FinalProject_group002.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COGS 108 - Final Project\n",
    "## (Un)Sheltered\n",
    "## Names\n",
    "Shirin Saifuddin: Data Cleaning, Initial Research, Proposal, Intro & Background, Conclusion\n",
    "\n",
    "Noor Bdairat: Data Wrangling, Proposal, Intro & Background, Data Analysis, Conclusion\n",
    "\n",
    "Shafeen Pittal: Data Cleaning, Data Analysis, Intro & Background, Conclusion, Proposal\n",
    "\n",
    "Vidya Raghvendra: Data Sets, Data Cleaning, Data Wrangling, Coding, Proposal\n",
    "\n",
    "Suha Kazi: Intro & Background, Privacy/Ethics Considerations, Conclusion & Discussion, Proposal\n",
    "\n",
    "## Group Members IDs\n",
    "\n",
    "- XXXXXXXXX\n",
    "- XXXXXXXXX\n",
    "- XXXXXXXXX\n",
    "- XXXXXXXXX\n",
    "- XXXXXXXXX\n",
    "\n",
    "## Introduction and Background:\n",
    "\n",
    "## Overview:\n",
    "\n",
    "Homelessness is a dire reality of our community, but one, that we consciously choose to ignore. In this project, we explore if a correlation exists between homelessness (dependent variable) and high school dropout rates (independent variable) throughout California. We had three phases to our analysis: first, we performed a single variable analysis, simply distributing the rates of each variable on its own; second, we performed a bivariate analysis between both variables by creating double-axis line plot; and third, we performed a bivariate regression to see if there was a statistically significant correlation to prove a correlation between our dependent and independent variables. We found that, with a Pearson correlation coefficient was .934 which is very close to 1, there is a correlation between high-school dropout rates and homelessness. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Research Question:\n",
    "\n",
    "To what extent are high school dropout rates correlated to homelessness across counties in California? \n",
    "\n",
    "## Background and Prior Work:\n",
    "\n",
    "The US Department of Housing and Urban Development defines a homeless individual as one who lives “in a place not meant for human habitation\" and a \"private place not designed for or ordinarily used as a regular sleeping accommodation for human beings.” This definition may seem far off as a theoretical concept, but in reality, homelessness is a reality is much closer to us than we think. In fact, California has the highest number of homeless residents amongst all 50 states in the U.S.— approximately 129,972 individuals are currently homeless in California. A significant percentage of this population comprises of homeless youth; an estimated ⅓ of the nation’s homeless youth reside specifically in our own state of California. Although we have such high rates of homelessness in our state, little research exists on what underlying factors have contributed to these high rates. There are many factors which could potentially lead to higher rates of homelessness in a locality.  \n",
    "\n",
    "Interestingly enough, there is much research showcasing how people who dropout of high school are more likely to commit crime and live in poverty. We are interested in exploring if these students are also more likely to suffer from homelessness. More specifically, we hope to find the extent that high school dropout rates influence and/or compare with the rates of homelessness across the state of California, in particular metropolitan areas such as Los Angeles, the Bay Area, and San Diego. \n",
    "    \n",
    "If society truly cared about those suffering from homelessness, based on the resources available to the privileged, we could have solved the social ill of homelessness by now. Instead, the homeless have drifted into the invisible and we have become desensitized to what has become a persistent problem in all of our cities. We hope that this research can help mitigate the gap in research to solve the plight of those suffering of homelessness. \n",
    "    \n",
    "    \n",
    "\n",
    "References:\n",
    "\n",
    "10 Facts About Youth Homelessness \n",
    "\n",
    "https://sdyouthservices.org/about/news_events/homeless-youth-awareness-month/facts-youth-homelessness/\n",
    "\n",
    "San Diego County Homelessness Report \n",
    "\n",
    "https://www.sandiegocounty.gov/content/dam/sdc/sdhcd/docs/ending-homelessness/Project%20One%20For%20All_Dashboard_July-Sep%202016%2011916.pdf\n",
    "\n",
    "Changes in the HUD Definition of “Homeless”\n",
    "\n",
    "https://endhomelessness.org/resource/changes-in-the-hud-definition-of-homeless/\n",
    "\n",
    "By the Numbers: High School Dropouts \n",
    "\n",
    "http://schoolsofthought.blogs.cnn.com/2012/06/20/by-the-numbers-high-school-dropouts/\n",
    "\n",
    "California County Homelessness\n",
    "\n",
    "https://calmatters.org/articles/homeless-in-california-what-the-data-reveals/ \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis:\n",
    "\n",
    "We hypothesize that there is a direct correlation between high school dropout rates and homelessness in California. \n",
    "\n",
    "This could be due to many reasons including the following: \n",
    "\n",
    "Most jobs that provide sustainable incomes require at least a high school diploma or GED. \n",
    "Data exists that high school dropouts are more prone to poverty, crime, and drugs. These are also all patterns that are prevalent amongst homeless individuals as well. Thus, there is a high probability that there is an overlap in these populations. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets\n",
    "\n",
    "Finding consistent data for these variables was extremely difficult. Although there is substantial data for each state, there is very limited data that is more specific to each county in California. Thus, it was hard to find data that matched up county-wise between drop out rates and homelessness rates. We tried to work around this finding by presenting the data as is in the single variate analysis, but when comparing the two data sets bivarateily, for the homelessness rates, in specific, we made our own data frame by manually collecting homeless rates across each county and inputting it into a data frame. We kept the drop out rate data consistent in each analysis. \n",
    "\n",
    "\n",
    "Dataset #1: Homelessness: Homelessness in the United States, 2007 to 2016; https://www.kaggle.com/adamschroeder/homelessness; Number of Observations: 8,096; This data set was obtained through Kaggle from the United States Department of Housing and Urban Development. This data set contains Point-in-Time estimates of homelessness nationally from 2007 to 2016. \n",
    "\n",
    "Dataset #2:\n",
    "\tKids Not Completing High School\n",
    "\n",
    "\thttps://www.kidsdata.org/topic/105/highschooldropouts/table#fmt=598&loc=2,127,347,1763,331,348,336,171,321,345,357,332,324,369,358,362,360,337,327,364,356,217,353,328,354,323,352,320,339,334,365,343,330,367,344,355,366,368,265,349,361,4,273,59,370,326,333,322,341,338,350,342,329,325,359,351,363,340,335&tf=84,79,73,67,64,46&sortType=asc\n",
    "\n",
    "Number of Observations: 59\n",
    "\n",
    "This data set was obtained through KidsData from the California Department of Education. This data set contains the number of students who did not complete high school per county in California starting from 2010 to 2015. \n",
    "\n",
    "\tDataset #3:\n",
    "\t\n",
    "\tCalifornia County Homeless\n",
    "\n",
    "\thttps://calmatters.org/articles/homeless-in-california-what-the-data-reveals/ \n",
    "\t\n",
    "\tNumber of Observations: 59\n",
    "\n",
    "\tThis data set was obtained through Cal Matters. This data set is a map that\n",
    "\tcontains the number of homeless people per population in the counties of\n",
    "\tCalifornia. \n",
    "\n",
    "\t\n",
    "\n",
    "We plan to combine these data sets to determine whether there is a correlation between the two sets. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning, Analysis, and Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Homelessness Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 746,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imports \n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objs as go \n",
    "import plotly.plotly as py\n",
    "from scipy import stats\n",
    "py.sign_in('vraghven','14G4fvHnTTB0I5FjDyv9')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 747,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.figure_factory as ff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 748,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "\n",
       "    .dataframe tbody tr 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>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>CoC Number</th>\n",
       "      <th>CoC Name</th>\n",
       "      <th>Measures</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>AK</td>\n",
       "      <td>AK-500</td>\n",
       "      <td>Anchorage CoC</td>\n",
       "      <td>Chronically Homeless Individuals</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>AK</td>\n",
       "      <td>AK-500</td>\n",
       "      <td>Anchorage CoC</td>\n",
       "      <td>Homeless Individuals</td>\n",
       "      <td>696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>AK</td>\n",
       "      <td>AK-500</td>\n",
       "      <td>Anchorage CoC</td>\n",
       "      <td>Homeless People in Families</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>AK</td>\n",
       "      <td>AK-500</td>\n",
       "      <td>Anchorage CoC</td>\n",
       "      <td>Sheltered Chronically Homeless Individuals</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>AK</td>\n",
       "      <td>AK-500</td>\n",
       "      <td>Anchorage CoC</td>\n",
       "      <td>Sheltered Homeless</td>\n",
       "      <td>842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year State CoC Number       CoC Name  \\\n",
       "0  1/1/2007    AK     AK-500  Anchorage CoC   \n",
       "1  1/1/2007    AK     AK-500  Anchorage CoC   \n",
       "2  1/1/2007    AK     AK-500  Anchorage CoC   \n",
       "3  1/1/2007    AK     AK-500  Anchorage CoC   \n",
       "4  1/1/2007    AK     AK-500  Anchorage CoC   \n",
       "\n",
       "                                     Measures Count  \n",
       "0            Chronically Homeless Individuals   224  \n",
       "1                        Homeless Individuals   696  \n",
       "2                 Homeless People in Families   278  \n",
       "3  Sheltered Chronically Homeless Individuals   187  \n",
       "4                          Sheltered Homeless   842  "
      ]
     },
     "execution_count": 748,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing data \n",
    "data = pd.read_csv('2007-2016-Homelessnewss-USA.csv')\n",
    "ogdata = pd.read_csv('2007-2016-Homelessnewss-USA.csv')\n",
    "ogdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 749,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>CoC Number</th>\n",
       "      <th>CoC Name</th>\n",
       "      <th>Measures</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>AK</td>\n",
       "      <td>AK-500</td>\n",
       "      <td>Anchorage CoC</td>\n",
       "      <td>Chronically Homeless Individuals</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>AK</td>\n",
       "      <td>AK-500</td>\n",
       "      <td>Anchorage CoC</td>\n",
       "      <td>Homeless Individuals</td>\n",
       "      <td>696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>AK</td>\n",
       "      <td>AK-500</td>\n",
       "      <td>Anchorage CoC</td>\n",
       "      <td>Homeless People in Families</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>AK</td>\n",
       "      <td>AK-500</td>\n",
       "      <td>Anchorage CoC</td>\n",
       "      <td>Sheltered Chronically Homeless Individuals</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>AK</td>\n",
       "      <td>AK-500</td>\n",
       "      <td>Anchorage CoC</td>\n",
       "      <td>Sheltered Homeless</td>\n",
       "      <td>842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year State CoC Number       CoC Name  \\\n",
       "0  1/1/2007    AK     AK-500  Anchorage CoC   \n",
       "1  1/1/2007    AK     AK-500  Anchorage CoC   \n",
       "2  1/1/2007    AK     AK-500  Anchorage CoC   \n",
       "3  1/1/2007    AK     AK-500  Anchorage CoC   \n",
       "4  1/1/2007    AK     AK-500  Anchorage CoC   \n",
       "\n",
       "                                     Measures Count  \n",
       "0            Chronically Homeless Individuals   224  \n",
       "1                        Homeless Individuals   696  \n",
       "2                 Homeless People in Families   278  \n",
       "3  Sheltered Chronically Homeless Individuals   187  \n",
       "4                          Sheltered Homeless   842  "
      ]
     },
     "execution_count": 749,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 750,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86529, 6)"
      ]
     },
     "execution_count": 750,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#size of first dataset\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 751,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 86529 entries, 0 to 86528\n",
      "Data columns (total 6 columns):\n",
      "Year          86529 non-null object\n",
      "State         86529 non-null object\n",
      "CoC Number    86529 non-null object\n",
      "CoC Name      86529 non-null object\n",
      "Measures      86529 non-null object\n",
      "Count         86529 non-null object\n",
      "dtypes: object(6)\n",
      "memory usage: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>CoC Number</th>\n",
       "      <th>CoC Name</th>\n",
       "      <th>Measures</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>CA-500</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Chronically Homeless Individuals</td>\n",
       "      <td>1,757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>CA-500</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Homeless Individuals</td>\n",
       "      <td>5,945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>CA-500</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Homeless People in Families</td>\n",
       "      <td>1,257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>CA-500</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Sheltered Chronically Homeless Individuals</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>CA-500</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Sheltered Homeless</td>\n",
       "      <td>2,101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year State CoC Number                                CoC Name  \\\n",
       "264  1/1/2007    CA     CA-500  San Jose/Santa Clara City & County CoC   \n",
       "265  1/1/2007    CA     CA-500  San Jose/Santa Clara City & County CoC   \n",
       "266  1/1/2007    CA     CA-500  San Jose/Santa Clara City & County CoC   \n",
       "267  1/1/2007    CA     CA-500  San Jose/Santa Clara City & County CoC   \n",
       "268  1/1/2007    CA     CA-500  San Jose/Santa Clara City & County CoC   \n",
       "\n",
       "                                       Measures  Count  \n",
       "264            Chronically Homeless Individuals  1,757  \n",
       "265                        Homeless Individuals  5,945  \n",
       "266                 Homeless People in Families  1,257  \n",
       "267  Sheltered Chronically Homeless Individuals    223  \n",
       "268                          Sheltered Homeless  2,101  "
      ]
     },
     "execution_count": 752,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filtering data by CA data only\n",
    "is_CA =  data['State']=='CA'\n",
    "data = data[is_CA]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 753,
   "metadata": {},
   "outputs": [],
   "source": [
    "#renaming columns\n",
    "data = data.rename(index=str, columns={\"CoC Name\": \"Cont. of Care Name\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping columns\n",
    "data = data.drop(['CoC Number'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 755,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'State', 'Cont. of Care Name', 'Measures', 'Count'], dtype='object')"
      ]
     },
     "execution_count": 755,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>Cont. of Care Name</th>\n",
       "      <th>Measures</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Chronically Homeless Individuals</td>\n",
       "      <td>1,757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Homeless Individuals</td>\n",
       "      <td>5,945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Homeless People in Families</td>\n",
       "      <td>1,257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Sheltered Chronically Homeless Individuals</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Sheltered Homeless</td>\n",
       "      <td>2,101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year State                      Cont. of Care Name  \\\n",
       "264  1/1/2007    CA  San Jose/Santa Clara City & County CoC   \n",
       "265  1/1/2007    CA  San Jose/Santa Clara City & County CoC   \n",
       "266  1/1/2007    CA  San Jose/Santa Clara City & County CoC   \n",
       "267  1/1/2007    CA  San Jose/Santa Clara City & County CoC   \n",
       "268  1/1/2007    CA  San Jose/Santa Clara City & County CoC   \n",
       "\n",
       "                                       Measures  Count  \n",
       "264            Chronically Homeless Individuals  1,757  \n",
       "265                        Homeless Individuals  5,945  \n",
       "266                 Homeless People in Families  1,257  \n",
       "267  Sheltered Chronically Homeless Individuals    223  \n",
       "268                          Sheltered Homeless  2,101  "
      ]
     },
     "execution_count": 756,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>Cont. of Care Name</th>\n",
       "      <th>Measures</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>7,202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>5,703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Oakland/Alameda County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>4,838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Sacramento City &amp; County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>2,452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Santa Rosa/Petaluma/Sonoma County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>1,314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year State                      Cont. of Care Name        Measures  \\\n",
       "271  1/1/2007    CA  San Jose/Santa Clara City & County CoC  Total Homeless   \n",
       "283  1/1/2007    CA                       San Francisco CoC  Total Homeless   \n",
       "295  1/1/2007    CA              Oakland/Alameda County CoC  Total Homeless   \n",
       "307  1/1/2007    CA            Sacramento City & County CoC  Total Homeless   \n",
       "319  1/1/2007    CA   Santa Rosa/Petaluma/Sonoma County CoC  Total Homeless   \n",
       "\n",
       "     Count  \n",
       "271  7,202  \n",
       "283  5,703  \n",
       "295  4,838  \n",
       "307  2,452  \n",
       "319  1,314  "
      ]
     },
     "execution_count": 757,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filtering measure of homelessness\n",
    "data_total =  data[data['Measures']== 'Total Homeless']\n",
    "data_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 758,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>Cont. of Care Name</th>\n",
       "      <th>Measures</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>7,202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>5,703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Oakland/Alameda County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>4,838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Sacramento City &amp; County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>2,452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Santa Rosa/Petaluma/Sonoma County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>1,314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year State                      Cont. of Care Name        Measures  \\\n",
       "271  1/1/2007    CA  San Jose/Santa Clara City & County CoC  Total Homeless   \n",
       "283  1/1/2007    CA                       San Francisco CoC  Total Homeless   \n",
       "295  1/1/2007    CA              Oakland/Alameda County CoC  Total Homeless   \n",
       "307  1/1/2007    CA            Sacramento City & County CoC  Total Homeless   \n",
       "319  1/1/2007    CA   Santa Rosa/Petaluma/Sonoma County CoC  Total Homeless   \n",
       "\n",
       "     Count  \n",
       "271  7,202  \n",
       "283  5,703  \n",
       "295  4,838  \n",
       "307  2,452  \n",
       "319  1,314  "
      ]
     },
     "execution_count": 758,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 759,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>Cont. of Care Name</th>\n",
       "      <th>Measures</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>7202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>5703.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Oakland/Alameda County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>4838.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Sacramento City &amp; County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>2452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Santa Rosa/Petaluma/Sonoma County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>1314.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year State                      Cont. of Care Name        Measures  \\\n",
       "271  1/1/2007    CA  San Jose/Santa Clara City & County CoC  Total Homeless   \n",
       "283  1/1/2007    CA                       San Francisco CoC  Total Homeless   \n",
       "295  1/1/2007    CA              Oakland/Alameda County CoC  Total Homeless   \n",
       "307  1/1/2007    CA            Sacramento City & County CoC  Total Homeless   \n",
       "319  1/1/2007    CA   Santa Rosa/Petaluma/Sonoma County CoC  Total Homeless   \n",
       "\n",
       "      Count  \n",
       "271  7202.0  \n",
       "283  5703.0  \n",
       "295  4838.0  \n",
       "307  2452.0  \n",
       "319  1314.0  "
      ]
     },
     "execution_count": 759,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#type casting count to float and taking out commas\n",
    "data_total['Count'] = data_total['Count'].str.replace(\",\",\"\").astype(float)\n",
    "data_total.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 760,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NCm5L2hM4AdgjIrYEtgE+qqPPkcDuQI+I6Eq6F2rMgs3MzMzMzMxs2eTgtpk1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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2aa125c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## creating a bar chart that displays the total \n",
    "#homeless population per county in California\n",
    "data_total['Count'] = data_total['Count'].astype('int64')\n",
    "f,ax = plt.subplots(figsize=(18,11))\n",
    "sns.barplot(x='Count',y='Cont. of Care Name',data=data_total,lw =2.5);\n",
    "ax.set(ylabel='County', xlabel= 'Total Homeless Population Count per County',);\n",
    "plt.title('Total Homeless Population Count',fontsize=(18));\n",
    "plt.xticks(major_ticks);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis\n",
    "Homelessness Plot:\n",
    "This bar graph displays the rates of the average homeless population over the years of 2010 - 2015 in each Californian county. We observe from this data that: \n",
    "\n",
    "LA has the most homeless people (expected)\n",
    "\n",
    "SD county and San Jose/Santa Clara have the same amount of homelessness (surprising)\n",
    "\n",
    "Urban counties have more homlessness compared with Rural counties (expected)\n",
    "\n",
    "LA county also has the most high school dropouts compared with other counties\n",
    "\n",
    "Total population of each county is not accounted for\n",
    "\n",
    "El Dorado homelessness & drop-outs are both very very low\n",
    "\n",
    "\n",
    "Based on the Homelessness bar graph, Los Angeles County has the highest population of homeless people. However, based on the huge population of Los Angeles County it’s not a huge percentage. We were surprised to see that San Diego County and Santa Clara County have the same amount of homeless people. One important thing that we noticed was that urban counties had a much higher number of homeless people when compared to rural counties. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropout Rate Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "\n",
    "dropout = pd.read_csv('Kidsdata-Students-Not-Completing-High-School-2010-2015.csv') \n",
    "dropout = dropout.dropna(axis = 1, how = 'all')\n",
    "\n",
    "dropout = dropout.dropna(axis = 0, how = 'all')\n",
    "dropout = dropout.iloc[:-4]\n",
    "dropout = dropout.fillna(0)\n",
    "dropout = dropout.rename(index=str, \n",
    "                         columns={\"Unnamed: 0\": \"Location\", \n",
    "                                  \"Unnamed: 1\": \"2010\", \n",
    "                                  \"Unnamed: 2\": \"2011\",\n",
    "                                  \"Unnamed: 3\": \"2012\",\n",
    "                                  \"Unnamed: 4\": \"2013\", \n",
    "                                  \"Unnamed: 5\": \"2014\",\n",
    "                                  \"Unnamed: 6\": \"2015\" })\n",
    "dropout = dropout.iloc[3:]\n",
    "\n",
    "\n",
    "##replacing unnecesarry characters in the datasets\n",
    "\n",
    "dropout[\"2010\"] = dropout[\"2010\"].str.replace(\",\",\"\")\n",
    "dropout[\"2011\"] = dropout[\"2011\"].str.replace(\",\",\"\")\n",
    "dropout[\"2012\"] = dropout[\"2012\"].str.replace(\",\",\"\")\n",
    "dropout[\"2013\"] = dropout[\"2013\"].str.replace(\",\",\"\")\n",
    "dropout[\"2014\"] = dropout[\"2014\"].str.replace(\",\",\"\")\n",
    "dropout[\"2015\"] = dropout[\"2015\"].str.replace(\",\",\"\")\n",
    "dropout[\"2010\"] = dropout[\"2010\"].str.replace(\"%\",\"\")\n",
    "dropout[\"2011\"] = dropout[\"2011\"].str.replace(\"%\",\"\")\n",
    "dropout[\"2012\"] = dropout[\"2012\"].str.replace(\"%\",\"\")\n",
    "dropout[\"2013\"] = dropout[\"2013\"].str.replace(\"%\",\"\")\n",
    "dropout[\"2014\"] = dropout[\"2014\"].str.replace(\"%\",\"\")\n",
    "dropout[\"2015\"] = dropout[\"2015\"].str.replace(\"%\",\"\")\n",
    "\n",
    "dropout['2010'] = pd.to_numeric(dropout['2010'],errors='coerce')\n",
    "dropout['2011'] = pd.to_numeric(dropout['2011'],errors='coerce')\n",
    "dropout['2012'] = pd.to_numeric(dropout['2012'],errors='coerce')\n",
    "dropout['2013'] = pd.to_numeric(dropout['2013'],errors='coerce')\n",
    "dropout['2014'] = pd.to_numeric(dropout['2014'],errors='coerce')\n",
    "dropout['2015'] = pd.to_numeric(dropout['2015'],errors='coerce')\n",
    "\n",
    "\n",
    "\n",
    "#take average of each row \n",
    "dropout['average'] = dropout.sum(axis=1)\n",
    "\n",
    "dropout = dropout.iloc[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>3047.0</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>1614.0</td>\n",
       "      <td>13180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Alpine County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Amador County</td>\n",
       "      <td>34.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Butte County</td>\n",
       "      <td>395.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>1681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Calaveras County</td>\n",
       "      <td>42.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>186.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Location    2010    2011    2012    2013    2014    2015  average\n",
       "11    Alameda County  3047.0  2567.0  2250.0  1924.0  1778.0  1614.0  13180.0\n",
       "12     Alpine County     NaN     NaN     NaN     NaN     NaN     NaN      0.0\n",
       "13     Amador County    34.0    48.0    27.0    44.0    36.0    31.0    220.0\n",
       "14      Butte County   395.0   291.0   278.0   245.0   256.0   216.0   1681.0\n",
       "15  Calaveras County    42.0    35.0    29.0    31.0    29.0    20.0    186.0"
      ]
     },
     "execution_count": 762,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropout.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization of Dropout Rates "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CgUAgEAgEAnMFMRAVCAQCgUAgEAgEAoFAIBCYK4hk5YFAIBAIBAKB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dXMCZjZF/ANmv9laBd6XzNbRtLBfRzXQsBngf/G3eDe2KCs/2Z8kt+phfvt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2bb85d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating a bar plot of average dropouts in California per county\n",
    "dropout.plot(kind='bar',x = 'Location', y = 'average', alpha=0.6, rot=90, \n",
    "             fontsize= 12, width=1, align='edge', figsize = (20,10), \n",
    "             title = 'Average droupout counts in each California County')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis of Dropout Rates \n",
    "High School Dropout Plot:\n",
    "\n",
    "Los Angeles County has the highest dropout rates\n",
    "\n",
    "Urban counties have higher dropout rates than\n",
    "\n",
    "Counties who dropout rates are zero: Alpine County, Mariposa County, Plumas County, Modoc County, Sierra County\n",
    "\n",
    "\n",
    "While observing this plot, we noticed that Los Angeles County has the highest high school dropout rates. This is most likely due to the fact that Los Angeles County has one of the highest county populations. We also noticed that throughout the plot, there is usually a higher number in high school dropout rates in urban counties compared to rural counties. This makes sense because there are higher populations in urban counties as compared to rural. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 764,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>Cont. of Care Name</th>\n",
       "      <th>Measures</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Jose/Santa Clara City &amp; County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>7202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>5703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Oakland/Alameda County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>4838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Sacramento City &amp; County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>2452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>1/1/2007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Santa Rosa/Petaluma/Sonoma County CoC</td>\n",
       "      <td>Total Homeless</td>\n",
       "      <td>1314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year State                      Cont. of Care Name        Measures  \\\n",
       "271  1/1/2007    CA  San Jose/Santa Clara City & County CoC  Total Homeless   \n",
       "283  1/1/2007    CA                       San Francisco CoC  Total Homeless   \n",
       "295  1/1/2007    CA              Oakland/Alameda County CoC  Total Homeless   \n",
       "307  1/1/2007    CA            Sacramento City & County CoC  Total Homeless   \n",
       "319  1/1/2007    CA   Santa Rosa/Petaluma/Sonoma County CoC  Total Homeless   \n",
       "\n",
       "     Count  \n",
       "271   7202  \n",
       "283   5703  \n",
       "295   4838  \n",
       "307   2452  \n",
       "319   1314  "
      ]
     },
     "execution_count": 764,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 765,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>3047.0</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>1614.0</td>\n",
       "      <td>13180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Alpine County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Amador County</td>\n",
       "      <td>34.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Butte County</td>\n",
       "      <td>395.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>1681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Calaveras County</td>\n",
       "      <td>42.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>186.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Location    2010    2011    2012    2013    2014    2015  average\n",
       "11    Alameda County  3047.0  2567.0  2250.0  1924.0  1778.0  1614.0  13180.0\n",
       "12     Alpine County     NaN     NaN     NaN     NaN     NaN     NaN      0.0\n",
       "13     Amador County    34.0    48.0    27.0    44.0    36.0    31.0    220.0\n",
       "14      Butte County   395.0   291.0   278.0   245.0   256.0   216.0   1681.0\n",
       "15  Calaveras County    42.0    35.0    29.0    31.0    29.0    20.0    186.0"
      ]
     },
     "execution_count": 765,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropout.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 766,
   "metadata": {},
   "outputs": [],
   "source": [
    "dropout[\"Location\"] = dropout[\"Location\"].str.replace(\"County\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 767,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alameda</td>\n",
       "      <td>3047.0</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>1614.0</td>\n",
       "      <td>13180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Alpine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Amador</td>\n",
       "      <td>34.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Butte</td>\n",
       "      <td>395.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>1681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Calaveras</td>\n",
       "      <td>42.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>186.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Location    2010    2011    2012    2013    2014    2015  average\n",
       "11    Alameda   3047.0  2567.0  2250.0  1924.0  1778.0  1614.0  13180.0\n",
       "12     Alpine      NaN     NaN     NaN     NaN     NaN     NaN      0.0\n",
       "13     Amador     34.0    48.0    27.0    44.0    36.0    31.0    220.0\n",
       "14      Butte    395.0   291.0   278.0   245.0   256.0   216.0   1681.0\n",
       "15  Calaveras     42.0    35.0    29.0    31.0    29.0    20.0    186.0"
      ]
     },
     "execution_count": 767,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropout.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 768,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_total = data_total.rename(index=str, columns={\"Cont. of Care Name\": \"County\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 769,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#replacing unneccasry characters\n",
    "data_total[\"County\"] = data_total[\"County\"].str.replace(\"CoC\",\"\")\n",
    "data_total[\"County\"] = data_total[\"County\"].str.replace(\"County\",\"\")\n",
    "data_total[\"County\"] = data_total[\"County\"].str.replace(\"City\",\"\")\n",
    "data_total[\"County\"] = data_total[\"County\"].str.replace(\"&\",\"\")\n",
    "data_total[\"County\"] = data_total[\"County\"].str.replace(\"and\",\"\")\n",
    "data_total[\"County\"] = data_total[\"County\"].str.replace(\"/\",\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 770,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unique_counties = list(dropout.Location)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging Datasets -- Dropout and Homelessness Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 771,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 771,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(unique_counties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 772,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location     object\n",
       "2010        float64\n",
       "2011        float64\n",
       "2012        float64\n",
       "2013        float64\n",
       "2014        float64\n",
       "2015        float64\n",
       "average     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 772,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new data frame containing number of homeless people per county\n",
    "#did not need to be cleaned because manually did it \n",
    "# gathered data from link provided above in intro & backbround portion\n",
    "stateData = {\n",
    "    'County': ['Statewide', 'Alameda', 'Alpine', 'Amador', 'Butte', \n",
    "               'Calaveras', 'Colusa',\n",
    "               'Contra Costa', 'Del Norte', 'El Dorado', \n",
    "               'Fresno', 'Glenn','Humboldt',\n",
    "               'Imperial','Inyo','Kern','Kings','Lake',\n",
    "               'Lassen','Los Angeles','Madera',\n",
    "               'Marin','Mariposa','Mendocino','Merced',\n",
    "               'Modoc','Mono','Monterey','Napa',\n",
    "               'Nevada','Orange', 'Placer','Plumas',\n",
    "               'Riverside','Sacramento','San Benito',\n",
    "               'San Bernardino', 'San Diego','San Francisco',\n",
    "               'San Joaquin','San Luis Obispo',\n",
    "               'San Mateo','Santa Barbara', 'Santa Clara',\n",
    "               'Santa Cruz','Shasta','Sierra',\n",
    "               'Siskiyou', 'Solano','Sonoma','Stanislaus',\n",
    "               'Sutter','Tehama','Trinity','Tulare',\n",
    "               'Tuolumne','Ventura','Yolo', 'Yuba'],\n",
    "    'Poulation': [39523613, 1645359, 1151, 38382, 226404, 45168, 22043, \n",
    "                  1139513, 27124, 185062, \n",
    "                  995975, 28731, 136953, 188334, 18619, 895112, \n",
    "                  149537, 64945, 30918, 10241279,\n",
    "                  156492, 263604, 18148, 89134, 274665, 9580, 13713, \n",
    "                  442365, 142408, 98828, \n",
    "                  3194024, 382837, 19819, 2384783, 1514770, 56854, \n",
    "                  2160256, 3316192, 874228, \n",
    "                  746868, 280101, 770203, 450663, 1938180, 276603, \n",
    "                  178605, 3207, 44688, 436023, \n",
    "                  505120, 548057, 96956, 63995, 13628, 471842, 54707, \n",
    "                  857386, 218896, 74577],\n",
    "    'Homeless':[129972, 5629, 157, 150, 1900, 221, 268, 2295, 63, 600,\n",
    "                2016, 92, 1470, 1413, 141,\n",
    "                810, 11199, 1300, 100, 57794, 260, 1117, 367, 1238,\n",
    "                454, 152, 63, 2800, 315,\n",
    "                238, 6860, 979, 134, 2406, 3665, 3364, 1866, \n",
    "                9160, 6858, 1542, 1125, 1253,\n",
    "                1860, 7394, 2249, 757, 152, 262, 1232, 2657, \n",
    "                1661, 760, 281, 176, 115, \n",
    "                700, 1152, 459, 512]\n",
    "}\n",
    "df = pd.DataFrame(stateData)\n",
    "dropout.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 773,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merging homeless population with high school dropout dataframe\n",
    "dropout['Homeless'] = [5629, 157, 150, 1900, 221, 268, 2295, 63, 600,\n",
    "                       2016, 92, 1470, 1413, \n",
    "                       141, 810, 11199, 1300, 100, 57794, 260,\n",
    "                       1117, 367, 1238, 454, 152, 63, \n",
    "                       2800, 315, 238, 6860, 979, 134, 2406, 3665, \n",
    "                       3364, 1866, 9160, 6858, 1542, \n",
    "                       1125, 1253, 1860, 7394, 2249, 757, 152, 262, \n",
    "                       1232, 2657, 1661, 760, 281, \n",
    "                       176, 115, 700, 1152, 459, 512]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 774,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>average</th>\n",
       "      <th>Homeless</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alameda</td>\n",
       "      <td>3047.0</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>1614.0</td>\n",
       "      <td>13180.0</td>\n",
       "      <td>5629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Alpine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Amador</td>\n",
       "      <td>34.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Butte</td>\n",
       "      <td>395.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>1681.0</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Calaveras</td>\n",
       "      <td>42.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Colusa</td>\n",
       "      <td>79.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Contra Costa</td>\n",
       "      <td>1826.0</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>1066.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>7467.0</td>\n",
       "      <td>2295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Del Norte</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>El Dorado</td>\n",
       "      <td>256.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Fresno</td>\n",
       "      <td>3276.0</td>\n",
       "      <td>2578.0</td>\n",
       "      <td>2222.0</td>\n",
       "      <td>2081.0</td>\n",
       "      <td>1929.0</td>\n",
       "      <td>1631.0</td>\n",
       "      <td>13717.0</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Glenn</td>\n",
       "      <td>67.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Humboldt</td>\n",
       "      <td>195.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>856.0</td>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Imperial</td>\n",
       "      <td>360.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>1576.0</td>\n",
       "      <td>1413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Inyo</td>\n",
       "      <td>259.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>398.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>503.0</td>\n",
       "      <td>2531.0</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Kern</td>\n",
       "      <td>2453.0</td>\n",
       "      <td>2282.0</td>\n",
       "      <td>2274.0</td>\n",
       "      <td>2226.0</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>1613.0</td>\n",
       "      <td>12759.0</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Kings</td>\n",
       "      <td>432.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>2157.0</td>\n",
       "      <td>11199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Lake</td>\n",
       "      <td>126.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Lassen</td>\n",
       "      <td>99.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>24747.0</td>\n",
       "      <td>21490.0</td>\n",
       "      <td>19443.0</td>\n",
       "      <td>16090.0</td>\n",
       "      <td>15920.0</td>\n",
       "      <td>15572.0</td>\n",
       "      <td>113262.0</td>\n",
       "      <td>57794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Madera</td>\n",
       "      <td>352.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>1838.0</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Marin</td>\n",
       "      <td>146.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>813.0</td>\n",
       "      <td>1117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Mariposa</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Mendocino</td>\n",
       "      <td>193.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>784.0</td>\n",
       "      <td>1238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Merced</td>\n",
       "      <td>731.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>2846.0</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Modoc</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Mono</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Monterey</td>\n",
       "      <td>847.0</td>\n",
       "      <td>717.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>3663.0</td>\n",
       "      <td>2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Napa</td>\n",
       "      <td>271.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>4607.0</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Orange</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>4021.0</td>\n",
       "      <td>3819.0</td>\n",
       "      <td>3049.0</td>\n",
       "      <td>2754.0</td>\n",
       "      <td>2311.0</td>\n",
       "      <td>21154.0</td>\n",
       "      <td>6860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Placer</td>\n",
       "      <td>484.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Plumas</td>\n",
       "      <td>34.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>4960.0</td>\n",
       "      <td>4488.0</td>\n",
       "      <td>3712.0</td>\n",
       "      <td>3148.0</td>\n",
       "      <td>3089.0</td>\n",
       "      <td>2496.0</td>\n",
       "      <td>21893.0</td>\n",
       "      <td>2406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>3696.0</td>\n",
       "      <td>3176.0</td>\n",
       "      <td>2591.0</td>\n",
       "      <td>2123.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>1899.0</td>\n",
       "      <td>15451.0</td>\n",
       "      <td>3665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>San Benito</td>\n",
       "      <td>93.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>529.0</td>\n",
       "      <td>3364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>San Bernardino</td>\n",
       "      <td>6406.0</td>\n",
       "      <td>5492.0</td>\n",
       "      <td>4510.0</td>\n",
       "      <td>4042.0</td>\n",
       "      <td>4066.0</td>\n",
       "      <td>3740.0</td>\n",
       "      <td>28256.0</td>\n",
       "      <td>1866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>San Diego</td>\n",
       "      <td>5421.0</td>\n",
       "      <td>4472.0</td>\n",
       "      <td>4049.0</td>\n",
       "      <td>3651.0</td>\n",
       "      <td>3899.0</td>\n",
       "      <td>3269.0</td>\n",
       "      <td>24761.0</td>\n",
       "      <td>9160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>953.0</td>\n",
       "      <td>2836.0</td>\n",
       "      <td>2467.0</td>\n",
       "      <td>2290.0</td>\n",
       "      <td>3577.0</td>\n",
       "      <td>894.0</td>\n",
       "      <td>13017.0</td>\n",
       "      <td>6858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>San Joaquin</td>\n",
       "      <td>2370.0</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>9701.0</td>\n",
       "      <td>1542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>San Luis Obispo</td>\n",
       "      <td>307.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>1125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>San Mateo</td>\n",
       "      <td>822.0</td>\n",
       "      <td>694.0</td>\n",
       "      <td>688.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>3561.0</td>\n",
       "      <td>1253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Santa Barbara</td>\n",
       "      <td>678.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>538.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>3275.0</td>\n",
       "      <td>1860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>2779.0</td>\n",
       "      <td>2836.0</td>\n",
       "      <td>2433.0</td>\n",
       "      <td>2224.0</td>\n",
       "      <td>2248.0</td>\n",
       "      <td>2290.0</td>\n",
       "      <td>14810.0</td>\n",
       "      <td>7394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Santa Cruz</td>\n",
       "      <td>355.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>1855.0</td>\n",
       "      <td>2249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Shasta</td>\n",
       "      <td>305.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Sierra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Siskiyou</td>\n",
       "      <td>52.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Solano</td>\n",
       "      <td>1259.0</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>851.0</td>\n",
       "      <td>688.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>4871.0</td>\n",
       "      <td>1232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Sonoma</td>\n",
       "      <td>941.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>4271.0</td>\n",
       "      <td>2657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Stanislaus</td>\n",
       "      <td>1697.0</td>\n",
       "      <td>1413.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>934.0</td>\n",
       "      <td>958.0</td>\n",
       "      <td>775.0</td>\n",
       "      <td>6951.0</td>\n",
       "      <td>1661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Sutter</td>\n",
       "      <td>288.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Tehama</td>\n",
       "      <td>157.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>671.0</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Trinity</td>\n",
       "      <td>21.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Tulare</td>\n",
       "      <td>1364.0</td>\n",
       "      <td>1118.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>901.0</td>\n",
       "      <td>908.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>6030.0</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Tuolumne</td>\n",
       "      <td>61.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Ventura</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>1345.0</td>\n",
       "      <td>1195.0</td>\n",
       "      <td>1215.0</td>\n",
       "      <td>1033.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>7221.0</td>\n",
       "      <td>1152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Yolo</td>\n",
       "      <td>378.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Yuba</td>\n",
       "      <td>304.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Location     2010     2011     2012     2013     2014     2015  \\\n",
       "11          Alameda    3047.0   2567.0   2250.0   1924.0   1778.0   1614.0   \n",
       "12           Alpine       NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "13           Amador      34.0     48.0     27.0     44.0     36.0     31.0   \n",
       "14            Butte     395.0    291.0    278.0    245.0    256.0    216.0   \n",
       "15        Calaveras      42.0     35.0     29.0     31.0     29.0     20.0   \n",
       "16           Colusa      79.0     62.0     45.0     41.0     53.0     20.0   \n",
       "17     Contra Costa    1826.0   1390.0   1330.0   1086.0   1066.0    769.0   \n",
       "18        Del Norte      70.0     60.0     34.0     37.0     42.0     73.0   \n",
       "19        El Dorado     256.0    184.0    138.0    148.0    140.0    112.0   \n",
       "20           Fresno    3276.0   2578.0   2222.0   2081.0   1929.0   1631.0   \n",
       "21            Glenn      67.0     62.0     58.0     59.0     46.0     83.0   \n",
       "22         Humboldt     195.0    213.0    167.0    168.0      NaN    113.0   \n",
       "23         Imperial     360.0    250.0    257.0    238.0    258.0    213.0   \n",
       "24             Inyo     259.0    369.0    484.0    398.0    518.0    503.0   \n",
       "25             Kern    2453.0   2282.0   2274.0   2226.0   1911.0   1613.0   \n",
       "26            Kings     432.0    431.0    379.0    341.0    300.0    274.0   \n",
       "27             Lake     126.0     94.0     59.0     76.0     92.0     70.0   \n",
       "28           Lassen      99.0     75.0     86.0     65.0     46.0     48.0   \n",
       "29      Los Angeles   24747.0  21490.0  19443.0  16090.0  15920.0  15572.0   \n",
       "30           Madera     352.0    311.0    294.0    325.0    311.0    245.0   \n",
       "31            Marin     146.0    146.0    138.0    113.0    149.0    121.0   \n",
       "32         Mariposa      18.0     12.0     16.0     15.0      NaN      NaN   \n",
       "33        Mendocino     193.0    132.0    108.0    105.0    150.0     96.0   \n",
       "34           Merced     731.0    443.0    440.0    424.0    444.0    364.0   \n",
       "35            Modoc      16.0     14.0     14.0     11.0      NaN      NaN   \n",
       "36             Mono      27.0     14.0     38.0    100.0     87.0    172.0   \n",
       "37         Monterey     847.0    717.0    594.0    518.0    651.0    336.0   \n",
       "38             Napa     271.0    213.0    160.0    181.0    165.0    104.0   \n",
       "39           Nevada    1010.0    952.0    673.0    646.0    663.0    663.0   \n",
       "40           Orange    5200.0   4021.0   3819.0   3049.0   2754.0   2311.0   \n",
       "41           Placer     484.0    323.0    322.0    307.0    274.0    276.0   \n",
       "42           Plumas      34.0     25.0     28.0     13.0     19.0     21.0   \n",
       "43        Riverside    4960.0   4488.0   3712.0   3148.0   3089.0   2496.0   \n",
       "44       Sacramento    3696.0   3176.0   2591.0   2123.0   1966.0   1899.0   \n",
       "45       San Benito      93.0     96.0     79.0     86.0     96.0     79.0   \n",
       "46   San Bernardino    6406.0   5492.0   4510.0   4042.0   4066.0   3740.0   \n",
       "47        San Diego    5421.0   4472.0   4049.0   3651.0   3899.0   3269.0   \n",
       "48    San Francisco     953.0   2836.0   2467.0   2290.0   3577.0    894.0   \n",
       "49      San Joaquin    2370.0   1973.0   1600.0   1248.0   1390.0   1120.0   \n",
       "50  San Luis Obispo     307.0    242.0    234.0    207.0    187.0    147.0   \n",
       "51        San Mateo     822.0    694.0    688.0    471.0    448.0    438.0   \n",
       "52    Santa Barbara     678.0    621.0    525.0    538.0    549.0    364.0   \n",
       "53      Santa Clara    2779.0   2836.0   2433.0   2224.0   2248.0   2290.0   \n",
       "54       Santa Cruz     355.0    382.0    338.0    284.0    276.0    220.0   \n",
       "55           Shasta     305.0    268.0    240.0    182.0    224.0    229.0   \n",
       "56           Sierra       NaN      NaN      NaN      NaN      NaN     22.0   \n",
       "57         Siskiyou      52.0     40.0     34.0     28.0     25.0     21.0   \n",
       "58           Solano    1259.0   1063.0    851.0    688.0    532.0    478.0   \n",
       "59           Sonoma     941.0    828.0    696.0    608.0    642.0    556.0   \n",
       "60       Stanislaus    1697.0   1413.0   1174.0    934.0    958.0    775.0   \n",
       "61           Sutter     288.0    209.0    184.0    153.0    168.0    146.0   \n",
       "62           Tehama     157.0    138.0     99.0    119.0    100.0     58.0   \n",
       "63          Trinity      21.0     11.0      NaN      NaN      NaN      NaN   \n",
       "64           Tulare    1364.0   1118.0   1030.0    901.0    908.0    709.0   \n",
       "65         Tuolumne      61.0     57.0     66.0     82.0     69.0     40.0   \n",
       "66          Ventura    1535.0   1345.0   1195.0   1215.0   1033.0    898.0   \n",
       "67             Yolo     378.0    309.0    238.0    213.0    202.0    176.0   \n",
       "68             Yuba     304.0    187.0    184.0    169.0    143.0    141.0   \n",
       "\n",
       "     average  Homeless  \n",
       "11   13180.0      5629  \n",
       "12       0.0       157  \n",
       "13     220.0       150  \n",
       "14    1681.0      1900  \n",
       "15     186.0       221  \n",
       "16     300.0       268  \n",
       "17    7467.0      2295  \n",
       "18     316.0        63  \n",
       "19     978.0       600  \n",
       "20   13717.0      2016  \n",
       "21     375.0        92  \n",
       "22     856.0      1470  \n",
       "23    1576.0      1413  \n",
       "24    2531.0       141  \n",
       "25   12759.0       810  \n",
       "26    2157.0     11199  \n",
       "27     517.0      1300  \n",
       "28     419.0       100  \n",
       "29  113262.0     57794  \n",
       "30    1838.0       260  \n",
       "31     813.0      1117  \n",
       "32      61.0       367  \n",
       "33     784.0      1238  \n",
       "34    2846.0       454  \n",
       "35      55.0       152  \n",
       "36     438.0        63  \n",
       "37    3663.0      2800  \n",
       "38    1094.0       315  \n",
       "39    4607.0       238  \n",
       "40   21154.0      6860  \n",
       "41    1986.0       979  \n",
       "42     140.0       134  \n",
       "43   21893.0      2406  \n",
       "44   15451.0      3665  \n",
       "45     529.0      3364  \n",
       "46   28256.0      1866  \n",
       "47   24761.0      9160  \n",
       "48   13017.0      6858  \n",
       "49    9701.0      1542  \n",
       "50    1324.0      1125  \n",
       "51    3561.0      1253  \n",
       "52    3275.0      1860  \n",
       "53   14810.0      7394  \n",
       "54    1855.0      2249  \n",
       "55    1448.0       757  \n",
       "56      22.0       152  \n",
       "57     200.0       262  \n",
       "58    4871.0      1232  \n",
       "59    4271.0      2657  \n",
       "60    6951.0      1661  \n",
       "61    1148.0       760  \n",
       "62     671.0       281  \n",
       "63      32.0       176  \n",
       "64    6030.0       115  \n",
       "65     375.0       700  \n",
       "66    7221.0      1152  \n",
       "67    1516.0       459  \n",
       "68    1128.0       512  "
      ]
     },
     "execution_count": 774,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visulaization (Bivariate Analysis) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 775,
   "metadata": {},
   "outputs": [
    {
     "data": {
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IOS3Jbarqu2PFuRlDwdp/pr93n0jP54vpAwPHpg/qnZXkJlW1DKs5r6wcf63065WvJfngEhR4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\nXvgXsNA4xi9EqWQUU4ztW0d+lnQIpcLUraM8p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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2c5f4898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dropout.plot(x = 'Location', y =['average', 'Homeless'], \n",
    "             figsize = (60,60), \n",
    "             grid = True, kind ='bar', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 776,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WN8LGJdPxyNZIAAAYOEEYANXqG2CT0SiTUbE1EgAABk4QBkC1Zv2pkeOSyXi0fA4AAAyT\nIAyAajWLrZDTccmOcVmeIgkAAAyTIAyAavVbISejUSbj0fIUSQAAYJgEYQBUq2+ATcYlk5FGGAAA\nDJ0gDIBqNctTI0eZjkeG5QMAwMAJwgCo1uqpkSWTcVnODAMAAIZJEAZAtfpTIqfjUSajUfY2GmEA\nADBkgjAAqrV6auQoOyYaYQAAMHSCMACq1TfCVoblmxEGAABDJwgDoFr9KZHT0SiTsVMjAQBg6ARh\nAFSrWdMIm45Gy1MkAQCAYRKEAVCt2WIm2GS8ODVSIwwAAAZNEAZAtfpG2HQ0ynQ8Ws4MAwAAhkkQ\nBkC1+gbYyrB8M8IAAGDoBGEAVGu2mAk2HY8ynZgRBgAAQycIA6Bay0bYqGS6aIR1nVYYAAAMlSAM\ngGr1M8LGo5LJeOVX3rwVhAEAwFAJwgCo1qztMh2XlLJyamSSNIIwAAAYLEEYANVq5m2miybYdLTy\n6ORIAAAYLkEYANWazbtMRitNsL4R5uRIAAAYLkEYANWarW2ELR4bjTAAABgsQRgA1Wrm3bIJNu0b\nYWaEAQDAYAnCAKjWrG0zWcwG6x81wgAAYLgEYQBUq5l3yyaYGWEAAIAgDIBqNW2byfoZYa1GGAAA\nDJUgDIBq7XNq5OJx1miEAQDAUAnCAKhWs59TI2caYQAAMFiCMACq1bRrT43sh+VrhAEAwFAJwgCo\n1mzeZtqfGrkIxJwaCQAAwyUIA6BazbzLdNI3whYzwlqNMAAAGCpBGADVmrVdJn0jbNRvjdQIAwCA\noRKEAVCtWdMum2D91siZGWEAADBYgjAAqtW07bIJtjw1UiMMAAAGSxAGQLWa+X5OjWwFYQAAMFSC\nMACqNWvbZQA2GdkaCQAAQycIA6BazbxbBmDLRpggDAAABksQBkC1ZvMuk74RttgiaWskAAAMlyAM\ngGo17eqpkdNRPyxfIwwAAIZKEAZAtVa2Ru7bCHNqJAAADJcgDIBqzearjbDl1khBGAAADJYgDIBq\nNW23HJJvayQAACAIA6BKbdtl3nbLJthoVDIeFcPyAQBgwARhAFRptgi8+kZYkkxGJY1GGAAADJYg\nDIAq9YHXZFSWr03HI1sjAQBgwARhAFRpGYStbYSNbY0EAIAhE4QBUKXVrZGrjbDJaJSZUyMBAGCw\nBGEAVGl1a+Tqr7od42JrJAAADJggDIAq9c2vydpG2HiURiMMAAAGSxAGQJWadqX5tc/WyHHJrNUI\nAwCAoRKEAVClvvm1dmvkdKQRBgAAQ7ahIKyU8pRSyhtLKR8rpXyrlNKVUt55iDVnl1L+rJRyYynl\n1lLKNaWUf1dKGR9kzRNKKbtLKTeVUm4upXyylPKMQ3zOM0opn1pcf9Ni/RMOcv14cR/XlFJuW9zf\nn5VSzj7ImjuVUv5zKeXzpZQ9pZR/LKX8YSnlIQe7NwCOnX4W2PpGWGNGGAAADNZGG2EvT3J+kjOS\n/N2hLi6lPCnJR5M8Jsl7krw5yY4kr09y8QHWnJ/kvUlOTfLOJL+b5AeSXFRKee0B1rw2yUVJvn9x\n/TuTnJbkvYv3W399WXz+6xf386bF/T0myUcX971+zXFJ/nuSVyT5VpL/luRDSX4uyVWllEcd6u8D\ngO++Znlq5Oqvusl4lL0aYQAAMFiTDV7375Ncn+RLSX4syUcOdGEp5a5ZCaXmSXZ1XXfV4vX/mOTy\nJE8ppTy167qL16y5X5LXJrkxySO6rrtu8foFST6d5IWllD/uuu7KNWvOTvLCJF9OcmbXdd9cvP6a\nJJ9J8tpSyvv691p4apKnJPl4knO7rtuzWPPWJH+V5HdLKZd3XfftNWtekOScJJcmOa/runax5pIk\nlyV5eynltP51AO4Y+kbYZLx2a6RGGAAADNmGGmFd132k67ovdl23kf96eEqSE5Nc3Idgi/fYk5Vm\nWZL82ro1z0pyXJI3rQ2uFuHWKxdP/826Nf3z/9KHYIs112WlgXZckl9Zt6b/3Jf3IdhizaeTXLK4\n76f0ry8aZP3nvGRt2NV13f+b5GNJHpqVcBCAO5D+1MjpaHVr5HQ8WjbFAACA4Tkaw/L/1eLxz/fz\ns48muTXJ2YsthxtZ84F112xpzeLzzl58/sc2+Dn/Msm/SPKFruu+uol7A+AYa/bTCJuMy7IpBgAA\nDM/RCMJOXjx+Yf0Puq5rknw1K1syH7DBNf87yS1JTiqlHJ8kpZQTktwnyc2Ln6/3xcXjg9e89sAk\n4yRfWdzHRtYc8L4OsgaAO4DZovk1GWuEAQAAK45GEHa3xeNNB/h5//rdt7Dmbusej8ZnHO4aAO4A\n+kbYdLSmEWZGGAAADNpGh+UfSf3/Nb+Z/xLZyprvxmccck0p5blJnpskJ554Ynbv3r2Jt4d63Xzz\nzb4PHFVX/8NK+ffqv/lMbvjSShj2zRv35KZvt3e4/+35PsAq3wdY4bsAq3wfOJKORhC2vr213l3X\nXdf/+70Wa244yJpvbfAz9tfk2up9bXbNPrque1uStyXJySef3O3atetAl8Kg7N69O74PHE3f+h9/\nn1z9NznrUWfmgd97lyTJe/7hb/J/9v7zHe5/e74PsMr3AVb4LsAq3weOpKOxNfLzi8fbzc0qpUyS\n3D9Jk+QrG1zz/UlOSHJ913W3JknXdbck+bskd178fL0HLR7Xzvb6UpJ5kgcs7mMjaw54XwdZA8Ad\nQLM4NXKyZmvkdDxavg4AAAzP0QjCLl88/uR+fvaYJMcn+XjXdd/Z4JqfWnfNltYsPu/ji8//0Q1+\nzpeT/G2SB5dS7r+JewPgGFs9NXLtsPySWWtGGAAADNXRCMIuTfKNJE8tpTyif7GUsjPJhYunb1m3\n5h1JvpPk/FLK/dasuUeS/7B4+tZ1a/rnL1tc16+5X5J/u3i/d6xb03/uhYv76decmeS8JP+U5I/7\n17uu69Z8zqtLKaM1a56UlUDt2iR/GQDuUPpTI6fjtcPyNcIAAGDINjQjrJTys0l+dvH0+xaPZ5VS\nLlr8+ze6rntRknRd961SynOyEojtLqVcnOTGJD+T5OTF65esff+u675aSnlxkjckuaqUckmSvUme\nkuSkJP+167or1635eCnldUlekOSaUsqlSXZkJdD6niTP77ruunV/lIuTPHnxvn9TSnlvknsu1oyT\nPKfrum+tW/O6JE9YrPlkKeXDSf5Fkl9IcmuSZ3Vd57+qAO5glqdGrg3Cxk6NBACAIdvosPwzkjxj\n3WsPWPyTJF9L8qL+B13XXVZK+bEkL0vy80l2ZmVG1wuSvGHRtNpH13VvLKVct3ifp2elrXZtkpd3\nXfcH+7upruteWEq5Jsn5WTmZsU3y10le03Xd+/ZzfVdK+aWsbJF8VpLnJ9mT5KNJLuy67uP7WfOd\nUsq/TvLSJL+c5N9nZWj/ZUn+U9d11+7v3gA4tmb9jLB9tkaOlk0xAABgeDYUhHVd9xtJfmMzb9x1\n3RVJHr/JNe9N8t5NrvmDJPsNyg5wfZPk9Yt/NrrmtiT/afEPANvArG+EjdZujSzL1wEAgOE5GjPC\nAOCYaw7QCJu3XfZTTAYAAAZAEAZAlfrTISejfU+NTKIVBgAAAyUIA6BKzbzNZFRSymoQNlkMzm/M\nCQMAgEEShAFQpabt9tkWmay2wzTCAABgmARhAFRpNm/3GZSfrMwIS1bnhwEAAMMiCAOgSs18P42w\nxfOm1QgDAIAhEoQBUKWmbZczwXp9Q2xvoxEGAABDJAgDoEqzeZfpaN9G2HSiEQYAAEMmCAOgSs28\nzXSy76+5yciMMAAAGDJBGABVmrXd8pTI3nTs1EgAABgyQRgAVZo17fKUyN6yEdZqhAEAwBAJwgCo\nUtMe+NRIjTAAABgmQRgAVZrN22UDrNc3xGZmhAEAwCAJwgCoUjPvljPBen0Q1miEAQDAIAnCAKhS\n096+EbbcGmlGGAAADJIgDIAqzea3nxE2HWmEAQDAkAnCAKhS0+7n1MhFMNaYEQYAAIMkCAOgSs28\ny2S0fkZYvzVSIwwAAIZIEAZAlWbz/TTCFlsjZ41GGAAADJEgDIAqNe3tZ4Qtt0Yalg8AAIMkCAOg\nSs28u10jbMfi+cywfAAAGCRBGABVWtkaub4R1p8aqREGAABDJAgDoEqzebucCdZb3RqpEQYAAEMk\nCAOgSs389jPCpiNbIwEAYMgEYQBUadbu59TIvhFmayQAAAySIAyAKjXzLpPRuhlhi+czQRgAAAyS\nIAyA6nRdl6btlsPxe6WUTMclMzPCAABgkARhAFSnH4Y/XdcIS5LJaGRrJAAADJQgDIDqNIth+Osb\nYSuvFcPyAQBgoARhAFRn1q40vqbj2zfCpuNRmlYjDAAAhkgQBkB1lo2w/W6NLMufAwAAwyIIA6A6\n/Qyw6eT2v+am41H2mhEGAACDJAgDoDqz5bD8/c8I0wgDAIBhEoQBUJ1Zs9L4mpgRBgAArCEIA6A6\nfdC131MjR06NBACAoRKEAVCdPuia7mdY/nQ8Ws4QAwAAhkUQBkB1lqdG7q8RNi5pWo0wAAAYIkEY\nANWZtQeZETYaZaYRBgAAgyQIA6A6zfzgp0aaEQYAAMMkCAOgOv0MsAOeGqkRBgAAgyQIA6A6s8UM\nsOl+gzCNMAAAGCpBGADVWTbC9rc1cjRK02qEAQDAEAnCAKhO3/iaHujUSI0wAAAYJEEYANXpG1/7\n3xo5Wp4qCQAADIsgDIDqzJbD8ve3NbJk1miEAQDAEAnCAKhOvzVyMrp9I2wyNiMMAACGShAGQHWa\ng8wI2+HUSAAAGCxBGADV6Rtfk/3MCJuMR8tTJQEAgGERhAFQneWpkaP9nxo5azXCAABgiARhAFSn\nmR+4ETYdaYQBAMBQCcIAqE6zaHztf2tkSdslc60wAAAYHEEYANWZLRpf+9sa2Q/Qn2mFAQDA4AjC\nAKhOM+8yKslotJ+tkYuWWKMRBgAAgyMIA6A6s7ZdNr/WmyxaYuaEAQDA8AjCAKhOM+8OGIT1jbD+\nZEkAAGA4BGEAVGc2b/c7KD9JJouArGk1wgAAYGgEYQBUZzbvllsg15ss5oY1GmEAADA4gjAAqtPM\n2+UWyPX6LZN7zQgDAIDBEYQBUJ2m7Q6yNVIjDAAAhkoQBkB1ZvM20wNsjewbYTONMAAAGBxBGADV\naeYHboT1WyabViMMAACGRhAGQHWatj3IsPzFqZEaYQAAMDiCMACqM5t3BxyW3zfFZmaEAQDA4AjC\nAKhO07aZjA8+I6xpNcIAAGBoBGEAVOegjbBR3wgThAEAwNAIwgCoTjNvl82v9VZPjbQ1EgAAhkYQ\nBkB1ZvNu2fxab7k1UhAGAACDIwgDoDqz+YFnhPXD8s0IAwCA4RGEAVCdpj3wjLDpyNZIAAAYKkEY\nANVp5m0mo0M0wgzLBwCAwRGEAVCd2bxbBl7r9a87NRIAAIZHEAZAdZq2XW6BXG+HUyMBAGCwBGEA\nVKc5aCNscWqkYfkAADA4gjAAqjObt5ke6NTIUb81UiMMAACGRhAGQHWatlsGXuv1AVkjCAMAgMER\nhAFQnWbeZTrZ/6+48aikFFsjAQBgiARhAFSl67rM2jbTAzTCkmQ6GmWvUyMBAGBwBGEAVGXedum6\n1aH4+zMZF1sjAQBggARhAFSlaVcCrgOdGpmszAlrNMIAAGBwBGEAVGW2CLimowP/ipuOS2atRhgA\nAAyNIAyAqvRbHg/WCJuMNMIAAGCIBGEAVGW2OA3SjDAAAGA9QRgAVekDroOeGjke2RoJAAADJAgD\noCqrWyMP0ggblcwaWyMBAGBoBGEAVKXfGjk91KmRrSAMAACGRhAGQFWWjbBDnRppRhgAAAyOIAyA\nqszm/bD8g5waqREGAACDJAgDoCrNYgj+jkPNCNMIAwCAwRGEAVCVjTTCpuNRmrlGGAAADI0gDICq\nLIOwg8wIm5gRBgAAgyQIA6Aq/bD8g50aORmNloEZAAAwHIIwAKrSD8GfHGRG2I5JWc4SAwAAhkMQ\nBkBV+i2Pk9HBG2FmhAEAwPAIwgCoyurWSDPCAACAfQnCAKjK6tbIg5waORotrwMAAIZDEAZAVfqm\n1/QQp0Y2GmEAADA4gjAAqtLP/jpoI2w8yl4zwgAAYHAEYQBUZbY4DfLgQZhGGAAADJEgDICq9I2w\nHQcdlm/fYUPbAAAgAElEQVRGGAAADJEgDICq9E2vyUGCsOlo5dTIrtMKAwCAIRGEAVCVfvbXZHTg\nrZF9SDZvBWEAADAkgjAAqtI3wqYH3Rq5EpI1gjAAABgUQRgAVWnaNqUk44M0wqajlV9/To4EAIBh\nEYQBUJXZvFsGXQeybIQ5ORIAAAZFEAZAVZp5uwy6DqTfNtlohAEAwKAIwgCoStN2Bx2UnyTTRVA2\nMyMMAAAGRRAGQFVm8/agg/KTZDLSCAMAgCEShAFQlWbeHXJrZP/zmRlhAAAwKIIwAKoya9tl4+tA\nljPCWo0wAAAYEkEYAFVp5l12TA61NXLRCGs0wgAAYEgEYQBUpWnbQw/LXwRlM40wAAAYFEEYAFXZ\n23SZHGJY/nQ5LF8jDAAAhkQQBkBVmrbNdIPD8p0aCQAAwyIIA6Aqzbw79NbI/tTIViMMAACGRBAG\nQFVm8/aQWyMny62RGmEAADAkgjAAqtK03Ya3Rs4EYQAAMCiCMACq0szbZePrQKaLxtjMsHwAABgU\nQRgAVZnND90I64OwptUIAwCAIRGEAVCVpj10I6wfpq8RBgAAwyIIA6AqzbxbzgA7kGUjTBAGAACD\nIggDoCqzts2OQ50auQjKbI0EAIBhEYQBUJUNNcJGhuUDAMAQCcIAqMps3maywUbYbK4RBgAAQyII\nA6Aqs3mX6WijM8IEYQAAMCSCMACq0mygETYdOzUSAACGSBAGQFVm7aFnhJVSMh4Vw/IBAGBgBGEA\nVKWZt8th+AczGZU0GmEAADAogjAAqtG2Xdouh2yEJStzwmyNBACAYRGEAVCN2WKr4/QQM8KSlbDM\nqZEAADAsgjAAqtFvdZwc4tTIlWtGZoQBAMDACMIAqMYyCNtAI2zHuNgaCQAAAyMIA6Aa/dbIHRuY\nETYZj9LYGgkAAIMiCAOgGptphE3GJbNWIwwAAIZEEAZANfrh9xuZETYdaYQBAMDQCMIAqEYfhG30\n1MjGjDAAABgUQRgA1WjafmvkxmaE7dUIAwCAQRGEAVCN1a2RGzs1UiMMAACGRRAGQDX6YGu6kUbY\naJSm1QgDAIAhEYQBUI0+2NrwqZEaYQAAMCiCMACq0Qdb042cGjnWCAMAgKERhAFQjX5r5IYaYSMz\nwgAAYGgEYQBUY7bcGrmxRphTIwEAYFgEYQBUo2947djgjDCNMAAAGBZBGADVaOaba4Q1GmEAADAo\ngjAAqtFvdZyMDv3rbToumbUaYQAAMCSCMACq0W91nG6gETYZaYQBAMDQCMIAqEazHJZvRhgAAHB7\ngjAAqjHrG2Ejp0YCAAC3JwgDoBqrw/I30AgblTRmhAEAwKAIwgCoRh9sbfTUyHnbpeuEYQAAMBSC\nMACqsbo1cmOnRq5dAwAA1E8QBkA1VrdGbuDUyMX2yX7APgAAUD9BGADVmPVbIzcwLL+/RiMMAACG\nQxAGQDWaeZvJqKSUjc0I69cAAADDIAgDoBpN2y0DrkOZmBEGAACDIwgDoBp7m3ZD88GS1UbYTCMM\nAAAGQxAGQDWatt1wI6w/NbJpNcIAAGAoBGEAVKOZdxsalJ8kk5EZYQAAMDSCMACqMZtvfEbY1Iww\nAAAYHEEYANVo2o3PCFs2wlqNMAAAGIqjGoSVUn66lPLBUsr1pZTbSilfKaX8USnlrANcf3Yp5c9K\nKTeWUm4tpVxTSvl3pZTxQT7jCaWU3aWUm0opN5dSPllKecYh7usZpZRPLa6/abH+CQe5fry4j2sW\nf44bF/d59sb/NgA42ja1NXLZCBOEAQDAUBy1IKyU8ltJ3pfk4Un+PMl/S/LXSZ6U5IpSyv+97von\nJfloksckeU+SNyfZkeT1SS4+wGecn+S9SU5N8s4kv5vkB5JcVEp57QHWvDbJRUm+f3H9O5OcluS9\ni/dbf31ZfP7rF/fzpsX9PSbJRxf3DcAdwGy+mWH5/amRtkYCAMBQTI7Gm5ZSvi/Ji5L8nySnd133\nj2t+9uNJLk9yQVZCqJRS7pqVUGqeZFfXdVctXv+Pi2ufUkp5atd1F695n/sleW2SG5M8ouu66xav\nX5Dk00leWEr5467rrlyz5uwkL0zy5SRndl33zcXrr0nymSSvLaW8r3+vhacmeUqSjyc5t+u6PYs1\nb03yV0l+t5Ryedd13z7MvzYADlPTdhveGtkHYY0gDAAABuNoNcLuu3jvT64NwZKk67qPJPl2khPX\nvPyUxfOL+xBsce2eJC9fPP21dZ/xrCTHJXnT2uBqEW69cvH036xb0z//L30ItlhzXVYaaMcl+ZV1\na/rPfXkfgi3WfDrJJYv7fkoAOOZm83Y5++tQllsjzQgDAIDBOFpB2BeT7E3yyFLKvdb+oJTymCR3\nSfKhNS//q8Xjn+/nvT6a5NYkZ5dSjtvgmg+su2ZLaxafd/bi8z+2ic8B4Bho5t3yNMhDmY40wgAA\nYGiOShDWdd2NSf6fJPdOcm0p5W2llFeVUv4wyQeT/Pckv7pmycmLxy/s572aJF/NyjbOB2xwzf9O\nckuSk0opxydJKeWEJPdJcvPi5+t9cfH44DWvPTDJOMlXFvexkTUAHCNNu/EZYX0jrDEsHwAABuOo\nzAhLkq7rfruUcl2Styd5zpoffSnJReu2TN5t8XjTAd6uf/3um1xzwuK6W4/iZ6xfs49SynOTPDdJ\nTjzxxOzevftAl8Kg3Hzzzb4PHHE3fPO2HD8tG/rf1t/fvBKA/Y//+b9ypxs+f5Tv7OB8H2CV7wOs\n8F2AVb4PHElHLQgrpbwkK7O63pCVkxb/IckPJXlVkneVUs7ouu4lG327xeNm9q9sZc0R/4yu696W\n5G1JcvLJJ3e7du3a5O1AnXbv3h3fB46011zzsdz7rjuza9eZh7z2um/ckvzV7jzowT+UXT980nfh\n7g7M9wFW+T7ACt8FWOX7wJF0VLZGllJ2JfmtJH/add0Luq77Std1t3Zd99dJfi7J32XlVMd+q2Pf\nrLrb7d8tSXLXdddtZs23Nnj9/tpfW7kvAI6RZr6JUyMnixlhhuUDAMBgHK1h+U9YPH5k/Q+6rrs1\nyacWn/1/LV7u96TcbtZWKWWS5P5JmiRfWfOjg635/qxsi7x+8Xnpuu6WrARwd178fL0HLR7Xzhz7\nUpJ5kgcs7mMjawA4RmZtm8kGZ4RNR4tTIw3LBwCAwThaQVh/uuOJB/h5//rexePli8ef3M+1j0ly\nfJKPd133nTWvH2zNT627ZktrFp/38cXn/+gmPgeAY6CZd8uA61D6wMywfAAAGI6jFYR9bPH43FLK\nfdb+oJTyU0nOSbInKyFTklya5BtJnlpKecSaa3cmuXDx9C3rPuMdSb6T5PxSyv3WrLlHkv+wePrW\ndWv65y9bXNevuV+Sf7t4v3esW9N/7oWL++nXnJnkvCT/lOSPA8Ax18w33ghbnhrZaoQBAMBQHK1h\n+Zcm+VCSf53ks6WU92RlWP5DsrJtsiR5add1NyRJ13XfKqU8Z7Fudynl4iQ3JvmZJCcvXr9k7Qd0\nXffVUsqLszKM/6pSyiVZaZg9JclJSf5r13VXrlvz8VLK65K8IMk1pZRLk+zISqD1PUme33Xddev+\nLBcnefLiff+mlPLeJPdcrBkneU7Xdd8KAMfcrO0y3eiMsNFKYGZrJAAADMdRCcK6rmtLKY/PSsvq\nqVkZkH98VsKtP0vyhq7rPrhuzWWllB9L8rIkP59kZ1ZmdL1gcf3t/kul67o3llKuS/KiJE/PSsPt\n2iQv77ruDw5wby8spVyT5Pwkz03SJvnrJK/puu59+7m+K6X8Ulbaa89K8vystNk+muTCrus+vn4N\nAMdGM28zGW2uETazNRIAAAbjaDXC0nXdLMlvL/7Z6Jorkjx+k5/z3iTv3eSaP0iy36DsANc3SV6/\n+AeAO6jNnBo5WcwSMyMMAACG42jNCAOA77pZ22a6wRlhpZRMxyUzM8IAAGAwBGEAVKOZb3xGWJJM\nRiONMAAAGBBBGABV6LouTdtteEZYsjInzLB8AAAYDkEYAFXoA63NNMKm41GaViMMAACGQhAGQBX6\nQGuywRlhycrA/EYjDAAABkMQBkAV+kZYfxrkRkzHo+w1IwwAAAZDEAZAFfqh9xs9NXLlWo0wAAAY\nEkEYAFVo2kUjbDOnRpoRBgAAgyIIA6AKs74RtplTI0dOjQQAgCERhAFQhX6L42YaYdPxaLmlEgAA\nqJ8gDIAqbOnUyHFZbqkEAADqJwgDoAr9FsfpZk6NHI2yt9EIAwCAoRCEAVCFfmvkZk6N1AgDAIBh\nEYQBUIXZcmukGWEAAMD+CcIAqMJsscVxM42w6dipkQAAMCSCMACq0G9xnGxiRthkNFoO2QcAAOon\nCAOgCrP5Fk+N1AgDAIDBEIQBUIXVYfmbmxE20wgDAIDBEIQBUIV+i+NktIlG2Khk1miEAQDAUAjC\nAKjCbCuNsIkZYQAAMCSCMACqsGyEbebUyJFTIwEAYEgEYQBUoQ+0NnVq5HiUZq4RBgAAQyEIA6AK\nq8PyN3dq5KzVCAMAgKEQhAFQhX5r5KZmhI00wgAAYEgEYQBUYbk1cpONsLZL5lphAAAwCIIwAKow\nm2+hEbYIzWZaYQAAMAiCMACq0G9xnIw2cWrkIjRrNMIAAGAQBGEAVGG2HJa/iVMjF6GZOWEAADAM\ngjAAqtC0bcajklI2szVy5do+RAMAAOomCAOgCs28y2S08RAsWR2s3584CQAA1E0QBkAVZvNuOfx+\no/rgbNZohAEAwBAIwgCoQtO2mWxiPliy5tRIjTAAABgEQRgAVZjNu02dGJmsBmGNGWEAADAIgjAA\nqtDM202dGJlk2SCbOTUSAAAGQRAGQBWadvMzwvrgrGk1wgAAYAgEYQBUYTbf/IywfitloxEGAACD\nIAgDoAqzeZvpJmeErW6N1AgDAIAhEIQBUIVm3m391EiNMAAAGARBGABVmLVdJpucETYZ9TPCBGEA\nADAEgjAAqtDM20xHW22E2RoJAABDIAgDoAqHszWyEYQBAMAgCMIAqMKsbZfB1kb1wZmtkQAAMAyC\nMACq0My75cyvjepPmbQ1EgAAhkEQBkAVZvN288PyF40wp0YCAMAwCMIAqELTdpluckbYcmukIAwA\nAAZBEAZAFZp5m8loc7/Wdjg1EgAABkUQBkAVZvNuC8PyF6dGGpYPAACDIAgDoAqzebv5rZGjfkaY\nRhgAAAyBIAyAKjRtt5z5tVF9g6wRhAEAwCAIwgCowmwLM8LGo5JSbI0EAIChEIQBUIVmvvlTI5Nk\nOhplr1MjAQBgEARhAFShadvl8PvNmIyLrZEAADAQgjAAtr2u61ZOjRxtoRE2HqXRCAMAgEEQhAGw\n7c3blUbXVhph03HJrNUIAwCAIRCEAbDtNcsgbPONsMlIIwwAAIZCEAbAtjdbBFnTTZ4amZgRBgAA\nQyIIA2Db64OsrTTCpmOnRgIAwFAIwgDY9mbtohG2lVMjRxphAAAwFIIwALa92SLImm6xEda0GmEA\nADAEgjAAtr1+2P1kCzPCpuOyDNIAAIC6CcIA2PZmhzEjbKIRBgAAgyEIA2Dbaw5zRphGGAAADIMg\nDIBtb3lq5GiLM8KcGgkAAIMgCANg25vND6MRZkYYAAAMhiAMgG2vabc+I2w6Hi2DNAAAoG6CMAC2\nvdlhnhrZB2kAAEDdBGEAbHv9jLDpVk6NHJkRBgAAQyEIA2Db60+NnJgRBgAAHIQgDIBtb3YYjbDp\naLQM0gAAgLoJwgDY9pwaCQAAbIQgDIBtr58RNhk5NRIAADgwQRgA297hNMKm47IM0gAAgLoJwgDY\n9pp20QjbyqmRYzPCAABgKARhAGx7zaIRNhltoRE2WpkR1nVaYQAAUDtBGADb3uGcGjlZbKect4Iw\nAAConSAMgG2v39o42eKpkSvvIQgDAIDaCcIA2PZmh3Nq5GI75V4nRwIAQPUEYQBse81ya+TWTo1c\n+x4AAEC9BGEAbHtN26aUZLyFRli/nbLRCAMAgOoJwgDY9mbzbkttsGS1ETYzIwwAAKonCANg22vm\nbaZbaIMlyWSkEQYAAEMhCANg25vN2y2dGJmsnho5MyMMAACqJwgDYNubtd1yi+Nm9VsqZxphAABQ\nPUEYANteM2+XWxw3azJyaiQAAAyFIAyAba+Zd8stjps1nSwaYa1GGAAA1E4QBsC2t7I1counRi6H\n5WuEAQBA7QRhAGx7K1sjt3hq5LjfGqkRBgAAtROEAbDtzebdlk+N7Ifsz1qNMAAAqJ0gDIBtr2nb\nLZ8a2Q/ZnzUaYQAAUDtBGADbXjPvDn9rpGH5AABQPUEYANvebN5ueVj+jsW6mWH5AABQPUEYANte\ncxinRvazxTTCAACgfoIwALa92bxdbnHcrH5LpUYYAADUTxAGwLY3m3fLofeb1TfJGkEYAABUTxAG\nwLbXzA/j1EjD8gEAYDAEYQBse03bLWd9bdZ00STb2wjCAACgdoIwALa92bzNdHS4jTBbIwEAoHaC\nMAC2vWbebXlY/uqMMI0wAAConSAMgG2vadutb40cOzUSAACGQhAGwLY3m3db3hpZSsl4VAzLBwCA\nARCEAbDtNfOtN8KSZDIqaTTCAACgeoIwALa9WdstZ31txXQ8yl4zwgAAoHqCMAC2vWbeLmd9bcVk\nrBEGAABDIAgDYFubt13aLpmMDq8RZkYYAADUTxAGwLY2W2xpnBxGI2w6Kk6NBACAARCEAbCtNe1K\ngHV4WyNHacwIAwCA6gnCANjW+gDrcLZGTsYls1YjDAAAaicIA2Bb67c0Hk4jbDrSCAMAgCEQhAGw\nrfVD7ifjw2yEmREGAADVE4QBsK01iwBrMjq8GWEzjTAAAKieIAyAba0PsKaH0QjbMS7LQA0AAKiX\nIAyAba0/NXJyOKdGjkbLLZYAAEC9BGEAbGuzI3VqpEYYAABUTxAGwLbWb2ncMTmMUyPHGmEAADAE\ngjAAtrUj0ggblcwajTAAAKidIAyAba3f0ng4M8Km41FmGmEAAFA9QRgA21q/pfFwTo2cOjUSAAAG\nQRAGwLbWB1iT0WGcGjkepZlrhAEAQO0EYQBsa/2MsMNthM1ajTAAAKidIAyAba1pD39G2GSkEQYA\nAEMgCANgWzsip0aaEQYAAIMgCANgW+sDrOlhnhq5VyMMAACqJwgDYFvrT42cHMaMsMmoLLdYAgAA\n9RKEAbCtzfpG2GGcGjkdjzJvu3SdMAwAAGomCANgW2uO0KmRyWqoBgAA1EkQBsC21odXh3Vq5CJE\n67dZAgAAdRKEAbCtzdrDb4RNRhphAAAwBIIwALa1/tTIyWHOCEuSmZMjAQCgaoIwALa1fkbY+DCC\nsH5bZaMRBgAAVROEAbCtzdou03H5/9m792hJ87o89M+vLj3dPTAzzIDCgIjI/SImosRRcKLGY5Tg\nkeCKyVmJ8cYyS80xEpMcRZOTqDEJkZiQFQ+cE3XFswI5Ei8YvMTgiIoiROUyCAgMIMwwMNNzoW+z\n6616zx/1vrV376596d5Vu/f79uez1qyaXft9q2pmzZ5e/fTz/f5SikYYAACwO0EYAJ1WTWcZDQ72\ny1l7amQ10wgDAIA+E4QB0GmTaX2gEyOTLIK0SiMMAAB6TRAGQKdVs9mBToxMNhthTo0EAIB+E4QB\n0GnVtD7QiZHJlkbYTCMMAAD6TBAGQKdNpvWBG2GjRSNMEAYAAH0mCAOg0+ajkQdrhB1bnBppNBIA\nAPpMEAZAp02ms4wO3Ahrl+ULwgAAoM8EYQB02mQVO8La0Ug7wgAAoNcEYQB0WjVdwamRA40wAAC4\nGgjCAOi0alYvGl2Xq72/siwfAAB6TRAGQKdNprNFo+tytcv2NwRhAADQa4IwADqtmq6gEWY0EgAA\nrgqCMAA6bTKrD3xq5HjUBGGW5QMAQK8JwgDotGo6y/iAp0a29080wgAAoNcEYQB02kpGI4ftaKRG\nGAAA9JkgDIBOm8xmBx6NXJwaOdMIAwCAPhOEAdBp1bTOsYPuCGuW5Ts1EgAA+k0QBkCnTaazjA64\nI2zRCLMjDAAAek0QBkCnTaYHPzWyDdLsCAMAgH4ThAHQadVslvEBl+WXUjIelkzsCAMAgF4ThAHQ\nadW0zmhw8F/ORoOBRhgAAPScIAyATptMD94IS+Z7wiZ2hAEAQK8JwgDotGpWL5bdH8R4OEg10wgD\nAIA+E4QB0Fl1XWc6W9VoZMmk0ggDAIA+E4QB0FntKOMqRiPHw0EmGmEAANBrgjAAOqsdZRwND/7L\n2XhYUtkRBgAAvSYIA6Cz2kbYaLCKZfl2hAEAQN8JwgDorGo6D66OjVa0I0wjDAAAek0QBkBnVbO2\nEbaK0cjBIlgDAAD66VCCsFLK80spry+l3FVKeah5/PVSytcsufaWUsobSymnSilnSynvLKV8Tyll\nuMvrv7CUclsp5YFSyulSyltLKd+0x2f6plLKHzTXP9Dc/8Jdrh82n+OdpZRzzed7Yynllkv7twHA\nqmxU7Y6wVYxGaoQBAEDfrT0IK6W8PMmbk7wgya8m+ddJ3pDkEUlu3Xbt12259ueT/Pskx5K8Mslr\nd3j972pe71lJfjbJa5LcnOSnSymv2OGeVyT56SSPaa7/2STPTvKG5vW2X1+a939l83le1Xy+FyR5\nc/O5AThkbSNsJadGDgaZaIQBAECvjdb54qWUb0jyz5L8RpIX13X96W3fH2/5++syD6WmSW6t6/rt\nzfM/mORNSV5SSvnGuq5fu+WeJyR5RZJTSZ5b1/WHm+f/aZK3JXlZKeX1dV3/3pZ7bknysiQfTPKF\ndV3f1zz/r5L8zySvKKX8cvtajW9M8pIkb0nyFXVdn2/u+ckkv5PkNaWUN23/5wNgvdpRxpWMRo5K\nzk8EYQAA0Gdra4SVUgZJ/kWSs0n+xrKQqK7ryZYvX5LkUUle24ZgzTXnk7y8+fLvbHuJb0lyTZJX\nbQ2umnDrR5svv2PbPe3XP9KGYM09H868gXZNkm/edk/7vi9vQ7DmnrcleV3zuV+y/Z8PgPVqRxlX\n0QgbDewIAwCAvlvnaOQtST4nyRuT3FdK+dpSyj8spfzvpZQvXnL9lzePv7rke2/OPFC7pZRyzT7v\n+ZVt11zWPc373dK8/29fwvsAsGbVbIWNMDvCAACg99Y5GvmFzePdSf4w8x1cC6WUNyd5SV3Xn2qe\nemrz+P7tL1TXdVVKuSPJM5M8Mcmf7OOeu0opZ5I8rpRysq7rs6WUa5M8Nsnpuq7vWvKZ/7R5fMqW\n556UZJjkQ3VdV/u8B4BD0AZXK1mWPxgsgjUAAKCf1tkI+4zm8TuSnEjylUkenvlS+1/LfNH8/7fl\n+uubxwd2eL32+Rsu457rtz2u4z1u2OH7AKxJO8o4Hh78l7PRsKTSCAMAgF5bZyNs2DyWzJtf72i+\nvr2U8vWZt7i+rJTyxVuX2e+i/eP+S/ldyuXcs9L3KKW8NMlLk+RRj3pUbrvttkv8KNBPp0+f9vPA\ngb3n3mmS5N3vfEcmHxvucfXu7r3nfB48M7si/136eYBNfh5gzs8CbPLzwCqtMwhrF9F/aEsIliSp\n6/pcKeXXknxrki9K8nu5uL213XXN49Zm1gNJHtncc+8u9zy47d6d3mNZ++tyPtdCXdevTvLqJHnq\nU59a33rrrTu8DFxdbrvttvh54MDe98nkbW/Lc7/gz+cLPvsRB3qp//apd+SO0/dckf8u/TzAJj8P\nMOdnATb5eWCV1jka+b7m8f4dvt8GZSe2XX/Rrq1SyijzxftVkg8teY9l9zwmybVJPlbX9dkkqev6\nTJKPJ3lY8/3tntw8bt059oEk0yRPbD7Hfu4B4BC0o4zHVjAaOR7ZEQYAAH23ziDszZkHV08upRxb\n8v1nNY8fbh7f1Dx+9ZJrX5DkZJK31HX90Jbnd7vnL2+75rLuad7vLc37P/8S3geANVucGrmCZfnj\ngVMjAQCg79YWhNV1fU+S12U+UvhDW79XSvlLSf6XzMcJf7V5+ueS3JPkG0spz91y7fEkP9x8+R+2\nvc1PJXkoyXeVUp6w5Z5HJPn+5suf3HZP+/UPNNe19zwhyXc2r/dT2+5p3/eHm8/T3vOFSf5akk8l\neX0AOFQbTXA1XsWpkcPBYvk+AADQT+vcEZYk35vkeZmHTi9I8gdJPjvJ12c+bvjtdV3fnyR1XT9Y\nSvn2zAOx20opr01yKsmLkjy1ef51W1+8rus7Sinfl+TfJnl7KeV1STaSvCTJ45L86+2L+Ou6fksp\n5cebz/bOUsrPJTmWeaB1Y5Lvruv6w9v+OV6b5MXN6/5RKeUNSW5q7hk2/xwPBoBD1QZXo8FqTo2c\nzDTCAACgz9YahNV1/clSyvOSvDzz8OsvJPl0kv+W5J/Xdf37267/hVLKlyX5gSR/NcnxzHd0fW+S\nf1vX9UW/Q6nr+t+VUj6c5O8n+VuZt9zek+TldV3/zA6f62WllHcm+a7MT3ScJfnDJP+qrutfXnJ9\nXUr565mPSH5Lku9Ocj7z8c8fruv6LZf0LwaAlWh3hK1mNHKQiUYYAAD02robYanr+lTmQdb37vP6\n303yNZf4Hm9I8oZLvOdnkiwNyna4vkryyuYvAI6ASbMjbLyCZfmjYUldJ9NZneHg4MEaAABw9Kxz\nWT4ArNWiEbaC4KoN07TCAACgvwRhAHRWG1qNVtAIaxfuV/aEAQBAbwnCAOisNrRayamRzcJ9J0cC\nAEB/CcIA6KxVnhrZhmmTqUYYAAD0lSAMgM5qQ6uVNMKa8cpqphEGAAB9JQgDoLOq2SzDQUkpqxiN\nbBphlUYYAAD0lSAMgM6qpvVK2mDJllMjNcIAAKC3BGEAdNbGdJbxCvaDJZtBWGVHGAAA9JYgDIDO\nqqZ1RitqhI0Wy/I1wgAAoK8EYQB0VjWbLZbcH1Q7YlnNNMIAAKCvBGEAdNZkWmc82Ecj7Lf+ZfKJ\nd+96yWjQjkZqhAEAQF+NrvQHAIDLVU330QirNpLf/JGkeih59LN2vKwdjdwQhAEAQG9phAHQWZPZ\nPiUbhsQAACAASURBVHaETc7OH6vzu15mWT4AAPSfIAyAzqr2c2rk5FzzeHbXyxZB2EwjDAAA+koQ\nBkBn7evUyDYAawOxHYwG7amRGmEAANBXgjAAOms+GrniRpggDAAAeksQBkBnzUcj92qEnbvwcQdt\ns8xoJAAA9JcgDIDOqqb1osm1o32ORra7xoxGAgBAfwnCAOisjelsHzvC9jca2b7OZKoRBgAAfSUI\nA6CzqtlsZY2wxWikIAwAAHpLEAZAZ1XTenHa44722Qg7NjQaCQAAfScIA6CzJtNVNsKaUyMtywcA\ngN4ShAHQWdWsvoQdYXsEYYN2R5hGGAAA9JUgDIDOmo9G7tUI299oZNssqwRhAADQW4IwADprPhq5\nVyOsCcBmVTKd7HjZcFBSilMjAQCgzwRhAHTWJY1GJnu3wgaDTOwIAwCA3hKEAdBZk+lsH6ORW8Kv\nPfaEjYfFaCQAAPSYIAyAzqqm9T5GI/ffCBsNB6mMRgIAQG8JwgDorGo2Wyy539EFQdjejbDJTCMM\nAAD6ShAGQCfVdZ3JtM5oryCs2n8QNhpohAEAQJ8JwgDopKppbo0H+xiNLMPm7/cajbQjDAAA+kwQ\nBkAntYHVno2wydnk5I3N3+81GjnIhkYYAAD0liAMgE6azOaB1b6W5Z+8qfn7PRphA40wAADoM0EY\nAJ20aITtZzRyEYTt3QirZhphAADQV4IwADqpXWp/aaORuzfCxsOSiUYYAAD0liAMgE6atMvyL2k0\nco9TIzXCAACg1wRhAHTSohE22OWXsrqet8BO7G9Z/migEQYAAH0mCAOgkyaLUyN3aYRV5+ePx69L\nBuN9jEYOMnFqJAAA9JYgDIBOqhanRu7yS1nbABufnP+152ikUyMBAKDPBGEAdFIbWO0ehDUNsPGJ\n+V8aYQAAcFUThAHQSRuLUyN3GY28oBF2Ys9G2HhYUs00wgAAoK8EYQB00qIRttuy/AsaYfsYjRwM\nFkv4AQCA/hGEAdBJ1SU1wvY3GjkaOjUSAAD6TBAGQCdNZu2OsN2CsLYRts/RyIEdYQAA0GeCMAA6\nadEI23U0cmsj7OS+GmF2hAEAQH8JwgDopHaEcbXL8jXCAACgzwRhAHRSNZsHVuPhPpblj47va1n+\neFgWS/gBAID+EYQB0EltYDUaXEojbK/RyMEiYAMAAPpHEAZAJ7UjjPtqhC1OjdxrWf781Mi61goD\nAIA+EoQB0EntUvt97QhbjEaeTXYJuUZNqDa1MB8AAHpJEAZAJ1X7bYSNTiSDwbwRliTV+R0vb0O1\niT1hAADQS4IwADppowmrxoPdgrDzmwHY+GTz3M7jke1rTewJAwCAXhKEAdBJbSNsz9HINgBrA7Fd\nFuaPm9dyciQAAPSTIAyATtrfjrCzl9QIa3eEtSEbAADQL4IwADppcWrkrqOR57YEYftvhE0sywcA\ngF4ShAHQSdW0zqAkg8FejbDto5G7NMIGGmEAANBngjAAOmkymy1GGXe+6NyS0cidG2Gbp0YKwgAA\noI8EYQB0UjWtM96tDZbssCx/l1Mjm2BtYlk+AAD0kiAMgE6qpvtphF3asvzxYlm+IAwAAPpIEAZA\nJ01m9WK5/c4XLVuWv9upke2yfKORAADQR4IwADqpms4Wy+13dMGy/H00wgYaYQAA0GeCMAA6qZrW\nGY8upxG297J8p0YCAEA/CcIA6KTJrF40uJaaTZPpQ5tNsNHx5sbddoS1o5EaYQAA0EeCMAA6aVLN\nFg2u5Rc0gVfbBBsMktGJ3RthTbA2qTTCAACgjwRhAHRSNdtjR9j2IKz9+30sy68sywcAgF4ShAHQ\nSZPpHqdGts2vdjSy/ftdgrBjw8HitQEAgP4RhAHQSdVsltHwchphuy3LHyxeGwAA6B9BGACdNJnW\nGQ0utRG2x2hk83oaYQAA0E+CMAA6qZrOMt5XI+z45nPjk7s2wtrXqwRhAADQS4IwADqpmtX7PDXy\nEhphw7YRZjQSAAD6SBAGQCfNRyN3a4S1o5Fbd4Ttvix/PGiX5QvCAACgjwRhAHTSfDTychphu4xG\njuavV82MRgIAQB8JwgDopPlo5KU2wvZalt/uCNMIAwCAPhKEAdBJk70aYdX5+eNFo5G7Lct3aiQA\nAPSZIAyATppMZ4udXssvaBth+1+WX0rJcFBSzTTCAACgjwRhAHRSNd3HqZFlkAyPbT43PpFMH0pm\n0x1vGw1KKo0wAADoJUEYAJ00H43crRF2bt4GK1vCsnZMcreTI4eDbNgRBgAAvSQIA6CTqlmd0WC3\nRtjZC/eDJZtjkrstzB9qhAEAQF8JwgDopPlo5F6NsO1BWNsI221h/sCOMAAA6ClBGACdNJntcWrk\n5OyFi/KT/Y1GDopTIwEAoKcEYQB0znRWp66T0a6nRi5rhLWjkTs3wkbDQSo7wgAAoJcEYQB0zqQJ\nqvY8NfIyGmGjYclkphEGAAB9JAgDoHOqJqjaezTy0pfljweDTCqNMAAA6CNBGACd044uXvpo5N7L\n8kfDsgjaAACAfhGEAdA57TL78Wi3IGzZsvx9NMKGg8XoJQAA0C+CMAA6pw2qxoO9doRdeiNsPCyp\nnBoJAAC9JAgDoHPaoGo03Gs08jKW5Q8GqWYaYQAA0EeCMAA6Z9IEVZe/LH/3HWETjTAAAOglQRgA\nnbNohO20LH86SWbVxUHY8FhSBnvuCNMIAwCAfhKEAdA57Y6w0U6NsLbxtX00spT5c7uORpZMKo0w\nAADoI0EYAJ1TzZpTI3cMwpqga3T84u+NT+yxLH+wGL0EAAD6RRAGQOdUbSNsp9HInRphSROE7TYa\n6dRIAADoK0EYAJ0zWZwauUcjbPuOsKQZjdxtWf5gEbQBAAD9IggDoHOqxamROzXC2iDs8hphk5lG\nGAAA9JEgDIDO2Tw1cq9l+Ts1wnZblq8RBgAAfSUIA6Bz2lMjd26EnZ8/7tgI2200sixGLwEAgH4R\nhAHQOW1QtXMQtlsjbK/RyMEiaAMAAPpFEAZA57Q7wtaxLH88LKnsCAMAgF4ShAHQOYtG2GCvRtil\nL8sfDQaZzurUtTAMAAD6RhAGQOe0y+wvvxG2+6mRSewJAwCAHhKEAdA5k2Z08fKCsGZZ/g6Nr1Gz\nd6wdvwQAAPpDEAZA57SNsF1HIwfjZDi++HvjE0k9TaaTpbeOBk0jrNIIAwCAvhGEAdA51XQfjbBl\n+8GSzed3WJjfnkQ50QgDAIDeEYQB0DltSNWGVhdfcHb5WGSy+fwOe8LacK2yIwwAAHpHEAZA5ywa\nYYPdGmE7BWH7bIRNNcIAAKBvBGEAdE67I2y4YxB2dpfRyN0bYe2pkdVMIwwAAPpGEAZA50xmdY4N\nBynlII2wHUYjmwX8lUYYAAD0jiAMgM6ZVLOdF+UnewRhbSNsp9HI5tRIO8IAAKB3BGEAdE41q3fe\nD5YcaDSybYTZEQYAAP0jCAOgcybT2c4nRiYHWpa/ODVyJggDAIC+EYQB0DnVtN7HaOTlNcKOLU6N\nNBoJAAB9IwgDoHMms9lihHH5BWcP0Ahrl+ULwgAAoG8EYQB0TjWtF0vtl9rXsvwddoS1y/KNRgIA\nQO8IwgDonGo2WzS3LlLXuzfCRrsHYeOBRhgAAPSVIAyAzplMdzk1snooSb1zEDYcJcNjey7Ld2ok\nAAD0jyAMgM6pdjs1sg24dlqWn8xDsp0aYYIwAADoLUEYAJ1TzXY5NbINuHZqhCXzkGyHRtjYsnwA\nAOgtQRgAnTPZtRHWBmGX1whbnBppWT4AAPSOIAyAzpnsdmpktd9G2E7L8tvRSI0wAADoG0EYAJ1T\nTWcZDfZqhO0WhJ3YZVl+OxqpEQYAAH0jCAOgc3ZthO13WX51fum32t1j1UwjDAAA+kYQBkDnVLOD\nNsJ2WZbfvO6GRhgAAPSOIAyAzqmmu50auc9G2I7L8sviPQAAgH4RhAHQOZPZfk6NvLxl+aNBG4Rp\nhAEAQN8IwgDonGpaLwKriyyCsL0aYctHI0spGQ9LJnaEAQBA7wjCAOicybRenO548Tfb0ci9To1c\n3ghLktFgoBEGAAA9JAgDoHOq2WyXUyObgGu0j2X59fLW12hYMrEjDAAAekcQBkDnzEcjd2mEjY4n\nO30/2WyLVeeXf3s4yEQjDAAAekcQBkDnTKazjEe7NMJ2G4tMNveH7bIw36mRAADQP4IwADpnMp1l\nvFsjbLdF+clmULbDwvzxcJDJTCMMAAD6RhAGQKfMZnVm9XyP11IraISNhxphAADQR4IwADqlbWqN\ndzw1cj9B2O6NsNFwkEojDAAAekcQBkCntE2t0WCnRtiljEbuvCPMqZEAANA/gjAAOmURhB2oEdaO\nRu68I6xyaiQAAPSOIAyATtkcjdylETbaIwgbHW+u3aERNtQIAwCAPhKEAdApm6ORq2iE7bAsfzDI\nRCMMAAB6RxAGQKe0AdXup0bud0fYDqORo5JqphEGAAB9IwgDoFPagGrX0cgDNsJGAzvCAACgjwRh\nAHRKG1AdbDRyj0aYHWEAANBLgjAAOqUNqJY2wmazpDq/92jkXsvyB4NUM40wAADoG0EYAJ1SLU6N\nXPJLWHV+/rhXI2wwmJ8suUMjzKmRAADQT4IwADplc1n+kl/C2obXXo2wZB6W7XRq5NCpkQAA0EeC\nMAA6ZTEaOVgyGtk2vPZqhCXzsGzHIKyk0ggDAIDeEYQB0CltQLV7I2w/Qdhuo5F2hAEAQB8JwgDo\nlMmsHY3crRF2wNHIgR1hAADQR4IwADqlWoxGHrQRdnL3RpgdYQAA0DuCMAA6pZquvxE2GpZMZhph\nAADQN4IwADqlDajGS4OwS22E7TQa6dRIAADoI0EYAJ2yaITtOhq530bYTqORJXWdTLXCAACgVwRh\nAHTK5qmRu41G7vfUyB0aYc2JlFphAADQL4IwADqlPTWyDasu/OZqluW3Y5eVRhgAAPSKIAyATlmc\nGrk0CFvRsvxm7NLJkQAA0C+CMAA6ZbLrqZHnkpRkdM3eLzQ+mUw3kml18bea155MNcIAAKBPBGEA\ndEobTo2XLss/Ow+4ypKQbLt2fLK6uBU2siMMAAB6SRAGQKdUezXC9rMfLNm8bsl45GjQ7AjTCAMA\ngF4RhAHQKZNmgX0bVl34zXP72w+WbF63ZGH+sVHTCJtphAEAQJ8IwgDolGo6y2hQUpaNP07OJuPj\n+3uhXRth7bJ8jTAAAOgTQRgAnVLN6uVjkckljkbu3AgbLZbla4QBAECfCMIA6JTJdLZ8UX6yuSx/\nP3ZphLWnRlYzjTAAAOgTQRgAnVJNV90I23k0UiMMAAD65VCDsFLK3yyl1M1f37bDNS8spdxWSnmg\nlHK6lPLWUso37fG631RK+YPm+gea+1+4y/XDUsr3lFLeWUo5V0o5VUp5Yynlll3uOVFK+T9LKe8r\npZwvpXyylPJfSilP3/+/AQAOqprNMhru1Ai7lGX5bSPMaCQAAFwtDi0IK6V8VpJ/l+T0Ltd8V5I3\nJHlWkp9N8pokNyf56VLKK3a45xVJfjrJY5rrfzbJs5O8oXm97deXJK9N8sokx5K8KsnPJ3lBkjeX\nUr5uyT3XJPnvSX4oyYNJfiLJbyT5+iRvL6U8b89/AQCsxGRaZ7zsxMikGY3cbyNst9FIy/IBAKCP\nRofxJk349FNJ7k3yX5P8/SXXPCHJK5KcSvLcuq4/3Dz/T5O8LcnLSimvr+v697bcc0uSlyX5YJIv\nrOv6vub5f5XkfyZ5RSnll9vXanxjkpckeUuSr6jr+nxzz08m+Z0krymlvKmu609vued7k3xJkp9L\n8tfqup4197wuyS8k+Y+llGe3zwOwPtV0lvFohz/Hqc6vZFn+Igib+d86AAD0yWE1wv5uki9P8s1J\nzuxwzbckuSbJq7YGV0249aPNl9+x7Z726x9pQ7Dmng8n+ffN633ztnv+TvP48jYEa+55W5LXJXlU\n5kFZkkWI177PP9gadtV1/YtJfjvJM5J82Q7/XACs0GRaZ7RrI+zgy/Lb159ohAEAQK+sPQhrdmj9\nWJKfqOv6zbtc+uXN468u+d6vbLvmsu5pRhxvSXI28wBrP+/zuUken+T9dV3fcQmfDYA1mExni8bW\nxd+8nGX5uzTCBGEAANAraw3CSimjJP8pyUeTfP8elz+1eXz/9m/UdX1X5k2yx5VSTjavfW2SxyY5\n3Xx/uz9tHp+y5bknJRkm+VBd19U+79nxc+1yDwBrUs12ODVyWiXTjf03wobjpAyXN8Ka1zcaCQAA\n/bLuHWE/lOTPJfnSuq4v/p3Gha5vHh/Y4fsPJLm2ue7sPq9Pkhsu8T1Wcc9CKeWlSV6aJI961KNy\n22237fAycHU5ffq0nwcuyyc/dT5nq/qi/36G1dk8P8kHPnpnPrbP/7a+dHAsd93x/nxw2/WfPDsP\nwN757vfk+vv/dMmdq+XnATb5eYA5Pwuwyc8Dq7S2IKyU8kWZt8D+9dYF9wd5yebxUudULuX6y3mP\nXe+p6/rVSV6dJE996lPrW2+99RJeGvrrtttui58HLsf/9f7fz4nZLLfeesuF3zj9yeR3kic97Vl5\n0hfeur8Xe9vD81mfeVM+a9t/i3fefy5585vypKc8Nbd+0eNX8rl34+cBNvl5gDk/C7DJzwOrtJbR\nyC0jke9P8oP7vK1tVl2/w/evax4f3Of1y5pc+32Pg94DwJpUs1lGgyW/fLW7vvY7GpnM94ktGY3c\n3BFmNBIAAPpkXTvCHpb5zqynJzlfSqnbv5L84+aa1zTP/Zvm6/c1jxft2iqlPCbzsciP1XV9Nknq\nuj6T5ONJHtZ8f7snN49bd3t9IMk0yRObsG4/9+z4uXa5B4A1mUx32BHWBlr7XZafzEOzpcvynRoJ\nAAB9tK7RyIeS/D87fO/PZ7437HcyD5nasck3JfmSJF+95bnWX95yzVZvSvI3m3t+aq976rp+qJTy\nliTPb/76zX28zwczX/b/lFLK5yw5OXKnzwbAGlSzHU6NXGEjbNQ2wizLBwCAXllLI6yu63N1XX/b\nsr+S/FJz2c80z72u+fqnMg/QvquU8oT2tUopj8jmiZM/ue2t2q9/oLmuvecJSb6zeb3tAdl/aB5/\nuJRyfMs9X5jkryX5VJLXb/lnqbe8z78spQy23PN1mQdq70nyWzv/GwFgVappndFglY2wJUHYQCMM\nAAD6aN2nRu5bXdd3lFK+L8m/TfL2UsrrkmwkeUmSx2XJ0v26rt9SSvnxJN+b5J2llJ9LcizzQOvG\nJN9d1/WHt73Va5O8uHndPyqlvCHJTc09wyTfXtf1g9vu+fEkL2zueWsp5X8keXySb8j8BMtvqeta\nbQDgEEymOzXC2iDsEhthZ++5+Onm9Sd2hAEAQK+sa0fYZanr+t8leVGS25P8rSQvTfKJJH+7ruu/\nv8M9L0vyt5vrXtrcd3uSv1LX9auWXF8n+euZh2dVku/OPBh7c5IX1HX9i0vueSjJVyb5p0luSPL3\nkvylJL+Q5Avrun7rZf9DA3BJqlm92OF1gcVo5KU0wpaPRg4HJaXM22cAAEB/HHojrK7rf5Lkn+zy\n/TckecMlvubPJPmZS7i+SvLK5q/93nMu80X//3ivawFYn0k1W+zwuvAbq1uWnyTjwSATO8IAAKBX\njlQjDAD2MtmzEXbwZfnJ/ORIjTAAAOgXQRgAnVJNZxkNVtkIWx6EjYaDVHaEAQBArwjCAOiUalpn\ntNJG2Nmkvrj5NR6WTGYaYQAA0CeCMAA6ZTLb5dTIMkyG4/2/2PhEUs+S6cZF3xoNNMIAAKBvBGEA\ndEo1rTMaLGuEnbu0Nliyef2ShfmjYcnEjjAAAOgVQRgAnVHXdapZvcOpkWcvbT9Ysnn9kj1h4+Eg\nE40wAADoFUEYAJ1RNTu7xjs2wi41CGsbYcuCMKdGAgBA3wjCAOiMNpjauRF2qaOROzfCRoNBqplG\nGAAA9IkgDIDOmDTB1HjpqZGrb4TZEQYAAP0iCAOgM9pG2PJTI88foBG2bFm+RhgAAPSNIAyAzmiX\n14+WNsJWuyx/NCiZVBphAADQJ4IwADqjDcLGg2WNsIOMRl7cCBsPB4tRTAAAoB8EYQB0xuay/J0a\nYStclu/USAAA6B1BGACd0e7sWn5q5BoaYVONMAAA6BNBGACd0Z7iOB7sdGrk6hph42FJNdMIAwCA\nPhGEAdAZm6OR2375qus1LMsfpNIIAwCAXhGEAdAZk9kOp0ZOJ0k9vfQgbDBMhtcsHY0cDcuigQYA\nAPSDIAyAzqgWo5Hbfvlqg6xLHY1M5uHZstHIgR1hAADQN4IwADqjHVW8qBHWBlmX2ghr79mhEWZH\nGAAA9IsgDIDOmDTB1PiiIGwNjTCnRgIAQO8IwgDojLYRNt6+LP9AjbCTO58aaUcYAAD0iiAMgM5o\nG1qji3aEtUHY5TbClo1GDlLNNMIAAKBPBGEAdEZ7iuPOo5GXuyNs2bL8+amRda0VBgAAfSEIA6Az\n2obWaOWjkcsbYfP3FIQBAEBfCMIA6Iy2ETYarL8R1p5MaU8YAAD0hyAMgM6oFqOR61+Wf6x5j4k9\nYQAA0BuCMAA6Y3M0cqdG2AqX5Q80wgAAoG8EYQB0xmJZ/o6nRq5yNLLZETbVCAMAgL4QhAHQGW0o\ndXEjrAmyRpc5GlmdS7aNQLYnU04sywcAgN4QhAHQGe0JjktHI4fHkuHo0l+0bZFV5y94ejTQCAMA\ngL4RhAHQGZMmlFo6Gnk5Y5HJ5l6xbeORbdg2EYQBAEBvCMIA6IxqWmdQksFgSSPschblJ5sB2raF\n+e3JlBPL8gEAoDcEYQB0xmQ6WwRUF6jOr7wRNl4syxeEAQBAXwjCAOiMybReHoRNzq28EbYYjZwZ\njQQAgL4QhAHQGdVsdvGi/KQZjbzcRlgbhG1rhA00wgAAoG8EYQB0xmRaL05zvPAbq1iWv7wR5tRI\nAADoD0EYAJ1RTWcZ79gIO+ho5PYdYfP32RCEAQBAbwjCAOiMalbvMBq5ikbYhUHYyGgkAAD0jiAM\ngM6YTGeL3V0XfmP1y/IXp0Zalg8AAL0hCAOgM6rpTo2wgyzLX94Ia0cjJxphAADQG4IwADqjms3W\nsCx/eSNspBEGAAC9IwgDoDMm0/riZfl1fbBl+aPjzYtv3xGmEQYAAH0jCAOgM6rZbNHU2nzy/Pzx\nchthpcxDtB12hE2cGgkAAL0hCAOgMybVkkZY2+S63EZYMg/RtjfCmvdxaiQAAPSHIAyAzpjMZoum\n1uaTTZPrchthSdMI27Ysf6ARBgAAfSMIA6Azqmm92N21sLJG2LbRyFHTCJtphAEAQF8IwgDojMl0\nyY6wNsBql95fjmWjkU0jrNIIAwCA3hCEAdAZ1Wy3HWEHHY3cvizfqZEAANA3gjAAOqOazhZNrYXF\njrDVLssvpWQ4KKlmGmEAANAXgjAAOmMyrRenOW4+uapG2LmLnh4NikYYAAD0iCAMgM6oZrPFaY4L\na1qWnyTj4cCpkQAA0COCMAA6o1raCGtHIw/SCLt4NDKZ7wmrNMIAAKA3BGEAdMZkOsv4olMjV9EI\n22E0cjiwIwwAAHpEEAZAZ1SzOqPBuhphS0Yj7QgDAIBeEYQB0BnVtM54tL0Rdn7+ODp++S88PpnM\nJsl0csHTo+EglR1hAADQG4IwADqhrutsTGcZL2uEjU4k25foX4q2TbZtPHI01AgDAIA+EYQB0AnT\n2TyQGi3bEXaQschkxyBsPHBqJAAA9IkgDIBOqBZB2PZG2LmDLcpPNu/ftidsNCyL9wUAALpPEAZA\nJ7TNrPH2EcjJ2fU1woYaYQAA0CeCMAA6oZru1gg7aBDWNsK2B2Fl8b4AAED3CcIA6ITJbN7MunhH\n2NkVjEa2jbBto5GDQaqZRhgAAPSFIAyATmibWRefGrm+RphTIwEAoF8EYQB0wuZo5LJTI9fTCLMj\nDAAA+kUQBkAntKOR44t2hK1vWf5oYEcYAAD0iSAMgE5YNMIuOjVylaOR2xpho8EigAMAALpPEAZA\nJ7QjissbYasajdx2aqRGGAAA9IogDIBO2AzC1tkI274sf5DKjjAAAOgNQRgAnVDN2mX5Wxphs2ky\nfejgjbDhOBmMlizLL5nMNMIAAKAvBGEAdELbCLtgR1jb4BofP/gbjE8m1fkLnhoNnBoJAAB9IggD\noBPaXV0X7AhbBGEHbIQl8/HKbY2w0dCOMAAA6BNBGACdUDWnN4627ghrg6uD7ghrX2P7svyhRhgA\nAPSJIAyATpg0zazRYFkjbBVB2MmlO8IqO8IAAKA3BGEAdMLmaOSyRtiqRiO3nRo5GGQ6q1PXwjAA\nAOgDQRgAnbA5GrnORtj20cj5e03sCQMAgF4QhAHQCW0YNV56auS6luXP36sN4QAAgG4ThAHQCdV0\nWSNsvcvy231kk0ojDAAA+kAQBkAnTGZLdoRV5+ePK2mELVuWP2jeWyMMAAD6QBAGQCdMqnkYNV5X\nI2x0fMmOsGY00o4wAADoBUEYAJ2wuSx/2Y6w9SzLHy2W5WuEAQBAHwjCAOiEdll+u7dr/mTbCFvh\nsvx6s/3Vts+qmUYYAAD0gSAMgE5oxxPH2xthZZAMjx38DcYnknqWTDcWT40G7WikRhgAAPSBIAyA\nTqhms5SSDC9ohJ2bt8FK2fnG/WpbZVsW5reNsA1BGAAA9IIgDIBOmEzrjAfbftmanF3NfrBk83W2\n7AnbbIQZjQQAgD4QhAHQCdV0tlhevzA5t8IgrG2EbQnCFjvCNMIAAKAPBGEAdEI1qy9clJ80jbAV\nLMpPtjTCNkcjjzX7yCYaYQAA0AuCMAA6YTKdXbgoPzmERpjRSAAA6BNBGACdUE3rHUYj19cIa99v\nYjQSAAB6QRAGQCdMZssaYetdlj+2LB8AAHpFEAZAJ0ym9SGNRi5phE01wgAAoA8EYQB0QjWdHdKy\n/C2NMEEYAAD0iiAMgE6YTOvF8vrNJ88lo+OreYMly/LHluUDAECvCMIA6IRqNls0tBbWvix/h10S\nHwAAIABJREFUsHhvAACg+wRhAHRCNa13GI1c57L8djRSIwwAAPpAEAZAJ0ymswtHI6eTZFatrhE2\nGCbDa5Y3wuwIA64S95/dyFe98rfyux+450p/FABYC0EYAJ1QzeoLRyPbwGpVjbD2tbY0wjZPjdQI\nA64OH/zUmbz/7tP5Bz/3zpx5qLrSHwcAVk4QBkAnzE+N3PLLVhtYrTQIO3lBI2zcvN/EjjDgKnHq\nzEaS5OP3n8srfv19V/jTAMDqCcIA6ITJdKdG2IpGI5OLGmHt+zk1ErhanDrzUJLkK5/+Gfnpt3w4\nf/TR+67wJwKA1RKEAdAJ1eywGmGbQdhw0AZhGmHA1eHephH2oy9+dh593fH8o9e/KxuV/wcC0B+C\nMAA6oZrWi51dSZLJ+fnjyhthm6ORpZSMhyWTmUYYcHW478xGjo8H+YyHH88P/6/Pyvvu/nR+8rc+\neKU/FgCsjCAMgE7YmM5ybOupkYewLD9JRoOBRhhw1bj3zEZuuvaaJMlXPP0z81eec3Ne9aYP5AOf\n/PQV/mQAsBqCMAA64eJGWDsaucpG2IXL8pP5yZFOjQSuFqfObOTGa48tvv7Hf+UZOXnNMP/o9e/K\nTDsWgB4QhAHQCdVsltEVaISNh4NMNMKAq8R9ZzbyiC1B2CMfdk1+8Gufkbd/5L78v2/9yBX8ZACw\nGoIwADphMq0zHixrhK1vWX6SjAbFqZHAVWM+Gnnsgude/Ocfm+c/+ZH5F7/6vtx5/7kd7gSAbhCE\nAdAJ1XSnRtj6luUnTSNsphEGXB22j0Ym84NDfvTrn53prM4P/sK7U9f+cACA7hKEAdAJk9lOO8LW\nPRqpEQZcHc5Ppjm7Mb0oCEuSz7rxZF72VU/J/3jvJ/PL77zrCnw6AFgNQRgAnVBNZxkPtjbC1jQa\nWZ1PtjTARsNBKo0w4Cpw6sxGkiwNwpLkm7/kc/Kcx12ff/JLt+e+5loA6BpBGABH3mxWZ1ZnWyPs\nbDIYJ8Px6t6oDdWqzVbYaODUSODqsFcQNhyU/Nhf/bw8cG6SH/5vf3KYHw0AVkYQBsCR1+7oGg+3\nNcJWuR8s2Xy9LeORTo0Erhb3NkHY9mX5Wz39MdflO77sc/P6P/xYfvtPP3VYHw0AVkYQBsCR1+7o\nGg22NcJWORaZbL7eloX5IzvCgKtEO+74iF2CsCT5ri9/Up74yGvz/T//rpzdqA7jowHAygjCADjy\nFkHYRY2wdQVhGmHA1Wc/jbAkOT4e5p+/+Nn5s1Pn8uO//v7D+GgAsDKCMACOvI0miDq2fUfY2kYj\nNxth42FJNdMIA/rv1JmHMhyUXHd8792Lz3viTfkbz3t8/uPv3pF3/Nn9h/DpAGA1BGEAHHntqY1X\nohE2GgxSaYQBV4FTZzbyiJPHMtg6hr6Lf/SXn5ZHPfya/MPXv1NzFoDOEIQBcOQt3xG2jiBseSPM\nqZHA1eDUmY3ceO3+T+K97vg4/+zrnpX3fuLTefWbP7TGTwYAqyMIA+DIa5sGF54auc5l+Rc2wjQd\ngKvBPAjbfT/Ydl/1zEfna5796PzE//jTfPBTp9f0yQBgdQRhABx57Y6u0fCwGmFbgjA7woCrxL1n\nNnLTtddc8n3/5EXPzPHRIP/Hf31XZv5/CcARJwgD4MhrG1mjwfYdYatelt82wraORmqEAVeH+85s\n5BGXMBrZ+oyHH8/Lv/YZ+YM7TuU/v+2ja/hkALA6gjAAjrx2R9j4olMj1z8aOR6WxfsD9NV0Vuf+\nc5PceBmNsCT5huc+Lrd87k35sTe+N5944PyKPx0ArI4gDIAjb+dTI1fdCLt4Wf5oOFi8P0Bf3Xd2\nI3Wd3HSJO8JapZT88xc/OxvTWX7wF9+duvYHCAAcTYIwAI689tTGcXtqZF2vpxE2uiZJubARNnBq\nJNB/p85sJMklL8vf6rNvujbf+5eekv/+nrvzK+/+xKo+GgCslCAMgCOvHU1cNMKqh5LUqw/CSpm3\nwi5Ylj9IZUcY0HOrCMKS5Fu/9HPyrMdelx/6xdvzwNnJKj4aAKyUIAyAI2+yGI1sGmFVE1StejQy\nmYdrF4xGaoQB/beqIGw0HOTHXvx5ue/sRn7kje9ZxUcDgJUShAFw5E2qeRB2rG2EtY2tVTfCkosa\nYePBYBHEAfTVvU0Qdrk7wrZ61mOvz7c//4n5L2//WH73A/cc+PUAYJUEYQAcedWsHY1sGmGTw2uE\njYeD1PX8RDWAvrqvCcJuOHnwICxJvucrn5wn3HQy3//z78q5jelKXhMAVkEQBsCRN2l2dI0GbSOs\nCarW0gg7sW1HWLngMwD00akzG3n48VGOjVbz24Pj42F+9MXPzkfuPZt/8xvvX8lrAsAqCMIAOPLa\nZfnjixphhzAa2bxnpREG9Ni9ZzZWMha51S2f+8h83effnP/0+x/RqgXgyBCEAXDkVYtl+dsbYYew\nLL9poTk5EuizU2ceOvCi/GW+9EmPzNmNae6458zKXxsALocgDIAjrz21cTw4jEbYiaWNsA1BGNBj\np85M1hKEPfPm65Mkt9/5wMpfGwAuhyAMgCOvbWMdTiPs5IWNsGHbCDPWA/TXuhphT/7Mh+XYcJD3\n3Pngyl8bAC6HIAyAI2/nUyMPYVl+00IThAF9Vdd1Tp3ZyI3XXrPy1x4PB3nKox+W2wVhABwRgjAA\njrzN0ci2EdYGYetqhJ1ffNmeoDaZGY0E+un0Q1Um0zo3Xjtey+s/8zHX5/Y7H0hd+wMFAK48QRgA\nR97maGTbCGtHI9fVCFu2LN9v4IB+OnVmI0nW0ghLkmc+9rrcd3aSux44v/fFALBmgjAAjrxJOxq5\nfVn+aB1B2MlkNkmmk/lbNOHbxLJ8oKfubYKwm9awIyxJnnnzdUliPBKAI0EQBsCRV01nGQ9LStnS\nCBsdTwZr+GWsbZk1YVt7amS7pwygb06dbhth6wnCnvbo61JKLMwH4EgQhAFw5E2ms8WI4vyJc+sZ\ni0wuCsLa99UIA/rq1Nn1BmHXXjPK5zzy2tx+5wNreX0AuBSCMACOvMm03twPljSNsHUFYSc33yNG\nI4H+29wRtp4gLEmeefP1RiMBOBIEYQAcedVslvHwyjTCjg0tywf67dSZjVwzGuTkseHa3uMZj7ku\nH7//XO5v2mcAcKUIwgA48qppvbkoP2mCsJPrebPto5FtEDbTCAP66dSZjdx47bHNPYxr0C7MtycM\ngCtNEAbAkTeZ1tsaYWcPoRHWjEYO2tFIjTCgn9ogbJ2cHAnAUSEIA+DIq2azbTvC1jka2e4Ia0+N\nNBoJ9Nu9hxCE3fSwa/Lo645bmA/AFScIA+DIu3g08uwhjEZalg9cHU6deWjtQVgyb4VphAFwpQnC\nADjyJtMrtyx/PBgsPgNAH913ZnJoQdgHP3U65zama38vANiJIAyAI6+a1UtGI9fVCGtHIy9shFUz\no5FA/zxUTXP6oSo3HUIQ9oybr8+sTt77Ca0wAK4cQRgAR95kOstocNjL8rfvCNMIA/rn1JmNJMmN\n116z9veyMB+Ao0AQBsCRV03rHLtgNPL8IS7Ld2ok0F+bQdh47e/1uEecyPUnxnnPXYIwAK4cQRgA\nR95kuuXUyNksqdY4GjkcJ4PRltHIphE20wgD+ucwG2GllDzjMRbmA3BlCcIAOPIms3oRSKU6P39c\nVyMsmYdsTSOsPa1SIwzoo80gbP07wpLkGTdfl/fe9aBxcwCuGEEYAEdeNZ1l3ARSbUC1tkZYMg/Z\nmkZYuyPMqZFAH917+nCDsGfefF0eqmb50D1nDuX9AGA7QRgAR1413XJqZBNQrbcRdmIRuA0HJaXM\nPwNA39x3diODktxwYv07wpLkmTdfnyS5/c4HDuX9AGA7QRgAR95kNtscjVw0wtY9Gnl288vhIBM7\nwuCq9sd/dn8+8cD5K/0xVu7eMxt5xMljGbSt2zX73Eddm2tGg9z+cXvCALgyBGEAHHnVtN4yGtk2\nwtY9Gnlu88tB0QiDq9y3/czb8wM//64r/TFW7tTpjUMbi0zmB5A87dEPtzAfgCtGEAbAkVdNr0Qj\nbDMIGw0HFjvDVez8ZJp7Tj+U33r/p3Jfs1y+L06d3cgjDjEIS5Jn3Hx9br/zgdS1P2AA4PAJwgA4\n8iazOuOLdoQdzrL8JBkPSyYzv2GDq9WnPv1QkqSa1fmVd3/iCn+a1Tp1ZiM3HXIQ9sybr8uD56t8\n7L5ze18MACsmCAPgyKums4wGh9kIu3A0cjQYZFJphMHV6u4H57vBBiX5pXd8/Ap/mtU6deZwRyOT\neRCWxHgkAFeEIAyAI+/CUyPbIGydjbDto5EllUYYXLU+0QRhX/2sR+etd5zqzdL86azOfWcPPwh7\n2qOvy6Ak73FyJABXgCAMgCNvMpvl2GJHWDsaue5G2LZTI+0Ig6vW3Q/ORyO/7flPTF0nv/zOO6/w\nJ1qNB85NUtc59CDsxLFhPvdRD8t77tIIA+DwCcIAOPImSxthh7csfzx0aiQs85o3fyh/9z//0ZX+\nGGt394Pnc2w0yJ/7rBvyrMdelze8ox9B2Kkz84DvsIOwZD4eaTQSgCtBEAbAkVbXdaazesuOsENs\nhDUnmo0Gg1QzjTDY7k3v/WR+40/u7v3pf3c/eD6Pvu54Sil50XNuzjs+9kDuuOfMlf5YB3bv6fkJ\nmDdde82hv/czbr4udz1wPqd6dgonAEefIAyAI23SNLHG2xtho+Pre9PxiSR1Uj20eO+JRhhc5KOn\nzubsxjQPnquu9EdZq7sfPJ/PvG4eFr3w825Okl60wu47Ow+hHnHt+NDf+5k3X58kud2eMAAOmSAM\ngCOtbWKNtu4IG59MSlnfm7aL+Jv22WioEQbbPVRNc+cD82D64/ef2+Pqbrv7wYfymdfNw/ebbziR\nL3rCjfmld9zZ+SbcvWeuXCPMyZEAXClrC8JKKTeVUr6tlPLzpZQPlFLOlVIeKKX8TinlW0spS9+7\nlHJLKeWNpZRTpZSzpZR3llK+p5Qy3OW9XlhKua15/dOllLeWUr5pj8/3TaWUP2iuf6C5/4W7XD9s\nPsc7m3+WU83nvGX//1YAuFRtE2s02NIIW+dYZLL5+k37bDQomVTd/g0vrNrH7zvXTg/nzh4HYXVd\nN42wzRbqiz7/5nzgk6fzJ3d9+gp+soM7dfrKNcJuOHksj73hhCAMgEO3zkbYNyR5TZLnJXlrkn+T\n5PVJnpXk/07yX0q58I/zSylfl+TNSV6Q5OeT/Pskx5K8Mslrl71JKeW7kryhed2fbd7z5iQ/XUp5\nxQ73vCLJTyd5THP9zyZ5dpI3NK+3/frSvP8rm8/zqubzvSDJm5vPDcAaVM1pjeNFI+zcZmNrXRaN\nsHOL955ohMEFPnJq82TVthnWR59+qMrZjeliNDJJvubZj8loUPJLHR+PvPfMRh52zSjXjHb88+a1\nesbN1xmNBODQrTMIe3+SFyV5XF3X/1td1/9HXdffkuRpSf4syV9N8uL24lLKdZmHUtMkt9Z1/a11\nXX9fks9P8ntJXlJK+catb1BKeUKSVyQ5leS5dV1/Z13Xfy/J5yX5YJKXlVK+eNs9tyR5WfP9z6vr\n+u/Vdf2dSb6geZ1XNK+71TcmeUmStyT5/Lquv6+u629N8hebz/uaUsrDL/vfFAA7qmZNI2yxI+zs\nITbC5r/Rd2okXOyj924GYX0ejfzkg+eT5IJG2I3XHsuXPvmRecM77sxs1t3/N9x3duOKnBjZeubN\n1+WOe87kzEP93jEHwNGytiCsrus31XX9hrquZ9ue/0SSn2y+vHXLt16S5FFJXlvX9du3XH8+ycub\nL//Otrf5liTXJHlVXdcf3nLPfUl+tPnyO7bd0379I8117T0fzryBdk2Sb952T/u+L28+T3vP25K8\nrvncLwkAKzdpG2GDrY2wQx6NHA4WnwOY++ipszkxHubxN57Mnfef3/uGjrr7wfmhGVuDsCR50XNu\nzsfvP5c//Oh9y27rhFNnrnQQdn3qOnnvJ4xHAnB4rtSy/EnzuPWPf768efzVJde/OcnZJLeUUrZu\n89ztnl/Zds1l3dO83y3N+//2JbwPACvQNrEubIQd1mjklkZYh1sfsA4fufdsHn/jyTz2hhO93hH2\niQfmId+jtwVhX/XMR+ea0aDT45H3nt7ITVe4EZZYmA/A4Tr0IKyUMkryt5ovt4ZRT20e37/9nrqu\nqyR3JBkleeI+77kryZkkjyulnGze+9okj01yuvn+dn/aPD5ly3NPSjJM8qHmc+znHgBW5OJTI6/E\nsvzBYlcZMPfRU2fy+JtO5uaeB2F3f3oehH3GdReerPiwa0b5iqd/Rt74rrs6+/+H+85u5BFXMAh7\nzPXH84iT49z+cUEYAIfnSjTCfizzxfZvrOv617Y8f33zuNPGzPb5Gy7jnuu3Pa7jPW7Y4fsAHEB7\nauS4PTWyOn/ojbDRsCw+BzA/SfGjp87ms288mcfecDx3P3i+t+PDdz9wPg8/PsrJY6OLvvei5zw2\n95zeyFs+eO8V+GQHU9d17j1zZRthpZQ88+br8567BGEAHJ6Lf0Vfo1LK3818Uf17k/zNS729ebyU\n34lczj0rfY9SykuTvDRJHvWoR+W22267xI8C/XT69Gk/D+zLHQ9MkyTv/ZPbc+Le9+WLHrw3n65v\nzJ+s8b+fa85/Ml+c5L3v/uN84t5H5p5PPpQz56Zr+2/WzwNdc//5Wc5PZjl/78czHSSzOvnFX78t\njzxx8D9jPWo/D+/+0Pk8fDRb+pkG0zonRsmrf+0PM7vzmotvPsLOVXU2qlnuu/vPctttd1+xz/Hw\n6UZ+/85JfuNNv5nRoOx9w1XkqP0swJXk54FVOrQgrJTynUl+Isl7knxFXdentl2yvb213XXbrmv/\n/pHNPcv+KK6958Et1+/2HsvaX5fzuRbqun51klcnyVOf+tT61ltv3eFl4Opy2223xc8D+/Hwj9yX\n/N5b8uee83m59amfkfzP5OTjnpDPXOd/P2fuSX4/edoTH5+nPe/W/Pf73pV33/eJtf036+eBrnnb\nh08lt/1evvIvPCeDUvJTt/9BHv+0z88Xfc6NB37to/bz8BPv+d088eGj3Hrr85Z+/2tPvSO/9u5P\n5C98yfNzfDw85E93+T5679nkN34zz33203Prcz/rin2OB274eH7ljj/OY5/+BXn6Y67b+4aryFH7\nWYAryc8Dq3Qoo5GllO9J8qok707yF5uTI7d7X/N40a6tZq/Y52S+XP9D+7znMUmuTfKxuq7PJkld\n12eSfDzJw5rvb/fk5nHrzrEPJJkmeWLzOfZzDwAr0u7eGS92hB3Gsvx2R1i7LN+pkbDVR+6d/2x8\n9k3X5uYb5j8vfd0TdvcD5y86MXKrFz3n5nz6oSq3ve9Th/ipDu7U2Y0kuaKjkYmF+QAcvrUHYaWU\nf5jklUn+OPMQ7JM7XPqm5vGrl3zvBf8/e3ce3lh+1/n+fY4WS5Zky6tsuVyuzXZ1ubureg+kEwhk\nIckkISRNQuAJl2EylzsDPAwMM/cCMw931jswcJ878zwwA8xAMmESkhBIN2lIQzIhnY3u6nZVt2tx\n7bbLuy1bkiVrOTrn/nF0VO4ql1fpnCP5+3qePJUu29Jptxfpo+/38wOagW8bhpHf4ce8+6732dPH\nlO/v2+X7f8su7kcIIUQVWKc1VlZm7CjL976xLN/nUSqnVwohYDKRRVWgLxokHjVDoukGDMJ03WAh\nnSfWcv+1x+893kFn2M+z56dtvLL9S2TMh9TtDgdhRzvDBH0eLszcr45XCCGEqK6aBmGKovwLzHL8\nVzDXIZe2ePcvAEvARxVFeXzDbQSAf1P+x9+962P+EMgDP6soypENH9MG/Er5H//LXR9j/fOvlt/P\n+pgjwD8u394f3vUx1v3+m/L1WB/zBPARYBH40y3+3YQQQuyRNYnl9ahQ0qBUqP1EmKqCN7ChLF+t\nnF4phIDJ5Qy9rUH8XpVmv5e2Zl9DToQtZwpourHlRJjXo/Leh3r56qUF0rmijVe3P8tr1kSYs91m\nHlXhZG9EJsKEEELYpmYdYYqi/CTwrzDXCl8Efl5R7inAvGUYxh8BGIaRUhTlE5iB2NcVRfkskADe\nDwyX//5PNn6wYRg3FUX5ZeA/AWcVRfkToAB8GDgE/JZhGN+562O+rSjKbwO/CLymKMoXAD9moNUO\n/JxhGLfuus7PAj9Svt1RRVGeAzrKH+MBPmEYhvz2FkKIGrAmsXweBbTyE+1aT4RZ92FNhKnmqZGG\nYbDJ7zIhDpyJRJaBjjuBdDwabMggbD6VA9gyCAN4/5k4n/zOBH99cZ4fefSQHZe2byvl1ci2kM/h\nKzHXI780OoOuG6hSmC+EEKLGalmWf7T8pwf4hfu8z98Cf2T9g2EYf64oyvcBvwp8CAhgdnT9IvCf\nDMO4Zy/FMIz/rCjKLeCfAh/HnHK7CPyaYRif3OxODcP4JUVRXgN+FvNERx14FfhNwzD+YpP3NxRF\n+THMFcm/D/wckAO+AfwbwzC+ff9PgxBCiP2wJrG8qloJpuwJwpor9+ct95NpumEGckIccJPLWd45\nEqv8czwaNMvXG8xC2grCtp6aevRwG33RIM+en6mbIGw5U8DvUQk32XqI/KZG4q18+ruTTK1kGegI\nOX05QgghGlzNfvMZhvHrwK/v4eO+Bbxnlx/zHPDcLj/mk8CmQdl93l/D7Dr7f3dzP0IIIfanuHEi\nrLyqWPPVSChPhFmrkWb4pZUM6uhQOCFqYi2vsZwp0N9+5/uwLxrku9c3O8C7vs0lzR6tntatJ8IU\nReF9p+P8wYs3SGQKjvdu7URizbxON0y5bizMlyBMCCFErdlyaqQQQgixV5WJMI/dE2F3ViP95Ymw\novSECVGZ/BpovxNYxKMB0nmNVB11ZO3EfCqHokBnePserfefjqPpBs+/PmvDle1fIlOgzSWB3VAs\ngkdVpDBfCCGELSQIE0II4WrWRJhXtXsirPnORJh6ZyJMiINuMpEBuKcjDGi4nrD5VI7OcBM+z/YP\nmR/ojXCiO8yz52ZsuLL9S2QLdLgkCAv4PAx2h6UwXwghhC0kCBNCCOFqd8rynZsIq3SElWQiTIjJ\nhBkQb1yNbOQgbLt+MIuiKHzgdJyXbiXq4vPgthXOU/EWLkoQJoQQwgYShAkhhHC1Yjl8MjvCyk8u\nvfaW5VsF+UVdJsKEmFjOEm320Rq8c9pgXzkIm17NOXVZNTGXyhOLbN0PttH7TscB+IvX3D8VZnWE\nucWp3hYW0nkW03mnL0UIIUSDkyBMCCGEq1lBmNkRZq1G2jURZq1GljvCNJkIE2IykWWg/Y3ryV3h\nJnwepS4moXZjIZUjtk1R/kZHOkOcPtTKs+fdHYQVNJ10XnNVEDYSbwWQnjAhhBA1J0GYEEIIV9P0\njadGOrUaWe4Ik7J8IZhMZDl818l+qqrQ0xpoqCCsoOksZwq7mggDcypsbDrFjcW1Gl3Z/q1kCwCu\nCsJObTg5UgghhKglCcKEEEK4mtXL5VVVx8ryK6dGSlm+OOC0ks70yjqH2+8No+OtwYYKwhbS5ppn\nT+vOOsIs7zsdR1Fw9VTY8poZhLmlLB+gNeijvz0oPWFCCCFqToIwIYQQrlYsuWEizCrLlyBMHGwz\nqzk03WCgPXTP2/qiQWYaqCNsPmV2VXW37G4iLNYS4Kmj7Tx7bgbDcOfPjETGDMLaXBSEAYz0tspq\npBBCiJqTIEwIIYSrabqOR1VQFMX+ibBSHvRSZTWyKKuR4oCbSGQAONxx7/dgPBpkLpVrmNNV51Nm\nqLfb1UiAD5zp48ZSxrVrfoms+ybCAEbiLdxazpLOFZ2+FCGEEA1MgjAhhBCuppUMvKoZRFFcB8UD\nHt/WH1QN1tRZcR2fKhNhQoDZDwZwuH3zIKykGyw0yKl/VhDWs4uyfMu7H+zB51Fcux6ZWDP/G7mp\nIwxgpM/sCbs0m3b4SoQQQjQyCcKEEEK4WrFk4CuvJlJcNye1FKX2d2xNnRXX70yENcikixB7Nbmc\nxe9V6dlkXTAeNf+uUXrC5lN5/B6VtubdB+/RZj9vHeziufMz6Lr7AvREpoCimNfpJnJypBBCCDtI\nECaEEMLVNF2vBFEUs/b0g8GGibCs2U+GBGFCTCxn6W8Loqr3htF9UfN7ZibZGD1h86kc3S1N5lr2\nHrz/TJzZZI6zEytVvrL9S2QLRIM+PJv8d3RSd6SJzrDftSulQgghGoMEYUIIIVytWDLMEyOhPBFm\ndxC2Xrl/WY0UB91kIstAx71F+QC9VhDWMBNhOWK7LMrf6O0PxAj4VJ49P13Fq6qORKbgurVIAEVR\nOBVvlSBMCCFETUkQJoQQwtW0kl6ZyKqsRtqhshqZraxmalKWLw4wwzCYTGQ37QcDCDd5aQ36GiYI\nm0vliLU07fnjQ01e3v5AjOdfn3PdNOnyWoGO0N7/3WppJN7CtYU0Bc1dnzMhhBCNQ4IwIYQQrlYs\n6Xd1hNk/EXZnNVImwsTBlcgUWMtr9w3CwCzMb5QgbCGV39dEGJinRyYyBb55balKV1UdiUyBtpAN\nh47swaneFoolgyvzUpgvhBCiNiQIE0II4WpF3djQEebERNg6XpkIE4KJ8omRAx33/x7siwaYXq3/\njrC1vMZaXtt3EPbWoU5aAl6eO+eu0yNXsgXaXTwRBnBR1iOFEELUiARhQgghXE0r6fgqHWHOlOV7\nVZkIE2KqHIQdhImw+ZQZ5m12OuZuNHk9vPvBXr5yYY5csVSNS9s3XTdYyRbpcGFHGMCRjhAhv0dO\njhRCCFEzEoQJIYRwNa1090SYXUFY+cm+lqusZrqt50cIO00sm0FY/xZBWG9rkOR6kbW8Ztdl1YQV\nhHXvoyPM8v4zcTKFEl+7vLDv26qG5HqRkm64siwfQFUVHuhtkcJ8IYQQNSNBmBBCCFczVyM3ToTZ\ntRq5YSKsHMTJqZHiIJtYztLTEiDg89z3feJRc4Jqts6nwqwgbL+rkQBvOtZBV6SJZ12/NGsOAAAg\nAElEQVSyHpnIFgBcG4SBuR55aTaFrsvPXCGEENUnQZgQQghXM1cjnZgI21iWLxNhQkwlshzeoh8M\noC9qft9M130QlgeqE4R5VIX3PtTL18YXSOWK+769/Upk6iEIayVTKHFrOeP0pQghhGhAEoQJIYRw\ntXtXI+0uy89WTo3UZDpBHGATicyW/WBgdoQBzNR5Yf58Kke4yUu4yVuV2/vAmTgFTecrY3NVub39\nWF5zfxB2qlyYL+uRQgghakGCMCGEEK5W1HVzIssw7C3L9/gBxTw1slzWr8lEmDigcsUS86k8A9sE\nYd2RJjyqUveF+fOpHLEq9INZzvRHOdzezLPnnV+PrIeJsKFYBJ9HkSBMCCFETUgQJoQQwtW0kmGe\n2lgqglGyLwhTFHMqrLhemQiTUyPFQTVpnRi5zWqk16PS0xJogCAsX5W1SIuiKLzvdC/fvr7M0lq+\nare7Fyt10BHm96oMdkfk5EghhBA1IUGYEEIIVyuWdLMsv2g+EbdtNRLM0K2YRVEUPKoiHWHiwJos\nnxi53WokmIX59d4RNpfMVTUIA3j/6T5KusHzr89W9XZ3a3mtQMjv2fLQAzcYibdwcSaFYcgLEEII\nIapLgjAhhBCupumGOZFVLD+xtmsiDCoTYQBeVZGOMHFgTZQnwgY6Qtu+bzwaZCZZv0GYYRgspKsf\nhA33RBiORRw/PTKRydMedu80mOVUvIXlTIGFtLMTdEIIIRqPBGFCCCFcrVgqd4RVJsLsDMKClfv1\neVSZCBMH1uRyhkiTl7Zm37bvG48GmUvmKNVpcLySLVIsGVXtCLO8/0ycsxMr3F7JVv22dyqRLdLe\n7P4gbCTeCiDrkUIIIapOgjAhhBCuZnaEqQ5NhAUr9+vzKGjSESYOqMlElsMdzSiKsu37xqNBiiXD\n8S6svZpLmide9lR5Igzg/afjADx33rn1yEQm7+p+MMsDvREALkxLYb4QQojqkiBMCCGEq5kTYRtX\nI+3sCNuwGulR0XSZCBMH00Qiu6N+MIC+qBkg1WtP2HzaDMK6axCE9bc388jhqKOnRybWCrSHqj/t\nVm2RgI8jHc1ycqQQQoiqkyBMCCGEq2m6gdejOL8aqSpyaqQ4kEq6we3E+rYnRlriUfN7tF5Pjpwv\nT4TVYjUSzKmwS7Mpri2ka3L7WzEMg+VMgfbQ9iuubjASb+XCrKxGCiGEqC4JwoQQQrhasaS7YjXS\n61HRpCNMHEDzqRyFks5A+/ZF+dAAQVjKXOnsjlR/IgzgvQ/3oio4Upq/XiyR1/S6mAgDszB/KrFO\ncr3o9KUIIYRoIBKECSGEcDWtZJ0aaU2E2b0aad6v1yMTYeJgmlg2vwd2uhrZEvARafIys5qr5WXV\nzHw6R0fIj99bm4fJ3ZEA33O8g2fPz2AY9v5MWV4rANBRBx1hACPxFgAuynqkEEKIKpIgTAghhKtp\nuo7X4/xEmE+VUyPFwTSZyAAwsMPVSDCnwuq2IyyZI1aDfrCN3vdwnFvLWa4urNX0fu6WyJhBWD2U\n5YOcHCmEEKI2JAgTQgjhWoZhUCwZ+FQnJ8LKQZhXQdNlIkwcPBPLWbyqQm/rzsOheDRQv6uR6VzN\n+sEsDx0yA55rdgdhWTMIa6uTIKwr0kR3pEkmwoQQQlSVBGFCCCFcq1QOnpydCCuvRspEmDigJhNZ\nDrUFze/DHYpHg3UbhM0l8zWfCDvaafat3Vi0OQirs9VIMNcj5eRIIYQQ1SRBmBBCCNfSKkGYcicI\n89oZhDWDrkGpiM+joElHmDiAJhNZ+nfYD2aJR4OsZItkC1qNrqo2iiWd5Uztg7Bmv5fe1gA3ljI1\nvZ+7VVYjw/UUhLVybXGNXLHk9KUIIYRoEBKECSGEcC1rAsunqqCtg8cPHq99F2BNnxWzeFUVTZeJ\nMHHwTCxnd9UPBtBXOTmyvgrzF9N5DIOaB2FgToXdWLQ3CFvOFPB5FCJNNv4c3adT8RZKusGV+bTT\nlyKEEKJBSBAmhBDCtawJLJ81EWbnWiRsCMLW8XoUCjIRJg6YZLZIcr3IQHtoVx8XrwRh9bUeOZ8y\ng7ue1tp2hAEc6wpxY3HN1pMjVzIF2pr9KIpi233ul3VypKxHuoeuG/zO16+xkK6voFsIISwShAkh\nhHAtayLM7AjL2luUD3fur5jF51HRpCNMHDCTCbMjb/erkeZEVf0FYXkAuiN2TISFSeW0yrqiHZYz\nhbo5MdLS39ZMpMkrJ0e6yMXZFL/xV+P86SvTTl+KEELsiQRhQgghXKuou2giTJWOMHHwTCTM1b3d\nrkbGWgKoSj0GYeaEix2rkce6zCm7mzb2hCUyeTrqqB8MQFUVHpDCfFexQsmrsq4qhKhTEoQJIYRw\nLWsCy6uWT410bCJsHZ9XpSgdYeKAmVg2J8IO73IizOdRibUEmEnW1+rUfCqHV1VsOVXxWOXkSPuC\nsJVskbbm+grCwFyPvDybrpwkLJw1Nm2GkpfnJAgTQtQnCcKEEEK4VrG08dTIrIMTYVl8MhEmDqCp\nRJbOcBOhPZSrx6PBupsIm0vl6I40oaq179A61NaMz6PYenLk8lrelpCv2kbirawXS9xcWnP6UgQw\nVp4Iu7a4JpUBQoi6JEGYEEII17JOafR5VIdWI+9MhHmlI0wcQBPLWQ637+37rh6DsIVUnlhr7dci\nATyqwkCHWZhvh2JJJ5XTaA/V/iCAapPCfPco6QaXZlN0hv0UNJ1b5alRIYSoJxKECSGEcC1rAsur\nKg6V5W+YCPMolc4yIQ6KyUSWgY7dnRhpiUfN1Ui9jr5v5lM5YjYU5VuOdYZs6whbKZfyt4d8ttxf\nNZ3oDuP3qhKEucCNxTVyRZ0PnOkD4Ir0hAkh6pAEYUIIIVzLOjXSuYmwjWX5auV6hDgICprOTHJ9\n1/1glr5okIKms2zjqYj7NZfKEWuxb2LqaFeIieWsLd1XiawVhNXfRJjPozIci8jJkS5grUV+4Ewc\nVYFx6QkTQtQhCcKEEEK4lqZv7Ahzsiw/i9cjHWHiYLm9ksUwdl+Ub4m3mkFyvaxHZgsa6Zxm22ok\nwPHOMIWSzvRK7T9HiTUrCKu/jjAw1yMvzKQwDPk57KSx6RQBn8qp3haOdIQkCBNC1CUJwoQQQrhW\n8Q2nRjpZlr+O3yMTYeJgmUiY3T8DHXsMwqL1FYTNp/IAtq5GHu0y106v21ACb03mdYTrNwhbzRbr\n7iTSRjM2neSB3ha8HpWhWERWI4UQdUmCMCHEtm4tZTj1L/+KsWlZSRD2siawfJWJMAdXIz1KZUJN\niINgslyCfXiPQVhfOQibrpsgzAxYemycCDvWaQZhNxdr3xO2Ul6NbGuuzyDsVLkw/6L0hDlG1w0u\nzqR4MN4KwFBPhFvLGXLFksNXJoQQuyNBmBBiWy/fSpAtlHjpZsLpSxEHTOXUSKss32tzEObxgeoz\nVyNVlZJuyFqOODAmE1mCPg9d4b11SrUEvYT8HmZW62OCxwrC7OwIaw/5aQl4uWHHRNiaFYTVX1k+\nwMmeFhQF6Qlz0NRKlnReq5ziebIngm7AtQV7Tj4VQohqkSBMCLEtq//h6oKMvwt7FbTyRBjlsm27\nJ8LA7AkrrptTaUBResLEATGxnOVwezOKouzp4xVFIR4N1tFqpBmEdbfYNxGmKArHusK2nByZyBSI\nNvvweurz4X+oycvRzpCcHOmgsWnzc/9gX3kiLBYBpDBfCFF/6vM3oRDCVuPl/ocr8/KKn7CXNRHm\nN8zuHtvL8sEM34rZypNH6QkTB8VkIrPntUhLbzTITLJegrA8zX4PkSavrfd7rDPEDRtWIxOZAu11\nuhZpGYm3ymqkg8Zmkvg8CoOxMABHOprxe1XpCRNC1B0JwmyULshKjahPl+esICwtX8PCVpWOsFL5\nibQjE2FBsyNMVd5wTUI0MsMwmExkGdjjiZGWvmigbibC5lI5Yi2BPU/A7dWxrhCzyRzZglbT+0lk\nCnV7YqRlJN7C9Oo6K+Xif2GvsekkQ7EITV4PAF6PyomucOVxohBC1AsJwmy0nDMqJxIJUS8SmQKL\n6TyH25tJ5zT5Gha2sqavnJ0Is1YjyxNhukyEica3mM6TK+r7ngiLtwZZWivURZn2Qipnaz+Y5Win\nOV1T6/XIRgnCAC7OylSY3QzD4MKGonzLcI+cHCmEqD8ShNlsXH5RiDpzec58sPm+070A8mBH2Mo6\npdGrl8u2HZsIy1aCMJkIEwfBRKJ8YuQ+J8Li5ZMjZ5PuL8yfT+WJ2dgPZjnWVT45ssZB2HKmQEe4\n3oMwM4T5zvVlh6/k4JlL5UhkCoz0tbzh74diEWaTOZLrRYeuTAghdk+CMJtdlRBB1BmrAPXvPRwH\nJAgT9tLKE2HektNB2DreSlm+TISJxje5bAZhAx2hfd2OFYS5fT3SMIzKaqTdjpQ/x7XsCTMMg5Vs\ngbY67whrD/n5oZEefu/FG/KY2mZWUf7IXRNhJ3vMwnx5fCiEqCcShNnIo8ipKqL+jM+laWv2cbIn\nQkfIz1UpzHfMQirHr/7Z66wX3L9iVC3WCY2+ykSYU6uR2cqpkdaUmhCNbCKRRVWgL7q/8Nn6+GmX\nB2HJ9SIFTXckCAv6PfRFgzWdCEuta5R0o+5XIwH+9Q8/SMjv4Zc+f77yYomovbHpJKoCD/RG3vD3\nQz1ycqQQov5IEGYjnwpXFiREEPXl8lya4Z4IimKeEnRlQR7oOOW512b547+b5Ds3lpy+FNtYp0Z6\n3DARpsqpkeLgmFzO0NsaxO/d30PFWGsTiuL+ibC5lPkzxomOMICjnSFuLNbuMeJyxuxZrPfVSICu\nSBP/9oMP8drtJL/79etOX86BcWEmyfGuMM3+N56qGm8NEGnyykSYEKKuSBBmI79H4ep8Gl2mCUSd\n0HWDK/NpTvaYfRBDsQjX5tfk5EiHjE6uAHBuKunwldjHmgi7E4Q5WZYvq5Hi4JhIZBnYZ1E+QJPX\nQ1e4yfVBmHUQTI8DE2Fg9oTdWMrU7PdronzKYr2vRlre81Av7zsd5z997SoXZ6Q43w5j0yke7Gu9\n5+8VRWGoJyInRwoh6ooEYTbyqZAtlFy/HiCEZWolS7ZQqvQ/DMYipPNaXZQeN6LRyVUAzk+tOnwl\n9rGK6b2l8s9NB8vyrYkwKcsXB8FUlYIwMHvCZlbd/XtjvjIR5kwQdrQzRDqnsbRWqMntW0FYR8iZ\nibda+FfvHyHa7OcXP3eOgiYvUNTS0lqeuVSucmrn3YZi5smR8kKpEKJeSBBmI395mkBGh0W9sF7d\nGy4HYcMxKUR1ynwqx/TqOn6vyvnbqwfmwaam6ygKqJo5reHMRJi5Gukrr4hZ65pCNKq1vBnI9O/z\nxEhLXzTo/omw8gs8XRFngqJjXWGgdidHWkFYewOsRlraQn7+/Qcf4vJcmv/8tatOX05DuzCzeVG+\nZTgWZjVbZDGdt/OyhBBizyQIs5Gv/NkelxBB1Amr+HSoHIANxcwH6lKYbz9rGuyHz8RZzRaZKJ/o\n1ugKJR2fR4Vi+d/XkYmwclm++VpGZV1TiEZVOTGyfX8nRlri0QDTq+uuDvDn0znamn0EfB5H7v9Y\np3VyZG1+vy5bQViDrEZa3n4qxocePcTvfP36gZqWttvYtFnJcOo+E2HD5QoNWY8UQtQLCcJspCpm\noaSECKJejM+lOdzeTKjJLEaNNvvpijTJRJgDRqdW8HtUPvbUAADnbx+MB/xaycCnKlAsT5N4HVhb\nKodvPoqVaxKikU0mykFYFVcj85pemUpyo7lk3rG1SDA/R36vWrOJsJVMgaDPQ9DvTNBXS//yfafo\njjTxS58/T654cE5VttOFmSQDHc20Bn2bvt16oVQeHwoh6oUEYTYbjEXkeGFRNy7PpSprkZahWFhO\nP3XA6OQqp+ItPBhvIejzcO6AvPKtlXS81kSYNwiqA7+2yuuYfsNcnSrKaqRocJMJM4yp1mpkPGqG\nyW7uCVtI5xwNwjyqwpGOZq4v1m41sj3UWNNgltagj//woYe5trDGb//1FacvpyGNTad48D5rkQAd\n4SY6w03yHEcIUTckCLPZcE+Ea4trlOTkSOFyuWKJW8vZSlG+ZbA7wjUpRLWVVtJ57fYqjxyO4vWo\nPNTXemBWQIq6YZ7WWFx3Zi0SKvfbpJvdJ0UpZRYNbmI5S7TZd9/pj93qKwdhbj4saC6ZI9bibJH8\n0c4QN5dqtxrZ0UD9YHd761AXH3vqML//4g3O3ko4fTkNJbleZDKRve9apOVkT0TqX4QQdUOCMJsN\ndocpaDoTy7V5xU+Iarm2YAa2906ERcjI6ae2ujyXJlfUefRwGwCn+1sZm0lRLDV+IKOVdPO0xuK6\nM0X5ULlfX3kiTJMXMkSDm0xkGajSNBhsnAhz5+8NraSztObsaiSYhfmTiSxaDX62JzIF2hqsH+xu\nv/KeB+iLBvmnnz9PtqA5fTkN42K5KP/BvvtPhMGdkyN1+R0phKgDEoTZzAoVZIdeuJ013n73RJgU\n5ttvdHIFgEcORwE43R+loOkHYgVBKxl4PYq5GunwRJhPL69GHoAAUhxsk4kshzuqU5QPlEvoVdcG\nYcuZArqB40HY0c4QxZLB7ZXqf54SmQIdDboaaQk3efnND5/m1nKW3/ircacvp2FcmDGL8ke2mQgb\n7gmTK+pMrRyMw3yEEPVNgjCbnegOoyhwRUIE4XLj82n8XpUjdz0ZGoxJmGu30clVuiJNlfWi04fM\nQGz0AKxHmquRqitWI/3lIEzK8kUj00o60yvrHG6v3veboijEo0Fmku4MwuaS5ve200HY8a7yyZE1\nWI9s5I6wjb7neAc/9eYj/NG3b/Hta0tOX05DGJtO0tsaoDO89eqwnBwphKgnEoTZrNnvpb+tWXbo\nhetdmk1xoitsFpVv0Br0EWtpkjDXRqNTqzzSH0VRFAAOtQXpCPkPRE+YuRppTYQ5uxrpLXeEaVKW\nLxrYzGoOTTcYaK/eRBiYPWHTLi3Ln0+Z19Xj+ESYOXF9o8qF+euFEuvFEu0N3BG20T9710mOdob4\n5S+8RjpXdPpy6t7YTIqRLYryLYPd5ZMjJQgTQtQBCcIcMBSLcFWCMOFy43Ppe9YiLVYPhKi9lUyB\nm0sZHin3g4E5XXGmP7r/IEzXzf+5WLFklE+NdH4izFtar1yTEI1qMmGuNR3uqG7wHG8NMuvS1Ugr\nCHO6LL895Cfa7OPGUnWDsES2YN5+g3eEWYJ+D//xmdPMJtf5d89fcvpy6lq2oHFjcW3btUiAUJOX\n/vagvNgvhKgLEoQ5YCgW5sZihoKcPCZcaiVTYCGd52Tv5kHYYHeEawtrUohqg3PlsMvqB7Oc7o9y\nbXFtf692/+lPw+c/vp/LqzlN1zecGumOiTDpCBONbCJhhjCHq1iWD2Zh/kI6T14rVfV2q2E+lcej\nKnRss/plh6OdIW5WeSIssVYOwg7AaqTlsYE2PvHWY3zmpSm+Pr7g9OXUrUuzaXRj+6J8y3Cs5UD0\nlwoh6p8EYQ4Y7omg6QY3q/yKnxDVYvU7WH0PdxuKhVkvlmpS6CveaHRyBY+q8PChNz4IPd0fxTDg\n9enk3m5Y1+HaV83/ldx7upZWMjasRjq0tlSeCPOUJ8KkI0w0ssnlLH6vWvU1wXjUvL35ZL6qt1sN\n86kcXeEmPKri9KVwrDNc9Y6w5Yz5Oe84IKuRln/y9iGGYmH++Z++RjIrK5J7YRXlP9i3/UQYmIX5\nN5cyrgy8hRBiIwnCHDDYLWXjwt3G58yjsu+3GimF+fZ5dXKVkz0Rmv3eN/z96XIwdn5qj0HY8lXI\nJ82AaX5sv5dZM4WS7oKyfHMyxqOVT410+TqpEPsxsZylvy2IWuVQyDrsY9qF65FzqZzja5GWY10h\n5lN5MvnqvUCRyJgTYW0HZDXSEvB5+K1nzrC0VuD/fu6C05dTl8amk3SE/DsOxodi8mK/EKI+SBDm\ngGNdITyqIiGCcK3x+TTRZh/dkc2fGAzGyoWoC/I1XEsl3eDc1Oo9a5EA0WY/RzqaOTe1srcbv332\nzv+femmPV1h7WiUIc7IsXybCxMExmcgy0FHdonwwVyMBZlwYhC2k8o6fGGk51ml+7qsZJFhBWEfI\nHWGfnR461Mo/ftsJvjg6zQsX5py+nLozNp1ipK+1cljPdk6WNwlkPVII4XYShDkg4PNwpKNZgjDh\nWpfn0gzHIvd94NMS8NHbGuCqnBxZU9cX11jLazzS37bp20/3R/c+ETZ9FppaINILU3+3j6usLU03\n8FY6whyaCPOaT5BVzQrCZCJMNCbDMJhMZKveDwbQ02p+H7kxCDMnwtwRhB3tMoOwahbmJzIFvKpC\nS9C7/Ts3oJ992wlO9bbwK3/2eiUUFNvLayWuLqR3VJRvOdoZwqsqEoQJIVxPgjCHmKfuSYgg3EfX\nDa5scWKkZVBOjqy50Ulz2muziTCAM/1R5lI55pK53d/47Zeh71Hof8rVE2HFkoFPMaCUd24iTFXB\nG0TR1vF5FIpySIRoUIlMgbW8VpMgLODz0BluYibpriAsVyyRXC+6ZjXySEcIRYEbi9V7jJjIFGgL\n+Xc81dNo/F6V3/7IaZLrRf7Fl9xbBeA2V+fXKJYMHozvrCgfzM/1sa6QPD4UQrieBGEOGYpFuLWc\nIVeUMknhLtOr62QKpfsW5VuGusNcW1ijJKFAzYxOrhJt9nG0c/M1pdP9ZkB2/vbq7m64kIX5i9D3\nuBmEJSchNbPfy60JraQTUsslx05NhFn3XVzHq6oyESYa1mQiC8BAR21C575ogOnVPQT3NbSQMovk\n3TIRFvB5iLcGq74a2X7A+sHudrKnhV94+xBffm2W58678/ed24xN764o3zLc01I5dEkIIdxKgjCH\nDMUiGAZcW5CpMOEud06M3HoibCgWIa/pTJWfOInqG51c5ZH+6H1fxT/V24JXVTg/tcsgbPYcGCU4\n9IQZhIFrp8I03SColFdZnJoIs+67uI7Xo1CUjjDRoKwgrBYTYWD2hLltNXIuZQZzbgnCwOySvbFY\n5SAsdLCDMID//a3HON0f5V98aYyFtLsCWTcam0kSCXh3/fNgOBbm9so6a1U88EEIIapNgjCHDPeU\ny8ZldFi4jHVi5HZBWKUwX76GayKVK3JlIc0jhzfvBwNzcuCB3hbO7TYIs4ryDz0OPQ+ZHVguDcKK\nJZ1mzIkN5yfCsvg8KkWZCBMNamLZDML6axyEGYZ7wuT5chBmdZi5wbHOEDeXMlX7PCUyBdrDEoR5\nPSq/9cxp1gslfuWLY676OnSjsekUI/GWXa/UDpVPFr8qjw+FEC4mQZhDBjpC+DyK9IQJ17k0l+ZQ\nW5Bw09aluoPWAx2ZaqyJ16aSGMb9+8Esp/tbee12En03K6q3X4boAIQ6weuH+KOuLczXShsnwpwO\nwtbxqoqcGika1mQiS09LgIDPU5Pbj0eDZAtmJ5dbWEFYLOKiIKwrzFpeYzGdr8rtLctqZMWJ7jC/\n/K5h/ubSPF98ddrpy3EtraRzeS7FyC76wSxycqQQoh5IEOYQn0fleFdYpmmE64zvoCgfINzkpS8a\nlK/hGhmdXEFR7vSA3c/pQ1HW8ho3lnYRSE6/Yq5FWvqfhNnz5smMLqPpuqtWI30elaIuE2GiMU0u\nZzlco34wMDvCwOyidIv5VI4mr+qqExWtXshqnByplXSS60VZjdzgp958lCeOtPHrz11g1mWHN7jF\njaUMuaK+634wgENtQYI+D+Py+FAI4WIShDloKBaRV0uEq+S1EjeXMpVX87YzGAvLVGONjE6tcqIr\nTEvAt+X7WRNj56aSO7vh1Aykps21SEv/U6AXYebcXi+3Zoolg6BrViPNUyNlIkw0qolEpmb9YGBO\nhAHMuKgwfz6Vp6c14KoTFY91lYOwKvSErWTN6bsOWY2s8KgK//GZ02glg3/2hddkRXITlaL8PUyE\nqarCUCwsz3GEEK4mQZiDhmJhplelTFK4h3UK5Hb9YJbhWITri3JyZLUZhsHo5Mq2a5EAxzrDhJu8\nOy/Mt/rB+jYGYU+af7pwPVIr6TThhomw8mqkR0WTiTDRgHLFEvOpPAO2BGHumcKZS+VctRYJEG8N\n0uRVubmbSd/7SGTMn58yEfZGAx0hfuU9J3nx6hKfeWnK6ctxnbHpFAGfyrGu8J4+frgnIhsDQghX\nkyDMQVImKdzGevVuJ6uRYPaEFTSdieXqnW4lzMLqlWxxy6J8i6oqPHyolfO3dxiETZ8F1WeW5FtC\nndB+3JWF+eZEmFs6wrJ4VYWCJsGvaDzWCcC1XI3sCPnxe1VXBWELqRwxFxXlg/lz/WhndU6OrARh\n0hF2jx9/aoA3n+jg3375Iumce3rr3GBsJsmp3hY86t4mJYdiEZbWCiytVafnTgghqk2CMAfdCcJk\ntUy4w/hcGr9H5Ui5n2Q7Q5WTI+VruJpGp1YAeHQHQRiYPWKXZlPkiqXt3/n2K9D7MPjueuLX/5Q5\nEeayFZGirtNUWY10fiLMJxNhokFZJ0bWcjVSURTirQHXdIQZhlGeCGty+lLucbR8cuR+VYIwWY28\nh6oq/OzbBskUSnzn+rLTl+Maum5waWZvRfkWa7PgiqxHCiFcSoIwB/W3NxPwqVImKVzj8lya491h\nfJ6d/Wg40W0GYTLVWF2vTqwSbvJWPr/bOX0oSrFkcHE2tfU7ljSYGX3jWqSl/0nILkHixh6uuDZK\nuoFhQJPhho6wZnMiTDrCRIOaKE+EDXTs7IWQvYpHg66ZCEvlNHJFnViLuybCwOwJm0xkKZb2F7wn\nMubPT1mN3NyjA1GCPg/furbk9KW4xmQiSzqv7ako32IFYfIcRwjhVhKEOcijKgx2yw69cI+dnhhp\nafZ76W8PcmVBJsKqaXRqhdP9rTteSbC6xLbtCVu8BMXMG4vyLf1PmX+6aD3SegLYZJSLtR1fjSyf\nGrnPJ6ZCuNFUIkukyUtb89YHdOyXGYS5oyx/IWVeh9tWIwGOdobRdKOysrpXy2Bcex4AACAASURB\nVOWJsDZZjdxUk9fDU8faeVGCsIqxGbMofz8TYV3hJtqaffIcRwjhWhKEOcw8dU9+SQjnJbNF5lK5\nHRflW4a6IzIRVkXrhRKXZtM80r+ztUiAWEuAnpbA9kGYVZS/WRDWdRKaWlxVmK+VD2G4MxHm5Gpk\nM5Ty+FW9cl1CNJKJ5QyHO5prfnpiPBpkPp1zRaA8ZwVhLlyNrNbJkSuZAi0B744nvWsivwYrt5y7\n/208faKTG4sZ10wqOm1sOoXPo1QqXPZCUcyPvyyrkUIIl5IgzGHDsQjzqTzJrJR0CmddnjPX6nYb\nhA2WT450w5OaRvD6dJKSbuzoxMiNTve3cv52cut3mj4LzR3QdvTet6kqHHrCVRNhWvlrym/kQVHB\n4+BEQ3karVnRKtclRCOZSGRr2g9m6YsGMAyYSzo/FTafMkP2HhdOhB0rd3XutydsOVOgI+xw0PeX\n/xz+8+Nw5QVnr+M+nh7sBOCbMhUGwIWZJMM9Efze/T1NPNkT4cpcGsNl3aNCCAEShDnOerXlyoK8\nYiKcZfU47GY1EszC/GLJkJMjq2R00izKP9O/2yAsys2lDKvZwv3f6fZZ6HsM7jfxcfhNsHARctsE\najYplru4/HrOnMiq8aTKlsrTaCE1X7kuIRpFSTe4nViv6YmRlnjUDJXdMH0zX54I6464LwiLNvtp\nD/m5sbS/6oFEpuBsP1ipCJf/AnQN/uQn4NpXnbuW+xiORegMN/HNqxKEGYbBhZkUI717X4u0DPVE\nyBRKrjkcQwghNpIgzGFDVpmkjA4Lh12aTdMS8NKzy9LgSpgrJ0dWxejkKkc6mnf9Cv6ZQ+WesPtN\nheVSsDi+eVG+pf9JwLizQukw63RGn5F3th8M7kyEUZTpR9Fw5lM5CiWdgfbaFuXDhiAs6fyT4/lU\njpaAl6Df4/SlbOpoZ2jfq5GJTMHZfrDJ70BuFd73/0HnIHz2Y3DzG85dzyYUReHpEx1869oS+gFf\nfZ9N5khkCvsqyrcMx+Q5jhDCvSQIc1i8NUC4ySsdS8Jx43MpTva07Lof5nhXGEVBuu6qwDAMXp1c\n4ZHDO+8Hszx0qBVF2aIwf+ZVwNi8H8zS95i5guiS9UjrdEafnnNNEBZU8tIRJhrOxLJZyG7HamS8\n1ZoIc341ci6Zc+VapOVYZ4gb+1yNTGQKdDg5EXb5efA0wYMfgo9/CdqOwP/8CEx8x7lr2sTTg10s\nZwoHvtNqbLpclN9XnYkwkJMjhRDuJEGYwxRFYTAWll8SwlGGYXBlfo2TvbsvRg36PRxub+aqTITt\n20wyx0I6v+t+MIBIwMeJrvD9g7DbL5t/9j12/xtpikBsxDWF+dbklc9ajXRS+f6blYJMhImGM5kw\nw5YBG1Yjg34P7SG/K9al5tN5YrucgrbT0a4Qi+k86dzeemQNw2AlW6A97FAQZhgw/jwc+35oCkOo\nEz7+LLTE4Y+fgamXnbmuTTx9wuwJ+9YB7wkbm0mhKvBAz/4nwloCPuKtAa4c8HBRCOFOEoS5wHAs\nImtlwlG3V9ZZy2u7Lsq3DHZHZCKsCqx+sN2cGLnR6f4o52+vbl5Me/sV6BiE4DYhW/9T5mqkXtrT\nNVSTNXnlLeXA6/CT1Y0TYdIRJhrMZCKLV1XotWk6Kh4NuKIjbCGVc3UQdqwzDOy9MD+V0yiWDNqd\nWo1cuAirEzD87jt/F4nBTz5nhmKf/hDMjDpzbXfpaQ1wojvMiwc8CLs4k+REd7hq68JDPXJypBDC\nnSQIc4HBWIREpsDSWt7pSxEHlNXfsNuifMtQLMzNpQwFTSZl9mN0cpUmr7qnyTwwg7CltcK9kxaG\nYZ4YeeiJ7W+k/ykopGHh0p6uoZqsryc3TYQFKFS6y4RoFBPLWQ61BfF67HlYGG8NOh6ElXSDhXSe\nWIvDJypu4VjX/k6OXMmYh6c4VpZ/+Xnzz41BGJgTYT/5HARb4VM/DHOv239tm3j6RCcv3VwmV3T+\nhSCnjE2nGInvfy3SMtwT4cZiRiaphRCuI0GYC1hlkjI6LJxireZaxfe7NRSLoOkGt+TkyH0ZnVzh\n4UOt+Pb4ZNQqzD9393rk6iRkFuHQFmuRlv4nzT9dsB5pTYR5Si7qCENOjRSNZzKRpd+GfjBLPBpk\nemV98+lVmyxn8pR0w9UTYQMdzSgKey7MX7aCMKdWI8e/bB7QEum5923RfjMM84fgUx+A+Yv2X99d\nnj7RSa6o82p5OvugWUznmUvlGInvfy3SMhyLUCjpcrK4EMJ1JAhzgaGYOfouq2XCKZfn0vRFg0QC\nvj19/KB8De9bXisxNpPi0T0U5VuGeyL4veq9PWGVfrAtivIt0QEIx9wRhJVfQfa6Igi7MxEmr2yL\nRjOxnLWlH8zSFw2SKZRI5TTb7vNu80lzCt/NQViT18OhtuCeC/MT5SDMkbL81Iy59njyPfd/n7Yj\nZhim+swwbPGKbZe3maeOteNRFb559WCuR16YMYvyH6xCUb7FeoFV1iOFEG4jQZgLdEWaiDb7GJee\nMOEQ88TIvU2DgXlypKogXXf7cHEmRUHT91SUb/F7VR6Mt3B+KvnGN0y/YnZsxUa2vxFFMafCXBCE\nWZNX5kSY06uRZhDXhHSEicaSzBZJrhcZaA/Zdp/xqPn9NJt0bj1yPmWeWunmIAzMnrCbS3v73Wqt\nRrY50RE2/pfmn8NbBGEAHcfNMAzgk++D5eu1va4tRAI+HumPHtjC/AszKQBOVXEi7ER3+fGhBGFC\nCJeRIMwFFEVhqDvCVZmmEQ4oaDo3FjN7LsoHCPg8DHSE5Gt4H0YnzSmuR/YxEQZmT9jr08nKNBVg\nlt/HHwHPDif++p+ClVuQnt/XteyX1cWlltZdMBFWDsKMPEXpCBMNZDKRBbB5NdIMn5zsCZtPm0FY\nj8uDsKOdIW4uZva0RmqtRnY4sRo5/jy0HYWuk9u/b9cQfPxLoBfhk+83f/845M0nOnltOslqtuDY\nNTjlwkySIx3NtOxxO2AzAZ+HI52hSgWHEEK4hQRhLjHUE2Z8Pu1oX4Y4mK4vrqHpxr6CMIDB7rCs\nRu7D6NQq8dbAvqcTzvRHWS+WuLpQniDQCjB7Hvp20A9m6X/K/PP2S/u6lv2yJq88mhsmwsqrkUYe\nwzCLtoVoBBMJc+3O7tVIgOnVnG33ebf5ZA5FgU6n+rN26HhXiEyhxEJ69wcqJTJ5Aj6VZr+3Ble2\nhXwabn4DTr7XnDLeidgpMwwrrJmTYatTtb3G+3jLYCeGAd+5vuzI/Tup2kX5luFYpHIokxBCuIUE\nYS4xHIuQzmnMpZx7UCgOpjsnRu5vFH4oFuHWcpa8dnBPW9qP0cmVfU+DAZwuF+ZXesLmX4dSHg7t\noB/M0nsaPH7H1yOtLi5Vc8FEmMcPiorfMJ+MSk+YaBTWRNhhGyfCOsNN+DyKsxNhqTyd4SbbTsrc\nq6OdZgfn9cXdr0cuZwq0O7EWee2rUCrce1rkdnoego//OawnzTAsNVOb69vC6f4o4SYvLx6w9chk\ntshkIstIX/XWIi3DPREmElnWC/L4UAjhHu7+7X+ADFonR0rHkrDZpbkUPo9SOaZ9rwZjYUq6sedj\n3g+yhXSO2yvr++oHswx0NNMa9N05OfL2K+afh57Y+Y14m8xVyimHJ8J0Ay8aiqE5PxGmKOBrxm/k\nKtcmRCOYXM7SGW4i1GTf1JCqKvS2Bh0NwuZSOdevRQIcLf9u3svv1pVMwZkTI8efh2Ab9L9p9x8b\nfwR+4k/Nk44/+X7bV/R9HpU3HWs/cD1hF2bLRfk1mggzDLi2IM9xhBDuIUGYS1inqkiZZH358muz\ndR/8jM+lOd4VxrfPV8WHJMzds3NV6gcDs3PwdH/0ThA2fRbCPdDSt7sb6n/SPPFL2/06TrUUSzpB\nyj0tTk+Ela/Br5ufD00mwkSDmFjOcrjd/u+veDTg8ERYjlhLk2P3v1O9LQECPpUbi7t/rJHIFGgP\n2fzvWCrCla/A0A+BZ4/hav8T8ONfgNS0eZpkxt5Q6ukTnUwsZ5kqT0seBBemzaL8kSoW5VuGeqyT\nI1NVv20hhNgrCcJcoj3kpzPcJB1LdWQxnednP/Mqv/FXl52+lH0Zn0vv68RIy7GuEB5VkcL8PRid\nWsXnUar2APRMf5Qr82myBQ1uv2yuRe60p8XS/5S52jJ7virXtBdaySBAOYhzSRDmK0+EFSQIEw1i\nMpFloMO+EyMt8WiQGQc7whbSedefGAnm9NyRjtCeXnRbzhToCNk8ETb5Xcitbn9a5HYGvgc+9iew\nchM+9cOQTVTn+nbg6cFOAF68enCmwi7MJIm3BugI7zA4XV+FP3gH3PrWtu96pCOE36vKcxwhhKtI\nEOYiwz1SNl5PvnppHsOAv72ySK5Yn70HyWyR2WSO4X32gwE0eT0c6WiWr+E9eHVihVPxVgI+T1Vu\n70x/K7oBl69PQOLG7oryLYeeNP90sCdM03UCijUR5vBqZPkafJWJMFmNFPWvoOnMJtdt7Qez9EWD\nzKVyjkxX5rUSiUyhLoIwgONdYW7soSNsJVOgze6OsPHnwdMEx39g/7d19K3w0f8JS1fgf3zQDF9s\ncLwrTE9L4ECtR47NpDi1m7XI8581D9T52/9n23f1qAqD3WHGZWNAiLq3mM7z+u2k05dRFRKEuchg\nd4Qr82vo0j1TF164OI9XVcgWSnyzTl81tI6zrsZEGJjrkVflgc6uaCWd124neaR///1glofLhfnz\nl8uv1O6mH8wSiUHbEUeDsELJcN1qpE8vd4RJECYawO2VLLphb1G+JR4NUtKNPZ2GuF8LKfM+62E1\nEuBoZ4iplXUK2s5Dw1yxRKZQosPOjjDDgMtfhmPfB03h6tzmiR+Ej3wa5i/Apz8Eudqv1ymKwptP\ndPKt60sH4oTgbEHj+uIaD+60KN8w4Ox/B8Vjng46f2HbDzFPjpTVSCHq3a8/e4Fn/uu3Sa4Xnb6U\nfZMgzEWGeyKsF0tMO9iZIXZmLa/xzWtL/NiTh4k0eXnh4pzTl7Qn1oOS4SoFYYOxCLeWM3U7IeeE\n8fk068VSVYryLZ3hJg61BTGmXgZFNcuH96L/KbMw33DmiYBW0glWViNdMhFWMoOwoi6rkaL+TZQ7\nkAY6nAnCAEd6wubLJ3TXy0TYsa4QJd2onPC5E4mM+SJCu52rkQsXYXVi/2uRdxt6J/zoJ2H2HPzx\nM5Cv/QtubxnsZDVb5OJM44c3l2ZTGMYuivInvg1L4/COfwXeIHz3d7f9kOGeCPOpPKvZwj6vVtQT\nw6HHj6I2sgWNr11eIFfUefbctNOXs28ShLmIVTY+LoX5rveNK4sUNJ33PtzL20528zeXFuryVcPL\nc2kiAS+9rdV5MjAUC6Mb7KnU96AaLRflP1qFovyNTvdHaV99Hboe2Psr8/1Pwtq8+cTGAVrJIKi4\nayLMKxNhooFYZeCH9xOE5ddgbXHXH9YXNX/vOPHi33xlIqw+grCjnbs/OdIKwmxdjRx/3vxz+N3V\nv+2T74UP/Tez9/IzH4VCbYvs33yi3BN2bfdf2/VmrFyU/2DfDoOws/8dAq3w+N+H0x+F1z637YEG\nVmG+HKh0cFydT/P0f/hfPHd+xulLEVXyt+OLrBdLRAJePvPSVN0HnRKEuchgzHyyemVBgjC3e+HC\nHG3NPh4faONdIz0kMgXO3rKvyLVarKJ8ZbdF6vdx5+RI+RreqdHJ1coEVzWd6WvhZOkK67FH934j\n/U+Zf069VJ2L2qWirm8oy3fDRFgQjzURJmX5ogFMLGcJ+jx07bQgezPP/Tz83vdDSdvVh/W2WhNh\n9hfmWxNhPXUShB3rNB8f7qYnzArCbF2NvPy82UkZ6anN7Y/8MHzwv8Ktb8LX/31t7qOsK9LEyZ7I\ngegJuzCTpDPs39mqcGYJLj0Lp38M/M3w1M9AKQ+v/OGWHzZcebG/8SfshPnz56c/eZbp1XX+6zeu\nO305okqeH5ujPeTnl94xxMXZFK9P13dXmARhLtIS8BFvDXBFJsJcrVjS+erlBd7+QAyvR+X7hrvw\ne1ReuDjv9KXtimEYjM+nq7YWCebJQF5VkSBsF0anVnjkcLRqYaTlTdEVokqGm4GTe7+R7lPgDzvW\nE6a5riOsGW/JnF6RIEw0gonlLIfbm/f+8yefNjuhUrfh+ld39aGhJi/RZp9jq5F+j0q02Wf7fe9F\na7OPjpB/TxNhtq1GpmZh5tXqr0Xe7eFn4NQH4NVP1Xwq7OkTnbx8a6Xh6x7Gps2i/B39HDj3x+aJ\n0o/9lPnP3SfNgxFe+gPQ7r/22NsaIBLwVrppReMqaDo/8+lXmEvl+PBjhxibTnFhpr4DE2H2Tn7t\n0jzvGonxI48dIuBT+ezLU05f1r5IEOYyg7GIjA273HdvLJPOabxzxHzFM9zk5c0nOnjh4lxdjYjO\nJHOkc1pVToy0+L0qRztD8jW8Q6vZAjcWM1XtB7MMaeMAvFw4tvcbUT1w6HEHg7CNHWFuCMLuTIRp\ndbgKLcTdphLZ/a1Fjv8laDlQfTD66V1/eLw16FgQ1t3SVPUXIGrpWFdoV7UDy9ZEmF1BmLUWefK9\ntb+vJ/8h5FZh7As1vZs3D3ZS0HReull/E/87lddKXJlP82B8B48FdR3O/iEMvNkMwCxv+kewNgcX\n//y+H6ooCsOxCFfm5PFhIzMMg1/789d56WaC3/zww/zaex/A71X5XJ0HJsKsBcoUSrz7wV5aAj7e\n+1CcZ8/NkMnvbhrcTSQIc5nhngjXFtccOU5c7MwLF+YJ+jy8ZbCz8nfvGulhKrHOpdn6eaXLGk+v\n1omRlqFYhKuy3rsjo1NmP9gj/dXtBwNomhslS5CvJfZ52/1PmSdC5e3/b1rUDcIeayLMDauRzaiy\nGikahGGY5esD+zkx8sKfQSQOT/wDMxTbpifobvFo0JGOsLlUrm7WIi1HO0Pc2MVE2EqmgEdVaAnY\nNPU2/pfQdhS69jGFvFMD3wvdI/DS79X0MJenjrbj8ygNvR55ZW4NTTd21g928+uwctPsBtvo+A9C\nxwn47u9s+d9jqCfC5blUXb1oLHbnD168yefO3ubnf+AEHzjTR7TZz7tGevjzczMNP1nZ6P5qbI7W\noI/vOd4BwI892c9aXuPLr806fGV7J0GYywzFIhQ0vXKSk3AXXTf464vzvHWok4DPU/n7H3wghqJQ\nV6dHWqGd1etVLYOxMJOJLOsF+YW3ndHJVVQFHj60w4La3bh9lpnwA5y7nd7fg87+J8HQYfqV6l3b\nDmklnWalfDyzWybCtHXAkLJ8UfcW03nWi6W9T4Str8K1vzF7mx79OOhFszR7F/qiAUcmwhZS+bop\nyrcc6wqztJYnldvZkfXLmQJtzT5U1Yapt3wabv6tuRZpx5SdosCTn4C512s6sdzs9/Lo4TZevNq4\nQdhYeWVtRydGnv3v0NwBD7zvjX+vqmZX2Mzolp2iJ3sipHJa5bAK0Vi+emmef/eXl3jPQz38wtuH\nKn//kcf7Sa4X665CRtyR10r89aV53nkqhs9jxkePDbRxojvMZ16edPjq9k6CMJcZKhfmX5Udeld6\nfTrJXCrHO0+9sQi2K9LEY4fbeOFC/fyQH59LE28N0Bqs7qvFQ7EIhgHXd1Hqe1CNTq4w3NNCqMlb\n3RsursP8GIXYoyTXi9xa3kew3vc4oDhSmF8sGYRUN3WEBVGMEj5KaLpMhIn6Zr3gdnivE2Hjz5td\nQSM/ArFTEH/EXI/cRfAejwZJ5TTSOwx3qmU+lau7IKxycuQO1yMTmbx9/WDXvmp+LZyscT/YRg//\nKDS1mlNhNfSWwU4uzqZYXmvM8ObCTJJIwEt/+za/Y1Oz5mEIZ34cvJuU6p/+MfMkye/+zn1vwnrh\nVXrCGs+l2RQ//5lRHoy38lvPnHlDAP+9xzs41BaU9cg69u1rZi3Qex7qrfydoih89Il+RidXGa/T\nfnMJwlzmRHcYRYFx2aF3pRcuzuFRFX7wge573vaukR4uzqYqx9G73fhcmpO91esHs1hhrhTmb03X\nDc5NrfJoDfrBmD0PukbLiTcBcL68grknwSh0P+BIT5im64SUgtk/5HFBqXV5PTNInqJMhIk6N1kO\nyAc6Qnu7gbEvQmu/2SMI8MhPwMIFmD2345uIR80n37NJ+06OTOeKZAqlnZ2Q5yLHu8z/TjeWdvb4\nMJEp0NZsYz9YsA3632TP/QH4Q+bX3MUvQbp20/hPD3YB8K3ryzW7DyeNTacYibds35c3+mkwSvDY\n/7b525vC5mTopedgdfPAQ06ObEyL6Tz/4JNnCQe8/P7HHyfo97zh7aqq8Mxj/Xzz2lLdPEcSb/T8\n67NEAl6+90THG/7+Rx49hM+j8Nk6nQqTIMxlmv1e+tuauSIdS670woV5njraTnSTB5fvOBUz36cO\nRn8Lms71xbWqnhhpGegI4fMoUpi/jRtLa6RzGo8crn4/GLfPAtA78haa/R7O7ScIA3M9cuplsyjX\nRlrJIKgU3DENBpXrCFCQjjBR9yYSWVQF+qJ7+P7KJuDG/zLXIq0n0A9+GLyBXZXmx6PmVJadPWHW\nWla9TYT1tzejKruZCCvQEbYhCCtpcOUrMPRD4KnydPN2nvhp0DV45Y9qdhcP9bXSEvDyrQZcj9RK\nOpdmU9uvReol83N87G3Qcfz+7/fkPwQMePn3N31zW8hPd6RJXuxvILliiZ/59CssZ/L8/scfp6d1\n85+rH378EIoCn3/lts1XWJ/+zz99jV/63HmnLwMwO3FfuDjPOx6I0eR9Y8jZHvLzzpEe/mx0ui47\n4CQIc6GhWIQrdTpi2MhuLK5xdWGNd5YDr7sd6QwxHIvwwgX394TdWDLLUatdlA/g86gc6wzLeu82\nXp0oF+XXYiLs9svQehhPSw8P9rVy/vZ+g7CnIJ+EpfHqXN8OFUsGza4KwsoTYUpeOsJE3ZtcztDb\nGsTv3cNDwct/YQYQIz9y5++CUTj59+D1z0NxZxNe1kSYnT1h8ynz2uotCGvyeuhvb+b6DgvzE5mC\nPauRk98xT3Acfnft7+tuHcfhxDvM7iqtUJO78KgK33u8k29eW2q4kvfrixnymr59Uf7Vv4bUbXj8\np7Z+v+hhsz/slT+CwuZfp8M9EdkYaBCGYfB/ffF1XplY4beeOcPDh+7/eLYvGuQtg1184ewUJTl1\ne0uL6Tyff+U2Xxy9ze0V5yfovnN9meR6kXdvWIvc6MeeOMxqtshX6uD5790kCHOhoViYm0sZCppM\nHLiJNen1jpGe+77Pu0ZivHwrQSJTmwdk1WLtctdiIgzMwnyZatza6NQKrUEfR/e6lrSV6Vfg0GMA\nnOmPcmEmtb+fJ/1PmX/avB5ZLOk046YgzLyOoEyEiQYwmcgysNei/LEvQtsRsxdso0d+AnJJMyjb\nge5IAI+qOBSE1ddqJJg9YTuZCCvpBqvrRdpDNvw7jj8Pnibz5EAnPPkPYW0eLj9Xs7t482An06vr\n++vbdKGx6XJRft82NRmv/CGEY+ZhCNt56v8wfwac/+ymbx6KmUGYhCH173e+fp0/G53mF98xxHsf\n3jwk2egjj/czk8zxzQY+hbUavnRuuvL98bmzzk/Q/eXYLCG/h7cMdm769u893kF/e5DPvlR/HXAS\nhNmoKb+8oxLZ4Z4Imm5wcxfHZIvae+HCHA/2tWy5RvLOkR50A/7mkrvXIy/PpfGqCsc6wzW5/aFY\nhKnEOtmCVpPbbwSjk6uc6Y9W/0Sv9Dwkp8ol93D6UJSCpnN5P50c7cfMk6JsLszXdJ2Akq9MYjlu\nQ0eYJg/iRZ3bcxCWWYKb3zCnwe7uFTr6fWZv2A7XIz2qQk9LgJlV+zrC5up0IgzgWKf5Qqm+zc+f\n1WwBw4D25hp3KxoGXP4yHPs+syPKCSfeDm1H4aXN1/Gq4S0nzCeA37y6WLP7cMKFmRRBn4ejWz0W\nXJ00V18f/fjOujoPvwl6z8Df/ZdN6xSGeyLkNZ1J6Yqqa381NstvfmWc95+O83M/cGJHH/P2U920\nNfukNH8LhmHwhVduc7o/yvcNdfG5l6fQHHzhVSvpfOXCPD/wQIyAz7Pp+6iqwkce7+c7N5a5VWfZ\nhQRhNvIXVmDyu9u+n5yq4j4LqRyjU6v3nBZ5t5F4C/HWgOtPjxyfS3O8K7y3lZgdsArzry1ID8Rm\n1vIa4/Pp2qxFTpv9YBx6AoDT/ebKw74K8xXFnAqzfSLMIOjCibCAUnD0gYkQ+7WW11haK9C/lxMj\nLz1rlmaPfPDet6kqnPkY3Pj6fQuz79YXDdraEbaQyhNp8lb/tF4bHO0KsV4sMZ/eOji0ptLbwzWe\nCFu4BKsTO5sUqhVVhSc/Ya5ozr5Wk7sY6GimLxrkxQbrCRubSfJAbwTPVi/Ivfop8zHAoz+5sxtV\nFHjTP4KlK3Dja/e8+U5hvjzHqVdj00n+yZ+c50x/lN/48MPbH7RQ1uT18MFHDvHCxTnXb8445cJM\nistzaT782CE++sRh5lI5vj7uXAD/0k1zy+k9D279/PeZx/vxqAp/cra+Qk4JwmxkKB44+9+2fb9j\nXSE8qiIdSy7yN5cWMAx458jm/WAWRVF450gPL15ddPU01PhcumZrkQCD5Qc6Upi/udemVjEMeLQm\nRfkvg+qF3ocB80lmZ7iJc1PJ/d1u/5OwfA0y9p2cpZV0ArhpIsxajZRTI0V9q5wY2b6H1eyxL0LH\nCeh5aPO3n/kYYMD5z+zo5uLRgO2rkd11uBYJcLyzfHLkNuuRy+UnmR217ggb/7L559AP1fZ+tnPm\nY+bviZd+ryY3rygKbxns5Ds3lhvmRRBdN7g4k9q6H6xUNIOwwXdCtH/nNz7yQXOV8ru/e8+bBmNh\nFEWCsHq1kMrxiU+dpa3Zx+99/LH7Tgndz0ee6KdYMviz0ekaXWF9+8Irt/F7VN73cC8/+EA3XZEm\nR09kfH5slqDPw/cPd2/5frGWAG8b7ubzZ2/XVXWIBGE2Kvoi5jHPma1fxTWIuQAAIABJREFUUWry\nejjS0Sy/JFzkhYtzHG5vrryStZV3jsTIazrfuOLOEfpUrsj06npNg7CB9mb8HlXC3PsYLU9nne6v\nRVH+WYg9WAltFEXhTH+VCvMBbtu3HqnpRjkIc8tEmLUaWUCz+QRNIarJWkva9Wpkeh4mvrX5WqSl\n7Qgcfau5HrmD75N4NMhcMmdbZ9BcKnffk83c7mhXOQjbZv3EmrZo2+SE66q6/Dz0PQYt2/cD1VSw\nDR7+UfOghmyiJnfx9GAn6ZzGa9P7fFHJJSYSWdby2tYnRo4/b/avPbZNSf7dvH544h/Atb+BxStv\neFOz38vh9mYpzK9DuWKJT3zqLMn1Ir//k4/THdn9z9Hhngin+6N87uWphjt8Yr8Kms6z52d4x6kY\n0WY/Po/KM48d4muXF5hN2vdikaWkG3zlwjxvO9lF0L994PnRJ/pZWsvz1UsLNlxddUgQZqOirwVK\nBRj9H9u+71AswlVZK3OFdK7It68t866R2I7Gf5880k5r0Ofa9UjrRNJanBhp8XpUjnWF5IHOfYxO\nrnCiO0xrsMr9LXoJZv5/9s47Pur6/uPPu8td9t4TErIgCSGQBARREAQBURFZiiJubWtra/trq1ar\nrbZq3dYtWlT2EJCliMoUEkJIAgkrkJ2Qve9y4/fHJxcIZt8M3PPx4BG9XL73hdx9x+vzer3eGR2x\nSD2JIR6cudBIfWvbwLcdlCScZmaMR7ZptDjorC8a6SixOcJsDG4KqoWQ0u9o5IlNoNNC/O09P2/U\nYhGZO7+v100GeTii1uq40KDs374MkIp6Jf4DuIGzBgLcHHCUyzh7oefrQ70Q5u1iQiGsvhRKjlg2\nFnkpKQ+CurXP/XT9ZfwwHyQS2HeFxCP1RflxPRXlpy0DtxCIurH/LzBmqRii8PP7v/hWtL+rYb2l\nNsyOTqfjyTWZHCuu4/UFo4jrSUDthfnJIeSVN5BZdGWIysZid14F1U0q7hgT0vHYwpQwtDpYY4HS\n/PTzNVxoUDIjvm8LHZNifPF3s2eVBR1s/cUmhJkRrVQBQ64VJ5ZeVkmj/V05V9VEa5vGTHtnozt+\nPHkBlUbLtB6mRV6KnUzKlOF+7MqtsEp76AkTT4zUIyYD2cTcy9HpdBwpqCXJFG6wC7mgaoSQ5E4P\nJ4Z6oNNBliEXHXJHCEw0a2G+WqPD3qqikWI/HGxTI20Mcs5XNePhJO+/GJ+9HnxjwW94z88bPhvs\n3fokSugH0JijJ0yr1VFe34r/IHWESSQSMTnSGhxhJ7eJr7GzTPca/SEgHoZMgMMfi0UhI+PlrCAu\nyI09V8jEu5ySeuQyCVF+3VwLVp2Bs7thzL0g7V/8DQAXX0iYJyLSl7n0YvxdOVfVbLvHGUS8uesU\nW46V8qfpsUzv4/1Qd8xODMJBLmWVrTS/E2vTi/B1te80nTHM24lrI31YdbjQ7JNWt2aVYm8nZXJs\nz7FIPXYyKfOTQ/nx5AWz1h0Ygk0IMzcp94lV0jO7enxatL8rOp2tbNwa2JFTjrezol99TtPjAqhr\naeNQvmks+oaQV1aPq71dj9MvjUFMgCvFtS00Ka23K80SFFQ3U92kIskk/WDtRfnBnYWwkSFi5e6o\nIYX5IOKRxemiN8QMtGl12OusKRop9sNJokRtc4TZGMQUVDczpL9usPoSUUge14sbDEDhBPFzRR1E\na88CfFD7ucgcF87VzSrUWh3+roOzIwxEj2xvHWHVTSpcHexMNhAHELFIz6FCGLUWUh8U19invjXJ\n5idE+pBRUHNFXNfklNQRE+Da/Xsk/TOQyGD03QN/kXGPQFuz6Bm7hJgAVzRaXa/vYxvWwebMEt74\n7hS3jw7mkesjDN6em4OcmQmBbM4sseo+ZXNS1ahkd24Fc5KCsZN1/kwuSg2juLaFPWacWqvV6tie\nXcb10b649GOwzPzkULQ6WD1ISvNtQpi5iZ0Nzn5wuOfS/JgAMXXPFi2zLEq1ht25FUwd7t/zVJ3L\nuC7KFwe5lJ05ZSbcu4GRV9ZAdIBrn6e8DJQoP/EetkV8O5NRIMQok02MdPAA72GdHvZwUhDu42zY\n5EgQhfnqVigzzWSuy1FrtCh0rdbjCLMTN+zOkjbabB1hNgYxBdXNhHn3syg/ZyOg6z0WqSdpMahb\nIGdDj08L8hDuLHN0oJTXi2mL/m6D0xEGEOHjTFFNM0p1926a6iYVXqYsylc2QP6PEDOr+644SxB7\nM7gGmqw0f2KkL20anVUucvYHnU5HdnFd9/1gaqVwc8bOBFcD3D8BCTB0Ihz6CDQXBQ99IiGv3BaP\nNJTC6mZmv72X367MYHVaodGPo5mFtTy5JpPkIZ68dHuC0e4dFiSH0qhUszXL+u6TLMHXR0tQa3XM\nHR3yi+/dOMIfb2cFKw6ZL3KYUVhLWX0rMxP61/8Y6uXExCgf1qQVmd3BNhBsQpi5sVOI1ZVTO3oc\nLT7E2xmFTEqeTQizKAfPVtOoVPc6LfJyHBUyJkb5svN4uVWVQep0OnLLGkzaD6YnumNypO09fCkZ\nBTU4K2Qd/z5GpShNxCK7uFAZFepheGF+SKr4aqZ4pFqtRWFNHWEyO5ApcJaqbI4wG4MWtUZLcU0L\nYV79/FzlbAD/BPCJ6tvzg8cIt1Av8UhXBzmuDnaU1Lb2b38GQIcQNkijkQARvi5odRcnf3aFyYWw\n07tE522slfSD6ZHJIfk+kbqoPG30zScP9URhJ2XPIO8JK6lrpaa5jbjuJkYe3wQt1eLf0lDGPQr1\nRZC7ueOhcB9n5DIJeWW2hVJD+e8Pp8kra2Df6Ur+tPYY17z0PTf85wee/TqbnTllBnXDlta18OD/\n0vB1teeDu8dgbzeAiGw3pIZ7Ee7jzGpbPBIQsciRIe5d1tYo7KTcMSaEXScqqKg3/XkSYFtWKQqZ\nlBuG9y0WeSkLU8zvYBsoNiHMEoy5F3Q6YTvuBnl72fgpW8eSRdmZU4aTQsaESJ/en3wZ0+MCKK1r\nJcuKJgyV1rXS0Ko2ixAW6uWEvZ20o5zfhiCjsJbEUI9+OQz7hLIBKk78IhapJzHEnfJ6JWV1BpxE\n3YPBPdR8hflaFVK01iOEAcgdcZKoUNs6wmwMUkpqW1FrdQzx6ocjrLZQTIyNu63vPyORCFdY0WG4\nkNfjU4M9HM3SEVZeLwr5B7MjLNyn98mRVU0qvE0phOVtE5MaQ8eZ7jUGyuglIJWLrjAj4yCXkTrU\ni32DvCdMX5QfH9RNUX76MvAMh/BJhr9Y9E0iQnvwvY6H5DIpw3xdbAulBlJa18La9CLmp4Rw+Kmp\nbP/dRJ6eNZwwLydWpxXx0PJ0kp7/ltv/u4/XduZxKL8albpv1y7NKjUPfJ5Gk1LNJ0tS8HYxbpxc\nIpEwLzmEQ+eqex3+caWTU1LH8dL6TiX5l7MgJRS1VseadNOX5ut0OrZll3FtlA9uDv0f6nXjCH+8\nnBWsPGT9IqdNCLMEHmEQPV1k5tWqbp8W7e9Knk1EsBharY5vj5czKcYXB3n/V0GmxPohlWBV0yPz\nOorye5gSZCRkUgmRfi6ctEUjO2ht03C8pN40sciSDED3i6J8PYnt5fyG94Slms0RJtW03xhbSzQS\nQO6Ek0SFyuYIszFIWbY/H4nk4jGhT+jjjX2NReoZuUD0DPXiCgvycDRLR5h+IcBvEHeEhfu2C2E9\n9CtVNylNV5SvUYtUQ9R04ZK1Nlz9hWB79EtQGv/6Y0KkD3nlDWZzZpiCnJJ6pBKI7epasCJXTHtN\nXgpSI9wmSmWQ+rBYQCtO73jYdo9jOB/+dBatDh6+bhgSiYTYADcemBjBZ0tTOfrsjax8aByPXj8M\nrQ7e2X2a+R8cYNTzO7nvs8N8sjefk+UNXaZWtFodv1+VyYnSet6+M8lkw7XuGB2CTCphtQUmIloT\n69KLUcikzB4Z1O1zInxdGBfhxcrDBWhNHDk8VlRHcW0LM+IHFotW2EmZOzqY706Um20a9ECxCWGW\nIvk+aKqAvG+6fUq0vwvFtS00XgGlnIORzKJaKhqUTBsxsAOBp7OC1HAvdh63nvx7rl4IM0Usrwui\n/V05ZVvx6yCruA61VkdSqCmL8sd0+e3hgW7IZRLjFObXF0Od6S9c5Jr2Gw0rc4Q52hxhNgYpGQU1\nfLb/HIvHDunfzU3OeggcBV79LEp28ROOkMyVPQ7ZCPJwMIsQVtHQio+LArls8F7+ujnI8XGxJ7+y\na5FHp9NR09SGl4uJhLCCA9BSY32xyEtJfQiU9XBsldE3rZ/otu/M4HWF5RTXEenngqOii0Xe9GUg\nU8Cou4z3gkmLQeEKB9/veEg/UKnBgOje1Uxlo5IVhwq4bVQwoV0MPrG3kzEuwpsnp8ew8VcTyHhm\nGu8vHsPto4PJr2zihS3Hmfb6T6S+uIsnVh1lXXpRx0LBa9+eZHtOGX+dOZwbYvtXDdMf/NwcmBzj\ny7ojRVftNVWbRsvXR4uZMtwPz15cvItSwyisbmH/mSqT7tPW7FLspBJuHDHw3/2ClDDUWh3rjli3\nyDl4rwQGO5FTwT2sx9J8fYeQTUiwDDuPl2MnlTA5pv/5aD3T4wI4Wd7Y66hzc5FXVk+guwPuTv23\nug6EKH8XSutaDeoouJLIKKgBYJQpHGFFaeA1DJy8uvy2g1zG8EA34xTmg1nikbIOIcy6HGGOKFEP\nghJQGzYupU2j5S/rs/B3deBPN8X0/Qer84XjtL9uMD1Ji8XC3+nvun1KkIcjNc1tJp8gVl6vxM91\n8MYi9fQ0ObJRqUal0ZouGpm3DWT2MGyKabZvDEJSIDBRlLQbuad1RKAbnk7yQd0Tll3STVG+qhmO\nroDht4Bz/ytBusXBTRwHctZDfSlwcUH2pK0CZkB8ujcfpVrLY5OH9f5kwN1Jzk3xAfzjtgR2PzmJ\nvf83mX/PTWBchDc/nrzAH9ZkMu6lXUx+9Qfe2X2ahSmh3H9tuIn/FmLK4IUGJbvzrL9PyhT8kHeB\nqiZVj7FIPdPjAvBwkpu0NF+n07Etq4zxkT54GOAqjvRzIXWoF6sOF1pVV/bl2IQwSyGVQfK9cG4P\nXDjZ5VP0q7W2DL1l2JFTxrgIb4NEI72abi3TI3PLGkxmce6KaD+9mGu70AExMTLMywkfI3ctoNOJ\niZHdxCL1jAr1IKu4zrBJLv7xQpgyQzxSrrVOR5iTREXbVbp6aWPw8uFPZ8kta+CF2+Jx7U/vhz4W\nOaIf/WCXEnUjOPv2GI8M9hCfcVMX5pfVtRIwiIvy9UT4OHe7wFbdJCo3vJxNEP/U6USSIeJ6sHcx\n/vaNhUQiXGEXTsC5vUbdtFQqYXykD/tOV1r1DV53XGhQUl6v7LooP2cDKOuMU5J/OWMfAq0G0oQB\noGNypC0e2W/qWtpYfuA8M+MDGeY7sM9hiKcTC1LCeHtREmlPTeWbx6/lrzNjCfF0ZNbIQJ6/Nd7k\n0+UBJsf64eNiz6qrtDR/bXohPi72XBft2+tzHeQy5o4OYefxMiobTRM5PF5aT0F1MzMHGIu8lAUp\noeRXNnHwrPVO2bUJYZYk6R5R6Jn2aZffDvV0wkEuta2WWIDTFY2cvdDU72mRlxPi6URckBs7j1u+\nJ6xNo+XMhUbzCmE2V2MnMgpqGW0KN1hdETSWd1uUrycxxINGpdqwYlKZXMQvzeAIk2vbT/RWJoQ5\noLRNjbQxqDh7oZE3d51iVkJg/+MOOevFscVzyMBeXCaHxIVwcjs0dr3qH9QhhJk2HlnR0Iq/2+Dt\nB9MT4etMVZOKuuZfuq0vCmEmcH5XnICacxAzw/jbNjbxc0Wh/6EPjb7piZE+lNcrOT0IO1BzSnoo\nyk/7FHxiYMh447+wV4R436R9Cm2tBHs44qSQ2Rb7B8DyA+doUKr77AbrDalUQlyQOw9dN4zl94/l\n3TtHo7Azj0Qgl0mZOyaY3Xnmm4hoLVQ1Ktl1ooI5SUF9jusvSg2lTaNjnYlK87dllSGTSpgWZ7gQ\nNjMhEFcHO1YdNp2DzVBsQpglcfGFEbdA5lfCjnwZUqmEKD9X20nCAuh7vaYONzwbPz0ugCMFNVQ0\nWPYAn1/ZRJtGZ5aJkXpCPB1xlMtsYi5iuk9ZfStJYaboBzssvvbiCDNqYX7pMVCZNvJrp7XOsnwH\nVKi1NkeYjcGBVqvjL+uzcLCT8uwtI/r3w5WnoSxr4LFIPaMWg1bdbWeTOYQwlVpLZaNqUE+M1BPu\nI1wgZ7voCTOpI0zfaxs9CIQwuSOMvgdyvzF6p6V+kvjeQTg9MqekHoARlwthpZnCWZ58n3DUmYJx\nj0JzFWStQSqV2ArzB0CzSs0ne/O5IdaPuK7irYOQ+cmhaLQ61h0ptvSumJVNmSWotTrm9iEWqSfS\nz5WUoZ6sNEHkUKfTsTWrlHERXngZIVrvqJAxJymYrdll1DZ3PxzQktiEMEuTfD+01kH2ui6/HeXv\nYjtJWICdOeWMDHHvuDg3hGlx/uh08N3xCiPs2cA5USoufmL8TT8xUo+0fXLkqQrbezijQIhPJpkY\nWZwuOlv843t8WoSPM672dmQWGaEwX6dpn1RpOhRW7AhrsznCbAwSVqUV8nN+NU/NGt7/fqyOWOSt\nhu2EX6xwlWUs77Kzyd/VHqnEtELYhfYoyZUghEX0MDmyql0IM0lHWN424Qh2CzT+tk1B8v2g00La\nMqNuNtTLiaHeTuwdhD1h2cV1DPV2+mU8Om0Z2DlA4gLTvfjQieI65eB7oNMR4+9KXjeTC210zVc/\nF1DT3MavJkdaeleMxjBfF1KGerImzbr7pIzNuiNFxAe7dT29tQcWpoSZJHJ4sryRs5VNzIg33vF9\nQUooKrWWDRnWKXLahDBLM2Q8+A7vyMxfToy/KxUNSqtVUq9EyutbOVpYy3Qj2EJB/A7DvJwsPj0y\nr6wBmVTCMD9ns75ulL+LzdUIHDlfg72dtN8nvD5RlCaKge16vvGRSiWMDHU33BEWkiK+mjAeqdPp\nsNfphTDrcoTZ65S2jjAbPdKm0ZJZWGvxi/qK+lZe3HqCcRFezE8O7f8GctZD6Dhw7/uKdbckLYYL\nuVB85BffspNJCXBzoNiEHWHl7bEbq4lGKhshd6s4fjdV9qvUPdTTCZlU0mVPmN4R1tsEsn5TXyoW\nXWKseFrk5XgOEXG89M9AbdxOnQmRPhw8WzXozgXZJXW/7AdTNkDWmotxUlMhkcDYR6AiB/J/IjrA\nleomFZWNtnucvqBUa/hoz1muifBmzBAT/p4swPzkUM5WNnH4XI2ld8UsnCitJ7u4njtG9//cOmtk\nIG4Odqw0cuRwa1YpEglGu/8FiAtyZ2SIOysPWafIaRPCLI1EImzIJRldXhxG26aqmJ1v2/u8phkw\nNvZSJBIJ00b4s/90lUXHROeVNRDh44y9XRfjsk1ItL8r5fVK6lqu7smRGYW1JAS7G793QdMGpUcv\nilO9kBjiQW5pA61tmoG/ppMX+ESbtDC/TaPDUWKdjjB7na0jzEbP/O3rHG59dx+f7M236H48uykH\npVrLS7eP7H/xcUUuVBw3PBapJ/52sHOEo12X5gd5OJrUEVZepxfCLOwI02rgyHJ4ewysXAQfT4FX\nhsFLofDetbDyLtjxFBz+WEzarDoD6s5CgcJOSpiXU5fRyJomFQo7Kc4KI5/rT24TXweTEAaQ+iA0\nV0LORqNudmKUD00qjeELSz3QotKwt7iNbVmlZBfXUdfcZtDNZF1zG4XVLb+cGJm1BlSNpinJv5yE\neeDkDT+/31HVYVss7Rtr04sor1deUW4wPbNGBuJib3fVlOavSy9CLpNwy6jgfv+sg1zG7aND2JZV\nRk2T8UTkbdmlpA71wtfVuItFC1PCyCtvMOmxcqDYWXoHbCBsyN89K1xhwaM7fSv6kpNEariXJfbu\nqmPn8XLCfZyJ9DPeRKTp8QF8vDefH/IuMDsxyGjb7Q+5ZQ2MtsAKUrS/+Hc8Vd5A8tCr8z2sUmvJ\nKq5jyTUDLJvuifJsULdCyJg+PX1UqAdqrY6cknrDVhRDU0X3ik5nkj4RtVaLA+0neDsrE8JQ0mbr\nCLPRDbtOlLPiUAH+bvb8c+sJgj0cmZFg/ijZjpwytmWX8cfpMYT7DMAJnLMBkBgei9Tj4C56UbPW\nwrR/gqKz0zPIw9Hw2HYPXHSEWVAIO/sD7HgayrPE4sWt74rutJp8UUJfcw4qT8Gpb0FziYNJIgW3\nEOFw8goHz6HMd9RwotQLmsOFi6f9OFzVpMLbWWH8iW+5W8FzKPgNN+52TU34JPCOEqX5Roz9XRPh\ng1QCe05VkmKCa5sWlYb7Pz/M/jMqPs66uFDuam9HiJcTIZ6OhHo6EerlSEj711BPJ5ztu7+1yylt\nL8oPvsSZrtPB4U8hIEHEXk2N3EEIbj+9SuwEMTwjt6yho3fNRteoNVre//EMiaEeTIj0tvTuGB0n\nhR2zEwPZmFHCc7eM6N9k40FGm0bLxqPF3BDrN+AuroWpoXy2/xzrjhTxwMQIg/fpdEUDJ8sbeW52\nP3tE+8DsxEBe2HKclYcKTdOTbAA2IcwacHAXKyTHVsO0f3SyJQe5O+Bib2dbLTET9a1tHDhTyX0T\nwo16ETk6zBNvZwU7j5dbRAhraG2juLaFO8eGmf21o/wuuhqvViHsRGk9KrWW0SYpyk8TX3uZGKln\nVHthfmZhrYFC2FjI+AKqToNP1MC30w1tGh2OWKMjTEQjNWoDHHU2rliqGpX837pjxAa4svqRa1i6\n7DC/W3UUPzd7xgwx3/GvvrWNv32dTWyAKw9dN4CLZJ1OxCKHTABX48UkSFosCvNzt8DI+Z2+FeTh\nyPbsMrRa07gty+qVyGUSvJxM0J3VGxdOwrfPiMmZ7mFwx6cQd3v3iwharZgEfKlAVt3+33nboamC\nR/XPffkPYO8uRLLIqTQ0TDFK0XEnlI2Q/yOkPGi6InVTIZUKV9i2P4lop5HEHncnOQkhHuw7Xcnv\nb4w2yjb16EWwA2erWBqvYO7kVIpqmimqaaGwupnCmhbOVzWx91QlLZe5uz2d5IReIpRdKpodOS9i\nZ51K1ovThTA76zXz/W6T74e9b+CVvQwv5xs5aetC7pVNmSUUVrfwt5vjjC9yWwnzk0NZcaiQLcdK\nWZRq/vsVc/Fj3gUqG1XcMWYAdQXtxAa4kRTmwcrDhdx/reH3rNuyRH3PTUbsB9Pj6iBndmIgm4+V\n8MzsEbj0INabG+vZk6udlPvhyOeQuVJMVWlHIpHYOpbMyO7cCto0OqbFGScWqUcmlTB1uD/fZJWi\nVGvMHk/Uv39i/M03MVKPbUQ2ZBSIi0+TrIQUp4OzL3j07aLBz82BQHcH4xTmg+gJM4kQpsVR7wiz\nKiFM7ItEc3WN+bbROzqdmM5Y36LmiwfG4uYg56N7kpn73n4e+DyN9Y9NGJgzawC8vD2XCw1KPrw7\nuc9j2TtRngOVJ2Hsw8bdsSHXgscQIaJfJoQFezig0mipbDJul5OeivpW/FwdkErNeBPZVAU//gsO\nfwIKZ5j6d9GRJO/FlSaVikJ6t0DRJXs5yka+2XOAjd/v59Wpbri3FIvf197X+JtsFZ94PwlMNN7f\n48wu0KggdpDFIvUkLoJdz8Ohj2CO8VxPEyN9eO/HM9S3tuFmJAdLs0rN/Z+l8XN+Fa/NT8Sz7jTx\nwe7EX97rhTjmVDWpLhHILopluaUNfHe8AtVlHWZB7g6dhdK0T0Hh8ovPo0lxC4T425Ec/YpRvtPJ\nu4qvD/uCVqvjvz+cITbAlSmxfpbeHZMxKtSDaH8XVh0uvKKFsHVHivB2VjApxteg7SxKCeNP646R\ndr7GYFfq1uwyxgzxJMDdNI7phalhrE4rYnNmiVX9bm0dYdZCYKJwdKR9+ovC1Jj28cLWWDJ3pbHz\neDk+LvYkhRpfsJge70+jUs2BM1VG33Zv5LavtsUEmF8Ik0olRF3lkyMzCmsJdHcwzQmm6LCI2PRj\nNSgxxMPwrL53FDh4mKwwX63R4ShRoZEqQGpe4bhH2ov7ZRrT3KzbGLysSS9i5/Fy/jg9pmMohpez\ngs+WpiCRSFi67BBVjaZ/3xw+V80XBwtYOiGcxNABTqnNWS/ieMONFIvUI5XCqLuEu6jmfKdv6ac0\nl5ioML+8oRU/cxXlq5Ww7y14K0mIYGPuhd8cgWt/17sI1hfsXfAMT+JbbTLHQhfDrFdhySa452uk\nWhVPV/xe9Iy1GalzLXerSCuEjjPO9syNgxskLhQT2puMN+lxQqQPGq2On400va1Zpea+zw63i2Cj\nmJPUc5G2RCLBx8WeUaEezE4M4rFJkbw4J4Hl94/l+ycnkfvCTRz8yxTWPnINry9I5Pc3RvOPOZdM\nl26pEf8mCfPA3szXh2MfAVUD8+1+5GR5g8mcoFcCO3LKOF3RyGOTI80r5JsZiUTC/ORQjhbWXrGL\n5zVNKr47Uc5tScEDW6S6hJsTRa/aikOGlebnVzZxorSeGfFGdH9fRlK7yLnSwH01NjYhzJpIuV+s\n6J3b0+nhKH9XaprbbFNVTIxSreGH3ApuHOFvkhPN+GE+OClk7Gwv4zcneWUNuNjbEeJpGWdNlL/r\nVT3wIaOglqSwAd6Q9kRLjYgm9jPqkRjqwfmqZsNKNqVS0RNmosL8No0WB5SoZRYutr6cdkeYTGO6\nUm8bg4/C6mb+vimHcRFe3H9teKfvDfF25uMlyZTWtfLA/9IMG1TRC0q1hj+vO0awh+PA41o6negH\nC78OXAxbse6SUYsACRz9qtPDF4Uw03y2yupaCTB1P5j+3+6dFBGFDBsLj+6Hm18z+r/lMF/Rv9lp\ncmTEJG7VvsoRn9vgwDvw/rWGH6M1aji1A6Kmg2wQB0lSHhSutiOfG22To4d44CiXsffUBYO3pRfB\nDuVX8/qCUdyW1P8S7cuRSiUEuDuQPNSLOUkhPD4lihtiL0k8ZK4SHaPJSw1+rX4TPBpCx3Ft1Vpa\nVaK+w8Yv0el0vLP7NOE+zsyyQNekubl9dAhymeSKLc3flFlCm0bwl28qAAAgAElEQVTH3AFMi7wc\nJ4Udt44K4ptjpdQ1D3wg2bbsUgCTdplKJBIWpoSRWVTH8ZJ6k71Of7EJYdZE3BzhsDj8SaeH9XG2\nU1eoOm4t7D9TRZNKY/RYpB4HuYxJMb58e7zc7CtfuWUNRPu7WKxXINrfhQsNSmqbrz4xt7JRSUF1\ns0lchhSni68hfesH09PRE2ZwPDIVLuQKQc7IqLU6HFGhlVlRLBI6ivvtbI6wAVFR33rFuZs1Wh2/\nX30UqUTCq/MSu1xIGR3myZsLkzhaWMvvVh5FY6JzwLu7z3DmQhP/nBPfY2l2j5RmQvVZcU1iCjzC\nIGKSEMIuGTphaiGsol5p2qL8ojT4dDqsuVdEze7eAHetAb9Yk7ycn6s9zgoZZy9cFMKUag0VSjkH\nhj8F93wtnGmfToedTw/cHVZ4UBzjB2ssUo9frBB3D38qxD0jYG8nIzXciz2nDXOZXS6C3TqASXL9\nRqcTKZTgZJFKsQTjHsWlpZgp0iPk2XrCuuSHkxfIKann0euHIbuC3WB6vJwV3DjCnw0ZxajUV95Q\norXpRYwIdGNEkFvvT+4Di1LDUKpF+f5A2Z5dRmKoB8Eepr3evn10MAo7KasOW48rzCaEWRNyR1Ek\nm7sFGso6HtZP3bNl6E3LzpxynBUyxg8z3TSW6XEBXGhQctSEk7EuR6fTkVfWQEyAcQ66AyHK/2Jh\n/tVGRoH4XZvEEVaUBkggaHSvT72UhBB3JBLILKwz7PX1PWH6wn4jotZocZQo0VirI0xrW73uL8v2\n5ZP64i6e33Lc0rtiVD786SyHz9Xw91vjCPFsn4R4/gCsvAvqSzqed1N8AM/MGsH2nDJe3HrC6Ptx\nsryB9344zW2jgpgUY0CPTM56kNrB8FuMt3OXk7QY6grg3E8dD7k52OFib2cSZ0iTUk2DUm0aIay2\nANbeDx9PEYX2s9+CR/bAsBuM/1qXIJFICPd15uwljrCaJuEK8HJRCLHxsQMwegnsfxvenzgwd1ju\nVpApYNgU4+y4JUl9COqL4OQ2o21yYpQPZy80DVjAbVapWbrMzCIYwPn9UJlnGTeYntib0bqFcJ9s\nu+0epwt0Oh3vfn+aIHcHozgEBwvzk0Opbo8QXknklTWQVVzHHWMMd4PpiQ92JyHYnRWHCga0yFhY\n3cyxojpmmjAWqcfDScFNcQFsyCg2qTO+P9iEMGsj+T4xSvvI8o6HfF3t8XCSX5UigrnQanV8e7yc\nSbF+Ji2ynxTjh51Uwo6cst6fbCTK65XUtbQRa4F+MD3RHULY1Xehk1FQg1wm6bLo1mCK0sA3VvSf\n9AMXezui/FwMd4QFjwGJzCQ9YWJqpAqtnZU5wtqFMLnW5gjrKzqdjtd25vH3zccJ9nBk2b5zrE0v\nsvRuGYWckjpe+zaPmQkBzNHfqNScg5V3ikWtL+Z2ckzed204SycM5ZO9+Szbl2+0/dBqdfx53TFc\n7O145mYDxp/ro30Rk8DJhFMuY28WE7Mzvuh4SCKREOThYBJHWHm96B3zN2ZHWGs9fPccvJ0sftcT\nn4THj8CYJWbrNQz3ceHshYvXhlXtgwa89WXo9q4w+w24e6OIwPXXHabTQd43EH492LsYe/fNT/QM\ncAuBQx8abZPXRvkAsHcArrAmpZp7lx3m8Dkzi2Ag3GD27mJ6qaWQ2SEd+xDXyI7TeO6I5fbDSvk5\nv5q08zU8fP0wFHZmumU/9R0c/hhazLdgfzkTo3wJcne44uKR644UYSeVcOuoIKNud1FqGLllDQPq\n/t2eLe5HZ5hgWmRXLEwNpb5VzdasUrO8Xm/YhDBrw3uYuABN/wy0Qi2VSCRE+7telSKCucgorKWy\nUcm0EaaJRepxd5RzzTBvduaUmy0edKJMZLEtUZSvJ8jdARd7u6su3tvapuHA2SpGBLrhIDfyjZFO\nJ6KRIQObgJUY4kFmYa1h70OFMwQkmEQIU2tFR5j1CWHC8SPX2qZG9gWNVsfTG7N56/vTLEgO5fsn\nr2f8MG/+uiGLTEMHNliY1jYNT6w6iqeTgn/eliCi58oGWLEIdBqY/SZUnoIVd3YSHp6eNYLpcf48\nv+W40RZFvvj5PEcKannm5hF4uxgg9hQfEQ4nU98cyx1EQfeJzZ1uuII8HE1Sll/WIYQZwRGmUQsR\n4a0k2Ps6xN0Gv0mHKc+YvXA8wseZ4tqWjtX16vbeRy/ny94DwyaLrrLR91ziDjvc+wtUnBDC7mCP\nReqR2UHKfZD/E1TkGmWTMf6u+LjYs6+fQliTUs3Szw6Tdq6aNxYmmVcEa6qEE5tEX5/CyXyv2xWj\n70EpcWBU6SrL7ocV8u7u0/i42LMgJdT0L1ZfCqvuhi/nwjd/gNfjYNv/CZermZFJJdwxJoSfTl0w\nWVTe3Kg1WtYfKeaGWD/DztFdcMuoIJwUsgGV5m/NLiU+2I0wb/McB66J8GaotxMrrUTktAlh1kjy\n/e3W7R0dD0X7u3DSNjnSZOzMKUMukzDZDGOJp8UFkF/ZxJkL5nH46XsXLOkIk0gkRPm7XDWuxpLa\nFl7ensv4f31PRkEtU4ebQGCtPgst1aLfYwAkhnp0jF03iNCxUJRutM4VPW3tUyN1VieEtTvCNDYh\nrDeUag2Pr8zgy58LeOT6YfxrbgL2djLeuXM0vi72PLw8nQsNg9dZ9+qOPE6WN/LKvEQ8nRWi72rD\nI6I3b95nYlLg7R9AwQFY90DH4pZMKuGNBUmMCvXg8RUZHCkwrGOvpLaFf2/LZWKUz0VX2kDJWQ9S\nuXmEj6TFwqWUva7jISGEGf/Gp6JevM8MFsLO/gDvT4AtT4BPNDy4G27/ENyNF3XpDxG+zuh0cL6q\nGbhUCJP/8skObkKcvXuDEGY/nQY7n4G2Ho5leVvF1+gZxt51yzF6Ccjs4fBHRtmcRCLh2khv9p2u\n7HP/a5NSxCHTzlXz5sIkbkk0rkOkV45+KQYHjLFgLFKPoyc5vrOYrPyBtvorKwpnCJmFtew5VckD\nE8ONv5B6KVqtEPbfHSvuO6f8TRzXhs8WndVvJYmY//kDYgHWTMxLDkWn44pxj/906gKVjUrmGjEW\nqcfF3o5bEoPYnFlKQ2vfS/NLalvIKKg1mxsM2ieDpoRyKL/abPfBPWETwqyRmJngGghpF0vzY/xd\naVCqO1Y1bRgPnU7HjpwyxkV44+bQxcVjT5zb26nPrS/c2C6K7Mgxzwk/r6wBfzd7PJwUZnm97oj2\nu7JdjTqdjoNnq3j0i3Qmvryb9388Q/IQT756YCy/viHS+C+o7+UKSRnQj+sL8wdipe5EaCq0NUFF\njmHbuQy1RosjSisUwsSqmUJnOxb3RJNSzf2fpfHNsVKemjmcP8+I7RjW4eWs4MN7xlDbouKxL9MH\nZSHu/tOVfLw3n3uuGcL10e3TAH94SUTkpr94sR8qfi7c9C/x+Dd/6LiRcFTI+PieZALcHXjg8zTO\nVzV180o9o9PpeGZjNlodvDgnwbCBKFqtiEVGTgFHEwz3uJzAUeAX1ykeGeTuQFWTCpXGuDdcRolG\nZq+H5XNEAf385bB0q5h8Z0EifPSTI8UNRbeOsEsZdoPoDku6G/a/BR/04A7L2yoi8G5X0LQ6Zx/x\nuTy6AloN7MlsZ0KkD5WNqj71XOlFsPSCGt5cmMRsc4tgWi2kLYOw8SYb5NBfquLvRSFRU7fnA0vv\nitXwzu7TuDvKWTxuiOle5EIefDZTCPtBieK4MPEP4rg25334XRZM/D2c3wfLboKPJkPWWtAMfEJh\nXwn1cmJCpDer0wrNPmDMFKxNL8LLWcFkQ/o7e2BRahgtbRq+PlrS+5PbuRiLNH0/2KXcMSYEO6l1\nTAa1CWHWiMxOrFid3tVhSdWXjdumqhif0xWNnKtqZnpcPw4EOh38+DJ8Ngs+ntov63CAuwOJoR7s\nNFNPWG5ZA7EWLMrXE+XvQlWTiqrGwesA6YoWlYYVhwqY8eYeFn54kANnq3hgYjg//nEyH96TzPhI\nH9NM6yxOA7kz+A0f0I/HBLhibyc1PJ6mL8wfSAlzD6i1OhxQoZNbmxDW7gjTKW0O3W6oblJx58c/\nc+BsFa/cMZIHr4v4xXPigtx5+Y5EDp+r4YVBVp5f19LGH9ZkEuHjzF9mtH/+cjbATy/DqMUw9pHO\nPzDuEbj2CUhfBj/+u+Nhbxd7lt2bgk6n495lhztEjP7wTVYpu3Ir+MO0aEK9DIw2FB2G+mLzdQZJ\nJMIVVnIEysV7QD85srrVuJ+tsvpWnBUyXPu72KUnZ6Nw9YWOhUf2wohbxP5bmKE+4nd+pn1yZHWT\nCqlE1DD0iIMb3PIWLF4PqmbhDvv2b53dYfWlIn4fcwW5wfSkPigWcDJXGmVzHT1hp3qORzYq1dy7\n7FC7CDbK/CIYQP4PUJMvOomthODIUfygScTt6MeQt93Su2Nxcsvq+fZ4OfeOH4rLQKf/9oRaCT/8\nC96/VsSfb/0v3LNJ1PNcilugcIg9cRxmvSai/+vuhzcTYd+bJu8Rm58cSlFNCwfOVpn0dUxNbbOK\n745XcOuoIJN1vY0McWd4oFu/4pHbskuJDXAlwte8/Y9+rg5MGe7HuvQiiy+E2oQwa2XMEpBIxYUz\nF8vGT10l0TJzsvO4cGbd2Nd+MK0Gtj4Ju/8pCn9VjfDZzf0Sw6aN8CezqI7SOtNm39s0Ws5UNFo0\nFqkn+gqbHFlY3cyLW08w7qVd/GV9FhKJhH/PTeDAn6fwlxnDDb8p7Y2iNAhKGnAps1wmJTHUg7VH\nitidWzHw/XAPAdcgo/eEtWm0OEisUQgTv1dHVKivgFVKY1NS28K89/dzorSe9xePYV5y990mtyQG\n8fD1ESw/eJ6VA+i2sBTPfp1NRYOS1xeMwlEhg9JM2PAohKTCza91LZBMeRZG3SVcY2mfdjwc4evC\nx0uSKa5t4aH/pfVrklJts4rnNuWQEOzOveOHGv4Xy1kvImPmFD5GzhdRzKNfAheFsKoW4362KuqV\nA49Fntgsbv5CkuGuNVZVGu/qIMfP1Z789smRVU0qPJwUyKR9FOkip8Bj+4Ugue9N+OC6i25j/WTF\nmFkm2HMLEzxa1Aoc+lA4pAwk0N2RYb7OPRbmNyrVLF12iCMFtby1MImbR1pABANx/HHyFmKulRDh\n68wLmiXUyX1hxQJY/3CnISNXG//dfQZnhYylE4Yaf+PnD4iOwB9eEpOBf50GSXf1LOwrnCDlfvjV\nYbhztRDMvv0bvDYCtv5JVHWYgOlxAbg7yq3COWQImzNLUGm0Rp0WeTkSiYQ7U0PJKaknq6h3p2t5\nfStp52vMGou8lIWpYVRZwWRQmxBmrbgFiYvRjC9ArcTLWYGvq71tvLAJ2JlTxqhQj75dJLe1wpp7\nxUSVCb+FBV+IVZS2pnYxrG8nA7377Nvjpj0AnKtsQqXRGlaUr1HD/nfg9XjI+HLAm+kQcysG73tY\np9Ox91QlD3yexnWv7OaTvflcG+nD6oevYevj17IgJUzcGJuatlYoyxI3Zgbw0u0JBLg5sPSzw/xj\ny/GBrcxIJCIeaXQhTIcjSiRyCxf5Xk67MOeIErWR41uDndMVjdzx3n4q6pUsvy+1T4sLf5oey8Qo\nH/72dQ7p563/xmdzZgkbj5bw+A1RJIZ6QGOFKMN38hLnA7tuImkSiehnipomIpInNnd8a8wQL95Y\nMIr0ghr+sDqzzzGQF7eeoKa5jX/NTcBOZuDlnFYjXE9RN/Z7Cq1BOPtAzE3CmaNpI7hdCCtsMO4q\ncVl9K34DiUXmbhXn/KAkuGut2cvw+0K4j3PH5MiaJhVezv2sQXBwh1vehsXrxMLeJzeKm9zjX4Pn\n0AG7jq2e1Ieg6rRwSBmBiVG+/JxfhVL9SzG7Uanm3k8vimCzRlooalpfKt7To+7q/lhlARzkMvAe\nxjP+78B1f4KsNfDuOMjbZuldMzvnKpvYcqyExeOGGLfSpLVORCCX3SQ6Au9aC3d8Ai6+fd+GVArR\n02HJZnh4D4y4tX1wyOj2HrH9Ru0Rc5DLuG1UENtzyqhrNn0c01SsTS8iNsCVuCATTI+/hFuTgnGQ\nS/mqDwuLO3LK0OlgZoJ5Y5F6rmufDDqQgn9jYhPCrJmU+6G5SlyMIArzrX7qXnO1OBgumwk15y29\nN71SWtdCZlEd0+L64AZrrYMv5opJO9NfghufFzc3gSPFSaGtWYhhVWd63VSknwsRvs7sNHFPWG57\nlHbAQlhJBnx8A+x8CrRq+Pox2PXCgFZQ/d3scXWwG5Q9YU1KNcsPnOPG139i8Sc/k1FQw68mRbL3\n/ybz7l2jSQ33Mk38sTvKjoG2zWAhbJivCxt/NYF7rhnCx3vzmfve/g5nQb8IHSsmzdUbbxyy6AhT\ndQhPVoOdEMwdJSqaVcYdEDCYySysZd77+1FptKx8eBxjI7z79HMyqYS3FyUR4O7Ao1+kd3Q5WSNl\nda08vTGbxFAPfjV5GKhVYspWcxUs/ApcezmPyOSiRD94DKy9H87t6/jWzIRA/jpjON9klfKv7b1P\ns9t/upLVaUU8ODHCOBfXBQegsQzi5hi+rf6SdDc0V8LJHQR5ODIyxJ1VeSo+3ZtvtPhxeX0rAf11\nhOVth9X3QGCiEInMKRD2gwhfl06OsH4LYXoip4qOoFF3CXfY2R+EG8wKIqAmIe42cPKBQ8YpzZ8Q\n6UNrm/YXgr5eBMsorOXtRRYSwbQaEXP99hkx0XbMvebfh16IDXAjvaiJzd5LKbzjG3TO3rBiIax/\nSNxbXCW898MZ7GRS7p8YbryNHt8E76RC+mdwza/F5zzqRsO2GTgS5rwHT2TDdU8KEWzZDPhwEhxb\nY7QesfkpoajUWjYeLTbK9szNqfIGMovqTOoG0+PmIOfmkUFsOlpMk7Ln69OtWaVE+rl0VC+ZG5lU\nwrzkUPaerqSwutki+wA2Icy6CZ8EXhFiagfCUXOyvNF6SwNLM+HDSehO7kBXliUKWE9ssfRe9ch3\n7Y6saSN6UcTrS4W4V/gz3P4xXPNY5+8HJMCSTWKVpY9i2LQRARw8W2XSVY68sgZkUgmRfv2Mcigb\nYftf4aMbxDCAeZ+L0szR98CeV0VMpKdJU10gkUg63sODhfzKJv6+OYdxL+7ima9zcFLI+M+8RPb9\n+QaenB5DoLuFRBp9dGWAEyMvxUEu4/lb4/ng7jEUVDdz81t72JDRzyk9+p6wIuP1hLVpNDhKVEis\nTQiTStHIHHBAybTXf+LN704NqNvpSmLvqUoWfXQQFwc71j4yvt/CjIeTKM9vVKp59Iv0Lh0Vlkar\n1fHHtZmo1Fpen5+InVQC3/weCg/Cbe9C0Ki+bUjhLKIlnkNgxSIovzhk4oGJ4Sy5Zggf/nSW/x04\n1+0mWts0/GVDFkO8nfjd1CjD/mJ6cjaAnSNE32Sc7fWHYVPAJQAyvkAmlbDiwXGM9pfx/Jbj/Hld\nlsHvB51O1/9o5MmdsPpuCIgXPVoOpl3JN4QIH2dqmtuoaVJR3aTCe6BCGIi/563vCOFv6ERxzr9S\nsbMXglDeNqMs3I6L8EImlbDvknhkQ2sbSy4RwWYmmFEEqzkvhI/V98Arw8T1XNYa0UF8eReUFTA5\n1o+aJhW/WZHBxOXVJBT/hRWOi9AcW0vzGynk/rCK2uYr+1xbUtvC+owiFqaE4udq4IRbgPoSYU5Y\nfbdwfj2wC6b/07jxbtcAuOFpeCIHbn4dVE2w/gF4YyTsfd3giGtckDvxwW6DNh659kgRdlIJtxk6\n0bmPLEoNo0mlYXNm96X5lY1KDuVXM9PMJfmXMz9FVGesSbPc79YmhFkzUqkosyw8COU5RPu70tKm\noajGtL1SAyLjS/hkGm1tKp50+Rd3yV6h0XkIrLoLtv1ZrJxbITtyyonwde5ZKKo8DZ9MEx1gd66C\nkfO6fl5AgnCGaZSiRL8XMWx6nD9qrY7v80znCsstayDcxxl7u37E9U7ugP+Og4PviovEXx0SK6cy\nOcx+C6b+XXTJ/O8WaOq5GPZy9K5Gay4Z12h17M6r4N5lh5j86g98cfA8Nwz3Y/1j4/n6VxOYOybE\ntKOs+0JxGrgFG3WS1/S4ALb9diJxQe48sSqT368+2uuKUgcBCcIpZcTCfJ2q/TinsLJoJCBVOHFr\nnCeJoR68/t1Jxv9rF3/7OpuCKsutalmKrVmlLP3sEGFeTqx7ZDxDfZwHtJ3YADf+My+RIwW1PPt1\njtUdI5YfPM+eU5U8NWu4KJY99CFkLIeJT4oJdP3ByUuIKwon4TKuFdEAiUTC32bHMXW4P89tyuk2\nOv/mrlOcr2rmpTkJxjkWadTCeR49zTL9VzI7SFwIp3ZCQznO9nb8apQ9j98Qyaq0Qu766GcqDRiy\nUtPchkqj7bsQdvo7WLVYRALv3gCOHgN+bXMQ4Ss+c2crm6huUuFpiBCmJ3Iq3LvFaqYKmozkpaKP\n95Ip7QPF1UFOUqhHR2F+Q2sb9y47TGZhLe+YQwRrrROLz1t+D28lwZsjYfNvxUTQmJkw9xN48rQY\nkmCF3DEmhJznp/PN49fy6rxEFo4bxhbve1ksfYlzrc7E/vAQu1+6jekvbmTpskO8vD2XTZklnK5o\nQK0ZfJOHu+LDn86i08HD1xsoVGq1wun4TqoYvHbj8/DgbtNOuVU4iXvWXx2CO9eATxR895zoEdv0\nuEiYDJAFyaEcL60nu9g4U17NhVqjZcORYibF+OHjYp4o8ugwD6L9XVjRg3C4M6ccrQ5mmFOY74Jg\nD0eui/JldVqRxT7DJhhFYcOojLpLRNHSPiU6/mkATpY3EOZtJTeHaiVs+z9IX8Z5t2TmVT6A1skX\nR4WUMSV/4H+hWxj783vCSTVvmeibsBLqmts4eLaqy4lmHRSlw1fzAIm4KOztJBIQL8Swz2cLMWzJ\nFvCJ7PKpiSEe+LnaszOnnDlJprHM5pXXMzKkjxfxDeWw/f+EM8A3Fu7bAWHjOj9HIoFrfyd+jxse\nho+niBOeb3SfXiLKz5UVzYVUNhpZGK0tgNZ6scIrk4vCZ5kC7BTiq0zRY7yjTaPl4NkqtmWXsTOn\nnMpGJb6u9vxuahR3jg0zzsqcMSk6bHAssiuCPBz56sGxvPX9ad75/hQZBWIVOz64FzeEnQKCRhu1\nJ0zXJoQwiRUKYRK5EwGOOj5dlMLJ8gY++uksKw4V8MXB88yID+Sh6yJEf9QVzpc/n+fpjdmMCfPk\nkyUpuDsNcCJfOzMSAvn15Eje2X2a+GB3046N7wenKxp5cesJJsf4ctfYMBEZ2/4XERub/NTANuoR\nKlw3n86A5bfD/TvBSThK3lo0ikUfHuQ3K46w6qFrOr2Xckrq+PCns8xPDmF8pI9x/oLn90LTBfNN\ni+yKpMWw7w04thIm/BapRMLvp8UQHeDKk2syueXtvXy0JHlAMVB93LZPQtiZ3cJB4RsNd28ER89+\nv565CW8Xn89UNFLbbKAj7GrDPQRiZ4nkRVMl+MeLhZ2A+AH97idE+vDW96coqmnm8RUZHCuq4507\nk7jJFIXUmjYRdzzzvXjfFqeL2KPcGYZeKzrQht0APtGDJt5qbycjLsi90+dcpxvLhdpFnPv+FW7J\n/i9TNMd5/cKjfHgqoWNgjb2dlGh/V2IDXIkNdGN4oCvDA9yMIwqbiQsNSlYcKmBOUnBHV+KAqDgh\nhKeiQxAxSbi0vHq4zzE2UqlYVImeJrpsf34fjq2GI59D4CghPsff0a9Fl1tGBfOPb06w6nAhUwbR\npdWe05VUNCi5Y4x53GAgFtQWpYbx983HySmp6/KcuS27lHAfZ6sYpLYoNZRHvjjCP745wfhh3sQE\nuBLq6YS0rwNfDMQmhFk7Tl4QfztkriJqohDC8sobmNrXCYempLZQWK5LjvCVYi7PVNzGnDFDeHrW\ncBR2Ul7enseC/XNY7D6M5y68i93714kIyfDZlt5zAHbnVaDW6pjW3b/lqe+EndjZV6wK99VK7h8n\nBDC9GHbvFrEychlSqYQbR/izIaOY1jaN0V1GjUo1hdUtzB/T/dQ2QKwcHfkcvn0W1K0w+WkxCMCu\nhwuIuNvEBeSKhfDJVFjwJYRP7HWfLk4/NVJPWHM17Po7pH8O9OIgkco7CWU6mZxmjYz6NgnVSglO\nWhm3SuQsdnbCJSaMgFlPofDrWsS0KI0XhPCX8qBJNm8nk/L7G6MZP8yb3608yu3/3c+fZ8SydMLQ\nnnvQQlPhwLsiMis3XDjUtQl3ldQKhTDkjqITEPGefmVeIk9Oj2HZvnN8+fN5vskqZWy4Fw9fH8Gk\naD+zndDNhU6n493dp3l150kmx/jy37vGGG1IxBM3RpNTUsdzm3KICXAlZaiXUbY7UNo0Wp5YdRQn\nhYx/3zESSU0+rF4ibi5v/0Bc9A8U/zhYtAKWz4Ev54l4vcIZJ4UdHy9J4fb39nH/54fZ8NgEQr2c\nUGu0/GV9Fp5Ocv4604gF5tnrxc1z1DTjbbO/+ERB6DgxIGj84x0P3zwyiKHezjz4vzTueO8Ar81P\n7PcqdlmHENbLivzZH8U5zWsY3P21uP4aBIR6OWEnlZBRWItWx8A7wq5Wpj4nai1O7eyYXgqAe2i7\nKJZwUSDzGNLjZ35ilA9v7jrFbe/uo7a5zbgimE4nkgZndwvxK38PqBqEoy0oCa59QghfISk9X78N\nMiQSCX6ebjD3BRg/D7eNj/Fs+Ys8nTSX02OeJadWxonSenLLGtidV8Ga9IvVDlOH+/H8rfEd02it\nmU/25tOm0fLopAG6wdpaRXXJ3jfEUI85H8DIBZYVQQMS4NZ3Ydo/hRiWvky4FHc8BQnzhCgWmNjr\nZtwd5cyID2Dj0WImThw87+216UV4Osm5Ida89+xzkoJ5aVsuKw8V8sJtnYWwmiYV+89U8fB1Eebt\nNu6GKcP9GRvuxWf7z/HZ/nMAOMplRPu7EO3vSkxA+x9/V3xd7Y2+zzYhbDCQfD9krsDt5AaC3IdY\nR2H+md3o1t6HUqnk8bYnyHG4jk/vS+D66IvTR567JY6b4i1CWtAAACAASURBVAP401onJtcHsNLz\nA4JXLYbUh2HaCxafWLPzeBl+rvYkduWYylwJX/9KRCPuWtd7CfLl+I8QAtjns0VnWDdi2PS4AL78\nuYB9pyuZMty4B8q8vhTlX8gTJ6WCA6IP5OY3unWw/YKQZHjgO/hyvriRu+UtGHVnjz8S7S9WgE6W\nNzC0b6/SNTodZK6Anc+I/oFxj0HYWLFCqlaCRnXxj1opHtcoUSlbKa6qo7iyjsraBqTaNpxlGgJd\npAx1luKp0CHVtkHJDnh/M4x9GK77o3VFY4rb+8FM4Ai7lHER3mz97UT+tDaT57ccZ9/pSl6Zl9j9\nTVboWOHoOPqlsMcberJqd4RZrxDWOaLu7+bAn2fE8usbIll5qIBP9+Zz32dpRPm58OB1Edw6Kqh/\nEWVrQdkoBjO0uyO0Wh0vfHOcZfvOMScpmJfvGInc0ImFlyCTSnhjYRK3vbuPR784wubfTLBcFx/w\n9q5TZBXX8f7i0fjJVfC/ReK9vegr40wQHDpBTO5afY+YTrjwK5DJ8XW1Z9m9qcx9bz9Llh1i/aPj\nWZte1OEwMdo0MU2bmGAZM8PyMeSku2DTby52ILYTH+zO17+ewCPL03n0yyP8dkoUv50S1WeBuaIv\njrD8PfDVAuGcWLIJnPs27MEakMukhHk5kXZOFIrbhLB+4j0MFq8V/91QDuVZwslSli2+ntwOuvbY\njsJVuMUuFcf8hncMdUkM9cDF3q5dBBvNTf3t4FGrQNkAyjrhclc24Ff+I3y9VjhR69rjTh5DIGGu\nEL7CrxsUzkWjEDgSHtoNe/6D7KdXiDn3EzE3vw6zLi6wX2hQkltWz+H8aj7ak8+Nr/3IH6fHcPc1\nQ5FZ6aJUXXMbXxw8z8yEQBG97y/n9opr+arTMHKh6AFzNpJj2Bg4esDYhyD1QZFoSFsmruHTl4k0\nQfJSUTGg6L5aYX5KKBuPlpBWLsOCSzZ9pq65jW9zyrlzbBgKO/M2UXk4KZiVEMjGjGL+OnN4p4XK\nb4+Xo9HqmGEKl+oAkMukrHr4GhqVak6VN3CyvIHcMvF1d96FTsK2p5O8QxzTO0Cj/F1xdxx4GsEm\nhA0GQpLFyfbwp0T7/4c8S5aNa7Ww9zV0u/9JPsHc3/oU140bx2s3xeJi/8u307gIb7b/biIvb/dj\n0n4v/umyhvmHPmiPSn4GXkacitIPWts0/JB3gTlJwb+8mN73lpisE36dcDoNdFKU3/B2Z9jNF2OS\nl0UIx0V442pvx46cMpMJYbEBXex/WyvsfQ32vCbsybf+V4hY/RUvPIeKSM/qe2Djo1B9VkSFutmO\nr6s97o5yTlY0MnSg120VuaKk+vw+sfJ580bx+eiG6iYV3x0vZ3tOGXtPVaLSaPF1tWd6kj83xQUy\nNsLrlzfyDeXw/QvC4XT0K5j8VxizVHTZWJqiwyCRCYu5ifFyVvDRPcl8vv8cL27NZcabP/HGgiSu\nGdbFTWL4RPBPEL+bjC/EKnvE9QN/cZU1O8KcOhxhl+Nib8cDEyNYMn4oW46V8MGPZ/nT2mO8uiOP\npRPCuXNs2C9P2m0tSLRWNBpcpxPi+JHlcHyjEJOHTUY9Yi5P5w5h5bFalk4YyjOzRpjE7ebuKOej\ne8Zw27v7eWR5OqsevsYivXxHCmp4Z/dp7hgTwk0j/GDlnVB5SjiEjRk1GT4bZv1HjLbf9Djc9l+Q\niCEnH92TzOKPf2bJssOcLGtgSqwfs4zZ65H/I7RUW2Za5OXEzRFVCxnLwa1zTNPP1YEVD43jqQ3Z\nvLnrFCfLG/jP/EScFL0fk8vrRb+YX3eOsPP74av5YoDBPZus6wayj0T4OvPdiQrAJoQZhKu/+BM5\n9eJjqma4cKKzOHZ0hXBjgXBk+URDQAJy/3j+d30oCmc34h2yIXs/KIWgJYQt/X/XXfZ4g/h/9S+H\nEI0AsHcX59hrn4Bhk80bdbM2ZHKY9GcRad34qOjzi58LM14BZ298Xe3xdfVlYpQv85JDeWpjNs9t\nPs7GoyX8a25C19fEFubzA+doVKr51eR+pBC0GjHo4ef34dweIY4uXg+RU0y2nwYjkYj0QGgq3PQi\nZK4SYtim3wiX2Mj5opu4i2v6ceHehHk58X1BK4sqmxjq7WQVjqbu2HysBJVGa5ZpkV2xMCWUDRnF\nbDlWwrzki8mgrdmlhHg6Eh88wM9Bfanoe1O3imORkUR4F3s7ksI8SQrrvL2qRiV55Q2cLGsgr7yR\nvLJ61h8ppvGSDuNAd4cO11h0P6dgWsFdnY1ekUiEK2zL75g8/Dz/POOKWqPFzoir8H2ipZa2dQ8j\nP72dTZrxfOD2W15ektprdMVJYXeJO8yN75ojebPiYxw+mIjklndEzM7M7D9TSbNKw7S4S1brtFoh\ngB14R1yQz/nAcNeaX+zFmOTnN4v+MN+Yjm8r7KRMjvXjuxMVaLQ6o65W5ZXV46SQEeJ5mZsifw9s\n+Z1YOUqYD9NfFNNkBoqjh+i62fIE/PSKEMNu/W+X8TgxOVIU5k/r77FT1Qw/vQz73waFC8x+E5Lu\n6TKmUFbXyo6cMrZnl/FzfhVaHYR4OnLPNUO4KT6A0WGePd/Au/qLyVmpD8GOv8LWJ+Hwx8LeHTW1\n+58zB0VpIlJlJoFIIpFw74RwUsK9+M2KDO78+CC/mRzJ41OiOh+D7F3h4R/FKt/ul8QwhWE3CEGs\nD9b3X7yuWjiuZPZWGGmQO4Kq5wUJuUzKnKQQbhsVzJ5TlXz401n+vT2Xd74/xaLUMO6bMISg2nQh\nGh7fxASdFOpuFcfDiMmWibY0VgjhN2O5OD4oXER8wdETbfY67DY9ynM6OQ+EXMewYfci0QwDqWn6\n8yL9XHltfiIPLU/nqQ3ZvDpvpFkvepuUan6/6iiB7o48O3sEfP8P4QyZ+aphAm93JN8nBPgf/yWO\nP1OfAyA13Iv/zE/kNysycFbIeOG2eOP+O2RvAHu3zjf+lsLeVZx7s9cjHTvzl9+2k/HKHSOJDXDl\nxa0nOP9eMx8tSe61T6esvhUvZ0XXjsyCg/DFHSLqv2SzYedCCxJ+yZAKmxBmZBROEDxG/NGj1ULt\nuYvCWFmWeC9lraH7JlmJ+Kw5uIn3ur0bOPkIUcu+/TEHt/b/vvi8wzlnSZmx2DoW4qyJgARRAL/3\ndfjxZcj/CWa9BiNu6XhKqJcTny9NYVNmCc9vPs7Nb+3l4esj+M0NUZYfetROk1LNp/vymTrcj+GB\nfRAnWmrFdcOhD6H2PLiFiCFWqQ/26KiyOhw9YdwjInlR+LNwiR1ZLq61g5OFSyzu9o5rXalUwn0T\nhvLc5uNMfvUH3B3lJIZ6MCrEnVFhHiSGeOBtpkL6vrA2vYgYf1figiwjvKaGexHh68zKw4UdQlhd\ncxv7TleydEJ4364jGsqg5KgQvkrbvzZeMsRHagdDxou+1NiZ4BFm9L+Ht4s9413sGT/s4gKVTqej\npK6Vk2UX3WN5ZQ3sP1OFSt2/0n3bUXWwkDAPdj7DpIbNPKtZyPnqZoYNxD47UMqyafriTuwbi3he\nvQT7CY+yfmp0v04kl7rDbjwwlA8d32HEmiVw/iGY9g+zRiV35pTjam/HNRHtzha1SkQhs1YL8eOm\nfxvW/XIpfrEiGvnZzeLPks2dJjFNjwtgU2YJ6edrSA03rJNEq9WRWVTL97kVfJNVRrS/60XBp7la\nCH0ZXxh/5Ugmh1veFhGD756DuiIR8eliZT3K3/X/27vvOKnq6//jr7OdZelViiBdUFCwgKiABXtL\nTGwxYksxJhprooklxkS/phrzM0pi1Nhi7IpiA+yKDQSUziIgve8CW2Y+vz/OXXYYZmEXtrLv5+Mx\nj7vce+fOnWE+c+8993zOh3FfLCX0rUIq66xX4OXrYP3XMOhc71qbtO2FqwsZP30Z42cs4/Ov1/lr\ntc/jJ6N6cdyAjgzo1LzqF5B7DfT/r5nj/LN79Nt+wTj69roZTSsegyWfVTxyaQ0a0KkFL15+ODe/\nMIO7J8zl/Xmr+es5B257EZqW7kWv9zvTT2be+QPcd6TfrR11Y5WGbLeo62F6fTyxy8z14uKVYGYc\n2acdR/Zpx/Ql63lqwgc0/fBPlH78FtgKYlnNSR90NiuXLGSvmeNg6mOQ0wL6nQz9T/ditzUZFIuV\nwrw34bOHPdATL/VaTYdf5UG5rKas31TCRXNGQ/Fkftd7Fn1Xvu71E7Obw76nehed7kdW+4Xa6AEd\nufKY3vzljTns37k5Y4bXXgbx7S9/xcI1m3ji0qE0m/O8Z9AOGQMHX1JzLzryF1CwzC/s8jr6RQJw\nyqBOZKQZzZtkVm+tm9JimPmijyhXDXX9qsUB58GURxkw4y7YO8PvOKeVn2eYGZcc0YNe7fP46eOf\nc+rf3uUf5w/Z4Q25FRu20L5ZivOLRZN91M7me0VBsPY18Y5qRWJ3qjZN68/F4B4rLc0DWK17bBN4\nYdMaWD7Ds2i3BrWiAFdm0106ryxcGFcQrCLpmTDiOv8Ne+7Hflwa8C048a6t54hmxmkHdObI3u34\n7biv+PvEebw8bRm3n7HfNhfXdeWxj75m3aaSnWeDrZwNk+/zbMSSQtj7MB8Nst/JDfv7YeYDc+09\nFI7/vZem+fTffk02/gYYdJb3yOjQnzHD9yF9zQIyO/Ri6uJ1fP71Ou6ZuJJozAS6tm7CoC4tOaCr\nPwZ0alFt9UurYu6KjUxZtI4bT9y3zrLWzIxzDt6b21/+itnLN9KnQzPe+Go5JbHACam6bBes2D7o\ntXFp2dY8iaPnUd4bpdOB/v826xW/Php/vT867O8BsX4nQceBNVafzszo3LIJnVs2YVS/8uN2aSxO\n/upN9L6z8ttqwC2nkcnOg0Fn0/Wzh2nJScxetrHWAmEbJz9C9itXURBvwk3Nf8+Ys85m/y5VH7kJ\nts0Ou/x/nThn47+5dPL9xL/+iLTvPlgr6d6xeOD1L5czql9777ddVOAHz3kT4KhfwxFXV3/jb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eQphEFCgzswzKvys7U/Z9vnYn6+Ulz0i++xgpazPpKZalWn9nzxHZFWOB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2c69c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dropout.plot(x = 'Location', y =['average', 'Homeless'],\n",
    "             figsize = (20,20), grid = True, fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 777,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>average</th>\n",
       "      <th>Homeless</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alameda</td>\n",
       "      <td>3047.0</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>1614.0</td>\n",
       "      <td>13180.0</td>\n",
       "      <td>5629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Alpine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Amador</td>\n",
       "      <td>34.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Butte</td>\n",
       "      <td>395.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>1681.0</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Calaveras</td>\n",
       "      <td>42.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Location    2010    2011    2012    2013    2014    2015  average  \\\n",
       "11    Alameda   3047.0  2567.0  2250.0  1924.0  1778.0  1614.0  13180.0   \n",
       "12     Alpine      NaN     NaN     NaN     NaN     NaN     NaN      0.0   \n",
       "13     Amador     34.0    48.0    27.0    44.0    36.0    31.0    220.0   \n",
       "14      Butte    395.0   291.0   278.0   245.0   256.0   216.0   1681.0   \n",
       "15  Calaveras     42.0    35.0    29.0    31.0    29.0    20.0    186.0   \n",
       "\n",
       "    Homeless  \n",
       "11      5629  \n",
       "12       157  \n",
       "13       150  \n",
       "14      1900  \n",
       "15       221  "
      ]
     },
     "execution_count": 777,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropout.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 778,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Homeless</th>\n",
       "      <th>Dropout Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Homeless</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.934344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dropout Average</th>\n",
       "      <td>0.934344</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                 Homeless  Dropout Average\n",
       "Homeless         1.000000         0.934344\n",
       "Dropout Average  0.934344         1.000000"
      ]
     },
     "execution_count": 778,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding p-value correlation coefficient between average \n",
    "#dropout rate and homeless population\n",
    "corr_df = pd.DataFrame(columns = ['Homeless', 'average'])\n",
    "corr_df['Homeless'] = dropout['Homeless']\n",
    "corr_df['Dropout Average'] = dropout['average']\n",
    "\n",
    "corr_df.corr(method = 'pearson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These line plots and bar graphs display the number of homeless per county versus number of dropouts per county.\n",
    "We observe that the counties with higher homeless populations also display a higher rate of drop-outs. There is a trend between peaks in homelessness and peaks in drop-out rates. When homelessness rates go down, the drop-out rates are also seen to fall, and when homeless rates increase, the drop-out rates are also seen to increase. Those counties such as, Mono and Alpine County, which have very low homeless rates, have almost non-existent drop-out rates. And those counties with higher dropout rates, such as Lost Angeles and San Bernardino, the drop-out rates are much more than the counties with low dropout rates. This demonstrates a clear positive correlation between rates of students leaving high school prematurely and rates of homelessness across the state. \n",
    "\n",
    "\n",
    "Most strongly supporting our hypothesis is our discovery of a very high Pearson correlation coefficient, clearly delineating a very high likelihood of a statistically significant correlation between our dependent variable, homelessness, and our independent variable, dropout rates. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Privacy/Ethics Considerations\n",
    "The datasets used were provided publicly on the data platform, Kaggle, or came from open source websites, ensuring that these were public platforms with public data accessible by any individual. According the Safe Harbour rules, no privacy concerns arise about these datasets. Because of this, we believe there is no concern about privacy. There was no personal information included in any of these datasets, instead the data are all simple count totals of various rates of homelessness or drop-out rates, so there is no concern of personal information. All data was anonymous.\n",
    "\n",
    "\n",
    "We acknowledge that any correlation in our data analysis of homelessness and drop-out rates doesn't necessarily equate causation. Those that drop out may not necessarily become homeless. There are many other factors that can cause a student to drop out that are unaccounted for in our research projects such as health concerns, poverty, special education needs, and other issues. There are also many other factors that can cause an individual to be homeless such as poverty, drugs, life circumstances, unsupportive government programs, and capitalism. \n",
    "\n",
    "\n",
    "We also recognize that our data is not comprehensive or holistic in measuring the actual rates of homelessness or drop-out rates. Many people are historically not measured in these surveys and dataset, and thus, the actual number of people experiencing these circumstances may be much higher. Similarly, we acknowledge that there may be potential biases that could have occurred in data collection, thereby also skewing the rates delineated. There may be many different methodologies in determining and classifying a homeless individual. The datasets we found did not provide the methodologies they used to collect the data. Because of this, we acknowledge that bias in counting who is homeless and a drop-out could have occurred and thus, the data and results could potentially be skewed based on the biases of those collecting the data. \n",
    "\n",
    "\n",
    "We evaluated this project based on the analyses performed in the data analysis section. We recognize the brevity of our analysis and the limitations of our results due to this. Nevertheless, the implications of this data will help increase understanding on the potential factors that may lead to homelessness. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion & Discussion\n",
    "Our data concludes that there is a very high likelihood of a statistically significant correlation between drop-out rates and homeless populations in each county in California. To prove this,  we saw a Pearson correlation coefficient of .934, which is very close to 1, or the value of a linear positive association. \n",
    "\n",
    "\n",
    "Moving forward, this result emphasizes the importance of supporting students in being able to successfully graduate high school. County representatives should promote better access to learning and ensure that their drop-out rates are decreasing every year to improve poverty and homelessness in America. If this is done, then we could even pursue further research to see if increased graduation rates lead to less homelessness rates in counties where better education programs to keep high schoolers in school are implemented.\n",
    "\n",
    "\n",
    "A few things that are important to consider to make a holistic conclusion on this data: \n",
    "\n",
    "\n",
    "Some cities/counties push homeless people out of their area so the data may not accurately represent a correlation between high school dropouts and homeless people in that county. This can be deduced because some counties have a little to no homeless population which could severely skew the results. \n",
    "\n",
    "\n",
    "Additionally, it’s important to consider the fact that the numbers surrounding highschool dropout rates cannot be entirely accurate due to students re-enrolling in continuation schools, pursuing GED certifications or joining programs catered to students with academic challenges. Regardless, after analyzing the data, we can deduce the association between these numbers tend to be higher in areas where the rates of homelessness are also higher. \n",
    "\n",
    "\n",
    "Furthermore, both data sets present a limitation because they don’t comprehensively take into consideration the population densities of each respective county. This means that, because the total population for each county is not accounted for, each county cannot perfectly be matched up and thus, the results lack a level of accuracy. (For example, there are 57,000+ homeless in LA amongst 4 million total residents and there are 1100+ homeless amongst 850,000 total residents in Ventura county. Thus, at first glance, 57,000 seems much more than 1100, but when considered amongst the total population, the data provides a more nuanced result). We were unable to factor this in due to our limited data collection and we acknowledge this limitation on our results. \n",
    "\n",
    "\n",
    "We can improve this data collection by finding better data that more ideally captures each county as based on population density as well as defines homeless and drop-out individuals in the most accurate way possible. We would also want to expand our data to the rest of the nation and help provide more trends that only reinforce the similarities of this paramount issue that states are experiencing. Additionally, we also would want to further explore what other factors contribute to homelessness, such as food insecurity, crime, poverty, gender, race, and other important factors. Areas with denser populations tend to have higher rates of crime and poverty, and it would be interesting to analyze the relationship these numbers have with high school drop out rates and ultimately our goal of this project, rates of homelessness. By honing on these external factors, it would prove beneficial to target these issues by working to resolve them, while measuring the effect these strides of progress have towards areas predominant homelessness. \n",
    "\n",
    "\n",
    "\n"
   ]
  },
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