SIT719_Prac06_Task02_Optional_PyOD.ipynb

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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.1"
    },
    "colab": {
      "name": "SIT719_Prac06_Task02_Optional_PyOD.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uRbiZWbgGcPn"
      },
      "source": [
        "# Outlier Detection using PyOD\n",
        "\n",
        "#prepared by Adnan Anwar, based on available information from the internet, thanks to the open-source community.\n",
        "\n",
        "#This is an optional task. We will demostrate for your understanding but it is not mendatory for you to practice. If you want to practice, please download and install PyOD library from the link below (for Jupyter notebook users):\n",
        "\n",
        "https://github.com/yzhao062/pyod/tree/master/pyod\n",
        "\n",
        "Please note, it is your responsibility to ensure the download process (follow the instruction from teh source code). As everyone has different configuration and platform (and it is not a mendatory task), we will not be able to provide support for this. Once you have successfully installed, you may try the following code to see the performance comparison."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Gy5oj-LGcPw",
        "outputId": "9c864836-6276-488a-9db3-923f6805e7c6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import numpy as np\n",
        "from scipy import stats\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import matplotlib.font_manager\n",
        "\n",
        "!pip install pyod  \n",
        "\n",
        "from pyod.models.abod import ABOD\n",
        "from pyod.models.knn import KNN\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pyod\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/37/50/94ac3c301b06e291ce52938e4a037b147cf01b40ff458dea5441ac42addf/pyod-0.8.7.tar.gz (101kB)\n",
            "\r\u001b[K     |███▎                            | 10kB 15.0MB/s eta 0:00:01\r\u001b[K     |██████▌                         | 20kB 12.7MB/s eta 0:00:01\r\u001b[K     |█████████▊                      | 30kB 9.6MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 40kB 8.5MB/s eta 0:00:01\r\u001b[K     |████████████████▏               | 51kB 5.5MB/s eta 0:00:01\r\u001b[K     |███████████████████▍            | 61kB 6.0MB/s eta 0:00:01\r\u001b[K     |██████████████████████▊         | 71kB 6.0MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 81kB 6.6MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▏  | 92kB 6.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 102kB 4.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from pyod) (1.0.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from pyod) (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.7/dist-packages (from pyod) (1.19.5)\n",
            "Requirement already satisfied: numba>=0.35 in /usr/local/lib/python3.7/dist-packages (from pyod) (0.51.2)\n",
            "Requirement already satisfied: pandas>=0.25 in /usr/local/lib/python3.7/dist-packages (from pyod) (1.1.5)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from pyod) (1.4.1)\n",
            "Requirement already satisfied: scikit_learn>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from pyod) (0.22.2.post1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from pyod) (1.15.0)\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.7/dist-packages (from pyod) (0.10.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pyod) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pyod) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pyod) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pyod) (2.8.1)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.35->pyod) (0.34.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.35->pyod) (54.2.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25->pyod) (2018.9)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from statsmodels->pyod) (0.5.1)\n",
            "Building wheels for collected packages: pyod\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-0.8.7-cp37-none-any.whl size=115979 sha256=dab980f3b1401ce7b3b597a7720a923921136f440a4d64768551cb4b2afb54a1\n",
            "  Stored in directory: /root/.cache/pip/wheels/b7/8a/c9/5fe3f32692ad445fe573cf5b953d7a0d55dc1f7c8e229ebda0\n",
            "Successfully built pyod\n",
            "Installing collected packages: pyod\n",
            "Successfully installed pyod-0.8.7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Osopc3kGcPw"
      },
      "source": [
        "\n",
        "from pyod.utils.data import generate_data, get_outliers_inliers\n",
        "\n",
        "#generate random data with two features\n",
        "X_train, Y_train = generate_data(n_train=200,train_only=True, n_features=2)\n",
        "\n",
        "\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aox59xutGcPx",
        "outputId": "47f15d41-dcd5-4c76-9ad3-339a9d840084",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "# by default the outlier fraction is 0.1 in generate data function \n",
        "outlier_fraction = 0.1\n",
        "\n",
        "# store outliers and inliers in different numpy arrays\n",
        "x_outliers, x_inliers = get_outliers_inliers(X_train,Y_train)\n",
        "\n",
        "n_inliers = len(x_inliers)\n",
        "n_outliers = len(x_outliers)\n",
        "\n",
        "#separate the two features and use it to plot the data \n",
        "F1 = X_train[:,[0]].reshape(-1,1)\n",
        "F2 = X_train[:,[1]].reshape(-1,1)\n",
        "\n",
        "# create a meshgrid \n",
        "xx , yy = np.meshgrid(np.linspace(-10, 10, 200), np.linspace(-10, 10, 200))\n",
        "\n",
        "# scatter plot \n",
        "plt.scatter(F1,F2)\n",
        "plt.xlabel('F1')\n",
        "plt.ylabel('F2') \n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'F2')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oPbPvBv4EeNnd3xfz+geBnwDPlg894O5fzi7CxeuWPkqRejXyDbrWTTTti1b1+1x2weC8m3YcAz6xfsXc3taNiIt1w9Zdi16rqROErlF8G7iixjn/5O5ryz8dlSSge/ooRerR6ppc0heqd/QXFrzP/XuLXP/+odSahRM/2mp0vMiGrbsW1EFq6ZUeg6CJwt0fAY6GjKHdOmXmpUgrtHq3taQvWmYLd8GLlgmpNfO7OFWalwwWk9w6Za2mxeqEGsUHzOxJ4CXgr9x9ovoEM9sEbAJYsWJFxuHV1g19lCL1aPU37KQ6xufu21fzfdJqFpW1hMUs9X3ZBYPzZl5Dd/YY5D1RPAG8x91fN7OrgFHg/OqT3H07sB1mRz1lG6KIROqtyS22jpE04a7yfeI29KkUJYNmk9voeJH79xbnJQkDrn9/930xDF2jSOXur7n76+XHDwEFMzsrcFgikqCemlwr6hj1vE9lt2+SKFHFqdV9FNcSSaqBdLpcJwozO9tstiJlZpcyG+8rYaMSkST11OSarWNUFpy37Tw4r3CdVPuLJu4lJYuk9aPq6T7qlUI2hB8e+z3gg8BZZnYYuB0oALj7N4AbgL8wsxNACbjJu22GoEiXWcyQ1yRxW6Dev7e4IDkkdWml7Svd7FaivTT0PWiicPeba7z+NeBrGYUjIhlo5gZbT8E5aT9tqD25r5kBJ2nJp9vkvZgtIl2mmRtsPa2QWsmk1aMPm22JdCIlipzqlPVhRBrVzA22nlZIiJpBrwx9V6LIoXqa0CKdrNEbbD2tkF6qGWQt16OeelWrZ7eKdLp6RlNpuZz2UYsih3pp2J1IvWq1QnqpZpA1JYocUhNapDm9UjPImrqeckhNaBHJE7UockhNaBHJEyWKnFITWkTyQokixzSXQkTyQIkipzSXQkTyQsXsnNJcChHJCyWKnNJcChHJCyWKnOqVvXhFJP+UKHJKcylEJC9UzM4pzaUQkbwIvcPd3cCfAC+7+/tiXjfgq8BVwHHgM+7+RLZRhqO5FCKSB6G7nr4NXJHy+pXA+eWfTcDfZxCTiIhUCJoo3P0R4GjKKdcC3/FZe4ABMzsnm+hERATCtyhqGQJerHh+uHxsHjPbZGZjZjY2OTmZWXAiIr0g74miLu6+3d2H3X14cHAwdDgiIl0l74miCJxX8fzc8jEREclI3hPFDuBPbdZ64Ji7HwkdlIhILwk9PPZ7wAeBs8zsMHA7UABw928ADzE7NPYQs8Nj/yxMpKdoRVcR6TVBE4W731zjdQc+m1E4NWlFVxHpRXnvesoVregqIr1IS3g0QCu69iZ1N0qvU4uiAVrRtfdE3Y3FqRLOqe7G0XENvpPeUVeiMLNCzLGzWh9OvmlF196j7kaRGonCzC4rj0Y6Ymb/YGYrK17+h3YGlkcj64a487o1DA30Y8DQQD93XrdG3RBdTN2NIrVrFP8D2OjuE2Z2A/CwmX2qvO6StT+8/NGKruFlWTNYPtBPMSYpqLtRekmtrqfT3X0CwN1/BIwA95jZCODtDk6kWtY1A3U3itRuUUyb2dnu/muAcsviQ8DPgPe2PTqRKmk1g3a0KrSBVDqNCOsNtRLFFuDdwK+jA+5+2Mz+I3BLOwMTiROiZqDuxniagNo7anU9/T93f7L6oLsfc/f/3qaYRBJpiHJ+NDsibHS8yIatu1i15UE2bN2locYdoFaiGI0emNn9bY5FpCbVDPKjmdad5qV0plqJonJk0x+1MxCRemiIcn4007rTvJTOVKtG4QmPRYJRzSAfNm9cPa9GAbVbd5qX0plqJYpLzOw1ZlsW/eXHlJ+7u7+9rdGJSG41MyJM81I6U2qicPe+tNdFpLc12rprphUi4Wn1WBHJjOaldKbQO9xdAXwV6AO+6e5bq17/DLCNU/tkf83dv5lpkCLSUqoxdZ5gicLM+oCvAx8BDgOPm9kOd3+q6tT73L1nJvdppquI5E3I/SguBQ65+zPu/hbwfeDagPEEpzHmIpJHIRPFEPBixfPD5WPVrjezfzWzH5nZeXEXMrNNZjZmZmOTk5PtiDUTGmMuInmU9x3ufgqsdPeLgYeBe+JOcvft7j7s7sODg4OZBthKGmMuInkUMlEUgcoWwrmcKloD4O6vuPub5affBN6fUWxBaB0jEcmjkIniceB8M1tlZqcDNwE7Kk8ws3Mqnl4DHMgwvsxpHSMRyaNgo57c/YSZ3QLsZHZ47N3l/S6+DIy5+w7gv5jZNcAJ4CjwmVDxZkFjzEUkj8y9u5ZwGh4e9rGxsdBhiIh0FDPb6+7Dca/lvZgtIiKBKVGIiEgqrfUkQWkmen7odyFJlCgkGO25nB/6XUgadT1JMJqJnh/6XUgaJQoJRjPR80O/C0mjRCHBaCZ6fuh3IWmUKCQYzUTPD/0uJI2K2RKMZqLnh34XkkYzs0VERDOzRUSkeUoUIiKSSolCRERSKVGIiEgqJQoREUkVNFGY2RVmdtDMDpnZlpjX32Zm95Vff8zMVmYfpYhIbwuWKMysD/g6cCVwIXCzmV1YddqfA6+6+78B7gL+NtsoRUQkZIviUuCQuz/j7m8B3weurTrnWuCe8uMfAR8yM8swRhGRnhcyUQwBL1Y8P1w+FnuOu58AjgHvqr6QmW0yszEzG5ucnGxTuCIivakritnuvt3dh919eHBwMHQ4IiJdJWSiKALnVTw/t3ws9hwzOw14B/BKJtGJiAgQNlE8DpxvZqvM7HTgJmBH1Tk7gE+XH98A7PJuW5xKRCTngq0e6+4nzOwWYCfQB9zt7hNm9mVgzN13AN8C/peZHQKOMptMREQkQ0GXGXf3h4CHqo59seLxG8DHs45LRERO6YpitoiItI8ShYiIpFKiEBGRVNoKVSQDo+NFbTMqHUuJQqTNRseL3PbAfkrTMwAUp0rc9sB+ACUL6QhKFCJttm3nwbkkESlNz7Bt58HYRKHWh+SNEoVIm700Var7uFofkkcqZou02fKB/rqPp7U+REJRopAFRseLbNi6i1VbHmTD1l2MjlcvwSWN2LxxNf2FvnnH+gt9bN64esG5jbQ+RLKirieZR10frRd9bvXUHZYP9FOMSQpJrRKRLChRyDyNFl6lPiPrhur6/DZvXD0vUUNy60MkK0oUMo+6PsJqpPUhkhUlCplHXR/h1dv6EMmKitkyTyOFVxHpDWpRyDzq+hCRakoUsoC6PkSkUpCuJzN7p5k9bGa/LP+5LOG8GTPbV/6p3iZVREQyEKpGsQX4R3c/H/jH8vM4JXdfW/65JrvwREQkEipRXAvcU358DzASKA4REakhVKJ4t7sfKT/+NfDuhPPOMLMxM9tjZonJxMw2lc8bm5ycbHmwIiK9rG3FbDP7OXB2zEtfqHzi7m5mnnCZ97h70cz+CNhlZvvd/VfVJ7n7dmA7wPDwcNK1RESkCW1LFO7+4aTXzOw3ZnaOux8xs3OAlxOuUSz/+YyZ/W9gHbAgUYiISPuE6nraAXy6/PjTwE+qTzCzZWb2tvLjs4ANwFOZRSgiIkC4RLEV+IiZ/RL4cPk5ZjZsZt8sn/NvgTEzexLYDWx1dyUKEZGMBZlw5+6vAB+KOT4G/Ofy438B1mQcmoiIVNFaTyIikkqJQkREUilRiIhIKiUKERFJpdVjc2Z0vLhgiW/Qst8iEo4SRY6Mjhfn7ZdcnCqx+YdPgsH0jM8du+2B/QBKFiKSCXU95ci2nQfnkkRk+qTPJYlIaXqGbTsPZhmaiPQwJYoceSlmr+pWnCsishjqeiqLqw1k3bWzfKCfYp0JYPlAf5ujERGZpRYFp2oDxakSzqk6wOh4MdM4Nm9cTX+hb96xwhKj0GfzjvUX+uaK3CIi7aZEQXxtIEQdYGTdEHdet4ahgX4MGBroZ9vHL2HbDZfMO3bndWtUyBaRzKjrieT+/hB1gJF1Q7FJQIlBREJRi4Lk/n7VAURElCiA+NqA6gAiIrPU9cSpbp3Qo55ERPJIiaIsqTYgItLrgnQ9mdnHzWzCzE6a2XDKeVeY2UEzO2RmW7KMUUREZoWqUfwCuA54JOkEM+sDvg5cCVwI3GxmF2YTnoiIREJthXoAwMzSTrsUOOTuz5TP/T5wLaB9s0VEMpTnUU9DwIsVzw+Xjy1gZpvMbMzMxiYnJzMJTkSkV7StRWFmPwfOjnnpC+7+k1a+l7tvB7YDDA8Pe43TRUSkAW1LFO7+4UVeogicV/H83PIxERHJUJ67nh4HzjezVWZ2OnATsCNwTCIiPSfU8NiPmdlh4APAg2a2s3x8uZk9BODuJ4BbgJ3AAeAH7j4RIl4RkV4WatTTj4Efxxx/Cbiq4vlDwEMZhiYiIlXy3PUkIiI5oEQhIiKplChERCSVFgUUkaDysF+9pFOiEJFgov3qo62Io/3qQbs65om6nkQkmLzsVy/plChEJJg87VcvyZQoRCQY7VffGZQoRCQY7VffGVTMFpFgtF99Z1CiEJGgtF99/qnrSUREUilRiIhIKiUKERFJpUQhIiKplChERCSVuXvoGFrKzCaB50PHkRNnAb8NHUQO6HM4RZ/FLH0Op0SfxXvcfTDuhK5LFHKKmY25+3DoOELT53CKPotZ+hxOqeezUNeTiIikUqIQEZFUShTdbXvoAHJCn8Mp+ixm6XM4peZnoRqFiIikUotCRERSKVGIiEgqJYouZmYfN7MJMztpZj05FNDMrjCzg2Z2yMy2hI4nBDO728xeNrNfhI4lNDM7z8x2m9lT5f83/jJ0TCGY2Rlm9n/N7Mny5/CltPOVKLrbL4DrgEdCBxKCmfUBXweuBC4EbjazC8NGFcS3gStCB5ETJ4DPu/uFwHrgsz3638SbwOXufgmwFrjCzNYnnaxE0cXc/YC79/Iu9ZcCh9z9GXd/C/g+cG3gmDLn7o8AR0PHkQfufsTdnyg//h1wAOi5zTB81uvlp4XyT+LIJiUK6WZDwIsVzw/TgzcFiWdmK4F1wGNhIwnDzPrMbB/wMvCwuyd+DtrhrsOZ2c+Bs2Ne+oK7/yTreEQ6gZmdCdwP3Orur4WOJwR3nwHWmtkA8GMze5+7x9axlCg6nLt/OHQMOVYEzqt4fm75mPQwMyswmyTudfcHQscTmrtPmdluZutYsYlCXU/SzR4HzjezVWZ2OnATsCNwTBKQmRnwLeCAu38ldDyhmNlguSWBmfUDHwGeTjpfiaKLmdnHzOww8AHgQTPbGTqmLLn7CeAWYCezRcsfuPtE2KiyZ2bfA/4PsNrMDpvZn4eOKaANwKeAy81sX/nnqtBBBXAOsNvM/pXZL1QPu/vPkk7WEh4iIpJKLQoREUmlRCEiIqmUKEREJJUShYiIpFKiEBGRVEoUIm1iZjMVQzD3mdlKM3tXefXS183sa6FjFKmHZmaLtE/J3ddWHjCzPwD+G/C+8o9I7qlFIZIhd/+9u/8z8EboWETqpRaFSPv0l1fnBHjW3T8WNBqRJilRiLTPgq4nkU6kricREUmlRCEiIqm0KKBIm5jZ6+5+Zszx54C3A6cDU8Afu/tTGYcnUjclChERSaWuJxERSaVEISIiqZQoREQklRKFiIikUqIQEZFUShQiIpJKiUJERFL9f853NzdOS87UAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rc_YEUo0GcPy"
      },
      "source": [
        "\n",
        "classifiers = {\n",
        "     'Angle-based Outlier Detector (ABOD)'   : ABOD(contamination=outlier_fraction),\n",
        "     'K Nearest Neighbors (KNN)' :  KNN(contamination=outlier_fraction)\n",
        "}\n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5r1GP7QGcPy",
        "outputId": "ad485867-f89a-4628-f7e9-3c2a34e127ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        }
      },
      "source": [
        "\n",
        "#set the figure size\n",
        "plt.figure(figsize=(10, 10))\n",
        "\n",
        "for i, (clf_name,clf) in enumerate(classifiers.items()) :\n",
        "    # fit the dataset to the model\n",
        "    clf.fit(X_train)\n",
        "\n",
        "    # predict raw anomaly score\n",
        "    scores_pred = clf.decision_function(X_train)*-1\n",
        "\n",
        "    # prediction of a datapoint category outlier or inlier\n",
        "    y_pred = clf.predict(X_train)\n",
        "\n",
        "    # no of errors in prediction\n",
        "    n_errors = (y_pred != Y_train).sum()\n",
        "    print('No of Errors : ',clf_name, n_errors)\n",
        "\n",
        "    # rest of the code is to create the visualization\n",
        "\n",
        "    # threshold value to consider a datapoint inlier or outlier\n",
        "    threshold = stats.scoreatpercentile(scores_pred,100 *outlier_fraction)\n",
        "\n",
        "    # decision function calculates the raw anomaly score for every point\n",
        "    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) * -1\n",
        "    Z = Z.reshape(xx.shape)\n",
        "\n",
        "    subplot = plt.subplot(1, 2, i + 1)\n",
        "\n",
        "    # fill blue colormap from minimum anomaly score to threshold value\n",
        "    subplot.contourf(xx, yy, Z, levels = np.linspace(Z.min(), threshold, 10),cmap=plt.cm.Blues_r)\n",
        "\n",
        "    # draw red contour line where anomaly score is equal to threshold\n",
        "    a = subplot.contour(xx, yy, Z, levels=[threshold],linewidths=2, colors='red')\n",
        "\n",
        "    # fill orange contour lines where range of anomaly score is from threshold to maximum anomaly score\n",
        "    subplot.contourf(xx, yy, Z, levels=[threshold, Z.max()],colors='orange')\n",
        "\n",
        "    # scatter plot of inliers with white dots\n",
        "    b = subplot.scatter(X_train[:-n_outliers, 0], X_train[:-n_outliers, 1], c='white',s=20, edgecolor='k') \n",
        "    # scatter plot of outliers with black dots\n",
        "    c = subplot.scatter(X_train[-n_outliers:, 0], X_train[-n_outliers:, 1], c='black',s=20, edgecolor='k')\n",
        "    subplot.axis('tight')\n",
        "\n",
        "    subplot.legend(\n",
        "        [a.collections[0], b, c],\n",
        "        ['learned decision function', 'true inliers', 'true outliers'],\n",
        "        prop=matplotlib.font_manager.FontProperties(size=10),\n",
        "        loc='lower right')\n",
        "\n",
        "    subplot.set_title(clf_name)\n",
        "    subplot.set_xlim((-10, 10))\n",
        "    subplot.set_ylim((-10, 10))\n",
        "plt.show() \n",
        "\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of Errors :  Angle-based Outlier Detector (ABOD) 7\n",
            "No of Errors :  K Nearest Neighbors (KNN) 4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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82x9qTlrJGNjOA/7sV7hWNf0Ad+PNyulwarg/jHom3qyWNXjB7328D0zrx27Em233Hbw9n1V4QwMdzXRZhdeZrMQb0rrE36MDb5LEVDPbiFeWj9wTGop3DFIVXin+dbYFqO/h7Z187i/7abYNeT2ANxT2MfAB3p5jR+v/O7y9pD/4z/UO3p7lMREfpv/zl7cUrwN9stVibgZ+7g8dtDcjqen5nsXbZk+YNxPsU7xq3s5ab96sqE/w9gbPdM79yX+ORrxgvB/ewbuVeKGr6birpsC8xsyaju9od5s65/6GdzzGY3jHXcxk2zFdLdbdObcA78Dqaf7zngyc7O9tdtb9eO9d84PVCOCPke9v59zf8Q5GjjyWZb15w/Gr8WZ2nuIP8YMX8GYDLwGb8MLaQ8BlrZ77bv/9uBqvgjIDOL5pSMbMsvC2d7ecV0/CwzlXgndQ+7fMrKNjX5seX4XXbw6IuK2jz/c/8d5/X+IN69Ww48MfOurrDgLe8d/zfwcud97xquAdb/aI/7nc4Ux+59wSvH4ucjTkTn+5L/ufiWK8mZVtOQfvM7cG77itJ2njO6QDz+Ed/7oOb2frdH+UpQ6vDzkBrz+5B/hexHdIW84Flvrb/xK/bdvxl/0w/tkA2rh/Pd7xjWOAv7Sq8jX5OS2Pb229jEa877bWjzkHLxAnNdvWP0tX+G/K5cA5zrnZQbdHkpOZPYY3iab1lPpAmdlleEP4252+RUQ85p1i5AvnXLunPwkDM8vBG43a3+3cJK9dec5xwHTnXHvH1iUNBbYuMLNv4FWWtuCdQPZSYGSs3sAiIhK//MMt1uJV9JtOODvBeWcSEGlTtwyJmtmfzKzczD6NuG2AeSeMXej/27+dvz3Pf8xCi5/rR07AO9C7aQjrNIU1kfiUhP2XBG8o3jnMNuGdGmqKwprsSLdU2MzsSLw33l+cc1/zb/sd3gHyt5jZdXizfFpf03AA3vFf4/EOAJ0LHOicW7fLjRIR6QT1XyISD7qlwuace4OW510C7wzWTQcaP4I3O7O1bwD/ct5JT9fhnZCw9ewQEZGoUf8lIvEgmhfLHeJPswZv5mNbJyEdTstZP8tpeYK/ZmZ2Md7FpyEl/UDrmdN9LRWRUHObVlY652L5oQ9d/5WSEZ7LKKZnRPs6623LyOj0tcpDJTM9Ptvdntr6xqCb0GV1dbFve31dA41rl+xyHxaTT51zzpnZLo29Oufuxzt9ASm9h7vMAzo6YbKIJJKaN27o6AocURWW/isrv71zMcdWbkFwO8v5efF1hbMxue2eNi4hfFlWFXQTdlrp8thfdKWspIL1f/2vXe7DonkettVmlgvg/1vexmNW0PIMz3m0PCOziEgQQtV/KawprIVRPK5jEO+j7vrcRDOw/Z1tZzQ+D+9Ef639EzjOzPr7s7COY9v1DEVEghKa/kthLb7C2pjcPnEZZLoq2dY3SN11Wo/H8a4zuYeZLTezC4BbgElmthA41v8dMxtvZg8COOfW4l037D3/Z6p/m4hITKj/2rEgw1q8SPbgEk/rHk87AJHi8sS5OoZNJLnUvHHDXOfc+KDb0R12pv8KQ3Ut6LAWD1+u8RRWYiFejm2L5fFs82/+xi73Ycl4LVEREemEoMNaPFBY2562SXQEMzdbREQ6FIbqWtDCXF1TKOlY0/YJc7UtP69vILNGu0oVNhER2U7QkwwU1hKDtlX3UWATEQmZoKtrGgptnwLIzgvzNgvzjkFrCmwiItIs6LAW5i/QMAePsAvzLNowv+ciKbCJiIRIkNW1oMNamIU1bMQbbceuU2ATEZFQCGulQyGje4Vxe4b1vRdJgU1EJCSSuboW1i/MMIaLRKDtuvMU2EREQiCZw1pYKVREV9i2b1h3GpoosImISKDC+EUZtjCRqMI2GSGM78UmCmwiIgFTdU2SXZhCW1gpsImIJKkwhLUwVjQUHoIRlu0exvckKLCJiAQqqOpaGMJaGIUlNCQrbf/2KbCJiEggwlbJUFgIhzC8DmF7b4ICm4hI0lF1bXthCAkiHVFgExEJSBDDoWEJa2GqYCishU8YXpMwvUdBgU1ERJJYGIKBtE2vTUsKbCIiAVB1LXgKBOEX9GsUlvcqKLCJiCSFsIS1sAg6CEjn6bXyKLCJiMRYkCfKFYlHQYa2sFTZFNhERBJcmKprYfjyU8UmPiX766bAJiISQykZmUE3Iakl+5e+dE0YdjQU2EREEpiqa9sorMW/ZH4NFdhERBJUmMJa0JL5iz7RBPVaBr3DocAmIiJRF+SXncJa4knG11SBTUQkAam6JtL9gtzxUGATEZGoUnVNoiHZXlsFNhGRBBOm6prCmkRTEK9xUO/ptECeVUREokJhLfnsk9uzS3/3Sdnmbm6JRJMCm4iIdLugw1qiVde6Gso6u8x4Dm9jcvvwZVlVTJ8zP68vpcs3xPQ5FdhERBJEWKprCmu7JhrhrLPPGa/BLYjQFmsKbCIi0m0U1romiJDWln1ye8ZtaIu1WFfZFNhERBJAGKprCms7LyxBLVK8VtsSvcqmwCYiIrss6LAWb8IY1FqL1+CWqHRaDxGROBeG6lrQ4qW6tk9uz7gIa5Hiqb2xfh/EckdFgU1ERHZJ0NW1eAhr8RjUIsVT2+Ph/dAVCmwiInEs6Opa0GEtHsRT2OlIvIfOaInVZ0CBTUREuiQMYS3M1ZREDTjxsE5hfl90lQKbiEicCrK6FoawFmbxEGp2RaKG0a6KxedBgU1EROLOmNw+oa2iJFOQCfO6hvX90VUKbCIicUjVtXAKc4CJljBX2xIptCmwiYhIp4UhrIX1SzisoSVWkn39o/3ZUGATEYkzQVXXwhDWwirZw0qTMG6HsAb8naXAJiIicSOMX75hDClBCvMQaTxTYBMRiSOqroWLgkn7wrRtYhX0o/k5UWATEZEOhSWsha26FqZAElbaRt1HgU1EJE4EfVUD2UZBpPPCsq3ivcqmwCYiIu1SdW17YQkg8UTbbNcpsImIxIEgqmthCWthouDRdWHYdmEK/jtLgU1EREItnr9kpaUwhLZYiMbOjgKbiEjIqboWDskSNqIt6O0YrzsACmwiItJCmMJaWL5cgw4ZiUbbc+cpsImIhJhmhgZP4SI6gtyusdgR6O4dHwU2ERFppupaSwpr0aXt23kKbCIiAoQrrIWBwkRiC8MOwc5QYBMRCSkNhwZHYS12Enlbd+dOkAKbiIiErroWZPUjkQNEWAW1zeOpyqbAJiISQqquSbJRUO6YApuISJJTdW0bhYbkE+33W3d9vqIa2MxsDzP7KOKnysyuaPWYiWa2IeIxv4xmm0REOiPI/iuZq2sKa8lNr0H70qK5cOfcAmA/ADNLBVYAz7bx0DedcydFsy0iIjsjWfqvMFXXFNYEvNfik7LNQTcjdGI5JHoMsNg5tyyGzyki0h0Ssv9SWBPxxMP7L5aB7TvA4+3cN8HMPjazF81s77YeYGYXm9n7Zva+q6+OXitFRLbXbf3X1pqqDp8oGYdDg/6yVHUtfPSabC8mgc3MMoBTgL+1cfcHwAjn3L7ANGBmW8twzt3vnBvvnBtv6dnRa6yISITu7r9SssKxJx+W6prCmrQn1q9N0O/FHYlVhe0E4APn3OrWdzjnqpxzm/z/zwLSzWxQjNolIrIjMeu/kq26FvQXpMKaxJNYBbbv0s5wgpkNNTPz/3+w36Y1MWqXiMiOJFz/FYbqWtBhTeKDqmzbRHWWKICZZQOTgB9G3HYJgHPuPuBbwBQzawC2AN9xzrlot0tEZEfUfyUuVdfih2aNeqIe2Jxz1cDAVrfdF/H/u4G7o90OEZGdFcv+K1bDoaquKaxJfIp6YBMREWmisBY9o/v12uFjFq3fFIOWdL9YVtnG5Pbhy7KOZ3MHQYFNRCRgyVJdCzqsJYrOBLPO/G28hrdkpcAmIiJRF4awFo/VtV0JZ51ddrwEt2Q/lk0XfxcRSQJBVtfCENbiyeh+vZp/YvV88SJWoTuM71lV2EREApRs514LSjxU14IMTvFWbUtGqrCJiCQ4VdfCLZbVtB0JSzs6kqxVNgU2ERGJirB84YW1uhamoBYpjG0SBTYRkcDEYjg0qOqawlrHwh6Kwhomm4T1dY0mBTYREUlIYf1SD3MQai2e2hoNYdnxAAU2EZGElezVtTCKxwAU1mpbWAN5tCiwiYgEIFFnh4YlrIXxyzyMoWdnxHv7450Cm4hIAgqiuhaWsBY2Ya1QdUXY1iUWwTws72sFNhGRGEvU6lpYhKm6FqZw050Sdb3CTIFNRER2WViqEGGS6KEmLOsXpoAeTQpsIiIJJtbDoQprySssoS3awvAeV2ATEYmh9AxdETCawlJtSZYgA+E4ri0sr3s0KbCJiCQQVdeCF3R4CUqyrnesKLCJiEhCCEOVJdlDS5DrH+3XP+idEwU2EZEEoepasJI9rEl0KbCJiMhOC1tYC7q6prC2jbZFdCiwiYiISLcKKrQl8rCoApuISAKI5XCoqmstqaLUNm2X7qXAJiIi0kUKJR0LYvsEHeCjRYFNRCTOqboWDIW1ztF26h4KbCIi0ilhC2tBUghJXkF9DhTYREQkLgVVXVNY23mx3maJOCyqwCYiEsdiNRwatupaIn4hJzoF3V2jwCYiInFFx61JZ0TzfRLEDowCm4hInErG6prCWnzTNuw6BTYREYkLCmuSzBTYRESkXWGprumYtcQRy/CbSO8bBTYRkTgU6wu9BynoL11V17pfImzTWO/MKLCJiEibwlBdU1gT8SiwiYhIKAUd1iS6YhWGE+V9pMAmIhJnYjEcGnR1LQxfsqquSZgosImISKiEIaxJbMR7lS2WOzYKbCIi0kKQ1bWwhDVV12JH27pzFNhEROJIMs0ODYoChISRApuIiDRTdU2CoJC8YwpsIiISuLCENQWHxBXvx7EpsImIxIlEHQ5VWBPQ9t+RtKAbICIi4RDEcGhYwlo8y+2b1enHlm2oiWJLJJoU2EREJOmFvbqzM6FsR8sJc2gb3a8Xi9Zvitry98ntySdlm6O2/GjSkKiISByI9nCoqmvhlNs3q9vCWjSXmexi8flRYBMRkZgLU1gLY3UtFqEqrKEtjK9HGCiwiYgkuaAvQyXbxLr6FdbQFk1h2lnYGQpsIiIhl2izQ8P0hRmmak5Q4UlDpPFBgU1ERGImTGEtTMIQmMIU3MIUpMNCgU1EJIkl83BoWEJBWEJSk7C1JxqiseMQ7c+SApuISIgl0nCoqmvbC2s4CkO7whKow0KBTUQkScWyuha2sBaGMBCGUNSRMA2RigKbiIhIzMVTEIqntiYyBTYREYmqsFXXZOcFFdqiWQmNt+PYFNhEREIqmsevabJBcOK1YhWv7U4UCmwiIhI1qq61FO+hJ4j2Bx2ww0KBTUREkkaQX/7xHtaaJMp6xBsFNhGREEqE4VBV17ZJtJCTKOsTT+9RBTYREel28fRFGG2JEm6CpGFRBTYRkaSiyQaxlchhLZHXbVdE6zMW9cBmZkvN7BMz+8jM3m/jfjOzu8xskZnNM7MDot0mEZHOUP/VNaquJQ+FtthJi9HzHO2cq2znvhOA3f2fQ4B7/X9FRMIg5v1XIl2OKixUXYt/o/v1YtH6Td2+3H1ye/JJ2eZuX253C8OQ6KnAX5ynGOhnZrlBN0pEpBPiqv+KxXBoGKtrCmvRlUzrGqRYBDYHvGxmc83s4jbuHw6URvy+3L+tBTO72MzeN7P3XX11lJoqItJCt/dfDZs3RKmp0trofr10sHqMKLRFXyyGRA93zq0ws8HAv8zsC+fcGzu7EOfc/cD9ACm9h7vubqSISBu6vf/qkTsmYfuvMFXXdL61xBStYdF4EPUKm3Nuhf9vOfAscHCrh6wA8iN+z/NvExEJVBD9V7SOX0um2aGqqgVDQXWbaHzeohrYzCzbzHo3/R84Dvi01cP+DnzPn21VBGxwzpVFs10iIjui/mvnhKW6FnRYU2iJT2F5/3Yk2kOiQ4BnzazpuR5zzr1kZpcAOOfuA2YBk4FFwGbgB1Fuk4hIZ6j/iiNBBzXx5PbNomxDTVSfI1mHRaMa2JxzXwH7tnH7fRH/d8Cl0WyHiMjOSqT+K9GHQ8MS1lRd88QitCWjMJzWQ0REiN/zr4ddPXgAACAASURBVAU5nKSwJslCgU1EROJSWMKabE8BtvspsImISJfFw8Ha0aZwEnvRCOvd/V7u7kMRFNhERBJYoh6/pupa+CnIdi8FNhGREIjH49eCqq6FKawplEisKLCJiEjcCFNYE4klBTYRkQSVaMOhYQtrqq7tWDS3UdjeD9GmwCYiIjst2ScbKKwlpjC/rxXYREQCFo/Hr8VaslVTEonCbfdQYBMRkZ0S6ypE2MKaAoh0VncelqDAJiKSgBLl+LWwhTUJl2R6fyiwiYiIdJKqa12j7bbrFNhERKTTYjkcmkzVE5EdSQu6ASIiySwaEw4SYThUYa1rBvfJbPF7eVVtQC2R7qbAJiIi0glhGdZrHcp29NiwhLbcvlmUbajp9uWO7teLRes3ddvy9sntySdlm7tted1FQ6IiItIpsRoOVXWtbYP7ZO5UWIv8O4l/CmwiIiIht6uhq6thT8JDgU1EJIHE+/FrYa2uBTkc2p1BK+jgFpZh5Vjqrs+kApuISEDi6QoHYb5kT7QlSlhrvVxV3OKLApuIiIRCWKtrQYlFoFJoix8KbCIiIu0IqroWyyAV69AWjW2aDGFfgU1EJEFE6/i1WAyHJsMXbmcFUfXSEGlLYTwEQIFNREQCpbAmsmMKbCIiAYinCQfJKojh0KCrXLF6/mScLbqrFNhERKRdYRwaSlRBh7UmYWmHtKTAJiIigdFwqEchSXZEgU1EJAHE+wlzk1kYw1oY27QjiR7+FdhERKRN0R4ODfMXrI6xin5o0zbeOQpsIiIxpgkH0iQeK1kSjLSgGyAiIsknjNU1VXy2N7hPJuVVtUE3Q1CFTUQk7un4tV2T2zcrKU/hIR0L2wxpVdhERGQ70fyyCkt1TRW1zlGVLRxUYRMRkaQTdFhTdc0T9OsQTxTYREQkaQQ1/Bnv4iVghqV6Gw0KbCIiMZSRkRp0EwIV5BeqgprEMwU2EZE4Fo0JB2E72Lo7hCmsxUu1qrV4bXeiUGATEZGYCKq6FqawJtJVCmwiIpKwFNbCT69R5yiwiYhIQgpjEIj3YcV4b388U2ATEZFm0Tp+LdbDoWEMayK7QoFNRCRO6QoHbVNYk0SkwCYiIhIDGk6MP2GaMa3AJiIiURXL4VBV16JPwTMYCmwiIgKEq5rQFQprksgU2ERERKJMVanYSdTLUymwiYhI1MTqy1PVtdhSAI09BTYRkTikGaLbKKzFP72GO6bAJiIicX/8mkiiU2ATEZG4pcqMJAsFNpEdyMofRVb+qKCbIRJ3EvXgb/HoOLbYUmATaSUyoGXlj+LgQ0eTW5Cj0CYSMvFSXVOwke6gwCbSjqz8UVw0cD333P0jjnKrg26OiIgksbSgGyASpLaqZgcfOhqAQ9I2ceOV/0VKNVxmd/H6qb9hSeniWDdRZDvdPUM0GhMOoj0cGi/VtaBVVlRQUrKUgoJCBuXkBN0c2QUKbJK0IsNaU0grXb6Bn04YxtLKdZx48XdJqfbu3+vz9xkx+HGWsFsQTRWROBXkcOizTz/FtVdexogRhSxbtpRbb5/GN7/17cDaI7tGgU2S1pgNK3ikZCb1GVkUvl+OS0+nbMTu7HHr32Cr95ja4cPJPO44+POfOeetvzJ7/I+xjOxgGy6S5FRd89iGDfT47VTSX59Nw/4HUH3vg833VVZUcO2VlzF79mzGjRvHvHnzOProozniqKNVaYtTCmySlLLyR/Gn+dPZc96cFrf3+fQjMO//pRlp5M+eDcuWwZ//zKjMTFztegU2EQmcVVYyYHwuVHq/px3yCVnPPcKaU+sBWPPGa1yZnc243FwAxo0bR0HBCEpKliqwxSkFNklKx6avY+8P3sRlwifHQU0fGGvQexmsPQLm1sJ37k9l9pYtjEvx5uak1NSQNXI/6ioqAm69SLhF8/g1Vdc82T/5MVTC5iFQcRpU5kBBFfBIOnWzTubQF57nUKB2/Hgy33qLeevWUVKyjIKCwoBb3r7cvlmUbagJuhmhpcAmSel7i2cDMK0xhZ++msWwYblUrF7BeYfV8ugfMyksGE5dYwlHHn4IBw8bzCzgINfA2PzBfKzAJiKdEK3j19LefIPMvz1BNXBSyiDe+Us1owuHUbJ8JefU1jGt7nnWA409YGBJCet2K2R6Vg9u/eP9SVNdG92vF4vWbwq6Gd1Kp/WQpJOVP4rsDd44wrzMHsyZM4eFCxfxxlvv8MDrWcyY+SJz5y3iP3Pex3BcfVoJW8dAylY4pWp+wK0X6V7xdEkqVdc8KXf8AYCK885jXu1WiouLmff5It54q5hZPXqx7r33KPnwQw5Kz6R6NPRv3Mofq6v5bmlJwC2XXRG1wGZm+WY228w+N7PPzOzyNh4z0cw2mNlH/s8vo9UekSa5BTmkba4CoO+QIYwbNw7wjvHIyxtOdnb2tt+HD6VsPdR9zfvbb638UCfQTRJh7cN00ff4ELXZobW1pM19kQZg3VlnUVhY2KIP6z9yJF85x7j99iN72FCe/jpsPBvoAdn1P41OmyQmollhawCucs7tBRQBl5rZXm087k3n3H7+z9QotkeErPxRHHNQPvu6zQCUrF7NvHnzAJg3bx7Ll6+guto7l8fvfvc7Fi8r587/jOLElzMAGL5gHrkFyTGkIOrDukKXo4qyzEw2/Qq+3iuDDT16sGTJkhZ92JIlSygsLNzWf709isIXsnj2bGCPYJsuuyZqx7A558qAMv//G81sPjAc+DxazynSGb/6x130XfwlAAtqaykqKmLYsGGUlZXR0NDApEmTyB8+lFXlayguLmbcuHF8/uqrcOyxpG5YH3DrJVbUh4WLhkO3yekHl36/jsknHEfjVmvRh9XX1/O1vcaysXpzc/81b948jj7yEA4fX9PtVZrBfTIpr6rt5qVKW2JyDJuZFQL7A++0cfcEM/vYzF40s707WMbFZva+mb3v6quj1FJJZFn5o/jB4E0M+NN9OIMfZaVRkp1NRkYGU6dO5Y477qBXr16MGDGCstWV9OnTp3moYa8JEwBI21QV5CpIQHa1D4vsv+o2KfQnumgNh6YsW8rAh9IBGFcAWBr//Oc/yczM5IQTTiAjI4O99tqLjdWbW/Rf48aNY99Bg6l+ATKeeToqbZPoi3pgM7NewAzgCudc62+7D4ARzrl9gWnAzPaW45y73zk33jk33tJ1HizZebmb13L7g5cAsHoCvDpsCEOHDmXUqFFMmjSJ6667jtdee41FixbxzjvvUFVVxWuvvQbAvIUL2Qyk1dUxsHpdcCshMdcdfVhk/5XRq1+X26Lj18Ivmlc26HHrb+AK4HV45VPIy8sjOzub/Px8HnvsMV5//XU++ugjiouLW/Zf8+ZRtXw1hf+AntdfE7X2SXRFNbCZWTpeR/dX59wzre93zlU55zb5/58FpJvZoGi2SZLXb9b/BzYCe0Pq2bBqdSVlZWUsWbKEGTNmkJOTQ27ESSZHjBjB8ccfT25uLkUTJvCGeWfUPW3tpwGuhcRSovdh8TJDVMOhwNatZDz3LDhgd+iRAcuXL6e6upply5aRm5tLbW0tFRUVjBs3jpEjR3LyySdTWFjIQQcdRGnv3qwHUpeXkqLZonEpmrNEDXgImO+cu72dxwz1H4eZHey3Z0202iTJq39uHt98+0nvl3Pg3wuMuoZGampq2LRpE1deeSUNDQ3svvvu3H777Vx22WUsWbKEQYMGsWHDBlJTU0k59VQADi1+WjNFk4D6sJ2XjBMOBvfJbPETLalfzCdlwwYYCE+WGFf8NZ3GxkZOPPFEnHN89dVXXHDBBeyxxx7N/dfWrVsxM9LS0thcU8OaPfcEYMtLL0atnRI90Txx7mHAucAnZvaRf9v1QAGAc+4+4FvAFDNrALYA33HOuSi2SZJQVv4oDk9bC7XQkAv/3gAXPpRGZlZPZs6cyamnnspzzz1HdnY21dXVTJ48md69e5OSksKAAQPYvHkzdXV1/OrVVzkOyFm8gK376li2JKA+TDoUywu7p739FgAb8uCCB1NJTe/B28Vv8sorr/Czn/2Mhx56iEmTJlFWVsbBBx9MSkoKI0eOpKysjBtuuIFbbrmFh5cu5ddA3fMzybzohzFru3SPaM4SfYvmqzK2+5i7gbuj1QaRJoc0rAbgucoUrn5sMPWN68gdPpjy8nKys7M544wzKCwsZOnSpQwYMIDq6uoWM6wmTJjAmoEDadi4kZHOMSxrM0uDXSWJMvVh4RDW4dBYhjWA9LfeBOCGz1NpSEll2JAhpKenc/3115Oamsptt93GZZddxi233EJqaipz5sxpcdH34cOHM2/DBigtJefTeegCUPFHVzqQhJdbkMNJJe8CsP/3L2D9xhruuOMOVqxYwQ9+8APWrVvHtGnTeOmll5g9ezaVlZXbnYwyNzeXRaWlLDIjBfju6P4BrpEkI004CE7rYc9YhzUA51fY3uuRzRNPPEFpaSnjx4/HzHjggQea+6+rrrqKYcOGtTyZbv/+LFq0iFfKy6kCssvLMV1iL+4osEnC++6wBnZ/61/UAbP69aOuro7p06fT2NhIfX09KSkp3Hbbbey5557Mnz+fvLw8li5d2uJklGvXriUrK4uFWd7e/lF1FTqOTSSBBRnOWpv55OOkrVwBwNqBAzn//PPp5/dlrfuvgQMHsmrVqu36L4DMnj1ZMxhvUF8j93FHgU0SWlb+KH74r4dIcfBYejq/eOAB5syZw8svv0xGRgZpaWnMmTOHuXPnMnv2bKZMmcKKFStobGxkwoQJHHDAARx99NHce++9DBs2jNl1dQCM+eyDgNdMJFyiMeEgiOHQsIS0JpUVFVxz9eW88+yzHJ6dzbJVq3jmmWfYvHkz6enp2/VflZWV1NTUbNd/5efns3nzZm7eG7gZ3ODBQa+a7CQFNkl4qV94J6YfcOqpDBo0iHHjxrF06VL69+9Pfn5+i6GDAQMGkJmZSUNDA1u3buXqq6/miy++YOzYsZSXl/OdBx4AYOg/n+JrA6M5Z0ckuuLllB6xFKag1qSkZCkjCnfjsNNO46jLL2fYsGFkZ2fTr1+/NvuvrKws0tPTt+u/Vq9ezUsvvcSjxT2YvyLglZIuUWCThFZTupgvDp4IQNGaNVRWVjJv3jwKCwtZt24dpaWlLYYOVq5cyQsvvMDtt99OQ0MDF154IYcddhhFRUXU1tay20knMbtXT6wW7q78t4ZFRRJA2KpqkQoKClm2zDtE46ijjqKsrIzq6mrWr1/fZv81c+ZMbr/9dhobG5kyZQoTJkxgwoQJ1NTUsHDhQvLyhvPk21C5enXAayY7SyUCSXj37nE8h3IffWbPZnjuQIqKith9991xztHY2EhRURHDhw9nxYoVTJw4kVNOOYW8vDzS09O5+uqrOfnkk6murubEE09kxowZ/LW+kTeBPWfcT8Y3J2i2lUgUhHV2aKwNysnh/h//hJID92dZv940NDRw+umnM2DAAJYvX95m/5Wfn096uneetqlTpzaf7uPII49kVlUVhy6EY/+9F9+5616++a1vB72K0kmqsEnCe+GLTWyecDhZwPOHrGHc8C188cUX9O7du/mkkk0nmSwuLmb27Nl8/PHHFBcX88c//pFevXqRnZ3N4MGDufLKK/nvRx6hZuRIMmthv7JPgl49EdkFYa2sRTp2/MGc1LCV7/XcwBFj6tm8eTPV1dWkpKS02X999NFHzJkzB+cckyZNAqC2tpb+/fuTOWAAAA/cfDPXXnkZlZotGjcU2CQpzD1yMgAjZkHjUq+wXFZWBsD06dN55ZVXABgwYECLY0KGDh1KUVERF110EatXr+amm27irLPOIuv44wGYVLVAw6IicSoewhoAWV470xy8sSAN5xwVFRWYWYf9V25uLlOnTmXPPfds7sN6jxkDwG4ZGRQUjKCkZGkgqyQ7T4FNEl5N6WL+dvCpbDjlDFLq4OcNDdT5sz1ra2u54IIL2HvvvRk8eDArV65scUzIkiVLeO6555ovqHzTTTdRUVHBssJCAA5dvTio1RLpsu6ecBCPl6SKm7AGOP90QtUboWEn+q+VK1fy4IMPNlfdiouL+ceHHwKw6sMPWbp0Cb2yewezUrLTFNgkKXy5aiNvH3USAG19VU2dOpXKykoGDx5MUVERo0ePpqioiL59+zJx4kRg2yysAw84gMN++UtqDA5d+QX7lb0euxURkV0WT2ENwGX1ACATaH3lsy1btnDuuedSWVnJkCFDKCoqYtSoURQVFdGnTx/y8vJaVN2y/PA3/f776devPycddxTPPv1UTNdHukaBTZLCu28vYsgn3rnTvmx135YtW3jggQcoLi7mvffeIyMjg6lTpzJ37lxqa2tb7LGWl5ezavVq7n78cbIumQLABW88jKurjuXqiCQ0TThoxQ9ZPQ1SrfVdWbz++uvMmjWLJUuW8Nhjj7FixQpmzZrFJ598wpo1a1r0YW7zZgDOOOccliz5itmzZ+tYtjihwCZJoaZ0MaM+eBuA4gF96dGjB3369KFHjx4MHjy4xfnZRo0axdlnn83YsWO59957mTBhQnPFrWfPnqSnp1FbWwvf9mZXfd0gvV92kKsnIp0Ub9U1AJftDTmnboGT9wcza+6//vSnP5GXl0d2ttcHDR8+nIKCAiZOnEhOTg733ntv86jBhAkTSG1oAOBr48cDXtVNx7LFBwU2SQqprpG+8+cC8OrmWmbNmsUrr7zCrFmz2LhxY/NeaGFhIUuWLGneIx07diypqaksX76cWbNmsWrVKubMKWbKlClseeghAF7EqF+vCpuIRIcbMID6I46iZl94f0EGvXv35t5772XZsmXsvfferFixgupqrw+qrq5m+fLlzX3YkCFDcM4xdepUli5dyrF/+hOXpqfzrh/w5s2bR0nJMgoKCoNaPekknYdNksLefbOhHtb3hMZevZk8eTLDhg1j5cqV3HjjjfTv35+ioiLy8vKor6/nyCOPZOTIkSxYsIDGxkby8vK2HcuWlcWb1dVkPvooW4Fp487EMlRhEwm7eKyuNal68RWmn59O1pp8Tjj661x44YUMGzaMtWvXctFFF3HKKacwZMiQ5t+PPvpoBgwYQGlpKXl5eZx99tkA5Jx3HrNvnsqjV1zByLumUVKyjFtvn8agnJyA17B7LVq/KegmdDtV2CSqdjusiN0OKwr81Bf7ZDcC8FGtUV3tHcNx3nnnMXr0aK655houuugirrvuOkpKSnj++edZuHAhV199NRkZGUyfPp3ly5fz2muvAbD22mvZu66OFODRr3+XZXueENBaiey8sF+SSsevta2yooLfv5hJRUUlTz31FLfffjtpaWksWLCAO++8kxdffJHS0lJmzJjBnXfeyYwZMygvL2f69OmsWLGCxx57jIqKCubNm0fZypW89O//8Ls7/8jbcz/VyXPjhAKbRE1W/iiOOSifYw7KD7opVK1eC0Bajx5c7l+P75JLLmHFihXNQwennXYaW7duZfLkyRx66KFcdtllnHfeeVx55ZXk5+czefJkdhsyhPSZMwG45bZnuOWQ71NTqlN7SPKKl1N6xHN1DWD1++9yxLAhXH/FFeTk5HDGGWdQUVHRfD7J3r1745xj8uTJ7L777pxxxhncf//9ZGVlYWbccMMNFBYW8vABB/DsfjWMycvjgAMPSrjKWiLTkKhETW5BDt8cO5j1FatZ1K+St2tyqAtoJlKa8ypsm2pqmDRpEtOmTaOsrIxbbrmFww8/nIEDB1JeXg5A3759KS0t5YknnuCCCy5g9uzZjBs3jnnz5jHt4PH0BrbsP56/VGZTVqKZVSKJrLKigpKSpRQUFAYabvZ97FH+vqCEL2pquLmykrKyMu655x6OPPJIevXqxZo1a+jfvz9r166ltLSUl156ib333ps99tiD4uJixo0bxydz5zJq/Hh6vglrGxtxO35aCREFNomqD159jsLLL+b5xkaOTkmjeMyppA0eF/N2OLy58OOGb2XvU06gR48+HHzwwaSmpjJs2DBWrVpFXV0dv/jFL/j973/P0KFDOe+88xg6dOi2cxj1789vG7cC8N43zqCspELVNZE40NXq2oynnuCKSy8mLT2dhvp67rznAU4/86xubl3nZOTmAvDk/95GRr8BFBUVMXjwYOrq6ujTpw9r167l0EMPZfz48dx1112cccYZDBo0iIEDBzb3Yft8/jkAG3cbievbN5D1kK7TkKhETcPm9Sz68SV8q7GRNOCXWxtoWPh8IOcsq09JBWBYH/jHj2vYtGENGRkZzJkzh7fffpvp06eTlpbG4MGD+clPfkJ5eTmPPPII5eXl3pCpc2yePJmchkY2Fx3Kf28dp7AmMTMmt0/QTUg6lRUVXHHpxWzZsoWNVVVs2bKFy390UWDnK2scty8AP9q9geqqtZx11lmUl5dTXFzM66+/zoMPPsirr77K4MGDqaqqYsaMGUybNo3KykqvD6uvp/ZnPwNg05T/DmQdZNeowiZRU79+Nb/0z/kDMAnYzYwVtetjPquyKbCtXgen3JlFbu5ASM1i/vz5HH300RQWFpKSksL111/P5s2bqa2t5eyzzyYnJ4eioiJOGzKEx5YuZUt2Nr+a8gdKX/4qpu0XkdgqKVlKWno6bNnSfFtaejolJUsDGRpt8ANb7ecweo8CHn/8cQry81v0YQ0NDVx99dXU19c3H8vW0NDAEUccwQVZWdxeXs76oblkXDwl5u2XXacKm0RNer8hbPb//6n/7wlbG8kauV/M29Jo3lt9UZkx+413eH7Wv1i1ahU//OEPmT17NnPnzmXOnDnU1NQwevRoevToQe9evXj66aeZO3cud++2GwAvHXQsz3+xUdU1kSiIxgzRrg6HFhQU0lBf3+K2hvr6wM5XtnpILktTUshz8MZ/X8kdd9xBSWkpU6ZMadGH1dfX06NHD3r16sWDDz7IV199xeL33+fqNWsASLv+F5CmWk08UmCTqKmorGfrmL0AWOy/034zOIcRo0bE/DQfW/3A1jMzk88++4wDDzyQhoYGqqqq+OyzzwDvjN+77747f/7znykuLmbjpk1UV1czNiuL/q+9Ri3w6KHf1kQDEV+8zBDtikE5Odx5zwPezpt/VYE773kgsIkHJctL+FOeN+N+3WWX8ctrr6W2tpaM9PQW1wrdY489uO+++9jk9185OTnYT3/KsMZGtux3ALXnfj+Q9suuU2CTqCob6ZXxDzwaGnKg36oyfr/4uZi3Y2Oad/Hk3C01/PAHP2DLli3U1tbinOP8889vPj/RsmXLKCwsZNy4cQwbNoxTTjmFm446CnOOWampfFKdpuqaSJI4/cyz+PDzxcx4/iU+/HxxYBMOwKv4/XHDej4zY61zZFdVAbC6vLz5HJFNfdikSZOa+68DDjiAr736KhdmpFJ2x92Qnh7YOsiuUWCTqPqwfwEAeVtg07ngDI577n72HhjbTuPT/gVsGrUHQx2cVlvb4r6amhoOP/xwJk6cSIN/zN28efMoKyvjnXfeYcrYsd5tg4dQtuCLmLZbRLquO869NignhwMOPAiAD+a+F9ikg0E5OVxy+VUc4xwHAEv92zMzMzn11FPZd999KSoq4pZbbqGsrIyysjLeff11pk+fzrwvv2TuXuNYbB08gYSeAptE1eK8MQBsmA/D7oaXDKwBznpzekyHRWuWf8XvxhwPwEPAORH39ejRg2nTprFgwQKGDh3K/vvvz4QJE2hoaKC+poaBH30EwMwNG3XNUJEkNOOpJ9h/r1GccfLx7L/XKJ7525OBtON7P7iQDVktj/NLSUmhuLiYBx98kCFDhnDLLbdQVFTEWbW1jD7mGA7q35+ysrK4uF5o2YaaoJsQagpsElVvbR2ES4Fe68Dq4GHvNGYUfPQaW2uqYtqWZ8d9g/mnfpd04G5gjL/zfcMNN3DcccdRVlbGunXryExr4LZTN/Ovg+soP3g8lJezNCWFhft/T9cMFUkyYTq9x6CcHO6690F69OjBHpnwJHDrVVcxduxYMjMzqaqq4uGf/5wn0lL4U2MjaevWMe3IIzn66KOjdr3Q8qraHT9IuoUCm0RVbXommwpHkQrsC2zwb++RYmytjm2Ht3z5Wq6Y9D9sPPYE+gHvDYOnvg733ngj++67L9886iju+va3+eGqSi6eCUe+Bcc2bMUZvHzRL0gvnBDT9opI8JpP7xGh6fQeQWg6ru6Ng47k28AJv/0N/ztgAMcddRQP/O53TLjySk7ZWI1LgVU33cKeT8zQ9UIThOb2StTVjtyd3l8tJg9oimiZW7eSkp0Dm2MX2mpKF/MucPN3r+O3xS/SZwmcuQTOpIY18+bR2yDjvvuaH19fNIG3jz6Vsv2LuPa55ZpsIBJF3X1Kj+66dmjYTu8BXqUt5aFHoGg3Rq9z/HjdOv4HWHrpFNJq6ykfOYr0mS+QPnIUBwTWSuluCmwSdXV9BgAwKBU2pgD1MLRgJClZfdgW4WLn3n+XsuHShzmv+Bn2Lp1P9oqFDKwFHFSP2ZPSPfblnwedwPQtud4pPEoU1kSSVdPpPS7/0UWkpqZRX1/HTbfcFvhF07cOz2PNF+vJePVf9LzuaqxiGaO21OPSIeMvj9E4MranTpLoU2CTqGiaUJCf15eGMu+adf0cpPr3b01JIbcghyUxDEJZ+aPILcihrKSC2Sm5zD70Uo49cDg/2m8w/Z74C8v2OYSPB+cz44NVlC7f0Hy+NYU1keR2+plnUbVhAz+79krS0tP5+XVX0btPn0BP8wFAdjZ1p5xG3TGTSP/Pm6S/+Tp1k0+icT/V1RKRAptETW6Btwe6KdV7m/XcCnX+pIMVSxfTsHl9zNoSOSO1KbQB/F9JBQtWj4b+h1NavAFYoKAmIi1UVlTwi59eTW1tLbX+aYEu/9FFHDnx64FX2gDIzqb+uOOpP+74oFsiUaTAJlG3eV0lALVAhn9bfYpRv351IO1pfaWCd99eBCigiUjbxjhcFwAAIABJREFUwnZd0bDQDNHYUmCTqBqT24dhPb2rDGwCmg4DrnWOtZti9/arKV3cXGVrqvw1KSupUFgTSSDdNeGgSRgnHkjy0Wk9JCpyC3I45iDvuncZ/b1JB0cYHOZntAH7HeJPOghWfl7f7QKciEiksF1XVJKTKmzS7ZoqWfvk9gRgwTfPZdD0P3B6HeBd+Ylnjj4fVsW+Ta2VLt/QfL+qbCLSntPPPIsjJ36dkpKlFBQUKqxJzKnCJlEx+WsD+NZ9v+GEL9/jsx59WfjjX8MI776qyafwckZBsA1sQywvlSUi8afpuqIKazp+LQiqsEm3yy3I4epn/sCAGU/S++V/wNP/4a5DzmD8xBMp+3wJr2XkUlb8VZcrWpHHou3M8We5BTnk5/VtcVtThS1y2aq0iUhlRYWqaRIqCmzS7U6d/zeGzvQujpxeVsYXd/6BL4/6Hl8CZA3fLiR1pK2qV25BDiu+XEyfxjqG7JPLR3R+hueYXO+4uS/L2r6OqcKaiMx46gmuuPRi0tLTaaiv5857HujWc64pDEpXKLBJt3J11Yz/+58A73C1NGDw09P5R+5RpPXst1PLam9WZ1pZMZv+/ls+TE9na0MD6QedD6lDOlxObkEOx4zPY/LSd3irz0jG5G5bZlOAVFgTkciLvTedxqM7z7kW7TAYr8o21ATdhNDTMWzSrVztej5O8/YDmvYGlqekUrbgi+bHtD4PWkdOG53JO3Pv4skhKzjmoHwOHZPNl4/9lpPra+m3eRMNdTXUvPMQrq56h8v60Wcvc8KVP+Bn910LbJsUISLSJJoXe48MgxurqtiyZQuX/+giKitif4k+iT8KbNKtLLMf92+FMv/3euApICXbO96ss1cRyMofRW7+IO5+ZRq9X36Bfa44n8vmz2JIQwWPbm1kJvAfoC9gqamk98vucHmn7NGLsb/+MQA93y/mmmf+l8PefJExQ3vvyuqKSILp7nOuVVZU8MHc95qHQaMVBmMp7BMOFq3fFHQTokKBTbqVZWSTeuKVnJOeQTXwZEoqQ//r5wwfM2qnz3d2cNYmsv/zRvPvWT//CX0++A/favDODTIaOAlwjY2kZHe87Ivff85Lj778Rx9g72umcMlXb243EUFEkld3nnNtxlNPsP9eozjj5OPZf69RfPLRR7sUBiPDn8TGJ2Wbg25CMwU26Xabe30N9+vn+P0fn2D134tpyD2kS8vpWV7a4vfPnOOXD01j3n4HshAoTk3l5YxMsg65gLoddGC5b70KwHfS4NY0+Kd5t4948E5wrkvtE5Fw2tUK0OlnnsWHny9mxvMv8eHni7t0jFlbw58/v+4qbrrlti6Fwcjwt9/Ykdx2628V3JKMJh1It6spXczqghzm9C6EZY0t7ou88PqODK1f0+L3t4DGlDR+c9bVnHXXOJ54+xP2pi8ffbKm3SHWrPxRDKjZSNbHc6kB/t4AT+Jd03QpkLtgPge++hDv9py4cyspIgltUE7OLk0yaO/6o/vstx8ffr54p2aJtjUR4uZf38D//v5m7rr3QU1aSBIKbBIVrUNZfl5fxuT24dX3Stv5i+1ZbmGL318HtjY08OGqFF6e/qF/65rWf7adffs6zMFygy1+Ma0OmAlMAdzfH2braQd0ul0iIjvS0bFwOxsG2wp/ADU1Nd06g7UzonH8mmaIdo6GRCUqakoXN4e2/Ly+fK1/Gj//+Dm+OSqr08so33cCq/K9yyNsBN5OzSDrkAuoqKxvUVHb0QSGrwbm41JgNwf98SYqpOEdAwcwPyWVrdUaWhCR7tOdx8K1Ff6axOOkBekaVdgkqspKKsjP68vPX7ib/n99mEsPKuLugu916m+/XLWRlx76O7sP6cVPH3mF3ssaScnq0xzQOnPetJrSxdQVFLF11BhSF37JWv/2CqCp2/zQOerXV2MZHc80FQnKl2VVzSd9lmB05WS33XX90abw9z9TLqSmpmU1aldmsEp8UYVNoiYyUPV+6q8ADHuvGDfzChrK53VqGXM3pTP1rQoqMwupq+j8Zaha2zxiZIvfm7rNhWY0TP6xwpokjTDNeosU5mGx1rM9n/nbk53+2+66/ujpZ57FR/O/4vpf/IqsrKxdrtp1RdhP55HoFNgkqmpKF1P7/vukRZTzJzfW0bDw+Q5Pdtv6FCA7c7LdSFn5o8jP68t70x5lTfkGFr/xAa+/v5S6Qi/APXHWNWzuvU+Xli2S7BL1fFeRwnSy20E5OVx57fV8NP+rXZrBKvFJQ6ISdXkfvtHi96/jnZ/N1a7vsLLVXUNAfTatp+dXCynvuw9lOQUsWr+JkmlPMGjBpzxeOQxKK7vleUQkPMqrahncJ3OXl9PebM+SkqWBXQd0V2ewhkmYK6thowqbRN2HrmXw+gBgayOW2fa1RbPyR3HMQfnNl47amYvFb8c5/vL4zyg6aQI93nkb8IaEHi3ZylVrhlNWWqlriIpIu7r7ygfxKl6GQxO56qvAJlH3Vd98Pu07uPn3R9Iy6FF0YYfVtZ9sWcDkj1/f5et9fm1dCT0+mgsO8q79HxZXruPLsipKl2/o8jCriMSH7ggZ3TnbU2RXaEhUYuLbX/85N//oMB598mXys0dud2qOSPsMSCH/vG9iDTDq4ZlAjy5VwbLyR3Fpxb+af0/78gv6rCyldHl9p69pKiKys7M9uzKjNBbL6qp4qa4lOlXYJCbKeg74//buOzyKqm/j+PckgYTeUUoAxfaiIF0QkBKadJCuYgUVfewNG2J7VB5QxIJi79hQUERBRBQFAZEiAgICASLSQ01IMu8fuwkbSELKzs7M7v25Li6yu7MzZ2Z3z977mzkzjJqzkz+qNCamZM67QsEXskb+MhmT5n/e1Zewb9X3hV5uwsrvsv7+F3jyqx8U1kSkwPI72rMoI0rtnJcbuf34NbeNqFZgk5DIPJFu5r/cwlLN8hZdF/sCWgYwKCOd9Onj8hxRmpO4+Lr0qXqAKtu2ZN33IbDs/SfJOJKssCYiQRfMEaVuGZ2q6pp7KLBJyOTnhLdRu7aSOUQhCigGlI+KwkrZW+Dl1du1LtvtdwDLRHFkw+8FnpeIyMlkjSgNUNgrEQRzXoWlsOYuCmwSUierbB0+5TSmmmNvy5+BNRkZuY4ozUlcfF0A5tVpztXRsBa4ElgMeY5OFXGrYF7loKgDeSR3wRxR6vToVC+GtXAeIQoKbOIyO3Ye5ZPuw7Nuj42KIeasXoW6EkHqKbWoN/ENzo+NY0qpMpiYWGLO7KmrGogEyRnlSzvdBFcJ5ohSJ0enejGsRQKNEhXXWVa/H30btuPQ3n9ZuC2amJ27Cj2vso3a89jU+Xw8dxl/rj9CqgNnJxeRyBGs64cGe15u4/YBB25ke2AzxnQFJgDRwKuWZT153OOxwNtAE2AXMMiyrI12t0vcbU16RShTEVNiB763Re4yd4EeL3HLPj71/50cXYPUHQuC20gJe+q/pDCCeSWCUF/VQNU197I1sBljooEXgE7AFmCRMWaaZVmrAia7BthjWdYZxpjBwFOALo4WwQJPaHuyY95yC2uZMq+SoJPkSkGp//K+YFyaSsQt7K6wNQfWWZa1AcAY8yHQGwjs8HoDD/v//gR43hhjLMuybG6buNCRxPVZISy/Ye34C8VnUkiTIlL/dRLr9h7QcWxhxMvVtXAfcAD2DzqoASQG3N7ivy/HaSzLSgP2AZWOn5ExZoQxZrExZrF1tGDn5BJvOZK4Pt/nScsMa/E1yxFfs1ye8xQpIFv6r9QDBT9FjYjdvBzW7OC2k+aCh0aJWpb1imVZTS3LamqKaZRfuImLr3vS3ZvHT58poVl81mkP4muWI6FZPJB75U0k1AL7r+KldVoZcZdQhzUNOCgcuwPbViA+4HZN/305TmOMiQHKcbKjzCWsBIavgoS2arWq8EX6Ap55fBgXWrs4q1pZLml8Kn3/r2q2apuqa1JI6r9ExDXsPoZtEXCmMeY0fB3bYGDocdNMA64AfgH6A3Mi5fgPKZrzymVw5r2jARhySRu6nVKd0rHFSD+vPl9c9UTWgAORQlL/5WFeGXDg9MXdtSvUfmuTkoMyH1sDm2VZacaYm4Bv8A2Lf92yrD+MMY8Aiy3Lmga8BrxjjFkH7MbXKUqEiIuvS/MLzwB8IzoLMlCg4ZrF2W6X274NgOjNm+jepgezOC14DZWIE67914qkQ7ragUt8+tGH3HrjCGKKFSPt6FEee3Ic9Rs2DLtzrgWyY3doJAw4gBCch82yrBnAjOPueyjg7yPAALvbIe41sF55rCjDxxRsZOeZf/0GwBjgVXzXHb0MeAQ4/Y0JcMmz2h0qRaL+S+wSeHF3Dh8G4I5bRlKqdGky0tOZ8OJk+g2w9wwxqq55i2cGHUh4qntKKa685Bz6X3ExtWOPFGigwGmb/wTgN3zD9/4GJgJHgDPW/knZnZtsaLGIyDE7d+zgtyWL2FnAq6jkdHF3gIMHDnD48GFuGTm8wPN0O68MNnDjCFFQYBMHxcXX5eolbxC9G8pv3MD+6zuxb9X3+X5+0qm+XZ5dAucJFMcX2g4c1OkTRMQ+n370IY3q1eWSnl1pVK8un308Jd/Pzeni7oFiihVj8+aNQWilhAsFNnGMObSHbvOmZ93+T9pRtn05His1f+fZm9Z5GABXAWOBB4Cf8b2pZ0VFkZiiE3pKeAjWQcsSPIG7NPcnJxe4KhZ4cffSpcuc8Hja0aPUqlUnyK32+Tc5RbtDPUiBTRwRF1+XzhX2UseyyPyNeSFQxkRhpeSvMjY/vSJLWnejBHAn8ChkDTP4s9NAouLKBr3dIuINdgeSnHZpFrQq1m/AIJauWs9nX33DuAkvUqJECcqULUuJEiWY8OLkoA88cDKo2bU7NFIGHEAIBh2IZDr+HGu7T2/ANqC6//bvwP6MdGJjT35i0SOJ66FWFR4YeB+92vYk9ccFVIo+QvNzqhDbpRsf/JQGqkqIiE1y2qVZmKpY5sXdGzdpRvdefRw9xYe4mwKbhMTxYa1arSqYCuX48qIejJj3JQBPm2hizuqFKZ6/K1lkjiidyOnQ6nTia5bjUONTAUgptiaIrRcRyS5zl+YtI4dnnZajqFWxzPAm4SOYhzMosIntcrpIe+aVCNZcN5rk075kzPrLmFnlAmJ27S7QvI8/Dciz/pPlJm3eoVN6iIit+g0YxEXtOniiKubkMWteGR0K7h0hCgpsEiLValUhvma5rGt+Zv7qWLk3nQHNF5BYfR+H5y8o0DyPJK7P8VJWBTmXm4iEr3+TU2y/4oGqYhIqCmxiq7j4ulSrVYWzKsfwwNIplP98PYkt2vJZo4uzQltBr3AQKLOKFhdfN9s8VF0TEQlvkTTgABTYxEaZ1a/4muV4a/Z4yk39CIAzZk3j3Hv28egFg/luUWJQdl8qoImIuI+Xdoe6nU7rIbaqVqsKtx1c6QtrsUAfwEDVpx6h5oZVTjdPRFzEji93nW9MwoUCm9giq7pWoywdXxvnu3OA/19n382hv3/jSNtEJLIotEl+uHnAAWiXqNgg87g1gI4HNxOz5k8oBztawNLlUKkcNAEqrv8T4p1tq4hIfuzcsYPly5diLEP98xtqoEE+2Lk71AvHrwX7CiUKbBI0gafvyDxtx8VzPwBgdR1ocAscTYfawEaAP1dCOx1/JiL2K8qI0U8/+pCbrruao/4T5RYrXpwXXn6dfgMGBbOJInnSLlEJqvGrPmDhW9fQ4Mg2zqpWljq/LwTgppW+sAaQ+Zsjbu9e0g8W7LxrIiJ22bljB78tWZTteqA7d+zglpHDs8IawNHUVG6+4dp8Xzc0EmmwQfApsEnQnL/te65Y+hVltm3h/LsGsPbHr0itVBmAUgHvtN7+/3+MimLb2rWhb6iIBI0Xdk3lx6cffUijenW5pGdXGtWry2cfTwF81wyNio4+Yfqo6OgCXTfUSTqG7+TcfvwaKLBJkBSvUoXhc9/IekMNzUhnx/P3s6VcJQDqpB+bdoj//w9MFFGldByIiBzjRGVm544d3HrjCA4fPsz+5GQOHz7MLSOHs3PHDmrVqkNGevoJz8lITy/wdUMjhd2vYbj8SCgoBTYJioyDO6htjt2OAupFR7GxUlUAHikHJaN8gw06A0eAVQPuJCqurAOtFZFIlFulafPmjcQUK5btvphixdi8eSOVq1ThuZdepVjA48WKF+e5l171xMCDUFfXtCvUPhp0IEFxdO9BJhNFazIA+Av4NN2iTNtBdPvtJ8qtXsWes+BwMvAPbLr0WlZyngYciDhgRdIh6lcr6XQzXKNWrTqkBRyjBpB29GhWBS3zmqFeGyWqXaHOCfYIUVCFTYLEFC/FZy2u5auoKH43cHt0DBW73caGtFI8cttzUAaKr4Vy/wCl4bWu1zrdZBGJQDmFmMpVqjDhxcmUKFGCMmXLUqJECSa8ODlbKKtcpQodEjrTvmMnT4Q1J6i6Zi9V2CRo0mKqcUnzO7FS9mJiy1Oi9Hn8+vM6EmtVYeNdn3Hloi9IK1aMKed14pdV+5xuroi4VNK+I1QrFxfSZWZW0TZv3kitWnU8H8rCtbpmx/FrXhhwAApsEmSmeClM8VKA7/xqmRdlX1yrCtu73kDiln1wlKBcP1QkkqxNSuasajrmMxhyOydb5SpVPB/UnKLqmv0U2MRWgaEtafOObPeLSHhYt/cAZ5Qv7XQzCqQoJ9IVcYICm9hO4UxE3ChcQ1u4jgz1yuk87BhwAApsEqEyL6OlMCkibhQYugoSKsM1rIkCm0SwjCPJZOzfioktn3XcnYi4Q6gGHnihypZTeNu5Y0fWAImMWGeObQyHsOaVAQegwCYRKuXvnzm88FWIiob0o8Sc1ZuYqg2cbpaIOCAzELk9uIGvrV9N/YgH77iRmGLFSDt6lEfHv0j3PgOcbpqtvLI71E46D5tEHCv1oC+spafC0cOQkUba2i+wUg863TSRkPFSZSFU3HIqjLzasXvXDh6840aOHDnMgf3JHDlymAdvH8nuXaG9EH04VNe8RoFNIo6VstdXWQtkon33i0ih2FEBcSIU/Juc4prglpOtiZtzvIzW1sTNDrVIAtk14AAU2CQCmdjykJ79MjRY6b77RURwb3CrEV8rx8to1YivFbI2hDpIa3eojwKbRBxTvBQxZ/WGqBiIjoWoGGLO6q2BByJygszgFqoAd7JlVKxUhUfHv0hcXAlKlylLXFwJHh3/IhUr6YS/BeW1wwI06EAiUkzVBkSXr5t1GS2FNfECXe3AeW4YVdq9zwBatmnH1sTN1IivpbAWIRTYJGIFXkZLRNzJieuKnowbQlvFSlUcCWraHeoc7RIVEZGgiKQv12DvInXrMXOSf3YOOAAFNhGRiOWVY3jcfAqJYIQsrwS1cKqueeW9H0iBTURERMTlFNhERESKwCsVsqIIp+qaVymwiYiI67l5t2hRRELYk+BQYBMRkaCJ1MqIgpd32HH8mt0DDkCBTUTEU0LxxSCFE66hTbtD3UGBTUQkgnlptFy47hYVyQ8FNhERkSApSJXNCxW5cKuueekHyvEU2EREJKjs/NL1QpXNC0FMgidUhykosImIiATZya5c4IVQF27VNa9TYBMREU/xQpUtkxeCmXiDApuIiEgIeSHEhWN1zcvHr4ECm4iI5wT7mBk7vsjs/gL2WpUtM6R5IayJOymwiYiIhIBXwlo4VtfCgQKbiIh4kpeqbOIsu3aHhvJE1gpsIiJiC1VOvEfVNfdSYBMREc9SlU0ihQKbiIh4fgSdeE+oqmvh8t5WYBMR8SBdBP4YVdnECaH+DCqwiYiIbUJVRVFoK7pQbkNV1wpOgU1ERERCRgMNCkeBTUREAO9XI1RlKzxtO/dTYBMREVuFsqKi4FFw4bgrFOz9AeLEMaQKbCIiIhFKAdc7FNhERDxKI0VzphCSPzpJrrfYEtiMMWONMauNMcuNMVONMeVzmW6jMWaFMeZ3Y8xiO9oiIlJQkdyH2bUbKdRf1gpteQv3sBZuu0PBvgrbLOA8y7IaAGuBUXlM296yrIaWZTW1qS0iIgWlPiwMKLTlTNvFm2wJbJZlfWtZVpr/5gKgph3LERGxg/qw8KFwkl0kbA+vj3bOTSiOYbsa+DqXxyzgW2PMEmPMiLxmYowZYYxZbIxZbB09GPRGiojkosh9WGD/lXpgry2N9AKnjmGKhJByMkn7jji2HXTsWnDEFPaJxpjZwKk5PHS/ZVlf+Ke5H0gD3stlNq0ty9pqjKkKzDLGrLYsa15OE1qW9QrwCkBUmRpWYdstIgKh7cMC+69ytf8vqP3X2qRkzqpWNpizZEXSIepXKxnUeTotad8RqpWLc7oZjoikwGp3dc3JgT6FDmyWZXXM63FjzJVADyDBsqwcOyjLsrb6///XGDMVaA7kGNhERILJqT4sNTW9UO2Voouk0OaWkKbqWvDYNUq0K3A30MuyrBzjrjGmlDGmTObfQGdgpR3tEREpCPVh9nH6C9wtQcYOmbs9w3kdI1mhK2wn8TwQi28XAcACy7KuN8ZUB161LKsbcAow1f94DPC+ZVkzbWqPiEhBqA8LY5mBxuvVNrcHs3A6lQc4f95DWwKbZVln5HL/NqCb/+8NwPl2LF9EpCjUh9l7HNu6vQc4o3xpW+ZdEF4Obm4PaxJ8utKBiIjHOf3L3+u0GzH4wq265gYKbCIiEnJOH8uWE68cA+b29oUjN/woUmATEQmxxC37nG6CnIRCUeGpumYPBTYREcmR3V+EbqyyBXJjaHNjmyQ0FNhERERyoYBUMOFYXXPD7lBQYBMRCQtu+VIREXsosImISK4ifbcouKfK5pZ25CYcq2tuosAmIuIADTzwFqfDktPLj1RuqlwrsImIiKO8UGUTd4m06hoosImIhA27qgGR+OWYE6eqXF6oril020+BTUREHKcv/Jx5IayFWqh+QLhpdygosImIiEt4IbQpQIlTFNhEROSktFs09LwSDkMZtCO1ugYKbCIijrFjpKgbv2gKQlU2kZwpsImIiLiMV0JhOFbX3EqBTUREXMULVTYJX26tUiuwiYhIvkR6hSOQnRUwr1TXQknvPQU2ERFH6Ti2nKnK5n56jUJLgU1ERFwpEgOBqmsnCmV1zc0/dhTYREQk37Rr6phghysvhbVIDNNOU2ATERHXUjBwn3AdGerm6hpAjNMNEBGR4FublMxZ1co63YygWLf3AGeUL+10M3KUtO8I1crFFWh6kcJQhU1ExGF2DDywk3aLFkzSviNZ/7xO1TXnKLCJiIjruXnXaE5BLJxCWiY3vwaRQIFNRCRM2Vk1cKLK5ubAEBjQwimkZQr1tlcV90QKbCIi4hluDm3hKty3ud27Q4N1yIMCm4iIC3jtODZwrgoS7gHCTZzY1qqu5UyBTUQkjHnhYOrCUGizXyRsYy99PhTYRESk0FQNCU9OhbVwez8Fs3KuwCYiIp4UCRUgJ0RKWPNSdQ0U2EREXMOLx7GBs1URhbbg0vZ0LwU2EZEw57VKQkEpZBTdur0HHN2O4VhdC/YPMAU2ERHxPIW2wtO28wYFNhERKTI3HCzudJXIayJ1e3m14qzAJiLiInYdx+bVL6nCiMQQUhBuC2puCPvBZsfnWIFNRESCwk1fvG4KJG7htqAmBRPjdANERETskBlOzihf2uGWOMvNIS0cBxvYRRU2EREJGjdV2TK5ObDYLZLX3Sl2HdagwCYi4jI6ji34InF3YKSt78l4/f2vwCYiIkHlxipbpkgJbl5YRze/T9xIgU1ERCKOFwKNBE+oqmt2Xq1EgU1EJIKE6ovLC9WTcA1tXlgvL7w/3EaBTUTEhbx6XVERtwmH6hoosImIiE28UEXxQjWqIMJtfeQYBTYRkQgTytFyXghtElqhfE94fWRoIAU2ERGX0m5RKQhV15wTis+qApuISARSle0YBZ3QUXWt8HRpKhEREQ9T4HRWqCrhqrCJiIjtVGWTUAq36hoosImIuJqdv95D/aXm9tDmRV4KmuH4+ofyOFMFNhEREbwVfsBb7dWxa0WnwCYiEsFUZRPxBgU2ERGXC7fTe7g5tHmlauWVdkL4VtdC/blUYBMRiXDhugtJnOfmcO41CmwiIhJy+iIvPC9V10IpnKtroMAmIuIJdn9BOFFlc2toc3MgcnPbjufW19erFNhERESO48Zg5MY25SbUYS3cq2ugwCYiIn6qskkw6DW1hwKbiIg4yq1f8G6qaLmpLXlx4rWMhOoa2BjYjDEPG2O2GmN+9//rlst0XY0xa4wx64wx99rVHhGR/HJr/xVup/cI5NbQ5gYKa7mLpBHOdlfYnrEsq6H/34zjHzTGRAMvABcD9YAhxph6NrdJRCQ/IrL/iqQvwPxwOiw5vfz8UuC2n9O7RJsD6yzL2mBZVirwIdDb4TaJiOSH+q8g05f+Mev2HvBMWHNKqH9cOF3hjrF5/jcZY4YBi4E7LMvac9zjNYDEgNtbgAtympExZgQwAoDYcsFvqYhIdrb0XzFlq9rQ1OBam5TMWdXKOrLsFUmHqF+tpCPLzs26vQc4o3zpoM0r3Choh0aRKmzGmNnGmJU5/OsNvATUBRoCScC4oizLsqxXLMtqallWU1OsVFFmJSLiWP8VU7JoPzid/pUfCm4MAEUJWpnVMoW14Im06hoUscJmWVbH/ExnjJkMfJnDQ1uB+IDbNf33iYjYSv1X3pyssoF7K23i48ZQHe7sHCVaLeBmX2BlDpMtAs40xpxmjCkODAam2dUmEZH8UP/lDgoF7hRpr4sbqmtg76CDp40xK4wxy4H2wG0AxpjqxpgZAJZlpQE3Ad8AfwIfWZb1h41tEhHJD1f3X6H6AnHDiNFICwdu54bXww3vSyfYNujAsqzLc7l/G9At4PYM4IQh8yIiTlH/5S5u3D0aidwQ1kLNLdU1cP60HiIi4mJuqWZEYlhwE21/5ymwiYh4kJt++YeKQoMz3LTdI+UyVDm6etKZAAAgAElEQVRRYBMRkTy5pcoG7goP4W5F0iFtbxdRYBMR8Si3VQBCRSHCfm7cxpFcXQMFNhERyQc3VdnAnYEiHKiq5l52X5pKRERslLhlH/E1I/NyfZnBQiNIi87tIc1tPxicoAqbiIjki1u/NN0eNtxO2y87N+4OBQU2EREpADeHNgWPgtM28w4FNhERj3NrRcAJCiD555VtFemDDTIpsImISIG4tcqWyStBRKQgFNhERMKAmysD4j5eCbWqrh2jwCYiIgXm9iqbSLhRYBMRCRNurxCEklcqSE7wyrZRdS07BTYRESkUVdlEQkeBTUQkjIS6UuDm0OaVSlIoeWWbqLp2IgU2ERGRCKCw5m0KbCIiIXQ0Nc32ZajKdoxXQordtB1y5pXqGiiwiYhIELg5tEU6L4U1vY9yp8AmIiJhzUuBJdi8tO6hDmteqq6BApuISFhy4stI1REpLIW1k1NgExEJsaTNO5xugm3cGtq8VGkKlkhcZzcK1uddgU1EJEx5sYpgp0gKMF5aV1XX8keBTUREgsqtVTZxn3B/rwSzmq7AJiLigFDtFnWqmuDWL2IvVZ4Kyyvr6MR7xKvVNVBgExERm7g1tIUzhTX3CPaPMgU2ERGHhHuVza28EmoKKlzXK1i8/jlQYBMREdtEQiXFDbwU1iLhPWHHjzEFNhGRCOBkdcGNX9BeCjjhxI3vBa9QYBMRcVA4n5MtkBu/qMMltHllPZx8D4TyB4tdn2kFNhGRCOH0MTxuDG1ep7AWORTYREQcFilVNnDfF7dXAk9OvNJ2p1/zcKiugQKbiEhEcbrK5kZeCT6BvNJmp8NaOFFgExFxAVXZJL8U1vIvnH6gxDjdABERCa3ELfuIr1nO0TasTUrmrGplHW1DoBVJh6hfraTTzciRVwJaIDeEtVCz+0eXKmwiIuIIt32puy0YrUg65Lo25YfbXtdwoQqbiIhLJG3eQbVaVUKyLDdU2cCdlbZMwa64eTF8FZSbwlq4DDbIpMAmIiKOcltoy5RbwMpPkIuEcHY8N4W1cKTAJiLiIpFYZQP3hracRGIYy4sbg1o4DTbIpGPYRETEFdz4xS9502sWuhHeCmwiIi4TylN8uK0SsTYpWSHAI9z6OrntPR0sCmwiIhHOjV9wbg0DolAdKJQ/rhTYRERcKJJOpJsbhQL3cftr4sYfH8GiwCYiIq79onN7QIgkei2yC/WPKgU2ERGXUpXNR0HBWV7ZBerWHx3BosAmIiKAu7/wvBAYJHI48WNKgU1ERLIotEkgr2zzcLuqQU4U2EREXEy7RcUpXglrkUKBTUREslGVTbwkEqproMAmIuJ6TnxJuDm0if28Eowj6X2qwCYiIp7ilTDhVV7ZvqEOa04fnqDAJiLiAaqyiTjH6bAGCmwiIpIHt4Y2r1SBvMYr29Wt70s7KbCJiHiEG37lS/hSWMuZWz53CmwiIpInt1YzvBIwRIJBgU1ExEOc+rWv0BbevLId3fo+DIUYpxsgIiIizvBKUANnwppbdoeCKmwiIp6jKlt2XgodUjhufe+FkgKbiIjkm744w4dXgq5T7zk3VddAgU1ExJOc/DJxY2jzSvhwC20v77HlGDZjzBTgbP/N8sBey7Ia5jDdRmA/kA6kWZbV1I72iIgUhPowb1qblMxZ1co63QzX81JYU3XtGFsCm2VZgzL/NsaMA/La4u0ty9ppRztERArDK31Y0uYdVKtVxYlFk7hlH/E1yzmybIkMbqzkOsnWXaLGGAMMBD6wczkiInZQH5Y3N36heql65ASvbB83vrecZvcxbG2A7ZZl/ZXL4xbwrTFmiTFmRF4zMsaMMMYsNsYsto4eDHpDRURyEJQ+LLD/yjgS3C9Mp3fd6IvVOxTW8sfpz1RuCr1L1BgzGzg1h4futyzrC//fQ8j7l2lry7K2GmOqArOMMasty5qX04SWZb0CvAIQVaaGVdh2i4hAaPuwwP4rptLpYdd/uW33qI5lO5HCmvcVOrBZltUxr8eNMTFAP6BJHvPY6v//X2PMVKA5kGNgExEJpnDqw5w8ls2tFNqOUVjLP7dW18DeXaIdgdWWZW3J6UFjTCljTJnMv4HOwEob2yMiUhDqwwrADV+2ciKvhDU5OTsD22CO25VgjKlujJnhv3kK8JMxZhnwK/CVZVkzbWyPiEhBeKoPc0NlwG2hLdLDipfW3w3vHTd8hvJi27VELcu6Mof7tgHd/H9vAM63a/kiIkWhPqxwdDybOyishR9d6UBEJEy4pULgti9gL4WXSOOW94pbPjt5UWATEZGgc8sXcSTySkDVe6RgFNhERMKImyoFbvpC9kqIKYq1ScmeWU83vTe8QoFNRCTMKLTlzCthpjC8sm6JW/a56j0B7vq85MW2QQciIiLgroEI4TQIwSshLZPbgprXKLCJiIQht51M122hDfBkcPNaSMvk1rDmleoaKLCJiEiIuCm0Qfbw44XwprAW2RTYRETClNuqbOC+0JYp1OHNq+GroNwc1rxUXQMFNhGRsKbQVnA5hamihLhICWfHU1jzOZK4PijzUWCTLOVLF+f+oY2oW60sUcbp1kikybBgfVIyj7+/lL0HUp1ujm0yUlOcboIrBDu0lYqBAWcW49SSURhb+q8ivG41YoPXDA9IT8vw/dGwsrMNyUN6esWgzzPDsvh7RwpPz9zC3sPpQZ+/AptkuX9oI5qddxoxxUthU48nkjvLomKlg9w/FO56ZaHTrQkrbqyyQXBD24Azi3FurarEli6HUf/lmNTU4AeVYDuammbLfC3LolKl/dwN3Dd1ExC86hroPGwSoG61sgpr4hxjiCleiroeOPi7qILZiXtdsHabnVoySmHNYZEc1gCMMUTHleG0KvZUVBXYJEuUQWFNnGWMdsfbxM0HWAcjtBmDwpqDvBDWQsEYQ5T/fRjsH2YKbCIiDnCiyubm0Cbe5ZWwZmd1LRQU2MRV2jQ50+kmZOmZcAF79+zOc5rpU6fw1KP3F2r+N4+4nP3JuVcWHn3gTjasW1uoeQdKTU1h5FWDGNq3E9/O+KLI88s0d/bMbO2b9NxYFv48L2jzl8ji5hGF+dXotFOdbkKWDk3PZfeunXlO89mH7/LIqDsKNf/hQy8hed/eXMPa6Lv+w/q1qws170CpKSlcO6Q3/bu0Zua0zwo9n+PD2vffzmDDX2uybr/0zH9ZOP+HQs//eHb8INOgAwlL6enpREdHO92MPD33yjt5Pv7gY/8LynLWrFoJwPtTZwVlfpnmfjeT1u06cvoZZwFw/c13BXX+Yg+3DkCQY7zQf01+/1Mg9+ramLETg7KcP/9YDsAn3/wUlPllmjtrBm06dOb0M88G4IbbRgV1/nZQYJNcNfm/GkGd35I/txZo+rdfe4nZM6eTmppK+45due4/dwJwx01Xsz1pG6kpKQwedg39Bl4G+Kpz/QZexq+//Mg9Dz7Bf0ZcyuDLr+GnubOJjY1j3AtvUKlyFfbs3sUTD9/L9iRfe24fNYaGjZuxd89u7r/zRnZs/4f6DZtgWVaO7Zr22RTefGUipcuW46yz61GseHGAXOd76OBBxj7+AKtWLscYw/AbbyOhc3d6JlzAO598TWxsHPfedh3/bk8iPT2Da2+4hc7dejNiWH9uvftB6p13PjO/+pw3Xp6IZVm0bpvAzXfen7XOOa1jpt27dvLgPTezZ/cuhvbtxNMTJnPDVYN455OvKV+hIqtWLuPZpx/llbc/4eXnx/FP0la2Jm5me9JWhgy7lsGXXwPAl59/zLtvvIwxcMZZ/0f/IcOY9/0sflu0gNcnTeDpCZN59aVnad2uIx279ODXX37k2bGPkp6WTr365zNq9H8pXjyWngkX0KPPAOZ9P4u0tDSeevZl6px+RoHeF+HkSOJ64uLrhny5bg1twRw1etYpZXJ9bPvYCewbdjUA5d5+nVPuuiXXaddu31+o5b/6wrN8PW0qqSkpdOrWk5vv9n1mR14xmH+2bSUl5QjDrr2BQf52NDrtVAYNu4qf583loSfHMXxwP4YNv4HvZ80kLi6OF9+aQuWqVdm9cwej776VbVu3AHDfo0/SpHlL9uzexR3XX832f7bRsEnzXPuvTz94h1eeG0eZsuU559zzKB7rO0A+t/kePHiAx+67k5W/LwVjuOnOUXTp0ZsOTc/lgy+/JzYujjtvuIrtSVvJSM/gulvuomuvflw1oDt3PvAY557fiBmff8Lk58eDZdEmoTO33zcGgOZn1+Cyq6/nh+9mEhdXggmvvU/lKlWz2rpr5w5G3TyCPbt30b9La555+R2uHdKLD7+aS4WKlfhj2VL+99gDvPHxV7w4/r8kbd3Cls0bSdq2hcuvuYFLr74egKkfvsfbr76AMYYzz6nHgKFX8cN3M1ny68+8+sJ4xr7wBq8+P442HTrT8eJeLJw/j2efHE16Whr1GjTivkfGUjw2lu4XNaJHv0H8OOcb0o6m8dTzr3Na3RP3CllpRwv1njkZ7RIVV1ow/wcSN/3NWx99xftTv+XPP5bz26IFADz02Dje/XQmb38ygw/feT1rt+XhQ4c4r0EjPvh8Ng2bNOfwoUPUP78xH3w+m0ZNWzD14/cA+N8TD3HpFcN5++MZPD1hMo896AuCk198hoaNm/PRl9/TvmNX/kk6MWDu/Hc7Lz//P157/wtee3cqG9Yf2yWY23xffelZSpcuw5Rp3/HhF7NpdkGrbPP8+afvqVL1VD74fDYfTZ/DhW3aZ3t8x7//MHHc40x68yPen/otq1b+ztzZM7PWOad1zFSxUmUeeHQsjZo05/2ps6hZq06e233ThnU8/+p7vPXRV0x+YTxpR4+y/q81vD5pApPe/IgPPp/Nnfc9wvmNmnFR+07cfNcDJ8w3JeUIY+67jf+Oe4kp074jPS2NTz54O+vxchUq8t5n39B/8OW88/qkPNsjkSccdo3+NPc7Nm1Yzycz5/LFnJ/5Y9lSFv3iqxA98eyLfDbrRz79Zh7vvDqJPbt3AXDo0EEaNG7GtO9/oekFF3Lo0EHOb+K/3bIVH737JgCPP3APV1x3I59+8wMTX3uXB26/CYAX/vckjS9oyVfzFtGpW0+2bUk8oV3/bv+HiWOf4IPps3h/+resC9hlmdt8Xxz/FKXLlGP6DwuZPncBLVpfBJAVCOfPnU3VU07l02/nM/W7X2jVLiH7Mv9J4pn/PsxrU6bx8Tc/8sey3/hu5pcAHD50kAaNm/Lpt/NpcsGFfPr+W9meW6lyFcaMfY7GzVvyyTc/EV/ntDy3+9/r/+Lldz/jg+lzeOmZpzh69CirV67k1RfG8fK7U5ny1Q/c9eATnN+kOW0TunLrvQ/z4Zdzia99bL4pKUd4+O6bePK5yXz09Y+kp6Xx8ftvZD1eoUIl3p/2Pf0vvYp3Xn0+z/YEmypskquCVsSCacH8H1gw/wcu7dcZgEOHDrF50980btaCD999nbmzvwZg+z/bSNz0N+UrVCQ6OpoOnbtnzaNYseK0adcJgP87tz4Lf/4RgF9/+ZG/A4LWwQMHOHTwIEsXL+Dp514FoHW7jpQtV/6Edq1cvpQmzVpSoWIlADpf3ItNGzfkOd9ff/mRJ8a9mHX/8fM948xzePapR3juf4/Tpl1HGjW9INvjf6xYlm2ZXXv047fFC2jXsWuu61hYrdomULx4LMWLx1KhUmV27drBooXzSejag/IVfCeaLFe+Qp7z2PT3eqrXqEXt03yVox59BvDx+28x9IrhAHTodLG/vQ34ftbXRWpvOFCVzR75rYztG3Z1VrUtWObP/Y75P8yhT4Lvx9mhgwfZuGE9zVq25p1XJzFrxnQAkrZtZdOG9VSoWIno6Gi69OidNY9ixYvTvrPvs3Jeg0bM/2EOAD/P+z5b0Dqwfz8HDx5g0YL5PP+67wdbu05dc/ycLl+yiOYXtqGivwrfrfclbNywLs/5/jJvLuNfPhZYjp/vmeecy/8efYDxT4ymbUIXmlxwYbbHVy77jWYtW1Gxku8kut37DmDJwp9J6NqDYsWL07ZjVwDq1W/ILz9+n78NnIuLOnSmeGwsxWNjqVi5Ctu3JbHolx/peHHvrP7zpP3XhnVUj69F7dN8lf8e/Qbz0buvcelVvmpdhy49APi/885nzjdfnvB8O0+MrcAmrmRZFleOuIlLBl2e7f7Fv/7Mr7/8yBsfTCeuRAlGDOtPiv8DUjw2NttxHzHFYrKG+UdFR5Oe7jvoNCMjgzc+nE5sbFxQ21zY+dY+rS7vfjqT+fPm8NKEp2nWojXDb7wtX8/NbR3zEh0TQ0aG70zkqSnZO5fixY+dPygqKpr09OCP/spcRlR0NGk2zF/yz62hze2XrjoZy7IYcfMdDD4uCC6c/yM/z/ueKV99R4mSJbm878Wk+D+DsbFx2fqvYjHFcu6/rAw+mjGH2Lgg918FmG/gcWt1Tj+Dj2bMY9733zJx7GNc0LotN9x6T76WGXPcOqal5aP/ij7Wf6WkHMn2WGD/FR2Vv/6woDIPgYmOirJl/nnRLlFxpZat2zHtsykcOngQgH+3J7F7104O7N9P2bLliCtRgo0b1rFy2W8FnneLVm2Z8u6xX4xr/vQdlN+oaQtmfjkVgPnz5pC8b+8Jzz2vQSN+W7SAvXt2k3b0KLMDfmHlNt8LLryIj99/M+v+4+e7499/iCtRgm69LuHyq69n9Z8rsi+zfsOsZaanp/PtV5/TuFnLAq93purVa2YdyPvdt1+ddPpmF7Tiu5lfZu163rd3DwAlS5XOen0C1T6tLtu2JZK46W8AZkz7lMbNWhS6vZFAJ9INL63bd+TT99/h4MEDAGxP2sauHTvYn7yPcuXKU6JkSdb/tYbflywq+LzbJvDOa8cOJfhzpe+z3KxFK6Z/9jEAP3z3bdbnNFCDJs1Y9MtP7Nm9i6NHjzJz+tSTzvfCi9rz3uuTs+7f8e+ubPP8958k4kqUoGe/QVx1/c38uWJZtsfrN2zC4gXz2bN7F+np6cz44lOatsh+WEhBVI+vxaoVvwMwa8a0XKfL3GXbrGUbZn/9RY79V+brE6j26WeQtCWRzf49JzM+/4gmzS88YTonKLCJK7Vo1Zau3ftw1ZBeDOqVwD23jODQwQNc2KYd6enp9O/elonjn+C88xsXeN533f8of65cxuDeHRnQox2fTvGN1hw+8jaWLl7IwB7t+X7W15xa7cRBF5WrnsKIm+7g6iG9uPrSPpx2+pknne81199CcvI+BvbswJA+HVm88Ods81y3djVXDOzB0L6dmPziM1xz/S0nLPOmO+7juisGMKRPJ845twHtEroUeL0zDb/xdsY98RCX9784XyPR6p55NldffzMjhvVnSJ+OPPOU74Dhzt16887rLzG0X2e2bN6YNX1sbByjHx/PPbddx6BeCZioKC4ZfHkucxenufXcbF4+lq11uwR69BvA4G4J9Gx7ATdfcxkHD+7nog6dSEtP5+LWTRj32GgaNmlW4Hnf//jTrPx9KT3btaBbm6Z88NZrANx4570sXjCf7hc1Y9ZX06heM/6E51Y95VRuunMUg7snMKRHJ+r6R0jmNd8bbr+b5H176HFRc3q2a8GiX7Kfuuev1asY0rMD/bu05qVnnuK640aLVznlVG6992GuHtiT/p1bU69+Qzp06U5h3XDrPTw1+l4GdWuXa/8VeAqPumedwzUjb2f4kF4M6t6W8Y8/CECXHn15e/LzDOnZPuvHJfj7r6cmcs9/rmHgxW0wUVH0H3plvtpm93WCTW4jSdwsqkwNK7bxdU43I+x8/nAnKler7XQzJMLtTNpEn4ezn4LkyLzRSyzLaupQk4Iqr/7LiWPZMrlx12hBdouOahpL9TqRO+I4FLxwglwnT46bGdjs6sNUYRMREVdW2rxcZQs3XghrTrK7ugYKbCIirqFj2U6k0Oas1NR0z4Q1r1966mQU2EREXMTJ0ObGKptIfrhhV6jdFNhERCSLG0ObqmzOUGXNXRTYRERcxuldowptorDmPgpsIiJyAjeGNgkNr4Q1NwjV7lBQYBMX2Z+8L9sJZoPthznf8ubkvK/9tm1rIgN7dgBg1cpljPWfs0ck1JyusrmRm6tsyfv28t4bk08+YSF9N/MrXnluXJ7TbNm8iR4XNQdgxe+/8dh9d+U5fU68FNYiqboGCmxSRHt27+KPFb9nXcC4KPbvT+bjgIuEB8rPJUtOpm2Hzlw5/KZ8T1/vvPO56/5H8z19MNoo4iZurLIFM7Tt3rmD5UuXsHtn0dczed8+PsglsAWjb0jo2p0RN9+R7+nrN2zMA0+Mzff0aWlpCmsFFMrqGiiwSRF8O+MLBvRoy/gnHmBAj7Z8O+OLIs1v4rgn2Jq4iaF9OzFh7KMs/vVnrr2sL7eNvJKBPdplq34BvPP6JF5+3veLc8vmjfxn+KVcdklXrr2sb9YFjQNNnzqFpx69H4CHR93K2Mcf5OohvejdqWW2S0xlWvzrz9x6/TAADh86xJj7b2fYwO4M7deZud99kzXP20ZeyfVXDmDkVYPY+e92hl/Wj6F9OzGwZweWLl5YpG0ikc0NVTY3hrZgmPH5p3Rr3ZTHR91Gt9ZNmfH5p0Wa37jHRrN509/07nAhT425n4Xzf2Ror85cf/lAurdpmq36BfDaixOYOPYJADZv3MA1g/vSr1MbhvbqzPq/1pww/88+fJdHRvkC2703X8dj993F4O4JJDSrz8zpn58w/cL5P3Ldpf0B38XnR91yA/27tKNPQitmf/1l1jyvv3wgw/p158r+Pdix/R+uuORi+ndpTd+Eliw57qosbuGGsOYEXfxdCmXP7l08/dj9/DB3Lg0aNGD58uW0bdeOZi1aU6FipULN8z933Mf6v9bw/lTfGaIX//ozq1etYMq0OdSoWYttWxNzfe7jo+9m1OgnqVXndFYu+40nHxnFpDc/znN5O3ds59X3PmfjhnXcfuNVdOzSI9dpX395As0uaMXox8ezP3kfVwzszgUt2wCwZtUKPvh8NuXKV+DdNybRonVbrrn+FtLT0zly5HAhtoSIu7jtIvFFvTj87p07eOTe25k79/us/qtdu/a0aH0RFSsXbj3veGAMf61exRdzfCFn4fwfWbV8GdN/WEh87Tps2bwp1+c+eMfNjBn7LHVOP4NlSxYx5p7befuzvK/z+++///D+9Fls+GstNwwbRNeefXKddtKzY2nRpi3/nfASyfv2MqBrOy68qD0Aq1YsY9r3v1CyVDneenkirdomMOLmO3391+FDhdgS9nJLWAt1dQ0U2KSQtm1NpHbtOjRo0ACABg0aUKtWbbZtTSx0YMvJufUbUqNmrTynOXTwIMuXLuHe245d7ic1NfWk826X0JWoqChOP+Osk+4SWTB/Hj/MmcW7b/gujpySmsI/SVsB38Xdy5WvAEC98xryyAN3kJaWRruELpz9f+edtB0ieTmSuN7RS1ZlCqfQtiVxM7Xr1M7ef9WuxZbEzYUObDmp36gJ8bXr5DnNwYMHWLp4IbdcOyzrvtR8hIGOXXsQFRXFGWefw84d/+Y57U8/zGHONzN4/cXnAEhJSSHJ/wO4Vdv2lCzl247nnt+Yh+68ibS0o3To0p1zzm1w0naEklvCmlMU2KRQqteIZ9OmjSxfvjzrF+rmzZuoXuPECw4XRYkSJbP+jo6OxrIysm6npBwBIMPKoHSZslmVufwqVrx41t8WeV9T17Isnn7uFeqclv1ahSuX/0ZcQBsbN2vB5Hc+5ae53zHmvtsYesUIevQZUKB2ibiV20JbYdWMr8WmjZuy91+bNlMzPu8fhwVVsuSxviEmJoaMwP7riC+UWRkZlC1bLqsyl1/FY2OP3TjZNcEti+def5fTzzgr293LfltM8dhjbWzaohVvfjKDeXO+5YHbRzJs+I306j+kQO2yi5vCmhPVNdAxbFJIFSpW4u4HHqdtu3ac37ARbdu14+4HHi9Sda1kyVIcOngg18crVarC7l072btnN6mpKfw0dzYApUuXoUbNeGbPnA74wtXa1X8Uuh05adm6LVPefQPL3zGuXrUyx+mStm6hYqUq9B14Kb37D2XNqhVBbYdEJjccy+ZGhR2AULFyFR56cjzt2rWnYaNGtGvXnoeeHF+k6lqp0qU5mFf/VaUqu3buYM/uXaSmpDB31tcAlC5Tlpq1avP1tKmAr/9a/Udw+43W7RJ497WXs/qvVSuWAZCWlpFtum1bNlOpSlX6D72CfkOG8efKZUFtR2G5Kaw5SRU2KbTO3XrTrEVrtm1NpHqN+CLvCi1foSLnN27GwJ4daHVRe1q1Tcj2eEyxYgwfeRtXDOpB1aqnUuf0Y9WuR8c+z5NjRvHapAmkpaXR+eLenHXOuUVqT6BrbriVcf8dzeDeHbEyMqheM55nJ504onXJop95+7VJxBSLoWTJUox5ckLQ2iCRTbtGc1bYXaPd+lxCi9YXsSXRV1kr6q7QChUr0bhZC3pc1Jw2CZ1o17FrtseLFSvGjbffy4Cu7TmlWjVOP/NYtWvsi6/x8D238dIzT5OWdpRuffpzzrn1i9SeQCNvv4cnHryHXu1akGFlUDO+NhPfmHLCdIt++Yk3J03091+lefzZl4LWhsJyW1hzqroGYKyTlVJdKKpMDSu28XUnn1AK5POHO1G5Wm2nmyERbmfSJvo8nH339pF5o5dYltXUoSYFVVH6LzcEtkxuCm0AL/apSvU6Z5x8QvHM6TvcFtYgf4HNrj5Mu0RFRDzCTbtGw/V0H+FOYa3wnKyugQKbiIgUkptCW/pxx2PJiRTWCs/psAYKbCIinuKmKhu4K7R5JZA4wSvbxo1hzS0U2EREPEahLXdeCSah5JVt4taw5obqGiiwiYhIECi0uZNXtoVbw5qbKLCJiHiQ26ps4L7Q5pWwEmyZ6+6V9XdzWHNLdQ0U2MRF9ifv4+P333S6GVmOb0/gxedXrVzG2McfdKhlIj4KbScXqqsqMjIAABFlSURBVOCSvG8v770x2fbl5Ob4kJa8by8fvvVq1uNbEzfRN6ElAH8sW8p/H7rbkXYGOpqaprBWAApsUiR7du/ijxW/s2f3riLPa//+ZD7+4MST0QKkpYX+Q51Xe+qddz533f9ovuflRPtFnOK20AY5B7fdO3ewfOmSk15LOD+S9+3jg1wCm52f/9wC6f7kfUx5+7Ucn3Pu+Y0Y9cjT+V6GHe13c1BzKwU2KbSZX06lR0Jzbrx6MD0SmvPNV58XaX4Txz3B1sRNDO3biQljH2Xxrz9z7WV9uW3klQzs0S5bhQvgndcn8fLz4wDYsnkj/xl+KZdd0pVrL+vLxg3rTpj/vr17uOOmqxncuyNXDurBX2tWAfDy8+N45/VJWdMN7NmBbVsTT2hPoMW//syt1/su1nz40CHG3H87wwZ2Z2i/zsz97hsApk+dwm0jr+T6Kwcw8qpB7Px3O8Mv68fQvp0Y2LMDSxcvLNL2EgF3VtnAnaENjgWczz+aQvsm53LVgF60b3IuX079uEjzHffYaDZv+pveHS7kqTH3s3D+jwzt1ZnrLx9I9zZN2bJ5Ez0uap41/WsvTmDi2CcA2LxxA9cM7ku/Tm0Y2qsz6/9ac8L89+7ZzcgrBtOzXQsGXtye1X+sJDU1nRfH/5c3J03Mmq5vQku2Jm7i2f+OIXHT3/Tv0ppxj2XfG7Dolx+58cpBABw6dJAH77iRIT06MKBrG+Z88xUAn3/0Hv+5ajDXDOrJtYN7sWP7P1xxycX079KavgktWbKwYNc+DeSFsOa26hro0lRSSHt27+LRB+8k5cgRUvBdhP2RB+6gecs2hb5E1X/uuI/1f63Juoj74l9/ZvWqFUyZNocaNWuxbWtirs99fPTdjBr9JLXqnM7KZb/x5COjmPRm9g745efHcfb/nce4519n0YKfGH3vLXleMP749uS2/NdfnkCzC1ox+vHx7E/exxUDu3NByzYArFm1gg8+n0258hV4941JtGjdlmuuv4X09HSOHDlcoO0jkhu3XLbqeG67jFWm3bt2Mvqu//g+g/7P4f233siFbdoV+hJVdzwwhr9Wr8q6iPvC+T+yavkypv+wkPjaddiyeVOuz33wjpsZM/ZZ6px+BsuWLGLMPbfz9me+4JRZPXv2ycc4u159np38Hgvn/8DdNw3nk29+ynWet44azbo1f2ZNszUx5+VPfm4czVtdxKPjXiB5316G9kygRZt2AKxauZzPvp1PuQoVeOvlibRqm8CIm+/09V+HDxV4G4HCWlEosEmhbNuaSLGYYllhDSAmphjbtiYW+Zqigc6t35AaNWvlOc2hgwdZvnQJ99527HI/qampJ0z3+2+/8vQE3y6LZi1as2/vHg4c2F/kNi6YP48f5szi3Td8VbqU1BT+SdoKwAUXXkS58hUAqHdeQx554A7S0tJol9CFs//vvCIvW0QKblviZmKKFcsKawAxxWLYuGEjpctWzLqvePHoIi2nfqMmxNeuk+2+zACWnmaRnp7B3j37WLpoITdfc3nANKkn7OZcumgB419+B4ALWrVl7549HNifXKT2Afw8bw5zZ33NWy/7qnQpKSn8s3ULAC3btKNcBV//de75jXnozptISztKhy7dOefcBgVelsJa0SiwSaFUrxHP0bSj2e5LSztK9RrxQV1OiRIls/6Ojo7Gso6dzTwlxRcWM6wMSpcpm2e1LC/R0dFkZBybb2oBP7CWZfH0c69Q57Ts1zFcufw34gLa37hZCya/8yk/zf2OMffdxtArRtCjz4BCtVnkeKqy5V/1+FqkHT2u/zqaRvX47D8OCzJY4ejRdCzr2HPS0tKJK1Ey63aGZUjPyKH/ysigTLlyeVbL8hIdHUNGDv1ifllYjH/lbU6re2a2+5cvXUyJkqWybjdt0Yo3P5nBvDnf8sDtIxk2/EZ69R+S7+V4Iay5nY5hk0KpULESDz02jti4OEqVLkNsXBwPPTauSNW1kiVLcejggVwfr1SpCrt37WTvnt2kpqbw09zZAJQuXYYaNeOZPXM64AtQa1f/ccLzGzW5gJnTPwN8u1vLVahI6dJlqF4jntWrVgCw+o8VbNuyOV/tydSydVumvPsGlmX55rFqZY7TJW3dQsVKVeg78FJ69x/KGv8yRYJFx7PlT8VKlRnzv4nExZWgdJkyxMWVYMz/JlKxUuVCz7NU6TIcPJh7xb5S5ars3rnD13+lpDDPf6xr6TJlqRFfm2++9B0DbFlWjn1D4+Yt+WrqR4DvGLQKFStSukxZqsfX4s8VywBYteL3rF2fJ2tPplYXJfD+G69k9V9/rlyW43TbtmymUpWq9B96Bf2GDMt1upx4Jay5uboGqrBJEXTp3ofmLduwbWsi1WvEF3lXaPkKFTm/cTMG9uxAq4va06ptQrbHY4oVY/jI27hiUA+qVj2VOqcfq2g9OvZ5nhwzitcmTSAtLY3OF/fmrHPOzfb8ETfeziMP3MHg3h2Ji4tjzH+fBaBD52589cUnDOzRnnPPb0StOqfn2J4BQ6/Msd3X3HAr4/47msG9O2JlZFC9ZjzPTjpxdOmSRT/z9muTiCkWQ8mSpRjz5ISibC4RKYJuvfvTonU7tiVupnp8rSKFNfD1Fw2btqBvQktat+/IRQmdsz1erFgxrr/1bob06EDVU6tRJ6Ci9eRzr/DYfXfwynNjSUtLo2uvfpxdr36254+8bRQP3nkj/TpdSIkSJXnsmZcA6HRxL6Z/8iF9ElpQv2ETavv7xePbM/iKa3Ns93W33MVTY0bRr1MrLCuDGvG1eeHNKSdMt+iXn3hz0kR//1Wax599KV/bRWEteExmqvaSqDI1rNjG1518QimQzx/uROVqtZ1uhkS4nUmb6PNw9t3bR+aNXmJZVlOHmhRUoei/3LhrFLB11+jEHpU5pZY71zuSRWJgs6sP0y5REZEw49ZdoxJZIjGs2UmBTUREQsJtx7KJfRTWgk+BTbJkWIAHd5FLGLEs3/tQisytVTa7QlsG4MVDfMQ5toQ1G/swBTbJsj4pmbTUgwpt4gzLIi31IOuTin5uKfFxa2izQ+K+NI4eSlZocwGvVNeCzuY+TKNEJcvj7y/l/qFQt1pZoozTrZFIk2H5fjQ8/v5Sp5sSVtx4fjY7zs32yq/7GAHEl9upSoSD0tMzTj6RC1jHnUc0GOzuwxTYJMveA6nc9Yqubyki9gt2aEtOsfjfj3uDNj8pOK8co+jVynORfogYYwYYY/4wxmQYY5oe99goY8w6Y8waY0yXXJ5/mjFmoX+6KcaY4kVpj4hIQURKH+bVLyiRYPPyZ6GoleOVQD9gXuCdxph6wGDgXKAr8KIxJqeLsj0FPGNZ1hnAHuCaIrZHRKQgIqYPc+MXlVcqMnJyXngt3fgZKIgiBTbLsv60LGtNDg/1Bj60LCvFsqy/gXVA88AJjDEG6AB84r/rLaBPUdojIlIQkdaHef0LS9wnafMOT4S1cGDXMWw1gAUBt7f47wtUCdhrWVZaHtNkMcaMAEb4b6YcmTc65ws22q8ysNOhZWv5zi4/ktfd6eWfHeLlBbUPc1H/xRGXfYZCfNRZJH+GnF5+JK87BKEPO2lgM8bMBk7N4aH7Lcv6oqgNyC/Lsl4BXvG3abFTl6lxctlavl77SF2+MWZxEZ7reB/mlv7L6eVH8rpH+vIjed0zl1/UeZw0sFmW1bEQ890KxAfcrum/L9AuoLwxJsb/CzWnaUREikR9mIiEA7tOVzMNGGyMiTXGnAacCfwaOIHlO7vh90B//11XACGr2ImI5EF9mIi4SlFP69HXGLMFaAl8ZYz5BsCyrD+Aj4BVwEzgRsuy0v3PmWGMqe6fxT3A7caYdfiOB3ktn4t+pSjtLiInl63l67WP1OXbsmyH+jC9jlp+JC4/ktc9KMs3uoyHiIiIiLvpCh4iIiIiLqfAJiIiIuJyrg1sbrlkjP+5v/v/bTTG/J7LdBuNMSv80xV5+G7AfB82xmwNaEO3XKbr6t8e64wx9wZx+WONMauNMcuNMVONMeVzmS5o63+ydfEfCD7F//hCY0ydoizvuHnHG2O+N8as8r//bslhmnbGmH0Br8lDwVq+f/55bkvj85x//ZcbYxoHablnB6zT78aYZGPMrcdNE9R1N8a8boz51xizMuC+isaYWcaYv/z/V8jluVf4p/nLGHNFUdoRbG7pv/zzcqwPi8T+yz8/R/qwSO6//PMO7z7MsixX/gP+D9+J5uYCTQPurwcsA2KB04D1QHQOz/8IGOz/exJwQxDaNA54KJfHNgKVbdgODwN3nmSaaP92OB0o7t8+9YK0/M5AjP/vp4Cn7Fz//KwLMBKY5P97MDAliNu7GtDY/3cZYG0Oy28HfBns1zq/2xLoBnwNGKAFsNCGNkQD/wC17Vx34CKgMbAy4L6ngXv9f9+b03sOqAhs8P9fwf93Bbtek0Ksl+v6L/+8QtqHRVr/ld/1sasPU/91wusQVn2YaytslssuGeOf50Dgg6LMxybNgXWWZW2wLCsV+BDfdioyy7K+tY6dyX0BvnNN2Sk/69Ib32sKvtc4wf/6FJllWUmWZf3m/3s/8Cd5XIHDIb2Bty2fBfjOBVYtyMtIANZblrUpyPPNxrKsecDu4+4OfH1z++x2AWZZlrXbsqw9wCx81/x0Bbf1XwHzdWMfFk79FzjYh6n/yibs+jDXBrY81AASA24X+bJX+dQG2G5Z1l+5PG4B3xpjlhjfZWiC6SZ/6fj1XEqr+dkmwXA1vl9GOQnW+udnXbKm8b/G+/C95kHl303RCFiYw8MtjTHLjDFfG2PODfKiT7YtQ/F6Dyb3L3Y71x3gFMuykvx//wOcksM0oXrPB5tT/Rc414dFUv8FLunDIrz/gjDsw+y6lmi+GBdcMqYA7RhC3r9MW1uWtdUYUxWYZYxZ7U/eRVo+8BLwKL4PwaP4dmlcnZ/55ld+1t8Ycz+QBryXy2wKvf5uZIwpDXwK3GpZVvJxD/+Gr8x+wH9Mzuf4TqwaLI5uS+M7XqoXMCqHh+1e92wsy7KMMa4895Bb+q8CtMWWPkz9l/tEcv8F4duHORrYLJdcMuZk7TDGxAD9gCZ5zGOr//9/jTFT8ZXF8/Umze92MMZMBr7M4aH8bJNCL98YcyXQA0iw/Dvfc5hHodf/OPlZl8xptvhfm3L4XvOgMMYUw9fZvWdZ1mfHPx7YAVqWNcMY86IxprJlWUG5sHA+tmWRXu98uBj4zbKs7Tm0zdZ199tujKlmWVaSf1fJvzlMsxXfsSiZauI7Xixk3NJ/5actdvZh6r9O4Ggfpv4LCNM+zIu7RJ24ZExHYLVlWVtyetAYU8oYUybzb3wHuq7MadqCOm7fft9c5rsIONP4RpYVx1cKnhak5XcF7gZ6WZZ1KJdpgrn++VmXafheU/C9xnNy64gLyn8cyWvAn5Zljc9lmlMzjzcxxjTH9zkKVmebn205DRhmfFoA+wLK78GQayXGznUPEPj65vbZ/QbobIyp4N/N1tl/n9s5dckrR/qwCOy/wME+TP1XlvDswyybRooU9R++D/cWIAXYDnwT8Nj9+EbhrAEuDrh/BlDd//fp+DrCdcDHQGwR2vImcP1x91UHZgQsa5n/3x/4SvHB2g7vACuA5f43QbXjl++/3Q3fiKD1QV7+Onz72X/3/5t0/PKDvf45rQvwCL5OFyDO/5qu87/GpwdxfVvj232zPGCduwHXZ74HgJv867kM34HMFwZx+Tluy+OWb4AX/NtnBQGjEIOw/FL4Oq9yAffZtu74OtUk4Kj/834NvmN5vgP+AmYDFf3TNgVeDXju1f73wDrgqmBtgyBtR9f0X/75vYkDfRgR2H/ltj6EoA8jwvsv//zDtg/TpalEREREXM6Lu0RFREREIooCm4iIiIjLKbCJiIiIuJwCm4iIiIjLKbCJiIiIuJwCm4iIiIjLKbCJiIiIuNz/A0I1IjNmXcvfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZEvgdRqGGcPz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}