TIme_matrix.ipynb

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  "metadata": {
    "colab": {
      "name": "TIme_matrix.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "MQdpXedQ34P_",
        "3NEJ1neK8VAL"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
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      "cell_type": "code",
      "metadata": {
        "id": "mcWLzC8AKD_x",
        "colab_type": "code",
        "outputId": "65a174e8-d96e-412c-a126-b1a7fd1380ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "!pip install ortools"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: ortools in /usr/local/lib/python3.6/dist-packages (7.4.7247)\n",
            "Requirement already satisfied: protobuf>=3.10.0 in /usr/local/lib/python3.6/dist-packages (from ortools) (3.10.0)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from ortools) (1.12.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.10.0->ortools) (42.0.2)\n"
          ],
          "name": "stdout"
        }
      ]
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      "metadata": {
        "id": "dQK7ufZVL_DA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import ortools\n",
        "from __future__ import print_function\n",
        "from ortools.constraint_solver import routing_enums_pb2\n",
        "from ortools.constraint_solver import pywrapcp"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pkMilyQjL7jb",
        "colab_type": "code",
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      "source": [
        "def create_data_model():\n",
        "  \"\"\"Stores the data for the problem.\"\"\"\n",
        "  data = {}\n",
        "  data['time_matrix'] = [\n",
        "      [0, 6, 9, 8, 7, 3, 6, 2, 3, 2, 6, 6, 4, 4, 5, 9, 7],\n",
        "      [6, 0, 8, 3, 2, 6, 8, 4, 8, 8, 13, 7, 5, 8, 12, 10, 14],\n",
        "      [9, 8, 0, 11, 10, 6, 3, 9, 5, 8, 4, 15, 14, 13, 9, 18, 9],\n",
        "      [8, 3, 11, 0, 1, 7, 10, 6, 10, 10, 14, 6, 7, 9, 14, 6, 16],\n",
        "      [7, 2, 10, 1, 0, 6, 9, 4, 8, 9, 13, 4, 6, 8, 12, 8, 14],\n",
        "      [3, 6, 6, 7, 6, 0, 2, 3, 2, 2, 7, 9, 7, 7, 6, 12, 8],\n",
        "      [6, 8, 3, 10, 9, 2, 0, 6, 2, 5, 4, 12, 10, 10, 6, 15, 5],\n",
        "      [2, 4, 9, 6, 4, 3, 6, 0, 4, 4, 8, 5, 4, 3, 7, 8, 10],\n",
        "      [3, 8, 5, 10, 8, 2, 2, 4, 0, 3, 4, 9, 8, 7, 3, 13, 6],\n",
        "      [2, 8, 8, 10, 9, 2, 5, 4, 3, 0, 4, 6, 5, 4, 3, 9, 5],\n",
        "      [6, 13, 4, 14, 13, 7, 4, 8, 4, 4, 0, 10, 9, 8, 4, 13, 4],\n",
        "      [6, 7, 15, 6, 4, 9, 12, 5, 9, 6, 10, 0, 1, 3, 7, 3, 10],\n",
        "      [4, 5, 14, 7, 6, 7, 10, 4, 8, 5, 9, 1, 0, 2, 6, 4, 8],\n",
        "      [4, 8, 13, 9, 8, 7, 10, 3, 7, 4, 8, 3, 2, 0, 4, 5, 6],\n",
        "      [5, 12, 9, 14, 12, 6, 6, 7, 3, 3, 4, 7, 6, 4, 0, 9, 2],\n",
        "      [9, 10, 18, 6, 8, 12, 15, 8, 13, 9, 13, 3, 4, 5, 9, 0, 9],\n",
        "      [7, 14, 9, 16, 14, 8, 5, 10, 6, 5, 4, 10, 8, 6, 2, 9, 0],\n",
        "  ]\n",
        "  data['time_windows'] = [\n",
        "      (0, 5),  # depot\n",
        "      (7, 12),  # 1\n",
        "      (10, 15),  # 2\n",
        "      (16, 18),  # 3\n",
        "      (10, 13),  # 4\n",
        "      (0, 5),  # 5\n",
        "      (5, 10),  # 6\n",
        "      (0, 4),  # 7\n",
        "      (5, 10),  # 8\n",
        "      (0, 3),  # 9\n",
        "      (10, 16),  # 10\n",
        "      (10, 15),  # 11\n",
        "      (0, 5),  # 12\n",
        "      (5, 10),  # 13\n",
        "      (7, 8),  # 14\n",
        "      (10, 15),  # 15\n",
        "      (11, 15),  # 16\n",
        "  ]\n",
        "\n",
        "  data['num_vehicles'] = 9\n",
        "\n",
        "  data['demands'] = [0, 1, 1, 2, 4, 2, 5 , 8, 8, 1, 2, 1, 2, 4, 4, 8, 0]\n",
        "\n",
        "  data['vehicle_capacities'] = [14, 12, 16, 12, 15, 20,12,14,20]\n",
        "  data['min_capacity'] = [int(0.85*data['vehicle_capacities'][i]) for i in range(data['num_vehicles'])]\n",
        "  data['depot'] = 0\n",
        "  #data['starts'] = [0]*data['num_vehicles']\n",
        "  #data['ends'] = [16]*data['num_vehicles']\n",
        "  \n",
        "  return data\n",
        "\n",
        "data = create_data_model()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUB6L1E7OKGF",
        "colab_type": "code",
        "outputId": "e4cec816-3eab-44cf-9dca-3809fe4542e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(data['time_matrix'])\n",
        "len(data['demands'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
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    {
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      "metadata": {
        "id": "O59yrVIlMCjb",
        "colab_type": "code",
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      },
      "source": [
        "def print_solution(data, manager, routing, assignment):\n",
        "    \"\"\"Prints assignment on console.\"\"\"\n",
        "    print(\"objective cost: \", assignment.ObjectiveValue())\n",
        "    print()\n",
        "    time_dimension = routing.GetDimensionOrDie('Time')\n",
        "    total_time = 0\n",
        "    total_load = 0\n",
        "    all_vehicles = []\n",
        "\n",
        "    for vehicle_id in range(data['num_vehicles']):\n",
        "        each_vehicle = []\n",
        "        vehicle_load = 0\n",
        "        index = routing.Start(vehicle_id)\n",
        "        plan_output = 'Route for vehicle {0} with min_capacity:{1}: and max_capacity = {2}\\n'.format(vehicle_id, data['min_capacity'][vehicle_id],data['vehicle_capacities'][vehicle_id])\n",
        "\n",
        "        while not routing.IsEnd(index):\n",
        "            node_index = manager.IndexToNode(index)\n",
        "            each_vehicle.append(node_index)\n",
        "\n",
        "            vehicle_load += data['demands'][node_index]\n",
        "            time_var = time_dimension.CumulVar(index)\n",
        "            plan_output += 'Node:{0},Time(mintime: {1}, maxtime: {2}),load:{3} -> '.format(manager.IndexToNode(index), assignment.Min(time_var), assignment.Max(time_var), vehicle_load)\n",
        "            index = assignment.Value(routing.NextVar(index))\n",
        "\n",
        "        time_var = time_dimension.CumulVar(index)\n",
        "\n",
        "        plan_output += 'Node:{0},Time(mintime: {1}, maxtime: {2}),load:{3} -> '.format(manager.IndexToNode(index), assignment.Min(time_var), assignment.Max(time_var), vehicle_load)\n",
        "        plan_output += 'Time of the route: {}min\\n'.format (assignment.Min(time_var))\n",
        "        \n",
        "        if vehicle_load !=0:\n",
        "          print(plan_output)\n",
        "          each_vehicle.append(manager.IndexToNode(index))\n",
        "          all_vehicles.append(each_vehicle)\n",
        "\n",
        "        total_time += assignment.Min(time_var)\n",
        "        total_load += vehicle_load\n",
        "    \n",
        "    print('Total time of all routes: {}min'.format(total_time))\n",
        "    print(\"total demand satisfied \", total_load)\n",
        "    return all_vehicles\n",
        " "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LG7qn5TXgHXU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vY2mnxDjKJf3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def main(guide_time  = 30, local_search = routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH):\n",
        "    \"\"\"Solve the VRP with time windows.\"\"\"\n",
        "    data = create_data_model()\n",
        "\n",
        "    ############# Manager ##########################################################################\n",
        "    manager = pywrapcp.RoutingIndexManager(len(data['time_matrix']), data['num_vehicles'], data['depot'])\n",
        "    routing = pywrapcp.RoutingModel(manager)\n",
        "\n",
        "    ##################    Amortized cost ###########################################################\n",
        "    #routing.SetAmortizedCostFactorsOfAllVehicles(linear_cost_factor= 10*6, quadratic_cost_factor=1)\n",
        "\n",
        "    \n",
        "    ################ Time Constraint   #############################################################\n",
        "    \n",
        "    def time_callback(from_index, to_index):\n",
        "        \"\"\"Returns the travel time between the two nodes.\"\"\"\n",
        "        # Convert from routing variable Index to time matrix NodeIndex.\n",
        "        from_node = manager.IndexToNode(from_index)\n",
        "        to_node = manager.IndexToNode(to_index)\n",
        "        return data['time_matrix'][from_node][to_node]\n",
        "\n",
        "    transit_callback_index = routing.RegisterTransitCallback(time_callback)\n",
        "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
        "    time = 'Time'\n",
        "    \n",
        "    routing.AddDimension(\n",
        "        transit_callback_index,\n",
        "        30,  # allow waiting time\n",
        "        100,  # maximum time per vehicle\n",
        "        False,  # Don't force start cumul to zero.\n",
        "        time)\n",
        "    time_dimension = routing.GetDimensionOrDie(time)\n",
        "\n",
        "    # Add time window constraints for each location except depot.\n",
        "    for location_idx, time_window in enumerate(data['time_windows']):\n",
        "        if location_idx == 0:\n",
        "            continue\n",
        "        index = manager.NodeToIndex(location_idx)\n",
        "        time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])\n",
        "\n",
        "    # Add time window constraints for each vehicle start node.\n",
        "    for vehicle_id in range(data['num_vehicles']):\n",
        "        index = routing.Start(vehicle_id)\n",
        "        time_dimension.CumulVar(index).SetRange(data['time_windows'][0][0], data['time_windows'][0][1])\n",
        "\n",
        "    for i in range(data['num_vehicles']):\n",
        "        routing.AddVariableMinimizedByFinalizer(\n",
        "            time_dimension.CumulVar(routing.Start(i)))\n",
        "        routing.AddVariableMinimizedByFinalizer(\n",
        "            time_dimension.CumulVar(routing.End(i)))\n",
        "        \n",
        "    ################################################################################################\n",
        "        \n",
        "\n",
        "    ###################### Capacity Constraint #####################################################\n",
        "\n",
        "\n",
        "    capacity_dim = 'capacity'\n",
        "    def demand_callback(from_index):\n",
        "        from_node = manager.IndexToNode(from_index)\n",
        "        return data['demands'][from_node]\n",
        "\n",
        "    demand_callback_index = routing.RegisterUnaryTransitCallback(demand_callback)\n",
        "    \n",
        "    routing.AddDimensionWithVehicleCapacity(demand_callback_index,0, data['vehicle_capacities'], True,capacity_dim)\n",
        "    \n",
        "     ####### min capactity\n",
        "    capacity_dimension = routing.GetDimensionOrDie(capacity_dim)\n",
        "    minimum_allowed_capacity = data['min_capacity']\n",
        "    for vehicle in range(data['num_vehicles']):\n",
        "         capacity_dimension.CumulVar(routing.End(vehicle)).RemoveInterval(1, minimum_allowed_capacity[vehicle])\n",
        "\n",
        "    ################################################################################################\n",
        "\n",
        "\n",
        "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
        "    search_parameters.first_solution_strategy = (routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
        "    search_parameters.local_search_metaheuristic = (local_search)\n",
        "    search_parameters.time_limit.seconds = guide_time\n",
        "\n",
        "    assignment = routing.SolveWithParameters(search_parameters)\n",
        "\n",
        "    if assignment:\n",
        "       all_vehicles =  print_solution(data, manager, routing, assignment)\n",
        "\n",
        "    return data, manager, routing, assignment, all_vehicles\n",
        "\n",
        "# data, manager, routing, assignment,all_vehicles = main()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzOH4rmkY4Z_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def get_cmap(n, name='hsv'):\n",
        "    '''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct \n",
        "    RGB color; the keyword argument name must be a standard mpl colormap name.'''\n",
        "    return plt.cm.get_cmap(name, n)\n",
        "\n",
        "\n",
        "def plot(plot_values):\n",
        "    n= len(plot_values)\n",
        "    cmap = get_cmap(len(data))\n",
        "    for i in range(n):\n",
        "        bus_i_route = plot_values[i].copy()\n",
        "        if len(plot_values[i]) != 1:\n",
        "            plt.figure(figsize = [10,4])\n",
        "            plt.title(\"route taken by bus\" + str(i) + \"is\" + str(bus_i_route))\n",
        "\n",
        "            X = bus_i_route[:-1]\n",
        "            Y = bus_i_route[1:]\n",
        "\n",
        "            plt.plot(X,Y, c = cmap(i))\n",
        "            for a,b in zip(X,Y): \n",
        "              plt.text(a, b, str(a), fontsize=15)\n",
        "    plt.show()\n",
        "    \n",
        "plot(all_vehicles)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xKxZ2ZIg66RK",
        "colab_type": "text"
      },
      "source": [
        "## Different options we have for local search in minima\n",
        "- AUTOMATIC  \n",
        "\n",
        "```\n",
        "routing_enums_pb2.LocalSearchMetaheuristic.AUTOMATIC\n",
        "```\n",
        "\n",
        "\n",
        "- GREEDY_DESCENT\n",
        "- GUIDED_LOCAL_SEARCH\n",
        "- SIMULATED_ANNEALING\t\n",
        "- TABU_SEARCH\n",
        "- OBJECTIVE_TABU_SEARCH \n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQdpXedQ34P_",
        "colab_type": "text"
      },
      "source": [
        "## 1. Results: GUIDED_LOCAL_SEARCH Keeping guide time  = 300"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WSjWEbWKTR3",
        "colab_type": "code",
        "outputId": "f3d4db6f-1ed3-4b33-a9bf-67998caa108f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#1 guide time = 300\n",
        "# this cell was run before and so by running it again will not produce the result\n",
        "#to run this with guided local search, uncomment the following lines and commend out the uncommented.\n",
        "\n",
        "#data2, manager2, routing2, assignment2,all_vehicles2 = main(guide_time = 180, local_search= routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n",
        "#plot(all_vehicles2)\n",
        "\n",
        "print_solution(data, manager, routing, assignment)\n",
        "plot(all_vehicles)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "objective cost:  71\n",
            "\n",
            "Route for vehicle 2 with min_capacity:13: and max_capacity = 16\n",
            "Node:0,Time(mintime: 0, maxtime: 0),load:0 -> Node:12,Time(mintime: 4, maxtime: 4),load:2 -> Node:13,Time(mintime: 6, maxtime: 6),load:6 -> Node:15,Time(mintime: 11, maxtime: 11),load:14 -> Node:11,Time(mintime: 14, maxtime: 14),load:15 -> Node:0,Time(mintime: 20, maxtime: 20),load:15 -> Time of the route: 20min\n",
            "\n",
            "Route for vehicle 4 with min_capacity:12: and max_capacity = 15\n",
            "Node:0,Time(mintime: 0, maxtime: 0),load:0 -> Node:7,Time(mintime: 2, maxtime: 4),load:8 -> Node:1,Time(mintime: 7, maxtime: 11),load:9 -> Node:4,Time(mintime: 10, maxtime: 13),load:13 -> Node:3,Time(mintime: 16, maxtime: 16),load:15 -> Node:0,Time(mintime: 24, maxtime: 24),load:15 -> Time of the route: 24min\n",
            "\n",
            "Route for vehicle 6 with min_capacity:10: and max_capacity = 12\n",
            "Node:0,Time(mintime: 0, maxtime: 0),load:0 -> Node:9,Time(mintime: 2, maxtime: 3),load:1 -> Node:5,Time(mintime: 4, maxtime: 5),load:3 -> Node:6,Time(mintime: 6, maxtime: 7),load:8 -> Node:2,Time(mintime: 10, maxtime: 10),load:9 -> Node:10,Time(mintime: 14, maxtime: 14),load:11 -> Node:0,Time(mintime: 20, maxtime: 20),load:11 -> Time of the route: 20min\n",
            "\n",
            "Route for vehicle 7 with min_capacity:11: and max_capacity = 14\n",
            "Node:0,Time(mintime: 0, maxtime: 0),load:0 -> Node:8,Time(mintime: 5, maxtime: 5),load:8 -> Node:14,Time(mintime: 8, maxtime: 8),load:12 -> Node:16,Time(mintime: 11, maxtime: 11),load:12 -> Node:0,Time(mintime: 18, maxtime: 18),load:12 -> Time of the route: 18min\n",
            "\n",
            "Total time of all routes: 82min\n",
            "total demand satisfied  53\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NN4T72v33f8N118WdKGlUQImIiEjluOMOGD0aBg+G3/wm7jRJpQJKREREku/+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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GwPR6CEwn4fG0EJg2Id94OnH+ZgUmERGRNnQYksxsA+AA4FIAd29097crXZj0jmJguiQE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\n4L+M/1lgAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FF9rTkQEMMaSulRBCGE0SKCHSkdOcohVN8MeP0YxjMMNtdobdTW4SiL/VUmAY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\nQImIiIgUkAqUiIiISAH9PxeA7/CqF86pAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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msy0iInm3o4nL/DOff9ho+b6ku9lGEjClUQvSQIyqMg1IremKcS4hD5DgJ3iz0x5I7ilA\niYhIzmXOqt3UxJHvNOpmq4Y9oWg0XfZqQRpMjH0VDJoVJ+ReEvyOJGfo13re6EyLiEi7Zc6q3VQL\n0ltNzKo9MApD5zUaqD0Eox/WaZf7l7txBHQDahWg8kpnWkREmvRhCy1Iq5qYVfvAaBzSSQT8XUYL\n0pBWZtWW7PTAGEfAHBL8F1VlO96r2ChAiYhUqIZZtZu+9UjTs2oPJsbHMcZmdLMNyXJWbcnehYTc\nTx3PkuIEgkKXUxEUoESkbPzqV7/i1ltv5dVXX6VXr16MGTOGW265hUMOOaTQpRVEw6zaH21BSgem\npmbVPixqMZpCuFcLUmfPqi3ZOZeQkDpqSShA5Ynl8w4sNTU1vmTJkrztT0Qqx4MPPsikSZP48pe/\nzOTJk1m/fj3f/OY36dOnD0uXLiUWixW6xE6xtYUWpKZm1T4E+8g8SA2X/RdyVm3J3jh28jopXqVa\nQTdHzGypu9c09Z5aoESkLNx7772MHDmS22+/fc9rPXv2ZNKkSaxcuZIjjzyygNV13C6cN5tpQWpq\nVu3epG9ee3TGYO2GwFTMs2pL9uKEfJE6/kyKv1ErVKdTgBKRsrB792569eq112u9e/cGoJjvdZ6M\nZs5+IyMcZXa5NZ5VuyvsaTk6KRqHlDlxZCnPqi3ZmUTAFaSvxlOA6nzqwhORsjB37lwuuOACZsyY\nwQUXXMCGDRu4/PLL6dKlC0888UTB6nKc92DPzWobX/bfeFZtAwZEV7MNbtSCVO6zakv2TmMHW4GX\nqC50KWWhpS48BSgRKRszZ87ksssuo64uPfJn1KhRzJ07d09LVGdpPKt240HbjWfV3h/2DMzOnAup\n0mfVluz9gHqupZ5XqebjlOe4v3zSGCgRKXsLFizgiiuu4Oqrr2b8+PG88847TJ8+ncmTJ/P4448T\nBB3v0mg8q3bjQduNZ9XuDnu61U5voptNs2pLZ5lMyLXUM4cE06gqdDllTS1QIlIWRo4cyZFHHsnM\nmTP3vLZy5UqOOOIIZs+eTTweb3bdxrNqN25BamlW7cYtSIMxDtCs2lJANewgBP6kbrysZdUCZWZ3\nAecCG9396EbvXQfcBvRz9825KFZEpCNWrFjBxRdfvNdrw4YNo3v37rz++ut7zard1GX/zc2qfSIB\nFzdqQRqgWbWliF1IyNepZy0p+qsbr9O0pQvvbuB24OeZL5rZAGAs8FbuyxIRaZ+Bhx3GU88t5VgS\ne1qQXly+nJ07d/Jvgw5iGtv3Wr5hVu2PYZxNl71akAYRo4cCkpSoeBSg7ifBlerG6zStBih3X2Rm\ng5p46/vANOCBHNckItKqt0gxjyTzSPAiKdZc8QVWXns9cw/pB+PHEryzkdi3v0e3QYcxZcIEhlG1\n15Vt+6NZtaU8DSPGcGLUkuTKQhdTxjo0iNzMJgHr3P1Fs5Y/gMxsKjAVYODAgR3ZnYgICZw/kmIu\nCeaS5M/RTUiGYJxGwOCvfIXVVdX84cd3sP4nd9Gnd29OOeUUbr75Zob02L/A1YvkV5yA77KbzTh9\n9UWhU7RpEHnUAvWQux9tZtXAAmCsu79vZquBmraMgdIgchFpj80480kwjySPkGAL6W99pxIwkYCJ\nhAzTgG2Rj3ieJCPZyZ105TK6FLqckpXraQwOBwYDDa1P/YHnzOyT7r6h42WKSKVznBdIMZckc0mw\nmBROekD3BYRMJORsAnoqMIm06FhiDMKoJaEA1UnaHaDc/WXggIbn7WmBEhFp7EOcx6PANI8k63EM\nOJ4Y06liAgEjiWn2bZF2MIw4Ibezm/dxeunnJ+faMo3BL4HRQF8zWwvc6O4zOrswESlfr0ZjmeaR\n5CmS7AZ6AucQMpGA8QQcoMuvRbISJ+Q/2c08ElysVqica8tVeBe38v6gnFUjImWpDmcRyT1dc69F\nM3cPJ8Y1dGEiIaOI0UXfkkVy5iRiHBR14ylA5Z5u5SIinWJdNM3AXBI8TpLtQDfgTAKuiQaAD1Ir\nk0iniWFMJuAeEuzE6a4vKDmlACUiOZHEWUyKedE0Ay9E0wwMxPhsNAD8DAKq9SEukjdxQn5MgkdJ\nMkm/8nNKZ1NEOuw9nEeiwPQwCd4lfZ+4k4nxvWgA+FHENM2ASIGcTkAfoJaEAlSO6WyKSJs5zssZ\n0wz8kRQpoC8wIRoAPpaQPgpMIkWhC8b5hDxAgnqcKv1s5owClIi0aDvOkxnTDKyJBoCPJMY3ogHg\nNcQI9MEsUpTihNxDgoUkGatf+zmjMykiH/FGxi1TFpKkDtgHGEvAjYSMJ+AQDQAXKQlnE9CDdDee\nAlTu6EyKCPU4v4+mGZhHghVRK9NQjH+kCxMJOJVAzf8iJag7xgRC7ifJj3C1FueIApRIhdpAivlR\n19yjJPkQqAJGE/AlAiYQ8jG1MomUhQsJ+E00bvEUgkKXUxYUoEQqRApnSUbX3NJomoFDMS6KphkY\nQ8A++nYqUnYmEFJFHbUkFKByRAFKpIxtxXksamWaT5KNODHgRGJ8hyomEnCMphkQKXv7YowloJYE\n/0GVfuZzQAFKpIw4znI8amVK8AdSJID9gHGETCBgHCH768NTpOLECXmIOp4nxUi1QmVNAUqkxO3E\nWUByz21TVkcDwI8hxlejaQZOIEao0CRS0c4jJIi68RSgsqcAJVKC3sqYzPJJkuwEqoGzCPha1NLU\nXwPARSRDX4zTCZhNgpvoWuhySp4ClEgJSOA8nTEAfFk0AHwIxuXRNAOnE9BNrUwi0oI4AVeSZDkp\njtSXrKwoQIkUqU0486PZvx8hwVbSP7CnEfD3VDGRkKGYBoOKSJtdQMiV1FNLgm9QVehySpoClEiR\ncJznM7rmniGFAwdhxAmZQMjZBPRUYBKRDjqUGCcRU4DKgVYDlJndBZwLbHT3o6PX/h04D6gHXge+\n4O5bO7NQkXL0YaNpBtbjGHA8MaZH0wwcR4yYQpOI5EickK9Sz2pSDFI3Xoe15czdDYxr9NpjwNHu\nfgzwKvC1HNclUpYcZyUpvk89Z7GT/dnOhexiNglOJeAeurKBahZTzQ1U8QkChScRyanJUdvJHBIF\nrqS0tdoC5e6LzGxQo9cezXj6J2BKbssSKR91OE9F95mbS4LXo2kGjiLGNdE0A6OI0UVBSUTy4HBi\njIi68a5VN16H5WIM1N8D9zX3pplNBaYCDBw4MAe7Eyl+a0ntmZfpCZJsB7oBZxLwT9E0A2o6F5FC\niRMynXo2kOIgfRZ1SFYBysy+ASSAmc0t4+53AHcA1NTUeDb7EylWSZzFGdMMvBhNM3AYxueiaQZG\nE1CtViYRKQJxAm4E7ifJFQpQHdLhAGVmnyc9uHyMuysYScV5D+fhKDA9TIL3gAA4hRjfiwaAD9d9\n5kSkCB1FjI9j1JLgCroUupyS1KEAZWbjgGnA6e6+I7cliRQnx3kpmmZgHgn+SIoU0A/jXEImEjCW\nkN4KTCJS5CyaHuU/2M17OPvpc6vd2jKNwS+B0UBfM1sL3Ej6qruuwGNmBvAnd7+iE+sUKYjtOE9E\nY5nmkWRtNAD8E8T4RjQA/HhNMyAiJShOyPfYzUMk+KxaodqtLVfhXdzEyzM6oRaRovB6xlimhSSp\nB/YFzibgW4SMJ+BgjRkQkRJ3PDH6R914ClDtp5nIpeLV4/yO5J6r5lZGrUzDMK6kCxMIOJWAKrUy\niUgZaejGu4PdbMPZR59x7aIAJRVpPSnmR4HpMZJ8CFQBown4RwImEnK4WplEpMzFCflvdvMwSaYo\nErSLzpZUhBTOsxldc89F0wwcinExIRMJGUNAD30DE5EKcgox+kXdeApQ7aOzJWVrK84j0eDv+STZ\nhBMDTiLGd6liAgHHaJoBEalgAcYkAu4jQR1OV30etpkClJQNx3klmmZgLgn+QIoksB8wLppm4BxC\n9tcHhIjIHnFC7iTB4ySZqFjQZjpTUtJ24CzImGbgzWgA+Ahi/Es0zcAJxAgUmkREmnQmAT2BWhIK\nUO2gMyUl582MVqYnSbIL6AGcRcDXo/vM9dcAcBGRNukaTQb8AAl+ihPqC2ebKEBJ0duN83Q0AHwe\nSZZFA8APx5ga3WfudAL13YuIdNCFhNxLgt+R5AxFgzbRWZKitJEUD5NkLkkeIcH7QBfgNAIuo4qJ\nhHwc0wBwEZEcOIeA7kCtAlSb6SxJUUjhPJ/RNfcsKRw4COPCaJqBswjoqcAkIpJzPTDGETCHBP9F\nlW5P1QYKUFIwH+A8FgWm+STZgGPAJ4nxLaqYSMCxus+ciEhexAmZQx3PkuIEgkKXU/QUoCRvHOdV\nfM9klr8jyW6gF3BONM3AOAIO0ABwEZG8O5eQkDpmk1CAagMFKOlUu3CeisYyzSXBG9E0A0cR49po\nmoFRxHTVh4hIgfXGGENALQm+R5XGmLZCAUpybm00lmleNDHbDqAbMIaAf46mGThMrUwiIkUnTsgX\nqeNlUhyjVqgWKUBJ1hI4izPuM/dSNM3AYRifj6YZOIOA7vo2IyJS1CYRcAXpSTUVoFrWaoAys7uA\nc4GN7n509Np+wH3AIGA18Gl339J5ZUqxeRfn4SgwPUKC94CA9I0pb42mGThS0wyIiJSUA4lxKjFq\nSTK90MUUubb0o9wNjGv02vXAE+7+ceCJ6LmUMcd5gSTfpZ6T2cEBbOcz1PE4Sc4j5D66spkeLKSa\nr1LFcN2kV0SkJMUJeZkUf4l6E6RprQYod18EvNfo5UnAPdHje4ALclyXFIFtOA+QYCq7GMAOjmMn\n36CeOuCbdGEx3dlANXfTjU/Thd4KTCIiJW9y1Dk1h0SBKyluHR0DdaC7r48ebwAObG5BM5sKTAUY\nOHBgB3cn+fJaxlimp0hSD+wLjCVgIiHjCThIA8BFRMrWQGLUEKOWBNOoKnQ5RSvrQeTu7mbmLbx/\nB3AHQE1NTbPLSWHU4ywiybxomoFXo2kGjsC4ii5MIOAUAqrUuiQiUjHihHydetaS0s3Zm9HRAPWO\nmR3s7uvN7GBgYy6Lks61ntSewPQYSbYBXYHRBFwZtTQN0Q+MiEjFaghQc0hwlVqhmtTRAPUg8Dng\nlujvB3JWkeRcEufZqGtuHkmeiwYG9se4hJAJhIwhoIdamUREBBhGjOHR1XhXFbqYItWWaQx+CYwG\n+prZWuBG0sHp12Z2GfAm8OnOLFLabwvOo9FYpvkk2Ez6ioGTiPHd6D5zf6Mr5UREpBlxAr7Lbjbh\n9NPvio9oNUC5+8XNvDUmx7VIFhxnWTQD+FwSPE2KJLAfMD66z9xYQvbXD4GIiLTBhYTcxG4eJMFl\ndCl0OUVHM5GXsB04T0aBaR5J3ooGgB9LjH+J7jN3AjEChSYREWmnEcQYjFGrANUkBagSszpjmoEF\nJNkF9ADOIuCb0X3mDtUAcBERyZJhxAn5Ibt5H6eXvozvRQGqyO3G+QPJPV1zy6NWpo9hfDG6z9xp\nBHTVf2wREcmxOCH/wW7mkeBitULtRQGqCL1DivnR3EyPkuB9oAtwGgH/EE0zMFStTCIi0slOJMZB\nGLMVoD5CAaoIpHCeyxgA/mw0zcDBGFMImUjIWQTsq1YmERHJoxjGZALuIcEOnGr9HtpDAapAPsB5\nNApM80nyDo4BJxDj36hiAgHHaZoBEREpsDghPybBoyS5QLFhD52JPHGclfieAeC/I0kC6A2cE00z\nMI5Qc22IiEhROZ2APkAtCQWoDDoTnWgXzsJoAPg8ErwRDQA/mhjX0YUJhIwiRqjQJCIiRaoLxvmE\nPECCelz3Ro2U7UjkV155hTFjxlBdXc0hhxzCDTfcQDKZ7PT9riHFT9nN+exkf7Yznl3MYDdHEuN/\n6MpqqnmZam6hK6cRKDyJiEjRu5CQrcBCOv/3aKkoyxaoLVu2cNZZZzF8+HAeeOABXn/9da677jpS\nqRQ33XRTTveVwPlTxtxML0cDwAdhfCGaZmA0Ad0VlEREpESdTUAP0t14Y8szOrRbWZ6Fn/zkJ+zc\nuZPa2lp69uzJ2WefzQcffMD06dOZNm0aPXv2zGr7m3EejgLTIyTYQvpEnkLArVQxkZAjMQ0AFxGR\nstANYyIh95PkR7jucEGZduHNnz+fc845Z6+gdNFFF7Fz506eeuqpdm/PcZ4nyXeoZxQ7OIDtXEod\nT5LkfEJ+TTc204MFdOerVDFcV8+JiEiZiRPwDs4fo56WSleWAWrFihUcccQRe702cOBAqqurWbFi\nRZu2sQ3nfhL8A7vozw5Gsk9u+YcAABGzSURBVJNvUs9u4Aa68AzdWU81d9ONTxFqinsRESlrEwip\nAmaTKHQpRaEsu/C2bNlC7969P/J6nz592LJlS7Pr/SUayzSPJE+RpB7oCYwlYAIh4wk4qDwzp4iI\nSIv2xRhLQC0J/pOqiu9pKcsA1VZ1OL/LuM/cX6JpBo7AuCoaAH4ygS7ZFBERIT2p5kPU8RwpPkFQ\n6HIKqiwDVJ8+fXj//fc/8vqWLVsI+vTmTnYzlwSPk2Qb0BU4g4CvRC1NQ9TKJCIi8hHnExJQRy0J\nBahsVjaza4HLAQdeBr7g7rtyUVg2jjjiiD1jnZI4z5Dil2tWsWPHDm46YhBQR3+MS6L7zJ1JQA+1\nMomIiLRof4zRUTfed+ha6HIKqsNNLWZ2KPAVoMbdjwYC4KJcFZaN0ePH8dtHHuGiDzdxENsZxU5u\nv+8+Yt27c+PpZ/IS3XmLan5CN84jVHgSERFpozghK3CWV/jVeNn2VYVAdzMLgWrg7exL6riHSHAq\nO7jpikvZ3rWK2vjfcszji7jsjl9QPf1mvvZP/8T0nn35G4KKH/wmIiLSERdEXXe1FX41XocDlLuv\nA24D3gLWA++7+6ONlzOzqWa2xMyWbNq0qeOVtsE2nG3A1/scwMwnHuPkJDx93oXMvfHbXHvttXzr\nW9/q1P2LiIiUu0OIcRKxig9Q5u4dW9GsDzAb+FtgK/AbYJa7/19z69TU1PiSJUs6tL+2cFwtSyIi\nIp3sNur5KvW8QTWDy/jCKzNb6u41Tb2XzVGfBaxy903uvhuoBUZlsb2sKTyJiIh0vsnRNWhzKrgV\nKpsA9RZwoplVm5kBY4DluSlLREREitXhxBhR4d142YyBWgzMAp4jPYVBDLgjR3WJiIhIEYsT8jQp\n1lfo1XhZdVy6+43ufoS7H+3ul7p7Xa4KExERkeJ1IQEOPECy0KUURPmO/BIREZFOM5wYQ7GK7cZT\ngBIREZF2M4w4IQtI8h4du6K/lClAiYiISIfECUmQnsi60ihAiYiISIfUEKM/xmwFKBEREZG2aejG\ne4Qk2yqsG08BSkRERDosTkgdML/CrsZTgBIREZEOO4UY/SrwajwFKBEREemwAOMCAh4iwa4K6sZT\ngBIREZGsxAnZBjxRQd14ClAiIiKSlTMJ6AkV1Y2nACUiIiJZqcI4j5AHSJCokG48BSgRERHJWpyQ\nd4FFFdKNpwAlIiIiWTuHgO5ArQKUiIiISNv0wBhHwBwSpCqgG08BSkRERHIiTsjbOM+QKnQpnU4B\nSkRERHLiXEK6UBlX42UVoMyst5nNMrMVZrbczE7KVWEiIiJSWnpjjCGglgRe5t142bZA/RfwsLsf\nAYwAlmdfkoiIiJSqOCGv47xc5t14HQ5QZtYLOA2YAeDu9e6+NVeFiYiISOmZRIBR/t142bRADQY2\nAT8zs+fN7E4z69F4ITObamZLzGzJpk2bstidiIiIFLsDiHEqMWaX+XQG2QSoEBgJ/NjdjwO2A9c3\nXsjd73D3Gnev6devXxa7ExERkVIQJ+TPpHi1jLvxsglQa4G17r44ej6LdKASERGRCjaZEIA5ZdyN\n1+EA5e4bgDVmNix6aQzwSk6qEhERkZI1kBg1xMp6HFS2V+FdBcw0s5eAY4HvZl+SiIiIlLo4Ic+Q\nYk2ZduNlFaDc/YVofNMx7n6Bu2/JVWEiIiJSui6MuvHuL9NWKM1ELiIiIjk3lBhHESvbmwsrQImI\niEiniBOwiCSbynBWcgUoERER6RRxQlLAA2XYjacAJSIiIp1iBDEGY2V5NZ4ClIiIiHQKw4gT8jhJ\n3i+zbjwFKBEREek0cUJ2A3PLrBVKAUpEREQ6zYnEOLgMu/EUoERERKTTxDAmEzKfJDvKqBtPAUpE\nREQ6VZyAHcCjZTQnlAKUiIiIdKrTCNgPyqobTwFKREREOlUXjPMJ+S0J6sukG08BSkRERDpdnJCt\nwIIy6cZTgBIREZFOdzYBPSifbjwFKBEREel03TAmEnI/SZJl0I2nACUiIiJ5ESdgI87TpApdStay\nDlBmFpjZ82b2UC4KEhERkfI0gZCulEc3Xi5aoK4GludgOyIiIlLG9sUYS0AtCbzEu/GyClBm1h+Y\nCNyZm3JERESknMUJeQvnuRLvxsu2BeoHwDRo/iyY2VQzW2JmSzZt2pTl7kRERKSUnUdIQOl343U4\nQJnZucBGd1/a0nLufoe717h7Tb9+/Tq6OxERESkD+2OMJmB2iXfjZdMCdTJwvpmtBn4FnGlm/5eT\nqkRERKRsxQlZibO8EgOUu3/N3fu7+yDgIuBJd/9MzioTERGRsnQBAVDa3XiaB0pERETy6hBinERM\nAcrdF7r7ubnYloiIiJS/OCHPk2JViV6NpxYoERERybs4IQBzSrQVSgFKRERE8m4IMY4t4W48BSgR\nEREpiDghT5NifQl24ylAiYiISEHECXDgfpKFLqXdFKBERESkIIYTYyhWkt14ClAiIiJSEIYRJ2QB\nSd4rsUk1FaBERESkYOKEJIHfllgrlAKUiIiIFEwNMQaUYDeeApSIiIgUTEM33iMk2VZC3XgKUCIi\nIlJQcULqgPkldDWeApSIiIgU1MnE6Fdi3XgKUCIiIlJQAcYFBDxEgl0l0o2nACUiIiIFFydkG/B4\niXTjKUCJiIhIwZ1JQE8omW48BSgREREpuCqM8wh5gASJEujGU4ASERGRohAn5D1gUQl043U4QJnZ\nADNbYGavmNkyM7s6l4WJiIhIZRlHQHegtpwDFJAArnP34cCJwJfNbHhuyhIREZFKU40xnoA5JEgV\neTdehwOUu6939+eixx8Cy4FDc1WYiIiIVJ44IW/jPEOq0KW0KCdjoMxsEHAcsLiJ96aa2RIzW7Jp\n06Zc7E5ERETK1ERCugCzi/xqvKwDlJntA8wGrnH3Dxq/7+53uHuNu9f069cv292JiIhIGeuNMYaA\nWhJ4EXfjZRWgzCwdEmGmu9fmpiQRERGpZHFC3sB5qYi78bK5Cs+AGcByd//P3JUkIiIilWwSAUZx\nT6qZTQvUycClwJlm9kL0Z0KO6hIREZEKdQAxTiVW1NMZhB1d0d1/D1gOaxEREREB4EJCrqaeV0kx\ntAjn/S6+ikRERKTiTY7aeOYUaTeeApSIiIgUnQHEOJ5Y0Y6DUoASERGRohQn5BlSrCnCq/EUoERE\nRKQoxYu4G08BSkRERIrSUGIcVaTdeApQIiIiUrTiBPyOFBuLrBtPAUpERESKVpyQFPBgkc0JpQAl\nIiIiRWsEMQZjRdeNpwAlIiIiRcswLiTkcZK83+jmwq+99hpf/OIXOeaYYwiCgNGjRze5jZdffplz\nzz2XXr16se+++/LJT36SpUuXZlWXApSIiIgUtTghu4G5jVqhli1bxrx58xg2bBhDhw5tct0XXniB\nUaNG0bt3b+677z5+85vfcN5557Fz586sajJ3b32pHKmpqfElS5bkbX8iIiJS+lI4/dnBKGLMovtf\nX0+liMXSbUFTpkxh8+bNLFy4cK91TzzxRIYMGcK9997b7v2a2VJ3r2nqPbVAiYiISFGLYUwmZD5J\ndmR04zWEp+a88sorLF68mKuuuqoTahIREREpcnECdgCPtONqvMWLFwOwZcsWRowYQRiGHH744cyY\nMSPrehSgREREpOidRsB+0K6r8TZs2ADAZz/7WS655BIee+wxxo0bx+WXX868efOyqifMam0RERGR\nPOiCcT4hc0hQj1OFtbpOwzjvyy+/nGnTpgFwxhlnsHz5cm6++WYmTJjQ4XrUAiUiIiIlIU7I+8CC\nNnbj9enTB0iHpkxnnnkmr7zySla1ZBWgzGycma00s9fM7PqsKhERERFpwdkE7EPbu/GOPPJI4K8t\nUQ3cvdUB6K3p8NpmFgA/AsYDw4GLzWx4VtWIiIiINKMbxkRC7idJktanYRo1ahR9+vThySef3Ov1\nJ554ghEjRmRVSzZjoD4JvObubwCY2a+ASUB2bWIiIiIizYgTcB8JnibFJ3bU7RkMvm7dOj744ANm\nzZoFwIQJE6iuruaGG25g2rRp9O7dm+OPP57Zs2ezaNEinnrqqazqyCZAHQqsyXi+Fjih8UJmNhWY\nCjBw4MAsdiciIiKVbjwh40hgwMaNG/nUpz611/sNz1etWsWgQYO45pprSKVS/PCHP2T69OkMGzaM\nWbNmceqpp2ZVR4dnIjezKcA4d788en4pcIK7X9ncOpqJXEREREpFZ81Evg4YkPG8f/SaiIiISFnL\nJkA9C3zczAabWRVwEfBgbsoSERERKV4dHgPl7gkzuxJ4BAiAu9x9Wc4qExERESlSWc1E7u7zgOzm\nQhcREREpMZqJXERERKSdFKBERERE2kkBSkRERKSdFKBERERE2qnDE2l2aGdmm4A3O3k3fYHNnbyP\nYlbJx1/Jxw6Vffw69spVycdfyccO+Tn+w9y9X1Nv5DVA5YOZLWlu1tBKUMnHX8nHDpV9/Dr2yjx2\nqOzjr+Rjh8Ifv7rwRERERNpJAUpERESkncoxQN1R6AIKrJKPv5KPHSr7+HXslauSj7+Sjx0KfPxl\nNwZKREREpLOVYwuUiIiISKdSgBIRERFpp7IKUGY2zsxWmtlrZnZ9oevJFzMbYGYLzOwVM1tmZlcX\nuqZ8M7PAzJ43s4cKXUu+mVlvM5tlZivMbLmZnVTomvLFzK6N/s//2cx+aWbdCl1TZzKzu8xso5n9\nOeO1/czsMTP7S/R3n0LW2JmaOf5/j/7vv2Rmc8ysdyFr7CxNHXvGe9eZmZtZ30LU1tmaO3Yzuyr6\nt19mZrfmu66yCVBmFgA/AsYDw4GLzWx4YavKmwRwnbsPB04EvlxBx97gamB5oYsokP8CHnb3I4AR\nVMh5MLNDga8ANe5+NBAAFxW2qk53NzCu0WvXA0+4+8eBJ6Ln5epuPnr8jwFHu/sxwKvA1/JdVJ7c\nzUePHTMbAIwF3sp3QXl0N42O3czOACYBI9z9KOC2fBdVNgEK+CTwmru/4e71wK9In9yy5+7r3f25\n6PGHpH+BHlrYqvLHzPoDE4E7C11LvplZL+A0YAaAu9e7+9bCVpVXIdDdzEKgGni7wPV0KndfBLzX\n6OVJwD3R43uAC/JaVB41dfzu/qi7J6KnfwL6572wPGjm3x7g+8A0oGyvCGvm2L8E3OLuddEyG/Nd\nVzkFqEOBNRnP11JBIaKBmQ0CjgMWF7aSvPoB6Q+QVKELKYDBwCbgZ1EX5p1m1qPQReWDu68j/a3z\nLWA98L67P1rYqgriQHdfHz3eABxYyGIK7O+B+YUuIl/MbBKwzt1fLHQtBTAUONXMFpvZU2Z2fL4L\nKKcAVfHMbB9gNnCNu39Q6HrywczOBTa6+9JC11IgITAS+LG7Hwdsp7y7cPaIxvpMIh0iDwF6mNln\nCltVYXl6XpqybYloiZl9g/RwhpmFriUfzKwa+DpwQ6FrKZAQ2I/0sJWvAr82M8tnAeUUoNYBAzKe\n949eqwhm1oV0eJrp7rWFriePTgbON7PVpLttzzSz/ytsSXm1Fljr7g0tjrNIB6pKcBawyt03uftu\noBYYVeCaCuEdMzsYIPo7710ZhWZmnwfOBS7xypnc8HDSXx5ejD7/+gPPmdlBBa0qf9YCtZ72DOke\niLwOoi+nAPUs8HEzG2xmVaQHkz5Y4JryIkrdM4Dl7v6fha4nn9z9a+7e390Hkf43f9LdK6YVwt03\nAGvMbFj00hjglQKWlE9vASeaWXX0MzCGChlA38iDwOeix58DHihgLXlnZuNId+Gf7+47Cl1Pvrj7\ny+5+gLsPij7/1gIjo8+ESnA/cAaAmQ0FqoDN+SygbAJUNIjwSuAR0h+iv3b3ZYWtKm9OBi4l3fry\nQvRnQqGLkry5CphpZi8BxwLfLXA9eRG1us0CngNeJv15Vta3tjCzXwJ/BIaZ2Vozuwy4BTjbzP5C\nulXulkLW2JmaOf7bgX2Bx6LPvp8UtMhO0syxV4Rmjv0uYEg0tcGvgM/lu/VRt3IRERERaaeyaYES\nERERyRcFKBEREZF2UoASERERaScFKBEREZF2UoASERERaScFKBEREZF2UoASERERaaf/Bz6WOw93\n3FaFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3NEJ1neK8VAL",
        "colab_type": "text"
      },
      "source": [
        "## USING TABU_SEARCH\n",
        "`routing_enums_pb2.LocalSearchMetaheuristic.TABU_SEARCH`\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HvpVURQy8Lpf",
        "colab_type": "code",
        "outputId": "78b17fd9-cbc1-4572-bcb2-50af69c702cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "data2, manager2, routing2, assignment2,all_vehicles2 = main(guide_time = 180, local_search= routing_enums_pb2.LocalSearchMetaheuristic.TABU_SEARCH)\n",
        "plot(all_vehicles2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "objective cost:  71\n",
            "\n",
            "Route for vehicle 2 with min_capacity:13: and max_capacity = 16\n",
            "Node:0,Time(mintime: 0, maxtime: 0),load:0 -> Node:7,Time(mintime: 2, maxtime: 4),load:8 -> Node:1,Time(mintime: 7, maxtime: 11),load:9 -> Node:4,Time(mintime: 10, maxtime: 13),load:13 -> Node:3,Time(mintime: 16, maxtime: 16),load:15 -> Node:0,Time(mintime: 24, maxtime: 24),load:15 -> Time of the route: 24min\n",
            "\n",
            "Route for vehicle 4 with min_capacity:12: and max_capacity = 15\n",
            "Node:0,Time(mintime: 0, maxtime: 0),load:0 -> Node:12,Time(mintime: 4, maxtime: 4),load:2 -> Node:13,Time(mintime: 6, maxtime: 6),load:6 -> Node:15,Time(mintime: 11, maxtime: 11),load:14 -> Node:11,Time(mintime: 14, maxtime: 14),load:15 -> Node:0,Time(mintime: 20, maxtime: 20),load:15 -> Time of the route: 20min\n",
            "\n",
            "Route for vehicle 6 with min_capacity:10: and max_capacity = 12\n",
            "Node:0,Time(mintime: 0, maxtime: 0),load:0 -> Node:9,Time(mintime: 2, maxtime: 3),load:1 -> Node:5,Time(mintime: 4, maxtime: 5),load:3 -> Node:6,Time(mintime: 6, maxtime: 7),load:8 -> Node:2,Time(mintime: 10, maxtime: 10),load:9 -> Node:10,Time(mintime: 14, maxtime: 14),load:11 -> Node:0,Time(mintime: 20, maxtime: 20),load:11 -> Time of the route: 20min\n",
            "\n",
            "Route for vehicle 7 with min_capacity:11: and max_capacity = 14\n",
            "Node:0,Time(mintime: 0, maxtime: 0),load:0 -> Node:8,Time(mintime: 5, maxtime: 5),load:8 -> Node:14,Time(mintime: 8, maxtime: 8),load:12 -> Node:16,Time(mintime: 11, maxtime: 11),load:12 -> Node:0,Time(mintime: 18, maxtime: 18),load:12 -> Time of the route: 18min\n",
            "\n",
            "Total time of all routes: 82min\n",
            "total demand satisfied  53\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+/fw4gXQpKip7OnnweVlTyhN1obp0ic6dkpr11VfRDlNsYAo6TApMIhlFIUkk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\nQmCC\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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REZG64v4ecekQ4vL3sGBMOkXBNl12PAt2ScdU7UxcOpao9G3cP+6y4/VGbRZQ7v448E4z\nq34KnAN4tUOJiIjUC49fIip+GY/vIshdQZCfiln/Lj+u2XoE+Yex8Bw8uo6ouAfu87v8uL1Fp8ZA\nmdkYYIG7P9uObU80s5lmNnPx4sWdOZyIiEiPFEe3ExW/BP4OQf4Rgtz32jVFQbWY5QjzlxHk7wR/\nKZ3q4JFuO34963ABZWZ9gfOBH7Vne3ef4O7D3X344MGDO3o4ERGRHse9RFQ6k7h0KNgwwjVmEYRf\nzSxPEB5IWHga7LPEpb2Jyz/WVAcV6kwL1ObAZsCzZjYXGALMMrP1qxlMRESklrzyyiucdNJJDBs2\njDAMGTFixCrrFy5cyNlnn832229Lv359aNjsp/zHt7bm7SW/wWyjbEI3YsFQwsJTWHAIcfl84tI3\ncV+Wdaweq8MFlLs/7+6fdfcGd28A3gB2cPe3qp5ORESkRsyePZvp06czdOhQttpqq9XWP/PMM/z2\nt5M5dOzr/O7OPJddfgp/fdrZbbcRvP/++xkkXp3Z2gT53xDkrsbj6entY57POlaP1J5pDCYDTwBD\nzewNMzu+62OJiIjUltGjRzN//nzuuOMOtt1221XWuTu77vw8L/z9Lc45Z332HPk0RxxxDdOmTWPe\nvHnceeedGaVeXTLVwXcJC4+Cf0BU3Ik4ujXrWD1Orq0N3P3wNtY3VC2NiIhIjQqC5tsc3N8nLp1A\n/7Vvw4IxBPlJmK0DwFZbbUXfvn158803uzNqu1jwFcI1ZhEVDyUuHYXHTxLkrsKskHW0HkEzkYuI\niHSSxy8TFXfC4zsIcj8myN/1SfEE8Nxzz7FixYpmu/xqgdn6hIVHsPBMPLqGqDgC9wVZx+oRVECJ\niIh0hi9JpyhYRJB/iCB3LmaffqzGccxpp53GlltuyQEHHJBh0NaZ5QnzVxHkbwd/Lp3q4NGsY9U8\nFVAiIiId4F7G4+dxnw32+XSKgj1X2+68887jiSee4Ne//jX5fOX3uutqQXhwOtXBZ4hLexGXr8Bd\nc2W3RAWUiIhIO7m/TVwaCf4PsA0JC4+T3NlsVddeey1XXHEFkyZNYqeddsogaedY8Pn0NjMHEpfP\nIS6NxX151rFqkgooERGRdvD4CaKPd8DjJyEYjtmWmK2x2nZ33nkn48eP5/LLL+fQQ6t7s+DuYNaP\nIH87Qe4qPL47nepgdtaxao4KKBERkVa4O3H5GqLiv4P1ISw8idmmzW47Y8YMjjzySMaPH8/3vve9\nbk5aPclUB2cSFv4A/m461cFtWceqKW1OYyAiItJbJGN+Pvr04SuIyxfg8a18+NE+PPDwIZj9kwUL\nFrB8+XKmTp0KwL777su8efP4xje+wdZbb82hhx7Kk08++cl+Bw8ezOabb57J11QJC/ZIpzo4hLh0\nWDrVweWY1f6Yrq5m3TlAbPjw4T5z5sxuO56IiPQ8yedSkaSI+RD8o0+f8xHe5HWyPn3OR7h/+rzl\n9zfaZpXXHzeTyAhy/8m8+Ufwuc81XwS99tprzJgxg+OOO67Z9cceeyw333xzx09GjXAvEZfPxqOr\nwb5CWLgdsw2yjtXlzOwZdx/e7DoVUCIi0lTy2VBm1UKjcWHSpFBpUrS0XeQ0LWJW3TdU8tkUAGum\njz5gfT59Th+syWus8bpGy1kTrA9m22LBjhXkqR9xNJm4dALQLymigj2yjtSlWiug1IUnIlLD3Mus\n2qXU/kKlrSKlrZYaiCtIbqxapDQtYtYE1uWTQiVoVLQ0W+S0tm7VosdMH21dJQgPx2wYUelAouLX\nku688AzMLOto3a5uf8pefPFFxo8fzxNPPMGAAQM44YQTuOCCCwjDMOtoItLDuMd0rEuopSKn4y01\nSStQJfrQcqHRB+jPJ8VI0FyR09b7P22paVwAQb5Xfqj2BhZsS1h4mrg0jrh8FhY/SZCfiFm/rKN1\nq7osoJYuXcpee+3FNttsw913382//vUvzjrrLOI45uKLL846noh0QtKl9DEdH/fS3i6llgsgKFWY\nvsAqxcVqrSkD+aQwCVYtWprrUmq9pabx9muoiJEuYdafIH8nHl1JXD6XqPgCYf5OLPh81tG6TV0W\nUNdddx0ffvghd911F/3792fkyJEsX76cCy+8kHPOOYf+/ftnHVGkR1p1cG97W1M601LT3PubG9zb\nETla7/YZ8MnzT7uTmmlpabE7qrkiZ+V6zRgj9cfMsNzZYDsSlw4jKn6ZIH8TQTg262jdoi4Hke+x\nxx5suOGGTJky5ZNlr7/+OptuuinTpk1j9OjRXZ5BpKt8Ori3nQN0q3qFUrUG97bWmtLOQqXF7qTm\n9r2GxsWIdCH3N4iKB4M/iYVnEeQurYvfuV43iHzOnDl87WtfW2XZJptsQt++fZkzZ44KKKkK94jO\nDtCt7AqlD6l8cG9rRUofPh3c27RLqfXWmLYLIM0dI1KPzIYQFh4jLp+JR1cRxTMJC1MwWz/raF2m\nLguopUuXMmDAgNWWr7vuuixdujSDRNJVVh3c2/EBut700mlvf0tNdQf3NtfS0o/OXKHUVkuNBveK\nSFcwKxDmryEOdiYunUj08Q6EhTuwYLeso3WJuiygpHt9Ori3ClcotfX+1bqTihWmXzm4d/Vun6RI\n+QxVuUJptSKnoHExIlKXgvCodKqDg4iKIwhyV2Hh+Lr7w60uC6h1112Xd999d7XlS5cuZd11180g\nUddLipgSLRUqVb1CabX3f1Rh+hytd/usA6xH11yhpGktRESqzYJh6VQHxxKXT0unOpiA2dpZR6ua\nNgsoM7sR2B9Y5O7bpcuuAEaT/Pn/L+A4d1/WlUE7Yuutt2bOnDmrLJs/fz4rVqxg66237tJju5fo\nWGtKta5Qqubg3uZaU9YGBrFqa0wLhUqHr1CqyzpeRKRXMxtAkP8tHl1GXP4hUfE5wvxdWLBV1tGq\noj2fXDcD1wC/arTsYeA8dy+b2WXAecD3qx+vc0aN2psrr7yK5cvn0q9fDvwjpkz5BWuuuQZ77J4n\njh6m4y017bsHE0QVpm/rCqUBtK9LqbnuqKZFTuP35+queVVERLJlFmC588C+RFw6nKg4nCA/iSD8\nZtbRKtZmAeXuj5tZQ5NlDzV6+SRQE5M+xOX/JS6fybeOK/O//wsHfnMzzj4LXn0NLroITv8urNVn\nH+I258Rbg9avUBr8yeuOXKHUdpdSQUWMiIjUnSDcCwueISqOJS4diMffJ8hd3KN7IKqR/D+A21pa\naWYnAidCMpVAV7JgByw8h4GD+/Dwg8v57ml3842D5jJgQF9OP20vLrjgGIJwrTbGzWhwr4iISLWZ\nbUJY+CNx+fSkW8//SpCfgtlns47WKe2aSDNtgbp35RioRst/AAwHDvR27Ki7JtIUERGR2hVHk4hL\nJwMDCQtTsWDnrCM1q7WJNDvd1GJm40gGlx/ZnuJJREREBCAIjyUsPAFWICruQVz+OZ0pJV555RVO\nOukkhg0bRhiGjBgxYrVtGhoaktvONHqsv37lE3x2qgvPzEYB5wD/7u4rKk4hIiIivYoFXyAsPENc\nOpq4fGo61cH1mPVt9z5mz57N9OnT2XnnnSmVWh7gfMQRRzB+/PhPXhcKhYqyQ/umMZgMjAAGmdkb\nwAUkV92tATycDnp+0t1PrjiNiIiI9Bpm6xLkp+HRJcTlHxEVn02nOtiiXe8fPXo0Y8aMAWDs2LEs\nWbKk2e022GADdt65ut2E7bkK7/BmFk+sagoRERHplZKpDn6YTnVwRDrVwa8IwgPafG8QZHfRly43\nExERkcwF4d6EhWfAtiAujSEq/SC9aXvlJk6cSKFQYJ111mHs2LHMmzev4n323AkYREREpK5Y0EBY\n+BNxeXzSred/JchPxmxQp/c5ZswYdt55Z4YMGcJLL73ERRddxO67787zzz/POuus0+n9qoASERGR\nmmHWhzD/S2Lbmbh8CtHHO6RTHXy5U/u7+uqrP3m+++67s+uuu/KFL3yBm266idNPP73TOdWFJyIi\nIjUnyB1PWPgzWEhU3J24fH2npjpoarvttmPo0KHMmjWrsnwVJxERERHpAhbsSFiYiQVfIy6fTFw6\nLr1nbYX7TeeDqoQKKBEREalZZgMJ8vdi4QV4/Cui4q54/Gqn9/fCCy8wZ84cdtxxx4pyaQyUiIiI\n1DSzkDB/IXHwZeLSkUTFpGXqw482YPr06QAsWLCA5cuXM3XqVAD23XdfHn30UW655Rb2339/Ntxw\nQ+bMmcPFF1/MJptswrhx4yrKpAJKREREegQLdgAbCP4OWIFFixZx8MEHr7LNytevvfYaG2+8MYsW\nLeL0009n2bJlDBw4kFGjRnHJJZfQv3//irKogBIREZGa576UqPh18LcIC49gtjENDbQ5sPz3v/99\nl+RRASUiIiI1zf0DouJ+4C8T5O/DgurelqUzVECJiIhIzXL/mLj0TfCnCPJ3EIR7ZR0JUAElIiIi\nNcq9TFw6Eo8fJsjdRBAemHWkT2gaAxEREak57k5cPgmP7yTI/ZQgNy7rSKtQASUiIiI1JSmevodH\nN2Lhjwhynb/lSldRASUiIiI1xaNL8OgnWDieIHdh1nGapQJKREREakZc/jlx+YdYcAxB7mcV33Kl\nq6iAEhERkZoQR7cSl0/FggMI8hMxq90ypc1kZnajmS0ysxcaLfuMmT1sZv9M/123a2OKiIhIPYuj\ne4hLx2LBVwnyt2FW2xMFtKe0uxkY1WTZucDv3X1L4PfpaxEREZEOi6MZxKWDwXYgyN+NWZ+sI7Wp\nzQLK3R8H3mmyeAwwKX0+CfhGlXOJiIhIL+DxTOLSaLDNCQv3Y9Yv60jt0tnOxfXcfWH6/C1gvZY2\nNLMTzWymmc1cvHhxJw8nIiIi9cbjF4mKo8AGERYewmxg1pHareLRWZ7cxa/FO/m5+wR3H+7uwwcP\nHlzp4URERKQOeDw3uTkwecL8I5htlHWkDulsAfW2mW0AkP67qHqRREREpJ65v0VU2gtYkbQ8BZtn\nHanDOltATQOOTZ8fC9xdnTgiIiJSz9yXEhX3Bn+LsDAdC/4t60id0p5pDCYDTwBDzewNMzseuBQY\naWb/BPZKX4uIiIi0yP0DouJ+4HMI8r/Dgp2zjtRpbU6y4O6Ht7BqzypnERERkTrl/jFx6UDwpwjy\ndxCEe2UdqSK1PUuViIiI9HjuEXHpKDx+iCB3I0F4YNaRKla7c6SLiIhIj+fuxOWT8HgqQe4nBLnj\nso5UFSqgREREpEskxdPZeDQRC/8fQe6MrCNVjQooERER6RIeXYJHV2HheILcRVnHqSoVUCIiIlJ1\ncfnnxOUfYsHRBLmfYWZZR6oqFVAiIiJSVXF0K3H5VCw4gCA/EbP6Kzfq7ysSERGRzMTRPcSlY7Hg\nqwT52zDLZx2pS6iAEhERkarw+DHi0iFgOxDk78asT9aRuowKKBEREamYxzOJiqPBPpfcosX6ZR2p\nS6mAEhERkYp4/BJRcRTYwOTmwDYo60hdTgWUiIiIdJrHc4mKI4EcYf5hzDbKOlK30K1cREREpFPc\n3yIqjQQ+ICw8hgVbZB2p26iAEhERkQ5zX0pU3Bv8TcLCI1gwLOtI3UoFlIiIiHSI+wdExf3A5xDk\n78WCXbKO1O1UQImIiEi7uX9MXDoQ/CmC/B0E4cisI2VCBZSIiIi0i3tEXDoKjx8iyN1IEB6YdaTM\n6Co8ERERaZO7E5dPwuOpBLmfEOSOyzpSpioqoMzsDDObbWYvmNlkq+cpR0VERHqppHg6B48mYuEP\nCXJnZB0pc50uoCyZ6OG7wHB33w4IgcOqFUxERERqg0c/xqMrsfBUgtx/Zh2nJlTahZcD1jSzHNAX\neLPySCIiIlIr4vIviMs/wIKjCHJXY2ZZR6oJnS6g3H0BcCXwOrAQeNfdH2q6nZmdaGYzzWzm4sWL\nO59UREREulUc/Ya4fAoWjCbI34iZhk6vVEkX3rrAGGAzYENgLTM7qul27j7B3Ye7+/DBgwd3PqmI\niIh0mzi6l7h0DBb8O0H+dszyWUeqKZWUknsBr7n7YncvAXcBu1YnloiIiGTF48eISweDfZEgPw1d\nI7a6Sgqo14GdzayvJR2iewIvVSeWiIiIZMHjmUTF0WCfIyzcj1m/rCPVpErGQD0FTAVmAc+n+5pQ\npVwiIiLSzTx+iag4CmwgYeEhzAZlHalmVTQTubtfAFxQpSwiIiKSEfd5RMWRQI4w/zDJbEXSEt3K\nRUREpJdzf5uouBfwAWHhMXq2UCEAABD4SURBVCzYIutINU8FlIiISC/mvoyouDf4m4SFR7BgWNaR\negQVUCIiIr2U+wdExf3AXyTI34cFu2QdqcdQASUiItILuReJSweBP0mQv50gHJl1pB5FBZSIiEgv\n4x4Rl47C4wcJchMJwoOyjtTjaE52ERGRXsTdicsn4fEdBLmrCHL/kXWkHkkFlIiISC+RFE/n4NFE\nLPwhQe7MrCP1WCqgREREegmPfoxHV2LhqQS5/8w6To+mAkpERKQXiMu/IC7/AAuOJMhdTXIXNuks\nFVAiIiJ1Lo4mE5dPwYLRBPmbMNPHf6V0BkVEROpYHN1HXDoGbA+C/G2Y5bOOVBdUQImIiNQpjx8j\nLo0F+wJhYRpma2YdqW6ogBIREalDHj9DVBwNthlh4X7M+mcdqa6ogBIREakzHs8hKo4CPkNYeAiz\nQVlHqjsqoEREROqI+zyi4kggTG4ObEOyjlSXdCsXERGROuH+NlFxL+B9wsJjWLBF1pHqlgooERGR\nOuC+jKi4N/ibSctTMCzrSHVNBZSIiEgP5/4BUXE/8BcJ8vdiwS5ZR6p7FY2BMrMBZjbVzOaY2Utm\npu+YiIhIN3IvJlMV+JME+d8QhF/POlKvUGkL1NXAA+4+1swKQN8qZBIREZF2cI+IS0fh8QMEuRsI\nwrFZR+o1Ol1Amdk6wB7AOAB3LwLF6sQSERGR1rg7cflkPL6DIHclQe74rCP1KpV04W0GLAZuMrO/\nmdkNZrZW043M7EQzm2lmMxcvXlzB4URERARWFk/fx6MbsPAHBLmzso7U61RSQOWAHYBfuPsXgQ+A\nc5tu5O4T3H24uw8fPHhwBYcTERERAI8uxaMrsPAUgtx/ZR2nV6qkgHoDeMPdn0pfTyUpqERERKSL\nxOVfEJfPx4IjCXL/g5llHalX6nQB5e5vAfPNbGi6aE/gxaqkEhERkdXE0WTi8ilYMJogfxNmuqFI\nViq9Cm88cGt6Bd6rwHGVRxIREZGm4ug+4tIxYHsQ5G/DLJ91pF6togLK3f8ODK9SFhEREWmGx48l\ncz3Z9oSFaZitmXWkXk9tfyIiIjXM41lExdFgDYSFBzDrn3UkQQWUiIhIzfJ4TnJ/Oz5DWHgYs0FZ\nR5KUCigREZEa5D6PqDgSCNPiaUjWkaQR3UxYRESkxri/nRZP7xMWZmDBlllHkiZUQImIiNQQ92VJ\nt50vSFqegu2zjiTNUAElIiJSI9xXEBX3B3+RIH8PFuyadSRpgQooERGRGuBeJC4dBP4EQf42gnDv\nrCNJK1RAiYiIZMw9Ii4dhccPEORuIAjHZh1J2qCr8ERERDLk7sTlk/H4DoLclQS547OOJO2gAkpE\nRCRDcflcPLoBC88nyJ2VdRxpJxVQIiIiGYnLl+LR5Vj4HYLcxVnHkQ5QASUiIpKBuHwdcfk8LDiC\nIPe/mFnWkaQDVECJiIh0sziaTFz+DhbsT5C/GTN9HPc0+o6JiIh0ozi6j7h0DNgeBPnbMctnHUk6\nQQWUiIhIN/H4ceLSWLDtCQvTMFsz60jSSSqgREREuoHHs4iKo8EaCAsPYNY/60hSARVQIiIiXczj\nOcn97Vg3ub+dDco6klSo4gLKzEIz+5uZ3VuNQCIiIvXEfR5RcSQQpMXTkKwjSRVUowXqNOClKuxH\nRESkrrgvSoun9wgLD2HBlllHkiqpqICypIzeD7ihOnFERETqg/uypNvO3yAs3IcF22cdSaqo0hao\nnwHnAHFLG5jZiWY208xmLl68uMLDiYiI1D73FcmAcZ9NkP8tFuyWdSSpsk4XUGa2P7DI3Z9pbTt3\nn+Duw919+ODBgzt7OBERkR7BvUhcOgj8LwT5WwnCvbOOJF2gkhao3YADzGwuMAX4mpndUpVUIiIi\nPZB7RFw6Go8fIMhdTxAenHUk6SKdLqDc/Tx3H+LuDcBhwB/c/aiqJRMREelB3J24/G08vp0gdwVB\n7oSsI0kX0jxQIiIiVRCXz8WjX2Lh+QS572UdR7pYrho7cfcZwIxq7EtERKSnicuX4tHlWPhtgtzF\nWceRbqAWKBERkQrE5euIy+dhweEEuWsws6wjSTdQASUiItJJcTSZuPwdLNiPID8JM32s9hb6TouI\niHRCHE0nLh0DtjtB/g7M8llHkm6kAkpERKSDPP5jMteTDSMs3IPZmllHkm6mAkpERKQDPJ5FVNwf\nrIGw8ABm/bOOJBlQASUiItJOHr9MVBwFDEhuDmy6w0ZvpQJKRESkHdxfJyqOBIyw8AhmG2cdSTJU\nlXmgRERE6pn7orR4Wk5YeAwLtsw6kmRMBZSIiEgr3JcRFfcGn09YeBgLts86ktQAFVAiIiItcF9B\nVBwNPpsgPw0Ldss6ktQIFVAiIiLNcC8mUxX4nwnyUwjCUVlHkhqiAkpERKQJ94i4dAweP0CQm0AQ\nHpJ1JKkxugpPRESkEXcnLn8Hj28jyF1OkPtW1pGkBqmAEhERaSQun4dHE7DwPILc2VnHkRqlAkpE\nRCQVly/Do8uw8NsEuf/OOo7UMBVQIiIiQFy+nrh8LhYcTpC7BjPLOpLUMBVQIiLS68XRFOLyt7Fg\nP4L8JMz08Sit6/RPiJltbGaPmtmLZjbbzE6rZjAREZHuEEfTiUtHg+1OkL8Ds3zWkaQHqGQagzJw\nlrvPMrN+wDNm9rC7v1ilbCIiIl3K4z8mcz3ZMMLCNMzWzDqS9BCdboFy94XuPit9/h7wErBRtYKJ\niIh0JY9nERX3B9uUsPAAZutkHUl6kKp08ppZA/BF4Klq7E9ERKQrefwyUXEUMCC5v50NzjqS9DAV\nF1BmtjZwJ3C6uy9vZv2JZjbTzGYuXry40sOJiIhUxP11ouJIwNLiaeOsI0kPVFEBZclIuzuBW939\nrua2cfcJ7j7c3YcPHqwKX0REsuO+KC2elhMWHsSCrbKOJD1UpweRWzJBxkTgJXf/SfUiiYiIVJ/7\nu0m3nc9PWp6CL2QdSXqwSlqgdgOOBr5mZn9PH/tWKZeIiEjVuK9IBoz7CwT5u7Bgt6wjSQ/X6RYo\nd/8ToGlaRUSkprkXiUtjwf9MkJ9CEI7KOpLUgUrmgRIREalp7hFx6Rg8vp8gN4EgPCTrSFInNFe9\niIjUJXcnLn8Hj28jyF1GkPtW1pGkjqiAEhGRuhSXz8OjCVh4LkHunKzjSJ1RASUiInUnLl+GR5dh\n4ckEuUuyjiN1SAWUiIjUlbg8gbh8LhYcRpC7hmTWHZHqUgElIiJ1I45uIy6fjAX7EuR/hVmYdSSp\nUyqgRESkLsTR/cSlo8C+QpC/g+RmGSJdQwWUiIj0eB7/kbh0ENgwwsI9mPXNOpLUORVQIiLSo3n8\nt2SWcduEsPAAZutkHUl6ARVQIiLSY3n8MlFxb2Cd5P52ppvWS/dQASUiIjXtlVde4aSTTmLYsGGE\nYciIESMAcH+dqDgSgOsnHsf++5/MwIEDMTNmzJiRXWDpFVRAiYhITZs9ezbTp09n6NChbLXVVgC4\nL0qLp3cJCw/y618/yDvvvMPee++dbVjpNXQvPBERqWmjR49mzJgxAIwdO5YlS94iKo4Cn09YeAgL\nvshf/vIXgiDghRdeYPLkyRknlt5ABZSIiNS0IGjcWRLh8QvgHxDkp2HBV5rZRqTrqYASEZEewb2I\nx08C7xLkpxCE+2QdSXoxlewiIlLz3CPi0rHgb2G2FUF4aNaRpJdTC5SIiNQ0dycun4LHUyDYDmxg\n1pFE1AIlIiK1LS6fj0fXY+G5mA3NOo4IUGEBZWajzOxlM3vFzM6tVigRERGAuHw5Hl2KhScR5C7J\nOo7IJzpdQFlyi+ufA/sA2wCHm9k21QomIiK9W1yeQFz+PhYcSpD7OWaWdSSRT1QyBurLwCvu/iqA\nmU0BxgAvViOYiIj0XnF0O3H5ZCzYh49K13H/tN8CsGDBApYvX87UqVMB2Hfffenbty8zZ85k7ty5\nzJ8/H4DHHnuMJUuW0NDQwPDhwzP7OqR+mbt37o1mY4FR7n5C+vpoYCd3P7XJdicCJwJssskmO86b\nN6+yxCIiUvc8/jNx+TKC/BTmzVvEZptt1ux2r732Gg0NDYwbN45Jkyattv7YY4/l5ptv7uK0Uq/M\n7Bl3b7YC7/ICqrHhw4f7zJkzO3U8ERERke7UWgFVySDyBcDGjV4PSZeJiIiI1LVKCqingS3NbDMz\nKwCHAdOqE0tERESkdnV6ELm7l83sVOBBIARudPfZVUsmIiIiUqMqmonc3acD06uURURERKRH0Ezk\nIiIiIh2kAkpERESkg1RAiYiIiHSQCigRERGRDur0RJqdOpjZYqCrpyIfBCzp4mP0JDofq9L5+JTO\nxap0Pj6lc7EqnY9V9abzsam7D25uRbcWUN3BzGa2NGtob6TzsSqdj0/pXKxK5+NTOher0vlYlc5H\nQl14IiIiIh2kAkpERESkg+qxgJqQdYAao/OxKp2PT+lcrErn41M6F6vS+ViVzgd1OAZKREREpKvV\nYwuUiIiISJdSASUiIiLSQXVVQJnZKDN72cxeMbNzs86TJTPb2MweNbMXzWy2mZ2WdaasmVloZn8z\ns3uzzpI1MxtgZlPNbI6ZvWRmu2SdKStmdkb6O/KCmU02sz5ZZ+pOZnajmS0ysxcaLfuMmT1sZv9M\n/103y4zdqYXzcUX6u/Kcmf3WzAZkmbE7NXc+Gq07y8zczAZlkS1rdVNAmVkI/BzYB9gGONzMtsk2\nVabKwFnuvg2wM3BKLz8fAKcBL2UdokZcDTzg7lsD29NLz4uZbQR8Fxju7tsBIXBYtqm63c3AqCbL\nzgV+7+5bAr9PX/cWN7P6+XgY2M7dhwH/AM7r7lAZupnVzwdmtjHwdeD17g5UK+qmgAK+DLzi7q+6\nexGYAozJOFNm3H2hu89Kn79H8gG5UbapsmNmQ4D9gBuyzpI1M1sH2AOYCODuRXdflm2qTOWANc0s\nB/QF3sw4T7dy98eBd5osHgNMSp9PAr7RraEy1Nz5cPeH3L2cvnwSGNLtwTLSws8HwE+Bc4BeeyVa\nPRVQGwHzG71+g15cMDRmZg3AF4Gnsk2SqZ+R/LLHWQepAZsBi4Gb0i7NG8xsraxDZcHdFwBXkvwV\nvRB4190fyjZVTVjP3Remz98C1ssyTI35D+D+rENkyczGAAvc/dmss2SpngooaYaZrQ3cCZzu7suz\nzpMFM9sfWOTuz2SdpUbkgB2AX7j7F4EP6F1dNJ9Ix/aMISkqNwTWMrOjsk1VWzyZ66bXtjI0ZmY/\nIBkecWvWWbJiZn2B84EfZZ0la/VUQC0ANm70eki6rNcyszxJ8XSru9+VdZ4M7QYcYGZzSbp2v2Zm\nt2QbKVNvAG+4+8oWyakkBVVvtBfwmrsvdvcScBewa8aZasHbZrYBQPrvoozzZM7MxgH7A0d6755A\ncXOSPzieTf9PHQLMMrP1M02VgXoqoJ4GtjSzzcysQDIQdFrGmTJjZkYyxuUld/9J1nmy5O7nufsQ\nd28g+bn4g7v32lYGd38LmG9mQ9NFewIvZhgpS68DO5tZ3/R3Zk966YD6JqYBx6bPjwXuzjBL5sxs\nFMkQgAPcfUXWebLk7s+7+2fdvSH9P/UNYIf0/5VepW4KqHSA36nAgyT/Ad7u7rOzTZWp3YCjSVpb\n/p4+9s06lNSM8cCtZvYc8AXgkozzZCJthZsKzAKeJ/k/sVfdpsLMJgNPAEPN7A0zOx64FBhpZv8k\naaW7NMuM3amF83EN0A94OP2/9LpMQ3ajFs6HoFu5iIiIiHRY3bRAiYiIiHQXFVAiIiIiHaQCSkRE\nRKSDVECJiIiIdJAKKBEREZEOUgElIiIi0kEqoEREREQ66P8DDmiMO91RMdkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FF9rTkQEMMaSulRBCGE0SKCHSkdOcohVN8MeP0YxjMMNtdobdTW4SiL/VUmAY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\nQImIiIgUkAqUiIiISAH9PxeA7/CqF86pAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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msy0iInm3o4nL/DOff9ho+b6ku9lGEjClUQvSQIyqMg1IremKcS4hD5DgJ3iz0x5I7ilA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\n3FaFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}