data-cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "understanding-hierarchy",
   "metadata": {},
   "source": [
    "# Analyzing NYC's Stormwater Flood Map - Extreme Flood Scenario\n",
    "## Objective: Data Cleaning, Inspection and Removing Polygons Less Than 0.25 Acres\n",
    "Note: I'm only examining flood polygons greater than or equal to 0.25 acres for this project.\n",
    "\n",
    "Mark Bauer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ahead-burlington",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import urllib.request\n",
    "import zipfile\n",
    "\n",
    "plt.rcParams['savefig.facecolor'] = 'white'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de1fc4b",
   "metadata": {},
   "source": [
    "An IPython magic extension for printing date and time stamps, version numbers, and hardware information.  \n",
    "Source: https://github.com/rasbt/watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "medium-german",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.13\n",
      "IPython version      : 8.4.0\n",
      "\n",
      "numpy     : 1.22.4\n",
      "pandas    : 1.4.2\n",
      "seaborn   : 0.11.2\n",
      "matplotlib: 3.5.2\n",
      "geopandas : 0.9.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%reload_ext watermark\n",
    "%watermark -v -p numpy,pandas,seaborn,matplotlib,geopandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1afd5340",
   "metadata": {},
   "source": [
    "![nyc-opendata-dashboard](images/nyc-opendata-dashboard.png)\n",
    "\n",
    "Screenshot of the data on NYC Open Data.  \n",
    "Source: https://data.cityofnewyork.us/City-Government/NYC-Stormwater-Flood-Map-Extreme-Flood-with-2080-S/w8eg-8ha6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3618d7b",
   "metadata": {},
   "source": [
    "![data-description](images/data-description.png)\n",
    "\n",
    "Screenshot of data description.  \n",
    "Source: https://data.cityofnewyork.us/City-Government/NYC-Stormwater-Flood-Map-Extreme-Flood-with-2080-S/w8eg-8ha6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "intelligent-winner",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buildings-hazard.csv      nta-hazard.csv            stormwater-data.zip\r\n",
      "daily-precipitation.csv   stormwater-data-nyc.zip\r\n",
      "hourly-precipitation.csv  \u001b[34mstormwater-data-source\u001b[m\u001b[m/\r\n"
     ]
    }
   ],
   "source": [
    "# download data from nyc open data using urllib package\n",
    "\n",
    "# url path from the web\n",
    "url = 'https://data.cityofnewyork.us/download/w8eg-8ha6/application%2Fzip'\n",
    "file_name = 'data/stormwater-data-nyc.zip'\n",
    "\n",
    "# assigning file name as 'stormwater-data-nyc.zip''\n",
    "urllib.request.urlretrieve(url, file_name)\n",
    "\n",
    "# sanity check of data\n",
    "%ls data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "vertical-breakfast",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mNYC Stormwater Flood Map - Extreme Flood with 2080 Sea Level Rise.gdb\u001b[m\u001b[m/\r\n",
      "NYC Stormwater Flood Map - Extreme Flood with 2080 Sea Level Rise.xlsx\r\n"
     ]
    }
   ],
   "source": [
    "path = 'data/stormwater-data-nyc.zip'\n",
    "directory = 'data/stormwater-data-source'\n",
    "\n",
    "# open zip file\n",
    "with zipfile.ZipFile(path, 'r') as file:\n",
    "    # extracting all items in our zipfile\n",
    "    file.extractall(directory)\n",
    "\n",
    "# list items\n",
    "%ls data/stormwater-data-source/    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5926d712",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GHCND_documentation.pdf\r\n",
      "NYC Stormwater Flood Map - Extreme Flood with 2080 Sea Level Rise.xlsx\r\n"
     ]
    }
   ],
   "source": [
    "# copy data dictionary to top-level directory\n",
    "!cp 'data/stormwater-data-source/NYC Stormwater Flood Map - Extreme Flood with 2080 Sea Level Rise.xlsx' documentation/.\n",
    "\n",
    "# sanity check of data dictionary\n",
    "%ls documentation/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "illegal-violin",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (3, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flooding_Category</th>\n",
       "      <th>Shape_Length</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.264384e+07</td>\n",
       "      <td>3.972152e+08</td>\n",
       "      <td>MULTIPOLYGON Z (((917206.497 120931.611 0.000,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.656489e+07</td>\n",
       "      <td>3.054550e+08</td>\n",
       "      <td>MULTIPOLYGON Z (((917270.790 120873.747 0.000,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>8.042525e+06</td>\n",
       "      <td>3.455846e+08</td>\n",
       "      <td>MULTIPOLYGON Z (((915290.556 120269.389 0.000,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Flooding_Category  Shape_Length    Shape_Area  \\\n",
       "0                  1  3.264384e+07  3.972152e+08   \n",
       "1                  2  1.656489e+07  3.054550e+08   \n",
       "2                  3  8.042525e+06  3.455846e+08   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON Z (((917206.497 120931.611 0.000,...  \n",
       "1  MULTIPOLYGON Z (((917270.790 120873.747 0.000,...  \n",
       "2  MULTIPOLYGON Z (((915290.556 120269.389 0.000,...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading in data\n",
    "path = 'data/stormwater-data-source/NYC Stormwater Flood Map - Extreme Flood with 2080 Sea Level Rise.gdb'\n",
    "gdf = gpd.read_file(path)\n",
    "\n",
    "# sanity check\n",
    "print('shape of data: {}'.format(gdf.shape))\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f4a8a2",
   "metadata": {},
   "source": [
    "The data is grouped by flood category. To have each row as an individual polygon, we need to explode these nested values into separate rows."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fea61c82",
   "metadata": {},
   "source": [
    "![data-dictionary](images/data-dictionary.png)\n",
    "\n",
    "Screenshot of data dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "honest-senate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 4 columns):\n",
      " #   Column             Non-Null Count  Dtype   \n",
      "---  ------             --------------  -----   \n",
      " 0   Flooding_Category  3 non-null      int64   \n",
      " 1   Shape_Length       3 non-null      float64 \n",
      " 2   Shape_Area         3 non-null      float64 \n",
      " 3   geometry           3 non-null      geometry\n",
      "dtypes: float64(2), geometry(1), int64(1)\n",
      "memory usage: 224.0 bytes\n"
     ]
    }
   ],
   "source": [
    "# summary of the data\n",
    "gdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "recent-fellow",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Derived Projected CRS: EPSG:2263>\n",
       "Name: NAD83 / New York Long Island (ftUS)\n",
       "Axis Info [cartesian]:\n",
       "- X[east]: Easting (US survey foot)\n",
       "- Y[north]: Northing (US survey foot)\n",
       "Area of Use:\n",
       "- name: United States (USA) - New York - counties of Bronx; Kings; Nassau; New York; Queens; Richmond; Suffolk.\n",
       "- bounds: (-74.26, 40.47, -71.8, 41.3)\n",
       "Coordinate Operation:\n",
       "- name: SPCS83 New York Long Island zone (US Survey feet)\n",
       "- method: Lambert Conic Conformal (2SP)\n",
       "Datum: North American Datum 1983\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine crs\n",
    "gdf.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65364088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    MultiPolygon\n",
       "1    MultiPolygon\n",
       "2    MultiPolygon\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine geom types\n",
    "gdf.geom_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "received-pottery",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/wWWu77aJRpQ9n5DjB/h3BFSCILzvw893MmwWNeS+5c7w843dJh88PLO3LxaPkY9H9gwEgO+HNG2XYrLv9+iaNookSEb7///25Q1SumBhchRpcF3b8ei5Hktb1X5nuzBEERJpQ6HS7FDMZQlVC1kSlOoNQiH47378FJbZBSFRafXw/YC/+elF/CBElSVE6KHIMpIIce+YqQSD/i/P36zw2ELxvl8ivX/M5JAfOYKw7/y0XQ9Du/09GYYhU1mDZs/Ecm434EbuJ8vdotmzyaXiqAP9IafX5NjCFBFFIgCqrS49xyUT01kYz1NrtumYDpIIiUUN8qk4mqpwZKqA7bjUTB9JM9htmey2bBRFIZQU/vRjM2TiBvlk31F8K0lPkgS30uY8P0AIge36TGXjtN8HBYxD4zLkvuTOWYvj+UzmEnv7OpbHhfU6XctFSBKlShWAszc3Kd6hVXTrIe9aLitbJQ7Pz1BudpFlGVkIIppGJm7Q7NikYxGEpNDudNEjGkEQ0u72SEZ1OqbFi0tljs9PIIWg4rE4WSBq6Agh9lpflts2pnN3YtwdeX/IksCIKKgCHPcOo3ifMjQuQ+47XM+nZ7t7neYMTd2rbg6CkJeXS8wVE8yNZIgoMsV8jjPXNzkyVQT6M5tqvU1hYGhiusrseJHRXBJjUNDYX071f0Aq3g9ZZxIGfhiwXW1y9vo6qqryys1NbDfAsiy+fnmLyVyMFy6v07NseqbFePp2z92e49F1vNdkEou7okeqLFFrd/Edl5Z1fxuYoXEZct8hhMC2bYIgpGe7d+WD2J7P0ckMCVUQhiH1joXleBBCfOBbcbyAazt1hBB0TJftWodmu8OrS1vE72gSdatwsNG1ePHcZSr1Js1Oj3K9QzwexbRsDk2N0DIdikmDx/flcUKJmfEiYTCQKJFu+3zShsKzNyp3vRdJ6huXOw3OSsOh1HHQFZlm5/5VchwalyH3Jal4FN/3iEZUWj0H2/VxPR/TdskloyRiBp4fkIpqvLqyyYn5kb1zV3fr7B/P4ngBjXYHVQqx/ZB8Mn7Xz7iVzJaKRnh5o025WuPI/BSyItNstdiutdAUmXzSYLVuoUcidFoNjs0UsBwLVZI4c+UGALvlGqVymUOF6OvCz6os9n5WEIQ8upBHCh0kAa3e/et7GRqXIfclbctB026Jm2lEVBnHD5Bf48O4tt3AdiVaZj+vxfd9rm3X2GqYaIrEZDGDrqls1zpcXS9j2y6+79M1bdSBr8S0HCaKOT51+ihBELAwnkNSdPLpJJIkSMV0PndyhqWdKl1X9GVLIjpb1SYPH9zHpWsrhECoRul5AZWOfdd7uXPWIkkCQ1M4OjfBZrmxFzK/HxkalyH3JfVBPsqdbO6UkO9YIq2Xm0zl4jxxZGovC7bS7HFsZoSDkzmaXYvVnRqKLPPA/BjFbJJLG7v9TF7PR1UkvvrqMr/5rVcRbo+pkVy/5igETwj0iIYQEkEI1bZJPhHl/HqFRquDoWuslVts1jpEkwlKjTZj6RhJQyUbe+NudNDP3XG8gNF86q48mPuNoXEZcl/hByFBCFHtbkEzxwsw4klc//bDmEvEiA18KBeXt/vn+y7FVBRVlkjFdKK6hhh0fssmohydHecb51cpNS1WdxucWijycx9+gMcOz+C6LjuVFl96+QbVZo/AD5Ckvm8nEzdQZImxpEE0avCdK2tc3qyjCJ/Qd6h0PUzLIqqI71kzlNBVdhtttGFt0ZAh7x5eEKApEqOZfuj5ykYFx/Ho2C6jqdjeg7tebhJR+9uu55NM9H0wiVhsLyLk+QGFdJyIpiIJge8HdE2b4zNFNDkkbaik4jF0PUI6kaDc6pFNRnlkcZKNcpMXLi3xW89cZLvWwg8CVnfrFHMpNEUmGtWxzRZPvbLMpY0KH31gHlWVWal03tT7nMwl6TneXcu8+42hcRlyX7G0XeX6Rome3fehHJzME0qCmKagKhK6pvQVFSV5Lzmu2mizMJqj0uoR01XapkPPcji3vI3n+biDGchUIcFus0NASD6VIJnoO19bPZsvPn+OK6u7yLJgPBdnZiyD6QdkYhG+/PJ1LizvEMoR3KDf8uHodJG/9Pkn2DeSQA59bm7soioaE3cURn43hBBIQuq3uLtPGRqXIfcNYRiC55KMJ+4SE6t3HGRJ0DYdVFni+m7f17K333TxPI+daoNyxyaiSCAEE/kMjU6PjuVRa/ZDvqWGSSYeZb3cYLvWpmva9GyXZCaPKTQqzR6m5fCh/eN85MgsT57cx6HJIkdnR9jYrdC0+jpJQgiEEOwbz5PLjVJrNIgb2l4TqDeDocl7s6z7kR9IcXGw/38QQoRCiPwdY0PFxSFvO64fcmBmjHxSZyTdXxbd3Gnguv1w7a1EOuU1S4lDUwVKjQ75VJwwBE1ViCgSaUNls9phdbeO43ms7tY5ND1C3NA4OlPk5eurVNsdYprChw5NcGIyxUa1Tc8NWK60kZT+gx/XQjZrLU4uTlEvb+H7Plc3SqxslpgYyXF8tsD1ikPXcveKFn8UeDMzl1uKi4foy4j8pYGqIkKIKeATwNqtg1+juPhp4P8UQtzKJLqluLg4eN0SONtTXAT+v/QVF7lDcfE08AjwdweNuof8CCIJsNwA3/OQJIHpuNSbbRpdC0Lo2S6+HzCR0vEGD/GZK8tc36qzXOkxnksymrqd/q9pCtVGg2zCYDSXQlFktstVur1BJEqKcO7mNjd2KlQabdKxvjaRY5nM5BN7D8/R+WkUSWEkqdNp1Nitd7mxUUUS/XsQQlDIxFle3yapq9S7Dj8K/CCKi9A3BH+du7WEhoqLQ95WgkFoVpYEjU4Hx+k/nGEQkk5EySR0tlsWluPjByGS3BeM36l2mB0vUmu1ODRdYKXUVwHu9izWqh1+/8wSs2MFpgtJABptk4YdEg4ei8PTRU7OjxKPxbi+vsvXXr3KE4emqXY91ks1ql2LWsdiZbvMRrXNRr3LzPw+VE0hE5Up1QdtN10X4ZrsNk1GkxF2miY9y77HO31/8ZYVF4UQPwFshmH46msOGyouDnlb8cMQTZFwXB8jEiEe73fR36o2KaRi9Gyfb9+sIEng+j7JmE7XsskkIuSTMSKyjOM4zI/mAPACQSYWIWtITOZTvHS9L4Vl6BrVZhtroFsUjShYgYTr+syMFbmx2+HpyxvcKNUwbZd8wiATi5CMRYlFJBRF5shkgZgewYgnSafTtHsW37myzicePkIhpbNZbZNP6Fze7bFW7b03H+i7xFtSXKS/VPo7wP/jXofeY2youDjkLREMZDeg3wIhE+8vaxwvwHR9NEmwVanxxGIBy7JodEwcxyWiqnRNm81Kk2hMp5DqG6Ras00oCdIxnTAIkRWFbKrv/F0r1RnLZ/jPX36edtdkZbvB05fXePbqFm4YoOvGINLk0vUEMV1DCIFPSCEVR5YkZAk0WeLk/ChThRQv3dhlJGvQNS2yyRiSJPj2jRJjyQjy/RxnfhO8VcXFBWAOeFUIsUJfCfFlIcQoQ8XFIW8jQdgvTuxYDoZ6S3oj4CvfOc9kIYNhRIjoBrWujaEbjOeSaJpKVNfIpuJM5FPsH89TqrUAaJh9OY9m16Zt+0iin5jnui4n58fIRiOcPDLPmWtrCCnkxHSeP/fkMSYyCR6aG8EMAh5ZHKMQvx3FabRtNmsdclGVaruHPojwPHt5g+VSgxvbTbqWjRNIrGxXmYj5nLm8iizEfa1L9L14S4qLYRieD8OwGIbhbBiGs/SNwINhGO4wVFwc8jZydnmXiKrgej6ZVD9C5DgOuh4hE4vgeAGZCBiaguO5CCHYKtfZavSXHL7v0+rZFDJ9v4ppW0QjCglDY7cX8OpalYlsFFVV2ag0UWSZ4/OT2KHO6YNTzI/nsR0XIWAqZ5DRNaotC5QIyzt9H44bBMQjKuc2m7TN220Snnxgjp96dJFHDkyhyArzY1kOThfxA0EhqeE7Dq9uNPecz+83fhDFxXsyVFwc8nbh+QGVRpvVUoOYru09hLtNk0cPzgL9Dv3NrkO3Z1JIxXEch1Qiiir6MwLT8Tlz7iK6KvPtK5vIcn9WIUkCVZZIGBH0QbLd/GiWEJ+W6fHk8f6EWQg4t7zNRqVBpdHmY8dn6HZaOLbFaLZv7MazCQIkpjJRhCzf1Z8lHY8S+i7fub5Ns2uRS8ZIRQ1MP+QPX7hAISpxY7PCtc3qu/KZvpt8zx66YRg+w719H3ceM/ua//894O/d47gzwNF7jFvAz77Btf81fenYIT9ChGG/x8mnT+1nZbtGq2uiRyLoYUgypiNJcl/u1HPY7QUczhuEYUilY+G4PuODB7/e7rF/bhrL9al2HQrJ25KqD0ymmEpH6Nk+MV3B0DVGwwBDj9AxXRxPAkkhHo+z0TDRw4CooTNWyNJy4NWVMgvFOHpEYzwb5XrZ5OJGDV1VmMon8P2AZy4ukYmqzBTjfOn5c/zYBx9gcbLAwekiX7zW4cxqkw8tFviPz1xlcTx7V4X0/c4wQ3fIDyW3xMHCMKQXwLNXt9FVmdVyk51aAwiRBCiqRixo02y1uLFd49pWk5F0gpB+xu5XXrqM4wVU2xafOD5NaiCOdml1l8WxNG4gkAYxgjAMObu0g+/7JKIasUhf9XCqkGS+mKTU89go1dFkga7A9ZrDyxsdlio9EDKm6/PkwRHG0n2nsyxLaITMTIwRiUR59PhBOrbPje0aS7tNPry/wFbL4embVf5vnz7+vjIsMDQuQ34IuTVrgX4C2qHxDLoI6ZgOC2NZPB+iuoamyDiej4nOvtEMjmVxcraAEVHQFIkLy9t84MgCc+MFDFUgC0Gl3aPVtZjMJZAEvLJSojVohh0EIaZjU+vcTnK7FQKPGwYfPjLHxeVNBILFkTQHEx4JNaRt+WRiGvWuw//21Rtc3WnRslx2mhazU+NEFJn5YpKZQpKRVJQDEznmikkemcvxwGSSxxZy7zvDAkPjMuSHkDslTaFvYBRsnnr5Kl975TqSPGisbdrEdI1jc6Ns1Lt84emzqAMVACEEE9k4h6aLCCFQZZnNRoeoKpGM6STjfSeu79hcWt3BdDx6loOqRtCEz1Z54NoLfDKxCAldRZElSkGcTDoFYYjl2EzkUzy+2K98+eyxMT60WOB6uUej6zKa0hnLxO4pJi+EIB+PkNQVgqDvX3q/OXaHxmXIDxW3uvr7QUhrIA5/7uYW+yZH+InThxjLxpnOJ6i3u9QHxYv5eAQIiCSTGKpMpdnDD0JSidv+FdtzSUo+47kktVqtX+1sujy4MI6kGexU6kiKgiQrfOncOl1XQgjBerm+V5msKRJHJ1JUOhaGEeHhI/vpOt5e20pNkTgxlebHH5jgzWibSZLg6GSG0ZTe71RH38hYrv+9Tr0vGBqXIT9U3NIiCsOQQIi+sZEkpkcLNLs9RjKJfn+WqE48ohIEIXpEQ5fgM489QBCE5JIGyzs1ap3bldP5ZIy/9q+/zYuXbtALlH57yqhGIqZzeCKDqun4rsPD+0Z5cCqJaZv4vs9IJnVXd7sTU2mqXY9XVmtUOzYt07srq3OhEKPasb/vTgmqLCEPlkbqfd4k6hZDxcUhPzT4QYgkBO2OyXatxf7pEWzXYyqfwHE9dN3gSy/f4Oh4gkxiCiSFlmkTVRWaruDctQ2ms/u5ut1BDUGVYaNUZ6KQ5uzNNU4cHOOhw/v2Qs+3KCQihHGNWxHkUI1RiKosldrMFRJ3HSuE4LGF3N7/J7Kx1+2/pej4/SJJAum7B2bvK94fJnLI+4IwDJEkQc/1mRnNAtC1HNKJGF3HJ6qpHBjPokUTCCFIxzRs2+HGdpW0LnNiJoMfhIwkdb690mS6mGIkk+Tpc9dZqfT4Mx89SkR5vf8D+kZBlvqvAxMZCskokxkD5Q2O/36pdmxWKvevTMhbYThzGfJDwa1ZCwBC7M0isom+qFg6GsFyPaaKaQBc10dVZbq2y0whzTOXlji9f5KIKhHVVX7s2AiVjo2hKjy4OI3tesgiuGdUJggCgmAwcxg4khVZQpEjP/D7qncdNiot0oZEpeMRhj5JI0Imqt33WtDfi+HMZcgPBWEYcmOnxlapRiqm07Xu1uuRJEE0otLu2ayV2rQshyAIGckkeHVlmxOzI3zzZpNbZY4j6RiFhM5Oy6Tj9rWdbwnOv/bnBiEoikQQht+3M9XxAiotE9Ppt8t0vNsRn2bPRZYFcmhT77rM5OJMpmN4ns9yqYHt+dS7DuW2TbVj3/Nn+0GI5we4fnDf1SENjcuQ9xzP7/dqmS+mabshuipzc7fB9XukxMd0nZfXKlxc2aXSanNzu8qBqSKlrsdD48brjt9XTJCOavcMBwN4QbjX1FuRJRRJ0Lbc72lkXD+g1LJYKnfo9nqYlsWl1QqOe9soJg0F23LodC2Oz42QMDSECMnEdcrNLt+5tEYmptHtmcihx9nl3deFo23PR5ElVFm6q6zgfmBoXIa85wThIJdFkTkw0XeWnt4/xuJE7q7jwjCk1Orw0YNFQiF48eYuu7UOV1d3mM7GGCtkcf3wrm94zw/6PXPfgNcuTBRZIqGrRBQJy/W/6wNtWRYHx5JM5NNEIyrzo0nOr/XzY0zHp9y2ySajpFIpHC9A1xQ6lst2vY2hyHzpcgnfD5gdSSPLMuVW7678HtcPcFwP378/Q9ND4zLkPcXzA1S5H3JuWy5+ELLbuLfjUwjB4mgGJ+iHaw+PZcjENA7PThCLGgjYC2MD+H5AGPh7P+dW/9oguL3EkCWB6bz+4RVCoKvyGxqXaqtHtT7obGfZ/MbT50hEdcbzCUoti816F8/q8OvfOkfgWDS7Peodi3TcYKaQ4oGFMf7MB+dpDaqoFVnh8YMTVDsOl7fq/O6LNzlzY5d218TzPMJBiP5+SrQbGpch7xm3HvClrTIX1nbZLtfxg4CoKr/Ov9AdpOirisxkNsUj+yfwJRnPMUlGNQR9v4ymSHtOW1mWUBSlPysaLC0AJEnq9+IdJOxFFOkNfRryPZyuttfvCZMadMRLxaM8un+S5y4t06hVCcMA03Yptyx+8tFDzI3nKaTimLbLhZXdvesk43F0rb9c67kuiiKzslvjlaVdTs/nUOWQfCIKksLzN6tsNcz7qkxgaFyGvGe4XsBOvcPKToNOt4uQZBzHIx6NvO4hikVuBzZLzQ7VVoe13Trj6Wg/8e6OxDMhBEHQ/4Yv1ds07+ixcgtDk/cybyVJoEj9rGB3kIZ/6/XaR9nzA5Z3GkiyYLyQ3ZvZLE6P8tjhOTRNJR/XOTadI5/Q2ay2WC33e+nmkwa/fWaJf/+VF9ipt+l2O3Qsj5vbDfxA8JVXbpCMCH7h8YMI4NhMke5gVnV6Pocq7m3sflgZGpch7wm3clom80k+dHyesUwC2/WIqNL3/HZOGSr/5g+e4eF9Y0xPTyPgruVLEPRDzrVWj5hhEFGku2YllutT6di0eube+J2zG+WO12vDxYoskU3GiKgq6+UG/+Hpq6zu1NltdAjDkNFsmrXNbRpdi9Vqj5ihslEq7537gYUCuqHzzPmbxKI6V9e22W60yRgqkqwwnuuLW+QGbTMtx+Xy2g4QoitgOXdH0e7kzkjVDwPDPJch7wmW66OrMp7nI0sSyXiU6Xjsnt/MruuBEKiDhDbHDXnyxByJ2O3WBmEY7vkjBP0yAiHJxI1+cyg/CCEIkGWJMyt1joxG0RSVRtdGkQSJ6JvPaSkm+9Ilk4UMp4VCPhMjNihFaFgeX7zeQl2x+fjBLJ2OxdRYEcv1kIXgg4dnUBWZK+sltspVxrLJfnW37xMRAd+5vsWTD8yhKAqm7ZKJ6VzdbvDN8ys0uxaP7BuhkE3vfRZ7n5EfIAR861qJJ/YX38qv5G1nOHMZ8p7QMl3WSnWevrzBN89eJfQCXOcN5DbE3eLto7kEnTsUF/uH9I+RRL8AUJElgsCjazmYjtcXsAcc16PValBudvn21XVECE/f/P6bGyqyhKHJFBIGscjt7naVVo/9o2mWq12mcwm8IGD/eI6Ncn92c2O7wm61QS4VY7zYbw8hKwpt2+eTJxeZKGb2HM9GRCUWjXBwPM1mvcvCRJ5Gq8XSdv119xOGoAyWd8/drNKyXr8UfLd5y4qLQoj/jxDiihDinBDit4UQ6TvOGSouDnlDNqvtQcvHOI/tH+PRQ7NEYzq6fneS29XtOr7v4zju62RQC/kcptk3MP/mqZe5tLpDs93h0lppzxBdubnK7zx9ltVqX4I1DEJs1yeQZJZLTbZKVS6vb/P8UpXd1t3G6s3SNU1q3dtG8ehUnslsDFUSbFVbbNY7rJYaNNtdbq7vMp1P8r8/dZmYJpMwIvzhq+vsVGqkDBVFkdFkQblpYrk+W5UW1a5Nx4XPnFpk/0SO2ckJpkfSr4ti3XJOf2BfgeulNss79e+6hHo3+EEUF58CjoZheBy4BvwtGCouDnljwjDED/ptKmVJYmm3QanZA0KiEe11x9uWw8puHSHLr6sJOjQ3SSQSYbdl8dihSRbH8xiRCDs7Zf7wuXOs7tR46vwGx+bGOTiWRNNkQgFOAEcnsjy8OM6xhWlM0+JPPzbDdvOtGZd4LEo2FqHTNdmtNtko9+Vlf/6RKZZ2ajz5wD4urm7z3IWbPLA4yWq1x4+fnCQRi1JptlnZaZCJ9cPoz128SSFhMJ6NsVGuM5ZLYNou45koHdPGdn2iuoYs+hGrOwnu8Ck9MptlKp9C195br8dbVlwMw/ArAwEzgOe5LRsyVFwcck+WKl2+fnGNVqdHVBXMFFL0HA8h3Tt7diQdZ240i67KPHNhmY7ZnyGEYYgfhmw2TGRJoKoGQpKQZJlTxw/w6UeO0rQ8DsyMYQ0yZg1NxXR8Gq0ms8UU2USUdrdDIqriWD2OjSfv+tlBEPCF71zm2xeWeWXt9cuQW1RbPeptk51aEy+Ecr3Bxc0a1XoDdeAQ/sypg/zU48dIx6N0e10m82kARrMpPnJghJmxPKulBu2eS63Tw/EC8ukkHctlMhsnoatk4walehvH81EVmTAI75qZtCyXbs8iCEIyUZVSs58rtF6qsVR6bxQG3rLi4mt2/RK3O/kPFReHvA7HC9B8m0JMwwt8xnJJUrEIUU1ip3rvhzcT03D8gI5pc3x2FFnqO253miYdyyOpq+TjEeKGimk5/MdnLlFqdJFlieOzRQ5N5fD8gHrHwnU9tkp12l2HcqPDytY2s8U0pw4uMD+W21t2OY7L05fW+Q9PXyKZSrFvIs/z17b559+8ec88mEREYqfVwzB0EkaEaDRGWvVpdC3KvYCdWgfL8UgkkjiOy4mFKWaLKaAfVj59eIZ61+JrL17g9KEZ8skYqtzP1zEdb0+83nFsLNui3OhRanSwLJNG18IZzGC6PYuzNzaQJEEmqvLC9R0A0vE4lZb5nvhg3pLiYhiGrTvG/w79pdN/vDV0j9OHios/4mw3TZbqDoZuIO74s8sk4hRyuXue44cCXVVIxgyQZdqmzfXdJiljUCsk4JlL6+xU63zt3BKbWyU6lss3XrnK8laJxdEsTq9FNKJSaXaYyicwnb7O88vLFZLRCBeXNtFVhUqzy83NCv/26xeIGzp/+sNHefzAOIV0nJ85vYDqd/k/v379dQYmGYuSishsVDtsVRrEIiqJeBRVktCEz+98+xwvXrrJF75ziadeucFOvUWl3V+CuV5f72ijVCOXy5KKGziuxzMXV5FF3+m9Vetw5voWuy2b8WKOVFxHj+jk0kkyCYMXbpYA+F+/cgNTieP7PpqqcGAsRatnETdUHtk3CmFI03Re9xm/k7xVxcVb478I/BjwJ8Lbn/pQcXHIXTR7LpWOzXhS7YvJd629ehnXD4jr6l3HX10vcX29spe9CpA0NCKahu/YrJYbKJJAkyUiqsxW0+ab10qYnkcmEvDEA/u5sdPCCUBLJOjaDl3Ho9mzeGhxgolChscOTqMpKnNjeXbrLS4sbbFS6TA7XiCb0OmYFmeubRIEATFN5tBIDM/3ubjVuutedVUGScUMJFyhcXGjhiHLxCIqO02TP/uJUzxx8iA/+8RJPnf6EDPFNKVak3KjjRcEfeOxXOVnHj8GQDpucPrAJN88v0oQhtzYqfPC5WUWiinaXZO4rhJR+9+5EUUmrkf45X93hvlCnHObLRpmf6l0ev84z15aGfi0IBvXSeoqjuPszXbead6S4uJg/NPA3wB+IgzDOxW1h4qLQ+6iYTq0eg5zI1kWRlOMZxNcXt3h0kYNMZicbtW6XF6vsF5ucml1m1RcRwhBq2fhByEdy0WSBKO5NPlUAs/zMDQZ2+qSjyl87Og0v/LJh0jHo6yUGmyUG1QadR47OI+uqewbz6PJEpqmkk3EGMuliBoRYlGdeqvDxx/cx7M3dvmDl5Z44eJN6h0LPww5v7yJaXvMT07w5x7fx/JrGj5JkmAsE0WWZL7+nVdRZMGR6QIPLY4Tk1xsL6Bcb7MzkJOtdCwiMhTSCb7+nYsQuJjB7WQ9P+i3fVBDm+eurHNoLMmPnz6MqsqoiornB2xUmlQbTQDGEionp1JMZXSq7R61ZodGz6HaNhlLxUkatx3lQgjWKi2+9vJVnr+y9o7/3t+MO/mW4uJ5IcTZwdjfBv4xEAGeGkSUnw/D8C+EYXhRCHFLcdHj9YqLvwoY9H00dyou/vuB4mKNfrSJMAxrQohbioswVFy877BcnzNLZWqVHWZSCsVcv4L48Ow4u80eEU3GdjwiCkwXUjx/fZuUHqGQ7tftNLsmr97Y5ujCOIokAYKULvdzShptfDfEEg4ZQyYWjZJJGEQNjwfnR0kYOm3TwYioVDs2MUPn5nYVz7HZPz3GH37nGo/uH+W3XlxBV9f55GKab11c4cXLbZrtFh99YJFK2+X65i6PHpoBYCFv0DQdUoOHNghCvn61REL2eOTQHF+9tMmhyTytnsMHj+4jGdNpCkHoeWxWmySiBrKQ2Kq1OLgwDmHAZw71k97CMGRpp85TZ2/iuh66KjBUCU8odEyHbDyCJGDfWI7fe+YcsyMpji/OcHq6C57N9ZLCestjcUIDNDKxCNd2WsxkDUIh863zSyQMlY8/uJ+m6bLb7DGSir5jv3txvzWg+V6cOnUqPHPmzHt9G0Pofwvbns8fPHeR+ckR1ncqRFWJeNRAi0QYy8QoNbrk0nGKsQiGEWGj3OD6VpmPHN+HGAi1/4c/OsOPPXoEISvoqowsCbo9h0bP5PzKLvl0gscOTGC53l39cRtdi+WNbQ7OTXPu5jrJeIzxdJyNSp161+LAVIGvnr3JZttnOqlxdDrHodlxAJZ269zcqrK5vc3PfPQ0cUPD9QPaHZNnz19lvFjk4PQIq7UeM9kokiT4+svXWDFV/vyH99E17X5fGNtFUwQj6Rih6HfI1VSFP3zxOnMjGVzPZmE0Tzwa4eyNLWzP54svr9DqdvgLn3mY/ZN92ZKXbmxQTCV59cYa0+MjzBUSeCFkYjqu23fW9hyfv/U7F/n8iQk+vJBF1zVsx+PcyjbZmE48Hmckdbvnjet6BGFIRLt7Wfr9IIR4KQzDU/faN8zQHfKOIQm4vrTK5x87zLHJLIenRpBljUIqSi4d5+JalYShkYvrGEZkUJuTpNyy8f1bKojw+ccfQFJUvMFD9O+/cYFzayW2K03MnkkiqnN1dWvPsJxb2uLG2jZLpQbnV3YxNJl4LIquKnQtC9/3ODJd4F999QIX1usczkj8zIeOcnBmDIBmu0O3Z3NsfoLPfvAUcUOj3u7xncurhGHAJx45RkwRXFjapBCTMTSZr7y8xMzkCCnFZ7PS5sJ6ie1aE01VySYMNE1DlWW0wT1OFjKs7lZIRKNsN1r86lMvU220eOTAJD/12D4emE5T71nYjsvFtQpC0ri2voMe0RlJGtRaXdKDkgUfCVVViWgqf/1TB/jn37yJpqlc3yjz9OU1js+NsTBRIK6r9Abha88PUFWFnTdob/G2/P7fsSsP+ZHH8QOul3vIskwQhCTiUSaLSRKxGDO5BI8cmGBmJINAcG2rTqNrEYYhxaTBVrVJq2tSavbQFBlVFsSMCJVGi2PjcQ5NZEnEDTLpJEenckwUsjR7Npbj0TId1hoWRybyHJnMUe3YFFMxbpTajBcy7J8ao9IwOb04yseOTFLIpSk3u3tZr6lEnJmRDEldJRnVsB2XUrPLB47MkkrE0DUVM4DRXJJCKo4kSTi+z4HRNH4Q8OyVdbLxKAsjaXzfpzfIz1nerfN7L1wB4IHZPJ966ADzoxkaLZvp0QwfO3UQ1w9JJ6L84qce4+T8GKulOt86d42YFrJd71BI66TjOjOjOeq9vrHVVRnf79dqzebj/OOfO8bNjW12mx0ePzRNz7RxHJdYRCGqKezU2twq4ZoZ9CQG9nKC3i6GhYtD3jH+0zcvMplU8YMAw+gveyKKzPVym6iuYahyvwpZknCDEE2WeHW1glA0JEWh2rYYz8Zp2x4JXSEMIfADDk6PcXl1i7oZ8skHFwAwPZ+4HuHi6jbzoxmQVCwvJF8o8OqNLR7aP87NjW32jyTodDskowYvXLzJwkiGk/uPkI7pVDs2q9tlxoo5EpG+tlGz3aXr+Lxw4QbVeh1DUwiFQrvXb/4N/VnAj53aR7Vr88cfP4jnuphuQCoWIRENUJT+cYmYQT6q8p2bZZJav/l41/GZKCQZzcQJw5DVchPTcSEPthcwO5JlYSxPpe3y2dOHsSybmzsNRjNxsnGd7UqDEMimEsiD4JogYGq0SMLoYFoWmWQcf5DH0+w56BENywto9Sy8wKeYjKJIgq7t0+rYFNLRt6VvzNC4DHlHaFsuZ3cs1hs2Ql3l4X0TaHLIy9fXyaQSSCJESAIhBELAofE0G7UOD80XqbQ63NhpcGJmDNMLUAkptyw6lsPqVpUPHokReD4fODRNEIQEhHzz7DXyMZVcfgTTDVCw+ML5FYQk8cBUmt1yhem0waWVbb5zfZN9E0U+duIAhZRBwugvL9KGyh+trGMFgnRUZzwbp9ox2d6t8OSDi4zlssiyhGnaRCLq3gN7qz2DMShgVOUIIQ43tqpEdR0/DJjOJ1nZ3OHUoVmapsM//aPLBJ7D505M8/Di+N7ntjiW4ezSFn/w3EUalksxEeGjJw/Q6FpYpsl2tc3Z9Rp/4iNHWSm32Km0ePjA1N5MxA9CcqkEqgSqppJJ9h3jt/rdtDpdpooZSi2LpunS7nRQBQQIeo6HFIZYZZfpO2Y0b5WhcRnyjrDbsrFaVQ7PzXN4ZoSYobG+s4uLwPVDNus99o+m9o6XJImb202m80lyiTgJw0DXFNoti6Sh8msvrLBRqvHLH95PTI/QCmVevb7KwZkJkjGdzz58mKhxu21Cp2fxwGyRmWKS3WYP4dmMZeM4cpR906AQoKsShmFQa3aodx1KrQ67tkyi1WEil2Sn2mRhPM/cSJbLN5aodDxyMZWRTIILS5sc3zd1r7cOQBgE7J8s0Ok5aJqC6wdEjDi+57NeavBnPzhPz3E4MFmk1rVp9WxG0zHCEPaP5ZgfyfDMlU0+dGwOx/PRVJWO5bDVcRjPxAiCgNlCEl0WfOvyFpoIGMkmyMZ1svF+AWg2GWWr1mY82xd22y1XkKW+AWz3LK6ubJEwIkTGczx/dZ2YphLT+2H7t4OhcRnyjnB9t821tkr08hqeb/Pw4hRXNltMFTPkEjoJQ8cLQsLA3ytK/PCRCdo9m2rXYibfr/UpJnWWK10iEZUTk3HiiTjr5Sb5mEG9a/VrixQZZeDXkSTBTq3FaDbJTmOHZqfH8dkCsWiadruD43qMZafYKdc5OF2kbbk4Tj+ZbaaY4LEDk5SbXaptkys3V4jGomRjGkcW52m0WjxzcQlPirJVqnJ4dvyeomk79RbJqNFPEIxqeH6AIstkYiq79RabtTYPLvSdx5btUml0ubBWIZ0weGgmi+n6/Oazl/nlTz5IRJN54eoutWaTiWKa3VqTDx0Y54XLa8SMCI2ew269xan904Qhe05e6Oe1JKM6lu2gqQqOUJnM9T/XXEyj0rGYG8nywrVNhKSwMJah2uiivE3d7oYO3SHvCCMpndGEyqPzGR5ZnOTrry4xlk8yW0wykUuSjGrc2K7SM21ubpa5tllhp9Hj/MoOSUPfW/MHQUjouhzNKXzw4CRLW1U6PZORpMHhqQLpRHSg/WNSbtvYns9oNonl+sR1DV3XiEX74VdJUVktN4kqgoPTozQtjxfPXqJj2jw0X2S6kKLWsdEVwZW1DT752ANMpKN87cwl1narqKrG6aMHOTk/wl/68ccQb6A2v11toqsKEtDoWHstIAqpOJfXy4zdIfeqR1T2T2T56cf28+TRKb5xcRVFlnnkwDjnV3aodmwOT+dBCGaKWWZH81ze7RCLxUjHY5yYLZLQNRw/JBvTqLZ6d91LXFcRgO36TOaSbJSqVBtt/u3TV/CRaNoeJ2ZG8HyPWttiYbJAtfv2lAkMjcuQd4QwhD//oVn2zUyzvFvn+EyebqPBarXLdrVNGIa0bNAUGYmAkaTBbr3FVqVONn7727fn+vz2mZtM5mIUMkkOjqc5MDVCLp2gbdn8+rMXOLu0zU69w2apSqnRf7hsL+CR/ePMjaTpDJp7B57DTq1JPhUnGdXIJww++fhDLEwU0HUNIQSxiMLNrQq6FsVy+nkgjz+wyNnVEiu7TSZzCRbHc2iaimXd3dzq0lqZf/mNq0wWMkiSQJYlJCFY3+0XZv7X567R9DWQJeqDyNidLG+WcQKBUBS+cn6Tg9NFVCnk156+TNUS1DsWj8wX+ezJORIRlfFcnEI6vpd8l4rpe8mHt3AcD1VVEUJwY7dJLp1CSBKvbluYtoskQdsNyMcNUskYjheST/zgSpMwNC5D3iFyMZWoKpB9l5GkDmFIN5DIxDSKmTim4zGRVDG9kNFcGtcPODJVYGF68q7r/NGLl1hv2HSJcHmzyctLW6zs1Dlz+QZrNZtW18EQPsdnChyZGaHTbgOgy6KfASwL4oPm3olEgpF8lgs31ljerlBq9gjCfmZsrdXl7M0trm5VeOHaJqcPTFBIxWj0HCpdj4fnRqiYHr/xrQuUmiaeHxCL9ttddno2l9bK/LNn1nj6Zo1CKg7AWqlOEHjU2y1+/9sXmC/GeXAmw76RNOlohK59d43PdtthOp8iY2j8zOlFmpZLo90lHRVIeIxl4yTjBpqqoKmCGxslNmo9CokopxfHuLpRxvPuDidrmoLjB7h+yEQ6yvOXVgmF4H/63EFcx8KyPTqWRypm4NkON3Zq95RaeSsMfS5D3hGeu77LY/M5JvJJah2TTEynUm/h+gGSgIiq4IUSzVaLzGgexwuot3uk9Nvfd5br8/DBGepUubjZ4PLqDh87WOS/vHCDw1N5Ou0KB/JR1qptjlouCUMjkBR6lsuzl1Z49MAUhn73t/BILsk3zlym1W4zkk+xOD2F5Qds7taRZQnLdkBS2ai2yacTJAyVtc0d8vk8leomZ0s+obzGZ07MUG12qTTbdF2Pf/FChVfWm0ykdZodk2RMZ7tcZ9nxEBGDI/PjzI9meXlpF0NTQXZJRzU2qm0mc32HayYe4cxKnVMLIYvj2UE/4ThCCF6+sQ13dJ8zdJ1Ky2IkEcGYGyMX1xlNR1mtdJgrJrEcD11TcLyAV66tcWRmBD2qc2CqQKtn0XZCjsyOs1Jpc2O3xecenOXC9RWO75+9S2nhB2FoXIa8IyzmdVQpRBaCkVR/qr5r+sxmdFw/6Oe3hC47bZuaVWZtY4uvXd7mr/30E3vX0FUZXdc5Mhrj2HSeTq3El642+MTRSZqOYCYdoWuafOTENJ2eRVxXMW2X33/+AvGoQSL2em3oyWyShck8u7syJw8u0LEcQgdMx+Naud+XJZ/M8O2bVdKGSiSicW55m9GeRzad5qFoyKWNKt3eJR6aLxLRFNpOyFbTpuf4lNo2XccjFRc8dHAWAWw3e0xm+7OZo9N5XM+j3LZJRzUmcwnaXRPTcUhFI3z5UonL67t89MgkrZ7DT59e5NJ2m3wmSWTQWc6yXfIxDTOucWltB9MN8EdzZOI60/m+oWp0LGLRCBFFwohoRPUIkiT1P6eoQS4mwI+xUjc5MBLn5naDgzNjqIpCRB0alyE/xJRbJgcm80iSwPN8nr28zuJ4Fj2iIot+JGNqNE+jt0G51aZca7KvEGc+f9tn4HgBjY7JydkCqiIRyjJ/47NHaPRcfuWffY0nT8zwkycnGLnDz3Bq/wRT+QQ79c4b3tvR2QkOTo2jyBJruzVmRzPoYRxNuDxxYj/FdIxmx+SZ67t4XhsjGuNayeRgRnB5ucETx2Z58mg/DL22U8F0Ah5byLFc6TKdjZEZRGwUWcLzPPJxne1qG4RgLBtHEoKRVD/K5Ho+y7sN0jGNiCIjE/DgTJ5iPMJiMU6ra/OhQxN8+8JNlndrjGaSe/k0kiwzM1ag3OyiKBJeECKkEBnB6MCYnVspc32nxmQuQSpusFGuY4dtFidyHJwsIKsaS5tlxnNJitk02/Uu+eTbU8w49LkMedsptSysAOJ6v3L40uou6zWT3VoTIUCWb4dvp0fzRDWNjx5foGuad+kEuX7Ai1dXqTb77QqiRoye45OORfhTH17g8EiEdPz1D0Ixk2A8m6DdvXdfXCEkMgmDuKGxMJ5jspBhciTPxx86SDEdw/ECbmyWuXp9CdwO+8YzWLbDrqeTiSnMZO8o/gvgwf1T/IUnFtAUiVrX4XqpvbffR9AxHUzXo9nu0OlZlJvdfqZvGHJutcyNSo/1cpN0IsahlMuTx2eZGc0xns+iqjJRTeHUgVnWt6vYTl/atdkxGculkCTB/EiaXMLA0GQ2SjUsx6NjOvzX5y9zeb3EwakxLm+U8f2AbCLKyfkiERleubHJvpEU5Z5LqVqj1Owylon/YL/8OxgalyFvO9mYhjH4y3r+6jqmG7A4EmNhvIjpBPQcD9N22aq28f2QhfEsaiyOaXv0rNth0FhEwZQTjObSABybynBmaYdfe+4GD82N8tC+cZwgZK1yu4GT5fr0HJ9CJkFUV3G9vkzrTqNHo+dwfbNCEASYlkPXdGh1en2FRklFhCGNVpuLa/1ix8+dPkIqHmMkm2E2BfszKn/s8SOkB3pJbdMhn4yjyBLTuSj/4ycPsNkw+YdP3aDasSm3LVw/RBEe5UabmBHhq+eWSUV1PM/n6kaVs8u7qLJgYTRLRFOZLua5uLZLOqrRsRwqbYtXrq+TjkU4ujBBLCKztF0lFTfQVZmUoe2FuoMQ5sbyeF7fuKR1lSPjCQK7g4eMj0TPF1zbrLBdbTGe7c/wPndyhovbbbaqbYy3yd8CQ+My5B0gCKHneKxX2rgezBXj7BsvYPsBKUMlqikYEZV8QqfW7rJWbfCtV5fZbMGL1zfvutZiIUatbeI4HovjeUYTGu1GnXbP4spmA8/10DWVzVqHC6sVXr6ygjyY/MiyjCBgo1Sna5r8y69fwbQdkrF+Al88GmF2NMeNrRJt06bd7dHqORycyHN4boIDMyPMjWRptLr8+AdOkM1lyCRjeGHAbrWBoUp851q/LbTpePzS43N85nCBtVqPl25uoUgSfhBQb3dBlql0PT54eJqlnQpX1nZIRVV+6pF9xDWZb15c5fxaiUcOTVOp1ml0TC4ubXJjo0RU17myvE02EUWSZEazdzcT7/RswjCkPFB97LghhXQMI6ITjydYrlkkozovXllhMpvkwX2TSFqEmBHhyy+cJ6JFOL1vlFP7Rt/Wv4Ohz2XI2442WP/rqsSHjkxR75ggyXTbHVqShAg9Oo5Pq90jDAMKcYOtapWfemyOM9c3+fCxOWAQIm63OXOtwWg6ysHpMRbG8ry01kLIMooEuYRBy7SQVZnxbIy4oeL7PpWGRddxkYUgm0qwXWsiC58XrqxRbphIIuCBhTGyyThrlQ7HpvIUMiMAPH1hlZNzRZAkLtxYpZiOs1mCWq3JfH6BnukwN9bv++tLMo12j3M31zg2P8n/8PFZEvEkr16+RqlUIp/PM57PY7TalJo9fvXry8wVkjy0MMLV7Sae7zOeVIkZRQ6M952966k0/+zLL/PYvhEe2j+FF0ByoBwpyxI6MutbOxTzORASW9UGE7kko9kEXdujWm9hugFd26WQjKGqGtc3yjxxeApbSFxa2yURNYhHdf7kJ08Tjah86Ojs2/53MDQuQ94RGl2bs6tVRhtd2pbHw/vGiOZSqJIgCFU0zUMKA5Aknjm/xOPH91HvBfzCR0/uXcN2Paoti8cPTXJ9o8zN7at8/gOH+OBCgSCwGcnFKLVMJrLxvYQ0XVOxXI9//swqn5iPslLpsDCeYyKf4i998jjNrsk3z61wcv/U3nLi5HSenhuytb3LxZVNDs5NsrxbI5/J8JEHD3Fxo8rUSJ5sPMJWqUImqhOGfRGyjx6e5NkrG4xlMkiyQiwapZjU+cTp41xb3cYLAjaqHcaySSRVIxap0rJ9cnEdSVLomj10TaXbcdAUCU3ReGh+lNB1iEYEzY5JIR1HIuT6ZgWz1yObijM1PspuvY2mSOyfGuHmboPpSATL9XGEwpiucHm7wYvLVX769Bwj6UlkSZBXJAwRMJrr13Vpyju3ePlBFBezQoinBkqIT90pVjZUXPzRJQhCLmw2WRhJ8dBcgf90ZodYRMUPQnqWizRIma+2rX5o1Pb55KmDHJ8ZJRWVKN7RdnG75fAnnzjEVC7O0ZkcsViE5y6vIimCfROj5NJxcom+/6NfXS3omRaaqvDff3yR8XySQ7NjHJspkjQihPRT8A/PjTNfTJKIGqzvVtltdXj15hqOrJHOZtltOcR1hbFMlK+/fIVMTEeSBBE9xkg+hxfAs5fX2ag0+Y0/ehbTtpkezZGMRhhN33aI7p8ZIx5RqHZMKo0WkoAHJhM4ZptSq4ehBExm47iez7Pnb+6d13McUskYvi8zXUzTNS0ur23z6rUVxgspRjJJ2t0uI5kEsqJi2g4RReIrryyz2zQZS2p849IGmZjOn/zQfsbSCXYabRK6iqbIe4blnebNzFxuKS6+LIRIAC8JIZ4C/gzw1TAM/74Q4m8CfxP4G69RXBwH/kgIsX/QR/eW4uLzwBfoC5x9kTsUF4UQP09fcfGP3aG4eIq+pMhLQojfGwikDfkhxHR9tus9pvJJfN/jxLjB9e06fuAR2iaH5qcxNAVDU1jarpFNRbmyUaHetRhP64iBokylbVOut5nLx1CBEAUtmmQ0IWH2evTcBK1Oj/Fckq5pETP6s4lY1GC12mEkrrBZ6/Lw/knOXF7GDBRkEfLw/kkOT2b37ldXBF89u82+os5EJs5sMYPreqiDXI9QUkhFVcIg4MyVJRKxKIEf0LU8oprCjz/xCFfXy1zbrLJvJE1Eu91tDqDR6jKRyzCR6xudXDLG7GiWjgsuKpZpo6oav/TJBwmCgFKjw1alzdGZIqvlBmEYUm00OTI7wZHZCVa3S+RSSVxbxvMDupZNs92l3PE5OBIFRXBps0ExriLHJdIxnYvr5XtqLr3TfE/jMujavz3YbgshLtMXJvs88JHBYf8W+AZ9NYA9xUVgedB0+xEhxAoDxUUAIcQtxcUvDs75nwbX+k3gn7xWcXFwzi3FxV/7Ad7zkHcQWRJkIoKMoXFtu8Enj07QcQIub9b4yOEZmqZLdNCISZIFGzs1QkXpd4tLRvdmNt+5ucvB0TT1rkMmplFtmzxzvcpUHFRFJh6P7yWmNXoO37m+TTwepdr1ODKeotJyOLttoimb7J8ewfJCsjEdTVO4vLrLTCFJNGqQz2b40PGAarVGvdnmD75zmQPT40wVUmiy4BOnDvblQ4CRQoFiQuPGdo1zS9uMJnXqzRbzk2Nk4zovL22gaxr7Jgpk4zrlegvHscimNda3S4zksyiyzPhAcbFj2sSNDI7r0exarJe2yWVSHJ4bpdZ1icfjrG2X2Tc5hh+EnLmyQjGXwvED6s0WmViBZ67ugmvx2KFpYprMP3nqAr/y5GHihk6l06XS7HF4IsdGo0cQBHuf77vB9+VzeY3i4sjA8BCG4bYQojg4bIL+zOQWt1QSXd6k4qIQYqi4eJ8igLNbTUYyBo8f7ieaXb6yyb68QURVyMsyO40ugR9wYm4MCKl1XV5avcJHByFny/E4NBpnbrQfFbFcn4gi8J0eP//YcSJ36Ep3bA8/7IurXd9tsFwxMfweF3ZNfvbhGepdlzDs94tpWQ70TGqWz7S4/ZAtjOWZLWa5ubmLpmjslnY5fXD6de0U9o2m+YMXr7FZ6xIAZzeaPHF4inRCx/c9NFkjqiroqoTnB2STMa6sbOGEEhPZBKVqC9O1iWgaXtC/p0q9RSoRJxXTSRoaitrXdhpNybQtl0BN4QYBQgiOLUyxVW0jC8FGw2K5skLTdPkzHzqEJAn+83NX+flHFwgF/MZz1/jA/iLxqEHHtPFdB9cPibyL8eEfWHHxXofeY2youPgjQqXVYy4XJ6L2DYDrB+wrJtAj/WVLvd1P1HI8D1mWkGWZQlLnsf2je53pX1qpUkjdzrqVJUHbdJlIavS8kNYg9AqgSIJWq8uF9SrzxRS/9KEFVEnCa1dY2qnhEeKEEsVUjFTMIBmPszia4trGLrW2udc313E99k+P8eETCxxdnN0zLJvVFtdWNtitd/D9gPF8ioV8hD//mdNEhcv5lV3Wa116viChS8h4RCMalu3g+LA4NwWei6xqTI5mySWT6LrBeDbJ7EiGkWySdFwnDAKqbQtByMp2ue9DCnx+57lzPH95Dd/ziagyk/k4X3npOoqAqzstfuaReWzH5YtnrvHxIzPkkjG2aj1SUYVKy8RyXb51dZd9Y7k9B/a7xQ+iuLg7EJdn8G9pMD5UXPwRxfUDFEXmkbksg5UEvufRND18BNv1FrlUnE7PZLvawPd9Wj2bjXKdsaQxKNSDwxMZ4kYE3/fp9CxUWcL3A/aPpXG8ANvvy2hAv/5oZjSLqih4nstu0wRZ8Asff5jj+6Y4MlXY0+ZRZQlNkUhENGwfNiptnru+g+MFGINs4qlilv1To3Qth2rbYnW7xv7ZSRRZYqfawPdD0ukMsiyYGs2R0mUuL6/iez5H5ic4ND9Ft2cR1TU0GfIJg/nJUXzXJQj6zbcvrW5T61psVOqUW13O3tigbVqkE1FqzS5RQ6dru5hewKMHZ6l1TH77pVUub5T42qVtFFnw8kabn390ngD4/ZeWmB/NIiTBRqXN/GiGjxyZ5cT8OBvbFT79wDRGRGW33t5Tunw3eMuKi9ytkviL3K2eOFRc/BHEdT26nQ5/dH6VhBHB8XwcP2QkFaXWc3nqwjYXt5pc3WkRhIJ2z+abF1aI6jpzI/0Ihu/7XFor4fr9pUCl1eOFq1tkUjEePzTJSMqgkIjQNm/3UokbGrGIIKSv0FjteuRTcaLa7VV/cFdFscqxuXF6ns/phSISAaVK9a73Um12KFWqFFJRwjDk7EqJyxtlVra2GU/HSOgqp/dP8sj+CR5enGEydztKFIvqNDtdlrbKhEFfwiMW1emYNkLAw/vGSRsa+UQMD4XpQr+heKVt0bQ8FCFxY6eJoSpYHpw+OEPTdLhcdpnMxpnIJvj8yQliho5t9RUPFkYzXFjeAllClcBybIIgwPGh5wXUuzbNnndX6cU7zfcURRNCPA48DZwHgsHw36bvd/l1YBpYA372Dsfr3wF+iX6k6a+GYfjFwfgp7lZc/G/DMAyFEDrw7+n7c2rAz4dhuDQ455cGPw/g74Vh+G++2/0ORdHeO7q2x7cv3ODIzAjj+QzVVo9/8LsvcrUp85efXODJw2Pf9XxnkKrftwPhXv3NLXG0etfBcj3G0lE2ax0mcwkaHZPnb+wwlo4S+D6ZZILVSptHFooYA+PStVx0TaFpOmRj/aLCMAxx/AAJ6PYs0sm7mywtbVd58coGzXaDkWwGTQrpBoIjkyOomiAdi0HokU+/cVjXsiyqrS7FTBJVVemYNl0nQIQ+iaiOLODmdo2vn71CLqpzdN8sR2aLfOHFqyiyQsYQuFIEp9dlcXqcbExlvdIim0qQT+icvbnB+c02D89lUbUIz1xa5qdPH+aV5R0URUWRYCITxw1DHB+imiChKqQSb5/K4ncTRRsqLg5523C8gKcvb3BitkCr1cFXdf7vv32eX3wwy8ceWEDTVHZbfe3nWrNFNBIhGdMp1ZosTOSR6JcOBCGI0Ee/w3F7i//0zFVmi0lE4BEGLq7jc7Vi8ScfP0i9Z3N5vcxHj83iByGaIrFV7xLVFBw/5MZmmaNzoyR1lY7tEY8odGyPrXKNZDSKKjzSqRT1rsV/+trL/MQHj2HaAVLg9vvLdh3yCQPLdokZr7+3O2m0e/QclwAJWYaoorJZqbPbtpkdyXJzp87h8TS253NlaYVPP3aSRtchE4+wXqqxXukyW0iQScZo9myWqiaTSY21ahPT9HADn5lCiiMz/TjKyze2iWiCnUqHAzOjFFNRLqyWmB1J8/y1dWIRlclMgslCmoj69s1evptxGWboDnnb0BSJY7NFzt1c5x/99jUeOJzn//iFkxQGiW6uH9Ds2Tx9fplPHp9mvJDqZ40mdGzPByHwPR9dU7i+VWcsG8e0A+IRCdvzUBUVScAXXt3g00fGODI1Rqltc3xfBEmWmMjGMc0uF1d3GEvH2WqYVLsOIwmVaCzKockcpt1fulU6DvMjaW5s17mw1abU3CKqwAcPTdDp2Hzy5D4mUlFMLyAeUZAkiZFU/3EJwuC7fQz812cv0bU9HlwcJ6FJnLm6wxNHpjEMA9H1cT2PhxbGuLxWIgh8kpkCQgiaXQtZFpy5UeLBfSNomkqjZ5M0NKJSB8v1KDV6PH21xI+dmGDfRI5yy6RtOozmkrxyfZNKq8MHj0zjeS4rlTZHpgs8NDfKMxfXGUsl9vxa7wZD4zLkbcVQZbq+zF/+yUMsTBb3DIvjBbS6FnP5OFNPHN2rvr3lC4koMj3HIxQCSRIcmMzz6kqVnm0SOg5fvtYkCEPWWy4fmE2ybzxD2/HYN5q664HZNzHC+bUqv/XqLlanzV/53Am6psNmrUUzVHnp2haLE3mOzRT5tacv07ADPjCfZeLYOJc36wjfZ7qYQI+oqKqCeg8Z5UT0dhOqW7kjpuUQCnhlqYQkXD734DyBrJBPGKzu1mh0TaIRjQem0nz94jpTeRMrgGqtg4LHRinB7EiaMAxJRjU8L+D81iYfPr7IKzc3eenmNp95eD+/eb7GvoTEY4dnaHUt/tU3rvDp4+PE9QRbtQZ/9hOn6Nkur97c5vBonHrXJhOPomoqixNZyq0exVTs9W/qHWBoXIa8rSR0lR97aP5147bnYzkO+VSUWwmsnZ6FEdG4WWqR1hVCSRBVZWxXoCkS+0aTfOnVBlEl5IPzCVarFqfnczyyb4SkoaKrMo2eQ0yTURWZWtehZ1nUek4/FCxpLO82ScU01uo2u6U14vEk10odIODwWIztpk251ubAVIEnDk++7r6/G34QUmn1GEnHKbdMfue5C8wXkqRiCb51cZmYrrMwlsUKFK6XehybiFBr9xjPZYjrMooSMJYd57e+dQEnWGOi2qaYiLA4nuWfffUif+XTxxECzq9so0uCUr3DJw+P9sPdmsLV9R3+/JOHSUQj/Obz1/jxh/dzdX2XmZEc13eqTORnUaWQSqNNVO77roy3qcvcm2FoXIa844RhyHq1SzZ2expguR5t02Gl3Ob6ZpmPn1wgqqlIkmCz0mSykMZ0PJ44NEYqGsGyHCTW+MixSbo9E13tzx7SUQ0/CLFcH8IA13P58MExHl8c4f946gr/v28u86GpCIfnJ8jpgkrbwoio2KZNzwv4zKn9QH/JZjrenhM4DEMs290LUb8W0/Ho2i5tK6C908R1PX7hiWPk0gm2a21UZYxqq4UbCp48NsVatY0eUVAUg512i4iiMZE2iMdizE+No6oyk9kEF9d3OTyRYyQRodmz2Wl2mRnNsLTV5NrGLj99+iiblSbPXVpBU2QShsbTF1b43EML+EGIZHk4rktAyGalRVxXeXWlwvG5URodk7Zpk4i+Pd39vxfDfi5D3nFsLyCm+HcJdumqQrVr07MsTu6bJKQv62p7AZOFNNCXGDVUGU2RScYN0tEoiiJT6ji47u18DVkS+EHIS1fWmBvpt0JoWi7HJtM4fsC+iTz7R1PMjOZ4cN84hgI906SY6WcAe55H4N8WZwNo9+y9+qJb9Oy+8Lvt+lS7DhdWKziex6urJerdHrFYrF+EeGkNXZVJRg2m8ikkScL3+/1jthtdupZNtWPy5fPbBGFIVHiUGx2evbhEQpEYz6XQFJmG5TGeTdDqOBi6xv6JERRJsH88y2bLZWE8z28/c5ZjcyO4tsXV9TK5RJznru3y4MIEjx6c5qsXtzi9OIqhKdR7Nvnku7MkguHMZci7gCYLQh/0wazA9QMqLYt0VGX/WGav0K/SMjEUwa0MvFhE7udqeD6qLOENIpsXVsuYjks2rqPJEtlElEvLm0QjEkEQUmn3iEdUnjg0ynK5ieP0c2JyCR3H9ZgezRLXCkSN/uxHUZS9B6FnWugRjeRrmnvvNLp0bJ9CEi6sVyEMyEUlnr22y4MzOSrtDrZt8+pGjQMTOdZLVYrZJLbrYtkWbdPBdn2Oz47wjXPLCEnhQ4cnuLlT43MPL7JTa/H8lWWmR9I0bY9f+NAh/sXXr/LijS0emhlhPBsjqkcQwKX1Eh87PMpOrcmh2QnyyRi//fwlPvvgfrqOT7Pb4VMnZ1kpNTg4mcN0AyrtDrqmoMrvnkN3OHMZ8o4ShiGeF5BJ3u47q8oSigiZzKdp2x7furTJtY0y+aRBqX67siSqKUgINEWm3LHRFZnNWouPnZhHCj0qtSaNTpfff/EKjXYXXZF5ZWmLeqvHRqWB5wf8Xz56iJ2GxfmVEkEQsFZuU0zF9gzLa4ka+j2L+7xA0Gj3SBkqluMzlU/TtT0kp03LcnEtk+evrGN12uiqzEg2RalpIkkyq9UOVzdLfPHMDV69sc7JuSIPzhUYTUeJqjIb5Sa5ZJTDM6Nc2Wpimyau6/DjJyZQApd4RMZ2bGQRIouQY7NjWG7Iet3i6Owo3768ziPzo0Q0hd1Gl9l8CtN2cDyfRttkJBOj2+vS6FrvahLdcOYy5B2lYzrYts16vcuk61LIpLBdj3Krg6IIsjGDuK4wW0wRhiFzE8W9c88u75I0VBaMHMWETj42jiQJHC8gaei0ZY/Z0Tz7p0bxg5AgDPmnX3mVz56YYSqXQJH7yXeyqvK1yztc3yzxwUPTb/rewzBkq9Yhm4jSc31qzSYwSqNew07r7FRqfOLBfVxcrdNxAj7z4BQbpXrfH2PabO+WiUY0ikmDXibBY4ensd2AaqvDf/n2KzwwnsBCZmEkheX5JONxjJbNrz93g6PTebabFjt1i0qzyUgmhRT6CKHheR4xQ+OJgxO8dH2VrmmTTY/h+gGvrtf5+ccW8PyAjYbFwbEMtuvT8WAuc1sm991gaFyGvCMEQUjXcmj0bMYycdq2Tyzazww9v1oC30cREkIIjkzl75l/oWsKrm0ShiFXNhvEoyq25TCZTzE1ksWyXbTBEkqWBK4b8JFDE8hC8Pz1HRqmAyF8+sQsD850MAwDx35zOsi26+P5/V65L19fp+FIHBiJc+7mFpValee6bT798CE6TshIVufx0XFihk407uLYNh3Hp2QGzIchmqIiyQFXN3YRnsv+2Sn+m0cPsFxu8+F9o3z51VUSus1ULsmJuSKNRpMvvbrGdD7F5x/Zx7cvr3J83wxu4FJr97i01SBlaFQbLQopg4OTRcptk9WdJgfz/TafiizzyWP9srztepsPHphESDK1tk32bZJr/V4MjcuQd4iQaquLF/Z9LNPF9J4BOTKZ41987SJt06GQTjBVSLLZ6CIFMFNI4AQhqWikX7AoIjx/eY2X1+p8+MAYf3Bhm5whYSghHjJPHp6kkDQotyx6lkM6qkEIZ27u0nDgcycmQUA0GgPPRYi7M9I9z0NRXuO4dTwUSSIIQw7OjiFklWdfWCZvSCyV2ixXbH7ug/M0XYlmq8mDi5OEITx7cZXdSomPPXwMRZb45sV1RjMxthsmza5PRINy28Ne3WVxPE8+6XJ1q87RqRwv3Syzb0SQiOp87gPHMPRrGLEYl9YrfO6Rg1xZ3eFLF3f4xccXWau0yag+86M5JCNO1OiLn/3eRo3PPjCFJElc3SwxlUshCRlVkoloKp4fkIzeI3HnHWJoXIa8rVQ6NrmYxlK5w//7D6/wD3/2OJZlYiQTe8eEQmIkEcGXdaptk5XdGsdmR8nHdW5s7hLRDDwvIKKqdO2AudEMbuBzcCrHarlM05bIRyCVSjOaTfBbz1/hU8dnmcwnaHZMNmtdcobMzGiaZs+l1OoXDOqKRP7O+9irY+pvd20P23F4ZbnERCbBmeUSP/fYfqYKKf7qp4+wtNNgvKAgRXTOLJVZqq1xbCzOdssmKgf9Fgehzs2NXebGR4jqCookUUwavNRu8+BckUary1gmQceyySWiuH5ATFf58KFRvnJujRNzBSKqzOlDs3hByGw+RrNjIeMzloywvF1mxIBINMnhuTEurpXpOjKNtsmBsTRGRKHeNumZPlE9wjfO3WSumMHQNZY3q0zlY6SSb5820XdjaFyGvG1c2GxyfatGvWux2w35U4/O4IUSCf3uPzNVkVE0HRyTXDHD3HgWgUDIEmOFHJmYjuMH5JIGPdvAcj2yiX4T7nwizsJ4nLMrFeYSOrbtUO95nLm5y/xYlt89c5PxbJqfe+IYbdslH9f3Zkxdu9/xLa6rfdkRIdBUhY7l8MpKmcDzOblvjFxCJ8TnkyfmiGgKpUaXsVyCpVKL51ZqHBvRmSmk+eOPHyKTjPZ1guwAN/Cpt3rc2NhhdhKK8QjXNiok4jofODLLubUyxXSMrWqTaMxAdC2K6QRCCNKJKJ8/tcC/+toFfvYDi0QiKqrvU275bFWbFLIZPn4yh+y6FDIxfu/F63zxlRU+dGicctvkxeU6Hzs6Tj4Z43dfuMqnH1yg2upRqrc5fWCGlVIDH/GuGRYYRouGvI0oIuRTx6f40P4RfvxYkWREcG2rxk69jeP0c0TCMGSt0sF1bZAE9a6NLguyMY2orhEfFATeauKkqzKGphKPGciyjKZGmMkliGuCfWMZEjGDR/ePk4lphCLk2ESaTxwu4gcBuah6ly8nIkMqpmM7Hpc2qrh+gO35bNU6TOVilNsdnj6/xMn5MTw/4MK1FbarDa7vNAiB0XQUp9NkLKHxxAMLRHSNMAxpWz5B4FOq94gqcGpxgqdevkEhFQdCLq3ukMskObNl8ep6g1w6Sa3RJZeIsry5ywvXdgjDkFCSQJKQZQXf9ftia/kUM2N5iuk4pVoPV8gkYlFCx8J1HXq2R8owyMdVep6g3u4xkU+x2zBZ3tyhkI7RdjzGUzrTxeRrf2XvKEPjMuRtYyIT5VvXSvz+N17ANLu4nker1aJjugghuLRe5R994Tz//A+fw3QDepbLVMYgGTOIDvSPFUmi1jGJ6wM9ZEngujYJXSMIQQpddF0jqUl7BsjqtXl4/ySFuMFsMU0+nSBhaHu+lFuqiz23r1vUdVzShkal1WWl1Ga31mEineDzDx/g1W0Ty7Kw3IBHj8yRTyUoN3uossSL17Y4OpFns+2wtlNBkwS/88I11sp1IprCaCZG13ZJxqJ8/Pg8pxbHmR/LkosaeAH88ocWODWbZyoXh9CjZdq03YAw8Ci3uqxt7tC0PDKxCA3Twvd96p0eqiRRbrSZLcbwA5+zyyWmRgscGEtTSMUo1xpEZEG3Z/LM+WWmc1HqXYsggA8cmqVn2qyXq8QiCh3zzTm03w6GxmXI20bC0HhwJssHHjnJuatL+KGMG4SEQmK3ZXJhvcyk7vDwwSlGEjqhGgEh4zgOrtfPuFVkiaR+O5rR6dlc36oT1SQsyyYSTWDaDul0aq9tYz6TxXJ9ohGNiVwS17+7ajkIfNqWi2k5PLx/kpiuk4xGEEIiaSg8cmCcF69v4ng+P/3QFM9c2eLs9VWWd+uUmh2yhqDaaPHIfJ6s4RLHY6Pa5tWVEtO5GA/MjfLN80vYtomiqOgRjUK6nwl7c2OLYjaGH4TkkgalZgfX83G8gHTM4PT+KSazcTZLNUYLWWJySBCEJKI6S7t1FEmQimnMjmSxvJBfffom3zx3nd1KlY7lYZo231mp8uD8CF85t8bDh+d45foqKUNBCEFEU2k0mkyPjbLV6GJowzyXIfchzU6XYjKGKiUZTzyAEtE5NpNno9rimxfXeHAuTy45Q9fx2K23yRgSqViEeqeLoXkUM31na+QOveJ4NMLx+TFUWQJNZS6iYTke240OM70U8cHSRBIMRMXu/r40LZua6REicF2fb19Z48G5Ub5+aZ2oFiEZUXnu8iqHJvP8zrde4U988jTXV7aomBLPL5WZyifp9DxWSw0mRgpoRgPf7BE3NKrtHtPFNN+8vM5EyuDaRoWPnFikYznEBzVJMyNF3AC6lkMQhigiwHM9QtfFc20u7DY5OJnlxvo2JxeniQQOQQgdyyWfitPt9rCRMa0OG5UGT+5L8MiBGWrtHlHDwHY9dCnk333rKo/NZ7m8tE4xnaLS7BCJp7AsmyPzE3gBmKbLWrnJ3GiWd4OhcRnytnErypuJR+m02hTjSUotG8fxODQ1QiwaIRnVSMc0FFlGlULSUY1M7I3zLhrtDunEbSdkRJERYcjxmWK/qRQQ0ZS+8bmDIAiptLpUWj0imsZOo0s+oeOhcO7mFul4jMcWRlAUmelCnBcurzA/mubaRoUf/+AxnuzZfOnsEjdKHU6NR7HMHtVGm1wqxVavy+p2Gd8Pue7DWDbBVCaKVGvzlbPLZGIaDy/2RSpqnR4ty2V+LM9Wo4vthhhGhJoN3766zQcPTbG00+DDJ/bzpRevsVzvUW00KaSTrJUbNLsmF1YrLI5nWSimWCm3+daldaZyKVzhkYxAIZ9lToVIaPMH18o8Op+n2u4wmXexrR6xWJSZfIJr21U+/eC+t/33/ka8mR66/1oIURJCXLhj7IQQ4nkhxNlB1/1H7tg3VFv8EcVyXEzTBGBqfARFUchGFfaNJlFDi7XdOh3LYWO3QlaXMFT5e2aMGoax51uBfusGVZEZy8RJRzUkSTCRuV2Md6ugsda1SUUjXN6oY1k2nu/RbHWYyyWIyCG9jsWN3SaW0++3W+u5/P65bf7l771AqdbkzM31frlB08P2AwJZwwtcUobEZx57gLNrNVabLlMjWZKGgpD6bR9mswY3NrZZ2irT6tl0LZvLG3V0ReKpl28SNSK02l3migkWx9JcXd/m2soa55Z3+MgD83z+4X2cX68SBj6SgLbl8/nHDjNRyDI7ksGQA544PEHckPCsLo4PtuMwW0ggNIO//hMPU+2YHBrPM5o2mCpmGEtF+0mNrs96pfkO/ObvzZuZufwq8E+Af3fH2D8A/p9hGH5RCPHZwf8/MlRb/NGmkE1h2t5dY+2eiabIFNJJsqFAVxW2fYlY9N61PXdiuT6tbg9FVlAVmVhEodnukU3GqLZNklENYzBrsT0fy+r3cclnkliOR89xubq6QcsLOTyaJBtVSBgqbjrLwozBF15ZpdGxeHBhhF/82Em++OJlXryxw1deuY6h6xyeyOOFIeWey6FiAknV0SMKUU1hNh9HUXTimkTou5y5sc5INsXabouDM5Pc2KqhKRIt06WY1Dm7WuH0/nE0WZCIR/G26/R6PY7MjnFqcYaO5fDsuSUOzY7Qczw0TcMLBee22khhSLVrk1Bhu95C1SLUGw0WJ/IEQcip6QzJWJRzy7tEJMFPPnq4nyNkmVzbrnF0ZoRK28LpdIlI372L3tvJm1Fc/Nads4lbw8CtuFaK23IfQ7XFH2GEEEQHUZ4gCBECMsk45WaXwHNxhErXsjk4kWOnaTGa0vH9gK1qk2I6QUS7+88xCAKius7Xz69QSBkUUgkkAp5/4QpjqQgPHpzF9wMuru4gKQoT6TgrpQZr9S77x3J849Imo9kEo3rITCbCt5eaPByJkIgaJIwIP3lqntVSk2qrhxHROH14llbPx8VjJB2n5fqMpXRapoca0Smk42yWKnx5q8F63eRnHl/AtB1qtTpPP3udFrCwb4qF8QwrrYBPFnNMFnP4vj+QXvVQpICnXrzEEw8sUm5bxKM6O+U6I/k0Hxv0lrnF4liWmCqxvNNAlTyOLC6gLK2RiSlMFGaxuz2kMODsWoO16iqdVpPpYppSvcW+ySLbNZv5QhrTDZnKJSgUMmSS7144+q36XP4q8GUhxD+kv7T6wGD8PVFbFEL8Cv1ZEdPTb74wbcg7hyQJXM/nay+8SipbJGmoTI2mUCWBadnEI/3O/rIskYobrzMs/WtI1Fomnz4535cwbVmYrk/X6vK19V2u77Yw5ShxIwJelzNXN/nUqQU2Sy1+57lLSKqGj8LJhVEyiTiqXKdnu6hahGbHpGXaIAQ3NiuEkkRElih1TGZGciyVOhydKdIwXfxQsFHtENgmhhFnMiJR7U0REdAVMh3TIqqDHsBa0+M3nrnCi2U4vdjggak0siwjy/0UfICPnjxIx7QYy8SRZYmJ0fwbfo7j+TTj+TSdjomQBaquUWr0mIvGyaRifOfaJvV2m7FMhocfPEHTcpjOZah3TCbzGV6+toaqaiRmChwaz3J+rcxjB94d0dK3Gor+i8BfC8NwCvhr9HWN4D1QW4ShKNoPE67rUm00AFjfLTM+UuDEvnHymQSK6BcjJmMGcV3d87ck32CJZDk+XhhwfavKxm6dpVKdiCw4fWQ/f/7HHuUjJ/dzcCTOje0Gh8aS/MKHj3Bjo4IXws88fpQ/+cRhJlMa19Z2sbyg72ju9dhpmvTcgMtbNTzgZtWkbXu8dHWdQ6MJjk5mSccNMvEIxbjGaCaKLAXEkmlmRtL83d+9SLtrcWmjQtd0ySeiWA48swSLxRjp/Ag9x+cbV0v3fF+qKpNJxr4vBcR43CCqR8ins6TiCdYrbQgD8gmNaETjqStl/ujlq1xZ3Wa53CYTN+hYDq9u1tmuVPlH/+UrSLKEK9692qK3alx+EbilvPgbwC2H7ruutjjkh4dgoGncsXy2ax3O31in0mhxfbNCMRXDiLz+D9u+o6PcTr171z5dk2l1LQ5NFZgdyzGdSzA9kqGQNEhFdQLPY76Y4M88Po8iAp69uMrpA1MYEZWz19dp9mxOHZqjYoV889IaW5U6mw2b5d0mT1/d5uJWl6srOxyaSGE7Lh99cD+5dAoh4NhECt+1EYpKKqrx+OFZVusmAvhTj84wFYfj82MUUgahZjA6ZnB0UaGQinK93EUMvhvfTukeIQT7xzMcmy3ieRZaROfQ1CgTkzNMj+ZJ5gp8dcXl117aYmm3xdXNEnLgY/sS/82HH+K5Vy5x7ubm23pP3423aly2gA8Ptp8Erg+2h2qLP6I4rofrh5iOjxdCvdni9JF9fOShIxyYyL3heRFVxvd9fN/n2uoG3UErSYBXl3fRIyoXVraodh0K6Th+EJIw+rktmYRONhkjnzDYP1mk2nNomR7HZ4s8dmSOmxvbnLm2ygcOTbFvNEvT8knGY3z00Ci65BMVHodnC7h+yEwuyU6zQ0wJyBoyXcdho1RnMpdgIpfi2nad8bhgaadGrd1l/3gOAazsNsink0wXUhybStM0XS5vNTk2meKV9QbbTett/6w1TeXRwwu4fkA0GuX5pSoXtjr8/a/c5A/Ob1PuOHQaZa4tb/DKq7tcv7nCje0Gth/y/2/vvaMkua77/s+r1NU5T84zO5sjFsAuIgkQEBhEkJQokpKVLYm2LFmWZUn+6fyOo/STbUXLkizaVrCyRIpZDAAJgCBy2ITd2TBxJ0/nXN2Vfn90bwJ2gQ0zCNz+nNNne6q66r2qrb793n333m/ecCgZ5hs3sg5czVL0XwPPAJuFEAtCiB8HfgL4TSHEEeDXaPk7XNc9TlOF8QTwVeCnWytF0JxK/W9gEpii6cyF5pQq3nL+/jzwy61zZYH/BLzQev3Hc87dNm8vXLcZVarIEqbtUm7YbB3qpiMWoVyto6nK62oUN0yL+VQexRuiZDTOj2aiPpXBRIia2SyWLYRgOVfGsh1mVouczVSoGDYNG56dmGM60+BPHj3EE8fnKFSqjPf34lM1VjJl+qM+PrB/hPm1NI7rIGyb3QNRPvP8LGFfsxj27Zt6aYhmQaWRzgS3bxsh7NdZzuSJB33kihXSuQLv2dZNOBqiarrg2qTzJcKRCEfnMqylswzEm2UWFnI1ji7kN0Sf2acpdEaDuMBHdnfws/eP0RXSMW2XuUyFX39smT97xcATlajXYXZ+jpGhfr739lH8lxlBbgRtxcWbCNtxN0QUq1KrY9sui5k8y8UGB8Y68XkvDYw7p+9zOZ47MQ2yh8W1LPFIiDu29lEzbWQhsG2H+XQeB0FXNMjkUhqvpmLaNrVajWAggCILxnvilGomDcvk+VNLzGWLfPI9e6hZLo+/MkPMp3PPziG+fXyKWDCILtvMrRX5zKEl7hwMs2esl6HOKGcWUyQjfmRZoVavkynXyefy7B7tZSWVZfv4MGfXCgx0hGmYNivFGka9hkfRePrwBKcqOjsGEvzeN8/wCw+MM5ep8onb+ogHvZe99vXAdV0WsiUeO53lP33pBKZ94Tu9vSfEvnCN3ZsGeGDPKOF1rufSlnO9yak3TCZXC+iyi9+r0xEOIK2DkTmn45wtlFBkhfl8DdOyGeuKEtBfu/rjui6GYeD1XviiVYwGf/y1F7Ftm11jA6RLZbb1d+D36YQ9Cq5jMblSoD8ZJqB7sF1IFypEfM38nVq9Qb5QpLujueLiOA6FSoPJ1QIraylu29yP5YCsSKxkCgx2xVElCdM0qdQtFtJ5YtEIxXKNiE9C03w4joWqSCiqh68//TIfu/82NFVh5uwiwwO9GA0LXVPIlAwifg8zy2kWMnnMapWunj7qjmBipcTHb+1HFmDbLuo6SqheDsO0OLVcJFuz+A9fOM5spsof/eAtPLC1c13+r6/E6xmXduLim0Cj0cxEPTyfp/yqILN1a8NqDs8zuRKGaVM2TDJFg3q9TrFcwa/J6JpGVzSIJDWLTZumfUPOPdd1sWwHx7axGnU6Ql4GEkHKxuUzby3HpWY3H3TLdjg2t8ZXnj5CyK8zlAygCouwJjOzWmDibAqvItBUDRQPlbpNrd5Ak0GSJTRNaSUoCjy6j8mlDI2GyamlLM+fmiedyWArOqqq0LBtNFlCkmBhLY8kXGaWVzk5t4giOcyvpvmjp84SC4fxaoKz6TIhr45PESQ6e5Faq1p6K4ZnaWUVAJ8mczZVRMgqu4YHyBsOOcNmV2+YHzwwiCpLSJK04YYFmlItuwdiRFSXbKV5/28bim2oYXkj2sZlAzFtB9t2kGWZYrnKL336KD//t4fX1Vvvui6fefQpUvkiK5kCiyUTXZXxaTKVukHecEjGIgx1RilUL+j9RII+JOmC4bsWbMfFtm1cFyQhSMQiBIMBkiEvmqoQDbw2V8h2XFRZIlWo0LCaq0q9sSDf8+793LdnjN6ubsp1mzu2DzPSGaYrHqZq2Tx6ZJqVdIFvHTnFF58/ha4q2E6zalymWGVyOYumyjx5eIJirUHMpyFw2DXSh1dV8Kgq86tpcpU6qqygeTwspAsM93Sxc7SPjkSCubkFRmIa3z4xj0/VGOsKMbGQZiFb5vbxLk5MzVIo1+hIJEnlisha8/q8HpXhzjCjXRFiQQ8ffdde7hnvOF/X961g70gnv/ah7cT8GlH/5QXd3izaxmUDUWWJqmFgWRY+3cOP3TWEJgsaDZPTKwUy5Tq1+pU999W6ydl0iXLdomKYnFouYNvN2iRlw+TY7CovTK0SDAR5ZTaF5bj0RZoPlCRJBHQPneELUxDHsS/5Fa3WLerX4WtczeZZyZZwXBfTNCnXLaxWmQNdkS4RFzNMmzPzaxeKRQmJf/j2ISZXC5TqNplSDU3T2DmY4PatgwS8GpqmYdYNyjULGQm/CnvHekHW+MILkziWzfxqDq8mM9AZI10x2To8wOeen2QxXUBRVTyyw2q+zOMTC0T8XjwKBP0+MrkSa8UahuVQNV1ePDFNJBxhW4eXuMfm0y9M4UoaPdEAW/viZMoNPF4/+UKBSq1OMhpi8HWC3t4OfGBPH7/1vTvf6m60s6I3mqDfd/79x24d4Pv29yOEYFOnyqMnVjAtmwe2d+MiUCTBaqHK6bUyYU3CaBgcmTjDbTs2U66U2TIyxLNTKXyyA67NaHecrx6awSvLaK5DHZVUrszkUpadI73EXuVEHOqIALCWzTOfqbJ3tIv6VVbDP4dh2hyenOfBW3egyBJ1x6Feq7OSrbC5P45ykWFxXZfp1Tzb+jtYSuUwbJdkQMejedGFg9GwOTS1guta7BhI4PdoZByHUqVKPKCjyXBgSw+ZSp1k0EtvIsqLk0s8eWaNlbLF6cUMO4Y6qBs19m8eIB4Nc3K5QFCRmVjJs5Sv8uDeUVLZHAvLqyzl6+wb7+PbxxfQNYXRniRly2H3aD/lap2jM8u8b98wC6kcw93NsgTxgAfDIxPze9CvYUSykivTGfEjhOC56Qy3j1x5OX69WcxV6Qj7WMtX6Ii8eQqLr6ZtXNaZmlHHq186LSjX6s0QdaBUqaF7NDRV4YHt3Xz1xdP83hee5a7tAwz3dPDK7CoxHTZ19qJpMXqiATpjIVRJolY3mZxb4KP37GYtm+OJiSV2DSZI5cvIisr2vgjHF3Ls39SLUW8wMZ8GVWPrQCfTKwVy5UprqOrQkwwzvZInogu8VxAIuxym7bBlsI90yUAIiAd0fF4P430eTLOBLOtYlo2qKjx9chHFaVBLhvD5fKwVayxlCsQjIZayRUZ6EmwfiHNkNs0XXpxjOOEj6tfJlIrUXQ27VqRgazy4vZuGK/BpCrqq8F1bowhNpWaYTJxdJVdzqLiLRH0e8qUqttdDxCPRFQsiXJNcqUo4EEQrN4h4Ne7Z0Ue5bmNaNh+9YwfFWp3PHDpOZ9hHMhoiGRWUaiZFw6RhOXSHvdes93NqrcbL80X2DESZz9UYLhl0BK/+Pt8Iz0xnef/Obv7kqRk+dms/8ctMU98M2tOidSRfbfBvP3eCs+nyJdsvqhiA36uTLlUpVhvUGhYP7R/n5z9yJ7eO95EIaNy/e4jxviSqKjO/miLq9+FRZCRJYLkuhYYEAmq2xGAiSDgY4I7tw3i9HmRZZXtflLOpZubs1tF+/F4fL58+y0hXmEjAS088wN6xPjRZRtNU4tHINV2jR5Ep1QxcqwG2Sc20m5nJqkLRsHhmYoFirU65WmNmKU2qWMLrUYn4PXQFNTyKzLGZJWzLxrJtIl6N7zkwxk8/tIvtAwmiAQ+JUICXF8oMDw4y2hniG8fm+dtvvMBqNs9sqkgsHMCjqPg8GrdsGuTBfaPk8iWW8zWceonhmIfHzqTQ3DpLmQoNy8KxTG7dPEi6aDCzUsCvqWQKJZ44Nsnsap67d41xYOcWJBcypRp+XSWkq3SFr09I7PRqifHOIJbt8OD2Tr5ybOWaz3G9fO8tfciS4Jah6DWNttab9shlnbBtm4nlIrmqyb//4gT/z/u3MNYRJF0ySFz0iyXLEj2xZmaq47iYVrNuhyRJGKaNBAQCARzHZbink4n5FFt9SRayZZ4/vYysaUyuFFAkia6oj4phIEs+soUK1ai36a+wbA6dnuH2baMMJIN0RLzYtkM44CPR+hXTVYW1fBXX9V/1l8d1XaoNk0jQjysrJEI6luNiOy6lSo10qU7AKxP2e5GEYEd/lJWiwfRyDrNRYzlTYOf4MCuFGouZInfsGGFmLY+mNjWhN/clsR0XAYx2J/FqgnRJ5cxqmXBEY2ktx87+GJmSgeaRyRXLDHTGCPt1HrplnErdxLu5k1K5xp4OheVKsyL/QEcEv9fDYqaIkD0sFw36Ek1toly+yuxKgQ8e3IrHozKXKePV9fM1fK+XZNDDy/N5vmdvL7IssaP3zS2OrSkStw+/eVOxy9E2LuuEg+DgaAKvIvF7j03xP755hh890EfNala2PxdA9tjhSYY7Ili49Cdi1CwLv6ZQN+o8emyO0USQ/o4wdcvlbLqAUTdJFw0+89RJJFXj+24f4vCZBd69bwseVWa1ZRf2jXbz1CvT7BvpJBkNkoz4qdbq+LwedLW5bBv3a9QaFkuZIpMruaao1hUC2y5Hw3aoVmscPr3AbVv6kGU/sgxr+Qor+TJeVaFsK+cT8k4s5DiwuZt0sYqETSIeJ1WocMtgnC8/Ncc3D0/i86g06ibpcp1scYZ7do0R8mp0RQMsZYuM98TY0hfHdV3q9Qa67iFdrCJLEt3RAH5dY26tyJmlNbqiYYY7whQbDpLqYUt/iM3dCTwemSOzqySCATIVg5BP48WpFRJhP/fs28TCWp7VTIFwKEgs6CUZuvGAt6MLBUzb4aO3XEipm8tUGIy/dT6QN5u2cVknlFY8wZ7BGJ+812Uk4WelaDAa9WDaLh4JbNth+1A3inBZKVR4YmKRcrVKQBWs5gssVQTv2tpHKODn7FqeiE8jHA8j4XA6U+eBcR+Fuku+UuaxI2fYOdqPaTlkygbVus39e8dJFUuXLRupyhKu66JKMNodQ1Vkvv7iaeL+bcRCV/fA27ZLdzzM/Xt9ZIolUoUq0YBOPKhzfD7F3dsGWCk2c2kWUgX6EiE64lGGe5qjgCNzabK5IvfsGOauPdv59NMn8KgSn7h3F8mgxlhXPy+dmSeoa3hkme5YkFKlhqQoeFQFTVMp1RqEfTqyBKeWqkiaSTLsI+7vY2YlzfG5FNNrGXriIVLFMqs5iUQ0yqaeBLoM4KA7DU6XK9yxuR+vrpHOl5grgfDYLMy9QvLArTf8PMyky8ykKzQaJh6PRsmw+Jm/OsS3fvHd15QN/U6mbVzWiXNOTIBbW8PRqN/D7zx6mi7d5qMHN5Ep1wloEgG/n2+dXGKgI8q923owGjbpQpGKYRIM+KjWTT73/Cm+e88gXzq6QGfIw8cOjqI6DaxGg4/fdzv1uoXHo+A4LqWaQW/UhyQJBpLR1/StWrfwtYpePzcxy75NAxyaWcMXiuK9hjn51FKahF/DRKK/I3p+1DO1nEd2bVZzJfoSYUzbwZUk7tk1csnxmdQq8XCI+dUMrmvSGfES8qoEPAqa5OKYDTb3JpEUhYVUgblMnoBHw+/3U6wWWEoVMBt1LFeg6D66QipPT2TZ1BXGq6rEI2FemlpmIBkBIRPUHZayZZ46ucTH37WHkmHSGQ2z6sKD+7uQlWZNmUenKnx4by8jyQAMrU/JjnzNZC5bxW65NXf2hikZ1iX+t+902sZlHXBdF3GZ6YUkCX7kziEePbHGXLbG1yfWSPpk3rW1h6DmYlZLPHEsy2BXkp5wiEC3hmU7nE0V+OidW3nq1BqFusNtyRC64nLo5ArVSIjBRJCA33u+jbD/ysN4y3ZwW0+0EIJQMMDvfPklfuHh28gVS9cUwdmbCLGQN0gGNeZSJcJelXylxmyqxLt3DPPtiUU6o0GmV/L0xpuV/NP5Eo5t8vyps/R1JGmYNo1GA79H5YMHtmFZFsgKfs1DzbRZzVfwqBZG3WTHUCfFah3TNFFkiaGeJLomI2yT+UyZQrHC4TOLvHBmEYSCP+Bna4dKvSGheTwEFJdENIKmymQLRXoSEXKVOoruxXDg8y/Oc/dYgqhPaxqWdcSyXVwXnp3J8O7NHcQDHjpCHp6aSvPuzR3r2tbblZtjfPZmcIWo25jfw3Bc56mpDFGvynLB4IWJWQY7E2wf7ua2LUOMdkYI+j24rsu3jk8xvZrHsV0emVjhg9s7CHo1SjWH9x7cwXv2jDKbygOwli+9bpfKdQvDqKMoFwzIcrbIfTv6UBWZcDDIbKp41ZcoJJmACkGPSiLo4fRShoVclTu39lOomXRGfOTKBqNdESRJsJyvgKxydGaN2zYPsWO4G4HNQHcH5brJWrGCbdtUTRejblBpmAzF/eRrFh3xMOlKnVLNwO9RyeaK1Eyb+XQZIat0xSPIssKOkV5cSUP3+RiMBxjpTpCIhgh5NWwElbrNUGeMasPmD7/yEosrq/zVs2fxyDI7ekLYrsuP3jl01ffgamlYDl5VZmqtcj4r+l89MH5NI8V3Ou2RyzoghMCy7EsCyC7m+HKR4USQY/NZbh1JsimmcnR2lWdPzrNlsJehjiDxgIdUyWBmOc/RZQOjVuOBzVH+59de5oO3jXHbliFenFomGvJRaTSjdB1xoT3bbubeeDSNmtFgLVdioDuB5rkwqqk3LKrlGge3DJAu1sgWS6iyxme/fYwPHNz+GnmOV+PVFNRwkHK9QVfEz87hbnRVRpYElZpBxTAZ7YpSt2y8qoIv4ufEQobnT05xYNsQ1YaFK2RmV3OM9HZiNCw8isRTx6fZ1p+kPxHHo8rU5/PgVdBVDVcWeFQZr8eDVxFIusz0WhHDcmhUSgx2drBzqIOesB9VlanWTQSQLht4FJnjsymEHWA2V2SkI4Bj2+zv9RLwyPT6oSO+MdrJQkDNtHl6Ks1H9vUSDyjMZar8rydn+PxP37khbb7daBuXdeJKqy4L2QqZikVvxOaH7xoFx8HnUeiIN9gzNoCmKXhVmWypwnMnz/LhO7aRnFggGgwSkExmo34GezpQZMFAIshSpsTWvgTlWoOQVyNfMYj4dWRZJuDzUq7WEZLMaF/yNUvMz55ZZc/mAfy6B4/j4FVDPHViHsNy+I3PPsO/evjA68ZFpEsGfTE/+UqVhuXgv0i8TBKCrpCOIkvnHZbFikFn2M/2TWOcWSsxnAxyy6Y+vvXKNJ3RILYrcIXCWG8Hx2aWObVmcOemJKoElXqd49OLyLLEEycWuW/XCKbj4tMUEkEvhZqJvyuIR1Op1OocnltBVzRGu8K4QkI4FrlilYPb+siXqtwy3EOhWsW0YbzLS65UpeZszCjCdV3OZqsAHFsssFwwsF2Xe8eTfN/+/jc4+juH9rRonUhXTf77N86QLV8aTt8X8/PdO7tIegXlWh0Jh2rNYEtvHF0WzCynWcvmUCWZch1SZZO9I11MLa7iC/i5Y1Mvh46f5Jnj0xiWiwMUqwa1holPU7AuUopwHJd8td4UNb9ollarGZycT3FqKU22VEMI0BQZ23a4dVMvW/uSfPTgGIu5ptG4EsmQjus2s26X0nkAppZzZEs1wn6dRDREutj8UuWKFT731FEa9Tq6DPMrafLVBiWjQXc0hCxJSLis5CssFwymKxpPTWU4uZSjNxYgoiv0d0TYs6mfvX0Rnj4xw+OHT+MIKBkNeqM+VFU9nyekyQpPnphjNpXnzGKKZ47PISkyjxyapmGZTC2uUa5YOK7EcjpLuVqjam2Md9VxYVdfmKG4j3LdIupT+cxLi2zuCpIMvjXRsm8F1yWK1tr+My3hs+NCiP960fabUhStJ+KlJ6wzlSryR09M8Q8vzPJ3z06xsJohIDXoiwVIBDz4fV40XUcSgprtEPGqFGoOMwsrLKVylAp5on5vswJatsLWgST33bKddKFKwKMgXBePIpErVambVlNF0LKpNUwKtQZ//uQEa4Uia6UL5RW9Xp3x3gQByWZ7fzPpzrZtMvkCq8UqmioI6Boxr0L+dYTKVUliajmLcGw6wn6ypRrdsSAhr0ahVkdTJCJ+nZMLab780iSpfJVPff0lnp2vEAgEsWyXbMmgVK5yaHoNn6epvnj7eA8/+8AWfv6BzWzuTVCuW/QkYuwa6qI36sfr8/HwbZv4kffs4/hsCkk0s7Lz5Rqu1SAa0EmEA6iKzBPHF5laSiEpKh63wZ3bBzFdBUnVsXBBUZnJNrWNNHljyhFUGxa//pFd/N4n9nFgJM5fPneWzx9eZLV4YyUvz2aq/H//OMHnXz7L7z56ep16u3FczcjlT2nqBZ1HCPFumnpDu1zX3Q78Rmv7xaJoDwF/IMR5x8A5UbRNrde5c54XRQN+m6YoGheJot1OswD4v2vV0n3bkq40GEkG+cDOLoQQjCV8zSry4RBCUVDVZrzHuXD+Ut2mvzPOxEKKQMDHg7v7WS4aOLjsGOmlblr84/MneeXsGuODXWTyzWS4U7NLRHweCtUGhtn09aiyjGnU2NYVxLZcZlbzlKr183375uFJupIh0uU6jtuqXpYu0xHU2TrQxTNTKSanZyjl8+cznF+NJAmSkQB+n5eaaeJRJJbSeQzTJtKq4K/IErYr8dx0mkg8zr/+0F387ANbuGU4QV/cT9CnU5c14l5Bw3Gp1i00WcJxXL51apmlfI3np9aYTxWomg7ZsoFPk8hV6pycXSHg9XB4aolnTp5lKd/U7ZmYW8O0LD5+1xY+cfdmEuEQ8ysZ1sp1XKPIsbMZIj6VE7NLNPJpDmwZwK9r9Ec2JtcnqKsMJfzs7Avzpz96G+W6hSQEn3158YbO6+Jy35YOfE6Frx+e4UtHFlhdS9GwHI4t5Pilv3uZX/vLrzK1nF+fC7lBrlcU7Z8Bv94SP8N13XMaCjetKJrjuAzFfbiuyx88MU1nSOfRUxmGEgV+8kAPHvnSW31oZgXHcalVyjx+ZJpMLk8wECTo03nqxDzv27+J8a4wZ1P5ZtwGTYOQK9e4fecmHj98hsHOKJrkoqt+VrIlVFVh31g3/ckohmmzmiuzms5QdSQ8Hg1dUfBrMg3LQnJdhvs60BQZx4UHtvfzraNTJBKComERu0ItkLDPg+O4RAPNL2bY32AlV2Ss50IZAsex8Xk0bMdGUQR+1cvxs2tMnF3hu/ZvZXtfDNuyOXF2jZ0DSQzTxqNIDES99MZ8dIe9rJZNKvU88aCX+UyJeNDPXCrH+w9so1Rr8JfffJl6Y5GardIVUNFUgSyCnF7OM5utMzzYw+1bh0nli9y3I0rAq3JQSEiyRESDWsMmkyvS/yaUT/iV92/lQ7//NE+cTvGhvb30x3xvfNBFrObLlE1Ilers6Y/w1YLB5//VA6QyOY4sl3nuuTW+eHSZWyN17rllhM6o/3yVwLeS6/W5jAN3t6YxTwghzoU0XknIrJerFEUDrlkU7e2A47ps7gqhqTLv2pykVDf5tQ/vZGuHF0lRkRWFQrHMwmqapWyJZ04t4fdoHF8usG/zIO/Zt4WIV+LZY6fAMphaXOOZk/Mcmlzi0ZcneeLwGaZX8/h0DVWW2Ld5iLMrebTWClUzbN1HZ7j5YOmqzGBHmLGBHlL5CgFVoiuis5IrY5kW4NKXCKN7NKYXV1nO5BCyQrHWFCx7vaLSYd8Fv0EyEmSsJ8FiKk/FMJlczrGtP8GP3jPO+28ZpWbaPHJkllgowO2bB6jWTSZXCpSMBqdmFvnKM0f50tNHqNUtHMdmPpWnNxbAFaBILmeWc3REghi1Kn3xIBNza5xdyfKJu7fz0O4x7t85wPtvG6dmWHzu6QlMG6yGwbaeCA3TIKi62I7DwmqOxXyNzkiAUrXKZKpMb0cM23E3XGrDo8j88MFBXpzL8YdPTF3TsavZAnWjzkjCj2qWkSVBtlSlZJj87ZNHOTDWgaeywr97cJCf+/BBDmwfwO9R3nLDAte/WqQAUeAAcCvwd0KIEd4iUbS3g+KiIkv4VEFIV+n0uvzE7T24psG+Hh8rmQzxaIxYKIBhuUQDHrxYxEM+ssUK79vRQ19XlKGuKPfuHOGZiTlemJih2JAw61X++Yfuec3DEvNrDPV1UDBsvLqLTz830pBe89lDywY7EyaaKtMdbzpT59IVNvVoCCHoS8Y4MbvI1qHupqNUkZrlK69QVFuSxPkv5LnPSELg11WiuszUcgZZQDLkQ5UlZEki7NNoWArfPn6W9+4bQZYlakJlYrXOfWNhnj89j1+TCakqQoZjZ1MoMZ1CoYxXlZCRiAa8IKl85flXuGPHMMmAF7faIB4OcjAUQuCiBwI8tGcIRZGpGiYNBH0+LyeNOnsHE7iO3Rx9mQoNyzlvnM9NBYUQG1LE/CuvrLCnP8LP3b/pmo4zalUGe7txHIeh3k5cFzZ1R4j6PXzg4C5wYbQ3zv17RljJNUd4hmlTqFTxyDKR4LWNktaT6zUuC8A/tPSFnhdCOECCGxNFW7iMKNq7XnXM45frjOu6nwI+Bc0C3dd5TTeMaTWjTwcSYVRFwrYsvD4/QsgEWnIOqxUT3aMiKyoRn8b7dw+QiEXOn0PXdd69dzOu6zK7mufkYponj0xSMyqEvF68wQjxkI/emB/hOnRHm3lB577sWksWtW7ZZIpl0rkSvQHBeH8HuuQS9nupmzajXZHmF0kWuKbAchyGOsLn+/FG+S/nDJgkNeVEfH4fq/kqDdslHvQTC+pkygYBj0rIq2C7EPFpOLbNt4/Ps3dTN993YIwvvTTNu/aMYTRsqg0H07LpiXrxKRAO6khAbyLI9FKGdNFhy2A3uDaLmSohXUXXNWYWV3C1AJIvzHKxjpAkspUGUZ9KsZCnVK0xmgwAAs3THHWZtWbUr+O64F64XnsD4vMblkOqVOfhPT10hK7Oz/N3L87zvp3dlB0Nx3E4u5qlvzNOplzn9OQUu7r8jHRFOTW/xq1jfaykcySizf8/x3EI+XQqtTqNhommvXkqixdzvcblczTF0B4XQowDGpCmKXD2V0KI3wJ6uCCKZgshSkKIA8BzNEXRfq91rnOiaM9wkSiaEOJrwK9d5MR9EPi319nfN4XVkkm1WiMY8HH05CR379+JbdusZnJ0JaN863SK339skr/5yQN84OA2spUGnYnYZb/IQgi64yEQMNx5wY89s5pFVmRmUyUGk82HqVmqwUVTFRqNBoqiIAF+zYOW1HhxJouuyCTCfgqGiWQ1KNcgFPCTKZTpiga4dcvIa/pwNfN2IQSaItAUibBXbcZ4pItkCyUKNRPTNBnpjrGSzhHoSRD0ynzm8eNoqssd24Z5+PbN1OoWTx4/S3fECwJiQQ/YJolQkI5wkNVClXypyF27NlOpGtyxZZCXliosHJ7hzs29nFwu0hdzmS04yJLgFz99lHdtTrKQq/H9tw1gVw1OrBns7Y9guS4TCzn2DF3e1+K4LvJlB83Xz0tzOYpGs/jUG+E4Lr/+1ZPYjkuhapKvNQgqDoslh4n0Cn1RH1sHOvm9rx7mR+7dQW8sQDDgp2E5yAImlzLkclnCQT+PvLKE6pr85PsPruv1XC1vaFxaomjvAhJCiAWaKzh/DPxxa3m6AfxwaxRzXAhxThTN4rWiaH8KeGk6ci8WRfvzlvM3S3O1Cdd1s0KIc6Jo8A4QRQv7VPojIWaXU+zf3hz+lioGwz2d2I7Lf/7yCf7p3SMIIUgXqmzqjlA3rSuOEmRcnj25QCTgw+9R0RSJ4c5m+cWaUWd6cYVNAz3oqszcSprBrgSapnHszCyxcJCw30so6OVH7tvJ1FIWRa6RrzUYSEbwAtNLa9TqDXoTYVZyRbqiN15zRAjBYDLcMjIlDp2e5bYtA5QaJou5KpYjSCa9PHligYFkjMPTyzx0yyZ2DHfjWiaLuRKqJJjONthbN/HpHhwXkrEErpDQPTo+vx+fk6UiZGqO4NbRTiRFIeCr89JilR++Y5BdfRFc12W5YBD2Kvg1SJfrPPL8cd57YPsV+y8Jsa76Tq7r8q0zKTpDOtt7wq/ZX65b/McvHidXNXEch/lcjcG4n39yYJDJtTIeRaanI86vfeMQpuXwwT09TK41+PDtm3h2apXnz8zzgd2D3L1rhGypwueePIRZrXNWRHl+Ose/fXBwXa7jeria1aJPXGHXP7nC538V+NXLbH8R2HGZ7Qbw0Suc649pGrJ3BB5F5vhSAbNcYqgrhhCCaDjAatEgV2mcHxqXqwa67OC6cGRqkX3jA8hCvCaJ0AG+7+4dLGdLeBQZTWkaoapRp1iqEA8HyZfK+HSdstG04TWjTjQcJFM2SURCHJ1aYKQ7geW6CFmmNxbExcUwTHRVIZVvBr2tZoqoQhAJ+pFbxu5GnIJNIxPi7JTF9OIasWi4qUqgSGjeIF7Z5E++cYjbxrpRFBldE1RR6E7EePKVGeYLdTLFCmuFCr0RH7WqxWI6z1yqSHfcz2iHF1cPkC5V6IkF+dpTL9IbD/MT9+zn1Epz1BT2qvREvMxnqyR9GvVyhfEO7+sKlMmSwLSddRu91C2HJ8+k2NQRZE/fa43LF48s8bXjqxRqF0Y1P3zHEP/3qSkO9mqcWDR48HcWmU5V6A7rrJYMTq+UWcvkeHSyiEcWfPLdXlwXNFnw3NE6aR0MUaQzHmAi/9bFybbD/9cJx3FIBjUSfpWV1WZFe8dxWMkVSdeaQ2PHhW9OrHHXSJRgIECh1uDJMyl8/gA7+uPnQ/kBag2LdNGgPxGgJxa4xLFaKFepGAa5hQyD/T1kKhV0uemQtBzQVY3TZ6eJBL0U6jZfPzTFWEeAVWBbXxzDtPDrCq+crVCoNo2Lqnl46fRZopEIt4x1X1MRqdfj7gP7mFtOcWwuxUAyiNers7s/RKZqUTYr6LqX5VwFv8fD7Moye0e66YsMA4KITyNfNamYDp3xKPlKA9m1cS0Hny/ImcUUsUgM2bXZOT6KcCz+6O/+kZ/86Hs5uVKiZJj0RX0kgx5UWWJ2bZkdY6Prcl1XS8N2+JE7hukO6/REL3Wu2rbNB3Z28VfPneXYYuH89q8eW+bUapnTaxIf3tvLS0vNRdPlgoHluPREdL5+pkDJsBmI6pQbLp9++jjYNvfsD9IRjxCKxCk3HELet8bfAm3jsi40ZTNcdE3Dtm2Ssaaz1HUhHgry7eklfvXLE0hCcDZbJbizm4ZlU642+GcP7Obw7ArffKWKa9XxexRu39IcynaEms7Hi7/olUqFE7NLmNUye8aHiYRClzjsgj4PlWqNdNnm6FyKoEeQyxdJbOnl809P8MopFU3X6I5HOLmYwW4YfO7pV1hOrRKK9TIQCq6bYTlHKBTGzds0bMGxhRwjXRGW0st0hL1U62ZTosSy0XUvuapJOpPHFYLplRzjA50cn10m7PWiqjKVmsHOsT7MWo3R7gSZcpWAr4NEVMIVgt3bJU4vpNja38HfPH+Wh3Z0EdJVSnWLmi1YTKWI1kMkY68dRWwEIV3le2/pu2RbtWHxtSPzvGtrD4fm88QukliN+lTOZitoikTMr7FSqDEQ9XJwJMZ7d3SxtdPPb39jmk0dPjoDKhGfitfrY6xPJ+rX6Ax78Wlvj6/126MX73AURT7/hVxJZwkGmpm2S/kqvVEfmiKxozfMj945RNzvIV9tsJqvosmCwUAIAfhVF9UXYnNvjEyhQtivX9bLr6oq24d68Hl9aJqMpl76X1iuNVBUlTu29DK1WuDIXIZY2MuZpSxbB7tYK1Yo1hpUl7MMJ/z4fF1EvTKJSIhIOMSWrvWv9Rr1a+zsi9CwbA6OxfmLJ0+jybBct3l6roQkoD8osZC1iAV0zixnKZky92ztIp3NEQ94KBoWmtkgGQvjlQU5w6SYT9PV1U+5btIV8vDoqTRBGhyaO8tId5zvvaWPTz05zT+7d5S6ZVMp5BjavA3bEZTKNYKBy0+PNjLs5dBcll//yglsB75xOstspsLWriA/e98I2A53bEoiCegIaHg1FddukIiEkOULIQa/+4m9rznvWtG46pWoN4u2cblBqg2LfKlCT7zpxFzNlSjXTEJ+LxoWQgjuHIuzszfEYMyH49hUjDqvnE3x3ftHkSTBWG+SbLlByKsQ0NXzxaGbCYjupVOiYglXyKgy58XHarU6qXyRbNlgz6Z+Al6NcMBL1TA5NLXEmZJEIi6RLxT47tvG0T06MytZnjkxi1GA7z84Qm6xRG9kA2MizBq5dI6G7OX+HQPMLy6glGocuGWEW0a6CHk1jDPzPHZ4ivfdMsrvPXKCnqBMV9RPo16l3rDo60rwyEuvkM4VmvIpVR+L6RweJYKuyIS8KofPgrdh8Ad/9VV27xpnT18Ux4WAJtPQIzw3V2A44SeTybBrtO+yfiVJXN1K2dVSt2wUSeKZ00v86ldOgySzvTtExWggC+gLSnzy3hGeOjHHYMxLLODFNE0Cfi+u67uqfrzdDAu0jcsN49MU1JCfummjyBLjA92cnJolnQ/QEQuzkKuSrzQYTgYAF03TUBSFQrnK4xNLxD0OSAqDyQhej8pqoXZeJVEI8ZoHKxoJoyiX/rd5vR5EQbBzpBnA/MLkKrsHYiDL9MdDTM036AmrbO7qQ5FlHMdmrDdBMuwnVSyTLjXoj3k3VP5TyB6EL8JoLMBaJo8wE6yWl+iN6Phbw/iwT+e9e4ZYTBXweT3ky1W8Pp2OUIgXZ2f58ydnmS3Bw8PLRDWZSCxMVLY5OTVPd1cnI4kwyYDGXz9boTMR5uiKwUh3g5WigU+V2dYXpzui41EkOgNdHJ5eZvdwN6Zlk6/UiPi9pMp1+mJ+TNtBXYfExoVclRcmV3jsdJZXlkvkayZdQYn3bI4z2hGiO+ojoKuUqwb37hpFlps/GJ7WPXk7RNpeL23jcoM4jnO+di5AwOclGgqRiDSnF4okYTkuCjaK0vShPH16hb1DSc7kGmzqiDHcGWJmrUgi7MN/BUmLWq2GoqiXtHVxH87lyJimxXw6z67BOELWSNUF7x4L8ezJFd69bwxVbcai1Go1dE1BklSQBH3RjY3kdCSZrb1R6paDHgjQLSRuVWRyhTK0+p4rlckJha54EK16HE8owtefPM58Ce7a00+vP4vfA4MD3QwP9jQ1iNaynFozeHziCB+7by+9ET8f2T9EttJgYqlAwTD5i2fn+Nn7NrG5K3hRj2QCHoWJs6s8M51hvDtKxG8S82vYtnO+4Pr1ULdsSrUGT5xKcWpuicPLNQ5u6uJn7htFVTUiXoXIq4qoB3xvv5HHjdI2LjfI5ULkkx0Xijx3hjzkqg0MG849TrcMJ7FMk3CwQW88QL3eYLgjhGEY6PrlHzKv10uj0bikvXND93N/Z4uVZph4MsQ/fPsYNvAjd43y0nQK05JYSWXJ5ovcvmUAr9dLqlDGEjLD15hIdy1U6iazmSplw0KVg2iKhOtC1bSQFI2vnFhhx1APkqxQqDXo6Qhzcj7DV444/OORLEWaeSD1k6tsG+xna8hDV0eIQCDIaE+C/eO92LZNqW6jawoBj8JAq0TtXZsSfPnoMo+fSnFwJM4945cW3/ZoKsenp7l38xBrJYOgLmM6LoZl4/e88SrL85OrfHNihY6QzlAySKrc4EvHVnl4dze9ES/VmsGe4Q52DcEdYx3oXh1Fks6HFHyn0zYuN8i3zmSYTJVxXbh7UxJJgphH4FMlFKWZQObTZErlKpgG4XCY40t59g8nObVSYKQzQqHu4vFwRcNyDk1rTlscx8GyHZazJQY6IpxZzmGaDoVanf54oFnOwRIMRXWyFZPt/XFyZYNCrc4LM2tMrxV5795hDFtiKH59ioKvh2k7PH18Fl/Ah+Za/Pbnj/BD79lGyfDiUSSMhoWsavzJczN8azKHopxAEw5WvYY+u4ZSLxOW4LADv3aHxtDwMOFQmB/6s0PsjZm8MBngz4VG0ivoDsjcNRpl01A/Ac+lj3NQV/n4bQN8/LbL55utli3u2T3OYq7Gpu4oR0/PMj7ci/wGq2W5co3ffeQU3zy6iKx7SFUsdnfITOYd/LqGg0DXFD5+xxj5ioEHi1BoY8ppvp1pG5cb5MBIjPu2djKdKpP0a+RqJrIsWM6V8Xt1ZEkQ1lXCvguOuXKlyqefm2L/QAjbtnGv4rt9cmaRRw+d4r592ynX60R9HkrlMi+cWWBxaZmP3X+Q1UyWz56coSp0HtrRyctTqyTCQWzXJepTGUgEOTG3wmBUp9CAkK5syK/omdUyo71xFEnhCy+cwqPC/370BH7tBJquk8oaZNEo1Ew0WXBwMMhCtspTc6vksxBNCm4ZgX99YBv5YoWOWARFFgx7TCpr0N9XZlNXmKRPYu/WUZKxMJIsY5j2NcmXjnSEyFfrxEM+HMfhjt3jb7iM+8TJFf7DF48znzMIa9CoNFBlidGoxq29ClU1zJ19HpLxIKoskQy9dYmDbzVt43IDVIwLofsBTcKvKwR9GtW6RdDnIV2qoyoSyaDnkgLeQpJJBJrZyAupPP0dsSu28crsKqcX1/DJoAcieDWJWCAIjk0s1M1gnyA/3E1XPIgiOeQNk+fOLCHoIeCROXR6jrPFBveOdzGbW+XunZvw+f1E/RqhG5QsvRKdIQ+5Sp3VXI5bR3swigX+8sUqW3plji8ahHQJ7AZeTUVT4csnMriNKrkCzEkSK1mHh98zwPaBJF6tF92jYtQb/Iv7B+mM+JG8YdaqNuOdQRJ+Dct1ka5j9BUPeK5apN0wbX7r6yf5+xfnkQV0+yViqoMXl/3DOj/98B3ob1GC4NuVtnG5ASzX5fRyiZ6ITsR7QbLV51EoVWokAhrL+SoVVcKvX1iJOTDWXKlQFJnBrkv1fMuGSb3RYCVXZjmdp9JoENEV7tw5RrlqoGkaZ84us2d8gJPzaQaSYRLRIA3LZj5nsJavYjuCLf1J+jtjrKSyzD01Qc5w+ciBLSwU6nRHvK+ZQqwn8YCHM2tlJtbqJIKCUsMl4IdyycanQKfucKIo84sPbeGh7Z3YZp3FbBlJ9/P0qWX8HhXX7+PZuRIhXUWVBV5NZv/OLSiSQFdlNl/U3kaLddRNi+//1DOcTZdISi5xD0R12NKp8N7bNjM2NPiOXtXZKNrG5QYwTJuucNNJ57loOF2rm3hUBVlq5tcs5WsYVv38r+RspsLWoS4APn94kYVcjbGkn+l0heGEj81JP9VKlZJhcO/uzSysZnAdl1g4QKFcJRYK0LActg02PZfVusXR2TWK5Qo/9uA+vnlsntm1PLbj8rXnXmHPcC8PH9zCmbUSXWF9Qw3LOQ6MxDkwEqfasHBsi56eCrrsokkC3eenVHdIhnSemclRrlvULYfOkMFDewbpDunXJNa2kZxczOLVZF6eL9IZUPDXLXb3wyceuIX+7s63TT/fjrSNyw3QEfRc9hdrNZNlqKfz/N89ES9nM1Vi/nOBWc1jPvPSPC/O5tjSHeL+rZ0crJuosoSuKXRHvCRyIRYzJXy6h5VijZ6YzFdfnmSoI0x/Z5RUsdYs8F02mFor4tOaVeoe2jvUzGtK5xnrTXD37hGem8kQ9WkkrnIasF74NIX37OjBcVxyVRPDtIn6NLrCb7+l12rdRJKk80mAJ1eKPD2Zoy/u4798eBtn5xf4qffuJ9haNm6PVl4fsdEl/t5s9u/f77744otvWfvzqxlq1Qrjw80VilzZIBrQqTVsDs83f6Ud12WlYPBHT0zzqR/az9buELIkOD2/xqZX6Q2tFUokQ4FmfIgqYxgNZFnw2JEZdN2L5DboTUY4Mp8lV7UJSg63b+2hPxHBtm2mV/Pkyg1ikQBjHcErdfum5Te+doqnp9Ls6A3zfXu7+MrRBX7wzlECugdJEtQth8b5IuiCkHfjAg3fiQghXnJdd//l9rVHLutIw7RYWUuxf0fTI5AvG6RzBU7PzFHFy0/9zXEsx2G8s6lf839+aB8xn4QsCZbWcvQmmwmPqXyZZCRAKl9EliReOjVPPOLHsV0QMNqTYPNAgk998xSyJNhesnlgRy+nFlfJ5it8+amjHNjcRygc4ZETKR7eP0hv5MplBr7TWSkYHJlL88CO3vPTGNd1+YfnzpApG+zoDnDXoJ+uWIBBr8lzpxe4f/cYrguqJIhuZFrEdzBt47KOHJk4w87xYapGA7/XQySgEwno0N/J3HKGT318M3/yXDM0v2hYbO2NcnYlS6pQJVt38NlNPR5Jkqg3LGQBy/kqt2zuZ2JmlXypwK3bx3Bdl6OzGT6xv5+lqoMqCSxXZmklw5nJNfoGovz3p5b5wTt83L2l86Y1LK7r8sUjSzwzneXvX5znoe0rPLitk6jfA5ZBIhLkPr+Pp6ZzZAsF5pcEH7p7N6blXjFSus3V0zYu10m1YV0SE2FaNjvGR9D11/o0KpUqfZ0xBrpiOLaDPxDgc89M8KXnZPLlCt9z924CXg+KLJhcSpOIhnh5cpGD2wYJB/wIIehKhogENFazJWRhM7O4gKr00xEJ4dg2z0xM4g8GUBI2X5kxOb5W459qCjv73tZSTxtCrW7x18+c4a9fXiVXtdjaHWyWJlAEfX4J2zWom3USAYEei7CrL0ihbBDRFRRFRdPavpT14LoVF1v7fkEI4QohEhdt+45XXHRd93wh54nZRV6eOIMiS9Qar62R2miYuEIhU6rxxLFZxvsSZPNlfu79t/CB27ewtS/O5OIaHkXgOC6a7iXq87BtqBvTNPna04eoGQ00RaKnI8ZirsKXX5pi20A3xfnTFEplnnnlDN+1ZxPIGl+fMcg34H/+k1vYN3BzGRbLdnjh9AITM/Ps7faxbyDKd22Jka+aGKbD1t4IjmsR8HsJB71E4glGkgHCfh9DXXE6kvH26s86cjUjlz8F/gfwfy/eKIToBx4Azl607WLFxR7gUSHEeKuO7jnFxWeBf6QpcPYVLlJcFEJ8nKbi4scuUlzcT1NS5CUhxBdc181d/+VeG4WayRMTy9w3HkNRNKqWS9Crcmoxx1hnsDmFUTzkHRfDtMkVm8vE5zBNm0KpRDIeQzFlgl4ZF4nBrhiKBJ9+5hQDYY2IT+f3P/sEquphpDtMd2QLIa+KZVncunMc3aNiOS7Ta0WG416WM0GenclzJB3h4Q7B4ysKP6konM46zOcMfuyuIW4fjt00qxnz2QovzeV59JVFEnaag+NdzFdkun0uIX8Av9fDR/Z28/CubqJBL9VGM5L3XJ3cdvDbxvCGIxfXdb9Fs3D2q/lt4Be5VEvovOKi67ozwDnFxW5aioutQt7nFBfPHfNnrfefBu5/teJiy6CcU1x8UzBMm2cm09y/Jcn8agpd1xA4pAoVhpMBphfXkGWZzX0JxjsCHJ1e5sWpZSbmVs6fwwbSFZNsuU61YRMPBpCAvkSQZ04u8L49g9yyeRDN4+EnP3AH3337OKVSjXypgmVZHJ9dRFdkcpU6RsPi0PQKkXCYbcM9fOl0hcm1Mr/5yBlemsvxH794gh29IR7Y1snP3LfppjEsJ1eKvP+/f5tf+Psj2NU8PsflmdkSn50o8j+fWebIcoWP7h/gR+4cIRZqpmD4PcqGaBO1uZTrSiwRQnwQWHRd98irdn1HKC6WqzVylQb3jMUxHUGp2hJnd1w+9/wZnnhlhlDoQsW23kSYrliIYrmE48JypkixVOb0YoaeWJhYwINpOwx1xYiGfCymStQbddZKdWRZoiPsQ0gyz56Y4yPvvgWPqmA6sH2oj2DAjyxJPH50ks6Ql3Sphi4afOK2fnb1Rfh/P7ANScCz0xl29kb4jY/uvqb8mnc63z6TpmhY/NJDW/g3Hz7IKbmDiZLGzt4wn/mpA/zux/eyqTN40xjbtxPX7NAVQviAX6GpI/Sa3ZfZ9o5TXNQ1lbpV58lTK4zGvfR3J5tZyOkcd4xEqZtQqtZwnCCSJFriYjLffcceAj4d13YIeFU2e72oksR8pkTMr3Po1DSaonF6cRXLgscOnWHHYCfbBzt54fQ8e7ePsJYtcnYtg6552DnaS7pY5djMMtt64nR3xLBdl4JjcceAlzsH/fR3RNndF+HfPLSZZPDNDZB7OzCxXEQS8PuPTfIXz86xuTPA998+wMN73rbKvzcN17NaNAoMA0davwZ9wMtCiNt4BysungsmFEKgKAquXeHerb2UDBOPKjO7uEJ/dwIhJJbTOWRZucT51xcPsJQpoeCi6SpCCDyKjOu69ER8TC+uIqkeVGEz2pVg20gvqZLBFx57lpBfJ+QR9EX8nJpdoj8RJl8xqNRNjs2skiuUePfuUQyjjiwEiYCOV/fyPX/4NOOdQT52az/7B6+c/PidzOnVEmGvyl1jMT6ws5v7tnSh3kQjt7cz12xcXNc9BnSc+1sIMQvsd103LYR4xyouvnrYnIiGWckW8GoqIZ+OGw4Q9DZHBrKq0nEZDd7uWIBHDk+xc7iLjqAP14XHjpzBsWxu2zKAz+8F22Ilk+XI1DJ+j8K+rSPMZ6uMRDxML6WIRUN0JqKsFRYoGSazazn2jg9imBambXNiMU/cJ2NYDv/5Qzu5a9PllQNvBizLYjDu53c+tof+qPc1xcrbvLVcl+Ki67r/53KfdV33Ham4eCXB9UQ4SK5YYn5xmQYyDhLRcIC+eIh8qcZKrkBfMoZHlRFCUK0ZDHbGmJhZZtGnU2/UuGfXGC9PzhMNN1eR6rZNIhxhMX2WgD9Oyaxx77ZeVFWhXKlRNBrMr2Xp74zxm/94lE8+uOt8CUpdVegMGdQaFgNdCXbc5IFesizzux/f23bOvk1p5xbRDNtfSBfoCPkwbQchSYT9Os9NzLCpJ8mpmXm2jvQRDTeduCuZApZt4fHoGA2LkNeDK0k0Gg1ypRq1ho3pOsykSgQVhxfOlnl4VxepUo1MoYLiC7CtJ0pfPMTk4hp7xnp5aXKJLf0dTC6mwXVIZXN4I0nuGu+8pK8Ti1m6owEivnaOS5u3nnZu0RugqQpVWyLgvxAmXzPqbO1PEg4G2T42RCjgJV+uEQl48Xk0VMWHtzVyqBoNUuU6g4kgiZAfSRJYtsPuoS40RWKkc4VkOMCm/k4My8UjC+pOM8R823A3+WqDRFBnenENj6JQbVj83SNz/NQP9FzSz0rdIuTV24alzTuCtnFpYTlgmBZ6a96uqCpqq3LcOYkJ13Wp1hqEWmJapu2wmi2SKdepFLIMJrYiSYIjZxbRPBrD3VFAYvNAF7liBZ+uobemYH7AcVxkIXAsm5oJPr+fqaUMLxw7xYom6A5cmKrVGxbluomq3hzFndu882k/qS2GEn7OLKQpVesspouUyhUapgXAzNIapu1gmgayIlFt6Su/eGqeJ0/MM5gIUK5bpPIlpuaXiHohl89ht7xNrusS9DWdwbbd1HSumzZCNIP1TMch5lewbQevJuENRfjfP3Ufft3Dc6eXmForMrGUY2YlR0fw7VcHpU2by9EeubSI+DSO2wqpYpXhzgim5QAup5fzeBWBpsjEQqGmIFlrdDPS28H+8T5URaYzHqNQdxns7kISLjVnlWK5jG2peDw6smgmN5ZqdUKiaWSEKuP3KBgNk0LNZmo5TXckyL/+8B1A0/AMJIOkskU0j87mns7XuYI2bd5etI3LRWztDhFrqQ5qrViJpO7SUALMr6Xp72gu+7quy9xqlpVsAam7A8u2KNYMvB6JpaxDUFeJBnwsLa/Q0ZFAkmRcSaDrHkI+D44LPl0jU6ogyyqpYo3BjjCxwNAlqoe6KvP0xDwBj8L+zQm8Wjt+o807h7ZxuQhdlWhYzWlLvmJQNW1wXFYWFlA8OrKk0JOIsLCS4hsvneCHH7oLWZZZyZYY6IiiSeD1eXAcqJk2m4YHOTY9j02e27cMUy5XqJsWFculLx4iW6iQrdmokqDesIgGXjvluW/3GC6cN3pt2rxTaBuXFoZp89LEFNGAl4HOOCFdpVoucnxujYWqxOaIiSQ3V4c6YlFu3bqJY9PLnJxfYbQzwq3bxxBCsJItIssyL56ZZ+9gEr+uMbmYw3IcVI8OssXc3CKzKzmErDLYGaEn4jsvUfJqNlK/uU2bjaRtXFo0bIdkOICDitfjQZJlejuTLK2skbJkfIEAHeFmMJuqKngUiXTdZFNPlP1bxwCwHZeuWAjHcTiwqQfLtNg5NsSO0cHzQXZmvU7Yp9MTitIZXn+1wzZt3i60jUuLkK5S8vvpTUYwbYdSzSSoy9y6ZwfbKlVOL2Yp12ospPIoAlazeV5aqNIRC1OfmGdLTxSvquLzeShXa0SCQXKlCtBMLSgUCqTKdbz+IL3d/vNL3m3afKfSfsIvQvXoLGZKrOWKTGUa/MJnT/CX3z9Gf2eCbQNxTAfm0lUObullfLCHO3e7TC9n+dSTM/xSVxDN0xzZCEXD51FwhcJyroIrBIWiwWBX/A3lQtu0+U6h/aTTXP15/Pg8Z/Mmn7htgIhXZTq9wGP/8nYCXh2/JqGqKqoruHfHAD5dY2YlSzqTZqCnh4YrMZc3KVZWGerp4umpDPeOJ8lUGwzG/QB0hbztEoptbipu+iC6fNng0RMrnF7M8t7tCQoVg1KtQVBzGe5OkIwE8Pl8qKqK69g4tkUul+eLL8+yc2wIXZX5kTuH+f3HZ+hKJJjLVDmbqTK5Vj5vWIC2YWlz03FTG5fVQo3VfIU7R+N878FxIsEgyWiQVLHOWGfoNZ+v1esspXJ4vD4+sLsfy2oK0W/pClI1bV6eyyBLYNo227pfe3ybNjcTN/W0yKNIjHZFsFyIh5pLvpbtMNAZwnaaIfoeVT5fkiEaDhENhzBNC0nzUHMkPLIELnzy3lFm0iV8usoP3D7YHqm0uem5aY1LpW6RLZaoaho98eYow3Ec0qUqmXwJj6YhhYN4VBnTdrHNpo6wbVn8xTMzWELhn79r9PxS8rs2d7DWE8JxINzOWm7T5uY1LjXTZqQ7cb68pe24GA2TTK5EsVJjtD9MpBXAVquWCfj9KIoMqsz7d/USCXpfE6PSTips0+YCN7XPBWB6KQVAyWggCYlYJIht18nn8pQr1ab4maRRrjRjVtJlg3jIR8Bz09rlNm2uiutSXBRC/DchxEkhxFEhxGeFEJGL9r0jFBfrlkOlVkdIClNLGXKlGoosUa0ZdCcS+H0e5lN5XprLcnixhK43Y1gSAb2dQNimzVVwNSOXP+W1YmSPADtc190FnKZVOPtViosPAX8ghDj3TTynuLip9Tp3zvOKizSF1v5L61znFBdvB24D/t1FxbpvmK6QTs2Goc4oVdNGkmSOn11Dk+DYfBbDkUjE42zrCdMT9eJpB7+1aXNNXJfiouu6X28JmEFTnvWcbMg7RnFRlgSVusVivkZ3xE+2YvBbX3yZ//X4KR66ZYzRrijJoIeAR2G8LarVps01sx4/xz8G/G3rfS9NY3OOcyqJJlepuCiEeNMUFwfjfp6aTHM2W2Vrd4h/+aE72NIVvKkUC9u02ShuyLgIIX6FpoTIX57bdJmPva0VF+8cS3DnNR3Rpk2bq+G6V4taDtYPAD/gXtAnuRHFRS6juHi5c70G13U/5bruftd19yeTyeu9pDZt2qwj1ytE/xDwS8AHXdetXrTrC8DHWytAw1xQXFwGSkKIAy1/yg8Bn7/omHMrQecVF4GvAQ8KIaItR+6DrW1t2rR5B3Bdios0V4c8wCMtR+ezrut+8p2quNimTZv1p6242KZNm+vm9RQXb/oI3TZt2mwMbePSpk2bDaFtXNq0abMhtI1LmzZtNoS2cWnTps2G8B23WiSESAFzb0JTCSD9JrTTbv/t24ebvX2AQdd1Lxu5+h1nXN4shBAvXmkJrt3+zdGHm739N6I9LWrTps2G0DYubdq02RDaxuX6+VS7/bect7oPN3v7r0vb59KmTZsNoT1yadOmzYbQNi40S0i0CopPCiF++TL7o61C5EeFEM8LIXa0tvcLIR4TQkwIIY4LIf7lRcf8eyHEohDicOv1vvVuv7VvtlX4/LAQ4sWLtseEEI+0ips/8kb1h2/gHmy+6BoPCyGKQoifu4578JpC8K/aL1qF3Sdbfdj3Rn2/lntwve2v4zNwI9e/Ls/AuuO67k39AmRgChgBNOAIsO1Vn/lvwL9rvd8CfKP1vhvY13ofpFmsfFvr738P/MJGtt/6exZIXOa8/xX45db7Xwb+y0b14VXnWaEZ+3DV96D12XuAfcArV9j/PpplOgRwAHjujfp+jffgetu/4WfgRtpfr2dgI17tkUtTWWDSdd1p13UbwN/QLBp+MduAbwC4rnsSGBJCdLquu+y67sut7SVggmuv83vd7b/BeS8ufP5nXCiIvpF9uB+Ycl33moMY3csUgn8VDwP/123yLBARzcLvr9f3q74H19v+Oj0DN3L9r8e1PAPrTtu4XF0h8CPARwCEELcBg1xathPR1FvaCzx30eZ/0RrC/vHrDElvtH0X+LoQ4iXRrCV8jk63WQGQ1r8dV2h/Pfpwjo8Df/2qbVdzD66GK/Xx9fp+Lffgets/zw08Azfa/no8A+tO27hcXSHwXweiQojDwM8Ah2hW2mueQIgA8Bng51zXLbY2/yEwCuwBloHf3KD273Rddx/wXuCnhRD3XKGd12M97oEGfBD4+4uOudp7cCN9vOpC7jfI67Zzg8/Ajba/Hs/AutNW+rqKQuCth+VHoelYA2ZaL4QQKs2H6i9d1/2Hi45ZPfdeCPG/gC9tRPuu6y61/l0TQnyW5jThW8DquWF7a/i8tlH3oMV7gZcvvu5ruAdXw5X6qL1O36/lHlxv++vxDNxQ++v0DKw77ZFLs0bvJiHEcOvX9+M0i4afRwgRae0D+KfAt1zXLba+ZP8HmHBd97dedczF8+EPA5ddBbjB9v1CiGDrM36aRczPtXNx4fMf5kJB9HXtw0Uf+QSvmhJdwz24Gr4A/FBr1eQAUGgN9V+v79dyD66r/XV6Bm6k/fV6BtafN9N7/HZ90fTEn6a56vArrW2fBD7Zen8QOAOcBP4BiLa230VzaHoUONx6va+178+BY619XwC6N6D9EZq+kCPA8XPHtvbFaTpgz7T+jW3EPWjt8wEZIPyqc17LPfhrmlOHcwJ6P/6q9gXw+63+HQP2v17fr/UeXG/76/gMXG/76/YMrPerHaHbpk2bDaE9LWrTps2G0DYubdq02RDaxqVNmzYbQtu4tGnTZkNoG5c2bW5C3ihR8jKf/z4hxAnRTM78q6s6pr1a1KbNzUcrirdMM19pxxt8dhPwd8B9ruvmhBAdruu+YUBee+TSps1NiHuZREkhxKgQ4qutHKUnhRBbWrt+Avh913VzrWOvKtK3bVzatGlzjk8BP+O67i3ALwB/0No+DowLIZ4SQjwrhHjoak7Wzi1q06bNucTLO4C/b2Y0AOBp/asAm4B30cxpelIIscN13fzrnbNtXNq0aQPNWUzedd09l9m3ADzruq4JzAghTtE0Ni+80QnbtGlzk+M2k1BnhBAfhfNlNXe3dn8OeHdre4LmNGn6jc7ZNi5t2tyECCH+GngG2CyEWBBC/DjwA8CPCyHOJUGeq+j3NSAjhDgBPAb8G9d1M2/YRnspuk2bNhtBe+TSpk2bDaFtXNq0abMhtI1LmzZtNoS2cWnTps2G0DYubdq02RDaxqVNmzYbQtu4tGnTZkNoG5c2bdpsCP8/8t2oZUmgflQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# examine plot\n",
    "gdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elegant-trouble",
   "metadata": {},
   "source": [
    "`Flooding_Category`: Category of flooding classified by depth of ponding or tidal inundation from sea level rise.\n",
    "\n",
    "\n",
    ">1 - Nuisance Flooding (greater or equal to 4 in. and less than 1 ft.)  \n",
    ">2 - Deep and Contiguous Flooding (1 ft. and greater)  \n",
    ">3 - Future High Tides 2080\n",
    "\n",
    "Source: Data dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5fe14f32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flooding_Category</th>\n",
       "      <th>Shape_Length</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.264384e+07</td>\n",
       "      <td>3.972152e+08</td>\n",
       "      <td>MULTIPOLYGON Z (((917206.497 120931.611 0.000,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.656489e+07</td>\n",
       "      <td>3.054550e+08</td>\n",
       "      <td>MULTIPOLYGON Z (((917270.790 120873.747 0.000,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>8.042525e+06</td>\n",
       "      <td>3.455846e+08</td>\n",
       "      <td>MULTIPOLYGON Z (((915290.556 120269.389 0.000,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Flooding_Category  Shape_Length    Shape_Area  \\\n",
       "0                  1  3.264384e+07  3.972152e+08   \n",
       "1                  2  1.656489e+07  3.054550e+08   \n",
       "2                  3  8.042525e+06  3.455846e+08   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON Z (((917206.497 120931.611 0.000,...  \n",
       "1  MULTIPOLYGON Z (((917270.790 120873.747 0.000,...  \n",
       "2  MULTIPOLYGON Z (((915290.556 120269.389 0.000,...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine flood category codes\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "defined-projector",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (3, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flooding_Category</th>\n",
       "      <th>Shape_Length</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>geometry</th>\n",
       "      <th>flood_classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.264384e+07</td>\n",
       "      <td>3.972152e+08</td>\n",
       "      <td>MULTIPOLYGON Z (((917206.497 120931.611 0.000,...</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.656489e+07</td>\n",
       "      <td>3.054550e+08</td>\n",
       "      <td>MULTIPOLYGON Z (((917270.790 120873.747 0.000,...</td>\n",
       "      <td>Deep and Contiguous Flooding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>8.042525e+06</td>\n",
       "      <td>3.455846e+08</td>\n",
       "      <td>MULTIPOLYGON Z (((915290.556 120269.389 0.000,...</td>\n",
       "      <td>Future High Tides 2080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Flooding_Category  Shape_Length    Shape_Area  \\\n",
       "0                  1  3.264384e+07  3.972152e+08   \n",
       "1                  2  1.656489e+07  3.054550e+08   \n",
       "2                  3  8.042525e+06  3.455846e+08   \n",
       "\n",
       "                                            geometry  \\\n",
       "0  MULTIPOLYGON Z (((917206.497 120931.611 0.000,...   \n",
       "1  MULTIPOLYGON Z (((917270.790 120873.747 0.000,...   \n",
       "2  MULTIPOLYGON Z (((915290.556 120269.389 0.000,...   \n",
       "\n",
       "           flood_classification  \n",
       "0             Nuisance Flooding  \n",
       "1  Deep and Contiguous Flooding  \n",
       "2        Future High Tides 2080  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mapping flood category codes to classifications, easier to read\n",
    "codes_dict = {\n",
    "    1:'Nuisance Flooding',\n",
    "    2:'Deep and Contiguous Flooding',\n",
    "    3:'Future High Tides 2080'\n",
    "}\n",
    "\n",
    "# add new column for flood classification\n",
    "gdf['flood_classification'] = gdf['Flooding_Category'].replace(codes_dict)\n",
    "\n",
    "# sanity check\n",
    "print('shape of data: {}'.format(gdf.shape))\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "wrapped-jacket",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (145429, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flooding_Category</th>\n",
       "      <th>Shape_Length</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>flood_classification</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>51.436905</td>\n",
       "      <td>165.359700</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>POLYGON Z ((917206.497 120931.611 0.000, 91720...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>37.622059</td>\n",
       "      <td>79.460913</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>POLYGON Z ((917309.366 120950.899 0.000, 91730...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>14.432754</td>\n",
       "      <td>4.925173</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>POLYGON Z ((917309.366 120950.899 0.000, 91730...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>54.817201</td>\n",
       "      <td>154.010850</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>POLYGON Z ((917277.219 120938.040 0.000, 91726...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>24.392395</td>\n",
       "      <td>14.030167</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>POLYGON Z ((917311.973 120963.757 0.000, 91730...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Flooding_Category  Shape_Length  Shape_Area flood_classification  \\\n",
       "0                  1     51.436905  165.359700    Nuisance Flooding   \n",
       "1                  1     37.622059   79.460913    Nuisance Flooding   \n",
       "2                  1     14.432754    4.925173    Nuisance Flooding   \n",
       "3                  1     54.817201  154.010850    Nuisance Flooding   \n",
       "4                  1     24.392395   14.030167    Nuisance Flooding   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON Z ((917206.497 120931.611 0.000, 91720...  \n",
       "1  POLYGON Z ((917309.366 120950.899 0.000, 91730...  \n",
       "2  POLYGON Z ((917309.366 120950.899 0.000, 91730...  \n",
       "3  POLYGON Z ((917277.219 120938.040 0.000, 91726...  \n",
       "4  POLYGON Z ((917311.973 120963.757 0.000, 91730...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# explode multipolygon so every row is a single polygon\n",
    "gdf = (\n",
    "    gdf\n",
    "    .explode()\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "# calculating area and length of geometries\n",
    "gdf['Shape_Area'] = gdf.area \n",
    "gdf['Shape_Length'] = gdf.length \n",
    "\n",
    "print('shape of data: {}'.format(gdf.shape))\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "76d8eac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Polygon    145429\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sanity check for geom types values and counts\n",
    "gdf.geom_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "392aa539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Derived Projected CRS: EPSG:2263>\n",
       "Name: NAD83 / New York Long Island (ftUS)\n",
       "Axis Info [cartesian]:\n",
       "- X[east]: Easting (US survey foot)\n",
       "- Y[north]: Northing (US survey foot)\n",
       "Area of Use:\n",
       "- name: United States (USA) - New York - counties of Bronx; Kings; Nassau; New York; Queens; Richmond; Suffolk.\n",
       "- bounds: (-74.26, 40.47, -71.8, 41.3)\n",
       "Coordinate Operation:\n",
       "- name: SPCS83 New York Long Island zone (US Survey feet)\n",
       "- method: Lambert Conic Conformal (2SP)\n",
       "Datum: North American Datum 1983\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine CRS\n",
    "gdf.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0d1e9c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flooding_Category</th>\n",
       "      <th>flood_classification</th>\n",
       "      <th>Shape_Length</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>51.436905</td>\n",
       "      <td>165.359700</td>\n",
       "      <td>POLYGON Z ((917206.497 120931.611 0.000, 91720...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>37.622059</td>\n",
       "      <td>79.460913</td>\n",
       "      <td>POLYGON Z ((917309.366 120950.899 0.000, 91730...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>14.432754</td>\n",
       "      <td>4.925173</td>\n",
       "      <td>POLYGON Z ((917309.366 120950.899 0.000, 91730...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>54.817201</td>\n",
       "      <td>154.010850</td>\n",
       "      <td>POLYGON Z ((917277.219 120938.040 0.000, 91726...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>24.392395</td>\n",
       "      <td>14.030167</td>\n",
       "      <td>POLYGON Z ((917311.973 120963.757 0.000, 91730...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Flooding_Category flood_classification  Shape_Length  Shape_Area  \\\n",
       "0                  1    Nuisance Flooding     51.436905  165.359700   \n",
       "1                  1    Nuisance Flooding     37.622059   79.460913   \n",
       "2                  1    Nuisance Flooding     14.432754    4.925173   \n",
       "3                  1    Nuisance Flooding     54.817201  154.010850   \n",
       "4                  1    Nuisance Flooding     24.392395   14.030167   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON Z ((917206.497 120931.611 0.000, 91720...  \n",
       "1  POLYGON Z ((917309.366 120950.899 0.000, 91730...  \n",
       "2  POLYGON Z ((917309.366 120950.899 0.000, 91730...  \n",
       "3  POLYGON Z ((917277.219 120938.040 0.000, 91726...  \n",
       "4  POLYGON Z ((917311.973 120963.757 0.000, 91730...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [\n",
    "    'Flooding_Category',\n",
    "    'flood_classification', \n",
    "    'Shape_Length',\n",
    "    'Shape_Area',\n",
    "    'geometry'\n",
    "]\n",
    "\n",
    "# rearrange column order\n",
    "gdf = gdf.loc[:, cols]\n",
    "\n",
    "# preview data\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "valuable-saver",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flood_classification</th>\n",
       "      <th>count_polygons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Deep and Contiguous Flooding</td>\n",
       "      <td>39742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Future High Tides 2080</td>\n",
       "      <td>7122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>98565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           flood_classification  count_polygons\n",
       "0  Deep and Contiguous Flooding           39742\n",
       "1        Future High Tides 2080            7122\n",
       "2             Nuisance Flooding           98565"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine count of polygons by flood classifications\n",
    "(gdf\n",
    " .groupby(by='flood_classification')[['flood_classification']]\n",
    " .count()\n",
    " .rename(columns={'flood_classification':'count_polygons'})\n",
    " .reset_index()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7b8a1870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flood_classification</th>\n",
       "      <th>shape_area_acres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Deep and Contiguous Flooding</td>\n",
       "      <td>7012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Future High Tides 2080</td>\n",
       "      <td>7934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>9119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           flood_classification  shape_area_acres\n",
       "0  Deep and Contiguous Flooding              7012\n",
       "1        Future High Tides 2080              7934\n",
       "2             Nuisance Flooding              9119"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine flooded area by flood classifications\n",
    "acres_conversion = 43560\n",
    "\n",
    "(gdf\n",
    " .groupby(by='flood_classification')[['Shape_Area']]\n",
    " .sum()\n",
    " .div(acres_conversion)\n",
    " .round(0)\n",
    " .astype(int)\n",
    " .rename(columns={'Shape_Area':'shape_area_acres'})\n",
    " .reset_index()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c788ab94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>flood_classification</th>\n",
       "      <th colspan=\"2\" halign=\"left\">shape_area_acres</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Deep and Contiguous Flooding</td>\n",
       "      <td>1.186102e-09</td>\n",
       "      <td>68.154537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Future High Tides 2080</td>\n",
       "      <td>4.305792e-09</td>\n",
       "      <td>1266.198909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>1.174981e-09</td>\n",
       "      <td>50.153468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           flood_classification shape_area_acres             \n",
       "                                             min          max\n",
       "0  Deep and Contiguous Flooding     1.186102e-09    68.154537\n",
       "1        Future High Tides 2080     4.305792e-09  1266.198909\n",
       "2             Nuisance Flooding     1.174981e-09    50.153468"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine flooded area min, max by flood classifications\n",
    "acres_conversion = 43560\n",
    "\n",
    "(gdf\n",
    " .groupby(by='flood_classification')[['Shape_Area']]\n",
    " .agg({'Shape_Area': ['min', 'max']})\n",
    " .div(acres_conversion)\n",
    " .rename(columns={'Shape_Area':'shape_area_acres'})\n",
    " .reset_index()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "519cb3d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "polygons less than 0.25 acres:\n",
      "-----\n",
      "count: 91.72% of total\n",
      "area: 13.31% of total\n"
     ]
    }
   ],
   "source": [
    "# a quick sanity check with small polygons < 0.25 acres\n",
    "acres_conversion = 43560\n",
    "ser = gdf['Shape_Area'] / acres_conversion\n",
    "\n",
    "perc_count = round(ser.loc[ser < .25].shape[0] / ser.shape[0] * 100, 2)\n",
    "perc_area = round(ser.loc[ser < .25].sum() / ser.sum() * 100, 2)\n",
    "\n",
    "print('polygons less than 0.25 acres:\\n-----')\n",
    "print('count: {}% of total'.format(perc_count))\n",
    "print('area: {}% of total'.format(perc_area))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b87186e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t7dHR0dFPGK+4bslyrlw36I/gVbaeW9v7DoXOzk5qPe6RoEjHU6RjgaE7noj4cYV5drOA0pstBjqBT5DrEQa2SCr1CG8l9QgDSCr1CK9MdT6b2loGfCn17pwO3B0Ru1Kdu8mSotsbfYxmNnzq+d/9XcCWiHgKQNK3gLcBT0oan3pvxgM70/7dwKRc/YlkQ1rdab28PF+nOw2DHQrsqiNmMxtZhqNH+A/lFer0Mpje4kb3FENjeotbsWexFWOC1ozLMVVXT4LzGDBd0oFkQ1QzgLXAc8Ac4PL0c3nafwXwdUlfJOtSngrcHxEvS9ojaTqwBjgfuC5XZw5wH3A2cE+ap2Nm+7ZG9gjX1FMMg+stbnRPMTSmt7gVexZbMSZozbgcU3X1zMFZI2kZ8CDQA/yM7Bf/YGCppLlkSdA5af8NkpYCD6f9L0rj5QAXko2/jyHrSl6Zym8Cbkvdz7vIxtzNbN8xHD3C3bwyDFaq09nYwzCz4VbXnxURcRnZ5Ly8F8l6cyrtvxBYWKF8LXBihfIXSAmSme2TSr24Q9YjLOmHwD/mrtB6D7Bg6A/NzIZSY/tNzcwGSdLtZD0pR0rqJvvj6XKGuEc4InZJ+gfgp2m/vy9NODazkcsJjpm1hIj4YB+bhrxHOCJuBm6uOVgza3l+FpWZmZkVjhMcMzMzKxwnOGZmZlY4TnDMzMyscJzgmJmZWeE4wTEzM7PCcYJjZmZmheMEx8zMzArHCY6ZmZkVjhMcMzMzKxwnOGZmZlY4TnDMzMyscJzgmJmZWeE4wTEzM7PCcYJjZmZmheMEx8zMzArHCY6ZmZkVjhMcMzMzKxwnOGZmZlY4TnDMrOVJ+rikDZLWS7pd0gGSxkm6W9Lm9PPw3P4LJHVJ2iTp9Fz5KZLWpW3XSlIq31/Snal8jaTJTThMM2sgJzhm1tIkTQAuBtoj4kRgFDAbmA+sioipwKr0GknT0vYTgJnA9ZJGpeZuAOYBU9MyM5XPBZ6JiOOAq4ArhuHQzGwIOcExs5FgNDBG0mjgQGA7MAtYnLYvBs5K67OAOyLixYjYAnQBp0oaD4yNiPsiIoBby+qU2loGzCj17pjZyDS62QGYmfUnIh6X9AXgMeB54K6IuEtSW0TsSPvskHRUqjIBWJ1rojuVvZTWy8tLdbaltnok7QaOAJ7OxyJpHlkPEG1tbXR2dlaNv20MXHpST+0HXINa3reavXv3NqSdRmrFmKA143JM1TnBMbOWlubWzAKmAM8C35B0Xn9VKpRFP+X91eldELEIWATQ3t4eHR0d/YSRuW7Jcq5c19hT7dZzq79vNZ2dndQS/3BqxZigNeNyTNXVNUQl6TBJyyQ9ImmjpNM88c/MGuxdwJaIeCoiXgK+BbwNeDINO5F+7kz7dwOTcvUnkg1pdaf18vJeddIw2KHAriE5GjMbFvXOwbkG+EFEvAF4E7ART/wzs8Z6DJgu6cD0x88MsnPNCmBO2mcOsDytrwBmpz+QppCdU+5Pw1l7JE1P7ZxfVqfU1tnAPWmejpmNUINOcCSNBd4J3AQQEb+LiGfxxD8za6CIWEP2+/8gsI7svLUIuBx4t6TNwLvTayJiA7AUeBj4AXBRRLycmrsQuJHs/PMosDKV3wQcIakL+BvSH2ZmNnLVMzB8LPAU8FVJbwIeAC4BRsTEP2j85L9mTq5qtcld9SrS8RTpWKA5xxMRlwGXlRW/SNabU2n/hcDCCuVrgRMrlL8AnFN/pGbWKupJcEYDJwMfjYg1kq6h/796WmriHzR+8l8jJv4NVqtN7qpXkY6nSMcCxTseMyumeubgdAPdqfsYsi7kk/HEPzMzM2uyQSc4EfEEsE3S8aloBtmYtyf+mZmZWVPVOz7zUWCJpP2AXwIfIkualkqaS3b1wzmQTfyTVJr418OrJ/7dAowhm/SXn/h3W5r4t4vsKiwzMzOzftWV4ETEQ0B7hU2e+GdmZmZN42dRmZmZWeE4wTEzM7PCcYJjZmZmheMEx8zMzArHCY6ZmZkVjhMcMzMzKxwnOGZmZlY4TnDMzMyscJzgmJmZWeE4wTEzM7PCcYJjZmZmheMEx8zMzArHCY6ZmZkVjhMcM2t5kg6TtEzSI5I2SjpN0jhJd0vanH4entt/gaQuSZsknZ4rP0XSurTtWklK5ftLujOVr5E0uQmHaWYN5ATHzEaCa4AfRMQbgDcBG4H5wKqImAqsSq+RNA2YDZwAzASulzQqtXMDMA+YmpaZqXwu8ExEHAdcBVwxHAdlZkPHCY6ZtTRJY4F3AjcBRMTvIuJZYBawOO22GDgrrc8C7oiIFyNiC9AFnCppPDA2Iu6LiABuLatTamsZMKPUu2NmI5MTHDNrdccCTwFflfQzSTdKOghoi4gdAOnnUWn/CcC2XP3uVDYhrZeX96oTET3AbuCIoTkcMxsOo5sdgJlZFaOBk4GPRsQaSdeQhqP6UKnnJfop769O74aleWRDXLS1tdHZ2dlPGJm2MXDpST1V9xuIWt63mr179zaknUZqxZigNeNyTNU5wTGzVtcNdEfEmvR6GVmC86Sk8RGxIw0/7cztPylXfyKwPZVPrFCer9MtaTRwKLCrPJCIWAQsAmhvb4+Ojo6qwV+3ZDlXrmvsqXbrudXft5rOzk5qiX84tWJM0JpxOabqPERlZi0tIp4Atkk6PhXNAB4GVgBzUtkcYHlaXwHMTldGTSGbTHx/GsbaI2l6ml9zflmdUltnA/ekeTpmNkK5B8fMRoKPAksk7Qf8EvgQ2R9oSyXNBR4DzgGIiA2SlpIlQT3ARRHxcmrnQuAWYAywMi2QTWC+TVIXWc/N7OE4KDMbOk5wzKzlRcRDQHuFTTP62H8hsLBC+VrgxArlL5ASJDMrBg9RmZmZWeE4wTEzM7PCcYJjZmZmheMEx8zMzAqn7gRH0qh0d9Hvptd+AJ6ZmZk1VSN6cC4he/BdiR+AZ2ZmZk1VV4IjaSJwJnBjrtgPwDMzM7Omqvc+OFcDfwsckivr9QA8SfkH4K3O7Vd60N1L1PgAPEmlB+A9nQ9iMM+HgcY/I6aZz+BotWeA1KtIx1OkY4HiHY+ZFdOgExxJ7wN2RsQDkjpqqVKhrCEPwBvM82Gg8c+IacTzYQar1Z4BUq8iHU+RjgWKdzxmVkz1/O/+duD9ks4ADgDGSvoaTXgAnpmZmVneoOfgRMSCiJgYEZPJJg/fExHn4QfgmZmZWZMNxbOoLscPwDMzM7MmakiCExGdQGda/zV+AJ6ZmZk1ke9kbGZmZoUzFENUZnVb9/huLpj/vYa1t/XyMxvWlpmZtT734JiZmVnhOMExMzOzwnGCY2ZmZoXjBMfMzMwKxwmOmY0IkkZJ+pmk76bX4yTdLWlz+nl4bt8FkrokbZJ0eq78FEnr0rZrSw/vTTcgvTOVr5E0edgP0MwaygmOmY0UlwAbc6/nA6siYiqwKr1G0jSym4KeAMwErpc0KtW5gezBvFPTMjOVzwWeiYjjgKuAK4b2UMxsqDnBMbOWJ2kicCZwY654FrA4rS8GzsqV3xERL0bEFqALODU9G29sRNyXHvlya1mdUlvLgBml3h0zG5l8HxwzGwmuBv4WOCRX1paeZUd6uO9RqXwCsDq3X3cqeymtl5eX6mxLbfVI2g0cATydD0LSPLIeINra2ujs7KwaeNsYuPSknqr7DUQt71vN3r17G9JOI7ViTNCacTmm6pzgmFlLk/Q+YGdEPCCpo5YqFcqin/L+6vQuiFgELAJob2+Pjo7q4Vy3ZDlXrmvsqXbrudXft5rOzk5qiX84tWJM0JpxOabqnOCYWat7O/B+SWcABwBjJX0NeFLS+NR7Mx7YmfbvBibl6k8EtqfyiRXK83W6JY0GDiV7wK+ZjVCeg2NmLS0iFkTExIiYTDZ5+J6IOA9YAcxJu80Blqf1FcDsdGXUFLLJxPen4aw9kqan+TXnl9UptXV2eo9X9eCY2cjhHhwzG6kuB5ZKmgs8BpwDEBEbJC0FHgZ6gIsi4uVU50LgFmAMsDItADcBt0nqIuu5mT1cB2FmQ8MJjpmNGBHRCXSm9V8DM/rYbyGwsEL5WuDECuUvkBIkMysGD1GZmZlZ4TjBMTMzs8JxgmNmZmaF4wTHzMzMCscJjpmZmRWOExwzMzMrHCc4ZmZmVjhOcMzMzKxwnOCYmZlZ4TjBMTMzs8JxgmNmZmaFM+gER9IkSfdK2ihpg6RLUvk4SXdL2px+Hp6rs0BSl6RNkk7PlZ8iaV3adm160i/pacB3pvI1kibXcaxmZma2j6inB6cHuDQi/hiYDlwkaRowH1gVEVOBVek1adts4ARgJnC9pFGprRuAecDUtMxM5XOBZyLiOOAq4Io64jUzM7N9xKATnIjYEREPpvU9wEZgAjALWJx2WwycldZnAXdExIsRsQXoAk6VNB4YGxH3RUQAt5bVKbW1DJhR6t0xMzMz68voRjSSho7eAqwB2iJiB2RJkKSj0m4TgNW5at2p7KW0Xl5eqrMttdUjaTdwBPB02fvPI+sBoq2tjc7OzpribhsDl57UU9O+taj1fYfC3r17m/r+jebvpnUV7XjMrJjqTnAkHQx8E/hYRPymnw6WShuin/L+6vQuiFgELAJob2+Pjo6OKlFnrluynCvXNSTHA2DrubW971Do7Oyk1uMeCfzdtK6iHY+ZFVNdV1FJeg1ZcrMkIr6Vip9Mw06knztTeTcwKVd9IrA9lU+sUN6rjqTRwKHArnpiNjMzs+Kr5yoqATcBGyPii7lNK4A5aX0OsDxXPjtdGTWFbDLx/Wk4a4+k6anN88vqlNo6G7gnzdMxMzMz61M9PThvB/4c+O+SHkrLGcDlwLslbQbenV4TERuApcDDwA+AiyLi5dTWhcCNZBOPHwVWpvKbgCMkdQF/Q7oiy8z2Hb4lhZkNxqAnOUTET6g8RwZgRh91FgILK5SvBU6sUP4CcM5gYzSzQijdkuJBSYcAD0i6G7iA7JYUl0uaT/YH0CfKbklxNPAjSa9Pf1CVbkmxGvg+2S0pVpK7JYWk2WS3pPizYT1KM2so38nYzFqab0lhZoPhBMfMRoz+bkkB5G9JsS1XrXTriQnUeEsKoHRLCjMboRp3Ha6Z2RBqhVtSDOaeW42+pxM05r5OrXg/o1aMCVozLsdUnRMcM2t5/d2SIt1QtFG3pOju75YUg7nnVqPv6QSNua9TK97PqBVjgtaMyzFV5yEqM2tpviWFmQ2Ge3DMrNWVbkmxTtJDqeyTZLegWCppLvAY6YrLiNggqXRLih5efUuKW4AxZFdP5W9JcVu6JcUusquwzGwEc4JjZi3Nt6Qws8HwEJWZmZkVjhMcMzMzKxwnOGZmZlY4TnDMzMyscJzgmJmZWeH4KiozsxFm8vzv1d3GpSf1cEGuna2Xn1l3m2atxD04ZmZmVjhOcMzMzKxwnOCYmZlZ4TjBMTMzs8LxJGOzAmvEZNRyt8w8qOFtmpk1mntwzMzMrHCc4JiZmVnhOMExMzOzwnGCY2ZmZoXjBMfMzMwKx1dRmZlZw6+486MfrNncg2NmZmaF4wTHzMzMCmdEJDiSZkraJKlL0vxmx2NmxePzjFmxtHyCI2kU8GXgvcA04IOSpjU3KjMrEp9nzIpnJEwyPhXoiohfAki6A5gFPNzUqMysSHyeabDBTFq+9KQeLuinnicu20CMhARnArAt97obeGt+B0nzgHnp5V5Jm2ps+0jg6bojLMVxRaNaGpSGHksL8HfTov7bFTUfzzFDHUsDVT3PwKDPNS35/V/cgnFVi6mJv8ct91nhmPIqnmtGQoKjCmXR60XEImDRgBuW1kZE+2ADayVFOhYo1vEU6VigeMeTVD3PwODONa36ebViXK0YE7RmXI6pupafg0P2l9Sk3OuJwPYmxWJmxeTzjFnBjIQE56fAVElTJO0HzAZWNDkmMysWn2fMCqblh6giokfSR4AfAqOAmyNiQ4OaH/CwVgsr0rFAsY6nSMcCxTueffU804pxtWJM0JpxOaYqFPGqYWYzMzOzEW0kDFGZmZmZDYgTHDMzMyucwic4km6WtFPS+j62S9K16fbsv5B08nDHWKsajqVD0m5JD6XlM8MdY60kTZJ0r6SNkjZIuqTCPiPpu6nleEbE9yPpAEn3S/p5OpbPVdhnxHw3zdTMxz9I2ippXfq3tjaVjZN0t6TN6efhuf0XpDg3STq9gXG86rw1mDgknZKOpyv926t0aX89MX1W0uO5388zhjmmiueQZn5W/cTU1M+qZhFR6AV4J3AysL6P7WcAK8nugzEdWNPsmOs4lg7gu82Os8ZjGQ+cnNYPAf4DmDaCv5tajmdEfD/p8z44rb8GWANMH6nfTRM/x1HAo8CxwH7Az8v/TQzx+28Fjiwr+ydgflqfD1yR1qel+PYHpqS4RzUojledtwYTB3A/cFr6N7cSeG+DY/os8L8q7DtcMVU8hzTzs+onpqZ+VrUuhe/BiYgfA7v62WUWcGtkVgOHSRo/PNENTA3HMmJExI6IeDCt7wE2kt1NNm8kfTe1HM+IkD7vvenla9JSfjXCiPlumugPj3+IiN8Bpcc/NNMsYHFaXwyclSu/IyJejIgtQBdZ/HXr47w1oDjSv62xEXFfZP9b3pqr06iY+jJcMfV1DmnaZzWI89qwfFa1KnyCU4NKt2gfkf8xJaeloYWVkk5odjC1kDQZeAtZT0HeiPxu+jkeGCHfj6RRkh4CdgJ3R0Qhvpth1uzPKIC7JD2g7BETAG0RsQOy/7yAo1L5cMc60DgmpPWhju8jacj15txQ0LDHVHYOaYnPqsJ5rSU+q/44wanxFu0jxIPAMRHxJuA64DvNDac6SQcD3wQ+FhG/Kd9coUpLfzdVjmfEfD8R8XJEvJnsjr6nSjqxbJcR9900QbM/o7dHxMlkT0i/SNI7+9m32bGW9BXHcMR3A/CfgTcDO4ArmxFTlXNIr12HK64KMbXEZ1WNE5wC3aI9In5TGlqIiO8Dr5F0ZJPD6pOk15D90iyJiG9V2GVEfTfVjmekfT8AEfEs0AnMLNs0or6bJmnqZxQR29PPncC3yYacniwNJaafO5sU60Dj6E7rQxZfRDyZEvvfA//CK0N0wxZTH+eQpn5WlWJqhc+qFk5wstuxn6/MdGB3qTtwpJH0utLMdEmnkn2/v25uVJWlOG8CNkbEF/vYbcR8N7Ucz0j5fiS9VtJhaX0M8C7gkbLdRsx300RNe/yDpIMkHVJaB94DrE/vPyftNgdYntZXALMl7S9pCjCVbFLoUBlQHOnf1h5J09Pv0Pm5Og1RNofsA2Sf17DF1M85pGmfVV8xNfuzqlmjZy232gLcTtaF9hJZFjkX+DDw4bRdwJfJZnuvA9qbHXMdx/IRYAPZLPbVwNuaHXM/x/IOsi7KXwAPpeWMEfzd1HI8I+L7Ad4I/Cwdy3rgM6l8RH43Tf4szyC78uRR4FPD+L7Hpn9nP0//5j6Vyo8AVgGb089xuTqfSnFuooFXuPRx3hpwHEB7+vf4KPAl0p34GxjTbenf8i/I/qMeP8wx9XUOadpn1U9MTf2sal38qAYzMzMrHA9RmZmZWeE4wTEzM7PCcYJjZmZmheMEx8zMzArHCY6ZmZkVjhMcMzMzKxwnOGZmZlY4TnDMzMyscJzgmJmZWeE4wTEzM7PCcYJjZmZmheMEx8zMzArHCY7VRNIFkn7S7DjMzMxq4QTHepH0Dkn/Lmm3pF2S/k3Sf2l2XH2RNEXS7yVd3+xYzMysdTjBsT+QNBb4LnAdMA6YAHwOeLGZcVVxPvAMMFvS/n3tJGn08IVkZmbN5gTH8l4PEBG3R8TLEfF8RNwVEb8o7SDpC5KekbRF0ntz5R+StFHSHkm/lPRXuW0dkrolfVLS05K2Sjo3t33/1O5jkp6U9BVJY2qM+Xzg08BLwJ/mN0gKSRdJ2gxsTmXvk/SQpGdTT9Ubc/vPl/RoOoaHJX1gQJ+emZm1DCc4lvcfwMuSFkt6r6TDy7a/FdgEHAn8E3CTJKVtO4H3AWOBDwFXSTo5V/d1qd4EYA6wSNLxadsVZMnVm4Hj0j6fqRaspD8BJgJ3AEvJkp1yZ6W4p6V4bgb+CjgC+GdgRa7n51HgT4BDyXquviZpfLU4zMys9Sgimh2DtRBJfwx8AngXWVLyfeB/Au8FPh0Rx6X9DgSeA8ZHxBMV2vkOcG9EXCOpA/gRcGhEPJe2LwXWAZ8H9gJvjIhH07bTgK9HxJQqsd4IHBkRZ6U6PwYmRMTOtD2AGRFxT3p9A/B0RPxdro1NwLyI+L8V2n8IuCwillf73MzMrLW4B8d6iYiNEXFBREwETgSOBq5Om5/I7ffbtHowQOrxWZ0mJj8LnEHWY1PyTCm5SX6V2n4tcCDwQBo2ehb4QSrvUxrCOgdYkuK5D3gM+B9lu27LrR8DXFp6n/Rek1IcSDo/N3z1bDr+IzEzsxHHCY71KSIeAW4h+4++T2mI55vAF4C2iDiMrOdHud0Ol3RQ7vV/ArYDTwPPAydExGFpOTQiDq4S3gfIhsOul/SEpCfIhrbKh6nyXZTbgIW59zksIg6MiNslHQP8C/AR4Ih0DOvLjsHMzEYIJzj2B5LeIOlSSRPT60nAB4HVVaruB+wPPAX0pMnH76mw3+ck7ZfmzrwP+EZE/J4ssbhK0lHpfSdIOr3Ke84hm09zEtncnTcDbwfeLOmkPur8C/BhSW9V5iBJZ0o6BDiILBl6KsXwIaokdmZm1rqc4FjeHrIJuWskPUeW2KwHLu2vUkTsAS4mm+j7DNkw0Yqy3Z5I27aTDSt9OPUQQTbnpwtYLek3ZPN1jqcPkiYAM4CrI+KJ3PIA2fDWnD7iXEs2n+hLKZYu4IK07WHgSuA+4EmyxOnf+jtuMzNrXZ5kbEMuTTL+WprXY2ZmNuTcg2NmZmaF4wTHWla6MeDeCsvKZsdmZmatzUNUZmZmVjjuwTEzM7PCKdwDCI888siYPHlyTfs+99xzHHTQQdV3HGatGFcrxgSOa6CGM64HHnjg6Yjo94aNZmZDpXAJzuTJk1m7dm1N+3Z2dtLR0TG0AQ1CK8bVijGB4xqo4YxL0q+G5Y3MzCrwEJWZmZkVjhMcMzMzKxwnOGZmZlY4TnDMzMyscJzgmJmZWeE4wTEzM7PCKdxl4gOx7vHdXDD/ew1rb+vlZzasLTMzMxs89+CYmZlZ4TjBMTMzs8KpmuBIulnSTknrc2XjJN0taXP6eXhu2wJJXZI2STo9V36KpHVp27WSlMr3l3RnKl8jaXKuzpz0HpslzWnYUZuZmVmh1dKDcwsws6xsPrAqIqYCq9JrJE0DZgMnpDrXSxqV6twAzAOmpqXU5lzgmYg4DrgKuCK1NQ64DHgrcCpwWT6RMjMzM+tL1QQnIn4M7CorngUsTuuLgbNy5XdExIsRsQXoAk6VNB4YGxH3RUQAt5bVKbW1DJiRendOB+6OiF0R8QxwN69OtMzMzMxeZbBXUbVFxA6AiNgh6ahUPgFYnduvO5W9lNbLy0t1tqW2eiTtBo7Il1eo04ukeWS9Q7S1tdHZ2VnbQYyBS0/qqWnfWtT6vtXs3bu3YW01SivGBI5roFo1LjOzRmv0ZeKqUBb9lA+2Tu/CiEXAIoD29vao9WnJ1y1ZzpXrGvcRbD23tvetphWfRN2KMYHjGqhWjcvMrNEGexXVk2nYifRzZyrvBibl9psIbE/lEyuU96ojaTRwKNmQWF9tmZmZmfVrsAnOCqB0VdMcYHmufHa6MmoK2WTi+9Nw1h5J09P8mvPL6pTaOhu4J83T+SHwHkmHp8nF70llZmZmZv2qOj4j6XagAzhSUjfZlU2XA0slzQUeA84BiIgNkpYCDwM9wEUR8XJq6kKyK7LGACvTAnATcJukLrKem9mprV2S/gH4adrv7yOifLKzmZmZ2atUTXAi4oN9bJrRx/4LgYUVytcCJ1Yof4GUIFXYdjNwc7UYzczMzPJ8J2MzMzMrHCc4ZmZmVjhOcMzMzKxwnOCYmZlZ4TjBMTMzs8JxgmNmZmaF4wTHzMzMCscJjpmZmRWOExwzMzMrHCc4ZmZmVjhOcMzMzKxwnOCYmZlZ4TjBMTMzs8JxgmNmZmaF4wTHzMzMCscJjpmZmRWOExwzMzMrHCc4ZmZmVjhOcMzMzKxwnOCYmZlZ4TjBMTMzs8JxgmNmZmaFU1eCI+njkjZIWi/pdkkHSBon6W5Jm9PPw3P7L5DUJWmTpNNz5adIWpe2XStJqXx/SXem8jWSJtcTr5mZme0bBp3gSJoAXAy0R8SJwChgNjAfWBURU4FV6TWSpqXtJwAzgesljUrN3QDMA6amZWYqnws8ExHHAVcBVww2XjMzM9t31DtENRoYI2k0cCCwHZgFLE7bFwNnpfVZwB0R8WJEbAG6gFMljQfGRsR9ERHArWV1Sm0tA2aUenfMzMzM+jJ6sBUj4nFJXwAeA54H7oqIuyS1RcSOtM8OSUelKhOA1bkmulPZS2m9vLxUZ1tqq0fSbuAI4Ol8LJLmkfUA0dbWRmdnZ03H0DYGLj2pp7YDrkGt71vN3r17G9ZWo7RiTOC4BqpV4zIza7RBJzhpbs0sYArwLPANSef1V6VCWfRT3l+d3gURi4BFAO3t7dHR0dFPGK+4bslyrlw36I/gVbaeW9v7VtPZ2UmtxzBcWjEmcFwD1apxmZk1Wj1DVO8CtkTEUxHxEvAt4G3Ak2nYifRzZ9q/G5iUqz+RbEirO62Xl/eqk4bBDgV21RGzmZmZ7QPqSXAeA6ZLOjDNi5kBbARWAHPSPnOA5Wl9BTA7XRk1hWwy8f1pOGuPpOmpnfPL6pTaOhu4J83TMTMzM+tTPXNw1khaBjwI9AA/IxsmOhhYKmkuWRJ0Ttp/g6SlwMNp/4si4uXU3IXALcAYYGVaAG4CbpPURdZzM3uw8ZqZmdm+o64JKBFxGXBZWfGLZL05lfZfCCysUL4WOLFC+QukBMnMzMysVr6TsZmZmRWOExwzMzMrHCc4ZmZmVjhOcMzMzKxwnOCYmZlZ4TjBMTMzs8JxgmNmZmaF4wTHzMzMCscJjpmZmRWOExwzMzMrHCc4ZmZmVjhOcMzMzKxwnOCYmZlZ4TjBMTMzs8JxgmNmZmaF4wTHzMzMCscJjpmZmRWOExwzMzMrHCc4ZmZmVjhOcMzMzKxwnOCYmZlZ4dSV4Eg6TNIySY9I2ijpNEnjJN0taXP6eXhu/wWSuiRtknR6rvwUSevStmslKZXvL+nOVL5G0uR64jUzM7N9Q709ONcAP4iINwBvAjYC84FVETEVWJVeI2kaMBs4AZgJXC9pVGrnBmAeMDUtM1P5XOCZiDgOuAq4os54zczMbB8w6ARH0ljgncBNABHxu4h4FpgFLE67LQbOSuuzgDsi4sWI2AJ0AadKGg+MjYj7IiKAW8vqlNpaBswo9e6YmZmZ9WV0HXWPBZ4CvirpTcADwCVAW0TsAIiIHZKOSvtPAFbn6nenspfSenl5qc621FaPpN3AEcDT+UAkzSPrAaKtrY3Ozs6aDqBtDFx6Uk9N+9ai1vetZu/evQ1rq1FaMSZwXAPVqnGZmTVaPQnOaOBk4KMRsUbSNaThqD5U6nmJfsr7q9O7IGIRsAigvb09Ojo6+gnjFdctWc6V6+r5CHrbem5t71tNZ2cntR7DcGnFmMBxDVSrxmVm1mj1zMHpBrojYk16vYws4XkyDTuRfu7M7T8pV38isD2VT6xQ3quOpNHAocCuOmI2MzOzfcCgE5yIeALYJun4VDQDeBhYAcxJZXOA5Wl9BTA7XRk1hWwy8f1pOGuPpOlpfs35ZXVKbZ0N3JPm6ZiZmZn1qd7xmY8CSyTtB/wS+BBZ0rRU0lzgMeAcgIjYIGkpWRLUA1wUES+ndi4EbgHGACvTAtkE5tskdZH13MyuM14zMzPbB9SV4ETEQ0B7hU0z+th/IbCwQvla4MQK5S+QEiQzMzOzWvlOxmZmZlY4TnDMzMyscJzgmJmZWeE4wTEzM7PCcYJjZmZmheMEx8zMzArHCY6ZmZkVjhMcMzMzKxwnOGZmZlY4TnDMzMyscJzgmJmZWeE4wTEzM7PCcYJjZmZmheMEx8zMzArHCY6ZmZkVjhMcMzMzKxwnOGZmZlY4TnDMzMyscJzgmJmZWeE4wTEzM7PCcYJjZmZmheMEx8zMzAqn7gRH0ihJP5P03fR6nKS7JW1OPw/P7btAUpekTZJOz5WfImld2natJKXy/SXdmcrXSJpcb7xmZmZWfI3owbkE2Jh7PR9YFRFTgVXpNZKmAbOBE4CZwPWSRqU6NwDzgKlpmZnK5wLPRMRxwFXAFQ2I18zMzAqurgRH0kTgTODGXPEsYHFaXwyclSu/IyJejIgtQBdwqqTxwNiIuC8iAri1rE6prWXAjFLvjpmZmVlfRtdZ/2rgb4FDcmVtEbEDICJ2SDoqlU8AVuf2605lL6X18vJSnW2prR5Ju4EjgKfzQUiaR9YDRFtbG52dnTUF3zYGLj2pp6Z9a1Hr+1azd+/ehrXVKK0YEziugWrVuMzMGm3QCY6k9wE7I+IBSR21VKlQFv2U91end0HEImARQHt7e3R01BIOXLdkOVeuqzfHe8XWc2t732o6Ozup9RiGSyvGBI5roFo1LjOzRqvnf/e3A++XdAZwADBW0teAJyWNT70344Gdaf9uYFKu/kRgeyqfWKE8X6db0mjgUGBXHTGbmZnZPmDQc3AiYkFETIyIyWSTh++JiPOAFcCctNscYHlaXwHMTldGTSGbTHx/Gs7aI2l6ml9zflmdUltnp/d4VQ+OmZmZWV7jxmdecTmwVNJc4DHgHICI2CBpKfAw0ANcFBEvpzoXArcAY4CVaQG4CbhNUhdZz83sIYjXzMzMCqYhCU5EdAKdaf3XwIw+9lsILKxQvhY4sUL5C6QEyczMzKxWvpOxmZmZFY4THDMzMyscJzhmZmZWOE5wzMzMrHCc4JiZmVnhOMExMzOzwnGCY2ZmZoXjBMfMzMwKxwmOmZmZFY4THDMzMyscJzhmZmZWOE5wzMzMrHCc4JiZmVnhOMExMzOzwnGCY2ZmZoXjBMfMzMwKxwmOmZmZFY4THDMzMyscJzhmZmZWOE5wzMzMrHCc4JiZmVnhDDrBkTRJ0r2SNkraIOmSVD5O0t2SNqefh+fqLJDUJWmTpNNz5adIWpe2XStJqXx/SXem8jWSJtdxrGZmZraPqKcHpwe4NCL+GJgOXCRpGjAfWBURU4FV6TVp22zgBGAmcL2kUamtG4B5wNS0zEzlc4FnIuI44CrgijriNTMzs33EoBOciNgREQ+m9T3ARmACMAtYnHZbDJyV1mcBd0TEixGxBegCTpU0HhgbEfdFRAC3ltUptbUMmFHq3TEzMzPry+hGNJKGjt4CrAHaImIHZEmQpKPSbhOA1blq3anspbReXl6qsy211SNpN3AE8HTZ+88j6wGira2Nzs7OmuJuGwOXntRT0761qPV9q9m7d2/D2mqUVowJHNdAtWpcZmaNVneCI+lg4JvAxyLiN/10sFTaEP2U91end0HEImARQHt7e3R0dFSJOnPdkuVcua4hOR4AW8+t7X2r6ezspNZjGC6tGBM4roFq1bjMzBqtrquoJL2GLLlZEhHfSsVPpmEn0s+dqbwbmJSrPhHYnsonVijvVUfSaOBQYFc9MZuZmVnx1XMVlYCbgI0R8cXcphXAnLQ+B1ieK5+droyaQjaZ+P40nLVH0vTU5vlldUptnQ3ck+bpmJmZmfWpnvGZtwN/DqyT9FAq+yRwObBU0lzgMeAcgIjYIGkp8DDZFVgXRcTLqd6FwC3AGGBlWiBLoG6T1EXWczO7jnjNzMxsHzHoBCcifkLlOTIAM/qosxBYWKF8LXBihfIXSAmSmZmZWa18J2MzMzMrHCc4ZmZmVjhOcMzMzKxwnOCYmZlZ4TjBMTMzs8JxgmNmZmaF4wTHzMzMCqdxD2IyJs//XkPaufSkHi5IbW29/MyGtGlmZrYvcQ+OmZmZFY4THDMzMyscJzhmZmZWOE5wzMzMrHCc4JiZmVnhOMExMzOzwnGCY2ZmZoXjBMfMzMwKxwmOmZmZFY7vZNziGnV35DzfHdnMzIrOPThmZmZWOE5wzMzMrHA8RLUPGsywV/4BoOU85GVmZq3GCY7VzfOEzMys1YyIBEfSTOAaYBRwY0Rc3uSQbIjVmjT117M01JyEmZm1rpZPcCSNAr4MvBvoBn4qaUVEPNzcyGxf118S1szEqz+NiMuJnZmNBCNhkvGpQFdE/DIifgfcAcxqckxmZmbWwhQRzY6hX5LOBmZGxF+m138OvDUiPpLbZx4wL708HthUY/NHAk83MNxGacW4WjEmcFwDNZxxHRMRrx2m9zIz66Xlh6gAVSjrlZVFxCJg0YAbltZGRPtgAxsqrRhXK8YEjmugWjUuM7NGGwlDVN3ApNzricD2JsViZmZmI8BISHB+CkyVNEXSfsBsYEWTYzIzM7MW1vJDVBHRI+kjwA/JLhO/OSI2NKj5AQ9rDZNWjKsVYwLHNVCtGpeZWUO1/CRjMzMzs4EaCUNUZmZmZgPiBMfMzMwKZ59McCTNlLRJUpek+cPwfpMk3Stpo6QNki5J5eMk3S1pc/p5eK7OghTfJkmn58pPkbQubbtWUqXL6AcS2yhJP5P03RaK6TBJyyQ9kj6z01okro+n72+9pNslHdCMuCTdLGmnpPW5sobFIWl/SXem8jWSJg/yIzMza56I2KcWsonKjwLHAvsBPwemDfF7jgdOTuuHAP8BTAP+CZifyucDV6T1aSmu/YEpKd5Radv9wGlk9wdaCby3ztj+Bvg68N30uhViWgz8ZVrfDzis2XEBE4AtwJj0eilwQTPiAt4JnAysz5U1LA7gr4GvpPXZwJ3D+TvqxYsXL41Y9sUenGF/9ENE7IiIB9P6HmAj2X+Ys8j+Myf9PCutzwLuiIgXI2IL0AWcKmk8MDYi7ouIAG7N1RkwSROBM4Ebc8XNjmks2X/gNwFExO8i4tlmx5WMBsZIGg0cSHY/pmGPKyJ+DOwqK25kHPm2lgEz6u39MjMbbvtigjMB2JZ73Z3KhkXq7n8LsAZoi4gdkCVBwFFVYpyQ1svLB+tq4G+B3+fKmh3TscBTwFfT0NmNkg5qdlwR8TjwBeAxYAewOyLuanZcOY2M4w91IqIH2A0c0YAYzcyGzb6Y4FR99MOQvbF0MPBN4GMR8Zv+dq1QFv2UDyaW9wE7I+KBWqsMdUzJaLLhlxsi4i3Ac2RDLk2NK81pmUU2zHM0cJCk85odVw0GE0fTfkfMzBplX0xwmvLoB0mvIUtulkTEt1Lxk2mogPRzZ5UYu9N6eflgvB14v6StZMN0/13S15ocU+l9uiNiTXq9jCzhaXZc7wK2RMRTEfES8C3gbS0QV0kj4/hDnTQcdyivHhIzM2tp+2KCM+yPfkjzF24CNkbEF3ObVgBz0vocYHmufHa6mmUKMBW4Pw097JE0PbV5fq7OgETEgoiYGBGTyT6DeyLivGbGlOJ6Atgm6fhUNAN4uNlxkQ1NTZd0YGpvBtlcqmbHVdLIOPJtnU32b8M9OGY2sjR7lnMzFuAMsiuZHgU+NQzv9w6yLv5fAA+l5QyyeQ2rgM3p57hcnU+l+DaRu8oGaAfWp21fIt2Nus74OnjlKqqmxwS8GVibPq/vAIe3SFyfAx5Jbd5GdmXSsMcF3E42D+glst6WuY2MAzgA+AbZhOT7gWOH4/fSixcvXhq5+FENZmZmVjj74hCVmZmZFZwTHDMzMyscJzhmZmZWOE5wzMzMrHCc4JiZmVnhOMExMzOzwnGCY2ZmZoXz/wNZkV41jR1GAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acres_conversion = 43560\n",
    "\n",
    "(gdf\n",
    " .loc[gdf['Shape_Area'] / acres_conversion < .25]\n",
    " .hist(figsize=(8, 6))\n",
    ")\n",
    "\n",
    "# note: units are still displayed as square feet in plots\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d88e0ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "acres_conversion = 43560\n",
    "hist_gdf = gdf['Shape_Area'] / acres_conversion\n",
    "\n",
    "(hist_gdf\n",
    " .loc[hist_gdf < 0.25]\n",
    " .hist(ax=ax)\n",
    ")\n",
    "\n",
    "plt.title('Distribution of Polygons Less Than 0.25 Acres')\n",
    "plt.xlabel('Acres')\n",
    "plt.ylabel('Count')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a4ae19a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (12036, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flooding_Category</th>\n",
       "      <th>flood_classification</th>\n",
       "      <th>Shape_Length</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>1695.857677</td>\n",
       "      <td>27852.535494</td>\n",
       "      <td>POLYGON Z ((916393.149 121765.469 0.000, 91640...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>1532.269198</td>\n",
       "      <td>18338.901687</td>\n",
       "      <td>POLYGON Z ((917251.502 121381.664 0.000, 91725...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>2327.963831</td>\n",
       "      <td>32127.388341</td>\n",
       "      <td>POLYGON Z ((916251.188 121441.271 0.000, 91625...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>3830.095703</td>\n",
       "      <td>48711.665246</td>\n",
       "      <td>POLYGON Z ((917377.109 122108.195 0.000, 91739...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Nuisance Flooding</td>\n",
       "      <td>4600.270710</td>\n",
       "      <td>65321.650657</td>\n",
       "      <td>POLYGON Z ((918159.499 121451.773 0.000, 91814...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Flooding_Category flood_classification  Shape_Length    Shape_Area  \\\n",
       "0                  1    Nuisance Flooding   1695.857677  27852.535494   \n",
       "1                  1    Nuisance Flooding   1532.269198  18338.901687   \n",
       "2                  1    Nuisance Flooding   2327.963831  32127.388341   \n",
       "3                  1    Nuisance Flooding   3830.095703  48711.665246   \n",
       "4                  1    Nuisance Flooding   4600.270710  65321.650657   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON Z ((916393.149 121765.469 0.000, 91640...  \n",
       "1  POLYGON Z ((917251.502 121381.664 0.000, 91725...  \n",
       "2  POLYGON Z ((916251.188 121441.271 0.000, 91625...  \n",
       "3  POLYGON Z ((917377.109 122108.195 0.000, 91739...  \n",
       "4  POLYGON Z ((918159.499 121451.773 0.000, 91814...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing polygons with less than 0.25 acres of flooding\n",
    "acres_conversion = 43560\n",
    "\n",
    "gdf = (\n",
    "    gdf\n",
    "    .loc[gdf['Shape_Area'] / acres_conversion >= .25]\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "print('shape of data: {}'.format(gdf.shape))\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7c359a4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Shape_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12036.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.733295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.038070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.250030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.347283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.538978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.048261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1266.198909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Shape_Area\n",
       "count  12036.000000\n",
       "mean       1.733295\n",
       "std       15.038070\n",
       "min        0.250030\n",
       "25%        0.347283\n",
       "50%        0.538978\n",
       "75%        1.048261\n",
       "max     1266.198909"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sanity check (acres)\n",
    "acres_conversion = 43560\n",
    "ser = gdf.loc[:, ['Shape_Area']] / acres_conversion\n",
    "\n",
    "# examining summary statistics of flood area\n",
    "ser.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fea98fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check\n",
    "gdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8a24e1d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buildings-hazard.csv      nta-hazard.csv            stormwater-data.gpkg\r\n",
      "daily-precipitation.csv   stormwater-data-nyc.zip   stormwater-data.zip\r\n",
      "hourly-precipitation.csv  \u001b[34mstormwater-data-source\u001b[m\u001b[m/\r\n"
     ]
    }
   ],
   "source": [
    "# save file\n",
    "filename = 'data/stormwater-data.gpkg'\n",
    "\n",
    "gdf.to_file(\n",
    "    filename=filename,\n",
    "    driver='GPKG',\n",
    "    layer='name',\n",
    "    index=False\n",
    ")\n",
    "\n",
    "# verify output\n",
    "%ls data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f467f100",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating: data/stormwater-data.gpkg (deflated 63%)\n",
      "buildings-hazard.csv      nta-hazard.csv            stormwater-data.gpkg\n",
      "daily-precipitation.csv   stormwater-data-nyc.zip   stormwater-data.zip\n",
      "hourly-precipitation.csv  \u001b[34mstormwater-data-source\u001b[m\u001b[m/\n"
     ]
    }
   ],
   "source": [
    "# zip .gpkg into zip folder\n",
    "!zip data/stormwater-data.zip data/stormwater-data.gpkg\n",
    "\n",
    "# verify zip\n",
    "%ls data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d8f13728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buildings-hazard.csv      hourly-precipitation.csv  \u001b[34mstormwater-data-source\u001b[m\u001b[m/\r\n",
      "daily-precipitation.csv   nta-hazard.csv            stormwater-data.zip\r\n"
     ]
    }
   ],
   "source": [
    "# remove copies of data to save storage\n",
    "!rm data/stormwater-data.gpkg data/stormwater-data-nyc.zip\n",
    "\n",
    "%ls data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31b32ed8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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