Enterprise mobility.ipynb

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   "source": [
    "import geopy.distance\n",
    "from pyproj import Transformer\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from random import choices\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "import requests\n",
    "from IPython.display import Image\n",
    "import json\n",
    "import polyline\n",
    "from copy import deepcopy\n",
    "import plotly.graph_objects as go\n",
    "from ortools.constraint_solver import pywrapcp\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "import requests\n",
    "import plotly_express as px\n",
    "import chart_studio\n",
    "import chart_studio.plotly as py\n",
    "\n",
    "load_dotenv()\n",
    "key = os.getenv('GCP_KEY_0X')\n",
    "token = os.getenv('MAPBOX_TOKEN')\n",
    "chart_studio_key = os.getenv('CHART_STUDIO')\n",
    "px.set_mapbox_access_token(token)\n",
    "chart_studio.tools.set_credentials_file(username='0xmarmelade', api_key=chart_studio_key)"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "# 1. Convert the Swiss coordinate system\n",
    "From Swissgrid coordinates (LV95) we project to classic World geodesic coordinates (WGS84)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
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   "source": [
    "def LV95_to_WGS84(coordinates_df: pd.DataFrame) -> pd.DataFrame:\n",
    "    transformer = Transformer.from_crs('epsg:2056', 'epsg:4326')\n",
    "    return pd.DataFrame(\n",
    "        np.transpose(\n",
    "            transformer.transform(coordinates_df['E_KOORD'], coordinates_df['N_KOORD'])\n",
    "        ),\n",
    "        columns=['latitude', 'longitude']\n",
    "    )\n",
    "raw_statpop_dataset = pd.read_excel('STATPOP2021.xlsx')\n",
    "wgs84_statpop_dataset = LV95_to_WGS84(raw_statpop_dataset).join(raw_statpop_dataset['B21BTOT']).reindex()\n",
    "wgs84_statpop_dataset.to_csv('STATPOP2021_WGS84_full.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
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   "source": [
    "nestle_coord = (46.466634011, 6.835639010)\n",
    "wgs84_statpop_dataset = pd.read_csv('STATPOP2021_WGS84_full.csv')\n",
    "distance_cutoff_km = 20\n",
    "\n",
    "pop_density_around_nestle = wgs84_statpop_dataset[wgs84_statpop_dataset.apply(\n",
    "    lambda row: geopy.distance.distance(\n",
    "        (row.latitude, row.longitude), nestle_coord).km < distance_cutoff_km,\n",
    "    axis=1)\n",
    "].reset_index(drop=True)\n",
    "\n",
    "pop_density_around_nestle['propability_density'] = (pop_density_around_nestle.B21BTOT / pop_density_around_nestle.B21BTOT.sum());\n",
    "pop_density_around_nestle.to_csv('STATPOP2021_WGS84_nestle.csv')"
   ]
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  {
   "cell_type": "markdown",
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   },
   "source": [
    "# 2. Data Generation"
   ]
  },
  {
   "cell_type": "markdown",
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    "collapsed": false
   },
   "source": [
    "Draw 24 employees location from the density function around Nestle's HQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_density_around_nestle = pd.read_csv('STATPOP2021_WGS84_nestle.csv')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [],
   "source": [
    "n_employees = 24\n",
    "n_nodes = n_employees + 1\n",
    "employee_indexes = choices(pop_density_around_nestle.index, pop_density_around_nestle.propability_density, k=n_employees)\n",
    "employee_locations = pop_density_around_nestle.iloc[employee_indexes][['latitude', 'longitude']]\n",
    "\n",
    "# We have to account for the possibility that more than one employees live at the same location\n",
    "employee_locations_grouped = employee_locations.groupby([employee_locations.index, 'latitude', 'longitude'],\n",
    "                                                        as_index=False).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "source": [
    "with open('./results/2022-12-16 153833.762017.json') as result_file:\n",
    "    data = json.load(result_file)\n",
    "employee_locations = pd.DataFrame(\n",
    "    {'latitude': list(zip(*data['employees']))[0][1:], 'longitude': list(zip(*data['employees']))[1][1:]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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   "source": [
    "coordinates_list = list(zip(employee_locations.latitude.tolist(), employee_locations.longitude.tolist()))\n",
    "coordinates_list = [nestle_coord] + coordinates_list"
   ]
  },
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   "execution_count": 11,
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           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'https://plotly.com/~0xmarmelade/1/'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = px.scatter_mapbox(employee_locations, lat='latitude', lon='longitude', mapbox_style='streets', zoom=9)\n",
    "fig.update_layout(margin={\"r\": 0, \"t\": 0, \"l\": 0, \"b\": 0}, showlegend=False, autosize=True)\n",
    "markers = go.Scattermapbox(\n",
    "    lat=[nestle_coord[0]],\n",
    "    lon=[nestle_coord[1]],\n",
    "    mode='markers',\n",
    "    text='Nestle Head Office',\n",
    "    marker={'size': [12], 'symbol': ['suitcase'], 'opacity': [0.9], }\n",
    ")\n",
    "fig.add_trace(markers)\n",
    "fig.show()\n",
    "\n",
    "\n",
    "py.plot(fig, filename='Employee locations', auto_open=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 2. Creating the distance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "url = 'https://routes.googleapis.com/distanceMatrix/v2:computeRouteMatrix'\n",
    "headers = {\n",
    "    'Content-Type': 'application/json',\n",
    "    'X-Goog-Api-Key': key,\n",
    "    'X-Goog-FieldMask': 'originIndex,destinationIndex,duration,distanceMeters,status,condition'\n",
    "}\n",
    "payload = str({\n",
    "    'origins': [{'waypoint': {\n",
    "        'location': {\n",
    "            'latLng': {\n",
    "                'latitude': latitude,\n",
    "                'longitude': longitude\n",
    "            }\n",
    "        }\n",
    "    }} for latitude, longitude in coordinates_list],\n",
    "    'destinations': [{'waypoint': {\n",
    "        'location': {\n",
    "            'latLng': {\n",
    "                'latitude': latitude,\n",
    "                'longitude': longitude\n",
    "            }\n",
    "        }\n",
    "    }} for latitude, longitude in coordinates_list],\n",
    "    'travelMode': 'DRIVE',\n",
    "    'routingPreference': 'TRAFFIC_AWARE'\n",
    "})\n",
    "response = requests.request('POST', url, headers=headers, data=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "distance_matrix_time = [[0] * n_nodes for _ in range(n_nodes)]\n",
    "distance_matrix_distance = [[0] * n_nodes for _ in range(n_nodes)]\n",
    "\n",
    "for distance in response.json():\n",
    "    distance_matrix_time[distance['originIndex']][distance['destinationIndex']] = int(distance['duration'][:-1])\n",
    "    if distance['originIndex'] == distance['destinationIndex']:\n",
    "        distance_matrix_time[distance['originIndex']][distance['destinationIndex']] = 0\n",
    "    else:\n",
    "        distance_matrix_distance[distance['originIndex']][distance['destinationIndex']] = distance['distanceMeters']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 4. Solving the VRP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_solution(data, manager, routing, solution, max_capacity) -> dict():\n",
    "    \"\"\"Prints solution on console.\"\"\"\n",
    "    print(\n",
    "        f'\\n\\n------- VRP SOLUTION {str(data[\"num_vehicles\"])} vehicles, max capacity {str(max_capacity)} ---------- \\n\\n\\n ')\n",
    "    if not solution:\n",
    "        print('No solution found !')\n",
    "        return {}\n",
    "\n",
    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
    "    max_route_time = 0\n",
    "    solution_dict = {\n",
    "        'routes': [{\n",
    "            'route': [],\n",
    "            'routeTotalTime': 0,\n",
    "            'routeTotalDist': 0\n",
    "        } for _ in range(data['num_vehicles'])],\n",
    "        'objectiveTime': 0,\n",
    "        'objectiveDist': 0\n",
    "    }\n",
    "\n",
    "    for vehicle_id in range(data['num_vehicles']):\n",
    "        index = routing.Start(vehicle_id)\n",
    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
    "        route_time = 0\n",
    "        route_distance = 0\n",
    "\n",
    "        while not routing.IsEnd(index):\n",
    "            plan_output += f' {manager.IndexToNode(index)} -> '\n",
    "            previous_index = index\n",
    "            index = solution.Value(routing.NextVar(index))\n",
    "            route_time += routing.GetArcCostForVehicle(\n",
    "                previous_index, index, vehicle_id)\n",
    "            route_distance += distance_matrix_distance[manager.IndexToNode(previous_index)][manager.IndexToNode(index)]\n",
    "\n",
    "            solution_dict['routes'][vehicle_id]['route'].append(manager.IndexToNode(previous_index))\n",
    "\n",
    "        plan_output += f'{manager.IndexToNode(index)}\\n'\n",
    "        plan_output += f'Time of the route: {route_time}s\\n'\n",
    "        solution_dict['routes'][vehicle_id]['routeTotalTime'] = route_time\n",
    "        solution_dict['routes'][vehicle_id]['routeTotalDist'] = route_distance\n",
    "        solution_dict['routes'][vehicle_id]['route'].append(manager.IndexToNode(index))\n",
    "        solution_dict['objectiveTime'] += route_time\n",
    "        solution_dict['objectiveDist'] += route_distance\n",
    "        print(plan_output)\n",
    "        max_route_time = max(route_time, max_route_time)\n",
    "    print(f'Maximum of the route distances: {max_route_time}s')\n",
    "    return solution_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Executes the VRP solver on the globally defined distance matrix\n",
    "# Inputs:   num_vehicles: the maximum number of vehicles used\n",
    "#           max_time_vehicle: each vehicle's maximum capacity (time)\n",
    "# Output:   JSON containing the found solution to the VRP\n",
    "# vrp_out = {'routes': [\n",
    "#               {'route': [0, 4, 5, 8, 0],\n",
    "#                'routeTotalTime': 5369,\n",
    "#                'routeTotalDist': 3985}\n",
    "#            ....],\n",
    "#          'objective': 366036\n",
    "#          ]},\n",
    "def vrp(num_vehicles, max_time_vehicle):\n",
    "    data = {\n",
    "        'distance_matrix': distance_matrix_time,\n",
    "        'num_vehicles': num_vehicles,\n",
    "        'office': 0\n",
    "    }\n",
    "\n",
    "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
    "                                           data['num_vehicles'],\n",
    "                                           data['office'])\n",
    "\n",
    "    routing = pywrapcp.RoutingModel(manager)\n",
    "\n",
    "    def distance_callback(origin: int, destination: int) -> int:\n",
    "        return data['distance_matrix'][manager.IndexToNode(origin)][manager.IndexToNode(destination)]\n",
    "\n",
    "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
    "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
    "\n",
    "    dimension_name = 'Distance'\n",
    "    routing.AddDimension(\n",
    "        transit_callback_index,\n",
    "        name=dimension_name,\n",
    "        slack_max=0,  # no slack\n",
    "        capacity=max_time_vehicle,\n",
    "        fix_start_cumul_to_zero=True,\n",
    "    )\n",
    "\n",
    "    distance_dimension = routing.GetDimensionOrDie(dimension_name)\n",
    "    distance_dimension.SetGlobalSpanCostCoefficient(100)\n",
    "\n",
    "    # Setting first solution heuristic\n",
    "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
    "    search_parameters.first_solution_strategy = (routing_enums_pb2.FirstSolutionStrategy.AUTOMATIC)\n",
    "    search_parameters.time_limit.seconds = 120\n",
    "\n",
    "    # Solve the problem\n",
    "    solution = routing.SolveWithParameters(search_parameters)\n",
    "    return get_solution(data, manager, routing, solution, max_time_vehicle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 5. Result Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "n_shuttles_to_test = [12]\n",
    "max_time_shuttle_to_test = [3800]\n",
    "\n",
    "test_results = {\n",
    "    'distance_matrix_time': distance_matrix_time,\n",
    "    'distance_matrix_distance': distance_matrix_distance,\n",
    "    'employees': coordinates_list,\n",
    "    'results': []\n",
    "}\n",
    "\n",
    "for n_shuttle in n_shuttles_to_test:\n",
    "    for max_time in max_time_shuttle_to_test:\n",
    "        result = vrp(n_shuttle, max_time)\n",
    "        if result != {}:\n",
    "            test_results['results'].append({\n",
    "                'num_vehicles': n_shuttle,\n",
    "                'max_time_shuttle': max_time,\n",
    "                'result': vrp(n_shuttle, max_time),\n",
    "            })\n",
    "        print(f'solution found!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "with open(f\"./results/{str(datetime.now()).replace(':', '')}.json\", 'w') as result_file:\n",
    "    result_file.write(json.dumps(test_results))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 6. Route Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_results = 0\n",
    "with open(f\"./results/2022-12-16 163709.548510.json\", 'r') as result_file:\n",
    "    test_results = json.load(result_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'num_vehicles': 5,\n",
       " 'max_time_shuttle': 6000,\n",
       " 'result': {'routes': [{'route': [0, 9, 21, 4, 2, 1, 0],\n",
       "    'routeTotalTime': 4621,\n",
       "    'routeTotalDist': 55919},\n",
       "   {'route': [0, 11, 17, 12, 16, 22, 0],\n",
       "    'routeTotalTime': 3795,\n",
       "    'routeTotalDist': 49224},\n",
       "   {'route': [0, 20, 14, 8, 3, 19, 0],\n",
       "    'routeTotalTime': 4770,\n",
       "    'routeTotalDist': 61399},\n",
       "   {'route': [0, 23, 15, 18, 6, 0],\n",
       "    'routeTotalTime': 4816,\n",
       "    'routeTotalDist': 61157},\n",
       "   {'route': [0, 13, 5, 24, 7, 10, 0],\n",
       "    'routeTotalTime': 3694,\n",
       "    'routeTotalDist': 46595}],\n",
       "  'objectiveTime': 21696,\n",
       "  'objectiveDist': 274294}}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_results['results'][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_run_to_draw = 2\n",
    "\n",
    "shuttle_routes = \\\n",
    "    [[\n",
    "        f'{str(coordinates_list[current_index][0])},' \\\n",
    "        f'{str(coordinates_list[current_index][1])}'\n",
    "        for current_index in route['route']]\n",
    "        for route in test_results['results'][test_run_to_draw]['result']['routes']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Function to give a list of directions to follow to go from origin to destination\n",
    "# Inputs:   origin: a string with the localisation point of the origin\n",
    "#           destination: a string with the localisation point of the destination\n",
    "# Output:   A list of tuple (locations) representing the path from origin to destination\n",
    "colors = [\"blue\", \"red\", \"green\", \"orange\", \"black\", \"brown\", 'gray', \"white\", '0xE5F5FE', 'purple', 'black']\n",
    "\n",
    "\n",
    "def get_directions(origin, destination):\n",
    "    url = f'https://maps.googleapis.com/maps/api/directions/json' \\\n",
    "          f'?origin={origin}' \\\n",
    "          f'&destination={destination}' \\\n",
    "          f'&key={key}' \\\n",
    "          f'&size=1280x1280'\n",
    "\n",
    "    response = requests.request(\"GET\", url)\n",
    "    travel_route_poly = response.json()['routes'][0]['overview_polyline']['points']\n",
    "    direction_dict = response.json()\n",
    "\n",
    "    lat = direction_dict[\"routes\"][0][\"legs\"][0][\"start_location\"][\"lat\"]\n",
    "    long = direction_dict[\"routes\"][0][\"legs\"][0][\"start_location\"][\"lng\"]\n",
    "    travel_list = [str(f'{lat},{long}')]\n",
    "\n",
    "    for l in direction_dict[\"routes\"][0][\"legs\"][0][\"steps\"]:\n",
    "        lat = l[\"end_location\"][\"lat\"]\n",
    "        long = l[\"end_location\"][\"lng\"]\n",
    "        tmp = f'{str(lat)[:-1]},{str(long)[:-1]}'\n",
    "        travel_list.append(str(tmp))\n",
    "\n",
    "    travel_list = \"|\".join(travel_list)\n",
    "\n",
    "    return  travel_list, polyline.decode(travel_route_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Funtion to create the path between all localisation points of a list\n",
    "# Inputs:   list: a list of strings with location points\n",
    "# Outputs:  a string with the path to follow\n",
    "def fct_give_parcours(list, color):\n",
    "    path_poly = []\n",
    "    path_str = []\n",
    "    start = list.pop(0)\n",
    "    while list:\n",
    "        tmp_str, tmp_poly = get_directions(start, list[0])\n",
    "        path_str.append(tmp_str)\n",
    "        path_poly += tmp_poly\n",
    "        start = list.pop(0)\n",
    "\n",
    "    parcours = \"|\".join(path_str)\n",
    "    travel_list = f'&path=color:{color}|weight:5|{parcours}'\n",
    "\n",
    "    return (travel_list, path_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Function to create a map with the localisations and path to follow\n",
    "# Inputs:   shuttles: a list of lists with strings of loaclisation points to visit\n",
    "#           paths: a string with the path to follow\n",
    "# Output:   A map with the localisations to visit (numbered from 0 (start)), and the path in a random color\n",
    "\n",
    "def fct_maps(shuttles, paths):\n",
    "    # Start with Nestle HQ Marker\n",
    "    labels_list = \"&markers=color:\" + \"0xCE0635\" + \"%7Clabel:\" + str('N') + \"%7C\" + str(shuttle_routes[0][0])\n",
    "    c = 0\n",
    "    for shuttle in shuttles:\n",
    "        labels_s = \"\"\n",
    "        # We only start labelling from 1st stop to keep the 0 special.\n",
    "        for i in range(len(shuttle))[1:]:\n",
    "            labels_s = labels_s + \"&markers=size:mid%7Ccolor:\" + colors[c] + \"%7Clabel:\" + str(i) + \"%7C\" + str(shuttle[i])\n",
    "        labels_list = labels_list + labels_s\n",
    "        c += 1\n",
    "\n",
    "    url = f'https://maps.googleapis.com/maps/api/staticmap' \\\n",
    "          f'?size=1400x800' \\\n",
    "          f'&zoom=11' \\\n",
    "          f'{labels_list}' \\\n",
    "          f'{paths}' \\\n",
    "          f'&key={key}'\n",
    "    print(str(len(url)))\n",
    "    response = requests.request(\"GET\", url)\n",
    "    print(response.status_code)\n",
    "    return Image(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Function to run the whole algorithm\n",
    "# Inputs:   shuttles: a list of lists with strings of loaclisation points to visit\n",
    "#           key: the maps api key\n",
    "# Output:   A map with the localisations to visit (numbered from 0 (start)), and the path in a random color\n",
    "def fct_run_maps(shuttles):\n",
    "    global all_paths_coords\n",
    "    paths = \"\"\n",
    "    c = 0\n",
    "\n",
    "    for s in deepcopy(shuttles):\n",
    "        path_str, path_coords = fct_give_parcours(s, colors[c])\n",
    "        to_append = pd.DataFrame(data={'lat': list(zip(*path_coords))[0], 'long': list(zip(*path_coords))[1],\n",
    "                                       'shuttle_route': [c] * len(path_coords)})\n",
    "        all_paths_coords = pd.concat([all_paths_coords, to_append], ignore_index=True)\n",
    "        paths = paths + path_str\n",
    "        c += 1\n",
    "\n",
    "    return (fct_maps(shuttles, paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11803\n",
      "200\n"
     ]
    },
    {
     "data": {
      "image/png": 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o1h5HK8kSk3F/yXKzrV0LB06AYXAUB/a0hqJkAhz5AEzmaREAKgsgDI0I8MMwCLldI3EImwGYNlCVUUCsIWk2SuegvcFQ4UcM8Ov3aKgQQIa/zu31k2Xlv8P+0XGZ48U7khhdVt5hFvGUpZ710/l2g4LvJEjr0TCZa8TztHU2m+1bCLdlKF83JEkoBprb4F8mIkeEqV2CU8cvbwRghtCXAGBoTaWxUTo3H7YOv2kWWD1fZcAdAODGeqcM/nprh4bw+qNiSVA5h4oJjHM9LyBewYQO80JVZjoLQGKfKhcVRI8HzByvzNP28Or6fNSdEHe2ijHFJOIem4piBGFXYapadz4a01aL/UfE8/LTBk0DAIP0QrsoQUCIVUqZxoHVTG5KLMxOOkgh/Po9DYAnn3711e917q7fPnEH4DEg6WgtfWfrI8W/HSoRgZS/kpwEjEUIA6Jmf4o95mj8PABO8/KqnVSAbEi+eu9kmrb3r71qDcMxA8V1QKYyUp3HugV9/gOdCZCQAVohOyOqRCD4TgPIv661qU/aq7TVFgBE/4MkmKd7s7TTD8Sr1GQ9Ew7cx2xwJpnDc0aJZcZBfVe5r7C7ZFDFdikUlhHAv/pcp3P79l1n/LWQjmX47ZqZAx/0DloSgirRhJfxYwZMqIj+9zPuf5IbDM1nMmVKyIs2cbMk5AFQnabd4XWtRcl4dULRIUonXabYj+l44nUxzpcNj5G2Kf7BNH8ymkdE+k5DUoki3A/xjAd1iIZvUM/jAOTu2CgBYIoChlmDibMKEkV+mJvMykNJeCEMaZkpVN97Ov58C/56ikPl5HPfe/WvbgMAkQQ6PbyZ0x6bN4OmInB3qDFttjxIEjRA50qylTYxNnFOmerUTAZ5RM53syTkAFCbpt0zr3IOSCG0XYVY9VFFOHfqtVqD/Z9CvkO1zZBVq9Xmo2qdUUs8tMVW3BSiTxq1enUc+T6ZVDxKVyX62H8e29OQ2lQagGGWV7jbYOBk4pqcIiwzQZH63nPFXy9M7tz59Kvf+4O7JwDAjSUN0Md9A1kUDPiIUc2Unsy9j3xxsflQv7KGEdtCVU6Qcfz8Xf1cj6ajKUsDoH45bZpOh6P6OQWlATK7JtWRBOAaIdUWIRXPW50wAjlv1wlpHEBIWpvtbRGvji9foV4LcrTZJSAgg7SrNAEghORVPSMAg8gY++jKgUUpIMej8TNEhbm4zvjrJSmxJ+vAgf8H2WAAvN1xepJDm8XvcK131EojUJLFbJWekGKkJywuNuYX/VDokhADJUVgLEpjnidlrvlahjRp0reEDoqJCKagAoRFK833NQBe6PvUvxgAa+e0Xqkg8shaSwKwBRFnDIAj8PaQ2ogHXIwYxYPwn9U1RhPr9TVQQrqNerXu1euMMNYYRkkUA3C10Wox4inutJ8A0I/MUbIXjhwYmDd1qmeivjihlDhCUPp9jfhTU2I7f8A48AalQAFv3zpxeY41654jJHYaBu2VOJICOOzFxh71QzLmbpCQtMUDgpUisNEzhS5xmueTNXDkmRVgAU7ijUBlLcjnZ/dVPSGeRlf0U0+HXuWMvUq9CIA84ILtjRgAqdeu1+ioOwYKyChnqz7vdj2ILF5lsmEMwAoDYGskcLI7PG8mSDCbD90JYNin7hw4Pg2je3PnTuKPWOzKiD+Fl9/6q1e/97kTJmYwEri+7kYCC8axirkjm2rsKwuQIacRBUwEGlOm9DEa1fAID9Ifr4GYyAPU0E8CRBK1TbZhToSKiaW72HlRvYX1TkKhjeI1ApFa3caQcdK6wYSwNYL7giDWVcVVZR72eG2Ff1AcGmNLTf/22TW7K2UIEyzYYwA8UAEoWPBBg1BvLuDZGEWkQplmwgA4qjHeXIc4uylZg7jiBICNA0jDFQtKBkSbM/RwuOIPpRq/kA9kBqO6xWSwAH/yQnx0Psc48HrExLDO7f+4vr5eTAJ7LsbmQ0EFD60H+1roGSiRENdNL8dMZ/Sox1RKphpS76DWZQeCB9kbe96lNwHqBSpkBGrmSCSUEsjJpJMRRSgwSjKHVNOuSILy2FmMxjYaY0b34ONSQDqqnrhvgr8SocOO4Xx23xWE3wXgz5cV7AzvMdiaXX+I6dRcCWlgYEVNBSAoIatt3H7DEwBs3+Ah7CEvCcUugDSZjCtt4icA9JjKUpsI0YsHRPtTe6yAey7wgjUmOeEKaQEZzMGffAAJwFuMA3+6g/LiBsPf+t3CZ2gV+3zZOD6Ce+V45/Q88pDU2iCiY3RMCzMNQHXsjiBDf46qIATvt9g3ZEpiFz9oHcP4eRxNxDDirdYhTxB3zisT0q0hItoUz7psMxmLqZ0gX1FaGwnVtFWDk/CqQocFLRXuu9Jn4POBBCYBcwnjjue1uTebXc28kQe1FdG2AqYTMMOwXyH1pTsRWKcUzDBzbqSJJiOP8iKhWBRvMibc3tFlwgcuQfwPzTCE2+qZCLg3MFle4uFsggnLSn/xVxOiW5BPBq34S44Q2zvrwIHfZ1Ph+48AgMUkUCgVRTWu+v2DvCz0VB0DnzIGBUaIBICVLhRrhVAtrgpC8H6rhnlaVciC5momCl6tNqiTUOerJnc22mSNwdkj3ao8C5DHNM1Wi5Gq1bngi7iP3bN+EOuwE8hF7NZXQPPwPd8DYT3whBZIUvM62DqfzR7yWCPBxH0Rqz6FVD80S4dYBJTElmkuF6DFXUa7y0otcXw7DhEVzzOem7PzXaPlJZ5EZxv0ogRQ0R38vBJutDCwRS6AW2AE7IhEzTsAwDtuT1JYZK2fi9F0BdgLhr5Ju0EUAK5VW4zxAgBRFTxge8ReD7OgPeRxI1qrH3iQzuStjpgmIHeya1UZDVmr1yrymuw/OKxVqVSqHOlMbuP7IAlMVyFG9RX8pJAVAt8c06VpgkD54LvNGdTmyC/OhUY0v3gW8r48fTqcnZO8Ajju+FuYAOpedPvD0yL8SVvso/U/ePV7n+YWG7DIwHiU+wjC2+uCv5yRqQDrhaTRrrdJlACQjA+qnAJyVRCyR/jeEWZBXwoARt211e5ldzTnABQ7GT1j7Ll6QLoqAL1ud87g5q1e5gAwAjMJApDhbyqMZcDcphSXvTatW8PZbHu3n1vz27dyTZ+UQODs/GlexIN7GKq/OAGk9r/UQYsCq6Qt9g4YATvgeMYj7wIAb+U+Avep9ZYDYBZ/vk/atVUmRR3UkV3WR5f1CVAiZMEHjCjNEwBSzILmUv6o28BNcwIsOKrJnbRRPaARsFMVgIwNAguGOthjwYJjAMYsGO10XQCgF8cLg3QFhcxCsVblgHDU4VZBjeqcuIjcfdoYnFmiUcUoEQY9XZQAptLp7ZEJMQBtFxI1Oh+tAwfuxeUEOAl0MMUshz8/nXsFzGNKVhskIJPVeq0mWDAoCUyz4KqgCkDMghYAnIMSwiT4SyIypXFnRNsVJvw3qgoLbq+ucS2YaQGrUjVVAMh1WAZASNPzVld4uk5SqiuiuEGb1e19JgHu5teoDnPpjUtpVxjN8/O89OMS+PNdb2l4k1SBE9uLFQJQuIKBAH7vexdJ6GanQA/pcQ14SQKYyf0D4SUAldEPQJGEdGcKqqNHoFoCL+AFwfuRx400wgyDaibUrENP/pzMR3OxEzTSMZRWGo9Jl8qzxmiG8aA8u8f0UG6GwX08D5udSOlHQkv1Vri1gT/hBTgL0dajZalDOPQ5JmXaARjkm3v9wkRIfqdmTk5wuTSQqXOnCv0Wvp/OZLCGJkgA2p8Bz2QSICOAJ36S1X1yK58EHiAAl8RfppAfdx+Bytjvy3RnHqTPlUdpdwbVEf4fyA2B+D+KRv6EVzaOVdGwL4p4En5WiJpmgDbnKAqlkVruCwLUS4nUUlfUdB0PcmhEWlfIDhXbwQbDU+KsVKUQYIETAnfPZzkEsFQakjHtTTjR+VyKH9regJBMzJs1sJL28/HHqyP7nQ1GAF/lHUBis0wuCUQNpLccALNeJJP2hq7OsDhPWp0lz/1oY3lovWblipqu40EQKU6TTxM3PthgOAG0q8HFQl7g4hHLJYCl0jCnYKVPHy9KQIsORlOSSg2Dkn2k7mWovO2lad+Iv7ce3ITx4C0054Tvdzqvvvq9EyaAhUmYw51Ca/Szx59x9gKMlSnlrCyh3U1NOUy6iL2i1ZoKRLMUeH5ePzHgNpg9TphsanDo8Ehh8WvuDnMIYCn8sUlicm+YuB25yxNKQPuig5FP4grRYezGpLQyimJGxPlM36zE+6ERgG8x7H0DBvv5LnSJ6PzHV1999YQSxgGDWNp5lGOKOXwGBDCLP6v5yqUSf/rirhXbMwDM1mBT6gMyISGiPPZYVJzhiSxggxFlOSxqsBN1c0Bpc2YvS1QKf0DmW3UaybpDvLozLwE9FR2MQpJUiE7cmAyA4zGhihgOUE0bJgOMayf9LP7+mqOPj5uvsRm//QcAQIZW9gJ+HAhvt0b30K+2FP5MURxW8ylEa1kr8xkHVYNac0cMVWyTR0KDSVgLx5qABhIplc1BkgIbjCwQbanh7ygUFPHp0307ASyHP5AgWvW4/PNq3UN3JZSAZsx3GikdjLgDNHZj0koNXJXCpNAVNaHVUu8CfOglzuDvWyr+GAK/RU4+xyTAdTAJIg0KRV7M+xsbG0wPYWBbOzB19VgGgL4hBNIewAbHBmUgCEY6t6/hU6wZKNFHTLKmBkBY0yI7AjcM2m9sbm6+MXy6JSUzIwCdrXxR/oHghbO9Sin8gQoMVtC4/DNFd+UNqEbPw2OVDkbCASrdmLw0tABgVdaEFqwjmCbgw8dK3fctHX8Mge+CBvIHj9AkiE5s6XI/6XQ21p8H5B2rWR5HwrP77PDHr5NHAOFHCQhiK0Kn7zGlvBp1PGNh1lWmZsV9RLn0h0DtIvjkeKPdxWNM4oKLABhPTc5zAwE0G6F9rxz+YIqI1ooo4u5KiIvF8Ni4g1HsAJVGfFEYVfGG8prQAW/5lR/km8YfQyDYoO8Im7R4Cx9ayfqdOy/Eppgkmu9A/LY4AP2UTIfXsc9fJLmkMwR9mAcnBOKlA3XKsq51BYCixqCPqeIJ9OLf+ONmwOYmAMaTY39umxH6oiT82F28S6IDkHB3JQZmQ3hs0sFIOkBNABxpNaHDwqRoSQC/f03ffSAA+GkkgCIDmdMhxnMPDvr/+2c6ncQUc3igh/QtjL+0ToEXsjvQVfOYMwTRbuN5npIby6ThTKasyX6VafKgAFBzekrU0Q+pCsCMGgzBMyXgkYPWwR4vHZoapeEHN2k0GEtdS8o/R4S7K4EFQxcppYORdIDqABS+0qKa0KkhQPd9+tpffHAuSODvIgEU+iCPJDvAOL6TDWDCKWu0TDnX8ZcYdhzePfWFOQG0Hh9pH5QbZYpdmOhl9jGK9MJLhvahAkuWZ0o+VQCo7RCgG364+fSpAsC0GuwXZU+kh9UYMzESwAXgB6vkktcni8s/R8JdSWu1MWrBDG9xhWjuABVuTNlQlftKi2pCp4YggA++/41vvEYlD/4elIORBgn1bToMgbcTUwzHnkgEUQGoGnaKIBik5Xy8kF0DMfkHXFharM76FxeJKUB9O98WzZsKTVFyQrRHUQD4oQLAtBo8LUP+xKMbv2fwocEIfeGVqECkvjol3S44KpPyz+iuhBLQaIZBR6RSITp2Y3o0rhXtxQ5P51dURMDzdyUA/wrjXmS1GaRFQum909FoYEupvqbgTzfsfCv3ATL44wB0JYBiuHTXMHfsUhCYk5WplexMAKhnKccsmFKVAqbUYN/ZJKne3lTOev7ztBcOMgVKX5zPX4TGZ7+flH/m7sppXJdGrxAt7c5MntBKQwd5NaENI4bKu/Q60UIQYVK4Ammsd8BD6B+tMwTeXb8Tu+QSMTABYMawk3P/0IC/Xl4ZE4uD1KXFldl9lyAwr8CYioCVeHXoPvdEAaZv2AFYngD2zcaYCRDAobJhYfgtEQbj2OXdPhKsPHj3A4kYHvgXS/fiEyHC7q4zLty5fSsnODVr2LFz4TBThZO3bCpJAN1aXPlmB16smeQ1aVX3rcS5mDphkvB7GhPATXFb7SEW+WAGVTiYNxkBnMfAWRx+C4fB8HOfCQD/4vugiEjAcBabLAqOwEMMOe0gAjdyAmMMhh3boYZWLwhAuxRtNUq4WNYsfVh9oY3kXUGd5hVffOpU3OVmdoiXVC68EAEU7z1RIr78+fzhbPh0LhC4DPyWIoAFvZULh6IFxxTwdzmDVdYtX/Kc3T66BWy4s37rbscYG5MQQHbRfBJoxl8vL4fLXrSE0GLJw9qJFdXjvK+gNlhckU6GdEUosyGaiLfECnPRQgSwj3oWw1xC8Cbzp3uzh2wL+hhLej0y07I4AQwWDeIX4zUBl9eu6bm0A36X71K/xwV/QRQET+4IIriuMGIhsTIMPIgJ3wcSgN94YLy3iSUiAO0MOK+4JOapF6zkPAtcQD27AUNtggFKCB5oKii9u42uuKeJLwU7QiD6SBCEC/cYZasV4CYgGMznwyEHIENgqeYLpkldnAAuHMQvh4FhClhp9Y441xFOt8767Q6HoCSC0zhBNogvuJsA8Kbx1oaFh/jLKaSYX7UpmNK85MS+VQzEAfTMasQIUgDET276blvYJjjg3jnEIMM8/r7gR/Z5QB525pxLCAIx/PnZ2c/FhqXw1y9RCsswaYtjl4/XMirDZ4V4lwqrBjYsdd5HSAOZPnz71p27d29tnIDHDwab6Ph69DsFAMxKZLx4Us50FroQcpNS+1YxEAbnsp4tCDH5Hc0w7DCT2eZ0C3Ph+v0Yg+xHv1zojj4IEY6tObvaRwi4iT+Zzz/avzo/539fzidLwMA1MMd87uLYleNBymjye9LQnJZwemsHMXk4udPhA4omwE/xCkECwHff/UY+AA1xSgjAHHEmcHJ55M6JVQyUdk8UpzBu/EKxhmcAeOGJIvV6eNH2vtKawReBRKVaaGjDn0IECtp1AwCgQCAbH9HZ9dVV8jdD5YL3WIYAujZ4zx03f1cD4IZ09qam7fC4f+wjtfTnAAAgAElEQVQpuhY52QBGLORBaRkMJAC/w3TqfAASowbSy6siEbpIywX6sFUMjIEZ++kuLmJinAEgE1S5PqEFWEIovtYdCTHo0gPW8i6k223xRrXzOYW6gRJxH11VKPsvASAb5UsK9ZcjgMszYBiPNb/FqzEAU2YJ7nFL6pGF9H1EIJgF1xOFRMqA7+LiP7UD0PDinADmPOrUSYfMz+m2iYHGPBo/aTmXbBQA5AtUz7GBSNR0g8JogWJn8UMRaBFVhzw+NpsjLFvOiB8bH15Vrq8ZAD+i8D8qdkSktJKzBAF8FgyYjcF/SRB483uMmsWPpl6+JyNQYxWBPXnMhZWUOakFP3jwQOLPqAVnq0Ag/nJLuTuWC8gP4jSLgRaPvx93fU22CQBOeCl1DYHNvWx3rsXLDfR5DABknZHxCAE4QkY88ujVdeUK6+h7sGkE9Ypgx5i6u2Hl0y1MAMmzYMD9QfM8VkRufprhKS6AYO6/GRMFmNhg6p+cPLqlIlDRamI74GuGqbWoILkKnaO9wFcjcI23zlzGKjnK180CkK9PHYBbZzIXSRnuCSnZR+UApBMo7PbRqAL1uagHBdzoNQ/VO7gB9bsqjfqNNXrJdtRLGoaXIIDuUbW5Y/d8tvefbkoFhAEwTvtQ2c5xUtAqhkiscJ7c4QjkOXNOnpDAbIPu5RtUXVeriKi3HGyIs8/RbjgCDSwYp1/HH6SjZzu0ugkOxsEAiN1qDxqUQnUarCLdHjHp75ID8HJ1Dg1FK22KQfERaYxL3WwZAvhsGPDp/mz/VIiBN0Gj3UicbAqhOEzq3CdMMon1vcsRiKrIaw6+YAjWTm0q1ECM3c/tx4bEMrckm9WZ+xkQgSSjhFwaRMDdhzIbWB3PAIBQXpPLgFDEt12vV0YUAXhwo9VqrFL2J1b0bbQn5SjgMirwM9FABjyrBUFz87MpAJpL7yd2OoW38Lz1dSwlnTbsZCVAn5o1kIJCsk5KcDLMgTMw5VG6BmEuGbjw9epjvETvHJuZ6ABs7psatC4HQBQBNQA2Gh9RCcDWDYiAFwCkk3Zj1Stxs4AuQQCfDQChsAMs2ZucADIAKgXxEy1EC76PUaIK+woCScqwk7mpCX/9Qg3E1hjOPmRpm7hycgD5gVisXINyQeYZ1sJS/uQA5NX39TRrJgIaWlQ/AwACC263BQDrbXopAejdmEORHA7A1gHU/nW/mVLiZpHh2J4wdyADhl++dZNpIAhAJc4lMUVr5e1jmGh05CRWRcJrzbDzj5m7mkJDi3wg/LyS04WqBZSuJbzpPBRChUtE2hMUp0h62Yjoi0QEjHeYRcAlAQhmmDYVSoho5FCvrQoAMhJYv9EWABxj3WjHm6GFsqTKrI8l42BwxpK00ptIAHUAJute73QUh8NpdCxGoE/fvZknABo7CSADLpiOIkqVHaGsm4FFIxLhNVAq4TnAWo+h4q446mcIIIqAprOXAGC3OxqNLqFtN8V6YHMPLC909CF9A6wv7I/uhFL4/0hE1TthSjgKF30uHM9ACZYMmI2bNzMAtH2ZXgJAbXusDBOaGHb+r+ykGul+fhCCvF/pBRsa3bBR4Mer38WYQIm6NqBCaj8WAZXDto0ioEuFF9vwp9AUkPBSLMGUkDkDDtqdz6/Or9D4HIny0kDkCYbPF1+V896FQ8Pkay1tBYwZMBufBRVEBaCv5rNqMmBPzHpWKRWC4F1KhSJiUEBCo2PAgQGXUoLzh6ivFfYd0cEor4LAFQ/cIFIEVA5L++HECJYIt+uHWsg4+qkDcLs1Z8OhEp4Fw9QkJMzaomQde7fqb/ZR2J6wcCgMWJRAvZUAUA8S05oqSGuxYWIFAjuECAA+zry+2TFVqAH3l/yQ2qCBKHLqu6WIw/HJ461AZid86xT+QATcMqTpLvPcKTOJiMSeYzRgcy7jUcXI1FHyo3QcmJ908liSAD4DDqwwYA7AO5B2KQCo6wmaDCgBaLKKPBIIfB8Do2/+dXq/b1H8iwXA8kpwztz5/HrU0QwB+l5CoFf6nncpREBtPptGEdANgGwyDcUh01KQMJMyGijjoSepW6l6BabUy7QbP+A9fiUelzFB8/OX5sAP1azSDpiREwAGdnk/9peZhGv/13c31m+zy5zcNFlgbPjr9xxaqSzh0koNwcqDyNEMgdwmRiA0qiFCBFSP2t2LS2JpwwWAAQYOpuvTZbbISQohI2TvqcaBxR5ZzBCCsAF5WL8QTQBMRAxU3X+55bx4iI8cA7aGlD9PTnjiOQegHgyotXUDe12Pv0L2600nR52NjY0XOp3v3jQkZEYWb26BD1ie/CwsnzAUDdhpGnnCpkQgUEBCMvgDAO6ZSlU5ABAPwRJ1Cv0y+KzjVTqRQmBmOeNVMHgGSSp22ginYWbqjJQgmBYWc5FjeU9wts22AsApjZRH0TobgcOsZ3uFabdCOhsvfLzT6TAAhjyD2ZfdVoktmsAFf4sowbYryV986kRVhSdYIJAB8EKIgNpR28PSABQ6gmSHPokbh/jGKP54mvwJCIEfpjiw2AeKs0RcZM0izC49n5PKyA2EzhXmbGN3mANAthYiGou0KRHwwre9AgIw6DAA3rlz8+bNqDuG1oGEd1vN4XiFFpj+s1SClciC0AnVUiDhCFyBurz9bKnF8gCUbeET2h63TjJ/3mSd+kwIZCSwCANT24ybSlfze8KXd+HOywqRDIBn6el6tKEZogMjeWDsUnBgw2MCAMPp33+88/zHP37zsyvQHLIBndvaUZjTh1uQ1PzhEo/vNtTw5siFkxC5FhGBDIAQm5Ap9VkagJBJGmLaiIoj2BJYGJwiKDP6wYTAAgiENgJvQk8C1tBpppelCLzHpzYYALWUc6GY6X4QgT9zXzsOwO5K5/lPdDrPP/dr0vBGVTJetXc1heG5lHZbwp6bGioAXdwUDA1QBw6eHhCw4oWcA6cOYwBslgGgkKHDzBqAtWo+RQFgc3b2cyMHVoafQ3szk6mA1VHDLVPm0DC2s6WV0gDkK/NYb60q0GKmwAkFfK7TufPxT7YnlFbH4EuP7JTG810A+OyUYBcAhkyI8rmIxo3O3G6LAJzzpqPpU7bOTZ5gGwBzA6VtXoYEgFAb9cMCDmxnkoY9Wny941Qvp4g0Z3uFAMSnPNb8IIxd9vjNTdMKAJyevMAB2OkcNVY9utaq5VYduLhwKq7qxCzd5s0OQJ+3KeiHkI0Lwcj9OPIcEYgAxK+X/bTmUAQLAPOzwSML5UoACKUBJwUc2K4mZE2qOiRdbSxLKSLb2fLCKQD6xscQ8prZUzwlq90T4L8MgCef/FsKvd28G9Uc8sfkqoLy5mLfs1OC1TobOgB9Ivt5tatj7CCsFJ8ABHIAYv+lzGXLADAff75tZwzAASMfAz//69urZgTZu+tQClznehnT2HZGCU4DUGiLh3pF/FgEND1jQNqV5577nXWGQRKNb9TrtY8ora3llQXwiurrxwBc+FVTQ600pAMwJGsVpjhNqNeYNCYAQFBLeavpEPx2CEDLdy0BwCA/u8Xa7jQGYH6DLnEVmwDoZ+Ng0szUldssoQqn7NA4UgAUrOooJQPKKTReNiQnL7wA3HdMmHQ96pIoIrVxjrDq+W4AfHZKsBbIpgNwOq5MCGkciIaFrRYldDSGdlRd9q/P9aAV81UH7gAswJ9v/fwSgAUdCguuknUHZNQJ10gDf/GYaoMdOg1Ayap0dHCbndWTE4An5HYHWwJi9yrvoJZTMhjLX+bjj+8sGY+fO2nK4+jWTAZAxoC9MW1Aq1fsZI1dWaCAMuSbIQItADTlBIv7pZZOURyKvd+zBGBugy75LrarZN0BWW3ZzRCDqemLqsIGO3QagMaQV+G1tepwJ2pXQ3BxthvjnCBJr7DDTS9P5lxk6N9X84UwFrzaaBN6Cb2rGzTpygIV4CHfbMKOzgOgcU8KgEX4s5OuGIB5Dbriq1g02dDkc87qxG50jVHKRc1jBjt0GoBSdTDwYDvv72hNDdHOmiMBQgyMEwCXtrvHI/V9NV8IU34Zwa5e0m6jXq0DAHlXlkuZb3YxpeGSACx0vtgJoPQY5TXoSm5jvkuQ9T6adFm35Y5z6UotU8Ngh04DMH4uzRfcF/27LNe9pfc0hIBK+3zjhOYDUOxcNngjGekvo8bFYYtq0mhcrnaha0tLdmWJ880u2IMsB8BCgpFDAKXxMadBV3JT820MlUmM1jziJPDwuVwMgAY7NPiCNQAmX+o4ac2FALRahTkHLno9ORuo0LgA8NkRwMyXUTkiaVeZErLWGEEjNASg6Moi880YDyQWAO7u2QGI0a9h4Lv4c/KK+HIADqwNutS3MnecMCXlE/fzU4NwE73TvKeHwQ6dAqBmBlQwctHPMcp1Mp3Vc4Q3T1w6B4Bi57MjgNmZVXzvU7IG/Vou5zU2EIC8K8uFzDdDM4z5wgyAZzYAYoQUxuQVvkeQ54XgAGyezwoJ4NTm/ssqIGb8uPBgTqyDxbRDgx06BUALsn0OQMtlb2Ubq1sRKIOgiwH4DAkgMRnl4laOYIgeg+mo603GkIKGXVngb55vBo9sB6ApJQknS0bThNOCz+qT3DdFAA6as6viRD4jKgwhhrb7uRhiOAdeEIAPs0pwCoBacaJeS4IEJ8H22BiSfyu10eKwiYOgiwH47AigbwxDiq8uXHG+qPcqurL4ootLHxFYFoCBew3msCBEG+d+93x2XjgdZlRkL291prmoFrxI9mIAPDXYoVMAnGpvKXvCccXBNqd3Mxy4b/M7JUkWOQDs8bL5z9ANZ5hXi19sagnJswDQkpSZXxldH2FRpjgu2eZsf1CIDiP70lVH3pfWBngHQ4yoTzldFIAWChhrsGkz4LEyCRYfoGYEVF/G8DZJFlIBAJ8lATTHeRgj8y3CmO/ZADg0AzBwLoBRXPAR1uzucPbQL6apJgBqiXBBwWMVPrR83MVa1Zjs0CkApiMI1pJJsAEQVZA72e0GBKpB0LkAfKYSoNk6ZozMt4m5F3YA7m9j8hk2rAhwQLhz5BpIVlzwEea+eX6261CXxfCBNAv4tNAc6aquLwpAgy29c1cFYPoVhDlaUEDj+jBy4My7w9DS0K0AfNYqsM06ZjAO2/sfmgHoQ0D0PhX1CSI+eOcKR19Bcc8XlJrRDVwcL2V4fHUeCxfFtCjUKna1LAZAkx0ammEmJaL7lhWCyDHHcVo4MIy0tUnLQrIDkL/pMyKAgV3FjNLe0JyebiYAsmfcPpvt7Rq/mVschV/s7EYd5CGYL4oltGwEnK9q+0VgZ+9fwKJjer1YlSyTHbq/rnZETwsBEiVIAc3qmo0D8zNUBOpp6PnxqM+GAGI5Hmvh3kx8ck4DAhMAGbnYmplTQvqOkWQOFZdh6oX2WEgts478ZB4dylYzJYahNEcnSuj1Yk1QTHZo6Iiu2FBSFjOhBnPjCab+pZ/uZN3GgWGE0KpKGDPSaeh5AHwWBDDMRR+MlEiU14DAAEBw9+QCsPgbuTRdEzoICO/FEpo95jl0eB5UbfMOTIK2FwOgyQ6NAEwIWMpn0BMATN6CT5kvu8RxI/Qt6y1DTDvFbOl0GnAeAJcmgLkFo+Oh+Uhz9dEsAJF7MgA2LQB0ieRxiTaLdRAHM12mxLzojBYSp7ZhnIDaNWVlhS4GQJMd+gSU4LvJ3/mpgYFIaoUadhCuGfDKRDmdhPHFaL9fXIdIHUsSwMAFff1U1aTcDkAAwCAMg6TyFBxujUftO1W1cWnjmuggDma6tCEQjZ2B01rEV+Lf3ic2n3LyRgtF5Zvt0KlGwCkx4pjTqQQ9U/QxYRXPWnX8/gaMDvoS7I/k66XOHMYyvRxdM8/FkST+Ne+WKxGESEeUyPnBeAsIhrEBsDiYNnCx1IDlavBQsK5iLSL7ekVyiDLiGTBPhbpeFopFMNuhoemRmhasT4oIC1TIV4BGBqxjvFZj3LvTucMkPYhmt3/AiJRsLrpMOqZfhn/HCDQwYF5chP+7QtuE1rtUlmPmCn4eAAtTuN30ZM6BRShgoRCoL7yAlkCfSmCN+WSajWAhAmG0Q29oSnCWuXMhULUgwwfmAGx/svPc73c6vz2iB9X6ak4MdEhLNhddvCpbUORazTwaJ5dZtuNDcRE/5CVGVmjDEwAMQPqFEqbT6eDh1dWZ7QsX4MuxVqTH6yEMxMMWHK3hglCt0GXx1Cn1m4ytBpSN0SKuOJMZEHUQNZSvIIZATB0CcLLyPADwUX10uToh7UYOCSxL0BbJRvKDKdaHKtmklyPQEMHokzX2SmRSGUfhdIV6lAOQQhInpeOJ1yVXs2vvjQ8tN8x/BddapR5kksnv5hfNikK6gtL1yBMVxhQuqGvswQIkkL3InkkJ3tCseOlpOT4S85AMHwFYqaww6scAOK2PDqrQOsWehnRRvtR9KQAyGRSxZ6hI5nQvatSAyZitK9KuQte2FUh25v91GbVv03qjVqtdX9dr9apnpta5r+CKP/8Cato247MKDk8k+Gn5dgyKEcewHlM0LyrPoiCvPrMxrYNkpUsemq+xUMj5YBTwk88BAP8HqY9aVWidYgfgpKxS61arCYasATpdqG8pv4SlaGpIam3M1yS1+gqSPwRgdUTnq7QO5ZdGo8r1eatdHoDOtZov/KZS07YwBkViyF+kHYMq/md8cmkdrXy5ciMB5DqIYkVJEfmeSQgEeDAAvr/x8XUGQDKpjrzqhDEmK++7KCvROX6fgHDsBYvbbIKQ9y4wXccnBw3GgT3abZMEgB400qp49RYE7F/X6i2PlAage61wb/dM+WyFXToEF12sG40KsbQnIMsiSkvpRgIInmBNB0kR+dhfpgmB75+cnKxMO53ff67zQqVeYyShVa1X29Yld1l2NpxKSELxqkVYbnw+D5hH7EkU6s/J0Ddhoi1l8m0CwBEAsHXJAUivu2urpSlgiVr1w301GTNycKbx65fSPvjQrYwpMTBrsixLAs0EMOWIy0S69noyO1PlwVAE4YXOxgb758TrTsYTboaxpvbPS2oULnY8gN/i6POnUYruCRVGz94kjXa9TaJ2zVNkQKbuE2glyAA46jZmV1hMxjCsAHQ3U/ZVAbDvYPxAEPkLdctOm400MdAUplHyJmYC+Gijc1f1g2ScOXFMtAJAQO3Gxsb6OoMgD4TjQe22W3sldWAHt7lPljJVo9QHOksKCKma4T5p11bJ01lr7UoBYLsKrXs5AD8CJWRcDoCFIdDKUAXAvjVWSb0p74q3gEySmVBVDDTZZXQSWMSLzAQwq4Ok3dlxmTYVgBhAY4/CSg2/rAoyLVKBfbJUw71+aLXPpnA9JauN2dmVt8qUkDGl448oUPtJ14vomIl+gw/p1cBqgbcV0ilBOTQB0CkXkukKJdJRtDdPn6SIgeagCeVF/ELPgZkA9u921GjUflbM7WlpSXwAADdyg2C04ZVsr+MXTOCy8OuTHA2RKjcf7O7Pxh9ez2ZXT99YYWLiFKq4T300RIN/JBxeXQ2fWn2QZgCWwd/pvl6NI3Sw/kZ0IQZsCp1IxMDIkm4XhxoWKj0WAti/ldJBMt7GmAUrWggH4K11jXdbh39RMnqW5DpRl4YfVk+y7+XMmY3p2f7Z7Opqtv+w+XT+0UoAERU6f7BWJhJTaU4RckdHSgA0RFtZXmARBcQ0p1IMtLjJpWCIM1ogV1gIYEYHycRbJJmZCQ8+Wd/obNzBJiMOwysZvp3LQfyikF2Xy+dewZfaCZ3NzofN5u7A84bN7RVErU6CmtasdBwmAJYq6pMSAN16pToI0IZh8fFxvmyNU5tyQWZKR10mleR5oLC2pmE7VsjXdJD0g/SOzADsWGKgsxNyURKAOTZogJ9LZ0j7wLL9hRISZBaFw9mwues9ZeDb2t4b8nhAXQiDjKScekEGAJbCHxMAU+GbhVYY/oYLuGktUhIXA61RY9itM2R35KFRBIUTGSrKf2DDz+n06exqaLoCJiSpglzmQZISGoopev12p2OPQdWHVzKH1G4jWxp+PCfWVQV9ONtrNre2Hu4NZ2xwAGpm+rxgQBhZAJbCn9rWVF7RQbkAbriADdpyCoiBfp5+gUkPpEIiutagWNKTRl0yZj8m3UlXdjbutkfjmWne9YQk04MwFiwAqAiBTAbsrPedBpxVKoveZoNeGn5hVCI4yR/8fIuBj0mBOIZ7HIAag8BW1Tn1ghwrAlkGY1pKKT2wUjrlugPNckg0SQ17VSImBhZeLkQAtutQU6dWnwM9rDboqFKti5qf9Xp9tf6h4SqgTdxWkZQNuT087GV58C13AMJJZQBoM9KGy8HPxy6ZbnEy/sUpo3xNAb7z4R7jwU0OQI1BWKsiiJEOS3TIgFPG7pmU2rmzMJpiXH1RuAXUQSjOdU+PnIRPB50PAfhRjRf2bLRopU0mq96oMpE1PyMyWB2YOM/JbS0fxCSL9hIenABwAwDopIL4CwDQvLW0a12bIXfid+ENt7b3zwX4QADcPR2InBBdV7S0qk7ePbWWyhGm5mwmGHDcAy4iGPRD8lx5dKHqkTlM1mHZYGhUZY2Jgl1CGCGEH/WDEUNlhDU/o+iqdjAzwbijd+kyyaItkxB41xmAXtlKSqZ0uOXgh+2aKXGUi7zB9hYKfef7HHxi+wp/EOUxCoww/XRYd7kGL4wDX0/72k1FaY4QG6BZaByfvrIAzC36UfzYQAEpI3RZAHq85me01rj60CgC3k2ZobM3W0sAmJiiwRe84eQEQapZUgbM9rBaAn4oJDtzb//p9tY+wq+51dxSGSwCUFufzWauEaaf0qdK1vVmKvCQn60krcirQul+Myw4Aylp+c8FrEu9IgAgXR2lWTCJurzmZ3v1w5mpZvOj2yACnuTe7MjEgwGAnfcdPirPRCoFwDBTXLk0/H7x9S/eu3fvi1//BSd+LsYL8f5NDr8hg9+A0aAUAHUOXGCEgRGpndjKaQZQkRzODpKCPERNXrHwc758y6rBeYB16J+E4fGUUTroK1BjSkiVKyEkmvCan6s3mBpi4EFMB76bbwUUMqAGQD+cPrq7vtF53yGnmivOpQCofSk2/aXh98sv3vvyV2F8+d6fPS0FX2+7CbLf/vbW7gBTuBR8rfRTHLjICIOPn7xLyf5WDP3NwZQGqm9aj5Ow9aLDOnolfcF5gHXp60i60B+mO6fcDFPpcjMMmRJe87M76ra7WQCCFVojgKaVcHyQAiBTJ6OTjfV1BwB6gmSWqyec8OCFIq7eFvDjEHzb/USf4Q9EPyb44d+n+2kKqE1PkREGR0wCy3b4AyN0ysMapDPeDFcU7KMct88127i0bgCbM3SU4ulaU8JkQG6I5jU+CTm7On86zYpBaATU3LmGd+qtJSTwwhfWkM76+u3O+0XP5cUsuxQAxSQGhC4S8Pf6va+qwx2BAn/bTUnVdofnOgVMceACI4z6LuUNI7wnSKgq7ukywybSJABbSgnJzyJ0v5R8xZDUx/qjMXL+0HDCo3RCphHtvWMGP5Gd7k8ER2PnbXSKlJAEf+UACGykuKyLZfxCxx9DYNvxrhJ/csOp3hdmJe2RKjLCiDfnHzcqHz2cKaKSgonpY0nxxbXjDD82V0hx12ekgAL9RvVHtbR4yhJAk7yJ9I8DMIgFKhcAqpno5awC3GqymOL7xS+nAPinX3Q7MY2/3X2mCSsYWEl5pIqNMDiAY0wLq+5lhiF2JA0qYgx1EBtLEFyS/3FKiA7yUD/dI8bc4ilLAI0133pHBwKATCCbiI3IgosAqDyFKfg450UW93m8I/D35Df06Z8LOfAXLideNPPxxwCoS0q7TSYCFl+XA7CoTH5m7h5m6+ileaEJYzEzLcGDCwqkl6Cl1pKBD40dTrIE0Kg89cAZjABke6UpGsrid07yH00FoM9TJ91QGC7cIdMPv84B+Ap9cv/pUwHAr7uc2tw+V/HXPJud6ZO2kgp4czDCwFisxMjA0JUrSvv1TDHyic3aFfIFqUWuTaxh2O7JtDmDCHiSJYBGZRXQJwCYxCNwAOa/40X6xVxRuGjAH2gtQgL881cYEaRSCnQ4F/A3i/E3YPgbphCwMhqpkg0YYR4Wi4ALljk0cmCdOhBTrFdCFZ1Ft4ID89ZPiEM5Fl7VDzPOanMsamcjQwCNUVNcBsTr92NTNBSl3DjJf/QL41aOwrwzFyOAwuScqCB/LimgCwARf02p0w6a57P9NLhWbmiFH3YfOomAC5a4WYgDa6XmXHlwPqn0p2BbsewMIZdV0Td4qNqUEI1T+NPpMPM2ICVmspEsdad7vYMjIwBvn+S/44V9F8kPiS9PAONAvxiATyn9c2cACvwJPA2YNNjMWJhXageqL6npYoRZlJqbOHCaUmUoa6B1RHflwblAZTzF6xKbFz0kFUoryV5+qWm3rgFwOunOrlIioA968kkJAB6bALjeyVftcgCYUzzU0Fi+eEzjlPIYgH9+35kCDrY1/O3updQPPlYaWjz81rm1NK8yFhRnTTwrY4QxTJz64R1Db/IotE/G1Vp9tU0gA4tn3k5FTyn8XQAQg56Rs8EjAgCxaYW4/bRbuboa6ucTsjYmv3YGIOPAMQCFZiEAmCtf5wHQntywQLov5viKeRQAvP/kq1/dkSSwAIC7W3szFX/7Rvz1V/Tky5za0NrbLNbMIMuB0xGjhlKoaUeJi/8ztz1mSKptSrqrUJupSyARdeyhs40NtoFGAMAogpoEESHeCE23DIBs07grGTED4PX1lXY+pd3agffru4sAUMAKAXg3/xVzCwLaskTK4w+5byh1uT9TtODfCC34z/JOZ+TvTMHfoDlMq79irOjmVScALqiCmOy2aTdI2r2enTin6JvcBsHTcYWJgKR1Ses8rnm11vYwKZ9CZbA5BQBSXhoSarPcaAMpGNVpBLsnBO2BDID0Kn1+q9oY/XrDFYBrKgAFrACAt/P5Sz4AzS9esrYkPwNkPynyvK3YAe8LAM2RW8AAACAASURBVL6TczonfzH+Tpn6MTQbV1bqqoNp4ATABVUQk902E6OmT6Bp4Tq4n/OL9DNixkgaI10HdUIO6rS+RrGTcm3UrmPRMASgh6UhaaVLu2s0QgCK3RFEYAEA4/Mj2maksXIJ9SVOXAF4dGAB4Ho+tcplwaGtr2g5/CH5C/gX4Fu++KcpT8hXczwhgvxJ/EEeuhVWK93uJJmfvNK8yVhMBTE6rtKkVM8QNk9cYdejKL+EUQxAqEIyqbAfEOhC2IZGvdWqNzgAeWlIulZtjalXr7cZAPnutXqdkhAAGJ8fRZODer2CVZ4eYWFKzQ7oxIL5E2NvhmmuhJNLAY0BWkHZlH4gf+IRpJnsnYwv2E4ABfkbbgn8MfJ3bm1KvjJVwzkyAAwMmv2CKshDk+MqjSXtW9ksB1EeAosT/BkLjqbAghMAEg7AOkNYq80ByEtDUjo+uLE273YvGQD5bq87uoQyTioASaMxIQhAghRQR6ANgMfcFYyzKUzRdwCAudkCXmkAlsUfKB/JCpaOgq/rzmC7H0SQv/OHWxxITPud7e9aTXt6n5AUACEXl2n2Qdpbu4gKcrpv8tyng2o1Lc5WSycnn9IlwT+kNcZwu6sxC2UABBbc8A4aTA3xOADHUBpydFmfAPMlU/gXd19SQttVcnVQu47Pj0i9TTwOwPd5eZdbCgLNAGzFAMQH5rhCAJ7krHAvvyuDAYAlS0r4qRK0Ug3paeEIX/6irRvOgJO/va1dQf6GjPzlMNUVDZoxAEPMUpuCGEoivUjwol6Q2blBCkibAVVA2mvp2BOyXfztPgNXHaoRgxLC9AwA0JgrIVyr4EoIloZkLJgpIWwagG9HbPeNMWHTAie2r6Pk/PZqvbbaZecw/ZoXuFIQWABAHlLEAXhXANC2iArwZ/D6lS1pkukaJlf7Y5UJ3+uaz77Yxdj74bamfeR5Nlb21N27TQ5AQnkhDykI8BqDvObgYioIEEDDc6SFZjVS0l5LxxqKPnUxNviQaM5Y7pRIMwwJiGKGoV32nzDDsH9HHpgJppMxmmFgUfiEDEYfzpTzQ3YwGUOqusc+9511vcuRnQWL54H9XAjkALR6NIrwZ6j2XtIAk21oFrvNlZDAe5ZIGG+4dYaJR5zW5GofYqzMzpUjgF9vDUT7ySDU40BE1WqIKQtKFw82E8DMilUAmJOjaQtFd+qQA/GPU+w/xNsQYb8UPzFEB1OlNCT7lyce+vyPKYpE/tOrq3NfPZ9bsNkKZTPUP0kh0ArAXvxAUghE6vnIJmUX4i8bIVjWAJjFfuJ8+s+SCX/5dfPJ3i7gb3+rKY1/edqHGCt754p5GAGYyP6GmfNRRI2MpUDAFWV5WwsBzKxYZbby6rnbZEOnJJ0ykTD9tJ6Earpe3UG7MJ+7u+tqlT8jAA8PYwDy50YezAFokXIc8NfXqxSULrlmunNiVBXmaJsJ2kf8xbbnAu1DjJWBWqsKAZisGZOsJR/R0HySkJZnIUFIAP1MSGcagMqfufV+zava0U1XJhawnwY7WiHjDk/aAPoraZeGQDMAj+IaqUrPwQ4/ywgbF/xh7JTixi5rMTPR3sStJMTAe2Nzy70mw985Sn+D3ebDvbN87UOMlaRaX0iifQBgEidiUerF/fRqN/4UqlUwEugbnu50/2rGBCWSCZJKMdOEOOUHbxiTalx6xOIo91FSX4TQIOnwlJoYP0FOR0Gg2T8WE0BR20kFoMnQ4IQ/8YT8gSKXimlCHJETy54fyIRGKhJW9PY9dIEQQ7aDf8FjX5qAvubwHLOAi+OqwAwDHYtOMYkibDKhUYmHNgMw3qi1JYY+oy0m21MTG27OrttjStYO0iEo+h0U+0p+OWNTkLJ7iRpSrqpZarIpvTZZNEPelyp+6gSBZqvRUQxA4VxMZMCOKarFd++KiZ/Fd+voA+pXUocf2BvmvmjClBLc+8Uvf/VPv9heywDQ9zwZezXAHOCz/aYD+etzOyAv2Ae32R5qVREKAKh14MU+o/W6R+tEho1wyRx6z51fzVrtiExqY8KDR/AA7AOlxTiptXFzvWn6Xh+sRu4laoIyPDjzID69MjBgLryrBwsE3rV4btZiAPJlzJWQRxyABlO7n+uCSz0hlb7cwkNFZFBE8LNE0CuM1CMaya8IKhZbITw2qO+//eWvfvntFtvJ/gjinAwGPx9zz/e3BiCfMPDtOqGvLwzRyIRhBhkA1XDAwJggpKDST5Yqb/NI56NKd4JhJmSKton5fEza3vWV51G0dVBv7I1pBAcQMh6pMrNCKvLLGaeaCYRCLXLmrCV4MHvdtNiwN7t6mvq0Yh50CUrSQOPdjg9lUrBYDtwM80jKgFkAllg0vFmCi/YbEjDKj1fntDuBBPyRR+fdyugy/orU8wiZezSCfiX/81dP7n313tNWfcL+IFDUHkgnwG8AuW+IP6g/f+rAeuVAAAITxootKQCaegimHJUx3REAvGxVWpfzWr1eIxSss9XRCKy+b9BWi3o1KG3RWq2uibCTVEM6hdQXTl9SW4K7fsMyBSzdczJBLklNApMAz1LURdLr1BpABN7uGCreHx+iHYZPKF9qSjxWx1SEtiwA3bxV0zF8NcKwV1mte+0b9WobvuAo/opzKIPDvl2jVv+tzSevvHjv3r0nLSCaHu8tSEKQTTn+zhj+0vXnC8eKnNQr+CgpAPaLARhTLQZAdKRGFUJHbUbH23S1SyaV0ajy9LpdBwBCjZ+q16pFIq5kBP6GtgpAiTqXno4hTjL3vfmYWuWcJeqeXMeEgpTSPICgHl8r7SOrf2c6KWOOUSfbc+G4dZQAUMiz3BPCAwJNLZlLsODQ3fkBcbYwKK202cdghPAGuoGo8hU9UvMuW/Q3X7r35N6fvXLv3uZ/q2GtpmqXeKt0DncS+NtFA5VDTpsyhC+YMe499oPp0SUBGMttKgBJu1FfbXmwuACAV9ddBOBqo9WqHrTqcdhJrX6g9bqI+W5xV/a+wB743qQDGPTy0MVETlwBiMtLh7U0wSQQjA1GWR/Rya1scJbslS4Lw4jLKwC8Y4y6dQYgKVE4aAoErl6pjGilG0XzVr2G4eDQMlF8Rdo48KqUdhsvvnjvyec3n7ABMmCrxXsLYixfcxuiDxB/5qYBOSMBIDz11kxPyjR8qAyjE5PFWTBBALZrHqknALy+fgMBWGEAZNS9HkUi7ISmGtLFhpSpGwdB7gvyoHAA+1OUBYtm372gDSJVU7lPk1SkkNdYSRpEGaRQjExIU0DepzUWAfGHp8Tk3wmXAKDvzH5xoscVkObqCEB644B0EYARib8iHdcO1qhX2XzyJQHApxyAvLcgGH53t85F8nmm/0Hx4AAEWyCoTel4VMO3jLIhyrxuiwAgEO/WGlN4WzELvr7iFBBCTrpzACDGlcy7DXgRlWpIiTJyhkjW1l/YMdG9oA1/NdUcr/tAeHluEl+X+pqtnf2OsVnmUgeaCOirWUm3jL5gNwAWdetIjZBATBmpcQBW5hBlBl9Q+YqkWh3T7n948oRJfy/+ydN5w+MApKvjiDAZsD8A/8fsYRMluX1X7VcOIQMy5MKnzAAwK1RlLQQcNcIM06a1mncB2kcLlRAE4IwD0KuCVAuPD+EkN7x5piGdxEa+DoKOW2g5gbbWzHQXBe2722t4IyVGowJpcFB9IIC1YBryDE8fXMHUj23tAQY7EKSA5otrImAc5AcA3DBiyAmAoaP2G78Dwxj7CFCBnQGwUUUWXKuNk68YkTWmT/7qxReBBQ945gEHIFNZag0SDrb3RfAz6LKlFBAYHIA8XciP0gCMXADIUQOGaDY8Cuo8U9PB7OJ1SeXyw9EVmYypYoYBOyfElUB0SaohXSi76eQ9M2PQJEJma64zVtA1okR1BX51GhD20IBa1QcScGNtSEcehGuR7iV7LNIQC4p47Shqr0GVhEznmUPo0qWJgBcxurjhxiQBuwAwLN3P1odP4UEPigmbzvGYMByyr5d8RUIm3vztJ09eeWVn898i+QUJ+5wUzDDEbz7E2pODbAMsp4EAZPOKRSYwJWRfkWAN39dY6zoQLh2ZoMhjQxrt7lrt6vrqHMgD5DgKE3QIlmO2Unk0ieGh8iO+/EIZJ8w9wl0DEU8RhYQtenjw89nV0yQJsw4uqZBW2D6f8a4WZcetkQingbYZ7SCffB6a/8rGNvJ9j0ELUUVApeDVLaiPZSTRDgAsHXvf7/dF1A88MiU8DkiEBPGGseyrffgERL+dnffC+Auy/XN+IgqAYABcRADscwCCZQFZN8SjNoeKEmUEYN9pG1tbXqPemMzMYoE1w/ytb74E45tvWR/ZgYVO86icKwBjvwwjdVCRnHQH10yc5bQBDbaQThNGlVUICGwwmQNIfETHwAnoHOIH/x66D/69aGwDOZ6x10uzAipy4y0w3Bjfz+sXjcX6KStnm+f18esMfzubm3oSiDALoQAIBsB0B1TXga44KdhgQPSAJHKEoQy1CWzmMADgUQTEA9N9LZaQt1566Ss/gPGVl15KQdAXorlTcYQ8o7RzdQ9JRhlLb4luNR9yg21VGGwZ851GlbU1Bq3VNWlpX6vXq6s8jfMTn/hEZ32lWk9yPKsQXq2Uh0YRUAHgHbBcG7WWYgq4WD/l5Gwzentvv5LC36eMYz9b98Vl8GAEQaRERL4fP4sh0d5I7cxECVTCZikC+JcCfhyCf6nuQlcH3MbNiJdjlHYEIFEc0xEDYKN1dY2l8illwEODbYTPVJmvsk2N2NJOmWB/wNM4O53nnl9fmdBLmeNJ0JF/GPvhuAioAXBjUQCGS/U6tyl+vbd3mAC4+bpC3ABuf8sG+zf+7VOfMoZIFo8VVbOTKSHSEu9HplJBhqtYLRu752YCaK6e9pcv/UAdKgJRLgCXg2PQVU5zZjcAxvcJQTpiAKycXl9js5Ao8ta4wZbSen2NViAlqTpuSEs7JWsNQpni3/jt33/+uedeqBA1xxPkxiPFEQz/KhXX7i4MwOUYcDYSUESG/4rhb1PDHwBwQMNw8Lc0pH/7D2E4oQDAkh4QOVbUlstxThKSmsDUI9pSGsPSeXdgIYDmDsFv6fhjCEy4MIcDWFkdY6l8azlaNwBGMuwR8uQQgKPrGQcgqQqDLbQrHDMAtuvtGthsuaWdtqsTJg3WaZ0B8Pef/3swqcU5ntU1wYLFPODbKNjqWAFYJANGC9efxJHiwIwLcm3qyQ4wYK0kOQIQ/hl8Khzwn2wshj8GQEV55ikh+DQhL02TQaCtNktkfHsbATQDUOG/kgvH+yTAS1QFthXkdvpOgbyPTxpQs2MNWDB2q2EArEy4wTZiH4mA56paawMAGwcMkvPL1S4hmMb5SegYBwCMczwJFtnqHWkiYBqAG48MD3RRQAGnyzFgXQVRzPvvbG4CCXysHIsAHAyA8dJ/gH9QBlzwxiun+4nyggDEb0dFH6ZMPx2bl9ZsG7ERQGMJiV8IAvjTf6OzHwkSGGdfuXflUUdg6iLvBMD4NZkyz7SPqnft3eDdahgAE4PtlHuAWqsEAMgt7Y1ViIsEJeQTn3jud24dgYcozvEEJQRLA4rbIGhcAFhAAIMlG55rHHiqePMGr2++wkhghgUjACdIAMOlAKimq8UATFhdOrJiau8ynqU10r5omC8DAL/JCeA2/emPZv8mSOA35c5ooXpcfVNuiQsAFRmVKfOjEZldXc9402DwncYG2yl2syGTMbgWwQzD2LU3YmM+H0eQIdzpTLpgeBc5nhBBx/jv0aG4Nn8WDYCdzoYhgKsgGGZJAVDzY6eIzi+RBKo8WLLgT4UT+Jf+7VIAHCrWw5gF9xNLTMARCOGxwvtglqx8g1fCXEYeyt+aACgI4E/eY0SQSimQxmNRC0OQ5k0OANSUKghxgGzDcCrb1ISxwdbn3WzAPMvAyY3xDKB8ogi2fovkGSH/VzXCaLGAOGwALCCAy1lgVA6cbeL19uYO48G/TI6VSsg/0AmTAf+BfGopAKoYkXnp+EJyI+ZhUm1kiovyBihpbJr7aKC7yBRrragg780kAJOnWHiFp8P7HQCYIufTpw5ezrR9wA+gXnnnJBMcBfg74r9qsYA4HpkBWEAAl7TAKIwteyV/wEgg04PjWhxebIbh/3xqSQAqGElKwwTKY/AA74hiAICoWo1bcJApr5qAGovCCE5Pd40EEBIMII4v+ygJAH9Cf5IGYOmmYOoUQsZ40lKiGIBpgXZ65uDlzEaRMjBBMGCmxhcD4AFPydRiAeU5hgjWfn5Gkl8mAss45CNOM6GEvv+YAfCVxBDt+WZD9IJ3XlG1BEt1Nl4hPpnegND04BaPUMr8g+bDvb3h+ewsEx+KtN5csDgG4I/omz/IALBsW0RthLBKJAqKAZi2dT+duXg5M1ywwwGYymCB0TtCAEo+oJC3EwbA2xkA+vkceLqcBSam3n4mHRSjy17fBE+IIIFMG3+2AFRXdm59yvQ7BjhCvV+NEGAxM292fjXjIStREvLCYeSb/D4SgD+6Hv7AAMAFiyLFI0Zg4cdK27oH+05hbpk4IQTgI35FnbLoImAagBt305cuSMlcUgOWRsCMJi2iG99GAHISyJfCAEoQDbcGGACzgANYGXp1rDIANA/UmsEv/XB7e0jx4gylsZUwjhIQCAyU7ipCC/7hv81+9KMfpbXg/lJiIA4BkOKEkCilIe2eOYUZZaJo764nLWs0PiyVYKnaK/TtBMwwWQDm3thflgD2RT96fQXF0bXvcAAiCeRPsr0HIfinGIFvrLjiTir0+oBbZ8sCEBe7yIxS6xzI9Akl8ZIIzUWSR+GI+wlu/EnGGSesk4tTQUFCC1PiMl6dbbc4y0wewR0FgCJvio8joYPIGymuOABgJx1BeJH/0uGSK5MbAdPiXxLdLQAIJJCT4lNGqGbbzYE1AMt3R6AOwHRWnDpcV1lAr4TITubJySHfpcwUEZJjUsAk4wlRObB2ocUGR2DRFbJ+7eZszyXQPIODWxta064wmzwgb5lQuBMwHaYBWGCDcU9gsF/AKP4JFA02dxCAr/e4Mj7YGjJOuW3Hn6kKkG2kALi/PAD7QJ7xueImkILs2KS4QKQV/WXGF/wLttp2T3EMYDwukdJruhHNZFlmR9aivu0WaJnxUq4DAJW/Y2VEmAGTEiEKwgCAG/p1CgjgssKxH2F2YboARIKhx5s7nAT+kj8ntNNiAmBOAKrvjkAdgLt7ywMQUpP5jNBJvDHkgVQ2PRYSKxkn/qZOApkEONhNjZ8NT99dfMEzElwUzZX1KTJJ26F5VFYUA266fit1f+RznAUrIWkKxAwALCCAi3fBlKdTQxVBFUEQkfqEAXBnjPOxdQ45mEX4c4RgBoC2Zq2+61uCb49ygqdod3Qa5lcuQE6sMeGvvPT4NI2/3TeHbw6HwzK1H9J3KUBvNrTxdG/mFulhAmCam+IDcACqd1Jj8pkQqCXK+IVekGXioAniL1ttyNe9cRAT88oroAhjEtJ2Hv5iALogcEWTbrBdtfnAwBGAWIoS5fhABSAx+E/4w8aJIYwTX6tM+KUs/Nhg8GP/391dEIMFbVxNwd27QzcKmA7pZABM983sK8qIdicVgIwCEizKLeNxCgC4jBcEnAzEVAxOB8/g9U1OBN/p9SEJqbndPMuTS0ogULMD5gLQMQ6PBzeA3SWkLnk0WGhZYNOn7yUIfOnHu2YAAhkc/uxNwODj4hukRpERxhBWtnvmGOubUgYeAQANbt1w4oXpOyU8+A4A8CSYCn9yFJHw388IA+TP2FcgDZ3Hb3MiuPn6G9vns7Mthr9i06gTAlfO0pZoS7dWRwAO9nBlsOXtlykPRglkWrH5eEMi8KX/8iZHHKofXBFJGDHw4SH793z2L+WmvIBdmQJrHa0wav+Ktx7chPF7nzUBsN/yuHddfZQYZADA27HtJgiY5OIFlshG8ciLcuCAamYw7bukN/QYDXyFEcH7X/spY8BbZ4slgBjGit5DbsvaKckRgDIAgUCxqmIAhhBdDD07oPeLd0mpJxSRr/zgzSFiLb0cHksccgyWFAiLdGBTtKOjFSbx6L/FsPcNGOznXxuOOwT7O4OWSrliAN4FZ7AaEu1zkDAMRsYJXdgNMjXEj1gHEwM3X2FE8Gv3d3Z+/gzx11/Ru2gyBWc5AMp+NBCC4sCBsTwYdC2S2fardU4CX/rjNwCAxmd5PFCkw5+hQOj6ugUc2CghOlphYjvMX3P08XHzW4YDpR062ZJEu3RupwDYD+ei5K4punZhI4wfWYifebyOANy8/7WdnR1GBJ8Z/voreh/h7eHsofnariy4KWJQCXHIZMXyYKHPxGAA4H/+9vB/6ZJ3AIEv/d9/PPzZbk6J9QSDbwICXYlgvhnRmFvlaoWRLv1vqfgzI3CN/1AAyGTAoyNUjjtMCV7XQ6ITdBok1MWMMLyArztyB5toB3wdCODLL+/8xBF/DkLgCu+kLj8gA+DeUgDsPxTS6XTqksuvAvCDb3/7zT8aDs9f+soPvvLSj785fLOArg1iXuxMAgtyio3Zpc5WGG6HeUvHH0NgNsVeuIIV5HhgHMSygQYAxivZlBG4UOM0xwK+yQACuPl2//SHOzuMCN6/77DgHdXgFd5PRKKuub8sAJvnqDNCyXAXCshY8FSw4CEH4PCPX/rBS9/8wk+HDi+JAmEJCpjPgc3wdLbCcJ9+Gn8MgdbDk1896R5pvWCoKCgTkgwEcBEOHNiK6lgHJ4CD/vb+eztfu3//5c3C+Xa2w/DSHOeSqdtdIa4AFFoIxPQ7AJDXKMbGgR8Mv/ntIZP83vwjBsAvfOHcUbQ+fXPI1GW3YwviacxOErGiXEZEA0kAX/uAfvDAQgKPpQwYv2LCZHsvbBgiUvlM6io6r3FOCjkw5veGPHgOzg9KldPm420kgB4EIfz0azsvK+HRtlEKgAyB0qyzPAC5yERoEEUOLBjMMNg4kKx+4Qtf+Pbwx3/8f3zhj1566aUv/J+u8jHTQlw5cEHlQHPKu5I2XTgYOxSg+w7d/YsPPhAk8EHqsEMBwEQgBTMgL1vZf7Rx5/l1cziMtnx6B8DIi2PxA5odxQ0dU3PACeDF1nB2/kNG//Q0YfNwNkTzH7I6wm7T5otzBSAIgeh1c2vchn0qoIjM05++Cda9N98Y/tGPX/rxFwaOxq3BLhDAE5dDwwJuZU44LVN0dkqnggB+/11GBKmFBx8dyweSWzxRsw3GLahtnn4hr5+KkkASdOiQjAm5y1jNLhI5ZeWEPxxIAF/v70Ltqpdf3lQzlOyjHACl8mp3hbgC0IdyhejZcGycxTv1hEz4+9nwvdov3vn2F4Zv/vgLHzgyCXcVJCyYeXNxhzIEEJKmFRHw3Q8KhMDEKKkKK51OVgiEODw1SuJ4DeF62C40QhetOpchVGAMQvjhy/fZ74UcGF+vDACl8ooANE64EwDBtX0lux+WevNzRvx23/wQKrq9N/zxN08dl+nAWQUpchhYytuUqrrNCFICwO/T71sAKGldIgR6vXhb/9HdzmdSQTRwhB+r6K0DuZHM2bak5ZxpLJ2u1E8IYHM22/vh/U0nDoxT6hSMIH6KibakJfXdAAjWZx/jxB5ubzdpGQ/FEMx+p1jRjXzw3rmrBIgqiBMBLKIV5iiFUgQQ3j8GIJMCLWpwryV/k0Kgf9E6SvZvdJ431JWeZJkto25MEzls5SBwyVAtPgecAIKF5Hzr5U1HDozv5XCMBODpPufBW0sAUFSkGsiUpBKf7hwIGaNkZQJ54FaogjjdpkhYCi0qcKmqswkAH1yf2uwwvRhs8T0vVAx1OneylfWTiPn4UBAAjziY1w77xuEvna7Ux+owEA0NHHhvCwjgpiEGpMRH00ccD9jkS50BsGm0OhQDMK6IhkmZ+2ezK+d6xQEwYHeHWjxOd4FwOh1aFEptNAKWI4AAQKEFf+ODDx48kL/rWrBC62Ih0FOFqkedzu1sYWlf1H8+bsUnA1E/lhc1cmLi3MDRPh5v8nwQ1E+37yM3Nkyfb6cduSiIAbjNCwxa8+IKAahV5Ds9Zcz82nX9+TPA0W7p4KrHEJ/qaIQOi57fZAUs23closIO+H3UN7kQePM19ZDDA+UPKaB5LZ0EfsbY2wG58KE8MlWN47iVpYL+sgUTYCgEcP/h39zfTNfq5XfKAaDvBkBhQN4ezvaNhsBCAKYC7pleveeq85OhNfAgd5yiH9jt2MKAWgMJLEsAFTuM1RNypB0vE4OPVPR0Oi88b4zjUteIyQKYhmC0bLpcHytESwI42xq+zD0i2clbFH8JABlgwCNszYubFgAwHXCPLtSpWz3JGTDg8gQwGJaJhCkkBtk2wqUbTzFV+zUHX3DyBoKI6aVfTrTmDiK28MFbuEaO4ibD2fVyfJC++vImGEEAHyMB3Hrv/v0iDmzYZbs0FmlKckJ4JZdUx+BkTIu6v6TmgwFw23UOFpIAfXJVCreFFQYzbYRLE0AMyHqQiobRJcA1/QQ+a4dHXk/nwbEtWo0tfKtPPCEB2sqxHanC5LMwwfQkAXzICGBzZ+dlIwfuL4I/XiYsASDP/0/1TE/GNPdlsglHTK3e7ueXq48PRQCWJYCEYsSqM26LEzrT37R85z0Mj7/5uzmhCC39T/ZQjG4dHXg9dcdJ5zOf4Tw4FVsY0p58efPCPlKEyWdhg+YEkKm9W8AZey/vMBKYw4GNe2yX7qUAOEBDTFIiMDVyAUhMqTz49RwCxk92h67GPHVE/2KMmLaO4pIcKVNgeQLIA7Iea6D5x7zDj9teHyFzoVHAX3fWn19/4aDfS8cW/iWVT2oXKHqCET8TE0xMAME+vDu4f3/n5RwObNxhv3YKgEwP3lZLBKZGHgBNnYnARdF3Spk5RQCWlgDpaSkC6NIkWF8s5QmgyFBTQgLzBEAoln84EulY2nd6v3N3ff35fzDEFr6L+2MBEDxPZJqqQCb07KVLIqLdXAAAIABJREFUZsH4pYjDAgK41X/nFUYC/6vhMKvttpAAqgB8CAC0ukJyAGiMcmqe81SeQj5wAlH1PytNAP3rcgQwU3LI9I7KJ4MIofKdf3hiXEy4dAsMDNXecnAU8w69+EFw0sHG6RaNOhYAk0iXbLpI8CxMMDIQlRPA/uuvMBKYqKh+KO6ah798AKqJ6UxreMjz4oyWaDsAzURuW3iEU9xZLYcl7vszSC4qrwKfIwCdjzdYWT6WDPxb9VudNs9nV8OyDyWTxIUicvNBemUeJ5aSgyNxBuBED530fw0AvCMJ4ClATKGoQgDEnHJIm0sHuRwd4C7MLZZXxKNKIJKXE+ZeuHd62/ugmg42X9l8eSdJEYxbg9pw5oA/HYAA7q1zsyXaCkBLTO62yCXThEBeXTVlr1lIAuyH5VQQk51ZB6Af0XPg6s3t7e2HbMbP98STsq9IXGuAC8FfiIE382J6pNaBVeNSsbsEuryu/54Mq3n3O9/5jvSq+LIja6gE9gVYaEy27Q7bPJFuio/t409KwiBy14p9QqAX5q9eQQT+V06V3mEAvL+T1J2Pmz3n4C/fBtM3ABAs0SbRxwrAyLywZDKjRh8hEjzdYvq0VFpbPM5/tiwBVAG422xeXZ3t7TNijI7sGWarAj3DslE0dM2+FbZftAbe/Gz6nENDHBO6K1Kxu+QEACg58AenCg/mTWrDUOnohwMDnaci5I9ejPjWUFBCjoOpczPXkKxV6rX6r57swLg/vLoahlP2x+b9f746C3nR9QAAiP0zfWyuaTBB5+KP/5IBYNNSIMsGQFvFoTiZUSGQwvcJXXiSJ3tvMQCiDXApAogA/KeP/jv8GHLUzc4ZAvf39vYe8txATrEDeG5HBErdEwHYSU/NUWwqPjjgH4B9SqjVkQJgCDx4QwLw+op+8H0VgCKuOXXxWCIkod8TsqafcGFxiBsbno4rE0IaLWgTt/Nk5+3rwYjMITN95zfdEbBe6PsJTcixQSjBPsLZ6tK5ABS/KADkhjsoLW4CoKVRh7V2+LZM5VGEwFgRVSuZnWMgltO0KGMgwmcchzHUgAHvD//pXxGAjOAxvru93QRUsyEPAX2T+oje0I18TPmCY3rIze/eVbwYchxC5NWxdMkFwOoMDkvS6SQAPH3tL969lgBsE1ofwX8GJIWQ+5FbRcp3rCXIAOgxlI3pn7x47/OvPFlp1KuN//Xei1968uQGI4yUNqDXPQCQNwi9hE6iqc6W5SKi+7HhzhYTbV46drrwUAJQOSSxBSvx+oOFKOAA3HBlbDCGuUDmywH40I7luICm1asjuoDIicL3PWEAfFXzYsiBIXzyTDKu1eqrXd4qOm4dPZ2Sk87GhqIEP5DRrTcbHgcg7yltehwBQHOEllOzW2TBqw2G9cvf2nnyZ/eeVN6YeZXXn2x+/smf1K9Iw7tsEdJq0LhBKGR1N8YaAJ1zQmIaLQx3lvIwZl++Pc2M69T8lalsvai8/DQG9EIADGdQlcNdBDS6QRQA5pw6TUR9MwKFwE6SowIAoESf9GIoZyQBgZgUOF6dU9kHm9DJmLAfv+50EgqoAnDMKSDDLBxtQpP4Uq0jwz6Xbt/8wYh3UKteHvyHL33p3uefVt64IitPAIBPGS4nlHYZSawDAHmD0EvYSrIAdLjTCpEavKCAlpBUYyxCTlKqkChxQihobohA5YBInrqIEsLEJkhI/5kzCzZEuuw23QCoDHMOkBDYJyTkve5RA7373ZspL8ZrhgtiZZKQ1A+wD3a3Wq+u0RGmCUKXJenSu373QcyCHzDowX8jyo5mhxn4kgBgzxQg6Njstk88tqJIo/HfXvwSG7+pvEHIbyEAr5+2G6uX0I+xhQAUDUInbKunfl73+oA81JG9iKhrZbHDmETAvKRoIVHiqcaoLF+KgeUBiILMKRblcD0l/aCnzeZwVhqA/ZAapHghsB/QiEnmjDhMRt7lSefVjBfD4JQThSFavA82+6qT6mgEvTjrlAHws+IS3/mAfiDMMDdPYwBWu8RbNZHAPBkwcvSOEGw5u9b45Ys7T+5vsqcTAPxVi1Hs+mhUYy+MAGxAWvcct3YViJStkArRtqFoW2iOCDSJgHmtYyQ9xcOMyy4QpXFK2wE5jxtgrUBHq4IuQAyazf3z2WyIAPzDj5UAIFtImVZqicB+wGjSGGhZa6XTcaqQoACwS7vQVnOtNapAs2Hvt5/7zLrhGpEEoFdhpKcyNtAF+a69VmZX6GqLnpI17BH6qy+9eO/Ft6HVO/n8kyf3P/9vY2wIOq+xgQDkDUIv4N9J8iwl0iqkEhIQSGezRgSaRMDcQJfdREKbWuyfIZLGcFASgNKUgNUqz93eUgmHZqx3j6HvfK+5m/aEFA6ft9NIvU4ssK8y0tUAWkY+fue7AjwPXvtAJicZ/MIKCx4pAIwYLrvrd9b/twwVPSUSgCMAYMvLTm3yqQ4yVkfej2aaVF+1vinItWMyBzPMK7+hb4Tk6eaTnVc2r6hI4+56kzH1PDTDkCjCrcm3WKhKvk8otleIe7ZqM5UVAfObt+2exQAM7UGpYMGfQUJcGX1WWEMwFmHoJlPHwYzIemfn+82F+vtwY2J6LmKBvdpqNVahofDJc89L+Y2+m2THPchcUCohHgCQSBZMods1PemsP/d76djCMAYgXR0T0jaw4ByfN/tkYMCOSFQYq841+19u7uzc37m6Gk79X+3svLzzz09Rm/SFqs5EXm6Ixr81/DnPqVqk/HQ4O2siADNaSFYELKizopQVDfKjogelAKhU5MbKWFdOWTf88Qe7D4H1njWb5e3eMHxpjkk9khDYW1WgScCu3r/ziQQ517b0zH5shmHyHwMgbd8Am5oAIDnpvPBcJxVbSPwEgG3GIxtZAKptJtN6cNLVT4vosmcWQ0bw1/4ZRbLXN7VABO0tshZo90nVquQ3z9jXgciHTI3AjOxQgKrYFdwvjMga/MwdgJodlYdjOTko4PF3m5AuyohfiQgafQhjYgaAQmD32I/JCKQ52vkdNwDKEtmMkU2AKo3mlM67ETTA9vu/fnRHV2VuhkxeHBMK/02oZvtJhkoAD1O2QM1dFscLHrWO4moh+oCEuK9/7T2Aw+CVJ4wYmu1eacCVCoNNtWkYMi68nU2My4iAhVXB1Lq2BQB809mkrHuSeA+Rf3Fgwj4dIOudLch65XsIrKfuKAV22qrWq21kpu//viMARZMA7HrNQwZkA2xwo/16Y0NB4M1BiH0/+bD4X/tq3cmeJV1YDPSIcjnxyJjdDtGA918Gu7D/qycMgD+yTO+iOcEwdAAy/nt+vjXLaCEZEbAwyk+ta5t/sDMAwYWsEWJew9xBsQuu95ZhvfL+8jVSdxQCe4QOUkbFGG8gJ64ATAYE0nipqJi7653vxrGF3+q7jDIxL2xBH63JzGIDAoEDv/w3DA0RfQLB0O+YL1OmlEBmpADYHzSHDIDpvKS0CFjcu3dbqeoY5R7tCsBsOwFOAknhfA+GV4z1PlyY9cYvLd8n3dWK+9D4DwzW86dlAQgWxijTEwR63fyujC10qHYHQ+USBSSQiTTzifh1LRuo04NQrK8NZ3v70f/cfHnn/suWoM1SxSzSYyWzZRda0ekADFIIcuhe/vAZAzA0VbVDAJ4XCYGgVQ2X7GrbV72oJD89Bl/29wwAfGA9KYmTTCEQmjYIAD52A6CvTfZa7rFHrSRzyeC4Qw78NVRJ33mZKcG2mkTPGIA8BVndEKSFuGLBP3EF94vKGLsA0GD+xbtwS0zuqRBazy3sS44kniEvQ1X6u5P04BiARmdcXxQMZ6eJWLUYgUBRT27f5Q6Rm39d1LNQDF1cP7YWUusd9dBQyN2LhsN6v3x9c3Pn/o8wNOB1xoDv/3twYBMAMRxBQUQmiKyw4aTmCrZbosXtdosA6BNLGBvy4Devcy8O5G9WqiK8ZSSrLsyztMlXda0VLeEXN+OThA4jtQjULL/JT3b7yEUFBHC0DvuHIiWgj3Xf0jrI418y+e+VnSdPXv45BuMDA97MaU24+LQaAAje4OZ2crfM13fIL9MAmB/LCQB8czhVRgosOQ0GOQ+2M3jI7DhnqodPytelTQ1l1dmrfCjGUbcKCQg/LlOGOgCFifDvuU/45mvgvuCzk/+cqbifA4EszS93rPNbGuqyIqKPgQ9ac33tHALkf3wfCkPbZsYSpOY2ryYAbu/P9rdipuVnqI+D6U0rLZ9vMwQADt9T8EcUc2nc4dQ4em8hD7Z9kgFk2e9z6Q8bwvqQnLNgvRQlpDo7I/0gCKfoX0geNb9CQl8+VKzZS8YuACicJJW7nXVGAm+GSaRWPgJT+qIsOnNwZD9Fw+zjd2L0QWOaH4JR+PRvGAPefKefM0zx+E7zagIgFuKK80IMNsDiS++qwXpBriHQwIL9kIPQYHrRxuNj7JX0gfm64PM9VywvXNInjhFxmZdWvrsukWKaUBRBtzvtyrkVEvop+CXIEQCUbuJPdj7+8Zs3b64okVr5la4N04UoTGjgUQqMjPaKfYN3XkfG+4Q3qN58/W9AHxj8BBhwbmuGDNpKByOoA4XAmIBlymI5cGAdgP2yAMTbotfSxn250Hx8/BY2qjFeFbjvlW564TmhCxUM0EOqQyUFzdrxaj23QkIKfglyhBIiA2X+n87HX/juzY+/cBJHauUCMEsdjhn0eElzyWfTFhd2DnBqQN8OoG9HoO+dQX/rDHpTD6EkzOt5dzV445YAIBcC48kmp7vN7a2tLUlMiouspDtMLgJAGMHUojs8Pj5+3Ov3jo//P+TBmbXpP4Xktr2fG+9b1DPdOFKM20erJFJqazXh9c/aKyRk4Pf/s/f2wW2d56GnSKkfu9w/sDd0cl3fJraZqOkdRx1mJp2MvJHShkpaZTya6TTK7drXGY8dWzYj1VLunes/1pXWM+kfjTcGyqiSWUAGF5gJTdEGKVtXtLUsK0YWhCuadoboEcFSPTjHF96FgBAZAgfm+98+z/O+7znv+QIOaGc6Y993bJHExwFwzg/P1/t8OPsrUgLKTK3Fe/fOffXeuTk7U6sjgL4Plx/0RZh9N8BHIfqk4sVi9CUM+qEo0uLpd7ApVgcN7KNt2xPTxSIjEH/h6CVSGbiepUyCGIxUVZBwTZjsWIvV3Qv2LeAPF/4gAN3PxsHXDThxrBjsLUQxIXzL96F57Xcng2yvQ+DvP+t/rse0sN+rRwVPvt8LgAEXR8EtLCytf29MVbwk+2hhRC6VaKAADNfAAax9WACFEZiMJ8bTsk6WihaRQS2KAHED2P5IAczPKwt1sPaS+tk5fpnUCsimYOdnO25IwN6P2dkhXdy719nFUO/gm77ew9mxHbcT0q8jgHNSBbe7ANg545l3jfG2ESwU3iXhJ+jjso+vFGZGaTePUI/UkIMa4QD20qTctcgIzKTAAOBLy6QSiUzJZrD7pta4q69Kx9TVXgHMz18B9WsTiF6IUhsCqm0TNz5WaL8mGMAoRqxv9dzrDAG0dzGcmw0rOBfPjm67wzA5hip47qs7RKZWZwDNLlukXBp6t0fMZcw75fS97dpuS+CmWOnFTqMZglH7kACSEZgpAYCrWpp0cHJlJRW3GRxHg7AjhGADKkm3HSPRxR767OJH49/EK4oIPKfkvrJVgV8BmQkGMGJpovdJPWJLAMpdDDyAjNQEh5Xs8IkSiKZMrUU4zP5FXjC30G5TplbY6tqP9rJfjsE3Avh7khSv914gQEuUOo1mCO3L2+OoLs9CIzABdiCwl3FMfIfBxipwmQgXhDgCtk3BCTqttQCl99aFEVwXrv2yRwCr9MMGEN0Qjb8Ts82siVWnt1JoANzaTu94s0fNTQDyXQxDNhDqMCnLll32lq/I1KLJSYuyZLjcKRJd6SqlL08qUyLEx2KV0eUzT46+HaBjsWV9Kh02mqFDY9QPCyA3AjGK5vlMwCBYgVwQavh7MIRibrpp8vlm/kj0WyMjB76J68DIyJIWudU9rmaV2v+oOphGC9OGHbxwxlbnRhgy5rZCgd0vr2sBgPvn5rBDwt/IHmpWLbQWw/GMvDkH+3Bqg90z/1aHlAQjwlfkvIjJyKdgTmAeTMDAKAuaYuOZF0bDNHDHztAfCkAyAtEF0kob9o2miQe06kkQfunVhqON/fQ4NXH0xfYBeGHkm84ayfVWFEIE5lU3BADmrflA8qqzVWthyGxjYF+h1wAiB7Dw+9TOxROj9i/HYfcSdj92jHaGNujhB4oWYlf547ucV0aPAYABWyXUJUP7u9HRLo2hQwGM8HaCASQjMEkjj0oykclo0mLNqqmjIBy3tXHKp41ld0rAAgH02k8u/ojAXnQwvAlDdYXRDU6ukP3taWpqhNlElW1tDUcJgDrrDVLBhX//+78fBQzHX/ICiPOD1dFxoQT2bNoaFv8iFkdBB48F5ESTIjz7VKfhSB17U34IABPUI6uYVAg0JYB8GUm3Nk6oCKZsRcg32t0AvuXmDwgs9qSDYZkFNRKDEzOxFiK52nDn/YX6uxgI7hnB3rznN1AA7gM35EdzEabJ2k6wt1HWIgLompsURmDUonPn4whiCcAbPgl4S6dOaS+MhgxHKvw6AaScwCIohnpGEsgBbNkA4jtXtLGSvqAm5FuMm9/qe/HyBwT2UhlcJQIdI/D/QS/k3BYKwIZnuHmH6TRGrecm3r1FsN9A7xUhnNt7sPujbbi9Vw0A3O85QDCB3UIw3g8jN7TzmHj65JNeHWvougFOcDoRqoELnX2NDxOIFp16Eyi5VlKiRqnKBaAKID4QtXGGVHHClj58ajo/LVQE7YpESwH42vXb1yWAb0fXwcIUqMKpExAmX9LOaSU+G8ctSDvO5zK6J/N7Vk/Ezn0NW6zNYb/xCADacHsv2xsB44MDCextLJyoysxjXuCrQNiyBzHMiyUI4qOjXbdBfg0AUggyoVEPDLAEM0mu+RQAlccWE4RgyU4iFE6ws1xd6b7B/d/X2G0mATxwIfqckKp4A+iKcAJLOOv63KLaj0asztmZtV4B7Cl8M8fXwb2Bc998S35FvZctbH61d/Xko8sct/PZwmQh/z0E0B3no7xsalh/NlQDy/cbBtqHSEYocB2cSZNhgkntWlL6IC0/gPhWsZDETmPlM2+U5YpESw382jdtAFEHRzQChSkqCARf5IrFNN4sUOlHY1+VTodq9wpg13lz6joo+Ls/ignovJkAAL+29w3vo30ERgnB2B9DZpgLhxiH0SzfdB2NUnKoR8ZYBw0c/I7t2yO9mTAAaT57vM7/ABmYrrsFYNXz+JU4yswUByDV8LTuc4WEHRNQATCaCq40XQsJvALSFYcWnlvx5IDxy9LpaGEa1aiE3dEDgAjf/v37D74R7eG2CxsAYIAI9V30HnYKZZM5e0AiAnimrj6E8+0A+KErusJXGIC0D50WAoWGoG8Qf60QALGaDvyVBBKAGtj9ll117MEARviMRrXZ9BGIWQIr2CgrCMBCZwkYKDRM2qwNfEIvEvAgghNN+OGyQzVeL/hgJAC718kqHwJrQPIOf1T+6wJQRLsBQC3RRQN3fqmKN50Ovtt0k/zpAVAtt+QiUGBRxF0RdIGlAGwGfN4iz14MHDGkepyBAEZwgo2mf5mY57BIOVlbNdbY9JLTkZhABU3BCZ4L6wvUdJ057Cwy3SIZf/yzOecHL36FZDBZV/vRiqSH3FKsLU+FXIQ6WbmQv8v5/GVlcDEJuQ+UKypyYhPpRvrvRjskInT7UPYgEXvVxE01+y4VQAPjJVUZtFdFIHav1DRHAQcCCDKw1FiNF4sp/4ghVQcHAhjhzAXw1yKweYE6Y1sAoHuPo7PODAqrCFuK2n27Y4WGWYkwbEmu/SQAIz/c4h1kxcyaClydCv7bbq6TD2JZuq7fgv+l7ecWkz1sU2NzsTxPh7EDfwRg0zGIxYskxwHATokI3ZZh7ZnyApgfmrJwVMelIbor7wLQYraOrZqmSwRilVF6i7mUn39hW5lMHPW1770ooWjhBasAHrjQ/eME8MeH2IsuRSimxyvukGOX5OcAueHataBwtV1vxHqYNdSjAASxtACrivmtcPkrVp9VMa3cMKvAgfbtn8s15YUUcLgA7CFRhwSBJx2BA+hId2EEFNPpdOMsAZiPenj3sgE0UODRdxkAHLJq8AH3AIB5/KIpAFYQPsfKa6IItDNLmoCVc5fHCbZXAjNo40o+gL0UHfzzER+AI291/zQB+PFjFsXQOAr+mOr8jG4dkf17a15dxlPnZb1RJXLsukcBWGNWX4wKjgD2ZpUAtJq79HYNTMD9c4uDOm+KX6nVmvAIq4b9qO0vQ3QHJDA/iDpwjDr2rVDAqZIEcJsa2AYQVHF+ap46edXye4YuWYZlDu2esvDmS0qDSq5gnUEom4oINJob46DgukjAQjGRaTQSqYBJS1agDrY1cHfJYvj5s5ROlUmzMkHBH7UdZjeV6TMCu+gyuH7RCJw72JMAJACxR1s/fKiFdZC0fXD+sdXR4t69e7+6D3uywR16m63rFltvlteZBb/ZpzaqXOYXwZuPeoX7GXZRlFDAWgMk0AtHtq+BbQCt6p49UwBchavgPfBJp+AWZk3BHw6AFvNe4bQjAsEASwGAjgisBr9mMY4N3gIyZFQ18XPfXnC5+2WVALbsL4h4C9wFqTeb4xT8UV6o6+aUwesppatmtDvPokH70LKTHDut/Ri8iy4AEXwEsN3H1nfFBgYJQOw4nd/3hS984d4vYT/KtV2YHh3LWu3Y2lp/Ve9fFwBGd0D4t8fbNLBQ5ACKTyVag4A9o8XTpSNHwlIBIywBoGFN7bZA5pk1AhB+sayheQAwP2SoKlhVsBzADUcEVpvNdKmkisCQF12hPP6ANC3VfvqGJxvmAmt2/RKbtuBr3vzbw7ie4ZVmvEFMEwEk38d5oQgbF6ZJo/zQPjGsbgqWT5Ywza4mFwjAg70IQEQbAGxnd7HcGtCnA4A6dpz+EpiAf/j5JjXFX2PlfgQQ/xgeHsyKeqjoDkiY9C5yR1fsGtkKGGRPWjtyZPtBGAfAPVP5fH5o3iIA8cZLu+FfBrKw4gBoeAEEUQPIoQgEYoE8DWSyraWbocTYlSSr7sx9V0eCkQMuAPF43cwYmYzDbh4+/NiPcT12+PCbQgCeu9lsbogNaEuq5sjpSeRrdM9NkC3mja4N+HoQgLw1DKy+vr7+QVCtk7FY3xoASB2n7wUX5N5L7bZsiq+TBFxg1Z0D0n6N6oCI5rJ5X0WSBJAbgSK+A/Z0GuypdxDApWgvEPCSNoC798C6JACcJwY5gEbNBtD0ANhCI6S0tQki0NJzcGc93UhQILDFcpgVGPyixVQmnrbL6FYziXiCQ6gO4y3kXQmpbRZuVdpLArgk8OMIPkfzQs6hANTEBrQpZ3n0UP8bJTvLFjVdd13f2B9VANo9aypk9bXbbHCwahGA2HH6D74yN/f5r1ikdakpflkC2L+zKr5mER0QQ5yUgPlJb/PNNgpZOQq4lCwktDNHjnyIbRACEBwntgcdDgr8IYDW7inQwhzAmi0BjaYbwBbTd2FR1tbWap3lsgzQW91M1/m3dVCHvy1yyhRnjBbarolEZtWp5cSyJoDQ7aspKYEjZS5Xu8gfkYzzzuEfq+vwaQ3w00AA1uGcTQhS+HjcbXVACF/2OPVaN7D3BeUPBCzTUfrg9+JbNq1YztIJwPWd1fcP7p37gy+8Z7VlU/wFHM1WBWU8ODw4zFVwxN1Bo0MEaYkDSBJQKOAkTexIvHjmiaPb18AcQPj6sEsAXHWqWhMATg2BHwIAmqCVq/acEDXKTKpu1yRja/2svK7daurYQLu4tdlg+hr6Z/jgNXLKHGdMvHUNvjqYopVQK4q1VCI+nlF2ey7MSAJHLsoX7vxVJgBbN938AYEvYiYCyD/cq5HbqRbz5SB++GXnznRLC3xjf+DumW9VmNNqlQIv+M5z/bGBfgQQROC/+fwf7rv387HYsN0UHy5IbGAnOiFV4YRE2x0UHfYCRtcUHADb9h4cbjusFAqJMwjgtjUwAjiEy2CA3NCUJQFE8BBA6xLcLAA0hGXnAKijTmDZcrZ/5/BajJVpTOcW/LOrrDhl/QM5L4DcEiuuJBPx+Hhas4vaEUKZun9hZloWJX3Dfl2z42ehACVT9K/UwthbC/VvKbVhH8Ew/ZPZP+xykre6mP0RBaB7lLy1QBKtYukL1nqTLbTxK46JCF/9rwvrsik+6Gh9obreXNfbMgxjRtmckR0ezwe2yFrimx3ghMjmmCkaGFNIHEUAAzWw4fsl6EFc8YIItMy8yeOAlrKqVjVvCADNpodAmpUCays7sLkJv2dxSKy+1re1mc0hgLvWwE9jsWHvbp+rMWARO3skUrZCRgifH3t5ZmRm+sb0iIgAOq/a4cNYVlZnTSYF4NktdvMFIQLPggAE/lZTGwrCBg1IjHBtoi8HwErHHZGIAtDtvRpy0EtFTICp4fvHdPz9c/Q3NcWH71SNX8KKDERHAdDsrAxuCABrMgS4ovFkEgIwJBnf/q3jC1f4cubZYhIC7nQbmKdwHT/TjgreY8gIRwCAsa3GQow1+wdzLbY1EJsEdaw6ZZ68DMxKdr+LlaRQyPVfvD4zM3Pi+NNPnwD+Tr2MBI78nHVMcBBXh6bTgt35PBeAZ9mLLzRKQgQ+r2kprbGadh0ANdNHLAKV/OmQjsF8dRWAJnc3ulkINWZSTbrCMkGLYso56REAlPxlQ+bR3OAJV1ZNJuIktVW6hi8ggCHlcM6v2z2f+evX8McOGdZteQAUKrgJAG6eBhibucH+JttcGEbNqzhlnsMq2fjqraCQn3/w6eMnZnAdf/jk9KlTpwjAgrL3FyYCQT8tZNdwiIsQgC+c/fGPX2TSCjyL5aNp9/MxBPjRikB3vLcSGorpKgBRH5qVWnenG15hH/rTCsyktm8ZymXvnqAjZ5se37WkAAAgAElEQVRdDmtNJDL+2hXZnVqmtT2BAAZpYBeA2yPwGscPAHTtLrRsGBgb4E4IALi1Aip4EgXfAsihbJacMhCEFJj3Hnmc8vf9K38Ahd9fgPibmZk+efIkADjyzZERQ6U+RARWaKM0ptsA4irdlACubpa0Er17kvQ4jI/HXD9KEeiN94buhnQTgJWIzhF9e6iu5H7vs3UFwFq3MLQ0NcMTCsZ42SWryoPK0eUEYMDzjA8LYP7aNfuwO+D6lz17XDzarO+K4TB5BBDUMf41AAyCE7KOw8pytGUUAGByNZjA12Zh3XPP3Z998PjMK2NjiCACaLpcn+APgy4ivS3dBvAm2/qJBFDfXAWDvYmjG2l6lYXDxy3jIxWBvtL6dgjb3QRgJeLGBZ8o79XBPJ3Z0BUAuwTF5etNhk8MGeMJL863VQC4hAAGmICGB8BeCVTxQwBbkkB1r5V+rlHIZa2xtbHCxF/wJ+hhvU5hGMa8QRhcqVV/OmDBKM8euGcW/r97ZORBsAHHEEHsDeMGMFgESgCzAzaAP3nh5q8kgGAT6P2xgYEsHyKO341dfKPqoxOBPrEV5oZ0EYBRO1SbQsVjk3z34bCZhq5HBdAW3JOhD6FkmDFTqSoWAI4hgKd9jzc+HID5vFum7mgTgSqCPCOmJXQyY+nGprahyMdWY3MzQ39XvGFoWqmSNyMfTGVgDwC8ji/+1mcffBoJRCU8UnGHHwM/DAAIBmfOARBtwLNMiMDDuRgbzLI2AEhDxEEsMz3GgxofmQj0qc0QPdpFAEZMpnE22Ba/5hWB9KmcnfguAEZIoaVkmDNqv0wOYHEUAfQn1vkB7IFAL34AoMG9ADeBLVUZx0uNRN2+GfkscaczLCfLR2CFXbwH1myZztZbB2aPP3gKCTx5aqbmBjBQrtgAxhQvWErAx54HA6F/wbJiWeGaL/QP5ywCsOeWaqHL36g9+HEdBaARWSLXbAGL7T323u8m0LAcJ7xzZo6dDxQ+sIZvBS+roHIAl4IBND4EgH78MCVfugFuBKUcxCwTTQLYlABmMEE/1Gst3umeRW6x8j0EINcHb4Eo/Kwk0CMBAw9pq+CYJkTgi1usJOOAmg2gcM2ZnosNEIDd81YiLpN1u4GvjgLQiJxQbTo+9hyJwH2eQ1btQ7U7HlIKwPPZ8McggGfeVQ/DAbyBAL7gBdAIAjAigYFfgh10mYIItGUgAJjSXAAyTWtktPDAnQtA/N6PEIBlYcmDP3LP3U8/jAienC67AQw6pGMD+ndCfnyYLaAKtixQwTREnA0CjX1CBW+nEaB/+UEOiX7sOxguAGvRE/rVl0MrcL+7OQwmjcqZmB0BFAKw88RCBHD5XfUWAhA0MwDonVzu68XxoebE4cKdEK8UctQtEZjKNNITbgCb6dVGqu6hRbacfIsAnJmZ4QBazLxA/F1sihNrokdyz2eRwFMnp3PlbiJQhGFy2f6B/9m3F7xiAYDruIOaFfulzijxCos+urTjdfShE9xy4Y2vuXr5uT5DD82Qaq6X20cVxu7uHrdsnkMA5G+ZC8B8B/FXoGSYM+7GHDTi4O1fF4AeOUhbcXYIuuwSgEIMTqQlgMwGEEtE0mm1llRtOfk5AI/dvn2BAGxX3uIKuNwUviRcjPJrQOBJIvDU9OtdRCAGotfW1prZ2MbC824R+NgzllVdZ+3qQjWW42GYNsPpvWQkGW0WXeyErgD+PIzIRaVIB4MPEb0joen+1izuoxYLi4r5SHu2vL48+OPVaMKKEID5DkOSCrQVvOwGcBw7zN9AAN/xAGiEAdiLGejxgukolo9AuUMGP20JyBQAN3AOTDqVLBqGWa1WDXfLyW/cOcOkCmYmV8DX8YkVfnow+HjhbpvAGQf8qnMdTOyISYu2QdEGbGxtupTwY4crOF6cTQ4v5Pp1iwp3TKaMm6uwXi59CA8BFznIn6gYc8EC0Gyzdg9fA6++X7x/P7b5eM/+ICJngLqbBnnjpJ/xmnIB2OXllrA/qgogVhcW8mMI4E03gMaHBjAfDKCqhfWyUMDof+gIjbQBuU6WW3YbaZpCnqGOCU1Py7+Rb8zAI/+YA8gV8AU6FmlY7vlW8hdmHhYE2ghWnavmsUhbTZYb3gTFryrhw9zZtqrZ2OA6n3zgCb3Y12G7ywyRoZYHbKCMWsHYAtCo8BhVyKzP0GvmF65YmQQMvi8UCI5VN6ixAO4qe45aa9tSnxcrzWe7EEgAqnVHVF2IluH3zzTirmhGUEPAngjM530EynxAm8CyruuoLFkZfwUuWiIMQ9JP/EOP3EhTf0qwBZtlX5nR52buLJcRQLN1N/I3kucA6nRmGFe2zdcdAi+6+fMWwdHLNrZWJ5rFwwp/IkmeMkTMgjtC4RAU6eQELzNUh7tyYgA/9r5LAIrJdJWQVP/QK2YGmK3gWSODPIqt35IjXis1kXqrvimsIHU+8GSnAMyrT33nEKxHj51x5VxRYwvM0fr+O+650YENKQNuyoe9ZN4PoKGk5IusAARP8od/17FJV92xCZWkhQ2NaofrzYBCy9fuvH4bAazMcgFYQJbhJQws7uGvZFTYP9gEvgI2YtV5X00/gM1MqZFqtlqnJYGHTzd5C3SAjp8CM+hqfxgz0OzwZOfFTOrjoVqApj2bM9gNCpcYgV8XQSAeFOvW0aFB+7YNtPG5jnJDgCfHqQCGrre/c+iRx3E9cujRp4rXnDuovS3uz33/pgtAIyKAfimn3u65xwGQvrIEIMk/koQkASewWaq9Q+wCsEmZ8I3UmgDwOqD1mky0bzFyQi4eIAFInQ1aXARKAJtgwdSfnxYETs/M2uXpAUXArLWRapS28I38WBQl/VjTuB6SSfLBG/3t7QNodoTX4CLJEG1keBuhmkVKsLPRF85fsHcjCJyDs4B5gh4hD8peTHUUdVgSwE7K94bAjyP4l1ede8YbqSJFp58oqQCGtIQO4c/PYCCUhgKgkpQPn7OMJJbRKQEnuBSXPknLCyAR+LJsOXn9tVZbNNv4Bo/C3IkRF1DAvLUGL3+zM7CqgM5qfGlaEPjy3Rf4e/S3wcIXbpQa/JkiHI250Cs8u4kpPPhWe9s7wpVuwhNbJViii9EideRlqAO7tcQ3elLAtA5KAkOcKvJIZL0UBzCf7RAAHDv0uLoO3XWnvGclDT4IZUk/0QgG0PtZQvgLloPeM6F0RrDJskUhyUCQgNo4k9DgT3fYEAiUAvA2/M+kDr5TARBbv6gAOoKtBQRqS0DgyVOnTv3s7pGfFymBNZ7S6htuBLWGrIz/5U8Pi1Ro7SaFwygr0wzb6EIrbFsjgq1wB9qU1ZQOEQDg3q8tRsm1MqIpYFf4ZHHf3r3379u/yEKzGVATiPR05n++Z73q5g8JlHdRUQVlyPwkMoDKm/atLmdC6Q1TbdqhP6GAwQ3WdZCA2nhT1cGeuHU9pZiAt1sSQA0chuZFzIC5Z0TEqEdGXivrFWbTjC3PGdPiq0VUw7gefPBhSuEvZTCZX1NeJb3aWN2Q6YqHH+PFINomCZ9KG02hsK4G26wPCQWaj9N2lnjdxbn9++f+24fiz1C7bFy+PKlacIv378WAYAcCncp6AjAk/5mv7zziAfDRQ/IuDEOTBj6aCAbQ92k68teRQDeAlabwc5lwhfnCOHSq7AShfQA26w6A151+Qxp4DOVZAPDAASVGPTJy20nBxugK/AIElpbGxihBZnpm5hd2LV18vC5fAvlL268IfsjhnwJ/5/g7r3UenEpDpXu1A2vBiaMSPrufB6lhOHgFyzcORigGDu+p7G7NMJl323CLAOD+vXvf6/BlMsVmCwE4XAhfSzZ/x9iysANfFfdhGJo0ME1Ld73vrgD2hh89VwHQ4DxQlFjX8U6MBVe1eDqu6XIbBP9peXfubABfUzr+gTOlzRKArhj1gZHrQpJybY4HBNK0F8f4mn75xMunEylN47MfMMyopcbTJZU/EIFkAcpBwd3dDMuFk91xLVQ1G0HeqxR97Yrr5ailahuDMAfvL3RdoU3lvdPhz3tHm7+xn1yRCDkNBGAHB7jwpA3gBzaAT8m4zKOPvkplIi9kAgH0fx7nj9748wJYsPc6dFcP7JXkyq0yjw2ylq94RAXwNVZ2SJsoNU75+HMIlNkO5NZsJBOJ+PN/S1r4mYefPj6jjYMGpik5cWo+Las+bAAPE4CidVd38WaQ5nJa/TkrkEG/92H4RZ+6zNp7c+7yjQ7nPPCOgC+Rt4pjkQC8/32PbJ70VRt1DXzaFuByXQL4+CFXXAYApIa37nceCKDz+zb4cwFYbUr55ztH1aZetnMEWzJlWppkF4QX3G69Bku0nNxI1Y/7+QMCvzmibvjhsXg1QvE0EkgBmQOzF0H3x1MlTVvFBvyZ8ZRqDzZvniUXRONvLkoPjorbbLPIVxVQuZuqYkzbu8tRaXeAT6Gja0fyUP68PdYu+zMIjOYiucL7FDMwj72u8nnPFFZWOD/c8dpLAH/AjtoAPuKKyzw5OhYHDaQUmIXseHTiz//C7iC0F0BTOBp6HeSOu66jigFqFUAlYbXZvD4iDEBcXAmDmEvNzACAATHqb464AnzMzoApnj6F/E1Pvzx7YLbc3DgbT6RhxRMJj0d8U6PFRwt0a0JkOsNSOXjKKbCwmoRxK46vipVrqgdURF+nF0EfuHs1cCh/vio7sAC9OvRW+307GMPfy7wUkpNuh5cVOucf2AC+++7jtgr2xGWePJ1Q+pMWCt0z/7rz576ZH87bIxrs/WQqni6tqjFwbNupECj3hJ3dkQDK4EDHZ47/0AHQ9k9UEUjPdty6KyvPAYGnvkvt104WE9hpEAgEdewmUPBXpSdZXTqRS/YCmgpVrOE+7Pggds1wJK/FBpsWLw+34D93qCWcrPcQwH2Fzit8qpDHtUWdetkLoM4qGIzhBOKEimFbRXt0NesWfzskPZAf2AD64jJ/Cz6ImovQDcAo4k+9SxzN1SUfsarDJccsA1X7226rR/LZMvG6T8+CnVc+MXPihN2I7bW2BPDAyIELTZVANVlg5fSY0n7tbOKsRHBCdbs5gKKfUac6H9oka4vt0oCsqvW+qmUNTmKNFUC23rasNex8wxtkWDpD8ai3repCZ/lXYYsRevKGX0GPVUf613cJq/CgxfuJwEW23iHHwFjrnIJqA/guXb13SAD64jKPZlwmYLfk+x744/eKo3kkIPBHg9/S8RVd1inj2F1DUbsqgqJKpOkdwIqEXZydnXnaEYAtJ0Kj6mA8hPo2n3O1X3te07geBlloW4EbGIA5l1wXzwhPdqIuRY5uawcBqFuWvs4GB2K7sC0kWxugTkD9sYFda2tUmFoGRnMDnQDEiPW+7v04jOgGYGAMj75o3Ay8//0F90Mm1QteyxW6LOEF/wAWe+cHiJvgb/kDVj8qdPKLODA1ytun1Qt+uOTBVAArGBTWKPqRqCuOCKsk4uMbMgboRlCODvG0nMSbgL+Z446D/FoIgE3X1/85T/u1v9W0iYRAUEQFib+k0n7VDOlz4U0G9NfyggruH8xZrJyl5h+DOcY73/Rjn9K1NUQvxmI5kJEdVDBFrCMCGHhHcJPdSQ+FwtR9Y+/er/zBV+Y8Ul/1Wbo30CzccOQdV8FCAB5jy0frdRmXcaZ0dHz7fPXIn+09qwDSpkQDuE9orpdiyfHG+IRwGFQ/hNuBREXVFezDvdzygdnjM3YXttu3vxkOoPP9f9OXcv/yP9/EDoMpQjClbTTrp8++8MILZ5eUDKLAnD/TlxLgn45ZAfE3ObCzzBYGYztjLDfIdjWp8w3jAFqY758btPr1cDXPI9Zz2wfQ12R3nlwIbysrbsLmz3+JrMAOM5j0te7bMd951GPx8R9nboIQZPI2lwnYtQdCx9iLTz/bB1MD0SyN/KXimicMw2hmk0sAisrNljNAWEkJHDld5xr4xPHXR2wB+M1wFewIgID2a+AP39SEKXg2MfrUkRdwHXlqdPR0bkG8wUrFlyHS9u+/+QG0dOykMDhY7l+wsjHW7l8Y4K2XwG60AbT6coOhGlhWBxGAnaMw4QrYc7vt02a5H5K/fLkwP5ljl4cnC/PnKTNmzkugQ6uR6+IC41o6FAggGYZ1G0BXOmrXpNPOsb8wAbnDNsAMK42h31RCN3T3cxnO7YxvKFtxthRUotIX7ZaTr1PJHAahn/6FuJHvv5VDAKzKV3rVFoBn2YtCBB4/MHtxg5uCwN1R4u/sEZojDwufxR1dVQYaMkXF/Tn8AOZ2ghMyPLg2ABgCgIM7qQUOquCmDSDcmgvTwHbEOhKAwTd7vid5ZRcXfkfoJs/D9XPO0sG9e/fPHXS53IrLbFWjzJV56pEQAI+xY/I2zd1mqqtc7RZ6DgR0ByYzYmisspUQ/IlROUazWsVorVFgOLk40eCkodFXdgxBJTdGhKMPzOCAYY0APH5TxqhhsfJrQV5wSyn0f8YWgL+SAD723QMHQAjW0RR8QSxN2xp1AKRQiWsjK7gSJKB+omYN74yBCm4OwIq12znQvW3hhEgA8dZqsAZW8mUieMGhDLv+DMuhqjBbLCzu2+vr2HFZYhuxM3HelY7gBAGdwPTjhzLuRntd0+67bn0EJUvvqNTalEG5hVOOSonELTmqxBmNyqoJrZHKgMzz+cJEoNwQES0nmxvp1Ubm7QMA4ExmzT8Zzh0HbKqRaFsAvtiQAP748MwBQhC8EcHfWe2pet0GEGe4mHZOQCHI+OMrIF5oYCejdavdthb06rplMZ3VLGxHqi+wvmZzgZfchWlgNV8mShgmeLlnduZDkljAUHTGcy3ev9ffseP8MKnedrvTJrCzrqhK+JC9NfLBu86N467NiAiFH9sZ6SVsQGuT85fC1+BfNQURLA2mnEBG3WLW1rAxkWsz19kQoZ1eIPDUCQDw4ngjcCfE1YmQOVNCJIA/YS84AE6/ghk1sxfrmgSwPlrfkABaMpDd5iFt0r6BvATFC0XvToNGBfLtDtwuHswtDA/g7AYMTC8MBGtg14bxtgGMsJFd4E0mlflwi3upd6r3UZfzeLhshOMVXCmBh16VcZkP6kd/8AMhFZ9EE9AZnfwhC9DDPj4H0NzE8F8pkcaTwUdFGCqAE6lGKS6aB7ZYXyw2sGtd3ZJrcYOQACQyS5szxwGbcrq0FrAXfF3tSsmUKSESwJs3f+wAeOPGtBCCR6QK/hWTEnDD3kihSh36pxYW7e2onJy2p4alD8YGm206csWKZa1a8MOVPxej7IQELa8GDnkQtgVUfMO5vVip6X3YMFxJPd8xEVBZp6QSfuQpGZc5Q1dzWcQB4/RtlrWgH7YFQvBJFwBqyN9qPM3LrvhnVgihuhDZH6ZFIy2GB+GftXUShLSbiu3aQASOXMCPoK81wAT84YHyRrrxsidGPTJywdWOmv/OxYAAMMF+4gLwxg0Sgq88JQl84eZpAWBi08GAD/kNz2Lu2CpKlWcmbh5bBsXnTExOcKcm1Ex/voyxXQA9cjkQwCp/0C1nb2BxLxLoCcVgCvTlKD6wWELsPfLoDX9c5vHHH80k0MZwJrn/WvjjANJ8rdX4Cg02N3waGPdHwD2ZUAEE77A8ENs1yLKxgVh/WXTNBwB/43+hNpa/cWJ25vgMTpjTPDFqcoGZKy2fAon44Q7L9pO4NNl96MY0CcGXpx9++CnCL62ljxzlAGacUAF3hjsotA4jDTzp95hzJ6axi/BDxW5EhyZmxZetb1IywjYugcczCirc0W1Ib9kEziGBi/4Pa0Ud3FWwzcBDWJa+5NsLfgesfvj8wnL+dfFHAKY4f0m1f6Kh8tdswiO0cQXA1kAWm/KxXXoWbCX4jXfNB/E2++nX1/pY+XOfm52Zmdaa2uq/nKdk/AM2f9VAAIlA4QX/BCCT/GHt2xISCOthWLYUPEqRmIZDoMm6Kdnwgg1P4ZL83sskPVMI14pBRVABge/tAtg9lczQlTpfe1L6IgUDLZVAmkJY7SXz+8YhUsDUF8ETl3nkqUZ8E79kIkr0a7EABYApDL9k4phcZ9gqv+IGMING4IaImvTBGmasfzCb3TWJ8xuyojXfP981Mjv7mRk2EPvcPbOzx2caGe3WHzvp+KoHLOOJLWVCsaluxNlxQEr8W0I9DBCeevp3/vKoIPDIafHOJYFGt/ajzArbNHYLTntvgo/TbPPeBqZIzDIxu9WbyVqJlI4V8JbcXxl/J3FUuw6ATogW82LmFtX3Tdo7F9UCpPWdRx5/9NAZAtATl3m0VIrzk0AjHxEVnCLf8+frtHg+YNF1FW0RqGhgDDZvog7WBDk4xgdHigKA2TUOoGjN95nPEYBs5q477p794enVRm7ErX0PjFx0on+e1BokMKD9Gs98eYcIvDEDpuXMKRCCZ1/EKempVEMh0OpSkmaGami3HDLUrWWcp1njlOAOJqpiPIo95U2s2uL+7QBoet6yd//NIMdDeXeqDqa8LM8BI2zDKWvskccfOXqGN+Zwx2VeBI3HD0VeksEnd3dNi+xpcQATKVWKyKY7Hg3MWLyEu3E2gM3+NYZZTGstDiB1zV9gn0YA714YnJ0FFTzbTOe8DogTgvYl1pAaftW3F3xWE2sJlfAsrYs3NY2mVCfjSKDYM+8WgkVXMphAz2ZEyATo/DXX3XhJHEOQA9g1I9q7fBNlPX0DdKXOl9YtOxhDiVnvewCMNrnLebVDjx9aHj1Tr1ZN03hbicv8ZNXOREAA8evG+41Vona5jrA4gHgFU44lLzrpVF0AMmqR1YjXeeAFnRDgrkmN8wWAC9Q1/38lAFuf/vRnPo3t2PwtY5w9uJa9oecakviMp/3a85q9Tp86dWp65J57MCSTS4pF4SOetta5CREvWw/ukusWgGEToEVvd6XPc8WJeHMAew4EdvzO2F6v+vbcInBRuQtTsip6oaf16KFHEUB+7u0cmUcwHV+GoS3LLe0/MgK5X7MD+Usom86MpLrCCYdQA0WdSWzxQDQGnzH2QvFo+F+nYYaYuwmmH5A3iw3JLzabgYnSbhHooq9apZJfNRXhJq76L3+1iSlYiODL1OvonpFXOYApzB9LJKk2raP2kaI9oE+4WwCaISALAejG05RbgLX3tgOgL0lbicLccuJ+LgBVN2T/nBKhxByGWsQotPxMTx16ygGwKeMyT2q4+V+0gXBHVj8qAm0AXfzht9pSeqPZLbFQBKZSduCOyT76tDnSkgJt1ll2qQjWity2W8aoBKoeSLNiXMnDD3f7NaU39U3Uus+BEOQE3vPyaRpVnYpnSpkUdurprIBtbvwE1lQMwgbJ5K+Kuz1nX4iH2vu9AWiIMif3reelD2Io+Lk7ojsbIpST4LI6L+fbCz0xUMTuqDaAGykRl6lTOxZ54toVj//70RAoIts7SuMJ90wFVPcWiD6+8WH3gsEvRcnVq4Brz6pFdZxCp1IdJv4DClhmJ1zFljG3RTr+BYU/2ewSlzEPixS/2n7tpuOxNJu/RAIVIXg3CsFzWiLxf2tx1T8L2QcxnQ/o6mjmTuCshQWyr5ICNgPOPRmDtfdRJR4sRFtG54bRhq4r+26ehlm2kn0DAVQJPD95udf6+zEE8JgAMFVaprjMsXczaTUb2hd/+SgIlFWeO1Y199wyw3J5B9Q8gyBJ0GZJXSUQ0wjmDT5ohCMopN+BA+jsylzA62rLGDd/Mp2wivxxzyf9U6f9mjvrurmS5AhmHSH4kvb38XQ6oWybm8HN+xUBycMptjun6EG797dvXbtOxw4Wj9SxCgGMuBUS1ruau8Ae/Lw+kR0KBDdkv3c/rtcZycUxBPBdOrv1TCP9CMZllpcTmjsb2rssd4lqpRaNe1OZ92gDeHphYaGqnA7DGs4RfbE1kWkgLj9Vi2TimsqfCdjo4n6Uguyi4G+22VRNwNdu35YAMrmjbHdCoifDgfKcv9WG6AP92PNN8R2w/eQi2X2gh1/mBN49rb0EnnDa8aKwsVZwR1P3JaegHn+g83UObQdTyCN/FSt8zluFIYAROiN0mLU5jwbcLd2TDuzNFzQUNwQA3Od+rO/FZPzYrLhD7eLPt0eXzxxb5gKwUZoYfQQswDOjPyk10oET/+zD8raE1KaVp8NVzEqtS4RGaXbndDragf3n16kOETssY/ZHLOsAqAhDtonlIqvxlKyQbBF/81U55UFvlpsXpQVYVgG8zdhrEsCy0mjQAdCQChh7wKRE+7WNpki9dpjnBCpCcGYpmUik0/HUyhX6YHiQwI6kfrTMtpgop3R5CeELHBDT6lyYzrBxS6StECtMT16+jNLP+xIVL1TKhvB+T1KW+lhK9LF3svGXipPVXLH4KB+zcmZ5+cwy6jVtdatRBx186D+duQHmFgBoUo6Q5+vg7IzTd1g0OEFnrM06a+aKXSGmtBo0doiR1usLFqvy/LdYVl8TALbItaWaRfi5UNrcxGYZ0jOwkL8rth2HWrg8e8D2QZSWMa/dbksALzLbiXHc7GZeKGDeg2iJ2q/9lKxMF3/NamFFIDjNW5/PjkyeRh2coColUzzIv2pBbFFGgX3WOnRju0bphh3VW3suYiS6Ux29ERBF8e3uOqHAg/s9Y0mUxwJxcE0tlhu2SEyt78zZjYz56JUKcTh2bHl5+V30QLbqry4vY1zmRrO0tQUq2NJBO1ruyHPNmlRvMGvqWeloG+LYWP7+DB+ArBobiA00RQp6bFdsYKCFAPIMg9jgwAAflLlT26SmVWkpuipG3mkriA3Oy+WLAkG40WUDSjf4otpt2haBQgFrogcWb7+2YQ+GcJZZsIUg+iI4gPOPn0cdjBsjUjYHXfXgTrlt+N4a8tdQMPJ5s6vZXYsKYDssVpzNF4KieP635YhAz2AmJRRvAHEDePWyMcuqgbAarA6vMytmtanovkYAmlau/7cOPQAI1lmjsfXqQ8vLxw6BQrYmc1ulRMHK7ozFgNsKTR8Qz9MHaVx7cMob+XdhpcqyWmYAACAASURBVH+WvaviApCmsE0OWtZwVgI4DLRRdeKuNVbup793ra3hBlyulG5Q3nRdDaA4XWKwpWp59h5yhFtMeMHXwQuWEvDAiDQAleJ29EHywgzhPbB4+7UNV9KCWEZBEYKz1ID66VOn0/GzyXPntLr9GC8eYcpVqt1OzXjz+QhD5yqLETeDw15nftLQA95kQAaF8zD3aDpDiWBWrJ2TcDVx3nN1oUozbEGTLfTxAaigLH/z3WKx0uzL1ZefBK9j+e361q+Wjy0fO7Y8uvyr9cFBfRMkYDYLYrSfj8Fts/WqDmqyvcD1ZfC2Ou+CQh1QgkI3+GGMIABjWZSDEsAs7fVicRgN/8O/gcaBWA54SIyXaPt1VSWDPAo7woeDCXG/jJUD4oBOZ6xNO70LRCfPfainGyLlhtqv/dJt/zkEciGYfG56hgC8++mXx84mzlG3DiEmfVc9tFSWy7YuY6QjDJk2onUnCh1zCBfGDBKAlYBCAlsH0zRX2/NpK++ytt6HrUWy5SwKQh3HR+3MlbN92SYDhfZb7y5/G5yPM8vffhLgA+ge+A+7pkH6LT906IFD3343F4vltiaMNgAIolOOwRWasI8rw/BCVUP0IFMMGjv8D3aMexV2kAkYDCDPMGgKANnaMLyLjXRc+CJbnrx6+68yhQIPgAgM2AmZFZxubIKzsaG4IaSBMzaA2H7tlyyAP7LwroAQPPeSpi29QgT+xczMtNyz+2WgF9IBINzasDr3frGihNcIwO6R6LDd2suGOodaee2AC+3o4PtRBArqXf3NawsxcJngomax4C/Lsrq1sIvoefVT7y4fehIAxDX67UOj8OOhYzfXfosAfGB5+dCDuLe61YDnZi0DC/Z38TG4w5a1cw0OgZxkc91HtdsGgb0BavJ5Bn4AUQVP5nQ48KRQwZNUnUgZBhzAhUHa88VwzDg17ktssjAEaTj6xWbZvxc8c1wQmKZjaG4AJ7QS7wLI2681gvgT8m0lqWkvnUuevjHDAZx58NSSaFlEjL75DDU4euZNAWBHvLrkEVYiFZr9NzQCuwMYMuawMGzoZhCAQa/thAJJBB4UF1eV8g6AYAPCddOHYwNA3476u4cOPfTQoUMAIP4LCB769ujysYc+NfCbXAICjVm80CAFdQGgrQktK4YAgjKc1LtnvtpJvnYKCDp9Hv4KFIbJNckJQdE6AACCD4J/rOHs8YFBxl+4FaP2KSi80qJ/R4aFIEixQByC7W0ZMzJz4vgJjFFvpBoZrN6c4Ao8kxhHFlMljetljlKDBRNIn+q/83yYcyAEZ48fn5k5/vQrN96RQvBNpcHRm90A7NYLv7sDQuu9aADWQnaaL5uBAjB4FpcSicHGrPyBrgOjCq6RCuYA7pxka0AfyD0E8CGUgMdGgTxA8MkHlh94aGtthw3gQ/X/I8vqpxeaQgWLXDsHQGsBlGGEk4JCkKxCOTLKK/8QQPC0wTwlsxIU8Hp1nelNCrvoKJowDLN+y6JSxfUFWYFZJ0uwlBjfCkSwLOIwZV/LmJkTQMoJuGui1EhtpFZJDU9sYTskbGk+3shwN4L3/2s0PckKUgnjNbEs7oskT0+PnHgaROBf/HBmWgjBn7oaHD3XEUCji/qNPPWaAOy+Fxc44vzyeVLAgZGioIM4xiJthrwPvmcbJyepuz0D0gkhAPs+qI8CffAf+hzga/zmu+8AfL8JBI4+sPybN7dyfeyGBHD5oe/9aqtUQ0HI1vvLOAa3jY4C1Uz3Ya8S9E8inBNK4xL7ngaN1/HyV9hBzjV3rO3iRJGuLLzVasXAMDr8b7WEx7E1QUIwhb6IH0GMRtNenEcJj5QvzgIpx0ENpwi1iQyoYcCwkUmUGukNADAt/NgSYFRyqWevGQiqSfgiyaVXHgSwnz5xYPYVQvCnngZHz3UA0OzaQL8ddXsrWllSUIXd/GQ+RAF7qobtCyvj1XN79819bc7ZL7C/SxiGickwTDZb/w8PHHoA6HvggWPL4Gc8cOMDDMNU2jlqAMYGd6GCXvuUBHD02w+9cxa8YLh31wITuXbCF+hjqC93rve4HUyB1CAAfZ9NveokgXCbjK+8I+W4JajFNf7BtVQilca4Cd4/e2L2xPHpF+lPpXPgSJnYnAE1fLzYoMQaLdUARby5Wm9uNTIa6OWE/cK/+hVrhhJoctW0SEIQ3N+/fRp18IEDB2amb7zzoi+p9c0wJ8QIH+5gAxN5fx8B7L4XF5y0g0QFvpGwURG3dF0HU/D+ffvm9s4ZNQp+YOmA/W5NkH8YiNbXm/Xl/7TMAyzLZ0bPLC/fyMEDqeMh/FxDbdfQ1zfXWEtn7x6jR545euzMkbH62hoeAmRSk8IwOhbuMx6GqVq9pT1UWNsM4s8PoOkGsCkSBQSATsiPW4KriRQiR0q0pKXGUxN1TUMxN3PixCtjL26oLWOogy+qZ4Bl5vjbdJQEylGStY1Sup7mWHLynU4gQWagcCaLxJ+WfuEVcEN+iMXDr0wHNDgKuY7dxV9EB8QGsPteXHAvw1wuuOanU4YzQLu4b9/eufvVSLTieqI6M806atrlM2fgvzMYeHm3aG+7wqcTLaa0zZJMrqvjo4/BY59YXn5XbMXJQLRZwZaxbb7JxzP3op4d7DEZDcCqiz/6VeB3JW+0VKOMhCAYghoGpxubpVWCMJ25ifSdIAizY0t2y5gLPAWVEwhSML3BAUzXKS9ws4GjGezKO2WjJMgMNGUu+5UVsvriqSXUwbieFgLwhRcb56QIfDWo+CyC+IvqgAgA90eIRAcLVD0bLABrnUfN7rsfa+Pcn8lt0i6dQf6OHTuGJYRjS+7MO5n47oS/qEfzu2NHjpw5+gR4KVeCzxp1eMd5jJV21HHM0pb18ucD0HBXTPJMgTyYKFU/DRtUz4SSUENN3EiNZ9AyLGWfPn78+NPoms7MnnhZ1IWM2HOIOYEzpRR3OzSaBcxAEqZpuJLboQ7aDHG1dDMaCODZRHpi+mkC8LtCALKbTAL42DMBzmQE8ecuT+q2IgIYdKMZ7IHgdeuk6eb2+nJSC3woD59d0rbecfDz0edU/qRKJWVXoFnNn37izJnvw5MCm72Ych4jLxJhkSahhcbfPQBSoq770ntymF2LGgpq1D2XMrdXkonx8dTGRexj8MMTACG6wzN2yxjxIWexff5MA11gLaWVOYBpOA6wZL+yq3eHh0GmcGG2tgDAiXg6nX6eOng4FuDWOdsKLASkQUcw7iI7ILgwM6CrCg68EOQBZwNbGoS74HyqzHceftPLh+OSfEDaN4S+gj0UfGK1IVOcuM1VWEloZ74Pz4r0uSlPiPuyHc5W2Nn2AFhREZNFG3Jql18baol4mqbJiInq7SvCVSkTgwdOzHzmjntmCcByqywOswYW4OxsmWFmjS4+NWMTuMWXVlLw3QLP42rLrCJWA5MBozaYk5VIvB4KoCduarLufWx7cUAKIjOgqxMSaNSR/MtPBj8++H0qU2Ue8rSEppRiUIxmkcy+MPoKsvgbFHBGayoANiWAN6J+dpGo0EmnBDtf+bwHQEu98ErZh6d+Q+CX0holGmikxdGig0dbdmIpMgjrM3fcPTMy8s2RPz4BzG2Ah1Kvg79w/Iez6LpsNjI26KCENzcbss8RFpoQeqIyoKkUMLVkPzfqw0aFyxtaOo4tfLXibAiA7vKfSOKvFwcE1uLXAMCuYRgzwKi7FaaA+VsNeqfuab9eUCokipZE+5yl0DdT5YZUYzVpKBqYAHwHAQx8ZocGCWgfhpb+B3yRsKHgDs/D3AJQlUAu/kB3gvdBu2kTVBk5jlmCaJo5BBKEKAGPA4Cki3+xubq5+SuesNpY3QQEtxoK45tbzLXcryvFIGU+GPyLblYYr5xv3sQWvvG0pv0iGEBVB0cTfz05IAW+KdE9EB2wE2eHoINbUwaZAd5pvwGiKn+D4zcWIv1w4WmtggGYku6IuOKFYvwMAhj41I4dOsywccb+b7zol+oGsOIRgM3gpaVSNL+tlE7quC8ihCAxi1nSV6o2ubFdM7M4JHP2ns/cccenZ7e2srvumZ29644s28rt7Osf3GBMH+zrG9AZy/ZNxvr6c8ovdNegbr8PB00DRQP2rmIyUJNOoAgEAkMAdHRw1zHU9jnrqcDijb2yZV+nPo0BW6i69Aqz+aAGLAENR/zTfn2DGYpj3fkzmlXTSIIBaKfyegAM+iDdWsRUgvSwjz+nX7QbQE+OVUf6Gtp4sgiw6eiLaHaWICtgyAZtuyoB2LfGygdGDvzwjjvuuuuOO165eNcdCOCu7FaubyA72DfIWH//8HB/fxO46xsE4HTnl2Z/3/Bg366mq5cbQ5FnCFli2B7KRjzNCfzxYz4AH3vG8SdDKzK8qycHpMBb9hGAooAzePmvzi07LJEPBDDAE/ZP+/2O5xGSvxudp/XCA9ON+umCdIilCk6eRQDHgp/UhUDD8n/BPbeoDctdAIZoYDd9GZyjBAo4lZTxaaPC90Uy8QlJIGrheUMCiMkx2DYGO8Z8DgH83I4sG+5b2NyKxZgey27iHyj4GPwy6fkl25fzvBEOIJnzhiwbZWw1nqFJDs5GnA3g4Z+uXMF5NoXI4q9HBwQXlggdnEP+roZfdf+4MEP3B2c9y/tVkNN+adAR72dqT/sVj+hq/smVyjYYiU+hg0krFxIvnjn6RKAPYnQF0C62CTmXnoENLgA9GjiEvlJmnKbE5O0dkvkrRSkENziBVdrByzcFgJghfc/nPveZOz7HAfxsX5bl+nZlTzcaW1tba9lsDG7IYrpuVv0l1jecBSmZ9QOI7xYvi+lo5qYWz2SQwIAGR0msGKmw6OKvRwcEF9WovYH8dQDQv7Wr8GdePh9pkpyc9nuzfuzYMbvJqXKJI5h/Yq2crqcqTftMwqnF/m7F+DtnjhwJwtf5lgRsbCh1mq4CLncrgE4Aels3q6su6UulxOdSAAQEtXiKotKJ1S1moEC/AjdaBGATAfz0HZ/+zKclgEgXyw309e3KbW0M9A3EdoUASCsMwIpjO7OmaRRW4hkNCPy/fHvBf3sOK0b+O4ss/np1QDiA4AYvIn/hAPobQpMCxsHgFVwhU849SlhagPV3FCvQuTuK+SfWefSAeTkC6eAqN1GTqTNnnngqyAcxOizX42jAbZsig/5WFOrEEHVQjenWwKoLotn0OcNzbPiuEYpFatMClmE8s9XiO8jwJhDAVqt88bO77gLNCwDetWumTHQhNbm+fjbZl21swg1bQQDqdoW8H0A8Wc6NdOqpTUf8MU+Do+/euPEOIKhtRhV/PTsguA7S/CLk7+q1sMf4dgQNHa5pRSzTCCPXrYQlgB98wD445gPQNv+uFLqtLAA4DhhixjL1l63wrb+Ehho4YCMuIn4SwhrvPxKgS3wAmtgXyOOCCADr2sREmtyONGYw0yvB9/x6HrBD8q7BGS/rOvy6Eqdd4RK4AptwBamlXKyPlxcDT63Zz9xxVzmLvS1RrQ4gXQN9RNrGYKAEnEQfpTnY9ALIs6LBCzHcAAKBqRIIQU+Do1PT0zeWTp/TtJXu10SC0nsrxn20LXuVr2CS/BWZyJ9p8xfqQLuVsATwneWjdWEDOgBGN//gtVbSjVQBBxPzvD2Lb6sV4zdRAwf4IL3wZ1MYbMvImTUcQMMn80TSFTgb6YymkfTj2xQmlmmL03zt2rz8tQwyTy8mEiI8IxIFW1KLrul9fbG+HeB7lPv6Bvr7yAbEaTCDDO+hG7LglbgBbA707Yr19et+APFtgBdS8QBYgHdQWk2nXA2OTmFbtxvTS1pSnXDYadXCDcBwwUh5Af+fOB3BD/G5ILrhAEhlZGFDWF0mqRKE+YGcayQAzAv+xt7u+iEngbuVTCNRUCYz0U5yW+Ma2O+D9Ixf56UAaPp0LgJYH6fRrQ3MDk2lxW5NtSCZ86zrt5u0N4emYGM1LiJ2uSytMtMHY1k9m2NsDX5Zy67RL4M82If3LLC1rN5oLMA98Avciw9hk4OxYd0Tj+SC2kQvpOkFEBtep0qZs0qDo5McwBvTp7CdVhQhWPHHqe1zHH6uqWlz+WonHezrrIUC0JD84aEvh036JUOqwsO8AsAfvPMDL4BXopt/FPZOaiVPCxgGIov7wAEmoAc6FT/+LeoWoHFVuRvUHsQG0BN2ZmxjAnc40phdMDGhORPLr4au26SxwQ/JlGwAu62W0ip1E1eDgfre1EoyLZsxH3/8bwO8kAAAC2CLJlLP2w2Ofkz8gQTEn6ejCMEAB8T5joeeYj44oZMANL3hFOx5LwWgGSG6hmoS/QXpBX/w7tF3ZRjmSfroUaJ/nD4OOhgsSdcODOZKYBQaNHBnAVgouCaHGBZf/t6LSg17xZrKK6e2Ro+XAHouc4ttUeve1EbTs65f7UIgeixxroKdbVzGuwjyY7daDkZ6md/DW8Dh5nIq3dhcBZGrOQ928ycVsuFsXFdd3RDMUibxmGxwlIqfBvoQQETwue5CMKADlsJfBwD3z839abgA9MVnb2EPLCkAHf4mFRxcHBkkYDCQJDuZggVYl2EYNPmim39ytmGikVkhG9D58CAWXwjxgd0q16V8DWvP0NDQnryPwIqVt1tfVayhKRXA/JADoF/MbIJDm6o3vasTfzaBth/jQTAII1HoJP7mya0lDbO8UlrTv1SX2AawWnAAFK3sb4oGR6XGRPynTz38MHbyyEYRggFZqgp1nQCcmzv4p2ECEB2yCna+chafCMkFoGpGnXewC5z9B0LQP1XmO71E/+btw6Ya6RW3DQhecOJoSBTabfK5bD8AcMoyp4bmLcMkyWZacg1xKukGANDAu0koqgBa7kjHBnq9q0EAdObPQ6CCoGTQbcfx38pNZ+owuj2p8fTEBG73aeOg/jW3DIaHy6U3mUjwMiSAgj78xDTx4fCPT4/iVAcg8C9/53c++1mph0OFYEAPaZeNHQ7gGyAAv/WntgC84mYH0x9uudv+ieN5+SvQyLcOAIK0eNu3F7zkMf866fPLw5K44ngj5ca1ZhWKPyENHHSGvAJQuQcBtKxLu7EJyPwUiHtzqnppymT5qSHSu6aFfw5NtS0rPzVPHbYUAN38baSxajwzESB/yle7LTeBaiZfS0VQTbTBlNegbRex45ziuHnBpb9EtqyQgKZNHy5qcDQ6SgOGzx75j79D6y9OST0ceG0CItXuL3o4gFifKwAEbK7Muy4gXI8g/LgANL1HtQkJGX9aY76pMt7oXycAnWMmSxiFUVUw+P+nj6AGDsnEcgGo3oMAFqzqUJ4ZQ1MgCVl+aPfUnqGaIQCEu/DvKVbds2dq9xReJAFgKrliMS2VStgL8CuVtnzGXyT+rl51PaGqdKJsqrnNrtZEQJcLLBBuCBy8qXRGa6wq3wRvZrRU6hZriTxMR36BCDz8KB+wrk2URp/8SyJwxBaCATZOQImw54veQQWDAPzW14UAxPiowo7JakH4cQFo+g/qSMBAAg1WcU+V+U7+bbf5F/4255XNlpUMmYDnVSfEYk+gBh4LVhEuD8R96hBAMPIMhqLw0m7gEByTPVOMq2DDmgLhWAUA8ac5hL1SBYAlTdssYaDPWaV0ejMgEaHcTf/6CRQJz65GWrLzryCqRQ9jDnqqLK7XQQ4qjlDLA6DsdSmWGx8A8Cni7+zE0Xp99EkSgvfc/fIpiaDn7AYVifj46wDgwW996+dcAPJNSpsdg7X1wOehADQDDzo5LwkMep7F3FNl/mHJY/6Fv83zjsFXpH24gtvXyY89ARoY3JvgeuQIAA5dyucvDVnGkEUAtgFA+Jx4fw1sQOATPhTYijaAq9zqz6gicENRkcILjUifh0Bdl5LPJQQxQu0SafAn2uVl+teNfbpRGh9Pa3VvIyMbbIc/j/Z85vBh0r804BrnC6MQxAkP00IPuy3BoI1iDxwdADQXQQASgNedTcq85CWYPxSAwfzxbr2hAIIIVKf9Lt3w8RfyNl1RxhRXwDaROOahsHQENPD3x/LBHRls19f7CrYKBgD34DIBwFrbBpDurxCAu/HuSw6AWmNzazMzgRe4paQeK75D8zaq3nKrFZVBBUBKDNSxv54PQZWnFtfE3D0suwCsp3m1Z4pccneCdFNpNWj6Bpk9d/hRMVRui4n5wv/xL6mdmy0Ei8oZDOp/7/2ehwNYWQQB+C0uAK84KRqc7Grw+BjMQui8jwAAXhaAVKgUFx+Nl7OtTPsd9UX/wo6ZVQFc0Rpp/Pxi8yV7+fL5wvyDT6EGPpLLLmwDQMuaGmIMgMNWay4A5f0kAblzzAGsVXZYC+tYUd+yayEZH0AjAihrMc7d9dbr11q9yMCqrq9fA5zyNIDhSsVX50YVJ06us9Tzum57HHxpZAk2+Kw6UaVif0d4tBp9Ed+pulVMCv6eGh1lYr7wkxeotzQXgoCgFIJh6tfoeIOyanMCwOsKfyQEDWaFjC/quI91GbTwZPZy/jyXhaa1rlsGdXrGMAZYGw/JqTL+6F+UzTGpgPMCSppSVxw9+sSxhx5dgtMRsicTsvtmh2FYe8+eKvymAti24H7Qym3wT9iloUrbmqoKG9CydljZHBNZC7Tp1WK5fuqDxS/z67HWbVzl1h/lWtfxFxCG8KOzNCxfu3YNzj5uz1VF1lbe8uQ6uNNc1CAhikKVwXqptLpa2tzMrIqm5fKpLfnFAQL5uaHEJhKjun7zHOfv6GjdHrA+WnhrRBGCzwkhWAsoUTeMHgRgwZIA5l1ZavPzBca2w19hHoMxjgquWDsHkb1Ym7VjKGHYhpgqc4bW8k1s7sN754YcddKlzrEUxKvgr4yNHgEB+BMyZ4JnLjkA2vksAkAeiK5Z1T1De4YuMRRvZAPCH+gFw09QvfD3FNw9ZXEAYe3AOVvrTezaujYwqWOJBVZjTA7yWVwgAeEBt1+/3WrpOtiDIAbh12uvR9fH18u3hEhwEcjcYRl/HEZ1R9garDpu1DXFvOI+If9sE7MsBad4XrWAIX2aKzyqLrj5wohHCBpBRUoBdHSSKw6Abv5A9FdDnhZhHx8vMM5Bhwu8MNCvs+pC31p5rW+hCm7NGpmBOOwSO1zVdWpvRr1zgw973lX2SQoYjj4vqpFR3uXHzix//8yZF5Jmh3fl4c8GUG7FwW9Gvmq1uaxut6123sQhfSD/8m0cfduuwA1kFuKf7R3Yg4s3X83uGqQpcAhga41hIl8fApgdGIj1X8PHXduJXZKwx+vA7duRnZKrV68Rgk2vxFOloBqt8S3WF4vF+ie3tjbTmHHdx+cl2u51K6B8yqCTPHH6rMqftsGTyUgPCyF46vRLmwHJVz3ylycA/8TH3/yVGrsV8pwIeSR4gWnbzLQGc8OTTM/2Zdfg/3JzIBb77UM4VWb0gUPffvJdpZG8bkXJT0lqqyk6Ote09CJLo8tHwQWRM1fOT4YUJQUAqCQjkBiuUKYBBjlN6iNjkPVgYdCpZuHdhtyE5ABSG3I5GSTXP5jDsdRrLQlgq/VHg/i4wclWa2AtF2u14LceALx6nfLzvRtwduGlbRgGl+Fx3tb6QRZvba4JCYjtC3XdEYHuVeXf8nPnzv3Leo7sP63eSI9LR+MtRQhOLwXkCfaaZWRY6AT/6dWrVzwAhrmT0V6CLjCyYVX7q+s7GbPwTPRZbC1nWQ/8+SOPP/Jd7G/6W6J9Lu+d2w46bt5j0iU1LYn8CAmI+n5p9MwyuCAT9oyuy5cLk8M+CN38dUt7ULaN3daj/IsDyDu/wX/DsRgmKg/2LygAAnCv9+Pj+kD1tthgLJuNDUbWwZzAJqXpKyq3Jey+llsK2iS1lIJhAhD+iWW3tmILWwTgIH5pcnY0x8MufcqXMAPwX3gIhNrYJNJ2ngHpYRSC09OYp7UScJJ74C9vvs8B9ArA+bzZEUDPbZemdoNZtHvqkgog8Y1NIQfWWRsQswBA+Dv27f986PFDz/5PDz30oN0+l/fODXrv3sYfEkBJGg0uXD7zxJmb6tgz3Az0IkbvOx8VQB9+hudWD4D62loL/Y/JXUSbD0AwAFkshp31ewPw6nUPKST9VJ/Y5R8r6Vo0iZMmFO/C97kVW9vq2wQG13YxvZ8FHMIWgFcIQDn0GQksJdK2sZefoUFL08IUdG+M9MhfIV957yAB6OVvvtARQPct83uGdvM1tMcORIOWnMd+p5jWi2PqwboCAHMDuvXA//6dQ9+577cfetBpn0u9cwMl4LznbxvAy7j1l6dULuDv6E17SqZcztax88YLPQAYzJ8zLdMNIKwypsrndrH+11vXAlTwoD45CG6I3pMKdgiUQtCZQecRfWriln03nPz+QV0AyPq2gEG2S88N2nKv5SaQPmbxHAEo52oIAp0zAHp4dvaAtARdQrBXAZivEYBf9xqAcHmqHQB033Bpt7ouSQJxq8zS+0Hy6f1NVL/4f3bYqj7w0MlD//nZL4LbkRN9vBeod24AgJe9nBQxE5AAGuY5D/mx0WPA3zsBQwrnPcLTJQC7AhjGnz0vWAFwGFVai1Hb1hzDMTkcwH6PE4KmbvSgoBdDkYba8u5qhPZhEBIQFr3Phc2+LWBwc3h4wJWrrwBYLdgAElaCwHEaOKtMs7o+e8B2h0EIXnGdm+j8wRW0Fn0AinyYdlh5XWf+BIEkoy4b1vCgValZsRwbAD08MKDf6o8N/NZD/+cX73v2vt92Gsnz3rl+APNeS66IDeJX8hxv4uvG6OhR5C8TD8jmymfzPE4o3nl+2wD67+FhGL3dXucT0OlCLixYLQq2vM6aQF7sw4RhPKvctHMSXMR5GmD5ASTrM8uau1ACAoNb+q5dntwY+y/+OVdsAHlX+SIOnNXiW/IEVFj7rRHFHX7O2RjpjT/ApE0AXlID0OJIoeUlnlfI7/aufEEagYa1XgXLztIxEFOF/5v4U19+7svPPvvsm6J9rmgkX21XfAB6ZVgyjcMIOT9802+J+DtTWgX+1anVMwAAIABJREFUAj47+Mr5rJqnuE0V7L+nsOM2jzOXb9+mMPP1Mv6KgeYyv+c22oDw93X+f7RAdIdlSytnJ83X/yoAQAr7rcH3ftca94I3twaGvakJLUUAFrF3r9xs4yKwiN0MU7aNXUPlSM6ITwj2yl+BEYCL7j04TnnEg0wJ7KZsAKckgOcLFdkpV7bX5f3Q/ubZZ+/78puyfS7/36r4LrUXEso7L6r4FDl/YAAmO3x4qcjzPQHYwaMjALsS08r97nbFXei63fL4Go4dJxuzOd5wCwPifJXXmnoL/1pjm5sDemNCdwEop68XcJDNS5p2TgIoBmsk42AG2jqGs0FBQSkET50Wj++FPwIQswHvX/QJwELkAndJHXNEoARw8nLIc9788o/uu++vgi63+gHcEWhQvzgdo6jiAw4I8ddIpAqdALQzJHoCUD1R7sejNdkdQFC527P3uhKIAvf6dcxz8CW2dF/lyYGtzRI2WPUCWCXWCD/bsxDpKGgGplN2d0v+8y1VCD63EmHeoO+0Fsy5ubn79xYCNHDEAuNLwgGuMNsWHLokALw8H/KkN+/70Zfve7YrgO4HoPmXdNFzhfP3TiOVKHQ0P7gI7BXADo/GQTXdYbn+Ucs/XE13eqtkMDqAuUG9ofG6FZG8IPIUSACCA6LyV9C5CCzGM+iHiAFStnBCPewVgh2XKQaO0086r18DAr/mdCsR91UsZtBwaMNbk1gQalWuKSkAGbPV8FRoPpZY/3gf6uDOAHqfj+3YVlw8YDEJ8ccdkAjbg5EAzOcjPTICgL+Wdd2bX3297Arm2ZUfjop1JcnQYzdLWDtA7fYxlVWgiTIHw1xqvp+chFoEJbwar+OvllL8lr+gCMFTS92EoCEqEOmnied2ERvDzBmYi49Jzlfk7ihjlmkZU+RCXFJrEgFLQ+0VKJirwRNsNWwDGFYsnEcJeJ/7ynoB9ESgi6mG5uYPHJBjmARY0rhx0ikCcP58IRpWhSBBqfoi9jP/tQAEAn2C9bpoSXjbl/16HRe6R6ptSLRtYQL3BCK4oU3U6TZLCkBVlAkRWEimGuKb7nZP31KF4HNdhCDmflRx/rK1e8iq4Klc3AsELuIRr1imYRkyP2Roj2m1rak9Vo2nbKoAUpaIG0AUgFO7LaGGbQCzYTr4WbAB7/vHAAAdDNzwFhOlRspNxdvHjh19AvgrxVfs54d+8qxTKBUdQNc3ww5jF/6VAbwaXGByvUvdicogV7qb2I1mIp1KZbTMOI6LaOrGiqZ5U+5FUUYROyhp6Ox5e3aiM2ILwVc71g4jXJdQqOWHhpA3y3pv//+2f26RkdQD3GQKJjP3YJ2Ydcm0Kvi3gerYMrhsNKr88TRziAMIt17ZfYnUsO0GFzDDOuSN/NWzAKC7TX6gzykvdxIMwHRRAcMwimeOcQdYbMF1i8FPwrch7xy3K4Du96Vs4/2rA7h9cpseVbyplLNsYWcu3ScAeRsCXCsYi1lNpIptbwqWSwie+ofOAAJYYOFN7cacytqlqTdAAX9lzpgCeKb2UNEXB5BhnZhlXLpUbbf3YE0i//VS/tIlhkWLl8ypS1UUmOSEXLIt3EvCCSl0tvL/pguAvABdXvEVLDRMJ23+8IHrY8eOPXEEHODxlPL8Tq8J+GWj+L5u8Wh8jAD0rOu3salHI5NKUe/g1TQ29EIAPSdE1qUlkcDSeKLuD5D8fCSaEES4MPO8OjQ11GbGEPz1lbm538OfUyqAjLWxSmJq99SePW2GJWLy1yHQtoDm1BA6HJidmUcA246PhQDmHQBDskNXgD9PHMYF0GTe4a+Ywjh8ypZ/1A/YuHEMDUB0gIt+fsPW+cuF7ssFYCh/HwMAkUFNdK/R4CTj+BsA8Jy36E36IUAgZvin46WAkwZ62BGCuULIQrimsPpwT36IsT2gcufu3Tv3e7uZNT+EIlCqYBCAU1gfZloWSDf4Oy9/3WMxAnB3FWtpURbvUQUgicA9BQfA4FBgpdgRQCehAVYSq21TtvgT/C2RAbhcsnfgjEgEhkeGugJY+DgCqKhlDSfATuD0rjoBV3FcDZvAFRwswQ1B33pLFYLTC+EAGuCG7L6U51U4lcXf+8rc7/0JA6PQQgAVJwQE4B4433uwHEf51aq1EcA91LsCAZwf2u1eQ/MKgMHLIgBdgUAFH15NIsXfKs40cMQffyA5IGIHzgVKVwDDHHMn7SZEAH5MAeTN4YjAdENLIIDNZvkWjbJxOgvpdjAGCURDMOAMXhhRhWBwWSTad3uAvioAiAAZb3AAaw6AJgKHiemgZ/dQNQQCaP+KNYoEYE0AWJjyADglrqP4EXTFmYEAugKBCj6XC44rCs6Hqn0Ff+CAkAFYimseUroB6F7qSTo/fJleWQUwgL+PHYCOEKynMunUWU37JbkopqvcV/YEKsZTDTIEgwjEoKAjBF8PA/DSbtDCJAHnrdocAvhfbQDNwq0qEMowS528Ystqcwkof/UDiEpYWXtsQcJ/ZgMIZAUE8D7VWLXxOV9Q+CumnN0Puvg2f0eewB04O0ktMoBKOwUlTUGU8J0fLqiy25WN4I3efHwAtPvS3MSRNYnEWa20yXAOtKviEqQgisFiIkVNh+OBPWLeUoXgy28FA1gdGjIIwKk9Vesr9+5XAbSqtwwEEB8MNuA8mIvYs4I5vwYAeMUFoKPKAsSMDSB6wa5AoKQnq8q/QlJrjLvickTDGBmA4ICMm8qzoxIocvkvFyhjbL4wD/+rXxIbMuMTAyAGrMvl2/WziRQOrUlpLyVXisCb5W74d4sTiDn6XkNwhUawr6ysnL/bEYLZC74tTCqzRtDy4AVjHeK96AV/3SIA80N7GHnBl6jjd8W6NLRnN3gd6AXbvwYAaO8HyxBMIe+6kOd9CAoA1TiMCo9zpbEG2LcnAQ7wU5QCmKioz46qgw0DC0ZE5ZIA0KHvfKGjAPy4AshXbulsghhMJP4ecPoXX0oKEmimiUBuCBYBvFQiHseZm/i0RCL+8GfDhaAsQuSViODzXtk/B8sCmjCSUgWh2xZ7MkRgNQ/P4IWKyq9gHso684KHwKGpvBODkRfrsq9AiBX+KgDAAu8Ao1zmYrphG4A2EuAAjx45gjtwm6by7OhGYMeHukKF7i24jz+AVy/mlk6fJZgIQa205Tl1OubCpKmjemk8juRhL3Z3h6afPni3IwRnXNc+b2JPP6wxxGQEo1IzCEAeFsfaV6timhVb8FICHzZiM9Rf0T6EQxTU1srSDNyTV9izr5Yv9MEKf+2Nw/DLPOx2n1P2/pvDA/XTeuLvwAFesSepGj0BWOj04PmghxYCNvA+lgAig68vnZYInjunaTf/ST0ft24VasxK0oCxhuYmz17aSVUIKnrYHxqh/rxzmHmg3Bg0mbXLkmnRV8RFcotA7ti6AHzTG4fhl9l9+dEFWfHwR/0Ejz4BDkiyYEvgHgHsLAMDSpkCBODHFkBkMCcRTCKCWgmHQoqp3uSbGMUk7+kvidPSXA/j03C75Oz37r77Z4LAn41IPZwPA9DTUMCMmgytLFLC2OBMAugm0OMKMzMAQP9BuQC0mzfhI3g/1SdebGARXM2ynyxXxCzGTrAGue2fLABhvb4kEUxihv5KaUuka4ESbmM/DkAwXWqsSvKwb2WxCLAmUzxY/fzTihCcsTe2fCf24Nzcwb3eLK6ex80VuBLeU8i7CXRezy1W2tYVbyAQcZh3b9ylVjEBwVLbj/B+0k/9pEQVCnKGsdEzgB13joM2S/wpNB9vAGHlzkpbEKXgOewHi5/71q0Ko1+KqTigl6KOqQZ1Eb/FC0gSqJ9LZx9++ulTthC84P8G8wXyb78PwO3MWwIRKDKhJYAeAtWXrvBItBII5C6IS/YkNYwBMlf/G97Q8oUUL1AQotrYBoCd01d9QjD/CQQQxODfp6QtiEGWZKmEpOie+KDBW9crriOffVdayT79vZMnT3IhOPJWMID7AMCv+fJY29sgkNeCSAJ9ALpdYWZ5AoF+HFYyjdUkDoxuO5+WN/Qdi5camVTBrk1QAYwuu+2e+UF3/g8ABYLJlAYIpoi/5EtacmVlsaA3aYCwYdy6hYPymryJlXp2Vmi/pNH4xczMqVMcwZ/dPfNWnpekCbYwJGMY96MX7G+wuo2Zh3uG9igABhDoFoGeQCA39JVHgAPSYDT6pG2/vSJv1XQa7sJAfIWHi4ztAWhzGKyN3dLYD6DxiQCQI6glznICtZcw2lzUwWtVEqCEtFLPTjGRoN6Y9ddnXjllC8GL1gKsqqjwqFmTVmV979y+L8z5r5rJevaELw3ZPRECRSBWuWHrSn752N8ggH/jAlDNn8YKdAwNYeGeBFAMdHgbBHwmYQ+GcvFnVnr94ri3O9R3q3juQQbMJwRAQDBVKqU5gbxgiea3ioWhOyGt3OdnhZyTtFacmZkWBP5sZHZHjHpHWFiIy/RBq2Lsn9t771yF+kPy+lzLomrdWu9zh11Xy5GEzh28dSX/vRaQEahWYQJ/WyDh8BhSGOc5f0sriQbGYMRgKMMLYI8JCYZ3KR/EFoKfaACvXsV62LO02YYJ+5zElX+SsRNDblz4DLziShIozM28IgicfvmOi01roZ+3I2iyhTZ7b+++vfcuYmMCS3QpaJbXLeyqW+ldCauXK1AE8nv5uy5+2ZuQpa7kamOzwo9hV8jzjuY3CgkwDjEGwwI6qJmVYIXaA4DKAWyR/AkH8OppzBUsXllZQX9YSEF7YIgzyy0wAysZvwmWIJ+8Of2p2dmy1ScasmCjhi99/gufBwCpNYu4ea2/qvevW9uKxSiXy95R874rkaL6T896AVSMrmSm0RD7r3YRKneAx/LFOO8SwaPQHnAqHxGA7rB4/pMO4NVcGk76Cso07ZxDIGeQJoZ3OOXJuLY2872THMDpl2fv2uUAuL5jbu6r9743GbOsyZi8eXh4MAt2orVtHVzoCGCea9oiFmaqgUAlCFiEjzshrrv8bEU50CElYjDkHBu/VgDVz+MLIXyiAERDsJGh/JfiitTDgsF/crIHghfmUb/+9MOA4PSnYN11z0UbwMnPz4EN+D7+XRWCcYFVdw4gfJGbw3QgsBBEoFjPPuvOCMw7Rj8glhKXvSaku5joBeAlNB6D4XF5rwbuFUCjI4DCFf4fAHICNV7/ZW7dVAlMao0tHpo2cORhGzwMzwXAPOo6+iJjn5qG9crsp3/XBeB7HgD7d2K9m7F9I9AjAr0E8qFHrtLgvBr1WMngKMI8V8Ai5XDMnugQLzXG8avIDD8+AsCPgj/u2Dl+0ScdwKtXk9SaBz+6xYziikqgphUNMW9TescuDIvxNAYFp298aoxswbvueq2801HBi0IFg+atws2Dw4PDGKoJ6MDfC4COzPACiL2ChieflZWZ+fOThXllEhcKwKQ4hIwGSQcEP0ujwevzA/CxAYzYesjoAmCn9YkDULgiBRrhVuCpgNInxtUA7ComTr1tOxgKRwLjgqX6xZlP3Rg7efLk9Kkdn/n0b7yFTsgwOiFz6IT0r7OF/tjATnRCquiEFGq9J8W4AHR0cBAOeapN/+vhYdcYOHqzKTsJUCaGSQcEfk2OUx4CjUj3QVPpDcAA7PgIKIe/8CKmTx6AV1eEK1IwhYcqGSQAXzq3YjfIcjCUlU0rqXiqVL57+sbY2MlTY6d+NnPPbHmh3bb27pv76hwf02FZ+kJ1vUnBGN3aXlKMwp9iNQXigD0CvS3aaCXtJMAKM+mpYgeODMYU1m7ROQjgpzcAg/gTY+CdB50PIfCTshPiWvPSFXFy9TmD5+zQjNqkzTAtZSu/mExoN19/5dQYrlOnXpk98Nr/a1RwJ27/IrWPLNBOnWXxlpGF7SXFeADsIAKpR2BAIHDFTgI0MBBdUB2QAvkg1A260g7GxzCMsFfszp/hTA1RPs35wCd/EgG8ek24Iobq9ir24NkEJgUq1SJGmznFdXoxFT87Pc0JPPnwZ+8ZuZBHAPfOBV+hbQdi8lFE4D/eFwhgEg0NYQAyer7igKBLzzOxCjXrQwJohALoNf+wUWZ+3vvkTySAQCB3RSquEcOLnEFe04SVdSmnZq6iCEFdLyRTz39PCMGTD959z8il/SABQwDcdiDGD2AAD1fue/bL993nuVHtggpinp53Qx1piO2yeSaW7SIFAJjvDqARCmBQItD5SV/H/Y85gLzNtb/D5rUkd0UqnimZV1b+PpXOpHGAfAoL67CkhLc5ooHspiTQMIqnTz7MEUQh+I0/RBkYco22G4jxARgoAr+MRqC7QRE2IUcbgyvgCj1fcUBQADbIBKy4Y++OCVixE+i3w5+hCkCnSI8z6JGBH28Ay63XYQW29BeuCM4JVgItydTmRlFLJM6m0qlMhoo70xktTdoYhaB4LCWu6v9w6uGTJ0kIfu/u3d/6k7k5K5i07RqB+UgE/jUGAp/9t3e6FwlvUsAWPV11QLgABBPDrQAKCj5mNACNcACVUnf5aeiHJ137Y+4Fl1t9sdjATp0PmsV/rt+WSy9tbmpJwRU6DbVaHWzDzYXY5j+tkEOcAfjwf61UKtUqRo1ChBZvUw/n9Vbh1nlM0iIh+KWvf+tb978fbO1Z29TBkQCsUDrCfXs8ABbFASxGzxYpgKIRRDKBFdFuE1jByVQB7ESgEQ6goT5GvJfAY3zsAWy1WoNZPuEE/rkN/zP9mn6NtV6/Vde3VlOGZa1jE36drelbm43M6kKMOq/WtdPnNO10Oj1xOg0/kyVWXbDoYVULfhNC7Z9fPkVCcOyZL3/967v/NJg0c9uxaD+BPv4Yz0n94r+T6M38Mf4rno8RGHiuTAEUT8JU7/iK1zhVTbgPB6A6vV5EA+0jeVu7fRIAjE0yfWdsINYU8++y/TuHmwMDcMvWZqnUxkmnzTUaD7WVKVprMda6Xm7ldn7qt3+q/flpTfsz7cU//+6fP6Mt9VnWAuUa4Nwifvp0Izd9EhE89V/u+9Lur18M1sG1MCvQpDBjx+xPL4GWS2yh+K786EfPfvmLX/p3//Z667U772y12HUbQIMr4LzqAHMBiKmoVrAARAlmGvZrd/mOBANouO92DuXVwB9/AGOxnYMtNvhHKAglgAMtNhlrtX43xja3WC7WsAYn1/o2tnIk+9ZiYDKWW1m99fqurVMntdPPaD89nc782bkX/0wrwUOau3L9Vdt20gv5HArBU8/+l/v+YPds8GxgIywxut2umBRljMCg5K+tOA4ms8xa5c0vP/ujL37xi//+S6+ev/NC+c7XbnMARQQGnrlk78DhwkbtlIvv7hHrRsjwvnAvHCoRaJW/wCN97AEEJ2RgkuZ8AjwCwBhXy9mYHovl+PTZtb7NxgL9TQBeb+m/G/v/2Xu730ayK8GzSAmzDdA5E7bjpYHNfjHQaBjdHrP/gs0qtF+6XNUfNuyZ9sJ2GyiMO92efZhy1bqcVlY+NLA9XVhpN9W2NWKZOQp7WEpDkUgRLcC5XDYXKoUIl8uelZYlRlETDGKJHn5YnGVE0Lxve869NyJufJGUUlIq0zwuM8lgkBIZP52ve865mRQxUidv3K3V7n7uC3/2gx/8Wb62+rkBUVOq34COLXSPfnJ76fabt2699i9vdOIvTyshDuGHkUEnmUFBBaLb1vKicV7NjBNifg/lD//wD8ASd1ED+hkYTXsvEICwOe24ChJomQprsVMCGHyX0GPxE2hBE/zMR8Fogil5sQBaxaLBdp/VqX21yuUmAAjhir1QsIspQmQ1bf23N5bAEK+CBtxY/UKttvTPCkKHGbZxakDg67dA/uW3D+IvjBNrhIWjbSe5GlEwwtRtM0cMXJe/6j/+oQvg7/0BGOLf5gCyDIzmlQAy8RSgGXABEwE8DX/xQAbrsLLBJ38TALwhuyaYbUGLAHIT3O2SgjwYHBoMQGJ3RwAgbgGRAmJTNlHBgNuSesIArN39Qu3tP//Bn9cGwh85ZmS2bi+99hoQ+Af/fmU9buagGVNq6HLEpZVIoHD9+Ntg7qg18mdv/vQPXQB/7yH57d/+77/4Msjnb35A+QsGILgKzBRgIAaJ42+GPOAs/Pmba+Ihd+NE9wc98wBi/9AeDUIyFrlBt6BFALsQhKTR2HZtLKQHACE+po+LaXiJRGR4BRzrplRQoWm4//MPPgexyBdqX1iqLb1ee1vc4do8NlQkEBD8H2q55bjBq+2wETYDkzMZgQmeon8BvZw2Bh9+n2VV+xsXwE8T+z9//eWvvoLy1Zc/r1ZDAQgtBGT75QVikCQF+Fj8hXdXj5kY2HrWAWSJ6C5Lw3TZFrQG3XO2aCgy9fbwILHwrD0EsIsvKRKyt2cXcdNau9u0DdCcXWK/t5p/u/aDfO3tH9CCfpHB4//8k1uUwH8Y1HNx06cDRpiWHTrhxapWQkW2f/18pdXqCa/+6S1mgj8JFvjBv+X4MQR/9rNAAFKtYjMc25Q62JcfC+Dj4ccN8M5OqeIBWLofGsHwbAPoLcWxRLS3BW2zuycV1QWV7sLYpAdojrq5u+tnqlHwyV3+MvoeH2xgbvqHblOTz6Cp/+QWRfCnOHRwJeIL+kbYpKnvuKikHU+gr0Fi6xp+iolACiCGIJmXXxHl5a8LK3AugGxPOBKOFUIAPjZ/7G1LOzsegmznkkDK+hkHMBFMCEkk1W6e/pXlXG1jJfe231Z3cMiz/UggIHjrcIBO1r1wMXCbW1wzRvf558QkZAQTFhNM/yPNQ3/rtde+9dpP4SpvBfkDAv9KCICrdJ0YuzGDdYrxCvBc+NN2qDxyJ849Clrg31QA6Sa0Z+EPJFev5/iko/W33/7hD3/49gcnuDNItXz7FkUQhymMh/V+6HqMCIdswjVjCZkkAKMvrdARqb8Pmvdbf0sv8ee/GgLwa1/1VuCorGywSlSxUjtBAcbvEDBrZQV/px0OYKnkveEcQJT9/TNvQ4vz9g4O6W44tdoPVuHO3aU6DsAsf5cRiPMIsuogNAGdu3jTygPD7iFct5986aWXXnzxpZf+x58HmDhcf+tbVIC/b/8jPXOL8/eN9/vvu37gX70nvggrUamL2vPT0C51PhjHhpkQgoQSfVPwcxUgAohmGIvzAxb4NxfAxxFKYGtYB/wgMv7zu7WlL9R+WDv8v1qNN5HAW2/WBkRSxvXaPVodjTdtCBvYGsb02hjqJLa9TMhPXgL5Cgr8+xPvrIPcW2++xuXNu/zcL3EASZ/8wgXw64E3dytRBQCj/BmGaSQkAYOJmsn0+QoQvEC+j7emBBTgHMAzCa3pbxEy/K27tdrqnwOAb6++DQx+sEUBvFUujyXF0MfDwWq/bJFO2XAcB26Rq5mqA7kpBgQqf8rp4wj+aQU032H1MPc24sc04FvrLqyuB/iNV8a/8LxA0QJjNxyLkHrhwbzBCb3m8WkBNEMniAqQAVihQ0bmAJ6DcAKNlPNBrbb09hIqwrdrq5/7wudeu/Xqa7de+3RFWqSZx1RaaugLOMpIXZAWFFxsnoE/FDTFPVMV8WMI/p8QBuW+fwfpo/NR/4a/QBMAfEUEUMwKeUFwBMDgjHxNS1CBcfy502Wq3tpvRAE+ogowuG81rRucA3g2WT8CArFyxjGdX9WXPlervb6EtTNvf/LWrU/C/259OlsfSAWSUglZPKj301hFY0iOM+u4LBZ0kDB/SOD3B/V1F7/f/5uK+IpYAI9Wct7brudYO6Y/GdoHKgjgcfxKcJQ//igYQrN7lagCrD4qiApwDuBZBQns09pBgErJgB5cevvPlpaW/hyiEOAP9OBPx4o8TumgJhVFSTXUdFZ1nBmLo924908j/AGBLx9g2EH5u/V/BF7TigVwcPCvcXFHklW/HZPO9xWIivDnEhgPYPBxSK95x33+dgQFqMwBPBcBAgeHKaznU5oKqLbvLK0igEurb772ydcQwFu3sjJJrQ7LCKDSHNdUKTMbgF7e5Scx/AGBn0PHD/ELtIKYPULiAPy8DOyhwD+P3HZMzEe2JwMYtcG0YCLAn5nEHxyoVEohAKuhM9tzAB9H1o+Gw0zBIXqaKBIZKcr4t9YhJKm9/Tk0wQjgq7cqz0Ekkj4cjstELteHqRkAFAvxX4oH8CVqfP/wH3CPnB7feILVpn79qxEAv8jxYwxuDYc57hLSfBD2LkdtqBavAUe4oi0AKODqvqparaCxBfjgZicAoJcF1OYAno+s18cGLaRGAImikPLix39riVYrvHrr1Vc/+Un0B/9gx4HwIyMTFaISeSqAIn6+AiTkL+G2/2tPBTLnz6ShNU2C4wJLueH8LALglwT8kEBlvMbTk2iDTafR6ZmiLfYBjMbBpAXQ+jquqgFojDJEjomv9UQFGADQtcGtOYCPKasb47FeHjjEaBDSbGI5tUFO3qvV3l5dveXJdx8BJYPB8ERvDOrBeXkT8RM8QEJ+GQDws7//XRyI1XayKSkjdRzQYwR0sEp6n/9aaCEkyB8Q+F/W+j3aWIU1XaYjlTEaNnGocCvAH7PBARtPcBnH63JjwIX4E7VeLH+4yaGgAOcAPpYU0QwPN2rttmHCRaU7wTm93v/zATD4po/grUe99nquDqeu+DsUByuUYvADC/wVD0DydQHAr7zEnL9eI9VxHLlARgbtqzJI4+ehteCPyxEZkEbZIZ1yRydtDiB2ZpVHnMBqEoBIH2HLdKVSQOFN4w8BDJjtOYDnJeXcxmBQy617QR6Xw4N1QQneuvXTyiHdFiznrs+Fi+S0GPEB7Ou/DgBYdQEEAwzqV85Iiw10AiT55cBisGeAVUN1VaDOurBSCxLptUcUQKeTkaRMxwnwR21wIAZx6DqOVvGVXinRAIcBFGsB7z/yLPAcwMeWcq6GCB4Iq+0ug6uiFrxzkFuBwHljedkdGykwqMXKix6A//fXyc8EAF/kKsnJpmXVIU0FXFCZAph1viYS+HEOIDGIB6AMERN2YXWwqJUwAMuqM5LUURBAVIEGZ25kAAAgAElEQVTBGATObLuMlXzepirAHU8BMg2oaK4CnAN4DqIighuAYCRtdnhwR0Bw9ft0A7qjHDKozSACgF/5JfnLGAAdo5BZaBJdlkCfIYCKcyIa4X/hGV4PQFniXVgjkpUkBNAE3abKUloZmX4LbwyApKWVWi0/yHB5m87fjr/DWMgCP+Ml+Zclq2hdNyhV4bjicEtA8O7yCt0bFhlcn8pg9SUBwL+0+hET7BiGMxhIspFeHSgugNbLPoEvSwkAYhfWiDR0HQCEWGSkZoyRhABW/S06MbcS2HaaVHd2ei2BKl8VTjbAIGEAS1mNAliZA3g+ggjW83EIapWf+gS++dZyrlbnDK5MYhBf6qUBAcCv/DtCvCiYAXg4LCz0B8OsrGeGQ1kaMwD7L3tG+Ks3YwGUIV5pYhcWdiRLEL44RMmOOhllFKgDRACDMYi2s+OYEbZmUICCCgxqQHNnDuB5yWoeEUSmoqmVR9/1EPz2ci6f3ziiO2V7DIJD7rDh1DhhFbdYardAviQC+JW+D+DXDnBQ5nJ9mF2UMotNG6KKDGrAMQXwm193m5Iy8RoQQhZsA2xjmzs8kCTVwLHCHED/Nw8C6DgIoBYDVwJ/pRgCBf5MFRTgHMDzkyJORavn1uMI1P7BI/C1by/j6DemB5HBe/k1lJVllLX8vX4bB6wikL98UQTwLz0AX/x7OBMdz/H4pHw4gDcqGycGZiIbtcEvv/n++8wIv6yXYwEkmLSxcL6NCbpQ1/WygVkZwxhVg4X4QQBHgEssgFwJhg1wCMhSsBQGYmC1DOp0DuA5ShFzMke59YNAKbR2uI56LoAgMJhzGQQY2Xh03ByWb529fnA4aSlubYNt9F7bqNX6bfOwX6+BTh2ibPz9+++//00k8Kvv9XqyFAEQLTAhNnF6uIcEE6eNxEdaVYIAklYpCUCqBCNQhg8EAKQGuDIH8JyF5mSONkCnrVAOD9ZzuRUALJ87EBG89eZduk32Ro2BFJKjWn6F7qL9VwnFCLfWwdxvrC37XfCH6zmmFAf/618DgS9/9ZWvfb5TNdUYAFWLiVltWaAJcadaI1LixxKBgaorgg1uo3gAgcAwbmZYI5bCAJrKzhzAcxdAkEFVr23kUctxqDbW1gMIAoMI2QqoQjYRmEpNwBBen49TgS+9hMVY/zPgFmp7QrdwY/j+9/765vt//dVXvvq+DZ5leCUOAKT0gdrrtCybAWiZhgigFwgFAGyTKiBFEjVgmL9q9CQBQNyK5NHOHMCLkPL6ytpa3lVutY3c2so6pmlqufXDyqaI4Jv/YYVK7j/w6ehgftc2jgQIP4gB8MV/zTqR/pfaRqT7+HDtv33zs599+Y+//vIrL//y1xDSSJG1YBXMryAMQCSwOhnAnkMBTDLApRB/ZiXuPK8QptXSmNKcA3gxUi6vAoeg3lbLRXy8iuaxngO7LCIIDLobQ/wju+bMlPoq8a9ejCjAf/stLq/dejMY77R77fYHn/36+9+7+cdfe/nl92/2CWnIoWoYGQMQCDx0i+JHSBMAbJktI2x/Ne1YPIbD3IAXUornL4hbyTTN+KRgidc7mBWlVJoDeJmiYoQCQcbayrfpDI8og5VjF0Iq96iEa6Jf+pPvfPtbtBXu0yDf+cmWWq7wDIf161/3+zf/+Hvvf+97X/rmyy9/9uV/1iSSGtCBWBidsQ24TamEm2J0AoEJIwygcRxaB6EAtmLxCwJYqpoxFtiNTbQq/B/LGXbmAF6ulHN5Zl5rd25RhDwGV+kuOW/XPvgvVkT+5KUgf7+TH9T/9rXXXv00BfDLILe/85Of/Oxn//v7VL758vdQ3v/SZ99//0t/oX+0oAYALBBgEqhTF3kAwswwAHN8HCTQMEO1WJSpFgk7ezGLIGhoE4IVL2n4iL9kDuBlSnF1JbeWrwGG9Z/eEhEEBt9m+9V90A8BaATbMp+vDepruTc//WkfQCo3Ab7v3fwmAvhvqA784s++/MW/UAFAIRst6XTNlxAjbWAuxo1HLHT2DNELPA6tA9OBDsgP6cXhFACwYiZaYJ/YLAd5DuClS1ldxSildvC3ojcIDp27X2IIwZ+96BP44u+8B8HM4Xc5f5/+ckBu3rz5/jf/+P2//+vvfenLX/zilxmAhYznBkqqmjGIBABmCtT/cwG0aQbQJxDNbyidjk10CIwpqsDYVTjKn5nEn8vs/Z05gE9UygcQpNx901eCqBC/fffu9/N5wPADkcAvvegHILcHG7WtT0f0n6AIP/tlfevL/x0DUFYl0IAqJ1DKECVLrIxCshJLRntGmHUKuASahhlZ0saB0lSrESei/gIAsjBDmwZgZQ7gFZD98sHqt0V3ECF8662VfNAUu4HIS3/yP9UH/+8tjt8bW4T0f/mzn1Hw/uLjrPMS3EGsLf0yAvgTGQBUrfsfcbviZIJrvouKkYJzssS2OYAW75bngYhhxHQL4GwvCk3VTQUK+IkWuBLqCE6ywffnAF4R2b7/3dd8oQ7ht99cx8FHHoPMDXzppd85ynnW9zsGz+T9uv/z70h+56WqFgrq1hbEJluqahkAl15QJJYBxK4Vu2nZmIYxwAWkXiBgaLstmgb+P27Ufwu7iBl5Tiui/wQAeZ5lQmUWf2Fhcw7glRFg0DPDFEL4/5t3VlEPNkGMf/ciM8A/uPuGi58qpJIDqRZJUVV1awnoAwLxaZuoNBKWbNsK5aBZGIJ33C51wzRiZ9ew8cEUGV6TKvIn6DtvLOo0FTjXgFdKth9uflcISDiK4BPeo8YYVeBLL/2Fi9+nv2swH47zF8w139i8rx7++62traWlD6kCbaqgAiWlaTWbzOWzuWblAMKtu5mTacTh507XrPgAlioTPMBJFtgFcO4DXjnZfsgV4a3XBH2IPuHK1xDAf+VZX52vYlCUitHVtoZprv8dJdBgqRxgFEJgHR81LW8FzksF+irQt7nufttMAbJBMtQGt5ywAoyEwJMsMGf30eYcwKsoHMLXGID+agnY4Bf/gvO3ZYn8WdHGSxnBWX9dIBA74cAyqx038SIuBdPbwNAkujftCPcRY76ht9cYNbJhAEXazOkWmAF4vzAH8KoKmuPXQvLySy/9CVd/h5ZHEfLnGmDVIobiqkCE5e6ST6DxY+oaqsdWiEDbBRCNrKvxvF2UcUwhtju7BprqOs0JWuDAAIQZLDDXgHMTfKUl4BOCMvz8iy/+qzeY+mNiu64cT/IpRFUMy604oLi8yQhsIKVcLKrZOHu4Hw/hUYjVGpEqcKfRDXT8bYS5LuSPSx6A8fztaDNY4EBL8RzAKywihG9ACPIqen9uZsZz5LgCLOigBInrBVZFAisdBPD+/fvqu2UQQufYeKtwHMB2i8B/xNF64Q10hCXiHRfApE44Xmy1MwOAyhzAp0HcwOSNV994441XtwL8BQBE0a0ggJzAO5WquomidnD/7kUDARQSMjY+GPVA8eGELdKL5mF4p1zFAzCBv8osFpi/+NH9OYBPiSCEb1C5a0T4EwAsECUEICXwzutq9REFsExS8DKw1orCBinBDU6UwbkyukNjYcsgphbFjwHIzSwRFGAQreosFngn8NI5gFddmrjTU5cBeJAzPAcwAqAiNL557CwtUSOM+N0HDxABlAoIoLEoZSSbpGRpMUtsXL6z1AylsxVch/P7lN1BCGQngb/ZLLCrAecAPhWyb+8Z9m7zkAI4qDMCiV/L4gNIdDkCYFV7/c7W1usUP9UA3iRpUaY+IJCG/0+VSTNFdBXLBK20McIyLbG5XuyUdxWbD2CYrJkUoJuIVuYAPg2yb1+X7f2u9VtvvPEd7LysB9bTAEO32EpICPIomMrW62CElzAC1uGFKV3XM1QDpnRCyhL9B9QiKxOUC40Fx2l7Jjc0QMkFiDDvL8oZs8CVOYDPlNh76W7XVhfRUSsPidFnCxfYXI7em8HzgAUKZIEBuOkD+Iga4TuYgkEAsRhacgFUXQB5maCayRYc0wMuNCvEQ444k/o9KlNCEBfAeRrmKZGmnVFtGwHDuX4n2ewYBwrhfdyCmyyWI52XkuQNxX90f1N9ncYh6ocugGyQm2uCKYC8TNBJLxhO0rAa0zO51WhjiG+Bp+o/D8B5NczTIft2Qba7qSbqKzCSxuLYTvP7+iIx0kSXIvzxfz/2o03QfHeoCqTLx4Tu303TME3cSIdwAFmZIHGyGW6AI6OSqGpz4WqTWC2HdahT1Z8P4NwEPyXS7aa6qozOHs71I4sGPPDuF2Riy1IMfn5plrqEcYiB9acECwEJtmJ6aRisF8RGTQvTMYIFdiMPjc6C1sSJ0DslMoq3wNp09TcH8KmTpo2FzRCm0rl+pJCVVfd+NgvOm7caxytSA9qQFgeCEb79fS97Q/wktC38HyhUF7kFDi59REaQaw6JSbVo0xLQl+MD7mMF5b53l95jO6OefX/U33Cx1UzaxgCDziFvLqbx/sBuGMRYXMSQxBAADPuDBd1Ql25vbf1djRPorSVbfl0gS243JFXPeu6jl/iLVj63nFZMz8es/LkAXsxKyL5tu1tBN+m97j6902W3uE/0fhcfsntcMNsKh7pNJk/6il8x6dppGe0nnetHSCZLyFiiY8nxPkLkuYHRUTAysQwahyyfhDuOSZBAMmqIeypVmT0NFJ66AMYFIaWZzO9Fm+B9+1i+dk0uAoFNu5i9loUAbrd7nC3yW7uo2l28offs7h4VOHS8B4cMRi/gORdB9u29rt3kqReWhBkOcDPYMQELTM2nW5PlViZYxHCR1C1LvQ1xyN17MQAGGAxW9m9G8PMQa3sAzqr0YgCsFO5fAIDdriQVi8o1INAuAn7KNQXu7V3bAwD3rgFy6jWgTL4G+u9a1oYnqACA2WsAHnt0bW8OYFDoatxuV0w/k0F9OO4XMu4hhp4klGYZLpKW9aGK6yFRFej5gpy/oPG+EcBP0G+m6wGWzsTfha4FM8q6CFdXghu7cO24C9QBU+ypIrB5fI3eUByZ4uMAojYsXpO7cwBjZb/r9XEANP3cWJUNT4tJogUuiKVZyBbGIatrVlS8uMTSI+7jZoi+koMFgzsVdzhCaYac36UDeHxN5oYUUdu3DfXY5QwB7BpwA39qKjvGngA/EQFEP9CGO8f2PFhJkH3RfFrD4XhcO/HsZ9AFNAwBQB3jkPeWa1asMCUY9R4/FtR9GiEtVp/qbU9zFvw8tbojXwCATWDqmgK+XpOpPYxJ8C5mAxRgr2mDXlQUBW7Q8HIAbQYge7lqz4OQZGmKqmtwNKjnTix6zAgACJpRFgC0VIxDassrtZMkBn0DXHCralAF+jquRUYIXMlsefydRQEGjXo2e+5BiG0oElhRmwG4izEt3JWoc6cCaYpsX1OL12w5S8l0D1MAIYQB4z03wBNlf7/Z9TTXkBHYBB2HnZe+BvTK8xmA72Ic8nOciZ7rxwPow2t5AMqlUtspUamMSJveERXZY6g/T7OeL4A8CaNeKzDzSpMtXcEEI4zFa4YBN4gd6Dtw+wwEUAIAu11Z6tpP+go/FQIYUjU4HtTzJ9j5i83nLoBKAfWYCKChqktLW/XBoL6xvHzvJAZAjz9V2NWrUmkRugbSIsQM2OPoUNTTqb8L8gFBwWGASyPcY6CQxR/0ptllAO5dy8oQByuIp2uCmwBgFvODGD7PHcBZBRk8yQ8GQ8zDqKqccedgFQINSsxFVNXbr9/dGLAtcnJhb9D2ASSKCGCbtCoVzSFOYC1uJ64a8FTq76KCEOBLLu6ht9e1Zalo70myLUbBoOSuoXpUMPfiAYgnZ2nipsBcwid9aZ8a2e/mAClc01XVH9P1YabFxBZNi6tAVb2zQnfsBDUId4Jq0ANQ14WB+hUT4l68aQWTMf597cz8XQyAAJF8DRGDwNbAe/IxtbQegE1bvob5aNCR1DmUsyBcZxrStQJ+T8bcC5xdinkkivDGy6Sh+LwxUz/J0/0S4RX55TUxInEBVIi4owPuI2K2CakGoRHvH89oiMP4uS97TpavB+XG8wF5GJDtad9HEzjao2sZ4A8e7+2hOoMjdKENa8v36VpI11D38IliNssBzILuw0WUeSI6RrrBzGjTW8FEqQFROQtVIGYaYobiszTfh/R5FrewQfwba8t5hiB2CPMXsqotNwrBbeRQB4bygQGCjmdSgkn8gQbcDiL2MMjfjSCdoY8Xg+4DCCuKKkWXLrPB2xfhn4fbuPCL+Xw0sftsubjLl97sJrPD9nwpLk7ArIipqX17r0iFHWzeOxrU77kqMDIUn/CJ+FwFqkSXCAQiG1wN9t08oPBCPwomVAUCLQEFGPIAj49PGX0E3MfHMsHb8ei6bfg3MPtXUBSOLqi6MLoKkxs3Hjx4gOxyCb7tk77+T1yadoqyhn/CTdolIkk4YtJGLdjcJ3VQaljSx4xwsDjQYjWoKmEqsqAggIQMakdDqgWBQCynMfRCDIC6CdFHxddfYg5GsLzaVAJLFa+u+hjF/ff42LiAesCmHRYaVHTZHZqp2d82LKuJJp1qSUC3SO+o6oOI1pUnal0X+qm+wVMsHED45op7aB3wG0yBI03sbtGwgbzB+OSQGDFuoGSwKnw9DYBS/1pHAOkGhfVafVA/XH19S010H1tVNo83uh1mcBU4mcBHJe3YCMjxMSt5rZRKj0AuoiC1yYsKVFxXk7JZxWDgqVmMLpq2qmKIUlRp5tnGNCDaZPAX97ozRMBB7fjA9RRk+cZ0B/UpFQYgWN4FKSNTFOFIsQvf5IK0cJ3oKTIEqqR05schFSjpNu15w5tmRvqIrBYkBDArSYtpUlvOb2G7CCewEHEf9RLflCFajxDWcMeGFkvecUNzi6ljzO9jm+Ck7+uaDLoMwgwAEFfgJHSXu10Jc88UT5VVHzRZ8da1bJcuhaBkz5yCeXjjunz92VSELoAqaL0UTRFQALt2umh3F3QEsCyNpSz1825IHklSgbFHiLJIZMVSPwKUWdQEW5kCsYd3sUjmzut3EtxHZ4e0RQcu2cRqBiDY5mvEjDyt9CjW+wtTfDEAZnl2j9a4qNeKNtiN4jVJpmWC166BZeDVB5JU7KrXFHoq2uDHKkTYfoCK8MEzByE3wXa3AL5f0QewmILv7boCAGKHL7ZVfvSjP4JA1h2K/5ECjiJPpVJp2SBpWbnxkRsFSacAZmVagr/1Og6PcQkMuY8tZ4cu+1amKsARbvjasWiVDLD4yHuCx8sT+LtQAOkKb9cusvxfVlavHdtUPwJx7ClkE9c/bJuxaj92IcL286gIn63AhQIIZleWu3YsgDbaVQDQKBQgfnDdOUmho8hTrN4qJSuKXChIKgJI90kitGGJEvjehx9G3Ee37rk0VQG2iamZLdxhu1QySiH64HWlZAN8UQCC55ct2E1aW2XI18Bs2MY11bhWsPcRT1wAyfIy1C5NE3oAnsMiyDOnCBFAmqpTbUMEkJtgAwAsUAAx0r0hcWMKqBFugrGeHxuLC2CCATNipXXCw2JOYM0KuYGSeuwEZ0EnKkCNWO5dPyMjji8K5gAvBUDwdjmA11iB/r5duGbAn7DdRAC7cMcFsMmyC/RUXI87n9+BKsIbz4giBNywU8hQ01Im7QO4T4MQmfaKZMAHVMY0FQNnfoQCKGUsBiCtYDUW4bha+CjGIXIaTtJ59MsIPNADeWxJJR22OXplmgIsESccBId2sIlZA968NBO8R1eDd7s2bmABD236LFhlH8AuzURjvKKeZy3g9kNUhM9CWNK0ad4ZICrae10GYBE7uWgapovZmW6DNIzxEV3zLVAVKCm8Wlp36+7BHDPkaBqG8wcv4FZYDEQkGdnVdhIHAQriEC3YoxmhLboI/KlPXTiAzf39XeoDUj9vH5CjguEG4pmV5GvU++uyjEz3HE2wINvPRn6Gr7yxJbh9fmTfzUyz3kJ7MBjm+7T6mZVm6ZhfNj70E3CGwaHTdQ9ATMcyAv9OzAZKHSx4xWXeqR6gScyAAhRmp8YBqFVNOOgrwAsDkDYCU6q6oAKROFR0uFkKqzuAUJiWv2B/nCz7AJ7/KtwznJ8JSDdXH9ASF4OVZqmzCKtTwNEJS9/3jTCtoAENaJSiiyARA9wTyxECxtetGfQPamx+qkjzhQNIQ11iYHdck97FZ/EgLQCUrxXAGBc9E3wx5YDPXFgSK0ZuUF/Jra3de3tr6z9JhVn4Y7s2WFtLd77z5Zs3Vw+5CpQ+pADuHPsWVYSOtHdKOMy3rVEDrMXzV2IvFY7y+ambnyqd11pwvDRdU0pX3cBu7B2TJlbCgOEoAoBIWbeLt/t2V5GvZVmQAoGz9BiJ6GnyLOZngrJfY5VWR7Xa7dtL6tbS0u07WxP5oxOnDX31prvZ682bt6n7+C6tUeW+XVgBllh9jcGKGAzBAEe8P/ZKBiCfnmpWfndHBPoiZsMEZnPsuuMPqM/Cal5Ys1JXnKMQXDe+GHnWFaFVW8vl87XaUX2LyZ07t5duT+GPYof8fecmk3+OeyrREkHH9LgSAdRIq8VHGJktYV/WuBkKQhqG7+BgbgaLaZ70cCJxfox3/wLlmcrPhKRrWSf9fu3e+spbb/5vd1dX3zsEObj7XlHXP/yQ1wTw2AOF7s7gAQjQMQA/ju6jQW2w7ZJVCgIY6w1GQl0OZckls+qqwIBRf9IAPgF5dvIzIWHtmcf8Ubm8ur6yBlZ5457RNLzNkXzdx6ZycADvqF8+7NP7X8SJWvwEV70FN6NxYvBLKjgVV0L4BOlgPetvIIAoz0h+JiSWoe2FDpUhNq7n+u5Icxzp60a/bCQHU3sIoHrCYfRPMOIAbDnT+fN9PBwpUxEJ3NyZA8jk2cvP7O3FHFxFJVjDqlPWyEkBPA4DiNR9IABosN73uPSfE52NFcavUopFk7qBm5tXygd8svKshyVUUAkOhtg8TG009QKL3liim14Uot7h9/GED5kKjFaaVkZEm8qfyK1IILqBpU8Feop/swFEeZbDEi6gBOt5JAoJ7LAwxLaZW+gCuKVufZnHwa4GxBPChaYmifBXKlUmEige9mzwHEBRWFjyDCvC8gZbJKEEvuuvgXgACpnAmzdBR+pNd2qRFVCCDok4gLH8VRJWR4DAahDQOYBcUBHKz6wizA1qfBhCk/Woq013JseJvioCuHpCLBdVQnAaVtNTeRqJzoYuxQMYaGAKuIHVIJ9zAAV5VvMzYISPBjnLJdDPxLibvjb12wjge/1BffnIGyONT7equOjBeGlHd6iJh08gMHLCHMDJ8mzmZ1AF5r05CLqYieHzUVEt3v47iFZqy2tD3MCVokrbMlvMDpcc1iEyG36VuEpApgODLuIcwKg8e/kZBJCFIcKYDpaIsd0BvVibuoKTY/LLG2OuGI1qm4zoZNSmEY0+KlOklAhoaQ7gFHnm8jMYhniTAT/kXiBSJgKo3l3BoR31tZUjtqFD0zBbGHaA8xcxv6Vp/E0QbQ7gDPJs5WfW64N8rV+7l1tbW8tT2j5EvWa4ayQsPXhvOY9KEOxwH41zx+QE7kQ2AX4c/sxjY56GmUmepfxMrj6o5Ws1Op5tldbIUPQgxqAJah4b93MMwY3l3AkQ2DMNo+03Hc1ufifyZ+zsHM8BnFWemfxM7ogWC27kN+r1rdu3vVQg3Sik2XSTMxzBep4h2D42YpJ/p8CtVApqy6ppVHZK2hzA08izkZ9ZXcutrK6Wy0Y+f7C1taT+nAEYikya1glHMLfSb+HODECgdgb76w+UFr0/E6y6oABLcwBnlGcpP2OdvH0H61W/n8vXyPAek9Xbt7eoVsTBvzk6v21jeb1qjggxSbtyGv5KoW27RPUHNl3bMf1R03MATyHPTH6myxqRDgf1+nBQ43Lnzu07qrsK0l/BgPho+bBahShk5FRLMxrgmO6lkoAfKsCS10YCz8wBPJ08I/mZrqHeWVpaugdBictf7T3QiUu6lyy8twI6MLderVbbxCGG4Wu1Gby+MIDmsTelzTRLxwKZcwBPL89Cfua/UpfvvVVBcGf1rbt9byuc5RqqQNy2FWKRqqGVZuQvAiF4fsax6Ykm1OvPATyjPP1l/Q/fQRE/wPY74AW+t+Ht4FBDFbhyUEUjTMxjLWxQZ4awUjEqflOS6W7xxU6aA3hm4WHJk/41zvrbUwAfBo+oS1v1jb6oAjdWUAU6pOOmjk+bgMZqmWPNC4FBdpifWJkDeA6CivApDUuYCgwdUW8fDHKGoALrGIZUTeJopzHAQdGOhfumxgCszAE8J2FhydOXn9mO2GBEUr07qN8TVSANQ6pt09d/vLFSMzSu4VjWJVY1mjTu8LShy7B/9jwPeC7yVOZnHsbY4HfeKdwbeG4gqkCaiakKDiBa0Sq4dcdaEKw4AI8N8ZEflAhTV+cAnpM8ffmZqAp8gAfu8BlHnheIBHqJYy+UADXounYJpaegI03RGfSjYndsOQbWcwDPUZ6y/MyDsApkOjHnG+EaFggCgZ7y4vy1DGTLCCi2CIDHzEZH04Ku6qSJnTmA5ytPU34mogLZASwdbPJsYI4ReBDgD926ljdkI2F9TvMTh5E6BvZKplbnAJ6/PC35mUgmhhOZQy/QYAyKBJY0XEvD/nbGYdCwltysC4imHRux9PGzDI+/OYAXJE9FfiaSjHZtcO1eE/nD/wsEYj6mhTmajruiEeq+LOHELLpLiJmwg6GXmNHceoQ5gBcmVz8siahABuBqjbbQMSvccQlE/dfGg4S0XAUoomVqJQN3gZuwfatvpWlNzBzAi5crnp/ZU9V33nmw7U6e5kSqG4O85S0JV1wCW8gkAf4cyp9mEm9ITAmLtgxzZ6IEJvVWSqZxXJkDeAlyhRVhs/tjJLBpF3F+LQqzyfnxwC1Tte3KDiWwtlxD/oiNdfpUAfpd6li1Wto5ng0/PicaQxpUg3MAL0OuaH6maS9I6v13impKsenE/X3c40/dq4+loTu9iDjVdu1oMBwOauV6pW0AACAASURBVCc2O9JmZX1AIt9MiRZMT+ZPpI/zRzXnHMDLkquYn2naqYyq3lflBcWme8HazR+DRlRP9FTZbjYMA+yqjjsRDwYnBikfDdmmI6AB204bnmy12LBUVIAT+QsO6QX+Nn+X7RQy14CXKletrB8AxK3l1IXrCjEWpIxkkUVZysiGklIsJb2oEDmTydD9YFdxL7A63eNwBAT2UDWC4iOaZnaoWpwWe/hLKMjf5s4O27xkDuBly1UKSwBAHVTgdVkB1G7YtqyQhR+pasYYp4ZEyRCiSoTIKt+Os7mopk/ACTQdnBgzcnBCPnLYbpkV7VEyf0H6fPs7j4KflFyZsAR3QZQUNfMOkJcqdm3gLQVhSUYfpsZjRRoOZUlRJBkB1KXBuJBS63XSq4L313JIGw2v0diZIqUgfWH+5gA+IXn4/BVQhAjgpvQjSZWCAKoENaA0HksAoKIyAIcDJVUY5momlgeitAiZRp9bPSNIOEM9B/CJyZNXhAhgNwM6UPJMcOodBNBKDVEDjgvycGw0DQCwIJF+Wk8bw+V1VqOPtncqgNP5mwP4ZOXJ5mfoRsTXM7g/Fw1CMhZJ3UcAjUzGUKSTkxMITBYNth8sBCEDRR6zEukWhCDEnI7fVP7mAD5xeYL5maZdtO0m6Lx31CZLw9jFpqr+WC12i5aBW+vae8WT4Xis68QgOhn09fFwjZZIa+JCSHLsW53G3xzAKyFPKj+Da3Dd4n3Qeg9ZIhr+Dzyq79CdYZu4VtLt7uWGw0GdTrI8OhoONnImBVCLRL5COUw095LA3xzAKyNPLD8TKguMVOrTsUZ53OuGOMv1QX3ZMCl/pUdR/EI1gdP5mwN4leQJhSXByugYAHdz9XqODjLKbwwGKzUKYHAADFvr4KoRZUb+5gBeNcH8zGX/yIAKjLaK7O4aeT5lH9uUNnJVil8psh1SiLiQ7MwBfDrkwfUbl6sDg2WBcQDu1mpsyPkJ2OCj5apWEgEMDh86HX9zAK+eQET8/OX+xKAKjLPBTW6ErRzY4OXDkgCg0AtSPT1/cwCvnGzLSMKl6sAYFfggdIprhGu5wSCXK8W3o5+BvzmAV04eXMfbGw8e931OI1TpucFPrA3evceM8MnyYFBbYY1HWjj0PQN/cwCvnNxg9vdSV0eCzD2IscGeEV6poQ3GOLdaLU3MuszE3xzAqybMAuOdy0Qw4PfFOYG7+31mhPNog9cRP00L8pfoAlZ35gA+PcIsMMr1S/QDAyow3gY3c0e42VIfbHB+vapVKgH+AEjzLPzNAbxqcsOLgLcvMxAJmF0GYJP2yjXdjrlmrTZYAydwDQGslHz+tInZF7OyMwfwaRJZHJVxeT82kIlhNtjeM+zdJr1FMcAG52wyHAwHK+ucvynsTVV/cwCvnDwUl0Gev7xQOJCJ2WYAXpexVy61Z9PSBPukNqwTIg3Gw1q73TPbPafVc0DgjtM6m/qbA3jl5MYN8dEl5mJEFcho7O6lATt1wSb2XtEmDWswbhh66uCE1BvlESHlzmikk4YxGpU7zqmj3zmAV1PkkNm9NCscUIHUI9y3M6ptywViY2+cJWcH48WCklKadkaSsHa1PBqliCKN1IV4AKervzmAV00ehgoRtm+c7X3O8qOFBRDuBBZku5syiCohh/riwEgT7BbRC44jqRxAklEXyqM4E1yahb85gFdLboSBu7zarO2IDf6v3VRXlW3CeuPI4kFBHgKA42FBltKKC2AjlR31ovhpM+E3B/CKifwEW5SE/DMDcN+WVQmsMOuNI1k500AAiZo5diQPwHJKRgCrZ1F/cwCvmDyMKQW8tNosUQVyG6xm0raNltjeM8jewuJ4PMSGzazTySgkXR41AMAFdUEd8WHQGs9HazPzNwfwSsmNGJfv8txAIRnNHMKuncZMjC1lpIU9Ymeyw+E4kzEaaSmzoBBlQZJoEFJe6LQDa3Kz4zcH8GqJ/EQHaAmZGNcG73Xt3X3sjQNFaGf27NzwSLeG7XLHMMioUR7pRO84TlkE8BT04S5KcwCvkGzHF+M/vCQsxUzMA2E1jvbG2UYhY3fvHdWGw3y73Wb5Z5Be24SH1TPoPvaKOYBXSG4kWNvLKksQVGCkIsZW5T17f3VjeTBY9kv/tLNaXtwthPqLcwCvkDxZCxxQgZGKmC7Y4P3d1fyKAKB2Zvp2Sm64PAfw6sh2cjvcJTWJ+MnoaEnWPhrj1dzKoLbCav+qZ6YP+KvOAbx6ciM53r2ksoTtCTaYyupKngPo41c6NX7ijl9zAK+OTLLAl+kFPgzeEwUBPFpmLUlnpQ87mcw5gFdPtic2pF9OeaqvAuPLohHAwcp6LFUzoxgo3p8DeGXk+YkZ50vKR/vJ6AfxAOZwx4Y4pTZT7Qs/1ZwDeAXl+mQ/73IiZD8TE2uD1Y3lOoTBBxGkZo9DQkMr5wBeFdmeWohwGUbYz8Swe6Gn93I5sMEbKyH6TsdfdQ7gVZTnp9nY7UvJR3sqMN4JPMwFVKDbGTxz/BHpX58DeFXk+tRMy6V0Cm9PtsG76yueCiydPg0Y4a86B/CKyHQLfEniJaPjAdxdcVXg6fmLTE2tlubVMFdFplpgetIl6MDtyTZ4t7zMVWDltOY3zB+tGpwDeEVkugVGuYxcjKf54gGkKrC+7AI4u/kNTS3X5jumXyW5MhZY0HwJNpgGwvnczhncPx8/74VzAK+G+CNhJssl5KO33WR0bCJmd3d1g6nAU/PnW1/BbP9mA9hs7sN/T/q3QLkxa73LJbSIuJmYJCdwA1Xgcum07l/VN76C/OYAyPel97an38cqc5vQMjcucKj7ZH65K2SBhWR0vA0u5upYlbpzOvdPyD4Hqlh/YwDk+9J3ve3pbUMlRQn+8wHs2gW7+UR+u1ktMMrF7yXiqsAkAAcA4MrBafgL75j5GwlgSgK6bG97elu94QLYpDsGYd+DbDebno68RJnZAu/ijjYX/dtsT7TB5TwD8BT8RYZoVZ9hAMurqysguRVRciv3Bql0v18byAvKcDBQDwfDvo7bMJdxTz69aJNusasTHSyyUezal0zg6SzwJXmBDxISMau1wXLtFADGT07ldlh71gAs52q1o0GMDMepbHY8NtJZhRgZaVEmujRm/41x+M6JnlqQCG5MuiCl9+zL/a1PY4F3L6E2cDtggx9el1Cuc2O8enR0GgArZrxUnxENqK4ybUf/v4rbWBzVahuD4bAu6MBcPn80TPXTw6EiK8pYVobDBcMFkKjScCwX9FSfAIBGumur8iWrwNNY4N3LKEvg3h+C+PxHJUlGkaQMRXBlUKMATvIB/fHlAfVXjSrBpxzA1VxA39Xqg3ru3r2jMSEnHeGjHh7W+sPUUFKHCw1ZIWmgcKEA9JXpf+7G9OPhIAUKUFHk9CXHIqeNgS/cCHMVCP9c5/gxBvEPJTfIA4D5dW0W/sKGN6IEryCARfTicitr6+VpZ67mjlDh5fNrqOPyG7VaLX/vxMaNRS2rVRXmZrfaDmg3VVIzBMfqAICKDrqP/eduTD8ej1NjBQBUlMsF8OHpd4e7aAR5MhqsrywKErg2yFEAq8kEJu7cEAHyKgAIwK1T3jh5uTzTavWN3CQGi6u5Oui83L1av29ZJ32Qe/dqJ7inI+4pZVWZiACSxYyKAMqF0ahseQAWZMdpNPUUOIpgghf6A6N4uSb4xunj2oveZZ1nYt4J8gcEPlw9GuTA0wEAq5Vp/EX8Py1y5IkCWC6v5nIroLnqlLd83rWnqNU26nhsbQ1du1W1XC4KL0Rm83XcuJHu4RgQzl9L5M9sMwCVNEEAjQUc8ekBOMLhO8d6ajQCMz1WFqUFdXBQnqp/z0/OkIW+6NpAnoz+qByWj+YHtYP1PAWwOs0AR7euiTA5DcB9caXg3ITpujVAj/JWrw2GFEIkD1TgGmi1kxr8neGx4XCIphWOY6CxugrKcgVQHYzHg1zfiuHPpv+aIn8UQB3QbBDSMAgZlRsjfOA04HY0chplh1jlEejF0XDQL5+Mh7WNPP4g+HmrQfYvQM5ggXcv3AhTFXiDK0AV90vnKvC9eq6KAObg6y2dUgEigNrpAOzaipG8PHXqhdRy0MoCevnc2lru3pgc5fKcPB+mWm5t5WhYHtb9QKNed9MqfXVoR/mzuQLsBPhr0XE6Dhun4/TadLi70+u503V6PXqshf/vOaB4a+6PORLZvyCleAYLvHvh+WiqAmUOoG6AX8wBlPOHCGBtpZpggxMiYC/u0E4BYLdr35C5CmxGVWE3qh5xQaHbdV147zVFIG99ZW0t713aYT2/Bo85cEQQO8DVCZjFo3trG0f1jSNUhxCqYrh7NFYVfnqAPspfpxXUfxRAQehEY35CwFX0omZwSgE5ULS1ms9+bbJTemY54zrwBY/rQBXoeoCGKniBYHs5gLhv+ikiEK4CK7MD2LSLRaVY7KIK3LeLkfRsV1xIZWIbxWLR4Gv68Br9ZDVHVZ5LXr1WO6rXfdoC6ssmJIyiDb6bDbcGWM7GCYF/x2XSNAzDNuAfS7f4W/CX2p12KwpVkL+2y1/V4y+6VkQ5PFhfZyiu5dkHqF8Ag2ezwBcu2wKAoCGsggvgIQC4xjQgMFg6jf5jAFZOAWAKU+AN4KnZJYrKFlF9Jdf1FlJ33RVUG+dmpm9Yx4bR728MFHVcByduiCsRw0FtDc1trS/iFmNGQyim6C3EDkTSibxIx3KmFxWIHhSIHtJN4p4rwhdkKqL/qlP4M4Vj9PmDg4N15pSeO4Nns8AoF5uPfugDqKqKTlwA4es4WHEBrGpiUYy4faYZL9XQc9MAxIolukK6Z4HSsVENku4elpQ0KYB0IRWRwSGa+7tlS1EGg34KmOvrJ+Ox0STGSb8Mmo8Y5WHUabOTCBSCChFA3CigDABmCIav+I+scE3aEeELMtWKM79TAWRH3Tv03wOPwXOMS84+lO1iJ1duvxNIwhAlFkBOIWIo4pe8detZAMQNS1KAFtlbkDIy0VHJqTaowrK7kDrGTEatntsYKsp4rAMmMhxojOGRhCcAPBlp0SDWqQmkJth2AVQlIA8HEzMA4R/4rfygI54/EUBf/Yn8JQBYDYPoM3h+BG5fTQu8i6UIngaMAjhYDn3hgSo/05wEoHYKAOmA/oJsqzJeahXUXMrQU10bSLBP2ELqmC6kjsdw4nhMFLCR6dVhX8F1V3yNlCVkUdcxC6eSWQgMOYIBDVhIAtCcwJ8Zq/7gnOo0AIUn/S8aGMyfI4Fnt8C7F1wb+PCdDCeQ6J4JluQ4AINVpsnqj9lg8eueCUAjbcsqXupuATwwgAlMsWRIkgoQsIXUcUYqNAFGyVDksl5YtIkuS4sSBZA5b3CCQWIA9BInsfD5AIKptRbBBFvUBIcBbE3iz4zFb8L5wdOCBzS8OcydH4GPNRb1QltEtt9xF+IUsF2uAtzEz48l0YfiNyTyN2ULzdMBSOvWAb90Fy+1LHdtCiBcflvXDW8hlYz1bLps6TqeBTZbt9I68uEDSPCEGP4QumT4KIAYCBl6Wsos0iBkMQbAiTy1pvI3CcAQfxRARmBuVX38aOTqWmBUgeGVOGaBq9VlbA0WvqBZ1Z9phpfjZgKQqAuybSNMqm0ggGRYlkh9MKYLqYQYVlkmDD16a6cNHeMDAUB+QnycIeQBnbZp8rygi6WOYhNDtwwL0zD6ImmCMrUM/A/TMxEDHPrErWn4TQIw9JgDiASuTJOZLvJjWWCQ7QtMB257SyE+f5sugIPlgzj+pu4gDDb4NACi9lFJN6VSAFVQQ2kAcDDUpcEJ6kE3umC3CCD4gIsysSHyyAgA2tEghClXUddxS9riqWk3DBYlA2Y/S8/lqRcaAE+maSp+EwAMPXb5QwJX1ibLbAA+9mDyiyxLeOithciCBwiyUsMxgfwbqs4Wffgq0L97eDg1EU3zyvae3bVB1wz7q4PGiaWPxxaljeCtodN7NCUM5hTUFYUDbhE4YjThSZudMEFsq+1d9SCBAWsNrqWCT9hiCtGcDNN0/hIBDD3WfADhIiBlb73lc/iWe7NCj84E4ONb4IsIQ9j+IM1uU1XFckBZkvjnX4coZLCyTr8i7RTmNySHldK0pThs1bHx/3qtPjiiSeUxruAOKW2UAdt2bw2jGQeZTaaIbYWyeJ0wgbYdfRPvydZkllpT8ZuQhgk81iIAvpV5VQLkXk3DTebVzAq7WXj11bdmBPBxLTDKuavAfXvPsEH97DXVH6kBAHXvw2PpOY1DzNOpP1EqU/uC95t4gU+wYiDPqwdyayv3ajVArWmReJdOyCHHwWbbwcVeFoSEAOkkvN7lUHyHThxASSZ1RgAjR6P8MQBfBfDA3QPg4L+3XqU3a5nZTfD1c6DnwXmHwvv2ddwjrpsy1OuqIgn8OVi2UTV7vX6NVin1HKdnVtHHabd7ralXIYjfzqPJACJ9J/17a2s5toxbqxFyUjOacemTBPpskaKI+ycC2A4B4RIYxNWO0MfZfTz8Iq+PHpwAIGq7IIDpzMKMJvh8GtLP3Qrbe2mweuoCse6rP/aMsLRlNZyygSVE5cZg3DTGddBD8LjTIM5xuTNqz04fVnJNnIyw37X6tVwut0EV39FGbi03CDIU5I9wx8wO0MfuRxZ9g0SyNEyYiBZKxxPvdZF1k051KoBT+avGnR8+pGkxJjgWQHZ8FgBnGso2A4HnjGDTxsUvuUCKEgAou01Jlo6rDCp2FS7IY0UaDxSlnHEcJUt7CRuj2ZXfDlV/SQDuG1gXmmeFyrX8Su5ezZjgg7HowILAlOZE4rSUHTifVyLbIoCt6ZQ4xPG5ADzbAGarZUYBOi1+UdgiTzD+SpVYE8wAfBUBxJuVt9ZWpNkAnG0o23QAz7ksYR/3CAYLzADkbqAkq3qqQ1SJyIrjLDQAwKGikPSxkzGa6Y6jys5sKhCrFybMhimvg7bbYEZ3A3RgrS+EFrZrPMOFVCSVJW4qkIUdXO0FghDXJHMAGcYkfjE3gb8QMPEm9LT8Vae/gE3U0YSz3SAENR5qwQUWf7BIZLYg5NxGwpxOBWrRQ3sgXMNrexh9prqqTBe8AEC6IIKKEBdU4cKxrkIOoFw4XuC9hE5rujOO9VslLWE4UVFdza2xMvk6Gt17/WaS5Qs5gABgWucA6gahmWLDsli+mFg6w65J+NMMQJbBCTURzcLfFHN5avwi4UYCfsCfcKKYhlkJpGHW6MGV6csk52SBKYIzn7mHkIWRDH3cpi2rkmp3i9IIACxQAGnnIL12rKsQABzDBW9kClnCegkFJzDhiqDtrWhaYDpWGcvkcZrFmm9011DxCW5/TIgRNrEpddFCANnUAbZIVs7AbZaoi1LawNtFlTT5UAIKINbJeNmWZBuMPW0jEqf/pgI4I3/Vaa/gX1m15Ud61amJ6I2cOoWFc7LAKLOMbdvb80ALELgX/T4cNZO2uwAguY8AgvpTaOfgyCmDCS5AGGIVsAxFIc7CQoM0FjqjTlkEUItmBUH5lSpaifoyPoA52k/rNl3QaONezeeMxMa6MT5eishZulwMSnDR4Ku0aYtkDCttE1UmwKCd4k8zALG2ChzdQBdHVNrA3shJKjWdDOCs/FWnvYIrQLPd9k+cuhSHTaOrE7XWOQ5lm7gkt7+naeGP5COoxXzgnpOWnSYCqHIAJQmuW8pBAFlXoZWWMhll5GQzzmikLEgLqhgGV1mBljCUEneXw2+SBXMegLxMvpbP51cwxWdY0+iLUYAUQCutK2zqwGKBAyir1iKoPvQPdFbUwp5mALJCGu+94i6+2QPNF9GNswI4M3/VKa9g35dWdRf1zOjaS+Q1h+u5GuZN15OrZs7RAieAp+3GoCd8LE3b24t9quU0Oo7pWHuko6rqppz5+CYW05VHmHMBNdgA6Pplo49JGANOxIWukRP97jXkrkp7kdjjUojA51Zya2v5e9jd3W82RaJIcqlolEubjvUB0jz/gDB7jP7BIvoHHEBxKAEvpHF/StQGw+ciTgwUMwI4O3/Vafyx7810G0qixQ/eqeLxdRxVU89HKvip0wMix1pgf2jr/v5pmg4fPL+L8YMv1NlLxm+ytHs9cOR6Tq/XUNUtRSqo5V675zhwiJiYjjZb/fpwcNSCB60W7TN0Wq24b19z4eP8cQA5gs/xq980wvQlp5ntKJmsak9KK9Sm6ja1rmhtFw1igHMIYQiYYEtiT3sTCXgxixVrg1uAXzvMRMC/ZXVWkTyM2HaUQFs8gMn8wZcFvnOkpjryhvx6m3hB8Bc7yG3Uh+PhvTK3hDp24q+Brw1S6xsncRNAMM3AToc7pxgRsncDranHoBbzF3E22dxc2lLV+5uYgzpGn8j9++LNmVWh58ZnMPabrJRCBGoUQFH1WWItVAJ/cYIANgFAYxH8AzC16B8Qks0QWiAD4QcPQvBpmwchoQqZwO/cJoifdwhbd0EcdAc90sAMwGNHQIjyNxqN2qFsXg//nvk/2P3rL7yY8MisTuKPA8gVYLsdh69/apX9Yp2yMaLK++DeUDfGGzlsxMeZXejyjMeG3hyeGOXGIMY+24rCAGza0t5pRgajV0d/iX2wvY/PnSePvntna2tzc/MRA9Bxo0EAsI4VMaGuV49B8fvj90oigPTucyH4rPgylBCegZVdBqWOCx5YsEfzLKDyDIMlYbxCGIs+QZ+mhXw8DeNrU4EKxG/E28hBAJNGGUUnUqPnoTYCK68o7vfhA5gahTRnr1PuwGkO/NMzHUPu9PynnALgOI0/+hVyAGNchQh/2RT8cXUc1vGuqOMhreIYZ41hPZ+v96UFKQ33wf+o+02F8C99gHVvXVYGkira3Vl14D5EGjd296rnL9qdO1tLPoAj9zqt52lhfqTt2nOU/RoihqDr9wn8ac/FAjaZvzhfkKeUgzUDNguXWemUfxsojxGtuQ8S8ymo4Elma5SVpFRGkkiqjCqrRWcbcACZS8LmHLgA4qVv0ZEHyEuvnFLAQ3GyKXBkHEllWPfwacDRwbEJqBrpSju9dUiIP/ZnkQCgcCoDsJHqjEbgbYyMcmeErcyHJ/3ycFzOKP3hgelkZcexwBwYffivUbTtrr23Zxex9xBvsPura+C9VBGOz6oEtT3t/vO756n6XOEAYi2qUa0S+AIO1dWbN2+u1jZWqmak7Vpk0P8D9gMP4X4MgLzGM9n8is/F5KeT8O3EmvLgkZj0JbfELbC2I2APTH250RjBo0YZ7C8FkODaONjmsoPPMABHDl56B2/R3PbK6QVgjqQXy8RB60gaHQNIxpkwo/KI4B0U0M7w0jIoZ08bCyvApmthZgAQ3sg4Zq2BtCqXtgYqC3J5ZPZGafiRhLYvuD2D8gK2O6NPsrBHz89ImCKFP7mF67P6gXvg7+3tXgSBqxzACgJonpQRPldWy41YAD0GQ/wFWNSiAPpaapr6iwFwQuzSAU+hE/N2gXVlYaYk1epU/bkuX9sBAHsjvCZZdqkaHEBjAR7B1ZLxmR6bg0UXx7n32TKdspRRnZEqSTopL+B6ugSuaEaW8OLzC28QfRGfKcNrFM/MCAUI7l96a1KwwnxTkk3L6og0Ffgb4a2Bo9EC+A9lCOt5m5XRYU+Mx4t6I21YaooUJNsusDLy67YtFUgKWyBmDkTw97y/vXv+AN7dunN7k9lgo+qj591rJwoyKEAX1YXV58JE2K5yChNo21G8gupwkvGmdiuGQa4L+euZwRVEyAEigEBXSnUMvFSjEbtUNPftOBBys2fIiA3axcVxktKJmmWLz6rsEEkFAIEDI41EkEWIkkBVpYieGmHP6GLZwWcUx5DcKEWsfxG6mxL5Yyq8B3gVMgtNsTVwNAL64L+sRCdRK6mUwp4YjrHbdDg4SI0lxe52U+wwWGL4/bENdmYAqf+X3T5/N/AtDuB99AFd6sgJmQ4gfFlaSeBOCynAavU5AbUAQH61VDDqDXAZdAIn+Y5e1UAsgx5v4NjRP5seLr0FvS0XwLIzSuEFdTopDiC6hSrzDuEZKUvcQbsku6A0COsf7YCSSTtp3cBO05RBO1XAEgKigENGUpuEP1NOZ1XHB1BsTwoDGAhcNFcBttp0kM1IlpvpcrA1EABsQCCVhh/QyXIAnQECOB7r+KkeC0Cm+6JGWDtzMtAD8PZ3EcBNzQewdnKz358BQLMUB6C7tM4ApOhE9JvX98OiijjM/JrAKfi58QX9JjiDrM6Plv212k5PbNxoxS1/zAigXm4AUmxxnJBGYSFrlctNXSJZRck62NKHz1hw4R0JX0ABJOVsusyeARBVKcMB1MTw2I/2vBKwsALUmEdOcF18lJX1DIQivgbUaXsq5qUk8E5lF0BHAhPcH6AJvhY2wacDkHKmPXgQ5g1j5Mdh8K4LYCkI4Ml0AM1SHIA+fwAgXJumGgNQjMJzrTOxxGzgDPi5SeZSKeEjitlLcKFGMUYuAGCsCaYAOj30ARsLDnH0ptTBpUunhx/SWkwbyFu6AbiyCw9xDAWwLI8woE43RiOVyPg2rgYUfy0h3WBWI5lD9s2yU0YkuyiBCbbE1kA0wVnwSHsQwUPMscijEwogBiELqTH6omkWhKRZEHI6AJnx1W5sh4tbdncfKz+zskUtMDqBvg8Il3y6BmxppRgCS2EAy0IZHyPJzZbE0iRaY9ujcAJ9HZ6Wo+uC0/hrCyFoIoD0UolBCF4tTg6cQ5PfCyqYYLh1TIiCQedIMlV4Kp1vw5wyDqDFQhr2DL3lAIoLSyKA0QVhzh9/fkQsvYFFFGWj00CDjGNZGx2CBe00922oEBOxJyAWhz+WRqO84GCCenCC7YeSQtMwxS78/5QaEHkLE7i/+1jR8QoLgtEJNFp+AExWpwIYzDzH8McA9JLClm7pbhLPCw5io4bgNIMk7jApLlwk/8fGA1hl1jfex2875U4Pb5wRxJIjTJzgFYT/3DRMB+5QAMv4ccodmowxaBqmA2rPgGtdJ4S4JwAAGMtJREFU7tA0DA7qHY0aHZxVTti7dUZs+YLAbcNxwut7QQA5gWH+PABZGrPd4qlMXFEd4fc0YmlJs93TGz12Vq/V7jmjjFqWss5hbTAcDur1vk29wX2+g1icrjvE9eTwMor72+w9uBFUeHR97hwA3Kwct138+p4CTAbQLIUBFCoBqz6Abm2eracWJY+oSeUIAVUY5q4V5M6TSimJQPdyovVNyvPSdTR6QwfpYoqZ3vZabiK6x5baMIJhaxBs9i57bbvVo4vpdN0ck9b4gOe7WVqbZr973jJuuEo1AGA7VgFqZvCE8Kqo4+t2+ZHwPPzJYL2dYx6ur9Hxb4OsmrxXE91UACd+1vLBekMfur2H57ok4gP46Lh3MyrT+KMAVkoBcTFwAaTVAyovWREydFPps6dyFyCwkqgBq8nWN3p26ACCNmLCoukExmeW8CvYo4Qxbz6BcQCKZznE/fOS74tvz4ZX01MPcjkcwD48UhPYW8u7s2brg1ouxgKj7O8+PD8CzboH4P1jJ79SCyaijSkOIAcwiJ/3uz5niLV5CKBoVskEG+zT12mZM36UJD+EXqfWKMn6Rs4OPsSaQVR1LKJOqHQ5pURqvlgyPABY+MPFAhg8Z+QSWFACP64lJBcPaRUXwBWoJKTtEpy92gYde5w7GtRzetQCo+xuXz+vJZE2GXoAbh6srMGvkF9epUtx6qGZtAziO4CuCQ7g530xLoDubkEkkI6JKbwS8oKng28KUogRz1aDsXSYJgON5lBbm/xT8HW9ECxn5w91XCvSYO29jfjnHVs/GwEw8DT8gfEPWS3JCXqXMZijxay8ph/ZW/HZW8utH7DRaOsA6sb6XsQCV2kgcoawg/3qwWMOIcP3XABvL2OF48qKO5rNnACgGdB4fuyhBT7sc2JtnsVMcMDORrnzbO/5wMc/OrwjmlGGHVVntDe4jTCOwjh6fmIEv8fnj/MlYhgHYHsmACPXEj4ne7vs/Zif75+KapBuBJXz2DsS2WNykKsPjnJ7EQuMP6DZLCYaHFaWxiIi76ADXzRWADsigSZ89fWaC+DS6+/hUKKc8O22ZuGPq8BKRdPCH9ULQmhtHgIYWuqYOtjFk5EnvNbkNADi6x2EK5lphmPPxRHLBmjF9EXwF8DQf6PWKQEMP89WGqlFv5+N+w3Ek5ka9Fp1Vhh7JiuMbLnn1Ab1fKu5v6e5ERitfMQyM9u6z8EKezY0l2BiWkA42HMKdJU65YTK4trrtZ8zAJeWtrbqOT6WjX8r0x1AL+TQtJgP+hzpNIjtDbnSJzPGaHSoVppOZEwocH7CcWwlX8BT85f0VcYTGAdgcpjMfx32l0PfTI4dJcVPZKtnqAZrPnsuPCAdRyjKGw4JwGRZmINyOmX2VK8s2fv79AWqUP7I3yOVddqOW8nWppkEYsgugLyKDc0LugzrtcMll7+tt1ZEFZz8pQn8VbRqwsdEALHGn0xQdMweYoxAIw+31LMdXi1jszSY8OJlbm0uUCZqkFMC2ApC1wqQRutRA2e1pgMY9wvTYgkH30spxP4aLn+sAtE8DNpcCo+E8zF4oqnXq4+Jqo4cw+mkDKdFaw1pqWNZcprNDl4I2XDaPdHewnuky47jVbIZbL5LmQHIatkAULz8CDMH8Dbwd+f11eDnSbLApsjfpMv0XJWPohKjWyxz7oS+Yvx9eH6MxZ1T4WrTv/VLBdA9riU8H+Qt4OeF+PP/tHlNZTVE4BQLnJSRalE3EP2GR/EqkP04umIU+0cF8OBwoDS8CU+/67osN4jZdnBJ0llQMZVuOKQsjWy7XMbSyQ5xCyO991AXOo5Xycbnu6QI/w9r2dAo8pwsAIhLca+D/nt900r6xIkO4ET+wgDSwDb6142+gEsfd/xnwAO+7MsF8BT8hdw8M4yfS1rLFGoCheOxnz8AYNwJGn4p2MSIb1SQlXgrTDtQklpFGYBOyquCTEsKDrHttEbqguM0gEwVFyDRsXcykpRxsARC4WuS/nvIWcdfRk8bTqbhAYiT4FVZUDHDw63bm0t3traWbm8GL2mCAhQN8GT+KIAtn73YIKAV6I5sz4hfFSOLGUYOnQt/YtWKUEE6k7lt0+l2ofCBM9ky/ZYQf+/hpLJ8cwp+9G3azAKDaIoca4a1wBCQ0JswAJVFvwpyTCeioE+eboAFJkQxnPICDS5V2wY2AcBU2VGzAQA76bJfSCQXjAXiAVigjdxeTS74DQdbS9QBfH1TJbEfOOw1a7PyRwE02UJGgtHAyQRtEb/ZssUooyQAzUmdFcEOIf7AjFM6cQBq0/lLzBz46o+dhvc1AUCfwPivgDEb07fp/WYa/wr5j3kkm9GpjlppCoCpVBpsrleEdoIjglRJapBs1llojEZGVsqk6EbIqoydsgAgFkY6AQCdwkLWA5DOd/EAZI3cgsUmd8H526IK8JEIYMI3eRr+KIDVyWlesSo5Fj+25Gq6H87x9oqmPixrqAxesl40NeC/U8sRn+UP8B92iv+k+KlMf4bkNAM8kT/xDTlJAoAugUm7jbRasV+yzx9vbnJbDR5lW+0IgaELJ/69VT0TTHwAewN05iAyJo2F8oIzchYKTpkCqGYMR6IAOlgYGQTQoV3cTI2OsH/bA/AY/EMIpr3L3nNWt+7QCGRzUwsAmPQdzs4fAzBJ0PYK5jahUgWQQK+XF5Cga+wepvEebag0gsBhiBZr62l3EBGsRZWbDsdtv2RtlDEAhlDxr9rp1Z/wSp+dwNvF82dOeDagSXHd0YFDhUKrHZ6px7RHVAHyRwhPD3wGx6uC7IEGHNLcGMlkCk7PSXVslQKoZJ1OBgFkhZEBANuOkeaVbJ0R3XXPD0KCHmN11Pvp0tJtMMDf3XxUFQBM+C5j+Uvs/Z8AIEaxQjK5NYqtE8WPE+qCJSwN42QbmAzEFLeUztJEtZsgHPFvgyo8T0MCqwsZ7AvCiuFeq8rfjz5AAFnDJG2jpJUxAQCr1aoWB2CIjenqL/AqgZ8gB5N+yCT+vN+uTeBB9n6rHVKBWrwC9B62WbWs6XhVkKCg0vSP1HQKKQPIkhe4CW6kpcyCQq7pXmGke8XK2Ira4JVsuEDYHPWwig3/c9Mw3u9OWhUMge8sYV8cET5uwh8x/P7sl+XfqfABAt9JC9dekgCkY1lEP62VWCjVa6Q68Mfod8HSnkf4JBm6ORerQ8ew3kt142wOHEdA2ybhRKdBHZQ2Gg/MMCgSNpIzxcofiO2XrI1yFKl4SuQvDr+W6WUuBfVXnSAT7MhkAAPVmN7JCKBcarWDKlArxQEonEDhYVePp2F67YHRGOCJmMxrw7faaNhFYjUwH9Ohl4PYvDCS/2y6XNL2KtlwhabFl1jwqXZgHcskZvWnr1MP8H5VBLCVyJ/GytxwVSxaWu6D09psk3gAQ7YXZZSY0wMAwcIajbguWFB21AUZSbqVoTM7UjpqeV0Cftoj1jxJ+ynhsdNIYfGm0lRQERo8naCkJV783KAVy14bpRMGIoE/4eLFxRrTsIpzJqefJ/JHq9BCBGoagfhGZhdReF0cf8Gf7a61+VWQ1f5gWD9gh9qGifx0sQi2zVcE4BR9rztpabQ9ac2gTXB3QrDAS9iX6QGYoABZE4jzqU8wYQDGf2897ROmCKCJlg2iUzNke5kkZ1TABKdl1SFNcErcLljHcbtgAUBw68qppq5iXyRhJfW4+gyv4M2TrJ/SQb2If8DgkVD3hqUT8AF7WxVr7lkbJRxSR+GrPY2/wEru6Uf3zoBq3Kzp0EAezf1NSUt7lDVDfw3BghEzsHH2hB+dOxrk+IKJabgrNwHRdh9O+pyTCjEx+lvPvbf1+p3bEJu7ACZ4MzyGR/23+SnUgBhvjhwcLOB44y7alBrHCQAoTMOIjXT5D2ZV5qF+SYd1wZZBk0lep43XBSulUikgj7CcAO9dwwKwLG+e9PopPQAluk7E0gn4AM2yQjp0r07WRlkwhGl0EwAUr92p+TOTZPaXVL1KYB9A1inrtLWC4s07Eg2wFvXdp/3oXL3ulagY8U5oMTuhPMshvcTnUBnl8ocQB79ZFQFM5g98wFZ7BAAic52dTQ1A3NFGYHIJ2TR3NAehaW3ulFwAWzQvMNr/zAsvbDdHsUUErWQAabOFQ7tgHSUEIETAVANiaJvBXhsfQF03ePOk109ppDCWAxPMAGTpBB9AWe7wNkqnnE37GtD7jiP8Ba+daIBPzV/g8SmQDVVl8sI4BqBy3xu2EODv9DPXD3ODo3WPwOPYc4oTCgR7DEAzJmVGbd9KbfCDux+sVasd4n7Q1gT+0IlAANutkfmJT21+AsD71KYDJpeQT3ziUzuYG9Hg+O9+QiPPtWm7DGjJ/ReYfIY4Meq4NXIB3PndnRCAuK7oYBcsYBgCkPQcBBB+NVxsJBmFpHViMBNM6OTAju71U45Ixg1CKIAQ26gpwQRLaKoRQFV2RooyCl+SaRkYgb9YTTkZv+rkMDfmJ7YC/AXKMpkJlh8FtjI2Z+Av6WcfbAw2vDIpw4g9Z/9BIoEcQFpq0wgqGKrzlgeD/Ar+BOINw4sLQISi0xYFsD36FBhiE0DzANzBjG7bgcdO6ROa8xxceNAHPeRvGzja/6PPsOhX2DCS/mojdsgD0H++50CQH+qCpQBmIfIH1qUyfjgHcwKLCt1NM0W9PcedHOj2U2IaRnLTMKjxaDpBybgdmGyvTq+NMgJgsOI/cun8QpaIroxe3PhrHg+BGfcill8JdkKUxPlkBNdB/JIHF1ZNq55eAYKs+24gOoJxp2jbN5II9ABMQaS40GAZrzavaoK7teGgtjEcDlnzAxbBteL4q3j8tRmAzggY640+tekDqDk0mQT/ghbUeswHNJ3OCy/ssxZCXD9jDiGq4laPtg7Cb+j6iAgg6yh0VzwwX4Izgx2/C9Zx3C5YoIqWo4Ht1onVpBmUMrEaNA0zamJWxu2npKkF2m1JmyixWRZONQyahsHHRtlpWISlYbyNocRveBJ/Qlt5yFmMXt/ESx5HQdyrWkyrlRIE+SMlXAkWo/IZ+JvgOohuIA9FIgQmdgf7ADpozNgUaPbVY5pMPxyPB/WGPiYWViMao1gF6E88wKVWBBAuKIDWDgLYoz8I/oXHoAGr7Advv7CNkTtd5MEkstXvW6yH8aR+gv2L8Ds1Oy6AcLTfBzD5p2VLcaYb9GNlc5s1Sbb4sOEqTWSTkfdnxbolmf13+ym5D4L1hLSJss3y2e6xVpu/v9s6GbkiswFoBhJyMRc4+YLHHBQeBn9SK6z+AgASYirZYKt71a0cPhuAATcwkcDnkwB0fABViW3GBZYIjI87dqyPaTTLkMCAFUaTDDDljwFotkefKI0cDqATALBENSAHsOP80QtN1h47wglBzi++8cor3/gF3PlVbnl5Ofcr4BJ9xM+88EcMwPdfeeWVv+47p626jznqnKVyOuGKaLFnBAH06lsSTpu0aVL0cIzabLk/J5m/EvJ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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_paths_coords = pd.DataFrame(columns=['lat', 'long', 'shuttle_route'])\n",
    "fct_run_maps(deepcopy(shuttle_routes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_markers = [(position, i) for i, route in enumerate(shuttle_routes) for position in route[1:-1]]\n",
    "all_markers_df = pd.DataFrame(list(zip(*all_markers))[0])[0].str.split(',', expand=True)\n",
    "all_markers_df['shuttle_route'] = list(zip(*all_markers))[1]\n",
    "all_markers_df.rename(columns={0: 'lat', 1: 'long'}, inplace=True)\n",
    "all_markers_df.lat = all_markers_df.lat.astype(float)\n",
    "all_markers_df.long = all_markers_df.long.astype(float)\n",
    "\n",
    "# We add the nestle HQ to the list, once\n",
    "all_markers_df = pd.concat([all_markers_df, pd.DataFrame([[nestle_coord[0], nestle_coord[1], len(shuttle_routes) + 1]],\n",
    "                                                         columns=['lat', 'long', 'shuttle_route'])], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plotly.com"
       },
       "data": [
        {
         "hovertemplate": "shuttle_route=0<br>lat=%{lat}<br>long=%{lon}<extra></extra>",
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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'https://plotly.com/~0xmarmelade/12/'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = px.line_mapbox(all_paths_coords, 'lat', 'long', color='shuttle_route', mapbox_style='streets', zoom=9)\n",
    "fig.update_layout(margin={\"r\": 0, \"t\": 0, \"l\": 0, \"b\": 0}, showlegend=False, autosize=True)\n",
    "n_shuttles = all_markers_df.shuttle_route.max()\n",
    "markers = go.Scattermapbox(\n",
    "    lat=all_markers_df.lat.tolist(),\n",
    "    lon=all_markers_df.long.tolist(),\n",
    "    mode='markers',\n",
    "    opacity=0.9,\n",
    "    marker={'size': ([15] * len(all_markers_df)) + [15], 'symbol': (['marker'] * len(all_markers_df)) + ['suitcase']}\n",
    ")\n",
    "fig.add_trace(markers)\n",
    "fig.show()\n",
    "py.plot(fig, filename='Shuttle route example', auto_open=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 7. Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = {}\n",
    "with open('./results/2022-12-16 163709.548510.json') as result_file:\n",
    "    data = json.load(result_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\"\"\"\n",
    "data {\n",
    "    'distance_matrix_time': distance_matrix_time,\n",
    "    'employees': coordinates_list,\n",
    "    'results': [\n",
    "         'num_vehicles': n_shuttle,\n",
    "         'max_time_shuttle': max_time,\n",
    "         'result': {\n",
    "            'routes': [{\n",
    "                'route': [],\n",
    "                'routeTotalTime': 0,\n",
    "                'routeTotalDist': 0\n",
    "                },\n",
    "                ... more routes\n",
    "            ]\n",
    "        'objectiveTime': 0,\n",
    "        'objectiveDist': 0\n",
    "            },\n",
    "        ... more results\n",
    "        ]\n",
    "    ]\n",
    "}\n",
    "\"\"\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from csv import writer\n",
    "### Dist & Emissions avoided\n",
    "# Inputs :  dist_matrix: the distance matrix\n",
    "#           vrp_out: the results of the vrp\n",
    "# Outputs:  - the avoided distance in meters\n",
    "#           - the avoided emissions in gCO2eq\n",
    "\n",
    "def fct_avoided_dist(dist_matrix, vrp_out):\n",
    "    EMISSION_CAR     = 0.118 #[kg/(m)]\n",
    "    EMISSION_SHUTTLE = 0.186 #[kg/(m*passanger)]\n",
    "\n",
    "    FIRST_EMPLOYEE = 1\n",
    "    COMPANY_COLUMN = 0\n",
    "\n",
    "    avoided_dist = 0\n",
    "    em_shuttles  = 0\n",
    "    em_cars      = 0\n",
    "    nb_employees = len(dist_matrix)\n",
    "\n",
    "    for s in range(len(vrp_out['routes'])):\n",
    "\n",
    "        dist_cars = 0\n",
    "        for i in range(FIRST_EMPLOYEE, len(vrp_out['routes'][s]['route'])-1):\n",
    "            dist_cars += dist_matrix[i][COMPANY_COLUMN]\n",
    "\n",
    "        avoided_dist += (dist_cars - vrp_out['routes'][s]['routeTotalDist'])\n",
    "        em_shuttles  += (vrp_out['routes'][s]['routeTotalDist'] * EMISSION_SHUTTLE)\n",
    "        em_cars      += (dist_cars * EMISSION_CAR)\n",
    "\n",
    "    avoided_em = (em_cars - em_shuttles)\n",
    "\n",
    "    return(avoided_dist, avoided_em)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "404661"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_cars = 0\n",
    "for employee_distance in data['distance_matrix_distance']:\n",
    "    dist_cars += employee_distance[0]\n",
    "dist_cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Overall time of work loss\n",
    "# Inputs :  time_matrix: the time matrix between all employees\n",
    "#           vrp_time: the time result of the vrp\n",
    "# Outputs:  - the overall time lost in seconds\n",
    "#           - mean time lost\n",
    "#           - the maximum time lost for all employees\n",
    "#           - the minimum time lost for all employees\n",
    "\n",
    "def fct_time_lost(time_matrix, vrp_out):\n",
    "    FIRST_SHUTTLE  = 0\n",
    "    FIRST_EMPLOYEE = 1\n",
    "    COMPANY_COLUMN = 0\n",
    "\n",
    "    tot_time_lost = 0\n",
    "    max_time_lost = 0\n",
    "    min_time_lost = vrp_out['routes'][FIRST_SHUTTLE]['routeTotalTime'] - time_matrix[vrp_out['routes'][FIRST_SHUTTLE]['route'][FIRST_EMPLOYEE]][COMPANY_COLUMN]\n",
    "    nb_employees  = len(time_matrix)\n",
    "\n",
    "    for s in range(len(vrp_out['routes'])):\n",
    "        for i in range(FIRST_EMPLOYEE, len(vrp_out['routes'][s]['route'])-1):\n",
    "            time_lost_i = (vrp_out['routes'][s]['routeTotalTime'] - time_matrix[i][COMPANY_COLUMN])\n",
    "            tot_time_lost  += time_lost_i\n",
    "\n",
    "            if time_lost_i > max_time_lost:\n",
    "                max_time_lost = time_lost_i\n",
    "\n",
    "            if time_lost_i < min_time_lost:\n",
    "                min_time_lost = time_lost_i\n",
    "\n",
    "    mean_time_lost = tot_time_lost/nb_employees\n",
    "\n",
    "    return(tot_time_lost, mean_time_lost, max_time_lost, min_time_lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Calculate & save results\n",
    "# Inputs:   create_file: a boolean : true to create a new file (and erase previous one), False to add a row to the existing file\n",
    "#           test_number: the name of the test, it will appear on the first column of the output\n",
    "#           dist_matrix: the distance matrix\n",
    "#           time_matrix: the time matrix\n",
    "#           vrp_out: the output dictionary of the vrp\n",
    "# Output:   - a print of the costs\n",
    "#           - a file called \"outputs-csv\" with the costs\n",
    "def fct_print_costs_save(create_file, test_number, dist_matrix, time_matrix, vrp_out):\n",
    "    if create_file == True:\n",
    "        with open('./results/metrics-outputs.csv', 'w', newline='') as file:\n",
    "            # 2. step\n",
    "            w = writer(file)\n",
    "            # 3. step\n",
    "            w.writerow([\"Test number; total dist[m]; total time[s]; avoided dist [m]; avoided emissions [gCO2eq]; total time lost [s]; mean time lost [s]; max_time_lost [s]; min_time_lost[s]\"])\n",
    "\n",
    "\n",
    "    [avoided_dist, avoided_em] = fct_avoided_dist(dist_matrix, vrp_out)\n",
    "    [tot_time_lost, mean_time_lost, max_time_lost, min_time_lost] = fct_time_lost(time_matrix, vrp_out)\n",
    "    newrow = [str(test_number)+ \";\" + str(vrp_out[\"objectiveDist\"]) + \";\" + str(vrp_out[\"objectiveTime\"]) + \";\" + str(avoided_dist)+\";\"+str(avoided_em)+\";\"+str(tot_time_lost)+\";\"+str(mean_time_lost)+\";\"+str(max_time_lost)+\";\"+str(min_time_lost)]\n",
    "\n",
    "    with open(f'./results/metrics-outputs.csv', 'a+', newline='') as write_obj:\n",
    "        # Create a writer object from csv module\n",
    "        csv_writer = writer(write_obj)\n",
    "        # Add contents of list as last row in the csv file\n",
    "        csv_writer.writerow(newrow)\n",
    "\n",
    "    print(\n",
    "        \"Total distance ---------- \" + str(vrp_out[\"objectiveDist\"]) + \" [m]\\n\"\n",
    "        \"Total time -------------- \" + str(vrp_out[\"objectiveTime\"]) + \" [s]\\n\"\n",
    "        \"avoided distance -------- \" + str(avoided_dist) + \" [m]\\n\"\n",
    "        \"avoided emissions ------- \" + str(avoided_em) + \" [kgCO2eq]\\n\"\n",
    "        \"total time lost --------- \" + str(tot_time_lost) + \" [s]\\n\"\n",
    "        \"mean time lost ---------- \" + str(mean_time_lost) + \" [s]\\n\"\n",
    "        \"maximum time lost ------- \" + str(max_time_lost) + \" [s]\\n\"\n",
    "        \"minimum time lost ------- \" + str(min_time_lost) + \" [s]\\n\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def export_result(dfjson):\n",
    "    title=' '.join(\n",
    "        [\"Test 1:\", str(dfjson['results'][0]['num_vehicles']), \"shuttles,\", str(dfjson['results'][0]['max_time_shuttle']), \"max.\"]\n",
    "    )\n",
    "\n",
    "    fct_print_costs_save(\n",
    "        True, title,\n",
    "        dfjson['distance_matrix_distance'],\n",
    "        dfjson['distance_matrix_time'],\n",
    "        dfjson['results'][0]['result']\n",
    "    )\n",
    "\n",
    "    for i in range(1,len(data['results'])):\n",
    "        title=' '.join(\n",
    "            [\"Test:\",str(i+1), str(dfjson['results'][i]['num_vehicles']), \"shuttles,\", str(dfjson['results'][i]['max_time_shuttle']), \"max.\"]\n",
    "        )\n",
    "\n",
    "        fct_print_costs_save(i==0, title,\n",
    "                             dfjson['distance_matrix_distance'],\n",
    "                             dfjson['distance_matrix_time'],\n",
    "                             dfjson['results'][i]['result']\n",
    "                             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total distance ---------- 283699 [m]\n",
      "Total time -------------- 21534 [s]\n",
      "avoided distance -------- 120962 [m]\n",
      "avoided emissions ------- -5018.016000000003 [kgCO2eq]\n",
      "total time lost --------- 93337 [s]\n",
      "mean time lost ---------- 3733.48 [s]\n",
      "maximum time lost ------- 4817 [s]\n",
      "minimum time lost ------- 3229 [s]\n",
      "\n",
      "Total distance ---------- 284445 [m]\n",
      "Total time -------------- 21324 [s]\n",
      "avoided distance -------- 120216 [m]\n",
      "avoided emissions ------- -5156.771999999997 [kgCO2eq]\n",
      "total time lost --------- 91169 [s]\n",
      "mean time lost ---------- 3646.76 [s]\n",
      "maximum time lost ------- 4845 [s]\n",
      "minimum time lost ------- 2908 [s]\n",
      "\n",
      "Total distance ---------- 274294 [m]\n",
      "Total time -------------- 21696 [s]\n",
      "avoided distance -------- 130367 [m]\n",
      "avoided emissions ------- -3268.6860000000015 [kgCO2eq]\n",
      "total time lost --------- 66111 [s]\n",
      "mean time lost ---------- 2644.44 [s]\n",
      "maximum time lost ------- 4183 [s]\n",
      "minimum time lost ------- 1566 [s]\n",
      "\n",
      "Total distance ---------- 302183 [m]\n",
      "Total time -------------- 23045 [s]\n",
      "avoided distance -------- 102478 [m]\n",
      "avoided emissions ------- -8456.04 [kgCO2eq]\n",
      "total time lost --------- 73966 [s]\n",
      "mean time lost ---------- 2958.64 [s]\n",
      "maximum time lost ------- 4087 [s]\n",
      "minimum time lost ------- 2229 [s]\n",
      "\n",
      "Total distance ---------- 315397 [m]\n",
      "Total time -------------- 24091 [s]\n",
      "avoided distance -------- 89264 [m]\n",
      "avoided emissions ------- -10913.843999999997 [kgCO2eq]\n",
      "total time lost --------- 54699 [s]\n",
      "mean time lost ---------- 2187.96 [s]\n",
      "maximum time lost ------- 3107 [s]\n",
      "minimum time lost ------- 1192 [s]\n",
      "\n",
      "Total distance ---------- 321572 [m]\n",
      "Total time -------------- 25013 [s]\n",
      "avoided distance -------- 83089 [m]\n",
      "avoided emissions ------- -12062.394 [kgCO2eq]\n",
      "total time lost --------- 59862 [s]\n",
      "mean time lost ---------- 2394.48 [s]\n",
      "maximum time lost ------- 3524 [s]\n",
      "minimum time lost ------- 1649 [s]\n",
      "\n",
      "Total distance ---------- 408419 [m]\n",
      "Total time -------------- 29588 [s]\n",
      "avoided distance -------- -3758 [m]\n",
      "avoided emissions ------- -28215.935999999994 [kgCO2eq]\n",
      "total time lost --------- 51704 [s]\n",
      "mean time lost ---------- 2068.16 [s]\n",
      "maximum time lost ------- 3208 [s]\n",
      "minimum time lost ------- 1548 [s]\n",
      "\n",
      "Total distance ---------- 405780 [m]\n",
      "Total time -------------- 29636 [s]\n",
      "avoided distance -------- -1119 [m]\n",
      "avoided emissions ------- -27725.082000000002 [kgCO2eq]\n",
      "total time lost --------- 55150 [s]\n",
      "mean time lost ---------- 2206.0 [s]\n",
      "maximum time lost ------- 3427 [s]\n",
      "minimum time lost ------- 321 [s]\n",
      "\n"
     ]
    }
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