Stock_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Stock Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNvnHoLz24AhlwODIvxjT+h",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RohanMathur17/Stock-Price-Prediction-Using-LSTMs/blob/main/Stock_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4pzj_QMGz_3"
      },
      "source": [
        "## **Stock Price Prediction Using LSTMs**\n",
        "\n",
        "### To predict the closing stock price of a company"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UML045-uGwrm"
      },
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import math\n",
        "import pandas_datareader as web\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense , LSTM\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('fivethirtyeight')"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VbgxnHSsHtYL"
      },
      "source": [
        "# Get the stock\n",
        "\n",
        "df = web.DataReader('GOOGL' , data_source= 'yahoo',\n",
        "                    start = '2012-01-01' ,end = '2020-12-31')  # Getting stock data of Google for the given date range"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "zo80CadmIDiS",
        "outputId": "25c623fa-b838-4a56-d4bb-31751ddb18fb"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012-01-03</th>\n",
              "      <td>334.409424</td>\n",
              "      <td>326.511505</td>\n",
              "      <td>326.796783</td>\n",
              "      <td>333.038025</td>\n",
              "      <td>7345647.0</td>\n",
              "      <td>333.038025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-04</th>\n",
              "      <td>335.460449</td>\n",
              "      <td>330.640656</td>\n",
              "      <td>332.847839</td>\n",
              "      <td>334.474487</td>\n",
              "      <td>5722272.0</td>\n",
              "      <td>334.474487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-05</th>\n",
              "      <td>332.317322</td>\n",
              "      <td>328.443451</td>\n",
              "      <td>331.396393</td>\n",
              "      <td>329.834839</td>\n",
              "      <td>6559234.0</td>\n",
              "      <td>329.834839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-06</th>\n",
              "      <td>330.330322</td>\n",
              "      <td>325.220215</td>\n",
              "      <td>329.904907</td>\n",
              "      <td>325.335327</td>\n",
              "      <td>5380414.0</td>\n",
              "      <td>325.335327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-09</th>\n",
              "      <td>323.823822</td>\n",
              "      <td>310.925934</td>\n",
              "      <td>323.573578</td>\n",
              "      <td>311.541534</td>\n",
              "      <td>11633555.0</td>\n",
              "      <td>311.541534</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  High         Low  ...      Volume   Adj Close\n",
              "Date                                ...                        \n",
              "2012-01-03  334.409424  326.511505  ...   7345647.0  333.038025\n",
              "2012-01-04  335.460449  330.640656  ...   5722272.0  334.474487\n",
              "2012-01-05  332.317322  328.443451  ...   6559234.0  329.834839\n",
              "2012-01-06  330.330322  325.220215  ...   5380414.0  325.335327\n",
              "2012-01-09  323.823822  310.925934  ...  11633555.0  311.541534\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mtUfRL9eIGv3",
        "outputId": "ab5f01e1-f8ab-4719-e08f-86df15b156b6"
      },
      "source": [
        "df.shape  # Getting the rows & columns "
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2265, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "Asl4rq5fITTR",
        "outputId": "a07abf14-e76c-42ba-d2b5-04d840dba8ab"
      },
      "source": [
        "# Visualize the opening price history\n",
        "\n",
        "plt.figure(figsize = (16, 8))\n",
        "plt.title('Opening Price History')\n",
        "plt.plot(df['Open'])\n",
        "\n",
        "plt.xlabel('Date' , fontsize = '18')\n",
        "plt.ylabel('Open Price' , fontsize = '18')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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+0p/+9Cf93//9n9q0aaPY2FjdeeedMhgMevXVV3XttddqypQp+vDDD9W7d2/t379fy5cvV3h4uObPn++ynauvJk6cqC+++ELHjh3T+PHj3fZsAQDAV4QaAAD4wWAw6PHHH9e1116rhQsXatOmTfryyy9lMBjUpUsXTZ48WVOmTNGZZ57p8T26deumF154QY8//rgWLVqkqqoq9e3bVw899JAGDRrkMv7ZZ5/VsGHDtGjRIm3cuFH5+fmKjY1VUlKSJk2a5LIDRVM655xztGrVKj3xxBNav369Nm7cqN69e+udd97Rjz/+qPT0dLc7vzSlbt26af369XrppZf08ccfa+HChYqIiFBycrImT57s0K+kc+fOWrNmjRYuXKhly5bpv//9r0wmkxITE3XWWWfpmWee0RVXXNEk1/H4449r9erV2r17t7744guFhoYqOTlZDz30kP7whz84hAfjx4/XoUOH9P777+uVV16RyWRSSkqKvcqob9++Wrt2rebNm6e1a9fqiy++UGxsrEaOHKkHHnjA5y1z3RkxYoQSExOVk5Oj3//+9/W+bgBA62YoKChwrVMEAAABt2HDBo0ePdrv3TxaismTJ+s///mPvv76a/Xo0aOpp4Mmkp2drfPOO099+vQJyiVJAIDmJbjqPwEAQLNms9l09OhRl+Pr1q3TBx98oHPOOYdAo5V76aWXZLFY7FUhAADUB8tPAABAwFgsFvXu3VsDBw5Ujx49FBoaqh9++EFr1qxReHi45s2b19RTRBPIysrS+++/r3379mnx4sVKTU312HMGAAB/EGoAAICAMRqNmjhxotavX69t27aprKxM8fHxuvbaa3X//ffbtwtF63LgwAHNmjVL0dHRGjBggJ5//nmFhvJ/QwEA9UdPDQAAAAAA0CzRUwMAAAAAADRLhBoAAAAAAKBZItQAAAAAAADNEqFGC5CZmdnUU4AT7klw4r4EH+5J8OGeBCfuS/DhngQf7knw4Z4Ep5Z2Xwg1AAAAAABAs0SoAQAAAAAAmiVCDQAAAAAA0CwRagAAAAAAgGaJUAMAAAAAADRLhBoAAAAAAKBZItQAAAAAAADNEqEGAAAAAABolgg1AAAAAABAs0SoAQAAAAAAmiVCDQAAAAAA0CwRagAAAAAAgGaJUAMAAAAAADRLhBoAAAAAAKBZItQAAAAAAADNEqEGAAAAAABolgg1AAAAAABo5t76sVRdFx9W6ntHlP5LeVNPp9EQagAAAAAA0IyVmqz665ZCFVXZdKzcqvGrT2jH8aqmnlajINQAAAAAAKAZO1RqUZnZZv/ZYpOmbi5owhk1HkINAAAAAACasfJagUaNb3JNOlBsboLZNK4mDTU2bdqkcePGqWfPnoqLi9PixYsdzpeUlGjatGnq1auXTjvtNF100UV6+eWXHcZUVlZq2rRp6t69u5KSkjRu3DhlZ2c7jMnKytLYsWOVlJSk7t2768EHH1RVVesoxQEAAAAAtGzlFtdQQ5Je3FXcyDNpfE0aapSWlqpXr16aO3euoqKiXM7PmDFDK1eu1Pz587VlyxY98MADmjVrlt577z37mOnTp2vFihVatGiR0tPTVVxcrLFjx8pisUiSLBaLxo4dq5KSEqWnp2vRokVavny5ZsyY0WjXCQAAAABAQ6lwU6khSSb3h1uUJg01hg0bpscee0xjxoxRSIjrVLZu3aqxY8dq4MCBOv3003XzzTfroosu0rZt2yRJhYWFevvtt/XEE09o8ODBSktL04IFC7Rnzx6tXbtWkrR69Wrt3btXCxYsUFpamgYPHqxZs2bprbfeUlFRUWNeLgAAAAAA9XK41KLdJ0yy2k4mFmUeQo0oo6GxptVkgrqnxqWXXqrPPvtMhw4dkiRt2bJF3333nYYOHSpJ2rFjh0wmk4YMGWJ/TXJyslJTU7VlyxZJ1cFIamqqkpOT7WOGDh2qyspK7dixoxGvBgAAAACAU7cyq0J9/3tUA5bl6I41J+zHPS0/sbaCSo3Qpp6AN88884zuv/9+nXvuuQoNrZ7qs88+qxEjRkiScnJyZDQaFR8f7/C6hIQE5eTk2MckJCQ4nI+Pj5fRaLSPcSczMzOQl9Lgmtt8WwPuSXDivgQf7knw4Z4EJ+5L8OGeBB/uSfDhngTW5M1RqrBUV18sP1ihz3b8pDNjbDp41CgpwmV8XkGhMjNzXY43p/vSo0cPr+eDOtRYsGCBtm7dqnfffVcpKSn68ssv9eijj6pr16666qqrGvSz6/rigklmZmazmm9rwD0JTtyX4MM9CT7ck+DEfQk+3JPgwz0JPtyTwCvc6LgpRk50Z43oEaO2phLpp0KX8ZFt2qpHjw7aV2TWQ5sLVGax6feJRbr+wjMba8oNLmhDjfLycj3xxBP617/+pauvvlqSdO6552r37t166aWXdNVVVykxMVEWi0V5eXnq2LGj/bW5ubnq37+/JCkxMdG+FKVGXl6eLBaLEhMTG++CAAAAAAAIoHZh1R0lKjwsPzFbq//7ga8KtOZwpSTp5/xwXdfXphBDy+i3EbQ9NUwmk0wmk4xGo8Nxo9Eoq7X6zqSlpSksLExr1qyxn8/OzlZGRob69esnSbrkkkuUkZHhsM3rmjVrFBERobS0tEa4EgAAAAAA6sdmcw0u2oRVBxOeGoWafm2qURNoSNLRyhD9XGRugBk2jSat1CgpKdG+ffskSVarVYcOHdKuXbvUvn17paSk6PLLL9esWbMUExOjlJQUbdq0Se+9955mzZolSYqNjdX48eM1c+ZMJSQkqH379poxY4Z69+6tQYMGSZKGDBminj17asqUKZo9e7by8/P12GOP6bbbblO7du2a6tIBAAAAAPBZsZv9WUN/LVPIKrW4fU2V1f17Vbof3iw1aaixfft2jR492v7znDlzNGfOHN1888169dVX9frrr2vWrFm68847lZ+fr5SUFM2YMUN33nmnw2uMRqMmTJigiooKDRw4UPPnz7dXeBiNRi1dulRTp07ViBEjFBkZqRtvvFFPPvlko18vAAAAAACnosBNQlGzvGRxZpnb1+zKq3J73NyCtkVp0lBjwIABKigo8Hi+U6dOeuWVV7y+R0REhObNm6d58+Z5HJOSkqKlS5ee8jwBAAAAAGhKC/eWuhx7bmexjpV7Lrs4XGZV8tuHXY6bPFRwNEdB2ygUAAAAAABUe+m7Epdjm3OqtDnHfTVGjRI3/TY89eBojoK2USgAAAAAAAi8ckvLKdUg1AAAAAAAoBUpp1IDAAAAAAA0R6WEGgAAAAAAIJhMPCdG486MqnMclRoAAAAAACCoRBkN+ttlcbrv3DZexxFqAAAAAACAoBJulKJDQ/TwBe28jmP5CQAAAAAAaBQ2m28hRHiIQZIUYZTCvTztU6kBAAAAAAAahdXHDKImqzAYDIqP9Py4T6gBAAAAAAAaha8ZREGl1f7nDhGeH/fbewk8mpvQpp4AAAAAAADwzOxjqUax6WSo0THSKMnsMubdC8p1dVqXQE2tybWceAYAAAAAgBbI10qNYtPJgR3dVGNEGqXu0S1n6YlEqAEAAAAAQFDztafGeR3C7H9211OjwiL92ku0xSDUAAAAAAAgiLlbfjKlV4zeGtzB/nN0qEETz4mx/+yuUuP2s6MbZoJNiJ4aAAAAAAAEMeflJ52iQjS3X5wk6b/D4rXjuEmjT49UQpTRPqa6p4aj+/u0lfnY8Qada2Mj1AAAAAAAIIhZnCo1Qg0n15AM7RKpoV0iXV4TE+a6zuSMdqHKPBb4+TUllp8AAAAAABDEnCs1Qnx4ko80trDmGR4QagAAAAAAEMQsVsefQ33IK5KiXZeftESEGgAAAAAANKC1hys0Zf0J/fP7Ellt/m+panZ6TagPW5hclBCmHrEnO05M6RXjZXTzRU8NAAAAAAAayMFis679PE+S9N7P5YoJM+iWHv4FDBanHMSXlSUGg0ErRnTUwr0lio80anJPQg0AAAAAAOCHp7YXOfx8z8YCv0MN5y1dfW2XcVq0UY9eGOvXZzU3LD8BAAAAAKCBHCiy1Ps9nCs1fFl+0loQagAAAAAA0EBMp9BDw5lLqEGmYUeoAQAAAABAAzFb6x5T93s4Lz8h1ahBqAEAAAAAQANxDiRO6T2cG4XyJG/HVwEAAAAAQAMxBaBSw+L0Hiw/OYlQAwAAAACABmIOSE8Np+UnNAq1I9QAAAAAAKCB1LenRqXFpoe2FDoco1LjJEINAAAAAAAaSFU9e2r8d1+ZMgvNDseo1DiJUAMAAAAAgAZS30qN+78scDlmJNOwI9QAAAAAAKCB1Hf3kyo3oQjLT04i1AAAAAAAoIHUd/cTdytNQll+YkeoAQAAAABAA7DZbCq31K9SIy7c9bGd5ScnEWoAAAAAABBgNptNf1ifX+/3aRfummAQapxEqAEAAAAAQIB9k2vSv/eVuxz3d+VIrJtKDZafnESoAQAAAABAgL35Y6nb42F+PoW7DTXINOwINQAAAAAACLAoD2tEwgz+JRKx7pafUKlhR6gBAAAAAECARXgINUL9fAqPcVOWYbXVr/loS0KoAQAAAABAgEV6WCMSiDiixESoUYNQAwAAAACAAIv2EGokRhn9eh938UVlPbeJbUkINQAAAAAACDBPzTwtVv8CCXcrTdw1D22t+CYAAAAAAAgwT9UU/hRZZBa63xb2trNjTnVaLQ6hBgAAAAAAAVZldX/cw2EXe/NNunJ5rttzFyWEndqkWiBCDQAAAAAAAszkYZmJ1cdUI/2XCpWZXd/j9Svby+DntrAtWZOGGps2bdK4cePUs2dPxcXFafHixS5jfvrpJ916663q2rWrOnfurIEDByojI8N+vrKyUtOmTVP37t2VlJSkcePGKTs72+E9srKyNHbsWCUlJal79+568MEHVVVV1eDXBwAAAABonTwvP/Ft/UmBh1KPEAINB00aapSWlqpXr16aO3euoqKiXM4fOHBAw4cP1+mnn67ly5frq6++0iOPPKKYmJPrh6ZPn64VK1Zo0aJFSk9PV3FxscaOHSuLxSJJslgsGjt2rEpKSpSenq5FixZp+fLlmjFjRqNdJwAAAACgdTF5qMio9LFRaIWHUIRMw1FoU374sGHDNGzYMEnS3Xff7XJ+9uzZGjJkiJ566in7sW7dutn/XFhYqLffflsvv/yyBg8eLElasGCB+vTpo7Vr12ro0KFavXq19u7dq927dys5OVmSNGvWLN1333169NFH1a5duwa8QgAAAABAa1TuIZRwt6TEnQoP48g0HAVtTw2r1arPPvtMqampuv7663XmmWdq8ODB+uCDD+xjduzYIZPJpCFDhtiPJScnKzU1VVu2bJEkbd26VampqfZAQ5KGDh2qyspK7dixo/EuCAAAAADQangKJSotnvttOI5zPyaEVMNBk1ZqeJObm6uSkhL97W9/08MPP6yZM2dq/fr1mjx5smJiYjR8+HDl5OTIaDQqPj7e4bUJCQnKycmRJOXk5CghIcHhfHx8vIxGo32MO5mZmYG/qAbU3ObbGnBPghP3JfhwT4IP9yQ4cV+CD/ck+HBPgk9rvif/3R/t8dyujJ/Uro6n8dzCcLl7ZD965Igyqyz1mltzui89evTwej5oQw3rry1hr7nmGt17772SpPPOO087duzQwoULNXz48Ab9/Lq+uGCSmZnZrObbGnBPghP3JfhwT4IP9yQ4cV+CD/ck+HBPgk9LvieVFptOVFqVGBkio5vSiawSs6RjHl/fKeUMJbfx/jgeuv+4pEqX412SOqtHV9eelL5qafclaJefxMfHKzQ0VKmpqQ7Hzz77bB06dEiSlJiYKIvFory8PIcxubm5SkxMtI/JzXXc2zcvL08Wi8U+BgAAAAAAX2SXWjRgWY56Lj2qUZ8dV7mbZSbrj7iGEbWV+tBXw1OjUHY/cRS0oUZ4eLj69u3rUhbz008/KSUlRZKUlpamsLAwrVmzxn4+OztbGRkZ6tevnyTpkksuUUZGhsM2r2vWrFFERITS0tIa4UoAAAAAAC3FK3tK9GOhWZL01bEq/fvnMpcx+ZUetj75VampPqGGD5NsRZp0+UlJSYn27dsnqXq5yaFDh7Rr1y61b99eKSkpuu+++zRhwgRddtllGmFjEA4AACAASURBVDhwoDZs2KAPPvhAixcvliTFxsZq/PjxmjlzphISEtS+fXvNmDFDvXv31qBBgyRJQ4YMUc+ePTVlyhTNnj1b+fn5euyxx3Tbbbex8wkAAAAAwC8v7ylx+PnF3cW6PTXG4dgvJd57XhR7CTXmf1+i/7e7WIfL3AcjZBqOmrRSY/v27Ro4cKAGDhyo8vJyzZkzRwMHDtTTTz8tSRo1apRefPFFvfTSS7rsssu0YMECzZ8/36Gfxpw5czRy5EhNmDBBI0aMUExMjN577z0ZjUZJktFo1NKlSxUdHa0RI0ZowoQJGj16tGbPnt0k1wwAAAAAaDlMbrKHn4vMXl9TanYfWBwqMWv6lkKPgYZEpYazJq3UGDBggAoKCryOueWWW3TLLbd4PB8REaF58+Zp3rx5HsekpKRo6dKlpzxPAAAAAADcMbvZnrXOUMNDpcaOPJPqWphCqOEoaHtqAAAAAAAQ7I6Wu1ZVZJc6Lj+5obvjbiWeGoW6yUdcGFiA4oBQAwAAAACAADFbbQ5LUowGKSHS8dG72N2aFUlVPqQabH7iiFADAAAAAIAAcd61JNJoUEyY46O3p+UnVR52PKmN5SeOCDUAAAAAAKgHm+1kGFHpFExEGA1qG+aYRHhaflLlfSdYSTzEO+P7AAAAAACgDjabTR8fLHd7rvaqkYPFjv00qiw2xYQ6hhqe3sc5EHGH5SeOCDUAAAAAAKjDq9+X6tbVJ9yeq114sWBvicO5ErPNZfnJ/mKL1h+pdHkfX3pq8BDviO8DAAAAAIA6PLy10OO52tu6Lv3ZtQrDuVJDkm5dnacip/UmVRaXYS5CKNVwQKgBAAAAAEA9eGiRYefcU0OSiqps+s3HubLW6sfhU6UGmYYDQg0AAAAAALyo3QjUHUsdYUSMm1BDkjIKzVp3+OQyFF92PyHTcESoAQAAAACAF8UetmCtUTuL6Bzt+pgdE+r50Tuz0Gz/c6UPlRqsPnFEqAEAAAAACAoHis06UGyue2Ajy6vwvtdq7eUnvduHOZx7MK2tx0oNSWpT61xRVd2hhg+5R6tCqAEAAAAAaHIv7CpW2vvHdMH7x/TynpK6X9CIjtcRamw5VmX/c5lTg40Bp0W47alRo02tnVEKKr1/juRb343WhFADAAAAANCkLFabnt5eJEmySZqxtdChgWZTO17hfVuSO9ae0OHS6jHlTn0xokMNXpef1D6VX+VDqOHDDimtCaEGAAAAAKBJ5VZYZXJ6ns8pr/sBv7HUVakhSQu+r64uOeE0tl24QRFGz6+rfd2+VGqYgyjsCQaEGgAAAACAJvPpL+U6Z+lRl+NZJcFTklBXTw1J+qHQrAqzTQed5t0pyiiDwaDpF7R1+7raO6cU+tBTw8TyEweEGgAAAACAJlFhtunmL064PZdbx5KPxuRLpUZhpVUjP811OBYTarD303gorZ3W/V+CesSGOoypvbGKc6VGzzjHsVJ1SIKTCDUAAAAAAE1iT77J47l8H5ZiNJbMorp3ZNmcU6Vtxx2v54KOYTLU2oP1/PhwXdjRcXcU86+VFyarTSW1mowaJG26NlEP16rwOK9DmNLiHV/f2rnGPgAAAAAANIIS50YatQRTqLEtt6ruQW68dHl7l2NhIY47oZh/vcxCpyahseEGhRgMmnZ+W/WIDVVuuVXjzop2CElAqAEAAAAAaCKVXlaYFFQGR++IcrPNp+Unzi7sGKYz2rk+cjtvhFLT+NN56UlcRPVAg8Gg686I9vvzWwuWnwAAAAAAmkSll6aXJebgqNTILq176Yk7bcPdP26HOlVq1BSr5DuFOHEeXg9HfEsAAAAAgEax7nClxq46rmlfFajYZFWVxXOoUWEOjkqN3Sc89/3wJibU/TKRMOdKjV+DnYIq95Ua8I7lJwAAAACABrf7hEljPj/+60+VMoZIfTp4bnpZ5iXwaEw/FJxapcaRMvdra0IN7ntquCw/oVLDJ3xLAAAAAIAGN2BZjsPP878vVZWXnhrBUqmRXep+klenROrqlEiPr9uZ577Co8ppyc0/9pSooNKqzTmOzUjbhdMQ1BdUagAAAAAAmoS3nhrlQRxqnNchTK8OaK/YcIN6/fuojpS59v+4KCHc7fvtL3Z8v+MVVvX78JiOlTu+R5SRUMMXVGoAAAAAAJqEt54a5UGy/MQ51Hh3aAd9MTpBcREhMhgM6t3e/RKaP5/Xxu3xc9u71hY4BxqSFO2hJwccEWoAAAAAABqUu6UkMaEGVXoLNYKgUsNms7mEGv0SwxVWaweTDm4aes67NFbDk90vTbn5LN+2Z40k1PAJy08AAAAAAA3q2+NVLsfKLTb9UuK5qUYwVGoUVtlUWitciTIa1N4pxEhuY3T4OSEyRJN7uq/SkKRO0UaP52pj+YlvqNQAAAAAADSoTUcrXY5ZbdLbmWUeXxMMlRpZTlUaXWKMMjjtXjL2zGiHbVoHdI7w+p6etnp1FkWlhk+o1AAAAAAANKgvj7lWatSlsUONYpNVVpsUGx6inHKLXv6uROudwpguMa5VFqlxYXr/N/Ga/32p4iND9MRF7bx+TojBoLZhBhWbvF9fJJUaPiHUAAAAAAA0GJPVpq05dYca/TuF66ta4UdjLj/5YF+Z7t6YL5NVmnNJrJb+XKZtx123ZHUXakjSlUmRujLJ8/auztr4EGpQqeEbQg0AAAAAQIP5ucjs0JfCE5PT9q6NWanx4JZCVVhO/tkTT6GGv9qGheiIXHc8qe00H3tvtHb01AAAAAAANJgTFd4f3ms4N8asskoWa8MHGzabTcd9nGNywEKNuqswUmOpQfAFoQYAAAAAoMEUVPkWGFhsrsFGYyxBKfGjIiRQlRptwrw/it/UPUoJUVRq+ILoBwAAAADQYPIrfQs1RqRE6ocCs0OQUWGxqU1YQ82sWlGV76FG5wAtCQn1kGlMPCdGQ7tEaHiy7/05WjtCDQAAAABAgynwITSIMEq3nR2jBd+XOhwva6C+GrtPmPTsjiIdKLZo9wnXhqCetPFh2YgvPF3X3b3a6EyWnfiFbwsAAAAA0GCe/raozjG7bzxNcREhLjt+VDRAqGGx2vS7L/KUVWLx+7WB2mbV5uGy4iLY8cRf9NQAAAAAADSIKoutzp1PurYxKvHX/hGRTqFGQ1RqbM8znVKgIUkRAQo1PGkXziO6v/jGAAAAAAAN4stjlXWO+UOvNvY/O/fG9KeJp6+Olp1aoCEFrlLDk7AQKjX8RagBAAAAAGgQz+wornPMLWdF2/98rNyxqejL35UEfE6FPu7G4k5EgDYk8dQoFP7jqwQAAAAABNzxKumrY1Vex/zlvDaKizj5WPqL07KQT7MqAj4v5+DEVxFGyWAITCXFQ2ntAvI+INQAAAAAADSAX8pdHzfX/V+CbjozSgZJ/RLDdVetpSeN5Uhp0/fTuPy08IC9V2vH7icAAAAAgIA7UuEYAlzbLUrnx4frnwM76J8DJZvN5lL5EGqQGmgXV7vDp9hTI5D9NEIMBkUapYpTb++BX1GpAQAAAAAIuFKLYwgQH+n4+OluKceDaW0dfr6gY1jA57WvyHxKrwsPcBPPSgKNgKBSAwAAAAAQcJVOrSt8qXQY0DlC2n6yuWhYgHpYWKw2fbC/XDkVVu0tOLVQIyo0sKGGc0EK+56cmiat1Ni0aZPGjRunnj17Ki4uTosXL/Y49v7771dcXJxeeuklh+OVlZWaNm2aunfvrqSkJI0bN07Z2dkOY7KysjR27FglJSWpe/fuevDBB1VV5b1hDQAAAADg1FU4hRq+hALOwUelNTBrUe77skCT1+drxtbCU36Pht7ONTxAO6u0Nk0aapSWlqpXr16aO3euoqKiPI5btmyZtm3bps6dO7ucmz59ulasWKFFixYpPT1dxcXFGjt2rCyW6loei8WisWPHqqSkROnp6Vq0aJGWL1+uGTNmNNh1AQAAAEBrV2l1DAGifAgFnJd4VFnqH2rYbDYtziyr9/v4Mv/6iAjw8pbWoklDjWHDhumxxx7TmDFjFBLifiq//PKL/vrXv+q1115TaKjjapnCwkK9/fbbeuKJJzR48GClpaVpwYIF2rNnj9auXStJWr16tfbu3asFCxYoLS1NgwcP1qxZs/TWW2+pqKiooS8RAAAAAFqlNw859sPwpVIjwqlaYW+BWf87VL9tXU0+7OB6WlTdj8aRAV5+4iyQu6u0JkHdKNRsNmvSpEmaOnWqUlNTXc7v2LFDJpNJQ4YMsR9LTk5WamqqtmzZIknaunWrUlNTlZycbB8zdOhQVVZWaseOHQ1/EQAAAAAAlfiQLoS7ebC/YVWevsg+9WCj3Idqj7Ni6243GRng5SEv9I9z+Pnvl8d5GAlvgrpR6Jw5c9ShQwdNnDjR7fmcnBwZjUbFx8c7HE9ISFBOTo59TEJCgsP5+Ph4GY1G+xh3MjMz6zn7xtXc5tsacE+CE/cl+HBPgg/3JDhxX4IP9yT4cE+CTbTDT3sO5ysz5pjXVxyvcn2dJN255rg+61d+SrPI8/CetU3qVKSNRyO9jjGXlyozM/+U5uDOBTZpUHy4thUYdWW8Rd3KD6mx/go3p39WevTo4fV80IYaGzZs0JIlS7Rhw4Ym+fy6vrhgkpmZ2azm2xpwT4IT9yX4cE+CD/ckOHFfgg/3JPhwT4LQRscNHO44v5N6dPEeHCRUWqWtR1yO55kMp3x/I0rM0lb3YUp0qEEvXhanMd2jpJ2Hvb5Px7i26tGjwynNwZOPegb07XzS0v5ZCdrlJxs3btTRo0eVmpqq+Ph4xcfHKysrSzNnzlSvXr0kSYmJibJYLMrLy3N4bW5urhITE+1jcnNzHc7n5eXJYrHYxwAAAAAAAsdmc13yMaBzRJ2va4i+EpVelp/85zfxuunMaBkMBnWI8P543NCNQnFqgjbUmDRpkjZt2qQNGzbY/9O5c2fdfffdWrZsmSQpLS1NYWFhWrNmjf112dnZysjIUL9+/SRJl1xyiTIyMhy2eV2zZo0iIiKUlpbWuBcFAAAAAK2A2SlHCDFIoT7s7uHcKDQQZn7jeYOIs9qdXLyw8Mr29j+7m6nRQKgRjJp0+UlJSYn27dsnSbJarTp06JB27dql9u3bKyUlxaUXRmhoqDp16mQvlYmNjdX48eM1c+ZMJSQkqH379poxY4Z69+6tQYMGSZKGDBminj17asqUKZo9e7by8/P12GOP6bbbblO7du0a9XoBAAAAoDVw3orV1+1KQwwGhRpcQ5FTVWyyKv0X901GO0SEqFP0yRRlaJdIvTm4g748WqlrukZpzOfHHcZb3VSfoOk1aaixfft2jR492v7znDlzNGfOHN1888169dVXfXqPOXPmyGg0asKECaqoqNDAgQM1f/58GY3VfzmNRqOWLl2qqVOnasSIEYqMjNSNN96oJ598skGuCQAAAABaO+eNTsL8WCMQYTTIHKBUI7fc844r0W62aB3TLUpjukW5He/DzrBoAk0aagwYMEAFBQU+j9+9e7fLsYiICM2bN0/z5s3z+LqUlBQtXbr0lOYIAAAAAPCP2amqwZelJzXCjVKpOTDzKPayjWyMm1DDGwo1glPQ7n4CAAAAAGg+bDabDAaD9uablONUIeFPpUak0SApMAlCqcnz+0SH+RdqeOk3iiZEqAEAAAAAOGW55Rb1+zBHJyo9V0WE+VOp4cfYupR4CzX8rNSgp0ZwCtrdTwAAAAAAwW/O9mKvgYbkf0+NQCnxsvwk2s/PIdIIToQaAAAAAIBT9npGaZ1j/KrUCGCokVnkuTlHu3D/Hoe7tmGhQzAi1AAAAAAAnJJKHxtN+FOp4c/YuvyQ7znUOC8+zOtrn+8fa/9zWIg0pVdMwOaFwCFqAgAAAAD4zWK16cwlR3wa60+lhqdCjZpGpP7wtvzkksRwr6+dkBojm03ak2/SrT1iFB9p9Ouz0TgINQAAAAAAfluVXaESs2+VGj1ifX/0NHoILopMNsWG+xZq5JRbdOsXJ7Q1t8rjmLR476FGiMGgST3b+PR5aDosPwEAAAAA+O2VPXX30qhxVy/fwwFPRR35dTQjre2dzDKvgYYkRfm5+wmCE6EGAAAAAMBvm49Vuhw7q51rRcYFHcPUN8F7VURtnrIGX/t3SNIT24p8HovmjVADAAAAAOCR2WrTvzJK9fT2ImWXWiRJ3+ebVOVUOHFT9yh9NrKjy+snn+Nfg02jh1INPzINtCL01AAAAAAAePTMjmLN21ksSXr3pzJ9e30n/b/dxS7j5g9srxCDQUaDYwBxQUffqzQkz41CCTXgDpUaAAAAAACPagINScoqsWjd4UptOOLYr+LNwR0U8muDz4cvaGc/flWXCPVs733rVGceQw1r4FKN2Re3q3sQmgUqNQAAAAAAbpW62RJ1S06Vssss9p9DDdKIlEj7zw+c31YXJoRp78HDmtQvye/PDPGw+4k/lRqdokJ0rNx9Y9GBnSN0aw//lsQgeBFqAAAAAADcyigwuxw7WOJ4rFOUURFO5RWDkiLVpdSiME9bmXjhqVLD7EelRtc2RpdQw2iQ8u7o4vd8ENxYfgIAAAAAcGtPvsnl2L9/Lnf4OTossFujeoou/KnUiAt3fdSlJ0fLRKgBAAAAAHBrb4FrqOEs2tMerKeoykP64E8oQYDRehBqAAAAAADcOlzqvi9FbYEONSo9hhq+JxWmADYVRXAj1AAAAAAAuMirsOijA+V1jgt4pYaHHMWf6gszmUarQagBAAAAAHBwtMyiIStyfRrbWJUa5rqLRuwsbsZe2y3qFGeEYEaoAQAAAABw8NoPpTpYYql7oKRAr/Tw3FPD9w8yuxn7YFrbU54TghehBgAAAADAwfbjVT6PDXT/ikoP7+fP8pOiKsfBa0cnqFf7sPpMC0GKUAMAAACAndVm0z+/L9EfN+Zr87HKpp4OmkCJyaovspvu3ld6KBBxt6TEHZvNpp+KzA7HQkMCu0QGwSO0qScAAAAAIHi8mVGmB7cUSpLe/alMe246TZ2ijU08KzSmT36p8Gt8oOOCKo+VGr6VauwtMLscC+XX+S0WtxYAAACA3Z+/KrD/2WyTXvqupAln0zxYrDbtOWHST4Wmpp5KQBwudS2VaBNqUPrVHbX9+k4u5wK90YjHRqF1fFCpyapndhRp4toTLud48G25qNQAAAAA4FFGQct4UG9IUzcX6I2MMhkN0ouXxWn82TFNPaV6OV7huM6jf6dwLRveUeHGxlnCUeVx+Yn3VOO+TQX67373W9BW+rFzCpoXAisAAAAAHlXxMOjVxqOVeiOjTFJ1I8sFe0ubeEanrspi053rT+jlPY7VOTd2j260QEPy3Ci0rkoNT4GGJJ0Tx+/zWypCDQAAAAAeeepvgGq//fy4w8/fnWi+lS2LM8v0759dg4G4cMdA49Ye0Q4/TzqnTUDn4emvnLe/ilYv/TZ6tw9VGI1CWyxCDQAAAACSpK/c7HYS6O06W5JvcqtaVCXLX2r1U6ntjHaOVQ5/7tNW3dpWN48dnhyhoV0iAjqPW5xCkxreGoWWmDyfu/ks9++HloEaHAAAAACSpJlfF7kcKzfbtOpQhWJCDeqXGC7jr7/xPlhs1ls/lur0tqG6tUe0Qgyt7zfh/zvkukuIc1VDc+IuFugYGaLz48Mcjp0ZG6rN13ZSkcmqhMgQGQJ876ed31af/lKhE06NMExeAqQiL+lSx0h272nJCDUAAAAASJL2umkKuiffrBtX5UmSHkprq+kXtFOF2abffJKrnPLqB8m8Cqv+fF7bRp1rMMh3032yuda12DxUQVzVJcJtYBUZalBkaMOEBd3ahurLaxP125XH9X3+ye1Zy7001SjyUqnRMZIFCi0ZdxcAAACAJKmutgPP7ChWmdmqFQfL7YGGJM3a5lrh0RoUuqkOaI6rdWw2m2Z8Xej23MDOgV1a4qvToo26tluUw7FiL6UaxV4rNXjsbcm4uwAAAAAkeS/vr/Hi7hLdvTG/4SfTDLirDrA0s1Cj0mLTsE9y9coe97u2nB8f3sgzOikmzPFx9W+7StwGSZL3So14Qo0WjbsLAAAAQFL1A25dnt1R7Db88LR8oSVz18fBWzPLYLQks0xf53reseXsJtwKtU2oa+nQ/O9L3Iz03lODUKNl4+4CAAAAkCT17RhW9yAP3PWXaMkKKq3aeLTK5XhzW37yWZbrFq41buge1aRboUa7CTXmbC92O7bYS6VGdCiPvS0ZdxcAAACApPotNThQbAngTIKbzWbT6M+Ouz3XUMtPfikxa/zqPI36NFebjrpuvVsXm82m2d8W6dx/H9XN/8vT4dLq+5XnIYxKiw/T3/rH1WvO9RUT5nug4qlSo3+npls+g8bB7icAAAAAJNVv6cSBYrP6JrSOB8jv883afcL9kg2rrTpACPQ2pw9uLtRnWdVbyN62+oR+GHeaX1UUv12ZpzWHq8OQQ6UWJUYV6e+Xt9eRUvdhwIqrO6ptWNP+DjzGjwqLrBL3oVpKG7Zzbemo1AAAAAAgqX5VBvtbUaXGgWKz1/OBXoJSZbHZAw2purrixwLvc6jtRIXFHmjUePenMklSboX7+xbjZulHY2vjR6XGz0Xuv4+mbHSKxkGlBgAAAABJ9Q01fH/Ibi5sNpssNinUqSLigIeqgBoWmxTI+oDv812rQkrNdfcwWXagXLevOeH2XJVVKjfb5G7VRv9O4QoJcKXJqfBr+Ymb7rURRmlk18hATglBiEoNAAAAAJIkcz1KDOqqXmhuskrMumJZjjq+eVhT1p9w2N0ls8DzbiFS4PtqfHvc9fMKq7x/SJnZqvu/9L71boab67j5rGi9dmUH/ybYQPypFqlyypnGnRmljWMS1a0tv8dv6Qg1AAAAAEiq37KJltYodMH3pdqTXx3UvPdzuTYdO7nTyY+F3gOcQG/r+u1x111Wcsu9f98/F1mUX+l9Hs/scNxJpEu0Ua8OaK8uMcHRh6KNHz09qpz+8t7Xp616xJ76bj5oPoitAAAAAEiSfFjR4FF2qUWVFpsijE2/bCEQ/rGnxOHnBd+XKCLEoAPFZm3JcQ0Zagt0pcaxMtcAY2tOlX7XI8bjaw6V1F0582mtPh2Sfz0sGoM/lRrHKxz/8obz6/tWg1ADAAAAgCTvFQaJUSHKKfecethUvWTjrBb62/EVByu04mCFy/FIoxRqMKjEfPK7C3ChhtuQZG8djUKPeblXngRbqBHuQ0BWarKqyztHTum1aBn8yq8OHTqke+65R7169VJCQoLWrVsnSTp+/Ljuueceffvttw0ySQAAAAANz1uFwaCkiDpfn+v02/IvsivUa+kRDV2RoyJ3HSlbgGHJkQpzWq0R6OUn7u7LPg+7fdQoPoXvu9wc4DSmETyxrcjt8Qg/trtF8+ZzqHHgwAENHjxYK1as0DnnnCOL5WQJVMeOHbV9+3a99dZbDTJJAAAAAA2rzGzVJ7+4ViJI1bthzLs0rs73qL0EIKPApOtX5ulwmVXbjpvUdfERWQNdwtBA/JnnTWdGy+i0U0igl5+4C0mOV1hl8dAE5WiZRY9+4/5h35vE6ODopeGPBXtL3R5vKcugUDefQ43Zs2crJCREX375pRYuXOjQ/VeShg0bps2bN/v14Zs2bdK4cePUs2dPxcXFafHixfZzJpNJM2fO1GWXXaakpCSlpqZq0qRJysrKcniPyspKTZs2Td27d1dSUpLGjRun7OxshzFZWVkaO3askpKS1L17dz344IOqqvK+Dg4AAABoTYauyHU51iXaqNvOjtaiKzsoNjxE/74q3ut7HK+15OGpb10fqrflet81JBjsOF6l367M83n8gM4Rcn5+Dnyo4XrMJqnI5P6Dxv3Pdf692tfdeWB8j2h/pxa0/OgximbO51u9du1aTZw4UcnJyTK42bM4JSVFhw8f9uvDS0tL1atXL82dO1dRUVEO58rKyrRz505NnTpV69at05IlS5Sdna0bbrhBZvPJUqvp06drxYoVWrRokdLT01VcXKyxY8faK0ksFovGjh2rkpISpaena9GiRVq+fLlmzJjh11wBAACAlqjCbNMNK4+77dHw0hVx+n+Xt1fSr7thXJUcoT+d20ZntQvVHWdH6+7ejo0qj1dU/39wq82m5W76T+zMC+5fLJqtNt24Kk9rD1f6/JrY8BDXUKM+28i44entDrrZRnf3CZN25LmGR5PPaaNVIxO8fs613aK8nm9OqNRoPXxuFFpcXKzTTjvN4/mqqiqHsMEXw4YN07BhwyRJd999t8O52NhYffTRRw7HXnjhBV166aXKyMhQ7969VVhYqLffflsvv/yyBg8eLElasGCB+vTpo7Vr12ro0KFavXq19u7dq927dys5OVmSNGvWLN1333169NFH1a5dO7/mDAAAALQkHx4o1/+y3T/EOz8XhhgMmnVxrGZdHCtJ+sd3jluC1vTU2OXmoVqSCquCe/nJ7hMml74g3nSIqP4dcUgTLD+RpEErcvXAeW306IXV9+PlPSWasbXQ7dg2YQZdnBiuduEGFbm5D09e3E7GFtSHIrQFXQu887lSo0uXLtq7d6/H8998843OOOOMgEzKk+Li6n9pxsVVr+fbsWOHTCaThgwZYh+TnJys1NRUbdmyRZK0detWpaam2gMNSRo6dKgqKyu1Y8eOBp0vAAAAEOy8VU/U9ZCbEOXYgyGvwqq9+SYNcrOURQr+RpT+Flg82rf6F6SHSh23XM0qdd2CtT68zev5XSW6/KNj6vrOYY+BhiQl/nqvojxUMJR6WMrS1J66JNbh57jwusOKdkG2iwsals+VGqNHj9Ybb7yh8ePH2ys2apahLFu2TB999JGmT5/eMLNUdSXII488ohEjRqhLly6SpJycHBmNRsXHO67tS0hIUE5Ojn1MQoJjmVV8fLyMRqN9jDuZmZkBvoKG1dzm2xpwT4IT9yX4cE+CD/ckOHFfgk9LuSdH88Ll6bHgl1AhdQAAIABJREFUyKFDyiz2XLlQfiJEUqT9519OlOjaT0vl6XenR/JOKDPzWD1m611978m+Qsfr8aZnG4v6GQ6r+iMde1G88s1RdUoN3FKbsvJIeft99J78uivmrcezlFlq0/HyKEmuD/2dTbkNcm/qe08uMUi1v1+b1erwnn3aRmh3sWO4NiqhqsX889lQmtP306NHD6/nfQ41HnjgAX3++ee66qqrdNlll8lgMOjFF1/Uk08+qW3btqlPnz6699576z1hd8xms+68804VFhbq3XffbZDPcFbXFxdMMjMzm9V8WwPuSXDivgQf7knw4Z4EJ+5L8HF3TzIKTHp+V7EyC806v0OYZl8SqzZB1q3QZrMpv9KqDpEnHwLDj5yQjpW7jDVIGtirmzp52RHj2NFK6fvj9p+/LvS+e0ZU2zj16FH3LiqnIhD/nGRlV0i7624SOv2CtvrLeW0VVlPJstFxo4L03FAtGXV6veZSW+j3OVLpqTdZTY4xauC5Z8oYYpDFaa6SFGmUbr2oe8CXnwTinuRXWqUtR+w/G0JCHN4zMiNHKj753TyY1lbT09q67QOJai3tf1N8/rdsu3bttHLlSo0fP17bt2+XzWbTmjVrlJmZqYkTJ2rFihWKjPQt1fSH2WzWxIkTtWfPHi1btkwdOnSwn0tMTJTFYlFenuO/eHJzc5WYmGgfk5vrWP6Wl5cni8ViHwMAAADUx8Fis/p9mKN//1yu7cdN+tePZZq8Lr+pp+Ugq8SsC/57TN3fParb1+TZdzOs8LAk5PGL2nkNNCQp2s9mjOWBbjYRYL4ujxmREnky0PDgsa89LwXxl7WejUfnD2zvNbA4Mj4paPtpOM/KuW7I+a/UsORIAo1Wxq/ouF27dnrmmWf0888/KzMzUz/++KP279+vefPmNUjDTZPJpAkTJmjPnj1asWKFOnXq5HA+LS1NYWFhWrNmjf1Ydna2MjIy1K9fP0nSJZdcooyMDIdtXtesWaOIiAilpaUFfM4AAABofaZscA0wPs2qULHJ96aTDW36lkIdKK7u9bDsQIU2Hq1eHlHhIWj4U5+2db5nVKh/D4+eApRg4Wvo0tkp7One1jX8+X/fldh3g6mv+mRB9/dpoytOi/B4vnN0SFCHAM5Zi3PPVOfvxs+/kmgBTrkermPHjkpISKjXPwAlJSXatWuXdu3aJavVqkOHDmnXrl3KysqS2WzW7bffrm+++UavvfaaDAaDjh07pmPHjqm8vLo8LjY2VuPHj9fMmTO1du1a7dy5U3/4wx/Uu3dvDRo0SJI0ZMgQ9ezZU1OmTNHOnTu1du1aPfbYY7rtttvY+QQAAAAB8dUx9/0T0n9x3da0qXzsNJcdx6vnXOYmaOgZ59sqdX9DjWCv1HD3XTi7sGOYvelmjfs8BEAPbi7UsgPl9qqYU1Wfr835o0d1dayuH3umYz+QYOMcalQ6fRnO302QFpygAfkcaixcuFBjxozxeP66667TG2+84deHb9++XQMHDtTAgQNVXl6uOXPmaODAgXr66aeVnZ2t9PR0HTlyRIMGDVJqaqr9Px988IH9PebMmaORI0dqwoQJGjFihGJiYvTee+/JaKz+F43RaNTSpUsVHR2tESNGaMKECRo9erRmz57t11wBAAAAfx0o9t7AsbDKqhUHy7W/qO5Gj/VhcbN8oezXp0F3lRrPXOpb3wtPO2l44vxAGkysNptWZjkGPzd0j3IZ9/tzYlyORXr4Hj7YX67b15zQ7G+L6jU3T1u6+uJsp4DqwbS2qmmpEhdu0N2929Rnag3OOaSoskpVtf4eOf/dNgZx1Qkahs+NQpcsWaILLrjA4/mzzjpL77zz/9m77/Ao6vwP4O/Zmm3pjQQSWuhNQZqKipyAiFhQseudh4oNu16xnSeIBVER689yhxVRUU9UigqIRBGkQ+gtPdkkmy3ZMr8/QkJ2d2ZLskl2k/freXwkM7Oz382kzWc/5b+48cYbQ37yM888E2azWXZ/oH0NtFotnnnmGTzzzDOyx3Tr1g0fffRRyOsiIiIiIgpHuk6BEpt/qUldgJv4GqcHYz4rxnGrBzqlgK8mp2J4mqZV1lctMa6zpq5+22+l3g0onxgRj3Fd5MsVmooLM1OjzB495Ti+Zq2p9MtmydD5l5VM7ObfR7DUFrjM5J3dVvxzeELAYwJpbiwoXi1gSo53YGZIigbrpmVgU3kdzsjU+mWdRBuFxKSWT/ZbcXVefXDJ93OjjK7evNQGQr7k+/btw4ABA2T39+vXD/v27YvIooiIiIiIYkmtRNAAABwB7nVf3W7BcWv9Tb7NLeLFbTWtsTQAgNnhH0wosbkbS1CauiDXPztBjj7MoMYusxPOFja9bA3ldjc+3Oc/AUavEjA192QQ47yuWqTG+QcBggWjyh2ekJuQSmnupyz/kgwkav1v+XolqDC9px6ZQRrBRgOpcpIP9lob/+2bxRJm8hB1ACFnarhcLtjt8jWBdrsdDocjIosiIiIiIooVHlFErcwNa6Ab+MVNbsyA+uadraWqzj+oscPswrw//AMpRnXod4VqhYApOXH4OsTeIQ43UFDlwoAkdcjP0RYKrdIZJHqVgNfGJWHUrlqIInB9X//SEwAYma5BD5MSB2rko1iHLS70TWze625O+UlanCImghbBSAU1XE0ul1+mBstPOp2QMzV69eqFH374QXb/6tWr0aNHj0isiYiIiIgoZshNDwGAQG/Ol0qUq7SWv/xY4bdtt9mJfVX+vTzCCWoAwJtnJeO+IcEnpTTYWuEMflAbk7uGcUoBepUCtw8y4Y7BJsRrpG+fVAoBa6alQyKJo9HBAAGPYJpTfjJJokwmFkkGNZoEedgolEIOakyfPh2rVq3Ck08+ibq6k2lqTqcTTz31FFatWoXp06e3yiKJiIiIiKJVoDGlgTI1pLI7PC2cktFUsdWNR3+twowV5dhX7X9D7fQAuyWCGuE2/9SpBPxjeDzMN2ajh8RoU1/bojCoUSlRngOEV15jVCvw6Xmpsvs3l0tPyAmFO0j86/o+/hNM7hsaeqApmkldgaaTkj0+nxuWn3Q+IZefzJo1C99//z2ee+45/N///R/69OkDANizZw8qKysxZswY3H777a22UCIiIiKiaBRoDGigRqFS1hTW4ays0Jp0BnPFinJsLg8/gCC0IH3/sREJuH61d1ZIapzCq0HooSATYVpTjdMDp1tEsk9KxSaJ3iJA+CNrx2TI99aYs6kG1/cxhFUSsr/ahUXbLSiXCboAwDlZWswdlYjPDtpQfaL5a4ZOgVxTyLd6UU3q69HZdPqJX08NRjU6m5AzNdRqNT777DM89thjyMrKwpYtW7BlyxZkZ2fjiSeewBdffAGNpnW6NRMRERERRavmlJ/IBTve2GmJxJJQbHU3K6CRY2xZD4Zp3XUovT4LswcbkagRMDpdgzkjvad+HKttfhlGS6w8ZsfAj4rQ84MivxGrciUxcqNa5SgEAbcOkO67AQBTl5eFfK4/yutw6qfFeGNXrcTz1P9fJQB3DzFBpxIwf0wiuugV6JugwocTUsJad6yp89Q3ur3gm1IU+ZRxMVOj8wkrfKdWq3HXXXfhrrvuaq31EBERERHFlECZGr7lJ7vNTry7p1b23eQjEbrhr5RoDBqKRWcmtfi51QoBj41IwGMj6oMZB30yM4pkmnK2tnt+NjeOtn32jxrcOsCAlBMZG3L9LpIkJocEEyhDoqDKhQPVLvSID3wb9p89tbhjnVl2/2+XZGBdkQNDUtQYmlL/xvKlPfW4tKd/GUpHVOcR8eauWqwt8s+w4UjXzoeXnIiIiIiomSodHlz6Xbns/qa1/xanB3/6uhSvbK/FS9ukMzIiNa2iKkC5gpxR6RqcnhmZ0pemEn2aax6zuvHv36tRbm/bjI1DFu/n+8ev1dhZ6TyxT7okpl9i+CUcuUGyXXyn3kh5bad/dkZTGToFru1jaAxodDZOj4h5m6VHILP8pPOR/S5dt24dAOD000/3+jiYhuOJiIiIiDq6a1eVo9QuH0BoWvv/foG1sedBKMe3RFWQ55HSnBv4UMRrBCgEoGnSyjN/1GDVMTtWTk1vlecMxQd7rfhonxXvnZMse13SdOEHmfoHGVe7dL8V/zg1PuAxwZqpdvYb97oA8TBOP+l8ZH9yXXDBBRAEAUVFRdBoNI0fyxFFEYIgoKLCf1wUEREREVFHI4qiZPp7U84m98q7zMEbZJYFCJCEwyxTftLVoMRRmRKXbsbWCWooBAEJGgGVDu/AwcYyJ/ZXu9AzSCmGKIr4/qgD5joPpnXXQduMpgmizFQZjwhcsyqy9y/dgzToPGhxw+kRoW7B3XdnL7Goccp/n7CnRucj+x338ssvQxAEqNVqr4+JiIiIiAgotgUPQDTtqWF1BT++PEJBjeMygYuhKWrZoIZR3Xp/68erFah0+D9vodUdNKgx748azNlUX2rw4V4rlk6UH5sqR2p8bnvxiPWvO0cmiOQIIVunJQGRjiBQyxgV71k7HdmfIFdffXXAj4mIiIiIOjPfBpgA8PyYRNyz/mSDR2eT+/jKEPpclDvcjRnQLSEXuAgUQJBJZogIjczb56F8ThoCGgCw6rgDhy0u2YCAHIsz/Bf3+riWN02VU1grH9SoCtLkNV3XydM0AlAIgJqfnk4npEtusVgwdepUvPfee629HiIiIiKimOA7MePcbC2Gpnj3U6hrkqmxvTJ4+YndHZmsAqkpKgkaAVfnyU/HcHlaL6ohlwQS7AZeqmykNIQMGV/fHrGHdfzF3XW4uIcu7OcJVcWJYI4oivjioA1PbapuHCv79/wqr2O1Pm09nh2d2GrrinU9TEooO3kWS2cUUojTaDRi06ZNuOyyy1p7PUREREREUc/qBm5ZX+m1bWCS2m8EaMMNeJHVLZs94avc7oGxhW83H/WZ5jEyTYM3zkoKOG60tXpqAJC90bQFCeDUSGRYuMNMKamq8+Cun+XHo/paMDYR1/c1hPUc4So/EdT4YK8Vs9bWr23e5hqkxin8+qo43MDxa7tg1TEHuptUGJQcuBFpZ9YvkZ+bzijkn5aDBw/Gnj17WnMtREREREQx4YUD/jdP3U0qZPmMZC20ueHyiPjX79UhnzsSfTV8Ayj/GZ8cMKABAOfnxLX4eeXIjUy975cqHAsQ7JH6XIRbSvJbaeBmrr5S41pevzCtu/fncrBPIKLyxOua7RNskWsUq1cpcEGujgGNIFprgg9Ft5C/Yx966CG89957+Omnn1pzPUREREREUe+zIv+byx4mJeJUAlKaZGt4xPosjYowAhXlIfSZCKS6zuM10lWjANKC9GE4NVUt2/ciEgKNsr1iRbnsvlK7f8BDKnsjkEJraBkyDbrowx/j6uvvp8Qjx1h/nqm5cZia6x3kaLjGQapv6ISXzwit5KYvMzU6pZBDWR9//DG6du2Kiy66CIMGDULv3r2h03nXmQmCgJdffjniiyQiIiIiihYWmXGSDaM8sw1Kr8DEsVp3Yw+Flpw/EPuJMo44leCX+ZBtUEIRpPHo/UNNYT9npGyrcMLi9ODDvVYUWT34cz8Dsgz1AQGpzIXqECIBS/db8djGaiRpFegVZLqKr/5JLb8x7pOoRv7FGbC4PEjRKvD2bqvX/nK7J2g/ETrpou463L42eAlRnwRmanRGIV/1999/v/HfW7duxdatW/2OYVCDiIiIiDq65TJNJ7ueeGc+26DElhNNH4HwgxrhZCKIoognNlZj/lYLMnQK/Gd8MgR4BzBSfMopbh9oxMvbLY0f9zApcU5W65WehOLx36rxxq5aAMBH+634Y3oGFIIgGdSwBxl5anOJuHOdGRaXiMMWN/4odwY8vqkre+uhU0UmYyVOJSBOVf814XsNKhwerC10hHQe3+aznZEuxCwi388zdQ4hBzUqKyuDH0RERERE1MG9u7tWcrv6RDPMrgbv8oVjte6w+mSEE9RYfsSO+VvrAxTFNg/mbqrB7CHeWRdanxvCOwYZscvsxI5KJ67qbcDfTjUFzeRobQ0BDQA4YnHjx+MOnJMdJ/l5cwQJahytdcHSjAky52ZrsWBs60wW8W0gW+HweE3GkSMAeGpkQqusKZYoFQLUCiBYEpPv55k6h5CCGh6PB2VlZUhISIBWq23tNRERERERRaU6t4j1xf6NJzdPz2j8d7ZPUONwrRuVrVB+Iooibl3j/cbjquMO3DrQ6LUtzieokaFXYsl5qSGvpz009MGQytRwBGmRURtGUKh/ogqDU9QYna7FjX31EFopuJPic7O9vrhO8uvI1zfnp2J0Bu+/gPpsDWeQQJAhQlk2FFuChrLmz5+PHj16oF+/fujWrRtmzpwJq9Ua7GFERERERB3OgRoXfJMAdl6R2dhPA/BvyvnGzlqEkzcQaq+FYpsHZokGnGafAIpvUCMWNLwCqUahjiA3tuFkujw9OhGvj0vGn/sZWi2gATSvLOLNs5IY0GgiLoSARWteQ4peAb+7PvzwQzzxxBNwOp0YOnQoEhISsGTJEjzwwANttT4iIiIioqhR5DNJY2yGxm9ahkbRshurfVXS40997TJL94r460/e2RuxGNQQT8QlJMtPApSWlNvd+OqQLeTnOT1DE/bamiO5GWUR4TY47eweHxHf3kugdhLwu+vdd99FdnY2fv31V6xevRrbt2/HpEmT8Mknn6C2VrqWkIiIiIioo7L4ZAGYNP5/TqtaWNa/v0a6vuJYrRu7zU64T2QqVAUYk9qUb0+NWNDwyiTLT2QyNQpqBYxYWozXdoZ2n/LAMBOULQxAhUqjFGBSh/dcucaWj5btSOQCQ9fm6bHigjTcNbj9JvhQ+wr4I3f79u24/vrrkZ2dDQDQaDS47777UFdXh4KCgjZZIBERERFRe6l0ePCXHyowamkxXtthQa1PloBRIiVe2cIU+DKJkosvDtpw6qdFGPVZCYYvLcbvpXVBp4A0iNQ0j7bkOhHLCKdR6L8LNKh0BP+cKATg3iFGPNDGY2zDbWLJppfedpmlM5gmdI3DiLS2ybih6BTwO8VisSAnJ8drW8PHNTU1rbcqIiIiIqIo8PK2Gnx6wIbdVS48uKHKb5yrQeLd93AzNR4+xfvmutIhwtUkG0EURVy/uqKxQebBGjduWVMZdApIA20MvuFfbm9oFOof4FlXVNe4v0GdW8R2S2gvdOl5Kfjn8ASo2ihLo0G4fTXYH8LbGJlSoRiM2VGEBfzOEkURCoX3IQ0fezyhd3AmIiIiIop2B2tc+HCvFQdrTr4j/L/D3kGMpQe8+zVITVtQBbgZlTrepFYgSeu9vaJJs88/yv17Z+ypcoU85UPf0nqYdrC2qA5OjwiJmAb2VLkwcmkJNpaenB6yvya0PiQAIKB97oJ9J6BQeM7Jkm6aqonB8iqKrKDdZzZt2uQ1xtViqZ+D/csvv6Cqqsrv+AsvvDCCyyMiIiIian17q5w4a1kpal0ijCoBP01LR3eTEjtlUt4bGCQCBsoA964mteBfwqIWYFIrUNlkVmmtUwR09f9+aZtF8lzmEKekpDdj8kak3TnIiBdlXoeU9cUOydKTBuUODx79rQpfTU4DAJTaQn/DNdzeFpESqFnoxG5x+LZJFtDwVHVbLKlDULf/lze1s6BBjVdffRWvvvqq3/a5c+d6pUSJoghBEFBRURHZFRIRERERtbKnN9c0BhssLhHP/lGDOwYZgz5OsvwkwD2zSaNAkc8NeHeTCjqfd5ttTUpL5LIQKh2h3cg3HTfbXm4daMSKY3bsqHTh3GwtDCoByw7ZZY+v8wBrixwBz7m26GSmRkWAz8WAJBV2VNZ/DrvoFRiS0j4Bg2SZ4NKEbC0eHmbyCmo8OiKhrZYVM+Tyktq6jIiiT8CfcAsXLmyrdRARERERtZtP9nuXlXy0zxpS80GpcpJAEzWMEkGQ3vEqxPmc57MDNgxICnzzva1CeqRrUylaBU7PbP8mil30Svx0YTosThHxGgF//bEy6GNuCuGYhjdWpXpvNHhjXDJ+KXHgeK0b1/UxtNtNsFymxnV9DBiWqsGn56Xg2yN2nNVFi3FdpEstyF87Jd5QFAkY1Ljqqqvaah1ERERERFFDQH0JRDD6MDM1HC7/95u76BV+mRrP/FGDCdlajMrQok+CCpvK/AMYPxfX+W1ranpPHWYPNsEQJfn5KoWAxBO9Q2whNjkNxuIS8UuxA4sLrLLHmDQC/tIveNZNa5MLauSa6hucnpsdh3Oz49pySTFFjMyXDHVA7Z+LRkREREQUZRRCaJkQRomeGoEyAYolej8IguCXqQEA/95Ug2WTtHDIJyHIWjctHQOTo7cvQ6iTW4K552ezX5aNr7goaSRplAku5Rp5SxYKua+YEPvlUgfG7yAiIiIi6tRKbP5RgzoPgjYJBcLvqWFQCyiXSACRuvH+o7w+E6MqxIagE7vFoatBifuHmpCpj+45rjaJjJXmCBbQAABtlAQ1pL5WEjUCEjkVJSTDU6XLqJqOP6bOiUENIiIiIupUlh204Y2dFqgVArIMSvw3QOlCgzgloBT8J5eE21PjjkFG3P/LyQmCz4yubwjpW34C1I96BYDqEIIaepWAjyakBD0uWkQqUyMUxkBRpjYk9bUSLQGXWDChqxaDk9XY6pNBxUwX4lcAEREREXUaXxy04YbVFbKp7HKGpWiQbVDi0wPemQHpOomRrgHuU6f31OOwxY0vDtowNkODq/P0AACdxA3v0Vo3Ri0txu6q4BkjmRLriGaR6qkRzFldtAGDTG1JL3GNpcqRSJpCEPDdlDTMWFGOHwvr050mdYtDj3je0nZ2/AogIiIiok7B5RHx2G9VYQc0AOCSHjpcmaeHWgF8uK8+sHFutha9JG6oAt1CJ2kV+NdpCfjXad4jO8/I1OD9vf4ZI6EENAAgXRfd5Sa+Tk3VNI5ZbS0mtYBHhse36nOEQ6rXSqwFo9qbTiXgi0mp2FhahwqHB+dkcUoMAfwuIiIiIqJO4evDdhyoCb/rZu94FW7oa4BJrcCr45KxeXoG3hhsx4cTUiAI/jeqckGT2YPlJ3Bc3kuProbmBybSYuzm+J4hJsnt+6/MjMj510xLR8GMLhgewljetiJVYvTYiASJIymY4Wka/KlrXLuN56XoEls//YiIiIiImmnJ/uC9M6R8MSkVmiY3pN1NKgxL8EAtc0Ml17fw8l562edQKQTcK3OjH4pYy9ToGa/CsBT/6SzJcUpoI/BSBiWpJCfKtKcBSSq/EpRp3XXttBqijiOs8pPa2losWbIE+/btQ0VFBUSfYcGCIODll1+O6AKJiIiIiFpqQ7EDXx6yN+ux8Zrwbo7lMjWClRooW/B2Y1pc7L1XeUqqGpvL/cfmxqsVKHV795qIUwLnZsfh68OhXUOpDJr2JggC8i9Ox/ytFjg9Ip4amSDZS4WIwhNyUGPjxo244oorUF5eLnsMgxpEREREFG0O1bgw8X9lzXqsAOmpFYEkygRBkoKM7mzJ/W2sZWoAgCgT/ZHKdLkmz4BnxyTC7RGR8u7xgOftmxC9bQO7GlV4bkxiey+DqEMJ+Tv+73//O+rq6vDOO+9g3LhxSEpKas11ERERERFFxLoih9+2y3vpkKBR4I2dtQEfq1XWT10IRzeZEZPBsgda0h8g1npqAIDcABS3RLTj9kH1/UiUCgGX99Lh4302v2Ma1MnV/xBRhxTyT7/Nmzfj9ttvx7Rp0xjQICIiIqKYUevyv8m9uLsO80Yl4NdL0rHzikxU3pCFhWf4v4NuD7+vKADgXyPCn7qhDvKXeTejfDZGegyWn8gNM5Xa3t10MlD0l76GgOdlnwqiziXkn34mkwnJycmtuRYiIiIiooizS6QETOoWB0EQkJegRhe9EoIg4Kre8o08w3VtH4NXkOLeIfKTTxoog2RyvHt2Mt46S/rNxbQOVH4it73BqQEmmiRrFbhlQPDPNRF1HCEHNaZOnYqVK1e25lqIiIiIiCJKFEVUObzvku8ZYpQsBREEwW86RXMlahX45E8pmNgtDrMGGjA7hMkmqiB/mXc1KnFpT+nAS3oMlp+MzfQOTjS8hmDVI2qFIJu1su6idGTqYy/AQ0TNF/JPv8ceewxlZWW4//77ceDAAb/JJ0RERERE0eKwxYXzvipF0jvH8eyWGq99WqV84MIqUarSXGdnxeGjCSl4amQiTMFqSwCoAmRqTM2Nk20GmqxVwBjC+aPN5T316BVf/5pUAvDqmfVZKKG0xBjXReu3bUKqC10Y0CDqdEL+6Zebm4uNGzfirbfewvDhw5GSkoLk5GSv/1JSUsJ68nXr1mHGjBno378/EhMTsXjxYq/9oihizpw56NevHzIzMzFlyhTs3LnT6xiz2YyZM2ciJycHOTk5mDlzJsxms9cx27dvx/nnn4/MzEz0798fTz/9NIMyRERERBFWZHXjmc3V+GCvtV3/1qp1evCnr0qRX1onuT8uQFAj1ycDoKep7W6SA2VqvHP2yTLwl316f+y8IrO1ltSqNEoBP1yYjvfOScaaaekYnx0HQLpRqK8EiQkzQYbLEFEHFfL0kxkzZkR83nNtbS0GDBiAK6+8Erfccovf/gULFmDhwoVYuHAh8vLyMG/ePFx88cX49ddfYTLVp/DddNNNOHr0KJYsWQIAuPPOO3HzzTfjo48+AgBUV1fj4osvxtixY7Fq1SoUFBTgtttug16vxx133BHR10NERETUWX28z4qZP1U2flxkdePuEEouWsPKYw4U2+TaUAbO1Lh3qAl3rjv5Bllbjt90yi8ZyiaTUa7JMyAvXoXjVjcmddMFfD3RzqRW4EKfxp7xGgXK7Cc/GVqJuFKCxj+CoVXwTUuizijkoMaiRYsi/uTnnXcezjvvPADArFmzvPaJoohFixZh9uzZmDZtWuMa8vLysGTJEtx4443YvXs3VqxYgeXLl2PkyJEAgPnz52Py5MkoKChAXl7/S/iGAAAgAElEQVQePvnkE9hsNixatAg6nQ4DBgzAnj178Morr+D222+PeKCGiIiIqLNxekSvgAYAfHXIFjSoUWx1w+IU0Ssh5D9JQ7K90hlwf6BMjct76rGtwom1RQ5c1F2Hs7P8yxxaS2kYo1ZGZbTdutrai6cn4qqVFY0fvz7Of1iBVFBDYhMRdQJR+61/6NAhFBcXY/z48Y3bdDodxo4diw0bNgAA8vPzYTQaMWrUqMZjRo8eDYPB4HXMmDFjoNOdjACfe+65KCwsxKFDh9ro1RARERF1TGV2N/p8WOi3fWNZ4MDC14dsGLqkCMOXFiNn8XHUBkpTCNPH+6wB9wfKbIhTCZg3OhE/X5SBB4bFt+kbYP0T1W32XNHsvK5xuGuQEQMSVbhtoBGTu8X5HSOdqdEWqyOiaBNWWNztduPjjz/G6tWrUVJSgscffxxDhw6F2WzGN998g7POOgtZWVkRWVhxcTEAIC0tzWt7WloaCgvrf3GWlJQgJSXF65eNIAhITU1FSUlJ4zG+a2o4Z0lJCbp37y75/AUFBRF5HW0l1tbbGfCaRCdel+jDaxJ9eE2iUzRelz+qFbhpi/8NZ4OCggLUuoDdtQr0NngQ3+Qvzyc3a2F319cVVNeJ6P3+caweY0NLKykO2wQcqNEFPMZcWoQCMfSsCDmRviYmERhs0mJrjXe9RbrGE5XXvzVdk1j/H1CNQ/tL/PZby5UAvLNVtAqx032eYgGvSXSKpeuSl5cXcH/IQQ2r1YpLLrkEGzZsgMFggNVqbWzIaTKZ8Pjjj+Oaa67BP/7xj5atOEoE+8RFk4ZSG4oevCbRidcl+vCaRB9ek+gUjdfF7REx/dNiANLBAYUAxGf3xJRlJSixeZClV+C7KWnoalTBI4rYufa41/E2j4DKhByMaWFZxZdbagBUBzxmYPds5ElMzwhHa12T73uKWHHMjmtW1ZdfKAVgwZmpyMsJHKjpbEpNDmBnmde2LK0Ydd8nnV00/uyijnddQk7Smjt3LjZt2oT//ve/2Lx5s1dHa6VSialTp2LlypURW1hGRgYAoLS01Gt7aWkp0tPTAQDp6ekoLy/3WosoiigrK/M6RuocDfuIiIiIKHxHat04ZJHPdvCIwEMbqlByomHncasHnx+0AQCKrNKlJuX2lpegbC0PXPYCAKlx0VunEKcScEGuDseu6YL/jE/GTxemYzIDGn6kyk9ydGwUStQZhfwT/fPPP8f111+PKVOmQKHwf1iPHj1w+PDhiC0sNzcXGRkZWL16deM2u92O9evXN/bQGDlyJCwWC/Lz8xuPyc/PR21trdcx69evh91ubzxm9erV6NKlC3JzcyO2XiIiIqLOJJQAxGcnghgNXt1RCwCodEg/1upq+U3p5nLpMa5NRXNQo4FBrcDUXB0GJrPPhhSTxEjXbrrI9WUhotgR8k/0oqIiDBo0SHa/Xq+HxWIJ68ktFgu2bNmCLVu2wOPx4OjRo9iyZQuOHDkCQRBw6623YsGCBVi2bBl27NiBWbNmwWAwYPr06QCAvn37YsKECbj77ruRn5+P/Px83H333Zg4cWJjOs306dOh0+kwa9Ys7NixA8uWLcMLL7yAWbNmcfIJERERUTM1J6ui14mmGtUyTUFbGtQotLpxoMY7e2TB2ERk6k7+yTsoWY20GAhqUGDpcUromjRgUQoA+6wSdU4h99RITk5ubNApZefOncjMzAzryTdt2oSpU6c2fjxnzhzMmTMHV155JRYtWoS77roLNpsN999/P8xmM4YPH46lS5fCZDo5HuzNN9/EAw88gEsvvRQAMHnyZMybN69xf0JCAj777DPcd999OOecc5CYmIjbbrsNt99+e1hrJSIiIqKTimzewYOLuutQZHXjlxL5TIluRiVcHhGXfVcuub+qrvnvtDvcIvp/VOS1bUCSCtf3NeDsLC0WbLVAIQB3DzHxja0OIE4lYPYQI+ZsqoFKAJ4fmwgg8NQbIuqYQg5qjBs3DosXL8Ydd9zht+/gwYNYvHgxrrjiirCe/Mwzz2xsNipFEAQ8/PDDePjhh2WPSUxMxOuvvx7weQYOHIhvvvkmrLURERERkbyNpd7Bi76JKtjcgTMt6twiPt5nhUUmI+NfG6tx12CT5L5g3i/wv6Htm1D/1n2uSXXippc6kgeHxePK3nqoBAFZBiViaJgDEUVQyLl3Dz30EMxmM8aPH4+33noLgiBg5cqVePzxx3HWWWdBo9Hg7rvvbs21EhEREVGU+NUnqDEyXQNnkKCGzS1iU4BGni4R8IjNK0F5apP/xJPeCSG/f0cxKseoQpZBGfxAIuqwQv5J37NnTyxbtgy33XYbnnrqKQDASy+9BADo378/XnvtNXTt2rV1VklERERE7S6/xIGvD9nx6QEbjtZ6l5+ckqJGnSdwQMLuEnE4wMQUACi2edBFH95Nqs0lolSix8elPTk1hIioowsrfD1s2DCsW7cOO3bswJ49eyCKInr27ImhQ4e21vqIiIiIKAq8usOChzZUSe5TK4AkrQJBYhqwuUVU1AYOavxrYzVeOTNJct/2CiccbhFDUtRQKU72xdhf7fI7dliKGv3YOZKIqMNrVk7egAEDMGDAgEivhYiIiIii0LdH7LIBDQBI0SogCELQoEatRKZGtl6JY9aT297fa8U/h8f7ZWs0Darc2FeP+WNPBj7K7P6BkkUygREiIupYwg5qFBYWYvny5Th48CAAoHv37pg4cSKysrIivTYiIiIiigKP/Cof0ACAZG19m7Zr++gDTj/ZVObdT0OvEpCpV3gFNQDg99I6TMn1Lh1ZuN3S+O+3d1txfo4Of+oaBwB+pSfTusehfxKzNIiIOoOwghrz5s3Ds88+C6fT+xeSSqXCvffei4ceeiiiiyMiIiKi9lVd58HuKv/yjqbGZmoBAJf00OO2tfKT7XzlGpXYbfY/99WrKrD1sgx0M9b/qeryiDjik+Fx2fflOK+rFhfk6nDnOu/nTItj40gios4i5KDG66+/jjlz5uDUU0/FrFmz0LdvXwDArl278Morr2DevHlITk7GzJkzW22xRERERNS2DtQEDmgAwH1D68ew6lRCkCO95RiV2CkR1ACA878pwytnJMEjAt8etUke891RB7476vDbnhoX8oA/IiKKcWEFNYYPH47ly5dDpTr5sEGDBmHatGmYOHEiXn/9dQY1iIiIiDqQSof/VBFfmU36Xzw0zIS5m2tCOneOUYXbBqq9SksaHLG4MXV5WegLbSJNx6AGEVFnEfJP/KNHj+LSSy/1Cmg0UKvVuOyyy3D06NGILo6IiIiI2lewoIZvbsY1eXp0N9UHOQYkqvDkafGyj80xKnFdHz3CTPAIKpXlJ0REnUbIQY2uXbvCYvGPojewWCzo2rVrRBZFRERERNGh0uE90qSnSYkEzckoxGvjvKeMdDWq8PNF6ci/OB0/TkvH5G7eDT+b6pOoRt9ENfZf1QX/HpkQsTU3BFWIiKjjCzmo8de//hXvvPMOioqK/PYdP34cb7/9NktPiIiIiDoQURQxd3O117Zp3XX48cJ0/PPUeHxwbjIu76X3e5xepUCfRDXUCgE945WSQQa1Ajg9UwMAiNcocNtAI/4zPjmkdaVoFRiYJF1FrVEAfRI4+YSIqLMIuadGfHw80tLSMHLkSFx++eXIy8sDAOzZsweffPIJevXqBZPJhA8++MDrcVdeeWVkV0xEREREbWLeHzUosXmXn4xI06C7SYV7TzQHDUYQBJybHYe3dtV6bR+ZroFR7f3+2gU5cSGd87o+elyTZ8CYz4tR51Mdc+sAY9gNS4mIKHaFHNSYNWtW47/feustv/2bN2/2Ogao/yXGoAYRERFRbPrsgP/UkYHJ4WdBjM3Q+AU1zjwxBrYpQQgtGKFVCuiVoMK80Ym4+2czRADnddXiwWHxGJ6mCXt9REQUu0IOanz55ZetuQ4iIiIiijK7JMatdtGH36/itHT/QMOApOaXiDQkZ9zQ14DzusbB5hLRKyHkP2uJiKgDCfmn/xlnnNGa6yAiIiKiKKNWAM4m5R19E1TQKsMv7ehmUGJEmhq/lToBAIkaAeO6+GdqAMBf+xnwhk9Why99kzVkGdgUlIioMws7pG21WlFdXY34+Hjo9f6NoYiIiIioY9ApBTg9J6ef+E46CZUgCHjtzGQ880c1BEHArIFGJGql+9XfN9SEw7Vu7Kty4ZYBBgxOVuPun83YcSJrRAAwvaf8RBUiIupcQgpqlJWV4cUXX8SyZctw+PDhxu05OTm48MILcccddyAtLa3VFklEREREbc/h8R7n2jex+SUjvRJUeHVc8OkmGXolPpqQ4rXt4z+l4La1ZuyvceHOQUZ0NbLUhIiI6gX9jfDrr7/i6quvRmlpKdRqNfr374/4+HhUV1ejoKAAL730Ej766CMsXrwYI0aMaIs1ExEREVErE0URdW7vbdp2qvToalThi0mp7fPkREQU1aTz/k4oLS3FjBkz4HA48Oyzz+LQoUNYt24dvvnmG6xbtw6HDh3Cc889B4fDgRkzZqC0tLSt1k1ERERErcglAk3zNFQCoAhxOgkREVFbCRjUeOmll1BTU4MvvvgCf/nLX6DTedcv6nQ6/PnPf8bnn3+OmpoavPzyy626WCIiIiJqGw63d+mJphkNQomIiFpbwKDGd999hyuuuALDhg0LeJJhw4bh8ssvx/LlyyO6OCIiIiJqH02nngCAJuBfjURERO0j4K+nw4cP47TTTgvpRCNGjMCRI0cisigiIiIial81PlENc50ocyQREVH7CRjUUCqVcDqdIZ3I5XJBqeSccCIiIqKOYNlBm9fH8RqWnxARUfQJGNTo2bMn1qxZE9KJ1q5dix49ekRkUURERETUvqqd3pkZ1czUICKiKBQwqDFlyhR8+eWXWLFiRcCTrFy5El9++SWmTp0a0cURERERUfuodHiXn5yRqWmnlRAREckLGNS49dZbkZ2djauvvhqPP/44Dh486LX/4MGDeOKJJ3DVVVchKysLt9xyS2uulYiIiIhayOry4LfSOr+eGb7KbN77r+1jaM1lERERNYsq0E6TyYSlS5dixowZeOGFF7BgwQIYjUbEx8ejpqYGNTU1EEURPXv2xAcffACTydRW6yYiIiKiMBVb3Tj7yxIUWusDFuumpWNgslry2FK72+vj9DiOPyEiougT9LdT7969sWbNGsydOxejR4+GSqVCcXExlEolxowZgzlz5mDNmjXo06dPW6yXiIiIiJrp5e2WxoAGAJz+RQn2V7skjy31ydRI1bEhPBERRZ+AmRoNdDodbr75Ztx8882tvR4iIiIiagVuj4iXtln8tv+3oBaPDE/w2iaKIopszNQgIqLox99ORERERDHI4RaxqawOhVZ30GNrnR5kvHdcct9Ri//jfyqsQ1WTaSc6pYAUBjWIiCgKhZSpQURERETRw+YScd7Xpdha4YQAYOEZibgqT7qR589FDjy4oQoumYmsFp8doiji379Xe22bnBMHlUKIxNKJiIgiikENIiIiohjz9WEbtlY4AQAigGf/qJEManxx0IbrV1cEPNcfZU5cuaIcK4/Z0cOkwuwhJuSX1nkdc22ePmJrJyIiiiQGNYiIiIhizEafoMP+GjccbhFaZX02RXWdB2cvK8H+muClKcesbhw7UcKyu8qFW9dUeu0/JVWNs7K0EVo5ERFRZLE4koiIiCjGSE0sOVRTv00UReQsLgwpoBGKq3rroRBYekJERNGJQQ0iIiKiGLPL7B/UOHAiiLHskD2iz5VrZGIvERFFLwY1iIiIiCKo2OrG05ur8e7uWrg9Mt05W8h33CpQn72xLIQeGvWZF6E/V7qOfy4SEVH0YuidiIiIKELcHhGT/1faWPpx2OLCP4cnRPQ5bC4RDonKkh+O2/HtUUfAx56WpsbfT43H6uN2FFo9IT1fuk7ZnGUSERG1CQY1iIiIiCJkXXGdVy+L57ZYIhbUsLlEfLTPir1V/qUnACQDGgMSVTi3axzOzdbitDQNDOr6rAsxxASSIclqdNEzU4OIiKIXgxpEREREEfL2rlq/bXurnOidoG7ReUURGPBxISod4ZWzfHJeKrIN/pkWH05Iwdlflnpte2R4PJ7YWN348bAUNT6ckAKBTUKJiCiKMahBREREFAFrixz47KDNb/u966vwxaTUFp37n3s0YQc09szIlC0dGZzsH2TRKIAj13TBqmMO9E1UoV9iywIxREREbYH5hEREREQR8O/fqyW3/1joaBy32hyFVje+LQ3vfagfL0wL2AtDqRAwMk3jtW1shhYmtQLTuusY0CAiopjBoAYRERFRGMwODxxu/6yJ9cV1so/5+nDwMau7zE6c8UUJkt85hsu+K0Olo76R5w/HpZt/ZspMJckxKjE0RSO5r6lHR8TDoKovLTk3W4tTUhnIICKi2MPyEyIiIqIQvbXLgr/lV8EjAq+PS8LFPfTIL3Hg0d+kszQaHLEEztSwuURc9n05jljqm4x+f8yBHu8XYkyGBr+WeAdLRqZp8O2UVHhEIOXd437nGp0ePKABAKdnarF5egaKbR70T1SxdwYREcUkZmoQERFRp7Xb7MSMFeW45NsybC7zz7QQRRGlNjdEUYTV5cEjv1bD4QacHuC2tWYctbhwy0+VAbM0AGDRjlr85YcK/FIsnXXx1SFbY0CjqfXFdXD5JIU8OiIegiBAqZAOQtw/zBRwLU2l6ZQYlKyWPRcREVG0i+qghtvtxpNPPokhQ4YgIyMDQ4YMwZNPPgmX6+S7HaIoYs6cOejXrx8yMzMxZcoU7Ny50+s8ZrMZM2fORE5ODnJycjBz5kyYzea2fjlEREQURdweEaM+K8HyI3asOu7A2V+W4s8/VMBzYt6p1eXBeV+XIu/DIpz9ZSk2FNehtkmEweoSMeiTYq8RroF8esCGad+WoarO47dvQ0ngoEhTI5r0wlg8Ptlr3/NjEpHXwkkrREREsSSqgxovvPAC3nzzTTz99NPIz8/H3Llz8cYbb+D5559vPGbBggVYuHAhnn76aaxatQppaWm4+OKLUVNT03jMTTfdhC1btmDJkiVYsmQJtmzZgptvvrk9XhIREVFMq3OLsPumDsSo874u9du29IANV64oBwB8ss+GX0udAIA/yp24ZU1lyOdOl+l34XADyyQmpOytDr2RqFZ5Mqvi/Jw4PDI8HqekqnHbQCOuztOHfB4iIqKOIKp7auTn52PSpEmYPHkyACA3NxeTJ0/Gxo0bAdRnaSxatAizZ8/GtGnTAACLFi1CXl4elixZghtvvBG7d+/GihUrsHz5cowcORIAMH/+fEyePBkFBQXIy8trnxdHREQUY746ZMMtP1XCKYp4ZnQirutjaO8lNcuxWjfeL6jFxjKn5P5vjzpww+oK1Di9MyqKbf4ZFnIuyNHho31Wr8yOBr7biq1urJcpSwlGEATcM8SEe4aEXnJCRETUkUR1psbo0aOxdu1a7NmzBwCwa9curFmzBn/6058AAIcOHUJxcTHGjx/f+BidToexY8diw4YNAOoDI0ajEaNGjfI6r8FgaDyGiIios7O6PFhT6IBEW4lGT2yshsUlwuEG7v7ZjDqJCSDRbnuFEwM/LsK/N9UEPO7zgzasPNa8QAMA/KWfAfEa6T4VKVrvP78W7bDAEVoFC54fk9jsNREREXVEUZ2pMXv2bFgsFowaNQpKpRIulwv33XcfbrrpJgBAcXExACAtLc3rcWlpaSgsLAQAlJSUICUlxaujtyAISE1NRUlJiexzFxQURPrltKpYW29nwGsSnXhdog+vSfuzu4GrN8XhsF0Bg1KH1+v2oo/RP2Cxp+pkaYNbBNbv2IesuJYHNorsAvZaBQyN98DUyn+ZvHZQDaB1ek7oFCLu7lmHccluaMoPQu2Jg9T7R8eLirDTVR/FUCmAVQe1AJSN+2f3qMPV2S7ct0ODHytOfkL0ShFDPMfBb5nowZ9f0YfXJPrwmkSnWLouwaorojqosXTpUnz44Yd488030a9fP2zduhUPPfQQcnJycN1117Xqc8dSWQrLaKIPr0l04nWJPrwm0eHDvVYcttf3i6h1C/jakoIppyR5HeP2iMBa7/GhaV1zkZfUsgDB1gonZnxdilqXiGy9Ej9fnI4ETeslki755TgA/0DM5b10+Hiff68LOVNy4jCpWxyyDUr0S1Qjy6D0OyZxRwkO2/1LXB7do8Wje+TPfcPwbuhuUuHTXiIWF1jx34JaCE47HjgtHSO6xoW8Rmpd/PkVfXhNog+vSXTqaNclqstPHnnkEdx+++249NJLMXDgQMyYMQO33XYb5s+fDwDIyMgAAJSWejf6Ki0tRXp6OgAgPT0d5eXlEMWTf8CIooiysrLGY4iIiDq6rw7ZMPbzYkz9phT7qrybUr66w+L18ft7rX6Pt0mUmpgdofeYkPP0purGHhPHrG78Z09ti88ZSN9E//dz7hlixOvjkrHzisyQz7P43BRc28eA8dlxkgENADCowx+TqhCAbifOp1IIuL6vAd9fkI6FgxyYwIAGERGRn6gOalitViiV3n8oKJVKeDz1f0Tl5uYiIyMDq1evbtxvt9uxfv36xh4aI0eOhMViQX5+fuMx+fn5qK2t9eqzQURE1FFZXR7c8lMldlS6sKaoDsOXFmNjaR3+/EMFblhdgaO1/g0dmr4ZAAAOiaBGZQSCGl8dtnt9LBVQiaQsvX8A4rQTI1K76JX42ynBG27O6KUL6bkMqvCDGkkaBZSK8B9HRETUWUV1+cmkSZPwwgsvIDc3F/369cOWLVuwcOFCzJgxA0B9b4xbb70Vzz//PPLy8tC7d288++yzMBgMmD59OgCgb9++mDBhAu6++2688MILAIC7774bEydO7FApN0RERL48oohn/qjB/+2qhcVn4sa5X/mPM22q1O5Buu5kAMAmMcXDHoONQqXWfGYXbeO/HxgWj2v7GPB/u2qx8pgdvRNUfmUpD58SH9Jz6ZsR1EiJi+r3m4iIiKJOVAc15s2bh3//+9+49957UVZWhoyMDFx//fV44IEHGo+56667YLPZcP/998NsNmP48OFYunQpTKaT77S8+eabeOCBB3DppZcCACZPnox58+a1+eshIiJqSy9vs2BOkCkfcnabXV5BDalgQF0LEzVe2uq/tmJry7M/AvENzswZmQCj2juQ0EWvxN9PjcffT60PXqTGmfHK9vqymL+dYkJuiN1Mm/P5UTJJg4iIKCxRHdQwmUyYO3cu5s6dK3uMIAh4+OGH8fDDD8sek5iYiNdff701lkhERBSVPKKIR36rbvbj91Q5vTIYbBIjR52e8DM1dpudWFxghUYh4Nkt/kENi6t1gxq+wZkRJ0pPAnlqZCJu6GOATiWgmzH0P51qnOG/lrhmZHcQERF1ZlEd1CAiIqLmWVdU16LH7zF7NxO1S5Sf1IVZfvLIr1V4cZsl4DEOd30/j6aj2CPJ93WEGkTokxj+lJe0OOkGooH8LcTSFiIiIqrHwk0iIqIOpLrOg/lbanD59+UtOs9rO72nkBy3+qdqhFJeUWJzY/a6SqS+cyxoQCOc8zaXb28RXfhxh5DdOsAQcP+AJP/3lrJlJqkQERGRNGZqEBERdQCiKOKNnbV4YENVxM65s9KJ/kn1GQoHa1x++4OVnxRa3ThlSRHsEqUrgdjdIrSt0Fyius6DwxbvxaS1YlRjZLoG/zw1Hp/ut2KHT+bLoGQ1tlU4/R7TT2LkLBEREcljpgYREVEHsHivNaIBDQDYZT55032g2j+oITXmtam3dtaGHdAI5bzNsb7YgZ7vF3pt06sExKtbr4eFIAi4d6gJP1+cgYNXdUGC5uRz/e0UE67J03sdP6OXDopWKrshIiLqqBjUICIiilGiKGJ/tQuVDg9WHHUEPPa7Kak4fm0X2f2zBxv9tlU6TgYXDtSEX37y0X5r4ANktEZQ47HfquHbFqS7UdlqvTt8JWoVWDstHU+NTMDXk1Nxfo4Ol/fSo+HZs/QKPD4ioU3WQkRE1JEwx5GIiCgGiaKIP/9Qic8O2hCvEVBdFzgQkKlXQq9SYHS6Br+UeDcR/c/4ZEzN1UEUgQVN+l7cs96M1cfteG1cknT5SZDgQ3PfOQkU1NhR6cTDG6pQ4/Tg1gFGXNZLL3tsgwq7G/kl/o1Tr+kTuOdFpHUzqjBr4Mng0bguWvw0LR2byupwdpYWGXr20yAiIgoXgxpEREQxaJfZhc8O2gAgaEADADJO9I5I1PqHGvok1P85kBLnv+/LQ3YM3V6LQxapTA3/5z1e64bNJSJRK0g2F23qtoFG9EtU4eENVV4NPH8qrEPvBP9pI6Io4q8/VmB7ZX2AZeZPlchLUGFYauCxrD8X18F3pc+NScCf+7ZtUEPK4GQ1BieHP1mFiIiI6jGoQUREFIN+OB643MRXQ+PNvgkqLD9ycvspqWrknQhq9JUZW/rk79WS250+5SeLC2pxxzozgvQPxT9PjcfswUYoFfVr+r/dtdhUdrJ/xz3rzTg/Jw4FVS6sPGbHOVlxOCtLi01lzsaABgCIAG5fZ8bKC9JkG4uKooj5W2q8tl3fR4+/9PMvtyEiIqLYw6AGERFRDErThV7ckdikQeUtA41YV+zAlnInLuqhw7xRiY3NKc/IDJzx4Mvq06RiwVaLbEBjcrc4TO+pw/k5OuhU3gEIg8o/IDH7ZzOWH7EDAF7YasGKC9Lw8T7/Hh3bKpwYsbQYGy/JgEYisPFLSR02lnlPGWnNiSdERETUthjUICIiikEJmtCDGg+fEt/47y56JVZckA63R2zMlGhgUCswNsmNnytDu+mvcXrw8rYarDrmQJJWgT1V/n03ACDHqMTbZycjTiJ4AQCj07VYW+Td86IhoNHg/l/M2GOWPv8Rixvp7x3HP06Nx31DTV77Lvq2zO/4NIkyGyIiIopN/K1OREQUg9xi4BqPhvBgNlIAACAASURBVPBBvEbAtO46v/2+AY0GF2ZIBw6kfHnIjn/8Wo1Vxx349IBN9rjpPXWyAQ0AmN7Lf32+NpU5Ues7vsTHk79X41CThqY1Tg8cEm09+ibyPR0iIqKOgr/ViYiIYpA7wDjV58YkYHS6FhtPTNXIDGOqRp4hyJzWZkiNC/z8/WR6eTTHymMO/Llf/Z83G0v9J570jlfhzExtxJ6PiIiI2hczNYiIiGKQXNLC+CwtLu+lx8BkNa7rY0COMbz3L7rFBZ+kEq7UEMo97hoUXuPOXvHSgZIKx8mgTKHVP0Dz26UZslkqREREFHsY1CAiIopBHpnyk6UTU2FSN//XuyAAL5+RKLkvlOBEcx/XJ8ySkEVnJuHDCcl+2w80KT8pt3vXnszs3/4jXImIiCiyGNQgIiKKQa7IV4k0GpYiPQUlMYzmpE2lhBDUkHtOOYkaBSZ10+Hdc7wDG4sLrBBPBHzK7N6fpFDWQURERLGFv92JiIhikFsiUeOR4fH+G5shSSv950GtywNtM6ahpsicr6le8aFnauiUArIN9Qs5XWIM7fTvywEAK445vLZnhdFbhIiIiGIDgxpEREQxyCVRfvLnvpEpr0jSSvecEBBagMJXsEahAKBVAgEGpHh5cJgJhhMlNqlxSgxJ9m40uvKYAwu21mBbhbNxmwDg3Oy4kNdMREREsYFBDSIiohjk8YlpXJOnR2IzAg5SdEr56EJKCAEKX4HGuTYQBEG2+em8UQnI1ivR1aDEhxOSMXuIyWv/2Vn+00we/a3a6+PRGRpkGZipQURE1NEwqEFERNRGKuxu/HjcAbOj5Q0xfEe6hprlEApBEDCpm39WQ7pOGXZfihFpLR/XOnOAEduvyMS2yzMxqZvOb//lvfRBz3Fqang9O4iIiCg2MKhBRETUBo7VunH6FyWY9m0ZTv20GMdr3cEfFMCyQzavjyM9pvTZ0Ql+2x4dHi9bfjJ3VALuG2LC7isycVH3+sBDvEbAEyP8zyPnNIkAyPvn+k848TUoWY2RaYGDFtf3CR74ICIiotjDoAYREVGE7TY7ccPqCsz8qaIxeLHsoA2F1vr0igqHB49trGr2+R/8xYzVx72bYAaoGGmWrkYViq7Nwl/6GdAvUYX7hphwVpZWNlPjlgFG/GN4PDL0Srx9dhJ+vzQDf0zPxNhM/9IQOTf49AR55YxETJbIGJFySU//DI6mMtgklIiIqEMKbyg8ERERBSSKIib9rxSVjvoGETsqXfjpwjS8ssPiddzH+2wwqc3412nx0KtCf4/hWK0br+2s9dse6aAGUN8L47kxiV7bpIIag30adQqCgJ5hTDNpcHWeAYOS1aiwezAqQxPW58Wolv8EnJ2lRUIzx9ESERFRdGNQg4iIKIJ+LHQ0BjQAYFuFE9sqnMiLV+GIxbvk5K1dtTA7PHjr7OAlFg32Vjklt0uNeG0NWokyl+fGhF5iEszQlOb1vjDKBEBmDzbi3qEmyX1EREQU+/i2BRERUQTdtc7st63Q6kGOUbr84dMDNrh9R5kEcKBGuheHxdk2UQ2p2MHI9NBLTFpLqs5/YYOS1XhsRAJMav65Q0RE1FHxtzwREVGEWJweHLL4Bx3MdZ6AmRQ1IQYkqus8eL/AKruvLVyQ6927YmZ/g8yRbes0iUahjrZKXyEiIqJ2w6AGERFRhKw65pDcbnZ4UFDlkn1cjTN4QGJrhRPDPy1Gfmmd5P4ye9sENbqbVHhmdAJ6xSsxuVsc7o+S0g6tUsC1ed4TTqZ1D9w8lIiIiGIfgxpEREQRctwqXRry6g4LfimRDkYAwM0/VaJI5rENFm23oDRA4OJoC0fEhuOv/Y3YeGkmPpiQgjRd9EwV+cep8ehqqF9PvFrAdRzjSkRE1OGxUSgREVGEyFU7yPXBaPBzcR3uWFuJT85LBQCU2tz48pAduSYlxmdpIQgC3t8rXXbS4NaBxmatuSPJ0CuxZlo6fi+rw/BUDRK1fO+GiIioo2NQg4iIKEI8YTT89PX9MQfsLhEuUcT535R5lav8fFF6wMfmGJWY3oOlFgCQpFXg3Oy49l4GERERtREGNYiIiCKkpX0p91Q5sbnc6dd/4/a1lZLHX5gbh9sGGtEvSY0EDbMSiIiIqPNhUIOIiChCWhrUWFdUh/3V/g1Ffy9z+m3LS1DhX6clINfEX+VERETUefFtHSIioghxi6FFNSZkayW3rzxmx/oADUWbWjctnQENIiIi6vQY1CAiIoqQUFpqfHt+KvolqiX3rTjmwLYK/6wMX9f10UOjFMJdHhEREVGHw6AGERFRhAQrP7m4uw4j0zXQq1sWkMg1MkODiIiICGBPDSIioojxBCg/+eRPKfhT1/qpHAZVC4MaJmWLHk9ERETUUTCoQUREFCFSmRrDUtS4Jk/fGNAAAH0Lgxo5RgY1iIiIiACWnxAREUWMb1Dj8RHx+OHCdNzU3+i1PdSgxj9OjZfczvITIiIionoMahAREUWI7/QThUzswqAK/uv3g3OTMbFbnN92nVJAuo6/vomIiIgABjWIiIgixu3x/lgpSEc1gmVqdDUoMTlHh7Q4/1/TDo8IQea8RERERJ0NgxpEREQR4jvSVS5TI9j0k4u66wAAyVr/X9P/Pi2hWWsjIiIi6ogY1CAiIooQn0QNKGXLT+SDGkaVgDsH1/fg0EicoE8i+2kQERERNWBQg4iIKELcPqka4ZSfxGsEjMnQ4O1zkpGuOznd5LQ0deO/jSoBI9M1EVotERERUezj2z1EREHsq3LhoMUFh1vEfwus6JOgwt9OiZd8F506N9/pJ3JfIjqJoMZHE1IwJkPrt/3xEQm46ccKVNeJmDMqASY1348gIiIiasCgBhFRAF8fsuHqVRVe2/4HoKrOg/ljk9pnURS1fIMa8tNP/HeoZQ4em6nFjiu6QBTZIJSIiIjIV9S/3VNUVIRbbrkFvXr1QkZGBkaNGoW1a9c27hdFEXPmzEG/fv2QmZmJKVOmYOfOnV7nMJvNmDlzJnJycpCTk4OZM2fCbDa39Ushohj0z1+rJLe/vduKyf8rxX3rzThscbXxqvztqHTiswNWWF2+XR2oLfmOdJXL1NBLjHQNloDBgAYRERGRv6gOapjNZkycOBGiKOLjjz/Ghg0bMG/ePKSlpTUes2DBAixcuBBPP/00Vq1ahbS0NFx88cWoqalpPOamm27Cli1bsGTJEixZsgRbtmzBzTff3B4viYhiRJ1bROLbx7C/xi17zPriOry5qxZDPimG3SXKHteaVhy1I3fxcYz9vAQ3/lCJ8V+WwuU7giME5XY3nv2jBgu3W9rttXQE/tNPpAMRcUr/bQxZEBEREYUvqstPXnzxRWRmZuK1115r3Na9e/fGf4uiiEWLFmH27NmYNm0aAGDRokXIy8vDkiVLcOONN2L37t1YsWIFli9fjpEjRwIA5s+fj8mTJ6OgoAB5eXlt+pqIKPpV13kw6X+lYT3m/l/MeOmMti1HOVjjwuUryr1upHeZXXh/rxXX9TF4HetbulBkdeO2tZXYVuHExG5xWHXMgaO19QGcbRVOLDqTpTXNEWpPDamsCybZEBEREYUvqjM1vv76awwfPhw33ngjevfujTPOOAOvv/46xBPpvYcOHUJxcTHGjx/f+BidToexY8diw4YNAID8/HwYjUaMGjWq8ZjRo0fDYDA0HkNE1FTO4kLsqAyvpOQ/BdZWWo00q8uDJ3+v9ssMAIA715nxS7EDQH0wY97maqS+exyJbx/DkxurUWZ348qV5Vh5zIFimwfv7bE2BjQA4IO9VuysdLbVS+lQfK9HOL1kmR9DREREFL6oztQ4ePAg3nrrLcyaNQuzZ8/G1q1b8eCDDwIAZs6cieLiYgDwKkdp+LiwsBAAUFJSgpSUFK93xQRBQGpqKkpKSmSfu6CgINIvp1XF2no7A16T6BToupQ6BPyvVAmgeSMzN2wvQHIrTtuscgILD6nxWZE66LEPrCnGW0Md2FCpwFPb4xq3P7ulBs9uqQnwyHp3ri7E8wMcUAiAppXD3x3pe6WqRoOmv1pLiotQ4JYuYTojSYu1lfV1KIkqEfrKQyiIknZPHemadCS8LtGH1yT68JpEH16T6BRL1yVYdUVUBzU8Hg9OOeUUPProowCAoUOHYv/+/XjzzTcxc+bMVn3uWCpLYRlN9OE1iU5y10UURTy4oQqv76yVfNwLYxMxo5cecSoBQz4pwmGL9E3qEV02RvXUR3TNQH1/jwc3mPH27tCzQbZblMjt2Rt3flsGoC7s5/y1Sokz1+uRqVPgv+emYERa60RrOtr3iu5wOVBub/w4O6sL8nJ1kse+kO7EPeurUFXnwaPD49E/O07yuLbW0a5JR8HrEn14TaIPr0n04TWJTh3tukR1UCMjIwN9+/b12tanTx8cPXq0cT8AlJaWolu3bo3HlJaWIj09HQCQnp6O8vJyr3pyURRRVlbWeAwRdU4Hql34qdCBbRVOvLFLOqDR1aDEDX1P9qeYOyoBV62skDy2oKp1pqB8fdgWVkADqO/t8OTv1VhfHH5Ao6kimwdPbKzGskmpLTpPZ+H2hDb9BAB6J6j5eSUiIiJqoajuqTF69Gjs3bvXa9vevXsbAxi5ubnIyMjA6tWrG/fb7XasX7++sYfGyJEjYbFYkJ+f33hMfn4+amtrvfpsEFHn8kuxA6ctLcZdP5tlAxoAMGdUgtfHZ2dp0d0kMboCwBGZDI6W+mifLaTjfO+fX9xmicjz/1ToQI0zOrtYLtxuwdBPinDVynKYHaGvcZfZieVHbGE9JhS+XwNJ2qj+NUtEREQU86L6r61Zs/6/vfsOb7ra/wD+zmiadKZNN1Cg0FJ22QUE2QgUUUAQ54+LqCCKXEBAEa8iVrjIUBkiIiggFQRZAoIiwwsWGRWZZZVVutM2nRnf3x+VQNqkOyEJ79fz8Dzku08PKc2753zOeBw7dgzz58/HlStX8OOPP2LFihV46aWXAJTUxhg3bhwWL16Mbdu24ezZsxg/fjzc3d0xfPhwAECTJk3Qp08fTJo0CfHx8YiPj8ekSZPQv39/pxpyQ0SV9+npXDz2UzoqWrnU11WMwaWmDrhJxfg1xh9TW3uWOX79pXw0WHcbLx/IrNVlUQsqca3+dV3xRAPz0xxqw0cncqx27eo6cLsI78RnI0mjx0/XC/HRyZJnvK7RYeLvWZh6RI20grJB054bhej6Yyqe3peJJ/akI6+WAhtBEHApx3S0TlNlxfVPiIiIiKj67DrUaNu2LdatW4ctW7agc+fOmD17Nt5++21jqAEAEydOxLhx4zB16lT07NkTd+7cwebNm+Hpee8Dx8qVK9GiRQsMGzYMw4YNQ4sWLUyWiSWih8MFjQgv7s/ArD8r9wH9z6Hmp6j5yiV4p60XLo8KKrNPXSzg+ysF2JpUudEVAKA1CHg7Xo0uW1LwwfHsMlMYMioYTVDXXYK4vn4IcrPet/RlZy2PZnlQXj5oOg1oxbk8GAQBL/yaiTUX8/Hl+TyMO5RV5ry1iXnGpVdPZWhrbURLdrGA4vu6ykMqgpIjNYiIiIisyq5ragBA//790b9/f4v7RSIRZsyYgRkzZlg8RqlUYsWKFdZ4PCJyECn5eoxOkEMrFFZ8MIDOgTL4ys1PM7lLJZfA11WMTDOhw+LTuRjZqHJFQzdfLcDSMyWhwVm1BhfUOrzdxgt6QUBLXxf8nVn+8qp3Pzg38yl/VMDg+nJsT7rX/m96+kIvCHj1UBaK9EB0gAxBbhL8eK3iQCa1QA8vFzHk0iqsWVqLruXqkFJQ9uv+881CnMq49/Xad6vIpKYSAJOvAQDMPZWL6VGeJsdUR3qh6agQlZyBBhEREZG12X2oQURUG/beKoRWqPyH1jCvyn17VMpEyCwqu/1slg5Lz2gwvrlHhdc4nGx6gZ3XC7HzuuXwxUUM3D9jIia0ZNWMp8Lc8E58NnK0ZaervNHCA29FeaJzYD4kIuC5cDe4u5R86G7jJ8OtPD06BcggEQGyQ8D35dTxmHA4C2sTSwqXNvCUYG0vFVr42naahaUVaJ7eV7aIa45WgLfsXt8Hu4mRnG8aiCTnGxDiXn6IVZH0QtNr+jHUICIiIrI6/sRFRE5PXWRAUm7Ving+bmEZztK8y5le8P7x7EoVojyWVrUVSjb2VRn/7iYVYfQ/q7PIpSIcHFJ2yoyvqxjTojzh4SLG+OYeeKWZhzHQAIAGnlJ0DXKFVCyCSCTCiu6+2NxPZXKNkH+mtpzJ1BoDDQC4lqvHI1tTcS1XB0GovToiFbmpqfxKM5mlwgYfWdk+S6rC9YCSVU42XMrHJwm5uPHPuQw1iIiIiGyPIzWIyGmdydSi69bUKp/Xp44r+tZ1rdSxni6WP7gW6YHj6cXoXUdu8ZhT6cU4r678B+qO/jL0CJFjz0A//JmuxYB6cgS63Rth0MBTiu7Brjj4z+iPEDcxzo4MrvT174oOlEEsAu6W97idb0B6oR7rL5lfWjZqUwoery/H6p6+ENdwGkdl/HrbzPAYC945lo11vXyN00sK9WXDl6s5OnQOrFyfA8Czv2Zi942S0TRLz2hw+IkAZJQONRQ1G/lBRERERBVjqEFETularq7CQMNVUhI83C/CW1qlD+b1Pcr/4Drs5wysetQHQ8PM19dYWc5ysuZ0DZIBADoFuqKThQ/h3/dRYfPVfHjLxBgYajlQKY+bVIyWvi5IuK8+xd6bRVhyxnJRzW1JhVh8WoNJrcquDFOb8nUG7Cpnek5pP10vxIHkIvQIKflamAs19twsxDPh7sbXf6YVo1gvoJ6HBDc0ehxPL0bnQFekF+pxOLnYGGgAJYVc557MgXepESB+LBJKREREZHUMNYjIKS0r58P3XSsf9cXzv5rWYHi8vgIe5Yy+KO3REFd8m2h+9MJdb/5PjX715Gav+/PNyn84B4CJLSsODORSkckH9OrqFCAzCTXMrSRS2vvHc9DGz8UYINQmrUHAjqQCHEsrRl4Vl8xdm5hvfKYCM6HG1muF0BoEZBYa8O8j6nJrmpizLakQrqXyrboVBF5EREREVHMMNYjI6QiCgF03LH8oDVCI8dfwIMilInQKkOGP1Hs1LR5vULUP4080UGDbtQJsS7J8vxytgH03i/BEQ9M6HblaA1LNrOBhyYLOSpsuEdo5UIYV56q+lOsTezIAAL/G+KOtv6xWniWjUI8RezNwPL38lWAs2XSlADuTbmNwfTmyiswHIv5rblf7+UqvgCOXADGVrMtCRERERNXHsbFE5DSyigz44qwGc07mWlwdo4WvC77s7mNcivSNFh64O2vgyQYKtFJV7UO4VCzCN71USHsxpNzjYk/mlNl2rYrFS6NUtl1hpFNA5WtMmBOzOx3rE6seipQmCEK5gcaLEW74uoePybbjQwNRerXZAr2A769UvFxtbXi6kRuC3ThSg4iIiMjaOFKDiJyCIAh45pcMHEkpu5JI3zqu+L6vCpcuXUJ4eLjJvkH1FTg5PAgZhXq0rMGypC7i8mtwXMjWoXncHbTzd8GYSHecztRi5jHToMNbJkKBTkCxoWSp2IktPfHRyRxoDcDoJm5o42fbUCPEXQJ/uRhpheZHk/jLxcjXCRanguTrBIw/rEZbfxkilfeeXW8Q8MPVAuQUG9DBzJfteFox5ifkwlcuxnvtvJBbLJQ7QsNPLsaTDd3wZEPTuiX1PSW4nFO14Ki2dA+uWSBERERERJXDUIOInMI5tc5soAEAj4XKjStfmFPHXYI67jX/rfrTjRTYcNnySIBb+XrcStJbnKoyrKEbXmnmjlMZWvQMcUWAQoLnwt0gEQG+8gfzW/9QD4nFUGN1T190CpDhYrYOXX60XJR13KEsfNdbhSA3CWbGZ+Nzk3onbsiIECD5JxQ6nlaMmF3pxroX+VoBYV7ltz3QwiojPUPkuJxT85Ei1dGrnBVviIiIiKj2cPoJETk0vUHAuSxtuR+qewTb5gPmy009anR+K5ULmihdMLKRGwL++aDur5A8sEADKBltYc78aG90DXKFVCxCMx8XDA+zXD/iZLoWkXF38OGJnFKBRomJ/1Mb/z75iNqkkOeWawX45K/yi7428jafz49vXrP+eCHCDceHBuLTrkqz+weFyjHCTLujA2Q2rX1CRERE9DDjT11E5LB23yhA600p6FxOoOEvF1f4m/7a0tZfhtdbVP+DdPtaKqpZm4rMrBQCAM+WWl3l407e6BQgg7dMhMfqmQ+R5ifkmt2+NjEfWoMAgyDgVEbVC4GGeZoPNcK8pOWGLQAwLcr8ajJjm7pjbiclGnlL8UKEO66MCsK0KE9EKqWI8JbiX03csfJRXyzr5oM3/ulzEYARYQpsH+BX5TYQERERUfVw+gkROSSDIGDKkWzczCu/ZoKvq7jcqSe17fUWHth/uwh/Z1b9w3mAwv5yZqmFWiGKUlU4/eQS7BnkD0EQIBKJ8MwvGfipCsuirr6Qh6fC3Co+0Ix65SyduqCzEsV6AefVOlzM1pXZ/3QjN8Rdzjcp2vrHkwFoojStX+Irl2BGGy/MaONV5hofdPDGBx28q/XsRERERFQzDDWIyCHd0OgrDDQAYIeNf2seoJDgt8H+yCk2wMdVjPabUypVrFIsAlR2OGXBYH6ghkV3A6RVj/oi6NvKL5G64K9c9AqpeJpQMx8p0goMxjofnQNlFoMXAPCSifFNL5Xx9ezj2cbpLI/Xl6OhlxRxfVRYdFoDb5kI06O8OHWEiIiIyIEw1CAiu6U1CFh+RoPrGj1GR7qjmY8LNFoDDiYXYaOFgpxiEfBOGy/cKdDjhQh3+FsoImlNUrHIWAdj10B/RGy4U+E5vUNcjcUy7cm0KE+MPZhV5fPkUhEG1JNj143KjdZIzjfgdCVGtyx9xAcZRQa8eywbni5i/DfafL0LS95t541Rjd2QUWhAh4CS6T5NlC5Y1s2ngjOJiIiIyB4x1CAiuyMIAuYn5GLOyXs1GL48n4c+dVxxNkuL2/nmV+MAgDdaeGBya/N1Eh6EAIUEY5u648tzllfhmNTSAxNb2s8z3y+mvgJA1UMNAIjt5F1uqOEiBrT3deUfqUUWj/WTi/H3U0GQ/zPtpXcNVhdp7O2CxpwtQkREROQUOMaWiOzO4TvFJoHGXftuFVkMNJr5SLG5nwrvtStb8+BBe6+dF/rWcTW7b9tjfnivvbfdTnlQSEU4+Li/ybbKFkNt4CnF8aGBZvd5SEXoV9c0mNh/23KocXbEvUCDiIiIiOgu+/wpmogeasfSiqt0vEwM/NDPD73qyG1aFLSyPFzE2NjPD9efDYbsvu+6rhKgox2ueFJaK5UM77TxRIBCjEeDXfFaFZZKbeQtxUuR7mW2T2zpgfBSS7GeV5ct5HmXTGJ//UpEREREDx5DDSKyOx8cz6nS8Zv6+SHYzfa1M6rKSyY2CQTmRysdZvTB1CgvXHw6GFsf80NQFb/WHi5l2ziltSdCKnmdFyKqtyoKERERETk/1tQgIrtyNqvyS6HWdZfgv9He6B5sfmqHPXqvvTeebuwGL5nYIYKY2uBipgCqSCRCsHvF7W/mI8W/W9lnvREiIiIievAYahCRXTmYbLmuQml7BvmjTiU+GNubJkqXB/0INuViYUygbzl1REY2UuCL7r4QBMEupxQRERERkX3g9BMisiuXc8rWVfi0qxJdg0xrT7T1c3HIQONh1NDTfH7eVGk5V1/cpWSJVQYaRERERFQejtQgIrtyzsz0k+fC3TCqsRviU4ux/3YRvF1EeC6ibPFJsk+D6yugcs1GRlHJyjXTokqmk/jKJXixrhZrbpqOXFn1qI/D1BohIiIiogeLoQYR2Q1BEHA2y3Skxp9DAyAWiSAWAV2DXNE1yHHqZ1AJuVSEPYP88OW5PIR6SvFK03uB1IQGWjzZPAgvH8xCaoEBa3r6YkgDxQN8WiIiIiJyJAw1iMhuZBYZkPnPb/MBQCERIcyL36acQWNvF8yNVprd1yNEjotPB9v4iYiIiIjIGbCmBhHZDY1WMHmtkoshZk0FIiIiIiKygKEGEdkNrcE01JDxOxQREREREZWDHxmIyG4UG0xfyyQcpUFERERERJYx1CAiu1GsNx2p4SJmqEFERERERJYx1CCiStFoDdhwKR/7bxVCEISKT6gGbamRGq4Sq9yGiIiIiIicBJcVIHoICIKAP1KL4eEiRgtflyqffy1Xh5F7M3Ahu2S51dntvfB6S8/afkwUGzhSg4iIiIiIKo8jNYgeAq//rsZjP6Xjka2pWHZGU6Vzc7UGRG1KMQYaAPDunzm4naev7cc0UyiUoQYREREREVnGkRpETi6ryID1l/KNr2fEZ8NXLsbIRm6VOr/71lSz25t9fwfbH/NDkV5AWqEBHfxd0Ni74lEg6iID1ibmQSWX4KkwBaT3BRfFpXISrn5CRERERETlYahB5OSua3QoNQACrxzMQmuVCyKV90IIjdaA6X9k42R6MUY3ccdLTT2QVqDH1VzLIzIG7043ef3TAD90CXI1e6wgCDh8p9jknF9vFeLLR32Nr8tMP+HqJ0REREREVA7+HpTIyaUWGMxu33K1wOT10jMarE3Mx5ksHaYezcafacWYl5BbpXsN3JVucd+IvRllQpCNVwrwzcU86A0CBEHg9BMiIiIiIqoShhpETi6lwPxIi3mncpFTbIDOIGD5WQ0+OnkvwBAA9NmRhi/P5VX5fs//mlEmnPj6fB723ioye/wbv6tRb10yum9LwwW1zmSfjKufEBERERFROTj9hMjJ7UwqNLtdANB3Rxoae0ux87r5Y6pje1IhHt2aio4BMgxv5AadQcCkI+pyz8nXCTidqcXpTK3Jdk8X5q5ERERERGQZQw0iJ/bLrULsumE5sLiQrTNZ1aQ8SpkI6mKh4gMBnFXrcFatwzeJ+WXqeVRFlKrqy88SEREREdHDg78GJXJSgiBg2M8ZtXa9rY/5G2Zc/wAAIABJREFU4fnwsiumuEtFGB6mMHtOTQINAIgOlNXsAkRERERE5NQ4UoPISW26UlDxQZX0YQcvtFbJ8NkjMsyLVkIhFeFytg7XNTo09XFBgEKMHUkFKLS8UIrR9WeDYRCAmN3p+LvUdJP7+bqK0diL36KIiIiIiMgyjtQgclLzzaxccmp4IP7dyqNK17nxXDAmtPA0vlZIS1YkaeQtRc86cgS5SSAWiUyWh7Wko78MXjIxlK5irO/ti4aeliuBDgyVQyTi6idERERERGQZQw0iO6Q1CNh0JR/zE3KRlFu5mhella6VEewmRgNPKWa188blUUEYUE9usv+JBgps7KuCenQdXHsmGJn/FwL16DqVLtY5vnnFYUmn+6aThHpIceDxAMzr5I3StwhSiPFeO69K3ZeIiIiIiB5eHNtNZIe+OKvBzGM5AIAPT+Qg/skARFRiJMRdp9KLy2z7tKuP8e8quQTf9VHhao4Ox9KK0TPEFf6Ke6MmlK5VzztHNHKDAGDOiRxc15ifh/JihGlNDi+ZGC8388BjoXJ025qK7GIBkUop9g8OMI4IISIiIiIisoShBpEd+uJcnsnrjltSsbCzEqMj3St1/qoLpuc3U0rRp45rmeMaeknRsBbrVoxs5IaRjdxQqBMw+aga6xLzjft+G+yPxt7mg5lQDynOjQzCuSwdWqtcIBUz0CAiIiIiooox1CCyM1qDgBtmRjpMOqKGr1yMIQ3MrzRyV4FOwDcX8022/bu1p03rU8ilInzeVYmprT1RrBcqNcrETSpGO3+udkJERERERJXnUDU1FixYAKVSialTpxq3CYKA2NhYREZGIigoCIMGDcK5c+dMzlOr1Xj55ZcRGhqK0NBQvPzyy1Cr1bZ+fKJKSSswWNz34v5MKL++hSf2pOPZXzLw2uEsXM25Vztj1/UCBH97u8x57R9AWCASidDAU1qlaTNERERERERV4TAjNY4dO4bVq1ejefPmJtsXL16MJUuWYMmSJQgPD8e8efPw5JNP4tixY/D0LFmx4aWXXsLNmzexadMmAMAbb7yBV155BXFxcTZvB5E5BkHA+kv5OJGmhaQSUeNvt4uMf7+So8Ougf4QBAFTj2abPT7EzfIqI0RERERERI7KIUZqZGdnY+zYsfj888+hVCqN2wVBwLJly/Dmm29iyJAhaNasGZYtWwaNRmMMMC5cuIB9+/Zh0aJF6NixIzp27IiFCxdiz549SExMfFBNIjLx3aV8TDisxqoLefiyVD2NihxJKcadfD323CzEzbyy01YivKWQSVijgoiIiIiInI9DhBp3Q4vu3bubbE9KSkJKSgp69epl3KZQKNClSxf88ccfAID4+Hh4eHigU6dOxmOio6Ph7u5uPIboQTIIAt6yMMKisiLj7uDpfZlm9y15xMfsdiIiIiIiIkdn99NP1qxZgytXrmDFihVl9qWkpAAA/P39Tbb7+/sjOTkZAJCamgqVSmVSJFEkEsHPzw+pqakW7+toozgc7XkfBpXpk8xiYHSCHHk6y/libGQRFlxxQVpx1TPIBgoDPNVJSKxZZuJU+F6xP+wT+8M+sU/sF/vDPrE/7BP7wz6xT47UL+Hh4eXut+tQIzExER988AF2794NFxfbFhus6AtnTxITEx3qeR8GlemTr85rMDm+/LRhfHN3jOtYB0+3NSA+tRjt/V3w0clcfHW+4ikq/27lgTGRHqjjznoad/G9Yn/YJ/aHfWKf2C/2h31if9gn9od9Yp+crV/sevpJfHw8MjIyEB0dDZVKBZVKhd9//x0rV66ESqWCr68vACAtLc3kvLS0NAQEBAAAAgICkJGRAUEQjPsFQUB6errxGHp4nM7U4tFtqei7IxV/pBRVfIKV5BQbMPmI+UAjwluKIIUY8zp546OOJTVkfFzF6F9PDpVcgnmdvNE5sOLVTGa182agQURERERETs2uQ41Bgwbhf//7Hw4dOmT806ZNGwwbNgyHDh1C48aNERgYiP379xvPKSwsxJEjR4w1NDp27AiNRoP4+HjjMfHx8cjLyzOps0HOTWsQsOCvXHTbmoqEDC2OpWkxfG8Gbmh0FZ9shWdpsfGO2X1TWnkifmggzj8djJebeZg9RiIWYWt/P0SpLI9e8nRhYVAiIiIiInJ+dj39RKlUmqx2AgBubm7w8fFBs2bNAADjxo3DggULEB4ejsaNG2P+/Plwd3fH8OHDAQBNmjRBnz59MGnSJCxatAgAMGnSJPTv39+phtyQZYIgoOG6ZGh0gsn2XK2AlhtT8GiwK1qpXDCzrRdcrbxKiEZrQPsfUpBTLJTZ1zlQhjdbmQ8ySpNJRFjd0xeP7UzDnQJDmf0z23rV+FmJiIiIiIjsnV2HGpUxceJEFBQUYOrUqVCr1WjXrh02b94MT09P4zErV67EW2+9hWHDhgEABgwYgHnz5j2oRyYb+/hUbplA434HkotwILkIAXIxXm/pafG46hAEwVik9pdbhRj2c4bZ4/7dygOz2nlX6doNPKU4/EQAfrtdBJlYhDf/p0ZmkQENPSV4Jtytxs9ORERERERk7xwu1Ni5c6fJa5FIhBkzZmDGjBkWz1EqlWZXT6GHw7cXKy6qCQAfnsyp1VBjy9V8vH5YDZlEhAktPLDpcr7FYwfXV1TrHn5yCYaHlQQYXYNkSMzWobVKBoWU00+IiIiIiMj5OVyoQVQVWoOA2/llp2eYU6Svvfv+ninGm2ezSl7oBHxwPMfisd/28kUbv4oLf1ZEJZdAJWdhUCIiIiIienjYdaFQopqabSZMGNJAbvZYlxq8G/7O1OJgchEMgoATacV486z5e5R2bmRQtUdpEBERERERPew4UoOclkEQsP5S2Skf/41WIrMwE4fuFJtsb+5jeTURSy6otZh9PAc7rhcCAB4NdoVBsFy/464wTwmOPBlo9cKkREREREREzoyhBjkdQRCwPakQH5/KQXqh6dST/vXkCFBIsH2AP85madHlx1TjvoJyiomas/lKPv51IMtk24Hkokqd+0KEOwMNIiIiIiKiGmKoQU5nRnw2lp81Xxx0dQ9f49/dShXTzNdXLtQ4k6nF8fRiTD2qrvYzess484uIiIiIiKimGGqQUzmSUmQx0Hi/vZfJqiDupUKNPK2AIr2AXdcLEegmRudA1zLXOJRchGE/p6O4crVH8VSYAhuvFJTZrnTlKA0iIiIiIqKaYqhBTuWr85aXbx0Yalq801smhosY0P4TUGQWGdDuhxTczCtZBmVGG09Mi/IyHq8zCJhyRF3pQAMAhjZUoFuwK9743XRUR6gH33pEREREREQ1xU9W5BTiU4sw4bAaF7N1Zvd/3cMH4d6mhUBlEhGiVC44lqY1brsbaABA7Mlc+LqKMbapBwDgu0v5uGDh+uZ4uYjQOdAV3jIRRABmHstGdrGAAfXkaONX9aKkREREREREZIqhBjms324X4stzedj5z8ojlrTzc8GTDd3M7usc6GoSapT2+d8aY6ix+0b590l+PgTbkwqw8XI+MnLzMTM6AErXktoZz0e446kwN6QW6lHPXQKRiNNPiIiIiIiIaoqhhoMzCAJOZIshydEhzOvh6c4Jh7OwNrHscq2lebqIENvJ2+L+LkEyfPq35fOTNHrMT8jFmot5uKHRWzxuxwA/KKQijGjkhhGN3JCYmIjwOqbTXeRSEaedEBERERER1SJ+wnJggiBg2M8Z2H9bDunfKVjazQcjGpkfkeBM8rSGSgUaT4UpENvJG35yicVjogPKFgMt7cMTORb3hbiJ8VaUFx4Jqvg6REREREREVLsYajiwhAwt9t8uAgDoBODN/6nRJVCGurU0GqBQJ2DFOQ0SMrQYHqbAgFBFrVy3ps5kWZ4ucteK7j4YHqaAuIJpHkpXMfrVdcXPN4uq/BypL4RAJuE0EiIiIiIiogeFoYYDO51p+uE+Xyfgs781mButrPG1tycV4PlfM42vf7hagB4hrmjp64LdNwrRp44r5nT0rjA0qG2CIGDeqdxyj9k90A/RZpZjtWTJIz5YfFqDIoOASS09EZ9ajP/7LbPcczr6yxhoEBERERERPWAMNRzY043dcDpTixXn7i1j+sW5PKy/lA9fVzGWdvNB12pMi7iao8Po/WU/1P92uwi//TMyJDFbh5a+Lngm3L36DaiClHw9msTdMbuvvb8LNFoBegEY18yjSoEGAPgrJPiw4726G61U5a9MIhYBb7f1rNI9iIiIiIiIqPaJH/QDUPW5iEX4sIM3fFwEk+25WgFJGj1m/JFdretuvVYAnVDxcZ//rTG7/cerBWj8XTKax93BgdvlrxhSGTc1OouBBgBMj/LC0ScDcWxoIP4VWfOQJcxLiqmty4YWwW5ibO6nwtkRQegRIjdzJhEREREREdkSQw0HJ5OI0MLT/Kocf2VWXHsiOV+PzVfy8fONQuRpDSjSC/jPccuFMe93Vq2DQTBNP7QGAZOPqJFeaMCtfD3e/J8aglCJhKQcs8sp1NnMR4qeIbVfpPOdtl649VwwogNkAACFRIQvuvuiVx05gtwsFx4lIiIiIiIi2+H0EycgLyeaMgiCse7F3XAhq8gAX7kESbk6PPZTGpLzDQCAAIUYqQWGKt37x6sFyNcLaK2SoaWvC05naJFRdO8aV3P1uJKjRyPv6v1T02gNiLtcYHH/6h6+kIitU9vC3UWMHQP88FeGFqGeknJXUSEiIiIiIiLbY6jhBHqo9Nibbr4rC3QC3F1EmJ+QW+7SpADMBhojwhR4rYUHntyTgcyisvv/dSCrwudrtzkF158NhpesagODruXqELUpxey+V5q6Y0YbLyhdrTvYSCoWoa2/zKr3ICIiIiIiourh9BMn0FOlR11386MICvQC1ibmVRhoWPJSU3e0VslwYlggZrf3qvYzdt6SWqXjNVqDxUDj6UYKfNzJ2+qBBhEREREREdk3fip0Ai5iIH5oADb2VZXZl6cVsPSM+YKeFfmuty86BpTUq1C6ivF6S08sfaR6y8Xeytdj4+X8co/RGgScTC/G1RwdDvyzykppOwf4YXl3X4hsvJQsERERERER2R9OP3ESblIx+taVI8JbiovZOuP2z//W4GyWrpwzzesaJMOAUEWZ7TH1FXjvzxykFVat9gYAjD2YheVnNege7IrxzT3gr7g3usQgCBjzWya2JZW/WkqXQE4FISIiIiIiohIMNZyMQmo6guHL83kVnjOltSfu5OuxNvHeSIoXIswvjeolE2PXQD90+TEVxVXPNXA8XYvj6VosPK3B6CZukEtE6OAvQ9zlfOy5aX50xl3bH/PjCA0iIiIiIiIyYqjhZCK8pUjIsLyU68y2XhjSQA4vFzFUcjGk960c8lKkO364WoDWKhcMa1h2lMZdjb1d8G5bL7z7Z0mdDrkEaObjghPp9+47p6M3tBUsD/v1hZIQZRkqDl6GhynQLbj2l24lIiIiIiIix8VQw8m8184LG6+YXwJ1UKgck1t5WBztEOUnQ5Rf5aZ3TGjhgUA3Cc6rtRjRyA2RShck5eqQUWhAGz8X4z2eDXdD+IY71WvMfZ4Pd6vxNYiIiIiIiMi5MNRwMnU9pNg10A8Dfko32S4WASsfrb0CmyKRCCMamQYN9T2lqO9pepy/QgJfV7HZ5WArK8RNjA4BrKVBREREREREprj6iROKDpChg7+L8bWPqwjHhwaWqbdhKy9G1GyUxZqeKrhJ+U+ViIiIiIiITHGkhhMSiUTY2NcPK8/nIbVAj1ebeaCh14Pr6nfbeSHSxwX7bhZanBpz1+6Bfng7Phsn0rXoHCjDD/0YaBAREREREZF5DDWclNJVjCmtPSs+0AbEIhFGNnLDyEZu+PwRAdOOqrHuUj7EIiA6oKT455VcHVZ090F0oCt+ifFHVpEBvnJJBVcmIiIiIiKihxlDDbIpV4kIi7r6YG600vi6NJFIxECDiIiIiIiIKsRQgx4Ic2EGERERERERUVWwWAEREREREREROSSGGkRERERERETkkBhqEBEREREREZFDYqhBRERERERERA6JoQYREREREREROSSGGkRERERERETkkBhqEBEREREREZFDYqhBRERERERERA6JoQYREREREREROSSGGkRERERERETkkBhqEBEREREREZFDYqhBRERERERERA6JoQYREREREREROSSGGkRERERERETkkBhqEBEREREREZFDYqhBRERERERERA5JpFarhQf9EEREREREREREVcWRGkRERERERETkkBhqEBEREREREZFDYqhBRERERERERA6JoQYREREREREROSSGGkRERERERETkkBhqPGALFixAz549Ua9ePTRq1AgjR47E2bNnTY4RBAGxsbGIjIxEUFAQBg0ahHPnzpkcM3/+fPTv3x8hISFQKpVl7nP69GmMGTMGzZs3R1BQENq3b4/FixfDYDBYtX2Oylb9kp6ejqFDhyIyMhIBAQFo3rw5pkyZguzsbKu2zxHZqk/ul5GRgaZNm0KpVCIjI6PW2+TobNknSqWyzJ9Vq1ZZrW2Oytbvk7i4ODzyyCMIDAxEWFgYXnnlFau0y9HZql/WrVtn9r2iVCpx4sQJq7bR0djyvXLixAkMGTIEoaGhCA0NxeOPP47jx49brW2OypZ9cuDAAfTr1w9169ZFREQE3nvvPeh0Oqu1zZHVRr8kJSVhwoQJaN26NYKCgtC6dWu8//77KCgoMLnOjRs3MHLkSISEhCAsLAxvvfUWiouLbdJOR2LLPpk2bRp69OiBwMBAtGzZ0ibtqw6GGg/Y4cOHMWbMGOzZswfbtm2DVCrFE088gaysLOMxixcvxpIlSzB37lz8+uuv8Pf3x5NPPonc3FzjMUVFRYiJicG4cePM3ufUqVNQqVRYvnw5jh49ihkzZuC///0vFi5caPU2OiJb9YtYLEZMTAw2bNiAP//8E0uXLsWBAwcwceJEq7fR0diqT+43fvx4u/4G/qDZuk8+/fRTXLhwwfhn1KhRVmubo7JlnyxfvhyzZs3C66+/jiNHjmD79u0YOHCgVdvnqGzVL0OHDjV5j1y4cAEjRoxAgwYN0KZNG6u305HYqk80Gg2GDRuGoKAg7Nu3D3v37kVQUBCGDh1qch2yXZ+cPn0aTz31FHr06IGDBw9i1apV2LVrF/7zn/9Yu4kOqTb6JTExEXq9HgsWLMDRo0cxb948bNiwAdOnTzdeQ6/XY+TIkdBoNPjpp5/w1VdfYdu2bXjnnXds3mZ7Z6s+AQCDwYBRo0bh6aeftmkbq0qkVquFB/0QdI9Go0FoaCjWrVuHAQMGQBAEREZGYuzYsZgyZQoAoKCgAOHh4Zg9ezZGjx5tcv7WrVvx4osvQq1WV3ivWbNm4cCBAzhw4IBV2uJMbNkvy5cvx8KFC3HhwgWrtMVZWLtPli1bhl27dmHy5MkYMmQILl++DJVKZfV2OTJr9olSqcSaNWswZMgQm7TFWVirT9RqNZo1a4Z169ahZ8+eNmuPs7DV/yn5+fmIjIzExIkTMXnyZKu1xxlYq09OnjyJnj174tSpU2jQoAEA4Nq1a4iKisL+/fsZNpXDWn3ywQcfYO/evTh06JBx265duzB69GgkJibC09PT+o1zYDXtl7tWrlyJOXPm4OrVqwCAvXv3YsSIETh9+jTq1q0LoGQ04BtvvIHExER4eXnZpoEOyFp9cr/PPvsMK1aswOnTp63aluriSA07o9FoYDAYjMPlkpKSkJKSgl69ehmPUSgU6NKlC/74448a3Ss3N7fC4fdUwlb9kpycjO3bt6Nr1641fmZnZ80+SUhIwOLFi7F8+XKIxfw2WVnWfp9Mnz4dYWFh6NmzJ1atWsXpc5VgrT7Zv38/9Ho9UlNT0alTJzRt2hTPPvssrl27VttNcEq2+j9ly5YtyM/Px3PPPVfjZ3Z21uqTxo0bw8/PD2vXrkVRURGKiorwzTffoG7duoiMjKz1djgTa/VJUVER5HK5yTaFQoHCwkKcOnWqdh7eidVWv5T+HBIfH48mTZoYAw0A6N27N4qKitgvFbBWnzgS/rRuZ6ZPn46WLVuiY8eOAICUlBQAgL+/v8lx/v7+SE1NrfZ9Tp06hfXr1+Nf//pX9R/2IWLtfhkzZgyCg4PRtGlTeHh4YMmSJTV/aCdnrT7Jy8vDmDFjMHfuXISEhNTeAz8ErPk+efvtt7Fq1Sr8+OOPGDp0KGbOnIlPPvmkdh7ciVmrT65duwaDwYD58+djzpw5WLt2LXQ6HWJiYpCfn197DXBStvq/fs2aNejfvz8CAwOr/7APCWv1iaenJ3bs2IEtW7YgODgYwcHB2Lx5M3788UcoFIraa4ATslaf9O7dG3/++Sfi4uKg0+lw+/ZtzJ071+QeZFlt9Mv169fx2WefYcyYMcZtqampZa6hUqkgkUhq9H3wYWCtPnEkDDXsyNtvv42jR4/i22+/hUQisdp9EhMTMXLkSIwbN45DuSvBFv3y0Ucf4cCBA1i/fj2SkpIwY8YMq9zHWVizT6ZNm4bo6Gi+N6rI2u+Tt956C507d0arVq3w+uuvY9q0afjss89q/T7OxJp9YjAYoNVqMXfuXPTp0wft2rXDihUrkJ6ejt27d9fqvZyNrf6vP3fuHOLj4/Hiiy9a7R7Owpp9UlBQgAkTJqB9+/bYt28f9uzZg1atWuGZZ55BXl5erd7LmVizT3r16oXZs2dj6tSpCAwMRPv27dGvXz8A4OjMCtRGv6SmpmL48OHo2bMnXnvttVp+wocP+6QE37l2YsaMGfjhhx+wbds245xLAMbfrqSlpZkcn5aWhoCAgCrf5+LFi4iJicHQoUNZEKkSbNUvgYGBiIiIwMCBA7Fw4UKsXr0aN2/erNGzOytr98ndcEmlUkGlUhnDjYiICMyePbvmDXBCtnqf3K9du3bIycnhb28ssHaf3L1OkyZNjNu8vb0RFBTE713lsOV7ZfXq1ahbty769OlT7ed9GFi7TzZu3IirV69i6dKlaNu2LTp06ICVK1fi5s2b2LFjR620wdnY4n0yYcIEJCUl4e+//8bly5eNRY7vvx+Zqo1+SUlJweDBg9G0aVN88cUXEIlExn0BAQFlrpGRkQG9Xl/jnxmclbX7xJEw1LAD06ZNM/6DjIiIMNlXv359BAYGYv/+/cZthYWFOHLkCDp16lSl+5w/fx4xMTEYMmQIYmNja+XZnZmt+qW0u3UCuIRVWbboky1btuDw4cM4dOgQDh06hE8//RQAsGPHDi5XacaDep+cPn0acrkc3t7eNbqOM7JFn0RHRwMALl26ZNym0WiQkpKCevXq1bAFzsmW75XCwkLExcXh2Wef5W+ey2GLPikoKIBIJDLpB7FYDJFIxLpAZtjyfSISiRAcHAyFQoFNmzahbt26aN26dY3b4Ixqo1/u3LmDmJgYRERE4KuvvoJUKjW5TseOHXHhwgXcunXLuG3//v1wdXVFVFSUlVrmuGzRJ47EcZ/cSUyZMgVxcXFYu3YtlEqlcQ6Uu7s7PDw8IBKJMG7cOCxYsADh4eFo3Lgx5s+fD3d3dwwfPtx4nRs3biArKwvXr18HAPz1118AgLCwMHh4eODcuXN4/PHH0a1bN0yePNlkziDn2pZlq37ZvXs3MjMzERUVBXd3d5w/fx6zZs1Chw4dEBYWZvuG2zFb9Unjxo1N7puRkQGgZKQGVz8xZas+2bVrF1JTU9GhQwcoFAocOnQIsbGxePHFF+Hq6mr7htsxW75PBg4ciOnTp2PhwoVQKpWIjY2Fn58f+vfvb/uG2zlb9ctdW7duRU5ODguElsNWfdKzZ0/MmjULkydPxiuvvAKDwYCFCxdCIpGge/futm+4HbPl++TTTz9F7969IRaLsX37dixatAhff/21VaeEOara6Jfk5GTExMQgKCgIsbGxxp+tAMDPzw8SiQS9evVC06ZN8eqrr+LDDz9EVlYWZs2ahRdeeIErn5Riqz4BgCtXrkCj0SA5ORlardb4foqMjIRMJrNxyy3jkq4PmKUKs9OmTTPWVRAEAR9//DFWr14NtVqNdu3aYf78+WjWrJnx+HHjxuG7774rc53t27ejW7duiI2NNRZBKq0yy4w+bGzVL7/99hvmzJmDCxcuoLi4GHXq1EFMTAwmTZrksNWHrcVWfVLaoUOHMHjwYC7paoat+mTfvn14//33cfXqVRgMBjRo0ADPP/88xo4d69C/VbAGW75PcnNz8c4772Dbtm0QBAHR0dH4+OOP0bBhQyu0zLHZ+vvXwIED4e7ujo0bN9ZyS5yHLftk//79mDt3Ls6ePQuRSISWLVvi3XffrfGINWdjyz4ZPHgwEhISUFxcjBYtWmDatGno27evFVrl+GqjX9atW2exVkNCQgLq168PoCSQmjJlCg4ePAi5XI6nnnoKs2fP5i8wSrFlnwwaNAi///57ucfYA4YaREREREREROSQONGSiIiIiIiIiBwSQw0iIiIiIiIickgMNYiIiIiIiIjIITHUICIiIiIiIiKHxFCDiIiIiIiIiBwSQw0iIiIiIiIickgMNYiIiIiIiIjIITHUICIiIrtw6NAhKJVK4x9fX1/Ur18fnTt3xquvvop9+/ZBEIRqX/+vv/5CbGwskpKSavGpiYiI6EGSPugHICIiIrrf8OHD0bdvXwiCAI1Gg8TEROzcuRMbNmxAjx49sHr1aiiVyipf9/Tp05g7dy4eeeQR1K9f3wpPTkRERLbGUIOIiIjsSuvWrTFy5EiTbR999BFmzZqFJUuW4KWXXsKmTZse0NMRERGRPeH0EyIiIrJ7EokEc+bMQefOnbFv3z4cOXIEAJCcnIx33nnHOPoiMDAQnTp1wqJFi6DX643nx8bG4rXXXgMADB482DjFZdy4ccZjioqK8MknnyA6OhqBgYEIDQ3FyJEjkZCQYNvGEhERUaVxpAYRERE5jOeeew5HjhzBzz//jM6dO+PMmTPYvn0l/YEXAAAD0klEQVQ7YmJi0LBhQ2i1Wvzyyy/4z3/+g2vXrmHRokUASoKMlJQUrF69GpMnT0ZERAQAoGHDhgAArVaLYcOGIT4+HiNHjsTYsWORk5ODNWvW4LHHHsNPP/2ENm3aPLB2ExERkXkMNYiIiMhhNG/eHABw6dIlAEDXrl2RkJAAkUhkPGb8+PF4+eWX8c0332D69OkICgpCixYt0KFDB6xevRo9evRAt27dTK67YsUKHD58GD/88AN69+5t3D5mzBh06dIFM2fOxM6dO23QQiIiIqoKTj8hIiIih+Hl5QUAyM3NBQAoFApjoFFcXIysrCxkZGSgd+/eMBgMOHnyZKWu+/333yMiIgJRUVHIyMgw/tFqtejRoweOHj2KgoIC6zSKiIiIqo0jNYiIiMhh5OTkAAA8PT0BADqdDgsXLsSGDRtw5cqVMku+qtXqSl334sWLKCgoQKNGjSwek5GRgbp161bzyYmIiMgaGGoQERGRwzhz5gwAIDw8HADw9ttvY8WKFRg6dCgmT54Mf39/uLi4ICEhAe+99x4MBkOlrisIApo1a4aPPvrI4jF+fn41bwARERHVKoYaRERE5DDWrl0LAOjXrx8AIC4uDl26dMGqVatMjrty5UqZc++vu1FaWFgYMjIy0L17d4jFnJ1LRETkKPi/NhEREdk9vV6PmTNn4siRI+jXrx+io6MBlCz1WnrKSV5eHpYuXVrmGu7u7gCArKysMvtGjRqFlJQULFmyxOz9U1NTa9oEIiIisgKO1CAiIiK7kpCQgLi4OACARqNBYmIidu7ciRs3bqBXr1748ssvjccOGTIEX3/9NUaPHo0ePXogNTUVa9euha+vb5nrtm3bFmKxGJ988gnUajXc3d1Rv359tG/fHq+++ir279+Pd999FwcPHkT37t3h6emJmzdv4sCBA3B1dcWOHTts9jUgIiKiyhGp1Wqh4sOIiIiIrOvQoUMYPHiw8bVYLIaHhwdCQkIQFRWF4cOHo0+fPibn5OfnIzY2Flu2bEFaWhrq1KmD559/Hm3btsWQIUOwZMkSPPvss8bj169fj8WLF+PKlSvQarUYNWoUli1bBqCk6OjKlSsRFxeHCxcuAACCgoLQrl07jBo1Cr169bLBV4GIiIiqgqEGERERERERETkk1tQgIiIiIiIiIofEUIOIiIiIiIiIHBJDDSIiIiIiIiJySAw1iIiIiIiIiMghMdQgIiIiIiIiIofEUIOIiIiIiIiIHBJDDSIiIiIiIiJySAw1iIiIiIiIiMghMdQgIiIiIiIiIofEUIOIiIiIiIiIHNL/A9dTufqvwM/yAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "CHndtrz_IxTC",
        "outputId": "75af3265-5320-4554-c2d4-4dd6f07ca4d6"
      },
      "source": [
        "# Visualize the close price history\n",
        "\n",
        "plt.figure(figsize = (16, 8))\n",
        "plt.title('Closing Price History')\n",
        "plt.plot(df['Close'])\n",
        "\n",
        "plt.xlabel('Date' , fontsize = '18')\n",
        "plt.ylabel('Close Price' , fontsize = '18')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8joCSSScI_8D"
      },
      "source": [
        "# Use a dataframe having only Close column\n",
        "\n",
        "data = df.filter(['Close'])\n",
        "\n",
        "# Convert data into np array\n",
        "\n",
        "dataset = data.values\n",
        "\n",
        "# Get the number of rows to train the model on\n",
        "\n",
        "training_data_len = math.ceil(len(dataset) * 0.8) # Specifying 80 percent as training data"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M6RBnYOGJohV",
        "outputId": "ea3c340b-e581-465d-9ecf-d4ceb18b6c6f"
      },
      "source": [
        "training_data_len"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1812"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7PgqVqSrJr4g"
      },
      "source": [
        "# Scaling the data\n",
        "\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "  # Scales values of dataset between 0 & 1\n",
        "\n",
        "scaled_data = scaler.fit_transform(dataset)"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xU4ntb8UJuq4",
        "outputId": "1d2b6ddd-57be-4f20-a4f2-677b1f11d2b5"
      },
      "source": [
        "scaled_data.shape"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2265, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-x-s5BFKYVf",
        "outputId": "a91bcfd8-9045-4245-c0c1-a8e1339a7dff"
      },
      "source": [
        "# Creating training dataset \n",
        "\n",
        "# Creating the scaled training dataset\n",
        "\n",
        "train_data = scaled_data[0:training_data_len , :]\n",
        "\n",
        "# Splitting train into x_train & y_train\n",
        "\n",
        "x_train = []    # Training features\n",
        "y_train = []    # Target variables\n",
        "\n",
        "for i in range(60 , len(train_data)):  # 60 days \n",
        "\n",
        "    x_train.append(train_data[i-60:i , 0])  # 60 values from position 0 to 59\n",
        "    y_train.append(train_data[i,0])         # 61st value with position 60\n",
        "\n",
        "    if (i <= 61):\n",
        "\n",
        "        print(x_train)\n",
        "        print(y_train)\n",
        "        print()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([0.03445147, 0.03538112, 0.03237843, 0.02946644, 0.02053937,\n",
            "       0.02075965, 0.02167308, 0.02286508, 0.02135888, 0.02252174,\n",
            "       0.02392427, 0.02608154, 0.00872625, 0.00857399, 0.00708724,\n",
            "       0.00338166, 0.00293141, 0.00677951, 0.00603775, 0.00682162,\n",
            "       0.00705485, 0.00844119, 0.01207551, 0.01620865, 0.01545717,\n",
            "       0.01645482, 0.01697633, 0.01517861, 0.01721602, 0.01642567,\n",
            "       0.01506522, 0.01537619, 0.01476723, 0.01779907, 0.01583614,\n",
            "       0.01524339, 0.01647102, 0.01627991, 0.01922106, 0.01917571,\n",
            "       0.02051994, 0.02014745, 0.01788005, 0.01487088, 0.01546689,\n",
            "       0.01557702, 0.01334524, 0.01493242, 0.01902345, 0.01844366,\n",
            "       0.02010858, 0.02137507, 0.02427087, 0.02411216, 0.02621435,\n",
            "       0.02818051, 0.02705976, 0.02924295, 0.0284947 , 0.03132572])]\n",
            "[0.028944952015694747]\n",
            "\n",
            "[array([0.03445147, 0.03538112, 0.03237843, 0.02946644, 0.02053937,\n",
            "       0.02075965, 0.02167308, 0.02286508, 0.02135888, 0.02252174,\n",
            "       0.02392427, 0.02608154, 0.00872625, 0.00857399, 0.00708724,\n",
            "       0.00338166, 0.00293141, 0.00677951, 0.00603775, 0.00682162,\n",
            "       0.00705485, 0.00844119, 0.01207551, 0.01620865, 0.01545717,\n",
            "       0.01645482, 0.01697633, 0.01517861, 0.01721602, 0.01642567,\n",
            "       0.01506522, 0.01537619, 0.01476723, 0.01779907, 0.01583614,\n",
            "       0.01524339, 0.01647102, 0.01627991, 0.01922106, 0.01917571,\n",
            "       0.02051994, 0.02014745, 0.01788005, 0.01487088, 0.01546689,\n",
            "       0.01557702, 0.01334524, 0.01493242, 0.01902345, 0.01844366,\n",
            "       0.02010858, 0.02137507, 0.02427087, 0.02411216, 0.02621435,\n",
            "       0.02818051, 0.02705976, 0.02924295, 0.0284947 , 0.03132572]), array([0.03538112, 0.03237843, 0.02946644, 0.02053937, 0.02075965,\n",
            "       0.02167308, 0.02286508, 0.02135888, 0.02252174, 0.02392427,\n",
            "       0.02608154, 0.00872625, 0.00857399, 0.00708724, 0.00338166,\n",
            "       0.00293141, 0.00677951, 0.00603775, 0.00682162, 0.00705485,\n",
            "       0.00844119, 0.01207551, 0.01620865, 0.01545717, 0.01645482,\n",
            "       0.01697633, 0.01517861, 0.01721602, 0.01642567, 0.01506522,\n",
            "       0.01537619, 0.01476723, 0.01779907, 0.01583614, 0.01524339,\n",
            "       0.01647102, 0.01627991, 0.01922106, 0.01917571, 0.02051994,\n",
            "       0.02014745, 0.01788005, 0.01487088, 0.01546689, 0.01557702,\n",
            "       0.01334524, 0.01493242, 0.01902345, 0.01844366, 0.02010858,\n",
            "       0.02137507, 0.02427087, 0.02411216, 0.02621435, 0.02818051,\n",
            "       0.02705976, 0.02924295, 0.0284947 , 0.03132572, 0.02894495])]\n",
            "[0.028944952015694747, 0.02662248667699532]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OvrzfGQZLyss",
        "outputId": "1ca37375-d2c2-4517-9232-16f17d7c15e2"
      },
      "source": [
        "train_data.shape"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1812, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mj66jM3aL-Hf",
        "outputId": "d9f2985d-6e62-4651-d56b-0bb57ab908c1"
      },
      "source": [
        "len(y_train)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1752"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ojs4xXNrMBqq"
      },
      "source": [
        "# Convert x_train & y_train into np arrays for putting into LSTM model\n",
        "\n",
        "x_train , y_train = np.array(x_train) , np.array(y_train)"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ngvKl4WcOOWb",
        "outputId": "d4494d51-8345-4610-da66-c26190a0992e"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1752,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NDQJc4YOF9u",
        "outputId": "9add9694-4d98-4657-c986-a037f3baecfd"
      },
      "source": [
        "# Reshaping the data\n",
        "\n",
        "# LSTM needs it in such a way which is 3d , right now ours is 2d\n",
        "\n",
        "x_train = np.reshape(x_train , (x_train.shape[0] , x_train.shape[1] , 1))\n",
        "x_train.shape"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1752, 60, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJoS7uXrONdu"
      },
      "source": [
        "# Building the LSTM models\n",
        "\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(LSTM(50 , return_sequences= True , input_shape =(x_train.shape[1],1)))\n",
        "model.add(LSTM(50 , return_sequences=False))\n",
        "\n",
        "model.add(Dense(25))\n",
        "model.add(Dense(1))"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "No_OEF3sQLJL"
      },
      "source": [
        "# Compiling the model\n",
        "\n",
        "model.compile(optimizer = 'adam' , loss = 'mean_squared_error')"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MHT8aUbaQWlV",
        "outputId": "532a565d-7454-4004-eb32-7ae68bd42180"
      },
      "source": [
        "# Training the model\n",
        "\n",
        "model.fit(x_train , y_train , batch_size = 1, epochs = 1)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1752/1752 [==============================] - 37s 20ms/step - loss: 0.0026\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f4c0bb5e450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vM0doha8Qt_B"
      },
      "source": [
        "# Creating test data set\n",
        "\n",
        "# Creating array from index 1752 to 2265\n",
        "\n",
        "test_data = scaled_data[training_data_len - 60 : ,:]\n",
        "\n",
        "# Creating x_test & y_test\n",
        "\n",
        "x_test = []\n",
        "y_test = []\n",
        "\n",
        "y_test = dataset[training_data_len: , :]  # All values that our model will predict\n",
        "\n",
        "for i in range(60 , len(test_data)):\n",
        "\n",
        "    x_test.append(test_data[i-60:i , 0])\n",
        "    "
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJerI0eORXX4"
      },
      "source": [
        "# Converting data again , to np array\n",
        "\n",
        "x_test = np.array(x_test)\n",
        "\n",
        "# Reshaping this data for LSTM model \n",
        "\n",
        "x_test = np.reshape(x_test , (x_test.shape[0] , x_test.shape[1] , 1))"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqvjrMd7SMtQ"
      },
      "source": [
        "# Get the predicted values\n",
        "\n",
        "predictions = model.predict(x_test)\n",
        "\n",
        "predictions = scaler.inverse_transform(predictions) # Unscaling the values from 0-1 to noraml ones that were already present"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ksjO_vSKSn-b",
        "outputId": "47a95fe5-97c6-401d-f460-9d520833d42b"
      },
      "source": [
        "# Evaluation of our model\n",
        "\n",
        "# Getting RMSE as a metric\n",
        "\n",
        "rmse=np.sqrt(np.mean(predictions- y_test)**2)\n",
        "\n",
        "rmse"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.739654473647903"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AjcuiqwcTOyI"
      },
      "source": [
        "#### **A good score**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "qedWk72kS_1K",
        "outputId": "7d021542-76f4-4cb1-9804-d825e58c4dd4"
      },
      "source": [
        "# Plotting the data\n",
        "\n",
        "train = data[:training_data_len]\n",
        "valid = data[training_data_len:]\n",
        "\n",
        "valid['Predictions'] = predictions\n",
        "\n",
        "plt.figure(figsize = (16 , 8))\n",
        "\n",
        "plt.title('Model')\n",
        "plt.xlabel('Date', fontsize = 18)\n",
        "plt.xlabel('Closing Price', fontsize = 18)\n",
        "\n",
        "plt.plot(train['Close'])\n",
        "plt.plot(valid[['Close', 'Predictions']])\n",
        "\n",
        "plt.legend(['Train' , 'Validation', 'Predictions'],\n",
        "           loc = 'lower right')\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "xxLtKh3aUCbh",
        "outputId": "49a531db-2a85-4b4c-fd6e-071e19841548"
      },
      "source": [
        "# Show the valid & predicted prices\n",
        "\n",
        "valid"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "      <th>Predictions</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-03-19</th>\n",
              "      <td>1202.459961</td>\n",
              "      <td>1200.518433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-20</th>\n",
              "      <td>1226.430054</td>\n",
              "      <td>1204.659668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-21</th>\n",
              "      <td>1236.130005</td>\n",
              "      <td>1210.828613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-22</th>\n",
              "      <td>1207.650024</td>\n",
              "      <td>1218.368164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-25</th>\n",
              "      <td>1197.380005</td>\n",
              "      <td>1223.120361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>1734.160034</td>\n",
              "      <td>1746.070312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>1773.959961</td>\n",
              "      <td>1742.601929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>1757.760010</td>\n",
              "      <td>1744.403198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>1736.250000</td>\n",
              "      <td>1746.743896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>1752.640015</td>\n",
              "      <td>1747.211670</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>453 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Close  Predictions\n",
              "Date                                \n",
              "2019-03-19  1202.459961  1200.518433\n",
              "2019-03-20  1226.430054  1204.659668\n",
              "2019-03-21  1236.130005  1210.828613\n",
              "2019-03-22  1207.650024  1218.368164\n",
              "2019-03-25  1197.380005  1223.120361\n",
              "...                 ...          ...\n",
              "2020-12-24  1734.160034  1746.070312\n",
              "2020-12-28  1773.959961  1742.601929\n",
              "2020-12-29  1757.760010  1744.403198\n",
              "2020-12-30  1736.250000  1746.743896\n",
              "2020-12-31  1752.640015  1747.211670\n",
              "\n",
              "[453 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAhJ6vZiUWsK"
      },
      "source": [
        ""
      ],
      "execution_count": 96,
      "outputs": []
    }
  ]
}