ARIMA_&_LSTM_on_Time_Series_Data_with_Fourier_Filtering.ipynb

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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.4"
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    "colab": {
      "name": "ARIMA & LSTM on Time Series Data with Fourier Filtering",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVUDDknvUxtU",
        "colab_type": "text"
      },
      "source": [
        "# An Attempt to Predict Stock Market Prices"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "En1fVJOVUxtf",
        "colab_type": "text"
      },
      "source": [
        "### **Data Description:** \n",
        "\n",
        "In this mission, we'll apply ARIMA on TCS stock market prices and LSTM on S&P500 time series data. \n",
        "\n",
        "**a) TCS**\n",
        "\n",
        "The TCS dataset is taken from Official NSE website.\n",
        "Timeline of Data recording : 1-1-2015 to 31-12-2015.\n",
        "Shape of Dataset: 248 x 15\n",
        "\n",
        "Column Descriptors:\n",
        "Date: date on which data is recorded\n",
        "Symbol: NSE symbol of the stock\n",
        "Series: Series of that stock | EQ - Equity\n",
        "\n",
        "**b) S&P 500**\n",
        "\n",
        "The S&P500 dataset is stored in sphist.csv. Each row in the file contains a **daily record of the price of the S&P500 Index from 1950 to 2015**. \n",
        "\n",
        "The columns of the dataset are:\n",
        "\n",
        "    Date -- The date of the record.\n",
        "    Open -- The opening price of the day (when trading starts).\n",
        "    High -- The highest trade price during the day.\n",
        "    Low -- The lowest trade price during the day.\n",
        "    Close -- The closing price for the day (when trading is finished).\n",
        "    Volume -- The number of shares traded.\n",
        "    Adj Close -- The daily closing price, adjusted for corporate actions.\n",
        "\n",
        "**We'll be using both datasets to develop a predictive model. For S&P 500, we'll train the model with data from 1950-2012, and try to make predictions from 2013-2015. For TCS, we will have a train and test data split.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzpdL-dc1yel",
        "colab_type": "code",
        "outputId": "cd38d83c-c13d-4a61-c99c-9f47845c482c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "import tensorflow as tf \n",
        "tf.test.gpu_device_name() "
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
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          "metadata": {
            "tags": []
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/device:GPU:0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JVA5x5eGUxuJ",
        "colab_type": "text"
      },
      "source": [
        "# Loading TCS Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8mGWXTZUxuN",
        "colab_type": "code",
        "outputId": "fd7ad34c-bb48-4299-ef58-1cf0527c9928",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "# Importing necessary modules\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Importing the statistics module\n",
        "from statistics import mean\n",
        "from statistics import median\n",
        "\n",
        "# To load the input data\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datetime import datetime\n",
        "from matplotlib import pyplot\n",
        "\n",
        "# used to format headings \n",
        "bold = '\\033[1m'\n",
        "end = '\\033[0m'\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "# Read the s&p 500 input data set and sorting based on date.\n",
        "sp500 = pd.read_csv(\"gdrive/My Drive/tseries/IT/tcs_stock.csv\", index_col=False)\n",
        "\n",
        "sp500[\"Date\"] = pd.to_datetime(sp500[\"Date\"])\n",
        "sp_sorted = sp500.sort_values(\"Date\")\n",
        "\n",
        "print(sp_sorted.head(3))\n",
        "# print(sp_sorted.tail(3))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n",
            "        Date Symbol Series  ...  Trades  Deliverable Volume  %Deliverble\n",
            "0 2015-01-01    TCS     EQ  ...    8002               52870       0.2883\n",
            "1 2015-01-02    TCS     EQ  ...   27585              309350       0.6683\n",
            "2 2015-01-05    TCS     EQ  ...   43234              456728       0.5207\n",
            "\n",
            "[3 rows x 15 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BasVF_TPUxuc",
        "colab_type": "text"
      },
      "source": [
        "From the sorted data, we can see that data since Jan 2015 is there in the input dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_92efK0jd8w8",
        "colab_type": "code",
        "outputId": "5ba039d5-8663-4ee5-c589-2a59bf7c0875",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "# Visualization Module\n",
        "\n",
        "close = sp_sorted['Close'][:175]\n",
        "x = np.arange(0, len(close))\n",
        "print(len(close))\n",
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "plt.plot(x, close, label=\"Close Price\")\n",
        "\n",
        "plt.legend()\n",
        "plt.title(\"TCS Closing Price vs Date\", fontsize=20)\n",
        "plt.ylabel(\"Close Price\", fontsize=15)\n",
        "plt.xlabel(\"Date\", fontsize=15)\n",
        "plt.show()\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "175\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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W19ezatWqZE9DREREREQkKcxs+2DjWkoqIiIiIiJynFNgKCIiIiIicpxTYCgiIiIiInKc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\nIpLj/j/h5pDYy+nUGgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ggzb3uQZx7FC",
        "colab_type": "text"
      },
      "source": [
        "To estimate the lag lets do a auto correlation plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZoJxo152feJd",
        "colab_type": "code",
        "outputId": "fe92b05f-ca26-4662-a71f-1f0f809a4f35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "from pandas.plotting import autocorrelation_plot\n",
        "from matplotlib import pyplot\n",
        "\n",
        "# close = np.real(filtered_sig)\n",
        "\n",
        "autocorrelation_plot(close)\n",
        "pyplot.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7mw7rH+Yv1T1HC+ocm+h0SbnuOVqg2w+fatGxiwhkPBBvO8U1qjqn5sO/8tSg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\nIFZAjDHGBMQKiDHGmIBYATHGGBOQ/w9puMlhniacwgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3MCEZLSef6u-",
        "colab_type": "text"
      },
      "source": [
        "There is a positive correlation with the first 500 lags. Hence, a reasonable value for autocorrelation lag seems to be around 250."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M7KH4UXQgehJ",
        "colab_type": "code",
        "outputId": "0108fc5f-49d9-460b-b9c9-0d4b50ed1f66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        }
      },
      "source": [
        "# https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/\n",
        "import warnings\n",
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "from sklearn.metrics import mean_squared_error\n",
        " \n",
        "X = close.values\n",
        "size = int(len(X) * 0.8)\n",
        "train, test = X[0:size], X[size:len(X)]\n",
        "history = [x for x in train]\n",
        "predictions = list()\n",
        "print(len(test))\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "for t in range(len(test)):\n",
        "\tmodel = ARIMA(history, order=(8,0,2))\n",
        "\tmodel_fit = model.fit(disp=0)\n",
        "\toutput = model_fit.forecast()\n",
        "\tyhat = output[0]\n",
        "\tpredictions.append(yhat)\n",
        "\tobs = test[t]\n",
        "\thistory.append(obs)\n",
        "\t# print('predicted=%f, expected=%f' % (yhat, obs))\n",
        "error = mean_squared_error(test, predictions)\n",
        "print('Test MSE: %.3f' % error)\n",
        "# plot\n",
        "pyplot.plot(test)\n",
        "pyplot.plot(predictions, color='red')\n",
        "pyplot.show()\n",
        "\n",
        "train_data = train.tolist()\n",
        "for i in range(len(predictions)):\n",
        "  train_data.append(predictions[i][0])\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "35\n",
            "Test MSE: 1180.102\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FUAeVNc87z5RMfuqpkxudNGA+v5bRo6FDBwbs2MCALkHsO1rU8M8h2iQJ+qJVS87MJyLY\nn5B2HpRHSEioPohrTyl45hlIT4d33jHHVqyAsDAYMqThGmzj5QVnnQU//khkiD8ZOcUN/xyiTZKg\nL1qEzzYe4PoPN6HrWJMmKdPDQVytISmpdmrH3tSp5uvZZ6GgwPT0p0xp8Hz+CTNmQHo6IwoPkXG8\nuM6vXQhHJOiLFuH7XZmsTM4+USLZExWVVew5XODZoqyMDMjPdx30weT2Dx+Ge+81vf7GSO3YWEsy\njEreQnF5JcdlFo9oABL0RYuw2xrsv9txyON79h0tpKyyiv51mbnjLL1jM24cnH8+vP+++XNjBv2e\nPaFfP3rHmn1zM45Likf8fhL0RbOXW1ROpnXK4rKdmR7f52rjlFqczdxx5KmnzPeICFMnpzHNmEGn\nLevxqSwnI0cGc8XvJ0FfNHtJmWaTkbMGdiblcAG/ZTlJ8fznPzB79ok/7s7Mx6Kgb+dA90+SmGgW\nWXXu7P7aUaPgoYdMisfTnbXqa/p0vIoKOS0jiXTp6YsGIEFfNHu2aZd/PqsfAN856+3Pnw///S/k\nmE3NkzLz6RnWHn8fL/dPYpu542kQf/55E/gb25QpaC8vHl31Ee2Wfw9VVe7vEcIFCfqi2UvOzCfY\n35vhUSHE9OjgOOhXVcH27WYWzq8mB747K9+zQVwwPX1PUjtNLTgY9fbbdCs4ylVP3Qn9+8Mrr5x4\nYxOiriToi2bPFEwLRinFOUMjSDyUZzYwt7d3r5lGCbBmDUVlFew/VuTZIG52Nhw50jyDPsBNN/HQ\nP5fw/JwnzDjC/fdDVBTcfvvJAWghPCRBXzRrNQumnTusKwDf7awxiyc+3nwPCYHVq/ktqwCtPa+h\nD7ifuXMKRYQFMb/3eFi71pR6vvxyUxJiyBCziOurryT1IzwiQV80aycKplmDd2RoACOiQ/m+Zoon\nPt4skrr2Wti8mZT9Zs/Z31VzpxmJ7BBATlE5haUVZiD5gw/MOoFnnzXloC++GF5//VQ3U7QAEvRF\ns5bsYNrluUMj2J6eS9oxuymM8fEm33322VBeTsGaX/H3sdC9Yzv3T5KYCO3bQ3R0Qze/wUSGBgBw\n0L4cQ1gYPPKISW1FR8PmzaeodaIlkaAvmjXbXHv73Py5QyMA+GGXXW8/Pt7UtJ8wAZSi3cZf6dc5\nCC+LB7NxEhLMRimNVU6hAUR1MEE/3VENHm9vs63j/v1N3CrREjXf/+VCYGbgdAvxJyTgZMG0Hp3a\nM7hrMMtsq3Nzc2HfPhP0O3SAoUOJ3rnFs0VZ0Hxn7tiJDDWfWJyuyu3eHQ4caMIWiZZKgr5o1pIy\n8+nvIHifNyyCbQdyOJRbbKZqwondq0rGjmdoWiIDOwW4f4K8PJMbb8aDuACdg/zw8VLOq212725e\nh6MtHYWw4zboK6WilVIrlVIJSqldSql77M7drZRKsh5/0Xqsp1KqWCkVZ/162+760UqpHUqpFKXU\na8rtpqWiLSu3Fkxz1GO3zeL5YWfmyZk71qB/YGgMgWXFnHbcg3RHUpL53sx7+haLomtIgOuefmUl\nHPK8NpFom7w9uKYCeEBrvU0pFQRsVUotB7oAM4ERWutSpZT9+vU9WuuRDh7rLeBmYCOwDDgH+O53\nvQLRau07YgqmOZp22Sc8kP5dAlm2M5Pr4uOhUyfo1g2A2B5D6Q/0S94GTHf9JC1g5o5NZGiA855+\njx7m+4EDZg6/EE647elrrQ9prbdZf84HEoFI4Hbgea11qfXcYVePo5TqCgRrrTdoUxh8HnDR72y/\naMVs5RecLbA6d2hXNu87Rvm2ONPLt35wjKtsR1qHrgRu3uD+SRISwMcH+vRpsHY3lsgObnr6IHl9\n4VadcvpKqZ7AKExPvT8wSSm1USm1Sil1ut2lvZRSsdbjk6zHIoF0u2vSrcccPc8tSqktSqkt2dnZ\ndWmiaEWSM/PxsiinBdPOHRaBqqxE7dp5IrVju2/PwFGotWtNWQZXEhPNVE9vTz70nlqRoQFk5ZdQ\nVuFgEZZtuqkEfeGGx0FfKRUILATu1VrnYVJDHYGxwIPAF9Yc/SGgu9Z6FHA/8JlSyoMVMidpredq\nrWO01jHh4eF1uVW0IkmZ+fQKa4+ft+OCaQO6BDGJHLxLS04Efa01u7MKOD56rCmtYMvZO9MCZu7Y\nRHYIQGvIzC2pfTIoyMxckqAv3PAo6CulfDAB/1Ot9SLr4XRgkTY2AVVAmNa6VGt9FEBrvRXYg/lU\nkAHYJxujrMeEcGh3Vr7LDc2VUlzifQSA3P5m9k368WIKSivwmmz9gLl6tfMnKCmB1NRmP3PHJirU\nNlffSV19mbYpPODJ7B0FvA8kaq1ftjv1FTDFek1/wBc4opQKV0p5WY/3BvoBqVrrQ0CeUmqs9TFn\nA0sa9NWIVqOorIIDx4rczrUfl59BucWLH6tCAfNGARB5+jDo0gXWrHF+8+7dpl5NC+rpg5u5+rJA\nS7jhSU9/AnAtMNVuGuZ5wAdAb6XUTmA+MMc6QDsZ2K6UigO+BG7TWh+zPtYdwHtACuYTgMzcEQ7t\nthZMcxf0w1KTONC5O9/uPg6cXMHbLyIYJk92HfRbQKE1e11DAlAK13P1pacv3HA7eqW1Xgs4m09/\njYPrF2JSQY4eawswtC4NFG2TbU9cd1UyVXw8RYNOY13KEXKLytmdlU9kaADB/j4waRL873+m92ub\n0mgvIcGUXujfvzFeQoPz9bbQOcjPdU8/J8csOAuu0zCaaENkRa5olpIy8wnw8SK6g4uCaUePQkYG\nHcfFUF6p+Skxi+TMk2WYmWTN6zvr7Scmmpo1/v4N2/hG5HKuvm3aZlpa0zVItDgS9EWzlJyVR/8u\ngVhcFUyzrsTtduZYuoX48/X2g+zJLjg5r3/YMFNf31XQbyGpHZtungR9SfEIFyToi2apWo/dGWvQ\nVyNHcvbQCH5Jzqa8Up9MCXl5maqbjoJ+RQUkJ7eYQVybyA4BHMopoarKwfoD+1W5QjghQV80O0cK\nSjlSUOZ+q8P4eLN9YOfOnGetxQM1Bn8nTTI9+pqL/FJToby8xQX9qNAAyiqryC4orX0yIsIsMpOg\nL1yQoC+anZODuG4GI2019IHR3TsQHuSHl0XRO7z9yWtsef21a6vf28Jm7tjYpm2mOxrM9fIydXck\n6AsXJOiLZifJwW5ZtZSXm9k31qBvsSjmjOvB9EFdqq/gjYkxA7U1Uzy2DcUHDmzIpje6E3X1Zdqm\nqKfmX3BEtDm7s/Lp2N6XsEBf5xclJUFZWbWaO3dN7Vf7Oj8/OOOM2kE/MREiI1vc1EaPFmi5Wpsg\n2jzp6YtmJynTlF9wud1CjRr6Lk2aBNu2QX7+yWMtcOYOQKCfNyEBPtX3yrUnm6kINyToi2alqkqb\nmjuezNzx84MBA9w/6OTJptzC+vW2J2lRhdZqcjtXXzZTES5I0BfNSvrxYorKKt2uxCU+HoYM8awk\n8rhxZpDTlvZIT4fCwhbZ0wepqy9+Hwn6olk5sXGKJ0Hfk9QOQGAgjBp1suJmC9otyxFbT1872itA\ngr5wQ4K+aFaSM/MA57tlAZCZCYcPex70weT1N26E0tKTM3daaNCP6hBAQWkFecUVtU9K0BduSNAX\nzUpSZj7RHQMI9HORtqnLIK7N5Mkm4G/ZYnr6YWHQQjfoiXRVV182UxFuSNAXzYq7jVOA+gX9iRPN\n9zVrTE+/hfbywcNpmxL0hRMS9EWzUVZRRWp2oWczd6KjTY/WU2FhJtCvXt2iZ+7AyZ6+LNAS9SFB\nXzQbe7ILqKjSDKhD+YU6mTwZVqyAY8da7MwdgI7tffH3scgOWqJeJOiLZsO21aHL9E5JiVmNW5+g\nP2mSyetDi+7pK6Xcz9W3baYiRA0S9EWzkZSZj49XjYJpNSUkmMVH9Q36Ni046ANEdmgnm6mIepGg\nL5qN5Mx8+oQH4uPl4r9lfQZxbbp3N1+BgaYaZQsWGSoLtET9SME10WwkZ+Yzuoebwdn4eGjXDvr0\nqd+TXHONCYau6vq0AJGh/hwtLKO4rJIAX6/qJyXoCxck6ItmIb+knIycYq46o7vrC+PjzTaIXl6u\nr3PmmWfqd18zc2LaZk4xfTsHVj/Ztav5+5GgLxyQ9I5oFmyDuC5r7mhd/5k7rYzLuvqymYpwQYK+\naBY82jglPR2OH5egjyzQEvUnQV80C7sz8wn08z6x8Mgh2yDu8OFN06hmrIt1a8gMR6UYwGySLnP1\nhQMS9EWzkJSZT/8ugZ5tnCJBH28vCxHB/q57+rKZinBAgr445bTWJGfle7YSt1evFrfFYWOJ7CCb\nqYi6k6AvTrnD+aXkFJUzoEug6wtlELeaKJmrL+pBgr445ZJPDOK66MEXFsJvv0nQtxPZIYDMvBIq\nKqtqn5Sgf8LOjFzuXxBHSbmkukCCvmgGkj2ZubNzp5myKUH/hMjQAKo0ZOaV1D4ZHW2+t/GgfzCn\nmBs+2syi2Ay27j9+qpvTLLgN+kqpaKXUSqVUglJql1LqHrtzdyulkqzHX7Q7/ohSKkUplayUOtvu\n+DnWYylKqYcb/uWIlmhF0mF6hbWnY3tf5xf9nvILrZTLaZvBwRAa2qaDfkFpBTd8tJmiMtPD35GR\n6/ziJUvg9NOhoKCJWnfqeNLTrwAe0FoPBsYCdyqlBiulpgAzgRFa6yHAPwGUUoOBWcAQ4BzgTaWU\nl1LKC3gDOBcYDFxpvVa0YenHi1ifepSLR0W6vnDTJhPIevZskna1BFJX37mKyiru/mwbvx0u4M2r\nT6N7x3bsSHcS9DMy4Prrza5q69c3bUNPAbdBX2t9SGu9zfpzPpAIRAK3A89rrUut5w5bb5kJzNda\nl2qt9wIpwBjrV4rWOlVrXQbMt14r2rAlcQcBXAf98nL46iu44AKwSEbSpluoLNBy5ulvE1mZnM2T\nM4cwuX84wyJD2J6RU/vCqioT8EtLzf+tdeuavrFNrE6/QUqpnsAoYCPQH5iklNqolFqllDrdelkk\nYF/TNd16zNlxR89zi1Jqi1JqS3Z2dl2aKFoQrTULt6UzpldHoju2c37h8uVw9CjMmtV0jWsB/H28\nCAv0c93Tb4MLtD5at5ePft3HzZN6cfUZPQAYFhVC2rFijheWVb/49dfN/6+XXzY1nX799RS0uGl5\nHPSVUoHAQuBerXUeplhbR0zK50HgC+VyZY3ntNZztdYxWuuY8Ba6ebVwLz49l9TsQi45zU1qZ/58\nk58++2zX17VBLufq9+jR5jZTWZGUxZPfJDB9cBcePvfkngnDI0OAGnn9hAR46CE4/3y45RYYPx42\nbGj1C9o8CvpKKR9MwP9Ua73IejgdWKSNTUAVEAZkANF2t0dZjzk7LtqoRdvS8fO2cO6wrs4vKi6G\nxYvhkkvA18VAbxvl0Vz9NrKZyq6Dudz1WSxDuoXw71kj8bKc7IMOqRn0y8rg6qvN3grvvWdKbU+Y\nAPn5ZqZYK+bJ7B0FvA8kaq1ftjv1FTDFek1/wBc4AiwFZiml/JRSvYB+wCZgM9BPKdVLKeWLGexd\n2pAvRrQcZRVVLI0/yIwhEQT7+zi/cNkyM6PiyiubrnEtiK2nr7WufbINzdXPyivhxo+2EBLgw3tz\nYmjnW71qfEiAD73C2rM93ZrXf+IJiIszAT8iwhwbP958b+V5fU96+hOAa4GpSqk469d5wAdAb6XU\nTsyg7Bxrr38X8AWQAHwP3Km1rtRaVwB3AT9gBoO/sF4r2qCVyYfJKSrnT+5SO59/Dl26wB/+0CTt\namkiQwMorajiSEFZ7ZNtJOgXlVVw48ebyS8p5/05p9Ml2N/hdcMiQ8wMnjVr4IUX4MYbYabdXJKe\nPc1eBK08r+92ExWt9VrAWa7+Gif3PAPU2q1Ca70MWFaXBorWadG2dMIC/ZjUN8z5RXl58M03Jt9a\n301TWrludtM2w4P8qp9sA5upVFZp/vx5HAkH83h/zukM7uZ8VffwqBBWbEqh8toH8erVC155pfoF\nSpnevvT0hWhYxwvLWJF0mItGdsPb1X64S5aYqXSS2nEq0tW0zTawmcpPiVn8lJjFY+cPZsrAzi6v\nHRYZwhM/zcWSlgaffAJBDlaAT5gA+/bBwYON0+BmQIK+aHLfbD9IeaXmT6e52Zz888/NDJSxY5um\nYS3QyW0TndTVb+Vz9bfsO4avt4VrxvZwe+3wzSu4bOdPbJ5168n8fU224604xSNBXzS5hdsyGBgR\n5PKjOEeOmPnTs2a1+E3MGxvlJiQAACAASURBVFNIgA9Bft5tdoFWXFoOQ7oF4+vtJpQdOkTAnbeT\nHNWf96fOdn7dqFHg7y9BX4iGsie7gLi0HC5x18tfuBAqKmRBlgfc1tVvpZuplFdWsSMjl1HRHVxf\nqDXccAMUFbHovueJy3JRX8fX19TgkaAvRMNYvC0Di4KZI7u5vvDzz2HgQCmw5oHI0ADSXfX0Kypa\n5WYqyZn5lJRXMbJ7qOsLlyyB77+Hl16iy5iRZOWVkuWoMqnN+PGwbZtZI9IKSdAXTaaqSrM4NoNJ\n/cLp7GRaHWAKYK1eLakdD7ldlQutMsUTm2bm3I+KdhP0ly83i7BuvZXhUdZFWs6Kr4EZzC0vNwXY\nWiEJ+qLJbNx7jIycYvdz87/4wnwkl9SORyJDA8gvqSCvpLz2yVY8Vz/2wHHCAn2Jsg5mO7VmDYwb\nB97eDO4WjEXBdldllseNM99b6dRNCfqiySzalk6gnzczBke4vnD+fDOgNmBA0zSshXNZV78Vb6YS\nl5bDyOgOuCz5dfy4KaswaRIA7Xy96dc5iB3pDipu2oSFmf97rTSvL0FfNIniskqW7TjEecMiCPB1\nsdBqzx5TO1/m5nvM5Vz9VrqZSm5ROanZhYxyl89ft858apw8+cShYVEh7MjIdVy6wmb8eBP0XV3T\nQknQF03ix4RMCssq3c/Nnz/ffL/iisZvVCtxcq5+25m2GZfuYT5/9Wrw8YExY04cGhYZwpGCMg7l\nuhjMnTDBlPPevbshmtusSNAXTWLhtgwiQwMY07Oj6wvnzze/cLZctHArPNCPYH9v5m9Oq10vHlpn\n0D+Qg1Km1+7SmjVmCmbAyby/7R6X2ye24uJrEvRF/ZWXQ2Ki28uy8kpY+1s2F4+KxGJxkX/dudN8\nSWqnTpRSvHblKPZkF3Dluxs4UlBa/QIPg35qdgFzPtjETR9v5qUfkvg6/iAph/OpqKxqpJbXX2za\ncfp3DiLIVYXWoiIzA8eaz7cZ3DUYL4tyPYNnwADo2LFV5vXdFlwTwqkHHoA334SkJOjb1+llS+Iy\nqNJwsSebpVgscOmlDdzQ1u8PAzrzwZzTuWneZmbN3cBnN51xclps9+5mQDM/33G9GeCX5MPc/Xks\nXhZF5yA/ViZnU1ll8tm+3hb6dQ5kYEQwAyOCGNg1iJgeHZ2PzRQWmnGZAQOgm5v1GPWgtSYuLYez\n3U0I2LjRrFGoEfT9fbzo3yXI9Qwei8XM4mmFPX0J+sIhrTWZeSV0DXEyHW7HDnjjDbPH6Ny58OKL\nTh9n4dYMRkaH0ic80NUTmgVZZ51lSimLOpvYL4yPrh/DDR9ZA//NY4kI8a8+bXPIkGr3aK15d00q\nz3+XRP8uQbw7O4boju0oragk5XAByZn5JGXmk3gojzW/ZbNwWzoAAT5e/GFAOOcMjWDqwM4E5efA\n11+bvYyXL4cSa7580CDzb3rWWaY8dqibHLwH9h0tIqeo3P2irDVrTm6OUsPwyBB+TMhEa+189s+E\nCfDttya336nT7253cyFBXzj00a/7ePrbRL66Y0LtvKnW8Oc/m1/gUaPgww/hqafAz6/W4yQcyiM5\nK5+nZg6pda6azZshNRUefbQBX0XbM7Z3J+bdMIbrPtzMFXPX89nNY4l0EvRLyit5ZNEOFsdmcN6w\nCP552YgTm4/4eXsxpFsIQ7pV/7c/WlDKjoxcfkrMYufqWLZ/8BbdUjYwOj0Ri66iMro7XrfcAtOm\nmU+AP/8MH3xg9qK1WCAm5uSbwIQJps5NHcWlHQdwP3Nn9WoYPtzhG82wqBAWbEkj/Xix8/2ZbXn9\n9evhggvq3M7mSnL6opbyyireXZ1KZZXmmWUJtae2LVwIv/wCTz9t9hg9cgQWLXL4WO+uTsXX28IF\nw918zJ8/38yy+NOfGuZFtGExPTvyyY1jOFZYxuVvr+dgsLXksF1ePzO3hCveWc/i2AwemN6fN646\nrdZuU450aufDH776kKf/djVfvTKHx1e+T3evcj6acjXnX/dv+l/9JlcNvZJPwoZR+Of7TPmDY8fM\n/5dHHwVvb/OpcNo06NDB7E/79tumPpCHYg/k0N7Xi36dHaeqADPetH59rdSOzXBPBnNPP920t5Xl\n9SXoi1qW7TjEwdwSpg3qwobUY/ycePjkyaIik8sfMcJsbnLWWdCnj/nFrWHLvmN8FXeQmyf1okN7\nF/vbVlbCggVw7rkN8vFfwKjuHfjsprEUlFZw2dK9aLvNVLbuP86Fr68l5XABc68dzd1n9XO9wMne\nggXw8MNm/v8rr0BqKl32JnP9Tx/zwtNzuO0PfcjMK+HxJbuY+q9fWBybTpWPL5x5Jjz5pMmRHz9+\ncnOcpCS4/XaziOy008w2hps3m7ShE3FpOQyPCq22B24tsbHm/6qToD8gIggfL8V2V4O57dqZT7IS\n9EVrZsvx9g5vz5tXn0bv8PY8+10i5bYZHC++aILHa6+ZTTosFrj1VvNROiHhxONUVmmeWLqLiGB/\n7pzifJAXgLVrzaYVMmunQQ2LCuHzm8dSXKU4FBRG3u5UvtiSxpVzN9DO14vFd05gxhA3g6H2KipM\nUB42zPx733sv9OoFmBlEQyNDePDsgax44A98eds4IoL9uW9BPH9661diDxw/+ThBQaaH/+9/Q0oK\n7NoFzz9vguzTT5s59VFRcPPN8MMP1ZpQUl5JwsE8z/L54DTo+3l7MSAiiB0ZLlbmgklBbdpkPjm0\nEhL0RTUbUo+xMyOPmyb2xtfbwiPnDiI1u5D5m9PMjkIvvGAWTtmtcOS660xJ2nfeOXFoweY0dh3M\n46/nD3KfNnj9dWjfHi68sFFeU1s2uFswn988lsyQziRv2sn/fbmdMb06suTOCfTv4iI94sjHH8Nv\nv5nxG4vr0BHTsyOL75jAPy8bQUZOMRe/+Sv3fxFXu7qlUjB4sEkTrl0LWVkwbx5MnGg+VZxzDiw7\nucPqroO5VFRpzxZl9e1rtox0YlhkKDvSPViZW1xsNlFvJSToi2reW5NKp/a+J4qiTRvUmTN6deTV\n5bspv+9+80v60kvVbwoPN9MsP/4YiorIKSrjpR+SGNOrIxcOd/5LB5iPzl9+CX/5iwn8osENiAii\nX8xgogqOcPOkXnx0/emEtnORbnOktNSkZ8aMgT/+0aNbLBbFpaOjWPmXP3D7H/rwTfwhpvzzF95Y\nmUJJuZP6/mFhcO21puje4cNmJte77544HXvA9Mxd9vSrqswbiJNevs3wqBDySio4cMzJrmPQKhdp\nSdAXJ6QczufnpMNcO64H/j5mDrZSikfPH8SAXZvw+Wox/PWvJ4t42bvtNsjNhQULeGX5bnKLy/n7\nhUNc54q1NuMDXbvCgw820qsSAEH9e9M17wiPnjPA9b7Ezsyda9J6Tz9d53LXgX7ePHTOQJbfP5lJ\n/cJ46Ydkpr28iu93uqnx7+8P11xj8v+HzbhSbFoOkaEBdA5yMesnMdEMHrsJ+sMizWCuy7x+ZKQp\nT92K8voS9MUJ76/di5+3hWt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3/VCLdlGbIHDZz64g+nFRifTrsiChVhGVVGCL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\n6DuO41QR/wD2G0XfwGHaQQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ER0qO/ZEQ4qAx/rmS8T8XQhwVQhwSQry6ZPw2Y+yoEOIT9X85Fz7jtfR9cyP6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\n/iqOOBfgefZngD7zq1AdxKtqiBUPi76z1Ij+mZIorwwQKT8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tnwLvz6Et6fgz4JBS6p1c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\nBoOhgDCibzAYDAWEEX2DwWAoIP4/SJYi7q3RHKgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bIE9_0ZvPt-W",
        "colab_type": "code",
        "outputId": "f26f83e2-cb16-4563-8fb1-bf47ca4a64e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "# Visualization with History + Prediction\n",
        "\n",
        "print(predictions)\n",
        "train_data = train.tolist()\n",
        "\n",
        "for i in range(len(predictions)):\n",
        "  train_data.append(predictions[i][0])\n",
        "# print(train.tolist().append(predictions))\n",
        "x_axis = np.arange(0, len(history))\n",
        "pyplot.plot(train_data, color='red', label='Predicted Data')\n",
        "pyplot.plot(close, color='blue', label='Original Data')\n",
        "pyplot.legend()\n",
        "pyplot.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([2490.52443477]), array([2512.00045077]), array([2503.73096076]), array([2514.84799144]), array([2522.90792719]), array([2490.96424284]), array([2515.28287872]), array([2512.85108822]), array([2518.34118115]), array([2555.88546433]), array([2576.02410738]), array([2547.00633004]), array([2568.1362717]), array([2602.20041169]), array([2666.40301412]), array([2617.15184829]), array([2690.09856517]), array([2667.36067071]), array([2713.67018456]), array([2708.40329047]), array([2665.10806049]), array([2660.08378312]), array([2559.87111996]), array([2548.38416718]), array([2571.07099052]), array([2574.75120059]), array([2571.65635111]), array([2568.11913359]), array([2537.05134819]), array([2586.32255476]), array([2603.19728876]), array([2551.59514782]), array([2538.71540007]), array([2536.65679083]), array([2549.9948131])]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0kZhcG2UTY+Nz7Tw42cWX73WyzXyWOz+xF4DP738lvwhewyd+/kpcPS6uuFn1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\nNxRgjs6p1AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqqG0cwl-NdP",
        "colab_type": "text"
      },
      "source": [
        "# LSTM on S&P500 Time Series Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DuPHZZw1LKuq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "14aa821a-dcf7-46bd-9ab3-9d6f51a0841f"
      },
      "source": [
        "\n",
        "# Read the s&p 500 input data set and sorting based on date.\n",
        "sp500 = pd.read_csv(\"gdrive/My Drive/tseries/sphist.csv\", index_col=False)\n",
        "\n",
        "sp500[\"Date\"] = pd.to_datetime(sp500[\"Date\"])\n",
        "sp_sorted = sp500.sort_values(\"Date\")\n",
        "\n",
        "print(sp_sorted.head(3))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            Date   Open   High    Low  Close     Volume  Adj Close\n",
            "16589 1950-01-03  16.66  16.66  16.66  16.66  1260000.0      16.66\n",
            "16588 1950-01-04  16.85  16.85  16.85  16.85  1890000.0      16.85\n",
            "16587 1950-01-05  16.93  16.93  16.93  16.93  2550000.0      16.93\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLfozc3GMjsD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "788240a7-8778-4953-d63e-64439bedb338"
      },
      "source": [
        "# Visualization Module\n",
        "\n",
        "close = sp_sorted['Close'][12000:]\n",
        "x = np.arange(0, len(close))\n",
        "print(len(close))\n",
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "plt.plot(x, close, label=\"Close Price\")\n",
        "\n",
        "plt.legend()\n",
        "plt.title(\"TCS Closing Price vs Date\", fontsize=20)\n",
        "plt.ylabel(\"Close Price\", fontsize=15)\n",
        "plt.xlabel(\"Date\", fontsize=15)\n",
        "plt.show()\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4590\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eSPus1FD9D+Z7M8xe+HotAJ2yKwPDthleYBicYjisV3tOObQbORmppNRTgJaS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0ttR5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\nUuf+BmG2BNizQ+/aAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fBqzNygSjkbT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "2c5305cd-0662-4c50-c0b0-e7c3fc9abc9e"
      },
      "source": [
        "autocorrelation_plot(close)\n",
        "pyplot.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SpHq7c29OArmhvnJVfaGJ/RYD9TXovh943j1hiEiuqtb7dE9EeuO6\nQpmjqsvdyg4BIbjGbN+pqg81+kWw8UCaw5vjOHRWVkdNszpqWkeqI4/HA+H71lcAYcAFuG5pNZpA\nVHVyI8EcFpHeqnrQSQZHGtguBvgPcH9N8nA++6AzWyYi/wbuasb3MMYY40XNacb7U/dlEYkFWnsf\naCEwB3jY+flu7Q1EJAR4G3ih9sNyt+QjwBXAxlbGY4wxpoU8uSlZBLR2DM6HgQtFZAcw2VlGRNJE\n5Glnm5m4Xlq8UUTWOlOKs+5lEdkAbADigd+1Mh5jjDEt1Jz3QN7j+wGkAoHhwILWHFRVc3DdCqtd\nvhK4xZl/CXipgf3Pb83xjTHGtF5znoH8r9t8Ja4kcq1vwjHGGNNRNOcZyGciMgb4AXANsBt409eB\nGWOMad8aexP9VGC2Mx3D1fWIqOqkNorNGGNMO9bYFchW4AvgclXNBBCRX7RJVMYYY9q9xlphzQAO\nAktF5CkRuYDv+8MyxhjTxTWYQFT1HVWdBQwDluLqF6uXiPxTRC5qqwCNMca0T02+B6KqRar6iqpO\nBZJxDWdbu+dcY4wxXUyLXiRU1VxVndeasUCMMcZ0DtY9pjHGGI9YAjHGGOMRSyDGGGM8YgnEGGOM\nRyyBGGOM8YglEGOMMR6xBGKMMcYjlkCMMcZ4xBKIMcYYj1gCMcYY4xG/JBAR6S4in4jIDudnXAPb\nVbmNh77QrXygiHwjIpki8pqIhLRd9MYYY8B/VyD3AktUdQiwxFmuT4mqpjjTNLfyPwGPqupgIBe4\n2bfhGmOMqc1fCWQ68Lwz/zxwRXN3FBEBzgfe8GR/Y4wx3iGq2vYHFclT1VhnXoDcmuVa21UCa4FK\n4GFVfUdE4oHlztUHItIX+EBVT2vgWHOBuQAJCQmp8+fP98l36iwKCwuJiorydxjtmtVR06yOmtaR\n6mjSpEmrVDWtdnljQ9q2iogsBhLrWXW/+4Kqqog0lMX6q2q2iAwCPhWRDUB+S+JQ1XnAPIC0tDRN\nT09vye5dTkZGBlZHjbM6aprVUdM6Qx35LIGo6uSG1onIYRHpraoHRaQ3cKSBz8h2fu4SkQxgDPAm\nECsiQapaiWuQq2yvfwFjjDGN8tczkIXAHGd+DvBu7Q1EJE5EQp35eGAisFld99yWAlc3tr8xxhjf\n8lcCeRi4UER2AJOdZUQkTUSedrYZDqwUkXW4EsbDqrrZWfcr4Jcikgn0AJ5p0+iNMcb47hZWY1Q1\nB6gzLK6qrgRucea/AkY1sP8uYJwvYzTGGNM4exPdGGOMRyyBGGOM8YglEGOMMR6xBGKMMcYjlkCM\nMcZ4xBKIMcYYj1gCMcYY4xFLIMYYYzxiCcQYY4xHLIEYY4zxiCUQY4wxHrEEYowxxiOWQIwxxnjE\nEogxxhiPWAIxxhjjEUsgxhhjPGIJxBhjjEcsgRhjjPGIXxKIiHQXkU9EZIfzM66ebSaJyFq3qVRE\nrnDWPSciu93WpbT9tzDGmK7NX1cg9wJLVHUIsMRZPomqLlXVFFVNAc4HioGP3Ta5u2a9qq5tk6iN\nMcZ8x18JZDrwvDP/PHBFE9tfDXygqsU+jcoYY0yz+SuBJKjqQWf+EJDQxPazgFdrlf1eRNaLyKMi\nEur1CI0xxjRKVNU3HyyyGEisZ9X9wPOqGuu2ba6q1nkO4qzrDawH+qhqhVvZISAEmAfsVNWHGth/\nLjAXICEhIXX+/Pmef6kuoLCwkKioKH+H0a5ZHTXN6qhpHamOJk2atEpV02qXB/nqgKo6uaF1InJY\nRHqr6kEnGRxp5KNmAm/XJA/ns2uuXspE5N/AXY3EMQ9XkiEtLU3T09Nb8C26noyMDKyOGmd11DSr\no6Z1hjry1y2shcAcZ34O8G4j286m1u0rJ+kgIoLr+clGH8RojDGmEf5KIA8DF4rIDmCys4yIpInI\n0zUbicgAoC/wWa39XxaRDcAGIB74XRvEbIwxxo3PbmE1RlVzgAvqKV8J3OK2vAdIqme7830ZnzHG\nmKbZm+jGGGM8YgnEGGOMRyyBGGOM8YglEGOMMR6xBGKMMcYjlkCMMcZ4xBKIMcYYj1gCMcYY4xFL\nIMYYYzxiCcQYY4xHLIEYY4zxiCUQY4wxHrEEYowxxiOWQIwxxnjEEogxxhiPWAIxxhjjEUsgxhhj\nPGIJxBhjjEcsgRhjjPGIXxKIiFwjIptEpFpE0hrZboqIbBORTBG51618oIh845S/JiIhbRO5McaY\nGv66AtkIzAA+b2gDEQkEngAuAUYAs0VkhLP6T8CjqjoYyAVu9m24xhhjavNLAlHVLaq6rYnNxgGZ\nqrpLVcuB+cB0ERHgfOANZ7vngSt8F60xxpj6BPk7gEYkAfvdlrOA8UAPIE9VK93Kkxr6EBGZC8x1\nFgtFpKnE1dXFA8f8HUQ7Z3XUNKujpnWkOupfX6HPEoiILAYS61l1v6q+66vj1qaq84B5bXW8jk5E\nVqpqg8+ljNVRc1gdNa0z1JHPEoiqTm7lR2QDfd2Wk52yHCBWRIKcq5CacmOMMW2oPTfjXQEMcVpc\nhQCzgIWqqsBS4GpnuzlAm13RGGOMcfFXM94rRSQLmAD8R0Q+csr7iMgiAOfq4g7gI2ALsEBVNzkf\n8SvglyKSieuZyDNt/R06Mbvd1zSro6ZZHTWtw9eRuP6gN8YYY1qmPd/CMsYY045ZAjHGGOMRSyCd\nnIg8KyJHRGSjW1l3EflERHY4P+OcchGRx50uYtaLyFi3feY42+8QkTn++C6+IiJ9RWSpiGx2utj5\nmVNu9eQQkTAR+VZE1jl19BunvN5uhUQk1FnOdNYPcPus+5zybSJysX++ke+ISKCIrBGR953lzltH\nqmpTJ56Ac4GxwEa3sj8D9zrz9wJ/cuYvBT4ABDgT+MYp7w7scn7GOfNx/v5uXqyj3sBYZz4a2I6r\n+xyrp+/rSIAoZz4Y+Mb57guAWU75v4D/cuZ/AvzLmZ8FvObMjwDWAaHAQGAnEOjv7+fluvol8Arw\nvrPcaevIrkA6OVX9HDheq3g6ri5g4OSuYKYDL6jLclzv2/QGLgY+UdXjqpoLfAJM8X30bUNVD6rq\name+AFervySsnr7jfNdCZzHYmZSGuxVyr7s3gAucboimA/NVtUxVdwOZuLot6hREJBm4DHjaWW6s\n66UOX0eWQLqmBFU96MwfAhKc+fq6j0lqpLzTcW4jjMH1F7bVkxvn1sxa4Aiu5LiThrsV+q4unPX5\nuJrcd+o6Ah4D7gGqneXGul7q8HVkCaSLU9c1s7XlBkQkCngT+LmqnnBfZ/UEqlqlqim4en8YBwzz\nc0jtiohcDhxR1VX+jqWtWALpmg47t1xwfh5xyhvqPqah8k5DRIJxJY+XVfUtp9jqqR6qmoerN4gJ\nON0KOavcv+93deGs74arG6LOXEcTgWkisgdX7+HnA3+lE9eRJZCuaSGuLmDg5K5gFgI3OK2MzgTy\nnVs4HwEXiUic0xLpIqesU3DuOz8DbFHVR9xWWT05RKSniMQ68+HAhbieFTXUrZB73V0NfOpcxS0E\nZjktkAYCQ4Bv2+Zb+Jaq3qeqyao6ANdD8U9V9To6cx35+ym+Tb6dgFeBg0AFrnupN+O6z7oE2AEs\nBro72wquQbx2AhuANLfPuQnXw7xM4Ef+/l5erqOzcd2eWg+sdaZLrZ5OqqPRwBqnjjYCv3bKB+H6\n5ZYJvA6EOuVhznKms36Q22fd79TdNuASf383H9VXOt+3wuq0dWRdmRhjjPGI3cIyxhjjEUsgxhhj\nPGIJxBhjjEcsgRhjjPGIJRBjjDEesQRiTBsQkcKmtzKmY7EEYowxxiOWQIzxExGZ6owDsUZEFotI\nglPe0xl/ZJOIPC0ie0Uk3t/xGlObJRBj/GcZcKaqjsHVd9I9TvkDuLq1GImrm+9+forPmEYFNb2J\nMcZHkoHXnI4aQ4DdTvnZwJUAqvqhiOT6KT5jGmVXIMb4z9+Av6vqKOBWXH0jGdNhWAIxxn+68X03\n3e7jp38JzAQQkYtwDY9rTLtjnSka0wZEpBo44Fb0CK7eVh8FcoFPgTNUNV1EeuHqRTkB+Bq4HBig\nqmVtG7UxjbMEYkw7IyKhQJWqVorIBOCf6hoJ0Jh2xR6iG9P+9AMWiEgAUA782M/xGFMvuwIxxhjj\nEXuIbowxxiOWQIwxxnjEEogxxhiPWAIxxhjjEUsgxhhjPPL/A0Hr6GxcU567AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TVJ92Zo_yirK",
        "colab_type": "text"
      },
      "source": [
        "From the above graph, we estimate the lag to be around 100."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z8Z2IiSG93eI",
        "colab_type": "code",
        "outputId": "79a8ffd0-1d5d-411a-f94d-86d12b546b6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# univariate LSTM example\n",
        "from numpy import array\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dense\n",
        "import warnings\n",
        "from sklearn.metrics import mean_squared_error\n",
        " \n",
        "# split a univariate sequence into samples\n",
        "def split_sequence(sequence, n_steps):\n",
        "\tX, y = list(), list()\n",
        "\tfor i in range(len(sequence)):\n",
        "\t\t# find the end of this pattern\n",
        "\t\tend_ix = i + n_steps\n",
        "\t\t# check if we are beyond the sequence\n",
        "\t\tif end_ix > len(sequence)-1:\n",
        "\t\t\tbreak\n",
        "\t\t# gather input and output parts of the pattern\n",
        "\t\tseq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
        "\t\tX.append(seq_x)\n",
        "\t\ty.append(seq_y)\n",
        "\treturn array(X), array(y)\n",
        " \n",
        "# define input sequence\n",
        "raw_seq = list(close)\n",
        "print(raw_seq)\n",
        "\n",
        "\n",
        "size = int(len(raw_seq) * 0.9)\n",
        "train, test = raw_seq[0:size], raw_seq[size:len(raw_seq)]\n",
        "history = [x for x in train]\n",
        "predictions = list()\n",
        "\n",
        "\n",
        "# choose a number of time steps\n",
        "n_steps = 100\n",
        "print('before split sequence')\n",
        "# split into samples\n",
        "X, y = split_sequence(raw_seq, n_steps)\n",
        "\n",
        "print('after split sequence')\n",
        "# reshape from [samples, timesteps] into [samples, timesteps, features]\n",
        "n_features = 1\n",
        "X = X.reshape((X.shape[0], X.shape[1], n_features))\n",
        "# define model\n",
        "model = Sequential()\n",
        "model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features)))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse')\n",
        "# fit model\n",
        "model.fit(X, y, epochs=200, verbose=0)\n",
        "print('model fitting over')\n",
        "\n",
        "count = 0\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "# demonstrate prediction\n",
        "for t in range(len(test)):\n",
        "\tx_input = array(raw_seq[-n_steps:])\n",
        "\tx_input = x_input.reshape((1, n_steps, n_features))\n",
        "\tyhat = model.predict(x_input, verbose=0)\n",
        "\tprint(yhat[0][0])\n",
        "\tpredictions.append(yhat[0][0])\n",
        " \t# as we predict the difference\n",
        "\traw_seq.append(yhat[0][0])\n",
        "\n",
        "\n",
        "error = mean_squared_error(test, predictions)\n",
        "print('Test MSE: %.3f' % error)\n",
        "pyplot.plot(test)\n",
        "pyplot.plot(predictions, color='red')\n",
        "pyplot.legend()\n",
        "pyplot.show()\n",
        "\n",
        "x_axis = np.arange(0, len(raw_seq))\n",
        "pyplot.plot(x_axis, raw_seq, color='red')\n",
        "pyplot.legend()\n",
        "pyplot.show()\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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1946.1700440000002, 1967.9000239999998, 1964.819946, 1935.0999760000002, 1968.890015, 1928.209961, 1906.130005, 1874.7399899999998, 1877.699951, 1862.4899899999998, 1862.7600100000002, 1886.7600100000002, 1904.0100100000002, 1941.280029, 1927.109985, 1950.819946, 1964.5799559999998, 1961.630005, 1985.050049, 1982.300049, 1994.6500239999998, 2018.050049, 2017.810059, 2012.0999760000002, 2023.569946, 2031.209961, 2031.9200440000002, 2038.2600100000002, 2039.680054, 2038.25, 2039.3299559999998, 2039.819946, 2041.319946, 2051.800049, 2048.719971, 2052.75, 2063.5, 2069.409912, 2067.030029, 2072.830078, 2067.560059, 2053.439941, 2066.550049, 2074.330078, 2071.919922, 2075.3701170000004, 2060.310059, 2059.820068, 2026.140015, 2035.3299559999998, 2002.3299559999998, 1989.630005, 1972.7399899999998, 2012.890015, 2061.22998, 2070.649902, 2078.540039, 2082.169922, 2081.8798829999996, 2088.77002, 2090.570068, 2080.350098, 2058.899902, 2058.199951, 2020.5799559999998, 2002.609985, 2025.9000239999998, 2062.139893, 2044.810059, 2028.2600100000002, 2023.030029, 2011.27002, 1992.6700440000002, 2019.4200440000002, 2022.550049, 2032.119995, 2063.149902, 2051.820068, 2057.090088, 2029.550049, 2002.160034, 2021.25, 1994.9899899999998, 2020.8499760000002, 2050.030029, 2041.5100100000002, 2062.52002, 2055.469971, 2046.7399899999998, 2068.590088, 2068.530029, 2088.47998, 2096.98999, 2100.340088, 2099.679932, 2097.449951, 2110.300049, 2109.659912, 2115.47998, 2113.860107, 2110.73999, 2104.5, 2117.389893, 2107.780029, 2098.530029, 2101.040039, 2071.26001, 2079.429932, 2044.160034, 2040.2399899999998, 2065.949951, 2053.399902, 2081.189941, 2074.280029, 2099.5, 2089.27002, 2108.100098, 2104.419922, 2091.5, 2061.050049, 2056.149902, 2061.02002, 2086.23999, 2067.889893, 2059.689941, 2066.959961, 2080.6201170000004, 2076.330078, 2081.899902, 2091.179932, 2102.060059, 2092.429932, 2095.840088, 2106.6298829999996, 2104.98999, 2081.179932, 2100.399902, 2097.290039, 2107.959961, 2112.929932, 2117.689941, 2108.919922, 2114.76001, 2106.850098, 2085.51001, 2108.290039, 2114.48999, 2089.459961, 2080.149902, 2088.0, 2116.100098, 2105.330078, 2099.1201170000004, 2098.47998, 2121.100098, 2122.72998, 2129.199951, 2127.830078, 2125.850098, 2130.820068, 2126.060059, 2104.199951, 2123.47998, 2120.790039, 2107.389893, 2111.72998, 2109.600098, 2114.070068, 2095.840088, 2092.830078, 2079.280029, 2080.149902, 2105.199951, 2108.860107, 2094.110107, 2084.429932, 2096.290039, 2100.439941, 2121.23999, 2109.98999, 2122.850098, 2124.199951, 2108.580078, 2102.310059, 2101.48999, 2057.639893, 2063.110107, 2077.419922, 2076.780029, 2068.76001, 2081.340088, 2046.680054, 2051.310059, 2076.6201170000004, 2099.600098, 2108.949951, 2107.399902, 2124.290039, 2126.639893, 2128.280029, 2119.209961, 2114.149902, 2102.149902, 2079.649902, 2067.639893, 2093.25, 2108.570068, 2108.6298829999996, 2103.840088, 2098.040039, 2093.320068, 2099.840088, 2083.560059, 2077.570068, 2104.179932, 2084.070068, 2086.050049, 2083.389893, 2091.540039, 2102.439941, 2096.919922, 2079.610107, 2035.72998, 1970.890015, 1893.209961, 1867.609985, 1940.5100100000002, 1987.660034, 1988.869995, 1972.180054, 1913.8499760000002, 1948.859985, 1951.130005, 1921.219971, 1969.410034, 1942.040039, 1952.290039, 1961.050049, 1953.030029, 1978.089966, 1995.310059, 1990.199951, 1958.030029, 1966.969971, 1942.7399899999998, 1938.7600100000002, 1932.2399899999998, 1931.339966, 1881.77002, 1884.089966, 1920.030029, 1923.819946, 1951.359985, 1987.050049, 1979.9200440000002, 1995.8299559999998, 2013.430054, 2014.890015, 2017.459961, 2003.689941, 1994.2399899999998, 2023.859985, 2033.109985, 2033.660034, 2030.77002, 2018.939941, 2052.51001, 2075.149902, 2071.179932, 2065.889893, 2090.350098, 2089.409912, 2079.360107, 2104.050049, 2109.790039, 2102.310059, 2099.929932, 2099.199951, 2078.580078, 2081.719971, 2075.0, 2045.969971, 2023.040039, 2053.189941, 2050.439941, 2083.580078, 2081.23999, 2089.169922, 2086.590088, 2089.139893, 2088.8701170000004, 2090.110107, 2080.409912, 2102.6298829999996, 2079.51001, 2049.6201170000004, 2091.689941, 2077.070068]\n",
            "before split sequence\n",
            "after split sequence\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "model fitting over\n",
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            "1637.9644\n",
            "1637.9631\n",
            "1637.9622\n",
            "1637.9612\n",
            "1637.9597\n",
            "1637.9583\n",
            "1637.9568\n",
            "1637.9551\n",
            "1637.9529\n",
            "1637.9506\n",
            "1637.9471\n",
            "1637.9438\n",
            "1637.9397\n",
            "1637.9343\n",
            "1637.9294\n",
            "1637.9238\n",
            "1637.9158\n",
            "1637.9081\n",
            "1637.8958\n",
            "1637.8829\n",
            "1637.8622\n",
            "1637.8467\n",
            "1637.8184\n",
            "1637.7926\n",
            "1637.7554\n",
            "1637.7217\n",
            "1637.661\n",
            "1637.603\n",
            "1637.533\n",
            "1637.4739\n",
            "1637.3545\n",
            "1637.257\n",
            "1637.1034\n",
            "1636.9618\n",
            "1636.7355\n",
            "1636.5168\n",
            "1636.2767\n",
            "1636.0369\n",
            "1635.7239\n",
            "1635.3552\n",
            "1634.8921\n",
            "1634.4574\n",
            "1634.2535\n",
            "1634.1384\n",
            "1634.0613\n",
            "1633.987\n",
            "1633.9167\n",
            "1633.8354\n",
            "1633.7251\n",
            "1633.606\n",
            "1633.4691\n",
            "1633.313\n",
            "1633.0808\n",
            "1632.8611\n",
            "1632.6165\n",
            "1632.3596\n",
            "1632.0148\n",
            "1631.6288\n",
            "1631.0946\n",
            "1630.603\n",
            "1629.9188\n",
            "1629.268\n",
            "1628.3589\n",
            "1627.6309\n",
            "1626.5104\n",
            "1625.4404\n",
            "1623.8955\n",
            "1622.2767\n",
            "1620.4191\n",
            "1618.3489\n",
            "1616.2253\n",
            "1613.946\n",
            "1611.7544\n",
            "1609.2341\n",
            "1606.2231\n",
            "1603.4683\n",
            "1600.3961\n",
            "1596.7158\n",
            "1591.676\n",
            "1586.2883\n",
            "1579.4471\n",
            "1572.9213\n",
            "1569.8352\n",
            "1569.1995\n",
            "1569.0043\n",
            "1568.9451\n",
            "1568.9261\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1568.9185\n",
            "Test MSE: 83339.615\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w/HndfgozE/nXN5fy9w8+5fVd5fj8upM12h8qGto4rReipJTi06pmrn90M2uuXsKa9UbE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\nBeSnD36BkoxEF34Nbq+fydli6Q8Zubk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pt5RSO5VSO5RS3zHbR9W1UErFK6XWK6W2mNfh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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cw+KMyVyEvr4+8b29+GLq8UJdb7ngneh0OfLI8n1+GG522BEjrHtn5Ei4+WYr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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRRB_NQtjlQf",
        "colab_type": "code",
        "outputId": "217528d5-7369-4e4d-b1db-149e8eb3fd95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "# Run this after prediction to find the significant signal movement.\n",
        "\n",
        "# Execute this block to get the significant signal using fourier filtering.\n",
        "\n",
        "from scipy import fftpack\n",
        "\n",
        "time_step = 1\n",
        "# period = 5.\n",
        "sig = raw_seq\n",
        "\n",
        "time_vec = np.arange(0, 5049, time_step)\n",
        "\n",
        "# The FFT of the signal\n",
        "sig_fft = fftpack.fft(sig)\n",
        "\n",
        "# And the power (sig_fft is of complex dtype)\n",
        "power = np.abs(sig_fft)\n",
        "\n",
        "# The corresponding frequencies\n",
        "sample_freq = fftpack.fftfreq(len(sig), d=time_step)\n",
        "\n",
        "\n",
        "# Find the peak frequency: we can focus on only the positive frequencies\n",
        "pos_mask = np.where(sample_freq > 0)\n",
        "freqs = sample_freq[pos_mask]\n",
        "peak_freq = freqs[power[pos_mask].argmax()]\n",
        "\n",
        "high_freq_fft = sig_fft.copy()\n",
        "high_freq_fft[np.abs(sample_freq) > peak_freq*5] = 0\n",
        "# high_freq_fft[np.abs(sample_freq) < peak_freq/4] = 0\n",
        "filtered_sig = fftpack.ifft(high_freq_fft)\n",
        "\n",
        "plt.figure(figsize=(20, 6))\n",
        "plt.plot(time_vec, sig, label='Original signal')\n",
        "plt.plot(time_vec, filtered_sig, linewidth=3, label='Filtered signal')\n",
        "plt.xlabel('Time [s]')\n",
        "plt.ylabel('Amplitude')\n",
        "\n",
        "plt.legend(loc='best')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f104923ae10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9y58eVyKmklPkosVTqsh4UalT44qDp8od26rqD14/vn19jm9fH4A5a7dz9stT\nWL1lFyf1uQZ+fwc2zgdfAXx1B1z0gVMMXI4oEUswWWsnE96+ubtBFYy3wJ8rmetN4M2qi05ERERE\nREQON4Vef4X901dtxR1MeNSpFQs5GzATrqJkU9gUf2fyG17BwIClIDhH3drlawl1aJTE0k25oSLb\n1aX0KXa/rdgaui6pv3T9gLYRfX/XZil4XIYlG3PB7YHhz8EbpwAWlk+CxZ9B5zMjGoMceqo37Soi\nIiIiIiJSBay1ZO0oqPDe8k15bMt36gzViXfBR1dA/hYANttUbvHeyFX/ncMrP69kztodACRUcCLc\n+9cex8Q/94/QJ9h37lJb5NZuyw9df7/YKXEc54nsaXYxbhfDuzUJnVxH83RIvyI84Ju7oTi/4ofl\nsKUEk4iIiIiIiNR4/522hkHP/Fyu3+My+AIBduQ7q3vqz3wK1k5xbhoX/0i8g2xSAViQtZOvF2wE\nqHCVUmqtWNJapEboE+w7dyXbz8ZPWwMQldPsOjVJJrfQx4KsnU7HoL9DYgPnOicLfns+4jHIoUUJ\nJhEREREREanxnvx2aeh6wvV9Q9cxbhfFvgA/LtnMAPc8PFOeCz908r2cdfb5oea+Fseubq69fJOP\nr2D1VVXr1txJtC3akON0JKQ6SaYSvz0HO9ZFPA45dCjBJCIiIiIiIjXWxp2F3PD2LHILfaG+XkfV\n5ec7BjDjnkHEuA1ev8Wbu5knPa+GH2x3Chz/1zJb4fzW0rxOAiN7NovmR9hvldWA6tg4CYjOCqYe\nLZ0EU3ZuUbgz7WJo0t259hXCd3+v4Ek5XCnBJCIiIiIiIjXWxLlZfDV/Y6j9+NnHAHBUvUQaJscT\n63FT5PVza/6LNDROfSUSG8JZr4LLVWa1T5EvQF6Rj9pxkTxw/eBVlmDaGSzyHekaTOCskopxG/KK\nwok9XG4Y8o9we+HHsGZKxGORQ4MSTCIiIiIiIlJjbc4Jr6C57dSjuahPyzL3XQZarHqfk8kId571\nCtR26gW1aZAY6l69ZRc78r1RWQF0MCqqwZQU72FLnvO7iIuJTvwl2w/LOKovdBkZbn/9NwhUfLqf\nHF4O7X81IiIiIiIiInuQnRdOMLVpULvc/aS8P7g89/VwR5/roP0poWatWA/92tYDwieyzVqzPULR\nVo3dVzDVinXToVESXr9TRCpaCbJYjwuvP1D+xqkPgyfeud44D+a+HZV4pHopwSQiIiIiIiI11pZS\nNYBO7tig7E1fMc/FvESCKQYgJ7k9nPJQuTku79+6TNuztyra1cy1W4Kpf7v6HB2svwTR2SIHEFvR\nCiaA1BbQ/9Zw+8f/g+L8qK+6bmwAACAASURBVMQk1efQ/lcjIiIiIiIiUoliX4Cpf2wFoHmdBGrF\n7lY76cfHOMa1GoAiG0PSReMgJr7cPKd2bkT92rGhdt3E2HJjDiWe3RJMgYCle/OUUDs2iiuYiita\nwQTQ7yan1hVA7nqY/mrF4+SwoQSTiIiIiIiI1Ejjp60B4P7hnZn8t4Flb676BX57PtR8wncBpnHX\nSucqvern0bMrH3cocO1Wg8lvLfUS40LtyoqAV7VKVzABxNWGAXeF25Ofg/xtUYlLqocSTCIiIiIi\nIlIjzVy1jdpxHi7v16rsjfxt8PG1gFOT6Bf/MYzzD97jXCV1i2LdLurXjtvj2Oq2ewLpvmGdiKmG\nwuSV1mAq0fNPUK+dc120E359JjqBSbVQgklERERERERqHH/A8s3CjfgDtmxNImvhi1udbVkACXW5\nzXsddi9ff0u2lUXrBLaDsfspcu0aJhET/B1Ea/USOEm5ospWMAG4Y2DQA+H2jNdh+5rIBybV4tD/\nlyMiIiIiIiKym405hQAUeP1lb8x9BxZ9Gm6f+S+yqUPLurX2OF9cjLNFLiEmOgWyD0bphNqMewYB\n4HE7X++jmF+idryH3ELfngd1OgOa93au/cXw42ORD0yqhRJMIiIiIiIiUuNsCiaY3vhTerhz60r4\n+s5wu9fl0HEYix4ezKS/nLjH+Uq2yCXEHvoJptIaJjtFyz1uJ7NkTPQyTCkJMews8O55kDFwaqmT\n++Z9ABvmRTYwqRZKMImIiIiIiEiNYa3lr+/P5eUfVwLQJDV4KpzfCx9fA8V5TrteexjsrJapFesh\nfi8rk0oSTPGempVgKhHjiv4KptRasWzOKSQQsHseeFQ/OHposGG1iukw5dn7EBEREREREZHoW7ct\nn2l/bOW89Bahvuy8Ij6ekxVqN0wKJph+fhKyMpxrVwyc8wbEJu7zu0pOkYuvYSuYSpSsYNr9hLlI\n6tO6Lu9MX8up//yZu4d2ona8B5cxGAMGZ/ESGFwGErr+lQ7LvsFgYdk3kJkBzdP38gapSZRgEhER\nERERkUPS3R/PZ/KKLfRoWYd2DWvj8weYuWp76H58jIv6tWNhzVT49enwgwPvg6Zp+/WukuLeCTWg\nyHdFYqohwTSie1N8fsvjXy3mqrcy9jr++Zi+nOmeAsCOLx8kb9QHNK+z59pYUnMowSQiIiIiIiKH\npJmrtwGwJa+I1FoxXP1WBnPW7gjdL/QGMEU5ztY4GzzNrNUJ0O/m/X5XaItcDSjyXRF3cItcSaIp\nGowxnNOrOQM7NmT55jx8/gAWCFiLtWBxtjSW/Hfr6jvwTx2J21hSN/zKVU++wilDzuK6k9pGLWaJ\nHCWYRERERERE5JCwdms+o/89gzsGd2DoMU0o9jtJo/xiH+mP/q/ih768HXauda7jU+HsV8G1/6uQ\nlm7MBcDupZzQoappajxDujTmpkHtov7uOomx9G5dd+8DOzaiKHcU7oXvA/BY6mcM/rojJ3doSIfG\nSRGOUiJNCSYRERERERE5JNw7cT5/bNnF9W/PLtN/50cVnzo2wvUbzP8g3HHGc5DS/IDevTLbKQ6+\nYWfBAT1f3eI8bl69tFd1h7FXcYPuhsUTIOCjQ8Fc+roWMnVlZyWYDgM1c3OpiIiIiIiIHHa8wRVL\nu9uSV1yur7nJ5tnE/4Q70i6GLmcf8LtfudhJzvj3diKaHJy6rZ0/q6B74ifw/eJN1RiQVBWtYBIR\nEREREZFDwuacor2O6dEylbsHH03vny+Ftc6qI+q0gqH/OKh3N0iKAyChhpwi9+olh/5qpUqdeAf8\n/i74izkmsITEdT9jbR9MFAuUS9XTCiYRERERERGpduu25fPHll17HPPImV346Lp+9M4aB2unOp3G\nDSPfgLiD22J1dKMkLji2BY+c2fWg5omWIV0bM6Rr4+oO48CktoCel4Wa19v3yNqeX40BSVVQgklE\nRERERESq3ZJgke09ubRvK9zrZ8OP/xfuHHAXtDj2oN8f63HxxDnd6NGyzkHPJfvghNvAEw9Ad9cf\nZM/5qpoDkoOlBJOIiIiIiIhE3Na8ItIf/Y4pK7Zw6rM/80HGujL37/zo9z0+73YZKMqDCVeC9Tud\nLY6D4/8aqZAlkpKblFnF1Pj3F2vuEX4CKMEkIiIiIiIiUXD3x/PZklfMRW9MZ/nmPO78aB5ZO5wT\n21Zv2cX2fC8AHpdTh+eE9vXL1BlKiHHDN3+D7aucjrhkGPk6uFVauMbqfzO4YgBokvM7rP61mgOS\ng6EEk4iIiIiIiETcL8uzy/VtyikEYMDTPwHQtVky0+4ZBMDFfVoypGtjHj3LqYk0zD0d5vw3/PCw\nZ6DOUZENWiIrpTn0CJ8ox89PVl8sctCUYBIREREREZGI87jKf/3MKfDi8wdC7cv6tqJ+7ThWPzGM\nIV2bANA4OZ7GbOXewGvhB485D7qNinjMEgX9b8VfkppY/SusnVa98cgBU4JJREREREREIiqn0Ete\nka9cf26hjy15xaH22T2alRtTywPPxrxCMnlOR0pLOP3piMUqUVa3NXPrnBZu//JU9cUiB0UJJhER\nEREREYmorO1OraXereuW6S/0+tmw07k3skczPO7yX1FbL3+Tfu5FTsO4YORrkJAa2YAlqmY2v5yA\ndWpvseJ/kDW7egOSA6IEk4iIiIiIiERUZjDBdM/pnWiUHBfqL/YH+GhWJgBXntC6/INZs2k8q9Rq\npRNug6P6RTRWib7i1LZ8ETgu3PGLVqjVRBFLMBlj3jTGbDbGLCjVl2aMmWaMmWuMyTDG9A72G2PM\nC8aYFcaYecaYnqWeucwYszz4c1lF7xIREREREZFD1+y12wFoXieBT/98PC9d5HzlK/YFMMGFK52b\nJJd9qCgPJlyFCThb6zYkHQMn/S1qMUv0JMV7eMl3Zrhj6ZewcUHlD8ghKZIrmMYBQ3brexJ4yFqb\nBvw92AYYCrQP/lwDvAJgjKkLPAD0AXoDDxhj6kQwZhEREREREakCs9Zs48Xvl/OnN2fwyk8rAaiX\nGEvjlHhO6tAAAK8/QEFxgGapCZiSTFOJb/4G25znbGxtGo0eD+6YqH4GiY6k+BiW2pbktxka7vxV\nq5hqGk+kJrbW/mKMabV7N1CSlk4B1gevzwTestZaYJoxJtUY0wQYAHxnrd0GYIz5Didp9W6k4hYR\nEREREZGDk7WjgHNemVqmr3acJ5REig3WWir2BdhV5CMxzl12goUTYc5/Q00z7BlMvQq20MlhISne\nSU2s6XIDnf742ulcOBEGroR6basxMtkfEUswVeJW4FtjzNM4q6dKNs82A9aVGpcZ7KusX0RERERE\nRA5Bu4p8nPHi5HL9Jx5dP3Qd43YSTcV+y5a8IpLiS61M2pkJn98cbnc9F7qdH7F4pfr1bFmH+BgX\nN/wY4KXE3nTeNQOwTB3/AB83uwOP2+BxuXC7DLEeFycd3YB+beuVX/Um1SraCabrgb9YaycYY0YB\nY4FTqmJiY8w1ONvraNmyZVVMKSIiIiIiIvvpf4s3sW1Xcbn+OE94lVJJYuCF75cDcOlxRzk3An74\n+Foo3Om0U1vC8GdBiYTDWoOkOF69pBdPfrOU1wrO4HlmANBrx9c8nn8mm2wd/AGLL2Ap8Pp5/Zc/\naNewNh0aJdEoOZ7h3ZvQs6Wq6VS3aCeYLgNuCV5/CLwRvM4CWpQa1zzYl4WzTa50/08VTWytfR14\nHSA9Pd1WVcAiIiIiIiKy74q8gQr73a7Kk0QpCcEVTJP/CWuCq5+MC0aOgfiUqg5RDkEDOjRkQIeG\nYI+HNyZC1ixi8fH5sQvg1IdC4wq9fj6Zk8Xnv69n8cYcvl+yiTd/W8Xrl/bitC6Nq/ETSCSLfFdk\nPXBS8HogsDx4/Rnwp+BpcscBO621G4BvgdOMMXWCxb1PC/aJiIiIiIjIISg7ryh0HUocAR/Nyqz0\nmYRYN2TOgp/+L9x54p3Q8rhKn5HDlDFw/F/C7Yw3wyvagPgYNxf2bsk7Vx/HD7cNYNZ9p9KxcRL3\nf7qAQq+/GgKWEhFLMBlj3gWmAh2MMZnGmCuBq4FnjDG/A48T3NIGfAX8AawAxgA3AASLez8CzAz+\nPFxS8FtEREREREQOPVtKJZh+uO2k0HXLurUqfSbVVQATroSAz+lo0QdOvCNiMcohrsMwqNfeuS7K\ngYx/Vzo0Mc7DXUM7simniJ+WZkcpQKlIxBJM1toLrbVNrLUx1trm1tqx1trJ1tpe1tru1to+1tpZ\nwbHWWvtna21ba+0x1tqMUvO8aa1tF/yp/G+ViIiIiIiIVJt5mTtYuH4n46asJtbjYtmjQ6lXO45B\nHRsC8J8repcZf8GxJVVSLCcseRS2r3Kacckw8nVwR7uiixwyXC7oX6rQ+7SXwVtY6fDj29WnXmIs\nn89bX+kYibxob5ETERERERGRw9CIf/3GsBcmYy0U+wLEepyvm8+M6s67Vx9H6/qJZcbfO6wTABe6\nf6Dlhm/CN4b/E+q0ilbYcqjqdj4kNXGu8zbBvPcqHepxuxjWrQnfLdpEXpEvSgHK7pRgEhERERER\nkYPi9Zct7N2vbb3QdWqtWPqWapdIio+ho1nLA563wp29RsMx50YqTKlJPHFw3A3h9m8vOKcMVuLk\nDg0p9gVYmLWz0jESWUowiYiIiIiIyEFZuy2/TPuvpx6994eK8ngp5nnijddpN+wCQ56IQHRSY/Ua\nDXHBUwS3rYQlX1Q6tGszZ9yC9TlRCEwqogSTiIiIiIiIHJRBz/xcpu1ymb0/9NXttHVtAMDvSYDz\nxkFMQgSikxorPhl6XxVuT34OrK1waIOkOOrUimHF5rwoBSe7U4JJREREREREqkTj5Ph9Gzjnbfj9\n3VBzx8lPQIN9WPUkR54+14E7zrlePxtW/VLp0Jb1Esncnl/pfYksJZhERERERETkgG3ODZ/u9beh\nHQBoVS+xsuGweQl8dXuo+ZH/ROr1Hx2p8KSmq90Qelwcbv/2XKVDGybFkZ1bFIWgpCJKMImIiIiI\niMgBW7bR2ZL00IgunN2jOaufGEbdxNiKBxfvgg9Hg9dZZbLGNOfZmGuiFKnUWP1uAhNMX6z8ATYu\nqHBYw6Q4NivBVG2UYBIREREREZEDtiXP+UJ/fPv6ex5oLXx+C2QvdtqeeFpc8z6/3jc8whFKjVe3\nDXQaEW5P/VeFwxokxbFtV3G5Uw0lOpRgEhERERERkf1iSxVa3p5fDECdWpWsWiox43WY/2G4ffrT\nuJp0xb0vBcFF+t0Uvp7/IeSsLzekQZJTq6kk6SnRpQSTiIiIiIiI7LP7Jy6g3b1fh5JMeYU+AJLi\nPZU/tHY6fHtPuN3zMuh5aSTDlMNN83Ro2c+5Dvhg+mvlhjRMcorMqw5T9VCCSURERERERPbZ+Glr\n8Acs2cFVIrlFPuJjXMS4K/l6mbsJPrzMSQoANEmDoU9GKVo5rPS7MXyd8W8oyi1zu+QUw/U7CqIZ\nlQTtIcUsIiIiIiIiUrHNOUU0TIont9BH7biYigf5ffDRFZC7wWkn1IHzx0NMfPQClcPH0UOhblvY\nthKKdsLs8dD3htDttg0TcbsMD3++iPHT1mAwGANul8HjchHjNrhdhtpxHnq2rMOpnRtRp7KC9LLf\nlGASERERERGRMgq9fq4ZP4vrTmxDv3Zli3e7XcZZwZRbRCBgeXfG2son+v5BWDM52DBwzlhIbRmx\nuOUw53I5q5i++IvTnvYK9L4G3E5qo1ash0fO7MrEOVkUegNYawlYCFiL12/x+QP4A5Zt+cW8N3Md\nsZ+6aNugNi3qJNDrqDr8qW8rEmLd1fgBazYlmERERERERKSMuet28MuybJZsyGHGvaeUuZcY6yan\n0Mfm3MI9F1Oe/xFMeTHcPvleaDcoQhHLEaP7hfDDo5C/FXauhcWfQtdzQrcv6tOSi/rsOYlprWXh\n+hwmzsnijy27WJmdx6RFm/h4dhYfXt+X5PhKVuTJHqkGk4iIiIiIiJSRU+AFYHNuUZkT40q7++P5\nrN9ZWPEEWbPh0z+H20cPgRNuq+ow5UgUkwDHXh1uT3kRKvk7WhljDF2bpXDf8M68OfpYvr9tAG+O\nTmfpplzG/PJHFQd85FCCSUREREREREIufH0aN74zJ9Q+41+TQ9eFXj85wVPjAhbOeuk3ADo2TgpP\nkLsR3rsYfMHkU732cPZrzvYmkapw7FXgCdbxWj8H1kw56CkHdmzEsG5NGDt5Fdt3FR/0fEci/QsX\nERERERERAFZt2cXUP7ZS7A+E+hZk5YSu36uk3tJbV/Z2LryF8N5FkLveacenwEXvQ0JqxGKWI1Dt\nBtD9gnC79FbMg3D9SW3JL/bz1YINVTLfkUYJJhEREREREQHg07lZFfYX+fwAPPj5IgD+cc4xZe7H\nx7idbUqf3wxZs5xO44LzxkG9thGLV45gfW8MXy/7GrKXHfSUXZom07ZBIp//vv6g5zoSKcEkIiIi\nIiIiBAKW5/63HICRPZrxzHndeWhEFwDygtviSpzWuXGZdqzbBVNegHnvhzsHPw5tB0Y2aDly1W8P\nHU4Pt6e9dNBTGmM4o3tTpq/axqacSuqLSaWUYBIREREREREWbwxvhXv2/DTO6dU8dGR7gddZwZQc\n72F0v1bUSYzlL6ccHRofu+wL+O6B8GQ9LoU+10UncDlylV7FNPddyMs+6CmHHdMEa+GnpZsPeq4j\njRJMIiIiIiIiR4iF63dy7fiM0Ja30jbscFZsPH1e91BfQkwwwVTsxx+w5Bb5SE5wjnC/cWA7AHqa\nZbg+uQYInuTVsi8MexaMieAnEQGO6gdNezrX/iKY+cZBT9m2QW3iY1ws25R30HMdaZRgEhERERER\nOUIMe2Ey3y7cRMbq7eXu5QdXKaW1CBfkrhVcwfRBxjpWbcnDWkgNJpjcLsNRZiNjYp8JnxhXty2c\n/zZ4YiP8SURwkpj9bgq3Z46B4vyDmtLlMrRtUJvlm5Vg2l9KMImIiIiIiBwBrLWh65vfnVOmDZBf\n5NRZKkkqAaEtcmN+XcXIl52j4FOCCSZ2bWVczD+oZ3Kddq16cPGHkFgvUh9BpLxOIyClpXOdvxV+\nf/egp2zboDZ/ZCvBtL+UYBIRERERETkCLN6QG7reuquY5ZvzCAQs+cVOYmlTThFQNsHUql5i6Don\nWOg7JSEGvAXw3oW0dm1ybnri4cL3dWKcRJ/bA31vCLenvgSBwEFN2aZBIlk7Cij0lt9KKpVTgklE\nREREROQIULDbl+Wb353Dg58vpPPfv8UfsMxZ52ybS4zzhMY0To4v84zLQHrLJJhwFaybDoDFwMgx\n0OLYCH8CkUr0uATiU5zrbSth2dcHNV2bBrWxFlZv3VUFwR05lGASERERERE5AuzILy7T9gUsb01d\nAzjJp7xCH2ktUolxh78mulxlC3V3b5ZM6v9uhyVfhPrM4Meh84gIRi6yF3FJ0OvycHvKiwc1XZv6\nzsq9P7KVYNofnr0PERERERERkZpu6y4nwfTLHSdz4lM/sqJUEeP8Ih/bdhXTqWlyued6HVWHWWu2\nA5brit6EuR+Hb/a9sez2JJHq0ufa4PY4L6ydCpkZ0Dz9gKZq08BJML0zfS1FPj8uY4h1u/C4XXjc\nhhiXi7qJsbRtmEicx72X2Y4cSjCJiIiIiIgcAd6fuQ6AerXLn/CWX+xnW34x9RLL3/vw2r60uecr\nbnJ/wuDcUsmlHpfCaY9GLF6R/ZLcFI45D35/x2lPeQFGvXVAU9WK9dC/XT0mr9jC5BVbKh3ncRm6\nNU/h2NZ1aV0vkfRWdWjXMOmA3nk4UIJJRERERETkMLdm667gKqSyRbxL5BX52JHvpU6t8gkml8vw\nJ/e33BbzUbiz0wg443nnmHiRQ0W/G8MJpsWfw7Y/oG6bA5rqrSv6kLW9AL+1+AMBfAGLz2/x+gN4\n/ZZNOYUsXJ/D9FVbGfvrKnwB51TGUzs34v9GHkP92nFV9alqDCWYREREREREDnPrthWEro0xNEiK\nIzu3KNS3fodzv6LVTWT8m4dj/hNutzkZznkDXNoaJIeYRl2g7SBY+T3YAEx7BU5/6oCmcrsMLevV\n2uOYM7o3BcDnD5C5vYAv5q3nxR9WcNGYaUz8c39qxR5ZKRcV+RYRERERETnMZW7PB+Cj6/oCMLxb\nkzL3NwWTTeVWMGX8G764Ndxufiyc/1/wHHmrM6SG6HdT+HrOfyF/W8Rf6XG7aFU/kRsHtmfMn9JZ\ntimPN35dFfH3HmqUYBIRERERETnMLd+cR6zbRVqLVAACwe08JR7+fCEAdUvXYNo9udS0B1z8EcTV\njni8IgeszQBofIxz7c2HjLFRff2JRzfgtM6NGPPrH+wq8kX13dUtYgkmY8ybxpjNxpgFu/XfZIxZ\nYoxZaIx5slT/3caYFcaYpcaYwaX6hwT7Vhhj7opUvCIiIiIiIoerRetz6NosGY/b+Qrot2UTTF6/\n0+5ScorcrHHlk0uXToSE1GiEK3LgjIF+N4fb018Hb2FUQ7j2pLbkFvqYMDszqu+tbpFcwTQOGFK6\nwxhzMnAm0N1a2wV4OtjfGbgA6BJ85mVjjNsY4wZeAoYCnYELg2NFRERERERkH+UUesusThrRvRkA\nNw9sV2Zcaq1YmDEGPr8l3NkkDS79RMklqTm6nA3Jzt9xdm2Gee9H9fU9W6bStVkyE2ZnRfW91S1i\nCSZr7S/A7psdrweesNYWBcdsDvafCbxnrS2y1q4CVgC9gz8rrLV/WGuLgfeCY0VERERERGQfrd2W\nT3J8TKjdu3VdVj8xjBFpTUN99w7tCD8/BV/dHn6wSRr8aSIk1IlmuCIHxx0Dx10fbk/9FwQCUXu9\nMYahXZvw+7odbNhZsPcHDhPRrsF0NHCCMWa6MeZnY8yxwf5mwLpS4zKDfZX1l2OMucYYk2GMycjO\nzo5A6CIiIiIiIjXHyuw8cgq9vDtjLbmFPrLzisqNiXGXfCW0nL3lFfjx0fDNZr2UXJKaq+dlEBfc\n8rllGSyfFNXXD+7SGID/Ld68l5GHj2ifmecB6gLHAccCHxhj2lTFxNba14HXAdLT0+1ehouIiIiI\niBzWBj3zM20bJJIQ6wYgv9hfbkyM24UbP//neYP6838O32h9ElzwNsQlRStckaoVnwy9LoMpLzrt\nKS9ChyF7fqYKtW2QSN3EWBZm7YzaO6tbtFcwZQIfW8cMIADUB7KAFqXGNQ/2VdYvIiIiIiIilSg5\nvWpl9i4WZOUA4Dam3LhEU8SrMc8xylMqudRxOFz8oZJLUvP1uQ5cwXU1ayZD1qyovdoYw9GNarN0\nU27U3lndop1gmgicDGCMORqIBbYAnwEXGGPijDGtgfbADGAm0N4Y09oYE4tTCPyzKMcsIiIiIiJS\no2zOLb8dLq3lbkW6czeS8t4ITnWX+tKddjGc9x/wxEU4QpEoSGkOXc8Jt6f8K6qv79AoiWUbc7H2\nyNhkFbEEkzHmXWAq0MEYk2mMuRJ4E2hjjFmAU7D7suBqpoXAB8Ai4Bvgz9Zav7XWB9wIfAssBj4I\njhUREREREZFKbMopeyx7n9Z1uWNwh1IDFsKYQbDh93Bfv5thxL/AHe1KKiIR1PfG8PWiibB9TdRe\n3aFxMruK/azdlh+1d1anSJ4id6G1tom1NsZa29xaO9ZaW2ytvcRa29Va29Na+0Op8Y9Za9taaztY\na78u1f+Vtfbo4L3HIhWviIiIiIjI4WDGqm1c8Pq0Mn0vXdwzXNB7xfcwdjDkZALgsy7u8V4Jpz0C\nrmhvchGJsCbdoM0A59oGYNrLUXt1t+YpAPzzu2Us3pATtfdWF/3fQ0RERKSa5RZ6+ed3yyiooACv\niMi+yin0klPoZdRrU8vdS02IAWvht+fh7XOhOFgXJjaJovPe5da7Ho9ytCJR1O+m8PXs8ZC/LSqv\n7dA4iY6Nk5g4dz0/Lj38T5PT2kcRERGRajZx7nqe/345z3+/nNVPDKvucESkhhr63K9k7Sio8J7H\nXwAf/xkWfhLuTG4GF31AYuOuJEYpRpFq0XYQNOwCmxeCdxfM+jeccFvEXxvjdvHFTcezKbeI2nGH\nf/pFK5hEREREIqDQ66fQu28rku6fuCDC0YjI4W5ldl6lyaWWZhO8cWrZ5FKL4+DqH6Bx1yhFKFKN\njCm7imn6a+ArXwg/EjxuF81SE0hJiInK+6qTEkwiIiIiVezF75fT8f5v6PbQpAN6/rcVW0JHjIuI\n7Ivn/re8wv6hrul8EXuvs3KjxLFXw2WfQ1Lj/2fvrOOrrP44/n5urZOuAaOlu8sApFQUDERQTMQu\nRP0p2B2YqJggSilISXd39xgwxjbWdfP5/fHcju1ubGxs5/163RfPiefcc8f23HO+5/v9fK/S7ASC\nckCr2yGslnKdfQkOzCnb+VRAhIFJIBAIBAKBoIT5eMVxAAwmS6F9lx9KdCmfTMpi9A/beHHu/lKZ\nm0AgqJho1ZJLeemETuxq+y/f6D4nXLJmsFIHwC1fw5CPQKMrg1kKBGWIRgddH3WUN09TdMkEJYYw\nMAkEAoFAIBBcAdvPpGK2uC5Q29WLtF/nGgr2RJq9Pd6l/MbCw4AS7iIQCAT+YLbI7IxLo2/Taozp\nVp9P++tosegWqhyb5egUGQMPLIP2o8tuogJBWdNxHOhClevko3ByZZlOp6IhDEwCgUAgEAgExWTt\nsSRGfbeFX7fEcT4t16655Ky9tDc+vcAxbJoMN11XA4CNJ1MAyBUZ5QQCgZ+cTMomPjWXQS2r82bN\nDdy2415IPmJvT48dBo9uhDodynCWAkE5ICgSOox1lDd9XnZzqYAIA5NAIBAIBAJBMVl2UAlv+23r\nWXq9v4Yn/9jDllOXOZqYZe8TXEDWmGOJWfy9NwGA6WM6urRl5htLYcYCgaAiYTBZmLroMKeSs6kv\nJTJ414OwbBKY8pUOmiAYPo3IMb9BYETZTlYgKC90exQktXIdtwES9pTtfCoQwsAkEJRT9sSn0X7q\nf2yynmQLBAKBoHyxTW72lwAAIABJREFU7OBFFu1TjEOnk3MASM0xcPf3W136+dJhir+cy8DP1tvL\nkiTRtEaovTy0Ta2SnrJAIKhg/Hc4kZ82neLAvPdZpptERNIOR2ONVvDwWuhwn5JBSyAQKETGQMvb\nHOXNX5bdXCoYwsAkEJRTjlzMIi3XyMxtZ0t87K2nL7Ngz/kSH1cgEAgqC+dSc3n0993kuIWx7Tyb\n5tFXb/Ie6nYxw5FOvFfjqgCoVY6l2bxdFzy0nQQCgcCZtRs2MFv3Fi/xE0GSQamU1NDnRXhoDVRv\nXrYTFAjKKz0mOq4PLYD0eN99BX5TqIFJkqRgSZJekyTpe2u5iSRJQ0t/agJB5cZsUU68s/JNvDz/\nABfS8wq5wz8MJgt3Td/KM3/uK5HxBAKBoDJy4EKG330nzTvgtT4jzxECN6F/IwCOXMy01+UZzczb\n7TgM2Hwqhfm7xeGAQCAADDmc/fN53k16jK6qo/Zqufp18NAquP4VkSVOICiI2u2hQW/lWjYrGeUE\nV4w/Hkw/AXqgu7V8AXir1GYkEAgAZWMBsOFECn9sj2fyfO8bFF+cTMpi3i7Pjcib/x4ukfkJBAJB\nZWXMj9uYMHN3of1U1ogUXwcEBrNykHDTdTXo0iDaax+9k1j4Pd9v49m/xOGAQFDpObqEnE87Uf/I\n92gl5RlhklVMM92K9PBaZeMsEAgKp9fTjuvdv0J2ctnNpYLgj4GpkSzLHwBGAFmWcwERxCsQlDL5\nRotbuWjZhG77ajPPzdlHtt6RHluWZX7b6gi5K+qYAoFAUNlJzzWw4YR/2nhf3qNka2pb17uwrtFq\nYJo8uAUatfclWYBGESE1mb3rOAkEgkpE8nGYOQpm301IXoK9erulGUMM76C64TXQBJThBAWCa4xG\nN0Cttsq1KR+2fVO286kA+GNgMkiSFATIAJIkNULxaBIIBKVInpvxJyvf5KOnd7KshqX0XIO97uzl\nXJc+s7aJWGOBQCAoCptPXXYpv31bK6/91r3Qj8Gta9GwaggxVUK89jGaFX0lrdr3uV2AVlmqpeeJ\njHICQaUlJwUWPwdfd4MTy+3VqXIo30c/x52G1zgmxxCsU5fhJAWCaxBJgl7POsrbf4B8/0PgBZ74\nY2B6HVgG1JMkaSawCnixVGclEAhIyzG4lFOyi2fX3RGXar8e8sUGl7apIlxOIBAI/OZkUjafrjhu\nLw9pU4vRXet77VvfalQK0KjIN5oZ/uVGftp0xqWPzYNJ5+S9NKFfI/o1q8a393ZwactzEhP/Y3s8\ngz5b7zM7nUAgqCAY82HDJ/B5O9jxg6ITA1iQmGXqz/X6jzlQbRgNqoYBoFGJIBOBoMi0GAZVGivX\n+gzY8WPZzucap1ADkyzLK4ARwDjgD6CTLMtrS3daAoEgPtXV2yhQ6/+plPNGxCbmffxSlj3b0WtD\nryuBGQoEAkHl4rk5+ziRlG0vP9CzoUt7dIgiqNuomsNjKVCrJt9oZv/5DKYsUoz6sixz4lIWRquB\nSOtkYHpxUHN+vr8LjasrG0abTlOOweHF+vL8AxxNzCI9z/UgQiAQVBDMRtj1M0zrCKumgCHL3iQ3\n7MPd0vtMNj1EOmGM79WQNtYw3CCdpowmLBBcw6jU0NNJi2nr12AsmeRKlRGfBiZJkjrYXkB94CKQ\nAMRY6wQCQSniHiJX2KlUrsHEV2tOYjRbuJSZ79H+nJMw7OiuMfZri0iBLRAIBH5xJMGR4e2WdrVp\nXUfZ1L00SEkDvuTJ3vxwXyd+eaCLvV+ARkWO3jXE+dctZ7np0/X8u/8iAFqN53LM5rlksobR5Ro8\nNfNy9UJHTyCoUJhNsHcWfNkJFj0FmU7JWqo2hbv/JOnWv9iWV5fJg5sT994Q2taLJCRAMSzViggs\no4kLBNc4be6E8DrKdU4y7Pm9bOdzDVOQmftj67+BQCdgH4q4dxtgJ46scgKBoBQwmCz0aFSFFrXC\n+XHjGfSFhEJ8s/YU01afJFin5rpa4S5tRxMz7Sm1m9cMI1CrpkNMJLvj0/lr5znu6hLjbUiBQCAQ\nWNl3Lt3uTaSS4PO7HFmaHuvXiMf6NQKgptsGL1CrJinLEeKcbzSz9KBiWNp5Ng3wrsGksdZ9vuoE\nw9rWZq6XrKDOXk0CgeAaxmKBQ/Nh7Xtw+YRrW3BV6DcJOo4DtZbTVh245jUda72Xb25O27oR9GhU\n5SpOWiCoQGh00H0iLH9ZKW/+AjreD2rhFVhUfHowybLcX5bl/iieSx1kWe4ky3JHoD1w4WpNUCCo\nrBhMFiKCtLw29Dru6RqD3mTh8Zm7+WvnOZd+m0+lMHHWbqatPgnAlEWHuXP6Vpc+3649Zb8e2qYW\nAG/eqgjTXszw9HYSCAQCgSvJTkaiZU/38fu+AI2K82mOkOeJs3az9XSqSx+tynM5Zgubi0/N5efN\nZ7wmZfhlc5zf8xAIBOUQswkOzIVve8K88a7GpaAouOF1eGofdHkI1FoAzqTkABDrFIobFqjlzs4x\nSJLQYBIIik3HsRAUrVynx8PBeWU7n2sUf0xyzWRZPmAryLJ8UJKkFqU4J4GgwpKZb0Sjkgj2I0be\nYLbYNxgBGhV6o5nFBy6y+MBFRnWqZ+837qcdPoVeA7Uq8o0Wl5TaE/opInYta0dY20WIhUAgEBTG\ntNXKxm/1c32JrRbq933/Hb5kv5YkWHkkyaOPyksIdHiQ43siId37QYCzZ5RAILiGMObDvlmw6XNI\ni3NtCwhXPCm6PQaBnh7pkxco27LaEUFXabICQSVBF6L83a15Wylv/ARajwQvh0AC3/hjYNovSdIP\ngC0QcTSwv/SmJBBUXNq88R+x1UL4cWxndBoVdSJ9Lw7OXs6lU33Fih6oVbuEyK04fAmLLBNbNYSI\nIK3Lyboz+UblnstOGemcNzKhAVqy9CLEQiAQCApj33klzLjWFWzqqoYGeDyvbdni3AnQOBI7bD/j\n8HjSqiWMVl2miCBtseciEAiuPoacdI4s+ow252chZV9ybdRaN7c9JireS27oTWaGT9sEwLgeDbwa\npgUCwRXS5SHF8GvIhuSjcHwpNB9S1rO6pvDHwHQ/8BjwlLW8Hvim1GYkEFRQ4i8rIRKnk3Po/9Fa\nAOLe8/7AOnJREZKdt/s8H49qS4BGZdf+APhx42l7iEVs1RCfBqYWtcLtY3kjQKNit1UDRCAQCCoz\nGXlG8gxmDw0lALNFRqOSeLhPLEE6/zN6uhPkJRto67qRhd53Id2RzSY6RMelTOWZn5UvDggEgmuC\nnBTY+g3ylu9oa8pyaTIFRJDQdCwxNz8DwdE+h3j2z332teDrw0Q2YIGgVAiKgk73w+ZpSnnDJ9Bs\nsOKCLPCLQv29ZFnOl2X5U1mWb7O+PpVlWYi2CARF5LGZu/zuezTR1SjkfJINkJnn2FTovGQfsjHt\n7nYu5Z6NXcUfL6TncTQxiwaTFvPblji/5ycQCAQVCaPZQtsp/9Ht3VVk5Bo92i9l5mOyyNSJurKQ\nFBnPrJ0hfhisMvKUOY3sWBeNk6u+e3Y6gUBQzkg/B0tehE9bwYaPCHAyLiVL0bxpHE2bjE/ps6Mb\n5kBPryVnFh+4aL8WWksCQSnS7XFQ65TrCzvhzLqync81RqEGJkmSzkiSdNr9dTUmJxBUJA4leHoS\nnUvN9dJTSSYC8OoQRe4sUOv6p5qQ4TjNbuRDCyQ0QENksM5evrFFDX57oKvP+b357xGfbQKBQFBR\nkWXZRUB7/Ylkjz42D6K6UcFFHn/pU73t1+dS8zzabenF/eHB3rEu3ky5BqGhJxCUSy4dhvmPwBft\nYPt3YHL83Z6x1OAl40P0zPuUH81DyEXxmszM8zRuOzO4dU0APhnVtvTmLRAIILwWtBvtKK99H2TP\nAyKBd/xZ1XRyug4ERgK+/TcFAoFXaoYHkpjp6vx3KTOfetGeGxaz9SE2qJWymHD3YGpdJ8Iu3O1L\npDs8UENUsI7IYC3puUaqhuoKjNc3iwenQCCoZMiyTP+P1hJ32WHs33AimWFta7v0G/ntFoACdfN8\n0aJWuM+2YW1r25M5+INW7XiG39iiOiuPJGGxyEKLRSAoD8gyxG+BjZ/BieWe7TVbMydoFG+daUKG\nwTM5S67RjDcfpgvpefy54xwGk0yLWuGM6FC35OcuEAhc6fUM7PkNLCaI3wxxG6Ch/xlkKzP+hMhd\ndnpdkGX5M0AoXQkERSTX4BnKEHc5l4MXMvhrxzmXerNFMfbYQiHcPZjSnUI48k1mr2HB/xvWErVK\nompoAAARwZ5isDXCAzzeUyAQCCoDsiyz7niyi3GpdkSgSwgywKnkbPt1cQxMvuhYP4ppd7cvsI/O\nzfik06i4q3M96kQG2T2f0nIN3m4VCARXC4sFji6GHwfATzd7Gpfq94TR8+CRDewK7Uegzrs4f54P\nj8RnZu/li1UnWHnkEgEFyCIIBIISJKo+tLvHUV77ftnN5RqjUA8mSZKc05uoUDya/PfnFggEgPdQ\nhjMp2Tw/Zx8AozrXs9ebrMYetfVU2l1nKSnL4QmVb7TQo1EVZj7YjX/3JzBx1h7evLWV3fspyeo1\nVTUkAHf+e7ov13+81p5l7kJ6XoluoAQCgaC8smDPBZ79a59LXWSwDpM1RtlgsvDMX3tdEiVcicB3\ncegaG233VgXF4PTe7W0A+GN7PP/sTXBJACEQCK4iJj3s/ws2fwEpx90aJSXzVM+noV5nQJFFmL3j\nHOGB3rdRvgxMZ1Nz7NfuB44CgaAU6f0c7J2leDGd3QhnNkDD3oXfV8nx5yn1sdPrXaADMKo0JyUQ\nVBS+WnOSu6dvJf5yLiaLTLDb5sSWatods3XDoLEamNRuLkq2DEKghMgFWkPohrapTdx7QxjTrb69\nPdOaZahjA0/H64hgLc1qhtnLHyw7Cign+ysPX+KNhYeEZ5NAIKhwHLyQ4WFcAiUEzfZcnr/7PIv3\nX+R0cg46tYr5E3oU+/28RbD5E9X2zb0d3ebnWLbZvJsMJmFgEgiuKvmZsOkL+LwtLJzoalxS66D9\nGJi4A+6aaTcuAXy5+iTgWJeBkgnYxtpjSV7frnlNR5itu2SCQCAoRaIaQNu7HeV1wovJH/zxRBov\ny7KLqLckSQ1LaT4CQYUgR28iz2jmw+XHADiXpoRgtKwdzo64NHs/542BLMv2rCB2Dyar3kZB+hr5\nRjOBXlJfu+NLDDw80OGqnWr1ZPp1y1leX3gIgPG9GnrViRIIBIJrBb3JzLN/7qNGeCBt60Xw48Yz\nLu2zHuyK3mzhy9Un7R5Mzpp5bepG0CGm4AxPBbH86T78vfcCX605BcCdneoxoX+jQu8LDdAwY1wn\nHvh5JwAaJw0mm2drSRiYjlzMZP/5dO7sHHPFYwkEFZa8NNj2HWz9GvIzXNt0YdD5Aej6mCIQ7AXb\n32+fptV4fkBTLucY+H3LWU6nKB5KH684zscrjrP6ub7EWtdse+LTWHfckXhAeDAJBFeZ3s/Bvj8U\nL6a4DRC3CRr0LOtZlWv8MTDNRfFacq/r6KWvQCAAhnyxwUXXY+vpywBUsYapjexYlzXHkjiZ5ND2\n0JssdkOR2SKjwoI27RRknCbm9AmeVO8nSspChQU1FsyoSCeUjNRQAuRacFEDVRqDzrsxyJdL9iN9\nY1l2KBGA2hFKeNwf2x0ZlfJ8iIgLBALBtcKG4ykuKb6rh7mGDHdvVAVJkvh27Sm7B1OWk5eB/gqN\nOE1qhDGuR0O7gen9O9r4fW9sVcfhQJDTYYLNwHQoIZMmNcI87isKN3++AYBRneqJ9OcCgTu5qYpR\nadt3oHfLCBxaA7o9Bp0egMCIAocJsx7ofTO6g11DrWejqvy585yLx/jCfQk8fWNTwHU9BsKDSSC4\n6kQ3hLZ3wZ7flfK696DBorKdUznHp4FJkqTmQEsgQpKkEU5N4WDNpykQVDB2xKUy8tstbJ50PbWL\nqUWUbzS7GJcAplndoqNCdACoJAmtWkV8qqPf5ZRE6qTvhrhN3HZoDeMCThHwnSLm3QJo4V0TUiEb\n+O4D5To6FmK6Q72uENuPqqEBpGTrfW4a2sdEEffeELq8vdIuFt6xfhRHE7MAxRtLIBAIrlXyjWaX\nZy1ASlYefarmIKWepk1EPtLGY5CTzOMZJ1CZ9TA7nDvOJ9NDayAfLbUs0bB4AYRUh7AaEFZLcZ2P\njgV1QQ9nB+4h0v6idvJg1TiFyNnEfp/+cy8ta4cXy8i0/3w6T/+5117OzDcREeTf5xEIKiLpuQae\n+XMva44ls/OZdlTdPx12/ACGbNeO0bHQ8ylocxdofW+LcvQm7puxnVGd6pJvNBMWqLEbl0AxFI/p\nVp9PVxy3e5GHWttXHL7E6qPJLuOdvZyDQCC4yvR+Hvb+AbIZzqyHs5uhfvHD5is6BXkwNQOGApHA\nMKf6LOChwgaWJGmG9f4kWZZbubU9B3wEVJNlOUVSdr6fA4OBXGCcLMu7rX3HAq9ab31LluVf/Plg\nAkFx+GVzHKB4HBU3Daw3MW8bMdZQM5UKLLJMXmoC96p3MEi1nZrfHQWUe6sDFPcQOfW08to7E4Bt\n1VuT32kgpDSDqo193haoVZNv9VaqHuZYLPkSnRQIBIJrgSd/WU/W6R08qj5FG9VpmkgXiJEuEZBt\nAh2QB6xS+toTEB+1GvZtNqE0YIeXwVUaiG4E1VtAvS5QrxvUbA0anUfXID9Cmb1RI9z75tW2GQW4\n+/tt7Hz1xiKPPfzLTS7l9FyDMDAJKi2yLPP6wkPsOXaaSZpFRE0fD+Y8105Vm0KfF6DlCFAXHgiy\n9fRldp1NY9fZNOpGBfmUNHD+ew4L1JCjN/HQrzs9+p1Ly/OoEwgEpUx0Q0WLaa/Vi2ntezB2YdnO\nqRzj88koy/I/wD+SJHWXZXlLMcb+GfgS+NW5UpKkesAAwNnn82agifXVFfgG6CpJUjTwOkrmOhnY\nJUnSQlmW0xAISgG5BPSsfYWU3da+Dlq1hISFptk76Jv3BzcG7EIj+Q69kMNqIVVrRqJUjXnH9BBc\nBbUmgPMZerSYiJByiCSbQXWN1DSeg7Q4JUbYCXXSAUKSDsDWj5RUuR3ug+tuAa2rh1aQVm03julN\njs+QLTyYBALBtYQ+G85uwnxyNblHV/NtxglUulJKVmAxQcox5XX4b6VOE6g8a5sOhCYDlIUpBWvp\nFYR7FlEb3WKr2K9TsvVe+xQV57BAgaCyMXvTUeoe+Jr1AYsIl/JsZ34A5EY2I/jGl+C6W0Hlv7F4\n/p4L9uvzaXl+ifsbzTI9319tL4/qVJfrm9fg0d93CVF/gaCs6GPVYpLNcGad8GIqgIJC5F6UZfkD\n4B5Jku52b5dl+cmCBpZleb0kSQ28NH0KvAj841R3C/CrLMsysFWSpEhJkmoB/YAVsiynWue0AhgE\n/FHQewsExSEuJceu0ZFvLP4XeL4PA1P1IGh6fi6rdT/Q8PQl8LI+SY9qw6zkBmyztGC/3Ig9b9wJ\nwOlTKXx4aBuxuhAWPN6TtlP+c7mvZq8ODGpVS0mZm7AXzm1VUmmeWQdmx6kYZzcpr/9eUzQDOo+3\nawbUjAgkISPP4/Mnl9DGRSAQCEqN9HNwZBEcWwLxW8FiRA2EgW9v0NCaim5dRB0IqQah1XlhyXn0\nsg49GgxoaV83nCf71AFjPuizIDsRsi5BVgJcPgUZ5zzHNeXDqVXKa+mLUK0FtBkJrUfSuUEU/ZpV\nL/LHm3pLS04luYboFDeM24bs5URl+5lUhk7byPQxHRnQsuYVjS8QXDOYDLDrZwateocordsZdo3W\nPHr+BpYndmJ2cA+6FsG4tPFECov3X3Sp85WY998nenEqOZunZu8l32gmPVeRSNCoJP43rKXdMOV8\nACgQCK4i0bGKFpM1QoRVU+H+pSB0Cz0oyLfziPVfT//MYiJJ0i3ABVmW97npwdQBnFdp5611vuoF\nghLntX8O2q+v5As8OUsxyNSJDOJCeh4BGHg7ZhcjTsxFlXUR3A6jt1mas9jcleXmzrwzYAAf/OL5\nJ6eWHNnkvIUv2FNXawIgpqvy6vmUsiE6sQIOzoNjSxWrO0BOEqyaAhs/he6PQ/eJ1I4M5FBCBp/8\nd4wZm84QGqAhx2AiKVMYmAQCQTkk7aziNXT4H7iwy2c3syxxXteQ+q17Q52OSghblcYQGO7Rd86i\nxS7lyCp1oFU733PQZ0Hycbi4B+K3wbltkH7WtU/yEWUhumoqc+r3hCrjwBTjNYzOF/d1b1Bge/Oa\nYZjMFhLS84mp4l/Wzxmb4jzqpv57GFAyiQoDk6DCI8vK+mjVVEg/i3OeyFOWWmxt+Dijx01k2ctL\nADidkkNXJ8/Bwvh2nSLq/9xNTfl4xXEAbm7l/e+qVZ0ImtUM46nZe12kCdY834/QAA2yLHNb+zqM\n6lSviB9SIBCUGH1fhP1/gcUI8Vvg5EpoclNZz6rcUVCI3CLrvyWieSRJUjAwGSU8rsSRJOlh4GGA\nmBiRZldQdDacSLFfX0nGoFnblOjPptVD6JC5iikhc4lOSnTpIwdGkNZkJCN3NuOU7LCZ+jrZsom8\nqn1YybVqH2lrA8Kg1QjllZUIe2cpYpWZVpdtfSasfRd2zqB31ftZqG/NF1ZB8nyjGZ1aRUae0d+P\nLhAIBKWLIVfxVNrzm5Iu2AdpoU2Yk96UjZZW7LA0Y/q9fajfpFqhw/84thPjnYz8qsJOJgPCoG5H\n5dX5QaUuPR5O/AfHlytioKZ8R3+bF+nyV6DT/UrmqbArN+R0qB/FB8uPMX39abZNvsGnbpONjFwj\nb1qNSd4objifQHDNcH4XLJsE57e7VCfI0XxquoMj1YYQmK+j2VmHR1NarsF9FJ/IssyJpCxubVeb\nJ25owoT+jVl7LInrm/v2YNSqVWhUErlWT/jaEYHUs2p3SpLEp3cWYOwWCASlT1QD6DgOdnyvlFdN\ngUY3KOK6AjsFhcgtQtE98oosy8OL+F6NgIaAzXupLrBbkqQuwAXA2SRf11p3ASVMzrl+rY/5TAem\nA3Tq1KmUxBYEFZXTya6hB77C3PyhVmQg7aSTfJj5HlV1+8HZPhNSHXo8gdTpATKyJE7tWOty74EL\nGV7HtC32fS36fRqYnAmrCb2fhe4T4cBfivfSZcWYRPYlBme/R6xUj8nSeHbLTdFpVOQazPy8OY7e\nTapyQ4sahb+HQCAQlAYX98HOGXBwvmeacACVBkNMb9442Zjho+7ng43pnMjOtmsK9fbDuARKBk1n\n5u0+z8ej2hZtrpExirGp84OKHtSxJbBvNpxeA7L18CInCda9Dxs+gQ5joNezEFl8zwSDycKGeOWQ\nJDlLX6iBaeiXvo1zoITlCAQVkswEWDkF9s92qTYGRPFe9hD217qDkd0ak3LgImuOJXPHtw4Z2rQc\n/w1Ma44lcSlTT9t6kYByUOjPOipIq+boReUZN753rN/vJxAIrhJ9noc9v4MpDxIPwOEF0Or2sp5V\nuaKgELmPSvKNZFk+gDU5FoAkSXFAJ2sWuYXAREmSZqOIfGfIsnxRkqTlwDuSJNlWfAOAl0tyXgIB\nQGKGcsL89I1N+HrNKQ8PpsMJmeQZTXSsH13wQPmZ9D/1AS8FLECV7mTnDK4CfV6EjmPt4tra3FyP\n248letk44ThF92VH0mmKsBnQ6KD9vUo2hH1/wOq3IEvRCGiuOsdc3RR+N9/I9+rR5KKEcGw+dZkb\nWtQgz2BGkvCZBUUgEAhKDLMJjv4L276D+M2e7ZIKYvsrC7tmN7PylJ5ZR3cza1Zcsd8yLNA1BPmt\nW1v56OknAaHQZpTyyroEe36FHTMUDSdQ3Ox3zoDdv0H70dD7OcVA5SfbJt/AwM/WozdZ0KqV7wG9\nyYwsy6w6kkSvJlW9Pq/PpToyUfVtWo07O9djwszd9jq1MDAJKhomPWz+QjHqGp3WX2oddHuMLbXG\n8uPvR1kwvB3tY6JYeyzJY4iCsgQ7k5Kt54GfFU/IqGD/Q2FBEfVfcywZgH7N/DOMCwSCq0hYTej2\nqHJQD7D6bWhxi19ZJSsLPt0eZFleZ3sBW1CS9KYCW6x1BSJJ0h/W+5pJknRekqTxBXRfApwGTgLf\nAxOsc0gF3kRJDrwDmGoT/BYISpKLVgPTLe3qYDBb+GbtKZf2wV9s4PZvCkmmeGwZ8tdd6ZYyH5XN\n+U+tgx5PwhO7lYeRU+Y258xA617opwyRmOV1aItVjNUWIjft7va8f3tre7u+OKLkKrViaHpiN1z/\nKia1MjeVJHOfZgXz5GfprdoPKCdqiRn5tPjfMnq8t7qgUQUCgeDKyE1VFm6ft4U5Yz2NS9GxcMP/\n4JlDMGa+YpgJjmbvuXSXbo2rh9KlYTRPXt/Y77d2N6yM7lqCIfdhNZT05k/vhzt+grqdHW0WI+z6\nGaZ1gpVvQL73wwZ3aoQHUisiiBy9yX4QkZln4lBCJg/+upMpiw4VeP9j/Rrx8/2dGdy6lku98GAS\nVChOr4NveigHas7GpeZD4fFtcNNUcmQlFC1IpxhkJS/ZAfwxMBnNFv7c4ZCPLeqB3GUnL6l6Uf7p\nqQkEgqtMz6cgQEmSROoph/C3ACjYgwkASZKGAN8Cp1BysTSUJOkRWZaXFnSfLMsemefc2hs4XcvA\n4z76zQBmFDZPgeBKSMxUDEw1Cwkr8Ep+Jix5AfbPdl2ONBkAN39gT1Htjs7JHckW4hZ32dOrCcBi\nFWeyhcgNa1sbgJfmHQCgXUxk0edtn0gw9HmB9dp+sOR5rlfvBaA6qfyme48fTTeTYpzEgE8Vu3Kq\nFxfxHL2Jlq8vZ0SHOnwySmgECASCYpCVCJunwc6fwJjj2qbSKOnBO4+HmO5es7bsOuua/WnG2M5+\nC15744WBzZBKIzuMWqvo4rW8DU6vVULl4q0HGGa9Ylzb8zv0fwXajyn0VLROZCDnUvPQWD2YDGYL\nKdbsn85h17Iss/5ECmcvO362t7arY/+M1cIC7EkqakYU47tQIChvZCfDf6/A/j9d62u0goHvQGxf\njGYLTSY5xP1YVEM4AAAgAElEQVSDCjAI5RpMPtvm7jrPjI1n6BZbhRmbztjrA7VF02ZpViOMY5eU\nw0abV6JAIChnBEVBzydh9ZtKed370OZO0IrvTvDDwAR8DPSXZfkkgCRJjYDFQIEGJoGgvGEwWfh6\n7UnGdm9AVIiry3JajoFgnZognZrbO9RlzbEkcvQmQgIK+ROJ3wbzH3LJGpQihzPVeB9f3PNWgakr\nnT2YCtNQMltcPZhsfHF3expXCyVYd+Vumaro+owzvsAQ8zamaH+mqmTVANAsJeHAMVbqHyWTul7v\n3Wnd2M3ffUEYmAQCQdFIi4NNn8OemYqBxZngqooQdqcHILyW19sBpi467GJgiokOLrZx6bfxXYgK\n1tGqTkSx7vcbSYJG/SG2nyIGvvINSLCGqeUkw79PK+Fzwz6HOh18DhMdouNQQqb9+2rd8WQCrN8v\nFifn1oX7EnhqtnKAEBaooX+z6jSrGWZv3/Bif2QZ2kxZXuAmWyAo91gsSjjqitch38mzMSAcrn9N\nMVSrlN/xOTvPu9waaQ1pk91kaEMDNCw/dMnnWz4/Zx+AR2KUoorCfjyqLUOnbQQoHQO3QCAoGbo9\nBtu+Vb6vMy8oSZR6TCzrWZUL/DGrZ9mMS1ZOA97jeASCcsjOuFR+2nSGP3fE89nKE/y0Oc6jT47B\nbF+chwVqSM0xcNMnBUSCmk2w5l34aZCLcWmeuRc36j/E0GJEgcYlcPVg0hVmYJJdPZhsDG9bm+tq\ne6baLg6hARpAYrGlG4P073MotLu9rbb+NP/oXmOYSglV0ZscbuKyLDN2xnb34QQCgaBgUk7Agkfh\niw6KIcXZuFT9Orj1GyUM7vpXCjQu5RvNdo+BV4e0ADw3h0Whd5NqpW9cckaSILYvPLgKRnwP4Y7M\noiTuhx9ugGWTFcFwL+g0KgwmCyeTlPZZ2+L5aVMc4BryF+/kIZuVbyIm2tUAF6hVDlmMZpntZ1Ix\nmoufTVUguJyt574Z21085q4KaWfh1+Gw6ClX41LLEfD4duj6sN24lK03MXnBAXuXOpFBRAQpOmzO\n0gM1wgPo0jDafk9BXEjPcykH+JOExYlCDzYFAkH5QBeihL3bWP+hEuIv8MvAtFOSpCWSJI2TJGks\nsAjYIUnSCEmSRpTy/ASCYvPdulN8s/YUd3y7hSmLDnMuTfnS9+aunGswEWKNu48MVhYXCVZdpoxc\nx2nUDxtOY85Kgt9uhXXvOTICBUbwYdhLPGecQDphfHZX4V48toV/27oRaAsR6badQrt7MJUkzjoB\nsQ0bUuXBBbxqvJ88WTnNC5b0TNN9yWua30jJcCwYnRdTQrdDIBAUSno8/P04fNVFSTQgO+ma1OkI\nd/0Bj26Cdvf45W5+1mo46dIgmvG9GvLdmI78NK5Lac2+9FCpFDHwiTuh/6ugsX522QJbv4Kvu8HJ\nVR63adUqF90WZ5w9kSKCXQXMW9TyfTix82wa9/0oDg4ExWfJgYusP57MqO+2cNBHhtwSRZZhx4+K\n1lKcU5bEqAYweh6M/MnDUP3ZiuMuZZtxCRxnhLMe6sq2yTdyewfFg/ufvRdc7tl3Lp3PVrqOY2Py\n4OZ0b1SlSB8jJEB4DwoE1wwdxynPGFAM2utLNEfaNYs/BqZA4BLQF+gHJANBwDBgaKnNTCC4Qt5d\nepT3lx21l6evPw14j6/P0ZsJsoaZNXcKGQBIz3Ms3BcvWUjm591dFy/1e8Kjm9gR0h+AsACNX6KO\nkiTx7xO9+HV8V68hcrMf7ma/blQ9BIDbO3oPUSsJApxC9m5rX4eakUH8br6J4Ya3OK9ynKiP1ywl\nas7tkK1kWEnLcRjgTBaZc6nedaQEAkElJ+uSolf3RQfY+7vDQA/QsA/c94/ixdN8sGJs8YN8o5mB\nn60HlCygkiQxsGVNGlcPLY1PcHXQBUPfF2DCFiV8zkbGOfh9hPIzNDieswV5wNq8X8+n5TJl0WGX\nttBA754SI9orz/stpy8Xb/4CAXAoQQmzv5Spt4d8eWPurvMeRpsikx6vHPwtfhYMVk8/SaUI8U7Y\nCk1u9LjldHI2P2xUPB/v6lwPUDzYbUy9pRUP94mla0PFQGTTJXtlwUGXcV6at5/PVp7wOq2brqtZ\n5DC3cLdMlgKBoByjCYAbpzjK26fD5VO++1cSCvXDlGX5/qsxEYGgJJFl3+ER7m7/j8/czcojl+hU\nPwrwTFOdZzQDMveqV/I/za/o7OFhEvSbpLhHqtRUC1fSyqqLIMpoC8Nwnm/L2uH8+kAXqoQG2Otq\nRQRx5t3BpRqP76wJZTApP6Oejauw6STcnDuFj7TfMlCtpN0NTtxO/jf9OXbDjyw472qQW34okQd7\nx5baPAUCwTVGbqqisbTtOzC5ho8Q2x/6T4Z6xfM4WnPUkUrc/dl9zRMdC2P+hv1/wfKXIddq8Nk+\nHU6tgRHToU4Hl2e3O7Zn+YO/7LRr+dkI8HFfw6ohJTN/QaXGlp3XhtFsQS1JSJKrtpBNu2h429pY\nZM9MjgUiy7B3Fix9CQxO6h1VmyohtnU7+bz1+o8dMgh9m1Zj9o5zhDt5MNWODGLy4Bb2cgdrMpUm\nbsbrC2luzzQngnVF90YK1Kp5YWAz6kWLDHICwTXBdbdAvW5wbquSEXbl63Dn72U9qzKl0CNCSZIa\nSpL0iSRJ8yVJWmh7XY3JCQTFxX1h48w7S46Snqt4JWXrTSw+cBFwnFy1d8rIZrbIPDJjMx9opvOW\n9id0kmJcMgdEwOg5ioHJGstfzWoQmnZ3+yLP13mxVSM80MW45K1PaRCgcSyEbBmIfr5f2fRlEcwj\nxmd4z3gXZlmZR2DOeRr+cxsnti4C4NM72wKgNwndDoFAAJj0sPEz+LwtbPrM1bhUryuM/Rfu+7vY\nxiWAdKug7rgeDWhVp2T06MoVkgRt74QJ26DZYEf95RPw402w4RMKSlJlMzBlugkPg+/06c5mKOGR\nKvDGH9vj2XKqYA+3pCxXwf6hX2wkdvIS3llyxGv/l+cfKFj70p28dJj7APwzwcm4JEGPJ+GRDQUa\nl5YdTHQp5xiUtZ1ziJw7kiQxokMdcg1ml/qsAjSZgophYAJ4vH9jhluzBQsEgnKOJMHAtx3lI4vg\n7Oaym085wB8f9L+BOGAaSkY520sgKLdczPB9ogSw5phy6r0zziHGFmo9/Q7WaXjFemqVm36JD/Jf\nZ5TGseg5aGnAsp5/QpObMJotduFUs0UmKlhL7ybVijXnd0e0BkBVRllDnE/Bb2hRA1C0PXo3qWqt\nlfjWPJyHjc9i0igna+FSLj9rP2CUeg03tKhBkFZNmg8tEIFAUEmQZTi2VNEMWvk66DMdbTVbwz1z\n4IHl0LD3Fb1NQnoeL89XBHpfHNSsYmdcCq0Gd82C4dNAa/Uwsphg1RSGH36WSLfcKzY9PIPVYzfQ\ny0bXlweTycnLd9L8/SUxe0EFIivfyMvzDzD6h60ebbIsk5lvxGS2cORipkvbsUvK7+gMqwA9wK6z\njjXY7B3nOJ2S498aIn4bfNsbDs131EU3Up4rA94sVLvtdIpDMP/t21pxc6uaDG9bmxcHNSvwvqqh\nAaRk6wv0kndGZGMUCCoJdTtBqzsc5eWvuKZxrWT4k6ogX5blL0p9JgJBCZKVX3CWj8w8E4M+W+8S\nMhDqlLkjNFBDrJRA4C+T6aqKs9fPNffhFeMD6Jek0qx5NmuPJfHW4iOsfLYvepPZxQuoqNhOzspK\nKNu22dCpVbSr5/DiurNzPTacSLGXV1k68luLb7l5/1PUlNLQSmY+0H4PW6sSHdyZ1FxhYBIIKi3J\nx2DZJDi12rW+ShMlG1yLW/zWVyqM7zectl9Xio2cJEGH+6BBL5j/CJxXRLgbpG7k34CjTDQ8yV65\nMQDzHuvB9A2nWbz/IjvjUjGZPTfEvgxMVcMcHrTO3wUCAcDi/YrXt0WGxIx8uzYRwEO/7mTlkSTe\nurWVz/ud/1Y3n/T0gjqdkk3HkGjvN1vMiojuuvddkwN0uA8GvadkdfKDHL0JtUri5Ns32w3TX/jh\nfV41VIfeZOHYpSwupOXRs3FVr/36N6tGkE7tVV9TIBBUUG58XfFeMushYTccnKsk7qiE+PPk+1yS\npNclSeouSVIH26vUZyYQXAG+DEyju8YAcDIpm6OJWZxIcpxihTpl7qifuZMFuv+hzYgDwCJLvGu8\nm+eNj6BHyaqWlJnPf4cuAcpJusFkIaCgWIVC6N+sOkNa1+LVoS0K71wK2DyY6kYHudS763YATNmh\n4Vb9VA5Z6jsq177LgznfsWD3OTafTPG4RyAQVGDy0mDpJPi6u6txKTACBr0PE7YgX3crGflm32MU\nkcvZijE7LEBTsb2X3ImOhfuXQPeJ9qq6Ugp/6aZwn3o5IFMnKohL1lDxO77dQryXUDdnw4Az93SJ\n4ffxXQHFo1cgcGaS1WsQoNu7q1h1RFkH7Tqbysojinf4XzvPATDn0e58dY/rlsFZl0jjxQBz3pem\nUVYi/DIM1r7jMC4FRsDIXxTPPj+NS+m5BpIy9YTo1EV+blS1yhcM+mwD43/ZSaKbHMOwtrV5//bW\nzBjXma9HdyzS2AKB4BonMga6T3CUV/wP9Fm++1dg/Fk5tAbGANcDNl8v2VoWCMol7ovpPa/dRFSI\nYhhacuAip5KzPe5R2TyHdv1C983PIkmKkSpP1vG08XGWWzq79M83mdluDbHLM5rRmywFZvMpjCCd\nmq9Gl53tVqtW8e29HWgfE+VSb/HhCp5IFUYaXudb7af0USsLzvs1y4mQcrjvBwsn37ul1OcsEAjK\nGIsZdv8Cq99yiFCDksGp4/3Q/xUIUbIwzdlxjhfn7eft21oxumt9HwP6x997LrBwXwJhgRo2T6qE\nyxG1VtF8qN8D0/xH0Rgy0Ulmpmp/obU6nqqBA3jihiaMnbHd49bpYzqy8WSKT+ORRq2iZ2Pl/+y/\nw5d4vH/jUv0ogmsHZ1F9G79uOcsNLWrwyYrj9rr95zMAaF0ngsAGas6nNefdpUpWX2cR+Rwv+kV5\nBi9G6LhNMPd+yL7kqIvpoQjdR9Yr0mdoN3UFAPXcDtP8wV0Av99Ha13Kj/aNpWXtiCKPKxAIKgi9\nnoU9MyEnCbIuwroPlLDdSoY/u+GRQKwsy31lWe5vfVXC1ZzgWuBMSg55BjMfLj/mUu8stBis05Dq\nLcZflmHNu7DoSSSLsuhJkqMYafgfmQ0GeXTPN1qcrhUD05V4MJUHBrWqRY1w11PtTvUdruruBrRc\nAnnQ+DyJdR0/nxHqjXyj/QyMBetgCQSCa5y4jfBdX/j3GVfjUv1e8Mh6GPqJ3bgEcDRROclbczT5\nit42OUvP03/uBRRv1QqXPa4oNB9C/B3L2G9paK8aqVoDvw6nrw+N4AEtazL1Ft8hTOBIKrHvXDrb\nz6QW2FdQeXj174MedeuOJ2MwWYixZj1r6xRWaQvDfKRvI3tdVackJtleDEz5RicDkyzD5mmK55LN\nuCSpoN9kGPdvkY1LzrSvF1V4Jzd8ef3ZaF6zAiYaEAgE/hMY7mpQ2vo1JB0tu/mUEf7shg8CIghf\nUO4xW2T6f7SWCTN3ebQ5a00E6dSkuekEqbAw4sJHsO49e91BSwOG66dyUI7lgzva8PHIttzbLYYe\njZQNk/MiKM9gVkLkrkCDqbxSLzqYpU8pYrydGnguyAxoibj3N+gw1l53k3o3zLoTDP5lIDpwPsPr\nyahAICiHpMfDX2Ph5yFwyREuQ0SMEq4y7l+o2Zq0HAMJ6Xmk5xo4mZTNjE1nAMjWe2Y085e959K5\n4eO19nK1MM+Mm5WN4BqNGGl4nX9xEk2P3wLf9+c6Ke6Kx3cW/RZUbmKrhaBWSfZ1kI2MPCN/bFfC\n4pzXW95C0JwP+NzXYgD5tky0+Znw133w36uOkLjgqjBmAfR7yZ7Btyj8uPGMy5yLSkFZ5gDUZaSh\nKRAIyhFt7oSY7sq1xQRLX1CM5ZUIfwxMkcBRSZKWS5K00Pr6p7QnJhAUlePWDCVrjimn47e2cxzf\nOi9ygnVq0nIcC4tawbCp4c80uzDPMVijGxhl+B+JKIuoQK2a2zvW5a1bW/PJqHYAvDDXkV1HCZEz\nX1GIXHmmWY0wpt7Skg9HtvVoa1svkqBAHQz7nIyODk0QzqyDWaPAkFPo+MO+3Mj9P+/wqvckEAjK\nCYZcWPMOfNkZDv/tqNcEKaFwE7djbD6cZYcSMVtk7v5+Kz3eW80DP+/gRqf045l5BSdhKIhbv9pE\nplVjb9rd7fnz4W7FHquiEBGkRY+O32tNhpumAtbvu4xzzNVNYYBqh73v8qf7+D2uLZuqN50cQeUj\nOUvPhhMpDGldyyPb7YEL6fbrJ69v4vX+3a/dxKCWNTmUkGGvS8s10qR6qEu/PINZOfH//no4stDR\nULez4hkZ26/Yn2HjCYf35IgOdYp8v69kAh/c0YY3b2lZ7HkJBIIKhCTB4I9Asj4vzqx3zXhZCfBn\n1fA6cBvwDvAJsAMQAfmCcsdzf+1zKQf6WAgcTcyyp24OJ4dp5qnUurjS0aHNnXD3bHJxuEI7h75F\nBisnWM7GkIw8I8cSs675EDlfqFQS93VvQJ3IIO7r7qqdEmILP5QkIoa9zerajzga4zZYPZkKNzKB\nkv5YIBCUM2QZDsyFLzsp2ZtMTsK2rUfCEzuh74ugDeK7dad49PfdNJq8xB4Stzs+3WW4zGL8nR+/\nlMWygxft5fBADcPa1ia2WmgBd1UOgnRq/ny4G9+N6Qw9n4J7/oIAJVQnWNLzrfYzq/g3NKsZ5ve4\ntlAng8nTg6nDmyt4ZcEBj3pBxeXBXxRDZdMaoXRt6Jrlbc7O8wB8eEcbejXxnlktOkRH81phZOab\n7F5xaTkGakcGuXj+1E5aCz/cCJdPOG7u8jCMWwIRRTcKOZOSbaBfs2qceXcwt7Qr+ljePLJeGNiM\nUZ3qMaZ7gyuam0AgqEDUbAVdnfZDy1+pVILfhe6GZVleB2QCQ4GfUcS9vy3daQkERefwxUyXstFL\nWmZwLJark8afuql0kpxiY7tPhFu/BY2OZ29qaq8OdAp9C9Sqae62SP9s5Qky802u2gEVFPfU1iEB\nrkKxu+o/wIemOx0VcRtg5kjQewqru5PjTdxTIBCUHQl7YcYgmDceMi846mu1hQeWw+0/QERde/X6\n474zSEYEabm3WwyZxQhNGfDpeh79fbe9rPdi9KjMdI2tQoT18IOmA+DBlRCl6DKpJJmp2l94WTMT\nLP7/3GyZRY1uIXIWi0xqjoGZ2+JZuC+BBpMWc+B8hrchBBWEfKOZfdb/4zHdGvB4/8b8Pr4rj/dX\ntJWWHkwEoFus4vX91yPd7aH1zkQFK8lWbOFpabkGokN0HJoykOn3duDVqBWMPPEiGKwbMW0wjPge\nBn8IGt0Vf47kLD3VQgNKNOvkvVeYsEAgEFRQ+k2CkOrKddZFRee3kuDTwCRJUlNJkl6XJOkoMA2I\nBySryPe0qzZDgcAPvGUdyTX4DsOIlRKYH/A6LVTnHJU3valk5VEpfxbO7tNatetixJd31JWEflwr\nuC/MOtZ31WUK1mn4ynQLxv6vOyrPbvLLyHTaS3Y/gUBQBmQnw8InYHo/OLfVXp2pjkI/+At4aC3E\nKOFphxIy6P/RWvbEp3E21bu34hvDrmPPazdhNMlk5ps4e9k/r0aAixmeCQPcjfwCN6o1g/Er2Cc7\nwpUe0SxWDIXG/AJudGD73nM35qU7GQif/GMPAJtP+TYsCq5tZm2Lp/lry+zliGAtKpVEryZVqRMZ\n7NLXponWpWE0LWp5Cl7bPMDTco00mLSY82l5RAZrCcTIgBNTeDDvJ1RYDwcjYmD8CmgzqkQ+x1dr\nTpKYmU9ooD8JtP2nonquCwSCKyQwAga85Shv+wYueOoEV0QKeioeRfFWGirLci+rUUm4FwjKJd42\nIL70fNpKJ5mre4O6krIgtkgauO076PmkSz/nTCfuRhV3Lx4b+aaK/yfi/nN9oGdDl7JNoyCn80Sr\nHoiV+M3wx10e2eUsTuM9P8c1zFEgEFxlTAbY/CVM6wC7fwXbZk+l5TvTEHrmfMgsYx+7IT4hPY8h\nX2zkTEoOt329mZRsAw/1bkjvJlUJdsreWTsyCJVKQmM1Wvy0KY5DCRmsOHzJfQYenE9zPDOevL4x\nIzrU4avRHUruM1dUQqvxVMBU/jN3dNQdmg+/3Qa5hWeGC/DhweTNmCTU8youkwsIhRzZqa5L2dfh\nm41IqweTsyZbXU0m/DIU9v1hr7PU6wYPrVbCTEoIW3Zh9RV6L/39eE/+eqS7vexrPSgQCAS0GeXQ\njZMt8M8TYK74ciAFPRVHABeBNZIkfS9J0g3YlSMFV4W8NNjydaVTni8OtjTKzqfa3RtV4ct72vPD\nfZ0cHU+tYZbubaIlq6eMNhjVPX9C27s8xixooWRx+j8JdzoN0xsrftiGxe33Uee2uLJtKnMNZkUP\nxNl6H7dByT5lcmSOcQ5tDAvUsmhfQinMWiAQFMqJFfBND/jvFdA7hRw3GQgTtvKuaTRZBDNl0WE6\nvbWCx2ft9jAQmS0yVUID+G18Vw5PHWSvt4Usvzb0OgBCAzQM+WIjD/26s9BppVmzTi2a2ItnBzTj\nk1HtqBsVXMhdAgBZG8Sjxmf4xXSTozJ+s5IBMCuxwHu1VnHvJ6xeSjl6E7O2xTNx1h6Pvu7fC4KK\nQba+YK9srVpF36bVAHikb2yh41V3y/p4nRTHXfvGwnmHEP2fpn6cHTobQqv5NceX5x/gjYWHCuwj\nO/1+PtavkV/j+qJdvUg6O2XULclwO4FAUMGQJBj6mRLuC5B0CDZ9VrZzugr4NDDJsvy3LMt3Ac2B\nNcDTQHVJkr6RJGnA1ZpgpeXUavi6Byx/GfbOKuvZlCv+2XuBqYsOu9RNmq+csDl7HY3r0YChbWpz\n43U1lIpDf8PMkYRIegDyNBEwdhE0ubHA92tYNcSjbkdcmv26i5PY5eDWNYv2Ya5BnPcRU4Z7Zk0J\ncjYwAfR4Am74n6PDieUw/yGwKO3JWXp708mkbPtmRiAQXCVSTiohrDPvcBXWrdIERs+F0X9hiW7k\nIsSbkm1g8f6LpOd6nsQ5Z4Va/0J/Jg9uzsCWynM4UKsmLEDD33sveNy37ngynd5aQY51U7v0wEUa\nTFrMR/8pnge28BqB/wRoVFhQ8bppHMndXnE0JB2GGQMhLc7nvZFBDs0bk9nC6B+2+fRm8fZ7ILj2\n2esm0O+NkADlOz9UV3jomfMhYD/VXubophCSbzVSSyqOtH2Zl0wPkW8p2BPKmT+2x/Pz5rgC+0yx\nrhkf6t2QKqEBBfb1B2FUEggEfhPdUMm0a2PdB5B8vOzmcxXwR+Q7R5blWbIsDwPqAnuAl0p9ZpWd\no4shy+rJsfQlSDtbtvMpY75ff5pu76zCaLbw1Oy9zNh0hjyDmbQcAyeTsmlTNwKAOpFBALxzW2vX\nBcCun2HOOLAoi2BjSC0CHl4BdTtREBte7M/fj/f0qHc+qYuwLsJvbVeblwY1v4JPeW1gO6l+fdh1\njO3RwKPdFiLnoovV+znlZePw34q+i8XiVazXn7AZgUBwheRnKJlNvu4KJ/5z1AeEw8B34LHNbFa1\n52JGHnGXc7yGHa87nkTtiEB+G9/FXuecqSymSjAP92nkkuo+SKd2CXuzMXbGdlKyDfbsZI/NVES9\nj19SPE6jQq5c5Ley4fAwldB3naiEg9tSJ6fFwY8DIemI13sjnAx6/+xNYO8538YGfwwRgmuLixl5\n3PvjNsBxeBbmRb/ocrbiYVgjPNCjzR1JkggP1HCneg0/aD+yH/gREA73zCGxxQOAVCwR/waTFnPE\nLdmLDZsBamib2kUe1xcd60cxsmPdwjsKBAJB10ehdnvl2myAPb+W7XxKmSIFDsuynCbL8nRZlm8o\nrQkJrNw0FaKtbryGLPh7QpGyv1Q03l5yhMTMfJKcvF0e+nUnry88xI2frEMlSXSIicRsNX7YdD6Q\nZdjwCSx6CrtKRJUmaB9agap6s0Lft150MBFBnqfmk5wMSbYFfO3IIJdNVEXF5jIf4uO0Mtha76HT\ncf1rygPWxt6ZxM2cSGaesjh1XrieTBJi3wJBqWExK/pK0zrCli/BYguDkaDDWM6O3sjS0BGYVVru\n+X4b3d9dbfcsfGGg63Nzd3w6daKC6N2kmt3QXz2s4I2me1itwW0z+ffeBHad9dQICtH579UgUHhx\nYHNCdGoWTuyphBW2vQvu/B3UVi+O7ET46WY47z1U8e3bFA2c5wrRx9sel0pWvvBiqki8+a/DU3zy\n4Basfb4f617o79HvsjWEtYEXb28PZJlfG67gfe33aCTl7z4/pLYi5t3kRvuzQV/MjLx7fBg6q4UF\ncHuHurStF1mscb0x77EefDiybYmNJxAIKjBqDQyfBsFV4ZavlMRSFZiKvxu+VtGFwIjpjpPGsxth\n61dlO6dywOVsh4Fp48kUFlr1ek4lZxMaqLWnvw7RaRTj0orXYNUUxwC12sEDyyCy3hXNQ5IkujSM\n5vH+jbDZlIIKEbesKNi0VKqEevcmsIXIvbv0KJMXHGD5IavOhyTBwHeh/Rh73wanZpLyj+I2Ws1J\nm0GIZgoEpcTZLfB9f8WDMCfZUR/THR5eC8O/oO/XB3ls5m4aTV5ibz6UkIlOrWKALeTYCVvI8IIJ\nPTnwxgAPA5I77t5LSVmeWc2+XnPKo06EpRSdPk2rcWjqINrUddpYNx8M984FnTWUMS8NfhkOp9d6\n3F+U77Vn/hRJGioSgRrl/35Cv0bUiQyiQdUQor14EZqsIvDhQYWEyJkMsOBR2p353l51hIYYxv0H\n1ZVDO9t3v8FcvAPVtceS7Nc23aVHf9tFcpa+8PkJBAJBaVKzNTxzENrfq+yJKjBiF1eeqdvJNaxo\n1VS4dNh3/0rAuVTHxqROZBCB1vSwWfkmwgI0dLIKL7apHQL/TITN0xw3N+itaC6FVC2Rufz1SHde\nGNjcHsRfNzYAACAASURBVDYSVElO118b0oJH+sTahT3dcd6QKIKwux2NKhUM+xxa3W6vmqBZyIPq\nxYQFOjzFLuc4DIkCgaAEyDgPc8fDT4PgopMhILwu3DED7l8KtdsVOITBbPHq0TmkdS0A1CrJ5e/Y\nX+bvvoAsy9R0CrFZdVTZKL55ayvevLUVW18WjtMlSsM+yvdhkFVD0Jij6HAdW+rSzVl3y529/7uJ\nf5/oRcvaSjr608nC87Si8OXqE8zfcwG1SuLFQc0LNO6arGugYG0BBpz8DJh5O+yfba9Kr9OPFi9v\nJLya48AvwGrU+m2Lf7IQspu4/H+HLyHLMmuOJdHw5SWcTs5mmfWQa87O836NKRAIBKWGNqisZ3BV\nEAam8k7fFxWvG1BiNuc/DEbP097KwuNOxorMPCP5TlnbQgM0PNgrlh0v9aLeysdg7++OG5sPVcRq\nA8NLfE6XMhVjSO3IyvHQqB4eyMuDW/gMBwx2M7QFu4XSyZKKX2u+zAqzI834q9qZdM9abi9/5cV7\nQSAQFANjHqx9H6Z1goNzHfWaQOg7CSbuUAy+1g1kRiFizdXDA1kwoQcn3r7ZXvfV6A4F3OHJxpdc\nw2w+WXGcLu+sIiPPSO8mrgcAY7rVZ0y3+tSMKFzfRVBE6nRQPHrDrLo0ZgP8eS8cXmjvMrCl78QV\nkcE6WtWJsIc3n07J4cZP1pGYUXnXKBUBs0Xmo/8UAdoOMYWHlPWxHjZ5Mz4DinF7xiA4s95R12Es\nkQ/Mg4BQl675JiU07j8/dRgPJXhqLq06ksSrCw4C8LxTaGdBv8sCgUAgKDmEgam8o9YqoXIa6+L6\n0gEls1wlwv2EykaWW/rc0EANKkMW1f4ZDUf/dTS0uxdG/gLa0tmgJKQrXlV1oyqHgakw6rj9HNzd\n0uNTc/nfv8eZaHySbRaHltXz+dPorxIZ5ASCEkGW4dAC+LILrH0HTE5haS1vUwxL/V8GXbDLbceT\nslzK7sYkgPYxUWjVKr4b05GPiqFBUjcqmEUTe3H0zUH20NjkLD15RjNVhJD31aVaM8XIFNVQKVtM\nSkKMg/MAJetfYUg4vFtOJmUzZ+e50pip4CoRn5oLwBPXN+aPh7oV2v+NYS1Z/0J/F1F4O8nHrULy\nTt7317+meDKrPT2eWtVWdNy6NIj2aPPGsC83etQZzBYuWNdlu500mZ4f2NSvMQUCgUBwZQgD07VA\ntWYw4C1HeecMODDXd/8Khr/pj6upsuCXYRC3wVHZ4wm45UuvC5mSon/z6oCfApeVAK2bZ5P7qWae\nVbxTj46HDM9xxBIDgAYLX2s/57W2WWhUkk/DokAg8E1SVj4Pf/gzcR/3VwwFGfH2Nkv1VjBuCYz8\nGSJjvN5vC3Ma0qYWsx7sajcm9W1aze6pYGNgy5rcUcwsSq3rRhCoVXNLW9esTsXJHiW4QqLqw/1L\noEpjpSybYd6DsG+2z1vev721/dosntUVio0nlQQdN11Xw6/EJTqNipgqwZ4NCXuUkNxMa2iaSgu3\nTYc+z/vUHwnSqakaGkDjGqFe293x9qu3I84zQUDdqCBqFJJ8QCAQCP7P3nmGR1G1YfiebekhhYSE\n0AnSewepglSVIipNsGHB3tHPjopdUewgojQVFJQuVXrvvUNoCYH0ZNt8P8723YQkpAHnvq69mDlz\nZuYkbKa8532fR1I0yADTtULLB6H+AOf63CfFzNANQNN3llyxT1XlHIN3PQBntzsbu70pAnPFLKT2\n/K212fH6rYQWQnvkeuWrIU0dy8F+7sG91Cxn5tnIW5pwsf80jCFCgyFAMTL0yAtUV09xLCmDbJPF\ny2FKIpHkQkYS2yaM5Jv0p6mW7pINGBjJrqZvEX/yZSYn5G3TnXA5G0WBz+9uQrt4Z7naz/e3Ysr9\nrYp8yGEeWQ83ipZdmSO0ogg+RtmySlUr/PkIbP2F8YObenW/u6UzQJldSMcvSdnjn51neO0vUV5W\n35ZNVCiO/QeTb4PMi2JdHwRDf4PGd19xV51GwWIpfNAywcNEAGDSyJZo8tATk0gkEknRIQNM1wqK\nIlKKI2qKdVMG/D4CjBmlO65Sok5MiGO5sXKYWYY3KZdlS8tXbELSNz9TImPRahTfqeE3MD1dtA7s\nIuh2MlxKG4P9tNzctCGGkXOEdSfgb05limEcwz6ZRZ3XFtJ7/H9IJJI8MBth7VcYP2tCj+wFaBXx\nN2dStZyrdz88sYXb1tXCioY3/96bqxjzqeRMxi89hF6r8cpELC7KBTpL4urEhPBMN2cZy2d3Swvw\nEiWkAoz4B6Lr2xpUmPs43TP/yXO3myqEuK3LfKZrl1UHnc6SeQm858n+efDrQDDaym39w2DEXKjZ\nNV+7azUKJmvhJ5Yyjd4Bz2qRMsNcIpFISgoZYLqW8A+Fu3526jFd2At/PgxXcSMu6ySl+3YTu/9m\noRfRVbOVGYaxlFdsQo86f7j7V2g+soRGKPGFTqthTC8xE+5Z8uJqPxxtT1mPrOlmmx2rJDPFMI5w\nUjl8QToTSSQ+UVU4sBC+aQuLX8VgduonrbI0pKdxHLcd6oNR756JsPN0CtM3nvQK/k5ZdxygRLMG\nQ20C0X0axrLw6Y5Ujgjk07saM7xNVfo3LVz5neQqCI6Ckf9ATCNHU8CiFxipXQgI0fXP73Z3G3zn\njgYlOkRJ8XE8SegvzXq0beEOsH0azBwOFtuzW0is0Piq1CLfh9BrFa9rU0FIyfKWVdBrZfaSRCKR\nlBQywHStEdMQen/sXN/3N6x4v/TGU8yMW7DfZ3u5AD2DtUv5Qf8JAYoRAKMhTNgu1+lTkkOU5MLD\nnWrSrW4FTB6p7iaXAFNVV92Gik3hnqlYNSKjIV5zhp8MHxGIdCSSXL/8u/c8z87cfuWOnlzYL7IE\npt8NFw87mo9YY/mu0vvca3qZI2ocrapHcDnL6Lbr0zO3M2b2LtYdueg+ln0XAPjwzkaUFDk2J1DX\nF8ABzSrxTj8ZtCg1AiNExklcc0fTm/opPKT9h3f6NaBf0zi37p5ljfJV/tpgd0IKt3yygrMpoqRs\nw9GLbDyezMh21WheNX8i226smwB/PSo0vAAiasD9iyC6boEOo9UomPMRYJq85pjPdnuAqV8TUQ6s\nUUApZqkEiUQikTiRAaZrkWbDofWjzvVVH163ot+RwSLYsOSZjo62d/vVo8aOT3lfP9FRCpLiH4fm\nwSVQueg1QiSFR69VMFvcsyHMLgGnEE/dqhqdOddtPFZVPAw20RzhG/3n5OSIINPaI0nc9uVqqfkh\nuW54cMpmZm9L4GIu2ZpeZCbD/Bfhm3ZwZKmjOVUN5B3TMAZpPmHo8FHYX/Pn7TxLq3dFv/vbV3c7\nVMLlTMfyr+tPcCxJlFzf1aLyVfxEBaNtzUgA7vMYm6SUCQiH4X9C5daOplf10+C/T3x2v7VeBcey\nLJErPCmZJrJ8lHgVBz+vPc6RxAzGztsHwHvzxb/dXf4v84WqwtJ3YNErzrYKDUVwKbxqgcel02jc\nNJgW7Tnns6z3zb/3uq3/r09dQvx1jgBT+WDhUHkVyVASiUQiKQQywHStcutYqHmLc33OaDixrvTG\nU0xYrSqBBi21bBoPAWQz9MRr1Dr4nbNTbBPKPb4CXbS0oC1r6LQar5lI1xI5TwFwAF3D/rxuHulY\n76TdybyxA4l/5R+enrGdXQkp7DubWmxjlkiKiiV7z7PG5sh0JU77EKZ1w2KGDd/Dl81g43eOLAEL\nGrIa3UvnnE/ZXmkoi57rRrCfjkPv9uI2D4e2bnWj3daT0p2ZTf8dEtorj3Wuma/xFhWVIwI5Pq4P\njSuHleh5JfnAvxwMmwVV2zvblr4NKz7w6vqyrSQaYMWBC9IFtBCYLVYav72Y/l+vKZHzGXTiFWDe\nzrMA+Om1NKsSRnsXcf8rYrXAvGfhP5fM+iptRZllcHTu++WByGASzwlmi5WHf9lCvwlreOmPnTz8\ny+Zc92tbM5IQP2eAKTRAamNKJBJJaVBsASZFUSYpinJBUZTdLm0fKYqyX1GUnYqi/KkoSpjLtjGK\nohxWFOWAoig9XNp72toOK4rycnGN95pDq4M7J0FkLbFuzoZpd8O53Xnvd42RZbIQoBfp9/F+l/nD\n8JYoC7RhrdkNRs4r9IOMpHjRuTwo2nHNYAry83aMCg808KulO1+Yna6JA7SreVEzjQtpIsvjfKos\nm5OUbdYeTuKhKZsZ+uMGEtPcs5MyjWYe/HkT1V6e52hbnVcg6vBS+LY9LHgBsi45z2GpR5+c99jR\n5E2SCeWBm6sTFSJm7fVaDV96uH+183hxTM5wBpgOnU+na51oXuxZB4nEgV8IDP2dxPLOTCZWvAfL\n3nXziHcVhd968rKj3FKSP44mphP/6gIA9p9Lu0LvosH1Xmy1qmTkmAl3Ed2/8gGMMOtB2DzJ2Var\nBwybDQGFDxjrtc4Sub22yaTUbDMzN59i0Z7zXv0NWg3Hx/WhfsVyjslIcOq7SSQSiaRkKc4MpslA\nT4+2JUADVVUbAQeBMQCKotQD7gHq2/b5WlEUraIoWmAC0AuoBwy29ZWAuIEP/Q2CbMGVnBT4dQAk\n+65Lvxa5nGnCX6+FU5tYHPQG9TUnnBvbPIZmyEzwCy69AUryRKdR3B5iwanBVLtCCEEG7wdAvVZD\nXFgAK2MfYFlQb0f7KN08HtIKN6PFPh4yJZKywh9bTjPkxw2O9ZPJzlK0lCwTb8zZ4/UC/tGiA95Z\nH0mHxcTBrwMg0alHl+Ifx7y6HzHE9Cr71SqMmb0LcJaEuNKsivuL3rgBDflycFOqRAQ6yvKOJ2Vw\nNCmD1Yfyl20lucEwBBE16i+o0cXZtupDkc1k+856Oo5JxZuCsfN0itv6l0sPFfs5M11KzdONZjKN\nFgJ9ZBX7xJgBMwbDntnOtoaD4J6pYAjMfb98oNUoJGcYGb/0EEcTRdluTKi/Wx/X4Pj0Uc7gZ2w5\nZ7++HhmcEolEIikZii3ApKrqKiDZo22xqqp2j/L1gN0i5g5ghqqqOaqqHgMOA61sn8Oqqh5VVdUI\nzLD1ldiJqCFS2P1CxXr6eZhyB1w+WbrjKiL2nklhiG4pTO6DJtNmn6vRwW3joef7IpNLUmbRaTWc\nTXHPNrIHmH57pC2aXGyQV7/UhVmPtcfY8yMWWlo62l/VT2OAZhWztyUU36Alkqvk+d93uK27Zty9\nMWc3v2857XM/h/tRZjIsfAW+bgMHFzo7GIL5Rj+clpffY/S2OOyv8XbtJHv2kis/39+Kfk0qsvl/\n3QC4p1UVbmtckYggAxczjFisKs/ZxluhnPf+EgkgggaDZ0B8N2fb6k9hyeugqug8XLr89FKBoSBk\neugufbLkYLGfM8todizf/d16jiVlEOwjq9h7x0vwS384/K+zrdUo6P89aK++LE2n0bDzdAqfLjnI\nvF2ifO+cyzX0xMUMhzA5QKNKziC6q+C8Xqth8n0teX9Aw6sek0QikUjyT2k+AdwPLLAtxwGnXLad\ntrXl1u6FoiijFEXZrCjK5sTExGIYbhkmtpF48NPZZm4un4Cf+lyzmUxp2SasVhWMGTyT/imj079y\nWt4GRMC9c6D5iNIdpCRfzNoqXqS3nXSW9dhd5Qza3C8/iqKgKAo5FoWnTKPZYHWW7Xyo/57e/juL\nacQSydXh6pJon3U/cC6NLh+vYHdCCqs8soQe7xJPp5uiADh/KUU4MY1vCusngNVut61A02Fsuu1f\nPkjrhRHxEteoUjm3Y0X7CDCF+Ov5/J6mXtlNp5Iz+e9QEnd+u5YtJ8Tf508jpUmCJA/0/nDPNFEG\nZWfteCHu7KG1J4WVC0Z+tdqKktQsM3ZzNbuuYaCPrGI30s6J58tTzgxNOr0MvT4ETdG8UsSGObOQ\n7NcmVy5nmjhiy2zq16SiW3lmoFuASaFz7WgGt6pSJOOSSCQSSf4olQCToiivAmZgalEdU1XV71VV\nbaGqaouoqKiiOuy1Q7X2cNcU0Nrq51NOwuQ+cPFI6Y6rgOw9k0rDNxczae5iLN935TZWOTdWaAAP\nLYNqN5feACUFwmgWL9vHL2Y42uwv4J4z3r7IyLGQg4GHjM+xzyqcrXSKlY/Vz/hv+fxiGLFEUnh+\nWnOMWq8ucKy/278BAF8sPcSxpAw+WLjfrbQDICxQzxNdatJLs4HIyR3Fy3r2ZWeHKm1h1HKye49n\n0NSjjuZK4QFMHNHS7VhB+S1vAS7axrHtpPNckUEF0F+R3Jjo/ODuX6F2H2fb+q8pt/J/uPrHWaXI\nd745cTHDkalTkhy/mEHX2u76lUGGPDKYko/BpB5wYY+zrecH0GUMjkhVERDjUubmeb0EYRRy0KZT\nNW5gI7dtoTZnWoNO49DvlEgkEknJUuIBJkVRRgJ9gaGqU3AiAXD1Ra5ka8utXeKLm3rA4OnOTKbU\nBJh4K5zaVOynHjVlM6Onbb3q4/Qev4rB2qUM2TYcbZJTc4Qmw+DBfyFCWllfixi0WpGVBmw4dhFw\nF4XNjf5NRcJiKkGMML7MaVWIFAcqOTRaOYrvZs3ncqb3A6hEUpL8veMM3648wrQNztLkiSNacEtd\nd7vvfWfFS5FOo9ChlvguV8/eQ+PFd/GN4QvKm844+h6zVsA6aArctwAqNnUT/v1+eHNWv9SVqBA/\nXuhRm651otn/jqfkYd5MGNLMq60gASrJDYzOAHf9DHVvdzT5bf2RsbpJKIgJBKtMYco3XT5eIf6t\nHeWWgVMcWKwq45ceousnK7iQlkODOPcsyNOXc3GzPLdbBJcuHRfrilaUxLV5pMjHqMuldN6O0WzF\nZLHip9MIjU4X4qODHX2UIgx6SSQSiST/lGiASVGUnsCLwO2qqma6bJoL3KMoip+iKNWBWsBGYBNQ\nS1GU6oqiGBBC4HNLcszXHPHdYMhM0AWI9cwk+Lkv7Pmr2E5ptlhZvPe8w+q20KSdZ6L+Y97XTyRQ\nESVx2aqeXc3fhX4TQB9QBKOVlAarDydR45X57D+XyprDF/O9n6uewgXCGW4cQ4ZO6C2UI42+Ox/n\n89krinq4Ekm+yTJaeGL6NsYt2M+hC+mO9q51RGaAq+hskk1Ue/Zj7XiuhYGv9OO5Zc0w9Ged1tuX\n1GDeMg3nVuNHpNXoDYrC8aQMFu85B8DCpztwa/0YR//RXeKZNLKl14vWlejZIMarzW5bLpFcEa1e\nONnW7+9oGqZbynu6iShYZYlcAbD/rgY0q8TT3WoV67lmbT3Np0sOOsSz23s4S6Zmmb13OrkBJvcW\nGp8gJjHvmQaN7y6WMWqvEBgymq3kmK0+r1ehAVevASWRSCSSq6PYniYVRZkOrANqK4pyWlGUB4Cv\ngBBgiaIo2xVF+RZAVdU9wG/AXmAhMFpVVYtNEPxxYBGwD/jN1leSFzU6C52iwEixbs6G30cIW2Gr\nJa89C8WPq51aT4WatVRV2D0LvmnLLdptjubD1ooMML6FsdHQohimpBR48zZh+jh9o8js2O5SjpNf\nPhjYkMc61wTgmBpLwIhZZKhCTyZOucj9x553s24vKEazlWxT0f9dSG4M7EEjT+yz5+vG3OKmfxRK\nOvHb3qfJnO701a53tFsUPd+b+9Ap51N+svTChI7ULBPnU7Pp/PEKvl4hyp2jQ9zdlAqLp+uXRFJg\ntHoY8KNwD7MxWLecD3XfY7H4CFRI3DCarfy6XjjjxkcH07dRLKM61uSZbjcBItuoqHnxD6d+4axH\n29KqegQVQsX1qWFcOd64zcOo+dASYRyTbXO58wuFYbOhdsEyJgtCbuYfdp79bTtp2WafOo7+OlkW\nJ5FIJKVNseXDq6o62EfzxDz6vwu866N9PiDFVgpKldbwwBKYOgiSbTpMqz6EU+th4EQIjs57/wJw\nyaVEKcNoJsS/ADNIycdg/vPubiTAT+YejDMPJgcDof6ybONapcNN7npo9sdl+wN0fri7pRDotL9g\nayq3YHu78bRa+xh6xUIVywmYdg/c+1e+stzm7jhDttHCXS1F9W3fL//jVHIW+/IoMTp9KZNNx5PJ\nMloZ0loKhkqcXEhzDzDFhPoz7aHWbm3/PtuRfuOX0zXtb57UzSZwS4bbdur3Z0Xco7w3113oNyXL\nxO4Ed/vysCKcof/32U6sP3qRm+PLU618UJEdV3IDodVB/++Eu+uO6QAM0q0iYdPLUH8yaOQLf25M\nXH2MDxYKKYDywQZHUDrI5uRW85X5LHmmI7UqhBTL+ZtXjQDEdUCv9S43Y9cf8OfDYLUFC4OihGtx\nbONiGY+d05dyKdOzcSnTxNwdCV7GBQABBpmFKZFIJKWNvBJfz0TWFLpF1Ts5246tgq/bioyhIhDh\nvJRh5LuVTuHZi+n51MPJSYcV44QNt0twKUmJYJhxDG+ZR5CDEJwNlgGmaxaNR6r7fJuQ6a6Egmcy\nzRzVhvlPdgDgYkxHnjc97Nx4aj38cT/kY9b8yenbeHHWTuwScAfPp5NlsqDm8vdgsarc/MFynpm5\ng1f+3FXgcUuuX1RVZeA3awFoWiWMmaPasP6VW6gRFezaibDjC5hueorX9b8QprgElyrbJgIGTaZ1\n8+aO5u+Gi+WULBMWj+/llWb3C0J8dDDD2lSVwSXJ1aHRwh0TmGnu7GiKOzkXZj+Ur2vyjUiO2eII\nLgE80qmmYzkuzDlRMn/XuSI75/GkDJ/tIf567+DSxh9g1oPO4FK5KnD/omIPLkHu43TFZFE5m5Lt\n1V7QUmGJRCKRFD3yzf16JzAChv8JKz+ElR8AqtBl+uN+MTvV6wMIK3xGxvqj7no6u8+k5P2yYjHB\nlsliLBmJLhsUaPUQg7bezDGr+9eyQBlRkjJFdY/vwn82i/ZBLSr76p4nrWtEOpb9dRrmWG+mvCmV\n1/S/isYD8+Gfp+H2L3N1tHEtOUjNMrPjtDPQlXA5i0rhgV77vCqDSmUCVVVJzTZTrgxpbLjqlfx4\nbwsiPWfUT2+GRa/CqfXEuraHV4Nub0G9Oxzf1WA/HdMfasOC3WepbPseDv1xAxLJNYFGy8vmB7Gg\nYYhumWjbPUuU5Q/8UZTTSRwccwmifDWkKZ1d3NxcryNFWcna9ZMVAPRuGMNDHWr47qSq4nlxxXvO\ntqg64jkytGLRDSYPrqZ8VzrHSSQSSekjA0w3AhqtsJGt0gbmjBbuciBeyA//C61GQYfnRDCqgHy8\n+AAAIX460nLMZOTkMluZkw7bfoF1EyDllPu2mEZw2+dYYptx4j/vashg6Wx03XHTVab8d68n3Lkm\nWnrTKCyHOzJ+Fxu2/QLBFeCW17z2OZuSRdv3lznWz6dlc++kjY719+fvZ8JQb2etGZvcv6+qqkp3\nmhLkz22nKReg5+TFTN78ey8N48oRFqjnv0NJ/PPEzV4uSCVJUoYoj3ukU0334FLyMVj6NuyZ7dZf\n9Q9D6fQitHxQ2L170LZmJG1rRpKQm5OTRFKGUdHwqvl+zGi5V7dENO79S2TB3PmTcJ+TAHA6WfyN\nN65Ujr6N3AM3/npncUFR3mrs8ysThjTzfQ+zWmHhy7DxO2dbXAsY+nuhng8Ly9UEmCKCxHesT8PY\nK/SUSCQSSXEhS+RuJGp2gcfWQ4sHnG0WI6z7Cj5rAPNfFC9GBeCIzYnk7yduBiDT6CGWfG4XLBwD\nn9UXDy6uwaXQOLhjAoxaAXHNSUrPwaq6P1xJrk+u9v9YURQWPNWBWtHBfKMbDo2HODf+9zFs+M5r\nnxEuwSSAtYfd9W4OuziA5cWKA4lX7iQpElRV5ZmZO7h/8maW7r8AwK6EFEcm3KI9zvKRz5YcZMPR\n/DsUFgXJGaIkuF1NW3ZdRpK4jn7V0j24pNFDm9EoT26DtqN9Bpdc8czSigkVwt63NS6ZDAKJpDDM\nerQdL/eqx+vmkUwyu2ja7f8HfrsXzL4F8a9HMnLMPDNzO4lpvn/m2dtOAzBxZEuvba5lXnofQtYF\nxWyxOrLN72pRyXdwyWKCP0e5B5dqdrUZxpRccAm8A0zfDmtG+/hIKpZzNzjwlc2qKAr73u7J+MFN\ni3WMEolEIskd+SZ/o+EfCn0/FbX0cS2c7aYM8WAxvglM6inq7y+fyv044KZZE2O78b/9927MJzfB\n8vfh25vFZ/3XkO2iuRMYKcpDntgCTYc5REDPp4p6+hd71KFGlNQEuV6w27W74lcETi91Y0NpWKkc\naTkWuH081LrVuXHBS7Db+YJvtaocPO8eQHrz771u6wfOp5GWbcr1fJ8MEtoT903exMqDMshUEvyx\n5bRj2Vcmo91FyGi28sXSQ9z9/XouZVxZB27N4aQicQ60a86V9zPDqo/giybiOmp1+R7V6wePb4Se\n7+X7RS3I4P73ER5k4Pi4PnwpX5okZZjmVcPp1SAWUHjbPBzaPu7ceHABzBwGJm/dnOuNkxczqf/G\nIv7clsCY2d4l1kazlfm7zhHipyMyyDury9UJLdes8AIwfukh7vleOFZW9lEGjjETZgyBXb872+r3\nh8EzwS/Yu38x4xlgMug0TH2wDSte6OLW/kTXeJ/7Bxi00iVTIpFIShEZYLpRqdJGCIAP+hmi6rpv\nO7lOOLt93gA+bySEHld+KDSbjiyDhC2QsIXMw6u5RbOF1yusxW/Z60zXj2Wn30PoJnWDleNE9pIr\n4dWgzyfw9G64+Wk3x68v/j3EpNUie6p1jQiWPde5eH9+SYlxb9uqXm1+uqK59IT660nNNgl9j0GT\noZJ9NliF2aPg6AoApm86mesxnuvudLSb5RLQcKV8sIH+TeMc66tkgKlEeH3OHsfygt3eYrdJ6SI7\n4EKa86X1salbee63HV4vZicvZlLv9YX8vvkUQ3/cQJ3XFnI0MX9Za3bmbE+g1qvzybJlaianZXCP\ndhm1f+sEy8aCMc3ZuUpbIeB9188QkYveSS54Zhh0qFW+QPtLJKWFxnFpV+DWsdD+aefGQ4tFIMN0\nfZeA/rbZOTnnq9x1zRGRgfnegIY+s4lcM3zTrjLAdDYli/HLDjvWD5xPc++QmQy/9Bf/N3aa3yfc\nNvMSoQAAIABJREFUhkuppFHnGWDSioCbXuveHiTlEyQSiaRMIq/ONzKKAvX7CaHZI8tg/TdwZCmo\nVmefyyfExwdBwEQDkAKsg7a+klK0flC3LzQZCjU652pZ/Nm/B0V3jUJ8tJgxGzegIVUifMy2Sa4p\nAg3el5miCzDpSMs2Y7ZY0RmCYMhvMKkHJB0UWSQzhsHIf/hgwflcj/F413hmbDpFwuUs3vx7LwOa\nVyLUJixvFwUf1qaqm3tXplE6I5UEWR5ZRgathuhQP2pGBXPqUiZJtgyicy5uQutspSC7E1JY9ExH\nR/vaI0lkGi288MdOR9t78/fx4wjvEpXceGrGdgDOXM6kZvIqeq56hSH64+BqelS+NnR7E2r3uioB\nlZ71Y1i45xz/61OXke2qFfo4EklJ4uYcqijib0GrFxl+IJ4xpt0Ng6eD4frMVP53n/N+cznTO6Ny\n+gYx4dG6uu+MRn+XDMaf1hznjdvqF3osCZfcA1zlXbXiLp2AqXeK+6Wdji9Al1eLVvypgHi6z9oD\nS57BuECDFPSWSCSSsogMMEnEg0T8LeKTngh7/+L8xtmUv7QNraXgM42JajmimvSBWt1FDX9AWJ79\nXUtV4sICHOVT97QqvLudpOzgy9VFVwS6EgCnbA/PT83czoQhzUQJ0rDZMPFWSDsjMkqm3kmbcu+w\nWVveoZljZ5xtBnnC0Gb0m7AGgEZvLmbTq92ICvFzBDg8H2T/2XmWOjGhDGpRyWcATXJ1WKwq7cYt\n9Wp/t38D+jWNQ6MoDP5hPYm2DCZfdtUaj1lwFwNBB1Eh/t6NHmQZLaw+nERUiHgxa6YcJOr3TyBx\nC26vh8Ex0OUVEUzXXv134qshTVm2/wLd61WQovKSawbP4ACKAl3/J3TI7M5kx1bClDvEhEAJ6/sU\nNycvZrL/nDNLyFcQKSk9h+rlg4gO9X398S9kCbnFqqJR3AMxGS66mGP7NXBm4p7dAVMHQbrL5EuP\n96HtY4U6d1Hi+R0y5DIhJe+9EolEUjaRV2eJG5bA8pib3U/r2RXRY+bQ6Fi4sBeSDsGl45CdAtkp\nWFDYdiaLTNWPji2aQLnKLLtUnlc36DlLBMf79833OS+6vPRXCM1b/FZy7RFgKL5K3FbVI/hzWwK7\nTqc4G8Mqw/DZIpMpOwUyEnlXeZUJ1b5k8j73/SvYHvBD/d0vhaOnbeW3h9s6MpUCbA+yP9zbgoem\nbCYt28wbc/fwxtw9HBzbK9cHYEnhOJaUwflUETxqUjmM7aeEhluwn84hehsV7MfmE8mcvJjJ2Hl7\nvY5hDwjZ+XObd/ljalbumlt2mo9dQqbRQg3lDN/qZ9JTuwlcKiSzlEACujwLbR4r0owMnVbDrfVj\niux4EklJoMntUtj5JZHBvOwdsX56E/zUS0wIlIvLZadrC4tVpeNHywH4bnhzPliwH7OPyHZatpk6\nMbk7qbqWgvmaoPFF3dcWkmWy8FCH6rzap56jfeMxp/HBsDa2cvXDS4XoutFWIqw1QP9vocHAfJ2r\nuFFx/5253l9DbFnLIDOYJBKJpKwi34okDo4kplPzlfnc8ZXI5DCho+vMTFaF9GF7vRe40HsijPyH\n80OWcGHwIu40vsmpvlPhjq+g80s07DaMs0TmKyvAlSQXl5VskzWPnpJrkQCPWcZudSsU2bHvaVmZ\nFlXDCfcUSo2uC0N+B7144Y9SLzL61LPsfLo+21/v7rAytgeYakS5C5meuJhBwuUsso3i+2h/yO9e\nrwKdbopy67u+hJ3LbgTmbE9wLH8wsBHTH2rD6C416VbP+d0JC9RzPjWHjh8tdwSjVrmIwK46mMjX\nK4T2yNrDSWw6fgmAZlXCOPZ+b3o1iGG1h5OgJ1/8e4ggYxLv6iay2PCiCC7ZsCg6fjL3IP2RTaKs\n5Dot95FICoKCMzhi8QyudHween0E9j6J+0W2aeJBrgfsgXAQ94wAg9ah12ZHVVXOpWb7dECz45qB\nlGWyYLI4n4tczVXsmCxWR7btD/85nYBTskxMWH4EEIEZALZNhWl3OYNL/uVg+J9lJrgE3tmmrr+r\nXW/2cASWZIBJIpFIyiYywCRxsMP2cOSa3n00KYN7J22k34Q1tHpvKSsOXKD1e0uZvVW8AIYHOl/s\no0L86HRTVIGzkFxtfGtK97jrjgoemSQRQbk/WBcURVGIDDaQ5UsTqUprGDKDbFWcL8p8jtDf+hNm\nSWZYa1F+WauCM7D0aOeajuXzqTm0H7eMP7YIsVbXB9lJHrbSqXk4z0kKxyGb41/d2FDio4NpWzOS\nF3rUcbPsXucR2Hu2+01UiXTXbPtw4QEA/tjqzF6a/Vh7FEXhaGIGKVkm3vWR/QSQmXYJVrzLSr9n\nGapbik5xvuTNtbTls9pTedsygsio6yP7QiIpalwDIw5aj4KBP4qSOYDU0yLbNGFLyQ6uiDlzOYvp\nG51mEiaLlUCDlkyPANPivedJyzbTtEre0gGexwbYdDyZ6mPme2VjztjkFBVvXKmcY3l3gsjsfa77\nTex641ZY+RHMeQystvtlaCXhKFzt5nyPpSTo10RcU1/oUZtlz3WikofznT0rTJbISSQSSdlEBpgk\nDk4lX1lv6a9tIrD03yFRI+I5CxcXHsCZywWzIf7hv6MAfHhnI97p16BA+0rKPjqthpmj2jgshX1M\nwF4VgQad10O8g+odedj0LEbVFiBKPgpT7uDZ9pEcfreXW8DixR61vSyj7e47AS4BJq1GoUXVcMd6\nerYU/C5KLmUYWbhHOMYteKpDrnbTeo9aHPu1aNHTHakbG+poV1WVjceSAaFrZGdoGxFk/OG/Y44X\nMQDMRtjwPX5fN+cp3Z8EKs4A+BpLfT6t9h1Pmp7gq+0WQv31XlpPEsmNjGt5U05uGckN74QhMx0Z\npmQlw+TbhNnINcKZy1l8/u9B3pizm6X7ztNu3DL+cHEhbVQpjACDjkwXjcmzKVnM2nIanUZhYLNK\n+T6XXaj7R9uz0oyNzoBStsnCa3/tdqz7uZTUnUzOBGBA4/Lw5yOwfKzzoBUawINLRLZvGaNPo1iO\nj+vD6C7xXtnFAFZHgElmMEkkEklZRAaYJADM3XHGzVo3N+yiyuuPihc2zwBTiL/Oyx78SlzOFBkg\ng5pXIsS/6LJbJGWH1jUiqWxzBPQltnw1+CpDsLPyYCJ7AlsytcrboNgeRhP3w5R+6HIuu/VVFAWj\nrxl3wGxxH/ROl4BEik3H53KmkQd/3syFtIIFWCXuzN1xJl/9Ysq5l+LaAz21Y0JoFOecxc80Wjif\nms0jnWrSt1FFR7vb8per+XXdceZP/xomtIIFL6DNcmZIpYfVYYTxJYaaXiGmbjtHe445l8CmRHKD\n4jqBkGd2Z/wtMGIuBNhEsE0ZQnR665TiHWAR8dGiA3z+7yF+XneCB37e7GhvGFeO4+P6EBXiR6Be\nS5bRTEaOGVVVGTlpE4v3nkev1VzR6GJUxxoMthmd5FisqKrq+N26ahK5ZoAD5Jid97AZG08SraRQ\n8a+7YOcMZ6fqneC+BRBakWsRi+0XEeQnM5gkEomkLCIDTBJUVeXJ6dtIuOzMYBrVsYbPvuc83JrC\nAt0DQgatBpPFytmULF75c1euL/6uKArSKekG4KYKQtQ0LqxgGl1XIlDvXYYAsGz/eUZM2khSupFT\n0V1h4A+g2C5553fBrwMhyz3I5Opo6EqHWuXd1m+pE+1Ytpcv/LLuBP/uO89Pa45fxU8jSbI5w23+\nX7c8+300qJHbencXba/XbqtHgziRxXQ+NRuTRXVqkNiICDKw8ZVbAGij2UuDBf3pfWAMXHJqmCSo\nkVy6dTxBT6xhpbUxof56ol1KPqVmnETiTnSIHzXKi8yklCuJ6FdqAfcvFKVaIEq35j4BS94Aa9n+\n2zp4Ps1nu6uod6BBy/GLmdR/YxGT1hzngG2frFzuM6680rsuQ2wBpvt+2kT1MfPZelLcr9Jcsmbt\nTpp2cmzHXnskiZyEXcw2vIZyeqOzQ7N7Yegf4B/KtYo90CYzmCQSiaRsIgNMEpYfuOC2PrZfAxq6\nZAAA/PpAawC3IBRAXFiA27peq8GqwuS1x5m24SRTN5y44vkvZRoJD5SZS9c7TSqHMfuxdjzWJb5I\njxto0JJlsjjS5jceS6bay/O4f7JzVjk0QCdETO+Y4NzxzFZhlZ2Z7Gj64h5nCZWdRzvXxN/DyeeL\ne5rycq86xIT6c8JWhvDJEiFU+82KI7kGqiR5o6oqX9rKEssH563lFh3iz763ezrWI1zKG4P9dDzR\ntRYAvcf/B+AVYAKIzjrCLwGfMMMwliaao472FDWQd01D6JrzCeHtRqBodax+qQsrXujCLXWjeeeO\n+oX/ISWS6xhFURjbX5S650ufLqq2KNWKaehsW/M5/DESTFcu2y8JVFWl2svzqPbyPEBoG+05k+qz\nr+t15kJaDkZbRtE7/zi13p68pVa+zqvXuU+62YPv6bYs8b93nGHw9+sBoV/ZpkYEyRlGjidloByY\nzyzDG1RSbEYGigZ6vAe3jQedhynGNcadzUVA0k+6t0okEkmZRF6dJQ4HJoBHOtVkWJuqBPk5X6ir\nRARSPsT7geTDgY28so7sqdv2tO2x8/Z5ZT15kpJlytNRRXL90KxKuFew5mq5mGEEYPMJ4RL289rj\nXn1C7aWXTYZA38+dG85uh59vhwzxEN67YSy9G7pbw/vSjDLoNDzSqSbNq4Wz50yqV1noiYuZhfxp\nbmy22P4P84urNpbB42UjzHZNsWcZVY10MRBIOQ1/PQbftKeD6hQXzlH1LAm/mw45n/ODpS8b3+jr\n2FYpPJCIIAOKojC0dVUigwzc27ZqgcYrkdwI+OnE36XRnM8spNCKcN9CuKmXs23vHJjcB9LOF8MI\nC8bpS85A1yeLD/DF0kM++7WqHsEHA52Zlbm5VHreY3JDn0sZ3elLmeSYLTwxfZujJO7XB1tTKTyQ\nxLQsZn32JG02PkmQXT/OEAKDZ0Lb0SJl/Bpn3ICG7HrzVpn1LpFIJGUUGWCScC4lG0WBPx9rx1O2\nmTW7O0fd2FBWvdiF2hVCvMrhAv28AwU6mw6K3WUOcBfQ9SDHbCHbZJUBJkmhWbxXvIDYNcRqRnuL\ngrplr7S4T8zi2q2yz++CyX0hXWTy1Y1xLx3I6xm2VbUIEtNyqP/GIrf2NOksVyi+WyWyiBY/0zHf\n+0wa2YKHfZT06rTu/3Fta0SKbLUlr8OXzWH7VLAJEltVhT8sHemS8wm/hjxIKsGMH9w01+uSRqOw\n8dVuvHW7zGSSSDwx2AIjJksBBPf8guGeqdD6UWdbwhb4vjOc3pzrbiXB+VTnJNmXyw673U+iQvz4\nX5+6vHFbPX57uC3VyzsD2XNGt/c61rLnOlEnJn/laYZcAkzZJivJtokVO8F+OqK0mUzUf8xz+j9Q\nbNe27ODKIkPsplvzdc5rAZ1WI/U6JRKJpAwjA0wStp68RK3oYJpWCXdkBNSuEIKfTuN4cVMUhfVj\nbnHbL8BHJkqaD0ettJzcX7aT0sVDkgwwSQrLiz1qA9DcxdnNk9oxIe4NzUdAv6+dmkyJ+8RseepZ\nalVw7xuch5Coqx6PK/bvtaRgpGebMWg1Dr2u/NC1TgXG9PZ2QmpaOdxRSjGsWXkM6z6DL5rAmi/A\n7JJVGd+N283v87zpEc5Qnq22LKqOHrpbnmg1ipxBl0h8YM8mNOVimpArGi30Gge9P3Zem9POwE+9\nYMvkoh1kAUj3yFC1Z2b9+2xHNr3ajQc71OC+9tW99mtcOcyrLSww/+VpvjKY7MLfnu6lAUl7GH3o\nAbpqtzva1lvrYXh4eZl0ipNIJBLJ9YsMMN3gpOeYWXvkIq2rR7q1hwcZ2P9OT/o1jXO0+eu1fDus\nmcMC3NcM0sWMHK+23NLkk9JzaD9O2BKHygCTpJC0rCZciOx6DL5cDKNDfAiLNxkC/b93usslHYTJ\nvWkTkQ7AiLZV0WsVR5DCF7m9LCSle/8dSPLGYlXZlZBC25qRV+6cDzQahY8H1GNbr5O8c3I4LH0b\nclyyKWObwL1zYdgspr32IKte6AJAWo4Icsmgt0RSOPS27MF8l8h50uohGPo7+NsCNBYj/P2UEAA3\nl/y1dcWBRMdyrehgsk0W6sWGEh+d/0C4nSAfmd+54ZmFCVA+WNxzpqyz61uq3KVdju6nWwnOcmaO\nLw6/h8e1r6MJiSrwGCUSiUQiuRqkx+cNztHEdCxW1Wf2h6/Z+Z4NYmlcOYxf1p3wuU+OD1clYy5p\n8kcTMxzLMsAkKSz2h3CzVWXmppNMXH3Mq09Abm4zjQaBVg+zHhAORslHCZvWh+PPzIYK9XnrjgZ5\nnttVWBrgsc41+XrFES6k5q07JnFitapM23iSd+ftI8tkIS484Mo7XfmgsGc2LBtL+CWP70NETbjl\nNah7B2hEUDLUX0+wwXk7NFqsMjtJIikk9swbY0EzmFyJ7wajVsDM4aKMGWDrFDi3G+6cBBHeGUNF\nyY5Tl/lt8yneur0+k226fnVjQ9EokGm05NvBrHX1CDYccxpJ2PWp8oPGxzXIfs/5Zf0JQknnPf1E\n+mo3gM1XwqgN4omsURyzdkFv8J5skUgkEomkuJEZTDc4b/8tnE2iQ/N2bHIltlwAL/asg1bj/fDz\nUq86Xm0m2yxmWraJL5cewmJz+zp9ySmELLMFJIXF/jJjtqi8NGuXzz6+yjkd1O8Hd00Bre1vIP0c\nTOoFJ9Ze8dye7of2F4Lxyw4XvDzkBmX5gQv876/dDuvu+ChvDa18o6pw6F/4vqMIGroGl0Ji4bYv\nYPQGqN/fEVyyo/FxPZNIJAXHr7Alcp5EVIcHFkPDQc62M1vh2w6w8/erO3YeHDiXxh0T1jB1w0kO\nJ6YTGWTAX6+hXmwoe86ksvbIxXw/s0wa2bLQ4/DlrtuhlshIaqXsY4HfGBFcshNVl7R7l7DI2pKD\n59O9jA8kEolEIikJ5N3nBicxPYdAg5Y21YumLKV8sB9H3+vt1mZ/yPxo0QE+WXKQ+bvOAkL7yU69\n2PyJXkokntgDnWarlT4NYx3tM0e1cSxf8UG7Th8YNgv8bN/DnBT4pT/sn5/nbp4lchYXyzlZJpc7\nlzONWK0qOWaLl77J8MI6s53aJMTapw6Ecy6BRv8w6P42PLkNmo8UGWtXoI6nZpdEIsk3jgymwpbI\nuWIIhAE/QI/3QWPLMjSmwewH4c9HISft6s/hwalk5+RXz8//IznTyKiONakR5RTwrlcxf88sQS4a\nfq/09p6AywtFUTj8bi/eucNpJuCvMfNJ5FymG8YSp1x0dm42Ah5aSmRVZ1/pZiqRSCSS0kAGmG5g\nTBYrJy5mMqBZXJHO3ms0Cr893JapD7Z2nAdEWjlAlu3fnadTaFczkuPj+hS5db3kxkGvcToW2bNg\nAFrXiKRltXCC8lnKQPUOMHIeBEWLdXM2zBwKW37OdReDTsPGV5zi91aryod3CpvqbB/lohJIzTbR\n5O0l1HhlPnVfW8ikNccd29rHR+ZqzZ0rF/bBjKEwsRucWO1s1wXAzc/CUzug/VOgv3Lp3bLnOvFe\n/4ZMf6jNFftKJBLf6Isqg8mOokDbx+D+xRDuUhq3Y5rIZjq+pmjOYyPbbHFbV1WoFhlIpEtJtJsz\n6RWItzmbVgj1oQV4BXRaDcPbVgOgkXKEmOk9GJgxA60iJjNU/zC46xe4fTwYgvI4kkQikUgkJYPU\nYLpBUVWVBjZr9eX7E6/Qu+C0qh6BasvmsGsw6WxBrFSbhfvJ5Ey3jBOJpDDYNZgsVqtD4PvDgSLI\n88sDrX3qguVKbCN4YJHIXrp0HFQr/P0kXDwM3d4ULkceBLrMUN9cqzwZOeLl5GRypptl9Y3OsaQM\nEi5lERnsfEmzqkLrJCxQz5zR7d1m+6/IpeOw8kPYMV38P9nR6MRsfqcXISSmQGOsERVMjasp0ZNI\nJBi0zqB/kVKpOTzyH8x7HnbOEG2XjsHk3tDiAej+FvhdffZhapa3dlGTymFsOu7UUirIterXB1rz\n7vx9dK9XoXADMmYyRjeVB7Xz0V50/k63aRvS9NGZUC4uj50lEolEIilZZAbTDcrvW06TY0tfH1PA\ntO38oigKBp0Gk8WK0Wzl330XABg7bx+p2SYuZ5qoHBFYLOeW3DjYA0wmi0patplb6kRzV8vKgHA+\nLOdDxyJPImqImfKYhs62teOF2GxOuld3e4bUTRWC6VArCn+9uKyOmLSxED/N9UuXj1cwbOIGen3x\nn9e2x7vEUzUyiPLB+dCCSzktHKW+bA7bp7oHlxrcCaM3Qt9PCxxckkgkRYPdRS6nKErkPPELgQHf\nibI5v3LO9s0TYUIbOLj4qk+x+XiyQ0fKTsWwABScmd7BBQgwxZTz58vBTQk0FHBOV1VFmfY3bXlY\nN8+RtZSj+PO2aTivBr8jg0sSiUQiKXPIANMNyt4zqY7lhnHl8uh5dRi0GoxmKx8t2u+mSXM5Q2Qx\nReRi8y6R5BedxlmOsfdsatEIm4ZUgPsWwE29nG0H5sGkniLA4YKiKGx9rTuzH2sPXEFQ/AYix2zh\nzOUswF3Q306bGhGO5fD8XAfSzsH8F2B8U9gyWbj+2YnvBg+vgjsnQmTNqx26RCK5ChRFQa9Vitfo\noNFdMHq9+zU69TRMGwRT74Kkw4U6rNFsZfa2BK/gmL9ei5/eeW+pFV3MOm2JB+HXgTBjsMjWtFO9\nEx9Wn8QkSy/0Bt/XzTUvd6VNjQjWvty1eMcokUgkEokPZIDpBiTLaHHY7gIFn1UrAIEGLZlGM9tP\nXXZrtwv76nXSuUlydWg1ClqNwpYTQjR+7ZGLV9gjn/iFwD1Toe3jzrbzu+D7LnB8tVvXiCCDY0a7\nkktW3gu/73Arq7iReG/ePtqNW0ZKpombP1jutu2hDtX59YHWjvU89UzSE2HRq/BFY9j4PViMzm1V\n28PI+UKgPbZxUf8IEomkkNgnl4qV0IoweDoMnAiBLkYlhxbB123EdSPrUu77++BCWjYAfRvFsviZ\njm7bXEv68yvyXWBSz8K85+CbtnBkqbPdPwxu/xLunUN2SBUArywrO3FhAcwY1ZaKYVfWnZNIJBKJ\npKiRGkw3IJ8uOeC2XhCxyoISGqAnJctEUrrRrd0RYCqooK9E4oNK4QEcOCfchF7vW6/oDqzRQo93\nofxNMO9ZkTWTcQF+vh26vQHtnhQCtC7EhQXQo34FFu05z+9bTpOYnsPk+1oV3ZjKOFarypfLDvPz\nuhMArD/mHfDz02nRufzt+xS/zUwWpYkbvgdThvu2Si2hy6tQo7PX718ikZQ+elt5fLGjKNDwTnEt\nWPoWbP0FUMFqgnVfwdYp0HY0tHkU/PPO1l59KImD58V95M7mlagc7l7Cr9NqGNS8kqMsu0jJuAhr\nPoONPwiDCTuKBlrcL653gSLrMypElBJr5bVPIpFIJGUQGWC6Afnhv2MANK0SxpT7WxWrg1uov46U\nLBOXMt0DTGk2oW8ZYJIUBRVC/dl4TGQKVSsOYe3mI4Q20+8jITMJVAsseR1ObYR+X3u9uGw76czY\nO3mDWUV/sHA/36066lh/+JctADx/6018vPgggEPo+8/H2rHvbBqNK4c5D5CRBOu/FoElo4cFeWwT\n8aJVq7sMLEkkZRi9toQCTHaCyosMnxYPwMKX4eQ60Z6TCiveF9eU1o+I7SHeYtvLD1zgvp82OdZj\nyvn7zBD6aFARZ0pePALrJsD2aWDOct9WtT30HCfMJ1ywZyadT81GIpFIJJKyhgww3WCsOZzkWP7T\nphlTnAQadKx2OacdewaTQQaYJEVAqL9TyDvQUEwB0+odhIPRbyPgtE3Ae/8/8N0eGPgjVGrh6Ppu\n/4Y8NGUzUExCt2UY1+CSK70axvJIp5os2XueHvWFAHfTKuE0rRIuOqSehbVfwpafwOQRlIuuD11e\ngTp9ZGBJIrkGMGg1pXPtq9hE6Oft+ROWvwcXD4n27BRY+QGs/gzqD4BWoyCumeN6MnPjKbfDxIYG\noLE53xa5TqXFDIf/ha0/w4EFgIfbXmwT6PoaxN/i83rXvKq4ZlrUInbpk0gkEomkCJABphuMn23a\nS8PaVCmR82k1vl8G07JliZyk6Ah1KfMstgATCM2PkfNE9tKGb0TbpWMw8Vbo9CJ0eB60OppVcWbk\nGEtyFr+MYtBpqBQegE6roZeLjgkAl07Ami9g2y/u+kogShM7j4F6/UAjrxUSybWCcJAtpQCIokCD\nAVDvDtj1B6wcB8m2wLfFCDtniE9kLWh0NzS80+s6HRog7in73+mJpiiC2lYrnNkKe/+CHTNFqbUn\nMQ2h44tQ97Y8A+k1ygfxRNd4ejWIzbWPRCKRSCSlRbEFmBRFmQT0BS6oqtrA1hYBzASqAceBu1RV\nvaQoigJ8AfQGMoGRqqpute0zAvif7bBjVVX9ubjGfL1jsaos3nsegLdvb1Ai5/QMMH12d2OembnD\nmcFUFI5fkhueYJcAU3hQMTsT6gzQaxxUbgl/Py1KMFSLKMM4tAQGfE9IueqO7jkmS/GOpwTYdvIS\ne86kMqxN1Tz7mXMJptWJCcFP5xH4SzwIaz6HnTPdHeEAKjSEjs9B3duFDpZEIrmmMGg1mEo7e1Oj\nhcZ3Q4OBsG8OrP/WmX0KIrtp+VhYPpYx1jja6hqzytqIndRCsQV4rkpCICVBlOodWwkHFvoOKgHU\n6iF0oqp3zFeGpqIoPHdr7cKPSyKRSCSSYqQ4M5gmA18BU1zaXgaWqqo6TlGUl23rLwG9gFq2T2vg\nG6C1LSD1BtACkUO8RVGUuaqqFswWRALAwt3nAKgc4Uz9Lm5cA0xdakcRFSzEfNMdGUyy3EVy9QS4\nZC2F+JVQYmaDgRDXAv58BE6uFW0Jm+Gbdhg6vYiOeMzorosMplG/bCExLYdON0WxYPdZrCo80qmm\nV7+ULJNjWa9VuDm+PMsPJJJltAXZVBWOrRKaI4cWeZ8orgV0fAFu6iFL4SSSaxi9TnG79h062M2p\nAAAgAElEQVQ6n0aAQUslD+HsEkGrE9frBgMhYQtsmgh754Ax3dGlliaBWpoEHmI+VhSY8BFUbAoR\nNSGiOoRVgYBwobdnCALVClYLmHOELl/6BUg9A0kHxefcLkg5lfuYgmNE8KvpcChfqwR+CRKJRCKR\nlAzF9iamquoqRVGqeTTfAXS2Lf8MrEAEmO4ApqiqqgLrFUUJUxQl1tZ3iaqqyQCKoiwBegLTi2vc\n1zOjp20FYEyvuiV2TleXE6sKfnqRsTR3xxkA76wGiaQQBLjMMislGZgIrwoj/xElXsvfE85F5mxY\n+ja7K9Zh8NnBnDDUL7nxFBOJaTkAJKXn8N78/QCkZpl4sWcdR5935+11GAj8eG8LutWrwMifRLbA\n8QuXYccM4ep0bpf3Cap1gI7PQ/VOMrAkkVwHaDUalu2/wD87z9C3UUW6f7YKgOPj+pTuwOKai0/v\nj1m/8FcyNk3jZs1u/BRncFyDCon7xacoCSwvguf1+kHNriLwJZFIJBLJdUZJ390qqKp61rZ8DrBb\necQBrlM9p21tubVLCkF4oJ5LmSa61/N2UCkutC4ZSio4XFlOJgsR3/IhxVzOJLkhsAcqH+pQ/Qo9\niwGNFjo8KwRZ5z4BZ3cA4J+8n9l+b/KHqTOkNYSQmJIfWxGQlJ7jWD6WlOFY/nrFEeZsP8NrfevS\ns0GsI7gEUDsmBICqfhk8pv2Le3VL4E/PxFMFaveG9k9ClTbF+jNIJJKSxWIV2UuPT9tG30YVS3k0\nPjAEsjGoC5+a4vAnhzaafdwdtp+q6TuorTmFliLIPNUHimBWlTYQ310YQciSX4lEIpFc55Ta9Imq\nqqqiKEWmAKkoyihgFECVKiUjYH2tYbaqjGxXrUSFtTccvehYVlXVS5MpIlAGmCRXj73UslSr0WIb\nw4PLYMO3sPxdMGWioDJIsxzz503RdXga2j0uyiuuIaauP+lYfva3HW7bEi5n8civW1n9UheXVpVK\nKZth6U+8efhvFL3JbR90AdB0KLR+FMrHF+PIJRJJaaG9BkT507LFtSkbP1ZYm9C02V08+u9BhjeP\n4p3WVji/G5KPwaXjkJognOiyU8CYCYpGBIu0epGZFBQFwVFCODyqtjAoiK4rtkskEolEcgNR0gGm\n84qixKqqetZWAmdXPEwAKrv0q2RrS8BZUmdvX+HrwKqqfg98D9CiRQvp3eqBqqqkZZsJLil9GhtJ\n6U5XKFXFzVUmLky4SkkkV4vd5cda2rbNWp0IItW9Dea/4NAZ0lkyYcV7sHkS3Pw0NBsBhlLQIikg\ne8+k8tm/B6/Y7+YPlhNBKvcGb+SR4FUoPx8GwC2cHFxBWIO3uB8CI4pnwBKJpEygc5lMyjGXTaOD\n05eyiA7x44KtBLhBXCiLnu5I5YgAMOhkZqVEIpFIJIWgpN/u5wIjbMsjgDku7fcqgjZAiq2UbhFw\nq6Io4YqihAO32tpuSP731y6WH8jFhQSYsz2B7p+uJCPH7LVt5iZRabj2SFKxje9KqKhUi3S+VDeu\nXK7UxiK5vrBnxlmsZSS2HF4Vhv5GQt+p7Le6xM7Tz8HCl+GLRkK3KSet9MaYD3afSclzux9GemvW\n84P+Yzb4jeZp8yT8Lx9271S5NfT/Dp7eJXSWZHBJIrnucc1WfuH3naU4Et8cTUxnwe5z1KoQTP2K\noQBEBvtROyaEQIPURpJIJBKJpLAU211UUZTpiOyj8oqinEa4wY0DflMU5QHgBHCXrft8oDdwGMgE\n7gNQVTVZUZR3gE22fm/bBb9vNLJNFn5df5Jf15/MVSTzqRnbAdh+6jLt48uz9kgSGTkWtywEuzZK\nadC6eiRhgQb+16cuY+ftK7VxSK4/7C8z5rISYLIR1rAnHf5QGaRdybO636mgXBYbMhJhyeuw8iNo\nMhhaPADRdfI+WCmQkmnyajNgYt5tKlsWTKa3diOhSqb3joYQ4ZDU/D6IaVACI5VIJGUJ1wymvCbG\nSounZ4rnpSyjhbqxoew5k0pMqH8pj0oikUgkkmuf4nSRG5zLplt89FWB0bkcZxIwqQiHdk1yIdUp\ntDtp9TFaVY+gQZwzA2jZ/vOO5SOJ6bStEcmQHzZ4Heet20vvZe/xLkJvJSJI6C4Zzde+fbukbGB/\nmbGWsQBToEGLFQ0zLV34y9Ke9T0SCN/2tdDzADCmwcbvxafqzdDoLqh3u7DDLgOkZIkAU3yolTG1\nEsjY+RedNdsJXZJFLV93j8qtofFgaHgn+JVeMFsikZQurhlMZe26DHAsURgWNIgrx5hedRnepiox\n5WSASSKRSCSSq0XmAV8jnE3Jciy//c9ewN3u9/CFdMfy63P2kGPyDt6E+Okw6EpH8+jn+1uhsT1w\nhtuEvXNkgElSRNi/W5bS1mDyQFEUHu5Ug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            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZwI7u4teaoe",
        "colab_type": "text"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "The prediction makes sense as the curve hits an all time high around 4500. Hence, the price should come down going forward. The LSTM predicted trend is also the same. \n"
      ]
    }
  ]
}