damage_shapefile_generation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "453f3a64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\n",
      "Requirement already satisfied: geopy in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (2.2.0)\n",
      "Requirement already satisfied: geographiclib<2,>=1.49 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from geopy) (1.52)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.1.2 is available.\n",
      "You should consider upgrading via the '/home/ec2-user/anaconda3/envs/tensorflow2_p38/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mLooking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\n",
      "Requirement already satisfied: geopandas in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (0.11.0)\n",
      "Requirement already satisfied: pyproj>=2.6.1.post1 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from geopandas) (3.3.1)\n",
      "Requirement already satisfied: fiona>=1.8 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from geopandas) (1.8.21)\n",
      "Requirement already satisfied: packaging in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from geopandas) (21.0)\n",
      "Requirement already satisfied: pandas>=1.0.0 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from geopandas) (1.3.4)\n",
      "Requirement already satisfied: shapely<2,>=1.7 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from geopandas) (1.8.2)\n",
      "Requirement already satisfied: click>=4.0 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (8.0.3)\n",
      "Requirement already satisfied: cligj>=0.5 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (0.7.2)\n",
      "Requirement already satisfied: attrs>=17 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (20.3.0)\n",
      "Requirement already satisfied: certifi in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (2021.10.8)\n",
      "Requirement already satisfied: click-plugins>=1.0 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (1.1.1)\n",
      "Requirement already satisfied: setuptools in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (59.1.1)\n",
      "Requirement already satisfied: munch in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (2.5.0)\n",
      "Requirement already satisfied: six>=1.7 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from fiona>=1.8->geopandas) (1.16.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from pandas>=1.0.0->geopandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from pandas>=1.0.0->geopandas) (2021.3)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from pandas>=1.0.0->geopandas) (1.20.3)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in /home/ec2-user/anaconda3/envs/tensorflow2_p38/lib/python3.8/site-packages (from packaging->geopandas) (3.0.6)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.1.2 is available.\n",
      "You should consider upgrading via the '/home/ec2-user/anaconda3/envs/tensorflow2_p38/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from matplotlib import colors\n",
    "%matplotlib inline\n",
    "\n",
    "from datetime import date\n",
    "from datetime import timedelta\n",
    "import zipfile\n",
    "import os\n",
    "import requests\n",
    "\n",
    "import numpy as np\n",
    "import cv2\n",
    "import math\n",
    "import random \n",
    "\n",
    "!pip install geopy\n",
    "import geopy.distance\n",
    "!pip install geopandas\n",
    "import geopandas  as gpd\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import cascaded_union, unary_union\n",
    "\n",
    "# Import specific function 'from_epsg' from fiona module\n",
    "from fiona.crs import from_epsg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca393802",
   "metadata": {},
   "source": [
    "# Damage Bucketing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98b28fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread('model7-diffdiff-results/Soufriere_St_Vincent_2020-04-05_360150_22385_dVV_dd.png', cv2.COLOR_BGR2GRAY )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a0ef5f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f75adddd7c0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5wCYopcfdoxiNFhPN5qfH9P3wSkn6pav444rJNP4WhTspvOvnhk7SZ15Cs2SPswOK1drvdd3dicF+hUjbF/agRbi7oIwehrFvz8SP/1+gBgfhOXhCF7257dBran3zDoPZ3ppcjPwe9Sr3KISfH2IXhVr+bHjLyomdu6g9+aYlD0FLSeqyjjCZu1GB7Qjp8RCxSEN6+wkTDJ2SA218Pvshts3wA9qGu0xm1OAgTD+sJPDtJT0mvA2XC7m175mUnQ0mKoQQMVLKMiFEDLBdBrhfIdI/A8b6zSh9aPftcfSQnOpxNYsFdH2nRoD19ET87ywla2kyKTft+ineVdm0HSFMZhpPGo/XIgj7cF2v2///QAfXMCGaoBVlXRdKo9/rqlfXEPJa79N9neFXJbm1+Dj8SyRqcBBZD2ZgqlOJWaTj982q3vclZb8h7M4+KZ8D57b9+1zgs07LTxdCWNrESNOA3ZYOVWy2gSVB2thz/gqISaOouWhgakqOw8fgPnDsTu1H2ZhP438TiPtl71RTlrpOxdEuWo9vGDRD0t8NAd9s6JYclF7vbmW0jpi3mMaDmglbVI573FAyXm4l5f1G/Bas2WUeiX6fKCHEO8D+QLgQogT4F/Ag8L4Q4kJgK3AKgJRyoxDifWATPgX1K6SUu+1pVEJDkI1Nu1fwcTdDKdhGRLa7R/rpHeH3+fI+1Yb7gtHSgmXBqr2358HQGXZHja9E9v+8pLq7vajeIF0uCs+IQWuBuBdzMTqRrmx/Oe6MQfifaxseDKpmTyN8nQN1bV6fF1pYLGDIv8UM/GChxUT/6S6748QpBC4uQq+q/lPYknpCb+QnrqMmIbxyQCrAewKeQyeCITH9sBLwUZc79xmOK1gj5NcCn65mL/inbXgnEPnKKpRlG/sdFRVC9NhG+v8BAxXPGBQUFXV4Wo+db0pAAHE35FI7307ea6O68S7+GRCaRuMJ43rM2jsiNFqiB8csvDth+m5FuwEA8E5MZ/KDK6gbrux0e/f/liegqLgOH4/1+7X/L9/afxc4j5nMiLvWkX95GqzLoenYsThDFCJeW4VQVTY/NAp7oUrCp2XoWwrR4mLZdEcctq0a8fcv6nGb2pAE8Op4S/dwHloIFItl7+gXEQJ1eBolR4QT80gP50UIlBEZbL4sCKlIts6+qUdP4G89dC40DSVliG8Cqw9jtl3ERCgCYfD/9q09UCg221/GhKPY7XitggVLx8A5QNAIFu7/CLWGysWN1xH44QqG3ZmDsJipPCKFcLsV77psMh9QMEICKL98Oo5YibVKEPPsChQ/K1LXcb4sqPk8iagn97ARGEC2vVcIgZY8BG9+4S4dghoRgWtUIrpFwfLNcmI25fS4njIig5DnKwh9JYTIj7LpjYf67x0OqCpNI8IRWu/umbBYEMNSAV/SxLxg+V+uQzhQCJN5tzXSqBERqMFBKFYrhTeN3S3bBF8zipYQ3/0DIXzufpuLKiaMoPTjEcT+KHjgv8+z8NhHyD/pefIPnk+M5s9PLcNoOb0Bx9Hj0RsaMaLDqD3QieFnAimRDY0ojQ5ivyll6Ns1BG7VyXtgPOHfgnvqMFzPxRD38R7uc1BU1IzUnf++lNRNjkYdkbFLh2HUN2BZV4ipue98ibEpl+K56UT9UNrn9OHf2ghIlwvbJ0v7dO2ly4WxoRPFlxComel7jbKRFhPd6/hp3ekT2PzEkN0yyiujwhChIRhOJykvD06bQJjMMHkUWkJ8O6OR0DTEhBHUfRyP8xWlCxWbGhJCzrxJGKM7PTAGvDf+JV5O/IOJZjcxmj/r3E6eqU8gx9PC/HlHEXNaPgErt6ElxhH/fBH7p+WiNPuurWNaOoWnxeItKELflIP/dxsIWyfYWB2NqcFF4HdZeLft2QSm58CxZN8euEuqS8Hr6/rtIVECArqwSe0I6XGjV9eg/N69DbkLDB3bJ0v71aL4WxmBQcty9YIts8Jw7zeq9xWEQEwaNWDN+V1B2XHJvX4WurqOmal5aPE9dl4PCsaG7HY31FtcMqjvquGhuO9rZObXm6k439eJqQxNIv9GDVUxaHkltkupUk+Lx1quIVZsar/h1fIazt94DstcHhoMN5+32Dj19ev4amY6V5xzJdG/1PrGYItLkCaNkgsTKDva0m7A7WuKiVnc4YYroSEEFjnxezWE/JMCOH5pHiU377yKT2coVitqZnq35da1Wxl2b8/8Dn3JtXWGvnEzei/u+3aUnT+KkusmDOh+VwMDd3mAaK/PCTiPmYz/hgqaR0ZRNk1l6H0d3WdqSIjPzRECNS0FI7+ovaSkBgZiZAxBrtzU9aJJSfLdy5FGH9ZYSgyTgmY2w87Ef4Ng44lY1dyrq2ZsyKbsshGI+t4tuRoRgVFXh/R6UdNS0LcU7fbeAW9ZOcYTk/muNZzIP1YhAdHQRMpDFuTqfCAfxwlTCPgjH9nURE2mP0PuW9GlvNcwfQgXpXzBWe9dTdhaieaUpPy2GaOhEeXX1V36KtpZdjpB2v2wbN4GCfF4i0vQy8oxNbegxUVRNT6ErytHYZlWA5NHIVZv3qXEr+F0InILfaFYWEh7C3bxeWnEP7u2x+/oEUG+ofrBQAjEhBHIFRu6LLaX6QRubsBobe13E9vOHUlIjhtbbjWyxYEwmQZNXb93ewJCUJemYQTaGPOv1eg22Z7Q0mKiKT1veMe6O7hYIjSY3DPsPerHSa+33wdFWbZx0E1Jit0nsa5FDUJOatn6Pj+Wqzd2aQpRM9NpPmUKanAQAHp1p1p6Vc1uFzHZDv8VRVhW5bfnU7zlFcjVGwFfuFA9y0H2XSk0HjuWiO+Luj2ELVEqM/y2kPxpC4HvLMH+9RpaJ6agJsT1SLaxI4wgGw0zhpBzha8rXXq96HV1GBuySbp9Ma03R1FfZ+c/782n6NbBTVBqcbE4j5nsSzTbbL55DGmg1zVgBPq1/0al7TSrgYHdQjS5vO/r2COkRK7qTkfv/8FSH0t12z1dctt0cl6ZgJw2ptu6cZ8UoTl1HOkRYMid0q7Yu42AlMQ8ughjbRbr7hxL+MqOnnyjuYXQLE/7enpufpc3j7dwK6nXL9npOXLp9aLFx1F+3fRuwyHdIATK6GGUXDEGMSJtp5pr1MDAAT0M+aeF89B/51H2WozPBe1s/KIiEGMzd1u+Q42KpPya6b6uv/KKXj0WNTaKMzOWYy9SCf69sMcyXdTSBpY7h9AS73uo5LgMnnj+aW778ROyH8vo/lBNG9MlLpYrN9IUpzJmRm7PgznLNiLdKrPXn8WQbwYpX6YoGJpv/1VnjmHObwvIf2MEalho+1taetzEPr8GFAXvqBQUP7/B7aM39Oe1CYE6uY5xQ7fSlNxpn215CW9JKcqvqzEvWO5jt+781bYwQZjMfeYx9m4j0AmWBSsIfnNJ+99GU9Og9OB3BjLQjv9h5Ww7ovuk3o5QGlpI/HAbcuPAhSkVu71DdFNVfU1H/SDp3hX8+8zzCba1Ur1vp1yBENQ9Lnnw4/k4jhzb5zaEyTwgQyE0DX0A93rxSQnMDlnJyWf/0iu1tVy5kYc3HsLhd/2KGh7G1sP8SdUUZlgV5u3/Rrc5+apx9m7no3GMmxvjvqX2pNHdDaahM+yajUSdWoRcvr73CT5FRRmb2eVzb3EJ/j9swnHUeGrHGuzvZ7B+5ks07dOJkkwIpNuN0dSEWLjmzyuxSkn8bToN/04kZE0HrUfrsRP65E0UmkbuwxNRh6eRO38EW/7bO63H38YIIOWfznajb8oh8MRyYt7Y0PeKUuItKsabXzjgNlctLpbNz2ZQfKmPy0+vqxtQ+CE9bsTitZgPK+liFLX4OORbEZy8aDaKp4/zJATOg8ew9eaJ/RoCb+k24h5c1Pt5b3u7tEZJQhQ/okwNiKau7dXegybAZF8SNvZxM1scEcjoCCJXeXFJLx6p+7j6lK63YuSq5m67S53v5fKHr0TokuyHhiMmjuzyueFwYDid1Fw8jdgfhW+/QnQVbJEGnhAr7inDuh7nmKG8/dSj5B4/j4drhzLsiysIXNupBXfKKEqv/2v4cYycfEy/rm1PKCp2O9Yqd68tworNhjFpBFqzoOzACM4YtQJ7Se8vmL0+MfhXY09ZfO+2MoZd1YzhcPRMuLADFLsdfXQqpooGX5Z/Bzcy695Ibp/8KW9cfwyWb1d1+UwNC8VoaER6vSg2G6G3FnJ33I88uGAW7JCUUsZmsvWIYIa8kN2NuluNiMCob0B63KhhoZSfkkHEc4tJf7qYkS1XEvebE7Wia9mq+BAzGGaSl4Hy+2rKDwnAaN6Mn3UES10hXPnp+aR84kQU7JBwW7KuS7JQaBrq0k1ErTaR8ZuLf0X+xomfXcN2M6bvP54ts1SC1psYPWsDLyf+Qeq1qVjXTWPIe6XoG9oy8lKi/tz1/DSdPhXzBeXEa778kSEVQlepbLo9nIzZ25AeN8qaHBILg/aoGnNv2PHFYjgcqOu2YPRinJXIcCIeLaD58UyCvt7AslXjid2wnt5eZXudEVADA31uVw9Z+ZaTpxCQ29hV209RESbtb9MA1A4pB5d41HW02hZkS3ejpAQEMDm1kARTDcWHqqR+63soig+yYG4UJL5fDM0t4PViOBxUPzqCaxNmE1ea3+2mFvklDPnI1aMKc+eYU6+tw9IoUaxWvMUlJN7Tc9kx9f6N6J14GY2mJtTAQMrv8jDFUocR7sG0pQwvvjLb9pq2YrP5+gyWrPMd17BU9CArYuEavv18OmeftwhTQ0fyUUjJOwc/h/UQL0M0nX9VTSLycyvl++gYAX7dHiShaShpySAE1cc7yBn5KQAOw83zvx3I8G9LCCqM8iU4hUAEBfY5nPOnQsouyeIdUTclllmhC1l5agKBH7UiFq/tc6p175sdUFRfhltKtLjYjiSTEDiPnoR9c00X1hxtSAI5l8eT/tw2vAW7sWNMUX2JIaezzxO+t0AND4OwEMoOiUQcWsPdw79gqrWKgx+f03Nf+W6AlpKEVBWE0z3w3gNFJfepiWw47ilsipllLg93H30W+sbNaNFR7Q+aYrXinj4C7aeV3TahhodxwC+FLK5NwXmKQK+oBEWl+rOhrJzwPkduPhJxtqB+WjwB+c3IlRu7b2NEBumvb+HAwE3sa60mRPXlMi4vnUrhQeYuBtp5zGTC5hTgPM7bJTmq2Gx4J2Vg3lrb/d4TAnVYKnp23p8exmopSXiigzAV13S5Ln+PKcKpo8l9dQy5T032JW7MndqBpcT6xbIeabMePvENyp+09NhcoUZF7lS2XCiCvOtTcew/vP+V9wLo1TXom/OIen4F0XN07ph3Hvu/MAdD86ku7QkBEW9+IZhNONN7F7vcEaq/nVsO/AKb4rsmr1bviyzwEXJ0ftMaTmePBgDAaGji+XX7ckP8twhz27U1dBqyw9ClQXZxNDLQzrAbNtIaa0dLSkSMG4GWPATwhVZZN/rzeMwKjrU72g0AwPKKRPQdjH7jEI1hARXQicPPddQktl06lmdef5rKp7p39yl+fjQND+3GF/FnNKAZ28pRlm3sYgB6oqFrP6Y9fkSDQO5lGkdnrkd42m5YT4cLp9hsXUpGalqKz1PYWsJ/Hj6bw+Kz8Uz2JXvExJGU3Dod9+GTOODHLeQ8Pg41I5WCB6YNuPdbGhJTo8Bj26tOUb8Ps/S40TfnEbnKyRVnfMHCqx7B/FILLSdN7rEVVQ0MJOe5yShj+jd2akREN4Nq5BXiCtZQU5O7GWGhad2PV1Uxi47rurA0Genuu7GnW0ecIgj6xY+bb7oMo6q6fXHG0yVMvfMKohaYEU0Ofl45gtsfe4V7fv6QUS9vQjramm+GJvD1AU+1fy/L7WDCylN5vC6J50a8ievwri9L4ZV88c4+HWGNomK7sRSvHxy24FpC/RzdfqfhcGD7eGmX3E3t+dOoOnvPcV+KiSMRmobhdHYNfxSVxikJvX5vr7rD059ys+7OsaRevxSjpaW98UFYLDgOHIl3mo93XYuLxZEeBiYNpCT8xSWs3icA9TdfckktryPigG0Ir0GD10bYkDrqxofjjXEjiwc4ZWboxD+wiID3lvT4sTJ62G5rYx4ohMWCGtq/zJWYNIrKa1uZu/BwDrn9emqeSKL0MIOy2RO6resZN5SQtSrG+r5bWdXMdJK+biJn7vguD6V0uQj8KYfGMZEUXTe2y8CTkpSAGhzc9dhsfjzw4UmsdPke/I/HvYg+o/cWbTU4iNJru2bl8+4dT9q5mwn4ZkPX3JGq0jREELK8ktzLE8g/4XkOtXmYYDHz0R+T0SsqUaxWsq/xJ7qtbO6ROpfnnkHUBXXM++QIolQ3FRc4u5QgI19aTuzDizseaEOn9b9xxP3SQtgKjZIfEvt1+YXFglQhbP0eZiFS1e5DZ4buM0i9YK9KDMoVG7BFRKALhbLrpyBVSPikHD03H+uXHVSFRkggfr9swrud7UfKLsw/3tJtbFs1lddfeJpUk5Nv5u2DrcLN/dM+5tlDTyFg6dYuDT2K3Y5z30xaw1VCNjQis7b0m2gUrW6MHiie9ySky4Xe6bjU4CCE3Q663sWVbkizE39rDfpmH/2YNiSBYV9XdTwwQrS7qcqvq4n4tW0gyGztfUx2WwVrHxpDUp2nG7W1XldHwFdryZijsXxSAjEn+vrre1LCLT0xCWsVFHtDmWBpJkmzkXeWRpo7A7G4h5ZcTaM5092F6WfIN25mn/gL9008twuXf9PoSOSwZhrHRPDtrIcBX7Z/n3UnkvC9pOWkKVhrPGTeU8EE9Sre3e85yr1BlNcHYptvIehDOP7fczjkshX8fs4kIp/x5VJ6KvuaFywHIQhbjI9foOez1g7pchH20sBIRXcWcsUGX47sinjSn+lHFKUT9hpPQGgaTB2NkeRrgFDc4AyVGP5+NJ02FTljLGU3TMdxwhSkRUXE9hGHSknqyxVcPO8qpr95I3WjDArOk5R7gzj0nt+oPiTZ91YNDESYzJRcMYa3X3iMnx58krmfvszWG7u/Mbsdb7MD6dm5gpHQtI4moV1A/nUjGPnlNnKu66qzF/j2EmRRKUgDYTLTmhHV5UYWE0dSOmcKOS+O6zgOoXSr1XeGXt+A/wdLfaw2Pbz1pNeLIiSJwfWIPvgaHLGSpqE6jbrvza8KhfCYBkRbFaDbfqtryLyrrItRtqwr5Lny/Wm6ualjjFlRqTjDidxipz5VJbjttzgMN2VZkUy9Zxmn3bMAr13FW1RM/Ccqlzx2Ddf8OovrRv7IT+New9qgE/byYpY+OZFzL/+6z05RLTqKbTdMQx2atEsEI2pwUI+dojs1FNQWkpx12K8UPuobLNIS4tEPGN9nGLlXGAFhMSN1Ha2iAbwGSIOopxaRcstixOYCZty8lLlvPccP1z5MfaqKXJ3dp946+PTY4x5bRvKtixk+t4z90nM5zn8Dr6ybRvj3BVR9OITAbzSUtCSGH7uZGM2fbxzhnDbvBqy1EmO/cT22Wip2O8qY4Wx9NhRlkCy6akgIwuT7rX3FwVpS4oCmGP1LJBPsBajOHhRy42MQZjPS48ZvfUkXIyBXbMA7oYmcw58n/4aRqCEhPoGMXeiJkLrOD2sy2bwsqc+GqYjVkpP2WcY5gR2xfHVlIGpQ70Zxx3542eqk6TQ/Wr+NRLYds1BVRsVu4/0zHueXKx4mRLWxxdPMCw3pJI4s457I5bxeMAX7mlKEpmFq1mlM0xn+31pemHscE965Hv/FhQCErq3nlReOxBveext34QVDeeOKx5j+cRaVl0/vSLz18LCJCSO6KwfRiRzU2dXrVGw2PPt1nxPoD3LaaHIui+f1dVMIfdMf6fVSNyOeyHsL0KIie/1ev0ZACJEghPhZCJElhNgohLimbXmoEOJ7IURu2/9DOn3nViFEnhBisxDisP724Qn0EUd4C7eiVnadtzYcDtZfnMnpL1/PtI9uICi/zRWV0qfN1ouFazp1Clvun4SamY63cCsV50Zx3JM3EbHAAlLidJuocAQgahtomh3B0PdmM8pShjAg6rW1eGxajw+5dLkQXoMxUdtQIsN72HPP0GKiMZpbfEIT/bDT6GUViOxCjH5CkvBXlvPqtAkk/3cN4EuWqiEhGPuOQ24t7Rj22WGWQY2MYL+kPExC5efzH6L1/cBBSWorVquvdm4yU3bDdIrvnI42JIHM+8uJ/6l3AyA0Db8qN9eH/96+zCU92HLNlFzYg86eEBj7jEUZPaz9bzUslNx7RiH9LMTOX9/e0KQkxXN13A+MtVhY5IygxNvMiY/dxBv5kzktbgXjn76GiHOrqdsnEefX8UTdk89vxz3C1d98ha1aZ+icxb5SI2CszSL2lzrEap+EvRoc1OX8OI+ezKijshlrsXBHeDY/3TqXpvkWhMncYxZers7G20MuSo2LwTU+FaF2fQwNhwPtx54rI52x40tCq3MQkgWp56zB9okvB6C6JOdF/UHJab2rEA1ElTgGiJFSrhJCBAAr8WkPngfUSikfFELcAoRIKW9uEyV9B58GYSzwA5DeF/W4JSVOHug8AaO+wXfjD7Su2tfIrqIiFNHrW0mYzF0EIoSmsXneOD48+BnOWHoRQx/wIEoqunXNKXY7wqQh/P3xbitHmDQUi2WP0aCrYaGIAH9ffLfDeXEfPon6VBORTy8CIciZN4mYnxWCvt6IbG1tzxTviOZTp/LSQ48y3Gxjq7eZN+snsKQumfUbExl+S3avv0UJCKDs/FH4H1lO0Pm+N/D1f3xPpqmBs8+9GvXnVSgBASh2W7fGmu3f9dhhzZVPYWrTqU3+7BIy/1tO3ZRYAt5f6nOvC33S2mpGKpPez+LzwlFEn1aIEhXBtqMSGHpGDq2nqF32oVitlL6bzLrJ71CptzD93RtJfygPo64OY8pIlGWbfC3XmkbOy6PJP2Q+ACtdbu445pyuxDPQ5d5S04fiiQnEXFiNtJiJer2SVxI7DNlCp8F/Co9BneUdVEOR0LRer1H/XxbUXDiVyE82d71Hp45my0l2bOXC1x8iBK3HTSJgSRELyp7ZOY7BNq3B7bqDTUKILHz6gsfh0yMAnyjpL8DNdBIlBQqEENtFSXvNipjqFKTiRvj5gdsDA5Uq6GsCy9D7nKrdcdRVer0Mn1vHWSXXogoQ23J9WvOKyrYbpxD/bR3G2qyOBGTbdKJ0G3s0QVh9TAbuAEH0vG1dDFrdudMwn1GB/UXf2LLQTAy7YSNGqxNDGmy7cRpCh5hHfcktNTMd4XDiLdxK0M9buOS66yg5RkerNJH2cgXZV0WQklnWpysvrFaaUgyaN0YS2JoFhuSiby/k0Inr0Zo9SECJCCP3viCGnt/Q5eZuPGIE71w/Fw8K4Ov/aDBaiftR4EyJwH9rK2pAAE2jI7FtKQTAG2bn7W9nYisVQCHerSXEfuym0JVGpLetjz4gwDdoFBmGd0UIT6UN4ZygbGzlAuFvQ1ZVIRauaW/N1meM4o19X2K7E/xryzAoKKbhrKmEfLyuIyTqdG9VPary/qineLF2OomWGmYHdw1P7MIDc4LRqzd3Wd5y0hSaY1WCCrxdEtvbIb3eHjsZhdncJTTTEuLB6+3q0QmFoPzuIaUn0MxPpz3MEc/d1LYTid+ny/psdx5U9kEIkQSMA5aygyipEKKzKGnnulqPoqRCiEuASwCs2NDF4AQ09wT0zXkk/jvP90dgIIrFAn5W4g4vYv+zc/jokYMJedVny9S0FESzA6OxaY+KT4S8tsQ3d77D8qZkQZRigKPN0o1JR67NAUNHsdk4+axfWFyTjHgxwDf5VtuA9PhGr/WqKmyfVpP+meKbm5eStGuLUEOD0fvIC+hVVaRe62sd3v6IpF++jK1WK9LZNk/vcnPysNUs3n9Kx5SnEGw7RGe42VdSXed2MtqsoiIYcdM6Lon4lYsfuobI5Q4CFxXibfN4FJeX4GwIfWVxe598zcHJ1I2UGOZ0IuevwhiRTGOKjaaTm4gJLGGrK5SxC65i2Lw1eB0OtIR4jKpqDKcTNSSEjEc2MMPa4X5/VDKWANdWwn4vxdsDiYecPobbMz4i2eTP/VEdyUtdGrzVFMlDmw7D0WwhIycXw+tFCQig6dBM7J+sQHVLnOEQ1Hf6qgvc+4+hYpKF+AcXt3t+xackElikY/+okxEwdLSfVrLj68fyxyaOv38OcWsHfk8O2AgIIfyBj4BrpZSNfYy9DkiUVEr5AvAC+NqGB3ocuwMDccNKLxjJqFM3sXDzUFLug+9uVWmOE4S0uYnSbiXvngBmJLeQ998p7TGYFh8HJm33tTD3Ehol3r2oLcnoKwMJr4HhcaMOT8MT6c9bmwwiP7Ti3+qLLbu5qVJ29bgMHX0nlYIMp9P3oN8wjcR3Cll8y2SKjlRJ/7mtrCclmfdVMCvzAN5O/pnRZisNRiu/toZxWcQv5HgiifmmFK93B3d67WbC1kJ7yCoENSMF0mwQmu1GulyojU6cp7l5YuQHBCutPLLtMDLvq8LbZsycaVGY2xqKZEIU10V8AfijS4OjNh+DOi8c6c3vtZyWf6IfSVoNy1wKky0dHawHbDiJgNkGcQW+lmQDQFHJfjId/+Am7J+A9YtlDPlicOfS9MNKkrLj2g0hQNzza5EuV8+DZkKgJca3H78+Ng3DLNDyyyi8fTqh+5ajPBeO36e9qwEOqDoghDDhMwBvSSk/bltc0ZYv2J432KtESfuCGJFG64Gj+izDxDyzgtx5wzGXmJGqwHREOQn3LUKL8ZUmjTWbCPjFxjPxP1JxqrM9QSmtZlqG9Z6J3Z2QHjcYus+grfO5oqKxBa+fhv9vNuwfLf3zFHykJP7pVXi3lWH+dgUZN6/pUtbzFm5l7ZfDqdMdNBitHLfpDG5bfwL3lhxFpNrzbIb0ejEmjcDYb5zvWknJ0DuWk3blsvZJQD0rF+/vodyVexwmYZBXHw7eDuNmKa5rT7DWjwzGKqDA08wlxTNRrrTj91nHw6FFR1E1exqF903DffgkAJI/c3HqkksY06lRslpvQTwV0aOhH/qaJOIZv52ieNvegLZjNcRoaen1OmrRUWy6Ncb3hxDUp9t47tqnKLoglcg1HkpLQ2mK6/tdPxAtQgG8DGRJKR/t9NF2UdIH6S5K+rYQ4lF8icHdI0o6QKXfgUBu2oK9IgRvHw+I9LgJfmMx4UMS2PSvKMLjJuAJEMR9VNg2j2Ai8s11TAq9luCyDq4DPa8ASz/ly90N4eeH8HgwnD7xDXPpNgZBcNb7dtv0GvqCGhzUzt7U2bPa0cvSkocQ/2MT+0y8mHVT3+CXkZ9Sp/ve1ps9lh57FNThaZw0/1sOsedxwMc3ErpWEP7hBmRbb78aGIje2EjkahdHnbeGUMVLVXEIYaEaFPuSheWPmAh+cjzW1QVwdhXn5J6BuDO0TVquo0tSi44i7asaPoj6ApNQ+fE0G3f89wIiF9XicQRgER1eQJBiJWROEe6lEV3ZfAwd9ZdVbC8sawnx6OFB7TRs/aE1PoCtl45i6DsulD/WDOg7RkMjqW+1hXn7jaPpqGZ+bRlG5GoP5gXLyfhWA1Xtc1x9IJ7ADOBs4EAhxJq2/47E9/AfIoTIBQ5p+xsp5UZguyjpAnaTKKnriImIiSN3iyyV9PROyLAj9PJKAteaCSxyE3z0NkZ+uY2iC1LRq6oxWloY8lIu4Z9u8glCHDUJOWNsdx7+ttLWnhoeMZqaegxtdoVmTBk9jOYvff0KvUFYLFScmjmg7cnGJkbO28gPk55HbetWDFSs2BQTr9fMQC8t6/adqqnhJJhqMAEyxI213kC2tqLFx7HlkanUvRdB7lNTyHhwI1NseZy9+Uwy/1PSPmouhw9l/qjXeX3+Exz6Wz7zhr1N0fJ4xOIdpNKFoHV0Al9uHsm4V65h1MLzONzm4uM7H+aWL94Dr2CZy0OW28Hh2UdRZziZl/xRe2Nbbyg/MoGymUE7nFgV74ETerwXrN+tZtjt2ajLu/MO9gbD4fBRjysqBceZmTf+LeZ/fjDWX3w5moGoIw+kOvAHPcf5AD1qh0kp7wPu62/b3Q4mIR7pcHQrywHYcmsQbg9G6wDLKdtzFp28h+0dYN6Coi7Lay6ahm4R7W2inSHdbqKf8C23rAvjy0umE7BvJfK/bW/IIH+oroXQIB56+lmWOlJ5/o2jiHuwY4JLsVhonZiCLacKY3eOO/cDMTwFuS67/xV7+m5pJaVFQxnmaur1LSJdLmzVRjvT8I4cd13Wdbr4oTiDeo+NJ+J/wF+xUqo7+KxpBOeELeQ/cSd1U+YJfXUJt1guIqjQS8ZvPk1INSWJ7KuiefnYF3i85BAmTdvKwzGLGP7RlaRfv7KLd9eYHkC6SWBT/Lk2pJBpa2eR+noN+g4eZf1ZU6k62EX6ub4y4ta7p+Oa4SFR82ebVyfue4W7H5+FNKlU/AfCFB/n2uYL/Ri2sXc1p8g31iLMpq7JO2lgza3A28ODKb3enjkxhUBNTe6Rhbkdhk7anBU8NO8EhtZsRh9E2XGv6BgEn+uWd0kCpWcN6/FzPdeXvOk3E6+oOI+eTNn101BTOzj9FauVgDcayb4npOt4pxCEnFGCpzspMQB1505t345eXUP8g0sJu9zdHvPpeQXo9Q3IohLOe/kaXn3iSBKe6uiB15KHUHP6OPyW5iL9eheUgO09CLtPFEVm9XHT9AO9ppbhN+dgrN/c53q2j5diK3Vg9KFwA764Nua0fMoOVzk97wQajFaiVAvj/QoIUlxIrQciTCmJeG4x5gXLEWYT3oMmkPWvUNae8jizV56J+3g3my8fxsi3rmbYU1XdQpeKqWBTzJR5m0lecBEhN2rdOP8dJ07hkts+Ifobc3vZOO5XJ1W67yFd0jqU0kMNZEExrXEBXJPxE682xlJjtLLqqMdp3a+HJqftv9nh6P5QS+kb8R1EaKsOT6NhbC8BXpu0mZg0ymdEcrb0+BLtC3vNAJESFop/CUS9n9Wt7DEYaEkJtIarxH9W1qW12HC5aLwogWGOarw76BCIu8JIzMntcb9hq+sxgjuRVhp6j4ouhtNJwr0+j6FLe4JhMO7yNfyWNI7kt/sOQfSxaWgVDf22RA8U25tjlKBA9JpaFLudzfePImizQuSzfRONqBERyKiw9n6IvuAJNGOxWNofwu0knjsa7O0DUOLcQCZdej1DppRwReLPrHcmILx9X3VhtxNxTwGnhGzmvaYkEh9XfH0cy9aTsgxETHTXHIYQEO5indvJktZ0hl+f2/5AKgEB4PFgOJ1UTFbY17aFD3I7PB6t2UOFbqbIazArcCPnHrGJwz46l9oslfdPOgCq65h3zAn8cvdj1KeaGDibQt9QrFaU2GhwubswNuubcvDf1Pv3pJ8FT4B5px/mvcYT0KtrsFfosItqKt78QkJeXdz9QZLSl0nuoRQkFq7p1ZVVqhsQnp03S96iYn4rSuWE4/6g6KS+bxexcE234xaahhqx82k+qesYjR2knfHDKvAEtvHo98bICxiNjYitAyvq+GWV0XREB+mn0eqk8IYxlNw6vev8hRCoUZHgcpN822KUg0t4cd8ZfHHgSLyFPZfo1Mx09P3Ho1dU0niUzufHTuajk/ejYoodNa4jJveWlbcLymwnFU2/PI/jP7uWYZZtlJ47AhSVlpOnkPlLCzn3j6XiqukIL0QogtTn8siZP5Gq2dOIfLKICRYzmSYnQYqZENXG4rHvMXV6NlTWoldUojklJqHSMNozoHOkBgf16+W59hnBBQt+IuB958AHzKRE35TTKwHLQLDXeALS5cLv02W75AXsCXhLt8Euyl27Km1cP3Ux74+cgDo8DSO3oIvrun0kuCe+fun1oldXd1s+YEjZ7uYaLS0EXKrjV7SYluMnI2ZXYn4gBKkKzEuyu7y5dxxb7nMXTc0o3k7uraHjGurEmmvtUipT/PyoOzAFS72O5RufVyQdrX22XDcOD+HIf/3Cm+8fRMJ9i32eiRC4T5mGXtq5g06gRYb7RFE25ICUKOGh3H/4ezxecgjxn5eijx/Odfe/w0n+jVxw4kJMwmCIZsYibDwdt5Sn45biOszTXgnYzji01dvMv7cdzskRK7j18nNI/HcVTYkKGir7j8qmYkQGcus2MIxew9WmA4YR8Hten70Y5l/XM+/Sk6lPtRDWvOsFtYFi7+MY3BE7UxrcjeXE/qClJOEaEop59ZZehU60+DgapsZjqILgdTXdeOeEyYwwm/Zo5+GOkNPGUHy9wb1jPmNZSwobjk8Y8Pz5gLY/fQym0to+tylMZkTmUERBaa+GQA0MpPKUEZibJV6rwDqrHNNDoZgXbkQJCe5opW0TgNleGRAmM2UfDGXt5Hc4f+u+5Dw6gvrTmtg47a0e99NsOKnQvQw19ZIcAir1Fqb8cDVD3lew3FjG18M+x4vOly1h/Ovls/D4S5LvWdmjBJoaGOhjJtrNEnEDhRoSwre1L/Y4O7B3GwEhqD1/KsG5TkzZJX1mn8H3Rs27JRNvjIvht2zrNj0nNA0xMh25Iaff+rcwmQekZ9dy0hSCVmwb8ANk7DMWU1Uz+ua8Aa2/uyEmjECtacII9qc1xk7NCBNeOyQ9ut6Xye6UVFLDw3a6i3DgB9S9igO+qUu9tq69vFV12TSeu+lJJltMzCo4gIaTzHgrq9sfKsVuxz1lGOalPo9GjYig+IUI1kx+E1UoNBtO/JXeS7TH5h7OvmG5zAndwquNkdgUFyfZ61CFwjKXh3FmX+R8zbYZXBHxMyPMfnikTuYbVxK9zCDg5xycE1KwrsjbadWrPQnPwRP49ftb926iUWEydyjBCsG2OdPZdsM0grc48dpUGEDnW9lZI1h/9pNsOOg5WsYmtG+3fW7bkBQdE4zzkP553pTgoH7XEZqGoQlk3cAuumK1UnuzA+uLDWjJQ9Di49h613Ty3hzXY9yvjsjwxZG7kSRUaWwFlxtjzSYs3ywndu4iEu9ZjNHU1C2r3M7J1wvKr5lOzYXTdu44xmaixcUiNFOPXlvTlETUkGDfH0JQN0ZnssXEjHUnsmRFBpv+k0jnCTFhNpN/qopI9I3y1h08lPDnbKxx++6bHQ2AS3qo1Fv4qDmQUUtnAXCk/wbqdAfzbz6e+586kzqjla3eZs57+Rq+cQRgEirPxi1hhNlXItzo9pL2eg3+eQ2gKph+WPmXGwA1LLRH2jvTD73nDPYaIyA9bmR+W9ZdShLmZ5P43lafztq3K3rVwesMR7TEIkw0GG5MzV7k9DHkPjSeyot9LaAYOgn3Lu4mztET+vM6wBevh/yxdcBjxIbTSeuScF5I/pSW4ZHU7JfA9CPXsXb/eVSc0J0AVW4pgrEZeA7pn+looNBz87trJfbiDfZHMuK1Qd0BOzcGu/nCANI+r6TlmJ4Nst/nK30NXUKgJSUSukrl8bokWr6KJuO29Qx/uLbLcet1dWQ+UAZVtQiTGUekQuGpEKX27M1NWHoeM965kTitjuZqO61eExGqwYPV07FWuZh01lpuLzuYw+bfhDNSJ1Dp/jttihfd34LYWobR8NfT0qvD08h7OoH640bjOGHKgNmJ9prEIHS0mqohIRjJsd0km3uDmpaCMzmUpC9bSEs5D9NGG4nLVqJGRpB+R13XBqMdB2d2ET0l8/pDrQEt0Ro14w2eifqJVxoycMR2f9sbTifK+lzMujEglaLOEBYLSnBQO1HGnkDSe9vQQ+wDOzYhUPz8fIZFSqIWC75yTGJocTPCZkPY7d1bcCMiKJydhjKuAeUPQYq5ksiVLb7cSSfqeefRk9l6vMHwRxrQi4ox9h3HghseIkbzR5c2PJ2ut4JAFQotFXaCigXDzW7yjnyeVxtjOfy+G4n5rAC1OZ/Ss6OhvIrExkXkvTmOKLWZ95tjiNYa8EiVaZZWDvv2WoZlZSOCg5CdKjCdobVJqf8p8HixLbIjFYOyU1ykfWse0OzIXmUEAJSRw0h9dQsrqx0E3jQMb5Af5oLKHiWXxbgRKFuKEU43ZdPMJL9VQ9THkdg/XISEP+/kDwKRqzwctuBa0s8sJuJKP257/UJaEmwkftRz3X5n+euky7VHDQD4yrE90pinD4XK6i6usRocTOGVw0n4j+93Bn+2ntBFYWTdEMOw5xLIPTeUlJt2UNW1++FMdZL2kIozwstNq0/CPtJG+OJOiV8hKJ3lJme/lxm75SoS8oooOcCPcNWPLZ5mjnnxJsI2dqL9PquZjdPeQmtUiVraxAa3hRlWhbMCi3nu6Brkb4EYZeXQSXsg/l0TxVODeDjnMOzPBmFfX0bWLXEE5qhkP55BaGQjESdrSFf3l8su3YP9EOPsCD2vgKinCkAIQr/uNBbeF/kOe1E4AIAQlB0QSoatnMp1UcjHm7j2lXeoOCKxx9Xlmk1smTOC8mdtTD9iHUI3CPyxb+rsvxqWb5aTftkqxM3B6Fm5sGw9/p+vpvDeaeQ/NG23dgz+Gci/e3w3LQc9r7BbbCy9XqKXdrjmRksLruRwZu27CNHYgv/W7p6Qt3ArGZdlY2gKFWe3MmfU90SsauoavkhJwusmJv33Koa8kE31uRN474JHMQmVY5bPJvHBZdg+XYbtk6X4/7CJYLsvz6FbJWJtDndddDGvN4ZjESY+Hz2f2vGh3Y7DXtDAFR9fhHV+CPZVWzFCAwjKUnHESH46+HEOjMuFzNTdLvCihgShxveviN0NUraHz0pAAM0ndcsFdsFeZQS0uFga03Xevf1I0h/Np+LDIdyw6hRqR/VCESQlUoPHRrxPWWsgsrFpQLmDnTq2IQm7fpHbvi9UFbliA+rwNJ+bHBRA5CqDuF+83ei8dzeUgAAqrp6OMrLn9uzBIu3JAqio6npuenjryNZWrGu7dlq2RJvJaY5Er6zyUaT1AMPhQF2yAa9H4xj/LdQP8+9Ilrbt07xgOVFPLsKbkcD513/JaLOVZS4Puq6Q+9BELL9EUfbpcArnJwE+QpAHj3gHNSIc85Isnnz4FMYtP53p315H8Lsr2vetxcX69rGtEq+/jtuuIP1t7PvGKlbf9iyrz3qMZJM/D0atJP+UQPT9xvVrxLXoqAEber2mtsfu1MHAaGoi4PM1fR/TLu1hN6LuvGl4rZB2tY9JxwtEPlsBz/nEFHp7NKKX6Ny8fjbBH6/ZJernPiEENfvEEfT2roUXcupotHoHVVPDCX11CQiB4/jJtEQpRC1pwFjTR2/oboBit5P/UjJPTXiR+7ech2VgKZc+YUQEs+WMZCJXGvhVuDGVN6Dn5nfRCYC2pqcewpOC+jDC9O4Z9dbjJ+O/oQo9z9dYlXZhFvvdNAf96GbEmQn4PxiItiqvXSdSHZHBwS/8zhXBvlLtOLPCyhkvoKDQLD1Eqp27IxX299vG8xnRVI8agscOydc4iCrJ6+J6F5+eRPzrHvTkaN48/DlqDvHn2sWnU1syiktCVuGQkn9VjGdpVRIpHzVSdItCoiMDlq1v34awWFBjovBuLR10j0Dnc7g97BqM8K7SpknR33Ox13gCQXmtBBV4umeqDb3XTL0aGMgx9/3IRXd+ihLRwfyrhofttrFjAKQk5KM1g29AUtQub0gtvwxR10jY+qb2dk+3v0L9dBeGuYcBGvCx+vYQd/cGoWnd2oHV1GTU8DCMlhZSb6rj4fPPwvJN/xWSAe1Plxxw0BpaolX87i0j67pwFKuVnEfG9sinvyPeHzWfzU925cVXbDaG37ae2ifV9m0YTieJ/1mMmu3PglFv0hphbjcAwmIh7w4rV4R0DDuZhIq/YsUlvTxTO7lLchAgUrVz+bwP+Or6h/jo4rkUnR7f3na8HfGvZmM0NlJwgj8jzS6qvIHEfm4idFYVM5+bw0VnXEnWQQHYjyjAGWnjtYnzGfv8+i704kpwEEZQp+uhaQPqPwFQUpPa267VyAg8+4zs+ws7QLpcGO7+25r3DiNg90NdnoV1Ud8TaztC6jpv5E0mxxndRRQz74Z0rnr3Q2oPS9sth6fY7b1aUy0h3jdz34NGgT5zDEzquHB6RaWvrbVT1SP4jcWknbOqy9ujM+TU0bi+jKbl5CkDCkfU6CjcU4a1U2QrVis106PJvjPNN2DT4kBxeemThXUQMDZvYdlr44j9KB/vMQ6G3bgBqRtYy9VufPo9IV7zA1PXh09oGqP9S7gz/UtI7qCnbD12Eu+c+xh/OEPw39Lsa+iaMZbC2ybgt9TOBnd3Iz1t8aV8WjC6nd24Tne0G4ST/BuJ1/xJMZlQptZRfXFXuTOjsZn8f40HCSeeeyUfnTQTvwo3ZbNGEL3cjVi01pf7kBIERCgu7oxYwqY7o3zXSlHRq2ow1mYhVNXHM6EqAw4r9ay8du/BW1wyIBryzpBebzvnZF9cFnuFEYhKroVRaYOWADdaWog5MYf1+wWi/dTxZkt7uohnjjiS4PcH/7YTJnNXLTdFRQzpxpPajuaxsRz+6u+oGSndPlN/XY3a4u7xovc0IKLFxXYzJqrDjXdeNIYqEOP7J/AwauuwZnVoDoiAAAwNkr7wtM0h1KDmlviadHYDpNdL5DOL8JaVozc2YjgcSI+bIY+t7fONJzSN8kO8KAispV0bhoxWJ09+cjRPnXJS+yizsFjwu3obYy0WVCQVd+u4DhyNYVZwRXpZNucJJlh8sfZ2nUOH4cb+oz+e5SFU6y04DDfjv7qWd5qi0KXBj62+c61LiXdlCNFfd+36rDt9AtbMelIf2Yz240r0TTkov68mct5irPkdnZRKQACWaifP1uyLv2LljAnLUPz88Bw4FnWYj+/fceRYLv/5B45bsLJdHblf9BU+9PDS6Q3bLhlL2cXje9/UgLe0B6EJg6oJ/buOPcLQfc06nW4ib+k29Nz8AbtdnSE9boyGTs0/ht5tBr0zrBWtvJI7DdHaw1tPSvSNm33DLJ0ssRoYSP6NI7o1c3hLt3W78DI7H1OzTtl+Bt6A/sMCw+HAW1be3uijV1UROn9xl44xva5up87NYNBtjHjamC7hQdWFk/j14Me5att0nLGeroZ3TDp3nfw+rXH2juuq6wjh+/fhNherJ73LxAdWYrt7G/ce+FEX+q/txsCmmMk4JxvdT+KQkhVuM9YyjbuXHcvYx67k3vyjAcjywPzzn6JpfNdMfEhWEwnXdSW5USMi0KIiaRke0X5sjUeM4NkP5nF/lC+peGjgBpToSLbta8Hw8x2Xf04d1354Pi7DhFR38bFTVOrPnNyrIVAzUrsoaMV9V429fC8vEW7LDSX8xT9vaqo/DIqcc/kGYk7Z0mcWV7HbITWpPaOtN7eQ8lj2gPYjXS6sy7cw7NlGtMUD46rbayCE76FJSkTLKsRoceA9aAK5r4/ngTkvsdwZy7LnxhGYZfJxA7ShId2fw2xb0a0dHpT0eslblcC1ZRNxGD4DdkrIMjxXh/CvFccCsMXTTNobl3Ft2UQWOn3hzn3xXxAzuYxEzZ+ZVlhzyRMEL7YQ+8hiCrf4QsixZo0MkwtniNrlwZIrNuAtKmm/bmp4GLWHDcVbWY31i477VfFK3FJpDzk+rJ2Esa0cv7G1vjZtQN+cT+p/1vHNceMxCnexf8XQCX5rWccLYwdPM+/cCA5++g+Ukb5wWN+Ug/+HvT9fe8cAkX+cnOKYPrCV+2l82BuhjBmO1BRKDwxCzKhjZEQ5xXPT22nK/79CzUil+UmdZpeZqDO30XLQcB549DlmWBW+clh57KJZqL+sah/WEpqGGhVJ1q0JBG5WifukqEuTmJqaTPa1kSw77lHCVTvNhpMx71+LtUrBM6aZkK/svodj8gia725m4eiP0aXBB81hnB7gKx17pM7IP87H/0c7/5rzGtOtVYS3VQ5+aVV4rnx/Vv+SQepT+RSdN5Sxx25i1TeZJD6wDCUgAL2hsdv9p0ZFor9tRlUMsotisGVZiHt4KcroDN9U4x5+xiqvnE7s19va6dmUgACEzQ+jprbLi+YH+eFePEDU0jGoogYHdddXb4MwmXEcN7HHrLM2JAHHCVN2+6EJi6VjsGknYazLZvNlfkgVjMUhGAjcAXvHqd+TMAqLCTivFfN7IQibH/4LC7jkxStJ//VcHr3kTNQ2WXHpcaPFx7H52XFsujeG4Y9WEPXUIiqOSOxSU8+5J4jcE+a1P7T+ipXj91vGkA/LkUV2qqYYqP52XGEWXhr2JuBTPT7Q1vHmNQmVzfu+zqJ/Pcm+1mp+bY2hwNPMdw4T1z4xmxVFiaw770lO+2Uln132EDfGfIuhtZU46+pQzF1zKYrNBu9qfJTxIQDmIgvXnfcx1RdN9pV8/4SXbEi2qws/o9HUhF5R6TMAQrD1X9NxHjO51+8PhHLcCvwGWNrW/1BK+S8hRCjwHpAEFAKnSinr2r5zK3AhPqGaq6WU3w74F0WEIS1m2GGiTWga+pRMFI/EaO7epy3rGvBa43a7pyBdLowt/ZCD9rdPKUl5R2LduAWjsYmm98IJLuyuymbsMxZhSMSitd0+E+NGoFY37FWt0ELTyJ07kbTXmpCrN/qk3kOC0LdVID1uFIuFpsmJuE+rg+8kelUV8Q90L/eqYaFUPmfnjqFf8N5Fh/vakU1mWmIFEWaTL3+hqMSH19Mq3Ry45mxOH7KSg/03cWTwWj67ZAq6v46lUqPx0OEcftevDDfbcEkPP7f6c3j3oToswoQuJL83pfPEnNPxz64lOn8FVc4JaPupnBlQyfDX55D6ahUpFRshJISyWcNp2aeFoO9sGGaIemcjMjmO+5NexV+x8kX6lywcopCmNfPr+Rupet26y70rwmJBpCVjbMrteo8JgRoZgV5R2eeEIIAzzoP1294rNQN5HbmAA6WUY4CxwOFCiKnALcCPUso04Me2v2kTJD0dGAEcDjwrhBhQKlNMGAEV1d3EIRWrla03T8a0Lh/r1yt7tK56YyOB7yzpU6CUyaOQM8YO5FC6QLrdfU5kqUOHoMVEt2f8FZutG1W39uNK9Lp6Wg4ZiSs5vMfav6nWgVbX8+Re2f5BOOarPare/lUQZjOBuQrKVh9duN7UhF5ahhIaTN6jU3F8GMpjjz9NZngFso+HQQT480jm+zy5+QDEYl+pVBkSR9i0cmjroBSKwKr5at6NLVaeW78vhhSMMTeSd+Y8Co57gd8ueJj5jzzKHeG+++cPp5X/5B0F+CoFWW4HlXoL69xOtniamV18MLlHh+P32TKkSaPmkySizijCQFJntBL/o7ud92Hb2cN5c84jrNr3eQ65eiGKx/d7HQkBPFp+CL+0KqhCwYzOImcsl0f9jDF21zxIgKpzx3PjZx+gjM7oslxOG03ja/4wdXTfG5CSYVevh6U9l6BhAEZA+rD91Wtq+0/iEx59rW35a/iUiqGTIKmUsgDYLkjaLxSnt0c5bsPpJPHrNsXiQbzlhaa1z1ZriXHEPllI6ENbu5TnFLu933KLMJtR/Ds1fEweRcVV09ubgcoPiuKa338k+BtfLVi63RQfEkD92dNQRg9rf+CFpuGxKZgrW5CerklBxWajamoYBaf1zCcY8+xKbJcKjEEyye5OqGGhFN43jZYFKVRdNg3D4SDymUUd2XMpcR4yjsCPvbxw3ItcnvQzo80qaz7LRO6o89fpnOtlFZz3xWxiAhtxHjUBbUgCWXPCqfs1uv1NqmQM5eL436k3vGTv8wafTZvHWIulPTR4tTGSKV9dR7LWUYWZaGnmgXSfYJZNMVNl2DjoiTncOOtSLrjieta+N9I3Vq2o1EwI4faMr8mvCiP9y9kcs+EcrMtyUTPT8XwUyK83PUKqSWOpy86qC0YR+uoytMR4lGsqGB+4ldtuu4TkLy5mqzeUk/wbmWQRlO7n35G0U9Q+OR17Q+SiOm79zyWI4q7j38rKbILOa4El61ACAnzyd73AcDr7DEsGlBhse5OvBFKBZ9okyOullMGd1qmTUoYIIZ4Glkgp32xb/jLwjZTywx222VmQdMI+4shu+1VTk5FllRgtLQNSw9kRrcdPxtToE25UM9PJu8uKyLOT8kCH+ISWkoRRXtnv7Hz7cWsaW98dxlPj3uHadadhLAvGFWaQN+s5dGmQ+coVJN2xGMVuRx+TSs2trYQ94Ifq8CBNKnJ57xZZaBpKekqfJcm/EvoB43n+1Se5t+wIKmeFddMJAMh5cRIFR72IS/re2g7Dw7HXXIe53ktznJngN3xhUMED0xj6bn07HRhCoKalMOLdfCbYC7nrk9NJuWO5T6I8MJCsx9PJOex5gPYsfJdjkwYLWm1kmqpZ5YrlJP+uHA8u6WGf1WcSebWbxrFR2EocaFt9zVtC02DccCon+hPx3JL2kq7UDTY/MZaNxz7NqXnHsWntEEaPLaDs5RSCX1+MsFjwzBhJ+RQLjlQ3Md9ptIYrnD77e24Oy2WZy8Ndp5zvmxMJC8U9OomiIyykvr17W8RdR0xCtyoE/JiF3tjou4+CgzDqGwaUGBzQ7ECbgtBYIUQw8IkQoq/+xd0mSCpLytrbHns1AH3wCQYs3YrR0IiBr0ySPEsFabQr3AI93si+kpDSxevY7j3ojY0EfeTP+uEJrJn8Jq2T3HzYnMiElafiXBSOte2FZ7S0IBatJfriKIzGEmpPGkPY0krohbKr6bSpNJ7WRNA7/n3SS/+V0H5fx0mP3ET04iZkfg/GTAgCI3xO4/a6vUU1MfuBD6nXbQSrDt5aeZBvejK5heahgdiVET6ZLinRc7bw44vT+PftS3lwm8CYMpLWSAulB8HGQ58C1HYDkOV2cM6G8zgkLpv7o9ZxT/Uo3vlqJuZhjWi/BHH0zU906R3I93jweymYyv1UPP6CbTPtxP6WhO3jCtTwMAoPCUDxAkJBi49h8zXxxP+oMyS1EptiJs5Wz9atyeRUD8Vqlz4mKK8X7aeVRJom8t7lT/DmlFH8cMBQXt53GjfPzGWCWaXoZkHiKb5hIFNNJPcd/xFPjj6IgJPsu41T0vrDWoSqtAuOiGGpZF8czLDnanznuh8MaoBISlkvhPgFX6xfIYSIaZMl3yOCpP0lVdTgIB+L68/Z6PUNPioxk4bR2uobQtqRQWcAoYSx3zjyL4bIsEZCr9DbjURn9qCQBZv5bs0knrrtAB6Y8jFNuh/Rl7XgLen0BldU1NQk9IJiFH87k65exVcrxhAQYyZhttZFBk2Li8VW4cb0ih375uo+GZebT51K8KJiZGMTIjAAo7pmzw1OdYIWE03t/klEPbmoVxIRZfQwnhr9TrflZwbUADXo0uBfF4ZiqZmGXm5gv7KYrbUhDDmnQ8Un5tsyFt/ox/7nLuPs6xaRatIJUvxwGDBl5SwWT3gTizARoBg0NFtxGL7qwelByyk/OJAwUwsfuseioFCtt6Ai8FcsHLPwCiL8FD7/98NEqjYaDScnfHcNANtOHMojF7yMU5q4LmkWJ05ZwdtRH/DMoWPZ4ojg5YZoDg3eSNmxQRR9nELdBA+QSv0UN8lvgN+ardy+7VBG+JfiyYjjhUmvA77KhLPB4ms7b3VibMjlxQtPxDHHQaA68I6//iA9bmSnEQFjQzZp1/i0GAZCuttvTkAIEdHmASCE8AMOBrLpECSF7oKkpwshLEKIZHaXIGkPkB4vXj9B5UmZKDYbea+OoPHjaEpu7XkuX01Nbu+p7w2l+/rxzb5Pc+PQ7/FE9cwzqNfVoWflkn5xFq8dMpOnPz0SvaqDFlwNDEQZmYZnnht9QRQ5t2ewaP54ENBUa8fYcdbe5UJbuAG/BWuQfn2MmQpB2VFuWuabyHosncO/3cDmh8f0+Xt2FVpMNPoB4ym4MIXyg3sPx9TwMHLPDsKQCg1Gz9yEqlD45ZS5rLviaf44cS7vpH2A1exBDu9QivIWFvPI1sP46e3JLG9NJqhN8ssiNG7K+K797R6v+ZMz83Uej1nBq42RnH/79Qyx1nJ/1DpyZr6OSajsu2Q2X7QkstXbytAnvAR/to7rio9FFQqBipWi49p+Y6vkEL9WxpjLmXvwuzwSs4ogxY9LQlahCMnrNx3LbauPJ+/7FGL+aODEcatoyJA8OuM9Kq9sRVgt/LB+OKWuEAyLyuw3Z3P+1n2p1FuwlJqp/zCanBfGocXF+NqOT9wyYEq69nMXFTloLUtvecWASpQD8QRigNfa8gIK8L6U8kshxGLgfSHEhcBW4BTwCZIKIbYLknoZoCCp0DTUhLge5Z57g9HSgqEKUi/YTONv0Tw95W0O8nNwb8xolj8fhXNcMk2JZkI2tyIWrsEoLO53Xj/xvys4TpvD1MPXo/tpfZ4gw+nEKCom+e6KLm24UtcRrW5q34+ndrKH4S/73LLtKb8dR3c6hwdye4zcE6Qk48pcWmcOwzxN45ygbDZMiWWrrasenpaUSMvwKB+X4i6WS/XqWlpikkh+y9e005sZ0GvrSb9vMw98eBZJT+ZyZvhi0rRmvm5JZZMjllbDTKtu4uTw5aySCo8WnMj0iHzSw6qoFx0VD8VuI9jcSvjJGzCJjmP3orOPXzG/Of05/9PZHDhjPc/F/44qFNLM5QRvaqLc3ZHwfbUxEvsCfw6ZUshSVzRaWR1ep4uSJp9giSoUzpyyhFUh4UgFGg0niZqNKLUaMKNLg8kf38DwuSWUPujhmpG/Mm/dUeScE8Dlgdks/WMSN3rORg93U/mMl+xx87AIEwXzf+fQd+dQcuNQDptzITGLPVx6+k+cMrqGV2fE8tS8E4n9pryLrmDjrKk0xyvEPtSVU0FMHIlc6QuV0HX6y99tT4JvvxeEqvrUp2rrEGYz9OIw7h0dgyJUTk+/mNKjool+vG95rB3ReMZUWiMUon9voOSwIFoSvQx/ohbKKim8aiTJrxUN3mVWVIRJG9TsthoYiN7U1M3yanGxO8VD2B8Uu52yC8dgqzAIeG9J1w+FQKgqjB3WI0+jGhKCbG3d5TBCDQzEGJqAK8qGecHy9uX6/uOJvj+fu+K+4uIrrsP2wzoApJRUnzWeS278jHMDi9BQ2ep1MPu0y2GJb52Gs6by04NPkuc1OP2l61l46dx2ERCA7xwmHj7vTFSXzvXvvMuhNg85nhauOfYi8k8NZuE5cwlX7fyragQfbRnLyimvssEtueO4c5BZ+aQvljwZuxyP1Jm4/CyU70O497r5jDFXE6911Rx4pj6BF3L2YcH4F9tDCPBJqb9eM4NzwhYy2qzzeUsUR9i3tXstLumhyOsm2x3Bo9ecyT1Pv8jMtpf4j60q1z1zKfFv5CJjwmgYHkzw7K1kF8WQdt7OqwgpViuFb6ShexVSzsv23btTR1M2wx9HjIHf0EY2HX/PXtwxiI8FdyAGQAkIQM1MR7FaUdNSCP5sHbFvZCGythA/dxkZV69C35yH3txC0nPZeEtKkV4vjbOmUn7d9F67EbvA0Ps1AEpAAGpqMo2zpsLU0YR/S4+swLKX5I8wmXvlCfDlNvpmnzFaWoh6clEXA1Bz4TS2PDIVxWbzSVL3YAAUqxX9QzsV7w/p0b0cKEOtsFjIemgYt3z4Drc+/WqXrkr1l1UUPJbBl02jcAUrPo/J5UJ6vER9XcCD3x/DsK8vp1J38HtrEmpeR2uwxy6wKWZOXHgZyS9tYb+VF/KbEwo8voTjoTYPs+d/TN7pdp4u9WlVeKSCaHEy9LEc7io/iE9b/Jlmz2PD1LewCBOjzSrex1rIfXg8+wR25G0cDgtzr3+eq5ecwenX3cDNFWO7/MYrgotZO/kdYjR/VKGwyBXK144Elrem8Fjs70y1qtgUM6cH1DH2u6t4ocHn0ViECV0KHi04FNsfmzn3u0sAyPG0cMkXFyN0qDgxFWkxoXglOduiCFw1cM6I7dDi46i5yEf5brhcxD+pkfKo0X7vlk/zx1InOWjmWm7P/Kb37Qx6z38xhKpQfGQ4Ca/VQW29z/Xpqbxn6L76taIiRqYTtLkJ1e2PjIns1o24M2g6LJOWKBVxWA1bmmx8kvADB4Vl0qWpVFEpvngEcY8tQ3q9vofbakEIAQkxuCPtqD93HXdWg4Ooeyec2lWRJN25bFDuvOqG6474iq8fHdUt86zFROMtK0cEBJDoX8fQwGoKekpOjRuOVlLVPam6/SfZbChRETQ9p3Bjwjd8Wj+e/0Yv5paZYUR0qmr4f7SC7xfEEdTsm49wHT6RolMNzDYPaY+1UjXOjnq44N2yyRgNHfkUw+QrLilFVryV1ejLhjJxoptaAza6W4lSDcZbmpl77Jscb/cZht8dqWTdHsqQOMmdUZ9x8o034lflZvHjS7kwZClxqo1Hhn5AaoaCTfEZV5NQ+Wj6c9QbVlKf9KLWN+IyNAo8zXgQ5HrCmGKpIVy1o0uDQ7OOx3KVBT3Ij4fffQGL6GpAn5/5GrW6P281hfFswX5oz4Rj+3EDusOBtcz3mGW7I1AdgqYJTi4b/ys/nzCGoOpGAtdrULt10BJ8rcOiaYkTbKfOMdU6YFtl+3bi3s6DVicl3wTxauyRwPIet7PXGAFlbCbC5em9pNGW5dTrG4idu2hQJ2x7Tda+sns8PmgIQcWV02gY50aoHigOJnKRYOYv1xC1uBQZEoKRFIva0IJsbMJWIUFVUSwW5PBklIp6jLBAop8rJuupEQThs+jbB2WEnx+zEleQmV7Coy8f03MJsxe4AwTPbd6XhKYdWouFwJMUhSgrx6hvYNvJkSAlhqN7mCI2bkEGdJfi0hLiyboljrRhpcib/FGesPH1xrEYQXaeebu2y8QfAIbewfxjMhN4SzEFaQuo1Fs4cPUcbPtWEab4saUynCS9o1LiiJak/XIeyV+1omakELZfGTbFzIgvrmT4o9WUHRbNlVd8zINfnMCo0x5mqMmf+QXTeWe/F3i7dipH3zuH6EVb8ZaUsvzAKH484AYSr8tBU3SG2SuYE7apvcw43GSi2XCQe7kZS0EA8gIzZ6dOoPRIncdmvktAm8FoNJwo/wlD37wOLSmBWc9dz5XnfMbs4A4P5lCbhzJvMWdcdh3+X69AqFsx2nocXEN9YcSxdgdHnfsM2R4XoYrOD2Ez2sOgnYFfdjnJKx3ogBifiaEoyE4cm+10bo2NaH2E/XuHERDQ+KCTpu+jidnBCGwX8FSjIgcdW6sRETTOTMH+0W6c1pOSmBdXEevn194Ftz229uILE5TmVp/KrjQIXVOHZ/oIqsZaiVzVinN0DNv21Tgt+Acq1sSiQ5dJOW95BV+dN5NHLzeTMFrFnBiK9se6jj6JPvIVjakGxyTmkIMNYbFQM2s8YW+vQrpciMW+eQTpcfc5f9CTZ6VmpJL5zhY+j/ocl/Qy8qrLSDtvuS9JKAQ/7JNArHtNrwZWej1sWpHE+zFB3PbZZWS8lo/8yEbmeVcQ/6O7i7cTs9hLhdeGqWwb8mU3Tyd/DFhRnAp6XgEx9U08aTqR1HmrONIzh83nz2Ph2HdREFxYlE78i0vwtt3wek0tAd9uwj7H4KjQtcy95Uwqbwvg8ZgV1OkOxn9/NelDypk1dhmpUyuYV3ASDanww8GPYBPwZmMqX1WNYtPPaQSkSJzXZpBwh45/icF4v0Lo5Pdt9TZzbeEJ2NeVIdNSKH3ITOy/oGp8MG/s8wzbI29VKESoBv+tnOnrkO31SvSPzveN8Bq4w629PtCdS9I7Yq/JCVSvjMJa091aCacbacguzUJafNyAKJr0qirsH+96dVINCekSoxtOJ3pdnS/W3SG5ZjQ1+TK/ho4WFYnYWuZrlTJg29Vu0u7ahHVYPc/fc2LPnYFSIpevx3+jhYpJKgXHmjGmjGyP32vOn0zFRRN6lJoa9lAB2ZdkoDc2Il0uAovcvgRhP9Di48h9egrFd07vfl6FoGZyBLdFLAR8brQ9qFMJUEqMllac+2Z2y2NoSYk+VmMpSb93M3etOZbYhTqepCi23OuPHNpC7bCOWFgNCyXtrk38fuHD1E+MRpyvce7c6/nNCS8f+wLGjDHoVVVEP74YYbUQsdqgWm/BJFRUoXDbyAVUXTq1S65FqAp2zcU+1gpm3LGUn96eTOrbs5nw2XUMvzYH9VITX720LwmmGt74z1z+OH8uQ03+xGj+nBNYyursJIZ81UTUuYUMj6hAbilCdUkKPeFUt80hvNwQzdGP34TjIJ8c2aQPslk88TXGvbqRz//9MFbRce/+5oSTN53Fgg+nItwdy3u6noOBsS4b7ec1O/XdvcITEJrWa/yrl5T5XMva+vZlPQmRaHGxYNK6k3vshuqH0dQ0ICpwxW7HOz4dU05pO5/gtpum8+pljzParFKtt3LG7OuI+brn2KwzEt8tAiFoHhtL3a0OguZmYlqyidDsVhS33u14FKsV6fH4Skpt0H5aOaA3TcuYWDYc/xS3lM8g5yFzu5ehJcSjRwVzxe0fUKwrHP/5ZUibl8zbSpGBgRTPHol9m0HoglzM367o1kTUkhlF6UyNlFsFel0dKRfpiAB/Nt0dR8G+L7LG5eLmRy9qX791Ygpz457ChIna4QqW2gic+zW1ZdYNzj/TTPoi38SmXt+AM0jB2en6nhlQQ8JN87i94lKstW5MVS14Q2z88IGNz+MmkPyJl9iGBsr2CcIVCs7JaViW5hD1fCmXDL2YP06ai1V0PBImoTJ3//domWnmnMBqtnqbuWj8Ffh/sJQHos7k/dNyWLs4DW+Ql2Hf1WJ43BhVNXwxbyamy3Xuj1pHs6Hx9LaDeCXxd9a4XIwxG4wNK+GLIRHk3mEj6dmxKH+sofLsMUS9n73zlPm7oKy1VxgB6fGC6PkHbK+/9yj33JaR1jfloFdUkvvwRIQ3jrR7NiBio/BE+KP8sWaXj0+YzcgBzBYIPysFx1oxNaeCSCUo16A52ctt+SdyScJvPLv1BFTnwBzA7YbOOzWORzLf5y77xWhSIhau6bFjz3A6YRAlzc4wNXsZ+8Y1JPzgRnN1lKm2npZI+GGl7G8r5OLc08m4xZdb8TY1ocXHcedFb3Gg3zZOrbwG808NvlbrsRkom4vQGxvx+z0b/0vjMGaORfl1tY+DcNzQ9u1HqR68/ub2m1DoEkNKbKqZ5899lmsnnEbWxPfa1//6iMe5duKl7aSsNVO93cp6M63wzVNPsMJl48oXZuOMMEh/IBu9roHyq6aQdnIRCXot76Z+woazTdx+0SVoP69ChrmJ2WFbujSI0+qYavV5U6GKhiPGSu0d09n/uFVsvmUEKT/7PE2j7QUm/O04YgS3hm0CFPwVKy8l/AooOKWGCZ1zwxby2DFLUYXCWUn7U3tcBA3pkojUeFjeuxHYkca9L2zvu5GqL4zqC3tNOLAzKD0knOwr2joAhUJgrsIBM9dRe8JIjPytaKt2YRCnbcpNi4km+5GRA6L91lPjCCgSHHT0Sh4762Xeun8u3x75GJPCiviydgyWM1rRfhpcLdjUYnDFvMuxfre2/xugP69HCFqPm9xlnFpoGoVHWEn5sLEbm62tUlKyKpYY1Y+xoSXk3j0CUuJ9v7Wiijs/mMXMpZcSe3ceOY9PoPDOCSgtLlBVSm/xMUU11NuouM7Zfj5LDvDjp0MfA2CTJwhrQUdlwLq+mJ9afc08M62wauJ7OAw3HqlzTtFMtukB5M2yt4csqW96+VfVCByGm63eDo4JBYX9/QwC9q/AiHBTeNkwHMdN5LrLPsSQgtKGIPwVK5MsgpoRFqo+S+ftfV5s/74uDc4pmsnop6/koZLDua1iNA/XDmXWluORquCi0xfwWOzvNCSbqT9rMlpinI/2/PGpnP7rKn674GFU0fFobf/3dmNSrgcytzYDl/TwaMJXeFNjSb15BXJlP0MjI1O70OirERHo+49H3787iWjegxPZfGUMRkj3JO+O2GuMgBoYOGgJrpBcD7atvhMrPW4in13EttPCCPsmz0cYOsDJwO3QoqPa5/XFuGGoUZHkXJeMsHn7fQC1uFiOnf8z4etb2bKfytUfXgDAbVuP45P396X8kjj06uo+t9ETrD+uI/bhRTtPDCpE+3nVYmOY9cBXHPnCL2jRHRTteowLlO63QtjKWrSUZkxCxWVoBKTWY3+6Cm1IAtLjJun2xSTeJ3kw/kuyTngamdFC07AQPCOG8MTFz3PI4lI+mPkc1w/7ETXI183n9ZMkm/wZt/x0Ll54LkZ1R7nWqG/gnYqOqfMCTzMTl/jOo5/q4fqnLiWqU1+U8utqVhyVxOh3rybbHYIuDep0B+NeuYZ/V2Xy9cg3yD9kPi9e8DQlR+s8k7c/N8R/y78yvwSgTHfQOEwn0OoiQOk4v2W6g983puMKlbyS8jknBa/gQHsWxfXBGCpsbI5l7IvXEJznoilBsOmOSKo+HMI7xz3FOYHV+AtTr63TNsWM0zDz60mjmb7qTF6pH0vVOLsvvDN01OCg3tuD127uQnoqY8OpybRi2dyRMFdsNp/GRLiHiBW094qoET2PqMNeEg4AiPBQZGFx/yt2guXr5cR93XWZt3CrT6PdPvgprc6jl3LlRnRFJf0ZC9Lp6rckaYQEstkRjdriwXA4SP1vNmevvpGQP7YSX7qo/9hcCITZjDFxOC1xVgK/2YjR4qDx+HEEfrp6UN2L26HFROMcHkfZDAuJD67AW1bBxxcfQkushaDAatSQQPSsXIbfsg2jsalbmJF9SQi/TZkL+N4mrWtCefaClzkncjYU+a6VUlzOtUXH83LyF8yf9CrrRyXw1u1HM9HSzEF++YCZYq8DYfV5UgEFUK238OaYV2iRGle+OwuIxqTqVK2JIqihhtcbw5lgLSbVZOHHyc/xemMqBwRlo54pKTjQhNE26qtER+IYFoWlViHNVMcxOaeSvSmBpN89LP50HMd9uJoQFWZYFQqOeIkGoxV/YUEVPq8hQFGJWKpgu72Oc069npdvfZyxFgs/OFJYf/jTAPgrfkxocwJnDV3Ou4GHMtRWxc9hOtv2scLYRuaO/pyT/BtZ59bJXHQ+uq5wSMpm7or+qYvyUbPhpEL38nvTRITHi3NRFC9tPJi7r3mPl3OOx/TjKgquHsG4w7KovmkYYuGaLtdjx0laY20WkWu7tnKLIXGUHRQBLTpBH65CSolit1M6Kw0e7+U+GcC99KdgMPXw/tA0Mw17iQP6mN3vCXL4UOTaTqxGho63qH/DVHPhNKJ+2kbegX6IZB2Jb8go4L0lvfba7wjnUZMoPcND9Mdm6tIUAvKHwIoNhPxWOOgmku2QThfWnHKS1rvRtzP0LFyDP1B25XSST83FeVJEr41BAYUKEarvCbgq/BdKDgz2JeKMDpOmV9fQelYC575+PBtKY0h91EvtoSofNiXzY+1wMgPKSLV0lKdiFpQyecT15J/k4waoKg/CnmNGt4K0S+qywnjvimm8MvwEznrsCy4MKmfuhkMwLwrg3AsXUGhJRlWD2Do/nlmpKzg68H08UuGo5bOJf0QlfflKpCExpo4kQPHgY8XzYXtb73b4CwuGCnp9AxGrmjjxi6vRIpx8Mu05/Dutu12s5IrgLPa7JZvnKw4g6Qsdr01ywVnftXMXRChebAsCCF/TxBZPCgccOYHWDBf3T/sYpzTx4p0nEFDooPAYfw79YAVqi4snkj4k2eRP9POvcvtdF2NugscTv+TwUTfSVpAZFPSsXCKzcokJD0Nv8x6VwABa4vf2PoEeIExmlLSknSLYsH2ytNdx174gV+8EpbcQ1Ez2Evmrgt7YiJK3E4+sENh+zSJ9uR96RSV2OggYentABwK9rg7ass2uIyeB9Kkig4+cckNpLKmu3pNGca9mMXm/c1g26XXiNQtvpXxD5k9Xkbaha+yql1dS8+I4olwS3Qrjjt7EQx+dQNJdy3j56ZkoboXU8qUoVitZd0cQ9ptC2XHNRKo2PjvoaaIP1VnjCubZ0gPxnO/HtqMSiPmujMeyDqI6bRn27/wJX93Ih2WHEuJXwrbjErl31OuYhJfRZiuHZR1N4unZSF3HmDqaLVcoXDXuZ5I0G3W6o332oFpvaWci0tsUmK66+QPuGXky0SMqSbtbQazYxEVfnMXiMR8BUOJt5qbiY9g/dDOXBG1jsgU2BedQWjWE0pMDOS+wkkq9hUXOKHJdQ3n+9idokWYuXHIelnUgHSqvlMygrCmA+LXV6DlbMM+cTk5jJEP8a0k2+bysfaxOxFlVxJ1VxwmHnY0nQHQl0hnASHCXa9KZs0LTiB1dTm9Xeq8ZIJoiDuqyTA0OouLUTMJf6E7IubdB2WGKbzBoPW4yXqvSfQhoN0JNH8o+H27g58untVdLhMmMkp7sE0fB9xuqTx9D5Nf5XRpLHCdMQSpQOUHhmzMf5tT75xD5xlpEso8yYvv3t0OYzCiB/hjNLUiXCzV9qI8dqqmJiqunc+GlX/HFJQeQe6GGyc9Dzn6vtX+32XAy6YXrGfJFA9UTA6mZ4kFp1jDMBuGJ9QQ94k/+ebDkwKe6uNnpr11G8q2LUUNCSPnOwdNxba3K0sMdFZP5V+Ri/BUrBZ7m9ofu9cZwIrUmDvBr5rOWcE71b+CcoplUnxtBc2YYFWc4+X36PKb9ciXWLD9MzfDktc8y0wrJn19C9JAaPhjxGvGaP+dv3ZffFo8gdL3AOKGGlRPeZ8a6E/HoKpcP/ZVHXzwZ0/7VeH4JJ/6zMmRZJUpEGDmz48g9Zx4b3a2c+Mb1mJoFCd/Ws23/YBQPRD6zqP2cihGpkJ2/00NfWkI8C7Y+vvcOEIkeklJ6QyPRn+f3sPbeh501AAABK0sJ+bXvEs6uomZKJO+/eBDq0o43uPS4uzzA9ceP5pE75mFEduVbsH2yFPsnK9ATnQw1+ZNxTja1J4+h5PAwpKl7I5L0uNFrattzGHrOFoy2kuLFs79gVVMi2qYiht+6lZRHDXI8HXkbf8XKoovn0jzUn7CXlpAxew2p1y/Hv0Dj5CGrqRxnJfQPC283jgB8bvpXDitaWhNaUiJ6QyMLcjL5tKUjI17kCGWV25dosyuCd5tC+M5hIlh1cIhfKxZh4lR/H7/D60N+o/QhM4pLknprIwc9NYdrx//EH5fN5bYr3+KCJeeR8sMFZD5QhturclPxMYx46nIa3FYMi0H4u2sJeC6IOt3BAdG5HBG/ifMCK7ln9us43Sbunf0qBWfGYLQ68W4tJXydxGG4iVINXjvzaZZd9Th5N5mJfXk9kc92vPykxw05heT9exxy2s7xR/TVJbp3hAOKoFvgK2WfrY79b1NFDQrc+eaLPwk9NT7tboS8u9LHPtPHOrWZgjl3X0bIpu6NTHLKSESFheTPLyFisUrI64sJZgBzGEKghoag19Sil1fwxNoD+XDa85x9zvXEvZsHUrLKGU+6qeMahag2gq/cindLJkXHBmFugITPKvho/7F8fe1DzPz5ajItpTQYrbikAdhZP+11Hv0yjc/vOpgh873csf4coi95igkWhetjv2vr2LMQqdq587PTGfK1i4Ybmzl2/PtdDtclPWiqTuTt+RwclsVvdelkWkvYpgvu2XA0GTeUo9fWQUQ4jVlhLIn2h2FO1i9OxewB99ThNCSbsCkmvi0ZTkJgHZ7wdUy0lHN0ykZ+aBjB5ad+xdLDkln623CGTyvAwCBctROuAqh4W0w9anIaDgep//a1fu8eKdkO7BVGQHr1npkJdwHCpCGCAtpj4oFAHZ4GVbU9cgDuCaghId2MlBochPDzQ7rdXcpBKCo1F04m8r2Ng2al6a286DpiErYVhehVVSTf7SP13NFQCJOZ5ngrGQ8XDMooazHRFJ6fguKF2IcWIb1ewj/1I3ofnY+vf4iDM64nY1gpp/jXsKND+u8hn3Hewddy0NEr2XLxUPScLYRfm8Ixcy8i9+CXUIVCsyHwFyaOsjkBhTmhWzjj8bUc8PYczA3wQ/MIJlg2Ms5i8E5TAsPNpSgomOsFdRkW6raYYYfy+g+tAURf5WTdBelkjYoi4Q6dm2Zcwiu3PYb2SxDeimy23jmFe898kwP9yttzDVPXnIzd7Obps96lSZqwCBMZoZWsfz+TMf5pCAMSH19D3kvp/HvfX7kqpIjqhK9wSsnXjjiGmSsYbbaiS4OIP3p/JHfF4+wLe0U4sCcgXa4+9QF7gifcHwbQay9M5i5NGzuNiO7cBsLfn+TP69jn51L0A3x3qZYQDxMzifyj2kdcsv0YRmRQcfX0AXMAdIYaForjynqfF0bvRK6KnxVniIIR3jVMkNPHoIztrpIsJo3CddQkhn1ZyZornuK+i1/FffgkAAI/WsH+L8zx3XRWg4KFiWS8fQWHZB1DgaeZswr357aK0Yw2qzhiDNY8OBa50cf7L4u3MTS0mmbp4srSKfgrVmyKGYfhbtcmDFXMBGTWMuKozcSY6inTW5ldfCCv3nAcw769jOl3XsmQl/OIWNFI3C+Sj5o7mIiq9RYezj8cWVtPytyNJF7VgJFXSHCem1DFy+PXPIecNooRB+dwkn8jIaqNar2FIzcfScOySIqrQkg1WZhs8Q0VvZn0C7Mu/J7ErxqIv3+RTyi2wdxuOMJVO/GaP0mmagzpuwY5HifhywbWS6JYrQNTJh7AjM1e4QnsTqgZqaDr/bZK9gTl99UDKsdtj3t3FXrOlm7LjOAAArViXvviQFKWrsUApM3aTo/dcbACqQqCCrx9U7H3EhbVHJlBw2oIqeidjVZMGoXbbiLitVUoIcEdbqgQOCMt+C/sPtWp1LdQd7uXDfWxmGJUjrU7WPfQ7ywsH4+xZhOJDyzjguXXoR6kkXyvb8LRdcQk7vnXEdReHEVVQCJTbx+GrVwhYMFajDYvRlgtGFLgkQZfLxnLwwdUE6A4efHxY1FdEH1BAfVOP44ZsoHbwtcwcdm5zF1zIggYcUsOFeUxRPxei3S0IlduxJ05DWunBqGHq2dgP6cV7Db0qmpkYyPGfuOIuXcL8Zo/8ZrBA/dU4zXU9irDkevOo2F1OMlfNKA8U8m5nx7M28k/U+JtpsFQsSluFJcHQkKoOyKDq/b9rtv5mmBWgU4Ps9F/ol5OG0Prvxto/iCGsBd7T5wLTUMJC+0YKe5tvb21OrCzUKMiwetFr6lFjYrEqK3f4zLcg0IfpR41PAzpaEUJDQFVGVCPQudtduYl2A4tIZ7ipwJoqvBn+M056G0kp4rV6hNz6eVYhKbRfPwE/IscHVoJQqDvN47SKzxEzbe2lxt3PJaC+6eS9IWDO958jTHmVoIUP5I/v4SM51tQHC5kUQlFN44n+e1teAuK0OLjcKdEoK3IwWhpQQ0LpfGtYLQnwrrsQ0wcyQsfP4dVCN5sGMW7Dx9G4FYXzpvrSQmqpvLKBBpTAzjujh8Z47eVr+tH8+Xi8UiTJHiDRtN0ByPjyqh5PAlrtZu33n6aSNWOw3DzemMyXxw2jrKjE6if4sZUbEZqkHPuPJrbaMX8lY5OPo/UGTn/ShCQcv9aDIcDZeQwNs+xccG4RcwOWUmtAce8fiO6TbLutCfbCU16gy4NRj99JfEP+KoCWkw00uPpFp5uF2416uoHFSLs3YKk/UGIAbu8ekVl+1tar6jcuwwAoEVF9urGybhIUFW8JaUDNwCTR+E6fCJC07obgPg4wt5vZPmk1wnIMbUbAOhblUaLjqLoneFcff+7yE7epJg4kq2HWrh11DfYVvVCCCslQ/+9GnXVZm6+YzYv1/sy+euPfpKXPnue4Pk1SN0g4d5F5J8VS85LEyg/MgF14Xpa989sy5M0YL/Ljm1h1/Kj0uKixVCIVO1cH5pPQzpYimoxXo2kbE4KcnU2Ae8t4aNHDmYfawPnhi0kfUQJ+4/N4s0bH+Hy0b+xYXky/gVNqA43F+WfxP4bjmf/O67hpUeORa+swusnyD/0ZR494xUA1rmdnJp7IqM+v5pmw8lvTt84sIFB7NRtRC/VfQYgIICiE0Kxr7fy9nsHEqL4kW6y8/Dpr/HLKXP7NQDgmzFwjfS1G2sJ8RSfkULNkd2lzKTXi7d0227LEQzYCAghVCHEaiHEl21/hwohvhdC5Lb9P6TTurcKIfKEEJuFEIft8kGOyqD0ugEpme318JZXdBuZltPHUPDANKSmdMsMKyOHUX3ptC4DTGpUJGU3TEdMGoVaUI7fH9k9hgTS5cai6HzZEkbcj/UDOj51eBqON6xsmP4atyw5CbG640GUy9eT/GkzL91yYhcXU42IoPmUDkVow+lESolftZdXXzmcje5W/BUrPzhSKHwyo90w+xdLTh63kugvi5ATM0m7axNbrh/mOz/L1mOkdZavAEor+KjRlyfJcjtA+jpNA95dgrmoGnXYUNTgICK+L2LKM9cTqrhZMOwrXkn8nSGa4OmfDiHtrnUYazYhV27EdUAVlsOLCXl1MRHvb0CfnEnAoeUcknUMN64+hejx5Zy+4iIaXVbGjCjCJFQyTS1EKA4swsR3mR/THKOiZqaTMy+VZZc+ys/XPIw0wblFB7LR3cp4SyUx6sC5AtRCn7dRdVACUoGwb7uHjLsbg8kJXANkAduzKdsFSR8UQtzS9vfNOwiSxgI/CCHSB0I73htERS1+VX3rBeyVEILmkyfj/9EK35+q2qNnsvVwG/4jalFedmO0eTztMwwWFWudbBfmBCAsGNUFanUj3qquKr9qVCQyOgxjfQ56VRXbzkjizoeOJd6u9VmA0eLjcKVFkXuuIH/Ey/zYamLYf1vaW0/bsWw9XZpvhaBp3xRqRqr4f9Bpua5j/mkNMd/rnCFv4N7LXuWH2kwCc5qQbeFLxLJacpsiyb1yCF6bpOmxUVjihI+5ubGxCzcC+OZLZvr/BsCR311Dxt3L26sZW58M5NPxL3Dk63NI/vdyEp+o5/7jD+PFhIXo0uCH1nBCkuto3X8EHruCVAXBCzro3TffNZzPT3yMXxzpvH/zEXjPcPP9yPe5sOgQHoz/sm1k2YSiKHik721tEirP3PI0oaqTeNWEKlQ+bE7Er1xSdudQrnVfjsdfY8K9K3kkpoNLUpcGlbqDIMXczUNQ0n1zDSGvLibMbkffSZUixWbDPXU41vyqfhPkA9UijMcnOnofcL2U8mghxGZg/04KRL9IKTPaZMmRUj7Q9t1vgbullL1mMAaUExhk2+ReASHwHDIB0/crUcPDMZKie9QiVIenUTc2jJB1dThj/FG8EvOaLV3c94Gi6fSpVB7rJOXpDolzoWm+KbU+zp8aHIQIDaHw9FheufgJznnjaobc1X+3puuIScx+/EPuf+EMAot07B/6uvW0+Diyb0gg6QsPxYea8c+sZfmEd/jdqXHPpedj+mElWkw0Zcclc9oVP3By4GquKzyZeqcfHkMh6A5rNyOgpiZzy3efMNMK91dn8Mc+UeiNjbQeP5mj/vMzN4flkuV2cM2syxCL1lJ5+XQ+vPkhhpr8Weg0iFBbiVIVHIZOvaGw3JkIQJknBJvi5qqQIhyGm2bp6dKROFA8U5/AV8dNQt9ShBYZTsuERGzLCjF/qPBp2rfU6T73/YuWRF674jiqxlr47OqHSDb54zDc5HkNvm0ewc/T4wZdBt4RakYqR32ylLlLDyX9fN+I+K7mBB4HbqJrn0KUlLIMoO3/kW3L44DOAW1J27IuEEJcIoRYIYRY4aHvCTnnMZOpvGzaTpXC/lJIiem7FSAlFSemkned2m1M1Nh3HOPfySb5ys1QUY351/Wov6zqYgCExdItj6CmD+1x9Drg3SWkXZSDur6j21J6vX0bgPAwsh5No2iuHWuN5O6jziTpnv7ZjwC2Hq5ytL0MvyqJoXX4Gp4hEbx13DOUTbNgrhfEBjZSZ7Syv59B1XhL23HpxHxVzI+XTOfEx28CICmwhrkZH/TYjUhdA4tb0mg2nNR47IjQYITJjPuSWt587RBebYwk3WSl6Aif+x3zQwVfNI9ki6eZGVaFdJOdIMWPGM2f4WYb5wRWc05gNTeH5XJViC/HYVPMhChWtnqb2+cLBooZfnmgKKipSWx+NIbHn36a1G/quTvxc9a4XIz/5hom/345caY68k9VsJcZWIVPKGXMW9cwa971nBG4ltIL+5L6HBj03AK+PmQUmf+u6nfdgciQHQ1USikHyoYxYEFSKeVEKeVEE30TdvhvqCB6Yd2AKL72VjiiBLFhDZRfML5LfL/tWjf3Rq5n3RfD0atr2sMFJSCA0puno0ZFknfveFqPndC15mv0cIMqKk2nTUWJCEMkxnb/vBOExYKamoyYNIqsh5LJOex5UsOqiXpvE/qmnIErQCuSefUjCP+ttIt73TTEymizzqEnLiP2dweOe2M5aO4ctnqbaR7m+416VRXe4hJMxdXEvrCW7IXJvJz4M6XeELTyerS4WLSEeJpPnYr78EnoNbW8+cYhTFx0MRsvzcTwt8HYDOID6lFd8O/Fx2AgOeWYPyi8bxq1kyP4pTqDJK0jJncY7vY3ck+o1lu4o3ICJ627gP/WDOf1xvB2bgCX9FCp+9xzj9TZZ92JvNvkC1PfbQohxxOJ9kIz1379OVn7vcxYi4UnY5cz1mJhhFnjy0OfZPXM5zjITyf76Gc55pafAfiuZgTW9AbuuvAt4jV/Rp2yqV0Ad2ehxcXgLd2Gt6gYZczwvtcdwPZmAMcKIY4ErECgEOJN/gRB0u0YjDTZ3oqU+UXUZ8UTVdDoo1Nrg2dzIMlVlzD8k8ouPQrS7cZWLmkdm0jYeghYXuJj0W1TTNbzCtoblhyTU7B8swIMnZCFxb469w78A8roYeScH0z4KkHwG4tR/O3kXRTN3JNeY5i5isxfryDt383o9Z17EVSqL5pM04EtDL24oINCXNN8XpnJhDQbvPTpoSQVdg0dmhJ9HP+PRC9jasYkQucvJkIfT72hcePUb/kqLM1XxRGCkpOGEFASj1+FaB/blX4W8i6N44wjf/s/9t47Poqy+/t/z8y27G56773Qe1UERRREmhVBQcCGgoJg77e9gFjAih1UVFAERAUE6b0T0kMSSO/JZtvM/P6YsCEmAbzv+3l+fp/X9/yjbGZnp1zXuc51zud8Pkz2+5aRPz9I6lYLUe8dpfGKToh1lWRPDyFpQTaHtyfz5dx36GRwohe8eCHkKPOm7GbC3Q9w9EA87iSZSrmJEMlCheKkStbj3wHOJkiyMC9oO48G7ThH+UjLgmxo8mZ7fQovhR5BL0gsSVuOXZUAPc+smIgr2knuVUubvyNx/5l+bDyVwpb+HxIkWehi0M7zm03P6uoB3BK4E29Rx/L4P6BZjtGmOMn4qBMBddrzlFKTEJyuvz0H3IVFWikxKBAl4z+kF1NV9TFVVaNUVY1DS/htUlX1Vv4BgqT/VBP0Bo0p6eyKLwg0do/Ae9V+1APpraoDSZ+V0enRDCgu0ya1IGgsMLJMwPL9eBXU4n+8zkO3LvboxJkfUsn8qB9BP7touCQRydESFbjPlLRLQHJytjcnbnoHtXnwy5VVJC3IZtF9k5j83HyS7shEzsj+y33oGHTnAUwmF7g02VvRYqHgmzQG7q5j2I4SXr18BQnftIVmqwIsrQ1DEkQqLtG+mzsNuhtMjLOmk/58ErroKMTuaUT+Vo7v/mKivs2h60+zudZcDksa2TD5dR4JPIi3oPLo8J8ZuKOazpsbKRouINQ3krS0FLm8HJ88yHSGtiIJrVIU9HUuzHHa3trZvB2K0Vnp2QFVXIXcyNiskdyUPpkHika2+luOq4EBxkpu899FnkvbKnQ3mIiQtGftDHGjOkVcqsydhZcwMe8K1m3pQ+xdxYw/flurc9374ww2/NKbrgZHK+wBwIYmP0J+L0AXGYHo7Y0r2Ipq+nuMW5534HbjLim9YOfhf4ITeAUYIQhCFjCi+d+oqnocOCtIup6LFCT9f8nynu1D3VWdQJYRenWhYnUy1al6xMQ4JP/WSsdyppYAVOMjKb0+RaNTX6FH6ddFo0jLyqMu2RupU7KWNffSMyQql0Oj3qbGacay4bjGDXh2z39u+fGcPIJUo+OU20nA0ZaEk1JdjbG0kYBPdrZbc1adTtIf7UrUlNModjuC3kDWc93YOfAjLrVm8GDASR5ffQuC3dEGRh3z9mFe+mU8NsXJ5D670cXH4pVuYrtdIVwy88FVn3Li8QiybvVD1Uu48wtwl5Tif0REL0j8nLKGGJ0Vs2hgyHfz6WIs4qHAQzTIRqz5Ejf/sY+CV72omj6I4JsLeG7vGO4sGM56mxFZVYjTmamPM2E1OahXnG0ISc+1CrmRu4sGoQBXBJ0k0aeCgyVRrY7ZaEthXWMs1656kAlvPEy3nVMocjd4zmvN0SM4RA46FY4s7k7tVQ5SPqygqW8C8xNbIwU/v24Jq6YsaENyAuBSdaCqVA+J4fSXUehL6zoW5Pkv2d9yAqqqblZV9drm/69UVXW4qqrJzf+tOue4F1VVTVRVNVVV1Y5F0P4fteCDCtafD6G63Ug1DVRXeqOzqfRbkc6lf5ym4aaBbb6jHE4n6IOdKDW1yI8EojuuhXCq203VdY28tO4r0t9MQZdbTO7MJLJdEo0uTdZMSknUFJz6dfPkDQS9AbFbM9BEEDAm1BEqidSkeQNa70DTqN7I5vOsMqqKbuN+T6a66eqerLxhEb6iFxFSPbNOX0rKqznIuQWtYNSC3oDobSXt7TNcff9sDlRHc+LJYGIXH+OFCZNZWJ3MVWYXeeM+5NAtb2IP1yaSoNOBCl22TuOSwzd5zrdw7BccaIpn5H2zKLoxCHv/Bqb4VBDs3YA9QKBsRQzBvxpJsZRxwh7JxiYjex0qogv85wiMfG4+Cd/dwwsVaYAWcue5GiiTG5FVhVJZRC8o+Ism5vjns68kGtNqXy49ch2vVibTecetLP5gPF/eOYa0RacxVSukhZSytLoFu6KvU0n62s7knXfivrGSmrHdEBpsnJ7mZLylgVqlyZNovMQk0slgJtPVyMiToz1bIICxlmqy74lFuL2MHX0/pfSKFi7I/1P2PwMx+D/MLN/vRnU4NB2CvFMkL3Hi9BXoY87jvoBDWO8u0lCQRiPKkF5UTR+E5K8lmJT6ethztFWJyF1ooVHVs2XEImx9YpFNOr6pHkDd8kjkyirsMX6U9/HFbdV7IgLV5UQ5K3MuiPhZmvi2PhF7gIhgNFIzIgVLdrVHmehCJlosyLMqmP7CXLrtnkQng5ktv/RCLi/Xog9VRejbFcnfHzE+mooRCbjzCzCv3I1yVQUJy1Tk+nqUw+l88cXVvFqZTIG7AaOgp26WhtPP+KgH1YOdJD9eh2mxP+ttWtg+2txAlduC9/7TyMG+6A9b+aHBh9OVvlhKFMJ/zCVgfyW/PjOUetnEYXsM9yyajaCoyL5eBH68k9SPali94HJ22WX675nGzBtmMnDTbMpkG2l6I5f5niTPrYXNdZUWgldnUL4/lPv8j+JjthP5Sxn6kloyXg5ky6vv8GnCatYWdfE0Ly15+F3u/mwV3wz6kP19VvDCCx9Rek0sscHV2BQnG2yh5Lhbk49muQIprffmiFMmZctUUrZMZb8Dxl+7k+qtYfTfdQcBJ/4z5eiLsf/negf+rolmM4LZ67/ePqyLiyFzZiTJrzQLSogSUlIcTfH+GCu0JhZ1cA9mff4dz5wYS/jU4g5xAYLRSO4zvUmfuphrTo5Fd6cOtbbOs/oKOh1I0nnJSCU/XxqHpGL3l/D74u+zNQn9unHdFxtZtGw8slFl4tg/2TupawsxiSiR/UV3Yr6UMG48RMFj/bEUqQR82v5vSYEBFC0NZU3vjxi1925ibi9AMJlahGQFgZyvepJ9+aeMSB+D4Q4RpawCISYCJbcA9+AuPPnxZwwyObj25HUYbnXhLi5BCg6mYHoysV/kIldUUXddbwx1MqYSG6fG+CJ3asRno5mQnVXc9eNaxlsacKguzrgdROiMGAU9ea4GRmybzTWpx3g7Yi+dtt9G7Gtw+1drmGAtwyhonYLb7QpdDY52w/qHSnrx61eDiFqWTfnoRHR2lYoeAidvXQzAUaeLJL3KmPSbeSR+PY+fGI8kqqzt8SnDdt9NzOSsf4tc1mP9u1HWz5vwX4up6xGC7/Z81hcv/h/cO/BvmBQagjKk1wWPq7u2O40DEy943N8yQaDwuiiMyXUQ6Kd9psgav9yv+1D3H0fQ6Sh/xMEVXlXE+lVT8EkUOct6ceq5wTSN6681QjWb6nITP6AQSRCZEb2VooVeCF4tA091X5gSXa6pxbIti6A/2vYk6KKjQBA01pp2Wk9FkwnXy7W8vGsUQUfdXDriKL8svKwVM5EUHIj3bi+88mtQFZWmcDeGm0tb3Qc0lyZDQ5Arq4ie08hdE+4m9jG7Jt9WXu5xbFJgANZdXrxamUzRn9G4807RNKwzbn8zqsOBbusRpm+7HRGR9Wk/kX97gnaf5eVEbmnAkRaB6nJS1h/Gvr6R01f64kxuYtWg91HGVlHbxZ9LTVolxK66uS19Ct/WhwMQr7eSMWwpb4ZrwKcI/1oaYsxcYS7CKOhpUOwUuzXsQXsOAGBVek/enbUE25dmGqIEJj3xC69f9yUAOe4mkvQqj5cMwb0kjHt3TCbCp477kjYTIlnoG1mA6NUB7fhFmttqIGJNIXJuAb7b81HOAzz7H4a+0Uy0WBBMxvO288qlZRi8TCje3u0ytZw164r/Lref1DmFrNsDMVRD1PUnkDuItAof6s+qnq9jFS28GfcDShwcckQQN7iChOluniu7jIPP9sf0sybP5n4plJQRM7ly+EHCfepo6BWJ1aD3sDTroqPOSyEFrYlHPdY86ZvG9cMWJBF4zuOQ/HxRo8Kp7uHHqOA/sX8QjmXDMU7VphKw80Ar8EfpuESMNaqnwpD2YQMohjZtrDU39KL/3P1kXx2gwVnz25JK6cLDyFgQRshPCluGRBBTu5Om8f3p/dQBKhxWqqYmIGflEvmTHsdwF1bRhNq8nOkS4ki/xYxqkkly9cRYIfLxycFMum0TTwadBLz4sedSdqRGEyRZOORwcMPKuQQcFTj+QBT4aP3854qHfJ6ynGceGom+GQJjFPToO+Cd2N6sMPXt4A/oYzSwueuPcA72x6a4mfDBQwQPO0NprTexhY0kTzmG3D2NV5+6mq3ReWRWhxCgnL/990Km334Md/PCcCEymP+RTgAARUUXGYH7THHHaLi/udVprxX3Yu2sRFRNtwBOTH6XLp/POu/v+1xWyqq6nvxQ0JPKaiv+f5jwz7RzapSJlMH5FNb44StqaD65ohL9tmPEb3KSZ7ViHxKB6Ze9KFYrxQ8OJnrFKbhYcM+5NrA7JQOseBfJ1N1WR/Tseo+iL0Dm4gS6Rp0h4A4nWx4ajPW33SiAtPlAG/SXI0AgcEsLzv2sHHzrhyTgsgjEmSrJCk+Evzhx0WymflQ3IuZm83PUEmZ+9YBni3R6qMhoQy2DvbN4q9ctWLNy8Sp1UKW4sYpw3+Sf+bBxDPJltSzs9hVPfjwFfXoOsaV+lJWHclO//bhUEwoKMTorMd6aM3yr9EqSnziEYrezKm0Q9cNNvBi26RyMAETprCyN2QZon2nS5m2dgE1xcueB6VhMTnb0+rrV3/Y7nPQxar0CK+5aQJQOJAQuf3YKgeN1KEdOEr2oJ3/clQK1evxs+ed9dReyv7OV+B+5HVAaG5Grq7Xa+XkmmvtU4XmjgDbH/wd8f2Izwst/RxGXPjqL5A+0VVkXGaFl7pshz6LFQtFjg+kTVMS6xy8n8BGJpFsPErh0J+LWg8Q/vhPX5SWEXZeJ1+q9qA1af33l5N6aTHt9PcZ1e9FFRnDytU50vT6dxm4RbajJBb3hgqwyUp0dr0oF76xaIiedahVJCEYj/ePyOXwyBjkrV4M/n8dC9zYPuvP8pi4yAsZU0sPrFMWXB2gsun26tByQHMtbb7zDioSNhElQ1qdF+jtxRRMrX7+Sx3ZfR/E1Gu5Ad7KA586MxKXK3OdXyOuzPuKTXp8x3tKAoGhYCDkrF/8sO1dveIAny/pw2KkpGxU3y5ZtTk/x1NGTXjlB3jCJ/svneTgE9jhcHd5PtWxj+ImxreDF87psYG/vFYgIrG40M6PgUsZnXU2j2lKF6WLwwlf0wiqaeDZtNfKv4WR93pvqFC/2XPEOwwccO++z/m/b/0gn8E8zKTkBtXkguQuL8Ptyp4cPIGt2LAt//JimkVoLbPYz3dkw8zVG+R/Gq9iGckwTO5GCgz0VAtHLC+dVvZF8fVDsdkpuTKV2hI3KvoGI3t7U3jqQ4jExTBm8nT070jCubz1B1Ut6UvVjLJXT2ylFDu1F3suDUIb2Qj6Rie9Xu1COnGyDE1CdTnKXpNLpjZaEqWhpv6lG0BuQDSL6osrzOmU5zJ/FXb5muJfMsnkLOPVEX4qe0MJ/ADLyeL5gDKARjo4avwvRWytpCjsPE7izlLt6bkM0aBsIubKK7QXx/NakXddwLwd9DBrzj19OyyZDfziX2O8Fdj3Vn9nP3M/wH+fz5BkNDDQkLQt1UA/qbhlI5ZjOiMGBBB1S6bF5Jg7Vxdq6nvQ/eKOHimxFg69n0vuIJh6PX0vq5hkeLcTBXlrPRqbLzqOf386m9FSmR2xtVlZua6PNdn7v9DMP9/uV6s7gK5rYcKizByKvi4/tWJbsv2T/6wT+A7NNGEDJA4PJe8lC5XVdNXCOKKGLjdZWRFEisEcZnQxmCq/SVsiU984wZMV8nn/2dsT8YkCbRBlvRtFpQy1lswajutyYT5Yi12kDq6aTSti3RvyP13H6rm48+PTX1CWq7BuXSMrzxxG9vBC7p2m0X2P6kzVFT/3eYELW5HjOf3YglfX0YvetC6hOvsDAUlV8l+1CzsoFQdC+Hx+NLjKCU/8aROUdgzyrftG8vtTG69vkJESzWSNvbTZHoInpn8ymwN1AF4MXx+58l/0DPiN/mpbUU+x27POCeac6lu12hVKHD2pKjOf7cs4pfijoydO91yD01TbaYZ+bePT96bhUGUkQOe5yckfudXhvbWmgUpKiefu9d5j75nICDlSTMm8fGTUh5Lga+CL2T979ZgnfvfoGf77yNg9tXMMbLy4hZK2RX22+jPU5yBudvmO8pQZZVXh83wQuO3oDG5skJEFEVkVCfjbyzJlROFQ3nQzalqGTwczRme+Sd/VSxlo67lU4ixGY4pPHpptfx6XKRK0XPc40664IKm45J8F9EZyBf9f+1wn8DRP0BuonDtRUfQWB0+NdHH5kCYnBFTgCBESLGUEUULybV0xFpmpfCEtrw/AqlhC9vXHnnSJx/i58v9rlKUuKFi/MFge/fT2QiLWnUV1OLWnWjP5LffwolvwGatK80TWqPLNsMqkL8nHnFyA3NJLzSRI3r9hE07h+WA+dIW3ucWKe3eFJytXe2JuS6b1BlIj45ChD3p1P6Ia2Wx8pNQnbdQPafF523yAyX+lJU4w3Z8bHcXTGu5huKEVoTo41hSo4/do+L8elnXG846DuloEgShiqHdx1yzqCRAMVciNuZOaduZRbJm7ykKoK6XmsPNOTlwtGs+dULCgaK/NZUlPDJwGE6OrJnKo9Y+PavcSsPMOMgssZeXI0016aS87PiZSNT/Jch5hZwIStM1mYOwLZ24gUE4X3fAPT7n+QL+qCSNFrpJ9GQc8wL4VLTCL+dxUQo6umj9HAZSYtUVjgtrGw3woeT1pHgk7LVVxldvHj6wsoavRjZUNC6+cpnH96Vcs2UlbPpEGxYxR0REpmzKKB0pubPJM9+a1cgn9obqkWhFZCsh2Zc2Q/3Ff0uSglbfiHOQHJxwcpsC0D7/+fJugNnv28oNdRPEzB6avXJkCNgWHHxpP/SzyRS4+h1Ncj6HTUdvFD6qyh9eKf28unT40j9pPsC+YnQg462m0UURobUY9lUjpEwTffTcxzO1rlAHQ6hQ1VnbFuzcZddLpNaO+yCHz/yGtkv9EPISqM6F+q2iWaKL8kmOLrnZx+ZHCrNuXggzZiuhRjqHXhU+BmVWMAZdXenr/H/+QkZk3bSo1sFPkqZTnvvfQWQu9OCAczeHujRjSlF0T2OSS2f9qHPdVxlHc3ISUnUDi7B6KgotzrTfL8ctSDxxF8ven16THKViVjLbAx96sZpH58Dvy5vJLM6hAyC8LoPeMI6uBa3F4CNVMGgSgheFtJfbAI8+hChJ2HOfkvf4qvCMCyKZ3vS7Wy+aLqOB4q6UWmS0MRrktd16rHoMjdgAKMtdgYbbajAAurEviiLghZVfkw6WuGmP8eC1ChLCJ4ybxd1YMCt41DTi25e2fX7Uh+flrZ1tebigldkIICESQJW/eo858UMB8upGCkAbl/5zYl2vbsH+EEBL2WAFJlGVz/Rpb7/6AJeh1iQiy6yAgUm43gnRJVaXqkyHCSH9iF8epTRC3Y40H4qbJCbYJIQ5LWIyCmJuKzNQ+5vGMwkmp30FhnoilI3/ExbjdpD6Vj3PgXhJ8iEzs1j6prVK1k2s6ePGRVJh9WXcrhmxYx+cdNFDzTfnkr6JvDpDx4mpiPTyJIoifkRlUZGXaCvHFeVHbR8fHt40m8K9fTbixtPoBy5KTWOHWOE/f67TCXf/owU9+ei1jTiGjxIulrOxua/HCp2or7+cMLuSF0H24z1HUPZuzN28jNCkM+kYn79BkkHx8ccUEMtGazr+9y8sZaiV903IOGFIxG8j+Jo1NAKTToOFAWSdSLEHzARm2iQNEjA+i65gyG70WEtCQkP1+mdNvN47OWkfFSZ+IslTQodj5cfg3Hbk/ltifnk+nS8juHzsmwB0tGT0vy5iaRdyqGcZPPESL11dyXP4EoncYpCPBDgw9HnBdG+kVLCosu+ZpQfS3eosB+exy77DKDzVkI/r6IZjMZ9wQh31CJ4G1FdbsxbjpywfO6i0tIWZiLbm86SlXNBY//R5QIVZcLBP62lPj/DVNsNjiHGjxoTwVqwRlc3ZMQThWCqrbqvVddTuI+ywWTkeJ7BhE3KZusNclE/RaEeqy5T1+UEPQ6TxlHsdtJmaG9XBWNaUjOyG3DRdhRJHGh5yZXVLLzuf6Uv7WNm6xlvISWqT/bmdjqXm02pMAAcp/ohblEIOSARP5YMz28Cjg5eTHFso1p22e3ey1ld/ShuodM8ucOhJ2HUR0OYp/RmHNlUSLvhf70HXqSB7ZOInSTjtKhMiFbdTQFCfS4Ph2nIjHebz8ryy/xnLNxaBq2YIntDSmMtRzisquOUPRDLBxqZk2OjiDMr47tm7rS6b1CFF8rBWN8WXzH+8z68B5G3byTh4K2U+Sn4/Yr5mIp9sUsFfHEykmoIS6eDN3MCZcB2UtFPZFNYIGFD+ZcxnOhW+liaImGzqIEAYZ5KQzz2gdYcam1XBV0giJ3k0fn8GpzGW9X9eBDhx+Phm5s07xUqzRhFgzUKApjLTZcaj16wcJdvmcAid77biasohilsZGkeXsR+nQGtxbdXSxx7t8RivlHRAL/k0yotyFEhqGr69jTu4tLkAtPI6gwMvg4M6auQzhVjOp2o4uPpfrnBHKf7Y3k19JRqLpb9APKLg2i6NEBNF7fdn/ekUmpSeS/2NKD4DFRQujTBadVREEjxniq6zq6rjmDlJKILi4G24QB6OJjkYf1RhcViRITjjPCRcSaQlBkIra6eWneVB4u6cvoA3eiP9F+b3tdAmSPeZ+i4W2rCIIk4du9kqIGPzo9lEtFLxC93AT8cISotWUU23zo5nOG2/dPI/GTlnyFvt5N4ME6Vqb3BOCNyA3c+u2vnH50MEKfLmQ+54d5ukz84ztRa+soG+yP26ySqq/DcEklx6alccPdc7jpmzk0xCr0ePgwsYYKUt45Red/lXHTXXO494X76TY0C7V3J+x9k+hpKcChKvzWZOG4s6nNvZxr8Xor9/id9jgAABmVj/8cRoGtfV7MUllBL0iESgZcqsyjJf3Y2NQSnX3W/XNsl6Rq/1Bk1L1HLwgE+0/sf53AeUzo0wVdbHSrELf6kmgGf38ce6S2J1aG9IL+3dp8V3W7CfpgJ9/fcRVlTh8Eo7aq5E2K5OW0lSixTdj7tiSvBKPRkxxrChLYMvN1yvq2/3p0UW3Y2qC4jJB9Cmdu66TxEZw9r15H9iRvhMnljN49k+4/zOFfX97C+s8Go54uQW2yU3yJQK9VuVy7eBPZCwIRMvJIvfuQp8xpXLsXr5/2cGKIifAbc9pHagoC+rgGTrocxKzVVmnJ39+TT1FdTirKfBgTeQTb1z78fsMbdIoqQQwNpvAVAz93+pbvvxpG7K3ZrfIVxowzCJn5KCUmLj8+DkVVmexdySd3v8WdX//M5K57UJ3a6mgfmEJVHzcJ31Yz+Ne5ON061ONZmH4/THCvUrJveZ8Kh4VHtt5IXf9o1PoGjL/sJWRXFfdHbmDe8m+ojzGwfNooJk67n7n7bmJTY1q772CXXfawDP3VfEUvsia8x+rk9e22MJ/dNhx06rg+ezQ/ZXRHaoZfuVSZZdUDMe/Lb/fcFzJdXMzFKROdY//rBM5jhY/DI3/8TMYTLdzvdXEijwQeZ/Bru8n8oB+nRpmwvFFC3iuDNBqnv7wAYcdhvv1jsCfbG/fOMRaOGEP490a8jresePXjetHwUB2i2UzMumpGPzGfpAWZ7V5XY/cIRHNrGmu5rg7LmoM0DLSR+WiixxGoDgcp75Vi/zmUxAeriPlNJv6rIsLe2qGBrkrLMFaJzAncxW9lnUl4yYVis7VLL6bYbB2Ho6qKmm6lXLYg2p0IOh0nn02FbqmeQxK/VJBQKd0RwfiFD1P5YSwnngokxLuBvp/MJWrJ4TYEGA19Yjh9Tw9S3y/H8JQvX9VpVFk2xcijP07mj2cu8VRBynobiPxNRDl2krQ5J4i5q0SLsGSZ8mpvHintSf09waTddwzrL4dbnJmqYhGcDPdy4LIKCLuOoN+wn6R/2Slytl3NZVXhmbxxFLo7zuGcWxnIdDV6KMrOtc/KhyBPBKXaiF7QnneF3MS2lwdq3Zl/fcSDelB7a1vsB2h0dMrQXmS8GIgyuO2idD77R+QEBJ3UFkD+DzDLah/uyJxJ6peVnsuL/baIlIR7yLz2fR4avYebMm7k2ZifyQ8P4L3lY9vs41FV/DIE6vvHYF5XpSUQ6+rwUVWcSeGIxSVIgQHo7yihpt6CX2o8Yr0Nvy92dvhIjOv2ehhfpcAA1Ca7Z4KmPHAa1W73aBYCyDn5hLxfQO2Evrzx+hKenjwd4ZzVNu6LU1xzej5+2U0Ihw95Pq+/eSDWgqZ2243PcuCd2/Kc8MZx7kq5Ffd9XuiDveg0Jx93actg1tU7+fa1q4n7QVPscV/em/kDfuPnKUNJLC9Cdv4FnSdKFA0X2XL9azw4YRxOuYHeXnnckHMV1c/GkrBpF4IkUTdxIGV9wZRQi88PTchoDktwODSmZUXF/xczR2cHolRo4CzRbEYd1AN9XgmKKJLvCsJP1HIkRY8OIuqPBipSLXTxahuGS4LIO0nfUuj2AWRkVcGNjIiIiNCmNDjl+FRmJ/7BNebCVnDkYEM9Jy7tyv6xb3o+D9dZqbqxEet3bdm1SwdacAyqx3dZ278pDQ1IO44To++GvrQammnbL8b+Ea3EvlKQ2l+9/P/vy2hjkp8vRIbBmbJWWn6Sny+m1QYOH0hk2djFDDRJ2BQnIx68H5+M2pY+/rMmCIhms0dii7Bgii8PZPydm9k2awCGwkqy7okkdk0T4rZDF319gk5H0YpUnE6JpJmFF5RhF7um8djqb3hh8tQOeQREsxnV5UYwGYnZ6KbcbsUxydAKUi0FBWIbkIBxbQsjsdijE4Uj/XH0sCHke+GOdJAy40i7EUX11EEEfnuQk4u6o6+SMBcLiG6V0E8Pto4EBIGixwZxfNYSz0dHnHYmL3mQplAFQRZQ9CpfjFtCviuYBRlX4mVw4XtTBYKvDyeeiEBXI5HwaNt25oq7BvH1E29wf85NJHhX8k7EDvrum4S4zp9fn3yDV8svxSw5eS64hfb8rF5AltvKgaY4Zvvl0mnr7fhstFDZW+aFK36gt7HQAxjy/JbciEmQ2GL3a1ZR1qxMbuSky9IGTbioOo7fhia2aW+XQkMQBOHCST9B0EqMBn2rBq5/tAyZ2h5z7r9posmE2DXtb++L2rOmAck8u+YrTk9tzdYq19RS+k4CqR9Vc9wRyX6HkyNOCb89ZxBrGrTk3Lm/r6qeDH7R1DQWrP2UiXf/zgTfA+SNN+IuOE38ozv/lgMALe9gqzfy66AluDvHXvB4e6SVKtmKZOs4w1zwQE9MG/wpuqcbtS4vvkj4mWHrT6IMPQe15nRhqG59jsKR/tw4aTOKLJD0Zjapd5/wOABdXEyrhGVjhIDaNYnJA3cS97ONhliFsO8y22wFRC8vbrh5i+fftUoTSTqRz2YuIvuW98m69T3COpXx2IP38Np7N1Ob7c8jietRUmJQ7Q50NRKKUcU5sh/K0F6IPTtz+tHBSEGB1Mdre/P1aWtZErmLYtlGY5OB2mSVWkXl9bCDHgdQ5G5gUt7lpC6/j9um3M9LN07m/eNDkAQRV5OegGM2Oj2bz0dzr2OTrWX741JlitwNeIsGrKIJb9HOfofTAzsOkdo6AIA7fE9SNzSpzedyaVmHDkAXHeVpnRd0ejIfTyPn7bAWSPZ57B/hBDoy9ZKeVN456G99J//h3mRN8/Og2f62nQPL9CqoxSK4iZ2Qi9ijUysAjaCCKgnUyma8RRfLqwailFeSfWcUXTbWUH53+7JpUcuyuWbD/ax490omv/sgEVuVtlsIOsbp/9XSHi5i+Jb70dX8Zc/ZDrzUfLyYxQWX4/bRRp5oMrVwCDRXER6//VsejP6VwGMu6ka56bV8Ltsrk6iNbxmtqiwjNrVe4SNf382ea+JIvT8fubzcM6HFrmmcWuhNU/8Wzgb/TBkh4xRrT3VBX1RJwip7y6rXHDXlvTSIgi/jucl3H6sbzXR9+16ufnQu2+wW+jQnWVc3mlE/C8F6rJyoH4uI/l3m8aW3Yw/xIu/eZJKePchlg46z9uPF9F50EK+3yzk4+x0y3ozBFaxtPXJcDcSvvour33uYWzrtZ+tNb5Co/2tJT8LP0ETEdhnZJHFqtC9zum4CYMfwt/jXsk8o/jAA2SiytlTbj8uqQqev7+OOcXfT5ZvZOFQXs9++l0nLH2jDMPRXq1Hc1EedM35FSUsan2dhU/ytiK5mpmaXk5QXT5AwNbNNY1l79o/YDnTELCT5+KDGRHiabC5kusgIagZH47PumLbyCgL2a/thWru/zUQTjEYEna5VjV1KTaL0DRHH9iCi3tyPoNNRN7obtsk1PJC6ia+nXA17NAUh59V9MR8vpnZgFKX9RNy+Mp1fPEPem37sGfgxDxcP49gL3fH6qS3RsqA3aNLiHbV7CgKnnh1E3Opa1AMnOhYOjY9FralDaWhEdTm1/gEF8m8IwNq3goAXTLDrSKvj/7XpO+Zn34hhxClqbhtETRpYulWREljOnWFbGO4lc132COxjnZ42XkFvQHW7tOtonqTtEpmcHaTNz1o0mXCuCcHfaKP62ViNELX52asuN/LQHhiOFbYkwQZ2J28OJL7souhZgaMDlpPnauDmpx/C/3MtpM9c2pdDV7/D+9XdGWo5SaYzlGf3jKXTU+WeaoYuIQ53kDfsPUbJ/YPY8/BbfFUXzaKTVzA0KoetpxOI9qvhy8QfGHdiMtaJNWQviWHTJYuZkTWRKEsND4RtIEKSNfLRvXdxSVQuT4T9jlkQCGpWJ3KpcnNbsWaHHA4PytClylwx617Mq3ajCwvluj8Os+D4lVwSncdH0dsBrb3YLLT0G2xskvizIY1lx/pjttiJnJinOVNRour2/gR/f7zjff5fnn171tF24KISg4Ig5AP1aOk7t6qqfQVBCAC+BeKAfOAmVVWrm49/DJjRfPz9qqr+ejG/81eT6+rg2MUlN0STieIxsdQNsWP9oXkVslqp7Kwjcm07x8dG4Q6yeqS6dGGh2OL8MH8i0TS5jlOP9iF6gw3ril14rzLwwivXkdJQ5UnWGX7bj1tVsawsIXmjDxj0lI5N5Ms+b2EVTSyJ3EXXqYlE/tT2t8/NsLeaYJ4DVBK+KqGuezCGwD4dtvHmTYok7ns9anU1gk5H1m3+LJnwMZd72bk5ZyT2Su9WyUWltJzlVQOx6J24BAFFB9ZT8MuUTzyyW8XuBhbErmJm8kxolkzzXK8oUXdzP4LvyefkmVCSH69pDXNuHoDlMwdhKVFoChRxNdSgvhKMcU+mJ5l51nlIfxxodX1ik4tP+y9j7ks3E2tpIPXTmQQfVAj4aT8qzXoHTRLDXplHbYrMXRMO83DWENJeqMVd2AJ8cufmo0T3Qp8Yh6qDY06VnqYCZqdt5tVfx3LghjexCkYeLrmUujXhePvKRH2q55p9DxO5oZqSeh1TRz0IAjh9IPaHMrZM6sUHd7bOLTxX3pP7AnYS3lwG7IjK3F1WwWtHrmL9wCWt8AQuVeK6z+bQbUQGKxI20s1Qh977OLdfupvDzjA+tA4Aux3J14fgbWXIDecBhZ1n8l/I/s524HJVVXue40nOCpImAxub/81fBElHAksEQTh/bP5vdEZJPj5InVNwjOoHG6Mw/upL2m2tIwbRaiFqU327D0ioa0CVWm7fFR/GqUkKZ4YIuLO9SfjiNNJBrUSnupwkztuFfOKckl3zpNXFROJOi8HZKYqQH7OZ/dj9xK+7g5cqUmF3a3rx9ky0WtrdushZufhsycbreMe6LcYaqOzfjAnolkr4DoUSty95bjsmyY0tuTUNuBgSxI3+e5kSsQNdfCxBXx8k9LuT3J59I0mbb6fI3cBRpz+5bl/ExrZRSuOEvnz+ygJWJ68n/bJPSX82EF1821xE2PfZ6Gwyrz72IT/2+oghC3bRcGXnC75nR7CZrgYHe3p9xx2RW0n6+AzWvAbEeE3LRkhLYuSAw7isoAu2M/qhB7HeXIWcmdPmHdfGmRjwQwY75i6kj9FAH6OBGT5FfDjmI6yCEUkQuSNwG789/DpF46MwHTxF5IdHUUWR9AfCqL/EhqVYJnZVOYgCg65uqyH5QshRwnVWXKrM5cfHkfjNPR6eAr0g0e3xwzRePwDbuL4E/Ghm2j1zW4GCUvUOAo/J1N8XQq8X72XQ9/N44OhEyhUjy0sH4EqLRkpNompZIPN++RFHczs6aMnpv5aJ/137T3IC49BESmn+7/hzPv9GVVWHqqp5QDZwXl1xJUn/t25I0OlouCKNskGBmMqbyMyMIPenRMofjyP5dQcosgZSMegR81omkeTjgy4sFEGnw11Wgb6kFl1CHADOAAPJi10kzdtN/KM7ceedQgwJuuDAVaxmapPMCKrKqTuSqY8WSfxK4c8Z/Yl8ffcF70WurkZ1uxFNpjbNU3JFZQu0t7lN+VwLeW83gT8dR+qUTOZ0K9W3NVDs8ueEM5TXon/G7fWX1ysIiILC9ZZqiq8OR7HbkaurOb06jvgPBGyqwFVmF4ebYlELi9tca2O4hElQPW27Xw75GFe4X5vjVJsNr325fFo6hB32WPZWxeKzt+iCTE/GkkayXdo9jjHXcfraSARZhTKN8qsp1ptjL3TH2bMBUVLwW5/eITlrbXMH8xa7dn2XHx9H8g8zGWRs8pTwOhnMTM+5gYgNVTi6xVD4eQzXLf+DiZftYPOl76K/pwQ1rxDVoOPP7CQP+/F2u8LrVYkejYPjTjeNyyJIXt7AGbklb7QkchdvvfEOi998C0EF4/p9PPH0naR+MpPxWVfjL5mpv60OsbSKkMU7SHpwNyETsnhw/iwOFUUy9oM/KL8kGJYHsb62O3Z/yZNglWvrUJrOn1sA2tWr/KtdrBNQgd8EQdgvCMJdzZ/91wRJ7RXOv6UzqLrdeP24h8ClO1H3HSNl5h7CF+5A3HLQQ2slenuTNyUaZ1dtpdJFRmBdp+fOrduRB3ZF7JZC0BcVFI3VNPtMpU1I9faWFT4slKy7IhHPIfQ8S3Bx1qTgYJI/y+HPV94m/26VKZN+59Ccd3n6k0/A3X7CryMTI8Jw9Izv+O8WM6LpL+GmIqOqKu4ACzdeuptjA5cx2fcgZW4fhn/5ED6HW/PUyYWnuXnTTO18zRG+Lj6WkVN24PWvYiKaI5L0xnAPCu+sCTodNV3d3H7HHIYcvpkCdwP3HL4VcY/mhLI+7406uAe66ChKpvXAnRLN9hNJXGsp4qeUnymYdOHqBXmF3PDLLG7KHU6B28YP818jf6wfck0triv7YHywGO9DxUzutI9bUve3IlsFWsGwQ/fJ/PjBMOYtm06t0kSTS4/fCRGb6iLP1cCwY+Pp+8xM1GkGlGMnaYzQ823vjxlmzmK4z3H8RB0fpyxD7ZKIcjidtPln+PDMZRS4G5i1YBZ/DIlh4W0TyXTZ6Wk08vGzb9L348PE6lpjHeZm3syUBQ/id1gjXAnclI//SZUjh+MA+LzHZ5x8I4LS2YMRrVbqJvZDZ1dIml3MO0eGEXSgFt+vdnH86iB88pqoXh6gjUNVvaBT1cXFcOrxNimAtsdd+M0AcImqqmcEQQgBfhcE4XyZuosWJAU+BPA1hKqq+/ycaJKfLwQFoJw6fVFNFHJqNKIT9LtOoOoNKP4+HNzmzxFHKgknMyi4I5Uvoz5mfKFW/lP3HWu1P3WXlJLwdCXKOXVuMSgA1en07Glt/eOYGriYzivuJ/CQwMdFV/LIrVl009tw+Zv+FhLLEROAPUBHRzo5HbYhu1wUDTOzPGg7oPXFj7dm8XKoC/VM63KS6nYT/ruO9wfFErKtXAPV+Ji5L3AbMaFWNKlJGB1wmI8ihrfa76tuN4nfujEUVmOaVMXQBXMI2K3HPqIXRbe5eKf/ckyXupi/8G5C3t2BY1Q/dBU6fEUvbIoTuWNwXcs9NjaSOvcwB5/rzZoxXXhz00hSfq4FvQGfp4ro4VfEXnsky1cPRUlsIrGquZNQp0NMjCP9YT9S7jwIioz5VCPDnzuISXTxR1Mw5ZXeJO9v4Pv6FN76dhxxrx7AaM/n7NsNOFjNcWcYKfoy7vh9Bl6ndTgCFcSbBELjBuCzKZOcqjCGHZ6Hly+kv5gMRoVASRva3Q0muoccBVpXdVZ2/oqrV87HEe6D8bQPOW8F81qvL/iiZDAARkFmzaWLCR6qcmnwfBZN+oTexioGrJlL4oduhGItIpPLy9HrJGp3JODTkMuFTPT2pnhkJJaiCyf+L2qcqqp6pvm/ZYIgrEIL7/97gqSyjGNUP40mqwPvlvNBLB/0/Yppv9+B31EdvrkujL+cA1YxmVCcLs/qa4vwwnBJJcqrdhpuHEDo7FySRhag2GzIaFp5Z2QJc3HHjUBtgC52B4Kkobx14WEUp+q55esHSPnXQRSHA+/Le1M0UdsTiq6/h32QthzE+m9WapKuysVXNJHnaiBeb2V6zg1YsgztSov5/nyUT3yuJSijJclVr0isbjR7GHA6G0pxxAUihvt5EqcAxvTTFE9IoPYSPx7otYF3q0eSOCWXmm9Teevdm8m50YsAmwqiRGVXPS9etwyAHn/eRfKHWSg6HWJ8DEp+UceOXJaJ3uRi1aYRpG4/RtV13Sm/oxd3+G9m+fLhRJbuIO6pMhAl1OZ3XfhQf6JHnEJfZkby9UGurkZs5gb84MAQEj9RSdmbgWKz8d2skSRk5ON2upCCAqm5MpnaBJHYHyv4ZPJYCq725oprjxAzqIrvlw0j+dosjslJeP/iIPL2MyhJEnO++Y6RZge1ShNFbpES2U53Q+uC/5928BPtdDdYeHzeMkyik1kbpvBu7y8YbbYzNnEDsqpwwBHNCHMBQZKFf01cjklwYRRETo5dTJeG+0k50jIG5Yoq4j/TtSKD7ciU+npCPt57URH2xUiTWwRB8D77/8BVwDH+i4Kk9ggvFENbKOS5ZtppZfq6OzGW6uCqKozVLZGDLioSuXcquogWYIR3ehXCWm2PLRsFuvmeabW/j3nzALd8+CAlAy2aMEjnlAvKQbuLS1omlk5H5G9VJDyzH8VuR/Ty4tQMha/renDZ6nm4rLpWQA3Jz/f8+YW/4wDOyQ0odjtHT8TgUF2sadBIO5clrWwh/mzndxpiNTER15V9yHzIhIjKo1/c7lHTideZKO9uoqK7lqcRzWaqpg+iemg8xrFlTOm2m56mAq4fvou9RxKJXHsGYecR9HUiLz7xMVKnJFQRJliqKJMbWdx/GcU3JSMFB5F5TwjOy7t3yJCjut0oeoEhr+8k471UGqIEOr1cxLYbu+GTrzlWXVQkUoAfurgYdOFhBB1zc1lQNjsueY/iyZ2Q/P3JnxBErLGCtFfqMeSWI0SGIeh06Dbtx32mmKJHB+D4xsItT/3CmrtfI+HzU5T1tWIPkVkas43VBV2J+baQY9uT8MsEJAm5roHM+w2MNGvP1lf0oovBy+MAPqyN8ACBLjPh+XxPYwKNipFvr17cCjG43ynz7P4xzC8aBcBN1lqCpUZ+aYxgm91E9wHZmlKVTocUHKwxTv2NbkLV7b6ocXUxOYFQYJsgCIfRJvNaVVXX898UJBVVTBXO8xIq2gNVAg6LxD63m9Abcj31b9Fkwl10Gl1NE3J4S2JNTs8i6ANttQtYk86vrw+heHoPpOBgmsb1R3W7ifs0Fy6rRuyaTPocX1zdE9r9bdFsbnNtpyfEUPeGE0RtYis2GxQb+eXBy0lZ2kDhCImMeXGe7LnqusALESXsY/pfmFlJEJCCW2f9E76T2e2wMNv/FC5V5pas6zGld8CcLAhMvPZPrvtiI11fOULOFZ8SrRMZPWEnZlFLIukFCdkL/LKbsQcJMXzx9AI2LHibsoxg9lwdzdwF9xBlqOa5K1aSM1XLqyR8WcwDByfiCLVi62JnaW0MYx+dh0V00Bit9UoIskDp3U009O04R9AYqkNEJffKT1h1z+tUDotGLTyD79rj1E4eSNefT5P9biR1vcJxf6Xj4Te/4Fqfw9QqKjPv+5FLt5zBHqKw7O7RyCezyZ0Ww9ifdlN7o7Y/lkKCSb46h42dVzPb/xTxeivvRu5myUPv8uW17/FCRRqhD7pxxAfj260Svwwbrr7JFDw5gJm9t1AmN3oc5rk2w6eoXUqx2YHbeHT9RCZ/d3+rRqJtjanEhFRxrDzc81kXgxfPH7uG9bXdeThqPfVDkhDjomm4pON80X9q/xiwUPzsp/AudLcLroHmRhm7ozW4JzgYVEVDmwkCgk7fYZgpBQVS/lkgzg1BPHHvMt6fdSPGnRlY1hlpmBsGRzuWfZKCgz3yWFJoCILBQNH1MSyc/QGv3zARsdEO1XXgcCDX11Nz60AaogS6XXuSzK9SCdtQqhF2nsd0cTFc98seVkwd4QEkXbQJArYJ/flw4ZuUylbu/uZu4p7oWGqs4OnBrJvxGgBrGrpwqD6aNyJ/8zSxyKpCz0WziHh9h6YYFBRI8uoyFoXv44jTzrLqgYzyOUKdYmLOzonEfSW2wjK4ruxD3o0i5nw9ccsLOTk3kuhfZc9276xGw/ns1L8GcfKO9wCtX+Chm+6EPUepuHsQO55+m07fzyb1sSOcfKMrmePeI89tZ9SqeRDoIDq0GsvdKu68U0ihIYzalMEM3yyueOR+fJftIvP9/mSPeR9JENncJPJl+WAeD19Pot7KSxWpLN1wOYICD4z6hRm+WfT48y5Gpx5jd1kslYdCiNzs4tS1EtvHLWC3Iwy7omeid8d9G7VKE5e9Po+6Hk4OXfVOK9WiYncDWW4rYVIj+S4/ehtrUACjIGIWDHT5YhbxT+xBFxp8Uei/jkwXHcX6gkX/3N4BANGlcuZmJ1KXVKTQEC08T9ZWZsnHB7myqsUBiBJCv26c/igIe884pM4p2Cb0R5DOczuygmNTEKG7G3lxyWRUQds35X2ZTFO4ucNBKeh0CDrJo4935qYkTr4UTF1nF/d8dxdifrP2gapo4CZVJeCHwzSl2dmXH0vY1yeQc9on4Wh1eadL+O62KxGP/T2eOgBUFfOqPVy79T6GeSksvPlTGm/omJAk4fNCyhUjq+q7s/6qLpy5QmHM8Vs9f5cEkZG37EQKCkR1OHCfPsOOd/rxp10LcV8NPcQwL4W5P08hecqBNmAm/Yb9dH6phNj302nsHEZgciVe2062iKVehDBG3E919NhzCy5VZm72TYhHNHluW6iADonlYxaT8X4anZ7OYe6ZwQSLAqJDIGnKIUxjilFKy9HFRnPyqXhm+GaxtDYZ/+N16GKjmX/ZL81MwQrzXrubo+91Y6892sMPcN2w3WRPep/Z/qcwiwayhn1GnKmCgJku4h/fhewlkrTczuDf5rDwoUl8Mm2sp3x4+fFxHqzAWfMVvdj+0ELyRn7cRrYsXGflMhOMW/oQr98+mTHHphAiWXCpCqsaAwjpVaopUP0HDgA47zbiH+EEBJ2OkK+OkPi2QuGoQBoGxlE2OJCTT/iT/eZAmgantjpeslrwefMMT3dei6oTSZ/tx8An96B2SdTovtsxubqa8IU7EHYeJmzRDgy/agM36MOdmtTX2XP7+Hjou0HbV519AWL3ND588C1yhn9K3rUfoZhU5Opq5KzcVh1fqtOJ7owRxSWiJMcgGi6cGlddTtR9x9pN5l2UqSqxn4vsdzgZbbZzemQHOzBBAEXFphiJNVSgWrxQbDZsP4Z6QlVZVXggaCtl41M8z8Fc6qavQYuynizrRuKKe4hd70YKCmw57znmPlXI6amdWLTkXer3BqM0tJ4YF7ydAyewLvdlXOYY3G+FaXkXs5nLxxxAEkRCpSZUp4ja0Eje+EAue3s+anQTotWKFOBPwZfxPLRpLb7RtayxBTPNN4MRX+2i7+pc7vBticr0DSqCAt5SE4O+n8e267uw8aOB5LlaX+/VlhM0dAtDHdSdgLmnqEkx0+mhHCy5dVQ+1sTlXg38aYfGZRGtsAJnzSqen+J95fQ3ePaLT1jXVdMrDJIsJOrLqV9/4Qag/9T+EXwCLj8jgqDh3CN2aQAHq9WCbOiEf6YD486TtMq163XcHLKXdHsEpqJ6Or0tc6IuHLX4JG5VQeyehnoi28Pn57qyF6a8qguG5ACuHolkT9KTOsfQZsUqHeRHZ73MHgd8WzWAPn2zaGyWLhP6dMEWZcF6vBw5O4/4x5rDcZ0OtUcqnKcH4GJN0Om0bG8H59FvPMCt+2YwOXUvwVvbcTyCgGNUX4SyJu7ZP5mE4Eoy7w4m+dkyRBcozec9Ldt4sWQETz/yOQ/HTCXxg1OcfRKvViaz//ZuJB8/gOuybh5aa8fIvliOnG7FW9jYr4mTzjCiNjb9/XtXVawrdqGsMmByN+szWCx0t5wgx9XAiJ/nobOJ1I/ugaDCLbdt5OPNw1AaGhAMeiYkHmFtbQ8aTvoT060Kq2jiwYCz7197NpIg0uOBw+z4vhdzV0wjYV0T6plSfPIDGXfwTo70/5pDDgcfV1xGhdNCj2cO8mToZgJFL5J734ulJJE73l5FpK6a/q8/QMg+G5X32D0NTuearCqclm3EtMM0BBpwqUJuxEf0YtbpASyI2EaszkVD3wsDgoCL6h3o8Kt/+xv/BywlohRXWssKrrqcyNXVhCzZgX7D/rZEmorKuqruLN1/CerJbOT0LG3wKdoEaUjyJff5fpq6r9XChDd/p+D6sIuCJ4tbD5Jy7952Q9aGWK3Da37mTRy/uzO1Di9Uh7Y6Zs01MPxf2+AvA0D09iZ6ST75zw9sN+kn6A00Xj+gXfhtm2sLDNDEPDrqJlNVYl+DX58Z6mm4Odd0YaGU99CTcacXQ+OyWZywgg/Gf0TF1+GEbCnhmdKhAMTorHwQtZOxFhsLJn1KxfBYRFll6KFbWfHulSiHTiBaLSS9fALVzxuhXzdEue0kNx734tlvbsGQXfy3Ia6CTtfMbeD0OBC5ooLX9l1NvaInNrWE725cROOUWs5c56SvOVeTB1RVXF1j+eb3Szk21ErEVplUfcfbjz7ep4gfnYtv90oKRnmR+Xw3zDsziXxS5bKjE7j5qznk3h7HgS2p3BG0lRDJoiX//J2U393EZO9KPiwZSsTSo6h6kVWXankMm+Jk5MnRLKqOI8fVQNcPZzH1zjl8U98+76BLldlmD0VBZXNhErWKkyDJgmJv+64lP1/ky3u3opETvUz/tpLxP8IJVMomVP35L0Xy86X0/sGarHV1NcUjJdJmHkd1uxF0OmzXDfCsSt7HKwnZr2A9WYXqdvNpzkCCD7nOuxoJegPu4X1wXdkHwdA+1DLxhSMM2zqb08dDqU+w0smvBCVKA0paDnixfPXQVnLdoME7t+QlsnHK61SNSvFw7p01MSGGt954hzOL2pe4PtfqL4ln8qqN2MZ1jAJT9x7FvKp9uHL51fFYi1RG9z7C25F/Eq+3MtxL5pLwXCirZM+iPjhUrb5eqzQhqwqjzXamPLqGkrvs+L/kRehKTXW46ppU6l0mKClH3XsU/W/72rAXx31ZQMKbxym/Op6iWT0veH/nWuVt/ai7tvtfbk5FLDOw3x5L0+fhTH9tDqE35JJ02yEeS59AyE7NyYsOmYQfm1Dq6zFvOsaE9EnMKe7bikIctNX5tbVjeTV2JTt6fY2ig9h1LggJouSyAHgvmITva8i9OYBxI3dxwB7DN/X+vFCRhtWniZV9PgTg0chfOLkwjfs//IbuBg2v0X/PNMTrGvh9eCqvlY7AHunCa08OT6+a2OY6ADJdTkaba9ELEvsHfEa5LPJSRSrRa9rOC9XuoOBqI/02nPHQjSmNjf82W/c/wgnU5nuTe4MeXVRkh2VCOS2WfpMPIzR7O7mmtqVn3dub2njJM8HkjGws3+9GzshGsdkIuS7HkwM416TAAA+2WvT15tR0BZ+nipCCg9r+/uW9cfVPRVUgeVk9vltyMYtOGhK08C78zZ3EPdNOZUORiXlfu673n3+LU0+0bqNQzEaeyh9Pbb7fBZ9TZWeJJ/+4Hsuagxc89q+mCwulLgH0NoVr/Q95KLTX2kwceqoXgtmLqtFa6FmrNLHN7k+dYifT1ciN3ic5OugLhr2/CzVcywE0RorsPJaEXNVBVlyUQBSQU2J48+nFNCR3LOzZnlX1UEl58DjVUwfhurIPkr8/uoQ4wnaqvPTLeAK3FBH8wR5PpBD8tJ6ANRqCUDyeh65M6z5VbDYsU+1kDPPipdPXANrkf7Uymb0OlbCuZZQrZj6sjSPxmzoMW4+R9aw3u554i5cXvo/9DRvHZyzm9bCDrCjui59kY7LvPvSSzNIqjRq9u8HE/EvW88hXt5PpamRFXS9EUcHZM5HG3jG8GL6BTVe/ScW4NIIOqUxbMJdBh69vpU3QxeDlaUvWITHp0HQ+XX8Flvy2uRTFbifuJxv9LTl0mXXsovoDzmf/iBKhrzFUDXj9YVRJJW1JFXJ6VusDmltYrWcciFuP/Edtk+ees+DbzrjyraS+XQiKQt60OG6+cTNbHxiIobwRVSeinsih8dpeqHeXY1gYgLG0UaMPEwQqZwwkeE81ypHz8x2IFgtdt9p4PewgCRumkzzlQKu/C0YjKCqil4mmwakYNxxsl5ZL9PZGtTsumnv+r9fgHJhGz9cOsiD8AIMOX09pmS9xy0RMB0+R/nw82WPep1i2saKuu2f/bFOcHvzA6kYz7196KXJpGVKXVLKmBhCxVW6VWD1ruthoTjwZRuenCsiflkjMuuq2tGvnsbyve5A59HOOO5v4taELX+f3paLEh87Pl9LQLRzHvVVUnAgibq0TacthUBVPpFc7eSD95+7n6BM9Wjn/6rXJ7On1Hc+Ud2Hv9SnYkoNwm0W8c+oRCktQGhopmtuH7bMWeLL4Oa4G1jV0IdlYwnAvm4dDcL3NyKt5I/mji4aR677nFiKfgauX72Sqzwn8JTPv10Ty5tHhZAz5gjnFfTn8RC/cXiLWnFrs4Va6vHCU18K3ep7vWatVmrhswTyivs7BXVbRws/QNQ1niAVTZoknD1XR24fAj3ddVM7lH00v5vI1ELIHBH8nlf3arsKSvy8NkSK63eltHYAota+5dlZEsx0TTdr+KeptHddesZfchf5M/2Mbe+5ZyJzA/Tz3ycek3+9D0dX+iL7eWLNqsT7mhX7DwZaBrKqEfH8CstvKef3VlMZGvt/XF1lV8Epve02qQ5vYqiyja2o7+T3nqa//txzA2WvQbdzPlvcG0KDYaVoXStq8U3gdLaLuS28yx7yHJIhE6aznJNDwDFCb4uSBjbcil1eii4uh4HkdhsQ63PdVoA7q0UYjIWN2JD7BDaguF1Gv7PxbDkAXHcX8nr8zNmskt74xj8WHhrKi2yf4HDMgny7GtGYPAfME/I8LlPYzIfn6IJ4dA4JA0421vB2xlzELNqEO7uE5r7w6iM6L72XDi0OQc/KxHD2D9y9HUdM1GvWiuX3YfN/rrcp4I795iF8uS+S1e29jj0OgTNaqNzO33Ib6Zgh7HC5cqoyy0x+h0c63BX3waa4E3ON3mhOXfka608bBZ3pjWL8X86rd5Ezy5+el73J/yCbmnhnqQRnudzj5oi4Iq2Bk84Nv0OOXYpwjWmjdcib78/lnb1E6ShNpVfcfJ/Cjnf9xwvkf4QQ6hZfhM6MI320mBKXtDcmVVUS8saMNBx1A3cR+ZL7Wq81eW/L14czdvdscD5D/SG8qbu6OuPUgRx/ugZfRqanOovLwmSu4xCRySbcsDDUqpz8OIXuKv/akFBl1cI+WbUdN7UWV9HTRUXgFNKGg4l2gIFos7YZwSmMj4paD0CMV+5jzdl+f10RvbyruGtRuItSrUhtwb89dwplbUsm/PYEt3b5vxZBzts498uRorsm4hs/qQjjo1JHySRMoMkp5JY4sH44M/JI/u31P+aMOTo9s3Ssh+7tpzPZFrqxCFxHeYem2PWvoEcHN3jkUfpeA6FKZ02sTs/NuJHzxPk+EJHubCP7xJBGv7UCurvaMDXVQd3SSzLL6QO7yO8Hdn63SBFGBoA92Ev3iDqwrdlFx50Bmb95AxYooiu/pg9ijE+MnbqXqnNuQVQWvUgG5sgqvo0U0KkbMosQPDT6E/67DZRE5ZI/l5pyRxCzNQA6wYl8bylGniwbFjqwqvFGVyrQnH8SSU03mh/3If3EQukaBesWNhMpwvxMelKGEypvv3kSX7VNZ2ZDAcyEHqYttqfIEHFMZsuZBQn7MvuhneTH2j3ACVYqeM7/EELapjMquwt8iGfE7UkPiSkebRgm5roHIr/5CMmIyIfTqwqybf8ZtEhD0BopnOni601r2OlSsgpEnwn6nWrZR5TATeNTGyl4f4d+lAvWgdi5bhAnxnJbVi7GTD0ZxYvBXiM0NlmpaHFJQc6VAlNo4MA5n4PXLAf5dc/ZPwRbefi+G2yTgUhVMggtVB7ZEZ4fquVVfxKBcVcEbn97AbX/eiZiejzysN9lPd2f1zQuQBJFi2UZ9th/eJ1uXJDstrCdhZRNS5xTsn+vIvvPCQprnmqKq1Mcp9J5+hKutJ6h9K6YlChIE8sdZ21RwpOBgii+14P2+LxuqO2MVTVxvraPXg4daCXNKXVIJPGqjRjazt/cK5s1cwbCv9nGT314ezLuh1TmFy6qRgoNxx4Zwz7rpjJ35AEvHXIX3ir3UTGzgLt8zmCQ3SnwEuddbMZfKPDzpLq6ZdT8vV3bmHv8jlA1UyZ4SxMnRS7h/whrsiQ5+tSWQqLdyk7WFD6Gn0YjUpBI78QTvvnsdI45fT+gfLZ2gvst2kXLvnnY1Cf4T+2c4gQwz0R8fRzXpSX4jEyklse3E6MCqe/iRc52x7YBvhvmea7lP9SL8vQKm+eTQOKSR+gm9aaox8dQHU/igbBiSIBKjs3JbzvUIU0R06fk8UjCexu0aPBnQEo5/oYJuGte/w4YY0WRC8XPxamUyAw9OpKKngGLQoQZojkTy90WKimj1nXMlyc41wWhEGdqL4nmDz/tMjHuziH/reLt/8/8lnber+vJt1QAC0l10SWrbYxAkeXFn4SUEHK5DdTmJfHUHqbMzyXm8K/M/XkbWbe95ePGeOTOSlOfTiXirdV5APp6B6JQ5ea8/N0fuI3TvxedxjJUO+mycTeqCfPIbArh63Vysv54DpVZVEhdmtIrCxB6dOLkgGsuwMhoidJx8qwuT8jQa+9fCN9MwKA7R2xvXVX2pW+BCl1nIu4/fxJ922FWfxCl7IN0NJpYkfOc5pySIfNnzU9Kfj6ch2ovUhw9j+nkPckY2gl6Hrc7EtZmjNAl1t0LKm7l4r9qPPcRIWS8dekHGV/Qi97oPSJ+yGKOg5w7fXHKvWsrtPlrTrUuVW4uXCoAiE76pHOPTPshZuaiDelw08ey/Y/8IJ6C63KCoFIwOQK6oRMkvbHcStGeCCr5Z4gUpxgW9AX2nOgyiG0kQSL/sUyp6CKTcuZ+IN3ZwcFk39jucFLgbyK0IRC4uQa6ppelGCa8yFedVfVqdTxd9zsomgOpjbTfEFyxm0t5s5M9RKQTfWop3SjWiS0bN12CccmX7MuFnTTSZKHh2MPkvDqL6pt5k36In6ufz887LdXUdMu7INbWsWjqM03Y/XBaRvEotInGpMr/Z9GS6GrmncChnxnujHmxxJM7+KXw98S1PB91Za5QNWrJSUdElxLXqllT3HSN51m6WPXwtXsVNFy073xRuIu2hAtzFJejG15A6+0CbbZdcWYVgNOIY1Q/l0p6INQ0kfApNv4VQ2VumdCAMD9DyEF6CAePsYrpuaWDZx4sYF3UYwcsLy8o9PD91Ght/7cWGnBSmFQxh9DsP82RZN1zNPW/dDSZOjllMVZeW8SXoDaguN7ErRGreiSH1sUrsYWYKbk1E6d+FNxYt5vgd73Kf/1HuLhrEfocWbf3YaKXTylmeHABozVrdDSYcqosCdwOqBIgSGXcHohglEASyZugR4v5eJPV37B+BGARt4MZ+V4wMiElx2md/qbn/1QSdDt8V+/BRVBxX96a0v56EL063Jr8EpJRE0ucEsrffm5xwWTzlsUnXbmHP0hTcufmEvruT+0ZNYk23zzGv9/Y4IXdJKSFbLLiDvTVH05yYbOgRgVdxiYfliKBAGsb2wvJD6xq9Eh/BpZ/sY+td/REPVcLvAVR2VwnQJbZiAu7IFIcDr96VpAWWUf11KH5fZSD/h4mgiF/LOEkaRrNKoNXGWpuJ+V9MJ355MUgSQm097pLWWPVT1+jbRcJFmmqoM3kjxUQybOUhNpalIV1HixMSBCy5tYz9bht6QebbO0YibD/U4bXpIiOY8dIqnvtjPL4nktHZVIJ3V4OXHnVv68Yq0duKcX4xZ36NISDdQMNdNZh1tazv8jl6QaBKAZdqYlzmGBo/imTEC+vxFQ0sz+mH69YAQvaFYdx5krhtNnRxMZwJjiNi70527+lL6Zc7MQkC3qKB58t7Y6gB9HokkxHBywu5vILCqyRO3PQOfXbfjtPh5N7uv/O+zygUVaRMthEkefFu5DYcqsKi6jhu8D7G8tFLkNqh3Oy08W7iPxcI3rIXMcAPQ2QjuloZRVVJm5uB/Ddh13/H/jFOAEDOzgOgsk8A6s0VBE32Ra6pbVdGW/T2pvDzGLqHnuFoWThTkjdxjfUY470eJOGxglbbg6r+wWSPW4IkWLjsnOc/M2AvOcuCqBil/Y55oS8bFkdReYkTr8oBHtCNnJWLrtQHrBYP5bNpzZ5WdEn5M1OJW1nZRjpMOJbNsu+vQBoGhl69CHl3R+v76NmZyh6+WEpc7WIZUFVCntFRRQTqiZYmHKlTMmdGBBOxvlQj2vwbJjQ2MeDWg1Q7zdwcspfHF08n5s0dba5dCgpErqoBRSZ0N/wwxofrra3Zn/tZ8zjaaSy62ibWFXdFVsQWdiRBoPDxQTj9VDqbThMpNfBBshf+2zu+tqrLYrjF+yduH/chjNP6FL4e1hfFKZGqdoVDJ1uSgxWViGPMRAnVSGt9WJ20Ej0SZtGKrCr4iiqf1kWjzvHFR9/AI8evpz7Lj9QF+dRe4kt9jAF9QyJSnV1jYWpePBrDjQRLRgbuuw3Ll774HK3EfR2cfK0To/oeIXdmNGpxCYKiSZbbGoxEhVbz0VfXkPhpDs+tmIw9yhvfxwr4MflXbKqTCpc3wZKRKF1rByCrCh/VRuO934Ru4w6k6ChcUYH4/GzGEeJCTwurlKDTXXSE/HfsH7EdwOKFLrJlX+z/7QGCZ2ghbdW0QWS90QKPPBtqKg0NyId9uT98Ay6XxKoXRnD3/DkkPrm3TX4gcNsZvmnQzvFZXQirG818U+/PNf+az5HlXT1cdYbNh3lq/zhOXvU+A5/a00o1R66ra8X5LugNNNw0EOfIfkh+vtjDtBBfFx1F/cQW0UjFbifmXzuI+egkoZ+2BvkIOh2Nrzax9aW3KbyqY3+sHjyuheaK7Ll/wWbHL8eFcqrj7jBdeBi6uBiKHhtM/cSBHg4+95kS8uam0t3nNKsqehO16pxziBK6qEjqJg2k4toUT/OTV7mTxw+ObwN7vd5aQfUTTeB0YbymGO8ZTuS6ZnYlo5Hu15wk8k+Zl8dOZNbYOwn8ruPoRxcXQ+mlKkZBzzvVsVTLNrwlO6lRpXw87FOmL/+Zmol9W0GQFZsNpbGRE0djqJJlzKKBjU0SSb/dSf+XZ/PZs2OpS/Xl2RWfkxhQQcq/TtDQNwafw+UELjtAWR8r1//wJxlPt4jJynoBu+rGrYj47j1D3gte7LhvAXnjPiTJq8zDxGwpFEldOpO0f1VjnS3i6GYja1EoSAKG3w5QUOuHS5WpkmV+zu9K/9cfIOXzma3kzusUO4u+G4elVEEKDSFzVjSXvL8XW4iAbBA1ZefmbaZtdPvVrv/U/hGRQERCBbnPBBKzIAj2HNfaV0tKkfz9Cb89j+p9cYC2+p++sxuRv1WhHM8gfKeTOQNuJvQLL0xrdgHtkBkC7vwCntk/hsnDPsOp6ihx+/HOp+OJ+Ehblc/6VtXtJnl2AT3fn86mAe8xYfR8fL/a1e41i7GRPPDC19gVPQca48jN0Qaj6nTht65O29aYzSBJKPX17cp5q243hQVBHEtR8T9+gYqIKCGlJlD4kh6vH33x/3wnxlOF7d6v576LS3CM7sfe+xYhCQL9wx8g7M0dGhvz9kNseHQI5v2nkEu1nIRyaU9yrzfx+MgfybEXcmB6N0/pTWpyE/6ZkaeGTWTibe95fmNVYwA1xwIJyN2jtbyeo1moOBzUPJCE6cB+lAsAvNRLejLiw80k288wKe9yMj9N493Ya3D5qAQdELhj4AzuufQPKnpBwO9WREkCRUFpstM0pg/vjPqcUMnADTlXUvtYFJ0Ly1BKNSUk9/A+JOntzIz4gzn33M07d7/PIXsMH3x3DQ/c9BMzfEuoGLGR76deSeDyAwRtL+b96u581ONLtqxJY3VABpKgLRSnHX5QpiWGwxZp40eIjebki0GcHPoxLlWmz/g5xJ/2w7ErkE71d9AzppDAd83oN+xA0BuYoD7I/Ak/8W7GUBzH/Qjf7cJlEXGlRWIqF5gTuJ+iG/3JfL6Lhg1prnx57yvivx8H/EOcwJmcIAa+olDR24eg/S3ccYgCda9Gk5JTAT4+IElEf5uPXFaBLjICh16gW2Axxy1BXKh1wudPL/4cCHf5atuKV6Lldskt5MoqRDGc+/InELi5kKar+uJ1tAjV5WrdLlxawbt5V/BVpy/pYypk51v9tM9dTuTmUpYQEYrgcHVMEgqkzTnBk0lTCTx54LwTWmhmMPL1stNo9bvA3WomWiyYztg46NRxiUlEHlILb7b83bwjs1UCUdxxFP1V/XlxwzgCD4gEpR9suabdRzEC6hUDeby0O5d5Z9DDUMnrL88k8cu92jsTBATDOd2Xqoq679hFXastzMgnWYMIe9WAM8BAYGUjgR8fQQoMQK6uJfjXID6aPxzFS6V6eAIll8sIeoW0+7Oovr2BK73q6frVAyS9cAyx4TCN1/RFHxeEuOUgqgBPlwxnuO8JhMHVDPNSGGLKpdttH9DX2AB48UhgFtaH7PyUORwF6OFVwECTRCfDEZbUpGFXddzvf5KpgTu4bcqDhO5qRDbrcPro8J9zisykpdySdxXH16YSu157prFLjuPsmUhWrxS8QhUC/P1xp8Ww4pZFTD18O1EzSkl/2UrVXQ1Yv/Yle6qE9YRWHnWrIpasKuRzwv+/bon/W/aPcALYmjT00/7WK7nS0Ih5WwZKkx3V7SL/XwPx71NO4HR/YldVsv5EKPf4nSC3PK3jcwsCYo9OyCYBRRXhnKZk0duK/Ndac2gI1yYc55dTnfDt78OLb3zA1F/votPTea1PK4mIbwdx+ZXz+eW6BTSOr8Pvi9Y/fTbH0d41SYHNlZDGRsTsAq1jTqdDDPBHqaxqlQ3XiE10VPQPQvkOQj5tv0FIFxVJ7oxYHIEyKV/aqH6uiTmJ69jblMBt267ActSkRRTBgcilZW0rCIpMwuIclJpaVIejdfu2qqKLiuTJMT8wwJTPzW/PpylEJTbfgePKXpT20RN4QsZpFfH/eu95966CTqcRltiatC2WKOGdU4/PHWXIFZWci/+UK6uaVZrc6GMb6RJWTMGeZDrNyyDvwa7IdXXEzK5lwpfjCDyqojY1kflBX567bBWfPDgBk06HV3oxv+zvzltjtnN9/6+19yyIREj13JRxEzkHognqUk5pfgB+vXQ0RqrE6qoBMxkuHd8+MxJThYtvUq8ielIu2+ctJMMl4i26CJVE6hWZpJ8fxOu0Dv2Aaor6iZjWpSA5oPN9x8jd35mID4+hOF0UXWGhm0FPrH81tu5xhEVXEWmtpWCKG69dwUS9e4Dene9nw/BFjJg1j+T7cy4aEfjv5gz+GTmBc00QPPz+qsOBXFfnaRKxnIEPO39FQ79Y9pbGcHD4YiZYy+i7YH8rMIhoNnv2v1LnFB74/ge2z1/IIXsMtUoTx51NxP0st6n3AzT2i6O/JZfA9yxYM2uZ8/pM0hbXtQFoyDW1GNfuJWneXq7+bQ5NBd5tztXhLfbuTPrrcR4shNLYCOHBuPumkP58KFnPt3TPCTodQpdkRH8//L/cg96mIlkt7UKlHUmh7LpzARnXL+G17z5ie48VjLWU8skn15A85QARr2tbAbm87X2LJhOC3oBcWtYh84/a1MRzO8Zy7z0PEP11Lu5gF5WdTdTG65h360oMdW4C91dSMa0fYo9O6KLayE2c8xAEOKf1tWCUH1nzUzqsh6sNjSQ8VMvBvUl4VcnIdXXEf1uOPKw3xdfGoBMVlFsqUfp14Ya++1hd3hPLgQJK7u1P5ushSA0iYzLGtjpnJ4OZJUnfELfWgf91RaTcu4fQ9/cQdFjlsVMTsClOvAUXik6gOtWIw0/Ax9CETZWZn30jV6+fg0tVqFdEwjeLxLy8B/duf3b1X8p192+ifKBCuLEWXb2IYrOR+XFXnp26DEkQeTV2JVPeW41pkT+20U6c64OJ3NyEYrdjyTAQpzMzYfBepIDWORhBb0Dy821VjpYCA6icMYi8Z/t1CLQ7N+fW5m8dv6X/+9Zw4wCYUY5BkjFd15Zrv7qHTHeDiXEvbyDJWIKv6KWpvQZuY+R7dxPziFUjDpEkaJ5gbh8Tq6t7Ifrv56tFo3g3bSTRv7kxbjrUbvhtPVjEC4tuJeJILu7iEoKP0XpF/KspMil37UPQ6bGN74/34VKU0nKKZ/Qk+EhTuw1P1Z190Bejhc5nM93pWeQ90ZM/h7zFkN/moF7SE0FWUHcdQT2cjtiMS9A3KVSN7YxfRqNHK1Dy96d8QhqiC54pHcKTIVu4/ch0xNUBGOtUota31lT46/VIQYFUf+mHa2UIQZ/tb9WfIJpMqJ0S4WgGcmUVKTOqQVVxixKdXrWgntbKpF+nX4NuywFkVUW+fDD5E/zxzfLFd1lbMJLqdqP6eYOiQmkZKDLeBQplg1VOLuhC6qwDLSuaKCFFR6BKInJeAanv6RCaHLiBzBmBbL7pDcIlM5IgMjHvCqpVL3a8MgC/3adp6BdOwg1ZFH6aRPCuCrpc0TqcLpMb2d4Uh6G0HrlZ+FP09cGrzEXJR/HcMGM861LXsfb1hVhFIyIC+W4bl267j+THqgm6XIdyjeZM7nn2e5Y2Xkfsd8XMm3A5r0Vs4rO6K9jy0iD0SQIlq9L4vdfbJOq1yoVN1VHh9qG0n4HYLU5C39V6AIofHMysqT8hCSKioKL8RX/QfWlXCq42ErPegbRZQ5UKPt7cMW81Cw9f2fE41Xc81S9WkNQP+BjoihaxTwcy+C8LklZ2E9FvDCNsVxNqU9vBE7lBIMl7GlHB1TwYX8K0giGkV4VSXuVNzhWfcvX71yJcY9ScR7P/EHYfI2+YiQceupNNT73GvMIxHDvdiajChNbags2m2ppQ9CBHBiHZHcjVHRNIaqCRFtILU7mTUzdF0nVsPa9GLsSlitz9wgMEfrqn1cQLXJeB31fVHiafs5YWWUKUzkpqQjFXfZDOBz9dTVxzXvIsR1xtrI7A445WNfP8WZ34cvoiJi1/gCOP9WS8Tx9C1xxGsWs4i45ScqLFAvHRCLUNVB4MQYiDqjd7kfZ4uqcSInh70xRmxniweVKevWZFblWalDYf0EqKCRGE7qrl1LW+GBqa3acoaQrEhdXI2XkIOh2yt4nK7lYCfb3InmThhqG7WLV+ELJRpf66vvicrEE4XcbpKWnMvPMnFv44lrA9QZy+3kXAJhMBn51BlSBKZ+Wd6lim+WbwZdzvlK5oYoc9kidXTiJtUB7dfM9g/1mhekQyjwR/xrniIAPWzKXT29Uo2adoGt+fwpHa5z+NfJt3Sodz6P3ufPOoPxOsLvSCRLVsI0pnZEqX3aweejlus7bqOlQXU3wqWPNIDnUPhPHboa78saEniS8eQbHZ8PHyIn9+T76K788Xf1yGoVrEHuMkc+QH3HLXEZ4fdyW/7O1H7FqVB+/63oMmbJINmqM8x3Rbj5C426AlRZs/k8+UsGr6cBKP57YZU2ftfIC0i40E3gLWq6p6gyAIBsAMPI4mSPqKIAiPogmSPvIXQdIIYIMgCCkXoh3XxcfiDFSI/6EO5XB6u6u05fvdJP4gIEgS7yeMwhHlh/cTZTzU/zdGnhxN3t5oEmQN5CL06ULmFG/SntH06mJ/rmXSJZPJzwpFjFGgrBKxa1ob2XO5upqwt3bgGt6HmF/clI4OaDezDyAG+CGXai9MdTkRth8iILg/n8SuZ+6Zy9n+Qy8a+7nxz+yuEWJsO4Sg0+HoGY9u41/OqapUfBxL2cuNjAk7ws6aRCL+/EsPviAQPu4UlXUxnIu9i/yziVssD5C0KFtTquEC0YvnZmUyp/uhBpqJ/9xJp1eP4ae3cdAcAc1OQK2vx5JeelFZaaW2HsWo49Q1Jlx+biwbjqMAYudkXvrkfe44MoXaggEYqiQS38+lcroXl85MpzAvjR3PDyBh9V5so3sT9lAOVwcexyS6sIh7+LmyJ4HHVAy1blJebQKdg+xXBvLmtZoUpll08GjxUN6N3E2Uzsr1lmq6TFzITR/MI8+dQFTDAepvrG+ZNKrCA2cGkbrUprWt9+/GI298weWmOvq/M4dvqvvzeNivXBvWg48Kh3Bt2gp2O/RUySGMMlfzSOBxJv9rH2dkM1ZBzy051/BszM88EvkL+V8H8XnxYPJPJqA0NiLodNSO7U7gcZllXkNJWGdHfziHspu6wEiNaHRJ5C6I3MVvV+npbazhrKNat7Mnya7W+Z/2IOWqwwG7jlzcO2/HLkZ8xAe4DFgKoKqqU1XVGv6LgqSgrcDJXzYhuGSUIa27AqXOKeccqKK63ciZOeg2HyK7IIRytzfSHXriH92pPaRLelL/YhPHrn+bhqEaSam6/zjGUadJuXcPaa/kIVfVUNvVr0O4sfFwPqeeTEGp7Vga/awDONcsvx2j60+zGe1/GJ98haA9ErnXG7l08R6yFw7EflUvdJvabw7y/+k4d+RezzSfHA6s69wWPKSqFK+Oxft0a+cgbjlI8junLthYYh/TH+fVLe3kit1OyjPHMaebKOtrZPd7vfnjpUtQzkkYKnb7eVeRVpfnciJuPUjkZhe+J3QoTVp5MWeSP2+VXIl+lT/GSglTpVa+7PzsKY480oOYWzIxr9yN6nZjya+npNGHteXdSDaUMHfzLZSMt+C/5gTSHwdQjp1EPZaJTy7st8Wz3+HkjW+vY8/i3iyqjsOlynxUG81ptw9NaXbswSpZz/fCvNqH9Y2xHHI4uObkWPaUxVB4lY+2h957jLemTmTUzFlE/lFPmpe2bXj7jg94M2kFH9Z0ZuqWGYy3NLCoqjP9X5zNK6Uj6GvUcAkrk35HUQXmZ99Ib+MZvklcjf5yTURVigzn9ZeWUDO5gSmj/+DOpSs5+WInqnoprTo3Aa4yuyiVReLX3UGf/TeRurTjsffftAuSigiC0BNNM/AE0APYDzwAnFZV1e+c46pVVfUXBOFdYJeqql81f74U+EVV1e//ct67gLsATJj7XCpcoyU7uiZrpcIDdVT28sE/ownd0dxWQB0EQVMdKiun8ZqeOL1F/L5o4dQrvX8wS+cuQi8oPDJuGoJboXhoICHvte69tk0YgPfmzFYhv2A0NvcyyIhmM4KXqcNIoMNnpjeQuagX3pkS+gYVn0IXDl8J33sLmRfzK/d+cxdxT7ZPBKELDyPzgXhifnei27hf2xP7+7a5BtFsBkFooZQSzq/gBNB4wwDqoyXcJoh6uQW52HDTQNYtfJNeG+8jZcaRjjPMgoDQuzPC8Zx227pbLk5CCgygdEISIV8dRoiNRMk+pTVh/RZKg9OIZWT7pK/KkF7oS+tQ8gqpu643fgfK2ieIFQQtaRYahHxS25ZIwYEU35BE+O+l5L5g5uSlX/J+TSSLPxnHNZN3cF/gNhZXXsotfrvpZBCZln8V1SPdKPX1SJ1TEJoc5E6NZNcdC1pxClTIjQzedi+bLlnMbRmTcX0QxqwXV9DZWEwXvQFJEKlVmrj07XnIRoi5/BS5ZYHETzyCFBrCqSXBPNTld0+Y36DYOeVW6WJoTSn3amUyX380grD39mnYgIsgzxGMRmqv64X/+ozzbl3hPyMV0QG9gfdUVe0FNKKF/h1eVzuftStIqqpqX1VV++oxIvbsrBFrHDpB4NKdiPU24qdncmqUF/I5CUJdVCSi2UzZiGhEsxnzqt2tHABA+JZqbthwH5MOzIDcInImBvDmvPcpu29Qq+Ms6w55HpxgNCL06UL+E73RxWpJOMVm+9sOALQVMfWho0StyCVw6U70v+3D+t1uqpbG0Flfy+DhxzrkMXQXl5Dw6E50G/cjdU5BHdCV6qtT2hxXeWMPTi7s3BLJXEQZyVLURNTKQiK2tWaw9d2SS7+t9+Dt29SqTVry8UFKSWx1bOYUb87ccwHkmiKj2mxU9dP4H+X0LC1K8PWhqMaP0qoWuTfBaGxF/iLtOYFacBoxOQ6/DZntOgApNUnjdfC2Ip/IRJAkTj80ACUmlJDFO5Cz8zGbtOTmmyvHYqhXeSp4FzE6K6N9D/FrQ1dsiosDp6NoujQNZUgvlHcb6ftjNofufKuNNoBZ0NM/9hSlsoFPUpbx3KsfM9G7mu4GE/0PTKRB0Rxi6F47oXtd6ESF+OAqpJRE5NIyoidls6Gqc6tzhkqKh8/xrBU7fYlYnqHlmS6SPUt1OAjYVoSS2H4lRvL3P3+VhovLCRQBRaqqnt2cfI/mBP5rgqSCXgcncxH6diXzfgPJS9woBzOov1IgIa0WISUROUPTZcucHYNiiCb1pewOPZ9yOJ2UO7XymuJ2k/h2Ni/+OoVAnR1deBjuklJtW3FOKaxxdE8+XPgmCgKzt81G34wjF02mVqueoNMheHmdFwAEzXBWm0073mhEaWzEaRWwq3BwWTfC5PPKM2pWXI6+yoDPztZdg1LnFC6fs5MuXkUs6zvqohSLRJOJ0scdhM/WGJXPNbm0jOTHvVDrGzzsyQByfT3Cud17qkrCKifGnLIL5giUxkbifoCT7yQRu1wEQcC0+SgxjzmgptyTrBRjIqntGYz1O214ld/em74zDjE2YDVPvz6NsN+Lqe4XRukACD4AAb/mkDcxBLeXiqE2iqbUYExZRrwGVpBnDSRuL4gmI3F+VeS4GtA1CjTEqB7e/2nbp2E+4sXsB47zy4D3cA0QCBYFrKKxOTxvzYvQoNgZk34z6qIQXnxEz8qk34nXt0xQVdX4GcTm0lzDrFr+SPmFe08PJCOqC7rm3LNTkTy5iHU7e2LNlwgdXcj6NK0SYFOcbH+3HwEVHStHdWTuwiI4n0bhhSTML/QDqqqWCIJQKAhCqqqqGcBwtK3BCTQh0ldoK0i6XBCEhWiJwQsKkoKA4nRR2dOb9wd/jGuwxP07byHlDTtC/mlPhlSQJEJ6lPJnt+9JDryD5NurzusxVbcbBAG1vh6Xj57pC1dhEGSWPHIjXqv3t/quIKtE6LQX6vSW0AO1tw6kdIhM6n0tnH9C1xTyHpeIvz2ndT+7t3e7jqF+Qh/sU6pR1gfx8fxF/G5LIXLtGVR/f+SKijYvSDAaUZ1axaEjJ5c1JZB1oYdYWvv3hCnSAsvY+VgynRYa2jQd/bXzEvDkX841afOBi4auGsttPNxvM68qIzEU6Yn7zQUZrVlxVKOBxjDJ03RkLlPoZCmmh6GCNU+8TuGjRnoYtEadihsbGTVxGo58meRZu2ka359Z074ncWg5P9T05evy/iBKKDYb9uu8mDFgLuaZFcxL/p349XdgyTCQvKGWsn4mzKKB+GbqtM1NIrG6OuL1VirkRrY0hZPtCOXzjAFYvRys6f4Zm9+M4JGNN/OQtRe7y+MYGX6CKIPW3Tn/9FX8kZFCcqOLcO86dtll/ljTm7iKKujRibpUX2INh5EEkXcjd1M2YROf13bng/UjOJ1sY689gvlbbsYcLrRK+F7IRLMZMSwEpbyyw0VJrq6GC2wTLopotDkv8DFgAHKBaWhbiRVADFAA3KiqalXz8U+glRHdwBxVVX853/l9hAB1gDAcKTmBsqGhOAIEoj9K13DTfxmEjmv6EfVkFneFbWbaj/eQ+nLOeRNiQr9uxC7ORlFF9pVE823PpbhUkRs/nkfsGwc8q3zdpIGU9Yc+fbM4sjGVuBf2UXlrH/wzm1q1vkqdU7jnpzU8c2Ispm/98Fmu1fAkP992e/il1CSuWrmPEZZ0bj82ldojgYTsVyjrJ2KoFoh8fTcostY8MzySHnceJevlzu1rMgpaZST7lb5k3LKETl/eR/xjzbkFQdBQh05XhzyE+d92J2PIF1yXPYLc75MJfaclRyL5+yP4WHGfKmzzPeXSnuizzrSbCO34wQvU3DYQVQD/L85DhPlXDUlBQEpLwhbvx4NvLWO0uYHP6iK42qI5jzEH70D62Z/gfbXYwyxIdpmiy004Yx3oSgzEP66VY6WkeE7OCWHl6Le5+as5xD29y3Mv2dNFJvfaQ5OsZ0H4ATY3ibycfw2r01axzW7i4VfvInTFCQRfH9KfDyb3yk+okBsZ8fJDeJ92UzRCIOlrB7qMQuqGJdEUKKJvULntibVM8s6gz8oHmTdiLb+Xd+bY3njW3biAFH1rEJRLlXGoLmoUN1fvvZvYe8s1ENdFbgOkwAAQNOqzi0UU/kdEo6qqHmrev3dXVXW8qqrVqqpWqqo6XFXV5Ob/Vp1z/Iuqqiaqqpp6IQfguRBvb5T8IgI/3unhjWsvQWVct5eqkQo/VffmxM3vcHpScvvna0aeiY0ONv7Zg6znOhP+uMqk5+ez1x7LJ9PfwT2gE6BNbN87ChFcAnXDG4h9bjd11/fGL9vepvddPpHJU4tvZ1/f5cx65jt0cRrpY4ckHhnZfPb+NTyYcyPei7yJf3wnlh92E75dJumaHNzDetI0rj/x35VSkwaHl3bD+2gZosXSRipdMBgQrRZSllbycmVnBDeeAaCLiyHz+W6IAX5tL0IQqJo+iLu7bOPVymRWJv2O05tWg0eurUMu0Sa5oNPBwBbUorj9sMcBiGYz7uGtCVbaM9eIPrz0zIf4ZdrOP0hVtbXTUlWEhiaM6w/w5Hu3s90hsuD4lbxWdjlnZCOXROQhyOAIMWP3lzAcyMYnTyX0Nz2JLx7T8AvDetPtuzzuHroJGQG/LJD8NClzW5iBtDcb2f7IANasG4BLlelhaMCpSLxS0QOL4MTvptOodgfuU4VErtKT42qgXlEJ2VePdXsO3jkSutJaqkck4/ARCf0hE7dJwE+y0e/P+zBWitzlm8/HCSuJ7lFMcHPfR56rgZcqUpl1egB6QcIqmojSWWk6bQW3G11cNI7R/Txo1/OZvU8CygrTRRO1nM/+EbBhwcuE+0c/imdpTkrQGzwlwrOT7FyT6+r4vTCVG7PHYKxtf4CdTbzJJzJJnL8L47q9yMczCPn+BK9+fQPPTJqGtOsEUmAAJ2f6sz5tLXNHr9G+p8j4by9CNrb/eKK+y+eZ8h70MxWgWi4sGuJdJJPsU05Znxaor+nnPRR8nUBVZyN1MTp+29Cb1CVnCPxoJ+7cfIiPbiOVrjocyDW1yOlZLPv+CuZetxqhVxekzim4IvzpNyATR5dodHExrWClurgY7nv4B/SCzKcrR9Cg2DFVtqVjUx0OLaLw86Uh5hzFoLPRQnAwOUuTKZ55fkFRx6h+1MyqJ1C0UdHT3LrEex6zX9uf0tmDSX80EmVwNwIyXCiqyInBX/F2xF76G/WsO9GFoG8PYzp4iqquAhXXdeHjZ9/kp1cXkD+3G1KnZMr6mFhxoC8r3r2Se/71AE6rQMYzqdz4y06+emMB+RMC8NqTg8tPwaXK+Etm1nf+nkutGdy6awYfJi8n9ymN5ddnZz77HVpiTfHSUXxTKv0nHmbimq38/voibKECSBKBxxt57sC1jEw5QcRlRYgIBEkW/ujyEz6iCZcqc82emXyT2xsfXRNLa8NwqTJlciOLRn1J3qw0cm+L4MqXt1L4aWS7ilTnls1NuzLJOh0Cclt0gKDT/S06sn8EbFg2iuQeiSTtu1O4gYZxvbDmNMDB42B3IPn5orrcnnKYaDZTX2plYMIBPhkRTvCW2DZ72o7203JNLTH/0sJgFVB1OmLXKoztPhIfvR01NQr2HePU5Bj09RC6xQCq4olKRIsF9+kzfLPhElY4LyUh+8KEoOYf95D9s0Sk3JL00cVGE3VLHhlb44l7Uvv8bNwj+fnSGOeDae1exJ6dNbruc1ZTyceHTldm8eqfo0nLPA5R4eiqGnk04hfmPnYT46L28f2MEQg7DmtfcLl58afriVtjJ0ZupPR2N9X9XYR903YL0zSuHw4fiaCtZ9rs/eXycpLnimAynj8vIEBdRgC3HJiLO17FL8cH/YmWP4ve3ghmLwRBQKmr9+RWGkMllj64CJMg89HAIex4px89DA1o2DTN/PwbUewOHMO68OPkBWyxJTNx3x14/e5N3M4qBJudyMUHECxmapf5UddkwnXEjxXj3m5mRrLy+e1vcWftAwzuc4Kv62PYUx/PcL8T+Ih2OkeWkKi38twN3/DFor64S0p59PeJ5E74gInv/0If0yl6Go04VBfb7BZEFxROTcLnihK+T1uOhEpKhKEVe1C10oQC6PVuPuz+FQNNEu9Ux3LEKTN3zlyKhgscu3MRekFi7pnBWL/zAWfNOQ9MayMvGhVM+EKttCvX1ZE09QjyX7YPgk5Hxvs9GdApl/pbg9rP9fzF/hGRgCoK3HL5dhp6ReIY1Q+vcidinpbtdJeU4u4ch/3HIFxXNUcKXiY6vVXLlxn9yRi2lLzJ5y+BtP3Blgml2GwY/ziC6+oatqcnEfDWaaTkBKyFCqZqBSkiFDFFW5GVob3Ifaw7oslE0qP7iH9850XJbHsSbOf8bmOXMK4JPoq1R9tmHrmmFtOaPdr3jme3Cafl+nrSNyZzRc8TnHqwB9lTg1ENOmQExoQf5WbvHAwvlaEL1xKH7qLTJDyyE3HrQcS96Yz8+iH6JOfj7hTX9lIFAf/jde0OHik5AVdieLt5g7NWO3kgp8YKpHxSQdz3VUT8KeOV9Zfmq24JFExL0qKOcxpkQn7K5IkbpjHv+js4eVcq9kAtaw9Q4G6g845bCVhoAVXhzBAd16yfw/LHRhN9wzGCPtiJUFyJPTEEuVcqBPhxVfhJeoSeYdzYHfQ06Mh0NfJceWc+KhvG/XevZFroVnbVJXJwSU8+vfEashxhfJO4GoB+pkJO3ZmMoNMRt8pNgbuBGb4l9DQaaVDsdF4xm5fuvB1DvUr0R+n4zZRZVdubUKktCMgs6LEKeo70/5rVtb1oUOz09spj4ncP4H20DPxcZLpUahU76/b0xGf5rlZtw1JwIIVjgvEp+Eu+oJ38gSrLxPwosvtEIqqXVn690JbhHxEJuM1wqCaKiq46jNUqwR/sa8Wj5/IxsDx1Gb+/HcdX00dDRiHyiUwiF/dmf1+ZaRN/5Y9VfS/ISdjGBIGGG/qja1IxVjt5bPA67vI9w3VLRyAuCcFpFbTMa6E20QuuMjHx2j/Z830Pygb6YqhXqYsXiXvv5N/GExjX7eVLy7XE359Do8WiRTmihDqgK8LOw57j2k3yqSqW0ypp1mKum7qPboYKJu+bx9SD0zg2cBkFbpmT+2NJqmpdChQtFoTYSFIWF9K02IRwum2vv6nKSdZcA8nTDK2SdbqoSE7MD+SFYSv56voRbeDWoDH+VncS0FdLHhUpy2l/lKbWuIQzQyzYImVN8v2crje5ohKaOztFsxnz8xHoBYlidwPXLH6YuA/TESxm3KpKwlOt25VFs5lTdySz474F1Coyiysv5bOdl/LY0DUMM2fxTk0XPnv/GrrecoInItbRyWBmbNZIGl0Gvn7udTbbkhhnzUEvmFhWH8hkb7jp5s3sWuiD6VA+Q9c9yIZRC0nUW3m8ZAjWApE731tJo2Lkuz3DyZxr5G2/ZQRJVmqVJrY0BRKmq+VAUxxL3xiL00fg1VlLyW4MhmD4pa4HSc8dAT9fxDIDbxRfzQDfPATftu9bLi0j4vWLpBlXVUxr9pCyVmiZQxdYqP4RTkB0QsOrUcScrkJwuNsQaXrty2XwxgfAJRLnraL38YbKKqTNB5j7yGyKh6qkuduuqILegKDXaYOwveSUIOKTWYfbx8QzX37CJSYtMPo2cT3HX3YjI/DoLdejn6mV1BIXZbP3s1SUrBMoQwYzdP5OHg/eyWPjLif9yb547c5Crq276Gytz7pjpCf3IKpRC/F04aGk360jedeF0X+hvxfxc/lwvo6Q+OOJhby/cBEBooxNMTD+0AxSFxUhd01Cym1ZUapGp1LVVSD+0baNU6BtQ0p6e6HTNyBFhrWGCysKnRbV8P6vN+BTXejBqYsmE47LulJ4u5uYkCqej1nHs19O9nytvW2ZsUalMbl5FevgPhuv6sqv3d8CvAiQjDTGuyEihPS7/Uj6JghFEsmeKqGr0pOyMJcTL0ZzZdfD+Ipe+IrwQsh+LrvyJKPNdmadvoLtn/XBFqXyVdxmHKqetTYTuesS6D3+GIl6K4m+JRS7VVY3+jPOchow0cucz+ZhkzH+cYSUmXuZ+ssUtnVfSWZdCPYglbGWUhZWdad0kC8Zw99BL2iFTpsi81tNN9bt6Un0LyoBa7Tt3uyI6cgWBa94AzP8d7Kn+30ou4+RusTA6R6+3BJ9gsW517T/wv8NafeL/e4/YjugeKlU3tVI5u1+GijoHBMtFopvTiVpqUzq7CN47ctFLir2/N36/V5SHzrapu4t6HRkLu1K9+22VokpKSiwBQWnyCiH09EdyGTGvqkemmm9INHTaKSP0cDvnX6maIymKSCXl3sQbGHv7eHQ3d2YmjOejbkpNIbrISgAQTo/9Xmr+25sJO6THM9qKJeWkbykpSsRUeowweM+VYjXT3sI21xBkVsTtAzXWem6ajZh81yotXWo+5spwyURuboaU7WMsUpASopv95xNA1P4c94Ctg5+D1fEOX3sqor79Bnk9CzMq/bgPtPy/ElL4NQ1OtwOicfj13LKGUTE1vNAigHJDvoyfStOhL/yI9h9JXxFLxoUO3cWDMfvqA4qqjFFNDLr8+/4evm7ZI/6kMsuO4rqcBKyRY8otKbyHm22U+xu4MiLPQj/PpuXr18GwEGHyKMfTCf63cPs+a0ry+oDkVWFckXHWEu1B1g01mJj+YdvUnJ3H0SzGeNr/uxxuPg+5QfUBBvPlffnWu/DzH/gW0QEKuRG0rbdhiQITAjYR/Qv2qp81kL2qURuBIfqJkZnpry3RRuDZhP5p4K54e45JH5U2Irb8v+G/SOcQLi1lkXdv0XVq21ER+QeSXw6/01S30wn471uXPVnLtmfdaHxhmbtO0XWEkvneDtBb8A5rAeP9luPr9SEepac02jk1J2pNKYGtvoNpbGRxAfKGfLwffR66d5WYhArGnwJ29VWakx1u2HvMQrrfCHTQvDGQuTsvL/H7CL+f+2dd3wVVfr/32fmttzUm15Jb0AooYMoiKhgASwgRbGhggUVLGvXta0FZK2gqKi4iGBBQURAWZTepIU0kpCQ3stNbpmZ3x8TEkKC4n53f7oun9eLVy5z7517zsyZ5zznOc/z+chk3heL1CuFmulD0Pp375D9J1nMSMGdtRk7tP3oMcZ9ei8/tagUuRvp2auArFuDKZnWEwb3Qvi2k52YV+8k/IUt0NC1hHV9tAGrMNGiaUiu1gfqFJIK2c+v7VpLvVMRdgdxK1ro/lgZN393E6sfOJ8Tde7QSoIRGIBss7URhAbsqyVxYREFD/RrO79jRBqGqEjUc/rQdOUgFAsUuRupVd1s3ptC+Dd6nkK3a47w0IEJnPvWfQx5+HZKbghDqakhYEspqZ7txsmhubjj+CCGfT4Xzw26/oCnpLvFgy0yjfF6oDnuk0o+PD6YEsVOkNTx3imaSpjBi+fvWozSOwFzUS0tmhEJCfKsfJHVizSTke0NcdxceB5bWoJ4ps+X+EomYgx1lF3XTOPVg9ok2FQDyC0qj5YNZmr+BYRsqUOyWPB+q4IeCceRHSrmpQ4Kb079xXtuiI0+rdjNv4I/hBGoybTwxNybSX22oHNykL+ZeaWj+TY7layLFjLJ+yCzem/Cfl2tTlPVmjQBevFN3nNDkCNC8cgu57MpI/nxnBDU1liB5nDQbcE+rHn1SD1TOhgcd0kpvku3Eb4ilwql4+xbOMpKlxAStXWefHHty+RN79bWjjPduxV9U1gy4Q0aEn3Qrq7C5dUxZVW127sO0Pn56m2XZDSHg/j7t3Hjx7fjL5lYlbiW+y9Zhd+E49THWXHnH0OprEIb1gepVwqyzYYaHoQhIlxPEDppBvbLcbLDIahVDaCqOrHr/UNwjNH5EyWrFXdq+5at+nMG1NRjKqzC3iOM5Dv2YF69s0NbNZcTpboW+5AERGRY2/fUymrCtrV7PeYN+zg+vhu3v7eCsY//wLePvESkwYtIgxd7LnsFR7R+TTW3G+NGX7qta8D2/ta22IOSk8ebn41hh8PFboeTAS/PZt2GdGJWu5ECbGiNTaytS2Nf6/r4gXNXY79iEBl3+7I2ZTUmIRj2xRySP59FjWLHrjrZ59THYm9TFTXJVuzx/qQZ7dxZdD4JC3Lx/MGT75o9WL25H8V3x3Lf8umc71GKWRiJMVgRApxeElorMYj/5iIaIg3cGPATUR41SLmFaIpKxqpkqhZGU51q5tP4b4kYU3Da6lbJ25ucm8MpvTyuy/f/FfwhYgKqpxnrl7twdxHt9DpYSsEzKQT4SqR7XEtqUBl7j0Xh+70Hknc5jt6xGDc3tg62GiRnHI6YQOQf9kBBZ0IN4emJZpDInOFNyuO+Ond9K3tO/gQf4t84yt37JwEQ7lNPTnEQxtN5+KqCx0EPyoZ48fC1n/DxO/qWktb8y+7wyVhaNRRTrZuGDYHILacXN5V9fFDq65EsForeD8e02g/bkRY94m8243kcrsiawIqklXx0bBBe1zVhKmtnSq5J8qCmuwd+WTZqzm0h8RUbijUE09Gytki0JuDBuTMZ/MgOSs71JVyJInivE9P3+9HQd2WkHYc6VIMJq4VjE6OwR6gkrD2NF9R6X8vPDSbIw4R6IAvN4cD842FESLBOaeZ2E7Z4H68cnczx4Qa8L2thuDWLNJOR83bfRMSOTFR0Axu2oYKS84Pw8++PapJQDQLv748Q++ROnvhgEscvCSVqRQHGpW4WTf2Mu49dSu5bvZgRMK+tcu82v+NMWjAfq2QEjEzMmErSffsQibHUXqayvSWECV56glSYwYv6i5swfGllyHtziF1eg5JgIfKaPFbV9MXQKGDbfuKP+DL66Bxq0jRUk0r8cjeqwYlSo2/DuguL8M8MYtyKe+g1MJe6i+MpHQrJjx2EiBBuW7Wapyt74mV0YO+ichRAczoJ2+LGmll5WrKY34o/hBEQdXYQXXdJLa/Ea68by/FibIe68/OVSSR+UIaSvQ8FMGzc3TYoNYeDmFV1ZM70wDBmCAlLa1D3d4xiV41JoD5OINns0Op1aKnx2J+34z7ojVJVQ8TEGp0kpH9PtOkmrH2r9Kq1LT9zKrq9l8PzG6Zy/Dxvoux6tP10SsXCYNAzI+12XY589yHyRnhicvyM0n8g3s8exz66MwOyIaYbuS/4EjM1E83tpqHUm9AryqlYE0LIZr3mP+jNrUhLfbju68soKrWRWJbXxtSLqlCXDJ9dPZ8Jn99N0q2ZqM3NSJrWtt/fPG4gx0dIJN6/h0M/BcEUkMqqMecWorbuEqj1jUjxMa0KRXpRk/tYEX654YTsOoUA5dS+q+D0FRyZRQhsLQAAO4JJREFU6U3ih2nw0z5Kbh+Ix0Xl+E1oJTa12zGv3kncavjm+RhWJ57LZUt+QN1qA6mInFcGo/m5CAhsYFff1zjicnDEGYJFcnLnmumk/PUoSm4BaROr2T8ijDE+GQRIHiyJWU/OX1e36SeeQJOmcn/RCHp5FVGQG0ySI5/aPn4YBVzqWYJZtFc3fjX4TQr7+/DAc7fQEuHFsWkKe+I/Z8Tzc4j/6BAK+tZuwOKtBKAvPRsv60PgnfkcvHgA8fdtR/LwoC7KTHSfYmocVsr7C76Z8BI3p06j8KiN8z2qGWypoMJX4r6Q6dCVEXA4MK/e+YsGQA4Kwj4gBuuPmR1L8E+DP4QR+CWovRIoOteL8BeKcflZMDYJXCE+SNldf14qquDNEZsYZK6hf8TtJEw75X23RtzCPJSKynY654PZyPN6kbTvKIrLiezjw/E7+uMY0sDBoa8y4PW7Edv0KjfJYkF1ONrcWKWsHMrKCd95ktchBO7z01ENAuvPhai1dUhBgdQNisDpKeF/qAFaqbhPJEBFv59L9f4YzI6O7jSAVluHOBzZlrSUNGs3sr8fNlHfYTAo9fXsy09ncEIeNd7elExOIeyjQyi1dXgVQK1qIeIHtUsj5ZlXT/KP5SguJ0pZOaHzO1cKSr7eZD3mTeILRmg1AmgamoDSQRaicyLbaNA6fM9qxSO3isjtNWhOp77kM5qQR1VRvzUYPyW/7bMnDKVSU4N0KJdXD44gbuEhmoelsOeq+fhKHiiaSqPmoIfJgx4mfZAfHLmRTY9HYoiJ4sDycJpDNb78eSiTr9tOvWZm+qY7OTT6TaxSeyalSQiqHFbm77wYPN0Y4mKw7a9le0s4Y6yV/LMFzm21AyGyxMzsS7BNLuLL5JU8VDaUPt/eSXSuS+e0PAVCCFLvO8gbkf/kUvc4ZD8/hJcnNcmC9SnLmV10AY0Z4XzXlMI/0z5npDSO245dzJLojRS7XeD+1+d5zW7Hml93xvGpP0RM4Bexbb8ezAJM5Y1EfleHob4FZWR6p1p30Flp79p5DSN234RW07FmXw4MoDZRQqnuWJeguZyYvt2FVlevMx17WLjh5jWsHvQmyxsjkdwgJ+trsBNqRb8IIXF0ssD2cAEYDDSfn4b4UEFya9iWbO2Si99dWoZ5TWcDAPoME7rtpJlW1ZmSuyqcSlrgIOvdFOrH9EB2aGgtulfRGAUz907FY1XXv6EeyNQ/K4SuQtSaaKQN64McpKs3KZVVJM7IRD3QcYvRc+V2vAtUaobqPAxNVw0i/5khSL10Kni1xYGSW4BSU6NTrLfGIQJetBL1zHbdu+jfUz9Z31TyZuuBMbW5mdhn3Ci1dcgOhasyJxK76haSvr+JGfmXtq3vAd45MAyCAzj3y0O4h9cRsdmNqQ6eLx7Dc5dfQ+rsbHr9YzZ1ajMZTn3NHyx78lHcaswVMu+d+x4zvl2PcKtsqOuOUcjc8doshvx8JQAFboF5poHc40EYhcyPbw0g+jOBNb8WpaoaQ2hIGy+CnBhHwdx0Nm3sxfl3zsJwk0TTsESaeoYR99zPnPfUPWQ/353AvXXMX30pDs3FA7FryX4zlT6v3MGUxfegHtVjQZ1SriUZqXfq6WMGvVLIfioNR5i3Xo16BvjDewInQzmchTIynWMXmtk+7WUuOzQNr0s7cq1rbjf+X3vQFO5JynuZbTOlHOBP9twkvAo5bZZf9tN9GDdyBwV2MzP9silTINxQw+o7X2CM4X4ij5yew6ADVIXUudm0BAWgFB3Fo6aWKr80fFd3FhgxhIaghAUi5RW1pfAKgwEpOR5hb6X20jTM6/eekWXXdh+i+sZBbLj0ZabPvretSjL62xaM7zW35WAIg4HcpweghDlIftGO1NCE8QMnjU8kI3+/R0/kAaTtBzsIYJxuqVM+1kHI12YQguC7jvJjwnf0rpxF6H7aYgKSpyeSv43iy7oRuN+O9OM+iucOwZHehN9aK7ZdIA5kE1fsjxYUpBu5zDwMcTGIHw/CxZDkKqT22iH0u/8YxpO2BD2sDuwJNs7xzOTyfj/zZvR5PBu6mS0t3ixoGou7vp7otU5WXBrLSwdHEx1QzYPR3zDCw8SsiasxCjdW4aBgfCBphixGHbwKg13Ddrcg7fJZCBUiyw4StD6E2anDCP5nBWpuPkprmfvhp6MweoWQ8GQzhZcE4Tm4koDxR3W6O5uNyhu8ibLVUh3Ym9Bvi9DqG1Fjwkn8sIbRvSZyvNyPpJU/49d6fdvGSXFnBeojt3uR+oBP57EoBM0R3tw/dhVfvjYAt9uN5OlJ3WVpGBtV+GpFp3PBf5ERkH18yPlLD+4dtwqL5GK7w0Z9ixnf0BCKro4mcplOEd5yfi8ar2wgwrcOdX57XrwWFUrioAIc34Wd9jeSXz3OxvzBbHzgJczCSjeDkW4GF4rWupbUNAxRkTSnhmLZmdvhJkje3hAfhbpPT5JXauug9aFWm5rw+Xhbl+SpmqbREm7F2hjQ9nnSkpmzYhnfN3Rnzwh/lNozd+3k1EQuHvgzFaoZa2FT229Km/Z2WDpISXE8OO5zJnnnM9j7JnyWR+D5mAPTz7kdPqe53TjGDqBsgBHJCTEf5OOKDuoUH4l/Q8XlqyAHB3GwOJR5/nH45p3UZkmm+JbeDJuyh3v9v6FW8WTxhDEgIOEpO7gbyFg0gKg1As91B9EUBTkwgPIJSbg8BeFvlbYZNKFpvLV1BEsKR3N45hsArO/3DhMs1/JMwaUcKQzlwQFrsWsKUYY6GnuE4NlopzraxAf3XU7c3iLyFvgTEqfXJczbOhqfQyaaIlUW3rQQgNXLhuJf4kbJzCFifj7qwB7UXZSK7eOd5K/yRG0uRFO1NgOX+nwVOTeGUtvLiuwE7YsANLfuMTWMTOLvfd4hzVTPsDG3E/CNHeHlSdZNXqQ8moXHxbUkaBoiwB/Z6qGLv5ygoj+ltkMYDfgcMnY5kUleXtiDDbz0xTjiKw+26XeU9xMo3sBXXY+Z/xojQFgwt132LQuzhyOtsaEaBZFLM8h4KglbTCXaYt2CWrflEGBIRBSB5m5fn6o/ZyAuNmFwnJ7kyF1QSMjb5YyQ5/LinW9zoVV3wfPddiK/b0QOCSbli2I85AL2jOy4fy8F2LC/2ITH7CQoKtUp0Qal0RThgednOzrkMUi9UiDnmE5fVlaOeU1FhwdPLq3i1s9uwVwjiGre/auXRhgM7QOyopr1uUn4GeyIrGOnlTZTvMxEGGuQkPg4fTFX5N5D0l9zuw4kCYj8oRnFKKE5HMh7MhGJcbrCUmu/ikZ58vz093nz6vHEX5/Fd3IYHie1XRgNqDI0uk0srxrISN8juH0sdPsgF3ufbmgyBIbXcevfNvBRzmhETj5KZRUBb+vZdifm/Popg6lKE/hkGKnv6WSHw4VFKEz/+Sbqj/pRUy2RsiiHT3qO4fnrLsZvm5nQvGr8vnDjaD6O9nwQ4Z/V8VnklxS7VeI+vxW/DBnPS0uJ9rCT6wxhoCWPuLFHyTHEEbsrnMJrYnjt9jdwaTK3DrwFuQUGXXCIQ++nE7hQb5+Sk0f0N34UD/egOcmBXGoiqHcqoqiM8nSJWctuwdAoiJ+3G8XhoH7KYL66dD5XVs0h/r0ilJIy3ElRlJzjScAhF6ZaZ5eBaM3hIHTBli6ZhdWGBgLX5OB/KARcLl3Xwukk6dkjKHX1nI4u9oxIRf7TOEEq0hUMoSFtkWj1vL64vAwUXKGR/Foz2r7DKOf1RXIqHS7YqZRgvxXqeX1Z9MGrxBr1NND4jTeQ8nAlOJyULPJjaFg+uSMM7SSf6MGv8mt7U93fjXBIpDyeTcbTCUTHl2MZW/RvkZSWvL2pH9MDr+UdRVK1YX2oSfbA/119QNZNG4zbQ7Q9QF3BEBfD4YcC2XnRAgJlT16vjWL18MT2bSlJ7si8ZDSR9XJfIn4A6+c7EOndcdosGNfrD7oyMp38S0zE37e9M1uSwQCyjOZ0UjttMHc9spzhHgVMPnwdFXtDSJinU6ULs5mS2/rROKCZwHWWTtyR2tDeTHt3NX6ynQc+vJ6x47bxQugu+u+aQvgdjdQOjqBkOATulghal4fmdJJ7dzILpywk2lDPmCX3Eb+wgOnf/8RErzq2tShMXn8bn49+ja8behNmrGWAR34beeg6u5HDjggu89JTi0/F8kZfnvhwKiE7XZjW7sQxdgDPvLaQUNnOVftuJuSqo0henuTem4JHhcCzRMH3uyMotXUUzx3K1rvnoaDxXl0qCzZdiFeegeTLs7g8aB/rqntSPVY7LU/F6ZD96iC6JZehvBWC9bN2unJDVCRrj73SJanIH8ITcER5Iltiu9bus5jbmHSlTXsxSzLBAQMRmYfRNI2Ts9NO4P9iAADkJhcuBOVKEy2ahqeXfj53aRlht0B2tySwdwzuqXY7gW/voDl4EB/fOJ/7l+r6hfXnxqEprXyFnp4IDws4XR1lzvv2QChKp+3MkyFZLIjIUOquacB3Y0AHCbWaZA88JpUifapTnPnvrISqmjbvoiuNOvfRfFIftjMyYAY2azPy/EBM1a0U50Jw7JFBxC3Ob8shkIMDmXLuFpIuLOFNy1V45zeTP95A4gb93jh9DEw4fzuHI8I7KBNLvVJwzrOT7l/I598PwhTdwFBLAeetv5ugkDo8UmsR3p5QUaHPcn/fiuzrg9rUDK3Mz3JQAK7EcBwBJl4+Mpo9A5Zyr7fGhiWDuWWKlcYmC82pXtTFyYQllaImCo4FxxH1eQnx/6jiwX5XEOdbReyLB9FMRopdNqCOXiaFjy5YSB+zmQeOJlC9NIrnUqH/0EyWxW5klIeDoZYjFCuCZyuTud1/H76SByXuRqZkTmVd989Qp/2Dv0RfSeruIMyVLRiFQrzRi2aHzpak1DiJeXQrDEzD8XQD2f26k/DUzwgNalU3kQYvFh4ezpgB+3nw0vV0M+jG5m+HIog2lJ75oG1F4od25AI7atXuDl5gV7s2J/CHMAKxfuUoXt66QsEp6MR5ryr4fbBVd4eEQDKbOz/0p8xiZwo5wB/h5YmWe5zr759Dc4BEYzcN31w4frmN0DdLdK+kXOeU7+CGA/TvzgXjdlKlWjFU6PqFniv1CH7LZQNxzKxmVtwPPP7jeJJm7G4n66iuR6tvPH27/Hw58vd4oj41ELbAhRoT1lZtB+D/3jbkFd4ojY16bkB2Xlub5OQEcm4IImFJZVt23QkoZeVE3eyG0CDqukuYZbmVl1Ei/CdHB/0B9/Filu4axMIRSyg93437JyspT2SheXkhDAZKB8k8GbydkSNn4/dhuxHIu9KfI92XATBiXAZDzdUM234bKbMzcfeORygaWkNle6O09tmvYdJgxA3lXB5xgHJnJWu+HIyzUY/AXzxiD3lXBFGyJogei0sIebqe8y01HGwIp8VtJOXKcobcmMvfM0YyLPAoRqGSfVU6ftOKuMr7IOCFVTLR09TMB/VR5FX4k7A6F/93y8m4eyh1c1fjK3lw7u7puH4MQJWh+Ao/JgZsx1OYiPGuZkOzlTGexfzY+wAZaT2x5FVy/6xZcE8F0QE1emRfUfV6mF2HqVkziPHXbWVd6RDOvWY3KpD4wUziVzTwzS1pJAwt5zrfAzSoGtI2X5TKQ12OB2EwgJC6rjDdceA3JxH9IYzA8fwgujmO/KI0d5cQEsJihpYWnauuf0/KBngTPSmX4+/H4f/eb2NuLbwxhcYEF0m37sT7k214A8FA1lsD2Tx2HsN73oPcKGPs1kToYgshj+aSWxNI0Bz9RhvKavlpUX9+GJtApNTxBrk8JcrKfHni6JWkPpHToVLyl+rzAQgJ4tPhbzHl2GxCdkHpGAuxu0+qNNQ03bMQAmG1Iurq0VoXjcfHBJNx7Wv0jJ9Ot0mdjWPNhUlU9APNAF6f6t5C7dSBBGwrQ2nqWGPgnWHiLyETMHq6aBjjIPgbQ7tHIulFO03hAr/WzwujicBB+mx2Ve4F5H6SxHk37MBicpF/bxrRz+/W2ZJaP+8YOwCXl9y23PHbkE3CffXM9c/kiyY/PotOJ8jWiCwk5odvIfnpGYSF1PJt/JcYhczFh6+iYVk4mgw/D3Dz6pjvuKx/FsGylSq1meuvC9C1Be2ByFQSZvDign3Tqa23YjxsRW1dCoVua+SVqn68v30YXllGVt75IklGTw45m1nT2JNF+4cT93eVV9zRPBPlSfF5gkkvb+HzL84h7v1CpkRv4VLPIlasjOXvmSMJv0Y3XE4fCDbV0xSp8lrEdsCLyH7FyG+qJN+ZzXeJA3lj+kWYawRp449Q97q1690YWUbI8mm5JH8r/hBGQDTY0cRv75CQZYTND2rrMIQEM+WjNVzlVcq4zPE0RgsCvb2RAv3BpQ/urDu6YTsCtiVdE1+G7Goh4gdXJ2Nk9HUQafBi/vn/YHn5AIqfTsBcYee5yFWUhXnwlDYR0B/mwEWFiHcNHbbVALyXbcPv+2CcqZFdqiH/Go67/ZBTGjB+byZuhZ0uYzmaprt9kowYkIY4kI02ogZZSFwaf5CDPZM7LDmE2Uxlb4F3viBsaYb+MApB9ZhmAvZ0rGNwjBmAz+hSinOCwMeFVmPu0Ia4T+oZ3GMy9h4tbTEZydebyyIO0Ki2UPJqAqGHq9l/pDfeFhktRlfnUYtbo/6SjOuuKm6N+SfvNk3AM0P3oA7fF82yt44y1buKQaNfwSIEiubB6ENXEv+Ohj00iCl3jKe00ZuHkr7h/oRrUSJbENVmzn36HmzZTh5a9D69Tc1k5ERwLL6RxcWjeeyjBJw+Aku1hnuwG0PvBuTAANylZahGmb21UXR/phStxcHk825gR/oyepg8uD6/H/E3ZOgZn0LglWEl8U4/ng3Zj2ucTOb5ISia4O6iC8l4rQdBRU6UvsnkTLVgDGrkre9GE7pdo2aSHZtsRdMESqmuBC2pKqpJI/yfzQyeepR1EenQhe6C5nD89gnzF/CrRkAIkYwuPHoCccBjwAf8GwVJtSG9kfdmtrn2ksVC0Z3pRLyyS7d4Xbj4mqKg1bZq5rW0cMgewQTPEuoXRdFt2Raaxg/EbZbwPVKHZpS5+/KvSbyqlHkHJ3VI2DFEReIuOo78fed9fICQ5RaSCmZirhJcMW0T+9JTiVlYyoX/uA/FQyO1Ib9ju04xAA3XDMZ39SGEEOROlkmpTvnF9X+na1Ncxsflg3k7/QNuvm06fp954XfQgup0IaUlUZnuR+A/9rZvoUmC0iHeNMxOZVP6q4AXktCo6eWH32ED9E1FMcsYfs4l4Ym9aIqKctKsEuDXSEuEH8aTvNHSwUZcGcEkztmJbPMl69VuZDwfRcrtjXoa9N5DBE0wUL2oD1JIEGpBIUplFW/9NJJ1Can4fq+zQltK/EFRaIjqjhLgDYXFbUbDd7bEqy+fz/I35tOg6UPzqCuQS6x1gIynkChwywxYextJS1pg2168hMCZ35OKWSbG9KlBumoJha4Azrdm8s4557Dr8f48OecmCscpeGUauTTjflQjhGU2IzW7kfZnE/S5ByVTUmkY5IGpLoKiWS5ej1zPo0Nm4Lv6EHVH4pH7SRS5G2FlAJojCzkwgOy5SRgbBR5NlbxVG4FLk1kWv4pBO69H2uRH6Mc6k7Tk6YlxbC9i52QixURR8oIBm6xvOwd6NNKYloiUkYfbZsUYasewJ4e104ZB3m8kyfkXcSa6A5lAHwAhhAwcBz5HFyD59wmSZhWitLToijRmM0SEEPV+dlsab+GtPYl6+1DHaKmqoNTU6CKf6QlMtb2GVfLA4SPwBrw2HkH4eKNWVqG1tLD68v44o2yYj+v8eYa4GJpSgpDzan+ReMH6+XZiP9ej84cuD6M53ola10Dcgx25AU9A8vZG8rTiLi1DTk3Eb381SkMDamMjwT/FQVb+aX9LDgmm7rw4vPOa2pSH1YYGau/qwd1PXoPrmCcD5uzGcY+BrcsG0W/iAYo/OUXD3mRi4s0beCgwkyI37HC4WJPfnW4/FKL0TeX55e/gK7m45bo7kTbtbaVLbzUCmobxnQDyrlZJWtd+zpiv6pEKynROO4OBsE/NuC0C1W7XWZGNBrSwYGYPWs+CR0aTPLMEze2m+/OlaAYZpaICQ1goGQ9H479X4sW5i3g8exzle/phqhNEf3wMraQc17c9iU1vj8T3MjUCMsfcjUyeMwefjDpSK/J1em5A9rdx7Hxv1ox8iffqEnn568u5evRPRPlI/CV4M5MbeiNt2kv3XRH0/Oo4hXYbT0R+TdAdgs0tgdzz1XUkvVtL8BXHWJm8Apem8k5dGoWuAKq7C2RHd3xyYVuLQqQBmsIFjnuH4lGpMeniH/nq/eGE3FDF356+hKjYCooCXRwY9DH7+ji4s+AurJ9vR7XbSVhUiBYfTeHYQJ7v8W5b/z6JW8eypUG8d9s4DJv3k3B/GG67XefX/P+E37ocGAXkappWIIQYB4xoPb4E+AF4gJMESYE8IcQJQdJfXKCf2JoSibFU9/bDo9KNqTWQ5eoVx4M3f8LfyyZ22jYC0NJTmb3wHyQZTSxv9CV4ey0qei49J0fh3QpVPSwEaOG0DI2m4dp66ksFYd8H4KeonYhJToYhIpzGvpGIq/JJbc7qMHMKsxmRHNs2u6sNDUieVuwTBuGVW4dobn/A6mMFtlMNzkk6ghlPxpB12etcmXMJjhHtx7Xdh6gsGkje5LcAvV6+v9SX43PiCd2ytU2SWk5NJGO2jVB7BrFrhpPwoYIpv5KIQIG7uBQtOggFwWFXIPljLCRst+BKi+ugSuS5eh+JpSkdm5hZ0CYHp1bXohqgbDDYNgZTssifJruZuOuP8N2F3emuFuJu9Ybc+cf05YnBgOZW0KwKM+d+SaKxjh97fYaSplKjNjP5wmvo5qlSXV1O8ubrOCf6KI+HrwWgQjGRajQhbqqg7v1gvJfp11kYdMMjueDaZ+bgf9CO4d5Gng3Zj6KZOOjUMFQ1o5nNaBYTKzP6YD5oJXzWN3gIE5d72hl69ctcmjKdn1JWIQsLux1OGhUL4cZaHCFuvLcU4O3jxcZZ3eltLWDtrS8QJnvQd/t15NsDsJapCIuF50Z+yjXeNZxQFG7SjJRNasbXNgTPa0q4JWYjQYZ6bv5kJvfsmkhFn7Wsr+6O3W0kqzKYyAYn6sAeyKW/bUvw34HfagSuAf7R+jpE07QSgFYpsuDW4xHAyRvZRa3HOuAUQdL2N3LysUSl4XGsri1gZDxSyKtPXU3g1pIuWW6lQ0d58qXp3JukYTsoCCrN7XJbLOemCJZMfZXJ384k6T07Xk/LeMVJlFzqZPJju1h160ikH/d12XFXtyCqUwxYv2/qlB8g+XjjPpiNbLPReF4iHl/swF1eiWdhEOQd76ClGLOyWi9AOgmG0BC0Jjt4WDD6tSAhMAgFh5DgFAdK0VSuzR9Ff98Cwv/Z0IGERJjNqK/bMVdaKLtYIrlxH+7hvWjqEYp1ey6KqiDvzWLuzNsx1TqIdzfQcl5PTN/tRQxIa/M8NIejU6KKEEIXBvHVtRDqY2SkIDtCCJr3+RPzbZNeCVjf0EkNp+a6gbisgtAPD2DNMTH2ghx2OoKRKEcFzlt3Nz0SjrO/Mhzfl73w27yfksRYzrvvbhYM/5i1tb14I2IbRlnBuvmYPgYkmax5/QjZJgib3y6i4vf5YC4NGgPAu/ErqHxexbm+H5oMIV+oqLLKyH3XcVPcT9zmd5xA2ZP1vT6iWYOBW28m6gWBobiaL17rxbj+e/nyyXSEW3CbRyE7muK5xKovuQ4OXsrTlSn0friQj3IGcrlnGbo2j45EYzM/DnsT6zlyG1ORS1N46oplrcYCXs0eQehsJ/69PDl2kYGUi7LJ/jqR8BfyuxyD/ymccbKQEMKErinYQ9O0MiFE7WlUiV8Htp6iSrxG07SVpzv3LyULnQpDXAyoKkpJWafUSTkwAIIDOH5RIOHfVZP/uBH/Tz3bos2yzcbRu1OJ2OTAsHE3so8PF249xp1+R7mnZBAZ9/TspNPX1j+jSRfrtPl08Bgkb29EVBjKYT1FVDpBGnqivaEhaAF+qB7GToVDwmBAUxQks5miO9KJ2FCHXFlH3nw/3Nne+OToDLxKZRWS1YrvdxaWxW7krdoIFmYPJ+gFS0d1pJBgRm7I5Zt7R2JctwtDbDQBH9dw7K/JnYqT5JBghMVM7cBwvD7djiE2uq1O4Zeuffzy43z7bX8SFpeAy91h/9lxyQCaZtYRNL0SpaoaOSGWkovCmHnHFzy3dSxJN+3GfX46Mc9kkv9QMrWJJobN2MWBR/pQMEbGVCsRv6g9N0GYzeQ+lY65RvCXGz7hr8smEf9mLq7YUI5d7Mnn019iYdW5ZF0bp9eVjEinNt6MagLruDL6Bx1jduAPeEsCm+SBisY/W0w88sgM3nlufgdV4KUNAbx/y+UYtmdQO6EPFz64mSeDDqFoKhNyxlL8YSxVw5zkXbSYXFcjxxUvepua8RJmZCHxzxa4/8hVzIj9kZt8S5mSN5JEr3Lu9N9BoKx7B9cVnMuPWQnc038Dd9oKyHU1csG6exjSPYedBdH4bvDAltWCvPXAGSeXCbMZVO2Mdgr+TwpErRgD7NE07URFQ1mrECn/V0HS3wLNYmLsmr0cfbyzMm7x5GTyrwxEckLOdTY2DnoToZ6kslNTQ/QTWzH8sA8AZ3oCcaZyDrmc7Hqp32kNALQy5FRUdFoyqA0NbQYA6GAAALJnx/HgV5+QPc2rA1WXMBjIeqc3pXcNQXh4ELm+Fg5kgqpyf891fD3lJcbP/h4C9PW+pii0uPWI/XU+eTRl2CgbYG0/pyST8VQ0myqTsGzT26MZDWzd3APz2s4JVa6kCJq6h+J9VM9PcIXbqLh18Gn7r19AhfN8Mhkxeh+Zs0KpGRbZ9pbjkgE89erbrOz1Li19YxFmM6bFdnY89Cq5LcEEbDGCkDDvzuH4zGgMG3cT+HMTNwb8yLT5X4GfE0sVFE+IaVPh0fomY2wU2LIUFuSczytTF1Ox2Icb31/FkRlvkGqy8krYLhrnuyi/Yyjyo+WseeIlGqNhdNgRrrbt4IAzmApFsNupYBQycYY6apMlwuWOxu7NvPNwWQ0U3ZFOU5jE+qfPIfGjmSR+N4OD+eEEvLOVhPdV4tbdhLckGGJW8JU8kIX+CM3cOxXXF0Fc5pWLQ3Oxf1UqH+wYyh3HLuP9+mCWNdjYvDeF5BftfPLYxdQoduKNXuRd8jbXh/yI7VsPApfsRNp8ZoViJ+Ae0gMpobNQyW/Bb1kOTKZ9KQC68Oi/SZD0zKHmFvDyztHMGf81q19O7MC+Er62lJoBwfzt6beIMzRyzsq5pOw6RURD0wAVqVcK7kcqef7R6/DbX41P7t7/07aLMJqQfL07bf/5ZcKsRbNIWV6M++RZVkj0T8invpsF8ZUvWm4hmttNY99IxnmuYLvDnx9mD0XO1B9gzeHg4K5YXAkKKxrDQYX6Xk5CW89ZOWMgay56masWziWyvlWgIiuXuAeOdjm7G48UIVVVo7XuuDh9jdTHQ2hqYqekItA1HYvO8eaoM4iFkVupmbSBy3+6p/VNQdXNTRxoieK2pZcRszuD8mnprIl7CbAwJ/Anbnn0R6bXzMFz5XbYq697NUmQ5Qrmep9i/Ias4KJR5ZiFkRq1hSV1vRhk3cQQs8K4rMs437+Ai60Ouqe9x/NlFzDAsr4tlTfcq44Z96xilIfCwL3XE//X/WxaPwT7iybiLeUsKLgAZV4INUlG0iYeZujF+9ui8zscLh7IuYrv0z5l8cvdmL/ycmKf3oPmcOC3NYLDj4UzecB2dg/UA4wpFUnkn2ci2NyxlPebAQtp6icRLHuystEH8zmVqA4T9df78WljOpUXxJKYa0eyt+BVYKBBUzkRzp25bRpJnx1EdeucBmpZBWpTE/VTBuNR6ca4btdpx53pQD7qLySanQnOyAgIIazAaODWkw4/DywXQtxEqyApgKZph4QQy9FVi93A7b+2M/BboDkcpMwp4I0Zl9GtpeO6Vck+iq26lpt6z0SKaSLxwb1oCTHUTR2M79L2MIUcHMTlyzZznU8e53rP7jCT/xqqZgyhMVIQv6RYlwtDd8ky5/fGXCkT/VjHwKXt/a3Y6LyDIAf6k1/nTdAdTtx5+W3Hrd8fYtSzc5BbwP/7judKnneMUZtn6sSpA3TGXtCJJ4ffspNXykcR80lXRq8zTuUiMH+zi0D/QThDvJEz2o9LVivVV/amOUiwYvaLbcKaXpIZ14wq+EIge3vjcsl8OvdibJ4KUd+2cH/AQqoUwcXP3kFzkGDNTS/QGC5zMnujvCeTpxZP5cq7XuNKr3pAXzsHy57c53/C45L5a8wXvFU+ErvqxCoEj4as57DLlxiDC1lIBJqa+L6hO6M8DlBd54mtqQnjhj0cvDKGr6YOxZnSTIiXTHOwhqfBgZ+xGZd2wjNwMjlyJ0YhM8Ajj7Ctbj0mYjajlJYh10eR4lHMXkcPVOD46ED6mTrX8p9I9wV4u2g476V9gLdwc8GsuSS9X0fA10cQZhMYDDgCPdoE0B8pT+OpAauYN2UiIT9UUDIyiODd3rDzAL7ZTUg1jSjoY6x+fF/8dpfp6fWSjGQy/ma9i65wpoKkdk3TAjRNqzvp2L9NkFSY2hNThNmM45IB1E8ZjCGya2UhpbKKyOe2dHK9AYSXJx5lgvhbC/SbWVWLpUbBEBeD68L+5L40mJr3vdndEMPgeXcTtPXX+QHkoCDynhuCISIc2zVFLJ8+j7JR7SXJmsNB6mO5xM1v3/s3hIV2EhTt0IfqGgLvUTulRatNTQS9ubXLbEf38WKsRXai5mYhNEhckIecnEBjjxC6mavZWxEJjn8xi0zT8P1oG6fWYgirB9MeXINlVAXxhvY19Af1EXi+5NuWrRhzbRbF5xow3VLKXcEbGeGhUuj2JXR9GTGfVTN2x0yag08pLPL2xuGvtbnUp0M/s4m3o35ir9PApuYwwgxejPJQ+MkhkfrWLHYt6MuKrD4AeHu2tPXHfTQfYz38MPxVvpv3dzZNe5GfKyOYatvG9PwLWNnoQ6DsyS2+xW2/U3CFhiEslMpr05FsNpKezuCZFVdzdKIfwmCgKVLvg111ErvqFgbvuwqA3Q4n6bsmsazBxurkr+hlshBr9CJ30lsM/GA/WlQImqKiuVzUJBoJlNuvZbyxnPWPvMzD3yxn0u3raYjRvRRt54EO9TThd+RwZHawrirk74eW1rUYL+jp4mJA2i9e1xP4QzALac521pyGcX35x5vzGXDv7jMS++x0Li8rIZcUUnxdD+QeyRx+JooZ8z+jeGw4l89fz6HJr3Jr7GZ2Lu1N2LwtSHWNFD48lJrpQ057zqZBsYwctQ81wIdquwe9TBaqhznb6rVBN0zCyxP7BJ0KvXxMLI4Bp79JmsOBdrwU54W/rvB7MgrG+vBx7Pd49aqiZFws2TcFUd7fwKriXmzs/SHHpsT8pvP9GpSqaj4p7MeOvp+2Payv1kTz9I+XITe7kZMT2voT++BWTBcVMe7TewGIMtThCvVFsxiIu78eZ5BC4aND2xiOSyYmsmrSy/rvaF0Vx3bEMIvU6jHo+Lk5mtgVlfh+vBNHnYVZxwejbvRHslppunIQxXOH0hyq8Vb1EF6s6sPi2v7IkkqF4smi6G/azmVXnW2aE3su+jvTN21l3kNvcuSxONTGJszVgjXTXsR+aTrJrxWRvOlGqlUn3lkG6pp0bYRbn55NyDWFvHPbBFY22XBoLmoUPeX3bv+dmF+tIeNv0RBowx6mtUmVPR18gGhDM2ZhoKfJwQSffViqO8cENKeT4tcTSHlTZ8VSKqvadnO6QuatgWTecGbPzx8ibRh0Dvsjd3jhFViPUQjWbOpHfNb2X//iKSgf6s/WlI+pTGxmYuEcUFWmelfxw7U/807mMBZvHUu3j44SUqafuzk1lHW3vsDwtfdgW9L1OS1rdnNsoxm1OZOmPUNwpSvsveA1xn46Fb+bvNui2a6oAKpTZKyAZ6kbY23LL8YZpJAgmgMNlD08lOZoFymvN+o03ifet1hAljt4PH45KnbVyd97LOPHmGQWr76Abk9sQfTtQf/x9xKU8wsrLyH0nPPfUtasadQ1t29xqajcaSvgpjGvkzda4c2KEeSO0HdEXBf0oybZTMABDbvqJMloQX28CsO4KtxNTRhqw1k74wUmHp2L/84K5t71CakmK0XuRlY09ORuW36Hny5x62vdsJNc7RPtKFOaeXvxJZiGaFQ/4IVA4YdV6ajDGuh3QwN7agrZlPQ1dxYPZc/oULSmJiSbH34mhSd738SSBfPwap0CLz40iUFB+bwYuhebbOWb6l5s/yqNsOGlNEzox6XX/sgFG+6m++5ilNJyjFlR3NttHFvunYdZGDEKC395YCkPXTge4x4Lz2WMYXDfd2hSJQqVFmQEj3b7ipBYJxuHxhBqqGNa/gje6raWMsXNk8Vj8TE42D2/L+Y6BYPSxT3UNLyXbYOkeKquG0DA0j2nZcgSRhMB+wUB+xq75B04FX8ITwBAqmtiyai3Wd73Hf5R353kV4t/VT7JfsUg6qa1R7SFwUBVX4Whj97B+Ifvw3tLHsZK3c6FW2pRd/sS/uIWnTqrNSDWEGEkTLaSnFDcSWijDScJnMgt8LeqHhiR+Sj1A1Rbu8tvKK8n4LD+gJnX7EQ4FIrvH4o6vG+nUzrGDqAhLRivYifN0S4Wnv8+Ul3H5Y3qdHW60d75zVSqTmY/eztfP3Y+CUtaK/AO5xC3vArvzV2UYp64PiZTmwDImUIYDPj8w4dFdeEccrp5vSaZQ85mrJKJHiYPSlt0RiXJ0xO/x44x+84VeBc6SN9yE7nuZsKs9UjBgcgB/oT/qPBC2SiarqoHo4FHNk8g+d2ZPFh0KVN89Aw5l6Zwx/FBODQX39ljWFQzkJ9aOg5lh+biy8ZU3J5gDxUcHf0uxkIzlgFVHBq2hKeDD7AmeQ2ykHgmdCONQ2NR7XbUqmoUPy8kl8aXjT2pU5txaC429FzB48Hty6+5oeswD6wmxa+cj156mYeCduCZZcJddBwpJhJHXAu7dybyZPkQdjv09lzpVU/m8A9IH3+QsLlOrnr0Pt6sPI/xq2bzt5KLuHrVXUQavJjsXcanVQPZv6I7EzKvZuqh66m8CHJmJeFR6abofJmWwI51Gx3GREERgXvqEClxnbQzTkBzObG9v7WN5erX8IcwAq5gT9TScu56ZRbjls1h5f0Xdi4h7gKeq/fh90n7GlZTFFJfqcL/3a34fbgVpaycgP0aWa4mHgrcR3O0q400E/QBXjlIQRYST8Ss0l3z0xmCVoRtbWHLpDR6fnEnS+v647Z5IIx6koiSk4fHl+0bIUcfNZF4aTbV9zch+vXocG7r9lysa/ZhOlCAucTI/QtmoBS1ehQX9EM9ry+oSqdZuz7Og83N0YSsP471ix0oR/SHXoqPprK/P+7kKH2LrQspK83hOCMK6pOvT8HDAzHNKGHBh+Op18y8tnE090y5jYlHR7Gy0Ycno74i634PpKAA9uZE8+znV2KsaSFmSgZ3TppJfr0/k775iYolgXiU2jk61odud9SiHMokeeZ+4p7bT+nDcfzl+EVsa1Ho9fadZN+ZzKZmKx8eH8Km+4Yy59FZNKrt5eJekgVPyUFLQgvGJn0pseCadwn2auwUX/CRLNRH6w9K+fS+LPpyITe+/DnfTh3K4Lfn8HpNMkbRntADuqTbwLACthTG4i0JvCQLC2YspPbawdT1DcbzoAVrTD3Ph+yml0nhyXJ9Sbe0IYCix5NQ847hv2wPBx7pw8B+2ewpiQKbHqspU5rZ91Yvgve2ULIuirJSP5TGJlSDxBNvvcMb4xbjUepoUy3q6h6q+w7rTFnH9PFiiI5C6tP9jO/rqfhDGAGvAH3tFPL3LcQ+uLVdxeY0jKonoDkcHZMkNK3jPv7gXrRcU8s+Rzgf1Udx5JI3MK2QMESE60ouzw1g/Zh5ZDjtPJhzJR55XQcJZT/f9tc/7EHJyCbxju18OX8kpYM9kCNC296XrNY26xz7okrtU92oKbCRM8UbQ2RE23tKVTWaorMGRz++hZBXt7Q98DUpZmzPHOvIpizJIAQBm4/z5OcTyZsaiZwYh+ThgezjQ10PGwgQW37GYFd+XTL9V4yd5O1N3uMD2Hzzi1wRvg9HgMqtu6/FO0/GkFlIxpfJtGhGEowG3hv6HqqPlZQ7M4j9y1Ya4/RYidh9hFDPeqZ6l5MeXEhdohfVF8WjNTWBEBx9qh8tXwRS1s/C0cdTuOfh24l9PRtpfw7zjl1ITnYYxnW78N9Vyc/OjszRV3kdQ3PKqEaQhURPUxWNTjMZTjvjsy9iWv4I7KqTZs2Jd5GCZLUSck0B/pKBEpcNp78H/7h+Pvf6t1fpOTQXP7WoPFTWi59W9iXmry7Oefc+PqgPJN3UQM0YO8WjFOxpzazrtwhZSFglE/cFbiPL1URWSyiN4Ubk0BA0hwPLDwfYUxjJZ/0W8dnwNwHwFBKGieU8tvhdPpn5MgaLGyktiaYoD7obmzjPw87xEVY0S0dtxi7hYdHjH91DaUjw/vXPnwZ/CHoxc1SUdl7ZOZ2kpotn9CF0wZZ/6ZyGmG7EflrGaxHbKVeaGLJiDg9d/AW5LcGsf2UY/h/v5ugHqVyYcIRdC/ri9+lepG4RUF3bYdtFDEhDe74G+U5rl1uJLZcNxOkl4fQWWGo1LJUuJJfaIfFIToyjIS0In31lVL1mwOevnhhLa8mbEkHUc9s7VUcKs5mK69OpjwfbYbDUKJRObsG2xhO/D7YiDAad+tzl0lOQhYRk0Vl4ziRzTBvam/xLrcQ9vvu0nxf9enDjP75mhEcxZiGhaho22cqGZpnHsscxNDiPr74ejM9RmHLfN5Q7fdg7vTvq/iPIPZJRDmXqlXZ/j8JdbyIt9RgNz0Yy+ZU1bKxOIWNFCuYLKriy2z4++nA00tAaBoYd4+hDKVT2NKOOqCXsRSNokDPNwuWDd3O1bQfJxmYsQqZFUxj45b1IzRI5U9/k9dooVtxzEYb7yzh6IIKYr1wcu9DE+NHb+CqnJ45qD+aP+hhFk3jpiSk4vQRbHv87ZnFKybTmwiyMFLkbubtgPPYrBPh6kXFvEE+PWkmTama0NauNeg7gpxaV67bcxKP9V1OrWHn33bGE/7MBbecBDJERFF0ZjeSC/tN/ZvPa3iyfPo9epvY4yyGnm6tW3I0a7ODynvsxS27WLhlK2Gs7fjV+I/v5oilqp1TtrvDvyBj8j8Fc1NSps5rTSciOroUzzwQF10RyuW0Pa+1mXqkcQvJblXw2uh/7JiUiuzTqrkon9BMzq/enIdRWVzn7aEcDYDZTl+hJgKUJUdO1G61JUD4Azr9tG87rqrFklSL9tL/DZ5Tso3iu3sfh+4KZFfcDCFCKSoheXdslA5LmcBCyMovUQXmseOpF6uIMRL8mEbBd39vX3G7Uhga9dFjT2mIWZ0oyIe0+grlaIOTT3365sp4Hvp/IlXfdy+LaHhhb3exRHgrxvpUcnhBJ7LN7sS3ZxrvvjuWRoF2UnOeve03l1SDJKFXVxE07QPwyN091+5L8CRJXe+WwLHYj9b0c1BwM5IvnRqEaoL7Kk5++6o1pRxaWao2hEXm0BJtpeaKe7PFvsq8qkkdm3cqFe2+kWnUTKHuye9x8vp34IvudLXzy0Bg8dh2lm2cNWVe/QeEMF3EPbGX3g/0Ie9eC3CjxWWU6849egE9OE7YsB+/VxaBoaoediRNGIdLgxd+6fYGyzMzxS8N4btSnTPWu4hbfYp4uuZhyRR+br9ZE8/iNN5N4/QFezriAu235LLr9VYrO90YYDBROjObdu1/hnruW06wYiVtWweSF9/JsZTJPVnQny+Wkj9nMvPFLSFiosH75QP4Wso+Fd72KFB/zq/dSqa07IwPwS/hDGAHQ8/odYwe0CThobjdia8dkIDnAv+39X0PUqz/zyiXjePmGKShIOF53UjskUpcW1+ClZ95g2nNfE/2ZwO8rPSgl9U5FTorXhU6TE8h+vi8PPPkRVS2eNKVHdVqeyEnxdHsgi61Xv0xuYyCJtgoKpkaT9U5fCh8d2mFdLvvbSEgq4QqvIhz+ZjSXsy1wI4cEcyrU2jpqXonmou0ziVhbgfTjvk6y7R3wK0unk6E5HIS/tKUjLZsQbbENYTBQPTSCm4ZsxnR7CTf4HsRLsnDDseEM3Hs15/jlkPuiH1WT+lI6ewiNMQpmYeCDOfMYtKmCAeuLyf2wF44x/UFVyLvcRB+zmRF9MmjSVI65G/Hdaybuga34Zjbw+PVL8T5koiVEofS6NNwesG5PGp4bMigsCGRpQzCVG8Mxrd1JfZatLTHHLAyMXjWHe268HY9Vu0FRSPcpQBYSrhp9nBjX7cK0difxc7dRMdKJz/RGpP3ZmA7ks/ily0lYfSsJ395ClqvzhBNv9OLb1K+5/dYvuNKrnQJt0489aVA13YAgcHkbkIMCCVlg4ZHyNJbVDCJy3i40t5um9GZ6mgSVbh/uCN1A/hV6TGpVURpLfjgXa6v8XrHLhtPXxPRrdeqNOGMLlUOCOrXpP4E/zBah2tCAahJdM+a0QqmuwdAtEvXX6LhozeHPzEELS2ekdwZ/C9nHoRebmdxtDhEbaqlQfHjnb+PwX78HKSgQtclO84vNHCv1J/WhEJT8QlIWOLnXMJWkHkVU9DJi7jYQ/yMO5O/1YGTWLUFsifqAIT/cQcLrCtnDvRgxcTevRWxnnd3Igg/HUDc2mcqxDgw5Hhi/gdeD0xAn91GSKRsfT+Ciig67IZrbjfXz7XT7UtZr+E8DqU93WkKsNIYbOyQYyUFBaE1NpxULORWOsf0pTzcS9dct0CelrcDmkcAjgJVlDTZ+2JdK9yePsbT/pTz04mdMGlaChISEQBYSvUwWegXphu2xEQdIdM0gcQ0Ep+oezFtRG1lU24OV911I6He7dJHTjDzm5YymJVjjidErabnAxPPfXUa3r/UxEb5e5qqxxTztp4EQKAF6TsnEo6PIXJlMyuKDbcFOYfNjdVkat/sVYvLvTDartrToVHRA4Zx0bMNLSZ1Wh1pbx8UL7uXohIVdXhs9majdyF45ahtWAbudCouWjWXEY3sY5XuYBz+bylyvLILkBh7pcT1SURm+P1n4a890vnv5HJb4X0z4/hbmvr2U0R7NDHBPJkTWDW+MqRKfBwu53S+DKXkXceDzVDzd7R6KNqR3p0kRISh4YgghO91Yvv7XM/P/EDEBIUQD8P+HRuX3RSBQ+auf+u/H2X7+MRGtaVon9+KP4glkdhWw+LNBCLHrbD//PPiz9PMPExM4i7M4i98HZ43AWZzF/zj+KEZg0e/dgP9PONvPPxf+FP38QwQGz+IszuL3wx/FEziLsziL3wm/uxEQQlwshMgUQuS06hf810IIESWE+F4IkSGEOCSEmN163F8I8Z0QIrv1r+2k7/ylte+ZQoiLfr/W/zYIIWQhxF4hxNet///T9RFACOEnhFghhDjSel+H/On6qmna7/YPPQMjF13VyAT8DHT/Pdv0f+xPGJDe+tobyAK6Ay8AD7YefxD4W+vr7q19NgOxrddC/r37cYZ9vRf4GPi69f9/uj62tn8JcHPraxPg92fr6+/tCQwEcjRNO6ppmhNYhi5e8l8JTdNKNE3b0/q6AchA11wYhz6YaP07vvV1m1CLpml56LrMA/+/NvpfgBAiErgEeOekw3+qPgIIIXyAc4HFAJqmOTVNq+VP1tff2whEACfnAHcpVPLfCCFEDNAX2M4pQi3oYsfw39v/V4D7oQNxzZ+tj6B7qBXAe61Ln3eEEJ78yfr6exuBrora/+u3K4QQXsBK4G5N036JxeO/rv9CiEuBck3Tdp/pV7o49ofu40kwAOnAm5qm9QWa0N3/0+G/sq+/txH4jwqV/B4QQhjRDcBSTdM+az38uwi1/IcwDLhcCJGPvnw7XwjxEX+uPp5AEVCkadoJsssV6EbhT9XX39sI7AQShRCxrTJn16CLl/xXQggh0NePGZqmzTvprRNCLdBZqOUaIYRZCBHLv1mo5T8BTdP+omlapKZpMej3a6OmadP4E/XxBDRNKwUKhRDJrYdGoetp/Ln6+ntHJoGx6FH0XODh37s9/8e+nIPu/u0H9rX+GwsEABuA7Na//id95+HWvmcCY37vPvzG/o6gfXfgz9rHPsCu1nv6BWD7s/X1bMbgWZzF/zh+7+XAWZzFWfzOOGsEzuIs/sdx1gicxVn8j+OsETiLs/gfx1kjcBZn8T+Os0bgLM7ifxxnjcBZnMX/OM4agbM4i/9x/D9znwkuHRZBXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2feffd53",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bucketize_image_diff(image, kernel=50):\n",
    "    image_size = image.shape[0]\n",
    "    iterations = image_size / kernel\n",
    "    image_len = image.shape[1]\n",
    "    copy = image.copy()\n",
    "    for row in range(0, image_len, kernel):\n",
    "        for col in range(0, image_len, kernel):\n",
    "            subset = copy[row:row+kernel, col:col+kernel]\n",
    "            num_1 = (subset==1).sum()\n",
    "            num_255 = (subset==255).sum()\n",
    "            num_0 = (subset==0).sum()\n",
    "\n",
    "            if num_255 >= (kernel**2)*0.3:\n",
    "                copy[row:row+kernel, col:col+50] = 255\n",
    "            elif num_255>=(kernel**2)*0.10 and num_1>=(kernel**2)*0.3:\n",
    "                copy[row:row+kernel, col:col+50] = 255\n",
    "            elif (num_255+num_1) >= (kernel**2)*0.4:\n",
    "                copy[row:row+kernel, col:col+50] = 100\n",
    "            else:\n",
    "                copy[row:row+kernel, col:col+50] = 0\n",
    "                \n",
    "    return copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "31b6c16b",
   "metadata": {},
   "outputs": [],
   "source": [
    "bucketized = bucketize_image_diff(image, kernel=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a7bc1c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f75a487c130>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a color map of fixed colors\n",
    "cmap = colors.ListedColormap(['green','orange', 'red'])\n",
    "bounds=[0,100,255]\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.imshow(bucketized, cmap=cmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf1d4d31",
   "metadata": {},
   "source": [
    "# Generate Shapefile from Bucketized Damage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "0abef34d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_meter_distance(x1, y1, x2=375, y2=375):\n",
    "    '''Return hypotenuse distance in meters between two pixel centers of blocks assuming 1 pixel = 30 m'''\n",
    "    a = abs(x1-x2)\n",
    "    b = abs(y1-y2)\n",
    "    return math.sqrt(a**2 + b**2)*30\n",
    "\n",
    "def get_damage_type(pixel):\n",
    "    if pixel == 255:\n",
    "        return 'high'\n",
    "    elif pixel== 1:\n",
    "        return 'med'\n",
    "    else:\n",
    "        return 'low'\n",
    "    \n",
    "\n",
    "def get_quadrant_subtractor(x1, y1):\n",
    "    '''Selecting quadrant based on image coordinates'''\n",
    "    if x1>=375 and y1<=375: # first quadrant\n",
    "        return 90\n",
    "    elif x1>=375 and y1>=375: # second quadrant\n",
    "        return 180\n",
    "    elif x1<=375 and y1>=375: # third quadrant\n",
    "        return 270\n",
    "    else:\n",
    "        return 360\n",
    "    \n",
    "def get_bearing(x1, y1, x2=375, y2=375):\n",
    "    '''Tutorial here - https://mathsathome.com/calculating-bearings/\n",
    "    Geopy radians takes degrees, so will convert to degrees at the end\n",
    "    '''\n",
    "    a = abs(x1-x2)\n",
    "    b = abs(y1-y2)\n",
    "#     print(f\"x-val: {a}, y-val:{b}\")\n",
    "    if a==0 and y1<375:\n",
    "        return 0\n",
    "    elif a==0 and y1>=375:\n",
    "        return 180\n",
    "    angle = math.atan(b/a)\n",
    "\n",
    "    bearing = get_quadrant_subtractor(x1, y1) - math.degrees(angle)\n",
    "    return bearing\n",
    "\n",
    "def get_lat_long(distance, bearing, lat, lon):\n",
    "    '''get the latitude and longitude of the bucketed damage point center to be used as the new center to generate shapefiles\n",
    "    \n",
    "    distance -> distance in meters\n",
    "    bearing -> bearing in degrees\n",
    "    lat -> latitude of original point in degrees\n",
    "    lon -> longitude of original point in degrees\n",
    "    \n",
    "    '''\n",
    "    R = 6378.1 #Radius of the Earth\n",
    "    brng = math.radians(bearing) #Bearing is 90 degrees converted to radians.\n",
    "    d = distance/1000 #Distance in km\n",
    "\n",
    "    #lat2  52.20444 - the lat result I'm hoping for\n",
    "    #lon2  0.36056 - the long result I'm hoping for.\n",
    "\n",
    "    lat1 = math.radians(lat) #Current lat point converted to radians\n",
    "    lon1 = math.radians(lon) #Current long point converted to radians\n",
    "\n",
    "    lat2 = math.asin( math.sin(lat1)*math.cos(d/R) +\n",
    "         math.cos(lat1)*math.sin(d/R)*math.cos(brng))\n",
    "\n",
    "    lon2 = lon1 + math.atan2(math.sin(brng)*math.sin(d/R)*math.cos(lat1),\n",
    "                 math.cos(d/R)-math.sin(lat1)*math.sin(lat2))\n",
    "\n",
    "    lat2 = math.degrees(lat2)\n",
    "    lon2 = math.degrees(lon2)\n",
    "\n",
    "    return lat2, lon2\n",
    "\n",
    "def generate_bearings(point_bearing, k=20, increment=10):\n",
    "    '''Generate a list of bearings based on the point_bearing\n",
    "    k -> integer, number of bearings\n",
    "    increment -> index, number of breaks in the range\n",
    "    '''\n",
    "    bearings = []\n",
    "    weights = []\n",
    "    range_len = len(range(0,360,increment))\n",
    "    quarter, half, three_quarters = int(range_len/4), int(2*range_len/4), int(3*range_len/4)\n",
    "    half_prob, three_quarter_prob = 0.5/quarter, 0.5/three_quarters\n",
    "    if point_bearing <= 90: # first quadrant\n",
    "        # 50% probability for the first quadrant\n",
    "        weights = [half_prob]*quarter + [three_quarter_prob]* three_quarters\n",
    "        \n",
    "    elif point_bearing <=180:\n",
    "        weights = [three_quarter_prob]*quarter + [half_prob]*quarter + [three_quarter_prob]*half\n",
    "    elif point_bearing <= 270:\n",
    "        weights= [three_quarter_prob]*half + [half_prob]*quarter + [three_quarter_prob]*quarter\n",
    "    else:\n",
    "        weights= [three_quarter_prob]*three_quarters + [half_prob]*quarter\n",
    "    while len(bearings) <= k:\n",
    "        rand_bearing = random.choices(range(0, 360, increment), weights= weights, k=1)[0]\n",
    "        if rand_bearing not in bearings:\n",
    "            bearings.append(rand_bearing)\n",
    "    return sorted(bearings)\n",
    "\n",
    "\n",
    "# def get_polygon(lat, lon, point_bearing):\n",
    "#     pts = []\n",
    "#     # base the bearing on the direction the block point is from the center point\n",
    "#     # 50% chance for values in the same bearing area and 50% for bearings in other quadrants \n",
    "#     bearings = generate_bearings(point_bearing,k=15, increment=10)\n",
    "#     num_bearings = 20\n",
    "#     for bearing in sorted(bearings):\n",
    "#          #calculate random meter distance to head to based on 1 to 25 pixels (block size)\n",
    "#         rand_dist = np.random.choice(range(100, 751, 1))\n",
    "#         pt =  geopy.distance.distance(meters=rand_dist)\\\n",
    "#                     .destination((lat, lon), bearing=bearing)[:2] # go the calculated distance based on the volcano distance and bearing (angle)\n",
    "#         pts.append([pt[1],pt[0]])\n",
    "        \n",
    "#     return Polygon(pts)\n",
    "\n",
    "def get_polygon(lat, lon, point_bearing):\n",
    "    ''' Create a square polygon that covers the entire damage area\n",
    "    '''\n",
    "    pts = []\n",
    "    for bearing in [45, 135, 225, 315]:\n",
    "        pt =  geopy.distance.distance(meters=math.sqrt((375**2) + (375**2))+750)\\\n",
    "                    .destination((lat, lon), bearing=bearing)[:2] # go the calculated distance based on the volcano distance and bearing (angle)\n",
    "        pts.append([pt[1],pt[0]])\n",
    "        \n",
    "    return Polygon(pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "fb65b900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"98.38319985524367 3.1611577074434436 0.01760028951264303 0.017684519453979863\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,6.339999934340867)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.00035369038907959725\" opacity=\"0.6\" d=\"M 98.40014516255432,3.1781872446954242 L 98.40014503449385,3.161812689645443 L 98.38385496550615,3.161812689645443 L 98.38385483744567,3.1781872446954242 L 98.40014516255432,3.1781872446954242 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x7f759ee01520>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_polygon(3.17, 98.392, 270)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f9ba177",
   "metadata": {},
   "source": [
    "# Full pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3fa60be6",
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = pd.read_csv('Sample Eruptions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b45fa975",
   "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>Volcano Number</th>\n",
       "      <th>Volcano Name</th>\n",
       "      <th>Eruption Number</th>\n",
       "      <th>Eruption Category</th>\n",
       "      <th>Area of Activity</th>\n",
       "      <th>VEI</th>\n",
       "      <th>VEI Modifier</th>\n",
       "      <th>Start Year Modifier</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Start Year</th>\n",
       "      <th>...</th>\n",
       "      <th>End Date</th>\n",
       "      <th>End Year</th>\n",
       "      <th>End Year Uncertainty</th>\n",
       "      <th>End Month</th>\n",
       "      <th>End Day Modifier</th>\n",
       "      <th>End Day</th>\n",
       "      <th>End Day Uncertainty</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Reference Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>233020</td>\n",
       "      <td>Fournaise, Piton de la</td>\n",
       "      <td>22343</td>\n",
       "      <td>Confirmed Eruption</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-2-10</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-4-6</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.244</td>\n",
       "      <td>55.708</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>273070</td>\n",
       "      <td>Taal</td>\n",
       "      <td>22344</td>\n",
       "      <td>Confirmed Eruption</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-1-12</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-1-22</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.002</td>\n",
       "      <td>120.993</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>282050</td>\n",
       "      <td>Kuchinoerabujima</td>\n",
       "      <td>22345</td>\n",
       "      <td>Confirmed Eruption</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-1-11</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-5-13</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.443</td>\n",
       "      <td>130.217</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>345020</td>\n",
       "      <td>Rincon de la Vieja</td>\n",
       "      <td>22346</td>\n",
       "      <td>Confirmed Eruption</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-1-30</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.830</td>\n",
       "      <td>-85.324</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>353010</td>\n",
       "      <td>Fernandina</td>\n",
       "      <td>22347</td>\n",
       "      <td>Confirmed Eruption</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-1-12</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-1-13</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.370</td>\n",
       "      <td>-91.550</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Volcano Number            Volcano Name  Eruption Number  \\\n",
       "0          233020  Fournaise, Piton de la            22343   \n",
       "1          273070                    Taal            22344   \n",
       "2          282050        Kuchinoerabujima            22345   \n",
       "3          345020      Rincon de la Vieja            22346   \n",
       "4          353010              Fernandina            22347   \n",
       "\n",
       "    Eruption Category Area of Activity  VEI  VEI Modifier  \\\n",
       "0  Confirmed Eruption              NaN    0           NaN   \n",
       "1  Confirmed Eruption              NaN    4           NaN   \n",
       "2  Confirmed Eruption              NaN    3           NaN   \n",
       "3  Confirmed Eruption              NaN    2           NaN   \n",
       "4  Confirmed Eruption              NaN    0           NaN   \n",
       "\n",
       "   Start Year Modifier Start Date  Start Year  ...    End Date  End Year  \\\n",
       "0                  NaN  2020-2-10        2020  ...    2020-4-6      2020   \n",
       "1                  NaN  2020-1-12        2020  ...   2020-1-22      2020   \n",
       "2                  NaN  2020-1-11        2020  ...   2020-5-13      2020   \n",
       "3                  NaN  2020-1-30        2020  ...  2020-12-13      2020   \n",
       "4                  NaN  2020-1-12        2020  ...   2020-1-13      2020   \n",
       "\n",
       "  End Year Uncertainty  End Month  End Day Modifier End Day  \\\n",
       "0                  NaN          4               NaN       6   \n",
       "1                  NaN          1               NaN      22   \n",
       "2                  NaN          5               NaN      13   \n",
       "3                  NaN         12               NaN      13   \n",
       "4                  NaN          1               NaN      13   \n",
       "\n",
       "  End Day Uncertainty Latitude  Longitude  Reference Year  \n",
       "0                 NaN  -21.244     55.708            2013  \n",
       "1                 NaN   14.002    120.993            2019  \n",
       "2                 NaN   30.443    130.217            2017  \n",
       "3                 NaN   10.830    -85.324            2013  \n",
       "4                 NaN   -0.370    -91.550            2019  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b7d3b759",
   "metadata": {},
   "outputs": [],
   "source": [
    "diffdiff_files = []\n",
    "for file in os.listdir(\"model7-diffdiff-results/\"):\n",
    "    if file.endswith(\".png\"):\n",
    "        diffdiff_files.append(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "3555daca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Soufriere_St_Vincent_2020-04-05_360150_22385_dVV_dd.png\n",
      "Sinabung_2018-10-24_261080_22365_dVV_dd.png\n",
      "Soufriere_St_Vincent_2021-03-28_360150_22385_dVV_dd.png\n",
      "Kilauea_2020-02-23_332010_22380_aVH_dd.png\n",
      "Rincon_de_la_Vieja_2020-03-12_345020_22346_aVH_dd.png\n",
      "Kilauea_2021-03-07_332010_22380_aVH_dd.png\n",
      "Rincon_de_la_Vieja_2019-03-21_345020_22346_dVV_dd.png\n",
      "Fournaise_Piton_de_la_2020-04-06_233020_22343_dVV_dd.png\n",
      "Soufriere_St_Vincent_2020-04-05_360150_22385_aVH_dd.png\n",
      "Kilauea_2021-02-21_332010_22380_aVH_dd.png\n",
      "Rincon_de_la_Vieja_2019-04-04_345020_22346_dVV_dd.png\n",
      "Turrialba_2019-08-22_345070_22369_aVH_dd.png\n",
      "Kilauea_2021-02-28_332010_22380_aVH_dd.png\n",
      "Fournaise_Piton_de_la_2019-03-18_233020_22343_dVV_dd.png\n",
      "Kilauea_2020-03-08_332010_22380_dVV_dd.png\n",
      "Kilauea_2020-03-01_332010_22380_aVH_dd.png\n",
      "Sinabung_2018-10-24_261080_22365_aVH_dd.png\n",
      "Sinabung_2018-10-03_261080_22365_dVV_dd.png\n",
      "Sinabung_2020-09-19_261080_22365_dVV_dd.png\n",
      "Fournaise_Piton_de_la_2020-04-06_233020_22343_aVH_dd.png\n",
      "Turrialba_2020-07-30_345070_22369_aVH_dd.png\n",
      "Sinabung_2020-09-19_261080_22365_aVH_dd.png\n",
      "Turrialba_2020-08-13_345070_22369_dVV_dd.png\n",
      "Sinabung_2018-10-03_261080_22365_aVH_dd.png\n",
      "Rincon_de_la_Vieja_2020-03-26_345020_22346_aVH_dd.png\n",
      "Soufriere_St_Vincent_2021-03-28_360150_22385_aVH_dd.png\n",
      "Kilauea_2020-03-01_332010_22380_dVV_dd.png\n",
      "Sinabung_2020-10-17_261080_22365_aVH_dd.png\n",
      "Kilauea_2020-03-15_332010_22380_dVV_dd.png\n",
      "Soufriere_St_Vincent_2021-04-18_360150_22385_dVV_dd.png\n",
      "Turrialba_2020-07-30_345070_22369_dVV_dd.png\n",
      "Kilauea_2021-03-14_332010_22380_dVV_dd.png\n",
      "Kilauea_2021-02-21_332010_22380_dVV_dd.png\n",
      "Fournaise_Piton_de_la_2020-03-16_233020_22343_aVH_dd.png\n",
      "Rincon_de_la_Vieja_2019-04-04_345020_22346_aVH_dd.png\n",
      "Soufriere_St_Vincent_2020-03-29_360150_22385_aVH_dd.png\n",
      "Sinabung_2020-10-03_261080_22365_dVV_dd.png\n",
      "Kilauea_2021-03-07_332010_22380_dVV_dd.png\n",
      "Kilauea_2020-02-23_332010_22380_dVV_dd.png\n",
      "Soufriere_St_Vincent_2021-04-04_360150_22385_aVH_dd.png\n",
      "Soufriere_St_Vincent_2021-04-04_360150_22385_dVV_dd.png\n",
      "Fournaise_Piton_de_la_2019-04-01_233020_22343_aVH_dd.png\n",
      "Soufriere_St_Vincent_2020-04-19_360150_22385_aVH_dd.png\n",
      "Kilauea_2021-02-28_332010_22380_dVV_dd.png\n",
      "Fournaise_Piton_de_la_2019-03-18_233020_22343_aVH_dd.png\n",
      "Rincon_de_la_Vieja_2020-03-26_345020_22346_dVV_dd.png\n",
      "Rincon_de_la_Vieja_2020-03-12_345020_22346_dVV_dd.png\n",
      "Fournaise_Piton_de_la_2020-03-16_233020_22343_dVV_dd.png\n",
      "Fournaise_Piton_de_la_2019-04-01_233020_22343_dVV_dd.png\n",
      "Turrialba_2019-08-08_345070_22369_aVH_dd.png\n",
      "Sinabung_2020-10-17_261080_22365_dVV_dd.png\n",
      "Soufriere_St_Vincent_2021-04-18_360150_22385_aVH_dd.png\n",
      "Soufriere_St_Vincent_2020-03-29_360150_22385_dVV_dd.png\n",
      "Kilauea_2020-03-08_332010_22380_aVH_dd.png\n",
      "Turrialba_2019-08-08_345070_22369_dVV_dd.png\n",
      "Turrialba_2020-08-13_345070_22369_aVH_dd.png\n",
      "Turrialba_2019-08-22_345070_22369_dVV_dd.png\n",
      "Kilauea_2020-03-15_332010_22380_aVH_dd.png\n",
      "Sinabung_2020-10-03_261080_22365_aVH_dd.png\n",
      "Kilauea_2021-03-14_332010_22380_aVH_dd.png\n",
      "Soufriere_St_Vincent_2020-04-19_360150_22385_dVV_dd.png\n",
      "Rincon_de_la_Vieja_2019-03-21_345020_22346_aVH_dd.png\n"
     ]
    }
   ],
   "source": [
    "newdata = gpd.GeoDataFrame()\n",
    "datarows = []\n",
    "for file in diffdiff_files:\n",
    "    print(file)\n",
    "    layer = -1\n",
    "    eruption_id = int(file.split('_')[-3])\n",
    "    data = targets[targets['Eruption Number'] == eruption_id]\n",
    "    vlat = data.iloc[0]['Latitude']\n",
    "    vlon = data.iloc[0]['Longitude']\n",
    "    v_name = data.iloc[0]['Volcano Name']\n",
    "    volc_num = data.iloc[0]['Volcano Number']\n",
    "    erupt_start_date = date(data.iloc[0][\"Start Year\"].astype(int),\n",
    "                            data.iloc[0][\"Start Month\"].astype(int),\n",
    "                            data.iloc[0][\"Start Day\"].astype(int))\n",
    "    diffdiff_date = file.split('_')[-5]\n",
    "    \n",
    "    path = 'model7-diffdiff-results/'\n",
    "    image = cv2.imread(path+file, cv2.COLOR_BGR2GRAY )\n",
    "    bucketized_diffdiff = bucketize_image_diff(image)\n",
    "    \n",
    "    # Iterate through diffdiff image and append a new row for each damage center\n",
    "    kernel=50\n",
    "    iterations = image.shape[1] / kernel\n",
    "    image_len = image.shape[1]\n",
    "#     polygons = []\n",
    "    for row in range(25, image_len, kernel):\n",
    "        for col in range(25, image_len, kernel):\n",
    "            if get_damage_type(image[row, col]) != 'low':\n",
    "                if get_damage_type(image[row, col]) == 'high':\n",
    "                    layer=255\n",
    "                else:\n",
    "                    layer = 254\n",
    "                    \n",
    "                meters = get_meter_distance(row, col)\n",
    "                bearing = get_bearing(row, col)\n",
    "                lat, lon = get_lat_long(meters, bearing, vlat, vlon)\n",
    "                polygon = get_polygon(lat, lon, bearing)\n",
    "                \n",
    "                datarows.append([polygon, v_name, int(volc_num),int(eruption_id),erupt_start_date.isoformat(), pd.to_datetime(diffdiff_date).date(), int(layer)])\n",
    "\n",
    "    newdata = gpd.GeoDataFrame(datarows)\n",
    "    newdata.columns = ['geometry', 'volc_name', 'volc_num', 'erupt_num', 'est_date', 'diffdiff_date', 'layer']\n",
    "    \n",
    "newdata = newdata[newdata.columns].dissolve(by=['volc_name', 'diffdiff_date', 'layer']).reset_index()\n",
    "newdata['year'] = pd.DatetimeIndex(newdata['diffdiff_date']).year\n",
    "newdata['month'] = pd.DatetimeIndex(newdata['diffdiff_date']).month\n",
    "newdata['diffdiff_date'] = newdata['diffdiff_date'].apply(lambda x: str(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "9f5dcc75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 47 entries, 0 to 46\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype   \n",
      "---  ------         --------------  -----   \n",
      " 0   volc_name      47 non-null     object  \n",
      " 1   diffdiff_date  47 non-null     object  \n",
      " 2   layer          47 non-null     int64   \n",
      " 3   geometry       47 non-null     geometry\n",
      " 4   volc_num       47 non-null     int64   \n",
      " 5   erupt_num      47 non-null     int64   \n",
      " 6   est_date       47 non-null     object  \n",
      " 7   year           47 non-null     int64   \n",
      " 8   month          47 non-null     int64   \n",
      "dtypes: geometry(1), int64(5), object(3)\n",
      "memory usage: 3.4+ KB\n"
     ]
    }
   ],
   "source": [
    "newdata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "3451540e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9857/1416888132.py:4: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n",
      "  newdata.to_file(output_file)\n"
     ]
    }
   ],
   "source": [
    "newdata.set_crs('EPSG:3857')\n",
    "newdata.set_geometry(col='geometry', inplace=True)\n",
    "output_file = f'damage_data_multipolygon.shp'\n",
    "newdata.to_file(output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "ac15dcc5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2020-12-20</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kilauea</td>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-155.39559 19.35889, -155.3956...</td>\n",
       "      <td>332010</td>\n",
       "      <td>22380</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kilauea</td>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-155.36406 19.33483, -155.3640...</td>\n",
       "      <td>332010</td>\n",
       "      <td>22380</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kilauea</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-155.39559 19.35889, -155.3956...</td>\n",
       "      <td>332010</td>\n",
       "      <td>22380</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kilauea</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((-155.34978 19.31848, -155.3670...</td>\n",
       "      <td>332010</td>\n",
       "      <td>22380</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Kilauea</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-155.37835 19.33195, -155.3955...</td>\n",
       "      <td>332010</td>\n",
       "      <td>22380</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kilauea</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((-155.35273 19.33485, -155.3355...</td>\n",
       "      <td>332010</td>\n",
       "      <td>22380</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kilauea</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-155.32418 19.36180, -155.3069...</td>\n",
       "      <td>332010</td>\n",
       "      <td>22380</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kilauea</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((-155.37834 19.31847, -155.3955...</td>\n",
       "      <td>332010</td>\n",
       "      <td>22380</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kilauea</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-155.34979 19.34543, -155.3670...</td>\n",
       "      <td>332010</td>\n",
       "      <td>22380</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kilauea</td>\n",
       "      <td>2021-03-14</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((-155.35274 19.34832, -155.3355...</td>\n",
       "      <td>332010</td>\n",
       "      <td>22380</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kilauea</td>\n",
       "      <td>2021-03-14</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-155.37835 19.33195, -155.3955...</td>\n",
       "      <td>332010</td>\n",
       "      <td>22380</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Rincon de la Vieja</td>\n",
       "      <td>2019-03-21</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-85.42829 10.77080, -85.41457 ...</td>\n",
       "      <td>345020</td>\n",
       "      <td>22346</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Rincon de la Vieja</td>\n",
       "      <td>2019-04-04</td>\n",
       "      <td>255</td>\n",
       "      <td>POLYGON ((-85.42829 10.76790, -85.42829 10.767...</td>\n",
       "      <td>345020</td>\n",
       "      <td>22346</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Rincon de la Vieja</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((-85.42828 10.72748, -85.42828 ...</td>\n",
       "      <td>345020</td>\n",
       "      <td>22346</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Rincon de la Vieja</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-85.39801 10.74385, -85.39801 ...</td>\n",
       "      <td>345020</td>\n",
       "      <td>22346</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Rincon de la Vieja</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((-85.42829 10.78427, -85.41458 ...</td>\n",
       "      <td>345020</td>\n",
       "      <td>22346</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Rincon de la Vieja</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-85.40085 10.75733, -85.38430 ...</td>\n",
       "      <td>345020</td>\n",
       "      <td>22346</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sinabung</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((98.30289 3.09444, 98.30289 3.1...</td>\n",
       "      <td>261080</td>\n",
       "      <td>22365</td>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Sinabung</td>\n",
       "      <td>2018-10-24</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((98.37036 3.08386, 98.37036 3.0...</td>\n",
       "      <td>261080</td>\n",
       "      <td>22365</td>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Sinabung</td>\n",
       "      <td>2020-09-19</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((98.31638 3.09734, 98.32988 3.0...</td>\n",
       "      <td>261080</td>\n",
       "      <td>22365</td>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Sinabung</td>\n",
       "      <td>2020-09-19</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((98.34337 3.09444, 98.34337 3.0...</td>\n",
       "      <td>261080</td>\n",
       "      <td>22365</td>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Sinabung</td>\n",
       "      <td>2020-10-03</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((98.31638 3.09734, 98.32988 3.0...</td>\n",
       "      <td>261080</td>\n",
       "      <td>22365</td>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Sinabung</td>\n",
       "      <td>2020-10-03</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((98.34337 3.09444, 98.34337 3.1...</td>\n",
       "      <td>261080</td>\n",
       "      <td>22365</td>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Sinabung</td>\n",
       "      <td>2020-10-17</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((98.32988 3.09734, 98.32988 3.1...</td>\n",
       "      <td>261080</td>\n",
       "      <td>22365</td>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Sinabung</td>\n",
       "      <td>2020-10-17</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((98.34337 3.09734, 98.35687 3.0...</td>\n",
       "      <td>261080</td>\n",
       "      <td>22365</td>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Soufriere St. Vincent</td>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-61.28526 13.27079, -61.27142 ...</td>\n",
       "      <td>360150</td>\n",
       "      <td>22385</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Soufriere St. Vincent</td>\n",
       "      <td>2020-04-05</td>\n",
       "      <td>255</td>\n",
       "      <td>POLYGON ((-61.27141 13.25732, -61.25757 13.257...</td>\n",
       "      <td>360150</td>\n",
       "      <td>22385</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Soufriere St. Vincent</td>\n",
       "      <td>2020-04-19</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-61.28526 13.27079, -61.27142 ...</td>\n",
       "      <td>360150</td>\n",
       "      <td>22385</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Soufriere St. Vincent</td>\n",
       "      <td>2021-03-28</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((-61.28526 13.25442, -61.28526 ...</td>\n",
       "      <td>360150</td>\n",
       "      <td>22385</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Soufriere St. Vincent</td>\n",
       "      <td>2021-03-28</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-61.27141 13.25442, -61.28526 ...</td>\n",
       "      <td>360150</td>\n",
       "      <td>22385</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Soufriere St. Vincent</td>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((-61.28526 13.25442, -61.28526 ...</td>\n",
       "      <td>360150</td>\n",
       "      <td>22385</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Soufriere St. Vincent</td>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-61.22988 13.24386, -61.21317 ...</td>\n",
       "      <td>360150</td>\n",
       "      <td>22385</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Soufriere St. Vincent</td>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((-61.28526 13.25442, -61.28526 ...</td>\n",
       "      <td>360150</td>\n",
       "      <td>22385</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Soufriere St. Vincent</td>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-61.22989 13.28139, -61.22989 ...</td>\n",
       "      <td>360150</td>\n",
       "      <td>22385</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Turrialba</td>\n",
       "      <td>2019-08-08</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-83.82998 9.96291, -83.84366 9...</td>\n",
       "      <td>345070</td>\n",
       "      <td>22369</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Turrialba</td>\n",
       "      <td>2019-08-22</td>\n",
       "      <td>255</td>\n",
       "      <td>POLYGON ((-83.85451 9.93595, -83.87102 9.93595...</td>\n",
       "      <td>345070</td>\n",
       "      <td>22369</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Turrialba</td>\n",
       "      <td>2020-07-30</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((-83.82998 9.95233, -83.82998 9...</td>\n",
       "      <td>345070</td>\n",
       "      <td>22369</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Turrialba</td>\n",
       "      <td>2020-07-30</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-83.82714 9.92248, -83.84365 9...</td>\n",
       "      <td>345070</td>\n",
       "      <td>22369</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Turrialba</td>\n",
       "      <td>2020-08-13</td>\n",
       "      <td>254</td>\n",
       "      <td>MULTIPOLYGON (((-83.82998 9.95233, -83.82998 9...</td>\n",
       "      <td>345070</td>\n",
       "      <td>22369</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Turrialba</td>\n",
       "      <td>2020-08-13</td>\n",
       "      <td>255</td>\n",
       "      <td>MULTIPOLYGON (((-83.81347 9.93596, -83.82998 9...</td>\n",
       "      <td>345070</td>\n",
       "      <td>22369</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 volc_name diffdiff_date  layer  \\\n",
       "0   Fournaise, Piton de la    2019-03-18    255   \n",
       "1   Fournaise, Piton de la    2019-04-01    255   \n",
       "2   Fournaise, Piton de la    2020-03-16    254   \n",
       "3   Fournaise, Piton de la    2020-03-16    255   \n",
       "4   Fournaise, Piton de la    2020-04-06    254   \n",
       "5   Fournaise, Piton de la    2020-04-06    255   \n",
       "6                  Kilauea    2020-02-23    255   \n",
       "7                  Kilauea    2020-03-01    255   \n",
       "8                  Kilauea    2020-03-08    255   \n",
       "9                  Kilauea    2020-03-15    255   \n",
       "10                 Kilauea    2021-02-21    254   \n",
       "11                 Kilauea    2021-02-21    255   \n",
       "12                 Kilauea    2021-02-28    254   \n",
       "13                 Kilauea    2021-02-28    255   \n",
       "14                 Kilauea    2021-03-07    254   \n",
       "15                 Kilauea    2021-03-07    255   \n",
       "16                 Kilauea    2021-03-14    254   \n",
       "17                 Kilauea    2021-03-14    255   \n",
       "18      Rincon de la Vieja    2019-03-21    255   \n",
       "19      Rincon de la Vieja    2019-04-04    255   \n",
       "20      Rincon de la Vieja    2020-03-12    254   \n",
       "21      Rincon de la Vieja    2020-03-12    255   \n",
       "22      Rincon de la Vieja    2020-03-26    254   \n",
       "23      Rincon de la Vieja    2020-03-26    255   \n",
       "24                Sinabung    2018-10-03    255   \n",
       "25                Sinabung    2018-10-24    255   \n",
       "26                Sinabung    2020-09-19    254   \n",
       "27                Sinabung    2020-09-19    255   \n",
       "28                Sinabung    2020-10-03    254   \n",
       "29                Sinabung    2020-10-03    255   \n",
       "30                Sinabung    2020-10-17    254   \n",
       "31                Sinabung    2020-10-17    255   \n",
       "32   Soufriere St. Vincent    2020-03-29    255   \n",
       "33   Soufriere St. Vincent    2020-04-05    255   \n",
       "34   Soufriere St. Vincent    2020-04-19    255   \n",
       "35   Soufriere St. Vincent    2021-03-28    254   \n",
       "36   Soufriere St. Vincent    2021-03-28    255   \n",
       "37   Soufriere St. Vincent    2021-04-04    254   \n",
       "38   Soufriere St. Vincent    2021-04-04    255   \n",
       "39   Soufriere St. Vincent    2021-04-18    254   \n",
       "40   Soufriere St. Vincent    2021-04-18    255   \n",
       "41               Turrialba    2019-08-08    255   \n",
       "42               Turrialba    2019-08-22    255   \n",
       "43               Turrialba    2020-07-30    254   \n",
       "44               Turrialba    2020-07-30    255   \n",
       "45               Turrialba    2020-08-13    254   \n",
       "46               Turrialba    2020-08-13    255   \n",
       "\n",
       "                                             geometry  volc_num  erupt_num  \\\n",
       "0   MULTIPOLYGON (((55.61247 -21.34648, 55.61247 -...    233020      22343   \n",
       "1   MULTIPOLYGON (((55.61248 -21.33300, 55.61248 -...    233020      22343   \n",
       "2   MULTIPOLYGON (((55.68481 -21.31667, 55.68481 -...    233020      22343   \n",
       "3   MULTIPOLYGON (((55.73119 -21.34650, 55.71374 -...    233020      22343   \n",
       "4   MULTIPOLYGON (((55.71673 -21.33015, 55.71673 -...    233020      22343   \n",
       "5   MULTIPOLYGON (((55.70226 -21.34650, 55.68481 -...    233020      22343   \n",
       "6   MULTIPOLYGON (((-155.38131 19.34830, -155.3813...    332010      22380   \n",
       "7   MULTIPOLYGON (((-155.39559 19.35889, -155.3956...    332010      22380   \n",
       "8   MULTIPOLYGON (((-155.36406 19.33483, -155.3640...    332010      22380   \n",
       "9   MULTIPOLYGON (((-155.39559 19.35889, -155.3956...    332010      22380   \n",
       "10  MULTIPOLYGON (((-155.34978 19.31848, -155.3670...    332010      22380   \n",
       "11  MULTIPOLYGON (((-155.37835 19.33195, -155.3955...    332010      22380   \n",
       "12  MULTIPOLYGON (((-155.35273 19.33485, -155.3355...    332010      22380   \n",
       "13  MULTIPOLYGON (((-155.32418 19.36180, -155.3069...    332010      22380   \n",
       "14  MULTIPOLYGON (((-155.37834 19.31847, -155.3955...    332010      22380   \n",
       "15  MULTIPOLYGON (((-155.34979 19.34543, -155.3670...    332010      22380   \n",
       "16  MULTIPOLYGON (((-155.35274 19.34832, -155.3355...    332010      22380   \n",
       "17  MULTIPOLYGON (((-155.37835 19.33195, -155.3955...    332010      22380   \n",
       "18  MULTIPOLYGON (((-85.42829 10.77080, -85.41457 ...    345020      22346   \n",
       "19  POLYGON ((-85.42829 10.76790, -85.42829 10.767...    345020      22346   \n",
       "20  MULTIPOLYGON (((-85.42828 10.72748, -85.42828 ...    345020      22346   \n",
       "21  MULTIPOLYGON (((-85.39801 10.74385, -85.39801 ...    345020      22346   \n",
       "22  MULTIPOLYGON (((-85.42829 10.78427, -85.41458 ...    345020      22346   \n",
       "23  MULTIPOLYGON (((-85.40085 10.75733, -85.38430 ...    345020      22346   \n",
       "24  MULTIPOLYGON (((98.30289 3.09444, 98.30289 3.1...    261080      22365   \n",
       "25  MULTIPOLYGON (((98.37036 3.08386, 98.37036 3.0...    261080      22365   \n",
       "26  MULTIPOLYGON (((98.31638 3.09734, 98.32988 3.0...    261080      22365   \n",
       "27  MULTIPOLYGON (((98.34337 3.09444, 98.34337 3.0...    261080      22365   \n",
       "28  MULTIPOLYGON (((98.31638 3.09734, 98.32988 3.0...    261080      22365   \n",
       "29  MULTIPOLYGON (((98.34337 3.09444, 98.34337 3.1...    261080      22365   \n",
       "30  MULTIPOLYGON (((98.32988 3.09734, 98.32988 3.1...    261080      22365   \n",
       "31  MULTIPOLYGON (((98.34337 3.09734, 98.35687 3.0...    261080      22365   \n",
       "32  MULTIPOLYGON (((-61.28526 13.27079, -61.27142 ...    360150      22385   \n",
       "33  POLYGON ((-61.27141 13.25732, -61.25757 13.257...    360150      22385   \n",
       "34  MULTIPOLYGON (((-61.28526 13.27079, -61.27142 ...    360150      22385   \n",
       "35  MULTIPOLYGON (((-61.28526 13.25442, -61.28526 ...    360150      22385   \n",
       "36  MULTIPOLYGON (((-61.27141 13.25442, -61.28526 ...    360150      22385   \n",
       "37  MULTIPOLYGON (((-61.28526 13.25442, -61.28526 ...    360150      22385   \n",
       "38  MULTIPOLYGON (((-61.22988 13.24386, -61.21317 ...    360150      22385   \n",
       "39  MULTIPOLYGON (((-61.28526 13.25442, -61.28526 ...    360150      22385   \n",
       "40  MULTIPOLYGON (((-61.22989 13.28139, -61.22989 ...    360150      22385   \n",
       "41  MULTIPOLYGON (((-83.82998 9.96291, -83.84366 9...    345070      22369   \n",
       "42  POLYGON ((-83.85451 9.93595, -83.87102 9.93595...    345070      22369   \n",
       "43  MULTIPOLYGON (((-83.82998 9.95233, -83.82998 9...    345070      22369   \n",
       "44  MULTIPOLYGON (((-83.82714 9.92248, -83.84365 9...    345070      22369   \n",
       "45  MULTIPOLYGON (((-83.82998 9.95233, -83.82998 9...    345070      22369   \n",
       "46  MULTIPOLYGON (((-83.81347 9.93596, -83.82998 9...    345070      22369   \n",
       "\n",
       "      est_date  year  month  \n",
       "0   2020-02-10  2019      3  \n",
       "1   2020-02-10  2019      4  \n",
       "2   2020-02-10  2020      3  \n",
       "3   2020-02-10  2020      3  \n",
       "4   2020-02-10  2020      4  \n",
       "5   2020-02-10  2020      4  \n",
       "6   2020-12-20  2020      2  \n",
       "7   2020-12-20  2020      3  \n",
       "8   2020-12-20  2020      3  \n",
       "9   2020-12-20  2020      3  \n",
       "10  2020-12-20  2021      2  \n",
       "11  2020-12-20  2021      2  \n",
       "12  2020-12-20  2021      2  \n",
       "13  2020-12-20  2021      2  \n",
       "14  2020-12-20  2021      3  \n",
       "15  2020-12-20  2021      3  \n",
       "16  2020-12-20  2021      3  \n",
       "17  2020-12-20  2021      3  \n",
       "18  2020-01-30  2019      3  \n",
       "19  2020-01-30  2019      4  \n",
       "20  2020-01-30  2020      3  \n",
       "21  2020-01-30  2020      3  \n",
       "22  2020-01-30  2020      3  \n",
       "23  2020-01-30  2020      3  \n",
       "24  2020-08-08  2018     10  \n",
       "25  2020-08-08  2018     10  \n",
       "26  2020-08-08  2020      9  \n",
       "27  2020-08-08  2020      9  \n",
       "28  2020-08-08  2020     10  \n",
       "29  2020-08-08  2020     10  \n",
       "30  2020-08-08  2020     10  \n",
       "31  2020-08-08  2020     10  \n",
       "32  2020-12-27  2020      3  \n",
       "33  2020-12-27  2020      4  \n",
       "34  2020-12-27  2020      4  \n",
       "35  2020-12-27  2021      3  \n",
       "36  2020-12-27  2021      3  \n",
       "37  2020-12-27  2021      4  \n",
       "38  2020-12-27  2021      4  \n",
       "39  2020-12-27  2021      4  \n",
       "40  2020-12-27  2021      4  \n",
       "41  2020-06-18  2019      8  \n",
       "42  2020-06-18  2019      8  \n",
       "43  2020-06-18  2020      7  \n",
       "44  2020-06-18  2020      7  \n",
       "45  2020-06-18  2020      8  \n",
       "46  2020-06-18  2020      8  "
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3de30e84",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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