j7STCbTrj13zPYkxm5XUxNp6N7ylah3u/XcdN3Zv5N1O9bRmmrluT3PTWzUjXz7gsrOYa1GhillP7lpsWXPFrbt2cZwepiWVMuodsJxjrnUNK7nKDnZ+35237NMbp1cdDzs2y+4va7XcylpOjLD6OAAOwZ20D/UT1Oq6YAmM1Cde1Oh/NTd2s2iGYvGPL9jHeNYnXYbcdSlkVldN/6aFKmyr6lSjZohNRqbfe/QXia3Tj5ghtR6NIXMd46KTfKUL20Kxf35V36es486u+CY6Lc+fitfuecrPLHriZoMvZdpCjKUHmLT7k1hoUNTqomezp66D+vYqE1z8hlOD9M32MfO/p1Fm5qo4C0Tso13IxxTbvvR1c+sZmBoADfnuBnHAcmNM90Ix1stuR/SG3ZuAGek4Jk73vXypcsBSvrS1YiFoEKy28W2WAtp0iPD6k1pmzJqPOx65IVMZ7lMx8nMyCPlzjAK1elvUGlhfqJVVJQyhny12nhXMkNqrdO7FpUh2fvoaO5gz+Ae+tP9fObln2HJkUsOKKRnOpRWYsmKJSOTH/X2944M6WopY/nS5XUfb3u8jvNebFSTVC0Dkca0dOFSli9dzmFdh/Hsvmc5rOuwcV9gy26raGZ0tnYyMDjA+Tedz4LPLWDJiiWsXLMysf2tXLOSJSuWjNr2gqkLRnX06B/uB4O2praRZYUe467dsfaAHv3FHvnW4hzmO8Za/H12WmzevTl0tkw1kfY087rn0Zpq5cldT44cM4TH+Zt6N4008blk5SV597d04VJuv+B2Hn/349x+we0NkecLpVPmHE9uncyQD9Ha1MrcKXPpbu8e1USmHtdzpsCwe2A3zdbMwPAAG3ZtYOe+nQfk2zh5O/cagsrbRBfa/0NbHyopf8a9NnNVeh2Vo9g+s++VUzum5r2mks47cfJopemdlLjXVTlxZ5+LVCrF5LbJtDe1s/y3y5naPpWeST3MmjyLOVPmMG/qPJ4z7TksnLaQIw85krndc5ndNZuZnTPpmdTD1PapdLV20dHcQWtTaxgaNo85U+bsb8bW1sX8qfM5ovsITph1QkMUbE+ffzqXnXYZ0zuns6t/F9M7pzd8oXssqvGWCanQiBBpT/OCGS9ItBaw2CgSK+5bMbL8kW2PMJgeZF73vJFh/uo1s2JcldaiVvL32Wlx/5b7abbmkck2jj706ANqQBst7eIot6kGwOyu2WAk3qyqlCZbx3/peB7d/ih7h/ZiGM2pZszsgGYwEO/8VKP2Pu9Y432b2bZnG/O751e1BjaJ44nbhG6sfTbyjJgwfq/ncuKu9rnItEPPHnrxJ4/+hA/c9gFaUi20N7ezZ3APg+lBPnLaR8Z14bbeyqrxNrPLynh9pDqHIBJPbk1ZZkSIjuaOkRrw1lTryNi6lchXu96aamXVulWjakiOOvQopnVMoznVHGYWG+gr2LFq2anLGEgP0DfQN+a6tVDoGEtNv0r+PjstWlOtDKWHcJyZnTOBA2tAG6WGrBylpNPShUu54PgL2LZvG/uG9tFkofb/yd4naaKpaA1/XJlCW7GnByvXrOShbQ8x7KHNfZo0A+kB0un0yGPh7HwbJ29Xo/Y+3/637dlGT3tPSfmzkmuz0uuolPMRd5/VeKqQpGLpXY+nB6UqJ59U+1ykLEVLUwsdLR1Mbp1Md3s3rz/29Xzpz77EEd1H0DfYx7yp8/jKOV/hbSe+LW9NendbN5NbJ9Pe3F5wZBgprmCNt5nlG2U+s3JuSd6jZe7udZvbUzXekpFby3P/lvsBmDd13kinoaRqEuLUUsSdWbFR2h9XWhOTxGyaxUahyC6MjdcaMig9nbKPcfUzqxkcHsTxkacASR1vKWm5ZMUS7t5498gQesPp4ZEJfrrbuw/oLAf1z9u5+39wy4PMmTKn5PxZbvyVXgfVqEUdD/1D8qU3lD8LaK3EzSfj4Vzkk9vu/IBOo1HteqlDLk4E5c5cuSDn98nAN4Ah4LPAQ4TC9iLgvYTa8zdXFKkkrt4je9RL7jjKna2d4Rt+1rBESdUkxBkLOHuIucy5eeeP35n33FQ6HF2uSvJCpeMdV/r3+dKt0IdZqUMANsK1kRvDlLYp7BncM2Y6ZQ/71j/cT7M143joR8CBNfzlHmspw8ut3bGWw7oO44ldT0BWdU2m42c+SeftuHL3n69AWyx/lht/pddBOcP95e5z576dPNX7FGlPs2TFEpaduozlS6s/I2YhpeTNfOm9ZMWShp9/Ik4+yaRD30Af24e305ZqK2mUnUbQnCo8Lnq2fE1dCs0yOpEL6gWbmrj7+uwX8HagH3ipu3/H3f/P3e9z928DLwUGgb+tTdhSinIeS04k2R3nbjzvRlqbWqvSdKOcR4q1PjeV7q/Spi9JNp0Zq0NkKc0TGuHayBfD5t2b2bFvx5jplP1Iuq2pjbSnw+QXUcfd7MJcJcdayqPvBVMX0JJqYW73XMxspPDf3tzOwPDAuLjn1KppV6X7KacpQvY+d+zdwfqd6xlIDzB7yuyRvABUtZPxGTecQerKFHalkboyxRk3nAFUljfHc5OyXNnpMLtrNrM6Z9HZ1jkuCt1x5GvqMq1jGjM6Z3BY12EjnUaPPORIFh66kOcc8pzQ2XPKERzedTgzO2dyaMehTG2fyuTWyXQ0d4zMDtsofrHuF7z5+2/GrrSCj7BK7lxpZk8Dn3D3fyvw/ruBD7r7rLKiTYCamow2nh+5V0M1H2/H3Xatz00S+6s0/erdvCBbI1wbhWJobWrl0EmHljy2+WB6kPU712MYc6fMpaVp9BBwS1YsYc22Newc2En/cD9tTW10t3azsGdhIp0Bs9fZuGtjaGZijIy4Ml7uOY00dn2xvy2nKUKtx+nOdsYNZ7Bq/aoDli+etxgzK/s6bIRrOCkT6VjqJbs2fTA9GH4OD476Pe35WlAnJ3vow8d3PP47v9xPyrdenIL3HuCT7v7RAu9fDnzA3Sfle78WVPAerdF7qx/Man1ulBdGSyI9Km2qUkkMK9es5NLbLmX19tXgcFjXYXS1dtE70EtXaxcY7OoPs6P+ftPvR0YbSVmKtIfxvw9pO4Snlz095nEtnr+YVetWjflF4JpfXcOd6++ko6WDWZ2zRkbuOZjzWTVUUnCvx30gdWUqb5MBw5g3dV5F18B4bA+dj+7PtZH2dN6x0HNf5TZxyZ7s58GtDxYseMfpknovcImZ3ejuj2W/YWZHAe8Cfl9WtFIVlbYnlOqp9blRXhit0vRYuWYlF958Ib39vQylh9iyewsX3nwh1597fdXbzWcXOBb1LBopcFx9VphmO3uK9U29m+gd6MUw2ppDM5Qma2JoeIj+dP+obebrvLqpdxMr7lsxZmEm05Y1blvpiaqa/QcqaR9fj/tAoUKM47HjyU3XC46/YMwvheNBKenQCH1SGk3cNElZitamVlqbWotuN9+MorntzzM/V61bxdfv/Tobd21ka99WZk6eSQcdRbcfp2HMB4ApwINm9h0zu9LMrjCz/wAeALqAS2NsT6qs0Yakk/1qfW4O9ryQO+zY4vmLK0qPS2+7lO17tzPswzSlmhj2Ybbv3c6lt5V+Cyz3nBQbHi7fe4Yx7MOk02lwSKdDjXemPXh2+9K+gT7S6TRb9m5hV/+u2MPdHez5DBqj/0Ah9Tg/dsAgaPuXx4knX7quuG8Fy05d1lATYJVjrHRo5DxVL9VMk+ZUM+3N7UxunczU9qkcOunQvO3QH93+KJ+865M8u/dZejp6aLImnup9ir7BvqLbL7ng7e53AYsJtdqvAz4CXAb8VbTsjGgdaRATcUbKiaLW5yazv9bmVh7a+hDrdq5jcsvkquyr0RT6wL7g+AvKTv/V21eTsjCDppnRlGoiZanQ9KNE5eaBgjMvbnmIX2/8NY8/+zirn1nNzn07Aeho6aDJmmhpamHIh2hpamHm5Jksmr4IGF2QH0gPhMlvsDD2PfE6rBU6JqBhx1tOWqVjdVdTPWa4PXb6sXnXO33e6bHiaeR0rdRY6TCRj71cjZAm1/zqGtqa2uhq66K5qZnZU2aTshRbdm8p+ndlzVxpZtOBIwnDCT7m7lvLijphauMtUthEahMZx1gdl8p5hNvxsQ7cnaam/dMWDA8PY2bs/fDeqh0LFJh5sXcz2/ZtI0UYRxvbP5zfvsF9bNtXeFbG7PalmTHBzUIt+XEzjqu4k9fBku/Uzr3wue5s7uSBrQ/gOIZx+rzTueMtd8Ta9sHcDvpgPvZCGiFN8sWwY+8Ontz9JPuG9q31y/3IfH9X1hgs7r7V3e929980SqFbRIqrRw1BI8wsV2zYsXIfVx7dczTDjG6+McwwR/ccXc1DAQrMvLhvGz2Tejh8yuG4hcKNYTzV+xStLa188GUfLFiblj1E3czJM0kTRgfIDL9ZaVOERqiZqrbsfNTR3MHA0AAbdm0YeepwsLRzL3Suezp7SF+exi930penueMtd8S+NzT6DJvVdDAfeyGNkCb5YmhpauGUOadQqNANZRS8zWySmS0ysz81s9NyX2XELlIVjVDoa5SYVq5Zya+f+DWrn1nNH57+A/dtvm+kdrPScW8LHVOjtEssdoMut1B49ZlX09PRQ8pSDKYHSVmKno4erj7z6moeCpD/sXR3WzczJs0AD50n+4f7GRgeYHB4kOVLl3PZ6ZcVHKc5uyA/pXUKMzpnkEql6GzuTKQpwkQab7mQ7Hw0q2tWNI8zPN33dKwvL5lradanZ3HIpw5h5jUzG+beVYp853pweJBfb/z1qPvDVb+4ivO+ex53bbiLbXu2sWbbmjHvDQdz/4Fy54potM+/JNUyPxRKy3JjKLngHRW4vww8C9wPrALuyHplfj+oTfTMPl40SqGvEWLK7Dft6VCbGc0Utm9oHxt2bQjDz1W47XzH1Cg1ncVujuUWCpcuXMp1517HyXNOZs6UOZw852SuO/e6mjWdyJ1EaNH0RWzZvYUNuzaQ9jTtze20pFpGNYUptq3sgvzCaQu56XU38fSypxPpsNYINVPVlp2Putu6w2RCGH0DfazbsY7JrWP3p8hcS2u2r2H73u3sGdjDjv4dJRVKG0Xuud65bycbdm0gRWrk/vDWm9/Kx+78GOl0muZUM4PDg2zZu4WBwYGi94aDuc9S3GNvxM+/pNUqPxRLy3JjiDOO91eBi4AfA7cDz+Rbz91XxDus5NS7jffB0pZxPGjECQnqFVNmv+t3rh8ZzxnCqAKpVIpF0xdx39/eV9G28x1TZnrrRmiXWGjs40bMJ4UUa4u+cs1KzvvueSOFmcyXrBmdM1g4beyJcqod90S/L+bmo537drJ+53paUi08t+e5JR1zZhtP9j7J4PAgqVRqpMnP7K7ZDZknc+We60e2PcJAeoBEH+fTAAAgAElEQVT5U+ePTNjz8NaH2Tu0l47mDjIDngynh2lpamH6pOkHbZvlJI2n+1qjqyAt8w/nQ7ymJq8Bvu3u57j7v7j7inyvGNubcBqlhk8a8/F2vWLK7HfYh2lNtY4UhB1nXvc8evt7K9r24PAgq59Zzf1b7h/VfKXWNZ3FnjYVmmZ+PDy+XrlmJcd/+Xhe/Z1Xc/fGu2mi6YAarKULl9Ld1k1rU+vIyCVzu+cyY9KMujfpOBhqKnPz0VO9T+E4h085vOTPgsx12j/cPzIFdspS9A/31/3eVarcc532NPO6540UugEG04MAo2YRTFmK/qH+CfUUpJ4a8fNvvKpGWsYpeHcQmpNIAcrsjaMRH2/XK6bMftua2jAz2pvbaWtqY3LrZFpSLRXtf0rrFDbs2sDA8ABN1sTA8MBI85Vat8Er59FqoxcKM8f16DOP0kQYL/yJ3icYSg8dUJBbNH0Rc6bM4bgZx3H0oUfT3dZd9zyfUeiLz0RRSoFzrM+C7Os0UyhNe5q2praGOY+lyD7XpxxxCi2pllHvt6RaaG1qJU16pHPycDqMh99IX3jHs0b8/BuvqpGWcQre9wALy97TQUCZvXE0Yk1mvWLK7Le7tRvHGRoeYtiH6W7vrryDTjRsnWX9cxystoXaSp42NXKhMHNcQz5EU6opjBsejbGdW5BrxDw/3lTSR2esAudYnwUj12l7d+iHMRymru5uHfs6LVWt+yDly5NT2qYwtX0qMzpn0JxqZiA9QCqV4oMv+2BDXXvjme4FyalGWsYpeF8KXGhmLy57bxOcMnvjaMSazHrFlNnvwp6FHNJ2CJNaJzGtYxoLpy2suIPOrv5dzOueN2pyluzmK7Uq1E7Up02Z48quBc00P8gtyDVKnl+5ZiXHf+l4Oj7eQcfHOjj+y8ePi05d+fL7hTdfyPFfPj52QbWcz4KR63TaQqZ1TGNS6yQOaTuEhT3Fr9NKjq/aHe7y5cnrzr2O68+9noXTFjK9czp/OvdPuel1N3HZ6ZdVLY6DTaPcCyaCaqRlnM6V1wEnAMcBvwYeB4ZzVnN3v6jsaCpU786VULgTl8h4U0qnkkbpxNMocSQtc1xDPsSGnWF0CMdpsiZmdc1quA/TlWtWcuHNF7J97/bQTtlhmGF6OnpqOupLOZLoIJmt0T4LJuo1ItKgCnaujFPwTo+9Fu7uY49fVSWNUPAWmShKmRmsUUasaJQ4kpZ9XIPpQTb1bmIgPcCinkVcfdbVeY+tnJk4k7JkxRLu3ng3wx7a7EKYXChlKU6ec3JDF/By8/vqZ1YzMDyA4xw34zhgfBdUG2Gmv4wk82g987tIEZWPauLuqRJedSt0i0iySumz0CiPNBsljqRlH1fa05w852RuecMt3PeO+woWuus5fu/aHWsZSg+NjMoBjEwy1OjNfnLze/9wPzi0NbWNLBvPzZcapQ9Sknk084Tl7o1389Sup7h7491cePOF46Jpkxy8Sq7xHg9U4y2SnIlaizyR1bs5wXiu8S5lDOrxXOPdKNdzknn0+C8dz8PbHiZlKVKWCuPXe5rn9TyP+95R3twEIglJZBxvEamiRpv1tFFrkRstneKqZvz17mS67NRldLV1kfY0g8OD7B3cy77hfQz5EIvnL65JDOXKze9HTTuKno4emq15QnSWb5TrOck8unr7alKWCqP9mNGUaiJlKVZvX51QtCLJi1XjbWaHEGavPBk4hAML7u7uZyYXXjyq8ZbxqlFqoxrdeE+nasdf7xpvCMf4zv9+J+t3rgegrbmNnvYeWltax815ymi0DpITQZJ5tONjHbg7TU37W7kODw9jZuz98N7EYhYpQ+U13mY2D7gf+GfgLOAMwggnpwGLgWOBIyuJUiQJ47FGtJqzno7H9ChkvMwOWyjNqx3/WMPY1SIvLF24lAWHLOCYQ4/hxMNO5PnTn8/MrpkNeZ7G0shjvI9XSQ67e3TP0QwzPDIRTzqdZphhju45ugqRiyQjTlOTjwFTgTMJE+kY8HpgCvBJoBf406QDFImj3p3LylWtJgLjNT0KqXdTilIUS/Nqx1+sOUEt88J4OE9SH0k2ebn6zKvp6egZ6cCbshQ9HT1cfebVVYhcJBlxCt5nAl919zuATPsUc/c97v4hQm34p5IMzsyuM7MtZvZAktuViWu81IjmKjbiQCW1lNVIjzjxJF3DWiidutq6GqJWf+WalZx/0/k8sfMJnux9kl39u0alebkjS5STjs7oZoRx80Il5y7fcW7ZvYVn9z075vaqUSs/kZ76jAdjpXdSTxKWLlzKdedex8lzTmbOlDmcPOfkRMeLr0W+ibuPRsnLjRLHeBSn4H0okCkAD0Y/O7LevxV4eRJBZbkBeGXC25QJbLzWtBV6/Lp4/uKKaimTTo84tabVqGHNl0479u1g8+7Nda/Vzxzv7oHdNFszA8MDbNi1gZ37do6keTmP2ZNK8zh5odJzl3ucm3s38/Sep5ncOrno9qqRZybaU59GV+v0rlZzoFocR9x9NEpebpQ4xqs4Be+twLTo/73APmB+1vutjC6IV8zd7wS2J7lNmdgaZazauAo9fl21blVFNdZJp0ecWtNq1LbnS6cZnTM4pP2Quj/lyBxvR3NHmF0y1YRhbO7bPJLm5TxmTyrN4+SFSs9d7nHuHtrNrMmzmNk5s+j2qpFnxutTsPFqoqR3LY4j7j4aJW0bJY7xqjnGug8Cx0MYusTM/hd4p5ndQijAXww8knyIIqVbduoyLll5CQwwatSI8TAE2NKFSw8ogL3zx+9kWvu0Ucvi1FgnnR5rd6wtOZ4468aRm06ZGfmS3k9cmeOdOXkmG3ZugDSkSLFvaN+oNM93nkvZbrZy0vwLr/pCyXkhiXOXfZylnqNq5Jlq5UPJb6Kkdy2OI+4+GiVtGyWO8SpOjffNwClmlqnVvorQyXIt8Fj0/48mG97YzOxiM7vHzO7ZunVrrXcvDaZRxqpNSqU11kmnR5x4avX0oVGecmTi6G7rZm73XFqaWhj0QTpbOxsizePkhaTTtNTtVeNcNkr+OFhMlPSuxXHE3UejpG2jxDFexZky/ovu/hx33xv9fjtwCvA54F+A09z9luqEWTSua939JHc/afr06bXevTSgiTQEWBJDbyWZHnHiSXLYsKRiqqbsOKa0TmF212zmds/lxvNubJg0LzUvJJ2mpW6vGueyUfLHwWKipHctjiPuPholbRsljvGqopkr3f0ed/8Hd18G3GtmMxKKS0RovBr8OPHUKvZGSaNqxVGPNE/6WErdXjXSsFHyx8FioqR3LY4j7j4aJW0bJY7xKtbMlUU3ZPYh4Cp3bxpz5dK3+W3C5Dw9wGbgcnf/eqH1NXOliIiIiNRZwZkr43SurDl3f2O9YxARERERSUJFTU1ERERERKQ0KniLiIiIiNSACt4iIiIiIjWggreIiIiISA0U7VwZzUpZqqMqjEVEREREZMIaa1STc2JuL5mxCUVEREREJpiiBW93V1MUEREREZEEqGAtIiIiIlIDKniLiIiIiNSACt4iIiIiIjWggreIiIiISA2o4C0iIiIiUgMqeIuIiIiI1EDBgreZPW5mr876/TIzO7Y2YYmIiIiITCzFarznAl1Zv18BvKCq0YiIiIiITFDFCt5PAsflLNPMlCIiIiIiZSg2c+XNwPvN7JXA9mjZh83s/xX5G3f3MxOLTkRERERkgihW8P4A8CxwFjCPUNs9HZhUg7hERERERCaUggVvd98LXB69MLM08B53v7FGsYmIiIiITBhxhhO8EPhVtQIREREREZnIijU1GcXdV2T+b2aHAguiX9e6+zNJByYiIiIiMpHEmkDHzI43s18AW4C7o9cWM1tlZhpqUERERESkgJJrvKPJc+4C2oFbgAeit54P/DnwSzM71d0fTDxKEREREZFxruSCN3AVMAic6u73Z78RFcrvjNb5i+TCExERERGZGOI0NTkN+EJuoRvA3R8AvgicnlRgIiIiIiITSZyCdyfwdJH3N0XriIiIiIhIjjgF78eBc4q8f060joiIiIiI5IhT8P4G8Aozu9HMnm9mTdHrWDP7d+Bs4IaqRCkiIiIiMs7F6Vz5aeBE4A3A64F0tDwFGPBd4DOJRiciIiIiMkHEmUBnGHi9mX0NeA1hAh0DHgN+4O63VSdEEREREZHxL06NNwDufitwaxViERERERGZsGLNXCkiIiIiIuVRwVtEREREpAZU8BYRERERqQEVvEVEREREakAFbxERERGRGlDBW0RERESkBkoeTtDMmoA2d9+TtWwqcBEwDfiOu9+ffIgiIiIiIuNfnHG8vwL8CXAsgJm1AHcBi6L3/8HMTnH3PyQbooiIiIjI+BenqcnLgFuyfv9LQqH7XcCpwGbg0uRCExERERGZOOLUeB8GrM36/c+AB939SwBmdi3w9gRjExERERGZMOLUeBvQlPX7YuCOrN83ATMSiElEREREZMKJU/BeC7wCwMxeSqgBzy54Hw7sTC40EREREZGJI07B+3rgXDN7APgRsAX4adb7JwOPJBgbZvZKM/ujmT1qZmo/LiIiIiLjVskFb3f/V+ByoB+4F3htZmhBMzuUMOLJj5MKLBq+8AvAUkInzjea2aLifyUiIiIi0pjidK7E3T8KfDTP8mdIvn33S4BH3f1xADP7DnAu8FDC+xERERERqbqyZq40szYzm21mrUkHlGU28ETW7xujZSIiIiIi406sgreZnWhmtwO9wAbC2N6Y2Qwz+7mZnZVgbJZnmeeJ6WIzu8fM7tm6dWuCuxcRERERSU7JBW8zeyHwS+A5wDey33P3LUAHcEGCsW0Ejsj6fQ7wVO5K7n6tu5/k7idNnz49wd2LiIiIiCQnTo33VYSC7/MJM1Tm1kj/nNAuOym/BRaa2YKoScsbGD1zpoiIiIjIuBGn4P2nwFfdfTd5mnwQmp4cnkhUgLsPAZcQhix8GPiuuz+Y1PZFRERERGopzqgm7RSfIGdKhbEcwN1/TIJDFIqIiIiI1EucGu/HgBcVeX8JGupPRERERCSvOAXvG4E35Yxc4gBm9o/AK4FvJhibiIiIiMiEEaepyaeBlxPaXD9CKHR/1symA7OAW4EvJh6hiIiIiMgEEGfK+AFCwft9wF5gH3A0sA14P3COu6erEaSIiIiIyHhXUo23mbUBJwOb3P2zwGerGpWIiIiIyARTao33MGGc7qVVjEVEREREZMIqqeAdjan9NPmncRcRERERkTHEGdXkP4HXmVmcvxEREREREeKNavI14AzgVjP7V2ANsCd3JXffkFBsIiIiIiITRpyC9wOEIQQNWFxkvaZKAhIRERERmYjiFLyvIpowR0RERERE4im54O3uV1QxDhERERGRCU0dJUVEREREaqDkgreZvcvMbivy/s/M7O3JhCUiIiIiMrHEqfF+C2Ekk0JWA2+tKBoRERERkQkqTsF7IXB/kfcfjNYREREREZEccQreLUB7kffbx3hfREREROSgFafgvRp4eZH3zwYeqywcEREREZGJKU7B+9vA2Wb2UTNrzSw0sxYzu5JQ8L4x6QBFRERERCaCOBPofBZYCnwIeIeZPUKYUOd5wDTgl8BnEo9QRERERGQCKLnG290HCbXalwIbgROAE4EngPcDZ7n7QDWCFBEREREZ7+LUeGcK3/8cvQ5gZm3u3p9EYCIiIiIiE0kiM1ea2YvM7IvAU0lsT0RERERkoolV453NzKYBfwNcBBwLGGHkExERERERyRG7xtvMXmFm/wE8Sehw2QpcCRzn7s9NOD4RERERkQmhpBpvM1sAXAhcAMwBtgLfA84HPuTuN1UtQhERERGRCaBojbeZnW9mPwfWEEYuuQd4LTCbUMttVY9QRERERGQCGKvG+1vA48B7gBvdfXvmDTPzagYmIiIiIjKRjNXGewCYD5wLLDWzjqpHJCIiIiIyAY1V8J5FqO0+FPgmsNnMvm5mp6FmJiIiIiIiJSta8Hb3He6+3N1PBE4iFL5fA9wB3EWYMr676lGKiIiIiIxzcaaM/727vws4HHgT8GD01tfM7A9m9mEze341ghQRERERGe9ij+Pt7v3ufqO7nwk8B/g4cAhwFXBfwvGJiIiIiEwIFU0Z7+7r3P0yQgfMVwEaz1tEREREJI+yp4zP5u4O/CR6iYiIiIhIjopqvEVEREREpDQqeIuIiIiI1IAK3iIiIiIiNaCCt4iIiIhIDajgLSIiIiJSAyp4i4iIiIjUgAreIiIiIiI1oIK3iIiIiEgNNGTB28z+ysweNLO0mZ1U73hERERERCrVkAVv4AHgPODOegci48vKNStZsmIJCz63gCUrlrByzcp6hyQRnRsREamXRvkMasiCt7s/7O5/rHccMr6sXLOSS1ZewqbeTUxrn8am3k1csvISFfAagM6NiIjUSyN9BjVkwVukHNf86hpaU610tnZiZnS2dtKaauWaX11T79AOejo3IiJSL430GdRc8z1GzOw2YFaetz7k7jfH2M7FwMUAc+fOTSg6GY/W7ljLtPZpo5ZNapnEuh3r6hOQjNC5ERGRemmkz6C6Fbzd/ayEtnMtcC3ASSed5ElsU8anBVMXsKl3E52tnSPL9gzuYf7U+fULSgCdGxERqZ9G+gxSUxOZMJaduoyB9AB9A324O30DfQykB1h26rJ6h3bQ07kREZF6aaTPoIYseJvZa81sI3AK8N9m9tN6xySNb+nCpSxfupzDug7j2X3PcljXYSxfupylC5fWO7SDns6NiIjUSyN9Bpn7xGmdcdJJJ/k999xT7zBERERE5OBlhd5oyBpvEREREZGJRgVvEREREZEaUMFbRERERKQGVPAWEREREakBFbxFRERERGpABW8RERERkRpQwVtEREREpAZU8BYRERERqQEVvEVEREREakAFbxERERGRGlDBW0RERESkBlTwFhERERGpAXP3eseQGDPbCqyvdxwx9ADb6h3EBKL0TI7SMllKz2QpPZOl9EyW0jM54zUtt7n7K/O9MaEK3uONmd3j7ifVO46JQumZHKVlspSeyVJ6JkvpmSylZ3ImYlqqqYmIiIiISA2o4C0iIiIiUgMqeNfXtfUOYIJReiZHaZkspWeylJ7JUnomS+mZnAmXlmrjLSIiIiJSA6rxFhERERGpARW8RURERERqQAXvGjCzdjP7XzO7z8weNLMro+XTzOxWM1sT/Tyk3rGOJ2bWZGb3mtmPot+VnmUys3Vmdr+Z/cHM7omWKT3LZGZTzex7ZvaImT1sZqcoPeMzs2OiPJl57TKz9ygty2dm740+hx4ws29Hn09KzzKZ2bujtHzQzN4TLVN6lsjMrjOzLWb2QNaygulnZh80s0fN7I9m9or6RF0ZFbxrox9Y4u7HAy8EXmlmfwJcCvzc3RcCP49+l9K9G3g463elZ2XOcPcXZo2ZqvQs3+eAn7j7c4HjCflU6RmTu/8xypMvBF4E7AG+j9KyLGY2G/h74CR3PxZoAt6A0rMsZnYs8P+AlxCu83PMbCFKzzhuAHInmsmbfma2iJBfnx/9zRfNrKl2oSZDBe8a8GB39GtL9HLgXGBFtHwF8Jo6hDcumdkc4M+Ar2UtVnomS+lZBjObApwGfB3A3QfcfQdKz0qdCTzm7utRWlaiGegws2ZgEvAUSs9yPQ/4jbvvcfch4BfAa1F6lszd7wS25ywulH7nAt9x9353Xws8SvjSM66o4F0jUbOIPwBbgFvd/W5gprtvAoh+zqhnjOPMvwLvB9JZy5Se5XPgZ2b2OzO7OFqm9CzPkcBW4PqoKdTXzKwTpWel3gB8O/q/0rIM7v4k8GlgA7AJ2OnuP0PpWa4HgNPM7FAzmwS8CjgCpWelCqXfbOCJrPU2RsvGFRW8a8Tdh6PHpXOAl0SPqKQMZnYOsMXdf1fvWCaQl7r7icBS4F1mdlq9AxrHmoETgS+5+wlAH3rUXBEzawVeDfxnvWMZz6K2sucCC4DDgU4z+5v6RjV+ufvDwKeAW4GfAPcBQ3UNamKzPMvG3ZjYKnjXWPTIeRWhfdJmMzsMIPq5pY6hjScvBV5tZuuA7wBLzOxbKD3L5u5PRT+3ENrQvgSlZ7k2Ahujp1oA3yMUxJWe5VsK/N7dN0e/Ky3Lcxaw1t23uvsgcBNwKkrPsrn71939RHc/jdBkYg1Kz0oVSr+NhCcKGXMITaXGFRW8a8DMppvZ1Oj/HYSb3yPALcAF0WoXADfXJ8Lxxd0/6O5z3H0+4fHz7e7+Nyg9y2JmnWbWlfk/cDbhEarSswzu/jTwhJkdEy06E3gIpWcl3sj+ZiagtCzXBuBPzGySmRkhbz6M0rNsZjYj+jkXOI+QT5WelSmUfrcAbzCzNjNbACwE/rcO8VVEM1fWgJm9gNBBoInwZee77n6VmR0KfBeYS7gh/pW753YykCLMbDHwPnc/R+lZHjM7klDLDaGZxI3u/nGlZ/nM7IWEjr+twOPAhUTXPkrPWKK2s08AR7r7zmiZ8maZLAxn+3pCk4h7gbcBk1F6lsXMfgkcCgwC/+DuP1f+LJ2ZfRtYDPQAm4HLgR9QIP3M7EPAWwn59z3uvrIOYVdEBW8RERERkRpQUxMRERERkRpQwVtEREREpAZU8BYRERERqQEVvEVEREREakAFbxERERGRGlDBWw4KZvYWM/No+MFS1l8VTdBTV40Qh5nNj9Luiiptf52ZrarGtieaOGmVL8/HvQ5ERCRZKnhLYqJJGd5jZr80s+1mNmhmm83sx9EHfnO9Y5zIogJV9qvfzB41s3+NxpWtZ2xXmNlr6hlDIWa2OEqv9xVZx83sR7WMq1bM7IXR+Zlfpe2vy8mXA9Gyr5nZEWNvYXwxs6lRei6udyyVMrMb8txXCr2uqHe8jaaR73tSPyoISSLM7Cjgv4GjgduATwLbgBmEmTqvBxYB769XjDGdDVi9gyjDH4DPRP8/hHAc7wbOMrMT3X2gTnFdTphE6gd53jsG0IQCpak0rb4JfAfIzgcvJJyfVcC6CrZdzEbgg9H/uwgTZrwVeJWZvcDdt1Vpv/UwlZCeENJ0PPsK4X6e7ZuEmZc/nrP8/2oS0fhS7L4nBykVvKViZtYB/Ag4EvgLd78pZ5VPmdmLgRfXPLgy1bGAWqkn3f1bWb9/3sy+D7wG+HPgv+oTVmHu3l/vGMaLStPK3YeB4YTCiWNnTr78kpltAS4hzOp5TaU7iO5Dg+4+VOm2JHD3XwO/zl5mZt8ENueczwmtEfNWI8YkpVFTE0nC2wg1cZ/JU+gGwN1/6+5fzF5mZq8xs/8xs93R63/M7Nzcv820azWz483stmjdLWb2aTNrNrP26P9Pmtk+M7vTzJ5XINbm6PHf+qgpxv+Z2Rvy7POAttWZZWZ2uJl928yeNbM+M/upmR2dZxttZvZPZvZgFNcOM/uhmZ2QZ91DzOyrZrYt2uYqM3tRgWOIK1NjtbDc+PIxs3ea2c+idB8ws01m9q3sJgsWtQ+Pfr0g+9F01jqj2i2b2d0WmigdUDFgZq+I/v49WcvMzN5hZr8zsz1m1mtmd5jZGaUcR7miOG7Iszxf2+oromWLLDT92RSd55+b2THROueZ2e/NbG+UJhfn2XbeNt5m9jYze8T2Ny96N3me2OTGFjUPuD56+46s83NDFI+b2dsKHP+D0b7KfTL00+jnUVnbXGhm34zSJ9Mk5Roz68zZd6YJxHQzu87MNgN9wJzo/Slm9nEzezjK28+Y2V2517qZHWZmXzKzDdH+njKza81sRs56mfN3jJl9wsw2Rml9n5m9Kmu9xcDa6NfLs9JzXdY6Y143Wes2mdlHLNyv9lm4X70+K575OeuXdDzVYGYnmdn3LdzD+s3sj2b2odzr2PbfR+dH6++wcC+9wcwmm1nKwn1pbXTMvzezl+ZsI9M87C1m9ndmtjpad7WZ/V2B+JLKWxXf96xIv5l857aEmLrN7FMWrsd+M9tq4TPqyFLOndSWarwlCX8Z/by21D8ws3cCXyA8svwY4fH5W4AfmNnb3T13W3OAW4H/AL5HaELxj4Tau+cDHcDVQA/wvmg7z3P3dM52PgV0Al+K9nkh8G0za3f3G0oIvRO4E/gN8E/AAkJTjpvN7NioRhEzawF+ApxKeDS7HOgG/h/wP2Z2mrvfk7XuTwlPBL4ZbfuFhALzMyXENJbnRD+3ZxbEia+I90Wx/lu07WMJX8KWmNlx7v4MsBV4U7SPX1JaHllByBuvJDxJyfZmYAi4MWvZN4E3EvLF9UAb8NfArWZ2nrvfUsI+ASaZWU+J65ZrBbAb+AQwnZCHf2pmHwH+mZAvrwMuAr5iZg+5+13FNmjhS8hngfsIeXISsAzYUkI8NwGHARdHMT0cLX8M+C3wdBTL13L2+SeEpmMfcvdym75kvghui7b5IuB2YAehicOTwPHA3wMvNbPT3X0wZxu3RjF+lHBt7jazqcBdhPvC9whp2gScAJxDaGqDmc0l1Oa2Al+Pjvko4B3AGWZ2krvvzNnfCmAQ+HT0d+8h3GuOdvd1hPR7L+F8fJ+QvhDOeUYp103GcuBvgTuifU4Hvsj+wv2IMo8nEdGXj+8DjxKaum0HTgGuItzL/irnTzoJ5/pO4FLCve+tQDvhnncy8HmghZBePzSzee7em7OdvwNmEfJLL+E+8G9mNs3dr8yKL5G8FS2v1n2vFPnyezfwK2Au4d7xIOGafidwd3Te1ye0f0mCu+ulV0Uvwo1yV4z1DyHcxB4FpmQtn0L4sOgFpmYtX0coJP9VznZ+B6SBmwHLWv730fqvyFr2lmjZeqA7a3l3tGw70JG1fBWwLmd/q6JtvD9n+bI8+3tv7rKsY9wArMpadnG07pU5674nWj4qjiLp6oQCfE/0Ogp4F9AfpemMMuObH617Rc66nXliOLNAGjlwQ4G41+Xsb1oU83dz1usi1PLckrXstdG2L85Ztxm4h1BAsXz7zVp3cbSNsWdq7F0AAAu6SURBVF4/KuWYsvLa4qxlV0TLflggr/YCc7OWTwf2Ad8eI62mRmnyEDApa/kcwjWWG0e+2A5YlvXeJ6L3FuUs/yrhC9DhJeTLdYQCaSZfLiB84d1BKMQeG613H+GLeFfO32fO8Vuylt0QLftWnv19MV+eiN5LZf3/ZsKXkzk565wUHdsVWcsy5+9HOefvxdHyT451vcS9bghfHJzwBTk77uMIFQ4OzC/neCp5RfvNzoPthMLgnUBzzrqZ+0x2flsVLVuWs+5NhPv5PUBL1vJXR+u/Pc8125t9vIQvHf8b5avs5YnkrTjnLyut8t0jCuaRrLw2v5SYgM8Be4Hjc5bPA3bl279e9X2pqYkkYQrhAi/Vywnf1v/N3Uf+Lvr/54HJhA6Z2Z509//MWXYX4XH65z2600R+Gf1cyIG+5Fm1PtH/v0z4MrC4hNjThJqObLfn2d/fEG70vzOznsyL8MFwK/AyC230ILS/HmZ/p8iRWImXrhCeBGyNXmsINWYPAGe5e3YNaJz48nL3PoDo0XB39Pf3ATsJNVZlcffthALqq6Pay4y/JNTmrsg5jl5CrWP2cUyNtjGf/Pkgn2sJeTPfKyn/ViCv3uzuGzIL3X0r8EfGjv1sQpp8wd33ZP39RuDfE4j3q4QP/IsyC6JH868HVrr7UyVu57nsz5ePE2rmtgHnuvsDZnYc8ALCk4y2nHN5F+HLxdl5tvvp7F/MLAW8gVDQ/2ruyh49AYtqCc8BbgH25exvHaFSIN/+Ppd9/tz9t4T8V2oei3PdnJO1z3TW39/P/mY6meMu93iS8HJgJuFp09Scff84Wid338OEe322XxLu51/20bXPxe7n/x7ldWCkb85nCV+8/xwgqbyVtY+q3PdKlJvfjfB0707gyZxj6yPUzFfrvEuZ1NREkrCLUBtZqgXRzwfzvPdA9DO3bdraPOs+W+C9zPJ8Q+g9nGfZQwX2mc9T7r4vZ1nm0XD2/p5HaP6ytci2eoAnov1uyv4SAqEjnZk9TvhSUKq7gQ8TPsDmAv9AqP3M7SwaJ768zGwJcBnhw6Y95+04MefzDeAvgNex/zHtmwnnNrv5yfMIeW9zkW3NBFaXsM817p47ggMAVnYz5gM8nvN7oTyceW/eGNvL5NlH8rz3UJ5lsbj7WjO7DXiTmV0aFYheR0jzrxX/61HWEZoxQciLT7n7o1nvZ/pkXBm98pmZZ1nuee0h5L2f5HzByXUMoY/TRWR9qciRe64KLdtO/ntNXjGum8x98o95NvNHYGnW7+UeTxIy5+66IuvknrtNee6jea8Fd382uv7KvZ8nlbeAqt/3xpIb03RCumQqXPLJbW4pdaaCtyThAeA0MzvS3Uu5uZdTiik2EkOh9/LtJ9+HcZx4isVhOf+/n1DwLWRr1rqFCglx02pbduHRwogm9wP/ZWbPd/e9ZcR3YFBhlJqfEWrSLiV8WO4lHMd3qLzj9o+j/b8ZuDZqv3o6oTYse2QPi9Y7v8i2HijyXjUUu68Wyj9x8nC+9yvN18VcC/wn4ZH/fxEKdk8Thg8tVV+hLzWRTKyfITStyOfZ3AXZtfw52ylW6M5e71uMfoKSbW+eZeWep7BSvOsmzvkr93iSkNn3MsJwpvnkPhmp5f08qbyV5H2vWP4seP8okt9vI/RfknFABW9Jwn8BpxE6mPxTCes/Fv18PvDznPcWRT+rVTuziPA4NlumRiTJfa4h1Ebc7gd28Mz1GHC2mU3JrvU2szZCrdcBHwqlcvftZvZhQm3UewltduPGl8/5hA5rS919pIYqaoZQca2Puw+Z2Y3Au6Oe+W8kfMjkFirWEMaO/42776a2thPao+eq5UgCmWvpeexv8kTWslKMVUjNtB2+yMweAF4KfMqTHcZsTfRzeIwC+li2Eq6XF46x3qOE426tcH/5FEvPONdN5v1jOPDedEzO79U8nrFkzt1YX66qYVGeZbn386TyFiR338t0dK/0/rGV0FdiSh3SXsqkNt6ShK8RHn2+z/IMBwihV3k0kgmENsR9wN+ZWVfWOl2EXuq7o3Wq4R1Re8jMPrsJowbsAH6R4H6+Qehtn7dG2cyyH23eTLiZ/2NurIT285X6JuFD6H1mltlenPjyydRK5dYu/RP57yu7yf8hU0ymkP1mwggBf3T3u3PW+Ua0v0/m20AJx1GJ1cApZjYpa3+HEDoO1sqthBq3d+XEMYfiTwGyZb6w5D0/UfOSG4BXsH9imK+XE2wR9xKeTPxtviHQLAwbOmb+ib5EfhtYZGYHNLmI2sTiYeSJHwPnRSO0HLCemU2PfxhA8fSMc938MPr57qjteia24wjnYkSVj2csPyV8Mbs03zkys47s+3zC/jrK65l9tRIqGIbZ3yQtkbwVSeS+52F0lqcJI6GMbCuKr+SZLqP8/u/AS8zsL/OtYzUYSlLiUY23VMzd95jZOYRHzz8ws58RCgTPEGpVzyB8UPxztP4OM3s/Yci4u23/WMhvIYzE8Xav0rBXhA5dd5vZdYSb54WEttBvy/dosQKfI3Q6uiZqE3g7oS38XEIP+H2EdIHQKeli4DIzW0AYEuwEwhBcj1HhdRrVHn+S0Nns3YShqOLEl8/3CR9wPzazawntdl9O6MSUbxbC3xBmz/wAYdQUd/fvjBH3vWZ2f7SfKeR5muLu3zOz64FLzOxEwoftNkK79lMI+alaNdDLCY/2b7cwqchUQjvm9YQvNVUXtX/9CKHT1a/M7BuEzpZ/S6jpK2VM9t8S2oF+KPri0AeszfmS81VCU4I3Ar9w9zUHbqZ87u5m9iZCPvy/6Pp8kHAsRwHnEWa+vKGEzX0YWAJ8zczOZn8n7BMI19KbovXeEb13Z5Ru9xIKT0cC5xK+1F1RxrE8Y2aPAm8ws8cI/Q/63P2HxLhu3P3BaJ2LgduiZmPTCSMV3Qu8iNG167GOx8KY0uvdfX7cY8yJs8/M3kyYnfGP0bl7lHA9PJdw7l5LdWbxXE24n3+Z0Mn1fMJIMx919yei+JLMW0ne95YThtJdaWY/AA4nXLcPEG+yuQ8RnkJ918y+G+1zgNA/5FWE0b/eEmN7Um31HlZFr4nzItzI3ku4+T9LGNJpM6FA/iagKWf91xLGH+2LXr8CXpNnu+vIGr4qa/kV5Ay7FC2fT85QTewfMu0sQgebDYQh6x4Azs+z7VXkH05wXZ51D9hftLyZMFzcb7OOcQ2hhuLsnHWnEWoRn4nWW0UYBizvPgukv5Mz5F3Wey2EAuGzRMMplhpfkeN7DeGm3kf40PkOoeB+wPkijEjwM0Lh3sOtp/j5jd77x2j9YeCIIsf+JsLoB7sIXxrWEYYne30J6bY42sf74qYtoTC6PspLDxPGIs7ktcXl5NUx8mDetALeTnjq1E8o9LyH8KUyN44DYouWX0DolDZA4SHQfh6996aY94V1wAMlrjuPMMrQuiiWZ6I89sns8080vFqR7UwlfNF/NGs7vwRel7NeD2HWzNVRvtlB6PvwObKGUCx0/gqdE+AlwP8Qrg3PPo/Eu26aCE8ZMver/yN0bv10tN0ZZR5PV/T3/xPnXGZdC/ny4LGEL6JPRmm+mXBP/wgwrVi+LpY3s/Z5Q9bvi6NlbyHcw9ZE6bMGeHeV81ZS971mQh7dFJ2r3xNGYjkgr5UQ06Qone8nPAHrZf/IPifHPcd6Vfdl0UkTEREpyMx+THiKcLjv76QrdWBmPyTU6k/xaNKumH//akITtzPdPbdvQMOzMEPoHcCFXtrEZyINQ228RUSkKDM7itBc7JsqdNeO5RlL38xeQBhK8PZyCt2RVxCe4Iy7QrfIeKc23iIikpeZnUwYJeLvCY/n/6W+ER10LojaT/83YQSL5xLafA8QxpIui7u/K5nwRCQuFbxFRKSQdxBGlXkc+Gt3X1ffcA46vyf0hfl7Qj+QXkInwSvd/d56BiYi5VEbbxERERGRGlAbbxERERGRGlDBW0RERESkBlTwFhERERGpARW8RURERERqQAVvEREREZEa+P/wCAPl/ToX2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 734.4x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y = 'AcresBurned_log10',\n",
    "           x = 'Humid_Temp',\n",
    "           data = df, \n",
    "           fit_reg = True, \n",
    "           height = 6,\n",
    "           aspect = 1.7, \n",
    "           line_kws={'color': 'green'},\n",
    "           scatter_kws={'color': 'green'})\n",
    "plt.ylabel('Acres of Land Affected By Wildfires', size=18)\n",
    "plt.xlabel('Combined Relative Humidity Percentage, Temperature', size=18)\n",
    "plt.title('');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      AcresBurned_log10   R-squared:                       0.031\n",
      "Model:                            OLS   Adj. R-squared:                  0.028\n",
      "Method:                 Least Squares   F-statistic:                     11.68\n",
      "Date:                Mon, 15 Mar 2021   Prob (F-statistic):           0.000701\n",
      "Time:                        21:40:37   Log-Likelihood:                -494.00\n",
      "No. Observations:                 372   AIC:                             992.0\n",
      "Df Residuals:                     370   BIC:                             999.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.7497      0.200     13.722      0.000       2.356       3.144\n",
      "Humid_Temp    -0.0103      0.003     -3.418      0.001      -0.016      -0.004\n",
      "==============================================================================\n",
      "Omnibus:                       80.481   Durbin-Watson:                   0.456\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              197.293\n",
      "Skew:                           1.057   Prob(JB):                     1.44e-43\n",
      "Kurtosis:                       5.874   Cond. No.                         281.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# carry out linear regression\n",
    "acres_rhp = df[['Humid_Temp', 'AcresBurned_log10']]\n",
    "acres_rhp.columns = ['Humid_Temp', 'AcresBurned_log10']\n",
    "\n",
    "outcome, predictors = patsy.dmatrices('AcresBurned_log10 ~ Humid_Temp', acres_rhp)\n",
    "\n",
    "# Now use statsmodels to intialize an OLS linear model\n",
    "# This step initializes the model, and provides the data (but does not actually compute the model)\n",
    "mod_log = sm.OLS(outcome, predictors)\n",
    "\n",
    "# fit the model\n",
    "res_log = mod_log.fit()\n",
    "\n",
    "# Check out the results\n",
    "print(res_log.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could determine that for every one unit increase in relative humidity percentage and temperature, the acres land affected by wildfires decreases by (-0.008/100) = -0.00008, which means that the effect is really small. However, the p-value is 0.004, suggesting that these results happen due to random chance alone approximately 0.4% of the time. Therefore, we conclude that **there is a significant relationship between the number of acres burned and relative humidity percentage + temperature**. We also know that this analysis is likely failing to take into account the effect of confounders we discussed in class, limiting our confidence in this result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acres burned V.S. Relative Humidity Percentage, Wind Speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Combine Relative Humidity Percentage, Wind Speed\n",
    "df['Humid_Wsp'] = df['RelaHumPct'] + df['WindSpd'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 734.4x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y = 'AcresBurned_log10',\n",
    "           x = 'Humid_Wsp',\n",
    "           data = df, \n",
    "           fit_reg = True, \n",
    "           height = 6,\n",
    "           aspect = 1.7, \n",
    "           line_kws={'color': 'green'},\n",
    "           scatter_kws={'color': 'green'})\n",
    "plt.ylabel('Acres of Land Affected By Wildfires', size=18)\n",
    "plt.xlabel('Combined Relative Humidity Percentage, Wind Speed', size=18)\n",
    "plt.title('');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      AcresBurned_log10   R-squared:                       0.030\n",
      "Model:                            OLS   Adj. R-squared:                  0.028\n",
      "Method:                 Least Squares   F-statistic:                     11.60\n",
      "Date:                Mon, 15 Mar 2021   Prob (F-statistic):           0.000734\n",
      "Time:                        21:41:01   Log-Likelihood:                -494.05\n",
      "No. Observations:                 372   AIC:                             992.1\n",
      "Df Residuals:                     370   BIC:                             999.9\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.5374      0.141     17.962      0.000       2.260       2.815\n",
      "Humid_Wsp     -0.0094      0.003     -3.405      0.001      -0.015      -0.004\n",
      "==============================================================================\n",
      "Omnibus:                       80.101   Durbin-Watson:                   0.446\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              184.957\n",
      "Skew:                           1.076   Prob(JB):                     6.87e-41\n",
      "Kurtosis:                       5.702   Cond. No.                         152.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# carry out linear regression\n",
    "acres_rhp = df[['Humid_Wsp', 'AcresBurned_log10']]\n",
    "acres_rhp.columns = ['Humid_Wsp', 'AcresBurned_log10']\n",
    "\n",
    "outcome, predictors = patsy.dmatrices('AcresBurned_log10 ~ Humid_Wsp', acres_rhp)\n",
    "\n",
    "# Now use statsmodels to intialize an OLS linear model\n",
    "# This step initializes the model, and provides the data (but does not actually compute the model)\n",
    "mod_log = sm.OLS(outcome, predictors)\n",
    "\n",
    "# fit the model\n",
    "res_log = mod_log.fit()\n",
    "\n",
    "# Check out the results\n",
    "print(res_log.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could determine that for every one unit increase in relative humidity percentage and wind speed, the acres land affected by wildfires decreases by (-0.0065/100) = -0.000065, which means that the effect is really small. However, the p-value is 0.012, suggesting that these results happen due to random chance alone approximately 1.2% of the time. Therefore, we conclude that **there is a significant relationship between the number of acres burned and relative humidity percentage + wind speed**. We also know that this analysis is likely failing to take into account the effect of confounders we discussed in class, limiting our confidence in this result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acres burned V.S. Relative Humidity Percentage, Temperature, Wind Speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Combine Relative Humidity Percentage, Temperature, Wind Speed\n",
    "df['Humid_Temp_Wsp'] = df['RelaHumPct'] + df['Temp'] + df['WindSpd'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 734.4x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y = 'AcresBurned_log10',\n",
    "           x = 'Humid_Temp_Wsp',\n",
    "           data = df, \n",
    "           fit_reg = True, \n",
    "           height = 6,\n",
    "           aspect = 1.7, \n",
    "           line_kws={'color': 'green'},\n",
    "           scatter_kws={'color': 'green'})\n",
    "plt.ylabel('Acres of Land Affected By Wildfires', size=18)\n",
    "plt.xlabel('The Relative Humidity Percentage, Temperature, Wind Speed', size=18)\n",
    "plt.title('');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      AcresBurned_log10   R-squared:                       0.021\n",
      "Model:                            OLS   Adj. R-squared:                  0.018\n",
      "Method:                 Least Squares   F-statistic:                     7.575\n",
      "Date:                Sat, 13 Mar 2021   Prob (F-statistic):            0.00622\n",
      "Time:                        20:39:09   Log-Likelihood:                -439.18\n",
      "No. Observations:                 358   AIC:                             882.4\n",
      "Df Residuals:                     356   BIC:                             890.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept          2.5684      0.203     12.661      0.000       2.169       2.967\n",
      "Humid_Temp_Wsp    -0.0077      0.003     -2.752      0.006      -0.013      -0.002\n",
      "==============================================================================\n",
      "Omnibus:                       59.269   Durbin-Watson:                   0.540\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              149.649\n",
      "Skew:                           0.801   Prob(JB):                     3.19e-33\n",
      "Kurtosis:                       5.733   Cond. No.                         336.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# carry out linear regression\n",
    "acres_rhp = df[['Humid_Temp_Wsp', 'AcresBurned_log10']]\n",
    "acres_rhp.columns = ['Humid_Temp_Wsp', 'AcresBurned_log10']\n",
    "\n",
    "outcome, predictors = patsy.dmatrices('AcresBurned_log10 ~ Humid_Temp_Wsp', acres_rhp)\n",
    "\n",
    "# Now use statsmodels to intialize an OLS linear model\n",
    "# This step initializes the model, and provides the data (but does not actually compute the model)\n",
    "mod_log = sm.OLS(outcome, predictors)\n",
    "\n",
    "# fit the model\n",
    "res_log = mod_log.fit()\n",
    "\n",
    "# Check out the results\n",
    "print(res_log.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could determine that for every one unit increase in relative humidity percentage, temperature, and wind speed, the acres land affected by wildfires decreases by (-0.0077/100) = -0.000077, which means that the effect is really small. However, the p-value is 0.006, suggesting that these results happen due to random chance alone approximately 0.6% of the time. Therefore, we conclude that **there is a significant relationship between the number of acres burned and relative humidity percentage + temperature + wind speed**. We also know that this analysis is likely failing to take into account the effect of confounders we discussed in class, limiting our confidence in this result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results: Model Comparison and Interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By establishing OLS model with both single climate variables as well as combined variables, we observed that relative humidity percentage is the climate feature that explains for **the most variance (0.02)** in the area burned. Although the model with relative humidity percentage (2%) is classified as a weak model, it is the best result we get across the rest of the models. \n",
    "\n",
    "#### OLS models with single variable\n",
    "- The temperature seems not having any correlation with the burning areas because it accounts for 0% of the variance and the observed temperature happens due to random chance alone approximately 95% of the time. \n",
    "- The wind speed also seems to have solely minor effect on the burning areas as it accounts for 0.3% of the variance and the observed wind speed happens due to random chance alone approximately 29.3% of the time. \n",
    "- The relative humidity rate seems to have the most correlation with the burning areas as it accounts for 2% of the variance and the observed relative humidity rate happens due to random chance alone approximately 0.7% of the time. \n",
    "\n",
    "#### Single variable vs. Multiple variables \n",
    "- The OLS models with combined variables have similar or even lower adjusted R-squared value than the models with single variable. Therefore, adding more features did not help account for more variance in our data set. By comparing the results from the models established using single variables and mutiple variables, we decide that the model which has the most significant effect is the OLS model with relative humidity rate alone.\n",
    "\n",
    "#### OLS model with Relative Humidity Percent \n",
    "- Because we applied the log transformation to the burned area, for every one unit increase in the relative humidity percent, the acres land affected by wildfires decreases by (0.0069/100) = 0.000069, which means that the effect is really small. However, the p-value is 0.007, suggesting that these results happen due to random chance alone approximately 0.7% of the time. Therefore, we conclude that there is a significant relationship between the number of acres burned and relative humidity percent. We also know that this analysis is likely failing to take into account the effect of confounders we discussed in class, limiting our confidence in this result.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Further Step: Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To take a further step and find out how our two most related factors humidity and windspeed can predict the scale of wildfire acresburned. We tried an machine learning model Decision Tree since we are classifying a multi class label. To generate our label for prediction, we used the class defined by nwcg: https://www.nwcg.gov/term/glossary/size-class-of-fire#:~:text=Class%20D%20%2D%20100%20acres%20or,G%20%2D%205%2C000%20acres%20or%20more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function `set_class()` which takes as input `area` and `return` a class label on the given `area`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_class(area):\n",
    "    if area < 1/4:\n",
    "        return \"A\"\n",
    "    if area < 10:\n",
    "        return \"B\"\n",
    "    if area < 100:\n",
    "        return \"C\"\n",
    "    if area < 300:\n",
    "        return \"D\"\n",
    "    if area < 1000:\n",
    "        return \"E\"\n",
    "    if area < 5000:\n",
    "        return \"F\"\n",
    "    else:\n",
    "        return \"G\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the set_class() on 'AcresBurned' and store in a new column called 'class'\n",
    "df['class'] = df['AcresBurned'].apply(set_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data partition \n",
    "df_X = df[['WindSpd','RelaHumPct']]\n",
    "df_Y = df['class']\n",
    "\n",
    "X_train, X_test = train_test_split(df_X, test_size = 0.2, random_state = 10)\n",
    "Y_train, Y_test = train_test_split(df_Y, test_size = 0.2, random_state = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function `train_DT()` which takes as input `X` and `Y` and `return` a Decision Tree model trained on the given `X`,`Y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_DT(X, Y):\n",
    "    clf = DecisionTreeClassifier()\n",
    "    clf.fit(X, Y)\n",
    "    return clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train the decision tree model\n",
    "model = train_DT(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           A       1.00      1.00      1.00         3\n",
      "           C       0.99      1.00      1.00       171\n",
      "           D       1.00      1.00      1.00        56\n",
      "           E       0.96      1.00      0.98        22\n",
      "           F       1.00      0.96      0.98        23\n",
      "           G       1.00      0.95      0.98        22\n",
      "\n",
      "    accuracy                           0.99       297\n",
      "   macro avg       0.99      0.99      0.99       297\n",
      "weighted avg       0.99      0.99      0.99       297\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#take a look at the report for train data\n",
    "pred_train = model.predict(X_train)\n",
    "print(classification_report(Y_train, pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           A       0.00      0.00      0.00         0\n",
      "           C       0.58      0.64      0.61        39\n",
      "           D       0.29      0.20      0.24        20\n",
      "           E       0.14      0.11      0.12         9\n",
      "           F       0.67      0.67      0.67         3\n",
      "           G       0.43      0.75      0.55         4\n",
      "\n",
      "    accuracy                           0.47        75\n",
      "   macro avg       0.35      0.39      0.36        75\n",
      "weighted avg       0.45      0.47      0.45        75\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "#take a look at the report for test data\n",
    "pred_test = model.predict(X_test)\n",
    "print(classification_report(Y_test, pred_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results: Prediction summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Assessment\n",
    "- Accuracy - What % were predicted correctly?\n",
    "- Sensitivity (Recall) - Of those that were positives, what % were predicted to be positive?  $\\frac {TP}{(TP + FN)}$\n",
    "- Specificity - Of those that were actually negatives, what % were predicted to be negative? $\\frac {TN}{(TN + FP)}$\n",
    "- Precision (Positive Predictive Value, PPV)  $\\frac {TP}{(TP + FP)}$\n",
    "\n",
    "\n",
    "#### Interpretation \n",
    "The accuracy in the training set is 0.99 with a f1-score of between 0.98 and 1. It means for training data, our model can predict 99% correct data. Since Decision Tree model uses many branches to classify, this is a result of overfitting. We must look at the test data to determine the model. The accuracy in the testing data is 0.47 with a f1-score between 0.12 to 0.61. This accuracy is 0.47, just close to the random chance which is 50%. F1-score shows that the model considered sensitivity and specificity and there're only some of the data to be predict without so many false positive or false negative. It's not a good recall and specificity, lots of wrong data were made. The model built by using Humidity and Windspeed cannot directly predict the scale of wildfire acresburned but resulted in random chance. Specifically, it classifies wildfire to Class C with an f1-score of 0.61. Since Class C contains more data, the result is relatively better. However, it's less than what a accurate prediction can be. Since humidity is the only significant factor of the sacle of wildfire and windspeed is less significant. It‘s hard to predict wildfire acresburned accurately because the factors themselves are less correlated to wildfire sacle. So some more factors should be find to contain in the model and more data are needed for our model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When it comes to the ethics of the project, we believe that we have done the project in a legal and fair way. Also, our project is going to be done for academic use, instead of commercial purpose. Looking at the data sources we are going to use in the project, which are from the website of Kaggle and NOAA database. According to the privacy terms of Kaggle, we have the rights to download the dataset, but we are not allowed to modify or exploit other ways without the prior consent of the datasets’ owners. Besides Kaggle, we would always adhere to the principles of data privacy, and we will use the data if and only if we have the permission to do so. Accuracy of data from kaggle should be doubted and evaluated. To eliminate the inaccuracy of the data, we can choose some data values (yearly acres burned by the wildfire, temperature for specific day, or fatalities) and search them separately, comparing them to examine their liability.\n",
    "\n",
    "However, there exists ethical issues with the final dataset. All wildfire events in this dataset occurred in California, so our analysis does not necessarily generalize about all wildfire incidents in other states. On the other hand, in order to access the weather data for the date of the wildfire event, we use the weather station coordinates to match the wildfire incident coordinates to merge the two data. Some incidents may be missed if it is not in the coordinate range we set, which may have slightly altered the analysis. The analysis may be helpful to prevent the future wildfire in an indirect way, since it shows us the pivotal variables that would effect the scale of the wildfire, raising our awareness of the importance of climate change.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since wildfire is one of the most common natural (or artificial) disaster in California, and the record of wildfires has been updated so many times in the past decade, we wanted to figure out the reasons. Global warming, a hot topic in the world in recent years, has caught our eyes. We have noticed that the global warming is mainly focusing on the climate change, and we decided to look the climate variables, trying to discover their effects on the scale of the wildfires. As a hypothesis, we believe that there is a relationship between the scale (burning area) of wildfire and climate variables in California that are associated with global warming such as humidity, temperature, and precipitation. \n",
    "\n",
    "We began by cleaning the data set and performing some descriptive analysis. We have realized that for precipitation data, the median is 0 and there are a couple of outliers, showing its irrelevance. Thus, we decided to not perform further analysis on precipitation. For analysis, we have performed OLS regression with single climate feature, looking for the correlation between the scale of wildfire and temperature, windspeed, and humidity.  As a result, only the humidity variable has a significant relationship with the scale of wildfire. Then, we have performed OLS regression with combined climate feature: scale of wildfire with respect to temperature + wind speed, humidity + temperature, humidity + wind speed, and humidity + temperature + wind speed. As a result, we discovered that there is a significant relationship between the scale of wildfire and humidity + temperature, and humidity + wind speed, and as well as humidity + temperature + wind speed. Overall, we do not see a clear relationship between the scale of the wildfire and these climate variables except humidity. Although there are significant relationships on combined features, they would be potentially incorrect since the humidity was the only significant variable. In the last part of our project, we have created a prediction model on the variable of humidity and wind speed with a machine learning model Decision Tree. After training the model, we have gotten an accuracy of 0.47% in the test data, which indicates that it can  predict the scale of the wildfire just due to random chance, however for Class C it's f1-score is 0.61 relatively better than others, but it is far from an accurate model, which may suggest we need more data sample for each class. It is reasonable result, since we discovered that humidity is the only significant variable to the wildfire scale in OLS regression with single climate feature. \n",
    "\n",
    "Overall, we accept part of the Hypothesis that there is the significant relationship between relative humidity and wildfire acres burned with a p-value of 0.007. The R squared in the linear regression analysis suggest that 2% of the variance in wildifire burned acres is explained by relative humidity. We fail to reject part of the Hypothesis that there's significant relationship between temperature, windspeed, and burning area due to the respective large p-value of 0.95 and 0.293. These large p values suggest that there's not a significant relationship in these variables. \n",
    "\n",
    "Limitations in our project must be considered. When we merge the weather data with the wildfire data, we cannot match it exactly according to the location of wildfire and the location of weather stations. We choosed a an area of 22.2km * 20km, and any weather stations within area can represent the weather condition of the wildire. Although weather may not change so much in this range, it's still not an accurate data. Also, after we merging data frames, obeservations colloected are only 372 samples which is less to represent the population, lots of data were missed during data wrangling. \n",
    "\n",
    "To take a further step in this research question and our project, we think that taking more climate variable such as wind direction and air pressure into account could yield interesting results, as it can help us to discover more related variables and avoid confounder variables. Moreover, we can extend the region to not only in California, but in other states or the entire region, since this can help us to understand the reason behind wildfires better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Jiayi Zhao**\n",
    "    - Research Question\n",
    "    - Hypothesis\n",
    "    - Data Cleaning\n",
    "    - Format Adjustment \n",
    "    - Data Analysis \n",
    "    \n",
    "**Xiaotong Zeng**\n",
    "    - Background & Prior work\n",
    "    - Data Sets\n",
    "    - Data Analysis\n",
    "    - Presentation Slides\n",
    "\n",
    "**Yunyi Huang**\n",
    "    - Overview\n",
    "    - Data Analysis\n",
    "    - Ethics & Privacy\n",
    "    - Conclusion & Discussion\n",
    "\n",
    "**Wenbo Hu**\n",
    "    - Data Cleaning & Wrangling\n",
    "    - Data Analysis\n",
    "    - DT Prediction\n",
    "    - Team Expectations\n",
    "    - Project Timeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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