{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zonal Statistics API Examples\n",
    "\n",
    "This notebook demonstrates two examples of retrieving data using the [Zonal Statistics](https://docs.climateengine.com/docs/build/html/zonal_statistics.html) family of endpoints.  This group of endpoints are used to generate bulk statistics of Climate Engine datasets over different geometries.\n",
    "\n",
    "The CE API token is read as an environment variable named `CE_API_KEY`.\n",
    "\n",
    "Climate Engine \\\n",
    "October 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from calendar import month_abbr\n",
    "import json\n",
    "import os\n",
    "import requests\n",
    "\n",
    "from folium import Map, GeoJson\n",
    "import folium\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "requests.packages.urllib3.disable_warnings(requests.packages.urllib3.exceptions.InsecureRequestWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set root URL for API requests\n",
    "root_url = 'https://geodata.dri.edu/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Authentication info\n",
    "headers = {'Authorization': os.getenv('CE_API_KEY')}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pixel Counts - 180-day Standard Precipitation Index\n",
    "\n",
    "In this example, the zonal_statistics/pixel_count/climate_engine_asset endpoint is used to collect zonal statistics of 180-day Standard Precipitation Index (SPI), a commonly used metric for hydrologic drought, across the state of California.  SPI pixel values are collected and binned to show the distribution of drought at a particular snapshot in time across the state.  This example captures drought from March - September (180 day period) for both 2020 and 2021 to show the drastic change in drought conditions across the state.\n",
    "\n",
    "Here, we're using the readily available Climate Engine assets, subselecting the state of California for generating the zonal stats.  The full list of built-in Climate Engine assets is available [here](https://docs.climateengine.com/docs/build/html/climate_engine_assets.html).\n",
    "\n",
    "Detailed documentation for the zonal_statistics/pixel_count/climate_engine_asset endpoint found [here](https://docs.climateengine.com/docs/build/html/zonal_statistics.html#rst-zonal-statistics-pixel-count-climate-engine-asset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "endpoint = 'zonal_statistics/pixel_count/climate_engine_asset'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up params dict for API call for 2020 data\n",
    "params = {\n",
    "    'dataset': 'GRIDMET_DROUGHT',\n",
    "    'variable': 'spi180d',\n",
    "    'bins': '[-2.5, -2, -1.5, -1, -0.5, 0.5, 1, 1.5, 2, 2.5]',\n",
    "    'end_date': '2020-09-02',\n",
    "    'region': 'states',\n",
    "    'sub_choices': '[\"California\"]',\n",
    "    'filter_by': 'Name'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Send API request\n",
    "r = requests.get(root_url + endpoint, params=params, headers=headers, verify=False)\n",
    "spi_2020_response = r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract data and build pandas dataframe\n",
    "variable_name = params[\"variable\"]\n",
    "spi_2020_df = pd.DataFrame(columns = ['Name', 'Date', '-4', '-3', '-2', '-1', '0', '1', '2', '3', '4'])\n",
    "\n",
    "for name in spi_2020_response:\n",
    "    for date in name:\n",
    "        a_subset = {key: date[key] for key in ['Name', 'Date']}\n",
    "        b_subset = date[variable_name]\n",
    "        a_subset.update(b_subset)\n",
    "        df = pd.DataFrame(a_subset, index=[0])\n",
    "        spi_2020_df = spi_2020_df.append(df)\n",
    "    \n",
    "# Convert pixel counts to percent values\n",
    "total_2020 = np.array(spi_2020_df.iloc[0,2:12].dropna()).sum()\n",
    "percents_2020 = np.array(spi_2020_df.iloc[0,2:12]) / total_2020 * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up params dict for API call for 2021 data\n",
    "params = {\n",
    "    'dataset': 'GRIDMET_DROUGHT',\n",
    "    'variable': 'spi180d',\n",
    "    'bins': '[-2.5, -2, -1.5, -1, -0.5, 0.5, 1, 1.5, 2, 2.5]',\n",
    "    'end_date': '2021-09-02',\n",
    "    'region': 'states',\n",
    "    'sub_choices': '[\"California\"]',\n",
    "    'filter_by': 'Name'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Send API request\n",
    "r = requests.get(root_url + endpoint, params=params, headers=headers, verify=False)\n",
    "spi_2021_response = r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract data and build pandas dataframe\n",
    "variable_name = params[\"variable\"]\n",
    "spi_2021_df = pd.DataFrame(columns = ['Name', 'Date', '-4', '-3', '-2', '-1', '0', '1', '2', '3', '4'])\n",
    "\n",
    "for name in spi_2021_response:\n",
    "    for date in name:\n",
    "        a_subset = {key: date[key] for key in ['Name', 'Date']}\n",
    "        b_subset = date[variable_name]\n",
    "        a_subset.update(b_subset)\n",
    "        df = pd.DataFrame(a_subset, index=[0])\n",
    "        spi_2021_df = spi_2021_df.append(df)\n",
    "    \n",
    "# Convert pixel counts to percent values\n",
    "total_2021 = np.array(spi_2021_df.iloc[0,2:12].dropna()).sum()\n",
    "percents_2021 = np.array(spi_2021_df.iloc[0,2:12]) / total_2021 * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the drought distributions together for 2020 and 2021\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(2,1,1)\n",
    "names = np.array(spi_2020_df.iloc[0,2:12].keys())\n",
    "colors = ['darkred', 'red', 'orange', 'yellow', 'grey','lightgreen','cyan', 'blue', 'darkblue', 'black']\n",
    "plt.bar(names, percents_2020, color=colors)\n",
    "plt.xticks([],[])\n",
    "plt.ylabel('% of State', fontsize=14)\n",
    "plt.ylim([0,40])\n",
    "plt.text(0.9, 0.8, '2020', horizontalalignment='center', verticalalignment='center', transform=plt.gca().transAxes, fontsize=28)\n",
    "plt.title('Percentage Of California by 180-day SPI Drought Level', fontsize=16)\n",
    "plt.subplot(2,1,2)\n",
    "plt.bar(names, percents_2021, color=colors)\n",
    "plt.ylabel('% of State', fontsize=14)\n",
    "plt.xlabel('Drought Level', fontsize=14)\n",
    "plt.ylim([0,40])\n",
    "plt.text(0.9, 0.8, '2021', horizontalalignment='center', verticalalignment='center', transform=plt.gca().transAxes, fontsize=28)\n",
    "plt.subplots_adjust(wspace=0, hspace=0.05)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Values - Aridity Index\n",
    "\n",
    "In this example, the zonal_statistics/values/climate_engine_asset endpoint is used to calculate aridity index (AI) for two hydrologic sub-basins in the US, one in the East and one in the West.  Smaller aridity index values indicate regions which suffer from a deficit of available water.  Zonal statistics of precipitation and potential evapotranspiration are collected and used to derive the aridity index.  This example plots the aridity index from 2000-2018 using daily precipitation and potential evapotranspiration data from the gridMET dataset.\n",
    "\n",
    "Here, we're using the readily available Climate Engine assets, subselecting the state of California for generating the zonal stats.  The full list of built-in Climate Engine assets is available [here](https://docs.climateengine.com/docs/build/html/climate_engine_assets.html).\n",
    "\n",
    "Detailed documentation for the zonal_statistics/values/climate_engine_asset endpoint found [here](https://docs.climateengine.com/docs/build/html/zonal_statistics.html#zonal-statistics-values-climate-engine-asset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "endpoint = 'zonal_statistics/values/climate_engine_asset'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the basin GeoJSON files for visualization\n",
    "east_poly_json = r'./eastern_huc8.geojson'\n",
    "with open(east_poly_json, 'r') as f:\n",
    "    east_polygon_dict = json.load(f)\n",
    "east_polygon_coords = east_polygon_dict['features'][0]['geometry']['coordinates']\n",
    "\n",
    "west_poly_json = r'./western_huc8.geojson'\n",
    "with open(west_poly_json, 'r') as f:\n",
    "    west_polygon_dict = json.load(f)\n",
    "west_polygon_coords = west_polygon_dict['features'][0]['geometry']['coordinates']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1ba16679460>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map basin outlines\n",
    "m = Map(\n",
    "    width='100%', \n",
    "    height='100%',\n",
    "    location=[37.0902, -105.7129],\n",
    "    zoom_start=4,\n",
    "    tiles=None\n",
    ")\n",
    "\n",
    "# Add satellite basemap\n",
    "tile = folium.TileLayer(\n",
    "        tiles = 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',\n",
    "        attr = 'Esri',\n",
    "        name = 'Esri Satellite',\n",
    "        overlay = False,\n",
    "        control = True\n",
    "       ).add_to(m)\n",
    "\n",
    "\n",
    "style1 = {'fillColor': 'None', 'color': '#ff00ff'}\n",
    "style2 = {'fillColor': 'None', 'color': '#00FFFFFF'}\n",
    "GeoJson(data=east_polygon_dict, name='Eastern HUC8', style_function=lambda x:style1).add_to(m)\n",
    "GeoJson(data=west_polygon_dict, name='Western HUC8', style_function=lambda x:style2).add_to(m)\n",
    "\n",
    "m.add_child(folium.LayerControl())\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Query to get annual precipitation and ETo totals\n",
    "# Each year is queried separately and then converted into a dataframe\n",
    "year_list = range(2000, 2019)\n",
    "precip_df = pd.DataFrame()\n",
    "eto_df = pd.DataFrame()\n",
    "for year in year_list:\n",
    "    # Set up parameters for precipitation API call\n",
    "    precip_params = {\n",
    "        'dataset': 'G',\n",
    "        'variable': 'pr',\n",
    "        'temporal_statistic': 'total',\n",
    "        'start_date': f'{str(year)}-01-01',\n",
    "        'end_date': f'{str(year)}-12-31',\n",
    "        'region': 'huc8',\n",
    "        'area_reducer': 'mean',\n",
    "        'sub_choices': '[\"Upper Stanislaus\", \"Upper Gasconade\"]',\n",
    "        'filter_by': 'Name'   \n",
    "    }\n",
    "\n",
    "    # Send request to the API\n",
    "    r = requests.get(root_url + endpoint, params=precip_params, headers=headers, verify=False)\n",
    "    precip_response = r.json()\n",
    "\n",
    "    # Convert to dataframe\n",
    "    for ts_id in precip_response:\n",
    "        df = pd.DataFrame(ts_id)\n",
    "        df['year'] = year\n",
    "        precip_df = precip_df.append(df)\n",
    "\n",
    "    # Set up parameters for potential evapotranspiration API call\n",
    "    eto_params = {\n",
    "        'dataset': 'G',\n",
    "        'variable': 'eto',\n",
    "        'temporal_statistic': 'total',\n",
    "        'start_date': f'{str(year)}-01-01',\n",
    "        'end_date': f'{str(year)}-12-31',\n",
    "        'region': 'huc8',\n",
    "        'area_reducer': 'mean',\n",
    "        'sub_choices': '[\"Upper Stanislaus\", \"Upper Gasconade\"]',\n",
    "        'filter_by': 'Name'   \n",
    "    }\n",
    "\n",
    "    # Send request to the API\n",
    "    r = requests.get(root_url + endpoint, params=eto_params, headers=headers, verify=False)\n",
    "    eto_response = r.json()\n",
    "\n",
    "    # Convert to dataframe\n",
    "    for ts_id in eto_response:\n",
    "        df = pd.DataFrame(ts_id)\n",
    "        df['year'] = year\n",
    "        eto_df = eto_df.append(df)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine datasets and calculate the aridity index\n",
    "final_df = pd.merge(precip_df, eto_df, on=['Name','year'], how='left')[['Name', 'pr (mm)', 'eto (mm)', 'year']]\n",
    "final_df['aridity_index'] = final_df['pr (mm)']/final_df['eto (mm)']\n",
    "\n",
    "# Separate values by region (using sub-basin name)\n",
    "west_df = final_df.loc[final_df.Name == \"Upper Stanislaus\"]\n",
    "east_df = final_df.loc[final_df.Name == \"Upper Gasconade\"]\n",
    "\n",
    "# Calculate mean aridity index values\n",
    "west_mean_aridity = west_df['pr (mm)'].sum()/ west_df['eto (mm)'].sum()\n",
    "east_mean_aridity = east_df['pr (mm)'].sum()/ east_df['eto (mm)'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3bhxTpkwBYMSIESxbtozOnTvz/fff8/DDD7Nr1y5AdWaJiorC2dmZiIgIli5dSnh4OFlZWbi7uwPw888/c+LECdq2bUt4eDgHDhygX79+VT5ec+bM4d1332Xw4ME89dRTZv1ba5qmaZqm3Uy9SKptZcCAAURFRREVFcUTTzzB+fPniYqKomnTpoSHhxMTE8Px48cZNWoUAEVFRbRp0waAnj17MmPGDCZOnMjEiRMr3f/OnTvLlFxcvXqVrKwsAMaPH4+Hh4fxtrFjx+Lm5oabmxutWrUiJSWF9u3bVxpvTk4OYWFhdO7cmZdffhlvb29j6cfx48d54YUXyMjIICsri9GjR1eIKysri6ioKKZOnWq8Li8vz/j91KlTcXZ2BlTZyRNPPMGMGTOYNGmSMaa+ffsavw8JCSE+Ph4vL69KH6+MjAwyMjIYPHgwADNnzmTr1q03/NtomqZpmimunr+Ki4cLHs09br6xVq/Vi6R6sY2Oa6hT/uWXX+jevTu+vr68+eabNGnShDlz5iClpFu3bsYykdI2b97Mvn372LhxIy+99JKxDKO04uJiDh06ZBzdLa1hw4ZlfnZzczN+7+zsXGk9c3h4OMuWLSM3N5cFCxbg7e3NyZMnyyTVs2fPZt26dQQHB7NixQr27NlTaVxeXl4cPXq00seldGzPPvssY8eOZcuWLYSHh7N9+/Yq463q8crIyKj0OJqmaZpWW/+5/T94d/Nm6ldTb76xVq/pmmoLGjBgAJs2baJ58+Y4OzvTvHlzMjIyOHjwIAMGDCAwMJC0tDRjklhQUMCJEycoLi4mMTGRYcOG8eqrr5KZmUlWVhaNGzc21hwD3H777bzzzjvGn6tKYk0VFhbGoUOHSEtLo1WrVggh8Pb2Zv369YSHhwNw7do12rRpQ0FBAStXrjTet3RsTZo0wd/fnzVr1gBqwZHo6OhKj3n27Fl69OjBM888Q2hoqLH2ujJVPV5eXl54eXkRGRkJUCYuTdM0TaupvKt5pJ1MI25XHLJY2joczc7ppNqCevTowaVLl+jfv3+Z65o2bUrLli1xdXVl7dq1PPPMMwQHBxMSEkJUVBRFRUXce++99OjRg169evHoo4/i5eVFREQE33zzjXGi4pIlSzh8+DA9e/aka9euLFu2rFbxNmvWDG9vb7p162a8LiwsjNTUVIKDgwF48cUX6devH+Hh4QQFBRm3mz59Oq+//jq9evXi7NmzrFy5kk8++YTg4GC6devG+vXrKz3m4sWL6d69Oz179sTFxYU77rijyviqerwAli9fzoIFCwgJCTFOatQ0TdO02kg5lgJATnoOab+m2Tgazd6JupCA9OnTRx4+fLjMdb/++itdunSxUUSaZt/0/4emadrN/bD0B7YuVHN0xr4/lj7z+9g4Is0eCCGOSCkrPBn0SLWmaZqmaVolko8m49HCg8ZtG3Nu3zlbh6PZuXoxUVHTNE3TNK26UqJTaB3cGk9vT87tO4eUEiGErcPS7JQeqdY0TdM0TSunuLCY1F9S8Qn2ocPgDlw7f42M+Axbh6XZMZ1Ua5qmaZqmlZN+Op3C3EJah7TGb5AfAAn7E2wclWbPdFKtaZqmaZpWTkq06vzhE+xDq26tcG/mruuqtRvSSbWmaZqmaVo5yUeTcXJxwruLN8JJ4DfQT49Uazekk2oLcnZ2JiQkxHh55ZVXqr2PPXv2GHsxm9u///1vHnvsMePP8+bNY+TIkcaf33nnHR599NEq779u3boyy6TXxp49exg3blyN7x8fH0/37t3NEoumaZqmpUSn4N3VG2dXZwA6DO5Aemw6WclZNo5Ms1e6+4cFeXh41HqVwz179tCoUSPjMuGmKCwspEGDm/9pw8PDy6w+GB0dTVFREUVFRTg7OxMVFcWECROqvP+6desYN24cXbt2NXtsmqZpmmZLyUeTuWX0LcafjXXVkQl0nWL6+55Wf+iRahv4xz/+QWhoKN27d+ehhx4yrgC4ZMkSunbtSs+ePZk+fTrx8fEsW7aMt99+27iKYlpaGpMnTyY0NJTQ0FAOHDgAwKJFi5g5cybh4eHMnDmTRYsWMXfuXIYOHUqnTp1YsmRJhThCQkKIjY0lJyeHzMxMPDw8CAkJ4ZdffgEgKiqK8PBwPvroI0JDQwkODmby5MlkZ2cTFRXFhg0beOqppwgJCeHs2bOcPXuWMWPG0Lt3bwYNGmRccnz27NnMnz+ffv368fTTT7N3717j6H2vXr2My5tnZWUxZcoUgoKCmDFjhvFxOXLkCEOGDKF3796MHj2aixcvGq8PDg4mODiYpUuXWvaPpmmaptUb11Ovk5WchU+wj/G6Nre1wcXTRddVa1WqF0OG2x7bRvLRZLPus3VIa8YsHnPDbXJycggJCTH+/NxzzzFt2jQWLlzIX//6VwBmzpzJpk2biIiI4JVXXiEuLg43NzcyMjLw8vJi/vz5NGrUiD//+c8A/OEPf+Dxxx9n4MCBJCQkMHr0aH799VcATp48SWRkJB4eHixatIhTp06xe/durl27RmBgIH/84x9xcXExxtOgQQN69erFjz/+SE5ODv369aNz585ERUXh7e2NlBJfX18mTZrEgw8+CMALL7zAJ598wiOPPML48eMZN24cU6ZMAWDEiBEsW7aMzp078/333/Pwww+za9cuAJKSkoiKisLZ2ZmIiAiWLl1KeHg4WVlZuLu7A/Dzzz9z4sQJ2rZtS3h4OAcOHKBfv3488sgjrF+/Hm9vb1avXs3zzz/Pp59+ypw5c3j33XcZPHgwTz31lBn+qpqmaZoGydEqZ2gd3Np4nbOLM+3D2uu6aq1K9SKptpWqyj92797Na6+9RnZ2NpcvX6Zbt25ERETQs2dPZsyYwcSJE5k4cWKl+9y5c2eZOuarV6+SlaXqu8aPH4+Hh4fxtrFjx+Lm5oabmxutWrUiJSWF9u3bl9nfgAEDiIqKIicnh7CwMDp37szLL7+Mt7e3seTk+PHjvPDCC2RkZJCVlcXo0aMrxJWVlUVUVBRTp041XpeXl2f8furUqTg7q7q08PBwnnjiCWbMmMGkSZOMMfXt29f4fUhICPHx8Xh5eXH8+HFGjRoFQFFREW3atCEjI4OMjAwGDx4MqA8nW7durfQx0zRN07TqMAzElR6pBlVXvWfRHnIzcnH3crdFaJodqxdJ9c1GlK0pNzeXhx9+mMOHD+Pr68uiRYvIzc0FYPPmzezbt4+NGzfy0ksvGcswSisuLubQoUPG0d3SGjZsWOZnNzc34/fOzs4UFhZWuE94eDjLli0jNzeXBQsW4O3tzcmTJ8sk1bNnz2bdunUEBwezYsUK9uzZU2lcXl5eVdaQl47t2WefZezYsWzZsoXw8HC2b99eZbxSSrp168bBgwfL7C8jI6PS42iapmlabaVEp9CkfRM8W3iWud5vkB9ISIxKpPOdnW0UnWavdE21lRkS6JYtW5KVlcXatWsBlZQmJiYybNgwXn31VTIzM8nKyqJx48bGmmOA22+/nXfeecf4c20nQoaFhXHo0CHS0tJo1aoVQgi8vb1Zv3494eHhAFy7do02bdpQUFBQZmJj6diaNGmCv78/a9asAUBKSXR0dKXHPHv2LD169OCZZ54hNDTUWHtdmcDAQNLS0oxJdUFBASdOnMDLywsvLy8iIyMBysSlaZqmabWRfDSZ1iGtK1zfvl97nFycdF21VimdVFuQoabacHn22Wfx8vLiwQcfpHv37owePZrQ0FBAlTXce++99OjRg169evHoo4/i5eVFREQE33zzjXGi4pIlSzh8+DA9e/aka9euLFu2rFYxNmvWDG9vb7p162a8LiwsjNTUVIKDgwF48cUX6devH+Hh4QQFBRm3mz59Oq+//jq9evXi7NmzrFy5kk8++YTg4GC6devG+vXrKz3m4sWL6d69Oz179sTFxYU77rijyvhcXV1Zu3YtzzzzDMHBwYSEhBhbDC5fvpwFCxYQEhJinNSoaZqmabVRmFvIpVOXKpR+ALh4utC2T1tdV61VStSFZKRPnz7y8OHDZa779ddf6dKli40i0jT7pv8/NE3TKnfhyAU+6vMRU9dMrbR13s5nd3LwrYM8m/EsLp4ulexBq+uEEEeklH3KX69HqjVN0zRN00qUXp68Mn6D/CguKCbp+yRrhqU5AJ1Ua5qmWVH+9XyuxF2xdRiaplUhOToZl4YuNL+leaW3+4X7gUCXgGgV6KRa0zTNinY8vYN3A94lZmOMrUPRNK0SKUdT8Onpg3ASld7u7uWOT08fPVlRq0An1ZqmaVZ0ZusZiguLWTNlDae3nrZ1OJqmlSKlJDk6ucrSD4MOgzuQdDCJooIiK0WmOQKdVGuaplnJlbgrZMRlMPTvQ/Hu5s3qu1bz287fbB2WpmklMs9lkpeZV2k7vdL8BvlRkF3AxZ8uWikyzRHopFrTNM1K4nfHA9B1Sldm7phJi4AWrBq/ivg98TaNS9M0pbLlySvTYVAHQNdVa2XppNpCHn/8cRYvXmz8efTo0TzwwAPGn5988kneeuutau1zz549xh7N5vbvf/+bxx57zPjzvHnzGDlypPHnd955h0cffbTK+69bt67M8um1sWfPHsaNG1fj+8fHx9O9e/dKrxdC8MILLxivu3TpEi4uLixcuLDGxzOV4Vg36y3+wAMPVPpYrlixwhjnsmXL+Pzzz43XX7hwwfwBa2YXtyuOhj4NadmlJZ4tPLlv530082/Gf8f9l4RI/easabaWfDQZBLTq0eqG2zVq3YjmnZvrumqtDJ1UW0h4eLgxAS4uLubSpUucOHHCeHtUVJRxGXBT1SSprmxp8sqUjhcgOjqazMxMiopUvdjN4q1JUm1qbObk7+/P5s2bjT+vWbOmzMI3lrRmzRr69+/PqlWrqtymqKiIjz/+mK5dK/ZGLW3+/Pncd999gE6qHYWUkrhdcfgP90cINQGqYauG3PfdfTRp34SVd6wk8WCijaPUtPotJTqFFp1b4NrQ9abbdhjcgYTIBGSx46/3oZmHTqotZMCAAcaltU+cOEH37t1p3LgxV65cIS8vj19//ZXbbruNI0eOMGTIEHr37s3o0aO5eFHVZy1ZsoSuXbvSs2dPpk+fTnx8PMuWLePtt982rq6YlpbG5MmTCQ0NJTQ0lAMHDgCwaNEiZs6cSXh4ODNnzmTRokXMnTuXoUOH0qlTJ5YsWVIh3pCQEGJjY8nJySEzMxMPDw9CQkL45ZdfAJVUh4eH89FHHxEaGkpwcDCTJ08mOzubqKgoNmzYwFNPPUVISAhnz57l7NmzjBkzht69ezNo0CDjUuSzZ89m/vz59OvXj6effpq9e/caV5zs1auXcdnzrKwspkyZQlBQEDNmzDCumFjV43XkyBGCg4MJDg5m6dKlVf5dPD096dKlC4bFglavXs3dd99tvL2qx/SHH34gLCyMXr16MWDAAGJiVOeGFStWMGnSJMaMGUPnzp15+umnqzz2qlWrePPNNzl//jxJSb/3N23UqBFPPvkkwcHBHDx4kKFDhxrjW758OQEBAfTt29cYi+Fv/MYbb7B27VoOHz7MjBkzCAkJYfPmzUycONG43Y4dO7jrrruqjEmznkunLpF1MQv/4f5lrm/UuhGzds2iUetGrByzkguH9QckTbOVlOiUm05SNPAb5EfulVxST6RaOCrNYUgpHf7Su3dvWd7JkyfL/DykksvSktuuV3H78pLb0yq5zRQdO3aU586dk8uWLZPvv/++fOGFF+TmzZtlZGSkHDhwoMzPz5dhYWEyNTVVSinll19+KefMmSOllLJNmzYyNzdXSinllStXpJRS/u1vf5Ovv/66cf/33HOP3L9/v5RSynPnzsmgoCDjdrfddpvMzs42/hwWFiZzc3NlWlqabN68uczPz68Q79ChQ+XevXvltm3b5DPPPCM//vhjuXTpUpmUlCR9fX2llFJeunTJuP3zzz8vlyxZIqWUctasWXLNmjXG24YPHy5jY2OllFIeOnRIDhs2zLjd2LFjZWFhoZRSynHjxsnIyEgppZTXrl2TBQUFcvfu3bJJkyYyMTFRFhUVyf79+8v9+/ff8PHq0aOH3Lt3r5RSyj//+c+yW7duFX6/uLg42a1bN7l+/Xr55JNPyoSEBDl8+HC5fPlyuWDBghs+ppmZmbKgoEBKKeWOHTvkpEmTpJRSLl++XPr7+8uMjAyZk5Mj/fz8ZEJCQoVjJyQkyFtvvVVKKeVzzz0n33jjDeNtgFy9erXx5yFDhsgff/xRXrhwQfr6+srU1FSZl5cnBwwYYIyz9HPBsL2UUhYXF8vAwEDjY3TPPffIDRs2VIin/P+HZnnfv/u9XMQiefns5Upvz0jIkIv9F8tXvF6RF366YOXoNE3LzcyVi1gk9720z6TtL/92WS5ikfxh6Q8WjkyzN8BhWUk+2sDWSX1dNmDAAKKiooiKiuKJJ57g/PnzREVF0bRpU8LDw4mJieH48eOMGjUKUKf+27RpA0DPnj2ZMWMGEydOLDPyWNrOnTvLlFxcvXqVrKwsAMaPH4+Hh4fxtrFjx+Lm5oabmxutWrUiJSWF9u3bVxpvTk4OYWFhdO7cmZdffhlvb29j6cfx48d54YUXyMjIICsri9GjR1eIKysri6ioKKZOnWq8Li8vz/j91KlTcXZ2BlTZyRNPPMGMGTOYNGmSMaa+ffsavw8JCSE+Ph4vL69KH6+MjAwyMjIYPHgwADNnzmTr1q1V/l3GjBnDX/7yF3x8fJg2bZpJj2lmZiazZs3i9OnTCCEoKCgwbjNixAiaNm0KQNeuXTl37hy+vr5l9lt6RHz69OnMnTuXJ598EgBnZ2cmT55cIc7vv/+eoUOH4u3tDcC0adOIjY2t8vcCEEIwc+ZM/vOf/zBnzhwOHjxorL3WbCt+VzxeHb1o1qlZpbc39W3KrF2zWD54OV+M+oJZu2fh08O0ETNN02ov5diNV1Isz6ujF43bNebcvnOEPhxqydA0B1Fvkuo9N7jN8ya3t7zJ7VUx1Cn/8ssvdO/eHV9fX958802aNGnCnDlzkFLSrVs3Y5lIaZs3b2bfvn1s3LiRl156yViGUVpxcTGHDh3C3d29wm0NGzYs87Obm5vxe2dn50rrmcPDw1m2bBm5ubksWLAAb29vTp48WSapnj17NuvWrSM4OJgVK1awZ8+eSuPy8vLi6NGjlT4upWN79tlnGTt2LFu2bCE8PJzt27dXGW9Vj1dGRkalx6mKq6srvXv35s033+TkyZNs2LChTOyVPaYLFy5k2LBhfPPNN8THxzN06FDjbaY8tqtWrSI5OZmVK1cCcOHCBU6fPk3nzp1xd3c3fsgwhzlz5hAREYG7uztTp06lQYN6829ut2SxJG53HEF3Bd1wO6+OXszaPYsVg1fw+YjPmb1nNt5dva0UpabVb8lHSzp/3KSdnoEQgg6DO3Bu7zmklMa5Elr9pWuqLWjAgAFs2rSJ5s2b4+zsTPPmzcnIyODgwYMMGDCAwMBA0tLSjEliQUEBJ06coLi4mMTERIYNG8arr75KZmYmWVlZNG7c2FhzDHD77bfzzjvvGH+uKok1VVhYGIcOHSItLY1WrVohhMDb25v169cTHh4OwLVr12jTpg0FBQXGBBEoE1uTJk3w9/dnzZo1gCoxio6OrvSYZ8+epUePHjzzzDOEhoYaa68rU9Xj5eXlhZeXF5GRkQBl4qrKk08+yauvvkrz5mWXoa3qMc3MzKRdu3aAqqOujtjYWLKysjh//jzx8fHEx8fz3HPP3XDCIkC/fv3Yu3cv6enpFBQUGB/P8so/L9q2bUvbtm355z//yZw5c6oVq2YZydHJ5F7JrVBPXZnmtzTnvl334eTsxOcjPic9Nt0KEWqalhydjEcLDxq3bWzyffwG+XHtwjWu/HbFgpFpjkIn1RbUo0cPLl26RP/+/ctc17RpU1q2bImrqytr167lmWeeITg4mJCQEKKioigqKuLee++lR48e9OrVi0cffRQvLy8iIiL45ptvjBMVlyxZwuHDh+nZsyddu3a9aau2m2nWrBne3t5lumGEhYWRmppKcHAwAC+++CL9+vUjPDycoKDfR92mT5/O66+/Tq9evTh79iwrV67kk08+ITg4mG7durF+/fpKj7l48WK6d+9Oz549cXFx4Y477qgyvqoeL1AT+hYsWEBISIhxUuONdOvWjVmzZlW4vqrH9Omnn+a5556jV69e1e5asmrVqgqTBSdPnnzTpLpNmzYsWrSIsLAwwsPD6dKlS6XbGSZ/hoSEkJOTA8CMGTPw9fWt8j6adcV9FweA/7CbJ9UALQNbct9391FcVMxnwz/j8tnLlgxP0zTU8uStg1tXa8S5w2Ddr1r7nTAlAbF3ffr0kYZuCQa//vqrTii0emvhwoX06tWL+++/v9Lb9f+Hda28cyUZcRks+HVBte6X8ksKnw37DNeGrszeOxuvjl6WCVDT6rniwmL+1fhf9Hm4D6PfrDhXqCqyWPJ6q9cJHB/IhE8nWDBCzZ4IIY5IKfuUv16PVGtaHdO7d2+OHTvGvffea+tQNKCooIhz+87hP8K0UerSfHr4cN/O+8i7lsdnwz8jMzHTAhFqmpZ+Op3C3MKbrqRYnnAS+A300yPVGqCTak2rc44cOcK+ffvKTKDUbOfCjxcouF5gUj11ZVqHtGbmtzPJSc/hs2GfcfX8VTNHqGladScpltZhcAcun7nMtYvXbr6xVqdZNakWQnwqhEgVQhyv4vYZQohjQohfhBBRQohga8anaZpmbnG74kBAhyEdaryPtn3acu/2e7mecp3PR3xOVnKWGSPUNC0lOgUnFydaBrWs9n39BvkBuq5as/5I9QpgzA1ujwOGSCl7AC8CH1ojKE3TNEuJ+y6O1iGt8WzhWav9tO/fnhlbZ3A18Sqfj/ic62nXzRShpmnJR5Np1a0Vzq7Vb2/aplcbXBq6cG7fOQtEpjkSqybVUsp9QJXT2KWUUVJKQ1+aQ0D7qrbVNE2zdwU5BSRGJda49KM8v4F+/GHzH7gSd4UvRn5Bdnq2WfarafVddZYnL8+pgRO+A3z1SLVm1zXV9wNVLosnhHhICHFYCHE4LS3NimFpmqaZJjEqkaL8ohpNUqxKx6Edmb5+OpdiLvHFqC/IuZJjtn1rWn2UlZJFVnJWjZNqUHXVKb+k6P/Hes4uk2ohxDBUUv1MVdtIKT+UUvaRUvYxLONsb5ydnQkJCTFeXnnllWrvY8+ePcZezOb273//m8cee8z487x58xg5cqTx53feeYdHH320yvuvW7euzJLetbFnzx7GjRtX4/vHx8fTvXv3Sq8XQvDCCy8Yr7t06RIuLi4sXLiwxsczleFYN+sh/sADD1T6WK5YscIY57Jly4xLjq9YsYILFy6YP2DNrOJ2xeHUwAm/gX5m3e8to25h2jfTSDuRxn9G/4fczFyz7l/T6pOUaLU8eU0mKRr4DfIDCYkHEs0VluaA7C6pFkL0BD4GJkgpHXopMQ8PD44ePWq8PPvss9XeR02SalMXJzEso24QHR1NZmYmRUVFAERFRRmXJ69MTZLq6i6cYg7+/v5s3rzZ+POaNWvKLHBjSWvWrKF///43XOilqKiIjz/+mK5du95wX/Pnz+e+++4DdFLtKOJ3xdOubzvcGpu/E0vnOzozde1Ukn9O5r93/pe8a3lmP4am1QfJ0SWdP6rZTq+0dn3b4ezqrOuq6zm7SqqFEH7A/4CZUspYW8djKf/4xz8IDQ2le/fuPPTQQ8YVAJcsWULXrl3p2bMn06dPJz4+nmXLlvH2228bV1FMS0tj8uTJhIaGEhoayoEDBwBYtGgRM2fOJDw8nJkzZ7Jo0SLmzp3L0KFD6dSpE0uWLKkQR0hICLGxseTk5JCZmYmHhwchISH88ssvgEqqw8PD+eijjwgNDSU4OJjJkyeTnZ1NVFQUGzZs4KmnniIkJISzZ89y9uxZxowZQ+/evRk0aJBxyXHDin/9+vXj6aefZu/evcbR+169ehmX2M7KymLKlCkEBQUxY8YM4+Ny5MgRhgwZQu/evRk9ejQXL140Xh8cHExwcDBLly6t8vH29PSkS5cuGBYIWr16NXfffbfx9qoe0x9++IGwsDB69erFgAEDiImJAVRCO2nSJMaMGUPnzp15+umnqzz2qlWrePPNNzl//jxJSUnG6xs1asSTTz5JcHAwBw8eZOjQocb4li9fTkBAAH379jXGYvgbv/HGG6xdu5bDhw8zY8YMQkJC2Lx5MxMnTjRut2PHjgorOGrWl3c1j/M/nqfj8I4WO0ZgRCBTVk8h6fskVo1bRf71fIsdS9PqqpSjKTTxbYJHc48a78PFw4W2oW11XXV9J6W02gVYBVwECoAkVInHfGB+ye0fA1eAoyWXw6bst3fv3rK8kydPlvl5+ZDlFS4/LP1BSill/vX8Sm//efnPUkopr6ddr3CbKZycnGRwcLDx8uWXX0oppUxPTzduc++998oNGzZIKaVs06aNzM3NlVJKeeXKFSmllH/729/k66+/btz+nnvukfv375dSSnnu3DkZFBRk3O62226T2dnZxp/DwsJkbm6uTEtLk82bN5f5+fkVYhw6dKjcu3ev3LZtm3zmmWfkxx9/LJcuXSqTkpKkr6+vlFLKS5cuGbd//vnn5ZIlS6SUUs6aNUuuWbPGeNvw4cNlbGyslFLKQ4cOyWHDhhm3Gzt2rCwsLJRSSjlu3DgZGRkppZTy2rVrsqCgQO7evVs2adJEJiYmyqKiItm/f3+5f/9+mZ+fL8PCwmRqaqqUUsovv/xSzpkzR0opZY8ePeTevXullFL++c9/lt26davw+8XFxclu3brJ9evXyyeffFImJCTI4cOHy+XLl8sFCxbc8DHNzMyUBQUFUkopd+zYISdNmiSllHL58uXS399fZmRkyJycHOnn5ycTEhIqHDshIUHeeuutUkopn3vuOfnGG28YbwPk6tWrjT8PGTJE/vjjj/LChQvS19dXpqamyry8PDlgwABjnKWfC4btpZSyuLhYBgYGGh+je+65x/icqkr5/w/N/GI2xshFLJK/7frN4sf65ctf5N+d/i4/G/6ZzL9e8f9c07SqLe22VP533H9rvZ+dz+2U/2jwD5mXlWeGqDR7VlV+2sDKCfw9N7n9AeABK4VjcYbyj/J2797Na6+9RnZ2NpcvX6Zbt25ERETQs2dPZsyYwcSJE8uMPJa2c+fOMiUXV69eJStL9awdP348Hh6/f9IeO3Ysbm5uuLm50apVK1JSUmjfvmxDlQEDBhAVFUVOTg5hYWF07tyZl19+GW9vb2Ppx/Hjx3nhhRfIyMggKyuL0aMrLuGalZVFVFQUU6dONV6Xl/f76eipU6fi7KxaFYWHh/PEE08wY8YMJk2aZIypb9++xu9DQkKIj4/Hy8uL48ePM2rUKECVSrRp04aMjAwyMjIYPHgwADNnzmTr1irntTJmzBj+8pe/4OPjw7Rp00x6TDMzM5k1axanT59GCEFBQYFxmxEjRtC0aVMAunbtyrlz5/D19S2z39Ij4tOnT2fu3Lk8+eSTgKq3nzx5coU4v//+e4YOHYphnsC0adOIjb3xSRshBDNnzuQ///kPc+bM4eDBg8baa8124nbF0cC9Ab5hvjffuJa6T+tOcUEx39z3DavvWs309dNp4G7Vl3dNc0iFuYVcOnWJoLuCar0vv0F+RP4rkvPfnzdbxx/NsdSbV93Ze2ZXeZuLp8sNb/ds6XnD26sjNzeXhx9+mMOHD+Pr68uiRYvIzVWTjDZv3sy+ffvYuHEjL730krEMo7Ti4mIOHTqEu7t7hdsaNmxY5ufSK+o5OztXWs8cHh7OsmXLyM3NZcGCBXh7e3Py5MkySfXs2bNZt24dwcHBrFixgj179lQal5eXV6UfIsrH9uyzzzJ27Fi2bNlCeHg427dvrzJeKSXdunXj4MGDZfaXkZFR6XGq4urqSu/evXnzzTc5efIkGzZsKBN7ZY/pwoULGTZsGN988w3x8fEMHTrUeJspj+2qVatITk5m5cqVAFy4cIHTp0/TuXNn3N3djR8yzGHOnDlERETg7u7O1KlTadCg3vxr2624XXH4hvtaLbnteW9PigqK2DB3A19N/oq7/3c3Ddz080DTbiT1RCqySNaqntrAd4Avwklwbt85nVTXU3ZVU10fGBLoli1bkpWVxdq1awGV2CUmJjJs2DBeffVVMjMzycrKonHjxsaaY4Dbb7+dd955x/hzVUmsqcLCwjh06BBpaWm0atUKIQTe3t6sX7+e8PBwAK5du0abNm0oKCgwJohAmdiaNGmCv78/a9asAVRZUXR0dKXHPHv2LD169OCZZ54hNDTUWHtdmcDAQNLS0oxJdUFBASdOnMDLywsvLy8iIyMBysRVlSeffJJXX32V5s2bl7m+qsc0MzOTdu3aAaqOujpiY2PJysri/PnzxMfHEx8fz3PPPXfDCYsA/fr1Y+/evaSnp1NQUGB8PMsr/7xo27Ytbdu25Z///Cdz5sypVqya+WVfyiYlOsXqb6y95vRi3AfjOL3lNGunraWooMiqx9c0R2NYnrw27fQM3Ju64xPso+uq6zGdVFtQTk5OmZZ6zz77LF5eXjz44IN0796d0aNHExoaCqiyhnvvvZcePXrQq1cvHn30Uby8vIiIiOCbb74xTlRcsmQJhw8fpmfPnnTt2vWmrdpuplmzZnh7e5fphhEWFkZqairBwWqV+BdffJF+/foRHh5OUNDvp8imT5/O66+/Tq9evTh79iwrV67kk08+ITg4mG7durF+/fpKj7l48WK6d+9Oz549cXFx4Y477qgyPldXV9auXcszzzxDcHAwISEhxo4ly5cvZ8GCBYSEhBgnNd5It27dmDVrVoXrq3pMn376aZ577jl69epV7a4lq1atqjBZcPLkyTdNqtu0acOiRYsICwsjPDycLl26VLqdYfJnSEgIOTmqL+qMGTPw9fWt8j6a9cTtjgOwyWhV74d6c8e7dxCzPob//eF/FBcWWz0GTXMUKdEpuDR0ofktzW++sQk6DO5A4kHVn16rf4QpyYi969OnjzR0TjD49ddfdXKh1SsLFy6kV69e3H///TfdVv9/WNamP27il5W/8MzlZ3BqYJuxi4NvH+TbJ76l+z3dueuLu3By1mMomlbeiiErKCoo4v6om79umuLk1ydZM2UNc6PmWmU+hWYbQogjUso+5a/Xr7KaVgf07t2bY8eOce+999o6FA3Vn7rjkI42S6gBwh4PY+SrIzm+6jgb5m5AFjv+AIqmmZOUkuTo5Fot+lJeh0EdAHQJSD2lZ7FoWh1w5MgRW4eglbiadJX02HR6z+9t61AIfzqcovwidv9lN06uTkR8EIFwErYOS9PsQua5TPIy88xST23QsFVDWgS24Ny+c4Q/HW62/WqOQSfVmqZpZmTLeurKDH5hMEX5Rex7cR/OLs7cufROhNCJtaYZJimac6QaVF31ia9OUFxUrMuu6pk6/deuC/XimmZu+v/CsuK+i8OjhQc+Pcw3+lVbQ/8+lPBnwjn8/mG2P75dPwc0jZLlyQW06t7KrPv1G+RHXmYeqcdTzbpfzf7V2ZFqd3d30tPTadGihR6V0bQSUkrS09Mr7XOu1Z6UkrhdcfgP87erMgshBCP+NYKi/CIOvX0IJxcnRr02Sr82avVaSnQKLTq3wLWhq1n322Hw73XV5uh/rTmOOptUt2/fnqSkJNLS0mwdiqbZFXd39wora2rmceXsFa4mXsX//+yj9KM0IQS3v3k7RflFHHzjIM4uzgx/abhOrLV6K/loMu1C25l9v14dvGji24Rz+87Rd2Ffs+9fs191Nql2cXHB39/+3tg0Tau74nbZVz11eUII7lhyB0UFRUT+KxLXxq4Mem6QrcPSNKvLzcwlIy6D2x64zSL77zC4A3HfxSGl1B9c65E6XVOtaZpmTXG74mjcrjHNO5tnIQlLEE6Cce+Po/v07uz+y24uHL5g65A0zepSjqUA5p+kaOA3yI+s5Cwun7lskf1r9kkn1ZqmaWYgi0vqqYf72/3IlHASjH1/LI18GrF+znoK86q3YqimObqUaJVUm7OdXmml66q1+kMn1ZqmaWaQeiKV7LRsuy39KM/dy51xH44j9Xgq+1/ab+twNM2qko8m49nSk8ZtG1tk/y2DWuLZ0pNz+85ZZP+afdJJtaZpmhnYez11ZQLGBtBzZk8i/xVp7NmrafVBSnQKPsE+FjurJITAb5CfHqmuZ3RSrWmaZgbxu+Jpfmtzmvo1tXUo1TJm8Rg8W3qyfs56igqKbB2OpllccWExqcdTLVb6YeA3yI8rv13h6vmrFj2OZj90Uq1pmlZLxYXFxO+Jp+PwjrYOpdo8mnsw9v2xJB9NJvKVSFuHo2kWlx6bTmFuocUmKRrouur6RyfVmqZptXTxp4vkXc1zqNKP0oImBtF9enf2vbiPlF9SbB2OpllUcnTJ8uQWXpildXBrXBu56rrqekQn1ZqmabVkrKce5phJNcAd79yBu5c76+esp7iw2NbhaJrFJB9NxtnVmZZBLS16HKcGTviG++qR6npEJ9Wapmm1FLcrjlY9WtGwVUNbh1Jjni09uXPpnVw8cpGoN6JsHY6mWUxKdAreXb1xdnW2+LH8BvmRejyV7PRsix9Lsz2dVGuaptVCYV4hCZEJDlv6UVq3qd3oMrkLe/62h7Rf02wdjqZZRPLRZIvXUxsY6qoTDyRa5XiabemkWtM0rRbOf3+ewpzCOpFUA9y59E5cG7uyYe4Giot0GYhWt2SlZHE95brFO38YtAtth7Ors66rrid0Uq1pmlYLv333G8JJGEekHF0jn0bcseQOkg4lcWjxIVuHo2lmZemVFMtr4N6Adv3a6brqekIn1ZqmabUQvyueNr3b4O7lbutQzKb7Pd0JHB/I7hd2kx6bbutwNM1sDIscWbrzR2l+g/y4cOQC+Vn5VjtmXVdcVEzS90m2DqMCnVRrmqbVUP71fJIOJeE/om6UfhgIIRi7bCwN3Buw4f4NyGJp65A0zSxSolNo4tsEj+YeVjtmh8EdkEWSpEP2lwQ6qu/+7zs+HfApKcfsqwWoTqo1TdNqKCEygeLC4jpTT11a4zaNGb14NAmRCfzw7g+2DkfTzMKakxQNfMN8EU5C11WbybGVx4h6LYre83vj09M6ZTym0km1pmlaDcXtisPJxQm/cD9bh2IRwfcFc+sdt/Ldc99x+exlW4ejabVSkFPApZhLVqunNnBr4kbrXq11XbUZXDh8gY0PbKTD4A6MWTzG1uFUoJNqTdO0Gor7Lg7fMF9cPF1sHYpFCCGI+DACpwZObHxwoy4D0Rxa2ok0ZJG0+kg1qLrqpENJFOYVWv3YdUVWchar71pNw1YNmbp2Ks4ulu8zXl06qdY0TauBnCs5XPzpIh2Hd7R1KBbVpH0Tbn/zduJ3x3PkwyO2Dkezguup14nZEGPrMMzOWsuTV6bD4A4U5hZy8chFqx+7LijMK+SryV+RczmH6eun09DbPhfa0km1pmlaDZzbew4kdBrRydahWFyv+3vRaWQndjy1g4xzGbYOR7OwA68d4MsJX3Il7oqtQzGrlOgUXBu50qxTM6sf22+gKhHTddXVJ6Vky4ItJEYlMmHFBJucaTCVTqo1TdNqIG5XHC6eLrTr287WoVicEIKIjyKQUqoyEKnLQOoyQ+JX10ark48m49PTB+EkrH7sht4Nadmlpa6rroEf3/uRnz/5mUHPD6Lb1G62DueGdFKtaZpWA3HfxeE3yA9nV/ur67MEr45ejHptFL/t+I2fP/3Z1uFoFpJ/PZ+LP6kShZh1dSepllKSEp1i9UmKpfkN8lMdgxx4pVIJ7Aes9RvE7Y5j25+2ERARwLB/DLPSUWtOJ9WapmnVlJWcRdrJtDrZSu9G+szvQ8ehHfn2iW+5mnTVLPv8FNApuv1IOpSELJK07dOWc/vOkZ2ebeuQzCIjPoO8q3k2LR3oMLgDeVfzSP0l1WYx1Nb/gMHAWisc60rcFdZMXUOLgBZM+s8km5xhqC6dVGuaplVT3O44gHqXVAsnQcTHERQXFrNp3qZal4FcBB4AHjVLdJo5JOxPAAEjXhmBLJac3nza1iGZhbWXJ69Mh8EdAMetq5bAqyXfb7PwsfKz8lk9cTWySDJ9/XTcmrhZ+IjmoZNqTdO0aorbFYe7lzute9nvhBlLaX5Lc4a/PJzTW05z7ItjtdrX/1Bv1JHACXMEp9VaQmQCrYNb4z/cn8btGnNq3Slbh2QWyUeTQUCr7q1sFkNT36Y07dDUYZPqfcCPQGNgB+p/1xJksWTd7HWkHk9lyuoptOjcwkJHMj+dVGuaplVT/K54Og7tiJNz/XwJ7fdIP3zDfdn2p21cu3itxvtZA3QEXIGPzBSbVnNFBUUkHUzCb5AfQggCJwRydvtZCnIKbB1araVEp9AioAWuDV1tGkeHwR1I2J/gkJN9XwO8gX8ASUCshY6z76V9/Pr1r4x6fRS33H6LhY5iGfXzHUHTNK2GMuIzuPLblTrfn/pGhJNgwqcTKMwtZPMfN9coQUhGjXzdB0wCPgNyzBumVk3JR5MpyC4wtn8LmhhEQXYBv+38zcaR1V5ydLJN+lOX5zfIj+up10mPTbd1KNVyHNiCKtUaX3LdDgsc59T6U+z56x56zuxJ/8f7W+AIlqWTak3TtGqI21U/66nLaxHQgmEvDiNmfQzHvzxe7fsbSj+mAvOADNTItWY7hnZvhqS645COuDVxc/gSkNzMXDLiMvAJsV09tYGhrtrRWuu9AXgCfwQ6Af6YP6lOPZ7KN/d+Q9vQtoz7YBxC2P/ExPJ0Uq1pmlYNcbviaOjTEO+u3rYOxeb6P96fdv3asfWRrWSlZFXrvmuAIKAbMAQIAD4wf4haNSREJtCsUzMat20MgLOrM53HdiZ2Y6xDt4FLOaYmKdrDSHWLgBY0bNXQoeqqk4CVqEnFhurmUcBuwFyFQTmXc/hywpe4NnJl2jfTcPFwMdOerUsn1ZqmaSaSUhK3Kw7/4f4OOYpibk7OTkz4dAL51/LZunCryfdLQZV+TAVEyeUhIAp1mlmzPiklCZEJ+A3yK3N90MQgstOySTqYZKPIai/5aMny5HawEp8QQvWrdqCR6sWos0qPl7puFHAN+MEM+y8uLGbttLVcTbrK3f+7mybtmphhr7ahk2pN0zQTpcekk3Uxq96XfpTm3dWbIYuGcHLtSU6uPWnSfb5BLR4xtdR1s1ATFj80f4iaCdJj08lOyzaWfhjcOuZWnFycHLoEJCU6Bc+WnjRq08jWoQCqrjojPoPMxExbh3JTGagzSNNQk4oNhqM+DJujBOTbp77lt52/MXbZWHzDfM2wR9vRSbWmaZqJfvtOTdjSSXVZ4U+F06Z3GzY/vJnsSzdfLGQNEAh0L3VdS2AK8DlQN5YbcSzGeupyI9VuTdzoNKITp9adcsiOFVCyPHmwj92cXXKkuuoPgCzgqXLXNwf6UPuk+uiKo3y/+Hv6PtqXXnN61XJvtqeTak3TNBPF74qnaYemePl72ToUu+LUwIkJyyeQm5HL1kdvXAaSCuxBJdDlU5x5QCbwlSWC1G4oITIBT29PWgRU7AkcOCGQK2evkHYyzQaR1U5xYTGpx1PtovTDwKenD25N3Oy+rjoPVfoxCgip5PZRwPdATddWTfo+iU3zNuE/3J/Rb46u4V7si06qNa2OKgQigA22DqSOkMWSuN26nroqPj18GPzCYI6vOs6p9VWXClRW+mEwCDV5UU9YtL6E/Qn4DfSr9LkdOD4QwCFLQNJj0ynKK7LpSorlOTk74Rvua/cj1f9Btb58uorbRwFFqA/J1XXtwjVW37Waxu0aM+WrKTg1qBvpqFV/CyHEp0KIVCFEpXNRhLJECHFGCHFMCHGbNePTtLokCtiEmgBm/5V79i85OpncK7n4j9ClH1UZ+NxAfIJ92Dx/MzmXK+86vQboDPSs5DbDhMVDQO3WatSq49qFa1z57UqFemqDxm0b065fO2LWx1g5stqzp0mKpfkN8iPtZJpJ5VK2UAy8DvQCRlSxTRiqzV51S0AKcwtZfddq8q7mMX39dDxbeNYiUvti7Y8GK4AxN7j9DtTrbWfUa+v7VohJ0+qkjUAD1On2RbYNpU4w9qceppPqqji7ODNh+QSyL2Wz/fHtFW5PQ7XhMnT9qMwswA09YdGaEiIrr6cuLXBCIBd+vMDV8zU92W8bydHJOLs60zKopa1DKcNYVx1pn6PVG4EY1Ch1Vf+rbsBgqpdUSynZNH8T5384z11f3IVPD/s5g2AOVk2qpZT7gMs32GQC8LlUDgFeQog21olO0+qWjagZ2g8C7wC/2DYchxf3XRwtg1oae/hqlWvTqw3hz4YT/Xk0sZvLLmR8o9IPg+Ylt38BXLdYlFppCZEJuHi63HA0N2hiEAAxGxxrtDrlaAre3bxxdnG2dShltO3TFmc3Z7utq34d1e1jyk22G4VKvhNN3O+hxYeI/iyaIYuG0OWuLrWI0D7ZWxFLO8r+bZJKrqtACPGQEOKwEOJwWprjTZ7QNEuKRb3QRQAvA02Bhaheo1r1FRUUcW7fuXq9NHl1DH5hMN7dvNk0bxO5mbnG69cAtwLBN7n/Q6jJT6stF6JWSsL+BNqHtb9h4tkyqCUtAloQs86xkmp7WZ68vAZuDWjfv71d1lUfKLk8gTrbeSOjSr6aMlp9dsdZdvx5B0F3BTHkL0NqE6Ldsrek2mRSyg+llH2klH28vfXKZppW2saSrxGoFbBeRi22scpmETm2Cz9eoOB6gW6lZ6IGbg2YsHwCWRez+PbJbwG4xM1LPwwGAl3QJSDWkJuZS8qxlCrrqQ2EEAROCCRud1yZD0r2LCs5i+sp1+1qkmJpfoP8uPjTRfKu5dk6lDJeR50xmmvCtt2B1tw8qb585jJrp63Fu6s3d31+F8Kpbk72trek+jxQuvN3+5LrNE2rho1AD6BDyc8PAL2BP1Pz9kf1WdyuOBDQcWhHW4fiMNqFtmPAUwP4+ZOfOfvtWdahOgXcqPTDQKDa630PRFswRg2SDiYhi+UN66kNgiYGUVxQzJmtZ6wQWe0lR9vnJEWDDoM7IIulXa1WeQpYjzqz2dCE7QUwEvgOVdpVmbxreXw54UuEEExfPx3XRq7mCdYO2VtSvQG4r6QLSH8gU0p50dZBaZojuQxEAuNLXecMLAUuAv+wRVAOLm5XHK1DWtepWerWMHTRUFoGtWTjgxv5+loenai8321lZgLu6PZ6lpYQmYBwFrTv1/6m27br146GrRo6TGu9lOgUALsdqfYN80U4C7uqq34T9X+3sBr3GYWahFxZxx5ZLPlm5jdcirnElK+m0KxTM3OEabes3VJvFXAQCBRCJAkh7hdCzBdCzC/ZZAvwG3AG+Ah42JrxaVpdsBU1IhhR7vp+wP3AvwHTFpPWAApyCkiMStSlHzXQwL0B4z8dT2ZiJg2e3mFS6YeBYcLif1ArummWkbA/gTa3tTFp9NDJ2YmA8QGc3nKawrxCK0RXO8lHk2nq1xSPZh62DqVSro1caXNbG7upq76IWtF0DlCdotqRJV8rKwHZs2gPMetjGP3WaDqN6FTbEO2etbt/3COlbCOldJFStpdSfiKlXCalXFZyu5RSLpBS3iKl7CGlPGzN+DStLtgI+AChldz2L6ARetJidSRGJVKUV6ST6hryDfOl0eP96bPsCMNK2hKaah5wDfjSIpFphXmFJH2fdNN66tKCJgaRfy2f+D3xlgvMTFKiU+x2lNrAb5AfSd8n2cWHlCWoRcOeqOb92gJdqZhUn1x7kn0v7iNkbgh9H+lrjhDtnr2Vf2iaVgsFwDZgLJX/c3sDL6EmjOmloE0TtysOpwZOJtWcapX77sXhXL21Oace2EB+Vr7J9xsAdENPWLSUi0cuUpRXVK3ndqcRnXBp6GL3JSAFOQVcOnXJbuupDToM7kBRXhEXfrxg0ziuohYGmYzq0FNdo4D9gGEKa3J0MutmraN9//aMfW9svVmF1qSkWggRcpPbTZl7ommahe1HrZ44/gbbzEOtkvUk+rS6KeJ3xdOubzvcGrvZOhSHdBnY4emC66fjyYjP4Lv/+87k+xomLP4I/Gyh+Oqzc/tVLa9fuOlJdQP3Btw65lZiN8Qii+33fFfaiTRksbT/keqSswS2rqv+CPXe8VQN7z8KlVBHAtmXsvlywpe4N3Pn7v/dTQO3mzXmqztMHak+JIT4U/krhRCeQohP0WfnNM0ubECtcjXyBtsYJi2eB160RlAOLO9qHud/PK/7U9fCOtQp5cmDOtB3YV9+eOcHYzJnCj1h0XISIxNpEdiChq1M6fPwu8AJgVy7cI0Lh207unojxs4fdtijujTPFp54d/O2aV11PrAYGErlZYOmGAK4ADsKilgzdQ1ZyVlM+2YajdvUr8WyTE2q3wTeFEJsFkJ4AwghbgN+Qp0tuM9C8WmaZiKJqqcewc1bIYUBs4G3UC2UtMqd23cOWSR1PXUtrEWtzNYbGPGvEXj5e7Fh7gYKsgtMur8XMA1Yiaqv1sxDFksSDiTUqKwpYGwAwlnYdQlI8tFkXBu5OkS3Cb9BfiQcSKC4qKqmdJb1JWqlvadrsY9GqPeV5Me3E78nnoiPImgXWunafXWaSUm1lPJ54HbUQljHhBBvAVFABtBLSrnSYhFqmmaSX1Gtc8p3/ajKq6jk+xH0pMWqxO2Kw9nNGd8w35tvrFVwBdjJ7wu+uDZ0Zfwn47l85jK7/rLL5P3MQ5Uq6VOi5pN2Mo3cK7nVmqRo4NHcg45DOhKz3n5XV0yJTsGnp49DLDLSYXAH8q/lG1sAWpMEXkMt4jKmlvsa8dEROi39kZAnwwieebN1U+smkycqSil3AaNRAwd/QvXkD5dS/maZ0DRNq44NJV/Hmbh9K+CfqKTna4tE5PjidsXhF+5HA/f6UxNoTutRk2enlLrOf5g/vef35tDbh0g8mGjSfvqjFjPSJSDmYyjB6TCow022rFzghEDSTqaRHptuzrDMQkqpkuoQ+66nNjD8DWxRV70VOIEapa7Nx4+EAwmwYAtnbr+FnFdvVIBYt5n8TiGEGA2sQK02uxl4EPifEGKulNL+/qs0rZ7ZCNyGWobUVPOBj1EtlO7AtBW06ovsS9mkRKcw/KXhtg7FYa1BrepZvk5z1KujOL35NBvmbmDez/Nu+qHFMGFxIXAEVUpyI8WFxeRcziE7PZuc9FJfL2Ubvy//c/71fO7++m5uHV2T3geOJzEykUZtGuHl71Wj+wdOCGTbn7Zxav0pwp8KN29wtZQRn0He1Ty7r6c2aNK+CV7+XiTsT6D/Y/2teuzXUe8Z02uxj8zETL6a9BVeHbz49svJuDk71Wp/jsykpLqk3ONPqPftuVLKy0KItcBnqHKQmSUj2Vo9JoulQ5xqs4bEg4k0at2IZv7WqedLQ62q9Ndq3q8BatLiQFSrvZfNHJcjM/Th1fXUNZOB6lv7KBVHwNyauBHxUQQrx6xkz6I9jHyl6pGtwtxCstOzuf1SNoHpOXyWno28SYKcm5Fb5f6cXZ3xaOGBZwtPPFp44N3VG48WHpz86iTHvjhWb5Lqc/vP0WFQhxq3OvPq4EXrXq2JWR9jd0l18lH7Xp68Mh0GdeD01tNIKa3Wfu4HYA9q0pxLDfdRkF3A6omrKcgpYNbuWexp5sEOVFlJfcwGTB2png8slFK+b7hCSrlTCNETWA58W419aXVM+ul0tj+2nYQDCQx7cRihD4fi5Fw/W6BnJWex/fHtHP/yOE39mjLv53l4NLf8al5bUC9iptZTlxaOmmn8BmryYoD5wnJov333G66NXWnbp62tQ3FIhtKPqvqt3jr6VkLmhhD1ehSyWJJ/Pb/SBLn0hMZ7Sr5uLvnq2sjVmCB7tvSk2S3NyvxcOnk2XOfS0KXSpKUot4iYjTEUFxbj1KBuv35lJmRyNfEqvk/Vbq5A4IRA9v59L1kpWTTyaWSm6GovJToF4SRo1b2VrUMxmd9gP6I/jyY9Jp2WQS2tcszXgaaosoOakFKy8cGNXPz5ItPXT8e7qzejgG+A09TP9xJTE+FQKeWJ8leWlH2MF0IsMG9YmiPIv57P/pf2c/DNgzi7OePTw4dtj24jekU0Y5eNrVczf2Wx5MiHR9j57E4KcwoJXRDKkQ+PsOGBDdz99d0WH3nYgFrV6rYa3v81VOuzR1E1dvVxhKG8+F3xdBjcoc4nWJayBvADbrSO2ug3R5N0MImoN6LwaOZhTISbtG+CT7CPSoZbehoT4/gWnsxp4cHfWnryUHMPs/a/DYgIIPrzaBKjEukwuGZ1xo6itvXUBkETg9i7aC+xm2K57f6avvqYX0p0Cs07N8fFs6bjr9ZXuq7aGkn1GdRcmmeBmja9i3o9il/++wvDXxpOYEQgoPpVgzpLpZPqKpROqIUQjYAWwAUpZUHJ7UstE55mj6SUnFxzkm+f/JarSVcJnhXMyFdG0tCnISe+OsH2x7fzcb+P6TO/D8NfGo5HM8uP1NpSyrEUNs3bRNKhJDoO68jY98fSMrAlzTo149snv+XH936k7wLLLdGahzpVNIOaJ8M+wD+Ax1DJ9V3mCMyBXU26SnpsOr3n36x6V6tMJuo5+Qg3fk66e7nz8PGHkVKadHarC9AGNQ/gj+YItJRbbr8FJxcnYjbG1PmkOiEyAbcmbrTqUbuRXJ+ePjTt0JSYdTF2lVQnH02mXT/HGtRp3rk5DX0acm7fOXo/ZPnXHUPJx6M1vP/pLafZ+exOuk7tysDnBhqvvwXVQnMHUB9HW00eghFCjBNC/IR6vTyLmoyNEOJjIcQfLBSfZmdST6Ty+YjPWTttLZ7ensw9MJeJKybSqHUjhBB0n9adhacW0u/Rfhz54AhLg5YS/UU0Uta9pm351/PZ8fQOPrjtAy6fuczEzydy33f30TJQjTL0f7w/ncd25tsnvjXW+FnCHlS7sZqUfpS2APVP/RiQXct9Obq43XGArqeuqQ3cuPSjNOEkTC4XE8BDqNUVD9c4usq5NXGj45COxG6MNfOe7U/C/gR8B/jWukxPCEHQxCDO7jhbreXnLSk3I5eM+Ay7X0mxPCEEHQZ1sMoiMKmorhP3ATWpOr8Uc4mv//A1rYNbM2H5hDJnYgVqtHo3atGn+sbUZconokrkLgHPlLtfHDDL7JFpdiU3M5ftT2xnWfAyko8mM/b9sTz444P4DqhYk+fWxI0xi8fw4OEH8fL3Yt196/h8+Oek/Zpmg8gtI3ZzLO91e4+o16MImR3CglMLCJ4ZXPbFRQgmrpiIZ0tP1k5ba7E3nY2AB1DbHhUNgHeBBOBftQ3KwcXvisejhQc+PRzrjdlerAF8gX4W2PcMwBPLtNcLiAggPSad9NN1t6FVzuUc0k6k4TvQPL3XgyYGUZRXxNlvz5plf7WVckz1enakSYoGfoP9yEzIJONchkWP8y7qDOefa3Df62nX+XLClzi7ODNt3TRcG7pW2GYUcBX4sXZhOiRTP6b+DVgupbwdtZplacdRfcO1OkgWS6I/j+bdwHc5tPgQtz1wG4/EPkKf+X1uOsrRplcb7o+6n3EfjCM5Opllwcv47v++M3klNXt07cI11kxdw6pxq3DxdGH2vtmM/3g8ni08K93es6Unk/47ictnLrP54c2VblMbEjUqeDsqsa6twaik5TVUzV19JKXkt+9+w3+Yv+5mUwOZwHZUb2pLPHpNURMWV6HeuM0pIEJVgdbl0eqEA2oktLb11AZ+A/3waO5hN6srOsry5JUx/E0sOVqdhUqqJwCB1bzv5bOX+XTAp2Sey2Tq2ql4dfCqdLvhqP/9HbUJ1EGZmlR3AVaXfF/+PP4VVI21Vsdc/PkiywctZ92sdTTzb8aDPz7IuGXj8GxZeQJZGeEk6P1QbxaeWkiPP/Qg8l+RLO26lJiN9rsSV2WKi4r5/p3veTfoXWI3xTL8peHMPzrfpDemjkM6Mvivgzn2xTGOfnbUrHEdAxKpfelHaa8DbqgemnWvaOfmrpy9wtXEq3Qc3tHWoTikjUA+ZRd8Mbd5qBIlcy/l28y/Gd7dvOt2Uh2ZgJOLE21DzdPVxqmBEwHjAojdFEtRQZFZ9lkbyUeT8fT2pFEb++lGYqpWPVrh1tTNoovAfIpK2qq7JPmFwxf4dMCn5FzO4b7v7qPjkI5VbtsC1UteJ9VVuwpUNR21I6pNrlZHZKdns+mPm/iw94dcPnOZCcsnMPfAXNr2rvmLcMNWDZm4YiKz987GtaErX47/ki8nfklmQqYZI7eMiz9d5JP+n7Dt0W34hvnyx+N/ZND/DcLZ1dnkfQx+YTAdh3Zky8NbuBRzyWyxbSz5OtZse1QTwRah2vRtvPGmdVLcLlVP3WlEJxtH4pjWAO1QqyBaSh+gF6oExNwf/AIiAji3/xw5V3LMvGf7kLA/gXah7XDxMF9njMCJgeReySUh0vL1wDeTEp1C6+DWVuv1bE5Ozk74hftZbKS6EHgL1UY1rBr3O731NCuGrsDF04W5UXMrLfssbyRwCLhWo0gdl6lJ9Q7gOSGEV6nrpBDCDbXI1VZzB6ZZX3FRMYc/OMy7Ae/y00c/0e/RfiyMWUjI7BCznQbvMLgD847OY+SrI/ltx28s7bKUA68dsIsRjvLyruWx7fFtfBT6EZmJmUxeNZkZ22bQ/Jbm1d6Xk7MTk1ZOwsXThbV3r6Uw1zxTODaiWpaZ+0TnI0A31Gh13Uwtqha3K47G7RrTvHP1/8713VV+L/2wZCNCw4TFaNQCFuYUGBGILJKc3W4fNcLmVJBTwIXDF8xWT21wy+230MC9gc1LQIoLi0k9nupwkxRL8xvsx6VTl7ieet3s+14DnKN6o9Q/L/+ZVRGraBHQgvsP3m+ciH8zo1BJ/J5qR+nYTH3dex71vh2D6mYkUe0Nj6JWuFxkgdg0K0o8mMjHfT9m8/zNtOrRink/z2PM4jG4e7mb/VjOLs6EPx3OwycfptOoTux8ZicfhHxg0VNe1XVq3Sne6/oe3y/+ntseuo2FpxbSfXr3Wo1+NG7bmImfTSTlWArbn9xe6xgvohKK8bXeU0UuqLq7eOBVC+zfXkkpidsVh/9wf4cc6bK1jagJUKZ0/aitPwANgQ/NvN92/drh2dKzTpaAnP/hPMUFxWarpzZwbehKp1GdiFkfY9NOT5diLlGUV+SQkxQNjHXVZh71l6i5MkHAOFO2l5K9L+5lw9wNdBrRidl7Z9OoteklNeGoeT71rQTEpKRaShmPWldiE+oDSBFqTtMhoJ+U8oKlAtQsKysli/Vz1vPpgE/JSsli8peTmbV7llW6Hnh18GL6uulM3zCd/Ov5rBiygnWz13E9zfyf0E2VmZjJlxO/ZPVdq3Fv5s7cqLmMe3+c2T5cdL6zM2FPhnH4vcOc/PpkrfZlmPZoznrq0oYC04FXgN8sdAx7k3o8ley0bN1Kr4bWoko/qnNquaaaoBLrL1GTI83FydmJzmM7c3rraYoLi824Z9szlBWYcvq+ugInBJJ5LpOU6BSz79tUhmM78kh12z5taeDewOyDTDtRo6BPcfPEr7iwmE3zNrHnr3sIvi+Yezbeg1tjt2odzw2VJO6sSbAOzOQzdFLKJCnl/VLK9lJKVyllGynlHClloiUD1CyjuLCYQ/8+xLsB73Js5THCnwlXo7HTajcaWxOBEYEsOLmAgc8N5Jf//sK7ge9y+IPDyGLrjXgUFxZz8K2DLO2ylLPfnmXkayN56MhD+IaZ/81nxMsjaNe3HRvu30BGfEaN97MRtWJdD3MFVok3UKPWj1nwGPbEUE+tk+rqu4aqA5yMZUs/SjNMWPyPmfcbEBGgaoQP2L5G2JwSIhNo1b0VHs3NvyBXYEQgCGxaApJ8NBlnV2erLfNtCc6uzrTv397sddWvoebLzLjJdvnX81l912p++ugnBv7fQCasmFCt+UOljQJ+BZJqdG/HJOrCohz+jf3l33r/rcx13e7uRujDoRRkF7DyzopzxENmhxAyO4TsS9l8NeWrCrf3+WMfuk/rTmZiJt/M/KbC7WFPhhEYEcilmEtsmrepwu2DXxhMp5GdSD6azLbHtlW4fcTLI/Ad4EtiVCLf/d93FW4fs3gMrUNa89vO39j3z30Vbh/3wThaBrYkZmMMB988WOH2u764i6a+TTm++jiH3y+7TEJuRi5FeUVcOnUJ7+7euHi4VFjOdcaWGbh4uvDjez9y4qsKK9Qze89sAKLeiCJ2U9nTpC4eLszYqv519764l7jv4src7tnCk7u/vhuAnc/tJOng7/9yBdkFZJ7L5Hrqddr1a0ezTs24dqHsVIcWAS2I+FCNz258aCPpsWV7yrYOac2YxWMA+N+9/+NqUtnGW+3D2jPyXyMB+GryV1yJv0J6TDoF1wvwaO5B8KxgRr81GoCVd6ykIKdsC8CAcQEM+PMAAFYMXVHhsTHluddhcAeWBS8DAa17lZ1UY8pzzy8ikM4xl5g1bxOdy91u7udeImqkujtqVndtnnsAd6+9G8+WnhxdcZSjK45WuN1Wzz2Ay2cu4+LhwiOnH2HbY9sqLNpj7udednrZZXb8R/gz5C9DAMs99yz1upcKfPDCYD4b2Ylbrfi6dwR1arsP5nvuHXzrIN/++VuatGtCs1uaGW+35HOvSfsmTPrPJACLPPfa9WvH4fcP02NGD7JTsy3y3Du+6jh5V/Nwb1bxzJ413nOjXo8i41xGpWUKtnzPheq97h147QCZ5zLxHfj7Aj21ee7lt/Bk3td38wrQ+wbPvetp13m/5/tcT75O887NadxWLWBe0+feddQiTT3D2vN4HXvdm7N3zhEpZZ/y21Y5oCCE+LQal0+q2o9mP4ryikg7mUZKdAr51/OZtm4aYU+EVUiobcnF04VOozpx1xd3kRGXwfFVx7l85jLFReY/DZubmUvy0WSSf0qmuKAY767etOrRqtI3BHNr1qkZoxePJv9aPhlxGdW+/3dALlW35DGn9qjFNs4AdetkeDkSrqde1630aigNaA4MsPJx26LevM3Zs7qBewPcvdwrvPk7suy0bPKv5Zu9nrq0wAmBpBxLMdtE7OpKPppMiwDH7/Dr3lS9B+Vl5pllf7FAY9SZnaoYelBnp2Xj3c3bmFDXRkPUmc74Wu/JcVQ5Ui2EiKdstyIvVN/9QiAdNWjVAFXOdkVKabP+U3369JGHD5t70dq6ozCvkEOLD7HvxX3IIkn4s+GEPx1u1pZKlpBzJYddz+/i8LLDNGrdiNFvj6bb3d1qXZ4ipeTk2pNs+9M2spKz6LuwL8P/ORy3JtWrGTOHTfM3ceSDI8zYNoNbR99q8v3mAf9FLXFqjai/Q7VI+jvwVysczxbO/3iej/t+zOQvJ9N9ml7PqjqyAG/gAeAdKx/7GiqxngIsN+N+f3j3B7Y+spWFMQvrRKL2/ZLv2fanbTyW8BhNfZta5Bjpp9N5N+BdRi8eTf8/WbKpYkVZyVm82eZNmxzb3PKv5/Oq16sMeGoAI14eUat9xQGdgcdRaxBU5sLhC/x37H8pLizmno33mLXmfgaqrvoi1isLswYhRPVGqqWUHaWU/lJKf2Am6nVzOuAhpWyDmth5D+o17V7LhK3V1pltZ1jWcxnfPfsdt4y6hYdPPszQvw21+4QawKOZB2PfG8sDhx6gcZvGfD39a1aOWVmrJYQz4jNYNW4Va+9eS6PWjXjg+we4Y8kdNkmoAUa/PZpW3VvxzcxvuHbRtI6eEjVjeDTWSagBRqA6OvwL9SJdFxlOmXYc2tG2gTigTagzJ9bo+lFeY9Qb92ogw4z7DRinVld0tIWqqpIQmUBTv6YWS6gBWnRugXc3b2LWW/8xc+SVFMtzbehKm9vamKWu+m1UovenKm6vSQ/q6hiFKg37xax7tV+mfnB4C/iXlPIrKWURgJSySEq5GtVxa7GF4tNq6ErcFb6c+CUr71iJlJIZW2cw7ZtpNPNvdvM725l2fdvxwA8PcMc7d5B0KIn3e7zPnkV7qnWKsaigiAOvHWBp16XE741n9NujefCHB2kX2s6Ckd+ci4cLU1ZPIT8rn2/u/cakMpefgAtYrutHVd5EvWA8buXjWkvcrjhadW9FIx/HW4nN1tageq6G2+j4D6H6qX9hxn16dfSiVfdWdaK1npSShP0J+A3ys/ixAicEcm7fOXIuW7fDvaEG3ZE7f5TmN9iP8z+cr1UpTTrwCapLTvtKbq9pD+rqGFXytb601jM1qe6BKqmszGnUHCbNDhTkFLBn0R7e6/oev+38jRGvjOCPv/yRW8eYXlpgj5ycnei7sC8LTi2gy6Qu7P37Xt7r/h5ntlf1tPxd4sFEPuz9ITuf2cmto29lwa8L6P9Yf5wa2MfJKO+u3tz57p3E7Yoj8l+RN91+A+of906LR1aWL/AXYD11b7WnwrxCEiIT8B+hu35UVxZq9c3JQM16BNTebaiJiuZeYTEgIoCEyASHX13xym9XyErOwm+g5ZPqoIlByCJJ7GbrfhhJiU6hqV9TPJqZv7OJLXQY3IGi/CLO/3C+xvt4D9Ud58/lrq9tD+rqaAd0of601jM1q0gG7q7itumA7RpTaoD6JzEsWLL373sJuiuIhTELGfjMQBq4NbB1eGbTuE1jJv93MjN3zMTJ2YmVY1ay5u41XD1fcZpSzpUcNs3fxKcDPiU3I5dp66Yx7ZtpFj39WVMhc0Lo8Yce7PnbHs7tv3F/0o2oPsDeVomsrCeAAOBR1CIfdcX5789TmFOoW+nVwBZsV/pR2jzgBFCxL0PNBUQEIIskZ7be/MO7PTOUEVhjpLpt77Y0btuYmHXWLQFJPprs0Iu+lOcXrv5WNe1XnQMsAcZSdtTTHD2oq2sUsA/1OlHXmZpULwbmCiE2CyFmCyHuKPm6BZiFKg/RbORSzCVW3rGS1XetxrWRK7N2z2LyfyfTpF0TW4dmMZ1GdmL+sfkMe3EYsRtjWRq0lEOLD1FcWIyUkl9W/cLSoKX89NFP9H+iPwtOLiBoQpCtw66SEIKxy8bSrFMz/veH/1XZdSAJ+Bnrl34YuKImop1B9bCuK+J2xSGcBB0GW64zQl21BvABBto4jumo+uoPzLjPdn3b4ent+KsrJkQm4N7MHe8ulv8oLpwEgRMCObPtTIXWaJZSkFNAekx6nSn9APBo7kGrHq1qXFe9AjWRvfSS5ObsQV0do1BJfpTFj2R7pq6o+G/gQVQZyKeoxdw+BboBD0oprT3hWysRvzee93u8T9LBJMb8ewzzfp5XbyZaNXBrwOAXBvPwiYfxG+TH9se382GfD/nP6P/wvz/8j6YdmvLg4QcZ/eZoXBu52jrcm3Jr7MaU1VO4nnqd9XPWV7rcr6E7q62SaoDbUaf6XwLsZ2H52on7Lo42vduYbeXM+uI66s3AlqUfBo1QM+a/Aq6YaZ9Ozk4EjA3gzLYzFBUUmWmv1pewPwG/gX4IJ+ss7BU0MYiC7IIK/ZItJfV4KrJY1qmkGtSZhcSoxGqv7FmEmgPTDxhUct31tOt8PuJzTm85zZ3v3cmIl0ZYbaG3IahWcfWhrro6Kyp+AnQoufQv+dpRSvmphWLTbqIov4jNf9xMU9+mLIxdSL9H+9lNnbA1NevUjD9s/gN3f303Oek5JB1K4o537+D+g/fTplcbW4dXLW1ua8Oo10cRuzGW75d8X+H2DcAtqBo1W3oLEKhyEEeXfz2fpENJuvSjBragRqBsXfph8BDqFPPnZtxnQEQAuRm5JB5wzMWDr6deJz023Sr11AYdh3bErYmb1VZXNCxPXpfKP0DVVedn5VdYCOhmvgHOopYkF/zegzolOoW7v76b0D+GWiDaqjVGlSzqpLocqSRKKX8o+er4yzE6sEP/PsSlXy8xZsmYet+xQAhBl0ldeOT0Izye+Dh9F/Q1rkTlaPo+0pfA8YHseGoHF45cMF5/HdiFGqW27kLyFfkBzwP/A761cSy1lRCZQHFhsZ6kWANrgFb8PhpmayFAX+BDzDdh8Zbbb8HZ1dlhW+slRFqvntrA2dWZznd2JnZjrEUW7iovOToZ10auDtnd6kYMC/VUp65aopYkvxWYiOpB/emAT8m5nMN9391H0ETblEGORHWuqnlDXMdgctYhhGgihJguhHhaCPHXcpe/WDJIraKrSVfZ+/e9BI4PJGBsgK3DsRsN3BsYV6NyVEIIxn86nkY+jVg7bS15V9WUwB2oyYG2LP0o7UnUogKP4NiTFuN2xeHk4mScGKSZJhtV+jEJ25d+lDYPOAkcMNP+XBu50nFYR4etq06ITKCBewPa9m5r1eMGTgzkeup1kg4l3XzjWko5moJPsI/VyluspXHbxjS7pVm16qr3Aj+iOn78ZuEe1NUxCpXwV1wgvm4xKakWQoSjVpr8L/AKsKiSi2ZF3z75LbJIMnrxaFuHolmAZwtPJq+aTEZ8Bpvmb0JKyUbUkqb2MirohppdHotaYMBRxe+KxzfMFxdP+18QyZ5sQSXW9lL6YTANaIJ5JywGRARw+fRlLsVcMuNerSNhfwLt+rWzyoS00jrf0RknFyeLl4DIYklydHKdq6c26DCoA+f2n0MWm3bu5TXU2aNeVuhBXR2hqPevul4CUp3uH/Gox8VdSulU7mJPAxV13m87f+PEVycY+H8D69zpLu13fgP9GPr3oRxfdZyfPv2ZTcAdgD2lfmNQpxhfBByx4jTnSg4Xjlyg4/COtg7F4axBtXUcbOtAymmImrC4BvOdajasruhoo9X5Wflc/PmiVeupDdyauOE/3J+YdTGVTro2l4z4DPKv5de5emoDv8F+5KTncOnUzT/Q/QJslZIFL+5lqxV6UFdHA2AYKqmuy3XDpibVXYAXpJRHpJT5lgxIu7Gi/CK2LNxCs1uaEf6UrdYv06xl4LMD8R/hz5ZHtiJPptlN6UdpbwPFqHIQR3Nu7zmQ6EmK1VS69MMeu+DPQ5UkmWuFRa8OXrTq4XirKyYdSkIWSavWU5cWOCGQy2cuc+lXy43w16XlyStTnbrqNwqLuWveJqQVe1BXxyhUx6iztg7EgkxNqhNQZ3s1Gzv49kHSY9K54507aOBuj29nmjk5OTsx6T+TKGrsxtRpaxllpb6v1dER+D/UyKCjrZoVtysOF08X2verbBFfrSrbUBNn7a30w6AnqkWVOVdYDIgIIOFAgtWX366Nc/vPIZwEvmG2qaUNHB8IYNESkOSjyQgnQavurSx2DFtqdkszGrVpdNO66t+u51N412qCrdyDujrqw5LlpibVfweeFULU3dVEHEBmYib7XtxH4IRAOt/R2dbhaFbSqHUjDnw+kVbHUzn02DZbh1Opp1Ct/h4BHOlUVtyuOPwG+dndm4+9WwO0RPWftVfzgFPAfjPtLzAiEFkkOb31tJn2aHmJkYn4BPvg1sQ2Y2JN2jWhXd92xKy3XOeUlOgUWgS0qLNzIoQQqq5637kqy2iup11n+YjPuXXLafpbuQd1ddyK6sWsk2oYh1o0K04IsUkI8Xm5y2cWjFErYZicOGbxGFuHollRPLBz9K24PhPOTx/+xImvTtg6pArcgX+jkph/2zgWU2WlZJF2Ik2XflRTDrAR+y39MLgbNTHKXBMW2/VtR8NWDR2mBKSooIikQ0k2qacuLXBCIOd/OM+1C9cssv+U6JQ6O0nRwG+wH1eTrpJ5LrPCbZfPXuajAZ8io1NI/vpuRlu5B3V1CNRo9S6g0MaxWIqpSfVA1Fm0q6hVFAdVctEs6OyOs5xcc5JBzw/Cq6OXrcPRrGhjyddJLw6jfVh7Nj64kSu/mWvNOPMZi2r393fgvI1jMUXcLrXam06qq8dQ+jHF1oHchCcwE1iLWq65toSToPPYzg6zumLyz8kUZBfYrJ7awNAXOWaD+UerczNyyYjPqLOTFA2qqqs29KDOvJzD59/dx8M26kFdHSOBTOCwrQOxEFOXKfe/yaWTpQOtzwrzCtm6cCvNb23OgD8PsHU4mpVtBAKBQBdnJq+ajHASrJ22lqJ8+3tjX4wagfizjeMwRdyuONy93Gndq26/IZvbGqAFaia/vZuHKkcy1wqLAREB5GXmGRdUsWfn9qsEzNYj1S27tKR55+YWqatOOaZWUqzrI9WturfC3cvd+DcFOF3Sg7qBpwuroubSZYAvwTaM0VQjUCPWdbUExDGXnKtnDr19iPRYPTmxProK7OH3BV+8Ongx/pPxXDh8gZ3P2d+0wE7As8CXwG4bx3Iz8bvi6Ti0o8OuvGkLhtKPu7Dv0g+D7sAAzLfC4i2j1OqKjlACkhiZSLNbmtG4TWObxiGEIHBCIHG74sjNzDXrvg3Ld9f1kWrhJPAb6EfCPvVh7udSPag9Dt5PTGBLnrZxjKZqCfTC8Sa1m6rKdxMhhFN1LtYMuj7JTFCTE4PuCuLWMbfaOhzNyrYDBcD4Utd1mdSF0AWhHHrrELGb7e/N/RnAH1iIit0eZcRncOW3K7o/dTVtB7Kw364flZkHxKBWmqst10au+A/3J3ZjrEV7L9eWlJKEyARj2YCtBU0MorigmDPbzph1v8nRyXh6e9pFH2ZL8xvsR3psOtse38aGkh7U9+2dzdutG3EbMNzWAVbDKOAg6rWkrrlRMlyIek805eJIE/4dyvYntiOlZPTbeuXE+mgj0BwIK3f97W/cjk+wD+tmrePq+as2iKxqHqgykJPAO7YNpUpxu3U9dU2sRT0fHaH0w2Aq4IX5JiwGRARw+cxl0mPMtbSM+aXHpJN9KRvfgbZblrq09v3b4+ntScw689ZVp0Sn0Dq4tV12ujA3wwek7xd/b+xB/W1jN2JQ3Zcc6REYhUoczfFB197c6AzeP7DAwjdCiDGoBgHOwMdSylfK3e4HfIZ6HXQGnpVSbjF3HI7g7Ldn+fXrXxn2z2F4dfCydTialRWhloK+k4r/qA3cGzBl9RQ+7P0h/5vxP+777j67KmOIQMW9CLgHaGPTaCqK+y6Ohq0a4t3V29ahOIxcYAOqq4YjNS/zAO4DlgFpqFUgayNgXABbFmwhZmMMLYNsu/RzVQy1t/YyUu3k7ETg+EBOrjlJUX6RWVpYFhcWk3o8lb6P9DVDhPavTe82+A30o+PwjgxdNBQhBK+h1gmw90nD5YWjOkbtQE1wr0uqTKqllIvMfTAhhDOwFPVBJQn4UQixQUp5stRmLwBfSSnfF0J0ReUVHc0dizlcBSzVuLswr5Ctj+jJifXZQdQyy1WtotgysCVj3xvLulnr2PfiPoYuGmq94G5CoD45d0ONovzHtuGUIaUkblcc/sP968UIl7l8C1zDsUo/DOYBS1CjNbWdRNvUryk+wT7Eboy121VtEyMTadiqIc07N7d1KEZBE4P4+ZOfid8Tzy2331Lr/V2KuURRXlGdXUmxPGcXZ+bsn2P8+QAQhTob6AjzG0pzBwZTNycrWntoqy9wRkr5W8ly518CE8ptI/k9V20KXLBifCY7i2pkvgTLrGN/8K2Dv09OdHO0fxnNHDagRgRvVPgTfF8wwfcFs+/FfcTvibdOYCa6FXgaWAnss3EspaXHpJN1MQv/Ebr0ozrWAM1wrNpNg66ovrDmmrAYEBFA4oFEstOzzbA38zu3/xx+A/3s6kOj/wh/XDxdzNYFpL5MUqzKa6guPHNutqGdGokqEXSE9qvVYe2kuh2QWOrnpJLrSlsE3CuESEKNUj9indCqxwd1CuNP/N62yVwMkxO7TOqiJyfWYxtRK9Y1vcl2dy69k+a3NufrP3zN9bTrVojMdM+hVtBaiP00+9f9qasvD/UhbyKOVfpR2kPAaczTlSZgXACyWHJmq3kn3pnD1fNXyYjLsJt6agMXDxduHXMrMRtikMW1/2iTEp2Cs6szLQJbmCE6x/Ir6v9xIdDQxrHUlGHJ8rrWBcR+ijB/dw+wQkrZHlWW+UVl3UWEEA8JIQ4LIQ6npaVZPchGwNfA/wEfAbejTtWbw/bHtwPoyYn12GnU6oRVlX6U5trIlSmrp5BzOYd1s9aZ5Q3LXDyBt4BfUJPc7EHcrjiadmiKl7+XrUNxGN+iyt0csfTDYApqpP1DM+yrXWg7GvrY5+qKhh7a9lJPXVrghECunb/GhSO1PwGdfDSZVt1b4exS+/psR/MmqoRiga0DqYWeqPkNOqmunfNA6Y/P7ak4+n8/8BWAlPIg6rlTYTaIlPJDKWUfKWUfb2/bTDZyAl5C1YseQtW21HYB6TPbzvDr/35l8F8G09TvZmOUWl1lWEXRlKQa1CnQ29+8nTNbz3Dw7YOWCqtGJgJtUbVetiaLJfG743U9dTWtQc0cH2HjOGrDA5gF/A9IreW+yqyuaGeLMCVEJuDS0MUuyyI6j+2McBZmKQGpD8uTV+YC8AUwl9pPurUlJ1QJyE4sU0JrK9ZOqn8EOgsh/IUQrsB01FmM0hIoee0WQnRBJdXWH4quhhmo1jDZqNZnm2u4H+PkxM7NCXuifBM1rT7ZiFq4ojoFCqEPhxJ0VxDfPfsd53+wn0o1J1THiK2o5WltKTk6mZzLObr0oxpKl3642jaUWnsI1cprhRn2FRgRSN7VvDKr3NmDhP0J+Ib54tTA/k5Ee7bwpMPgDsSsr11rvazkLK6nXq+XSfUSVCndE7YOxAxGAcnAcVsHYkYm/dcJIcwyZCqlLESVAW1HlQV9JaU8IYT4hxDCsL7Fk8CDQohoYBUwW9pzl/0S/YAfUJOzIlCnZ6obdNQbUVw+c1lPTqznrgD7MX2U2kAIwfhPxtO4XWPWTl9r9tXLamMaat7BehvHoeupq28n6sOQI5d+GHQBBqFKQIprua9Oozrh7OZM7Cb7KQHJzcwl5ViK3dVTlxY4IZC0E2mkn655wWR9naR4FXgfVcpU+/4ptmeoq65LXUBM/Sh7QQjxiRAitLYHlFJukVIGSClvkVK+VHLdX6WUG0q+PymlDJdSBkspQ6SU39b2mNbii0qGJqPaNt2PGuUxRca5DPa/tJ8uk7tw62g9ObE+24bqUV3dpBrAo5kHk1dNJjMhk40PbrSbVd/6oSYs2roEJH5XPC2DWtK4rW2XbnYkhtKPkTaOw1zmobo37arlflwb2t/qiolRiSDts57aIGhCEECtRquTo1VS7dOzfo1Uf4RKrJ+ydSBm0h4Ion4m1a+jPlQcEkL8XDJJsO6vC1oDDYHVwF+B5ag3IlPq97Y/vh0hBKPf0pMT67sNqFq5mi5p4Bvmy/CXhnNyzUmOfHjEjJHVnECVgOzAfBN6q6uooIhz+87ppcmrwXB2YQKOX/phMBnViswcExYDIgK4cvYKl05dMsPeai8hMgGnBk6061e+qZb98OroReuQ1rVKqlOOptC0Q1M8mnmYMTL7lg+8jVrNtI+NYzGnUajyWVMHIO2dSUl1yUIwHYG7UHXy7wHnhRDvCyFCLBWco3IC/o4alTuMSo5+ucH2p7ee5tQ3p/TkRI0CVO3xONRyojUV/lQ4t9x+C9sf2873S74nITKBvKu2fdmajqoF/MbKxy3MLeTCkQtEvRFFfla+Lv2ohp1ABnWj9MPAHTVh8RsgpZb7ChgXAGA3XUAS9ifQ5rY2uDa0749AgRMCSTiQwPXUmrUATY5OrjeLvhh8herqUNvFi+zNSCAHtZBNXWBy4a6Ushg1iLZBCNEBeAA1AfUhIcRhVKnPKillXfnAUWvTUHVPE4ABqEUwxpfbpjBXTU5sEdhCT07UiETVr9ak9KM04SSY+PlEVgxZwbY/bTNe7+XvRevg1vgE++AT7EPr4NZ4+XtZpRNGL9Scg9WoFw9zk1KSdTGL5OhkUqJT1OVYCpdiLiGL1On5hj4NdVJdDWtQK3HVldIPg4dQrR6XA8/WYj9NfZvSOqS1Wl3xaduurliYV8j5H84TuqDWVZoWFzQxiL1/30vsplh6ze1VrfsW5BSQHpNO16ldLRSd/ZGo52sXYIyNYzG3oagBpJ2oUXhHV9PZcFeBy0AW6sxuU+AT4EUhxD1Sykgzxefw+qBankwsufwLtcqcIYWJeiOKK2evcO+39+LsWv/6bWplbUSdZh91sw1N0MinEQt+XcDVpKukRKeUSTZPrT9lnEnr2tgVn56/J9k+wT606t7K7KNdAvVB81+okqhWtdhXUX4RaSfT1O90LMX4e2Vf+n2Fu6YdmtI6uDVdJncx/n7Nb2mOcNKt9EyRD6xDDQq42TYUswtELaz0Eer1uDZ9MgIiAtj/0n6y07PxbOFplvhq4sLhCxTlFdl1PbWBT7APTTs05dS6U9VOqlOPpyKLZb0aqd4D/Ix6vtpfT5faaQL0R5UGvmTjWMyhWkm1ECIcNc9jCupM7kpgipTyFyFEIKpM7QOgm7kDdWRtUTVDc1GjIidQD1RuvJqc2HVKV24ZVRfm8mq1IVGngoajFhcyByEETX2b0tS3qfFUNUD+9XxSj6eWSbaPfXGMw+8dLrkjtOjcosyItk+wD03aN6nVqPY01AvnWuBhE++TlZJVJnFOjk7m0q+XKC5U/RsauDegVY9WBE4I/D3Wnj64e7nXOE4NvqPulX6UNg/4A+r3rM2H2ICIAPa9uI/TW04TPDPYPMHVQMJ+teiLb7j9dv4wEEIQOCGQnz78ifzr+dX6AJ8SrYp26lM7vbdQ82zutXUgFjIKVTJ7GWhu41hqy6SkWgjxCOqMWVdUK7yngM+llNcM20gpY4QQf0O9RmnleAD/RX3a+Atqxbz5j21DOAluf+t2m8am2YdTqK4E1ug/6trQlfb92tO+X3vjdbJYkhGfUWZE++KRi5xcc9K4jUdzD+OoryGB9e7qTQN30z6fd0edwlxNxaS6qKCI9Jj0MsdPjk7mesrvdZeN2zWmdXBrAsYFGI/fvHNznJzr2viN7a1FjSLV1VenSahVxT6gdkl1295tadS6EbEbY22bVEcm0DKoJQ29HWPh6qCJQfyw5AfOfnuWLnd1Mfl+yUeTcW3sSjP/ZhaMzn6cAjYBi1DzAeqiUajfbxdqxNaRmTpS/QbqTOBCKeXeG2x3GvhHbYOqqwTwAiqpWLTlNPHrY+jyygia+urJiVr1V1E0N+EkaNapGc06NSvzJpd3NY+UX1LKjGr/9NFPFGQXqPs5C1oGtaxQq92odcXxdoGasPhqejY/RqdQWCqBTjuZZlydztnVGe9u3nS+o7Nxnz49fWx6er0+KUC94I+n7pV+GLgBs4HFqAUoalpMYFhd8cRXJyjKL7JJGZ8sliQeSKTLFNOTU1vrMKgD7s3ciVkXU62kOiU6BZ+ePvWmjGsx6rn6RxvHYUl9UR/gd1B/kmo/KeVNJ0pLKc+jRvG1G5iQW8hvj27lQmAL7n88jAaoUROtftsIhKD6ndsTtyZu+IX74RfuZ7yuuKiYK2evlBlVPrf/HL/89/c+Nw1bNTQmxC06t+BK3BVSj6XiHp3M0+evsaVku0atG+ET7EOn2zvh01Ml5C0CW+DsoucY2Mou1KnYulr6YfAgasToU+D/arGfgIgAfv7kZ87tO0enkZ3ME1w1pJ5IJTcj1yHqqQ2cGjgRMC6A2E2xFBcWm7QCpCyWJEcnE3yf7c4IWNMl4DNgJrWbg2LvGqAmKdaFftWmJtUHhRB3SSmjy98ghOgObJBSWv+VxEEdeP0A2WevMGPHTA65OjMZeBF4nt8nMGr1yyVUS6HnbR2IiZycnWgR0IIWAS3oNvX3KRQ5l3NIOVZ2UuQP7/xAUV4RTg2caNmlJZ2H+bMy2Ie8YB++DG5Nw1aOcbq6PlkDNKbuln4YBKDezD9CzXepaRFRp5FqdcWYjTE2SaoN9dR+A/1usqV9CZoYxLEvjpEQmUDHoR1vun1GfAb51/LrTT31+0Au8LitA7GCUaie+Gdx7NUiTU2qO1L1WUB31GJpmgmuxF0h8uVIut3djT4jO7EbNVryF9QExk9R9dda/bIFtWyyrUo/zMWjuQcdh3Ys8wZZVFDE1aSrNGnXxHhq/BRqZPASasEkzX4UoHo4j6fu1nCWNg9VkrQDqOnSW64NXek0ohOxG2MZs3iMVVpUlpYQmUDjto3x8vey6nFr65bbb8HZzZlT606ZlFQblyevB50/coF3gTtQk9nqOkPbzh04dlJdnQ/mVa3D2gc1SVwzwfbHtiOcBbe/qcaA3IHPUW3GVqPaPF2wXXiajWwE2gC9bR2IBTi7ONPMv1mZWtNpJV+/sk1I2g3sRpV+OHpto6nuQnVW+KCW+wmICCAjLoNLv1p3dUUpJQn7E/Ab5Gf1ZL62XBu5csuoW4hZH2PSUu/J0ckIJ0Gr7nW5GEJZhWo9ao2J6/YgAFX6uNPWgdRSlUm1EOJxIUSCECIBlVBvNPxc6pIGLAW2VbUf7Xexm2KJ2RDDkL8OoUn7JsbrBerU4zfASSAUtRKjVj/kAdtRqyjWlx4WnVDP89W2DkSrYA2qpWNNR20djStqwuIG4BCqV2xNGFpWxmys+fLbNZGZkMnVpKsOV/phEDghkIz4DFKO3Xx9y5ToFFoEtMDF08UKkdmOYbGXnsAIG8diLQJVArILKLJxLLVxo/fw31Dt8b5D/b6HS/1suHyNKvd50LJhOr6CnAK2PrqVlkEt6f9Y/0q3mYCqq3UBBqNH8eqLvcA1HL/0o7qmAUeAM7YORDMylH5EUL/K0OahkuswwAt1xvBpVFvBRKo+TVtak/ZNaN2rtdWXLDfWUw9yzKQ6ICIABJxad+qm2yYfTaZ1SN0v/dgBHEeNUjvWuYfaGQVcQb0vOKoqa6qllOtRdeOGU0r/kFLGWSmuOufAawfIiMtg5s6ZN2y51BP4AdUNZBpq5Pqv1J8RzPpoI6oMqL6MSBjcDfwZNVrtKBM067q9QDp1v+tHebcAMcA+1Ovv98C/UatKgirN6gv0K7n0QbUAKy8gIoD9/9xP9qVsPFtap/1jQmQCbk3cHLYkopFPI3wH+BKzPoahfxta5Xa5Gblknsukz/w+1gvORt5EPefusXUgVmZ4D9yB+n9zRCblalLKOTqhrrkrv10h8l+RdJvWjU4jbj4zvBXqNMBsVH/CaUD2je6gOSyJSqpHAfWtA7MvMABdAmJPDKUfY2wdiA34AjNQyfQh4CoqwX4H9WZ/EjW5dgRqNLsbapXcD1BLSBcCgRGByGLJ6S2nrRZ3wv4EfMN9HXoBpMAJgST/nEzGuYwqt0mOLpmkWMdHqo8D3wILUWdP6hNvoBeO3VqvypFqIcRfgY+llBdKvr8RKaV80byh1R3bHtuGUwMn4+REU7ihOoF0Ry1f+RvqtEH7G91JczjHgXPU35HaacCfUMu0Os6yFXVTIfA/VG1/fSr9qIobqu4/FJXggJrA+SNqJPsH1Afi5SW3eQC9b2vD8DaN+G5jLF73BeOHZU/fZ6dnk3YyjR4zeljwKJYXNDGInU/vJGZ9DP0e7VfpNvVlefK3Uc+lebYOxEZGoR6DLNQHfEdzo5Z6i1ATEC+UfH8jEtVqWSsnZmMMsRtjGfX6KJq0q+yEYdUE8CQq2ZiOenFfhzr9qNUNG0q+jrNpFLYzFXgMNVq9yKaRaHtRLQ7rW+lHdTRHTeA0TOKUQBy/l4x87ySIHhdA1y+Pc0t+ES1dnY1lI31Rr+FeZown8UAi4Lj11AYtOrfAu6v3DZPq5KPJeHp7VrpSa12RDPwHeABoYeNYbGUk8BqqFOtOG8dSE1WeL5JSOkkpfyj1/Y0ueumzShTkFLDtT9to2aUl/f5U81T4TuAg6tPrEOC/ZopPs72NqPrMNrYOxEbaoJ7TqzFtMphmOWtQPcPvsHUgDkSgOtlMR42uRQEvjQvA7Vo+b+yNZzRwGngBtZBOM9QgySzgPdSErPzKdmyihMgEnF2daRfarhZ7sQ+BEwKJ3xtPzuWcSm9PiU6hdUhrh2sbWB3voSYLP2bjOGxpIOoskaO21nPcIiwHcOBVNTnxznfvrPWSy91QoyH9UHV/z6MWC9EcVwrqbzre1oHY2DTUYjDHbB1IPWYo/RiLLv2orYCRnWjg3oDAjbF8hiptuoKqk/0n0Bl1CngBv094HIBqo7UKSKjGsRL2J9A2tC0N3E1dx81+BU0MQhZVXo9eVFBE6onUOl36kYNKqsejniP1lQcwCMetq9ZJtYVcPnuZyFci6T69O/7D/c2yz5aoJ9oDwMvAZFTdkeaYNqNGZ+tbK73yJgPO6AmLtrQPSEOXfpiDi6cL/iP8id0Ya1zQxAtVK/o8quQrGYhHPecXoN6IlwF/QC1ffNyE4xRkF3Dh8AWH7U9dXts+bWnUplGlrfXSY9Ipyiuq05MUP0d13qkvi73cyCjU/8BFWwdSAzda/KVYCFFk6sWaQds7KSXbHt2Gs4szo94YZdZ9uwIfomaobwDCURPdNMezAdVxINjWgdiYN6qjgi4BsZ01qO4zjljDaI8CIgLIiM8g7URapbcLoAOqreSbQCSq28h+1P/AFhOOcf6H8xQXFjt8PbWBcBIETgjkzLYzFOaWXYLH2Pmjji5PXowqH+qNGqWt7wxZkyOWgNxopPofpS4vAudRk58/Q9WRf17y8/mSbbQSsRtjOb3lNEMWDan25ERTCOBR1AvvOdQEmINmP4pmSbmosw7jqF/N/asyDdXhxpGb/juqIn4v/ahvbR0txbC6Yuwm0xeCcUHVk3bDtGTi3P5zIMB3gG9NQrRLQRODKLhewG/f/Vbm+uSjyTi7OdMisG5O39uC6pP+JPr9ANRAkzeOWQJyo4mKi6SUf5dS/h1VcncO6CClnCulfE5KOQd1piqBmq/sWucYJid6d/WuchazuYxG9VNtDAxDnz53JLtQvcfrez21wV2opEI/h61vP5CKLv0wpybtmtDmtjY1Wl1xJOpvknuT7RIjE2nVvRUezepOFXzHoR1xbexaoQQkJTqFVt1a1Xpukr16C9Uud4qtA7ETTqizlztxvLOXptZUzwNel1KWWYNESnkdeAOYb+7AHFXkK5FkxGdw59LaT040RRAqse6DmoH+Mo73JKyPNqI6LQy1cRz2ohmqO8Jq9ARca1uDmhykSz/MKyAigMSDiVxPu16t+41EJdRRN9imuLCYxKjEOlNPbdDArQGd7+xM7IZYiovUK4GUkuSjyXV2kuLPwG7U2WcXG8diT0ahaqpP2DqQajI1qW5J1Yv7uFJ/WyqWcfnMZQ68eoDu93Sn49COVjtuS9QKjIauIHOpXZsmzbIMqyjejlqeXFOmAYmoD4madRQBX6NKPxraOJa6JiAiACTVXl1xCGri7o1KQFKOpZCflV9n6qlLC5oYxPXU65z//jwAWclZZKdl19lJim+hFjl50NaB2JmRJV8dra7a1KT6MPB3IUTb0lcKIdqh1mz40cxxORwpJVsf3YqzizO3v2H6yonm4gZ8gfpjrEAlbJetHoVmip9RExHqe9eP8iagnse6BMR6IlGtHXXph/m1ua0Njds2rnYJSGOgPzdOJs7tV9PT69pINcCtd9yKk4uTsQSkLq+keB74Ergf8y4KVBf4AQE4Xl21qUn1o0Bb4DchxB4hxGohxB7gLNAatdJwvRazIYYzW88w9O9Dady2sU1iEMDfUCsyHQTCgDM2iUS7kY2ov9VYWwdiZ5qgShDWoEZQNcvTpR+WI4Sg87jOnN1+lsK86k07GokaybpSxe2JkYk07dCUpr5Naxum3XFv6o7/MH9OrTtlLP2Autn54x1UuVu9T6CqMAq10qsjnXkXhj6aN91QiBaoFor9UQuhXUTlbm9LKdMtFqEJGvfpI3sfPlzmuruBh1GTwSp7w5hdcrlE5ZMD/sjvp6NnVnL7k6iRxhjgoaJizv94ASdnQZvebRFOghdQL4xHqXx1pJdRDf+jgP+r5PbFQAhqtOKfldz+ARCIStDerOT2R1CF8LmoRvLlX3rXospGVpRcytuC6gTwHvBVJbfvKfn6BrCp3G0ewNaS719ElaaU1gJ1yhngOSp2LmmP+mAA6rE7Wu72AFRbQYCHgPLjQCGoxw/gXiCp3O1hwL9Kvp+M6g1a2gjgLyXf34Fqyl/aOODPJd8PpaKbPffiUZ9QN1D75968Sm639XPvC1SrwNXA+5XcfqPnXipqsYzdwEn0c8/cz73Z/P66Nxn1+zdFdZyA+v3cA/O/7uWkZ5PySyo+PX1o39zD5Ofe3agPPN1K4oXfn3tSSsL+c4ysPm1p2cXbeP8QHOe5d7PXvYnnr5J++jLtQtuScS6TvKt5vN2/fZ167hWhngPNUH9n/Z6rlH7uXULVVM/n98d0KBVZM98zPPf2CnFEStmn/LYmL8NUkjg/b+r29UlmQiaFuYVqCVUn+2iI0xf4HtXzMhr1YlD3Tp45njxUG53K3hQ09eLvgXpj6mHjWOq6q6glkb1vtqFWY+5e7ggnQU56NjQ3vUtHa9Rp5Cv8nlQbXDl7hYKcQtya1t0ZGZ4tPUk/fZnsS9nkZ+Xj2qiqKV2OKxmVWNedhojm51XytXqzEmzL5JFqe9anTx95uNxItbWkn07n/e7v03VqVyb9Z5JNYriRK8Ak1Kfcv6Jqru0j7a+fPkB96v4F6G7jWOzVNNRI9QWq8alfq7Z5qMUG0lATpTTLWBWxipRfUvhT3J8QwvRX33GoEcHyo4I/L/+ZDXM38PCJh/HuWnc/En3U9yOK8opIPZ7KoBcGMezvw2wdktkUoUZ/fbhxlxdNLXBXiBoktCeiipHqG62ouEsIEVTq+xtdyp9tqBeMKye6OTPqdfOunGguzYDtqFMf/0B1CLlZ/1PNcjYC/vx+ul2raDoq0dtt60DqKIn6cP0h6lStTqgtKyAigMxzmaQeT63W/UaiRujKr5ibEJmAR3MPWgaVH8OuWwInBJJyLAVZLOtcPfUG1GJXeknymxvFjecX2JsbTVQs/ZHaqeTnqi6mTnisU2LWx3BmW8nkxDa2mZxoClfgU1RN2SrUi3Xli+dqlnQdVesWgT5bcCN3oDog6C4g5leIat31d1TrzfdsG069YFxdsZpdQAwtxcqPWCXsT8BvoJ/dlBpaStDEIOP3da2d3luowZW7bB2IAxiFmszpKIMsN1pRcZiU8lTJ90NLfq7yYr2Q7UNBtlo5sVX3VvRd2NfW4dyUQE1Q+Aq1FHR/4NQN76GZ207+v717j5eqLvc4/nlggyIXEQHlKihskvKOKAmKOiioqJ1K1CwtzSw161jHS6dSq3PUUtP05K3TyS6amRioiXcDlRQVUVEQFTegCF4QMZHb7/zxrJFhsy8ze2bWmjXzfb9e+7U3M7PXepg9s+ZZv/X8np9fJVArvZZtibfX+yvpmvVd6T4EjgZ+g08KuhEtNhGHrn270mevPgUtWQ5+NWs7Nm2tt+qtVbz78rsMGF39lbi9hveix5AedOzake6DuicdTsk8gbeyPAvvRy4tG4kPsqSltV6rI8xm1tHMJpvZ/nEElBYfvPEBW2y9RWwrJ5bKF/EzvlX4jNwHkw2npkzF28bpjdS6ScAK0nMgrXTLgYPwDgHX4qVg1T3OWVnqJ9azeOZiPlyW/+qKho9W38/GVUYbZjQAsMOYHUodYsUxM8ZeOJYx54+pqlH5y/GOO19LOpCU6IB3/EjLZ0GrSXUIYQ3+3q7JEo/m9BjSg9Nmn8YO+6fv4LYvXvTfFzgULw2R8tqAt0EaT/NLk8pGh+Azv1UCUrxX8ck+c4DbUeeZJAybOKxNqytmS/Wej/7dMKOBuk519NmzT6lDrEi7HL8Lo88dnXQYJfM63lrvVHz0VfIzDl8U5bWkA8lDvonyo3guJjnSfPY8CJ91fCC+mtN5bBwNkdKbha9cp9KP/HTEu9bcgSbWFuMp/IrUO3ht7lHJhlOztt9je7r2K3x1xYOj79kSkIbpDfTfpz/tO6bn6qhsdFX0/cxEo0ifbBuINIxW55tUnw2cbGZnmFl/M2tvZu1yv8oZpJTH1sBd+MjVxfgl98YN/6U0puBvtglJB5Iik4APgHuSDiSl7sUvm3bCR0U+m2g0tc3MqD+inlfuLWx1xQH4GgP3Ax9/8DFLn1laE/XU1WglcAO+UIn+goUZhi9QU01J9XPATsCV+BWMNfi6AdkvzSdKqQ74SkWX4RPDxuIjqlJaU/FL8NsmHUiKHIQvfHFL0oGk0E3A4fhB+zHgUy0/XGJQP7GeNavWsPDhhQX9XgZfqnnhzMWEDaEm6qmr0Y34IIHa6BXO8NHqB/Ae35Us37UVLsLbm0oVMvyNviPex3ofvP5Xi5OUxut4PevPkw4kZerwJW1/j3eu6JxsOKkQgEvwcq6D8RrqbolGJFmDDxpMXac65k+dz5BDh+T9exngGuDx6Q1YO6P/vv3LFqOUxzp8RHJ/YLPVQiQv44DfAk8DeyccS0vySqpDCBeUOQ6pAEcD/8DrfvfD2+8dmmRABXoTL7P4EP8/DE02nE/cGX1XPXXhjsVXobwLv2wqzVuPt+m6Bjge/wDSpNjK0aFTB3bM7Mj8qfOZ8KsJea+uOBa/pLxwRgN9d9+eLbptUc4wpQxuBxqAXyUdSIrlzi+o5KS66FpoMzvAzNRAokrshXcGGYRfPr420WhaNw8fmRuFdzM5DZ8AUA/sgi/NPptkL7NMwRP8YQnGkFZjgO1RF5DWfISfdFwDfA8f3VdCXXnqJ9bzfsP7LHsu/9UVuwMj16xnw8zFqqdOoYCXVw7Bl56XtukN7Ebl11W3Kak2syFmdpGZvYa3PdYgUhUZgDenPxT4Jl4aUil1TBvwpP88YGe8VvRc/PLaT/HWUwuBX+L1yz8D9sBLW87G/19x/l8+AB5Go9Rt1R7vrX43PtFHNvce3oJwMnAFXmakmeOVKbu64ryp8wr6vcwzb9L+o3X0Uj116jyGL/jyXfS+LNY4fNL1v5IOpAV5/43NbGszO9XMHsUHCH+AH8+/iQ8SShXpCvwNb/1zBd7ebFVCsawBpuEvtP54b8dfRD9fjV9WexJ/QX4a2AG/DP4wsBSfIDI8euwYoB/e8WQa5Z9he2+0jyPLvJ9qNglvqzcl6UAqUAMwGv/QvgX4TqLRSGu69ulK3xF9C26tVz/dF31pGD2wHGFJGV0G9ABOTDqQKjAO/zz9R9KBtKDFpDpql3eYmf0ZL1m9Fs9Zroke8p0QwnUhBA0iVaE6vK/mVXhd8P7Akpj2vRKv6T4e6IUvmvJ7vNb7D8Ay/DLQ6bTcnqgX3of7LnwRhZuBA4A/RtvsDZyAdz7Jf62z/E0BtonilrYZhZ9AqQRkU8/hbfKW4CeIulyYDvUT61nyxBJWvZX/MEX7GQ28O6QHj2zfpYyRSam9gvfaPw1NtC6FMcAWVHYJSLNJtZldhh+vp+KlQJPxPGQgXqrappVPzGy8mc0zswVmdm4zjznGzOaa2Qtm9qe27EdK50w8OXwZ7wwyu0z7WQpcDxyGJ8OT8EkJx+BJ/dvAX/AOJdu0Yfvd8Ilvf462NRUfgb8H+ALevu1ovB3Zu23/b3xiPV62MIH82+zI5trhr4Vp+KUx8asw2XXmpuOT2SQd6ifWF7S6YtgQWDyjgXWjB36yCIykw5X4sf+MpAOpEp3w414qk2q8BKg3nhcMDCF8KYRwbwhhA22c92Vm7fFR7gn4FfnjzGx4o8cMxUtm9wshfBpd0awIh+P1yIa/qO9s+eF5mw9cio+49cXLMubhifx0/PLIDdH+tyzRPom2dQS+RPtS4EHg6/gKdCfiL/xxwP8Ab7RxHzPx5F311MWbhDfEvyPhOCrBn/H5Dv2Bx/EJuZIe2+++Pd36d8u7BOTteW/z0Tsf0X/MQObS9uORxOs9/PPleKA2FpWPxzj8Kt3SpANpRktJ9W/weVaHA/PM7GozG1nk/kYCC0IIr4YQ1uBlgI1Xzv06cE0I4T2AEEL+06SlrHbDazc/hf/RrqTws6sN0TbOx8+qhgHn4HVSF+FvlgV4zfRofKJaudXhy7VfhdeoPgH8B7AILy/ph5cgXBrFlq+p0bbHlzLYGjUCn2xa6wvB/BK/2jISP+lUL4j0MTOGHjHUV1dc3frqig1RPfWYqJ76gbJGJ6VyPV5S+N2kA6ky2SXLK/V90GxSHUL4Ot7N6kvALHwQ8XEzexHPg9oyWt0Pz1WyFke35aoH6s3sUTObaWZN5iTRpMlZZjZr+fLlbQhF2qIPvrrXkfglhDPxzhstWYNP2PsWngTsgyeoffG+na/jL7D/xBecaVNdUYkY3gPzv4CXgLl4V5G1+It+KD4y+GNab9U3Ba9D7162aGuH4WVAD+C18bVmA94q77t4ydJ9+OQnSadhE4ex9sO1ea2u2DCjgc69OzNqaA96gkpAUmANPkiTwQejpHR2xzt7VWoJSIsTFUMIq0MIN4cQsrXU5+Gloufin3MXm9kJZlbKK/N1eO4yFjgOuMHMujcR2/UhhBEhhBG9evUq4e6lNZ3xiX3fw2t5jmTzdmcfsLH+uTd+ufp3+Ijv7/GJhvfjtWaVPJ99Z7yryCy8Vd8VeDLzE7xV3054q75H8cQn6xXgRVT6UUqT8IPP7UkHErM1wJfxLgKn4xN4S3nAlfgNPmgwHbbqkFdrvYbpDQwcPZD2ZhyMHze1vHFluxUv09GS5KXXDl8I5j4q832Qd0u9EMKbIYRLQwifwa8+XoMnvzfhpa/5WMKmVyz7s3lDicXAlBDC2hDCa3jZbaUsjieRdng/3GvxUejReD1ytv65Jz6yeC++1PQUvL74NrzbRhpH2XbAR+cfweu5bsCT7qvx/3928ZlpbEz8lFSXzm54uVAtdQFZiU/c/RNwMX5lJ46SKCmvui3r2HGcr64YQvOpwcrFK1mxcAUDx/jQQwZP1l6KJ0xpgwBcjn82pGlF4jQZh78PXkw6kCa0qRd5CGFWCOFMPI/4PD4ZPR9PAkPNbLCZdcTLAxu3n72DaDK7mfXEy0FebUucUn7fwGeyvo7XvZ6KH/DPwHtJLsWL8yfiM3erRW/gFDZt1bc/3u5vPF6TPRwfyZbSMHy0+mHyP4tPszfw19Qj+MjFOSRbGiWlVT+xnpWLVvLWnLeafUzDDK+nzk2qQSUglexh4Bl8lFqLvZRHtq66EktAivqbR6PJk0MIn8vz8evwfGsafpJxawjhhWh1xuz6GNOAd8xsLr5a4/dDCO8UE6eU1yH45L4rgTn4ZL7L8J6StTCqlm3VdyueYE/BTy5+kmRQVWoSPhJ0W9KBlNlLeEecBXinnS8nG46UwdDD/AJsS11AGmY00LFLR7bfbXsABuEn6kqqK9fleEvYE5IOpIrtgJcvVGJSbS1dekqLESNGhFmzZiUdhojEYBdga7zFYzV6DL+yU4dfBdor2XCkjG4YeQNmxin/PKXJ+6/d7Vo6b9eZL9+78bTqNLwc6F3U/77SzMO7Y12AT2aX8jkdn6f1LtAxgf2b2VMhhBGNb9fVCRFJlWPxiaGLWntgCv0Nn4SzLd6DWgl1dftkdcWlm6+uuHrFat567i0GNlqaPINPBH8ynhClAFfgK/59M+lAasA4YDsq73NASbWIpMqk6PtfEo2i9K7D2+Xtip807JhsOBKDYROHATD/rs1LQBY9tgjCxnrqrAPx2nqVgFSWt/GR0y/jc26kvI7Cu2xV2rwlJdUikipDgD2pni4gAfghfln/MHx1TzUJrQ3b7bYd3QZ04+U7N1+y/PXpr9Ourh399+m/ye3b4q9/JdWV5dfAarTYS1wqddK2kmoRSZ1J+OTYtLcFWgucjC8wdDIwGe8DL7XBzKg/or7J1RUXzVhEn7360GGrDpv9XgYvD9q8aESSsBpvrToB7/oktUtJtYikzjHR91sTjaI4q/BLmL/FJzXdgCae1aL6ifWs/ddaXnvotU9uW7d6HUueWLJZPXVWBj8hmx5PiNKKm/EFzc5OOhBJnJJqEUmdQcC+pLcEZBleGzsNuB7vFlCplzOlvAYfOJgOnTts0lrvjVlvsH7N+s3qqbP2wyfEqQQkednFXnYFDko4FkmekmoRSaVJwGy8jVWavIL3oH4BX+nq64lGI0mr27KOncbtxPw7N66u+Pr01wEYuF/TSXUnfBVXJdXJuw94Hl/sRSfGoqRaRFLpi/iHWJpGq1/Fl4t9D5+QqGXsBXJWV3zWV1dcNGMRPXfuyVY9t2r2dzL4YlvNr8cocbgM6AMcl3QgUhGUVItIKvXDR+vSklQvxEs+/oUvFbtvotFIJRl6uK+uOG/qPDas30DDow3N1lNnZZcsf7DMsUnzngfuxZeJTmIBEqk8SqpFJLUmAXPxD7dK1oAn1B/gl+x3TTYcqTBdtutCv5H9mD91PstfWM7H73/cbD111h7ANqgEJElX4KU430g6EKkYSqpFJLW+gB/EKnm0ejGeUL+H11/ukWw4UqHqJ9bzxpNv8MJfXgBodaS6PT4x7j58spzEaynwB+CreO9wEVBSLSIpth2esP6Zykws3sDjexu/TKxlx6U59RPrAZh5xUy69utK90HdW/2dDL5M84KyRiZN+R+8reF3Eo5DKouSahFJtUnAy8AzSQfSyFI8oV4K3AOMTDYcqXDb7eqrK679cC0DRw/ErPVeEtm6apWAxOsjPKk+EhiacCxSWZRUi0iq/Ru+aEollYAswy/NLwH+DoxKNhxJATP7ZLS6tXrqrJ2AHVBSHbebgHfwNnoiuZRUi0iqbYuP2N1KZZSALAcOBl4H7sY7lIjkY5fjd6GuUx07HbJTXo83/LX/ILC+nIElZArwLXzFwjcTjiVrAz5BcS9gTMKxSOVRUi0iqXcs3rLuiYTjeAdPchYAU4H9kw1HUmbgfgM5/8Pz2XZo/lPfMsAK4OlyBZWQtcA3gV8DxwN9gZ2j224luf7cd+MLTp2NFnuRzSmpFpHUOxrvE5tkCci7eIIzDx9h05LF0hb51FLnyr7Oqq0EZDI+0fcO/GT5UmAw3nFjErA98GngdOA2/ApRHC4H+uOdh0QaU1ItIqm3NTAeH8HakMD+VwCH4D2z7wDGJRCD1KbeeN/zakuqr8JrxicCewPfx0eJ3wNmAhcDA4Df4aur9gZ2Ab4N3I5fNSq1Z/CFm84COpRh+5J+SqpFpCpMwicGPhrzft8HDsWXjL4dT+5F4pQBZuCrdVaDp/H38RlsnqTUAfsA5+Bddd4DHgP+C18u/DfA54GewG54y7u/RY8r1uVAF+CUEmxLqpOSahGpCkfiq5vFWQLyATABTwJuAw6Pcd8iWRlgDfGfUJbLr4DO+MIqremAd9c5D+8F/x5+gvFToBdwHV4eti2wJ96xYyp+dakQS4BbgJOB7gX+rtQOJdUiUhW64EntbcTTCWEVcBhe7/lnPKkXScIYPLmshhKQZcCfgBPxsq5CdQT2A36APx8rgH8AF+LJcLa/9LbACOB7wF3Ayla2ezVeWnZWG2KS2qGkWkSqxiS8K8AjZd7Ph3gC/zje7uvfyrw/kZZ0wUdrqyGpvgEfdT+jRNvbAj/p+CHeenAF8HD07y74qPgRwDb4Ak3n4L3lP8jZxirgWvx9PrhEcUl1qks6ABGRUjkMv2x8C+XrvvEvfKRrBt6J4Itl2o9IITLAj4G38XriNFqLjyQfgrfPK4ctgQOiL/DVEWfiExAfxntQXwq0x0eyD8RPolegxV6kdRqpFpGqsRWe8P4V/4AutdV4feZDeNeB48qwD5G2yOCLHz2UdCBFyLbR+3aM++yEJ84X4WUiK/AR/3PxUcdf4KPZo9DKqNI6JdUiUlWOxXtGP1Di7X4MfA7/wP1f4IQSb1+kGHsDXUl3CUi2jd6EBGPYCl8R9af41agV+LHk1gRjkvRQUi0iVeVQfIJTKbuAfIy36boHuB44qYTbFimFOnzENa1J9VM030YvSZ3xUrL+SQciqVBJr10RkaJtgZdoTMaT4WKtwSdA3oVPVlKPWqlUGeDV6CttCmmjJ1KplFSLSNWZhC/KMq3I7azF66b/hrfU+kaR2xMpp0z0vdSlT+W2DO+icxJta6MnUimUVItI1ckAPSiuBGQdXjd9O/BL4PTiwxIpq08BfUlfCUip2+iJJEVJtYhUnQ54DfQUvGVWodYDX8EnJ/0CLfgg6WD4CeUD+EIlaZDbRu9TCcciUiwl1SJSlSbhizbcXeDvrcfrOm8GLgbOLnFcIuWUAd4Bnk06kDwl0UZPpFyUVItIVToA6I0vBJOvDfhExN/jLbXOKUNcIuV0cPQ9LSUgldBGT6RUlFSLSFWqA76Ad+1YlcfjN+ATEf8PuAD4QbkCEymjvsBw0pFUV2obPZG20utYRKrWsXhN9dRWHhfwiYg34sn0j8ocl0g5ZYDp+AqglUxt9KTaKKkWkaq1H9CPlruABLye81q83OMn+IQvkbTK4CeTjycdSAvURk+qkZJqEala7YAvAn/H+1Y3FoDv4j2ozwb+GyXUkn4HAO2p7BIQtdGTaqSkWkSq2iT8w/uORrcH4PvAlXjLvJ+jhFqqQzdgHyo3qVYbPalWSqpFpKrtA+zApiUgATgfuAyvpb4CJdRSXTLALOC9pANpwu2ojZ5UJyXVIlLVDB+tvg/v3wvwY7wH9Tfwll5KqKXaZPCONg8nHEdTfoXa6El1UlItIlVvEr7s+GTgInwy4sn4JWgdBKUa7YN31qi0EhC10ZNqVpd0ACIi5bYHMATv7vEu3nHgevShLtWrIz5hsdKSarXRk2qmzxQRqXrZEpB3gRPwftQ6+Em1ywDzgYakA4mojZ5UO32uiEhNOAf4A/BbvN2YSLXLRN8fSDSKjdRGT6qdkmoRqQldgS+hmjepHZ8BelMZJSBqoye1IPak2szGm9k8M1tgZue28LjPm1kwsxFxxiciIlINDB+tvh9vI5kktdGTWhBrUm1m7YFr8E46w4HjzGx4E4/riq/H8M844xMREakmGbyW+fmE41AbPakFcY9UjwQWhBBeDSGsAW4BjmricT8BLgFWxxmciIhINTk4+p5kCYja6EmtiPv13Q9YlPPvxdFtnzCzPYEBIYS7WtqQmZ1qZrPMbNby5ctLH6mIiEjKDQTqSTapVhs9qRUVddJoZu2Ay4GzW3tsCOH6EMKIEMKIXr16lT84ERGRFMoAj+CdN+KmNnpSS+JOqpcAA3L+3T+6LasrPmH5YTNbCOwLTNFkRRERkbbJAB+SzCQltdGTWhJ3Uv0kMNTMBptZR+BYYEr2zhDC+yGEniGEQSGEQcBM4MgQwqyY4xQREakKY/EP+7hLQNRGT2pNrEl1CGEdfsI6DXgRuDWE8IKZXWRmR8YZi4iISC3YBhhB/Em12uhJrYl9HYQQwt3A3Y1u+1Ezjx0bR0wiIiLVLIO31FoJdItpn2qjJ7WmoiYqioiISOllgPX4hMU4ZNvonYkSDakdeq2LiIhUuVFAJ+IrAcm20Tsppv2JVAIl1SIiIlVuS2AM8STVaqMntUpJtYiISA3IAHPxyYPlpDZ6UquUVIuIiNSATPT9gTLuI9tG71DURk9qj5JqERGRGrAbsC3lLQHJttE7s4z7EKlUSqpFRERqQDvgYDypDmXax1WojZ7ULiXVIiIiNSKDjyS/VIZtPwU8htroSe3S615ERKRGZOuqy1ECojZ6UuuUVIuIiNSIwcCOlD6pVhs9ESXVIiIiNSUDPASsK+E2r0dt9ESUVIuIiNSQDPAB8GSJtrcW+DVqoyeipFpERKSGHAgYpSsBURs9EaekWkREpIb0BPagdEm12uiJOCXVIiIiNSYDPA6sKnI7aqMnspHeAyIiIjUmg9dCTy9yO78CuqA2eiKgpFpERKTmjAa2oLgSkGwbvRNRGz0RUFItIiJSczoB+1FcUq02eiKbUlItIiJSgw4G5gBvteF31UZPZHNKqkVERGpQdsnyB9vwu9k2et8uXTgiqaekWkREpAbthddCt6UE5CpgCDC+pBGJpJuSahERkRrUHjgIT6pDAb+XbaN3BkoiRHLp/SAiIlKjMkAD8EoBv6M2eiJNU1ItIiJSo7J11fmWgKiNnkjzlFSLiIjUqKHAAPJPqtVGT6R5SqpFRERqlOGj1Q8C61t5rNroibRMSbWIiEgNywDvAc+08ji10RNpmZJqERGRGnZw9L21EhC10RNpmZJqERGRGrYdsAstJ9VqoyfSOr03REREalwGmAF81Mz9aqMn0jol1SIiIjUuA3wMPNrEfdk2eiehNnoiLVFSLSIiUuP2B+pougREbfRE8qOkWkREpMZ1AUaxeVKd20ZvWNxBiaSMkmoREREhAzwNvJNzm9roieRPSbWIiIiQAQLwUM5taqMnkj8l1SIiIsLeQFc2loCojZ5IYfQ+EREREToAY9mYVKuNnkhhlFSLiIgI4CUgrwBPoDZ6IoVSUi0iIiKAJ9UAJ6I2eiKFUlItIiIiAOwM9AFeQm30RAqlpFpEREQAMDaOVquNnkhh6pIOQERERCrHmUAP1EZPpFBKqkVEROQTe0dfIlKY2Ms/zGy8mc0zswVmdm4T9/+7mc01szlm9oCZ7RB3jCIiIiIihYg1qTaz9sA1wARgOHCcmQ1v9LBngBEhhF2B24BL44xRRERERKRQcY9UjwQWhBBeDSGsAW4Bjsp9QAjhoRDCv6J/zgT6xxyjiIiIiEhB4k6q+wGLcv69OLqtOScDfy9rRCIiIiIiRarYiYpmdgIwAjigmftPBU4FGDhwYIyRiYiIiIhsKu6R6iXAgJx/949u24SZZYAfAEeGED5uakMhhOtDCCNCCCN69epVlmBFRERERPIRd1L9JDDUzAabWUfgWGBK7gPMbA/gOjyhXhZzfCIiIiIiBYs1qQ4hrAPOAKYBLwK3hhBeMLOLzOzI6GE/B7oAfzGz2WY2pZnNiYiIiIhUhNhrqkMIdwN3N7rtRzk/Zzb7JRERERGRChb74i8iIiIiItVGSbWIiIiISJGUVIuIiIiIFElJtYiIiIhIkSyEkHQMRTOz5cDrCe2+J/B2QvuupBhAcTSmODZVCXFUQgygOBpTHJUVAyiOxhTHpiohjiRj2CGEsNkiKVWRVCfJzGaFEEbUegyKQ3GkIY5KiEFxKI5Kj0FxKI40xFEJMTSm8g8RERERkSIpqRYRERERKZKS6uJdn3QAVEYMoDgaUxybqoQ4KiEGUByNKY6NKiEGUByNKY5NVUIclRDDJlRTLSIiIiJSJI1Ui4iIiIgUSUl1I2Y2wMweMrO5ZvaCmZ0V3d7DzO4zs5ej79tEt5uZXWVmC8xsjpntmbOtE6PHv2xmJyYYxz1mtsLM7kwiBjPb3cwej7Yxx8wmJRTHDmb2tJnNjrZzWhJx5Gyvm5ktNrOrk4rDzNZHz8dsM5uSYBwDzexeM3sx2t6gOGMwswNznofZZrbazI5O6Lm4NNrGi9FjLKE4LjGz56Ovcr9nP2V+jPjYzL7XaFvjzWxeFOO5Ccbxv2a2zMyeLySGUsbR3HZijmFLM3vCzJ6NtnNhEs9Fzvbam9kzVv7Pt5ZeGwvN7DnzY8esBOPobma3mdlL5sePUXHHYWbDbNNj6Uoz+04Cz8V3o208b2Y3m9mW+T4XRQkh6CvnC+gD7Bn93BWYDwwHLgXOjW4/F7gk+vkw4O+AAfsC/4xu7wG8Gn3fJvp5m7jjiO47GJgI3JnQc1EPDI1+7gu8CXRPII6OwBbRz12AhUDfJP4m0f1XAn8Crk7i7xLdtyrp90p038PAuJy/zVZJ/E1y3rvv5htDiV+jnwUeBdpHX48DYxOI43DgPqAO6Aw8CXQrYxy9gb2BnwHfy9lOe+AVYEf8/fssMDzuOKL79gf2BJ6P4b3S3PPR5HZijsGALtHPHYB/Avsm8TeJ7v93/Dha7s+3ll4bC4Gehb4uyhDH74BTop87Ut7P2Rb/Ljnv36V4T+c4X6P9gNeATtG/bwVOasvfp+C/Zxw7SfMX8DdgHDAP6JPzh58X/XwdcFzO4+dF9x8HXJdz+yaPiyuOnH+PLfSgU+oYcm5/lijJTioOYFuggQKS6lLGAewF3AKcRIFJdYnjaHNSXcL3ynBgRpIxNNrGqcAfE3ouRgFPAZ2ArYBZwM4JxPF94Ic5t/8GOKZcceQ87gI2/XAcBUzL+fd5wHlxx5Fz+yDakFSXOo7G20kqhug1+jSwTxLPBdAfeAA4iDJ/vrUSx0LamFSXKg5gazyRtCTjaHTfIcCjCTwX/YBF+ABJHXAncEgpnpfWvlT+0QLzS9B74Gfi24UQ3ozuWgpsF/2c/eNlLY5ua+72uOMoiVLFYGYj8TPoV5KII7q8NCe6/5IQwhtxx2Fm7YDLgM0uZcYZR/TzlmY2y8xmWgHlDiWOox5YYWa3R5dxf25m7WOOIdexwM2F7r8UcYQQHgcewq/mvIknlC/GHQd+4jvezLYys57AgcCAMsbRnLiPo2VXqjgabSfWGKKSi9nAMuC+EELBMZQiDuCXwH8AG9qy/xLGEYB7zewpMzs1oTgGA8uB30bH0RvNrHMCceRq87G0mBhCCEuAX+ADZ28C74cQ7m1LHIVSUt0MM+sC/BX4TghhZe59wU+FQq3EUaoYzKwP8HvgqyGEgg+CpYgjhLAohLArMAQ40cwK/jAtQRzfAu4OISwudN8ljgP8stwI4Hjgl2a2UwJx1AFj8JOMvfFL/SfFHEN2O32AXYBphey/VHGY2RBgZ3wErh9wkJmNiTuO6APobuAx/EPxcWB93HGUSrXF0dJ24oghhLA+hLA7/jodaWafKSSGUsRhZkcAy0IITxW671LGERkdQtgTmACcbmb7JxBHHV6e9OsQwh7Ah3ipRNxxZLfTETgS+EvcMUQ110fhJxp9gc5mdkKhcbSFkuommFkH/A/6xxDC7dHNb0UfuNkP3mXR7UvYdASnf3Rbc7fHHUdRShWDmXUD7gJ+EEKYmVQcWdEI9fN4Mhd3HKOAM8xsIX42/RUzuziBOLJn9IQQXsXrmvdIII7FwOwQwqshhHXAHfiHQ5wxZB0DTA4hrM13/yWO43PAzBDCqhDCKrzeeVQCcRBC+FkIYfcQwji8jnZ+GeNoTtzH0bIpVRzNbCfWGLJCCCvwKyvjE4hjP+DI6Dh6C34C+ocE4sg9ji4DJgMjE4hjMbA456rBbRRwHC1hHFkTgKdDCG8lEEMGeC2EsDw6lt+Oz1cpOyXVjZiZ4fWDL4YQLs+5awpwYvTziXitT/b2r5jbF7/M8CY+0nWImW0TnTUdQgGjXyWMo81KFUN0xjoZuCmEcFuCcfQ3s07RNrcBRuO1WrHGEUL4UghhYAhhED46e1MIIe8RhRI+H9uY2RbRNnviH1Jz444DnwTX3cx6RY87KN84yvA+OY42XK4sYRwNwAFmVhd9uBwA5F3+UcLXRnsz2zba5q7ArkDel0/bEEdzngSGmtng6DhybLSNuOMoSqniaGE7ccbQy8y6Rz93wmteX4o7jhDCeSGE/tFx9FjgwRBC3qORJXw+OptZ1+zP+Gd93h1iSvh8LAUWmdmw6KaDKe/xvDUFH0tLGEMDsK95+Zrhz0WbyugKFmIo3E7TF55oBWAOMDv6Ogyf2PYA8DJwP9AjerwB1+A1ws8BI3K29TVgQfT11QTjmI7XWn2En80eGmcMwAnA2pxtzAZ2j/u5wA/+c/B60TnAqUn9TXK2eRKFd/8o1fPx2ejfz0bfT07wNZr92zwH/B/QMYEYBuGjoO2SOm7gs+Wvwz8A5gKXJxTHltH+5wIzKeD92sY4tsePTSuBFdHP3aL7DsNHyV/Br3QlFcfNeH3m2uj2vN8vpYqjue3EHMOuwDPRdp4HfpTU3yRnm2MpvPtHqZ6PHfFj6LPACyT7Gt0dn9w8B7/iV0jHsVLG0Rl4B9g6wefiQvxk73m87HSLQmJp65dWVBQRERERKZLKP0REREREiqSkWkRERESkSEqqRURERESKpKRaRERERKRISqpFRERERIqkpFpEJIXM7DYze9eaWBXUzMaa2QYzOyuJ2EREapFa6omIpFCUTM/FF734Ys7tnfA+r8uAMSGEDQmFKCJSUzRSLSKSQsGX/z0L+IKZHZ1z1wX4ct5fK3dCbWYdohXLRERqnpJqEZGUCiH8AbgLuMbMtjazPYGzgQtCCPMAzOxUM3vWzFab2dtm9hsz65G7HTM7w8wej8pJVpjZTDM7vNFjBplZMLNvmdmlZvYG8DHQPZb/rIhIhVP5h4hIiplZP3x55Mn4MsXrgH1DCOvN7GI8yb4KmAb0A36KL+f72RDC+mgbv8CXRl8I1AETgdOBCSGEe6LHDAJeA94AngRuxJdVvzeE8FEM/1URkYqmpFpEJOXM7BTgBmAtsFcI4bkoCX4FuDCEcFHOY/cDZgCfCyHc0cS22uFXMe8GPgohHBXdPghPqp+J9qEPDxGRHCr/EBFJuRDCjcCbwB0hhOeim8fhx/g/mlld9gv4J/ABsH/2981sLzO708zewke610a/P6yJ3d2hhFpEZHNKqkVEqsOa6Curd/R9AZ4k5351BbYFMLMBwANAD+BM4LPA3sA9wJZN7OfNMsQuIpJ6dUkHICIiZfFO9P0Q4L0W7h8PbA0cE0JYnL3TzLZqZrsapRYRaYKSahGR6nQfsAEYGEK4r4XHZZPntdkbzKwe2A+f0CgiInlQUi0iUoVCCK+Y2SXA1WY2DHgEWA0MwOulbwwhPATcj9dR32RmlwF9gAuBBlQiKCKSNyXVIiJVKoRwvpm9iLfHOx0v3ViE11C/HD3mBTP7EnARMAXvGHIuXhYyNoGwRURSSS31RERERESKpEt7IiIiIiJFUlItIiIiIlIkJdUiIiIiIkVSUi0iIiIiUiQl1SIiIiIiRVJSLSIiIiJSJCXVIiIiIiJFUlItIiIiIlIkJdUiIiIiIkX6f4I7ea2zYaWOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot AI values (yearly and means together)\n",
    "plt.figure(figsize=(12,6))\n",
    "west_color = 'cyan'\n",
    "east_color = 'purple'\n",
    "plt.plot(west_df[\"year\"], west_df[\"aridity_index\"], label=\"Western Watershed\", color=west_color)\n",
    "plt.plot(east_df[\"year\"], east_df[\"aridity_index\"], label=\"Eastern Watershed\", color=east_color)\n",
    "plt.axhline(y=west_mean_aridity, color=west_color, linestyle='--', label=\"Western Watershed Mean Aridity\")\n",
    "plt.axhline(y=east_mean_aridity, color=east_color, linestyle='--', label=\"Eastern Watershed Mean Aridity\")\n",
    "plt.legend()\n",
    "plt.title(f'Yearly Aridity Index', fontsize=18)\n",
    "plt.xticks(ticks=range(2000,2019))\n",
    "plt.xlabel('Year', fontsize=16)\n",
    "plt.ylabel('Aridity Index', fontsize=16)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}