{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network distance tying it all together\n",
    "Here, we will calculate the walking distance for students in public schools to their nearest subway entrance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "from shapely.geometry import box, LineString, Point,MultiPoint\n",
    "import os\n",
    "import sys\n",
    "from scipy.spatial import cKDTree\n",
    "import numpy as np\n",
    "from zipfile import ZipFile as zzip\n",
    "import requests\n",
    "from rtree import index\n",
    "import rtree\n",
    "import itertools\n",
    "from shapely import wkt\n",
    "from shapely.ops import split, snap\n",
    "import time\n",
    "\n",
    "sys.path.append(os.path.realpath('..'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Work\\\\Box Sync\\\\GIS_exploration\\\\code'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the 2019 \"NYC LION Single Line Street Base Map\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = r\"https://www1.nyc.gov/assets/planning/download/zip/data-maps/open-data/nyclion_19b.zip\"\n",
    "filename = r\"nyclion_19b.zip\"\n",
    "r = requests.get(url)\n",
    "# open method to open a file on your system and write the contents\n",
    "with open(\"../input_data/\"+filename, \"wb\") as file:\n",
    "    file.write(r.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Name                                             Modified             Size\n",
      "lion/lion.gdb/a00000001.freelist               2019-05-13 12:48:56         4440\n",
      "lion/lion.gdb/a00000001.gdbindexes             2019-05-13 12:42:56          110\n",
      "lion/lion.gdb/a00000001.gdbtable               2019-05-13 12:48:56          385\n",
      "lion/lion.gdb/a00000001.gdbtablx               2019-05-13 12:48:56         5152\n",
      "lion/lion.gdb/a00000001.TablesByName.atx       2019-05-13 12:48:56         4118\n",
      "lion/lion.gdb/a00000002.gdbtable               2019-05-13 12:42:56         2055\n",
      "lion/lion.gdb/a00000002.gdbtablx               2019-05-13 12:42:56         5152\n",
      "lion/lion.gdb/a00000003.gdbindexes             2019-05-13 12:42:56           42\n",
      "lion/lion.gdb/a00000003.gdbtable               2019-05-13 12:44:22         1825\n",
      "lion/lion.gdb/a00000003.gdbtablx               2019-05-13 12:44:22         5152\n",
      "lion/lion.gdb/a00000004.CatItemsByPhysicalName.atx 2019-05-13 12:48:56         4118\n",
      "lion/lion.gdb/a00000004.CatItemsByType.atx     2019-05-13 12:48:56         4118\n",
      "lion/lion.gdb/a00000004.FDO_UUID.atx           2019-05-13 12:48:56         4118\n",
      "lion/lion.gdb/a00000004.freelist               2019-05-13 12:49:38        57688\n",
      "lion/lion.gdb/a00000004.gdbindexes             2019-05-13 12:42:56          310\n",
      "lion/lion.gdb/a00000004.gdbtable               2019-05-13 12:49:38       399741\n",
      "lion/lion.gdb/a00000004.gdbtablx               2019-05-13 12:49:38         5152\n",
      "lion/lion.gdb/a00000004.spx                    2019-05-13 12:49:30        45078\n",
      "lion/lion.gdb/a00000005.CatItemTypesByName.atx 2019-05-13 12:42:56        12310\n",
      "lion/lion.gdb/a00000005.CatItemTypesByParentTypeID.atx 2019-05-13 12:42:56         4118\n",
      "lion/lion.gdb/a00000005.CatItemTypesByUUID.atx 2019-05-13 12:42:56         4118\n",
      "lion/lion.gdb/a00000005.gdbindexes             2019-05-13 12:42:56          296\n",
      "lion/lion.gdb/a00000005.gdbtable               2019-05-13 12:42:56         1803\n",
      "lion/lion.gdb/a00000005.gdbtablx               2019-05-13 12:42:56         5152\n",
      "lion/lion.gdb/a00000006.CatRelsByDestinationID.atx 2019-05-13 12:48:56         4118\n",
      "lion/lion.gdb/a00000006.CatRelsByOriginID.atx  2019-05-13 12:48:56         4118\n",
      "lion/lion.gdb/a00000006.CatRelsByType.atx      2019-05-13 12:48:56         4118\n",
      "lion/lion.gdb/a00000006.FDO_UUID.atx           2019-05-13 12:48:56         4118\n",
      "lion/lion.gdb/a00000006.freelist               2019-05-13 12:48:56         4440\n",
      "lion/lion.gdb/a00000006.gdbindexes             2019-05-13 12:42:56          318\n",
      "lion/lion.gdb/a00000006.gdbtable               2019-05-13 12:48:56          701\n",
      "lion/lion.gdb/a00000006.gdbtablx               2019-05-13 12:48:56         5152\n",
      "lion/lion.gdb/a00000007.CatRelTypesByBackwardLabel.atx 2019-05-13 12:42:56         4118\n",
      "lion/lion.gdb/a00000007.CatRelTypesByDestItemTypeID.atx 2019-05-13 12:42:56         4118\n",
      "lion/lion.gdb/a00000007.CatRelTypesByForwardLabel.atx 2019-05-13 12:42:56         4118\n",
      "lion/lion.gdb/a00000007.CatRelTypesByName.atx  2019-05-13 12:42:56         4118\n",
      "lion/lion.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx 2019-05-13 12:42:56         4118\n",
      "lion/lion.gdb/a00000007.CatRelTypesByUUID.atx  2019-05-13 12:42:56         4118\n",
      "lion/lion.gdb/a00000007.gdbindexes             2019-05-13 12:42:56          602\n",
      "lion/lion.gdb/a00000007.gdbtable               2019-05-13 12:42:56         2504\n",
      "lion/lion.gdb/a00000007.gdbtablx               2019-05-13 12:42:56         5152\n",
      "lion/lion.gdb/a0000000a.gdbindexes             2019-05-13 12:44:48          116\n",
      "lion/lion.gdb/a0000000a.gdbtable               2019-05-13 12:44:48      3247250\n",
      "lion/lion.gdb/a0000000a.gdbtablx               2019-05-13 12:44:48       665632\n",
      "lion/lion.gdb/a0000000a.spx                    2019-05-13 12:44:48      1609750\n",
      "lion/lion.gdb/a0000000b.gdbindexes             2019-05-13 12:44:56           66\n",
      "lion/lion.gdb/a0000000b.gdbtable               2019-05-13 12:45:24      5491953\n",
      "lion/lion.gdb/a0000000b.gdbtablx               2019-05-13 12:45:24      1172512\n",
      "lion/lion.gdb/a0000000c.gdbindexes             2019-05-13 12:45:30           66\n",
      "lion/lion.gdb/a0000000c.gdbtable               2019-05-13 12:45:42      6406110\n",
      "lion/lion.gdb/a0000000c.gdbtablx               2019-05-13 12:45:42       527392\n",
      "lion/lion.gdb/a0000000d.freelist               2019-05-13 12:48:44        20824\n",
      "lion/lion.gdb/a0000000d.gdbindexes             2019-05-13 12:48:30          116\n",
      "lion/lion.gdb/a0000000d.gdbtable               2019-05-13 12:48:44    126137796\n",
      "lion/lion.gdb/a0000000d.gdbtablx               2019-05-13 12:48:44      1136672\n",
      "lion/lion.gdb/a0000000d.spx                    2019-05-13 12:48:30      4362262\n",
      "lion/lion.gdb/gdb                              2019-05-13 12:42:56            4\n",
      "lion/lion.gdb/timestamps                       2019-05-13 12:47:14          400\n",
      "lion/LION - Street Name Labels.lyr             2013-09-24 18:10:22        18432\n",
      "lion/LION Streets - Generic.lyr                2017-02-10 07:51:36        21504\n",
      "lion/LION Streets - Roadbeds.lyr               2017-02-10 07:54:38        21504\n",
      "lion/LION - Generic.lyr                        2017-02-10 07:48:08        24576\n",
      "lion/LION - Roadbeds.lyr                       2017-02-10 07:49:32        24576\n",
      "lion/LION - Nodes.lyr                          2012-08-17 13:19:50        11776\n",
      "lion/LION - Street Direction Arrows (ArcGIS 10x).lyr 2013-01-15 10:58:06        22528\n",
      "lion/LION - Street Direction Arrows (Requires Maplex).lyr 2013-03-12 14:22:44        22528\n",
      "lion/TrafficRepArrows.style                    2012-09-27 08:32:40       598016\n",
      "lion/ReadMe.txt                                2014-03-17 15:27:04         7758\n",
      "altnames_metadata.html                         2019-05-13 12:49:22        38976\n",
      "altnames_metadata.pdf                          2019-05-13 14:35:58       129541\n",
      "lion_metadata.html                             2019-05-13 12:49:12       199830\n",
      "lion_metadata.pdf                              2019-05-13 14:35:22       483768\n",
      "node_metadata.html                             2019-05-13 12:49:32        42396\n",
      "node_metadata.pdf                              2019-05-13 14:34:28       135027\n",
      "node_stname_metadata.html                      2019-05-13 12:49:40        32999\n",
      "node_stname_metadata.pdf                       2019-05-13 14:36:56       123835\n",
      "Extracting all the files now...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "fp = \"../input_data/\"\n",
    "foldername = \"nyclion_19b\"\n",
    "with zzip(fp+filename, 'r') as file: \n",
    "        #printing all the contents of the zip file \n",
    "        #file.printdir() \n",
    "        path = \"../input_data/\"+foldername\n",
    "        os.mkdir(path)\n",
    "        # extracting all the files \n",
    "        print('Extracting all the files now...') \n",
    "        file.extractall(fp+foldername) \n",
    "        print('Done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the lion layer into a geopandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = \"../input_data/nyclion_19b/lion/lion.gdb\"\n",
    "lion_gdf = gpd.read_file(fp, driver='FileGDB', layer='lion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing the non pedestrian accessible roads/paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "lion_gdf['todrop'] = (lion_gdf['NonPed'] == 'V') |(lion_gdf['FeatureTyp'].isin(['F','9','1','7','3'])) |(lion_gdf['TrafDir'].isin([' ']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "lion_gdf = lion_gdf.loc[lion_gdf['todrop'] == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:2263'}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lion_gdf.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vnJQ1hwWo7YxT353g4mPG8fSWNm6+4jiOm1K93wL50kiQE6dVc+K0ana09lMSjrG5qT8rrHKDX9Y+TF0DhsJx3G5Q5UXB/OP+OQp4eksrEyoiTB9dwoSKf/98TEGQGjFBEITDiIzlsKt9gP54mj+9VMsHjhlLVXEQcCNGAe9bwZdGDpB2oLUvxWBaZ53uP3TseGaOLslem8u9axpwHIdPnjo1b/24KZXcfc0ixpcXMWdcOcdO3r8Iy6WxJ86D65oZXxGm0Jhxh5GGrr4OtBw9Ii3pF/gHDJW97tkdHXz81lWc/pPnuW9N4wHvTRBeLyLEBEEQDiNW7e3CcjSxtKYrnubbj+yg0+uINA13/E8koLJiJmyC4zjs6hj0jrvRpBN/9ALVxSEmV+VHjRJpmzV7e3jv0eOZPqpkxPMrikLEUhar67pJW4Xk1L7545I9gOapbR0FZ0gCRINGVkQaFK4VMw1FQA2lK+2cSQJ41zgavnbvBmb91+Nc/qflNPcWroUThDeKCDFBEITDiCc2txEJuv/pj6eHhFBR0OD9x06gpjSSNzjb0WTd6B0Ns8a4NV8OsLutn2Mn5deI/e8zr/DM9nY+ctK+66zeMXsUZZEgT20r3HFZiI5YirtW1lNdHMJy9n3eYMYZqv/yfiry50vajs56p8GQtUUu/lgly9G8vKuLc/7nBf7fbStZWdu1z2cnM69NWAoCiBATBEE4bGjrS/LU1lbmjisjHBj6z/+EijDfuWgeP3z/AvoG8yM/UyujWaFSFApw7TtmZo+VRIIYnrrRWvOHJbu5eckePnLiZCKGwef/tobG7viIfSyaXk1RyOT2ZXsPeO9/XlpL2tZZvzHFSF8wcn4PqHxfscyw+ZLDMdRw24yha4OGIm05PLe9g0tvXs77f/sSX757PVaOInxsYwsX/vplfvv8KzjOqyhFQRiGCDFBEITDhB8/uZ22/hRr63s5eVolAS9K1BFL8837N/GtBzbQ7/mqTq50LRwa+1xhpoE/XXEcv3hmR/Z+06qjblpPa3759Cv88PHtfObM6Zw+u4Zr/rKC9v5kdmRRLoah+NRp0+geTLO7o7AhbC598TTPbGllxqhiol40z/cJ21eJma3JisRCp7gCK//8fQm1jBdBMxSEDVjb0M/Gxl4u//MKbn5xNwDbm3vZ1THAzxbv5N2/fJ5nt7WyfM++o2eC4CNCTBAE4TAhkbaoLgpSHDR4YWcX8yeUUxw0SNsaBdy32rWDMICvvWsO5dEA/hhHBXzqjtXs6RiKcD2/s5tLfvMyF/76JW5e4gqS25fVcdvSWlAmbf1Jjp9ceM7kuXPHUts5yGMbWwoez+VvK+vZ2TFIW3+ShOdL4UeswjkmsMNNXC1H560FhimyzLDAVa6oMxiKuAWUaxxbFDJJedccP7mSv199MtecOYNfPLWd376wJ3vtjo4kn/3LGq69cw2bG935lfarheOEwxqxrxAEQTgMcBxN50CasRVRGnvijAoHWN/Qm40K5do4ACzb08no0hCxpJV1pa8pCaO1pr4niangJx9cwI8e305/MkPQUIQDilljSrhi0VS6BlJ895GtzJ1QUXA/o0vDzB9fyuJNTXzh7Jmv2j15xz/3Eg64cyFhqDPShGw9m6Pdjk89zEHf0UNGrqahsOyRgqg4BHPGVZBI25hotrQMkKvRLA2WrfH7LhXwjzWN7GwfYGJFmEc2tQFQGlQMZNzC/7QDXfEMrf1pWvvbue7eDZSEA5w5u4Yvnzub6hIxjRVcRIgJgiAcBjje7MeLjx7HK60x+hyLiqIAvfEh93wHN/pTFDa4a2VjVsyYCiJBk0XTq7h/TRMAnzp1Cst2d9E1mGbuuBIuOWYCnz5zqH7s18/uBGBiZWEvri3N/WxvjWE5sGxXO6fOGlPwvI5Yipa+FKEC+Rsb8tSj5bj1XJlh0SdfVKUKiDBDwfTqEu79zClZMfjc9jZ+8+xO1jX05X0GGe/6qTVFJJJp1jX0sqUBJpYHuf69C5g9poRz/ufFbLTOAI6ZXE5FNMSHFk7kiU2t3Lu2kae2tlMcMnnv0eO56ozplEZGWoAIhw+SmhQEQTgMMA1FTUmIeMZh2qhigDwRBu4XgqWhP+nkFa6bpmIwbXP36qbsGKG/rWrk/rVNXH7SZHa2D4LK/zpZMKGcqdVFvLyzo+B+yqKBbDRua3P/Pve9zBtCnucNVqD2yxs5iePPpjxQezINtV2DbGvqzS6dPWcMReEAGvczqSwKUBQOEDBdv7HazjitA+5nlwYa+zI8trGZhze0UF0SxgQqowEuXzSZ0kiQGx7ewn9dMJcXr3sH937mFD51+lRqSsPcs6aB8365hLX1PQe4WeHtiETEBEEQ3ua09yf5+J9X0jmQ5qWdHXzuzBmsKevhxZ2deeflpuP8qE7AgLQ1MpI0kLKpKgrwwNp6HBseWt9IRVGQE6dVMbEiwqmzamjuTbKitpuzjxwZ7drTMUhpNEwilqKpL73Pvb/SFqMkZDLgWW0YCgw0NvniLDfYZSpQSuFonZ0lua8+RgcYSGu+/uAWFk2vYlxpGK0Vy3Z1YQKRsElpJEB9d3KfewyairquQRZvaeWr588hGjBYOLWK2WNKuf6hLexodedhGobB/AkVzJ9QwadPm8G6hh7CAZN548v2eW/h7Y8IMUEQhLc5tZ2DvNLmioFkxuEXz+zMRpD2hz8KaLhjPUAkaNCXcNd3dwxy3b0b3a5GDR88YSJohxW7u0ikbaKhoQmVluXwk8e30TngjkfqLDAmyefZ7e1Ul4QY6E6gcGu+TEMRMDS249WKqSEh5v9U2i3Ut3XhrkmfoKEIBw02NfaxqbEvW/TvC7dowE2PGp6gC5iu4W0s5RAyYe64Mj5w/CQCBvz6ud109Kf45gVHZu/fOZAilrRGPNcwFMdPqdrnvn7/4i5+8dQrbLzhPDQq7/MT3l5IalIQBOFtzqzRxXmCJFe4wNB6OKCytVj+l4PtFbtr3MiP4V1vAF2xNDWlIcIB95gCEhmHhOVw1/J6bA3bWvs582fP88zWNtr6k8SSGe5e00B9b9wdMaTg5BnVBfe9u2OA7a0xwkFXhGjc7kW/VitXYPmvQzkKM3fg977EmKk0Fx8zFoCpVVEiQROVc/Zgymbh1Kpsw0LG1sS81smMDWFDc+fyer778FZGlYT50MKJefefXFVEdclrrwG74qQpzB1fzvce3cpJP3yaC371Ine8Bt814dBhv0JMKTVJKfW8UmqbUmqLUuo/co59QSm1w1v/ac76N5VSu7xj78pZP99b26WU+kbO+jSl1Aql1E6l1N1KqZC3HvZ+3+Udn/qveuOCIAiHCw+uc20plHI7DX1x5ePPXExbGj8L6c9t9F8D2J6ai4YCOLj1ZL3xNEkLBtN2vnWEdp+TsjUDyQzX3bued/9qCef+4gW+++BmUhkHW0MkoJhYGS2471uX1rJoWhV1XUOWGSlbEzLcwny/0TL3uWlPpJmGGuGeX4i0DbvbXa+0vd0JUpaD4+isdYWD4qgxJUQD+V+XEe+eK+pjxNMWN15yFH+76iRmji7NnvP89nb+9HLtPhsWXo3iSJAHrz2VdfW9JC03+vfTxdv50ePbXvO9hLc2B5KatICvaK3XKqVKgTVKqaeBMcDFwAKtdUopNRpAKTUXuAyYB4wHnlFKzfbu9VvgXKARWKWUelhrvRX4CfBLrfXflVK/Bz4F3OT97NFaz1RKXead9+F/zVsXBEF4+9OXyPDs9naCpuKocSWsbXRTlA5k66cUbgdicUgxmB6SLMPFi/b+z2DKykbVbMsVcfY+irAUEDANerzGAIUroPzGxkRG8+y2dgZSNoumV1FdEgZgd/sA21pijCsPY+U41UfMIQsKWw9F9xT5kT4rp3MyYIDj5NeJmQrOOaKG8RUR7l7TnF33/b5GlwTpT9okLYffvVyXPV4UgFDQJGVpApZDSdikqSdBccikJJof+XpkQzO2o/n2hXMLfzgHwI8/sIAbH93KmroeVn3rnVQXGLIuHNrsNyKmtW7RWq/1XseAbcAE4LPAj7XWKe+YPzTsYuDvWuuU1roW2AWc6P3ZpbXeo7VOA38HLlZuv/DZwL3e9bcD78u51+3e63uBc9Srmc0IgiAIeXzt3g2sqO0iYEB9TyJbGxYJKMqiQZTh2UAA6RwR5huaBrwommkM+XMVBY2cYn6VJ3AiASPrfn/0hDI00J+0KA0PpRednGs1cOfyOr5yz3q+89BmUt4g8AfWNbGuvoeAYWRFnqEgaWtyK66GD+s2jfw0ZMBQeSLR/9KrKArQn7R4aXdP1iQWIBwwGFUSYmJlEQsmjDSjjVvQm7BJZhymjy6hN2njAN+8b0PeYPCM7bCuoZdwwKA49PrLsY+ZVMHZc0a771VrDEMqit5uvKa/US81eCywApgNnO6lDF9USp3gnTYBaMi5rNFb29d6NdCrtbaGrefdyzve550vCIIg7IcN9T2squ1mclWEREbTOWhlo0eWAz3xTJ5IyeRe7DnSO0DAzI94DWYcPF2F5WgmV0X561UnseH68/i/jx5LIuNQGglw85ULs9fEUnbeF87vPnIMoYDhpf/cJoLHN7Vy7+pG7lhWy7r6Hk6cWsmTm1uz1/gCzremMMhpJvD367hizHsL6GHu+j7dgxbL9/YymMxkz3VTtJrrzp/DhsY+VtX15O3ZUDB/XAngjkdKpCzmjXNTkcmMw8ra7uy5z+9oIxo0+OWlx2AesJdGYQZTFmfOrmF0mZjAvh05YJmulCoB7gO+pLXuV0oFgEpgEXAC8A+l1HT2PdarkOjbVw2l//8zr3Ysd29XA1cDTJ48eT/vRBAE4fCgrjtOdzxDd3xIYkUCYDlqSMEUIDd16Gi3KN0nBKiAgYlGoRlXHqG+O8GcsaW8+Eo7//H39QRNxQ0XHsnHb13l1qR51/pazlDwpX9sIGO7LvSG8tKjGq5/aHO2MP7zZ89gee1Ijy1/69prGsidE6lxRWbIdOu/bMDQOU773k/Ts9vvGEjndYQayuBHj28jHDBI2w6587uf+MKpRMNB3v2rF4kETfqTGRp7XVuLjAMXLRiXPfeRDa10DqS4IGft9dKbyLC+oe8N30d4a3JAETGlVBBXhP1Va32/t9wI3K9dVuL+77vGW5+Uc/lEoPlV1juBCk/Y5a6Te413vBzoZhha6z9orRdqrReOGjXqQN6SIAjC25Z7VjfQn8yMGGw9raaIgGmg0JiGojRsElBQUxJiZo1bUO53P+aSm3qMRgwylkPCE1FfPMd10//i39fxlX9sAODio8fzp5f30tgd55wja0bsz9FuUb3GTR0Gcmq7nBzR9MSGhhHXQs6/xj0Blrtf/+2mc8SjXw8HQ9Eyy3FFWtbuApheU8T4yhB9iQzxjIOVU1cWNuGetU3c8OBm0hZ0DVr0JW0vVeuKyQEvuqa1ZmVtF8dN3rc9xWuhayB14Aa1wiHHgXRNKuAWYJvW+hc5hx7Ere3CK8YP4Yqqh4HLvI7HacAsYCWwCpjldUiGcAv6H9Zaa+B54IPefT8OPOS9ftj7He/4c975giAIQgG+/cAmvnn/Rv7w4h5Om1nD2PJINvXR3BPHQJFxoLo0TDRkMqY8wsIplezqjDO9pohoULnzGVW+wImY7u+xpIODG5W64KixfPiEyXzuHTNYW9eNozWhgGJKVZRQwOAbF8zhhR2dIzeZQ8BUXPeu2dkvo2xkCtjdtW+jVyC7wVyRko145ZzmW24AeREunfPTTWtqajuTQynQnHukbLjl5b08v7OTjKMJKCiPmAQMiGfc68uLvUaDjgHa+lP7tOV4LTy/vZ3FW9q44uSpb/hewluTA4mInQpcAZytlFrv/bkA+DMwXSm1Gbfw/uNedGwL8A9gK/AkcK3W2vZqvD4PLMYt+P+Hdy7A14H/VErtwq0Bu8VbvwWo9tb/E8haXgiCIAj52I5mTFmYiqIQy/d0cunvl1EUCnD0pHLGlYcpjQQJBgzGl0c498gxXLpwMr+67FjquuMEDdjTGc+66PuRqXDAVTBJe0iwBAxFVVGIX192LEopvnb+HIpDAdeuwtL8zzO7aOiK88cle0i/Sjfl2LIwT3/pTJIWlEQCGPAeUr0AACAASURBVEBF1JVQgVeJAIU9VRU0FEFT5UW1fHSOkHQguw8/FVpoP3u7E3ndlqbhnnv0hFK+f9FcjhxXyphSt2vxfcdO4KiJFWjvvleePCV7XUufm66cVFXYluNAaOiO84PHtnHt39Yya3QJnz9r5v4vEg5J9lsjprV+mX174V2+j2t+APygwPrjwOMF1vfgdlUOX08CH9rfHgVBEAT4xJ9X8pI3m7FzIE1J2CAcMHny2+dS1zXAV+7ZRCJt8ZuPHsf0USXEEhlO+MHTZGxd0PYBwDQMhqq8/BoszfUXzSXg5fluX7aXzsEMC6dUMq2miHvWNJG0bHq688r/Afdf/453n9b+FJ+5czXtsTSxpIUC+hLusyqLoG2w8Pv0vcIsO3+vyit9M7wat9z6NP/Z5NS/eb+igcoik+64nbfu1569Z8EEHtzYwNaWGBFPIc4cU8IXz57JGT9/kUjA4LvvnZ+9dvGWVopCJsdNriz8BvZB2nJYvKWVB9Y18cKOdhZNr+b8+WP56nlHEApIt+TbFRlxJAiC8DZAa03XQIqQaWAo+MWHj+bpLe08vNH1sppaU8qNF8+nqjjE2HK3+640GiRoGiQtm2gAEpZbO5VLOjPU7egfmlFTxPuOdZvbd7T1csPDbnJja1MfGxv7MA3F3HElrKkfGuadG50ygHDQIJFx2NISy3Y1+vqoIhrAtR0bORqoJKgYyOhst6W/t1wPsWyjAfmjmRxyfsnZl4I8Eeaf5hfCrGvo4dw541lbtwPb0YQMRWtvgjN+/qL7eXhD1H1Kw0FOmVFDRVFoxP73xfI9XXzjvo3s7YoztizCZ86cwZUnT83+XQlvX0SICYIgvA14fFMLxZEA37noSN5/7EQsW3P9Q1s4fVYNAdOguTfB9JoiIsM8rX76waP56eLt1HbG89aDBowrj9DSn8TJ1yhccfIUbMct+H/XL5dm1+OeivvYSZN4eH1z3jW+cML7mbacrEgyvQHd/u/XXziPny7eTiw1UogNZFw7ioChsL2C/2jQIG052WgbuOnLVE7EzDeS1Xqojd/B9Q2znPzuyOF86ZyZ7O5MEA2afO6s6UQDJr98ZidFQYN4xuGk6fmRL0drluzsIG05+41k7W6PceeKem5dupcJFVH+dOVCzpoz+g1bXgiHDiLEBEEQDnGe3OymszpiKT564hRMQ3Hr0lpSlsNXzzsCrTU3ProVheZXHz6aYHDInf3dR42jN57kmw9szbtnxoH6niQBYyimFFDu6KIfPbGDjliajoGhYd2fOHE8t61sJgA8t72dWCpfvTlAadh0/cSGzbr006EaGF8e4ZwjR/GtBzfv8/1qr+vS35nt6OwwcKU1AYOsCPPPcQUfI0Y4HT2xnO1tA2g088eXsXT3iMZ8uuM275o3lrO+M5poyOT6hzaTtmx8H9i2vqHPQWvN2PIwacvh1qW1XHPmjILv4fFNLdz84m60hp54mi+ePZPPvmOmDPc+DBEhJgiCcAjTn8xw7V/XMLo0zG8+dlw2krKpqY9Y0uKh9U289EoHL+1sZyDl0N6/nGvPns1Zc8YAsKs9xrc9EXbBkdVMHlXOHcvriHv+D5aj3aiR7RANme5MSe1w0wu7s9GnmaOK2NbhFqhfdcY07lnTCOSnBcE1dQ2ari/Z8PotgHAAvnfxPJ7Z1k4iY+eNQzIVBAMG2tFYtsY0FVq7tW1Dosx9Wsp2I14RL2Ll72V42nVyZYTV9T3u3EpcQWeqoUYFf/8zRkUxDZUVSb7dRth0n5X1ErMd/t9tqxhIZZhaVcSPn9jO7LGlnHXE6Lzn/n1lPd+4fxOzx5QwtizCxqY+Pnj8JBFhhylS/ScIgnAI89jGFmwNv79iIcdPGfKt+vr5c5hQEeWPL9XyxOYWBlKuClld388nb1vNNXesYtWeDi743yVZQXTtuUfyjQuOZOv3z6coOwrJIGU5lEWDJDK2F43K9xb7/sVHsaK2m/JoAGUoKj0bB1+E5WbnfHNY/5lGznkG0DOQZnGOm77KmSeZ9AaFO7jiy1Aqz8jVdobmTjqQFWG5+FsJKmjtT2bFmWkoltf2YOf4mPnZwTV1PZz642f53kOb+MKdq3lqaysnTauixpuLOckbWr5sdxcv7ezkpGnV/M+Hj+aIsaVcc8cant3Wln3+YMriofXNXHDUWJ74jzP42vlzqC4O5UUXhcMLiYgJgiAcwmxu6uOUGdUsmJg/F3FMWYQlXzuLgaRFU2+cT9++iqa+VDbKs3hrO4u3tmfPNxWEvC7Ihq44cU+NJD2l0pPjzp8b6fruhUfwqdtWuudmbG5bujdvdiO4kSi/k7EkbDLgpS0Dhpei9G62cFo196+ppzOeGXqG4XVC4oory9HuHEwF9j5sJQut5hbsl0UCrgFryiZgaCwnv1tUQV769AePbaepN8mt/6zPnnP+/DGsrXNd//30o+mNQj5lRg3HT6ni7qtP5rI/Lue6ezfy+8uP4/jJldz46Fb+uaeL+z93CqahmD+hnJXfeidikXn4IkJMEAThECWetrhnTSNXLJqCUiOLu01DUV4UZHNTmrZYKtshqCHbJelz3JTybPffi6+0ETIVadtN1YUDJpbjZG0jfMlwxfFjeXlnFwmv8MqyNDb5gsZP8TnaTekNpHKtMBQhQ5OyoSRk8tLOLj524iS+dNQ4PnHHKtIZje1AWIEKqKzNht/96GsnP2KW+zz/p5/ezHO60A7xtJNNSQ5HMxTxC5mKxt5k9vMKGrBwahX3rGoiZWuKQyZHTawAoKLIrb0b8JoMyouC/Pajx3LJ75ZyxS0rqSwK0tqf4lOnTcuztnDTya+tOD+Ztrjhka0s3tJKX9ydohAyDX54yXw+uHDSfq8X3jqIEBMEQThEeWpLG2nL4by5Y0Yc64tnCAUUNz66lcc2tWA5MH98KTvaYmg7X4QBfPncORiGGxF7eENrVnTZGhIZu2AdS0VxmL+sGUojDq/5yu1OJOdncchgMO1gOxrDgNKwwlQwpbqI9x03kdljSokGA6QybhQupclW2fvRKn9vkDN7kpE/de4CEA0qYiknu7dc4ZaLUm5HpeM5tvqfl+3A8j3d2fMTaZsP/u5lPnHKFFbV9XLqjGrmjivL3mf6qBIe/cLpfOWe9ays7aE0HODb7zmywKd54Gxu6uOav6yhqTdB0FRMH1VCJGDQ0p/k+CmvzbtMOPiIEBMEQThEuX9NIyUhk/7E0Cig1Xu7ufGxrWxo6KPUEzwObhpwTFmY3R0DZOzhsgNeeqWTU2bU0NobZ9Xe7qzwKg6bpCyHjFcQHwkaZGyN5Wj+b0ndPvdmKvfctO1QHDDcGjUvajXo2dxXFgWpiAap7Yozf3wR93/utKzdQyJtuS7+CkJBg1TawcarBRtmyPoqzhMjGF8epbYrjtaFr/NFmePA6Iow9d2JvOOhgIFSmkTGm0AArK7vY3X9RgC+du5sptbk+4pNqiri7qtP5qQfPM1n3zGzYPTyQNndMcAlv1uK7WiuPHkK371oblZAC4cm8rcnCIJwCNKXyLBkVycDaZuv3ruR1Xu7uOmFXVxzx2q2NvYBMJhxsmJDA89u78wKiHnjSvjdR49hujfs+7RZ7nDuF3d2YRgKBzhlZjWfOn06oYBB0HSjVomMG8maVl2U3UuurPCTbI5XXB8OmAx6ESjfUX98WZBxZSEsR3P2nFF8/73z2Nwc4761jdn7lEbcNJ/WEPfE5L7SiPsi14rLUDCqNMjuzjjOq1yUe6i5J5H3Jfn1c6Zx9zWLsg0HN19+LGNLTKI5zY4PbmikEMv3dFNdGqE0Gix4/ECwLIer71hNxtZ8973z+P7F80WEvQ2QiJggCMIhyH1rGrKv+xIWH755ObbO70LMFRy2k5+C++iiqVywYAIXLJjA1pY+5o5zi/13t8ewHU1p2KSlN8HNL+4mkXGYWBmmqcft7NPA3q4hA9jhusYvdLc1DKaGqsYChhvhSlqa7niGcEDx0Ppm+hIZpldHeXZbC2vqurly0VRiSTcXaO/jGQdCbv27oaAj5qY6/fSmb1NRCAM3G3rqjGqaewZp6kvymyV7+d/na7EcGFUc5F3zx/PIxlY21HXR0OdGJVOZwne8bVktrX1J3nPUuP3uu65zkMv/vIKMpbngqHFccuwE5k8o45sPbmJ3xyCXHj+RK2UI+NsGkdKCIAiHGFpr/rhkDyEDRpcEs3MRYSjq5ONHhXLLwU+aWslHThgq6PZFGMCyPV2YhuLzZ8+kN2FhOZrisEljTyrvvoXkRtBbV0qN+Fd+dVEAy3GHh3fHPZFlu4LM0YpYyqauM8Hmpn6+/cBGzJx9gzvg+7UQMFSeO73luOlS01u3tVtM7zYjqLxnKdyUrKHcWrH63hRpGwYzmuvfO49IEHrjGb738BYe3diSFWFAwYhXc2+CZ7a1c+kJk4iG9h//uOXlWqZUFXHUxHLuXF7HRb95mfN+uYQH1jYSDZr8+ANHvabPQnhrI0JMEAThEGPxljZa+lMopZhUVcTc8aWUhofyY7mvfTNUf57j6NIQXzv/iH2mtHrjGRxHs6O1n/EVETK2JpHOL8NXQFXUpDScfw/f4KI4bGB6WzAUnDmrOustlnsPy6umDwcNOgbS7OmK05fIcMTYMlKeBYYv+DKOzivUH46p3IibabjnWI4eMcDc1q6I9df74hkMpUhZ+d2gGqgqDjJ/fDltfQkuO2FSVqTVlIR54avncPKsUfx9ZR2GIisai4KKWWNKRuzt1qW12I7mikVTCm8+h7b+JHeuqOO4yZX88cqFrPrWO/nhJUcxtaYYy4Hz5o2RdOTbDPnbFARBOITQWvN/z+3EUO4Yn7rOQcZXFBENuf85N5XrYK9wo0K59g3+CKH6njgX/d/LOMOESnssSWNPAhO4f10LW5v7CQdUXorTv9/VZ85EDxvQqHBTj4m0kxU1EyvCnDyjhvruQQCmV7vmp7m+Xsm0TThgcNYRo2jpSzJ9dEneuG8/kqVxhaXKWfdf29qNetkOr1oDlvdZ4gq8QjT1puiJp/n5pcdyybET0MCJUys5Y1YNY8sj3P7JEzhv7hgcLwUbUBDPaAZTw3tHoalnEFPBFX9awa+eeYXm3sTIB3o8sqEZR8N7j3GHqpcXBfnoSZOZMaoY01CcP29kh6xwaCNCTBAE4RDi4Q3NbGnuz4qNzrjFsp0ddMQyWYNUv07McjShgIHjDEVt1jf28437NqO1Jp7JFw07WmMAWAx9OaQtTWnIdKNqDFlS/PjJHQx4ITBfDPkdjWlb4wfR6npS/PjJHZSG3ZRcT8KiLDKUnguZbmNAynI43WsY2NzUnz3upwdzf89aTuynxisSgNKwQUlOhDCQE00KvEq6szwaxNHwwd8vY0NDLydMrWRqTQnRsJt63NLcz0MbW1G445os73Mvi+anHpfu6uTxze1MqS6itivOL5/Zyak/eY6rbl/F01vbyFj5fwfL93QzY1QxM0fnR9a6B9Nu7V7k9Rf7C29NRIgJgiAcIvQnM/zw8W1EgyZeWRNTqyMoQ2EabjQoYLhRpqDp14VpHIZqyBSQthy6B1N854FNLN/VCcCdy2v5wSObss9y72Fko0aOZ/eQ2wzgkzshMfdYRTSYFUF+XVhPPMP4ikh2L74txsxRRYyvcKNlCyaWURYJZMWWo905kDCUatXkR8T8fQQMt7tTGZC0XKuMeNrG9Paetp1sOtF5FTf7eNrimEkVnDitihsf20ZD1yCvtLoC0XE0X7xrbfbc8RXhrI1Grt2F1ppbXt7D2XNG8+gXTue3Hz2OSVVRtIZntrXzxbvWsuC7i/nm/RvZ2znI6r3drNrbzZE5PmQ+p850Reo9qwt3ZQqHLtI1KQiCcIhw0wu7ae93i+bHloYZzNjc+N55XHXH2uzMRNv7mbHddJlhDDnPg1tDNaWqiJOmVbGqrpcnbltJ0iosSPzxRsmcadmmofLSeQGV9VodwXFTKljqCb3cU7a3DlBkQtwLBk2pjFLXFec//76OskiAFbu76U9abjSMfNd8vN99MaZxBZiD12Hp1ZJFAgYJx8lPqyp3dmXuWmnYJFYgnZjIODy2qYVPnjKFmqIQj2xqYdGMahxHs6dzgCZv0DdAQ2+KyqjJ/AkVfOykoTqwJze3srmpn+9cOJeicID3LBjHu+aNYUVtN1/429rs2Ki7VjZw10q3C7YyGmDW6JF1ZhctGMedy+tYsrOD+9Y08v7jJrwhPzLhrYNExARBEA4BOmIpbnm5llGlYUaXhrn6zOlUF4e47t7NpHIcTnMFj6XB8SJO4Kb1gqbBU18+kx9+4Gh+9sGjKQ4NfZkr8LoIDYLG0FoukZCR98Vhmvv+GllX38vo0ggKd/xOyCD7vHiO9jlibAkZB2Jph/6khaVtSsJmnr2E/9NUQ8LS8gSanXPc300y4wyNOCr04Xik7ZG2rkZOpK2+O0HAq5NbsrOLZbs7iQZN0lZ+M8FAwubl3V209rsWH7ajeWRjM6PLwlyQY1kRMA1OnVnDf19yFJ88dSpfPGcWV50+jZBpUB4NUhwy2drSjzVsX4Zh8KvLjmVceZSfLd4hIuxthETEBEEQDgH+7HXepS2H8+ePZTBto7VGGa4U8FOTw0nmCB6t4ROnTsU0DSzbYW19N11xV7Bkxw5psHMiYLkCx8H1Bct9TMZyCo4IArcDc2xZhCPHlXL85EruWllPPD0ymvb0tg4AIgacML2Gq06fzpV/XkkkYGA5TrZbMuPku+pHzPz3h3feviJ0w5cDhnLHLCm/CUHhaJ2tPTOAl3Z2ZGvUegbTvLyzkyU7O7P38DtSF06p4svnHcGJ06oAuGd1A49vauV3Hzs2z0bD54KjxuUJtPcdM4EL/+9lTp42mie3tPHkllYuXDA+75rxFVEe+cJp1HYOFn6DwiGJCDFBEIS3OM29CZa80sGsUcVsbxvgntUN2BqKgm4KMho0sB2Ngy448mfeuBLStiYYMPn6+UfS2BPn4t8spWvQ9b9yC/uHOh2HC6vcew4vq3q1EUMa2OY1APTFM3z+rBncv66J+h43recLpqxFhYajJpazq30AAFtrtONGvGxNNkoX8K4ZLsJy7+mLN6UU2tu038igFARMd4i434Xp5Lw5P6rm4DYe+J2i1cUhRpWF6fE+t1FFBh8+aRoXHT2eI8YO1XXtaO3n+49u5cRpVbx7/v4NXAHmTyjnogXj2N0eQwFfvGsdIdPgvHlj884zDTWikP/ni7ezem8Pf7/m5AN6lvDWQoSYIAjCW5yfLd7B9tYY2tGML4/Q3JekKGQQ92Y2Bk1XMAQMsAqoohmjS/nGu+dQUxImbTlc948NWRHmk8zxixgeOcoVZsMFmjLA1PmRKoCyiEl/0s462Df1Jfnf53YXHFMEUFMc5IRpVXzqtGks3dlJ0BNKuWQc1wft3FmVrG4cYGe7Gxny33LugHG/g1Npnd3/kOAC29LZtGWuAM0Vln7HpnbgzNmj+NDCSZSETK57cQMV0QCXnjiFr75rTt4eW/oSfPLWVZw8vZofXHLUa0oh/uLDx/DSzk6++9Bm6nsSXP2XNZw0rYpbP3ECReGRX9cr9nTS0hvn4Q3NdMTS9CXSlEdDB/w8n4GURUmB+wtvDlIjJgiC8BZm+Z4uHljXlLVG6OhPsmh6FTNqSjCUa/8wf3wpVVGjYGqyNGzSE88wrjzKDQ9v4fqHNvPP2m4Agp4JKnjdl/vYg98tmUtVcdDrXFQjRBhAvx+uGnbM/zVqwu8vPw6AyqjJbZ9YyE2XL6S6JExd9yB2oZvidl0+vq2LHe2DOMP2Vigyp/1zVOH0qd996acPc0VdNGhy7KQK7rzqJG795IlccNQ4ntnWTihgMKmqmA2NvXn3GkhZfPLWVfQnLb587mzGlkcKvod9ETQNzp4zmheuO4trzpiGAlbUdjPvhsV85i9rSKSH1HLasvnIH1dw3X2bmTO2lEe+cNrrEmGbm/o446fPs6au+zVfK/xrECEmCILwFubRjc2UhAyqS4KEPNXUM5iitT+B43l2rW/ooyfhphYDBnzylMmAZ9dgOfz2I8dy96oGNjf18fKujuy9MxrGlAWZVhMlldEFI2H+z+Eip3vQ7fiz9iGYfHKvyxV6CRuaehKeMS3Mn1SVPXbWEaOJBPO/ngIGFAUNgoZBX8LCVBAKGEQDilBA5d27OGQSMlVWZMLIiB3kd2Hajs7WhSlg7vgSbrr8eO797Kmc4llHAHz+nJmcPKOGtliSus5BbNvGdjQDyQxfvGsdr7TF+N3HjmP+hPKRDzxADEPxzQvmsubb53LspAo08OSWVs7++Qt864GNxJIZLr15OY527T9+f/nCEenKA2VaTTFBU/HLp3e+7v0KbwyJRQqCILyFCQdMBtIOA+mhVGJvwqJrMEPY8N3hh0RF2IT1DX2Au5ayNb3JDI9ubGHu+HJ6BlM09gxZLzT1ZoAM0QLfBoXSkT5+Sq9AmRbgWkoMPxbw7CMwFWlb87Mnt6E1ZCybD920lI8tmsL588cxf2IFV50+nV8/tyt7reua77ipQjxvsUJ5WGAwbeeNQwqZisqiIINphy+/cxY/W7yDqdVRtrflF70Xh00syyYcDBBPO3zhrrWcNbsawzBRwNqGXnoG05RGgphK09yX4pxfLGFvV5yioEnSsvnexfM5Y/aofXwqr42qkhAPXHsqHbEkn//bWnrjaR5Y18ziLa30edYXBq5we70UhwNcunASv3l+F/3JDGViGPumI0JMEAThLUosmeHuVXXZ390UGuDYro+WcgUNDAkfwzBZ5wmxiqIg8yeUc++aRl7e1cl/XzyP7z26teCzEpZ7D1Wg6zC39gpGFvMXwi5wXkZ7+7Q1kysjBE2D3Z1xMg6squvFUDB7TClzx5fTm8iMuF4zslkgF198+bYXfhQsbWs6YmkcoLk3TtJyaOrJHzM0rTLMpSdO4rx545lSXcy3H9zMxsYetrcOsqNtIG8fvQmLI8aW0NyXJho0GF0apj2W4oYL5x7QPMnXyqjSCHdfcwqO4/D7F3bz6KYW+hIWoAmaYNkOgVexEdkfJ0+vdj3PGvvyon/Cm4OkJgVBEN6iPLi+mYHUkASytdsF2Bu3yDjkFbPbuF2FlUVDEY2BpEUqY/P7F3dTVRRkyc5OggW+sA08by6GvLlyGR532p8Iw9tLofP8KFlxJMiP3z8PU0FVUYCZo4rY3T7Ijx/fyv1r6nluW2v2GhNXgA530h/+2jd/HeH8r4bew+3/rKMyGsi66vvi7esXzKW1P8MX7lpP50CKl3d10tyXYlfHYLaODNzPKmwa7GgdoKY4SEufa7B78xXH88nTph3AJ1MYrTX9yUz295a+BMlhI6gMw+BzZ8/ijk+dlPV7G1sefUMiDGBaTRFBU+U9X3jzkIiYIAjCWxDLdnhhezuTKqM0eNGboAGpnHBVbrcfns9WY0+SSMCgPBqguiTMluY+cDRp2+aprW3Za303eg1EQiY1UYP6vkzefffF8AjZ8GPhoEEiM3IguAnZYd6nTqviw39Y5c2ZtNnTGcfRsLK2m654mprSMI29KUJe96SJwhoWDgvkuPz7Lvu+1YR/am5kDFyvNcNQTB9VwqbmWHYQenN/itv/WUdRyOTKW1bS2JMgaCgsZ8j6wr9fyuuK6BvM8L7jJ/LtC+dSHn1jKb1HN7Zw27K9vGf+OJr7Ety/tpFZY0r526cXjfAhqykJ89l3zOKXz7xCa3+Kpbs6syOQXg8Pb2hha0uMiZVFb+g9CK8PiYgJgiC8Bfn6fRt5dns7uSX0vrYxldsNaTDUDZjbOTiluoi2WJrq4iApS5N2yIuswZAbvQLiaZu4BblS4tWqjhzIzroczpHjyzD00LNCpv9TUZJTiPbXlfVZMTeY1tl6LkPBJ06exlfOdW0h0rbODjD3Mbz7ZRydjVSBK8z8+jFfmAWGfctp4LjJlVx24sTs2tlzRnPT87soDpkkMzam4Rb/l0WM7LQBlXO9Twb49KmT3rAIA/fvbE1dDz9/egcv7GijO55hXX0P//3oVhxnpDS+6vRpzBxdTMpy+Mrd69g0rIPzQMjYDq29Ce5d08D7j53whhoMhNePCDFBEIQ3kWTaZt4NT3LJb5dyx7Ja4jmWBMmMzdNbW/nRY1t4enMTAKm0m54ygDGlIUKmO27Hd7j3v6P9iE1Z2CSetrli0RR2tsWyAqaQbooEDEojJmVhg87BDI530qvVgBnq1d3rd7b2M5jjSeZt320aSAwd8CNZxSH3a2hqjRuN+eI5s/nQCZM5ZlIFY8sihMyRO9cMDQvPjXY5ekjQ5daLDd9/bWec7z68DYCgoXAcjeVoptUU8+33HEl9dxzTgFgqZ0xSzjb8l5GAwYwx/xrxsmBiBV84eya2bVPXFScaVIwuDfLXFXUkrZEtEcXhAJ88ZRqlkQCtsTQX/WYpT21pJVXg3H3xnQc3877fLaWtP8Xo0vC/5H0Irx1JTQqCILyJPL2tjcGUzbqGXtY19HL9w1uZUh3lvUdPYFNTLy/s6KQyGqTfa5KMpe1sIX5bLJ037LoQlUUhWmMpxpWHaY1lKAub9KfsvPNNb7RPxnZIWq6wmDUqSn1XoqDNQy7FAUUskx+dyo21pR0vSjesVmtE4b2GoyeUsb01xlHjS5k+qoQxpVE+fcZ0wO1QVEpnmxFy71MaCdCftPKGmYMrunyh6Gj3GcMFmaNhV8dA9ppLT5jEtpZ+Fkws54eXHMWo0jB/W9ngufvr7DX+s3ONZj920mRM0/Tej2ZFbTcZ2+H0Wa+va/IDx03gntUN9CczpDIOnQMWs8eUUhQq/FV96QmTuPHRzdnfP3vnGj54/ER+8sGjD+h5acuhezBN2tZsbu5/XXsW3jgSERMEQXgTuXDBOJ75zzP5j3NmMq26CENBZyzNb5/fxZJXOikOGfQk3FqtCWVBkhnH1I7WwAAAIABJREFU7YYEIoHC5qkAkaBBwHAjTafOqKZ7MM3EigghL4doeGN9wPXMgvy6p4aeFH72stAj/BQjOfVKvigcji/CgjlRpDwhiD9uSGNpmD66lK7BDNNGFWeFzT9WNdDSlxrxJaVwmxBy95+9r+FGC3OjYP7avtjQ0Mva+l6W7eqiuiRM50A6O2IJ3BRsrg2G7zcGcNac0dnzOgdS/L/bVnHXyvp9P2w/TK0p4ZiJ5aQy7szPpOVw7Vkz93m+djTR4JBIUwruXt1IRyx1QM+79uyZlHvp4pW1ndlB5sKbiwgxQRCENxGl3FmBXz73CJ6/7ix2/eAC7r7mZGqKQxSHzP/P3nnH2VHW+//9zMzp20uSTd8USEIKkBAgkY4gXcQC9o4ioqhX5Nr1XttVvIheC6g/QQULXZoQIBAgQHohve5udrO9nT4zz++PmTlnztmzmwVCf96vV15kz7Rn5ujOJ9/y+TLJLZg2BPQn811s3tzD4TCQLG6s5UBfism1MW5ZuZ/eZJYB30DGYvNVTyfpwomOiKLP/UgJQZx0nYfTxen8feHkKr9Gc7r6grrj3l90rljIEVtrWwbQkTz8wkGiAY3WPsffbDBtct/GVi45dgKyaDGemz44gq5wjbKguxEYkpotttyKBB2PsFnjygkH9FyqOKCLXCRNuOdLW7IgRRnyFcptOtBPImOxeGpt7rPirsfRMKk2lhOYV50xk3PmDT+rMm3Z9CXN3NBy04a6mEHXYCo3X3MkptXFcs8wGjQokQVWvAooIaZQKBSvIZomOGp8Be2DGT66tJHbPn0iVRENKWHA1WGekCmO7HiuBRpOJKxzME1DZZhHtrQRMjTqy0OEArpj31DiveyJCkvmOyihdJTLtiFT6nN35w3NfdgyP5jbkpDMWJjuuf3v+MF0XqBkbGfbmv291MacET2egJk1rrwgqlaM51XmX4vEEbH+2I5/vJEt8+75CydXsbPd8QiLu8Vs37lnM+DWoLnn8Z4PFM7yfHZPfizQwd4k0aDOT/+9latuXcM51y9n8X8/wsrdXcPfQAnevXASAA2VIU6eOXwn5G8e38nlN6+Cou+2O27yjutXcMeaplFd75pz5zB7bBl3XrEU/WXaYCheGqpGTKFQKF5jvBeprgl2tg8SDhj0JvOyxxzmOMt20mUBXWNSTQSkxdTaKCt3dzNzTBkH+5P5mY8lMDSn3kniCKgix4mcqMrapR30PVd305ZkbekKwnyNmD/16Sca0smatmNLIZy5ifGMlYuIVUeDHDW+guse3u6IA9tZmFc4X7DMouIz2yeavE1SOs/JtGVue0gXXLJoAv95x2an+cC9RrdvGLo9TKZOFxAwRC6V98P7X+DmZ/aSzDrP4N71rY73mCb488p9nDCttvSJSnDkuHL++NHjGEybLJxSPex+921sZXNL/5B79X6+9o6NLJpcw5T64UcfCSG4+NiJXHzsxGH3UbzyKCGmUCgUrzH7upxROyt3dzFzTDlt/RmCuqAyEqBjcGgcSnPfujYwoSrCZ0+Zxk8e2k5nPAMkmT+hgg0t/QxnquC9tIWUI3ZIesIspDmizy8Ig7rAtGSBrYQ/6jTcNcGJiI2vCFIVDbG1bQBbQiQAV585E3Bq2CKG40UW0HyCylu371xeQb7/+n53fe9zWzoiLKhrjKsMsb87yXUP7XDScRL2dycZTJm578J/vmIsCcKU/PaJPfxr/QF2d+Vd+nNpUzddOFyh/Uj4a8+GQ9dELtLoF2AepsUQ3zXF6xMVh1QoFIrXmMa6GB8+cQrNPUkiQQ0dpx4slTHzRfI+vKhOTIdPLJlCLBSgM54hpEPEgIN9SaoiBqV80kOGyBvBasIZmyQKU27FtUJZe2hULmNJbCAS0JhcHQYKhVCuJgsnVVgsCQ70ZzjQl6Q87NxgMgvHTnEGfwsBXfGM6xU29Fj/z34vNcMVbQXF+u5CTNvZXhYUDKayTpNE3Bl75GXkPnzTM6SLCtZL1c0JHPuOtGnnRJgmnPMb7jOVEmxbcufaZlbv6+Fwc9Vp09AYXizWlwWYPqb8sF9XcfhRQkyhUCheY4QQhAyNrsEMTV1xDLdQfCBjk7EKi+r9HDWxkncunMS9Gw5w4fxxLJleR2NtjFTWJJ42SxbdG5rzi39iVYis6cwqLB4LVNwTMFwvXcjQuOK0GZwwLV/LFNAKo1F+rzOjqFJ+MGnS56ZOa6P5yNG2tn5a+1N85ewjWTzVSc+VKrbPTQfweakVD8D27kVz9+tJWnQnTGwJv3jvAm649Bgy7g3u6YoPieb5xWXxZ/i22dIRe6Ytc89TCKfWbPm2gxxupo+tZEpdDCDntea90KsiOhceo9KNbxSUEFMoFIrXAbqmYdmSJ3d2k7ZkgXO9JwK8Dj6Pjy6dxobmPlr7UpRFAjy2vZOJNVEiQaPAx0sA48oDHFEfw7Id8dLc68xITAxXgOZjuHp5y7TZ3T7Aw+7opIDmCB9d5OvLIC/kijv5/Jc+e26+O/Ca2zcwriJMbyLLvu54gU2G7RNWwaDG3PEVLJxa5Z4/b80B+a7KqohBZVgnFhQFXY8/e2Q7Z88dR9RdbE8yb57rv2fPbqOYkGvb728AcLzGoKEyzNGTKqkIGQSNEmHNl8mU2hgPfOFtRAIi3w0rYNGUKtZ88yy+ft6cw35NxSuDEmIKhULxOiAa1Fg0tRrTtAgZWq6bEAqL3j09UBUxeMfcBh7c3Mb2g4Nsax3gjCPr2dE+SE/SKhAOEmgfyLK9I557aQ8peh+B4dJfmg53rmslns4S0p0Upu0W6WdtiBa1PNpyaFQP4MRp1fzXO+eStWy+/Pf1bD4wQCZr8sen9nCwP+PUZPn2F8Cc8eX8/fIT+ddVJ3HLJ07g/PkNhIpOHtahLORYUsQzFv1pWSBmm3uT/PLRHaz75hkFa/R3kApACobYXgAFLv7eMRLQhSCVdQazP3j1yVx5+sxhnuDLIxwwWDq9PndDtnRqxzRNvdrfSKhvS6FQKF4HdA5m2NjSR3NfMteNVyoF5vHZkxq5fU0zd61pJhzQWL2/l9V7O9jTlSRt2kOO9Qq7TZ+og+FnRpYSHlA4uzFjOTVXGRs8Rwr/+RLZwlVICiNLAkeY9cazPLipjf/31F7uXNvMWXPG0jGQIZm1hxTihw3Bg198G/+66mTmTahCSsmO1j7GlQepLw8VvNTiFqSyNlmLXPrRfy7Lhj8/s5dn9/bxH2cNFUteJM4zifU/krKgjmnZhA0tdz53WhMZU9KdMNnQ3Mfze7t5JfnK2UcUpHx3tw/Q3p96Ra+pOLyI0Zi+vZFYtGiRXLVq1Wu9DIVCoRg1UkrOuf5JykIG+7riJFJZEqYcIqa8DrmwIfj8GUfwl6f3cGAgQ0CHQ3mHhgxB2jcgstQcRoCyAFx6/FRqY0H+9nwTe7uTzB1fxt7OBIMZOzcyyJJw9pyxvPOYcVx564aClGDxekciajhr1zVBMKCxeGoNOzvi7O1KOOt0T+Q/fUXAEZQpEzStsNHAT1B35kj6LS1KUR7SiYUMeuJpfBZnQ0YoDXd/QV0gpSyw/xhfGeLMo8Zx9RlHUO36o71SvP/GlTy9y/ErCxsaJx1Rz40fXvSKXlPx4hBCrJZSlvxSVERMoVAoXkMyps3Xbt/gDrK26YpnCIcc44lQ0W9oTxMsmlzF8q3ttA06nZJZC0KHKEMajQgDiIaD/Oe5c/jsaTMJu/Xz0WCAuBtSMnziZFFjNY+80ElAE5w/fxwRw2kyCLgpQp3CCFlx9E0A4yqjBAxBVkIibfHM7i4O9iWJBZ3uQ6/Yf1J1hPEVznPpzzq1bRKoDBuUhXQCJUJ4GUvmDGX9FKdHB9IWbf2jGwskcIrwnft06+JsR4TVRQ0MzRF23YMZbn56Hzc/vXtU5305fPuCObl7Sls2j29rpyc+uvtRvPYoIaZQKBSvIcu2HOS+jW0gJetdd/qeeIa6shDlkdJOYM/u62VDi7PvyUeMYXpdrKC70mO49KK/4L2YnniGrQf76Ymn2dqeJKjD5gP9ORHoZRuDumDptFruWNvC7IYKehMmkVAAWzqdggHNKcY3fSk9syi9J4HdXQkSWYllO6IpkbGxJcQzTvdhSIOaWIDmniSt/YWGHPMnVnDykWPJmDbZkQZKFlEqyrVkei3VUed56wIieuGwb++/Eteawv2TtaHC/Z46Eya27Qg7bzTTz5ftYsfBgVGv7aVw5LgK6stD4K7NtCSL/usRlaJ8g6CEmEKhULyG3LWuhcG0SdIdsRMLCgKGRtdgms54KScwR+h4Iuv775zLdy86inBAI+JrVdQYGvUq1mV+V/aJVc6LPGvD9rZBLr/FKfHIWKVnJs5uqODauzYhgW0HBxhMm4xxxUAkoBWkC/3LkJQWiDkDVENDiPwRaRt64tmcHYSf9c39/HtjS24Gp3f3AV/Iq5QWLX7xTawK8/P3HU1DVdSZkWlA0hpqHlt8Pu8eB1KFM0GDuiDjRiCrIgbP7nlxY45eCv/3wYXMG1/hrNGNWr4S/mWKw48SYgqFQvEasmxLOwJo6XGMQeMZSdq0R6xpKgsKTNtmwYQKxlaE+cWyHaSzNgl/kdJwnhPe5iJfrAN9acfcVRO09SfZ2T6Y2+6PIIU1Z2j3N8+fw86Dg2jCSa/+7L0LuP+qk/jokqmMr4o46buipfh/vvzkaUyrixENaAjyXmazJ1Qyqbr0WB6/sPTOGfd5YNhAZVjH8i045wNG4X5lvrxv+0Ca7niGslAAS0Iim9+/lIg0tMI0q5Q4w83dbRlLMrk6wrS6KB88YSpnHzX84O7DxbGTq/n2hUfl1gNQFX1la9MUhwclxBQKheI1YvvBASJB3Sl+fxHHXbJwMhkLvnrObPZ0xnlub0/Ot8v7pV4qU+dYK7h/LzZtdW0nLFvy4MY2BtJmwfkAQoChw7S6GN+4axPxjMXixlr+651zmV5fhqYJrj5zJkeOK6cspBWIyXcePZ5rzz0id60TGqvZ3Rnnc6fN4Lx5DQR0wcz6GB9/WyO9ycwQEefhRc5ChlZSa8bTVu66xeIroAsEENShxmcgGwsZrNnXw7T6slxU0Xav5Y/eea79Qohc92kA167DDZ8JnNq2jGVx7blz+MrZR+bShq80i6bWsOzLp2BoUBsLMqeh4lW5ruLloWZNKhQKxWvEv9YdIJ42h0R6/G7uxXVKR4yN8cSOTs6f38CS6bX88and6DgpPDi0N9hwXYBeEbotYfOBvlwHoP98aSAiBAODCfb2mRia4JhJldSWBVnyw2Uc6EtRGTZIZk0yRcqyvT/FwX6nE3JGfZSfPrydi48ez/nzxjGYTnPhggY+cPwUJlZGuOy4idzwmFPk7qUHdRyx6q0/NUyrpCnz3Y6GTsE6sq5RbsaCph5nhqcunMjR/z2+i+OmVFFbFqS5J1XwrIq/k6zvIfqTx6YNhnBq2zoHM1SED7+R66GYXl/Gjh+c96pfV/HSUUJMoVAoXiNeaOsfEpmSw/wgcTr0TEsSCxqcMWsMQgge29aJxeisIkZCAmUhg2TKKTgvPp+GI8qmjC1nfEWEpv6DjK+K8I9VTfx6eb4zsC9lDkm1SODp3Xk/rd0dCSzgjCPqOP1nT+SigbevOcCMMWXs7xwccrx3j55YDOgiJ4iK12pJRzAVazWBkzq0rPw9BTRBTzxDfyqLodfkRFgkoNFQGWZ3Z6JgeLg3ZNz7zN+BKWVevGVt+OTNq9n4nbNRKEZCpSYVCoXiNUBKyUAyQywAsUDpX8XFMR9NwJ6uBLVlQS46egIAW1v7nPO9hDUUX3UgZTpjh8TQ83nje9Y39dPck8DQNWpiAfpTTgoz7Kb9Sq27GE94/eLxPQUp2fEVAfoTGTIlBn1D3lwVCqNS0ncvXu2Wl74M+m5SFxDUtZz3V3UsSMqS9Caz2NKJsh09sZJZY2OYtuQHF89jUnWEmDt53dCcAeH++jBLgnAFmN+RH5zn+Up3TCre+CghplAoFK8BG5t72dTSx2AW4tlDSRcvJeYIkdOOrEPTBH2JLAOp4avLDvUL3n9VT2jB0PRlNKBhkRcZGw8MkDZtNrf0ETI0QrogY+UNaEfqE4iNkIfp6M/SPpjJ20UcouHAj1fL5dVu2dJx1c/YTueit23A9UNLW5KueCZ3fFDXOKGxlnXNfVz99llkLcllNz5LY12My0+ZxncvnMM75jYQCuj4LNnQKJyZ6SdkaGxq6Rv9TSjekqjUpEKhULwG3LG2hUx29HEsWzqzG1NZyep9PXx0Kaza112yVsqrLZtcE2FvdzKXVhyJkVYypjzI3u4UDRVBWvvz4iVrQ9a1ohc4HZeWPXQigEdtLEjQ0KgzNDp6EySKNKRXb+XVhQV0jWyJcU3FGAwVj2UhHQQk0xa9STO3Rm8qQMHxGvy/jx2XG0ouBFRHDOIZi+svPYbqWJCOgTQtPUmyWatwPUV5UZ18ZCxt2uzqKEyzKhTFqIiYQqFQvMq09aW4fXULFbEghgZzxx+6u602FuRjS6YigKTr63XfxtaS+3p1Z3u7k+gCwsHCX/XBUfzm9wej9nan0IBJVZFh95c4HZf+47wOR49ExqK1L0VAEzkR1lgbJaSLId5iEsi6ItOfuS0VJDMZ6vWVyNoMpKwhIs5fgO/xi0uPYcmMOibVRikLGezvTtBQFSFjSQK6c/G/r9rP757cQ9o3jF13Ba+h5b3LPG3p7fN/j+0qsWKFIo+KiCkUCsWrzG3P7WcgbWJknHTZpgP9uW2VYYM+t+5Ky73oHVuIyrCTIuxPOAXlB3sTuW5Cb38pC0WJ7brV+8nYzr/CJcNHwrzPGypDdA5myFqS5/Y7abZoQMMQknjGiX7pmkDXJKbtFMh767Zk4YzL9y+eRG8yy+1rWnLX2d+dcIrrcQSNEE4xvb/eysvcFhflG5rzbFJm4V14ohDyotTQnLmT/u5HS8K3zp/NufPHAzCxKsrYihB/WLGHk2bW8kLrAN+8ayMbmnpp6k0WXMO/FueeZS7yWPD8gVTKJBxWr1tFaVRETKFQKF5FBlJZ7lnf4rjPl1BB1bFAvujd3R4Lalx2/BR+8tBWAOrKojT3JNh0oC/XTQiAdDyyRkOxYPB3/wXdHwwNOvrTZK3CSFcqa9OfkQQDAhvI2hLbzncpVkTyosM/4/KCBeN5ckdHwTq8CJWNc3xA03K1YcURNd0XNisL6Vg2OVf9ke4T3G7HonCaLuDM2eNyPw+ms7x99hjiaZMVO7sYUxbkznUH2NWVKLiOJ2Cd7k1fNyel69r+9OyeEdeoeGujhJhCoVC8ivz9+Sb2dyWojhol02x7u5I5geSl5AZSFmf9/HE6+rIEgSMbKqiMBKh1ZyN6+9vA8dNrqQprGFo+6uWn1DX9NVYaTjQpqDvdgN58yOIoD0DSV+Pm7zfoSxRaWOgCxleGae9P0pvw1WuVWEvWsnNr8dz2c9f1eX0kMtaQcUm6cGq0St2jJR1/r7pYgO9dNAcpQXdFn2VLfv34Dk744aPc9OQe0qaTQm0fzHuNFTwvd+ECp4HCWZvzXykhVHTADx/YXmJFCoWDEmIKhULxKtExkOZ/HtpG2ID2vsywacGA7qbObCgLOg71uzoSpCQYAUFrb4rycIBzFkwoPE7A9y+ch67ruRSlX6hoPluK3FxGCrv+bFwrBum4xRvCSS96wikgHOHmHe9FrRyPLudiksLmgKCh8bYZdfz+qb1kLLvkPt6a/PVXxeSGlfsiZYaWf41Zklx3pzHM260znuVbd7+ADWQsm9tW7uD4793PTx/aTjSoc9lxE/m/S4/m0yc3Mr0+RiSgFwz9Fq4/Wf76efsM7xnZxeZwwLxvP1h6QYq3PIcUYkKISUKIx4QQW4QQm4UQXyja/hUhhBRC1Lk/CyHEL4QQO4UQG4QQx/r2/YgQYof75yO+zxcKITa6x/xCCCe4K4SoEUI87O7/sBCi+vDdukKhULy63L+xlZRpc/2lRw+xPPALJs+mQggoCwcK9ktkJbetambOtx7gbyv3FWzLSjjlp4/TFc/mokmeeAmIvFCCvAjyO8PnRBD5NKMpIWXKnHDKSke45YrTZT5VZ9mlpWUya3PfxgOs2duTu45T31UYvbKBYEDPnXc4vPozGN5h33LFki5EwX2HdagO5199v3qimc6Uc77xFSGe29fHb1bs4dZn9xMJCD598jRCAT0nYqXMiy8JRN316prIPaNSax9IWzy48cDwN6V4yzKaiJgJfFlKORs4AficEGIOOCINeDuw37f/OcBM98+ngV+7+9YA3waOBxYD3/YJq1+7+3rHvcP9/GvAMinlTGCZ+7NCoVC84djVMci/1rcwrjzIJ25ZN2S7U/Q+9MPkMB5jqayN7oZ9QjqsvOZUjhpXelg2OALKX+ckKOxGBKgtc4ZEy1H4d6WL1IYENK2wjstDw2kY8NKcAnKF/dVuetU7LpG2mFxTujtTAFUhQW3UcAeUO58bmigwWfVjSYntE4jRkEFPyqYyrGEA06oDTKkOcVRDGVXRENsODrCppZ/+lMWmA4Pc/MxeaqJGwRgqv/ZLZG2CbtenF8UbRo+yeIqKJSiGckghJqVslVKucf8+AGwBvHj4z4GvUpjGvwi4WTqsBKqEEA3A2cDDUspuKWUP8DDwDndbhZTyGemYuNwMvNN3rj+5f/+T73OFQqF4Q1EdCfL8vl7aBjIlt0uZj+J4hAKCgWS25P67f3geX3z7LMARF7c+38RfPnUik6pCwxqqujrLuR5OXVckJ+YEWcvOpRoDmmNzMVwtFwytxbJsCkSPhw2Mr4pQVxZ0XhYiH1HrS5oENYEunIYAoTn3HNIdgelHEzB/UjXXvW+BU+9VFnKvK4dEoYK6KBhNBE6atdutUetLOQ/7qEm1XLxwMptbB9ngmq+mTZuADosba+hJZGnqTQ9776YtkVI6EbERonhnzxlDTcXw9h+Kty4vqp9WCDEVOAZ4VghxIdAipVwvCttEJgBNvp+b3c9G+ry5xOcAY6WUreAIQiHEmBezXoVCoXi9EAxoTK+PsqsjMeJ+/nd51iptxLpogvNCX7m7k6qIgWXZ/OrxXUSDAZp60/laJd8xtbEgWcvGqwjzCvAtKYmFdOJpCztlFhTKe7ytsYItB+N0Fjuw+vDOV0qLVIQNpJR0uMXvtgTpXsArovcYWx6kPBxg58FBJ8JG3p7DkvDEzm5eaOkjEhDE02bumRmawPSJwLTb6emtSZCfQZnbTQhmjinn0sWTuX9jK/G0ScQQtA5ksCyoiebTwoagoMu12CIknhn6TYV18AYffO2cI3OfX/P31fxtTRsAJzRWc9vlS0o8NcVbhVEX6wshyoDbgS/i/D/568C3Su1a4jP5Ej4fNUKITwshVgkhVnV0dBz6AIVCoXgVkVLy3t88PawIK56TaGgQCWoFwsLPXy8/GYBx5QHSpsmXzj6CikiQ65eV7s776ImT+c0HjiWZtXKpQe/MGUuScN3xhzP6f3JPf24MU8GQa98+uiaGpDo9TMumOhYk7KugL+4M9TjQlyadNbFwInalpF9X0kJKScbMbz1tVj0nTqsF8vYb/lSv33LCuw/Tltz2/H7GVISZN6GKrniGVl/E8tGt7Uyvj/L7jyxk5w/P46+fPJ6jJ1VRGSmMYZSqCROAFPkFbGoZ4DfLdzL1a/flRBjAyj09OUGpeGsyKiEmhAjgiLC/SCnvAKYDjcB6IcReYCKwRggxDieiNcl3+ETgwCE+n1jic4CDbuoS97/tpdYnpfydlHKRlHJRfX39aG5JoVAoXjX+saqZF1qHDn+OuirEi6WY0p1daIM1TLX6Z0+aTDDoCIELj56AJnSQGvMnVpDI2oQMraAj0RDw8aVTaepNogvB/PHlQ/6lO5zFRcj3hshmJYY2fP2TaTuGrv6h2+AILQnMbqhgSm0UQxMFXmelSuC6BlMFBfZ+IgGBrglSJgj3WF3Awy+088zuLiBfC2f5zj0kjereRzTkPMtPndRYkFZdNKmSDd85m2VfPo0zXK+xJTPquOtzS/nLJ46nsTZMKcs27zoSJ8U5uTrCcVOqOXPOWH70wLaS99TWnyr5ueKtwWi6JgXwe2CLlPI6ACnlRinlGCnlVCnlVBwxdayUsg24B/iw2z15AtDnphcfAs4SQlS7RfpnAQ+52waEECe41/owcLd7+XsAr7vyI77PFQqF4g3BhqYerr1jQ8ltCZ8KETiu+jZO+ixjySGGpvUxg2vOm5f7eebYKuIZi4GUyRbXnT9t2gXHmBJO/ukT/Pzf20iZNusPDBWExXhyJO0TMpGgIxDLfOrMbxGhCSgP63kDVe9zTRAyNB7e3MqezjiWLcmUCHP5i+0XTa0bNhqYzspcd2bal0aNBHTKQ/lIlT/q59lOFBPQBfu7k1i2ZFZDBcc11lBfHmJyTYSxVVHCgUKpZduS257bx1f+sZbmrhSfOnkKdbHCffyrDuqC6WNiPL+vh5ue3F3yfgBuf37fsNsUb35GExFbCnwIOF0Isc79c+4I+98P7AZ2AjcCVwBIKbuB7wPPu3++534G8FngJveYXcAD7uc/At4uhNiB0535oxdxbwqFQvGa0pfM8qE/PDeiFYOHBAbcFJX3i9mUhbYWfUmTG5btIOW6iAYNjaAGz+7u5KCbUhPAmPJQ7pjJVc7fm3pffNSlzmc662XsBtK2r1A9b4kR0AWRoEFRzTBpU9KfNOlLWWQsWSCI/HsePbkKgGhQ47HtnSXXIxh+eHkya+WeH+QFkS4cEea/lif6spakOhpgX1ccgA8ePwVDEzR1J6mJ+TobXFr7Ulx7xya2HkxQFjF4eHM7vUmLoC6ojQx9nWYsyfN7ehhXEWb5MPcE8MhmZWvxVuaQxfpSyhWUruPy7zPV93cJfG6Y/f4A/KHE56uAuSU+7wLOONQaFQqF4vVGMmPxrX+upS85uvrJXX2fAAAgAElEQVQfQT7t5y8K94s4U8LPH9nO4sZqjp9WBzhzI1fs6s7tUxaAgwP5Lr+UGyErqOcSI/t0efS7My8DmpNC9M6ja07aT0IuchUNGqSzFiFdkCiKZnni6X0LJ/C31S0F2zTA0AWr9/UyqSpMU28q1zxQjF9c+Z9RRIeku3s0qJPwhdyK79MQhWIulbXYcXCAafVlTKuPkbVsJEMHpYPTHOGdLp6x0AXUxYK0D2boSkoCYmid3fnzxnLGnAY+99c1BZ/7v5OmvtKdtIq3BspZX6FQKF4BfvzgVu7ePPrmoeFqovzYrg/XUzuHj64M+NwuKkI67YPZIQ77lhxaJF+KTE5s5aNIQddZvljY9SSy9CbNgnTrEIQgGtSoc+dpGhrEwkaukD7iLiqetigbJkygkR+7VBd1olZfOGsW9WVOh+OS6bUEdZFL62ru+vz3b0s49ch67r5yKX1Jk61tg4BjIZI1HQuPkD60AuyShZO4+JgJzGkoZ/GUKmrLQvQlMrnvxRNh/m/yttUH+NGDL3Dm7DEFNWWS/DP9/GnTh39mijc9ahy8QqFQHGY2tfRx+5q8K8/bptWwYrcTtRJATSxAVzyvmKbUhNnXPbrUoSXhhkd3cefaAySypaNtAQHVZSE6B9PO6J0SEbDRRMQ8JPkIVEZCWIOUT29ph4iwecKktT/Fw1efgi3hohueRAjoSZq5SGBLXz6SNzhMINEGwgakTBhIO5Gkf65qoj9loWvwyJZ2wgGNCVURxlSEeXZPd24Yuq5pOYPc53Z38e27NztGrK4ozZgWZglPMj8/f9/RgBNJ+9TNq9jeHs/fpCvIip/Hro4kuzqSjCsP0j2YIVMU+bx340GuOGPW8BdVvKlRETGFQqE4jKSyFt+8axODqbySeGp3PnUooUCEQWkPquEI6gIdaOpJ0jVY2ux13sRyokEdW0LQEEyqiTCxqrDmabjux9HgiTDPZWukIBg4wkQTsL8zTkNlhInVEcZVRehKmAXriGcsokEdQ8D4iqFxAk/Qpcx8d6kAdnbEyVg2lg2NdVFSWZvFjbWcOL0md2zGKpxSkMjaNHXHsSV0DjoCcE9XAs0NnY2pyNfZlSIc0Hn3wokE3Y4Fz8zV8WbL37c/OtY2kBdhQK57dGvbIOv2d6N4a6KEmEKhUBwmpJR89fb1rG3qLUjdeX+PBhwHe3+KKqgL+uIj1wj5mgHJWBITmDs2xm2fOq6khcKGlgH2djmeZWlTsr87SXOvc41So4DKQ1rBy2C4cUEemoApNRHCIWPkAmIXSzp/9nYn2dbWjxAiVwxfEwvw8aVT+el7FrDimtP408cXM29iJY315UPO4++C9GY6+uvGAPZ1JQho8LdVTfzvIztLrkcAFSGNrniWirDBt86fg5SSf29uy3VklocPnTA6d14D8ydWDvncP+oo1z1a4vislZ9buaczWbBtU0sfciSrfsWbBiXEFAqF4jBxy8p9rByhfsvQdSdiQr4eqiaiD2uk6uE1A9ZG87Jrc3ucE6aPKVnr5Z+F6DcxhXwnpl/ATamNEfJZNdSWFUbPgnqh3LIlHOhLEc+YL8p9WwDXuFYe58xtAGBGfRnJrM3cCRVMrI5y3NQaGuvK2NGeN7/16rz89+ShC0fM2Lazzgvmj+eo8ZUFNWElRnjSn7bRgeveuwBN02juSfLE9k5OnllPY22UBza0kTiE0WpA13jvoklUuQavupZ38Pfu1/uvTaHA1QWEDA3TdsR5R3+Cf65q4sQfPMJ7f/M059+wgnOvf5JndnWNuAbFGx8lxBQKheIwsLN9kO//6wXi2dLpQl1zCsEzliQA/Oe5s2ioDOUiMHqJ0JLfpyuoa455q0vY0EhnzJLO8wDXv3dezirCawTwLmHLvGN9LKSzq32QZNbKCYWDRfMwi4eFg2P9IEqosECpG3GRwIbmfj72+2d4//GT+fYFc9jdmeCONc1ccMMKbnpyN809CWaOLadjMJ0TU170CxgSAbSkIzxtd50nTq+lOhYovN9h1vOhJVM4c45j1mpaNkJIHtzcRnN3gv5UFmuEiJRlSx7c1Mo961qIu12aVtGFJORq9ACEK9R04firpUybsCEYUx7iJ//ewVf+uYGBtMlze3sI6ILt7QPc8OiOYdegeHOgivUVCoXiMHDDozuoi4VodV3Si2c9WjakXdEVCgredexEbn56H722EyMrVSDuj2xlLJuOeCYnLmIhg6be5LD1WVf/fWMucuRZTPgvYWjO+dNpC3/cx7NVKLa88M9t9OwvikWgAKQtOWF6NSt39QxZk3eOx3Z0c8OybfzxqX2ccmQ9F8wfzx+e3st/3beF/7pvCxOqw0iZFzJTasI0xCy2tGbpGSFIZWjwrbs3kbEkQV0UCEhwIlKaRs5Q9opTZ+S2lYUDTKsvY8uBfnRNcP1lR1MeDlBM2rS4Z90B7l7XwqaWfnrdoezeMyl+xtJ2hK+uOalIHffZWRJDwNfOnc2lx03m3b9+mq1t/cTTFgaO0A1q8LlTVUflmx0VEVMoFIqXyVM7O7l73QGiwZErpjzNFM9ILvrlU+zoiNMxULo+LFKq+Iu8GOqLZ/j7c4WO7EFd5H6pH6r837QhqEFDtTNAXNectKUnJEqJMO/uRuoqNCV8/tQZ1McKRYxGoYXEdY/soidpcte6Vj5x82qOb6zlniuX8h9nH4ltQ3U0wOSaMCuuOY2z5o0nFK2i3LWo0HBEVXHwzbSdqNiM+ijvWTiRD584ZcjaTMs5/pJjJzCmIpzbVl8e4jOnTCdtSf77XfMYWxEpeX9N3Qm+fscGVuzsoj+VLXgmpV6o3vflBdf84tWU8ItlOwgHdD576nTKwwHChjPMuTYW5ItnHcnSmWps35sdJcQUCoXiZfLvzW1URwstKYYTQppwXsy7O+ME9eEHZSeHyzm6ZCXcuKJQiGUsic0hHLh9SCFo6nGKxIvTan50nJdFKf1VEdJz9+Bd9wO/f56Oos5Qf3qxFL96bAfzJ1bxudNmsOKa0/nE0qlURkJ8/a5NRAydgK4xvrqMWEDk7DSKzydyaxB85tQZvG/hJBprCwWVd5tTa2ND1rC/O0FVNMCFCyYMu86m7iRek6tdFAGzccSt11wR0HXGVoaZNTZGVWSoUz9AdzzLuqZuzpnbwInTakiZUBMx+NxpMwoidoo3L0qIKRQKxcvgsa3t/OmZfcyfWEGv66IfMvI1WdGiAhCvXiioa1iWzDnW+wu5i7sWS3m91kSG71gcbQF91lUyM+pjjBvBrsEwtJLCMqABIm9fMXoTjqGkTUki4zw/XRNcecYRXHzMBB7b1sGR48r50SXzue3yJZw7fwIBXeSeo0b+ReZFn3Z2xLnolysYzJjcecVSptYUirGKaIDLFk8asoaHNrUxc0wZ+gjmuqccUc+0uuiQ76QqYnDRggZ+++HjWHntmTzypZNZee3pPHPtGTx49alcdeYMd56oNuR7+8wta9A0wUXHTEACi6fV8vG3NY7yySne6CghplAoFC8R25b8z0PbmFIbZZ/PfiDtOnUumlJBceOd7QqvlGnn0lQVEWPIKCOPM46s5Vh3DqOfi46ZkOvEiwS0Uf8yjxmFEbOwBndfuZT3LBo+CpRyi9WKBUR9ecgxUhX5aJT+Mt4qW1r7C37+yJKpzBhTxvXLdnDs9x/mrrUtfP282Vx+ynTKgo7CtSktALsTWR7f1k5VLMRj/3Ea3zx/dm5bbSxIXXl4yDFZW44owsApsv/86TMLOlEDGnzzvNlcf9mxnDZrDNWxEDPGlFMZzUfBPrqkkee+fiZ3fu4k/vSxRVT7cs9t/WnOv345x02p4aYPL+Q3H1o04hoUby6UEFMoFIqXyE0rdvNCaz9Xn3kE+7oTQ7avbRrIia3iqA04wqU8pBEL6sNGsdY1DxBPmyycWE7Ml8fMZE1mjS0jGtRJZu0hYqSUnCgL6cTNwojZl88+klgowNtmjinZuel/SXjHlYcNnrnmFK44ZQaxoJ5LW+pavhZquDWMxPLtnaza203GFX66JvjlZcews30QAfxm+S5SGYsvn3Uk/3mu40Q/QpMm6ayNlJLOwTQfXdJIuZszHC7ld/ZR4zj7qHGHXOd588fzl08u5jOnTOOmDy9k23+dwyWLJh/yuPryENPqyzj5yLHcd9XJzByTT49uah3kHT9fnuviVLx1EG82w7hFixbJVatWvdbLUCgUb3JsW3Lmdcs5fdYYsmaWP61sLthe3HU4HP6B1V5UqVhUBdzuu4ABwoIMcMVp0xBC5/plI9sbVBrQ50blijs5AT62ZBJfP28uTT0JzvjZ8iGO+153pccRY8r4x2eXUBkJIKXk6O/9m/6kiaGLXKrTQ9cEUsrcOUf7TKbWhnn8P87I/Xzt7Ru4b2MribRJeSRARSTAvAmV/GtD64jnqY0GSGQtklmbR798Crs6Blmzr5er3z6ToDFMN8SryPN7uvjAjSvxD1ZY8eVTmFhf9totSvGKIIRYLaUsGepU9hUKhULxEvj3CwfZ3Rnn8+MruPrv64ds9wuOUlYKHv6i/FIiDPI1WKYrqCbVRPjA8Y1UxQJsbevnoc0Hh11nny81WnxuDbjt+WZaejNMqgmXHHvkF2GTayLcfeXbiLizeYQQzJ9YxXO7u3J+aB5OutKZ2zhaAeaxtyvFP1ft591ulOmHl8znAydM4f6NB0hlbXZ1xDnYn2bBxEo+d/oMPn3z6pLn6UpkCeiCWePKqC1zolFvfx1FnI5rrOXr583ixw9sJeF+T2/72XLqYgblkSBhQ6djMM23L5jDBSM0ECje2CghplAoFC8S25b88rEdTKqJcMOy7UO2v332GB7e0p77ORbUyByqDZKhYiUa0EiZ9hCBdMkx4xlb6dQ47W0fKNj2YkSPEJAxJY9v78Ao5c5atJYHv3ByToR5LJlex4odnTl7i6m1YfZ2pZyaN9/8xZHWVx7SueSYCfzHWTP41r+2cvuaA3z33hdyQgxg7oRK5k4YOk4IIBKAZAkfXUMTWLbkqPGVVEaGeoK9HvjI0uksbqzj87euoakrQdqG7rhJd9xkYnWEzsEMn791Hd//1ws88dXTCQde+0ie4vCiasQUCoXiRXL3+hbChs6JjTXs7iqcESiAK0+fQcgQuRopz3n9UBQLlER2qAgD+NMz+9nY0svSHz3Kto58bdpIIizotmIGcN3eyRuQCiQJ35wlDZg7Pp8e04Szlmho6L/dP3vqdD6ydAoxV6Dt6UrlOhp1TWBo+edA0RDsirDBmbPHsvZbZ3FEQyVn/O9TPLXTGekzkLbY0zk4zN0UMqO+ouTntpsWfWhTC8u2DB81fK2ZPb6SR758Gtt+cB6rvnYa/33xXCJBHdO2+eDxTndn+0CG0376OP9ctV/NoHyToYSYQqFQvAiSGYufPLiNeMbinnUtQ7ZL4KJfPU1lSPM527+0a3m/oIvr0XsSWS684SlaepP4A1SyaF//3zOmRAdsUdjZaMt8l6eGkyaxgU0HHBE0uTrCidNqAOgcTJVc53cumMuliycjgcsWT6Lem1UpJaYt8yaxsnCN/SkTTcALrf385MGtxEIGbf3p3HnPum556QdTxGdOnVbyc0/EDmbgL8/sGdW5XmvqqqJcdvwUbv30CQQMnfs2teW29fen+OZdG7n0dyt5eoSZpoo3FkqIKRQKxYvg14/vpLUvxYdOmESqKNA1JpaXPu1xC11znO5LRbVGg2fOWupwCbzr6PE8/KVTmFHvdN8NZ7rqYeFEtzzz1uIXgA25cUcCuHTReJZ/9TRmjnUiTqv39g577g8cP5mTZ9Zx63NNuVmV/uHX3p+ZY8oK1hhPZfn14ztJZi1OmllPQBecMrPWWc8on5suiqVqHm/LE9u7ePYNNEB7/sQqvnX+HI6bWkN9LEBQF8QlJE14dk8377/pWa66de1rvUzFYUAJMYVCoRglTd0Jnt3TzXsWTuQ3jw3tVmyPSyI+iwlNyoIC+UNYVJXEr0X81UEC+NemNsrDQR7+0in84rJjOHZK1bDHemRtqHZdZod7AWjAladN4zsXzUcIwdzxjhDb0NI37Dqn1Zfx7QuP4tsXzGHBpIqcrURAc2dGuhMFuuLpguOe2t3NA5sOkjFtXmjtZ9a4Cja2OH5ifh+ukWioKj2OyC9iTeDJnR2jOt/rhTNmj+V3H1rEk9eczkXzhzYZ3LP+AIPx0iOyFG8clBBTKBSKUXLdw9tZ29TLZ06exv7ewhegVyOV9E3hjrqf5eqrRxHhqSsLcPcVS5heGx2yzR+Ak0DGtHlmZwdCCM49agzT6suI+Gz5qyMBtOK6rKDg2ClVlAdAH6buuzoa4NfLd3PRr57CtiUnzqjlpJl1HDe1esS1T68v42NLG/nRuxYQMjTmT6zkvPnjiRoaUjpirCueJeCrG/PMaiXw3J5uJlSF6U44Mxz/+NHRGZsumFTNkqlVzoQCDUKGhi7yjzugC7505ky+cvasUZ3v9UY4aPDj9xzDJccO7ZwMvj57EBQvAtU1qVAoFKNg9b4e7lzbwpWnzeBrd2ws2Ob8i7ZQZRkCxlWG6GtPkLUcMRQL6wwU5zOLmFoTY8aYMibWRNnVNdQk1k9Agwc2txEO6HzpHxtImxbjq6N89uRpNFRFiWeyXH7LmoJjJtVGeXRLJ6VWYWiCmliQ9oE0AtjbOch3793Mdy+ayy2fOH7EtfiZ3VDBI18+laChUVcW4oq/rGb59g7Chs43zpvN2XPH8f17N/OP1S30xDN87R0z+dGDToTxQdeKY1pdlAWTRhZ+fv76maUAbG/r559rmglqGuGgjqFrTK2NcubssaM+1+sRTRP89D0LePfCiaxr6uW3y3exdEYdweDoooaK1y/K0FWhUCgOgZSST/zpeUwLfvCuo3jbjx8fsk/IEKRN6dRCCae+6ZhJlaxtctJ5OlAW1uk7hBDTxcjDsYsJABWxIF3xDJccO4H/efd8NM2Rht3xDDcs28Efn95bcMysMRFMG3Z2FnZ81kQDuWgUOH5l/33xPE6aWT/6BZVg6Y+WUV8e4tMnTaO1P83G5j6e3NFBNKjT1JPk0S+fwqq9PVxz+wYkzsim1V8/k2hYhXsUbw6UoatCoVC8DB7Z0s6jWzv44bvmcelvny65j+WqJ4nTHVgd1ekcdOqhDNcZ/1AiDEYWYaUK97NAKp3h5o8u4uRZhVGfmliQb194FJ3xFPeuz3ffbW1PuusSmLbM2Vn0J7MEDeF0WGrw+dNnvGwRBs6cxf/3zF6++68XONifpqEyzNlHjWPBpEo2NPeha4L3HjeJJTNquWtNM1eeccTLvqZC8UZBCTGFQqEYAcuW/PShbUytjTKrPkJzUW1YyBUuNoUjhHoSFr0JR3i9VPuK0RLU4X+X7SBtw9vnDE3B/c+7j6axdie/fWI3ad9iTHfItWVLqiIGfSkTy5RMronQHc8wb+LQYeMvhfcumsTYijBVUYNZDRWM8Q3cft9x+f0mVkeVCFO85VBCTKFQKEbg3vUHqIwYvPvYqVz2+6FlD6YpMTRBtnjED4XF9f5oll+wjYnpZGzBQMrEkk7dV3YY4ea/wvsXjuFva9oxAF3XWNfcx6duXsXEqhBBQ7C70/H8GlsW5NlvvJ0vnXUkfcksf3pmX8HaLFtSFwvQGc+nJI9vrOF7F80b4qL/UqmMBrjw6PGH5VyvZ0zL5q61LcyfWMkR40qbzCoUxaiuSYVCoRiGrGVz63P76U+ZzpxDN5rk70K0cMTMhIoQkP+lWpyEzIkwkRdhlWGd+ZOq6U2auZTkcCLMjwbEM865shJSpuMgr2uC5t50ToQBHBzMMOsb95NImfx55T4AbvzYQv76ycXUxQLEgjpd8SyG22UYMjQuWDDhsImwtxLv+r+n+N49G7niL6t5cNPIA8kVCg8lxBQKhWIY/vrsfp7b083588bx+I68GWhxnZYN2NLOiSy/UKsM6znDVSg0KR1IW2xo7s2ZnQ7r61VkQWEDd29spzzsJDXiGeek177jyJJeZSlTcvT3HsLQBGPLgyyeUsvKPT10xrMc31jD0hm1mBKqogFiIR39pRielSCVtUhlRzfe6Y3Ob5fvYE9nnERWcrA/ww/v38L7f/s0375702u9NMXrHJWaVCgUihIMpLLctbaFdx4zgQd8Y2aKiRowe3wF+zoTOZHlF2p9KYtY2OJPHz2WT9+yhoyV325LaB90vOx1oFQwrJQzv+Z2ZcZTJt5s7Yqwzrb2QWwJIR3SlrPfPz51PJf87lkytrOygwMZlv54GWFD4z0LJ/LDd80jmbW47t/b+fOz+7j/qpOYObb8JTwxhzX7ulm9r5f1zT08u7uLYyZV8/2L5zG2Inzog9+gfOeezdyxtpmBtCM6B9ImA2mTfd1Jnt7Tw+Pb2nnkS6cQMFSUUTEUFRFTKBSKEtz4xG7WNvXy8aWN9CaGdy9PmY5JaTJrIih0v/f+pZvKmNy9vo20Be88uoHz541lUnVkSJTLr7dCI7yzbelYZKTt/OzG/pRFWUDD0AVTassYVx7ClnDl39az7MtLmVCRt4JIZyU9CZPLT5mGoWuUhwN864I5PPCFlyfCWnqSfPqW1fxm+S7u39hGx2CWh7e0c+EvV9DSM7In2huJZ4oc+h/c3Ep/0hXUmsj9b8AbtN7Uk+SDv3+WLa39Jc830v++DgeDKZNLf/cMNz6xC9t+hTtHFC8aFRFTKBSKItoHUty0Yg/nzB3Lf9/3Ah2Dw78oy8IGLT1JNCGQyMICfTd0lTYl925oRRewvzvBLZ88ASElx3z/36Tc4Y5akX9YTRT6UxDPlr6uIB8Z87y3po8to74sxI72QeZPqmJCTYTffmghfQmTqmiAln7nZCnTpjyk84Xb1nH355Zi6BpCCGaMeekiDOCXj+6gczDD+MpwTlVK4GB/mo//v+e49dNLqIm9sQ1I//OODfz1uSYALppbj4WgrS8/tsmy8wPUbRvChiPW1+7r4Yq/rOYvnzyB8b6RTDc9sYv1Lb3MbShn04EBLpw3HguoiARYMqPusK175e5uVu7uZn/XIN+/eMFhO6/i5aOEmEKhUBTxxxV7mFobY+2+btoGhlFCwBmz6li2tdP9aagBWNaWBDSBJSVZV2Wt3t/HB256luMmV5L2RBhD/cN6E5C0ICwg5W7zuh0jAZ1M1sICvnj6dB7Z2kF9eYjZDZW09qX41EmN/OnpfYytDPHFW9eyYlf3kLXpmmDzgX4e2NTGBQsOT0fjc3u70QTMaSgna1p0xLPOjEkJ2w7G+djvn2VhYw2LG2s5elIV4ypfvXRl1rL5nwe2snz7QT6ydDqXHjcJ7SXUwjX41nz3pg68eePRgEYia2NoriB3CgcJhgzAImVK9ncleNuPH+WdR4/nB++aT1c8zR+f3ktLb4p/bzpI2pLcv6ENTYOqWJBfvX8hixtrXvI9/+25fazY1YlpScZVhEibJrc938yTO7t5/D9Oe8nnVRxelBBTKBQKHzvbB/ndk3u45NgJ/H1V6VSSBkQCgvceNwVD03hiRydVEYO2/kyBHAsb8J6FE6mIBrnxid1kLElAg6aOQdbu783tVypZlHRDaymZt77wom1JtwA+EtB4ZGsHIUPjjNljWbGjk+Mba/jY0qmcO6+Bz/91zRARdtGCBh7e3EoibRLQBY9taz9sQixrOVYej23t4MiGcjKW5MGrT6a9P81Fv3qK9Qf62Xignz88tRdwRM0HT5jC5Sc7KdJXkq/dvp7b1xwA4Bt3bWTFzg5qokECuuCso8ZRFQ0wu6HykOf5/BlHsGxrB+uanO/PG07jddSavoI+G0hmLMIBjaxl5cT2fRvbuOqMI2jrTzLghkTTlsxNVbBs6BjI8PsVu1nf1Et52GBafdmLFmVfv3MTpoSgBhkbYobTlbu3K8H6/d0smPzSRZ7i8KGEmEKhUPj46UPbCBsajXVDh257aK7X1+W3rKY8pHPuvAbOn9fAJ29eVRDZSpnwt1VNZHz5yopIgL7E8FG2F0N1NEjGtDlxWi1nzK7nzJ89wamzxjC+KsoT2ztp7k0NOeb+jW38+JL5fOkfGwBJx0B66IlfAomMyf7uBJURg76kibQl8ydW8LvHd3L89NrcfuUhjUDAoK4sRGtfkv95aBsvHOjnfy89msArKMaWb+/M/V0X8OiWg2RMiaHDrc/uI2XBp06aylffMfuQ6xhXGYKm/M8CR5B5/nBeyhjAtiUp938AhgAhBLoGH/r9s5SHjVwqUxeOH5ywbUwbAprg0a3trNzdRV/SEc2fP20mV505c9T33FgXY0dH3G3UgLiZ33bXugNKiL1OUMX6CoVC4bJ6bzc9iQxXnDaDf29sKbmPhuOU771AExmLO9a28Jk/ryoo1PaK7U3LeQF7v2y741myPrFWHjx0esxft+9ZXVRHA0RDOl848wi+fv4c7l3XypzxFXzx9Bns7hjgz8/sLjhH2D1J1pZ89fb80PIzZx2eYdjt/WmiQY24G+HZcnCQNfv7+MeaFq7489rcfl86axYBXaMvkUYTgnccNZb7Nrby7l8/TTJzeARqMT9+cGsu8mRozveXMiXhgMC2wZs89cen9vG9e18Y8Vwt3Qke2nSw4LOgLtA1UdANa7hfuCUd0W5J509tWYBExiaeMWnuSZKx7Nx+GdPGtp3vN2s76WzL225L9nXHX9R9X3n6UNHm/W/pvg2tdI9Q+6h49VBCTKFQKHAGe/982Q5ChsZT29tZ21L6peelEb2XbSigIaVjF5GxnGJ9AXzp7UeyaEoVNnD23LG544o7IwcypYdL+n85m+45ddd0VQJHjivnDx85jvPmN5DOWoytCNM5kOayG1dy+s+eYFNr4fpTbr2ZwB1t5H7+gRMmj/YRjYhp2wR1jZpogLHlQWpjAd517AR+cPE85jXko4vtAymm1ERo7c/Qm8gysTqCocGG5j6u/sszErcAACAASURBVNtaHtjUykDq8Aiy1Xu7OOUny/j147tyo53GVoRy30EiKzF9j19KyRjXmLcUd65pYulPHhtSDRgOarmUpIY3w9MR4OB8b5GARtAQtPVnEEBfIkt/ysT2RcQg3z0rgPPnjSMSMjhhWg3vXzyZn75n9EX2tm1z2/P7EeT/IWBogo8umUJQg/bBDO+/6ZlRn0/xyqFSkwqFQgE8tq2dFTs6mTkmxo724SMPAhhTFqArnsWW5CIWfiTwwwe3YWjQWBvlvo0Hh+wDzgtyuMSg/6wCOGJsjG0HnXVNqAzxmw8upCoa5HfLd/Kbx3dRGQsST5sENI0jxsTY7t6DV3cE+aJ/gIAh+N75Rx222qwx5SEyliQS0Llk4QRuWbmfoKFxze3rsX352t89sTu3Lgk8vauL8+Y1cPf6Vh7c3MGDmzuYWB3mhsuO5ZjJ1S95PU3dcb5z7wvs607lauwEcLB/+CiQAKxh7B2ue3grv1i2q+Q2y7KoCOv0pyyCuiDt3a9wOhWEgHTWRtOclGPWlgQNjbRpl2jxyEc9H9/ejmVLthzoY0NzH2v29/CrDyyksc4xCJZSYtoyl0q1bMnerjj3rGvhvo0H6Es4UUBTwviKAIZu8I/VzblU5da2QR7c1MY75o4b8VkqXlmUEFMoFG95bFty++pmZo4pY2f74Ij7CuDgYD5i43p45iJWlRGDLu8FaMOertL+WQEKa3b88yeLkcAxk6po7UsjBNz/hZOpjAb52UNbufHJ3aRMSbfrY1UR0hlIZ9E1p+jbL8T8fPeC2bzv+CnD3md3PMMjWw4yY0wZCyZUoh9CsFVEgnzqbY08sKmNW59rJhzQ+f2KvcwaV05vIkNbvyM5szYE3TWFdWjujvNC60DuPNURjZaeFB/74/M8/40zX1Ld2D3rmvnqPzfkhq1XRQP0unV5/mJ63Wf/Ac5/71xzgPcumlxgMQFO2nI4BjNw8swqntjRlRNhmsinr8GJnGYsRzgZmsC0ZK7Q39DyRf/eOnRNMJh2biCZtRDAjvZB7l3XzFVnHskzu7r44t/WcrA/TSSgEzQ0BlLOPw6Ob6whlbHRkMwbX86GAwP0JE0iQYFZJDRjo0iNK15ZlBBTKBRvee7dcID7NrZRG9FLRij8DLe9Mmxg2ja//MBCrrl9A/u7kwXbY4G8J1hQB80CfwLOez1GdKdj0ut087htVQsCuO59C6iMBlm9t4tfP74rl1oL6oKMJelPWwXF4pkidffJpZP5xgXzkL43/41P7ua3y51oT0U4wDlHjeVvq5rojGcpD+pUxAK8b9EkrjrjiBGfzRfOPIKjJlRw7e0b6ejPENIF/aksFx/TwK+X73Wuv2Qyd6w7gC4EXfEsKcsmYmgkTZux5UHH7iKZoTeZ5fk93S/JS+uONQdImflU4UAqiwQC7mxOP/4fLQn7uhPE02bBPmv395A1h8pkL9I2riLEyTNq6RjIsKXNEZW2b4C76U41CAc00m7Hqy0lEkew6ZpGxrQLRLP/+/HWmbUkf352PwsmVnLdIzspDwc4Z24Dy7YcxLJtxpaXUV8eYu74MrrjKUxb0pc2OfWIOibVRPnX+gMkih7Ah/+wij0/Om+0j1bxCqBqxBQKxVuajGnxhxV7mDGmjK5kvr1xuDiB9xqrCOV/fQY06E2ZpE3JZ25ZzYGe5JDj/MasGQuG9jM6eEsoFlAAYyvDXLRgAlJKrr1zY06ETagMEzby6xlpkM7UujLA6d4DmPq1+/jv+7YwtTZGbSzI7s44v1q+m053wQMZi//P3nnHy1HV/f99Zmb77Te3pPeQRkIgCSRU6R0BEbBgV+w+oj8fH9RHsKGgWFBURDpIEQGlBUgCgSSkh/Tebu93996tM3N+f0zZ2b17bwLqI2U/rxfc3ZkzZ86cmez5zLd8vh2xFM9uaua4Hyxi2c62QftWFMHZM4Yza1Q5miaYUldCU0+SpTuySvR+v0ZKN6gqCTC5roTKkEbErpm5cFINR4/KuiP3db654HQHH184DrCsSiaWsCqAzLuphSyFmgI+NduwL5mhK5aitiyAKrJB+E5bFWiJpvjZ8zvZ0Rpz70NItUiY01NAUwhoVjyhlJLzjx6OX7EIm+OiNGS2rqghLYud46Z00BZLc8er+xlZHuCCGbW8sLWFQ90JmnpT7Gzr47U9nfx5+UF2tcWJJ9NkMpJtLTHuW3mQk6fUsHBCJZOGhRlVaVn8hsoOLuL/BkWLWBFFFPGexu9f3sPutj7OmFaT45b0rtFeC5ODkF8jmrLijTL2Qq+bkpRu8r2Lp3OgM4GU0tXMeqsIqlagvQKcflQNiiL47hOb2GnHi5UFNX582XQ+dtc695h8q48DVcBV80a738f/99Pu55MnlvCrJYcKHcYpk4eR1E22tfTx0T+v5v3HjOCXV80ZdMxnTKtjU2OUzU0xykMa21qyhOp3S/dSGfbR0huntjREd0InYJOXtK4zc0QZr+xoI1mIJR0hFk4axrfOPYpfvbgTw1MGSjezVizIum0VcIVZhRBc9OtlxNKWhUoToPlUEmmDkCZIZCQ+VZAxJAFN0JeW+FVciRJHT8wQ1pmccyXSBmndwJQQ8atcPX8Mf3+jueD4BZZESn74YcgnuP6C6Wxt7OXB1Q1U7O2iN5G13mnCigcLapZumV9TaYklUQX4VPjHG828b2otj3x+LmG/Slss9Z4pyv52RtEiVkQRRbxnkUgb/HrxbhJpgwUTqgdtJ23ZAbBIGUBrLBv0PbmuhL0/Pp+9Pzmf7T88j2sWjOe7F07nK2dMzrGgQOEfXRUoCShu344FJKRkpRWEgCvnjWZPm2XdcBBN6jy6er/7fdaI3DJFTl8lfpU7rjkOTbPev5/deMglCSePj/CHVxtdsllua11EAip/+Oix/OzyY1gwoZpR5UEE8OK2Nr5w32qW7+mgEC6ZM5K54yoZNyxMb0LPsejUl/npjmeIp012tVsEbe7YSqrDGs09CZ7Y0EjGlAwvD3LB0cML9n84+DWFEyZUu+7JgJq1LuW7IsFyCzvyEhlDkjZMakv9GNKq59lvu3v7MxIT3CoJfXbGq0PCvJbItJ5LJE0swi6xYr4+9KfXCz4LprTHk0fCBJZlb/G2NtYe7EERMGtkOY987niqw5rbBiCWMpgzppwLZw9n7tgKFNvyJoAl29v4+F2rkBLGVIWZ8k/UFi3iXwOR74d+p2Pu3LlyzZo1/+lhFFFEEe8AfOWhdTy1sZlTJlfjUxVe2t4+oI03bsf5HPaJnFibq+aN5qbLZw049umNTXzxofUDtuejNqLSkzBy3JEBWxojmspu1BRBbWmApt7kAFJRCEEBurAsQbd9aA4XzrIU9PuTOrNueH6Aa85JGAj5BMmMZc1RBZSHNWJx3bW0qQJGVQY50JVkfHWYJQXK5SQzBmv2d7F0Rzt/enVfzj4nsUGXMGFYhN5Emk6P79anwqOfO5FjxlQc5gotNPcmeG5zCx2xFA+vOYQ0Tc6eOZyHVh0qaM08HDSbuHhLTx1pF042aKFzOuWeHAudA6ep44bUFMsy5wT4C2mS1rOVFQRw+TH13HLVcQC09ya56Lev0hpNWWMGZo8sZ0drjA8dPybHKutXFdKGyUdOGMMP33/0EV5VEf8shBBrpZRzC+0ruiaLKKKI9ySiyQzPbGomoML6A12D6nl5yYohLTLkZMMFVCtrMuwbaNswTck3//rGkGOw9KYkvXkkDCxLjJ42CagwsiLI+bNGcPvSvTT1WtFlN106k+uf3JKTBZiPpC06dtHsepeEAVz5xxUF46McIpbISMIKxE0r7qurX6cqrNEV16kt9XPDxdPJ6DpfeXgzB7oKZ4UGfSonTa7h1hd3AvDbD89h5ohyMobJI2sOsf5AN+sO9tDSm+DM6fWMGxZBN0xURXDhrBEcVX/klpqXd7Zzw9+3EvIrJNNWvNVDdmHuwabHK2kh7Wt3NNp8quWCHFUZpDWaJG1k63wOBmfunHnNJ4ACK5Yw5NeIBDQae5KUBzV6k1nXonP+kqCf/nSGaLLwGQWgaNnlu6Y8yA/eP4NbF+1kb0ccUzeJBFVqy4JkDJOqsI+eRAYhYcGEKibVlXLnq/s4/+jhLJz4ryssXsRbQ5GIFVFEEe9J3PHKXnQTzju6nr9vagEGuq4KQTclztLpSFfcu/IAiYzJf501hdoyqyj0r17cQTw9dPyNbkoEFuly4F3AnUX57k8tYExVmOHlIa5/You1EKsKflVBSgMps1mXAisw/H1Ta3h2cys+VXDtKZOs8xkmf3xlD5ubsjU0vdfszRWM2x1mDGuM3XGdulI/f/38iYyqCjPpf56xjhde204u9rT3sfZAj+1mzBLB/zl/unV90pJweCvFt724cu5ohpf5+cID6y2rE9ZFqYpAKJDR5YD7KvP+mmTlLBIZybkz6vj+xdP56l82EE8bjKoMsWRHO9I0iQR8BHwqIyqCrD3Q45Yn8iuCpG6gm7kkTLPjvVIG+ExJU0+SkoBGyjCJ+BX6bRZeU+KjP23SFU9TgNtnISCdF9t11vTh/HzRLsASj23uSRH0Kfxl1UGOn1DNq7s7ATh2bCWfPmkCL25r5duPb+K5r55CyD9UekcR/24UiVgRRRTxnkN7LMXyPZ18+PgxvLormwXoXax9dikc77agprjB2GGfQiLjFHqGh1Yf4uE1h3j5m6dRGvTxq8WFxT/BVlG3zTFCWIu0IsBvB4M7uHreGK6/cBphv/VTXVMacMf565d2EVQlhinQVEE4oDFvTCWv7ukgmjSYP7aSgKaydEc704aXEUtkOOvWl109r0LXnI+gBmOqIkypK+PZTc20xtKc8Yul9jVbR268/sxBj7/ukY0A/OCSGQX3CyEYgscdMYQQzBlbbQnsSoj4FTK6SdqU+AXMGFnGoc54jvWpEBxrVllQI542qC8P8/DnFrr7D3bGSWZ0rn9iE3NGV9IWS7GpMcroqhD9iQwd/Wm8KheqbT11kgTAKolVGlSJ2mPxXn5HnyWzccL4KtYf6gbTKhJvyFxiZ0pYtntgfN7lx47iR89sA6AkoNIaS7lVGKoifp7Z1MKvX9rFXa/tY+64Sl7c1s5Pn9vO9y8ufH+K+L9BkYgVUUQR7znctngXGw71cOLEag50FRaSyJhQGlCJpQzXauSQsGFhjQ5btPWlr53M/u4EX3pwHYmMybce20RXPEt2VDsuyOt5NGS2ULRlubHImJeE/c95U/jsqbm1As+YVs+CiVWs2NNFQ3fCJVEpQ2KYOgsn14ACz2xq5Yant7sxbbcv3c1j6xoGkLBCCPsU4hmTkE8ACjvb+tnZ1s+lc0ay9kAXmqLQ2Z8ipessve5USiL+gv3sbI2x4VAPoypDnDn936/cXhb08cur5jBhWISIXyMcUCkJaG7lgM/cs5oXt1mk2xuT5SWiToyWYUpqSoPu9qO//zxXzh3Ndy60LHmr9/fQnzKoigSoKfFTFvRxsDOOaWa1w8g5kyuyjymt4H9vC8cK6rhI97RFSemWIn/GtFzYpieeW2Bpj8WSGUqDPnf7ZceOZENDN3ta+5hQEyHkV2iLpbh/5UGuO2syT21sRlOgJ6Gz/mAvc8dWcvfy/Rw7poKLjxn5luf+4dUH+fbjmzAl/OzyGXxw3ri33Nd7EcVg/SKKKOI9hT1tMT565+tUlwQwDIOtLYNrVVkZj4ITxldyqCfJAVslvzSgksgYlAV9rLr+TDRVYdW+Tj74h5VHNAa/aimrl3pihPKV9bfdcDahgG/AsVJKDnbG+cvqA7y4rZ2aiMbyfT2AFdsksGpenjG1li+dPok/vLyH57YULrE0lCt2TFWIez4xn8tuX05ZQOOsGfV89tQJVIX99MbThAIaIZ/l0irknrzoN8vY1Bjl7k/M47Sjao9oXv6dOPvWl9nZmls1wSFH+VAEDCsJMLwiBFKytTlKechHXVkQKWFrc5QSv0LaBF03EYpwLVuqkKQMm9R53MzeufbZsYHOd29JqpKASkXIR0NPkg8cO5KKsJ8HVh5AUyGWMnP6eeYrJzN9RFnO2G96dhv3LD9AVcRHQFP41EnjufHvW1w3elATXDVvDFuaY2xq6GH+hCpae1P84srZzBhR/qbntak7zsKfLnG/BzWF7T887033827HUMH6RfmKIooo4j2Fve39dPSl2N4UdfWt8iUmHOimJeXwvYtmMHtUNoMvkbbigO7/9AloqoJhGHz23jWD9uPAWazThiWDkB+o7WBERaAgCQOL9IwdFuFb503nha+fyoOfO5FfXWkVg84YEkUIPnDsKO78+DzmjKkkErAcH1PrSjiqJlu253DxcFPrIlz4m1dJZkzaYgnuWr4fJGiqghTw5PpG/vepLdzw961u4WoHW5t62dQYZUxV6G1BwiDXCuWgEAkbVRni0jkjmVJXQnnIR2XEz0WzRjBrVAX1ZUFGVAS57NiRDCsNIKV1HyvDPquQumKRMCfkTcqsPpn3VJm8+YqmshmjfSmDxp4kPkXwyq4OntrYhG6YfOF9k9x+VGGdo7FnYKJEeyzFvHGVXDpnJHs74kyvLyfo0whogpMmVVNdEiCazBBQLTK4pzXGoe44196/luhbKLb+2fvW5nxP6ibJ9NAu4CJyUXRNFlFEEe8pbG2Okh9DP5jQJ1hB2Lcs2sGirdlYMl3CN86a7Foj7nptPz0JnTHlfmIZSXe88II2mEuMvO/9KYOWaIL6shBHgkvmjCKeMfnViztpjab4xEnjALjl+e38dV0jpUGNK+aO4uZFO91jfJ4SSk7GoHcci7Z1uPvG10Q4c1oddy3fz5dPmcjxP16MYUo0AadPqxsQ5/XkhkaGlwf58aVvH3kEwzRdIlzIHQlWIfQTJlRzyxWzh+7LMJh0/XNuP139lqacUxlJkA3+F7ZMh7PdfQZsa5wA2vMKkVvyF5K2WMo95h9vtLj7h0U0Wvt0DnUNrOAwe3QF33tyC584cTxT61v52N2rmDWqnKvnj+GCWSO49Hev8vSmZlRFoa7UT2Ov5a6WsRQ/fmYbN102UIZlMCzd0cbW5uiA7fesOMDnTp14xP2811G0iBVRRBHvGbTFkvx28a6cbWoBi4U3i6wtls4hYQ5ueWEXF/16GR19SX738h4EcNc18wYlYY6VRFOEe67BkgV7EzpLC2iaDYWr54/h+a+dyqlTavjR09v44yt7uG3JHoI+hc+ePJ5fvrCDZDZ4KUcuY8bwUlf/Kh+fOHEcL113GgC3L93D9BsXufIdJrCxoZtV+zrd9m3RJPcsP8DFx4zgpMk1b+oa/p2I2vUmCxFg0/6rG9JWwB+s/LqF7zyxJeceCpl9jhztMSfIvrok4Gayqp4b7nyWFC61lE8at3kyXaePrEAV0BodGN94yeyRlPkFX/nLelp7k/g1hW+cM5ULZo2gqy9JbzxDxpD4FWiLpfDZJ0pkTP6+oYmVezoH9DkY7ly2r6A8yE+f3c79K/YN3FFEQRSJWBFFFPGewS9f3DVAr6vQItiXMtBE4XqTVeGsy3BTU5S5P3yJrv4Ml88Zydf/tnnQcztuMG/QtYK1kGueGCOAGfURFm0pXP5mKJSHfUyojbB8Tyc/f347PlXwoXmj+PkLu3J00vLFCra0xNALsTDg8XUNPLLqIE9saMzZrikCRQjaomk+fe8a7l+5H4DbFu8mY0o+PH/smx7/vxN1pSE0JUt+Ffs/TREoih3PBTy9qZm22GCVQC38dV2D+1nY8hvu/bW3a6o1y6mMpY2W0k0U4ZlkacVrOVDzHrb8++F9bF/e0UFAUznYFUc3TB5f28AXH1jLlsYeysM+MtKKQZxcX8bvPnwcvfEUX3xgLafevJSmniRSQjRpWAXJ7fP4BCR1g9tf3j3ktXuxuaGr4HYT+M6TW/nO3zZimiZPrG+grQBpLMJCkYgVUUQR7wnsbInxl1XZ0kDHjCodsPiBRVIEtlUjr8HI8iBdtsXrvk/Oyx4j4KUdbWxs6B30/E5GnNeCoEuLoOmmtApI27/IW1r6Wb6nk67YQNdTNJnh4dUHC1ptXtjawl2v7ke1s+2+cfZRPL6ucUA7AygPZiNTvGPK16/qTuj899820ZqXcWlI6Qacx5IG33tiC3cu28u9Kw9wzOgKxlS/vYpJf/mMSTn6Xqb9n27KbFFwCZNqS6gI+dhwqKegxelv6xpIe9m7sLIsTbL3z68KErbOV08i41oQvS5x3ZSY0qpbqSoQfBNaXrWlPsrDGluaevn4Xav59uNv8MymFr5jvwhMH1HGvPGVLJhQzY+f2cqXHlzPM5taiKUMdNNEEVYtS1WxnktNseZieFkQ/QhrfKbTBt3JoS2H97/ewJr9Xdy2eDeX3LaM5ze3DNn+vYoiESuiiCLeE7h96W6m1JWiCCgNKIQDfq5ZOI6vnZErEeEsphIrQ9FBUFO4+vixVIZ9fOvcozh5Si3Th0co8St8+bQJg7okIRuH5CxbihjoesqYloSFo9JfGVJ5cHVuEe6eeJrTbl7Ct/66iVN+toTdbTEAehMZbnl+B5+/fy0S+O/zjsKU8OdX99IziDp7IU2tiF8hYw5cGPIV4h1rkoNTJg/jnKPrXXmIb5591KBz8Z/CUfWlOdbMfDj3Y3x1iP60wft/+1qO5cvBD/6xNee74ZEmUexe0nlkphC1EVixiRlDYtj1LP0F3gwKua9bYhmae1Mc6kqwYk8HKcMixBsbe3l87SEauxO8vreTX720izcaeomljKxor2mNWTez9SxLgz5uvGQm758zihV7O2npPbz1avnewnVG89HRl2J3ez/N0TRffWgtv12y6/AHvcdQJGJFFFHEux4r9nTyxIYmUrpBQLFkAFbu7SSZNjh2bHmOFcgpzOxTRY44pyLglZ1tVEd8fP40K4OtNZpiZGWYU6bWD/pj6qyt3vgg086mU4CAJqgI+XC8VPGMSW2Jn/qKEvpSuRaHnz67jS67JmN3PM2n7l7DB3+/gk/etYrblux2x/u7xbs5c1pNTmHyw0EVuArvQ4msOjFVhsQmtSr/e9EMbr58Nge74nzkhDGcMHHwAur/KeiGdK2ZhXDixEoAtjb3UVcW5OiR5by0LTc2sLE7Tlc845I255Y61kWzQBqmXxVZd6hnXoWSJbiKsBNECgRcOTUvC8HJoHT7FPD9p7bQEUu5mazSvk/OC4b31qr2MxhNZHh07SF2tcaQwL0r9g9yxixGVR6ZxfMPr+wFLJKfMuD2JbuZ8d1nMM2hrWnvJRSJWBFFFPGuhmlKfvT0VoI+hYauOLoBYb/AlJYa/jV/XuMR4LQWLL9qWSqcZdGvgG6arNrfTVd/mnuX7+O8X75CUNNojaX46kPrckoMeWHYQp1OOSNnQXTW3Iwu6UlkcmKC6spDrD/Uw3kz63KuY1+HpYOlACndpC+ZYldbjNZoEk+4Ed1Jgy1N0YIxbl44++tK/ZQGVJeQFqxD6Ymtco6VEuIZg28+tpHFO1pp7Elw0qS3Z+3CjGESzNMXcQjn8LIA9356IWOrwzT2JGiNJjlzWh3rDnbT0Zd1yf5tfSMRv4oE5o2rxJRQHfG51kU3gN/+e/Gs4YwfFsa0g/dN6bGEymxbpyLAYF5B99nKu6ECK+nCp1ovDoYJ0ZSBLrPVGspCmtW/mduHIiwpkpKgVfvyYGc/mxp7WDihmvtXHqB3CNIKMKmulOHlgSHbAOxojuFTQBGCcdVh+tImSV1y1R0ri3FjNopErIgiinhX48mNjWxuipLRTcsVBCTSclDtHt2E/Jd1TYGMrQ/VFdf53lNb2d4SI+xXuOjoOg71WAvKYJmHXjgLoiKycUpeq0Z1xMf+zn5OnjyM2aMtK01aNznzF0vZ2GBlzjnxZp1xA0PXaYkmBwR3N/emDjsWZ39/SsenqTmENB/e2Cp3DMK6no0Nvfzs2W3UlASoKz384vyfQE88jSGzF+hXsm7W7kSGY25cRENXHE0RPL+lhTOm1SIlLN5uWcX2dfTz6u4OEBD0KUytLyWoKTn1RPPn6I3GHtpsq6RjrdTs87pCrx4zmYJFqAbLps03uCm2nhjSenFwyLgqIORX8CuWlc3bnymzNTWHRXwMK/HhUxVu/+hcXv3WGVx/wXRiKZ2fv7DjsHN665XHHLZN2pR86sRxxFIGLb0JqxaohNX7ujnr1lf4zeJd6MZ72zp2WCImhBgthFgihNgmhNgihPiqvf1mIcR2IcQbQoi/CSEqPMd8WwixWwixQwhxjmf7ufa23UKI//ZsHy+EeF0IsUsI8bAQwm9vD9jfd9v7x/0rL76IIop4d6O5J8EP/7GV8qCK7slSk+QWuM5HPqn5zMkTrYDmvO1XHDeKv6zKxhHlWzQE2eB/B2VBKyjb25f3uM7+DKmMwf9eNAPdMFm6vY14OkNAU9FM09X8cpaueFq6i3xQzZ5rKGtY/g9/X9qkOz60G7OQWK0zblNKmnpTdPSl+OQ9a/jaX9by+Dorvq01muSLD64b0t3Vm8j82xfjM6fXU1uSLVuUNqGuLMCxYyo4eVINc0ZVcPLkGiJ+lbuX76c/pVMd8fP85mZ64xmuvX8t6w5apY2SGZPH1zdSXx4sGNflwKcoWRehvU03yb05HnYlsS2xsnBsmCpwyZYCjK4MuVpkYJE6nyLsBAAF3cSqfiDz+rCzRw0JBzoSdPanuXf5ARRFMH1EGV85fTIvbWtjyY6Bsi1enDBhGHd9Yt6QbUwJv1+2H4CEbpmHHbmQ3kSGny/ayXm/WsaqfYUzMN8LOBKLmA5cJ6WcBpwAfFEIMR14AZgppZwF7AS+DWDvuwqYAZwL/E4IoQohVOC3wHnAdOBquy3AT4FbpZSTgW7gU/b2TwHdUspJwK12uyKKKKKII8KCmxbT2Z/BpyqUBzWSBTQajhk+tAUnoAn+65ypnDyl1rVcja8OoQC/WryLzMAugax4p7Po5Wqr6AAAIABJREFUOAQqmjRyY4XyjlMF3HDJTNK6ydcf2cjvXt7Da7s7iWcMSiIBTCCSl2Hn9JE0BgqzVoYGZuMVojyHkc7K2e+cz5kPzb5YRUBfMsMTG1r4+iNv8MLmZn6+aAfPbW5h1d7CC+32lihn/XwpC37y4tAD+CcQTWY47ebFNOYFobf1plhzsIc3GnrY1hJj6c52fJpCWzTJlX9cSWd/mqbeBB/843J2tcaI+FWOHllG0KeQTBt09adJpE3XMiXIJVA+VSFj5CZLWEkg1me/KnKsljmfCzxXTvFvZ96beqysWkVR8KuC06bUkDElSV0ipUQo0NWXxsv9DAkpw+q/sy+NgfU8hTyBkteeOpGMbvCtR9ezskBxcS/ed5jKCWMr/EQ8fdeWBaiO+FCEpa0GsKutj288vJ5Xd7057bx3Cw5LxKSUzVLKdfbnGLANGCmlXCSldF4qVwKj7M+XAH+RUqaklPuA3cB8+7/dUsq9Uso08BfgEmEVKTsdeMw+/h7g/Z6+7rE/PwacIQoVNSuiiCKKyMNvXsqqyHf1Z+hN6gVddRuahy6Eff25kzFNycZDPR5y40MogmS6MAtzSBiev44LyuuW8iraO/jkieOYMbyULz+0juc2N7NqXxfHjKnk+vOn4dNUJg4LuwsYWG/KQ7kguxMGYS33ZzNfomIo5P/geq/NsAPJddMid4oictybdyzbRzShY5iSg125NT1NU/L4uga+/+QW2vvStPdliCfSmKbJ7rbYv9RCdrAzTmdfBrs0JrUlVqFyE1h7/ek889WTeeTaBbz49VP4zVVzSOuSc2fUc8bUGnY097GjpQ9FQCpjsK0pymlTaplYE6E/paNLSdCnoiqC0qBKXVkAxZ6nrS0xUoMJtGGXupIDrZiSwe+pkyghsax6Eite0K8prDnQ7ZK0/pRBxrCkSsqCmutKduDELQL0pw1OtGP7lu5o5QO/X0533CKZX390I409A2VUAH709FZO/dniQa8PoCwcJGNbcsESju2OZzClNW5nSAd7klx77yrW7T9yQdl3C95UjJjtGpwDvJ6365PAs/bnkYA357rB3jbY9mqgx0PqnO05fdn7e+32RRRRRBFD4pcvZNPk/5kl/ftP7+S5Lc109qcxpSUAagrByPIAhYUhcksZOX8NO1g/N3NO5GiVnXVUNR89fjRX/H4FLb0JZo8qJ+JXSaQNzp5Rz5LrTuWL75uEXxWHDcR3zwGkTEllSHPdaIZpJSTkQxW5xMtLuvKvDbJEUtpt8zWodrZGOf/oegD2tGcLbvcmMnzi7tV8/ZGNlId9jK2y1OcX3PQC07/3HGf+4hWO+s6z/HbxTv4VmFxXwqPXLuCqeWOswugi66I+79evURXxM6YqzKTaUhZMGsY1C8by3JYWXtre7lqTdNPKaFUUaOpNcKAr4VqoyoIaSEsIuLk35db3hFy3c/49qwhpVEd8+GxB38PF9HmRb0nrSxl0xzNZdzHWPfarEE3q+FThvgBoinWvK8I+NEXw9TMncfLEYdyzfB9feWgDPYk0k+tKOGNaPbGUzkf+9LpbxsmLR9c0uCRNERDIYxQasKUpyqyR5e48ZnTDlWjJv+a+DFz7wDo2HOp5EzPxzscREzEhRAnwV+BrUsqoZ/v1WC9lDzibChwu38L2ofrKH9tnhRBrhBBr2tvfm6bNIoooIhdekqSKgcrlRwpTwhceWO9+96uC48dV0RQd2pLmjany2vG9C7NhSpe8VIQ0vn3+NC647TVShmT++Go6+9NMqS+lpsRyn6qqwvDykPXDKXIzGAtBscdrmJYwq3MuEwbU23TG5iVog/1AO+d03K5Q+Ec7mjL46XNW0HfSlsbo7Etx9R9X8PJO67d6+c42WuzEgt4UrvvYkHDzol0s+MkLtBcQtn0zCGgqU4eXcdzYKjKGZGRFkB9fOpOgJmiLpbji9uW8vreTe1fs46N/WsFL21oHXCtAQLWqCWxq6LWV8q370JcyKAlqSAnDSvwEfYqbfRvyKWiee6UqVtygqghmjarg3Bn1+LRcF+VQcWdvBhkje58NUxK26xn57CKYx42pQDclv3pxN+f8+hVu+PtW4mmDxu4ku9r6eWpjExVBK6Pym49uzNHVA/j+xTOotp9NTYESj0hwaUBBx3pGkhmT48dVIrCKgjsyKU5vIc2a47BfxTAkX394Pdfet4boYeIW3y04IiImhPBhkbAHpJSPe7Z/DLgQ+LDM3qEGYLTn8FFA0xDbO4AKIYSWtz2nL3t/OTAg0EBK+Ucp5Vwp5dyamrdPbbMiiijiP4M3DnXnfB9KGmAwaMLSPspHPGPy8JqDZAbp0FlCnULiToYaDHyLVO1tIU3hxktmctfKg8TtRerlHe3s7YgzLOInElDZ295HRjf5xYs7aI2l7dI62eDrQuOwCJekOmIJmQ5lGVTsfvIzJwtdpdeyJ/LaetubEvoSFmGtLvWzdEcbZ9/6ClubY26bWEYOeW+ae9OcfNNi+lNDyykcCZwC1Tta+7h7+X5mDLeKtq852MOVf1zJ957cyvI9XezvjBPQsq5kYc+vT1Os+CvP9VmWMJ3ueAZFgF9VaO9LW4KphuWK0z0B6oZpvSQYpuSVXR08uOoQiYzp3sdClkUHg2VTDob8exG3AxpTusQwJS9ub0dTrPF09mdsGQ1JyKe45zrUkyTsV3lpextLd+YaOt4/ZyQXzxqOgkX4pCcrNZbKuh23tcQ49aga1yrmHZdPEQR9PmpLfKQyBkndYG9HnOe2tHL1HSvf3AW/Q3EkWZMCuBPYJqX8hWf7ucC3gIullHHPIU8BV9kZj+OBycAqYDUw2c6Q9GMF9D9lE7glwAfs4z8GPOnp62P25w8Ai2U+JS+iiCKKyMPFv12e810pQFa8Aqv+AgucX1hxTCpw+lFVOft6EoWdkvmLjLPw+FQxwCInsBbAsE/h3Jn1HDO6gq1NMXecJlAe0vjwCWP54oPrOP3nLzP1u8+yvamXiF/F51Pckj05OmhiYIxadzxzWCuLozXlV6wEhUKtnYB0VREueXJij1SlMCHstUVpy4I+Pn7Xajo9Lq66UitWayjZDLCSEK69bw2H8uLM3iz+5/xpLL7uVPrTBlfNH80N7z+azywc7d6bkRUBN/7Kie3S1KzkSH8q975XR3zu9ZcHVHyqkqM75rT2zqWmCDRFuM+fI+fgqN07CR6F4AgBe3Ek3MzrIldFNhFAFQML0EtpkceULt1zxVIG5UGNf2xsIh9fOmMyk+tLEFjkK6hma5lK+3yGhGPHVDJ7VFmB0UnShknahJKARn/aZJj94rC7LcbM/32OZe/yIP4jsYidCHwUOF0IscH+73zgNqAUeMHe9nsAKeUW4BFgK/Ac8EUppWHHeH0JeB4r4P8Ruy1YhO7rQojdWDFgd9rb7wSq7e1fB1zJiyKKKKKIQli6PbeeXdBnxcYo+cW+TekuwIVi7jPSCmofUx3mpsvmUBHyMXtE6ZDnLuSes8Q1rT2qp7i3xJLTOHHSML574XSuf2Izmxp6GFZqSSzMHFHKbVcfy389vIHNjb34VGtBi6UlKd0go+e6d5z+FSHcguXO4mpKyzKWH//l/ezEeyUNyyJT6I1Xytwgb+92wyyceVkZUpk5opQDBUjUkSr/jywPsLcjzmk3L2XJ9tbDHzAEhpeH+MtnT+DCWSOYObKc6y+exTULxyKAxp4UQcVT7mhYGGR2pnyqYHSldX80YSWBhH1WwcbelEFSN0kbEp9itS1IZpF2fcusCzZ/rh3CpCl2UXLPvnxTxJEQMZdk2ecT9vMhhHRJZr7KvyDXijyiMsjj6xsHiLCWh/zcePFMrpw3mvfPGcWfPj6fKcNL3P1OH9//+xZGVoazcWoiW5EgnjZIZgz60wZjq0JWFiqWa7UvZfDjp3PLSr3bIN5tBqa5c+fKNWvW/KeHUUQRRfyHMOG/ny5oUVAobGnwq2JAbUAVCPlVDNPkunOm8umTJzDl+qcLxlUVQmlAJZYyEEB5UM2p9+i4KqXd7tYrj6EnofONRzdy9MhStrfEyBiw6Ksnc9UgQdKaHZuUb0nya4K0LvGrgrKQj3hKJ2438p5XU0VB95eC1a+UVl9euQ9VWFbCxCDmGuE59khWlcHuR34bsPS+/KrCge4EE4aFmVBTQtCncssVswn6jrxY9lC4+Dev8EZj1mXqWHLAmm9dWvey176XKlmLl08RVEb8dMRS+FRB0KfSm9Rdy6TXQik8/ecnVDoyGPmENt/S6syz9zghGTR5ZLB+/Kqwi48Xbq/ZN9URhTUk3HLFbD5w3KjCB9ho7Orn6jtW0tiTdOdQFfCh48dy38oDAARUGF4R4kBnAkk2Li7os/7dOXFkqrBkQD6+cCwXHzOC6SMqCp3ybQ8hxFop5dxC+4rK+kUUUcS7Bl9/aO3gbp1BtudbigBMYZXuSeiS3y/dw4HOfo4ff+QJ2zHbhSVhQNFthwxFfDBjRBlzx1Xxh5f3IIAtjRYJu2reaGJpfQAJcywVQZ8gX91BU3CtLLop6U2kiWeycTpey4uTwenAsZ450ggmA61epUEf2358Aeu+cxY3XjKD04+qYViJn5BPIaAKfKqVPVhT4h8wn5ptkjlubHYRNbGIqGP9yUeJX6CpAhPo6E/TYGfn7e2Is3JPB/94o5mbnt028MC3iKe+fArfOW8ydjw7hoST7JqZurQyAFO6dS8VAZGgxsiKkBWYr1pB/5oqSBmSeFqnrjRgueaU3EB8xVPiSpDrzjVlYRKm5U2QlAOPy9eJze8DBiZfOPIZUDjWUJdW4XvL/SqIBNQj0voaWRXhlW+dwUeOtwibY219YkNW/DhlwIHOhEtCSoOWBEg0qRP2q7a2mWD26DIunDWC+1Yc4JLbXuO7T24imnh3BfEPVuWjiCKKKOIdh8c3tgy6z4nFKehyy/vuUyxLgSIsEnDqzUv/ZW+t2ZgcwS+unMMvXtjJrrY+d199WYDrL5iGguD48VW8biuOe11FibQ1Nkfc03ENeoPITZv/5VtPIM+aguV9y7eK6Gau1aq+zIrnqirxc82CcVyzYBwAty/dzU+f28HEYRH60zqt0YGllRxSt/ZAT86YYikjx/LkheNOBdzECIe09aVNjh1TwbLdHWw41MMxo/95K4lpmnz61Cnsbk/yyNpDmBJW7etEUyAS0OhN6OiOyJK0yk75VRhdFWZXax9VYY2euG65E1WF1ljKSpiwSa8TL2Wa1vU7851fWB5vHJjdvlAx8HzC5iVZCpY0inOcJGt18R7mtZAZ0rKq2QmVOc8TWNUTSvwqHX0pDMNEVQv/i+hP6Szb1cGKPe08uq4pe04JMfulJOJTkALiadO14iXTBkk7maCzL0NZUCPg89HSk6I0kKDfTmi4b8VBOmIpvnT6ZGaMKC84hncaikSsiCKKeFfgJ/94I+e7qghMMzfDTfMQLGdbIeiGHGBB+2e0yLwLXnlQRRXwrfOmcs9r+7hvxYGctndcM5c7XtnLfSsPEEtk3GO9QzWwFk2wFlCfApGgj2giQ1XET0efZTFwCkoXgqpYi62zSGrKwMU9oCkkdRMJ7GjtJ57WCftzl41lO60yOLd8cDaPr2vg/pUHDzsfXiKYUwjb0yZeoGSBJOvOa4kmiacNbnl+O/d96njeitZ3PJnh0tuXc6grjiIEwyuC/PGjc3l4jSV5mTYhqCn0JvSc+TGxZBgae5KkDUlAU4inTTRVIW2YSGly4oRK1h/qIZGROeRbyoGuQCt2MFuqyq9Y7nLH6ZrfPt/F6J0fYY/POchpa+Yd6/x1SneZno2VYT8fnD+akRUh1hzo4sl1TVRF/CQyBrvarIzTDx0/lpCtdbKvo58XtrZwoDPOo2saSBsmw8sCJNKF/9UkM2aOG1UT0O+53ybQk9TBDkfr8FiGw36VZze30tST5OYrZjOlbui4zXcCiq7JIooo4h2PTYe6+MOrh3K2GebAgHPHMiNlrlaT45JxCdq/eHzS7rssoDKlvoz5E2oYO6yER9c2DBjjVx/ewLiqABVh/4CC0CPKfQQFhPJ0pzIm9MQzSAnRRAa/av+4i1wriQMBOa7N/O8OdMPErwo0Kx6dJdtz3VIPrz6EIQVfet8k5oypJOqpXuBM7+HokWQg0RwM3r6ae5JMqI6wsaGX57e8uQB+KSU3PbuVE256iR2tfZjSJGUY9Ccz3PnqPuaMyVpakrb6ez5JLQ1opA3JpccMt0oZmSYBn8KEmgiaAqsO9JDySF04BeGd7yGfmnUJmzKnf8cS6Lix39S12X8FgwvyaoqVoRhUcTNvx1eHOaquhJKQgiENHl51kO89uYW/rW/CANr70kSTBroh+cHT2zjxpy9x49+3cNYvXuZ9tyzlsbUN7Grt45oFY3noMyfw8v97H58/bWLOuQW2FpuanRPFjpXzPqc++7n32ckKznwoinCLrG9s6OWS217l2U3Nb3KG3n4oBusXUUQR72i8vqeVK+/I/TfvuNS8AdUONGHpQSXs+Kl/xS9gfj8lAZW+PKmDj58whpX7u+nqT/Ho507grFuXWQWe7f21JT5GVITY1hwj5Sw8eS7D902u5KLZI/nGY5uBXCtHTkB4AVdjfl9eeAP5vdejCKdeookp4bJjR/KLDx4DWOr4p9+yhOHlIabUldCbyLC1KUpL1HLJMcT53iryLWe1JX4URZDMmNz98bkcM7ZqsENdSCn5xN2rWLqjwy2gLRTBpu+fwxcfWEcqYyIUybJdA0vtBFSYMaKcTY29bqKET1gZtmGfQmnQx6SaCCv2dSElBH0KqYyZE79VFlRR7WSJPjuhw5Tg1yDlqUTvdWcKkXUzOkHzDga7r2G/imFKMrrpPidlAYVYyiyYpemdV8gtv+Ulk6aEcMAq4pRMG5wxrY7546s4fnwlR4+qzOl3e0uUc3+5zJY8schrd0LPaaMCnzllAg++vp9oKstGHQuk14InhPUCFfapCEWQyJjE0wafOmkc/3P+9BxJmrcbhgrWLxKxIooo4h0LKSXjv/3MYdvlE7LB4pLct/JB9g8Fx7rhHOclRnURhUgoyN6OON88ewoHu+I8traB8pBKV9wg5FP46+cXMn1EOR+5Yzmv7uke0Ed9qUZLTEcBasOC+ZNqeWVXJ70JHbWAWzHfBTXYNQdt96NzTH5fw0v96EB7LE11xM/a754FwOW/e431B62YL01T0DzWiv8LONcTUkEoCqZh8oG5o/jexUfjLyRoBmQMk8/es4YlO9tzyjONrw7z2OcXUhn20xpNUBrUmPn9F9zjqoIqsYxJxpAEfQp+IenXLaurApSHfPQmMpjA9OGlbG+ODbDyOS8HjiXWm60osMSD+wq48pxnUrWDHB3XrC9PfNf7rPhVYWdEgm6aBWVF/KpAVSDhcQk6VrtCrtCAphAJqHT1Z5g/toILZo/ksuNGURIYOsLpnF8sZUdbv3u/rH8nAsPDPcZUBulNZoglDEyslyUnuUTBSnAwzOy/L+daSwIaibSBIiTjh4V59NoTKQ/7hxzPfwrFrMkiiijiXYl5P1jkfh7sZVgVljK6d3eh+n8Cy4Lh7A9qypBlkfL3OQuYwwG8a1lrv8nBzjgLJ1bxkQVjeXxdA6aErrhlEbn82FFMtwOP7/rkCUypjQzoY2p9OedPryKERSqbepPU2IKihdyKziLmjZHzwhl+0rNKSwYSuoyEj50wDoDO/jSJtMHu1hibm6KY2Or9uklF2OcGmReatze72Aj7mMHEKdzEBQOOHllG0oQHVjVw7A8WccNTm9lwsDubRWqYPPD6ARb+5CWW2OrwjtK9APZ1xvngH1YgBAyvCFMS9POV0ye54+5KGm7CgGlKfJqGkJKAPbhum4T5VcHW5lhO6Sdv6JqC5RZ3shW9z2whEuYcX1cW4OgRZW6JIrDKXXlrg3pvb9qQ9KVNknphEgZQVxYk5NNy7pVTOxOyKv9O30nd5PSptUjguPHVXLNw3GFJGMCZM4bnvARIwKdlXY8ADT1JkmnDtdyZ0soUVYALZg13hW4Naemz+RVrLvtSOoaUKAoc6Epw5q2vcLDznxP9/U+gGKxfRBFFvCORSOt0xLNuDseak09KDNs04QTq58O7JeExMaQ9Lp1CKGRd0lRLhHLAdgXmja/i/k8dz9ce3kDGzJKkERVBbrhkptv2b+sb2dnWP8DdudR2lanA504Yxy8X73PHN5jxztmfbz0Z7JhCbi6BYFRFkFEVQRp6khz3wxdASlJ27JTjDmrpSVolfjTI8z7lEMIjRSEr32BYtb+HcZUBpJRE05IHVx3kruUHmF5fwoIJ1aw+0E1rNEl7X9rNTDSx7pdfUYj4FRZOrM4J+K8I+6yKCEi8CiQZQ9KXNjCkpUwf8gsSadMtJ+W9ZsV+5oTns/QE0eeXvipEqlQBrdEULXm1TZMFyhEciTabc55D3Qn3mKAmcuLZwMrW9H4PaoK/rW9kTFWYR9Yc4uTJw1gwcRgATT0JFm9v44q5owhoudT56vmjeXZTMwc6+q05V7Dcv2SfSSktOQtNySYzzB5ZzqamKHs7+ghqlgVNYOnjHT+ukhe2tbq6fikdBJKMbnDKzUvxq4KdPzr/CGbi7YGiRayIIop4R+IDv1vmfnbIgJBWeR7vW35tid8Sz7TdSFA4gDyfKLzZgH1HCbwQzppex92fmM+qfV3s7+ijOqSiKIKqiJ+nvnSSO/7WaJL7lh/IGU/+WIMBhSffaGF4RdA9bz4cN6lif85fsx319Hw4chje8woBNz69nfkTKplWX+KWwHH60U1bKR6LnFZ5XEPe80gKz/tgRkeHhCk4JapETmUCLyTQ0JuiM2EwrS7C2dNrmTmyjJ1tfdy14gBvNEZJ6QblIY3Zoyo4bWoNCqAblrsy6PdxypTanD6rIwEyRi4JAwiqUB3xWxZQ07rnqn2Rqsjej1BAzZGdcBNFPGNWGHoRdu5doZguJ9DdOx/iCFd0IazMzJBPQSjkCPfmnMTGmMogl84ZzV8/v5CnvrQQn6rw7cc3cc/y/XzvyU2cdstivvfkZm5dtHNAN6Mqwzz9lZPx+xUkA59Fxx0a1ARXzxtFTYmP8rCPdYd67NhHwadPmkBZ0MfIihAdfWneaIyim5Y717Fk15UFXHKaNiTPbR5cyubthiIRK6KIIt5xiMaTbGmxStxee9JoPrZgrGXxkqAYuUWk2/rS7neTrDsoUICJZONxjnwsTqD8UHHC+zv6WbGrg4/e+Tpt0RTRtMnY6hD3fmIekYDG3zc00dqb5JN3rWJzU681BmHrbeX11Z8y2dORoKU3SVBTXMI4oJalJMdFhqedVdw595qdv/kWsbZYiqqIj40He/nJZbOYXBvJ1Z7CWkg0RaAIaIxmpQac8wxmuTsSYmba/eumzMmEdYxXQdvNZUirFuTagz3sau2nIuTj1Cm1lId8+BSBlILL54zkoc+ewBXHjaI0qKEIixikDZNTp9QAlqX1h//Ywv97bEPOmJz5TRjQFUsS0AQSq06iacc7WbUgBcPLA1x4dL0bqzaUxdKbcOFclypwEwkKwXHTOQkWTlPDtFx3DvKfSedZNaUlWpw25ACNOXdsnu1Th5fxk8uPprokwGW3r6C5N8n+zjg3PbuN+1YcJK1b7e98bS87W6ID+tJUwZRaq+yRU+jeO6i6Ej81pUGSGZNYyiSR1lk4cRhJ3WRYaYB1h3ro6s/Q1BMnoCq0x5KYEvrTJlfNG8VZ0+vsguEm5SEfZ06rde/nOwFFIlZEEUW84zDnxpfcz//v/Jms3tdJypCUBBRSMmuZ0IT1w59Tqw9rsUsV8C268VRDmMOcvryLnMRKwfcpuYRoWFjl1CnDKA/5+ez9a8mY0BJLE/IpXHrMKGaOquCmZ7dz78r9nPjTxexsjeWQHCkHJ3mGzI3vMmSWEEmyyQnOscLTDnLH6XWD5v8FaI9ZC+83H3uDeNpA9cQPKYqwZRAKl8pRlcLWN+cc+YcU4gUO4bMKZpNzfsea4xCKlAG7W/voiKXY2RolnjbImJKAT+H0afUEfSpnTKtnYo0VhxfxK5w1rQ5FwGNrDvKxP6/ir+sac8pZDS8P5Fqe7AsqC/nQFEF9WdAdu6pImntTPLKmkZRuulY8d77EQLIpsAiUE2doSFvSIa+t9znWFGHVuXTOK5z+s0cUuh/uM25Y9S697lHvuZx/Qyqwu62PdMZgS1OURFp3ryNtuxidygmGCZ+9by35SYA+VeG8o0cQ0gQGua5qw4SEbtLZl+SVne3E0wYpXbLhYDcCWLqjnY6+FMePryJlQMowSetWndiLZ4/gR5fO4jdXH8sX3jeJyrCPMVUhbv7AbFfj7J2AIhEroogi3lHY3dLrkoygJvjOE5vZ1GTVCDQM0xPUaxfcJtfNmK8hVghDxTN5A4rz2xoyS4iOG13Bmu+dyz2fPJ6KsM+NHxJYC+wlc0ayen8Xj6w+SFN3HN20akTm6335VQYEdou8Nq6lg4GlibyCqe7iqgh3u0OGgp5+vBhfHaI3acVEHeyKM298hau67tRGTMuBxzmQsnA8nRfeY50i14pLLKxYLomVpaibthDtECRDB8pCGhfNHsnYyiAK8L8XTeekyVZMk19TOGN6PVJCyKdRVxbkvF8t474VBxhdFebHlx3NFXOz9RSbelMo9oAUYWUa6oYkGs9QW6rR0pt0ryNt5Mp/GKZEiNwC7GDfB5F1zTn3zfto5uuIeZ9j047Tc5JDTCx5jXReYF3+8+TtK+grPH+QvWcG1r8jVVU4Z0Y9V88fzfHjq6grC2Jg1SSVHotgY3ecP7+2j3xcOXcUIb/GhGFhykMax40poyyoMa2+hPqyAAC9yQxlIY3ykEZf2nDHE01kWLmvC78q8KmCsF8hHNCYWBOmLWbFzn3mlIn84aNzefKLJ1EZeXtmTg6GIhEroogi3lE485evup+TuuRzpoIoAAAgAElEQVTh1Vkh14SedVlZwb+FCULBmJi3gHxC5CyyFWEfv7/mOAC2Nvbwys4Ot50EehMGH/rTSq57ZCMSaOy1FpP+TK6iv7Svw68qLnFyFm5vG8eaMZh71GkjbTeh4+Lz9pOksIUqltTdedRNyaLNra470JRW8LpuDrTeAAXLKw02PgdOgWlnLq1zDGx3OJQEfFw1fzS72+OE/QrvO6ouZ/+k2giqIujoT/PzF3ZiSMm1p03ilitm41cVHlnT4FqcIFtmqcxmL47Vqi2Wodpe+F23KdZLQmlQda8hR/uLbF1P594511swCcQWNs0nad5EFIH1rAxwRzqEloFxjyl9oGU0n6yB9YLTFrXI5lfOOIqHP7eAZ796MgE7vsywrXfOOe5Ytpf+VCbnXJWRAB9bOI7h5QGCfpWNh6KYprQqCCgKwyvCaIqgxK9iGqYdF2jFeLZEU0T8KmlbPiSjm/QndW59cTcn/OQlzr31Ff7nb5uoCGsuYX4noZg1WUQRRbxjcPeyPe7n06YMY+nOjgELlymzIpte7aGhak2+GeS78VRPTT+wrG3XnjaRmlLLXfWzRTuJZwxqSv20x9J8+dTxHD+pll+8sIN1tg5XIdREfERTOrphxSE5GEz/zCFawt7gJUDO/sHK3BS6Ngfd8UzWOmJKOuM604eXsqe9n5TH+uK1vLl4C5PtuHnfDAple25s6OH3S3YjgVOPqiUc0NANk6U72rl3xX5e2ZUlx7+9+hjOPXoEAsnetj6SGYOgTyHuiSx3uu9JWAQj7FOJ2+2cklIuBKR0iWH7uB2C4vwV9kQ7br103k11ZCmcedCdOlSFrlnkWkHznw+vbEQ+slIVVjmw/OcjYGdTtsVS3PCPLdz+4eMQQqAbJmsPdDOyKsS+joQbd+hcazSe5qVt7Vx8zIjsuUzJ9pYYbzTEuGBWPSdMqKYqEmB8TYSR5SFiyQx/Xd9AU3eSqoife1fspyWaot2e237bV5xIGujYCTqm5aLc2RZjZ1uMl3e0MaYqzB+vmUtpnrnPMEx+8tw2AqrKdWdPQVHePnaoIhEroogi3jG48ent7uf1h3oYWR4gmdYZURVhU6MVJBxQLcsAZIOawSYmhZjGm4RzuLMQOqKeYMWIPfSZ+cy0FcafeaOJaCLDpJoIu9v7mT++kuvOm05KN/jmOVO5/vE32NsZd/v2jj2aMjAcvSkKB947cNyS7hhlroBr/nGSwttHl2sc7M3VnnBIrLtoC0ikDUKDKMF72/4r7I6OpU8RViB8PmmBXBKgCEsAtD9tsKO1DwF09iU57eYlJDIGrdEUAVVQFtSYXBuhO65z0pQaUrrBiTctJqMbHDu2ivrSAPu7EgPGUhX2EU1mSOkG88ZV8pPLZ/JfD21w3ePe8WRs6QqX4DgETBEo9nXohhxAJJ2YOEdV35F0KDSf+RIfzgtHXYlGS1/2BjnHOuLGTtB8IXe28y1lM8GMIVm2o41nNjXzxQfX58yHEHYVC0WQMSXSBENKNhzqyiFiGxp6eNbOZPzL6gZ64hm+cc5URleGASgP+/nkiROy7Q/10LK1FQm2OK2kNKgSSxhW3KdwSK1g4rAIIyqCLN3ZQWNPkubuOKXDcwuCf+beNaze34VfU3h1dwfnzKjn4yeOG1A79T+B//wIiiiiiCKOALqRq+vVm9DpTejMHF7CmVNr2d4cdfWI8qEJgW6bwwpZT0ZXhkhmDPft20FVWKPb1irLXwS9fTjjmlAbobokSEBT2dTYw2+X7GJPe5ykblJXFuDBT58AwGNrG7j+b5tzxD4hO/agZutPKSANx/KW1UjzliTSRNZy4iVfXgVysNw8ioDKcIBRlUH2dvTTHdcpD2n02sJf+SQMshIYTl+6hL60Tm1pkJ5klkQ65/lXlDVygrkFVsYdSAwpMAzpapdJm6FKu3C5Zs+PLrGYhmESS2aoK9VYta8HTRUcN7aSGy+ZiZSSa+9fx/um1nHG1Fqe29zC1uYo3fEMYZ/glV0dXLNgLNN6k6w71E1rLPtcdMczCAEVIY31B7t5cn0T3zpvKh/782pqSgM5el/5vN+5N4qADB53ob09oAoiAY3jxlVSHvRTVeKjJKDRE9fZ39HHsl3tbqH2webZ2d7Rrxd873DdjUd4LzSb8PRnZE4mqQPHCivtKvSarVm3dHsb152dIRKwXbm2u9EZ33NbWtnU2MutV85h/viBpanq7LgxsIhiTWmAVs/c1kU0RlSWsKmxh51tfexs60NTYM7oCmrKgzl93bZ4F6/v7aQ/Y0LKoLO/l61NvbywtYXHv3DiWyoY/69EkYgVUUQR7wjc9uIO97N3gdnS0sfm5t0AnDq5ilX7ui23pKeOoy6lu+gVWsCaehMF1em74jqlAZVYAXZXaJH71InjGDfMStN/8PWDCCFQhSSgKTzw6ePRbF2M82bWc+eyfeztKKwC7o1hU4QTqJ49p9co5HXj5VcMkFhWttqyIB19GRb918mMrrKyBf+0bC8/fHobv7l6Dvva+3jo9QM09CQZURGmMuJj3cEeMoZ0iYI3060jliaZzI0BKoShDJBDuUW9chdpw7p3QpHuNifGTXVik2SuZciJe2roSZDxiK0+9NkFAOxrjzGizMcti3Zyy6KdBDSFiTVhFCBul/x5cOUBSoNqjotXOmOU0GkT9N8u2cOdy/ahqsINHPe2F1hz532CHEtT/iNXGvKx+vozCxKD/qTOST97yX0xKAQvEXcSPFzyx9BW1cHgJL6kDehPS44ZUcIXzziKz9+/Fl1apY+Q0s1CztjPaWdfiv/36EbOnjkcKSU9iRSlAY3epE7YJ4hnJJ19aXa1RgsSsf5UlkiWBjRaoyk+eeI4/rr2EL1Jg6ZohqZoN4qAC2bWc6ArzuamKEfVl3LJb17jpstnsXDSML7/1Bvct+KQG8fmyLpkTFh/qJeMIfEPpRXyf4AiESuiiCLeEfj1kr3uZ+9i4l0oX97V5S58Y6uC7O+yAoy9Qq6FFqJCJMxpX4iE5Y/BwRlHWaKgzT0J/rb2EBK4/LjRfPn0SQyvCLvt7nptP3s7sur5ToFjr2vSgbceobSvRbVdNWGfSjxtENIEkYCPmlIfu9v6GV0Voiuuc9mckVx39lH8dV0Dj61toDueYUSFZFtzlKU72hldGeKUKbWcMqWWK+eP5bLfvcae9j6iCV9O3Fu+FVECsczhl3THFacodqJAAaLo7dMb3+ZXRbaskPu/bFuHdPhV23Vngl+x+xHWsU7BbbAsiusOdPGZe9fQ2Z9LIv2KyT2fOp7P3b2azc0xS6RWQnfi8HYjU1pK8aOrw+zvjKMpguqIj9ZYOkucyRKhoKaQMkzXfey4nqV93Uu2t3L6tPoB50noOhnDHLRmqDMW97M9R84kex/xN+Oht8hw9vuGpj7uWbEfXVrWMrfCgicWM+y3Xl4WbW3l6c2tDCvxM31EKUIRjK4M2ZYtK/Zxv8c178WUuhKbhCn0JnW+duZkTDNbKN1rgV26s51IQGVYyf9n77zD7Ljq8/85M3Pr3u3alXbVu2Q125JtuTdcMNWU8KMZHCAQEkrohBAcEgghkNikQCgGG7ABh27cbWxLsiyrWL1LK23ve/f2MjPn98eUO7ftyk5iZJ77Po+tu1POnHNm7j3vfMv79fGjbVbyzvzWED977hR3PdNTcMVXcO96ddf+UKgRsRpqqOFlAWchCdqyFNXK3zgL36nxTFmQuhM/c6Y4k0PXdNazrz/OT953ES0NFtn6yE+eJ2PA4hkhPnH9cloiBTdLKqvzxOEht31Lnb4gfRBUKVNzd44VWAtHa32A685pZ0ZdgO9tOcUHrlzMB65c7B772z39fPmBQ7x5w1zCAY2rl7dx5+YuPnXfXvom08QzllVl3exGfrGrl9ZIgMVtdZw/t5mDA3GG4rmi7LsX6250ZTdkeRvee+KV0nAmvVIsWFADu+tF7TnPgkkhsF1gEYOAgBmNQfqiGd7wza2AJW1x0znt/GRnPwDxHHz0nt3UhXwFC5zd9pvWz+a+nX0IIOxXSJbUhFw/t5GBWJbeaNq19DjB+879dbJVoVz7DWxrmbRqef7Z3Tt57bmzaa7z0xDUuH71LOY0hfi3x4+RLxErLoXzjLiEXQgMz1PslPkqskSe4XfCjW8T8OyJMXujZY0M+FXSNltTsSovmFhu46agj7tuvZBIQGNOS5j7dvTwtYcPk9MFsazBZKqyZTWvS3yqIJ41ufXSBXz4mqWs/4dH8WkKdX6VsWSeC+Y3seN0lGTOIJ83yNnjCPsE9+3o5RfP901pkdUUy/IWKdXxeIlRI2I11FDDywqKIjCnSatzswjtv312ILFpW1AqLfIVr0V5pqEXfhUODiZ464Vz2WjX3TNMSyizo8HPD997sUvCNh0bYX/fJO31AY4PJaz2HYuR5zp5oziQ2guJJUR7/TmzuO21q5hM5bh3ew97eyaKjtveNUZAU5jfarkh41kdCZwcjhMJ+9wg8D19k/z9/QeZSOWZ02SRFatGYoEcOv2sNGV1PvAalxSshdp7bCkBc3SvXPIkC2PzZnP6VVEmuuv0ybm3mrACySWWxcshFN57FQpqLJtZb41NWHpW//jGdXSNJrhvVz8hv0oia7Cta4z185sI+hQy+UI80/auwtyKCoxleUc977l8Ebf99gCTdkalIbECyzNG1WLrzuamsEbezrDM6JYl7hfP97mWz9sfO8aMSIDRRPaMXgzcY4Tlni+ev/IWKmXXVoLjXpWyIImhmxa5S+cMGgIqsaxBXloHOTIr0UyeQ4Nx3rTe0mV7ywVz2XRsxA3crwtoJLM6dXYB8T09UXomUjx4YAhNUVjSFuaTNyxHUQTfvmU9b/mvZ5nbGGAsmWf76SgLW8P0R9O01mmMJvI0hv1E03n+7YnjU86XYzkdS+ZqRKyGGmqo4YUgVcEl5gSsh3wCv6owmSl2X+Q9C9CZkjCws7Kw0vgraY8taY8wMJnlUzescLepiuDeP7uYVN6gMWT9wG85PspjBwf57Z5BxlM5dzF2LEau7ASFWKJqDpOGgOBvXrUSsDLNXrtuNndvPUV/NE1nUwiA3T2T9Eczrrp493iK8VSOnIRxmzkpUqJhBZ/XB1SGYhlCtmSDIqEppDKZNopi0kq1qEo8fBZ59JAhsLPqPBavqQp5S8+/Tt3Long4s9C20y/vXSnNEp1ZHyDgU+gZT3PJoiYuWdLOX16zlKHJNF/87UEMCTndmnFFwPZTUToaA/RNZt3+nxpPuW2mdenGyjnXWD27iZvWdvLMiTF+tK3bfRYTFcya3ioHhrQkWL74utVkdJOxeI7P/2Yfxz0F3/OGRWgmktmicZ8JvFY4L0otYIIC0Z6qbcft6BOQtRte0lZH12iSiF+lsylEZjSFKa3ao8698KmCe5/rdomYEIIPXbOU3d1Rgj7BL57v5ac7ejj4xRs5Phzn/T/cQdiv0TWaRFUE585rdjMbL1jQyv6/uwEpJRd++XGSWYPhWJqAT6U/lkcBRhI55jYHuXbFTH6w9fSUc2SYkt6xlPvC8ofC2SOkUUMNNdQwBdqrvDYqFALWBYLrz7Hia6ZbsBqD0//8udYXWXBleNE1muIzr1xRpuTt0xSXhI0mMnzq53u4e2s3153TzpK24h99ryyB5vEHevvvve68ljq3SDjAOzbOo70+wDceP0YsnedA/ySRoMpr13W4xxwdTBBL60Xtm1gK9GDFwRlmQbRUArG0UdQHxy06FUqPd8bn7Ks049WazNuLuUMUvO16CZuD0ji/uU1+LpjfQMSvcnosyXA8z6ZjI7z/B9u44V+f5vdHRvCJQj9zdjZiKpfHrxYqJHjhkAtvcPfnfrmf9921nVsvW8j7r1hIJOhzEyy8EPZcOEKsn7tpBXe++0LmtdbhVxUGYmnqbcLhWAWdazpB9lM906WF3N1nquQ4r9XQOc6bzVkNzn3ImRC2xz8Yy7JxcSvtDUF6J9JIZJnVTQC9EylOjCTcbSs6Gvjkjcvpn8ySyuosbqtjcDJNLm/V7eybSNpxk5Kl7cXfl7Bfoy7g4yq7lmQqL4lldBQBHc1WtmTPRIbf7u6rOkfeuY1lp086+b9GzSJWQw01vCyg+ykwBw+862/WMHnAdnlMh8nMFKYZGxKI+AQJ2wrnVwvClz4BGxc28abz50zZxnAsy2gsgwTu3zvAilkRgj6FdL74+pJiy50XhglvOK+TrtEUz/dE+fyv9vMPN68hqxv0TaSRUvLwgUEe3NePT1UYTeYJago9E0kWzYiwqztquZamCPYysUQ3nb44vfMr1uKriuL6nKXWmdK/vS5dR3W/kjGyUqanA8XeKGXBvXymFqGeaI6sPk46b+l0HR9Jcmo0WZRlmpeU6TgksiZhv0bO0MssSk7/sp5GZjYGePTQMItm1PGZm1Zy8aIZfH9LF8+eHC1KvHDOWNwW5tM3ruQVK2fyr48d5b4dPQzHs8yo8zORtGLLlJKJWDGrnhPD8bJEDq91zntriyyJCgiznMSV/l1J1qWU9DoWyYwhCflUklmdXafHMUy4fFkbubzBpuNjbr8s/TfBUCzLQDTD4raI2/brz5tD2K/yqf/ey7GhOAf6ovxiVx+j8Rx+n8KMoIqmaYyWZKI6+OLrVvG7fYNu/xoCCgPRDOfMquPgYJKxdOHHwhlbpfGdGKmcufxSokbEaqihhpcFJj3amt5YIsfSoigwpylUJsLpxQtx7TjHJ/OWC8+nWW67gALzZkRorvPz5Teei6pObVlb2h6htT7IaDxLMmewq3tySstDKXwC3nHxfD79ypVIKbn6a0/y423dPHF4CFPCaCJb4u6zVuuMbrKtK8q2rqgrfumVoChFvV8hnjMJaYK0h2jkTGgOakxkKutSgWVdTGTNyqr/0yzwUFgoFaXYsuW1RjoktVKpHqdNQcGdLIHhhEHEr5I3TFbODHNoKFV0vPMMzaj38+9vOZd3fn87Ib+GblR2KzoZjg7RkcB4wnI1/9emLk6OJVk/v5m/efU5/GJXL9/d1EVDSGM8mUcCQZ/CzedZxP2jP93N/r5J5rWGyegmwx4NO1PCrIYAMyIBsrrJwYE4qzvrOTQYL5of7zx44/OcuQ3Zyvh++9+p7oFzj1RhkV6EFdhfKa4s5FOZ3xrm9GgCn6bRUufnmeNjpPOGew9CmlVyrDmo8Z23nOvW+fTi+lUdfHfTSZ47FeVzvzrA2jkNAFy3sp09vTG6x1P851Mn+ei1y/B7ingfHJjkFzt7C/3RQFFULlzQyLNdE2XXQXrun/1S4LhuqxG9lxI1IlZDDTW8LOBd5EtdU5Y1B06Np6su1N7jvSi1BDhSElDIktMBaUsHZE0YmEjwl9ecS0djaNp+3/7YEfqjmSK3USXCUqnfmgKrOhvYdHSY57snmEjrDMayqMIqRF06DimtZAaBLCq27bijqs2LzzY9RfwK2Xz5UbGcTmNQs4L+S+KwnH5OJangFFnPeVyfPjuDzzlGiAIJK022KGqvyhgcAlA6j6m8gQnc/tbzueXO7a4oqGswElb9yKWzGuhsCpHXdVTFTzKXKWpfEQJTWgW8vZzGG3P46MFhHj04zD89eITmsIYppSuVoQhoClnaZQtaw/SMp3nzhjl8/PplvO/unUyG8nSNJVnQGuY/3n4+81rC1Ad9DMcyHBiIsai1jlu+v43usbTb92VtYY6NpAoWTI/bURWWHp3EykAsjbcrnduAJgpF1Y3C7DtuVm9cWTJn0D2eQtVUMnmDjiZLIPjN6+fwzIkR+qJZ0rr1XKyd08TlS9uq3DWo91ku/PFYlkcOjrCio543nD+Xhw7scI/58E+f51vv3MBoIsvtjx3ll7v6SOcLZNkwYTyVr0zCsOPi7IfKkbCQ9hxNZqrrsr1UqMWI1VBDDX9UKLISnIHlqTQRzmtd8koVGLI4GPwqWzNsKty5+STferLLPR8KbrpSBP1KUZ+Xtkf47rsuoC6g0T2RZndvjO6xVFUdKSfWzF1MS1x+CrilYZxt2NvzJsRzBomcSSV5MNOEvKFblhIb3ri18ZRZNdYLYHZTsCxJwrFwudqlHhLhHFmyqwii5LOJ7TouOc6vCnyq4J5t3dz2mlVl7RjSck9d8dXHOT2Won8yR89EgYQJTztQnIzg7FOFlUHr7W/GJgohn4IqLCmJr7xxLd955wZOjVlB7b/eY7mSf/UXl2JIq+d/99pVrOpsdGsltjcEuXp5O/Nawxh6cdze0ZEUjkHWsWQpWLF8jmVMESCUylbJgCa4dsUMPn7dUjoaQ0XJEM79LK0hKoSVEXrxolaQ0FYf4LmTY/hUwbMnRomm9EJ8oAmnRhPIKfQxOlstyZdXrJ7F9s+9ggc+dDmKYsUrzmmyYr729ET54I93suEfHuPXu/tI54yifpUoirhWr6I4O7Nwb8COxxTQN1FZx+ylRI2I1VBDDWc9popt8kIALXWWPIPC9PpITor9dG2W4tz5rW4wfjU8uK+fu7Z0VSwlU3rNZe11fOOt57FhfjNXLW/jq29ay7Ur2vnTH2zn2ZPj5IxCrJUhLdfQC4Fp/+fID3j74LjyvLFcYLnRoGARSeWLY8S8hNXJuutoDFYkZF1jlru4dJcT2+R1mUqsLNip3KiU7HM+V8qIzegS05DctbWbnzx3uqgPCtDZoKEKSOSkayXxtuIcn7Ethd5+Op8NaYueehpP5a1kg7VzGmmrD5A3JZ1NIa5Z2U5DUEMCF8xvpsEmXPGMjl8VXFmF4D96cIjeyVzZduc+qIog5FeoCyjkdFkghdKyGDnzCuAX8MrVM/nBrRfxvXdfxIeuXVbUd0l1CZLOxiC6ITkyGOPV6zrpi1pVKQxT0hPNkMwZRc/WydEU++06sJXwwasWoSnw2KFBIn7Fkqexx7TIjikbiGXZdHSU16zrIJ4xCNjPZunddt4TnJeS0v2CwvOsm5KZ9YGKrteXGjUiVkMNNZz1OD40WfR3oMovl8SSZzBloRAxFOollqKai7ASvOf/65+cO2V/pZR89hf7OG1bVrznFrlB7X/fdtE8XrFyFv/59vM5p6ORv/7FXr6z+aR1rCwsHgLrTd6wVyo7lOcFodKy4120Apqw9Ll0E2/4m/cy3swzr4UumsxNSX69Fso6T8yPQwYdAn3Z0jauWt5WdC+8Fo5q8Fcxyzlk+KljYyxsLbiTTaA/prvZmZcsaiRgd6sjbFm5nGdoqtQOvyrQFFFx7Nu6Jtz6kz9+9jR3bemiPqjRGvFz159eiGK3H03nMUxJ10iCLcdH2X5yhOFYBtOUDEym+drDhype25nTnCGJZwwSWdNS6bf3e12w0nPSlcvbWTW7gURW57muMXpKFO6d+S797jhac5qi8OrVs9zvmgAiAZXGkFZ0flBTGE1Uj8PqaAozrzVEzoCP/dQqKH7hwhYagxpbT46xfl4TYb9CIqvzyP4Bq+h8Bfc5VNCsE8XPnHe3YrslM7npqyf8X0NMZTJ8OWLDhg1yx44d0x9YQw01vGzwlQcO8K2nT72ocytlg/kEFV1wXqgCwj6Fuc0hDg4laa9TGU4arO6o5/6PXDHluXdvOcFtvz085eLtoM6v0l7vZyKlE0tbQd1NIR8T6Tx+TeGN583m3u09Zed5LVhCWNmcToYhvLCkhBeKgKaQ9ZjEgj4F0zSZbk3zxt/V+VWSnhOaghqqatVUNBF2mSLzf6WIOFiLciSo8bmbVvLTHd3sOF1M7i9f2koub7DtVJTWsMbX/2Qdg7EcR4fiPHtyjL6JdFk80VTlhirBKTmk2Nm3a+Y0okjBYDztxvy1NwSIJnP4VIU1cxp5vnuChqCfkSpkRhWF+Lozte6WHlcf1NxqC85xpaTFuQ9hn0JznY/6oJ9oKsd4MgcI3n7RXC5c2MqaOY185r/3su3UOKYp0VTrWbnz3Ru4ZsXMiv2aiCXZ+JUnCfhV9t52IwCP7h/gAz/exerZDUTTeU6PpSt+l0vnwrvfietzXmIUm9mb9lwFVAj5NXZ/4YYp5+1/A0KInVLKDZX21YL1a6ihhrMejx8acT97y9xMhbAtTlrpXbMuqBKdpo6gKS0Bz4NDSXxK4c26tT447bXvePyEGzhe5xeYiLLSOE0ByOnQGFSZ2xKifzKKCTQENSbSeRQBEb/C+fMa+eXzvWT1cukGd9F5kexLYAXqNwYtVfQqZTXLkNVN5jT5GIrlMU3LbaeKAjks6pvnWrpZCKZP5gzC9r1UFYjaN9UiNwUbXTWyE7QLUXtnVbUv5MbjUZgSXcJkWmdRWx3/+IZ1/N1vD7D5+Jjb/qZjY247C2dEuHjxDAK+whL5sx3dfOq/9xX1wZtpKMTUYrVQKDHk6LXt7pl0+wmW9e3mc2czmsjSEvGzeEaEvGHyfE+UGXU+pBCMJ3LF+mklPjhH5qMSVE+ChBfxki9UKQnzq4JwwMr+TOVNtIzBaCKBYcIbz5/NfTt7iWcNXrnG0q677XWr+PbTJxlL5Hj88DACeOTAUBERk1LaBbcVmhvqCAU0UjmDTM7g7q2n+d2+Pua3hNjTW3BrVnqmnHtc6k61xiFdAVtJgYA5yBogcwZ53cRXKhL4EqJGxGqooYazGn95zy6OebR+vGvGVG/IqbxZJnPh7qtUzNE+LmDXevQpoGkSPWcFsw8mrHNWdjRM22fH0xDwKZjApUtmcHo8zUg84yrbp7JgCOiP5eiPjduaS5DJ6W5fUjmT720+XVHVX1FAeGJhKrllKlWCcrIrnWDlt2+czxc8QezJTJ7r//UpBiezGEBDAGIVjDG90WIhTEN6Cj9XmhP736aQyrhNglN6odamt50i9XrHiuRpQ1MFQb9GJp3n0kUtbDk57l5jKouJBP76l/t5+K+u5M53X8AHf7SLx2yi4K2eoKmiiIQBjCey7nNUWrPRlMXxiA7Z9D53Xh02J1apyIIpoSXs47M3rcHx8jAAACAASURBVCy67sBkmv19MVRVYVl7hD09E8SyhQlz2nLrVpqStbPr2dsXLxu/KWXV56ISFOB16zq5f28/hm7gF5CTEMvo3LCqnScOjXDfzl6uO2cmv93TT31AJRL0cWwowY7T427NTYDVswvfG90w+cR9e9BUha+9eR1PHB4imtZRgNW3PTQlofW6SSvFgRWPt/o+y8UvyRmSiVSO9obpX7D+r1AjYjXUUMNZjblN1X8gA5olt+CVLfDKFzi/w07BYjeou8IPtLNo6qZFYkwTUvY64mhsAbzu3I7ykz14aP8AfvvtesWsBlZ2NpBI5+kdT9IaCTC7Mci+/jh+v8KlS9vY1R0lk9eJ2+TQ6XtrWOPbt6znmRMT1B/V2H5qoqifXqkH7xgcq9RUi60EzpvXyAeuWlLmLjoxmqRvMkvYp5A3JCYqUF1DzAuHDHjFXCM+hZRhWuTFkIynC+WnHOJWSg6s+D6QjqS83ZhDWnRTMiPiQwjYcXrCyhKsYDar1N/ecYvU7+2d4PHDw+5xXrJ7zcpyF9p4Ml+IU5MFQlWJ6FciYaXSEV5LltciVopVnQ0ENIWhWNaV3nCgUHgZCdi1OU0oImGtdT78imQ0oZOX1WPdZtb7GIrn6WwM0D+ZpaXOx3gyz++PjBD2q2R0s+jcrSfGqQ9paAIeP2QVsX++O8ru3knXBe1+JwX85Lluvrf5FJ1NQToaAvxqdz8fe8VSNh0b5k9/sMOdNyScO7cRIQQhTWE8leXIYJI6n0CXhfvkzJTzvDuky6kl6yWoTvwhHheu6SHSR4fjNSJWQw011FANXr2g8n1mUdyLXxWu28eL0hT8SpAUWxYUexWZ2xxkwqMFtWzm1Baxn23vYTBuMbgbV8/i/VcuBuDuraeY3RTiqw8dIqTAfR+4iJWdLUgpOTWaZOvJcQ70RTkyFGfH6SiTGZ3Xf3MbYMdPiWKxTjdGTBSn5kvnQwUIHGsYXLl8ZsWYnX29lrssZ5joJuSzutu2Fz7boqBQseCBe07cDqz2Zr465MWsQMJcFXSzMJbSe6dglbFpCvuYSMmiZ8BZgKtZx0xd8tc/383e3ljBShdU6WwJ0zeWJJ0zmWeXypFSImxfa2dTGE21xqwqgtmNQZJZndFknnktIbK6wUQyh2NsLXMfYt0r1b5flTTPRuJZ3vifWxhL5MjldRRVIZ7JuyS9lNCZnjEKJCpWYsLaOY3ste/jmKcg6FQF799z+WK+/MBhN1ZtPJknoAmS2TxQ7u6MZXRawj6Gk3kuX9LKzefPYcepcfb2TroWLZ8dS6gogv39FjmcSGbY3S3pbAxy97On+ZfHjhW1+6sPXsrauU3u3//88GEOD56gIexnNJEHpFu03rDJlNca6YxPsU2ojgXY0Rp04B3N4weHuWxJda2z/2vUiFgNNdRwViOeKrg3KrkipeffnCHLAslLj5sKhYBk4RYX92pKnTe3saLVwotbL13AwGSGaDrPrZfMB+DwYIxvPHaMUbuEjSpg/gyL0AkhWNgWYWFbBJiHlJJ9fZN85cFDPHPCcrlFKwTFqVgExnFPOu1OFTzu7NLN6nUjjw7H7XJI1duxtMdsy4TH9OME4M+qVxhJVFbaL+2LKQtB/PUBhXTOxGsIw3OcA0OCoZuMJ7P4FMt17CVjToFqr1XK+ZyWcM/2Qh3C33/iCha0RhBCcM3Xfk/XaIp7nuvhi/cf5s5bL2DFLOs+Xby4hZBfZTKtYxiSf3nLuXzh1wcYTea5ZuVM/vY1q+gaTXDHY8e4f++ARdiENVeOBVZI3CxD7xw4/xoS9vfHME0Tw7SsgqJkfyUIcAmgIuDe923kdf+2meOjBZd+nQazmkKcGC1kR7aGfVa2poRnjo+ydGaEY0MJLlsyg3dePJ/d3VG+s+kkulmItdowv4kdp6OAlfwwryXEgb5JNh0f83bJzSRFU6gPaq6b0onNLBXMBfjln19cRMIALlzQwn8pJ4hldK5e3sbDB4dsV3BhMkoLmSuiwvMryy2XAB97xWI+/IoVlSf2JUJNvqKGGmo4q9HWGHY/VwrWLYVumPhVRy3+xSGrS3wVGv+XN62Z9tzLl7Xz4EevYOtnr0VTFf7qJ8/zJ9/a6pIwsBbUa7/+FB++d5dbX9CBEIK1c5pIZg3a6/3MiPhoCpW/MzvUrJLlZToowAULWsq2x9J57tnWXZEIFXey8NF7Tz76imXc+a4N5E2Vpe31RALT652ZFGLE4lmzyK3pkEWJRTwDthCWpkBbxM/bLpjH68+bg6qIsv5KSZH8hnNu0NMlAQxNZlyrV0BTkMDTx8YYi2cIeSQ2FrdFeP25s1k4I8w7LprHuXObiaYta9PymZbe1aGBOPfvHUA3JZ+8YTnH//FVRW5wR8/N7WOF+cjqJnnbYpY3y8VKK8Gx5jaHNaSEv//tAd5ywWwAGux69EmdIhIGMJbKE/YpCGDTsVEC9oR96Jol3LBqFtedM5OOxiD1QZU/v3Ixj3z0cu74f+e7xKF73BIaHrfrOmqKKIisGpKwTyGgCWY3Bqr23TleU2B5hfjLSxa3IoBE1igjrlCweHlRyRLq8QS7hOycjoY/OAmDmkWshhpqOMtxYqhy2ZJqMCQYRkEZvPRt+YzboNh11FGvMb+98Yzb+N3ePj50z+6qMTmDkxke2DfA7p4on7xhBa9Z1wnAYwcHWdwWYU/vJAJYNCOMIa36e/ft6i9rx7EIQeHNerq1uyHkwzRNfvBMFztOTXBkMM7AZJpE1ijKRKuGakHQX3rgEAFNIa+bGKYkUSEN01fFfVzadsinuBl+zhgV04qZetclC7jhnFk0hn08tK+fn3vqDjqQFAje0hkBhmJ5MkjXcuRY7/o8paLWzm7g0GACgHdcPI/5LXXuPk1VuO21q+idSBMJaKiKIGm7bVd1NqIbJnt6ohhSsm5OAzfZGYSlWb6OVTdokz6wrDuuS83zvDruNcf9Jqh+b1vr/IzYVqf79/a7FqOYzfP9imURa434eb67IN0Rz5lcuriVj12/jGXtES7/5yf554eP8KP3XsSqzgbSOZ2NC1s5PZbird/ZxngyV/3ZkJKAao0vZ8KI7RbdUyFxwD0Fa1xL2iJoSvnb03A8S0dTiO7xNCdHEsxpDjEwmXFd3dWexUrPsGMxFral8rpzpq+O8VKgRsRqqKGGsxonRqqXIKlGsgKaIOcEHr1AEuaFd9H70LXLXtC5f3HP7in3O4tr93iarz18mHTOwKcq/NXPdvPWC+byw/dcyLvufI7eaAYhJJuPG+5Cogprkf74dct57bmdHBlK8MzxMZ4+OkxLnZ/OxiC/2zdIpkr6WTSd543ferZom19ViNjkpMjiUDIPXqLmtTI4cNzCqazOey6Zz0+295D0CHDqZ2i2iwQ1JtM6Dl1xMhWbgxqvWdvBwweGeOzQEPfvHZi2rZOj2bIKB44o7u2PHOE1azvwaypLZjYA/QRU2LCwvEi1EIK5LQULraO2/6F7d9E9nqa5zs/itgi//svL3WOuWdnEA/ui7t8OcYgENVbMamA0kSWe0emLpt39FRMyKrjlvRjxZCgmKpjRPnLdUkbjOe7fO1Dkwr5gQTM/ft9GhiYz/MPvDqEAO09PcOPtTxNN5UjmDB49NGy55KWc8sVGl6CXTHQkYBWE3zC/kQP9cSuu027DebYkcHI0ye+PDHPDqllF53/rqZMMTGZQBBwfSbJhfhO9E2l3vxMr6UhUVINCoYqDKS3r5zs3LpjijJcONSJWQw01nNXITLFwV5IMAMu16B5T4TzvsU6A83RWoO2nJnjbxoXT9zdvMJbMcsmiFp45OT5lu4oiuGHlTN5/5WKe6xrjSw8cRgBXrWjj8qVtfPeWDXz+1/sZTWQZ8GTMBX0qP/mzi1kzx7LQdTaFuXp5O5971UqklGw+Psqvd/fjV2BZWx3nL2rl7q3d7thnRFSWzWpm3dxGNi5qZWVHA73jKV7/n88U9U9TBOtm13N4KOHqoHnJl9ca58AZb86E7z9zGhMIa5DV7RJL086gheF4jpkNfoZiOWZGAkyk8uim5LnTE6z74qPU+VWiqXzRNaFyHGGldI+MbiUa9EUz/NsTx/j49Ss4NBjHpwrCfrVI+b8aVGENfnAyg6bCaCLHaCLHRV9+jEsWz6AhqBJLlM8NQGtII+ATtNT5kJ4OO/235EwEUkpMadWqNKWcsui504qTAOFs62zw8+9PnCSrG2Vzs79vki3HRnnPXdvRVIU6v4oQcMqjtC848zJjpUhkTa5Z3sYTR0ZQBbSENYI+lf7JbNFY8obk2RNjZUQsnskXWVB3nI4WZauKKgRVUiyD4o3LE8ArVrQzo766y/SlRI2I1VBDDWctpJREk+X19dz9ns9nomLvPc+xsBiebVOha/TMigP/dHsPX/jNAffH1UsQoCTo3JC01vlZN7eJWCZPS52f8WSOD/xwFxcvbuELr1nNWy+cx9ceOeqes3Z2Azeu6WBVZ+XsTSEEJ4eTtmyA4I53bGA4luGHW7utcauCe/7sUpa01xed9/D+wSISI7DkFP72NauZUR/gc7/ax5NHRovOyZsFIuvApyoITLJGIf4nVSGt0llMXeJB+T0csv1qApOVHfU83zNpu/Akum66EhhCFIRKHRJzJrzBud69z/Xw8etXcHIkwcpZ9WR1k9sfO8YVy6Z2XTmetHSJVshQLMsvn+8rO9571Pp59fz37mFyhuTC+YUAdaffprR0v7DHOLMhwFAsa1VQqACJRcDaIj7GUnk003KLpvKWVl01pPMm7//RTjK6yT++9hwuWNDKk4eGuHvbabrH02X9rgYrps8S2Y14qiZcd047jx4cdrNkL1rUyor2CHc8caLofgd8Cg1BjZ7xFLObgvxwazfffOo4wxVE7KrFiim2acy5HZUsuSYWefvAVYvPYFQvDWrB+jXUUMNZi60nxiou4pUgSv6dClb8UOXlRRVQHxD4S34d9/dOVjy+FGvmNPK2C+e55ERQKEBe2jcTODVmZbZdvrSNz77SChxuCGlsPTHOn/1wB++/YlHROWG/xgevWuLWKCwbm5R89aGDdp1KyRd+vZ89PVFWd1gB5ZctbmWxXUzZi5/v6uUVK9r4/KtXsnxmPQhY2h6huS7A7OYwfrVQBDziVws1CEu6kTNMDNPa6NfKxxzUBKs7691FtDmsVSRhXvTH8pwcsRTW6wMafk1hUZsVv2VI6142hbRCjNwZGm+CPgVFwHgyh2lKRuJZjgwliKXzjCZy7OqeOj6xw5NIAhZx3biohfPmNnHxYqte4iy7tOXMuuIHyhAqOUPSENSKqjU49UNVUUg2MKRluZuuQLUuYX5bPWG/z6peYClPFBVi1xRRVH9RVeBtF87llo3zGIim+fR/7+WbTx2jbzxd2nwZHKkQp99OxaqE7d7WFBhNZPFrCiG/gqYIHtw/xL+WkDCw3LzfeOI4N9z+FBv/8XG+/OAhBmPZsuOCHvOR870C654bpkRVi+fZm0DgtHXTmg7WzCnOzvxDomYRq6GGGs5aXLKkQpwOld/QK70le4/3K6CqgnSFIpPeNgOqIJ6tcMw0shUOGkM+7nmuu2K/REnnFYFbEPqHW45z+xMnrWLEdhbaV25eg09TXauCTxE82zXOD7Z08e5LK7tJH9g3QNIeY86EzcfH2GxLC6gCLlrUYpFCz3CGYxl2dUf55A3Lec9li3jPZYvI6Sa6aRL2a2w6NsIThyzxU1NCKm/g9ymWKKa0FnNNwZX8cCZUUxXqVFEUtJ/RJSeHE66FIpvVixZbvyp4x0VzuPOZniJr22TGanvhjDAHBuIcH0niVwWqojC/OchALGMRGM4se7Q+qLr6XAL4yfZu3nrhPG5/7Cg5Q9JSp3H/nn7On9dc8fxjg3H8WrH70jAl+3ujtNcHGI5bFjtd0QCdoWQxpfjpDivxIpbReXD/oLvdiXUy4Iz9uF5X3baT40WnObFYqmLFnBlSum5Oq89w15YuPGL9qFixXUIRRG0BXm93HGupYUqX9GpKsajujLBGPKfTXhdklz5JW0RjJDf1W5UE1s1p4rx5TeR1k9ZIgK8/csSVJ7FeoIqPd/qj2JbRUumaSuT1tteeM2U/XmrUiFgNNdTwssILjVRxjs951C+denyOJSZk16XUgJWdDRzsnyRrWK4eRbGy3vKmZPPRES5bNrXwY0dD0G2/NIaldE0wJfRHUyz6zO8qWoTmzbCsPt96+/lc9bWnyOR1OhpD/P1vDxLP5CsmEDxzYqyqhcmQ8M2nTvCqdZ3MaS5kBO7qtoLJL1nc6m7zawp+FDJ5g4//bE+x8r0sBKr7VcsdlPJUPHLcZ+mcyYULrVg5L1J2YxJLVqElbAXmO1bDn+2w3HqVYrv29lmWsdY6HyGfSm80w5HhpNsenJlr0rRJg0P2bvvNAV5/bifr57ewrWucsWSOQ4Mx/uq6ZdQHfYAV//eZn+9ly/FRUjmjTGxYYFml+iezZHSTG1fN5Pnu4rFbtAyuX9HG0eEEPRPpMtV917orysWIKw1LtXXYqg3ZdP9nETO9JNreIWHndNTzvssWctPaDgI+jclUniv++feksjq6lCiyEFzvJTiq86Aj0RRL++v0WBJFVTk1bmVMjiQKJOzihS10jSUZjWXLxIAbgyrv2LiAkE/h/t29bgyi48YWpW8zFLTjKmURlH4Xls+KMCPyh1PRr4Saa7KGGmr4o0KdX6moAeaFI0bq/ECn7F97HdjZbSmDBzRLTTPocb385U+e50D/1C7KcEBj3byCzIUsITClSOWKS8e0Rfy0hn10NAbpHbfi0lRV5ZvvPJ9kzuTwYAwT+Pqjx3jv95/D9MiF7+mZ4MRwgqkQTRv8dHt30bYTI9Y5m46NcN+OHrpGC3IDd24+yXC8QrFJLKIqpSRnWvOjUqzfpqmCfb1WcHVQs1xTDhx3mQQiAR9XLJvB0ll1qFIWZf05WmKlt3Qsmac3mnHbKIoXPAO2riiWm0sRlrU0b0h+trOPD169mB+/90I3u+6qrz7BG/5zC1975DCf+NkefrW7n2g6z4ULm9FKOiWBrJ0V2BT20RfNMBS3GGrEJ2gMqqi2Ee35vknSebMsYNxpUvHwDUn5GB3BWihYiRSsl4zpjLcKsLA1xJvP76QpoLCm04oXHI5nuXn9XLfOZmPYx123XsCc5qCrTl/Ujt0HQxayZa9e0Y6mCPomswzHsxweKsRWBoF6v2Br1zjJrO7OhRdbToxx839u4cbbn+Zrjx5FAyJ+YV9PuBZYIXBjBJ15q6RRXBon9uCHLy8/6A+MmkWshhpqOGsxOFkcpzJdZiPgZvedCYKaQkY3XfFQR8spb0LeNJnbFGAwapGQGQEQmsLHf7aHhz56xZTt3nrJIvb07KK0OlMlS03Yr5DOm+720YSj05SndyLNRfZxc5vqeNtF87hzU5c7B5tPjHHZP/0eU0qCPpW+iRR+tXzBXN5eR2t9gGdPjKMpEPT5ivYfHrDI5b8+esxNZFjUVsfnX3UOX334KNWgS4t8gUU4gz6BT1WYzBiEfQqKnRBxz3svpCHo409/sIOJdM4lwu11KmNJg3g6wzMnMkXZroU5s6wsPkWUBcWfKbw1D53EgAWtdWQNyVgiS043MXIGhoRnj4/x6ZtWsqgtzPGRFGMpncxgnL5ommgqz2duWM4/PXyErC5d61WRKxDI6ybpvEkya8e1+RUuXNjMseEEWUMiDJPReGU9LjegHNA0K+/PlJRprznPi+I5B2G9ZExVyugvrlrEx69fjmJnGvzLI0cI+FTGUqcYiGa549FDfOS6QuHx7afGuWHVLL71dBdg1wCVTjJBod2gAp9+1Up+uauP/bbVshQZIGMr3MZLNOZa6/xMJHPEsybxbNadCwkkclaVAsNW+DcltEb8TCRyGLJg4a7mknbu++ymoDvuswlnX49qqKGGGmz0TySL/n4xy3B4Col9R2dLQsWFqyeaxfG4paXKzefORkqYSFa2EDm4cGELy0tqUvpUi6SU9iaZM623fPtv71t+70Qaw7RqUd5wx9MENIVvv3M9dQEVVcC6OY2kcjqDsSy9EynCAY36oJ+6gEpz2EdT2IcqBCnd5JkT4/g1Qc6EnafH2XpijLQtSJrI6nQ0BgqldEzJ0aEE7/r+c0T8SpnlRxGWS9JrjRBYsiGTdtxVTjfxqQqGKfnrX+7nVf++haFE1ppnaZX7qfMpmMBERmKa5VavkGrXnJSWa7lKVaZp4dZ1lAXScnAwwfGhBLG0bhFZu1D7f206ySvveJrjtn7d7EY/yZzB3OYQmmLFkgU0wXNdY+SruAMdvjirIcC5cxsJ+jXetnEBt71uDcvaI1y4sJm5LSGCtqq9A1UUSLQurTnM6rKqAK5DVBxZEIcYVXqWnXukqWoRGfnY9cv5i6uX8KXXnYOqwHc3ny4qH/SeyxZx6yULuWXjPMCKKfNm1jrImPD1hw+zvy9W9iIgKL+3pRhL5lwCaiUqFFdLcOqsOteOJnNIYb1M+ex751dF2bOq2iSsMeTjP96+fppe/GEgZAWf6ssZGzZskDt27PhDd6OGGmr4X8C2E6O85TtW4euACt6X6Kky7er8gmTuf/7bFtLAjptHwcpmVBWFpz51NZFAdYfCvzxyhG88cdwNkBZYi0T2TGsQ2ZgR8VPn18joBpOpHIva6/nglYu5duVM7t/Tx2/2DPDey+axvy/BX1y7lFQmz9u+9yy7e2J0Ngb45zetAyG4dMkMLv+nJ8jqBuPJvBvfs3xmhIxuMhLPct3KmQzHM2wtieeqhKAmyNo6XIqKa/mrJPapKbBubgNBVWXbqQl0s9yKZIeZuQtx0T4gEtJIZ3WX4DjusBcD57mpZF2t8ymu+GxAU9ANk/aGALohSecNmkI+hmLZsgLY1TCzwU9ON5lI6dT5VXyKIJ7VrVqYgFCmrulZ1vcKFlUnUL3afGhKIQHAlFaW6vbPXYemlr+gXPZPT9A7kea9ly/gb161qmhfTjf54I93cqAvRjKnk9NN90WmPaIxnKgciO+dZ29SARWelenOn0q3TmBZZF+ztoP+aIZnTo4XEcZ3XjSXL968dvoL/h9BCLFTSrmh0r6aa7KGGmo4e+F5uzU9P8nqNAtYMierLrhnWvKoJaSSyBWYn4nlTuls1KYkYQD9k3ZBY0+Mj5eEVXOxFi1awhYIpaABdbA/xofufR6fqpCzJ2DT8VGCKly5bAar5zazqqOR3T0x+iezfPQnu3jXpYsYjWcZimW4ankb3eNpvvT61UTTebadHGNff4zReJZIUOOOt17M3//mAN975pR7zcag6lq5HGRsRmRglZOCytmKznh2no7x+ZtW8mzXhHdaXHjPU+wTnW0GVhapImBNZwPHR5IYpomhS7cc0FREpLQ/pcKe3tNyhslli5rpnkhbLmIFOhtDvHXDHL76yBFmN4eY1Rhix+kzK7s1Fs+5wqpZ3SCLcPvpBNCfibvdgVd134EbqF4BTvF27+50zuSZE2NcUSHp5O4/vYBrvv409z57mk9ct5ygv/Cc+zWF777rArrHkozEs0TTeRJZHd0wOTwwyXc2n674clSpaxLLInom8N4n1XZBeuFV1U/nJf+9q98lYEFNIeRTuWH1TN516SLOVtSIWA011HDWYvORIfez1z0jShbuSpxMlvzrbq+yAJQucNG0UdSuhuVeXD+/vFh2KT77yhVsXNTCJ+/bW/Ea1dagogXW84eXPAbsuLbWOh+L2iJsPzWBEIKnj42yem4zX3rDWm4+bzZ3PdtNS9jnisEK4OBAnN6JNDnDZEl7hG8+eYIdpydQBPx4Wze/er7PdTk6011KwipBoXJ1AglIE9ojfp7rGj0jwlFNtsGUsKcvRkAVrJrdyLo5jTx2aIieiYybbTld+85+VRGYpkRVRFH2X96E7aejXLm8nfPmNXLjqlksbLMC2bMmfP7X+7lmRTsBVaAqwk3yKIUrteAh4l6B1kp9qgZv5q2gMNfV4FWTd85zpCbAcsc/eWS4IhFb1FbPxrkNPNsT47J/fIxlnY1ct3Imc1rquGRxK3UBjXmtdcxpDqMogn29k7zv7u0M2RIszouPQ3idYH5TFj/D1dyURRUvPM+gM0cZXRaNBQoWwqagxtzWOha0hlnaXs8Vy2awdk5TVb29swk1IlZDDTWctXAtSxT/SBdJKVQ5d6oFrpK1zI3NktZCV5pt9Y6N87h7Wzdrz0AIsi6gsbqzkTnNIaQ0rXIustxFU4pq273rd0Y38auCVDZPvd9SI9cNnatWtLHt5BgXLWplw0LrP4B3X7qQzcdG+LtfH2AgmmZWQ4C3fmcbGxe1sOP0BO+7bCHf2dxFc1ijPuijezz9ghcGr5WpFIoiGE7kODIUL5JimOr+TEWqTFOyqzvK3p4oX7p5Df3RND9+rpuJZK7IMlSpDbfos/23bkuMSArFtnOGZE1nPX9+1dKic//fhfPoHk+xpzdqWTfti1WysJa6XQ1pJUB84vrlfPnBw1P2sfTFQlJoX1KZhHnJmnNu0KeQ1yWakEVk06cIjg6VZ9bG03nu3nqKlD2u0bTB2Ilxnusat9zJAlrCfpbNjKCpCh+/bhm/2NXLUCyLKgrfScVjrfYmFHiJpPPdK4XTd4c7+RVbdsYDLwkLaYIbV3fw4WuXsrCCSPHLBbUYsRpqqOGsxfovPsRYqnjpmSo2bDr9qOkU3KvBKyz63F9fS3tDdR2iRFbn5v/YwivXdPCRa5bwqn/bxOHBwsJX+kZfCbObgvRFCyRU8yx0zUGBiZWZ6LcDhCIBlfkzIuR0k99VSM9PJHOs/vtHUQQ88bHL+eXuQe54/BgCmFGnMZLUCWqWu7POr5LOGxVjuaaDN17H0bYSWG4tKa2g8zNtr9I8qQJCPmHVrZSSsE/hvZcvYsnMer6/+SQTyRwnx9JF8YSlcUWlJZkqjbEx3+ZtwQAAIABJREFUpLHnCzdU7Fcik+drjxzlh1tP0RT2MZ7MTzkmTQGJ4MZVM2kO+3jq6Aj90YxLzkoFRyvFVJXiogWNbDtlZbo2BDRiWR1NKbghQz7rHvqUYrelE7T+wasW877LFxFN5fjNnj6+8tARMnmTSEAl5BNFml9BTWF2c4gZkQA53aRnPElrJEDekIwlMsQzxZZjR3bD2abZz0GRpa7CuLzfXTfLlfLva70PLls+k3dePJ9LFk+t6Xc2oRYjVkMNNbwsUUrCoDKRcn7kpxXxpELMGJaERDXZC68raGFLcEoSBnZJFQHfePwYTxwaYmDCkuBwLCNnUjxZK0mx91oAoxmJxEBTwO9TSeUMVnU28up1nVy9onJtxJ/bdQ9NCdffvpmcIfmnN63mS/cfZiRpLbpZu3ZjImsUWQmrkVefKsgbsohMeIVFHRJm1QCEVZ0NPN8zecZB9pXmyZAWwTJMq/h1ImfyX5tOcvtb1jGnJUwyq7O8vY4TI8mic4rasP/1ur6g+LlIZnUmEhmaKwh/RoI+Our9SCwtM1VYrvJqmvG6CQFV8rt9g+41NNv8Np0hxLtXAVoifuLZPDlT0BbxM5LIEcvqlqguVuKElBJDmsxuCtEXTRdZ/MASLxYCXvvvm0nldE7aNVTDfgVTwnjSInVgEbuMbnJiJEkmpzOeypPVTUaT+TJrlXMdq0B5ofO6WUyyHGIctkWUS+EQNyg8dwFNYU1nA29eP4c/uXCeLer6x4MaEauhhhpe9nBdGp7PVd18Ff5O5qxUAA3IT3H8ba9bM21fvv7IEdf1c2gg5rrCnH+9AePV0DuRqurCc/6+ZkU7ly9r47qVs5jVWJ0cHh+O8+9PHQdgZsRHzoSVzSGkacU5edtVKLi4HF01UcLEVAFvXD+buc0hFrXVs/PUGHc+YwnEllrRpD3unG7SXaIg/2LgE+V6WumcyZ2bT3F4IMZkxirH09HgpyHk4/BQElNCW52PsWS+aN5L44+8lhrdhDufOc3Hr19e1ofRRJb/2nQCxS4T5FMVyzVXJV4MCjUYnWt5C5SXotIUXbl0BsmczonhBDldsqcnWnSubkraIj5iGRNH9GEwlibso1DxQELEZ1lbv/yA5R4N2loP9X4rmCuXN9i4sIU9vZPohsF1a9p5cJ9V2qpvMsuC1jCnxlJWXcnS7EWPi9bbt1IrtTNNpSRMem8EhfiyDfOb+NLNa1gys3KR+z8G1IhYDTXUcFbCPAPLkRdnUtZmKjjxN16y5CUVAU3hyuWVLU5eDHji2hzy5bThVyAUUBFCYSJVSvksTJUB6LR12ZJWvn3LBdMPCviXR4+SzZusmBnh8FCC1jo/Bwfj3PtcN/UBjXROd7MgnT5DgfCUZqcaEt5/5RK3cPjyWQ3cv3eQ4USOqTAan3r/mSAvbVV1iZuNKIEdpydYNKOOyUwSBPTHcvTHrOsJYCSZR7MD9CuhlPQuaAnx/S1dXLyohUuWFLu/ZkQCfPVN5/GjbafYcmyMrG5OW8lhusdyOhfwU8dGAfjBrRewuzvKdzadKLLgSgnD8TxzmkP02BbYgKaQs8WKFUXQ0RBgPJljT8+kW/3ANCWtdT58isJgPIumwJaT48xuCjKeMHn6yCg3rZnFA/usWpinxlJWFQITfB7ZEih897yWxlKrI1hkWpflz7mmChpCPibt56gxpPGbv7ycuS3FhdX/GFETdK2hhhrOShwdLFfn9iuUCTZCITPLixfDyZw6es4SN79JcxfpdbPrpzy3L5rmG48fJZHRCVQo3QJgIMjkJVcvrx7bUjqOSklfr17bOWVfvHho/yDxrI7fZ3Xq629ei25I1s5p5KlPXc27LllQOHgKQqEKgcBybb3921u48qtP0BdNs6Q9wpvWz7b6ijV/Xg1dBVjSXoemCnyKIKAJfCXz0xhUEcB5cxqZDoZZnI0IVtzXlUutOa0WOB/0KTQGK9seJMXPy1A8Szpr8LOSUlAOXnHOTG5a00nelNQHNQzsuqRML1zqoNRyWAqt5Mav7qzn8qVtdI0laQj5i/ZFgiqzGgI01xUqJjQFNXQJbRGNNbMbSGYNq4KABJ+qWNZLReBTBRnDIKRB2KdS51OQpkRRLEtfc0gtup85c+owAG8yRKUXirwsuEqDPkssWAC6IRlL5FwXbzpnTGnp/WNCzSJWQw01nJV4+MCg+9mJGylVV3cWs2oBwP9TnIoWIn/OnTs1SfjRs6f55pMnivrh9Nvtp5QIIXho/2BFa0EpSgmmBBa0hLn5vNlTnndyJIEiBHc8fgQB3HLxfO7Z1k1z2BKkbY34uWFVB0IIVnVWrotZ2jVTWhmG6bxVvgd0To8mmd0U4pZLFvLzXX0M2VavIr0rAYpQiAQ0PnDVYlZ1NtIU9tEUsop2nxxJ8NjhYb711En29U1dx9OBSoE0O9crJXdeKMLKZB2KZQlqCgLTFeqthHTepKXOx47uSfeeleLihS201vkZS1pjbgqqZHSzyLpoD/+MXOSlKA3iz+QM3vytLezqLp+jRMagPqCxv7fw8jKeylvF7HMmJ0eS5HSrhFMkqCGkpKMhQMDeL6QEoRD0K5hS0B/LEtAEiqby5NGxiuTSESquNJ7pxqZgWdVU1SpLpAmLoDlwXPm7Tk9w0aLWas380aBmEauhhhrOSvx27wBg/UjpHveYUYEsvFgSVvoDWM2aoQAbFla3Yo0msty56WTZObppF8a2tznFkVN5E78dEa2KylY+KB9TfUChJ5rmB890lR07Es9y5+YuXvvvm7nm60/xH78/zqajIxgSbjhnlhXvheA3e/rJ5k02LGhmcDLDoYG4O+6AKty4IS+E54NPgfqA4OrlM7h4sbVIzmoM8d7LF7olewxZKNZtSugaTRLP6tx66UIuWdzKQDTDx36yixtuf5p33vkc337KmrupSkl6i7kbFFsKdQn3bu+teq4pYVZDkC+8+hxMWSBhfqXyIiiAaCrPeCLD9zaVzzXA3NY6JtM57NrwTGaMMhIGL/y5LOWTIU3w+Meu4HXnzeFgv0W0SvssgYHJbFEMnGonUyRyJrGMTl1AY1ZDgFRWJ2uYLO+oJ5kzaQ6pJDIGumEymcwjTcvfKKUkmdEZiWfL4sG81y0tReRFte+TkxGZt+fLIWEC67mp8ytcuayNummEk/9YMO0ohRBzgbuBWVjz920p5R1CiBbgp8AC4BTwJ1LKCWG9OtwB3ASkgHdLKXfZbb0L+Bu76X+QUt5lb18P/AAIAQ8AH5FSymrX+B+PuoYaajjrEE3leNU3NiOEVUPx9JiV+TZd8D1ML3JZDaXry8YFjRwZSTCRNIquZQITqeoxTl/53UEU+wzpOUdQTC68ekppO1i5klVMAfw+Bd2wNKACAuqCGuM2gxi2BTQTWZ1HDgzyq939bD42gimt7MTP3bSS16zrpCns4zubuth2apyAAlctb+Oh/QOsmNXAh+7ZxbMnx8jakhIKkKlionNdaBICmqAlHKRrNMUzJ8a4dMkMhmMZdnVPuq5JBWziZ2Fua4hMzmR/b5Qfbj3Nb/f2l5WqqQYBrJvdwJ9fvYSNC1tZ//ePopfMmwI0hX1EpzBz7emdZE9vsTUpZ8sqNARVYh7hWmmPNZuX/MMDhxhP5vjUK1eUtXlORwN7+2JTFtmuhBXtYcaTOcZSVrkjvypobwjQO5Epe459msIPt57ivp19RIIa/rxO3hSk8yYKltVVUQWqEEVxYxl7gh2rbCydt6xQwqoJenw4yVgiSzqnoqgCISXhgEY8a1jB+IY193nPffSO0NlWarlz93uC951MSceabUj7u+c51Qf8+TVLePzQMG/fOI+3XTT/jOfz5Y5pdcSEEB1Ah5RylxCiHtgJvB54NzAupfyKEOIzQLOU8tNCiJuAD2ERsYuAO6SUF9mkagewAWv6dwLrbfL2HPAR4FksIvYNKeWDQoivVrrGVP2t6YjVUMPLE1uOj/L2724r265RXRqgErx6RS8Es+o07njLGj5x3x564mYZ8fvZn13EhYtmlJ33+MFB3nP3Tvfv6axzZ2K9c8RFneMuXdjAhoWt/HBbL4ap015fx8LWOp46NsKymREmUnlef+5sXn9eJ0vaC7Fs8UyeNbc9wuymECGfwumxJA1BjawhSXpkKqqVfarUV2ebADZ9+mqeOT7Gf/z+OAOTKXIGtNT5aYv4OTKUcMdx7fIZPHl01HWFncm9UQV0Nga55ZIFvO2iedQFrPinh/f38/4fPV92/GVLWth8fPo6mVOhkoCog1sunscXS7JmP/vzvdy7vedFXcOvWmTnHRfNZXl7hJb6IH9xT/m4HLjaWp6klHUdIfYMpK2anx4ZEYEVf2WYFpH3ciVNsa2wqopuWA7eWY0hesbTzG0KEM+ZTKQK2milSTDeZ8L73JQ+s15rmFOSbKr77tR1rQ9oRIIaWz977RRHv/zwP9IRk1IOAAP257gQ4hAwG3gdcJV92F3Ak8Cn7e13S4vhPSuEaLLJ3FXAo1LKcbtTjwI3CiGeBBqklFvt7XdjEb0Hp7hGDTXU8EeGU2PJitvFC/Q7vhgSBjCY1HnLnYWFMKjBtSvaePDACCawYUHl0kb/9sTxF3Sd6fpWGjumKbClK8bRoQQTKYvEmGaGoViGt2yYw3nzmnn9ebPL4piklBzojdEQ1OiLWpl0s5uCvGZtJw/tHyCbT4MQ6IY8YxLmjXmb1RDg1u9v59hwgvXzmuhoCrLt5Djnz2vknRsX8uF7dzKZMWgO+3jsiJX1p1djORUwpznEgx+9grC/eJm6YXUnb7tghHtKXJGnRys/Py8EObOyyCrA3Vu7yeRMvvrmde628+Y38fjhIUzTtEpBSTmtpa8l4kcIwcCkpUj/8529pKfwyTr3wWnWK3p6eCRLQ1Ajnil+VZFYFldNQFBVSHl8+0G/RjKjI4RJ3pD4fQqnx63no3cy6z57znUdIuZImxQZ/jyfy5JMPAr7esmcqIBfg5xesGQ7xsxUzqAx7OMXu3p5w/lzqs7LHxNekANWCLEAOA/YBsy0SRpSygEhhJPXPRvwviL02tum2t5bYTtTXKOGGmr4I0PXSHnZFSgO4j0TTHX4C5G4SOtwZCiBIWHt7AYUpTQyB7afGuekTQCctislDkz3d6W6eu547A2TGYuEqUJYgp2NYZ48Osrdz3bzxd8dZMunryHkUznQH+N3+wb43d4BhmIZmkMa6+e3saQ9woevXUp90Me6uU2MJrKcHEly/95+RjzSE9v/+hV8/ZGD/GxnP8iCSKvVN4EqrADrwVgWJZ7lttecwy0XL6B7PMVQLMOitght9QHecsE8vrupq0ym40wthkKIMhLm4MtvXMeBvkn29MfdbQO2u/Z/CmnKMjV+B7/Y1UtbQ5BP3rCcTM4imRfMb+HxI0O2uK11XEiFdBVf+WAsVxBMlVQkYY6Fq6MxyETKKq7tVBrwErOAJuwsXUHGkEUu0qAm0FTFdVHWB1TiWYNk1mI8Gd0K2B9LFObNeQadKguqKE42KS06XslV6cZDluqMAQEVMoZFvkq9yI7wr5SS3ok0f/vr/bTXB7hs6ctHPf/F4oyJmBAiAvwc+KiUMjaFsm2lHfJFbD9j/H/23jtMkqs+9/9U6DB5dsLmvFqtdqVdCeUsEZQQIILBYDBgkgwXw7V/RF8cLiYZX4MxwTaYbEAGCSMkhFDOOW7QanPenZynU4Xz++PUqT5VXd0zC1xLu7fe55mnpyucOudUdZ+3v+H9GobxPuB9AEuXLj2aU1OkSPEiwc7+yUgpH4W4srvuGjtqy1dwwpUnz+fWLX0s68qzb6RU9/Adg9JS8IGXrkzc/6EfP0UxUOuMZzfGL6uI2kz9jhNFtTiG8UxNNuet7OKe7UO8ZEknl57YS3erzb/cvZNfbjyCAewfKXDh6l4++LITuHBVFwvntETavGr9AkBmpX33ob2Rfa/4x7swTVMKuroiYs3wfamkL4RUtvcE7BuexjQNlve0sLynep0PveJEntg3yjP7x2pqJyoY1ArcKuKnrHj18LMPXMBZn7mN8ZI8O2uaCN+vibGKl9yZCR61JELBFXD/9gGOjBfZenic/okSJVdQCn4tKNKcRML0+x63EMVhmQaOL8hlLM5Y1sK924fwfBGplSmAyZIXPFuCnGXgA3//+lP4+M83I4SsknDS/Db2Dk9TdHzachaFshfO0dB0JZFwllxpTVMkTCfj+v86kp7p5qyFU5HXMwxJwpI+46CKoleTMCquT8b6fyOfcFajNAwjgyRhPxJC/DzY3B+4HFUc2UCw/SCwRDt9MXB4hu2LE7Y3ukYEQohvCiHOFEKc2dt7/LPnFCmOR2ztn0r+gtb+r/drfLaQFiV4fO8wOdtgZAYRUoU/+8mzvP4bD/LgjsFwW/94ieHpCnYQgaz/ojRir1AlWEn91rcl6YaF7RowWnC4ZXM/ZcfjqX0j/OSxfXz5jl189e5d7BsusHe4gG1AxXHY2T/JPduHuXXzER7eOcTOgUlGpiv4vuDIeJFv3rer5hrjZZ/RosuC9ixZ26RZy6IUQR/U+mgANzx1kJ0DkzXttOZsLl3djWUlD8g0AhV/NTbkvVnS1ULWMvB9GJos1J2LrG3xmz+/NHxfcGtJGFStlKYh2280v7PBpkMT3LrxENv7pxgvuJhBlqGtWTWbg+xRHfWe1+ZM7TIsgoelNWvy0StOCvscFwjOmJLsGIaBYcosyeZchq5mGQcIsK1vkgUdeVxf4HoC3bDreCJxPiwDetqy4f+tWUsLzq8/NyBJhR2cV6h4mLaUxTCTPhQBmrT5Up8T1xP0tOYaX+w4wWyyJg3g28BWIcSXtF2/BN4BfCF4vVHb/kHDMK5DBuuPB27F3wCfMwxjTnDc5cAnhRAjhmFMGoZxLtLl+XbgqzNcI0WKFMcZ1i1oo3+iTGsGpmKi879tAL4OFd/kCVkjEKBcp8WWrIVtSkmCDYvaOTRWYuPBMa794ZNYlslbz15KR7ONLwQVZQ2h1to1kyUsvvhYs3SdZiwDIWRmYnuTzWgh6uep+PDInjEe2TMW2a7rXuWs5EVV9XNgysVR1ZqRLyt7m9k5KEvcKDdYczbDjU8f5v+7orYc0NWnLubbD+1nvOhExguBsnpw/WYbCq688MBEKXSvveM7T3Luym46mjOcvLCddQvbmd+eD+PhOpuz5IAyMuXeDSxJ+jjCeT8Kq1gjCKRbrbc9R7HkgIhmv1qGFEq1tb7kbZOSK7McW/NWaMkCWeonE3H/VpNTNh2c4C/+86mIVVidF/JbIZh2BK05eaM+fN3T1ecpEEDuC6o9FLUbHn/29GtI17N8TlwBY6XGOckmkLVlbJ3rVzMiLVPKteRtA9OQbm3Xh9aswVSl+qCrDGL9M2JbkPldWfMxgtlkTV4I3A9sonqf/hJJmn4KLAX2A28MSJUBfA24Eilf8SdCiCeCtt4VnAvwWSHEd4PtZ1KVr/g18GeBfEV30jUa9TfNmkyR4tjEwzsH+R8/fppXrJ3HT5+UYaNJ5KVeEerfJ+a2ZhiYcljR3cLNH7qQlpzNE3uH+fLt23ly35h03dQJ6k6CGkfONrFMg5LjhRlmkcUnlvkmqGacof2fMeGalyzmo1esYW5bjpP/5laKWjZizpIZh/tGSgjg5SfNZcfgFPuGC7QEBc6V/pVlGhH9K9WHjHZdn2qWn4GMTfIDImibkM9YNGcsBqYqYayPaRh84qqT+Lf7djNecCi5Pi0ZEy8QSM1YRoSQQJVwq7Eu72khZ8u4N4WulizrFrQzpznDQ7uGQlINVaKZJJYbBp0fRZzgTMiaQaagUXWtQW28VN4KJBtEoLFWJwYNJMESoqq11ZQxKDiiJqZQxWypsapX24CWfIbJkhP2Z0FHjr7xMmhZjgq/jYt/TpMkkz7RuYxnOFtGVZxVUB131pZu7zjibsvtn7kq1Ns71tEoa3JGInasISViKVIce9h6ZIIP/eQp+ibKlCtuKCHwW8WBNUDOgt7WDH0TDq4g0fqm4w2nL+If33RaZFuh7PCv9+3mew/sZqKcvJoqspjRrCK2KcnR/tGSjI0KFkU9RkonXXEogmQHx7xe69v/99NnuOnZw5IYGdCcM4MC0HDOijm8+exl/PWNm5kuuyFBactZfOUtL+FlJ83jxmcO8R+P7OWpvWOsCKxecehWnUtP6uXQWJEd/VMNSc2aea1SVLTkYprQP1FmfkeOE+e10ZqzuX/HYKjfFbdgZS2D/3X1Ot5x/nKmyi5bj0zw3OEJnt4/yr3bB+vW6lTzr8KY44Ts9/1MKWQDJVbPr71m1jJoylhkLBPHl9mKhUp03IogWkj1+8mSGz4XTXaVLOtNN9nSKhnRVNMIp0m0Judskc8YYdwbQHezLStbuB5lV9S1TltE9fxMoClrUXa8kIyZGjlrBEXKX7ehl398y1mJ1Q2OJfxO8hUpUqRI8X8bmw6Ns2NgmlMWtLH5SDXe6LddMJWlJ26x8nzom3TCX91JJKwjbzFe8jhnWQcfu7JWxLM5l+EvLlvDyFSF/3g0uRahWkAd7fquD/tHpYtIULVMWEExaoNkEtbVkuF77zyLD/74afaPFkMX1mN7RhieKtPdmuM1py3ihqcOhW1PlHwypoFAMDhZ5p5tfUyW3IhVIp8xaQmyEq85bRGGEDx/ZJL9Q7UkzEAGcKv273p+kJaMURPuc+ayOVz//vMBqFQqeMisvULZxbIMuluyZGzJWAYnS1zz9QcjQqpQJagVT3D7c3284/zltOZsOpsylByXx/eOJJIw3VIaFwuNB7kfLXIm6JxbtXPO8k42H55kuuIR8Cpas6BCDxXpkORIMDJdob3JplDxsAywLMjbNmXXC+O1PAHjNXIU8l7qsAyZ+ai7KvU6j76Qc6mPOZ4cEbccKhJXckToJrcM2Z+4GztpLgXSbe5ojfpCkM9aTJU9cpZB2RNkzfpETPVBXe6mzYN8ZKzEwjlNySccB0iJWIoUKV5wHB4rcuK8VrYcqQ36PhoofaKim6z47db7KR+gNWeysreNdQvb+fArVjO3rX7R4f2jv7tuFYDn11o6dIxMO/z1LzYxVY4uzgMTJe7aOsAbz1rCS5Z2srynmUOjBXImLJjTgucLdg8V2BX8LezMc3isFLrEBqccPvCjJ3n/pSfwzvOX8/e/2SZV1RP6oCxVKmPOBKYDi4lazLuabF596oLwnGxWBns3ZWFOS7amzZ7WHCu6mzk8VgqD6D0RdZ09tXeYf759G/fsGGJ7/1Q4B0kkYGVvE/uGi+H4DMAwwfejJKwmdiyAbs3J2SanLGhlx8A0E2UP0zAwEZE4rbmtWf7lbWfS1Zrjr2/czHWPHaDi+ej5H81Zi5asxUkL2nlw5zBzWjKctaKLB7YPMlXx8VyouG4QY2WysCPH4fFySHqr1rIq5dVj3gQy0N3zfbyg8/pjHyc7AmmZ9f3k/SqWMWOB4wXxasFnJk7adHKn/vcBXydhyPgvy5DuWWWd8zHobZXu05IromKw2jV6WjJcecr845qEQVprMkWKFC8C7B6YYs/Q9FFZK2zgj86uFr82SNYniqOnJcOSOU2ctWwOV58yjze+ZAGnL+3kNacu5IGPv5yff+B8Pvu69Q1JWMnxuG/78Ix9bM8lf8XqYS+zGfMzhyYZKTihtcM2oewJHtkj+9Cez/CT95zLvNYcBQf2Dk2zZ6gQ+YIfnKyV6Riedvg/v3me1379AQ6PlSJWJYPqAhHPmBNELS8AXa05FnTUn7M4DMPgghN6Q6uR40frFhrIAP5/uXcXowWHlZo0RtKcvW7DQqwgnVORA6FZcUSdV4XeNpulnVkMZID50wcmmChLdlP0RM28DE9XuGubTOT/9DWnsO0zV7KqO5rlV6p49E9WeObAOK4vGJ52OGVhZyjzYZvSgiSlQAQl1+dPL15Ba1aOI2sZ5CwoOF5ozVL9DoVQHV8moQBZQ8bvNYIiqiaweE6eV6+fx9+8ai3rF7aFsWcVr5odq1pTZFb9qRjD+DzGr67ckCVP/jgSyB8fg1MVyq60BOuxdap6AMBo0eHKUxZwvCO1iKVIkeIFx2TZxT2KWn0gA6TveK6qaBM/2zahJZdheXczB4anGC96/MMbT+X1Z/zuat0/D9yASVDZmSAXHx2K6MwkAVC/bRlzNr+9iYrr8dzhcX786F6GphwGJ0sMTFVCt1zc+OcEfenI27xqw3x++sRBXF+6t547PBkeH5IYAteWqJ3bpDu1c3Ca9/3wKW798EWctKB9VuO54IRuvnlfhrGiJJn6M6D+a8mZ3PkXl3Dnc0d4b0JpI4W9oyVW9rSyY2ASxxOJAfv1YBvQNykZvIrFq3eLJEGR7refPX6AN5y+WMpHGAZ//ZoNvOO7j4fHukJarFTWKMiyU5NlL9TpUiN1PMHh8TJfvWcXva05piplXL8a/6WGopPlrAEVIRfyCvJ/o1HldGR/XrpmLp993XoE8PT+UZ47MkH/ZDliTctaJo5XTQCxDEmelaW53jOsjm+yDcquIJcxw6xI/RgVD2kFiR2OX61paQT+6bxtsn5RR8PxHA9IiViKFCleUAghgqLNtTIMjeAJGEgI8lq/sJ0vv/k0Vva0YgY/re/bNsDbv/s4//7A7t+ZiP3g4b14vs/aBW1sjblSVfyVcu+YRnQBimuiqdcZPKYA9LRmOXv5HLpaczy0c4jRgstgX4W//K8tkePacyY522JwOjmYfbzkcv+OoTBwHKqkSxE41Sflqpot3n3B8lmTMID1izo5YV4LT+wdSxy/AUyWfH7+1AFWzW1jUWeetpzNW89ewt/evDXsI8DGAyO8+5LVfPKGTXS3ZBkrViKT2pSRGauO69Gat5nX3sTfv2ED6xd34no+D+0Y5KPXb8QXPoPT9Z9DKRArG36+b5K3f/tR3nPRSi5ZM5dL1szl8pPncseWgfBeFx3833yxAAAgAElEQVSffKAobwBP7R+lOWOFBEvP4jQBhExqgGTZE/0Z8qgG5CskzWPWhI7mLKt6W1jQ2cTfv+FUvnTbVq577CCTFRfhU0M8Hd+PWqqE/KwCYcJIo2dWVQyo9wMra1lgCYqOT29blr6JcvW5Cy5cqPh8/+G9fOjlJza40rGPlIilSJHiBcVkscKe4ekICWu3YKKxdFENWrImpy7ppOwIHt87Gil+feHqXk5d3MH+4QJbDo1z8m/5K3twssjf3LiFl540lynNB9qeM5ko+6HbR7l39BSyuOxGaOEILAJx2Yo4hqYq3LK5v+5+td2yLFpz9YkYVJMG9HPVomtrpXT0wO56WNSZ5d0XncD8thz/++bnuGXTER7+y1cA4Ps+B0aL7B8p0JbPMK89x4KOJiquz/t/9CQTRYehyVpR3fYcuL5J1jaYLnt85PpNmMD8jjy+EPzb/bvI2TKzb83cJrYNFNk2WOTA8CRNWRFqpeUzJhsWdfDFP1jP8p62muso2JbJxSfNY82CNnYPTpOxXDyvGhOnNNdcEbUEjZdc7t85zJbDE7z3opW8/6UncO3Fq7htS1R7PJuxKAVBXI/vHQWqRbIjoYyBFa8tZzFd8WqIcNaEXEYGvqtgf4RBzjYx8BkvVxuzgPbmDFevX8Cbz17KyQvb+cbdu/jKXTs4fWkH/3rf3sS5UJZElThiUNUsU4RPkTCVDNAoc1a3qHnaM/bWc5bykSvW8POnDvLFX28FolppiuxtPjRev/HjBCkRS5EixQuKXzx7hH3D0Uw9nYTNJtPtirVz+Yc3nUrOtrj2P57kMzc/R3ve5uoNCwFZt+/VGxbylTt38PW7d/KNt51x1P10HJdrvvYQAI/sHubEeW0cGCvSnrfIW1Cv1KFKw69HaFb0tLB7aLrGQlYPOoHTLRbKkjVacCJZhTO1py+QBrVJDopAxvW3QrkF0+Qfbn2ekivlMlqyFn3jJe7eNsCPHtnLnsFpHF+QMQ3yWYtXbVhI33iRO7cmFkoB5FxmLJ+CVuXIBw6P18a5bRuoHvTVu/eE4/jwy1fz4VesPirZg+7WHA/tHMYVMLcty3jRodk2ma54kozV+XEwUnD46l07eHrfCKct66oh3aoMVtaW7j6VKauC4uP3qBCQMILt+UC+ouKDcOX5IJC5C4JyQPK6m23yWYsF7XkGJiucf0I3zTmLb963m76JEiXHpeL6/OCh5GxfU3sW9AB63eJmBhMshBZg3wCuyggWVcFi24T7dw7y1P5RSo4XxuJB1WWpyN5YcXbVL45lpEQsRYoULyjGC07DWJ6ZvugzlsGnXn0y7U0yM+9f33YGn/nVc3z8+o0YGLxygwz2feNZS/jH27dx745Bfvb4Ps47oZfWnE3eNtkxMMVf/tcmeT0hGC+6fPa1p3DxmrnhdUqOFxIBIXw+9/pTeNVXHmCi5DFBfcQJmKIFalwHRqbJmDKt30o4PhynUV0QBdHswtkQt3rZgklzr1OXUBIidpy6frxW57Tj8dlfbeFXG/siY6l4gmnH5/sP72vQ2yqSCIqORsKs6xe2866LVhy19tTrT1/Mb7b041Y8RqcreD6Yto8FdDRlQle4bcprz2myGQ4suQXH5/bnB7nteVkGSye4biDCW3ZlW64ng9aD6kgh2RIYWAgKroiQuZIr9eFMU8aSteQsSo5PS9ZgOhDd686bWAb0j5c5NFbGNOBnTxzEF7LvK3pasAyD5T3NDEzWWkTVM1XPLa22Z2yTcixArJ71NP7Mqb+SK9jWN1U916ge7/nR+96UOf5pyvE/whQpUryo8fbzlvPQriEe3t2waEYNWjImX3rjBjb3TbGws5rens9YfPKqtRwYKfDpm7dw86ZDrJnXTtn1qTiyHuFHb9gcxnMt65ayB3qQvQF86/7drFvUTk+rzAScqlR/tRcdwU3PHGb9ojaePRSNE1PyAPqipBZVfWFSkMaAoOafIb+U9dhmdY4jahc2O1B2jwd0Q9SiofYdjap8vcNCvaqgfWXlUMd7Pty0sW92F4lBLwel+pC1TCoJAmuNxrHx8ASP7h7msnXzw217h6bZP1LglEUddCXIaWw7MsH1Tx4kbxuUneo9qHgCw5RyHwrqORkuuBHSUI/gCqrt+XWONwyDUxe28fi+cWxTWhp1wtPTlmNVbyuP7BnGcT1MA+a05GjK+kyWHIZLPvkMiODGSMuTQcYUTJUdbMPg2+86mybL5IIv3lWbSULtPVfZi215m7HAFR8nYeq8pNsRuiUDC2C9e6Zb/0JiFmybbT3YYxmpsn6KFClecJQcj/M/fyeTRYeMLUu6NELOgps/dDGr5yXH/Qgh+PjPnuGnTx2e8dpJYp/qF35z1mLzp68Mj918YJRrvvEQnqhPai5Y1cXAZIkdA9Ldqh+nyF6SBSFuKdNRr6xT0na9TJIS0NRxNK7K36Yc0GxLUCmiyAz9iWM2rmqQVQzu//jLMBC85ZuPsunQGAXHZ+38Nt5wxmLecPoihqZk0fbt/VN87qYtHJks4wZSFXlLkuR4jJO6d2jjrBFG1fap/ibdC3UsSEvTJ69cw/++eaus2xhYzQwhQw3V+TbQ0mQzVXLxhfwseKJawaG3JcNEya25lm3CqzYs5Atv2MAvnj7Ip2/eGqr7N0KNpEkQQ5i0X4dehinsgxF1cza6Jsjzl3Y1ce/HXjbzSS9ypCWOUqRI8aLHl27bxvcf2oPneTWK93kTSsG3fbMFp6/o4ofvPreu6+lnT+znkzdsmvWXvk/VwhPHtRet4JNXrwvfHxkrcvEX746o5uvobLZpsQ0OTchBxC1Ts+mPsgzUlVAgwXqhHS+LckNzxmK06EUIZvy8o5F50M/xZzGm+PXqlXBKij8TQo7BNKU6vwGsm9fMhuU9jE6WuG/bAAu7muluzfP43pFQSV5vpz0vywoNTztsWNTOW89dxu3P9TM4VSZrmTy9f4yulgwDk5XQQtqISOrjsU0DAxHWkFQ1POU4qwkP4fEJJCQsRwQIA9oyJhMVv5p4YcgxdTZnGAo+FO05g4ly7czrczivPUf/RDmict/ZZLGyt41FnU185rWn8LpvPMjuhCoKUI0Dy1hSgkKOV+5TPySS3NxJ8xW/J7OBfg9W9rRw10cuPboGXoRISxylSJHiRY+/uHwNl6+bz1/duJFnDkxEvuAVCWvNWjz2ly/DN8yG8T83PXtkViQsa8lafQZVEhZfiO/f0c+rDy3ilCDTckFnE1s/fTlnfOYOxhPcO2MFN4wZ0zPKdEuVvijHFzM9c6weWrJWxFWqn0dwzbILZTdKwvRgbEWmjoaExWN9IuSPWgITX6x9v3a8RnDu2oVtrOppYWl3C3Pb8pQcl1W9rdy1bYAfPXqA3rYcl29YzIdevhqAL976PJsPj/PH5y7jU69ay6u++iBZjTg0ZSwMDIanpUbZxkMTbLxhE4YhY6Z6W3Ocs6KLQ6PTWERdh/VgBsTI9WuTGmzTkHNKtVpCU1Cz0ad6v9W9j1tihYCJgMi1Zgym3UBDLPhb3JHl4HilLgmzDem+lgTOIJ8xKDsirIHZ29bEm85YzCf+azNZ2+Kt5yzjc7dsTbz/PmAKcIJOm8GYVfak7lptRMbUuBpBWYkjMh6a+r/1/4Ds/P8DQ0yRIsWxglMWd/Cpq9eRtaNfTj0tWT515Uls/vSVNOeztObq/4Y8ODIdSgTMhIonyNlGREX+xPmtZKwqyXuur8Df/nIz0yUnXGBt2+a8Vb3hMe15m1MXV/Wz4vFhEF2447pPVn1Omajjlc+YkXOSFqt43I5ALvzqUFWEOQm2WXtV29TIV3BikhyHQiah4/E+WSYs72nmxPltfO51G/jnPzqDj1xxEm8/fznvu+QEXr5uPn9+2RoWdOS5av183nfxyvDcD718Nf3jJT77q63Mac7xh2cu5sxlc2jPGcH1fa5aP581c1toz9vYJqzqbeGd5y3npWvm0pq3efbgOHtHSsxWKcUTtUKmliED8Uuuh2FIl6aFdCW6fjU2UE1HKAGhTUTGMuhutrACQqUKfF+2ViaLDE07HByvxkrNb81E5tgXUQvl4fESFUeEMipzmjLc9ucX8+ZzlvE/X7GaG546SLHi0dNaGyunu11DwhXrs0EtKfttoCyfski7EbarkzfPh+PNcxdHahFLkSLFiwpnLO/mFx+4kAOjRaaLFV592iLsoFD0bHDP9kGKzuxFyMoaK7KAbX1TNYvLU/vHOPfzd3Hpml6uWr8Ay5DWJoXu1iwrelrZfGiiJkh7NkuIfo6yMDQFNTP1GCXTkJaboWmHnG3guYL2vM1Eya2xSCW6L7WFT49virsZE+t01rEYKZde/AwV5qeIZj5jYiJJx3hJiqr+6SUree9Fq7AbmD22HpngyHiJK9bNJ5+pPgf5jMWnXrWOL92+nd62HCfMbeWnTxxkeVeeiXKJiYrguscPMr8tyyVrejl3RRf/+eQhbtl8hNv+50X86JH9HBwpMFVHdkS5quMuReUuVPOhXJNG8HrS/DYOj5cY0XTcMoHlVYcJ5DMGpmFw5SkLeGrfCOPFIm5wzaxlcHtM4uOE3hZ2Dk4zHJS7si1wAjk7V1Tvr21GSxkNTju89wePc8fWQb72llPpasnyT3fu4PK1vfx6y0B4jxQ5N2nsUhTU36fX7FRZwPqh7TkrIleh9uVtg4xlYRgGnu9TqPi4vsDxPMqOTz47+++AYw0pEUuRIsWLDmsXdrB24dGLrgohuO6xfXX3Z8xoRiIk1++LwxdQdFxu2niEWzYdqXHnuK7gF8/IxACloq6gSE/GMvCCQHDblItUWeuLctEI4MqT5/HuC1fwx996JHTLejE3oiKQhYpcibOx68bXSSPYGCeHs4n10tvQXZ0GMh6pb6IS6kXF21rYnuWVGxZxwtxWzl7Wycp57Vz3+H7edMZiTHNmp4zSROtty9Xsu2h1L+ev6glKDvnYJqxb0MG7L17Fp36xRVqYLJP7tw+xfmEHGxa1sWdwinM+dxdAqH2mu2wVTCGfhzjxvPCEHjYfHmeq7CXKOGw5PElPa5bTFnfy7MExspYRKsWDdFdmLZOzlnfRns/w6y19XP/UobAftgHtTTYThWqB83OWd/DI3nF2Dk4HWm9ynuN1VdVl4j8GTOCOrVJW44M/eTbc98CuYZoyJgXHj1i7tMpL1TZixKzeM6N/hhZ25jgwVo48F5MaCdPbq7iCguNWQwQCi9tY0aHouCkRS5EiRYpjAd95YC+bD0/V3R8nYRZBELIBbfkMQ4EV44SuDIcmnMhCp1tAamAEsTRIMqTohb54qaBpuZwY2LZJWYvz0oPYb9vSz4M7B0MSVg9qUc5ZiWoEEdSzzkVchUaydS7pWIEkf+MlDz9GwgztmCXdrXzqVevQ8eazljbuLEjhU8Pg15uOkM+YdYuwu77PgztH+PGj+3F8+JtrTmFue56v3rGD/qkKQ1Nlyq7gH257HterEitVG1u5bBUUcfapZv7p2Ds0xdvOWcb1Tx6gf6JMT1uOvolySCAsA1nQ2vP5+QfO47E9o3z/oT0MTzu89MQuTlvaxY8f3c99OwaxLZmIoJ4TT0DGNBgvuvjAqp4mXnvqIn7xzEEyhrSSBjkgDZMs/MA6ZlBL4HVMlDzmt2cpuZUwoUS/h3HCHs7RLAPwVaH6pGdIuSDV/dCTX/TYysmSRyn+wT3OkBKxFClSHDf4j0ej1rB6WXoKHlopl1LVldTa0kKuOEXRra05qBYW3b13ICgZ5CMXeKWzZVoQb8ID8AVuLNg+sugBU+WZFx91TrkBCZutZADIfut6ajOd9sazlvAfjxwI3+vB/MpNtungKEKIoxZX3XJ4goWdebYemeC1py3iq3ftoOR6PLN/jD8+byltuQxPHxjjkV3DlFyfguNz4QldfPfBvRwZLzAUlDlS8Vb6dBtIuQjP8UO3nLIM6rOugul17Bst8S/37sLxBCcvbOfrbz2db923i188c5hiuaqIf9lJ83jJ0i72Dk4xVXIpuz73bB/iqQOTDEyWA/FSPwxoVyV9FCExDTg0Vuaf796J40Nvk8FgUYT91y1YTZlayZck66+CImlmMD8L2vMcilUtaHTvZ5sFqbJIkzBbwg9w87MHee8lq2d30WMQqXxFihQpjgsMT5Y547N3RLbNlF4fx9xWqZ7embdwfZ+pSvLZMnsuqqekYBEooPvVrMzfB2YzFnWMruGkiFgGqF99Enpbs4xMV2QpGuq7aRWylnSx6dmbulSHbhVb2pXn3o++7KjI2OCklF8453N3hi5AZQXKWtDZlGO8WKG7NUdTxmSs6DJZcnB9kSgXYgV9ciGUdVClg+JIsjYlzf/iOU384gPnM1l22bh/nId2D/LUXlkiqVj26J9yEl3EofuvzrX0Pte7D3o7Ml5Mzq0zw/OmrFm6pp1pGJiGSCRuuuu23jMYt4aKOvsifZ+lVQ3g6299CVevXzi7g1+kSOUrUqRIcdzjgz95MvJe1+OaLRVS6uljM/j5fGXGSIIWyD3TophwamJfmywoNuhSqEdl1Lra3IBYNSJhbTmLiuOFQrWz6bbnCaY8LypPEfyTMQ0MA5bMaeLIWJH9IyU++rOn+T9vOn3mhgOomLBv/vEZ/PWNW9g3UqA5Z+F5UHA8BoIo+yPjpRkXfqgSmgUdOfrG5blZ24xIfKjzMYwgG1LGnukZpnr7B0eLvOVbj7JrcIp2TX2+EQSENSbj90qHGfRZdzHG21GbVOmj2cgg+EIWqZ8MLK6SsAtpQaNK6oQQYb1HNW6VwKCTM9Ooyluo9nQSnnRvGn18FJSF0AB6W5Pd0scLUvmKFClSHPM4ODLFU/uikhWGMfusRYXfh+1KX7Qbtad/+arFViFuN2pEwqBaFii0dsQaaHS6gQygHg/8m76IymlkTNANWUrYM6nNrCXPdXzB6nmt/Pkr1vDLP7sIgBueOsLkb1HA+ZI1c1m/qIPethyTJY9CnYxYffFPghrT6HQlnO+pshcGhSsIpA6Y7wtKrk8mltEZb3/HwBS+gLGiW3PfbO1UA6mDB9JNqiyXJtH7rS6n1/jULUcG0KT9r1yMTbYxq4oGWdtgouyHhMkKZElKnkysUNZCP5YJqyxYNdYvoWl+aXF3SfchJI4zGEb1hIRsRv2kOn6RWsRSpEhxzOPj128kXq3l9+QRnDXqWWKyprRK6e4y3TV1NGWEGln3BNKC4/sCN7ZgKqX3pHb09pS7KhK/Yxp05yxGCy6CWh0tgNOWdHDGsjm8esMCfrWpj46mDJetm8ea+VJb7bwVXTy8Z4Rrf/AkP772vNkNNoDj+dyzfSBCalpzFlNlLyLIq6w1TRmLsuNFSImeuej6guasRdHxEjXBfAE528AVsjh3KWnAdWBbUm1fPYuuLzXZfF9myxa1hzRrVePWFJmSJDC5T0q5vy0LEwGfVbfJ8amp9FDvWRHacfr1bNPgyESF1XNbwuxM1UA8Vs42QATzbamOi6hYcPzz15qBghsllpZpIIK50WGaIAINNuELxoqN7LnHPlIiliJFihcU/3THdp47NM6DO4dYNKeJ3vYsV568kD86ewmWNXPK+iM7Bnlwd9UapsdJ/XeQMd3dEt8GEI9XNmL7dRdNUluNtsdRUErowUlKMywey5bUV2WFiKPiCqbx6MzblD2B43qUfUL9MgE8c2Ccy9bN47SlMiswjn/94zM44+9u56E9I1z7g8f4t7efPcNIqvjNlj7Kjo8fMLG8beJ5Qmpt2Sa+74UuPtMgUUNOj9NzfVg0p4nn+2R2bTyuLZxnIchZjRMh4s+YckXr5FppsunxXAJJwlQMmE7M46gWdJdUqOwbrOqymSh5DBaiZ8WLyysYwOlLO3hy/7gs46T1Tx2r+rl3aBo7kFpR7ZlUE1/CKgGKpAVlofS5SPrclf1qOSfV63o/DsIEmqBfE9PHNxFLXZMpUqR4wTA0WeJHD+/jtq0DTDs+2wemeXDnKH914xYu+PwdVGZRlPje7YOR9+GXeexX/GzQbMCGxVX9Mgv4X69cy5ym6m9Wg0DUM3a97uYM5y6fE24zkEHhsiZh9HhVGCCuYP/7KufiU7XsqIW0EWaKoyu6guGiS0ve5qNXnsQtH7qQ6957DnZwbnPW5IcP7+ct33qYB3YM1pyfz1h89Mo1APzmuUG+e/+uWY/lvJXdCASFiodlSO2voutjmgbFirR8maaBZVYJgWVAS0zaX70zDJjflo+4cuNjL7sC129MwkykBUy/her/sFSPUX1WBLVk2gv6GxHijT206t6VHQ8z6NvcjtYICbPN4NpBXFt8PAIiWmbKxZh03ys+ZE0zYqX1kVY3IxiLHfQrq7lRw0LxJD/HSv9YH6ttRgt8xyGQ1jGrUemJ4wBp1mSKFCleMLzj249y746h8H18Yei04ZnPXN2wjU0Hxnj11x8Mz4dki089NNp/2dq5fOsdZ/HhnzzNjc9KwVZlbVLB8ZDsWlSuss6mDOPFaPbcnOYMowWnRlpCLzw+U79nGouBlGhwXb9h7JBuyYir8+v1MQG+844zednaeeH76584wK6BKc5c3sV1Txzg9uf6AVjS1cSGxR2ctriTu7cN8OS+UQQGFdcP3Z9ZA5ryNl0tWW76HxfQ2lRbbsd1fe7dMcB3H9zHI7uHwpqEZpCxKoCe1ixDU7WxZ1nLwESE+moZM1pbM2tJy4uypB1tYWqof55uZYzvblQBQf2vnot4wfik6+lyIyBJcUErHK4/Y3FLWFveZlVvCwdGC3TkbZqzNjsHp2nPZ2jOwP6RchgLOFPlhtnMYbwN9eyZSMKlj0N/Lm/50PmsWzinceMvcjTKmkwtYilSpGiI7z+4m1d+5T7e+4PHGZgozXzCLHH1P98XIWEgs/d0jLlQqjTORFu/pJMfvusMAJozRlgnUpGkmdbX+P580IWOvM1X/0hm+Z2/qjvcr6xN4fuECyyb0xQSqmJFulXM4M82jVAtPq6coP8ujjebs6Nf16YBJy9opy1vhV/kiohmLGkVcVwfW4vcV4YF26we64naAHGF0K1mQEeTzbpYtYM/OHMJH3/lWl6+bh7fevuZ/PKDF7BmXisHRorcsrGPz97yPA/tGqHsCiquT2eTzYdefgKWARUB40WXPUMFLv3HeyL1BA+NFXnP9x/jNV9/gPd8/0ke3BmQMBQBrroAFQlTFi6FihfVLnN8ORADeQ96WnPhQl+PQBjanw5lCdPPy2i3R7eIxqHHBta4EAMLmgj6qax1qpUkEqY0yNTYC5VqNqT+qs5XbTVnTD56xYnYpkHF8XE9+Omfns+N/+N88hmT/iknkpBhmNVrtuWitU71vtkNWEWkBqsR3a5ImF4LFeR8fOPu3fUbPQ6QxoilSJGiLh7bM8K/P7CXA6NFnjsyycjUk9zwgQt+53Z/tfEwWw5P1myfKHuctridg0MTDAWc78BIgdXz22uO1XH9k4cAWDu/jacPToZWgEaCruoXd3wxVBaU8ZLLqf/7Nnpbs5h10rziWWQK+0aLAMxvtejIZ9k9XMRDLpr1SgHV65+CExuIL2Dn4CRlV2CbMnOtusjLoHGBjPEKxxqY3JR+lDy2mvVWD61Zi39802nM72gsI7BhcSd/ct5yPvGLzbRkYUl3G5etnce5K7u47tH9/GpzH8u6W9n1+at57tAYtmHy1m8/wuCUw2u+9gDfeedZ/OSx/fz86UPsHypEBHJBuqiEXw2G19d8RVzCMQnpUtURFm03DYYbxB1ZQYB9PSkP11PtyDl1vGQNLhWblhSzZQQMqz1nMV7ywv7L+1FtX2UPhn3TrEiuL3842JZFyfUi44cgozFO9oPXacdnqixdu+ef0MOtW/p56f+5h5LjUXY8Fnc1sWOgEPYjY5kUfR/Xh6JTa2VVSQUz5Taoe6Nb5vS+hc8wVdmOluzxTVWO79GlSJHid8KJ81qZDmJzzlrexV+9et3MJ82A/okSf/vLLbRmDEqeqPnifubgROR9R3Oty0qHEIKbn+0DYGNQdFvZ1RR3SXKbGMHqaCFFPpNQdn0OjiVbAa2gAU9b/N581hImig7bj4xzYKxEa1OW/iknLICtrDFSCkDqNGWCxbYpY0WlGVT/TKnplGS1qRYsry71BuB6fjQWKSCMiogYRDWh6q2dyjX5xjOX8grNJdkIZ6yQVrP2pjy//vDF4fb1izsZnHZ4bM8w15y2iHWLOgG4+cMXcc7n7mLToQnO+uyddORtxkvVO5LLWEwHzMv3BUZCTBdEg9T1uUpK2qiXCamOVfOUtc3gPkWfUxVk7/mQtaE5Y1Hx/IgFEe2ex/srgvMRMFXyIn3UA9qTuhl3pZY86MhUyXXWNqKF7I3qeTo6m2z+5PwV7Oif4rG9IwD0T0gR3RW9rQxNyufeBJqzFtMBWVQFz0Mir8aT8MPnxLkt9E+UKTo+TvBMhvFpRm18XlKFAwMoNyqPcRwgJWIpUqSoi87mLDe8/3xGpyusX9xRo6l0tCg5Lp++aQvjxUqN3EQSeluzicWedXz7/l2hC6USLBDxppU2lm4BU4ucy+zisSwDLMukEpzoxeJrTcPgx48dIBvoMAlg56C0jKn2TUMGlleCYtMAtpBCmnF9rHDtESKysEf6BLRkCSsAqMNCC5ImW6Gfrru7PJFMVkBa785Y2sknrjqpwcxUMTBZ4t3ffxqQQqslxyOfsXj2wBh3Pz/AI7uHefnauZFzyo7glIVtbA4spI7rhXFOQEjCILpwJ1mrdFqq3tfLnJ3XlmG67IVleBRJ0MlAUTNz6SRD9UggyXDG9EI3qLJ2GUZUU07tE1SJWhJxhOTMRx0Zy8T1/VDrazwoStqctShUPDJaXFhSMkLGMvjOO84mn7X45CvX8rZ/f4ScZdCUgfNX9XL39kHKwa8HJbsRjjl0QUqSrvra3ZJhpFC1MmZtk1s+fDF3bxvgJ4/u565t1UrwLvMAACAASURBVCQOobWTlG2pi8QKYHDy9xcS8WJESsRSpEjRECt6WljR0/I7t+N5Hld++T72jkTJSSPc8uGLZiyLc8vm/sj7eJtxbaxwIQ9eDTO5VFEcngCh/TLXF3uQFi6Q5MWO1fmzTQPHl9YzLyByavzlmPUmvnB6AlVTvLZPVDWl9L6E+31Rk1zgawt0aAHSrDA6LOADl64k2yjwR8Pnb9nKwdFC2Jen9o9y9vIu3vhvD1Nxfa44eR6vPU2WqvF9wX8+vp/vPLiXHQNTtOYsSo5HxZdB+mo+RNCYH7SpArsbGUlmvpswMOnIGCui814v2zZ+Pf0YxweDqgtY9TkJOvFIOkSVXVL79JqRigyqkk9xqdPpiierAczwPDue4Gt3b+M7f3IuvW055rXl2D9SYH5HE0/vH6PoSLdsk2VS8fwIUbSDoDnbMvCFoKNJypiMTEcLfP/zH56KbZlctm4+l62bT994ibd/52Fc1+PwhBMW8o6Sr4DcxT4DR+pYpY8XpEQsRYoU/y340E+eDkkY1M+4MoGPXL6aPzx7Gd2tydawPYPTfO+h3ewfLvL0/rFIO2rRDq+jXUhfKNQx1lFkjifF4Kg+q6BuPSC6avGovYYfe41bpZIW7NmQV/0YA2mBUy6zuEK7biFM4jU+8Ne/fI7Tl3Uzp6Wxi/iB7QP86tnDkWv84plDfPeBPTgBcfjNln6OjJf4yOUn8s37dvHMgQkqrkdrzmK67GFbRIL2k6xZPjKmzTQ0IqJZoOLoarIZCVTv41ZBZRWNz7tpSPITF8KF6jX1ObYCN28965sq11PPQqeT+njtS0XCDKA5azMVaLepuVB98sPjq+c3el7u2jbMG77xANe//wLyGYuK4/N8/3S4Xwgouj7tOYuJsieL2KNpiAlZ+mmk4AaSGVXyePKCNq6M1Yac35Hntj9/Ka7nc//2IX7z3BFuePJQ2F8ZNybCfut9Lx6FqO6xiJSIpUiR4v86XNetsVzp8AUs62rm9acvYtOhcf700tV1A+QBPvWLjTy4aySxHai1/ED9RSkesN9ICDZchIXmajQCa4lOcoIALEM7XtmUZhLuVH1NMmrEg7fVOOPkS/UNEY0LE9qrOj5pqMrysmZuMws6m2ckYWXH46M3bKTiR0nBTx8/WO27AUu7m9l4cJz3/uBxylpgnud52IFLd7aomZ86pxYcv+5uS5NM0N2GSi9LMW3LrAa+63OnLGlJ3dYLr/tCymWUvVqrp/7eoHGtT52E6fCJFpjPWXDJ6m5uf344uaEAT+4f55zP3cHNH7yQO7beFdmnrlN2tDJQmlXVNA1MTwSxbNEElLOWd/HAjkHOXtFdY021LZOXrp3LS9fO5dITe/jCrdvZO1youbbKBnV9+d1wPCMlYilSpPhvgfqSvmBVF4/tGgmLULdkLf7mNSfzpjOXzK4dIXhKs4IlQS3SOW1xSlrA4qQEoougCqRXgcVxEuUHJ+skB6TbLWsZ8hwtjiYp1kvXDgvHGLzGNaLimXkGVYJnBZYvOxizbZk4rl9DuPRL6ZYa0IPV5fumbIbv/EljFXwhBB+74VnGgizEtpzJeFkTGwU+9sqTOHlBO39383MAIQkzqVqylFo+RlS7TI1TJ5EzwTIMfARCyMB8nRTp0OdWaNt0KxdoSR9EnwH1rCQRYRVUT3BdJQ6b5HrOaKr19byK+mZFlHWLnaeRobIH9+0cpj0L0040Vi0+hwOTFc7+/F205WVAfvwZL/vQlgFHmDLJQah5qpYminf5uscPcNPGwzRnbd5w+iJef8ZilnbVhjdcuX4RV65fxB3PHeY9P3g6ss+gen+uWr8geVKOE6RELEWKFP/XYds289ty9E2WI5as1582ny+8/jSy2ZlLGbmuzzfu2UFn3gqDqOOLim5DE0g3TegyDLarjDeYeVEPLRUiGqvT05plZLpSkwQQnoeULsgFfi43EB/VxVotU8bYNArnmSlZTFlwZIyXbEgRTz+WOamIYMY0KGs+z3okFODAaBEhRMM4veufOMBtW/rxfZ+cCVMaCVs6J8e333kOf3/r83z7/t0MTMaEVwOLYi5jUaxIq1gpkONw/Vrr0WztZV5gjYzEwDUONawhezp50eVB4lYrNa+iOqUh9OdDIWkMEYJtBAQ8IZlFt7AaVMmXigFc1pXjwGgZT0DJBRVZ1WQbFF3RUHR1slQ/e6Yln6VvshKJ2Qt/JCT8QKm4PmXXZ2Ta4Rv37OJnTx7kghN6eM0G6a7snyxx9YaF5DPyc//E3vGa2Da9m8vmNE7YOdaRKuunSJHivw3v/M4jPLFvjNaczQcuXcnbz185q/M8z+Oarz3I5iO12mNxhPXxNLedoS1Ac1stBqa8xHMS20tYvC5b28vtW2UWWJI8gF6jT1modJehbUpZBCGq8heWIV2avi+JjycaL5zxfco6kmRhU1AEUF1XBfKrl5BcGFV36uUnz+WLf3Aa7U2Zmvamyi5XfPk++iZK5CxJqEYLLplgsJ96zTq+csd2RgpVP6SeEaejNWfjeHIBV5Y6mD35mglxlfpGsKgmByShkQsxvM/UWlshIHUiJgZrgDCq1sm4Ba/R8xnpd+BSVWFm+g+VzryF4wmmHf+3qiSgfojYwXOatQ0KFUn2VYIBRK2AtiLSQUcytoHnCjykfMb7Lz2BV6ydyzu/9zgHRorRKg9aH+c027zpzKV88pVrj67TLyI0UtZPiViKFCle9LjkH+5k33DjzKn44pLkxjKAPGBlIaEqTg0MoCNvUnBEpHB0PViGjJ3RY52yMSIGUj7AiW2LLGBagH2cPFXblRltSX0+mm91tchb2vyJ2P6etiz/ee35Ndmzd2zt52PXP4tpGIwXKng+tDdlKLuuFPf0oha/ev3S+/DfUah9NlAWH/2+zIYQNZp/paafJCnRmrOYCvyX8p5HyWq9HxY6FnbmmSpVmCxJgqRbcQFaMlJ+xYmdm5SUUG9MJlKrrORKi285dsN0C7EZ/KCo155hQFPGpOz6eL6sHqGkXVQ2s5rz81d18eP3nle3jy92pCWOUqRIcUxhZ/8k33twDwCXfenekITN4F2KQI+JUa8CKCJJ2JwmM8x2hOQvQwFMlv1ZkTCQFhdFwrJBwxVP0JwxyWjeV8cTtOXtmoDtamJB9XpSQFPUEBTXq7pn1atpyEVVjx1T7debuzB7UyRbcHxgcLLCdY/uqzl3V/8kI9MOQ1MVnMCaM1Z0KDqCiitC9576q7fgKAtm9vdQ3HkmF+RsoZMg1WTMgxheT+92/LnToeY4rgaSsw1sU5YNUuWp4iRML9mV5Mh/yeJ2HvrEy1k1ty3sQ/yZmXZ8hGFgG9GC8zPJtwigtyXD4jlNQFVIOGmu1TUzZq3Wnt4eyM9LoeKH1s9KENPoa31SBb8XdDQ17OOxjDRGLEWKFC8KDEyWuOf5QQ6NFfnKnTsA+K+nD7JrYCo8pt5yYSG/1DMWeF5MviL2ahqQz1iYpokq1GJQ/ZWvQ3fX6dtALjSVmHlEf1vR3EqmYYSuGhWcXaxUrR+NhEnrOS1C6QI94F6AG5ygtxEPKk+sNED9+RXAjx7dz/P9U3zr7WeStU3GCw7feWBHeG7cVadf3wzcWfEgfICeFpuhaTexJBFErVCzsfapeU5oqi70uMFwWzAepb8Wh0q8iA8pXm4q3ue4WxLk/ZsoOvjA+oXtPHNgPKL/Fj8nbtEC2Hp4gumSy6LOZp7ePx72P25lDO+BEKyck2f3aFVBv561L2vBuSu7mXZ8RqbKYSZq0v1SPyrin43ZQJFUz1fZq7J2rPAFz/dNzHj+sYqUiKVIkeIFxZ6hae58rp+bNx6qKW+08eBEzcKbtGDYgYvEEpKY6LUTVTFmlfDlC5jblmXvcFTTrDRDWZjQnRK8YhiYiBldaj6EZaIUCXJ8sM2qa7IpY1B0qu+VgKtpygxA5dpsy9t0t2TYo/VdZc/ZpohIQqi5sjT3lIr90iUv1GtkbDE0ZUxKjse92wdZ/7e3YiK4YHU3Q1Oy4ZxVrdGp3GoCQuV3T4BZh1EOTScXmErqS/x9vf7q/CBJ9yveVlKYun4//Vh78ezGqLyFqvJZvY7ez5rniCo5MoFNB6skCiEL2RcC5qXcklnbxHH8SL9dDLb2jTM8VQ77byBd5V6dh3O84obxXYpAzWnOUCg7WGa15FbFg5s39UlrnGbNSyK86lLxjFwd9e6b7rY1BHTkLUaKLh15m5EGtUGPdaSuyRQpUryg2HhwjC/c+nxIwnS3S5KbJ+mHtuOJMB5GLZAhmfIJ3WYAS+bkOTJWppFgv21qQcOxvqggbldTbp3JcymQC5bK4BNE+6TKyag+KYuH8InIb0yXXQ5oorgg3VqeXyVhutvLp1byQu9To/c6io7PK0+Zy3suWIrnC4ou3LF1OCQCesKdnJvajM+ZDCQmsLyr6n7S57se4sQga8KJ85p5+Um99LRkwvNF7JyZjGWZBitjaIk0VL3RwAIWGppEjStYv566x5FtqOxRM3xGFEqByr06xxdB1mTC83torMSGxR0RsulrjNECPn7FidzyZxdgAaPTLq05m/NWdPGui1by5TedSs42KXuy5JY+DaoVz5dZmO+9aDlnLG2Xc5EwT/Fi5TqSCLU+PpA/rlTN0VzG5NQlnXVaO/aRErEUKVK8oLjmtEW89Zyqhpj+K1//grYbBP9E3EBCSjSERZRjx5ZdH9M0QgmAeKvqMo0EWDNKIyy2oihrkOqrIgm5GWKf9DitpO1h38yoBaI9a/DmsxYzv72a3u/60NEknR0ZI8hyo+q+bc7UJwkg3VBJ+NXmAfonHW75s4vJzmLliJffmQlZ24hUXoDZxQQqwmcAq+a2ccuHLuHb7zybP730BGzLID71thld+PRYr/ntOUwDFs5pZlFHPvF6dnCCFHKtkmQ9qN8VsYQHo3ot9dzpj7MiILosS9geVQumQtn1aYrdKF8IDo4WOWNZFy256j6d1G399BW8/6WrGZouk7FN1i1soyVn83z/JL1tOV77kkWsnttKPmPSmrMxEu7z6YvaeO7vruLai1fx5IGJsI8qLrGRmy3p86bGn7GMMEZw3bzmSFzk0FSFE+e1Nmj52EZKxFKkSPGCYnCyzI6BabKWGX5JxxdPKwhYb83V1xvTLUyGGaUA+sK2oCNfJVsJq4IS35SEqna/ZUJTxiITdDLehB9kfxlIQmYaMpNwpjj02XwZx+qCM1ERfP/hA/RNlCPbx4vSkuBopMBDvhacZItEKK7qRbfrbtqbNh7h8q/clxj/UzMPM4zFiP2vskh1xPupB63HIYCtfZM8s38UgEWdTVhGrU5bXDZDEMhVCJgqe/gCRqbKuL4r4/uQ5EslE6zoaWZZdzPNGTNyz5JIeXyfbuENLbcJ5+iWIb2f+nHTQdkh/RrjhQqXnzyfGz94IR+5bDU5y4j044ov38Mnrt/IxSfO47QlnVy6Zi4j0xXGCg7fe2gvf/6fz/Dei1dimwZTZReEVpM1eN0xOMmDO4f49M1bwrm0VJ8B0zKkfEkCkqyRBpKE9bRkmNeWoyVnMVyQ185ZhJ+jlT3HLxFLY8RSpEjxgiJjGZRdn7XzW3n2kPyF7YmqfIBtwrkrutk9OIVlGmGKfz0IwDJMelpMBAbDQWyJig27fN0C5rWPcdtz/Q11oGQcl0HWIpI1aRkGxYoblmBKEvP0fS90CzVlLWzLlGroWtfjcWW2aVBJCGbPWiaO55PPmKHFpNmGigvJ0VVHj3CBTFj8ZysnMZvDlLBo/Hh1HUUIlYUl3uZsdLXe9u1HednaeVx1ynwypkUZL1KhQBFOU2iWyOBiU2WXpV159o+UmCqrgPFqR9bMb+emP7uQkuOx5dA4j+8b4Qu/3gbMTrhVj8cDzVIm6rtMM0Y0OF+NXSAXcFe79nRFPhEV1+O2LUeoeCLS5p7RMvueOMBbzlnCddeeh+cL/v3+XayZ18LCzmZ+tekImw6NcfrSOTy9f5TJ2GctY8JkBd727cci23WL9GwzjMPxGJKE97Q10ZI1mdeZ55n9YzJzUsgEnJU9zZy1vOuo2j2WkBKxFClSvKDobM7ys2vP5dofPgGHqtuVsrrrwyO7hmdFOnT9rf94z3mUXZ/7tg/ytbt2sH5xB4WKx5fu2I4QgiaN2OjQlxHXF6GkgBssak7wavgiFAmNo+wFVh4fWi2TiaJDnFo0ZS0KFa9qLQkW5Xwse9PxZUq/3ldbQLQ6XzJasgbTlZkXxpaMwXRSKt7viHgR9niWnRUQHRXUr5OReFyVPs+J7uIgRrDiCm7Z1Mctm/pCN6RuBVPJC5FYRK3twYkyK3ua2T1U4GNXrGF+e44v3rqNjG1xzWlSGT6fsThjeRd941rShKivxxWSy/g4YodaSLmG8BkjSsJ0rTc70JZT1wZJ2g+NFXj1Vx/E86ttGNp9mNee5dQlc4K5MLj+T2XR7zd/6xEcT3BopMBowaG7NcdkufqUKZdsEtR16j1Bjfapqge7BidZv7CDrYcnwvvl+lL4dXlPK4vmHL/yFalrMkWKFC84Hto1zGN7pUtJ/75XC8xsLT+KhBUdn92DU3zxN8/z1bt2kLUMDo0V+Z+vWB26hdqyRqjt1chr6AkiAq06UVDxPgp6nJBALpYZ06BQ8ah4ourODFytUTebbLkcy95c3BldgAwAy2y4MCrUI2HxPjfnZ3adNkIYH2QaNS45nTRZhlFzf1VQt9lgNRJCzoVlVt2EcTh+rRvN9WtJm+KzSXbVJtuk5Ar6J0qsmdfK1RsW8E937mS64vGBS1fyrgtWRI5/YOdwKNIK0cB4hQUd+ZBkxwWHbdMgp91ID0ICpR8H1Ii4ur4gH2QVWEBT1uRPLlzBv9y9EyEEK3ub5Q8FovfhyESFu7b2h+2csriTE+a10dNs02TB4q5mRqYd+idKYfsgCWFS4ofeP/29jpkovi+knljR8fFjv2zGii7nn9A9QwvHNlIiliJFihccE0UXO1iJV/Y0A9Uv9nhg9Ux8oSljsqAjz7/eu4u3nr2U81Z1U/EER8bLfP3uHbhBfb6Jsh/GXAmkdUChnrhrfHvcamPGiIYdxOio4H1TS5krOX606HSwWqs2z1rWiWEYjExEA9hN4JQlneQy1ozipXOaM5Fg9KR++0IKttbTS4v/r95b2sKr4oMUEa4nIKuC29V+PRswnmWp4rPUPVfFyLtbsyGhVbDNwFpEVTojCfXIq9pcDgRFDeCvXrWWI2Ml9g0X+PQ1p/Dms5eF7miFR/YMR8omJV33yHiprhacZVaJtyKzSYXEgfA6qn8A0xUf2zTwgWsvXklL1g5lJtbO74hKbBiwoD0LwPt+KKvPPLRzMNx/3bXnU/Jg56C0ghUdn5Lj05FvTBNCN2vs9bfBxkPjFGOWWduAM5fO+R1affEjdU2mSJHiBcdV6+fTN1Fkz/A0/+uqtfzo4f38wx3bqLh+1IpgVK1Zk5Xqoq6TiNOXdPDGs5byVzdu4QM/fpqlXfmQIDx7cBITuObUefxy00CkD7o1wzAN2rMWEyU3ovuU5BJT2zJGNSBe9StnW4wUHQRCxog1UDCPWwI2HZrA9wWZvBUJLvOAh7TC6QoL22z6Jt1Qpd4AhFb4Wy9Ares/WXXK0AiqrsX4XoGuUyVlNvzYvnqI7BfVepfxOpBqHGGNSAGdeZMzl3fxq019kTZdH+a3ZxgrOHhBXKEZ6K9FjqvTr3COgtfpis9tW/pwfJmYcdm6eTXnTJddhibLQWZsrVitHseWpEsnACfeIQNypkEmIGiOSNYg058xEVjQ/umOndz8bB+FshTHvXnTEdpyFtOB+7s1b4dF110fln/iV3TmLTqbc+wdKdTNch0v+bTnbSZKyXZp5Y4N61vOtjCmhvaswUQlef6W9TSzam7b0TV4jCG1iKVIkeIFh2EYvOvClfzdNevJZ21u2nwE3/PpDApNKwtAS85mZU8LkxVBzjbparGZ35Enp6U39k0U2TtcoDVI7z8yVoqQOR/4r2f7a0iR/s7zBZMaCZtN6Z2KXyVaSk2/XHFwPIHrw5zmbLg/XEi181V3VBq/60ux2LFScnKCFfv2FgTislRV7seDYGvbhLac/N19Ym9zRCdLJ2F5C5Z35TlzSVvYJ12HSy8JpcbhJbj/kqAsYDWyDUTlGVoysKwrT2cukIjwNQFaw+SLb9jAmnmtNa7UvgmHsitJxtnLu0PLJ0BrzmT9ojZaA92NJAufDul2Nnh09whvPmsJLblam8UzB8aYLHu4PjUkDKJzIqUuqu9l4ffqs5UxjTB+zBUC34jqlSmLasYyIlY9n6qLde38Ng6PFUPyaRpEgu0nim4NQR4reewdKdT0N46JkhR+NZDWm4h72Rc0a52KZ6XOBoWEihZKOqMla5PL1M+WPh6QErEUKVK86LCos4k5rTlesqyT0xZ3hESr7Hjc8RcX809/eCrnr+pmquRxaKyEq3377xkq8YOH9pLPmjRlzKqqfJ1rReKltP+V5SFjUpN9puP6a8/hVesXRIiBFFIV6ElnQ1OVoNSPfK9qCsb7IHwhi3wH7+u5+TJaUFWzDYWAsOVtwlVVteH6UCzLhXj7YIFincTTkgd7R0o8e2Aycb8iZcpd2GgBiRMlFaukG9/ipE5tu/MvLuWcVXPD94oQjxdd+ifLuJ6faHVTtTb7JyX5bs5adLdk8Hw4OFqiOSeJvYlMiohfW6Ejb1NyfXYPTfO+S1Ymju+6x/fP6BpWfTfiBFSAgYEdFIR3fBHGFZoYeL587hT9U6TSFyIq5CqkRbOjyeZjV62h4skMUaVzBzIjU1V10PtUr78yOUW+10lfyRWhDEpbTEYmTqSOBu15qyaZQhF8A5nMc7wjJWIpUqR40eGLf7CBl66Zy51bByk6Pi2BMFFL1sY0TV77ksUMT1XobcvxmlMXIAKy0tVk4gMjBYc9Q8Uw01BAXSV9QyM6SctJUoByYKgD4J3fe4KvvfV0dn3+av7tbS+JHBdvz9OIiOOJRCuEK6SVwfOFFGM1qIlNgiihKbhgBjpsQlSTG1oy0orRnjNnTHjI21WykMTTdGuYIql6/+MLvE6Ukopi1/u/tz2PZZlhmSrdFZe3TXYOTHHh6l4ZtB/rowz+F+wanAZkaamMZfEvbz2D05d2IoJGPaipK6qP0xcCz/c5ZWE7v465QRXu3TZYU+ZIJy4qUF4R0GpBd3lPHV/geyJiJZTuOEHZFZiWST5nkbGtwMIqG4vHkHlCMF50ac1mAIO189uwTSNMalD3Uie0guo90bXyLFVRQhFBTdtP9dEyDc5cUSsl8duSiYmYxVcvTyaQz+7xjuN/hClSpDjm0JKz+cIbNvC5153CzoFJhgMTzvffdSYAjuuxZ2iKpXOa2DMwhS+gqyXDv7/j7LptJoVnqeLXgkAOQNunFi4Zb1Td3py1eO/Fq5jTLNnYVNnj5o1Sd+Os5d0Rt99sv2DjXVPuJVeockG1nVcuRdW10VKgym5ZWIE7q+SpGoIzWyxKbnXMOsGa0yTdnWoRt806gfxG9UUP5Jd9jV7LimVX6k0cGStx1T/dy+3PD9TsL7k+7/vhk3z/4X24or47zQIWdeY5a9kcLlzdzVfv3sFdzw/+/+ydd3gc5dXFf9O2qTdLLpJ7N+7gBqYajOkQSggEAgESQnogoYQUIJQEQofQMSTUhI5pxrjhbnDvki1blq3etk/5/piys6uVbUpiw7fneWytZqe8886u5sy9556LZrgsQNIMwNYltUc13lu7h6qGIK+s2NlpPVXT8aSYpbpTqGDNlSutaO/fJmYCZuseO0poFxvYRQuaqhNTDWKaTsArYWB+HlLNYUUBxpbnsqctjKqb7vrhuJ60rr2+YY1BEhJE3vFXExLHtisk7eIFML+Tigh9ivyWHQt4rZS37C4d/QKw59C9qU1o7QeniuKsL77jbxgyRCyDDDI4ZHHhhN7kWFVbIjCy3HwSr2wI4lUkVte0saUhyKBu2VxweDlj+xQxqFsgaR/7Sh85BqFCep2PgHmjct9yfbJAjlehORRnYl+z/92vXloFQGG2l16FAcuWQHQE5zld9Q36CrDHmxpVCsY0JzKk6ebNu6MLG4tUzZfdB9ON5rBlq+AcN32ay13V50RtXJEeN1ItGtzjN4mJwQWH9yL7S96hBAHq2iJ8trOFf6+oYeWOFrK9UpLWL/X4BQEZHbPowqeIhOI6oZjGhtp2ttV3JK27vraNhmA8aR9uo1Ww9V4JmxI7EmZXeBpAyG5pJCRXQwJEdYhpOrpuOI7/TlTKFUUzDNjbFuPlZTvxySLNYdV5zybNdoEG1hhTr7NNnlNT5e5K4mDELAJo6IhR2xJ2xiigo+mdOxgcCOyUv3tT+zNrL7x6av8vvuNvGDJELIMMMjik0bvQFI4fO7jEWTa4LJf7vzuGoiyFP546jLd/dhTXTh9KayjOYT3zkrY/gGBQlzcRO37itl24cmo/Jg8oonuejxOGlOGRRGKawf2zNwPQ0B5F1Q26ZXssvY1IRNOd/oypdhx2akiRhLQtlf6bEIRk0f++9ENSmvfc6UmB5J6KkJjX1HX2dZoxHeraQjQ21NKRJuSV6+K0hVly2nY6qhXRUV2ETxAEDEzLj7Ic89rk+WRnXOeOK6dHng9JFhCBLI8ZhfIrIr99dbVjngpwzwebOrXJSk3TukmuHXW1fdM69RQ1SNIFQrKlgSIJlOV4k6ov3ZGxho4oiyqbiFjhLd0wj+/WuItWuy1ZMB8mfFaLJjvS6U7BO2TSNSKbwAUjKrWtiZZabdFE1EwRzeicG119ntwaPfu8bN2kX0k8fPl9GY1YBhlkkMFBxfpas+3Rgxcm668m9y9m3m+P54IJvfHIIh+u38OlTy/h35/VMrQ0YYKajmO5o2Tup/79ZVd0A/a0xRjeI589bRFeWbmLX54wAIB7P9oCQEWRGZGrI/AtZwAAIABJREFUtwTjiiQ6lZOQfMM2MFvtSAJJ63QFMeW13dPSI4lIokD3PA//uXqyeV6uk+nqvGxvLvd4ALIVsPqGY8vh3NYc6WBHs9z7sU1Y7YIDRy/lPg8h+RoANEfgw6rOSjWvAG1aIqXVFFTTav9sIiQJUJ7vwwDOGdeL/ICHXgV+uuf7iOnQalkyGMCxg4r45xUTuGHGMF64ciIXTqjALwtE4jrLdzRz1cxlzPy0CoAlVc1Jmi/7HJPmoov5stezo3MCpvjdNg0eWpbN9yZUMKF/IUVZHvpbEd72qIpoFWjY85o4nuGkn3N9svsNDCCgiOT4FPweCY9sRmt9skh+QCHHI6EI5j4DHtH5bvhkEY+UIGs5XgmvZKYt3cTajbgOZ47pyaWTKuieozhzkQpFNDsg2LAjqPYchOM6AiYx219BxLcBGR+xDDLI4JDF3E11xHWD0lwvfq+yz3U/Wl9HXXuUPL/MtvrwPtd1RyQ0K3VkRy3cguZ0iFgusH84bRhPzK/izvc345VFoqrOL178jI21ZsWhrUHWNPNm7yYf7t6HcQ4saged00myKBDFvGGhQVG2DxHI90m0WgNQBLN5coea2M5wba/rBoaQmJNCP1QUZLO9qYOwmizct7ftCu7OAmDdYPdzbroBgmF08oNz79PWVEUN6FfspTjLx8rqVlTDbKg+oV8RvYsCvLqihrr2qHP9BMPsH6lI8PTC7WR5JDyySJ7f1NGN7pXLhj0dhOI6L6zYxbnjKvh0awMtoRj9u/npVRAgEo+zsyXGBxvq+WBDPU8v2I6q6050L7US1J4nt9bOI5nr6LpJOiUMYprZxF7XDQRBoGe+jwsn9Obc8b3oluNja10HRVke2iJxXltZQ31HFEUSyfJIFGd7CMY0qurbWVLVTJ/CLKYOKWHFjiY21nagG2Y60+r9TlTVicRjic+g9Rk2LV50p42SLAqOfiyq6mb0UoSARybLI9Fq4HjaSdbnKqSCB4hhRrlufnOD02qswK/QEk5O4UokR998sohg6ORneahti5HjkYhpGoosMr53IUJXVTbfIgjGgf4F+IZg/PjxxvLlyw/2MDLIIIOvgIaOKJ9ua+B3r64hFNe442zT2XxfMAyDK2Yu56MNnUXebqQjE7aGxzYptU0qbcKS75NotojN/d8dzemjegIwf0sdFz+5LMm8M52fpX0rsQ1p7b6IjlnpPkecPE53JCbLK9MRVR0SM7hbAEkU2FwXdIxNDT25RZStVerquG6zV4AsCbwKtEXMm3IsTdTODs4ktTMSSap8/Kqw58wmsV4J+pdks70pxPIbpxHwytzz4UYenlOJpidsHuz5KggoFAY8hOIaoahKW0RFkQRG9sxheXUbQ8tyaI/EqWmJIEtm1EjTdcaU57On1VyW7fPQLcdHnl8m4JHxygKCILCiqoEVO9soyvYQjWsEo5rTQ1MU6GTMClCc5aEpFEM3wCPCUYO68eSlh3d5/tWNITNFmedDEEwC/ae31rG1roPbzx5BRVE2U+74iJqWKIUBBUmC+va4s72bHKZ+7uw5tasz0xWUioIZgXX3PPVKEE1TYhtQRHyKRGs4DgYoslmB2RFN/vCU5ijsbY8jAdk+iZhqENd1y2PNbHa/7MYTKMr2djkv3xQIgrDCMIzx6d7LRMQyyCCDQw5zNtZx7aurARhUms35h1fsd5v69iiLKhv3u146UpBaXabqBl7BjMDkuUiYJOCQMICjBnbjR0f349G5lZTmeNnbHkV0ERCA8gI/9e0R04fJTsHYaUojQWJSx5WOMLpjA3bTZ/eyTXWJJs02OUtEZczG5bp1sC4JWcpBgxooHhGVrp1b3aJ8JxqWZl1FSG5i/UWQ6geX65Pp3y2HjXs7mLl4Oz86egBT+pdw/+xt5mmkHKc5FCcYjTOwWy517VHKC/2EYxrLq83U97b6DqfZdlwziFuRn08rm5EE6JHn519XjMUjifg8Ch6XoK+qrp3bZ23kpBGlDCrLpao+SK5f4bevrGJvRyzt+bRH4ugG9MzzEoxpXDo5/YOGqunc+9FmHpyzDUUSGFOex+QBJfhkiZmLd3DzqcOoKMoG4KTh3Zm5aDttkbhjeWITWCdKSHKRimHYBSlWOjVNdM9OKUdTvFxsEuaO8IJZhKAbhhPh1FQIiGZlc0c47pD5vS6i2OqysTD3Za60YkczJw4vSzs33xbsl4gJgvAUcCpQZxjGCGvZaOBRwIf5sHW1YRhLBTOGeB8wAwgBlxqGsdLa5hLgJmu3txqG8ay1fBzwDOAH3gV+bhiGIQhCIfAS0AfYDpxnGEbz13DOGWSQwSGOowaW8PzlR1BRGKC8MHBA6YmCLA9eSSCY5j37ZgTJLZGcNJ1gm2wmeEjUMFM3950/mkufXYEowAe/nNpp35dO7ss/5lUyoV8Bb67agwQErJYwIiZBjKgGeX6FcEwjlspQhPSpSY8kEtV0FFEgbt053am7uG6gxTVHBG77Rtk3OfucvJZpqLvdj5Mx7CLyUeyXmdi/mLctD63RPXP4ZGure8jJlW5dkCt7Pa8sEFWNtP5kBwJJwIkwZXslxlQUWGnEOnQDPtvRAkCh1VtTAAJeiZiqM7FvIRqwcGsjgiCyua6dPkUBNF2nORTHIwuWTYRBns+MMKaej25AUyjG+Ns+pijLQ0s4TkVhgFG98uhZ4CcS02gJm9rBId1zOaxnPoZhcNa4XrywdCeRuOaY0HpFuHhyH4pzvJw6sgcn3D2HiApvr66lT3EW5YUJuwbDMLjxtbW8tHwnAauSs7E9ysNztqBIEmeO6sGlk/vw6ZZ6Hl9QxZxN9YzomUNptpdFlY3ESEQR8wMKDR1mmtCJ+NqEjOTomE8RaY8mPPjKC/y0R6K0hF32H9Y+PBLEXBfWjtq6fdr6FWdR3RTihKHFfLKpnlhKuySNxPcy9bO1ty3yhT4r30QcSETsGeBBYKZr2V3AnwzDmCUIwgzr92OAk4GB1r8JwCPABItU/QEYjznHKwRBeNMiVo8AVwKLMYnYdGAW8DtgtmEYdwiC8Dvr999+pbPNIIMMvhEoy/NRluf7Qtt8uL6WplB621L3g7x9kxVdf/HtG1IqnyjL83L96+voXRTgyqn9nZ53mqYxa81e1ta2oukGRVkePtnUYFYWioLTl0/HtCAQgKiqoel6Jy1UuopNScBF2LoOIbm3VQ3TJiNmhSnsCEU0DUuy9WGiINA938eOxpBzlJgBkZjK0soGZ/0F21qTtk/VqpmmpGbPytToouxqbL0/i4PUtG6WRyRoMUtVN73BThpWyg0zhrFwawOvr6oFINeqLFi1qxVFFtA0A78i0hHV2FLXQbZXRgIMXSeuQ1VDMDEW1wHbrT6NqTCs/7O9EsU5Hi6cUMG2+g7aIyqPzjVToaPL8xhUmo1hGCypbGJHU5ALxldwxqiefLarhRXbm6hpDlHbGuHE4d05wjJFtTnJS8t3URDw8LsZQ+mIxGnoiPHisp28tHwnPz1uAC8srSYUj2EIAjENYprGz6cN4M5ZG3l0XiW27+namnZ2+cNke2VK8xRqWyIoosA7PzuKiX/52CTuLusTOxKmiLYJbXKPTlEw23NFVQ1TCWbbupjrxLRESy37Gqd+xq+a2pfXPt9tpW1V5zrbaXB7eztC5yZlh6cxj/22Yb9EzDCMeYIg9EldDNg1qnnAbuv1GcBMwxSeLRYEIV8QhO6YJO1DwzCaAARB+BCYLgjCJ0CuYRiLrOUzgTMxidgZ1nYAzwKfkCFiGWSQAbB8exNjyvMQRRFBEHj200r+8OaGLtc3G26br2UhfYWfDfuJPs8nsbU+xIzDyrjupCH0cRlLHnv3XBo7YvTI91Oa6yOu6kRiGmV5Xmpcpf129ko1QLMiIumMRCE5EpAc+Upocex1UtvlZHtl2qMqETWZ6ImYui5b/+OmdmaUzTCrC8XkptUBv8dpEG2P3z6kRxac6ja3LYMiCeia0Smioeo44u2uIAnJ1ZS2vuyMUT1ZVNnI9kYz5TqkNIebTh3OQ3O2cveHm53tm0NxXlpWzR3vbkDVTH1YfUccnyTQGo5T2xZFJBEtdKfmbCNTA+hT6EfVDaqbk6Mwdsr1phlD+N7EPknvReIa0+6ZS37Aw4rqFu56byMAn1U3m1o2WaR3UYAJfYsY1jOXJVXNBCNmSm57fZuzn58dN4ACv8yQm96lOMdHTUuYgCxyw8lDuGJqP15YWg3ArpZEIcrzi6p5YsF2ehcGeOjC0WR7BZZXt/PcpztYXdNKXUecLI/ES1dNpDTXz6+O78/dVuoWOrvtRzUIKAJ+j0xUjTlpzN3NQRpcDzn2lbQ/U6lc3/35O2FIER9vqmfNrlYKAjKaRbTsik739Ugq8rDw7e4yaeLLasR+AbwvCMLfMOd6srW8J+C2Id5lLdvX8l1plgOUGoZRC2AYRq0gCN2+5FgzyCCDbxFW72rhO48uAqAwIPOd8eU8Nq+qy/XtNIx901CNBBlzwyZg9s0gHNMY2TOXhy4cm5Qajak67ZE4/YsDPHbJeF5ZXkN9W5hNdUHaIyoFAYWWkJkCUiQJTdcRSFhTCPsrPUwDm4QJmNqoqKo6kRSPJNAeNX+Jp4t+CQKSYHS6WdrDqGwMddrGJmF2pMvtx2WkRL3smXG7sKceI5WEjSiGne1gc9ZObXusuXp5xU5HvwQwe1M9Zz+yEN0wkny8zhjVg9ve3UCzXSZo7S+iGc7OBdG6qRsJLZT7egOIokhLMNpJzK5jWirc9s4Gzh1fkaQPW7+7jZ3NYTqiKuc+uoiSHC+/mjaIk4aVMWdTHetq29i8t4PNe01TWI8scMVzK5g6sJhVu8yUapZH4pfTBtH/+nfRgQK/yE+PO4yxFQUMLDWjsHHrA5TtkcnKllBVjScWbMcrCbz8o0mU5poR5D4l+XxnXDmPz9/G7e9uJBjTmPHAQib2zueu80Yxf2sjy3a00C1boS2iElYNx7gYTH1XXI+7p5Esr+wQMXfBSJINifW7/X2zL+jA0jwemlvJuN75fF7d4kS/7OIYySooEcTOukJBgJveWMdLV03i24wvS8R+DPzSMIx/C4JwHvAkcALp7WqML7H8C0EQhCsx05tUVOxf1JtBBhl8c9GvMECeT6ItotEUUnl8HyQMEmkYu1pMN9JXhaUGbGI6VDd1cNd7GzlnXC8GWGnJT7c10K8kh58c05/Vu9qYtbaWoCWSaYu6PehxBMvu1I07ZiRgGm2mc5pPV30pidAt18POJtPVK9srEY1rnSod7XShZuBEqZx9CMnO6vZobGd1NwFRxOTUpihAlkdBDcc7jc29P0hUiKZieDeF3Oxsuolh5KBKY7DzDd621bDJgXs3q3a2IolmU+sfHd2P4wZ3Y2R5Pj998XPAJKaabpDjk2kJp2iROsvzkixLdjUHieyjKWdM1fl4016mD+/uLHtqYaVzvr+dPoRLJvcm4DFvrT+cajYMj8RUXllRzRPztyMAu1sjLN3eRIeVRr7+5MEYhsGUgUXM39LImt3BTgUqtk7wu4eX85vpQzj6zo+BOOMr8h0S5sYVR/Xn8il96XfDLAAW72hh6l/nOu83BuNOpDP1Mumua16cJVPvKjiwr1GnFLs1j7rrop88oozl1S2cMLiYtbVtSbKATkUdrmvjFJoYsKyqiVAkTsC3b/uabzK+rKHrJcB/rNevAHaDt11AuWu9Xphpy30t75VmOcBeK62J9bPLmnTDMB4zDGO8YRjjS0pKulotgwwy+Bbgvo+3ELJE6gcSXHIq+vaj9y/O7uzg3RLWeW7xDu56fyPLtjexfncbTy6oIt/yrjpxeBlT+hfS2J6cyvJL5s0kqupJVgoAhutGZZ6DkfaJ1Gu5i/usn2Y6EbbUhYhYUYxgVDMd5FNImJ16daeebNjE0LbscKdD3V5qkKiKc/uDNaeQMIPO18Cd+k1FVVMcvywwuW8x4WiC9bj7NKp6ckP0LFd+6rrpg1B1g7PH9OSk4WUc0a8InyLxzKWHc964ntx61gjOGNMj6TFfcP1MSuum/IyoZqsju1enJAhOdamIOc+toUSlH8CSyiYAZv3sSH58TH8CHpmVO5q415U69XlkxlYUsaMpzPamMDHNIGSR93PGdOeiSX0RRZENNS3pJw0cZ3+fV6K+PYrPss+I6gbvr60lnRXVLW+tdV5PG5KstXKn5kVMAisKoIhCUlFFTDMSrZhcc5muQMNtMisCK6tbaOiIsnpXM/WuVLc94andCdydA+zj6cDlzy7rfLBvEb5sRGw3cDSmbus4YIu1/E3gGkEQXsQU67daacX3gb8IglBgrXcicL1hGE2CILQLgjARWAJ8H3jAta9LgDusn298ybFmkEEG3xKc/dBCVu5sQQSOGVzMJ5sa9rsNRvITeFdocD31u5/2O6IaczbUsXRrPbKiUJjl4V8/nMiKHc289fkuZq3bS1iD4d1zWGeZuQ7vlc/nO1vTlkPa0ScE034i1U2/NFvhN9OHcO2ra7j3/NHMXr+Ht6zqxdT9ZFs+YqnL90dQU1OLbkNbex+p67uNVe0bpHsf9nap2546ooSwCrM31gOgIbBiZysfb25KWtdd2erGgJIAreE4wQ6TAP1z8XaGdc/h8iP7MtzVzuqYId04Zkg3Kus7uOXt9bRbdgip6U4j5dzta2GnT4uzvLSE1GRi45qb1EhVYzCGKEBpnumA3xyMcfYjZur83tlbEAVYc9MxqCmFGrJoiuInDSjh8fmV3Pv+BoKuS/ngx1sQBAFFEjhnbC9n3G99XstH6+vY1RLm6mMGsHFPO1c9v5KjBhbzh9OGM6BbtrOPpxeZiiBJgMcvNdN7j87ZzB3vb+kU0bQF+opoFjrErSITEOhXHKBPYRZzt9QnRbXs6GmCQFvnJsDAshw21LZTEFBoDmlO1MedurTT9AJmZa0oCOiGQVQ1kF2pypXVXRPUbwP2a+gqCMILmKL5YmAvZvXjJkybChmIYNpXrLDsKx7ErHwMAT8wDGO5tZ/LgBus3d5mGMbT1vLxJOwrZgE/tewrioCXgQqgGjjXFvvvCxlD1wwy+HagqSNGoStKdff7G3lgjik0PpBI2JDSHJ64dDz3zd7Mgi2N1LaaUauAInD+4RXMXFTt9JAEV4TIAEkCVet8DEUSuGpqf4Z2z+XuDzcRUCRkUeDM0d25+8Mtpk2BDr3yfeztiBGz7kz2DdhnOfCbrVsE4paA3z0Gw4A8y5HcJ4umiDqFrQkpr9PZe0nwpe0i3Cj0yzSF95Gv2w965nkpzPYRjkbY2hDt5DklAEcPLGZ1TRNNocQb4yvyWF7d2nmHmN5yZ4zuyU+OHZC0fGdTiPP/sYjGjqjVvslwUqRm4YI5c7rRdZPqMeV5rNrZmmR8Wl7gcwT8FXkK864/0Vn/tAfmk+tTGNe7gIZgjLq2KB9t2Ntpv32LAkweUMzqnS3sbYvw9/NHU1GURY98Pxc9vohFVV27M91yxnD+9v4mpyVTwCMxsV8RT116OKpmRm3v+XAz4ZjGZUf25afHDWDibR8RtJjt9jtOSdrf9oY2jrt7fto0o217AjCgJIsh3XN5f90eJvYtZElVk7mNlXK3p9DWEtpzaxO0nvk+alrS20+4v8OKlRa3I7u2nYaB6davG7DmjyfhVb650v19GbpmnPUzyCCDQw6fbm3ge08u4Yqj+nLDjGF8srGOS58x0xPuG0VX6FPo46NfHYvsElXf/u56PJLESSPKGNEzj4Vb6/nR8yvpiJjO9KqR7KeUTuM0Y0Q3DERW7WyhIMvDy1dMJMuvcPcHm3jg463k+WRaIypDy7LZ2RSmw0o/uaNJTgTBSnd15cElugiEjlk5aTeM7lngI9/nYVtjkLiqoafxWk3njZbkp2adrB0JS21PZMOvJLupfxEoQK5P5MQR3dhWH2FdTQuGYXbYsRN8XgkkUXTSX+kgAHedPZzdrTHunb2F0hwPi2+clrTO9oYgFz6+mGBM4/Ij+/Do3ErC1vzvq5MAJEdM+xf72dYQdpanWpuUBETe+vmxSfYqlfUdHHe3qb/ySCJnjunBzacNZ+vuJs78R/q0Wo5HZM2fT6YpGOPnL35GQUDhnnNHIssymm72jrzsmWUs397Mgt8ey4WPL2aTJfi3j/Ofqycxomc+YHaj+Ot7m3h5xU6Ks73Ut5uVEFP7FzLzikl8Vt3Mr19exSkju3PqyB4YusbJ9y+kIKBwzbH9+cusTZ2KKnJ9Mn8/bxSXz1zBOWN7snlvG0cOLEEQ4IO1e6lvj9IWUZ30Jpifw2GlWejAjsYQYVfeXBHN75n7+ysK5rlEVB2v5Z1nwx1pff8XUxlcltPFFTz0sS8ilmn6nUEGGRxy8CoihgFPzKvid/9ZzePzEiX3+yNhfhn+eeWkJBIGcP2MYfz6pMGMsNJZUwaU8O7PjmJSv8IkWwf7n31P8rh28/66Omat3cOetgiH9ykky28KiE8d2QOPLDqC6uZQHFXXkW1244o22MTIMJJJWMAjcuOMIVw8sYIsj4QkChQEFIcAxDXLo0mEjojKzpYwqqab5CzNPKTzRnO702uWutp+P7WC0E5BRtIQpFRNm1t35X7PEKAxovPmqr30LgowujwPzUhuuRTVzEo9uzF4KhQsM9KQys+nDaLAL7OnPcZVMxMP3OtqWvnOo4uIqDr/umICRw/qRiimJV1LJ3pD5xufYSTOYW9bFJ8iOtYjqenW+pDOuY8spCOS0Ipt3GOmpGdedgRLbjieu74zimyvzOi+3cjxJaI42V6J358yFIB2y0vjpL/PZeWOZn5y7ABk2VQLSZY+bXFlI9+bUEFRttdJ03mtgcU0ncufWc6dszYQiqkUZ3u58zsjee3qKfSwSKJfhutmDEPTDW54bS2VDUEe+HgrJ907j+n3L8QA2sJxzhlfwRtXTybHk3wNglGVa174jJ75flrDcdojGr84fhDXnTSUj359DG9cM4XhPXIShrvAeeN68dBF4+ldnO2kO+0UcPd8f5IeqiCgYBjmuZgWJnrS9bHnXhKgKRjl24oMEcsggwwOCUTiGn97fyPH/nU251gaGx14celOVuxsoXvu/vvN+WSB+y4YS8/8wAEds7wwwJOXHsHpo3p0es/+4+jurWiX3c+99hj+ePpwZ/ngshwuPKLCSUXuaTPd9O0Ig53mCnhkJz2WiphqcNu7G1lV3UwwphHXDJpDiYbJOrYVBLSEVVrD8SS7ClvrpYgCsrjvP+7OPq1z81s3dwHole9lcGkgiZS6j3H84KKkfUlYkTdL4O5e3w6GBOMGy6tbqG6JEtU7R6aunNKbSyZXdFpemqPw1GVH0Kc4iwVbTT3gY5eMRwIWbK3nqueW8+gnW/nTW+sYXZ7Hy1dNZHiPPAaX5SAK5g1cSpkIwUU+bYIgioJDXIMxnUhcd6KPznm6LtvOlgiLqxLttHY1mxYgoyvyKchKpNM37W53tGrHDChi7Z+mc/lR/cjxmXTkxHs+ob4jRjCmMbgsFzdimk5cM5z0vG4N0J2mbmyP8sjcyiRR/ujyfF67egp3nTOSgNfDaQ8u4ILHFrGhto2HLhzL2z89kocuHMu108yKTq8ikedXGN4rn6d+MJHuOXKSaP7mU4fzyEVjkUSBw/sUJqUH+xRnc/GkPs5DRa5P5trpQ0CA+ZsbEiat1r72tEYozfM6JL85FHe8ygRBMB8UhM4PFpoBG2vb+LYiQ8QyyCCDgw5dN7jmXyt5cM42qhqTNSU5PoloXKf5AJ6ILz+qHyeO6L7f9dzweyT+fv5oxlbkJxEBmzy5EZDN6Iy7DY2q6qzc0cS7a3Y7xCt1O1kERRLpke8j1yfjkUUmWo7hfkUi1yej6gZ9i7PY3tBhkirX9vbr9BTOes9Kr8Ut24cDSSba5xtWE+1sdrVE2bS3s7dYaY7COeN6Ut0cSZons0EzVs/CZGLoft3cESE/oCQts/gIjy3cwVMLq5OO951xPVnw2+M5alAJI3vmke2RqWsN89aqPQwozWZwaQ57WsPM3VxPeVGAu84Z6ViM+BSJbI9k6cTM/UkWQXV7wtkEwW0fYgADSwIOSbMrKFNTyO+vTRRQFGaZDwn/WlKN6kqtnfHwfOf1Mz+cCICqqgy0BPUtYZVuOV5G9EgmYZCwXZGs8WopoWCZRGRx8Za9rK1J6OlEUeC8w8v5+DfHcOnkPuxoDOGTRRqDUYZ2z+WUkd156lPTwnNMRYGz3Xn/WMzudpU/nDqEbjkefLLALW+vpSjLw7rdbZ0KQwDG9S7kkYvG8f1JFTzzg8MpCCjMWrMHt0WL/TOmGbRHNLK8kkOQnbMy7O+OYJ138nFmLt6eNLffJmSIWAYZZHBQYRgGt76zgY821DGmV34n0tEeMVNMkS6U5xWFfgQBjuhTwLUnDflSYxAEgeOGmJ7RNonyWiJhN3RB5MiBxQC0ReJc8tQSBtw0i3MeXZTkRA+msN8r2a7y5vls3tthpi01ncVVTXhkAUGA/IAZ9ahqCNIWM5w0j3PcNGMWSRQYOFEF17L92XUcCPoV+lh6w3Fsue1kjh1aypuf78YrSxwzqMiJuhUGFBQBTh9ZRo5Pdq6bGZ0zXxtAS0RnTY2ZwhME8CkCI3vlJx0voIj8YEpvPvn10fzt3NEoshl96VeSzcY9bRxx+8e8u6aG0eX59C/JZtWuNoqyPNx25ggKs5MjpscOLcUrixQGFCb1K+Ss0T1QdZLMae2xSmJy5CzLpyRImtHZkBagoT3xYDB9RBlTB5Vwx6yNnPrAAhZbzec33jqDPkV+1txo9ih9av42Btz0PiurW+iR5+PM0T0IRuOsr23jg3XJlbE28ZKsC+lLSbW7vw4ej5c739voRGRt5PkV/nDacJ67/AjGVBRw8xvrOO2BBSzb3sT543shiwJPXzLWWd8myje/tZFLJ/WhMFshFDc48s459LWikqlkbECDZaGvAAAgAElEQVS3bE4aUcafzziMsb0LkSURURSIqbpT/CJacy2LAu0RlWyP1Mkjbl8USxYgEtNRuxJUfsPxZe0rMsgggwy+Fjz8yTaeWljF1IHFLN7W6IiobFH7/v701rREkEWBx76fVgd7wBhQkpNUWq+qBgFLqG6PIRLXqWlsZ+veVi57ZgXVzaao2w5W2NETVTOIO+1+DDyiST7OGduDjXvaWbe7neOGlLBoawOKJBCJa3gk00LBJlX2/rDnwEhDztJMji1KT6elc4oEBNMwNd3c2nMuArIs4/PIXPbMMuZvaaAwoLBtbxsdkQDlBQHq2kOcP6GCnc0RVlW3IOhaki9Z6n1TEiDPL9MUUlEkCd0QnGKEwoDCf66ektRKykYkprKjyZzrU0d2Z1FVM15J5CfH9ufX0wanTffed8EYqhtDlOZ68SoSmm6wsrqZ6qYQkNwtQE9Jl3aEY6afluVnZhNb9/lU1bc7r7O9Ms/+4HDeX7eXW95ezwWPLea0UT24YcYQPrn2OABmb9jDn98x2x+V5Xp592dT0AyBx+ZXAXD18yvYenuiutEjiQgCtFi+ZccN6cbmvR3IIkwZUMy43gWcfFgZ3bI9fL6rnU17EuNJxeCyXP51xQTeWVNrdgd4dBFnju7Bwt8dh6IkopSf3XwiY/78Ac2hOHd9sBmfLHLC4BI+2lTP/C0NSILA0spGjhta2uWxAH50dH8EDO7+YDMxV9srr2T6nrVG1E7f7cTn1RZUmi/tisqAVyKiavg839zKya6QqZrMIIMMDhrq2iJc9dwKsr0S87cmNDf9S7LY0Rjap82Ajd4FPnY0RxhSlsNjF4+noujA9GGpWFfTyikPLCDXJxOKqZ38vbqC245BlqybupGoRHQblboF41leiXBMQxAFirI87GmLdtpvqoAeEn0ZU6fFXfFpE6muTsEhW11UEwY8IqPK8whFNUpyfHy8sY7bzz4MXYfrX1uDIMAtpw1l/pZ6tuxtZ8qAEv65dBdZXpFQTE9bCep2//eIpvZugmWJYI/n/vNHcfqYXknbPb1gG396e6Pze3muTFPE4N4LRjNtWFkXZ5geZz60wPR3IxFRREgQMTu9m2/Zh9gQBRhUmuOI8gHy/TLzrjuOXH+y43s4pvHI3G08OncbkiBwzXEDuPzIvtS3Rzjqrk/I98usuGkakhWC6/O7dwAzIjfvumOT9I3fe2Ixm/a0M/vXx5DnV1ha2UjfkixKcjo76R8oQjGVh+ds47F5lSiSwE+PH8hlU/o6bZv2tIY54e65TsUvmOlRO0LnU0TevGYKg0o7p1Pd+GDdHv789nom9y+iKNvLrFU1DOqeS3tEZVzvfN5ft5ctdUEETDPZuGaSXttKwyOaUb88n4JfEfn+5D5cdfSAfR7zUEbGviKDDDI4ZLBiexPPL95OUY6P0hwvd763iT6FPrY1hB1y4FdEgrH9MyFFgptPG07vwix++sJnADx44RiOGvjFO2x0ROKc/uBCqhqC+43CgXnjvPe80Zw2uifff3IJ87c0oEgmsbELDb2SgCBCJG4kN+O2mEe6/nr2e7rrd1Ew06eGFcVxO+CnI2SQxs6CZPPNrs5RBKYOLmFMRR6PzNmGKEAobnDm6B68/rnZ+KRPkR9NVynNCbCxthVFlinM8rC7JUxprpftTQmd32+mDeCBj7cS1Uwy5vdIBKMa2T6ZNssXK88n0RrR6FsUYM61xzrb3v/RFu75yHSol0UBvyLQPc/PX84eyfg+yU7xB4K7P9jEgx9v7fLc7TmSRYEsr0xHJI4gmJExnyIwvneh88AgC/CXsw/jvBSDVxs7m0Lc9s4G3lu3h95FAX5/yjACikjfbtl0z/Oj6zoPztnG/bO3OKnPw3rl8tY1Rzn7WLOrldMeXMBvpw/hx8f0/8Lnuy/saAxyy9vr+WhDHf2Ks7j5tGH0L8nmmn+tYE1NG/efP5K/fbCV7U0JvaAiQNyAPJ/MrWeO4LTRPbvcf1VDkOPv/oQ/nj6c70/q0+n90x6Yz5qatrSfRxHzu3FEnyJ+deJARvfKd9LU31Rk7CsyyCCDQwbn/mMRr31eyxPzq7jnQ/MmtNUiYbam5EBImCSYlg5/fGMdta1h3vjJZIqyPNz42tqkfncHimyfQntExadI+9VXTRtawra/nEJcN7jkySUsrWpEFCCmJUewoppBJJ7Q+tjViYZhash0PbltDFhRMOu1HbFSdbOht2ok+kRKYueKRicqlqZYwB0ts1/Lac5z2rBSVE3jwY+2ElENFBGKshTe+Hw3XlnkPz+exKT+xdQ2x2kKxrh+xhB0w6CyIURpjpxEwgaXZlOS5aGvlW7UMaNO/btlMbJXLopoVrr2KgjgkwWqGkMs3Go68M/dXM/Dn5hNWxTJrMY8a2w5T15yxJciYQDdcrxJ+jDBsl0wU8rmzR9MTZhXFh19mAGE4wa3nXUYhX5T0eNVJHrvI/paXhjg0YvH8fzlE1AkkR/OXM4/5lc5rY1+/PxK7vlwMwYwxPLHWrOrjZ/+MxFIiFksvSxv/xXDXxS9i7J44pLDefoHh6MbBpc+vYwZ982jsiHErWeO4NTR5Xxy3bFsv+MUjhtcQo5XQpBEAh6J1ojKT1/8nMueWdrld61vcRbnjuvF0qr0PuzXnTSYbI+EIpndA0pyvBzWM5dzxvbgx8f045WrJvHClRM5vE/RN56E7Q+ZiFgGGRxiMAyD1nCcHJ/iCHW/TXh1xU5+88rqtO8dSCsiMFMZMS0RZZIEOGNMTy6Z1JvvPbGEa44dwFVH90+qkDsQ/HDmMj5aX7dfbVquT2b6iDJmrdlDR1RNchi3o1mipRcrLwzwnXG9eHD2ZqJaQvjtlUwPLa9stpNJTRHaUTC7F6VgtX+BRDqxq7lKN/7UJs1u2GnM44cUM7q8gL9/mNwCRxbhuMHduOWsw3hxyQ6eWFBFe1TDI0B+loJhGASjKiHVPPbk/oW0RzVW72ql0K8wpiKP2a52VD3yvOxujTrH7luSxWkjy7h3tukXF/CYKU4bw3vkcu/5oxlY+tUMPZdWNXLVcyvoiMbR9K61dADHDy5h/tYGFEnEr4h4FIk3r5mCpsOzn27niL6FHDO42wEdN67pPLdoB3//cDMRVeOCwyt4bvEOCrM8zLv2WFrCMY68cw5gXvfNt5yELMu8t3YPP3p+Be/87EiG98jbz1G+HKobQ/z+jbXM3VzP9OGl/PbkoQ5xTrfui8t28OqKGuqsYoXB3bJ47Pumb1gqLnpiMVFV55UfTU67v7+8u56PN9bxq2mDOKJPIcVfIeV6qGNfEbGMWD+DDA4hfLKpjitmLkfXTW3UyF55XH3sAKYMKGbljmbK8nwM+oo3o4ONc8b24rFPtrC5PtzpPbvCan/xsPICP789eSg3vrbWaWfzn5U1NLRHGF1ewB3vbaIpFOdX0wbh+wJtUYoCXjySGdnaFyJxnZeX70paZhtR6tbPkiyFlrDKjsYQd763qRM5shtqpyNhighHD+7G8B55+BWRvIDCw3O2EVN1sn0yPkVC1w2qm0OEY1onQub+1fbJSlf5Z0MHRnTP4eONDSzf3oKRMlhVB0WW+Ms761E1naA1QTEDgnGN/oU+1taaaUYBePayCYgCXP3Plby3bi89C7MQaUgY57rGogNNwRiXHdmfT7c1sWpnM6GY7pDtq47ux29OHIySagj2JTC4NJfmlKbdYM0RZlRU03XaohqF2V7+eu4o+hZnMbgsB68rKnPd9C9WnatIIpcd2ZfTRvXg4TlbeebT7QCMq8gn4BHJ9gXI8cq0R1V0AybeMYflN01ziPd/44Esqmo8NreSB+dsRRYFbjplKJdO7oO8j3muKApw3fShXDd9KE/Or+SWdzawqS7I0X+by61njuCiib2T1pdFgYJcH4ZhpH0oumHGMG6YMexrP7dvGjIRsQwyOATQFo7xnxW7uG/2FlrCatcaFgGumtqfq4/tT65P6WKtQx87m4Kc/fCn1HfEnPSbwIF5X9kYW5HPPeeP4qrnVrJpT7uTlgt4JKYPL2XWur0M6JbNIxeNo2e+f7/7q2uLcMnTS9hQ2+GQpnRRJDdHsdNbNq8Y0T2bdbUdiXOy7j2aYd5MBQzHysLevyKLad3rf3nCQH5+wqAux3vvh5u57+MtztzZSP3s9C4MsMOl80kdP8Dg0iw27g0mVUymjsg0pJW4+pgB3P3BJjQDCrwCGgJt0eS1fzVtID87fhCabvCj51cwe8NexvXOZ/XOVqLWhIqC6TQ/pryAeVsamPWLo2jqiHHRk0tMPZxhcMfZIznv8PIu5+DL4OrnVzBnUz3ZXokeBX6GlObQs8BPeUGAId1zaOiIcvGTy+hfksXsXx/ztR7bxjzrgSuqGeT4ZO48eyQzRnZ3hPsAM0aUceXUfvzhrfVcc+wApg3bd6XiF8XiykYueGwxMw4r4/enDqN73v6/I248Mb+SO2ZtRBYNLKkfI3vm8sqPJjumr298VsOf3l7H708dxlkpRRj/35AR62eQwSEMVdM54Z5P2NEYRrZ6AfpkgZhqdLoZ2lGCod1zeejCMfQr6ZwOONTR3BFhzK2zk5YdaErSXTGoSALfGdeLtbtaWFfb7mwvWO+dPLyUN1fvYXBpFjefNoLJA4rT7nNnc4gHZm/hsx1NbKsPOUzFnnt38+z9pSzzfQIdUZMZ2cTSMGBoWQ7ratPbC6QV3AtweJ9CXrpqUpfHuu+jLfzdErIrkmmZkW5sWYpARDU6pTJN13mRvsUBdjYHCcWSt3ZXVoJJzq47aTDtUZXnFu+gJawmmYq64VMEbj3zML4zrpxwTOP8f3zK2t1tZHlkbpgxmOtfWwfAkO7Z5HhkJEkkz6/wyPfG8ue3NvBpZQM/PKof543/ekkYmKn/+VsaGFKWQ7fczqkwTTfof8O7+BWJDbdM/9qPbyOm6vz236t5/bMaRyf2wyN785tXEy751500mMfmVdIzz8t/fnLk1970et3u1i+d8myPxAnFNBRJ4NmF23ll5S52W1Yyz11+BJP6F6PrBifeO4/ibA8vXtn1Z/n/AzJi/Qwy+C8jGtdoC8dp6Ijyt/c2cONrq5m1ZneXQlZdN1i+vYnfv76WCX/5iPr2KAYwqnsWPfN8GAgM65HrRFSyZDPVFdNM8fTOphBnP/Kp09j3m4KYqjPh9o87Ld8fCRNwRaesOYlrBi8s3cmW+o6k7Q1MB+83Vu/BwGw8fNGTS/jnkh24HzzbI3FufXs9R905h5eX72JLfcjx8HITYHeWcl8ZIo8ILRFTUK/qJqEWrEiYAWSl+B91Vb1om1/u75bbPd/n9EO0Pcvc+7URjBtJFhpgkrOh3XN59ceT6F/kJxIzty/JUhzPLFE0xyAY5pwc0beQpz/dzqNzt9ESNulXOhIGZpXo/bO3sGBLPX6PxGPfH48AdERVirJ9jj/axtoOlu1oQRIENu1p563Vu5m3pY5zx/f6r5AwMLV2UweVpCVhYF4vryQQ0/QvVfRxoPDIIn8/fzSfXHsMgy1rjGtfXUvvwkQBwN8/3ERLOM76PR1c+vRSalrCX+uYvoruLMenUJrrozDLyy9PHMyC645jVK88VN3gu48v4ba31yOKAhP6FibZfmTQGZmIWAYZfAnEVY0FWxt5YWk1VQ0d7GmLUJLlpbIxuTWMJMCNpwwl1+9BFKChI8ramjYWbq0nqup0RDWnR1/cIlljKvJZur0FgKMHldAajrJhdxv9u+Ww3oqqKFarllNGdufc8eVM7p8+2nOooT0UZdQtHx1Q9GtfcJMYt9mmJCY8oez0Wp5PItunUNMS4bRRPfjz6cN4adlOHp27jdaI6oi17fUlO60oJqo492foLYsCOT45SX+kWN5IHhFkSaQ0z0dVQ+fWQfb52FFB+1A+WeC2M0cQ083my63hOG3hOMGYhqppbN0bZG2tWf7vlc2o14FgfHkefz5rBM98uoMjB5TwzqpdfLixPmEi67LUcFte2NGxL3Ltuud6eeayIxhclsv7a/dw1fMr8EgCZ47pybKqRqoaw85xFEmgLM9PRWGAmZcd0WVPzv8Fzn5oAZ/vauX66UO44uiv1zaiK7y9aje/+88aOqKq89AhCiYRBsBatuaPJ5JzCMsSHp+3jdveNX3ffj1tIHXtMd5avZvPbz7xII/s4CKTmswgg68R/5i3lcfmVhJXTdGy3QjaK4tEunABTfKQAgwhfbVWnk9kRI8c1uzucFr7dIXibIXRvfJZUtXEb04cwCVTDq7ZoWEYPDq3khyfzPcmVHRZsdjcEeOfS3dw9webD8yvS0g023Zrm9wEAexWQgn9kW4kG6ra6+V5JYJxM9rhtolwEwzZIiOp48tSRIJxvUuvLrBE+5ZdhKaTdFNNR2JSZ8mpwMQ0iAXBGeuBkKB9pU9FAe4+9zBuf2cDdcH08SxJBMFynrcrO93asSyvRFQ1iGn6PisxbYyryOfRi8dRkuPj/tlbuOfDzfTK83HkoBJeXLbTWW9i30IWVzUx87IjmDroi/vAfZ1YUtnI+Y8tpijgYcXN0/5nx9V1nZvfWMe/llajG+bfFLvJd55P4voZwzh7bC/HfPVQxcKtDXz/qSUoksj3JlTw1MLtrP3jSWR5///WB2ZSkxl8LahqCDJz0XanBcz/N8Q1nT+8vobb391EYzBOezRBwmQRIqpOn7x9/6GxH/LdJEwRE2koURCpagjT5iJhXQUGwjGNZZX1tEc1/vDWJt5dXftVTu8r44WlO7nzvY3c9Ppa+l7/Lh9v3Jt2vYJsD5GYhiTsu4m1DcP1M6nlD8mEwyZhdnoR0hOX9qhmrus6uFtfluOTUaT0icGgJaoXBNJeH9t81SSBwn7HYp+D+58zJnB6Ix5oEYMo7LvCThLhVy+v6ZKEiZawTdUNZMEkYbZfma15a49qxK2QWVckzD2Cz3a28Oe31hOOqfzs+IGcO64nqgHbG4P8cEqiym5xVROlOZ6DTsIAJvQrwiMJNIZiVNb/79Jqoihy61mHsej64xlVnueQMIDWiMZ3xh36JAzMFkx/PG04kbjOf1bWYBhwz4ebD/awDlkc+lc0g0MGr67Yyc1vrOOshxeyZlfrwR7O/xTNwRjT753Hs4urkww4sz0iXpdb5smje3e1CwKKSI9s0fnS5XhE/LKpJepZ6CfPK9EcVtnrah7tlwWnP1+WYh61d6FZ3RSM6bS6+kw/tbDqazrbL46V1c384c21HD2oxHEAv+yZ5cze0JmMvbysmpdW7EQ1uo7cQGKObQIjsv/oC3S9T5v4uVNsHovZiS6C1x5RiWsJZZib19g2B7apqpwS5dLtNKZh6rZEAWTrGJIoIIvm9TQJU2L/6YKH7vNP1+ooHcz1OovuB5ZkM6x7LrIo7tNRXzdcmjhrRU23jGW72CYdkgilAR9u2MsvX/qcuvYwfz13NCcfVsbiyiZaIyolWTIea0dnjenaqf1/jUlWuv/KmSv+58cuzfXxxk+O5PGLx1EYMB/ufIq4T2uJQw0XT+rDtKGlNIfilOX5+NeSatoina1DMsgQsQy+AH5z4mDuu2A0ta0RTn9oATe/sZZWqx9bXNPZWNvGu2tqWbClnuZgLGnbUCT2XxW+Tr3rY06+b97Xtj/DMNha186bq2r481vrmHT7bLbVB5FFAUUyDSdlUSCm6uQHZHL8HlNwvLctaT8eSXCsDGQJDFFGtoJmYVVHMwQGdstmd0uEYEwzNU4GDCgOUJbjwZZgR2MqMc1AFgX2tHb23yoOKDx68div7fy/CEIxlaufX4lPFgnHVF5cWu2819iR/DlYuaOZO9/bRH17LHU3neAmJ6KQxk7B+im5idI+9meTJ3f1oG0Km1pNqLrSku4Ik5Yq5DfAlxKhEEVzHb9iOrNruoFHMgs0VN0w92EkdFiyJKKkiWK507Dp4F4uW4TP1re5cdLwUt786RQrpdh1bC31HXfMzB1ATGdx0dWNxF4eieu8t24vR94xB1XVuemUYVw0sYLXPttNtt/L+L5FjCnP47sTun6Q+V/j0e+NAWBrffC/+rdrX5g2vIwXr5rM6PJ8Lp3c96CM4avgHxePoyzPR31bhHBc4+mD+LB4KOP/b8I2gy8MQRA4Y3RPjh3SjXs+2MzMRdtZXNkIBuxuDdMj349hmKmdcFwjquqcOaY7CzY3snpXC4ok0C3Hz5VH9+OM0T3wKV/Px681FKO6Kcyg0oSVg2EYaLrB6l0tvLumloVbGzlmcAkzF1Xx6EXjOWrQvh2x31hZw69fXe00urWR45Npj6h4ZAmfIhCOqexpizkeVqFoIpIyfXgZPQv8vLKsGtWAUERDFDQiqkkYVB3y/BK7mkOoukFxtkJDRxwRqGwImcJxS6sUx74BGqR6jQrAX84ZSXH2wXGljqk6UVUjGNP4rLqFuOumdd2/V/POmhruPX8sta1hfv/GWhqD+ydhdiNt+6bvvg+aUSaIqQmRvo10PqypUTBZNHsHGq7juKEZoGDOOZiRLRu2OaquGwiCXR2ZvAObYEXiuku3JmBgpNWJxa0BCJhkSrPy3YaR0LmRJnqYlJY16BQutAXwYyoK+N2/17Bse3Oa2UkPp3ABc07tOfRZmqWSbA/BmEYopnXyf0vS8gmJSBuYKc+T7p3H7N8cww0zhnLxxN60hOKc/9hirps+mN5F6R3dDwb8XoWiLA+NwRi3vr2em08fflDGMag0h9d/MuWgHPurQhQFnrpkPDPuXwCY370MOiMj1s/gS2NtTSt//3AzszfWURSQuf2cUfQuCtASjnP7uxuorA/SFlE7iYdzPBJZPpnbzjqM44d+dZNCWwB81dS+FGV5+OeSaqKqTrZPYXtD0Ownhxn9ADNKdeMpw7hkch9nH62hOOG4Rlmej9vfWceLy3fRGu6so8n2SkRjGoYAhVke6ttjScafAY9IXDOIawZXH92HLXUhPtvZQkNHDK8Efo9MNK4iiSKqphPRwCtCdB8iIPf8pd7Ic3ymGeiNpww7qE/MP3x2OR9ZacgZh5Vx1dR+bNvbzvWvryWqGhRnyciiyJ72mNn3ULc8tlL2cyCu+jZSKyf3l+YUrJXs9kOpbvZdbneAY3ITPlFIJnii9Rk8kNSqfV4eSSCqGU5PytSHAntsqdoyN2RR4MIJFby6vJpQ/MD+1tvXwF3Nm4rCgEzPggAb97QTd7WaSr1+IubDRKpf7Zmje3DvBWbE6e4PNvHgnK0suf74Li0lDhbmbqrjkqeXoUgCW26bcbCH843FlDtmU9MSwSuLrP3jid/63pHpkGlxlMF/BSN65lGS46UgoDDzhxOSPGle/8mRtEfi3PT6WiJxnTU1LexuMZsBt8c02mMav3hxJT8/fjA/nNrvK42joSNCtkfi3yuq6YgaRFSzqm1PWzTJ+XxoaTYb93YQ0wxun7WBfiVZLKtq4p21tWyrC+KTRWZefgQfb6gjGNWQgfG9c1m8I5Fu7Ihq5Pkk2iIasijilQUUyYwS2OklryQS1zQenrsdgPyAwvFDSpi9sZ5oWKV/cRY7mkIIgoAkGA4J68rF3U0ybBJma5tU1SCuwYfr9h40ItbQHnW0YF5ZJMcn09AR4+01u4lalgoNQRURGNwtQG1rpJMTuw2bJLnF82m1SSkpuP2RMYPk9VN9tZKiOCn7c+9TFMyoqK4bxDWduGY418wRtLv0XIpo9od0X1f7mtqfS/utSf0KWbGjGUEUEAzDsaNwe4TZ2zvn5BqXICSicQBjy3M4fVQ5d7y34YCtLSBBpAxL55buc9kUUmkKtTljsUmYuzWSHc3V9c7X5vXPdwM6fz13DK8s38WRA4oPORIGZpspSTTnYVllA4f3+2bYxBxK+GBdLe2WNiyq6lz+7HJmXj7hII/q0EJGI5aBA10/0FhEAruawyiSSI807TFyfApNwRi7mkMOCXMjGNO564ON/O39jXREu7KG7GqsBtvqOnh6QSWvrayhI6bRENTwyuatwe8RKQwoTrQqxyuyra7D/B3zxvDE/EpW7Wple0MQRQKfIvHdxxazpSFE/25Z3HLWCM6f0KfTsVutisbdrREiqkEwppn6H00nFtfpsPrwCZheUOMqClhT04ZHEvDLAtsagk7lqX2DKwzI+D1SJz1QV0JuTTesIgEryncQK6k+3lTnvI6qOi8t28VVz61gzqZG53xkSze1qS5EOE07Hxu2mamZVnP5hKWsZ0ez3Df3JMJEMtFKzYiIrvXtdIkdtbKXp6MuugFtYZVgVCOqJhMs+xj2pZAEiFuaMLeOzQDHisKOOvkUkXW729A0g4AiU16Y5egLU8eSet4eSTC9zlKmtbYtxh/fXn/AJKwrHXi6KJ4sCkmEVbKur+D6lzoef8pntDCgMHdzPUXZCj+Y0ueAxngwcInVP/EXL31+kEfyzUIkrvG7f6/m96+vQxAEpg015SDztjSwq6njII/u0EKGiGUAQE1LiKE3v08oduCEqD0SZ93uFurao5z18ELeWrW70zp5foVt9R1ptQECIIsiD87Zxpg/vc+oP73Ppj1t7KgPUt0YorYlTG1rmC1721m0rYFXlldzx6wN/PLFz5h8x2zOeXghf3p7A+0uXVZcM8lPMKbTEooT8EioOoRiOjErCqJh6poWbG2ksqGDXJ9CXIPWcBzNgG45Hva2Rtiwp43qxhY8rjFLliVBYUDBr4goounvY2uNVMMS6AOH983nvgvG8uczhhOOqXgVEa8ic/GEcgTMp2y/LFAYkDllZA+icT3pBuuTBVNgnuZGqBmmjUDYOvVpw/ateftv4l9Lqk3LB9dfE9XSYBnO7wmiYkdIUqGIAjoJz7C4S/CVSt3SUQtHTC50kfa0SK2UIvx3xnoAqUp7/waQ65OTKmjt6JadktQMnCipZiSObY/V1r6pukEkrjO0ew4DSgIUBGRyvBIxzTigSsmYq7WR+2tW27rvrgup18AmThSdpRwAACAASURBVCKdiWsq7DkDSwcmmJ95SRCSrrs7ehd25WrvOXcUN58+kvmb68n3ezhq4MG3rOgKvz9tOH5ZoKY1ymsraw72cL4xeHtVDe+t20NzKMb3J/Xm8UsOZ0x5LgC3vrPxII/u0EImNZkBADXNYU4Y2o2AZ98fieZgjFW7Whhdns9t72ygOWQSt+2NIfa2dY565XjlpIbGHhFiumnlEFV1Yqpm3cAF2sIq5zzyKXFNJ6oaZHtl4qqKTzHJVDhmOkumuznZ6awcv8TeNisiJUDU8quSJNBU6yZqpY4Mw6CmKYxOwsDTK4uEYxrtUY3nF1UTUECQQdZsU04zwtESiqNjkq5miw35ZJGyPB+yKFDfHkUWJKYNK0UUBV7/yRR+8s+V1LZFWPZ/7J13vB1Vuf6/a2Z2O72nnPQeAiGd0A3CBeQCdlFEUSwoF/u193KveEHRnygXUbDTpKkIBCI1BAikh/R2cpLT6+57ZtbvjzVr9ux99kmCN0rJeT4fyD7TZ/bsWc+87/M+754+GqsitA9kmD+hhh9eMp9bntqDZRZqadK2ImqpQERDX79ivGvxxEN+b/8s7OiIs7al74i0XTodWCyon1gbpTeVozoWpqU3VTLtGMRwKUg/kiWHpjcLLDCGOb7gdkOeG34qp0xLEcKPYupt9adtv0qxJDEUykpCp/akzO+71PLP7u4dcm6lNGrDpmuB2WOq2HRgoMTcoRiO47nkI4oSqI5azG2u5smdKsJZFbXoT+df2FwJrpc+tQM/zlIpzbAB91x1CnOaa+kYTPPH51p479IJhF7FtgxCCN5/ymRufGIXn7ljLRfPG4NhvHqP99WCUdUxbNvlvONH8x9nTQfg1CkNrGkZ4Pk9Pa/w0b26MHI3jQCAJZPrueHShYddbl9PkstveZ6VO7v58/oDRCyDuz92Mt9/6wlcdvJQMmCZha/WroSQUOanrvSiI0JQFjEJWQbxTN7o0xDKUHIwraqzdDqnPGwOeWOvCJtcfdZUkhlniIbGBfzMpzcwRC0vslWh4l3arsAQStOFt97UpkpCQmBLlX5pqIxgCIiFTUJF5yalpCeRpbEijARW7urmhG8+yHt/sYqv3beJfb1JsjmHzniGnCOJhgy6E1n+tHo/j2xuJ2srE80gdFrJ9KIp4RJNfwWwdt+RV8QdTTywQZnIHoqEmUIVJEC+ii6Ivb1pBtIOfYkMUxpi1MYsRlWq7yViGT4ZCV7uQ0VsigsairVfet1gNEug9E0+8ZHCT6FqCwqNkJE/lmI7C/8YIN8aycstl9J56fPS1hP6vg+mSYtJWCnMHl1J2DJ46TAkLGSKIzLR1dWadWUWnzx7Ghnv/CUUkDANSwTSlN4OSrUoWvnFZcxprgXgl0/uxnZdLjt50hEc0SuLL75pNhVh5cH26ZEU5RHh9OmNrP7q2fzwHfOIWCZZ2+UXT+0GoDuRI5UZ8RTTGCFiI3hZOG5sFTUxi5uf3EU653LWrCYWTKzjkiUTiJSohPnC+bM5dVod4FWSSchJ9ZCWqIGhzGuGrMv4HanmDXgRh+I0UyLrDImKLZhYS85WbWsqoqbvDaWbFkM+smCibCAsU2ldysOm78MkpaQsbBH2GJHrCuKBarOuwQyuVMdgu6qizQBipooGDKRtVu3uZcAbrBJZl6d2djNnbCWNFWFyjiSZzZGzbYSUbG9PcP2j29nVlcCR6vqYqPRo1BK+L5Yjlcljsd/U2GpF+nqTh7eEONpwXMnvVu1BAOXFDDK4nFRVodrGwEUN3EU8lsGsS9tAhu6kjRCC2pjJoglq0BaB6IpO6WkUE5zieyOYstTrard4TR50alI36DaEHEK0NXJu/lgEKiqqCZSG7lkJ6h4vDvgEdWiOBNsT/euXDUOIYfdffM7TG8uJZ7KqSXWJZSwBE2qjXH3WNOpiSjc5oe7wwviIZZDI2Fzz4Fae252PYJQ6KkfmdYA6BVtcbVkZNvjdM2ogbulJcuvKPVw8r5nJDa8ey4pDYZ53L9637iCvN7eBfxaiYQvL0waGTMEP3n4ik2rCLJtR7xfyjGCEiI3gCPGNezdw7UNb+fI9G2ioiLBmXx/VMYtPnzPjkOtVRCy+8e9zmDWqgpmjK/3ptiN516Jmrj5rKmUhk0TG8QeeiGUQ8RiIFjqHTUF5yGBCbYxYyKChPExzdQRQ89sHMtz4xG4ytksq4xAyDcKG8oJyBUQ8wgQgDEjb4DowvjamSFVgMGzpSfjRqQ0H8xGGiGXiShhXqwYxKRXRQihyGRxcQUVO9D6f3KZaEdlS6eKytktV1GJ0VRhXSk4YW0lUkz+gYzCrKvK89RvLQ7T1p+lN2f61AUhlXRZMqOGUaf/6aq6ndnTRMZjlghPGYARIw3D0QaIsPyKWgRimCiGddQmZgvbBDImsy6VLJzB7VLmfbixGMQkbLmVZnKoMCsmDETOdWtMVg+Xh0mX2BviaONdVabnisVnzkHTO9ftWmuLwhRWGl9LMeuk+HQ21jLzDvWkIxtVEeOOsRnZ2JmjpzQwhoPrPxsoIgxmHu1a30BPPUhUx+PmlC1k289D3TDLnknE4ogFT4ukAvX+Lq0QBJjeW8fj2Xv68toWv3LsR0xD857kzD7vtVwuCz7r71oxoxV4uhBBcPL+Zx754Drd8cCk15eHDr3SMYISIjeCw2No2yD1rD/Dzx3bw7K5uls1sor48RNg0uPGxnazc0XXI9WeMruLBT5/Jf73lBC4+cQzTG8qZ1VTO5gP9LN/UTjKbwzIkDeVhmipCJDK26nHnra9F0ImcyxfPn03WdknlbFr7M1iG4I2zm9jREceTmmFLVb2X1a1ZpBI0O6jBM2gU+vzePj8qBmrw1X5LxfqWRNahImphCBWp0oRICa6LmkYLFTnRk7a1J+hJqFC8iswI2uM5uuJZGspCtA2kqSv3iKVO43n7iJiq91211+okEjL9yrzelM0pU+sPq+37Z+D+ta2cOrWenmSWeMCOInjZgtWLAuhJZJWdg1f1qV3rg020c55IXQIf/8MadnQlfQ+wSBGJkd6Gi0lYMWkLasTkMMtovzlB/rvP2ooKD5Ewibyez5b5qK0BVEWG90hypCoUCaI4mKjtL4J9Kx2ZJzjlYYM3zxtDMid5ZmfXkNRoMV+NZ2ySmRwHBzLkgMWT6/n2Xzbz5Pahv1u/avUwqd/hoCsng9AE9L1LJ9Hal+D6R3fyxLZOvnj+LMbWDK22frVi4cQ6KqPqd3btwyN9E0dw9DBCxI5h9CWVtcSh8PX7NvKhXz9POudSFTN556Lx/GrlHizTZMboSv6+tYOv3bfxiEL18yfU8uYF49jelaClM0F5CJoqTfrTLmkbDgxk6Enm/EHQASbXxwhbhp/mqCqzvLSSIGIKls1q5N/mjCHnNUbWY5weFPUArPU3QiirAJ2WuuK0SXz0zCkqRaqtL0IGplFak5TM2HQOpolFTHSxpiHUYFrwYxJCpWi0t1hIoIMrg2nHJ35ZF/rSDr2JHJ2DGWpiIWxXpUxtCWlH6eS2tg+qKJ0BA2nHjz4A3P3i/sNe+6ONeDrHlrZBKqIWK3d2F2ivgtCpP8AvbnBciSEEyayDdCXlYVEypaY9rLKO9AXxpVr0FIvlS6UnoYRZbCBCBnnCL/OzfLJVbMWgb/fi3ejU4suBLUtHEVVBSeG8mAm1sRD3rDlAbyJLahiT1qD+bTDjECgs5tEtnWxo7WdyQ9mQ9bSm81A/Z31Mh4p8FsOR8F2vwnl3V4IzZjRymWcL8VrCmV5D8ta+oW3GRjCCfxQjVZPHKNI5hy/8aT39qRzvWDiety0cV3K553b30BVX+pMPLJrCzx7bycxRldxx5clURCza+tPs71XmpEeCnO0SNkAa0JFw2d3Z71eyhbx2MNoKImwKdnerB56OZHz+zvVkHcg5qi9jZdjiq3evoywsQApyjutXqGmdjiQ/2LlSkvUGL9OAFVs6iIZMBlM2hiEIef0jEcrDqS5m0ZfMYQplzFlTFqYzngWcAm2R4322DFWA4Pr2DWpfmZzMi8SF1tSo883YLpaXqdP2IaahiJuOzvUksiSyDhEj3wOwtjxEdyJHMufSl8xSU/avC/U/8lIHmw4MFGjW9AAtgagpSDt5Cws9PetIr2m2Oi8XSGTzQ3eoyIU9OKgHP4c87aBOXWvyrslWcSpS70vjSExjNbT+qyDdJgrPN4icXViXGbEEtq0islHLIO2RyaowDGShMmISDzAly8DXWyayDjFLEAmZDKZtUg7sP4wthUTpcQwhShJXU8D33jyXN80dzY8f3c4vntjlv+jYXuum4YiYvpZ+VJF8FOxw/HPAO8e68jDfvmjOET8zXk2YN76Gv6w/iATSOfuotWkbwbGNkYjYMYoX9vaypytOTzzLZ+9cx48f2eYburb05KNkP33PfCq8cPyvnt5NRcTi1g8spiKipo2ujrJoUt0R7bM/lWNnZ5ysC0kbTp1axfRRVXgZOXJuoUBaDwb1HsGorwhzoD/tDwYGsG5/L2kHklkVEcu5ha7pjlTrAX5vQFBVk7YLnQMZehJZHKAsYhKxlLHrmOookxvLOX5cNdI7NsAjYQoxL6oVJBu217pHp8uCAvM5zVUsnlRLTVmYiqjyitJwpEfepGRyfVlBaxkB9CZzmIDhCcMtj5kKlJVGa++/9g397hdbCBmwdn+/Py0YSUoX9z0sErIPN2gXe70WN5sGTwgu89fWkQGPssB6weMqOBby95ZuJq7XL+WiUEzC1DZURFb6f3vLApUxK7AcmMLAAZpropw7Z5RvOmxaFrd/ZCnzx1djCQj7RF1w5RumsGB8NSEDxtZEyeTcw7ZHCh57zpHDNvg2DYM3zx9LxDL5/LmzeOxzy7j8lImMrlI/RFcOLaTQkBQat7qB6QDeo2JIlSso8mwKuOfjpzDpNSLQL0ZQ5/rj5SPpyREcHYwQsWMUOzvjbG1PcMsHFzN3XDV3rG7hC3/aQDrn8Jk71tIxqDzBpjVVcsv7F/tVbh2DGW57voVcca7mCPDEtk6ueXArUU/nc/vzbTy3ux/HVf5CoCImevzIuSp6UlMRwkX5iEUsQZmXWrxoXjMLJtb5g0KFR4yiloF2eaiMmPQmclRHrYLB2RSCqohBPOvQF89gCehP2RgeY2jrS7G7M86qXd0UWwbp/WVywSiG8LUwoNochQJ6pskN5Vx4YjONlVEWTKhh+afP5HcfPol3Lmpm9phKmirDhAxBzoXuRIac4/oDoiDvf1UVsfwoXDRs+gPgnu5Dp5iPJjoHUjyzq5uX04hBEx99PvozlLZ1KFi36N9SQvBDpdIs7wvT5KAgyub9oe+N4G0drFgthislmcAMy3PBl0Ai4+QLQ4CUd5/ELIMHN7Uzo1GlBFNZh1+v3M2LLQOELQPH04RJR3LDip08vauHnAs7u1Jkin5vxdfJAKSX0jYOsRxA1nH56/q8+XJzbYxvXnQ8179rHosn1RILGf45F6vdgv5ixZdlwfga7r3qNEZVRvxqUEvApLoYnztnOpMayjl9esOrqrH3y8XCibX+S8V96w6+sgczgtcNRojYMYr2gTQhQ/D0tk5yjks0ZHLnC/u5+Ian2dg6wM6OBAAdg2l+smI7Y2tjTKyvoLEywg+Xb+Ozd6xlnzf4r9rVzR3PtwzbIundN61iztcf5OFNB3nXonHY3qCSdSELJHL4qcBiuChTVoD6cgukEu2HDMH29gE2tPb7g0JPModAeZRlHAhbygsqGjJJ5fLeRwKlNcp56c+cVKlLUFWQqaxDQ2UE6fkd6DEw74+Utx3QkF5KNeIxQMsUBaLsrG0zp7mKtv4UCybUUh6xmDO2hh+8fR7XvG0u7YNZsh4baKyMYgjPVd+7BrGwiWlApyf4dyR09ucNdF/c+68zSDw4kMF2VAQRhtcKBb9PP5VlBKJIAmKWKCRGw2xLk9xSQnDI+8IV71dbphQTLkm+ArEUTCFwOLQOyjTyNhy2dzNIIGVL3yZl/oRqZo2uIGwK+tM5wqZgh/fbStuSBza2E8/YJLzKSleq1LP22YPSmreS2jRUatE9xHIa//HHtVz00yeJp/MR3qVTG/j9h5byjQvnMHNUOZbIG9CGRH57+toF8W+zG7n7qlP528Z2OuOqx2tjRZgFk+o4MJDh2uXb6U5kaelJvaatH8rCFosmKhuLUgbWIxjBP4KRBPcxigc3tJFzJV+9bxMzRlXy5/84jd8+u5frHtqKaRjUlIVIZm0+9OvVbG+Pc+eVS+kczPLN+zfSVBXhgQ1trHipg7cubOau1fuJhS329yT5zLkz6UtmueLXq7Edl+aaGLs6B0nlHJ7a3s35J4z2Sc9p0+qJZ2xqy8I8vrUTUyhti440hE2BZQg6BtOYAvb1ZlT7FdSAs/HAINVlIaojgqwrsAxIZPJtgrLejuIZ2+8vKVFViDVlYfrSNtkAmwqZgqhpkHFdOuIZvxWPXi+oQ9IDlOUN9A6AC3ZWacfSubyxrBCQcSSfum0tx42p4p2Lxxd8F9+4b6P6IOG84xo52J/BdV2iloFEcvzYal7Y1+cvHzGV15qB9Eda61/oTL7D69mJdH2/tlIopaHShMXw0sS2F/V0GaoPg8L1gw2ytY5QX+Ng94Hg8RSnKIPRON8LrIQmSndQyO9PHYlv0grUxEKETANLwGDGZkx1lF2dCbIuqiDElazb3+9HeDvjOb+yV1+DUq782q2/OO0XPG9QRK9Ul4DitKGeVqzr2tQ6wBuufZwvnTeLi+eNwbIsLNPgkiUTOOe4UfziyZ3c/lwLvSkbXROgtxO8rpefPIFvXnwCAOPrYkRDBtMaK+hP2zy/p4dLFk9gXG2MDfv7WP5SB+v393Pi+JoSR/7aQL2npThcqngEIzhSjBCxYxRdcSX4daWkpTfF6r09bNjfj2WqAeSyXz7LlIZyNrb2c9Nlizi+WT04T592Bv/75G5ufHwXlgG/fWYfEkjmslREVYTkoU1tvLBXOb1v2N/P+LoYrlS9Kf/wXIsvUr9g7hgm1JUrfRoQC5mMqopwoDdFxpFUhQ1SjhK5az13QZpKgJSSgYxEIMmSNwvVA4WL8hBrqozQPpjBdiQ5V0WsykMGOdv1B72cI5GOg00+VCxRBM0O9PMLkoWgxVJwADRNQc5VVX9zxlZRXRZi9Z4+vv7vx9FQEfHXeWFvL2ta+v1jdYB1rZ53mRdh3HQgr8OqipgqLZbNm3dGLIP5E/51A9sBr2IsnpNDCIwA3z3fKUEy/O+viCAF2xrpwb6YxPlEQhRWxx4JgsRLEz59H5Zqe6R7Y+YJh6r0jIUN+tMOMUu5gwfRn074nwV5XaHeD3jkSJNnb3rxNTxU1aUr870gi1O0QhRWfgZRapOOVEUgn7trPZ+9az2fPWc6V79ReWXVV0T44vnHMWtUFbev3seafX2k7cJoWyxkcOsHFnPSlLwf2dsXjufUaQ2875fP0TmY4abLFnHOcaPUMUjJlrZBZo+pGv4EXwM4fUYDD25qA+CF3T0snHxkGtkRjGA4jKQmj0H0JbOkbQfLgF9dvoTqWIgP3rqav21s45bLF3Pf1aeRzjns7kryzYvmcLb3IM3aLilb8vE3TONXly8mbJp+eglgxZZOvnbPeq57eCugBjwXeMfiCaz8wjI+e84MTmyuQkrVR/LGx3Zy6c3PsmZfH4ZQFWIH+lKKYAGJnEMiq6wtjEAUIWwpHYzjKp2NTt/pwVT7OgGcd1wTlywZT2NFhJBhUFsWwpWwvy/DYMZRjZlNQdiA8oiJ6+2noSL/jmJIOeyAH8zQBAfAdE7612VifTnxjMO7TxrP5MaK/PJS8rtn9vptZywDTp5cz/jaaEGT9JzH9sKGqjyLe80m9TnWloc47/gxw3/hRxndiawftQkb+EUHOtXnSpCikDgdCnodW6qOAsEopEbBdZZqmzpVqaV4osS+TOFFjgLROd+SQkqfMGpELOGnGz96xmQm1sVU1NNV+sX+tLrLUoEuPwZD06YZ2/WJkaoEHnoRgtFYbYES8rRmwxUUSo+h2nIoSTWEIneHMOQH8jYiUBhlvG75dk7+3sO8uKfbX/bNC8bx+w+dzP+840Tmj6/2tZxnTK/j7587s4CEgZIyXHLTKvb1JLn5fXkSBqoI4bVOwgBOChCvH68YEeyP4P+OESJ2DKKmLExteZizZjZxxoxGlkyuxXGVyeaG1n6e3tFFPONw8fyxvC/QB+6O1S2c/N8reHZXD4sn1fGXT5zOCeNqSNuS0VURVu3u4bfPttAxqHQnsbDFty6aw3uWTGBsbRkfO2s6BwcyIATxjM3eHhVZybnSN7DUff2iIYOcowZZPaCBN3B4OhiDfC/GmKfNqggbmEKljRorwzyzq5sd7YNsahsgmXPoTeZ8U1DblUyojTG6OsqoqggRy6A8bBE2lOBaO90Lb3ALmqxrAqAHMVMMJQHagHN/b5I1+/r44KmTC+b/dtVeHtzUhvSsLkKmwXUPb2Pm6EquOG0yVVGTkCGwzHxFZknIw4y8RxkRy/CJkCOVT1WxjimoLS/VjmhIOs77t2Mw67ciAgoIjP7ok73A5yAJDMKRDGm0HbUEEctQRNISNJSH/OPK2FJ1WZBw27Mtqgm5MbSFURCGIQq0b/qc9TUQKNKtCSZFy4EiWLZUUVlH5iNfZhEjkwxfmKBTvY7MO/8PZ+L/puNHc/q0ev9vvZeDgzneeuMqfrNyt6/lNAzBhSc285srTqK+MkpdeZib3reE0dWFPmSPvtTOu29axcH+NH/8yNJXpNvDvwLTmiqZMUq9UK3e88r0eB3B6wsjROwYRU0szIotHXzj/k3c9UIrV5w2mQtOGMN3//oS3/rzZs6e3cSXzp/tL7+jI841D24hnrF5fFsnoFqn/O6KJUxrqmAwnfMdwmMhg3cvGc/F85o5c0YjdV4ri8F0jo7BDK6XDgqWuoMafHKO5Cvnz+Y9S8ZjS5XWq/SUwqYQBVqtIC9xXZfGihCprKt8ii6ewyfOnkHOkaza3YuFarfkkm8x40roTeU42J+hpS9DTyJHOmeTdSGRk6RtyaiqMLYL71o8nmwgfzW1sSwvpvYISTC6AIpYCWBKfTkzRlVw5+q88eqfVrfw9fs2kbFV26Pjx1aSyrm8ZWEzx42p4vnd3WRs6VXn5UX7oPRH+rqZAvpTWQ72/euqJre2DWIYRkHqsUB/dYinyrCRRZGvyNPRK0U+pb9eMckq9gmD/APNNPIkLng4IaG0g7qvqe1IuhK5ksfVm7bVPekRZWRpMma7EscznwX1nYSMQtsN4RWjDEeZBfg6Rv/8wPeh09MPVWAQhLZyKUXeBXDvuoM8vaO74DiD+PEj2znrusdZvSdfBFIZDfHMl97InVeeTLSo+fxDm9q44terEULwv5ctZIHXl/H1io+dORVQbaBGMIL/K0aI2DEI23HJ2i6mYXD7c/s4bkwVX3rTbD6+bCohw6sYcyUZz5jyYH+K9//qOUwB5WGTzsF8tVBlNMQ1b5tLxnZ516Jm/n3uGFI5lz8+10JrX5KmqrweamvbIKOrogggHDJwvUhO8cD9w0e2ce+aVspDBuv2D5DISS/6ooag0dURGjxvME3mMo6kM54jEjbojGf51p83c+1DW0nmVO/ChC1J5hwsQ/gNuQEG0zYS6ZPI4oGrfSCL7UruW7O/IOVzoC+VJwYecWgoD/uDmjJ2daivCPOhM6cyf0INf9t4kD1dcX66YjtfvX+j7/wfswStfSlCpmBucy2pnMOLLf24rhuIDOX3nfYe/gZQGTVI5Vzee/OztP6LyNje7gSu1jwV6bqUc75K8fnkgdIPmiABCLYJgnxT7lIo3pYfQRJ5suq4+NV52thX7ydIam1ZGOksBb/ikkIyGDLyhEuby+rjybn583GBiLaEKMF69CRtChycNhz5HG4bRwK9CU3WBPlUpUZ3Mse+niRX/nY11z28pWDe1EB6HaBzMM0X/rSeueOq+cvVp7FsZtPLOJrXJpZOzUcTU5ncIZYcwQgOjxEidgzCNAQfPn0KteUhxtZE2XxwgJ/+fQcf+c0L1FdE+fQ503np4ACfu2MtO9oHed8vn6MvlaXWqxb64GlTCra3YGItp09rpDvp8ONL5vOnj51CQ3mYFVs6mfet5dz42E4ANh0coG0gjYsiE1lHqtSbIQoHZcfhjitP4WfvXcBHzpjM1MYyZoyq5MOnT6a+PEQ0ZPhv5PUVUSojJjUxE0PA2JoYV71hCt2JLL3JHOUhZYwa8VrrWKZyfYe86Fn3NoTSuqSQAfMm1DJrdF7fkszJAkE/QFcii8RLWUqY0FDB8185m9ljqvjQaVMQwFnXPs61D2/DcaCqzCBsqoG7N2ljCoHtuoypVf33gv0rbRdmeumQyojas2HAQEpRit3dSW5btfdl3AX/OOrLIzheqk1DXwNdFZm1VZGF6ieZX65URKky0BDgSB5I2lD0UNCRNY2gsW7wO7YMOGtWExfOHUNIDN2/PgdN1IP7zbmF/UQtQxALGSXPIZ11sCh9fkFiqI+pmG8dLgomhFqvWIt2qCgcKE2aBCpjIbUdKDjOrkSO/7diJ8uufYwNAfPeIH64fBvTmyq47h0nDomUvR7x3O5urvztC/7fv3nmX/O7G8HrFyNE7BiEEIJ3nzSBVV8+mwc+eQbnzB7Fj5Zvo30gzU3vW8ilJ00k57is2NLJxTc8za7OONOaKtjXk+SGSxcwtal8iBfQBXPH8OCmNnZ3xVk4sZbnvvJGPv6GqThS8v0Ht7DwO8v50+p9BetELFVZKCVM8frehU3Bf5w9g2lNlZw5cxRfftNxPPyZZfz1E6dh2w4DyRwdA1kOeE7yB/vSpG2XVMbBldDSk+KGx3b5AvJETmlmGipChE1BWcikynM+r4iafiSFEhov/wwFPL2zh4vnN/PZc6YTNgWLJ9YwoS7i3KpulAAAIABJREFU/4DC3khtCDhuTCUXnjiWa94212/j8reNB5lcF1P2GZbBvR8/maWTGykLh3AcSXXUJOe4PLW9i8UTan3Xdp8ACDj/+DEIYMYoRQgrwoY/gIcE/ObZvfQm8r5Q/yxMa6qgKmoVpCOD+i2tp3Jk3q9tOMd7AXiSQj+ypOxJSu/7UKQimCoORpaC61hGwEVfqP08uqWTx7d1UlUW5i+fOI2KiOE3d3a9c7Al1MQsmmtjVIZLH4XtSrK2659XdbSwc4JNYZXtcNDnbgiVrrSMQ6d79XFqjZhGRdhECHHIyKLu3tAVz/ppz6mjynyyq6/V7q4Emw/2DdlGbyLD9vY4iybWMX1U5ZD5ryfYtstlv3yWd/7vKtYFSOkfnt93iLVGMILDY4SIHeMImXmRiO1KfvHELq6+bQ2DaYfvvPl4Zo2pRAjBupZ+vnPx8Zw+vZGrfr+Gz965roCM3bJyD3XlId+7yzAMPn/eLF786tmcOaOR7kSW9a2DBfvO2Kql0bJZjXzo9Ml8+uzp1JWF6I4X9tLL2i7ff2Azv3lmHzmpUk4hE8pCwredyLhqYM14vSyTnp+XHkgO9GepLVN6r/60gyFU8+xJdbEh18Q3bvX+Lg+raNs1D7zE1KZKfnrpAtbs62NPTwbTFMoU1jsXU8BAyuGH75zH/Am1SCn5yj0b+Plju3jopU4aq8L84cNLOK65htljqji+uYqa8jCRkIkj4e9bO9jUOuATOFtCVVhw+dIJbGjtRaIGBIDedMAw1oX+lMP/W7Hj5d8ELxNZxyXtu8WraXPGVjGqKuRHnHxndlG6mlEj0A1oiGXFcE3EIU+wBEOjQFLmxf9Kn6W2VUzwfK2hI7Ftm+5Elg/c+jxTmyr47puP56K5owkHNFSLJ9fz1BfO4oOnTy19MhRadjRVRoZdrhi6urNAI+YVGthukeN/YKEg0bSK9F7xrOMXCVhG/n7W6xRfb83htrYnCVvGEF3ejY/tJJ4JlIsCf9twkN5klgtP/NdV7b4SyNoub7juMZ7c3sWY6ij3fPwUKryXvZbukQbgI/i/YYSIHeOIZ2zaB9J8/d9n8/nzZvLXDQd5ansX37xoDu9YNJ6Tp9T7pfGxsMF3/7qZR15qpzeRZcWWDn87HzhlEtOaKnwheX8qxw1/38F/3rWe9S29mKiBJlx0x+UcySMvdfClezZx81O7iEVC9CZsfv/sXlp6kjy+pZ3zr3+cXzy1FwcVIYpnlet4Klf4rq9F+Fk3PyAGU47tgxkG0jbZgGu5rtx0ZX7ZifVlXLJ4nF/m35dyqC0LE7Lgqt+/yB9W7fXbHuUcT6htQmXE4orTpnDf1aex8UA/n7tzLf955zqilkEq5xALm/zwnfMZXV3Gou8uJ2IZ/Ohd86iJhaiMWIypDFMWEty//gA5x6XcK1IYyEr+vOEgNWUhamMWW9oH/L6AeNdUD6odg//8QaEqFvJJeNpWD5ENrQO0DwzVyujvQUqV8goShfKwQbJwXFfnIXSlYX56UPhuikI9mV1COKXrKsKWoYooZOmonF4268AbZjbSm8iwtytJLGTyyJZOlkypp6YshCHg2xfNAeC06Y3+sQRtVYrPY2dnXrMXjMIZRdFXX2PG4VOQMLRqUn/3tjt0fV1AYgeikpJ8SlJSuq+k7goRnLe7O8V37t9Y0NT88W1djKqOvi5sKQ6Ft/38afb3pjh9egNPf2EZ8yfUssSzsXBhSIZgBCN4ORgxdD3GUR0LcdeVpxC2DGzH5dGXOpjaWM67l4znoU1t3PDYTt44q4l42ubTt68D4JSp9fx9ayfNtTHeOFv5BK3c1c2OjgST6st5ansXX75nA/G0zZSmcsKWiYNNxDLI2K7vu6R5lI5SpLIO+7oT7O1OcP+6A4yriXCgP+P7LWlXcVPkvaC0sz3gV8Jp6DGkWPcVNhRZ0wNR0AE+ZMC5x4/hC+fNorUvzYt7e8k5Dt2BlN+G1gG/grIsJEjmJK4D/z53DF9802w2tfbz9fs2sralUFPznYvmMLm+jJ+t2EHIEPzgwa38z8PbmNZYzp7uFK6r0ngrd6pKNd1hwBRQH7NYtauPeNbBdVTPTwLnos/zL+vbeMOMFt6+qNC9/2giagkyDsRCgrqyEK396tpox3Z9zMEqUolKzWnUxkwG0qV84YcSpUJj1SI7DJEnJsH9a6S9eyJoMhuMAoVMpSG0JTy+tRMJZFM2V/9xDXXlIc6c2cRTO7pprokypiaG67qs8dpJ6XsneExmIEQlAuTelSolLaGg9ZXejlGkUSzl9u+ff+C4Tc8U1jS8AoWiZQ1vWy6F2wt6mB3KIb543u0vtLJiawfXXzKfU6c1sq83yclT6v0I7usRN/x9BxtaB5g5qpLfXnGSP/3iec2s2KIqyP+yrpUL5417pQ5xBK9xiNcbk1+0aJFcvXr1K30Yr1nYjosjJdva4rzjxpXMHFPFbR9eymNbO/j471/0H/TLZjbyi/ctUk78UnLK91ewYEIt71o8np8/tpOeRJZr3zmXMVURFn1vBQBLJ1Wzak9/QZuWiNfSKBYSSATZnFvwpl48EEQt5XJvS6iImEgp1WArhy6rB1wDit7+hd+HkhLz6yvCDKRt5e3kughg6ZR6nt3dgwH8+9zR7OxKsbZFaWbKQgaRkEFv0uYdC8aypyfJ83v6iIUN0h5LsgwoC5tURsPs95zpm8otmusq2Hygj4wDURMaK8PE07afdhSoPpPJgHeGQBFQKQSTG8rY1ZGgOmYwsaHSd+mfM6aSv37yjH/oHjgcHtvaweW3PD/k+1Eic1UUAYrUlorS/CMIkif9OdgKKHhPDbe+aQiklC+rNU1deRhTqBeWhz51Bh/7/Yss39yer+4kH30zDeF73IHSQI6qjLCvV1UZV0RMpZnzDkCA71EmPdPgUi2Phjuf4hcMAtN8kb5QDRo0TxJC4LpyyLUcDsUkWMMENnz7XI77+kN8+uwZfPLs6Udw1K895ByXRd99hGTW5sWvnUNlNOTP60/mmP/th3FRHS/Wf+u8V+5AR/CqhxDiBSnlolLzRlKTIyiAZRpELJPOeBpHSs6e3cSWgwN85o51jK6OAlAVNVk8qc7vb7i7K8HB/jTzJ9Tw+bvW0ZPIcPtHl3JCcw3vufk5AN5y4lg/cmJ7vfhU37180sm2XUKWQbnnJ1D8jqC1WOoYLBrKw6S9Zsk60oG/tcIy/aAOxpHSJ2EC1e5I/xAcqUxF0zkXx/G27cKzu3uQUpJxJH/d0Mbe7gRTG8sxUF5CvV6O7c4XD/D8HkXQcrbMa6ZcZRJrGMpsFqAjYdPam8R1VWQr40B7f5b+gPbLECCkO0T/o7Vye7sTlIUNZo+tYU1Lv5/y2nRwkJP+65HDfNv/GGaOrqSxIszoqkhhtaurzHY1XKkqO4tTX9ooF/LeYf7fwwRWJIro+J81CfT1jaXXCxYRBElYsYg/uKz+tyxsUldm8dYFzYQtg/bBDNs7BguISzCm5xY13M7YkjavMbsA4hnHj77qY3Jc6ZuwulJFuiLW0IsQvIbBz1bw/ILHIrU5csAAV+bNknWEUF/L4b4DbSxbDAdY8r3lgHpxeb3ibxvbiKdzfPPCOQUkDKC6LMQVp00EVMeLtft6Sm1iBCM4LEaI2Ah8tPaleHhTG7bjMq2xkiWT67ju4W28/9bniIVNXFfy5nnKbf89J03w13t6p2qJcrA/jSEE17z9RGrKwjy9rZ3t7XEsQ3DlsqnEA3476ZyKNM0eU8mP3nkilVELWyqx/fTRlZzQXKXeNL3qtZDhDaSoVGQiY1NbHikQhZcYv4ag1Nu/SvHk/9Yfg/oyNWCqAeuEcdWcf8JoehJZKiL5Fac2RP3PtRETq4iB2BKmNFSwYHwN46sjnDunifH1ZYRDhp8qndhYTtABwDIEaVsWpJ0k+bY9WQeiYZOnd/ZQGVFVoLqor30gw0nfW866lqPr/j26KkrGdhlbWzbkHINExJEqGlMcNQv279EkWfukBaMvxaRMR9qCfRZLRWv05g2GpjT1Jg2Rr0QURfNd6VVUOpLysMWe7iSbDw7yrT9vYm9Xaa+2UsQOVNrYFIV6Ns0fS7neZx1JJlBa6ZM2mff6ChYEFFyvkkeWhz7flOe5oSsjg9sLGSpKPaoq4uvZ9Hda/H3EM2pOzjlEKPI1jnvXtDK2Nsa7l0woOb88Gvav+5t/9syIVmwE/xBGiNgIfNy7Zj+/eHIXn7xtLWURk99dcRLvWTKedNZhcn0ZErjunfP43LmzqCnLvwVvbx9kSkM5d7+4n5mjK5k3vgYpJVd4Xjvfvvg4YmETU+Qd4UE9/Le3D9I2kOa3V5zE/Ak1NFaE2bC/j02tA6oCMW1THjF9zybIR0PWtvQpvZnhufIHnoHDPQ61d1hQaC1Rb/g6EhfxCg7KIyaVEbNgQJfAhXObecv8cfQmc7x76SRACfx74ln/3HozDo7t+vvQQ9WOjjiP7+iiL20zmMrx3qUTiYZMIqZByOt8nXHyP8yM1/Km2A8qGInpjOeImKrVEOS1WCET2gezXHzDSv5wFD3GhBCcOq2B8rBFY8WhKwP196D7KMZCBpmcW/DgKQsZVEQKow2aEAX/hjy5ChIqXTXpXx+ZJxC+074IiNMNz07CHZqa058dLy29rnWA5ZvbCRuCR19qB1GacElAFD1NdaRJk0ZtR6GXL66ELH4YB8maS56I6nnBYzeN/D1WETEC1hfCj6YGo4ay6F+NnAtnzxnN/VedwsT6cpA6rTuU9I4uN1kwocY39309YiCVozoW8sl9a1+KX6/cw8d+9wLLrn2M6x/Zzujq/LPwLTc8/Qod6QheyxghYiPw8bEzp3LZ0ok8vq2D//fodoQQvNQ2yKSGctbt7+ct85v99JCG40o2tfZTETHpTea4+o1KK3L1H18knZOMq43xnpMmMaGunP+9bKEanALkJJF1+cmjO/j8Xes5Z1YTH182lYaKcMGDP5nJ046o51BeVx7yjTAdNz8wVUXy4aTitJfep454BNNEQkIm52CAn7aMe95kxe/7vakMDR4BqS/XDv+CwUzeCV8Lx3WbI42OgTSOKxnMOKzb38+T2zo5fUYjtuuSc6Vfaaf3KVAFAZURc4jr+tLJtX4VasaBch0K884rYpn+8l+7byN3PN/C0UJFxCLnurx1QbPfu/NQEALmNVeR9jSALuqa/fLyRWz+zvnc9L6FhLyIj44giUDUKr8h9Y8kT8psd6i5bNANH/JdGUBF6aDw4VeKXAUjZVlXRSWL21iZBkSs/HaDP49gulxHlySF6cTgPorvM0H+5SNI6PTf+hpB/jcAkMm5AesL+bJ1eite6uDuNQf4+XsX+kQwSBr1tK6Uw0sHBznYnx66kdcJ3rpgHGHT4K0/W8kHbnmO36/ayzfu38TGA/3MHFXJp86ezo8vWcCXz5sJwNr9/Ty9tf0VPuoRvNYwQsRG4MMwDC6a18x5x4/hjtX72XygnzX7+phQG8N2JdObhho2rtzZxQv7+mjtTVERMfjCnev438d38Jf1bQDc87GTAUhmbb5+30Y/uhNENqdIyU8e3caPl29lMG37zb6hcBAJGUrP1ZPIpzkNoQiaJN+AGrzUi8jvzydd5FNi2obClmCZhZ5UElXJqRHzImXd8SxVUZPmmij/9YBq/7K1PeFXb2oS5nqMIHi6liXyZrNZl7vXHCBqmZwytZ7qqFUwGOvBNpmTTB+VtweQQMRUDYf1QH3atHos72T0uWVyDqOrIj4xueuF/X4j5/8rZo+p4ukd3cxtrmZ0dZRQ4ElS7K1uCbj5fYvpiGcLojK2Kzl5imoVs2RyPU9/4SxOGKtS0l5w0CcAxam4YDRIa538lGQgFRi89sVateA6ssRywcibQN1joaKNOC5oay3p/U9HXSEflZOoiJRlCmxZGI0t3r9G8JsKzrcC0S83sC1DqHs4eI0Pl60PmQJLFJ5nMutwzYNbOdCf4oZLFxXo8wR50nfqtAaWTK7zbWNej9A9c3U09e0Lx/H3z72BJz9/FjdetpBPnT2DxZPr+cgbplEZUdfo8ltGisVG8PLw+v0FjeAfxhWnTSZiGVx+y/MA7PO8tj531zpO/NZDfOWeDfTElfD+t8/sJWIJBjM28YxLMufwk0e3A3DZ0ok0VCnD1C/+aT2bDypDVz1QWIbwXe0tAyIhk/60SyLrcsHcZt66oJlLFuVLwlf+55ksnFhHJGRgGco8VdskJD3NmWnkncRVex1RkFYcXRVWA1mJkS9tw3FjS7uDV0YMP1J2zwv7OeX7f6e1b2gkoCxsIgOjX8aRvrda1FKFEEEyZAD3rGnllGn1TKyPEQkM9Fp/NWt0JdvaC60whFDz9Wms2tlNOpe3BtHXuX0gQ12ZRU3MYnfnIIPpo9MX760LmqmIWNyz9gCWIQrbCQWPE7jqrOl0xDPs7Un5+jYBXHRiM2XhvINOU1WU+68+jS+fP5spDeXD7tsnYUUaJlcqoi5lnlAHv+ZgmyO9jaCkp8z7ngqWCxCmVM71WzgNd1yaBBZ3END3q2UK//4TlPbwCkI/oIP6RzsQ0QumzR0JtlM61RpEcJ9vnNnEZ/9tpp+y1edsCPjs7Wuxpct/nDUN15X+uRlCpeI/duY0MrbDpgMDhz6J1zCEELz/lEnce9Wp3PqBJUxprGDyMPfmi187lzCQA7589/p/6XEeLTyw/iCf+uMaVu3qfqUP5ZjCCBEbwRDMHlPFjZctoM5rYt02kObSkyZwytR6EhmH3z+7jwXfXc5p16xg+eZ2TJSofuHEGt61sJlUzqUiYvLti+f423xqe5f/ELcMqC0LYbvS12Vpx/uQCTOaynl4czvfe8sJfP/tJ/rbOOV/HmerR0hsVxI2hR8R8AdnmW/g3VQZLnjTNwW0DRRGZYq1tXpQ0fqckPe2n8i6zBxVQX2ZxWDWJWO7BWkn/TmZzY+E+riSHoGrLbNYNquJlJ3vU+miChSe2tbF1vY40UAH6rqyEBFLUBW1SGbdggHUdSHtheBUulfZgBgeuQ0Zyj4hJNR17UvZdCdz/HTFzqMiKK4pC/Oh0yfT1p9m8aRaP92rr51PIAyYUBPh98/sybeD8qKXxWluUOfx4TOm8PaFhZ5MxalH8Kwrik4l54nhXVeWFMMP3V9+27qZuo56lbJuONSV80lMieO1XdXPNJl1MUSeJBbzOq2j8/fvTS/F/1zy8w91rvpY9OXW26qMwI8umceDm1UqzY8+eiSxJ5nj1yv38IllU6mvCKlCEFNw8tR6fnPFEpZOradjIMPj2zr5y/oDh7gyxwZClsmdV50CwB+eayGdfe00A39mZxfrWnr50fJt3LvuAO+9+dkhHU5G8M/DCBEbQUksndLAg586gy+9aRa24/DuJeP5w4eXsuXb5/G1C2YzrjbG/t4U05oqSNqSU6c18MLePn706E5cCT+7dKFv8tibyNKTVA8l3ROvL5kjZAgGMw6ON4ibQlUB7u5Oks45PL61c8hxtfVnfUPMlGcPoQcIwNcfAbQNZFRFpFApyKBxK+SjZzETv1lzzlURGwleA2NJgzcIdQxmaKiK0eCV64uigU0jOD0466pl0/jhO+fx4CdPLUg7XXnGFDYe6CfnSAZStj9wjqsr8yvowqZBfXmI6qhJ2BRk3cLB3nYVuXNd6TeiTtuSrFQ6Pp0q/d2ze/jLutbhvvaXhY+eMQUJ3LfuIJPrYwWRRwmMqozwnTfP4bN/2sja1gFc4PixVT4R+egZU4bd9pVvmMYnzpoG5FN7Lwd+apihLvbBz8Wms6CqJXWkyU/7MdR2IwgjEJ3V1YgFx+PtJ2QUmqkWI+dIMvpNQea3e6jzFxzaQ604rQswtbGcF75yHrGwxfzxKu1teWl//f1FLcGqXT2c9oPH+OaFx/PVC2bzwdOmcMN7FjChTkWFxniWNr95Zi8H+0da/Zw4vhb9LvVvP3rsFT2WI8Wn/riGK3+7mrf9fCUHB1IYKO/C3R1xBtM5pJQs39zG/t7SFcOl8NjWdr7/t81HTQrxeseIs/4IDon1+3pI5iRvuWElFVGLNx0/iivPnMZj2zqJp3O8bWEz1z28jbcuGMfW9kG2tMVprAhzxoxGfxs7O+PEQgYRyyBtu+RsV0UzAiNDcGzS5OnuF/fzb3NGc8PpcNWTap5EeZ052jEdNVjlXInluYxDPkLgRx287Yc9lb6OIAGYlsnSKfU8+lIHsZCh0k+uJOEVCXTFczRVhLni9CnMGFXB5PpydnQm+NljO9jZMchg2ikgVqXSnhETbn++hXhW9WmsiZr0ec7yv1u1h3i28IEVMmF9Sx9hU/CDt59ATyLHs7t6+MmK7UxuKGdnZ9wffIOpNAMoC5kkc0orFwkZSFf655qxJQOZ0o72LxexsMVNly3kop8+XWCOawiluXvHwma+eu/mgnU+dPoUbn5qFzWxENMO0SS6J57lBq9v5pEYtgahq2pdme+8YBnKaPZQmqzgvGDUTU9L29JPebpFzqulvnOdggzOyw1zDjoKp6J8agUXVURyqNPWLxZaUydKRPGKMX98JfdcdYYfGX3pYBxQhsIZoUxpJXnC2BXP8Ok71vKr9y/mQ6fnf9fJrM2G1n4WjK/hud09PLChjStOm3zonR8DuPatc/nEnetp6c2SsR2/aObVioMDabKOKurIZh1MUzCYtvnSPRvY3pmgNmaANDhlWj03vLekH2kBfvHETr73wBZqYhZdg1kmNpRz9VmvT8Pfo4WRiNgIDgmt6wqZBr3JHL9/bj+n/49qfvuJs6ZSVx4h50hcJNva1QP9Xi88r7G7K4EpBFnbVTomLyKi6UDEa5w9qT7fgNuV8NS2DnZ3xrnggguA/M2aK9JYhU1ByDRw3HzKLxYyhzSD1tu1pYoghQw1gNWXhxlXG/N1QJahRM/TRlUwf0I142qiJHMurX0pbnuuhbKoxdnHjeLuj5/K9Zcs4O0Lx7FwYg2e5RmhIvNVU6iqxg0HBvn+37Zw/SPb/cFSm3cGD9UUkHNUmi7rSK5bvp2DA2muf2Qbx42p4sb3LuTMmU1UFjfu9JDyGnILVLrNDaQiDeCmx3fSOXh0Kt2aqqJc87a5gKShPMyyWU0gFfG8d23rkD6Qq3Z2srF1gDNnNA27TSklX7tvY4HWTpOwUpqqYkPSYMWpLqCwPY0TgXnDwTQKKxt1X0a9HZ1mDG6rWMQvKSSOur9kqf0KYHxdmWq15G37SCJhej+A78gfLDAYDjNG1QDw1Xs2ctr3H6VzQN0L8Zwk50i1LRmw2vCiql+/f2PhcUtFxuZPrMUyBPetbeWZnSPaoosWqvZiEjjQ++qvKL3tI0s5aXIdIaGjuYKJdTEOeBHOgZRLX9pm+ugKNuzvI511cF1J1nbJ5ByW/c8K5n7zQbLZLG+54Sl+8uh2opa6Z+55sZXrHt7Gw5sOvrIn+SrHCBEbwSER8cTUm79zHis+cwZnzWqk0jMx/e+/bePzd63HEPCRW1bjSvjAqRNori0Us24+OEA86xT0/StwIHckjivZ061++FpjlMhJWnoSHOhN8uU3zUTruoODoANcPL+ZsdV5l3eBqp50PJ2QaeQHa9dVA40jVXTClarxtx6MQKU854+v4rNnT+Nt88bSGc+SyNj85pm9LN/czqU3rfKXPX16A/Mm1DK2Jkbaq57TqUFQJLNUHz4dlcrYkkTW9XsNCvIRrqwjaaoI84k3TuORze3YUnLq9Eb++Nw+Hn2pg8Hs0FhJsIuAbxMSSGO6qOKLr9y94ailDU6f3kBv0qYqatCfylIWVr0o9/flNSaap9y2WqVFD2UCevOTu/jrhoM+qQhevWDkNGIJv1AgaKxbnIJGb8PbULAitxRpcdw8gQN8gf5w4nqJiuLq+aFAN4Fgr0lXlt6hBLrjmYLKU+2MX3D8wxxvqe2Vwqx6izuvPJmvX3gcAE2VEdr70uzqHppy0iQ25KXvXQm7u5L0BHquvtQ2QFU0hCslF504lq1tgzy9o+sIjvD1D01i71nbgn2kodxXCEIIbv3gSZx7whgqohZZx2VvT6pAamAacO+ag7zjxpXM+vqDzPzq37j4hqc46XuPsrs7xUDa4S+bDrKmpZ/BjIN0lf2Pg/o93PD3na/kKb7qMULERnBIhAJGTlOaKvnV5UtY/83z+PL5M6kpUyacrlSVQtURk29ceAKgjFo7BlLs6Ijz57Wt/nI6ShXz7ON/dflCptTHClrPSBTBksD7blnNGT/4O9f8bSs5l4LBqipqEgsZrNjSwe7ulC9e1oOsDrfrFkiSIqKCGjCrIhZ/3dhOlae/AljfGufK36/lR49s9zVpetd7uhP+Mdz05C7+66+b6RrMcuWZU3jT8aM4cVyVryfKeFEOgLfNH1NQ/Rb18qeSvAlr8SDaFc9ywY+f5L41B5hQGyOTs7npyd0Fy0yqixQQBF1NF2xwHRzAI5bBvp4EOzvjHA0YhuDz580g5wpMQzCQHUoFimVRtzy9i7tW7xuy3JPbO/mxV3WrEVxVfweVYdOPIAEkMoWDXZDAWd42XBcvfa0Gn2KyNhyCKctSurPgci6KiA8nA5Ny6LqWkfesKz6H4mMonl483xLDt4maNWEUiyfVUe4Zn33ynBlUxkqnzXR61nGlX+UKkMrmW7f/6JHt9KdsamIhPnXOdDK26z8TjmXYjutH+3/y6E7O+/HjHOx79evnLj1pgv/91sYsvyfqjFEVTG+q4EBfyi8QMgx46eAg/V4VdsiAz92uIqYVYZNM4GU058LB/hS7j9Lz5vWIwxIxIcSvhBAdQoiNRdOvFkJsFUJsEkL8IDD9S0KIHd68cwPTz/Om7RBCfDEwfbIQ4lkhxHYhxO1CiLA3PeL9vcObP+lonPAIXh6mNVUwriZWME0IwUfOnMbzXz2H1V9e5k+/48qlrNrVzXnXP8G51z/BRT99indGe2gyAAAgAElEQVTcuJLeVP7hbbsS15VURCxuumwBd7/QivD8r8Jm4Q05tamckOe7JGSeUFVHTWrLQliGspTo9YT/luHZBBgqVVQWNgibyjG/OmZRFlKeSQ1lFqMqw1SXWZ7dhIqcDaQdqstCnDWzwa907Era/qAkhPov58IPl29V8wcznDCuimjY4K8b2nhgYzvr9g/gBkxGI5ayLnhsaxc1ZXlZpkBSVza8TFP7kdmO+nfqqEp+UUTCptSFSOVUlM8SKk0brKaTFEYfDQFl4cIOB0cD71o8kTNmNvLs7l6/xdJwuGjuaLIOfOP+zVz70FbSXip1T2ecj/3uxQID3yABCXnnBjCYdQrScMW8RxNuQSHJ1cTNcSXhQ0h3DPLO9AUpTe+zKfD94ApQgmjhHYeu0CyOrKmXBdVBIWSKkt5mep+T6suYObqi9PZRUSyfLBYtc9+aVn711C4AsrbLRT99ip7UUL2gEVjXMIRPKgTw65V7AKX7fHJ7F46UDKRyPLxJVV4GtaHHKn748NaCv3d0JnnPzc+8Qkdz5HhmRzeWqe7p3pTtGyX3JzN0DmZxpaQqavrtvyCvS8256jdXGTGJZ50hz5Z4KscVtz7vv9SOoBBHIta/Ffgp8Bs9QQixDLgYmCulzAghmrzpxwGXAHOAscAjQogZ3mo3AOcA+4HnhRD3Syk3A9cAP5JS3iaEuBG4Avi592+vlHKaEOISb7l3/V9PeAQvD4YQ7O9LIT2BezEu//UL/udLb36WimiYPV6qo20g65OJqClIez9eW0J3IsPX799EW79KX5WHDbK2y+jqCK39GRZPquUPH17KWdc+RktvyvcSsAyD/rSDKRw/WlceNjEMQU9CtfoRElzvYZF1wHYd3HxGha5knhj65+n92zmY5cltQ9MrOsKk0TGgjvvCE8eyqytBWchiakM5/cksGdvFcYPFBAJHSiIhg95k/kCytiTjDD0WDTfwb9SAJ7Z0DIma7B+wyXki8rApOO/40Ty0qY141sVEif4zToCMSaiKhdnSnuCinz7FsplN/PyywwtwD4eKiMVnz5nJQxvbSWRyOLY7bFRo5c4ebrx0Hss3d3LL07t5eHMbtivZ15UoSAlCnowC/ht68XwdPS2FgmiaKEz9ar6nCz6CUVlDF34UXXD9Z84FO1u66KGUWF56/wuZgoqIRTJj45Q4H53iLIbSkcVo6U3RVBUmbApqyyzaB3MF56kLVoLXLXgM//XASyycUEv7YIb1+/v9bQdF/rrYAiiwOpHg64ae260aXJeHTX759B4Eyu9uxiEKMI4FPLTxID97fNeQ6fH00SmQ+Wdi5a5usrYzpDCmJ6WeMS7qhSEWVn10+5K2r33EVS/SSe83YRogXRU5y7lgmiaJnM3e7gTTj/F7pBQOGxGTUj4BFLeV/xjwfSllxlumw5t+MXCblDIjpdwN7ACWeP/tkFLuklJmgduAi4Ua2c8C7vLW/zXw5sC2fu19vgt4oyjFBEbwT8O1D23lrhf28/E3TC05/8mtHWw+MOBHALoSNnu6kyyeWEuzF0UrDxvKZyymOL8Wp7sS2vvzGqJE1iXnwufOm628y/rTdA5mOOCF9C1TsHRyHcc3VzFjlIoI6DL/wbRNTyLHyVPryDiStKvSkq7nal8srD4UBKpnZfEPIzioHTe6ks+do94v5k+o5dYPLOGGSxdw6weXsP6b53LZ0gmETMOPpujj7EnkMISR16tRetAuhZxUDaSLYdvqHEdVRfjJu+fzkTOn+dE8B6iMWUOE7Joop23J3za18/0HXmLVzv+7tqe2PMxPL51P1pFURIdPUXUlsrz75uf5w3N7aKyI0FgRYXdngoqoRXWs8N1Qu+zDMHoub7ouvAgupwXvoKKFmlcJhrZOCnIinZITRdvSHnh61WI7NkXg8h50pZBzJH3JHLkSX3wwule8uoruCS6YO4Yn/vMs3nPSeNoHcyydXFuooXMlDRVhP3JhCJjWWMY7F42jJhbCduHtN64kaiqNGHg2LjJ/zLGwybiaKKOrIn4nDB1d1Aa829vjlIVNHv7U6fzyfYv4+LKp/PaKk0qf9DGCZ3d389HfvThkuiHwdXmvZmw80E/OVZ6QWqJRGbWwHUXCVNcGVU0+kLSpjln+b8oyBXPG1fi/o5DIR1IrIwaDGZvOwSxX/eEF2vqO3AbjWME/qhGbAZzupQwfF0Is9qY3A8GGdvu9acNNrwf6pJR20fSCbXnz+73lh0AI8REhxGohxOrOzqHeUyN4+cg5Liu2dLBoYi2feON0PxqmLSd6E1nef+vzuBJmNJUVrNs+kOT6d83jv99yArPGVCIMQXdcvblnbJUGDJv5Po1BtPYkqIxahEzBr57aiSNhelMZ6ZzLqt09vLivz3eP12O2aaiBeHtbnIiVd93XTZ019MBqGRAS2vRU+YfpBfXwWMx59HZMQ7BsVhMNVVF/XtZ2+ev6A+zqjHP/2v3cunIPWdvF0mlC6Qm4cRG4VEZNwoaqLCxGMdkIm4JRFWEcWXpw12nI/X1pPnX7Wvb2xAuMUjvjtu9rJSis7quKCEKG4MYndvHlezYM3fg/gJMm13P9JfOIZ+yhfSKLYDuwpyfJ1WdNY/f3L+AL583CLPGuZRTpuYJCe9MQXvQqv4D/Hcr892gH5imild9Gsf4q6KWlCUh+wDGIDZPT1NtVKdCicyD/HQS1ZsXraxTff4ZQNgN/XneAXV0JvnHh8Zw+vYEtbXGOb/7/7J13eBzl1fZ/U7ZqV71ZzUVy771hAzYtCS2EFiAQIKGFAG/ykpBASEJJJQRIKAECIUBC76ZXY9x7tyRblmT1ru27U74/Zme0K62MTXi/gNF9XWB7d9pOeZ4z59znvvvsr2aWZvLwRbN4+oqFjC/KYGppJg98ZxZ/OHMqm24+ngynTEyDy57cxJXHjMJlE1F13TpmWYAzZ5by2jWLeOGqhfzqlAnouvECVZLl4icnjgOgzR+mNMtFcXYaSycUcP2J48jzHtwA/khHKKJYeogmLl80koUVOcwoy/rvHdgh4ujRRllZ0wUUXSfHLeO0ieTGr6tDkixdQ1Ew5gJb/AVn6dg8WrpD1m+PqEaW3mmTLP6mrkNlS4Brn95yRBvFfxZ8Vh0xGcgC5gGzgWcEQRjF4DzSVENyfw5x4ud8ynfJH+r6g8CDALNmzRq6wv8BwjGV93a1MrbQS2m2izU1nX26RprOJY+tY/6oHB78yBBuXVCeTXO8TFfgtXPh3OE8vraeP79bycp4K7tDEqgYlk4gqnCgM4imGbydaaUZvLu7L3CeNMzDJ3s7kSWRP58zjd+9sRtBgB8fN5Yr/rUJMAKJ6nZDl8wff8DNClF7QkeXxEAhUHNiNU2f0SHLKdGjakmTJAxUVdcxBtjcNJkzphfxp7f3MKbAy62v7aTVF6E8100opuILx9B0wy4nHNMQdCNTJOgqvSEVCfBHVeySaQvUV0YaluHgmDF5/HvdAetYphSnE1J0/njmVMYXZ3DBw6vZ05Ka9OqLqFz2z40pH5wcj512fzSpvCcKIoJgGJ0rqk6bL0ye15li7cPDyVOKGJ6dxrVPb2JfWwC7ODCbV5Ruozuskm6TaemNcPe7Vdz7QRVRVR9w7k1el1lCS8xEmQO6eW3tkmBIMMS/T2UiYG5/sAHGCNYFqyyn6X06ZuHBhMBSwBY/FoCCdDut/hjqQSYgsd9v67+kue/v/H0Nz1w+n5OnDOPjqnYuGDucXU0+FlXkcsvpkyjNNl6MXvvhUYY1kekTKQis+MnRTL7lPcIxjUdW1HD2zBKeWldnlWkV3dC1O39eGRX5Xs6bO5yIolGY7mTx2Dyy3MbLU7svOqjVz1cVj6+uS8qs5qTZOGVaMTcUjU9J6/iiYWReGjYR/JEYmgZuh80wdNd1HJKhw5fukglFFTTNGG/AeObe3NlqiGPbRKLxLngxHuAn8lVtolHWvuGFrdx2+uQj2qf0cPBZz8IB4AXdwFqMc50b/7w0YbkSoPEgn7cDmYIgyP0+J3Gd+PcZDCyRDuFzxLr9nWyq6+LapzZx88vbOXpMHqVZbva2GhP/m9ub+XBPG0+sqaUjrpQ/psDLvjaji7A7pLBkYiG/P3MydR1B6+ZSdahu87G/I5jEAUoMwgDaAzHa/BHOmFHM1NIssuIZs0kl6cwdabxRxlSdLJcNdJ2STCdj8j1kuW0DJlUVY9J220RsojAgMyaJAhLQFYxZchai0KfQT8JEbf5pkwTOnlnKoyv389Dyvfzomc10BqI4ZJED3SGCUZVgRCPdaUMWBbwumxGEaSphxUjRZ7iMkl1U1cn32plY5EWIl7s6/FF8YYW7zplq7XN9XQ87GntZXdNBntfB8eP79LckQSA3zdheYnbN5EKZ5z87zUa7P4pNFEiwdqQ7rCLEr0dLb/hz9ZebXJLBG9cu4vffmsxJk4uYWpJBurNv520BhZw0BzPKMtnW0Mk/V9UYPEQOXq7V+l0X0x/RRFTVEYTk7loSlje3IRIP6hK+N0nqGkbwp+p93baq3idWO5gemAmPmTFLiKrOmF5sBftmwlIWjeaRxFJnYiYuEZIgWL+pzRdhyR0fcs+7lThlkb++X8388hzuPm+6FYRZv6lf54DX7eT1Hx4FQH1XmPruED9YMppsd18J22GT6Iq/1NhlkcuPLue06cVWENbhj7C+tvMrzwfrj/6xVo7bRpsv/KUIwvwRhS313cTincUum0hPKGaUJnUoyHCQ6bYjCgKiICYFnNGEF59wTCMvzWZUJHSjGzgRMQ1sssBLmxroCAxZKJn4rBmxlzC4XR/Gyfh2jKDqFeBfgiDciUHWHw2sxRh/RguCMBJowCD0n6frui4IwgfAmRi8sYuAl+P7eCX+71Xx79/XPw+TvCGkRCymcvPL26ntCHLe3DKeWltLuy9CZauffe0Bxhd6eOCjvYzKdbOvPYgkCEwvzeDJNYYEwe3fnMSNL27n4kfXkeGSOdAdQhYNaQhfWCHP4yQYVekJxeK6T5L1kF44r4wfLBnN+v1dBKIKZ8V9Bv0RBZcsketxUt3alwVqi5c5fdEwTgnCqnGD2SVDFVyWDXFXSRQIxM3A+2e61IQsC/SVjqzPBWPyS+TyhBWdDyvbqWrzI0siwzMcVOR7WTq+EFkSUFWdW5ftxC6LZDjtlOakkeWy8ezGBuySQCSmo4SjllBnmy+CJIpIgkB6mkx7IMabO1v4MG7tJGEEBXleG5luOze/vJ3VCYKZmq7THojhsYv4o5qVAVR1KEx3cNrUYayp6SCmgi+kGL+lX0lueF4ac0Zm8+LGBvzhwRsHPgscssQ5s8s4Z3YZYBC/93cE2VzfxZ5mP5UtPrYe6GFPcy8um0RplpvNB3pwyQKhhIjdJiYr0pulVjGBVGwGOWZwZZal+2dEE/+eOJqYy+r0BVqJ9kdy3DxeFgUiipYyy2duIxTvGtP0vjLV3z6uscqhmm4EkJquD8i6DobE+9DMuHYEo4DA+XPL+PWpE5E/rRYcx4TiDG47fSI3vbSD93e3MWt4Fq9fezS7mnqoaQ9w6rQicj2DZ0Zf2HiATJedb0we9qn70jSdlzc3sHB0LvmfQ7b1i4z6zmSJiuMnDuPYcQX/paM5PIgCpDnkuDOHSGdItdwyABq6I6Ab3dkHywnbJYFmX5SDNCQTVXQynDKn3PMxz1+1kOE5Q5nVTw3EBEH4N3AMkCsIwgHgl8AjwCNxSYsocFE8SNohCMIzwE6M6tMPdF1X49u5GngLY355RNf1HfFd/BR4ShCE24BNwN/jn/8deFwQhGqMTNi5n8PvHcIg+PYjq/CHFXTd8I0TBcNbcViGk9uX7eLXr2ynJ6RYb/NZbpnvLy7no8o2/rW2juc3HADiaW3dCH6mlmawvyNEababA51GNkwUDL5JTNGwxYOXyxePoiDdyTemJA/sNa1+YprGrct2keGy0xGIkeGUiSqqNVGHVfj27FI+qmxjamkGW+q7aYw3AZR47TT1RJLe3uyisY4JPeHPJFK/0Df5CYKR5YipOtsbe8n32ukIRKnrCKLqcMq0Yk6aZBz7CZMK+WB3K13BCKdNK+bfa2oN7zabRE880BFFQzlfFATqu0IUpDuQzSyLpoOkJw12MUXnzncqjeaDhLdrWzwY8Ef7gk2z+6+5N8I7u9qoyE/jzBkl3LJsJy29YeJKEdbv7glECERUQ1T2//jNXRAERuamJZW0NE3nT+/s4f4P9nKg2xDVDSk6bpshTWLyrkyrIugLeJLM2+n7LvE6um0SYSVZoytViTOJn6XH3RHiPEOz6cLyLRVSN06Y514Ujc7dpA5QvY+jp+vgtEuEYyp6Qjahf+B4MJiTZKZb4voTxh5yEGbignkj2FzXw3MbD/CHtyqZMTyLY8cVcOynrNcbjvH0+gMsHZ/P2GHpB102HFP58TNbWLatCYcssvOWk1IavR8JUBSN6lZf0mdjh31xM4a+cAxvQkON2y7zwAUzufzx9XyYoms8VUndBoiyiKYbxEhB75OFSdVQPCrbSV13GF2HkKIiSzJFGUd2cH6o+NRATNf1bw/y1QWDLH87cHuKz18HXk/x+T6Mrsr+n4eBsz7t+Ibwn8MfiLK+pq+V3SGLXLZ4FLNHZONxyOxs7OXP71ai6jA828WFC0aysa6by5/YwPHj8zllyjBe3drEiBw3rb1hxhV6eeGqyZRkpaFpOo09IRq6QtzzfhWfVHcQiXf6mXwcV6o6ElCQ4aShJ8yTa+oYFZ+8M912VE0lENHoChmZsTyvg8aeMD9YUkFPKGYFYs29kSRRUxhcssAsSZnThKIlfzapOIOIorGxrpuYqhnq60AwqvGrV3ZQlu1meVUbk4syOHlqkfUbXtzchEPu04YSBVBV4s0Exs5b4hw7SYAMtw1/JJZ0HhJ12IQETlk0RSCSGHTuaw/QHQwzd2QOmS47mqZjlyX2dwSxCxDVocWv8PKmRkoyHJx0CBmOzxuiKHD9ieO4bFE5nUHD0H1jXRefVLfzxvZm9HjWqH/mKJFHZv7dzAYmtt+bnps2DNFh04cyMQhL5WOZlIHTdPT4v00OWyqY5z6mGVkFpR+fTCcuTyEYnb5gBHsiydm3Q4G5bFdQ4ZqnNvLAd2ZZHY2HijvOnsqOxh52Nfs498E1rL9p6UEzYVFF48YXt1HTHuBPZ0096Lbb/RG+99h6thzo5tSpw3hlSxPVrX7GFn5xg5P/BL9+dccAuZb7P9jLqVOLU6/w/wGb67r441t7+NPZUynM6NOCrGrx8YuXtvPLUycyPiGYliURf0TFJvY1s0wo9LCzeSAnVSL+PGk6OW47Hf4oLruhISaq4BAhokGGU0TXoTeicaA7jE0yuLO6otOpxGjsCQ9lxBhS1h8C4Emz47YZsgoLK3JZd9Nx/PiEsSwek8eM4VkomoYvrFCY7uB7iyv47sKR/PGsKVx/4lhWVHewvLINSTBkEeaNyuHBC2dTErc5EkWBkiw3c0flpCzD6MAHe1oHHBPAxGHp1jr72g0eWm1nkAPdEbpCMSto+uv71cwoyyQnzU52Qidm//JUInnfnLTNkhaA0yaS4ZLRMWQ3nPEAUdXh8qNHce/5M/DaJXriIq9gyGw090Y49a8r+OfK/fzm9V2omk5XMML3H1tLY08Ipy2BUxGPPlWdfir7Rk0tGNW4fFE5Ttnw3pwX58aJGEGwToqOupRnz0BnUOXu9yrZ3thLpy/CvFHZZDhEYv0mjaIsl8Vf+28gw21jZG4aYwu9fHtOGX89bwarf7aUn5w0luJM5wD+jXlfmMbaidfR6ohM4F9pQrKGmFWSFozlBYwgzVSmT9ydTp8vauK+B4MsGr6q/TmG5raShGj15O8PB7JgHPPyqg6+cc8KNO1gRaPUeOO6xZYa/pzb30NRUpenW31hLn1sHVvqu7n1tElMLc0cdJtVLT5Ov/cTdjf38sAFM7nuOEPqZVNd12Ef35cFH6ewdmr1/Xc5UPd+WM0nezt4am2yg8XHVe2srumkpj0wYJ0ZZZlIomg9T1VtA5exxXmZLptBAekORVGBqGoEcR5n34jkC2uWnI6GwSGLD3XowO7Gns/r536pMRSIDQGAHbecxNenFLFybzvbDyQ/HBfOH8ErP1zI6p8fx3lzDb6PTRSZMyKLu8+dSlGmE7ss4nXK/M/xY3DaUjMEmrpDKbMJ7+1JrWE1c3g2ogBOySC+pjtlXLIhuWA+zACzR2Zx6rQiRuSmWel2k6xuHkri3xNhkr11QNB1uuPZpxllWaS7+rb19o4WzntotaHoDhTEW7o1jFKrIZ+gM7U0k1BM5cUNDaysbqc3pNATUrFLfXZGZnkxsdQWVnQkWSAUU1kyPh9dh/0dIdbWdFnHOSInmYhtItFncXi2C0+/eKo3bGSFwho8s+4APRGj5DepyEu+1whctzX0UtsxcND9byLP6+CKoyt4/NK5TBiWTnaaDXtcqsQhGTxDRevTuTIzZWamVddBiHslqvEPXTYxadCTRSEpODeV6Q8Wa31adU3RdJREfpkkDFgncfv9g8NDhdlMAFDTHuD2ZbsOcwsGNtx0PFI8q7j0zo8HfL96bwfH3vEhq/d1cPWS0dYYkAorqto54/6VRBSNZy6fz4kTCxmZm0aGy8bm+u7PdHxfBiwZN9DEviOhi/twEY6p7G8PsLGui0dW7OOG5zZz97uV3NPP/utgSvUdfmP/JQkNHPWdQZ5eux+bSJJvqIlrlo5hQXmO8TIipBZR1jWzUmDs2ykbmdioajxzvWEN03FMB0uSxnwxjiU8Gw75YGyyrw4+K1l/CEcYBEHgt2dMRtU0rnt6E89escBKGYuiwLjCvhT2G9ubuOmFbXQEYzhlgbCis3RcHlsbevnWA6v42qQC7jxrKpKU/JC19IaNTkZZtN6SACpTpL4BTpxUSOYrMp0hhXCwr1xnEw3bI6PrTKczEOMv71Xzq1d2MibfY+mC6XpfyUmWBCKpBpWEzEoiQXxjbRfuuCefWfoakeNmf0fQKKUKkGYTsdtEeoIKGU6ZYEzj5KlFrN7XwaMr9xGIGZppumYMyibBW05I/UNyxk4WYcuBHmyyiKprVvkypsFHVam7GnsTfBbrO0Mp9bvMfSQO27ubfHidMpIEVxxd/oUtEYzK8/DcFfP5xUvbWV7VTqsvggQoqkq220ZPKIZNMAb4RKkJMDgrZiZUwOBWmWVpTU+WvzCDd/McSSaPLJ5WtQK2QaI001y+Pz/GnMwMn1VjQ4IgoOn6AGPvQylPyvF0ReIcLAB//2Q/CypyWTr+8Ajikiiw5oalzP/D+9R2Bnn0k31cvHAUAB/uaeWKxzcwusDDPy6eQ45ncK2wp9bWcdNL2ynP8/DIxbMtUWdBEJhWmsmmuiM3ELv86HKKs1zcvmyXlTEt9Hz26fX2Zbt4en19ykDrkeVVSLKMP6JSke9h2TWLUm5jcUUue5p9fH1SIQCRmMpvlu1gT2sQmySkbM5x2SUuX1zOu7taB70XzbXMIVmWBNKdMqGYgoRg2R+BUVnwJ3AoEn+NTYS5o1JKg37lMJQRG4IFj0Pm+hPHEVN1rn92K03dqTMkm+u76AjGrEzODV8bx8MXzebNaxcxozSTlzc3Me7mt1hT0xc4xFSNUExD00gKwsAoe6SCwyZx3wXTrQ4cCSjLdlnG4aJgEO/3tvnpCcUYnuOmqtVviajq9E2amq5jk4yHP9Mlc9KEfKYUZ1AQF2b1Om2k2fpyEoGYZvWj/2BJBTd+Yzz3XzCLNT9fysobllKem0YgptEVt0vyOiXyvU5uemEbXf4wrb6old7X4ptVTMK2qesEZLkknLKITTTEbsV4Y0BplssKwlJBBJxyn4l6IhLFSs3gwvx3f3SFFC5bXM61x41J8e0XBy67zB/PmsoVx5QbRPt45qszGEMUjQHe7FBMnLsksc+f0jydOn2yDooViAtWx6VV4tRJupdM2GUx5cCpakYQJtDneZp4zhVNR8d4iYhp+gBOUf8pV8K4X9Ns4BLBK8N5c0vI8coDgjAzG3j9c1uJqYNnSQZDboaTN65dRIZL5rbXdvG713cC8Ic391CS7ebBC2cNGoRpms7v39zNDS9sY0FFLs9dOd8KwkxMK82kstWHP/L5duZ+UVCQ7uSUKUVJZes3rlt82NtZs6+DZVub2FDXSSx+keV+D253RKcjEKMow8kZ0w0O2h1v7WbkDcsYecMyRsT/u+v9agJRlb+8X01vOMZFj67lrR0GDURRdaoSmgueWV/HafeuYNHv3+fGl7Yd9IWg/zjSHYyhqDoxFVQ9mUOZGIRlumTy0vpezmMa/GNlzaGfnCMYQxmxISRhZG4a/7h4Dvd/WMVlj2/i7nOmMCo/uTuqKB682CT4yUnj+d4i4+05x+Pgye/N4dR7P2FHo49z/raab04v5s/nTGNvm5H1MnWYXDaJiKoTUTRr4mjpDeO2S0ndPPPK8ynP91DZ6kcXjLfrshwXlS0BK8j63lGjGF3g4frntlqDRJbbhqZp6Aj0hhXcdhlV05lfnsOfzp7Wp+elqNR3hfi4so1fvbqTkkwnE4d5eW9PG1FFI9/roLU3TG1HkE11Xexs6uX6E8ZSkOHE65TwOGSaeiK09EaQxBiZbhvzynMZkZNGdYs/3nWHxcnSgXB8gDXI2xrhhExcVIX/fW5bUpYsz2OnxZ9cRtAA44W2b12BvoDEJICDYS/VE1KsIMPrNDpAdR1mlWbwwyWjD+XW+K9DEAQuWTiSkblp/OW9KrY19KDFJ4BJxeksLM/moeX7kxXxEzJekmiI+ZqkeWu7kBT0ynFlVTMI6w9F1fqsl/TkAEoSAV2wDJP7r24q8JjirakmPCsQBDKdNi5ZMBx/TOWjXc0sLs/iuTVGh7KZvbOynTqEIrEkZffDQUW+l9+fMZkrntzEA8trGJPvpr4zyLdmlkX8dSIAACAASURBVDAsw5VyncTOyPPmlnHLIDIa08oy0XXYeqCbBeW5n+0Av+DITrNT4JFp8Stku21kph1+R+B3H12Lohp2b2YmN7H7NjHbX9MRZF65kVF6fFXtoMHT/R/t443tTezv6JPX0IGPKts58/6V7G7uJaJoKAkdvCYcsoCm6CS2D+kJ36U7ZQJRFVXTOWZMHhtqO0lzyDT3Rgb4nXaHkoPw82cVcdGCkYd2Yo5wDGXEhjAAU0szuWyx0YF44l0r2FDbR7LtDER5Z1crTlnk5ClFVhBmQpIkll2zmJtPnoAAvLipgRm3vM32+m5LLDWmGd1sEUVDEoxy0T3vVvK1u5Zz+l9W8MN/beThj/dZZNLiLGMS0HRo6g6zpyUQ5wIZcgIvb2nk3g+qLRLpNyYX8O6PjmbzL0/krnOnUZ6XRkxRKclycufZU9lU12VNiHZZojzPw2OragF4+KLZdIYUREEgqqh0BqKs2tvB46v28+KmBl7d0sSiP7xvTMYaqHGT5phmBLGCAPd9UE1jdwhBgEyXREyD6aUZFjfMJvY9djFFtwRnLWHP+J/ZLgkNI1izS8absTcFn94uC33mu8QzOXqfJVJiCUIHfGHj3I8b5uXh784elNP3RcWxY/P5xckT0HUduyySZpeYU5bOYyv2Gwbpcp8lUmJQYtoP9Uf/z9R4tsoM9FOR7iEuDNtvBFU1IysARkI1cR2Th2WuO9jEmfh5RzDGn96t5v6PatjZGuKKf20jmmI5E2fMKEbsf1CHgZMmF3HqFKOU9ePndqDpOoPJN7b5Ipz74Gpe397EjV8fz+2nTxpURmNaiUHuP5LLk7IkkhEPvs6dXfqZhFwN2Rzj76nOuvlyYOlOx2+oUNR4xl0JSvUjc93W/VfbkaxxBtAdjLK+tgt/RE1yo0hEpF8QloioojN5mJdgVCOiaFTkp5HvdSCJIh6bOKjemF2AnDSZ5zY1s+EIbuA4HAwFYkNIiRnDszh9+jCyPTaufnIDP31uC7e+up2z7v+E1Xs7sEkgi+Kgli2XHDWSNT9fQpbbRmcwxv8+v41oPAthlm4yXbJlAv6XD6rpDMbY2xFk2bYmblu2i7+8V0W7P8Kfzp7G1ycbk4Om65TnpVGW7aYky8W5c0qZUmJIS5hJji31PXidNgRBYMm4AhaW5xBRde4+dwY17UFuW7aLt3e0WMe6pb6LmvYAYwu8jBuWzq2nT+KjnxzLjLJMhmU4uXjhCH73rSncd/5Mvja5gHyvg6313QSjKq3+qOXf2OaP0BNSeH5jA76IGi9BGfvYVN/DsHSDGB9KqCtFNCjOclKc6TA6JxPQGTLIRr0RBVkUOWZsDqdPKyLL0beciDEgJko8mMbUFk+q3yXyOiWGZ7v5x8Vz8Dj+e52S/wmml2Xx2CVzcEiG1+Qjqw5YBOEl4/KtSSUxm3Co06Ke8J8Jl01I0mkrjDc59KfwSIJx/j02o+s2UTer/3WwS0LK7JUsJgd4On0Ddb5bYnimDY9DtLomE/Hk2gMDuuQOF/ecN5OJhWnoQCCqsrZmoKFJVYuPb97X1xn5/cWjDhp4ZKXZKcp0suUIJuwDnBLXQmz3p6ZbfBpW3rCUb04ztnGwphDzvt58wDifph9k4thS0x5Ex8hcIQy8/xVtcCmWQ4EOvF/Vd288vGI/e9uDNHSH8KewArNLAvleB1EdOgIKEVXjQNfAAPGriKHS5BBS4v4P97KiqgNF0QlEo7y48YClWyUCvojGRQuHH1SgMT/dxaabT+C6pzfx0qZGhPgkluWW6Q4qyKJOMF6zc4o6mmp0ucmikWl6YVMDbrvIradP5tKFI3l9WzMOWWDx6BzW7OtClkWuWTKaP79bSW8oiqZjZOqmFmGXRTr8Eeb85j3cdomJRemku2y8sqUBAfjXmhr2dwS4bPEofv7idgBuPX0i3cEoY/K9iKLAPy+Ziw7WW35hhpM/njmNldXt/PGt3dR2hRlT4EHXoarVT4c/OmCwC0RUbPFB8EC30c5enOmksTtsTfQN3WHcskCG24YWiBFO8oM0JvsFo7KobA3y8e4OHA6jaJGopWWqz4v0ZX68cfeCxPl/emk6m+p7eeqyGWS6B5quf5mwsCKP644fy62vGXym+aOyiCg6Wxp6UpcFP+N+9ISVzeRQq6+vVJy4L9MKSQE0dfBsEhg8HVHsKyOb20niuAGzh2fwzWmF3P/Jfkoy3HQGYtR3+wfNONzwwjaKspwsHj2wk+9Q8eRlC1j0+/fwRTR2NfuSfEhX7+vge4+tw2WXeeby+UwpGVzKAoxy7D9W7qexO8yNXx//mY/py4Arj6ngqfX1KNpni3Ay3XbOmFnKi5ubDIrqp9y0Dy3fR77HaVlS9cfIXDc17cEBn5ubHoyGan5vlwzB1oM0ZwKpy/QAk4d5WTwun6mlmRR4HRSku/ioshV/JEa+18miuNH4Vx1DGbEhDMArWxq5/8NqdjT2EIrGsEti8nggwDGjcyhIT80b6Y+7zpnOx9cfQ0ac+2US3P0J7WW+GNhtIoqmE1Z0YvGJaUVVB4+sqCHDbeOsWSWoqs7jq+qoavVR1eLjzAdW8eyGAwTi6vKLx+Ry7VKDeO6yS0wrycAXVqhs8XHsHR/y2tZGCrx2Vu7tYtmWRva1+tnZ2EtZlotJxRmc+cAqnlhTS0RRKb/xDc57eDXbG5LlPBZU5HLCxEJy3DaKM10EIzE8Dgkdw0dtZlm65atonrdowgls6A7jiGuUSRiyGTFVJxBVk4Iw6PNFXF3TSUNXiAh9XZLmIGqWRsEgp5v8KF+/IAxgd5OfM6YXHzE+gd+ZN5wTJhTwwyUVPPG9+Rw9Np/mngj2/gznQXCwjEDi4BhTkzlfiRNO/3MsCYZeUlTVB+qWJSynYQRd5nWUxIEDsgqsru3h129UUdceYUeTn93N/iTx4fU3LmVqcTKP86rHN7KnuXfwH/cpuOKJDfgSunGP+t37hOLPa22Hwc/85yVzPjUIU1SNG1/azq9fNYLl3U29vLjpwGc+ri86ZEkkpui8urXx0xdOQHcwygUPr6Y7GGV6WRa/+Mb4QbtzE1HfGeKyJzYgSyJ53oHNFBfNH5FyvcE2LWKMm2kOKe5SMXgQtqgil2EZTt66bhGaDukuGzkeG9lumUuPGskrVy/klWsWcf2J4zhhQiFTS7MozHByzuwyLj2qnFOmFn/pXwY/LwwFYkMYgNe3NOILKzhliaBidL6YE71Lhpu/PoF/XDqP3IO0svdHaU4aa25cynlzDA2iVN1+SpwLJQqGTpQO7O8Mcuc7ezjpruU0dga4emkFz125gI2/OJ6vTx6GLxwjz2PnvvOm89szJvOLkyfgskvous6epl72tRtNAhHFaAyobQ/SG1bQNJ2uUJTesMKUkgyuXlrBeQ+tobrVz5OraznvodWAIdgaTdGFdvq0IjLddj6ubKOlN8LFC0fgkIz9VLb4GVfgtrqdxsXVqyX6TMVjioZLFg3Db9GQ1gj20z0wy04afca6AiSp9Ju+lQAlGU7LlmmwgVbV4eaTJxy2Jc4XFXZZ5MELZ/HjE8YiigJzRmYzflg6Yw8h0Mx22yjLMrI804q9TClORxYFsuKClBqQ7jTMsBN1wRJhliITkRhLJ3jIY0vo4EyESIKcRvyz/lW+YNRoEBib6yIt7vBu7qYnrAxouPBHVb7/2Dra/Z9NVHR7Qy/nzC7lwfOnAxBRdU66azmapjMiJ42YqvPLl3cQjqXwsonDF45x6WPr+deaOmYNNwK2v3ywl9++vgt/OMZf3qtiT7Nv0PW/rJhSkoFDlmgfpBsc4Jp/b2L0ja/T1G2U5v720T5WVHcw9zfvcec7u9nW0DOglJ0IS6g4/qcvotKWQkD2V6/uPKRJPsMpWduLxFTDfgsjAzxhmCepc1MWjMpDc28ITdO5+eXtFKQ7SHPI3HLaJNbddAK/OHkCU0oyvxSG518EHBmj8RA+N3T6I+xo6kUWjQHeaROxx+8SCRg3LINvzS75TNt2yBK3nDaRa5dWDChp2uOltUWj8wzBVkmyHv5AVEMCPtnXxR1vV/HQxzUgCFxxdDm+sMrXJg3j61OKOHdOGaXZaTy+upYrn9jIN+9fRSCiMrk43coeeB0yc0fm4LSLNHSHeWNHM1sO9LCryYeqaThtInvbA2yoNbgXum6Iuw44T8EYe9uNpgFFh9e2NDC2MJ08jx1NgN3NAUuHbFujkZkw1KeN0VXVTT6HQEQxMif94z2h3zkyCd5CPEum68Z2JLP02XNwXorXIbHshwvJTDty30Lnjcrh5R8sYMn4AmRxcF6YJBjBzv7OMAKwsCKH2WUZiOg4bH2Mjd6wYumOmYFvIlR9IE+sv69l37I62e6BnDyTgG2VmSXBEsFMOmZgTV0vvoiKCHjsBgft9W1N3PH27gHL13WFue3V7YRjhy8ZsaA8h5o2PydMLuK1Hy4EDFeLK57YwNxROdxx9lTW7u/kx89usTpTE9HYHeKsB1axorqd354xmbu/Pd36rsMf5ZwHVnLfh3v520d7D/vYvui4ZOFIJgzzsrsltT6iruu09IRwSCINnQE+rmpD03V+dPwYXDaJR1bU8tLmg2fUDkegxFw2yyVRmuVIErm2SwIOCXI9TpyyaI0lmulKokNVa4D8dCd2SeSoihwmFmcwvSyTQFRlSmkmI3M9XHVMBR/8+Gi+MbnoiPUT/b/EEEdsCElYUd1Ouz9iTDxAJKYxLMNJY49RTsvxOPA4PvttE4wq/O2jfZaEA8SFTGWJaFTlwz1tqDpEVNWynQFIqJKwbFsT62taOX/+KGaOyOKKYyqS9jEs3YnTLlKUYVjj/OrUiZz74GqcsojDJvHBnlZUzdAO6wlGmF6SztPr6pFFAbdNojMYY1yhl93NPr45PbVXXG8oRkG6g55gBBmo6wwzrtBGmz/Kj44bwwd7WmnoDtKbEBuJGNkvTTOkEQydKMGSTkicziTB4BBJgkCGS6YnGLNsdkyundn0oANuWcBllzhqdC617QE2HRhYlnrowlmMLjy4UfORgKiq8/iqWoN3JaQWYFV16ArGrBb7J1bXEYhoKBjZJK9Txh9WLEspQTdU+YNxixZlkGyFeb+a18fkjJnBVmcwhssmEI3pVnlREIWkphezDNofWR477f6oNVk+8J1ZXPD3tayv6UyZDQF4fXsLNmkbfzhr2mFnJ3ricgOTijO5/fSJ3PjSDt7e2cJf36vi6qWj2dPcy0PL9/HXD6q5ZmlfRm57Qw+XPraOYETlHxfPtnhATpuIpmrIkkhVq5+oBpuOQPL+xOIM1u7v4oWNBziqok+qI6ZqvLipgX+vqWV7Yy8xVefBFfvZ1tBDU08YWQDPf9DAfMeZk7k+Ln1jE6Aww8GB7giSCC6bzJiCdJw2kUy3gxZfhHyvg3SnDU017CkcNomIoqLrOm67REG6i+w0G16njdEFXgrTnRRmOCjJdOP9L9qhHYkYCsS+QqhpD9DYGaS+O8RDH+9jybh8rj9xHPZ4baUrEOGpNbWGKWvCeo09YUQgzSHx4HdmfuZ0s67rLP3TR1YQJgA2GaIKBKMqckJQYqqjJ7b5J3J0Wvwq97xXjdcpW6KsJo6bUMCeFh+baru4/Ohy7v9wLzluG76oQpsvzLxROVx61Eja/BEeWbEPVRewSwKCIHDxUSP4aE+7NUFcctTIlL9lR2Mvrb1GwJrhstEbirGjyYddgr8tryai6EZXEn1vpBqGrpUtnl3RNCMrlki6Byx7EXQ4aVIBp08r5uZXttPmiyQZXRtEfuODgKaT53Vyy2mTueyf6wYEC785fRLzjlD9pv54Y1sTvaEYI3PSLI/SRFj6Wwn3Vk880nfIAv64M3ymS07SPoqqmqXtZJUc+xGUzeYKMHT2DDmL5P2HYnpyaSl+DRPvg1RxXppdoh1w22V8EYUF5Tm47RJbG3os+YJEOCSjpPjsxkZKc9K4Zumhi/b6wopVAgU4f94INtR28cKmRu58t5KjRufQHYyi6jp3vlNJhsvGRQtG8N6uFn74701kumw8d+WCJJPv8jwPOxp7Kcx0UpTp5KiKXCZ/CsfsywhTo/CFjQ3cefY0wLCJ+s3ru9jaj2/6/q4Wqwyt6NB9GMlLtywQTHjIIzEVt10iEFXRBKjvjjA638OYQg/TSzMpzUqjJMtFYYaTTJcNKQU9QVE1oqp22AbyQ/jPMHS2v0L4y3tVvLKlEVXTkSV46OMaGnvC3HveDHY09nDxo+sIRmJJk4DVXYPhWfi1u1fwzOXzyUhRYkmEomrsbQvQ7o/gtkvsafbxhzd30xn3xThmTC4fV7WTbpdpVxQcssGvcskCIUW3JrPEY9Exskg5aTK9YSVuWaTz4qYD7Gzs5bRpRazZ18mafR2s2tfO3FG5qJrOuv1dvHzVQp5YXcOcUTnML8/F67Rx7VMb6Q2rhvxFTKMrGOOqY0bzwyUHn7CeWV/PU+vqSHNIRBU9bg4u0xtWUTQdTdNQdKOT0fwdiQFZrJ82Qv/KjpkJnFOew73nzwSgvivALa/ttr53O8R4R5ORuUh3ytx7/nScNolQVE0Kws6eMYzz5g0/6G86kiCJAjFNZ288CEvMUgFWJirxtNslgaiqE1F0JFFARGdErocF5Tk8u66etkC0rwR5EO5O4qWNqQyojZr7FRPvDcE4ZuVgpCCgttPgE/kjCg5ZpDMQRdd1IjGVBeW5vJ/g2Wpaj5m4850qSjJdnDGz9KD7AEOkdVtDD6dMLUr6/M5zplPd6jeszO5fxYSidBySyJjidH716g52NfXyzPp6JhZl8PeLZpHf7wXp6DF57GjsJcttY29bgH9/f94RyyH6n+PG8Od3K7n7nUoOdPl5e1eblWFMhNJvLDgYzBcDWQCPU8YXL5ubq9/2+m5CMY0ZpRkcNSaPBeW5TCpKx+M89OyVLIlHDH/0y4ShQOwrhGuOqyCiGLpAFfkeVu3rJMtl4+on18flFyIUeJ1kewScskhla8Aq7eR57Fy0YAR3vF3J46v3c3UKNXZF1Vhe1cYrmxt5f3crvQlCookDxqwRmfzsa+P4uGoF7UEFj0MkHDXV5o0lnTaRcNwX0FzPIRkcr46gYk2qXUGF65/dik0SeXVLI829fSWa93e18r2jRvLej48m1+PgplMmJR1vJKYxoSidl35wFGf/bRWxQ9DV0TSd93e1Ut0awC4a+j2BiIosgizoxDRwOUR8Ec1ScYcEEjb9SpBAIt3Z1KmyCyKnJkyElxxVTmNPhDe3NdPQEyIQ0cjIsDG5OJO3drbgCyus3ttBptvGloa+suS4Qg+/P2s6XyW8tKkBWRTITrNz88nj+ceK/WzoVwLTSW6514HSLBfD0p1MKskAXee6E8aS7rRx9THlTPjV24AhmJnojNAfJrcGDK5XVNVx20WC0eTMp5aQRtN0Q+oCsPho5uep9qEDOR472xp78Dhk2vxRzps7nGuWjOaXr+5gy4HepCDMxE+e30ZJdhpzRmYf9Pw9uaYOf0RJWZZ/8QdHMeXXbxGIqGxv6EUUBToDETJdNl7e3MDsEdk8evHslBmVaSWZpNklRuWlsa2hhxue38rvz5x60GP5smLR6Gz+uVLmz/1Muk3keuwcXZ7JtkY/lW0D5SX6Q8Lg2AooSJKIP6wkZVo9dhFV1zl/Tik//do40l1HLg/0SMRQIPYVwogcD/eeP5N/ranlrnerEAV4Yk0dsijgdUqcNKmQZduayUmzU5btprI1YOkb2SSRyhajw6k7FKOhO5TkJ/f6tiZufW0nTT1hstw2jp9QyKLRRnvzVU9uAKAjEMNtE9lQ281Jd6+wJhl/xAi4vHbB0iqLxDTLJNucwCIqxIIKggBpMgQUcNtEfrh0NE6bhCwKBCIqbrvI/R/upak3wrcfWsO1x43muhReisVZLqrbjKzJsWPzufOdPextC1CR7xn0HIqiwP0XzODyJ9azvqaT3mAUf0wnyykSjkdUZtu/mRDR6bMrSdqWMLBsZU7Up04zmg8ScdM3JnDZolH89PmtNHSHOHd2GXtafCwdl0eWy04gpvH1e1ZYyxekO3jpB0cdsVmHwXDOnDKOHpvPhfOHc8fblWyo704dNCWUeWOqzt++M5MJRRkDl0v4e0xLLtv3LwEL9GW7xHh9ORjVcEpY98fB4JRFYpp+UJ9RMLp5f/bcVnwRIyvyyuZG7jlvBi9etZCKG99Ibc2k6Vz31Cb+9f15jMhNbfDe6gvzt4/2Mm9UdsqATRIFVv9sKdN+/Xa8wUSnrjNEml1CFAT2twfoDSkDArF2f4RfvbqDTLeds2aV8uqWRnxHqO9kR2+YHz21xaJ8mDDHAgFo90d5fkur9V2aXcTrtNHcG+H0qUW8tqURpd/K3cGI5dYgCJBuF8lLdzKjLIuL5g9nUsnApqIhfDkwFIh9xfDvtXXc8uoOYqqhxi5jkDr/eckcRuV5WX3rOyiazorqDo4bX8CJE/N5+OP97GnxsX5/FyOyXdR3BLn11Z3cf8EMBEGgutXHVU9uZEpJBr88ZSJLxuVbg1Bjd4iOQIzCdEPqYmyhhx+dMI6rnlyPL5zc++OL9uXpTQNmu2TY94QVHUk05ARiioYeLwJNLsnkyn5kfYALF4xk9d4OLnp0LXe9W8XafZ088b25ltnzzsYent9wgGPHFQBw8cIRnD69aFBPvUQIgsBfz53OxJvfsjwHu8MaGS4bPaFkQxAdo6SgqCphRbfKq1FtYElSipO2HZJBvE+F/HQnj3x3NoqmY0soIaiazm9f32GVP9IcEk99f96Xzr7o88Ab25p5aXMDt722wxK59TpES3/NhBQ3fJREQ0y3sSecMhALJUg0SKKIEk9nyWI8e5UQYGnW/0iSPRkQhPVPjcYR1XSLuG9mx1JlxjQdmn1Rst22uGaZynf+voYHL5jBJQuH8/CK2pTnprEnzHkPrWb5T44dUIL6pLqd/312C/6Iwo1fn5ByfTCaXJ66bB5n/c2QeMn12Pn1qRMZmevhrAdW8t1H1/L05fMtrlRM1bjqyY10BKI8f+UCSrNd5HocdA4iQvplRVcgzP8+u5UPdrel7Go0r2Gux05b3DtWBmTJePEszjQ8Gt/YkRyEmZO0LInke+3MGZXDzOFZTC/LYlyh9yv3onUkYqgY/BWBqulUtfh4YvV+S/bg9GnDOH5iPg1dYS5/fCMn3/MxveEYTlnEZROZNTyLFzY2sqfFx+IxuaS7ZOq7QnxU2cqYhAHgjW3NADxwwUxOmlSY9Ca4Iy7dEFWNmaitN8jlj28YEISZQZdDFkhzSEiCYGiX6YbAq0MScNkkuoMK4aiGXZKwSQLTywYn+84rz2HNz5cyPNtNfVeQe97vKxO8tLmBEbkerj9xLABOm3RIQZiJzQd60AXD07AiLw1JhEAkZtkUJcocjMpxogui0XygGUFYTtpA3oYaN4pWVJ1/rKzl9td24k+RNRAEISkIA2Pf5uQriwK/O2MyI/MGz+wdyRg/zEtump2KfC9Lx+Wz/ZYT+dassgHLqZrxMjI8y4UvHKNxELsVX0JwHU3o9tX15GDa65CTAqbBkloThnn6snH0mbQb29Rxx+8hHeM59TqTr7UkGPxDWTS6kAHe3NHKx1XtTPzl29x08iRmlg4MKE3MK89JCsKCEYVfvryd37+5G4cs8szl85lcMvj6ALNH5nDpfOOctvujvLuzGU3XuXTRSKpbfVz2z/X4wsZ5u33ZLtbWdHLraZPY1+7nvIfW0OqLsPVAD99/bB3v7Gw52K6+8Hh5Yz1L7viQ6be+x3sJQViq8MghCQRjKmfPNMq+gmgE6YoOla1Gdj7xkS9Md3D27DJ+ddoknrl8AR/+ZAl/Onsa580dzvhh6UNB2BGCoYzYVwRNPSGO//Ny7LJIgdfJPedNZ3pZFoGIQtVfP0HTdaKqZkgmxKOI3725G3dcAX55ZTsVeR5UHWYWZ/Cj4/tKfR9VtjEsw8neNj9FmcnBTGtc1DAYz0a0+RWKMt1oukZ9VzhpsjI1tiRBIxYPShAEBHSiqk5MVREFQ4G/K6QgCvDtOQMn2ERkuu28/+PFnP/QGjYlGMx2B2OUZbus491c38WW+h6+PbsEu+3TH4vG7jCKpjMs3UF9Z8jIurhkOgJKUslxmNdGTWeY0iw3e5p9VmmiI5DaSlfEeEMuyXDx2Kr9vLmjmd+cMfmgViC6rnPmfZ9Y/146Lp9TpqaW3fgq4PKjy7n86HJ0Xbcmqp+cOI7NdV1sqk/uWtOBqrgFzB/f2oOq61y8MLlTti3RzijOmZxSkkFVq99Smwc4a2Yxj6ys/VTu9a4mP1L8HjFlTMxyk1GC161smCBAb7+XFtPUXQDCCQ+QXYKoClc+sZ4rjx3Ng8v3MrU0A7dDotMfY+m4fArTnYxLyPptqe/mZy9uJc/jZPHoPK5eUnHIWdRfnDaZjfXdbDrQy4ubm3hnVxsxVcNtk9jV1Mt3/r6W06cX8Y+V+zlzRjF3vbsHWRSp6woxe3gm62q7WVPTSVWrn2mlmSmV4b+oaOgOcv2zW9hyoIdAJHXNOdV9EFF1ZFHluY0NgKGdKACTir0UZ7qZWJROQbqL/HQHZdluRuS4/yMT9yF8OTAUiH1FUJzp4qiKXD6pbufOc6YyPS5SmuaQeedHi60J65J/rKOqxUcopnHOrBKeXm/YkTx/5Xx8YYXvPrqOnn4DT08ohj+i8PDHNUkBQyiqsmxrE4sqcllRbXR0aTocPTafXI+dO96uTNqOS4KQCiZF35RoKM12Ud8ZQhIg3W2jIxDDLgncd8EMhuek5rokQpIkdrX4OGVKERFFxSFLNPeGLcL0qr3t/ODJDQzLdNPcE+KnX/t0P7xHV+4n02UzRDrT7LT5I3QHlCRF9Fy3TJMvxnfnD6emPcjuuIq4EM+AmMGalBC4qQAaFGW6EASBqKJx5RMbqMg3dHx+mjV+EAAAIABJREFU/60pAzpWv3bXcovwOybfwwPfmfmpx/9VQGK2wGWX+Ov5MzjhzuUEEoIncwkd8EUU7n63akAgtrvFyOpmxj1SReLOEP0Eyh5ZWWtkQvXkBoz+MDNdJiwbJL1PV878ejB7mVTNdsNzPNR2BNhc101zb4Q/njmVsmw3YUXFKRp11KaeEH9fsQ+7JLKtoYdn1h8g1+PghpPGs3jM4fv+vXj1Iqb9+i26Qwr+iBJvNBC4ZOEoHl6xj+0N3cwZmYXXaSPX6+CcWWW8urWR7Qd6GFPgocMfZX9HkFgK94ovElRV494Pqlle2cLOJj/BFKbWiUi8r/rDfAdz20W+NaOE644bQ85huJQM4cjDUCD2FYEgCFS2+Dh1WhGzR+YM+M5ERb6HFVVG0NQdL8m8/+OjGZXnoScYQxRgT7OPxu6QlU2aXJzBvvbAAFHJt3Y0s3JvBw+cP4OP44FYTIMl4/K57unNTC5Kp7YjQG88sAupRnYgHDM5OEZLf31nyMoOdARiSKLA/RfMZOn4gkP+/f9z3Bh+9epO6ruCZLntRGMqs0Zks6W+i4sfWYsOXDhvOOd8SoYN4JOqNrbUdyNg+Kv5lCiKqlvCn+YQ3Rk0iNQtvgg5HpslYWFqTYkCZMUDy8SOOlWHVfs6rXOwsCKHnpDCmzua6QxEeOaKBdaxnP/QakvBWxQ4oiUB/lMUZ7r5+CdLOO7Oj+gMGlkuHZPrZUhe9Lez0nWdpm4jyO2OX09BFNha342tHxl7RI6Lpp4wkUHUXtNsAoFY33eJkibmscQSMlyfomaRBLdNoLknDIJAU2+Ept4Ix935EYIQ795U9CSdPoDReR5u+No4zp9bhvcwJA76Y+Mvjqfi529YorUiAu/sauGuc6Zx00vb6QkqpDlkth7oJd3ZxO4mIzPcFYjisAnc/s1JAzLpXxSs2NPCL17dyf72YFJQ5ZQEMtPsdAWicRmdZAx26URgdEEaVx87mqUTCob0uoYADAViXykIwuC8FROj8z1EVY1Ml43a9iB2WSQQUbn3g2q+NaOEijwPla1+HllRw00nG4TeCxeM4IVNDexs6qWpJ2RxrSKKEWAlljomFaUzbpiXzkCUpePy2dbYG7eiERCEvvKkXQSvU+LxS+byt+X72N7Yy764pdB95884rCAMYHFFDkvG5tLYGWDV3g4EAapb/fz1g73IIhxVkXdIQZiu6zy6ch9um0iex05U1egJaZYUgnl6xXizga7Du7taGFvgZUppOtvqe/tEO/W+EuXBrssdZ00lz+vkrAdWMjGhrPTuzmY+2dth/fvZK+aT8yUq7/w3kO2xM29kJq/v6OtYO31aMXZJYGN9N09+b17S8o98sp8Hlu+3/q0DhV47HoeMwy6xNcHBwC6JpNtl2pTUZedIrM84fGROGlFVJaKotPtjzB6RwZ4mH6qqo6LjlGWcdpGSTDdFGU4+rm7nW9MKGZbl4d4Pqqko8PD9ReW8vaOJOaNyOCuBAxeIKNR2BNnZ1MuBriBv7WimriOA12kny23jhIkFHD+xkImfE8dIFEW2/PI45v32fQJRDUXXqWnz8++1dZw6tZDHVtVT3+HDKYusqDbuV5dNYGppLg9d+NkFov+vEIup/OHtPTy7vj5J0Nfs4gYIq3qSVM7B4JAFphVncPGiUSwanUfaf+BMMoQjE0N3xFcIJ00s5Ik1dcwansUJEwrwumw4ZDGJ+G0qYRdmOOjwR5BFAV8kxh/f2sMf39pjLffPVbVctGAEpdluppVmUpGfRnVrgHd2tnDh/BEATCs1yp/v7+4j447L99AbUqjIT+PZDUbZM9/rJKyo2CVDpDKq6kQ16AooXPf0Zh67ZC6n3fcJmg53nj2VEycWHvZvv/313XxU2Z6UZYgoMQo9Npr9Ma48ZtQhbeetHc28u6udGSXpbG/yEVV1bHEBUegrM2p6nx5UTNWp6wyiqBpzRmbR0BOmvrOPGG66CICpmN+XuVA0nTm3v8fI3DTOnFmMphtdaOFIjCse32Bt4+aTxzNz+MH1oYZg4MZTJtHs28jGOkNbbHVNO91BhUy3nbqOYFKZaFZZpnV93TbITHMSU3WyPA6ruULHmKQPdIct4/b+bglAUiecTTKMmoMRFacsEohonDdvBDPKsphWlkm+N1kMNRGXLi63/n58imchzSEzoSidCUWGnVUq6ZbPG16Xg5U3LGXGbe8Qiz9kK6o7rMCrMDONwnQH6/Z3ctGCkWi6zmOraqnrDB4SveCzYmNtJ2/vbGZ7Q2/cmUJHEkWcskCm28600kxaesN8uLuVkpw09jT20BuPtkTBINebnL1ov2pkplNC1XVLrqY/slwy1ywdw2nTisgeKj0O4SAYCsS+Qrj+pHFUt/n55Ss7eGRFDbWdQUtywR4n6Otx3ovLJtEVjKFoOrOHZ3Hb6ZNo6Q2T4bLx3Pp6drf4+dkL23jie3MBWFieS3VrwNIaAyjJMjJjNR19goULx+RRke/h7nOmc9Gja+kMROkMRAgrOk5ZjAc2Rrk0qupUtgY47s4P8Ed1jhufzxkzDs9wfGV1O7ube/moqi8IK8t2IYsCdR1BRhd4iem+JPFZE1vqu1lT08Fl8YlP13X+vqIGmySwrdGHoumG72O/IMwkXida6ATCCiqGDpUtXm5cUd1hEPu15ABOEnTGD/Ny8YIR/OR5wztuX3uAP7xlcOrqOoO8sqXR0q86dkwelxx1aIHklx2KqrG9sYf/eXoznf4IE4oyaPVFsEsCu5v9nDltGH88d8ZBt1Gc6eKFqxZy7B/eo6YzzIEuI7Phj4Ro6g2TKH97x9t7rCD7qiVjCEdV/vrBXuaMzE6yAtMRrCAMUmc4EzMqla2BJB7RziYflS0+7LJEml2iOMtJWXZanMAuEI4piKKIqmqsrekgFNPo8EdZPCaXP5097T8qLX5eyHDbefu6xSy9czkADllE13VG5Dh5+0fHsG5/J/9eW8dlR48iFFV5el09P3luK09fPv9zP5auQJQ/v7OHN3c00+GPDlrm/aiy3bqGTb5kOY1lVy+gxR/jj2/spqrVNyAQ604hDOe2SZwzu4SzZ5cxftiR7+s6hM8HQ4HYVwgeh8wTl85lc303e1v9NPaEue+DaqCvJAiGPYog9PkY9oQVzp9bxs7GHv78bhULR+fRHoiyr81vrSMIht5XQ1cITdMRRYE0h0ye12EtJwtGme7kqUVkuWU64lo6bruMJCgGfyq+XDh+PDrgjxp/X/gZvBI3H+jmzrcrrd9ianV9a0YxL21qZH1dN+lxU1sT7b4w37xvJY09IcpzPVy8cCQ2SaS2I0BVi99644dkTpiqG8bQoQRHAJOXYw7ZG2p7EAXY2x5kfKGHsKJy7JgC/rm6FkHXLSJ3daufNl+UUblu6jpDSdyet7Y3WTy6wnQ7j1w8+7DPy5cJPaEoP31+G7sae6nrDCaV2Nft70TXDWFfHXhucxO1Xct55srFn7rdW0+fzP8+t4Xm3r4JuKY9gK7rdAai1HeFWFvTV/q9/8O9OOO8sPX7O2lPKE3FPq3mjxGEeR0SvjgnUscIwHWM36NooERVQlGVDn+ULfW9VlAPA/XEnLLI2ztbeXFTg5WF/m+jPN/Lz782jt+8sZuIonHR/DIeW1XHa1sbOXlKEbNH9GVt37xu8f+JltjyPa3ctmwX+9oDxstSwneyGHcyMOkBCd/1z2Ke/9BqYrpgeDce5PKK/4+98w6To67/+Os7Zev1nly/9N4boSQCoYhSFEQEFURUkJ+CiqioWMCuCIJSVFCqCoj0kgAJJCE9uZRLcknuLtf73t72Kb8/ZnZvL4UEpYZ5Pc89dzc7Mzu7s+U9n/L+CKvu9Ysn1DCnKi/lVejgcLQ4QuxDhhCCGRW5qa7JSxdWEYgkME14dmsbNz9ThxCCxp4IMyuslvLP3L2anR2DFGaoxDSDjU39BCJxbjpvCmBFhO5d2QDAhqY+dNNEsj/+YgmdhG79bZjwcl0Xs37yIhlu64tzelk2N358Irk+N5+6ayVdiThleX52dw0Nay7KdHHyhGKe397BR6eOOGiG3eEIhOPsah9AM0wyXBJet4JbtiYIXDCngoIMNz97Zge6abCzbYACv4s/v76PFbu6aOmLgICOYJS69gEmjcjmzlf3pnybDme0mRRhqcL7YfYCVpQv2WzXPZjgyasXsrVlAEURrNjdzY62IKZpO70v38OK6xZR1xbkir+tIRBLCmPri1yV4YmvnvC+q7F5O3m9vptvP7qF9v5IKgKY3qzosUcOjcjxUiZMGnvDrGkM0jMQIT9reAF4z2CMrsEYY4oy6Q5Gyfa7uOGsSXzzH5uIaVaX7sNrmrj95d1keVyMyHaT3iAciukpq4KuYOywpqxvRnL7pD0FQmCkGbiaaT9Jl34rzS1S0Wqw7DO2NAdQJGjuPbT/2XvFFSeNYvnuLl6r7+G+VU2U53q54+U93LdyH+NLsvjx2ZMRQlCe56M8z/e23W8krvGHZbt5cM1++sJDdXomds1m0vZDiNRFT3pZwIFiqzd6+M5ItyKR4Vb4zpnjOWPyCKfuy+F/wnn1fMjJ9KiptMYVJ44iFNP5/dLd5PokguE4G5v6mV6ew0cnl2AIk6sXjeKOV/fx1JY2bn1pN4UZLjaleTNdfnxNquZsS3M/HlVOGTvm+VWimkm2VyWuWR1o21oHuOGJragC/C4FzW/SYXuPJbn/8vkEoxqf+ONKalsCnHwEIWaaJves2Md9K/fR3B9FwmoYmDgik1MnjiDLo/DTp7ZT1x4klDDI9KjENJ1Fv36FcDROsj7XNGEwqvHLZ3YQ101qWwZSkcMDP7TTr6YlAW4F4ppl1JjsjovrQ7MsJSxhcMbvl/PVxWO4bGENJdle/vLaPtr6o7hViQU1+UTiBr94ficHWhUpAq4/YwLFRylKP6h8/eFNdA3GKMhw0TMYH9YMYdiCdVZlDpKQ6AxGKMpw09AX47ktzSwYV8xLOzpYt6+f0UUZPL+9g31dIWTJ6t5VJCstnP5FHUloJDSDzoEoP/r4BK56YCOaaQm+j00bwbNb21k8roBLF47i7uV7eHbbWzMjTZ8brie7ObAiY4osEUs3jCVdJAx/wW1ptt5zsiTYtL/3LR3Du8HfLpvHSb96mf19EQYiVtQrFH2LqvUtUNsS4JfP1bG2oTcVLU4nvUEG00zNeD2cPUg6igCvS2FOVS7nzCgl06MwpjiTsty3T0Q6fLgRpvnOvTneC2bPnm2uW7fuvT6MDxSGYQwzDbzorlWs3NtLabaHvkiCzT9YMqxV3zRNalsC3LV8L819YTbtD6S+GLM9CiNyvEQTOo094WHpuV+dP5U7Xt5DXyhOYYZKeyBKwjCRhUkoAVV5bpr7YmT7VCaWZBAzoL4zxPobTmEgojHv5heZUpbDQ5fPRznAOkA3TDY09fH81nae2dpG50B0WAH8iCwXPzp7ChW5Xs647XUUCWKalbaoyvOhqoK9nSF000rNRtIsCNJtBtKDIAf+nR7RkMVQwb5ui7GCTBeddh1KKnVpbzgyx0t7IMrC0QV8Y8lYvvT39bT0Rzlr6ghe2dlJTNNJTtpxKxLV+V6eu2bRf3/SPwBEEzqzfvICobhBea6H/X1DAj0ZyRDAVYtq+Of6FgKRRGog94Ekn2/VFmEw1BghSQIJa45opluhLxxHM0z2/uyjVF3/NAowpTyHUyYU8eiGFsDyjmvsCadG1Rzq/pJpRd0c7hX3djOq0M+/vryAXP/7qyDcNE2m/PB54rrJ0mtPIq7rVBf431aDUk03+MvrDdz5ym56wkPRasGhn+9D+XulR8XS39OqZF2onjO9lLNnlDKt/PBTPBwcjoQQYr1pmrMPdZsTEfuQc92/NvPYhmbWfu9Usr0qT25p5ROzyhhbksnUsmxmVeQd5JckhGBqWQ4/PXsyJ//2lWFfMoGoxoDtIA+W23eypurFbR387bI5/OX1Bh54oyk1LibPpyDQaOyNMbk0i0/OKmd0kZ/P3LOGK06sQQhrFuA5M8p4eO1+PnHnSp646viUgNzQ1McP/7OV2jQrgWTqJ1kPMn5ENjc+uZ3ewTi6YaIbQwKrNRAmoUNehkIgYs2ETH44+1wS4biBW5HQdCNVjJ8UXEkO/MxPCjDThNIcD10D0ZQIU9KK+ivzveT6XKxrstJM6xu6KcyYwk/PnsSl963nhW1tmAak2U9x/qwyfvixw88CPFbwqDKZHheRRJSoXQgvpz2vyTFSD67Zj6bphxVhMHR+irPcNPdbtV3Wa2OoYEiWYGS2m57Q0PqysFLA1QV+ekMJ9nZbKfO93WHejNTrI5miNq39H8m31KpBM4hqUOBXufFjkxhfkskpt6zgsoXVPLm5mVMmFvP9MyfRGoiwcX8f3/pXLZv2B1g8vujNd/4uI4TgipNG8dsXd3HHK/X87BNT39b9724P8o1/brImHCSM1MVPcurAsCg11nv9UFo4PbotBGS4FMrzvJw6sYQL5pRRmuNEvhzeWRwh9iGnNNeLZsDHbnuNmkIfy3dbxcmnTyrhxo+XH3a7XzxXx13L96IbJl5VojLPR47fTWt/hKbeMGdNHcGru7oIpnUjLqvrJJLQOWd6KV9YWMmfXt1nFSobICsKI30Sv/jkVCry/Jz2u+XUFPi5Jq31/uZzJ7O+sZfN+wOM/d7T5PndnDi2kGdqWwnHjdSIF7A+cN32HLczpxRTlZ/B8t3dwwrtVdmKjsR060O7e3DoWIUQ+FUIxa2ar/SUUfoIIzkt4pW8X7D2KwvLSsCjyBRmeWjptyI6yWBbhksmw+1ic/MAFXleIrEEXSGNC+5aRdA2HE0+HlUWJHSTy4+v5nsfnXBM14Wlc+rEYv6+upHeUMISwPZzl6zPk4Beux7IpwBCEE4c6uvWojsYs86VsBot0jO+ugH1XWFku/Hk7ld3o5vW8t9+ajqX/nXNf/UYFCzriqMxj49qBsfV5FGS46VrIMqNT27jwSvm8/PzpjC/Jp++cIyH1jTz2IZWTp1YRCimU5rjfd8Od//q4lHc/nI9j25o4aZzJ79t0bB/rm3guke3kbxGTNZlJs98MiqNvfzNnnoj7b1cne/l88eP4twZpU7dl8O7hjPE6kPOl06ooSTTRXN/JCXCAF7Y3s5ty3Zxym9e5buPbRm2Tc9gjD++socTx1hdjF88oYbnrjmJh6+YzwljClhQk8etF86g5ID6pf989TgGowm+/egWXtnZmSqWFZLglAklPH/NIsYVZ/GDJ7bSGojwq/On4nUNfcFIksSVi0ejSIK4bn2A6oZJRX4GOT41JVoy3DLZXoWobnUxPre1gzte2cPIbG/qw9klC3R9+Bd7lkch22utkNDNlAt6+te6zFA0S5GGBJnBwVfcugmqgPaBaEqEpTMY19nSEiBhmJTmeHnky8fhVSXa+qP0RoYkQrZXIaGbfPfM8R8qEQbwnTPGWeOgGB6FTP+tStYHWdyAcMJEwjo/yXPtVQX2yFQSOikH+OQzLACPKqXW100rYnrzs5ZdSNyAiT94jld3dSEA7wHfz8k0pISd7sR6bSSHeWtAjr2RjCWqk0jCXs9eNKU0iwevWMBvL5jOt04fT3G2l7UNfVw4t4KqAj83nTuFT80pY/G4IrY0D9DaH+XShVWMKnrnvLj+FyRJ4pOzyojrBrcuq/+f92eaJo9uaOY7j26zJxFYP+mvjdR5NA6aQjUMkfZZUJbr4UsnjeLPl87j4vmVjghzeFdxasQ+xCR0g+1tAc7+w0pg+AgWIaxW+wG7Q++Vby6iqsD6sO8Kxphz00sU+F10h+L85OxJXGK3z3/pb+tY29BHllehoWd4+ubXn5xK+0CUX7+wCwk7aqUIfvmJqXx8RhmGYfLJP61kQ1M//3fymGGDxZMYhsnDaxu5ddkezp42ki+fNIqzbl1Of0Sz59UJagr9zK3O48E3muzBz/DM105gZX0PNz653Xp8DHWmqZIVpUr3/Uonfd3030civS7JNO2Umn1bsjg8fT9TRmZhYrK1dciLzatKFGS4+fbp4zlr2sijuNdjj8aeEF+8by17O0Mc6PaWXvMFlvCVJSuSqCoSHlUwuzKPtft6CcV0VAmSzXA+VWJEtpcZFTksreskFNOsOY9p52VGWRa94QQdwRixNN+wdEYVetnTFWFsUQaDMY0ppdl4XTIuRUIzDCaUZFOY6UKRJWZW5HLjf7aR0A3K87yMK85kRkUuY4syGYxrSJIgK80TrCsYw+eSP9DCIBzXmPLD5/G5FWpvPO2/2odpmizf3cVdy/fyen0PFXlemv6HblEJyPGpTC7NZtG4Is6ePtKZ9+jwjvJmNWKOEPuQUt8Z5LJ719HUOySWVNmKGFTl+2jsCSNLAlWWiCR0fn7eFAKROD97didbblzC81vb+df6ZjoGojT3hbns+Bq+sWQsd7+6lztX7CUc13HJgiml2axp6AOgNNtD+0CMwkwX7QMxqyswodEVjPPlRaPY1hJg5Z4eYprOY1cupDRt/txgTGNP5yB7ugZ5ZE0jbzT0c+bkEhrsUS4wlCY8aWwBe7pCNPdFyPWpfGRcEav39tARjGEYJtlehUBkyLfMsLcVAkxjeLrqwOLdAxuyDizCloCzpo3gyc1tqdSZccB+krUrSdPX9G3Td5/tVfGoEr/8xFROGvf+qv95L3hkbRPbWwe4b1Vjalmy+w2s8zMyQ2buqGJermvFQCammYQOOGnJ5z/Pr7Lh+0sAGPu9p4nr1sXHYExPpZ+nlWez2e4KHl+Syc72IG5FYlxJJp8/rpJF44rJ9btswf/hiVS+Vb76wHpe2dXNd88Yz0XzK9/StvWdQT5152oGYxqqLBhV4KeufcCKhB3m4ulwCKzI96yqHC6ZX82icYXOeXN4V3CEmMMwOgeifPS219B0A0WSGIgmiGkGXlXmvsvm8pm7V1Oa66UrGCOu6aniaElAjt/F/Oo84prBmOJMzp1Ryj0r9vHIuv14VZkbPz6Jbz+6hUkjM/nYtFK+dGIN1d95BoCbzpnE9/9tpRROm1SCIgue2tKGV5WJ2C2Bp08u4dWdXXhdMmdNHUG2V2VLc4DX6rtTnlzJeql51Xlcf8Z4HlzdSG8kwY6WftoG4iCsTk3DMMl0K/RHLauMMcUZ7OoYpCjDRedgHFlYzQQRzUpPeFWZYFR703qSdEPIQ0XGvnRiDd85cwKr9/Zw9YMb6LLvJynWjhRNk4CKfA/NfTGmV+Tw8/OmMLoo8022+PAR1wye39rGLUt3syfNby4dj2zbE+gHd7n6VEFCt1KQsytzaeoJ0R6M41Eg3++2bSR0PjK+mFyfi/wMN5X5PirzfJTn+3Ar7896rPczHQNR5t28lKJMN2u+d8pRbaMbJj9/dgf3rNiXOm+LxxXx/LYODjNX/U3xKIKZlblcceIoThrrCDCHdxena/JDTEI3uO5fW9jaEiDX5yKmG2zeb83YK8/1ENUM5lXlsry+h0hCp7U/QsKw2vN7Q/FhEaCxJZns6wrxdG07AC/u6OT+1Y3cftFM5lbn8Y1/bubO5XsAy3aisSc87MPu+09sw8Cq4XpuWztCwA0fncAlCyp5anMbmR6FUycWU985yM+freOpLW30heNUF/i5/IRqZlbkUprj4ZpHNrOve5C1+3o5946V+FSJcPqBmlYKUzfB71EIRDVMYFfHIKoEyQSTLAkElqdQXDeJ61pqePehEAxZJiStCdKvYy4/oZLrzxgPWA7tkahVcH+gCDvUHMIkZ88YwYvbOjl7+kh+dPZkMj7AKal3Cpci8bHppXxseim9oThfuHct21oDxHUTxT4nfo9KOK7b6WqLZE2iV5WpLvAyMseLS5WpLsggktA4eUIRk0uzKcv24nY5z/vbSXGWh9mVuaxr7OOVnZ0sOkKEd0dbgC/cu47WQDRlOXHGlJHUtQUPulA63Fs2JbxdVkr44vmVLJlY4jjfO7zvcCJixziX3PMGK+q7Act/qjLfR3sgmpqt+MtPTOXelQ2p9N5Fs0fy4LpWCjJczKrMpSsYJcvjYm51HmW5PmZV5tIXjgGC7z5emzKW/PHZkwiEE/zmxV2U5XrpHozhcyksv24xk3/4fOp4BJDlVcn0KPzzSwsYkTPc/TydUExDlgQeVSYQTqAqgsGoxim/fZUBO8qVrDMzdGuM0IECRxV2/Zf9v98lE0qbCZgSR7x5Z9WRIlm/+eQUPjG7AoCH3mjipqe3MXjgcDreXIRNKslgZ2eI82aW8uOzJ79vO+Heb0QTOmsbeqlrG2BvV4g39vWk7CXKcr1U5fvwqDIFGS6q8v1MLs1mTlUebuf5fVfZ1RFkye+WU13g5+VvLjrkOjFN59pHNqUu9pLTKACuPWU0Y0uy+PL9G47q/lyyYGZlDp+eW8FHp4xEkZ3eNIf3Dici9iHlJ09uZ0V9N7k+lWll2Wxo6mdXxyCSsOrAGnrCPL6xhYaeUGoGY1SDX58/lW/+cwtnTR3Bk5tbqW0Z4JVdXYA1U+20ScVcMLucJ65ayDWPbOLfm1r5wRPbeP7rJ7Krc5Cnt7RSmOnmswuqaOgOkelRCEY1rj99HJ9fWI0iiaP6UEwWKPcMxrjmkU2EYwn29YQZiGpU5XuJxnUGI3EidhX8oa5zXaqMqRmYphUhU2QxzNQxqYkOlEwH1nbJdqrTwCoGT7OfQpHA71apax/gR//Zxqq9ltN5UYbK4nGF/GdLOxE7YmccEB1L/s72yDT0RhhTnMn3z5roiLC3gEeVOWFMISeMKXyvD8XhTRhbnMlYuzxga0uAyaXZw25/aUcHVz2wgZhmWcZMLcviU3MqeGpLGyv39LByTy9XLh6DKob76h2ISxaMLvQzrSKXH318Mi7FEWAO72+ciNgxyvbWAc68dQWyEJTnemkPRrn21LHMr8mnusDPNY9sZlldBy7ZmtU3pTSL2pYbnfapAAAgAElEQVQBvKrE5JHZNPSGee3bi1Ftw1TDhIfWNLF8VxcJ3eCOi2dy/Gjri++yv65l2c5OppZlc+nCKq55ZDM1BX6W2Ve9C25eSttAlO9/dAJfOKHmv3o8d7+yh5ueqyPLo3DujFJu/Pgk9nSF+PRdq+gNxVMeQsl0YdLc0S0Lorr5ps7mKS+wA6JVyf+9ikTE9hE7MDJWluuhIxAjYdejRRI6p04o4vMLq5lbnYcQgoRu8NsXdvGnV/cctuvSq0jk+l08ftXCY35skcOHl9V7erjw7tWMLvTxx4tno8gSdW0D3PHqHrY1B1INNDefO5kL51lF/dGEzvjvP4dLFlTm+9jTGTpk9FoR4HdJfOHE0XzxhJph1jcODu81TkTsQ8htyywPpJE5Hpr7I9x76VyOt32/APrCcTI9KoGIZYZ5/Kh8alsGiCQMWgNRrv7I6FRR8uyqPAAmjcxi2o9eQDNMOgdiqX0tmVTMsp2d7O0KMaEkC7DqeJp6Qjywpik1KDtxBEfLmKYjEAddwcYSOmua+nDJgoGoxqkTixFCMLoog7U3nMrahl7ueLmeFbu60Mzh400SxpAXmEcRKVd9tyoR1yyn/OQIGtP2/UqYQwaRAvC5ZQwsU9d0AeVVJe78zExe39PDr57bSSiuUZHn5crFY5hSNnS1r8oS3z5jPEsmFfPjJ7excX9g2FiVDLeCW5W497K5jghz+MDS1DXIn17dTTCmccmCaubWDH3exBI6/ZEE80flk+WRaewJc+rvlg/bXgDzqnK499K5eN1DFh77ugfJ86tE4hq7O0Nk2J2tSWQhGFPs59NzKzl3RilZXhUHhw8SjhA7BukNxXl2qzWMuGcwznkzS1MiTDdMbvj3VtY39nH9GeN5traNnR1BXt5l1ZG5FImzpo7g4nkHt5h7VJmCTDceReLk8cWp5Ul/scGYRq7Pqi1b39jHNf/YzPrGPvz2lenh0m2v7uric38Zci1fef1HGGnXjpmmye9e3MW2ln5OGlOA160wInu4WJlTlcdfPj+HW1/axf1vNA2b/5cyXLU7P4WQMDGIJAw8qoSpGZRkufnemRO5/eXd7GwfBEiZiKoSFGa66Akd3J03uiiT217ew3PbOlAly0qhqTfCZ+5ZxWfmVXLl4tGpgeoAMypy+e0F0/nawxvoHEzQHYyiG9YV/52XzGJssdMd6fDBZNWeLi6+Z03q/fZkbSduAYXZLnL9btr6InSHD3SBG24/8szVxzMhLV25uz3Adx7fSl1rgMGESb5fRdMTfOnEGtY29LFmXw95fhefnFXOl04a9YH2WnP4cOO8co9B/rVuPzBUB3bujLLUbT99ejsPrWliycRiPregio6BKFuaA9S1WyaiRRkuvnbymEN2FkkCppZlI7BmPyYpyHBRU+hnb1cIj0vmgcvn8drubnpCMX56zmQuumslISyriAP56VPbuee1fQCcPL4In1shP8NFQjdQZYnblu3mkXX76QsnGF0Md1wwHfUQ9WVCCL526jiWTCrm7D+8Ttw4OAVoFfZL6IYV2YomDPJ9Cn+8ZDbLd3bS2BtBx64BM4Z8w3a3hw5KW14ws5TPLKhiV0eQZXWdqJIglLBqWwaiOveubGDtvl6+sngUJ08oSW1XXZjBv76ykMfWN3PTs3VIwM/Om8rC0QU4OHwQ0XSDr/x97UGp/5gJzf1xmvsPPRi92C8TSRgMxK0N71yxl8p8H+G4zst1nTT3RYhqBn6XRLZHoieUYGS2h9Mmj+Dqk8eyvzdMjk8ddrHj4PBBxBFixyA3P1sHQGW+j+a+CDMqcgBY39jHX19v4PPHVXHjxycRjmu8sK3Dnssm0E3LtuJwtRUt/RFe2NbBWVNKuPYfmxhdmMGFc8oZXZTJ419ZSEzXyfIoCCE4ZeJQxMzvUumL6JRkD3eu7hyI8nRtGwALR+fz58/PAazI2rm3Wz5nXcEYg3EdjyLR3h/hD8vqueYQjvsAkbjO95/YjqrK6HEdwxzedeVRJUJxy6wzaUMRTRjc8Hgt+zoHCCfsOZJ2ytCw19HhoKKubL/K+BGZrN7bg2lCKGHgU2XCth9aTDNY19TPtx+t5ayp3Vy5eDTPbGnjH+v2E9OMlP/VBbPLOHPqiCOdUgeH9y2yJBiMvbVaY0WC2z8zh1nVeVz7yCYe39TKk5tbU2Iuwy1j6Ab5fhWBybTyPM6dWcrpk0pSjT7lec4wbodjA0eIHWN0Dw7VbrX0RZhWnpNKCT6ytgmAa5dYQuamp3fQ0h+xa6pMCjJc5PjcXP9oLb/45NRh++0djHHD41sBeKq2PTWf7/dLd/PZBZVcfkLNYeubBu0aMZcy/Mr1lqW76RmMMXlkFq/X97C+sZeZFblc8bd11HcO4lasRgKwTDxDcY3fL92N3y3xxRNGIYQgpunohkk4qvGDJ7eysalvmG/X1LIctrYEiGoGwaiOkpwxCbjsKNb21gF0E3K9MjHdtDocD3C+Txb0Z7kFkqTw4Bv7eb2+h53twVSNWTihWyaxikTU7pLsHoxz78pGXt7ZSSSuE00Y+F0KHlWiMMPNzedOecvn2MHh7SQQjHDFAxu57oxxzKzIe8tGp7phUl3oob4zekiLl0wJLj6hmruX77Nny0okdIObnq3j8asW8tGpI3hhewehuM7IHA+t/VHrfSdLLBpXRGW+nytOrHE6iR2OWRwhdoxxvz3+ZXxJJoYJX108CrDmvT29pY0LZpeR5VFZuqODB95oQpUECcNEAH+6eBZ/fm0fK/da9WL94ThPbW7lic2ttPVH6AxaIs+jQFSzokkl2R7uWbGPh9c08Y0l47h4fuVB1hTV+T5qW4NkHTAt+aK5Faza04NhWKLlyc1txDWTlXt6KMvx0txvzZJLpgXbAlFKMl387sV64prJVz8yht88X8e9rzeAEKiywK0IJElmXlUOe7pDnD+7jPI8L49vbMWlSCS0tIaB1JBn6/dARMcA8nwq/eEEhjlkW6GbVt3YnOpCXt7ZydUfGc0fX9mT2jb9d1w7eCZhY08EjyJRWeCnMMPNyj3d/OmSWY63kcN7ylf+vo5nt1n1pJ/442pOHJPPHRfPPioj4WAkztcf2cyK3Z2kWfPhkgU+VTCtIg9ZmOzqGOTBN5qQZMvZXhGwvL6X2pYAy+o6uPLBjZRkefj1GeOZVZnLQDTB/t4wo4synaiXw4cCR4gdY9yydDcAo4syeGpLW2o8zmu7uwnHdc6ZXkpcM/jxk9uAoa7C688Yz+yqPFbv7eHZre28XNfBX19voDUQIZbQae2PplrGbS9YTCxxJGEV1f/4qe38fVUjFy+o5PTJJYzI9tIfirOpeQBZEmR7Xanj3N9reZh1BaOosoTA8hH6x9omZCFo6x8a6JsURIpkDYRUJbj39QZyfC4KM72cMKaAtkCEvd0RopoJaKxr6Oem8yZz1rRSjqspYNP+AM29YbK9MhHNxDAsI4kMlyAcN5CkoXRlTziBYteJaWmFYW5FsLSuk+tOH8eYosxUyjPpwea206C6ybCuyCSV+V52tQfp8ET52XlTmTRyuI+Sg8O7xT/XNXHjE1sJHWDItXx3D+39EUYfpnHENE1+/9Ju7rLnySaRBFTkemnojeBWZCoL/Ly6q5vCTBeBcAKE4N7Pz+G40YVc/aBlyKoZJt97vJb5NXn8/lMzyPVbnw9FWR5nrJfDhwrncvwYY4ldmxWMJpAEqcHZL2y3asHGFGfyu5d20dg7JHQWjyvkihNruOWlXfxr3X5Kczxceu86TOAnZ0/h7s/OxueShtlKuOxoUjKJocoSqizY0x3ix09u52O3vcYvn93B7J++iIn1oZt+lR1J6Px9VSOjCjPoCycwgcGoRn6Gmzy/So5PQbF3nulReOVbi9j4/VMZVZhBXDfpDsW54d9bmVOdx58vnceTV5/IzedNxq9ax/TRaSWcNa0UgPJ8H6dNKsI0TUIxnSy3Qo5PJcurEI4beJQh0ZT8WkqKsiSSgJhmkuNVSGgG969qSN2mG5ZPWUw3h3mZccD2bYEYAssw91Nzyo/6nDo4vF3ous4Ff1rFt/5Ve5AIS3LfqoZh/5umybObWzjp5y9Q/Z1nuGXpbsJxHYEV7Z5VkYMsBK2BKFcuGsVVi0dR3zlItlehKMNFXDcpynDTM5jgNy/s5MktbVw0r4LPLagiHNf51mnjUyLMweHDiBMRO8ZI1mn1hxP43QqSJDBNk7p2a4TRTU9v59+bWlPrj8h288eLZ/GPtfu55aXdyJLgtgunE9UM5lTlUZ7nYyCcIMur0j4QQxYgTBBJfwcAASF7nE9yIHf3YJw7Xt077NgiaVfQY4oyePCL8/jBE1ZkThJW+nQgmjhoBJAkYFNTH819ESu9qBupWY+/eX4nf710Dooscd7Mck6dUMLz29o5b+ZQp+jGpj5errM8xjChczBOoV+hO6ThkQWaCS7J5MCJROmHkeNVCUYTBCIav3vJijp6VUEkcbBZrGkOPTUAmS6JWVV5bGsLcsmCKk6ZVIKDwztJbUs/j65vZntrgLJcL16XwgNv7EcFEkfY9h9r9/OtJeN48I1GHly7n/19kWEXFi5ZUJTloabAz4r6bloHoozI8TCrIof2QISlOwZYMCqfa04Zy8SRWXz2z29Q1x7k649sRDfhhDH5/PBjE3ErMlefPJp8R4Q5fMhxnPWPMS66ezUr9/QwutBPWyDKx6eP5PX6Hpp6rdl7PlVCM0ziuolLgmevOYnuwRifunM1YEVrPjlreLTGMAw+dtsK9nQOIiSJSMJAtidfJzsMs70qhmkSTJsBmU4yurRwdAFzq3J4o6GP5r4w+3sjKePUw70S060jFFmg6SYSVlG8Igv+/LnZzKs5tP1DXDP4xB2vU9s6gCIgy+fizInFPFnbSiCqp/ad41WIJXSimnnI40j6HUlYAlBP68hUgKRDkssWosl9SAJyfS56QnHmVuXywOXzUBWn6NjhnSOuGUz4/rOHnSTxVhFAeZ6XjoEYMc3gu2eO467l+5CE4PxZZXxkfBE/fWYHW1sCGIaJLAkeu3Ihk0ZmUdsS4Dcv7GLT/j7imsmU0iy+e+YEplfkvj0H5+DwAcFx1v8QsdP2A6u37REeWrM/dZsAwnY3n0+V8KiC6x+tZV2jNRvxqsWjDhJhAJIkMaMyj2BMt6I9hokkQXNfFFmAIkmEYxrqIWa6Vef76AhEiOsmp08p4aXtHbyxtxtZknCrEi4JorolWEzz0IO3TXPodt3+dvG7JVyyQk84zqfueoOL5pZz83lTD9r2xW1t1LYOILCGf4diCR5eu5+yfC+B6FB6tj9ysNlkOjqWk37U9goD7DqzIREGENfN1Dw9gLIcD019UQB+8clpjghzeMdZuqPjsIPlBeBTxWHTkklqCnzkZ7i5avEoFo2zyh0uvHMlq/f18cbeXjyqhECwor6bp2vbaOwNc/qkEj4zv4LH1rfw6q5Orn9sC1tbBlhQk8fF8ypZMqnE8iF8i12ZDg7HOo4QO8YIxzUUSQwrMk9iAi5ZItOjcPfnZlPXNsB3bUuKM6eM4FunjT/sfn96zhQ2NvZRU+jH71LY2jbAK3Wd3LJ0Nx5ZEIob6LaHVrJGanyJn4buMDE7yvRyXRcxzbSFjEEgMiS7dNOaFZd+2LK9r+Sy5E2KBPl+F232mCVFgkfXN7OnK8SoooyUJUQkrvH4xtZh28Y066+2vkhqjNHRIAuIJAwkOxLoka2mhfQB4gAzy7O5/4vzmfiD5wHoGoxz1yWzaOwJU21PIHBweCf582v7DhtdznCBx6USShzaZBWs99ayby4+aPmC0fms3tfHsrouZPszpqrAzyXzKplWkU0wqvPohmae39ZO5h6VHJ/KT86exNkzSslyTFcdHA6LI8SOMS6cU85fVzYetLwq38eVi0YzoyKH0UUZCCF4cXt76vbqfO8R9z2jciidML08hwKfwv2rG/C6ZOKajoklbAwTTh5XyNKdXan1XbIgFNfJ8SgEohqKJJEwhse/rG5DgWGaGHadlc+OsoXtSJRiu9039kZTA7R1A2TZGnTePRijpT9CeyDCzU/vYFNz/7D7SKZATSHIcEsEYzoHkkw5zqzIYtP+AetY0kYlYcIJo/IZiGns7QpRmuOlvjOIR5W58ezJnHvHytS+vnHqGJY4NWEO7wI7Wvu5b1UjW1v6D7tOMA7B+OFFGFgTJQ7FeTPK+d2L9QhhNd98ek45H5lQxOq9vdyydDct/RE8qsRHp4zkonkVzKzIcaJfDg5HgSPEjiHimkFZro/JI7PY3xsmENVSheSTR2ZRmutljN2WvnxXJ39ZMVRMf/sre5lRkcspE48sGtoDUX7+zHb+vdlyxc8HsrxDA8QL/Crr9/cO28awaxH77RoytyoT141h6cjkB3w6kYSRElyyRMrPy8BKV3oUifmj8lm1pwdVgpa+EL94djvbW4Ps6QoNq9VK37UizIOK0pIiTZYEPkmyCpQPE1rwuRXu/cJ8NjT1MbMil+5gjMbeEKZpptLDflXi8hNHH/H5dHD4X3iuto3HNrTw+p5uQvGDLyzeMocRT2W5XuZWZNPQGyEUS/DYxhYeWrufmgI/40sy+caSsZw6sdgZOeTg8BZxhNj7FMMw2dTcT02BnxzfkbuKNu/v4/aX9/DC9g5UWZDtVfG7JCrzfVTl+1lW10lTb5jp5dmYJqzY3U1MZ1iU6cv3b+D16z9yWIf81+u7Ccc1SnO8KREG0BtKpPSKADBNwjFjWAF+uqeWCfRHEghg0bh8draHaA1EhwmlpHBKijBFhoRueXkl04uGCVcuHs1Vi0cTiCS49uGNLNvVzdOb2xGSYMKITOo7gsSNITuJ5F3EddA060trdKGPlv6o5agPuBTBJ2aWs7m5PyUSJWHViIUTBmOLMvjJOZMBmGkXHRdkuinIdPPEppbUY/jjJbOOeN4cHP4XWvojXP9oLf3RI/VCHj0fmzrykMuFEPzjyuMBeGNvD429YSryfEwpzXYGbjs4/A847573Kc9va+f6x2oJRBKUZHn4wvHVXDCnnGzv0NXmvu4Qf1/VwNId7fSGEgRjOlX5PsYUZ3LR3HJerutie9sAz2ztoDLXS23LAFtaLBuL0mwPBX6V7lCCTI9KbziBYZp877Et3P25OYdMKdz6ktX95DqgKN/vltENE9NOKXaHrajXlNJstrcNpKJcPgXCdmW7SwKXKrOleYCe0NCXSFK8pYsyr0smlprhOHSDDFwyv5KuYIzL7lvLrjbrsXlVQY7fTXNfJGVJcWBgK203FGZ6OG5UIX9/oxGXLJhQksU3loyjoTfEeX94jZhuHc/lJ1SjSBK/eXE3t7xUnxJj6dz8lGXH4XdJnDi26KDbHRzeTgYiiaMWYX4ZvB4FVRJ4XTI5PoUsj4t4Qmd3V5gMr4sTxxbwlUVHjuLOq8lnXk3+/3r4Dg4OOELsfUswphGIJPjsgkp2tge56ZkdPLWllT9dPIsROV5ueLyW57a1MxDRUvVXxVluBJDlUVg8vpjp5bnEdYNMj4LPpfCDf9fyt9VNZHkEFXk+6juDyJKgptBPqCWAppus2N3NHa/Uc9XiMcOOZ0tzP/Nq8qlrHyAQHZ7+iGkGPlUQjFm2EkMpPvC5ZAZjlvqK6ZYAy/W76QjGiMf0VKgqGQFLN0PN87nI9Co09oSRAa8CmmEVCZtYNWRrG3vJ9bnY3jKQil5ppknHQDRlyiqEbTlhWPUvduAr5YS/am8vfrfC108eQzShc93p4xFC8EpdJz6XQiKioUpwzvRyqgv9BGM6dy3fy9SybM6fPdRlunR7Ox2D1pfi01ef8Da9EhwcDs0ja5v46VPbyfIoBKPaYQv0XbLAMOH1751yVNF1BweHdxfHWf99Ql8oRlt/hGV1HexsD3LKhGJkSZDpUXjkSwt4+IvzqW0JcP8bjfSH4rxW3033YBy3IojqVh1Vx0CMxt4wz2xppTMYJdfvojjLg89l6e2P2imHgajJG/t6yfG7OG9GKbMqclDtCFhMN7nr1b2s2G0V2pumyd9XNfDVBzZw67L6YSLMLVvbCCxxkyxql+xg2pbmAANRLVXAr8iChAHRhJ4qCFZly85BCGuEUfqQ7aim0xOM4lEEsiyIaEMjmZL834MbuOvVPeRnDH3BRDXreASgyqR8yjzKkAjzKhIuReaez8+mKNPNSzs6eXRDC5ceX52KBi4eV0x/RMMAYgZ8+u5VBKMJrjttHAtq8vnev7eytSWQut/HNlppydmVuVQVZvw3LwMHh6OiLxTn24/WctzoAm67aCafnnuw7Yxqvz/jukm2V019Djg4OLy/OKIQE0L8RQjRKYTYeojbvimEMIUQBfb/QghxqxCiXgixRQgxM23dzwkhdts/n0tbPksIUWtvc6uwvwWFEHlCiBft9V8UQhyTDoCmabJiZyfXPLyRE365jMvuXcdptyznpR0dLKjJ55nadkzTZP6ofObX5PPc1nZ2dgQZsGusZCFS3lqKZAmOoiwPeYe48p1cmk1ZrlX/ZQC7O0MUZqgsretEM4xUZ2AorvP1hzdx+8v16IbJP9btp7kvMmxfElYBfrZXQRKWyWqeT0GVBXJSiQlhOfHb22j2HQSiGpphCbZgLGF3PpoYphXpcsuWoAvHdUJxw54fmfacpf0dSZis3ttDf2R4ekaRrMeo6VYUzKPK2NlNynPcxA2TaeU5nDy+mNe/vZiTxhbS1Bvm5N+8StiO4E0cmUVFni91/O0DMa5+aD1CCP5w0QzKcrzst41yAW7/zCw23HAq//rKcYc93w4Obwd+t8y44gxGZHs4aWwhp08egSqs95TPJaPKghHZXh784jxqb1zCqu+cfFBJgYODw/uDo3ln3gucfuBCIUQ5cCrQlLb4DGCM/XMF8Ed73Tzgh8A8YC7wwzRh9Ud73eR2yfu6HlhqmuYYYKn9/zFDfzhOQjfY1RHkVy/s4rX6HjQDKvN9TCjJ5Pcv7eaMySXs6w5RZ3fhlWR58CkyX394AwPRBF6XTChupSRk7OJ2E/rCcW56ZjtgCZzvP17Lx29bwXE/W0qzbS6a5N7X91GZ50eSZSSswb2aYdIXjnPrSztp7Y+wrzs01NmIlSI0sOqsdN0kqhmASSCiocoSki3EDMMagJ0yQDUhw2NdlSfrwDTDSp14FJGqC9PNofo0E+tFGtetUUIAp00sYnZlNoqwarFG5njRdRO3/UUjGJoVaWJFwQZjOroJxZlujh9ThG6YfOmkGgBURea+y+Zy4ZxyglGNa/+xOXX/XztlDEsmFqUewys7e/jRk9vIz3Dz/DUncsaUEcOez7wMJ/Xj8M7jUmROHFvI4xtbiSZ0jh9dQEmulxyfi4WjCzhneimPX3kcx40qINOjOiLMweF9zBHfnaZpLgd6D3HT74DrGB6gOBv4m2mxGsgRQowATgNeNE2z1zTNPuBF4HT7tizTNFeZ1qylvwHnpO3rPvvv+9KWf+DpGIhy8m9e5Z4Ve/ny/RvY3hpIFY/3huLsaA/S0h+hIt+HJODZ2jYaukM8vrGF7R1B2gbiuGUrYqQb9klM6zJM6AYvbu8gHNfQDINH1jezvW2AhGFy+fHVw44lrEFTX5hPzykn26uwvy+CbO9LN+AL965hMM1rywSSJvSKEAzaw3/jOrYw1Imluc/DkD2FCWj6UPukLCBDFeimSVSz6svSzWilA7bHtGrMXt7ZRW8ogbBNVne0D4KwatUkhjotverwhoPCDBc/PWcyT9e2ccn8So4fPXws0s/Om4LfJbOsroNAOM7qPV3c8Uo9J08oJs+vpoTg/asbeXpzK6rsfLk5vHd88cQaln9rMR5VRpIEPzl7Mk9ctZC7PzubX50/jfwM93t9iA4ODkfBf1U0IIT4ONBimubmA7rrSoH9af8328vebHnzIZYDFJum2QZgmmabEOKYaUErzvIwqzKXW17aTVmOF7cqo9n+P8Ho0MCcf65rZl51Pk/XtlGe50UIS7zoWIXvAGOLM2jqDSFhMpjAFicm/eEEK+u7OWViCXd/djbrG3pZOLqAOVW5PPzGPuyaciQgFNN5aG0T0bglZNyKRFQz0EzY3RXmQJI+Xj6XRCBqGbnm+lRCdkrPJZOyxkhtY4u7lDu9XTc2mBhKqxoGYJqp7SR7mUvYAtMWh24FGrrDeF0S4biBKlmRQNM+LkUCjyIzmOapNLrQx7NfOwlVkdg8sfiQXaFCCOZV5bJsVzfTfvwihZluMAzuWr4Hn0shmjAIx3UM00qpOji8lxRlDreZWTTumPmIdHD4UPGWL+mFED7ge8APDnXzIZaZ/8Xyt3pMVwgh1gkh1nV1dR15g/cBPz57Mi5ZIqYbhOI6hRkqAphTlcvEEZbp6pqGXs6cUsKerhA/fnI7soBsr0JRhgsTa15kQ2+YhG6iyArZHjmV3otpBqv2WoHMk8YWcu2SccyryUeSJC6cZ0XFXJJgXEkGijDRNAOXIqEDumGkiuYPRTJClSzcd0kQjSXIz3Azszwbw04tpu8ieVxuZaggX2CJNsvLTFgmrZJVtA9WetGnCnQsEZbErUhMr8gmapu9ZnldZHiUlA+lZjBMhC0anceL1y5KzcJ8M7fvsSXWcz+60M/5s8q4YE4FCR3290VSI4q8qsSkkTmHf4IcHBwcHByOkv8mtzIKqAY2CyEagDJggxCiBCuild6+Uwa0HmF52SGWA3TYqUvs352HOyDTNO8yTXO2aZqzCwsL/4uH9O5Tku3hujPG09wXweeSyfJa4mpLc4DCDDeV+T7aA1E2NvWT53eR43MhhCAUM+gcjKMb1tgfVbJOYkzTmTwiK7X/hG6ybEcHeloqMMlZU0dY6UTDpL4rRHMghipLdq3XULQtKVe8toCRpUMr57gBER1Mw6CpZxBFHlpLsguI0wv2k5ErAyulKQTE7dxsXDcxbHsKgHDCTLnpgxquzKEAACAASURBVPVb0012d4RQZYFLsrYP2WOThkXhgBs/PpF7L19w1KNWzrOHnmuGyXWnj+faU8dy1eLRrLz+IzzwxfksmVTMo1cuZEpZ9lHtz8HBwcHB4c14y0LMNM1a0zSLTNOsMk2zCktMzTRNsx34D/BZu3tyPhCw04vPA0uEELl2kf4S4Hn7tqAQYr7dLflZ4An7rv4DJLsrP5e2/JjhM3MrmFWZi2lCMJrAo0rENINXd3ezv8dKCT62sYUL55Rzwexy4rpJODHcwyscN4gZlvBa09gHWCdVlaBrMHbQrEWAxu5BkuVNmm6ZsPrdCmn6ieR0H1lATDdSMx2TxfNJFGGlKAHag3GiupV+9NjizTStCFSykzLdfcIlWT+GaaUTXbLAp0pk+Q7OmBtYadgZlTlWN6Zk1ZXFDegdjIMkrJFJ6ccmCyaPfGuCaXShFfVKjmuSZYkL5pQzMsdLtlflzktmMzFN8Do4ODg4OPwvHLFGTAjxELAIKBBCNAM/NE3zz4dZ/RngTKAeCAOXApim2SuE+Amw1l7vx6ZpJhsAvoLVmekFnrV/AH4O/EMI8QWszszz39Ij+wAgSYKfnzeF7/97K7WtAXwumahtdOVRBZ8/rprPHldNtlfl+F8sI9MlEbSt4lPjg+xokW5CtkemP6Jb0SYDtJjOit3dzKrMAyAQTvCv9U38e2NrauRQ0iqiOxRHAryKIGJHp4T9k2wCSC4zGDJGNUxSxflFmS46gnG7a9FI1YXphjksouV1ywzGdHL9bvpCMWu/wnpE0YRJzD64Aq9Md8QSnrKAcEyjOxglrhnENdP2CRO4ZUEkYaTmag4hOP/OVUwryyHbqzIyx8uSScUcV5NPbziOJMRB45zqOoL28fy3Z9XBwcHBweHoOaIQM03z00e4vSrtbxO46jDr/QX4yyGWrwMOmhVjmmYPcPKRju+DzpjiTObW5LN6Xy+hmI5LFmR5VV689iRybS+wx9bvJ9Mt06MP714ESxRNHZlJdyhBeyBqRZiEwKNIDMZ0ntjYzNdPGUswmuDL969jW8sAgzEtJeSS/qyKXRCf7F50qxKRhIFblVODhBVpaGZkyp1eFWR6XJRkedjSEsAtC4TgIO+v5JBu3STVhRmMaUiSoMivcvL4Qh5aaxmiFvpdFPhdBOMaiv2cRDWT7sEYAsvGQtOHvMwQEgfcHQCnTiikoiCD2uYAfeE46xv7eGhNE36XTFGmm+b+CIUZbv591UKKsjzUtQ9w3b8s64oJJU7Uy8HBwcHhncexWn4fcMXxVfxnYwvtgQiLJxRx3WnjUyIM4GfP7iSuaQTTXO2TQsoENrcGU/9bJWEmcWEJlkBEY0/nIIFognUNvSQMKwqW5khhRbBsIZOMYhl2DnFsSQZb9gdSnl/plGS5QEDXQIyuYAy3IpHQDIQQqc7KdJK2GMn7TOgGN50zhdJcD197aENqvYFInJhmMGB3kGqGiVeV0A0TVZKIaEMDxUsyXBimSTA2PGUrAeNLsrj6lLGpZTFNZ8WubpbVdfDgmv3MKM9hZ0eQ/3toI7OrcvnTK3tSobAvLxr1ZqfMwcHBwcHhbcERYu8humHy9JZWfvX8TmKaztWnjOHKRaOHFZbXdw7SNRgj16cisKwi1LTIVDJ9WJnvI9fnYkd7gEjCJK4ZKJJ1H16XTCAcw6PIJOL6QSIsM21WXXIcUMzO8W1tDjC2JBNVktjRNmCZtAIuRaJ9IE5BhppKXeqGYYkv07SK9O39JVOGmmGNHNLs+9d0k10dA1z/2BZUaWi9uA4xXUvNiQSI2unPpLGrboJbgo5g/KAOz1kVOVx3xnjmVQ8fSuxWZE6ZWMzJE4rY0NSPW5E4ZUIx/9ncyvqmPjQTsj0KN587hRPGfDCaPhwcHBwcPtg4Quw9ojsY49p/bGJXxyAVeT6+edpY5h4gHAAKM92MLc5gdmUu1QV+/rO5lV0dg/gVkbJoEJJgX0+YfXaBv1sWJHSTGRW5zK/J55zbXyMU01MpxlyvQjBmjRlSpOHeZTCU9nTLMKUsl2+fMY7ZlXls3t/PBX9caYklWwn2hxLk+1W6QwlkSVipQoYEXfoOZQEJHfL9CuG4VdN1z2sN1mMQlrGrlBY1S9bAmcN3g5HsohQC84DZk1W5Xn7+yamMKco87HMvhOCWC6dz+i0rAKtAf09XiG+cOpZPz6ugwDHCdHBwcHB4l3CE2DtAfWeQkTneQw7ZNQyTv67cx6+f20lBppurFo/ikgVVh91Xtlfl+a+faJmfxjRe2N6OhEkoPpT409PEiCQg26diGCZ9tjjqDMZTt6uS1U1Yme+juTeCbs93tOZWWpGmLI9COK7hUiQuO76KjoEYe7sGyfW7MFNdAhYuVSIQ0chwySR0IyXAVMnal0sW5Ptd9IcTZHpVSnO87GwPEteMYYX1himQhTls2YEu+0b6bzEUtUuS4ZL5v1PHvqkISzK+JIscn0p/OMHe7hBluV6uPnnMEbdzcHBwcHB4O3GE2NuMaZqce/tKBuMaFXk+xhVnMq7E+hlfkslTm9t4cE0TEc1gf1+Ee17bx9qGPqaV5zCvOo8JI7KGhmbbCCFY9IulhBMaPSErepVcIylFFNuc9dpTx/L7pfVsaQ7QHUrQ2BMati/NgN5Qgq7BIWd4lx1BS86FHIhaacHBmMGVD2xMFelPKMnAQCAwUSTrdyRukOdTCIQ1NIYK+pPF/IYBHQMxynO9hBM6W1sCxPUh9/zUcelWWjOZajVhmAjL9qn0hRMYcFB3pAA8KlxyXCXnzSzjaLnrktlccOcqhBBke9Wj3s7BwcHBweHtwhFibzOGCb86fxo724Ps6ghS1z7ASzs6Uuk2VRacPKGYz86vZFNzP1v2B1jb0Msb+3r4yVPbOWFMAZIQTCvPYXp5NrIkcNlmqz0hDY8i0E3LN+xA0dLUE+b/HtpEQjfxKJbU+c+mViQxNALIxJoJmexclOxtD0z/Jf3CrH1by3a0W/5jJpAwTGRJcP6skbxc10EyuZkctu23uy6FafmUNfRGUs9RevelbI8nSk9HplKjiiChmbgVGLB9vfyKNR8zHRO4/aJZfGRCyVs6V7s7LasK3TD5wgnVR1jbwcHBwcHh7ccRYm8zsiQ4fXIJp08eEgXRhM6erkF2tge545V6qvN9HDe6gOPShk63BSKs2dfLzvYgy+o6+cOy3Rgm5PtVFEmQlEpRzSTXqxCIagjTmjuZ7ZEpyvKwrzuEaVrRIkkMjfJJF4GmaRW+J1N8dpPlsNossETYgcu8itWxmD5Y+6ktrYTT5g8lo1WDdnG9S5GoyfPR1h8hENNTjQZJcXgI438A8n0qPWFLfMU0UtGyqH7wDCxJMKyj9Gho7g1xw+NbAZhensO5M44+kubg4ODg4PB24Qixd5CtLQEeeKORedV5TByZzdnTRnLHy/Usq+ukpT9CttfFGZNLKMp0Mbo4i7OnW/POrzt9PKGYxi+freOlHR20BKLIDAmjkbk++lsHhs18zPUZKed7AEkITDsalcQyWrXyj+kGrSbDRVfSYV+zU5WKbKmmiB3GMoAMt0xcM8jxucgwTHpCcQyDVHozGdmKJAx6QjECsf9v787j66rKhY//1j7zOZmHZuqQznSipbSlpVBKBWSSKhcBFUVAZRBxFhV99d6rvBfF914EmS4ooyIoCMo8z6V0pnOTNGnTpJnHc3LGvd4/9j4nJ2nS0tImoTzfzydwsvZw9l5nJXm61trPSmCo3iFLhZ2Iln0Dviyvk5ZQrDehLNb6kpF+88qcCrwug2DU5Ml1tSw/rowPozsSZ9nvXk8tVv7ENSd+qOOEEEKIw00CsSOopiXEk2v38PSGerROZptX9EQTbG/oRgMPrqjBoeDWL87l7FklqWMDHidej4O9XVbm+QSAhoKAk9buMChwYGXgn1aSxVdPHM93H1mXWhxba90vy3zvOo/pXUrJJKtpufqtoUJ7u9XzZS0llGTNH0tgAHUdEdwOcBmKsB1NKbu3K2laSRatFS2psmTg5TCsBpgcpjToO0ctfZjSNPt2nbkM0Ci6o6Z9Pf3GK/fje4+sI5rQuAzFez9Z9qHXoRRCCCEOt0NZ9Ft8SOccW8J/X3QcsYSJwqQ0x4fLoTCBLJ+TTLeBxx4ufGlLAzsautBpEUwwEseprDlVyVAhmoD6zmhqXlU4pvnMrFJ+/o+NpI0Q9h1mtA/W0Gc9yfT9XGkb0uM3Zb8nWEFgcnuypwqs4cVoXKfObaYFXF6XwZVLJpDhcVhLIzlU6l4SJhiqtwmaWMFZhr12ZbbXSF1D/2uKmeAydOp6Ntd3UdXUzYForXlxSwMAr/5wKW6X/FtECCHE8JG/QkfYp2cWk/2Ui+buCLtagsRMOGfGKFpDcWpaQ7SFYuR5DR5fs4fH1+wh2+fkzBnFlGT7eGtHM6bdi6WxhuJM03pKsT0Ux+NUXLRgLN2xBG09vT1CTkP1SWmRymY/QMdPcq+eWO/akPayjzjSJtV7XQatabPkNdacsWjCRNm9ai76DjV+Yf4Y/u34MRw3Nof7LlvAv935LqapU+cHrOPpDe6cDmu5ovGFfqqaQqnhyfSs/sl9026Zmz9/LBMKMw74efzgsfVo4OyZxYzO9R9wfyGEEOJIkkBsCFwwbzRPrq2joTMMaOo6I/zs3OmU5wfIz/Bgmia7Wq3J+q9sbeTlLY2MyfPT2BUhQW/PU2Gml/nluTy1oR6vw1qce1dLkLipyXIrOqN2D5HZmxtM2UFRKshKu67+yxAlt5saCjPcgEl3xKQky0NVi/XUoyJtGSTsYU1tzRmLxBOU5/gJxxK0h6K8U9nML5fPZFNdJ53hOFleJ+09cdz2sGpyen36fLWQPYmsuimUur7+k/OTxxjAd0+fzFkzS5hUtP/cYY1dYf741k7+vmYPpdlefnfhnP3uL4QQQgwFCcSGwOrqdna39ZDhcRBwK9bt7uDm57fz4BULADAMg/KCAOUFAS6cPwaAWDzBLS/v4LZXK1Pn+czsYlbVtFnHKAi4Dd6tauWsmW6iaRPC0gOc9Lla6T1P6fOv0jkNWDQhn4IsL/9cV8dtX5rL2zua2N2+m5j9HslATCfM1FOQwUiCyaMCPPz1RfxjTS2/fnYrTd1RlvzmFVqDMd758TJOKM/j+S2NxM2+AWAyKHMYCmXn2UjQN6dYfxceX8ZlJ41nWkn2gHX+h1crCEbiJLSmIxTjH2tricQ1OX4Xf7t6ET63Y8DjhBBCiKEkc8SGwLXLJuEyFKFogoJMDyio7wjRFowOeozL6aA0x8eEggAA3142kW+eOolrT50EWHm8glGTTK+L4iwfj12zmBlF1r7HFGeydEpBn/Ml14NMl/69W1kT4P1uJ6tqWnl8zR5+uXwGn55RTLbPhUvpPsfMGZNNVMNZs0rI8lnx/ANXnEBhpofq1hB5PhfBSIK9HREicZMt9Z1cfMJY3E61z2LgyaArZloPGCS/HyhYdBhw9owivnHKpEGDMIDbX63g9tcquev1Kv62ejcahQauXDKB0hwZkhRCCDEySCA2BBZPKiChNSXZXmaV5RBwGdS3h5l/48ssvPFl7nitcsDj/G4nVc1WZvwx+QGy/R6WTSvmwnmjiZlwy8WzefDyBVx32mRmleXw9aWTKM32cuWSidz71QV40zp9kkOK0De4+dKCMdz31flMK80iZkJH2FqD8hsnT+C+t3dy9YOreGVbEz1xq+cKrNQYpTk+Tp5cwLSSLP5xzWI08NiqWrTWnDKlkGPHZPd5n7+v2cOdr1USjevU9aSuy74mtz3bf7BeMANr3tntX57HpFH7nw+28d8/TdWNZ7PmZ6cxb1wekbjJ6dOLuHrppP0eJ4QQQgwlGZocAm9sbyLX5yQST7CrNcTMshw21nXyhRNG09kTozBz4EWmW0NWj9moTDc/f2IDu1tDfPf0qfhcVkg0d2weY/J6e3emFmdS1xEmbpo4DEV+hps9Hfv2ujkMxbg8P985bTJdkThX3P9+n3lkpta8X91CZVOQHY1BK1O+26A7arJwfB4/+PQUZpTm4HUZqdQPJ07M59HVu1k8qYBvPLgaQ1m9VwE7i393OJYaVoXeJzgd9kS2aEITTVhPXpoDRGFuB2R63fzH8lkfqs6VUigFVz20hpXVrRxTnMmdX5r7oY4VQgghhooEYkdYJJ7gdy9soz0Ux+GA5XNK97vId7r739oJkFq0+9ZXKvjn+noaOnvI87upbg72CcRau6393tjexAnj8ynJ8acCseR8MacdHGV4HPz6mS00dEZSxyttpY7I8rtZv7ujTy6x8jwv25pCrN7VRkdPfJ85VsvnlHDbK5U0d0e49eI5PLWhnqtPmciT6/bw0Hu7eHFL4z7rSyZ0MoeZTk3MdzoUcXvdy4DLwAR8LoPLF4/n0sXjMYz+Zxnc757fysrqVkZlevjXt07C4ZAOYCGEECOL/GU6wqqagmxv6CKBlY9reung85r664r0XbbH1NbTf6GoSTAa54M9HX22j823grJ3KptZ8ttXWVXTjqEgw9UbvCTs4cfKpiANnREunleGNy2HWEGGm7ZgNC1DvxW8VTSHufOS4/E6Hfz07xu45aXt7O0Ip45bOKGA3W09PLiihs/MKeN/vzKPueNy+fqSCWTbY6Tpecw8/ebKJ4O+ZBAGcOH8Mbz4vVN44psnce2nppDpPbiFuZ9cXwfATefPxClBmBBCiBFIesSOsDe2N1mLX2MFIlOKeuc2aa35r2e3UNsaYlxBgH+tr6O5O0Y0YVpLEQFep2JyUSYf7OkErKcTFRCLm6zZ1TvUt21vF89ttBb4bgnGei9AQ3esb+4uAwhGE7gdUNkUpCzXS1VzDx6nQXVrb3AFyaFK8LnghPEF3HPpPC66ewUPvFvDba/s4JUfnMqYPD/j8gO4HIpNdZ19js/0uAin3X8yyIoMsjRkcrvf7SBuQlmO75Az3//56wvZ0dDFqccUHdLxQgghxJEmgdgR9tjqWqB38nl6r84Fd7zL6rRgaiAXHlfERQsnce6tb6UlPbXmVK2taeOO1yr4p93z09gV2Wf4L9nTZGjQypofFkvYQ4EadrWGaAtF8bsNEqamKMtNU1fUmjOWdnxXxGRjbTsLJxVw2eJy/vR2NRMLAjjspymjcZMxuX6qmoNEowncaUOXsbRJX8lFwcHKsh/rvw4T4HXB98+YwqWLyj/S8kOjc/2StFUIIcSIJuM1R1AwEmdXSzD1vcuhOP/2tznmZ88y4+dPp4KwqYVecu3hu1klfm696NjUMf9+/lxmlGVz7bKJgBUYJXOGtYRi3PTcNiobu9nb0UNbMIoyrA81+cEqwO8y0HaXVDLwMbHyfzV1RSnM9DCzLIeiLC8/POOYVBCWHiK5HIrv/209jZ1hrlwygZJsD5XNQf7wagV3v17Jub9/k7NnFZPpdfBudSumqWkLRlmzq43jxuamrie5KDjQN8lZ2jVfs3QyV5w0QYYThRBCHPXkL90RFPA4+b/nzyLb68TttCahr93dTjhukj56uK0pTFvYGqv7oD7Et/66AYeC7542MdUj9P0zjuHpaxcxscC3z/vE7ZxiJ04swO9yUpLt6TPR/pJF43A7FIYB88pzAGu5pOR6kTPKclhf205Naw+toSjleT5MrGWNMjwODGUFcHs7wmxr6KK2rYf6jggOBS9sbmBTfSeVzUH+9FYVwUiCh1dU89k/vM1X/riSpVML+eapE3E6e5taMit++rJFDmVdy4LyXM6YUXyYPgEhhBBiZJNA7Ag7f+5oRuf5icY100uzePf6T3H9mVMozvKQ53OwdEoBc8fmMCrDnTrG41BcvGAs3z7tmD7nmjE6jytOnkiGx4HbaS0YDlbAV5DhYWNdB4sm5rGnI8JXTxyXOs5A8aMzp+JyGKkksibgMhQzy7J4eUsjYXtpof/3wnaml1kPFDgMxcTCQCqdxKgsN9OKM5lXnsc9Xzme06aNIhw32VzXiWlqgjGNqeGVrU1s3NPBB3s6uO4va3m3spWy7L4BpAm4nIrPzi4mP+DmvNmlXLG4nP/9ynyOKc46nB+BEEIIMWLJHLEjTCnF108ez/ceXU9lYzcra1oIRkwaOiPMKsvivstPAODhFTX87MmNPP2tkxiXHyDg2fejeWhFDTc+vdmaeG+a+DxOiCYoyHDxqWOKeHjlbkJRq2ftz+/tIttj0BExeXVbA0opnIaisilEptvA43ZYwdueTk6fXsSmPR3UdYSJJUye+WAvXqdBMJJgQ601+b4w4KKxK8b/eXITt19yPMeNzeWf6+uIJTQ7Grt77xerhy7H60Argzd2NBOMxHD1SztRmOHmtGmj+NGZ08gNuBFCCCE+iZTW+06W/jibN2+eXrVq1XBfRh81LUGuenA1W/Z2pcoUkB9wUZztRWvY0x6mJxzj5+dOY055PlOKMnGnDefd+1YVt7y4nWDMJMvjoCMcx7QX247GTXL8rlS+scIMF03dsdSTig5lTccqzvZgak1DZ5Qsr4OA10Vde5gsr5POcJyZpVnc/PnZbG/sZnSOj/PveAe/y6AnZuJxGhjKWpR7bJ4Pl8PA61Rsbwxy7OgsfvmZmTy0ooZXtjbSZOcz++nZU2nujrK2pg0NrKppB+D4cTnc9G/HMmnU/hfqFkIIIY4GSqnVWut5A22TockhMC4/wDnHlgBWT9CYXB8+t0FzMMbGui421XfR3hMjouHW16o499a3uOO1itTxr25t5D//tYWEaeKwp9AnJ9QHIwncTiMVhDmUlStsWnEmPpeBy1AUZXkxgaauCC3dVo6wjnCCgMvBdcsmMbM0C5/Lwca6TgxDcd7sUuaOy+WSE8YSN01mlGYRTZhE7TQUu1p7KMz08M1lU/C5HLSH4kwtzuKmC2Zz9qyS1HX/9wvbufuNnWyobac4y+r1+uqisfz96sUShAkhhBBIIDZkkk8Avv6jU3nz+mWcMqUgNS48q6x3TlRTV4Trlk3ijOnWhHXTNLn5ha0A9MQ0cQ3d9vCjidXj1R1JkOtzMLnQT2mOh/aeGIsm5HHLF+YyoTDAqEy3taajoTDtpxZdBuxoCvLSlgbOmFlETyxBjt/FlCIrQHp1ayNVzd3EE9AdjlrDocpaGeDp607iL19fyF2vVxKKWslhlYJQNM7p00YxvdhafLwnrnErK2fY7NG5PPedk/nFeTOPcE0LIYQQHx8yR2yIhGNW8ORxWmkqCjJ8uFwO4rEEOb7eOVKZPifF2V6mlVrB2Zrd7Wyp78LACuZiCRO30yCWsM6X53MQipnkBTw8cMVC7n6jkj+9U8Pa3W0sP2409R1hxuX5cRiK8QUBphZlMqM0m3+s28OOvV00dIb53HFjeGpdPX4791c8bvLIyhreqWzF61DsbguTsHvgXt7SwI7GLi5fPJ76jh60Br/boD0Y5b9f2s6Kqhaau6Pk+FycMrWAZVNHUZ7vZ9boHAxD4n4hhBAinQRiQ6SlO4rf7WBFVTP5GR6ausJ4nAYJrbnipPF8UNeBaep91nFcVd2WemrR6YAL543ltOmjaA3G+MVTm+iJJQjHNZXNIT5z21t09dhrS2rFjNIsnr7uJF7c0oBevYf7L19AfoaHnc1B7ny9ElOBNjVZXifBSIKWYJQt9Z1c9qf32dtpZdiPJHQqn5gGQtEEm+q6+NHfNjA2z0+PO0FXJMEbO5oIRhLsaQ9jKLj/sgUsmJA/hDUshBBCfPxIIDZE2nuiROMm33t0fWqhbacCj9vB/e/WcPbMYh59fzcG0GLP9wL46onlbK7rpK49xENfW4jX5aCisZt7Xt9AdzhOUZYXXyJBTzROc3fvcetqO7j6wdU0BqNs39tFcZabPW0h8jM8xBMmrSFrMn9rT5yHV9RQkOnm7YoWzvn9mwCpif4ayPY66QjHU9+DlZg1bmridoLYu9+oRKO4fHE5V54ykRy/PAkphBBCHIgEYkMgGovT1BXhvNnFXDR/HE3dEW59pYKaliABt5O8gIt3KlqI21FOdyRGIpHA4XDgdTn43YWzcRqKaNzkV//azIub9xKMJJgzNovlc0aT6XJyw5MbSYZJLofCoWD1rlb8HhcZHge1bT28sLmBY8fkMrkokx+fOZX/em4bAD97clPqWtNWI6Is28uejjBdkXiqzLATweb4XTx21UJe2tLE2l0tPLNhL4smFfLlReUShAkhhBAfkgRiQ2DL3m5WVLXy+4tmc4I9XFfXHuaVrQ3cf/kCPE4Hm/e0c/Gdb9MZg1teqeCu1yu54dwZXLJwHE5D8fymvfzq6S34XA5OnlLIdcsmYxiKggwPAC9ubeStiia6IwlGZbjZ2xEhaiQIRk2icRMN/GNdHVrDXW9UkeWx8no5jb4Z7sfketndZg1Lmrr3CU2PAVcsmcissiyOH5fHqCwvAJcsHMd5c0r5z+XH4h8g95kQQgghBid/OY8QrTUvbG7A5VDs2NuFx6lYu7uN844bDVg5xOrbw7y2tZHFkwq48dmtTCrJIdProKMnjlMpHlm5iwferSbL62RPe5hMr5OfnzOdk6cU7vN+WT4XPdEETgP2dkasJY6Ug5JsFzUtPQA0dYWZUpyBU2k6wppsj0FnxIrCkulWx+YFAEVbKEq9PYQKEDGhobOHH53ZN9s/QFbaQuZCCCGE+PAkEDtCWoJRrnxwNQAep0EkrvnLyt1sruvi91+cS1mOn5rWEN99dD3HFGVQ09rD/zl3GsvtQC1hav61vo47Xq9kd1sP31gygUsXlQ+6EPassiweX6OIm5rxBT6Wzx7N/7y8g4BbpXq98gMewlGT0XkBKpqCzBqTy1sVLfhcDkDTEzNZv7udX3xmOj9+YmPq3Arricmz0nKECSGEEOKjk3wCR4DWmnW7Wlk6pcBaWNu0ep3Ccc171W1c/qeVOA24cskE6ynE+k5aglFG5wVS53AYiuXHlXHmzGIacSgINAAAFBdJREFUOiOce2zpoEEYwLxxucRNTbbPRV7Ay5SiDLLcis4ea36XBuo7wvz0iQ8wtebhKxbQ3JUcgkyQ73ehsHKUxbUmz2e9V8Bl4HM7eOkHSzltmizGLYQQQhxOEogdRuFYgnvfrOCzf3ibrz2whrcqWtDAlOJMnGlrLW6q7+IbD75PY2ePtTZjQlOU6WF8QWCfcz62qpaTJhVQZM/JGsyU4iwmF2UQS5iEonH+982ddMc0ptZo3fsUpN/j5NzZpSyeXEiuPb8sGoe6jgiGggy3g7IcP3+4ZD4BJxRmern/sgUDXpsQQgghPhoZmjwM6tpD3PV6FU+t20NbT+8ThnFTM7UoQHsoRtzUVtSrrMnv7aEET6yrJz/gIhhJ8PNzp5E3wOLXAY+D3kxeg3MYihPKc/lzQzfxhObY8mxKs728uaOJzkgCn8sgEjNJmCa/f7mC4iwP1546ic7QFrbv7SKmrXQaOX43Y/L8jC8IcP3Z01kyZRTlEoQJIYQQR4QEYh/Rzc9v5YXNDexo6O4TLmV7HXSGE2xrCGIocDsUsYTVOwWQ4TGYVJjBp2cWcfOLFfz1/d2cO7tsn/N/9rgy7nmzis5wLDUpPho3+ywIntQTMzGB/7loNtNLs6lpCbGyugUiCXpi1vBoJGbiNOBnT2zC43LwwzOmcOOzW0Bba1TWtveknpb8yonjD2tdCSGEEKIvCcQ+gje3N/HHt6sJxxJorB6lZC6wjrDdCxU3MTVE7cSnCsj0GnSGTVp74rxV1UosYc0dG8j88jx+89w2ntlQR67fzcrqVh54p5qXf7CUwgwPa3e18W5VK+ceW8pn55RS3RxkR0MXHeE4Vz+0hlA0nnpfDWTaWfRNNLFYgv98egsOw9qaDCRN88A9cEIIIYT46CQQO0SmqVFaYyidSoJqasjyGMS1IhRNEI2bKA1uA2KmNXyYbaencChoC0ZpC1rZ8KNxkw9q25k1Oif1Hu9WNjOtJItxeX5+98J2OsMxInFNcZaXs295k+IsH41dYcKxBD6Xg6tOmcj3H1vPQyt2sa62g/EFAY4bU8Sjq2vRgMtQTC/NZmJhBh/UtrGrLUxrMIrbARkeB7EEFGd5CHgcA9yxEEIIIQ43CcQO0QPvVvOrpzdjmlZvU37ARWswhtvlIBKOY2AtA1SS7aG5K4LbqYjENRMKM9hU10nc1HRF4kwaFaAjbAVmd79Rxa1fnJt6jxuf2YrPZTB3bA7PbaxnbK6PquYQZTleFk8s4/4Vu+gMx5lVls3oPB/RhElZjo8NezpYOCGP2y853uqB81nZ9c+bU8bEwozU+WvbQvzyqU2srmnjjR8spbI5RG7ARWmOf6irUwghhPhEkkDsEHX0RNEmJJPSNwdjGEB3OE7A7aQjEQMN9R12UlRTM7HAT0VTkFDMpCjLTUNnlO0NQdwOhdaa93e29Dm/01CsrG7jxs+N5ol1dTgcBgWZHlbvaueHZx7DV04sZ82uNvZ2RvjX+jr++NZO1u3uwOdycMM501Nzyn5+7vQB72F0rp97Lp1PMBIn4HEyZ6wsTSSEEEIMJQnEDtG4/ACJfmUmUJrtpaqlB4ehMLTGBI4pCrC9MUhNSwi/24ECGjqj9nwxJ0unFPLi1gamFGdy/7s7Of+40dz9RhVrd7djKAhHE+Taeb7uuuR4vnrf+3zt/vcJRRKMyfdT0xKiLMfH0qkF/PSsacwrz8NIS5dxIAFZmkgIIYQYFvIX+CCZpklFYzePrNyN01B4XQbBiDVZ3wB2tfbgUNYcMg34XIqdzUFMbU2W77L39bsMEqaVTmJcQYBIzOSNHS2srmnnlhd38M9vn8jG2nY+qOvgr6t343Ya1LSG+Ozt7wDgdlgLb589s5gL54+lPN+PUh8++BJCCCHE8JNA7CCt3NnKdX9dR2NnhEy3A0OB320QjFqpI5IT9x3KmpzvcznpjsSZUOCjujmECWR5nUTiCZSCcMwErTlt2ihe2NxIKJogL+Di9JvfZHxBBtEYNHb20BWOkx/wcNUpY3EaBr95fht/+cYiphZnDmd1CCGEEOIjkMz6B+n96jYa7cWww3GTrnCCYNTcZz+3w8DrMgBNNKEJhmMYhiLT4yA/w4U2NS6Hg4SGisYuXt/WmPowgpEEJ0zMpyzXR3c0Tnc4wXXLJtPQFeHqpZOYUGglWDW1JpYw+eVTG7n5+a28W9myz3UIIYQQYuSSQOwgleX6cNgjgBrNviGY9RRl1DTpDCdoDVlPUDYHY5imJsfvoqa5h7i2hikBnt3USCRhDV1ecVI5L37/FG656DgKMt24HIpJRRl95nwlhyCjcZNvP7KW+96pIdPrYmZZ1hG9dyGEEEIcXhKIHaSlU0cxJs8HQDwtCkuvSG3/J32+vFIKj1PRFU6Q6XNi2us/TizwYwCLJuQyuSjAB3s6yQ94yPK5yPK4cChFdUuw99xa47ADsVte3sEzH+zlZ+dM48pTJpJpPyUphBBCiI8HCcQOUl7AzXmzS/E5rWAoGWupfk8pJjSp5YwMQ2Gamp64JhJPcMkJ4zh5UgEaGJsfQAMN7T1sbwiyqa6DVjvJ6wXzxrBoYh5zRudSlutn/rhcHl9Ti8tpkOV18srWRr6wYCxfO3nC0Ny8EEIIIQ4rmax/CK5eOpl3KltYVdOeWhYo0W9ZIIdhBWJaW4t/J0VjJvPL87h4/ljOv+NtMuzUEVWtYQC6Iwneq2rhrFklTBqVwb2XLiASN3E5FI+vqeWGJzYycZSVlPX7Z0zhqlMmHvkbFkIIIcQRIT1ih8DjVPRfjtFpKOtJyeT8Mc0++wAkgJue20JJtpfCLC917T343dbHEHAZeJwGa2paU/sbhsLnduB0GNz2xbmMzvWRMDWLJ+Zx7amTcDnkIxRCCCE+rqRH7BA8v6mBNbva+5TF+0Vd+wRqypo7ltDQFY7zmxe2srmuk6IsD27DIO7ULJ5cyPradmrbewZ832yfi6euPZmVO5s5ZeooyRsmhBBCfMxJIHYIool9n5VUkBqmVIBhgGnvpqFPjrFoNMbW+i4AOntinDy5kC8vHMfiSQUHzIif7Xdx+oySw3EbQgghhBhmEogdguVzyvigtoN73tqZKlNYPVY5fheNnT2EYn27xHTat82hBFecNJ7LTirHZRgsnJCPU4YYhRBCiE8cCcQO0fVnHUNLd4RnNu4lGjeZVpLFrz83kxmlWfz6ma3c9051n/2V6g3GTGBBeS4+j6SbEEIIIT7JJBA7RC6Hwa/Pn0V9Z5j3d7YSS8QJeBw8sXYPm+o6UFjBV3I40tSQ4Tb4/LwxLJiQL0GYEEIIIQ4ciCml/gicCzRqrWfaZb8FPgNEgUrgMq11u73tJ8AVWA8IXqe1ft4uPxO4BXAA92it/8suHw88AuQBa4Ava62jSikP8ABwPNACXKS1rj5M931Y+N1OrlwykTU1bexs6eGah9ZiovG7HcwozWTB+Hx8Lgfrats5bkw23zt9KoYhQ5BCCCGEsCitB8ixkL6DUkuAbuCBtEDsDOAVrXVcKXUTgNb6eqXUdOAvwAKgFHgJmGKfajtwOlALvA98QWu9WSn1KPC41voRpdSdwHqt9R1KqWuAY7XWVymlLgY+p7W+6EA3NG/ePL1q1aqDrYdDForGeW7jXrbUdzC9JJs5Y3IoLwjIE41CCCGEAEAptVprPW+gbQfsEdNav6GUKu9X9kLatyuAC+zXy4FHtNYRYKdSqgIrKAOo0FpX2Rf0CLBcKbUFWAZ80d7nfuCXwB32uX5pl/8NuE0ppfSBIsch5nc7OX/uaGD0cF+KEEIIIT5mDsc42eXAs/brMmB32rZau2yw8nygXWsd71fe51z29g57fyGEEEKIo8JHCsSUUjcAceDhZNEAu+lDKN/fuQa6jm8opVYppVY1NTXt/6KFEEIIIUaIQw7ElFKXYk3i/1LacGEtMCZtt9FA3X7Km4EcpZSzX3mfc9nbs4FWBqC1vltrPU9rPa+wsPBQb0kIIYQQYkgdUiBmPwF5PXCe1jqUtukp4GKllMd+GnIysBJrcv5kpdR4pZQbuBh4yg7gXqV3jtmlwJNp57rUfn0B1sMBI2p+mBBCCCHER/Fh0lf8BVgKFCilaoFfAD8BPMCL9tOBK7TWV2mtN9lPQW7GGrL8ptY6YZ/nWuB5rPQVf9Rab7Lf4nrgEaXUr4C1wL12+b3Ag/aE/1as4E0IIYQQ4qhxwPQVHzdDnb5CCCGEEGJ/9pe+QrKLCiGEEEIMEwnEhBBCCCGGiQRiQgghhBDDRAIxIYQQQohhIoGYEEIIIcQwkUBMCCGEEGKYSCAmhBBCCDFMJBATQgghhBgmEogJIYQQQgwTCcSEEEIIIYaJBGJCCCGEEMNEAjEhhBBCiGEigZgQQgghxDCRQEwIIYQQYphIICaEEEIIMUwkEBNCCCGEGCYSiAkhhBBCDBOltR7uazislFJNQM1wX8chKgCah/sihpnUgdQBSB2A1AFIHYDUARwddTBOa1040IajLhD7OFNKrdJazxvu6xhOUgdSByB1AFIHIHUAUgdw9NeBDE0KIYQQQgwTCcSEEEIIIYaJBGIjy93DfQEjgNSB1AFIHYDUAUgdgNQBHOV1IHPEhBBCCCGGifSICSGEEEIMEwnEDgOl1LeVUhuVUpuUUt+xy36rlNqqlNqglHpCKZWTtv9PlFIVSqltSqlPp5WfaZdVKKV+nFY+Xin1nlJqh1Lqr0opt13usb+vsLeXD91d9zVQHaRt+4FSSiulCuzvlVLq9/Z1b1BKzU3b91L7PncopS5NKz9eKfWBfczvlVLKLs9TSr1o7/+iUip3qO65v8HqQCn1Lftz3aSU+k1a+VHXDuzrGejnYY5SaoVSap1SapVSaoFdflS0BaXUH5VSjUqpjWllA17PUNzz/t5jhNTBl+zr2qCUekcpNTvtmMPW/gf7GRsJdZC2fb5SKqGUuiCt7BPRDuxtS5X1e2GTUur1tPKPbTs4aFpr+foIX8BMYCPgB5zAS8Bk4AzAae9zE3CT/Xo6sB7wAOOBSsBhf1UCEwC3vc90+5hHgYvt13cCV9uvrwHutF9fDPx1JNWBvW0M8DxWbrcCu+xs4FlAAQuB9+zyPKDK/n+u/TrX3rYSWGQf8yxwll3+G+DH9usfJ+t5pNQBcKr92mPvN+pobQcHqIcX0j6zs4HXjqa2ACwB5gIb08oGvJ6huOfB3mME1cGJafd2VlodHLb2zyA/YyOlDtLu9xXgGeCCT2A7yAE2A2Pt75O/Hz/W7eCg62y4L+Dj/gV8Hrgn7fufAz/qt8/ngIft1z8BfpK27Xn7B2sR8Hxa+U/sL4WVyC4Z1KX2Sx5rv3ba+6mRVAfA34DZQDW9gdhdwBfS9t8GlABfAO5KK7/LLisBtqaVp/ZLHmu/LgG2jaR2YP/SOG2A/Y+6dnCAengeuCjt8/vz0dYWgHL6/vEZ8HqG4p4He4+RUgf9jskF9vRv1x+1/Q/2MzaS6gD4DvBN4D56A7FPTDvACp5+NcDxH/t2cDBfMjT50W0Eliil8pVSfqx/gYzpt8/lWP8qASgDdqdtq7XLBivPB9q11vF+5X3OZW/vsPcfagPWgVLqPKxfsOv77X+wdVBmv+5fDlCkta4HsP8/6vDc0kEbrB1MAU62u8pfV0rNt/c/GtsBDF4P3wF+q5TaDdyM9csRjs62kDTY9QzFPQ92rqH2YT6TKzjw78dDaf8jug6UUmVY/0i/s9/+n6R2MAXIVUq9ppRarZT6il1+NLaDQTmH+wI+7rTWW5RSNwEvAt1YXaDJRoJS6gb7+4eTRQOdhoHn6+n97L+/cw2p/dTBDVhDtP0Ndt0HWz5i7KcOnFj/4l8IzAceVUpN4ChsB7Dferga+K7W+u9KqQuBe4HTOArbwocwFPf8sagnpdSpWIHYScmiAXY71PY/0uvgf4DrtdYJe6pX0iepHTiB44FPAT7gXaXUCj5Z7UB6xA4HrfW9Wuu5WuslQCuwA6wJl8C5wJe03SeKFY2n95iNBur2U94M5CilnP3K+5zL3p5tv/+QG6AOqrHG49crpaqxrnuNUqqYg6+DWvt1/3KABqVUCYD9/8bDe2cf3iDtoBZ4XFtWAibWumlHZTuAQevhUuBxe5fHgAX266OyLRzgeobingc711Ab9DNRSh0L3AMs11q32MWHs/2P9DqYBzxi/368ALhdKfVZPlntoBZ4Tmsd1Fo3A29gTWU5GtvB4IZ7bPRo+KJ3guFYYCtWD8iZWJMQC/vtO4O+EwersCYmOu3X4+mdnDjDPuYx+k5OvMZ+/U36Tk58dCTVQb/t1fTOETuHvhNIV9rlecBOu/5y7dd59rb37X2Tk1TPtst/S99JoL8ZSXUAXAX8h10+BauLXB2t7WA/9bAFWGqXfwpYfbS1BfadFzPg9QzFPQ/2HiOoDsYCFcCJ/Y4/bO2fQX7GRkod9DvmPvpO1v+ktINpwMv25+7Hmtow82hoBwdVX8N9AUfDF/AmVtC1HviUXVaB9Ud3nf11Z9r+N2A9ubEN+6kXu/xsYLu97Ya08glYT8tU2I0w+QSe1/6+wt4+YSTVQb/t1fQGYgr4g32fHwDz0va73L6fCuCytPJ59g9pJXAbvcmI8+0f5B32//NGUh3Yv0Qesq99DbDsaG4H+6mHk4DVdtl7wPFHU1sA/gLUAzGsf4FfMdj1DMU97+89Rkgd3AO00fv7cdWRaP8M8jM2Euqg33H3YQdin6R2YO//Q6zfFxuB7xwN7eBgvySzvhBCCCHEMJE5YkIIIYQQw0QCMSGEEEKIYSKBmBBCCCHEMJFATAghhBBimEggJoQQQggxTCQQE0IIIYQYJhKICSGEEEIMEwnEhBBCCCGGyf8HHHfhlxj7W5wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lion_gdf.plot(figsize = (10,10), markersize = 1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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",
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       "\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>Street</th>\n",
       "      <th>SAFStreetName</th>\n",
       "      <th>FeatureTyp</th>\n",
       "      <th>SegmentTyp</th>\n",
       "      <th>IncExFlag</th>\n",
       "      <th>RB_Layer</th>\n",
       "      <th>NonPed</th>\n",
       "      <th>TrafDir</th>\n",
       "      <th>TrafSrc</th>\n",
       "      <th>SpecAddr</th>\n",
       "      <th>...</th>\n",
       "      <th>RLo_Hyphen</th>\n",
       "      <th>RHi_Hyphen</th>\n",
       "      <th>FromLeft</th>\n",
       "      <th>ToLeft</th>\n",
       "      <th>FromRight</th>\n",
       "      <th>ToRight</th>\n",
       "      <th>Join_ID</th>\n",
       "      <th>SHAPE_Length</th>\n",
       "      <th>geometry</th>\n",
       "      <th>todrop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EAST 168 STREET</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>U</td>\n",
       "      <td></td>\n",
       "      <td>B</td>\n",
       "      <td></td>\n",
       "      <td>T</td>\n",
       "      <td>DOT</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>596</td>\n",
       "      <td>716</td>\n",
       "      <td>599</td>\n",
       "      <td>699</td>\n",
       "      <td>596</td>\n",
       "      <td>716</td>\n",
       "      <td>2251001000000</td>\n",
       "      <td>396.030947</td>\n",
       "      <td>(LINESTRING (1010964.446978778 241812.26140345...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WEST 192 STREET</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>U</td>\n",
       "      <td></td>\n",
       "      <td>B</td>\n",
       "      <td></td>\n",
       "      <td>A</td>\n",
       "      <td>DOT</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>99</td>\n",
       "      <td>58</td>\n",
       "      <td>98</td>\n",
       "      <td>63</td>\n",
       "      <td>99</td>\n",
       "      <td>2798401000000</td>\n",
       "      <td>279.360514</td>\n",
       "      <td>(LINESTRING (1011576.686607853 255023.58303095...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UNION AVENUE</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>U</td>\n",
       "      <td></td>\n",
       "      <td>B</td>\n",
       "      <td></td>\n",
       "      <td>W</td>\n",
       "      <td>DOT</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>1016</td>\n",
       "      <td>1084</td>\n",
       "      <td>1017</td>\n",
       "      <td>1079</td>\n",
       "      <td>1016</td>\n",
       "      <td>1084</td>\n",
       "      <td>2728001000000</td>\n",
       "      <td>618.327133</td>\n",
       "      <td>(LINESTRING (1011600.676209003 239639.74280026...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNION AVENUE</td>\n",
       "      <td>BEHAGEN PLAYGROUND</td>\n",
       "      <td>0</td>\n",
       "      <td>U</td>\n",
       "      <td></td>\n",
       "      <td>B</td>\n",
       "      <td></td>\n",
       "      <td>W</td>\n",
       "      <td>DOT</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21279501000000N</td>\n",
       "      <td>618.327133</td>\n",
       "      <td>(LINESTRING (1011600.676209003 239639.74280026...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DELAFIELD AVENUE</td>\n",
       "      <td></td>\n",
       "      <td>6</td>\n",
       "      <td>U</td>\n",
       "      <td></td>\n",
       "      <td>B</td>\n",
       "      <td></td>\n",
       "      <td>T</td>\n",
       "      <td>DOT</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>4600</td>\n",
       "      <td>4664</td>\n",
       "      <td>4601</td>\n",
       "      <td>4645</td>\n",
       "      <td>4600</td>\n",
       "      <td>4664</td>\n",
       "      <td>2187601000000</td>\n",
       "      <td>670.281037</td>\n",
       "      <td>(LINESTRING (1009974.212831751 264857.13919802...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 119 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Street                     SAFStreetName FeatureTyp SegmentTyp  \\\n",
       "0   EAST 168 STREET                                            0          U   \n",
       "1   WEST 192 STREET                                            0          U   \n",
       "2      UNION AVENUE                                            0          U   \n",
       "3      UNION AVENUE  BEHAGEN PLAYGROUND                        0          U   \n",
       "4  DELAFIELD AVENUE                                            6          U   \n",
       "\n",
       "  IncExFlag RB_Layer NonPed TrafDir TrafSrc SpecAddr  ... RLo_Hyphen  \\\n",
       "0                  B              T     DOT           ...        596   \n",
       "1                  B              A     DOT           ...         63   \n",
       "2                  B              W     DOT           ...       1016   \n",
       "3                  B              W     DOT        N  ...       None   \n",
       "4                  B              T     DOT           ...       4600   \n",
       "\n",
       "  RHi_Hyphen FromLeft ToLeft FromRight ToRight          Join_ID SHAPE_Length  \\\n",
       "0        716      599    699       596     716    2251001000000   396.030947   \n",
       "1         99       58     98        63      99    2798401000000   279.360514   \n",
       "2       1084     1017   1079      1016    1084    2728001000000   618.327133   \n",
       "3       None        0      0         0       0  21279501000000N   618.327133   \n",
       "4       4664     4601   4645      4600    4664    2187601000000   670.281037   \n",
       "\n",
       "                                            geometry todrop  \n",
       "0  (LINESTRING (1010964.446978778 241812.26140345...  False  \n",
       "1  (LINESTRING (1011576.686607853 255023.58303095...  False  \n",
       "2  (LINESTRING (1011600.676209003 239639.74280026...  False  \n",
       "3  (LINESTRING (1011600.676209003 239639.74280026...  False  \n",
       "4  (LINESTRING (1009974.212831751 264857.13919802...  False  \n",
       "\n",
       "[5 rows x 119 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lion_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = r\"../intermediate_data/nyclion_19b/\"\n",
    "os.mkdir(fp)\n",
    "lion_gdf[['geometry']].to_file(fp+\"nyclion_19b.shp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the public school location data from NYC Open Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = r\"https://data.cityofnewyork.us/download/jfju-ynrr/application%2Fzip\"\n",
    "filename = r\"Public_School_Locations.zip\"\n",
    "r = requests.get(url)\n",
    "# open method to open a file on your system and write the contents\n",
    "with open(\"../input_data/\"+filename, \"wb\") as file:\n",
    "    file.write(r.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Name                                             Modified             Size\n",
      "Public_Schools_Points_2011-2012A.dbf           2011-08-26 10:30:16       771337\n",
      "Public_Schools_Points_2011-2012A.prj           2010-10-12 11:55:12          562\n",
      "Public_Schools_Points_2011-2012A.sbn           2011-08-03 11:23:00        16580\n",
      "Public_Schools_Points_2011-2012A.sbx           2011-08-03 11:23:00          868\n",
      "Public_Schools_Points_2011-2012A.shp           2011-08-03 11:23:00        47952\n",
      "Public_Schools_Points_2011-2012A.shp.xml       2011-08-26 10:32:50        28413\n",
      "Public_Schools_Points_2011-2012A.shx           2011-08-03 11:23:00        13772\n",
      "Extracting all the files now...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "fp = \"../input_data/\"\n",
    "foldername = \"Public_School_Locations\"\n",
    "with zzip(fp+filename, 'r') as file: \n",
    "        # printing all the contents of the zip file \n",
    "        file.printdir() \n",
    "        path = \"../input_data/\"+foldername\n",
    "        os.mkdir(path)\n",
    "        # extracting all the files \n",
    "        print('Extracting all the files now...') \n",
    "        file.extractall(fp+foldername) \n",
    "        print('Done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the school point shapefile into a geopandas geodataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = \"../input_data/Public_School_Locations/Public_Schools_Points_2011-2012A.shp\"\n",
    "schools = gpd.read_file(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:2263'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ATS_CODE</th>\n",
       "      <th>BORO</th>\n",
       "      <th>BORONUM</th>\n",
       "      <th>LOC_CODE</th>\n",
       "      <th>SCHOOLNAME</th>\n",
       "      <th>SCH_TYPE</th>\n",
       "      <th>MANAGED_BY</th>\n",
       "      <th>GEO_DISTRI</th>\n",
       "      <th>ADMIN_DIST</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>STATE_CODE</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>PRINCIPAL</th>\n",
       "      <th>PRIN_PH</th>\n",
       "      <th>FAX</th>\n",
       "      <th>GRADES</th>\n",
       "      <th>City</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15K001</td>\n",
       "      <td>K</td>\n",
       "      <td>2.0</td>\n",
       "      <td>K001</td>\n",
       "      <td>P.S. 001 THE BERGEN</td>\n",
       "      <td>Elementary</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>309 47 STREET</td>\n",
       "      <td>NY</td>\n",
       "      <td>11220</td>\n",
       "      <td>Jennifer Eusanio</td>\n",
       "      <td>718-567-7661</td>\n",
       "      <td>718-567-9771</td>\n",
       "      <td>PK,0K,01,02,03,04,05,SE</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>POINT (980985.0541713729 175780.758994163)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17K002</td>\n",
       "      <td>K</td>\n",
       "      <td>2.0</td>\n",
       "      <td>K002</td>\n",
       "      <td>M.S. 002</td>\n",
       "      <td>Junior High-Intermediate-Middle</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>655 PARKSIDE AVENUE</td>\n",
       "      <td>NY</td>\n",
       "      <td>11226</td>\n",
       "      <td>ADRIENNE SPENCER</td>\n",
       "      <td>718-462-6992</td>\n",
       "      <td>718-284-7717</td>\n",
       "      <td>06,07,08,SE</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>POINT (997785.1364296663 178431.910832498)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21K095</td>\n",
       "      <td>K</td>\n",
       "      <td>2.0</td>\n",
       "      <td>K095</td>\n",
       "      <td>P.S. 095 THE GRAVESEND</td>\n",
       "      <td>K-8</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>345 VAN SICKLEN STREET</td>\n",
       "      <td>NY</td>\n",
       "      <td>11223</td>\n",
       "      <td>Janet Ndzibah</td>\n",
       "      <td>718-449-5050</td>\n",
       "      <td>718-449-3047</td>\n",
       "      <td>PK,0K,01,02,03,04,05,06,07,08,SE</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>POINT (991224.9830063033 156323.3612131244)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21K096</td>\n",
       "      <td>K</td>\n",
       "      <td>2.0</td>\n",
       "      <td>K096</td>\n",
       "      <td>I.S. 096 SETH LOW</td>\n",
       "      <td>Junior High-Intermediate-Middle</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>99 AVENUE P</td>\n",
       "      <td>NY</td>\n",
       "      <td>11204</td>\n",
       "      <td>Denise Sandra Levinsky</td>\n",
       "      <td>718-236-1344</td>\n",
       "      <td>718-236-2397</td>\n",
       "      <td>06,07,08,SE</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>POINT (988439.6813609767 160709.9926236183)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21K097</td>\n",
       "      <td>K</td>\n",
       "      <td>2.0</td>\n",
       "      <td>K097</td>\n",
       "      <td>P.S. 97 THE HIGHLAWN</td>\n",
       "      <td>Elementary</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>1855 STILLWELL AVENUE</td>\n",
       "      <td>NY</td>\n",
       "      <td>11223</td>\n",
       "      <td>KRISTINE MUSTILLO</td>\n",
       "      <td>718-372-7393</td>\n",
       "      <td>718-372-3842</td>\n",
       "      <td>PK,0K,01,02,03,04,05,SE</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>POINT (988205.0729096507 158329.5913366843)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ATS_CODE BORO  BORONUM LOC_CODE              SCHOOLNAME  \\\n",
       "0  15K001          K      2.0     K001     P.S. 001 THE BERGEN   \n",
       "1  17K002          K      2.0     K002                M.S. 002   \n",
       "2  21K095          K      2.0     K095  P.S. 095 THE GRAVESEND   \n",
       "3  21K096          K      2.0     K096       I.S. 096 SETH LOW   \n",
       "4  21K097          K      2.0     K097    P.S. 97 THE HIGHLAWN   \n",
       "\n",
       "                          SCH_TYPE  MANAGED_BY  GEO_DISTRI  ADMIN_DIST  \\\n",
       "0                       Elementary           1          15          15   \n",
       "1  Junior High-Intermediate-Middle           1          17          17   \n",
       "2                              K-8           1          21          21   \n",
       "3  Junior High-Intermediate-Middle           1          21          21   \n",
       "4                       Elementary           1          21          21   \n",
       "\n",
       "                  ADDRESS STATE_CODE    ZIP               PRINCIPAL  \\\n",
       "0           309 47 STREET         NY  11220        Jennifer Eusanio   \n",
       "1     655 PARKSIDE AVENUE         NY  11226        ADRIENNE SPENCER   \n",
       "2  345 VAN SICKLEN STREET         NY  11223           Janet Ndzibah   \n",
       "3             99 AVENUE P         NY  11204  Denise Sandra Levinsky   \n",
       "4   1855 STILLWELL AVENUE         NY  11223       KRISTINE MUSTILLO   \n",
       "\n",
       "        PRIN_PH           FAX                            GRADES      City  \\\n",
       "0  718-567-7661  718-567-9771           PK,0K,01,02,03,04,05,SE  BROOKLYN   \n",
       "1  718-462-6992  718-284-7717                       06,07,08,SE  BROOKLYN   \n",
       "2  718-449-5050  718-449-3047  PK,0K,01,02,03,04,05,06,07,08,SE  BROOKLYN   \n",
       "3  718-236-1344  718-236-2397                       06,07,08,SE  BROOKLYN   \n",
       "4  718-372-7393  718-372-3842           PK,0K,01,02,03,04,05,SE  BROOKLYN   \n",
       "\n",
       "                                      geometry  \n",
       "0   POINT (980985.0541713729 175780.758994163)  \n",
       "1   POINT (997785.1364296663 178431.910832498)  \n",
       "2  POINT (991224.9830063033 156323.3612131244)  \n",
       "3  POINT (988439.6813609767 160709.9926236183)  \n",
       "4  POINT (988205.0729096507 158329.5913366843)  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    902\n",
       "1.0    348\n",
       "4.0    341\n",
       "5.0     73\n",
       "3.0     43\n",
       "0.0      2\n",
       "Name: BORONUM, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools['BORONUM'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "schools.plot(figsize = (10,10), markersize = 1, color = 'blue');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the subway information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the subway entract shapefile into a geopandas geodataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = r\"https://data.cityofnewyork.us/api/geospatial/drex-xx56?method=export&format=Shapefile\"\n",
    "subway = gpd.read_file(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:4326'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subway.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "subway = subway.to_crs({'init': 'epsg:2263'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>line</th>\n",
       "      <th>name</th>\n",
       "      <th>objectid</th>\n",
       "      <th>url</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Birchall Ave &amp; Sagamore St at NW corner</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (1020670.668616476 248679.9905504963)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Birchall Ave &amp; Sagamore St at NE corner</td>\n",
       "      <td>1735.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (1020710.253511059 248665.112299423)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Morris Park Ave &amp; 180th St at NW corner</td>\n",
       "      <td>1736.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (1019251.986414479 245782.8697043241)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Morris Park Ave &amp; 180th St at NW corner</td>\n",
       "      <td>1737.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (1019419.817771734 245866.9102186682)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Boston Rd &amp; 178th St at SW corner</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (1017557.721758398 245631.8322767556)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  line                                     name  objectid  \\\n",
       "0  2-5  Birchall Ave & Sagamore St at NW corner    1734.0   \n",
       "1  2-5  Birchall Ave & Sagamore St at NE corner    1735.0   \n",
       "2  2-5  Morris Park Ave & 180th St at NW corner    1736.0   \n",
       "3  2-5  Morris Park Ave & 180th St at NW corner    1737.0   \n",
       "4  2-5        Boston Rd & 178th St at SW corner    1738.0   \n",
       "\n",
       "                                 url  \\\n",
       "0  http://web.mta.info/nyct/service/   \n",
       "1  http://web.mta.info/nyct/service/   \n",
       "2  http://web.mta.info/nyct/service/   \n",
       "3  http://web.mta.info/nyct/service/   \n",
       "4  http://web.mta.info/nyct/service/   \n",
       "\n",
       "                                      geometry  \n",
       "0  POINT (1020670.668616476 248679.9905504963)  \n",
       "1   POINT (1020710.253511059 248665.112299423)  \n",
       "2  POINT (1019251.986414479 245782.8697043241)  \n",
       "3  POINT (1019419.817771734 245866.9102186682)  \n",
       "4  POINT (1017557.721758398 245631.8322767556)  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subway.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "subway = subway.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "subway['index'] = subway.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subway.plot(figsize = (10,10), markersize = 1, color = 'red');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding these points into the street network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = r\"../intermediate_data/nyclion_19b/\"\n",
    "lion_graph = nx.read_shp(fp+\"nyclion_19b.shp\", simplify = True, geom_attrs = True, strict = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = r\"../intermediate_data/nyclion_19b/\"\n",
    "nx.write_shp(lion_graph, fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = r\"../intermediate_data/nyclion_19b/\"\n",
    "# load as GeoDataFrame\n",
    "nodes = gpd.read_file(fp+'nodes.shp')\n",
    "edges = gpd.read_file(fp+'edges.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes.crs = {'init': 'epsg:2263'}\n",
    "edges.crs = {'init': 'epsg:2263'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a point geopandas dataframe of these points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "points_to_add = gpd.GeoDataFrame(subway[['geometry']].append(schools[['geometry']], sort = False), crs = {'init': 'epsg:2263'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "points_to_add = points_to_add.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "points_to_add['key'] = points_to_add.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This AWESOME function `connect_poi()` is adapted from [Yuwen Chang](https://github.com/ywnch/toolbox)\n",
    "\n",
    "This is slightly modified from Yuwen's version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def connect_poi(pois, nodes, edges, key_col=None, path=None, threshold=600, knn=5):\n",
    "    \"\"\"Connect and integrate a set of POIs into an existing road network.\n",
    "    Given a road network in the form of two GeoDataFrames: nodes and edges,\n",
    "    link each POI to the nearest edge (road segment) based on its projection\n",
    "    point (PP) and generate a new integrated road network including the POIs,\n",
    "    the projected points, and the connection edge.\n",
    "    Args:\n",
    "        pois (GeoDataFrame): a gdf of POI (geom: Point)\n",
    "        nodes (GeoDataFrame): a gdf of road network nodes (geom: Point)\n",
    "        edges (GeoDataFrame): a gdf of road network edges (geom: LineString)\n",
    "        key_col (str): a unique key column of pois should be provided,\n",
    "                       e.g., 'index', 'osmid', 'poi_number', etc.\n",
    "                       Currently, this will be renamed into 'osmid' in the output.\n",
    "                       [NOTE] For use in pandana, you may want to ensure this\n",
    "                              column is numeric-only to avoid processing errors.\n",
    "                              Preferably use unique integers (int or str) only,\n",
    "                              and be aware not to intersect with the node key,\n",
    "                              'osmid' if you use OSM data, in the nodes gdf.\n",
    "        path (str): directory path to use for saving files (nodes and edges).\n",
    "                      Outputs will NOT be saved if this arg is not specified.\n",
    "        threshold (int): the max length of a POI connection edge, POIs with\n",
    "                         connection edge beyond this length will be removed.\n",
    "                         The unit is in meters as crs epsg is set to 3857 by\n",
    "                         default during processing.\n",
    "        knn (int): k nearest neighbors to query for the nearest edge.\n",
    "                   Consider increasing this number up to 10 if the connection\n",
    "                   output is slightly unreasonable. But higher knn number will\n",
    "                   slow down the process.\n",
    "    Returns:\n",
    "        nodes (GeoDataFrame): the original gdf with POIs and PPs appended\n",
    "        edges (GeoDataFrame): the original gdf with connection edges appended\n",
    "                              and existing edges updated (if PPs are present)\n",
    "    Note:\n",
    "        1. Make sure all three input GeoDataFrames have defined crs attribute.\n",
    "           Try something like `gdf.crs` or `gdf.crs = {'init': 'epsg:4326'}`.\n",
    "           They will then be converted into epsg:3857 for processing.\n",
    "    \"\"\"\n",
    "\n",
    "    ## STAGE 0: initialization\n",
    "    # 0-1: helper functions\n",
    "    def find_kne(point, lines):\n",
    "        dists = np.array(list(map(lambda l: l.distance(point), lines)))\n",
    "        kne_pos = dists.argsort()[0]\n",
    "        kne = lines.iloc[[kne_pos]]\n",
    "        kne_idx = kne.index[0]\n",
    "        return kne_idx, kne.values[0]\n",
    "\n",
    "    def get_pp(point, line):\n",
    "        \"\"\"Get the projected point (pp) of 'point' on 'line'.\"\"\"\n",
    "        # project new Point to be interpolated\n",
    "        pp = line.interpolate(line.project(point))  # PP as a Point\n",
    "        return pp\n",
    "\n",
    "    def split_line(line, pps):\n",
    "        \"\"\"Split 'line' by all intersecting 'pps' (as multipoint).\n",
    "        Returns:\n",
    "            new_lines (list): a list of all line segments after the split\n",
    "        \"\"\"\n",
    "        # IMPORTANT FIX for ensuring intersection between splitters and the line\n",
    "        # but no need for updating edges_meter manually because the old lines will be\n",
    "        # replaced anyway\n",
    "        line = snap(line, pps, 1e-8)  # slow?\n",
    "\n",
    "        try:\n",
    "            new_lines = list(split(line, pps))  # split into segments\n",
    "            return new_lines\n",
    "        except TypeError as e:\n",
    "            print('Error when splitting line: {}\\n{}\\n{}\\n'.format(e, line, pps))\n",
    "            return []\n",
    "\n",
    "    def update_nodes(nodes, new_points, ptype):\n",
    "        \"\"\"Update nodes with a list (pp) or a GeoDataFrame (poi) of new_points.\n",
    "        \n",
    "        Args:\n",
    "            ptype: type of Point list to append, 'pp' or 'poi'\n",
    "        \"\"\"\n",
    "        # create gdf of new nodes (projected PAPs)\n",
    "        if ptype == 'pp':\n",
    "            new_nodes = gpd.GeoDataFrame(new_points, columns=['geometry'],\n",
    "                                         crs={'init': 'epsg:2263'})\n",
    "            n = len(new_nodes)\n",
    "            new_nodes['highway'] = node_highway_pp\n",
    "            new_nodes['osmid'] = [str(osmid_prefix + i) for i in range(n)]\n",
    "\n",
    "        # create gdf of new nodes (original POIs)\n",
    "        elif ptype == 'poi':\n",
    "            new_nodes = new_points[['geometry', key_col]]\n",
    "            new_nodes.columns = ['geometry', 'osmid']\n",
    "            new_nodes['highway'] = node_highway_poi\n",
    "            new_nodes['osmid'].astype(str, inplace=True)\n",
    "\n",
    "        else:\n",
    "            print(\"Unknown ptype when updating nodes.\")\n",
    "\n",
    "        # merge new nodes (it is safe to ignore the index for nodes)\n",
    "        gdfs = [nodes, new_nodes]\n",
    "        nodes = gpd.GeoDataFrame(pd.concat(gdfs, ignore_index=True, sort=False),\n",
    "                                 crs=gdfs[0].crs)\n",
    "\n",
    "        return nodes, new_nodes  # all nodes, newly added nodes only\n",
    "\n",
    "    def update_edges(edges, new_lines, replace):\n",
    "        \"\"\"\n",
    "        Update edge info by adding new_lines; or,\n",
    "        replace existing ones with new_lines (n-split segments).\n",
    "        Args:\n",
    "            replace: treat new_lines (flat list) as newly added edges if False,\n",
    "                     else replace existing edges with new_lines (often a nested list)\n",
    "        \n",
    "        Note:\n",
    "            kne_idx refers to 'fid in Rtree'/'label'/'loc', not positional iloc\n",
    "        \"\"\"\n",
    "        # for interpolation (split by pp): replicate old line\n",
    "        if replace:\n",
    "            # create a flattened gdf with all line segs and corresponding kne_idx\n",
    "            kne_idxs = list(line_pps_dict.keys())\n",
    "            lens = [len(item) for item in new_lines]\n",
    "            new_lines_gdf = gpd.GeoDataFrame(\n",
    "                {'kne_idx': np.repeat(kne_idxs, lens),\n",
    "                 'geometry': list(itertools.chain.from_iterable(new_lines))})\n",
    "            # merge to inherit the data of the replaced line\n",
    "            cols = list(edges.columns)\n",
    "            cols.remove('geometry')  # don't include the old geometry\n",
    "            new_edges = new_lines_gdf.merge(edges[cols], how='left',\n",
    "                                            left_on='kne_idx', right_index=True)\n",
    "            new_edges.drop('kne_idx', axis=1, inplace=True)\n",
    "            new_lines = new_edges['geometry']  # now a flatten list\n",
    "        # for connection (to external poi): append new lines\n",
    "        else:\n",
    "            new_edges = gpd.GeoDataFrame(pois[[key_col]], geometry=new_lines,\n",
    "                                         columns=[key_col, 'geometry'])\n",
    "            new_edges['oneway'] = False\n",
    "            new_edges['highway'] = edge_highway\n",
    "\n",
    "        # update features (a bit slow)\n",
    "        new_edges['length'] = [l.length for l in new_lines]\n",
    "        new_edges['from'] = new_edges['geometry'].map(\n",
    "            lambda x: nodes_id_dict.get(list(x.coords)[0], 'None'))\n",
    "        new_edges['to'] = new_edges['geometry'].map(\n",
    "            lambda x: nodes_id_dict.get(list(x.coords)[-1], 'None'))\n",
    "        new_edges['osmid'] = ['_'.join(s) for s in zip(new_edges['from'],\n",
    "                                                       new_edges['to'])]\n",
    "\n",
    "        # remember to reindex to prevent duplication when concat\n",
    "        start = edges.index[-1] + 1\n",
    "        stop = start + len(new_edges)\n",
    "        new_edges.index = range(start, stop)\n",
    "\n",
    "        # for interpolation: remove existing edges\n",
    "        if replace:\n",
    "            edges = edges.drop(kne_idxs, axis=0)\n",
    "        # for connection: filter invalid links\n",
    "        else:\n",
    "            valid_pos = np.where(new_edges['length'] <= threshold)[0]\n",
    "            n = len(new_edges)\n",
    "            n_fault = n - len(valid_pos)\n",
    "            f_pct = n_fault / n * 100\n",
    "            print(\"Remove faulty projections: {}/{} ({:.2f}%)\".format(n_fault,\n",
    "                                                                      n,\n",
    "                                                                      f_pct))\n",
    "            new_edges = new_edges.iloc[valid_pos]  # use 'iloc' here\n",
    "\n",
    "        # merge new edges\n",
    "        dfs = [edges, new_edges]\n",
    "        edges = gpd.GeoDataFrame(pd.concat(dfs, ignore_index=False, sort=False),\n",
    "                                 crs=dfs[0].crs)\n",
    "\n",
    "        # all edges, newly added edges only\n",
    "        return edges, new_edges\n",
    "\n",
    "    # 0-2: configurations\n",
    "    # set poi arguments\n",
    "    node_highway_pp = 'projected_pap'  # POI Access Point\n",
    "    node_highway_poi = 'poi'\n",
    "    edge_highway = 'projected_footway'\n",
    "    osmid_prefix = 9990000000\n",
    "\n",
    "    # convert CRS\n",
    "    pois_meter = pois.to_crs(epsg=2263)\n",
    "    nodes_meter = nodes.to_crs(epsg=2263)\n",
    "    edges_meter = edges.to_crs(epsg=2263)\n",
    "\n",
    "    # build rtree\n",
    "    print(\"Building rtree...\")\n",
    "    Rtree = rtree.index.Index()\n",
    "    [Rtree.insert(fid, geom.bounds) for fid, geom in\n",
    "     edges_meter['geometry'].iteritems()];\n",
    "\n",
    "    ## STAGE 1: interpolation\n",
    "    # 1-1: update external nodes (pois)\n",
    "    print(\"Updating external nodes...\")\n",
    "    nodes_meter, _ = update_nodes(nodes_meter, pois_meter, ptype='poi')\n",
    "\n",
    "    # 1-2: update internal nodes (interpolated pps)\n",
    "    # locate nearest edge (kne) and projected point (pp)\n",
    "    print(\"Projecting POIs to the network...\")\n",
    "    pois_meter['near_idx'] = [list(Rtree.nearest(point.bounds, knn)) for point in\n",
    "                              pois_meter['geometry']]  # slow\n",
    "    pois_meter['near_lines'] = [edges_meter['geometry'][near_idx] for near_idx in\n",
    "                                pois_meter['near_idx']]  # very slow\n",
    "    pois_meter['kne_idx'], knes = zip(\n",
    "        *[find_kne(point, near_lines) for point, near_lines in\n",
    "          zip(pois_meter['geometry'], pois_meter['near_lines'])])  # slow\n",
    "    pois_meter['pp'] = [get_pp(point, kne) for point, kne in\n",
    "                        zip(pois_meter['geometry'], knes)]\n",
    "\n",
    "    # update nodes\n",
    "    print(\"Updating internal nodes...\")\n",
    "    nodes_meter, _ = update_nodes(nodes_meter, list(pois_meter['pp']),\n",
    "                                  ptype='pp')\n",
    "    nodes_coord = nodes_meter['geometry'].map(lambda x: x.coords[0])\n",
    "    nodes_id_dict = dict(zip(nodes_coord, nodes_meter['osmid'].astype('str')))\n",
    "\n",
    "    # 1-3: update internal edges (split line segments)\n",
    "    print(\"Updating internal edges...\")\n",
    "    # split\n",
    "    line_pps_dict = {k: MultiPoint(list(v)) for k, v in\n",
    "                     pois_meter.groupby(['kne_idx'])['pp']}\n",
    "    new_lines = [split_line(edges_meter['geometry'][idx], pps) for idx, pps in\n",
    "                 line_pps_dict.items()]  # bit slow\n",
    "    edges_meter, _ = update_edges(edges_meter, new_lines, replace=True)\n",
    "\n",
    "    ## STAGE 2: connection\n",
    "    # 2-1: update external edges (projected footways connected to pois)\n",
    "    # establish new_edges\n",
    "    print(\"Updating external links...\")\n",
    "    pps_gdf = nodes_meter[nodes_meter['highway'] == node_highway_pp]\n",
    "    new_lines = [LineString([p1, p2]) for p1, p2 in\n",
    "                 zip(pois_meter['geometry'], pps_gdf['geometry'])]\n",
    "    edges_meter, _ = update_edges(edges_meter, new_lines, replace=False)\n",
    "\n",
    "    ## STAGE 3: output\n",
    "    # convert CRS\n",
    "    nodes = nodes_meter.to_crs(epsg=2263)\n",
    "    edges = edges_meter.to_crs(epsg=2263)\n",
    "\n",
    "    # preprocess for pandana\n",
    "    nodes.index = nodes['osmid']  # IMPORTANT\n",
    "    nodes['x'] = [p.x for p in nodes['geometry']]\n",
    "    nodes['y'] = [p.y for p in nodes['geometry']]\n",
    "\n",
    "    # edges.reset_index(drop=True, inplace=True)\n",
    "    edges['weight'] = edges['geometry'].length\n",
    "    edges['weight'] = edges['weight'].astype(float)\n",
    "\n",
    "    # report issues\n",
    "    # - examine key duplication\n",
    "    if len(nodes_meter) != len(nodes_id_dict):\n",
    "        print(\"NOTE: duplication in node coordinates keys\")\n",
    "        print(\"Nodes count:\", len(nodes_meter))\n",
    "        print(\"Node coordinates key count:\", len(nodes_id_dict))\n",
    "    # - examine missing nodes\n",
    "    print(\"Missing 'from' nodes:\", len(edges[edges['from'] == 'None']))\n",
    "    print(\"Missing 'to' nodes:\", len(edges[edges['to'] == 'None']))\n",
    "\n",
    "    # save and return\n",
    "    if path:\n",
    "        nodes.to_file(path+'/new_nodes.shp')\n",
    "        edges[['geometry','weight']].to_file(path+'/new_edges.shp')\n",
    "        print(\"new_nodes.shp and new_edges.shp are written into {}\".format(path))\n",
    "\n",
    "    return nodes, edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building rtree...\n",
      "Updating external nodes...\n",
      "Projecting POIs to the network...\n",
      "Updating internal nodes...\n",
      "Updating internal edges...\n",
      "Updating external links...\n",
      "Remove faulty projections: 2/3637 (0.05%)\n",
      "NOTE: duplication in node coordinates keys\n",
      "Nodes count: 110931\n",
      "Node coordinates key count: 110868\n",
      "Missing 'from' nodes: 0\n",
      "Missing 'to' nodes: 0\n",
      "new_nodes.shp and new_edges.shp are written into ../intermediate_data/nyclion_19b/\n"
     ]
    }
   ],
   "source": [
    "fp = r\"../intermediate_data/nyclion_19b/\"\n",
    "new_nodes, new_edges =  connect_poi(points_to_add, nodes, edges, key_col='key', path=fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading in the modified street network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "shp_file = r\"../intermediate_data/nyclion_19b/new_edges.shp\"\n",
    "lion_gdf = gpd.read_file(shp_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "shp_file = r\"../intermediate_data/nyclion_19b/new_edges.shp\"\n",
    "lion_graph = nx.read_shp(shp_file, simplify = True, geom_attrs = True, strict = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Convert to undirected graph\n",
    "\n",
    "And create a nodes dataframe from graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = lion_graph.to_undirected()\n",
    "G = nx.convert_node_labels_to_integers(G, first_label = 0, label_attribute='pos')\n",
    "pos = nx.get_node_attributes(G, 'pos')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_df = pd.DataFrame(pos.items(), columns=['node_id', 'geometry'])\n",
    "node_df['geometry'] = node_df['geometry'].apply(Point)\n",
    "node_gdf = gpd.GeoDataFrame(node_df, geometry='geometry')\n",
    "node_gdf.crs = {'init': 'epsg:2263'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Locating the nodes for each location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use cDKTree to locate the nodes of the school and subways."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the tree \n",
    "#coord_array = np.array(list(zip(geodataframe.geometry.x, geodataframe.geometry.y)) )\n",
    "#tree = cKDTree(coord_array)\n",
    "\n",
    "def ckdnearest2(point_gemo, tree, kneighbors):\n",
    "    \"\"\"\n",
    "    This function read in a coordinate xy position, and a declared cdktree.\n",
    "    The aim is to find the closest point to the coordinate xy position from the tree \n",
    "    The function will return the distance between the closest point to the coordinate and the ID of that closest point.\n",
    "    \"\"\"\n",
    "    coordinate_xy = (point_gemo.x,point_gemo.y)\n",
    "    dist, idx = tree.query(coordinate_xy,k=kneighbors)\n",
    "    if kneighbors == 1:\n",
    "        dist = int(dist)\n",
    "        idx = int(idx)    \n",
    "    return dist, idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "coord_array = np.array(list(zip(node_gdf.geometry.x, node_gdf.geometry.y)) )\n",
    "node_tree = cKDTree(coord_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "subway[['distance_to_node','node_id']] = subway.apply(lambda row: ckdnearest2( row['geometry'], node_tree, 1),\n",
    "                                                     axis = 1,\n",
    "                                                     result_type = 'expand')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1928\n",
       "Name: distance_to_node, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subway['distance_to_node'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "subway.drop('distance_to_node', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools[['distance_to_node','node_id']] = schools.apply(lambda row: ckdnearest2( row['geometry'], node_tree, 1),\n",
    "                                                        axis = 1,\n",
    "                                                        result_type = 'expand')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1707\n",
       "21949       1\n",
       "11187       1\n",
       "Name: distance_to_node, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools['distance_to_node'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two schools that are really far when they should be at a distance of zero from the nodes. Recall when I ran the `connect_poi()` function that it `Remove faulty projections: 2/3637 (0.05%)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14a6824de48>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "#new_edges.plot(ax=ax)\n",
    "schools.loc[schools['distance_to_node']>0].plot(ax=ax, color = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove schools that are not in NYC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools = schools[schools['distance_to_node'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools.drop('distance_to_node', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding the nearest 2 subway entrances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to find the closest 2 subway entrances to each school"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the tree for subway location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "coord_array = np.array(list(zip(subway.geometry.x, subway.geometry.y)) )\n",
    "subway_tree = cKDTree(coord_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools[['distance_to_subway_nodes','subway_ids']] = schools.apply(lambda row: ckdnearest2( row['geometry'], subway_tree, 2),\n",
    "                                                        axis = 1,\n",
    "                                                        result_type = 'expand')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse out the subway_node_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools['closest_subway_id1'], schools['closest_subway_id2'] = zip(*schools['subway_ids'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>ATS_CODE</th>\n",
       "      <th>BORO</th>\n",
       "      <th>BORONUM</th>\n",
       "      <th>LOC_CODE</th>\n",
       "      <th>SCHOOLNAME</th>\n",
       "      <th>SCH_TYPE</th>\n",
       "      <th>MANAGED_BY</th>\n",
       "      <th>GEO_DISTRI</th>\n",
       "      <th>ADMIN_DIST</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>...</th>\n",
       "      <th>PRIN_PH</th>\n",
       "      <th>FAX</th>\n",
       "      <th>GRADES</th>\n",
       "      <th>City</th>\n",
       "      <th>geometry</th>\n",
       "      <th>node_id</th>\n",
       "      <th>distance_to_subway_nodes</th>\n",
       "      <th>subway_ids</th>\n",
       "      <th>closest_subway_id1</th>\n",
       "      <th>closest_subway_id2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15K001</td>\n",
       "      <td>K</td>\n",
       "      <td>2.0</td>\n",
       "      <td>K001</td>\n",
       "      <td>P.S. 001 THE BERGEN</td>\n",
       "      <td>Elementary</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>309 47 STREET</td>\n",
       "      <td>...</td>\n",
       "      <td>718-567-7661</td>\n",
       "      <td>718-567-9771</td>\n",
       "      <td>PK,0K,01,02,03,04,05,SE</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>POINT (980985.0541713729 175780.758994163)</td>\n",
       "      <td>109165</td>\n",
       "      <td>[574.0166423762518, 617.2474703341181]</td>\n",
       "      <td>[1744, 1746]</td>\n",
       "      <td>1744</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17K002</td>\n",
       "      <td>K</td>\n",
       "      <td>2.0</td>\n",
       "      <td>K002</td>\n",
       "      <td>M.S. 002</td>\n",
       "      <td>Junior High-Intermediate-Middle</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>655 PARKSIDE AVENUE</td>\n",
       "      <td>...</td>\n",
       "      <td>718-462-6992</td>\n",
       "      <td>718-284-7717</td>\n",
       "      <td>06,07,08,SE</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>POINT (997785.1364296663 178431.910832498)</td>\n",
       "      <td>109166</td>\n",
       "      <td>[258.5002370023575, 442.8355620846949]</td>\n",
       "      <td>[1349, 749]</td>\n",
       "      <td>1349</td>\n",
       "      <td>749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21K095</td>\n",
       "      <td>K</td>\n",
       "      <td>2.0</td>\n",
       "      <td>K095</td>\n",
       "      <td>P.S. 095 THE GRAVESEND</td>\n",
       "      <td>K-8</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>345 VAN SICKLEN STREET</td>\n",
       "      <td>...</td>\n",
       "      <td>718-449-5050</td>\n",
       "      <td>718-449-3047</td>\n",
       "      <td>PK,0K,01,02,03,04,05,06,07,08,SE</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>POINT (991224.9830063033 156323.3612131244)</td>\n",
       "      <td>109167</td>\n",
       "      <td>[431.5770408752747, 492.43651609913985]</td>\n",
       "      <td>[1522, 1591]</td>\n",
       "      <td>1522</td>\n",
       "      <td>1591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21K096</td>\n",
       "      <td>K</td>\n",
       "      <td>2.0</td>\n",
       "      <td>K096</td>\n",
       "      <td>I.S. 096 SETH LOW</td>\n",
       "      <td>Junior High-Intermediate-Middle</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>99 AVENUE P</td>\n",
       "      <td>...</td>\n",
       "      <td>718-236-1344</td>\n",
       "      <td>718-236-2397</td>\n",
       "      <td>06,07,08,SE</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>POINT (988439.6813609767 160709.9926236183)</td>\n",
       "      <td>109168</td>\n",
       "      <td>[1366.7287949334989, 1549.5096241139383]</td>\n",
       "      <td>[443, 203]</td>\n",
       "      <td>443</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21K097</td>\n",
       "      <td>K</td>\n",
       "      <td>2.0</td>\n",
       "      <td>K097</td>\n",
       "      <td>P.S. 97 THE HIGHLAWN</td>\n",
       "      <td>Elementary</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>1855 STILLWELL AVENUE</td>\n",
       "      <td>...</td>\n",
       "      <td>718-372-7393</td>\n",
       "      <td>718-372-3842</td>\n",
       "      <td>PK,0K,01,02,03,04,05,SE</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>POINT (988205.0729096507 158329.5913366843)</td>\n",
       "      <td>109169</td>\n",
       "      <td>[1224.1544066200531, 1270.3197366749077]</td>\n",
       "      <td>[190, 189]</td>\n",
       "      <td>190</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ATS_CODE BORO  BORONUM LOC_CODE              SCHOOLNAME  \\\n",
       "0  15K001          K      2.0     K001     P.S. 001 THE BERGEN   \n",
       "1  17K002          K      2.0     K002                M.S. 002   \n",
       "2  21K095          K      2.0     K095  P.S. 095 THE GRAVESEND   \n",
       "3  21K096          K      2.0     K096       I.S. 096 SETH LOW   \n",
       "4  21K097          K      2.0     K097    P.S. 97 THE HIGHLAWN   \n",
       "\n",
       "                          SCH_TYPE  MANAGED_BY  GEO_DISTRI  ADMIN_DIST  \\\n",
       "0                       Elementary           1          15          15   \n",
       "1  Junior High-Intermediate-Middle           1          17          17   \n",
       "2                              K-8           1          21          21   \n",
       "3  Junior High-Intermediate-Middle           1          21          21   \n",
       "4                       Elementary           1          21          21   \n",
       "\n",
       "                  ADDRESS  ...       PRIN_PH           FAX  \\\n",
       "0           309 47 STREET  ...  718-567-7661  718-567-9771   \n",
       "1     655 PARKSIDE AVENUE  ...  718-462-6992  718-284-7717   \n",
       "2  345 VAN SICKLEN STREET  ...  718-449-5050  718-449-3047   \n",
       "3             99 AVENUE P  ...  718-236-1344  718-236-2397   \n",
       "4   1855 STILLWELL AVENUE  ...  718-372-7393  718-372-3842   \n",
       "\n",
       "                             GRADES      City  \\\n",
       "0           PK,0K,01,02,03,04,05,SE  BROOKLYN   \n",
       "1                       06,07,08,SE  BROOKLYN   \n",
       "2  PK,0K,01,02,03,04,05,06,07,08,SE  BROOKLYN   \n",
       "3                       06,07,08,SE  BROOKLYN   \n",
       "4           PK,0K,01,02,03,04,05,SE  BROOKLYN   \n",
       "\n",
       "                                      geometry node_id  \\\n",
       "0   POINT (980985.0541713729 175780.758994163)  109165   \n",
       "1   POINT (997785.1364296663 178431.910832498)  109166   \n",
       "2  POINT (991224.9830063033 156323.3612131244)  109167   \n",
       "3  POINT (988439.6813609767 160709.9926236183)  109168   \n",
       "4  POINT (988205.0729096507 158329.5913366843)  109169   \n",
       "\n",
       "                   distance_to_subway_nodes    subway_ids  closest_subway_id1  \\\n",
       "0    [574.0166423762518, 617.2474703341181]  [1744, 1746]                1744   \n",
       "1    [258.5002370023575, 442.8355620846949]   [1349, 749]                1349   \n",
       "2   [431.5770408752747, 492.43651609913985]  [1522, 1591]                1522   \n",
       "3  [1366.7287949334989, 1549.5096241139383]    [443, 203]                 443   \n",
       "4  [1224.1544066200531, 1270.3197366749077]    [190, 189]                 190   \n",
       "\n",
       "  closest_subway_id2  \n",
       "0               1746  \n",
       "1                749  \n",
       "2               1591  \n",
       "3                203  \n",
       "4                189  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>index</th>\n",
       "      <th>line</th>\n",
       "      <th>name</th>\n",
       "      <th>objectid</th>\n",
       "      <th>url</th>\n",
       "      <th>geometry</th>\n",
       "      <th>node_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2-5</td>\n",
       "      <td>Birchall Ave &amp; Sagamore St at NW corner</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (1020670.668616476 248679.9905504963)</td>\n",
       "      <td>107248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2-5</td>\n",
       "      <td>Birchall Ave &amp; Sagamore St at NE corner</td>\n",
       "      <td>1735.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (1020710.253511059 248665.112299423)</td>\n",
       "      <td>107249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2-5</td>\n",
       "      <td>Morris Park Ave &amp; 180th St at NW corner</td>\n",
       "      <td>1736.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (1019251.986414479 245782.8697043241)</td>\n",
       "      <td>107250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2-5</td>\n",
       "      <td>Morris Park Ave &amp; 180th St at NW corner</td>\n",
       "      <td>1737.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (1019419.817771734 245866.9102186682)</td>\n",
       "      <td>107251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2-5</td>\n",
       "      <td>Boston Rd &amp; 178th St at SW corner</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (1017557.721758398 245631.8322767556)</td>\n",
       "      <td>107252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index line                                     name  objectid  \\\n",
       "0      0  2-5  Birchall Ave & Sagamore St at NW corner    1734.0   \n",
       "1      1  2-5  Birchall Ave & Sagamore St at NE corner    1735.0   \n",
       "2      2  2-5  Morris Park Ave & 180th St at NW corner    1736.0   \n",
       "3      3  2-5  Morris Park Ave & 180th St at NW corner    1737.0   \n",
       "4      4  2-5        Boston Rd & 178th St at SW corner    1738.0   \n",
       "\n",
       "                                 url  \\\n",
       "0  http://web.mta.info/nyct/service/   \n",
       "1  http://web.mta.info/nyct/service/   \n",
       "2  http://web.mta.info/nyct/service/   \n",
       "3  http://web.mta.info/nyct/service/   \n",
       "4  http://web.mta.info/nyct/service/   \n",
       "\n",
       "                                      geometry  node_id  \n",
       "0  POINT (1020670.668616476 248679.9905504963)   107248  \n",
       "1   POINT (1020710.253511059 248665.112299423)   107249  \n",
       "2  POINT (1019251.986414479 245782.8697043241)   107250  \n",
       "3  POINT (1019419.817771734 245866.9102186682)   107251  \n",
       "4  POINT (1017557.721758398 245631.8322767556)   107252  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subway.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging in the respective node_ids for the nearest subway locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools = schools.merge(subway[['index','node_id']], \n",
    "                        how = 'left', \n",
    "                        left_on = 'closest_subway_id1', \n",
    "                        right_on = 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools.drop('index', axis = 1, inplace = True)\n",
    "schools.rename(columns={\"node_id_y\": \"subway_node_id1\"},inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools = schools.merge(subway[['index','node_id']], \n",
    "                        how = 'left', \n",
    "                        left_on = 'closest_subway_id2', \n",
    "                        right_on = 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools.drop('index', axis = 1, inplace = True)\n",
    "schools.rename(columns={\"node_id\": \"subway_node_id2\"},inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools.rename(columns={\"node_id_x\": \"node_id\"},inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def network_distance(graph, node_gdf, origin_node, target_node, weight_name):\n",
    "    \"\"\"\"\n",
    "    graph is the networkx graph\n",
    "    node_gdf is the geodataframe of nodes. This will allow us to create the LineString.\n",
    "    origin_node is the starting node to begin the network distance\n",
    "    target_node is the end node to end the network distance\n",
    "    weight is the distance measure\n",
    "\n",
    "    We use the shortest_path function from networkx.\n",
    "    Use the nodes on the shortest path and extract the coordinates from the node_gdf\n",
    "    Use the geometry from the route_nodes to create the line string\n",
    "    From the line string we will extract the length\n",
    "    \"\"\"    \n",
    "    \n",
    "    try:\n",
    "        route = nx.shortest_path(G=graph, source=origin_node, target=target_node, weight = weight_name)\n",
    "        route_nodes = node_gdf.loc[route]\n",
    "        route_line = LineString(list(route_nodes.geometry.values))\n",
    "    except:\n",
    "        route_line = LineString()\n",
    "        \n",
    "    route_line_dist = int(route_line.length)\n",
    "    return route_line, route_line_dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It took 0.002848712139659458 hours\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "schools[['route1_geom', 'route1_dist']] = schools.apply(\n",
    "        lambda row: network_distance(G, node_gdf, row['node_id'], target_node = row['subway_node_id1'],weight_name = 'weight'), \n",
    "        axis = 1, \n",
    "        result_type = 'expand')\n",
    "\n",
    "t1 = time.time()\n",
    "time_difference = (((t1-t0)/60)/60)\n",
    "print(\"It took {0} hours\".format(time_difference))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It took 0.0029577416181564334 hours\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "schools[['route2_geom', 'route2_dist']] = schools.apply(\n",
    "        lambda row: network_distance(G, node_gdf, row['node_id'], target_node = row['subway_node_id2'],weight_name = 'weight'), \n",
    "        axis = 1, \n",
    "        result_type = 'expand')\n",
    "\n",
    "t1 = time.time()\n",
    "time_difference = (((t1-t0)/60)/60)\n",
    "print(\"It took {0} hours\".format(time_difference))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine which route is shorter between the two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools.loc[(schools['route1_dist'] <= schools['route2_dist']), 'shortest_network_distance'] = schools['route1_dist'] \n",
    "schools.loc[(schools['route1_dist'] > schools['route2_dist']) , 'shortest_network_distance'] = schools['route2_dist']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools.loc[(schools['route1_dist'] <= schools['route2_dist']), 'shortest_network_geom'] = schools['route1_geom'] \n",
    "schools.loc[(schools['route1_dist'] > schools['route2_dist']), 'shortest_network_geom'] = schools['route2_geom']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    1707\n",
       "Name: route1_dist, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools['route1_dist'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ATS_CODE', 'BORO', 'BORONUM', 'LOC_CODE', 'SCHOOLNAME', 'SCH_TYPE',\n",
       "       'MANAGED_BY', 'GEO_DISTRI', 'ADMIN_DIST', 'ADDRESS', 'STATE_CODE',\n",
       "       'ZIP', 'PRINCIPAL', 'PRIN_PH', 'FAX', 'GRADES', 'City', 'geometry',\n",
       "       'node_id', 'distance_to_subway_nodes', 'subway_ids',\n",
       "       'closest_subway_id1', 'closest_subway_id2', 'subway_node_id1',\n",
       "       'subway_node_id2', 'route1_geom', 'route1_dist', 'route2_geom',\n",
       "       'route2_dist', 'shortest_network_distance', 'shortest_network_geom'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>node_id</th>\n",
       "      <th>subway_node_id1</th>\n",
       "      <th>subway_node_id2</th>\n",
       "      <th>route1_geom</th>\n",
       "      <th>route1_dist</th>\n",
       "      <th>route2_geom</th>\n",
       "      <th>route2_dist</th>\n",
       "      <th>shortest_network_distance</th>\n",
       "      <th>shortest_network_geom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>110157</td>\n",
       "      <td>108875</td>\n",
       "      <td>107599</td>\n",
       "      <td>GEOMETRYCOLLECTION EMPTY</td>\n",
       "      <td>0</td>\n",
       "      <td>GEOMETRYCOLLECTION EMPTY</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GEOMETRYCOLLECTION EMPTY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>110182</td>\n",
       "      <td>108875</td>\n",
       "      <td>107599</td>\n",
       "      <td>GEOMETRYCOLLECTION EMPTY</td>\n",
       "      <td>0</td>\n",
       "      <td>GEOMETRYCOLLECTION EMPTY</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GEOMETRYCOLLECTION EMPTY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1042</th>\n",
       "      <td>110206</td>\n",
       "      <td>108871</td>\n",
       "      <td>107599</td>\n",
       "      <td>LINESTRING (929431.2955717515 137631.694279956...</td>\n",
       "      <td>4601</td>\n",
       "      <td>GEOMETRYCOLLECTION EMPTY</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GEOMETRYCOLLECTION EMPTY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1613</th>\n",
       "      <td>110772</td>\n",
       "      <td>108750</td>\n",
       "      <td>107347</td>\n",
       "      <td>GEOMETRYCOLLECTION EMPTY</td>\n",
       "      <td>0</td>\n",
       "      <td>GEOMETRYCOLLECTION EMPTY</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GEOMETRYCOLLECTION EMPTY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      node_id  subway_node_id1  subway_node_id2  \\\n",
       "993    110157           108875           107599   \n",
       "1018   110182           108875           107599   \n",
       "1042   110206           108871           107599   \n",
       "1613   110772           108750           107347   \n",
       "\n",
       "                                            route1_geom  route1_dist  \\\n",
       "993                            GEOMETRYCOLLECTION EMPTY            0   \n",
       "1018                           GEOMETRYCOLLECTION EMPTY            0   \n",
       "1042  LINESTRING (929431.2955717515 137631.694279956...         4601   \n",
       "1613                           GEOMETRYCOLLECTION EMPTY            0   \n",
       "\n",
       "                   route2_geom  route2_dist  shortest_network_distance  \\\n",
       "993   GEOMETRYCOLLECTION EMPTY            0                        0.0   \n",
       "1018  GEOMETRYCOLLECTION EMPTY            0                        0.0   \n",
       "1042  GEOMETRYCOLLECTION EMPTY            0                        0.0   \n",
       "1613  GEOMETRYCOLLECTION EMPTY            0                        0.0   \n",
       "\n",
       "         shortest_network_geom  \n",
       "993   GEOMETRYCOLLECTION EMPTY  \n",
       "1018  GEOMETRYCOLLECTION EMPTY  \n",
       "1042  GEOMETRYCOLLECTION EMPTY  \n",
       "1613  GEOMETRYCOLLECTION EMPTY  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools[['node_id','subway_node_id1','subway_node_id2','route1_geom','route1_dist','route2_geom','route2_dist','shortest_network_distance','shortest_network_geom']].loc[schools['shortest_network_distance'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = r\"../intermediate_data/school_subway_route.shp\"\n",
    "schools = schools.set_geometry('shortest_network_geom')\n",
    "schools[['node_id','shortest_network_geom']].to_file(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = r\"../intermediate_data/school.shp\"\n",
    "schools = schools.set_geometry('geometry')\n",
    "schools[['node_id','geometry']].to_file(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = r\"../intermediate_data/subway.shp\"\n",
    "subway = subway.set_geometry('geometry')\n",
    "subway['geometry'].to_file(fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the schools, subway and routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "\n",
    "schools.plot(ax=ax, color = 'blue', markersize = 9, label = 'school')\n",
    "subway.plot(ax=ax, color = 'red', markersize = 9, label = 'subway')\n",
    "schools = schools.set_geometry('shortest_network_geom')\n",
    "schools.plot(ax=ax, color = 'black', linewidth=1, label = 'school routes')\n",
    "#Set default back\n",
    "schools = schools.set_geometry('geometry')\n",
    "\n",
    "plt.title(\"Routes from NYC schools to nearest subway entrance\")\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "ax.set_axis_off()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I can see that there are 3 blue dots in Staten Island that should have a route drawn, but the networkx shortest path function did not find a path for these origin nodes. Need to figure out why. The other blue dot south of Manhattan is on Governors island. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}