Second Home Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    "# Table of Contents\n",
    " <p><div class=\"lev2\"><a href=\"#Base-Criteria\"><span class=\"toc-item-num\">0.1&nbsp;&nbsp;</span>Base Criteria</a></div><div class=\"lev3\"><a href=\"#Existing-climate-for-vacation-rentals:\"><span class=\"toc-item-num\">0.1.1&nbsp;&nbsp;</span>Existing climate for vacation rentals:</a></div><div class=\"lev2\"><a href=\"#Add-seasonal-data\"><span class=\"toc-item-num\">0.2&nbsp;&nbsp;</span>Add seasonal data</a></div><div class=\"lev2\"><a href=\"#Basis-for-proximity\"><span class=\"toc-item-num\">0.3&nbsp;&nbsp;</span>Basis for proximity</a></div><div class=\"lev4\"><a href=\"#Plot-where-the-current-locations-are.\"><span class=\"toc-item-num\">0.3.0.1&nbsp;&nbsp;</span>Plot where the current locations are.</a></div><div class=\"lev3\"><a href=\"#Investment:\"><span class=\"toc-item-num\">0.3.1&nbsp;&nbsp;</span>Investment:</a></div><div class=\"lev3\"><a href=\"#Revenue-(Price-to-Rent)\"><span class=\"toc-item-num\">0.3.2&nbsp;&nbsp;</span>Revenue (Price to Rent)</a></div><div class=\"lev3\"><a href=\"#Home-Price\"><span class=\"toc-item-num\">0.3.3&nbsp;&nbsp;</span>Home Price</a></div><div class=\"lev2\"><a href=\"#Renters\"><span class=\"toc-item-num\">0.4&nbsp;&nbsp;</span>Renters</a></div><div class=\"lev2\"><a href=\"#Base-Analysis\"><span class=\"toc-item-num\">0.5&nbsp;&nbsp;</span>Base Analysis</a></div><div class=\"lev2\"><a href=\"#Where-are-they-located?\"><span class=\"toc-item-num\">0.6&nbsp;&nbsp;</span>Where are they located?</a></div><div class=\"lev2\"><a href=\"#Key-Takeaways\"><span class=\"toc-item-num\">0.7&nbsp;&nbsp;</span>Key Takeaways</a></div><div class=\"lev2\"><a href=\"#Above-a-certain-population\"><span class=\"toc-item-num\">0.8&nbsp;&nbsp;</span>Above a certain population</a></div><div class=\"lev2\"><a href=\"#Why-is-Everything-in-South-Florida?\"><span class=\"toc-item-num\">0.9&nbsp;&nbsp;</span>Why is Everything in South Florida?</a></div><div class=\"lev2\"><a href=\"#Near-a-coastline\"><span class=\"toc-item-num\">0.10&nbsp;&nbsp;</span>Near a coastline</a></div><div class=\"lev2\"><a href=\"#Near-Major-Airport\"><span class=\"toc-item-num\">0.11&nbsp;&nbsp;</span>Near Major Airport</a></div><div class=\"lev3\"><a href=\"#Accessibility:\"><span class=\"toc-item-num\">0.11.1&nbsp;&nbsp;</span>Accessibility:</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base Criteria\n",
    "1. **Growth:** Have property values in the region historically gone up?\n",
    "2. **Revenue:** Can rent as a short-term vacation rental? And do households in the region generate sizeable short-term rental rates?\n",
    "3. **Value:** Is the house in an affordable area?\n",
    "4. **Existing Climate:** Are there other vacation rentals in the region?\n",
    "5. **Vacation Friendly:** Is it near vacation activities, like ski resorts, lakes, beaches, etc?\n",
    "6. **Accessible:** Is it a near major airport? This is especially important if the house is across country from the owner.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import statsmodels.api as sm  \n",
    "\n",
    "from matplotlib.patches import Circle, Rectangle, Arc\n",
    "from geopy.geocoders import Nominatim\n",
    "from descartes import PolygonPatch\n",
    "from pyproj import Proj, transform\n",
    "import json\n",
    "from shapely.geometry import mapping, shape, LineString, Point\n",
    "\n",
    "\n",
    "import seaborn as sb  \n",
    "sb.set_style('darkgrid')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "def plot_polygon(ax, coordlist, edgecolor, color='white', alpha=0.5):    \n",
    "    poly = {\"type\": \"Polygon\", \"coordinates\": coordlist}\n",
    "    ax.add_patch(PolygonPatch(poly, facecolor='white', edgecolor='black', linewidth=1.0, alpha=alpha))\n",
    "    ax.plot(coordlist[:, 0],coordlist[:, 1], color='w')\n",
    "\n",
    "\n",
    "def plot_multipolygon(ax, geom, color='red'):\n",
    "    \"\"\" Can safely call with either Polygon or Multipolygon geometry\n",
    "    change from original\n",
    "    \"\"\"\n",
    "    if geom['type'] == 'Polygon':\n",
    "        a = np.asarray(geom['coordinates'])\n",
    "        plot_polygon(ax, a, color)\n",
    "    elif geom['type'] == 'MultiPolygon':\n",
    "         for g in geom['coordinates']:\n",
    "            a = np.asarray(g)\n",
    "            plot_polygon(ax, a, color)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_us_outline(ax):\n",
    "    with open('data/us_states.geojson') as data_file:    \n",
    "        data = json.load(data_file)\n",
    "        \n",
    "    for feature in data['features']:\n",
    "        plot_multipolygon(ax, feature['geometry'], color='black')\n",
    "    \n",
    "    #set the graph axes to the feature extents\n",
    "    ax.set_xlim(-160.6708333, -65.9038889)\n",
    "    ax.set_ylim(18.0638889, 50.0002778)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zillow  Home value time series data for US cities http://files.zillowstatic.com/research/public/Zip/Zip_Zhvi_AllHomes.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10919"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/City_Zhvi_AllHomes.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zillow Price to Rent Ratio Time Series Data for US cities- http://files.zillowstatic.com/ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10919"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_price_2_rent = pd.read_csv('data/City_PriceToRentRatio_AllHomes.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Existing climate for vacation rentals:\n",
    "Area with vacation rentals? Analyze only cities that are in a county where more than 20% of the households are for recreational use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11383"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CountyState'] = df['CountyName'] + \" County, \" + df['State']\n",
    "\n",
    "df_price_2_rent['CityState'] = df_price_2_rent['RegionName'] + ', ' + df_price_2_rent['State']\n",
    "\n",
    "# join on the combination of the city and state as there are multiple Hollywoods\n",
    "\n",
    "df['CityState'] = df['RegionName'] + ', ' + df['State']\n",
    "new_df = pd.merge(df, df_price_2_rent, on='CityState', how='inner')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add seasonal data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10401"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dd31518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Vacation or not occupied\n",
    "xl = pd.ExcelFile('data/seasonal_rates.csv')\n",
    "df_seasonal = xl.parse('seasonal_rates.csv')\n",
    "plt.hist(df_seasonal['seasonal use'], bins=50)\n",
    "\n",
    "\n",
    "# df_seasonal = df_seasonal[df_seasonal['seasonal use'] > 20.0]\n",
    "\n",
    "df_seasonal['CountyState'] = df_seasonal['Geographic area'] + ', ' + df_seasonal['State Abbr']\n",
    "seasonal_df = pd.merge(new_df, df_seasonal, on='CountyState', how='inner')\n",
    "len(seasonal_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hrbfeQldXF37zm99AlmW4XC7YbDZ4PB60tbWhuLgYXq8Xra2tGBkZQUVFBUpLS6HT6TKx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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b28470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(seasonal_df['For sale only'], bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basis for proximity\n",
    "If you are buying a vacation home, proximity to attractions is important. I included a lat/long to check for reference.\n",
    "\n",
    "Data: US cities with population and latitude and longitude https://www.maxmind.com/en/free-world-cities-database\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# add geo location\n",
    "df_geo = pd.read_csv('data/us_cities.csv')\n",
    "\n",
    "# combine the city and state for merging\n",
    "df_geo['CityState'] = df_geo['AccentCity'] + ', ' + df_geo['Region']\n",
    "df_geo = pd.merge(seasonal_df, df_geo, on='CityState', how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot where the current locations are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/h927d/HCC88za1YGer2eCxfKefPNN3E4HDidLvbs2c3MmXNIT+96v599doj2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/CTn/zkSp+riorKFeLuu81UVCTx73/fTUPDcUCIjtJSmfR0MQaiJ52dIjUwfrxwQ3Y63QuFu3tK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\neEjBSURERMRDCk4iIiIiHvp/KfviiKKDmzAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11828ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = plt.gca()\n",
    "add_us_outline(ax)\n",
    "ax.scatter(df_geo['Longitude'], df_geo['Latitude'], marker='o', edgecolor='w');\n",
    "df_geo.to_csv('out.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tLere82H5qg0Twe9sev7557Vr1y7dfPPNMsbIZrNpxYoVWr16tXp6ejRx4kSt\nXr1aNptNv//971VXVydjjBYvXqy8vDx1dnZq6dKlamtrU0JCgtatW6exY8eqqalJzz//vPr6+pSd\nna0nnnhC0hd/PdDY2ChjjJYtW6Zp06YNOXNpaamqq6tls9n07LPPRl3OX/3qV/rwww9ljNFTTz2l\n8ePHq7KyMqpynj9/XsuWLdO5c+d08eJFzZs3T1OmTImqnKdOndJTTz2l7du36+OPP47oe3327Fkt\nXbpUFy5c0JgxY7Ru3TolJib2m/O1117T97//fX3nO9+Rw+GQzWbT9OnT9dOf/nTYc17+Wn7d2HBn\n/Gqm06dPq7KyUp9//rmcTqfWrVsnp9MZFTkv4buYAACWOFEOAGCJggAAWKIgAACWKAgAgCUKAgBg\niYIAAFiiIAAAligIAICl/we4+1LdzOl5PQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11828c7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_geo['Population'].dropna(), bins=100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investment: \n",
    "looking for upward trending growth in the value of the homes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_geo['growth'] = (df_geo['2016-03_x'] - df_geo['2011-03_x']) / df_geo['2011-03_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1158993c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_geo['growth'].dropna(), bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# calculate growth\n",
    "\n",
    "max_growth, min_growth = max(df_geo['growth']), min(df_geo['growth'])\n",
    "difference = max_growth - min_growth\n",
    "\n",
    "df_geo['growth_index'] = 100*(df_geo['growth'] - min_growth) / difference\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Revenue (Price to Rent)\n",
    "The Price to Rent Ratio is the median ratio between a home's value and annualized rent.\n",
    "\n",
    "It would be great to have this for the annual rate for investment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pnCi+GzduoKqqCtu2bcPatWsBAIsXL1bN/MfKr6b5f/PNN3H06FEAQE5ODvR6\nPZYuXYqOjrsr4Gb6/H89v06nw3PPPYeuri4Aic2/IksgSCpfE+feoxB0Oh22bt2KwsJCpWPJunr1\nKrZs2YKTJ0+it7cXu3btQjgcht1uR319PXS62CsgZop781++fBl79uyB0WiE1WrFCy+8ALPZrHTE\nmPbt24e//vWvWLhwISRJgk6nQ21tLerr61Ux/7Hyb9myBQcOHFDF/A8PD6OmpgY3btzA6OgoNmzY\ngIULF6Kurk4V8x8r/8MPP5zU859r3RARaZx6dowTEdGksOiJiDSORU9EpHEseiIijWPRExFpHIue\niEjjWPRERBrHoici0rj/B7NBHVNc2JokAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1134c0dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_geo['2016-03_y'].dropna(), bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "max_revenue, min_revenue = max(df_geo['2016-03_y']), min(df_geo['2016-03_y'])\n",
    "difference = max_revenue - min_revenue\n",
    "\n",
    "df_geo['revenue_index'] = 100*(max_revenue - df_geo['2016-03_y']) / difference\n",
    "\n",
    "df_geo['old_revenue_index'] = 100*(df_geo['2016-03_y'] - min_revenue) / difference\n",
    "\n",
    "# df_geo[['revenue_index', '2016-03_y']].sort('revenue_index', ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Home Price\n",
    "The lower the price the higher the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The median home value is $169000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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MmMGuXbtij0+cOEFBQQEAfr+f119/nfb2dvLz80lKSsKyLLxeLx0dHbS1teH3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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d660940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_geo['2016-03_x'], bins=50);\n",
    "print(\"The median home value is $\" + str(df_geo['2016-03_x'].median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "max_price, min_price = max(df_geo['2016-03_x']), min(df_geo['2016-03_x'])\n",
    "difference = max_price - min_price\n",
    "df_geo['price_index'] = 100*(max_price - df_geo['2016-03_x'] ) / difference\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Renters\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the percentage of renters in the area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "min_seasonal, max_seasonal = min(df_geo['seasonal use']), max(df_geo['seasonal use'])\n",
    "difference = max_seasonal - min_seasonal\n",
    "df_geo['env_index'] = 100*(df_geo['seasonal use'] - min_seasonal) / difference\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base Analysis\n",
    "Subsequent analysis will use this the analysis as the basis and focus on regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName_x</th>\n",
       "      <th>State_x</th>\n",
       "      <th>index</th>\n",
       "      <th>Population</th>\n",
       "      <th>Home Value in USD</th>\n",
       "      <th>Price to Rent</th>\n",
       "      <th>growth rate (5 years)</th>\n",
       "      <th>monthly rent</th>\n",
       "      <th>seasonal use</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>Lehigh Acres</td>\n",
       "      <td>FL</td>\n",
       "      <td>86.148716</td>\n",
       "      <td>48534</td>\n",
       "      <td>126900</td>\n",
       "      <td>9.26</td>\n",
       "      <td>114.72 %</td>\n",
       "      <td>1142</td>\n",
       "      <td>55.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>Golden Gate</td>\n",
       "      <td>FL</td>\n",
       "      <td>85.933024</td>\n",
       "      <td>25388</td>\n",
       "      <td>202600</td>\n",
       "      <td>8.40</td>\n",
       "      <td>102.80 %</td>\n",
       "      <td>2009</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1525</th>\n",
       "      <td>Golden Lakes</td>\n",
       "      <td>FL</td>\n",
       "      <td>83.505419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70000</td>\n",
       "      <td>5.09</td>\n",
       "      <td>79.95 %</td>\n",
       "      <td>1146</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1517</th>\n",
       "      <td>Palm Springs</td>\n",
       "      <td>FL</td>\n",
       "      <td>83.336956</td>\n",
       "      <td>14329</td>\n",
       "      <td>124800</td>\n",
       "      <td>7.87</td>\n",
       "      <td>95.00 %</td>\n",
       "      <td>1321</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4095</th>\n",
       "      <td>Kensington Park</td>\n",
       "      <td>FL</td>\n",
       "      <td>83.121668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158600</td>\n",
       "      <td>9.03</td>\n",
       "      <td>95.08 %</td>\n",
       "      <td>1463</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>Leisure City</td>\n",
       "      <td>FL</td>\n",
       "      <td>82.661397</td>\n",
       "      <td>23770</td>\n",
       "      <td>136600</td>\n",
       "      <td>7.49</td>\n",
       "      <td>100.59 %</td>\n",
       "      <td>1519</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4425</th>\n",
       "      <td>Yeehaw Junction</td>\n",
       "      <td>FL</td>\n",
       "      <td>81.276911</td>\n",
       "      <td>24080</td>\n",
       "      <td>158500</td>\n",
       "      <td>10.45</td>\n",
       "      <td>93.29 %</td>\n",
       "      <td>1263</td>\n",
       "      <td>57.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4426</th>\n",
       "      <td>Poinciana</td>\n",
       "      <td>FL</td>\n",
       "      <td>81.054447</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132400</td>\n",
       "      <td>9.82</td>\n",
       "      <td>88.07 %</td>\n",
       "      <td>1123</td>\n",
       "      <td>57.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>Broadview Park</td>\n",
       "      <td>FL</td>\n",
       "      <td>81.009600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158300</td>\n",
       "      <td>8.18</td>\n",
       "      <td>87.78 %</td>\n",
       "      <td>1612</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>North Lauderdale</td>\n",
       "      <td>FL</td>\n",
       "      <td>80.975046</td>\n",
       "      <td>34267</td>\n",
       "      <td>143200</td>\n",
       "      <td>7.90</td>\n",
       "      <td>85.73 %</td>\n",
       "      <td>1510</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RegionName_x State_x      index  Population  Home Value in USD  \\\n",
       "1437      Lehigh Acres      FL  86.148716       48534             126900   \n",
       "5571       Golden Gate      FL  85.933024       25388             202600   \n",
       "1525      Golden Lakes      FL  83.505419         NaN              70000   \n",
       "1517      Palm Springs      FL  83.336956       14329             124800   \n",
       "4095   Kensington Park      FL  83.121668         NaN             158600   \n",
       "658       Leisure City      FL  82.661397       23770             136600   \n",
       "4425   Yeehaw Junction      FL  81.276911       24080             158500   \n",
       "4426         Poinciana      FL  81.054447         NaN             132400   \n",
       "1929    Broadview Park      FL  81.009600         NaN             158300   \n",
       "1920  North Lauderdale      FL  80.975046       34267             143200   \n",
       "\n",
       "      Price to Rent growth rate (5 years)  monthly rent  seasonal use  \n",
       "1437           9.26              114.72 %          1142          55.8  \n",
       "5571           8.40              102.80 %          2009          69.4  \n",
       "1525           5.09               79.95 %          1146          50.0  \n",
       "1517           7.87               95.00 %          1321          50.0  \n",
       "4095           9.03               95.08 %          1463          59.5  \n",
       "658            7.49              100.59 %          1519          31.4  \n",
       "4425          10.45               93.29 %          1263          57.6  \n",
       "4426           9.82               88.07 %          1123          57.6  \n",
       "1929           8.18               87.78 %          1612          44.3  \n",
       "1920           7.90               85.73 %          1510          44.3  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_geo['index'] = (.30 * df_geo['growth_index']) + (.30 * df_geo['price_index']) + (.30 * df_geo['revenue_index']) + (.10 * df_geo['env_index'])\n",
    "\n",
    "\n",
    "# sort descending from the highest index down and limit the top 10\n",
    "df_out = df_geo.sort('index', ascending=False).head(10)\n",
    "df_out['Home Value in USD'] = df_out['2016-03_x']\n",
    "df_out['Price to Rent'] = df_out['2016-03_y']\n",
    "df_out['growth rate (5 years)'] = df_out['growth'].apply(lambda x: str(\"%.2f\" % (x * 100)) + \" %\")\n",
    "df_out['monthly rent'] = (df_out['2016-03_x'] / df_out['2016-03_y']).apply(lambda x: int(x / 12))\n",
    "\n",
    "df_out[['RegionName_x', 'State_x', 'index', 'Population', 'Home Value in USD', 'Price to Rent', 'growth rate (5 years)', 'monthly rent','seasonal use']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Where are they located?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ubm4OHRqrCqCuDIYUCgUKhaLB9SxY8Dc8PX3a/PN6JavVRllZBQqF4z7kDAYL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\nIiIiIvLS/wBobHmB9BAHzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1164c6a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = plt.gca()\n",
    "add_us_outline(ax)\n",
    "ax.scatter(df_out['Longitude'], df_out['Latitude'], marker='o');\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Key Takeaways\n",
    "\n",
    "1. A lot of small towns\n",
    "2. price to rent is not the best since these would most likely be short term rentals. \n",
    "3. everything is in South Florida near but not in major metropolitan areas -- Tampa, Orlando, Miami/Fort Lauderdale\n",
    "4. Why is Leigh Acres so desirable? (http://www.huffingtonpost.com/raymond-schillinger/lehigh-acres-florida-a-pa_b_817783.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Above a certain population\n",
    "I have not heard of these cities. Find cities with populations above 100K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName_x</th>\n",
       "      <th>State_x</th>\n",
       "      <th>Population</th>\n",
       "      <th>index</th>\n",
       "      <th>2016-03_x</th>\n",
       "      <th>2016-03_y</th>\n",
       "      <th>growth</th>\n",
       "      <th>seasonal use</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>Cape Coral</td>\n",
       "      <td>FL</td>\n",
       "      <td>130807</td>\n",
       "      <td>77.971541</td>\n",
       "      <td>200200</td>\n",
       "      <td>11.03</td>\n",
       "      <td>0.798742</td>\n",
       "      <td>55.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>San Bernardino</td>\n",
       "      <td>CA</td>\n",
       "      <td>202381</td>\n",
       "      <td>74.532503</td>\n",
       "      <td>223300</td>\n",
       "      <td>13.55</td>\n",
       "      <td>0.845455</td>\n",
       "      <td>38.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>Hollywood</td>\n",
       "      <td>FL</td>\n",
       "      <td>144821</td>\n",
       "      <td>74.503549</td>\n",
       "      <td>220000</td>\n",
       "      <td>10.45</td>\n",
       "      <td>0.646707</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2433</th>\n",
       "      <td>Richmond</td>\n",
       "      <td>CA</td>\n",
       "      <td>103041</td>\n",
       "      <td>74.081780</td>\n",
       "      <td>397400</td>\n",
       "      <td>15.02</td>\n",
       "      <td>1.114955</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>FL</td>\n",
       "      <td>246316</td>\n",
       "      <td>73.298085</td>\n",
       "      <td>156100</td>\n",
       "      <td>10.10</td>\n",
       "      <td>0.542490</td>\n",
       "      <td>44.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1074</th>\n",
       "      <td>Moreno Valley</td>\n",
       "      <td>CA</td>\n",
       "      <td>168629</td>\n",
       "      <td>72.577724</td>\n",
       "      <td>269200</td>\n",
       "      <td>14.17</td>\n",
       "      <td>0.746918</td>\n",
       "      <td>44.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>Hialeah</td>\n",
       "      <td>FL</td>\n",
       "      <td>224840</td>\n",
       "      <td>72.528938</td>\n",
       "      <td>184900</td>\n",
       "      <td>8.87</td>\n",
       "      <td>0.520559</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>Glendale</td>\n",
       "      <td>AZ</td>\n",
       "      <td>240112</td>\n",
       "      <td>72.123423</td>\n",
       "      <td>192500</td>\n",
       "      <td>13.32</td>\n",
       "      <td>0.753188</td>\n",
       "      <td>28.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>Pembroke Pines</td>\n",
       "      <td>FL</td>\n",
       "      <td>155862</td>\n",
       "      <td>72.082024</td>\n",
       "      <td>253500</td>\n",
       "      <td>10.87</td>\n",
       "      <td>0.541033</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td>Miramar</td>\n",
       "      <td>FL</td>\n",
       "      <td>112922</td>\n",
       "      <td>72.034339</td>\n",
       "      <td>237200</td>\n",
       "      <td>10.48</td>\n",
       "      <td>0.513720</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RegionName_x State_x  Population      index  2016-03_x  2016-03_y  \\\n",
       "1436        Cape Coral      FL      130807  77.971541     200200      11.03   \n",
       "1371    San Bernardino      CA      202381  74.532503     223300      13.55   \n",
       "1906         Hollywood      FL      144821  74.503549     220000      10.45   \n",
       "2433          Richmond      CA      103041  74.081780     397400      15.02   \n",
       "1312  Saint Petersburg      FL      246316  73.298085     156100      10.10   \n",
       "1074     Moreno Valley      CA      168629  72.577724     269200      14.17   \n",
       "631            Hialeah      FL      224840  72.528938     184900       8.87   \n",
       "238           Glendale      AZ      240112  72.123423     192500      13.32   \n",
       "1905    Pembroke Pines      FL      155862  72.082024     253500      10.87   \n",
       "1907           Miramar      FL      112922  72.034339     237200      10.48   \n",
       "\n",
       "        growth  seasonal use  \n",
       "1436  0.798742          55.8  \n",
       "1371  0.845455          38.7  \n",
       "1906  0.646707          44.3  \n",
       "2433  1.114955           9.6  \n",
       "1312  0.542490          44.6  \n",
       "1074  0.746918          44.2  \n",
       "631   0.520559          31.4  \n",
       "238   0.753188          28.1  \n",
       "1905  0.541033          44.3  \n",
       "1907  0.513720          44.3  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort descending from the highest index down and limit the top 10\n",
    "\n",
    "\n",
    "df_out = df_geo[df_geo['Population'] > 100000].sort('index', ascending=False).head(10)\n",
    "\n",
    "df_out[['RegionName_x', 'State_x', 'Population', 'index', '2016-03_x', '2016-03_y', 'growth', 'seasonal use']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why is Everything in South Florida?\n",
    "\n",
    "\n",
    "What is the trend in the home values? South Florida was hit hard by the recession/housing market crisis. What has happened since."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "year_lst = ['1996-04', '1996-05', '1996-06', '1996-07', '1996-08', '1996-09', '1996-10', '1996-11', '1996-12', '1997-01', '1997-02', '1997-03', '1997-04', '1997-05', '1997-06', '1997-07', '1997-08', '1997-09', '1997-10', '1997-11', '1997-12', '1998-01', '1998-02', '1998-03', '1998-04', '1998-05', '1998-06', '1998-07', '1998-08', '1998-09', '1998-10', '1998-11', '1998-12', '1999-01', '1999-02', '1999-03', '1999-04', '1999-05', '1999-06', '1999-07', '1999-08', '1999-09', '1999-10', '1999-11', '1999-12', '2000-01', '2000-02', '2000-03', '2000-04', '2000-05', '2000-06', '2000-07', '2000-08', '2000-09', '2000-10', '2000-11', '2000-12', '2001-01', '2001-02', '2001-03', '2001-04', '2001-05', '2001-06', '2001-07', '2001-08', '2001-09', '2001-10', '2001-11', '2001-12', '2002-01', '2002-02', '2002-03', '2002-04', '2002-05', '2002-06', '2002-07', '2002-08', '2002-09', '2002-10', '2002-11', '2002-12', '2003-01', '2003-02', '2003-03', '2003-04', '2003-05', '2003-06', '2003-07', '2003-08', '2003-09', '2003-10', '2003-11', '2003-12', '2004-01', '2004-02', '2004-03', '2004-04', '2004-05', '2004-06', '2004-07', '2004-08', '2004-09', '2004-10', '2004-11', '2004-12', '2005-01', '2005-02', '2005-03', '2005-04', '2005-05', '2005-06', '2005-07', '2005-08', '2005-09', '2005-10', '2005-11', '2005-12', '2006-01', '2006-02', '2006-03', '2006-04', '2006-05', '2006-06', '2006-07', '2006-08', '2006-09', '2006-10', '2006-11', '2006-12', '2007-01', '2007-02', '2007-03', '2007-04', '2007-05', '2007-06', '2007-07', '2007-08', '2007-09', '2007-10', '2007-11', '2007-12', '2008-01', '2008-02', '2008-03', '2008-04', '2008-05', '2008-06', '2008-07', '2008-08', '2008-09', '2008-10', '2008-11', '2008-12', '2009-01', '2009-02', '2009-03', '2009-04', '2009-05', '2009-06', '2009-07', '2009-08', '2009-09', '2009-10', '2009-11', '2009-12', '2010-01', '2010-02', '2010-03', '2010-04', '2010-05', '2010-06', '2010-07', '2010-08', '2010-09', '2010-10_x','2010-11_x', '2010-12_x', '2011-01_x', '2011-02_x', '2011-03_x', '2011-04_x', '2011-05_x', '2011-06_x', '2011-07_x', '2011-08_x', '2011-09_x', '2011-10_x', '2011-11_x', '2011-12_x', '2012-01_x', '2012-02_x', '2012-03_x', '2012-04_x', '2012-05_x', '2012-06_x', '2012-07_x', '2012-08_x', '2012-09_x', '2012-10_x', '2012-11_x', '2012-12_x', '2013-01_x', '2013-02_x', '2013-03_x', '2013-04_x','2013-05_x', '2013-06_x', '2013-07_x', '2013-08_x', '2013-09_x', '2013-10_x', '2013-11_x', '2013-12_x', '2014-01_x', '2014-02_x', '2014-03_x', '2014-04_x', '2014-05_x', '2014-06_x', '2014-07_x', '2014-08_x', '2014-09_x', '2014-10_x', '2014-11_x', '2014-12_x', '2015-01_x', '2015-02_x', '2015-03_x', '2015-04_x', '2015-05_x', '2015-06_x', '2015-07_x', '2015-08_x', '2015-09_x', '2015-10_x', '2015-11_x', '2015-12_x', '2016-01_x', '2016-02_x', '2016-03_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ldaXaL4QQQgjRJDTo7Uu1Ws3s2bN54403GDFiBFFRUTz//PN88803BAcHs2jRIkwmE0aj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\naTkR6I3aibvQ60TGlEhCI0zUlHXRXG87b3tBEAiPX45CAL0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P8PZ/Fa8IAn1BUeQTSokp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120239b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index, row in df_geo.sort('index', ascending=False).head(10).iterrows():\n",
    "    \n",
    "    start_month = int(year_lst[0].split(\"-\")[1])\n",
    "    start_year = int(year_lst[0].split(\"-\")[0])\n",
    "    date_range = pd.date_range(start=pd.datetime(start_year, start_month, 1), periods=len(year_lst), freq='M')\n",
    "    over_time = pd.Series(row[year_lst].tolist(),index=date_range)\n",
    "    \n",
    "    over_time.plot(figsize=(10, 5), title=\"Home Values over Time (~1997 to Present)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JHdW5JJQJIYQQH8NAbJCN3s1s6ttMS7ANAA0aJnsmMKdsJjPLpmM32Ea5lOJY\nSKUz7O0Ns2Z7H5t3e2nt9JPq66U04aMs6WdcwseC5AD29IEQlrGY0YydgmfGLJzTZwx7fgllQggh\nxIfUG/WyqW8zm7yb87cmNWiY5B7PKWWNzCprxGl0jHIpxcFUOk2yp5tEZyfZaASVSqHSabKp1L7X\n+54zWdBqSKQhGEvhj6bxRXLPGQWudISGhI+FqQD6g/qJae0WzBOmY5s6E1vDNAwVlUc9gENCmRBC\nCHEUkpkU63o2sLrzn3SGu4HcrcmGoknMKmtkRsk0WT+yQCilSPsGSXR0kOxoJ9HZkXvd0w2ZQ5eq\nGo4JKN/3GPIZOg3aUgvGqjLMdeOxjWvEXDcOvdP5kcstoUwIIYQYRjAZ4rWONbzeuYZwKoJWo6Wx\npIFZpTNoLGnAarCOdhE/8ZRSpLxeYk3bie5oIrazibTPN2QfjcmEecwYjNW1pIrK8GWN9IXT9AaS\ndAeT+ONZMhotaY2ODFocpiyV9gjltkEq7BFKLFEMOi1GUzlF9Q1oy6djsJQe02lMjiqUvfvuu9x3\n3308/vjjbN++nW9+85vU19cDsHz5cpYsWcITTzzBqlWrMBgMXHvttSxatIhEIsGNN97IwMAAdrud\nn/zkJ3g8HjZt2sQ999yDXq/njDPOYMWKFQA8+OCDrF69Gr1ez8qVK5kxY/h7r0IIIcTx0hnu5h/t\nr/NOz0bSKoNVb+EzYz7FWTWn4za5Rrt4n2hKKVL9XmI7mnIhrKmJtG8wv13ncGKaMYtEUQU+azE9\nehdtSSO9/jh9fVHS3Wr/noAJl01HdU2GcpuPMks3Fc4wbnMCrc6IyV6H2X4qJvsYjNZKNMdxndEj\nhrLf/OY3/OlPf8Jmy3VS3LJlC1dffTVf+cpX8vv09/fz+OOP88wzzxCPx1m+fDkLFizgD3/4A5Mm\nTWLFihU8//zzPPTQQ9xyyy3cfvvtPPjgg9TU1HDNNdfQ1NRENpvlnXfe4cknn6S7u5vrrruOp556\n6phfsBBCCHE46WyarQM7eK3jnzT5dgFQZinhnNozmVc5B5POOMol/GTKJhLE97YS37Ob2J7dxPfs\nIRMK5rdrbHaSkxrpc1azS1/C1rCRcDQN0f175AKUxaSjtsxOqctAuSNGqaWXYkMzNn14335aTPZa\nzI6ZmB31GK1VaEZwmasjhrIxY8bwy1/+ku9///sAbN26ldbWVl566SXq6+tZuXIl7733HnPmzEGv\n12O326nb2yCfAAAgAElEQVSvr6epqYn169fzjW98A4CzzjqLhx9+mHA4TCqVoqamBoCFCxfy5ptv\nYjQaWbBgAQCVlZVks1l8Ph8ej+d4XbsQQgiBUor2UCdv9aznnd5NhFMRIDfT/qfqzmRa8RRZf3KE\nqHSalG+QlNdLyusl0dFOfM9uEh3tkD3QoT7rcOGvnUKbqYz3skV0aRyQ1YA/t73EpWdirZuKYitl\nLj1F1jAeQz+GbBepWBfZdCR/Lp3RhcU5B7NjAmZHPVqdaaQvO++Ioey8886js7Mz//PMmTP5whe+\nwNSpU3nkkUd48MEHaWhowOE4MMrEarUSDoeJRCLY7blOjzabjVAoNOS9/e+3t7djNptxu92HnENC\nmRBCiOPBnwiwrmcjb/Wsp3vfLPt2g41zahcyv2IuNY6qUS7hyScTi5Hx+0j7/aR9PtJ+H6l+bz6E\npQYHhoQvAHR6kmU19NnL2Jn1sD3rJKTP3b3TaKCy1MK8UiM1xVqqPIpKVwKzLko60UUi2kUm6Yco\nZIEEoDM4sbimYLKPweIcj95UXDDLW33ojv6LFy/OB7DFixdz1113cdpppxEOh/P7RCIRnE4ndrud\nSCSSf8/hcGCz2Q7Z1+VyYTAY8vu+f/+jUVoqw45HgtTzyJG6HhlSzyOnEOo6nUnT7Gtju3c3m3ub\n2NzXhFIKvVbP/JrZnFU/j1Mqp6HX6ka7qB9ZIdSzUop4dzehpp2Edu4k1tFJYmCQ5OAg2Xj8sMcZ\nPB4ckyeh3MUM6my0xg1sCerZk7GT0eS+E4tZw7jyLGOKQlTb+yi19GHUvW80ZQqS/ZDc96POYMVZ\nPBmbqxarsxabqwaD6aOPjny/41HXHzqUff3rX+fWW2+lsbGRNWvWMG3aNBobG3nggQdIJpMkEgma\nm5uZOHEis2bNYvXq1TQ2NrJ69Wrmzp2L3W7HaDTS3t5OTU0Nb7zxBitWrECn03Hfffdx9dVX093d\njVJqSMvZcI5HZzsx1PHq1CgOJXU9MqSeR85o1XUsHaclsJc9/hb2BFppDbaRyqbz28c66zitYg5z\nymdi2zeC0jcQPdzpCt5o1XM2Hife0pzr69W8h3hzM5nw0HLoHA4MpWXo3W70Hg86V+5Z73aTsnvY\nE9GxtTPMttZBvN4Dwc1pTjPVE6DWPUidJ0iZPYp2X6OWzuBEb6xCq7ehM9hyz3p7/rXe6ERncOVb\nwVKAPwj7+5d9HB+nrocLcx86lN1xxx3ccccdGAwGSktL+fGPf4zNZuOKK67g8ssvRynF9773PYxG\nI8uXL+emm27i8ssvx2g0cv/99+fPccMNN5DNZlmwYEF+lOWcOXO47LLLUErxox/96CNdrBBCiE+e\nWDpOR6iL9nBn7jnUSXekF0VulJ0GDVX2Cia4xzLeVc9491gZQfkxpAMBwps2EH7nHaI7tg+55agv\nKcExdSrmcROwjB+PsaYWrcEA5FrR+gNxdncG2NMZZPf6AG29e9g/FtJi0jG9Nkudo41xRf0UWxMY\nzB4M5hIM5vHoTSUYLKUYTCVoT8JBFxqllDryboVN/to9/qRVYeRIXY8MqeeRc6zrOp5O0BZqpzXQ\nTlu4k/ZQJ/2xgSH7GLUGah3VjN8Xwsa56k/6NSc/Tj1nlUIDw/atSg0OEt6wnvCGd4jt2gn74oOp\nfizWyVMwj5+AZdx49PvucimliMTTdHrDNHcFc0GsK0gwksyfU6fVMKHaxeRqA3WOFjy8i06r0Bkc\nOErnYS+ZjVZn/kjXdDwVTEuZEEIIMVKUUnhj/bQE2mgO7qUlsJeucE++BQzAprcy2TOBGkcVtfZq\nah3VlFlLZMTkYaTSWdr7wrR0B2ntDtLSE6K7P4ICtBoNWq0GnTb37MxEmRhoYWKwhfJIX/4coZIa\nomOnkp7UiLGkhFA0hd+XwL+6A39oD75wAn84SSo9tNO+x2Fi7uRSxle7GFflpMI2QGxgLfHQHgAM\n5jIcZadj80xHcwL37fuoJJQJIYQoCKlsmt5IH12RHrrCPXSGu9kbaieSOtDPy6DVM841hrGuMYx1\n1lHnrMFjchfM6LlCFI6l2Nw8wK6OAC3dQTr6wmSyB0KtyahjXLUTnVZLNqvQJuNUefcwxruT8kAn\nWhRZNLRZK2iy1rHTXkdYbwUf8JYX8A75PI0GnDYj1SU23HYTZR4L46tdjK9yUuTMtXql4l58HX/F\n582FMZN9DM6yMzA7J3yiv0sJZUIIIUaUUgpfwp8PXl2R3HNv1Ev2oMWdi80eGoomMdY5hrGuOmrs\nVeg+gS0oH1Z/IMbGXf1s2tXPjjY/2X23GvU6DXXlDsZWOhhb6aS+0kllkRUyaaJb3iO4dg2Rdzeh\n0rkBEebxE3DOm4997mlMcTo5M5UhEksR3veIxNPEEmnsFgMehwm33YTTZkCn/eBWymw6hr9nNWHv\nOkBhdozDVXkOJlv1SFVNQZNQJoQQ4riJpxPs6O9ja+eeISEslh46NYJJZ2SMo5YqewXV9kqqbBVU\n2ytkXcmjpJSiuTPAP97ey8adXtr6Dkw9Na7KyayJJUytL6K2zI5elwtM2USCyJbN9P1pI+F3N5GN\n5qalMlZU4ph/Oo558zGWlg35HJNBh8mgy7d4HX35soT7NxDofoVsJobeVIS7+jwszkkndctYMpGm\nvzdMf28Yb2+Iwb4I/99N5xx2fwllQgghjhmlFB3hbrYP7GDb4A72BFqHtH5p0FBmLWVK0SSqbZX5\nEFZkdksfsA8hncmytzfErvYAuzr87OoIEI6lgFzn+enjipg9sZSZE0rwOA7MUJ8OBgm8u5Hwxg1E\nt29DpXLH6D0eXAsW4ph/Oqa6Mcc0KMVDLfg6XiQV70OjNeKuWoyj9DQ02pMngiiliIQSDPRFGPDu\nC2E9IYL+oX986A3D/46fPDUihBBiVIRTEZoGd7FtYAfbB3cSTOZGpWnQUOeoYXrlJDy6YqrtFVRY\nyzHqDKNc4hOLUorBYIKugQh7OgPsbPfT3BUk+b5O9MVOM3OnljOlxkXjuGIsptx/75lIhMjWXcRb\nmols2Ux8z+78qEljdQ32U2ZhnzUb05j6YxrElFIkIm0Ee98kHtwNgK14Fu7Kc9AZ7Ec4urAl4mna\nWwbZs8vLgDfMYF+EAW+EZCI9ZD+TWU/1GDcl5Q5Kyu2UlttxFQ3f8iuhTAghxIeWyWbYMrCdN7re\nYvvAzvxoSIfBzmkVs5laNJkpRRNxGO0y/chRSmeyDATjdPdH6RqI0NUfoXsgQtdAlEQyM2Tf6lIb\nk2rcTKxxManWTZHTTJHDQOeGrcRXb8Lf2kK8tYVUX++BgzQaLBMmYp81G9spszGWlXGsKZUlFthB\nsPdNktEuAEz2OjzVn8ZoPTGWrVJKEQrECfrjBAMxgv44IX/uOeCLkYgPDV8aDbg8FmrqPRSX2Sgu\ntVFS7sDuNH3ooCuhTAghxFEbiA3yz663WdO9jsC+FrF6Zx0zSqYytXgy1fZKuQ25TzariCfTRBNp\novs6xMcSGQKRBL5QbsoIfziBP5TAF4qjQkHs6ShaFFqVRasUei3McBgo9hgpdhgpM0OJPoUu1kum\nLUhmS5BgMIAvGGRnOJRvBQPQWm1Yp07DXD8W89ixmCdMRO84NksMHXqtKSKD7xLqW0s6MQiAxTUZ\nZ9kZmOy1x+UzP65MJkvQH8PXH8U3EMXXH8HXH8U/GCV90FQeAFqdBqfLTHmVk8oaF1a7keIyO55i\nK3rDsRl8IqFMCCHEsDLZDJsHtvNG51qaBnehUFj0Zs6uWcCCqtOotleOdhFHRSKVoc8Xo3cwSq8v\nSs9glN7BGIOhONF4mvhBrVsohScVojgVwJ0K4U6FmZQK4UmHcaXC6FXmgz/oIAe3OWotFnROJ7Yx\ntWgra3IhrH4shrKy496JPp0MEBnYRKh/Hdl0FDQ6bMWzcJadjsFcclw/eziZdJZoJEkknCASShIN\nJ/Kvc88Jgv442ezQ+fP1ei3uYivuYisutwWn24xz37PNcaDl63i1/kooE0II8YFi6RhvdL7FK+1v\nEEgGARjnGsOCqnnMLpuB8SRc5uZwEskMe7oC7OoIsLszQPdAhMFg4pD9tBoNHoeJUrcFi0mPQ5eh\nMthFWX8r7t5mjJHAocdYbRgqazGUlqJ3e9Do9Wh0OtBq0Wi1Q15rrVZ0Tid6pwudw4nO6UBryH0P\nI3WbOJuOEfVvJ+LbTCK8FwCNzoyzfAGO0tPQGY7voujxWArfQBT/QJRIOEEskiQaSRGLJIlFc68P\n7t91MLNFT0mFnaJiG54SK559zw6XeVRHg0ooE0IIMYQv7ueVjjd4s/Mt4pkEJp2Rs2sWsLBqHlX2\nitEu3ogIRZPs6sh1qt/VEaCtNzRkwlWPw8SUOjcVRVbK9z0qiqwUO01kujqIbtlMZMtmYnt2QybX\nAqa1WrHOPRXzmLEYSktzj5JSdDbbaF3mUVPZNLHATiK+zcSCu2Ffq57JXofV04jNMx2tznSEs3w4\n8ViKgb4wvv4ogwORfbcZI8QiqcMeY7YasDmMlFbYsdiM2OwmbI79zyZs9txrnf6j3WLPKIU/kSLk\nj9AbjJLKZElks6SyimQmSyKrSGcPvfX5fl+SZZaEEEIcSVe4h5faVvNO7yYyKoPT6OAzYz7Fwur5\nJ/W6kYlUhrbeEC3dodyyQ91Ben2x/HadVkN9hYOJNW4m1rqYWOPGbhk6gjTp7SP4+ot0rPknqf59\nM9xrNJjrx2Kd3ohteiPm+rG5Vq8ThMqmiYeaifq3E/U3obK5lkGDuQxbUSNWz3T0xmOzqHs2q/D1\nR+jtCtLTGaS3M4B/MHbIfg6XmbrxjlzLVrEVu9OExWrEajNgthrQHmbS2g9DKUUwlaY/nqI/nmIg\nnsy9TiQZTKTIfswVw780a+xht0koE0KITzClFDt8u3ml/XW2DDQBUG4tY3Hd2ZxaMQvDSTSX1H5e\nf4xtrYM0dwVp6Q7R1R/Jz3gPYDXpmVbvYWKtm0k1bsZWOTF9QEfuTDRK+J11BNe8mVugG9CYTDjm\nnY5txkxsU6ehcxzfW3nHWjabIh7cTdS/nVhgJyqbWzxcZ3BiK5mDtagRo6X8I58/ncoQCiYIB+OE\ngwmC/hh93SF6u4Kk3tcHz2DUUVPvobTSQVFJLoC5i6wYjMc21GaVoj+eoisapyuSoCuaoDuaIJY5\ntLXLqtdSbTVTYjZQ5rKQjqcx6rQYtVqMWs2+1xoMWi0f9QboyfevTQghxBFFU1HW9qzn9c419EX7\nARjvque8MYuYVjzlpBpBmUhl2NHmZ0vzAJtbBukdPLCWplGvZVy1k7EVzvzSQ2Uey2H7Fal0mmjT\nNoL//Cfhjetzk69qNFimNOA6YyH22XPQmj/cbPejLZMKEw+1Eg1sJx7cjcrum4TW6MbqnoPV3YDR\nWn1InWSzingsRSyaJBlPk0ikSSYyJBPp/CORyBCLJAkH44SCCeLRD7716C6yUF7toqLaSXmVE0+J\nDa322Pbtimcy9EaT9MQS9ESTdEUT9MQSpA5q+io2GRjntFJqNlBiNlKy79mqPxAIpaO/EEKIj60t\n1MHrHWtY17uJVDaFXqtnXsUczqw+nbGuutEu3jEzGIzzzg4vW5oH2NHuJ7VvigOTUcesiSVMH1vE\nhBo3VSXWw67TuF8mFiO6+T3CmzYQ2fwe2VjutpqhvALnGQtwzj8DQ3Hxcb+mY0EpRTrpIxFuyz0i\n7aQTA/ntelMxVncDVncDGkMZg94InU0hAv49+zrUJ4lFc53q47HU+2fgGJZOp8HuNFNSZsfuNOFw\nmrE7Tbn3yu2YLcd2QuFgMk1rKJYLYLEkPdEE/uTQzv9aDZSZjVRZTVRaTVTZzFRajZhH8RazhDIh\nhDjJhZMRNvdv442ut2gNtgFQbC7izOr5nF55KnZj4Xc0P1rZrOKl9R08/doekqlcEKsptdM4rojG\nccVMqHHl134cTsrnI7JpI+FNG4g2bc931teXlOBcsBDHqfMwjxtf0Os2ZjMJ0gkf6aSfdGKQZLSL\nRLiNTPrAupgarRGzYzwGay2JdD2Dg3p2vReir7uTQe/OQ6aMADCadFisRlxFVqw2AxarEaNZj8mk\nx2jSYzTpMJoO/Gy2GrBYDce1rpRSeOMptvnCbPdHaI8MXd7IrtcxwWmlwmKkwmqi3GKkzGLEcAz6\noB0smorhTwQIJIL4EwH8iSCBZO51MBHi3gtuPuyxEsqEEOIko5SiK9LDlv7tbBnYTkugDYVCg4bp\nxVM4q+YMGoomnVS3KAE6+yP89vnt7OkKYrcY+MI5E5g1sXTI2o+HozIZ4s17iOwbNZnY25rfZqob\ng33WbOynzMZYU1MwQUypLJlUMB+8kv4IQV8v6aSPdMJHNnNoR3mt3o7VPRWlq8Uf8NDfr6N3SxBv\nT4h0qvnAfjoNJeV2yiodlFY6KSqxYrEasdgM6PWFMVghqxTt4Tjb/BG2+cIMJHK3RrXAOIeFyS4b\nVbZcALMbjm3cUUrhTwToifTRHemlJ9pHT6SXnkgf4VTksMfpj9BHU0KZEEKcBOLpOLv9LWwdaGJz\n/3Z8CT+QW39yrGsM04unMKf8FEosRaNc0mMvncnywtq9/PmfraQzitMayrh88SSctuHnUUsNDhDd\nsoXI1s1Et23N35ZEp8MypWFfEJuFoTg3CapSimw6QireTyYVJpMOk01HyKQiZNKR/GuVTaLVmdHo\nTGh1JrQ6M1qdCY3OjFZrQqu3oNVZ0OktaPVWtLr9z7k5slQ2TSYTI5uOkk3HyGZiZNMxMuno+0JY\nLoihPmD6BY0WvdGD0VqF3lQEOhehkB2f30x/e5reriBBfww4ENqKSm2UVTr2PZwUldrQHUWL4miI\npTO81RdgbZ+fYCrXgmnUapjmsTPVbWOy2zak/9fHkVVZBuP+XOCK5gJYb6SPnlgfsdTQ1jgNGkos\nRdQ7a3Gb3biNLlwmJ26Tc9+zC6t++FHMEsqEEOIEE0qG6Qh10R7upD3USUeoC29sIL/+pEVvYU7Z\nTKaXNDC1eDJ2w8lze/JgrT1BHv1rEx3eMC67kSs/M5lZE0uH7KPSaZK9vSS7u3KPrk4S7e0ke7rz\n+xhKSnHMPx3btEYsk6egdClScS+x+G6CbWtJx72k4v0f2PqUp9Gh09vRGmyoTIJMIpIfvXh0NGi0\n+nxH++Fo9VaMlkr0Jg96oxu9yUNRSRXdPeD3aej15hbJHvRGCPhiwGD+WJNZT934IsqrnFRUOymr\ndGI0FX4c8CdSvNnrZ503QDKrMOm0zC1xMtVjY7zT+pFuRWayGUKpcP5WY+45yGDcR0+0j95IH8mD\nvg+tRkulo4xSdymVtjIqbOVUWMsos5Zi1H28vnGF/y0IIcQnWDydoC3UTkugjZZgG+2hTvyJobPC\nW/QWJrrHMcZZy7TiKYxzjUGnLYxbTB+VymRIdneRDgRAZVGZLGQzqEwWlc2QSaVZ39TL9j1eKrIZ\nFlbYmFHtRL+5B++GFNl0mozfnwthfb1w0ISeGpMJ6/QZ2PbNIaYtdpIItxAP7iGw51UyqYNH1mnQ\nm4ow2eswmEvQGZzoDHa0ehs6vQ2dwYZGe+gC1EplUZkE2UycbP45nmsFy+Ravw60hkXJZtNodeaD\nWtEs6Pa9Vlo78biFUDCDzx8j2BEnuG+x7FBg55BpJSAXwKrq3PsWybZTXu3CXXT40aWFqDua4PVu\nH+8NhsgCToOOc6s8nFrmHLZTvlKKSCrKYNzHYMKPL+7PvY7nXvsTAYLJUP6PmYMZtHrKrWVU2Mqo\nsJbnA1ippZiKcreMvhRCiJOZUgpvbICWwF5agm20BPbSGe4e8p+Gy+hgWvEUah3V1NqrqHFUU2z2\nnFD/yR5MKUWqr5d4awvxlhbirS0k2vaiksO3MtXtewDQD8Eth+6jtVoxjx2HsaoKU2U1xqpKjJXV\n6NxuUvEe4sHdDAafJ9ndCfvqWau3YnE3YDCXHniYitB8hDnbNBotGn0uWH0Y+5cS8vVFDiyWPdBP\nONj5gfsbjDqKS2w4PRaKymwUl9ooLrVjtRtP2N+NllCMV7sG2RXMTWFSZjGyoMzJOIeWRCZGe3Av\noVSYUDJMKBkimAwTToYJ7vs5kAyROkyro16jw21yMc41Brcpd5tx/y1Gl9GJx+ymyOwe8X6XEsqE\nEGIUJTNJdvh27+uU3zSkFUyv1TPWNYaxrjrGOcdQ76rDbTo2M6iPNpVOE1r3NsE1bxJvbSEbPTB3\nGFotxqrq3KLaxcW5WfB1OhQatuz1s7nFTwaYUl/M3OlVGM0mNAYDWoMBjd6AxqBHYzCgs9vROV1D\nQkkmHSPsfZvQtnfIpvd3yNZgstVgdo7H7JyA0VI5okEmEU/R3RGguz1AX3fosEsJ2exGqurcQxbJ\n3v9sthgoK3OOyNqXx4NSinAqQn9sgN2BXtb1ddIXHUCpCAZtEr0mSVs0xq6+I98O1mq0OAw2Kqyl\nFJk9eMzufSHLQ5HZjcfkwWG0jUjgUkrhDyfp9IZp94bp8kb4wVfnHXZ/CWVCCDHCBuM+tvQ3sWVg\nOzt9u0llc/Mn2fRWZpfNYJyrnrGuOmrsVUccrXWiycZjBF57Dd9LL5IezPVzMpSXY2ucgbl+LOb6\ncZjq6tCaho6Y7PCG+c1fttE2aKSotparL2hgav3RD1pIJ4OE+tYSHliPyqbQ6izYimdhcYzH7Bj7\noVuyPo5wMJ4PYd0dAQa9Q0frvX8poaISK+7i3ILZJvOJ/buglCKYDNMX9dIX8+KNDtAX9eKNDdAf\nHySZ+eDApVUGzAYbLksJNoN138OGzWDFYbDjMNpwGB04jHYcRjtWvWVURhZnlaKrP0JzV5D2vjCd\n3jAd3gjh2JH7CO53Yn/DQghxAggkguz2t7An0Mou3x66Ij35bVW2CqaXNDC9uIF6Z+0J3xfscNIB\nP/6XX8L/6j/IRqNojEbcn1qM57zPYCgtPexx2azixbfbeOb1ZtIZxcLGSr547kSsRxlQUolBgr3/\nJDL4LqgMOoMDR+U52Itno9UNPzrzWIlFk3Tu9dPR6qOj1UcocGDUnl6vparOTWWti8oaF+VVJ0an\n+8NJZJL5vlu+uJ/BhJ/+WC589UX7iWcShxxj1Bkx6lwojRWNxonHXMT88hpmFFdSZPZ87M7zx0s6\nk6W1J8SuDj+72gPs6vATiR+YoFYDlHosTKp1U1Nqo6rYhvUIs+2euN+8EEIUIKUUfVEvewKtuSDm\nb6E/fmDkm0GrZ2rxZBqLG5hW3ECxxTOKpT3+kj097H7id/T941VUOo3O4aD40mW4zzkXnd0+7LGd\n3jCP/W0HuzsDOG1GvnL+FE6ZWHJ0nxvrI9jzOlH/NkChNxXhLDsDW9GMj9Q37MNIpTL0dARob/HR\n2eqjv+/AZK0ms576icVU1uSCWEm5vWCnnthPKUUsHSeUDBFKRQgmQ/l+XKF9fbh8iVwQi6SiH3gO\nvVZPmaWEMmsJpZYSyqyleMzF7A7qWdefIK2g1mTgvOpiphfZ0RZgP7h0JktzV5BtrYO09ITZsXeQ\nZPrAAJISl5kZ40uYWOOirtxBdYmNTCpDW/Mge3cPsPmdTpKJDOcsmnjYz5BQJoQQH1FWZemLemkP\nddEe6qQ93EVnqItI+sB/TBa9henFUxjvHssE91hqHTUn5SLfB4u3tjL4wl8Ib1gPSmEoK8fz6c/g\nPGMhWuMHt1Alkhma2nxsaR5kc8sAfb7c9BP/P3tvFhxLetZ9/jJr3/dFUmmXjo509q23Y+i2jY0B\n8xEQzHjsCG6YgODCXIwHMAQBhogJ4MIENwbmgoiZL3xBgGH4wKYZGnvcbbvtts/eOrv2raTat6wl\nK7e5yDolqc/SZz86p+sXkZGpUiqr9FZV5j+f93n+z6n9cX7tZ6fw3kcrHk1tUNl8Gyl/DjCwuRL4\nEx8z2wY9oSktuaWytVFhqzMlmdmsomudogGLwMBwkNRIiNRIiGjC99h7Oj4qDaVBup6h3CpTblep\nyNVdFhGVdrU7xX43bKKNsDPEkC9FyLEjf8sZJNLJ67o1pWgYBu8XJf51NUdVkfHZLHyiP8zJaADL\nHhobwzDYyNe5ulzi6nKRG6tl5I4vmiDAQNTLvsEA+waDTKaChHwODMOgkK2zslDg4rfnyaSr3eP5\nAk6mDibv+Zwv/pmhR48ePR4Tmq5xozTPbP4aa7V1NqTN2zyMYq4I+8OTTARHGQ+O0udJvHDO+XfD\nMAyaN65T/I9/p3HFLIV0DI8w8rlfRZ+YQfiAj5Si6mSKDS4vFbm8VODmWhm1I2acdgvH98V49UCS\nE1N3n97cfm4NKXeW8tY7GFoLqyNCcOBncPn3Pfak/XpN3pETVqaQ3Z0TFk14uyIsmQpgs+2NKWnD\nMKi0q11vuzXJvJkotkp33F9AwG/30udJ4rf78Nu93dwtfyd/69bPHqv7vsZ5syHzzdUcy7UmVkHg\n4/1hXk+GsO+RaKHUVJhdLDC7WODacolKfTvPrS/iZmY4zMxoiNeODdKUtqehNU3n5pUMl36yRj5j\nRkYFAfoHAwxNRBgZjxCMfPgY9URZjx49etwD3dCZKy1yLnuJi7nZ7vSMKIj0eRKkvP2mPYVvgAFv\nHy6r8xm/4qePoevU379E8c1v0VpcAMC1fxrPJz/DZjDFGQ3WfrBMWZIpSTLlWpuyJN+WAD2U8HJo\nLMLB0TDjA/fXoxKgWZ2ntPEWaiuPYHEQHPg0vtgpBOHRxZDcUshu1jpLldxmjbq0faG2WAQzH6yb\nExbYMwn5iqawUltnoZPPuFpdp6ZIu/bx2jxMh/cx4DXzt3a6z/ts3seW49hUNb69UeC9bAUDmA56\n+IXBGGHns80XuxUNuzSf59JCgYWNSrfJut9j55UDCQ6MhJkeDhH2b3+3vS4bTalFq6lw9WKay+c2\nqEttBAFG90UZ3x9jaCyM4wH/v73xyenRo0ePPYRu6CxWVjifvcT57PvU2uaFzGf38nrqNY7FDjPi\nH2TdrOAAACAASURBVMS2RxOQnxaGrlM78xOKb36L9sY6AK3RaW4On+BSy0f6PwsYFG77O5fDQtDr\nYDDuJeJ3MjUU5OBomID3w3tU7kRpFShtvEWrOgcIeKMnCCTfwPKQHQx03aCQldhcr5BJmwKsUtrt\n4O/22hmZiJBMmSIslvRhse6NKE+93eBy/hoLlWUWykus1NZRd0w7hp0hjgQPdvztzJuJgN3/RO0/\ndMPgfL7Kf64XqKsaEYeNzw7FmAo+uy4TqqZzbaVkCrH5AoWqGfESBBgfCHBkPMLh8SipmOeuY1PI\nSXzvrZvcmN1CVXRsdguHT6Y4dHIAf/DhK3l7oqxHjx49MKvGrhfnuJy/xpXCNSpt0+/JY3Pzsf6X\nOZE4wkRw7CMzFXkvDFWl+t6PyH7rmxj5LLogcDMwzg/8M+QtIVgHu63J1FCQ8YEAU6MRLIZByOcg\n6LXjtD/8pccwdFq1RaTCBZrlG4COwztCKPWz2F2JBzqWqmhkN2udqcgyWxvVXY74DqeV1Eio2xMy\n1ufHex/NzZ8GklI3pyBrnVZbUppsI981GhYQSPn6GQ+MMB4cZTwwSsDhe2qvzzAMrpfrfCddJN2Q\nsYsCP5uKcDoRxPoQ7ZAelbaicWWpyNkbOS7O52nKplh1Oay8NB3nyHiUg2NhfO57V+RmN6uc++EK\ny/MFMMDnd3Dop1LsP9z3WCKkPVHWo0ePjyyFZpHZwjUu568xV17sRhW8Ng+v9J3kRPwIU6GJF9am\n4kFR5DZz//5faO/8F456GQ2RWf8kPwodxBGPM9kf4DMDfsb7A6TiHiydi28s5ntkU1NVLiEVLlIv\nXkJTzORpmzNOoO91XIH99xXtaTWVXQn52a1aNyEfIBh2mRGwwSDJAT+B0LNpR6QbOg2l2XGrr3Uc\n6iWq7Rpb9SxrtY1uw/lbuKxOZuKTDLoHmQiMMhIYeiZT6YZhcL1S5zsbphgTgKNhHz87GCXwCGL8\nYZDbGrOLBc7eyHJpoYDcEdxhv4OPHerj2GSUidT9TZNvrlc49+4ya0tm/t3AUJADx/sZ3RdFfIwi\nsyfKevTo8ZGhrjSYLy8yV1pk/uwCa9XthtQD3j4ORaY5GJ1m2D/Yi4h1aMoq56+myb/9Nqkb7+FT\nGyCIXAztp3b0p9h3aJQ/GYs88NTjvTAMAwwNXZNp1RaQCheQpRUABNGBN3oCT/godnf/PUVTrdIx\naV03hdhOk1ZBgGjCR1/KzAdLpgK4PY/uW6bpGmW5Sl2tU1caO5ZbPzdp621UXUXRVVRdRdUVVF1D\n0RVkrY2k1NEN/a7P4bf7mIlMMeQdIOUbYNDXT8QZfqaO/oZhcKPS4DsbBTY6YuxQ2Msn+sMkXE8v\nuigrGu8vFPjJtQyzC4WuZUUs6OTksQFOTMUZ7fPdl9g2DIP0apmz766QXjVFcP9QkJOnhzlyYpB8\nXvqQIzw4PVHWo0ePF5aG0mC+vMTN8gJzpcVdfSRtFhsHI9McjO7nYGSakDP4jF/t3kHVdGYXCrx3\nOY12/j1ezV1kWmuiila2pl4i8pmf55enh7DdI5fKMAxUuUC7kUarK9SqNQxdwdAVdK2Nod9aFHRd\nMbe1dnebDzSJdniH8UaO4QpOI4p3zuW7X5PW/sEA8b5HM2lVdJVsI8dWPcNmPctWPcNWI0u2kUcz\ntA8/QAcBAZtoxSpasYlW7BY7w87wjurG7QpHn81L3B0l4PA/9Ot+3MiazmKtwXfTRdbrpjHswZCX\nTw48PTGmqBqzi0V+ci3DpflC17YiGXZzcn+ck1MxBuPe+456GobB2lKJcz9cZmvdjMoOjoY4cXqE\nvpTZ5uxJRVB7oqxHjx4vDBW5xkLFNGxdKC+xvkOEWUUrE8FR9oXGmQyOc3J8hkqx9SFH/OigGwZz\na2Xeu5rh7LUMg/kFfrp4gbBSQ7fasL/xKUZ/8bPMBG7vvWkYBppSoV1PIzfStBtp2o1NDP129/Y7\nIYh2BNGOaLFjtXkQRTuCaEMQ7dicMTyRI9gct7dUUhWNzfVKV4TdsiIAsDssjExE6Bt8eJNW3dCp\nyFWyjTzZpulIf8uZPtcs7GoUD+Cw2En5+om5Ing7bYA83XVnsXpwWO1YBVOEPS9T4w1VI9dsk221\nyTY7S6tNpb1dSHAwZEbGku4nL8YUVePqcokz17NcmMvRlE0hFg+6ODUd56XpxD0T9e+E3FK4cTnD\n1YtpSnmzynp4IsKJ14ZJ9D8dIdwTZT169HguMQyDbDPPQtmsNJuvLJFvblf6WUUr48ER9gXHmQyN\nM+of2lUtabZu6YmyRkvhO+c3eOfiBsWqzEgjzedKF4k38yCKBN74BJHP/jeswd2RRF2TaVUXaFRu\n0Kot7mjubWJ1RLG7p3B4+glH+6hJakd82RAt9u62IFjv+8KpqTqZdJWN1TLp1TKZjQraLZNWUaB/\naNukNZb0fmiuj27o1Np1SnKJYqu8qz1QvlUk18jf5kMHZvHHWGCYpCdB0hOnz22ug47AM8lBu19a\nmkZRVim2FEqyQnHHUmmr3L0BkIF2h1/6bRYm/C7iLgcnon76nrAYK9VkLi3keX++wNWVIm3FnJqM\n+B28fnSAl6bjDCfub2ryFoZhsLVR5erFNAvXc2iqjigKTEzHOPryELHk0yuOgJ4o69Gjx3NCWa6w\nUl1ntbrGSm2d1er6B5zznRyI7GciYJq2Dvk/Gs75D0tFknnr7BrfPb9Bq60xrBX5n6VZgjkzd8v3\n0itEfumXsSe2Kxo1RaJZudERYkvQmaaz2Hy4gtM43P3YO4to2b5AB2M+FOHBc500VSezWSW9WmZj\npUwmXUXb2dYm7mVgJEhqJExfKoDNfveok6y1WattsFJdY6W6xpq0QbFV3mUZsRO7xU7CHSPmNlsC\nxTutgeJusyn2XscwDIqywlKtyWK1yVKtSUW58//qsVpIuOz3bG3ktorEXXZiTjtxl524047T+mSj\nfLpusLRV5f35ApcW8qzuiIT2RdwcGY9yfCrGeP+D23rcKSoWCLmYPtrH1MHkY8kvfBh6Z6wePXrs\nKTRdI9fMd/N0VjsX0kq7umu/qCvCdGRft+T/o+Sc/yjkyk3+3x+v8v33N/E1y7yspDmqb+HKrALg\nPniI6K/8Ks6hYQDUdpVG6TKN8jXajY3ucWzOBK7gPtyB/dhcyUeOEOm6QalQJ9c1aq1RyEro+naI\nJhL30D8UZGAoSN9gEOc92i5lGjluFOdZqa2xWl1ns57ZNd3osrro9yR3tQMKO0OEHea21/ZgU197\ngVxD5myuYgqxWnPX1KLHamFfwE3YYdu1hBw2HHvITX8xXWVho8JCusJiukqrUzFptQgcGA1zeDzC\nkfEI8dCDC+NKqcHyfIGV+QKbaxV03ehGxaaP9DMwHHzm73lPlPXo0eOZcGv6ca22sZ0s3ciSbeRu\nqzwL2P0cjh5g2J9i2DfIkD/1XEQr9hIbOYk3f7TMyoWrTEir/K/NdUKtjq2CIODaP03ks/8N9/5p\ndF2hXpylXrxkRsQwAAGHdxhXYAp3YAqr49EaqSuK1o2AZTer5LZqqMr2+y6KApG4l+SAn/6hIP1D\n9xZhABW5yrnMRc5kLrBa2xaQdtHGWGCYYf8gw74UQ/5BYq7IM78APyqqbrAiNblernOjUiff2p5q\ndVstHAx5GfW5GPO7iDvte+r/VVSN9VydlUzNFGEbVbaKu5uZJ8NuJlMBDo9HmRkJ4XrAwgxN09la\nr7AyX2B5oUCluG0EHEv6GN8fY+rQs4uK3YmeKOvRo8dToaW2WK6usVRZZbm6wlJ1tduy6BZOi5Nh\nX4qEJ06fJ0HSHSfl6yfouD25/KOKoapodQlNqnfWErokodUl9FYLQ9PA0DE0HUPTKFaarGeqSJU6\nx5tbvK6ZFybBZsd99BjeY8fxHD6CxetDllYorPwbjfLVTgUk2N0DeCJHcAdnsFgfXggbhkG50GB1\nscjaUpH0armbDyYIEIp6iCV9XaPWSMx7X075TbXJxdwVzm5d4EZpHgMDURA5ENnPkdgBRvxDJN3x\n5yah/sOQFJWblQbXy3Xmqg1kzRSydlHgaCJAymFnzOci/iHTkU+TpqyymqmxmpFYydRYzdTYLDTQ\ndkRBnXYL08MhxgcCTAz4GesP3FcD+g+i6zrryyXmrmRZns/T7hQAWG0io5NRhiciDI2H8TxGC5fH\nSU+U9ejR44lQkavMlReZKy2wVF0lLW3tmj6KOMNMh/cx7B+k35Mk6Yk/8ZYvTxpD0zAUBcMwEEQR\nwWIBUbytEfeHoTWbKJkM7cwWSmaLdmaLdiaDks2gNxoffoAdWIDhW6/P5cZ37GP4jh3HPXMA0eFA\nUySkwgWk1QtobTNyZrEF8MRewhM+gs0ZeaDn24muG6wtFfnx24vcvJpBqm5XY0biHobGwqRGwiT6\nfdgewFhU0zWuFm/w463zzOavdvPCRv3DnEoe43j8MD6796Ff915D1nTeL9Y4n6+yKrW636KQw8rx\niJ/9QTejPhd9icAz8ynrvlZFYzVTY2mzxvJmlaXNKpkPtKqy20RG+nwMJXwMJ3yM9fnpj3oQxYf7\n7huGQXazxtyVDPPXsjQbZsTQ53cwdTDJ8ESE/sHgnmmHdS96oqxHjx6PhWq7xlxpgZsdc9ZMI9v9\nnU20MRYYYSwwzGhgiBH/8FNt+fKoGJqGvL5Ga2Ge5uIC8toautzCUFRThKkKhqLQ7WT8QQRhW5yJ\nFgSLiCBaQBR2CDdzrTcbaNXq7YewWrHF41iGhrF4vVg83u5a9HiweL20sHJ+ocjZm3lqsgaihQNj\nUV470s9gIoA1HEawWDAMA7m+ipQ+S6NyDQwdQbThCR/BEz6MwzvySOJYbilcf3+Ly+c3qJbNCleH\n08rEdIzB0TCDo2E8D9iuyDAM1qU0P948x5nMBSTFrPZMuOOcShzjVPIoUdfDC8i9hmEYrEgtzuYr\nzBYlFN1AAIZ9LqYDHqaCHmJO2zO9iVE1nY1cnaWO+FrarJHO19F3fA/cDivTwyGGEz6GEl6Gkz4S\nIfdDC7CdlIsNbl7JMHcl0/2cOV02Dh7vZ/JAgsRDFAA8a3qirEePHg+EbugUmiW2Ghk26xm26llW\nqmts7RBhdoudmfAU+0LjTATHGPINPFfTR5ok0Zy7SXNhntbiAq3lJYx2u/t7weHE4nYjOhwIXi+C\n1YpgsyHabAhWKwgChm6ArmHoOmidtW5OKZrbGobWeUzXTGGn64h2B44DB7EnktiSSeyJJPZEAms4\ncteI20ZO4r/OrvHDyxlUTcftiPL6K/188niKsH+71Y6uyUi5C0j5sygt8/2yOWN4o6fwhA/tqph8\nGEr5OrPnNrhx2WzSbLWKTB/p45WfHsPusj7UhbgiVzmTucCPN8+Rrm8BZhusN1KneTl5gkHfwHN3\n4b0X1bbKhUKVc/lqN0csZLdyIubneMRP0PHgU3qPA90w2Co0WNqssrxZY2mrympGQtW28wDtVpGx\nAT+jST+jfT5G+/zEH3OrKl03WF0oMHtug/Vls+WR1SYyORNn8kCC1Ejogf3o9hI9UdajR49dGIZB\nXW1QkatU5CpluUpZLpNp5NisZ8g2cigfsBGwW+xMh/d1PcGeNxEGoJbLSBfOUTt3lubNG6B3LjaC\ngL1/ANf4OM6xCVzj49gSyQeeknzcGIbBleUib/1kjctLRQDiIRefOjnI6UPJXU2/240tpMJ56sX3\nO7liIu7gAbyxkzg8Q4900dR1g9XFArNnty+SXr+Dg6cHmD7Sh9Nle+Del7lGgcudnqS38sQsgoWj\nsYO8lDzBgcgU1hfI7kTTDW5U6pzNV7lZrqMDVkHgSNjHyZifUZ/rqeaHabrOZqGxnQe2VWM1W+sa\ntAJYRIGBmIfRPj+jfX5Gkj4GYtv9Th83d4q+9g8GmD7az+hk5IGmv/cyz/1/8c9X3qTfnmLYP9jz\nJOrR4wHQdI2N+mbX+yvTyFGWq1Ta1bt7N4k20zDTnaDPEyfpiZP0JIg6w8+dCANQCgWk82eRzp+j\nOT/XnX50jo3jOXQY5/gEztExLC7XM36l2yiqxo+uZPivM2ts5M0pvKnBIJ9+aZAj49FuNErX2jTK\nV5Dy57tWFhabH2/iNbyR41hsj5ZzVcrXuXF5i5tXMtRrZhSxfyjIoRMDjExGHqhJs6ZrLFaWO83h\nr++a+h72D/Jy8gQnEkfw2jyP9Jr3Gtlmm3P5CufzNeqqKXj63Q5OxvwcCftwPWEfMMMwqNTbZIqN\nrghbyUis5ySUHX5wApCMuDk6sR0BG0p4sT3h1wed6Ov5DW7MmtFXSyf6eujEAJH4i5M3eIvnXsX8\nw+VvArdyVoa7LVSG/akX6k6qR49HwTAMso0cy9U1VmprrFTXWZfSu8SXgIDf7qXfkyTg8BN0BAg6\n/ATs5nbcHSXkDD73XmDtbBbp3FnS719AmpszHxQEXJP78B4/iff4CWzh21v6PGsq9TbfPb/Ody9s\nUGsoWESBVw8k+PSpIYZ3uI5vR8Vmu22OnP5JvNHjuPyTCI/w/rWaCnNXM9yYzZDbMiNfdoeFmaN9\nHDx+/xdJRVNYlzZZqa2xUF7iWvEmTdWMfthFG4eiMxyKTHMguv+Fq7yVNZ3ZYo2zuSqrdfN/dllE\nXo0HORl7Mq74jZZCptRkq9ggU2x01k22Sg3k9u4+nbciYLeS8IcTPlJxz67I65NG13VW5gtcPp++\na/T1RUUwjLtlpj4f/GT9ImdXLnOztNDNNwDziz0aGCbmjhJ2mKaApjlgkIDdf8e7et3QkbU2ba2N\nrMk01RZNtUWrs25qLZpKk6bWQtU1REHEIogIgoBFsCAKYvcxq2jt9jaz7ljMny2Inf3v9Pd20Ybd\nYsdhse8ZYfmg0w89Hp7HNdaGYbBaW+dCdpaLuVlyO1oQiYLIgLePYZ8ZZR72D75QtgEfpL2Zpnbu\nLNK5s8hrpkkqooh7ahrviRN4jx3HGtibDcnXcxJvnVnjvStmvpjHaeX1owN88kSKUCdZ3tA16qXL\nSPmzO6JiPjyRY3gjx7DaH17YtGWVtaUSc1cyrCwU0HUDQYDBsTBTB5OMTEaw3iNioukabUedCyvX\nTTf92job0uYuL7qwM9RpDj/NvuDYrnZYLwrr9RZnchUuFWq0O0n7E343J2J+ZoIerI847aeoOgoC\n1xZyu0RXptig1ri9VZTNKpIIuUiE3SRCbhJhF0NxcwrS+oxyshqSzLVLm1y5uEm9Zt5Q9A0GOHQi\nxei+B4u+Pmke5Twdi929yGlvXPEfgZdSRxl1jAMgtetmCX55gZulBW6U5rlRmr/tbwQEgo4ADovd\nFGF6G1lr33XK5lkiCiIOiwNHR6Q5LU4CDn8nkrEdxbj1s8v6eJMqezxf6IbOUmWVi7lZLmRnKcmm\nxYHdYudY7BAToTGGfYOkvH0v5IXvFoaqIq+vIV26iHTuDO102vyFxYL74GF8J08y/Mmfoizvze+K\nYRhcXiry1pk1ruzIF/v0qUFOH+zD0WknpKkNpPw5pNwZNFUChEeOihmGQSErmX5ii0W2NqpdV/1I\nzMPUoSSTM3Hc9/B5aqktrhZv8n7uCpcL12mq25YIVtHKkC/VNQIe9g+ScMdeyPOWrOlcLNQ4k6uQ\nbpgiI2i38lNRPyeiD5e0bxgGxarMek7qLHXWsxJbxd2+XwCiIBANOhnt83eFVyLsJhlyE/I79oSP\nmWEYbK5VuHJhg8UbeXTdwGa3cOB4PweO9ROJvXhTlPfiuRdlO/HaPRyLH+JY/BAATbXVbTBbbJUp\nydvNZoutMjVFwi7a8dl9REVT9NyKUNktdlxWZ2dx4by1bTHXVtGKgYFmaOiGjm4YnbWGpuuohoqi\nq6idRfnA2jB09Dv8vaZrKLrSida1d4nGhtqk0CqxUlu76xhYBQs+uw+f3dNZe/HZvPjtXgIOP3F3\nnIQ71mnG3ON55k5VkNeLN6m0zbs3p8XJqcRxjsUPMh2eemHfc0PXaW9tIS8v0VpepLW8hLy6iqGa\nN1mC1Yrn6DF8J07hOXIEi9vMS7L5fbDHor9tReO9qxneOrNGupMvtn8oyKdPDXF4ItK9iCqtPLXs\nj6kXL2EYKoLowBd/BV/sJaz2B4v4GYaBVJXZ2qiwtlhkbalEo75daRrv8zE4FmZsX5Ro4u53+BW5\nxmz+CpfyV7hZnEft9MUMOYK8MniMhD3JsD9Fvye5Z2YAnhQfjIqJwEzQw6lYgMmA+77EUFvRyJab\nt003buTqNOXdAQSH3cJIn4+JwRABl41k2BRgsaDrmUW9PoxKqcn8tSxzVzPd3pOhqJuDxwfYdyCB\n/QHd+18U7uu/vnTpEl/96lf5+te/zurqKr//+7+PKIpMTk7yla98BYB//Md/5B/+4R+w2Wz81m/9\nFm+88QayLPO7v/u7FAoFvF4vf/EXf0EoFOLixYv82Z/9GVarlddee40vfvGLAHzta1/jnXfewWq1\n8gd/8AccPnz4kf45l9WJy5uk35t8pOPsJQzDoKE2KcuVbmVcRa5SbleoyBWqbYlaW2Kz0zPwTggI\nRFxhku6Oa3onYbvPk8Rh2TvtJl5kdEOnqbaoK3XqSmN7URs48hbqdfmOf6foKtl7VEF6rG5e7TvF\n0dhBpsKTL0zxi6GqqOUSSrGIWiigFPKoxSLtzBbyyjJ6q7W9s8WCYyCFc3QU99Q0nsOHEZ17J1H/\nTmRLDd6+mOb7l9LUW2onXyzJp08NdvPFDMOgVVukmv0xraqZC2exB/HFXsIbOXZfdha6blApNchn\npB1LjVZz+3Pk8tjYdzDRMXYN4XLf/Zwga23ObJ3nvc2zLFfXuubAA94+jkQPcDh2gJS3n3jc/8Kn\nPzRUjYuFGufyVTZ3RMVejwU4EfXjv0NOVqOlkis3zaXSJFduke1MORarMh/MLRIFgUTYxYHRMIMx\nD6mYl1TcSyTgRBSEPZ9mUqu0WLieZf5arpuTaPaejHPgeD99qcALGTF9ED70jP13f/d3/Ou//ise\nj3l3+ed//ud86Utf4uTJk3zlK1/h29/+NkePHuXrX/86//Iv/0Kr1eLzn/88p0+f5u///u/Zt28f\nX/ziF3nzzTf5m7/5G/7wD/+QP/mTP+FrX/saqVSK3/zN3+T69evous7Zs2f5xje+webmJr/927/N\nP/3TPz3xAXjeEAQBj82Nx+ZmwNt31/0Mw6ClydQ6Iq3WrlGSK2w1zCbPW/WsWXJeuLZ9bATi7hiD\nvn5S3n4GfQMM+gZ6PQYfEN3QqbZr3YhssVXqRGnLlFplynKFutLY5W7/oNh2VEGagjpO0h0n6oo8\nd3lhequFWi6jlkuopdL2drmzXSyilkt3NmYVBOzJPpwjozhGR8314CCibe/fXOi6wfuLBb57foPL\niwUMwOe28QuvDvOJ4zvzxVTqpSvUsu+htDIA2D0p/PFXcQWm7jlFWa/JbG1UyaQrZNJV8hlpV39J\nAH/QSf9QiHifj9RIiGjC+6EXxkKzyDsbP+RH6TM01CaiIDIRHOVI7CCHojNEXXuvUOJJoBsGC9UG\nZ/NVrpbqaIaBKJhRsZfiASb8bnTdIFNscKPQYDNfJ12oky2ZQqzeunPKTNBrZ2oo2M33eh4iX3dD\nqrZYvJln4VqWrQ3TFFkQYHA0xMR0nNF9URzOFzOK/zB8qCgbHh7mr//6r/m93/s9AK5cucLJkycB\n+Omf/mneffddRFHkxIkTWK1WvF4vIyMjXL9+nXPnzvEbv/Eb3X3/9m//FkmSUBSFVCoFwMc+9jHe\nffdd7HY7p0+fBqCvrw9d1ymVSoRCj9b09qOKIAjd6de4O3rHfaR2na1Gls26Of2VljZZq6XJNLKc\nzVzs7hdyBBmLDOISPN2p0O7UqN382WlxvrB3OKqu7igAMdcNtbnt49WuUtkZuWxXb2uofQu7xU7I\nESDhjuGxeboC22N1d7ejYT+VSvOOfy8KInHX810Faeg68soy0sULSBfO007fOaILgChiDYVwTUxi\njUSwhSPmOhLBGo5gi0QRHXuzh93dqNbbfP/9NG9fSFOomhG+iVSATxwb4MRUHFunFYyZL3aWWu4s\neidfzB08gC/+Mg5P6rbjappOPiOxtVEhs1Elk67uamt0q79kNOElmvASS/iIxL04nPcXTTUMg7ny\nAm+vvcv7+asYGPhsXn5u5Gf42MDLL1yV5N0wDIOirHChYLY9KrdNYRVz2ph0OfHKOsXNJm/NFvi/\n86YA0z9wQ2G1CEQDLsYHAsQCLmJBJ7GgKbqiQedTrXR83Oi6QXazyupCkZX5AvmsBJifv/6hIJMz\nphC7VwT2o8yHvvOf+tSn2NjYPmnuLNb0eDxIkkS9Xsfn2841cLvd3ce9Xm9331qttuuxW4+vra3h\ndDoJBoO3HaMnyp4cXruHCfsoE8HR7mO38pTWpA3Wa2nWahusSRucS8/e81iiIOKyOLdz7zq5eLe2\n3R8QHuZiihKbaL1j3t2t7ZbWqX5Vm7sqYhtqs1ugcbfcPUEQdlXF7twGoZPPt71o3W2Ntq4ga+27\nCqw7jUHA7mfYlyLYqfQNO25V/YYIO4O476MQIxbzkbPu3SmIh8FQVRo3riNdOE/90gXUklnmLlit\nuKdnsEaiWINBrKEQ1kBnHQxh8fmeuUnro6LpOkubNa4uFbm6XGQhXUXTDRw2C28c7eeNYwMM7cjV\nUlq5Tr7Y+zvyxV7t5IvtFj5mZWSRpbk8K/NF2jtyjZwuG8MTEZIDfhL9fuJ9fmz2B4+iKprCTzLn\neXvt3W6F+5AvxRup0xxPHHlhpsjvhN4RYOmGzGZdJt0wl1ueYhYDvE2NZrrOtfUqs9pu8eV2WBnr\n99MXcdMf9dAX8dAfcRPuTDe+KMgtlfVlU4StLBZpdao9RYvA4GiI4YkI41OxexaH9DB54G/TzpLU\ner2O3+/H6/UiSdIdH6/X693HfD5fV8jt3DcQCGCz2br77tz/frhXeWmPBydBgBlGdj1WkyXKoSkN\nsQAAIABJREFUrSqVVpWKXKPSqnV+rlFpVZHaDRpKk4bSJN8q0FLvnBP1JLllOWKzWLGJNpx2BxhG\nR2iZBRS6rqF1xJdh6KYViWiKtFvbVsGCRbARsPhxWh04rHYcVgdOqwOnxdx22ZyEXQHCriAhV5Cw\nK4DP4X1s0asX5TPdWFtn/Z/+meJPzqJ1GmlbvV5iH3+DyMunCB498kyNWZ/EOBuGwXpW4uLNHJfm\ncswu5Gl0pqkEASZSQT5+YpBPnBzE0/FbMgwDqbRIZvkdKnkzpcDuChMf+hjRgVNYrNutkqRqi5tX\nM1y/vMXSzTxap82NP+Dk0PEBBkfDpIZDhCLuR4peS3Kdtxa+x3/MvU2lVcUiiJweOsnPTX6cycjo\nAx97L3ymW6pGviGTa7QpNNvImo6q6yi6Ya41A0XXUXWDckthrdagpe6+KTNkDbkiI+ebtDJNDN3A\nbrMw2h9gpM/PSJ+f4aSfwaSPkM/x1GcQnsY4G4ZBJl1l/nqWhRs51paK3Qpdr8/BsZeGmJyJM7Yv\n9kIn7D+JsX7g0ZqZmeHMmTOcOnWK733ve7zyyiscOnSIv/qrv6LdbiPLMouLi0xOTnLs2DHeeecd\nDh06xDvvvMPJkyfxer3Y7XbW1tZIpVL84Ac/4Itf/CIWi4WvfvWr/Pqv/zqbm5sYhrErcnYv9nJi\n44tCLOajVTVwij4SLuBDrqO6oXf93Rpqc3cyu9Kgrm4nuKu62vFvs3V93Xb6u90rAmd6udk63m/P\nKKKiQrsGhVr9w/e9D/Z6su79oBQLFP7tf1B99wdgGFijUYKvncZ79DiuyX0IFgs6UJRUkJ7N//oo\n46xqOoVqq5Ok3dpO1u78vLM6LhFy8fJ0gpmREFNDIbwdIdaQWtRrDZrl61SzP6TdMG07PpgvViwp\naJrMwvUcVy5ssLW+3aw8EvMwMhlldF90Vy6YZujk8xIPQ6FZ4rtr3+fdzZ/Q1tq4rE4+PfxxXk+9\nZk5RGjzwsZ/2Z7qt6cxXG2zUZYqy0l1uRbjuC8PAaGm0yjKKpKDW2ii1NgGnjdE+P4NjcQZf8ZKK\neYkFXbf19dRkhbx8uz/Yk+RJjnOrqbC2VLxjhW4s6WN4PMzwRIRY0tf9HFaqd07DeBHYMz5lX/7y\nl/mjP/ojFEVhfHycz3zmMwiCwK/92q/xhS98AcMw+NKXvoTdbufzn/88X/7yl/nCF76A3W7nL//y\nLwH40z/9U37nd34HXdc5ffp0t8ryxIkTfO5zn8MwDP74j//4vl7P0v/132m2VASLBUQRwWIxpztE\nEcFqxeL1YvEHsPr9WHz+F2I65HlAFETcNjdum5vIs34xPZ4amiRRfPNblP+/b2OoKvb+AaK/8qt4\njhx9bnMOGy2F1YzUbUGzmq2xmW/clicEZkPmWNDFwFiYmZEwMyMhooHb72B0XaFeuEQt+yPUtjmV\n6wrsx594FYdnsLuf3FK4enGT2XPr262MBgOM7IsyOhnFH3x8Uca1Wppvr77N+ez76IZO0BHgs6Of\n5nT/Szh3ROr2KpKicr1c51q5zlylgbrj/REFCNlt9LsdhB02wg4bQYcViwG5UpONrMRaRmJtq4Ys\nqxi6ga4aeOwWRvr8jKbCjCbNKNitAowXGaWtks/WyWdq5Lc6VbrZbSHuctvYdyDB4FiYwdF7V+j2\neDCee0f//+N/+xesuoJVb+9YFKyauW3XmjjUxvaiN7F73Fj8fuzJJO4DB/EcPIQt3JMO9+JFiN48\nLzyPY623WpS+/Ral//wP9GYTazhC5Jd+Gf+rr+3Zm6A7jbNhGGzk6lxeKrKQrrCaqZErt3bt47BZ\nSMU9JEPubnK2uTjxe+z3FJ+aIiHlz1HLn0FXGyBY8IaP4Iu/is25fQ6qlBq8f2aD67ObqIqOzW5h\n/6Ekh06mCIQenxBTNIWLucv8IP0e8+UlAPo9SX5m6HVOJo4+tkreJ/WZLrTaXC3XuVaSWJFa3Xrm\nuNPOTMjDmN9NxGEjYLciCgJSU2F+vcLcepmb62WWN2u7DFeTYTf7BgNMpoJMDASIh54vM+4HHee2\nrFKrtKiWm5SLza5FSrm4O8JlsYrEkj6GxsIMjYXvq0L3RWfPRMr2GrGEl2ZTpa3oSIqOfh852Taj\njV1t4kxX8SzP4vmXHxAM2IhPDRM4cgjX5ORzUVLfo8ezxlBVKt//HoVv/g+0ahWL10fsf/kCgdc/\njmh7Psrc6y2Fq8slZhcLXF4sUJa2p2W8LhsHRkIMJXydxUsi5L5tqupeGIaBXF9Dyp+lUb4Kho5g\nceJPfAxf7KVuY3BN09lYKXHlQprlObMlltfv4NDHzH5/j9M2YKue5d30j/nx5jnqqpnrNxWa4JND\nrzMT3renL7jVtsr7xRqXCjU2On5gAjDkdTIT9DId8hB1mufvQqXF9fkCc+sV5tbK3QbuYHp+DSe9\nTKaCnSWA3/Ninfd13aBek6mWm1TLLaqVzrrzc+sO7ZfsDgv9Q8FulW404SUUce+pFkcvMs99pAx2\n55SpqkZb1mjLKnJLpSHJ1Gtt6pJMvSZTl25tt3dVKt3CodbxKFWCXguxPh/9+/qJzoxj9X60Wj18\nkOcxevO88jyMtWEYSBfOk//nb6BkthAcDkKf/gyhT3/mmSbu3w+3omE301Xem91kIV3pWqB5XTYO\njoY5OBZm/1DokRK1dU2mXppFyp1FaWUBsDljeKMn8YSPIFrsqKrG+lKJxRs5luYK3XNSvM/H4VMp\nxqZiWB6TL5WiKVzIzfJu+sfdqJjP5uWVvpO81v/SXa1zHgeP+pluaRpXSnUuFaosVJsYgAhMBNwc\nDHnZH/TgtlrYzNe52RFgc+tlCjssQew2kfH+AJOpAPsGg4z1+59r64lbaKrejXSpbZ3NjXJXdNUq\nrW4C/k5EUcAXdOIPuvAHnfgD5jqa8OILvLj2Ro+TXqTsPrFaLVitFtz3ccfTaiqU8nVKhQaFbI3C\nWp5y0UnR6qGoweI6sF7C9tb3CRlVon6BZCpI38wQnuFBRNejVTf16PE80pyfI/eNf6C1MA+iSODj\nnyDy2V/CGti7PlWGYbCSqXH2eo5zN7JkSub0jCDAeH+Ag2NhDo1FGE74HigKdieUZo5a/qzZAklv\nAyLu4AG8sZM4PEOoqs7SXJHFmzlW5gsobTP53ONzMHUowcR0nES//7GcWxRN4XppjgvZWd7PX+32\noNwfmuT0wMscjs7s2ZZHiq5zs9LgUqHG9XK9myM25HFyJOJjJuAhX2hwc7HMD9dWmFsv7zJj9bps\nHJuMsm/QjIQNJbzPnfHqTlpNhXxGolxoUC42KJeaVIoNapXWHX2VnW4b0aT3NuHlD7rw+ByP/Dnv\n8WR47iNlf/R//pCZ4SDHJmNEAo8nGbUtq2QXt0hfXyOzUSUvCbTYFnmCoeNv5Uk0VugXi7h9TrOQ\nwO/H6g+Y61CoY3QZNYsLnnPx9jxEb14U9upYt7e2yP8/30A6fw4A7/ETRH/lV7En795Z4lmiGwaL\n6Spnr2c5fzNHvmLmhtltIofHIrxxcojhmBvPY5gWNAydZnWOWvYnyJIZhbLYfHijJ7D7DpPP6myu\nVdhcN531tY7Ngj/oZGwqxthUjHjf4zlPtLU2Vwo3uJib5XL+Gi2t0/LHEeBU4hin+18m5n66ObT3\n+5mWNZ0blTqXixI3K3XanShPzGnjSNhHn2hlY6PK1eUS11dLtNrb1ZTRgJPJVJB9g2YkLBl+Pm+a\nDcOcctzZCiuXqe0yAr6Fy2MjGHITCLsIht0MDodBND9XL7IVxV7gSUXKnntR9ov/+792t4cSXo5P\nxjg6GWUw/ngTEWuVFunra6Tnt8hmmxRlGyAgGDrh5iaJ2gIxaRWrcfuUqGC1bruRhyPYEwmco2M4\nR8cQnXu/qgn2rlB4EdlrY60UCxTf/Hcq33sbdB3n+ASxX/0crsnJZ/3SbkPXDebWy5y9nuP8XI5S\nzbyQOe0Wjk5EOTEV5+BYGIfN8ljGWVdbSMUL1HJn0NplDAN0yzhNdYZi0cvmeoVCVtoVyYjEPQxP\nRBjbF3tsCdNtTWE2f5Xz2fe5WrhOWzdzhSLOMEfjBzkWO8ywP/XMbGPuNdYtVeNauc7lkrSrajLi\nsDHuceKoa6yvVri2XNyV75cIuZgeCTM1aOaDhf3Px7n0gzQkmexmzVy2auS2arflerk8NqIJH9GE\nl3DUQzDsIhBy39aNYa+dO15keqLsLhQqTb7z3jLn5/JcXyl1K2miASdHJ6McHo8wNRjEZn28/QAb\nksz8tRxzVzNkN803xmIRGOpzMhzRiYlV9HIRtZDvNFHOo9U+8AYKAvaBFK7xcZxjE7jGJ7AlEnvy\n7q73ZX967JWxbm9tUvyPN6m+90PQNGyJBNFf+Z/wHj+xpz6jqqZzfbXEuRs5LtzMUe1c0DxOK0cn\no5ycijMzEu62L7rFo4yz3MiytXyO3PoKtZodqe6l0YpQq9lQdvSWFC0C8T4/fYMB+lIBkgP+x5aw\nrxs6N0rznNm6wKXc5W5ELO6Ocix2mKPxgwx6B/bEe7VzrDXDIF2Xmas2WKg2WJWa3DLCD1otBBRo\nZpusrZS7ohrMvqAzI2FmhkNM38VqZK+jtFUy6RrZzWpXiNVruyNgvoCz0wbLawqxpBfPfTrh75Vz\nx0eBnii7C6WtSzRkN1ZHmJYiMrtY4MJcjtnFAk3ZDG3brSL7h0McHDXzRh53mXO52GDuSoa5q1kq\nnVwVu8PC0FiYkckoQ2MRHE4reruNWiwipzdoLczTWlygtbyEoWzfFYleL87hERxDwziHhnEMDWGL\nxZ+5rUDvy/70eNZj3VpZpvjmt8xpSsPAlkgS/rlfwP/KqwjWvTElUqy2uLle5spSkYtz+W4ukc9t\n4/i+GCen4kwNBe+ZQ3Q/46xpOpVSk1K+QSkvkdvaopSrUatZ0PXdN3qiRSAYdhOOuonEvfSlAsT6\nfFgf4w2hYRis1tY5k7nAucwlqm3z9YccQU4lj3EycZR+T3JPCLFbGIaB7rbzk+UcC9UGi7UmLW1b\nuLo0UAstsstl5Nr2udDvtjE+YNpTzIyESMW9z11romaj3Z223lqvkNuq7Yqaujw24kk/8T4f8X4f\nsaTvkTy/nvW546NET5TdhXNv/W5322LzYXVEsDkiYAuxVPBwY8Pg6mqDdGHbaygWdHJwLMLBkTBT\nQyHc99mQ98MwDIPcVo25q1mWbuapdXJYRFFgYDjIyGSUkYkI3h1hdkNVkdfXaC7M01pYoLW4gJLP\n7Tqu6HTiGBzCMTSMY3AQWyyOLRbHGgw+NbHW+7I/PZ7FWBuGQfPmDYpvfovGlcsAOIaGCf/8Z83I\n2DO8KTAMg/WMxLXFAsurZdKZGnJTwYaADXBYRUIeB363DYdVRNN0dNVA1XREUcBms2C1idjsls62\nBZvdgs/vRKq10FQDTdPRVH3XWqrKVErN26rXLBYNn08lHA8QTSYJR72Eom78QecTsQ0wDIN1Kc3F\n7Cznc++TbeQB8FjdHEsc5lTiGGOB4T3ToL6haqzXW6zXZdbrLdZqTeo7RBiyRjPfpFVs0S7KGKqO\nKAgMJrxM9AcYH/AzNhAg9hxVARqGgVSVKRcbVIpN8lmJzbXyLr8vURSIJX30DQY6vUh9eB5zG6be\nefrp0RNldyGz8n3KhTSqXEBpFdGUyh33qzTtLJRiLOQjzOe8yKp5AhMFGEk4mRmJcGAszvhA4LFU\n6BiGQTFXZ+lmnqW5PPnMthtyLOklNRImNRIimfLfdietSRLy2iqt1RXk1VXktRXam5t8sMRGsFqx\nRqPYonFssRj2WAxLIIDF4zU7GXi8iF4vovPRT269L/vT42mOtWEYNK7MUvjmv5nVlIBraj/hn/8s\n7pkDz+SiWCw3mb28xepSkWq+gS5rD1QmLloELBYRi0VE1w1URbujLcCHYbcL+Hxt3M4CXm8dn08h\nPjBCYvg4dnf8gY/3IBiGwXJ1jYu5WS5mZ8m3igDYRBuHozOcSh5jOrzvmVdOKrpOui6zVm+xXm+x\nKrUot3fn1WotlXalTbvYol1sYdMhFfOSinlIxc02RcNJHw7b400xedwYhkGzoVApNigXm1RKt9bm\non2gR6bNbiE54DenrVMB4v1+bE/4f+ydp58ePVF2D3YOjK4rqHKxu2iKhKbW0dU6mlJHU+so7Sbr\nFS+LhSALhSAbFR+GYV587BadsYTB/pSbqeE4Y0PD2B6DkWyt0mJ5Ps/SzTyba5XuRcJiFelLBUiN\nhEiNhO6a+KvLMvL6Gu2NDZR8zlxyOdq5LLr0IX3oLBYsHs8uobZTuFk8HkSvF3s8jr2v32xZ9QF6\nX/anx9MYa8MwqF+6SOFb/4a8bFYLeo4cJfzzn8U1PvFEn3snsqKxsFbmxvUcW+sV5EoLu6YjYH4H\ndAxUUcDhsuEPOInFPCRiXrw+Bx6fA5fbhtVmwWIRsFhNIXan74+m6aiKhtLWUBRz2+t1Uqs1TQHX\n+VtDLdGSriNXr6EreQQBbM4E3thJPKFDiJYnZy7a1tosVla4nL/GhdwsZdm8wXRY7ByKznA0doiZ\nyBSOJ/ga7oVhGJTbKqtSi5Vak8Vqg5yssPMCoisaSrXdXTyGwGjEy/R4hLDbTirmIbLHI2CGYVAu\nNshnJCrFJuWSGf0qFxu05dt7Z9rsFjPxPuwmGDLXoYibSNzz1A1Xe+fpp0dPlN2DBx0Yw9DR1QZq\nu4zSylOt5ri5XuVmWmc+6yJfd3f3tYoaA8E2IwkrE/0BJocHiMUGEB5hqkBpq6TXKqwvl1hfLlHM\nbbtMO11WYn1+IjGPucS9BCPuexpIao1GV6Rp1SpaXUKTJLS6hC5JaPV69zG90bgt4rYTwW43c9pG\nRnF2Fls8Tjzu733ZnxJP8sRq6DrS+XMU//3fkNfWAPCeOEn4F34R59DwE3nOnUhNhbn1MjcWzcbG\nRqWFF7oizAB0hwV/zMPQaIiDMwmiIfc9j/mw3BpnpVWgUb5Co3S1a/IqCFZcwf34oiexewafiIho\nawqLlWXmyovMlRZYrq6hGeZF32V1cTg6w7H4IfaHJrFZnn53hKaskq02uVqQWGi0yKkqyo5hMHQD\npdYRYJU2NllnOOxhrM9n9ovs8xPo+EXuZbGgaTqFrGTmfnXyv1rN3dWPoigQCLm61hOBkLkOhl24\nPqS11tNkL4/zi0ZPlN2Dx/kh1LU2ufwWV5a2WEhXWM6obFXsGGx/6QJOmf6QRshrJxzwEg6GiIaj\nhHwuQj4HLof1gb6kDUlmfaXMxnKJ9ZXSbX40oigQjLgJxzwEQy4zMtC5s7dYRaxWsRspANMWwNAN\n9M5iGOZjFotAvM+H16Z3RZsm3VpqtNNpWstLtNMbu4Sb6PbgmxxHiCWx9/fj6BvA3teH5SPe5eBJ\n8SROrIaqUjt3huK/f5N2Og2CgO/Uy4R/4RdxDAw81ue6hW4YZIoNljarzK1XmF8tIxcbhBHwsy3E\nLB4bsX4/E1MxpvZFsT9hl3XD0FGaGSz6Btn18yjNLfMXggWXfwJ36AAu/77HGhVTdJVcI89WI8uG\ntMlcaZGV6ipqR4QJCAz5UkyGxtgfmmQyNPbEpyYbLZVMqUGm2GCr2CBXblKW2pQabeoOAUvYgSPs\nQrCY75MmaygVGaOuEhJFBtxOBiIe+qMe+qNuIv67R8D2mlgo5ess3MiRXi2TSVdRd1TMev0O+lIB\n4n1+ghFTeHn9zufCbHWvjfOLTE+U3YMn/SFsym3ml1eZW8uwuFlnOQeN9t1PmDYLeJwWXA4bbqcd\nt9OGy2HB7bDicljN3Im7fb8NaMsKck2hLbVRGm20pgKyBg+RF3MnfAEn/UNBBoaC9A8F8X3AdFeX\nZVory8jLS7SWl2gtLaHksrcdx+L3Y+8fwN7Xjy0UQnA4EB1ORIcDwWHvbosOB6LLZXZAsO+du8q9\nyuM8sarlEpXvvUP5e2+jlcsgivhfeY3wz38WezL5WJ4DzCmfQrXF8maNpa0qy5s1ljcr6G0dL3SE\nmMCteK8v7GL/wST7DyZ2Fb48CXRdoV3fQK6vIktryPW1jtM+gIjTP44ndABXYArRcn/WA3d9LkNn\nq55lXUqzVc+y1ciyVc+QaxbQje0Lv4DAoK+fydA4+4LjjAdHcFmfjMVDW9FY3qqxmK6SLtTJFE0h\nVt3hhSXYRJxRF864C3vYidARIHbVIIrIsMPBWMjNQMxL4CEiQ3tBLEg1mfmrWeauZnbl+IaibvpS\nAfoGg/SlAredD58n9sI4f1ToibJ78Cwq1ar1JvnCJoVijkK5TLEqUZEUqi0bNdlOU7EiqxZaqrWb\nr/aoOAA7Zs83EVPX7VyL0M3vMD6wOO0WvHYrXgPEloqhbb/tvoCTgeEgY1MxhsbCdzzhhtwi6dmb\ntDc3aW9u0E6naW9u3lYp+qFYLIguF5aOSBNdLqzBII6BFPbUII5UCmvozq/ho8KjnlgNw6B54zrl\n734H6cJ50HVElwv/q6cJfurT2GOPlqSuqDqbhTrrOYn1XJ31rMRqpobUUPAAHsCLgF8QsHzANHVi\nOs7EdBx/8P9n781jZMnuOt9P7BEZudaWtd297233Zne724yxPR4be/SE+Acj8Rj0NMNisNsSDGM3\nDYYZ2Z4ng/3+QKM3LCP+wsMiMIvRwICQzMyjjQcYjE3b7m56u3vta+6xx3l/RGRW5a2qu1bVreo+\nHyl0Tm63Is6NzPjGbz24GlNCCMLePF7zFfzOVcLeAmwTRLo1ilU8yfjUg0TKCbR7EEO9qMfl1jUu\nN69yuXmNK63r+Ik/9J6C7jDp1plyJ5gsTDDp1jldPknB2P81EEKw1vS5ON/k4nyLiwtNrq90BvUb\nIWstNV5xGJtwMcdsfEejSTr47ZguWDxSK/JIrciEsz/WwvslFgI/4tIra7z64jIL1xpA5nk4cWaE\n849McOLMCLZz+K7hg0KKssNDirKbcFROQiFSIn+NyFvOEg3CBpG/iec16XoefqwRxDpR0rcXKFkZ\nD7OCZlbQ8820KpimhaFrGLqKrinZXFPQVJVUCJK+e1JkY5JvHS+i0Q7YbAc0OgGbnSB73AnZbPmE\neYaQA5SBEgolQO8HVpsaxoRLabJIsWBSdAyKjsFMvYzvhViGimVomKaGbWhoSUS0vETSapEGAWng\nI4KANAi3zQNSr0fieaSeR+r1SD2PpNdDBDtbh6iFwjaRdgL71Cms2RNHpkbWfiGEQATBsBu526E6\nUqIrDLRyGa1cuWX2rEhTUt8n7XXpfOubNP+//0m4uACAOXuC6vs/QPmfvfOOu0eEUcLKpsdS7t6a\nW+0wv9plca2HjsABbMBBoawqmOmwAbhYtqhPl6nPlDl5dpTa6MHEhgGINMZvX8ZrvorXfIUk7ltC\nFMzCFJZ7Aqt4Css9gWa4wJ3/qPpxwHxnkeudea635rncusZyb9iCXC+Mc6Z8ihPlGabdOpNunZKx\nv91F+gghWG/6XF3ORPG15TaXl9q0ultV73VN4VS9xLmZCmemSlhVi6Uk5pVmj2Uve58CnCzaPFQt\n8mityMg+FbfdzmFnFC9eb/LCN+a58toaSX4DOjlb5vzDdc69ZfyeaoEdZaQoOzykKLsJx+EkTNOI\nJGwSB5tEwTqxv0bkrxL5q6Q33FkDKKqJblbRzHIm2oxKLt7KaHoRzXBR1DurcSOEoNWLWG14w9um\nR2ujh9uLqZG5VnwESwjWgJudIIoCppEJNMvQsrmpZeLN1Aeja+sUHQPXNgZCz3V0XFPF6rWIFuYI\n5uYI5q4TzM8RLS8PxbUpuo45ewL7TD8B4Szm1NR9L6p7O6RBQHDtKv7ly/hXLhHMz2dJF90OIt7Z\nlutGFMNAK2W9VTXXRYQhSa83ELep7+9Yq+KT76D6/u/CPvfAnueIEIJeELPe9NloBaw0vEF80fJm\nj408trFAJuAdFAqKggMoN5wUqpbVYJrMRVh9pkKxdG+uwFuRJgFe8zW85st4rdcHLklVc3AqF3Aq\nD2KXzuzpktzrR1UIQSfqZgKsPc/19jxznQVWemuIbd8GSzM5XT7JmcopzpRPcrpykmIu+Pb9WFPB\n0kaPa8ttri63uZYLse0NuAFqJYtz02XOzJQZmXDRCgarQchSL2ShF9CNsxg2XVF4oFzg4ZrLg1WX\nknGwNzyHIRbCIObVF5d54RvzbK71AKiNFjj/SJ3zDx+sdfaoIEXZ4SFF2R789je/xCOlR5gtTd/v\nXbkrhBCkcTezsPmrRP4acdjIBFzURCQ7LUkDFA1Nd9GMIqru5nP3hnkRTXdR9Vs35xVCsLLS4fn/\nfZ3LL68iUoFuaZRnHJy6Safn4YUhQRgThAlBlBLGKUEkCGOFMFEJY4UoUUnvwGWrKFByDMquOdgq\npsJY1KTSXsXdXMRYniNdWoBkKyVdsWzsU6cGWaLWmTMYY+P31fUpkoRg7jr+5UuDeLwbEycUK29g\nXyyiukW04la5Eq1YxHV0mgurxK0mSatF3GplWbWtZibiFCWL0SsUhtzAaqGANT1L+d3vQRRcWt2I\nVi+k2Q1pd7Nxox3kIsxnveUPNXTuowNTtsGopmIECWyrv6TlVetrYy610QK1sWxeqTk3zRDet/VN\nE7z26/Q2vo3XfBWR95rVzRpO5UGc6oNY7onbyo4uVg1eun6Zld4aK73VfFxjxVvFi4dvlBzdZrY4\nzWxpmhPFGU6UZph0Jw6kYGsUp8yvdbi23OHqUmYBu77aIYyG62BN1BxO1UtM112KIw6JrbERxyz1\nQtb8kPSGf7di6JwtOzxULXK+UsA6hP+vPgcpFtZXO7z4jQVefXGZKExQVYWzD47zyNunmZqtvKlC\nIaQoOzykKNuD//OLHwNgpjjFOyef5KnJJyibex/wcSNNfOKwRRI2sjFqkcRdkqiT1V6Lu6RRd3Bx\n2hsFVXdQVQtFNVBUA1UzUdRsU1WDNI0G9dy6nYCLF0e4en2SJNHR9YjpqVVmplaoVVub/36gAAAg\nAElEQVQM/86pKNsyxYSAJFUIEy0Xaiq9MEuO8CKDXqTjhfkYGfRCnW5k0wkNgmjvC4UmEmbiBmdE\ng+lwg9HOCm5nfchlphRc7NNncM5m1jRrdhZ9ZPTALGpxq4V/8fWsI0O/bVa45T7aq8TI9guFEFn1\n+SBKCcKEYtlmeaVNnAiiOCFKUuI4nwcBXqzQCxM8P6YXxHjbto6XCTFvl3pK2ylYOiNlm7GKTa1k\nUkRB6Ub0Nno0cysDQME1OXF2hBNnakxMlShVnEPPQhNCEHSv0dt4gV7jJdIkq5KuW6O4tUdxqg9h\n2MNiPBUp7bDLhr/JZtBgw99kw2+w6TfYzOfduLfjb+mKxlhhjLozxqRbZ7Y0zcnSDKP2wcQ5CiFY\nbXiD+K+L8y3mVodjwDRVYWrU5VS9yEy9mAswlZUgYq7rs+YP1wqzVJV6wWTSMak7FpMFi0nHxNnn\n/r93wn6LhTQVXH51jW//wxyLc1k9t2LZ4uHHp3norZMUbrNX5BsNKcoODynK9uCH/9sfYXCJln8F\nQYqqqDw8coF/NvUUj40+dF/q+xw2QghEGg4JtSTqbpv3n+8h0pA0jRBJCDvupTMU1UIzMmtbnBa5\ndKnCxddNvPwa5pY0zj1Y4fxDY4xNjmbC7hYXrDSNSPPivUnc2TbvEvvrhN5SZjFMVLqhQTc06MVF\nfDFGLx2hExVpeQaNbkijHdDqhgjATCPqwTpT/jpTwRpT/jrVeLiYbqIZBOVR4tEJ1PEpzOkp3BOz\nlKancF0Ly9T27Kkn0pSk2yHe3CRuNIgbmySNBuHyMv6l14lWtyU6KApqfYp05hTBxCzd2hTNQo1u\nmNLxMsHU9SK6fpRbGvMtTEn34WuoqQquY1AumJTd3PJYMKm4JqVCZoEcLVvUShZ+OxzUyVu43iDK\nLWaqqjA5W+Hk2RFOnBlhdMK9L5aGQbB+42W6my8OOnWoepFC9WG0yoP0VIdG2GLTbwwE14a/yUbQ\noOE3BuUmbsRUDWp2jcnyGFW9yoQzzkRhjInCOCN29UDbFbV7IfOr3YEAu7TQHMqC1DWFk/USp+ol\nTtazSvfjNYeLHZ9vbbR5tdkj3nauWKrKjGsx69rMuBYzrk3NvLOSPIfBfomFMIh5+VtLfOsf5gZt\n7E6cqfHIEzOcemDk0Iu1HjWkKDs8pCjbg//nb1/h9c0uaeqRJpdQ04u0w2Ugczk8PPIgD40+yMMj\nF6hY5fu8t0cLkSZbIi0NUVQdVXdR1Z1CdnS0yPP/cI3XXlzm0qtrg4v46LjL+UfqnDw3QqXm3FPz\n5SRqE/aWCL3FfFwiCRuD1xVFx3RPYJdOYzgn8Rin2Y0HSQ2NTpbg0NtooC3P4W4uUu1tMho2GY2a\n6GKnCE1QSBSNRNVIVB2haghNB03DiAMsv4O6y+cAQt1ixZ1gzhrnij7Cgj1GqN46gNgyNKw8UWK3\nGLxyySaOEgwtq0Fn6CqGlo+6im3m5VVsfVBmxbF0TH33avZCCFoNn8W55qAWXq+zZc2rjjiDjhIz\np2qY1v1JqOgH6/caL7O2+TLLYZv1JKUtVDy9RFcxaMUhzbBFmIR7/jtls0TNrjJiVRmxa9nc3pq7\nuSv/oC5gQgganZCF9S6La10W1nssrHVZXO/S7g0XJR0pW5ybrnBuJuv5eHKihKGrRGnKK40e395o\n83KzS5RbziZsk3NlJxdhNmO2cSyadN/rWrebPt/++hz/9M1FwiBB11UuPDbJW5+aoTZ6MHF8xxEp\nyg4PKcpuwsvX1/nGept/XGvRCGOSZAM1vUgUX8SLtxZtpjjFwyMP8vDog5ytnLrvfeOOE9tPwDhK\nuPL6Oq+9tMy1ixtDfQVLFTtrOZK3G8nmBdySeVeCLYl7BJ1r+J0rBO0rg4rrAIpqDLLq7NJpzMI0\nijL8N6I4odWNaHZ82gtL+PPzJEuLsLaM1mlCHEEcoyQxSpqgJjFamqCJhEAzaWsFOnqBju7Q1gt0\ntAJtvUBLd2lZZVzHwHW2Jy9szbcSG/Sh12/VW/Vef1ijMGFlscXyQoul+Wz0t4kBp2AMibD7WZdp\ns7vIpdVvMtd4jaXuCmtxxHqaEuzxq1QyilStMhWrTMWqULHKjNg1RqwqNTvbjNv8Xt/rOqdCsNH0\nWVjvsrDWGxJhXjAcTqAA41WH6TGXqdECZ6bKnJupUNuWDBGlKa83e3x7o8NLjQ5h/r0asw0eGynx\n1pEided4uuXuZq23Z1FeemUVITJ3+qNPzvDw41Nv2AzKe0GKssNDirKb0F+YVAiutD3+cb3Ntzfa\nBElKmjaIk+so6Tx+tIggs/BYmsmF2jneUrvAW0YeoF6Y2NPknwpBL05oRQntMKYVxXhx5nJKEaQi\ne08qQOSPi4ZGxdSpmQYVU6dk6mjH4I52L/Y6AX0v4tIrq6wstrNGvZvekBVmO7aj4xYtCiULt2ji\nFrP+hcWyRblqU6rYtxRumUi7it++QtC5QuRvuQ+3RNrpXKRN7RBpt0MqBEGYDMX13IimKtimdiBu\notv9sidxSrPhDRok9/v1ra90hjppFcsWkzNl6tMVZk5VGRm/Py7JKOxwZf0FLm6+ypX2Ate9Jo10\n2MWoojBuV5ksTTPlTjJZmGDUGaFqlSmbpX29kbqTH9U4SVlY63J5scXlxSwDcnG9uyP4XlMVJmoO\n03ml+6mxAtOjLpMjBcxdmlGHScorzS4vbnZ4udEdCLGaqQ+E2FThzrKsjyK3u9ZpmrJwrcmlV1e5\n/Moavby8x+iEy9vecYIHHpoYdC6R7ESKssNDirKbsNvChEnK660e17s+812fuW6AF4fEySJxfJ04\nvk4qWoP366pLyTpBxT5J1T6JrRfpxgmtMKYdxST3uEoqUDZ1qqZOzTIYtU3GbIOxfH6YmVB3w52c\ngGEQ09z0aG5mQqG54dHtBHQ7Id12MHB97oZbMilXnXyzKVdsCkULxzUouCa2YwxdoJKom4m0zhWC\nztUbRJqJWZjEdKYwnEnMwiSGPQ4ohEGM70X0uhFeN8TrhYN5rxvi9SLSdHe3JYCqKBiWjmVpmJY+\n2CxLx7zxOTt7Tjc0FBGSpgGIFCFSIEUIkT0mBZFSqxVpNAOiSMHrCXrdZLB12iGtTY/GhjeIqdmO\npimMT5WoT1dyIVbGvaE0hRApIglIk4A08bMt3ydF0VAUHVQ9m6s6iqLnY16iWFGzuaJmrZIUFSGS\nPE6wQxJ38fwGi71llnvrLPmbXO9tshgFbLcfOYrCrF3iZHGKE7UHmak+wERh7NAs2Hud00GYsNrw\nuL7SyUTYUotryx2ibVmouqYyNVoYWL76Imyi5tzSEuonCS83MiH2arM3cE2OWAaP1LJaYbPu8Rdi\n27nZ70eSpMxf3eTSK2tcfnVt0HfSdgzOXBjj/MMTTJ+svqHW46CQouzwkKJsD/7097+JbqoUyzal\nskWpYuOWrB3p+UII1vNspfluwFzXZ93bpBddpxfOEcbzCLF1kVPVGrpWp2hOMerMMGqPUrEMyoZO\n2dQp6CqqoqCioCrZRVpVsjt9FOhEMY0wphHENMKIRhjTDDIr224LXja0gVCbdCymC1nW1FERa/v5\nZQ+DmG4npNcJ6LYD2q2AVsOj1fBpNzw67WDPnumKAnbBoFAwcVwTy9ZRVQVFUfKswJg06SCSNknU\nIgwiolAnjAzCyCCKDKLo1l0WFIWbZhn2e4reKboWo+sJiiKyTc1HsrmqCKJYx/dNkmRvcWJZIUU3\nwHVDiqWIUjGmWEooFmNUZUvgCZEOCb40Cba1GLp3AiFYTVLWkpT1bWP7hsVRgAnD5qQ7zpnKaR4Y\nfZTpyun7Y7GLExqdkFRVef3qBqsNj5VB3T5/qPgqZN/tmXGXM/1G25NlZsbdW4ovyOPLwphrHZ/r\nXZ9rHY/FXjC4yRu3cyE2UmLKeeO2IBsfL7Gy0qLXDdlc67G53s3HHmvLbcI8W7jgmpx5cIyzF8aZ\nPll50wfu3ylSlB0eUpTtwf/9zJ/u+rxbNAfiTFGzC7aiKpl4UhWyBCtl0Lg7ESlNdZM1fZF1c5GG\ntUqqbFl09MSk5I9Q9EYo9Wq4QQ1btzEMbbDp5tZc07eahauakjcNV1E0BU9V6GnQUQQtkdKIEzai\nmGY4LNgUYNQ2mC5kIm2qkGVXFe5DavthftmTJKXT8mk1fFqNzB3q9aLcghXidbP5zSxuN6IoYJop\nhhFj6D6GEWGaEZYZDkbLinEKGgXXxnELKKqCSGOESBAiRqQxiASRxiRJQBT4xLFOFGvEsZ7NI504\n1ojyx3FiEccmcWLkr2vEsYoQStYoPh9FSt44HkxTwXZSbDvFcRIcJ8G2I2wrwnZCCo6Prsf5vvX3\nK3+M2GbF2mbVIhsVzUIdbDaKZqOq2WNF0YaONRuzx3ESsRH2WAo7LIcey2GPldCjsUvAfVm3qdtV\nJgtZWYnJ0gynKg/gGAcfu5akKRutYFAYeaOVd7XIO1s0OiEdL9r1s5qqMFq2Ga/ajFcdpkZdzkyV\nOVEvZv1qb4EQgk6csOqFzHcDrnU9rnV82tHWeaoqWRujBysujx7jGLG9iOOETiug0/JpNwPaLZ9O\n06fbDllZahMGO0v3VGoOJ8+NcO7BceozlWPR+PuoIkXZ4SFF2R6sLbe5dnUj//LnPwKtgHbTp9sJ\nSO/W76imBMUOXrFB192k624SmsN1jdRExwhtjMDBDJxsHjoYgYOa6qipipKqKEJFTTUUkT/eoxu5\naqkoZQtGHeKKmfWk02D7JUQh+1G/UHG5UCkwW7QPJVbtKH7Z4yghDOIsli8X1yJvO9V/bFoatmNg\nWltlAkSaZIV6gzWSqE0StrNx27az7puau/Byl55qohlFNKM02HSjPJireiGrB3cX/zdHYa3jNOZq\na45XNy/yWuMil5pXidJhMVMyiswUp5guTjLlTjLl1pl0xw+ssXafJE1ZbfgsrHVZ2ugNdadYbwZ7\nlhdxLI1q0RpsM/USrqkyXnUYrzqMlC2027DMpELQCmNW/ZAVL2SlP3ohXjLs8i4ZGieLNiddh5NF\nm2nXwjjG1p80Tem0gsENU39sN33aTR+vt7vgVVSFSs3ZKjg86jIyVqA6UkC/DcEruT2Owm/Hm4WD\nEmXHPv1wrF5C3OI3rn+h7l+sBxdukcXgKIoysKb1XWG70QzaXGlljYcXu0tZbaSgQce5s6bcCgoK\nKqrIRoSCIhQQStbTKFWgp6D08ucUBVQNoapg6Lza03h1Tc8q+qNR0k1GHJsJx6Fo2liqiamZWNrW\nmG0Wtm7j5Ntxzz7VDe2uftAVVctizQqTu74uhED0W1/1hdgB1q46CsRpzLX2HK9uXuK1zYtcbF4Z\nEmHT7iQny7PMuJNMF6eYKU5RMosHuk89P2a95bO0kZWUWFjrsrDeZXmjR7zLzVbFNTk7XR5YusYq\nDqMVm1rJolo0sc3h8/1WP6q9OGHND1nzo8G47oesBdEgDqxP36p9puQw7phMOhYnizbVI1gz7FYE\nfrRDdG0XX7tpXk1TKJZtRsZdSmWbYiULJymWbUoVizNnx9nY7B7+wUgkx4zjfVW+TRRFQdMUtHu8\nIatYJd42/ihvG3906Hkv9tj0m1tVw4MGYRISpzFRGhPnW5TGRGlEnCakZPE+iUhJt239x0makKTZ\nOHg9y+2EbV67GAhiWPPh1c07Ox5D1bdEmuYMxJqj29i6TUF3Bq/XgxpBNx0SeaZmYqompmYcaMHN\nw0ZRFJQDtvbcb5I04Wp7jtc2L/Ja4xIXG5cJbxBh52tnuVA9xwPVsxTN/a0Ftb0P61rDY72V9d9c\nz9s/bbT8XbsSWIbGiYki06MuU2MuUyMFJmqZALPM2/+Cp0Kw4YVcbXtZvGcYsZnHffZjQP1kZ6KH\noSqM2SajlsGEY2ZbHguqHzMLWBjErC13WFvusLrcZnOtS6vhE/i7dwcpuCb16TLlqkOpalOuOlTy\nsVC8eTyczJiUSG6PN4UoO2gc3cEpOkwXd7e87CdCCDw/YLPRpdHosNnssNTpsZAEbOgJng2pmoDI\nCsLqXR/N89F8H5UY3QXNEWAlpFpMpET0Io8Nb3PPCui3g6kauWCzhix0/bEv8hzNHrLWObqDazgU\nzeKgqKdk/wmTkPnOIq81LvFqbgnbXoB10q1zoXqW87VznK+e3RcrWBQnrDX9LJB+MwuiX214rDYz\nV+ON5ST62KbGaMVmtGwzUraZrGX1vabHXGql289KDJOUjSDaddsMYpI93JymqlC1DE6bRpYhbZuM\n5mPZOJgyKAeN70WsLLZZW25nImypTasxnL2r6Srlis3kTCXLfO5nQFcdShUb4w5Er0Qi2YkQgnhj\nA97I7ss3G4qiUHBsCo7NzNTojtfDJOGVtQ4vb3S41PNpWimCzKJmBgnKuo8+H2I1AnQvQVMVamMF\nxuolJmZdJk65CDPBj328weah2bDRbBMkAWEaEsQhQRoSJmH2XBIS5FsjaBIkIckdijxVUSkZRUpm\ntpXNEkXTpWyWtj1fomwWKRoumiovEjeSipQ1b4OF7hLznUUWOkssdBdZ7a0jtqWRTBYmOF87x4Xa\nOR6onrnrfrFdP2Jpo5eLLo/V/tj02WwHu37GMjXqtUIey2UP3Iyj5Wwr2Hf2s+TFCSteuCPGqxHu\nYfHRVaYKJpNlB0coVE2dqqVTNQ2qpo6t7d4Z4bjQt4CtLLZYWWyzstjeUT7FsnVmTlUZnywxVi8y\nVi9SqRVkkL1Esk+INCVcWiS4epXg2lX869cIrl0l7fWY/m9/tOfnpCh7g2FqGo/VKzxWrwCwGUS8\n2uzxWrPLxbZH19LoTmeuKCsVOJ2Y9kqPxSvrGC8soQKTMxVOnx/jzIUZKhMF4O6CGuM0JogDmqHP\nZtCjGfRoRT1aoUc38ujmgi9MfJK0R5T0CJMeS71V5joLt/z3XaNA0XCHrXJqZq3rP6epGqqioikq\nKiqqqqIq6uA5XTUwVB1d1THyTc83W7Nwchfu7VaJP2hu1Wh72Vvb0YKooDucq55mpjjFucppHqie\no2Ldvgjzw5jVhs/yRo/lzR5LGz2WNzyWNnq7ZjIqCoyUbN5ysprFduXia6JaYLxqU3TuLAGin9W4\nud3a5WfjehANZTf2KRkaZ0sO47bJiGVQswxGbIMRU8fOs5ffCEHRaSrYXO+yPN/KtsUWm2vDCUmW\nrecN5cuMTxYZq5colt9YddAkbz5EmpL6Hqnvk/oBIgxIg4A08BFBSBoEiCRG0XUUw0DRDVTDyOfZ\nc6pTQCu6qLaDcpfhB2kYEq0sEy4vES0vEy4tES4tEMzNIcLh32KjXqfw8KN7/EsZxz77EnYvHivZ\nSSoES3kczZW2x5WON3RBUwRYUQrNzIpm9CJqhs4D0xXe+dQpLCdz3fS7F6Qis72kQtCJkm2uoWFX\nUXiTyvh7IUSEEB6p8BCpB8JDV0N0xUMRPqnwiFOPOPVJ0ohwH2tv7Yau6jjaVrydo9s74uu2C0Jd\n1VAVDU3JrC6aoqIqWi4IlT1jDeM0QjVho93Ci72BtbJvuezF3p4WSEM1GHdGBxmRM8Uppt1Jqlbl\nphdgIQStbphZvPJaXWvbMhpbu2TUaarCWNVhsuZQHylQrzmM17IsxtGyfVs1vCA7d9rbOmW0ophW\nPm+HCc0oprHHOaQAFVMfxHVNOCbj+ejcRtmY4yjKAj/KWmfl7bNWFluDGl8AuqEyPlliYqrExFSZ\niakSpYp93wXYcVzr48hhrrNIU+LGJtHyMtHqKmkcQZIg0hTSFJEk2ZimoCiolpVvNoploloWimmh\nmhYijjIRFQakfkAaBqS+jwhDUs8j6XRIup3BmHa6JN0Od1UscjdUFa3gohZdNLeIViyiOs6eCV4C\ngdpt052bz9yRN6JpWNMzWCdPYZ08iX3yFNaJE6h2Fqv8hi6JAVKU3S1CCDaDmCsdj6sdj+VeyFoQ\n0ot3ifXpXxQVMlPIbWCqysBK4eoahcGmUtA1nHyuKQp+nOInKX6S5GO2eXFKO8q6KmQX62TXWCBL\nVZguaEw4KuO2yogFhrI9SWJnQkWSJpkwElsJGVtjRBAHWy7cxB9y6d5YHuIg2Z6QUdALVK3KUIPt\nEbvKiFXDNW4dkxdECQtrXa6vdJhb7TC30mFutburxUtTFcYq9sDa1RdgkyMFRiu3J7zCJM2KKIfR\nUCHlrLByRCuKuZlmtzWVWt4FY6Rv7crnVVO/p+D6oy4UbrSCLS20aKwPW8EqIw6T02XqMxXq02VG\nxt0j6YI86mv9RmG/11kIQdJpZxagviVoeYlwaYlodWWHJejAUVU0NxNOqutm4sl2BoJPyUWfapko\nlo2ia4g4RkTR1pjP077gy8Ve2hd+3W5WMPI20GsjGPU6Zn0Ssz6JMZnNjdExFH1v78obuiSG5O5R\nFCW7yNkGbx8rD57vxQnreRmA5W7A9Y0um36E70WkSYoiMquabesUCgaua1J2zPzCqTNqmdQsHVff\n/6BoIQRektLM21+teiFzeYeGy52Iy52t95YMjRnXZta1mHVtTu1j4d0kTQiSMI+vC/L4uoggCbJ4\nujxrNsum7YtDkc0RmbtU2XKVbnebTo7V8NtJlkCyS+mSKE3pxQm9OBubUcKiF+PFmwRJSiwEcSoI\nkpRWL6TlRXSDmF4YE/QikjAlDRPSfCzXLM6cqjJTc6hXHCZqmZtxpGTveYFPUkE7igf70Ylu6F4R\nxmwG0Y66XX0UoGTozBZsKmbWJaPfLaOU940tGTrmEelocdAIIWg3fdZXu6wutne1ghmmxsypKvWZ\nMpPTFeozZWzHuI97LXkjIJKEpNMmbjS2RNc2AZb2ejs+o1gW5uQUZr2OUZ/EGJ9AtSxQVRRVRdG0\nwRxNgzTNLGG5ezENwtwq5pOGYeZKtDKrmWJvWdQyoWWjFYv37Ga87fXou0U976bvq5+eYqO9/zfn\n0lImuS36bVJWFttcfX2dq6+vs7aypYBKFZuxepHxepGxPHjYLR5utXIvTobaaM11A1rRcLB3zcqE\nwKxrM1kwhyx4xk1q1N2KOBVEaUqQpISpIE63xNFgzOdJvt3YyD7JH+uWzmbH32Y9HLYg3lgja78x\nVAVTVTG1fFRVDFUhSLbEYHCLO0lDVQaB81VLp2Ia1EydiqlTzduV6ffZonO/rDeBH7G+2mVjtcv6\nSicbV7s7OlRURxzqM1s9TGtjR9MKdjtIS9nhMD5eYnlhnaTT3bL8bB/bLZJWm7jVJGm1sm0vN6Cm\nYY5PYExOYk7UtyxCk5NoFdmLVFb0vwnyy37w7HYCdlo+Vy9ucPXiOsvzrUEj4T6OazBWLzE2UaRc\ns7OikmWbYtnCOKQq3q0wHjSkz4Sav6f1RleUIdeqqjAknFIhSMnGRAjCRBCmKWGa3tQFd69oClia\nhq0qaICSQBolhH6M14votPIG6lGKSFJEKjBUhemRAifGS5yqu5ydLFOvOniJoBPFdKKETpzQ7c+j\nhF6SEKaCKMmOaev4suM1VIWClrmcnRvc0UVDzwRYLrqcY5DBeBCunjBICPyIXifc6i4y1G3EH7J+\nQRYNUB0tMDpeZHTCZXSiSH36jWUFk6Js/+ln9/mXL+NfuUxw5TLh4gKp79/6w4BacNHLZbR808sV\njIk65mQdY2ISY3Q0s3hJdkWKspsgv+wHz61OQCEE3XaQF6LsDOohdVq7l0WwHYNSJav4XXBNTFvH\nsnRMS8O09G2blj+vY5j37g7tx9HNdX1W/Sx+zoszQbLdJbi9cKjKVsN5RVFQAU1RMDUVc5tVyVBV\nLFXFyOe6oqCrytaYz7V8U5Ts3+k3tAfoeREoKouLLVotn41GwFpeYLV9GwH302NZv8bpscJttQy6\nXVIhBvt4XEiSlDhK9+zoUa0UWF/vDLXn2v6eOE6JwoQ4ysYoyrY4n/tehO/FBF7m2g/8mPQm6tww\nNUqV7KZkZMxlZNxldLxIbbTwhi+uKkXZvZP6Pr1/ehHv9dfxr1zGv3IFEWwTYJpG4cQsuKU8WN1F\nzYPWtWIxe65UQiuV0cvlm8Y8SW6NbLO0B//9q5dwTZXJWoGR8t4xMJKDRVGU3Apmc/r82OB534tY\nX+nQbgV0mv6Q9WBjrcfqUucm/+pOTEvH2ibcbMegUDJxi9agCb1btHBL5lC/y+372Y+juxn9ZAI1\n/8x+IYSg68csb+SlJTZ7LG14g3ITuxVU1VSF0YrNyXqJ8bsMuL9XjpIgS5IUr5s3qe+EdDsB3XZA\nN5/32iGdToC/Rx/G/URRspITlmNQrjnYtoHl6BRcM283ZOUWYgvrFuecRHIjcbNJ95vP03n+G/Re\nehER5+EYioI5NYV9+gz26TNYp89inZilPj0qxe8x59iLsl//428P5rqmUu+n6I841GvZRWusbDNS\ntjD2KchbcvvYjsHMqdqurwkh8HoRXi8kDLLm4mEQE/hxPt/2XDD8XHsXN9CN6IZKdaTA6LjLSN81\nNO7iuDdvCQPcVZP3OEnxghgviGl0wkG7oPWmz3orYKPls9byCcKd+20a2Y1FfaTA6ZkKrqkNiqve\nLOD+OBDHCUmcEscpSZySJClJLPIxez6OkoE1amueEkUJgRfR64Z43ZBeNyIMdi8K20c3VNxSZo0y\nrcy6ur2vrapmvW4LBZMwjHf2vlUz66Wmq+iGhmFqGPmoGxqGoWKYGpZtYNnHr7el5GgTLi/R+cdv\n0Hn+H/Evvj6I9zJnZik+8QSFtzyMffr0oLyC5I3FsRdlz/xfT/L61Y0hy8P82u6Nb8uuyWjZYiSv\nHF4tWriOTtExBpvrGLi2vq+uH8nuKIpCwTUpuOZdfV4Ige9FdNu5haQT5haTgG47pNP22Vzvsbbc\nAZYHn7MLBqO562hk3GV0wqU25g7FucVJSqsb0u5FNLshrW6YZTLmY6cX0csFWH/cq21Qn4KlM15x\nGKvYg5uGvsWruq134O2axdN0S9zEcZplxirKwAW832IhTdNMRHczId0XSl4vwqZjevAAABlvSURB\nVO9F24RzJp77j9NdmoffDbZj4JZMxieLOK6JUzAouLmVtLRlKTWt2/tZky41ya0QQiDiCNGvnRUE\ng0Kliq7nNbcsVMvM5qZ529mBaRAQLi4QzF0nmJsjnJ8jmLtO0s7PSUXBeeA8xSfejvv42zEnJg7w\nSCVHhWMvyt739lkeOVEZPO43Ou67g9abw42Or690uLx46x9ix9IxtOyuWVOVLKaoP1ezmCB12921\ndsPrt7ocbn/vjZ9Xdvn3+vugaQq6pmLoKoamom8fdRV927+pKsOf1zQVx9RwLP1QXF6Q9/pKBLD3\nhVnJ9/NORYSiKDgFE6dgMlbfvVdjmgoaGz2Wl7IYt/XVDs11j/mrDeavNobfa6iEqkI7TWlGCT4Q\nMtT/fQhNVXAsnYKlU3UtCraOY+k4lkbFtRgtW4xW7MFNgHObYgG2SiRsrvXYXO/mY4/mpjewPN0q\nGtQwM3Fm2XmsnqmjajstRv3HQojcSpVuWaz6Vqsw2bNR9Y3oupq5lm2dcsXGtHR0IztH1fxc1TQV\nbdtoGBq6mY2GoQ1ZqGxHxy4YqPJGSbJPJN0uwfwcSaNB3GqRtJrE7dYgIzFuNUm7XdIguOMCpYpp\nZqUdtlWOH4yGgaJpRGtrRCvLO/5tY3wc58KDuI+9Dfdtb0Mvlff4K5I3KsdelN2IoihUXJOKa3Lh\nRHXH66kQtLshay2fVjek40V0vZiOF+XziK4f0fFikjQlTQVJKkiFIIrSQTBw/7n+/LilS5i6mgsI\nPRcWmVjbEhb6QHAULJ2JTY/VtS5BlGRbmAzNvTDGC5Ihy1HPz8bkNlITFchEZS44t4+atk3A3iA0\nhcisWlGcEuUWo6j/OE4J42RXC5YKOEABcFCyeZRgo2AD42wJAEVTMAsGjmtRqlhUaw61WgG3YGxL\nUNhKTLgdcZll6sVZfF1zW5ZeHnO3ttwhvqGIr6oqlKo2JdMaEjTbR5GKHRarbjtgcy2+43M0E0uZ\nUCoUzYHr1ymYFFxjMHdcA6dgDNZAe5PUFpMcfYQQJM0G/rWrBNeuEVy9in/9KvHa2p6fUXQdrVTG\nGB9HyWtlDQqTmhaqnVWiF3Gc190KtlWjz+puiSDILGxxROL1BkVL+0VJ1YKLc/4C1uws5swJrNlZ\nrJkZ6ZKUvPFE2a1QFYVK0aKyzzW0hNgSafEt3DViUGZhK+MrzcsO3Cj6knT4uSQVw8LjBiEy+Owu\nn4+SFD9IBqIpE04Ra03vlvt8J5hGJvhKBYOJmoNtajcNFE9SQZyk28SVII4Ten5ElIgdx7QbqqLk\nIk4ZiDm7YGAaNpahYZsapqFhGxqWoWGaambhKlpUSxa1okXFNUnDhI21rIZUq5mJpL5oarTbNJba\nXL/V8Vsamp61UxpYonJBqagKIhV02sGOulR9dD2LhauNFaiNudRGs3m56ty14OlbwbZnGKaCbMyz\nDRWFPGYqs1Qd5zg2yZsXEcd0v/0tWv/77/BeeZmk3Rp6XSuVKDzyKNaJkxgjI2jlSl4SIisNoTq3\n7oxx1/uWJIg4ztycMhZRsgtvOlF2UCj9UgcqGMdwVaM4oZdburZbufoCTjN04jDGMlQsMxM2lqEN\n5nbuFj1o16gQmVWyL9IAdF3ZvxhAx6BUsTl1bnTH391u2ep2wp1JCP5WYkKSpAPxkyQCkVtZ+xVo\nShWbUtmiWLEH2Xn98dSZMdbX7ywr9VYoioJhHsMTUyK5DUSa4r3+Gu2/+1va//A10l4WV6yPjOA+\n8fa89+BJrFOn0av3r/Cpommy9pfkpshfaQkAhq5R0TUqewTdH5WgaCWv73XYVhxFUfJsO4PRid3j\n1/YLaaGSSG6PYO46rb/7W9p//3eDxtBapUr1X/4flN/5nVgnT0mLlORYIUWZRCKRSI4NIo7pfOPr\nbP6PL2clIwDVtim/6z2U3vmdFN7y0IH3R5RIDgopyiQSiURy5InbLZrP/RWNv/qfJI0sc7rw6Fup\nvOc9uG99HNW8u9I6EslRQooyiUQikRxZ/KtXaPyPL9P++/+NiGNU26b6gX9J9f0fwJycvN+7J5Hs\nK3ctyr7v+76PYjGLrZmdneXpp5/mk5/8JKqqcv78eT796U8D8Pu///t88YtfxDAMnn76ad73vvcR\nBAHPPvss6+vrFItFPv/5z1Or1Xj++ef5xV/8RXRd513vehc/8RM/sT9HKZFIJJJjgRCCcHEhq2r/\nj98guHIZAKM+SfW7PkD5Xe9Bc2TpCMkbk7sSZWEYAvCbv/mbg+c+9rGP8YlPfIKnnnqKT3/60/zl\nX/4ljz/+OL/1W7/FH//xH+P7Pj/4gz/Iu9/9bn73d3+XCxcu8BM/8RP8+Z//Ob/2a7/Gv//3/57P\nfOYz/Mqv/Aqzs7N85CMf4eWXX+Ytb3nL/hypRCKRSI4kIk3xL16k8/zX6Tz/j0TLeQcOVaXw6Fup\nffCDFB5+VMaKSd7w3JUoe/nll+n1enz4wx8mSRI+/vGP89JLL/HUU08B8N73vpf/9b/+F6qq8uST\nT6LrOsVikdOnT/Pyyy/z9a9/nR//8R8fvPe//Jf/QqfTIYoiZmdnAXjPe97D3/zN30hRJpFIJG8w\n4naLcHGRcGEe/8plut/85qCemGJZFJ98iuLjT+A+9ja04sFmO0skR4m7EmW2bfPhD3+Y7//+7+fK\nlSv8+I//+KD+EoDrunQ6HbrdLqVSafB8oVAYPN93fbquS7vdHnqu//zc3Nwt92Xlf/4Vvl3CGB9H\nq9y/+jMSiUQiyRBCkHa7xJubxI1Nom6DjdcuEy4uEC4skHSGy+to5TKV9/4L3MefoPDQw6iGDNqX\nvDm5K1F2+vRpTp06NZhXq1VeeumlwevdbpdyuUyxWKTT6ez6fLfbHTxXKpUGQu7G996K1/7fXx7M\nVdPEqk9gT9ax6/Wh0arX0az9reL/ZmN8vHTrN0n2BbnWh4Nc57tDJAnB2jrewgL+wiL+0hLB+gbh\nRn/bzNoK3YiiYE/WKT/0IIUTs9l28iTu2TPSNblPyHP68DiItb4rUfalL32JV155hU9/+tMsLy/T\n6XR497vfzd///d/zHd/xHXzlK1/hne98J4899hj/6T/9J8IwJAgCLl26xPnz53niiSd47rnneOyx\nx3juued46qmnKBaLmKbJ9evXmZ2d5atf/eptBfqf+9hH2bh8nWh1hWh1lWB1Be/67hY2rVLBGBvH\nGB/HGJ/AGBvDGB1DHxlFr9VQDeNuluNNwVEpHvtmQK714SDX+dYIIYiWl/AuXiRcXCBaXiZcXiJa\nWUbEuzSoV1X0SgVzZha9VkOvVtGrNUbPniAojmDUJ3eUrvAAb717OAf0Bkee04fHvaz1zcScIsSd\nt9KO45if//mfZ35+HkVRePbZZ6lWq/yH//AfiKKIc+fO8dnPfhZFUfiDP/gDvvjFLyKE4GMf+xgf\n/OAH8X2fn/3Zn2V1dRXTNPmlX/olRkdH+da3vsUv/MIvkKYp7373u/l3/+7f3db+3LgwSbdLtLqa\nCbW11XyeP95YHzSFHV4JBa1cwRgdQR8Zy8fRXLSNYIyMorrum9Y9Kr/sh4dc68NBrvNOEs/Dv3wJ\n/9JF/Iuv4126SNodFkyq42DUJzHrdcz6ZDafmECvjaCVy7tavORaHw5ynQ+PIyXKjhp3sjAijok2\nN4hWVojX14k21ojXN4g21rPHmxuQ7N4oWrHsgVjTazX0cgWttNXINmtqW0EtFG5pihdpiohjRBQh\n4igfY0QUo+gaimWhWjaqZaHo97+cnPyyHx5yrQ+HN/M6J563ZfVaXiJcXiaYu064MA/bLgnG+Dj2\n2Qewz53Dmj2BWZ/MhNcd3py+mdf6MJHrfHgclCi7/1f7Q0bRdczxCczxiV1fF2lK0moSrecibWOd\neGOdaGMje7y+TriwcPM/oqo3bTor0nRP4bcrmoZqWdlWcDEmJjDzO9XsjvXufiglEsnxIPX97Ddp\ncyO7gUsTSFKESLMxTRBJCklMOrjB236zF5GGAfH6OuHKMkmzueNvKKaJc/4C9tlzOOcewD57Dr1S\nuQ9HK5G8eXnTibJboagqerWGXq3BuQd2fU/qe8SNBnGrRdJqkbSag3nczsZdXaSDP6KgGEa26Tqq\nYaDo/ccaIk5Ig4A0DBB+Nqa+jwgD4o11wvk5bozA6LsUrNkTOOfOYZ97AHNySgbPSiTHACEE8eYm\nwbWrhEuL224E14jWN0h7+xRzpSgYY2NYjzyauR4nJwc3ePrIqPy9kEjuM1KU3QWq7WBOOpiTU4f+\nt4UQJO127nJYIlxaGrghwrnrBFcu0/rqV7L9dBzss+e23fmeRSu4h77PEolkC5GmRCsrBNeu4l+7\nSnDtKsG1azvKREBWs8sYHUU/exZjZBR9ZATVtEBTUVQtH7fPtaEbvqEbP8NAq1RlQpNEcoSRouyY\noSgKejmLY3POXxh6TSQJwfwc/sWLeJdex794kd6LL9B78YX+h7FOncZ9+BEKDz2M/cADsh6Q5MiR\nBgHRysogyy/xvCE33FAcphCoto3qOGhOAdVxUAuFrbltb4vPNFGt7LGyz8JEpCmkKSJJEGlK2u1s\nhTxs3BAKsbaGyLui9DHGxnEuXMA6eQprZgZ9dOxNn1wkkbwZkaLsDYSiadgnT2GfPEX1/d8FQNJu\n4/UzqV57Fe/SRYIrl9n48/8+iCEpPPQwhYcfwZo9Id0XkkNBCEG8sUG4OJ9Vdl9eHlh/442Ng98B\nReHiPdQtFAIQmQgjTYeC42+GWnCzrMWZWeyTpzIRdvKktGBLJBJAirI3PFqpRPFtj1N82+NAFjDc\ne/UVev/0Er2XXhyypKkFF+eBB3DOX8A5fwHr1Gnp6jgGiDgm9bzMorRb7ahtqJaJalootpXFMR6g\nFSazGHWJ222ilWXChUyABYsLhIsLiCDY8Rm9NoLzlofyOKdJjHodrVi8wR1n5HGYOigKqe9lx9/z\nSL1etvU8Eq+HCALSwCcNQtLAzx9nm5bGxPFNYj9vRZ7Qo6gq9Ec1cyeqTiFzO46O5m7HUYzREVRb\nNtKWSCR786YriSEZJm426L38T/Reegnv1ZeJVlcHrymGgX3mLM75C9Tf/hg9w0WvVlGdwrF3qYgk\nIQ3DYVfY9ky1KLqp9UNRVRTTzEqWbC9fYpq3tTZCiOzv5mIhDYKBYCiaCo2ldRKvR+plQiPp9efe\nNtGRzW90hd02ipLv/9a+q9vFzw1CSFH3Pq40ikg7HZJuh6TTJel0SL3ermuo6HqWlDI9jTk9gzk5\nhTk5iTFRRz3ErhuyfMDhIdf6cJDrfHjIOmU3QZ6E+0fc2MR77bXM1fnaqwRz13dcWBXTzDNUq4NR\ndW5hAciDkRVNhcGYBylru4mArZEkIY3iG8RThIhi0ijckaG6m1VEBP33BIgwuKVF6a5RlEyY3aQk\nCmlKGgS37fLagaYNxU9tj6e6aayUENl63WJddm2Pcyf7ViyiucXBqBbdrAzN9Azm1BTG2PjN1+eQ\nkBeww0Ou9eEg1/nwkHXKJIeCXq1Resd3UHrHdwCQ9Hr4l15HW1mgOb+UNxhu5OJt5e6FxSGy3aKl\n12pbAd+GkQmoG91i21xjeyLEsLDJXWSib/W6SUkURVFy65o1cCWqpjXYx/JYFS9RUQsOqlNAdQpo\nBScTYU4h27+DdDsKkQvhYSsiN/mvVvRMjCmWfeytqBKJRHK/kKJMclO0QgH30bcyPv5u7BvuCkQc\nE7eaxJubN3WhCSFAiEFQ9GBM87FvBYtvKHyZW8MUrZ/mr2/Vcxuk+ptbAid3I2aPcyFmWscueeF+\n3+0qigK6jqbrgIyBkkgkksNCijLJXaPoOsZIFsgskUgkEonk3jheJgSJRCKRSCSSNyhSlEkkEolE\nIpEcAaQok0gkEolEIjkCSFEmkUgkEolEcgSQokwikUgkEonkCCBFmUQikUgkEskRQIoyiUQikUgk\nkiOAFGUSiUQikUgkRwApyiQSiUQikUiOAFKUSSQSiUQikRwBpCiTSCQSiUQiOQJIUSaRSCQSiURy\nBJCiTCKRSCQSieQIIEWZRCKRSCQSyRFAijKJRCKRSCSSI4AUZRKJRCKRSCRHACnKJBKJRCKRSI4A\nUpRJJBKJRCKRHAGkKJNIJBKJRCI5AkhRJpFIJBKJRHIEkKJMIpFIJBKJ5AggRZlEIpFIJBLJEUCK\nMolEIpFIJJIjgBRlEolEIpFIJEcAKcokEolEIpFIjgBSlEkkEolEIpEcAaQok0gkEolEIjkCSFEm\nkUgkEolEcgSQokwikUgkEonkCCBFmUQikUgkEskRQIoyiUQikUgkkiOAFGUSiUQikUgkRwApyiQS\niUQikUiOAFKUSSQSiUQikRwBpCiTSCQSiUQiOQJIUSaRSCQSiURyBJCiTCKRSCQSieQIoN/vHbgR\nIQSf+cxneOWVVzBNk1/4hV/gxIkT93u3JBKJRCKRSA6UI2cp+8u//EvCMOT3fu/3eOaZZ/jc5z53\nv3dJIpFIJBKJ5MA5cqLs61//Ov/8n/9zAN72trfxwgsv3Oc9kkgkEolEIjl4jpwo63Q6lEqlwWNd\n10nT9D7ukUQikUgkEsnBc+RiyorFIt1ud/A4TVNU9ebacXy8dNPXJfuDXOfDQ6714SDX+fCQa304\nyHU+PA5irY+cpeztb387zz33HADPP/88Fy5cuM97JJFIJBKJRHLwKEIIcb93Yjvbsy8BPve5z3Hm\nzJn7vFcSiUQikUgkB8uRE2USiUQikUgkb0aOnPtSIpFIJBKJ5M2IFGUSiUQikUgkRwApyiQSiUQi\nkUiOAEeuJAZAHMf8/M//PPPz80RRxNNPP80DDzzAJz/5SVRV5fz583z6058evH9jY4Mf/MEf5E//\n9E8xTZMgCHj22WdZX1+nWCzy+c9/nlqtdh+P6Ghyr+vc58tf/jJ/8Rd/wS/90i/dj8M4FtzrWnc6\nHX76p3+abrdLFEV88pOf5PHHH7+PR3Q0udd19jyPZ555hlarhWmafP7zn2diYuI+HtHRZb9+Py5e\nvMgP/MAP8Dd/8zdDz0sy9mOd3/ve93L69GkAnnjiCT7+8Y/fj0M58tzrWqdpyuc+9zlefPFFoiji\n3/7bfzsohn/biCPIH/3RH4lf/MVfFEII0Ww2xfve9z7x9NNPi6997WtCCCE+9alPiS9/+ctCCCH+\n+q//Wnzv936vePLJJ0UQBEIIIX7jN35D/PIv/7IQQog/+7M/E5/97Gfvw1Ecfe51nYUQ4rOf/az4\n7u/+bvGJT3zi8A/gGHGva/2f//N/Fv/1v/5XIYQQly5dEh/60Ifuw1Ecfe51nb/whS+IX/3VXxVC\nCPGlL31J/nbchP34/Wi32+IjH/mIeNe73jX0vGSLe13nq1eviqeffvr+7Pwx417X+ktf+pL4j//x\nPwohhFhaWhK/8Ru/ccf7cCTdl9/93d/NT/3UTwGQJAmapvHSSy/x1FNPAZnq/9u//VsANE3jC1/4\nApVKZfD5r3/967z3ve/d8V7JMPe6zpDVlfvMZz5zqPt9HLnXtf6RH/kR/tW/+ldAdjdnWdYhH8Hx\n4F7X+Yd+6If42Mc+BsDCwsKO812yxX78fnzqU5/iE5/4BLZtH+7OHyPudZ1feOEFlpeX+Tf/5t/w\n0Y9+lMuXLx/+QRwT7nWtv/rVrzIxMcFHP/pRPvWpT/GBD3zgjvfhSIoyx3EoFAp0Oh1+6qd+io9/\n/OOIbZU7XNel3W4D8J3f+Z1UKpWh1zudDsVicfDeTqdzuAdwTLjXdYbsJJbcmntd62KxiGmarK6u\n8jM/8zM888wzh34Mx4H9OKcVReGHf/iH+Z3f+R0++MEPHur+Hyfuda1/5Vd+hfe97308+OCDO/4P\nJFvc6zr3RcJv/uZv8pGPfIRnn3320I/huHCva725ucm1a9f49V//dX7sx36Mn/u5n7vjfTiSogxg\ncXGRH/qhH+JDH/oQ3/M93zPUaqnb7VIul4feryjKYL69VVO32x3qpSkZ5l7WWXJn3Otav/LKK/zo\nj/4ozzzzzODOTbKT/Tinv/CFL/Dbv/3b/ORP/uSB7+9x5l7W+k/+5E/4wz/8Q/71v/7XrK2t8eEP\nf/jQ9vu4cS/r/Oijj/Jd3/VdADz55JOsrq4ezk4fU+5lravVKu9///sBeMc73sGVK1fu+O8fSVHW\n/4I+++yzfOhDHwLgoYce4mtf+xoAX/nKV3jyySeHPrNdrW5v1fTcc8/JC9ge3Os6S26fe13r119/\n/f9v745RFAbCKI6/ELGwsLLRNkewzQE8gBL0FOlS2ERQbMRuhDQBYy1Y23mLnCOlFrHaZbdwWZkl\nTNj/rw988wjhDcMQxXGs3W6nMAybG7xlbHPOskyXy0WS1Ov15Pt+Q5O3j23W1+tVRVHodDppMBgo\nz/Pmhm8R25yNMToej5Kksiw1HA4bmrx9bLMej8ef3aMsS41Go7dncPL2ZZZlqqpKh8NBxhh5nqfl\ncqn1eq3H46EgCDSZTL4987WtzudzJUmixWKhbrfLrcAXbHPG79lmvd/vdb/ftdlsVNe1+v2+jDFN\nL8N5tjlPp1MlSaLz+ay6rrXdbpteQmv85ffD8zw2fC/Y5vxxZHm73dTpdHinf2Cb9Ww2U5qmiqJI\nkrRard6egd8sAQAAOMDJ40sAAID/hlIGAADgAEoZAACAAyhlAAAADqCUAQAAOIBSBgAA4ABKGQAA\ngAMoZQAAAA54AmRkyN3ytZSpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1164e2358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "for index, row in df_geo.sort('index', ascending=False).head(10).iterrows():\n",
    "    \n",
    "    start_month = int(year_lst[0].split(\"-\")[1])\n",
    "    start_year = int(year_lst[0].split(\"-\")[0])\n",
    "    date_range = pd.date_range(start=pd.datetime(start_year, start_month, 1), periods=len(year_lst), freq='M')\n",
    "    over_time = pd.Series(row[year_lst].tolist(),index=date_range)\n",
    "\n",
    "    recent_dates = over_time[over_time.index.year >= 2010]\n",
    "    recent_dates.plot(figsize=(10, 5), title=\"Home Values over Time Since 2010\");\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "source": [
    "What is the rolling average for growth since recession in South Florida? How do we know that South Florida is not doomed to repeat the problem?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# df_foreclosure = pd.read_csv('data/City_HomesSoldAsForeclosures-Ratio_AllHomes.csv')\n",
    "\n",
    "\n",
    "\n",
    "# df_foreclosure = df_foreclosure.rename(columns=lambda x: x + \"_f\")\n",
    "# df_foreclosure['CityState'] = df_foreclosure['RegionName_f'] + ', ' + df_foreclosure['State_f']\n",
    "\n",
    "# # df_foreclosure = df_foreclosure.drop(['RegionID_f', 'RegionName_f', 'State_f', 'Metro_f', 'CountyName_f', 'SizeRank_f'],inplace=True,axis=1)\n",
    "\n",
    "# df_geo = pd.merge(df_geo, df_foreclosure, on='CityState', how='inner')\n",
    "\n",
    "# df_geo.to_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# fore_year_lst_ = [ str(j) + \"-0\" + str(k) + \"_f\" if k < 10 else str(j) + \"-\" + str(k) + \"_f\" for j in range(1997,2016)  for k in range(1,13)]\n",
    "\n",
    "# plt.hist(df_geo['2009-09_f'].dropna(), bins=50);\n",
    "\n",
    "# # df_geo['2009-09_f'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# plt.hist(df_geo['2015-09_f'].dropna(), bins=50);\n",
    "\n",
    "# df_geo['2015-09_f'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Near a coastline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-99-f0f22766750e>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-99-f0f22766750e>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    data = json.load(data_file)\u001b[0m\n\u001b[0m                               ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "        \n",
    "def convert_lines():\n",
    "    with open('data/us_coastlines.geojson') as data_file:    \n",
    "        data = json.load(data_file)\n",
    "            \n",
    "    point_to_line={}\n",
    "    count = 0\n",
    "\n",
    "    for feature in data['features']:\n",
    "        \n",
    "        feature_name = feature['properties']['NAME'] +\" \"+ feature['properties']['MTFCC']\n",
    "        \n",
    "\n",
    "        if feature_name not in ['Arctic', 'Caribe' ]:\n",
    "            geom = feature['geometry']['coordinates']\n",
    "            coordlist = np.asarray(geom)\n",
    "            x, y = transform(wgs84, cap, coordlist[:, 0],coordlist[:, 1])\n",
    "            \n",
    "            line = LineString(zip(x, y))\n",
    "\n",
    "            point_to_line[str(count) + \" \" + feature_name]=line\n",
    "            count+=1\n",
    "            \n",
    "    return point_to_line\n",
    "\n",
    "def measure_distance(points_to_line):\n",
    "    lines = []\n",
    "    oceans = []\n",
    "    cities = []\n",
    "    \n",
    "    for index, row in df_geo.iterrows():\n",
    "        p = Point(row['x_feet'],row['y_feet'])\n",
    "  \n",
    "        near_shore = False\n",
    "        ocean_name = None\n",
    "    \n",
    "        \n",
    "        for name in points_to_line:\n",
    "            line = points_to_line[name]\n",
    "            dist = p.distance(line)\n",
    "            \n",
    "            if dist < (5280 * 10):\n",
    "                near_shore = True\n",
    "                ocean_name = name\n",
    "                break\n",
    "        \n",
    "        lines.append(near_shore)\n",
    "        oceans.append(ocean_name)\n",
    "        cities.append(row['CityState'])\n",
    "    return lines, oceans, cities\n",
    "                \n",
    "                    \n",
    "               \n",
    "# points_to_line = convert_lines()\n",
    "\n",
    "# lines, oceans, cities = measure_distance(points_to_line)\n",
    "\n",
    "# coast_df = pd.DataFrame({'cities' : cities,\n",
    "#  'lines' : lines,\n",
    "#  'oceans': oceans\n",
    "#   })\n",
    "\n",
    "# coast_df.to_csv('close2oceans.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# merge the files together\n",
    "\n",
    "coast_df = pd.read_csv('close2oceans.csv')\n",
    "\n",
    "coast_df['CityState'] = coast_df['cities']\n",
    "df_geo = pd.merge(df_geo, coast_df, on='CityState', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName_x</th>\n",
       "      <th>State_x</th>\n",
       "      <th>ocean name</th>\n",
       "      <th>index</th>\n",
       "      <th>2016-03_x</th>\n",
       "      <th>2016-03_y</th>\n",
       "      <th>growth</th>\n",
       "      <th>seasonal use</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>Golden Gate</td>\n",
       "      <td>FL</td>\n",
       "      <td>Gulf</td>\n",
       "      <td>85.933024</td>\n",
       "      <td>202600</td>\n",
       "      <td>8.40</td>\n",
       "      <td>1.028028</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>Golden Lakes</td>\n",
       "      <td>FL</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>83.505419</td>\n",
       "      <td>70000</td>\n",
       "      <td>5.09</td>\n",
       "      <td>0.799486</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>Palm Springs</td>\n",
       "      <td>FL</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>83.336956</td>\n",
       "      <td>124800</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>Kensington Park</td>\n",
       "      <td>FL</td>\n",
       "      <td>Gulf</td>\n",
       "      <td>83.121668</td>\n",
       "      <td>158600</td>\n",
       "      <td>9.03</td>\n",
       "      <td>0.950800</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Leisure City</td>\n",
       "      <td>FL</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>82.661397</td>\n",
       "      <td>136600</td>\n",
       "      <td>7.49</td>\n",
       "      <td>1.005874</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>Broadview Park</td>\n",
       "      <td>FL</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>81.009600</td>\n",
       "      <td>158300</td>\n",
       "      <td>8.18</td>\n",
       "      <td>0.877817</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>North Lauderdale</td>\n",
       "      <td>FL</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>80.975046</td>\n",
       "      <td>143200</td>\n",
       "      <td>7.90</td>\n",
       "      <td>0.857328</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Naranja</td>\n",
       "      <td>FL</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>80.963786</td>\n",
       "      <td>157700</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0.968789</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>North Sarasota</td>\n",
       "      <td>FL</td>\n",
       "      <td>Gulf</td>\n",
       "      <td>79.780089</td>\n",
       "      <td>128800</td>\n",
       "      <td>7.71</td>\n",
       "      <td>0.685864</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3472</th>\n",
       "      <td>Laurium</td>\n",
       "      <td>MI</td>\n",
       "      <td>Great</td>\n",
       "      <td>79.652994</td>\n",
       "      <td>71600</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.623583</td>\n",
       "      <td>62.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RegionName_x State_x ocean name      index  2016-03_x  2016-03_y  \\\n",
       "1903       Golden Gate      FL       Gulf  85.933024     202600       8.40   \n",
       "364       Golden Lakes      FL   Atlantic  83.505419      70000       5.09   \n",
       "356       Palm Springs      FL   Atlantic  83.336956     124800       7.87   \n",
       "1105   Kensington Park      FL       Gulf  83.121668     158600       9.03   \n",
       "106       Leisure City      FL   Atlantic  82.661397     136600       7.49   \n",
       "485     Broadview Park      FL   Atlantic  81.009600     158300       8.18   \n",
       "476   North Lauderdale      FL   Atlantic  80.975046     143200       7.90   \n",
       "131            Naranja      FL   Atlantic  80.963786     157700       8.50   \n",
       "1098    North Sarasota      FL       Gulf  79.780089     128800       7.71   \n",
       "3472           Laurium      MI      Great  79.652994      71600       7.30   \n",
       "\n",
       "        growth  seasonal use  \n",
       "1903  1.028028          69.4  \n",
       "364   0.799486          50.0  \n",
       "356   0.950000          50.0  \n",
       "1105  0.950800          59.5  \n",
       "106   1.005874          31.4  \n",
       "485   0.877817          44.3  \n",
       "476   0.857328          44.3  \n",
       "131   0.968789          31.4  \n",
       "1098  0.685864          59.5  \n",
       "3472  0.623583          62.7  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_out = df_geo[df_geo['lines'] == True]\n",
    "df_out['ocean name'] = df_out['oceans'].apply(lambda x: x.split(' ')[1])\n",
    "df_out = df_out[['RegionName_x', 'State_x','ocean name', 'index', '2016-03_x', '2016-03_y', 'growth', 'seasonal use']].sort('index', ascending=False).head(10)\n",
    "df_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is just like the one above. \n",
    "\n",
    "What about for California."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName_x</th>\n",
       "      <th>State_x</th>\n",
       "      <th>ocean name</th>\n",
       "      <th>index</th>\n",
       "      <th>2016-03_x</th>\n",
       "      <th>2016-03_y</th>\n",
       "      <th>growth</th>\n",
       "      <th>seasonal use</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>Boulder Creek</td>\n",
       "      <td>CA</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>69.845936</td>\n",
       "      <td>499300</td>\n",
       "      <td>16.34</td>\n",
       "      <td>0.698877</td>\n",
       "      <td>55.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>Watsonville</td>\n",
       "      <td>CA</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>69.360017</td>\n",
       "      <td>471200</td>\n",
       "      <td>15.91</td>\n",
       "      <td>0.641812</td>\n",
       "      <td>55.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>Salinas</td>\n",
       "      <td>CA</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>68.915325</td>\n",
       "      <td>386500</td>\n",
       "      <td>16.01</td>\n",
       "      <td>0.695919</td>\n",
       "      <td>39.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>Guadalupe</td>\n",
       "      <td>CA</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>68.254704</td>\n",
       "      <td>238700</td>\n",
       "      <td>13.32</td>\n",
       "      <td>0.528169</td>\n",
       "      <td>31.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>Guerneville</td>\n",
       "      <td>CA</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>68.160935</td>\n",
       "      <td>369800</td>\n",
       "      <td>14.84</td>\n",
       "      <td>0.560338</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>National City</td>\n",
       "      <td>CA</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>68.032306</td>\n",
       "      <td>355000</td>\n",
       "      <td>15.23</td>\n",
       "      <td>0.672950</td>\n",
       "      <td>26.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>Monte Rio</td>\n",
       "      <td>CA</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>66.846068</td>\n",
       "      <td>351500</td>\n",
       "      <td>14.76</td>\n",
       "      <td>0.477512</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>Felton</td>\n",
       "      <td>CA</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>65.830678</td>\n",
       "      <td>524700</td>\n",
       "      <td>16.74</td>\n",
       "      <td>0.500429</td>\n",
       "      <td>55.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>Ben Lomond</td>\n",
       "      <td>CA</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>65.754377</td>\n",
       "      <td>608200</td>\n",
       "      <td>17.92</td>\n",
       "      <td>0.579330</td>\n",
       "      <td>55.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>Oceano</td>\n",
       "      <td>CA</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>65.548792</td>\n",
       "      <td>390500</td>\n",
       "      <td>17.28</td>\n",
       "      <td>0.447905</td>\n",
       "      <td>60.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RegionName_x State_x ocean name      index  2016-03_x  2016-03_y  \\\n",
       "792   Boulder Creek      CA    Pacific  69.845936     499300      16.34   \n",
       "789     Watsonville      CA    Pacific  69.360017     471200      15.91   \n",
       "438         Salinas      CA    Pacific  68.915325     386500      16.01   \n",
       "558       Guadalupe      CA    Pacific  68.254704     238700      13.32   \n",
       "414     Guerneville      CA    Pacific  68.160935     369800      14.84   \n",
       "36    National City      CA    Pacific  68.032306     355000      15.23   \n",
       "420       Monte Rio      CA    Pacific  66.846068     351500      14.76   \n",
       "796          Felton      CA    Pacific  65.830678     524700      16.74   \n",
       "795      Ben Lomond      CA    Pacific  65.754377     608200      17.92   \n",
       "1277         Oceano      CA    Pacific  65.548792     390500      17.28   \n",
       "\n",
       "        growth  seasonal use  \n",
       "792   0.698877          55.4  \n",
       "789   0.641812          55.4  \n",
       "438   0.695919          39.4  \n",
       "558   0.528169          31.3  \n",
       "414   0.560338          44.0  \n",
       "36    0.672950          26.7  \n",
       "420   0.477512          44.0  \n",
       "796   0.500429          55.4  \n",
       "795   0.579330          55.4  \n",
       "1277  0.447905          60.6  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "df_out = df_geo[(df_geo['lines'] == True) & (df_geo['State_x'] == 'CA')]\n",
    "df_out['ocean name'] = df_out['oceans'].apply(lambda x: x.split(' ')[1])\n",
    "df_out[['RegionName_x', 'State_x','ocean name', 'index', '2016-03_x', '2016-03_y', 'growth', 'seasonal use']].sort('index', ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Near Major Airport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessibility: \n",
    "Is it near a major airport? Find cities that are within at least 20 miles of major commercial airport."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load Airport Data\n",
    "df_airports = pd.read_csv('data/us_airports.csv')\n",
    "\n",
    "# There are airports in every little town.\n",
    "# remove non major airports\n",
    "\n",
    "major_airports_codes = [\"GA\",\"LAX\",\"ORD\",\"DFW\",\"JFK\",\"DEN\",\"SFO\",\"CLT\",\"LAS\",\"PHX\",\"IAH\",\"MIA\",\"SEA\",\"EWR\",\"MCO\",\"MSP\",\"DTW\",\"BOS\",\"PHL\",\"LGA\",\"FLL\",\"BWI\",\"IAD\",\"MDW\",\"SLC\",\"DCA\",\"HNL\",\"SAN\",\"TPA\",\"PDX\",\"STL\",\"HOU\",\"BNA\",\"AUS\",\"OAK\",\"MCI\",\"MSY\",\"RDU\",\"SJC\",\"SNA\",\"DAL\",\"SMF\",\"SJU\",\"SAT\",\"RSW\",\"PIT\",\"CLE\",\"IND\",\"MKE\",\"CMH\",\"OGG\",\"PBI\",\"BDL\",\"CVG\",\"JAX\",\"ANC\",\"BUF\",\"ABQ\",\"ONT\",\"OMA\",\"BUR\"]\n",
    "\n",
    "df_airports = df_airports[df_airports['code'].isin(major_airports_codes)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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adQ0ul4u33noLl8vk7hhqqWYDJ0mS+OGHH8jIyODpp5/mX//6l3ti57Rp03jl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120172e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sanity Check\n",
    "# make sure airports are in parts of the states that make sense\n",
    "# validate that the data is correct (lat, lons for airports fall on the map)\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = plt.gca()\n",
    "add_us_outline(ax)\n",
    "ax.scatter(df_airports['lon'], df_airports['lat'], marker='o');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#converted units\n",
    "cap = Proj('+datum=NAD83 +lat_0=32.10 +lat_1=33.53 '\n",
    "    '+lat_2=32.47 +lon_0=-116.15 +no_defs +proj=lcc +units=us-ft '\n",
    "    '+x_0=2000000 +y_0=500000', preserve_units=True)\n",
    "\n",
    "wgs84 = Proj(init='epsg:4326')\n",
    "\n",
    "def transform_lat_lon(row):\n",
    " \n",
    "    try:\n",
    "        x, y = transform(wgs84, cap, row['Longitude'], row['Latitude'])\n",
    "        return x,y\n",
    "    except:\n",
    "        return np.nan \n",
    "\n",
    "def transform_lat_lon2(row):\n",
    "\n",
    "    try:\n",
    "        x, y = transform(wgs84, cap, row['lon'], row['lat'])\n",
    "        return x,y\n",
    "    except:\n",
    "        return np.nan \n",
    "    \n",
    "    \n",
    "# gett city geo    \n",
    "df_geo['x_feet'], df_geo['y_feet'] = zip(*df_geo.apply(transform_lat_lon, axis=1).tolist())\n",
    "\n",
    "df_airports['x_feet'], df_airports['y_feet'] = zip(*df_airports.apply(transform_lat_lon2, axis=1).tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import math\n",
    "feet_to_mile = 5280.0\n",
    "radius = 25\n",
    "\n",
    "def check_location(row):\n",
    "    center_x, center_y = row['x_feet'], row['y_feet']\n",
    "    airport_close = False\n",
    "    \n",
    "    for index, airport in df_airports.iterrows():\n",
    "        x, y = airport['x_feet'], airport['y_feet'] \n",
    "        eucl_dist = math.sqrt((x - center_x) ** 2 + (y - center_y) ** 2) / feet_to_mile\n",
    "        if eucl_dist < radius:\n",
    "            airport_close = True\n",
    "            break\n",
    "    \n",
    "    return airport_close\n",
    "\n",
    "\n",
    "df_geo['near_airport'] = df_geo.apply(check_location, axis=1)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName_x</th>\n",
       "      <th>State_x</th>\n",
       "      <th>Population</th>\n",
       "      <th>index</th>\n",
       "      <th>2016-03_x</th>\n",
       "      <th>2016-03_y</th>\n",
       "      <th>growth</th>\n",
       "      <th>seasonal use</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>Lehigh Acres</td>\n",
       "      <td>FL</td>\n",
       "      <td>48534</td>\n",
       "      <td>86.148716</td>\n",
       "      <td>126900</td>\n",
       "      <td>9.26</td>\n",
       "      <td>1.147208</td>\n",
       "      <td>55.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>Golden Gate</td>\n",
       "      <td>FL</td>\n",
       "      <td>25388</td>\n",
       "      <td>85.933024</td>\n",
       "      <td>202600</td>\n",
       "      <td>8.40</td>\n",
       "      <td>1.028028</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>Golden Lakes</td>\n",
       "      <td>FL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.505419</td>\n",
       "      <td>70000</td>\n",
       "      <td>5.09</td>\n",
       "      <td>0.799486</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>Palm Springs</td>\n",
       "      <td>FL</td>\n",
       "      <td>14329</td>\n",
       "      <td>83.336956</td>\n",
       "      <td>124800</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Leisure City</td>\n",
       "      <td>FL</td>\n",
       "      <td>23770</td>\n",
       "      <td>82.661397</td>\n",
       "      <td>136600</td>\n",
       "      <td>7.49</td>\n",
       "      <td>1.005874</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>Poinciana</td>\n",
       "      <td>FL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.054447</td>\n",
       "      <td>132400</td>\n",
       "      <td>9.82</td>\n",
       "      <td>0.880682</td>\n",
       "      <td>57.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>Broadview Park</td>\n",
       "      <td>FL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.009600</td>\n",
       "      <td>158300</td>\n",
       "      <td>8.18</td>\n",
       "      <td>0.877817</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>North Lauderdale</td>\n",
       "      <td>FL</td>\n",
       "      <td>34267</td>\n",
       "      <td>80.975046</td>\n",
       "      <td>143200</td>\n",
       "      <td>7.90</td>\n",
       "      <td>0.857328</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Naranja</td>\n",
       "      <td>FL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.963786</td>\n",
       "      <td>157700</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0.968789</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Youngtown</td>\n",
       "      <td>AZ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.624740</td>\n",
       "      <td>110900</td>\n",
       "      <td>9.62</td>\n",
       "      <td>1.016364</td>\n",
       "      <td>28.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RegionName_x State_x  Population      index  2016-03_x  2016-03_y  \\\n",
       "335       Lehigh Acres      FL       48534  86.148716     126900       9.26   \n",
       "1903       Golden Gate      FL       25388  85.933024     202600       8.40   \n",
       "364       Golden Lakes      FL         NaN  83.505419      70000       5.09   \n",
       "356       Palm Springs      FL       14329  83.336956     124800       7.87   \n",
       "106       Leisure City      FL       23770  82.661397     136600       7.49   \n",
       "1288         Poinciana      FL         NaN  81.054447     132400       9.82   \n",
       "485     Broadview Park      FL         NaN  81.009600     158300       8.18   \n",
       "476   North Lauderdale      FL       34267  80.975046     143200       7.90   \n",
       "131            Naranja      FL         NaN  80.963786     157700       8.50   \n",
       "24           Youngtown      AZ         NaN  80.624740     110900       9.62   \n",
       "\n",
       "        growth  seasonal use  \n",
       "335   1.147208          55.8  \n",
       "1903  1.028028          69.4  \n",
       "364   0.799486          50.0  \n",
       "356   0.950000          50.0  \n",
       "106   1.005874          31.4  \n",
       "1288  0.880682          57.6  \n",
       "485   0.877817          44.3  \n",
       "476   0.857328          44.3  \n",
       "131   0.968789          31.4  \n",
       "24    1.016364          28.1  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "df_geo_near_airport = df_geo[df_geo['near_airport'] == True]\n",
    "\n",
    "# sort descending from the highest index down and limit the top 10\n",
    "\n",
    "df_out = df_geo_near_airport.sort('index', ascending=False).head(10)\n",
    "df_out[['RegionName_x', 'State_x','Population', 'index', '2016-03_x', '2016-03_y', 'growth', 'seasonal use']].head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ubm4OHRqrCqCuDIYUCgUKhaLB9SxY8Dc8PX3a/PN6JavVRllZBQqF4z7kDAYL\nkqRw6uNwtRMnTpCamk5sbDfWrl2Pl5cnXbr0QKs1o9Ve+f7mRmRkB0pL1UybNoOoqPaXgwVVg9rr\n7x+MLLtRUqJz2PtmS74H5+fns2bNStq3j2TWrNn4+fmhVCrtr32lUmlfiGI0Gtm9ew+zZz9Q79/q\niRPHSEvLQKczIstKtFqd057/2Ngu/PTTdv7wh2vnadcVgNY5VLdkyRIWL17M4sWLufHGG3nrrbd4\n6KGHOHbsGAB79+6lZ8+ezWy6IAhNpdFoGrxCrbWcO3eO119/jRkz/uiQyZ0AAQEBPPDAXLZs2cIb\nb7xW7xyvsrIyFix4kwMH9mOzVfYCqVQqVKrmTRKtiSRJKJVKe/nu7u4olcpG1uM6c5ycKT8/D5PJ\n1KR96prCarXyyy87CA+PpLi4gKlTp2Gx2EhMTLrm2LNnz5Cbm8Of/3w30dEdGj1k7uPjQ1JSEjk5\n2Y68hBaRlZXJ999/w7hx45g2bQbBwcH2LyBVr/0rV+8qlUpsNhtWq6Xesr/77jtuuWUCULmopaJC\n75RrMBgMnDmTat+EuTEaPbPy1Vdf5dVXX8XNzY3Q0FBee+21RlcqCIJjVH3Da8vWr/+RUaNGMX78\nLQ4ttyqNwoMPPsCxY8fo06dyQq7FYqGoqIDQ0HYUFOSjVKrYtGkDJ0+eID09vc55SELTSZLs8DlO\ngwcPYffuX3jnnbeYMmUa3bt3d2oyzIKCSxQUFKLXG/nDH/7AqVMpxMX1rnERws6d2xg/fnyzvrhI\nkoTF4lqvx+zsi/z44w9Mnz6dmJjYBp2jVqtxd/cgOLj+pJOhoaEUFRUB4O7ugdlsalZ7r2YwGNi+\nfTspKSfp3LkrAwcOanQZDQ6cFi1aZP952bJlja5IEATHq6jQERwc0NrNqJVarebQoUM8//wLTquj\nY8dYTpw4zsmTx6moqODMmVQyMjLw9PREp9OhVCrx9/fn2WefY9WqVRw8eACLxbFvxo5WuSls218p\n6WxKpZKHHnqUJUsW8c9/vkf//gN44IG5TqvPbDYjSRIjR46gc+fOrFmzhgceqHmTbE9PzyYnY6xS\nWlpGQIBzNkd2Bo1GzerVK5k8eUqDgyaoTD4pSRJpaWfp2rVbnccWFRXZV7q5u7s7LHDSaDRs2rSB\njIwM/P2DGDXqJrp27dakHufrcy2vIPxOGI1G3N1bbuJsY6Wnp+Hurqr3zbI5xo0bxzvvvIW3tw9D\nhw7Fzc04i7FTAAAgAElEQVSNt956h6CgYLy8vCgtLbVvr9C+fQdmz74bs9m1Vqy5AlmWHN7jBODv\n78/DDz9KVlYW7777Fo899ggPPPCAU/KSRUW154UXXkShUJCWlkZ4eHitmfTLy8vx92/elxa9voLA\nwKZv/dGSSks1bNq0noSEfnTp0qVR5yqVStq3j+LixQu1vhdkZmayYsU35Obm2INjlcq92T3EBQUF\nbNiwjpycXCIj22OzSYwePZbo6A5NLlMEToLgwkpLNY3eJbwl+fj4olar+emnjYwZc5PD5jhdqV+/\nBJ599nlCQ8NqTJR45Z5UAQEBBAYGuVSeJKFShw4deP/9D9i4cT3//e9/+eij/zqlnqrXaElJMX5+\nNQdGBQUFWK2WJs2PqXLkyGFSU1Pp08fxmyM7WlraWTZu/JG+fePtmyI3llqtISio5iAxIyOd+fNf\npm/fvrzxxt/tw5/u7u6YTA0PnIxGIyUlJfYeK5vNxvfff0d+fj4jR46hX7/+DnkPapmdEwVBcAqL\nxdKmczh17dqVCRMm8p//fMTixV86rZ6+feNbNLu00DoUCgVjx44DbHz++WdOrauiogKDoeaJycnJ\nexg4cGCTP4QtFgsbNmxg0qSpTUrA2JJOnUph48a13HrrRMaNG9eka87Pz6O8XEt0dHSN92dmZnLD\nDTfw9NPPVJszplKpsFobHjht3ryZ//1vIfn5+VgsFnbt+oVLly5xzz33M2BA05+vq4nASRBcmF6v\nx8Oj7e62Lssyf/rTn+nW7QZ8fdtGz1jlPnBiqM5Vubu788ILL3Py5AnefnsBmZmZDq+jpKSE/fv3\nM2zYtb0r58+f4+LFLBISmtZTZLFY2LlzB97e3nTsGINGo2bjxnUsX76E3Nyc5jbd4c6cOU1cXC96\n9OjR5DIOHjxI585dak1FYDAYMZmuncukUqkatfhFo1Fjtdr45JOF/P3vf+Po0WPcdNPNhIaGNrnt\nNRFDdYLgomw2G+Xl5S6zXUNERGRrN0G4TkRFRfHCCy+xcuV3vP76q3zwwb/x8vJyWPnnz5+jY8dY\ngoKuXTHn7e2DJMlNmltos1X2lOn1BsaOvZm1a38gIyOdXr3i6NcvntWrVzJ79gNtphfZZrNx/vw5\nIiMjmlWOj483FRUVtd6/a9eOGlM+VAZO9X/J2bz5Z06fPkVxcQkJCYn4+vpQVlbGqFFjnDIsL3qc\nBMFFlZeXo1IpcXNza+2m1MloNJKXl0vXrl1buylAVSZvsWLN1QUHB3P//XPo3bs3X3zh2GG7S5cK\nCAmpOZt0SEgIer0eg6HxSRk1Gg2lpWXMnv0AZWWl6PU6HnnkEW69deLl4eZIUlNPNbf5DhUYGERx\ncXGTrrdKp05dyM7OrjHfmtFoJCPjHEOHDrvmPjc3t2rJZK9UWFjI+fPnWLFiOXv27KG8vIL77pvD\noUP7KSwsZPTosU6byyh6nATBRZWUFNf4jbit0el0yLJMaGjjtjUQXI8zcyzVJj6+H+vW/ejQMv39\n/bh0qbDG+y5cyMLHx7tJvUJlZaWYTEby8/M4fPggY8aMsWewB4iJieHSpfwmt9vRJEli1qx7WbZs\nMTk52cTGdmpSOZWTvCuH465+3P7zn3+TkJBQ4zBeZUJNN/R6vb1H0Ww289NPmzh48BCSBElJQ3jo\noZvt98+aNbvaY+oMInASBBdVVFTo8LF7Z7BarUhS2+ncFivqri9VewM60rlz54iNrXnJ/fnz54iL\n69mkOiMiIklMHMDSpV/Rq1cc3bpVX5rv7e2NTnehSW12lspcaG7o9U3vcTp27CgAhw4dZNCgwfbb\nS0tLOXLkMK+//mat53p4uKPVavHy8sJms7Fv317OnDnLXXfNQql0u+Y9sCUm24vASRBcVGVOlM6t\n3YwGkeW2FazUt0WL0Diff/4p6elpPProQ9dsL1PfY92QAKSuMrRaLeXl5Q1vbD0sFgvnz2cybtyE\nGu+Pje3E9u2bGTVqTKPLViqVjB49lsTEJDw9Pa+59pCQEAoLLzWp3Y5ms9k4eHA/e/bswtfXp1mr\nVpOSBpGcvI9t27ZWC5wyMtIJCQmlQ4facyr5+vpSUHCJsLAwTp48wdatW5k8eXqrzpkUgZMguCCt\ntozMzHPccsu1m1MKrs/VesWMRiODBg3h2Wefr/H+quupLQCqK3FmfcN/5eXlPPnkY2RmZtKxY8cG\ntrh26enptGvXDk/PmlerRkW1p7S0lLKysiYPCdW2oCMsrB1qdQlWq9Whw57l5eWkp5/l/PlzlJVp\n7BOufXx8CQkJJSFhAN7e3pf3k7NiMpnYv38f58+nM3fu3GZnSPfx8SEhYQDHjv1a7fYLFy4SHh5e\nT9t1WCwWSktLOXDgAD169KJLl9adLykCJ0FwQampp4mNjXGZFXVC47lSygQfHx/y8vIICGj57X8C\nAgKYOHESH374L9599x/NLi8nJ5v27WvvAZFlmc6du3Hs2FGGDBna7PquLlulcsNgMNQauDXWyZMn\n2LjxR+LienLjjTcQEhKCQqHAZrOh0ZSSkZHOV199hkqlorRUgyRJKBRKunbtzO2339HsoKmKXq9H\npaq+ElGtLq43+Bw/fjyfffYZEydORKlUMXr0WIe0pzlE4CQILkij0RAZKRI+NoWLdea4hDFjxvLj\nj2vRarWtEsxnZ2dz4403OqQss9mMm1vdqQbi4xNYs2YlgwcPcXjvoJ+fP2p1icMCp9JSDfHx8dx6\n68Rr7ouIiKR79+707dsXSZIIC6ucH2Q2mx2+lZPBoMfDo3qZFy9m06dP3VvnTJgwkW++Wc6pUykM\nHTqq1i1wWpIInATBBV26lEePHje0djMarK0t/3fGnmqOZDQa2bNnNwUFBTXeX9OcsSsn4EtS/cN9\nKpUSo7F6VuYrh9J8fHyZMOHWBmVbNhpNjUpU6Gj5+XlMnDjJIWWpVCrUak2dx7Rr1w5Zlrl0KZ92\n7eoeamqsbt26kpJywmFzeBQKJVlZWXVOor/6S5gztkYyGg3XpE4pLVXXe55KpSIhIYHs7Byn7nnZ\nGCJwEgQXo9VqKSwsoH37mrcvEFyfLMsYDAZMJmOdx10dkF45vFdfsOrursRgqB44XRmQ/fzzT5w4\ncZxp02YQExPDsWNH8fT0RKfTYTab6d27j713KSXlJO3bd2i1oeOoqPakpp6mZ8+4ZpcVHh5BWlp6\nvccFB4dSXFzi8MDJ09OLkpIyh5UXF9eLw4f3c/LkSeLimv/4NJXRaMTN7bdUBEVFRWRkZPDYY0/U\neo7ZbGb79m1oNKXce+/9rZLuoiYicBIEF1NSUkxgYGCbyS4sOJ6bm4oRI0YyYcKtTqvD29ud8vLa\nl5jPmnUP33yznL///W907NiB9PR0goKCkGUZna6CsLBQxo69icTEJA4e3M/gwYNrLcvZBgxI5Mcf\n1zB16vRmlxUZGUlRUSFabRk+PrXPv3F3V2E0Nn2Jfm1KSkoIC3NczjMvLy969uxNenpaqwZOWq0W\nvV7P4cMHcXNzo6CgEEmSKC8vp7i4GA8PD9zd3e29XXl5eTz33F+RJBmVSkVwcM0bBLcGETgJgosJ\nCgpGo9Fw4UIW0dG1T2IVhObw8PDg7rvvYfToMWzYsI6HHnrE3rtitVpZsWI5K1asYNGiRZjNJgYM\nGNhqbU1I6M+qVd/z/PPPMGrUGMaN+0OTy/L29kahkOsdzjWZzCgUjv8I1Wg0hIe3d1h5KSknOX78\nCFOnTnNYmU1RXl7O2bNnKCwsxGw2YTKZ0Wg0vPbaq8iyhMlkxmIxo1QqUSqVGAwGunbtxksv/R/f\nffcdS5Z8xYwZtxMYWPN+dy1JBE6C4GK8vb3p0aMXGRkZInBqAputdTJcu6ro6GgeeGButdtkWeaO\nO+7kjjvuZMGCNzl9+hRfffUFN9/cOukxfHx8eOONv5OcvI8vvvgMo9FEcHAQNpsVhUKJLMsoFAqU\nSiUKhQKFQolSqbAHPlcOcVosFkpK1Pj5+ddZp06nxdfXcUOTe/fuYcuWLciyzNixjnkcMzPPs23b\nz9x+++106ND8VA3NUVBQyCOPPEpCwoBaj7FarRiNRvR6PWazmZCQEMxmM3p9BaWl6gbtW9cSROAk\nCC5Ir6+gXbu203XtSpyRafr37JlnnuOFF55ly5Yt7N+fTGJi6/Q8+fj4MGbMWHbu3M7Chf+hb9++\nyLICi8VyOT+RBYvFgsVivfxz5f9QfSK9xWKlqKiozlxKNpuN4uJih/V+2Gw2tm/fQXx8f7p06drs\nDYttNhvJyXs5fvwoEydOavWgSa/XU1BwiRtuqHvloyzLeHh4VFs5t23bVoqLS5g799E2k35FBE6C\n4IIsFgtKpfjzbaq2lsnclcmyzIIF7/DSS89TWlra2s3hySfn8fbbC+jSpRt3331Po88vKytj4cKP\n6wyuCwsLcXNzc8ieaCaTiY0bNxIUFNSkbORXKy0tZdu2zZSVaZg92/n7tjXEkSOHiY6ObnTgU7nF\nyj5GjRrbZoImANFfLQguKCAgkNzcvNZuhiDYJSYmsXfvntZuBgEBAdx883h++WUnGRn1r467miRJ\nWK3WOuc4HTlykN694xzSc3nqVAq5uXlMm3Z7s8pRq0vYsWMHr7zyArm5F5k9+742ETQB/PrrEXr2\n7Nmkc+Pi4vjll+2cOpXi4FY1nQicBMEFde7chfT0tFbNnSMIV+rW7QbS09PQaOrOgdQShg0bzqRJ\nk5k//+VG94L5+PgQFdWew4cP1nh/QUEB586lkZTkmFWEly5dIiYmtlnDcxcvXmDp0q/Iy8sjKiqK\nTp26tKke6dOnT6PX6zl27ChnzpwhPz+P/Pw8cnNzyc/Po7i4GK1Wi9FYPf2GJElMmTKVmTNnsnv3\nTqxWK8XFRWzevImioqJWuhoxVCcILqldu3AkScHTTz+Bt7cPkiRhs9kwmYxYLFYMBj1ubm5YrTYs\nFjNWqw1ZlpAkCVmumiQr299cKzdm/e17VNVk2eLi4suZtmUkSbL/u5LVasVms12RPNFWLZGi2Wwh\nLy+Xhx+ee/ncmr+lX7mfmc1mtbe16vaKCh1Wq43AwED7NdlsViwWKzabFZvtt3ZLkowsS9XaUXV/\nbm6OCDidoHv37oSGhvHFF5/xxBNPtWpbJEli0qRJbNnyExcuZDU6v9PIkSNYvXpNjSsFf/31EP37\nD3BIZm+dTsexY8eYPLnxaRTMZjPp6Wls2fIzCoXMxImTSEjoQ3Z2LqtXr2Lz5k3Ab0lNr9wmsKaO\nsqr7r5woL0lyrZn2FQoFt902lTFj6h9e1GjUHD9+jDNnUjEYjFy6lI8kSfj6+mK12jCbzVgsle8T\nISHBmM0W+vcfwDPPPAdAaGgYanUJOp2O/Px8jh49wpEjh/nrX2veG9HZROAkCC5IkiTi4xPQ6coY\nMWKkfSJrVR4UT09PjEYTsizj5uaGLMuXAxwrZrPlcoBlsSdYNBpN17xhAnz77Qr69+9Hv34DLidU\ntF6TWFGWFZeDMtk+d+jK4EqWJfs5lZN0a0/MWLkKSnFFIGgGKtu1c+dOsrMvMmXKVBQKN3vgJ0ky\nCoVsb0NVORZLVfD1W1skScFrr82vFiQKjjNnzlxef/1VzGZzm+jx0Ov1deZiqk1ERCQajQadTlet\nJyg/P4+zZ1N54IE5zW6bxWLhm2+WExvbhfDwiAafp9WWcfTorxw/fhSr1UJKygn+97/P7I/3n/98\nN2PGjL1mEcSVfwc1/Q3+9rdb/QtUbX+v585lsGjRlyiVMiNGjKqzzX5+/syZ8xA9evQAKueRKZXK\na4LPJ554jDvuuANZVvD22wvst3t6ehIbG8uOHVuZMGESWVmZnDhxFKPR2Cr57Fr/lS0IQpOUl2vp\n0KEjAwYkOq2OHTt2EB4eTvfujtkHrDlSU1PR6/X069e/WeUolW71HyQ0SZcuXfH09OTYsV+b/Tw1\n15o1q9HpKoiObnyGfVmWiY2N4eTJ4wwYMBCbzcapUyl8+OH7yLLEG2+8Vuu5VT08ZrOZG27oTlJS\nEhUVFej1evR6PRUVOvR6PZmZmZSXVzBv3h+BykzaXl5etfZkWSwWdu3ayYkTR+nVqzd33jkTi8XK\nG2+8Wi1I9fLyokuXro2+5sbq0qUrPj6+/Pe//2bgwEF17iFnNBqrXVdtc6+USiUqlQe9e/fG3d2d\n5OR9DByYhCzLjBo1imXLlrFt2xby8nIBKCoqdNjWNI0hAidBcFF9+sTz2WcfU1paip+fX2s3p0WI\nNAJtn8FgbJUPs6v9/PMmnn56XpNzdnl6erBkyZfk5eWhVhdjs1l5+OGHUSoV9t6cK4eCofrrMzk5\nmfXr13L+fAYqlQp3d3dUKnf7cvt27dqRm5vHN98sZcSI0SxbtoTevfsyfvyEa9pitVrZuHEdBoOe\nuXMftAce2dnZTbo2Rxk0aDCff/4ZGo0aD4/at54xGo14e9c/tCnLMjZb5YrhRx99gn/8413+/e//\ncu5cBmFhYYwYMYKff/7J/ribTCaHXUtjiMBJEFyUt7c3HTrEkpWV5dStFK7+cBCEulRU6NiwYQOz\nZ9/X4nXbbDbS09NYufJ7tNpyevRo2kouqBzO6tSpE9HRkQwY0I+YmNhGBWHl5TpycnJ4/fW/1dne\n48ePsWbN9xiNBi5ezMRisaBQKCgoKCAt7Qyenp5kZKQhSRK33377NRvltragoCCOHj3KuHG1B05m\nsxFPT+96y6oKSK1WK7GxsXh6evLBB/9EqVRhtZpRqdzp27c/3t4+7NixlcDAQEdeSoOJwEkQXFhA\nQAAaTf07jDdHW+nlqW/TWqFteOGFl3jjjde49dYJhIU5dgPc2hiNRt59921Onz6FLMskJQ3mvffe\nb9b8F5vNio+PD4MHD2nS+VcvTqiJJEn07t2H6OgOGAx6tm/fxurVKzEY9JSWqrnhhu7odGVER7dn\nyJChNc4ba+0vNjNn3slHH/2bH35Yxb/+9eE1gZ3VWjmvsiGrBnNzc6io0LN8+TLS0tLo2vUGKioq\nKCsrpaiokK5db2DIkGEYjUZCQ0Px9W2dnnYROAmCC6uo0BEZ2a61myEIdr179yEoKJjCwuIWC5xO\nnDhGZuZ53nnnPft+eq2tMQsQqnpOpkyZxqFDBwkICKRTp051zhtqK/r2jWfs2JtYtOhLnnrqCebN\ne4aOHX/LVK7ValEoFPbeurKyMnJzczEY9BgMBgoKCsjJyeHs2bNoNKXs2rWLDh1ieeSRJ/Hw8KC4\nuIiPPvonsbGdUChkDh8+yK5dOwB4+unnWuWLnQicBMFF2Ww28vJySUpy3uRwQWiqs2dT7auonE2r\nLcdkMuPp2bytSq7W0nsauru7N7mHq7UYjUZ++mkj7777Pqmpp3nllZeZNGkyt902GVmWuXjxIiaT\nmbS0sxQVFbFr1y94eHjZJ8Kr1erLKUXg5Zdf4+TJY5w7l24PGgMDg7jppvHExyfg7u7OiRPHUSrd\nWnWfThE4CYKLMhqNqNUlhIX9XnqcbA5LI2A0GqioqKheus1mzxZd9XNVfqqaPkCrhkiqjgMupz+Q\n7d+Cq36u7wO4cjjDbK/faDSg1ZZx6dKlamkiauPu7kFAQEC9x7WUmJgYysrKWqy+IUOG8ssvO1i9\nehV//vPdLVZvfRry3Lm6d999hx49etK9e3duvPFG4uLi+OSThezevYuHHnqEl156gcGDB7N8+XLa\ntYtg3LgJdOnSFavVyrZtm0lPP4vVarscRJWQkJBYbfqBJEnVko3GxfUiLq5Xa1yqnQicBMFFubu7\n4+3tQ2lpKcHBwa3dHJcRHBzMSy+9ePm3qvkhVYHOb3moqiapFhcXExJSfUPlK3PkVB575X1csV2H\n7fLvtnr3x7sy4NJqy0lJSWHz5s32++sakigqKmThwk+vaWdriYmJITc3t8XqUygUdOjQEa1W22J1\nthWtOQdx165fSEs7w/vvf2BvR5cuXfnb3xawZMlXvPnmGwQGBpKenkHPnnG0a9eOfft2s2/fLjQa\nDeHhEUyePJXVq1chSZVfUloilUJzicBJEFyURqPGYjERFOSYHdrbusokn83/kFiw4O0GH1tcXMxT\nTz3Ol18ubna9zvTggw9gMBhauxl2QUHBnDx5ssXqu3jxItu2bWXevGcdWm5d+9XVp60sqnCmwMBA\nJEnC39+/2u1KpZL27Tvg4XGIdu3CiYqKIjQ0FKPRwOjRo/Hy8kSjKeX06RQ2btxAv36J9O0bj79/\n2+k1rYsInATBRR0+fIhevfo4/Q26tVftCPVra8/R+fPnCAjwr/9ABzl27FdiYjq12JyqhmpjT4vD\nffPNcpKSBl1zu81m4+eff2LatOkMHz4ChUIBVAaiZ8+eZcOG9ZSXV9C3bz9GjLjJJSbBX0kEToLg\ngkwmEykpx7n//gecWk/VVi1tgdXquDlO15u21rsRH9+Pd999m2++WU5gYACyrLi8LU7l6qort9Wp\n2gbIbDbbf7dYrFgsZkwmExaLFbPZhMFgxGw2YzQa0OsNeHt7c/fd9/DVV1+wY8d27rrrzw6/juZO\nDnf207Jw4ceUlDg3HUldiouLSUpKuub2l156ngsXLhAcHEJy8j4MBj3nzp1Ho1Hj7e1LYuIgunbt\n1uZetw0lAidBcEHnzmUQHh7RpiYEX48q5zK1ditcz4ABiZSVaVm9ehU9e8ZhtVqv+mfBZqsMLCr3\nGlTYl6xXBivS5e03KvciVKlUqFQqlEo3AgODcHf3YOPG9RiNBnbs2M6HH37k8DQErpA37NSpFDw8\n3Fut/jFjxrBixTeMHz/BHgRlZmZy+vRp2rfvwO7du4iOjsHd3Z2BA4cQGBhIQEDrJK10JBE4CYIL\nUqvVLZI1ty19I7TZHDPHSWgZjz32BHv37ubFF192SvmDBiXx2GMPo9OVOyV3k81mbdbrvyWGT4OC\ngtDpyp1eT21uu20Ky5cvY/36H5kwYSJQufjilltuJScnhz59+tG3b79Wa5+zNKgfsqioiJEjR3Lu\n3DmysrK48847+dOf/sSrr77q7PYJglCDiIgIsrMvOv3NWZLa3vyZllS5nLztB2tt8Tnq168fJ0+e\n4OefNzml/HbtwunVqxejR491SvmVKyGbPlRXGXg5d2jZZDLSmq9PWZb5v/97hW+/XUFa2lkAfHx8\nuO++vxAfH98mX5eOUO+zajabmT9/vn3y1t///neeeuoplixZgtVqrbZcVhCElhEW1g6Lxcb27duc\n+uZUOa+o7QcOQtsTFhbG3LkPsWLFN9fkzHIUrbackSNHOaXs5vY4WSxWpybQvHDhAjqdrlWH6gB6\n9oyjX7/+vPPOW9Vuz8/PJzQ0rJVa5Vz1DtW99dZbzJw5k4ULF2Kz2UhJSaF///4ADB8+nD179jB2\nrHMifkEQaubu7s706X/ku+++wdvbm8TEga3dJKEVGQwGNm3aYJ/zVltPx5W3e3goqagwNrnOmuqo\naSg1NzePRYu+Ys6cuU2uqzaenp6UlZU6vFyoSn/RnB4n537pSE7eR/fuN5KX13L5sq5mtVpZunQx\nhw4dYMaMP1a7/dKlgt9n4LRy5UqCg4MZMmQIH3/8MVA9r4W3t3eDssMGBnqhVCqq3RYa6tuU9gpt\nnHheW05oqC8zZ85g2bJlDBzYv0GbaDaWm1vlZF1v79b9VlvZFgUeHqoWbYtO545SqWgT118XjUZN\nUVEBJlP1XE51rYh0xAd7Tb2dVQFHVd1JSYns27cbrVZDz549mTZtWrPqvFKfPr05cuQQt9xys8PK\nrOLu7obR2PTnXqmUnPp6TU8/Q0REOwoLL1Wro6Veq1arlc8//5wjRw7xwQf/IjIy0n6fWq0mKMiP\n9u3bRkJWR6s3cJIkid27d5Oamsqzzz5LSUmJ/f7y8nL8/OrfnbikRFft99BQXwoKWi4dv9AyxPPa\n8tzd/enQoTOrVq1m8uSpDi/fYDAjSRLl5a2fXNFoNGMwmFu0LTqdAbPZ0iauvy7+/gHce+9fCAkJ\nbfA53t7uLXZd2dnZLF78Fd9/vxI/vwAGDx7qkHLDw9uzfv0GysoqHD4sZjCYMJma/tzr9c07vy5m\ns5mTJ1N48smn+fXXo/Y6WuI5XbnyO9avX09JSWVG/blzH8bfP7havXl5BciyyqU/D+rqBKgzcFqy\nZIn951mzZvHqq6/y9ttvc+DAAQYMGMDOnTtrzOEgCELL6devP8uWLcJsNqNUioWyv0eSJLfpibhR\nUVFMnz6D+fNfprTUcduiDBw4kIUL/8O+fXscFow5ijOH6oqKCjGZTERFtefSpUvMmfMXABQKGYul\n8nWgVpfg5+d/uS3Vex4NBgMWiwV3d3dUKhUgXU4NIdnbfuXryWarvE2vr0CtVtO//wBiYmJ48cWX\na7zGCxcuNCqIdzWNfpd99tlnefnllzGZTHTu3Jmbb3Z8F6kgCA0XEBCISuVBcXExYWGOnVNgtTZv\ngqwgVPn666UkJg506GeGJEk8/viTvP/+e/j7B9CzZ5zDym6u5k4ur43ZbOb9999l6NChhIWF8ckn\nn9u32/HyUqHTVc5be/TRh3jllddQKhVoNKW8+ur/MWzYCLKzL5CamoqnpycVFRUMHTqMW2659Zph\n3St78Krmrv3661G2bv2Z7OyLzJp1T43XZzAYSE7ex5Qptzv82tuKBgdOixYtsv+8eHHb3rdJEH5P\nbDYbRqPRKb1NznrzFxzv6l6FtkajURMe7vh8S337xjNt2h/54IN/8t57/8THx8fhdTSFo3uc9Ho9\nVquVf/3rfXx9/Zkz5yGAantVVg3VWa1WlEolkZGRfPvtCtav/5Ho6A6cO5eBTqfjzjvvYtKkyWzd\nuoXVq1dxzz2z7dui1MRisbB/fzJ+fn6YTJVZ3PV6fY3HHjiwn/btOzr8S1xbIvYvEAQXV1FRgdVq\ndspmvzZb83LZCEKVefOeYdu2raSkpDi87IkTJ9K3bzxPP/0khYWFDinz3LlzNCdHkiO3CCouLmbW\nrNCtXPoAACAASURBVD9x1113oNdXMHfuQ3V+UarqKd68+Sc2bdrArbdO4rXX3uCtt97hzjvvYunS\nJXzyyf8YOXIU5eU61q5dXetQr8Vi4aOPPmTz5s3odOUYjUa6d+/O1q1buHjxQrVj09LS2LdvHwMH\nDnbIdbdV4h1REFych4cHVquN8nLHZxB29pLqxqgM4lq+LW3l+usiSRJtZEvBWkVERDJixEiWL/+a\n0lLHphCQJIm5cx9iyJDBvPfe2w4p8+LFi/Tq1bvJ5zvqS0dpaSmzZ99NYmIiL7zwEi+//ArBwcF1\nnlO1tc3XX3/N88+/yPTpMy7PZYJffz3CiBEjSUlJ4eOPP8JiMbFs2dd8+un/aqx71aqVqNUaxo8f\nT58+famoqGD27L9gsVhZtGgRP/64FrVaTWbmeVavXs3EiVMICbk+V9NVEYGTILg4WZbp2DGW06dP\nObzsqo1Y24LWaEbltbf9wMlV3HHHnRiNRu65588O3zxakiRuueVWsrMvNrtsq9VKYWEB0dHRTS5D\no9GQnLyXpUuXsHHjhiaXs3LldyQk9GfevGcYMCCxQUPySqWS6dNn8OabC+jSpav9dp1Ox5EjRxg4\nMIm//30BJ0+moFZrmDDhVlJSUq5JVJqRkc7JkylIkkTv3n3w8/NDr9cTFhaGv78/48dPpKREzb//\n/SEbN25k1KixREd3aPK1ugoROAnCdaBjxxguXLhQ/4GNJMsKdDpd/QcKraoywG3jXU5AQEAAL774\nMt7eXk4ZAvbw8ESvN3LhwgX7FiBNIcsy4eERzfoycvp0Cn5+/hw/fozPPvuULVsat8tGbm4ujz/+\nGBs3bqBTp86NOleWZWbMuJ2IiAj7bTabjWXLlhIWFkpCQmXetw8//Ijvv/+BMWNuAmw8/PBcVq/+\ngaysLADi4nrZe3n1er2918pkMuHn54ebmxtJSUMAG3q9ga5duzWqna5KBE6CcB2oqNBjNpscXu5N\nN93EypUrOXHiuMPLbixZllp8x3qLRUyOdwZnPaY+Pj7ExMTwxBOP8tprr/Dcc39FrVY3qayePeM4\ne/ZMk9sSFBRMVFQUCxa8zVNPPc2aNbXPI6rJkiWL8fPzxcPDg/j45m2Uazab+eKLz0hO3sdjjz1p\nD1qVSiVKpZKoqCjef/8Dhg4dzltvvcnzzz+DxWIhPT398nA99s2ElUolOp2OpKQkjh07gsGgx2q1\nMXbsH+qcYH49EYGTIFwH4uP7kZOTx9Gjvzq03AEDEpkzZw7/+Me7TpnU2xiVm662bBAjVhU6i/Me\n0wcffJjHH3+ChQs/pbS0lJ07dzSpnPDwiEZv55KWdpaUlBPs3v0LR44cse+YMWjQYIqLi/nb317n\ns88+QautnssqKyuLQ4cO2H83m80cOnSA2bP/wpdfLqZHjx5NugaAQ4cOcPfdf2LDhvW8++77da52\nmz37PmJjO9GuXTiyLJOamno5pxP2YTw3NxUVFTp69epNYWEBaWlnefrp54iN7dTkNroakS1PEK4D\n7u7uTJ48nRUrviYoKMih8wyGDx+OVqvjvffe4bnnnv/ddMeD2OTYGRw9t+lqsbGxxMbGUlxcTHFx\nCTffPL5J5Zw9m4qnZ8O3MdJoNLz88ouXe3HcmDp1OiNGjLDf/95775OcnMyOHdt4+eUX0Gq1dO9+\nI9HR0WzYsP7/2bvzuKjq9Q/gn3NmhwGGfZdFUEAQF5BFc9+3LDXTq2kuaevtlrllmeVSdq+3a4tZ\n95fdrNyyXMtyzxXcSkURVERFZZ8VZp/fHwiJIAzDLMg879fLlzpzzvc8cBAev+f7fR5UVlZi7dpv\nIBKJ7s0IMVYp37B//34kJ3fD8OEjGu30cerUKWg0WkyfPgMMw8Bg0NWsLayeLePzqxIngSAIzzwz\nGatWrcLAgZZ9jh9VNONESCvh4+OD3r374eeff7Z6N/pevfpg/PgJ+OCD93Ht2hWrjm2uqtkf+37L\nakm7ChtStauuZSzib4y9Pqd3796GSCSsWZfTFEVFRTh37hwmTJho9jksW1W9/csvv8Lq1WswYkTt\nRMXPzw8jRozAu+8uQUREJJ5++m/gcFgcOXIE06bNgL9/AD755D/QaDT45puvIRDwLdqYodfrMXXq\nZOzatQN5eXm4eDELU6ZMrbVIvD4VFRX47LNPMGzYMMTFdQAAeHh41Kyvqv764vP5qKiorHmfx+NC\np7P+MoGWjGacCGlFYmPjUFh4F19//RWmT38OPB7PamP37z8AGo0GH3zwPhYseAthYWFWG7ulqqqH\n4+goWhd7VaP//fff0aPHYxadu27d/5CcnGx2bTSNRoPly5ciNDQUfD6/wYXvLi4ueOWVVwEA/fr1\nq3k9Pj4BS5e+i9mz/wG5XIFZs16wqHH39et5UKlU2LhxAwIDg5CUlAyJRNLoeUVFhfcaeouRm5uL\n6Oho+Pj44vDho2jTJhRRUVEAAA8Pd3z22acQiUTgcFiUlZVDo9FY9XtNS0czToS0IgzDoE+ffhCJ\nXJGZmWH18YcNG47Bg4fi/feXoqCgwOrjE2INSqUSR48exYgRjzf53Pz8fJw/fw5jxz5l1vF6vR6/\n/LILCoUCy5evsHi3oEQiQUpKGuRyOZ5//kWkpqZZNM7u3b+ga9dkLFjwFhISOmLatBlmnScWu4HP\n52PTpo1Yv/57VFRUrWMSiYS4desWZDIZ9Ho95s5dgNdeex1Tp07DqFGjERwcAldXV4tifVRR4kRI\nK5SU1A2ZmSdrpvr1er3V6jE98cST6N27L5YvX4ri4iKrjNlS8fl86PUGR4fRqEfhcaI9VTepFQqF\nTTrPaDRi/vw5GDNmLAIDgx56nMFggF6vBwBs3rwR3367DhMnTmp22yOJRILo6GikpqZadE//+OMP\nHDt2FJMmPYOYmBhMnDjJ7M+Bj48Pli17H4sWvYO7d+9iyZL3sHbtV3Bzc4OXlw8+/vg/WL58GTQa\nDaKiotGhQzwEAj46dEhwuq8/SpwIaYVCQ8OgVCpgMplQVlaG999fjm+/XWe15GncuKeRnJyE5cuX\noqyszCpjtkRisfihPblampZSqLQx1dvbbcnNzQ19+vTBO++81aQq5VUVt03o2LHTQ4/RarV4773F\neOGFmdBoNDh79gwWLFiIlJTUZsddUFAAgUBg8fk///wznnjiyVr1m5pCIpEgPj4BU6dORXR0NI4e\nPYq7d+9AKBRCpzPAZDLVrGcyGAy4fDkHERFNqzHVGlDiREgrVFZWBpPJhHXrvkFpaSmMRiPy869b\n9RqTJ1ctOP3gg2VQKBRWHbs+9l4YDlQt+HVEmxfSfJMmTUa7du0xe/Y/zK7ldPjw7/Dz84Ofnx+U\nSiXOnDkDqVQKk8mEvXt/w2effYJff92NW7duIiQkFFOnTsaNGzcaXXhtrtGjx+DixUv4888/m3xu\nQUEBzp07hz59+jV+cAMYhkFSUjKefXYqpkyZgooKNaKj24HH4yM3NxerV3+Cv//9JUyePBGHDx9y\nql221WhxOCGtkJ+fH1588VX8+uvP2LhxPRiGgaenp9Wn1GfNegH//ve/8OGH72P+/IUQiURWHf9+\njqiMbeut89bCMI9OrPbaHcnn8zFz5vN47bW/49lnn8Hrr7/R6GLxgoIClJaWYOrUyeBwOOBwuBAI\n+IiIiMTly5dRUaECj8dDr169MWPGTOTk5EAkEjW6zd9cYrEYMTGx+P33Q0hMTDT7PI1Gg48++heG\nDh1q1T5xPXo8BqFQiN27d6Nfv0Hw9vZCRsZxDBw4ALduFSApKdWiXYuPOppxIqQVYhgGIpEIo0aN\nRmJiVdXhsrJyqzcCZlkW//jH6+By+Vi58p/QarVWHf9+jiiASazPnv3/GIbB++9/iDfemIs1a1bj\n5MnMBo8vLy9DUVERBg0agqefnoCvvvoaU6dOx4kTxzFt2nT83/99jddem40ZM2YCANq1a9esfnb1\nmTlzFk6fPon8/Hyzzzl27AiMRhOeeeYZq8bCMAwkEk94enohMbETOnfuig4d4lFZqUFsbDySkrpZ\n9XqPCkqcCGnlwsMjwDBV7UN++2231cdnWRYLFrwJlUqJTz9dBYPBdouphULbzWg92h6dhNLeM4cC\ngQDp6d3x3HOzsGrVRw89TqPRICMjAx9//BkmT56CYcOGg2VZpKSkYuvWHejR4zGIxWJ06dLVpvFK\nJBJ065aCNWtWQyaTmXXOiRMnrJ7AVVOrK+Hu7gEAOHPmNAYMGICysjL07t2v2YvhH1WUOBHSylVU\nVEAgEILDYZGVlWWTa3C5XLz99mLcuHELX365xiYLlXU6bc1OJkKaKi0tHTqd7qGL/Q0GAzgctkWU\n2Zg16wV4e3th6tTJDZYV0Wq1KCgoQFFREdq2tc46qwd5eXlBqVSgtLQUOp0WfL4A3t4+TtOXrj7O\nmS4S4kTCwsKh1xswY8YMm+4QEwqFeO+9JZg37w18//23+NvfJll1fB6Pb/fEyR47wIh9cLlcREdH\nY9++vRg2bHit99au/T8cOnQQYrEb/P19HRRhbXK5Amq1GhcunEe3bim13isrK8NHH62EQiHHrVsF\nSEiIx7Bhw2wSx40bN1BaWoI7d24jJCQYOTnZCA93vp1096PEiZBWzt3dA35+flCr1QgPj7DptcRi\nMd59dynmzp0NNzcPjBw50qrjO2KBOGkdDAYDbty4gd69q3adKRQKuLi4QKFQYPfuX/DWW28jPr6j\ng6OsavfyyScfg8fjYs6c+diw4Xt07/4Y5HIZPvlkFUaMGIkzZ87g4sUsTJ06HYMHDwGXy7W48GZD\nTCYT9u3bC6PRBJlMCpZlkZOTi2ef7WP1az1KKHEixAmwLAul0roLwx/Gy8sL77zzHhYunA8PDzf0\n6uXc32RJXY4omMjhcODn5w8AOHkyEx99tBIBAYEYO3Yc3N09WsT6uV27dmLTpg1IT++OSZMmw8XF\nBXfv3sHixYvA5XLRpUsX/PHHWfTu3QezZ88xuyWMpRiGudd6JQAajQbXrl1FeHik01UKfxAlToQ4\ngbS0Hti58yeEhobCw8PD5tcLDg7G3LkL8MEHy+Dm5oYuXZKaPabRaERxcTHy8vIeeow5M1LVRQ4B\ngMfj1fxvvXo7//3b+isqKqBWa2rtcDJn27+rqyu4XO69axkbjI1h2Hv1otia8Q0GPYxGU8351WMY\nDIaaitjVvcFYloVGo4FSqYBUKoXRaLxXOdtYs9bMYDDWub5IxIdarb83Rv1xNcTS2b+SkhLodLbb\nfdmQoUOHYuPGjVAqFXj++RehUCjw1VdfQqlUoLy83CExVbtyJRfffbcO7767pFZdqHHjnsaQIUPB\n5/ObXAndGpKTu+HXX39Damp3nDqVgbS0HnaPoaWhxIkQJ9CmTRiSktKwdu1aCIV8DB8+AiEhttmF\nUy0mJgYvvfQyPvnkY8yduwDt27dv1nh37hTg9OnTuHXrZr3v157FqG9Gw3TvOLbmWINBD4PBeO89\nps44er0ORUVF+OCD5QBgVjkEk8mEioqKmkSoerz7ExGGYWqSmvsTnGosywHLMmAYpla8paUl0Gq1\n8PPzq9m9aDKZUFRUiHfffQcCgeDeOVWfg+prVr1W++PjcBjodH/tgLR15fHqj1mtrkRZmWOSlPT0\nHrhy5Qo2b94ElaoCQ4YMRa9evXHpUha6dk12SEzVli1bihEjRtZbTNNadaIs4erqCoVCjvLyMkyZ\nMgPe3t4Oi6WloMSJECfRrVsKQkNDce3aVfz++2FMmDDB5tfs2jUZEyZMxMqVH+Ktt95BSEiIxWPx\n+Xw888wUjBzZ9MatliopKcHcubPx2Wef2+2aDdmy5QfcunUTf//7P5o9lqurACqVxgpRNU1R0V3M\nnz/f7tcFqkoTzJgxE1lZF+Dv7wcAcHFxcWjSlJeXh/ffXwovLy+MHTvOYXE8jL9/AEaNegIHDx5A\neXkZhgwZ3vhJrRwlToQ4kcDAILAsi6tXc+x2zf79B6C8vAwffvg+Fi161+brMlo7R7SeaU2kUilu\n376NuLgO9b539uwZJCR0rKnAbTQaodfrodfrodVqax6buru7N6uOUV5eHn7//RAOHNiH1NQ0TJ/+\nXIuti9SuXTu4u7vhhx+2ODqUFqFl3iVCiE1otVp89903iI+v+0PDlsaOHYfi4mL8858rsHDh23Bx\ncbHr9VuT1rCz0BENiTUaDfbs+RXbt28Hl8urt1XI+vXfISPjBNRqNTgcTs3jUA6HA4ZhweVy7tV7\n4kCj0YDL5YLPF4DH44LL5cFkMkKr1YHL5cDFxRUikahmHR2HwwWXywGXy0Vubi6uXr0CV1cxfHy8\nUVRUhBUrPgAAGI2GmseparUaUqkUubk5GDnycbi6iiEUCuHi4gqBgA8Ohwt3dze4ubmDz+fBxUWA\nykpdnR121cle9etVa+iMNevoHmQ0GqHVamA0Gmutvbt58wak0nJIJJ5WuiuPJkqcCHEiWq0WJpMR\nI0ZYt0yAOWbNegHLli3Bf/6zEm+8Ma/J/7tmGNYhP3Bbkry8a8jMzEBpaUm975s7G8XlcrFgwTxw\nOAJrhme2oqIivPrqK/W+d/9Stap7bqzz5+ovA4b562N+MKFkGBZ8Ph+zZ8+Bj48Pzp49g3/965/g\n8Xhwd3fHSy+9UGvdF8MwKCwsxIABAzB+/N/A4XBqNg7UtwvQZDJBqVRCqayqt6TV6sCyDFxcXKHV\naiCTyaBQKKDX66DX66HT6WAwGKHX6xAUFIQRI0ZCKBTAYDDCaDTAYDDWrGu7/+uczxfgP/9ZCXd3\nN2i1epSXl+HWrVvQ6apmv1QqFSoqVNDrDeByWeh0+prND9Wq1+ZVv87hsDVr5+5ft/fXx8mAz+fV\niaW8vBw3blynxMnRARBC7EcsFsPDQ4KSkhL4+/vb9dosy2LevAWYP38OvvxyDWbNeqHeH0jk4ZRK\nBbp3745u3VLNOv7+hrr3//mjj/6N4uJiBARYvubMUm5uHnBxEWH69OmobxG/OTNq939M1RMmD+4M\nNBqBH37YiG++qeovx+Gw8PLywtKly8HlcmvtNKxKKKpmYCIj25o1I8owDNzc3ODm5tbosZYyGo34\n7LNP4O7ugYkTJzd6vK3XrR04sB9yucJm4z8qKHEixMn4+wcgK+s8/Pz87J64cLlcLFr0Lt5443X8\n8MOmJi2GZVmmVTymag4ej4eOHTshLS29WeN8883X1gnIAiKRCK6urggKCrH5ercLF84jNzcHBoMB\na9d+BXd3D8TExNr0mta0c+cOZGVdwLhxTzs6FABVayQzM086OgyHo8SJECfTs2dvbNv2E3g8Ph57\nrKfdry8Wi/HOO4uxYME8+Pj4ok+fvvUeV1BQgLVr/w9yuQy9evVBWVkZYmLi7BwtsQVvb2/cvn3L\nponTyZOZ+PXXX7B8+QoAgFKpRK9evW12PWsxGo3YsmUzTp8+ievX8zFnzlyr1EGzhvDwcGzbthU6\nna6mjpgzosSJECfj4SHBE0+Mxtdf/xft2rW3+yM7oGrWa+7cBVi+fAm8vLyRmJhY55i1a/8PQqEQ\nUVFRWL/+ewAmvPHGPLvHSqzP29sHt2/fsVmLE4PBgFWrPkJwcAiOHTsClUoFlUrVohOnoqIiLF++\nFDKZDBKJBzp16owOHRLQuXNXR4dWQygUok2bNsjOvoSEBMe3p3EUSpwIcUJubu5ISemO/fv3Yfx4\n29dzqk+7du3w3HOz8PHHH+Gtt95BWFhYrfeFQiHCw8MxZsxTiI9PgFars+l6kkdBaylF4Ovri8LC\nuzYbn2VZ9O7dB2q1GsXFxaioqEBlZWVNy5WWpqysDDNmTEVYWBgMBj2ef/5FREe3c3RY9erSpSuO\nHDlCiRMhxPkkJHTE2bOnkJOTg3btHPNNOi0tHYWFRfjwww+wePF7taoSh4dHICfnMgAgPj7BIfEB\njumr1tr5+Pjg2rVrNhufYRhMmzYDQNVO0nXr/ofAwMAWWQbj4sWLWLfuf2jXrj2WL/+gxdZyqhYV\nFYVdu3agrKwUXl7OWUW8dfz3hRDSZCKRCD169MLRo0dQUVHhsDhGjRqFuLg4rFz5ITSav3YEcbk8\nuLg4dzPRB7WWxfFeXt6QSqV2udamTRuwc+dOzJ27oMUlJZs3b8TSpe8iNjYWr776WouLrz4cDgdx\ncfE1/6lxRpQ4EeLEYmJiIRZ7YMuWHxxaI2nWrBfA5fLw6acf18TRpk0o8vKuOiwmwLyGvqTpvLy8\nIJPJbH4dg8GA8+fPITEx0WGzqg/Kzs7GsmVL8frrr+LAgQOYN28+nnlmCoKDgx0dmtmCg4NQXFzk\n6DAchhInQpwYh8PBkCHDUFmpxo4dO6DX6x0SB8uymD//TVy/fh3ff/8tAEAsdqPE5QEmk3mNhls6\nb29vKJVKm1/nwoULuHr1KiZPftbm1zLHsWNHsGLFMrRv3x5DhgzDkiXLkJBQd2NES6fX62lXXUOM\nRiMWLlyIvLw8sCyLxYsXQ6fTYebMmQgPDwcAjB8/HkOGDLF1rIQQG2BZFqNGjcYPP2xEXl4eoqPr\ndme3B6FQiLfffgdz585GQEAgunfvAblchuLiIvj6+jkkJmIbEokn1OpKm1/n4sUL8PPzR0REhM2v\nZY4NG9Zj7NinMWTIUEeH0ixyuRxisfNu1Gg0cdq/fz8YhsH69euRmZmJlStXok+fPpg6dSqmTJli\nhxAJIbYmFruBy+VCJpPCZDI5bEG0j48P5s9/E0uWvAs/P1/4+Pjh5s0blDi1MlWPY23/NZaVlVVn\nt6Yj5OfnY+7cN8Dn89C5c5cGjy0svIuDBw9CJBKBzxdALHaFi4sLxGI3SCQSSCSSevvs2YPJZMKl\nS5dw6tQp9O070CExtASNJk79+/dH375VBeoKCgrg4eGBrKws5OXlYe/evQgLC8Obb77ZIncrEELM\nl5raHTt2bIVcLkffvv0cFkdUVDSmTp2OVatWQa/XQaVy3MJ1Yhs6nQ4cjm1XiphMJpSVlSEgIMCm\n1zHHn3/+gZiYGLz44kv1/ifAaDTi9OlT2LhxPYqLi9GxYyJYlgOdTovKyqpSChUVlaioUEGpVEKl\nUmH58vdtVgerPgqFAlu2/ACtVofu3XsiKsoxM9MtgVlL+KvWH8zHnj17sGrVKhQWFuKpp55CXFwc\nPv/8c3z88ceYO3eurWMlhNhQVFQ0hg4dgdzcS44OBT179sKtWzexdu1X6NjRcWtAHuwyT6xDr9fb\nPHFaseJ9nD9/Dt7e3njvvcV13q/ehFD9u0qlQmFhYU1BWJZla35xOJyaXwzD1vq64HI5947jIDAw\n8F7DXkOtRrtnzpyGQCDAL7/8cl/DXQMqK6vqTF25kguJxBOPPfYYhgwZ1uBExK1bt7Bo0UKsWLEC\nX3zxXwiFwuZ/shpRWlqK7777FvHxiUhJSXP6Eh1m731cvnw5Zs+ejbFjx2LDhg3w86vKmgcMGIAl\nS5Y0eK6npwu4XE6t13x9nff5aGtG9/XRlpOjhbu7K1xdBbVef/Dv9jBjxjT8/PNOeHjUjcdeKisF\nYFnGYdd/EJfLQijkNzue6u/Hjvq4FAoWPB7PZtcvKyvDtWtXkJLSDYMGDah3x2j1D//q34uLi7F+\n/XoMGzYE3t7eMBqNMBiqkiC9Xl/zy2Qy1Uq6qt9fs2YNhgwZAoFAAA6HUyu5SkjoAJPJBJY1ATDB\naDSCw+HA3V2M337bjdGjR2PatGlmJert27fFhg3rMX/+fBw4sAcDBw4En8+veXxn7c9pRUUFduz4\nCf3790FycrJVx35UNZo4bd26FYWFhZg5cyYEAgEYhsHLL7+MN998Ex07dsTx48fRoUOHBscoL689\n1e7r64biYuqw3NrQfX308fluuHnzTK0O67buuN6QgIAg/PjjVjz55BiHXF+l0sBoNDns43+QXm+A\nWq1tdjx6vQEAHPZxyeUVMJkYm11/9uw5CAwMxiuvvNqkfni//bYHQUGhFlXt/vnnXzBw4BBERrY1\n+5yPPvoX0tK6Y9y4v6GyUtek60VGRuPHH7diw4ZNMBiqkjoulwOj0QSRSIRly96Hv7/ljymrk8ON\nGzfCxycIYWHtner7e0OTAI0mToMHD8a8efMwceJE6PV6LFy4EIGBgVi8eDF4PB58fX3x7rvvWjVg\nQohjCAQC6PVN+wZuSy+//HcsWvQWhgwZBpFI5OhwiJXY8lFddnY2dDodFi58u8kFJdVqDSQSiUXX\nDQgIQH5+fpMSp7t3CxEbG2vRI+EJE/6GCRP+VvN3k8kEgYADpVKNLVt+wHvvLcY///nvJj3KUyqV\nyMw8gcuXL6O8XAqj0QhXV1dMn/6E0z+eu1+jX1VCoRAfffRRndfXr19vk4AIIY6jUqlQXl6O4uJi\nSCQScDicxk+yobCwMHh6SvDHH2eRlpbu0FhaE4VCgf/+97+YP/8th9xjo9Fgsx/E169fh7e3V5OT\nJqPRCLW6Eh4eliZOgbh162aTzpk583ksX74EDMPiySdHQywWW3RtoOqRI4/HA59vxNNPj8edO7ex\nYsVyLFy4yKzE7ObNG/jhhy2Ijm6HQYOGw8vLG3v3/oY2bcIc/n2gpWn59d0JIXbTtm0UUlN74Msv\nvwDAgGEYxMfHQCQSQyQSoXPnLnbfQdulS1esX/8dEhI6NusHS2tgMlmn0e9nn63BO+8sxJdfroGr\nqytu3bqFGTOeg4+PrxWibFzVjJNtfhjfvXsbXl4+TT5PKpVCIBBYvNXf3z8AWVlZTTonIiICS5Ys\nx6JFC7Fv3x6sWvUpPDw8LLr+/RiGwfPPv4i3316IDRu+x4QJExs8/o8/zmLfvv0YOHAw2rb9a7fc\n0KHDmx1La0RbRgghNRiGQWpqOqZPfx5///vrmDnzRbRt2xY6nRG3bxfiv//9EmVlZXaNafz4v8HP\nLwD//ve/7Hrd1szd3R2LFy/G5cuXkZmZiatXr+B//1trt+sbDAarJIAP+uKLz/Hrr7uRnt70DJKk\nSgAAIABJREFU2cmSkhK4u1uetPj7+6OsrKTJ5/n5+WH16i8QEhKCjIzjFl//QSKRCK+++hr279+H\n06dPPvS4nJwcHDhwEOPGTaiVNJGHo8SJEFKHWCwGwzAQCoVITk5Gr159MGzYCCQmdsG2bVvt2teO\nZVmIxa6orLR9pWlnEhAQgH//+z/4+ONP8f77HyIjIwPLli3B7t2/4Pz5P1FWVoa8vGu1Gi9biy1m\nnG7evIk9e37D66/PtuixbmlpCTw83C2+fkBAYLMaFw8ZMgz79++3+Pz6BAcHY+bM5/Hxxx+jsPBu\nnffVajV27tyOESNGwcvL26rXbs3oUR0hpBaTyYTs7EuorKxAcHAI8vMvQyqtAMMwcHFxhUajxYYN\n65GSkgKRSASW5dTUvrGFZcuWQKvV4JVXXrXZNR4VJpNtevf5+PjgnXfew5Urudiz51fcvHkLLi4i\nKBQK8Pl8vPLKq1ZdY2YwGK1eI2vfvj3w8fFBUlI3i84vKSmFu7tl65uAqkRUpVLd293W9B+tSUnJ\n+PLLL3Dx4kXExcVZHMeDkpO7YdiwYXjvvXexcuVHtR5FXrp0EUFBoQgKenQaDLcElDgRQmoxGAzY\ntWsbWJaFyWRCeXkJvLx8YDJVJVXPPjsD16/nYefOndDrDTAaDRg6dJhVv9lXKygoQG5uDv71r3/b\nbf1NS2eropxxcXGIi4vDyJGPw2g04scftyA5uRu2bfsJq1d/CoZhcfDgfowbNx6hoaFgGAaZmRmI\ni+sAV1fXOsmCwWBAbm4OIiPb4uzZsxAKhUhMTLz3ns6qM05FRUX46acf0bt3H4vHkErLIJFY/qiO\nz+fDzc0dd+7cQWhoaJPPFwqFGDFiJNav/w6LFi22KPl6mDFjnsKNGzfwz39+iHnz5kMmk4FlWVy+\nfBlRUTFWu46zoMSJEFILl8vFrFkvIzPzOC5fzsaTTz4OhUKD0NA24PH4EIvF8Pb2QdeuVcXwcnNz\ncPr0aZskTpcuXURISAglTfcYjaaaytO2xLIsxowZCwB46aVX8OKLz+Pjjz8Cl8vFwoXzwePxwLIs\n5HI5PDw8oNPp8MYbc9C+fSx+/nkn/vjjLIqKilBaWgp/f3/cvn0HAgEfY8c+hVGjnrw342S9j0Ol\nUsHV1bVZs5Ll5VIEBQU2Kw4/P1/cvJlvUeIEAMOHj8Dhw7/jk09WYebM561WgoNhGEybNgOzZ7+G\nt99+Cz4+vtBo1NBoNOjff4hVruFMKHEihNQhFovRt+8A9O07oNHCpiKRCNev52HXrl0IDg5Cp06d\nrRaHUChs0sxEZaUae/deh1QqgESixoABEc1qSWE02ubRmKWqtvHbd2kqy7J4++13cP16HtLS0rF5\n8yacPn0K+fnXMXTocCgUcty5cwfLli2FSFTVJeLJJ8ciIMAf8fEJuHDhPDw9vaDTabF06RLk5V1D\n+/ax4HCs9+Nn797fwOPxmjWGXC5DTEz7Zo3h7x+AgoICi88XCoVYunQ5lixZjA0bvsezz05rVjzV\nVCoVNmxYj/T0HujWLRWRkVHQ6/UoKiqkPrMWoMSJENIsQUHB6NGjN5RKOfbt2w+GYdCxY6JV6vQo\nlSoIBOYnPnv3Xkd2dkcADO7eNQE4hxEjWs+jCIPBaNVHOOYKDAxEYGDVbMzYsU9h9Ogx+PHHLRg8\neEhNiYhjx47g1KlTmD79uVo/jO9PpJcsWYbvvluHL774HJ6eXrh48QJEIhdEREQ2K76DBw8gJia2\nWY//lEol3NwsXxwOVO2Qu337drPGEIvFeO21NzBnzuvo3btPsz83JpMJW7duRVhYJHr1+utRJpfL\npbVNFqJddYSQZrlyJRdxcXHo128gRo8eh8OHj+DYsaNWGTs7Ows+PubX5JFKBQCqEzbm3t9bD5PJ\nuo+4LFX9KO/+ulrp6T3wyiuvNjiDERwcjDlz5mH58hUAgA8//BDz5s1Bbm6OxbGo1WrodDpMnz7D\n4jEAoLKyslm76gDA19fXKuU6/Pz8MHLk41i2bCnu3q27G64psrKyIJPJ8NhjvZodF6lCiRMhxGIa\njQY7dvyEL79cDblcBj8/P4wZMw4ZGZnIyDjRrLGPHTuCzMxMPPHEE2afI5GoAVSXSjBBImkZPeas\npao57KP/oCA2NhZr1/4Pa9f+D8HBIaiosLzUhFAohI+PD65fv96smCorKyAWN3/Gqby8vFljVHvy\nyTGIiWmPbdt+sngMk8mEgwcPoH//QTbbVOCM6DNJCDHLli2bcObMqVqvCQQCpKSkw2QyIT8/HwDg\n7u6BceMm4OjRY8jPv27x9XJycpCY2Al+fuY3Kh0wIAIxMecQEJCNmJhzGDAg3OLrt0R6vaHZa3la\no4CAAFy5ktusMdRqdbOrdvv5+UMulzVrjPslJSXj1KmTFtcwk8lk0Gp1CAmxbLE6qR8lToSQRplM\nJpSUlKCkpLjOez169MTLL7+GhISONa95enqhb98B2L17t8XFMoOCgqFSKZt0TtWW7hhMmhSBESNi\nmrUwvCUyGo12Xxz+KPDy8m5Wkq7X66HX65vd0sfb2wcGgwEVFRXNGqdar159IBa7YcOG7y06/9at\nW3B3b94sGqnr0Z/zJYTYHMMwmDnzhTqvS6XlYFm23lYV0dHtcPz4Edy4kY+wsPAmX1Oj0UIuV+Di\nxQuIi4u3JOxWx2RyzOJwW9JqNcjKOg+ZTFqzoaB6HRfLcsDhcMDhsGCYqj9XP3FiGPbecSwOHTqE\nUaNGWRxDaWkJXF3Fza4txTAMvLy8cePGDcTEWGdTwiuvvIqlS9+DRqPBzJnPm7XpQiaT4fz5czh0\n6BBGjx5nlTjIX1rXv0BCiF1lZ2fjyJGDmDRpap3q4QzDICwsAjdu3LAocerTpw+ysy/ik08+wWef\nfW6liB9tRqP9Wt3Yi0qlQkZGBrKzs2tmJ6t/NxqNNb9MJhMMBkNN9fTqgqwmkwlqtbpZ7U5KS8vg\n5ubW/A8GVT3rbt26abXEKSIiAqtWfYLnnpuGxx8fhcDAoEbP+fXXX3Dp0mX06tUXbdqEWSUO8hdK\nnAghFuvcuQuOHDmIb775Cm+8Mb/O+yzLQqls2uO2amKxGM88Mxmvvvp3mEwmq5Q3eNSZTPYpgGlP\nnp5emDz52Zqq4pZ4993FzZotkkqtlzgFBQXiypUr6N9/gFXGAwAXFxfExMTi+PHjePLJ0Q0em5ub\ni/z8G3jmmWfh72/++kBiPkqcCCEWEwgEmDr1Ody4kV/v+0KhCOfOncHgwQ9PfLRaLeRyORQKORQK\nxb1fSpw/fw4nT2Zg9OgxlDTdU5U40RqnBxUXF9XUmbJEaWl5s2s4VUtK6obVqz+zylj3i4mJRUHB\nrQaPyc/Px7ZtWzF8+ChKmmyIEidCSLN4eXk/tLN6VFQUjh79HadOnYSrqxhyuQxSqQwymRRSqRRy\nuQw6nQFisRhubm4Qi6t+ubqKUVJSgoUL36616NwRaDF2y9ev3wD8+ecZi8+XyaTN3lFXLSYm9t7X\nuRQSieVNgx/k5+eHjIzjqKysrNOKxWAw4OjRI/jjj9Po02eARY/GifkocSKE2IyXlzfS0x/DsWPH\n4OsbAHd3N7i7eyAwMATu7u5wc3OHSCSqM6NkNBpx8OA+sCx7r3aR9RrCktYnMTERmzath16vt2jx\nvEwma1Kh1YZwuVykpqZhw4bvMWtW3Q0VlurWLQW7d/+Mzz//DP/4x+s1rx88eAAXL16EWOyGKVOm\ngMtt3s5A0jhKnAghNpWSkoqUlNQmncOyLJ588ilkZh7Hvn37kZqagk6dOkMgsG8l8OqFyKRl8/X1\nhVpdib1792Dw4KY3rVUoFIiICLdaPDExsdi79zerjQdUldqYPXsuXn31ZSgUCohEIiiVSpw/fx4h\nIWEYOHAw/PzcG+wrSayDEidCSIsUGdkWkZFtcefObZw8mYHDhw+ja9cuSErqZrWFvMTxrLF8jc/n\nw9PTCzKZZTvrVCoFxGLrfU15enrh8uUcaLVa8Pl8q43r5eUFT09P5OZeBsOw2LlzJ0QiFyQnd6N1\ngHZEiRMhpEULDAzCyJFPQCotx+nTJ/H556vRvn17pKamwc/Pz9Hh2RXDMDAaaRbsQYcPH4ZWq0Xf\nvv0sOr+iotKqhSJPnDiGyMgIm9TcEgiEyMnJQWBgEIRCEWbMeJ6qydsZJU6EkEeCROKJfv0GIj39\nMfzxxxmsW7cOQUGBSE9PR5s2YS3uf9x//vlnTTXrB6un3x9rU+JWq9VWia2lae4jUbW6Er6+vvD1\ntSyRrqhQWW1xOABcv34dEyZMtMkOyEGDhmD16k8gFrth2bIPKWlyAEqcCCGPFJFIhLS07khK6oaL\nFy9g+/YdcHWteq19+/ZW/2FlaUL23/9+AVdXV/j6+tS7M6+hZOFhXWq6dOnS6goaBgQE4bffdiMh\noaNFmwDkcjnWr/8OM2bMtDiGyspKqz3+raioQFFRIcLDI6wy3oP69esHwITs7BxIJJ42uQZpGCVO\nhJBHEo/HQ2JiZ3Ts2AlXruTi6NGj2L9/H1JTU9GxY6JV/iduNJrqrMGp3vqtVmsaPNdg0MPFxQUv\nvvhKq+uZZ00vv/wKXnvtVZw5cxrJyd2afP7kyZPQs2dP9OzZq1lxsKx1dm5mZ2dDIpHAy8vLKuM9\nqLS0FGfPnsWkSc/aZHzSOCpQQgh5pDEMg+jodpgw4RkMGDAEly/n4JNPVt1Lbqz/aEsqlSIjIxMC\ngajBXwMGDIWfXwB27Nhm9RhaE6FQiJSUVJw5c7rJ5xYUFIDP5+HZZ6c161Ft1doxg8Xn38/NTQyl\nUmWztWj79u1F164pVivYSZqOZpwIIa1GSEgoQkJCUVJSgszM4/j441Xo2rULkpNTrLoTTyAQIDk5\npdHj9Ho9vvjiM5SVldlsBqI1cHERoaiosMnnffnlGiQmJja70CSXy4NGo23WGNUiIiLB43GRk5Nj\ntX519ysrK0XXro1/7RHboRknQkir4+Pjg6FDR2DixClQqdT4/PPV2LVrF8rKyuwaB5fLRadOXbBr\n1y4oFFRf52G6dk3C6dOnUFJS3KTzbt++hUGDBjf7+lwuB1ptw49ezR+LCx6PZ5MZp5KSEiiVKmqn\n4mA040QIabU8PCTo128gUlO748yZU/jqq/9DSEgIXF1da+10e3DXG1D1+EahkEOt1mDHju01x2i1\nmiY9FkpNTce+fb9h9epPMWXKVKcroWCOyMi2aN8+BhkZJzBs2Aizz1MoVGjbNrrZ1+dwuNBqdc0e\nB6haA1daWobw8HCrjHe/8+fPITY2nvoVOhglToSQVs/V1RWPPdYL3bql4sqV3HrWs9SXCJng7x8E\nP78guLm53UuWqo7r3Nn8Rcwsy2LAgMHw8vLGjz9uwXPPzaQffPWIjY3Fvn17MXTocLMS0+++Wwc+\nn2eVApM8HhdarXXWw0mlUnA4DFxcXKwy3v2UShXc3a3X/45YhhInQojTEAgE6NAh3iHX7tIlCXl5\n17Bp0waMHTuO+u89YPjwkdi0aSMuX85GTExso8erVCp07ZpklQSFy+VBq7XOGicXFxcIBCJcvHgR\ncXFxVhkTAIqKipCVdQFTp1pedoFYByVOhBBiBwzD4IknxmDz5g04e/YMkpKSHR1SiyIWixEa2gZ6\nvd6s4z09PbF1609YvnxpnfeqH6tW18pSq9UYNGgIevR4rN6xeDxenUd1v/66G6dPn6wpU8CyLBiG\nAcNU/c6yVTOQDMPAYDDAaDTc+92IysoK7N79s1UTp8LCuwgLi4BYTE18HY0SJ0IIsRMOh4M+ffpj\ny5aNcHf3QLt27RwdUouiVqvB45n36G3YsBFwc/MAl1t35q664GhVcgNs3rwRV6/mPjRx4nK50Olq\nJ06bNm1EWlpaTcFRo9EEk8kIo9EIo/GvxMxoNIHD4YDL5YDD4YDD4cLVVYxr166Y90E3oKKiAhUV\nFfDx8bmXrNEj3paAEidCCLEjf39/DB48FNu3b0NMTCyGDRvW4trFOEJZWRkqKioQGhpq1vEuLi4Y\nPNi8HXU//vgDEhM7P/T9qhmn2o/qwsPDweVyMXDgILOucb+UlBTMnDkdWVkX4O8fAIlE0uS+dUaj\nEXPmvI7i4mIkJnaC0WhEt27dmxwLsT5KnAghxM4iI6Mwffrz2LRpPX74YRPGjHnK6ZOnPXt+Q4cO\nHWyyqFomkzXYqobH40On0yE3Nxd79vyGv/1tIiZPfhbvvbcYHTrEN7miuVgsxsCBg7FmzedQqZSo\nqKgAl8sFny+AUCiASOQCFxcXiMVucHd3h6enJzw9PeHj4wNvbx/4+/tDqVRCLlfgP//5BL/9thsn\nT2ZCKi1v7qeCWAElToQQ4gB8Ph9jxozD//73f8jNzXX6x3ZHjhzGU0+Ns8nYXl5euHw5G2lp6fW+\nz+NxoNPpcPlyNoqLi3HjRj4SEjoiODgIMpnMomtOnjwFkydPAVC15kqlUkGpVEKpVEAuV0ChkEOp\nVEAqlaG8vBTXrl2FXC6DTCZHWVkpAgODYDAY4OrqimeemYLQ0FAYjfSoriWgxIkQQhxEKBSiQ4cE\nFBTcdPrESaNRw9vbNtXVH3/8CWzevBGpqWlgGAaVlZW4evUqPDzc4ePjC5MJuH27AAKBECKRS816\np/z8fCQna2AymVBSUoLCwkJoNGq4uooREhJi9kJthmEgFovvHd948UqpVIrnnpsGlmUhEokAAJ6e\nXsjJaf66KdJ8jSZORqMRCxcuRF5eHliWxeLFi8Hn8zFv3jywLIvo6GgsWrTIHrESQkirExnZFtu3\nb0FaWnenbQasVqshlysQHGze+qam6tWrN7755n/Iy7uGS5cu4eLFiwgKCkFpaTFkMhkUCgVu3rwJ\nb29fDBo0FLt374aHhwQ6nR7nz59HTs5lsCwX/v6BEAoFUCqV2L59G0JCQtG3bx8EBARaNV4PDw8k\nJSWDw2Fr1ka5u7ujokJl1esQyzSaOO3fvx8Mw2D9+vXIzMzEypUrYTKZ8NprryEpKQmLFi3C3r17\n0b9/f3vESwghrUpQUDCCg9vgwoXzTluiQCgUwtNTgoKCAnh4eFh9fJZl4ePjg9WrP0NSUgrGjBkP\nf39/AFWVvisrKwAw0Gg00Go10Ov1+O23PejRoxd69OgJb29veHjULjyp1+uRlXUe3377HXr16tnk\ndVANYRgGc+bMq/Wal5e33VsGkfo1mjj1798fffv2BQDcvn0bHh4eOHbsGJKSkgAAPXv2xLFjxyhx\nIoQQC/n6+kMmkzo6DIcSCoV1SgJYy82bN+DqKgbLcpCffx3Z2ZfwxhvzAVSViBCLqxpAVz96e/31\neY0u1udyuUhM7Izw8Ahs3rwBcrkcffv2q/c8qVSKgwf34+7dQqSlpSExsVOTPwYPDw9otVpUVlbW\nPL4jjmHWSjOWZTF//nwsWbIEw4cPr9XXydXVlZpXEkJIM7AsC53uwTYwziMr6wIUCoXZpQiaQq/X\n4+uvv0ZwcCh8fLzRpk0IwsIevsMOQJN2OHp4SDB+/CRcuXIVO3fuhMFQ+z5KpVJs3rwRKpUa6ek9\ncejQIezYsd3sQp/3x+Tn54vS0pImnUesz+zF4cuXL8fs2bMxZswYaDR/dZFWqVRwd3dv8FxPT5c6\nRcp8fd2aGCp5FNB9bZ3ovtpWfHw0duzYAZGIZ3aRQ6PRiMOHD+PmzZtITU2FQCCAv78/9Hq92Vv6\nXV0FzQnbarZs2YSRI0cgNLRqrZDJZLJaeQaTiY9+/Xrj6NGjGDZsGFQqFdRqtVW/pn193TBz5jSs\nXLkSWm1v+Pj43Lu2CStXfgEej4eRI4cjMjISXbvGY/PmzThzJhN9+vRp0nUiI8OgVsvg6/vwiuT0\nb9X2Gk2ctm7disLCQsycORMCgQAsyyI+Ph6ZmZno1q0bfv/9d6SmpjY4Rnl5Ra2/+/q6obiYZqla\nG7qvrRPdV9sTiTyh05lw/vwlREVFNXp8dnY2du/eDblchs6du+Knn3aAy+WgtLQUOp0ecXExGD16\nbIPJh6urACqV5qHv20tlZSX+/PM8xGJ3vP/+P6FUKmEw6BESEozU1HS0b9++zseRlZWFM2dOY+DA\ngfD3b3yXWnp6T8TExKO8vBxbtmzF2LHjbfI1zbJ83L1bDJHIDXq9Hlu2bIZQKMYzz0wFy7I110xK\n6o6NG79FdHQsPD09zR5fLPbAlSs3EBVVf79F+rdqPQ0loI0mToMHD8a8efMwceJE6PV6LFy4EJGR\nkVi4cCF0Oh3atm1rdvVWQgghdTEMg6io9jh5MhORkZENzjqZTCYcOnQQyckpSEzsXOvY6jVCX331\nBbKyLiA+PsHmsVtKrVbj8OHDOHv2DGJjY9GmTSRSUtIgFovB5XJx7dpV7N9/ACdPZiIlJRXh4eGQ\nSqWQy+X45ZefoVSqsH37dsyY8ZxZ1ysrK8P69d+jR4/eDRbDbI6YmDisW7cOMTHtUVhYhICAoJqk\n6X5eXt7o1CkJGzduwKxZz5s9fps2YTh69JhVZ+RI0zWaOAmFQnz00Ud1Xl+3bp1NAiKEEGeUnNwN\nP/20GevWfYMxY8bC1dW13uPy8q7BYDChU6cudX548ng8AEB6eg/s2bMHYrEY4eERNo+9KUwmE/74\n4ywOHjyA8PAoTJkyo956SNHR7dC2bRSyss7j2LFj+PHHH8HlcuDiIoZarQbDMLhz5w6OHDn80B50\n9ystLUFwcChSUhp+QtIcPXv2Rnp6D5w6dRLx8Z0b/Nx36tQFJ09moLy83OxZJ19fX5hMJpSVlcHb\n29taYZMm4rzzzjvv2PoiFRW1ewC5ugrqvEYefXRfWye6r/bBsixiYuJw585t5OZeRvv2MfXOKvz5\n559wcREjIiLyoWP5+flDIBBh584diI2NrXfNE5/PdciC9EOHDuLcufMYNmwUEhM7gc9/eFNfhmHg\n7x+ADh0S0LlzVyQlpaBTpy4QCITo2jUZsbEd8PPPOxEREVGnXMD9TCYTuFwuTpw4jpKSYkgkXg9N\nTJuLZVmEhIRCImk4GeLxeDCZgJ07tyEpKdmsXnYMw6C8vBwymQwhIXUX0tO/VetpaP0f1W8nhJAW\ngmVZ9OrVF3K5CmvWrEZGxok6x1y7ds2sXWEJCR3Rq1cffPPNNxa3DbG227cLcOJEBgYNGlZTR8lc\nfD4fXC4XLMuia9dkREREIjKyLUJC2uDIkcO1dnvf7/z5c1i+fBm2bt2K3r17w81NjG+/Xdsi+r6l\npqbB3z+g3vv8MDEx7ZGbe9mGUZHGUOJECCEtCJ/Px9ixT6N//yHIyMjE9u3boFJVVYy+fj0PxcXF\n8PIy7zFNYmJnxMTE4cCBAw9NLOzl6tWr+P777zFkyHD4+flZbdz09B6QSuX44os1uHTpUq33zp37\nE3v27MWECZPRtWsKDh36He3aRSM+Ph4//fSD1WJojm7d0nDo0CFs2LDerAKXYWHhUKkUKC+nYpiO\nQo/qiNXQfW2d6L7aH8MwcHd3R2xsB9y4kY8TJ47D19cHmzZtwuDBwxEUFGz2WP7+ATh4cD/CwtrU\nKh1jz0d1d+7cxubNmzFy5JMNPmK0hIeHBB07doKrqxjbtv2Eu3fv4PTp08jIyMCVK1fw5JPj4Ovr\nCz8/P3C5XGzfvhXFxUWIiqpaQ+Vo7u7u0Gg0KC4uQXFxIWJjH15qAKj62qioUOHmzVuIiGhb6z36\nt2o9DT2qoya/hBDSQolEIvTvPwhnzpzCli0/Ijg4FFFR0U0eIzS0DUpKShAcHGKjSB9OJpNh48ZN\n6NdvUL3rcqyhqm9qO4waVVVnUCQSgc/nw8/PHxzOXzUEtVodDAYTgoKC0KtXX5vE0lQsy6Jv3/4o\nLLyL7dt/gtFobLSWV1JSN3zxxRp0794TAkHLqMXlTChxIoSQFoxhGHTtmoyuXS3vYxceHonTp0+j\nY8dEu25jv3DhAn75ZReSk9PRrl17m1+vsdmslJRUdO2aZNZCbHvz8JBALpeZlTh5eHigbdtInDv3\nB5KTU+wUIalGa5wIIaSVi4vrgLKycpSX22dBtF6vx+7du7F//36MGTMB3bq1nB/uLTFpAv4qJXF/\nZ46GpKd3x5kzp5rcuoU0HyVOhBDSyjEMg7Zto3DihPm7tyyVl3cNa9Z8jvJyKSZOnNLk3XPOisPh\nwN8/ACUlxWYdHxAQiMDAAJw//6eNIyMPosSJEEKcQHp6D2Rlna/ThNZaTCYT9u3bi23btqNnzz54\n/PEnIRQKbXKt1orP50OvN//+JCR0xJUrOTaMiNSHEidCCHECbm7u8PDwxM2bN2wy/sGDB3Dt2nVM\nnDgFbds2bQE7qXq8effuXQQHm79jsqiosMHCn8Q2WubDXkIIIVYXHh6JCxeyrNqGxWg0Yu/evbh0\n6SLGj59Ub5Vy0rj8/Dz4+vrUO0tXWanG3r3XIZUKIJGoMWBABIRCIZRKpVlNjol10YwTIYQ4ic6d\nuyIvLw9Hjhy2yngmkwm7du1EQcFtTJz4LMTih3eUJw07fPgQUlPT6n1v797ryM7uiLt3Y5CdnYg9\ne64DANzc3KBQKOwYJQEocSKEEKfh6uqKp5+eiOPHj6O0tLTZ4505cxq3bt3GE0+MgUgkskKEzovP\n58PNrW6zYwCQSgUAqstIMPf+DgQHh6Cg4KZ9AiQ1KHEihBAn4urqioSETjh06FCzxlEoFDhw4ABG\njHi8wUa9pGl0Ol2d1yQSNYDqljkmSCRVJQvCw8NRUlIMpZJmneyJEidCCHEyHTsm4urVq1Cr1RaP\nkZmZgaiodmb3zSMNEwpFOHHiON5/fzlOnz5V670BAyIQE3MOAQHZiIk5hwEDwgFU1X6KiYnBxYsX\nHRCx86LEiRBCnIxE4on4+Hhs3rwJcrm8yedXVlbi9OnTSE/vYYPonFPXrsm4fDkHRqOpphhmNaFQ\niBEjYjBpUgRGjIiptYA8Lq4Drl6lkgT2RIkTIYQ4oYEDB8LdXYKvvvq/Jp+blXUBYWEAIpGlAAAS\n1klEQVQRtBjcivz8/GEwGBEQ4I+EhI5mnxcaGori4uJ6H/ER26DEiRBCnBCHw0G/fgMhl8shk8ma\ndO7FixcRF9fBRpE5J4ZhwOGwGDp0aJP6CVY1M/bFnTu3bRgduR8lToQQ4qQYhgHDME2qJq7RaHD7\n9h20aRNuu8CckFAoRFxcPHJzm/7YLSIiHPn5160dEnkISpwIIcRJMQyD4OBQ3LiRb/Y5RUWF8PLy\nqrMOhzRfjx49cerUabMb/VaLjm5HrVfsiBInQghxYiEhoZBKpWYfr1SqIBa72zAi5+Xm5o6goJAm\nzzoFB4egslJl0UJ/0nSUOBFCiBNjGAZSqflrnPR6HXg86tZlK7GxHXD27NkmncMwDMLDI5CXl2ej\nqMj9KHEihBAn1qVLEnJyLqOoqMis43k8PrRarY2jcl7t2rVHcXGJ2fejslKNHTuykZXFwT/+8T3K\ny82fPSSWocSJEEKcmEgkQlJSKo4eNa9/nUTiAZmMfjjbCofDQWxsB1y8mAXgr8Ro3bo87NhxqU7R\n0qo+du2gVnvgzz9N6NPnG0qebIwSJ0IIcXIxMTG4ceOWWcd6e/tAJpNBr9fbOCrn5e3tA6m0HMDD\nG/xWq+pbdwUMEwOGicPt2wswZ84B+wftRChxIoQQJ+fu7oHKygoUFxc3eiyPx4Ovrw/VDbIhX19f\nFBTchslkemiD32pVfez4MBr1MBq5ABjk59PifVuixIkQQpwcy7KIje2A9eu/Q37+dZhMpgaPDw1t\ngzt37tgpOufj5+cPDoeHpUuX4OrVfaivwW+1AQMi4OZWCI1GjspKNwAmhIXR7jpbosSJEEIIkpNT\nEB0di2+++Qb79u1tsIVHQIA/iosL7Ridc2EYBk89NR6DB49EZmYmOJwTEAqPIjr6VE2D32omExAQ\nIAbwB/j8nzFkyOdYsaKPQ+J2FpQ4EUIIgUTiiV69+mDGjOeRk5OLVas+wsWLF+s91svLixaI25hQ\nKMS//52NS5fSUV4eCrU6HRwOv1aDXwDYvfsqcnM7o6wsCHL5swB48PSUOCZoJ0HFOAghhNRwd/fA\nlCnTcffuHWzdugV5edcAAGKxGHFxHeDr6ws3N3coFAoHR9r65ee7Q6Vyg1JZCHf3kDrrmwDg1i0G\nAAO5XAEgDMeP03yIrdFnmBBCSB0BAYEYP34ihEJX8HhCyOVKfP31WuTlXUNW1gUoFLSOxtbCwmSo\nqAhBYeFN1Le+qUoFABM0Gi0APoBSu8bojGjGiRBCSL08PCRIS+te8/eIiEh8++234PP5GD16nAMj\ncw4rVvSFVrsDV69mwMXlGCZPfqnOMaGhfOTmXgCQD6AYaWliu8fpbBhTY9snrKC4uPaUrq+vW53X\nyKOP7mvrRPe1dbL0vpaUlIBlGXh5edsgKlIfrVaLzMwTuHLlMmbOnAUOh1PznlqtxpYtZ7Bu3Xp4\neCRg5cpRtMbJCnx93R76Hj2qI4QQYjYfHx9KmuyMz+cjPb0HRCIXnDhxvOb18nIZvv76HH74YR8u\nXLiGDz8cTEmTHVDiRAghhLRwLMsiLa0HLl26VPPaxo2XIZd3x/XrXMhkkzFw4BYHRug8KHEihBBC\nHgHBwSGQyeQoL69qx6JWuwNgwOMBEslVlJUFOTZAJ9Hg4nC9Xo8FCxagoKAAOp0Os2bNQmBgIGbO\nnInw8HAAwPjx4zFkyBB7xEoIIYQ4LQ6HAx6PB4PBAAAQCKRQq7WIiuqAy5c9EBh4xsEROocGE6ft\n27fD09MTK1asgEwmw6hRo/Diiy9i6tSpmDJlip1CJIQQQggAcLlcZGScwI0b+SgsLMahQ6shk7nC\n1zcEBw9OdXR4TqHBxGnIkCEYPHgwAMBoNILL5SIrKwvXrl3D3r17ERYWhjfffBMuLi52CZYQQghx\nZv37D0JW1gV06ZKC4uJCcDgsunRJRp8+/WgHrJ2YVY5AqVTihRdewLhx46DVatG+fXvExcXh888/\nh0wmw9y5cxs8X683gMvlNHgMIYQQQsxz9epVrFu3DgCwYMEC8Pl8B0fkPBotgHnnzh289NJLmDhx\nIoYNGwaFQgE3t6r6BgMGDMCSJUsavUh5eUWtv1NW3DrRfW2d6L62TnRfHx1GoxFnzpwCj8dDhw4J\nOHHiGDIyjsFkMmHSpGchk2kAaOieWlFDdZwaTJxKSkowbdo0vP3220hNTQUATJ8+HQsXLkRCQgKO\nHz+ODh06WDdaQgghhAAANBoNdu3aBpVKCalUihMnjkOlkkMoFGHChEnw9PRydIhOp8HEac2aNZDL\n5fjss8/w6aefgmEYLFiwAMuWLQOPx4Ovry/effdde8VKCCGEOIUNG75DZWUFNBotKiuVABhwuTzE\nx3eEi4sICQmJ4HKpa5ojUMsVYjV0X1snuq+tE93Xlm3r1h9w9OgReHp6oUuXJKSlpcPDo+Gq4HRP\nrcfiR3WEEEIIsb9Ro8Zg6NCR4HA4tXrTEcejxIkQQghpgWinXMtELVcIIYQQQsxEiRMhhBBCiJko\ncSKEEEIIMRMlToQQQgghZqLEiRBCCCHETJQ4EUIIIYSYiRInQgghhBAzUeJECCGEEGImSpwIIYQQ\nQsxEiRMhhBBCiJkocSKEEEIIMRMlToQQQgghZqLEiRBCCCHETJQ4EUIIIYSYiRInQgghhBAzUeJE\nCCGEEGImhyZOJpMJOp0ORqPRkWEQQgghhJiF66gLl5eX4ccfN0MqLUdgYDAmTJjkqFAIIYQQQsxi\n9xknvV4Po9GIY8eOIDY2BnPmzEVR0V0YDAZ7h0IIIYQQ0iR2nXHSaDRYtWol+vXrBaVSCR6Pg5s3\nb0Ii8QSHw7FnKIQQQgghTWbXGadjx46AZRlERkbi8cefREVFJb7//jt06ZJkzzAIIYQQQixi1xmn\nsLAwiEQiREdHo7hYgVGjxsBkMoFhmIeeU15ehps3b6Bjx052jJQQQgghpC67Jk6RkVGIjIyq9VpD\nSVNe3jXs2/cbZDIpAgKC4OfnZ+sQCSGEEEIeqkXXcdq1azv8/HzQs2dPbN/+Iy0gJ4QQQohDtejE\nqVu3NDAMg7CwMMjlUuTm5jg6JEIIIYQ4sRadOHXq1Bm5uVf/v707j42y2sM4/p0uUzrTlYKRJaEs\n9SIISClQU66RBA0VCSRsxVC9CCRe09gFTAzEAUwTjdXkmoAbaaxgq4LxGq7xj6qJIgUESwHD2ovE\nlsW0lC7MjC1t59w/iHNTKGXAttN5eT7/vadvp7/kCeTpmdN3KCsrA2ycO/crAJcvX6apqRG32x3c\nAUVEROSeErQHYAbCbreTl7eO9vZ2zp79L6NGJdPW1kZJyTb/2ajoaAfZ2f8gNjYuyNOKiIiI1Q3o\nHSeAsLAwoqKimDBhIk6nk87OTgYNiiYhIREAj8dNVVUl7e3tQZ5URERErG5A7zh1x+FwkJOTx6lT\nJ6msPERcXBw//3yQX345RkJCAvPnLyQuLj7YY4qIiIgFDfgdp1uprj5NXd0lzp+vISwsjOTkUYwf\n/zc+/bSUurq6YI8nIiIiFhSyxSk1NY2wsAgee2wOEydOorm5mRkzZnLffUM4e7Y62OOJiIiIBYVs\ncRoxYiS5uWtJTEzk7Nlqzp37lbff/hetrdeYNGlysMcTERERCwq5M043+u2333C73SxatIzhw0fo\nfJOIiIj0mR6LU0dHB+vXr+fChQu0t7fz/PPPM27cOF5++WXCwsJISUlh48aN/TVrt2bOTGfmzPSg\nziAiIiL3hh6L0+7du0lMTOSNN96gpaWFBQsWMH78eAoKCkhLS2Pjxo18++23zJkzp7/mDUhp6Xac\nTifz5y8kPDw82OOIiIiIRfR4xikzM5Pc3FwAOjs7CQ8P58SJE6SlpQHw6KOPsn///r6f8g7V1f1O\nR8c1PvxwG2fOnMYYE+yRRERExAJ6LE7R0dE4HA7cbje5ubnk5+d3KSFOp5OrV6/2+ZB3Kjl5NBMm\nTOCJJ57gwIEf+frr/+Dz+YI9loiIiIS42x4Ov3TpEjk5OaxYsYJ58+ZRVFTk/5rH4yEu7vYfdZKY\n6CAioutbZkOHxt7FuLfn9XpparpMSspchg8fzpQpEyktLeXEicPMnj27T36m/F9f5SrBpVytSbla\njzLtez0Wp8uXL7Nq1SpcLhfp6dcPYD/44IMcOnSI6dOns2fPHv96TxobvV2uhw6Npb6+b3aqdu/+\nN2PGPEB8fBIeTxsAjzwyi+LiYsaPf5iIiJD/Q8IBqy9zleBRrtakXK1Hmfaengpojy3i/fffp6Wl\nhXfeeYetW7dis9nYsGEDhYWFtLe3M3bsWObOndvrA/8V9fV1pKVN67KWlDSE+Pg4jh07QmpqWpAm\nExERkVBnM/1wcvrGBtyXrfjAgX14vVd56qn5XdbPnDnDV199xZIly0lKSuqTn32v02871qRcrUm5\nWo8y7T097TiF7JPDb2XUqGRqampuWh89ejQREeFcudIQhKlERETECix34Of++4fhdrvxer04HA46\nOjq4cOE8P/zwA8OGjWDcuJRgjygiIiIhynLFCa4/8dxut3Pt2jV27vyM+vp6pkxJZcaMdGw2W7DH\nExERkRBlueLU0dGBMQabzcauXbuw26NZvfqfeoK4iIiI/GWWO+MUGRlJcvIY3nyziNraGjIz56k0\niYiISK+w3I4TwMKFi9i3by/x8QkqTSIiItJrLFmcbDYbGRl/D/YYIiIiYjGWe6tOREREpK9YcsdJ\nRERE5Faam5v46af9JCQMxm6PxGazER4egcMRjcPhvPuPXBERERGxmh9/3ENbm4fo6EE0Nrqx2Wy0\ntrbR2enj6tVmJk164Jbfq+IkIiIi9wyfz8epU8fJzs5m9Ogxd/z9OuMkIiIilvb775f4448/AAgL\nC2PSpIepra29q9dScRIRERHLam1t5eOPS6io2NNlLSlpCHB9B8oYgzEmoNezmUDvFBEREbnHacdJ\nREREJEAqTiIiIiIBUnESERERCZCKk4iIiEiAVJxEREREAqTiJCIiIhIgFScRERGRAPVbcfrmm29Y\nu3at/7qmpoaVK1eSnZ3N6tWraWlpAWDLli0sWbKE5cuXc+zYsf4aT+7CjZn+6b333qOgoMB/rUxD\ny4257t+/n6ysLLKzs8nNzaWtrQ1QrqHmxlyPHj3K0qVLefrpp9myZYt/XbmGltbWVl544QVWrFjB\nc889R0NDAwBHjhzpNl/pBaYfFBYWmszMTFNQUOBfe+aZZ8zRo0eNMcaUl5ebqqoqc/z4cfPss88a\nY4y5ePGiWbRoUX+MJ3ehu0yNMeb77783WVlZ/nVlGlq6y3Xu3LmmoaHBGGPMW2+9ZXbs2KFcQ0x3\nuS5YsMDU1tYaY4xZs2aNOXnypHINQZ988okpKioyxhizc+dO8/rrrxtjus9Xeke/7DilpqayadMm\n/3VbWxtXrlzhu+++Izs7m6qqKqZMmUJlZSUZGRkADBs2DJ/PR2NjY3+MKHfoxkzh+i7irl27ePHF\nF/1ryjS0dJfrjh07GDx4MAAdHR1ERUUp1xBzY65ut5v29nZGjhwJwKxZs6ioqFCuISgqKorm5mbg\neq6RkZHd5rtv375gjmkpEb35Yp9//jkfffRRl7XXXnuNzMxMDh486F9ramqiuroal8tFfn4+GzZs\n4IsvvsDtdpOYmOi/z+Fw3LQm/SvQTL1eL5s3b6aoqIjq6mr/ujIdmALNFWDIkOuf51ReXs7BgwfJ\ny8ujuLhYuQ5Agebq8XiIiYnxXzudTmpraxk0aBAJCQn+deU6sHSXr8vl4oMPPmDevHk0NzdTVlbW\nbb7nz5/v73Etq1eL0+LFi1m8ePFt70tISCAmJobp06cDMHv2bCoqKhg7dixut9t/n8fjITY2tjdH\nlDsUaKYVFRU0NDSQl5dHS0sL9fX1bNu2jdjYWDwej/8+ZTowBJrrn0pKSigvL6e4uBi73U5MTIxy\nHYACzdXpdN70f218fDyRkZHKdQDrLl+Xy8XKlStZunQpp0+fJicnh7KyspvyjYuL6+9xLSsof1UX\nFRVFcnIylZWVABw6dIiUlBSmTp1KRUUFxhguXryIMabLbz8ycD3++ON8+eWXbN++nfXr15Oens6a\nNWuYOnUqe/fuVaYh7N133+Xw4cOUlJQQHx8PXH/rR7mGrpiYGOx2O7W1tRhj2Lt3L9OmTdO/1xDk\n9Xr9u0uDBw/27zZ1l6/0jl7dcboThYWFvPrqq/h8PkaMGMFLL71EREQE06ZNY9myZRhjcLlcwRpP\nesnEiROVaQhraGhg69atPPTQQ6xatQqbzcaTTz5JVlaWcg1xmzdvZt26dfh8PjIyMpg8eTKAcg0x\n+fn5vPLKK5SWltLZ2UlhYSEAmzZt6jZf+etsxhgT7CFEREREQoEegCkiIiISIBUnERERkQCpOImI\niIgESMVJREREJEAqTiIiIiIBUnESERERCZCKk4iIiEiA/gcr7GRJyhCj1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b000f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = plt.gca()\n",
    "add_us_outline(ax)\n",
    "ax.scatter(df_out['Longitude'], df_out['Latitude'], marker='o');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  },
  "toc": {
   "toc_cell": true,
   "toc_number_sections": true,
   "toc_threshold": 6,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}