Coastal_Change_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coastal Change\n",
    "\n",
    "*****\n",
    "### This Notebook:  \n",
    "\n",
    "- Highlights a simple method for detecting coastal change between two timeframes\n",
    "- Visualizes water classification for two timeframes\n",
    "- Visualizes water classification changes between timeframes\n",
    "------  \n",
    "### Motivation:  \n",
    "\n",
    "International agencies like World Bank, UNEP, and USAID are currently reporting and addressing the problem of coastal erosion near Lomé, Togo.  The links listed below are references from these agencies regarding coastal erosion in Togo and coastal erosion as a world wide phenomena.  \n",
    "\n",
    "- *\"West Africa Coastal Erosion Project launched in Togo\" (2016) *  [- link](http://www.ndf.fi/news/west-africa-coastal-erosion-project-launched-togo)\n",
    "- *Agreement to Erosion Adaptation Project* (2016)  [- link](http://pubdocs.worldbank.org/en/493191479316551864/WACA-Lom%C3%A9-Communiqu%C3%A9-2016-English.pdf)\n",
    "- World Bank WACA program brochure (2015)  [- link](http://pubdocs.worldbank.org/en/622041448394069174/1606426-WACA-Brochure.pdf)\n",
    "- UNEP - Technologies for climate change adaption (2010)  [- link](http://www.unep.org/pdf/TNAhandbook_CoastalErosionFlooding.pdf)  \n",
    "- USAID - Adapting to Coastal Climate Change (2009) -  [- link](http://www.crc.uri.edu/download/CoastalAdaptationGuide.pdf)  \n",
    "- UNEP - Coastal Erosion and Climate Change in Western Africa(2002) -  [- link](http://www.unep.org/dewa/africa/publications/aeo-1/121.htm)\n",
    "  \n",
    "-------\n",
    "### Algorithmic Profile:  \n",
    "  \n",
    "- This algorithm generates a water change product.\n",
    "- The product is derived from Landsat7 Collection 1, Tier 2 sr imagery taken from USGS data holdings.    \n",
    "- Utilizes WOFS for water detection.\n",
    "-------  \n",
    "### Process:  \n",
    "\n",
    "- Load in a year's worth of data for years `y1` and `y2`, \n",
    "- Reduce noise by temporal compositing of both `y1` and `y2` to yield `y1_composite` and `y2_composite` .(This removes clouds, scanline errors, and statistical anomalies)      \n",
    "- Run WOFS water classifier on `y1_composite` and `y2_composite`, to yield `y1_water` and `y2_water`. These outputs contain a 1 for water, and 0 for not water.     \n",
    "- Subtract `y1_water` from `y2_water` to yield a `coastal_change` product. Where 0 represents no change, 1 represents water gain, and -1 represents water loss. \n",
    "*****  \n",
    "\n",
    "> **Flow Diagram**\n",
    "![](diagrams/rainy_demo/algo_nb3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "\n",
    "import datacube\n",
    "import utils.data_cube_utilities.dc_utilities as utils\n",
    "\n",
    "from utils.data_cube_utilities.dc_mosaic import create_mosaic, ls7_unpack_qa\n",
    "from utils.data_cube_utilities.dc_water_classifier import wofs_classify\n",
    "\n",
    "# import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start time: 2019-09-27 22:25:02.812737\n"
     ]
    }
   ],
   "source": [
    "start_time = datetime.now()\n",
    "print(\"Start time: \" + str(start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize data cube object\n",
    "dc = datacube.Datacube(config='/home/localuser/.datacube.conf', app='dc-coastal-erosion')\n",
    "\n",
    "# Set query parameters\n",
    "platform        = 'LANDSAT_7'\n",
    "product_type    = 'ls7_ledaps_ghana'\n",
    "\n",
    "# Select minimum and maximum longitudes and latitudes.\n",
    "lon = (0.0520, 0.3458)\n",
    "lat = (5.6581, 5.8113)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f00775489b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils.data_cube_utilities.dc_display_map import display_map\n",
    "display_map(lat, lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_range = ('2000-01-01', '2000-12-31')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "measurements = ['red', 'green', 'blue', 'nir', 'swir1', 'swir2', 'pixel_qa']\n",
    "# Retrieve data from Data Cube\n",
    "dataset_in_2000 = dc.load(platform=platform,\n",
    "                          product=product_type,\n",
    "                          time=time_range,\n",
    "                          lon=lon, lat=lat, measurements=measurements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_range = ('2015-01-01', '2015-12-31')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve data from Data Cube\n",
    "dataset_in_2015 = dc.load(platform=platform,\n",
    "                          product=product_type,\n",
    "                          time=time_range,\n",
    "                          lon=lon, lat=lat, measurements=measurements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only keep pixels that are clear or have water.\n",
    "clear_xarray  = ls7_unpack_qa(dataset_in_2000.pixel_qa, \"clear\")  \n",
    "water_xarray  = ls7_unpack_qa(dataset_in_2000.pixel_qa, \"water\")\n",
    "clean_mask_2000 = np.logical_or(clear_xarray.values.astype(bool),\n",
    "                           water_xarray.values.astype(bool))\n",
    "\n",
    "clear_xarray  = ls7_unpack_qa(dataset_in_2015.pixel_qa, \"clear\")  \n",
    "water_xarray  = ls7_unpack_qa(dataset_in_2015.pixel_qa, \"water\")\n",
    "clean_mask_2015 = np.logical_or(clear_xarray.values.astype(bool),\n",
    "                           water_xarray.values.astype(bool))\n",
    "\n",
    "# Remove noise from images by using appropriate data within the dataset to replace \"dirty\" data.\n",
    "mosaic_2000 = create_mosaic(dataset_in_2000, clean_mask=clean_mask_2000)\n",
    "mosaic_2015 = create_mosaic(dataset_in_2015, clean_mask=clean_mask_2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "water_class_2000 = wofs_classify(mosaic_2000, mosaic=True, no_data=np.nan)\n",
    "water_class_2015 = wofs_classify(mosaic_2015, mosaic=True, no_data=np.nan)\n",
    "\n",
    "wofs_2000 = water_class_2000.wofs.values\n",
    "wofs_2015 = water_class_2015.wofs.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "coastal_change = water_class_2015 - water_class_2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "def plot_data_array_with_aspect(da):\n",
    "    fig, ax = plt.subplots(figsize=(15,8))\n",
    "    ax.set_aspect('equal')\n",
    "    da.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wN79IYxZ8y1VrSgc6EhEREQ2Jqztdk9Yis/UnNFFCU10EKnZA5EviMKbAiaprPcxU1Vuyv7cCuBizSapcTsaOXfCeAuC7qvoTVf0tgI8DWN5mWV04GzcRERGlwGw9ym99MBMltN2VzrftEHZAVNY1MGWyYC76bSpabVkSkT0AzKnqHdn/xwE4y7HcXgCeAOD5xsPfB/DorHveLwE8GcBG+7Vd4pUFIiIi6pPd42XtkrunpdnScnDk6rLm48oyZ6+jDXXX73qdq6seTU/b3fD2BXCxiOTb+pCqfkpEzgAAVX13ttyzMBuT9Iv8har6ZRG5CMBXMcuScS2y7nZdcw3+IyIiIurSwuUrdwqI1vbU+lG19aUsYUNoxryujKq+196ktJPQarCkqjcCeITj8Xdb98+HIyW4qr4WwGtDt3fUYQfiGxFHLY2p+ZWIiIjSsHD5SqzYtB6rt27GqvnF27vTpVp5D2mhCc1+11fyrdSTfjHBQ32jO3Jbrlqz/WaKFfik+iEhIiIai1Xzi0eTgCnfj3yfVs0vxpar1gQnZqjSNa4NsepFvmQLrqx5XaUVd22Xpmdo8yy1KrRJtayZmB8WImobv2uI3HwpsIfOLLcd6Pn2yTX3UJE2vzPKJnH1aVKmohTe/H4MJyKYW7Cg72Ika1LBElBeARlan1kiIiLqJkgqa60yu8VVadnKM9PlLUfmPpj1jrLxR746Stt1E992fV3TXMkdQgI9ezu8aERDIKpRpybq1dFLl+qGDRt2erxKP9OyLyJ+cImoT/wOoqmyg4yutmmyt++az8in625jsYRcRHbVs0IvPuffafZfez1l+9fH3Ei7z89fo6rLWt1IBEcddH/97GtOi77evV/85iT2v6nRjVnyadKn19WHloioLUVXcfkdRFPU9fgke0yU3TJkPx4SxKX42bVTfrv2wRfM2GOOzNe7WpHMx0K2a2srUHKVKTnCeZaamEQ3PN8H2Fa3Py4RUQxlV1B9V3CJxswe6xO7dcnXvc+1nabbDv3cDqFiXrUMZssQsPOFZtc0LK6gJn+8aH1liup5Vb87OdcSTSJYKtNXH2AiIpuvy6+rL7/9GqIxMscHNQlWfIFW7KAoxmczpMtZTHZg4ipPlbTe5v/5ul3zMoUen6pd8OquZ7yEqcMb4JHzmO4Hioj6UJT1aghXmYlSY6bkBvrJnpe3kMRmTpNiptJusq2QCWPLutq5ymmvu6hbndna5Fp36D6WJaQgqmLyLUvsekdEQ1LWokQ0NWXjlXxJFkLnKepCyOe4ad0jNIlV2TyUdtc31//2a831+uZL8m3fXE9oxjy7G5+vK59rnVNslZdszBLVM4lseGV8XwRT+RARUb/KMkmFVG6IpqQsS13X6lzUqDqxal+f8zFOqRJr7Gcq2fCWHvwAvfL1p0df78JTX5/E/jfFMBPuZmIioj4V/Yjb3W+IxizPQLdqfvH2VgWz1ajvQKmuOgkUzFtX7O+Zsos3RVxjmuosU9Xk63cizIbXALvhsRseEQ2Q3eLt6g7D7ypKRX6+uiZkdZ3H3q51xjKpBklNFM1HZIv9HRE6jUpRq01I97w2pmuJmR0vVUzwUB+PnGUqHxoiGoaygdOu/v+jmPeDRsfV4mEGPQuXr8TaJcfucL9Iyq1GdZUlSshVqeR33RplJpwwy1AXv+eobxyzRETUIzNYKsvaxZZwGpqisXWucUWuiwO+VqSUtFmhd41tCp1/qCiomkKrStk49KbHIJkxS4fsr194U/xzdI+T/zKJ/W9q8t3wiIj6ZFZ6zPlkfJM15qZQ0aFh8nWZstmBz6r5xVhh/J8vk2qA1AVXq7J97Msy0YUmM3Bl4rTHdKf2nVPWXTG1/aF+sBseEdEAmAETEJZCl6hNTSZsz19rJmcwjS1IKkq6Unbf95gt5P0oa2WyW1pcqb/tdbnSc5vramOC3Jj4XZmZWxD/NhEMloiIepZXRPJsXwCcFUyiLlWtZJpjY+zX5sHR2IIkn6K50srmOnIFXWUteWbgYo8VMtcVklTBl3XPF1gVjU+qO1aq6DWh6+NYJ5MAc3PxbxPBbnhERAOxeuvm7dm+8gqlnT2MqA+r5hdvT87gmm7DDPhNUzp3feOKXF3BfHOrFXWtcy3res7V9c7XcmRn2vSV29eVrWw7oV2HXdvzCQ3iqwT77NZMRaYTFhIRJcTulkfUNbOl09U91GwBMVtCec7O2C0xrpYZ3zJlY41CxoyFdM9zvZ+u/XAFQ74WsLLugkWJakL2yyxX0f2Q13WZJbBXAsiCBdFvU8FseEREA8XKJ7Wp6Cp+SGY7tnruKKTS7Ws5KlrWXH9oEBHaStKkRaWotcnXDbGsG2FZQoai19Up92Sy4T34AN3w1ldEX+/8s16RxP43xWCJiIiInGnsc1MPiqqk6S4LmnzjlXwV+LLua2XlKitvyHNVyhTyfKxymY8VPddWmdMJlg7UDW/7P9HXO3/Sy5LY/6YYLBERDdDC5SuxYtP67fenXlml9o1hvqM2lbXA+Z4PGZ9U9JqmrSd1NQmm8seA4nFTVcsasmzVwM63DqD8mCcTLB16oG54+19EX+/8CS9JYv+b4pglIqIBMgMlYMeKLLPkUSz2WKOpZKurwxyj40q4UPS6kGXsrHVVxu/U2WboOmxF3ezsMVi+ZBMh66qy3dD9MMtnPuYKWMta82g6GCwREQ2EL104K7AUW36u8dyKo6xy7ctEV8bXwuGq9PvKFaJKAODaF9dj+Xp9z/nW5SpLlVa9kKQXrsQWrqDPvp9q1jyBQObmot+mYjp7SkSUgHzgfNkyRFXYgTiDpHa4Wp7KWohcAUKXWdrKtlMl61yMQKLKcfK9ps6xK2oFa9LKR+ljsERENCCuQIkT1FJdbEEarqqpvWMwW0iKuse5yhCa5ML8v+o4Kx9XsFIUxNUJnspawZLuficA5hbEv00EEzwQEQ1EaEDESi+FYGrv9oV2/xqKosxxbW5vCNooSyoJHo5+yEG64Z2vib7e3Z/+4iT2vym2LBERdSC/wu9qJQqZT4mVXgrF+bm640v2UGeC1NiKsvOZXc7aLMNQAiUgbNxX2XuX7iS2wpalBhgsERH1JLR7nVnpZQWYyvAc6YcvRXiRNrt5lXXtqzMOp0pSiqbaXk9Rsgff64oSeQwdEzzUN509JSLqkZmW2UziYI4l4bgkqovnTj98rRO++YSK1pO39pi3oQnZryaT0drr6fMYpBoUUXwMloiIOuaaM8lX2bVbCZjsgYAdzwOOTeqOL5jxJWswAyBfi0VRC4YvWBhSIBUjOPIFmEMOWIb0HpQSdsNrgsESEVFNIVd/m0wmG5JGnKaJrZH98E0W65ow1ff9UDYfk2uOorLWmy7GH9mabsuVqtteb4z9GXLARWnYpe8CEBGlps4PeNHVf99EtOZf+38ing/dc827UzTpbJ3Mc64Aouz1dgAXkua7qSYtSvZxsZNO5I91oW6WvC1XrcHu8+9roURtkEm1BMXGliUioobsrjltjDdgCwJR/1wtPWZXO/u5Mr7AKKSlpSiwGHJriiuwM4MnX4tT2+UpM9RxZEEEkAULot+mgvMsERE14LoianL9EFeZT4njUYjS4JvktUmFP7TVw9UNsI1Ao431+o5TlwGTS8h3ejLzLB32IP3iP70p+np3e/wpSex/U2xZIiJqwLxKGpJWtij4MTPjMVAiSov9+S8am+SqiFcZ3+RaztXaFUPdiXdD5yvyJbMo2lYb++bavuuWJgHm5uLfJqL1liURuQnAHQDuAnCnHYGKyKsAPC+7uwuAxQD2UdXbReTeAN4D4AgACmCFqn7Rty22LBHRkNlXZe0WphiBESckJepPUctLlVYioDiBREgZmrYCuV5ftn+AfxxXbDFbnnz7mq/ftz/ptCwdol9875ujr3e3xz03if1vqquw8EmqusR1QFX17Oy5JQDOBHClqt6ePf0OAJ9S1cMAPAIAf/2JaDRcacGJaBxc6cVdY5tcKbSbdqmLEayEpvK2U6Pny5W1/sTIpucLcOqsy15H2i1JFgFThzcwtGx4pwC4AABEZC8AjwfwAgBQ1d8A+E1vJSMiqshu5bF/eH0tS01anNiiNE5dXKmnMKEtGnZw5Oqm5woymorVmhTa/c61fEjGu6YtcK7l64zvslVpTUuHQCYU3MRW2LIkIltKbneIyHdKtqEALheRa0Tk9IJtzQM4HsDHsocOBvATAO8TkWtF5D0iskeFfSMiGiy2IlGoZDNwjYjZClS1xaFo+ZD1dFlRtwM81/bt87FKUOU7l33rCDn3qx6fOssnnQmvRyJyvIh8W0RuEJFXO55/hYh8S0S+LiKfEZGDjOfuEpFN2W1dtyXfUVk3vP9U1YUFtz0B/KJkHY9T1aUAng5gpYg83rPcCQA2GF3wdgGwFMC7VPWobDuuA326iGwUkY0/ue22kqIQERGlZctVa7Bi0/q+i5GMNhMb+AIHV1e0fPmyRAcxyxmLmdCgKPGCL8hxHS8z0AztZlg1cKqynGv5oi6SZtCUZPDUcYIHEVkAYA1m9f/DAZwiIodbi10LYJmqPhzARQD+3njul/kwHVU9Md6BqK4sWPq9gHUULqOqt2R/bwVwMYBjPIuejKwLXuZmADer6pez+xdhFjzZ6z9PVZep6rJ9Fi0KKC4RUXfYLY5i4HkULlZLjF05BtwZ0+z5gVzd0eqU1dWCY6+7Tpe1MlVSn1dpZQttXbMD0KpitDT5kjrY2U+p0DEAblDVG7OhNB8GcJK5gKp+VlW3Zne/BGD/jssYpDBYUtUby1ZQtIyI7CEie+b/AzgOwDcdy+0F4AkALjHW+2MAPxCRh2YPPRnAt8rKQ0SUAl/lt40MeURUj90SUtYlrWrLUihX9z+75Sd2l7W+ggFfQNJ0LFbR/dAyJdealJPZmKXYtxL7AfiBcf/m7DGfFwL4N+P+PbOeY18SkWfW2/E4grLhicizReR6Efm5MVZpS8BL9wXwBRH5GoCvAPikqn5KRM4QkTOM5Z4F4HJVtbv0/RmAfxGRrwNYAiD+jFpERC0q+nHN51Uy51cionS4WhhcLTO+ynrR94MrGYKry1iV1qlQyQYFHnWCWF8XxDRblKStbHiL8qEw2c2bm6CwdCLPB7AMwNnGwwdlWbT/AMBqETmk8WGoKWieJRG5AcAJqjroX3POs0RERERTEmteJd96qz4Xa3v2frky6zXdhm9brueBncdQ/XbT+5KYZ+jowx+iX/6Xd0Zf7z2WPt27/yLyGACvU9WnZffPBABVfbO13FMAnAPgCdmQHde6zgfwr6p6UcTiBwudZ+m/hh4oEREREcXSZetKk221ESiZ6wXCytc06UHR+KAq48Dqls2XNt0XKCVF0HmCBwBXAzhURA4WkV0xy02wQ1Y7ETkKwLkATjQDJRHZW0R2y/5fBOCx6HEoTlnq8GeLyLMBbBSRj4jIKflj2eNERGRhWvB6kquA0KhVGW8UY4LVtl4fOxCzgwp7HilfRrmm5bDHjYUGQKFjuaq0WqXbHa87qnongJcAuAzAZgAXqup1InKWiOTZ7c4GcC8AH7VShC/GLPb4GoDPAniLqvYWLJVNSnuC8f9WzBI05BTAx6OXiIgocRx/FIbBEQ1d00xvIZq0CoVW7Jtsp2y8j71+O2AqSxMeWi5XC0/IvldpUQrVRktemwQCWdD9pLSqeimAS63H/sb4/yme110F4Mh2SxcudMzSY1V1Q9ljfeOYJSIaipDB15RepYOIdub7vvN1bauaVbDKNouW942Bqmv3+fkkxiwte9hD9csf+cfo693lyKcksf9NhY5ZOifwMSKiyUp2ssIeJT/RYyB2zUzL0M7DrroD1l2/3XpUlAmwqLXH7g5XpO58Sr408D6uua2Gdn6UErSVDW8SysYsPUZEXglgHxF5hXF7HYDpHCUiogiS+4HtUNKDpwvkQRK7ZqYldla5pkJbWrpopXWNQTK37Qt47HFHvufMx1yJFuxl2uaa2yo9raUOn4SyMUu7YjbwahcAexqPbwHwnLYKRUSUmtgTQk6Nr7vO0BV1LVo1v5hBErWWrc61HZ9Y2w8ZL+RL9Z2/pspcUWXjrfrqxsvv8mkpDJZU9UoR+QKAh6uOCbL2AAAgAElEQVTq6zsqExHRKLhaS/gj62cOEB+qVfOLsXbJsdvvm2W1gyMGSukJSUBQp4Le9znd9pxIIUGUGSjFOn59H9eUSHmqb/IoPXKqeheAB3RQFiKiJIT0WXf9iPOHvVh+fIY8vmf11s3bAzq7vAyO0hfyuZ3y5zj/7vONO/I9VtZFr2oZmrw+dP2xlqP0hYaZm0RknYj8IedZIiKa6WKiximwW5NSCzpSKy9RXXYLUlEWu7JECkWJHoq+M7sMVs3v7zoZ+wZDOGapidBg6Z4AfgrgWMzmXjoBwDPaKhQR0ZCFXB1lgLSjVfOLB91iRMV4Pk9PSOa7ouVC+BK7lAUibY5VciV0GMX5L3PxbxMRtKeqeprjtqLtwhERpcB1xbFqetqxY+sLtSWVimwq5cyFBgkhgU3o9kL0kdRhVEETVRYULInI/iJysYjcmt0+JiL7t104IqLUMDhKFytCfkM+r4dcNlMq5TSVldmXoa7KOmKXqU0pvoczwpalBkL39H0A1mGW6OEBAD6RPUZENGmuLimsdPevqHuk/Zw5aJ3Gb8yfz9j7Vmd9oS1NscrqSyrRxrEY87lDfqKq5QuJbFLVJWWP9e3opUt1w4YNfReDiCbEToXrSo3LtOHdKRr3kPQAbSKPIQT6QyhDzp7fyZX2vcr4qCK7z89fo6rL6pe2G8uOPFy/vO6D0de7y4OOTmL/myqblDb3UxF5PoALsvunYJbwgYiI4J90kdxiT9jqC1pNQ6nMUXUx5joaq66OQ9Exr1KGtt+70HU3LUNS3/OCSXWbiy30yK0A8PsAfgzgRwCeA+C0tgpFRJSKslS6FC7PmFc1a56r5Y4V6XEZ2lxHU+ySFTI/UtWEEG0fwyppy6uUhcH6tAS1LKnq9wCc2HJZiIiSxu521diTudotTWbrkxlAmctNrcI6BSlURMsujIz5u6Bon4a+v0UXVIZe9mZkNtcS1RKaDW8fEflLETlPRNbmt7YLR0SUEld6Wd//Y+NrDfI9bgZI+TJmy1IeKLlamvL7ZcdzzMd76GLNvTNkZUFDKvth6/Jzk9IxijXOidIT2g3vEgB7Afg0gE8aNyKiyQvpnjJ2vvFHrpYh+zlXy5IZRJmP5Tebq/LCCk1/eOzT1eUYqJQkf07PzcW/TUTons6r6l+o6oWq+rH81mrJiIgSwsQCO/K1ErmWyS1cvhJrlxy7Q/CU39YuOda5HfMKfmqVL0pDk7EtfTDHUw2lrKkdQ1PKZac4QoOlfxWR3221JEREIzTFQMlWlPUuD6LycSq+dN+u47hi0/oduuTxWFMbQiZeHaKhfSbMIC7lboopllsBqMxFv01FaOrwlwH4SxH5NYDfYpaEUFV1YWslIyKipNhJGOwWJTto2qEFybPOrtIAxxA7HToN0xDOtSJDK5+vZWZo5bSlEBAHE2Hq8AaCjpyq7qmqc6q6u6ouzO5vD5RE5GHtFZGIKF321dQpWL11MxYuX7lTdzpbWZpw3/Nm8DWk4GRIZRmiPiqfo6rwtqSv9N0p4Xk0bbHCzH+OtB4iouTYP6RTm+/HDGqKAob8OBWNXbLHOTEAGY8+PgtT+PyFVOSLluniGOXd7vJtTeF9GRyZi3+biNBueGWYvJ2IyCP1ikFR648vBfiWkox1ZSnFi/QdQLG7Hbn0NUYodJtm+focz9THdqt8Zs3xk2xRIiBesKSR1kNElJxUWpLsCoMvSx2wcxrvKusuWz71QCP18tP0TGsC1p1VDZTGh2OWmuCRIyJKhDn+qYzZnS1XmGChpMtbUeuSWT6isSo7v9kSkT4zUBrbe8lsePXFaln6TaT1EBGRh+uHPK+g2VdDzclgi1qO7NYm+/GQIGl70FVlZ4gGJkarwjhbJabJDH75vk6bqJb3oBORxwLYpKq/EJHnA1gK4B2q+r22C1jF0UuX6oYNG/ouxuCNt5mZqD/558r8fDUZ2xKSMMFcLmRZIupGypVs87sM2PkiTdE+ja1+UbY/u8/PX6OqyzosUi1HP+JI/dLl66Kvd9ffeVAS+99UaBvauwBsFZFHAHglgP8E8IHWSkWtGdsXGdHQmJ+vKsGL3YITmgkupAsd0RgMoVtUlTKk+ltrZ6yr0v13bN3YUn0PKa7QYOlOnTVBnQTgH1R1DYA92ysWtYUffKJ2lF1tLcNgh6hY1d+vMVTW+9Yk5TfrGwMi0s5tIkKDpTtE5EwAfwjgkyIyB+Ae7RWLiGgcfK25IWOBqDlXoguiulIOAHzB45iCyjHtS3ScZ6m20D19LoBfA1ihqj8GsD+As1srFRFRYswf6bzbij2viYktSe3jeK5pCwls2qpc29uu0pWtLb7jkXIAaBvTvtBwBCV4AAAROQjAoar6aRGZB7BAVe9otXQVMcEDEQ2JWTnijzhNScoJDmJzBUk8LtW5Wun7HoedTIKHJQ/XL3760ujr3W2fA5LY/6aCWpZE5I8BXATg3Oyh/QD8v7YKRUSUIru7FytENGRttHT03XoyNEM/HkMvn8n1fcrvWOpCaDe8lQAeC2ALAKjq9QDu11ahiIhSZGelWzW/GCs2rceKTes5ZoYmwUwE0GdF1hUE9BEYuBIk9F3BL2rxTil4qqtoH8e7/wLMzcW/TUToPEtfVtVHici1qnqUiOwC4Kuq+vCA194E4A4Ad2GWVW+Z9fyrADwvu7sLgMUA9lHV27PnFwDYCOAWVX1G0bbYDY+IuuBK8+2b+DV0viSiIem7e9NY2MeR3RPTZI8/zf9PpxveI/SL6/8t+np3u+9+Sex/U6Fh4ZUi8pcAdheRpwL4KIBPVNjOk1R1ieuAqurZ2XNLAJwJ4Mo8UMq8DABrGUSUDLOFKf/LliVKSYzKfFnrzniv4t+NXcfGwXzPknz/BMyG10Donr4awE8AfAPAiwFcCuCvWijPKQAuyO+IyP4A/jeA97SwLSKiaIomh2XARFNUFigkWemsiS1K4zKE7IbVCIOlBnYJWUhVt4nIBwF8XlW/XXEbCuByEVEA56rqea6Fsgx7xwN4ifHwagB/joIJcEXkdACnA8ABBxxQsWhERGFWzS/eqbtdla517IZHNF0Mksbl7m547+u5JNSF0Gx4JwLYBOBT2f0lIrIucBuPU9WlAJ4OYKWIPN6z3AkANhhjlZ4B4FZVvaZo5ap6nqouU9Vl+yxaFFgkIqJwdoDka0Eioh2Zc465nqNiPEYUDVuWagvd09cCOAbAfwOAqm4CcHDIC1X1luzvrQAuztbjcjKMLniYZd87MUsQ8WEAx2atW0REnWJwRFSPnQ3Ofs4n9SChrPyh++dKDkFE3QoNln6rqj+3HitNoycie4jInvn/AI4D8E3HcnsBeAKAS7avXPVMVd1fVR+IWSC1XlWfH1heIiIi6lDT1qOiVqiuhZShaJmybnd1uuWxKx81oTIX/ZYSETlERHbL/n+iiLxURO4d8trQPb1ORP4AwAIROVREzgFwVcDr9gXwBRH5GoCvAPikqn5KRM4QkTOM5Z4F4HJV/UVgeYiIWjOEyhpRaopaj6p+pvoODEK233cZq+L32oQJEzwA+BiAu0TkwQDOA3AAgA+FvDAowQOAPwPwGgC/zlZ8GYC/LXuRqt4I4BGOx99t3T8fwPkF6/kcgM8FlpWIqJHUKkFEQ1flM1U2UWpfn8+hzT1VtTxDKjtRD7ap6p0i8iwA56jqOSJybcgLS8PCbFLYs1T1Nar6yOz2V6r6q6alJiIiIgJQOLYpf7xuhT9Gq0qdbbfZmtNW8DPFFqhJTOsgEv+Wlt+KyCkATgXwr9lj9wh5YWmwpKp3AXhc/bIREaVhipUEoiFyjV9qEhxUfW3Rd0GV74mhtOakWOYuMYHPJJwG4DEA3qiq3xWRgwH8c8gLQzscXisi60TkD0Xk2fmtbmmJiIZoipUEoiEyW5P6uIhR9F2Q4vdErDKb78WULi6l3/I03TFLIvKZ7N9TVfWlqnoBAKjqd1X170LWETpm6Z4AfgrgWOMxBfDx0MISEQ3R0MYhEKUqrzzH/Dzxszks5vsxpfdmDC1PfWSvE5HjAbwDwAIA71HVt1jP7wbgAwCOxizOeK6q3pQ9dyaAFwK4C8BLVfWymsW4v4gsx2w6og8D2KH/oKp+tWwFQcGSqp5Wr3xERMNjBkhT+sEnahM/S9PluujEC1HTluU8WAPgqQBuBnC1iKxT1W8Zi70QwM9U9cEicjKAvwPwXBE5HLNpgx4G4AEAPi0iD8mGBlX1NwD+GsD+AN5mPafYsSHIKShYEpF3Oh7+OYCNqnqJ4zkiosHJf7z5A05EFE/VSYd9GGC1qPuWpWMA3JBlxkbWqnMSADNYOgnA67L/LwLwDyIi2eMfVtVfA/iuiNyQre+LVQuhqhcBuEhE/lpV31BnR0KP3D0BLAFwfXZ7OGYR2gtFZHWdDRMRdaWN7kFENF1TGq/TBt/xM7+jeYyTsEhENhq3043n9gPwA+P+zdljcC2jqndi1hBz38DXVqKqbxCRE0XkrdntGaGvDQ2WHg7gSap6jqqeA+ApAA7DbDLZ46oXmYioOwySiOKbcmWW3ynNuI6faz6tWFkJp05FWrkBuE1Vlxm38/reVx8ReTOAl2HWsvUtAC8TkTeFvDY0WNobwL2M+3sAuE/Wd/DXFcpKiUk/AwwREbWBAQPFVLUrX5Xzzw6sGGh14hYABxj3988ecy4jIrsA2AuzRA8hr63qfwN4qqquVdW1AI4HENS6FBos/T2ATSLyPhE5H8C1AM4WkT0AfLpGgSkRY8gAQ9PEQJ+IaFpCuve57jdZdxIU0BZuJa4GcKiIHCwiu2KWsGGdtcw6zCaJBYDnAFivqpo9frKI7JbNh3QogK9EOBL3Nv7fK/RFQcGSqr4XwHIA/w/AxQAep6rvUdVfqOqrKhWTiKgDDPSJxq9JBTbpyi85+YKgpu+1mXgizQtxim0a/1a4xdkYpJcAuAzAZgAXqup1InKWiJyYLfZeAPfNEji8AsCrs9deB+BCzLrLfQrAypqZ8ExvAvBVETlfRN4P4BoAbwx5oWhAaJhlpngegAep6lkiciCA31HVGFFeNEcvXaobNmzouxhE1IP8B4xBEhERFamTdc/1mt3n569R1WUxy9aGpUuX6ue/EL9+vOceaew/AIjIBwF8B8DPANwE4GpV/XHIa0Mnpf1HANswy0V+FoA7AHwMwCOrFpaIKKbtme4YJBERUYCiQMkXSKU+Rq+8aWT03gvgfwE4EcAhAK4Vkc+r6jvKXhg6ZulRqroSwK8AQFV/BmDXmoUlIoqG8yYRdY9d2GisQn5PeP6nR1U/i1m3u78G8E8AlgH4k5DXhgZLv81m4lUAEJF9MGtpIiLqDH+giIZhyhco+D10tzTH7zSX2vmvALZp/FtKROQzADYAeC6AbwN4pKoeFvLa0GDpnZgldrifiLwRwBcwGyhFA8UvcxoL81xO7QeKaKym/BvDloe7cYxoOlQ1+i0xXwfwGwBHYDZ/7BEisnvIC0Oz4f0LgD8H8GYAPwLwTFX9aL2yUhdYqaQxqDMIlyhUmxXaqVSWqVh+HtQ9H3geTbf1iuJS1Zer6uMBPBuzuZzeB+C/Q15bmA1PRO5TsuHbK5SzdcyGR0RERERdSCUb3pKlS3X9lV+Ivt77Ltwjif0HABF5CWYJHo7GLBvevwP4d1VdX/baspalawBszP7+BLOUe9dn/19Tv8jUp4XLV/JqFQ0Gz0WiMPysTFeM+aR4/tDE3RPA2wAcpqpPUdXXhwRKQEmwpKoHq+qDAHwawAmqukhV7wvgGQAub1pq6o+raxODKOoDu9kRheFnZXq2T43Q4L3PX8vzZ9q0hVtKVPWtqvrlbLLcSkITPDxaVS81NvhvAJZX3Rh1zxf8uAIjpmAmIiIajrq/yfbve9GFUF4knYAWMuGllg2vidBg6Yci8lci8sDs9hoAP2yzYBQHgx/qE1sriYaLn83xyZMhhP72M4kOUbnQYOkUAPtglj7849n/p7RVKIqvrBWJP5rUBrZWEg0XP5vj40vlbb/Xdbv3hdQVuqhPFGXIY/Y8N6YOry80dfjtqvoyVT1KVZeq6qqhZcKjYvxRpJgYXBONk/nZHkrFmOKrWycIeV2VVq26iuZ34txPFFthsCQirytbQcgyNHwMpqgKni9E42R+tmNWjIlyMZJWUDUKYFsLt6koa1l6kYi8ouD2SgAnd1FQimfFplmmxFXzi7c3V/PqIPmY5wkRtYvfxZSykDTldYIkfi6oT2XB0j8B2LPgdq9sGYqo6ZdC2etXb92MVfOLtzdVr5pfjBWb1rNSTDvIz4XVWzezWwNRR3i1nVLWNE15Xn+x6yL2GOtY2f2mVOdRjX+bil2KnlTV13dVkKkLTfPZ9IfUznyTB05EpoXLV2ILAyQiIgpgXoBtIq+fFK0rX8aXya+onmSXc0oXAqeU6ju20Gx41BL7CklZMFR2xcRsAncta6/fDJTYgjAOMVoIeXU7vi67kbDLCqUmpPsWDVfdukOV3ypXVt+yZczHFi5fub2cvhYsIhcGSz2pGiTly2y5ao03ELLX52q2zpdhUEQmVlDa12UAymCXUlOl+1bRZOuUlip1kdB6ki+ociUvWbvk2ODtp2zWbY6pw+tisNSDOn1tfVdQXIFTSN9eXk0Zt9AfIGYlIqLU+L6v+D1GQHgPnaIJeYvGRbH+ND2FY5ZyIvIQAO8CsK+qHiEiDwdwoqr+baulmwjXlRD7+aL7rserXmGzkz7QNLByQUQUpqhyTeH6rmvYLU2u97XoOTM5VkqmlOo7tqBgCbOMd68CcC4AqOrXReRDABgsVVTWfc7W5Is55LWpfuipHv7YExHVw+/OOIZ2cTafTsWl6D1fvXUzzp2fb6NIrZhQr7noQrvhzavqV6zH7oxdmDEym3JDW3vy8UZNAyVzAKN9M4MkBkrj4voBYnc7KpJK8okhjUkZUlloOHhehDF/p/qugwwlaKPhCm1Zuk1EDsFsEmCIyHMA/Ki1Uo2I2ZTre96X6MGs4JotAnW62AHlX0j8whgvBklUJJXkE0M6j4dUFupGSMt86udFHy0+vu2FlmVIrVRDpQC2sWmpttBgaSWA8wAcJiK3APgugOe3VqoRqjIuyW4FyL+g7eCpaL35a30Ti/Z9JYeIaGzYzXXcpvDeNgk6Ygctdp3FtW4GStQFqZL6T0T2ADCnqndUeM1NAO4AcBeAO1V1mfX8qwA8L7u7C4DFAPYBsAeADwDYF7Og+DxVfUfRto5eulQ3bNgQWrTOubLW+QIgorpYYaNYeC61j5W97vGYUyy7z89fY9drh+jIJUv1kk9fGX29h+yzMIn9b6pwzJKIvMK8AXgxgD827od6kqoucR1QVT07e24JgDMBXKmqt2M2JuqVqno4gEcDWCkih1fY5uDYOf7N4ImVEoqB5xHF1Ma5xDEdO2KlvXspHvMh9gYZYpm6lNr+b9P4t6koS/CwZ3ZbBuBPAOyX3c4AsLSF8pwC4AIAUNUfqepXs//vALA52/ZomEkc7HFJRHXw/BmXMQYWTc7RMR4PKpdapbQNZZOn9vHZSCHobHLumK91rSeF/ac4CoMlVX29qr4ewP4AlqrqK1X1lQCOBnBg4DYUwOUico2InO5bSETmARwP4GOO5x4I4CgAXw7cZhLsbnfmGKWiCdGIpoDnP4NfW1fHg+fesEyhUlpWqS+aPLXo+dTkxyFWgNxkrLb52jGcg6rxb1MRmjp8XwC/Me7/JnssxONUdSmAp2PWle7xnuVOALAh64K3nYjcC7MAapWqbrFfJCKni8hGEdn4k9tuCyzSsDAwoqa6On+6PE/H8uNfhJ/7Yer63KtSieM5M05VKuO+DLr2c3X0fX7lx6GN4MReZ9NWJ7Z4TkdQggcReQ2A3wdwcfbQMwFcqKpvqrQxkdcB+B9VfavjuYsBfFRVP2Q8dg8A/wrgMlV9W9n6h57gIefLZOdKDT6FCiMRERHFx+79brGSfKSS4OGIJUfpxy6Pn+DhsH33SmL/mwpqWVLVNwI4DcDPsttpIYGSiOwhInvm/wM4DsA3HcvtBeAJAC4xHhMA7wWwOSRQSlHZFRzXF1zfV31oWNgqSVNWdcLvKuv03R+zKe3rVIQESq4WkhitJrHOp6qtOCHLjqFbHXUnKFgSkQMB3IZZy9LFAH6aPVZmXwBfEJGvAfgKgE+q6qdE5AwROcNY7lkALlfVXxiPPRbAHwI4VkQ2ZbffDSlvKoq+xHxN63UnpqXx4TlAfRjSeWeP94y5Ttf9Ie17G9gCMU2rt27e6dxuEkzEHkdlzxMJMOlHZS2MV5rSmKXQbnjfwCxRAwDsDuBgAN9W1Ye1WLbKUuyG53qsbO4ldtMjdq0g6pf5GeTnkahY25+Xom51bc6rlUw3vEccpRdeFr8b3sPuz25426nqkar68Ox2KIBjAHyx3aJNgxkMmVdJXf+P/aomhWHFjKh/9rx5RGPlS6Ed0rqT11tWbFofv2CGomBo9dbNrbREsXVrOkKz4e0gm//oUZHLQgZXYGS3NNkJImgaWDEjIureVCvHvhTargDFPkb575W5rP0b5juuC5ev3Ok517Ih9aCqLUsh46RSGvekYDe8JkLHLL3CuP0fEfkQgB+2XLbRalrZZUsTEdGwTf37eQz7b1eW88rxVIOmEHUCCN9rtly1ZqfnXMvWqVOFBEJF3fpC1kHjEdqytKdx2w3AJwGc1FahpqKoO5UvIGLr0nSN4X0ewz5Qf1I6f6beAlx3/10ZDsve97bOC19lOaUWhVTUCTyqZMnzBb51tDkXVJu2QaPfpiI0WPqWqr4+u71RVf8Fs0lkqaay1qGiLwC2LE3PWMYple0Dz2nyGctngIq5MhyWve88L4ahSUtL3RYp83X8/SjGbnj1hQZLZwY+RhW5vuTzzC1lfXPbSJtL1Ceey+TDc4OqGmPlechdv/puaSnqaRNStpBjO+TjT+0pDJZE5Okicg6A/UTkncbtfAB3dlLCkXN9sH0farN7gt1VYYw/CnQ3VhSJpj1hLFXH780dDbWi7ytXnfLWec/zRBIhrVSpjltTANtUo9+moqxl6YcANgL4FYBrjNs6AE9rt2jjV/ShNj+IZnDkwu4p48YKIaUg9Dxtcj7bV45933t1x7jws0amIZ4PMcbaDI2vJ00X5V01v9iZSKKoTrVw+crBHktqR2GwpKpfU9X3AzhEVd9v3D6uqj/rqIyj5vtA5h9E12zy9sRueQViiF/sRKkK+TxN6TNXtq+hF2yqXtixB/qHvL7uGJeQdU/pPZ8K33va5fjgqi0VY+gyZpavy+Cj6XaTvDitwF3b4t+moqwb3oXZv9eKyNftWwflm6yiLzl7JuwqWYMoLXw/+xOjUt6VLs6Tvva1yeSvbRyXobznFF9Z0ORbvqznR4jQSnteNwjpDtZ160fV4KzO3Ecx1D0uKf8esxteM2Xd8F6W/X0GZtnv7Bu1xDeBW1m6cf6Qjw/f036l8AMZ4xzpcz/bGnsZelxc203hfR+rrltE6iZLCnmd67km51bIvEM513GMeWxjpuN2rc9WdV+b8k2wS9NT1g3vR9m/f6qq3zNvAP60/eJRruxDumLT+u3/D73pncKwshZH0/EpU/mB7HI/XfPHdV0G1/bLHrPxM7qjvq/8xyxDU0XnRlfnues4xmhtslu3YhnaeKxxjUtS3KXxb1MRmjr8qY7Hnh6zIFQsH4Rod0nJb+aHelwf8GmbSkW9TTyGw8P5tsZpCL89QygD0N73jhkMdhUYDiUArarNlrVUj8mQiMh9ROQKEbk++7u3Y5klIvJFEbkuGwL0XOO580XkuyKyKbstaausZWOW/kREvgHgodZ4pe8C4JillpkZYswfgFXzi7dXJnwzWPODTFSMQVS4tr9PfC1NbWsalPEcoljqjG3qKjCs0vUvN6Q6SBuJJIYSlIca6JilVwP4jKoeCuAz2X3bVgB/pKoPA3A8gNUicm/j+Vep6pLstqlpgXzKWpY+hNnYpHXYcazS0ar6/LYKRXfzNaPnP9K+D2xqH2SiOppUdvtovRhSBaKKtr9PhtT9jsil7c9uyLlol6Hud1jo61L9vsrl31tVvr/sfS46BqkfnwE4CcD7s//fD+CZ9gKq+h1VvT77/4cAbgWwT2clzJSNWfq5qt6kqqdk45R+iVmAei8RObCTEk5IlRaismx5lDbOneUXMs9OiD6Ob+ygY0qf9Tr7muLxYcr6mT4ron3N+VOmylxARcrmEPJtr4qujleM86So5aloP4ZwTgQbZurwfY3cCD8GsG/RwiJyDIBdAfyn8fAbsx5vbxeR3RqXyCNozJKInCAi1wP4LoArAdwE4N/aKhT52d3yFi5fibVLjt3+P8CrpSnKu1Pm7y/fQ7/Y2aVSNqXzpM6+molv2hbrHOwjZX0bn5+mldg+K6JJVYIrcL3PVTO++c6VkBaZNjLx2e9VnQvMZe+3b7hDSlrshrdIRDYat9PN7YrIp0Xkm47bSTuUT1WzYjqJyP0B/DOA01Q1D9POBHAYgEcCuA+Av4h2wCyhCR7+FsCjAXxHVQ8G8GQAX2qrUFPl+8DmH9JV84tH+yU+dau3bsbqrZuxdsmxnGC4Bju9Po9f94ZYmQipBAHlwULI+VQ1gBnS8Woj6O5rktEuXpcq+3sSKH+ffOMJ7cddLTK+1OIxjnvVIQhNW8nsceO03W2qusy4nWc+qapPUdUjHLdLAPxXFgTlwdCtrg2IyEIAnwTwGlX9krHuH+nMrwG8D8Axbe1kaLD0W1X9KYA5EZlT1c8CWNZWoWhHrn63ZiuSmXaXlcR05V3vOF9WM2XHj92c2pHihZy8zGWft9SDiZTUqYjWPZZ1K8BtLdvlOkNb6O3lynqw2N34mnRprDpeqMp4o/z50GNpLpvqZ3eAqcPXATg1+/9UAJfYC4jIrgAuBvABVb3Iei4PtASz8U7fbHm5rncAACAASURBVFogH9GAnRWRT2cFeTOARZhFf49U1eVtFayOo5cu1Q0bNvRdjFrKWo3y5+2xLBzbkj6+h/1I9bizhbk/qZ4zbZjysRjDZ9C3DzH3bczHKbf7/Pw1qjr4xoOHHrlE33XxFdHX++RD71d7/0XkvgAuBHAggO8B+H1VvV1ElgE4Q1VfJCLPx6zV6DrjpS9Q1U0ish6zZA8CYFP2mv9psj8+oS1LJ2GW3OHlAD6F2eCqE9oo0FSVXeFyXQFta9Z7oilItaLXReWD3yl3i5VQZMjqdEMc4rHoqntUjJaRKtrYr7KLs1Ufc50j9jZC6ixD++5JPdjLzcYsxb81KpPqT1X1yap6aNZd7/bs8Y2q+qLs/w+q6j2M9ODbU4Sr6rGqemTWre/5bQVKQGDLUipSblnK2c285lWN/EoeEzmkz3wv+T52K5Vjnko5qdwYrrB3gef8juqeN67XxToHh3ou91WuVFqWHnLEEj3nY5dHX+/xh+2bxP43VTYp7R0issVxu0NEtnRVyClyzdJtjk3iD0qa7ECX72P3hnzMzc/9kMsZquurxENN7jHEyuUQhLaYVH1P67TEhIyP6Xpgf5OxWL7kCk2FZp/zPdfWMay6f7HPERq3snmW9lTVhY7bnqq6sKtCpiB2akn+uI7TGCrA1J5UPvehQUnX5zsvJMXRVcBp9poA/OeLLwObr5yxP0d1JjftW92yVq3HFGW4c5XBDuRiTLRb5zV1jk9K779NET9t+LYR9UwrEzpmiQI1yZLj+gKxU1YSEQ3B0IKSqhWmIbZA1RVzX/roChe6PTvjq2sMb9k4Gh9f5jbbEFug6nKVt2j8dNFF4Sr1FNcwg1zoubBw+cpa8xKar7Ol8r5R9xgsRRbSRB3SCsUAKV1jqoRVEWO/x3DsxrAPZfoOlMoSDgyx1SsG3++Gr+WlDl+q6KEoa33yPRayHyHzchUt03cLVN1WodC5icou3lYNFpscpy1XrQmet8lMLJG/rklK81xqwdVdGv82FQyWIimatMwMjnxXcFz3KU0pVsJiiLHfdWePr7pMm6b6/sdu3SgSMi9SShm3TKvmF3fWvSxEauez7xiZ+1G3ktv28bfrCTG7xLm2E5vv+LQ9dqnsfXGNEQ5tSbTZ80ilYpYNj93w6mKw1JK6H8T8tTQcqV096lPbxyqk4hbzKjuFi1mpbjvwrtLVJ8YyVazeurn2/jc5bqHjgcagye9zm8fFbumJMdluneebqnohuK9Jfu0xcyHbzD9jRRc1aHwYLHWIle70LFy+ksFrgC5mNq/6w5TaFfHY+H3TXJXgPHX2fgz1okOT87qoZ4ePvf9tvN9VM8o1Wa5vrt+KqnM+xbZi03rn4/l7v3bJsTuVJbl6gQJ3bdPot6lgsNSivGuefcufy5lN7mVfGvltaD9gY5DKxItD5Jt8sIkuKim+bY1B2z/mXR6ztibgHuP73kQK34FNzuuYn4nQbp4h527ZOKiQgCHmvrUZoFQtZ1ErVaxylnXdzP/WTUZB6WOw1DMzQCrqb2xf0eCHtLkuK+Nj03RMSci6Y70fId2L+N5X1+Uxa2teMnNs05ADp67K1tfnoM3KeVHW2RC+JBGh3Txd567r/SwqZ5Nug3V03WoSY8hC00yGbbxuSDhmqRkGSz2oMgjSftwMqMbwAe5TjIrBVN+DNitVbVSI21w/tacsSIiZ/a2N8yJWkFOlbCl+J4UmJmiybt/9KnzdtcrYCZ9c76evnHW7OA/pPAjJ/ts0kCmrV7l67oR2f0yuyx1Fx2CpZ75+u2YwlHffy/vNmo+RXxc/FlN/D4b0g9y3IbdMpKpp1rvY2ho3F3MfUv5OSrnsRcr2q6yLXheTrlbNwFfluz+kLHUC5rKhC/l6fcuFpIEv2nZqmDq8PgZLifBdjSK3kC9R6sdYg4qmn88UuoMNkZmdKhbfe+B6j+u8XzG6AC9cvrJSy1vd8yrFSmFbfC0gVRI0uFo3Qi4K+FTNYhhrjse2WgPrTm4ba51Vt50KdsNrhsFSh6p0nctbjvLXpPbB7AObzbtX5Ypp7LFIY2N3B0sxaOqizF3MQeQ7R10BSp3zOcbEr1uuWtOokm3z/Tbxu3RHsVsommaCs7sGln1/hE5Am2+/rN4Scn7EDKjsLo1l26q6bV4cIBcGSx2r2hzN7nbhUjlOKVaC63INdG5qTMev6KpwikFl7DKHJuOoek7UnVMoJECpo4v3umwbXX1/pl4ZbTpxbBVV6gt2WULOqZBAqOlYKd/F3hhJHVzZhduc4ylpqti2Lf5tKhgsdaAocUPRc6lU/rsyli+1FCvBXQmptI7p+FVJPmF30xtT0OgT+l5XPSeKjnsb3zMh71n+XFvfcwuXrxz8d+jQywfEmzjWpUlGN9+5VfS+58FQ2flZp2uc636VDH9dnQtNE2nQNLQeLInITSLyDRHZJCIbHc+/Kntuk4h8U0TuEpH7ZM8dLyLfFpEbROTVbZe1DSFzKLm+FKb6ga07FwWNw5gCodjsbno8Vs3U7XYWEqSa6164fGXQe5Ynq2jre27LVWt2SBIUqssxKVWylZVp2h2rqapjm4DqLUmmLVetcT6XP160bd/Fgrbf+6rdF7toxSvbRgoBvYuCCR6a6Kpl6UmqukRVl9lPqOrZ2XNLAJwJ4EpVvV1EFgBYA+DpAA4HcIqIHN5ReaOwr1SMbSbuOsoGJVetAI75WFE6ql7trWMKLUldqptlzDdfjvm/WdFzfaf5zpeYAbBrG1XKkrMr2rGmrWgy0Wqd17Z5sS30gmefF/xCxkbZAZfvNX21UoaOj6oSKJsXtEOz66WKCR7qG1o3vFMAXJD9fwyAG1T1RlX9DYAPAzipt5LVZH/xsBUpboVgasduqKYetJbNAB8DW5LiqDquI1TZpKOmLq6aV81qFmrtkmN3eE2VgLNuEp6yjHJlr2mztcS33raSGvieb9KlLeR4xr5YE3N99nnlCpR954DZnbLK+Kap/+ZNTRfBkgK4XESuEZHTfQuJyDyA4wF8LHtoPwA/MBa5OXvMft3pIrJRRDb+5LbbIha7PtcXlytQSjV5Q+iXROjgbEpX3avMU24lmfK+2/o4FnW6ovn4WmpCW3B8rVE+dZIKVBnjVqelrMp3et0gsc7vZJWsb2Y56gQ9rt4jMX/by45bWV0iRsY6O1lP08QPsdLwA9W7L9bNQBi6jiGadcPT6Lep6CJYepyqLsWsO91KEXm8Z7kTAGxQ1durrFxVz1PVZaq6bJ9Fi5qWtbbQFJtN+iQPhRn45d3qXD/IDIzGzexmWvWcrXIVfmz4ubhbH8ei7W2WVdLNLkxlZfG1qFT5vJWNl7Jb2lwp/osCvTpJCeyKfsw5oOr03jC7X4UmKHBtI/R9CbnIVKc1qEnLR4x6h1kvKFt/aDfRnFnHqNrVjqgp0Q4jQxF5HYD/UdW3Op67GMBHVfVD2f3HAHidqj4tu38mAKjqm33rP3rpUt2wYUMbRS9Ud6DwEFTJusd5cobNHEjeNmYOoiFLJZtoWeKfqvsQ+zvALkOXx9Xel6rbLlu+bN+63NcutjWUz4T5vsY4X33rMH+jQve9zjHafX7+Gtd4/KF54OKH61+9/xPR1/vHj3pgEvvfVKstSyKyh4jsmf8P4DgA33QstxeAJwC4xHj4agCHisjBIrIrgJMBrGuzvHW5rkQN4UvJ5LuSZZezKAFDW3OMUBxdvjdDPMeHZmotZkOSyrlZp+tUaFaz0NcUqZskoaiFI7Qsdutz1ffU15KRfy7L9q1qYiaX0Nc2SWRR9Bqza2HMz4Sry2Kd9zXGb1a+jlXzi3dotV29dfP2Lre+c8HXcjvGsUnMhtdM293w9gXwBRH5GoCvAPikqn5KRM4QkTOM5Z4F4HJV/UX+gKreCeAlAC4DsBnAhap6Xcvlra1p5XEoH0gGRNS1sQYVsT9HZWNexqDP/WlaMW1DnbmXfGNmi9ZfZd1Nlw1ND55XfoE4n6V8u67uhaHjVOpus+3X290aq7aq1O2yF2MsVJGy7Lm20LFUZcelKDHXUOpq1K1Ou+G1ra9ueLm6XZPaah7vslsWUZ9SPddTLXcq2jq+vu/sJt19YvwONA0u2uyqFfO96LIbcEhXyaF0cXNpeq423U6fQs650O6XvsdT6YZ30OKH66vfF79z1p8+5uAk9r+poaUOT1rV1iXzCmDdrGJFWAmjqUj1XE+13H2yrzTb9+2EBW2om2mtaLm80l0n412uac+AmN3BbLGnjHCVtehccD1WNxOfHayFtpaFLBOatCJ0O2XnamiiiaYJJ0I1TXxhqhooAcXHq8lnk9LGYCmyph8ifggptrF11aJ2pPLdU5a6Oh+n0PS8j/m5KatMm5XvOhnv6qjyfjcNosxj2eZ5Zo5fAdxjckPSP7ve+yrnQ9Wst3Y3uqrbaSOYsp+vk32vzjlcp2dO1edCWyZdFznqjJ8bAkX8tOFMHU6N+K4MlbUecdA8tYGtFxSizndP1YCii8C9LFV2mfw7uurrm1SmY33vxwiAqijLNpf/NY9lyGuqCh2jVZYAo+i9b/N7NDRgCX2+SfBUhSvwrBq81B0v5btf9FxZkFR0HuVj6Mz0+kldiFTgrm0a/TYVDJZaYH/AQvryFl3h6vMDmdSXARGVsisETT7jdgWybF0pBO51g4iQfTOzdbWhy7TeZeq0kNXNtlf0upCyurKmhXbhi5H0wrVMk/PEdezbHFPkSzDhK1foumIsV2X7ZWPTtly1BqvmF2PFpvUAsP0vjR+DpQ40+YLqOztdCpUbqidmIJxKFy7a+fso5mc8te+Los9AlXM69LMUO7V3X8yr6z5dBoV2r42qrRt29z3XNvLH8m5YbQWDbXXDbNKVsup4pSZdCcsC1qJtuLq3xjwP86Bp6Ek9XBRsWWqCwVJkIV9I5gc4/+CVDVomii1mZqqUfjRSkdJ3QEplNRV9BlzjXULXYy5rv85cr2/OH5e+j3HeDSlXdOx84zp8Y7WayH9DfWOR7Kv/rp4fVd6Hsu/NOu9TrKC9jqrjq0K00epTV931ugLnkPFuNE4MliLydW/xDXw0P2xlg5aJhmpoPxptViq7rLCm9B0Qq6x9BwRFquxj6MSbddfpE6ti7fvNCi2vbzlXC0RRmZueD6vmF2/vYmc/bpbHLq8dRNmvK+JqpfKxU727tmkH1118RuokTTDFyqpYtp6ysWpNyjG27HfKMUuNcJ6lltXNckNERMNXNJdLrLmF6qynymu6bB02txWSlcwuW2hZhzyH2cLlK3do8fK1wjV9T+qsI9a5kK+nj54H9jlW9fi6nvOdh6nMs7T/Q4/Ul513cfT1/vkTD01i/5tiy1Jk5pUIVzpYohSM4UoaDU+f3Y3aUjR+p+i5Kvtbp9Lvek2Vgfd2MoqyMUGh+2N3ZSq7+u9qfarSwuMT2kJT1KXSJaRsKzatd3YfNFWtM7jKVraOqq1UVVqc7K5rTVurypb3XZiumhHP95x9HqZYp2PLUn1sWYpsyFeziJoa0vikoX3WYh2boe3XEJgpe5scmy7O37zrV2h2vKbvdR/ni7nNkMpn22VwPe56fkjfX0VCMvzF+Dz0qWwfY79XdvDta/Wqut1UWpb2e+gR+qf/+PHo6/2rpzw0if1vii1LkaX6xUUUYkgVjb4/a2X95euus4+Kb5Pn22SP6ej7PQ8RY2xPGfPcM9dRNMamytigKlf6Q1qHQlRdNk/jbMuPx4pN62t9Ruu2toRkXqvT+uYS8nnwbcucI6jKOOvQ9YcqGxMUa9yTuT67tcvXglQm1VZwqo/BUiSxU1QSDdGQB+B3rU5wVDUFb11V3qeyCnvMAKXqd2TMbfsytNmG/j2e70deTjtDXWh3JJN9nEMr6z52a1NIQFG2TXu/XGNIitbXNL27jyudeMg+9pHlzj5X7OQXVfY9pHudi6tLZZ1U43WFXEAoOk+TTfrABA+NMFgiomApXNk3DS2466plbqjvU18tk1W6KzUpY8j4lqbnZL4fvkxuVVQtS+j4K19WspCAoijQKBoz4gpE7IyzZvmrbt8npFWkSstJm+x5G10p782LCiHjjHz74WulKhqzFqtlLlTReDHfMSgba0bjxGApIn54iIZlqEFDalJIx15UkQod7B8rkCnaZlnXqaZlCP0d8o3pKWIubwcuVctdJ7FBWapxezl7WbP8vpYXV6rxsm2VnV9t1A2q9mYJScqx5ao1pVn6yp6zu86WtfBV6fIX+v4Xrb9qi9hY6nWclLYZBkuRjOUDRUTtabPrRpvrbjPojLXuGN/BVcrSRgDZ9+9IWQU4ZFmgWVcl3zgaXzl86lz9t1teqggZ7xPzM1p1/0LH51RZpx2w+cZchk5m7GtlCi1TrIyNZetOEedZaobBEhHtpK0fhroVnzbL0aU2K8NN1900xfXQujxWESs1dxP2FXnX8azbtc9+3p7np2xdvgpwWUrvkC5adgXXbj0ws+5tuWpN7fPcV5GuM964qFJe1PrRVgtTlcdjbmv11s07tMT59i8/1/JjbZ5jvpY8+/H8NUVdHF1j9MyWpNAxfOY5kXqQRHEwdXgEQ0vfObTyENF49Tn5aZ/fdU32O79vp+AOTUBR9RjGPu6h8v2ruv2QY1tlnavm3encYx+Xvo5zVWY5y8YexdiG+Zgvrb7rM1FlG1WPve8z6VoO2LnVKw+Kz8X3kkidff9Dj9AXrL4w+nrf8oyHJbH/TbFlKZIhpd9loDROvMIVX0hmpDrrGqK2voPqZNAag6qTvprP5VfNy8bQmOv1XVkP5WoJKtqe+df1XNm2gB3HrZSdf67WpKLtVe2ClqcSb9q9y3ff1So2VL4kC1W/D6vuqzm+yz4ffIGK77y1s1u6zjHz/bbXU5QgxPe4PXZqTN9nVIwtSw3ZPwpE1J+UW1VDr3S6Xhc6CaqpzsSWTY9v0evt/W3rvTS30/f5Ym4/1oS7TStxoeWoux3f68qORd3zo6zlpOl+tNUy49pebu2SY7Fi0/pGXRJ9wUnM1s2qrX9Vl7XPGQDO77Q2P+epTEr7O4c+TP/obfFbls4+8Ygk9r8pBksNtPEB7PvHm6htPMfj6vp4dlUZb1sXV4bb2obvGJZtLx+rVLXrUoz3rEq3pyYV+SIxz726FzeGqK3uiHUC+LJl2+jSaa87D7xcAam9vlSCpX0f/DB93v/9SPT1vv2ZRyax/02xG97ADKESQdSmsmxJU9O0205q3xlDKW9INrchcJXFdwyLBr/nrytbJmR7dT6voWNkqlbaq6R9d7VW1X2vywKlqgkC+jjn2moVs5Mr2Nuz/3e91qVO0oWQ9Ph2F9c8K6Ir+YQ9XxfVJyL3EZErROT67O/enuXuEpFN2W2d8fjBIvJlEblBRD4iIru2VVYGSzWw6x1RXFP+LNWpqBRlSmu70hVa3tAKRZWKR1cVyrKAou56ioSOd2q6/Xzi0fx5s0ui7zW2vMIYUvl1ZVCrM1bQNa4mL0v+tyj4cQWFvvE6QHHmTrv8eWuEuZwvYPCVJ3S5mMz3Ivb4Tdf4oNBsdD75+xVyrKoEY75xVK7Hze7LqQVNA0wd/moAn1HVQwF8Jrvv8ktVXZLdTjQe/zsAb1fVBwP4GYAXNi2QD4OliobShYSIpsv1HeSbBLIvvu9JX4WkbDkAO1XsgbCWEd/6irgqvm0q2oavQu97rIhvLqGi3zVfOvGQMTt2C4zdClO0b2Xrcz3nCnTtcWq+LnS+c8Q+Nq5ujFXnaApt2XQlGAg956uUIySQKdpOUfKE0NdVfczWNBiz0+m70vnngdKKTetZF2zuJADvz/5/P4Bnhr5QRATAsQAuqvP6qhgsVcBAiYYmtStbfejyGBVV4mkm9Du0aLmiLHK+ilrV7+463/Wh73loq4zNVQGM3dLnUmciUdc+2cFMUQU7VNmxs7fp6opoBjx5Brb8f992zGxsrlao0JYH33Hy7YudWKJo+aJtVFU2Vig0u5yv1anotU3K3+S19nlvtmI2afHtw0Anpd1XVX+U/f9jAPt6lruniGwUkS+JSB4Q3RfAf6vqndn9mwHs17RAPgyWAjFQoiHiOVmuy2MUmgq6Lyn9uJcJrfw3fR/Kjpmr4lf2miaTqxZtv2i75lVzs4WirXMiZP983cDs+0VlrNoyFVImu5XWTlMN7Njtz/4/ry+Yc2jl67HLuXbJsY0nhA7Zp6bd7OpmDLRf6+tG5zsX7e6SVVUZm1h1G0P8ju/BoiyQyW+nm0+KyKdF5JuO20nmcjrLNueLvg7Kkkj8AYDVInJIO7vix2ApECulRHGNqeKeiqZZ7IakjUykLmXHzFVZr5L5K18+ZKxNWSuErxLqW65ozE7RNvPHfS2pvsCnyme+alewqse86Dm74m4HPeY2XceyStlc3ffs8WP2uVXWylNWhq4q+VW242t1jF3WKt0Fx/YbdZdq9BuA21R1mXE7z9ymqj5FVY9w3C4B8F8icn8AyP7e6iq3qt6S/b0RwOcAHAXgpwDuLSK7ZIvtD+CWFg4bAAZLQYZWSaB+8DyIq4sf7DG9Z33vS0hw0lYZu9j3PrI0usa+FF3tLvvMFI3zCi1L2eNmJdYcy5FXdl0VeV+yA3PZ0C5eVb43Qru42cGJL/AJ3bbdhc8c+xISGNvBmb1dV5a2IlXGAIW0QLUVRMTolpmvpyyorjIGagxBkyJ+F7wI3fDWATg1+/9UAJfYC4jI3iKyW/b/IgCPBfCtrCXqswCeU/T6WBgsBWCrEgHjPA9SzOhThS/LUZuaZHcr+lFO4fwrK2PdJAt97XusAftVXttkPXYl3eQ79q7HQ7q92ds1n68zBstVwXVV3KtU/EODSzM4qdMK5uMaL1fUKhQ67seVvvr/t3fm0ZdV1Z3/bkGFAhUUNQEHUEEKB0pAnFiIJQLqakBb4phWcWgMDsR2XNGVpYlpjN0tmkaFYKlpFaIoLUujoKKiOFEgMljSEEGFOCEao6Uo1O4/3j146rDPdIf37n3v+1nrrd/v3nvume97Z5893FDLV+sXZJ1PCcrhudhxDbVzPzXuvqDTdUxpbjcYJwJ4gohcBeDQ5hgicoCInNakWQtgo4h8GzPh6ERV/U5z7bUAXikiV2Pmw/TeoSpKYYmQFaavRejYBa5F+S3lWOYf4VgEtdT18NoUBMR5UfKMWb41OVLvjWmrZWi7I28JC+ECPbVQL62f718Uq0NOkFsE/jj54at9n2pfy9eWmHBR4/8zJqwxTQmQYxnvPhljgAdV/bmqPl5V92zM9W5szm9U1Rc2/39VVR+iqvs2f9/r3f89VT1QVR+gqseo6k2dKpSAwtLIGfsilEyfPhakq7ioHeLZ9H/A2+Y/Fm1h7kWmpdHuxkBpf4bCRU1wiByhdsKvm6XByBEuvH1ii8sSszZ/od3G5CmHlb+rm6XliPn9dPGnidXJuicncLSdA76A5J8Pten+/KgNilKiJQznSmwsUrTxlyuljbYqFYSitnyyHFBYGjljWzQQ0pWuC/kh7ejbLlyGoK1JW+37XubFvE0ia83LUpT2p6UZSZm/td39dz4rTjvSJh9fo2SFvrZ8blw5qTEs3cUPKdUSxXycrAWuJeylzMZSC+QUpf5Wfn6hxqx2bro56aIcWi9O9bVnMU2ajzX2qTns01UYjeVlHaf83LpqhVICYol/3ZgZm2ZpSsjMR2o52H+//fSCCy5YdDVIDzBUO1k0fc/BNhGspgCf1eEpmTvuRZmlc6x03ELtTNud+jZ59xERzeXbx/OXyyPWhpJ72tYvdZ//EtXSAC25lxNb16f+3dZmXB3br1lzURPWetTc7X776JPe/E+95/vBP3/4JNrfFWqWBmIMZjBTZlUXX32p9/swgVh12s7BtiGo+2AR3ztTelZr53ptsITYPZYZVM1YlcydEg1TaHp3wpq12XpYGqJQO5IyT6rRuljXc2liflW+oBUKZUM5+reJoNdVW5G6L9QuOWLBIfx3ceV8Dn386Hwlfm4hpYE8avMtJafZqvWTI8sHhaWBmNICgmzNIgXdRezO1ZQ5hU2ARddxEc9+GBChTR8MvQgYOv+SNtf6hFlmZ7nd99TCyvIt8Rlq7sZ8mrou0GN//TJLhZOYIFZyn6WBSp0f6nt2jIEBrLaG88Ad+y/ItYKxxHyfrJdxn7BmLTasW19txpjyl0qZLvbpH1jrPzcJdJShwyfDSgtLi15UkXEyBkF3iB/dPvLL7VCOgTGMXwk1fRf6lqQWK77pTQ21726JkbP3d8TaUlOOX1btuNek94XQVHhuH+eTlPNN8rU7MROnrpRoH1Jl5Ralqfc7hcJJbAEc280vXWxb35nz2Hxq66sT9mmtRqbNvMjdE/N3DIWp2Et6/XMnbd6U/L2o3dzIHae0Q6EZXRcNc60vHlkO6LNEyMSZur14imVuW18symfIGpu+6zJU28K6L7reQ9entOy25cae05zPkS989vWcp3yAxvh9UuqzlAtEEcs3/L9rPS2scbQElA3r1t+6kRP614Vzb14+o1adrXkbu38qPkt33WOtPuGvP9B7vh95/iMm0f6uDK5ZEpFrReQyEblERDZG0hzSXL9CRL7knf/L5tzlInK6iGw3dH0JGTu53dMa85exmxH0aQ4R29EeE2PU1NXQZXFjjUcuv758T0qd31PHJfnFwrp3FZRqTNfC8v2yY+HJU2WGeVh5u/DV4YtThxAIa32AUhqdlPanJt9UmtJ6WlqroTRQtaTeVxVGUnTpQzO7mGlgjBq/wZTGNBb9LqfVmhqqwM1btPfPqjAvM7zHqeo6S/oUkZ0AvAvAkar6IADHNOd3A/ByAAeo6oMBbAPgGW0rMPVFCCGOGofhNg7JU3xWSn7I2vz4zVugarN4XJRpZKz/hnKeB9IL/VJqfWIcYT/7zvA1eeR8lXLRyCxCAaFUkAsFGJ+cWWZKk+TX4aTNm27tK998yy1SpIbGxQAAIABJREFU7/zo428ty3o3kEVboSWlaSopr2a+lZh9tTHpKtUWpcwgawThmrbH/KNcHv4zUyMoWvMq9b1nfddb45zyvcuZRY5xs40Mxxh8lp4F4OOq+gMAUNWfete2BbC9iGwLYA2Af6vNnG+CJ6SOIZ6VKQpgQH+7iTVaia50Gb9cvdr4GZTcX+rL0GW32VGrecjlU0Osfsdect5thLg2mjYrXWpR5/uVhOnCskr8mfzj0Ok/FAycL5e/gPbvSQlrMW2AX36NJsgt3MN8YwEjcnlZeXclbE/op1Xa1tpNtD7qbj1zbQVPP4pjqJlyx9ZzZn3H+Jremo2YMZpz5lDwPUtdGNxnSUSuAfALzMbqFFU9Nbh+EoDbA3gQgDsBeIeq/lNz7RUA3gLgtwDOVdVnp8qizxIh44Tv4umO1YdD9usQec9jHvhl9O2DMw/CnfxUG9qa7HXRyJX41/RRXml+YUCTvvxecv43tf1QWtaQ9/RJ333Qhtz4lvhZhemsIC6p9k3FZ2mn3dfqIW94X+/5fuJFj5pE+7syD83SQaq6H4AnAjheRA4Orm8LYH8ATwZwOIA3isheIrIzgKMA7AFgVwA7iMhzwsxF5MUislFENv7shhsGbQghq0DMr6ILFJS6Yfl3WOf6HLc+xiwVtW8etDFpAxbnn+C0LaUmjqX9WRscwMKvV1/maG1Mmfxw1C5QAGCbOFpas9wzEgqFYRus4y4mWanAA7l7upqCdTF1bKuhjc2BNm3Jzf+wbjFz15j5asxEc5KWEkrNUhcGF5ZU9frm708BnAXgwCDJdQDOUdXfqOoNAM4HsC+AQwFco6o/U9U/APg4gEcb+Z+qqgeo6gF332WXIZtCyEpAwSbNIn4orQVJuLsOzG/sSsN+19YnZT5TumBZ1PytDQBg3Z8K2wyko8qlSPkm1sznkoVtzuwvV69cfuG7qkp8jvxyLF8zvy/aCCFdhOtY/Uv9kSxKg1ZYbU35KJWYisbKdedic6DEx6ukjBTWi5jbmBa38Vck02ZQYUlEdhCRO7n/ARwG4PIg2ScAHCQi24rIGgCPALAJwA8APFJE1oiIAHh8c55MnEnuyqwYU3ifkk+NNqzrbuwYhcnUy2hTdvkWNf1T8j6n2vzC+9y5cFEZG4c249s2YICPpREq9fNx97fFeoForlx3T412qmSR3qUdlg9ObqMgTFeqEXFYplwpfym32I69P6svQbILlm9Qat7mgmCE18K/pW2uDWJh5WGlq3l+fT89AFGfOb+NoX9UH1q9eaPgS2m7MKjPkojcDzNtEjAzt/uwqr5FRI4DAFV9T5Pu1QCeD2ALgNNU9aTm/JsAPB3AzQC+BeCFqnpTrDz6LBGyOizaZn+e9NnWtn4cufu6+IdMyafthDV/fF8MUOdLZr1fxso/ZQKWEiLmSanPSsrMrFboivkR1dah5Hmq8RVb5PzNzYchvidTPlx+XcbwHZ2apzVzCLD9Iafis3SX++6tj3zte3vP99zjD5pE+7syqGZJVb+nqvs2nwep6lua8+9xglJz/DZV3UdVH+wEpeb8X6vq3s35P08JSoSQ1WKIH+GxatC6trU0NHWK3H2+lqlGo9K2TtZYpXZ7u46tvxAMTcFKy/PNxmKktAJuQZfLI7frndLEluyY57QFJdqENnO6bbvDfkzlUxIRskRQauOLE5rPldybmw9DfE9amj1rbtZq3oYgZuYX02Cm6hdq8Mf6e0H6ZwyhwwkhZBR00YzUpp/nD22bl67GzqWuhSZzIV0X+WFZYT9aflwu3647/zV1t4IN1I65tehsY8Zk9emvvnpy0u+ixJTNWlxawRFK8i3114kJYaX9U5Jv+E6o2PWa+sXSWPXz+7BkztVuTITXrfGrNWuL1bWNuWlJe/x61vo3te1f/697vqeiDQdmL6WlGV57KCwRQkhHan80a35oF2kbn6rjEAuF2h1/148li/uu77rqEkQBmC2yLR8vV/ehhOdSwTW1iEwtSmNmTCmTp9K6hOXGNBr+31h9U9dz2pCUj4o//yx/qJRPkDUnY4KuFZTgpM2bov5VOb8aX8AMhdwSobxEGO6yORKOV6yeOfPLcGxCQT+ldXRpx2BOSBbL4O9Zmif0WSJtmZLPxLIw5h+gedWty3tCatLMi5p3HC3ymZtH2UOPS99t6KO+fbXZ8jMp8XEqMbNz6cN0bXyr/Lz8/Gr6IeXfBqD4eRrL90DKR6hLfrFrQHm/D+FTNRWfpTvfZ299+Kv+sfd8z3vFwZNof1eoWSIE44xwNiRjsLWu/ZGap4alrx/5HLXvCXHETM8WQVt/qK7PXC7KX4nvQWk5bQh3/GuiBuY0I0CZj1gNNfMoplWo1STE2mmZ1aV8SmJmeCltmC9cWXXP9b+VX1if1H2pcP++qZfDiniYMvsrjYpYS85sr9ZkNFWv3Jwq8ZGy0seEaqttpXUdO6rAli3a+2dVoLBEyAoyReFw0UJBija2+TV5hZSE7HYMLRjH5lLJHCtpa+p9SqkySrUBqTJcOaX5lN5nYZn/hNdjL/ltazbVlpipWs19lhlViRYhRcwUMGdCaKVLLfpTQoplPmZhmY+Gi3hrM6BEY+vyt84D6dDoOXJ96l8rEThSfVn6HRozxYwRE2ZLBa+xaPHI/KCwRAhZSuapPevzh7M0r9LF+JgF4xJNQGxH3U9bE0zBej9OygyqhJLFYR8R/9oG6gDaaY66ClipfGoFLt+0y9JI+feXCixhPfvQhrQR1hxOiEnNPT/AhC/0uDJigSkcTtiq/X50fe73VUygbKtdKhWIY4TmdTktWE0dY+aW00Gh2v9nVaDPEiFkYczDrn0VKPVfGbNv3tjf05QKEW3Nva51qrm/bdpc2OucD88YfOr8usa0BF2EobAsS8Av9dEJ07j3brm6ueNQ81Pqt1RTbx///V+5+eHy6vOZ6zKP2via1cyHMJ/weCo+S3e69wN1/1ee2nu+X3rlIZNof1eoWSKELIy+TNUWISjNS3NVUk6fWqZF7ZZ2WXiVmsp1GbNYGTGTnvCdLLHyU2aGXeqWKyu12N2wbv1tds+tBWaJZrCLX0gJlobD0nRY7an1S4lpw2rMB8M54tfLjUcsNHXqnV7WPPOj5sVw88Aap1ADFdPmdvX1aWPCWUOuzFKTPXc8LY3SH9Et2vtnVaCwRFoxhgABq8oq933JjmDb/qlxwAfmZ95WW07M9KaUPgXPPuZqF78mYOv21LxAtqQfS8y9wl36WtO6tgszK19/Ae7qcsKatbdZbPv3lphUxepoLaJjDvah8FKSp1VGiZO/+xsT5IYyy4vl6dfFN8ULBRZr/rk0sfHOEb43ytd0uTxC07sS00O/DbG0tbj21grplmYo5SsVllc7x0YJAzx0gmZ4hKwAYza/Giv+j+Iq9d88zGvG3p+pxXKq7m1MgmJlb1i3vnfTytp69GW21sVUrTZtbOxy5lSl9N0WX4guEZxL65yKtufyAurDrJ+wZu1WZoKpDQh3zb/HPxeORzjnY35Cpf2fa4t1PjVPrDInY4Z3rwfqule8p/d8v/Ka9ZNof1eoWSJkBRjzwnSslGgkShhKE+g0AiXparDMx3z60FL5ZcQcr2Pn5oGldXCkooz5wnWYX0p7FeZRYiKUWwxblCwuS8a3dk6HPjg1Zk85jVN4PacxsszvauZZqGnrahLmNDmx565UA+LubTMvwnJymqPQTNAKvGJpJ0uiV1rPjqUlCrVDbcYh1Jj5+Vt5TlKj1KAAdEv/n1WBmiVCCOlIuIsa+1HtQ6PSZ+CAsWt4fPoyf6ndnR+yf8YUmKMPrVO4C1+qHRvatCmmLYgJIjmtS07bkCrbIhzfmu8Q6+W1seNSbRhQ5ouV0t7Frrs5YWmI3P+x7yhLQ2W1uW+tYWo+TEWztOO9HqjrXta/ZumC162GZonCEiGEDEifC902JmBD0qdZV5f6T9GPIDS/qlkw+/fX0mUhCdzWHKpNGalFc07gKsnbr2dXuph9+feX5lsjZDpi2qTQlNiliQkDQD4CYsk1SyOTEor8aHxWniWRKC1BK5YmvFYrRPrnTsH3JyEs7HivB+q+L3137/l+9fWPn0T7u0JhiRAyKsa+6O+DvjUFU9IQTYE+58Oix6ZGC9I1fyAdbjpVl1CDANiO+SHW4ji8N1cX65qPZXLXxf+pyzhY/WsJ3CkBwwljfqjynFamRLAomWulAkqt0BsKXCXEhCerDrG0U9IsUVhqD32WlpRVjphGpklJmNswbV/zvHbh0qb8WLSxPkj50bSlaxtj93X18QjzHcLHpit+ndv6joQR6tpi+XvEfHba+ICEwoUfPS0sw6qLf59/LdSIbFi3fqu+se4L7/Xb5fzjUj5H/jX/E+Yd1t0SumJ9bKW38J8/K6qcjx/hMDwX5nfS5k1mSPKccFL7HZnLxxoj99eNU0w4DtO6NsQ0XLG+C9NvWLd+K/8rvw5T01zfBkbD6wQ1S4SQwcntMnbNZxnpGuWsjaO3K7Ov8SotL3e+1kStr3rUMFSftXXYt2irZUqlzUVz69NUs02eIaEGoqbebcoN+xywTRlzWo6S8od4Tmo0PCWamq5azhLNVmh+GGqcSn1MY+X6fTIZzdJue+mDX/Ku3vP9xhufMIn2d4WaJULI4JTsVpbsZFsmMiFT0qr6u5jh+a6L4/DdKyX969L3tbtcWl7uvKU9yI1zzTzoQxAJI745Sn0hYsTeodMWa8GeI+WTYmk1/HThM5sqLyZslqRLEWrp/Dq5zYFwvljj2XX+W5oqq07WcUn5seckpp2NaQD9/y0NT5gmNqdi3+/hvKjRaKb6wNcCWRonN85tnidLw0hWBwpLhJC54hYu4adkAQzkF3mWv0TX+g5FKJz4P/AllJgu+v07D1ILMf98ylSpRrBrez0sM3WuZA7kQq7HmOeiK2ZCVvqM1D5LlglTauFtnc/5taTmk8MJnCesWXubZ849GzFNZsy01AqXHbYn5gMTI6WBaoNrr/X9csKatVu9ANcnNFlMCTWlc8qlK9EMhefCY8t0L/wbKyM0E63ReqaOx84sdLj2/lkVKCwRQuZGyqRoqEVmanezj0W5RVsBq2bhnIuM5+hbM5HD0k5Zi5PUIqvrIrEGq8/Dc7n5OiVtpkWJAOOPS0wrkMsjR2oRm9MwpXb7LeE33JjwfYRivj/W/ymzM2t+57C0cW3JlX/sJefdKlD5Gy/u3U+h0BY+u5bwVCIQdpkb/jiH8zHXv9Z3UpvfhDAvsvxQWCKEzIXSxWVfgQEsUguZXL1qiAknfS6qS16O2jdtgg7UarVyY+QYSpCyTLJK52uJiU/OWb8tJXm2XQimFqKp8yUCVU7A8RfJlhanxMwvXCTHtC3ub01fhkKkPx8sLUisjmFdU89Bl++RUCB1eacCZbg5G/a/tamRElxSwleYxpVVqpmzhLCU0B+WHZ5P3T/UMzwoCmxR7f2zKjDAAyFk7vQRGa70B9kqO1XmokM990nXtoQBH/xjoJ0mLBdEooQu91r1AtLzsCbIQpe6xcy/Ssvuow6pelnj7pdVGsY7pj0p0RCUti1m0pVbzFvaTf+cPxfC58LvA+uaT2ysS8Y5FgCl9HkP+z8nHFv9UUJqDErrEsvXT1tav651t5hKgIcddt1L177oH3rP96I3HzGJ9neFmiVCSC/U7LQ507AuC/nanW6gTHgYSlAqNVPx6aqJ6iNIhK8Z8heB/sKxxqfH8hmpxbq3xoTGv9f3Z0mR8kkKtXptg0/EAluU1K+NH4Z1v1VOah6lTCZj1yxTTGun3/pr5WmlramXpa0K0zp+9dWTbzVTs3DX/Gem5DmpeVateVKzMeILfyVzpVSYCTVpVj/HhDDftC/ULoVlhPUO/09plMJzseMSjSVZHahZImRBLJMGw6d0h7Bk17FPSk2kFjUmqbK71qsvLU4upHdIWy1SLr1V7jzGribk8Alr1m71ss9UXu4YaPdepr6fo5gGyeGft0Iz15STE6hK65laDA+hqarp49K52XVe5zRMbqyAfvssp7GL3R8z6bO0erE6lZoADsEJa9biFHx/EpqVHXbdS/d+wTt7z/fiv33iJNrfFQpLhJC5kzPdmdeP3TwYYhG/yD4KFy5DCSl95VtjstRHv3aZz13Nq3L0PW9KF8ltzbi61KVm4Q4s1mG/dhPCurd0roQCP7C1FiUm4PuCliNlypg6n5oPJWZ0NaZ2JfVpg7t/SmZ4FJbaQzM8QpaARUTj6lKm/+NsORa3JWZ+MQ9i5fQpSPgRqxZlFhKawPQVqj28Lxcko3T+heZzqfcC9VmeI/b+pTAwhJV/rq5tI0imxig0PYqlTS1Cw/us+Zo7tsovmVuhwGS1KUbf3x+l98bML31iQUfCORIL/BKm983ZQtM21+9h1ECXZsO69beam+Y0gi6v0u/1WouEXPmx78pUHiXnp7ahp6rYsqX/z6pAYYmQOTKUUJOKjDYUXYWArj82uR/AvsopZR7ltA2uMASxhUTbeuXM7hwlwo5VNyt9iemX6/O2z1isntYCuUbw9E0caxd7Kd+N0P/DX0C7xXEY4dBaoOYWqSnNQO5cqr25hWxK62AtoHMas9T5Pjc1YkJ/6X2h39uGdetvHU8n+DicQOTjC1AuzxLzy9x3cuin5JeVmj8134MldSwVqKYoKDlUtffPqkBhiZA5Mi9/GMsXYsy0WVDkFlt9al4WpcWxSNntz3Osa0yc2hIuDEvbF/rUlKRLsWHd+mLNV0m5IbG8wkVsLA2wtRbGXwR3JRRUT9q89Xt4UsKNJYjUCNmhYJUS8tx1q8xYP6SuW/W1yvMX9paw1vfCus1vSEwj7YQdtxkQfndawpBfvi9Q1WgkQ3zhOtRyheli+dVoDEuEsFx6slpQWCJkyelbQGuzIM/92JQuKErNcGryLKHtDnHtPbG+zb33x7V1CN+oLvfUjEGJNqi2fV18fWJ9bF0Po/IB5SGgc8Ty8f1H3OLSnwcx8z8Lp2UI8TVqMSEk1AyEC90S7Z1LZwl4loBkmdpZGoo+iD37YXv9zzwX1zETTnctxBeO3BwJzWpP2rzp1r/+fWGe/iZGTtNo/R8KWDEhyR/XcIxzAnSuPqGwVatdnBK6pf/PqkBhiRBSRZvFZ18/Nn3l00b71KbsUg2HI2bq1VVoaEvXNs/jvhQ5R3cg7WuWChPu5+3/by0qrXLcgtX3+3BmTiXk+qvU58sJlGFQirCM2GLVr4+1qC3xDXH315jmhZsiqcV2V6wNmFJTv67EfObC0PcW4RwOxzW0QPAFbH9eujys4A+xDQmnFU1pH2PCbmp+xYTSmNBUulGXEsqtdEMI5mS8UFgiZAkpXXBNwUSvb8Kd6Xn1QbgwzpELJV5yrobUO4vavjeopr3zwNoVjy1s/f6PCUapMsKFamx32zd1cjv6tdSYDvn+Jv6xVTcft5B2f0NtkrsWap1S+ZbOjVAgSqVZNmI+RzX3WyZ2VrpYtDvrOQjNPn2c0O/mdmjG6o+j/x1smVHW+AqVaCZz+EK+1Wdj+j6rQRUM8NABCkuETIwSk4vYrnhIycKs9Mchly5lLlJKlx+qmDlQWy2NVZdc+0LTllzammtdtU2hdiQ0zbEITc5CIcs/Xzr2qTHuOn9qgjTkIpLF/vcXnOFzGDOLKiGnlUmdK/HZCBeEofBcElij1o8rZbJWYk7l0i3TLn+qP6xnMjWH3XduzEQyLC/1zFvlOE3TsZect5Wm1p135p3ue8/VJfR18tvit9MXoiwswSgU4GNzP5w3sX4In6PUZgCpQ0TuKiKfFZGrmr87G2keJyKXeJ/ficjRzbX3i8g13rV1Q9WVwhIhLVikRqYmulYfxBYo1v8x/N32LouaLj9O/u53H1g/xvMyjwvLDY9zJmallAYXCHeGQ41KjcYkNcZ9969lelaaPmeGl0tXS8ncj42XZbIUzt9QexP6RIVpY4vRWJ0sTallIlbqJ2KZR01RYIpt4oTPc6x9OaEppt1xglT4vPqCsvs/FGicyZ77PvfN8/xn3TcR9DVN4fMSluHKtuag3zcpgTo2j3yrAj+/2IZDqGXymdp80y3a+6cjrwPweVXdE8Dnm+Ot66z6BVVdp6rrAKwHsBnAuV6SV7vrqnpJ1wrFoLBESAvmuSgeI9bOW6kg08YUp88fpb7Hbl67i7mAAz45E7NSasMTx45rNxdSwrh1XOqfkGKRu8R9ze9UePQ2z2dsIe8f12hKY5pHq84xEy+/7Jg/yZSIfR+GQkxKYALi2uZYn/jmeb5mKCXsu7ROiKnRFod1iQnR4VimTEJdf9R8v8Q0SL75nY81z6z7R4+OUlg6CsAHmv8/AODoTPqnAfi0qm7uWnAtFJYIIZ0oNfeL7eKX7BimfpSGsCfPOecPRUojlNPKDfnDHTOhLD1XahbqiJnB+Me+03ntYrnG9KsNtcJhSb1LzVxTQrUlaPo78THtU6weJf1lRe0Ly/GjqfnjmYtmOKnFKsoEe19I8v3Kjr3kvFu1ODmT5lQZ1vwINYmurJjgEqZ3+Ybl+kJGOG9Cs05f8AkFxlBo9M17XV1yZcb6w7U/pi11Zbj8+tiYWTJ2EZGN3ufFFffeU1V/1Pz/YwD3zKR/BoDTg3NvEZFLReTtInLHirKroLBECEmSM/eq8VPI7XK32SFOLazbErapTxOfWIhmV24Yhjesxzzx69VGy1RroljTx+Fiq60wFp5vM86hoOGf78tkNzf2boxywoW/2AtN70rmV+0cjJk3uXJD8ytLK1riKzYVwu84S/h03wO+6Zwv1LhxTmnd/DzD59AXrC3hwhIcLAHC1xaFgUrCeqS+p32tjv/uJ1df/71eoRYqZkLnlxO2y/WrJeD5dQqPS01Px4lii/b/AXCDqh7gfU71SxWRz4nI5cbnqK1qN3vDbVRVJSJ/CuAhAM7xTr8ewN4AHg7grgBe209f3RYKSwVM88GoY4o/OlNjqn2cMj0oWQCn/Dn6IPd8hgvWVPoSW/iu5KKPxUyphopwmNNWleQX83kIF7zWwtA3t4r1can2so9nzPehSOEWeJag0ZefUgkl89k/9heyXbWybZ+JULPkzwX//1w0wlS0SEsjsQhSbXXn/OsOJxSFpmb+cSw6nTt2WrqUKV5YvqVh9PO08kvN8ZJ55eeZErQsLWkoMFkbHuH3mAs+4bfff9fYhnXrbxWoLIGsdGOBAKp6qKo+2Ph8AsBPGiHICUM/TWT1ZwDOUtU/eHn/SGfcBOB9AA4cqh0yE+aWg/33208vuOCCRVeDEFKA+wGytFOp3cKSPPukRHvWNr8h6jskNX0RpvWP2/Rpl3HoewxjxBZXufRjo6961eZjabvCa+F532StS52HmiOhUFTTJzXzaMO69ck+iD1/fr86SsssTeuXU5NXaRmxuWHNFael8tO7/nMbLv5f/16/z1we269Zc5GqHlDUCQtku3s+QHd/xv/sPd8r33l06/aLyNsA/FxVTxSR1wG4q6q+JpL26wBer6pf8M79qar+SEQEwNsB/E5VbxMkog8G1yyJyLUiclkT1m9jJM0hzfUrRORL3vmdRORMEfmuiGwSkUcNXV8yTaamtZlaffvC+kHOaVqAdH/V/shb99aEkO46dkNr2mop1boB8XDa4X3WwtNfaLRZlOZCebfBam+XPNuYkdZqdLpEOizx4QD6mZcl9bP8Z9x9YdTKmBAamh62Hb8+8rDw/V3ccaxvSjR9Ma23r4WytExOALBM2VJa3dJ65e6z2m19d1uarZTG37831IxZc8WfV36gCD/YhcPXUPrfX75f3WQYZ4CHEwE8QUSuAnBocwwROUBETnOJRGR3APcG8KXg/g+JyGUALgOwC4C/7VqhGPMyw3tcE9bvNtKniOwE4F0AjlTVBwE4xrv8DgCfUdW9AewLYGKzk8yLqUWnS5mPLDM1u5D+3xIn7zZ9lxLYYoxhrtW0tY0vT6lwCtgRt2r82NpcLy0nl85qb0w4LjHNs0i1pY05TyzSYW5Bad1jmTX1Raxtfjkx00m3oLdM1ixBwF1zedYSG6PQjM+fCyX3+1hmZVaf++0pHZ+c2bDrN0uDbwlP4XWrvqEQaBHTWKU0Rta8CTchUoJkrl6+UOkLQM5HzLd2cJ9YCPNl/70eGlX9uao+XlX3bMz1bmzOb1TVF3rprlXV3VR1S3D/elV9SGPW9xxV/fVQdR2Dz9KzAHxcVX8AAKr6UwAQkbsAOBjAe5vzv1fVXy6slmQhtP0yiv2ojZ0xLMRj9L2YStFmhz40kRiaGoGib/oU7sId9ZIIZF21L2FQhjC/1Fi2FVpSlPrehW1PCSgu3TzN/4ByIcry6xiScIFsaTnc9ZT/iZ9HX+aC4Xy0zLB8DVbpmIZCXW7enrR5063ajlhUw5iA4cqwhB2XJjS5swSTlGbGT2+lseaapTFKCe1W3qm5mhLGYu2y0vpCURjpz5+z8zLpHYItW7T3z6owD2FJAZwrIhdFQgruBWBnEflik+a/NOf3APAzAO8TkW+JyGkiskN4s4i82IUs/NkNNwzXCrIQ2n4p1f6okTxjNjtImeK5XcN5UtpXfdXLtbHtJoG/SCx5ZsLFRFtyz6k1lpYmq5RUv8SuWeaE1kI27Pt5mQ2mFshhmpIF+1CEdfSFz1AAdR8rUEjfgpJPLnhETKNU8zvjt9ea33648LC8nNlwaKoYCkOWhiYUYmL3hG0I6xSOR2guWqNFzaVLbQBY8yIlfFnCeUwj6r4fF/UMkcUxD2HpIFXdD8ATARwvIgcH17cFsD+AJwM4HMAbRWSv5vx+AN6tqg8D8BvYb/c91YUsvPsuuwzZDkImwaK1aWP7IfE1GX1Sq8my0vVVL9fG0k2CmCanprxFjHNu4WaRMrHziZmEWQtk3xQuNBX187FMFkvx740trq1z1mI3XDyHplXzGMvYrn4ooPt19U2mQhaxeVOz05MgAAAPeUlEQVQiOPs4LZHf32F7Q2HD96mJ5R/77okJcynhyv8bCk9WvuGGhTWfwrqXmIimiJnzWVqu1HxJacPc/7F5ZQnpfW0azQtV7f2zKgwuLKnq9c3fnwI4C7cN7XcdgHNU9TeqegOA8zHzT7oOwHWq+o0m3ZmYCU+EAFi8UDBWFq1NW8QiZpFar1jZMTMaH39hMkSY45TdfldSfT7PZzPn/9fWZDHmSxMz0bHyqZ2Xfpn+vV1DM6fMmGp2/GtI+ZXEdvpji/1Fa8VSxBbMTqiOmUg6zVkobKQ0F/57l2Lluf/98t1fX6vll2+ZtvrHoTDrj0VK2HBClWUCaJWVmjOpORq7Fs4zq+7+udrnYMzWFiGq/Qd36CHAw2QYVFgSkR1E5E7ufwCHAbg8SPYJAAeJyLYisgbAIwBsUtUfA/ihiDywSfd4AN8Zsr6rztSEj0ULBbUM5ZQ/FlI/tvMor22aNpQ4eMfmZ8x2PmcC1IY+f8xr5qS1813iazTEvC8Zq9T4+WMR8ycpLTuXpo3myVqAWgvYcN513e3PUaIRsjRmvuDkBBG3qF/04tTvM0tL4acJtUfh/Hehvq08YtqlME/fl8bPKybA+WZkLlS2O++b4KX6OiV4p9KmtNGpsiytVaz/LeEoVWdL45pry6LnIFkMQ2uW7gngKyLybQDfBPApVf2MiBwnIscBgKpuAvAZAJc2aU5TVSdQvQyz0ICXAlgH4O8Gru9KMy/hY4qCQB/EomyVpJ0CljlQjtqFmiVk1NSptA5ucePIma3lzHFidar54c35K1hp22ItqkMn+BTWItjyNaqJNBf6tuRMjPxd9Rw1UfX8RWaqfJ9wPvn3xvKq2QxIPXsxrcbQiz5rgRrWN+x3954g3wTPT7dozVLOpyV1n/WdAWzt25MLIOBrgkKByhecfKHepQ/rbJURCtCWEB4KUynzuBKzUQtfSLLM32JmdNZxmL6thsoSihc9H2thgIf2LNVLaUXkZwC+v+h6kGp2AcDoHNOH47gccByXA47j9OEYjp/7qurdF12JHHe8+/1116e8tfd8r/3HYybxUt6ubLvoCvTJFCYsuS0isnEVHrZlh+O4HHAclwOO4/ThGJI+0S23LLoKk2UM71kihBBCCCGEkNGxVJolQgghhBBCiIcqNUsdoLBExsCpi64A6QWO43LAcVwOOI7Th2NIekFBYakLNMMjC0dV+YOwBHAclwOO43LAcZw+HENCxgE1S4QQQgghhCwrCugt1Cy1hZolMigicoSIXCkiV4vI64zrB4vIxSJys4g8Lbj2XBG5qvk8d361JiEdx/EWEbmk+Zw9v1oTn4IxfKWIfEdELhWRz4vIfb1rfBZHQsdx5LM4EgrG8TgRuawZq6+IyD7etdc3910pIofPt+aErB5L9Z4lMi5EZBsA/w/AEwBcB+BCAM9U1e94aXYHcGcArwJwtqqe2Zy/K4CNAA4AoAAuArC/qv5ijk0g6DaOzbVfq+qO86wz2ZrCMXwcgG+o6mYReQmAQ1T16XwWx0OXcWyu8VkcAYXjeGdV/VXz/5EA/kJVj2iEptMBHAhgVwCfA7CXqlJtQKLc4W676z2OeGPv+V7/4ReuxHuWqFkiQ3IggKtV9Xuq+nsAZwA4yk+gqteq6qUAtgT3Hg7gs6p6Y7Mo+yyAI+ZRaXIbuowjGQclY/gFVd3cHH4dwL2a//ksjocu40jGQ8k4/so73AGzjQo06c5Q1ZtU9RoAVzf5ERKniYbX92dVoLBEhmQ3AD/0jq9rzg19L+mXrmOxnYhsFJGvi8jR/VaNFFI7hi8A8OmW95Lh6DKOAJ/FsVA0jiJyvIj8K4C/B/DymnsJIf3BAA+EkKG5r6peLyL3A3CeiFymqv+66EoRGxF5DmYmd49ddF1IeyLjyGdxQqjqyQBOFpFnAXgDAPoLktaskiaob6hZIkNyPYB7e8f3as4NfS/pl05joarXN3+/B+CLAB7WZ+VIEUVjKCKHAvgrAEeq6k0195K50GUc+SyOh9pn6gwAThPI55GQOUNhiQzJhQD2FJE9ROQOAJ4BoDQC0zkADhORnUVkZwCHNefI/Gk9js343bH5fxcAjwHwnfRdZACyYygiDwNwCmYL7J96l/gsjofW48hncVSUjOOe3uGTAVzV/H82gGeIyB1FZA8AewL45hzqTCaMeyktfZbaQTM8MhiqerOIvBSzhdU2ADao6hUi8mYAG1X1bBF5OICzAOwM4D+JyJtU9UGqeqOI/A1mPyoA8GZVvXEhDVlxuowjgLUAThGRLZhtzpzoR3wi86FkDAG8DcCOAD4qIgDwA1U9ks/ieOgyjuCzOBoKx/GljYbwDwB+gcYEr0n3EcwE3ZsBHM9IeCSL0gyvCwwdTgghhBBCyJJy+53vo3db/996z/cnHz9hJUKHU7NECCGEEELI0qLYQs1Sa+izRAghhBBCCCEG1CwRQgghhBCyrDQvpSXtoGaJEEIIIYQQQgyoWSKEEEIIIWRJUTAaXhcoLBFCCCGEELKsqEJvobDUFprhEUKIh4j8eoA8jxSR1zX/Hy0i+7TI44siUhyitUl/pYgcaVzbXUQur63DVBGR54nIrt7xh0TkRhF52iLrRQghZPxQs0QIIQPTvGTy7ObwaACfxOylkkPzbFXdOGQBIrLNBF6K+TwAlwP4NwBQ1WeLyPsXWSFCCJknNMNrDzVLhBBiIDPeJiKXi8hlIvL05vwhjdbmTBH5bqOlkObak5pzF4nIO0Xkk83554nI/xaRRwM4EsDbROQSEbm/rzESkV1E5Nrm/+1F5AwR2SQiZwHY3qvbYSLyNRG5WEQ+KiI7FrRnfxH5toh8G8Dx3vltmnZeKCKXish/bc7fTkTe1bTnsyLyL04TIyLXishbReRiAMc07fhM0+4vi8jeTbq7i8jHmrwvFJHHNOcf27T/EhH5lojcKVHvV3t1e5N3/v825V0hIi/22vJ+b8z+sqnzAQA+1JS3fawsQgghJISaJUIIsXkqgHUA9gWwC4ALReT85trDADwIM03FBQAeIyIbAZwC4GBVvUZETg8zVNWvisjZAD6pqmcCQCNnWbwEwGZVXSsiDwVwcZN+FwBvAHCoqv5GRF4L4JUA3pxpz/sAvFRVzxeRt3nnXwDg31X14SJyRwAXiMi5APYHsDuAfQDcA8AmABu8+36uqvs1dfo8gONU9SoReQSAdwFYD+AdAN6uql8RkfsAOAfAWgCvAnC8ql7QCHq/syosIocB2BPAgQAEwNkicrCqng/gWFW9sRF+LhSRjzX13U1VH9zcv5Oq/lJEXgrgVUNr2QghZJQwdHgnKCwRQojNQQBOb0zMfiIiXwLwcAC/AvBNVb0OAETkEswW6b8G8D1Vvaa5/3QAL+5Q/sEA3gkAqnqpiFzanH8kZgLMBY2gdQcAX0tlJCI7AdipETIA4P8AeGLz/2EAHur579wFMwHlIAAfVdUtAH4sIl8Isv3nJu8dATwawEc9we+Ozd9DAezjnb9zk/4CAP9LRD4E4OOuLw0Oaz7fao53bOp2PoCXi8hTmvP3bs5fCeB+IvIPAD4F4NxUvxBCyGpAYakLFJYIIaSem7z/b0G379Kb8UeT6O0K0guAz6rqMzuUGeb3MlU9Z6uTIk/K3Peb5u/tAPxSVdcZaW4H4JGqGmqOThSRTwF4EmZC3+Gq+t1I3f67qp4S1O0QzASxR6nqZhH5IoDtVPUXIrIvgMMBHAfgzwAcm2kHIYQQEoU+S4QQYvNlAE9v/GDujpmm55uJ9E6rsXtz/PRIuv8A4PvoXIuZyRsA+NHZzgfwLAAQkQcDeGhz/uuYmf09oLm2g4jslWqIqv4SwC9F5KDm1LO9y+cAeImI3L7Jby8R2QEz7c9/bnyX7gngkEjevwJwjYgc09wvjcACzDQ7L3NpRWRd8/f+qnqZqr4VwIUA9o5U/RwAxzqfLBHZTUTugZn26xeNoLQ3Zto2Z6J4O1X9GGamivs1+YR9TgghK8PsPUtbev+sChSWCCHE5iwAlwL4NoDzALxGVX8cS6yqvwXwFwA+IyIXYbZA/3cj6RkAXt0ENrg/gP+BmbDyLcx8oxzvBrCjiGzCzB/poqacn2EW3e30xjTva4gLGz7PB3ByYzboO0qdhllkvotlFk78FMw0ZR8DcF1z7YOY+UxZ7QFmwtcLmuARVwA4qjn/cgAHNMEZvoOZtgcATmiCMFwK4A8APm1lqqrnAvgwgK+JyGUAzsRM6PkMgG2bvjkRMwESAHYD8MWmjR8E8Prm/PsBvIcBHgghhNQiqrroOhBCyFIgIjuq6q9l5qRzMoCrVPXtC6rLF9ExqIHXnrthplV7TEpgnBIyCx1+a6ANQghZVra505/oDvs/t/d8/+NLf3+Rqha//2+qULNECCH98aJGq3EFZqZip2TSD8mNAN4vxktpK/hk054vA/ibJRKUPgTgsYhE4SOEkGVDt9zS+2dVYIAHQgjpiUaLtBBNUoiqPrWHPA7poSpZROQhmEXo87lJVR8xRHmq+ux8KkIIIYTCEiGEkAWjqpdh9k4rQgghfaOKLSukCeobmuERQgghhBBCiAE1S4QQQgghhCwpCkBvoWapLdQsEUIIIYQQQogBNUuEEEIIIYQsK6orFb2ubygsEUIIIYQQsrRQWOoCzfAIIYQQQgghc0NEjhGRK0Rki4hEX2wrIkeIyJUicrWIvM47v4eIfKM5/88icoeh6kphiRBCCCGEkCVmhC+lvRzAUwGcH0sgItsAOBnAEwHsA+CZIrJPc/mtAN6uqg8A8AsAL+haoRgUlgghhBBCCCFzQ1U3qeqVmWQHArhaVb+nqr8HcAaAo0REAKwHcGaT7gMAjh6qrvRZIoQQQgghZFmZboCH3QD80Du+DsAjANwNwC9V9Wbv/G5DVYLCEiGEEEIIIUuK/vbn5/zhkvftMkDW24nIRu/4VFU91R2IyOcA/Ilx31+p6icGqM8gUFgihBBCCCFkSVHVIxZU7qEds7gewL2943s1534OYCcR2bbRLrnzg0CfJUIIIYQQQsjYuBDAnk3kuzsAeAaAs1VVAXwBwNOadM8FMJimisISIYQQQgghZG6IyFNE5DoAjwLwKRE5pzm/q4j8CwA0WqOXAjgHwCYAH1HVK5osXgvglSJyNWY+TO8drK4z4YwQQgghhBBCiA81S4QQQgghhBBiQGGJEEIIIYQQQgwoLBFCCCGEEEKIAYUlQgghhBBCCDGgsEQIIYQQQgghBhSWCCGEEEIIIcSAwhIhhBBCCCGEGFBYIoQQQgghhBCD/w+D7FiaXFtDTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# -1 -> water to coast\n",
    "#  0 -> no change\n",
    "#  1 -> coast to water (Coastal Erosion)\n",
    "\n",
    "plot_data_array_with_aspect( coastal_change.wofs )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "coastline_2000 = np.zeros(wofs_2000.shape)\n",
    "\n",
    "for i in range(wofs_2000.shape[0]):\n",
    "    for j in range(wofs_2000.shape[1]):\n",
    "        pixel = wofs_2000[i,j]\n",
    "        if pixel == 0 and np.nansum(wofs_2000[i-1:i+2, j-1:j+2]) >= 1 and np.nansum(wofs_2000[i-1:i+2, j-1:j+2]) <= 5:\n",
    "            coastline_2000[i,j] = 1\n",
    "            \n",
    "coastline_2015 = np.zeros(wofs_2015.shape)\n",
    "\n",
    "for i in range(wofs_2015.shape[0]):\n",
    "    for j in range(wofs_2015.shape[1]):\n",
    "        pixel = wofs_2015[i,j]\n",
    "        if pixel == 0 and np.nansum(wofs_2015[i-1:i+2, j-1:j+2]) >= 1 and np.nansum(wofs_2015[i-1:i+2, j-1:j+2]) <= 5:\n",
    "            coastline_2015[i,j] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import imshow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize =  (15,8))\n",
    "a=fig.add_subplot(1,2,1) \n",
    "\n",
    "imgplot = plt.imshow(wofs_2000, cmap='Blues',\n",
    "                     extent=[water_class_2000.longitude.values.min(), \n",
    "                             water_class_2000.longitude.values.max(),\n",
    "                             water_class_2000.latitude.values.min(),\n",
    "                             water_class_2000.latitude.values.max()])\n",
    "a.set_title('wofs - 2000')\n",
    "plt.colorbar(ticks=[0,1], orientation ='horizontal')\n",
    "a=fig.add_subplot(1,2,2)\n",
    "imgplot = plt.imshow(wofs_2015, cmap='Blues',\n",
    "                     extent=[water_class_2015.longitude.values.min(), \n",
    "                             water_class_2015.longitude.values.max(),\n",
    "                             water_class_2015.latitude.values.min(),\n",
    "                             water_class_2015.latitude.values.max()])\n",
    "#imgplot.set_clim(0.0,1.0)\n",
    "a.set_title('wofs - 2015')\n",
    "plt.colorbar(ticks=[0,1], orientation='horizontal')\n",
    "plt.savefig('wofs_compare.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0068b9c978>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize =  (15,8))\n",
    "plt.imshow(coastline_2000, cmap='Greens')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0068b75860>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize =  (15,8))\n",
    "plt.imshow(coastline_2015, cmap='Purples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (latitude: 569, longitude: 1092)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 5.811 5.811 5.811 5.81 ... 5.659 5.658 5.658\n",
       "  * longitude  (longitude) float64 0.05188 0.05215 0.05242 ... 0.3456 0.3459\n",
       "Data variables:\n",
       "    red        (latitude, longitude) int16 1648 1609 1628 1628 ... 446 497 411\n",
       "    green      (latitude, longitude) int16 1449 1428 1449 1428 ... 536 516 477\n",
       "    blue       (latitude, longitude) int16 1354 1312 1354 1397 ... 605 570 536\n",
       "    nir        (latitude, longitude) int16 1998 1997 1997 1997 ... 436 436 397\n",
       "    swir1      (latitude, longitude) int16 2603 2628 2603 2578 ... 368 391 368\n",
       "    swir2      (latitude, longitude) int16 2003 2055 1977 1977 ... 303 328 328\n",
       "    pixel_qa   (latitude, longitude) int32 66 66 66 66 66 66 ... 68 68 68 68 68"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mosaic_2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mosaic_rgb_2000 = np.array([mosaic_2000.red.values, mosaic_2000.green.values, mosaic_2000.blue.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1648, 1609, 1628, ..., 1661, 1621, 1583],\n",
       "        [1609, 1628, 1628, ..., 1641, 1621, 1661],\n",
       "        [1609, 1609, 1609, ..., 1641, 1661, 1641],\n",
       "        ...,\n",
       "        [ 244,  226,  262, ...,  428,  446,  428],\n",
       "        [ 225,  262,  244, ...,  428,  428,  428],\n",
       "        [ 244,  262,  262, ...,  446,  497,  411]],\n",
       "\n",
       "       [[1449, 1428, 1449, ..., 1421, 1400, 1400],\n",
       "        [1449, 1428, 1449, ..., 1400, 1400, 1443],\n",
       "        [1449, 1428, 1449, ..., 1400, 1421, 1421],\n",
       "        ...,\n",
       "        [ 361,  381,  381, ...,  497,  477,  497],\n",
       "        [ 381,  361,  361, ...,  497,  477,  458],\n",
       "        [ 381,  381,  361, ...,  536,  516,  477]],\n",
       "\n",
       "       [[1354, 1312, 1354, ..., 1285, 1306, 1389],\n",
       "        [1375, 1354, 1354, ..., 1306, 1327, 1327],\n",
       "        [1375, 1354, 1354, ..., 1306, 1327, 1306],\n",
       "        ...,\n",
       "        [ 434,  434,  435, ...,  554,  519,  536],\n",
       "        [ 472,  434,  435, ...,  554,  519,  519],\n",
       "        [ 472,  453,  435, ...,  605,  570,  536]]], dtype=int16)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mosaic_rgb_2000"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}