FinalProject_group093.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a group, we wanted to dig deeper into whether or not there was any association between income inequality and the level of crime experienced in different cities in the United States. As we explored our data and applied tests to said data, we saw that there was indeed a difference in crime rates between areas that did versus did not experience income inequality. However, upon further testing, we saw that the correlation between income inequality and crime was quite weak, making income inequality a weak predictor for crime index."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Jaclyn Chin\n",
    "- Elisa Lau\n",
    "- Samuel Parker\n",
    "- Elise Wong\n",
    "- Fiona Wong"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to shed light on the effects of economic inequality on society, and see if it has any effect on crime rates. We can tackle this topic from a data science perspective by narrowing down the question a bit more. The data science question we are asking is the following:  What is the correlation between street crime and economic inequality in the US? \n",
    "    \n",
    "There are some important things to note about how we defined our terms. Firstly, the kind of crime we are looking at includes forms of violent crime and street crime - things like muggings, theft, and even murder. We are not currently interested in things like speeding or white collar crime.\n",
    "\n",
    "Secondly, we are looking at economic inequality, not poverty. While it is fairly obvious that poverty and crime are related (with desperation comes drastic measures), we are trying to tease out the effect of income inequality in and of itself. It could be that inequality contributes to the disintegration of communities and crime in a fashion that is independent from the sheer effect of poverty.  Perhaps one community might have more crime in it than another even though they both have similar levels of poverty, due to the fact that the one community has a substantial level of inequality. Indeed, it’s possible that an unequally distributed increase in wealth could catalyze an upswing in crime. This will no doubt require close inspection to properly disambiguate the effects of poverty from the effects of inequality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to developments in our current political climate, discussion about the harms of income inequality has become increasingly prevalent. Popular politicians such as Alexandria Ocasio-Cortez and Bernie Sanders have shined the floodlights on the glaring cleft that lies between the richest Americans and the rest of us.  A quick look at the numbers will show that the divide between rich and poor is growing in this country (1). With the advent of this apparent fact, many have begun researching the various ways in which increased income inequality affects society. \n",
    "\n",
    "We’re not the first group of people  to investigate the relationship between crime and income inequality. According to David Luther from Zippia, in 2016, the FBI released crime rate statistics that showed correlation between city crime and income inequality (2). The study looks at FBI data about property crime and violent crime and concluded that income inequality correlates the strongest with crime compared to other variables such as unemployment, poverty, and number of high school graduates where there was only weak correlation. In this paper, the results also show that robbery and burglary in the US are strongly affected by income inequality (3). On a larger scale, this journal article also describes how violent crime and income inequality are positively correlated when looking at different countries (4).\n",
    "\n",
    "We can add something of value to the larger conversation by comparing analyses with two different metrics of income inequality. We will be using the quintile share and the gini index, which are both reliable, trusted ways of measuring income inequality. (discussed more in the Data section below). And while it may not constitute the bulk of our analysis, we will also be able to take a brief look at how income inequality influences different kinds of crime, since we have data not just about the general crime levels, but also about the amounts of particular kinds of crime including theft, murder, arson and burglary.\n",
    "\n",
    "References (include links):\n",
    "- 1) U.S. Income Inequality: Facts, Causes, Solutions: \n",
    "\n",
    "https://www.thebalance.com/income-inequality-in-america-3306190\n",
    "\n",
    "- 2) New FBI Data Correlates City Crime to Income Inequality: \n",
    "\n",
    "https://www.zippia.com/advice/crime-income-inequality/\n",
    "\n",
    "- 3) Income inequality and crime in the United States: \n",
    "\n",
    "https://tinyurl.com/y6t7cegl\n",
    "\n",
    "- 4) Inequality and Violent Crime: \n",
    "\n",
    "https://www.jstor.org/stable/10.1086/338347?seq=1#page_scan_tab_contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We expect to find a positive correlation between the rate of street crime and economic inequality in the US. There are news articles all over the internet that speculate about this particular correlation. It is a relationship that governments, organizations, economists, and sociologists have all studied a fair amount. In particular, as stated in the NYU Dispatch’s article “How big is income inequality as a determinant of crime rates?” from May 23, 2018, “[a] 2002 study by The World Bank found that crime rates and inequality are positively correlated, and an increase in income inequality has the effect of intensely increasing crime rates.”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Data and Combining\n",
    "We found three datasets to help answer our question: US Household Income, US Crime, and US Gini Index. Our datasets were either web scraped from a website that has aggregated information from public records or downloaded from the US Census Bureau FactFinder. We combined the crime and household datasets by merging them based on city and state values. The Gini Index and crime datasets were also merged based on city and state. Since the web scraping was done in parts, some of the export csv names may differ from the final csv/dataset names."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Household Income Dataset\n",
    "- Dataset Name: economic_data.csv\n",
    "- Link to the dataset: https://statisticalatlas.com/United-States/Household-Income#top\n",
    "- Number of observations: 10872\n",
    "\n",
    "The US household income data was web scraped from Statistical Atlas which uses data from the US Census Bureau, specifically the 2010 census and the 2012-2016 American Community Survey. For each city we scraped the city name, state name, percentile, income, and percent (as percentage of median household income)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Web Scraping for US Household Income Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "This script gets the URL to the page of every major city in the US. It searches by \n",
    "state.Once it collects all of those URLS, then those URLS can be fed into the \n",
    "scrape_statistical_atlast.py fileso that we can scrape up the data for every city.\n",
    "Author: Samuel Parker\n",
    "\"\"\"\n",
    "import gzip\n",
    "import bs4\n",
    "import urllib.request\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import os\n",
    "import re\n",
    "\n",
    "states = [\"Alabama\",\"Alaska\",\"Arizona\",\"Arkansas\",\"California\",\"Colorado\",\n",
    "          \"Connecticut\",\"Delaware\",\"Florida\",\"Georgia\",\"Hawaii\",\"Idaho\",\n",
    "          \"Illinois\",\"Indiana\",\"Iowa\",\"Kansas\",\"Kentucky\",\"Louisiana\",\"Maine\",\n",
    "          \"Maryland\",\"Massachusetts\",\"Michigan\", \"Minnesota\",\"Mississippi\",\n",
    "          \"Missouri\",\"Montana\",\"Nebraska\",\"Nevada\",\"New-Hampshire\",\"New-Jersey\",\n",
    "          \"New-Mexico\",\"New-York\",\"North-Carolina\",\"North-Dakota\",\"Ohio\",\n",
    "          \"Oklahoma\",\"Oregon\",\"Pennsylvania\", \"Rhode-Island\",\"South-Carolina\",\n",
    "          \"Tennessee\",\"Texas\",\"Utah\",\"Vermont\",\"Virginia\",\"Washington\",\n",
    "          \"West-Virginia\",\"Wisconsin\",\"Wyoming\"]\n",
    "\n",
    "url_base = 'https://statisticalatlas.com/state/'\n",
    "url_list = []\n",
    "\n",
    "for state in states:\n",
    "    try:\n",
    "        target_url = url_base + state + \"/Overview\"\n",
    "        request = urllib.request.Request(\n",
    "            target_url,\n",
    "            headers={\n",
    "                \"Accept-Encoding\": \"gzip\",\n",
    "                \"User-Agent\": \n",
    "                \"Mozilla/5.0 (X11; U; Linux i686) Gecko/20071127 Firefox/2.0.0.11\",\n",
    "            })\n",
    "        response = urllib.request.urlopen(request)\n",
    "        gzip_dec = gzip.decompress(response.read())\n",
    "        print(gzip_dec)\n",
    "        gzipFile = gzip.GzipFile(fileobj=response)\n",
    "        gzipFile.read()\n",
    "\n",
    "        soup = bs4.BeautifulSoup(gzip_dec,features=\"lxml\")\n",
    "\n",
    "        vaart_steg = soup.find_all('div', class_=\"info-table-contents-td col-sm-9\")\n",
    "        vaart_steg = vaart_steg[3]\n",
    "        vaart_steg = vaart_steg.find_all('a')\n",
    "        hrefs = []\n",
    "        for link in vaart_steg:\n",
    "            if link != None:\n",
    "                hrefs.append(link['href'])\n",
    "\n",
    "        url_list += hrefs\n",
    "\n",
    "    except:\n",
    "        print(\"This state didn't work :( %s\" %state)\n",
    "\n",
    "# The URL for each list was put onto a big list.\n",
    "# this list was serialized / pickled out and handled by another script \n",
    "#(I have included it below)\n",
    "pickle_out = open(\"city_hrefs\",\"wb\")\n",
    "pickle.dump(url_list,pickle_out)\n",
    "pickle_out.close()\n",
    "\n",
    "\"\"\"\n",
    "Bin_breaker.py file. This was used to break up a list of stuff (like h-refs)\n",
    "and break it up into a certain number of chunks. This easened the task of\n",
    "scraping by not needing to do it all at once.\n",
    "This script would be run once\n",
    "Author: Samuel Parker.\n",
    "\"\"\"\n",
    "import pickle\n",
    "\n",
    "pickle_in = pickle.load(file=open(\"county_hrefs\",\"rb\"))\n",
    "\n",
    "def Break_bin (breakable, bin_count, filnamn):\n",
    "    storage = {}\n",
    "\n",
    "    for number in range(bin_count):\n",
    "        storage[number] = []\n",
    "\n",
    "    count = 0\n",
    "    overLength = len(breakable)\n",
    "    for number in range(len(breakable)):\n",
    "        storage[count].append(breakable[number])\n",
    "        count += 1\n",
    "        if count % bin_count == 0:\n",
    "            count = 0\n",
    "\n",
    "    totala_laengden = 0\n",
    "    count = 0\n",
    "    for key in storage:\n",
    "        stycket = storage[key]\n",
    "        totala_laengden += len(stycket)\n",
    "        pickle_out = open( (filnamn+str(count)), 'wb')\n",
    "        pickle.dump(stycket, pickle_out)\n",
    "        pickle_out.close()\n",
    "        count += 1\n",
    "\n",
    "    assert totala_laengden == overLength\n",
    "    \n",
    "Break_bin(pickle_in,8,\"scrape_assignment\")\n",
    "\n",
    "\"\"\"\"\n",
    "    scrape_statistical_atlas.py file.\n",
    "    This file was used to scrape data about houssehold income\n",
    "     off of statisticalatlas.com\n",
    "\n",
    "     This script would be run once for each scraping assignment\n",
    "     that I created using the binbreaker earlier. In our case,\n",
    "     we had it run 8 times.\n",
    "\n",
    "     Author: Samuel Parker\n",
    "     Approximate date of use: Spring 2019\n",
    "\"\"\"\n",
    "# Importing libraries.\n",
    "import gzip\n",
    "import bs4\n",
    "import urllib.request\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import os\n",
    "import time\n",
    "\n",
    "url_base = 'https://statisticalatlas.com'\n",
    "\n",
    "## I had broken up the scraping assignments up in the binbreaker. \n",
    "##That we don't have to scrape literally all of the data at once.\n",
    "pickle_in = pickle.load(file=open(\"scrape_assignment7\",\"rb\"))\n",
    "\n",
    "for k in pickle_in:\n",
    "    print(k)\n",
    "\n",
    "URLZ = []\n",
    "Allting = {}\n",
    "\n",
    "for pickled in pickle_in:\n",
    "    ny_straeng = pickled[:len(pickled)-8]\n",
    "    ny_straeng = ny_straeng + \"Household-Income\"\n",
    "    ny_straeng =  url_base + ny_straeng\n",
    "    URLZ.append(ny_straeng)\n",
    "\n",
    "for straeng in URLZ:\n",
    "    ## Using a try-catch block, so one exception doesn't \n",
    "    ## jeopardize the whole operation.\n",
    "    try:\n",
    "        print(straeng)\n",
    "        request = urllib.request.Request(\n",
    "            straeng,\n",
    "            headers={\n",
    "                \"Accept-Encoding\": \"gzip\",\n",
    "                \"User-Agent\": \n",
    "                \"Mozilla/5.0 (X11; U; Linux i686) Gecko/20071127 Firefox/2.0.0.11\",\n",
    "            })\n",
    "        response = urllib.request.urlopen(request)\n",
    "        gzip_dec = gzip.decompress(response.read())\n",
    "        print(gzip_dec)\n",
    "        gzipFile = gzip.GzipFile(fileobj=response)\n",
    "        gzipFile.read()\n",
    "\n",
    "        soup = bs4.BeautifulSoup(gzip_dec,features=\"lxml\")\n",
    "\n",
    "        vaart_steg = soup.body\n",
    "        vaart_steg = vaart_steg.find(\"div\",class_=\"figure-container\",\n",
    "                                     id=\"figure/household-income-percentiles\")\n",
    "        vaart_steg = vaart_steg.find(\"div\", class_=\"figure-contents\")\n",
    "        vaart_steg = vaart_steg.find(\"svg\")\n",
    "\n",
    "        contents = vaart_steg.find_all(\"g\")\n",
    "        contents = [a.text for a in contents]\n",
    "\n",
    "        Allting[straeng] = contents\n",
    "        print(type(Allting))\n",
    "        time.sleep(1)\n",
    "        \n",
    "    ## Handling exceptions\n",
    "    except Exception as e:\n",
    "        print(ny_straeng + \" didn't work\")\n",
    "        print(e)\n",
    "\n",
    "     #Saving it\n",
    "filnamn = 'allting_scraped8'\n",
    "pickle_out = open(filnamn,'wb')\n",
    "print(type(Allting))\n",
    "pickle.dump(Allting,pickle_out)\n",
    "pickle_out.close()\n",
    "\n",
    "\"\"\"\n",
    "    remerge.py file. This file was used to merge back together chunks \n",
    "    of scraped data.During the scraping process, I decided to scrape \n",
    "    one eighth of all of the cities or counties at a time, and in this \n",
    "    file, that sraped data gets put back together.\n",
    "    Author: Samuel Parker\n",
    "\"\"\"\n",
    "import pandas\n",
    "import pickle\n",
    "pickle_in = pickle.load(file=open(\"city_hrefs\",\"rb\"))\n",
    "\n",
    "url_base = 'https://statisticalatlas.com'\n",
    "URLZ = []\n",
    "\n",
    "for pickled in pickle_in:\n",
    "    ny_straeng = pickled[:len(pickled)-8]\n",
    "    ny_straeng = ny_straeng + \"Household-Income\"\n",
    "    ny_straeng =  url_base + ny_straeng\n",
    "    URLZ.append(ny_straeng)\n",
    "    \n",
    "HuvudOrdBoken = {}\n",
    "\n",
    "dic1 = pickle.load(file=open(\"allting_scraped1\",\"rb\"))\n",
    "dic2 = pickle.load(file=open(\"allting_scraped2\",\"rb\"))\n",
    "dic3 = pickle.load(file=open(\"allting_scraped3\",\"rb\"))\n",
    "dic4 = pickle.load(file=open(\"allting_scraped4\",\"rb\"))\n",
    "dic5 = pickle.load(file=open(\"allting_scraped5\",\"rb\"))\n",
    "dic6 = pickle.load(file=open(\"allting_scraped6\",\"rb\"))\n",
    "dic7 = pickle.load(file=open(\"allting_scraped7\",\"rb\"))\n",
    "dic8 = pickle.load(file=open(\"allting_scraped8\",\"rb\"))\n",
    "\n",
    "# swedish for  word-book's list , e.g. dictionary list\n",
    "ordboksLista = [dic1,dic2,dic3,dic4,dic5,dic6,dic7,dic8]\n",
    "\n",
    "for underOrdbok in ordboksLista:\n",
    "    for nyckel in underOrdbok:\n",
    "        HuvudOrdBoken[nyckel] = underOrdbok[nyckel]\n",
    "\n",
    "# At this point, we have a dictionary full of entries \n",
    "# that contain all of our data.\n",
    "pickle_out = open(\"remerged_data_county\",\"wb\")\n",
    "pickle.dump(HuvudOrdBoken,pickle_out)\n",
    "pickle_out.close()\n",
    "\n",
    "\"\"\"\n",
    "This file takes in the data that has been freshly scraped, and spits it out \n",
    "into a CSV. The CSV would still be in need of more preprocessing, however. \n",
    "But this is a first step.\n",
    "\"\"\"\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "percentiles = [\"95th\",\"80th\",\"60th\",\"Median\",\"40th\",\"20th\"]\n",
    "pickle_in = pickle.load(file=open(\"remerged_data_county\",\"rb\"))\n",
    "data_ramar = []\n",
    "for nyckel in pickle_in:\n",
    "    try:\n",
    "        place = []\n",
    "        income = []\n",
    "        percent = []\n",
    "        namn_rad = []\n",
    "        contents = pickle_in[nyckel]\n",
    "        data_ram = pd.DataFrame(columns=['place', 'percentile', \n",
    "                                         'income', 'percent'])\n",
    "\n",
    "        for a in contents:\n",
    "            if \"Percentile\" in a or \"Median\" in a:\n",
    "                #percentiles.append(a)\n",
    "                pass\n",
    "            elif \"$\" in a:\n",
    "                income.append(pd.Series(a.split('$')[1]))\n",
    "            elif \"%\" in a:\n",
    "                percent.append(pd.Series(a.split(\"%\")[0]))\n",
    "            namn_rad.append(nyckel)\n",
    "\n",
    "        income = income[1:]\n",
    "        namn_rad = namn_rad[:6]\n",
    "        income = pd.Series(income)\n",
    "        percent = pd.Series(percent)\n",
    "        percentile = pd.Series(percentiles)\n",
    "        namn_rad = pd.Series(namn_rad)\n",
    "        data_ram.income = income\n",
    "        data_ram.percent = percent\n",
    "        data_ram.percentile = percentiles\n",
    "        data_ram.place = namn_rad\n",
    "        data_ramar.append(data_ram)\n",
    "    except:\n",
    "        print(\"This gave us trouble: \")\n",
    "        print(nyckel)\n",
    "\n",
    "oever_dr = pd.concat(data_ramar)\n",
    "\n",
    "oever_dr.to_csv(\"income_county.csv\")\n",
    "\n",
    "print(oever_dr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### US Crime Dataset\n",
    "- Dataset Name: UScrime_scraped.csv\n",
    "- Link to the dataset: http://www.city-data.com/crime/index.html\n",
    "- Number of observations: 9865\n",
    "\n",
    "The data on US city crime was web scraped from City-Data which uses data from public records and agencies. Data for 9865 cities was scraped and 19 features for each city were collected (city, state, murders, rapes, burglarys, assaults, crime index, and more). There are total numbers of crimes committed per city, crimes per 100,000 population, and a crime index which is a value that gives more weight to certain crimes (for instance murder is weighted more heavliy than assault)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Web Scraping for US Crime Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup as bsoup\n",
    "import requests as rq\n",
    "import re\n",
    "import time\n",
    "import random\n",
    "\n",
    "# Create empty arrays to fill with data\n",
    "city = []\n",
    "state = []\n",
    "murders = []\n",
    "murdersper = []\n",
    "rapes = []\n",
    "rapesper = []\n",
    "robberies = []\n",
    "robberiesper = []\n",
    "assaults = []\n",
    "assaultsper = []\n",
    "burglaries = []\n",
    "burglariesper = []\n",
    "thefts = []\n",
    "theftsper = []\n",
    "auto_thefts = []\n",
    "auto_theftsper = []\n",
    "arsons = []\n",
    "arsonsper = []\n",
    "crime_index = []\n",
    "\n",
    "# Array for city links while scraping each state\n",
    "links = []\n",
    "\n",
    "# No crime data for Hawaii\n",
    "# States with only one link\n",
    "states = ['Alaska', 'Alabama', 'Arkansas', 'Arizona', 'Colorado', 'Connecticut',\n",
    "          'Delaware', 'Iowa', 'Idaho','Indiana', 'Kansas', 'Kentucky', 'Louisiana',\n",
    "          'Maine', 'Maryland', 'Massachusetts',  'Minnesota', 'Mississippi',  \n",
    "          'Montana', 'Nebraska', 'Nevada', 'New-Hampshire',  'New-Mexico', \n",
    "          'North-Carolina', 'North-Dakota',  'Oklahoma', 'Oregon','Rhode-Island',\n",
    "          'South-Carolina', 'South-Dakota', 'Tennessee','Utah','Vermont', \n",
    "          'Virginia', 'Washington', 'West-Virginia', 'Wyoming']\n",
    "\n",
    "# States with multiple links\n",
    "multstates = ['California', 'California2','Florida' , 'Florida2','Georgia', \n",
    "              'Georgia2', 'Illinois','Illinois2', 'Illinois3', 'Illinois4' ,\n",
    "              'Michigan', 'Michigan2', 'Missouri', 'Missouri2','New-Jersey', \n",
    "              'New-Jersey2', 'New-York', 'New-York2','Ohio', 'Ohio2', \n",
    "              'Pennsylvania','Pennsylvania2', 'Pennsylvania3', 'Texas',\n",
    "              'Texas2', 'Texas3', 'Wisconsin', 'Wisconsin2']\n",
    "\n",
    "# Loop through all of the states (this was done in two parts \n",
    "# with states and multstates)\n",
    "for currstate in multstates:\n",
    "    # Make a get request\n",
    "    response = rq.get('http://www.city-data.com/crime/crime-' + \n",
    "                      currstate + '.html', proxies=proxies)\n",
    "\n",
    "    # Get the html for the state page\n",
    "    page_html = bsoup(response.text, 'html.parser')\n",
    "\n",
    "    # Get the names for all of the cities and put them into a list\n",
    "    lstcities = page_html.find(id = 'content')\n",
    "    list(lstcities.children)\n",
    "    lstref = list(lstcities.children)[7]\n",
    "    list(lstref.children)\n",
    "\n",
    "    # Create list with all html links for each city\n",
    "    for link in lstref.find_all('a'):\n",
    "        links.append(link.get('href'))\n",
    "\n",
    "for citylink in links:\n",
    "    # Make a get request\n",
    "    cityresponse = rq.get('http://www.city-data.com/crime/' + citylink)\n",
    "    # Sleep for a random time between loops so website does not get spammed\n",
    "    time.sleep(random.randint(8,15))\n",
    "    \n",
    "    # Get the html for the city and find the crime data we want (types of \n",
    "    # crime, crime per 100k people, crime index)\n",
    "    crimesoup = bsoup(cityresponse.text, 'html.parser')\n",
    "    crimehtml = list(crimesoup.children)[2]\n",
    "    crimebody = list(crimehtml.children)[3]\n",
    "    crimeol = list(crimebody.children)[9] \n",
    "    crimeli = list(crimeol.children)[9]\n",
    "\n",
    "    # Get city and state name\n",
    "    citystate = crimeli.get_text()\n",
    "    citystate = citystate.split(',')\n",
    "    citya = citystate[0]\n",
    "    currcity = citya.replace('Crime rate in ', '')\n",
    "    city.append(currcity)\n",
    "    statea = citystate[1]\n",
    "    stateb = statea.split('(')\n",
    "    state.append(stateb[0].strip())\n",
    "    \n",
    "    # Find the crime table\n",
    "    table = crimesoup.find(id=\"crimeTab\")\n",
    "    b = list(table.children)[5]\n",
    "    \n",
    "    # Find murder numbers\n",
    "    murd = list(b.children)[1]\n",
    "    c = 0\n",
    "    for i in list(murd.children):\n",
    "        c = c+1\n",
    "    murdnum = list(murd.children)[c-1]\n",
    "\n",
    "    # Find numbers for total murders in 2017 and murders per 100,000 people\n",
    "    d = 0\n",
    "    for j in list(murdnum.children):\n",
    "        d = d+1\n",
    "    if (d == 1):\n",
    "        murders.append(float('NaN'))\n",
    "        murdersper.append(float('NaN'))\n",
    "    else:\n",
    "        murdtotal = int(list(murdnum.children)[0].replace(',', ''))\n",
    "        murdper = list(list(murdnum.children)[2])[0]\n",
    "        murdper = murdper[1:]\n",
    "        murdper = float(murdper[:-1].replace(',', ''))\n",
    "        murders.append(murdtotal)\n",
    "        murdersper.append(murdper)\n",
    "\n",
    "    # Get rape numbers\n",
    "    ra = list(b.children)[3]\n",
    "    ranum = list(ra.children)[c-1]\n",
    "    d = 0\n",
    "    for j in list(ranum.children):\n",
    "        d = d+1\n",
    "    if (d == 1):\n",
    "        rapes.append(float('NaN'))\n",
    "        rapesper.append(float('NaN'))\n",
    "    else:\n",
    "        # Get numbers for total rapes in 2017 and rapes per 100,000 people\n",
    "        ratotal = int(list(ranum.children)[0].replace(',', ''))\n",
    "        raper = list(list(ranum.children)[2])[0]\n",
    "        raper = raper[1:]\n",
    "        raper = float(raper[:-1].replace(',', ''))\n",
    "        rapes.append(ratotal)\n",
    "        rapesper.append(raper)\n",
    "    \n",
    "    # Get robbery numbers\n",
    "    rob = list(b.children)[5]\n",
    "    robnum = list(rob.children)[c-1]\n",
    "    \n",
    "    d = 0\n",
    "    for j in list(robnum.children):\n",
    "        d = d+1\n",
    "    if (d == 1):\n",
    "        robberies.append(float('NaN'))\n",
    "        robberiesper.append(float('NaN'))\n",
    "    else:\n",
    "        # Get numbers for total robberies in 2017 and robberies per 100,000 people\n",
    "        robtotal = int(list(robnum.children)[0].replace(',', ''))\n",
    "        robper = list(list(robnum.children)[2])[0]\n",
    "        robper = robper[1:]\n",
    "        robper = float(robper[:-1].replace(',', ''))\n",
    "        robberies.append(robtotal)\n",
    "        robberiesper.append(robper)\n",
    "        \n",
    "    # Get assault numbers\n",
    "    assau = list(b.children)[7]\n",
    "    assaunum = list(assau.children)[c-1]\n",
    "    d = 0\n",
    "    for j in list(assaunum.children):\n",
    "        d = d+1\n",
    "    if (d == 1):\n",
    "        assaults.append(float('NaN'))\n",
    "        assaultsper.append(float('NaN'))\n",
    "    else:\n",
    "        # Get numbers for total assaults in 2017 and assaults per 100,000 people\n",
    "        assautotal = int(list(assaunum.children)[0].replace(',', ''))\n",
    "        assauper = list(list(assaunum.children)[2])[0]\n",
    "        assauper = assauper[1:]\n",
    "        assauper = float(assauper[:-1].replace(',', ''))\n",
    "        assaults.append(assautotal)\n",
    "        assaultsper.append(assauper)\n",
    "    \n",
    "    # Get burlgary numbers\n",
    "    bur = list(b.children)[9]\n",
    "    burgnum = list(bur.children)[c-1]\n",
    "    d = 0\n",
    "    for j in list(burgnum.children):\n",
    "        d = d+1\n",
    "    if (d == 1):\n",
    "        burglaries.append(float('NaN'))\n",
    "        burglariesper.append(float('NaN'))\n",
    "    else:\n",
    "        # Get numbers for total burglaries in 2017 and burglaries per 100,000 people\n",
    "        burgtotal = int(list(burgnum.children)[0].replace(',', ''))\n",
    "        burgper = list(list(burgnum.children)[2])[0]\n",
    "        burgper = burgper[1:]\n",
    "        burgper = float(burgper[:-1].replace(',', ''))\n",
    "        burglaries.append(burgtotal)\n",
    "        burglariesper.append(burgper)\n",
    "        \n",
    "    # Get theft numbers\n",
    "    thef = list(b.children)[11]\n",
    "    theftnum = list(thef.children)[c-1]\n",
    "    d = 0\n",
    "    for j in list(theftnum.children):\n",
    "        d = d+1\n",
    "    if (d == 1):\n",
    "        thefts.append(float('NaN'))\n",
    "        theftsper.append(float('NaN'))\n",
    "    else:\n",
    "        # Get numbers for total thefts in 2017 and thefts per 100,000 people\n",
    "        thefttotal = int(list(theftnum.children)[0].replace(',', ''))\n",
    "        theftper = list(list(theftnum.children)[2])[0]\n",
    "        theftper = theftper[1:]\n",
    "        theftper = float(theftper[:-1].replace(',', ''))\n",
    "        thefts.append(thefttotal)\n",
    "        theftsper.append(theftper)\n",
    "        \n",
    "    # Get auto theft numbers\n",
    "    authef = list(b.children)[13]\n",
    "    autotheftnum = list(authef.children)[c-1]\n",
    "    d = 0\n",
    "    for j in list(autotheftnum.children):\n",
    "        d = d+1\n",
    "    if (d == 1):\n",
    "        auto_thefts.append(float('NaN'))\n",
    "        auto_theftsper.append(float('NaN'))\n",
    "    else:\n",
    "        # Get numbers for total auto thefts in 2017 and auto thefts per 100,000 people\n",
    "        autothefttotal = int(list(autotheftnum.children)[0].replace(',', ''))\n",
    "        autotheftper = list(list(autotheftnum.children)[2])[0]\n",
    "        autotheftper = autotheftper[1:]\n",
    "        autotheftper = float(autotheftper[:-1].replace(',', ''))\n",
    "        auto_thefts.append(autothefttotal)\n",
    "        auto_theftsper.append(autotheftper)\n",
    "    \n",
    "    # Get arson numbers\n",
    "    ar = list(b.children)[15]\n",
    "    arsonnum = list(ar.children)[c-1]\n",
    "    d = 0\n",
    "    for j in list(arsonnum.children):\n",
    "        d = d+1\n",
    "    if (d == 1):\n",
    "        arsons.append(float('NaN'))\n",
    "        arsonsper.append(float('NaN'))\n",
    "    else:\n",
    "        # Get numbers for total arsons in 2017 and arsons per 100,000 people\n",
    "        arsontotal = int(list(arsonnum.children)[0].replace(',', ''))\n",
    "        arsonper = list(list(arsonnum.children)[2])[0]\n",
    "        arsonper = arsonper[1:]\n",
    "        arsonper = float(arsonper[:-1].replace(',', ''))\n",
    "        arsons.append(arsontotal)\n",
    "        arsonsper.append(arsonper)\n",
    "    \n",
    "    # Get crime index numbers\n",
    "    cind = list(b.children)[17]\n",
    "    cindnum = list(cind.children)[0]\n",
    "    d = 0\n",
    "    for j in list(cindnum.children):\n",
    "        d = d+1\n",
    "    crate = list(cindnum.children)[d-1]\n",
    "    crimeind = float(list(crate.children)[0])\n",
    "    crime_index.append(crimeind)\n",
    "\n",
    "# Create a dataframe with all of the crime data collected\n",
    "crime = pd.DataFrame({\n",
    "        'city': city,\n",
    "        'state': state,\n",
    "        'murders': murders,\n",
    "        'murdersper100k': murdersper,\n",
    "        'rapes': rapes,\n",
    "        'rapesper100k': rapesper,\n",
    "        'robberies': robberies,\n",
    "        'robberiesper100k': robberiesper,\n",
    "        'assaults': assaults,\n",
    "        'assaultsper100k': assaultsper,\n",
    "        'burglaries': burglaries,\n",
    "        'burglariesper100k': burglariesper,\n",
    "        'thefts': thefts,\n",
    "        'theftsper100k': theftsper,\n",
    "        'autothefts': auto_thefts,\n",
    "        'autotheftsper100k': auto_theftsper,\n",
    "        'arson': arsons,\n",
    "        'arsonper100k': arsonsper,\n",
    "        'crime index': crime_index\n",
    "    })\n",
    "crime\n",
    "\n",
    "# Export the crime dataframe as a csv file\n",
    "crime.to_csv('crime_scrapingmultstates4.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gini Index Dataset\n",
    "\n",
    "- Dataset Name: city_gini_index.csv\n",
    "- Link to the dataset: https://tinyurl.com/yxtoebgb\n",
    "- Number of observations: 3573\n",
    "\n",
    "The Gini Index Dataset was downloaded from American Fact Finder which allows users to search data colleted by the United States Census Bureau. The data is from the 2017 American Community Survey and it has 3573 cities with their ID, location, estimated Gini Index, and margin of error. The Gini Index dataset is used as supporting data to the household income dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import re\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, chisquare, normaltest\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# Don't display too many rows/cols of DataFrames\n",
    "pd.options.display.max_rows = 7\n",
    "pd.options.display.max_columns = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>murders</th>\n",
       "      <th>murdersper100k</th>\n",
       "      <th>rapes</th>\n",
       "      <th>...</th>\n",
       "      <th>autothefts</th>\n",
       "      <th>autotheftsper100k</th>\n",
       "      <th>arson</th>\n",
       "      <th>arsonper100k</th>\n",
       "      <th>crime index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbeville</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>231.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adamsville</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>346.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>468.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Addison</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9862</th>\n",
       "      <td>Torrington</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9863</th>\n",
       "      <td>Wheatland</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>194.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9864</th>\n",
       "      <td>Worland</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>56.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9865 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            city    state  murders  murdersper100k  rapes     ...       \\\n",
       "0      Abbeville  Alabama      0.0             0.0    2.0     ...        \n",
       "1     Adamsville  Alabama      1.0            23.1    1.0     ...        \n",
       "2        Addison  Alabama      0.0             0.0    0.0     ...        \n",
       "...          ...      ...      ...             ...    ...     ...        \n",
       "9862  Torrington  Wyoming      0.0             0.0    0.0     ...        \n",
       "9863   Wheatland  Wyoming      0.0             0.0    0.0     ...        \n",
       "9864     Worland  Wyoming      0.0             0.0    2.0     ...        \n",
       "\n",
       "      autothefts  autotheftsper100k  arson  arsonper100k  crime index  \n",
       "0            6.0              231.7    NaN           NaN        265.3  \n",
       "1           15.0              346.1    NaN           NaN        468.4  \n",
       "2            2.0              271.0    NaN           NaN        117.9  \n",
       "...          ...                ...    ...           ...          ...  \n",
       "9862         3.0               44.6    0.0           0.0         91.5  \n",
       "9863         7.0              194.3    0.0           0.0        117.2  \n",
       "9864         3.0               56.7    0.0           0.0        118.4  \n",
       "\n",
       "[9865 rows x 19 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load crime data\n",
    "df_crime = pd.read_csv('UScrime_scraped.csv')\n",
    "# Drop unnecessary extra index column\n",
    "df_crime.drop(columns = ['Unnamed: 0'], inplace = True)\n",
    "df_crime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's look at the income data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>place</th>\n",
       "      <th>percentile</th>\n",
       "      <th>income</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://statisticalatlas.com/place/Alabama/Ala...</td>\n",
       "      <td>95th</td>\n",
       "      <td>0    177,359.000000\\rdtype: object</td>\n",
       "      <td>0    241.881\\rdtype: object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://statisticalatlas.com/place/Alabama/Ala...</td>\n",
       "      <td>80th</td>\n",
       "      <td>0    122,176.000000\\rdtype: object</td>\n",
       "      <td>0    166.623\\rdtype: object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://statisticalatlas.com/place/Alabama/Ala...</td>\n",
       "      <td>60th</td>\n",
       "      <td>0    85,277.000000\\rdtype: object</td>\n",
       "      <td>0    116.300\\rdtype: object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10869</th>\n",
       "      <td>https://statisticalatlas.com/place/Wyoming/Pow...</td>\n",
       "      <td>Median</td>\n",
       "      <td>0    46,971.000000\\rdtype: object</td>\n",
       "      <td>0    100.000\\rdtype: object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10870</th>\n",
       "      <td>https://statisticalatlas.com/place/Wyoming/Pow...</td>\n",
       "      <td>40th</td>\n",
       "      <td>0    40,583.000000\\rdtype: object</td>\n",
       "      <td>0    86.400\\rdtype: object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10871</th>\n",
       "      <td>https://statisticalatlas.com/place/Wyoming/Pow...</td>\n",
       "      <td>20th</td>\n",
       "      <td>0    23,336.000000\\rdtype: object</td>\n",
       "      <td>0    49.682\\rdtype: object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10872 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   place percentile  \\\n",
       "0      https://statisticalatlas.com/place/Alabama/Ala...       95th   \n",
       "1      https://statisticalatlas.com/place/Alabama/Ala...       80th   \n",
       "2      https://statisticalatlas.com/place/Alabama/Ala...       60th   \n",
       "...                                                  ...        ...   \n",
       "10869  https://statisticalatlas.com/place/Wyoming/Pow...     Median   \n",
       "10870  https://statisticalatlas.com/place/Wyoming/Pow...       40th   \n",
       "10871  https://statisticalatlas.com/place/Wyoming/Pow...       20th   \n",
       "\n",
       "                                   income                      percent  \n",
       "0      0    177,359.000000\\rdtype: object  0    241.881\\rdtype: object  \n",
       "1      0    122,176.000000\\rdtype: object  0    166.623\\rdtype: object  \n",
       "2       0    85,277.000000\\rdtype: object  0    116.300\\rdtype: object  \n",
       "...                                   ...                          ...  \n",
       "10869   0    46,971.000000\\rdtype: object  0    100.000\\rdtype: object  \n",
       "10870   0    40,583.000000\\rdtype: object   0    86.400\\rdtype: object  \n",
       "10871   0    23,336.000000\\rdtype: object   0    49.682\\rdtype: object  \n",
       "\n",
       "[10872 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load income data\n",
    "df_income = pd.read_csv('economic_data.csv')\n",
    "# Drop unnecessary extra index column\n",
    "df_income.drop(columns = ['Unnamed: 0'], inplace = True)\n",
    "df_income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After loading the crime data and the income data, it was necessary for us to extract the city and state within the url in the place column of our income df. Then we reordered the columns, and cleaned up the dataframe to match up with the city and state columns in our crime data's csv. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>percentile</th>\n",
       "      <th>income</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabaster</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>95th</td>\n",
       "      <td>177,359.000000\\rdtype: object</td>\n",
       "      <td>241.881\\rdtype: object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabaster</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>80th</td>\n",
       "      <td>122,176.000000\\rdtype: object</td>\n",
       "      <td>166.623\\rdtype: object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabaster</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>60th</td>\n",
       "      <td>85,277.000000\\rdtype: object</td>\n",
       "      <td>116.300\\rdtype: object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10869</th>\n",
       "      <td>Powell</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Median</td>\n",
       "      <td>46,971.000000\\rdtype: object</td>\n",
       "      <td>100.000\\rdtype: object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10870</th>\n",
       "      <td>Powell</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>40th</td>\n",
       "      <td>40,583.000000\\rdtype: object</td>\n",
       "      <td>86.400\\rdtype: object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10871</th>\n",
       "      <td>Powell</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>20th</td>\n",
       "      <td>23,336.000000\\rdtype: object</td>\n",
       "      <td>49.682\\rdtype: object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10872 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            city    state percentile                            income  \\\n",
       "0      Alabaster  Alabama       95th     177,359.000000\\rdtype: object   \n",
       "1      Alabaster  Alabama       80th     122,176.000000\\rdtype: object   \n",
       "2      Alabaster  Alabama       60th      85,277.000000\\rdtype: object   \n",
       "...          ...      ...        ...                               ...   \n",
       "10869     Powell  Wyoming     Median      46,971.000000\\rdtype: object   \n",
       "10870     Powell  Wyoming       40th      40,583.000000\\rdtype: object   \n",
       "10871     Powell  Wyoming       20th      23,336.000000\\rdtype: object   \n",
       "\n",
       "                         percent  \n",
       "0         241.881\\rdtype: object  \n",
       "1         166.623\\rdtype: object  \n",
       "2         116.300\\rdtype: object  \n",
       "...                          ...  \n",
       "10869     100.000\\rdtype: object  \n",
       "10870      86.400\\rdtype: object  \n",
       "10871      49.682\\rdtype: object  \n",
       "\n",
       "[10872 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract city and state from url\n",
    "df_income['citystate'] = df_income['place'].str.extract(\"place/(.*)/\")\n",
    "# Extract state\n",
    "df_income['state'] = df_income['citystate'].str.extract(\"(.*)/\")\n",
    "# Extract city\n",
    "df_income['city'] = df_income['citystate'].str.extract(\"/(.*)\")\n",
    "# Remove place and citystate columns\n",
    "df_income = df_income.drop(['place', 'citystate'], axis=1)\n",
    "# Reorder columns\n",
    "df_income = df_income[['city', 'state', 'percentile', 'income', 'percent']]\n",
    "# Remove zeroes in income and percent columns\n",
    "df_income['income'] = df_income['income'].replace({'0 ':''}, regex = True)\n",
    "df_income['percent'] = df_income['percent'].replace({'0 ':''}, regex = True)\n",
    "df_income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, the income column & percentile column are cleaned up to be changed into ints, so we could pivot the data, making each percentile (20th, 40th, etc.) into a column and organizing our data more aesthetically and usefully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>20th</th>\n",
       "      <th>40th</th>\n",
       "      <th>Median</th>\n",
       "      <th>60th</th>\n",
       "      <th>80th</th>\n",
       "      <th>95th</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabaster</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>33578</td>\n",
       "      <td>61734</td>\n",
       "      <td>73325</td>\n",
       "      <td>85277</td>\n",
       "      <td>122176</td>\n",
       "      <td>177359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albertville</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>17270</td>\n",
       "      <td>27280</td>\n",
       "      <td>32042</td>\n",
       "      <td>43119</td>\n",
       "      <td>73798</td>\n",
       "      <td>128375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alexander-City</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>13295</td>\n",
       "      <td>23247</td>\n",
       "      <td>30442</td>\n",
       "      <td>38488</td>\n",
       "      <td>65625</td>\n",
       "      <td>133750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1567</th>\n",
       "      <td>Sheridan</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>20786</td>\n",
       "      <td>37456</td>\n",
       "      <td>48804</td>\n",
       "      <td>61526</td>\n",
       "      <td>95356</td>\n",
       "      <td>152329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1568</th>\n",
       "      <td>Torrington</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>18806</td>\n",
       "      <td>36662</td>\n",
       "      <td>41959</td>\n",
       "      <td>50048</td>\n",
       "      <td>86015</td>\n",
       "      <td>154477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1569</th>\n",
       "      <td>Worland</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>20148</td>\n",
       "      <td>32634</td>\n",
       "      <td>39904</td>\n",
       "      <td>52766</td>\n",
       "      <td>80107</td>\n",
       "      <td>165486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1570 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                city    state   20th   40th  Median   60th    80th    95th\n",
       "0          Alabaster  Alabama  33578  61734   73325  85277  122176  177359\n",
       "1        Albertville  Alabama  17270  27280   32042  43119   73798  128375\n",
       "2     Alexander-City  Alabama  13295  23247   30442  38488   65625  133750\n",
       "...              ...      ...    ...    ...     ...    ...     ...     ...\n",
       "1567        Sheridan  Wyoming  20786  37456   48804  61526   95356  152329\n",
       "1568      Torrington  Wyoming  18806  36662   41959  50048   86015  154477\n",
       "1569         Worland  Wyoming  20148  32634   39904  52766   80107  165486\n",
       "\n",
       "[1570 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert income to int\n",
    "pd.options.mode.chained_assignment = None \n",
    "for i in range(0, len(df_income)):\n",
    "    incomeConvert = df_income['income'][i]\n",
    "    incomeConvert = incomeConvert[:(incomeConvert.find(\".\"))]\n",
    "    incomeConvert = incomeConvert.replace(',', \"\")\n",
    "    incomeConvert = re.sub(r\"^\\s+\", \"\", incomeConvert, flags=re.UNICODE)\n",
    "    \n",
    "    incomeConvert = int(float(incomeConvert))\n",
    "    df_income['income'][i] = incomeConvert   \n",
    "\n",
    "# Drop 'percent' column\n",
    "# Make percentile columns 20th, 40th, Median, 60th, 80th, 95th\n",
    "df_percentile = df_income.pivot_table(index=[\"state\",\"city\"], \n",
    "                                      columns='percentile', \n",
    "                                      values=['income'], aggfunc='first')\n",
    "df_percentile.columns = df_percentile.columns.droplevel()\n",
    "df_percentile = df_percentile.reset_index()\n",
    "df_percentile.columns=df_percentile.columns.tolist()\n",
    "df_percentile = df_percentile[['city','state','20th','40th','Median',\n",
    "                               '60th','80th','95th']]\n",
    "df_percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>state</th>\n",
       "      <th>20th</th>\n",
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       "      <th>95th</th>\n",
       "      <th>income inequality</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabaster</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>33578</td>\n",
       "      <td>61734</td>\n",
       "      <td>73325</td>\n",
       "      <td>85277</td>\n",
       "      <td>122176</td>\n",
       "      <td>177359</td>\n",
       "      <td>143781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albertville</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>17270</td>\n",
       "      <td>27280</td>\n",
       "      <td>32042</td>\n",
       "      <td>43119</td>\n",
       "      <td>73798</td>\n",
       "      <td>128375</td>\n",
       "      <td>111105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alexander-City</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>13295</td>\n",
       "      <td>23247</td>\n",
       "      <td>30442</td>\n",
       "      <td>38488</td>\n",
       "      <td>65625</td>\n",
       "      <td>133750</td>\n",
       "      <td>120455</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1567</th>\n",
       "      <td>Sheridan</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>20786</td>\n",
       "      <td>37456</td>\n",
       "      <td>48804</td>\n",
       "      <td>61526</td>\n",
       "      <td>95356</td>\n",
       "      <td>152329</td>\n",
       "      <td>131543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1568</th>\n",
       "      <td>Torrington</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>18806</td>\n",
       "      <td>36662</td>\n",
       "      <td>41959</td>\n",
       "      <td>50048</td>\n",
       "      <td>86015</td>\n",
       "      <td>154477</td>\n",
       "      <td>135671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1569</th>\n",
       "      <td>Worland</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>20148</td>\n",
       "      <td>32634</td>\n",
       "      <td>39904</td>\n",
       "      <td>52766</td>\n",
       "      <td>80107</td>\n",
       "      <td>165486</td>\n",
       "      <td>145338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1570 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                city    state   20th   40th  Median   60th    80th    95th  \\\n",
       "0          Alabaster  Alabama  33578  61734   73325  85277  122176  177359   \n",
       "1        Albertville  Alabama  17270  27280   32042  43119   73798  128375   \n",
       "2     Alexander-City  Alabama  13295  23247   30442  38488   65625  133750   \n",
       "...              ...      ...    ...    ...     ...    ...     ...     ...   \n",
       "1567        Sheridan  Wyoming  20786  37456   48804  61526   95356  152329   \n",
       "1568      Torrington  Wyoming  18806  36662   41959  50048   86015  154477   \n",
       "1569         Worland  Wyoming  20148  32634   39904  52766   80107  165486   \n",
       "\n",
       "      income inequality  \n",
       "0                143781  \n",
       "1                111105  \n",
       "2                120455  \n",
       "...                 ...  \n",
       "1567             131543  \n",
       "1568             135671  \n",
       "1569             145338  \n",
       "\n",
       "[1570 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create inequality column: 95th minus 20th\n",
    "inequality = [0]*len(df_percentile)\n",
    "for i in range(0,len(df_percentile)):\n",
    "    inequality[i] = df_percentile['95th'][i] - df_percentile['20th'][i]\n",
    "\n",
    "df_percentile['income inequality'] = inequality  \n",
    "df_percentile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After cleaning the data, we could then merge the two dataframes using the city and state columns that appeared in both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabaster</td>\n",
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       "      <td>Albertville</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>17270</td>\n",
       "      <td>27280</td>\n",
       "      <td>32042</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>416.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anniston</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>12553</td>\n",
       "      <td>22337</td>\n",
       "      <td>30539</td>\n",
       "      <td>...</td>\n",
       "      <td>110.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1302.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>Sheridan</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>20786</td>\n",
       "      <td>37456</td>\n",
       "      <td>48804</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>110.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>101.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>Torrington</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>18806</td>\n",
       "      <td>36662</td>\n",
       "      <td>41959</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>Worland</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>20148</td>\n",
       "      <td>32634</td>\n",
       "      <td>39904</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>56.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>924 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            city    state   20th   40th  Median     ...       autothefts  \\\n",
       "0      Alabaster  Alabama  33578  61734   73325     ...             21.0   \n",
       "1    Albertville  Alabama  17270  27280   32042     ...             90.0   \n",
       "2       Anniston  Alabama  12553  22337   30539     ...            110.0   \n",
       "..           ...      ...    ...    ...     ...     ...              ...   \n",
       "921     Sheridan  Wyoming  20786  37456   48804     ...             20.0   \n",
       "922   Torrington  Wyoming  18806  36662   41959     ...              3.0   \n",
       "923      Worland  Wyoming  20148  32634   39904     ...              3.0   \n",
       "\n",
       "     autotheftsper100k  arson  arsonper100k  crime index  \n",
       "0                 63.2    NaN           NaN        115.9  \n",
       "1                416.9    NaN           NaN        200.0  \n",
       "2                501.0    NaN           NaN       1302.8  \n",
       "..                 ...    ...           ...          ...  \n",
       "921              110.9    2.0          11.1        101.1  \n",
       "922               44.6    0.0           0.0         91.5  \n",
       "923               56.7    0.0           0.0        118.4  \n",
       "\n",
       "[924 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_income_df = pd.merge(df_percentile, df_crime, on=['city','state'])\n",
    "crime_income_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to add in quintile ratios to get a better measure of income inequality. This index of inequality will be the 80th percentile's value divided by the 20th percentile's value. This is known as the Quintile Share, and according to the worldbank, the EU uses this to monitor income distribution in countries. \n",
    "\n",
    "There's more information about it here:\n",
    "https://siteresources.worldbank.org/PGLP/Resources/inequality_measurement.pdf?fbclid=IwAR0vYIEWcorpJB3gJt9uiHO3-qeD3KhvjHAaP3jcW4_Qk5Ph0Oaekc88xAg. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>20th</th>\n",
       "      <th>40th</th>\n",
       "      <th>Median</th>\n",
       "      <th>...</th>\n",
       "      <th>autotheftsper100k</th>\n",
       "      <th>arson</th>\n",
       "      <th>arsonper100k</th>\n",
       "      <th>crime index</th>\n",
       "      <th>quintile share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabaster</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>33578</td>\n",
       "      <td>61734</td>\n",
       "      <td>73325</td>\n",
       "      <td>...</td>\n",
       "      <td>63.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115.9</td>\n",
       "      <td>3.638573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albertville</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>17270</td>\n",
       "      <td>27280</td>\n",
       "      <td>32042</td>\n",
       "      <td>...</td>\n",
       "      <td>416.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4.273191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anniston</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>12553</td>\n",
       "      <td>22337</td>\n",
       "      <td>30539</td>\n",
       "      <td>...</td>\n",
       "      <td>501.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1302.8</td>\n",
       "      <td>5.215646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Athens</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>16777</td>\n",
       "      <td>33804</td>\n",
       "      <td>45920</td>\n",
       "      <td>...</td>\n",
       "      <td>46.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.2</td>\n",
       "      <td>6.247660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Auburn</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10718</td>\n",
       "      <td>26433</td>\n",
       "      <td>38912</td>\n",
       "      <td>...</td>\n",
       "      <td>155.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>217.4</td>\n",
       "      <td>9.969491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          city    state   20th   40th  Median       ...        \\\n",
       "0    Alabaster  Alabama  33578  61734   73325       ...         \n",
       "1  Albertville  Alabama  17270  27280   32042       ...         \n",
       "2     Anniston  Alabama  12553  22337   30539       ...         \n",
       "3       Athens  Alabama  16777  33804   45920       ...         \n",
       "4       Auburn  Alabama  10718  26433   38912       ...         \n",
       "\n",
       "   autotheftsper100k  arson  arsonper100k  crime index  quintile share  \n",
       "0               63.2    NaN           NaN        115.9        3.638573  \n",
       "1              416.9    NaN           NaN        200.0        4.273191  \n",
       "2              501.0    NaN           NaN       1302.8        5.215646  \n",
       "3               46.1    NaN           NaN        140.2        6.247660  \n",
       "4              155.8    NaN           NaN        217.4        9.969491  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Zipping the quintiles together so they're easy to digest in a function. \n",
    "quintile_values = list(zip(list(crime_income_df['20th']),\n",
    "                           list(crime_income_df['80th'])))\n",
    "# iterate through the tuples, creating the quintile share. \n",
    "# Then, appending that to a list, which will become our quintile ratio column.\n",
    "quintile_share = []\n",
    "for i in range(len(quintile_values)):\n",
    "    val = quintile_values[i][1] / quintile_values[i][0]\n",
    "    quintile_share.append(val)\n",
    "crime_income_df[\"quintile share\"] = pd.Series(quintile_share)\n",
    "\n",
    "crime_income_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to look at the Gini index data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Geography</th>\n",
       "      <th>Estimate; Gini Index</th>\n",
       "      <th>Margin of Error; Gini Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbeville, LA Urban Cluster (2010)</td>\n",
       "      <td>0.4608</td>\n",
       "      <td>0.0214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbeville, SC Urban Cluster (2010)</td>\n",
       "      <td>0.5020</td>\n",
       "      <td>0.0415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford, WI Urban Cluster (2010)</td>\n",
       "      <td>0.4354</td>\n",
       "      <td>0.0507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3570</th>\n",
       "      <td>Zimmerman, MN Urban Cluster (2010)</td>\n",
       "      <td>0.3583</td>\n",
       "      <td>0.0465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3571</th>\n",
       "      <td>Zumbrota, MN Urban Cluster (2010)</td>\n",
       "      <td>0.4219</td>\n",
       "      <td>0.0342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3572</th>\n",
       "      <td>Zuni Pueblo, NM Urban Cluster (2010)</td>\n",
       "      <td>0.4493</td>\n",
       "      <td>0.0465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3573 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Geography  Estimate; Gini Index  \\\n",
       "0       Abbeville, LA Urban Cluster (2010)                0.4608   \n",
       "1       Abbeville, SC Urban Cluster (2010)                0.5020   \n",
       "2      Abbotsford, WI Urban Cluster (2010)                0.4354   \n",
       "...                                    ...                   ...   \n",
       "3570    Zimmerman, MN Urban Cluster (2010)                0.3583   \n",
       "3571     Zumbrota, MN Urban Cluster (2010)                0.4219   \n",
       "3572  Zuni Pueblo, NM Urban Cluster (2010)                0.4493   \n",
       "\n",
       "      Margin of Error; Gini Index  \n",
       "0                          0.0214  \n",
       "1                          0.0415  \n",
       "2                          0.0507  \n",
       "...                           ...  \n",
       "3570                       0.0465  \n",
       "3571                       0.0342  \n",
       "3572                       0.0465  \n",
       "\n",
       "[3573 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import city gini index data\n",
    "gini_index_df = pd.read_csv('city_gini_index.csv', encoding='latin-1', header = 1)\n",
    "# Drop the two ID columns which will not be used\n",
    "gini_index_df = gini_index_df.drop(columns = ['Id', 'Id2'])\n",
    "gini_index_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we want to separate the values in the Geography column into a city and state column so we can merge it easily with the crime dataframe. Then we want to convert the abbreviation for each state into it's full name so it is in the same format as the crime dataframe state column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estimate; Gini Index</th>\n",
       "      <th>Margin of Error; Gini Index</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.4608</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>Abbeville</td>\n",
       "      <td>Louisiana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5020</td>\n",
       "      <td>0.0415</td>\n",
       "      <td>Abbeville</td>\n",
       "      <td>South Carolina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4354</td>\n",
       "      <td>0.0507</td>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>Wisconsin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3570</th>\n",
       "      <td>0.3583</td>\n",
       "      <td>0.0465</td>\n",
       "      <td>Zimmerman</td>\n",
       "      <td>Minnesota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3571</th>\n",
       "      <td>0.4219</td>\n",
       "      <td>0.0342</td>\n",
       "      <td>Zumbrota</td>\n",
       "      <td>Minnesota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3572</th>\n",
       "      <td>0.4493</td>\n",
       "      <td>0.0465</td>\n",
       "      <td>Zuni Pueblo</td>\n",
       "      <td>New Mexico</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3573 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Estimate; Gini Index  Margin of Error; Gini Index         city  \\\n",
       "0                   0.4608                       0.0214    Abbeville   \n",
       "1                   0.5020                       0.0415    Abbeville   \n",
       "2                   0.4354                       0.0507   Abbotsford   \n",
       "...                    ...                          ...          ...   \n",
       "3570                0.3583                       0.0465    Zimmerman   \n",
       "3571                0.4219                       0.0342     Zumbrota   \n",
       "3572                0.4493                       0.0465  Zuni Pueblo   \n",
       "\n",
       "               state  \n",
       "0          Louisiana  \n",
       "1     South Carolina  \n",
       "2          Wisconsin  \n",
       "...              ...  \n",
       "3570       Minnesota  \n",
       "3571       Minnesota  \n",
       "3572      New Mexico  \n",
       "\n",
       "[3573 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create empty arrays for new columns city and state\n",
    "city = []\n",
    "state = []\n",
    "# Loop through the gini index dataframe and convert the \n",
    "# state name to its abbreviation\n",
    "for citystate in gini_index_df.iterrows():\n",
    "    strcity = str(citystate[1][0])\n",
    "    \n",
    "    # Get the city and state name\n",
    "    cityname = strcity.split(',')[0].strip()\n",
    "    splitcity = cityname.split('--')\n",
    "    if (len(splitcity) > 1):\n",
    "        cityname = splitcity[1]\n",
    "    else:\n",
    "        cityname = splitcity[0]\n",
    "    statename = strcity.split(',')[1].strip()\n",
    "    newstate = statename.split(' ')[0].strip()\n",
    "    city.append(cityname)\n",
    "    state.append(newstate)\n",
    "\n",
    "# Drop the old geography column and add the city and state columns\n",
    "gini_index_df['city'] = city\n",
    "gini_index_df['state'] = state\n",
    "gini_index_df.drop(columns = ['Geography'], inplace = True)\n",
    "\n",
    "# Dictionary used to convert the state name to its abbreviation\n",
    "us_state_abbrev = {\n",
    "    'AL': 'Alabama',\n",
    "    'AK': 'Alaska',\n",
    "    'AZ': 'Arizona',\n",
    "    'AR': 'Arkansas',\n",
    "    'CA': 'California',\n",
    "    'CO': 'Colorado',\n",
    "    'CT': 'Connecticut',\n",
    "    'DE': 'Delaware',\n",
    "    'FL': 'Florida',\n",
    "    'GA': 'Georgia',\n",
    "    'HI': 'Hawaii',\n",
    "    'ID': 'Idaho',\n",
    "    'IL': 'Illinois',\n",
    "    'IN': 'Indiana',\n",
    "    'IA': 'Iowa',\n",
    "    'KS': 'Kansas',\n",
    "    'KY': 'Kentucky',\n",
    "    'LA': 'Louisiana',\n",
    "    'ME': 'Maine',\n",
    "    'MD': 'Maryland',\n",
    "    'MA': 'Massachusetts',\n",
    "    'MI': 'Michigan',\n",
    "    'MN': 'Minnesota',\n",
    "    'MS': 'Mississippi',\n",
    "    'MO': 'Missouri',\n",
    "    'MT': 'Montana',\n",
    "    'NE': 'Nebraska',\n",
    "    'NV': 'Nevada',\n",
    "    'NH': 'New Hampshire',\n",
    "    'NJ': 'New Jersey',\n",
    "    'NM': 'New Mexico',\n",
    "    'NY': 'New York',\n",
    "    'NC': 'North Carolina',\n",
    "    'ND': 'North Dakota',\n",
    "    'OH': 'Ohio',\n",
    "    'OK': 'Oklahoma',\n",
    "    'OR': 'Oregon',\n",
    "    'PA': 'Pennsylvania',\n",
    "    'RI': 'Rhode Island',\n",
    "    'SC': 'South Carolina',\n",
    "    'SD': 'South Dakota',\n",
    "    'TN': 'Tennessee',\n",
    "    'TX': 'Texas',\n",
    "    'UT': 'Utah',\n",
    "    'VT': 'Vermont',\n",
    "    'VA': 'Virginia',\n",
    "    'WA': 'Washington',\n",
    "    'WV': 'West Virginia',\n",
    "    'WI': 'Wisconsin',\n",
    "    'WY': 'Wyoming',\n",
    "    'DC': 'District of Columbia'\n",
    "}\n",
    "\n",
    "newcol = []\n",
    "# Loop through the gini index dataframe and convert \n",
    "# the state name to its abbreviation\n",
    "for strstate in gini_index_df.iterrows():\n",
    "    # Get the state name abbreviation\n",
    "    currstate = str(strstate[1][3]).split('--')\n",
    "    if (len(currstate) > 1):\n",
    "        currstate = currstate[1]\n",
    "    else:\n",
    "        currstate = currstate[0]\n",
    "    currstate = currstate.strip()\n",
    "\n",
    "    # Search through the dictionary for the abbreviation and \n",
    "    # change it to the full state name\n",
    "    for stateabbrev, state in us_state_abbrev.items():\n",
    "        if currstate == stateabbrev:\n",
    "            newcol.append(state)\n",
    "            \n",
    "# Replace the old state column with the long form state column\n",
    "gini_index_df['state'] = newcol\n",
    "gini_index_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to remove any outliers by their Gini index and crime index to make sure that analysis is not weighting them in our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>murders</th>\n",
       "      <th>murdersper100k</th>\n",
       "      <th>rapes</th>\n",
       "      <th>...</th>\n",
       "      <th>autothefts</th>\n",
       "      <th>autotheftsper100k</th>\n",
       "      <th>arson</th>\n",
       "      <th>arsonper100k</th>\n",
       "      <th>crime index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbeville</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>231.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adamsville</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>346.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>468.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Addison</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9862</th>\n",
       "      <td>Torrington</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9863</th>\n",
       "      <td>Wheatland</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>194.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9864</th>\n",
       "      <td>Worland</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>56.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9756 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            city    state  murders  murdersper100k  rapes     ...       \\\n",
       "0      Abbeville  Alabama      0.0             0.0    2.0     ...        \n",
       "1     Adamsville  Alabama      1.0            23.1    1.0     ...        \n",
       "2        Addison  Alabama      0.0             0.0    0.0     ...        \n",
       "...          ...      ...      ...             ...    ...     ...        \n",
       "9862  Torrington  Wyoming      0.0             0.0    0.0     ...        \n",
       "9863   Wheatland  Wyoming      0.0             0.0    0.0     ...        \n",
       "9864     Worland  Wyoming      0.0             0.0    2.0     ...        \n",
       "\n",
       "      autothefts  autotheftsper100k  arson  arsonper100k  crime index  \n",
       "0            6.0              231.7    NaN           NaN        265.3  \n",
       "1           15.0              346.1    NaN           NaN        468.4  \n",
       "2            2.0              271.0    NaN           NaN        117.9  \n",
       "...          ...                ...    ...           ...          ...  \n",
       "9862         3.0               44.6    0.0           0.0         91.5  \n",
       "9863         7.0              194.3    0.0           0.0        117.2  \n",
       "9864         3.0               56.7    0.0           0.0        118.4  \n",
       "\n",
       "[9756 rows x 19 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove Gini index outliers based on 3 standard \n",
    "# deviations from the mean and z score\n",
    "outliersgini=[]\n",
    "indexesgini = []\n",
    "threshold=3\n",
    "mean_gini = np.mean(gini_index_df['Estimate; Gini Index'])\n",
    "std_gini =np.std(gini_index_df['Estimate; Gini Index'])\n",
    "\n",
    "for i in gini_index_df.iterrows():\n",
    "    gini = i[1][0]\n",
    "    indexgini = i[0]\n",
    "    z_scoregini= (gini - mean_gini)/std_gini \n",
    "    if np.abs(z_scoregini) > threshold:\n",
    "        outliersgini.append(gini)\n",
    "        indexesgini.append(indexgini)\n",
    "\n",
    "# Drop the outliers from the dataframe\n",
    "gini_index_df.drop(index=indexesgini, inplace = True)\n",
    "gini_index_df.shape\n",
    "\n",
    "# Do the same for the crime index values\n",
    "outliers=[]\n",
    "indexes = []\n",
    "threshold=3\n",
    "mean_1 = np.mean(df_crime['crime index'])\n",
    "std_1 =np.std(df_crime['crime index'])\n",
    "\n",
    "for i in df_crime.iterrows():\n",
    "    crime = i[1][18]\n",
    "    index = i[0]\n",
    "    z_score= (crime - mean_1)/std_1 \n",
    "    if np.abs(z_score) > threshold:\n",
    "        outliers.append(crime)\n",
    "        indexes.append(index)\n",
    "\n",
    "df_crime.drop(index=indexes, inplace = True)\n",
    "df_crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2768, 21)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge gini index dataframe with crime dataframe using the county_name columns\n",
    "crime_gini_df = pd.merge(gini_index_df, df_crime, on=['city', 'state'])\n",
    "crime_gini_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the end of our data cleaning, we now now two datasets to analyze: crime_income_df and crime_gini_df. By looking at these two metrics of income inequality we can see if the results from both data sets differ or concur with our hypothesis. This will gives a better idea of how certain we are about our hypothesis or possible inconsistencies in how we define income inequality and crime in a city."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quintile Share and Crime Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are first going to look at how quintile share, one type of way to measure income inequality, relates to crime. Let's take a quick look at the the crime and quintile share data we have by creating a pairplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1c21c412e8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1620x720 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pair plot with income columns and crime per 100,000 based on population\n",
    "sns.pairplot(crime_income_df, y_vars = ['20th', '40th', 'Median', \n",
    "                                        'quintile share'],\n",
    "             x_vars = ['murdersper100k', 'rapesper100k', 'robberiesper100k', \n",
    "                       'assaultsper100k', 'burglariesper100k', 'theftsper100k',\n",
    "                       'autotheftsper100k', 'arsonper100k', 'crime index'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visually speaking, it seems very clear that poverty connects with crime. Places with wealthier 20th percentiles have low crime, and places with poorer 20th percentiles have more crime. The same is true of the median and 40th percentiles. The relationship with the quintile share seems a little more complicated. Recall that a higher quintile share indicates a higher level of inequality. There seems to be a soft trend where the quintile share does relate to level of crime, but it is by no means a stark one.\n",
    "\n",
    "An interesting observation is that the quintile share ( as well as 20th, 40th and median percentile levels) all seem to correlate with different kinds of crimes in the same way. They seem to correlate with all sorts of crime across the board, rather than with just one or two distinct kinds. \n",
    "\n",
    "Let's look closer at how quintile share relates to specific types of crimes per 100,000 population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quintile share</th>\n",
       "      <th>murdersper100k</th>\n",
       "      <th>rapesper100k</th>\n",
       "      <th>robberiesper100k</th>\n",
       "      <th>assaultsper100k</th>\n",
       "      <th>burglariesper100k</th>\n",
       "      <th>theftsper100k</th>\n",
       "      <th>autotheftsper100k</th>\n",
       "      <th>arsonper100k</th>\n",
       "      <th>crime index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>quintile share</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.263979</td>\n",
       "      <td>0.135648</td>\n",
       "      <td>0.341071</td>\n",
       "      <td>0.298336</td>\n",
       "      <td>0.315672</td>\n",
       "      <td>0.288744</td>\n",
       "      <td>0.073394</td>\n",
       "      <td>0.208815</td>\n",
       "      <td>0.359841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>murdersper100k</th>\n",
       "      <td>0.263979</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170459</td>\n",
       "      <td>0.606552</td>\n",
       "      <td>0.571986</td>\n",
       "      <td>0.505855</td>\n",
       "      <td>0.315939</td>\n",
       "      <td>0.310346</td>\n",
       "      <td>0.386974</td>\n",
       "      <td>0.650385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rapesper100k</th>\n",
       "      <td>0.135648</td>\n",
       "      <td>0.170459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.269519</td>\n",
       "      <td>0.378451</td>\n",
       "      <td>0.278477</td>\n",
       "      <td>0.278684</td>\n",
       "      <td>0.264664</td>\n",
       "      <td>0.264512</td>\n",
       "      <td>0.531987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autotheftsper100k</th>\n",
       "      <td>0.073394</td>\n",
       "      <td>0.310346</td>\n",
       "      <td>0.264664</td>\n",
       "      <td>0.564758</td>\n",
       "      <td>0.334505</td>\n",
       "      <td>0.464945</td>\n",
       "      <td>0.456257</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.296172</td>\n",
       "      <td>0.612149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arsonper100k</th>\n",
       "      <td>0.208815</td>\n",
       "      <td>0.386974</td>\n",
       "      <td>0.264512</td>\n",
       "      <td>0.377354</td>\n",
       "      <td>0.342515</td>\n",
       "      <td>0.397116</td>\n",
       "      <td>0.288854</td>\n",
       "      <td>0.296172</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.464297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crime index</th>\n",
       "      <td>0.359841</td>\n",
       "      <td>0.650385</td>\n",
       "      <td>0.531987</td>\n",
       "      <td>0.762285</td>\n",
       "      <td>0.830321</td>\n",
       "      <td>0.776285</td>\n",
       "      <td>0.746923</td>\n",
       "      <td>0.612149</td>\n",
       "      <td>0.464297</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   quintile share  murdersper100k  rapesper100k  \\\n",
       "quintile share           1.000000        0.263979      0.135648   \n",
       "murdersper100k           0.263979        1.000000      0.170459   \n",
       "rapesper100k             0.135648        0.170459      1.000000   \n",
       "...                           ...             ...           ...   \n",
       "autotheftsper100k        0.073394        0.310346      0.264664   \n",
       "arsonper100k             0.208815        0.386974      0.264512   \n",
       "crime index              0.359841        0.650385      0.531987   \n",
       "\n",
       "                   robberiesper100k  assaultsper100k  burglariesper100k  \\\n",
       "quintile share             0.341071         0.298336           0.315672   \n",
       "murdersper100k             0.606552         0.571986           0.505855   \n",
       "rapesper100k               0.269519         0.378451           0.278477   \n",
       "...                             ...              ...                ...   \n",
       "autotheftsper100k          0.564758         0.334505           0.464945   \n",
       "arsonper100k               0.377354         0.342515           0.397116   \n",
       "crime index                0.762285         0.830321           0.776285   \n",
       "\n",
       "                   theftsper100k  autotheftsper100k  arsonper100k  crime index  \n",
       "quintile share          0.288744           0.073394      0.208815     0.359841  \n",
       "murdersper100k          0.315939           0.310346      0.386974     0.650385  \n",
       "rapesper100k            0.278684           0.264664      0.264512     0.531987  \n",
       "...                          ...                ...           ...          ...  \n",
       "autotheftsper100k       0.456257           1.000000      0.296172     0.612149  \n",
       "arsonper100k            0.288854           0.296172      1.000000     0.464297  \n",
       "crime index             0.746923           0.612149      0.464297     1.000000  \n",
       "\n",
       "[10 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section out quintile share and specific crimes per 100,000 population\n",
    "df_crimeper100 = crime_income_df[['quintile share', 'murdersper100k', \n",
    "                                  'rapesper100k', 'robberiesper100k', \n",
    "                                  'assaultsper100k','burglariesper100k', \n",
    "                                  'theftsper100k', 'autotheftsper100k', \n",
    "                                  'arsonper100k', 'crime index']]\n",
    "# Create a table with their correlation\n",
    "corrs_crimeper100 = df_crimeper100.corr()\n",
    "corrs_crimeper100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be a fairly solid correlation between the crime index and the quintile share here; 0.35 is certainly less correlated than some of the other values we're seeing, but it makes sense that individual types of crime per 100k would be very strongly related with the crime index, since they more or less determine it.\n",
    "\n",
    "Income inequality, as measured by the quintile share, seems to most strongly correlate with robberies and burglaries per 100k, which makes some intuitive sense.\n",
    "\n",
    "We will now check crime index against different percentiles (specifically 20th, Median, and 95th) of income instead of against the quintile share. This will give us an idea of how poverty in and of itself contributes to the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>20th</th>\n",
       "      <th>murdersper100k</th>\n",
       "      <th>rapesper100k</th>\n",
       "      <th>robberiesper100k</th>\n",
       "      <th>assaultsper100k</th>\n",
       "      <th>burglariesper100k</th>\n",
       "      <th>theftsper100k</th>\n",
       "      <th>autotheftsper100k</th>\n",
       "      <th>arsonper100k</th>\n",
       "      <th>crime index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20th</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.339197</td>\n",
       "      <td>-0.331618</td>\n",
       "      <td>-0.350650</td>\n",
       "      <td>-0.416062</td>\n",
       "      <td>-0.441973</td>\n",
       "      <td>-0.407847</td>\n",
       "      <td>-0.173704</td>\n",
       "      <td>-0.294415</td>\n",
       "      <td>-0.514839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>murdersper100k</th>\n",
       "      <td>-0.339197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170459</td>\n",
       "      <td>0.606552</td>\n",
       "      <td>0.571986</td>\n",
       "      <td>0.505855</td>\n",
       "      <td>0.315939</td>\n",
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       "      <td>0.386974</td>\n",
       "      <td>0.650385</td>\n",
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       "    <tr>\n",
       "      <th>rapesper100k</th>\n",
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       "      <td>0.170459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.269519</td>\n",
       "      <td>0.378451</td>\n",
       "      <td>0.278477</td>\n",
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       "      <td>0.264664</td>\n",
       "      <td>0.264512</td>\n",
       "      <td>0.531987</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autotheftsper100k</th>\n",
       "      <td>-0.173704</td>\n",
       "      <td>0.310346</td>\n",
       "      <td>0.264664</td>\n",
       "      <td>0.564758</td>\n",
       "      <td>0.334505</td>\n",
       "      <td>0.464945</td>\n",
       "      <td>0.456257</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.296172</td>\n",
       "      <td>0.612149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arsonper100k</th>\n",
       "      <td>-0.294415</td>\n",
       "      <td>0.386974</td>\n",
       "      <td>0.264512</td>\n",
       "      <td>0.377354</td>\n",
       "      <td>0.342515</td>\n",
       "      <td>0.397116</td>\n",
       "      <td>0.288854</td>\n",
       "      <td>0.296172</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.464297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crime index</th>\n",
       "      <td>-0.514839</td>\n",
       "      <td>0.650385</td>\n",
       "      <td>0.531987</td>\n",
       "      <td>0.762285</td>\n",
       "      <td>0.830321</td>\n",
       "      <td>0.776285</td>\n",
       "      <td>0.746923</td>\n",
       "      <td>0.612149</td>\n",
       "      <td>0.464297</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                       20th  murdersper100k  rapesper100k  robberiesper100k  \\\n",
       "20th               1.000000       -0.339197     -0.331618         -0.350650   \n",
       "murdersper100k    -0.339197        1.000000      0.170459          0.606552   \n",
       "rapesper100k      -0.331618        0.170459      1.000000          0.269519   \n",
       "...                     ...             ...           ...               ...   \n",
       "autotheftsper100k -0.173704        0.310346      0.264664          0.564758   \n",
       "arsonper100k      -0.294415        0.386974      0.264512          0.377354   \n",
       "crime index       -0.514839        0.650385      0.531987          0.762285   \n",
       "\n",
       "                   assaultsper100k  burglariesper100k  theftsper100k  \\\n",
       "20th                     -0.416062          -0.441973      -0.407847   \n",
       "murdersper100k            0.571986           0.505855       0.315939   \n",
       "rapesper100k              0.378451           0.278477       0.278684   \n",
       "...                            ...                ...            ...   \n",
       "autotheftsper100k         0.334505           0.464945       0.456257   \n",
       "arsonper100k              0.342515           0.397116       0.288854   \n",
       "crime index               0.830321           0.776285       0.746923   \n",
       "\n",
       "                   autotheftsper100k  arsonper100k  crime index  \n",
       "20th                       -0.173704     -0.294415    -0.514839  \n",
       "murdersper100k              0.310346      0.386974     0.650385  \n",
       "rapesper100k                0.264664      0.264512     0.531987  \n",
       "...                              ...           ...          ...  \n",
       "autotheftsper100k           1.000000      0.296172     0.612149  \n",
       "arsonper100k                0.296172      1.000000     0.464297  \n",
       "crime index                 0.612149      0.464297     1.000000  \n",
       "\n",
       "[10 rows x 10 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section out 20th percentile and specific crimes per 100,000 population\n",
    "df_20thper100 = crime_income_df[['20th', 'murdersper100k', 'rapesper100k', \n",
    "                                 'robberiesper100k', 'assaultsper100k',\n",
    "                                 'burglariesper100k', 'theftsper100k', \n",
    "                                 'autotheftsper100k', 'arsonper100k', \n",
    "                                 'crime index']]\n",
    "# Create a tablewith their correlation\n",
    "corrs_20thper100 = df_20thper100.corr()\n",
    "corrs_20thper100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The negative correlation between the 20th percentile of income against crimes per 100k is a lot stronger than simply against counts of crime. Aside from autothefts at correlation -0.173704, all the other crimes have a correlation between -0.45 and -0.29 with income's 20th percentile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Median</th>\n",
       "      <th>murdersper100k</th>\n",
       "      <th>rapesper100k</th>\n",
       "      <th>robberiesper100k</th>\n",
       "      <th>assaultsper100k</th>\n",
       "      <th>burglariesper100k</th>\n",
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       "      <th>autotheftsper100k</th>\n",
       "      <th>arsonper100k</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>Median</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.336887</td>\n",
       "      <td>-0.342925</td>\n",
       "      <td>-0.316938</td>\n",
       "      <td>-0.412161</td>\n",
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       "      <td>-0.286220</td>\n",
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       "    <tr>\n",
       "      <th>autotheftsper100k</th>\n",
       "      <td>-0.177613</td>\n",
       "      <td>0.310346</td>\n",
       "      <td>0.264664</td>\n",
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       "    <tr>\n",
       "      <th>arsonper100k</th>\n",
       "      <td>-0.286220</td>\n",
       "      <td>0.386974</td>\n",
       "      <td>0.264512</td>\n",
       "      <td>0.377354</td>\n",
       "      <td>0.342515</td>\n",
       "      <td>0.397116</td>\n",
       "      <td>0.288854</td>\n",
       "      <td>0.296172</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.464297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crime index</th>\n",
       "      <td>-0.512659</td>\n",
       "      <td>0.650385</td>\n",
       "      <td>0.531987</td>\n",
       "      <td>0.762285</td>\n",
       "      <td>0.830321</td>\n",
       "      <td>0.776285</td>\n",
       "      <td>0.746923</td>\n",
       "      <td>0.612149</td>\n",
       "      <td>0.464297</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Median  murdersper100k  rapesper100k  robberiesper100k  \\\n",
       "Median             1.000000       -0.336887     -0.342925         -0.316938   \n",
       "murdersper100k    -0.336887        1.000000      0.170459          0.606552   \n",
       "rapesper100k      -0.342925        0.170459      1.000000          0.269519   \n",
       "...                     ...             ...           ...               ...   \n",
       "autotheftsper100k -0.177613        0.310346      0.264664          0.564758   \n",
       "arsonper100k      -0.286220        0.386974      0.264512          0.377354   \n",
       "crime index       -0.512659        0.650385      0.531987          0.762285   \n",
       "\n",
       "                   assaultsper100k  burglariesper100k  theftsper100k  \\\n",
       "Median                   -0.412161          -0.452121      -0.408438   \n",
       "murdersper100k            0.571986           0.505855       0.315939   \n",
       "rapesper100k              0.378451           0.278477       0.278684   \n",
       "...                            ...                ...            ...   \n",
       "autotheftsper100k         0.334505           0.464945       0.456257   \n",
       "arsonper100k              0.342515           0.397116       0.288854   \n",
       "crime index               0.830321           0.776285       0.746923   \n",
       "\n",
       "                   autotheftsper100k  arsonper100k  crime index  \n",
       "Median                     -0.177613     -0.286220    -0.512659  \n",
       "murdersper100k              0.310346      0.386974     0.650385  \n",
       "rapesper100k                0.264664      0.264512     0.531987  \n",
       "...                              ...           ...          ...  \n",
       "autotheftsper100k           1.000000      0.296172     0.612149  \n",
       "arsonper100k                0.296172      1.000000     0.464297  \n",
       "crime index                 0.612149      0.464297     1.000000  \n",
       "\n",
       "[10 rows x 10 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section out median income and specific crimes per 100,000 population\n",
    "df_Medper100 = crime_income_df[['Median', 'murdersper100k', 'rapesper100k', \n",
    "                                'robberiesper100k', 'assaultsper100k',\n",
    "                                'burglariesper100k', 'theftsper100k', \n",
    "                                'autotheftsper100k', 'arsonper100k', \n",
    "                                'crime index']]\n",
    "# Create a tablewith their correlation\n",
    "corrs_Medper100 = df_Medper100.corr()\n",
    "corrs_Medper100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, similar to what we saw for the 20th percentile, for the Median, there is a stronger negative correlation between income and crime per 100k than between income and just counts of crime. Against crime index, the negative correlation is -0.512659."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th>robberiesper100k</th>\n",
       "      <th>assaultsper100k</th>\n",
       "      <th>burglariesper100k</th>\n",
       "      <th>theftsper100k</th>\n",
       "      <th>autotheftsper100k</th>\n",
       "      <th>arsonper100k</th>\n",
       "      <th>crime index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95th</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.240329</td>\n",
       "      <td>-0.302745</td>\n",
       "      <td>-0.130805</td>\n",
       "      <td>-0.302601</td>\n",
       "      <td>-0.342421</td>\n",
       "      <td>-0.257003</td>\n",
       "      <td>-0.091805</td>\n",
       "      <td>-0.238494</td>\n",
       "      <td>-0.354678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>murdersper100k</th>\n",
       "      <td>-0.240329</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170459</td>\n",
       "      <td>0.606552</td>\n",
       "      <td>0.571986</td>\n",
       "      <td>0.505855</td>\n",
       "      <td>0.315939</td>\n",
       "      <td>0.310346</td>\n",
       "      <td>0.386974</td>\n",
       "      <td>0.650385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rapesper100k</th>\n",
       "      <td>-0.302745</td>\n",
       "      <td>0.170459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.269519</td>\n",
       "      <td>0.378451</td>\n",
       "      <td>0.278477</td>\n",
       "      <td>0.278684</td>\n",
       "      <td>0.264664</td>\n",
       "      <td>0.264512</td>\n",
       "      <td>0.531987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autotheftsper100k</th>\n",
       "      <td>-0.091805</td>\n",
       "      <td>0.310346</td>\n",
       "      <td>0.264664</td>\n",
       "      <td>0.564758</td>\n",
       "      <td>0.334505</td>\n",
       "      <td>0.464945</td>\n",
       "      <td>0.456257</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.296172</td>\n",
       "      <td>0.612149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arsonper100k</th>\n",
       "      <td>-0.238494</td>\n",
       "      <td>0.386974</td>\n",
       "      <td>0.264512</td>\n",
       "      <td>0.377354</td>\n",
       "      <td>0.342515</td>\n",
       "      <td>0.397116</td>\n",
       "      <td>0.288854</td>\n",
       "      <td>0.296172</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.464297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crime index</th>\n",
       "      <td>-0.354678</td>\n",
       "      <td>0.650385</td>\n",
       "      <td>0.531987</td>\n",
       "      <td>0.762285</td>\n",
       "      <td>0.830321</td>\n",
       "      <td>0.776285</td>\n",
       "      <td>0.746923</td>\n",
       "      <td>0.612149</td>\n",
       "      <td>0.464297</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       95th  murdersper100k  rapesper100k  robberiesper100k  \\\n",
       "95th               1.000000       -0.240329     -0.302745         -0.130805   \n",
       "murdersper100k    -0.240329        1.000000      0.170459          0.606552   \n",
       "rapesper100k      -0.302745        0.170459      1.000000          0.269519   \n",
       "...                     ...             ...           ...               ...   \n",
       "autotheftsper100k -0.091805        0.310346      0.264664          0.564758   \n",
       "arsonper100k      -0.238494        0.386974      0.264512          0.377354   \n",
       "crime index       -0.354678        0.650385      0.531987          0.762285   \n",
       "\n",
       "                   assaultsper100k  burglariesper100k  theftsper100k  \\\n",
       "95th                     -0.302601          -0.342421      -0.257003   \n",
       "murdersper100k            0.571986           0.505855       0.315939   \n",
       "rapesper100k              0.378451           0.278477       0.278684   \n",
       "...                            ...                ...            ...   \n",
       "autotheftsper100k         0.334505           0.464945       0.456257   \n",
       "arsonper100k              0.342515           0.397116       0.288854   \n",
       "crime index               0.830321           0.776285       0.746923   \n",
       "\n",
       "                   autotheftsper100k  arsonper100k  crime index  \n",
       "95th                       -0.091805     -0.238494    -0.354678  \n",
       "murdersper100k              0.310346      0.386974     0.650385  \n",
       "rapesper100k                0.264664      0.264512     0.531987  \n",
       "...                              ...           ...          ...  \n",
       "autotheftsper100k           1.000000      0.296172     0.612149  \n",
       "arsonper100k                0.296172      1.000000     0.464297  \n",
       "crime index                 0.612149      0.464297     1.000000  \n",
       "\n",
       "[10 rows x 10 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section out 95th percentile income and specific crimes per 100,000 population\n",
    "df_95thper100 = crime_income_df[['95th', 'murdersper100k', 'rapesper100k', \n",
    "                                 'robberiesper100k', 'assaultsper100k',\n",
    "                            'burglariesper100k', 'theftsper100k', \n",
    "                                 'autotheftsper100k', 'arsonper100k', \n",
    "                                 'crime index']]\n",
    "# Create a tablewith their correlation\n",
    "corrs_95thper100 = df_95thper100.corr()\n",
    "corrs_95thper100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation is negative once again though when we check income's 95th percentile against the crimes per 100k in the population. Against the crime index, we have a correlation of -0.354678.\n",
    "\n",
    "As we have seen from these correlation tables, looking at just one percentile does not quite capture the idea of income inequality so we will be using the quintile share values which better takes into account the percent of income controlled by a certain portion of the population, not just the income of a percentile.\n",
    "\n",
    "Now let's create a new column which categorizes each location as a place where there is high inequality (1) or low inequality (0). This will help determine if quintile share is an independently influencing factor on crime rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>20th</th>\n",
       "      <th>40th</th>\n",
       "      <th>Median</th>\n",
       "      <th>...</th>\n",
       "      <th>arson</th>\n",
       "      <th>arsonper100k</th>\n",
       "      <th>crime index</th>\n",
       "      <th>quintile share</th>\n",
       "      <th>inequality exists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabaster</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>33578</td>\n",
       "      <td>61734</td>\n",
       "      <td>73325</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115.9</td>\n",
       "      <td>3.638573</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albertville</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>17270</td>\n",
       "      <td>27280</td>\n",
       "      <td>32042</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4.273191</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anniston</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>12553</td>\n",
       "      <td>22337</td>\n",
       "      <td>30539</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1302.8</td>\n",
       "      <td>5.215646</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Athens</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>16777</td>\n",
       "      <td>33804</td>\n",
       "      <td>45920</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.2</td>\n",
       "      <td>6.247660</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Auburn</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10718</td>\n",
       "      <td>26433</td>\n",
       "      <td>38912</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>217.4</td>\n",
       "      <td>9.969491</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          city    state   20th   40th  Median        ...          arson  \\\n",
       "0    Alabaster  Alabama  33578  61734   73325        ...            NaN   \n",
       "1  Albertville  Alabama  17270  27280   32042        ...            NaN   \n",
       "2     Anniston  Alabama  12553  22337   30539        ...            NaN   \n",
       "3       Athens  Alabama  16777  33804   45920        ...            NaN   \n",
       "4       Auburn  Alabama  10718  26433   38912        ...            NaN   \n",
       "\n",
       "   arsonper100k  crime index  quintile share  inequality exists  \n",
       "0           NaN        115.9        3.638573                  0  \n",
       "1           NaN        200.0        4.273191                  0  \n",
       "2           NaN       1302.8        5.215646                  1  \n",
       "3           NaN        140.2        6.247660                  1  \n",
       "4           NaN        217.4        9.969491                  1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create 'ineq_exists' column: '1' if exists, '0' if doesn't\n",
    "# Income inequality defined as >= median of all income inequalities\n",
    "income_ineq_median = crime_income_df[\"quintile share\"].median()\n",
    "\n",
    "ineq_exists = [0]*len(crime_income_df)\n",
    "for i in range(0,len(crime_income_df)):\n",
    "    if crime_income_df[\"quintile share\"][i] >= income_ineq_median:\n",
    "        ineq_exists[i] = 1\n",
    "    else:\n",
    "        ineq_exists[i] = 0\n",
    "crime_income_df['inequality exists'] = ineq_exists  \n",
    "crime_income_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see below, the distribution of crime index for the two groups of low and high income inequality are not normal so we must transform them inorder to run tests to check if there is a significant influnce income inequality has on crime.\n",
    "\n",
    "We normalize the data by using the normalize funtion from sklearn and doing a cuberoot transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The distribution of crime index for income inequality existing is not approximately normal.\n",
      "The distribution of crime index for income inequality not existing is not approximately normal.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Side-by-side histogram plots of crime index in \n",
    "# areas with and without income inequality\n",
    "fig, axes = plt.subplots(1, 2)\n",
    "\n",
    "crime_income_df[crime_income_df[\"inequality exists\"] == 1][\"crime index\"].plot \\\n",
    ".hist(ax = axes[0], \n",
    "      title = 'High Income Inequality-Income Inequality Exists')\n",
    "crime_income_df[crime_income_df[\"inequality exists\"] == 0][\"crime index\"].plot \\\n",
    ".hist(ax = axes[1], \n",
    "      title = 'Low Income Inequality-Income Inequality Doesn''t Exist')\n",
    "# Test if there is a normal distribution with a p test\n",
    "crime_ineq = crime_income_df[crime_income_df[\"inequality exists\"] == 1] \\\n",
    "[\"crime index\"].values\n",
    "crime_no_ineq = crime_income_df[crime_income_df[\"inequality exists\"] == 0] \\\n",
    "[\"crime index\"].values\n",
    "st_ineq, p_ineq = normaltest(crime_ineq)\n",
    "st_no_ineq, p_no_ineq = normaltest(crime_no_ineq)\n",
    "axes[0].set_xlabel('Crime Index')\n",
    "axes[1].set_xlabel('Crime Index')\n",
    "\n",
    "fig.set_size_inches(20, 4)\n",
    "\n",
    "is_n_ineq = bool(p_ineq > 0.01)\n",
    "if is_n_ineq == True:\n",
    "    print('The distribution of crime index for income inequality existing \\\n",
    "is approximately normal.')\n",
    "else:\n",
    "    print('The distribution of crime index for income inequality existing is \\\n",
    "not approximately normal.')\n",
    "    \n",
    "is_n_no_ineq = bool(p_no_ineq > 0.01)\n",
    "if is_n_no_ineq == True:\n",
    "    print('The distribution of crime index for income inequality not existing \\\n",
    "is approximately normal.')\n",
    "else:\n",
    "    print('The distribution of crime index for income inequality not existing \\\n",
    "is not approximately normal.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The distribution of crime index for income inequality existing is approximately normal.\n",
      "The distribution of crime index for income inequality not existing is approximately normal.\n",
      "[0.5475311] [0.03667113]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalizing and cube root\n",
    "crime_ineq = pd.Series(crime_ineq)\n",
    "crime_no_ineq = pd.Series(crime_no_ineq)\n",
    "\n",
    "crime_ineq =    pd.DataFrame(preprocessing.normalize([crime_ineq],norm='max'))\n",
    "crime_ineq = crime_ineq.transpose()\n",
    "crime_ineq = pd.DataFrame(np.cbrt(crime_ineq))\n",
    "\n",
    "crime_no_ineq = pd.DataFrame(preprocessing.normalize([crime_no_ineq],norm='max'))\n",
    "crime_no_ineq = crime_no_ineq.transpose()\n",
    "crime_no_ineq = pd.DataFrame(np.cbrt(crime_no_ineq))\n",
    "\n",
    "fig, axes = plt.subplots(1, 2)\n",
    "crime_ineq.plot.hist(ax = axes[0], \n",
    "    title = 'High Income Inequality-Income Inequality Exists')\n",
    "crime_no_ineq.plot.hist(ax = axes[1], \n",
    "    title = 'Low Income Inequality-Income Inequality Doesn''t Exist')\n",
    "axes[0].set_xlabel('Crime Index')\n",
    "axes[1].set_xlabel('Crime Index')\n",
    "\n",
    "fig.set_size_inches(20, 4)\n",
    "\n",
    "# Check for normality again\n",
    "st_ineq, p_ineq = normaltest(crime_ineq)\n",
    "st_no_ineq, p_no_ineq = normaltest(crime_no_ineq)\n",
    "\n",
    "is_n_ineq = bool(p_ineq > 0.01)\n",
    "if is_n_ineq == True:\n",
    "    print('The distribution of crime index for income inequality \\\n",
    "existing is approximately normal.')\n",
    "else:\n",
    "    print('The distribution of crime index for income inequality \\\n",
    "existing is not approximately normal.')\n",
    "    \n",
    "is_n_no_ineq = bool(p_no_ineq > 0.01)\n",
    "if is_n_no_ineq == True:\n",
    "    print('The distribution of crime index for income inequality \\\n",
    "not existing is approximately normal.')\n",
    "else:\n",
    "    print('The distribution of crime index for income inequality \\\n",
    "not existing is not approximately normal.')\n",
    "    \n",
    "print(p_ineq,p_no_ineq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Success! Now that we have approximately normal distributions, we can do some more testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a significant difference.\n"
     ]
    }
   ],
   "source": [
    "# Check for significant difference\n",
    "t_val, p_val = ttest_ind(crime_ineq, crime_no_ineq)\n",
    "if p_val < 0.01:\n",
    "    print('There is a significant difference.')\n",
    "else:\n",
    "    print('There is NOT a significant difference.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After performing a 2-sample t-test, we can conclude that there is indeed a significant difference in crime index between areas where income inequality exists and areas where income inequality doesn't exist. But maybe this effect is due to a confound, namely poverty in and of itself so we should look at poverty values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "561\n",
      "363\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>20th</th>\n",
       "      <th>40th</th>\n",
       "      <th>Median</th>\n",
       "      <th>...</th>\n",
       "      <th>arsonper100k</th>\n",
       "      <th>crime index</th>\n",
       "      <th>quintile share</th>\n",
       "      <th>inequality exists</th>\n",
       "      <th>poverty exists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabaster</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>33578</td>\n",
       "      <td>61734</td>\n",
       "      <td>73325</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115.9</td>\n",
       "      <td>3.638573</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albertville</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>17270</td>\n",
       "      <td>27280</td>\n",
       "      <td>32042</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4.273191</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anniston</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>12553</td>\n",
       "      <td>22337</td>\n",
       "      <td>30539</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1302.8</td>\n",
       "      <td>5.215646</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Athens</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>16777</td>\n",
       "      <td>33804</td>\n",
       "      <td>45920</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.2</td>\n",
       "      <td>6.247660</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Auburn</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>10718</td>\n",
       "      <td>26433</td>\n",
       "      <td>38912</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>217.4</td>\n",
       "      <td>9.969491</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          city    state   20th   40th  Median       ...        arsonper100k  \\\n",
       "0    Alabaster  Alabama  33578  61734   73325       ...                 NaN   \n",
       "1  Albertville  Alabama  17270  27280   32042       ...                 NaN   \n",
       "2     Anniston  Alabama  12553  22337   30539       ...                 NaN   \n",
       "3       Athens  Alabama  16777  33804   45920       ...                 NaN   \n",
       "4       Auburn  Alabama  10718  26433   38912       ...                 NaN   \n",
       "\n",
       "   crime index  quintile share  inequality exists  poverty exists  \n",
       "0        115.9        3.638573                  0               1  \n",
       "1        200.0        4.273191                  0               0  \n",
       "2       1302.8        5.215646                  1               0  \n",
       "3        140.2        6.247660                  1               0  \n",
       "4        217.4        9.969491                  1               0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create 'poverty_exists' column: '1' if exists, '0' if doesn't\n",
    "# poverty defined as 20th percentile income < mean of all 20th \n",
    "# percentile incomes\n",
    "mean20th = crime_income_df[\"20th\"].mean()\n",
    "\n",
    "pov_exists = [0]*len(crime_income_df)\n",
    "for i in range(0,len(crime_income_df)):\n",
    "    if crime_income_df[\"20th\"][i] < mean20th:\n",
    "        pov_exists[i] = 0\n",
    "    else:\n",
    "        pov_exists[i] = 1\n",
    "crime_income_df['poverty exists'] = pov_exists \n",
    "print(len(crime_income_df[crime_income_df['poverty exists'] == 0]))\n",
    "print(len(crime_income_df[crime_income_df['poverty exists'] == 1]))\n",
    "crime_income_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, based on our defintion of poverty, 561 cities do not experience poverty while 363 cities do.\n",
    "\n",
    "Using `value_counts` from pandas, extract the number of 'poverty exists' and 'poverty doesn't exist', separately for 'income inequality exists' and 'income inequality doesn't exist'. \n",
    "\n",
    "To do so:\n",
    "\n",
    "- select from `crime_income_df` income inequality, 0 and 1 separately, extract the poverty exists column, and use the `value_counts` method.\n",
    "- Save the counts for each 'poverty does/doesn't exist' for 'income inequality exists' to a variable called `pov_ineq`\n",
    "- Save the counts for each 'poverty does/doesn't exist' for 'income inequality doesn't exist' to a variable called `pov_no_ineq`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    409\n",
      "1     53\n",
      "Name: poverty exists, dtype: int64\n",
      "1    310\n",
      "0    152\n",
      "Name: poverty exists, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "pov_ineq = crime_income_df[crime_income_df['inequality exists'] == 1] \\\n",
    "['poverty exists'].value_counts()\n",
    "pov_no_ineq = crime_income_df[crime_income_df['inequality exists'] == 0] \\\n",
    "['poverty exists'].value_counts()\n",
    "print(pov_ineq)\n",
    "print(pov_no_ineq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we saw from above is that of all the areas WITH income inequality, 409 cities do not experience poverty while 53 do. Meanwhile in all the areas WITHOUT income inequality, 310 cities DO experience poverty while 152 do not. \n",
    "\n",
    "Let's find the ratio of poverty existing, the proportion of cities with poverty, in 'income inequality exists' and 'income inequality doesn't exist'. This will be value between 0.0 and 1.0, calculated as #(pov == 1) / (#(pov == 0) + #(pov == 1) - done separately for income inequality existing and not existing.\n",
    "\n",
    "We will use the `pov_ineq` and `pov_no_ineq` variables to calculate these.\n",
    "\n",
    "Save the ratio of poverty existing in 'inequality exists' to a variable `r_ineq`.\n",
    "\n",
    "Save the ratio of poverty existing in 'inequality doesn't exist' to a variable `r_no_ineq`.\n",
    "\n",
    "Note: keep these numbers as ratios (they should be decimal numbers, less than 1).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11471861471861472\n",
      "0.670995670995671\n"
     ]
    }
   ],
   "source": [
    "r_ineq = pov_ineq[1]/(pov_ineq[0] + pov_ineq[1])\n",
    "r_no_ineq = pov_no_ineq[1]/(pov_no_ineq[0] + pov_no_ineq[1])\n",
    "\n",
    "print(r_ineq)\n",
    "print(r_no_ineq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ratio of poverty existing in the two categories appear very different, but we will use a chi-squared test to see whether the difference in poverty-ratio between 'income inequality does/doesn't exist' is significantly different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a significant difference in ratios!\n"
     ]
    }
   ],
   "source": [
    "chisq, p_val_chi = stats.chisquare(np.array([pov_ineq.values, \n",
    "                                             pov_no_ineq.values]), axis=None)\n",
    "\n",
    "if p_val_chi < 0.01:\n",
    "    print('There is a significant difference in ratios!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now take a look at these comparisons in pivot tables. \n",
    "\n",
    "We'll use the pandas `pivot_table` method to create pivot table and assign it to a variable `pv`.\n",
    "\n",
    "We'll set the values as 'crime index', and the indices as 'poverty exists' and 'inequality exists' in the pivot table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>crime index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poverty exists</th>\n",
       "      <th>inequality exists</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>276.013158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>352.225672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>186.009032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>181.984906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  crime index\n",
       "poverty exists inequality exists             \n",
       "0              0                   276.013158\n",
       "               1                   352.225672\n",
       "1              0                   186.009032\n",
       "               1                   181.984906"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv = pd.pivot_table(crime_income_df, values='crime index', index=[ \\\n",
    "'poverty exists', 'inequality exists'])\n",
    "pv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Short recap:\n",
    "- Our initial hypothesis suggested there is a significant difference between crime index of counties with and without income inequality. \n",
    "- However, further analyses suggested there may be a confounding variable, as there is also a significantly different poverty balance between cities with and without income inequality. \n",
    "\n",
    "Checking the average crime index, per 'inequality exists', split up by 'poverty exists', suggests there may not be a difference between 'inequality exists', other than what is explained by 'poverty exists'. \n",
    "\n",
    "Now we want to statistically ask this question: is there still a difference in crime index between 'inequality exists', when controlling for differences in 'poverty exists'? \n",
    "\n",
    "We will need to make some linear models, using Ordinary Least Squares (OLS).\n",
    "\n",
    "We will do this using the method that is outlined in the 'LinearModels' Tutorial, using patsy, and statsmodels. \n",
    "- Create design matrices with `patsy.dmatrices`\n",
    "- Iniliaize an OLS model with `sm.OLS`\n",
    "- Fit the OLS model\n",
    "- Check the summary for results. \n",
    "\n",
    "First let's only look at quintile share inequality and crime index and based on the model we create (using alpha value of 0.01), check if income inequality significantly predicts crime index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income inequality significantly predicts crime index.\n",
      "\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             crimeindex   R-squared:                       0.126\n",
      "Model:                            OLS   Adj. R-squared:                  0.125\n",
      "Method:                 Least Squares   F-statistic:                     133.0\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):           7.81e-29\n",
      "Time:                        23:24:56   Log-Likelihood:                -5966.1\n",
      "No. Observations:                 924   AIC:                         1.194e+04\n",
      "Df Residuals:                     922   BIC:                         1.195e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept          215.6208      7.179     30.035      0.000     201.532     229.710\n",
      "inequalityexists   117.0751     10.153     11.531      0.000      97.150     137.000\n",
      "==============================================================================\n",
      "Omnibus:                      292.501   Durbin-Watson:                   1.614\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1135.375\n",
      "Skew:                           1.463   Prob(JB):                    2.86e-247\n",
      "Kurtosis:                       7.574   Cond. No.                         2.62\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# rename some columns so that dmatrices() function can be used in next step\n",
    "crime_income_df.rename(columns={'crime index':'crimeindex',\n",
    "                                   'inequality exists':'inequalityexists', \n",
    "                                   'poverty exists':'povertyexists',\n",
    "                                    'quintile share':'quintileshare'}, \n",
    "                                   inplace=True)\n",
    "\n",
    "# First look at how quintile share inequality affects crime index\n",
    "outcome_1, predictors_1 = patsy.dmatrices('crimeindex ~ inequalityexists',\n",
    "                                          crime_income_df)\n",
    "mod_1 = sm.OLS(outcome_1, predictors_1)\n",
    "res_1 = mod_1.fit()\n",
    "\n",
    "lm_1 = bool(res_1.pvalues[1] < 0.01)\n",
    "if lm_1 == True:\n",
    "    print(\"Income inequality significantly predicts crime index.\\n\")\n",
    "else:\n",
    "    print(\"Income inequality doesn't significantly predict crime index.\\n\") \n",
    "\n",
    "print(res_1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at how both poverty and quintile share inequality affect crime index. Then check if it is a significant prediction based on the same alpha value of 0.01."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income inequality significantly predicts crime index.\n",
      "\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             crimeindex   R-squared:                       0.205\n",
      "Model:                            OLS   Adj. R-squared:                  0.203\n",
      "Method:                 Least Squares   F-statistic:                     118.6\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):           1.45e-46\n",
      "Time:                        23:24:56   Log-Likelihood:                -5922.4\n",
      "No. Observations:                 924   AIC:                         1.185e+04\n",
      "Df Residuals:                     921   BIC:                         1.187e+04\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept          292.9769     10.607     27.620      0.000     272.159     313.794\n",
      "inequalityexists    52.9444     11.788      4.491      0.000      29.810      76.079\n",
      "povertyexists     -115.2856     12.068     -9.553      0.000    -138.970     -91.601\n",
      "==============================================================================\n",
      "Omnibus:                      312.565   Durbin-Watson:                   1.612\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1384.544\n",
      "Skew:                           1.522   Prob(JB):                    2.24e-301\n",
      "Kurtosis:                       8.167   Cond. No.                         4.42\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Look at how quintile share inequality and poverty affects crime index\n",
    "outcome_2, predictors_2 = patsy.dmatrices('crimeindex ~ inequalityexists \\\n",
    "                                          + povertyexists', crime_income_df)\n",
    "mod_2 = sm.OLS(outcome_2, predictors_2)\n",
    "res_2 = mod_2.fit()\n",
    "\n",
    "lm_2 = bool(res_1.pvalues[1] < 0.01)\n",
    "if lm_1 == True:\n",
    "    print(\"Income inequality significantly predicts crime index.\\n\")\n",
    "else:\n",
    "    print(\"Income inequality doesn't significantly predict crime index.\\n\") \n",
    "\n",
    "print(res_2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cities with income inequality and cities without income \n",
      " inequality have a systematically different crime index.\n"
     ]
    }
   ],
   "source": [
    "ans = (lm_1 == True and lm_2 == True)\n",
    "if ans == True:\n",
    "    print(\"Cities with income inequality and cities without income \\n \\\n",
    "inequality have a systematically different crime index.\")\n",
    "else:\n",
    "    print(\"Cities with income inequality and cities without income \\n \\\n",
    "inequality don't have a systematically different crime index.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try a model that predicts the crime index values based off of the quintile share."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quintile Share significantly predicts crime index.\n",
      "\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             crimeindex   R-squared:                       0.129\n",
      "Model:                            OLS   Adj. R-squared:                  0.129\n",
      "Method:                 Least Squares   F-statistic:                     137.1\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):           1.25e-29\n",
      "Time:                        23:24:56   Log-Likelihood:                -5964.3\n",
      "No. Observations:                 924   AIC:                         1.193e+04\n",
      "Df Residuals:                     922   BIC:                         1.194e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept         7.3352     23.341      0.314      0.753     -38.472      53.142\n",
      "quintileshare    57.9963      4.952     11.711      0.000      48.277      67.716\n",
      "==============================================================================\n",
      "Omnibus:                      288.899   Durbin-Watson:                   1.716\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1148.417\n",
      "Skew:                           1.434   Prob(JB):                    4.21e-250\n",
      "Kurtosis:                       7.647   Cond. No.                         22.6\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Linear regression using the continuous values for crime index and quintile share\n",
    "outcome_3, predictors_3 = patsy.dmatrices('crimeindex ~ quintileshare', \n",
    "                                          crime_income_df)\n",
    "mod_3 = sm.OLS(outcome_3, predictors_3)\n",
    "res_3 = mod_3.fit()\n",
    "lm_3 = bool(res_3.pvalues[1] < 0.01)\n",
    "if lm_3 == True:\n",
    "    print(\"Quintile Share significantly predicts crime index.\\n\")\n",
    "else:\n",
    "    print(\"Quintile Share doesn't significantly predict crime index.\\n\") \n",
    "\n",
    "print(res_3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QQYKaxcJb3vIW/uzP/oydO3fS29tbXF+uwN6IC205V1qlFNdffz1/+7d/O3uDLUGrqjSLjkTEYvOaRt66tYN3vG49b93aweY1jcWZhg4S1EwGpdSU/nbv3j3tvm+88UY+/elP85rXvGbU+ssuu6yoatq5cyetra00NDSMWv+jH/2I/v5+AK644gruvvtuurq6gMBG8vLLNWVInxJ6xqFZUhSDBJORstuTUYtUPsfhnjSb1zTO8eg0mtF0dHTw0Y9+dNz6z3zmM7znPe/hvPPOI5FIcNtttwGB7eO6667jggsuYPv27axbtw6AzZs389nPfpYrr7wS3/exbZt//ud/Zv369bMy7iVZc3zbtm1KF3JantzzVCdRwyBqV3bJzTseed/nrVs75nBkmoXC/v37Oeecc+Z7GPNOuesgIruVUtsm2lerqjRLCh0kqNHMPlpwaJYUOkhQo5l9tODQLCl0kKBGM/towaFZUuggQY1m9pk1wSEit4pIl4g8XbLu70XkgIjsE5H/EpGmkm1/ISIHReQ5EfntkvVvCtcdFJFPztZ4NUuDkSDBnOtzaihH3vHwlSLveJwaypFz/WKQoEajmRqzOeP4GvCmMevuB16tlDoPeB74CwAR2Qy8HTg33OdfRMQUERP4Z+AqYDNwXdhWo6nISJDg1nVN5H2fnlSevO+zdV0T12xZrYP/NJppMmuvXUqph0Rkw5h1Py1ZfBz4/fDztcBdSqk88JKIHAQuCrcdVEodAhCRu8K2z87WuDVLg5EgQR2roZkOs5W6xjRNXvOa1+A4DpZlcf311/Onf/qnGEbld/nDhw/z2GOP8Y53vGPK/c4U82njuBH4Ufj5NOBoybbOcF2l9eMQkfeKyC4R2dXd3T0Lw9VoNMuJnlSee/eeYM+RAaKGwcq6KFHDYM+RAe7de4KeVH7Kx47H4+zZs4dnnnmG+++/n/vuu4+bb7656j6HDx/mm9/85pT7nEnmRXCIyP8CXGAkfWO5OoaqyvrxK5W6RSm1TSm1beXKlTMzUI1GsyyZy9Q1bW1t3HLLLfzTP/0TSikOHz7MpZdeygUXXMAFF1zAY489BsAnP/lJHn74Yc4//3y++MUvVmw3F8y5hVBErgfeDFyhXglb7wTWljTrAI6Hnyut12g0mllhrlPXnH766fi+T1dXF21tbdx///3EYjFeeOEFrrvuOnbt2sXnPvc5Pv/5z/ODH/wAgEwmU7bdXDCngkNE3gR8AtiulMqUbPo+8E0R+QKwBjgTeJJgxnGmiGwEjhEY0OdfwafRaJY0k6lvP1N2tJH3aMdx+PCHP1ysq/H888+XbV9ru9lg1gSHiNwJ7ABaRaQTuInAiyoK3B+mBn5cKfV+pdQzIvJtAqO3C3xIKeWFx/kw8BPABG5VSj0zW2PWaDQaCFLX1NdVfzzalsFgqnqwaa0cOnQI0zRpa2vj5ptvpr29nb179+L7PrFYrOw+X/ziF2tqNxvMplfVdWVWf7VK+78G/rrM+vuA+2ZwaBqNRlOVkdQ11ZJlzlTqmu7ubt7//vfz4Q9/GBFhcHCQjo4ODMPgtttuK5aAra+vZ3h4uLhfpXZzgY4c12g0mjHMduqabDbL+eefz7nnnssb3/hGrrzySm666SYAPvjBD3Lbbbdx8cUX8/zzz5NMBlkOzjvvPCzLYsuWLXzxi1+s2G4u0GnVNRrNsqKWtOqZgsu9e08QswyS0fGKmXTeJef6XLNl9aLNQqDTqms0Gs0MolPXVGd5nrVGo9FMQC317Zcry/fMNRrNskUpRejZWZWlmrpmuiYKrarSaDTLilgsRm9v77QfnosVpRS9vb3Tct/VMw6NRrOs6OjooLOzk+Wc0y4Wi9HR0THl/bXg0Gg0ywrbttm4ceN8D2NRo1VVGo1Go5kUWnBoNBqNZlJowaHRaDSaSaEFh0aj0WgmhRYcGo1Go5kUWnBoNBqNZlJowaHRaDSaSaEFh0aj0WgmhRYcGo1Go5kUWnBoNBqNZlJowaHRaDSaSaEFh0aj0WgmhRYcGo1Go5kUsyY4RORWEekSkadL1rWIyP0i8kL4vzlcLyLyjyJyUET2icgFJftcH7Z/QUSun63xaiZPpuDy7PFB7nmqk28+8TL3PNXJs8cHyRTc+R6aRqOZRWZzxvE14E1j1n0SeEApdSbwQLgMcBVwZvj3XuDLEAga4CbgdcBFwE0jwkYzv/Sk8ty79wR7jgwQNQxW1kWJGgZ7jgxw794T9KTy8z1EjUYzS8xaPQ6l1EMismHM6muBHeHn24CdwCfC9beroCTX4yLSJCKrw7b3K6X6AETkfgJhdOdsjVszMZmCywP7u4hZBslkpLg+apu02SbpfLD9mi2rl3Vd5nJkCm6xhnWu4BGLmGxqr1/2Naw1i4u5tnG0K6VOAIT/28L1pwFHS9p1husqrdfMI4d70nieTzJa/kGXjFq4ns/hnvQcj2xho2dpmqXCQjGOl6sar6qsH38AkfeKyC4R2bWcS0LOBQdODdMYt6u2aYrbHDg1PEcjWviUztLaGmJEbRMRCWZpDTFilsED+7u0fUizKJhrwXEqVEER/u8K13cCa0vadQDHq6wfh1LqFqXUNqXUtpUrV874wDWvkCt4RKzqt45tGeQK3hyNaOGjZ2mapcRcC47vAyOeUdcD3ytZ/+7Qu+piYDBUZf0EuFJEmkOj+JXhOs08EouYFFy/ahvH9YlFzDka0cJHz9I0S4nZdMe9E/gFcLaIdIrIHwKfA35LRF4AfitcBrgPOAQcBP4N+CBAaBT/38Avw7+/GjGUa+aPTe31DGadqm0Gsg6b2uvnaEQLHz1L0ywlZtOr6roKm64o01YBH6pwnFuBW2dwaJppsqE1ya+PDZHOu2VVL+m8i2UabGhNzsPoFiYjs7SoXXkWpmdpmsWC9v/TTJpExOKKc9p4YH8XqXyOpriNbRk4rs9A1sEyDa44p23ZupeWc7mNiNCVyrO2OVFxv4Gsw9Z1TXM4Uo1maizPX7Zm2rTWRblmy+riA3Iw5RCLmGxd17SsYxJ6Unke2N+F5/k0xm3q6ywKrk/3cJ5njg9hm8Kqhvi4/fQsTbOYWJ6/bs2MkIhYbF7TyOY1jfM9lAVBtcDIjpYElinsOTLI5tN82utiepamWbTou1SjmYBao72LLrclQqOUVY1xzvUUK+ui5H1fz9I0ixZ9p2qWPdUEQ6bglVU97TkywK+PDXHFOW201kWB2lxu2+oDofHWrR1zcWoazaygBYdm3lgIeZsq2ST2HBlg95F+Co6itS5SU06uXMGjvq76uG3LYDBV3ZVZo1noLJSUI5plxkLI2zRRGpDhjMMzxwewjHKZb8ZHe+vASM1yQQsOzZyzUPI2TZQGpDdTIGIadA1XFmKl0d46MFKzXNCqKs2cM5ERORm1SOVzHO5Jj/PYmkn11kQ2iYIbqK86+zOsbSkff1GqetKBkZrlgp5xaOacqeZtmmn11kRpQEa2FbzK6qdS1dNIYGTO9Tk1lCPvePhKkXc8Tg3lyLm+drnVLAm04NDMOVPJ2zQb6q2JbBIdTXEGsw4Rs/JYx6qeRgIjt65rIu/79KTy5H2freuauGbL6qIHlkazmNGvPpo5Zyp5m6aj3qrEpvZ69hwZoK3CONoaYuzrHGRdS/mHfSXVkw6M1Cx19IxDM+dMxYg8G2nJN7QmMU2DdL78LMX1FK/uaKQ+bo9TPR3ty3CoJ03e9fjuU8e456lOnj0+qAsxaZYFWnBo5pyJHtjl3uRnIy35RDaJobzDOavqsSw4MZjl0UM97Ds2wInBLAPZAk0JmxWJiC4Bq1l2aFWVZs6ZSnbdmUpLXs4r68y2QEAd7ssU04BsXJngxa4Mh3syNMZtWjuixWSFTx8f4vx1jaOSFVYKCtRoliL6ztbMC5PNrjuRPQImTkteKUr8wIlhzFBYtdZFyRRc7t17goaYNcqtNmqb+CgMgRdOpWlORIhao8czFVuLRrPY0IJDM29Mxog8NkYi73p0DeXoHMhScH0UihXJKG0N5Q3Z1TLXjp0pVDPEH+vP0pyMkM67dA3lWNsyPiZjxNaiBYdmqaJtHJpFQak94mB3ikcP9vJiVwoUKAXKh2TE4oH93WVtDBNFiZemD6lmiC94PpYh1EUtOgeyZdvoErCapY4WHJpFQ2tdlCvOWUm24GEISJhD6oyVdbz+jFZOX1lXMZ5jMl5Z1QzxEdPA9RWmIRVjQHQ+Ks1SZ0JVlYh8HfiwUmowXF4P3KqUGlc7XLP0WAgZbEvpGsrTXh/lNaeVVwNVsjFMJnNtNUP8ac1xDnWnqYtaFYWLLgGrWerUMuN4BHhCRK4WkT8G7gf+YXaHpVkILIQMtmOZajzHZDLXVoszaa+PYRlCb7pAR5MuAatZnkwoOJRSXwH+CPge8FfAZUqpe2d7YJr5ZaFksB3LVOM5JhN0WC3OJGqbvKqtLrCrCDoflWZZMqHgEJF3AbcC7wa+BtwnIlum06mI/D8i8oyIPC0id4pITEQ2isgTIvKCiHxLRCJh22i4fDDcvmE6fWtqYzLG5LlkqjUvJhN0OFFgoG2ZvPey02lLRnn0UA/3/fo4jx7qoTFmccU5K3U+Ks2SR5RS1RuIfBd4r1KqK1y+CLhFKXX+lDoUOY1A/bVZKZUVkW8D9wFXA/cope4SkX8F9iqlviwiHwTOU0q9X0TeDvyeUuq/V+tj27ZtateuXVMZnibknqc6iRpG1YC7vONNWAZ1pm0kzx4fDOI5GmIV25wayrF1XdM4d9iROA7X8ysGHZY+9CuNvT5m8diLfcV4kIhlUHB9BrPOqHgQjWaxISK7lVLbJmo34S9XKfW7Y5afDIXHdLCAuIg4QAI4AbwBeEe4/TbgM8CXgWvDzwB3A/8kIqImkniaaTETZVCrlWUdW6+7VqZT82KyQYfl4kxGggNriQfR6irNUqUWVdVZIvKAiDwdLp8H/PlUO1RKHQM+DxwhEBiDwG5gQCk1okfoBE4LP58GHA33dcP2K8qM870isktEdnV3d091eJqQ6ZZBnS0byXRrXowIg7du7eAdr1vPW7d2sHlNY80P+YWqwtNo5pJavKr+DfgLwAFQSu0D3j7VDkWkmWAWsRFYAySBq8o0HZlRlCv4PG62oZS6RSm1TSm1beXKlVMdniZkumVQZ/MBO581L2YjS69Gs9io5TUrEaqnStdNx5XmjcBLSqluABG5B3g90CQiVjir6ACOh+07gbVAp4hYQCPQN43+NTUw3TKok3nATiU1x3zVvJgJFZ5Gs9ipZcbRIyJnEL7li8jvE6iYpsoR4GIRSUggja4AngV+Dvx+2OZ6AvdfgO+Hy4Tbf6btG7PPdFVCs5EGfSEwXRWeRrMUqGXG8SHgFmCTiBwDXgL+x1Q7VEo9ISJ3A78imLk8FR7/h8BdIvLZcN1Xw12+CnxdRA4SzDSmrCZbqCy06OwRJmtMLmWm0qAvNGYiS69Gs9iZ0B232FAkCRhKqQWvvF1M7rhjPY+WimvndNxmFzKjvKoqqPCG8g7nrm7gcF9mQb0IaDQTMW13XBH5nxXWA6CU+sKUR6cBJpfqe7E9cKZrI1moTFSEKud6oIQDJ4ZnzAVZo1loVFNC14d/24APELjFnga8H9g8+0Nb+ixl187p2kgWMpW8us5ZXU/ENGmtiyyoNC0azUxT8VerlLoZQER+ClwwoqISkc8A35mT0S1xZtvzaL6Zjo1koVPOq2v3y32cGspyuNej4PpELIOOpnggRCxTVwfULBlq+eWuAwolywVgw6yMZpmxHFw758ttdq7pSeX59i87ERRNiQjxmInrKw51pzncm2XL2kYaYvaifhHQaEaoRXB8HXhSRP6LwCX394DbZ3VUy4Sl6nk0X8yXd9qIrUopRWt9tGgHtEyhMREh73jsPTrIRRubF/2LgEYDtaVV/2vgRqAfGADeo5T6m9ke2HJgutHZmleYz9ohI7aq+piF64/3UozaJp7v0zWU0y8CmiVBra9hewiC/iwAEVmnlDoya6NaJix0z6OFGl8ylvn2ThuxVY1UB2xKRMa1GalRHrFMHeOhWfTUUjr2I8BNwCnAI8gdpYDzZndoS5+JXDtHUn3Px0N6NjLblmMmhFPROy05/oG3DRHNAAAgAElEQVQNlcvJzhQjtqr2+hhHejPkHI/YGPWjaQh9mcXpgqzRjKWWX+ZHgbOVUr2zPZjlyEL0PJqrN/iZEk7z7Z1Waqs6r6OJfZ0DDDgeyaiFZQiurxjMFBBh0bogazSl1HIHHyVIZa6ZJRaa59FcvMHPpHCab++00jQkDXGbCze00DWcp7M/Q6bgEzENWuuivEEH/2mWCLUIjkPAThH5IVC0MOrI8aXLXLzBT1c4laq4fnVkgKRtsr41QXt9rKyX2lSM0rWq0Ta0Jtn98gDPnxymN52n4AXC4rTmOO31MVxfkXN9Nq1umFT/Gs1CpRbBcST8i4R/miXOXLzB1yqc9h4bKLYfeXivqo/yYk8aE6ExbrN5VR3Pn0pxqDvNkd4M53U00TDm2JNNPDgZNVqm4FHwPF7oGiZqGTTGbRTw/Klhfn1skFevaeLarWu0ikqzZKildOzNczEQzcJhLuJLxgqnvONxajjHsf5s8Y29IW7zYtcwyqP48B7OOdy9u5OIZXLRxhaitkl7Y5yX+3JETEEB+zoHuHBDS3H8k/VOm4waDeCB/V20JqO8cXM7XUM5OgeyFNwglcy65ggRW0hoF1zNEqJaksN7KVNpbwSl1FtmZUSaeWcuUoeXCqehrMO+zgFcX5GMWsRsk5zj8cjBbkwRYrZZFAIDWYdExCJum8WguqhlsmVtI3uPDuL5Pq7nc3Iox6qG2JS80yajRgNGtV3bkmRty2gBdWpIpxnRLC2q/ZI+P2ej0Cwo5iK+ZEQ4NRLMECzToC72iqBKFVwMDNY0xUbNII71Z0lGLWzTIOsU6BrKsbYlSUPM5qKNzXQN5XipN82Bk0M010Wm5J022fKwSznfmEZTjmpJDh+cy4FoFg5zEV8yIpxe7s3g+mqU0AA4NZgjahu01cdI5V26hvOsbUlQ8PxijMRIUN3IG37UMlnbkuS05gQ9qTxv3doxpbFN1saz1PONaTRj0dY6TVlmO75kRDj9/Y+fQ1C4no9pCJ6vSOVd8q7POasbsMygYFJnf4a1LQkipoHrK2xTMA0hXRhfxtVxfQwJiklNJbBwsjYenW9Ms9zQgkNTkdmOL2mti7JpdT3K9zncl6F3uEAq71IXtTAMGMw42KaBZQiZUECUpvXwfFW2rvnRgSzZgofvD0wpsLCajSfvenQN5Xj6xBCrG2LEbINcIRBylYSHLiWrWWrULDhEJKmUWnwVhTQLmsaETaHgYSA0JyN0tCSwDOHUkMHRvgz9mQIdzcFMAxiV1iPneJyxcrSdpT+T5+CpFBdtbKYl+YpwmExgYSUbz1DOYe/RQbIFl5hpsKm9noLv8/DzPfSkC1y0oWWcG3A67+KjyDke9zzVuaBzfmk0tTJhdlwReb2IPAvsD5e3iMi/zPrINMuCDS0Jnjzch2UaNCUi2KaBiNBaFyMZtVEqiIdoqw9ql4+k9UjlXdJ5l8aEPaqyYGd/lrPa60YJjVJqqapYrnph1nHZdbifTMElETF57foWYhGLhliES89aCcDDB7sZyhZGjacnnafgKA6cGJ7zrL0azWxRy+vOF4HfBr4PoJTaKyKXzeqoNMuGnOPRn3HoywS1wgSImCYFL6ii15PKoxSkCg6+UqRyDoe60ziuT0sywq6X+2lJRuhoinPO6npODQWxIEf7M6Oit0vVSLV4OY218RzqTuG4HpvXNNJWHx11vIaYzaVntrL/xBDHBnOsCG0a56yu55njwzTErFEzl6VQU16zsFBKoZTCMCacC8wINd2tSqmjI8VpQrzZGY5mOdGTyvO9PSdoiln0ZRwKns9AxsH1/HAGYrOyLoZtCk8e6scQ4eRQjpV1UX7zzBXUR20Krs9g1iFd8HjqyCBH+jK010exQiN6uWhy2zLoGsiPM55vaEmggJf7MqNUSm86dxU/fuYk57QHdoy863G0L10M9BspEXv6yiSIFL25nj0+iClUrSmvS8lqpoJSihdeeIGdO3fy4IMPsnPnTm6//XauuOKKOem/piSHIvJ6QIlIBPgTQrXVVBGRJuDfgVcTBBneCDwHfIugLO1h4G1KqX4JJNaXgKuBDHCDUupX0+lfM/+UVs1b05ygpd5jX+cgpgimbaIUeL7P5tOaiNkmJwayHOvP8brTm2lOjLZdNArseyHIw9kQtxARRATbFJoSEXKOx77OAc5eVc8Lp4Z54nAfJwdyfC1msb45wWs3tNCUsLnj+ZdRCi46vYWVddFRBvXBbIGNK5JFO4fvB5HhpSViRYSOlnhxbLOR82ux1EjRzCxKKZ577rmikNi5cycnT54c1Wbnzp0LSnC8n+DBfRrQCfwU+NA0+/0S8GOl1O+HwigB/CXwgFLqcyLySeCTwCeAq4Azw7/XAV8O/2sWMWOr5qXzLsmIWbRlAKTzwUzCMg18pfA8n1TOHSU4ALqGcthGkG4kbpmh7SOC6/kMZAv0pgqcGMry02dPooCC49Mct4nHLI4P5Tjy1DGaEhavPq2RuG1x4ESKizbao1RKv+pOUR8LtkVMIRp7Jap8pERsKufwYlc6tINYM57za65qpGjmH6UUBw4cGDWjOHXqVNV9du7cOTeDo7ZcVT3AO2eqQxFpAC4DbgiPXwAKInItsCNsdhuwk0BwXAvcrpRSwOMi0iQiq5VSJ2ZqTJq5Z2zVvN5UYVzxo6ht0pvKYxnBDGJFMlIM+Btxi+0cyHLgxBARy2BFMoKvFLZpMpApcHwgh698DBF6hwpkXBfbNPGUIh4ziVrB30CmwLGBHHVRm1ef1ojneMWIdAhUSivrouw9MoBtGqOERimur1hZFymqnmYy59d8VznUzA0nT57kT/7kT3jwwQfp6uqa1L5PP/00juNg29VnuTNBLRUANwIfIVAhFdtPI1fV6UA38B8isgXYTVAsqn1EGCilTohIW9j+NIKaICN0hutGCQ4ReS/wXoB169ZNcWiamaSaWmVs1byM49I05oFsipByHCxDiNkGUctgOO+OUxfZpkHENOkezpN3FRdvbOGHvz7OUM5FRCg4HjnXwxRBGVAfsxnOuth1JoYEhZZMA3rTeQayDk1xe1REOsDpK5P88nAfm1eVT42eczwsQzh9ZbKoeprJnF/zXeVQMzc0NTVx7733ksvlJmybSCS45JJL2L59Ozt27GDbtm1zIjSgNlXVd4GvAvcC48N0p9bnBcBHlFJPiMiXCNRSlZAy68YlX1RK3QLcArBt27aKyRmXO3OlIy+nVhnOO9z36xN0p/JkCh7NCZuNK5KcvjLJns5+uocKWKZgGwbRiEGu4JF1PFY3JOgezpEteMSjQXLDUnWRbRogEItYZAp57v5VJ4LQGI+Qd1yGsh4FV2GZkDAgahoUfJ+c45GIWPhKEbVNhnMuvak8K5KRcRHpdVGblmQEHxjIFEZV90vnXSxDOK+jibqYVXSxncmcX/Nd5VAzPXzf5+mnny6qnlauXMm//uu/jmsXi8W4+OKLy6qdkslkUVBs376dCy+8ENu2i7/pHzzTNWd2r1qOmlNK/eMM9tkJdCqlngiX7yYQHKdGVFAishroKmm/tmT/DuD4DI5n2TCXdcTHqlWCWcIQvu9jGxKoh/oyPPliL30Zl1jEQClFTJkUxOfUsIOIcP5pjbQ3RFH4HDyVpilps7IuSn3jK0bohrjN4Z40g1mHvlQeV/m0JKIkYxYNcZuhvAt4RCyTVN4laptYhkG2EAgOQwSUwvMVju+XjUh3XJ/mZIQtHY0MZt1R1f3OWFlXdNHNO15R9TSTOb/mu8qhZnL4vs++ffuKguKhhx6ir6+vuL2trY0vf/nLjPFWBWDHjh3s3LmTuro6LrnkEnbs2MGOHTu44IILxs0o5svuVYvg+JKI3ERgFC+tADglzyal1EkROSoiZyulngOuAJ4N/64HPhf+/164y/eBD4vIXQRG8UFt35g8c6kjH6tWybtecZbgG0bwkA9jN1xPYRuQybs4vk/W9PH84EcQs0wyro/rK+qjNsmoRd716E7lWVEXDTLkFlx6hvOBS27exbYN8CQUFtCYiCAENpKIZeD5inTOoSkRxVXBxDQeCVK7m0Yw20nl3XER6QNZh9ef3spQ1mFtS4K1LYmy5z5W9TRTOb/mokaKZup4nsfevXtHCYqBgYGK7bu6ujhw4ADnnHPOuG033HADV199NVu3bsWyKt8f82n3quVorwHeBbyBV1RVKlyeKh8BvhF6VB0C3kMQxf5tEflDgoqDfxC2vY/AFfcggTvue6bR77KlFh15XzrDz549RUGpaU15S9Uq+dAV9uCpIQzToHc4TzJq4bg+lgguiqzno3yFbRqkcw6egmzBozFu43o+vakCdVGLNU0xXuhK4biKnlSe1mSEF7tSuL5iVWOMF04NYxmCL4FXCgRvZPHwYev7YBrCcN5DSR4Dg3TUxTYNCp5Pc9yiPmpjGgZtDaXeXYFK6eIzWnhgf/ekVU8zkfNrLmqkaCbP7bffzne+8x0efvhhBgcHJ7XvQw89VFZwrF+/nvXr10+4/3zavWp5GvwecHro/TQjKKX2ANvKbBrnhBx6U03X/XfZM5GOfCjrcLBrmGdO+Fx+Vtu0prwjapWRAk0vdA0Tj1pkHRdQ9GUCG4fvK2zLwDQCFU+24AFCImKgUKTzHum8S0syytZ1jSQiNscGshQcn+P9WXIFj4Lns64lSWPC5uRgDssUBjMOWcdHRIhbBk1Jm6N9WVK5AohgGQIKTBPSORfX94mZQsQycZVi0+o6bNMg73ijVEqtddFZTzdfiZmyl+g4kJnlscce4wc/+EFNbRsbG7n00ku5/PLL2bFjB1u2bJlW3/Np96rlTtkLNPGKzUGzCKmmIx+ZFcRsE8OQojpkqlPeWMRkOOewr3MQyzSwLYOoaXBq0MUDBtIF8q5PzvGJRQxiZvDAjloGhhiAAjGwDYhHDExDONaf56x2m42tSY72ZvAVJKMmOdfi18cGcHyf7lSexphNfdyiLmaRjNiIAdmCWywrW3B9BEhGDJJRk+G8i+fB6qYYH9pxBu2NcQ73ZehJ5cuqlGY73XwlZsJeouNAasd1XXbv3l2MobjjjjtoaWkZ12779u185StfKXuM5uZmLrvssqLX03nnnYdpzpwqcT7tXrXc5e3AARH5JaNtHLp07CKimo781HAO11dBHEUZH7ZKU95Kb6/rWxL8fH9XsUCTbRhkHI/eTB5RQs71g9w6gOspMr4LBPU1oqbCUT62EYzFNAK7hK+CYL6meITjZo7BdIFHD/aSiBjEIxauB8rz6ezPYhqwsj6K5/uYIvRng/TsK+qi9KXy+ArSBZ+sW2BVQ5Rt65uJRy1e7EnTn3WJRUzOX1tZEMx2uvlKTCS0oHINEkDHgVTBcRx27dpVFBSPPvooqVSquP3hhx/m2muvHbff9u3bi59bWlqKQmL79u285jWvmdXcUfNp96rlDrlpxnvVzDnVdOQj5VjLGYVHGDvlrfb26qmgJkZzOI1ujFvsOzaEgZDzAhWSIQYRy0epwGDm+QpQDDoeng8RyydimRRMj3jEIGab9KYKtNbFaIjbHOwaxvF8miyL/nSBVMEDQzANsEKvrWTUwvOh4Hr4NqQ9H8TgjLYkqxtiCJDKuRzqzqAENrYmObu9YUG/hVcSWhPNJtY2x3QcSAmFQoFdu3YVjdmPPvoo6XTljMk7d+4sKzjWrFnDrbfeyrZt2zj33HPnLMkgzK/dq5bIcV1CdglQTUde8AL1zVijcCmlU95avDkc16fgBdlte9J5hrIO+TAQzzIMgntdKHgK3/dxPQIJEs54HE/heC45x2VVQwylwPF98o7HUNahrT5KzvHoThWKMRp10Qj1McVgxsHxfXIFj7zrI4ZAKJAa4kEqkOODWdrqo/Sk87TURamPWhzty1Dw/JrfwheKvaCW7+N7e07wutObqx6nnD58oZzjTNDZ2cltt93Gzp07eeyxx8hkMjXv++CDlR+D73nP/PjrzGSc0GSp+M2LyCNKqUtEZJjRAXdCYLMuH0KrWZBU05FnCx6eBds2NBO1yr+9lE55a/HmSERMGuM2nf1ZjvVlqY+Zxeht0wgEhikgKBwPJLBX45fE3VmGYAj0pgsc6knRlLApeArH82lJRhnKFcg4PhFTjUpXYtcZpPIF+jIuAoiviBqCZRt4PvSlC9imkMo7xC2r6MElMC7VSKW38IVkL6jl+8g5LgPpAg0V0qXAeH34QjrHmaCrq4tPfepTNbdva2srqp127NgxewObIjMZJzRZKh5RKXVJ+L9+xnvVzAuVdOQ7zlpJd7pAQ6yyh0bplHfEm6M0X1RpevHGhE3cMvjR0yfpaI5jmgatdZHQXuHTl3ZRyqfg+hiAaYDng6NeqSxmGWBIkL9f+dCXKtBeH2XL2gYOnBzE9RSmYZCIWGTyLgXPxzIMBDAMwTAMDAnOOV1wcTxFvRmsUwiup+gaKtBWH0R/v9ybJh4x+fnz3Vx+FrQ1xIha5ri38EzBZf+JIb79y06UUtTHrFE1P+bDXlCbd02Ew30Z1q2oq9im9OVgseXGyuVyPPHEEzz44INs2rSJt73tbePabNmyhcbGxopus6tWrSoKiR07dnD22WeXDdBbSMyXs0bVo4qIAexTSr16VnrXzDnldOSZgsu9e0/UPOXNFTyUpdjXOTQuvfjeowMc6klTcD0GMw6mwHA+iOjOOT4x2yRuG7iGYJpC3vEQHyCYgUSt4KHuK1CoojBzfEXBUxwbyGIYgk8Q3W2J0FIXIR/OnHylMETwPB9LCGp7GBLYUIQg5ToQsYRMweXkYJbmZIQVdVHaklGyrseh7jSHe7NsWduIbQqHulPc81QnPcN5XuxKA0Gm3vbGeNmaH3NtL6jFu2btigRPH6seZ1D6crDQc2Nls1meeOKJYorxxx9/nHw+8N25+uqrywoO0zS59NJLi+6zq1evLgqJ7du3c9ZZZy14QVGO+XDWqHq3KaV8EdkrIuuUUkfmalCamWUiPfVkp7yGwO6XB4iYgcrpRHeKvOszlC1wrD+LAHkvUEul80Elv4LrYwgM531inkkiarEiGuFoPktbQ5TBrIPreTiewjKEaMTEdRWZQhCg5yvozxT4z93HOKutLgjwEwFxEIRkxCJZ8nbVmwoCDV0FlmEQswI1mC8KUwRQ+ASzDdsQVjfGwRCilkljIkLe8XjiUF/Qzlec3pqksy+LbcLTx4bIOj7Hh3IkIxYr6iLYhsm+zgEu3NBC1B4/U5lNavGuaUnYRG2j5peDhZYbK5vN8otf/KJozH788ccpFMqHlj388MO4rls26voDH/gA11xzDZdffjmvetWrFqWgWAjUMo9ZDTwjIk8CRbcD7Y67sKgkHOpjNo+92DuhnnoyU966iEX3UI6BbIHhnIdPUCcjnXeDhIGWSc71qbMtLEuwfYNC+AAWEQxDMEUwzODtfzAXlIW1DMEnUIdYYmDaikxB4anA3pGwTBzDp70xyrPHh2lJRIhaQt71MY1XHpp5x0MkOE7W8TBEaEhEcDwVxnJ4YTyHjylQn4gQtUzSeYdVYQ4s0xBe7ktTHzV5/RkrGcw4ZByXvpRDOuehBI70pqmL2nT2ZaiL2axpjtM1nGdtS2JO80bV4l2TdXyu3bKGo/25ml4OFkJurJ///Of87Gc/Y+fOnTz55JMVBcVYhoeH2bNnD9u2jY8xvvrqq2d6mMuSWgTHzbM+Cs20qGTEfOKlPp45NsT5axuLD0SorKeupMYqFUhiwCMvdPPU0f7iw9l1PZQIjqcwDcHxfJQSMILUHx6wsj7GUM5BKUXO8XBcn75UkFDQLUDENPAIBITj+bi+jyFBKvW869OcCDLTJqMmhhj81uZV/OSZE+SdIJeVYYAQqJ9MMVi/IknXUI6YZSKGEDFNXM/FNIRI1GbAKxCzLExTUH4QGGiK0BS+ZfdnC4Ai6/i01Ud54nAvXUN5DIFs6IWGGEiYlTeVd9l/fIiYJaxtScxp3qhavWvOX9fM+euo6eVgIeTG+vjHP87u3btrbr9u3bqijWLDhg2zNi5Nda+qVxHUyHhwzPrLgGOzPTBNbVQzYqIUhsDBrhTNici4h4BlCEd709z6yCFaktFxKqyeVJ4f/fokpwazDOdcBnMFnj8xxIs9mWD2YAAZJ7AdEHhAKQU5F6JmIABMMfB9xWC2QMF1gxgN08C2hMGsIm4ZFMIAPwDbNBEBx/NJF9zwAS34StGbzlMftXjipV42tiZxPIWPz1DWoS+tqIvbnLEiyZrmGMcH8xzrz9JSZ7O2OYHrKfrSMJAtAMGMJxIR2utjpAouqZzDme11RcF3tDdDxDRpawiy3vamCyil6Ms4JCPBTMY2hKzjk4xKoPPPFXj2xDCXndk2p3mjJqtqrEUfPtsxAqlUikcffZQ9e/bwiU98omyb7du3VxUcGzZsGGXM1sJi7qg24/gHgnKuY8mE266ZlRFpJkU1I2bnQJaWZIRU3i2qUEYYySOVcz0ieWNc4Nvrz2jhp8908UJXkDjQUz5Pdw7Rl87j+ipwc1WCQqEU+CpYFxXBNIKAjLyjyDtBfij8IKWIWIGB2nEDNVFDPEK24JLKuxhioJSPSDDL8EMzRsyUooG7L50n6/oM5Rwaoza+UqxtSeIpn0TUIhGzyDqKV69uIGmZHB3IopQiETWpjyVZ7cXpTxfoHMgQsyx8BWsa4yQjFi/3ZqiLBjaL5mSElqRNKufyi0M9PH9yGM9XGAh1MYu84wU2kxJP9ahtkck7vNybYUV9lLaGaMVI7pn2dplp75qZjhEYHh7m0UcfLRqzd+3ahed5ALzzne+ko6Nj3D47duzgC1/4QnH59NNPHxWZXUsiQM3sUO1u2qCU2jd2pVJql4hsmLURaSZFNSNmwfWJx0ySUYvO/kxRcIzkprJMgxVRi6FcUPsiaps0Crzck+Yz33+G7qE8jQmbqGXw3KlhBrNOGOENSCAsgtkGEEaA5x0fDzAIqur5oceUD7iOHyQXNIKZiQhkHAeFELdNElGTwayL5wbBepaAhJHg+TBpYcYJDO1dTp5hyyEeMWlORLBMk0LBZ/MZ9Vz/+o0kIkFA3//30+eClO2uTz70uFrVEKMpYYeZcy3ObK8nbpvFokyGCFHL4MCJFG31UWzDoCFmc2Igi2kauBmHZNRiOBdcD08pjPB626bJy30Z3nhuOw/s78bzgnxcuYLLgZNDPPRcN1Hb4Notazh/XRCQN1MBdjPpXTPdGIGhoSEeeeSRYgqP3bt3FwXFWB588EHe+c7x1akvvfRSbrzxRi6//HK2b9/O2rVry+ytmQ+q3ZnlQ4gD4lW2aeaAEdvDYwd7iVnBQ7+jKV6MPwCIWAauHxidMyUV7UZyU9XFTFzPLxYtKi3JemooD0rRn3YYyjn0pwvYpoEbvmF7/kgkKNhmIAS8kjBRn+DmMgxww65HZI6pgs+WQDrvYxpCQzwoqLS+JUHXcI5U3itGkQepQxTpXIGsG9TvUEDO8cm5Pv1HBjANIWabDOYKnL2qnjdsWsXalgQfu/Is7t7dSedAFtsQEqGgefbEMFHL5FUr64oPP9sUmhIR0jmH504OE7EM1rYksEyDhrjF8cEgpYkQeIslohZx20T5ioznoVRQYvb0lUmeOjJAzDLwTBlV5rYhapPOu9y9u5O9R4eI2ELcMhdkgN1kZjFDQ0M8/PDDxRnFr371K3y/toKhlQRHU1MTX/3qV2fsfDQzRzXB8UsR+WOl1L+VrgzrZdRusdLMOKXG8KRtErMNRGRU/EFDzKajKc6h7jSJqEXEfCWHzkhuKqCYn6q02FI0FiHvBinN2+rjmIYUU374fhBvodQrxVncMS+SgbNr8GebgYpppK0pYJiBlBERfD8wnuccn5hlUBezOT6YxTYDu0bctojYBl1DORxPYUvg8VRwFa4iWBZBKUXB8Tjal+VzP3qOjuYkZ7XXs7Ylyfu2n8GBE0M89mIPh3uzHOkLCuxcfHoLvWmHgUxQ78MM1WFdqTymCCvrooHgNYOAwKN9QaAj4uO5PpYJLc0JXM/HlAgtyQhnttVR8PwgjiRq8quXBkaVuQVIxmyyjseezn6aExEuPbO1KOwXWoBdrbOYO++8k/e///2TOvZZZ53Fjh07eMtb5s9BcymlVJlLql2ZPwX+S0TeySuCYhsQIajRoZkHxhrDc2HAWlMiUow/2Ht0kIs2NtPWEONwb5a+dIHNq1/JEFPwgkC8vOMV81Md7c/Qk8pRcH1yrsepwRyeCqKzR9xsDT9QT/lqVFopYHROGjOcZbhh+xEMwJDAiyliB8WTlEB91MRxfZQpDOeC7LS2CANZB8fz6U8VcNzgOGIGeazCRWzLQPnBDEgphWEouoay/OMDL/CZt2ymtS5GpuDxQlealkSUjSvq2H2kHwH604EraXtDlIGsQ7oQzL5s0+D8dU24vsL1fAYcj7qIRXPcYiDnBsIuPHkBVjfEidkGCsG2DZy8S2Pcpmsoh+/7o4TGCI7vM5x1WFkXHZXmZIT5DrAbS19fHw8//DCbNm3i7LPPHre9lpQcmzZtKtonLrvsMtasWTMLI62dpZZSZS6plnLkFPB6EbkcGIkc/6FS6mdzMjJNWcYaw9vrYxzpzZBzPGK2GcYuFIoPozPbk+w5EkQM5x0P2zIwEHpTeUzTYFVDlEdf7OXJQ71BTEPcDiKw/aAGd67o2QRuqGsaUTmVCovSz144vRCC2YAXGkBEAtUVBAkVbRXMRJQCV0HG8TBzga/+YM4hapvYYWEmP5zhuO4r/QiQDVVwYUwfosBTHg8918WffafAG85upzddoKM5zoowgaPj+TTEbOKRwMjdk3K4aOMreboefL6L+qjFUN7lwg0tdA3nOdyTpj4e4eX+YDbUFI8Qi5isb0mQd30UwpntycArjKBMbedAtqxhGQii6k2DuqhF50B2nOCAuQ2wG0tvby8PPfRQ0WtyJ/wAACAASURBVEaxb98+lFJ8+tOf5uabx3von3XWWbS3t3Pq1Knius2bNxeN2ZdddhmrVq2ay1OoymJLqbLQqCU77s+Bn8/BWDQ1sPfYAMM5hxe7UxQ8n4hpsKIuwsnBHDnHIxm1SEZMXupNE7FMbNPko298FcM5t6inbm2I0h8WUzo2kOP4QPAwtEyD4wPZwFXVNhDXD1xh/aCfrOfhlaioKiEE0eW2KTTEbQYyDn6YRkQIgv5s00BQ5ByfrBPUGVd+sC1f8Ci4iogJg1k3iNOQVwTWCIpgFjMifEZWmqGQeub/Z+9NgyS70vO855y75lJZe1Vv1etg6QEawMxgegYYjgYa0CRHojU2NbQYpGiNQ2Hqh2xLYdmW6F/+QUXQEbaohWE6RqFwiENJNEdikGLYtMQYCRgugwEwAzQaQAPorbqqumuvyj3vds7xj3MzK6ururu60Ut1o96I7q7OvPfmycxb5zvn+97vfa/V8aQ1p1puJOwfDKm2Ey4uNwlch4kBWyRXqdq06vddSZTZ9xx4tv4gBBwYKrCvEnJ+qcF6O6XVUnywWOdzh4cZKvl4jsPLJyf43vnlXrd8IdyezhorTeBYk6pqRzG71tqi+TU2EBAl2xeU7zaWl5c3BYqzZ89ue9yNVGKFEHzzm9+k2Wz2AsXExMS9HPLHwm6XVNnt2AulDxFWmjFvXFqj6LuUApcwZwIt1uO8yO3wwXydTqbAGE4fHeWLJ0YYK9uV9tGxEtMrLd6cXuM/nlui4EvKgYcxBt+16rWu4wAmd9hze4VthA0sSbo5bHTrHd3UlRTgOrZ24Uhhdwo5VVcDmdZ4jqQZpzY4uPY8V8recQXfIdWatVzrynOkbfDLNNe9/LZBTGN3Fa4jWWsm+K5DrZ1wdq7KSMknThSrjZh6lFIOXI6OlDat+g8NFXh7tsZzU0ObGGjlPAh8tuAxu9YmyNNai/WEl56Y4Mn9FYq+2+uB6JETnK2yFkoZBgd8GnHK3Lot3Pdrfl1abnF+qcWT+++NxujS0hLf+973ehIe77777o7Oe+2114iiiDDcyp351V/91bs9zHuG3Sap8rBhL3A8JOhurYuBQ9l3cfNit+fYtMiV1TaZ0nz6QAVXCjqZ9a347rllXj5pV37dfG4nURwfK7LcSji/1CBwJQOBy3IjRUqBK2Svg1oDcWrrIrBR1zDYIOHkTq+afPVv7LOZBp1oOkmyaXJXGqSwkcaVIBH4OTMp8ByUsSHIcyRSKKS0NNfrNhs3RLeOoo1NrVk5E5v+6jb3jQ2ERKpNM2eLXatGDBbsr8KhoQKea1+/FDibGGhdaG0YHwh76a3FekToOb2URrcHYrToM1+LGCxuXtVGqWK0HCAwfLTQ5MBgYdMxriMYLPos1iNWmwntJPtY6ZLrC8AOGf/lV54iieNbn9yHZ555hpdeeolWq7Vt4HiYsBskVR5m7AWOhwTdrfWJsTKXllu9iSZVmukVKwmeZNBKFK4UnBgvMVEJacUZf/juPBhBJXQplXwurjQZGwgZHQhpxilxqllvpygN5cChFHoYbVhpJYwUPVLfpdZJ8tSNslpN2CDhCEnoS5pxhuNI0IY4r1xrbHDJ40QPoedgtMYIiVYao62GVOBIioFHwZNcXE4JXIEQAt8RtFOFuUX0sDsXke9cBGXfoRnZBkTfdRgq+rQTxXApYLQUcGm5aeVRgEpoOb5vz9bwHMnPfv4Q0ytt3r9WZ7Bgd2VKG/s+peTZqUEwMLPW4spKmzem1zgxUUZgKAUeUkIzyahFVubE7t5sn4grBV86McorHy6jtGaisrUAG6WKkm/FEu8kXbKwsMDly5d57NRntykAexw7+Swfvv36jT9LIXj22Wd7xewvf/nLjI6O3tYYdjN2g6TKw4wHFjiEEA7wJnDVGPPTQohjwG8DI8CPgF80xiRCiAD4TeBzwCrwV4wx0w9o2A8Mva218Jhe7RCnisBzbP3A5DUDAQu1DkdGyz0nv1Lgcn6pAQb2D9qGs17uXQgqoUfsaNZ0gu/ayU4bQ9H3KPkOqTI0co2pwHVQSiOlYLgY2A5xYbccnSQjU7rXyxE4kObpeSc3aeo+Z7QVLgxdSZRz/UfLPm5eT1lvZ5ah5Ag0Ak9Apq0WlrpJ8BDYtJgy4HmCwYLPtVpEtz4tsDuntVbM3FrHBj4p6CQZ9Y6tuj83NUQpcJheafPyyXFm11u044x6lOK7sheQ41TzxvRar4t+odYhzBtMfDfh5IEBJsohmbKpxPW29Us/OFQgU5o3r6xzrd4hcCQza232D4a91GM3uDxzaMg2Iu4gXXLt2rVefeLVV1/lww8/ZN/+/fza771GwXO2FICf/+KXNgUOIQSf+cxn+MpXvtJjPQ0P39wx8GHGg7RdfRTwIHccfws4B3R5ov8r8GvGmN8WQvyfwF8HfiP/d90Y8ykhxM/lx/2VBzHgB4nu1jrJNGNlnx9cXiNRimYno1J0cR3Rs2t9dspOMt2C6zuzNVKlmKt2KHgOV2sdRgoecab5cKFBtZ0Cxkpz+C7CQCtWFDzH0lOloBB4LNQ6KGVIM8N6O8H3bMW6a8Kk+nNSQiBk7rwnBbEyGG07rKXMdxe+iyMtw6rkuziOpNqO+4rhVlG3rqw3ucCmoro9Ir2X6ntM5WZQgSvItKZbr2lGKZ1Y0U6tTDtA0Xd6gWatndJJFYlSxG3F+aUms2st1toJR4aLHBou9lan/XWPwBN8sFAn9JxeLSlOFR/MNzl9bJjhks/UaMpT+yucvVbjzEwVKQWPTZSt4q/vsFCP+XCxwb5KgcGCy4nxMhMDViNLG7NtuuTq1au9IPHKK69w/vz5LccszM9z7colnnnq5JbnPvOFH+P1P/4ujz3zBf7iT77Mz3/9Jxka+uRMkg/SdvVRwAMJHEKIQ8BfBP4+8N8LK4r/VeDn80P+OfC/YAPH1/OfAf418OtCCGHMrRIXjxZC32G1GfPBQoNMG56YLNOMU96aqbLaiKl1Uk6MlRkp25Xl65fX0VojJNQ6CUpbkUBPSgJH8OaVNartzNJYsRpMWkA71aRaE7guqTJMDnqst4QtThtQ2kqJxJkmysDLnfrMJstXcLCbESMgVjbN4woIfZkHhVzJVoJSVsZiIHBJM4ObB6tGpKxfOLaWA8Kq2Oav2SUcifyPxu50Qs+xXeRtKwniCNv70Vaprf+kCqNtcHQkDBc9BILLyy0uLDWRQnBsvEg7URwZLnJuvsF8LeoZNfXXPZYbsa0ZjW9MMNdTotdaCZeXm7x/tYErJQOhi+dKfE/iuw7Hx8tEqSJTms8eHt6UPummS2ZnZ3n11Vd7geLChQs7um8un31j28Dx7Okv8X/8zr8nThWx1p+ooAEP1nb1UcCD+lT+IfA/AV3KyChQNcZ0WfpzwMH854PALIAxJhNC1PLjV+7fcB88jo4U+a3XrjBSCnqF2oLvcmTUFrSNgZWmldvodoA7nstHi01cIfF9QSX06SSKy8tNWolCGYMrJKXAI04VGEHgSTqJIs1SYjdDrBsmKgEfLTYB6xcuhEClNi2VaRs8+qE0RLqrVZX3amD/0rHGd6y7n4VGCkGiNK04I840k2WfTGlbI8mZVgZQ2j6GhoxuTcNawTpSYLTBcXK/DylpJhlKaVpqo/Zh8vK+xvT8z2udjPGBgLVWzFApoBJ6LFRj9g3CoZFhrtVitNE9o6b+zvvFWkTBcxkqbNScqu2U5UbEpZUmR0dKVDsJnUQxWQkZGwh6rKlaJ6XtKvYNFgg9h2qqtohRVjsp3/3Nf8Bf/o1/fFv3i+M4HH3yFAMDN2dlfZILwA/KdvVRwH3/ZIQQPw0sGWN+KIR4qfvwNoeaHTzXf91fAn4JrC7/owYDvXRNP0bKPou1CN+xwWS9naC1IQh9VpoR2mgKgX1Oac1aO2KtnRIlGo3tjFaOnUAcKXr0UaMNrpQkyrDatCv10HOJMk2c2p2KAz0F2+6q3/T9QW/9ojJjm/g8oXFdQZyCIwyJUlZuXcByM0FIQSlw6CS2KJ5kdsr3+rSvbDAB1zE98cVMgSsNq42IVOVjE9Z6NnQkrrSdHzJXZxRYG9pOklHwAqJUMVYO6KQZUaoJXIdnpwY5M1tjrZ0wt94mzqwd7UojYrUVM1T0+GCxjtHQTjNCz6XgSaSCq7UOqbLaX1MjRYQQPU0sTwo+XGwSuJLVhau0ZIlLK01O7qtwcLhAObDsuS+d/iz/12/c/P5wXZfnn3++JzH+4osv8kcXagRS3vS8T3oB+EHYrj4KeBAh9UvAXxJC/AWskGIFuwMZEkK4+a7jEHAtP34OmALmhBAuMAisXX9RY8y3gG8BPP/8849cGuvKWpvTx0f4YL5JJ93QVir7Llcyu/p/fF+Zi0stTubyIqvNxB4T2trIzFqbWjvJVc5t74Y2dqVfyHtDupPVSiPqsZQCTzJgbEe5JwUxOeU17whP1Ebg6KL7BXj5Sl9dx6xKDejMELj2zFTZ+ozvOlYSRdjax+GRkGvVGK2MDVZSIITppcZszwYMFRwCT9JOMpqRzWG5jk2hOcKq9GbaIKQhzazSrswDSsl3rfyJMaRKU48SFusRjSjl+5dWODRU4NmpCsv1mCvrbau0qxRRoikGDr7n4Ai4Wu+gjSFONW7Z79ngVkKPlUbM3HqHx337va0tzHHp7OuceePP+L1336SzvsiP/63/nQOnXgTgTM7u+qWvHKew76tb7gfXdTl9+nSP9fTiiy9SLpc3HfPkpN4rAO/hnuC+Bw5jzC8DvwyQ7zj+B2PMLwghvgN8A8us+mvA7+en/Nv8/9/Pn/8Pn7T6Btji+Hg54PQxq4E0V+30tJW+dGKUhXqMUoZWkqGNZqEWcXG5ietIJsoBUZJtpI2MLYRLYXsmXMf2cYyWg55goBCQZrazO9F5kTpKexLn3b4NV+Yig3nvRn9PhxDgucLSc6/7xrrNgmAlSbpPB65AKYMUtiFwqZHgOIIh30Xn7oEGK5ToCBvUokThuhKQGCMoePTUcpuxDSJSawQ2aKicSyx0Xqj3BJ1E0EwUxijacWaFCo3h8nKLhartxfjskSEGCh5P7hvgt167wmQlpOi7LNYjYm2DUcl3yZThWjWiErp56i/Day9z7sxbvD59lqvnfkh1eX7Ldzz/wQ+ZeOoFEPDc1CDl0OWtmSr/6bP7OXnyJKOjoz0JjxdeeIFS6eaF270C8B7uFXZTEu/vAr8thPgV4C2gq6f8z4BvCyEuYHcaP/eAxvdA0c87nxopbdE2Oj6umFtr8+Finbdma/hSMFLyGCwEdhWeKAJHUvAdokQhpSDTtoFPImw9oN5hqODjORJPOlTjCN+z2laJ1vnEvSEwmFttkGIL31LYYnhXTdvkTXiZZlsYY9NnXcV3YSDOrEJplFqjpzg1+J4k0io/3uA5AmMEmbLKtUNFj3as2DcUWNdBKegkCmWsr0aqdO5vbgOn1cyybC8hIM5MLhGSUPAk4PRSSZ4raSWKRmT1wE4fG96UNhwqeiw3IqrttMfWMsbQXp5j6cpZGpfOMP/hj2ivL93yO167cIYfPzm56btdrFvZi7Nnz+I4t5dS2isA7+Fe4YHeMcaYV4BX8p8vAae3OSYCfva+DmwX4la888B18DzJ45MVMmWYqISsNCMWalHPnKgeZ7STDAR5/UCjNAS5H0ejndnVdqZ7k77GECcZIq8PRGr7KJBdz5GlW4PYKG5f/1yWixL2p7hSZXcBviMZLLhcrca5QZKgGLjEmW0KUXmuKkoVrShDOtbV0GDpwnGqiFKFFKInzqgMFD0wUmwEECBTdjcSZYb9QwGVMCBVikrRx5USN5BEaca7V2s4QvPdc4vUOikXlpsUfYeJUkCUZigt6azO89o/+W+Ja7fH3XBcj6BYZrS0WQbj48pe7BWA93AvsHfXPCTYSdqh1k45OlLiWq1DlCqGCj5z6xHLjRjPkbiOxHckkdHEmV3BGwOd1Ob3tTGIjN4OYLBgjZ5iBVppS6Pd4Xg1UHAl5dBlvZ3ceNdx/Xl570amNMuNzQJ/aWZTTZk2ede6DXqZMmgN1XZKpnVPN6u/IbCLKLWBTErb9OZ7Tu+coi9xpYMxtoZTyovGSaZYqEW0k4xzCw0KnktIQlAo0EoUC80YZaDkSQb2H0RF7Vt+Pq7nM3biFBOPfYZjpz7PU889z+dOTFAMNgeOu8F62isA7+FuYy9w7CLcylTm+rSDxnBtvcNHy02MlZ7liYkBntg3wIcLDZqRQhkbFKI0I4vtROoIS8NVZHSy7cfiCCs8KBE2naS2Cgxej65SbRedTNNpJrf1GShtKa1S5LUO7E5BKIOUygYJNjSyegKLxp7XDRKutGPRevO4eoZSAFKQGcNo0WelGVtKbDuBIhwYCi1NOFNcWW3RXpph9cLbXLjwFvXL7zB+4il++u/8QwqeDWT7KyFLjZhnp4Z459Tn+PDNP970vrwg5ODjz3Dq8y/w6c98Ef/A43y41EFKyUuPj3NopNiTde/HJ531tIfdib3A8YDRDRZvXFnb6CoeL3NwuIBAbDGV6aYd3ryyztszVRwBj0+UOThU4JWPljgzV6PaTghdyXwtptqJiTJNmtm0jZTQyaDgCuI8aFyfSrI+GtBJFY6wtQBXShrJzSPHTncjt4IUEmOsN60QIPJqu9LgOICyAcFgd0uwQQnu7jYsTRdkzqzql2R3sDWjQ8MFAlcSp4bAt82AlYLPk/vKfPjBB8y89wYrH71N9fIZ0mZ10xgXPnwbrRWe65DEKb7rIYClRsSJZ05z8Z03ePyZz/Hs518knHqawSMnKYQhL5wYQWJNqoYHIkq+y/HxzWyofuxW1lP/IqfeTmnEWU+na7Do7bnoPeLY+1YfILoOZJ044/xSg5Gij+tI5tY7XKvFPDs12BMq7JrKgM3rv3ethusIBkIX35WsthMu5k1lrdimNgLPIVM5g8oReNKhHLjUOpHVpIKeBEh3Iha570WqQafWJ1sZa4V6v5DmEiN50zgFT+Z0XbNFbgQgdCHJtroSgi1+4wBZLncCDBQ9QscGjGcOVagELq++/iOuvPsmly6+zfcunyVqrN90jEm7wbWLH3LosU/bLvVOymOTZS6ttPjUS3+Zp3/i5/mxJyeRCJYaMdVOymjZZ6WZ0IpTDIKi73JxuUWmDEdGi1sE93Yr66nfOU8KuLzSIs7UJp2uPRe9Rxt7geMBod+BrBVb/23Hkay3U1ZbMVGiuLjc5MuPjXJoyPpavz27zuxaxOxqCwE9T+x3Zqu8c7VuqZ+pIvRcOknGQjWyq3Rp6akZKa04w2hD2seM0n0xoUt07havG5Etpl9vonSv0C+G2H3NLNUEckNWBDZqGEJ0pdrteY7Ii+7kj+XKu902OEtMMnieRBnN9//VP+bNP/pd2vXNO4pbwfFDzp0/jxk9wvhAgNKGgucwGHocnRxgtOz3bHC/cHyEo2Ml2oniu+eWCBzJYMHDdyWVgsvrl9Y4v9zgC0dHGCkHu5r11H/fur7DG9NreI5kILS1mX6drkyZPRe9RxR73+YDQr8D2TtzVaQQnF9soA0EnqRS8FjvJPzJhVWMWWHAd/hXr0ccGSmy2oop+m5PivzDpSatJKXWsbTSVm7zqjSQGy3Feb3AkX0SIDtAl3L7oNFfX3EEOE5eqc/rNY5jVX/1pi2JyKVGrLe5ypsVnVQTutbNsFlr7ShoSL9A6fDTDB1/hgNPfpb9n3qKWMNyI2ax3iHwXFJtODFa4hdfONwTPOziRlal4+WQH//0JFdWWrw1V+XkvgqVorct6+lWNbCdHvNx0H/fzqy1tniVXK/Tteei92hiL3A8IPQ7kLWSjMVahOc6vV6AJFPU2ylaG0YHfN5fqKO0FeRbbyWEnmR2tc21aodWklEKPOpZgjGGOKcwdefP7gqcfOcR30yb/B5hO+vX2zm3WwSHvP8j7ySXWAl1YwSBZ6VJlMpQK9O0rr5HbfodWlfe5ch//Rs4peHcR0Sz3krxXUE68cS2r+kERSpHT+FPPUVh6hThgU/he55V1NWGa/WEZmwl6D1HMlyUjJUDKgWvZ57Vn6K5lVVpwbeS6ivNmEpxqzNdf3pow1dDb0oJAbc85lZpo1sFnv77tl+zqx+BKzkzV7NNqnHGe/M1/uoXjuzVPB4h7H2LDwj9DmRRosgMFPOgobTVNvIcSYqm3rHif2XfpVLwWWunLNZipBSkGjKVW7MaSLXGcxxSpXuSILAhMhinD2b78HFSXd16h87fjhU/tN7gjrAdh8nCJRrT71C/fIb2zHvouLXpGsncuwRPfBlP2omtnWg6mSE4+BRgA0X5yCkmnvws448/h6ocYT22TZMm7/aTwlJ+08yyvoQEYQyx0Sw1E+Rigy8eHyF05ZYUzY2sSutRypnZGlprhkKPdqIIpNw02Rd9Z9vdSuA5THjOtmZd2x1zq7TRToJT/32bKE14XV2mk2RcWmnRThT7KiGjRZ+1drJX83jEsBc4HhA2OZAJsVFcgJ5siOM4pKldWdvCtS39DhetKxxAJ1HEmWa9HZNktr9BaN1bjXcLxnnWCsXuwXaNgTc6rht4HAkqU8QLF6jNvUsy+y7t2ffQ8c17J5pXzjLw1J9DYtNVgWv7WoLiMM/97X+KPzrFUCmkFWUorMZUQUExcGjHGe1EEaWarEsiMAZfWGVegfVYb8UZ716t8+KJUbJYb0rRbGdVGmeqp2QchD7GWFmX6yf7T02Ubrpb2c6sa7tjbpY2ulEq7fqxSEHvvvUd66nu5Z7qWe5GKYFKaDXPUqUp+u4Wksd2wetep9n2cPew9208IPR3ghc86z+RZFZ7qtHJrNhfpjDaEGvbCV03grn1Nq4UNDopOvfn1kbTSazGVKYBx2CMwHUFaWZNkTD3r8B9M/QHC8mtA5kr6dUt1NJFll79TaK599FJ57ZeN1m8SOA5hI4gU1ZWnbyr3B87YvtijH3M5A2IniuIU7sblAIitVFkBzsu3xUErsNA6BKlipVmzFI9YmIg3NTxvZ1V6VI9QmtNENqJOtMG39l4he5k//2LKxwbvTFlF+zORZjthKQ3cLMu9Ful0rpjGQxdap2UCc/h4HCBS8sthnIb4/VOknvGW+IGWGbYiZxufLPgtZPdzv3YqewFr51h75O4T7j+hpQCFhsxrmNpmYdGilxd79CKFYnSeMZapVY7VgNppGQlvzt5PaQZq1ygz8knYIMxG2qxXl/XW7YLAkYXoyUPIaAdKzrd5fvNYMAVkBgw0qN96Yc7eh2/VKFy9BTFI89QOnqKyv4TjAxYK9fQt41+bi4jn2a2c9xzJKk0dBJrj+tJQTVWuI5AC4HE4LkC2aX55u4eBU/iSIkjNWvNmLlqh4PDxU0d39tJxsxVN9cI+ifZLoYKHm/NVnlissLNoHewKrhZF/qNUmnXj6WepDiOpBVnTA6EzKxaDa/Qc1hrJrngpWCo4BOlClcKJgaCTde4PnjtdLdzr9lZuyV4PQzYCxz3ATe6IWtBxuuX16kUrKz3iYkya62EuWrH+lRIu+UvhR5RkpEqw3orITPg5ukBmU+qEoHjCHSmUeQS4mJXEKJ6kFgpD4NV3ZXCugSqNCWaP088+y7RzFnCo59h8As/A9jY120h8cemcIqDqHZty7W90iCHn/oczz7/Ikef/jzzzigrzbQXGAYLPvuGQhZqEZ4UVha+SxrIM4Vhnn6pRQlaGZzcy8IRVlDRvgfRtVlHG9FLH6ZK4zuSuWrb1p6UZmLACkwWfXeLZEycKhaqEXGmbPrLwGDoMFjY/CvpuRKB2bJb2fLZSnHLHcfNutC3S6Vt+YxdiYng5aesgkGmNE/uG+D9hQbNKGW9lVAOXQ6PFmj2+6b3jXu74LXT3c69ZGftluD1sGDvE7jHuNkNeWKszEjR4/JKi1rHcvcHix4vPznJdN5UFSvNtfUO9SgjcCW1Tmqd7qRlErlSEno2vTJc9ri6HvWkzbtRY6e1hHsNKWyzYaMV0Zr7gGjmLPHMu8RXz2GyeONAozYCRz5wCfieQ+nIKern/gRZHKQ49TTlY88wdPw5JqaOkSE4dmKMp/ZXeExpzs7WqEcptU6KBpbqce+aBd+h2koIfYeRwCfOtA1sucR8qmxwKwYSrenpWxkMLoJEG6QQFH1JJ9UIsVEoHgg9klQTJZo/ODPfW6l2JWMW6k1mVlustxNCz8EYjcDqZr0zV+tZ1IKd7A+PlnrpoRuhEnq3/JJv1oW+XSrtenQDz/XCicfHSjTijHqUUgxcPCk5NFrs+aZvd41+7HS383HEHm+F3RC8HibsBY57jFvdkMPFgGTI8GOfGmN2PSJTmoIvefdqDaMNK82ERpThO1DwHKotK9qXpDbxnypNIZCEnsN6M+01woHtd+jiATBwezBZSjz/IWruXRbn3qU1c12guA7x1Q8wWYpw7WQigNCzMiSDL/4cAy/+PJUDRwldh0wpPClJjbWfvbrWwZMSKQWh71CPM4aLPuvthHaSUfCtUZTEpqmkgGenhnhrttozkrJuiAYpBeMDAVqDNg3aiRVTxLEmUIFng0qcKUZLQZ7ucqiEHqHncHJ/hUxvNMHZ4DHOt79/BSFhuOxTbSZMjZYYKni4jiRKVc+iNvAcqp2UL50Y5fxS66YCl5ODIRhxx94bt1Jfhs2BZzvhxPev1ew1KuG258ep4tx8ndCX/MsfXOnVD+rtlINDhRu+Ltx7i9vdELweJuwFjnuMnd6QC4140yquGDhcXmpRCh2k9GlGGdVO0pMvd/KisTLQjjUdtM23BxITW7E/IaxOUzdm3O+dR+2179C5/BbJtQ8w2c7FDk2WEC+cJzz0aQA8YTu+2zHIsaP4ArQytFWGlBKNRiVQDlxW2wkHVYHhwLfy8gMBV6sdCp5Ea81yPSJRNmAcGS1xaLiAFIIT4yXOLzZsD4yx9Q5rWqXwHMljhygQ2wAAIABJREFUExUur7TRaIQRONJ6qhsDnusgpaCTZIwNBBQ82UvRBLBppbpUj5kcCDl1cIg4U7x+eR3fsWZawCbv8ZGSlaA5PFokShW/f+YaUaIZLLocHSkyVPLpJBrXkXztaStHc6feG3fD9Olm16h3Ul6fXiNOFV9+fIyBwOvVD84t1AldydjA9gEH7r3Y405TdZ9Uf/brsRc47jFu54bsX8VdXrGKt83YSnrHeU1A9nYRAilMbu+aW7FqaEQazwFMrgorc1ruDurQdxudC28QX31/R8c65RGCqVOEh09ROHwKb/hAT1ZESogSq1/l5iv9TBvbBGg0noGOUkxWQqQUzK632TdYwEsVx8fLHBwK+d6FFVqJouA7fGZyAJUHh9BzmKj4JEqhNERpRqXgErourSQBJKPlgFo7JjO6J+te8qzZU62VIjC0ooxS6PLykxM8MVnZlKLpX6n2LyT6/cz77YADV/LetRqfPzbKZw4P8d1zyyil+cLREartlCurbd6ZqxF4Ll//zH6emxruBYQ79d64G6ZPN7pGM0r54wsrBK7Dlx8ft2k1NuoHR9pFXp9e4+UnJ2+YKrvXYo+3k6rbw17guOe4kxtypRnz4XyDajtjdMCnUvBoOwqlDL7WJKnBcWzBVgjsXsOYDelwDSMln0ackXTVY+/ie9JpRHztQ+KZs6QrM4z9Z7/cKxL3Izj89A0Dh1MeJTx8iuDwKcKpp3GHD2y6Rr9yiLqOBtu7hiN6ciqea+nIgWMFB8HuQKZXW9TaGS4C35VobQg8h+PjZQJXMrve4YP5JgVf8uceH6cZZXiO6Emsv3+txoeLdZLUEhsElsHUThSLtYhUaYZLPmOlgE8frDBc9Ld82P0r1esXEpXQ4/Sx4U12wJ4j2DcU8vLJcb57bnlTfaxS8Dk8alf9rThjdi3iuamN1/o43ht3w/Rpu2usthIODhU4ub+yrXT8kbES55eaXFlt8/i+gS3P3w+xx9tN1X3SsRc47jFu94bsFtPLgUuaGYzecKpLMbhSkqBQuSWrlCCN2WAIsdFL2NcS8LF2GzqNiK9+QDxzlmj2LPG1j0BvGHlk1QW84f1bzgunTlH//u/YsQyM2UAxdYrw8NO4Q/u3DTY3gszFC7UBV1vqsVXytcGz6ElasaLYlyKJM8WPrqxboUbsueXA4cpam6vViOPjJZ6bGuL16TVOjJXZNxjy7rUaZ2arrLdT67uuDJkyHB8vM1zywUAjSllqxNQ6Cb52GCuHnD42jO84XFpuMb3a4dmpwd7Kun9hsN1CInA32wHHqSLWmqV6/LEKtnfSk3A3TJ+uv8bvvjVHIOW2QQPs+z99fIS3Z2oMFr0HYnF7L/zZH+WekId79A8BbveGfHtmnbeurPHetXqPVWWpoQKlbWHckfQKu93ucLApKyFsF/N6x3Lq76SuoZOI+Oo5GyRmzhLPn98UKK5HNHN228ARHDzJ6Nf+O4LDz+AOTt5WoIDNNRkhrFkTWD2qrgQ8xgbPTqLRJsNtwnilQKo0712rc60aMVBw8RwHtKYYeKTKEGcZ0yttGp2EYuDywUKdf/feAo04w3ckZd9BG2jFMVlmSFLr6Z5qjSMkgSs5uX+QVpwRp4p2oiiWPQaLPnFqO8JPHxsmcJ1NC4PbWUh8nILtbupJ2Em6drQU8OT+gd77vt8Wt3fbn303ff73AnuB4x7jdm7IjxYb/NofnccYGCp61DvgSsF8O0Jp2yVuMHjSZv8zo61JETb33y3oNmOFVnaHslMky9O03n/F0mMXzoPeuThJPHuWgWd/Ysvj0g8pP7P18Z2if/T96ri9YJIHDZmrAVdcSStRlJOUKytNZtfbeI7AFRJhDIOloNdj0YwyVhoxc+twcKjMpZUGI6WAwaLfcwxcrcdEqUIbuLTS4vmjI5a91k5YacZU2wlDRZ9mnLFQixgrh6RKU48yZtdarDQihks+o+WAiYqdJG5nIfH2bPWOCrYPoifhZqvrnaZrB4veA7W4vVv+7J+EnpCHc9QPGXZyQ640I7716iUcCSOlgE6qmFlro7RVXzVGo4xVRupkGmHo0UZTY4UOo1TjSoHnQHybolTJ4kXqr/3rHR/vDk72itnhkVO3+YncPbhSIoTVSeokioGCS62TcWl5CYOgHDiEnqQUeqRKsVRNehLzytjP7Ny1dXS+pam2rGyGbfpTJJn9WQhYbyccGCrSSRTl0MMYWG/FDBZd4lSzUOuw3IwhbyZcbccMFn2KgbNJMXenC4k7Ldje756EW62up4ZDplfad1w/uJ8pnztJ1V0/vrVWTJRoTu7fvtv/UegJ2Qsc9wm3uiFfu7BKpjTl0CNWmoV6B5EHBJHnZDJlelpAAGSamD7fbSDNU1j90HGLaO594pmzFB57gfDQyS2vH07dfPJ3h/YTTD1tA8Xhp3ErE7f1/u8GnJxVprXVuPIcQaYt28qRVlVYCkE58Ki1ExwpEEJS72RIaZ34BFZKRGBrQ61EkWUQelYmPPScvKYkaEQpSZ4m9B3BSiPmwFCRzGgCHIQUxKn9XkYGXC4sN2nFGY4UVAoew0WPL31qlMDdvMrc6cr2Tgu297MnYSer64srrTvuMdntKZ/txvfBQp0k07wxvbapmbMfD3tPyH0PHEKIKeA3gX3YjMC3jDH/SAgxAvzfwFFgGvgvjDHrws6a/wj4C0Ab+KYx5kf3e9x3G9evUv7gzDUmBwIGQ49LK01qnZTAd3FdTb2TApY11I+uz0b/owZIO81eoIhmz5IsXrJ8XAAhtg0c7uAEzuAkqrZo/z98IC9mP004dQq3MnZPPofbgTKbg6QjAS1QGIq+Q8F3EQjGBnyqnYQ4tfTbeifl6nqK5zr4eVeklaA3KGVvwk5q8KQlGeQq6j3ZFq0NGVCPM+JM4QqJxjrSKm1Yqlvv8IHA5cBgAWUs66oVK+JUE7jOllXmTla2d1qwvZ89CTvb3WQcmygyuxbdVv1gt6d8bjQ+Y2CsFBBlelMzZz8e9p6QB7HjyIC/Y4z5kRBiAPihEOKPgG8C3zXG/KoQ4u8Bfw/4u8DXgMfyP18AfiP/96HFdquUVGnW2wkCwVozAWNXxZ3ENqBpA57SJHproNBRk3j2PaKZd4hm37WB4gb1jWjm7A3HNfSln0c4LsHU07gDo3fzLd8R+jdO272bKDX4LpQ8h9FSyEDBJdOaSuhT9u3KFOwvaaetrPuhBGMMnVSTZmpTR73SoIy2Zlpdna8NSxMcIWyQCFy7G0Gw1koQQrDUiAhcByOg7Ls4AkbKwaaJo+BL/sMHi5tSLkdHihjgylp72zTMnRRs72dPwo4bXOvxbdcPdrsMyI3G57u2z6i/mXNqpLjpmIe9J+S+Bw5jzDwwn//cEEKcAw4CXwdeyg/758Ar2MDxdeA3jV1uvyaEGBJC7M+v89BhpRnx7e/PsNqMEQJ8R3JwuEDoOniuY2sYicLzBK04JVGaTGmSzGwUiFVG59KbRDNniWbOkixdZqfcqWThAjpuI4PilufKp16+e2/0LqBffr37/+6fbiOg9YKwciNrzYSB0N7Sw2WfepTaHYq2opDWSdDQTm0BSEqBVKbnU94NFNoYXGGva7B+E5kyFFzrL6G0TXGttxIcAeMDAVIIpBRUWwlrzYSRUsDEQEgzzlhqxAwWPd6eqVHvJBwbLTNQdlltxvzWa1cwBk4fH2G8HGybhrndCfd+9iTcaYPrTrDbZUBuNL5DQ1ZufrDoUwpc5tbbWwLHw94T8kBrHEKIo8BngB8Ak91gYIyZF0J0k+gHgdm+0+byxzYFDiHELwG/BHD48OF7Ou47xUoz5tvfv8KVlTaTg2FP1vvScgtHCtaaMfuHighpue2NKCbJbNDohwGW/+B/w6TRjl/bGz1McNimnZAP10qnj1CFBEZKXs/YKkoyMm1wpULn6bg4U/iO5MBggVaqrNptbh9oMGhtMHnvi+/kir3dvhhsikobg0RgBBhtU2GTgyGtJGO1FVPyHVLl5raxPq04Y1MmMecOl/ImRFZt4BotW+G/OFV8sGCZXAI4O1fnyGiBpXpMomzd5lq10/Mvv50J9170JNwI93J3s9tlQG40volKyPRqhzhV+LnbZD/uR0PjvcYDCxxCiDLwb4C/bYyp34Tjv90TW5bXxphvAd8CeP7553eDGOwmdPOhq62YfYNhT5vIc4Q1whGG71/oUA5SvLTJxTM/Im3VGXjuJ+zKGLvjMGDTSQdPEk2/dcPX88aOEB5+2jKfpp7GKT28q5t+dG+TdqKscVVe9Mi0xsmptpeWmuwfLnDq0BBX1lpEcUbNJLRyHxTPERQ8j0hlpJntj/GEzaEa7Jyv8h9UXu84OVHixU+NY4xheqXFSMnnwnLD+nt4dvJs5VpVJd+aOlU7CaOlgJVcd8oA+wdDZtZavDNX41qtQyX0KPku89U2tXbCoZEioWf9x6dXWnz7+1f4xReO3lYBeLsUlzaGa9UO55eaaAPPHR5ieqX1sZlJ93J3s9tlQG40vn4pmUYUE7gO2pj72tB4r/FARi6E8LBB418YY343f3ixm4ISQuwHlvLH54A+UQUOAdfu32jvDrr5UIHAvY721KyuMnP2DZZf/1PeePdNGvOXAZCFAQae+08Am3/vL4SHh09tChze+NG+YvbTOMWHk61xM3RZVa0kI8ls4VpKgTaGOFMUPBchoJVmtKKU2fU2WoPnChzpMDYgre9FwSP0XK6stXF8QyfJiPNFa7+0iRA2JWYMxJmmk1jGVDWyKTFHSh6bKFMpeGRKc36xgZTW1Cn0YLWZMFiwfR7Wm8NSrIUQ1DspQwUPZeDcQh0pIPQzW1/BBrf9gyGrrfiOCsD9Ka43rqxxZqaKlILHJsocHC4gEHfMTOondtTbKecW6hxpFzkyVtrSHf5xVte7XQbkZuPrSsmcm68TeA4rzfi+NjTeazwIVpUA/hlwzhjzD/qe+rfAXwN+Nf/39/se/2+EEL+NLYrXHsb6Rjcf6ruS9dVlZt7/ERffeZ2L7/yApZmL256jOw06i9OEk8dQbE7ZFI4/j2qt22Bx6KlHMlBcD5UXOAymt/OQGFxH4rl2wk2V7exuxYqiryn5DgXPJfI1B4YKVNspIu/LCF1BlGqbugJCFxAbhkilwEUZez2JYLER4QhJ0XN4PC9gz61bC1vXkRwcKXBuvkEjSpHC7mSszL0hziN/wXcJPYe5ahtXOrSjFD9v3FysW1n97m7UkdbPvOvlfbt5/H4Dqa88PrElbXUnzKTriR0HhwqEruT16TXOLzU5fXyE0VJwV1bX9zPldie41fgyZZgaKT3UjX43woN4N18CfhE4K4R4O3/sf8YGjN8RQvx1YAb42fy5/xdLxb2ApeP+V/d3uB8fi4uL/Mf/5/eYPvsGP/zBnzF/5cKOz01m36W8/wSZ2pwn9SePMzL5N+72UHcVtpNLyU35KAUOvuMQZZqB0KHoW9pnM8oo+naCXmlG6FJAKfQ4eWCQhVqE7ygcR6By741GlOFISSBskVxIK78uhKCTZmgDx8cKVAoeRhtefGIMjabi26LoQi0iShWJUpybb1DrJD0yA8BSI2a47DMYujRi1TN78qREGUMjsrsMIewOp9pJe6t/pQ2+Kz9WAfhuMpNuRD8dGwh5+clJrqy2eXumxpP7Bxgseh97dX23ZUDuNnb7+O4lHgSr6k+4sVjrFlpPzqb6m/d0UPcYv/Irv8Kv//qv7/wEIRmeeozSkVOMfuoUxpUkSlsToU8IJPQih+h7rBS4tBPb0a20xpEgjCBwJEHRJ0o1KjdXKvoOf/7Jcc4vNnu1DQOst1JGSx5HRooMlTwuLTVxhEAZ2y2eKoPnWAmXou+gtGG5HtFKFC+fnGCpHvUMi545NMSfXlzhrZl1PCntytNAI8oo+g6PTVYYL3v84PIqzx4a7r2/kbLPYs1KybiuIM4UI0Wf1WbcCxzNOOPEeOljFYDvJjPpZkEo8Bwe37cRMO4Wy+luyYDcK+z28d0rPJrv6j7j6tWrvPLKK3zta19jZGSk93g3F+wc/PTNLyAk5YOPMXD0FGOPPUfx0NOUKxVrOCTtJJhmEqX1rrCAvZfwchMOV4peak4AibIsqExpKzGiDUJotIL1jpUJcaVg30DAeCXk0/srpFozPhDyxxdWENgV4qHhAq0kQylbt3hsosz8eodMaaJME7oOQ0UnDySGSsEjcB0GQpcsZ1f1pygCT9JOMgZDF40gzRtDhksepw4N4QhBtZORKMNaM+HgSE4XLvis1GMMVrRRCslA6BErSxWOU4UjJROV8GMVgO8mM+lB0WPvhmLvvcRuH9+9wF7guAPMzs7y6quv8sorr/Dqq69y4YJNPX3nO9/hG9/4BrCRC661E7LxJzdfQEgK+z9F+egzDB57htKRp5FBieGiz/HxEsvNmKV6hADqkQKT9yFktuP5UQ0eAmtIJXPxQlcKW59IFalSCAm+J1GxIjEGX0gcYYvjnUShjKHgOyDsan1qpMCZ2RoTpYC1doIrJcYYPOlw6sAAS42I+WrEaMlnuREzVPIYLvj4jmSg4FLybQEcrINd4IpeSuflkxP84bvzvDdX46OFBr4rcaUk8KzkyYnxcm+12UkV+yoBC42YUuj2DJsODhdYbMY0opRjY5bnLxFU2wmOlDw7NUjgOizWozsuAN9NZtJup8fu4f5hL3DsADMzM70g8corr3Dp0qVtj3vllVf4xje+0csFCywF0i8PcewLP0F5dBI1eRIx+TiJDHGkQGtIJIjU9iEs1CIyrUmVphUrXAmdVPcc/B7VoAGbLW5dIXClzHcHOpcYkXm6zvpxRIldnQeeJHQlnrDF5EYnwwznXfVaM1EJqXYy0kwjpA3CriM5MFSk6LscHi7ypxdXODZWohRsrKiV1tSihGorpRVnPLmvzHc/WNooxhrBSivOnRlFr4FQmM252HLg0ogcPMfhxHipZ9jku5Kf/PQk5+brtBNFtZ2yfyjkxHiJiUrY07j6OAXgu8lM2u302D3cP+wFjm0wPT29KVBMT0/v6LxXX33Vnp/ngqNMkWlbAP2Jv/n3caTk/GKda7UIlSiMNghpJx1tDAv1mOGSYazk4zuCutLEKbgOKAniEa9xOKLbPwFCChwpMJkhM5YBJYVl0mQ5NVljg0OUaJJMU/AcEqWpOIJ9lYDlRkwpcHEdybHxIpeX2zQ7KYdHihhjrBmW0qTK8NhkmTjVNOOU0HNQygbxONM4juDAYMi+wQIXl5r8mx/NkaSGsbLP/sGC9YB37I4DIMksC+qxyQFcR+JIYW1qfclIKegZNnVxcKi4yY+7HHikmWaxHn3sAuvdZCY9KHrso2yI9LBi71O/Du+99x5PP/30bZ3jui6f//zn+epXv4rWmjen15heafGnF1dRRtOKMiYrBUZKPu0kw2hN6EmUhsCV+SrO6tskqWK1GVu2j2O7ox1pV+ACaCWPVp2j6ytusMVoz2gSBWFOrx3KC8apMpscDTW2WO5J0MJKgjRixRP7fJ6YLLPcjFltWaHDTBs8KXOJdUExcKhHKb4reXyyjJASKeH9uRqNWNGKMxZqHRwpqRRcCp7LaNnnymqbKFO8dmGFTqr46WcO4LuSwYLPaivBDewAfdfSgbsMKaUNRgi+/uwBZte3Cv11UsVTBwY5MVFkoR7fVc7/3WT+PAh67G5Xx/2kQlyvuPoo4PnnnzdvvvnmHZ2rtWZ8fJy1tbUbHuN5HqdPn+all17ipZde4oUXXqBUsr8sHy02+B+/c4ZMadZaCa6AVqos3RKB60Ar1hhjiDIrLaHMRuNZ6FkV3CSzO41S4JIqQ+BKMqWpReqRChz98PI0kjYwVvLthO86NNoJnVSBgEzZ+kfXEVHAhlwuMFRw2T8UstayhevRos9A0SdJrebX1GiJLx4f6dm6dm1an5wc4PVLaxjgzFy119Vd9l3WO1Z8UmnNgaGQ5UZCvZOyf6jAE5Nl5tY7LDVi20+SR7dM2/6QJ/ZVWKxHHBgK+RtfOQHwQFbPd2vV3p3IM6VvGITu1kTeTjL+4My8pf/eIFBFmX4k+yQeFIQQPzTGPH+r4/Y+7euw1k458cxp1l75/3qPuZ7Piaee49Of/SJ/9T//KX7qq1+hWCz2fhn/3UfrRMkKmdb80fuLNCKb7vBdBymgIATtXJJba4PjQJQY+r2WDLYrup3asCCxxWErqgfNON2k1PooQhvLMBoteihtg6bWhoGiR9q0zodto3q0WmVAC7vCF4DRmmonQxCzb6iA70qWGzHNWPH4/jKTAwW0NtvaunZX06ErGS35TFZCAM4vNmw3tIFMCIaLAYuNmNFyQCNKma9FSGl1sa7VOsSZ7dWQQDtTLNYjjIFvfO5Qb3J7EAycu8X8uZ/0092ujvtJxl7g6EO3qP38iy8Rt2o8+/kXeeb5Fzj57OcIwkJvhYPrb7uF/t5HS1xebuG5toms6Du0EkXgOsSpRhtNpAxSbe4Ch7yQe52/hjbW88EYQ6rYJAH+qMGm46y0ijbamiO5HlMjJUZLPv/+3CIqUxQ8SaxsfUKIboCFVNlUl9GGTj55P31wkOmVFsZAva3YN2D7DTppwlI9YqQU9FIr/SmdlWbMvkrIejsl1QZtFFLYOonrSLxuF6KwqcR9lZCVZsLEQECmDdV2QiNVpJntVv/G5w5tUUd9mHG/6Ke7XR33k4y9wNGH7grnZ37hm/zML3xzy/PdFc4H83XOL7W2dNC+N9+gHLr4rmStGTMYWuXTVGk0hizv4NNssG76sixAPgl2damMIc4MWpNLgm8NOA8zHGy3dNczHQShJzHGOujtHy4SZ4pLqy3GywFz1Ta+46C0QuTCjxowRuA4uTug0gjhELgO7VhxdKzE9EqLZpyy0oyYrBQIXMm783VOHx3dlN/vrqaXGzb1dLXaJnAdJioBQwW/JwUyWvZ7boGlwKXaTjh9bJilesRctUPBd2inij//+ARffUQ7h+8H9ui/uxd7d3QfdrrC+dOLq4wW/S1b6Go7pRJaFk/Rd6lHCYnSNKIMg9ksoIfN1Yvc80EK6xzULX1rbADBgBHgS2llwx/iXccWCZE8Yjp5w1/Bt/0NnVTZGoYyNlAYK6W+2BAEriTONMZoy7ZyRI+FlWYKR0A5cCgGDiutmCcHKjw2OcBqM2a5EVPw7fezvxJumxsv+i5ffXKSt2eqGGPF6q5Xbh4q+EyvtBkve7hS0E6sy9/USImpkVJvZ7oXND4e9ui/uxd7d3UfdrrCmVltcXx0K3PEkfS6m6M0b0jzHBKliK7T5HcdK7SntEFpyPLA0t2NdCdYNzcPSnOPhocZ/e/L+mr4hL6D5wiqrRSEQOs8iIL9DLXh8X1lqu2U2bUOUWoLuwUkmbYF9XaqQFn9J88VjBR9mlHGajMhUwbXsY/tHwr5yuMTvYL4jSb1br3DGNuh7jmbA4fShiNjRVwpWW0+mrLZuwG7XR33k4y9OztHO8lYa8V8sGAnDN+VHBoq9BqxukgzjUHgu3LLNQ4OF5hZ7aC1RgqJ6xgQguFiSF2mSGClmdjdhDK9NFV3Qu2GFgkoupOsoeh1g8fGa/Wf9zDEE1faVFtXeypwBZXQoRT61NsJldAlTjWxtgwyKQQ/9qlR5msRcWoZPE8dGOCN6XU6iSLwHCqBi5CCWBkcIRgpebRixXIjYm69g8HQSlKGCgGNTornSuqdlE6qbjrZdOsd16odplda7B8MeyyuZmxFEb9wbJTAlY+sbPZuwG5Xx/0kY+/uZoNi2G0kGysFPWe+6dUOz04N9uib1U7K0dHCtlvozx0e5vxiE2MsrRZjG/scIRC5ZWk3WBhsgOif+Lv/7waQki8QwiHNFAiB61i9pu7OpCt6uJ2K7G5BN8ABFDzbs2IneZ8DwyUanZREa3wpGS7b1F/gSo6MFXly/yDHxsosNWJm1tsEnsPPnT7MH569xmorJVYazwjGSx6RMqTKEGeaTGuksNIfjpRWJdcYTh0c5PXpNZ46MHjLyWasHPCLLxzm29+/wmorRuSLheu7uh9V2ezdgE+y+uxuxyf+E++Xij65v8Ib02tW7M5zGCz6xKnq0Tdt2kPywokxPpxvbNlCHx8vM1kJuLreodFRDJd84tQQK0UnVShlKHrWLjZRG+mqrm1pF92gMjYQIhDMVzs9q1NBTkM1dhWf7aBavq08Ofen0G5Vaa1GVKK0/RkrF14JvV5AFtLatyZK40rJyckKYFlQUyNFJgYCYq35qaf28dnDQ/zTP77MQj1CCCiHLiNSMlftEHoS13FpxZn1AReC0HOIUsWF5Rae63BiorhlsrlRn8M3PneIP7u4tqVv4W50de/h1vikqs/udnziP/XrueLPHBrinbkq1VRRCixDqhHFnJuvMzVSyicKhwtLrS1b6MB1ODFexpeClWaKRKDRtCNFMXBwpLTpjijF7XpfsyFc2L8692Qu/122jXAAoedgtCbJebkqZ1uJnMbaDTiweSfTk/LIH3Pyc4Sx52d6864ncAUmdypsJ+qOA4yAXMlWI3PW1JGxIvO1CGWg3kko+A4FX7Jcj5GOZKTgc3isxKHr6KvVjhUC/IMz8yil+Uv/f3t3HmNXfR1w/Hvu8rZ5b+Z5FsYb9mBjbAjUNnETsBFlS4oolCahahqyUDVK/2jTNG0VhapS2z/aRlVTNRLpQmkCSRFRQ2mTIpSAaKJK7GYLYINSbzBe8IzHs7/tvnv6x71vmBnP9mzj98bvfP7xzJ377vvNleee99vO2baavUfGKAfRxsCDg+MUKiEJT/CISrRGu8kDIEpjDtCTTXBstDTj2gvtTnZdh50buxgrVuzB1SCtmH222bX8//rZK6na0z6/2NfJ8bES/Scnp1bMJH13xpDEfF3oEFjfleWaTRlOTpaZKAe8eOgkKzIJ2pIubx4bQ1VIuoLrO4zHDzaIHujtaRcHoVxxqSiZAAAOjUlEQVQNKVeq9J8skPKcKBCoIo5DmxetPArjgtg9uSSFcsBkpUq1Gk3SiyOUKlFxIs8VJIxqltc4Eu++jjPzuUAm4dCWih6amYRHW9Jj/8A49Szmqs1l1Cbyp8rkqpJOuoQhbF+3gu62JEMTJUYLAarRUueubJKVHWl29K2YMa80UYoKKu0bnKA96U8F+fYN/tQS2ONjZVwHerNJNFqgRsp36WlPTmW5LQdVDpyYYMuq9qlrz1ecKOm7UxXynt53gtu2rrIHlzGxlg8cc62kqg2P1DZthaoMjpdmfLqcrwt9/SUXMDhWmvGJOajC0HiRUKEzHv4KQiUIlZTnRNXlUCbLVdpTCXJxtbnxUoWB8TKuG+2MFkfwHaEUhFG5UV8oB8pEqYrjOFyQdRktVgmq0cokxwsRlGKoUxvl/LhGt6qQSrgkPIdSEG9SjLsebQmXdV0ZfNeh/2SBark6Y2hrehCZftyhlgoEHIlyS9VWHFVDpc13SXkOV6zp4DNXr+f4aGlqaKgShgyNl+nI+CRd55RVSht7MhwcnDylh1dbAvv64RF8V8imE6zIzL3T2HeEk/EwVI3tTjamfi0fOM5krfhcXehafp3pw1gdaY+OdJaJcpXxYkA64ZLxXUrV98aoQlXSvpDLREEj5btMBlX6utooBSGT5SqqSk8uies4FMsBhTgAjRer5DM+ac/BcULK1SrjhQpuXMEuJ9HE7mgxwJfod65UQ7JJDz9OsphwozmHIC5Xujqf5uhokRUZL8pEqyFVjVKblypRjRAn7tnUyqSKA9W4+BSAK1GGW9HoZ2OlAN932bo2H9+71Cn3br6x7B+9cWzBPTYpz6U95TM0UZo3cFTiSaItvbmpY7Y72Zj6tXzgONtrxedaCbIqn+b/jo/hOQ6bV7WzvquN4+MlBseLtPkeiFCsRPMgY8WASqVKPp3AKUTzDBURNvRkgajKne86HBsJyTjCyfESiNKTTTIcJ1UshkouneDCzgzZuGJdNVT2D45TLFeZLId4rkQpUUoBoYQUKiEdaaE7m+DK9Xk29uQoBVUeffUIT+0bpBJAxnfJJn2CMGS0WJlKDyK8N0TlCHiOg+MICdchCKOEjo4Iad/l5st6OTZS5L9fPXpKQryFxrIX22OzZWWO3YeGCDXaqV9LNjjdifEyW1blZqyost3JxtSv5QPH+7FWfPYwludESflWd6RY3x31IJ7bP8Th4QIaBnhetCvcE49cwiOfT9OdS/LuaIFyELKiLcHlq6Nx+eFChRPjJXJpn5OTZbqySUTgkpU5Dp+cYN/ABABr8in6ujJkk15UU8JxuOWKVbzyzggAW9d28NaxsakexoGBcaqhMlKo8PZQgZ5civaUz4cu6mS4UOG1wyP4noPrCq7rkAwcSoFC6OC7IQ7guA7phEtvLkmxElIIqiTUwXeE7lyStO9y+Zo8yXju4Mm9x5e8lHWxnuHmVTle7h+hIyUE1XAq2WCtBOzwRBkRuGtX34z3s93JxtSv5QPH+7VWfPan59rKneHJCvm0zzWbukh4wgsHTlAsBeTjjKxbVuZYuyJD0nfJJj2e2z/ERd2ZqTxJ3dnk1Kf0oBqy99go6zozXLqqnSvXr6CvM8PwZIUf7zlGsRyS9EM2dGfItyUolEM29WYpB8rzB4fIp3w60j7VUOnKJjkyXGRNZxrfEV55e5ht6zpQgd6OJK7kGRgrMlmJ9kmkfJe2hJBMuASB4jmQS/uMF8uUqiGZlEcicFjRlsBzonoZV2/snnpA1zt3sFjPsD2V4MMXdbLn6ChJ1yHhRZUAx4IqhXKVrrYkX7xpE5f0ttd1XbDdycbMZvU4Yueiytjs93AEjo6UWJNP0dl2ag2DYyMF/vPlw2xemaWzLXXKz0cLZSbLIV+9ZTPd2Zk/X+j32Xt0lJ+8eZxCpRrNb8S75DsyPiOTAf0nJxkcL7GpN8sNW3rJpXwe/dkRHn/jGEnPpSPtocB4MWCsGJD0ozmTtO+y6+IuEp7DU/tOgCpJzyWX8kn7DjunBQ54rxbGx7evXdK9W0pthp0bO3n1nRGe3j/IZCkgk/TYuaGbqzZ2nnKP6rmubfIzrWCp9TiWTeAQkZuBbxAlVb1PVb8237lnUsjpXFusME5fd4bvv9BPuRrSk01MJfkbGC+TcB2+8EsbuGTaZO9SPPJyP0nHWXB4ZvZDfbIc8Oz+Qb73/DvxewsrO5Jc3J2jLe2hYbQxJOW55NM+z+wfpC3hMVGu4jnCL6zN0z5rErq2Wu1TH15/Vu7V6RYROpfFiYxpZudVIScRcYFvAh8B+oEXROSHqrqnsS07c0vZGbuxp41n9w3x9P5BTkyUyCQ9PnpZ77yfohdzOhPCUdbYlVy1oXvengxMq24XKK6EbOzJckEuOWeQqnfu4P3aRWy7k42pz7LocYjI1cCfq+ovx9/fDaCqfz3X+cupx9EIp9PjqNeeIyPR3EH7/IHt3dEi29flbZmrMU1iqT2OU9csNqc1wDvTvu+Pj00RkS+IyG4R2T0wMHBOG7fcbOnNMVJYeHnpcKEyY79Dvfq623Bdh4lpO+Ons8ymxixfyyVwyBzHZnSVVPVeVd2hqjt6enrOUbOWp3PxUK+tVivGCQFLlSqhKqVKVIe7GISWINCYZWq5BI5+4MJp368FjjSoLcveuXqo1+YOtq/LUwpDBsdLlMKQ7evy3LZ1lU04G7NMLZePey8Am0TkIuAw8EngU41t0vJ2riaELbOpMeefZRE4VDUQkd8Dfky0HPdbqvpGg5u17NlD3RhzOpZF4ABQ1ceAxxrdDmOMaXXLZY7DGGNMk7DAYYwxpi7LYgNgvURkADi0wCndwOA5as5yY/dmYXZ/5mf3ZmHL4f6sV9VF9zOcl4FjMSKyeym7I1uR3ZuF2f2Zn92bhZ1P98eGqowxxtTFAocxxpi6tGrguLfRDWhidm8WZvdnfnZvFnbe3J+WnOMwxhhz+lq1x2GMMeY0WeAwxhhTl5YJHCJyoYj8RET2isgbIvKlRrep2YiIKyIvi8ijjW5LsxGRvIg8LCJvxv+Hrm50m5qJiHw5/rt6XUQeEpH6S1OeR0TkWyJyXERen3asU0SeEJGfx/+uaGQbz0TLBA4gAP5IVS8FrgJ+V0Qua3Cbms2XgL2NbkST+gbwI1XdAmzF7tMUEVkD/D6wQ1UvJ0pE+snGtqrh7gdunnXsq8CTqroJeDL+fllqmcChqkdV9aX46zGiP/w1C7+qdYjIWuBXgPsa3ZZmIyLtwLXAvwKoallVhxvbqqbjAWkR8YAMLV4vR1X/Fxiadfh24IH46weAXzunjTqLWiZwTCcifcB24LnGtqSp/D3wFSBsdEOa0AZgAPh2PJR3n4hYzduYqh4G/hZ4GzgKjKjq441tVVPqVdWjEH2QBS5ocHtOW8sFDhHJAv8B/IGqjja6Pc1ARG4Fjqvqi41uS5PygCuBf1TV7cAEy3iY4WyLx+pvBy4CVgNtIvLpxrbKvJ9aKnCIiE8UNB5U1Uca3Z4msgv4VRE5CHwPuEFE/q2xTWoq/UC/qtZ6qA8TBRITuQk4oKoDqloBHgF2NrhNzehdEVkFEP97vMHtOW0tEzhERIjGqPeq6t81uj3NRFXvVtW1qtpHNKn5P6pqnxhjqnoMeEdENseHbgT2NLBJzeZt4CoRycR/Zzdiiwfm8kPgc/HXnwN+0MC2nJFlUwHwLNgFfAZ4TUReiY/9SVxZ0JjFfBF4UEQSwH7gtxrcnqahqs+JyMPAS0SrF1/mPEqvcTpE5CHgOqBbRPqBPwO+Bvy7iPw2UbD99ca18MxYyhFjjDF1aZmhKmOMMWeHBQ5jjDF1scBhjDGmLhY4jDHG1MUChzHGmLpY4DAtQUTWisgP4syk+0XkHhFJLuF1j4lIfpFz7hKR1dO+v6+WQFNEDopIdx3tvDVOa/KqiOwRkd+Jj98vIncs9TrGvJ8scJjzXrwp7RHgv+LMpJuANPA3i71WVW9ZQkLDu4hSbdRe83lVrXuDYJzZ4F7gNlXdSpRP7af1XmeO64qI2N+6OWvsP5NpBTcARVX9NoCqVoEvA58VkWzcY7indrKIPCoi18VfHxSRbhHpi+tw/Etcd+JxEUnHvYAdRJsDX4mP/VREdsxuhIh8WkSej8/7ZxFxZ52SI9qUeyJuZ0lV35r282tF5Om4x3RHfM2siDwpIi+JyGsicnt8vNbefyDamHehiHxURJ6Jz/1+nLfNmLpZ4DCt4APAjASOcYLLg8DFdVxnE/BNVf0AMAx8QlUfBnYDd6rqNlUtzPVCEbkU+A1gl6puA6rAnbPaNESUluJQXAzpzlk9hVXANcCtRLuQAYrAx1T1SuB64OtxDwtgM/CdaYkZ/xS4KT53N/CHdfzuxkxppZQjpnUJMFeKBJnj2EIOqGotXc2LQF8dr70R+CDwQvxcTzNHkjtV/byIXEGUOPCPgY8QDYVBNNQWAntEpHfa7/BXInItUUr8NUDtZ4dU9dn466uAy4Cn4vdPAM/U0X5jpljgMK3gDeAT0w/ExZl6gbeAy5nZ+56v7Glp2tdVoof/UgnwgKrevdiJqvoaUU617wIHeC9wTH//WtC7E+gBPqiqlTjDca39E7POf0JVf7OONhszJxuqMq3gSSAjIp+FqLY68HXgnnho6SCwTUQcEbkQ+FCd1x8jmp9YrA13iMgFcRs6RWT99BPi+Yrrph3aBhxa5LodRLVUKiJyPbB+nvOeBXaJyMXxe2VE5JJFrm3MnCxwmPOeRpk8P0b04P450eRzqKp/GZ/yFNEn+9eIKtm9VOdb3A/8U21yfJ427CGaY3hcRH4GPEE0ZzGdAF8RkbfiDM5/wXu9jfk8COwQkd1EvY8353n/gfhaD8Xv/yywZfFfzZhTWXZc03JEZCfwEPBxq3poTP0scBhjjKmLDVUZY4ypiwUOY4wxdbHAYYwxpi4WOIwxxtTFAocxxpi6WOAwxhhTl/8Hr3y9ZY21pbUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the orginal data (as before)\n",
    "plt.scatter(crime_income_df['quintileshare'], crime_income_df['crimeindex'],\n",
    "            alpha=0.3, label='Data', s=100);\n",
    "\n",
    "# Generate and plot the model fit line\n",
    "xs = np.linspace(crime_income_df['quintileshare'].min(), \n",
    "                 crime_income_df['quintileshare'].max())\n",
    "ys = (57.9963 * xs) - 7.3352\n",
    "plt.plot(xs, ys, '--k', linewidth=4, label='Model')\n",
    "\n",
    "plt.title('Quintile Share and Crime Index Linear Regression')\n",
    "plt.xlabel('Quintile Share')\n",
    "plt.ylabel('Crime Index')\n",
    "plt.legend();\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quintileshare</th>\n",
       "      <th>crimeindex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>quintileshare</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crimeindex</th>\n",
       "      <td>0.123457</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               quintileshare  crimeindex\n",
       "quintileshare       1.000000    0.123457\n",
       "crimeindex          0.123457    1.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the correlation between the high and low inequality groups\n",
    "# and crime index\n",
    "df_ineq_exists = pd.DataFrame(crime_income_df \\\n",
    "                            [crime_income_df['inequalityexists'] == 1])\n",
    "df_no_ineq_exists = pd.DataFrame(crime_income_df \\\n",
    "                                 [crime_income_df['inequalityexists'] == 0])\n",
    "\n",
    "df_ineq_ineqcrime = df_ineq_exists[['quintileshare', 'crimeindex']]\n",
    "df_no_ineq_ineqcrime = df_no_ineq_exists[['quintileshare', 'crimeindex']]\n",
    "\n",
    "corrs_ineq_ineqcrime = df_ineq_ineqcrime.corr()\n",
    "corrs_no_ineq_ineqcrime = df_no_ineq_ineqcrime.corr()\n",
    "\n",
    "corrs_ineq_ineqcrime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quintileshare</th>\n",
       "      <th>crimeindex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>quintileshare</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.285439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crimeindex</th>\n",
       "      <td>0.285439</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               quintileshare  crimeindex\n",
       "quintileshare       1.000000    0.285439\n",
       "crimeindex          0.285439    1.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs_no_ineq_ineqcrime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a weak positive correlation between quintile share and crime index.\n",
    "\n",
    "### Summary of Quintile Share Analysis\n",
    "Looking at both quintile share alone as well as quintile share and poverty together, we can see that both of these seem to be independent and significant influences on the level of crime. The second linear model (with the variable name mod_2) shows that there is a systematic difference with respect to crime levels between cities with income inequality and cities without income inequality. Model 3 (variable name mod_3) shows a predictive model based off of the quintile share. It gives us sense of the strength of this relationship. While it is certainly significant, the correlation is somewhat weak."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gini Index Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the Gini index data, another metric of determining income inequality which represents wealth distribution. First let's check if the Gini index and crime index data are normal to see if we can immediately run tests on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00013370632925130914\n",
      "The Gini index distribution is not approximately normal.\n",
      "8.454322024579447e-62\n",
      "The crime index distribution is not approximately normal.\n"
     ]
    },
    {
     "data": {
      "image/png": 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rW9/lHw98ysUvpfPsl/OYsXgN8YSWy5CKxcy47rrrct4PGzaMtLS0Ms3hvPPOY8yYMfnGmzVrRocOHWjVqhX9+vVj8eLFOcePP/54/v777wLbfeSRR9i4cWOp5CwiIiJSHKrBik8jiUSkxDnnmJaxhvFTlzBh6hL+Wre50Ndmxx0LV25k4cqNfDgruVFPXdZyUvhbTg1/yQE2j8BHsaatCTgBKe9SU1MZO3YsN910E/Xr1y/y9bFYjEik9H5EP/DAA5x++uk453jkkUfo0aMHM2bMICUlhXfeeWe71z7yyCP06dOHatWqlVp+IlIMabWDzmDn6GeriJQA1WDFp04iESkxsXiCidOWMuLz3/nlz3Ul1u4qavFi/FhejB9LC8tgQHgS/wx/RdTiJXYPqXyaDp5Uqu0vuO+EAo9FIhEGDBjAww8/zN13+2YKsXDhQi644AKWL19OgwYNGDlyJHvvvTfnnXcedevW5eeff6Zz587UrFmT+fPns3TpUn777TceeughvvvuO959910aNWrEhAkTiEaj3HHHHUyYMIFNmzbRrVs3nn766ULPRTczrrnmGsaNG8e7775L7969adq0Kenp6VStWpUzzzyTjIwM4vE4t956K8uWLWPJkiX06NGD+vXr8+mnnxbreygiIiKVj2qwHSvPNZimm4lIscXiCV75biFHDPuMq1+fUqgOotpVo7S2P+hgv9Pe5tHaFrIbO75urmvMDbFLOGLzQ7wcO4osFy6JjyBS4i677DJeffVV1qzxPx2//PLL6devH9OmTePcc8/lyiuvzDn222+/8dFHH/Hggw8C8PvvvzNp0iTefvtt+vTpQ48ePZg+fTpVq1Zl0qRJOe39+OOPzJgxg02bNjFx4kSKqnPnzvzyyy++2HvvvUfDhg2ZOnUqM2bMoFevXlx55ZU0bNiQTz/9VB1EIiIiUi6pBisejSQSkWL5Zu4Kbp8wi1+XFdzBUz0lTPeW9TmoaV26NK1Ly91rUD01ku+Q+PWuCvPdXvyY2I/vE635JtGOdVTf5rzFNODW2AWMjPfi9sgLHB6eUaKfS6S4atWqRb9+/XjssceoWrVqTvzbb79l7NixAPTt25cbbrgh59gZZ5xBOLy14/O4444jGo3Svn174vE4vXr1AqB9+/YsWLAAgE8//ZShQ4eyceNGVq1aRbt27TjppJOKlGty53S/9u3bM2jQIG688UZOPPFEDj/88CK1KSIiIhIE1WDFo04iEdkpy9dtZsj4Gbwz/c98j4cMjmy9O6d0akTP1ntQNaVwI35qWCbtbT7tQ/O5gPfY7CJ8mujEm/HD+TTRkVief7bmuYb0zb6Z4+LfMyT6Enva6mJ/NpGScvXVV9O5c2fOP//8As/JPSy5enV/h2hqaioAoVCIaDSac24oFCIWi5GZmcmll15Keno6TZo0IS0tjczMzCLn+fPPP9OzZ09frFWrVkyePJl33nmHm266iWOOOYbbbrutgBZEREREyg/VYDtPnUQiUmQfzlrG4DensXJD1jbHUiIhTj+wMQMOb07T+tuOACqqVIvRK/wjvcI/kuHq80zsREbHjyCLFN957yYO4ZvN7bg3+izHh38o9n2l4tvefPWyUrduXc4880yee+45LrjgAgC6devG6NGj6du3L6+++irdu3ff6fa3FCP169dn/fr1jBkzhtNPP73Q1zvnePzxx1m6dGnOE7ItlixZQt26denTpw81atTghRdeAKBmzZqsW7dupxaDFBERkcpPNdiOlecaTGsSiUihbcqKc+OYaVz8Unq+HUSndm7Elzf04J5/ti+RDqK8GtsK7oi+wFepV3NO+GOMhO/4GmpwafbVDMq+hPWuSonfX2RnXHfddaxYsSLn/WOPPcbIkSM54IADePnll3n00Ud3uu06depw8cUX0759e0455RQOOuigQl13/fXX52y/+uOPP/Lpp5+SkuLveJ0+fToHH3wwHTt25O677+aWW24BYMCAARx33HH06NEDgIsuuoj09PSd/gwiIiIipUE12M6x/ObABaVLly5OhaZI+bRo1UYueXkys5au3eZY+0a1ub13OzrvvVvRGi3mNr1TE825Lfs8proW2xzb1xbzbPRBmoXynw5XLNqmt1ybPXs2bdq0CToNIf8/CzOb7JzrElBKkg/VX5VMMX+2BkY/W0UqPNVg5UdxarBSHUlkZteY2Uwzm2Fmo8xMj/ZFKqCv567g5OFfbdNBFAkZ1x7dinGXdit6B1EJ6BCax7iUIfwn8gopZPuO/e4a0TvrTj6PH1DmeYmIiIiIiFREpdZJZGaNgCuBLs65/YEwcHZp3U9ESseoH/6g3/M/sHqjvxOmeYPqjL20G1f2bEkkHNzM1ZA5Lo68w7iU22hhGb5ja6nO+dk38FysVwFXi4iIiIiIyBal/ZtdBKhqZhGgGrCklO8nIiXEOcejH83hprHTiSf801J7tduT8Zd354DGdQLKblvtQguZmPIfTgl95YsnCHFnrB/3ZZ9NOZpdKyIiIiIiUu6UWieRc24xMAz4A1gKrHHOfVBa9xORkhNPOG59ewYPf/SbL24Gg45pxVN9OlMjtfxtjljFsnk4+iQ3R14llGdR6xHxkxkcu5iY03r9IiIiIiIi+SnN6Wa7Ab2BZkBDoLqZ9cnnvAFmlm5m6cuXLy+tdESkkOIJx3VvTOGV7/7wxVMjIZ7ucyCXH9kSMwsoux0zgwGRSTwfHUp1NvmOvR7vweXZV5LtwgFlJyIiIiIiUn6V5lCAo4D5zrnlAGY2FugGvJL7JOfcM8AzkNxdoxTzEam8Smgnk7gzrsv+N28luvvitdjA86EH6PK/3+B/JXKrUndEeBqj7C7Oy7qRVdTKib+XOJgrsy/nsehwohYPMEMREREREZHypTQ7if4ADjWzasAmoCeg/VVFyqm4MwZlD9ymg2hPVvJSyn20Ci0OKLOdd0BoPv9LuZ1+WYNZTIOc+LuJQ7gmO8Ej0SeIWGI7LUilUNLbQRdym+Zx48Zx6qmnMnv2bFq3br3dc1944QWOOeYYGjZsuFMpffbZZwwbNoyJEyduE+/duzfNmzdn48aN7LHHHtxwww2ceOKJO2wvJSWFbt267VQ+IiIiIqrBKmYNVmqdRM65781sDPATEAN+xhsxJCLli3Nwc+wixiUO98UbsoLRKXexd+ivgDIrvn1DSxmTejtnZ93CQrdnTnxioiuR7DgPRZ8iZBrEKCVv1KhRdO/endGjR5OWlrbdc1944QX233//nS5Qtufwww/PKVymTJnCKaecQtWqVenZs2eB13z22WfUqFFDnUSy6yrpX2xERKTMqAYrnlJdwdU5N8Q519o5t79zrq9zbnNp3k9Eds4DsbN4Pd7DF9uLlYyq4B1EW+xlqxiVchdNzP9Z3kp0585YH+16JiVu/fr1fP311zz33HOMHj3ad2zo0KG0b9+eDh06MHjwYMaMGUN6ejrnnnsuHTt2ZNOmTTRt2pQVK1YAkJ6ezhFHHAHADz/8QLdu3ejUqRPdunXj119/LVJeHTt25LbbbmP48OEATJgwgUMOOYROnTpx1FFHsWzZMhYsWMCIESN4+OGH6dixI19++WW+54mIiIiUN6rBiq/8bU8kImXq+Vgvnoz39sX2YiWjU+5kn0rQQbRFQ6+j6KzNt/qmno2MH8cetpqBkYnbuVqkaN566y169epFq1atqFu3Lj/99BOdO3fm3Xff5a233uL777+nWrVqrFq1irp16zJ8+HCGDRtGly5dtttu69at+eKLL4hEInz00UfcfPPNvPnmm0XKrXPnzjzwwAMAdO/ene+++w4z49lnn2Xo0KE8+OCDDBw4kBo1ajBo0CAAVq9ene95IiIlIdNFWbx8PSvWbWbF+izWbMomO54gO56cEl49NUKN1Ai1qkZpWLsKjXerRtUUbUIhIttSDVZ86iQS2YWNj3fljlg/X6wua3kl5Z5K1UG0RWNLTp87LSuNv9gtJ35f7F80sDWcFv4ywOykMhk1ahRXX301AGeffTajRo2ic+fOfPTRR5x//vlUq1YNgLp16xap3TVr1tC/f3/mzJmDmZGdnV3k3FyuoXMZGRmcddZZLF26lKysLJo1a5bvNYU9T0RkR9a7KvyUaEl6Yj9muX2Y4xrxh9sd9+DnRWqnfo1U2uxVk/aNanNA49ocuE9dGtRMLaWsRaSiUA1WfOokEtlFTU60ZFD2QF+sGpmMTBnKvqGlAWVV+pqElvNiyn2cmTWEdVTLid+YfTF7sZJu4VkBZieVwcqVK/nkk0+YMWMGZkY8HsfMGDp0KM45zGyHbUQiERKJ5BP0zMzMnPitt95Kjx49GDduHAsWLMgZAl0UP//8M23atAHgiiuu4Nprr+Xkk0/ms88+K3DefmHPExHJyzn41TXhg0QXPo53ZrprRqIEVrxYsX4zX87ZzJdzVuTE2uxVi/9rWZ8erXfn4KZ1CYV2/O+tiFQeqsFKRqmuSSQi5dOiRH0GZF1LFtGcWJQYI6IP0yE0L8DMykab0CKeiT5IClufAMSI8O/sq5mf2HM7V4rs2JgxY+jXrx8LFy5kwYIFLFq0iGbNmvHVV19xzDHH8Pzzz7Nx40YAVq1aBUDNmjVZt25dThtNmzZl8uTJAL6hzGvWrKFRo0ZAcqHFopo2bRp33nknl1122Tbtvfjiiznn5c2noPNERAqyKFGfh7NP44ish+iVdT8Pxc5gqtu3RDqICjJ76Vqe/mIeZz/zHV3v+5g7J85iekbhdkMSkYpPNVjJ0EgikV3MOleVC7OvZyX+nVvujz7D/4WnB5RV2esans0jPMGl2VfnxNZQgwuzBzEuZQi1bUOA2UmJKuR2qSVl1KhRDB482Bc77bTTeO2113jqqaeYMmUKXbp0ISUlheOPP5577rmH8847j4EDB1K1alW+/fZbhgwZwoUXXsg999zDIYccktPODTfcQP/+/XnooYc48sgjC5XPl19+SadOndi4cSO77747jz32WM6uGmlpaZxxxhk0atSIQw89lPnz5wNw0kkncfrpp/P222/z+OOPF3ieiEhucWd8kOjCq/Gj+DrRDlfIDiEjQcM61dmjVir1aqRSt1oKKZEQYW8k0MasGOs3x1i1IYuM1ZtYuiaTeGL7u04sW7uZ576az3NfzeeAxrXpe+g+nNShIVWiWstIpMyoBquQNZi5crStT5cuXVx6enrQaYhUPIXcqjfhjIuyr+OTRGdf/IrwOK6L/q80Miv3noydxNDYOb5Y99B0XojeT8QS+V9Uxj/wpGhmz56dM5RXgpXfn4WZTXbObX91SClTqr/yUcifq5KU6aK8GT+c/8ZPZIHb8YjcFpbBQaHfODD0K63tD/a1pVS9fXmh75cdT7Bw5UZmLF7D9MVrSF+wimmL1+xwt9LdqkXp360p5x/WjNpVo9s/WUSKTDVY+VGcGkwjiUR2IcPjp2zTQXRC6FuuiYwJKKPg/Ts8gbmJxoxNHJ4T+yrRnmGxMxkcHb2dK0VERHZtWS7MqHhPHo+dwgrqFHheCll0D83gmFA6R4Z/Zncr3sOWaDhEi91r0GL3GpzSKTkNY/WGLL7+fQUfzVrGB7OWsTErvs11qzdm88hHc3juq/mcf1gzLjisKXWqpRQrFxGRykadRCK7iM/jB/Bw7DRfrIP9zoPREYSs/IwoLGtmcG/0vyzM2p3Jbr+c+Ij4yXQKzeXYsJ6ui4iI5JZwxvhENx6MncEit3uB53WwuZwV/owTw99SyzaVak67VU/hxAMacuIBDdmUFeeTX/5izORFfPbb8m1GGK3LjPHYx3N48ZsFXNmzJX0P3YeUiJZqFREBdRKJ7BIyXH2uyr7MtzZAXdYyIuVhqljRt2+sbFItxoiUhzlp8938Sb2c+KDsgexn/6FpaFmA2cnOKOwOFlJ6ytN0dhEpOdMTzbgl+wKmun3zPR4lRu/w11wYfoc2oUVlnF1S1ZQwJxywFyccsBcLV27gle8W8vqPi1ibGfOdt2ZTNndOnMUr3y3k5uPbcFSb3fWzQ6SYVIMFr7g1mLrMRSq5TBfl0qyr+JuaObEQCR6PPs5etirAzMqXBraWJ1IeI8LWAnId1RiYfTWbnIaiVyRVqlRh5cqV6qTPO/XoAAAgAElEQVQIkHOOlStXUqVKlaBTKRfMrJeZ/Wpmc81scD7HU83sde/492bWNNexm7z4r2Z2rBdrYmafmtlsM5tpZlflOj/NzBab2RTvdXxZfEap/Na46tyafR4nZ92ZbwdRVTIZEJ7Il6lXMSz6dGAdRHntU686/zmhLV8PPpLrj92POtW2XYto/ooNXPxSOhe/NJmla0p3xJNIZaYaLHglUYNpJJFIJXdHrC/T8hRz10Xe4LDwzIAyKr8ODM3h5shr3BHrlxP7xe3DLdnnMyz6NHooUjE0btyYjIwMli8v/CKoUvKqVKlC48aNg04jcGYWBp4AjgYygB/NbLxzblau0y4EVjvnWpjZ2cD9wFlm1hY4G2gHNAQ+MrNWQAy4zjn3k5nVBCab2Ye52nzYOTesbD6h7Areix/ELdkXsIJtF/SOEONf4U+4PDKu2GsNlaaaVaJc1qMF/bs15cVvFvDUZ7+zfrN/ZNFHs5fx3byV3NhrP849ZB9CIf3gFykK1WDlQ3FrMHUSiVRib8UP47X4Ub7YUaF0/h2eEFBG5d/54feYnGjJpETXnNibiX/QJf4b50Q+DTAzKaxoNEqzZs2CTkNki4OBuc65eQBmNhroDeTuJOoNpHlfjwGGW3Ksfm9gtHNuMzDfzOYCBzvnvgWWAjjn1pnZbKBRnjZFim2Nq0Zadn/G5drcIbfjQt8zODKKfUJ/lXFmO69GaoTLerTgzC5NeOjD33j9xz9I5Br0sH5zjFvfnsn4qUt46MyONKlbLbhkRSoY1WCVg6abiVRSfyR255bs832xfezPXX6h6h0xg/uj/6W5LfHFb4/1Y26iYUBZiUgF1gjIPe8mw4vle45zLgasAeoV5lpvalon4Ptc4cvNbJqZPW9mu+WXlJkNMLN0M0vXE1/Jz5fx/Tl28/35dhA1tT95MXofT6U8WqE6iHJrUDOVe09tz6QrD+fAfbb9a/LjgtUc/+iXvD1lcQDZiYgER51EIpVQzIW4Kvsy1rP16VcqWTwVfYTatjHAzCqGGpbJiOjDVCUzJ5ZJKldmX85mpwGYIlIk+c1XydtTX9A5273WzGoAbwJXO+fWeuGngH2BjiRHGz2YX1LOuWecc12cc10aNGiw/U8gu5SYC3F/9ln0zb7Zt5kDJKeWXRV+k/dSbuQf4WkBZViy2uxVi/9d0pU7e7ejekrYd2zd5hhXjZ7C1aN/Zm2mNvoQkV2DOolEKqHHYqfys2vpi/0n8iptQ38ElFHF0yq0mDujI32xWa4pD8bODCgjEamgMoAmud43BpYUdI6ZRYDawKrtXWtmUZIdRK8658ZuOcE5t8w5F3fOJYD/kpzuJlIoy1wd/pX1H56K997m2H72B2+n3Mo10Tcr3c6ooZDRt2tTPrz2Hxyx37adpm9NWcLJj3/FL3+uzedqEZHKRZ1EIpXMD4n9GB4/xRfrGfqJvuEPA8qo4jot9CUnhr71xZ6Jn8hXc1YElJGIVEA/Ai3NrJmZpZBciHp8nnPGA/29r08HPnHJrWHGA2d7u581A1oCP3jrFT0HzHbOPZS7ITPbK9fbfwIzSvwTSaX0dbwdx2++lx9cG188RIKB4fGMT7mFdqGFAWVXNhrWqcrI8w7i9pPbkRLx/5q0YOVGTnnia00/E5FKr9Q6icxsv1zbr04xs7VmdnVp3U9EkgtMXpN1KYlcf7UbsJqh2plrp5jB3dHnaYi/U+jaN6awekNWQFmJSEXirTF0OfA+MBt4wzk308zuMLOTvdOeA+p5C1NfCwz2rp0JvEFyQer3gMucc3HgMKAvcGQ+W90PNbPpZjYN6AFcUzafVCoq5+D5WC/6Zt/Eyjy7l+3Oakal3MXg6GhSLVZAC5WLmdG/W1MmXtGd1nvW9B3LzE5w1egppI2fSXY8EVCGIiKly5IPqkr5JsntXxcDhzjnCnwE0aVLF5eenl7q+YhUOmnJou7yrCuYmGtXLoCXovfyf+HpQWRVaXyXaM05WbfgcnW+HdtuD0b0ORBT75tIkZjZZOdcl6DzkK1Uf+Ujbdut3iujLBfmttj5jI4fuc2x7qHpPBJ9gvpWRlOs0taUzX2KIDM7zu0TZjHqh22n63dtXo8RfQ6kdrVoAJmJiBRdYWuwsppu1hP4fXsdRCJSPBPjh2zTQXRReJI6iErAoaFfuDTsnx3y/sxl/C89I6CMREREimeVq0mfrJu36SAyElwdGcOL0fvKroOonKoSDXPvqe0ZetoB20w/+3beSv751NcsXLkhoOxEREpHWXUSnQ2Myu+AtmAVKb7lrha3Zl/gi7W1BVwfeT2gjCqfqyNvcoD97ovdOXEWS/7eFFBGIiIiO2deYk9Oybpjm/WHarCR56PDuDoylrCV/myDiuLMg5rw5sBuNKpT1Reft3wD/3zyG9IXrAooMxGRklfqnUTeIo0nA//L77i2YBUpHucct2RfwGq2zptPIZuHo0/uMusHlIWoxXk0+gRVycyJrdscY/DY6ZTFtF0REZGSMDXRnNOz0vjD7eGL723LGJcyhB7hKQFlVr61b1ybCVd056Cmu/niqzZk8a//fs+EqXk3LRQRqZjKYiTRccBPzrllZXAvkV3O+KlLeD/h3+H46sgY9gtpKlRJaxb6kxvyjM764rflvJG+KKCMRERECu/z+AGck3ULq6jlix9is3gr5TZahrRz1/bUrZ7CKxcdQu+ODX3xrHiCK0f/zMvfaWUNEan4yqKT6BwKmGomIsXz19pMbnt7pi/WweYyIDwpoIwqv/7hDzi4WV1f7K6JszXtTEREyrVx8cO4MHsQG6nii58e/pyXU+6lrq0LKLOKJTUS5pGzOnJVz5a+uHNw61szeOzjORphLCIVWql2EplZNeBoYGxp3kdkV+Sc4+Zx01mzKTsnlkIWD0ZHEDFty1paQuZ44PQDqBoN58Q07UxERMqzkbFjuSb7MmJEfPFLw2/zQORpUiweUGYVk5lxzdGteOjMDkRC/l1OH/rwN+6YOItEQjWBiFRMpdpJ5Jzb6Jyr55wrf3tailRwb01ZzEez//LFBkX+R4uQ5sSXtn3qVefGXvv5Yl/8tpzXf9S0MxERKV9GxE7k9lh/X8xIkBZ5gRuir2NWwIWyQ6d2bsx/+3WhStT/K9XIrxcwaMxU4uooEpEKqKx2NxORErRy/WbumDDLF+tsv3Fh+J2AMtr19OvalEPyTjubNJs/12QWcIWIiEjZeiz2T+6L/csXixLj8ejjnBf5IKCsKpcerXfn5QsPoWYV/yitsT8t5to3phCLa3S3iFQs6iQSqYDunjSb1Ru3TjNLJYsHok9ru9oyFAoZD5zewTftbP3mGEPGzwgwKxERkeT6OMOyz+Ch2Bm+eBU281z0AU4Mfx9QZpXTQU3r8sYlXalfI9UXf3vKEq55Y6o6ikSkQlEnkUgF8+Wc5Yz92b/7yFWRN9k3tDSgjHZde9erts20s/dnLuO9GX8GlJGIiOzqnIP7YucwPP5PX7wamYyMDuX/wtMDyqxya7NXLd78d1ca1anqi0+YuoSrXteIIhGpONRJJFKBbMqK859x/pEqrfesycWaZhaYvl2b0rFJHV/strdnsDYzu4ArRERESs8DsbN4On6SL1aDjbyUch9dw7MDymrXsE+96owecOg2HUWTpi3lqtFTyFZHkYhUAOokEqlAHv14Dn+s2pjz3gzuPbU9Ue1KEphwyLjvtPa+3U3+WreZoe/9EmBWIiKyKxoe682T8d6+WC028ErKvXQJ/RZQVruWJnWrMXrAoTTeLU9H0fSlXPP6FC1mLSLlnjqJRCqImUvW8N8v5/li/bs2pdPeuwWUkWzRes9aXPKP5r7YK9/9QfqCVQFlJCIiu5pnY8czLHaWL1ab9byWcjcdQ78HlNWuaUtHUZO6/o6iidOWcvPY6TinjiIRKb/USSRSAcQTjpvGTvc9fdqrdhUGHbvfdq6SsnTFkS1pVr+6LzZ47HQ2xzTKS0REStcrsZ7cFevji9VkIy+n3Mv+oQXBJLWLa7xbNV4f0JW961bzxV9PX8SdE2ero0hEyi11EolUAC9+s4BpGWt8sTt670+N1EgBV0hZqxINc88/2/tic/9az4jP5hVwhYiISPGNiR/OLbELfbGqZDIyZSgHhOYHlJUANKxTldcuPoS9alfxxZ//ej6PfDQnoKxERLZPnUQi5dzivzcx7INffbHj9t+To9vuEVBGUpCu+9bjzC6NfbEnPpvLwpUbAspIREQqs/fjXbgh+xJfLIUsnosO0xpE5UTj3arx6kWHUL9Gii/+6Mdz+O8XepAkIuWPOolEyrm08TPZmLV1ylLN1AhpJ7cLMCPZnpuPb+MrBLNiCdLGz9SwchERKVE/JPbjyuzLSeQq56PEeDr6CN3CswLMTPJq3qAGL11wCLWq+EeA3/3ObF7/8Y+AshIRyZ86iUTKsc9+/YsPZy3zxW48rjV71KpSwBUStDrVUrjpuDa+2Ke/Luf9mcsKuEJERKRofk005qKsQWxm60OJEAkeiz5Oj/CUADOTgrRtWIsXLjiYailhX/zmcTP4eLZqBBEpP9RJJFJOZcUS3DHB/ySw0951+NfBeweUkRTWqZ0bcXDTur7YHRNmsmFzLKCMRESksshw9emXNZi1+DdLuDfyLMeFfwwoKymMznvvxrP9upAS2forWDzhuOy1n/jpj9UBZiYispU6iUTKqRe+mc+8FVvXsjGDO07en1DIAsxKCsPMuPOU/Ynk+rNasiaTxz7RIpUiIrLzVrsa9MsazDL8DyIGRV7nrMhnwSQlRdKtRX0eP6cTucu5zOwEF77wI78vXx9cYiIiHnUSiZRDf63N5NE8u16cfVAT2jeuHVBGUlT77VmTC7s388We+3I+c5atCygjERGpyDa6VM7Pup55rqEv3i/8AZeF3w4oK9kZx7bbkzt67++Lrd6YTf/nf+CvtZkBZSUikqROIpFy6L73fmFDrsWqa1WJMOiY/QLMSHbGlT1b+ra9jSUct7w1Q4tYi4hIkcSdcWX25UxxLX3xE0LfMSTyIqZBxhVOn0P34fIeLXyxjNWbOG/kj6zLzA4oKxGRUu4kMrM6ZjbGzH4xs9lm1rU07ydSGUxeuJqxPy32xa49uhX1aqQGlJHsrOqpEW47sa0v9v38Vbw1ZXEBV4iIiGzrrlgfPkoc6It1Dc3koeiThE0PHiqq645pxRkHNvbFZi1dy6Wv/kR2PBFQViKyqyvtkUSPAu8551oDHYDZpXw/kQotkXCkjZ/pi7XaowZ9Dt0noIykuHrtvyf/aNXAF7t70mzWbNJTQhER2bEXY8cwMn6cL9bGFvB09CFSTRsiVGRmxj2ntueI/fx1wpdzVjBk/EyNPBaRQERKq2EzqwX8H3AegHMuC8gqrfuJVAZvpC9i+uI1vljaye2IhDUztKIyM24/uR3HPPIFWbHkU8EV67N48INft1mPQEREJLdP4h25PdbPF9uTlYxMeYBatimgrEpJWgVddzFtzY7P2Y5oOMST53bmnGe+Y2rG1rZe+/4PmtevzkWHNy9uhiIiRVKav3k2B5YDI83sZzN71syq5z3JzAaYWbqZpS9fvrwU0xEp39Zsymbo+7/6Yie034tu+9YPKCMpKU3rV+fSI/b1xV75biGzl64NKCMRESnvZib24YrsK0jkKterkclzKcPY07RdemVSLSXCs/0PolGdqr743e/M5oOZfwaUlYjsqkqzkygCdAaecs51AjYAg/Oe5Jx7xjnXxTnXpUGDBnkPi+wyHvnoN1Zt2DrYrko0xE3Htw4wIylJA/+xL/vUq5bzPuEgTUPJRXYJZtbLzH41s7lmtk0tZGapZva6d/x7M2ua69hNXvxXMzvWizUxs0+99R5nmtlVuc6va2Yfmtkc77+7lcVnlJL1p9uNC7MGsYGtnQYhEgyPPka70MIAM5PS0qBmKiPPP4iaqVsnejgHV42ewozFxRutJCJSFKXZSZQBZDjnvvfejyHZaSQiefz65zpe+tZf9P37Hy1ovFu1Aq6QiqZKNMytJ2y7iPWk6UsDykhEyoKZhYEngOOAtsA5ZtY2z2kXAqudcy2Ah4H7vWvbAmcD7YBewJNeezHgOudcG+BQ4LJcbQ4GPnbOtQQ+Jp8HdFK+bXCpXJg1iD+p54sPibzEkeEpAWUlZaHVHjUZfm5nwqGt29Vtyo5z4Ys/8ueazAAzE5FdSal1Ejnn/gQWmdmWfbt7ArNK634iFZVzjtsnzCSe2DqipPFuVbnkH5qDXtn0bLP7NotY3zNpNhuztPCoSCV2MDDXOTfPW59xNNA7zzm9gRe9r8cAPc3MvPho59xm59x8YC5wsHNuqXPuJwDn3DqSG4M0yqetF4FTSulzSSmIO+Oq7MuY6Zr54ueF36N/5IOAspKy9I9WDUg7uZ0vtmztZi588Uc2bFa9ICKlr7RXw70CeNXMpgEdgXtK+X4iFc57M/7km99X+mK3nNCWKtFwQBlJaTEzbjupLZFcTwiXrMlkxGe/B5iViJSyRsCiXO8z2Nqhs805zrkYsAaoV5hrvalpnYAtI7f3cM4t9dpaCuxeAp9BysiDsTP5KNHFFzsqNJlbIy8HlJEEoe+h+3DBYf6OwplL1nLV6CkkEpqmLiKlq1Q7iZxzU7z1hg5wzp3inNMqeyK5bMqKc9ek2b5Y9xb1ObbdHgFlJKVt3wY1uKC7v/Ab8cU8Fq3aGFBGIlLKLJ9Y3t/yCjpnu9eaWQ3gTeBq51yRVsLXxiHlz9vxrjwZ9w8ya2fzeTQ6nLCpY2BX858T2tCztb+P96PZy3jow98CykhEdhXaV1skQCM+/53Ff2/dwjYSMtJObktyloFUVlcc2YL6NVJz3mfFEtw5UbNxRSqpDKBJrveNgSUFnWNmEaA2sGp715pZlGQH0avOubG5zllmZnt55+wF/JVfUto4pHyZnmjGDdmX+GL1+ZtnUx6kum0OKCsJUjhkPHZOJ9ruVcsXH/7pXCZOy/tPiIhIyYns+BSRXUha7TK71aJEfUZkDQNScmL9bRItnjynzHKQYNSsEmXwca0Z9L+pObEPZi3ji9+W83+t9MuaSCXzI9DSzJoBi0kuRP2vPOeMB/oD3wKnA58455yZjQdeM7OHgIZAS+AHb72i54DZzrmHCmjrPu+/b5fOx5KS8perzcVZ17I5Vz2QQjZPpzzMXrYqwMwkaNVTIzzbvwsnD/+KFeu37oB7/f+m0ax+ddo1LLu6VUR2HRpJJBKQe2Ln+grC+qzhqsibAWYkZenUTo3otHcdX+z2CTPJjicCykhESoO3xtDlwPskF5h+wzk308zuMLOTvdOeA+qZ2VzgWrwdyZxzM4E3SG788R5wmXMuDhwG9AWONLMp3ut4r637gKPNbA5wtPdeyqnNLsLArGu22cns7shzHBiaE1BWUp40rFOVEX0OJBr273g24KXJrFivUWYiUvLUSSQSgK/j7Xg3cYgvdkNkNLVsUwFXSGUTChlpJ7Uj98zC35dv4MVvFgSWk4iUDufcO865Vs65fZ1zd3ux25xz472vM51zZzjnWjjnDnbOzct17d3edfs55971Yl8558xb87Gj93rHO7bSOdfTOdfS+6+GopRTzsF/Yhfyk2vli18YfoczIl8ElJWUR12a1uXO3vv7Yov/3sSlr/xEVkwPl0SkZKmTSKSMZbswabH+vlgHm8vpYRWEu5oOTepw5oFNfLFHP5rD8nV6MigiUtk9H+/FmPg/fLHDQ9O4KfJaQBlJeXb2wXvTv+s+vtgPC1aRNmFmQBmJSGWlTiKRMvZy/GjmuMa+2O3RFwlp55Jd0vW99qNm6tbl4dZtjvHA+78EmJGIiJS2L+LtuTvWxxdran8yPPo4EdPIEMnfLSe2pWtz/9TE177/g5e/WxhQRiJSGamTSKQMrXC1eDh2mi92RvgzOoZ+DygjCVr9GqlcfbR/qsEb6RlMWfR3QBmJiEhpmp/Yk8uzrySRqwyvwUaejQ6jtm0IMDMp76LhEE+e25kmdav64rePn8l381YGlJWIVDbqJBIpQw/EzmId1XPe12QjN0RGB5iRlAf9uu5Di91r+GJDxs8kkdDoMhGRymSDS+WS7GtYm6sWMBI8Fh1Oi5C2NZcd2616Cv/t14VqKeGcWCzhuPTVn1j8t9a2FJHi22EnkZl9XJiYiGzf1ERz3siz9sBVkTdpYGsDykjKi2g4xJCT2vpiUxf9zZs/ZQSUkYjkRzWRFIdzcGP2AH5z/rXoboi8zpHhKQFlJRVR6z1r8dCZHX2xVRuy+Pcrk8nMjgeUlYhUFgV2EplZFTOrC9Q3s93MrK73ago0LKsERSqDhDOGZPfH5for18Iy6B/+IMCspDw5vGUDjm23hy92/3u/sC4zO6CMRGQL1URSEp6LH8/ERFdf7MTQtwwMTwgoI6nIeu2/J9cc5Z+uPi1jDbe9PQPnNBJZRHbe9kYSXQJMBlp7/93yeht4ovRTE6k8xia6M8W19MWGRF4ianraI1vdckJbUiJb/1lesT6Lxz+ZG2BGIuJRTSTF8m28DffGzvHF9rM/GBp9BrOAkpIK74ojW3BUG/8DpjfSM3jthz8CykhEKoMCO4mcc48655oBg5xzzZ1zzbxXB+fc8DLMUaRCW+eqcl+2vzA8NvQDh4dnBJSRlFdN6lZj4P8198VGfj2fecvXB5SRiIBqIimepa4ul2dfSZyta8jUZANPRx+mmm0OMDOp6EIh46GzOtCsfnVfPG38TH76Y3VAWYlIRbfDNYmcc4+bWTcz+5eZ9dvyKovkRCqDx2L/ZAV1ct6nkMUtkVcDzEjKs4FH7MtetavkvM+OO+6cOCvAjERkC9VEUlSbXYSBWVezktq++CPRJ2kaWhZQVlKZ1KoS5em+B/oWss6OO/79ymSWr1MnpIgUXWEWrn4ZGAZ0Bw7yXl1KOS+RSmFuoiEj4718sYHhiTQJLQ8oIynvqqVEuOn4Nr7Yp78u55Nf9MuESNBUE0lRpcX6M9W18MWuCr9Jz/DPAWUklVGrPWrywOkdfLFlazdz2Ws/kR1PBJSViFRUkUKc0wVo67QCmkiROAe3x/oRy/XXrCEr+HdkfIBZSUVw0gF78cq3C/lhwaqc2J0TZ9O9RQPfmkUiUuZUE0mhvR47glHxnr5Yj9DPXBUZG1BGUpmdcMBeTFvcnKc/n5cT+2H+Ku55ZzZDTmoXYGYiUtEU5reNGcCepZ2ISGXzYeJAvkwc4Iv9J/oqVS0roIykojAzbjuprW8x0/krNjDy6/nBJSUioJpICmlqojm3xs7zxfa2ZTwSfZKQqY9RSsf1x+zHYS3q+WIjv17AWz8vDigjEamICtNJVB+YZWbvm9n4La/CNG5mC8xsuplNMbP04qUqUnFkuih3xvr6Yl1DMzk+9H1AGUlFs3+j2px90N6+2OOfzOWvdZkBZSQiFKMmkl3HSleTf2ddTRYpObEqbObp6EPUtg0BZiaVXSQc4rGzO9GoTlVffPDYacxasjagrESkoinMdLO0Yt6jh3NuRTHbEKlQno0fzyK3e877MHGGRF7SNrdSJIOOacWkaUtYmxkDYP3mGEPf+5VhZ3TYwZUiUkrSgk5AyreYC3FF9hUsob4vfn/0v7QJLQooK9mV1KuRyog+B3LaiG/IiiXXI8rMTnDJK+lMuLw7daql7KAFEdnVFWZ3s8/ze5VFciIV0RJXlydivX2xvuEPaa3iUIqoXo1Urjm6lS82ZnIGUxb9HVBGIrs21USyIw/EzuSbxP6+2AXhd+gd/iagjGRX1L5xbe4+xf//4aJVm7hq9BTiCU13FJHtK8zuZuvMbK33yjSzuJkVdryiAz4ws8lmNqCA9geYWbqZpS9frh2fpOK7J/tcNrF1C/O6rOWayJsBZiQVWZ9D96Hl7jV8sbTxM0moyBMpc8WsiaSSmxQ/hKfjJ/tiB9tsboqMCigj2ZWd0aUJfQ71T1v//LflPPLRbwFlJCIVRWFGEtV0ztXyXlWA04DhhWz/MOdcZ+A44DIz+7982n/GOdfFOdelQYMGRUpepLz5LtGaiYmuvtj1kde1BoHstGg4tM2uJFMW/c1YLUIpUuaKWRNJJTYn0Yjrsy/xxfZgFcNTHiNq8YCykl3dbSe2o9PedXyxxz+Zy4ezlgWUkYhUBEXeS9k59xZwZCHPXeL99y9gHHBwUe8nUlHEXIi07PN8sf1tPmeGPwskH6k8ureszzFt9/DF7n/vF9ZvjgWUkYhA0WoiqbzWuqpckn0NG3ONIo4S48mUR9nd1gSYmezqUiIhnjr3QOrXSPXFr319CvNX6AGmiOSvMNPNTs31Ot3M7iM5jWxH11U3s5pbvgaOIbl1rEil9Fq8J784/7De26MvENZWt1ICbjmhLSmRrf9kL1+3mcc/mRNgRiK7np2tiaTySjhjUPZA5rmGvviQyIscGNK/0RK8PWtX4Yl/dSIc2rp7yrrNMQa+PJkNetgkIvkozEiik3K9jgXWAb23e0XSHsBXZjYV+AGY5Jx7b2cTFSnPVrmaPBg7wxc7NfSlCkQpMXvXq8bFhzfzxZ7/ar6eBIqUrZ2tiaSSGhE/kQ8SB/lip4c/59zwxwFlJLKtQ5rX4z/Ht/HFfl22jhvfnIZz6ucWEb/Ijk5wzp2/Mw075+YB2qdZdgnDYmewhq2LC1dnE4OjWqhSStalR7TgzcmL+XNtJgDZccddE2fx3HkH7eBKESkJO1sTSeX0VXx/hsXO8sX2t/ncFXkeswIuEgnI+Yc1ZWrG37w9ZUlObOK0pXRsUoeLDm8eYGYiUt7ssJPIzBoDjwOHkRxS/RVwlXMuo5RzE6kQZiT2YVTcvyTFlZFx7G7aprzSSqsdyG2rAzfFu3EVl+fEPv7lLz679XCOCE/bcQNpWhtDpDhUE8kWi109rsy+nESuQfl1WMdT0YepYtkBZiaSPzPj3lPb8+uf6/jlz2hRF1IAACAASURBVHU58Xvf/YX9G9Xm0Ob1AsxORMqTwkw3GwmMBxoCjYAJXkxkl+ccpGWfh8v1V6m5LeH88LsBZiWV2cmhb+hiv/pid8T6keXCAWUksktRTSRsdhEuzbqaVdTKiRkJHo0+QZPQigAzE9m+aikRRvQ5kJpVto4TiCccl7/2E3+uyQwwMxEpTwrTSdTAOTfSORfzXi8A2qteBHg7cRjpbj9f7NbIy//P3n3HR1Wlfxz/PNNC770IKIggCggW1F1XWQUrdtEV2VVXXfvuz3VR1xXFhq66Kpa1oGBD7Ciuir2D9I4gvXcSICSZmfP7IyPkppGQTG4y+b5fr3nlznPLPPcyCWeeueccIpruVpLEDIaGR2HEd8cWu1aMivXzMSuRakNtIuHO6CXMcAd4Yn8LvclxJbmjU8Rn7ZvU5j8X9PDENm7P5i+vTCErqvariJSsSLTRzC42s2DicTGwKdmJiVR2210N7s25yBP7fWAKxwdn+JSRVBfdAku5IPilJ/ZY9Gw2uHqF7yAi5UVtompubPQ4Xo393hPrG5jKNcH3fMpIpPT6dmnO9X07eWLTlm9l2AdzfcpIRCqTkhSJLgXOB9YCa4BzEzGRam1E9EzW03D38wg5/DP0so8ZSXVyU2gsddkzs1kGtXgw3wCqIlLu1CaqxmbH2/PPqHfs8na2lofDTxIwzRAlVcuNfTvxu87eGyFf/nE5b07REGsi1d1ei0TOueXOuTOcc02dc82cc2c655ZVRHIildUv8ZY8HzvFE7s8+CHtA+t8ykiqmyaWzg2htz2xN2LHMTPewaeMRFKf2kTV1xZXh6tybiSbyO5YDbJ4OvwI9W2nj5mJ7JtAwPjPBT3Yr1EtT/y2d2Yxe5UmuhCpzoosEpnZA2Z2VSHxv5rZ8OSmJVJ5OQdDo4PJyTM5YAs2cU3oXR+zkupocPATOtqeb/wcAYbmDMbpC22RclXWNpGZ9TezBWa2yMyGFLI+zcxeT6yfaGbt86y7JRFfYGb98sRHmtl6M5ud71hDzWyVmU1PPLzfaEipxZxxQ841rHTNPPH7ws/RJbDCp6xEyq5BrQhPX9yLGuE9HwmzonGufGkKW3Zk+5iZiPgpVMy604BuhcQfBWYC/0hKRiKV3Mfx3nwTP9QTuy38CrUty6eMpLoKW4x/hV7ikpxbdsemugN5N34MZwW/8zEzkZSzz20iMwsCTwAnAiuBn8xsnHMu7+AflwFbnHMdzWwgMBy4wMy6AgOBg8mdUe1TMzvQORcDXgRGAKMLedlHnHP/LuU5ShEejZ7N1/Huntjg4Mf6Oyu5htb3O4N9MzT3bqGurepx39mH8NfX94ypuWprJtePmcaLfzqCYMD8ylBEfFJcdzPnnIsXEowD+msh1VKmizAsZ5An1icwh9MCP/qUkVR3vw3O4veByZ7YfTkXsd3V8CkjkZRUljbREcAi59xi51w2MAYYkG+bAcCoxPKbQF8zs0R8jHMuyzm3BFiUOB7Oua+Bzft6QlIyn8V68ljsHE/sMPuZ2zQGoaSQs3q24Y9Ht/fEvlm4kUcm/OxPQiLiq+KKRDvNrFP+YCKWmbyURCqvp6KnsyrPbMdBYtwZehFT2VR8dHvoZSLk7H6+noY8Ec3/GVREyqAsbaLWQN4+SSsTsUK3cc5FgW1A4xLuW5hrzWxmoktaw8I2MLMrzGyymU3esGFDCQ5Z/SyLN+OvOVd7Yk3YxpORR4mYpgqX1HLrKV3o3c7752LEF4v4ZM5anzISEb8UVyT6F/A/M/ujmR2SePwJGJ9YJ1KtLIs34+nY6Z7YH4Mfc2BglU8ZieRqF1jPZcEPPbHnY6ewNN7cp4xEUk5Z2kSFfY2Qf+SworYpyb75PQUcAPQgdwa2hwrbyDn3jHOut3Oud9OmTQvbpFrLdBGuyrmRdGrvjgWJ8Xj4MVrYFh8zE0mOSCjAk384jKZ10zzx/xs7g8UbtvuUlYj4ocgikXPuf8CZwPHk9nt/EfgdcI5z7sOi9hNJVcOigzyzmjRhKzeE3vIxI5E9rgm9RzP2fHDJJswdUQ1iLVIeytgmWgm0zfO8DbC6qG3MLATUJ7crWUn2zZ/rOudcLNEV7lkS3dOk5Jxz3JZzGfNce098SOg1+gTn+ZOUSAVoVq8GT/7hMEJ5xiHKyIpy5UtT2JEV9TEzEalIxd1JhHNutnNusHOuV+Ix2Dk3q6KSE6ksPo/14NN4L0/s1vCr1DP1vJTKoY7t4pbwq57YV/EefBw/3KeMRFJLGdpEPwGdzKyDmUXIHYh6XL5txgGDE8vnAp8751wiPjAx+1kHoBMwqbgXM7OWeZ6eBcwualsp3MsTl/N2/Dee2CmBiVwe1HekkvoOb9+If57axRNbuH47N781E6dvnkSqhWKLRCICu1yYO6OXeGK9bQFnBb71KSORwp0Z+I4jzPst9105g9jp0orYQ0SSLTHG0LXAx8A8YKxzbo6Z3WVmZyQ2ex5obGaLgL8BQxL7zgHGAnOBj4BrEjObYWavAT8Anc1spZldljjWA2Y2y8xmknvn018r5ERTxNTlW7jr/Tme2AG2igfC/9X4g1JtDD66PWf2aOWJjZ+5hue/XeJTRiJSkUJ+JyBS2T0bO5VlrsXu5wHi3BnWYNVS+ZjBsPALnJp9L9HEn/fVNOGx6FkMCY/xOTuR6ivRJe3DfLF/5VneBZxXxL73APcUEr+wiO0HFRaXvdu4PYurX55KTmzP3RK1yeS/4UeoY7t8zEykYpkZ9519KPPXZjB/bcbu+H3/m8/BrerT54DGPmYnIsm21zuJzKxRWV7AzIJmNs3MPijLcUT8sNI1KTBL1MXBTzk4sMynjESK1zmwkkuDH3liz8VOYVG8VRF7iEhJlbVNJJVXTizO1a9MZW26txj0YPi/dAwUOwyUSEqqGQny30G9qFdjzz0Fsbjj2lensmabhlsQSWUl6W420czeMLNTzPbp3okbyL29WqTKuTvnYnaxp6tOI9L5v9AbPmYksnc3hN6iBZt2P48S4vbonzSWgEjZlbVNJJXUPePnMWnJZk/siuAHnBIsdggokZTWrnFtHh3Y03P3/KYd2fzl5alkRWP+JSYiSVWSItGBwDPAIGCRmd1rZgeW5OBm1gY4FXhu31MU8cfXsUP4KO6dFOYfoTHUtx0+ZSRSMrUti9vDL3tiP8QPZtwMfRsuUkb73CaSyuuNySt48fulnlifwBxuDqmbrsjxBzXjhr6dPLHpK7Zy1/tzfcpIRJJtr0Uil2tCou/75eTOvjHJzL4ysz572f0/wM1AvKgNzOwKM5tsZpM3bNhQmtxFkmaXC3N79E+eWHdbxHnBr3zKSKR0TglM5DeBmZ7YPePnkbErx6eMRKq+MraJpBKasWIrt73rnQCudYOajAg/RsiKbL6KVCvXn9CJEw5q5om9MnE5Yyev8CkjEUmmvQ5cbWaNgYvJ/dZsHXAduVOy9gDeADoUsd9pwHrn3BQz+11Rx3fOPUPut3L07t1bfSGkUngyOsAzWLUR567wiwRMb1GpGszgztCL9M8eTjZhANZnZPHIhIX86/SuPmcnUjXta5uoWhta3+8MirTB1eOqrHvIZs8gvGlk89+dt9I4kFHMniLVSyBgPHJ+D8544luWbdq5O/7Pd2fTpUU9DmlTeX/PRaT0StLd7AegHnCmc+5U59zbzrmoc24y8HQx+x0DnGFmS4ExwAlm9nIx24tUCovirXgqdoYnNij4Kd0Di33KSGTf7B9YyxVB75wBo35Yyrw16f4kJFL17WubSCqZHBfkmuwbWIN3lqbh4WfpFljqT1IilVj9WmGevrgXNcJ7Pj5mR+Nc9fIUtuzI9jEzESlvJSkS/dM5N8w5t/LXgJmdB+CcG17UTs65W5xzbZxz7YGBwOfOuYvLmrBIMjkHt0f/RE6em+yasoWbQq/7mJXIvrsm9B6t2dOVNxZ33P7ubOJx3RUnsg/2qU0klc/d0YuZ5Lp4YpcFP+TM4Hc+ZSRS+XVpWY/h5xzqia3amsn1Y6YRU7tCJGWUpEg0pJDYLeWdiEhl8G78GH6IH+yJ3R5+mXqmqT6laqpp2QwNj/LEJi/bwltTVxaxh4gUQ22iFPBG9LeMivXzxI4OzOaW0Ks+ZSRSdQzo0Zo/HdPeE/tm4UYenrDAn4REpNwVOSaRmZ0MnAK0NrPH8qyqB0RL8yLOuS+BL/chP5EKs3VnNnfneG92+01gJqcHfvApI5HycWJwKn1jU/ksftju2H3/m0/fLs1pVDviY2YiVUN5tonEXzPi+3Nb9FJPrDUbGBF+XANVi5TQrad0Yc6qdCYt3bw79sQXv3BI6/r079bSx8xEpDwUdyfRamAysAuYkucxDuhXzH4iVdLwjxawiT0D70XIZljoBcx8TEqknAwNjSIttOdP/uYd2dwzfp6PGYlUKWoTpYANrh5XZv+VbPYUx2uQxX8jD9PINFC1SEmFgwFG/KEnzeqmeeJ/fX0Gc1dr3EORqq7IIpFzboZzbhRwgHNuVJ7H2865LRWYo0jSTVm2mdcmLffErg29R/vAOp8yEilfbQMbuL5vJ0/srakr+W7RRp8yEqk61Caq+rITA1WvLXSg6mU+ZSVSdTWrW4OnLj6MUGDPt6mZOTH+PHoyG7dn+ZiZiJRVkUUiMxubWJxmZjPzPGaZ2cwKyk8k6XJicW57Z7Yntr+t5srg+z5lJJIcf/7N/nRuXtcTu/WdWezKifmUkUjVoDZR1ZY7KcWlBQaqvjw4ngHB733KSqTq69WuEXcN6OaJrdqaydUvTyU7qu6bIlVVcd3Nbkj8PA04Pc/j1+ciKWHkt0uYv9Z7m/ndoZGkmYaZkNQSCQW49+xDPF0ol23ayWOfLfQvKZGqQW2iKuyFWH9ejx3viR0dmM2Q0Gs+ZSSSOi46cj8G92nniU1aupl/vTcb5zTjmUhVVFyRqLaZHeOcW5b3AexHMQNei1QlSzfu4OEJP3tiZwe+4ejgXJ8yEkmuXu0aMugob2Puma8XM2+NxhAQKYbaRFXUV7FDuTvqnZRiP1vHE+HHNFC1SDm5/bSuHNPR25VzzE8rePH7pf4kJCJlUlyR6D9AYaP4ZSbWiVRp8bjjH2/NJCvP7bD12c6t4Vd8zEok+f7erzMt6tXY/Twadwx5exaxuL7xEymC2kRV0KJ4K67NuY54nuZuHXbyfPjfNLTtPmYmklpCwQBPXHQY7RrX8sSHfTCXbxZu8CkrEdlXxRWJ2jvnCvSzd85NBtonLSORCjLmpxVMXLLZE/tX+CWamO6okNRWt0aYuwYc7InNWLGVl35Y6ks+IlWA2kRVzFZXm8tzbiKD2rtjAeI8Hh5Bp8AqHzMTSU0NakV47pLe1Enbc3Nl3ME1r0xl8QYVZUWqkuKKRDWKWVezvBMRqUhrtmVy34fe6b9/e2BTzg5841NGIhXrpINb0P/gFp7Ygx8vYPXWTJ8yEqnU1CaqQnJckGtybmCp8/6NuzX0KscHp/uUlUjq69S8Lo9d2MMz9mH6riiXj57Mtswc/xITkVIprkj0k5n9OX/QzC4DpiQvJZHkcs5x+7uzycjaMzB1rUiQe8/q5vlPTSTVDT3jYOrm+cZvR3ZMA02KFE5toipkWHQQ38W9My6dF/ySy4If+pSRSPVxwkHN+Uf/gzyxxRt2cP1r09StXaSKKK5IdCPwJzP70sweSjy+Ai5nzywfIlXOBzPX8Om89Z7Yzf0606ZhrSL2EElNLerX4OaTvQ25T+et573pq33KSKTSUpuoingp+ntGx07yxHrbAu4OjdQXQSIV5Mrf7s/ZPVt7Yl/9vIFhH2hiGJGqoMgikXNunXPuaOBOYGnicadzro9zbm3FpCdSvjbvyGbouDmeWK92DRnUp70/CYn47A9H7Eevdg09sTvGzWF9+i6fMhKpfNQmqhq+iXXjzuglnlhrNvB05BHSLFrEXiJS3syMe88+hB5tG3jiL36/lBe+W+JTViJSUsXdSQSAc+4L59zjicfnFZGUSLIM+2Aum3Zk734eCQYYfs4hBAP6elGqp0DAGH7OoURCe/472JaZw63vqNuZSH5qE1VeC+JtuDrnRqLs6UJbi108G3lIE1KI+KBGOMgzg3rRsr53SLdhH8zl07nrfMpKREpir0UikVTxxfz1vDPNO6PJdSd0pGOzuj5lJFI5dGxWh5tOOtAT+3TeOt6drhmARKTyW+8a8Kfsm8lgT7dxI84j4SfpGljuY2Yi1VuzejV4fvDh1I4Ed8fiDq57bRqzV23zMTMRKY6KRFItbNuZw5C3vbMXH9SiLlced4BPGYlULpcduz899/PeFj503Fx1OxORSm2nS+Oy7JtYTRNP/NbQq/QLTvYpKxH5VddW9Rjxh8PIe9N+Zk6My0b9xJptmlFVpDJSkUiqhaHvz2Fdetbu5wGDB871drERqc6CAePBc7sX0u1slrqdiUilFHPG9TnXMsvt74lfHJzA5ZrJTKTSOL5zM+4c4J1xcF16Fpe+OJntWRovTKSySdonZDOrYWaTzGyGmc0xszuT9Voixflo9poC3cyuOu4ADm3ToIg9RKqnwrudFeymKSJSGQyLDuLTeC9P7PjANIaGRmkmM5FKZtBR7bj82A6e2Lw16Vz36lSisbhPWYlIYZJ5G0UWcIJzrjvQA+hvZkcl8fVECti4PYvb3pntiR3Uoi43/L6TTxmJVG6XHbs/hxXodjaHdep2JiKVyMhof16M9ffEutpSRoQfI2T6wClSGd1yShdO7NrcE/tiwQbufH+u7loWqUSSViRyubYnnoYTD/32S4VxznHbO7M8s5mFAsZD53cnLRQsZk+R6isYMB48rztpebqdpe+Kcuvb6nYmIpXDJ7FeDIte7Im1YBMjIw9S27KK2EtE/BYMGI8O7MEhret74i/9uIynv1rsU1Yikl9SB2Qxs6CZTQfWAxOccxOT+Xoieb07fRUfz/FOsXlD304c3Kp+EXuICMABTetw00mdPbHP5q/n9Z9W+JSRSNVmZv3NbIGZLTKzIYWsTzOz1xPrJ5pZ+zzrbknEF5hZvzzxkWa23sxm5ztWIzObYGYLEz8bJvPcKtrk+IFcl3MdLk8TtjaZjIw8SAvb4mNmIlIStSIhnh/cm1b1a3jiwz+az5tTVvqUlYjkldQikXMu5pzrAbQBjjCzbvm3MbMrzGyymU3esGFDMtORamT11kzueG+OJ9a9TX3+8jvNZiZSEpce24Fe7byfLe98fy5LNu7wKSORqsnMgsATwMlAV+BCM+uab7PLgC3OuY7AI8DwxL5dgYHAwUB/4MnE8QBeTMTyGwJ85pzrBHyWeJ4Sfo635tLsv5NFZHcsSIwnwo9qqnuRKqRZvRqM/NPh1E0LeeL/eGsmXyxY71NWIvKrCpnayTm3FfiSQhozzrlnnHO9nXO9mzZtWhHpSIqLxR1/fX066bv2zJaQFgrw0Pk9CAU1m5lISQQDxr/P606tyJ6umZk5MW58fTo5GmBSpDSOABY55xY757KBMcCAfNsMAEYllt8E+pqZJeJjnHNZzrklwKLE8XDOfQ1sLuT18h5rFHBmeZ6MX1a7RgzOHkI6tT3xu0Mj+V1wpk9Zici+OqhFPZ65pDeRPG3zWNxx9ctTmb5iq4+ZiUgyZzdramYNEss1gd8D85P1eiK/+u/XvzBxibfd/Pd+nenYrI5PGYlUTR2a1OZfp3lveJixYiuPf77Ip4xEqqTWQN6+misTsUK3cc5FgW1A4xLum19z59yaxLHWAM0K26gq3cm91dXmkuwhrKGxJ/630BtcGPrCp6xEpKz6HNCY/wzs4ZmNMDMnxqUv/sTiDduL3lFEkiqZt1W0BL4ws5nAT+SOSfRBEl9PhBkrtvLwJz97Ysd2bMKlx3QoYg8RKc4Fh7flpHwzkYz4fCFTlhV2A4OIFKKwydjzjwJf1DYl2XefVJU7uTNdhEuz/84i18YTHxT8hOuC7/iUlYiUl1MOacnQ0w/2xDbvyOaSkZNYn6GZVUX8ENr7JvvGOTcT6Jms44vktyMryo2vTyca39N+blgrzEPndycQKKydLSJ7Y2bcf86hTFvxNRsycmcNiju48fXp/O+G31InLWn/jYikipVA2zzP2wCri9hmpZmFgPrkdiUryb75rTOzls65NWbWktzJQ6qkqAtwbc71THUHeuKnBCYyNDTKc/eBiJTS0MozkctgYH3wfJ6I7ekdu3JLJn+893nGRIZRzzL3bDx0W8UnKFLNaIAWSRl3FTKo7vBzDqV5vRpF7CEiJdGodoR/n9fdE1uxOZN/vTu7iD1EJI+fgE5m1sHMIuQORD0u3zbjyP2cBHAu8LlzziXiAxOzn3UAOgGT9vJ6eY81GHivHM6hwsWd8Y+cP/NZ/DBP/Eiby8PhJwlaudxQJSKVxE2hsZwX/NITm+vac3n2TWS6SOE7iUhSqEgkKeF/s9bw+mTv9NwXHbkfJx3cwqeMRFLLcQc25Y9Ht/fE3p62StPViuxFYoyha4GPgXnAWOfcHDO7y8zOSGz2PNDYzBYBfyMxI5lzbg4wFpgLfARc45yLAZjZa8APQGczW2lmlyWOdT9wopktBE5MPK9SnIOh0cG8FT/OE+9iS3k28hA1LMenzEQkWczgvtBznBCY6olPcl24MuevZDnduSxSUfTbJlXe8k07ufkt78wmBzStze2n5p9hWETKYsjJB/H9Lxv5ed2ewSRvf3c23dvUp1Pzuj5mJlK5Oec+BD7MF/tXnuVdwHlF7HsPcE8h8QuL2H4T0Lcs+frJORgeHcjo2EmeeBtbz6jIA95uJyKSUkIWZ0T4cS7OvsXTzfTreHduyLmWEeHH9OFVpALoTiKp0rKiMa55dSoZeaa7DweNRwf2pGaeqbtFpOxqhIOMuOgwaoT3/NeRmRPj2lenkZkd8zEzEUkVT8QG8HTsDE+sKVt4KXw/zUzTYoukulqWxQuRB+lqSz3xj+JHcHPOlcTj6moqkmwqEkmVdu/4ecxa5R3AbsjJXejWuvIMxieSSg5sXpe7zujmiS1Yl8Gd78/xKSMRSRUjo/35d/QCT6whGbwcuY8OgbU+ZSUiFa2+7WB05H72N+84/W/Hf8Md4+aQO2SbiCSLikRSZX0wczWjfljmifU7uDmXHtPen4REqonzerfhrJ6tPbExP63gvemrfMpIRKq616O/467oJZ5YXXYyOnI/nQMa+0ykumli6bwSuZfWbPDEX/pxGcM/WqBCkUgSqUgkVdKSjTsY8tYsT6xto5o8cG53THPiiiSVmTHszG7s36S2J37r27NYtH57EXuJiBTu3dgxDIle7onVIIuRkQc5JLDEp6xExG8tbTOvRu6hGVs88ae/+oVHPl2oQpFIkqhIJFXOrpwY17wyle1Ze8YhigQDPHHRYdSvGfYxM5Hqo05aiBEXHUYktOe/kR3ZMa58abLnd1NEpDjvxo7hbzl/weVpkkbI4dnwQxweWOBjZiJSGbQLrOflyL00JMMTf+yzhSoUiSSJikRSpTjnuO2d2cxdk+6J33ZqFw5t08CnrESqp66t6vGv07yzCP6yYQc3jZ2hRpuI7NU7iQJRPE9zNEiMEeHH+E1wto+ZiUhlcmBgFaMj91OXnZ64CkUiyaEikVQpo75fyltTvWMTnHpISy7p086njESqtz8cuR9nH+Ydn+ijOWt5+qvFPmUkIlXBO7Fj+L9CCkSPhkdwUnCKj5mJSGV0SGAJoyP3UTct5ImrUCRS/lQkkirjx8WbGDZ+nie2f5Pa3HfOIRqHSMQnZsa9Zx1C15b1PPEHP57PNws3FLGXiFRnRd1B9Fh4BKcFJ/qYmYhUZj0DvzD6siNUKBJJMhWJpEpYtTWTa16ZSiy+549/nbQQz1zSi3o1NA6RiJ9qhIP8d1AvGtTa87sYd3D9a9NYsXlnMXuKSHXzduzYAmMQ/VogOlUFIhHZi577NSyyUPTwhJ9VKBIpByoSSaW3KyfGVS9NYdOObE/8kQt60LFZXZ+yEpG82jaqxWMDe5L3pr4tO3O44qUp7NBA1iICvBzty//lXFWgQPR4+HEViESkxIoqFD3++SKGfTBPhSKRMlKRSCo15xy3vD2LWau2eeI39O3EiV2b+5SViBTmtwc25aaTOnti89akc+Pr04nH1WATqc6eip7OP6OXFVogOiU4ycfMRKQqKqpQNPK7JQx5a5an94GIlI6KRFKpjfh8Ee9MW+WJ/b5Lc27o28mnjESkOFf/7gD6Hewt4E6Yu47hH8/3KSMR8ZNz8EDOBQyPXuiJh4gyIvyYCkQiss967teQly4/kno1vIWi1yev4Pox08iOxn3KTKRqC+19ExF/vDd9FQ9N+NkTO6BpbR65oDuBgAaqFim1ofWT/hIGPOzSONfuYJ5rvzv+368Wc8B3/+D80Ff7duCh2/a+jYhUKvG4Y2j0j4yOneSJp5HN0+H/cHxwuk+ZiUiq6NG2AWOu6MMlIyeycfueoSnGz1zDjqwoT/2hFzUjQR8zFKl6knYnkZm1NbMvzGyemc0xsxuS9VqSeiYv3czf35jpiTWoFebZS3pTVwNVi1RqtS2L5yP/pilbPPHbopfxY/wgn7ISkYoUjcW56Y0ZBQpEddjJqMj9KhCJSLnp2qoeY6/sQ6v6NTzxLxdsYPALk8jYleNTZiJVUzK7m0WB/3POdQGOAq4xs65JfD1JEcs27eCKl6aQHdtzi2g4aPz34l7s37SOj5mJSEm1ss08F3mINPZ8q5dDiKuy/8rieAsfMxORZMuKxrj6lam8na+7eAMyeDVyD0cF1P1URMrX/k3r8MZfjqZDk9qe+KQlmznv6R9Yl77Lp8xEqp6kdTdzzq0B1iSWM8xsHtAamJus15RKZB+7tWx1tflT9p1sdq088QcCIzhy9IVF7CUilVH3wGIeCj/FtTl7biTdSl0G5wzhrcgdNDN1IRNJRQvXbeerrFZ0/QAAIABJREFUnzd4Ys3YwkuR++gcWOlTViKS6lo3qMnYK/sw6PmJzF+bsTs+f20GZz3xHS9eegQHNtfMyCJ7UyEDV5tZe6AnUGB+UzO7wswmm9nkDRs25F8t1Uimi3BZ9k0szlcguiH4FmcFv/MpKxEpi9OCE/lb6A1PbIVrxh+z/0GGq+lTViKSTN1a1+fpi3sRSowf2MbW80bkThWIRCTpmtZNY8wVR9Fzvwae+Optuzj3qe/5cfEmnzITqTqSXiQyszrAW8CNzrn0/Oudc88453o753o3bdo02elIJZXjgvwl50amOO/02WcGvuXG0Fs+ZSUi5eG64DucE/AOWD3XtefKnL+S5TR/gkgqOv6gZvxnYA8623LejNxJu8B6v1MSkWqiQa0Ir15+FL/v0swTT98V5ZLnJ/HBzNU+ZSZSNSS1SGRmYXILRK84595O5mtJ1RV3xk05V/FlvIcnfoTNY3j4GUwTmYlUaWZwf/g5jg9M88S/j3fjbzl/Ie70Sy6Sik47tBUfRG6jhW3Z+8YiIuWoZiTI0xf34qIj9/PEs2Nxrn11Gs98/QvOOZ+yE6nckjm7mQHPA/Occw8n63WkanMO7ooO4r34MZ54F1vKc5F/k2ZRnzITkfIUthhPhB+jhy30xMfH+zA0Ohi100RSU9hifqcgItVUKBjgnjO78fd+nQusu/fD+dz85kyyovobJZJfMu8kOgYYBJxgZtMTj1OS+HpSBT0eO4sXY/09sXa2ltGR+6lnmT5lJSLJUMuyGBn5N/ub9zbv0bGTuD96oQpFIiIiUq7MjGuO78hD53XfPU7ar96YspI/PDuRjduzfMpOpHJKWpHIOfetc86cc4c653okHh8m6/Wk6nk6ehoPR8/zxJqxhZfD99HUCgxfJSIpoJFlMDpyP83Z7In/N3Z6gb8HIiIiIuXhnF5teOFPh1O3hncsxMnLtjBgxHfMW6PPHiK/qpDZzUTyeyZ6KvdHL/LE6rGD0ZH7aRvQLHciqayNbWRUZDgNyPDEH4+dxWPRs3zKSkRERFLZbzo15Z2rj6F941qe+KqtmZzz1Pd8PGetT5mJVC4qEkmFezZ6CvdG/+CJ1WQXIyMPclBghU9ZiUhFOiiwgpcj91GPHZ74w9HzeDp6mk9ZiYiISCrr2KwO715zDEcf0NgT35kd48qXpvDAR/OJxuI+ZSdSOWjuYalQz0VP5p7oxZ5YTXbxQuQBegd+9ikrEfFDt8BSRkfu5+LsW9jOnm/1fr3L8KrQB36lJiIiIpXR0PplPkQDYJQLcldwEC/FTvKse/LLX5j+9Xs8Fh5Bk/Ic/mLotvI7lkiS6U4iqTDPRk/h7uggT6wGWYwMP8hRgfk+ZSUifuoR+IVRkeHUYpcnfn/0Ih7KOU+DWYuIiEi5C1uMYeEXGRYaSRDvDGffx7txWtY9TIl38ic5EZ+pSCRJ5xw8mHN+gTuIfi0Q9QnO8ykzEakMegUW8kLkAWrgnV3k8dhZ3Bm9hLizIvYUqRrMrL+ZLTCzRWY2pJD1aWb2emL9RDNrn2fdLYn4AjPrt7djmtmLZrYkz8yyPZJ9fiIiVdWg0Ke8FrmbZmzxxNfSmAuyb2dktL++sJJqR0UiSaqYM26LXsoTsTM98TSyGRl+kKODc33KTEQqkyMD83kxMpzaZHriL8b6MyR6ObG4WmhSNZlZEHgCOBnoClxoZl3zbXYZsMU51xF4BBie2LcrMBA4GOgPPGlmwRIc8+95ZpadnsTTExGp8o4ILOCDtFs50ryfS6KEuCt6CZfn3MQmV9en7EQqnopEkjTZLsj1Odfyauz3nnhtMnkh/IAKRCLicVRgPq9E7qU+2z3xsbHjuf61aWRFY0XsKVKpHQEscs4tds5lA2OAAfm2GQCMSiy/CfQ1M0vExzjnspxzS4BFieOV5JgiIlJCzWwbr0Tu5crg+wXWfRY/jP5Z9/NNrJsPmYlUPBWJJCm2uxpcnnMT4+N9PPGGZPBq5B4ViESkUD0Cv/B6ZBhN2OqJj5+1hkHPTWLLjmyfMhPZZ62BvFN3rkzECt3GORcFtgGNi9l3b8e8x8xmmtkjZpZWWFJmdoWZTTazyRs2bCj9WYmIpJiQxbkl/BpPhx+mLjs96zbQkEE5t3JvzkVku6BPGYpUDBWJpNyt2prJudl38HW8uyfegk28EbmT7oHFPmUmIlXBQYEVjI3cRSs2euKTlm7mnKe+Z/mmnUXsKVIpFTaoVv7+k0VtU9o4wC3AQcDhQCPgH4Ul5Zx7xjnX2znXu2nTpoVtIiJSLfUPTmZ85FZ62MIC656JncbZ2XexIN7Gh8xEKoaKRFKuZqzYyoAR3zHftfPEO9ga3ky7k46B1T5lJiJVyf6BtbyRdif7m/dvxuKNOzjrye+YunxLEXuKVDorgbZ5nrcB8v9nuHsbMwsB9YHNxexb5DGdc2tcrizgBXK7pomISCnsF1jPG5G7uC74Dkbcs26268Dp2ffwRHQAUaeP05J69K6WcvPhrDVc8MwPbNzunaHoEFvMG5E7aWMbi9hTRKSg1raJtyN3cIR5Z0DctCObC5/5kfEz1/iUmUip/AR0MrMOZhYhdyDqcfm2GQcMTiyfC3zunHOJ+MDE7GcdgE7ApOKOaWYtEz8NOBOYndSzExFJUWGL8X/hN3gtcjct2eRZl02YB6MXcJbuKpIUpCKRlFk87nj8s4Vc/cpUduV4K+39ApNyxxexdJ+yE5GqrIHt4KXIfQzo0coTz4rGuebVqdz3v3lEY/Ei9hbxX2KMoWuBj4F5wFjn3Bwzu8vMzkhs9jzQ2MwWAX8DhiT2nQOMBeYCHwHXOOdiRR0zcaxXzGwWMAtoAtxdEecpIpKqjgrM539pQzg18GOBdbPc/pyWfS8jogPI0VhFkiIs94uqyqF3795u8uTJfqchpbBtZw5/HTudz+evL7DuyuA4/hF6nYBVnveYiFRN7o6tPPTJz4z4YlGBdcd2bMJjF/akUe2ID5lJaZnZFOdcb7/zkD2S2v4aWj85xxUR8cH42JHcnvMnNlOvwLpOtpK7wyM5MjC/4I5Dt1VAdiLFK2kbTHcSyT6bvWobp434pkCBKBQwhoee4ZbwGBWIRKRcmBk39evMA+ccSjjoHbP320UbOf3xb5m9Sg0wERERSZ5TgxP5JO1mTglMLLBuoWvDBdn/4qacK9nk6vqQnUj5UJFI9snYn1Zw9lPfs2JzpifesFaY0ZcewQWhL/1JTERS2vmHt+X1K/vQvJ53Vu9VWzM5+6nvGfX9UirTHbIiIiKSWppYOk9GHuWJ8KM0ouCQGm/GjuOErId4LXo8cVfYZJQilVvSikRmNtLM1puZBkxMIdt25nDda9O4+a2ZZEe944B0b9uAD67/DUd3bOJTdiJSHRy2X0Pev+5YDm/f0BPPjsa5Y9wc/jx6Mpt3ZPuUnYiIiFQHpwYnMiHt75wX/LLAum3U4Zbonzk9+25+iHWp+OREyiCZdxK9CPRP4vGlgn2/aCP9H/2a92cUnMZ+0FHtGHvlUbRuUNOHzESkumlWtwavXH4Ug/u0K7Du03nr6f+fr/l2oWZUFBERkeRpbBk8GH6GsZE7OdBWFFg/x3XgwpzbuWL0ZJZs3OFDhiKll7QikXPua2Bzso4vFScrGuOe8XO56LmJrNm2y7OuRjjAIxd0Z9iZ3UgLaUR/Eak4kVCAOwd049GBPaiTFvKsW5+RxaCRExn2wVwys2M+ZSgiIiLVwRGBBYyP3MqQ0KvUZFeB9Z/MXcdJj3zFsA/mskV3O0sl5/uYRGZ2hZlNNrPJGzZs8DsdyWfKss2c9ti3PPvNkgLrurasx7hrj+Wsnm18yExEJNeAHq358Prf0KNtA0/cOXj+2yX0+8/XfP+L7ioSERGR5AlbjKtCHzAh7WZOC/xQYH1OzPH8t0v4zQNf8PCEn9mWmeNDliJ753uRyDn3jHOut3Oud9OmTf1ORxLSd+Xwz3dncc5TP7Bw/XbPOjO46rgDeOeaozmwuUbuFxH/7de4Fm9c1Ydrj++I5RsjcvnmnVz07ERueXsW6bvUIBMREZHkaWMbGRF5nLcid9DDFhZYvz0rymOfLeS3D3zBE18sYkdW1IcsRYoW2vsmUp045/h4zjruGDebdelZBda3blCTh87vzlH7N/YhOxGRooWDAW7q15ljOzXh/8bOYNVW7+yLr01azufz13HbqV05/dCWWP5qkoiIiEg56RVYyDuROxgX78MDOQNZhfeGiG2ZOTz48QJGfjyJy0Mf8ofgp9SzzCKO5pOh2/zOQHygIpHs9vO6DIZ9MJdvihjs9bxebfjnaV2pXzNcwZmJSLU3tH6JNz0K+MSl8WDwAkbFTsLluWl2XXoW1782jZfHvMod4dEcHFiWhGTzUONKRESk2jKDAcEf6BeYzOjYSTwdPZ3N1PNss4n6DI9eyJPRAfwh+CmXhj6imW31KWORJHY3M7PXgB+Azma20swuS9ZrSdls3pHN7e/O5uRHvym0QNS+cS1evfxIHjyvuwpEIlIl1LYshoZHMzYyjP2t4IyMk1wXTs++h9tyLmWzU7dZERERSZ4alsMVofF8nXYjN4Vepx4FZzrLoBZPx87g2KxHuSXnMhbHW/iQqUgS7yRyzl2YrGNL+cjMjvHyj8t4/POFpO8q2Bc2FDCuPG5/rjuhEzXCmrlMRKqewwML+DByC49Fz+bZ2Knk5PlvL06AV2K/573Y0fw5NJ7Lgv+jjhWckURERESkPNSxXVwbeo9BwQk8Hz2F52Mns4Oanm2yCfNarC+vxfry28AMBgc/4XeB6QTN+ZS1VDfqblYN7cqJ8dqk5Tz55S9syCg47hDAUfs3YugZB3NQi3qFrhcRqSpqWA43h1/n3OBX3BW9hC/jPTzrt1OLR6LnMSraj6tD73Fx8FNqmAa4FhERkeSobzv5W/hNLg39j5diJ/JitB+bKNi1/ut4d76Od6etrWdQcALnB7+kgRW8C0mkPJlzlaci2bt3bzd58mS/00hZu3JivDFlJU98voi16YV/W96ucS1uPaULJ3VtXrZBXUsxfoiISEX6PNaDu6KXsNQVfht3Czbx59B4Bga/oLYVXkgvMY1JVICZTXHO9fY7D9kjqe0vtQdERPYq00V4I3Ycz8ROZaVrVuR2aWTTL/AT5wW/4ujAnOTfXaR2TEopaRtMdxJVA1t2ZPPyj8sY9cMyNm4v/ANP3bQQ1/XtyOCj25MWUtcyEUldJwSnc0xgNqNi/XgyegZb8Y5JtJbGDItewuPRs7gk+Al/DH1CI8vwKVsRERFJdTUtm0tCE7go+Bnj40fxQrQf012nAttlEWFc/BjGxY+hFRs5O/gN5wa/pn1gnQ9ZS6rSnUSVXRm+gVsab87I2MmMjR3HLtIK3aYmuxgc/IQrQuP1IUhEqp10V5PnoqfwfOyUAmMC/KoGWZwb/JpBwQl0Dqws3QvoG7gCdCdR5aM7iUREKp8Z8f0ZHT2R9+N9yCZS7LaH2c+cGvyRU4KTaGmbyy8JtWNSSknbYCoSVXalbFxluyCfxnvxaqwv38YPKXK7NLIZFJzAVaH3aWLpZc1SRKRK2+Tq8lT0DF6KnUhWMQ2xI2weg0IT6Bf4iYjF9n5gNa4KUJGo8lGRSESk8trk6vJ67He8GutbbFe0X/W2BYmC0USa29ayvbjaMSlF3c2qmV/iLXkz9lveiB3HRhoUuV0ddjIw+AVXhMbTrKx/NEREUkRjy+Cf4Ve4KvQ+L0b7MTp2EunULrDdJNeFSTldaMJWzgp+y1nBb+kaWO5DxiIiIlIdNLYMrg69z1XBD/gx3oU3Y8fxYfyIInuKTHadmRztzJ3RwXS3RfQNTuOEwFQOtmWUZchZqT50J1FlV8w3cGtdQ96P9eG92DHMdh2KPUwLNnFp6CMGBj+nnmWWd5YiIillu6vBa7ETeC56CutoVOy2B9kyzg5+yxnB72lhW7wr9Q1cAbqTqPLRnUQiIlVLhqvJ+NiRvBE7jimuc4n2ac5mTghO4/jAdI4KzC3ZZ0K1Y1KKupulinyNqxXxpnwS78Unsd5McgfhCBS7e09byCWhTzg18GPJukaIiMhu2S7IJ/HevBQ9kYmu6163P8x+pn/wJ/oFfqJdYL0aV4VQkajyUZFIRKTqWuma8L/YEXwQO4oZrmOJ9gkQ5xBbzDGBORwdmEOvwM/UtOyCG6odk1JUJEoR0TsaMsMdwFex7nwS78V8126v+9RlJ2cFv2Vg8HN1gxARKSc/x1vzcuxE3o4dy3Zq7XX7g2wZfX93Ascd2Iye+zUgHCy+qF9dqEhU+ahIJCKSGlbEm/Jh/EjGx45kpjugxPtFyKGHLaJ34GcOCyzksMDC3EmNVCRKKSoSVVHOORat3863izby3aKNTJy3lIwSfBgx4hwdmMuAwHecFvyRWlb4VPciIlI2mS7CJ/HevB07lm/ihxLfyx2dkFu8Pzowm98EZnFEYD4dbTUBqzz//xaQxEahikSVj4pEIiKpZ71rwBexHnwW78k38UPIpEap9m9vazmsRy96tmtIjzYN6NS8DjXCwSRlKxVBRaIqIisaY87qdKYt38q05VuYtGQz6zNKXuA51H7hjOD3nB78oeyj14uISKmsdw0YFzua92N9mFGKb+wakEHvwAKOCCzg8MB8utlSwpWpS7CKRNWKikQiIqltlwszMd6FL+I9+C7ejYWuTamPEQoYHZvVoWvLenRtlfs4uGV96tcKJyFjSQYViSqhrGiMReu3s2BtBrNWbWPa8q3MXZ1Odixe4mMEiNPbFnBScAonBibnjnkhIiK+W+0a8UmsNx/FD2dSvEuJ7jD6VRrZHGTL6RZYSjdbQrfAUg60FaRZNIkZF0NFompFRSIRkeplvWvAD/EufB/vxvfxg1nhmu3zsZqzmQMCq+loqznAVtPRVtExsIpmbNVsavuiErTBQknLoBrLisZYsXknSzbuZOH6DOavyWD+2nR+2bCDWLz0RbnGbOPowBx+G5hJ3+C03P6hIiJSqbSyzfwx9Al/5BM2ubp8Ez+Ur2OH8nX8UDZS/AflLCLMcB2ZEdsz4GSYKB1tJZ1sFR0DiUaXraK9rdVEBCIiIrLPmtlWBgR/YEDwBwBWxJswxXVmarwTU+OdmOf2I0bJupatoxHr4o34nm6eeF120s7W0dbW09Y2JH7mLre2jdSwnHI/LykfKhLtg3jcsXFHFqu37mLN1kxWb9vF8k07WLJpJ0s2bmfVlkz2oRa0W61IkCM7NOKYjk04ZsIZdLaVlXvsChER8WhsGZwZ/I4zg98Rd8Zc146v44fwU/wgJscPJIPaez1GDiHmufbMc+0hzw2nQWLsZ+tpk2hwtbENtLGNu583IV3f3ImIiEiJtQ1spC0bOTP4HQA7XRozXQemxjsxPd6ROfH2rKJpqY6ZQS1muw7Mdh0KXd+czbS0TbSwLTS3LTSzLbnL5D5vbpupS6baND5QkSiP7GiczTuy2bg9i43bs9i0PZtNO7LYuD2bDRlZrN6ayeptmazdtoucWPkVbdo0rEnP/RrSs20DeuzXgG6t6hMJJbopfLai3F5HREQqXsAc3Wwp3QJLgfeJOWOBa8vkeGcmJYpGa2lc4uPFCLLEtWSJa1no+hpk0dI205StNLWtNLM8P9lGU9tKI8ugAdv1LZ6IiIgUUMuyOMrmc1Rg/u7YNlebufH9mOPaMzfejrmuPYtcK6L7WFJYRyPWuUZQzMfqGmTRmHQaWQYNLYNG5P5sbOk0JGN3vAE7qGs7qctO6rBLN1iUUUoXiVZs3sm8Nemk74qSnpnDtswc0nflkJ4ZTfzM2b0uPTOHjKzkj/3QukFNDmpRl4Na1qV7m9yiULO6pRtpXkREqq6gObracroGlnMJE4DcsQHmxNsz27Vndrw9s+MdSv2N3a92kZZbRKJlsQ0vyG18NWQ79W07DW07DdhOA9tOg4/mc37vtnRosvc7nkRERCT11bcd9AnOow/zdsdyXJBlrjmLXCt+ca34Jd569/IOapb5NXeRxiqasso13WubJq867KQumbsLR3UtM/FzJ7XZRU2yqWFZ1CSbWmTtXq5JFjUt8TPPcoQoYaKEiFWLO5uSWiQys/7Ao0AQeM45d38yXy+/j+es5e7x8/a+YRK0ZBMdAmvoYGs5yJZzUGAFB9oK6u/aCUvJfYiIiJA7NkCz4HSOZ/ru2GZXl4WuNYvirVjkWrPIteaXeCtW06TcXncXaawhjTWusbfx9eUvHHdgUxWJysne2kNmlgaMBnoBm4ALnHNLE+tuAS4DYsD1zrmPizummXUAxgCNgKnAIOdcdrLPUUREqp+wxehoq+nIak/cOVhHQ5a7ZqxwzVjhmrIi3jSx3Iy1NMSVYoKP0tpOLbZTK7d9A6UqMBXHiBMmSiTxCBMlYtE8sZx8sRzCxAgSTzxihCz3eYi8P2OEft3m04X0bt+QYzqWX3uvtJJWJDKzIPAEcCKwEvjJzMY55+Ym6zXzq1cjedPx1WMHrWwjrWxT4rGRDraWDraWdraOmqb2mIiI7LtGlsGRNp8j89zqDbDDpbHMNWela8oK15SVicevy9upVS6v37BWpFyOU92VsD10GbDFOdfRzAYCw4ELzKwrMBA4GGgFfGpmByb2KeqYw4FHnHNjzOzpxLGfSv6ZioiI5DKDFuSOMXQECwqsz3Ih1rjGrKUh61xD1ruGrHWNEssNWEfu8ywqV1vEESCbCNl58yqsAFWWotSnP3PVcQekZpEIOAJY5JxbDGBmY4ABQMUViWqW7vQCxGlEOk0sncaWTmO20djSaWLbaEI6LWwzrWwTLW0TdWxXkrIWEREpWm3Lyu2uxvIC65yDbdRmvWvAeteQ9TRgg2vAeteADa4+62nARlefra4uW6ld7DgCDWol74uWaqYk7aEBwNDE8pvACDOzRHyMcy4LWGJmixLHo7Bjmtk84ATgosQ2oxLHVZFIREQqjTSL0t7W0Z51RW7jHGRQky2uLpupy2ZXj82uLluoy+bdsdx4OrXY7mqSQa1y6ebmt1DA3z5tySwStQbyjrq8Ejgy/0ZmdgVwReLpdjMrWGqsQEsKDzcBNlZoIlWfrlnp6HqVnq5Z6eh6lV4VvWbpwJoyH6XF8FLvUtLr1a7UR67aStIe2r2Ncy5qZtuAxon4j/n2bZ1YLuyYjYGtzrloIdt7VGD7q4r+Hu2T6nKuOs/UU13OtbqcJ6TMuaZDMYUkUuY8vf4+HP7uDZXXeZaoDZbMIlFh5a8CN145554BnkliHmVmZpOdc739zqMq0TUrHV2v0tM1Kx1dr9LTNSsdXa8ilaQ9VNQ2RcULG8ihuO0LBiuo/VWd3hfV5Vx1nqmnupxrdTlPqD7nqvNMjuSNFpX77VXbPM/bQL4RrURERERSW0naQ7u3MbMQUB/YXMy+RcU3Ag0SxyjqtURERESKlMwi0U9AJzPrYGYRcgdeHJfE1xMRERGpbErSHhoHDE4snwt87pxzifhAM0tLzFrWCZhU1DET+3yROAaJY76XxHMTERGRFJO07maJPvXXAh+TOz3rSOfcnGS9XpJV6u5wlZSuWenoepWerlnp6HqVnq5Z6eh6FaKo9pCZ3QVMds6NA54HXkoMTL2Z3KIPie3GkjvIdRS4xjkXAyimjfUPYIyZ3Q1MSxzbT9XpfVFdzlXnmXqqy7lWl/OE6nOuOs8ksNwvnUREREREREREpDpLZnczERERERERERGpIlQkEhERERERERERFYnMrL+ZLTCzRWY2pJD1V5nZLDObbmbfmlnXPOtuSey3wMz6VWzm/tjX62Vm7c0sMxGfbmZPV3z2/tjbNcuz3blm5sysd56Y3mNFb+e5XnqPFft7+Ucz25Dn2lyeZ91gM1uYeAzOv28qKuP1iuWJV5vJGErye2lm55vZXDObY2av5olXu/eY5Crp3/OqwMxGmtl6M5udJ9bIzCYk3tsTzKxhIm5m9ljivGea2WH+ZV46ZtbWzL4ws3mJ3+UbEvFUPNcaZjbJzGYkzvXORLyDmU1MnOvrljs4PJY7gPzriXOdaGbt/cy/tMwsaGbTzOyDxPNUPc+ltuezyORELBXfvw3M7E0zm5/4fe2TaudpZp3ztLmmm1m6md2Yauf5KzP7a+Jv0Wwzey3xN8qf31PnXLV9kDvY4y/A/kAEmAF0zbdNvTzLZwAfJZa7JrZPAzokjhP0+5wq8fVqD8z2+xwq4zVLbFcX+Br4Eeit99g+XS+9x4r+vfwjMKKQfRsBixM/GyaWG/p9TpX1eiXWbff7HCrpNetE7iDJDRPPm1XX95geJX/fVKUH8FvgsLz/zwAPAEMSy0OA4YnlU4D/AQYcBUz0O/9SnGdL4LDEcl3g50R7JBXP1YA6ieUwMDFxDmOBgYn408BfEstXA08nlgcCr/t9DqU8378BrwIfJJ6n6nkuBZrki6Xi+3cUcHliOQI0SMXzzHO+QWAt0C4VzxNoDSwBaiaejyW3PerL72l1v5PoCGCRc26xcy4bGAMMyLuBcy49z9PawK8jfQ8AxjjnspxzS4BFieOlsrJcr+pqr9csYRi5f/B25YnpPVa661VdlfSaFaYfMME5t9k5twWYAPRPUp6VRVmuV3VVkmv2Z+CJxPsI59z6RLw6vsckV0r9rjnnviZ35rm8BpD7QY3EzzPzxEe7XD8CDcysZcVkWjbOuTXOuamJ5QxgHrkfXlLxXJ1zbnviaTjxcMAJwJuJeP5z/fUavAn0NTOroHTLxMzaAKcCzyWeGyl4nsVIqfevmdUjt3D9PIBzLts5t5UUO898+gK/OOeWkbrnGQJqmlkIqAWswaff0+peJGoNrMjzfGUi5mFm15jZL+R+KL2+NPummLJcL4AOidtcvzKz3yQ31Upjr9fMzHoCbZ1zH5R23xRUlusFeo9B0e+TcxK33r5pZm1LuW8qKcv1AqhhZpPN7EczO7MQajZzAAALzklEQVSQ/VJRSa7ZgcCBZvZd4tr0L8W+kpqqw799c+fcGsgtrgDNEvGUOPdE94We5N5hk5LnarldsKYD68ktYv8CbHXORROb5D2f3eeaWL8NaFyxGe+z/wA3A/HE88ak5nlCbqHvEzObYmZXJGKp9v7dH9gAvJBo9z5nZrVJvfPMayDwWmI55c7TObcK+DewnNzi0DZgCj79nlb3IlFh1bYCd744555wzh0A/AP4Z2n2TTFluV5rgP2ccz1J3O6aqIKnumKvmZkFgEeA/yvtvimqLNdL77E98r9P3gfaO+cOBT5lzzcPeo/lKun1gtz3WG/gIuA/ZnZActKsVEpyzULkdjn7HXAh8JyZNSjhvpKaqvO/fZU/dzOrA7wF3JjvLvECmxYSqzLn6pyLOed6AG3IvfutS2GbJX5WyXM1s9OA9c65KXnDhWxapc8zj2Occ4cBJwPXmNlvi9m2qp5riNzur08l2r07yO12VZSqep4AJMbhOQN4Y2+bFhKrEueZGFdpALlDjLQit0fOyYVsWiG/p9W9SLQSyPsNcRtgdTHbj2HPLV6l3TcV7PP1SnSZ2pRYnkLuNzUHJinPymRv16wu0A340syWktt/dpzlDsas91gprpfeY7sVeJ845zY557IST58FepV03xRUluuFc271/7d35zF2VmUcx78/FlsotQhF0BAtxZZ9KdAGKatgFQ2FStWSIiINIkHQKInBakUxQsSwaCAiiBJlUSi0jRiK0o4gRdpSu28sBa1gMagooFDp4x/nue07l7mzlHZmuPP7JDfz3vMu55x33jv3mfOec978+RTQQrnL3uw6c52sBaZHxPocHruK0mjUF68xK/rC735dbShD/qwNs3xL113S9pQGolsj4u5Mbsq61uRQnRZKXLFzDveA1vXZWNdcP4g3DkHsjUYDYzNuuoMyfOUamq+eQKvv6eeBeyiNf812/a4F1kbEo/n+LkqjUbPVs+ZkYEFErMv3zVjPk4A1EfG3iFgP3A0cRQ99Tvt6I9E8YFjOGv42Sje2Vk+rkTSs8vajwOO5PAOYkDOL70UJhud2Q5l70mafL0m7Sdo2l4dSztdT3VLqntXuOYuIFyNicEQMiYghlImYx0bEfHyNdel8+Rpr93NZHY89ljLHBMBMYIykd+QdjDGZ1sw2+3zleeqXy4Mpgffybil1z+rwnAHTgBNg47kZTvn89cVrzIrOXDdvdTOA2hP7Pg1Mr6SfpeJI4MXa0IjeLue0+DGwIiKuqqxqxrrulj0ekbQD5Z+0FcBsYHxuVl/X2jkYD8yKiF7fSyEiLomIPTNumkAp90SarJ4AkgZIGlhbpnznLKXJrt+I+CvwZ0n7ZNKJlHikqepZcQabhppBc9bzT8CRknbMv8O132nPfE6jF8zm3ZMvyizoqym9DiZn2rco/3gCXAssAxbmL+mAyr6Tc79VwMk9XZfefL6A0zN9EbAAOKWn69Jbzlndti3k07p8jXXtfPkaa/dzeXnl3MwG9q3sew5lUvQngM/0dF168/mi3NFZkulLgEk9XZdedM4EXEUJaJaQT+Loq9eYX42vm7fqi/IPynPAesod3EmU+R8eoNwQewDYJbcVcF3We0n1e723v4CjKUMWFmcstzB/j81Y14MpT2VcTGlImJLpQyk35Z6gDG/pl+n98/0TuX5oT9dhM+p8PJuebtZ09cw6LcrXssr3VTNev4cC8/P6nUZ5gmgz1nNH4AVgUCWt6eqZ5f8msDL/Hv2M8oTrHvmcKjMxMzMzMzMzM7M+rK8PNzMzMzMzMzMzM9xIZGZmZmZmZmZmuJHIzMzMzMzMzMxwI5GZmZmZmZmZmeFGIjMzMzMzMzMzw41EZk1N0u6SbpP0lKTHJD0iaVyuO0LS9ztxjDkN0l/qYlmOl/SrruzTxjEGSHpB0qC69GmSPrE18zYzMzNri6Q9JN0h6UlJyyX9WtLwBtu2GVe9yfy7HOdIulTSxVsg30fq0raTtE7Su7Zm3ma29biRyKxJSRIwDXgwIoZGxOHABGBPgIiYHxEXdXSciDhq65a08yLiZeB+4LRaWjYYHQ24EcjMzMy6VcZb9wAtEbF3ROwPfBXYvW67baF3xVVbwIPAnpKGVNJOApZGxHM9UiIze9PcSGTWvD4AvBYRP6wlRMQzEfEDaH3XKe/o3CypJXsdbWw86qjHUB6nRdJdklZKujUDJiR9ONN+D3ysss+AzG+epD9KOjXTvyTp5lw+SNJSSTvWZXk7pbGrZhxwX0S8ImmUpDl5zDmS9mmjvK3uXmUeQ3L5TElzJS2UdEMtoDMzMzNr4ARgfV28tTAiHsoYabak24AlsCmuynW/k/RLSaslXSFpYsYhSyTtndvtJmlqxkzzJI1urzAdxHSTJa2S9Ftgn0r63pLuy17nD0naN9OnSzorl8+TdGs1r4jYANwJfLKSPIESqyHp3CzzoqxDfUxHlvOIXB4s6elc3lbSlbn/Yknntf9rMLMtxY1EZs3rAGBBF7bfF/gQMAr4hqTtu7DvCOCLwP7AUGC0pP7AjcApwDHAHpXtJwOzImIkJbi6UtIA4BrgfSpD4n4CnBcRr9TldR9wuKRd8/3GYARYCRwbESOAKcB3OlsBSftRgpzREXEo8DowsbP7m5mZWZ90IPBYO+tHAZOzh1G9Q4AvAAcBnwKGR8Qo4CbgwtzmWuDqjJlOz3UdeUNMJ6nWo3wE5cbdyMr2PwIuzF7nFwPXZ/pngSmSjgG+XClT1cabd5L6AR8Bpua6uyNiZEQcAqwAJnWi7DWTgBez3iOBcyXt1YX9zWwzbdfTBTCz7iHpOsqwrNfyC7fevRHxKvCqpOcp3aTXdvLwcyNibeazEBgCvASsiYjHM/3nlGADYAwwttKjpz/wnohYIelsYDFwQ0Q8XJ9RRLwmaQYwXtJU4FDKEDSAQcAtkoYBAXSloetE4HBgXnaE2gF4vgv7m5mZmdWbGxFrGqybVxuWJelJNsUzSyg30aAM39o/YxOAt0saGBH/bifPtmK6Y4B7ajffMpZC0k7AUcCdlTz6AUTEOklTgNnAuIj4e31GETFP0k7Ze3s/4A8R8Y9cfaCkbwM7AzsBM9spc70xwMGSxuf7QcAwoNG5NLMtxI1EZs1rGeWOEwARcYGkwcD8Btu/Wll+na79fWi0bzTYXsDpEbGqjXXDKA1M724nv9uBr+VxpkfE+ky/DJgdEeNyCFlLG/v+j9a9KPtXynRLRFzSTr5mZmZmVcuA8e2sf7mdddX4aUPl/QY2xVLbAO+PiP90oUxdicu2Af6ZvajbchDwAu3HZXdQehPtx6be3QA/BU6LiEV5E/D4NvatxmX9K+mi9G7qSsOSmW0BHm5m1rxmAf0lnV9Je8NY8K1oJbBXbUw9cEZl3UzgwsrcRSPy5yBKt+pjgV0rd4/qzaY0Jl1A62BkEPCXXD67wb5PA4dlfocBta7LD1B6J70z1+0i6b0d1tLMzMz6sllAP0nn1hIkjZR03BY6/v3A5yvHbtSY05EHgXGSdpA0kDIdABHxL2CNpI/n8SXpkFweBZxMGaJ2cTvDvW4HzqTMhzmjkj4QeC6nMGg0hP9pSk9uaN3YNhM4vzb9gaThOTWBmW1lbiQya1IREZSngB0naY2kucAtwFe6Kf//UoaX3asycfUzldWXUYaCLZa0NN8DXA1cHxGrKWPRr6g12tQdewNlvPuulKCn5rvA5ZIeBhpNOj0V2CWHxZ0PrM5jLqf0Trpf0mLgN0DDx7eamZmZZbw1DvigpCclLQMuBZ7dQllcBByRkzcvBz63meVcAPwCWEiJhR6qrJ4ITJK0iNIz6tScX+hG4JyIeJYyJ9HNtRt8dcdeDrxCmW+y2nPq68CjlJhqZYOifY/SGDQHGFxJvwlYDizIWPEGPArGrFuo/F0zMzMzMzMzM7O+zD2JzMzMzMzMzMzMjURmZmZmZmZmZuZGIjMzMzMzMzMzw41EZmZmZmZmZmaGG4nMzMzMzMzMzAw3EpmZmZmZmZmZGW4kMjMzMzMzMzMz4P9e2bpXRZvQugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, let us see if the Gini Index values are normal\n",
    "gini = crime_gini_df['Estimate; Gini Index']\n",
    "# Test to see if the gini index values are normal\n",
    "st_gini, p_gini = normaltest(gini)\n",
    "print(p_gini)\n",
    "if p_gini > 0.01:\n",
    "    print('The Gini index distribution is approximately normal.')\n",
    "else:\n",
    "    print('The Gini index distribution is not approximately normal.')\n",
    "    \n",
    "# See if the crime index values are normal\n",
    "crimeindex = crime_gini_df['crime index']\n",
    "# Test to see if the crime index values are normal\n",
    "st_crime, p_crime = normaltest(crimeindex)\n",
    "print(p_crime)\n",
    "if p_crime > 0.01:\n",
    "    print('The crime index distribution is approximately normal.')\n",
    "else:\n",
    "    print('The crime index distribution is not approximately normal.')\n",
    "    \n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "ax0, ax1 = axes.flatten()\n",
    "\n",
    "xs0 = np.arange(min(gini), max(gini), 0.001)\n",
    "fit0 = stats.norm.pdf(xs0, np.mean(gini), np.std(gini))\n",
    "ax0.plot(xs0, fit0, label = 'Normal Dist.', lw = 4)\n",
    "ax0.hist(gini, density = True, label = 'Actual Data');\n",
    "ax0.set_title('Gini Index Distribution')\n",
    "ax0.set_xlabel('Gini Index Value')\n",
    "ax0.set_ylabel('City Count')\n",
    "ax0.legend();\n",
    "\n",
    "xs01 = np.arange(min(crimeindex), max(crimeindex), 0.001)\n",
    "fit01 = stats.norm.pdf(xs01, np.mean(crimeindex), np.std(crimeindex))\n",
    "ax1.plot(xs01, fit01, label = 'Normal Dist.', lw = 4)\n",
    "ax1.hist(crimeindex, density = True, label = 'Actual Data');\n",
    "ax1.set_title('Crime Index Distribution')\n",
    "ax1.set_xlabel('Crime Index Value')\n",
    "ax1.set_ylabel('City Count')\n",
    "ax1.legend();\n",
    "\n",
    "fig.set_size_inches(20, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since it is not normal, we are going to transform the data by taking a value to the power of 1/2.2 and test for normality again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.023377379473207744\n",
      "The Gini index distribution is approximately normal.\n",
      "0.1170495969245683\n",
      "The crime index distribution is approximately normal.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transform the Gini index and crime index so they are normal\n",
    "transform_gini = []\n",
    "transform_crime = []\n",
    "for i in range(len(gini)):\n",
    "    y = gini[i]**(1/2.2)\n",
    "    k = crimeindex[i]**(1/2.2)\n",
    "    transform_gini.append(y)\n",
    "    transform_crime.append(k)\n",
    "\n",
    "# Test Gini index for normality again\n",
    "st_gini_transform, p_gini_transform = normaltest(transform_gini)\n",
    "print(p_gini_transform)\n",
    "if p_gini_transform > 0.01:\n",
    "    print('The Gini index distribution is approximately normal.')\n",
    "else:\n",
    "    print('The Gini index distribution is not approximately normal.')\n",
    "\n",
    "# Test crime index for normality again\n",
    "st_crime_transform, p_crime_transform = normaltest(transform_crime)\n",
    "print(p_crime_transform)\n",
    "if p_crime_transform > 0.01:\n",
    "    print('The crime index distribution is approximately normal.')\n",
    "else:\n",
    "    print('The crime index distribution is not approximately normal.')\n",
    "\n",
    "fig1, axes1 = plt.subplots(nrows=1, ncols=2)\n",
    "ax2, ax3 = axes1.flatten()\n",
    "    \n",
    "xs = np.arange(min(transform_gini), max(transform_gini), 0.001)\n",
    "fit = stats.norm.pdf(xs, np.mean(transform_gini), np.std(transform_gini))\n",
    "ax2.plot(xs, fit, label = 'Normal Dist.', lw = 4)\n",
    "ax2.hist(transform_gini, density = True, label = 'Actual Data');\n",
    "ax2.set_title('Transformed Gini Index Distribution')\n",
    "ax2.set_xlabel('Transformed Gini Index Value')\n",
    "ax2.set_ylabel('City Count')\n",
    "ax2.legend();\n",
    "\n",
    "xs1 = np.arange(min(transform_crime), max(transform_crime), 0.001)\n",
    "fit1 = stats.norm.pdf(xs1, np.mean(transform_crime), np.std(transform_crime))\n",
    "ax3.plot(xs1, fit1, label = 'Normal Dist.', lw = 4)\n",
    "ax3.hist(transform_crime, density = True, label = 'Actual Data');\n",
    "ax3.set_title('Transformed Crime Index Distribution')\n",
    "ax3.set_xlabel('Transformed Crime Index Value')\n",
    "ax3.set_ylabel('City Count')\n",
    "ax3.legend();\n",
    "\n",
    "fig1.set_size_inches(20, 4)\n",
    "\n",
    "# Create a new column for the transformed Gini Index \n",
    "# and rewrite crime index column\n",
    "crime_gini_df['TransformedGini'] = transform_gini\n",
    "crime_gini_df['crime index'] = transform_crime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the Gini index and crime index are normalized, lets look at how they are related and run some tests. First we create a table of correlation between crime index and Gini index. As we can see, there is a correlation of 0.33 and it is slightly less than the correlation we saw between quintile share and crime index but it is still a weak positive correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crime index</th>\n",
       "      <th>TransformedGini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crime index</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.329588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TransformedGini</th>\n",
       "      <td>0.329588</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 crime index  TransformedGini\n",
       "crime index         1.000000         0.329588\n",
       "TransformedGini     0.329588         1.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation between crime index and Gini index\n",
    "corrleation = crime_gini_df[['crime index', 'TransformedGini']].corr()\n",
    "corrleation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to how we looked at quintile share and crime index, we want to look at a linear model to see if Gini index significantly can determind crime index. We will use Ordinary Least Squares (OLS) again for our modeling using patsy and statsmodels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gini Index significantly predicts crime index.\n",
      "\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             crimeindex   R-squared:                       0.109\n",
      "Model:                            OLS   Adj. R-squared:                  0.108\n",
      "Method:                 Least Squares   F-statistic:                     337.1\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):           3.92e-71\n",
      "Time:                        23:25:00   Log-Likelihood:                -7246.6\n",
      "No. Observations:                2768   AIC:                         1.450e+04\n",
      "Df Residuals:                    2766   BIC:                         1.451e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept         -12.1309      1.295     -9.366      0.000     -14.671      -9.591\n",
      "TransformedGini    34.4819      1.878     18.360      0.000      30.799      38.165\n",
      "==============================================================================\n",
      "Omnibus:                        8.272   Durbin-Watson:                   2.081\n",
      "Prob(Omnibus):                  0.016   Jarque-Bera (JB):                8.236\n",
      "Skew:                          -0.132   Prob(JB):                       0.0163\n",
      "Kurtosis:                       3.044   Cond. No.                         43.9\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Rename crime index column to check \n",
    "crime_gini_df.rename(columns = {'crime index': 'crimeindex'}, \n",
    "                     inplace = True)\n",
    "outcome, predictors = patsy.dmatrices('crimeindex ~ TransformedGini', \n",
    "                                      crime_gini_df)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "# fit the model\n",
    "res = mod.fit()\n",
    "\n",
    "lm = bool(res.pvalues[1] < 0.01)\n",
    "if lm == True:\n",
    "    print(\"Gini Index significantly predicts crime index.\\n\")\n",
    "else:\n",
    "    print(\"Gini Index doesn't significantly predict crime index.\\n\") \n",
    "\n",
    "## look at the results\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EEB7wu8BvGmNe6j28KITY09sN7AGWNnutMebXgV8HePbZZ3fWW41wS7iZJMLG4tjB6RLfuNSkk+RbfjnvJI9b9F2e2Dt2W05kpzBcxKyGHs8fmhh0+V6qRwSuQ8F3OTY3xsJadx3/H66PmruZ4pWzqxh1c1mLTpKR5opqLzL3pBzoJYGljTaznDi7xrbJcs1qlHJ0d5VqwePEQp2lZkI3zYnSnFjZVKoUYDzDUjOm7kjGi96g0S1Kc1Y7CaevNjHGnscVgno3WxeBD1NY4e4Wc3eKmNDf4W2GfRMFzi53GCt4dNLNGUx38xp3AjtWIxA2JPj3wCljzL8beur3gU/1/v0p4Pd2ag0j7AxutZFrqzzuxrz6g8qouB1srF8ErsP+yRLffniawzNlDk+XmC6HzE0U1/H5+xhu/EoyxesX1igGzrbotwbBcDvEVNmylAByrWl0UxabCWdX2oO+hqVWwmTJx3cl1YLHcwcnMcKw0kno9hrPyoFLJfSIezu/XBsWmwlLzXhQSI1TTZprxkIPT0qacc6FWkQ9SgfraUQprhR87ewK/+XkVU5cqhNn6q7w7XeKmHAjmuquSogrBVGSb1oLu5sF653CTn7z/g7wSeAbQojjvcf+JfBLwP8rhPhvgQvAP9jBNYywA7iVL1s/6tqYx71Znvt+4m6kmm5Uv+imCuXaQnDgOsyN9yLK4rUodjhqXmzFtBPFU/s3j2A3RrmVwCVwLSsp7OkIdRLF1XpMI8nIco0UkCmXcysdLqxGlAKXb91bHdRyEqVodjOMsfltKSzTTQpB6DpIKVmLUjxHoDSDHeLRPVVemV8lzjWh5zBVDnCk4Oxyh/1T1pBeqEXsGw9xHUnBczi6u8LpKy3OLHXuWCDuRpF7H7eTr78RTTXwHI7NjfOX7yyzb7xAkqm7Uqu6l9ixlRlj/gq2bNN8cafOO8LO43a/bA9iCmcj7qaS5VbO77sem2G5k1INPZJckSrNudUO1DoUPIepsg8GvmV3FYCzyx1KgXvDGsuw460WPWtcr7a4EqXUWglCGFajjG6W4UhJ6ElSZegkOQbreP7ynRUqgUu16LGwGhH1IlkHMAaacUamNOXAw5AChmro0YqzdemYY3PjnFioU88UpcCl5DsUfId3l9os93YeuTY8uqvM3ERhkHe/G3z7nSIm3Cy96UjBMwcm+eDeCvOr0QMd6GyGB3t1IzyQ2Kkv2/3GdmsfLx6dYamZbGvHsJnz6xc0rza7nFnsoLXm8FSJhbUucaaYX+kgEBzdW2WxGROlir/zgal1fQQbMex4+9Hrsbkx/vytJQyCTBkQhqlSgAEyZXcc9W6K0oZLdctsqgQOCEE3VcSZxnEgzq0jkAKkgVwpMiXQ2tAwGeNFf90OsZ9aWmolnFlqcW65g8ZQ8By+/dAkY6WATpJzpdFlphIM/o5uN38/jJ1qMNsOQ+37PrSb6XLAM49M3tba7ydGjmCEW8a97ua8V9hOofFKs81/+toFdlWC6yd9na9zZKZ400lfRd/lhSOT/MqfvoMQMFnycaXg0LTkaiOmEWdMFn1OXm7xD5+bw5EQ3sAJwHrH249erzYSqgWfA1M+r8zXmC75FAOPXBnLdxew3GMFha6D50raiaKba7S2xWGVGaSUGG3IgULvHIErKPs+7TSnFWfXNU8GnsNsJeB8rcMH940RuJLjF9cYLwUDHaPh7mqwKbDzKxGvX6zzwgembov5tZPEhIcpvXmreHhXfgvYKXqh4zh86EMfIssyXNflU5/6FP/sn/0zpNy6Bj8/P89Xv/pVfvzHf/y2z3u/sdMsoPuFm9U+ktzKNkgBH9p3LWINPIcw1/zN/Cp/e36V/+qx9ZO+NnMQvhA8NlvGcyULaxFRaiUlnj04yVjBpd7NOHm5yZfesjuUhdUuR/dUt9yFDTvefvT6b/7zW+RaETfsuoWARpwTuA6eI9DKoIwGI/FdiVDQiDImSn4v3WPo5iC1XZvvycGwniQ3FDyYKgdUCy6X6l3mJossrEacutpkuWXlMjCCD8yWGS96GMQ6dlToOdQzxbmVDivthFwbir6DMnqg03Or6bid6i0Zfv8HPb15O3jPO4KdnF5UKBQ4ftzWwZeWlvjxH/9xGo0Gn/nMZ7Z8zfz8PJ/73Oceakew01+2zXAvegVuVvtYasZIzHXyIH3FznJvmtmltS7tuMXpqy2aSU6zmzJR9PnBJ/eyd6xAmmv+7O0lAkfy4Ucm2T95jXfe7GacWKiTa8NYwSNKcg5Pj/GXb6+w0kl5/uDkgBaa5IqlZsy7K22iRCEdiDOFAc4stjhfa3OxHpGmimac47sOxcCBzBCl/XTPtWvJco3G4EoJBqSUOFLhSkEYOGSZZQQ5UlAJPaoFl+lywGO7ynzjSpN3VyIu1jq4rqQcuDS7tqbwjUsNyqHL7oqtDbjOtXP6ruSvz9V4fHeVcuiQKz1QOx0TcH6lw7/5k9OW1lr0tnXP38uR+05hxxvK7gaeffZZ8+qrr657bDuNVDvVXNJHuVym3b6mGnn27Fmee+45VlZWOH/+PJ/85CfpdCyF8ld/9Vd54YUX+MhHPsKpU6c4dOgQn/rUp/iRH/mRTY/bLu5nY929auS6lea1O7mGr72zQsF3ODRVsjTNDemYr51dGehPf/uRa8b7wmqHs8sdxos+i80ur1+sM1X0KQUuy80uBkE7UVSLHj/6zD72jRf5cs8R5NpcGx2ZKV6ZX8V17GAcYwyrnZTHdpU5s9zmzNUWCvjuR2eYqvi8ealFJ8kpBw7PHJggU3ZXEmcazxEcv7hGmhmMgGY3RWuD40iMMXiOJFOaou8hpdUQqnVSlFL4ngvGdjyrns5SteDjCCuHrYxmouizu1rg40/txXXgP33tAkXPoVLw8Hpsp/M129QWZznNrmKs6BK6DuMln6lSwHjRY62Tcr7W4blDU0C/ka7EWNHnjYsNtNZkSvPYrgq7quFduefvJzwQDWX3GzvVXLIVDh8+jNaapaUlZmdn+eIXv0gYhpw5c4ZPfOITvPrqq/zSL/0S//bf/lv+8A//EIAoijY97mHAvdgm32rz2q1go4N5fHeVM0stzi53mK91eXL/GNVwvVyCMXBkprzuffqKnd0058RCAykE0xVrtIywRt3zHOI050/eXOS/fnafbbaSkjjPWWol7J8sDiSgyz1JhlaccbEe4UpBNfB45sAE87UOf3txjXqUc2SmyFP7xxkruiw3E/7ynRWUNqx2Eoyx8wgQoLXBdSSx1nhAakBqQ5IbpMgZLwUobdBaU/RdEmUIHIl2wWQKRwqC3o7PYB3EeNHn40/tYboc8O6STalluSbNFUKAIwRC2GuIUrsbkUL01mM7yZdbMUmu16mdGqz09cunl8EYCr5DteByvhZxYKq0rXv+IHaaP+h4T38qt8N3v1P0d1hZlvGzP/uzg7kCb7/99qbHb/e49ytuxZkfnC5t2wBs5mB2V0MurkYDuYA3LjZ4/tDEYGdgMBgDs5X1kWiqbLrkm1carEUJvuOwsBbRTXPC3nkl4DiCXGveutIa9A4MdxAPS0B305zXzq+hjWGllSCwUfvusRDPkcyUQwq+S8F3eONik5VWjBACR1q+/5V6RK5t+sdxhHU8yqCwjxkstztKNYUgp9Uz4lGqMAY6jiBwBYEnMUYMJLGNgXLg8fieCtNlS2d9e7nNWGjprZ1UUWsnJFoROJJmN2OyHOBKSa700H2wKbbLjS7H9lapR+mA2WSVUQ2VgofqXf/lPObJ/Va++kYB3GiQ/e3hPe0Idqq5ZCucPXsWx3GYnZ3lM5/5DLt27eKNN95Aa00Ybs4B/+Vf/uVtHfd+xXad+avn1/jGpea2DcBmDqbfGNTP0edKs9josqtaoN7NmCoFlHx3kMZZbMVcWuty6kqTVpxTaye4jqASukghaCf5IHqWUuAK27V7+mqLY/vHma91UUpfGx2pbBNWN83563M1Fupdir7EERLZo3Rerkd0M83B6TJxlvPXZ5eZLAY0k4xq6FHvpESpItegtMYYgcYMmsgEAs8RZEojpUApQxTnGARSSqTWGIkVwzOGNDPMVHwqBRetDZkyPL6ngiMESa44v9IZdJB3M8VUOeTwTBnPkVypd2nFGRLrWJUxSAmHZoosrHaZX+3QiDLO1To8+0hApjXVwGO1ndrJbULgCtFzpnrAMAo8Z9MAbqd2j++HHcZ74yq2wL3kuy8vL/PTP/3T/OzP/ixCCBqNBnNzc0gp+exnPzsYOVmpVGi1rrW3b3XcCBY3c+ZJpriwFvHyqUUOT5ephC77JgrsqoQ3NABbOZhhDvz8SoeTV9uMlwOePjDObDXg5VPLXG10eWepbbt/hyQFkkyR5OBKh06qSHODMZrVTkolcJiqhFbBM1eDeQKvzq+R5ookU7iOpBVnnFhocqEWUfYdCp6HxEpDNJMcrTSpMlxajfBcSZTmRIntAtbG0M01viNIMoMGPMfKVQghSHOF0YaxkodIBAiDwJBrCH2BRJDkGnRPn86A7wmSTBOniqLvMl50mSr5tBPFn55cRAg4PFNipZmgDbyz3CJKcyaKPo1uRqXg0U0VqWWrojWcWWyTZJrdlZAPTJdY6VjJi1on5YP7qmRa4zvX7nmcKfaMFQby1fsni5sGcDuRCn6/7DDe045gp/nu3W6Xp556akAf/eQnP8k//+f/HICf+Zmf4Ud/9Ef57d/+bb77u7+bUslSKY8dO4brujz55JP8xE/8xJbHjWBxI2feZ9hcaXZxhWCmEpBrw9nlDhdqEcfmxglcSa2TcPJyk4trHQ7PlHl8V4VmlLFvaATjMALPYf9kkX0TBVbayTrFyBeOTPErf3oGIWCq5LPaSQkcyZq2BjXwJHGmqBRcAk/STXO6mcaTUPIdMm0GPQHV0OMDu0rMlkMSrSn5Dt+81KaTZhR9h2LgIQRoA1Fqc/V5DkmmaYoUkUhSpelmikxpjAZlIBrWLTK2thG4dmehjCFNNWNFK5UtjCFKDe1EgbFpIw22mxgAMeD976qGVEKXxWZCpgxPzY3zyFSRpVbMVzo1Lq5GeI49vtXNLf3U5IDAcyRa2+E+kyWfQzMlxgteryCe0UlzSr7DueVosHtwhN25OEIwVrROu59G2yyAu1up4P4O4I1LdV45u0oxcDgyXQbhXdN4ekgmj20XD/fqb4Kd5rvfKHp/9NFHB8PqAX7xF38RAM/zePnll9cdu9lxI1hs5cyHh6w345y5ySJCiHWjIL9+tobr2Gi4T8Xs89NPXW0SupLpbcweHkYrzvjgvioYM1ASdRxBJXCZKHl0ElvwzHLLzOkKgTSGONcYrPN6er8NPDpJTsn3+J4e1TZKc/6n33odo20XrsHWnNpJTjvJSZVG24CdKDUY1EDDRWtruB0B+ZAf0L26RtKjfXquINeGVpyTK0OuDMhex7AZkKIQgOdJtNFEac7lRpe//8wcBc/h7EqbiYLPXE8Zdazo0epawTjXcRDCylG0kwzP7VFRheDRXWVmKyFTQxF0pjSHZkqcW+7guRJjNAXfGlkpBY4QHJwu4fXYTlFP3XOzAG67qeClesLJy41NUz1RqgY7gFacUfRdyr67KYHgbpNN7ife047gfvDdR7i72MqZ9xk2Xi90ndqQDnCl4MJqxO7xkH3jxYEGfj+aeyQq8jfzq7z4+K5tNWn1cXqxxWw56O0aSqS5JskVUiRMlDy+eblJrjRRZhhzPKaKPs1uRivOOXmpQeA6+J7kwlqHku+t+/sr+ra2kChFN9VEqUJpTa5AYQvO/SLvsMHOlU0D9X8fRq5tdI+wqqRSWEeQZDY9lWQ96QhH4Gl7nO4xjpJMU/IloStpdzN+628ucGS2xGwloFq8di8aUcb+qQJew9YFojTvjT4w5D0HCPDW1SbtJGelnTBV9hkv+HSSnEcmSzSjjKutmFZsVU0roceBiQJ7xosDOmpfiG+rAO5Gu8d+Tef01SbvLnU4eanBB2bL7BsvIIQYNP6lSjFdCiiVfN5dblPqzYkYK/okmbqOQHC3ySb3Czs5s/iBQL+55OkD4yRas9JOSLTm6QPj/OCTe94T+b2HAVGac/Jyg5deX+BzXz/PS68vbGt04FYS1udXIvKpkVBJAAAgAElEQVRevvzQTOm6Jq+1borr2IlaYA3bcD7/kekSxsD5WrTpebcyNhvHbvqOZKWVEngORd9SUAu+bSwr+g6J0ggJUtr8+6O7S1YFNMp54cjUur+/lXbClWZMrgxRT+un7wSGMTy6QPd++tisK0gBmbY7nFxrem0C5NqyiDJjtYeUMWhj6AlZow00E22F6YRg73jIwckSV+oxxy80aMY2R79Q7zJdCviW3WXGih7F3i7KGMs6KngOEyUfpQ1RT5b7aiPmm5ebtOKMC6sdDFDwHA5Pl9k7XuADs2VW2hm1djKYG92IUgqes6Vs+VYzrJvdjFfmV3l7scW55YgDE0UmywEX17q8en6NJNfMVkNa3Yw3FxqDhrdU6XWd0IHnoLRmqRkPHrudEasPIh7qUNiY67s8N8N7tS38YWgGhDsvuG3WKdrJFE/sLrNrrMBSM75Oxnm1nVLwHdJenqSd5ByZuWbUA9fh+cOTHL/QYKzobXu3uDHq3DdR4M1LDaY8u/5K6PHYrjJLzYS1bopWls55dHeF3eMhx/aNc2mty5V6l1/90jt8/Kk9hJ7DmcUWX3prmUtrEY1u1nudNdh99P+pbuO293cFyhhMbncKmb7mQvq1Acy1XUV/95Eb2FvxOTBZoh5nTJVt30E/Ou6Po1zr5lRDt1fY1WS5/VmJUqSwKbpcG5rdnEwrWt2cC6sdDkwW2VUNbfor14Suw+5qgYrvstKx86LjTKO05tB0kUxr/ss3r17H3tls9zicQky7Ga4j2DUWbqp3VOskBK4cjJv0ew1/3lAndDlwWah3B+MoH0Zxxc3w0DqCMAyp1WpMTU1tyxm812CMoVarPfB007tF6dvozEPfsaMDe/N1r5sL3NPHcR1BkikcKa+TcZ4qBTy+p8LTB8a3LUWwsWaxqxLaqDBXFDyXNFdcbSa0uhm5MvhSMlb02FsNWWqlg96BmUrApbWIf/+XZxkr+FQLLmeuNhEGlL5WsB3OA92u25cCfMc2c+XKkGhD6Eoyfa3msNV7CyDJDWtRxjcvN1AGPrS3Sie1ReATF+ucX43AGGrtlGrBI/A0caYIPIk2ggOTRdailHrHitxdrneZKAWUfAdtDEobFps2LVSPcsqhi9KG6XJA4NhmukemfDCCJDfruss3BhMbU8FXmjFxrnC1YamT8Ohsed3YzL7e0VIrIUpzMqX5s7eXeWSyQyvOyHLD/sni4DWOFOumkD2M4oqb4aF1BHNzcywsLLC8vHy/l3LfEIYhc3P3dgbqjbAZ39oXgijNmSoVN33N7Rbchg1yn4r5xsUG3SylHLg4UtKOc8aLPqkyPLl/7DrJiCzXjBW9dQ6mfw3/+ZtXN+WMb4w6A8/h2w5N8pV3atSjlPMrEYlSOMLy8jGadpzzF++s8NT+McqBy1o35Wo95txKh0QpqqHHwmqXwLOyD64jSHNDbq7P+d8OtIFubpCA66x/z81mbpkN/+5dBknP2c3XOry92EYbm257ZLJIlCi6ac5alKINVEKX8YJHtWhTQvUoY6ocstKK8ZBMlHyWGjFCQq2dEHgucaZwXcGRmSKtWHG+1kFKwUTJx3ccqqG7rk60WTCxcff41tXmQDZEabOuU7yPUuByZqnFxbUYYQxCwljo4TuSk5ebNOOcx3aXKXjuuhTjxvThw9xv8GCv7gbwPI9Dhw7d72WM0MNW6Z8/f2sJz3UYK3ibfgnh9gpufYO8FiW045yFepckVySZZdgYY0iU5sm5MeYmi5tq+W+M5rabwtoYdR6cKvHm5QZfe6cOCKoFjzjVFDyJkII4tznks8sRcWbQGK7WY7ppTpwpOrEiybWN0A24QiBYXxQexlaP3wwayJV1BnGuEViW0c1STQIQEs7VOpQDD0dAO0lJe8yoNNesdVIcRxB6Dq60PQlrUUontewk13EoBw5x5hJlmixXCCmQRnO1GbN7rEDouZQCO8t5/6SV8eimOe+utNk3XrSqp0ONfKmyu759EwUEYhBMDO8e41QxUw4QQrBQ714negeAsZTjvWMhy52UwJUgBAXf5ejeKu8stvnmpSYf3FslzhQHJossNuN16cOHvd/goXUEIzw4uFH6J/QdAkdex7YYxu10dxd9l6cPjPPrXz5LpjTTZZ+pokecO6y07fSvp/aPs3ussOk5N4vmNrsGgDhXnL3S5Otnazx/aIJjc+PrhtM0I1vULAUeEyWfwHVY6yQgBNpApmyapBlnUDdkChrdjG6W40qJK7FpG22NbmYMjugZe3O90b/dFFHf/OW2ZWDw/jeDFNDqZmQGpoqBFaLTNoUlsJ3RoS8tddYw6M/QubDCjqnhg/uK1Dp2VvGBiQKOI2l28wGtthi47OpJd9Ta6UC+wuvRg5NcrVNmLQUuoecM+kaMMUiH64KJ4ZrOZiNBAWqdBEfATDXkciNmZshg+45kpurzzmKbr5+rEXgO0+WA5w5N8vie6oD2u1N6WPcKD+aqRniocKOOTt+ROE5PXK1XhNuIrQpuN9pqA7x+oc7zhyboJIqFtYhmkuM7kqf2j1MKHFqxoplktJP8hsXgKM350slFXj1Xs+msXpRZ8BzeutoaGB7LXMk4fqE+UMB8Yu8YJy83+KNvXObxPVUypWnF1slEaU7gyh51NSfPFe04swJ1WW4NqTAkqV5HBzVci9LvJh1gYPxvAZY0IwaRdJQq2on9/ISwMhzdTFMJXBxhi6urUcpYTydICMlE2Ta+JbnGk3bH5EjJgakiZxZb7K6GGG1wpP2sYn2NhdNOcqZKvi2wN2wU3hflAwZ9I900540LdaIP5+uM7XAKcWMtKVOaepTxxsU65dDn9JUW1dDFk1YFVQpYWI3IevdfSPjBD+1FGziz1OHAlE353Gtxy53AyBGMcMe4UUfnvgkbhW1kWwxjs4Lbzbba+ydClLK0v8kS7J+8vgaRqZijeyqEnrNlMbh/nlfma4wVvEGU+fZii4u1iIPTZSZ6X/BK6LHUTjgyW1kX5Z1ebGEMFFxJ0XcZK/hEac6ZxRaZsj0NnTQHDZldGEpbI2tMj6o5tO4+W+h+c8JEby2uI8iMwZcQZzlxqhFCk/Qa3DxHWWlraTuB48wOoJks+kSZwgVWWglSCGYqIU5vcJPvOIBVNs2N/QS0MXi95wdF/krAUjNhuhKscwLD8BybhttobDfWdPq1pLWoy3I7Ic81Gij5EoGhWvBxHclk0eNvL9btffUdpoo+QjJoQNx4/++1uOXdxsgRjHDHuFFH565KyIVaRDYkrjaMzfj629lq/97xK3zb4YkbrqvgS7767gp7J4qbFu+Gz1PwHAqendfrOcIaYQEL9Yg4UzTibMDDny3bub/vLLW5uNZhsRGT5Zqllp2ylSpLjQw8pzfsRWA0GCvvg+6rf0oQxiqGqp4shOZa49j9hCMg9CSZMpje2gzQSZV1AKanR4RNabW6+UD5tOg7rHZSuqnlI4WupFr0eWy2zFqUobRlFUkhOTxToh6lGKyeUpwqZioB9SjFkZIn94+x3Ix560qLR26gANBOch6bKV9nbDdrKv3QXIUvnVoiSm1qbrYSsnuswHTZjtGMM8W5WsTceGFg+HOl19Fth6P8ey1uuRMYOYIR7hg36ujsK3q+dn6VWGmSTN2Ur7+drXac5dQ7KdVw82OacWabnroph6bKmxbvlprx4Dy+K9cVElfbKb7rcGktoh3nTFcCHE/STgx//tYyBjg8XSJKcpQ2rLRj6lFOteRhel25pAp69EjV0/EJXMun18KgFIhh4n4P+iZewBU7kzrqwxF2t6K1FaUDgdurYVhFU6zRR6CMGaxFYl9ji/VQDaHgu1YuQgh8zyHNE4SA3WMh4wWftcg62KlyQJIpUqUpBuuHA9W7KeXQQymN51g567Vuymo7JdOaXBmkECitOXW1CbDO4W9kEs2vRBQDl+8/OMlsNRz0ofQpoqHnUGsn+ENaVBv7UOBalH8vxS13CiNHMMId42biftWCxwdmK8xUAhKtr0vRAOu0X9683OCRiSJjBW/LL9d4wWd+NeLA1PohMUlu6wV/+c4Kaa8J6YunFil4dkzj3HiBsit4+dQSxujBln54PsBaN+XCaodWrCx1EsF0JSBKraEaL/gIAVcadn7AfC1CG4HBsNZKMT2tfVcKAtchNRpHWj2goufQThVyyJjfzPDDenZPvsPbBdWL9vPe1qUQSEq+w2o3s5o/WC0KbfqydL3GM22dgRD2J8k1U5WQRpSTZDkCwQ89vZfTV2zdxQChK5COnZnsu5K9BQ9jbLcyQDl0KfkeL3xgmrPLHerdLsutBIStPyWZptZOKYUuF1e77J8sbjrveJhJ9NLrCxzdfW3+82Z9KI4jaEQpe8YKW/ah9KP8p/aP76i45b3AyBGMcMfYjrhfMXAH4mrD2KwWoLXhYq84eGxufDCjdxj7p4q8eamx7rFmnPHGxQYrrZgsN9SjlFLgUOskeFIy16tXSCnZMx7STjKe3GcZIrPVkNNX28xfaeL0pmhJAZ6UtJOMhdUIRwocR7DcismNTWPEmS2AGmFsc1KUW/E3aXcCNqUAGAg8YWmbwgxqADez6RLo6bZd6/69B5DCymIYZWcYx8oMRlX2n9dc63zeeB2eFHQzzaW1Dlrb+/yVM8v4Djy5f5xaO+XMUhtt4LmDE5y+2iR0HQqu1TaKc83xiw08R/JPPnqYXGmU0by50ET0+sFyrcm1Yf+kpZ7Wo5ROYtMvs9VwS7bOxlTOZn0oniOJMrUuRbVZH0roOzsubnkvcFOtISHEp4UQztDvZSHE/7WzyxrhYcJWekBJplhsxltqwwzn6Gerdn6AEIJS4NpB8I7kxEKdJLtey2Wy6BF4VoAM7E7gjYsNfEfQzRSNyKadZqsFqqGPI+VgApjvCC7UIpabMWk+ZFqFIc01i62ExWZMrZ2y3IytHn+muNKIqTVTFptdLq91ubwWcaUZ04wzhBHkyhC4wlI/jY2QY2W7eouBQyV00QYc5KYGfbPmMY3VCZLCzha4F3AFBK6LJyVCCGKlaHezntSCsCmgXpHbsN6hGWzzWa4NSmuSzPS6jSUG+Pr8Kl99t0YYOPzkdxziX37scQ5Mlvh7T+zm2UcmAWj27ulT+8d5+sAYf3TiMq+cX+Xr79ZYbHbZXQ15fFeVXdWQku8Qei6ZslLbRd9lqZUANoWYKz0YnNNHP5UzjGro8fyhCY7MlMi0HgzvOTJT4vlDE5v2wNS7GY/vqtz23/+DhO2srAz8tRDiJ4BdwP8B/J87uagRto8HpZtxMz2gm8k1bFULGOZ799v/N7KCupnmh57cy8W1mHYS044zlFJIz2OplaCNZm68ZEckak03y1lpJTTijEro0U1zmlHG20sdip7E60kWrLYTkt70LqENuTHkaU4nUWitiRxJEDg4Utqir4ZWkuNmmtCTuK6LSRS5tjr6whi0ASkMiTKUfIdWkpNv4gm22h0YILlHumb9SD/NFUobXAcCxyXLDbm2jk4bSSe5Rnn1HciGehNcaYXuQlfiuw6eYz+vaugSpYpvPzKFNmIwWrTP/ioFCrDy3mmuObPcptXN0NpwzBtjV7VAuhZxbrlD6CVoNIHr0E6ydXLV/ZkFsDlbZ6tUZuBaRdn9kyUurHVoRDmTpWBbfSi38/f/IOGmqzPG/AshxN8FXgHqwHcZY0aDdR8APGjdjLcq7rcV7W44Zzs80xcspfDMYpuTV5q8Ou+QGzuQZX6lje86VEIPpdSgkSzJFUvNxMoheA5Rkg/GSiLg0aJtADu30rHdoq6gEtjRko4joEf/7KbK5ua1RmQQeC5SShytbaHSGLqpLXRKrP6+KwUG0Rvqboe+30pq53Y7iO8E/XpF0itIONg5y8YYstwQ9ZrePEfguRJtrIOwFRILpcFxep+RsCmccuAipW2wa8d2mH3faI4VvEFaT2s7zN71BaeutNDa4ErB+dUI1xE8sadKs5tztRGzsNbl4FSJPdUCY0XvupkFsDlbZzupnJLv8XeP7uar79a2LWH/MItb3tQRCCFeAH4Z+EXgW4F/J4T474wxV3d6cSNsjfdCN+NWtLvhnG2e5uiePPLVepc/OXmVhbWI8aJPObWRPQbaiWZf0eeRqRK1TsrVRowjBbW2bQzyXYdc2y+xIyWBZxvHZC+VVGsnaK3RStJNMzLVj4DtlLH+0JZMg2euKb/KHvVTG6vsmeQaKSFwJUpbI6gMeEJyqxn++00h7Sswa6xBzXMr6WDz9QYfe535BhnmHDAKmlFK6Ds4QuAV7Y5rquSxUO/yzP6JwS7WD8UgrRf0WGAr7RgpoNKbA3BuucOju8oIYQv305WAVCmmSj7TlWvBTn9mQV+K4vxKRCe7fqe8nTkl0+WAHyz7D22UfyvYzpX878AnjDHfABBC/EPgy8C37OTCRrgx3gvdjDei3VVDjyf3V3nzUpNTV5r89qsXeHupjSME+yeLlHyPS2uRNbKOIPQktXaK63SYLNn00PmVNqHvUvRdlDGsdVJacY4jBVJAoeRjtCHN1MDYaeyM3sCVSGFpgxvTOLnWtGONdCzDRulrxba+8U5yS7PsF4WT7NZ2A/cafebPutkGBow2vYlpLpGxKS/fk6SZHZwTuBKtzXVOS2PTZlmuEYFgtZPiO4Kju6t0kpwrzZi3rjYxCOLU0k3nhtJ/tXZKOMTgEcLOK+gkVkgQYO94gcuNLlPlgHqUUeskNOOM6aLPnzRjir1o/4nd5U2ZRNtJ5QxH+cNp2OMX6w+VqNzNsJ3Vf8QYM5geYoz5ghDiSzu4phG2gfdCN+ONaKc2VdCkHqW8cGSKtxbbVEKXJNU0u4pMAQJKvmsLk0ZhNHQSq5c/WbQTpqx6pqDVzViLUsCyYPyewM9iK0Zi89k2vy0G4ya7mR7o8w8bOt1Lhue9jIMjrmn5yyF+fT/NstHA3g/cbD+y1fO5gTzVuI4i9ARxZgXxfFeQaSvs50mJKw1Jbta9hyNtwTzN816aSCIlLKxZamjBdzg4WeRPTi7iu3YmQ9+oDg+wjzPFnvECcWYHxcSZIvQcJks+87UOpy43kdJOXwt72kS5NnRXI5QxzNc6ONLOtD44WeKP37zCjz4zd0upnActDXu3sR1HMCGE+AVgzhjzMSHEE8DzwH/Y0ZWNcEM8jN2MGwvbQtqRk54rmChe+xL1GUDGaCZ6ud+1dgJGUA4tz/xSvct0bzfkSjscPdGKKFVUc0PouVRDD60tjdTSQe3g8YJnpQ2qBc9SQZVNAcVZL4rH0I7VQIJ542hqie0Stgwgi34Tluzx782QA9moI3Q/cDNHtFktuk9ZFUCUKnzHiggabZBSQKaQwspEx5mC2A6WMcZ+DqInodFJNKXApuC+ePIqlV5N4LlHJhkv+daAu5ahNL/S4dFdFTwprTPVlrY6VfLpZooP7q7ylXdrNpVnDLVOShpqfCkpBS7jJZ/FZsxKK6HZzSkXXHZXQ5sm7GQsNlaZKHocmSnxwpGZbX1274U07M2wnVX/B+A3gf+l9/sZ4AuMHMF9xcPWzbhVRNXyc75+do0P7Cqzf7yAxvD6+TW+sVAncB0OTZc4fbWF6VlRKUBIgTKaRKlBN2j/c8iVpp3mPL67wuV6RCfNKXouhaLDmHLt9C8DkyUPKXp9s0JQDmWP8mg/t8xYg6/U9ca7t1EAerOEe8bSdQSeFHgOQ07lGu6nI9gM212PlX8AUKS9z3y2GA6YURKIEjXoPXCF6TXYWbaUTaVlZFqTKigow2Ij5qvvrjC/EpEqbaN2IXqpJs1yK2WxZUXm9o6HLDa7hK7D6astSr6DP17gzGKLoutQ9Fx8VzI7FnCxFrHWsZIVe8bDXt3GUC24VB0rNNfoZvzuq5d4av/Etgz3eyENezNsxxHMGmM+J4T4FwDGmEwI8fAP6XzIcbNuXri9bsadoKPeKKI6PFNmvOhxqR5zpdnl5OUWF9c6HJgqMlsJEULwzkqbZjfDGEhiRadH/WxGGWNFj2rgUeg5vN1jIVOlAM+xDWSnrmZMlDwKvks3tXLGcW4VMqUQpJmiEnq4joPnaJRW1zp+t7iezfLhGBDKYPS14S8bvyT3Oz00DDH0/205A9PrOIYBzTZKbG2lm2o0GrfHlOqziKxqqd052YH2El/ajuBiwaXeSam168xWA2bKAVmP8//ucpupcmCnqkm4XLdF3/GCy3OHJpmu2G7fasHnW3aVKfhuT57CMFbwyZQhyRW+66CMoRlng6ZEr6eGW+ts33C/F9KwN8N2vtkdIcQkvb8XIcRzQGtHVzXCTbET3Yw7lQftR1Su73BhtXPdUJFdlZDxgke9m/LRx2Z49bzDWOgNVM2qoUea2ahR9yZEBa4kyTVpZljKEgquQ8F3qAQex+bG2T9ZZG68iMZwYKJIo5txshnTim3Bs+D1egGAdprjOZJ94wW+camBEDbK7+NmHcCy30BmQBorpyyEebDC/w0wbF4X6I1GuM6JCWy3tSNs7j/Thn7hMFH62hwFaQhdB42xgnu9jyHNQTr29yRTxJmi4Dvkykp0u45DklmhPkdKlNa23yPTKKnJJdS7Oa+er/Ot+6zB3z0WWrVYpWkmGRdrERfrEUpDNXDRrp3Klm3Q8PBcSZIqTvdqEjcLfB7GNOyt4qadxcDPAX8AHBZCfBn4PPA/7OiqRrgp7nY341ZdvkFPxz10JS/3FBtvFacXW0gBr8yvDsS9qqGH60jOLnd4ZX6VtU7CpXqv87cnANfHWOjRSqxqpcDOjXUdiRQCR9p0TDvNiVNFOXSZrQQDWYsn5yZ483LTyhVIwZ6qNR7NOKfZTZkq+jgIap2EVmy17ycKntUG4sbzfAcwttjsyl6EbcxAmfNBRb+mMYzB0HpxfZdz32FYHSGBK4eMR48dJYQtlMe5ts5Q9vcF9j+elBR8lzjXtJOcVqxwpEApQ5LlRKmyPReqJ9UqBeXQsr4C17EGPFesRSlP7q8yFrq04owziy0WG7HtP0CglSbKFKvthExpnA0XmueKasFjpZXwB29c4fiFOoGUzJSDAbvoD964wkrbdihv1om8EQ9SGvZ2sJ2GsleFEN8NHMXe05PGmHTHVzbCTXE3uxl3Mg/ajDLOrXTwthgqEmeK1y7W2dsT9dpsklSu7HCQNDdkyrJTSoHN+ebKas44Dqx1Ev7LyUVCX/L3ntjN+VqHaq/AbGsAhulSwGw5oBXnNOLcCp5JK0pXDBwur0U4jlwnO7wV5FBuRQrLode9CV4PMkyP4eRLK5IKN59a1n9OYkgzQzlw6KQ2lebQ28D1Po84Vah+r4WwhXpHgCPsbANX2r6CDuC5guV2SuBaOQutDJkURHHOwZkyJd9hoR7hConSxub5o5zZSsCfnl7EEZJupsiVJsrVoMcD4EojZqzgcn61jSskhV5fQzl0eOtKiyMzZWqd5Lodaq7NoAD8yGSRPzu9RDdTpLnGdyVz44WBOio8+KJyN8OWlkII8fEtnjoghMAY8/s7tKYRbgF3q5vxTvKgN6srtBKbm69sMbM49BySnrInXK8G2Ygzip7TU7s0FHxJvZPSzaxxd5EUPdu12kxyDk6Vaac5//Fr8zS6fZnjkHJo/9yV1nTSHCGsJHSl4PGdj07x2vk6i40ujW6OIwyhIwYdtlvtCgaduD3Lr2HgBG69hezeoE9nNfoa02k7CqgAmYEst9cmlcaT1wrqZsiLqKF8mjLWOcY9emmu7KxjIWxaKc1tH4LvuLhCUirbaXDa2FkGjpS4wm63rAS2YGEtohTYsaSha9lMvpBUA5dupqhH2WABAg9P2lRit6MoBy7NbkaaaS77znVjLy/UIo7NjZMrzfGLa7y7FHG53mUs9BgLvcFx87UuT+4fwxHigReVuxluFDL+g97/p4EXgD/D/g19FNtQNnIE7yHcbh50O3UFsdng3Q1wpED3IvCNapD9bt2x0KfRTUlSzUTJSgsXfYflVkycKbqpYrERc6nYZfdYiCsFY6HH2ZUOaR4xXQkIPMlKKx1Eq6EnKfsOS42YhbWIPeMhrUQRetI6kTi/6XB3WG/wHcfm07fzuvuBQbqG69c4FNTf8JZpIB7Sw+6/RkqBg6C7YUsksI13/z977x4kZ3ae9/3OOd+tL9Nzx/2OvZLLBXZ5X9qkJJJSJMtmqMR2lFhy2YmdVFmOy045Udl/OCn9YVclLl/iJFWyo9iSJSc2y2XZiVS2KFKkSC5vuwssl9wFd7HAAAMM5t7X737OyR/n657BzACYxS6WAHaeKhRmer7u/rr76/Oe932f93mSypYz19qVloSToq4FTo4i9NWIOloUmldv9BiPPArjeg710Hlgx3nJhcUehydqdJPS9SOMYWYsZH1QVIHA9ZfiQlMPPUci8ARr/YxvX1pjsh5wtZO4TGCixtHJ+ihDfXm+zeMHx/jtlxZ47vQ0f+SRWV6eb9NOnNVoq+YzyEr+8IfLPHtikp9+6sGljsJtAoG19hcAhBD/FniftfZa9fth3LTxbSGE+DXgZ4Ela+1T1W3/I/AXgOXqsL9hrf2dt/MC9vDO4G7oqLvlV/tKEXjqJr33zcgKzWTdZz0pef7iCnFekhaGQhsKa1jqpiSFxpeGVs3nQCsiCjyyUnN5JXZeuJ4iyQvSwjUO0xVNUmomawH1QNEIFFlpWB04tlDguT5FPVDERcnywLmLvb7YRwhBvzKcuZvFfAex1PsGw1LW0A5zmBEU1bo99D243cveKVBowLNgtSXHjlzWhscYNqwvbVVKGspY58by+FQda3H2kcb5PgeeGGUvdV9xPc4ptOHUdJ2kMCNDm0bo0YkLukmBpxT10M2fpIUh9AWR77G/FZLkhuVexkovc2Y2EsYCj9LCm8t9Xr/RcxpVviIvNb3EbUKGZIwPn5hiqZcxvx4T52YUQN5/sPVAD5PB7lhDp4ZBoMJ1dicv8U+BfwT8+pbb/5619vq40NEAACAASURBVH/Z3entYTPupdLo3dBRd9tXyLXhyUNjvLbQH+m9K+m07ftZSa4toa9YHuQMspLFrlsMEAJfwGOzTUchTUtmmiFR4FEaw1I3Iys1ke8R5wWeEqMvrZSS9TilEXhMNgJW+xlKSvJC06hKVBYYpAXXuym9tKAWKHJtiDxJOymqevj9Wd65WwzVRYeL+XBhHmK3QWAnGDZKTMNAsPn929x/GMZKgdNhcmwhwXqSsz7I8T1VXdOCNNeENcl4ze3uX7raYTxSGOvyGt938tPvP9yi5ntcuNFlqhHSTQuWu27RH2QlrcijHRvGam5IMS8tsiYR2lBqyErL3NqAJw+2qIUeL13rcKAZ0U2KSkpbcXSqfpMSblZoLq/FPFtJaMOQLHCfswW2YDerx1eFEP8fji1kgf8M+Oqd7mSt/aoQ4sTbOrs9jLC5BBMFkjQvee1Gl69eWCb0JZ87c4izx3Y3ILMT7oaO6mz6JFfXBiPp4K2NtImaz3qcYQ185OQkS92U+XbCIHfHHp2scWUtxnqKT5ye4buX1xHCOVMpIUhLzY1eRjP0iHNDJy1p1TTL/ZS1Qc6gKFEI8tLQrPk0Q49eUtKaDGj4ivVBTjNyg2RCgK+UG1oClnrO/GY89FDCGaIoAWsDd6yxD1cQANe/8DaJyW3thzv/5NtLYt8Km/82DDaqihxmh2M27mcYpAXHp+ss9xxjLBoFKEtaaEfpFYKDYxFJ4fo7kXRN5ulmwKmDTWq+u24LY2j4HjMNt0v/6fcf5OhUnW9fWmV1UGBzjQqhk5Zoa+nE7vNuhIo4L1kbOGcyY2D/eMTL820+fGJqx2x2a7n0V37lV/jd3/1dvvGNb9zmnbr/sJtV4y/h+gV/tPr914EvvI3n/CUhxC8C3wX+O2vt+tt4rPcENpdgtBI3yfW2Qler/MIL81xcjvnpDxy4qzR1t4qMmwPNSi9jfi3BWsfoqUVqWyOtGXo0Qh+lJKW2I733IS4u91jqpSSloZsWaGuYiAKK0pBh8aVk31jI6dkGX39zlcVOyms3CqeVLyXCQrnJMtHtSp3JvKrki22lkzDsI1gsCqdnZIwlNxaBoJ0UaG1HJZOHLQgMUdrt1NEhpBAIaUeloreCrVPUQ60hIWDrWIXAnYMnwQrJQidjrO7z6L4xjk01uLjcZ5C7PkLoCxqBhwAGRUkr8mnHBY/MNlmPC6dftJbw2H6Fp+Sox5CXTol0X6VOeuFGj7Ga73pOwtGP46zEYAmkI8MGnmK1nzNVDwg8wVQjICvNNk+M4a5/a7l0amqK559/nsXFRfbv3//W38QfEe44R2Ad/qW19i9X//6V3czPemv4P4DTwFlgAfi7tzpQCPEXhRDfFUJ8d3l5+VaHvScwGshSG3K94/XAySsIQSPyHTsnKe6a6w8bdNRnjk2QGcNKPyMzhmeOTfDHzxy8KcDEecnFpQFScNO5eEoyXg8IqnPtZ276d6eZh+V+yn/4/g06Scmp6QbaWFqRT1Y6RbnTs00eP9DiyFSdQa55fP8YU40AJSTaVjr3kc9UI2CyEeJJxybS2rLQTuhn5WgmodR2pJi50s+41klpJxXlMC1J8tJ5zVevTz+sUaDCrVhCud45CAybyDthp9uldI1jUT2X3XKsxTXqI18RKpf5rfedMGAr8nj6cIsT0/WK7ilZj3M8JTg8UWe67vo+C50UbaHQrsS40s+w1jJe81kfFCR5yUdPTo928mmpCaSoBtU0hyci4kxjKxnxoa5SWmgGueZjJ6fJqx7B61dv8PXf/13+4a/8Mn/uj32CH5z7LrDhUjbEZz7zGQC+9KUHS5dzN34EnwP+DnCITaQCa23rrT6ZtXZx0+P+Y+D/vc2xvwr8KsCHPvSh+5R/8e5gSO1c6qYYY0aa7ZvRCD1WBxnjdf9taZ7slo56eWXAbDNwtfQdEPqKpMh5c3nAz3zg4LaZh6V2xmsLPaYbESdn6vie4sp6XOnGKIrScGk55rH9TTwpiHPDWOgR+ZKpZh1PytGg0EI7cdTAohpGymCq6aEUlEleMZKcXwDWlUP8qmuaaUdhdKbrerRAvqcvuB0wrPfvXNq5GRL3/jZCn74tRhTczccGysk9DPKSSCmkEqRFCQS8PN9BSMH+ZsiRyToLHTdo6KQrXH9hoh5UC5HraxyaCFjqZ9QCNwsyUfcZizxObSplRp4iN5ZQCZSQNAOPsZqHxZLmhtJarLXUA4/D4x7tN8/z5S99iYUffJu1y69i7UaEfPH5r3Lifc9sK5c+9thjHD16lG9+85v8/M///Nt5y99V7KY09HeBzw/9CN4OhBAHrbUL1a+fB155u4/5XsCQ2jlfTd7uhOFi+W5pnry22OPUbIOX5zsjWeCt8KVguZ+Nviibg8wPrncIleTicn8kJTFM6T3h3K/yrKCd5IzXAgIlSSr9GCxMN0IWu26adLoZcmW1TyfO0cbgKUUUSOJM089KrLYYLGFlToMVZNrctCse6gXt4da4U5LkCfcWausyC7+q7cNGv2DYdxEI8sJsfOa4nX1e6EpS2vXFHtnX4EYXmqGHNrDYTRmvBRyeqFEPnctZmmuU8Dk2WeNjJ6dpJwXHpzVYV+6bwNXyT882eGGuzXQz5NnjEyx2UidZ4itqdUX7+kXmzn+LZO4c33j9JYosveVr/ebX/oCf+bN/eVu5VAjBd77zHfbt2/f23ux3GbsJBIt3EwSEEP8C+DFgRggxD/wt4MeEEGdx18tl4L9+q4/7XsSQ2pmXhlq0M6tn6Mx0rzVPhsylb7yxSuS5Ia7VQU4jUK5EVImODbISKV15Z6cG9jDLOTzppoiboYcUgisrA5Qn8ISkHihnHyklp2ebvLnS50ArIitNZY1o6acFK/0MIQQTjYD2wFEMb3RSPOFoigWV5boAa50Ewt6a/85B4IKAlJuos9ZJVys1+nVExfWlk/ouLQRSIqWgFjg/CG0seamrbE/w/Rs99xlW1/cgM2SlHllTPrp/jNV+xkI34eC4GpUyh5uPzVP3x2cbzK3H7B8LmGqERBRc/Prv8/JLz7P6+gvkvd23K+cuvMJnH59kaod+3IPUGxhiN4HgO0KI3wT+DZANb7zTZLG1dqe86P98a6e3B9igdg7r3Z7aXpUdZCWnZ5v3VPNkM3Op4Ssi32nIG2NdY08WKCkIlFu4x2veLbuSwyxn/1jEhYUeP1jtuPKDckFASEEnLch6pmrYaS4u9ym14ZF9Y4yFrpdwfr6DwGUb5I77n1VG8n7oszbIELhSVVHavSDwDkPgZCoQbgNgcTv/euBRaKcIOsRwkM1UtfhhQBDCeTuDEwD0pPNC9iT0KqLCUi8j9BWeFJWfhIOvJPtbEYW2/PlPnNiWCW8tc3781Ay/+pU3ubIW01KGr/2zv40ud9dTO376MZ79+Kc4dfbj/Ok/8ZNMtZp396bdh9hNIJjGZXObJScse5PF7xqG1M7peuDojvWbewRpofGkYN9YeM80T7YOj6WlHu3kEdDL3M780f1NjkzW2Ndy9oG3OpebhLwEWARSwr6xiJVeSlnJDhhreXMlJi3c5HLDdwb1g6xEa8vj+8e41o6ZX4/Jqz6Btc4PIC/zET0yLzXZfTzo9aBC4JhBFjeg5eH04vJS4ymBNG5Bz7XLGka0XANauJKmtTBIS8Yjj1qgkNJ5DieZJs4N9cBJgQgLk/WAXlbe5GYWZyVRsF3iIc9znn/+eVZXV/m5n/s5AB7bP8Yv/8zjfPXCMr/36iITJ55i9Y1zO7625tQ+Tp/5GJ/68U/z3Cd/jOlZt9PPCs3VvuHD9/KNfZexG9G5X3g3TmQPt8aQ2vm7ryzw+lIfJcWoeTbISjwpnDaKsfdM82Tr8Nj+sYgLN3p8/3oXJZ1eENYZlLy5PODCYp9H9zdveS7DLCctNb6SvO9Qi05csDLImGiE5IVhkBV4nsIXcGSyzuMHmpy/2sUYy0Td2RTOr8f006HEwAZDxW6ilQLYvSDwjmOoKjrc9EsLUSDxlawa987ox7DRaA49Rb2a8tbGkBXG+UcHilrooaSjdQZKslJkDO0/W5FPkpduMKzmo6Tg4nKfg62ITBv+5AePUPMV3/ve9/i93/s9vvjFL/KVr3yFOI45fPgwn//85zcNeQkKA3/0kVl6P/kZfrMKBGGtwdQjZ3nig5/g/R/6BM88/RT7KyXezXjQJad3wu1E5/4et2mfWWv/2j05oz3siJlmyH/y7BFOzzb47ZcWWI9jJmoBJ2caTNYdHW4nrv87hW2idNUufrPcQOBJ1gY5hyZq7hh76+nKYZbz5kKXRujhK8nMWMhMxfnOCs2NTkojUkzUAgpjaEXBTRpEvbRgtZ+RaYsvBeUmVsfWC/chZ4P+yLB5clgCvqfwpdMOssay3M/QxukJaesUS611xjXWWCf3bcCT7vPvJgWelE5KvdDUQgXGlQwDXzmp8GZIYZzERNZepn/pJf7Bv3mRz3/pSywtLW07x2vXrnHhwgWeeOKJbZntpz77x1BC8OzHP8njT53la5fWsAaeOz1zS7mVB11yeifcbsXYY/TcZ6gHHs+dnuXs0cmbpCaQ4q6kp98KtorSLXVTQiV4ctNOvijdINfp2eaoTHUrKqt7LdP8+1cWSAuNUpJQCcZrAb4ShL6j9o1Fbvc3yF2TsBPnWAzrcc7VtYQ4NygJnlSVh4G97ywhH1ZYNiaIAYQStCIfVVmAJnmJ70mUNkglsNVwmTYQ+R6FLvCVQChXAhwLPdbjguVOSj9zO25jLEIJzFC4sEhY+8F55i+9xBvnnmft+uVdnesXv/hFnnjiiW2Z7cnHnuTkY0+Ojjs90+Tc1c62AbLNeNAlp3fC7UTn9hq79yneKenpt4KtonRDKqu3aSc/9AYYfoEm4JZU1pV+xjcurtKq+bQij35WkmnDjW7CVCPk7LEJvn+9i1dpEmkL3760vjFRPe3zwtwq4Ch7hTGYig+6FwTePQwbv0o4xk8vy6n7vpMJCT03nKUtSW7wlOsnhL4EpOshCGcs5CinhjjTxFmJNVAPPZSkKh9JFr78z5n74q87/exd4uDBg3zmM5/h/e9/P3BnufV9rYhG6OwydwoEd+P89yDgwdVN3cO7iq2idJuprIU2tOOCq2sDJusBz19c4fBkjdlm6DKWCkPq6flrbb7z5prbLQrBtU5KFEgiTzHVDKh7itcWelUj0tJOcuJMc6AVjYbpCm0oSicHUWonJwB7QeBHBW2djESSGfI8J/QkpS/pJOWILVSv+kiDrBxN8prK2kxiubg8oNSGA+M+/dxNg5eVlEgr8ujOHLxjEGg2m3zqU5/is5/9LJ/97Gd58sknbxKAu5Pceugpnj0+wTfeWGWxuzuplYcBD9er2cM9w1ZRuiGVNdeayysDstLJ8h6ZrIEQvLk84OJSn8cOuvH7zdTTXloggRvt1BnRJyUHvQjpCRY7KaqaCZhpBKwPcpLCmYkMs5FhQKncDLF3UMzcw73D0FNg2KQvtEF6kl5WYlJ7kxR1ri2BMCghkRWzSAgB6RrJmy9x9Ycvsv76Czzxp/576qc/iLaWQAkmagGRr5h+7IPbnl8pxcc+9jE+85nP8NnPfpaPfOQj+P7NO/7Nqr0vXmnT8BXHZ+rsH9veCAaoeYqPnJzk6SMTb9v570HBw/eK9nBPsFWUbl8z5NJyn4VeRqkNkac4MdPAr6z7JuoBi92UtX7OSj/l919dHjXoLtzosdLPCTxFM/JphgU3OhlTDR8LdJOCy2sDxkKfmWZIM1KjdL7Qhssrzvd4vBYQpyWlcYyVvYbwjwYCJxlRaBcYcm0oNuk2CSBUoLVloDUiH2Cuv0r3zRfpvvEi6crVmx5v+Ycv8NgTHyEvClJt6FIgJJw+fpRXDp/CV4LjH/goP/YTn+Gv/eLnODAzxa2w1TjpfQea/HCxf5MTWWtLqWjYA3i3y68/SuxGa+gR4H8DDlhrzwghngb+mLX2b9/zs9vDfYXNekEvz7dZ6uckuebUbJOJmjOjHyIrNPXAY7zu882Lazc16Fb6GRZL4LnjG6HPeF3TqdRAhRiykdyO8NuX1gg9hcCSaU2pYaoR0Aw86qFPmuuqR/CjeFfe2zC4ICzZMLXZ7HImAa0L4usXGLx5jmTuHMm1H962xNN+/UU6cYkSjpYc+BJR6UP91N/4J8xOTnByps4vfPzEbZV2dzJO2j9eY24tJVCuob1VYvph7QHcCbvJCP4J8DdwwQDgezhvgr1A8B7E5kZ1ri2vLfRGw8PW2pHZjJKSM0fHCZXkq68vcWqmycWVPnlpeGO5z0TNpxYYlJSUxtDPSgRwbLpBe1AwN8hZHeT00pK1vhtor4c+pdYESrLYzRgOWEeBRBROjXSvRPTuYbjYKwmZ3pABV4DJU7rn/z3Z3EskV17BFrfW7dmKfH0B8gE6aJBlJVlZsm6cQf1MK+TkTINf+PixO8qt72SctNkGVRtHbrjRTTnQih7qHsCdsJtX27DWfmPYcLHWWiHEwzVN8RDhXrqYbYUnBc+dnmK5l91kNnN6tjEypmknOT+8MQAc+8c1fkvW44zFnk/Nkyz2U5LMYIVlvp24BUVblBSsxa6EVGiLFK55LIVlohGgtaWTVNIDnqSOM0i/X72CHyZspugOmUOegLz6WSpF+w9/Y3cBQEoaR56kcfIsrdPPUj/8OHEJJiuc2qgWtGqC9aQkChTPPTLNTDO648PeiiHUinzOHG3xyrUOr1zr8vybq+xvhTx+oMVPP3WA+kM2I7Ab7GZlWBVCnKT63IUQ/zFw456e1R7uCjsZyfeygt/53gLL/YzTs01mxsJ3LDBEgUIgtpnNDJGVmhfn2njKcqOTIYQl8hXjtYDlXspCO0Fbi6+kkyEwEGclpYbAl9SVcMYkgYfOCrppia8EhXZG5mGldZQVBm0tSkoizzAo3l4keNjsKd9pSMBkMcmV71HGHcbO/CQCKIYaQp7EqIDasaeIL353x8cIZo7ROv0MY6eeoXb8Kbyw6STELQxK4/SEqunwsmKlTTdDnjrU4l99Z57Ts407BoNbMYS6ScG3Lq0xtxozFjq56vcdHKeTFnzhhXleXejzuWcOPfA+xG8Fu1kJfgknFveEEGIOZyjz4Ahtv0ewUz20mxaVJIOh7imurSfMNALOXWnzvWtdPv3kvrd1sd/J5/jqesyVtQHdtGRtUDAW+TRrlsiXxEUJVLt8bbBCoKSgtCCERWtDbC1jkY8Ubmo5L0skAo2ln5dk2ilWZqVmvOamq9Py7acDe0FgO6wuyK5fIL18nnTuJbLrrs4voyZjH/g0QqpRWajQBk8KmqeeHQUC1Zykdvws9ZNnqZ04Qzi+j0YgqfmKQVZSGIPWtvIEcJRgBCghUJ4zIlJCMt2MuN5J+ebFNX72zKHbnvPW2RdwvasX5ta43k5pRT4WSzcteXNlQGFcIPrOJTef8vMfPfqeKRHtRmvoDeAnhBDjgLDWtu/9ae3hrWJrPTQr9cjNbMi9b8c5naTk6FSdQeYCxx8/c/Ad8znOSs38WsyrN7pcbye8uRwjhHWG855EY2kPctqV7IBfNZeTwkBlS1lqgy/B4FRNjXUs9NI4YxHPE5iiJM7KkfmIMZY4dwJle4v4OwNrLcXKnFv4L79EenXnOr9J+2SLF6kdfMyVhGQ1H2As9Uc/wrSxNE49QzBzlMJsSJL4EqbrIYk2G0J01jmFeZJR7am0rhSopEBbzUtX11FS8JvfmuPUbOO2me1OG5XFXspKPyPOC9bijJVeTs1XSBExFnkIIVgb5HztjSWePNjkuUdm78Xbe99hN6yhFvBngBOAt6lXsKc1dB9haz10JzezRugxvx5zdKpOI/ToZ+nbdjMbUkoXun1+eKPH9XaMxRnAS2ERAjpxQTPy8UpnXzgohr6AjrnhKxDC7Q6H3PJQKZKiJC20k5bONVJYcuPcT7RxvHSLGZUUdsKe3MTuUfZWNhb+ufPowe70+dPL5wgPPkagKpYQzhfZGz/A/uc+T6kthXbSHxLwPEGr5pNopzBqrBtQHHobKymIvA0GWlE6cbrSd6KGRyYilgf5KLN97vQ0vbTY1hfb1wpRSo42KgBvLPaZW41BQL8qNbbqPr20ZJBp9rVCJhsBcVby2+evc/bY5HsiK9jNK/wd4EUcW2hvw3WfYms9dL6dEHiS5b7j8hfG4AlB6CmyQhP66h1xM5tphnz6yVl+7WuXud5OUEqgNUzUfOqBYj0u8CQkuaZZcxLSFrfzC5REAJm1ZKWhbXKGgqHGusU9L92O0OCULEXlgh4owFracelEyxS3XPX3gsGd0f7ab9H5+m+9pft4U0eonXyG6OgHADAarITIk2TaYozr5RgLnhIo4TSDDrUihIBeWpKVmrRSIHWWoha7Ka5r40xpQk/gKYEVztCmHnrsa0Xc6CT8gy++zvsPt9jXDEd9sX9z7hpzKwMslrVBzng94P0HWry+3MNgKUv3HDPN0GWmypUol7oZB8ZDpBCkuXlbG6UHCbsJBHVr7X97z89kD28LW+uh3aRgtZ9jrCX0FXXlkWkn1vady2s8fWSCZuSx1M74wfXO22IZLXUzJPDIviYT9YALN7ooKejnJWuDHCEUQhrywqAazjxc4L7gnhQEnkJKjTWOWVSYagcpAJzRjERghEUJ15TU1pW/bDVIlt9CZnovAGzAateXEWo7kybYd/KO91eNSaITZ4mOnyU6fgavNQNUWkPSjQYMvaEbgesZaO0ovTVfEecaTwjaaYEUAoGgVklSm0qiwlfO9U5bNzUuhCMThJUbniecbMUzxybICs0bS313nVTXeTetGsErMVT2pE8eaHG9m/LvXr7OelLiV1aY4zUfuUl+wlOStND00pKarxive++K7ev9gN18039LCPHncEbzmx3KuvfsrPbwlrG5HpqVmoVuSigljXDjS19qw7GpBp6SvDzf5uRsg9cWeoRKjlhGeWnu2EzeSlF95XqHblywv+VYHIUxBMqjGTijkSR3EtmDvGRuNUZbwyCrXKE8SeArxsOQtNDkpSbP7MhDWFnINy3nJRt8dV26mvTear8zXJ3/CuncOdLL50ivvsL0T/8VGk/8kW3HRsc+AELeNOgl/Ijo2Afcwn/iDP7M8Zt0e0bPA6OALHHew9popHS7/PHII/I90tKQF5pCGwKlcCODjDwLjLbUPNcv0FqTGydkJyvFUq80dHx3TYee4Pd+cIOFjvOtPj9vGK/7vDDXHjWCfU/Szwp6qcZWUtet0MNaS14a0sKQlTkTle0luL7V2iDn9GyTE1P1m7SyHmbsJhD0gb8P/AobXzkLHLtXJ7WHt47Njdu1QcZ45eI1RKENSoiR1+tKWvAHF5b48PFp9rU2aHihr9hXMTl2aibvRFG1xrLUSxlkmlP7GiNDciWd9tCrC336WQ64L2OkFEI4JUpt3WSqJ50DlUWMNOq3at0Pf7ZQHbuHrSh7K6Rz593CP3ce3V+76e/p5XPbAoEEiJqEh54ADLUTzxAeP0N46PEds4etpTbJhtaQpsoMhKXuKQSQFJZcF2ij0RY84T7jtHCZwGbEpXt8bexoUtlWj6mNQRvXZ/r2pXWUgvG6j0Cw0En58mvLJIWbSParHoOnJBeWuhydbDjxOGPoppq8MGjr2Gjrcc50I3SlKWtHxkcTjQB2CHwPI3YTCP468Ki1drvjwx7uG2xu3L6y0GW6GRBnmiTXaOuCwImZxmjnE2clvbTg+PTOmus7NZN3oqgOn7sRui/kpeWYqYbPSj+jEUoCTzFe89DGUGiNwQ2FTTcC1uPKY1hrTGIQUiClwBgnV+BJsJUnsi8loS8JPEmcFQxyt5sU7/Gulcli0qvfcwv/5fMUq1due3w6t2HLuNlUCGD/f/638ZWCKkjfClsDsN1ym6gW8LzU1ELPsYAslLra7VvnHU1lT7qTfLiSG/LWw6W4GXl4SjDbDFBCMrc2oDntIQS0ItcbWGinHNskH53m2tmeWksvKTFYxxJCkGvnkuZYTML5WmvDZCPkQycmSXL90PkO3Aq7CQQ/APbKQA8AhlpAV9cGxLlmeizkRielFXkcHI+IfEWpnZzDapJzdLJ+SxcmYFszeaeRfYDDkzUWOilxrhl+naVw0r1xUeIryWzlPDZW81jp5jQixdRYBFiuribEhXb1WluxRwQEniL0pFs0rEVK13BUSqFESWndDvS9BlNkdL/1BdLL58muv/aW9PlNkaKTLqrWuim9l4CVaiQZ/VYwvM/mx1O4Gj/GkmlLUujK29gFmUCBUk42YvPZS9wGYGh4U/MkSjm1UomgPchZq/nsa0UIoJeVBEpyYDziRjfFWEuaa4JK/LCTFISeczyrhYq0UsObaQYU2hLnBUmpWelnPHZgjKl6jWePT6KEuKPm0Ls5xX+vsZuzzYGXhBBf4uYewR599D5EPfA4ta9JKCVh1S9Y6qbMtxO6aTGSgMgKzdhtDDpguzfrrUb2949FTFYUvMATdJKCk7N1Li3HrPQyZ1AOTNcDjkzV8WXCqdk6C+0UJd1k8FIvo+4rQl+x1E0ptKOFllWJwOLUK1GMvInfqxCeT++l38HEnTsf60dER58iOvHMbev8wx24rLSf1CZp6d3gpoyAqneDoDC28h24+RhtoCj1qCk87DG4iRIXVox1FGEPw2Q9JM0LSmO5tBqz1EsJlOLqWsypmTEmagGr/ZxASdbiHCsEvbRgsfK6CJSkETmBQosTszs6FbLYTZ13cmk5MlGjHngESpGW5raaQzuVSHfTX7tfsVv66O/c6xPZwzuHzY3j0FM7SkC8stBl/9jOF2pWaBZ7KXMrMYNiY6fTjQsOD/2INyH0FR88PkVpVrm8GqON5uRMgxMzNW50Y+KiZKoRcmymzqOzTU7M1Lm2ljBe83l9scfV1RghcGYmQ/lKQIqKRWQtEiilayJnRflQ85jL3mrFaxtnTAAAIABJREFU419j/KP/6ba/CyGJjp8hfvWr2+8sJOHBx0YL/63q/FuhDYSeK9Vo7TIyJTYaubBzUBiGFF+6cpC2jBb+UlhqUo7ooGLTfYy1oyDgK0m+qRZlsVRDvkgsNd9DYBkUepQleEIQFyVlacnKklwbppsBi92ExW7mFGylu6ascYQDHbuN0HDyWUrBwfEa7TgnLTKurCf8ibOH+PDxqdvu6m9VIr1Tf+1+xm4mi/csKx8wDBvHa4OMflZybT0h18445vBkjWbocWSiRuhtLwt1k4KX59uUxmm8vO+Ayy7OXWnz6o0ukSeZGduu8dKq+Xzy0VmOTPT5wY0u63GORXBiusGTB1ucmm2Oni8rNZeWYxY6CUu9FLB4yhndJFlOmm8sPUN+uTYWU1qsKR86Ublb1fmFF9D64J9AeMG2+0THz44CgTd1hNqJs27xP/YUMnzrEsoGyEpX7RfClXascEQiWS3sO2kwbZSBnDn9aLGvSjnWCqwwlR2lHTmTDT9CxzLaUI0VuMCQVbV7Y91AomyIkWNdL3GDhoGSzDQd5/+V+TZTzYCFTkqaa9ZkRiP0ODARcb2TUFOKwhjizHB8us7hyRqdpKAwBiUFn35iP4cma/zZj9+ZRnurEukQ78Sw5ruNWwYCIcS/sNb+vBDiJXbYDFhrn72nZ7aHu0Y98Hjm2AS/+pU3KbRhphkwFnpkpeH81Q6+kvzix4/x6o3+TVOXWaF5eb6NpyS+ghjIjeXFq+vkpaGfFPyH79/gZ88colXb/iUIfcVUM+Qv/8Sjoy/AD653OHelvT3oCOvKENWiEGcGr7TYilI4LC+oTQtMLXADaHHxYOcDVpdkCxdGC/+t6vy2zEmvvUrt+Jltf6uf/jDiZ/7qTXz+t31e1T+qxdr3HLun1C4jg4rbDxRmw3eAat5DVCUecIGg5kukhKIUWGvcY8oqy6g+92JYGqoe36mN2tHPw/mCTpK7DAJo1hS+lGSlYS0uGK/7DDJNOx4Qeoq6ryoKqqWfOv/jOC+R0s0t7B+PODBe48B4jbTQlNrwxMExRnrqd8CdfI9he3/tfsftMoK/Xv2/PTfdw32NOC956Uqbj56apJ+WzLcT11TzJGePjtOMPF690ee509N84+Iq/cx5sy50U9JS41UDXQjLtTVnUl+LFIEn+c6lNb7wwjwnZhooKQg8yZGJGvtaEaW22xpsW/WIwMlfBFJycqbOy/PrgMCTG1/+Yc1Y4BYUoy0CwUTdJykMSWEYjg88CMmBtZZi9aqTbqj4/DZPdnXf9PJLOwYC1Zyk+YFPv6PnKbb8b6sd/LBMVJhqoa9+Hy7qSBBC4FeDXaV1DeFuqpFVQKgFkqx0UhJSgoegqKLGMHgohj0C9xyi+oAtjnEkqvJTVhhK4TYKvgfX1hNakQcCTu9r8shsk05SML+esBpnCARIy76xkMl6QD8tKLSbZfGk4OkjEyTF7hlCd/I9hu39tfsdt3w11tp5IYQC/ndr7U+9i+e0h7eJYeo63YqYrIc7SkQvdlN6aTFyHHttsceFG11qgeLwRI0rawl139vGKmqEPgudhFIbnjo8jicEF270eHm+w1NHxvnc2cM31UXrgcdzp6f4wgvzzLcTfCm4ujZAIpnvJBTaEgWSeuCabUmuK/VROypVgGOZCARlVUt+kHKCzjf+bzpf+83d30FIgoOPUjvxDLVHP3bvTmwLNvcCJIAVZMbiCYhCRShBl675W488V9KxbjYEINea3OjR4u1XAcIwTHgsgedq8wooKwqwFIyGxobln+F5bEYgnMyENhakwUOSZE4uJS40xliOT9bp5yWdpCAKJCeiBlIILJZm6NFNCnpZSakNp2eb7BsLGeQlS72Ml+fbnLvaviP7ZydV060oSkP0APka3DasWWu1ECIXQrT2JonvL9yOuvZWU9fhvzTXzDZD3lzp0x7kLGlDYQy+dJPHS72MZqA4MlWnFXqsxTljkU8j9Dg2FYIVzK0O+Orry6NzOjAW8uqNHmluyHLNlV7KpaUBCEvoe4zXfAZpSTcpRgNGWxcAxyFxmjGegMiDwX222TJZDEIig+39k/Dwk3e8v6vzn6nq/B+4qzr/OwmLkwAZ1unz0v1clk7htTA5pdnYzYMTD/SF26XnpesxBJ6TkjDGDWrVAo/ZsYA415Q2p9giG+5V08hFaUfU4GEfIvTcIKKSboORFBqrLYU21AOFryRX1pygnKckZWlZTVOSXGOs5ZF9Y0zUAw5P1PnoqWmK0jDfTnh9sc+jB5qMhX4ld3579s+d5Ndhw/f4QcFuJ4vPCyH+AzAY3rhHH/3R4U7UtU6Sc3L69gvJTqlrFChWBhl/+MYKAvdlqyuPXBu+f71LO84Yq0b39zVDGqHik4/tA1yT+csXFvnhYpfIU64xaCxXVgck2nBypsFUM+BGJ8X3JXFekicFaa7Jd1HfMQaEAiNAmx+9jJyr879eKXWeI7t+gemf+ks0n/7JbcdGR96H8AJsmY9uk/UJohNnqB0/S3TiLF7r/pI7vonqiWvoGruRNWTVKq02UT9LCz5uCLAVOfqythasYJCVhL5isu4T+YrI99Da4jcE6wMXVBCuHqikxA+GPSRNkllCX1AP1E0Zg8TNlQjcxqgZeBTGUgscBdlYd523ak6BtB0XpIXmfYdDVvoZQrr7ffTUJJP1jcX+Tuyfncqdm/Eg+h7vJhB8sfq3hx8RNu/+O3HBaws9jk/VOT69MRC2+eJ9cbnPTCPYsaE7xE6p6/GpOv/8+Tmwzs4PIUZzCN20wFeKflZypFFnpZ9xvW04e3SS0JN869Iqa/2cNZHz1OEJPCmYX4u51kkJPMnV1YTCGPppQTP06MQ5aWF3VeKRwi0yNjdEgXSlgXcZ1lrK1XmSauFPr3xvW50/uXxux0AgvIDo5LNYXYwWfn/2xI58/vsVeodMDTZsKiVV+aeKDOWwzgPUfI+xyFGZa75CG8taP2NQFohcVM1j90ASNyXcqrkNxxtLPVQ1k5BpjSckSVFirGQs8kly51XdTzU135WrFpZiSus2C1IIIt/NEUjhzmu5m7F/LGKQFWhtqfs7L4O3Yv9snuIf9td8zw1QPqi+x7djDc0A01vpo0KIJ4E9uYl3CVt3/9YYBJbrFfXy6SMTtDaVgRqh58o7ywPOHrt1INgpdR02Z72K6metZambIqVESYFCUOAUIKPApekvz7eZagTO9i/yXCmpkrFY6KXUAoUnJZdX+zRCRTP0We5nuw4CwIguqnHNRv0uNQjK/lql2+OavFt1e7YinTuPtQYh5La/zX7+bz5QC/9W3Cr0Drs4w0avto49FAWKZhSQ5CWlMU7WuTTkpmC6HoCEmvLwQ0U/rTICnNbPRN0nDDxXXvQVYShItVOvzdA0As8p2AqnO6QLS+C78uXcSkycF3hKIKWTJEly5zswyBXTzQBfCqxxQ4x5aUZqvK0dyqm3Yv8Mp/hHG7R+QRQonjk28dBNFv9D4B8DF7bcfgL4mzizmj3cQ+w0uHKtkzJRD0aSuS/Pt3n6yDjtpBjNC2hjWenlHJuuM9XYPjR2q9T18lrMR05M8fWLq6MAoI11InDVVO900zXXAk9wYLxGaSwvXlnD91ywgI1FsBuXRL4k005vvps48w83Y3B3uNfMUasL1v/gn5Jefoli5fa6PVshoyZ60MZrTm3724McBHYDCyOnsVAJjBV40kmO59owSEsErul8vRNTaCcvLYwTf1NSYq3bwV9dT9jfCplshDxRb9GOc3ppSSJKhHCEBW0tEsFYqDDGUBpY6eckhaYReXgVvTTJNVhL4HkEShL5iol6wLVOirUw0whJS8PL820+fGJqWwP4duyfeuCN+msPOm4XCM5Ya7+89UZr7e8KIf7ne3hOe6iw0+BKXhq8QLDST1nt56z2M87Ptzk0XuPAeEQr8im0YW2Q8+1L6zy6v8mRidquUtdhs/jHn5jly68t8/pi34mCWctsKyArDKGSxLlGCcFEzcfi6HsnZ5ukhebA+KZmaVXzHcpQr8YZCjEqJ9yX1E/pEb/+TXRn8c6H3ud1/ncVVZwzlkrnxxkJKekMiDrGkBYaYw1xPtSTMpS5rfo/btJ4uhGQFpqxyOPpI+NOKM5aksKQl5b9rWgkjZKVzr1uX6vGYiemNAa/YhXVA4WUggYeStnKDhVy7QbIBrkh8NwQY+Qr2oVmqZdxdOpmEcYHjf1zt7hdILjd3+48s76Ht42d2D/awqsLPWTFjMhKjUUwyEsurcScnK3jSzdB/MSBMebXE7p5gU25Y+o6pMW1ooAff2Ify72M0BPUAg8LzK/F9DM3mHNixvka2EoqoCwNUkgmNvUlxqOAdpzRSdyOrhyaDPyIMKrzz51Dd5eZ/PE/v+0YIQS1E2fpn//32//mh4RHn3pg6/z3GhLHFEIImqGHtYahDmFWuvJQN3P00lrgOTMbXCO6KBy9s5+V7G9FpLljA0npJo1PzTbY3wrBQjctsAgmah77xiIKY7DWCdt1koJCu7JmM/SIfMXaIEcK4ewwrQsUw/mXN5cHjNeDm2xcN+NBY//cLW632F8UQvyUtfamb4QQ4ieBS/f2tPYA2wdXslLTSwqMsYzVAzpJ7iiLuHS5KA2XlmNmWwFP7B9jsh6Sl5Yzhyd2lb5upsW1Ip/HD4wxyAo6SUlhDNNjIeuDnEPjNfwqCKSFpuYr2knJsekabyz3q8EyNxE0307IdtCdf7dwyzq/kIw/96d3pGlGx6tAICTBgUcr+YazhIef2JVuz3sRwjpGlxKQFyW1sYDQ8+hnBZ24QFTTCYESzm2MqvdjKok5C/2sJJCupo8QCCQfOTnJU4dKzl3t0Ip8phuBU9E1djQQlmtDJymQiUBJQZxppjdRPo21KCDThsAT9LOS07MN9rUiLq8mZIV28ub5zXXHB5H9c7e4XSD4a8C/E0J8BXihuu1DwCeBP36vT2wP2wdXlropzdAjzkry0tBLS5QUG8JfnmQQ58SZHpnNvJVR9620uOPTdd5cHnBg3O2SskITF5pjUzWutRNW+zmLvZTJhs/caoyxlpmxkHrg5H4vLvcph1z0e/EG7QCTxaTz36/kG25T57eG9Mr3qO8wsBWdfIbZz/9Nx+ePmvf4jB8ODKUplHIGQ+txgZKlY69FPovdhPU4R+DKMUFlWtMvC4x1mlKlgQLDWj+nWfNJi5I//OEyz56Y5K985lHOX23zlQtLI92s4UDYNy+tUg88Qk9ydS3BU4K81G6zgnPm09oiJCx0UgxwdMqJJ545Os75qx16aUboKYy1DzT7525xu8ni14QQHwB+AXiquvlbwC9Za3c3H7+Ht4XjU3W+/NoSSaHJS8PcWsy+ZsjhyRrX1hPi3O2IphshpTFkhXYNupo30vZ5K6PuO9HipBQM0oLCOMexDx6fBOB6O6MReJyeblJozdog51o74crawPHHjSXJ9Wg46F7BGk2+8EOSy86OMbv+mnNR3wXSy+d2DAQqalJ/7OPv9Kk+tBBs2FQ6L+CA2bGQwljSwrA6SMm1JpAS35PEhSavrimsY/44cUHnYV0YizWGSysDnjk6wfsPtjg6VWe6GTDINZEn8ZSjJ//eqzf4/rUOeVUOmmoGHGw1uLyWkBYFSkqUEBTClYUm6j6nphvMryVcb2ecOTrOR05O8upCl9BXrPSzB5r9c7e402RximMO7eFdxko/4wfXe1xvJ4xHPuORj7WWlX6GEsKpJ6YFvbREVC5PB8ZrtCI3cTnEW212baXFHZmscXG5z2wz5NRsA18Jnr+4RqENjUBRCxQvXWnTCDzWRYHBNYezXKOtxfeUkxCwzm/4rWA3DeXut/817a/8s90/phcSHn0/tRNnqZ368Fs8oz3sBCVdk1jh+jC9rHSZgXDkhqJ0PsVjgaK0rjlsKp8CADZpGgnh+gdPHhwnKw3NyOPyWsyzx6dGG5XfPneNFy6tsdLP3JBaI2S5lzLINUvdjDg3TNZ9jLWs9HOiQJHFhqlGwNOHJ6gFGyKL5692eOrwGEenGg+UbPQ7jffmq77PMaKN+oJTMw2+eWmNojR004Kx0EMby9zagMhTtCKPQ+P1kQF3qR0bYohhs+utuCltpcVtvu+FxR5FqXnfoXHG6x7/+sXrRJ5kuV/QjDwGOcS5RuOkAnwpEL6rvwaSkUvV7RZ4AYQK0iqe6f46KA9VG9t2bHTs6du/mVWdPzpxltqJM4SHnkR4e3X+dwJKQOjLkUBgWhiMdjv7NNdkpXFNYilQAvqF+/ClgHzTgNpwTsRxjQSnZ+o0Ih+d5LyxNLip3g+WG+2Mq+sJaalRWclY6NEMfTwh6WclnThHSWgG3mjy+dBkxOnZJllp8NSGH8F6L2N+PeEXPn7iPRsE4B4GAiHErwE/CyxZa5+qbpsC/h/cLMJl4E9Za9fv1Tk8qLi8MqCd5FxfTzHG8Pj+MQZpyWuLXd5cHuB5kplGwOmRymLMcs81tfLScHrWNbeGza6xyOffnV+4azelzYHhX780z5P7W4S+4uragEJrcmtpJwVKCiLflaXyMnVUwaJEDoXHrNOj2ZSwbHO2AiBP6My/wuCSK/cUK3NMfvov0PrQ57adW3DgEWTYwGQj9RO8yUNEJ55xC/+xp1F7df53HBJHCw2UJMk1hXbidEo5enBhLMZaGoHzAUgKEFhqvkQJSVaWN0lPK+n+TdYDZiu/i1qgWOymo4x2pZ/xG8/P8fpil7GaR0t4dOOSdpzTTUqiQNGq+xRak2SaQVYSeYpG6HFqpsknHpmmE5fMr8duY6Ik7z84RqvhP1BuYvcC9zIE/lPgHwG/vum2XwZ+31r7d4QQv1z9/j/cw3N4IPHyfJu5lZhGoAgjR8f0lGS8HpBXOy5rnF/r6X1NLq8MSEvNhYUux6YbjNd9FrspnpI8d3qKb1xcvWs3pWE28PJ8m/l2wrfeXGO2GTAzFtJNXI/i0mpCIJ3HK1Ra9QKsEQisG0rDyUQMlcSGZR+Bq/OXN35IfOkcydx50muvgbm5kJRePrdjIBBSUX/ij2LyhOj4GWonzuKN73sHPoU93AkGR9kshq5yAsLA49B4RD3wWOykxEWBsC44GKdkR1j5HIhN7mdOmg6Cag6gn+e0+zmdtOD6esyLc2t8f6HL6iCjNIZuXIAA31N4VuIpSV45j4We50zpreWZY5NOejor6cRF5da3QRE1Vbn1vY7bSUzsaEgzxJ2Maay1XxVCnNhy8+eAH6t+/mfAH7AXCLZhvp0gsTdNObbjAoHg8ETEYjcjLZ2IVuQrTsw0uNFJudFNqQcKhBg1u96Om9JQ3qKd5MytxEgsdV/Rjguut93zJXlJnGtCTzKUCCi0KwGY4UrP0LGKakGwlGvzrsE7d5507mVsHt/2PUmvvoLVJUJtv2Sn/6Nfeitv7x4qvJ2hPoGTIgGBVQZjLIVxC/nwuk2Lkl6qEcI1g4dloaI6dvg4bkEXBEqyOijIrncYr/lobZluBByaqPGlV5e41nYcleV+TuQp/KoEmuaOWVca54fhSTcBbyyjKfxm6DHfTrZJsr9XBsbuhNtlBENDmv8GV777jer3/wLo3eXz7bfWLgBYaxeEEHtbtx2wNsipbRl1Xx1khL7Ek5JDExHrg5x2UowM6T90YpLxuhOK+7lnjozudytJ6qEv8ZB99P2FDn/mo8dHPYNhn6LQJa/Md+hljnnUy8rKXN55xVprKQ3o3IzYI5tT/UJv6NsLNMu/8786Pn9vZfdviJD408fQg/X39vTuXUJVTm9Un9VW34HbUXtv+XfhnMtqgUQKySDTVXCQlKVhJclZHRROh0i6BrHzJ1Z4yvlKaDaMiIZByVhDWgiMzan5Hp98bB+tKCApuozXfM7NdzDGIDdtCLS1+ELgSTe4pqRikGvGaz7fm28TeApPOhnrrNA3b7DeIwNjd8Lt6KMXAYQQz1lrP7HpTy8JIb4O/E/38sSEEH8R+IsAx44du5dPdd9hqh7Qjgs2i4eW2hJUpRclnejbI2Mhn3psI5bulObu5Ka02Ze4EXpM1gPW4vymnsFSN6Wd5Lwy32GpmzHZ8LEWBmlKLylQSuIrSScrUJXGjN1kVQjOaGQoSGYBKRX5jTd2FQT8yUNEw0GuvTr/24IC6oEizjWetGyem7rTfIfYkjYIcL0A6RzCaoGqJCM0oYK81G6o0JjRpmBoXq+tW+itdcHAajsqB/meJM5KQKCEAeVxerbB4/sdQSDXhlbkY61rPhelAc/pbcW5BusMjZR0GUE3Kdg3FoKpMhcB7SS/SWDuvTQwdifspkfQFEJ8zFr7TQAhxEeBu/1WLgohDlbZwEFuo2Jqrf1V4FcBPvShD92XsjT3CoenaqwPctLCqS8CzpnJWrxqVB4L082byz07pblbh9I2+xI3I3dbqQ2N0GNfKxr1DLKiZG4lppcWTDR8lJR00pzSaJo1n6wwJLkzkvckmEKTLLxOcvklyvYN9v/sXx3ZDQoJdV8xOxbSf+xD3Fi+vO01q/o49RNniI6fJTi+V+d/pxHn5cg4fitG1pQ73G/r8Uq6LEFJSbPm0Qg8Su02H2kJSVngVf2CYTloGBAA0sJSGkcgGPYDPCmRCDwpCXyFFJaxSLG/FY2uWykEi92EONd0kwKByzYCTxF6kqzQCARpoUmykolGwMnpJoU2XF4Z0EvdIKSSghfm1nhk3xj10HvPDIzdCbt5B/4r4P8SQkS4ayUFtou07A7/FvizwN+p/v/tu3ychxpnDk/Qi0uutxPahaYRekzVAxa6KUo4wbfZsXCb+cxOae5WN6XFXkpp7CgIAKwnORO1gOffXCEvDXGh6ScFE/Wg8qd1X+N+UlIaJxEQKEnvxhzd179Lcvk88ZY6f/nJXyRoTSNk5T9clRIaJ8/C17+A8ELqx54iOnGG5qlnCPadqCQJ9P0pRvcAIzcblNwtKgqj0oxX2Urm2o7kQLzKF9hUfF9d1fgDz5Uoi9LQLnOEEIRKUFa1/6HD6FDWfOhBHShJWhpKDfVAOP0frWlFHkJIPE8SehKtDXlpuLjcpxYoJmo+y72M1X5G5Els5NNLC+LCHdeszGGy0jkeB54cUahrgcfRqTqrg5x6qMi1IdWG2bGQn9gLAiPc8V2w1n4HeEoIMV39vrqbBxZC/AtcY3hGCDEP/C1cAPiXQoj/ErgC/Mm7PO+HGkOph4m6zyDTzK/HSEk1ORnRijwsYiQjAbfWRdkqG3FtPbnJVakd51xdTZDTgvGa70zqleSly+scnHA7qGEmMuissPjKd+i88QLrr79I1lm+5WuI587jP/0To22mNZbs/2fvzYMsv677vs+997e839t7nZ6Znh0ABxCIhQQhkhIpUyStsmyZSZzYsstyHDtyElclqSiJyn/Yf6TKWVypVMpLkkplsVPeyopLlizLskoUI5EmYZKAiB0EBpgNPdPTe7/1t9+bP87vvemeDT0ghgQGOFXkzDTect/r3++ec8/5LkVJ4/ijPPwX/kfyhQdxSgzH/Up9LC3sR0ngHoRGfg1ZCaYyDphs9hqo+8LUdSgC4yr/X6j5ulKKdVincKXYlhalIysKfE8TGkOz5tOKfFZ2xtOEkpduShDTCMM98DUK8aIuHNiyxDmYaYYstmtc68UMYtEZKp2jVZPr9JtvbqG1tI9qzjBICuaaITPOiUR1KrLovlJEniEKPJo1X1jCvsFozRfOLorZEnIqTq39KAnsiXf8JpRSC8BfB4465/6YUuoR4Gnn3N+70/Occ3/6Nv/pi3e9yg9Z7JV6CDzNJ47P4Hua7WHKty9u06sq/7VezMXtMb1xQS3QfOXxI3d8rWGaMM4KZuoBRWnZiSUJnF5oSPVfRc031AOPIhlz6fXvsf3Gc6y//ixbl88d+DMkl55n9vEv4pSrzMxF7MsLQsITH8eUjrK0lKiptrz5SMjzPY3J11mBe7CVx7CnpLpXCjwjm7RC0an706HtpDXpkETtVa2cvJorTewiO3Vf9P6NwShNZi2G6xaWunIXy61FFzLYnTiRKaWYa4c4B9d2pe1jnZMTqFLMN0PhAvga3xiSrKB0jsLK9RIFhsj3GCY5Mw2fh5c6vLiyS1yURJ5mfZjyhYcWWGzXppIrcHeyKx+WOEhK/HvAP+Q6zPMcQgr7e/dmSR/suBsG753idg5Iv/DpE+zGOb/98hppXlAPPZZaAf2s4FeeXeHXX7jKVx4/whPHZ6bvt/e1XlntsT3OaIQe3ShAz6l9SQDEE7ZT93jz936d7/yj/+nAazb1NvUTj8uQ98STWOvQ1e7uGakSi1JIRkUpHgUeqtKFtwzTuxWh+CjuFA5p+UxCa/C1WDYGvsJTiiPdiKdOzvLqap/tYcYwFcmSk/MNNgcJuZUK2gGzzVCUZXVlHWodWis2hjmdWjU8VgrPM4xTIRoaFM1QvIvHmcVV6CGjwHiG2UZIYDT9RPyEtZJk047k+nxrYzit6seZ+BG0I4/13ZR+UhJ6hvlWKD4EztGo+Xx8oUMr9Okn+U1wUfgIMnqrOMjOtOic+0dKqf8awDmXK6UOpur1IYvbmcp/+8I2v/3KNWabAX6F+DlIcriVA9I4K/iNF1b57Jk5xnnBN97Y5JWrfeK8QKOoBZr/fTfm8x9b5CtPHJ0yJiev9Wd//ITMDNo1vvXWBtnWCr20SWfu0PQ9RmnBo0c6mCc/w3f+0e0/r/ICmicepX7qSZbOPkU5e5ykEHSTZ6SHXFauU2kuAnSldfTinLJSJY18TT3w2RoVPzQLyg9TlFbY3FJlS38/Lyzz9ZDA0yx2Is5vjrBWyGG1wFDzDMMkJ7fiH1xrhdOqepzlvLY6ELRPdUpV1RC6Vhm9OAdRaPCKkqKaOShkjmC1wjlHGHg0A0NRWoxSlWmNuOH5RrM8G1XtrJKaMgxTUQOdiQKOdCLOzLcYZSVbw1T8DEph1B+bjXh7O54xgWXEAAAgAElEQVR6DtwqPoKM3hwHSQSjShrCASilPsW75xF8oGNvtd8f52yPMnbGKcNEKqY4K3j4SJsHF1vTo2haWi5ujomzgs1BymdOz6GUOrC8w40xIYjFzvKrf7DCzignMIpG4IETka/L2zHfOCcQzT/99PF9ySYqhzzzO/+c1579Jt/91u/T37zGT/6pv8zH/sifpyjlJu7UDCfmuhStw0TdBeLdahagFPUjD9I89STtBz5B9+SP0WmKkf0oLSG7fiNP/FqiwBB5hkGSo5ToD2EdedWjTnPLWj8m/ai0uCcxmQ3UPDFy6UQ+aWEJjcEY2B6mdBuB8FaUSDAbI5r+pZUN9vhcg9AzFNZWSrLCRxiPM4rS0qz5hEbTbkdc3BqRWYtXXQChr4kCTW9coHD4WjPbDHloqUUr9Hl7J6YfZ6S5pVUTL+KHDrU4vdBgvZ+Slw6jHAvNEOssF7dHzEQBu+OM5bk6ZxYEwJhby7HZBmlesrqbsDXKeOTwzdpUt5ulvVcn+Q9qHOQT/lfAbwCnK2+Co1wnm31oYm+1rxW8utrn4uYQa6EeerRCw9Yw49+8tc1qL+HHT80RepoX3u4RGEWrXWN3nLExzDg2Wz+QvMOt4vtrA2q+5ndeW2NnlIvY1p7memQ02iiu7sQ8f2mHs3M+/Ysv89WvfpWvfvWrvPjiize95rnvPcNDf+TPi/E6Isb1e99f50i3zgM/8Ufp93Y48fFPc+axpwkabTYGKas9UUUtccw1Q5xLSHKH72k8rTBG0a78atNc7AEboaEsHUlpUcqR5UIqyj9KAvc8rLM0AtGBmqmHHJ+N+O6FbcLAkeYl/STHN4aap6lVQ+A4K9iNc85vDAk8zSgp8T1VXauOnbHFQ9MIDE6J0qd1DmsdaVXde1oRp0LiWmgGPHVyFk8rzm+NWGjWePJYF6MVr632yYqSwsKPn56jXfM5s9BioR3y7EWRl8gLOVXWPMPGMOVaPyHwDUe7NT51UnyiQ9/wwGKT59/u4ZS0te5k0zrOCp6/vMOvv3CVNLe0I4+Ts3Vqvn7XxdoHMQ6CGnpWKfUF4GFk/vSqcy675yt7H8VeE3kvMHzrrU1WdxO69RC/aoFc2BxzZCZCo7i6m/DcpR2Oz0ZYa6d6QTfa4d1J3uF2kVQ46rfWhsRZIR6sSlELjFR1tmR46TUuv/wdXr74PP/bmy9RFncejG289RLZeES90SQrS97eHdMfFySF5RN/4j9mrZdSOsdqAk0nmPETsw1OzDWYb8kN8vzbO7y5PqRT89BG060IO1ujjKwQotAwKacDyEZoxObRwuge+hV8FNKP97VikBTMNgOaoeE7F3fICsuhTo1enItSaG7pJTmR0SglbTzrhNXeDn20FoG5euDJdV9mNAKDbwzX+iL/oLUiUjIctg4yK2J0S3WPT56YYbYR4pzjWLfOmYUGK7sxo8zSjnziXLHUjmjXfNK85PzmiH99bpO3NgYCCa3UdVd2YmlN+Zo8L3lzfchsPaQd+Vgr+kP/+ZceZJDk++ZrN3oMbA5Tfuula3zv8jadms9CI6SwjgubY/R2wuPHOhil7rpY+yDGQVBDGvgSohjqAZ9XSuGc+1v3eG3vm9ir13N5e1S5LymBPSIohNxakrygE4VkZcnuOGOUFiztgXh6Wt1kh3c3DmIgg9zff2OD3TinHsgQrXSO7WHCuX/81+m/9QeUyeidX2iypiDk+COfJBn2MGEkNoBFTuhpLm2PCT3NkW7EQiPAApuDlGFacLQbsTlKp4nA04rDHdF3WeslU7hqXlo8o2VOUFhc1U+OMxEISz4aDNzzUAhKqJfkvHK1z2w9QGs41KmxuhuzPc7xjKIZeJRWfk9xVuJVp7p+UhDnJY1QNuPI1wyzEr86ia7sjCgclSyKo1Qyk2rVfEJPY7TixFydXlww10TM5UNTCcBJiyYtSr766hqHWjX6cc5zl7Z5a2OEc6I31K+KilbNox2JYMkok7ZoUcBLV3pc2hrzJ586yk+dXWC+Kffd7e6rSXE3iHPqgUejgpZ6RtGpB6R5ybMXdzgxF3FubcjbOyNOLzTv23bRQT7NryOtxpf44TkOvq9ir17PlZ2YOC8J/f2DqJpn6I0lEdR8wzgTO8nlbjR9TGGvy0RM4m6gbOOsYGuYkRUW30gLp3Ti3YqDfLD9jklAKcVTTz3FsUef5pOf+TzLZx/nX722g1JyrNZKkZZy6qh5emoo3o18As+QW0foGTYG0lu+/rrQCAS7PdsMp4JkgSduZXlZEmciO+ApkcOIC4f66DBwz+K6fg+Upau+e5FnODZXZ3eU0UsKapPfeyEM836co7XCORjnIt3gnKO0lnoo4m6hpzjcClkfpuIoVj3W02rqod2seAB1X06rmxUkNcnLqVT6JIrS8ehyB5ziG29uUJaOmqfZHmcYo4l8j9BoSieVfLvmU5SOdi3gyGyAco75ZsiFrTHFaxvv2M6ZFHdbo3Qfr2YSpXOc3xjSG2csdWqM04JQ37/tooMkgpPOuY/f85W8j2OvXk9WitLihG07iXbksz5IAKHDuwo7L4qI8thRWkyHW5M4CJRtNBrxjW98g3/ya7/Jc8+/xMf/4n9P4GnSQpiVUCFwTj7B8PIrNz2/c2iZ4x//cT7/hZ/mz3zlZ3niwWV+7XtXWGiGrOyMOTFXxwFbw5TUii1mzTN06j556Qg9zc4oY64VMkwKSusY5znbo5QstwS+rmj/jk49YJyKMmozNCgnJiVJYfeJnanqLx+NB+5duD1/+p7YRM7VfTwjVfowKwTi68mwNc5KRllBUcqsyCmF7yD0NM2az7HZhrRHteKlqz2yvCQvmZrPW+tQWqG1ZpznuMrAcr4pMtBL7YhhWpDmJZ3KQWxv7/4rTxzl0taInXHKi1d6gCjZzjVC+kmOMT5GKfHsLi2durj2hUYzzgu69UCQT55+x3bOpLjLSjuVcZnERJaiUTGRa76hn+QHlm7/IMZBPsVvK6V+2jn3tXu+mvdp7NXrCYyeVuLenmQQBabCysswWSk41AoYpQWdekBSVUuLrf1VxK2gbGVZ8txzz/E7v/M7fPWrX+Vb3/oWWXZ9LPOV/2TIbhjS24mxFSHLWUftxOPAP8REbZqnnqB+8glOP/FpPv34w3QjnzgveWPH8fYLqxTWkRWWld2YTiSbw3wzpCgtm/2U7TgjH4jh9+F2jfVByvY4Y2ec4ZyIi2kN5bYQxuqhR5KXtMKSQ52IUZqjtGKclxTOMZGsn5yHJk5lH8W9C4XMByYyD3PNgFbgkZWWUVpirZO/D6/LehgFWgkX2VqLRdGJapyYq/PIkQ79JOPrr2+S5xbP02RFlQz2kMTK0pIXAhldbAdsjyreSj3gx450ObNY51o/vaU/8NfPbfDwUptenNOp+by+NkBrxW6SMdHQLZ1AXTuRT1pWlqhaktsosweavU2Ku8AI5NXfA7jYHKT0kpy8dCR5QWEt3cgnLYS38G5me+/3OEgi+AbwG0opB2RMCgDnZu/pyt5HsVev5+hMxGovESXH8HqbpyjFSawfFwzTgpmGz8eWWlzeTljrJzQCw2PL3X0SuBMo24m5OufOnZsie772ta+xu7t72/Vsvf5dHv3EHxUp6lEGWkw9msfO8uBf+jt4CydBaYHjNQK2RhlLnYikknCoeZqruzFxXnCtl5AWVtQiLcR5yTgXPSPnHHXfY2ecsTFIaYQGT2uGSV6hQxSpsUS+wTcaZx29uCDOR3QbPo3AsDXURL4hLwoqJYmP+AI/xJAkICiuOC1ZaEjrcrWf0I9zsolMeLUPlhZKrAyYKxz+OMvxPRnUvrY6IC1LwsAwTgrQisCIGZEDIYVpReQLW9nXAkV1FPyhjy2+o77PdIOuOAmzzYC1XoKnNBZRUk1zK8ZHgKdEcG6pE+3jDuydvd0IDdUKXlvt83IpnJYJFHUmCshLy2urfUSzSFpaIqnh+M6FHR4/1qFd8+96tvd+j4Mkgv8Z+Bwf4hnBXr2eQ60aM3WfQVKQl3aKGtJKs9SO6EYlq/2EZmgIfY+zh1tsDTO6kQzOJsfht6+t89qz32TrjWf55d/7GpcvXz7wel787jf5pT/x53hxZVc8WCc3m/HwD53GOWiGHt3IpxfnjFKB3XVqPhc2hxybXcTTiu9c2GYQ53SigNAYVgbjqkoUA5pxJuf+3VE+5QcU1k4rKKNFisIoVWnBGDlGpwU4x9XdFK1gsR0SZ+WURAYfnQYOGu/kF3Cn5zlEVqJV94gCU/1uS452I76/OqikwZkO8PdG6aAdaEpbJRIF6/2EQZLjaTkJeJ6iyErxHK6QSaDQGrLCkVFIS0cr2qHHp8/MvmMrZXL6Xu5GnN8QzsBmX04OwyTH+CJvYbQiK0paNWkXdSOfYVpMZw+T2duNJE/nOZ67tMvV3THjtOBjS22GWcHK9phrOqF00tBqBB5p9fqB0RybrVNaxwtv93j61Mx9J1NxkERwDviec7cSsP1wxF69nqK0fPxoh8w6Lm0Msc5RD31OzEXsjjPSwvL0qdl9rN69FckEyrb16rf4W3/14M5aZ86c4ROf/hyHH3man/pDf4h25DPXCGkEHoO0IC0StBYpX5ERgLgQxyZXYbtfXe2xNcx4YWWXUVow0wiYiQJ6cY7WUq7XfI+4sPTigoeWWgyTnNW8JAq8arOQKi0vHaHWU6JRYS1pJREQVvML0TXyaYQ+3bpPP5be7kdx8Hi339be53lGiIYoR2+c040CmjVDMixlhqXl5GArdVFbmdfsjkXnpxmGNGoeq72EorS4qq1YDz3SIq9+5wprpW1T5NJSDIxmZ5ix0Apphh6/e4Ah7vT03a5xcUsYwifnG7y5PmSndGhlsU6uvbzy6Dg53xA7VK2nQox5YVGaKey70QhIi5IXV/o0AsPZpTavXO1zaWvMifk6V3ZitkYZ46yoZK0tub3++p7ReAbiPGO9n7DYqt1XMhXqnfZ3pdTfRaCj/xKYup78MOGjTz31lHv22Wd/WG9327gVs3h3nDJISozRnJyL+OyZec4ebhMaxXPPPcfc3Bxnzpy56bVWVlY4duzYbd9rbm6OL37xi3zpS1/iS1/6EqdOnZrKS9Q8TSP0+Gd/sEI/yXEKLm+OZVaA3PSNwMM3SjZe5yidwtpSTG08jarMPebbIc3A41ovFS0aIwShjWHKXDNkGBdsjVOW2oJ+urg1oh0aHIp6YPa1lbSWROIbTW5L+hXM1tMaV6E9stJOlS8/tJXFDyEmJwJPi4yH74mcQz0UBno79Li4PSa3MuuyUOmFAyhsdXqbawj6px4YyhLWBjF5Icb09cDIZuxdlxNJczttNUWBh1bw9OlZlrt1HlvukhT2jkPWvdd46aQCL60l9DQbw5TXrvbpxRn1wOfHltv4WrHeT3HAqYUmDy6IZ/f59RG9JGOQlMw1A5a7EVlpWdmO6VTaWnFe8MqVPrONgMOdGi9d7bEzTLFWPsDHj3ZYatemXtwgLeDcWk7PN3nyePd93xpSSj3nnHvqnR53kBPBSvW/9g+8qg943Er7ZxLOOd58802++i//Cf/tnj7/L//yL/M3/sbfuOnxy8vLnD17lu9///sAhGHI5z73Ob785S/zpS99iSeeeEKq9Bvef6+SaM1TnOunoiKpFUXhqAeKcWoZpQX1UKCbo7SQY7GWQVd/nFMPDd16QCsUD4BGaGiGprp5FZ5RXNmNsRa60eQycVIRYphtVF6wyM2xNkiYiUKWKmx6WTqWujWubI/pZ5lADKmczN7j38tHsT8U4HtQlmId6XuGwCjmmxG9OGcY5wJB9jTGSuVrqieK3agkBOfEiKY3zujHYkZzpFMjt471fso4LwmM9O5rgSHJLdoI1DTyPRbbIb7WDOKC5VP1Aw1Zbzx9P36sze4o4+L2GIXisWMdHjva5bsXt1nrJfgVz2WuGaBQvHK1z+XtsZgpoUiKkq1RymurfYZpztFOnWv9hNyKpPZSW0hkk8++PFunGXi0I58o8PYlARCJlO3x/edsdsdEoJQygO+c+ys/pPV8oGJjY4Pf/d3fnQ55L126dNNjfuXXfpMf//n/9JZElF/8xV9kfX2dL3/5y3z2s58liqKbnn9jTJREv7/a5/ffWKfmC5mtU/cJs5JhWhIFRqCAqRUBOK2mkNaabxjoXMhunYh64DNKLc3QsNAK6MUFiS0JPM0Diy0Co7nWi+klhejJ1Hzq4f4bJC4EgTLTCAg9QyfyWWiHKODctSGlE5RQpYY8hY/uTQg3/vujuPvQCHqttCWlU9QD6flEgYdCEvaJuTrn1ocMk4JO3WCdwZZiKJ/mJQUT8xkBIMS5RStRqG2GBk8bPGuZb8HVnYSstGjrML7IThgls6Jjc3WcczRDaSlO0HIHGbLeqLxbCzyeOD4zvYdArqdz6wM8rWiEnkhZ5CUrOzGltewMcxxCRqv7Ai9941rKWi/lY0ttmqFH6Rw744yscPz0WRFd9JQg3Z48NsOLK7tTYyhPCxS8N85QivvO2eyOn8Q5V1Yicx/a2Nff7w85//KzXHn5Ozz3zNd58YUX3vH5F994haS3y/NpeRMR5Zd+6Zfueg0TQaxAKZ46PsvVXoxDkBSrvZjta320Euy3AoZpSasmN2OnLoO1Ce67UfU4O5HH5ijl2GydpY7IX8RZwdYopSgdo7RkmOSMUbRrYgzuaz1FQA3inGbNo1EN+izwyeMzPP/2DqGvSTOL52uctSilSAorblfIpjOBlko1en+GAiJfUZbiGXynpDcBMk58AyZWj54WdM7twlX/r6qGv3PQqhnZJLOCfiJs4dl6QJYXNMKAum/ojXMGaY7SGt+JdLizTiQcSkcj8mjWPIrSkhU5hycIHV9jrQUnv1MBTyiOzkRoIC0F1nl8rj69Vg46ZL3T6fvVqz0iX/O5B+ZZH6Ss7IwZZ1aAD3WPJBPEUWHt1ORma5hXJ2TL6m7M6cUmntbUAg+wvLiyy+FOjQubIxqhnAg+dXJ23+sHFcT6p+8zMhkcrDX0PaXUrwL/LzClrTrn/vk9W9X7JCaIg6Io+Zt/5Rd5/pmvk+cHl1lqdWZ48tM/SZmOOby48K6IKLeTtv7/3lgnNJqHDrW4sDnCM4qzh9topRgkOWuDlN04I81LGoEhrOkK3lcQGE3Nk6M+CNrDKCWSD5Uc8KtX+7RCQ5zLDKARevTGOUlR9ftHKc3QQyOs1SOdGkkuCJITsw3y0vL6NanYkirz5NYRGoEWTqQ2GqEmziyFvX+TwCTECEZwQOkdPuzU4tEobOmmto/vBNdwQJJZnJrISjiGSYGjoBkaDrUjziw0K9hwzOYg5ceOdNgeZ8S54Ei11tjSUTiHy6V9Mt8MCY3BWgg8QRHNNkRO5OruGKdkDmGMoeF7VftQ88jhNqGneXDxugroe+EFMCGDhb7h2Gx9qt31zFub1TUvUhQ7Y1E1LZ3FOrG0HMQ547xknJa0I02SlxzuRNP2UFpYjs/KJn/j649S0d86e/j+65IfZDc6hCSAn93zM4f4D9+3sVdortEIwNl3TAJeEHD07JOceewznH7iM/zMT32abv165XC3RJSb1lBFWFH2Q6O5sDniseUOvbhgZWeM0YqdOGOxFXJmscHLKz28yvjDZAVHu3XaNX+q4S6DXUc99Hhsucu3z2/y4tUeaWHZGio8T5yg4rzk1EIDZx27cUGSQz/OWWiGLHUiRmlJaQtC35Bby2+9fI1+XFSDZBEvKywYLI3IpxF67I4z4rzqRwO+vk40e7ewyfdrBFr69XFeorUmmiB5bsGu9rQkac/TWFtMTwX2AL2zEgT9ZeS0lZYV1NcYfK3EX9gzHOlGXNgc8eKVXdqhh681pXWU1YY4Uw8orbSFTEUyMFpRWMuDh1r0Kn2iwlqSvGS2IfOhYZxzbK5BN/KlKi/tPhLle+EFsJfpvzey0tIb51OplMAT9NzmMCeoZOFHqbCp+0lOFBiMEmc2gLe3Yx5d7hAYw1o/oRv5d1QuvZ/iIOqjv/DDWMj7LfYKzQF84tOf57vfuIFcrRTHHniEz37+C3ziM59n2DlNVK/jGyG5vHRlwNOnvH02eXdDRLlxDXtjYiCeFAW9uBBp61ZIkpekhSUKDFrB4U6NtHC0qxtzkOQstELOLDa5shOTFiXDpOSTJ7qkZSkQwcLia8VudbN7WlPzBVKXFiWFtZycr7PQCpmpB5xbG7A2SGhVktgvvr0jvdqq/+NrhbKAEYx2kpXMt0IOdWoM4qLagEqUQshJ5f01L2jXFEWpGKYlvr4+IwmrTWZS7U8SQukkKWrcXZPvJi02rUUgziGOcDjZKMdZztYwF7JW3ZfhvauSjIbDrRoz9QBQbI5S8b7ISlo1TVGW1CtUWKfu45AK/0ovxvcUjx5pc3U3IfS02JJqtY9EeTsvgLuNvUz/vREYTVpaWp7BOkfNk4p+ZVeUUX2jadc8tka5aBhpKc5eXe1TVlIS/9FPnWGuGdwE975RufR+i4Oojx4B/ibwk9WPvg78F865q/dyYT/q2Cs0B/CJz3wegKXl43zyMz/FsY8/jb/8KAsLC3zm9DwAv//GeiW2JlX7BHO81y7vbogoN65hb0wIN3ulrdcGCUopHjzU5OLmiGFayHBwbUSc5SglvdOZKKATBdQDj0tbQzaG0gf97sVtdsc5pbUkld5QzdeMc0tvXDAwBR9bbGGMJk4LPK1Z2YlZaEmyqfmarVHKRj8lLcS8HCsDOKXBc5CVUiVe2Ylp1qRye+BQi/V+QlFKpWadvWMv/F7HDzrIVgjaBiaMXSHilQ5cCQorLGsnm75j/wDdIdfPIM73DdgPEpPTFCAkPhz91KEo8FPRhJogvpa6dbLc4nDMt0N2xpIgklzIf4HR9LKC9X6y58M5XlrZZbWfCETUM5yer3Nirsml7bGolmrNsZmI0wsNmjWPNC/f04p6wjXoVCS3ld2YrLAMkpxBXFDzDHlpWerUqAcey92IJC8ZZSVxbqkHkqi2RznDpGSm7rHQCvGM4VtvbfHFhxdvO5+4X+Mgv5G/C/xT4M9W//6F6mc/c68W9X6IG4+fpx56mP/nt/4NR46fBKQfaSqkwiRu1C1phh4ru/G+RHA3PdLbHYGB64Sb0k5JWld2YhqhVGzHZ+tsjTJmGwFKab6/2if0RDdokOTEWcHKTszmMONLDx8C69gYiGetXx35ndNsjjI0ilZNJKVXdmNOzzfwPcM4LXj5So8vP3yI0wsN/uDSDuv9pGrzKGzlZJVWQ8SJbeEERjrOxLd4lOa0ah6jCvHkUpknvB/C1+As3I2bstGgkeTnHJUjmKr4HPKzovLu3ZvvJp/YOmFzAwSVF+8kMR4kKZTIe5bVA+vK0gilHbg7zomzksOdmtibVjaiDSMM2lFWiM5P3SfyNGmh6ceFaExZhyFke5QLlwDLdpGRO8vnHzrE0W7EKC3opzk/drjNxe3xLfWEftA4Od/gmfNbvHiuh1+hhqKa6ICdXx/x5vqQo7MR3UhO0ovtkCu7CZ4uGaSy9m7kc2QmEvmT0rI5zPmJB9sHEqy7H+NAMwLn3P+x59//p1Lq4JTYD2jcePxUSk2TAMgxu67NPl/UozNSpU/M4CciWHvjbnqktzoCp3nJ2iDhyk7MKCu4sjOmERix+6vkJnbHGUZrvnB2kXals/6Fswu8fm3Iq6s9NkcZc62AuWbA5x+apxsF/OPvisRFYDShr8kKx6Cq+o1WU3+BcVYySEvqgWG1l+BpzaCqro5062yNM7JEdIWKsur1O0gLh67EzybJoCihESjiTOCqMtDzibOSH2Vz6MZ3VjfsvjduxprriKeJlpOqNlgHGCVChZ5RlIWbai5NILU3vV/1p0VgnUZD5GniSivqVo/du3bF9XlCaKRNNIHw6so3+squyKB4nsY3IhmxXbWCtNYEFeqsHQVopYmzAgdcGyR4RuEbg3Mw0wio+x6//fI1/vgTh5lv1himIm397zy5fLAv/C5jnBVc2025sDmqWl8eC82QZs3j9EKDF1Z6DGKRgDHVXOTtnTGjpJChtnI0I5/dcUZfKWYbAb6nudZLOT3fpEjtfSUod5C4tbvz/thWSv28uh5/Cti+1wv7UcfZQy168e1bOIHR9OJ8n9/AoVZtirwBbjLQvtse6Y1r6Mc53724zfmNEZ7RHGqFHOnWmG+FfPviNr04Iy5E6/3pUzPTJADQrgV86uQsP//Ucf79nzjFn//sKR490uFQO2JtIHjwrLACE7TCN8hLi8VNK9fcihH99ihlvhGyPc5YbIe8vRNjncwlQqPxPFVtFmrfTmXd9SQgpCdF6HlopXlgsUk98FAaSvv+GBNrQGmFMRqv+hwG8f/19PV+vNGCu/c0zNREf0lXbF3f03TrHkqJNhNc/x4m6CBfC1R0ku4nyUEjM5PAM3ie/P2Gr1TWqfY/x2g5STT8CTySqt1jKUsr8yWjMEZOnRv9hPVBSqA1rtIKSiuZkcg3LM9ELLTDqdHQRMvq5FyDU/MNFlo1lIJvvrlFWpTTOdi9iM1hyt9/5hIbg4RHj7Q5NddAAZe2RryxNuCBxRb/wWdPMtvwubQ9Ym2QcHlrzFIrFCZ86ejWA/xKGysvHWu9hFNzdRTSarqX63+/xkFOBH8B+F+B/wW51v4N8Bfv5aLeD7FXaO5WxhVzjZDtUTbVNgHp6z623J0SUYrS8tCh5rvuke5dg6cVL67sCpu3JltGmpc0w4CnT81QlI7zm0NmooDlCu52q5icSG4022kEHsMkIfKFIWoqAbG8tIRGY5Qiy8tKOE9MyRWyIaz2YmbqktwCX5MMSpQS9cmJhMmNla9v4GinxoNLbcZpga8VZw+3eGWlJ7DWH7EmUaW0gLVOJMerdk/NUwS+pihhnBdoBFUVVIbvubVkGRRWFDydlXaMA/I9rbFJTAe1yCbe8GWTzgpR2Jy0YOAUQQYAACAASURBVGqenj52LylvkoiK8voAeiIV4pyQyOqBJ0iZOEcbIUZpDdvV0DjQCm3EJUwSmqbmG5K85O3tMU8c77DYbNFLCtZDD18rlmf3FzOtmnBR1vsJ862Q8xtDfvV7K++pEfwERbc1SlnqiPRDFHgstOQeTPKSzWHKqfkGP/fYEa70EhSOorDsjOW6v7QZUzpLWsg1LnwAEVrsRj4ruzFHZ+r3laDcQeK2JwKl1H9X/fUTzrmfdc7NOefmnXN/zDl34Ye0vh9ZTKjuSWFZ6yekFb4+zUvW+gmtyOfR5Q5Fuf+wPiGiHOlEOBRKa1JrefJ4l597/PBdEVH2ruG11T5JIRtxUVp2xxlZ6Xj8WGeqkd6p++zEwhq+Vew9kSRVOwakzbXQDBH+jaJbwel8T3DsWWlxTmCCXoW8UMBDh1okRYlDeAhpUZJWLmSuOknsa6GoChqpxNFtoVPD05p64LE5yjk+16BbD1io4IaTje5HEZM5xiQflU6SWpxbrFU8uNDkcFuEx/LCMq4MejxjpvOQvBQCWFbJGJfuzg0vC1PDocDXU95AUTqSXPR29n6nrnpOUfkB+Aa6db9KDAINniTsvDplmcqhbJyW5KUM+LWR90wKS1FYjKdohh6LrRrLM3WS3DLKCkJfBA3zW5zYdMVDObcx4pm3tlnbTQi1ZqEZTp29fuOFVTaH6U3PPWhMUHQKNQVl7I2aL+5p64OUZs1nrhFwZKbOlx9Z4vhcneVunblWwOFuxIm5BsszddqRMOW3hmmlaGrfE67DBy3ulJ5/Tin114C/CvzqD2k976u4kep+I5RsnJVT3Z8bMcdzrZA/+fSxH5iBOFnD//2vzxOkwgcIPM2ZhQaL7do+aOqhZg1PZySFveWa9p5IbjTbAdDKsdaP8YxU85GnsdZVPWQZ+M41Aj55fJbHljus7Iz5rZev0QwMaWlZ76egxKZwnBZk1YYxqTaUk7lJYBRGwzAumYnkMc45lrsRnUbAIMmJPEVWSjL5USCIxGRFkoGpmNoOIWmBYyfOsU7RCDwyVRAXFt+YqlgoRL9tSuw62MRDK9nAQTb/woqKp3YTDzoR+ouzkmLPKUIDoacqJVhJQOIVDNoJAzwtyuunAaXwK6noSYVelJaitKSlo+WkGGiEcpK4vD1msV2j5SnqoWFnfLOvnHUiDX1hY8jyjJjYoGBlZzxF9TgcV3fH/MJnTkw9he8mJqfYiVfBxPlvb0xQdIutUPSPbvA3mGuErPWTfV4iRilSW05bue8F1+GDFndKBL+DzALqSqm9MwGBPX9IjGnuRHWvB94dE8V7hTqoBx6zjZCPHWqLfMBtwq9sBA+ypr1mO926zzff3KJVkxNFUVoCz+CQ/nQr9NCR4oGFJgutkMeWO4S+YaYhbag0L1jdTbCV1kwzMKR5SZbLbjXZx40Cp8Qg3KHZjlOKTUs9EFvMx5a7DNOSf7gxrOQNygN1iN4LnaIbX8PTMmSdWGw6JevHSounE/kcaodsDnNCDy5tjamHPsMkJ87Ev0Eq8btbQ1xYQWgVwiEIfFWZBMmwdyIZ7SlES8iJsFoUeBxq12iEhvMbI7LCkllL3fdo1TyswJemMtNpNceKMxloO0c1sJc2T6dC3DhsdTK0VYtMTQmJ/h69qclpMS4LGoGhFmi+c2EHW7HSo5okqYubY/7+M5erZHB3RdJkU59ApycqonvD04pxZve1QPf6G3TrPhuDZN/6Jw5nw7TgSNVyup8E5Q4St92pnHO/pJT6L4F/AfzxH96SPlhxp0TxXsbtSDR7426OtJP5w/Yo5VovITCK0Pc5G3qsbI8ZpgVKKWbrghTq1HyaocejR+VzrvUTPKP5z774IN84t8E/fXYF5xxpUTKufG8Dcx0qKTLH1anAaLRSRJ6Y3whhyeNaLybJpQXRCHyGaTntiWtu72/8XiSCG59fONB7TjRFacmcVNrtmpD1xM9Z0Y8LPnF8hlpg+Pb5bbD7OQIHXkP1+MAIZlVrGSA3Qo+ylPYTyLXQDAy+ERZ3UCjOLDbxjWGQ5NO2VqhEHsI3mt1xBk78eEvHFJ5bOtHx943AfbOkYJAIS12MhAQO3Y58tkcZzdDj9HyTi9sj8bgubCUpIaeCU3MNzh5u8drqUK6p2vXN2jOKpU6NrUo25W4hmpN7YAKdTvPypvuhqDSW9m7me/0NbOVvcHFzRFqUMgvJCjo1sZVt1f37lj18p7gjaqgyo/l3AeucKxFfgp/hR9e6/dDGO6GYQAbBS+2Q33hhlecv796xRzuZP1zZTRimJSfnG2IwU1oWWiHLMxFnFhrM1APmWyK09cTJLllp9808HjrU4k8/fZzPPjBHIxR/AlUNUKPAiNRxhYqBqrXiHEHllRBnBbONgHYU8C9eXGWuGTLXDFlshcxEviBqJvjMPaH2/M/TIuHwXoedVuDSmPe0WC/O1EMGaSFSxkbTSzK2RhmXt8ZsjzNKwDfqXSenOCvJStH5KayTmYrvMd8ImW0GdGs+RmtakQxuu/WAnXHO5kCIX4FR04LgWi9md5yS5ZY0F62crLAEnsL3dCUZrSuRQoVvBA6c5iJQl2TCJi4rG9MJcMJZ8STICpmddSMfrDDHv/76Bt+/1uPC1oiNQUq+51jnaYWqWmAXN0c3ffY7xeQeCD3D48c6ZKVjd5xJInJCVFztJcw1w+lmfnK+gTGaopqnybzGcmq+wVKrxjgVZr7va/69Ty3zJz6xfN8Jyh0kDupZ/HmlVAf4feB7wM8Df+5eLux+j1spit4JWfFOKKZRWmBxvLU+pl3zbtImWvTNTaJ3882Qw52Quq9ZH6YstWvEeYnCEfoejdBI377ug1L7cOGT9b9wZZcr2zEvreyyPcoqiJ6IehmlRBcp8BglOXHhwFYEK99gFCy1a5xZbDBbD4nzkiSzfPL4DL/6B1corKMeGpwVeeV+ZSiuqyOA72mpcO1+HZ4JLHPys3dzYpg81zhB5eTWoRwEviErSxSKrPLOBYVWml6aCVdCa5y7GfN/p9iLBCodtCKfNCsoncFoQ+ksy906o6zAOcdsMyTyDINazigraAYem6OMtCiZawTsxgWt0GNjmPL2dkzgKZGkrhJwYR0aR2qhHGdCrLJONIZwRIFHkhc4FCcXGix3IgrXZpDkPHd5l8Ao5poBZxaamEmvXsEwzdkd5RydiTBasdZP2Bgk0+u6qBjL78bzd+890K75PH1qZsosHmUygzg1X+cXPnN8upnfyd8gKSxHZiK+8vgRnjg+86E7BeyNg3xy7ZwbK6X+AvB3nHP/g1Lq+Xu9sPs5bqco+vzl3ZukqidxoynNrQbBZ+YbXNwc3zJRwK1F76yD0wtNzuxRiLwxrHP70B6T9ffGGZe2RiglQ9M34xG1wKDReFo2bF8LasUzhsAVGAX10OPhpTaRbzjUDhnnlkGas95PeWOtj9GasoJflk6R25JhLJwGca6SNWmtUdbhmUn7RpA5Su0ngd3YptfX/9MdN2uNKIA6pGqekKx2xwXLcxEfP9rhzc0h7cijn2SMs1wgnvZgEhkT6OfEGKxw0AoNraoN52oeg7iYSnX006zSpJaNd5IoZ+sBpxaa5KVlbRDzykqfJLe0a4oj3YirO2NakU9pYX0g6BiD+BAr5UgL0XqaqftiPO8ZenHGUjviZx5d4mjFlVnrJxztCEAhzktGaVmdMhzr/YQJmEhsSy0138MLJVlf3Bzx4KEWo7TgzELzXXn+3uoeODpTZ7FV2weGuPneMTy42OCbb21x+e2RJLe5iH/riaOcPdz+UCeASRwoEVSeBH8G+EvVzz5c2Kr3MO6kKHqrqn1vvBOK6V+9co2ar7m8PeLKTkxWiob60ZmIQxXWepjk/INvX+LRIx1qgWF7lFLzNe3azYO3SeydPUzWr3Bc3Y2JAo+ab7iyOyasPAdKJ8PUQZxTVB4Ezlki39CKfOq+oRv5tKKAYVZwrZdU0FJVkfTq1ALDblwQVLaceWlRlqkloq81WV7KJlqpYpZO7DJDT2MtxNYSaKZSC5MNt4TpqUKp6715uF6ZR55U9qEvLm9pVpIUJUaJm9tPnpnnY4faPHtph/lmyGtX+ygF9VCkN+4m6oEofxoLZxaadOs+m4OMYVpWzFhhBF/eilE4OnWfozMRS52Qi5tjTlaYfgX0xyWL7RqPHu3Qj3Pe2hzi+6aaC0jV7GuNX02do+rzJ3nlJ12U1EPDUqvGv/3k0X3XRTfy+eb5TX7i9DxpaXnhbZF4iPOSeiDSJpe3x4yygu1RNh3m+kaTFiXrg4ROzWexFb5riOY73QM33jN7i67Tcw3OHmqRFZZenHNufcTxuduDOu721P5BjoN8ml8C/hvgN51zLyulTiPtoo/iXcSdFEXhnaWq7zSc3hykXNkRw+9G6E1x1ec3Rry+OgAlQzRrHQvNkKywJJnlG29s8rmHFvYxkffG2jBhsVnjV7+3wvn1Idf6CXP1gKQoma+e44DZesBqX+wtHdCoeVhrUVphPUMjMNXwV7E+Snn69Cy//vxVmqFH4Bk2Bwlp7rjWT9gd5ZTWEaclRaW30wgMBlHUzEtXMZ0h1Iow8KqZg8hrN0MPiyUKPNK0xFYATK1E5kGG1qIjpKo/CwdRoClLgUJm1pInAhc1Sga40oZSfPvCDr4Rhc8L1eDROYfvGW4Bcb9lOGQOERpDimWxLa2WXmX043BkJWwOMjytWOqEU6esN9YG7I5zDndCatXAdDfOyIqSmqc51K5xqF3j/OZQkDSpcFCagRHf6sDgVae1iZT0oXaNR46IT29u7U3Fge9pxmmBQ7yEJ8Pga9f6hL683vHZOm+ui3XpkZmIwIj3sLWOjUHK0ydnWRskvHq1z1JXrqm73VwPCtD4QYqud3Nq/yDHQWSovwZ8bc+/zwN/+V4u6n6OOymKTuLd9E/HWcFbG0PqnqG1B1bnGyEHvbrVw6F46FATlGjhhL7h4cNtNkcZ37mwzecenN/HSwC41o959coAfUSx0AqnCKKXr/TwjBDZIl/07DuRz/ZY+uRpbqkFGoVhsR3ST3OGsYicnZxrMNsM6MU547RglBZkpZww6r4mzospeiYtKuSQEs2YZuQxTHKB/2khEQVGUzgIjGF2JmS27nNxc0w79IlzizGabiSyD2lhseNMNvQqYYVai+VidRzoNHwhEOYQ+koeiyQRpR1nFhrkRcmvPPs23chHKUkcRekERePkyDw5ZdyqS2QmHAUtlXoj9HjsaIf5pmzsi0iVvj7IONatk5VOJETykuVunUGSUVjLWj9lviXGMZe2xwRa0DITK9HQN7SMBnLmmyHjrGCQ5DQr3Sjr3JSRvDxTZ5QWDNOCMws3wyfzwlIPPa7sxFhrCWsBRWnZGWVTNJLRMjtIi5KdUVYNtMVXOCtKXr82YJyVBJ5U1wp1zzbXd1t0/SAJ5IMaB5GhfgA5FZzc+3jn3B++d8u6f+NOiqKTuJv+6eT4+rvfX2etl5AXluW5OjNRMN0MduIMrQXFstpL+NTJmenzQ9/w9MlZvvHmBi+u7DJbD1gbJIyzkiS3DNKCnzg9N5WtyEtLu+bje2J0cmFjzEOHmsw25YRgtOjymBAWmiFpYTnSrbNoLW+tDWlHhuXZiLgo+cabm2gtyJRhWqBwJIUwXn0jLFqlFWnpMMpRWkW652sxWtOq+XhGU/eNEOGUJIduI+Cx5Q5Xd2PWByl5IZvPBII7zsT0wBhpvagK2RT6mronp4goEAOgrDLxySszeBFws5RWeuO6kuAolcZHWiG+j9gkWmEFT9pTe5FOvqc5PlNnpuGzspNgtCK3ljMLDdLS8tJKTxBDgUcNKqtFx9mlNkVpeXt7zMYw4cLGiDOLTWbrAcvdCH+f/0XA5kDgwVpJNT1MCwJjqFfX4TgraNV8SiuIGqP1PumUSezGOZ89NcfvndtgvhESV9feKC3wfUNoRF+pN5akY7RmuVunGXqkRcnbW2M8I1pMjx/rUPPl/e/V5vpui64f9NT+QYyDfOP/FPi/gH/A7aHcH8UB473kA+w9vm72U04vNHhzfcSV7ZhNL+XkfIMo8NgeZuIcVjj6SXHTTd6OfJ5c7vKvz2+yPczwjKIeeNR90Wm5vD2mWw9oR/5UatszCq0VeV6yG2fMRAGb/ZRDrZCrvRitlChnKunfD5OcmYbP8Zk613pCPnPWMdsIeXM0oLSOmm/ICkdhC/KyxNcKPZGqcDBMSkJfksTRmYg0t3TrPoFnhIyVl0S+ZmecM1v3Obc2pFkztGsea/2yat9Q2RdWGP3AoLRoKmklGlJxLvDNbsOnqDZ/rcWmcbFVoygdl7cFk54WMjPwPYNSAv1UTmQlQk+hUBVzW/D6gS/JuSgEsvvI4Q413/CxpQ71wPDEMUnSz5zfZJwVU3jqMC4YVVDbjWEy1ZTqRD4ljseOdoFdlFIiQRJnbA0zxlnBbpLTrnmVxpCiG/nkZckgEQnztBBZ6s1hMt2kbzwZTuRJPv3AHF97fZ0kK3h7J0ZrzVw7ZHeUoYyHK6UdN98MGKYFx2Yi1oeiFFqvGR453LqJEQ/3ZnN9t0XX3SaQ+2GWcJBVWufc377nK/mQxF5G7+3iIBT3G4+vr13r0/R9HjzU5MLGmKQoeXN9yMeWWqSFwB21EkLPjTdhmpe8uTmiGwX84UeWpj9/5vwmy7N1Cut4cWWXT52cnUptd2o+F7eEwXqtn7A8U6dV8yrtfUMzNGR5QRSIwcpcM+QnzsyhteL5t3eJs5IeOc455hsBcVayPkxJi0J0+0vxQtZK2icTqKi1DmeY9uF9Y8jLUjxnEUe219eGJLkjLxyn59uEvuGZt7ZY3U1EtrhmaDjHMBWzElNh8WueZmecTc3L09xO+/hKicJoXpSs92Pi3OIrOZU0woBRmmNdJQShocgdRSl9eaccBkWn7pHlllEmcgb9RAazj1Yb37cvbk/hwVkhyJutNJP2jVbUPEOz5nN1J+bC+lCkI0rLoVY4nQVc68f0xmIRCoqZekBgNGv9hNVeTLce0I18BklOXvFGWqFPJwrIC0voa9YHKaHRt5QnmW+GPH68y7ff2mKQFsw0Ahq+YddRnTQ0i22ZZXiFIvAMjx3tMkxKfvz0zB1BCe+mJXqneLdF190kkPtllnCQRPDrSqm/BPwzYIohdM7179mq7uM4CB/gIBT3i5sj4rRglMomfX5jSOgZFtohpxak13tpe8yV3RiUYqEpMrz9OOeZtzb3IYqywpIVwu7dG1lhiWrSctnNS9YHKYdaNV6/NmC1F9OPc1COUWq5qsZTAb7AUwzSAuvk8z6+PMOhdkicC9ns7OE2c/WA9UHC+Y0RFsd8K6QXZ4wqRq705KXn7BmpyIscMufIraiVAmwPU3xPs9SpcXq+ycrOWBKdc/i+kL8ADrUj0swyTHOaNR9rBSsfeppRJht2UA1ZHz7S5uuvb1TMWqgFumqziUrlKJPE6pS0W5SCbj0kLUqyIhN4ZjUzaEUe47So3kcGE83QMNsIWWiFXNmN2RhkNEPD9jDlN1+6inWOnVHOyu6Ypm8IfQ9PKRY78ll6sfAphknBfDtkphEwEwVczWIubo6p+ZpmzWe+EdKpixzEG2sDzm8M2R6lFJUt42JTpKUfWGxOTxIPLDbZGGYM0hybcEtEzqdOzPL8xR2OzdbpxTlF6ZhrSqso9I0YNmUli82QV672+NSpOc4sNmiFd66y3w2k9E7xbouugyYQpblvZgkHWd1/WP351/b8zAHH3/vl3P9xED7AQSju3720zbn1gWD4Q4+j3Tqr/YS1XsKmUrIBH+2SW8tT3YhXrvZ5Y23IXGVTuBdRdHFrxHwj4GOH9nMJ9op77RXzosLxWwejRAbAoBjEGbl1RL7hE8c7nJhrcrWfcG59QL1m+NSJWU7ON/i1710h8PSU9g8yDNbG0KxZeuNStCiqGYE4wQmePvIUaeEorGW+FTJISqyT6nmU5uRWWjJbo4InjnXwjGZzmOBpGQJ7nnAanHXMNANOzzXp1H1GacFSO2S1lzDKSo7M1Dl3rU+j5k9hpxNlUKXUVNGzGXroSm478g1lhaIKjMwVTs43ubA5RDmxdWxWyXauGeJpuLwdM1v3WetLG+3oTI1BIgNdZx3DrERpzWKnhtGK1V1pxzQ8RW+cYS2cmmuwPFtnnBUc7UZ06/70uhCVTs1yN6IRaC5sjnFOiHwLrbDq39upv3A7kuc+tty9bWV+cr6B0pqZyOdw57ofh7Skcq71BLrcijw69YCfe/ww/+qVa/s217Qo99lMBp5msRlOjeTfi3i3RddBE0in5tFPivtilnAQ1NCx9/pNlVIXgQEycyicc0+91+/xfo67xULfGOOs4IXLMtidGI/MNAI2hylepUN/cVMGiFlh6dR9Lm+PwTlqgeH8xlDkEbRmthlU0trpTTfhXnGviZjX2iBBaRGjW2yHbA0FTbMzStFGERmDw3JhO+aRo12+8P+39+bRcWX3fefn95Z6tWMHARIkm1s32VKrV7XUsjS2rMSR4jiOHCW2E+vE8aJkjp3JcpIzS2bm6GSc2H9MHHvOOCfWJD5J7GPHjsbLtC3ZlmzLsrVE3epu9spWN9lkEw2QWAuF2t5654/7qljYCyQIEMD9nIODQlW9qnfrFd6977d8v+OjWkY5Sjpj615xPXy8j4W6z0zVx7EFv6G0yFmiE6da00fH9j3dAYXnCqVshvuGStiW/qecrbZoRnoVeqwvh2PbDKTCafO1gKyrHbXKuQxRrBjt9zh3pIRra1nvVhjzjWuLHC17LDVCWkFEnGrZ9+VcPNdmsRFox7Y07wHagKcv77LUCPVVlmtRb+kch+PYuhqnP6cN1AWWmyGIblJbaoaoRCd0b1YbnBkpMdaXZ6xP23iqNJntWBYLNR2yShRkLL16dywhVnRyPvONgFLW4cRgnoxjM7nYoBHoK78HxsqMljy+8uYcVT/kSCnbsTg9M1JktOR1TtJbhWjyGYdHTvRzaXqZVqT1h+w0z6J7R2BiIIeIkE2lzrtPrtVWyMXrS2sE6V6dXua+4QJzNX9Hwim3u+jqdQKpBdFdqQDcC3q6XhGR88CDQCfLqJT61Tt87w8rpebu8DX2LXciVnd1rp5aH94S2HHTlc3VuXqnbnu+5lPIOlyZqVPKOlSbEZMLTco5h7yjJQWuzjaoNELOjpZYakQrYrjd4l6WpZOe7yw2idLEatHTzWE5z+aNmzVcW8eEVaKoBxEvXl9iquLz8PG+jrbMg0f7VpwUylmXD58f4U8uzbLY8AmSBEeErGsx4LnYYmkNHQHLsrEtvdLSTWNJRwnUTS06x/uyOLbN46cGeWu2TqUR0IpiSuk/ux/GxGnISoCrczVemVrSVURAw88xUsyCwMnBQsfy0HEs4jhBpdU3SkHBs4kT7eeQoCtutC+zlpAe8GziWDvL2ZbOL7QifWJerMdp3sHmxpI2fQ+iW7UYti0MlzyaQUwzjLXonB+Q81wt6pf2ghztv5XzCdJJa2bZ56kzwxxfx6DIsoRixmFiINdZjU9WGoDqJHF7CdE8cXKAONG6UZOVJvP1kOmqbhh7YKxIzrGZq/m0goSnL07zgTND2LbFYsPn5XeW1wjSRUnCQN7lWH92R8Mpt7Po6nUC+cNXbqxwIFyPnQ533S16KR/9X4HvAs4Df4AWnftz4E4nAsNtcunmMudGikwuNldI8eYzDueOlFhqhNxcbnFlrsFHLowQRorhoseJwTzNMNbVJFGMa1mcGslzpM9jutLi6nx9xcmjLe518foSi8s+7xovcX2xyVzNJ07AloRjgzmeubqI59zyb1aWblzTeQjh4vUlHj5e7qyMVq+4ytkMHz4/CgrmagFK6SY017JSXX8tbd3uiC16NiLCuSMlKs2Q+ZpPnCgW6gFLzZBSVml7wr4sAlxfaLAcxNo7II4ZyGcQ4OL1Clfm6thtkbZEsdgICULVCfuM9WXJuhbT1RZBonAVHO3LstgM8UNtYrJQ80lSwbU4DSMNl7KU26EZEcqey2I9IJ9x8BztMR0lilzBIYwShgoeszUtoObY2gDG9SyyrsXZQpFr8w2uztd0T0Q+w/GB3BoZ5m6joY0Io5i3K01c21qxGtchwqauGLKtLavW2scwm4b4vvHWIheOZDpXFa1QK3teGC8TJYqvXp7nA2cG+ew3J6k0Asb6slooLlEdB752aOpmdWfDKbez6OplAtlpReC9pJcp9/uBR4DnlFKfFJFx4Bfv8H0V8IciooBfVEp9ZvUT0gT1pwBOnDDpiG5aQcyxgRxTS/4aKV7XthgueTr52Aj5kQ+e5t9+6U1cSyh4LgXPXWMKEsXahW260lzzXuWsy7uPlZhcbFIuuPizNVphwskhrUxaaQbESUK+ax8SRcfJynNtmmFApR50wljrrbiKnsMHzg3x8lSF5VZa165U6volqETRiCJUALWWMFLKEsYJw2kS/PJMjVLOxbGE+VrACwsVmunq+9yoLqMdKnpcvF6h6Dm8fmOZ64t1gjjGVhatUFFKu7GHSx7XFxos1iOGSxbDpSznx/uYXfa5WW1pKY2ZGlOBDveEsdIyEOn4s55N1rGJleLkcB7HEeq+vqoCHfZpVzmVPIfQ1YYorUBbmg4XtcrojaUWIjDWl+NIOYvrCA8d6+/o6Fcage45SOPtC6nAXMFzeHuhzpFSdsV3ww91ZVYxY6+YRHR4S4cIL15f4uRwjvefGlr3u9ddKrnUDHhuVuc/4kRRLOtj0n1i91wbD6j5LZZbEWP9WfKuzcyy3wlbbTc0tVtsNYHsVAXgvUAvE0FTKRWLSCQiJeAGcPoO3/fblFJTIjIKfEFELimlvtz9hHRy+AzAE088cady8weKbMZGkM5qvRneitPGiaKWVuw8cqKffMZhoa7VJTfCsS1ODxe4MlfnZnX9S+FPPnWfLh081s8vfeUtJ6O9ggAAIABJREFUBvI66TxfC8g6Ngm3BKjCWJuruJY+YRU9h6sLDR45cauRrVsI7IXrix0hsL/15Al+96Vpip5LKetQ9yNuLrX0Sd2xyKYJbD+M+eqbczx0rK+jujlWyvLSO7qYrZh1yHsOrSjhlakqIsK7j/VxcijPXM3nymyNehDpUFYq4B/ECSrRlUrlnEs9CGmFEa9OLTFc9AjjhJllrc2UsS1c20o1dnTMXymdLzk+mNeNZEHM7HKTjG3xdq1O1tWNVW1Z5oLn0IxiLTERxgwUMsyn8fGBXIYblRaxSpittZit+tRbMS9PVpgYypNztFRELmN3DGAGix5TFZ++nMOV2Tpvzzc6q2zQ5jnFjC6Nba/Yu/Fcm0qzxVIjXLdqbXWp5KmhAsOFDJ97eZqGry1L+3LOmhM73Dq5q2RrkcP9Ek7ZqQrAe4FeJoLnRaQf+CXgWaAKPHcnb6qUmkp/z4jIbwFPAl/efCtDm85KpJxdI8XbtrFUouO4oDWAKo2Q3MYl3FiWxSPHBzquThvFUu8bLnCsP8d0pcVoR5PGpdIMsV07zR9oT9mhNOFnW8JSI+J8WpW0mRBYrOC/OzfKt24uM1fzub7QwHN15Yubhj6KWYfhUpYwSnjm6gIj5SxRoqgF2tR8uRUSJwmWZZN1LBLPQSUJL05W+NhDY1ydq6dXKlFqMi8Mlz0KnotKtMrmUFFf7QRxpCuElpJOgrjhx1RbAeWsFn+r+xHNKMaxLB44UmKsnOXSzSqOJdhi8e5j/bw6tUS11cSxLEqeQ9FzqAcxtVZE5OhQWDnr4sc6T+DYFqWcy9sLdYKwgW3BmdE8M8sBb97QPQQfvjC6wgCmFcbcN5zHTidgAV6YrPDIRB/NMOHaQoMPnBvGs21enKxQSf0F2vpFdT8iYwuDxcya2PlGsgvlXIbxvhxe2mj42ImBdUMl7ZP7QQqn7FQF4L3Apnso2hfx00qpCvALIvIHQFkpddsTgYgU0NLWy+nt7wL+xe2+3mFk9Urk+GCB44O3Vh11P+pU6QAcG8yxWA/WXQWCjueiFPeN5LeMpeYzDp94fIKf/+Kb3Ky2sBCyrkBDn0hc29JXC5ZeBQI0/Ihsxkp9nrfWcQmiDE+eHuQbl+eZqwXkXKHS0OqYrmMzkM9godVKFxohjVDH/xMF/Xk4Us7SinQFi6/S5HPWZaBg8fJklamlVurBbNNfcCl6bloCC9jQCrUsRBTp5q9yzmViII9r6W7pxXrAzLJPLqNDcUfKWYaKGfq7ZD1cy8IWWGgETAzkGC1nqaYnB9fWMtuLdR8/TogSG1Ewt9yinFZu1VohC7WA9xzroxHoHo6MY3NiMM9yK8K1heeuLVDOumTdDJWGFqZ736kh3RSWLg4WlgPeWWrxkfOjBHHCcMFDRHjvfYPMLPsrKovOjBQZLmZYXkc9dTPZhYxj4VgWrShiZtlfN0ndPrkfpHAK3HkF4L3CpnuplFIi8rvA4+nfb+7Aex4Bfiv13nWAX1VK/f4OvO6hYbsrkYeP9bPciJiqNNddBTqWcGJI9x30wvHBAv/wL5zjs9+cZLHhU6mHDBQzqfyyIChODhVQ6Dh2PYj4G49PkM84vDq11IOOS8SF8RLPXl3g5GAeEWGkpPc1UYooVtT8kGU/BhRKKWxbm8i8NVMDgf58hoF8pmPzeH2xyVAhw81qi7MjRWZqLZqB1tTPuw5WlxG669jcTMtR+/IuZ0dKJEp1Euxnj5RYbIaIUoyWPIaLHpVmwOWuslxLhForQCzh5nKL4aKH59goFIuNgEpDW0qWPEfnCRLFVLVFK07oW6gTJYrRcgbQVwofPDvSCfH4aXPf516aQiU69LY6HNNeHPhhjJ8kPHi0r+Pf67k2nmtzfDC/5qTth/G6q/HNZBfaZcbtXpP1JoL2yf0ghVPa7JZd7d2kl+nqGyLy2J1cBXSTqpc+vBOvdZjZzkqk/c/Xn3ep+/GaVWDBs3WMfhv/fMcH8/y9bz/Npekqv/6M9ivOuhY516EVxtrRK0442pejlHM7+YFedVyuLjTwHIvTQ7p5CXTuYakRMrPcYqrS0Cd61yKIFdVmhOfoE1ySpPX6Cir1gChWuI5WR51eajJS8qi2wtTDVzFfDxgpeZ2rAguoNEKyrsVg3uNUl5pn5/MveMwut7gyV2ehHpKoJFVCdYiVohHETFdbnBoq8OL1CjU/Yma5iS1aUdWztQJsKWMTo81sPEd3ejdC3emczzgUMtoE5vnriyu8JY4P5jk7WiSXsXnqzPCGn2V3vP1OVuObyS60y4zjOFm3Yqn75H6QwikHiQ0/bRFxlFIR8EHgx0XkMlAn9e5QSj22S/to2IBeVyLd/3wZx+KxEwMr/vkUsuKfbz0RrZODeV13v9BYIax1frzMP/voAx0rwK3+sbej49JO9uZSQ+J2RZQSXavvuTZvz9eZrjZTNzPIomWpk0QnzpthzEI9YLiY4eq8/vudSpOlVkjdj7BFqPkRzTCmmLHJeQ6g8MOIwXyOC0dLayYB0A1Ts/UWkwsNho97uM6tyc0WwXN0Qnyq0mS2FnCknOVYf443ZmrM1wJc29JeApnU59mxKHouH33XOM0g5spsTV+xBSuv4LqTwNmMQyNYGcZpVxC9NV9nvq6vPHKuhVjwwGiJGHVbq/HNYvvtMuNnry4SRDF+GG/6HTgo4ZSDhGh/+nUeEHlOKfWYiJxZ73Gl1OW7umddPPHEE+rZZ5/drbc7sPSikri6MiTjWMzVfb5xZQERePK+QYZSU5ulZojd+Se3e1Jg/M3nJ/FS56+NaIczMiJ88bWZjgR2m9dvVAkTxUItYKaqSzgzjha3s20LCx3eaYWRru9XilLWptKIcGzBs21Gyhlmqlqd07J0B6znWLQiLS+dd20+dP8IQ8W1csygr06+9uYMk5VWp0zSFm2g44cxQaQlJsb7cmnMXREl8MZN3Uyl0M8dKujcgufqDueTQ0XOjxf57eff4cGxMoV1zIJaYUwUJwwVM1xbaPDRd40DdDp264G2/bTQPQU6TOZycqigE8miyDr2hpP2el29r04tdQoUNuLtxTqjxSxBkuxbFc6Dhoh8sxflhs2OjsDunvANd5etriBWJ3L9KObyXI0/f2MelK6V/9zL00wM5LHTTuOhgsfnX7rBX3/8WE9XJ9sJT4yWPb70rTmqzYByV8lTK4o7DmYiwumRIguNQIvgKUUiUGloyQorVV1dqIdkHJvjAznm6yHX55vksw7jfVmqrZBGoOgvOIz2OQykMhRhsnFjlmtbOI7N0b4shYxDM4xpqQRbLAoZh3xGkc849OVcqn5IGCb4kXbCyXsOuYxDEMX057S8d90PGevLEScJb83UyKVaUKAnnUojZL7uE8Va+ymT+jUc68+lK3nduGeJYqEWknXstBv7VjnrVKXJg0fL+JHiwniJqwuNnlfjvcT2CxmX7zRhnX3JZkdsRET+yUYPKqV+9i7sj2EP6a4Maa8u52otBN3YdWOpRd2PqfsRD0/049gWU0tN6rPLnBnJ84GzI1u+x3aShfmMw6e+/TSf+dMrVJoNRooZreLZilhqBuQzLhODOfpyLsWsQ861eWu2piUfEoXtWCRKd9MqJRzrz+G5DsVsQj2IUkcxxVg5RzOMOTtSYrTk8Z6Jfl6/UeWNmWX6c96GlVZJAt/14DilnMvkYqOj6DoxkOetuRoAV+bq1FoR+YxDsxXiOhY1P9IhspxLtRWSTa8m2lVWL00tcXIwj2VZVOoBU0tNEqVNc9rWj4v1gNlln//tr1zgtRs1rs/U8cMotfGMiZVgp+KDrm3h2lBJDeczjhYd/L5HJ3r+bpjY/sFms6NmA0XSKwPDwaedyPWjuONJ244Lz1R9XRpatGmFCdfmG5w7UqI/n8G1hN+5OMUjJwa2PBFsdUJJFJwZyfP7r9zohBc+/thRbi61+Ppb81yeazG7rPdltJxKP8cJnmMz3pdjqJDRfr7NgDjtcBZbkc+65NJ9CyOtTxTFiT6pNkKOlLPYov0MXplaYqkZUmmGXF9oMJB3O8J77UqrKNFdwxODOcq5zJpKmW/drHJzqYUl2hPgxFCem2+0yNjgx9p2s9II8GybpJB07CWVUtRaIWdHiuQ8m6dfmEZEUcrqEtckDT1lM9pw5+I7S5wZLvCl12+yUA9YqAf053QYaKjodbqQgU5Vz2MnBm6rc9fE9tfnoBvTTCulTH3/PmEnvoztRO7kYqPjSRsmCVGk4+yOrf2Eq02trlkLI04NFXSnaiPsWR9moxPKqeE8l+fqXJ1rrDD5uDbXoBkmPDBW5pGJAZ4rLjK37GOLUI8Trs/XOTFUIOPYZBxthVjKOoRxwonBAhcnKxS6VvWxUriiHW/6cy5horgwVuKNmRrXF7VEw2jJI04SYiXMVFta9dOxOiv3I305zozktTdybu0Ym2GiHdosbdmZcx2O9mdp+DFTlSZhKk6Xy9mcO3IrId3wtaZ/f1436Z0Y0i8+XwtoJbp8dawvq7uSw4Tnri5ys6In1NNDBV6cWsIWYbEeaDG+romgrSB7J527B6FUcic5DMY05kpgn7BTX8Z2ZchkpdkJ27iWxXyzhQALNa0r5Fha677px9xYajFVaTHWl93WKnP1CaURRDx9cZqy564IGXmuTR/w4jtaqPZD54bpy7rkMzaTC9paseHrSqBj/Tkt3yBoS8dlH0HLPDuizeBdx8YCQqWI4gSlFOWsw7XFBp6rBd1A6y8NFj0eOzHAtbk61xYbnB0tUs67nQkW4Ppiq9OLcXO5xTuLWov/7fk6YaytOPvT/MaRcpYbSy0eGC8zU/XTk77VkXCu+9qO8uMPH6UVJryz2KQvl9HVUquS1rO1Fg0/ZqiQoRnG5DMOkYKsY6dSIzrU1z3JRIkiY1v7pnP3XucgmdxvpqH6kV3bC8Nt0/1lHC1rkTER0V/GcpasY/FHr82sKTNcj/NHSiw1Q4LUqARgqKhPNNVmqA1ZLCHvOWmYAgqeNj1v+BFLjdvXh+nkJ9bJG9xcbmnrQ0uvzo8N5EgSuP9IkWNp124z7b4dLma4MFai4Wu5DT9OuHC0xFDZY6DgoRKFY1kEYcyRcpYjqbFKM4gZ6zJZqfkRE/05PMfm/rEy7znWz5OnBvm+Ryd48Gif9nROw1xztYAvXrrJt24uY1tCPvU+qPkRfhR1auv7cxkssbDQEtP9ea0PVW2FRGnPxWMnBvmL7x7Dti2WW1HnOHTjhzENP6boOdrWMk46UhdDRS01kXF0LmGu5jO77HPpRpXnry+yUA94bbrKfes0fRm2x2bfWdChuLb8+r3OhhOBUmphN3fEcHvs5JfxvuECtm2hUJ2Klf5chlgpwkQBKpWUSEXmLJ1DyKbJx7p/+xPBZo1m76ThmqLnMFlpcqSU7cTrh4tZHpro5zvPj3JqJE9fuvoWgXeP9zFezjJezpGxLLKOzbGBPCeH82lJpafzBGm5aH9X565tWStKJduiaavJZ2wyrnBupJiGa2IipbhvOM9TZ4bI2BavTlVpBBG2JUwMplVKfsT9R8o8eLSP958a4vRIkaGSx8ceGmO4mOUDZwapNAOeubbAxclFLt1Y4sZSk9laiyC1wOzLuZ1VfvszyTk2luhVPyK8Mr3EzWqLJIGia5PP2LxTafLKdJW5mr9mPIbe2Y7J/b3O5q4Khnuenfwytle4QwVt2RjGSarE6RBGMX6cUMw5WEDTjzqmJs0o5vpig5lqq6crj/VoBfGGJh9B6sbVnng81+Y9E/3aGrEREMa6Kzfr2JweKfK+08P8y+97iPPHyviRdh87OZQnjGJml1tEieLRE/20opjFRohKjWpAS2IEsdK6/E6XvHcqE72aq3N1bIT7x8o8dXqYb79/lKdOD/OeiX5sER482sdQIcNC3afaCnEtiw+fH+HDD4zoXgnb4sp8ncWGTyOI+PIbs3z1zVm+9Pocp4aKDORdPEeH7KYrTRp+zIXxEnYq7Ff3IyYG8p3PRAEDRZdGEDO10CCOFQpFnKqTOrbujyh7bs9Xiob12ew722aj7829xr0duDJsyXY6dXthuOjxyadO8stfe5v5mp/6BGtdGscSLefQCqm1Ysb6tM6OAH6UsFALePri9IqcRK9J7M06VzOpsmXbNxignHNXCKdV08aw7gqWiYEcZ0YK/M7z07TCiKMDOYoZrWVUDyLG+zyeODnO89cXaUUxti2cGSl0nLq62SiuvtFE3JFdSBQTg3miOFkhBVH3IxzHImPb2EKneW/ZD/mvz07i2MJ4OUvdj1Eo8q7DUH+GnGvz2vQyInRyE6Mlb81nEgQLLNYtIqUoeg5DxQynhlaOrdtP9yBUvuw2B0lJ1Rzhfc7d+DIOF7N88qmTHdkIESCBa4sNklaIINw/WiDraV0hW4STQwVcWzo5ie95eJxGEPecxN6s0ezYgBY1Azgzckv+oFs47Wa1xaMnVhqu5zMOHzgzwiPHBzY9yT18vH/LrtntavB0u7tFgRbLS5S6VSaLAiWUs86KsN5SI8S2hRtLPpMLTU4NF7ix1Owkjx3LopzTPRNBnPDBsyMrjn37M5lcbFDwHB4YK61Qpu2mfaU4Ws4eiMqX3eYgKama0NA+p53g3YxKM+x4AfRKu8Tz0RP9jKYibScH86kzmYtYFnGiGC/nOHdEq3NODOQ7OYlL09VtJbHb+Yn6OhLIR0rab0Arcq49WW+lWNmuUPq+Ryf4W+87uSLhu9V7b/X67Yl4PcpZlydPDXBiMI9YwlzNx08SHj3Rz7vGy+Rca01u5635OjNVn5xrk3F1Q9y5sXLaDGd3zHEGihkePTGIvU4yGWC5pU13NpvcXMdiqRHuWLHBYeNOvjf3GmYi2OfczS9j+wT6Ix88zVNnR3j85CD9hQyPnhjg3cf6OD9WZrjkESdqRYiiP+fylcvz20pit/MTrUjbZvph3GmeqjRDzo2WOHekSKURrnjsZrVFK0ruqKt1s/fe6vW3mog9x6aYdfmh951cMQldXWisG1KarwepVpJOwM/V/Y7Y3vmxMo+cGOD0cAHbEj720NiG+ywiXDhaWhPi6iaMEup+eGAqX3abO/ne3Gvc+3to2JTdaP3vfo+55YCxsreu8Xg7ROE6FldmamTGSlyeqxGkNpMT/bkVMerV3rRbda4CO9bVul5M/Nxo+h6pBo9Y0Jd1qAURT1+cou5rpdaS53T6CUbLXmci3o6i50YhpeVWhJfmQWwR/HVknbOunSqqbvx5nR8v8fr05gUCbeXZXosNTBPZWg5Kt/X+2EvDpuzGl7H9HjPLLeaqPkkYr2s8DrBQ83l7sUEho1fDuazdkVC+Ot/k4eN9lLPuuknsrTpXd6KrdaMGvEvTyx01VYA/em2GaitCLHhrtk4QxSB6lX9hTMeHbdvi0RP9PP92ZVsT8Ua5nZKnfZozjk2stMDcalpRwmDa+LbR59UIIt6cqW85QZU8p6fKl/3gIbxXHIRuazMRHBB248uYzzh85PzopolVP4z5b1cXGCt5FLJup6vVsYW+fAY/1DpGT54aAMWuV1T00g36+ZdugCjKnovjaXN4z7E6DmGtMObSjWXee98gUaJ4/u0KH7kwwkzV33AiXn0FMl8P8MOYC+PlFeGboWKGWkuX5gZxzPgq/Yq2reixwXV0Lbro9Urxy2/M9lxsYCqLDi7m6Bm2xVbqodfmG6DgPcf7ub7Q7Eg2tNGm8QEz1RYZx971iorNvHdBx8TfuLkMAuPHc1xfqHd0l9pkXZtKahd5fDBPzW8xU/XXTMTtE+ez1xb11YPA2dEix/pzZB2LP39zjrlawJOnBimnvgOnhgo0fR1jDmJF3rPXhOF6tRXt5Uqx18qXUyN5nr44bSqLDihmIjBsi61WmtcWGjx5epCy5zJdadEK4zUyzkXP4fJcjYeODex6RUUvDXjLrQglurO6W3epm25/3vVi6O3wUyOIeONmjcG8i21bXF9sMr2kzWw+eHaEb1xd4M++NcuH7h+m6Ln05d1OddR9QwUWGwHVVriurWgvK/StrhR7kQVPUFyeaehS132uqWNYH3PUDNtms5VmECcMFzxEdAL5xckKlXCl3WLNj2gG8a5WVLRPml97c057Gzu3/H9Xh0VipSB17guihFx27Wq5reQJa2Po3eGnhq+wBbLpOPvzWgvoxckK771vkA+dHea16SpTlSaDBR2C+RvvneDyTANL4NRwYV1b0e30aGxGLyGkM8MFrs41Nq0s6m5OM+w/zERguC02WmleurnciTmv7v5tBFpS4cRAjnLB3bVQQndyOJexyTk2iKzw/y3nVnoOqzRHm3F0V/PqpG1b4wfWNux1h59efKey5gS6OrR0YbyMnyQrjGIeOR5tWj319MXpHVO93CqE9Puv3DCVRQccMxEYdpTVMefu7t82N6utnmLcvbJZiARYY795ZbZOXz6zZnXevjIoZZ2OCPtEf67z/G7qfsSZkSKwtnu0O/y00RVFd2hpu9VTr04tbZnn2O4KfbP322kZE8O9h2koM+wou91tOVfzefriNC+8XcGzLEaKHp5l8cLblfT+ReJUtO7thTpX5xu8MVPj5cklZpd97DRcNbPsd/bvSF+OI31Z6n7EaDmLZVn44S3hsFYYdxro1htPtxhZ+4piNVq3SYeWtisBstuql5t1T7fZL5o6hvUxE4FhR9nNbstevBh+5+IUUZzwzNUFrszWybo2Dx4toYC35mpculFF0OGc9v597KExPvbucVqRNo0/P17EjxLmllvMLbcI4oTzYyUqzXDd8XSfOCdSc/nVdIeWtisBstuql3dLxsRw72BCQ4YdZ7e6LXspBa21Ip65tsCRco5iGqJxbYsL4yUqzYAbSy3emFlmrC+3Zv+6x3BqpLCiszizSQ19d3isrULqh/GKpHQ7tHQ7V0i7rXrZS2XRftHUMayPmQgMd4XdaHDrJUQiwMyyz8mh4or7ndT+cbiYZW65xbHB/Jp9vd0xrD5xtlVIm2FA0dNeyiqtSrqdK6TdVr3cDRkTw95iQkOGfUsvIRLHgjheG6NfjbD1c3pldXjMsy2euK+f4wM5Zus+i42A+8dLvO/0IN/z8Pi2q6f2QvWyW43WT5IVSqq3MwbDvYWZwg37ll5CJI5t47n2mtBMGz+MyTg2BW/zK4vtsjo81gpiRvqyfOj+kTsOj+3VCv0gaOoY1sdMBIZ9Sy8hEiXw2Ml+qs24E5qxLSFOG9tsy9JyzZu8xu1yN0+cB0X10nBvYL4thn1LL0nMif4cA7kM58c8ZqotJitN6oGWxW7bUlYa+7PixazQDTuFmQgM+5ZeQiSfeHyCr15eIIoVxwcLa2wbTcWLwWAmAsM+p5cQyUcyjql4MRg2wXz7DfuerUIkJp5uMGyO+Q8wHApMPN1g2BjTR2AwGAyHHDMRGAwGwyHHTAQGg8FwyDE5AsOm3I5hefc2S41whVhbOe/umeH53TRfN8buhv2MtMWvdvVNRT4K/DxgA/9eKfUzmz3/iSeeUM8+++yu7JvhFt3OXn05l4xjEUQJS80QOy27XK0x072NWPDa1DJBFIOA59hcGCuRKDbc/l4ay73w2gbDnSAi31RKPbHV83Y9NCQiNvALwMeAB4EfFJEHd3s/DJvTi9Z/25x9vW368i6Xpmt4jsVwSat8urbFpRtaMXS97e+lsdwLr20w7BZ7kSN4EnhTKXVFKRUA/wX43j3YD8MmdLT+NzEsj+KEq3P1dbeZqbZIkpWCcFnX7riBrbf9vTSWe+G1DYbdYi8mgmPA9a6/J9P7DPcQt2OH2L3NZKW57smx7dW73vZ3i7tp7bjbtpEGw91gLyYCWee+NYkKEfmUiDwrIs/Ozs7uwm4ZurkdO8TubYJI+wSvpturdyftFDfjblo77rZtpMFwN9iLiWASON719wQwtfpJSqnPKKWeUEo9MTIysms7Z9DcjmF59zYbmbZ3e/XuluH53TRfN8buhoPAXkwEzwDnROSUiGSAHwD+vz3YD8Mm3I5hefc2G5m21/2IiYH8utvfLe6m+boxdjccBHZ9IlBKRcBPAn8AvAb8hlLqld3eD8Pm3I4dYvc2o+UslmXhh12hozDGsYTRkrer8s9309pxL2wjDYadZk86i5VSn1NK3a+UOqOU+pd7sQ+GzVntu+uHMYlS+GHMzWprXdP17m0qjZDz40X8KGFuucXccosgTjg/VqLSDG/LtH03x3IvvLbBsFvsSUPZdjENZXuH6Sze+9c2GG6XXhvKzERgMBgMB5R7trPYYDAYDPcWZiIwGAyGQ46ZCAwGg+GQsy9yBCIyC1zb4mnDwNwu7M69xmEc92EcMxzOcZsx3xknlVJbduTui4mgF0Tk2V6SIgeNwzjuwzhmOJzjNmPeHUxoyGAwGA45ZiIwGAyGQ85Bmgg+s9c7sEccxnEfxjHD4Ry3GfMucGByBAaDwWC4PQ7SFYHBYDAYboN9MRGIyEdF5HUReVNE/qd1Hv9hEZkVkRfSnx/reizuun/fyF1vNeb0OX9TRF4VkVdE5Fe77v87IvJG+vN3dm+v75w7HPeBPNYi8m+6xvUtEal0PXZgj/UW4z6ox/qEiPyJiDwvIi+KyF/ueux/Trd7XUT+0o7umFLqnv4BbOAycBrIABeBB1c954eB/3uD7Wt7PYa7NOZzwPPAQPr3aPp7ELiS/h5Ibw/s9Zju9rgP8rFe9fx/APzSYTjWG437IB9rdH7gv09vPwhc7bp9EfCAU+nr2Du1b/vhiuAwmt33MuYfB35BKbUIoJSaSe//S8AXlFIL6WNfAD66S/t9p9zJuPcr2/1+/yDwa+ntg36su+ke936llzEroJze7uOWe+P3Av9FKeUrpd4C3kxfb0fYDxNBr2b3fz29lPqsiHRbYWZT7+Ovi8hfu6t7unP0Mub7gftF5Cvp2D66jW3vVe5k3HBwjzUAInISvRr84+1gSkSaAAAGpklEQVRuew9yJ+OGg3usPw38kIhMAp9DXwn1uu1tsx+E0nsxu38a+DWllC8ifx/4T8B3po+dUEpNichp4I9F5CWl1OW7uL87QS9jdtBhku9A+z7/mYi8u8dt71Vue9xKqQoH91i3+QHgs0qptu3bQT/WbVaPGw7usf5B4D8qpf61iDwF/PJu/F/vhyuCLc3ulVLzSik//fP/AR7vemwq/X0F+BLw6N3c2R1iyzGnz/kdpVSYXiq+jj5B9rLtvcqdjPsgH+s2P8DK8MhBP9ZtVo/7IB/rHwV+A0Ap9TUgi9YeurvHeq8TKD0kWBx0EuwUtxIs71r1nPGu2x8Hvp7eHgC89PYw8AabJKTulZ8ex/xR4D91je06MIROHL6Vjn0gvT2412PahXEf2GOdPu8B4Cpp709634E+1puM+8Aea+DzwA+nty+gT/YCvIuVyeIr7GCyeM8/nB4/wL8MfAudKf/n6X3/Avir6e2fBl5JP6g/Ac6n938AeCm9/yXgR/d6LDs4ZgF+Fng1HdsPdG37I+hk0pvA393rsezGuA/ysU7//jTwM+tse2CP9UbjPsjHGl0d9JV0bC8A39W17T9Pt3sd+NhO7pfpLDYYDIZDzn7IERgMBoPhLmImAoPBYDjkmInAYDAYDjlmIjAYDIZDjpkIDAaD4ZBjJgLDXUFEhrrUIW+IyDtdf2fu0nv+bKpI+jN34/V7eP+zIvLCBo89ICK/JyKXReSbIvLHIvLB9LGPi8g/2+K1j4vIr2/nPTd5rR8TkZ/bzjaGg81+kJgw7EOUUvPAIwAi8mm0WuT/2f0cERF0o1Byp++XvtaPAUNKqbDHbRylVHSn793D++SB3wX+kVLq99L73oP+fP5cKfVbW72GUuo68P13dUcNhxZzRWDYVdIV7Msi8u+A54BxEflMKiD2ioj8713PnRSRT3dps9+f3v+dInIxvbp4TkQKwO8BBeAZEfmEiJxKdd1fFJEviMhEuu2viMi/FpE/Af6ViPyUiPxHEflDEbkqIn8tffzldAXvpNu9V0T+NF3Nf15EjnTd/6KIfA34+xsM+5PAl9uTAIBS6kWl1H9OX6OzQk/37+dF5KsickVEPt71uW268k9f57Mi8gei/Ql+etVj3xKRLwHv77r/iIj8Zvr5f0NE3p/e/29F5H9Jb393+lmup3djOAjsdaed+Tn4P+ju0H+a3j4LJMB7ux4fTH87wJ+RygWg9VXa2uz/A/Dv0tufB96X3i6idd4doNL1mp8H/nZ6+1No0TKAXwF+G7DSv38K+NN0+8eBBvAX08eeBv4Kuq3/q8Bwev/fBj6T3n4F+Lb09r8BXlhn/P8X8BObfD4/Bvxc1/79GrqD+j3Apa7Pbb3X7tyfvs4bQAnIoeU3jqJ1aa6hpTgywNe73u/Xgfent+8DXk5vF9Dd29+B7oQ9tdffI/Nz935MaMiwF1xWSj3T9fcPisiPok/GR9Ft9q+mj/1m+vub6PZ80C34Pyfanez/VUrV2iv3Lt6HPokD/Gfg/+h67L+qleGozymlIhF5CUAp9YX0/pfQJ8cLaK2XL6aLYhuYFJFhIKeU+kr6/F8GPrzV4EU7ap0BXlFK/c11nvLbSp+NXxSR7UoNf1EptZy+zyXgBHoi+COlw3WIyG+k9wP8BeCBrsX+gIjklFJ10Uq+fwz8A6UF/gwHFDMRGPaCevuGiJwD/iHwpFKqIiK/glZcbNNWlY1Jv69KqZ9KT6bfjQ4FfQdacG3b77/qPRIg6Lo/Sd9TgBeVUh/q3iidCHrRaHmFLhMRpdRfTUMwP7XB8/2u29sNx3Rv2/nM2Hg/Bf3ZB+s89hAwj56cDQcYkyMw7DVlYBmoisg42nVrU0TkjNIx9p9G21Y+sM7Tvg60V9s/BHz5DvbxVeCYiDyZvn9GRN6llJoDWqJ140GHjNbjl4HvEJHv7rovfwf7s12+DnxERAbTiq1PdD32ReAn2n+ISDvBfxodjnsE+F4ReWIX99ewy5iJwLDXPIc+0b6M9pL4yuZPB+CfpsncF4EK8IfrPOcngU+lz/l+4B/f7g4q7XXxCeBnReQievJ5X/rw3wV+MU0W1zbYvgF8D/CTaQL4a8D/CPyr292n7aCUmkRffXwd/Vk92/XwTwDflia8XwV+PE0K/xLwj5VS0+jcw38QEW839tew+xj1UYPBYDjkmCsCg8FgOOSYicBgMBgOOWYiMBgMhkOOmQgMBoPhkGMmAoPBYDjkmInAYDAYDjlmIjAYDIZDjpkIDAaD4ZDz/wPTlVxGjsUOggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the orginal data (as before)\n",
    "plt.scatter(crime_gini_df['TransformedGini'], crime_gini_df['crimeindex'],\n",
    "            alpha=0.3, label='Data', s=100);\n",
    "\n",
    "# Generate and plot the model fit line\n",
    "xs2 = np.linspace(crime_gini_df['TransformedGini'].min(), \n",
    "                  crime_gini_df['TransformedGini'].max())\n",
    "ys2 = (34.4819 * xs2) - 12.1309\n",
    "plt.plot(xs2, ys2, '--k', linewidth=4, label='Model')\n",
    "\n",
    "plt.xlabel('Transformed Gini Index')\n",
    "plt.ylabel('Transformed Crime Index')\n",
    "plt.legend();\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's quickly look at how the Gini index is also related to specific types of crimes. As we can see in the correlation table below, there the highest positive correlations besides crime index are with thefts, robberies, and burglaries per 100,000 population. This would make sense in areas with high income inequality that property crimes are also high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TransformedGini         1.000000\n",
       "Estimate; Gini Index    0.999032\n",
       "crimeindex              0.329588\n",
       "theftsper100k           0.288551\n",
       "robberiesper100k        0.286284\n",
       "burglariesper100k       0.270550\n",
       "Name: TransformedGini, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the correlation and section out the transformed gini index value\n",
    "corr = crime_gini_df.corr()\n",
    "ginicorr = corr['TransformedGini']\n",
    "# Sort correlation from highest to lowest and look at the top six values\n",
    "sortedcorr = ginicorr.sort_values(ascending = False)\n",
    "sortedcorr.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Gini Index Analysis\n",
    "\n",
    "The Gini Index tells a fairly similar story as the quintile share does. Although it does so weakly, Income inequality significantly correlates with crime levels. Our analysis of the gini index data adds strength to our quintile share analysis, because it ensures us that our results were not a mere artifact of our particular way of quantifying income inequality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For common problems among ethics and privacy when collecting data, many did not apply to our study, such as informed consent and collection bias, due to fact that we did not use human subjects for our projects and are using public & government datasets. However, ethical issues we may need to consider would include the disclosure of data of minors, for example the perpetrator of a juvenile crime. Perhaps we can use data where crimes of minors are included as long as identities are anonymous or unknown. At first, we considered focusing our project solely on the crimes performed by persons of legal age, thus removing information about minors from our datasets. All of the data sets we have looked at right now are public datasets, so we should be fine as far as permissions are concerned. Any identifying information have already been redacted to maintain confidentiality. If we encounter any data that seems to be a breach of confidentiality, we will follow the Safe Harbour method to remove any personally identifiable information. Using a default ethics checklist to cross-reference possible issues helped us think more abbout who will be affected by our research and what ways we can use to ensure prevention in harming others.\n",
    "\n",
    "In terms of bias, there could be bias in the crime reporting and data that we are looking at since it is collected by humans. We also have to be aware of the bias and cultural issues around reporting certain crimes such as rape or hate crimes. For other ethical issues to consider is honest representation of the data and consider if the data we've grabbed from public sources and the government are always reliable. For crime, data is collected from public records and the other source is the US Census Bureau, a public government source.\n",
    "\n",
    "In terms of whether our research can indirectly harm others and serve as a source for others to profit off of, it's possible that there's always a chance to represent a source incorrectly. Our analysis results could also be used for laws and policies that negatively affect people. However, what we'll do to at least control some of this possibility is to not make outlandish claims from our data and stick with a factual conclusion truly based on what the data is showing. \n",
    "\n",
    "References:\n",
    "- 1) Data Science Ethics Checklist: http://deon.drivendata.org/#default-checklist\n",
    "- 2) Institute for Social Research: https://www.icpsr.umich.edu/icpsrweb/\n",
    "- 3) U.S. Census Bureau: https://census.gov/"
   ]
  },
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   "source": [
    "# Conclusion & Discussion"
   ]
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   "source": [
    "What we had wanted to answer was whether or not income inequality played a role in crime levels in United States cities. To answer this question, we first gathered data from three sources, which you can read more about in the Datasets section. One dataset observed the number of crimes committed as well as the number of crimes committed per 100,000 persons in the population for each city. Crimes taken into account include murder, rape, robbery, assault, burglary, theft, auto theft, and arson; the dataset included a crime index value for each city reflecting the crimes committed. Another dataset measured each city's Gini index, which is a statistical measure used to gauge economic inequality, measuring income distribution among a population. The remaining dataset read incomes at different percentiles for each city.\n",
    "\n",
    "For our analysis, we first checked for correlation visually and numerically. To get a better look of the comparison between cities with and without income inequality, we split our data accordingly and first checked if our data was normally distributed. It wasn't, so we transformed our data so that we could apply parametric tests. From there we saw a significant difference in crime index where there was and wasn't income inequality. We then went on to check if poverty had any influence. While the ratio of poverty existing was significantly different between the two, it appears that it does not alter the fact that income inequality has an affect on crime index. Although we found a positive relationship for crime index on income inequality, it's but a weak one, so income inequality should not be a reliable predictor of criminal activity. We ran similar tests against the Gini index to further support our findings.\n",
    "\t\n",
    "Our project could be biased and inaccurate if our data is affected by biased reporting of criminal activity, government withholding of information, falsified income reportings, and the list goes on. Although we face limitations on what information we could get ahold of and on how accurate the information is, based on what we gathered, we have concluded that there is indeed a positive relationship between the two-- that crime index does in fact seem to increase with income inequality. Can we say that income inequality directly causes crime though? Probably not, as there could be many environmental or political factors to consider. From our analysis, we actually saw that more cities in areas without income inequality experience poverty, yet this did not affect crime being significantly affected by income inequality. Our findings could possibly encourage the public to understand other possible economic influences on crime levels aside from poverty. A less wealthy neighborhood doesn't necessarily mean more crimes are bound to occur. Rather, society might see that crimes occur more often in areas where the rich are much richer than the poor are poor, or in other words, areas where income inequality is undeniably evident."
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