{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "recurrent-neural-networks.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oA6GZzYgRBsu"
      },
      "source": [
        "<img src=\"https://www.kaunokolegija.lt/kk_wp_content/uploads/sites/5/2020/05/kaunas-university-of-applied-sciences.png\" width=\"300\"/> \n",
        "\n",
        "------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QBqbb8BRFzu"
      },
      "source": [
        "# Recurrent Neural Networks\n",
        "\n",
        "A case study of univariate time series analysis.\n",
        "\n",
        "### Practical Session\n",
        "\n",
        "<br/> Prof. Dr. Georgios K. Ouzounis\n",
        "<br/> email: georgios.ouzounis@go.kauko.lt\n",
        "<br/>last update: June 25, 2021\n",
        "\n",
        "\n",
        "--------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TdhSL4xTROUn"
      },
      "source": [
        "## Contents\n",
        "\n",
        "1. [Challenge](#challenge)\n",
        "2. [Download the data](#download-the-data)\n",
        "3. [Visualize the stock price history](#visualize-the-stock-price-history)\n",
        "4. [Data transforms and preprocessing](#data-transforms-and-preprocessing)\n",
        "5. [Build the RNN](#build-the-rnn)\n",
        "6. [Train the RNN](#train-the-rnn)\n",
        "7. [Improve the RNN](#improve-the-rnn)\n",
        "8. [Fine tune the RNN](#fine-tune-the-rnn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CU-lVwMmRTNK"
      },
      "source": [
        "## Challenge <a name=\"challenge\"></a>\n",
        "\n",
        "<img src=\"https://miro.medium.com/max/3504/1*NpT5pyemQQsGEHXbfS51Zw.png\" width=\"600\" align=\"left\"/>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WehN_1mKRhu4"
      },
      "source": [
        "Given a 5-year history of any stock traded in NASDAQ predict the stock prices for the period of the recent-most month that are not included in the historical data.\n",
        "\n",
        "To address this challenge we will employ [**univariate time series analysis**](www.homepages.ucl.ac.uk/~uctpsc0/Teaching/GR03/TS1.pdf) with [**recurrent neural networks**](https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mReQATWhIv6d"
      },
      "source": [
        "## Download the data  <a name=\"download-the-data\"></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGl4xmaGRYCc"
      },
      "source": [
        "# importing the libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hvLG67JMRlJf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d4af5bf-8824-49d3-d6ed-921fb271dc54"
      },
      "source": [
        "# install yahoo-finance\n",
        "!pip install yfinance"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting yfinance\n",
            "  Downloading https://files.pythonhosted.org/packages/5e/4e/88d31f5509edcbc51bcbb7eeae72516b17ada1bc2ad5b496e2d05d62c696/yfinance-0.1.60.tar.gz\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.9)\n",
            "Collecting lxml>=4.5.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/30/c0/d0526314971fc661b083ab135747dc68446a3022686da8c16d25fcf6ef07/lxml-4.6.3-cp37-cp37m-manylinux2014_x86_64.whl (6.3MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3MB 27.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2021.5.30)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
            "Building wheels for collected packages: yfinance\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.60-py2.py3-none-any.whl size=23819 sha256=de567c9e1d56b3f51d8234a5b5b230d25a14fd924b18fc926824a0c6d6bea0c6\n",
            "  Stored in directory: /root/.cache/pip/wheels/f0/be/a4/846f02c5985562250917b0ab7b33fff737c8e6e8cd5209aa3b\n",
            "Successfully built yfinance\n",
            "Installing collected packages: lxml, yfinance\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed lxml-4.6.3 yfinance-0.1.60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBW1a1O3RnKx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "740f576b-9995-4335-e767-e20f47acc255"
      },
      "source": [
        "# copy some custom code files \n",
        "!wget https://raw.githubusercontent.com/georgiosouzounis/deep-learning-lectures/main/code/NASDAQ_io.py"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-07-04 05:02:15--  https://raw.githubusercontent.com/georgiosouzounis/deep-learning-lectures/main/code/NASDAQ_io.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1716 (1.7K) [text/plain]\n",
            "Saving to: ‘NASDAQ_io.py’\n",
            "\n",
            "\rNASDAQ_io.py          0%[                    ]       0  --.-KB/s               \rNASDAQ_io.py        100%[===================>]   1.68K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-07-04 05:02:15 (37.2 MB/s) - ‘NASDAQ_io.py’ saved [1716/1716]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CB8DpuO6_sp1"
      },
      "source": [
        "# import necessary functions\n",
        "from NASDAQ_io import * #getStockTickerSymbols, searchBySymbol, getStockPriceHistory, getDateTime"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jalyBe1iRs2G"
      },
      "source": [
        "# get the companies listed in NASDAQ\n",
        "companies = getStockTickerSymbols()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nn2ULPRPRvGq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "outputId": "16d9f5c5-0608-47b7-bb3b-3be0ca56cd78"
      },
      "source": [
        "# view the companies\n",
        "companies"
      ],
      "execution_count": 6,
      "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",
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              "    }\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>Symbol</th>\n",
              "      <th>Company Name</th>\n",
              "      <th>Security Name</th>\n",
              "      <th>Market Category</th>\n",
              "      <th>Test Issue</th>\n",
              "      <th>Financial Status</th>\n",
              "      <th>Round Lot Size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AAIT</td>\n",
              "      <td>iShares MSCI All Country Asia Information Tech...</td>\n",
              "      <td>iShares MSCI All Country Asia Information Tech...</td>\n",
              "      <td>G</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AAL</td>\n",
              "      <td>American Airlines Group, Inc.</td>\n",
              "      <td>American Airlines Group, Inc. - Common Stock</td>\n",
              "      <td>Q</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AAME</td>\n",
              "      <td>Atlantic American Corporation</td>\n",
              "      <td>Atlantic American Corporation - Common Stock</td>\n",
              "      <td>G</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AAOI</td>\n",
              "      <td>Applied Optoelectronics, Inc.</td>\n",
              "      <td>Applied Optoelectronics, Inc. - Common Stock</td>\n",
              "      <td>G</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAON</td>\n",
              "      <td>AAON, Inc.</td>\n",
              "      <td>AAON, Inc. - Common Stock</td>\n",
              "      <td>Q</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2962</th>\n",
              "      <td>ZN</td>\n",
              "      <td>Zion Oil &amp; Gas Inc</td>\n",
              "      <td>Zion Oil &amp; Gas Inc - Common Stock</td>\n",
              "      <td>G</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2963</th>\n",
              "      <td>ZNGA</td>\n",
              "      <td>Zynga Inc.</td>\n",
              "      <td>Zynga Inc. - Class A Common Stock</td>\n",
              "      <td>Q</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2964</th>\n",
              "      <td>ZSPH</td>\n",
              "      <td>ZS Pharma, Inc.</td>\n",
              "      <td>ZS Pharma, Inc. - Common Stock</td>\n",
              "      <td>G</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2965</th>\n",
              "      <td>ZU</td>\n",
              "      <td>zulily, inc.</td>\n",
              "      <td>zulily, inc. - Class A Common Stock</td>\n",
              "      <td>Q</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2966</th>\n",
              "      <td>ZUMZ</td>\n",
              "      <td>Zumiez Inc.</td>\n",
              "      <td>Zumiez Inc. - Common Stock</td>\n",
              "      <td>Q</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2967 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Symbol  ... Round Lot Size\n",
              "0      AAIT  ...          100.0\n",
              "1       AAL  ...          100.0\n",
              "2      AAME  ...          100.0\n",
              "3      AAOI  ...          100.0\n",
              "4      AAON  ...          100.0\n",
              "...     ...  ...            ...\n",
              "2962     ZN  ...          100.0\n",
              "2963   ZNGA  ...          100.0\n",
              "2964   ZSPH  ...          100.0\n",
              "2965     ZU  ...          100.0\n",
              "2966   ZUMZ  ...          100.0\n",
              "\n",
              "[2967 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0FSCohxRv7z"
      },
      "source": [
        "# Let us go for Tesla\n",
        "symbol = 'TSLA'"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1YTwxW9v_A3E"
      },
      "source": [
        "# check if the symbol exists; if it doesn't the dataframe will be empty\n",
        "df = searchBySymbol(companies, symbol)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BK4aLl4m99D5"
      },
      "source": [
        "# set the strat and end date for our training data\n",
        "start_date = getDateTime(2016,1,1)\n",
        "end_date = getDateTime(2021,5,31)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EGy3NceYR2yM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73e768ac-a4ae-4c7b-9952-d5a2bd380315"
      },
      "source": [
        "# get the stock history\n",
        "stock_history = getStockPriceHistory(df, start_date, end_date)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 : TSLA,"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1N84CzJrbhq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "a5c24a1d-eaff-4f1b-9321-03f380da6762"
      },
      "source": [
        "# view the contents of the dataframe\n",
        "stock_history"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Name</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",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-01-04</th>\n",
              "      <td>46.144001</td>\n",
              "      <td>46.276001</td>\n",
              "      <td>43.799999</td>\n",
              "      <td>44.681999</td>\n",
              "      <td>44.681999</td>\n",
              "      <td>34135500</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-05</th>\n",
              "      <td>45.271999</td>\n",
              "      <td>45.377998</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.686001</td>\n",
              "      <td>44.686001</td>\n",
              "      <td>15934000</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-06</th>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.009998</td>\n",
              "      <td>43.195999</td>\n",
              "      <td>43.807999</td>\n",
              "      <td>43.807999</td>\n",
              "      <td>18895500</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-07</th>\n",
              "      <td>42.838001</td>\n",
              "      <td>43.688000</td>\n",
              "      <td>42.734001</td>\n",
              "      <td>43.130001</td>\n",
              "      <td>43.130001</td>\n",
              "      <td>17771500</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-08</th>\n",
              "      <td>43.571999</td>\n",
              "      <td>44.088001</td>\n",
              "      <td>42.153999</td>\n",
              "      <td>42.200001</td>\n",
              "      <td>42.200001</td>\n",
              "      <td>18140500</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-24</th>\n",
              "      <td>581.599976</td>\n",
              "      <td>614.479980</td>\n",
              "      <td>573.650024</td>\n",
              "      <td>606.440002</td>\n",
              "      <td>606.440002</td>\n",
              "      <td>34558100</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-25</th>\n",
              "      <td>607.309998</td>\n",
              "      <td>613.989990</td>\n",
              "      <td>595.710022</td>\n",
              "      <td>604.690002</td>\n",
              "      <td>604.690002</td>\n",
              "      <td>28005900</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-26</th>\n",
              "      <td>607.559998</td>\n",
              "      <td>626.169983</td>\n",
              "      <td>601.500000</td>\n",
              "      <td>619.130005</td>\n",
              "      <td>619.130005</td>\n",
              "      <td>28639300</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-27</th>\n",
              "      <td>620.239990</td>\n",
              "      <td>631.130005</td>\n",
              "      <td>616.210022</td>\n",
              "      <td>630.849976</td>\n",
              "      <td>630.849976</td>\n",
              "      <td>26370600</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-28</th>\n",
              "      <td>628.500000</td>\n",
              "      <td>635.590027</td>\n",
              "      <td>622.380005</td>\n",
              "      <td>625.219971</td>\n",
              "      <td>625.219971</td>\n",
              "      <td>22737000</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1361 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low  ...   Adj Close    Volume  Name\n",
              "Date                                            ...                            \n",
              "2016-01-04   46.144001   46.276001   43.799999  ...   44.681999  34135500  TSLA\n",
              "2016-01-05   45.271999   45.377998   44.000000  ...   44.686001  15934000  TSLA\n",
              "2016-01-06   44.000000   44.009998   43.195999  ...   43.807999  18895500  TSLA\n",
              "2016-01-07   42.838001   43.688000   42.734001  ...   43.130001  17771500  TSLA\n",
              "2016-01-08   43.571999   44.088001   42.153999  ...   42.200001  18140500  TSLA\n",
              "...                ...         ...         ...  ...         ...       ...   ...\n",
              "2021-05-24  581.599976  614.479980  573.650024  ...  606.440002  34558100  TSLA\n",
              "2021-05-25  607.309998  613.989990  595.710022  ...  604.690002  28005900  TSLA\n",
              "2021-05-26  607.559998  626.169983  601.500000  ...  619.130005  28639300  TSLA\n",
              "2021-05-27  620.239990  631.130005  616.210022  ...  630.849976  26370600  TSLA\n",
              "2021-05-28  628.500000  635.590027  622.380005  ...  625.219971  22737000  TSLA\n",
              "\n",
              "[1361 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qwetf-Nt5eVR"
      },
      "source": [
        "### a quick visualization example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "RV-gww_E4nHT",
        "outputId": "7252e26a-ae96-4b27-9743-cb8603317582"
      },
      "source": [
        "# let us plot a quick chart to see the history of the stock in the timeframe specified  \n",
        "stock_history['Close'].plot(figsize=(16, 12))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcff729a190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 687
        },
        "id": "KonITMNU5B4q",
        "outputId": "075a237f-1501-4523-ebdb-d639a40ab5d8"
      },
      "source": [
        "# use truncate() to zoom into a specific date range\n",
        "stock_history.truncate(before='2020-03-01')['Close'].plot(figsize=(16, 12))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcff67350d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxMdovBWR8sB"
      },
      "source": [
        "# The Date is set as the dataframe index. This is not very elegant!\n",
        "# Use the reset_index() function to make this the first column in \n",
        "# your dataframe and reset the index\n",
        "stock_history.reset_index(inplace=True)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Qj9FFRvTiIm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "898161aa-4ec0-4864-9a87-e5255976c2e1"
      },
      "source": [
        "# confirm the change\n",
        "stock_history"
      ],
      "execution_count": 15,
      "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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>46.144001</td>\n",
              "      <td>46.276001</td>\n",
              "      <td>43.799999</td>\n",
              "      <td>44.681999</td>\n",
              "      <td>44.681999</td>\n",
              "      <td>34135500</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-01-05</td>\n",
              "      <td>45.271999</td>\n",
              "      <td>45.377998</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.686001</td>\n",
              "      <td>44.686001</td>\n",
              "      <td>15934000</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-01-06</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.009998</td>\n",
              "      <td>43.195999</td>\n",
              "      <td>43.807999</td>\n",
              "      <td>43.807999</td>\n",
              "      <td>18895500</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-01-07</td>\n",
              "      <td>42.838001</td>\n",
              "      <td>43.688000</td>\n",
              "      <td>42.734001</td>\n",
              "      <td>43.130001</td>\n",
              "      <td>43.130001</td>\n",
              "      <td>17771500</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-01-08</td>\n",
              "      <td>43.571999</td>\n",
              "      <td>44.088001</td>\n",
              "      <td>42.153999</td>\n",
              "      <td>42.200001</td>\n",
              "      <td>42.200001</td>\n",
              "      <td>18140500</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1356</th>\n",
              "      <td>2021-05-24</td>\n",
              "      <td>581.599976</td>\n",
              "      <td>614.479980</td>\n",
              "      <td>573.650024</td>\n",
              "      <td>606.440002</td>\n",
              "      <td>606.440002</td>\n",
              "      <td>34558100</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1357</th>\n",
              "      <td>2021-05-25</td>\n",
              "      <td>607.309998</td>\n",
              "      <td>613.989990</td>\n",
              "      <td>595.710022</td>\n",
              "      <td>604.690002</td>\n",
              "      <td>604.690002</td>\n",
              "      <td>28005900</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1358</th>\n",
              "      <td>2021-05-26</td>\n",
              "      <td>607.559998</td>\n",
              "      <td>626.169983</td>\n",
              "      <td>601.500000</td>\n",
              "      <td>619.130005</td>\n",
              "      <td>619.130005</td>\n",
              "      <td>28639300</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1359</th>\n",
              "      <td>2021-05-27</td>\n",
              "      <td>620.239990</td>\n",
              "      <td>631.130005</td>\n",
              "      <td>616.210022</td>\n",
              "      <td>630.849976</td>\n",
              "      <td>630.849976</td>\n",
              "      <td>26370600</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1360</th>\n",
              "      <td>2021-05-28</td>\n",
              "      <td>628.500000</td>\n",
              "      <td>635.590027</td>\n",
              "      <td>622.380005</td>\n",
              "      <td>625.219971</td>\n",
              "      <td>625.219971</td>\n",
              "      <td>22737000</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1361 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date        Open        High  ...   Adj Close    Volume  Name\n",
              "0    2016-01-04   46.144001   46.276001  ...   44.681999  34135500  TSLA\n",
              "1    2016-01-05   45.271999   45.377998  ...   44.686001  15934000  TSLA\n",
              "2    2016-01-06   44.000000   44.009998  ...   43.807999  18895500  TSLA\n",
              "3    2016-01-07   42.838001   43.688000  ...   43.130001  17771500  TSLA\n",
              "4    2016-01-08   43.571999   44.088001  ...   42.200001  18140500  TSLA\n",
              "...         ...         ...         ...  ...         ...       ...   ...\n",
              "1356 2021-05-24  581.599976  614.479980  ...  606.440002  34558100  TSLA\n",
              "1357 2021-05-25  607.309998  613.989990  ...  604.690002  28005900  TSLA\n",
              "1358 2021-05-26  607.559998  626.169983  ...  619.130005  28639300  TSLA\n",
              "1359 2021-05-27  620.239990  631.130005  ...  630.849976  26370600  TSLA\n",
              "1360 2021-05-28  628.500000  635.590027  ...  625.219971  22737000  TSLA\n",
              "\n",
              "[1361 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nk4-N88VooQp"
      },
      "source": [
        "## Visualize the stock price history <a name=\"visualize-the-stock-price-history\"></a>\n",
        "\n",
        "The visualization code is a modified version of the original, preseneted by [Trifunovic Uros](https://trifunovic-uros.medium.com/) in [Medium.com](https://medium.com/analytics-vidhya/visualizing-historical-stock-price-and-volume-from-scratch-46029b2c5ef9) on Mar. 23, 2021.\n",
        "\n",
        "Start by getting a copy of the code in  the local path"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NmUA0JEMSCB2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aea26703-9cb3-4ede-86e9-c91eb8f59dca"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/georgiosouzounis/deep-learning-lectures/main/code/stock_price_chart.py"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-07-04 05:02:36--  https://raw.githubusercontent.com/georgiosouzounis/deep-learning-lectures/main/code/stock_price_chart.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2753 (2.7K) [text/plain]\n",
            "Saving to: ‘stock_price_chart.py’\n",
            "\n",
            "\rstock_price_chart.p   0%[                    ]       0  --.-KB/s               \rstock_price_chart.p 100%[===================>]   2.69K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-07-04 05:02:36 (35.3 MB/s) - ‘stock_price_chart.py’ saved [2753/2753]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9MafdA7SV8j"
      },
      "source": [
        "# import the get_charts() function\n",
        "from stock_price_chart import get_charts"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyWd_ja3Sqgu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "outputId": "57842b90-1ebe-4688-daac-0c2d57210ef3"
      },
      "source": [
        "# plot the contents of our dataframe\n",
        "get_charts(stock_history)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bDwB5sb6kXkfnaFsq+6OmzweqfkbrpRb9uwzLfro71TZqXxh7/uaxQ8wh83SlnlQ9T+rrvM/63In1o0v0WLp+LKxmesoJ0W1yekpt+nE2FnCokgkSBU6exc7nvrE+p/qXrs/E9p3r9/cCoCge3Ij68SMwFRjh2PK3IiIiIpLQ2AGOx6Lb5JSBXKSu9A9O89igRBsAwsD/OAz868PAPxC7+vcV8BfHVlSoC7Oi23SrO6Rd8aEam2QoJjoQmJ/tCiZpzMQGMNUt9QjwZXRbGgb+pAw/79Swj5w/o7oQC24MAg5ONxUnkhrU75LmsRHA52HlEo65ShVO7JXmsV5AfDpRMnD0R4Aw8OdiA6ARGfoHVgS2Nn6bOOZvo2OWAe8CO6QZeA7HgkypYxZiUzz2w86X1M/k6HEvur8+3/OkWWHgX40V1e2N1dcByyYqwOpnxPuSWlr0wuj+r6L7H2HTBNINPlOfWTb1bLKVa//6YAGONxPth2PTGr6kanbJdKBnIuhBdL8HVc+Tejnvczx3Uv3Ijx6L97kA+/cp7bnt2BLMBwAfhIG/Tpta9CN1DjRjXc0TtzW9d+VYAefafH97AvkZ/j9qHvWvsf/vFhEREdkgNfYfSamim+c6rvfrdA0c1xsarcCRybvYShvj4oGAaJ77edgfmk9F2zolimcSFbb8BhssFFA3JmLzrU+J/khP9WkzogFkji6M33Fc72AsUPJk+uY1i6Y+PI/VI0nWvyD2x/V7WKbNyekybRzXax4FEqozEZs//gfHlsZMPbcdVpx1ZdSmzkSFTSdiQZVDwsCvborBU9iV3tOjOf2pfRyATZ2pTd2UlH9Ht1WKwUb77ollPQBQQ+DoYWDL6HmpfTTD3r+l8f3kIgz81xLHjBcwfBj7XiRXuTkLK/yZqh9RBBya5if1vX0huv90rO8dHFuhoktNfXRcr1WmehvYVBWorGXwTYa+XB49Pj66/3r0+ldhgdbNou9V3CnRbcV7m0u/M8ipf8B1Gdp/Gr3mQ7HMg5SHo9uzqCp1P34u1+d5n+25Q/R7eZo+/y7aR6Z+HIN9/vdmeDzXfqSmKR4XbxhlHv0aywiZCRAG/kwsKHGo43o9Ym17YJ/JS4mphbl8fz/FglqHJvrRD1vJ6qM0q8KIiIiICI1cgyMM/FWO6+2Ppf8+6bjei9igdBF2RXg0Nsf6r9XsozSaXvIEMN1xvbuwwqWHY1fHrolWUAH7g/hsx/WewP5QLcZS3H8JPBIGfnIeem1f1yLH9a4ArsGWc/Wp/CP7C2An7A/6bCwEfhP94TwZK7B5KlZR//L17Orp2Jzz5x3XewC74liILZk7C7ggDPxyx/WOxgp8fujYqjGfRK+nP1aI7yKqWUUlDPyljuudj80df9NxvVTb46J9nBQv3FpHJmK1Gh4GOkY1IuKmpVbvCQN/geN6l2KFBSdFc+57YhkUn2E1ECo4rjcEODC6m8ocONpxvdS8+NtSrycM/NT+jnRc7zlsed0+2MBmLtl/hn/BBjwPOa53E3ZF+EhseckT46vbRH08HVtSEmwQ2MdxvdRyqh+EgT8hi2PeDYwDbnJsRZQZ2LK3BwNXhYE/K3qNxUCQfLJTuYrKV2HgJx8/GLgPm2ZyeQ396I59tx/EUvR/AfR1XO9y4BzsM0oFLJZl6MvC6NeP0vTl/7Aitw85rncbdu7vi2WkjE/Uvkjbb8eWcT06upvKVDrAcb1UFsi/w8D/Ntf+hYGfbhnS1OfbJ/lawsB/1nG9Z4Bzomk7r2NZBicAfhj4r8ba1tt5T5bnTtSPjxxb9er0qM7Ec1iG2hnAK0SrU6VxAhbk8TM8nlM/otd7DJbNty3wGraU9e+wc/C0qP5GypnYSl1To/MG7HudT5R9FZPL9/carKis77jenthSub2wgFsBdr6KiIiISBqNncGRuhK2AzZQaYPNT78L+wOxDCvyd3EN+5gAONgf5edhf0wWYH84xp87OfrZH7syej02GDkX+8O2zoSBfy0WQGgX9ee46HjjoybZBlOKsCKHraL9nIhdEd8tSn1enz5+gwVb7sVWQvkbNv+7I/BirN372GfkYwOc27BlTkdggY0wi2PdgQVDlmJ1BS6Lfj84DPy71ud1ZDA0uj0Sy6JI/oxK9O9GbCDUCVvZ5RRsqcg90qTp74gt9/pnKlfYOD62LVmz4RgsC2dL4OboOAG2ZG5WNQ7CwF+EDSqfxApu3gp0AI4IAz/dFexzY/1pidUOSN0/JMtjrsUG/v/E3sfbsVomfyC71V/qSioQNAgbhJ4HHIZ9px4Bxq5PcDIM/O+wc/lx7LO5Bfuszo3uZ6Mfle/vEdG238S29cvwvPpwKHA19tndAeyFfV7HJxvW13lfi3PnLOz9Hhy1PQL7d2b/aKpJFY7r7Rrt7/Ew8JdkeiNy6UcY+N9iU1f+jQXXb8O+t99jWWB3JNpPw/5tnoUtL/xnLHA+Kgz8DxJts/7+hoE/HdgVy3g6BPsM/4AFoJ0w8J/J9HpFREREfu7yysuzTSSQuhBd6Tsd6J5hdZR428lA3zDw+zZA10SaBMf1jsO+F5c3bk9ERERERGRD0ugZHBureO2N2Lbu2NX8j2sKboiIiIiIiIhI9hq1BsdGbk/H9a7H0t5/wKYI/A5oS6JoqIjk5H0qVyoSEREREREBFOCoTzOx5Wd/B3TGiuG9DVwbBv6kxuyYSFMW1YQRERERERGpQjU4RERERERERKTJUw0OEREREREREWnyFOAQERERERERkSavSQU4HNcrd1zv/sbux8aqvt5fx/Uuj/bdt673LSIiIiIiIgIbQJFRx/W2wFYVGQX0BtYAPwJvAfeHgf9yI3avwTiutwlwFjA5DPzJjdydnw3H9Q4EDgZ2ATYHlgGfAjeEgf9ChuccA5wNDACWAxOAi8LAX5BoNxY4BBgKbAu0AkZX9/k6rjcIuAQYDXQCFgDTgZPDwJ+X5WvqAfwF+BW2as8nwHVh4D+aaNcW+GPUvx2BnsArYeDvmc1xEvvqAFwF/AYrqvsV8HfgzjDwMxb6cVyvNfAx0A+4PQz803M9dmJ/f8H+LekPdADmAx9gn+fkGp7bHfvsNwHOCwP/hiyPeVJ0zKHAVkB+GPh5Gdr+ETgA2Br7fBcDnwG3hoH/RJr2md67ojDw22bZv8nAHhkeHhYG/tuJ9ll/lo7r5QNnAidhK0UtAB4B/hQGflE2/RMRERERqSuNmsHhuN5OwEfAYcCL2KDxCuAlYFdscPhzsQlwGbBnI/fj5+YuYCQWpDgTuAUb6D/vuN7FycaO650NPIAFQs4E/gkcAUx2XK9NovlvgeOBZsCMmjriuN4vgXeAIcCtwCnYwDIfaJ/Ni3FcrxPwKjY4/UfUx5XAI47rjUs07wJcDgzHggAl2RwjzTFbAhOBk4H/An8APgfuwM7p6lwJdK3NcTMYAXwIXIe9f/8AtgBedlzv6Bqeexu1C/peBByIBVPm1NB2OLbE7c1R/24EWgOPO653aYbnTAWOTvyckGMfF6bZx9HA1/FGtfgsbwZuwgJDfwAeBc4AJkTBDxERERGRBtPYGRyXYX/cbx8G/gfJBx3X26zhu1Rx7BZAszDwf2qsPtTEcb12YeCvaOx+NHFHhYH/UnyD43p/B94DLnNc744w8JdE27tgV7anA04Y+KXR9unA01gw4ZrYri4GTgoDf43jeucC22fqhON63YCHgMnAgWHgF9fy9VyIZUMcGAb+hGjf9wKvAzc4rvdoGPgro7Zzgc3DwP8harcy3Q6zcCIwDDgjDPzbom13O673GPB/juvdFwb+t8knOa63I5a1dD420F9v6bJPHNe7FctCuAj4d7rnxTJ5LgT+muNh9wS+CwO/zHG9Z4Be1fTv8DTHvgULbJ3vuN41qfMq5usw8P0c+5RUlOU+sv4sHdcbjAU1Hg8DvyIY7bjeN1iA7gjsnBYRERERaRCNHeDYCliULrgBEAb+j+m2O663C5aCvxPwE/AEcFZs4IbjegOwK4l7YFNfUlfR/xEG/j2J/V2OBVu2wa6MHgZ0BxzsynwrLJX/t8CW0TGnYmnY78X2kx8d83hskFmODSJfxaYYpB20Oq63J5CainOZ43qpK6XfhoHfN6pd8Q2W3TIDGxAOwq6wHpfLa42ONxgbUO6OTQl6HsueSctxvcOxgcx20b4/Aq4PAz9ItMsHLgB+H71/M4FrM+23muMdBJyHBQTKseyCv4aB/1Si3SzsavjJ0esZBZRhV6BPz3T+xCWDG9G2VdFA9RxsKsEb0UMHYQG52+KD0DDwJziu9zXgEQtwhIE/O7tXDNFr6AScHwZ+cTR1o7gWgY6jgK9SwY2oH6WO690GjAf2xaYQEAb+GuCHHPef6ZirgLsT22/BMkkOJxE0cFyvWdT+BeBxMgQ4oukS3YGFYeAvrE3nwsBf6bjeIqBjhmO0A27Hsj2m12L/s2rTr9jzSxzXm41NY2oBJAMcqcyKlvF/43IVfT/bAiuqmTaUy2d5JJAXPRZ3N/bvs4cCHCIiIiLSgBo7hfgroLPjer/J4TnbA89gA5FzsKktJ2Bp0nF7YgPeZ7DB8qVAMXY18qIM+34Qq8VwIxbQmBtlcryABUBexwIBf8ECDK9F02xSLsZStmdhA/3zsODLLlj9hUxmUBlgeILK9PGzEu0OwgZhL2ABjedzfa2O6/XDgjO7Y9Mf/oRNVchUb+Iq4D/Aimi/F2IDoEcd1zst0fwmbID/HRaEeRIbOB5YzWtPHu9U7D3ohE1f+HP0+5OO6/0+zVN6YlkP30Wv/SFsIDY+22NmkLoKH697MSy6fT1N+zeAAVFdi9rYF6vnsYnjeu8DRcBPjutNdVxvWPVPNVENiZ5UBmSS/YPK11AnokHzjsB7abKd3sICVOmOmaphUlPNjYOx70dOtTkc1+viuF43x/W2izJyBgLPZWh+LRa4W2dKUn1xXK+T43pdHdcb6Ljen4B9gJczZIy52HduheN68x3Xuy0K/OSiJzZVaRmw0nG9x6PAaLxPuX6Ww7CA4lvxhtFz36eOzzURERERkZo0dgbHVcBY4DHH9b7EMh2mY4U2M9UsGALsEgb+m9H9fzqu1x4Y57jeObErnP8OA//O+BMd17sZq+9xoeN6N6S5Or4UGBMGfknsOWdjAYR9wsD/X2z7HVhxxBuorJtxMDAjDPzkgP7C6t6EMPDnOa73JBYc+bCaVPLBwJA0700ur/Vq7Er2XqkCro7r3Y5dRd8hsY8dsUHftWHg/1/soVuj/l7ruN74MPBXOK63NRZ0eQnYOzZ943GgShHDTBzX64hdHf4K2DkM/OXR9n9gU0ZudFzvkTDwl8ae1h84PAz8R2L7KQNOdVxv6zDwP8/m2Il+bIcFSaaGgf9N7KEe0W26zIzZ2NXsHsAXuR4TyxRpjgWaHsUCO32xgqOTHdcbHgb+JzXso6b+gQ1061JHoDDdMaOpOQuTx4yCbFcAV4aBP8up49V1oiBTvODraqzWyjlp2o7AamEcFQb+Msf16rIr1fkCK+AJVvvkMeDUNO3ews6HmVgdln2xYM8ejuvtmmVGxzfAa1htklJg52gfjuN6u4WB/1HULtfPsgeWWbMmzTFnA7s6rtcyDPy1WfRRRERERGS9NWqAIwz81x3XG4plS/wKGBf94LjeVOC4MPC/Tjzt9VhwI+Ul7A//vljQgXgFf8f1CoA22AD0RWwqxwBsqkXcLfHgRsTDVjl4J6rBEDcRONZxvcIw8FdjV0e3jAYNr2bxFuTq2XSBn2xfa3SF9gDg7fjqNGHglzuu91csQyTut9hV2wfSvPangV9j2SkvRr/nATclpm+867jeRGDvLF7f2Kjvt6aCG9E+lkd1FG4BxgDxqTFz4sGNyEvYYHErrEBi1hzX64oFe1Zj9QjiWke36QZ0PyXa5KodlkXwYBj4x8X68w42felP2PSA6tRn/2pzzNRxk8e8Eytumcy6WkcY+PcD9+fYp9XYudQc6IOdx22jfsS/Ky2w6RQTw8D/b47HWF+/AQqwgMGhWGChHVUDM4SBv3PieeMd1/sQC1SeGd1WKwz8ZHHZwHG9p7HMp5uw9wpy/yxb19A21UYBDhERERFpEI2dwUF09fA4AMf1+i+nz+0AACAASURBVGAD8hOxKRRPOa43NHEFMBnwAFgU3aauiKau4l6O1dPYPM1z0s3HT3flfSA2+FiQ5rGULsD3wP9h0zKmOq43BxtAPAsEdXQVM21mQA6vtRs20PssTZtP02wbiAUt0rVP2TS63SK6zbTvbAIc/aLbdJkKqW1bJLZndT5kw7EVSCZiV6b3CwM/+X6vim5bYYPouIJEm1ytxj6b++Mbw8Cf7Ljed8RW13HWLb67Ngz8xYn+Ja1X/6LAT7PYptJoWdzqjpk6bsUxHdfzsAH1qPUopFqtKMA2KXbMe7Dv4kuO6+0YO+4FWAZQMrBX78LAnxK7e5/jeg9jU94GpYraVuN6bMrcfmQR4Mhw/KmO600BRscCtDl9ltHv3appm2ojIiIiItIgGrsGRxVh4H8bBv54LMjxGlb0c3ii2ToF+GLyYr8/hKWkP4ddwd0HG1jdHD2e7rWn+2M8D8v0GFvNz4Ko/69jRUhdrI7E9lhdj/ejwfP6yjRYqM1rzUYelsGR2l+6n0kZn90wsj0fqhV9PpOwbJeD0hUfpXIJ0HTTPHpi71VNy4Rmkir2ma4w6lyqBuTmJn4ez7J/kH76SjamJ46ZKsa5BAvOrHPMqDhvl9Qxo/s3Yefpj47r9Xdcrz+WZQHQIdq2SS37mFYU8HgQ+/dkVNSX7tj0qweAvFhfUq+jc7QtufRvfXkA2AzL7KhWFKCZg72362MWFrRKnVtZf5aROUCX6LGkntj0FWVviIiIiEiDafQMjnSiKRNvAiOpRc2AaIC0P1ab4uTEY2Ny3N2XQFfgpTDwy2pqHM2Jfyz6SRXNvB0rhHp9NU/NtKpBtXJ8rQuwQoMDWNegNNu+xIIb31VTEyUllUkxAKuhUdO+q9vHYCDMsI90GRvrJRbcGAQcHK+1kjAdWyFmF6wmQtwI4PP1WOXiLey960U0zSqmFzA/dn9s4vElAGHgz41W4xiRZv+pbVnVQ0njt1gmU8rq6Jhljuu9C+zguF6rRD2G4ViQKXXMQuy7tF/0k+RFP+dhtW3qUqrvqUDjpliWwUnRT9KF0c+hVJ0SVV+S/csomobWi/TFZHOxFVb/YzHk/FmCfR/2jh6bmujf9kA8S0VEREREpN41agaH43pjHddbJ8jiuF4hlVMa0k2dqEnqqn6VK/jRVdtkXYWajMeurK5ToDDa56ax39NdUX03uq1p4JIaGOea6ZH1a42uZD8D7OS43uhY2zxs1ZOkf0e31zi2rGcV8deO1eQoB86Jt40KlWYbVJqI1Uj4g2NLd6b20Q5bpnZl1KbORIVNJ2JBlUPCwH++muZPYQP70xOv8QBs6syD69GV1HudDFIdgAX5KlYACQN/UuLnndhTHsbqwBwQ20cz7P1bSuaVRKoVBv5riWO+ljhmayz4E3cWNoBO1bcowgIGyZ9Ucc0XovtPx/rewXG9ARm+W1U4rtfRseVUk9vbYAHG+Iof32Toy+XR4+Oj++lWzKkVx/XaOGlW2Yk+n9SKRG/EtmeaYvVnLDg9IcPj8X13yPDd3Q8LIE9MrJiS7WdJ9Hs566729LtoH+vzfRARERERyVljZ3DcjKWCP41NA1mF1ZA4CvgFMD5W4T9r0aoeLwKe43qrsSuNfbArtd+QW22Gv2FXzK93XG8vrIDlcqA34GDF9FLBghmO670BvImlb3fHBgprsaVWq+vzIsf1ZgJHOK73FbY8aVEY+NUOYmrxWi/BCro+47jebdjUiAOwK+vJfU93XO9ybND3vuN6j8Ze11CssGvLqO1njq3GcjpW6+AxbH7+6cAHJFZoyfBaljqudz6W8fKm43r3Rw8dh9VKOCkM/GU17SdHE7GlMR8GOkY1IuKmpQrdhoG/wHG9S7HsgklR3YSeWJHcz7AiqBUc1xtC5RK5I6Pbox3X2y36/bbU6wkDP7W/Ix3Xew4LRPXBAhNzqRx41+Qv2MD8Icf1bsKmFByJLdl5Yhj4KxJ9PB1ITQlpAfRxXO+S6P4HNZ1/kbux4sA3ObYiygzs3DgYuCoM/FnRaywmTTaEU7mKyldh4CcfPxi4D1t15fIa+rEHtqrSY1iGzQqsrsvRWMbDFWHgfxv1ZVmGviyMfv0o2Zfou3AZMC4qfprafgCwXXS3f7Qt9R4uDQP/79HvWwGvOK4XYMVvF2Pnz5HYKjoPhIFfkQkBXOLYKi8vY8sgt8Xe19HYvzG3Jfp3HNF7FQb+5dHm0djnMgHLfirBMi48YCHrBiey+izB6ielvvOOrZb0HFa35wzgFWzqnIiIiIhIg2nsAMc52OobuwGHYAOtZdhyhteR++oJcR422DsAOBabbnExUIwNArISBn5xdLXzVGygdEX00BzsavADseY3YoOBM4AO2LSCN7BlVj/I4nC/xYI+12BXQL8li6u05PBaw8D/ynG93aO+/gFbBeH56LXNS+44DPwrHNd7O3pNZ2GrnMzHplGckWh+JlZD4vfYdJwvsSvTW5FFgCM63h2O683FpilcFm3+AJs68mQ2+8jR0Oj2yOgnaRyxaTFh4N/ouN4i4GzgVizY9QhwYZrpKTtiV9vjjo/97mPne8ox2Gs9HjsPUoPwi8PAz6q2RxQoG4mdD6dhg+JPgSMyrBRyLpU1MMBWIkr1+QGyOP/CwF8bTYe6CnsPO2PTlP6ABasaykdYf/fEvkutsYKz04GTw8B/dj33n8oqStYxOQT73sWl3sNvgVSA4wcsU2d3LGDQDvuM34vaJwMCk7FpU8di72kpld/tmxKZF5n69zk2rWR/bFpOi6gfdwLXhIFf5bXU4rM8C6vl8Xts2tFCLPDyp2ym9ImIiIiI1KW88vJalX4QEflZiepTrAgDf4/G7ks6URbFtsCg+lqhRkRERERkQ9bYGRwiIhs8x/W6YdNQdm7svqQT1TLaCzhBwQ0RERER+blSBoeIiIiIiIiINHmNuoqKiIiIiIiIiEhdUIBDRERERERERJo8BThEREREREREpMlrzCKjKv4hIiIiIiIiUg/y8vL+V15evk9j96MhKYNDREREREREZOPTpbE70NAU4BARERERERGRJk8BDhERERERERFp8hTgEBEREREREZEmTwEOEREREREREWnyFOAQERERERERkSZPAQ4RERERERERafIU4BARERERERGRJk8BDhERERERERFp8hTgEBEREREREZEmTwEOEREREREREWnyFOAQERERERERkSZPAQ4RERERERERafIU4BARERERERGRJk8BDhERERERERFp8hTgEBEREREREZEmTwEOEREREREREWnyFOAQERERERGR9VK0BsrLG7sX8nOnAIeIiIiIiIjU2owfYdh1cOmExu6J/NwpwCEiIiIiIiIZzV0GFzwBX85P//iFT8LqYnhwesP2SyRJAQ4RERERERHJ6LzH4ZF34dB70j8+Z1nD9kckEwU4REREREREJKNvF9vtstXpH19T3HB9EamOAhwiIiIiIiKSUcvm1T/+U0nV+7MWKatDGkcNp6qIiIiIiIj8nLVolvmxklIoLq28v3otjL7Ffv/mz/XbL5EkZXCIiIiIiIhIRi2rCXAsSUxbWbCy8veysvrpj0gmCnCIiIiIiIhIRtVNUVm0sur9pbGAxyrV5pAGpgCHiIiIiIiIZFRdBseioqr3F8fur1xTP/0RyUQBDhEREREREcmo2gyOosz3v14Iv7kLnvygfvolkqQioyIiIiIiIpJRLhkcy3+q/P3sAOavgPe+h4O2q5++icRtMAGO5cuXM3/+fIqLNVHr56hFixZ069aN9u3bN3ZXREREREQkprpVVBYmanAsi9XgmL+ifvojkskGEeBYvnw58+bNo2fPnhQWFpKXl9fYXZIGVF5ezurVq5k9ezaAghwiIiIiIhuQ+BSV8nKID9cWr6raNp7BkVLQon76JZK0QdTgmD9/Pj179qR169YKbvwM5eXl0bp1a3r27Mn8+fMbuzsiIiIiIhKTHxui3RjCS59X3k+topLK8kgX4KhuiotIXdogAhzFxcUUFhY2djekkRUWFmqKkoiIiIjIBqa0rPL321+BE/zK+6kaHD062O3y2BSVlOU/Vd2HSH3ZIAIcgDI3ROeAiIiIiMgGqCRNcKK8HG4K4d3v7X7PTew2XQYHwIoM20Xq0gYT4BAREREREZENT7rsi7nL4bbJlffTBTia51dmdixNk9khUtc2iCKjG6MXXp7C9bffVXG/sKCA7pt2Y78xe3LA3g7NmmWeiJZ67oN33Mxm3bo2RHdFRERERETSSpfBMe2rqve7tLHb1Coqg7vDP4+CcePt/tqS+uufSIoCHPXsT388g66dO7Fq1Wpeef1Nbrt3PEuWLWfcEW7G54zYcXtuu+ZyOnXcpAF7KiIiIiIisq50GRyvxgIcrVtWrpSSmoqyY2/L6mgeXddNFyQRqWsKcNSz/n1707P7ZgDstP22zP5xHk8897+0AY6SkhKaNWvGJh3as0kHLZUqIiIiIiKNL20Gx9eVvz97Krw4w35fscZuUyunpFZXWVtaf/0TSVGAo4Ft3X8LPvhkBp99+RWnXXQZZ5x4LD8uWEg45TUWL13GE/fdyWvT30k7ReXZiS/z9IuT+G72HFq2aMEWfXpz4lGHMXjALwD4ac0axj/yBK+8/iYLFy+mS6dO7OvsyZEHH0B+vsqtiIiIiIhI7tJlcCyIlod9cBz07QytEiPL1P1UgKNYAQ5pAApwNLAf5y0gPz+fgoICAB58/Gm23nILzj7pBMrKymjZokXa5935wEM8OuE5fuXswbGHH0J+Xh4zvpjJvIWLGAyUlpZywZ+v49sf5nC0exD9evdixpdf8e/gSZavXMkpx/62AV+liIiIiIhsLDJNL8nLg2162O8FiWFMYXQ/FeAoUYBDGoACHPWsrKyM0tJSVq3+icnT3uDVt6YzYuiOFLRqCUDHDu258vyzql0idfbcH3ns2ec5ZP99OPU4r2L7iKE7VPz+0quv8/FnX3DzlZcwZNAAAHYcsg0A4x99nCMO2p+OHTrUx0sUEREREZGNWLoMDoAtu0B7u25LQWJk2b7QbjVFRRrSBh3g6HdpY/fAfPPn2j/3uDPPr/g9Pz8PZ7eRnDrOY9VqKy88cvhO1QY3AN798BPKysrZf+xeGdtMf+9DNu3ahcFbb0VpaeW/HkO324Z/PfwoM76Yya7Dhtb+hYiIiIiIyM9SpgyO7XtV/p6cotIhCny0yK9+HyJ1aYMOcGwMrjj/LLp26kTrwgI27dqFli0tcyMV4Oi8Sc0rpSxfaRPcunbqlLHNkuXLmbdgIXsffmz6faxYmWvXRUREREREMmZwbBcLcLSoIYOjWMvESgPYoAMc65M5saHot3mvilVU0qo+eQOA9u3aArBw8WI279kjfZu2benerSuXnvOHtI/Hi5WKiIiIiIhkK1P9jO16Vv6eCmSkpKauaJlYaUgbdIBDzNAh25Cfn8czk17OWCx02A5DmPrmdAoLC+idIQgiIiIiIiKSq3QZHC2bw9abxu5nCHCoBoc0JAU4moAem23KIfv9iuCZ51m9+id22WlH8vPz+XzmV2zeswejR45gzO678r+Xp3DuFddy6AG/Yss+vSkuKWXuvHlMm/4uV15wNgWtWjX2SxERERERkSYmXfbFtj0syJGSKYNDy8RKQ1KAo4k4+dij6LnZpjz1v0m8OHkqBQWt2KL35gzdblsAmjdvznWXnM/DT0zg2Ykv8+P8BRQUtKLHpt3YecftadFcH7WIiIiIiOQumcHxhz1hn0FVt9UU4NAysdIQ8srLyxvr2BUHnjFjBgMHDmysfsgGROeCiIiIiMiGZeQNMGdZ5f2PL4E2ieTwGT/Cvrfb7y2aweeXQV4eXPYMjH8T/rQvjNul4foskJeX9055eflOjd2PhpTf2B0QERERERGRDVcyg6NlmuTweA2O9gUW3IBYBoeKjEoDUIBDREREREREMkoGJ5qnGUW2SAQ4ktuveQG+X1L3fROJU4BDREREREREMkpmcKSyM+KqBDgK028/6aG67ZdIkgIcIiIiIiIiklG6ZWKTasrgAKvTIVKfFOAQERERERGRjLKpn5GswZGSnM6i1VSkPmntUBEREREREckolcFx1QHQc5P0bZpnCHAsKqra7uuF8ItN67Z/IikKcIiIiIiIiEhGqQyOI3aCZhnmAMSnonSI1eCYvbRqu4/nKsDRVDmudzZwIlAOfASMA7oD/wE6A+8AR4eBv9ZxvVbAeGAosAg4PAz8WfXdR01RERERERERkbTKyqCs3H7PT1NcNCU+FaVtq8rft+hStd0nc+qub9JwHNfrCZwB7BQG/jZAM+AI4Drg5jDw+wNLgBOip5wALIm23xy1q3cKcIiIiIiIiEhapVFwo3l++tVTUuKPtW5Z+fspo+D8sXDl/nb/47l130dpMM2BQsf1mgOtgbnAXkAQPf4AcFD0+6+j+0SPO47rVXMG1Q0FOERERERERCStVP2NTFNT0iloUfl721YW5NhnsN1/axYsW11n3ZMGEgb+bOAG4DsssLEMm5KyNAz8kqjZD0DP6PeewPfRc0ui9p3ru5+NVoNj+fLllJTY+1BWVlbxu/y8lZWVsXjx4sbuhoiIiIiIAEVrATrRLK+cxYuX1NC6EwCla1ayePHaKo80iz1+0J2lPHbssjruqSS1bFXQxXG9t2vx1LvCwL8rvsFxvY5YVkY/YCnwKLDP+veybjVagKN9+/YVv8+bN4/mzTeeeqcvvDyF62+38+H+W69n8x7dqzz+wSczOOeyqwH4658uZOiQbdbZx3lX/oV3P/yYU8d5HLJfbufN3+8dz5x587nm/86t2Oa4Xtq2/7z+avr361Nxv6ysjP88+QzPTHyJxUuXsXmP7hx96EGMGjG8os2aNWvxTjub044/mj13HZFT32qSn59Pp06d6nSfIiIiIiJSO82ibIvmzfKy/ju9a8e2pGu6SSEsXQ2zFjfT3/wNYO2anxaGgb9THe1uDPBNGPgLABzXexwYCWziuF7zKEujFzA7aj8b2Bz4IZrS0gErNlqvNp6owgaodWEBk155lXFHHlpl+4uTp9K6sIBVq39K+7wFixbx/sefADDxlVdzCnDM+XEeEyaG3HrVZes89svRu7P/WKfKtl49Nqty/77/BDz69HMcf+ShbLVlP15+7XWuvPE2rr7oj+y84/YAtGrVksN/vT/3PvQIuw3faaMKTomIiIiISKXUCirNc5ii0q4g/fb7j4GD/mm/l5dXX9NDNjjfASMc12sNrAYc4G3gZcDFVlI5Fngqav90dP/16PGXwsAvr+9OqgZHPdpt52FMmjqN8vLKz3HNmrVMeeMtdh8xLOPzJr7yGmVl5ey843Z8+fUsvvnu+6yP+dizL7Bln95s3X+LdR7r0qkTg37Rv8pPQavKEsdLli3j0aef44iDD+CwX+/HDtsM4pyTTmD7wQO52/9vlX39cvQoFixczKtv1SbjSUREREREmoJcanCcOwbGDICd+6Z/fLteUBjV51ixpk66Jw0kDPw3sWKh72JLxOYDdwEXAOc4rjcTq7Fxb/SUe4HO0fZzgAsbop+69F6Pxo4aycRXXuWjGZ8zZNAAAF59623KysvZfefh/O/lqWmf9+LkqfTdvBenjjuaN9/9gBcnT+WkY46q8Xhri4uZNOU1jnYPrlV/337/I4pLShgzamSV7WNGjeT6O+5m7rz5dN+0GwDt2rZhp+235blJk+t8moqIiIiIiGwYcsngOG2Pmtt0aQvfL4GFK6F9hkwP2TCFgX8ZkJwq8DUwPE3bn4BDk9vrmzI46tGmXbswZODWTJryWsW2ia9MZbfhO1FY0Crtc2Z8MZPv58xlzKiR9Oq+GYN+sRXh1GmUpkKn1ZjxxUxWFq1i24Fbp3386f+F7HPEcex71PH88fJr+PDTz6o8Puv7H2jRogU9N9u0yva+m/cC4NsfZlfZPmTgAD789DPWrq1aQEhERERERDYOtVlFpTpd2trtgpV1sz+RuA0/g8Pvl/mxna+GraLMhi8fgjcvztzW+6by9+cOgMUfp2/X/wgYca39vugj6Lxtbv1NGLvH7tw5/kFOP/5oVhQV8c6Hn3DtxedlbP/iK1PJz8+ryKLYe8/duOWu+3jnw48YvsN21R7r0y9mkpeXxxZ9eq/z2JhRIxkxdAc6d9yEeQsX8shTz3HuFdfy10svYPttBgGwYmURbdu0Ji8xGa5d2zYVj8f179eH4pISvvx6FoMH/KLmN0NERERERJqU2tTgqE7vjvDe9zBrUeapLCK1pQyOerbHrsMpLi7h9bffI5wyjU6bdGDHbQenbbu2uJiXX3uD7bcZTNfOVlV49MgRtGjRghcnp5/OErdoyRJaFxbSosW6cauLzjiF0SNHMGTQAMaO2o2/XXUpnTtuwn3/CWr92jpEK+EsXLK01vsQEREREZENV2mp3dZVBseWXe125oK62Z9I3IafwRHPvKjOVkdVZnPUZN8J2bVbz+wNgNaFhYwcPpSJU17lx/kLcXYfSX5++n8dXn/7PVasLGK34UNZWVSZLTFsu22ZNv1dilatok3r1hmPtba4OG1wI1O/RgzdnufDVyq2tW3ThpVFqygvL6+SxZHK3EhlcqS0atXSjqspKiIiIiIiG6W6zuDYsovdfrOwbvYnErfhBzg2AmP32I2Lr72BsrJyLjn7tIztUlkat97zALfe88A6j0+e9ib7jRmd8fnt27alqGhVbp2LBTL6bt6T4uJi5vw4j57dK5ePnRXV3ujTq2eVp65YYRPn2rdrl9sxRURERESkSajrGhwdo+u1K+OrqJSugWZpahSuXQFrl0GbHpCnyQdSMwU4GsDQIduyxy4707ZNm4qCnUlLli1j+vsfsuuwoRyy3y/XefzqW27nxclTqw1w9O7Zg+KSEhYsWkTXzp2r7VPRqlW88c77DIgtJztshyE0b96McOo0jjnsNxXbwymv0a93r4oVVFLmzre8ss17dq/2WCIiIiIi0jRVZHA0q5v9FVoSOKvWYoGN966DuVNh32ehWUuY+yosfB8WvAtzXrbGHQfCbrdBhy3rphOy0VKAowE0a5bPJWefXm2bcMo0SktLcfffh+0GD1zn8b333J3/PvUsc+bNp0ci0JCSWor2sy+/rhLgeOSpZ/l+zly232ZQRZHRR59+jsVLl3LRGadUtOvYoQPu/r/ioScmUFhYwFb9+jJ52pu89/Gn/PmCc9Y53mdffkWXTh0z9kdERERERJq2us7gaN3CbletBd69Bj4fb9kZ89+EbjvbwhErv7NG+S0ts2PJDHjjAtj70SoZ6CJJCnBsIF58ZSo9NutWEaRI2mevPfjPk88wcfJUjj38kLRtNuvWlQH9t+T1d95l9xHDKrb36tmdV996m1ffepuiVatpXVjINgO24txTfseArapGQY8/8jAKCwp4/Nn/sWTpMnr16M6l5/yBXXbaYZ3jvfHOe4weuct6vGoREREREdmQldR1gCPK4OhQ9gN8+TCQB2Mfhm7DoeQn6HcwfP8CdN8NBv4emhfA02Og/ZaW8dG8oG46IhulvPLy8sY6dsWBZ8yYwcCB62YtSO5eeHkKd9z3bx65++8UtEozj62OzPhiJmdccgX/uuWvbN6j7qao6FwQEREREdlwTPsafnsfjOgHDx+//vtbXAS/vH4BN/S4iD1ah9DvIBh5c/VPKi6CFm2qbyPryMvLe6e8vHynxu5HQ1Kllo3M2FEj6dyxI0+/MKlej/PwkxPYe4/d6zS4ISIiIiIiG5a6zuBos+Zrwn6OBTfyW8KQs2p+koIbkiUFODYyzZo147zTfl+xhGt9WLNmLf379uH4ow6tt2OIiIiIiEjjK63jZWJbLnmPVnlreGf1jpSMeRTa9cn+ye9dB8/sAyt/qJvOyEZHNTg2QoN+0Z9Bv+hfb/tv1apllVVWRERERERk41RSard1lcGRt+UhjHp4b8pKfiJs35X2uTx56Rew9HNY8A60Tb86JUD4OXRrC9v2XO/uShOjDA4RERERERFJq64zOADKWrRjYWlXVq/N8Yldh9rtgrczNpm9FE704cA7a98/aboU4BAREREREZG06rQGx9oVUFZKm2g2/ariHJ9fEeB4J2OTRUW165psHBTgEBERERERkbTqLIOjvBxePxce3YF9Wk8AyD2Do/N2kN/CpqmsXZG2SV1mmkjTo49fRERERERE0qqzDI5P/wnfvwhla5mdPwiAVbkGOJoXQKfBUF4GC99L2yTez/LyWvZVmiwFOERERERERCStVBCizfos0vjjNFsBBWDXm1jSYkvbd65TVCCraSopxaW12L80aVpFRURERERERNIqigIcrdcnwPHts3Y76PfQZ19at7C7q9bUYl/ddoY1S2Dro9M+nJpSA7CmBFpqxPuzoo9bRERERERE0qqTDI5FH9ltz9FAZbCkVhkcvcbA5mMzPlySCHC0q8UhpOlSgKMevPDyFK6//S4A7r/1ejbv0b3K4x98MoNzLrsagL/+6UKGDtlmnX2cd+VfePfDjzl1nMch++2T0/H/fu945sybzzX/d27Ftnse/C9ffPUNX3w9ixUrV3Leab9nn9Gj0j7/2Ykv8+iE5/hx/gI27dYFd79fccAvnXXavfrW2/z7kSf4dvYcOnZoz35jRnPkwQfSLDbx7dLrbqZLp00483fjcnoNIiIiIiLS+FIBjtatarmD0jWw9DMgDzrZuCcV4Mi5yChAXl61D5fEpqWsKanF/qVJUw2OetS6sIBJr7y6zvYXJ0+ldWFBxuctWLSI9z/+BICJaZ5fnTk/zmPCxJBjD/tNle1PPj+RNWvXMmLo9tU+/9mJL3PzXf9i9xHDuPaS89ljl5352z338/T/JlVpN/39D7nihr+xdf8tuPbi8/jNfr/Ef+wp7n3okSrtjjn0YJ6dNJnv58zN6XWIiIiIArtdnQAAIABJREFUiEjjK1rfDI7ildBnP8veaNEWWM8MjpSZj8Bzv4bv/ldlczKD48PZ4PwNps5cj2NJk6EARz3abedhTJo6jfJY+d41a9Yy5Y232H3EsIzPm/jKa5SVlbPzjtvx5dez+Oa777M+5mPPvsCWfXqzdf8tqmx/evxd/O2qP3G0e1DG55aWlvKvhx9lzKiRnHDUYeywzSCOP/JQfrnn7tz3n4CSksoQ6D3+f9lmwNacc/IJ7LDNIA49YF+O+s2BPPbs8yxesrSi3VZb9KV/vz48/uwLWb8GERERERHZMKTqZNS6BkdBZxh5M4y+t2JTYVSD45oX4LT/1HK/q+fD4g9h7tQqm+M1OH4qhlP/A18vhGMeqOVxpElRgKMejR01knkLFvLRjM8rtr361tuUlZez+87DMz7vxclT6bt5L04dd3TF/WysLS5m0pTX2Gu3Xdd5LD+/5o/6k8+/ZOny5YwZNbLK9jF77MbyFSv56LMvAJi/cBEzZ33LmN2rths7aiQlJaW89d4HVbaPHjmCcOo01qypTQ6aiIiIiIg0ljopMpoQ39dzn9RyOdfNorHIj69V2RzP4NjvDpi7rPJ+0RooK0M2Ygpw1KNNu3ZhyMCtmTSl8ks38ZWp7DZ8JwoL0k9im/HFTL6fM5cxo0bSq/tmDPrFVoRTp1FaWvM3ccYXM1lZtIptB25dq/5++/1sAPr17lVle9/Ne1Z5fNb3P9j2RLvum3ajoFUrvv1hdpXtQwYOoGjVaj794sta9UtERERERBrH6mgaSa2nqMx7A1bNqxLFKEzsK4uhzro6b2tTXlbMgqI5GfdVFgue7PgXOCuoxbGkydjgi4zmLboh42P/bDOW3xdsB8BdP33ASUUTM7Yt71xZcHPo0n/zbum8tO1+12oId7XdG4B3Sn5kaPPNatPtCmP32J07xz/I6ccfzYqiIt758BOuvfi8jO1ffGUq+fl5FVkUe++5G7fcdR/vfPgRw3fYrtpjffrFTPLy8tiiT+9a9XX5ypUAtGvTpsr29m1trtyK6PEVK4usXduq7QDatmnN8ujxlC379iY/P49Pv5jJDtsOrlXfRERERESk4a1XBkfpGgiPgbISOPzDihocyWBJaRk0b5bjvvObQ7fhMPslePcvsMtfoXlBlQyOpLUlMOEjuPWwHI8lTYYyOOrZHrsOp7i4hNfffo9wyjQ6bdKBHTMM8tcWF/Pya2+w/TaD6dq5E2DTO1q0aJHVNJVFS5bQurCQFi02rLhV8+bNadO6NYtitTlERERERGTDt9CucdI+8xoJmS39AsqKoX2/iuAGVNbgSKkuKFGtQb+DZgXw7QR48TAoWVW7bBDZaGxYI+E04pkX1fl9wXYV2Rw1eWeTo7Nqt77ZGwCtCwsZOXwoE6e8yo/zF+LsPjJjPYzX336PFSuL2G34UFYWVWZBDNtuW6ZNf5eiVato07p1xmOtLS5er+BGKiNjRVERnVtWhlUrMjuiTI62UYbHikSmBsDKolW0T5PZ0bJlS9asVQ0OEREREZGmYtVamL0UWjSD3p1qsYOvH7PbrjtV2ZzMBql1UGLTEbDP4zDtPOiyHaxdTnFp5vESQLvaLncrTcIGH+DYGIzdYzcuvvYGysrKueTs0zK2S2Vp3HrPA9x6z7plfidPe5P9xozO+Pz2bdtSVLSq1v3su7nV1Jj1/Ww6d+xYsT1Ve6NPVIsjVZNj1vc/MHjrrSra/Th/AT+tWUOfXj3X2feKlSvp0K5drfsmIiIiIiINa+YCu+3X2YIcOflpEcz8r/0+YFyVh5IBjlpncAB0HAj7ToC8PKDmYEl1ay+UlpfRLE+THJoyBTgawNAh27LHLjvTtk2biiBC0pJly5j+/ofsOmwoh+z3y3Uev/qW23lx8tRqAxy9e/aguKSEBYsW0bVz55z7OegX/enQvh3h1NcYOmSbiu2Tpr5Gu7Zt2WbrXwBWPHXLvr0Jp06r0p9JU16jefNm69QKWbxkKWvXFrN5j+4590lERERERBrHl/Pttn/XWjz58/FQ+hP03As6DqjyUJ1NUUmJghvxfQ3vC2/NWrfpmhL4qGQBb5bMZctmmzC6hdUvXFa2hqHL/s3j7X7NkOa1ecGyIVCAowE0a5bPJWefXm2bcMo0SktLcfffh+0GD1zn8b333J3/PvUsc+bNp8em3dLuY8gg+4fjsy+/XifA8cEnM1i6fAVLllodjC+++obCAptIt8cutmRt8+bNGXe4y9/uuZ8unTqx47aDee/jT3nhpSmcfvwxVaa/nHDUYVx87Y3c9M972WvkLvw/e3ce51R1/3/8lWQymX2DYd9BEGRRkEVZXCKuKC7BrVGrbdXaaltba1vt8m2rtrWbrb8uLrW24z5WRbEIREEEQcEFFUSQRfZ19i3J5P7+uMlMZiaZSWYyC+n7+XjM457ce+45B4s+ms98zuds3b6Toudf4tLzz6EgP6/J3Ju2fB5cX/tOdxERERERka4XyuA4LvLXj9YdWBN8+UstHiU0gyOkai8cWIujbhowkAG5YLU0PUUF4OCEdUwsWw5AH0sGH+d9mUJrBq/6tvF5oJQTyx7nJFtffpd5Gqfb23d4g3QfBTh6iCUrVjKgX5+GIEVz5555Gk+/+ApLl6/kuisui9inX59Cjh81krfXv8fsGVObPHv8mef5cOOnDZ9fWryUlxabp854iosa7l94jhMs8NzL/+XZlxbRp3cvbv3Ktcw/d26T8aZPPpGffvc2/vXcCyx5YyX5eblcfelFfOnS+S3WtWb9+4weMZyB/Tte00RERERERLrGZ8EMjnYFOFJzwJEPeaNbPEpYDY5w7/8adiykb5+fA9eQZoe+2bCvvLFL1ehPOTpnOQAX2Ucyyz4QG2b2xyWpx3FN6jie8W7mvfoDOMuf42fpp3KV43hG2fJbzic9ksUwjLZ7dY6GiTdt2sTYsS2zFiR+i994k7889m+effhB0hzdX0HH6/Wy4Gvf5KZrr+Z85+lt9tffBRERERGRnuG038MXJfDaN2F033YOYhhNtpAAlFTD5PsaP7/+bbPOR4d89iS8cxc7si7gjPUP4p4GG/fBe7sgkOKl9JRVlJ/4PtgC/DJ1DndlT4s4TI3h45c1a7i3Zm3DvbvTZ/CLjFkdXGDXs1gs6w3DOLntnslDFVSSzNw5M+mVn8/Cxcu6eykAvLz0dfJyczjn9NndvRQREREREYlRjRd2lYLNCsM6EnxoFtwAyGhWg+PpdbDgYSir6cA8facD0KdmLQ5LLTYrDMg1H5XMWUH5lPVgDZD7znRutkyNOky6xc49GbN5JfsS5tlHMMKayw2O8VH7S8+iLSpJxmazccc3bmTLtu3dvRQAUlPsfP8bN2GzxVt2WUREREREusu2w2byxbBekBrvt8b6OrCkgDXyd4Dm4z29Dspr4f1dcHrLHS2xyRkBWYPJqNzFk4OvZgV/pyJYLDRv1SxmjQ6wc8kEqrcNwDez7eEuSB3JBakj8RsBUnSyyjFD/0sloXGjR7WomdFdLjzH2eQoWRERERER6fk6VH9j8+Pw9FjY8EDEx82TOsprzWudvx1zhQ96+sNU2AYwOf19rqy4gRSLWRXBVpfOqwPPoeDogLjnUXDj2KL/tURERERERKSJDp2gUrkLAj6z0Ggcan3tmCtc3hie7fMiB/2FZAe2UTfzFU4YWc0jbvOxI5g54u1IIEV6NAU4REREREREpIktoQyOwna8XPGFec0aHNdrHcrgCKq0FHLTnr9z1sn38kDKZiyXLsY5xnwW2hpz10L4eG/H55KeRwEOERERERERaWJLMINjVHszOACyhsT1WvMAx86jMOf38Nx7sY/hD8B662jeLfgcGxZ+knFKw7NQBse7O+HCv8a1NDlGKMAhIiIiIiIiDep88MVRsFpgRO84Xw7UQ9Ues93BDI7vvwC7SsxrrOoDUDn2EwKWABekDGPq2/dA6RagHcVS5ZijAIeIiIiIiIg02FsOAQP65zZmPcSs5gAEvJBWCCnpcb3avAZHe7aR+ANQNXozADfs3wk7F8H6XwCQpgBH0lOAQ0RERERERBocrjSvhVnteLkyWH8jO77tKdA0gyMQgGpv4+cn3oltjKO2Srx9D5AaSOHsIW6wpsK+t6BqLwWZTft2uKip9DgKcIiIiIiIiEiDUICjd3sCHLmjYNafYNyNrXZLs7e8FwpwVNXBCb9s+uzul2Ob/v30bQBMqhtKenofGDwXMGDb8y0CNnvLYhtTjh1K0ukEi994k/v/30MA/PNP9zN4QP8mzz/8ZBO3//QeAH7zkx8wZeL4FmPc8fNf8d6Gj7nlejeXXXBuXPM/+Oi/2HvgIPf+6HsAbN66jUXL3mDDxk85ePgIuTnZTDh+DNdf5aJ/36ZVgwKBAE+/+AqvLH2do6VlDB7Qn2sWXMycGdNazLNo6Rs89/Kr7D94iL59euO64DwuPMfZ8NwwDG6+427OmHUKV148L64/g4iIiIiIdI8jVea1XQGOtN4w7MI2u900Cx54o+m9UIBjw572Z1fkfHY8aVmbuKDX8eaNkQvMbSpbn6Vf7xsBR0PfPaXtqDEiPZoyODpRRnoay1a81eL+kuUryUhPi/reoSNH+ODjTwBYGuH91uzdf4CXl3q47vJLG+69sWoNO3bt5pLzz+a+u+7gq1+6gi3bd/D1O3/CwcNHmrz/2NPF/OvZ/3DxuXO57647GDt6JD//3Z9Z+94HTfotWvoGf3joH8yeMZX77v4+p50ynQce+ScLX1vW0MdisXDNgot56oWFlFdUxvXnEBERERGRzvedYvjJK03vNWRwZLbsnyi3ng5D8pveqw0GOCJld8Tq4NFU+r54GddnBgMc/WZB9jCo2s3l+85levqahr7K4Eg+CnB0olnTp7Js5WoMw2i4V1fn5c017zB7xtSo7y1dsYpAwGD65Els2baD7V/sinnO5xctZuTQIYwZNaLh3pUXz+NP9/yU+efOZdIJY3HOPpVf3f19KquqWLSsMWxaUlbGcwtf5cpLLuTy+Rdw0vhx3H7TVzjxhLE8XPRMQ7/6+nr+8dRznDVnJl+5+nJOGj+OG65awDmnz+axp4vx+xs3z51y8hRS7XZe9SyP+c8gIiIiIiKdr7IOXvwQ/r3WrHkRUlpjXvMz4hxwz3JYcTPsX91mV5sVTmp2yEpdMGuj+WkqbTEMeH13NSU1AfaXgzWQQr8ci/nQaoNZD0DmQCw2O31SDja8t7c0vnmk51OAoxPNnTOTA4cO89GmzQ333npnHQHDYPb0lls+QpYsX8mwwYO45fprGj7HwuvzsezNVZw569Qm9/Nyc1r07VvYm9ycbA4fLWm4t+6Dj/D5/Zw1Z2aTvmfNmcn2L3ax74D5H4NPNm+htLy8Zb/TZlFeUclHn37WcM9ms3LaKdMV4BARERER6WF89Y3tyrCCnnXtzaT4/DnY9Roc/jCm7rZm30ZD88Yb4Pi/V2HevsUMPPIQ1f32UJjV7EjYXhNh/ht8fvJLvFxxUcPtPcrgSDoKcHSivoW9mTh2DMveXNVwb+mKlcyadjLpaY6I72z6bCu79u7jrDkzGdS/H+NGH4dn5Wrq6wMR+zd/t7Kqmgljx7TZd+fuPZSWlTN04ICGezt27cZutzOwX98mfYcNHtTwDsDOXeZ1+JBBzfoNbPI8ZMK449mzbz97DxxERERERER6Bm9YIKG8prEdqn8R9xGxB9ea16Hnx9Q9JUqAI7SuKTEexPKPDVXUDNtOTWo19tI8BuRG6GS1k5XZ9NhaZXAkn55fZHT4j6M/u+ciuDq41ePJd+GuhdH7bv9FY/vCv0Y/VPnKk+G++Wb7oz0wYWB8621m7mmz+du/nuCbN1xDRVUV6zd8wn133RG1/5IVK7FaLQ3ZEWefPos/PvQY6zd8xLSTJrU618bPtmKxWBgxtPX/EtTX1/PHhx4jLyeH85ynN9yvqKwiKzMDi8XSpH92VmbDc4DySnNTXnZm0015OVlZwX5N620cN3woYAZgBjQraioiIiIiIt0jPIOjvLaxHQpwxJXB4a+B2iPmsaxZg9vuT9sZHDnRyxY2CASgcsynYDVI3zYcW01m5AAHkO2AXGspPyj8FRtqJ7Kq9OqY1inHDmVwdLLTTp2Gz+fn7XXv43lzNQV5uUyecELEvl6fjzdWreHE8SdQ2KsAgDNmzsBut8e0TeVISQkZ6enY7a3Hrf70yON8snkLP7zt6w3Bi86Um5Ntri9sO4yIiIiIiHSv8K0gZWEZHN72bFGp3G1eMweAJbavmSm2yOsJXbMjJ7038BsBfly2lpKZbwKQtcn8njUgL3L/LAecm72YK/Oe4d5+d/G99G8RqFEaRzLp+Rkc4ZkXrbl6amM2R1te/nps/TqYvQGQkZ7OzGlTWPrmW+w/eBjn7JlYrZH/hX973ftUVFYxa9oUKquqGu5PnTSB1e++R1V1NZkZ0Sv9eH2+NoMbDxc9zaJlb3DnN2/i5BMnNHmWlZlJZVU1hmE0yeIIZW6EgiENGR1VVfRKTW3o15DZkdX0PClHsE+d14uIiIiIiPQM3mgZHMEAQ1xbVKpCAY7Yv0NF26ISuma0EeD4Wc1q7g2sgRTI/nASGVtGA0TN4EizwzNlV2C3+Phh4X1cmL2QwKvr4MyHIX9czOuWnksZHF1g7mmzWPveB2z/Yhdnnz4rar9QlsafHnmc+dfd1PCzet171Hm9LF+9ttV5crKyqKqqjvr8iedf4ukXX+GbN1zD3NNarmPY4IH4fD727j/Q5P6OYO2NoYMGBvuZtTd2NKu1Eaq9MXRw0/+olQcDJKFMDhERERER6X7eKBkcDVtU4gpw7DOvcQQ4mmdo1DY7RaW1AIvfCPBo7UcAFL48n15vzMWC+UvagVECHObvcC0UlV7D+TteZbt3GNaavbDoAlh8KdQpm+NY1/MzOJLAlIkTOO2U6WRlZjYEB5orKSvj3Q82cOrUKVx2wTktnt/zx//HkuUrueCsM6LOM2TgAHx+P4eOHKGwV68mz/6z6DX+8dRz3HDVAi4+7+yI7089aSIpKTY8K1dz7eWXNtz3vLmK4UMG0T9YP2Pc6FHk5mTjWbmKKRPHN/RbtnIV2VlZjB8zusm4+w8eAmDwgP5R1y4iIiIiIl0rWgZHLAGGFjL6weBzofdJMb8ytOlXlhYZHK3Nn2KxsiHvOm798HPWfn5ck2fRtqiE2+kbxpd3/5Pi6X+k8PDLcGQDHHgbhpwX8/ql51GAowvYbFbu/s43W+3jeXM19fX1uOady6QTxrZ4fvbps3nmpUXsPXAwaqHOieOOB+DTLduaBDhef+tt/vLPIqaeOJGTJpzAxs+2NjzLSE9vOP0kPzcX17zzePKFl0lPT+O44cNYvnot73+8kV/ceXvDOykpKVx/hYsHHvknvQsKmDzhBN7/eCOLX3+Tb95wbYttMpu2bCUlxcbY0aNa/wclIiIiIiJdps0MjnhqcAw60/yJQ7+cpp+bn6ISHuAIP/a13ghgs1gptGbQ//Om2+4B+kfJ4AiXnwFfVA9l7YA/MG/GN8Fqg+xhca1feh4FOHqIJStWMqBfn4YgRXPnnnkaT7/4CkuXr+S6Ky6L2Kdfn0KOHzWSt9e/x+wZjfVI3v1gA4Zh8O4HG3j3gw1N3pk07nh+//O7Gz7fcNXlpKel8Z9Fr1FSWsagAf358e23csrJTSOxF57jBAs89/J/efalRfTp3Ytbv3It88+d22Jda9Z/wCknTybN0cYmOhERERER6TLhGRxlETI44gpwtMO4fubVaoGAYc5rGPCH1837jhS482z49RIYZp7BwMf+Q1xT+V9W5l5JliWVXc3OMThzNPRq5RyFF28yD9TcuN88iPP9XTDnuJGNJ7aUb4flX4XBZ8NJdyb0zyudz2IYRnfN3TDxpk2bGDu2ZdaCxG/xG2/yl8f+zbMPP9gjAgqHj5Zw1c238eu772Ry2HaWaPR3QURERESka7y2EW5+ymzPnwh/XGC2p/4aDlfC2u9Dn1jL6JVsBEcBpPeJ+RQVgL1lZoDjlPvNgMZb3zXnB/j+XDh7LJz1JxjeC179BtxY81/+7fuE+zJm84P06Uz7NRyqbBzvz5fDvJZJHS08sgruWWy2++fA6juCD3YthRU3mu15SyDvuIjvHwssFst6wzBO7u51dCUVGU0yc+fMpFd+PgsXL+vupQDw7EuLmDhubEzBDRERERER6Tq+aDU42lNkdMkV8J9TwFse1xoG5EKf4CGMoQyOkN2ljUfJHq6Ck/9czZO1n2IBFqSO4WCFGdywhx03O7ZfbPOeH/b1ZF/4kvtOb6wjsvFvcf1ZpPspwJFkbDYbd3zjRhyO1LY7dzLDMMjPy+W2r17X3UsREREREZFm6sJqcHSoyKi3AnyVYEuD1BgKYDRjtUKqreWaUm2NR8lW1MKekR9Rb6vnfPsIhpLH9N+YzwbmmZkfz30VRhbGNmfzo2QbAiupOTDzj2YWyvaX4LMnIVDf4n3pmVSDIwmNGz2KcT2goKfFYuGqSy7s7mWIiIiIiEgETWpwBIuMltea962WpoU9W1VzwLxm9AudxRo3R4o5b42v8d4tpzUGHgJ2LxUTPwDgeutJVHkb+2U5zCDHwBhOT4mm1gfpod8RZw+B0dfA5sfhnbugei+c+L32Dy5dRhkcIiIiIiIi/4Oan6KydBNc+aj5edqwOGIV1fvMa0b/dq8lFEypDgYu+udAYZaZwVGfXsX+BU9Tn1OB/WgBo8uHNVm7z99yvHiV1jS7cfJP4dTfm+19K5vunZEeq8cEOLqx2Kn0EPo7ICIiIiLSdcIzOA5Vwo1Pwqb9ZmHRH54Tx0DV+81rRowFMCIIbYcJZXCEgispNqgdvAt/ThkppXn0WXgJ+8ss1IWtfUzf9s35j2sa2y0CHBYLjLjEPEnlzH+2OzNFulaP2KKSkpKC3+/Hbu/kc4ikR/P7/aSk9Ii/kiIiIiIiSc8bIfPhlOHw2DXgiOerWVXHMzhCR9KGAhy24K/i7VbI/Ox40j8fiZHqw1aTQa2vsRAqwM/buSv+jNFmpso7Oxq36LRwws3tG1y6RY/I4EhLS6OysrLtjpLUKioqSEtLa7ujiIiIiIh0WKQAx/DecQY3ILEZHMEtKtawDA4Aa70dW00GYGaehIqRjukLuentnpa84Lul1e0fQ3qOHhHgKCws5NChQ1RXV2ubwv8gwzCorq7m8OHDFBbGWPZYREREREQ6xBvhcJBsRzsGmvQdOPs5GDy33WuJtkXlVf9WAnZvk751/sa1x3zSSxQNAY5oGRxyTOkR+wHS0tLo27cv+/fvp66urruXI93A4XDQt29fZXCIiIiIiHQRX4QAR057/u94eqH50wGOZkVGrRZY49vLxZUvYrs2i0H/uJEB2Vb2lZsBjriPso0iVwGOpNIjAhwAubm55ObGf2ayiIiIiIiItO2TvfDPNfD9uVCY3RgkCJcdb4DDMBJSgLN5DY7K3vs4t+J5AM70j2beGVYq6+DhVWb9jdDaYz7KNopQBkfUGhxyTOkxAQ4RERERERHpPPP+2ti+/9LIW1TizuBY9W3wVsDkH0De6HavLbwGh4HBpmlLqTDquCz1OJ4YOQfHKPi9x+xT52+sH9LhLSpmWQ9lcCSJHlGDQ0RERERERLpGSbCgZihI0Cuz8VlOPAU7672waynsfQPsWR1aU3gNjrr+e6koOEhvSzpPZF2Aw5LSpE94kdGEbVFRkdGkoACHiIiIiIjI/5A+2eY1FOAYnN/4rDCeOEX1XqivgcwB5k8HhLaoVPugYuIHAHzVMaEhuAGN21ESWYNDRUaTiwIcIiIiIiIiSa4+0NjuHQxihLao9M1ufDYqnlqhVXvNa+bADq0NGgMVJUYNVcd9BgbcnDYpYp/NBxqDM4mqwaEMjuSgGhwiIiIiIiJJriTsC7zNAncvhGWfmp/zw7aohDIpYhIKcGR0LHsDGoMXRnU6/Z+9il5j9zL0rNyIfd76HIYWNL3XXrnBGhwqMpocFOAQERERERFJcuFf4L318MS7jZ/PHQf+ejjtuDgHTWAGhz0lAFip8YHjYD9GpfaDs5r2CQ9mrNza8l57aItKclGAQ0REREREJMmFBzhqfU2fZaaap6rErToU4Oh4BseLw5ZyMMvL4R2nATlYI5w8Gx7MsNvMa6qtY/NmpkKKNVjc1AeOeDJYpMdRgENERERERCTJldU2tveVN33W7joW/WaBJQUKxrd7XQBHAjWsy9uIP7+e6s9nAWCNUC0yvI5IqH5IXFtqIrBYzJNUjlSZ/4z6KMBxTFOAQ0REREREJMmFZ3DsKW36rN1ZEMPmmT8dVFS3Eb+1nrQdw7CVmEe6RMrgCP8zHK0yrxmpHZ6evGCAo7S68YSZcPcshqxU+NaZHZ9LOpcCHCIiIiIiIkmutLUARzd+K3zLt5u7qt8CIPvjCVQHt89ECnAM7dXYrvKa1/QEZFzkBQuNRqrDUVELj6wy21+blZiAinQeHRMrIiIiIiKS5MrDvrwfqWr6rF0BDn817FoKJZ+2e00lgVrmVbxAFT7mVI0lY+toaloJcMwcAYPzm97r6BYVMLeoQOQAR/jpM1sPdXwu6VwKcIiIiIiIiCS51k4JcbRni0r5dlhxI6z6drvX9Jx3M2VGHTNTBvK98vOwYGkogBopwGGxwJVTmt5LSAZHMMAR6ajY8ADHZwc6Ppd0LgU4REREREREklykL+8hlgjBhDaFjojN6N+u9QDUGfX0sqTzNccEMuzmV9Pq4NaTSAEOgPRmW0QSGeCIFAQKv2d0fCrpZKrBISIiIiIikuQifXm322DiQCjIaMeAJZvMa+bAdq/p1vTJ3Jw2CQN4N/jNNBCMIkQLcGQ2C3AkdItKdctnoQyOCyfAgskdn0s6lwIcIiIiIiIiSa48QoDjma/ASYPbMVh9HWx5wmwPntvJnqYvAAAgAElEQVShddkt5v4YR7NvptECHM2LfCbkFJVggKe1LSp57QkCSZfTFhUREREREZEkFymDozDCkagx2bEQag5C3hjoPyfu1w8Fqnm09iPKAnUN91oEOKJ8Ux3br+nnTt+iEgxw5Kd3fB7pfApwiIiIiIiIJLlI2QmFWe0YyAjAxofM9rgb4y7gUWV4cZY/y1erXmNu+XMEDHNPSqwZHCMLYcqQxs+JCHCExgid4BKuJPjPTRkcxwYFOERERERERJKYYUQOcDQPKsSkeh9kDYG+p8CwC+N+/XnvFj6qP8xx1nwW5lyCNRggaV5LI1qAA+CKsJNUElGDIzRGbYQAR0MGhwIcxwQFOERERERERJJYrQ+89QkaLHMgnPEonPUEWOOPLqzw7QLgprSJ9LNmNtyPNYMD4ILxje1sR9xLaKG1AEeJAhzHFBUZFRERERERSWKRakvcdnoHB23X2bLwpm83AKfZm1Y3jSfAkZEKr38b6nzgSEQGR3Du5gGOQADe+txs56kGxzFBAQ4REREREZEkFml7ynec7Rysah/Y7ODoFXeQY2+gkq2BUrItqZxo69PkWTxbVACG94pr6lY1ZHD4m95fsqmxrQwOcLrcecAjwHjAAG4ANgPPAMOAHcDlnuKiEqfLbQEeAM4HqoEve4qL3uvsNWqLioiIiIiISBILZXAkYjsH7/wYiqfCrtfifjW0PWVmygBSLE2/iqbamvZtZ4JIu0TborJqW2O7VyZiBiwWe4qLjgcmAZuAHwAeT3HRcYAn+BngPOC44M+NwF+7YoEKcIiIiIiIiCSx8mCAY9ow+OWF8OT1HRiser95zejXer8Ixtt6c2faNL7kGNfimdXaNMhh68JvqqEtKnXNMjhCAY8LJ0BmIoJDxzCny50LzAEeBfAUF3k9xUWlwHzg8WC3x4GLg+35wL88xUWGp7hoDZDndLn7d/Y6tUVFREREREQkiZWGHXX6pWkdHKx6n3nNGBD3qxNSCvlVSmHU546UxmKobW1RSaRoGRz7yszrdZl7oK5PYgp+HLuGA4eAx5wu9yRgPfAtoK+nuCj4l4L9QN9geyCwK+z93cF7++hE3RbgKC8vx+/3t91RRERERERE2u3z/elAOlm2Go4ejVCQI1b1dRTUHcWw2CipsUDt0Zhe22yU0ps0elnSWu2XassjtMnA563j6NGq9q81Dr56gAJqfQZHjpQ0bI/ZXZJLXl0dk374T+rvS6X8H1di9D529qqkOtJ6O13ude149SFPcdFDze6lAJOBWz3FRWudLvcDNG5HAcBTXGQ4XW6jnctNiG4LcOTk5HTX1CIiIiIiIv8zdlWY10lD0yko6MBxIBU7AbBk9KOgV/RMjOZ+VLaU5f5dvJbl4uzUYVH75WfCkeCxrGlpDgoKum5fiM0K9QEL2bkFpKaAYcCBSrjrg2WkVNTCxAHkHzeoa4uDdJC3rvawp7jo5AQNtxvY7SkuWhv8XIwZ4DjgdLn7e4qL9gW3oBwMPt8DhB+VMyh4r1OpBoeIiIiIiEgS2xL8yjm6T+v92lQV2p4SeymFGsPHav9eLMCUlL6t9i0IS47oyhocEHZUbHCTQWl5PZd+8g5Xb1lnLuanFxxTwY1E8xQX7Qd2OV3uMcFbTmAjsBC4LnjvOuClYHshcK3T5bY4Xe4ZQFnYVpZOoxocIiIiIiIiSSxUg6NPdgcHqo4/wPG2fx9e6jnR1ode1tazR8JPKunKGhxg1uGo8sLSTXDZiQYp33iKe97ZbD782iw4rqPRoaRwK/CE0+VOBbYB12MmTTzrdLm/AuwELg/2fRXziNitmMfEdqS0bcwU4BAREREREUlioeKZaR2tkTnwdDjrKbDHXodiue8LAM6wD26jZ9MAR1cnS4T+2XzvP3Due++SvWozJanpPDX/Qm6544SuXUwP5Sku+gCItOXFGaGvAXyj0xfVjLaoiIiIiIiIJJmdR+H3Hqj2QmWdea/DAQ5HPvSbAb0mxPzKGz7zII3T4wxwdHUGhyPsV/8fl9qpTEnlrukXsXvmBPMMWzkmKINDREREREQkySx4GA5Vwp+XN96z27p2DdWGj7X+fViAOSmD2uzfpAZHN5a7uK3+JOovPo4jaVl8N7f71iHxU4BDREREREQkyRyq7IRBP7gfjACM/Qqk9W6z+4FAFdmWVIZZc8mztn5ELEBBRmO7q7eonLXpA/ZWWXll2EQOVgBpWQBkt71s6UEU4BAREREREUki/vpOGnjLM1B3BMbEVi9yuC2Pj/K+zAf+g213BnpnNba7dIvK/nJue+MVMr11HMjI4d0+wyKuSXo+BThERERERESSyK7SThjUX2sGNywpkNYr5tcGWLMYkBpblCA8g6PLAhzvfQE/fplMbx1LBh3Pu4VDmzw+d1wXrUMSQtVSREREREREksiWA50w6P63zGvuKLC2XczjNe92Dgeq45qiSQ2OrvimunEfXPEobNzPwawcfjp1XpO9Me5pXbQOSRhlcIiIiIiIiCSRLYc6YdDtL5rX4fPb7LovUMm5Fc+TY0mlJP9WrDEW1MgPy+AIGO1ZZJx+tQT8AbhoAl8eOJ/9FY4mjzNSu2ANklCKR4mIiIiIiCSRI1UJHtBbDruXme1hF7XZfb3fTCGZbOsbc3ADmmZLlNXEtcL4vbkVVm41q4j+bB5kOlp06c6TXKR9FOAQERERERFJIlV1CR5w9zKor4O+MyBzQJvdQwGOKSl92z1lSXy7W+I3ojfMnwi3zIH8DO6/BAblwd+uauzi7axirdJptEVFREREREQkiVQGAxwjesO2w2Z7+rAODJgxAEa7oc/0mLonIsBR2tkZHIPy4I8LwDD3wpwwAFZ+t2kXBTiOPQpwiIiIiIiIJJFQgOPbZ8C/1sKIQvjp+R0YsN8M8ydGCcngSPQ2m5An3oFzxjWe/9rKFhqfAhzHHAU4REREREREkkiV17z2zYHnvta1c+8PVLHXqCTbksooa367xymvTeCiQt7ZAXe/DH9ebqZr2Fs/DSZTRUaPOarBISIiIiIikkQqg8GBrJZ1M9tn7wo48hEE/G12/aTe3BMz2dYnrgKjIQ9eYa77/kvjfrVtf3rDvF4xpdXgxt+vhjmjzPIccmyxGEZXnL8TUbdNLCIiIiIikqxm/Q72lMKK78CQgg4OFqiHp44Hww9XboKUtDZfKQnUcsSoYZStfRkcgQBYE/mr+Do//PQVeGY9ZDvM7I3c9ARO0DNZLJb1hmGc3N3r6EraoiIiIiIiIpJEQqeoJCSDo/awGdxw9IopuAGQb00jn9j6RpLQ4AbAA6+bwY3UFLjv4v+J4Mb/Km1RERERERERSSKhGhwJqSFRtTc4WP82uz5f9xm1RtvbWLpUjReeXGe2H7sGLhjfveuRTqUMDhERERERkSRRH2g8/SM1Ed/2qoMBjozWAxw1ho+rKl9hiDWHT/NuIMXSQ36XvmEv1Phg0iA4dUR3r0YicLrcVuB04DRgGJAOHALeA5Z4iot2xTqWAhwiIiIiIiJJwhtMoHCktHoCauwqdprX7CGtdnvHvx8fAbIs9p4T3ACYPgze/h4cquzulUgzTpc7HbgduAUoAD4A9gI1wHDgQuDvTpd7CfBzT3HRmrbGVIBDREREREQkSdQFAxwJyd4AKN9mXrNbz35Y6dsNwGz7oARNnAChaqUFmeaP9DRbgLeBGzEzNXzNOzhd7qHA1cAzTpf7l57ioodbG1ABDhERERERkSThDW5PcSTqm14ogyOnjQCHfw8As1MGJmjiDnpqHazcCg9e3glVSyVBzvUUF33cWgdPcdFO4D6ny/07YGhbAyrAISIiIiIikiRCW1RSbQkacO6TULmr1RocfiPAal8wwNETMjj89fAHj7ktZfV2mDWyu1ckEbQV3GjW14uZ8dEqBThERERERESSRF1YDY6EsNrbzN74sP4glfgYac2jvzUrQRN3wOrtZnBjWC+YqcKixwKnyz0CuBgYARjANuBFT3HR9njGUa6OiIiIiIhIkkhoDY7q/RBo+9jXd/z7AZht7yHbU178wLxeMilBlValMzld7u8CnwL3A5cBC4DfApudLvd34hlLAQ4REREREZEkkbAMjoAPXr8eFl8Clbtb7XqzYxLb877G3emndHDSBKj2wmubzPb8Sd27FmmT0+WeBfwGM7hR6Cku6u8pLuoH9AF+B9zvdLlnxjqetqiIiIiIiIgkiVCR0Q5ncGx6FEo/hazBkNar1a4Wi4VhttwOTpggCzeYQY7Jg2FoQXevRtr2deBfnuKiu8JveoqLjgA/dLrc/TGPkV0Vy2DK4BAREREREUkSDRkcHSky6q2ADQ+Y7Wm/hJT0iN1KArV4fDupMrwdmCyB6vzw4Aqz7ZrcvWuRWM0A/tnK838G+8REAQ4REREREZEk4U3EFpXSTVBfCwXjYcCcqN2W+nZwVvlzuCoWdmCyBHKkwENXwy1z4AoFOI4R/TALikbzORD9CJ9mtEVFREREREQkSSSkyGjpZ+Y17/hWu73pM2tzzEzpIcVFAcb1N3/kWJEO1LXy3As4Yh1MAQ4REREREZEkkZgMjs3mNW90q91W+vcAMNs+qAOTJYBhwNJP4bTjEng+rnShC5wud1mUZ3nxDKT/9UVERERERJJEQoqMln1uXgvGR+1SGqjlo/pD2LEyLaVfByZLgHd3wk1Pwpi+8N9v6GjYY8+jbTw3Yh1IAQ4REREREZEk0bBFpSNFRs8qgsovID164GK5fxcGMDWlH+kWewcm66AdR+CexWb7rOMV3DjGeIqLEloXVAEOERERERGRJFEdPNAkrSMxB4sVsodFffxFfTlfr1wGwHn24R2YqIPe+Ay+/pQZ1emVCe5p3bcW6REU4BAREREREUkShyvNa+/Mdg5waL25NcUWva5jnsXB+JTejDUKuCN9ajsn6iDDgF8vMYMb88bDz+aZQQ45pjhd7sFAjqe46JOwe2cAPwaygP94iot+Fet4OiZWREREREQkSTQEOLLa8fL+NbD0anj9y1Af/WCLHKuDRdmX8mL2xTgs3fQ783d2wuYD5h/0d5cpuHHs+j1wTeiD0+UeArwM9AH2AT93uty3xjqYAhwiIiIiIiJJ4nCVeY07wFG1F1bcCAGveXqKNbXV7qkWGznWmE/vTLxdRyHLAVed3MGKqtLNpgGLwj5/CTOwcaKnuGg+8CPg+lgH098EERERERGRJBHK4CiMN8Cx6zXwVUD/WTDlJ1GLdf68ejW9rOm4U8eR250BDtdkOPcECAS6bw2SCH2AnWGfTwde9BQXBcvlshC4K9bBlMEhIiIiIiKSJNq9ReXgO+Z16DywRj6CxWvUc1/NO3yzykM93RRYqPE2trMckJPePeuQRCkFeoV9ngqsCftsEEdihjI4REREREREkkB9AEqqzXZBPCUpDAMOvGu2+0Q/iWRD/SFq8TPamk+BtRsCC3V+OOOPMGM43Dcf0lvfRiPHhLXAd5wu9w3AAiATeD3s+WhgV6yDKcAhIiIiIiKSBI5WQ8CA/AywR07CiKz8c6g7Aul9Wj0edq1/HwAz7P07ttD2evVjOFABnx3s4Dm40oP8BPAANZg7TO71FBeVhD2/Elge62AKcIiIiIiIiCSBQxXmNe4jYn1V0Gc6ZA2KWnsDYI0vGOBIGdDOFXaAYcC/1prta6e3uk45dniKizY4Xe6xwExgv6e4aG2zLk8DG2MdTwEOERERERGRJHCovfU3ek+Cs582gwitWOPfC8CMlC7O4PDVw88WwQe7ITcd5k/s2vmlU3mKiw4DL0V5tijS/WgU4BAREREREYnT+7tgzXa4aRZYe8DRDfvL4cv/MttxnaBiGI3ZEK1kRaz372droJR0Uhhv693+hbbHQ2/Bk++CIwV+c7FqbyQRp8v9pyiPyoDNwHOe4qK6WMfrAf8qioiIiIiIHFt+s9T8+XBPd6/E9MQ7je2YMzjq62DJ5XC07R0AhZYMzrIP5ea0Sdgt8RT46KD6gBncAPjz5XD2uK6bW7rChCg/84C/Ap84Xe4hsQ6mDA4REREREZE4lVSZ1/La7l1HiC3sV9cxBzjKtsKhdfDWbXDRsla7DrHlsCTbha+rj4f1+uHyybBmBzjHdO3c0uk8xUVnRHvmdLlzgCeAXwFXxzKeMjhERERERETiVBFMmq/1de86QsIDHPkZMb5Usin4wvExdbdYLKR2ZfYGmNtRvnUmPHVDz9gLJF3GU1xUDvwCmBXrO/obIiIiIiIiEqcqr3mt6SEBjsqwKgUxHzBy9CPzmj+21W4/r17NX2s/oNLwtm9x7VXtbbPwqSS9w0BerJ0V4BAREREREYmDYUBVMKDQUwIcJdWN7ZGx1gA99J557T0lapdqw8fPa97mm1We9i+uPQ5WwKV/hz8v79p5pac5BdgWa2fV4BAREREREYlDnR/8wVIUPWWLytFgTZB54+HkoTG84K82t6hYbNA7+rGr7/kPUI/BJFshWZYuPL3khy/B5oNQ/xF8dSZk6OSUZOR0uSdHeZQLTAF+APw01vEU4BAREREREYlD+HaQnhLgCK3j8ujJGE0d3gBGPRSMh5ToRTvW+PcBMD2lfwdXGIcD5bD8M7DboOjLCm4kt3WAAUTaWHUY+C3wl1gHU4BDREREREQkDuEBjp6yRcVbb17tMdcADUDhydD7xFZ7rfHvBWBGVwY4XvgQAgbMHQ19c7puXukOw6PcL/cUF5XEO5gCHCIiIiIiInGo6oEBjtCWmdRYAxz9TjV/2hDK4JiRMqCdK4uTYcDz75vty07qmjml23iKi3YmcjwVGRUREREREYlDZdhhIj1li4ovmMGRksBTXL+oL2dPoJJci4MxtoLEDdyadV/A1kPQKxNOH901c0rSUIBDREREREQkDj2xBofPb17b3KIS8MOq2+HIR22OmW9N40L7SGamDMAa89mzHZRiBZsVLp4Uz34bEUBbVEREREREROLSE7eo+IJbVOxt/Qp7/yrY/gIcWg8XvQ7W6EGEbEsqL2TPZ3+gKnELbctJg+E3l8CZyt6Q+CnAISIiIiIiEoeeWGQ0tEXF3tY3vL0rzeuwea0GN0JsFisDbdkdW1y8Lm298KlINNqiIiIiIiIiEoeeuEXFH6rB0WYGx1vmtd/sqF2eqtvEqWVP8u+6TxKzuLYYBrgfg3sXQ3lt18wpSUkZHCIiIiIiInHoiQGO0DGxrZ6iUnMISjdDSgYURj+hZJlvJ2/793JZ4LjELjKaD3bDqm3w6QH43lldM6f0KE6XuwC4B3ACfWiWjOEpLorpvGAFOEREREREROLQE2twhI6JbfUUlYPvmNfCyWBzRO32pm83AKfZBydoda3w1cOfl5vty06CVH1F/R/1KHAS8BCwFzDaM4j+9oiIiIiIiMQh/JjYRAU4/PWwvwIG5bXv/YYaHK0GONaZ18KpUbvsDVSyNVBKtiWVE2192reYWBkG3PYsvPEZZDnAPa1z55OezAnM9RQXre3IIKrBISIiIiIiEofKsDIRidqicuuzMPt3sGZ7+94P1eBo9RSVrMFQMAH6Rg8kLPRuBWBmygBSLJ38dXHdF7B4I2SnQdGXYXB+584nPdlBoLKjgyjAISIiIiIiEoeqsAyORAU4Fm80r0+vi/9dw2iswdHqFpWxN8D5C6HvjIiPn6n7lFurXgdgfuqo+BcSrxc/NK9fmgqTBnX+fNKT3QX83OlyZ3VkEG1RERERERERiUNnHhMbaEflgfpg/Q2rBWwd+BX2Z/Ul+AnwvbSTuckxqf0DxSpgmDU3Lu6CuaSnuxsYBhx0utw7gSb/ZnmKiybGMogCHCIiIiIiInEID3DU+SEQAGuCcuNDwYp4tFl/Y9dS2L0UJv8QHNG3gdydPoNTUgbgtA/BYrHEv5B43TcffnSOuUVF/tcVJ2IQBThERERERETiEH6KCkCtHzJSEzN2ezI4fMGgSNQjYveugM+fg+yhMP4bUcexWCyclTo0/gXEyzBCEyq4IQB4iov+LxHjqAaHiIiIiIhIHEIZHKEkh+Z1OP6yAn70kpmN8dBb8Pa2ps+bZ2mEfw5lY8TD11b9jUPrzWufyMVFywJ1LPPupNbwxz95vAwD7l8KD67o/Lnkf44yOERERERERGL01udQWmO2CzLgSFXLOhz3L2tsPxUsGrr9F+b18TVw72vw3Fdh4kDzXnnYqSyhsePhb22LirccSjeD1W6eoBLBUt8OFlS+zFz7UJbkLIh/AfFYtQ3+utIsFnL+CTCysHPnkx7L6XKXAyM8xUWHnS53BRA1f8lTXJQTy5gKcIiIiIiIiMTomn82tnPSzABHtJNUnopwIsrPFpnXexfD018x20fCDsc8UB7/mhoyOCLl5x96DzCgYDyktNwOYhgGj9d9AsAZ9iHxTx6vxeZc3DRLwQ25FagItr+ZiAEV4BAREREREWmH1OC3qfBtJUaE30FHqo0Rnm2xYW9j+2CFOUY8NT5DR8RGrMHxxX/Na/9ZEd+9t2Ytr/i2kYWdK1PHxD5pexgGeDab7XPHde5c0uN5iosej9TuCNXgEBERERERiVFBRmM7FFAID3B4I9TQcET4tXJ4gOOdHU3fL6mOb03+aDU46r2w6zWzPeyiFu8dCdTw05pVWICnsucx3JYX38Tx+mQf7C+HvtkwfkDnziU9ntPljuuonlj6K8AhIiIiIiISo8EF5vUn5zcGKcIDHHURtqs0r9EBje96/fDaxqbP9pZFn7+qDt7d2TRTxButBoc1BSbcCvnjIHdUi7E8vp3UY3CmfQjzUkdGnzQRDAOefc9sn3V8fCkqkqw+dbrcbqfL7Witk9PlHut0uR8GftDWgNqiIiIiIiIiEqPKYEHQWSMbAxPhWRt1EQ4i8QfMIEh4ACJUL2NlsGjpmL4wth+8+CG8tTV6gsMtT8ObW+G3l8JlJwXnD87ZIlPEYoXjb4Ah50Uca4lvJwBn24dFniyRDlTACx+Y7Ysmdv58ciy4EfgN8KDT5fYA64C9QC2QD4wDZgGjgT8BD7Y1oAIcIiIiIiIiMaoIHhGb5QjLwmgjwAFmIdLwAEeovfBD8zp/ollz88UP4dVP4OY5kcd5c6t5fWZ9WIAjOH9DgKP6IFitkNbbzJTIjBwtOcc+jArDy3n24ZEnS6R+OfDAAqj2wrRhnT+f9Hie4qIVwHSny30qcBVwBTAUSAcOA+8D/wCKPMVFpbGMqQCHiIiIiIhIjCqDAY7stMg1OFoLcGSHHWJitzWtuXnhBCjMgsxU+Ggv7CqBwfnR1xF+tGxozlDRUz78Lex4GU69H4bOizrGAscYFjg6ubBouDO7cC45ZniKi1YDqxMxlmpwiIiIiIiIxMBfbyYgWCyQYQ+rwREW1Ih2ZGyND+oDjZ+tVjhcBVVeyE2HQfngsIMzGAN49ZPW11IRKcBhA8o+h20vQMALBRPi+vN1irIaKH4PDlW23VekgxTgEBERERERiUF1MHiRYTcDFBGLjDbL4AjV2qjxmcGREJ8f9gST7gfmNt6fHawFujHs6NhI9pY1FhoN1eCYbH0dFl8Cht88NSV7aMR3DcPgDzXrWOT9nFojSspJoiz/DO54Ab7zXOfOI4ICHCIiIiIiIjEJZWekp5rX0JaQ1mpw9M4yrzU+OBp2/OvG/fCr4AmuA8NOZx0QbC/8qLHeRiAs8yM77LyJj/Y2zplqqeOawPfBVwGD5sLJP4v65/h97Tpur16Oq+Jlyoy6qP0S4o3PzOsZ2p4inU8BDhERERERkRiEjntNCwY2IhUZrW0W4CgMBjhqfXCwovH+5gOwdofZHt238X6/nMb2dY/DL/8L034DhypgxxHID+xkavo7pFuqWfRx4/znZi0mmyOQPxZO+zs4wtJCwnzsP8Qd1SsAeDzrXPpaM2P807dDfQBWbDHbZ4zuvHlEglRkVEREREREJAYNGRx28xpxi0qzGhx5GcH7fli3M/K4105vbIcHOAAeDZZe3PnO04zZ+ytWjChreLZhzxSM2keo8+dxee6z5s3j3GaRkCie8W7GAL7imMDljuOj9ksIz2bzDNxhBTCid+fOJYIyOERERERERGJS03yLSjDAEb4tJdTOSIVH3Y19qr3w4IrI44ayPELvDe8FYJBtLW+4P6BmNdmWMqoCGWypMwt1TExdT+VbP8PrC3Drvj+zLONHMPyiVv8M//GaGRVXpnZycGPnUfj+C2b7iimdO5ckBafLneZ0uV1Ol/tOp8udF7w30ulyF8Q6hgIcIiIiIiIiMQhlcDiabVG5d3HjCSmhQqQXjDdPRQ31+WhP9HGbJ1z850ZYeNrDPDPsGqyY6SFv97ufH6a8w5St6/l9xlLu9C2iNuCAg2vx1lspqS/g/eyvgT0rwgymzfVH2Vh/hHxLGqfZB8XzR4/fr14zT1BxjoEbZ3XuXHLMc7rco4BNwN+Ae4BQUOPrwG9iHUcBDhERERERkRg036JiC/s2FaqvETq+NSfNvIYCHO9+Efs8eanVTDjyIGPtG7hzygYAyn0ODvoLqTPSuPREqM4Yx/W7H2NF6RR2HjXfc7RRgODFYPbGPPsI7BZb7Atqj6unwpdnwM/mmUfOiLTuj8BSoC9QE3Z/IXBGrIOoBoeIiIiIiEgMGoqMBgMcTY59DdbhKI8S4FgfDHB8dSY8sqqNiXYtMU9D6X0iZbaTAKiqg5rgfOmpZnBlTc0prKk5hTHBuVPb+HZ3k2MSQ6w5jLDmtd4xEWaPajzzVqRtpwIzPMVF9U6XO/z+F8CAWAdRgENERERERKQNVXVhNTiCAY5QMAMagx2he9nBAEdKMMBhGOb1gvFw+nHgD8DdC+Ebp0WYbFuwdsXwS8k6YDYfexuGBJP2M+xQFRZc2XzQvLaVwZFnTeMqx9jWO4l0H3uEe0OAsgj3I1KAQ0REREREpBWbD8D5/88sAAqNGRwVkQIcweT6UAZHathOELsNxvZrDAncW+gAACAASURBVESs/G6EyaoPwv63wGqHYfPILDVvH602DyQBM4NjaISyi9ECHB/5D1Fm1DGrs+tuhPx2GQzKg4smNv5DE2ndEuB24CvBz4bT5c4B/g9YFOsg2gwlIiIiIiLSiiWbIGBAZZ35OS1SBkcwu6N5DY6UsG9c4cGNiPzVsPp2MAIw4HRw5OMNO6ElEMwCSbfDbafD/IlNX0+L9Ptv4M+17+Msf443fHEUAmmvshr465vwk1ca01ZE2nY7MMvpcm8G0oBngB1AP+AHsQ6iDA4REREREZFWhE5ICQltUbl6KqzdYbYbMjiCQZCcdPNqD8vgGNm7jYmq90PJJkjrDSfdCTRucQmXkWqO/8cFcM44WPapucYzR7fs6zXqed77GV7qKbSkt7GABFi51YzETB0MmY7On0+6jNPltgHrgD2e4qJ5Tpd7OPA00AtYD1zjKS7yOl1uB/AvYApwBLjCU1y0o7WxPcVFe50u94nAVcBkzGSMh4AnPMVFNa29G04BDhERERERkVaETkgJKcg0rxdNNAuGfrS3sQBoRbMtKuEBjqy0NibKGQHOf0NKmtkGFpwE/9csQT89LFPjvBPMn2iW+nZw1KjlBFsvxqcUtrGADjpQDvcsNttnjuncuaQ7fAvzKNec4OdfA3/wFBc97XS5/4a5veSvwWuJp7holNPlvjLY74q2Bg8GMv4R/GkXbVERERERERFpxYGwAEevTLjsxMbPY/ub16pmRUYjBjhiKUdRMK4huAFmEsS545p2SY+yFSWSp+s2A3BVahcUF/3u87C/HKYOhetmdP580mWcLvcg4ALgkeBnC3AmUBzs8jhwcbA9P/iZ4HNnsH9r49/jdLlvjnD/ZqfL/YtY16kAh4iIiIiISCuOVJnXx68Dz7caMzgAMoNBi9AJK60FOFrdsXFgLXjLIz4KD2hkOSJvW4mkyvDyoncLAFc4Ojmj4kA5rNpmFgL521VtH+kix5o/At8HQhu2egGlnuKiUJWY3cDAYHsgsAsg+Lws2L811wDvR7i/Hrg21kV229+68vJy/H5/2x1FRERERES6UWVNDpCCI1BGfU09R8MqAljq04F0jpRVc/BwLVXeAqwWg7qqEnzV4KtLAzLMvv4qjh6tazlBfS35r18PAR+lZy7GcOQ3eWw1MjDrLsIJfX0cPVrRcoxmAobBVSylEh9TKKSgzOAoR9v3DyAGqf/9hCzAO3UQldRBpD+ndKlUR1pvp8u9rh2vPuQpLnoo9MHpcs8DDnqKi9Y7Xe7TE7bApvoAhyLcPwL0jXWQbgtw5OTktN1JRERERESkm3mDv7Pu1zuXgmbHsxYEv9YYtgzsGWYgIzvNQu9eZsfc7Ma+ffIzKQhP/wjZtRTqa6BgIvn9R7Z4nB82xqmj7BQ0X0QU59eM4sOaIzyWcz4FKbG9025XzYDRA0lNS4l5fdK5vHW1hz3FRScnYKiZwEVOl/t8zEhbDvAAkOd0uVOCWRqDgD3B/nuAwcBup8udAuRiBipa8wUwG9jW7P4czOyQmChvSEREREREpBWh7ScZEWpfZAS3qFR7W25PgRi2qNSVwoe/M9tDzo44f/i8Jw+Nbc0A30k/mWsdJ9DL2gWnpzjscOqItvvJMcdTXPRD4IcAwQyO73mKi77kdLmfA1yYJ6lcB7wUfGVh8PPbweeve4qL2joz+O/AH5wudyrwevCeE7gPs0hpTFSDQ0REREREpBW1wQBHWisBjhoflDc7QQWaBjgymhcZNQxYcTOUboackXCcO+L84d8MTxwU19K7Jrgh/6vuBG53utxbMWtsPBq8/yjQK3j/duAHbQ3kKS76HWaQ40/AZ8GfB4CHPcVFv4l1QcrgEBERERERicIwGjM4IgY4gveq6iJncKSE/Uo5q3kGx5ENcHAtOPLN42EduRHXsO1w2HxtnMRSaXiZXvYEF9pHcm/GbKyWVg+vSIwn34XXN5snp8we1fnzSbfxFBctB5YH29uAaRH61AIL2jH2D50u9y+B0LlBmzzFRZXxjKEAh4iIiIiISBS+eqgPmJkY9ginl6SHZ3BECHCkhr2TG3YfgO3/Ma/DL4HM/lHXcM10WPQx3DKn7fU+WPs+G+uPkGdxdE1wo6oOHlkF24/ABeM7fz5Jap7ioirg3fa+rwCHiIiIiIhIFK1tT4HGY2LDa3BkhwUyrGEZHAPzmr08wgVGAEa6Wl3D9GHw4Y+ajhvJX2rf54fVKwG4LW1y650T5a6FZnBjdB8474SumVOSgtPlXgi4PcVF5cF2VJ7iootiGVMBDhERERERkShC21PSowQ42srgOFrVsm+DXhPMnxjktFFKw2vUc3f1KgD+knkWVziOj2ncDtlVAi9tMKM/f7kyehRIJLIjNJaYOUrTcjPtogCHiIiIiIhIFG0FOMJrcFRECHBYI+0SqdwNVjtk9E3YOhf7tlNi1DLB1puvp52YsHFbtepz8zpnFIws7Jo5JWl4iouuD/t4C1DnKS6q78iYCnCIiIiIiIhE8fz75rW0JvLzjGDh0CYZHGHZFpeeCO/tCitP4a2ApVeCLQ3mPgXpiQkMPFG3CYAvOcYmZLyYrN5mXnU8rHSA0+W2AWXAJGBjR8bSMbEiIiIiIiJRPLjCvJZFC3AEMziqvZGPiU1Phd9dBmeOCd7Y/gJU7QFbqhnkSADDMLBiIRUbV6V2UYDDMMzIDcBMBTik/YJZGzuBNs4IapsCHCIiIiIiIlEUZJjXy06K/DwjrMhoKMsjajFQw4AtT5ntE26B1OyErNFisfBU9jwO5d/CEFtOQsaMYVLw3AZP3aDtKZIIvwB+5XS5e3dkEG1RERERERERiSIleMzrd86M/NyRYtbZ8NbDW8GSFANyowy2fzWUfgqOXjB4bkLWt9q3h1PtAwHIsToSMmbMHHaYMbxr55Rk9T1gOLDH6XLvBqrCH3qKiybGMogCHCIiIiIiIhEYBpRUm+1emZH7WCxmFkdlHfjq4eQhcEL/CB3rvfDRA2b7+OvA1vFgxMf+Q8wuf5qlOQs40z6kw+PFxFcP33oOvj4bJgzsmjnlf8Hz6BQVERERERGRzlFRa36fz0ht/QTUdLsZ4ACYOtQMerSw9kdw8F1IzYHR1yZkfb+tXUcAgxe9W7ouwPH6ZvjvJ7BpHyz7FthU9UA6zlNc9LNEjKMAh4iIiIiISARHgtkboToc0WSmwqFg+7g+UTqdcDMc+RBO/R04ou1hid2e+gqerNuEFQvfTpvS4fFiFtqHc8mJCm5Ihzld7gzgfuBiwA4sA27zFBcdbs94CnCIiIiIiIhEcDRYBaAgyvaUkPSwsx8yo50DkTsKLlgMVluH12UYBr+pfRcfARakjmaELa/DY8YsdDTszJFdN6cks/8Dvgw8Afx/9u47Tqrq7uP4Z/psX5alL7036SCigBR7F40Fa+waTVNjYvQxPqkmjxpbbDEmxFiwi6iIoEZFpVfpveyyvU6/zx9nl13YNjtbYNfv+/Xa171z595zzyCLM98553d8wKXAU8BFsTSmyE1ERERERKQGOeUBR231NyrEVwk13Ed+hVyyv3K/CcKNiGVxXcmH/NW3HICfe8c1us2oHSiEbdkmxTlO9TekSVwA/HDh3Dk3LJw753bgTOC86bNmx/TLooBDRERERESkBtGO4Kg14MjfCG9PgQ8uMEVGm8Cbgc383b+WOJz8M/F0xrtqqmjaTL4on54yoRe4Gh/WiADdgc8rHiycO+cbIAR0jaUxTVERERERERGpQW60IziqFCB1V3zuL9gKCy6DSBASuoGjtrkrDTPW2Yk7veMY5EjjCs/QJmkzahXTU07Q9BRpMg7gyPQvRIxZhQIOERERERGRGuRWFBltQA2OQyM41j4O/lzoMhkmPtRkferpSOFPCVOarL0GGdwZRmTApD5H5/7SFtmAOdNnzfZXOeYFnp0+a3ZpxYGFc+ecE01jCjhERERERERqcKgGRxSrqFRwO4BIGPZ9ag6MvQ+c3ibpj2VZ2Gpcg7aFXDfJ/Ig0nRdrODYn1sYUcIiIiIiIiNQgllVUXA4gewX48yCxJyQ3frSDZVk87V/Fk76V3OQdwc2ekUc36BBpIgvnzrmmKdtTwCEiIiIiIlKDaKeoVK3B4XECOxaaBxnToQmCiH/413JzyccArA/lYPO2cLjx0ALYmw83nmSmqYgco7SKioiIiIiISA2iLjLqqdx3O4GU/tBxPGTMbHQfQlaEB8q+AuCR+JN5PHFGo9tssPfXwturIRBq+XuLNIBGcIiIiIiIiBzBsiprcKTVU4Oj2ioqfS4wP03g3cBWdkYK6WdP5Ufe0U3SZoPsyIEduZASB0NbcElakRhoBIeIiIiIiMgRSgPgD5kpJ/H1rPBa8byNCG57sEn78ZhvOQC3eUdhPxp1NxZvNtuT+oLTUfe5IkeZAg4REREREZEjVNTfaJ9QfxmNuPIRHDekPUPcp7PBl9MkfVgQ2MGi0G4ScHG1Z1iTtNlgn24y26kDjs79RRpAU1RERERERESOkBPlCioAHpcZvXF16j+wHcyE7JWmwGgjneTKYJKzG+e5+5Fi99R/QVMrDcCX283+5P4tf3+RBlLAISIiIiIicoTcKOtvADjtcJx3NZ1dmRDfFbpNa5I+eG1OFiZfhMd2lD62vbvGFBYd3hU6JB6dPog0gAIOERERERGRI+Q2YASH0w4zExeYB91nNHpp2OxIKSk2Dy6b4+iFGwCzRsFX2+CycUevDyINoBocIiIiIiIiR8ipUoOjPg6bVRlwNMHSsLeXfEL3vKdZENjR6LYaxWGHRy6C8b2Obj9EoqSAQ0REREREvhfufgsu+ztEIvWfe6DAbNPrm5kRKGL09psY4NlMQTgZOk1oVB9zImW8HthMllXKAEe7RrUVswUbYP1+s1auSCuiKSoiIiIiItLmWRa8uszs78mHHml1n7/loNn261BPw04v7kAmEWcSjuP/DHZXzH0stYJcVTyfAGFOdfWipyMl5rZiFo7AL9+B7GKYfysM6tzyfRCJkQIOERERERFp8yqWfQUoC9Z/fkXA0b9jPSfaXXDSY9ixkZiYEXP/AG4o/oh5wW20t8Xxx/jJjWorZl9tN+FGj3YwsNPR6YNIjBRwiIiIiIhIm5dZWLlfUFb3uaEwHCg0tUIzUqNoPLF7o/oGkBfx8WpgI3ZsfJr8A4Y60xvdZoO9vBQeeN/snzms0cVSRVqaAg4REREREWnzsooq9/fm131ugc9sU7ymzmat9nwMkSB0OgE8jZtO8lZgM0EiTHf1ODrhxudb4J63zf55I+C2qS3fB5FGUpFRERERERFp8w5UGcHx09frPje/fDpLanw9ja59Cj67BXJWNapvAIMd7ZntHsLVnmGNbismr5QXKLn5JHh4FsS7j04/RBpBIzhERERERKTNqzqCA8AfAk8tn4byygOOdnUFHJEw5K03++1HNLp/x7u6cryra6PbiUkkYiqv2mwwu3GrwIgcTQo4RERERESkzcs8IuAo9kcRcMTV0WDRDgj7IKFro6enFFsBEm1HccSE3Q5v3Qg7c6HrUVi5RaSJaIqKiIiIiIi0eVWnqIAJOGpTUYS0zikqFaM32g1pVL/m+jfSJ+9ZVoayGtVOzKr+QfSsZ+1ckWOcAg4REREREWlz9ubD3W/Cliy46kX4ZOPhz+cUw46cmq8tLC8ymuyt4wZ5G8y23eCY+1gY8XNV8XwOWmW8G9gaczsxyy2BGY/CpsyWv7dIM1DAISIiIiIibc7vPoBXl8PMx+CzLZXHu7cz2wufhZMfgdV7q18bCJltbVNYAMheYbaNGMExN7CJUkJMdmZwb9zxMbcTs7dWmbk7D3/S8vcWaQYKOEREREREpM0JhKsfs9mgd/vDjy3YUPu17toCjrAfHF5wxkOn2ItyvuhfB8DVnqHYbLaY24mJZZkECOCc41r23iLNREVGRURERESkzclIrX7M66y+Mkqcq/p5/vIRHG5HLY07PDDtBSjNAk+7mPq3PZzPZ6E9xOHkQveAmNpolNV7YWMmpMXD9IEtf3+RZqARHCIiIiIi0uZUhBRVWUCi5/BjizZXP69iBIervq+D4zvG0jWgcvTGBe7+JNs99ZzdDF5aarbnj6xjqIpI66K/ySIiIiIi0ubUFHCc2Ld6wLF0p6nRkRYPb6+G04ZU1uCocQRH0U7w5UK7QeCsax3Zuu2PlABwpWdozG3ExLLgDx/Bq8vMnJ2LR7fs/UWakQIOERERERFpcyoCjh7t4JKx0DEJpg6Al5dWP/eaf0LEMvufboIxPc1+jUVGt74Ka5+EobfAqDtj7t8TCTMY4+zETFfPmNuISVYRLNkOTjv89hwY0Kll7y/SjBRwiIiIiIhIm1MRcPzqdDilykquR47ggMpwA2DzQRja1ezXOIIjZ63Zth/WqP45bXZu8I5oVBsx6ZQMb98EB4ugQ1LL31+kGakGh4iIiIiItDm+oNkeOQqjpoADzGwNV3mgsSfPbKuVprAsyC0PONIaHnBYlsWPShYyL7CVsBVp8PVNSuGGtEEKOEREREREpM2pGMFxZMCRVCXguHNG5f7wrjCsfOTGd5lmW20ER8le8OeCOxUSMhrcpy9D+3jct4Ibihc0+NomsXAjbMo8OvcWaQEKOEREREREpM2pCDi8RywDm+it3B/WrXL/xL4wrrwcRrHfbKuN4MheYbbpo8yQjwb6l389AJd7BuOwtfBHsc1ZcNcbcOrjsH5/y95bpIUo4BARERERkTYnmhEcfdMr90f3gNnjD88tXEeO4DgUcIxscH8+De5mTnnAcYVnSIOvb5R1++CCZyC3FMb3gsGdW/b+Ii1EAYeIiIiIiLQ5/tpqcJSP4HDYoVMSXDMRJveDk/pC93YwfWDludVGcBRsMdsODVtadWUoi1ML51JCkMvdgxnu7NCg6xvtzx+bYSmnDIYXrohp9IlIa6BVVEREREREpM2pbQRHx0RIcEOfdHA64L4zDn/+/BHw8Xdmv1oNjmkvQvEuiGvY0qp/9S3HT5jL3IN5MfH0Bl3baOv2weLNEO+GP5xntiJtlAIOERERERFpU8oCkFVs9o+swZHggY/vgHhX9evAjOKocGQ4gs0GST0b1JeIZfFVaB8A98dPbPnaG09+ZraXjoV28S17b5EWpoBDRERERETalOv+DeHyVVhrCjI6J9d+beeUyv1DIzjyN0LIB2lDwd6wj1B2m421KVezJLSfAY60Bl3baOEIBMLmhVw3qWXvLXIUqAaHiIiIiIi0GdnF8OW2ysdxDZyR0b7KIIeIVb6z/ln44DzY+M+Y+uSw2Znk6lb/iU3NYTfTUn42o+5UR6SNUMAhIiIiIiJtxvJdjbvebq+cmnIoE8j82mw7TWhQW8/4VrE0dKBxHYrFwSKwytOZ9glww4kt3weRo0BTVEREREREpM3YnV+5/+cLYmvjm7uhNADJcUDxHijZA64kSB0UdRtz/Ru5sWQB7WxeNqVeS7q9hepf+ENw9lPQLRWeudwEHCLfExrBISIiIiIibca+8oDjF6fChaNiayPZW2X0Rlb56I2O48F+5LIqtfurbwUAP/eObblwI7MQbnkZMotMQpOmoqLy/aIRHCIiIiIi0mbsLQ84uqXUfV7UMr8x2wZMT9kbLuK/oT14cHCbN8aUpaFCYbjyRdiUBYkes/6tzdYy9xY5RijgEBERERGRNmNfgdl2S22iBjOXmG2n46O+5PXAZizgDHcfku2eJupIPV5aasKNHu3gletUVFS+lzRFRURERERE2oxDIziaIuCIBCGug6m/0W5I1Je9EvgOgIvdA5ugE1HYkw9//tjs33Oqwg353tIIDhERERERaRPKApBbCm4HpDdFbU27C2b+B/Z9HnX9jQOREr4M7cOLk7PcfZqgE1Eo8UORD6YNgFOjD2JE2hoFHCIiIiIi0iZUTE/pkmKWe20SdhdkTIv69I62eP437kRWhrNItLmbqBP1GNgJbj4Jrp6ouhvyvaaAQ0RERERE2oS95QFH18YWGLUsWP8MdJ4IacMbFBrYbTZ+GTeBfMvfyE7UI7sYsopgSBfz+K5Tmvd+Iq2AanCIiIiIiEib0GT1N/I2wIo/wCfXgBWO6pLMSAm5kTIAbDYb7ezeRnaiDqv2wHX/hnP/Bm+var77iLQyCjhERERERKRN2NdUAcfOd8225xlgj27Q+/+UfklG3tO85N/QyJvXY8VumPWsCTlS4mBMz+a9n0groikqIiIiIiLSJlSM4GjUFJWQD7a/ZfZ7nlXv6ZmREv5StpS/+9cSIMxIR4dG3Ly+voXhV+9AKALnj4D7zzQhh4gACjhERERERKSNqCgyGvMIDl8ufH4rlB6A1IHQcVydp4etCJMLXmZTJA+AWzwjGeJMj/HmUXjxa9hwADJS4bfnQFwLFTEVaSUUcIiIiIiISJtwaARHrAHHkl9A5hLwpsPEh8BW94z+T4K72BTJo7s9iTeSzmWss3OMN47CR+vhtx+Y/QfOUrghUgMFHCIiIiIi0uqFI3Cg0Ox3TY6hgVAZHFwGNiec9iYkZtR7yZzAegCu9Qxr3nAD4IS+0DERLh4D0wY2771EWikFHCIiIiIi0uplFZnSFOmJ4HHF0IAzDi74EnLWRBVuAOwKFwFwuWdIDDdsoEQPLP4JeGN5cSLfDwo4RERERESk1SswK7TSPr4RjTg80HFs1KcvSvkBByIldLYnNOKmddhwAD5cDzeeaKakKNwQqZMCDhERERERafWK/Wab6I3hYn8e2F3gSmzwpc0Wbry2HH7xFkQs2JQFT17SPPcRaUPqrpojIiIiIiLSChwKODwNvNCfD59cDXPHw9bXorpkd7iQf/nXNfBGDXCwGB5834Qbs8fD785pvnuJtCEawSEiIiIiIq1eTAGHZcHi6yFnNcR3hU4T670kYllcUvweX4b2kRfxc3vc6Ng6XJc/fQRFfjh5ADx4dtO3L9JGKeAQEREREZFWryiWgCNvAxxcCu5UOHUuJHSp95IvQnv5MrSPzrYELvMMiq2ztQmFYc63MHcFuB3w6zOatn2RNk4Bh4iIiIiItHoxjeDY9obZ9jo7qnADYF5gGwCXeAaRbm9MRdMa/Osb+M37Zv/WKdC7fdO2L9LGKeAQEREREZFWr8hntknRBhyREOx42+z3uSDq+8wLmoDjLHefBvQuStdMhMGdIa8UTmuBpWdF2hgFHCIiIiIi0uo1eBWV/f8FXzYk94H2I6K6ZFe4kLXhbJJsbk5yZsTW0foc37t52hX5HlDAISIiIiIirV5uidmmxkV5QXIvGPxDSOwJNltUl1SM3pjp6onb5mh4J2vz/lrokgKjujddmyLfQwo4RERERESk1duTb7bdUqO8IKkXjLm3QfdIsLkY7kjnTFcTTU+xLLjrTVNUFODZy2FGExcuFfkeUcAhIiIiIiKt3t5oAw5/PjjjwNGQaqTGlZ6hXOkZimVZDe9gTRZsMOFGnAt+PgOmDWiadkW+p+xHuwMiIiIiIiKNEQhBZhHYbdA5uZ6TVz8Mc8fBjvdivp8tyiktdfKH4Hcfmv27T4FrTwC7Pp6JNIZ+g0REREREpFXLLzOzPdrFg6uu0hiRMOycD8EiSOoRdfv3lHzGxUXvsCqU1fjOAkQi8Nhi2JkL/TrAZeOapl2R7zkFHCIiIiIi0qrll5ptvQVGs74B30FI7AFpw6NqO2xFeNG/jtcCmwgQblxHK7ywBJ741Ozfe3o9qYyIREsBh4iIiIiItGoFPrNNqS/g2Fk+LaXnWVGvnPJFaC/7rRJ621MY6+gceyerumg0DO0Cj14EU/o3TZsioiKjIiIiIiLSukU1giN/I2x/2+z3OjPqtl/xbwTgYvfApqm9AZDshXdvjjpkEZHoKOAQEREREZFjVlkAZj0LUwfAnTNrPqewvhEc/gJY9EMIlUDPsyF1cFT39lkh5gY2AfADz8AG9vwIlgULvoPJ/cDrUrghrcr0WbO7A/8EOgEW8MzCuXMenT5rdhrwCtAL2AFcvHDunLzps2bbgEeBM4BS4OqFc+csb+5+aoqKiIiIiIgcsxZvhvUH4MnPaj8nv8xsaw04fNmQNhTSR8PEP0UVLuRHfJxSOJcsq5RBjjRGOjo2vPMV8krhshfgxpdg5l9NaiPSuoSAny2cO2cIcDxw6/RZs4cAvwAWLpw7pz+wsPwxwOlA//KfG4CnWqKTGsEhIiIiIiLHLEeVr2QtCxZtgnvfgUl9YU8+XDUB9uab59vF19JISl+Y8rRpIMqRE9+GDrAjUkA3eyKvJp7duOkpf/gIlmw3c2jumglx7tjbEjkKFs6dsx/YX75fNH3W7A1AN+BcYGr5aS8Ci4G7y4//c+HcORawZPqs2anTZ83uUt5OszlqAUdhYSGhUOho3V5ERERERFqBwkIXkATA/31YyuNfmBRj7grz/JLt0DkpDDgY3L6Q3Nym+YwxhmSWWhdSQID2hQ5yyY2pHce6AyS/tgycdgqevYhIn/aQG1tbIg3h9njTp8+avTSGS59ZOHfOM7U9OX3W7F7AKOBroFOV0OIAZgoLmPBjd5XL9pQfa5sBR3Jy8tG6tYiIiIiItBKWq3K/ItwAGN0dlpd/fDpQ5CDJC1OHJldfcTVrqam90WkiOBo+cqIRE1PAF4S/vGYqFlx7AqljtWKKtJyA35e9cO6csU3Z5vRZsxOB14EfL5w7p3D6rNmHnls4d441fdZsqynv11CqwSEiIiIiIsesIl/1Y78/F1677vBjU/pRPdwAWPcUfHI1bHs9qvsFrTB/LPuag5HSBvf1MJYFP3geVu2Bjknwo6mNa0/kKJs+a7YLE278e+HcOW+UH86cPmt2l/LnuwBZ5cf3At2rXJ5RfqxZqQaHiIiIiIgcs4r9hz/+4QlwSQ3fSZ/Qt4aLC7bC/v8CNsiYUe+9ciJlXFn8Pu8Ht/OyfyMrUq+McVj8LgAAIABJREFUqc+AqfVx6VgziuPhWZDoib0tkaOsfFWU54ENC+fO+b8qT70DXAX8oXz7dpXjt02fNftlYAJQ0Nz1N0ABh4iIiIiIHKNCYXj4k8OPTaoSZFw/CZ79Auw2OG3IERcX7YQFl0IkAN1PhbgO9d7vsuJ5fBTcQarNw/8lTG10//nBGDh/JHj0sUtavUnAFcCa6bNmryw/9ktMsPHq9FmzfwjsBC4uf+59zBKxWzDLxF7TEp20WdZRmyJzVOfmiIiIiIjIsW3VHjjv6cOPrb0XEqoMhgiEIBSB+CPLa3x1F2x9DTqfAFOfA2dta8gaXwT3cmLhf0i2uVmVchW9HCmxdToSgX0FkNEututFmojNZltmWVaT1uA41qkGh4iIiIiIHJOW7Tr8cZzr8HADwO2sIdwIlcLO983++AfrDTcAflP2FQC3e0fHHm5kF8Otr8DUR+AvH8fWhojETGOlRERERETamBI/3PYKnDcCzh1xtHsTu4qA4w/nQVo8DO0a5YW7PzIrp3QYA8l96j3948BOPgruIBEXP/GOia2zwTBc/U9Yt99MSRnaJbZ2RCRmCjhERERERNqYt1bB4s3mZ+bgGkY4tAKWVRlwjOkO/RqyXmvPM8GVCI7oCnt2dyThws7tcaNJs9c/2qNGz31hwo2MVHjpWuiuKSoiLU0Bh4iIiIhIK2VZZrGOujw43yyr2lrsyYfzn4ZpAyCzCFLioE96Axuxu6JaNaXCQEcabyedx3RXzwbeCJi/Dv72OawuXwHzt+cq3BA5SlSDQ0RERESkFQqE4PQn4O43qz+XX1a5/+2OFutSk5jztSll8epy83h0d7BH86nFsmDtE7Dq4aju47dCfBnce+jx6e4+uG2OhnU2EoFXl5lwI94NP5kGk/s1rA0RaTIawSEiIiIi0got3w0bM83PH88//Lncksr9rdlQUGZGQrQGu/IOfzymR5QX7v8MVv4ZbE7odzEkdKv11IKIn5+VLuZ5/xrui5vIA/GTYuus3Q5/vwK+3gEjM8Driq0dEWkSCjhERERERFqhcKT253JKDn+8am/rGFhQte5GhVHdo7gw7IelD5r9426vM9zYFylmfMEc9kaKsWNjqiuaG9TBZoPjezeuDRFpEpqiIiIiIiLSCoWt2p/bV2C2FbUrVu5u/v40hT35kFUE7eJhUh9TymJURh0XWBZseB7ePAkKt5oVU4bcUOc97ij5hL2RYkY4OvBl8mWc7Ip2iEgVW7Lg52/A+v0Nv1ZEmo1GcIiIiIiItEKhcOV+1WKjuSVm+orTDtdMhF+/Cyv3HJ0+NtTSnWY7ujs8fZnZd9T1lezqh2HNY2Y/qRdMeqTOlVP+G9zD3MAm4nDybtL5dHckN7yTBwrhgmegyA+r9sDbN7XOZWpE2iCN4BARERERaYV8wcr9QJWw48MNZvrKpL4wtb85tmK3CUGONZYFH62Hg0Xm8fLykSZje5pgo85wIxKCnDVgc8CkR+GcT6D98FpPz4v4mF38PgB3xo2LLdwAeHSRCTdO6ANv3ahwQ+QYohEcIiIiIiKtUFmVgMMfAk/5O/t5a832jKHQLRWSvWZVlewS6JDY8v0ECIbhoQXQMw1mja7s62vL4e63zP6Fo+D1FWZ/eNcoGrU7YepzkLMSOoyp9/RUm4fBjjTibU7uihsX2wvZkmVWTXHY4TdnQULto0VEpOUp4BARERERaYVKApX7/pDZbsuGL7aa6SmnDDbTVnq1N6uY7sg5egHH0p3w7Bdm/6nP4bfnwCvLYP66ynMqwg2AAR3raCxnLST3BlcC2B1RhRsANpuNFxJPJ8XmJs4Ww2oneaVw/zyIWHDZWOjboeFtiEiz0hQVEREREZFWqPSIgCMcgemPmscT+0BqvNnv3d5sd+S0TL+CYbjjVej968oAY3f50q92G+zNh6v/eXi4cc1EuHVK5eP02oKYkA8WXwdvTYaiHXX2w7IsFgd38dOSRRRE/AB0tifEFm4U+eCsJ+HLbZDogTtObngbItLsNIJDRERERKQVOizgCJralxUuqTKooV/5QIOWWvDjpW/hnTVm/5aXYfuDsGafeXzeCHhjZeW5950BZw+vDDQyUqFLSmXB1Go2vwRlmdBuCCTWvfrJA2Vf8kDZVwAsD2WyOOWS2F9UkhdeuQ6e+S9cMR46JsXelog0GwUcIiIiIiKt0JEjOBZsMPt90uGMYZXPjS7PAZbtapl+bc0+/PGWLJjzjdmf0Mv0e1MWvPLD6iM1LhlbS6OWBRv/ASv+YB6P+CnYah+M/nZgCw+UfYUdG7+Mm8Ct3lGxvJTDZaSauhsicsxSwCEiIiIi0grllFTuv7kKniuvcXHk4ILBnc12R27L9Gv3EfepqL0BZnWUi6MrmXFEox/C0t+Y/UHXQLdpNZ62NHSA//g38KzfDCH5ffxJ3BU3PoYbYkKVTzZBZqEZEmPX7H6RY50CDhERERGRVmh/QeX+c1VChDOHHX5eYvlCH6UB85ndsiAYqVzJpLHW74dVe81UkwR35XSUCq8uN9tnLjOjSxosEoKVfzb7o+6GoTfVeFqxFeDswjc5YJnk51L3IO70xrhaCsBfF8Eji8z+piz4nzNjb0tEWoQCDhERERGRVqhqzQ0woyP+70LolnL4cZcD3E4IhOCLbfDAPCj0wSd3NH6V0yIfXPmiGU3y5wXwwFlmPyUOhnU1K7oADOwEMwbFeJOcNVC009TcGHTtocMRy+Jx3wrGODsxydWNRJub95LP5wXfWn7gGcQkZzdstRbzqMdnW+DRxaYYyD2nwpUxjgIRkRalgENEREREpJWxLNh/RMDx+3Oge7uaz09wm4Djp3PhYLE5tnIPTOrbuH4s+K5yqkxuKfzoVbM/rAukeCvPm9SnjsKh9ekwCs79BEozweHGb4X4Zenn/Mf/HfutEi5w92eSqxsAY5ydGZPYOfYXVOgzVVKf/NT8Id9xMlw/Kfb2RKRFKeAQEREREWllckpMYFHh4VnQr2Pt5ye4Ia+0MtwAWNUEAcfnW8x2ZIYJTCoM6wr5ZZWPx/eKofHSLIgvf1GJ3SGxO/kRH+cVvcWnIXOznvZkrvQMjanv1fiCcPkLsLZ8js2Zw+D2qU3Ttoi0CAUcIiIiIiKtTEX9jd7t4dnLoW+Hus9PcFc/tmpv4/oQiVQGHOeNODzgGN4Nvt1Z+XhczwY2XnYQ3p0Bfc6HMfeRjZ+flCzm3eBWCiw/XWwJvJZ0Dic4u8Y+DeVIDjuM6QEFZabextT+Kiwq0sroN1ZEREREpJWpGnDUF27A4bU2RnU325V7zCyMWG3PMSNJOidXH6ExoRfsyKl8nJbQwMZXPgTBIijeA3YHPivMp6HdFFh+Jji7sCTlcia5GlFjo6o9eWbrcphg472bYdpAhRsirZB+a0VEREREWpl95fU3uqTUfV6F+CojOM4cZlZWySoydTNilVlktj3ToH2VAGNoF0hPhKkDzONJfRrQaCQIax6Dra+xObE9X4+6BYAMRxILki9ifco1LEm5nB6O5Ng7XtV3B+D0JypDDoDkuKZpW0RanKaoiIiIiIi0MhUjOKINOBKrjOAY1tUs17p6L2zLPjycaIjc8uKiaQmQWiUTePmHZnvFeLOiy0n9GtDo17/C2voa73TszZWjzmCW8yATyp8a6EiLraO1KSiDm/4DxX54fx3ccGLTti8iLU4Bh4iIiIhIK9PQgKPqFJUhnc3UloqAo8H1Mcpllwcc7RPMMrS3T4VQpDJMcdhh5uDo27OyvuHv/rU8OPVKdsYlARDBwrKspquzUeGlb+GJT2FfgUl8rpxQ/zUicsxTwCEiIiIi0socCjiinKkxfSC8vsKUmUjymhEcANsOWrDvM0jqDUk9zEF/AfgOgs1h1nZ1xIMrAZzxh631mlsl4AD4yfTYXsvecBGvBTbxsbWCecOnAdDBFsedceP4uXdc04cbry2HX71j9vt3gKcuBa+rae8hIkeFAg4RERERkVZmXwNHcJw2BP5yIfQtDzb6pEOyvYBxe+6H7Ldhxn8qA44Nz8LaJ6o3ktwH+l4EQ28CoLi4mDvaP8ekQCLs7QcON6T0h7goqp5WKDuI3RPHQ2Xfss9ZjDcS4amEGVwZNwp7UwcbYGpu3Pee2b/vDLhqgoqJirQhCjhERERERFqRVXtMwJHoiT7gsNnggpHlD8J+Ju/7MSv6fYjdZlESScDrTMBRcXJppgkzImEgAqEyCBRC4TbIXHIo4Nid7ePZ9EchG1hU5WZxHcGdDCf+FdodMUel7CDkrOHVslUMyN7MyJ2f0mXGS8xNOYfV4YOc6uxBL2e7mP9s6rW/EJx2mDUKrpnYfPcRkaNCAYeIiIiISCuybJfZnjUcPLG8m1/+e5IPfgA2+Kr0eH6TdR/3Fw3m+PLRHZzwUPVrIiHY/1+wmxjEF4Rv9iTwWOpt3DR8E65ICYT9kLcByrLMD1XWoP3mPtjzMcGyTO4YfCJP9RzOL/LyGBkqg+wVTEy/momurjG8mAY6eQC8dwt0TGz+e4lIi1PAISIiIiLSiuSU176Itv7GYXa8BxtfBJuT83e8ykrfKKCypket7E7oNvXQwxW7oTAUx4eun/GjaVXOi4ShdD+ESiGpsnqpb98iftetOy93mc7mhBQ8lsXgrmfCkIcgMSOGF9IAizaZNXEvHm2GsvRs4tVYROSYoYBDRERERKQVySs127RYlnfNXmm2I3/Gyu9GHTpc4GtYM0u2m+3xvY94wu6oFljkRXyce+KVfG4zKUo3eyKvJ57LhPQuDbtpQxX64K+L4D9LoTRgqqHOGNS89xSRo0oBh4iIiIhIK1KxeklafAwXD73R1McYcgN3nAyPltfOKCxrWDNLdpjthF71n3t20Zt8YSugmz2RFxJOY6qrOy6bo/4LGyMYhhtfqkxizj0Opg1o3nuKyFGngENEREREpBXJaegIjqIdEN/VrHIS1wGOux2AO04Gfwj+9jkUNCDg8AVh5W4z22N8r/rPH+fsTLEV4N2k8+nuiGVeTQOFwvCb90240SERnp8Nw7s1/31F5KhTwCEiIiIicozbnQff7DCFRSvqZbSPJuA4uAI+uxmccTDtBUjqdegpmw16tzf7DZmismI3BMIwpDOkxB3+3OZwHg+VfctUV3cu85gVVH4TP4l4puCwtcByrL4gTPoz5JaC2wFPX6ZwQ+R7RAGHiIiIiMgxzBeEi56FzCL4w0eQXWzCjYpwolZZ38CCS8GKQNpxkFD9g35FQNGQERzf7jTbCUfU33jTv5mrSuZTZAV40b+OYY50jnN2IMnmjr7xWATDEI6A12V+eqdDcgnccwqM6t689xaRY4oCDhERERGRY9jafSbcABNuAJw3Apz1lbFY+zcTbvS9CMY/CHZXtVNSy+t41LuKShWr9prtyAywLItn/Kt53LeCteFsAGa5B3Bv3PEMd6TX0UoT2ZQJt74C10yEy8aZY/+4EhI9zX9vETnmKOAQERERETnG+INwsBgy2sFX5XUyx/aEXmkm7Lhlcj0NFO2AfYvB7oZRvwBHzR/4h3eFOBes2w978iEjtfK5JdthSBdI9lYe25YNn2w0+wO7hpld/AEvBTYAkIiLB+In8RPvGGw2WywvO3or98D8dfCvr6EsaFKXioBD4YbI95YCDhERERGRY8x982DucvjX1fCPr8yxm0+CaQOjuNifB9/+BrCg1zngTav11Hg3jOsJn22B9fsqA44l2+HSv8PATvDBbRCxLFaHD/L4nlwi7t5M6OKhf5ode4mNBFw8lTiDH7gH4W7u1VEiEXhsMTyyqPLY+SPg/jOa974i0ioo4BAREREROYaUBeDVZWb/8hfMdkwPODnaVU7tLshZbUZtDLmu3tN7p5uAY0du5bH/boVQUiHfZHzHyTty+dK5nUBcCXSHtCHTOK/HaOx2G08nzuRXkQkMctRXEKQJ+ENw/tOw4YCpkHrFeDh9qFmrtrlHjIhIq6CAQ0RERETkGPLp5urH7pzZgM/wrkSY9BdI7A7Jfeo9vVd7iDiCbM9xAuYmczstYs81y8Fusbj8vPiyJBKzOxGK2BjQsfyYzdUy4QaAxwmWBalx8MhFMKV/y9xXRFoNBRwiIiIiIseQ+esOfzxtoBmkUC9fNnjLC3t2nRLVvTIjJfy9x8fsunULGxbcACQBsJ8isGwkbByIZ28GnsxOpBd0pshnIyUOhp8X9cuJ3aJN8PBCuPEkOHOYOfbYxdAlBRJUZ0NEqlPAISIiIiJyDFi/H67/N+wrX9Fk0Y8hpwSGdI7iYl8OvHsKpI+GEx8FV0Kdp68LZTM3sImnfCvJ9JRCxMbmcB4VAUf31ccT2TWN309P5NerzTXlC7lw4UjwVF+QpWn4g/DfbfDeGnhrlTm2ZHtlwNGvYzPdWETaAgUcIiIiIiLHgEc+qQw3hnQ2U0d6RTP7Y+8i+PLnprhosBCc8XWe/lFgB+cUvYmfMABTnN3Z+cwZlBYm4QuC1wXW/o44S2Fqf3j7Jjj3b5XXXzo2xhdYl6wiMzfn8cWwK88cczvhJ9PgqgnNcEMRaYsUcIiIiIiIHAOySyr3zxsZ5UW+bPjixxAohE7Hwwl/rrNYx5rQwUPhxiz3AGZ7hnCWqw8zXHZ2ADtzzcopBWXm/OQ4SK2Slwzr2kyDKNbsg7veNPt90+GUwXDhKOjboRluJiJtlQIOEREREZGjrDQA6/aZ/b9dCjMHRXnh8j+acKPLZJj2j3orkfa0J2PDxs2eETyeMAN7+fm928OOHPPTJx1KAuCwQ5Ln8CZTvA1/bdXsKzDDVUoD8PgPzLGBHeG0ITCxjxki4mrm5WZFpE1SwCEiIiIichTllsD/zINAGI7rBqcOifLCnNWwbS7Y3TDuf2oNN7aH8+llT8Fms5Fs9/Be8vmc7OxxKNwA6JlWfm5OldEb3som2yeYeiATesf2GgmE4L218M0OeHs1+IKQ4IZIBOx2yGgHT10aY+MiIoYCDhERERGRFrYzF9LiIckLP3/DLBjidsDNJzWgkdIDZtv/EkiunjzkRMq4teRj5gY2cY1nGH9LmInDZme6q2e1cytmgqzbByf1NfspcZXPv34DfLgerp3YgP4B+EPw6jJ4/kvzoiucOQxum9KAtW9FROqngENEREREpIXkl8LP3oBPNpoA4a6ZsHizqaf5wW1mqkjUMmbC8B/BoGurPWVZFlcVz2decBsObESwCGNR28SPyf3M9r21MK98mdoOiZXP90yDG06Msl/+YOUyK047/O8HZgRH33S4ZCwc39sU8xARaWIKOEREREREWkBBGVz5oqmnWfH4V++Y/cvHRRFuFGyF7W9B7/Mgpa8Z/TDip4edYlkWX4T28px/DfOC22hn8/J1yuX0d7Srs+keaaY5yzI/U/vD3ac08AVuOABPfAofbYDl90CixxTyuHaiKexx/ghwqraGiDQfBRwiIiIiIs1sZy5c+y/Ylm1GQ7x4Fdz3Lny2BSb1gV/UFyZsexO+uhOsMGx5Gc76ALzVE5FbSz7mKf8qAGzA8wmn1htu1OSFK6M8cW8+PLYYPv7OFOkAE2psz4bh3czjBiclIiKxUcAhIiIiItLMHphnwo1OSfDStdA1BZ6bDWv2mhygxkVDAkWw/mnIXAIHlwMW9DoH+lxQY7gBMNrZiUS/ixu9I7jcM5hRzk5R99Gy6jmh6tQTgOvmwOdbTHVUMAVFLhgJN50EnZOjvq+ISFNRwCEibV5pAN5dA1P6m/dbn2+BfyyBoV3gsnHH1nuwr7bBq8thcxb85UIYGP37UpEafbPDjAxPT6z31FYtFIZw5PDPXiLHipe+NUVEAebeYMINMKHG6B61XOTLho9nQ/5G89jmhJE/h6E3Vjs1K1JCR3sCANd5j+M8dz/S7fGN63QwDJsy4dtd5n9KGw6YCqSrf1X5i5ZfZsKNc4bDrVOgf0cVDRWRo0oBh4i0WR9/B12SzcjZDzeYIGPmYFPM3R8yBd4eWwy/PccEHUfbe2vgR69WPv7B8/DezWblPGm7LAteXwGp8TBjUNO2vTHT/D3yOGHdr82o8bbIH4JZz8KePJh3a+WHR5FjwYYDlXU2zhgKGalRXuhOhfjOEAnB6F9ChzHgqf6Xe35gG7OK3uHviafxA4/5R6Qx4cbQnH385tv34LX95perKocd9uRXLrny0AWmzkaHNp6gikiroYBDRNqkb3bA9f82xdsj5UNuDxTCv742+1P6w9KdUBIwbzw3ZZn5z94Yvv3dnQff7oR73zH3m9wf1u+HTsnwfxdClyPejwZCkFUMXZPBXv6B8+GF8NfFZn9qf1NRv6AMHpwPT192+PVlAdhy0IzuKAlA0FdMqifE6gNucv0eThnSOgu4WVb0X/xFIrB2v/mzbc3vq/cVwG/nw/vlKxbcdwZc09AlGOuwtryQoT9kvkEe3wsGtJIvWCuGytfU19KACYUWfGdeYyBkfhfA/C796fzW8RqldYpE4PWVMCoD+nWs+9z1++HMJysf33NaHScX7YKtr0DGDEgfBXYnnPgYYIG7+lDDMivI64HN3FS8gFJCLA0dOBRwRGVfASzfBV/vgG4pcNNk0w23l9HZu805vdJgVHczh6ZvB/Oik7yVbTRoyRcRkeZns+qdbNdsjtqNRaRtKywz3+ZuPlh5bEwPmD0eduWaD3inDjEfkO58s/Kc3u3hteuhfULd7Vf9IL4jBy5/6iA97FuIt5cSby8lwV5CnK2MBHsJwzqH6D7+cn4yrwN9O8D5vbfwypJSNhem4vCmkpycxPZsG0V+097IDHj5h5CXfYDXXvk3iQkH6dM3iDMuQKktyMFgmD2lIf5VfDH7/RkkrR/O/3S8n6va/ZPZx80g2+0lbLPjtcBjcxLvTCa184mc7erLTHcv2L2AQk8CRUk96eTtgtPWsl/pWxa8vdqMrsksNB+8/3g+LNsFv34XxvaAZy6HJA+ELTPy4Ejf7oSfvW6CpW6p8P6tkFzl/XY4YkZN1/ff8WiLRGD6X83foarW3AvFfnhtOVw0OrYpVLkl8J+lZkj8sl2HP3fXTLh5cuz9bkr+oAkX+3eEf39rQscbTzL//W94yfy+DuwET11qPn99vcOMxnp/LRwsrr3daIOi3BITlE3sDev2m79vgzs32cuTNiSzEB5dZP6erN8PoYg5fu/p8MMTar5m60G4+DnILTV1Nxbcfng2cEjWN7D2Kdi32DxOGQBnvg/22sPqZaEDXFj0DjsjhQDMdg/hn4mnY6sv2QuETEL+n6Xw8tLKF9IhEb78Of+7wMHzX1j8b9pWLr+2qxlaJiKtls1mW2ZZ1tij3Y+WpIBDRNqMl741tTbW74dCnzl28gCz9N3scdW/abMiFlt27SNry7es3ZpJfmmExNE3c+uU8hNW/BGK90DYD6FSAr5iMnOKcUWK+cYzmzMvuY3ffQA7V3/McxnX19qvadsWsC3SC1vYycNdfszMdvN4o1MfCpweCpweDtqTKHK4yYzrTEn3wfwlYSqjy/zw9hTuHjiRP/UZXWO7jsIkuv/9Rm5Ke4ob056m/8wLyfbE1XjuH+JP4m7vePjPIF7vkMGs0adjsyzahwKkhy3SLDtp3s4keTryQuJpeCzA5uBh3zJ2RgpxYseFnc2ZdpZtt1NUZmcIHbi/f1+mD4S8iI/XA5vMeTY7Tux4bA7iLDc797tJKWyPr8jB52tDLD7owxcXBstGx+ISehUUEBcK4Q2G8QbD9PK4IBShyGdx+5NjSE80o1ly/7EMV24Rc78NQcSO04rgsQIMTi1g2GntSTq1M5FwiCf/nc+QRVsY3jFA546dweGGOBe4Q5DghouOh47lw2p254EvaD51JHog3lU5rKaZ/f5DeOa/Zv/8EfCmWfSAm04y4UZOiVlp4bfnwKS+DWv70U/gkUWHH0uLNx+0EtwmFOqR1vjXEKtIBDZmwY9ega3Z9Z9/Qh8TZr22vPLY8K7mz6XQB2cOMyHFO6vhx3NNXYPPf2pGUQGEy/Lx5W4nLpSFvWw/FO8mJ7+Y362bzhvZZnWHSfH/5ecd/sLg3h3xJKRBcl+zDKc3HRK61lrQUdo2X9BkAY8vNr8/R4pzmVF2E3ub1U+3HjSZwa5cMzqwIoh760YYkVHlwtIs2PAs7JoPJXvNMUccdJsCg66FjrXPm7y/9Av+WPYNfsIMdbTnVu8orvMMx2WrZ/Te/HXwi7cq/wdpt5nhjIM6wTnHwaDOBEKwco8J290a5y3S6n0fAw790yUix6RwpLJegGWZN1wuh3kf5jziPdx3B+DPH8PCjZXHxvaAP19oPiAepmgHbHsD9n6KrXAL/UOl9AcmJUAw3snM5Tdzy2T4cD0MXrOInvbKRt1AdwfggEDeDs5+CvYXgDswnMLk8eS3S2FJciobvQnsdrtZZXeyyW6j4JR38O7oww+3n4fly2CzazhXjqjlK7/QbraE8xidOAD/4NsptRJoX1CG3e/EEXRhC7po53BzXLcUluxIZUQG3Hn9zWzLvpl7g1tZsAKW77BwOctIcedw09RS3tzbidV53YhM9WPPmIblDtAhUEa2y0u2y0N2+bQcWyQHb2EO/whNgQ1zYe2/eXP6VD5PNV85TlgTYvD2CGeVWiSVWrTbnc6exzfwhT2fTmMTuP6OrebPKWCx4vJi4vwWcX443mcRV2bHHYlwFXDjtQN55uoDAFzwsp+/vuir8Y8iYrdIK/4IR7FFQjDIx68F6b3dxo9rOHe3cy83jHQTHw4ytKudmes6wzqAzGrnrvM+QcHwPnjdKfR+OoF2HxQdfkKCGxI8MLq7GToA5lvPq/4Jbof5i+h2mn2P0+xfOMp8IgBYvRe+2l7l+Srne10wdQAbM+GFr6BPwUHumWkx/TgnI5xOHv3CwZyFDoJ2B3a7g525dq54EeZeZwoRVp22YVlmpEpqXPXpGHvyK/ddDouFZ75KBhtYsrFRMWRnAAAgAElEQVSA0uJCSt8thPZF4PBidZmCbeRPzMnhAJTuA08auJKaZZ7H6ytMuFOxmmRVDrv53QfzO/yXC+Gsp+DLbYefN74X/Oea6lnUuQOzWT9oD898N5JfvGWWuQy8czbuwrUcOaCnPdCbDoAJOByEGeldCftr6fispZUhx8qHoGg3JPeGlH4Q3wniOkJiD2jhUVHSNLbnmPpMDrsJHCtKTNz6MnyyqfK8i0ebEYBFPhOmlQXhyhfNcz3awa68w9vtnAwf/aiGkRvZK2DDc2bflQQDr4ABV5iaG0coiPix22wk2dwALAtl4ifMDZ7j+GvCNDy2Ot7O+4KVcy/7pEOR3/yPcVR3uPkkGHB4JWu30/x+iYi0VhrBISItKhQ2XxrV9CX5wSIzLeHhT+CtlWbY712nwP/Oh+e/NOf0TYfzRsAlY82qELvz4NTHzJvMOJd5btYo897NVrQNDiyBpO7Q5STTwK4P4bObKm/qToEOY4gk9ebFb+N4cN9PuHmynSc/g+kJHxNvLyVguSm24shKcGBLcxJMj7CWMkoT/LT7chLu4hTW3As3+Ofx78CGGl/3OCuDb9IvAcBvhbim+ANSbR6ScJIUCZOEk1R7PF3dHRnl7HhYgbhIxAzNj1jQLt6EPHa7OW6zgQ3LFCBI9ADw6WZ4/YHV9CjOJTngIznoIyngY0KnAOm2oBnWcuOJbNibh/27NfT58SLs/jCOUPV+f/Tr1awfAEGbg5nPdWXk4pornga65XP940GCDi8Bm5e5Z1cvZhKxAS6LN270cc9F8ViWxYzP4bqXocwLZfFe3B4PyTk96Rv6hCR3Ho6/VX7p8ON/++mUaxFyQMgBp8f1JbDRzuDgPOaPSODKy80wh07ZEWYtDBKxm3vaLbg1azJDCj7AdnA7p93RlQ97mQ8Rv3/Mx/mLgiSXmNAmsayyv8WjEunzrBevzU37EgcrJu+u8bUDfPnQCbx+updUm5eT/7GLEx9aW+N5Ea+LS+//GV9vcULQyTfzH6PjwZwazy2efQK/GHY689bC7zJ2cdGTc8iLuCizO3F6HfgtG2ErgDfOotMfeuFKD0CwCN7IZe/XAb4tGcz4/k46pTlw7P8IyrIhvRBO+A6AUNBJ6L3j2RHuzvbwEHqkWgyK9+HY/ynYLbKn5ZE7rg+2uC7Eb0om4VsLK7kLOD04nU6SbBbYbUQSEtl13YhD/U5cvB17IILD48LuceGN8+LyesDjxGqXwOQX4tmTDzYrgsdl440bbSR5zEIMqXFw/tNmf94tkJYAb62y+PEbIWyOIL84LcSY3kE6OyJk+HOgYCdZJVv4wr2f/NK97AkX48qP5+XcH2CzLIZ3ifDjFXMYau1gV6An/xrcnWWd2lEcSqZ9lp1+xcn0SE9j4x4o3OfiuAM+zhtYSLe0Qh6cnIMVLCYSKmPopyGCcSey5aBFoivC7KIv6ZG7HYDFQ708eWIXQjYb7bK9TFiWTr4tjcKQRdckO7c4e4M3BewOzpm6hc3xZYSsCGOWldJpfwhskOBwM8XTk1OT+0OCm/1pNj7pVUaGPYkOeMmwJZHsrGlugzSVW142AxwABnaE+86EV5bCO2tM6PHoRaZAaNXM7953YO6Kw+twel2VmcIPT4BLx0K3xDLIXglF26F/eVGlsB+W3AP9L4H0MTVORym1gjxY+hV/8S3liYQZXO89DoBPg7uxsJjqqmX5lSXbzfST7w6A120S0opvBrZk1V84RETajO/jCA4FHCLS7Ir9Zpjurly4602wAXefAuN6QnaJWT2k2G/eKFZ8e1uhXbz57B6o4cN3SpyZugAwqY/FI+fkkp63EDKXQO46KCj/2q3PBXDCX8o7sxu+ewG6TsFKO44st5tvQgfYFSnklfU+VuX4cOWmkbTWfGALpuSz/+KXiMSVmU/KR+g092Ju69GDe0+HP5V9w6fB3Qx3dKCHI4m+9lQGOdLoaI8nztYEa1fuyIHPtkBWEezMMX+o2cWmUJwvBJvuP/Qmtvj0p0n8bk+NzWw/eRRzL72Apz6HgbkHmD/viUPP+RxOfA4XfoeTiNNi0/Vd2ZVQTKTkACPWF9N1L7Tr4ME1YCqReDdbS910zvoT8UlZOPpV+eo7MwVcYf4v/0f0mHIFs453wZ634aufV55jd0FcB0jsaaYATPgtOExIs+CzZTz/3yDFNg9FThelTgf3XBRHx44OigkRJsJQRzoZpOAPwc5IAc/u3E9uMETHdkFKrBDvbwqSFwyBPUza5ydzzUQ4ZZDFE8mvsjWSjS8SoDRUht8K4nfHU2yP41rXEB7JS4T5V7M0pQPjTjsbAGfI4qQVYdxBC08APEGL+0Inkr/RR+d4i+fPLuEPXcwwgylLQ5z9eRBPANwhi67BOM6iBwTCrD1oZ/izlf9d5t9eQu+9ETxBcAUt2gddeANhCIVZdk6E4+8yU2lmLAkx//Yq6csR7nwqi6V94kgMB/nlb+OZ+EXNH4bzR8XT65F4Sp0R4oIhCqYW1trmI3cV8JOLzQeoO1/086fHah5pY3kC2L/ocOjx5vOK6LcnUuO5n1/el8l35GCz7Jy6IsT8mwrxV4wgssBt2bEsCyw4850MFnUpxkeE1+4qYdYnNfwjAGQPzqXDv3oD0D4/QvaMohrPA7j8p3156TIzJ+aev/v43ZP+Gs+LpHpxfOw59Hj72YX02l/z25YPz45w2v0m+Ju5JMhHt9Uwj6Hcia+5+aK3mUZW52s6vgsdHjftJBdbFEwtpMRroyzBjj/BQafEdjgTvJDgZufNo3CO6UVXeyK2ZbtMIZN4jylkUjH1KtlrailoeZlanfxI9Xo4FX42/f/Zu/Mwuaoy8ePfU72nu9NZSUL2hEAIkUU2AUUgoIAILhFB44hbnHFH/amjMzrj6Kij474RRQFbgdgioqLgNCgICIR9h7BlI/venV7r/P44FbpJupNOUunu6v5+nqefruXWrXOrTlXd+973vAc+dEr3j31sZcpIqiyF2QdCQzO0trUwuv0ZeLIWnv41ZFsgFMHZ18HwWd2uK8bIX1qf58fND3Bb63JWx9QPvjrkVXy64vjuG7F8Y5oa7G9PvTSdsbQYrnp3ivpLGnQGY4DDISqS9qvGllTw84kdRgp8/DfdP2bWWDhodDpztiF3rHDSNPjpvPS4ux5fy7EVd3PntuOBEVSXw09mfI6KP1350hWVVMH402DsiWyNLVRSQqiaCMd8nrdu+T1/avwFW7ZPvQAwOf0NXzqNf6o8gg+fAm0VJYzbmBoxMVPNhEw1B2YqmV00ikmhhllzR3B8bhjMpyqO41MVx+35i7ShMe2crtyc8qSXrE8V7Z5aA+84Dt6dG87y8Ar4wh+6XseQ0jSuekRKxK86/3Di6qlsLK3gqaZyfvFoGVtLytlWVMKqIUN57pb0sKdqRnPsBZ9lcyimJVNMeWngLS/vmG2G1R1PMeEgqPsalOTqGmSAGQBcy7L17fzHbzfx/Ip1zB6xjlcetJ7ilvXMPGI2Z51ckaJaY45PMwKU1UDNIVAxqtt0/uOPO5pvPgjPrIPXTEtFI0dXd73pxUUwixq+NfOlB2/NY+Hn/4Cv3Ziu//wO+PkdgXed8FZ+cXa67Zwf5opLEikj8DtgyMFP876pc9m2cjlP3XYrJaylqb2RpkwRTZli3rv+h2SrhvO6Z8bzw3EX88qh13Pa0gkM3zqFzdVj2ThjOPcdNpSG8qFsLa1iRmY4J3Iq9z3XzPzfQKbx5xSVthKL2jnruy8dOPHzyjO56JYvweo7uX3yy2jLpGqg/3dcCaP/UkxFM1Q0Rcpbijj26tP59wO+zubmSq6ZOoVnRqb09TXvbWPy67KUtEFpK7wmTOSC4oMhBF4Y1cqmyjvSkwV47+eGMry0lMa2wJZWqL7nOGYOqeCCo7JsPvpxDsquJmbbePKIDF/8UAmt7UMIrTBkazXnPP88BxU/DaUtTG5MwYAYArcdE3lqWhFlrUWUtkRmtQ5lxIYXIFbyfKYBMpFIOzEXtSxr7fwKZNl+grxlyxKaxqX5NGMmZe1EIAbIZALFtEEmUFo9knOby6gpqWH6kAPZNPZO2kNgY0ugPZOB1nLayTB9TIYp2akMv+UwYlGWoycv5dljl9CeCbRnoKm5hBVrRhKLMoTqYk545kDa2gJPrQ78fuajjJjYSpYMWQJFW6sJreVkQ+Cm7AGMvr4GsoGydUu57PhltLaXkcnCy2rWcFzTvdC2DWLgsseeoXVpG8Uxy4jqKTSc8wZippTm9jbKt6ygsilCcwnNk0t4c5jIqkwboakB2ExlU6SyqR3WtdN56NVP3tjClzfewIhQzjfqs7zrx2voUk0F3P/ZjusX16X/U0emYiWjq1Jl3pFVMLoSyvIQlC0QTa3w/Po0C9al74Br7ktfxbPGpVovb+m6DNKLXlKYtnUrwxZ9Cpb+BWKnANbwQ1MmYeWEnR6/3V9bl/Dvjbfx97blL952eNFoflx5BieUHNh9A55YBa//EbS2p+sVJTD/lam+xowDXszuk6TBwAwOSS9asxWGluVvvzbGFJC4Nlc8MRPg9S9Ls2hA2pfePg7/g69OGR3DKtIsJys2wRnfzTK55Hm+eMpSjqq4n6oNfydufo7QlHbgF7/sMn7+/Kt52zFw2KqvwRNX0D7qKJZMnsOzwydxR0UZd7Wv4ZnsJh5vX8/jw97N9KJ0wPSRhnq+13Qf1aGUY4rGcEjRCEZmKmhvKOcV1cM5rzwNd8jGyKrYwIhQvutxzt29AGsb0tiblZvTHvSqzakC3b+d1bHcMV/tuiABwPtOgs/m5hV8YlWKPIyqgonD0+DuUVUwbigM7bq4KKSd91O/nZpw9KS0D1xSBBe9Ig3JnjkmFc9raoMJ6eVhzdY008mS9ems5qxx8KFX774kw55M9bo7be0po2df+2N7Fr731zQDAqT08z9/OCW/HPu13T/+43Pg0lsaGc4qRhWv5f5tR9JGatS3xn+aE8tvZlTRWjJhh5+1ia9h1VGX8OlrYeXzj/PnqWfttO62EGgNGcLZ10PNNErIUPT4ZbB1Ce3DDiFbM5WWMJTljVWUDRnKgSOG0JoJtMR2KrLlFGdSfOzC61fz0IYmYkkr2dIWYkkro4e38dFXFXFk8WhekTs42hZbuXNdA7c/UsbMYWW8ZmaGqrL0Wn/75lSod0NjKh3ylfPg8PFw+nd3/foEslRntjCsaCNHjt7It8/dSKgYDSNyZ6mfvRZuSzU+IimE0ZYJtIYiYhZ43c3EsmHEEKj563xYdTsE2FRSSsnQqZRXTiRTNQHGvAImvmb3b1jOxkY47Ttpe65+T6or8Pz6NKvFm45MWWSdPb0G/vnKNMHEjqrK4PZPpoSIGx5NSVSnz4Sf3JY+T5WlcMnfd35cSRF87FR42bhmrrh5DWvXrmF8yXIumr2MYyZlYfYH0oIxwm9fmeqf7Ki4kvaZ72HttHeyautG1q59mlOfupfQPhQyY3j3y1u5dtgaNsQm5tzZxmv+0crQBhjaEDli21AOa6yCLU00Di3m65cdzrHFYzmpeDxDj/g6YWvXGSx88nRerLZ885Pwjb+kujSVpVBRmgKqFSXp/8dP6/iQ/vXJVJyirDjd1rn+zMiqjgyS9mz6Yior3rmoUh9YvBrO+F6aEfXmi3v4oBhTtuCqO9Jwk5f/a7o92wZXHQbZVqgcD2NPgFnzU60W0m/K8uwWbm9bwd/blnNa8STeWJZCxb9ufoLzt/6eYaGMT1UcxxtLD+LgzAgy279UNzelOdAfW5nSF7f/jsQI518KI4ekjv662R0VdiUNaoMxg8MAhyQ2b4PPXgd/zJUNmHdcOvgbVZUOAvb05E+Mqejnnx+FZ9ams2IXHANvPTql7963FEZXRiZUbaW1YQ0lW56ArcuhaQ0UV8LhHwHgpseaOe2emTutv624nMcnnMTSQ95KzbBZnFgyHmKWNbGJaRt+wlZad3pMhsCvq17Pm8oOBmBddhsBGB7Kdz+tXley2VSsbUNjKgSyYhMcMR5m5k7lXXN/mve0sWXnx2YCPPLvHYXf3lubqkKOHZoCF9NGpTkFp4xMle66mit1DzXnXpJBdFJ2J82tMPtLqc7Lg59Lx2IfXpjG108bCau3puKBS9anIexdOWUG/PWpdLnufSlgtHQD/PMv29i0biWzyh9lUskSJpUs4b6mo7h28xsBOLTsUeomzaU0tFIc2ohlwwlDxsHII2DC6TD2FVC899MxNrfCA8tTd/zB39KQrl++e8+nyt3QCJ//PfyhixIi22dhAfj9v6Q6hfN/tfNrdf0HO85oNzTDV/64jQtHXccBTYt47rnnmFr6LCOL1xNKqlJxxdf+Og1RAlh6IzRvTLOXDJ0OZcP2bAN28OgLaRraNxyx+2W3e349vP6Hqc7Nu14Bk0fCObN3fxz+lRtSNtD2ApXX3N8pE2oHo6tSRtp9S1Nt2mkj27nmmmso37CI0cVrGFW8lpFF6xhbspYiWrhv5Cc48swPcdOTUL7yRk5a+v6OlYUMcciBLB91CI8ccBhLJp7MQ3Ejt7QuY3754Xyg/CgAFjY/zlu3dmSAveG2LLPWlfHKZeVMXp9h5voSMusa09y5H58Dc3NpC1ctgn/9Xfcb/sQXOqbbeNOCtFFdefNR8I035R6zCs78frpclOkoxjusIg2l+fK5KaoKcO8SWLUlZacNLU+ZKKMq8zrFx62LU6HQE6bCr969iwVjhBdugTX3pAyNjameDcVD4K0PdWSkLV4IB578YsHQ21qXc2/bKu5pX8V1LU+zIXYM9/qnsllcXpVSylZkt/K31qWcWTKV4Zny9GX0k9tSle0l6ztmP9mu84etpc1pTyTtxABH7zLAsR+s3ZrOUr7t2B1SJqVu1D+Rjq+78+FT0r5ud1rb04Fiezad2BtTDf95fTroCWSZULqKr5y+jJOOmg7lubEc9/9vmh6vvYuzh5UHwhtv67j+5zdxb9Vwrj9gKrcPG81zxYFnYwNNpFTcE4oP5PaaVLStNbZTuv5bjM9UMTVTw8uKRvHqkonMKBrOjKLhL1ag71ZbO2xqSqd+N25LO/ot7els2HbzLoPlG2D5po504O0+/Zo0xyekanUfuCrtsI+phlHVKRV87FA4oDqtc8hu2qO8e/2P0kifoyamA/RrH3jpyertnl0H42vSkPZrH4AXNsOXXg8vG58+M8WZlP293crN8PW/pIPazqrK0gFsTUX6LB0yBsi2pwOh/TBDST5ks3DCN1KWwnbvOTGdLP7bU+kYc/tw/pO+kWJ7kI5ff3MffOqMlAn06oPT9S9en+4/+zC4/pFU4/Zn8/KY6rMfrNmSEhb29SN6yd/hqzeky+cdDhOGpwBUZyVF8Iqp6SC7pCgFU7ZPGQyR6swWIoGt2TRG66DSp3j90Os5evQaDi9/gOptj5JSYUj96sInOwpW3vZxaGuEEYfxwPAJ/KoicnNRIw+3r2UbHcMnyimmYcRHX8wU+FTD3xidqWBKpoZxmwNTVkTGbiumuLEtBW0bW6CxFZpa4J9P7tiYb9WnqHZTG7S0EZvbWb2ujYpsK0Nffyh87LS03CMr4C0/TdU5s13sDv7xAx0Bjs9cC1ffs/MyZcVwzGSovSj3UsX04zOyMn3HHlCdonIjKlNEqfyl0d3mtpQMcfh4uPGxVB/qjUfAN+d2/V6ybQ3c/B5Y/9CLN7UNGcPS6eeyYsR0nh89i+eyDSzLbmFJdgtXVJ3FiEzKrHv9pt/wh9ZnXuzzZz9YzIkvVHDclqEctq6MAzeFlMW3fGOa+3h7ZsYjK9I0Qp23+dCxabunjYLTD+l+7J4kYYCjtxng2A8+dHU6C19dDpdcmC6v2JTOxM84IJ2pDwGKAsweD3MO6esWqzf849lUwHNMNUwakfYnm1qh/nG4LTf94pSR8N/nphIPBw5LO/bbK8p/881px3zlpvTYGaMjs0ZuYvEL2zjj0rQTWhaa+NzoLzOyeB2jitYypmQN40tXUrz9TNWrvgeTz0mXn7gc7v4PKKqA8pHEmulsqJnKM1Uj+XtlJffUjOETFcdwZHGq9P7phr/xP013v2SbpmVqmF40jKmZGi6p6sg1X5ttfMkMJC+KMZ1+3l6Uc/a4jh3D7/01nSXb0kURxZGVsOgzHdc7H9FVlaUqqGOHphf2rMM6PlTbWtIe9LC9Pyuv/Jv/S/jL4y+97bfv75jhdV8t25C6xaIl6fhq1th+kYG/x5ZugJO/mS7f+OH0+9GV7/0VvlmfMrSOngj/77e7X/fFp8FHTs1bU/u1GOHu59P35rGTU7LCkvUpyHHdQ+l7eLuy4pQZM+OAjpmjTjs4ZQx1FQOA9Ht+5TtbOH70MlYteYy7HnyautaP8NpZ8LZjIiw8Is2s01nNDOKk17J14hk8Wj2a29uWsyW28PkhJ+baHKle/10ausiEGxkq+ELFCXy4ImV3PNW+gTvbXuCAMIQpRUMZ1VrDtfcWccvilHQAaSRFVVnKmuoyptXWnr4rm9o6gsuzxqaIOaRxUzc/me7b0gQbcsHntuxLUy42N8ERX+7+zfje+XDOy2hsgfqv3MekP97F1qJSWstK2RhKaSgp4+DJJRx7WBl89JRUqBrgitWwqYn2tibaF9dR2lYBZZNZH4bwqTOzXHpW+oC/6t42vvf1bZS0QUkbTMwOobwtQEs72S3b+GT9cUwcPZYzS6Zy6Lv/kCJaXTn1YPjZO9Llrc1Qe1eKKB40OgVs+nFgUFL/Y4Cjdxng2Evt2TSrxNBORfJjTAewn+rBzmVnJ01Lx2RvOrJjX0KFLcaOKeou+0c6+/zg8l0/5i0vh6+e99KpW5ta4VXfhHGtD3JExQNMLXmWWeWPMrZ4JWOKV1GRaeKJ1tmc+czvAagobufhaTN2rkNQNhKqJsDsDxInnJ6Gg7RupT3bzodb72JJdgt3tr3A2vjSGSJ+XnkmF5WnzIk/tDzNDa3PcXLxBA4pGsHkzFBqMmVpp3jTtrTju7kpDf14eW7avOY2+PRv02noNVtTinPnAMY33wxvPDJd/tXd8Lnr0o5jTXlHmvSwijSO+SvndexUPrwCyos7okAqKP94Fi78Wcf1A2vg1o93PW3xYPfnR1LmyQnTul+muS29pidNS8eb/3R5OqDvbNbYNGTj6TUpFvgf56SRWINdU2v6SvrV3XD3Ejj/5XDu4R33N7d1jE7b2gwvbEpfcdNHpxP7n7gmjfR45fRUi/ibN720mPPHT4u8b/Ziyrc8lA7Wty6BNfdBc26qkBO/AdPenC4v/yusvgvGnkB79TQuL17HvW2reSE2sDrbyLPZTbyQbSBLpLbqbN5eluqr/Kjpfj7Q8H8v2a7QUkKmuYxMcxkH1l5EyJWNfe/H7+X+1a1MqSzjmNHljAjlDGkvZ1xJOQcUDaFqdxl2ncXIT+tb+fUdbYyaMIRhFfDc0mYueOZeji7fQvXmrWRXbaFsSyOlmxoYtnUL2R9cwILyQ7nsH3DB7Tfz8Qdv6nrV1XD3N+/jvhJYWj2Giz8wmZGrdg72AHz5XWX88EOjmFBUxbm3ZfncB5/qvs03fyydSQD40S1pCteaIenMw4jKFLwYVwPjh+352DJJ6oYBjt5lgGMPxZhOMl92Rzpe+/RrUlB/zRb45d3w96fTcicflHY0NzSmOlQnH5ROLtfdl47LTpqe1vWnR6GMbUwpfY73vux53nzo2lQUK9sGsT0Vx5qSpkiktQEe/FbHfdlWiJGYKeXBF4r55fq38P7Xz2L6aGDLc7DleaienNaR6fmg/xjTto2qhLueT9ma1z4Ah42DOTPTDt6UkWkn76DRkeLiSJZICZm9q6MwAMSYzrY+uDzVs/zt/elyUaZjytXSYjhxaspsXbEplXuoKU8HLucdkbJ3iTFNq/r0b2DyWTDqKDY2wtM3/DdHN/xkp+fd0l7Fw82zedvSK/nt+9N7dNMfr6SltJJxsypZWV3G4pLAYraxOLuRxe0bqQwlPDDsnS+uo2b9d9kcWyhrjhz7bIZDtlZw5NZqDm+o5rBtVYxsDGnP/p2vSJ0Y0injqxalDr5th53Ogw+AGz7c8cLM/OJL55etLk87kwdUp3W+5tB0e2Mu22JoeXrhNKDFmEYeXfdgOvg+bBeTE2jPtLbDpbfB0o1pRNb5L99l/Vvtg3UNcOLXU1/u7KRpHZl5o6pSMdRXTU8nM0JsTcGMtfel6bNzhS+5+z9SZt12xZWphsSwmem3fOp5tMcsq2MjVaGUKkr50yPw1WefZfX0R1gdG9hSsYm26i0vTqddmS3jvvhh3lObK1P0rp/QUrOpy205+vExfHLVSN56eAN3xY18oGozo9uzjGlrZVzlNCZUHcyITAWVG57myIf+SqapjYeXt7K1rI2iohZKspGQDXxw+Y/TNDsRPj3y60zOrKA5lrEtltFYXkpjNgOlzazMzuTwCdOpKNrKqheWcd5jf2LMli2EliKumHwQ75w/6cW2vet3LVRti0BgQulQPjX8xBeLqK6dWsmoGblltzSlDS3OpLStkk5/1WXWx5DUJwxw9K6CCnB8+rfpwPsdx6fswd7WnoVv3wTf/1v3yxRl4GtvSNkY3R7rtzakgENRKdfcD4c++k4Ozd7S9bLjXkXrKVfwx4fhwPINHHd39/Ok/cvyH3J3PIufzoMDl/6QA57+OgCRIkLVeKieRLZqMqtrDmL91POYVTY8nbm6ZRXfL3qYzPAmWkpbWdbUSrasCUqbac9A5QMvZ+gDL2diyRJGTP87fz51JdlMJJuJtGeATtv5ymvfx6i2Gs6uqOOGY5fyyLAswwiMjBmGxwxDY4aa0pEcXnMUF5TNhNYGWlbdwQMlUFpcSUlJJaXFVZSWDKU0U05pKKI6lFIS+ld++crNadzw7APTAcRFV8At3WS6VpbCp14Dc4/aIdlgezBj01Ow6WlYcUsqltaWqyB4wrbAHOsAACAASURBVDeI488ltLbDc/9H0+rbuWXCWLYOnUhTSyXtz7Xw7KZW2lq3URQbeQtTmdVWA01tfOMNWf5f++0A/HNdM0c8maW8JTKkCUZthlO3jCRsaISTpnPFlw9hCMUcv7SEiWf8vPuN/sVF6TQlwP/cCD+6NV0uzqRTzMMq0lHUlBEvHUB9/cNQVQ4HVKWgxnDTeyUNHP/++zSCYUhpitNe9Io0pfJtz6Risc+u61j2B2+Fs2d3s6LVi1KB1zWLUqZHU8cDFzUdz1uev4o5h0B5aObi+Hae2jaRNU1VbM2mv4ZsJVuy1TSPeTX/9fZqttJM47p7OWTtU1x33zaWrW3i1kNbaRjSRihvZF3xEO6Jh5ItbyJbvo3/XnED/+/Z+wC47oApnHf067rd5mU3Xcb45jTr1OuPPps/HDC1y+XmvLCa/7v/1wAsLa9i0qnv7HI5gBvuvo7XrFsO407my9OO5FdVJRxdNplpRcOYlBnKscVjOaRoBKX9bH9AknbHAEfvKqgAx+nfgafXpmOj956YitO9bHw6a70/T/g+tByeWpMqsd+/LD3/l89NKa0Pr4AZo9Px3crN8NFTO4qevyhG2PgErLwdVt6W/s+5Ag44Nt1/z5d44dGbebRhCqvaxjBzXAlDh5RAppiGsql84Ym38sAyKA3NXDDqcq45oYm2TKA9E8iGFGiImSzrYw0Vd5xGxdLJvGnob8gc+Td+NXMUbUXQksnQnCmiKVNMDIFMwxAOvfwDNLfBQ9Nncshpb2VJRddFsmY+cTxzlr6Kk8NChg79AWcd+/qX3J+JWTIx0pYpYsKCf6a4sYprJr2Rf3vleK4/YEqX6zynZBq/H/omsusfZ8VNc5l46kXdvv4XPfVGvnfUdKqW/4Yvtj3M14cPoYwMV368gZEbIxQFQiZDSVEJs0tGp9SSNx7Jp8/czDbaOOjBrZz1/WfJAJmQSbN2UEYNpZCNrPzuOdw9dAtVoYSZX7ubmkUrKYpQlE0nwTIRyEaajprCH+aew9X3wrNPbOXXN/yETIxkgBAjgUhZceqLV7zhDcx400GcOA0qf3k7JZffDrGdmG0jFpURyMWFwvPc+pl7uKtmDCvLhnDxxZMYtr6I4rZA6Q6JEe0Xn0rx2+8F4Ly/tnLtJxu7fc2uvuVcPlt1PwcVDed/P7SU2beu73rBzuOMNzTC23+eAhU1FSmToro8RWiqyuHMWR057Wu3prHawyrS/QYsJA1S7dk0Om9YRcrYPGh0R6LApm2pPuefH03XT5qeghw13WTUtGdTfdAv/AGWLF3GyZW3ML5kOU82H8x1W84D4GVlD3LdlPO6bU88/UrC2FekK4u+BI9f2uVyG4qm8Im2m3ntrPR7NPLu99ASS2nIVrIuM4RnqqtYU1rOurJSHi6ZxJqKErKlzZSXb+Ttt5QwvLiUEw8q4fIjV/LXkgaaibQTyWaKaGyNtMfIKU1DuYkD2NbUzI3LG7lo9mqKMzAkZhiXqWRM6WjGZioZSzlvCwdySPlEKNrDKcMkqZ8zwNG7CirAsej5tJNw6e0735cJqYDW9qnHXzc7ZSQu2whVpXteY/CFTanq+nUPvfT2UVXwP29Mx4VrtqT1b69k/xKtW2HRf8HWpeksfaczMQCc/COYdOaLV+9ZAnN3HoHwoprcJBDtxS3Un//dbpd75X1n03bfLDY0wqajFrHypL/utExFc6BtywgOrL2ITAjcOeMkrp46gVZgWOs2hrc1UdPWTlm2jCEHX8j46W9mdGYIrPoHzU/+ioaiIkpDEcUU0daWoSRTRENrhscn/BuVFSWs2RKofP6XPLNpBY83ZmkpzdI0JFI9IsuGbKR5xIFMHnEih64+lN/f9gznVH+FL5w0nvailBGSzUBbJtCSybA+1jDiD29kZuMkvjPxU1w5aSlfm340AMvP3MyBa7vuwvGslYz8wnQ2FBfzultb+cPF3QcDLv/lbC46JE2p97uLGzj31rYul7t5/MGc+19jaTlgJQduaOPZdzzT7TrXfKaY17yuhBjbmf/zFj7QXWLEMPjwVVm+PyoFDpa8bjMTV3VsU0sxlJaVplTc953EWW9bTTnFvPzBZuZ9YwmZshIyZSUUl5dSU1HJkPKKNAfpx0/r6PT/93iKvpUXp/XUDOmoaj+8wsIvkrSfLd0Ap307DV0FeP8r4TOvfekyzW1w3o9fWsMD4LRD4A2HpymIp4+Gp1/YxozMfbxy7ApGlW2lqL2B4mxD2u9o3QqHvR+G5QotL70x1fUoqoDi3F9ReZopq2oiDO0o7rJ8I7zvl2nmms++NtUTW7ymox2vngFffUM6sRMjnHFo99u7aVuKg08fvQ8vmiQNEAY4eldBBTi2u+OZFOi4opu57bebPAKeX5+GEPzlI7vP8rh1MdTdm+ojLFqy8/2fPB3ePfkmKpb9DtpboH0bNG+Als2pQnrZCHj9jWnhGOE3x0NTbu+g4gAYeyKMPSn9r9x5wPl9S9OZm/uXpWGkre2pLcs3wiVvS4GUtpilruVJigmUhKL0nyKKQ4YSMhxSNJzRoZJshC00cfUjzXzpD8WE9gyhvYjQXsw7js3wn69LZRUymRQY2l+eWQtzvrPrZSpLoTULZUUwdVTaMXp+fWRIponjp5dyx3NFNLXC0RWLOKjscSrLNlJVspFJa7dQSQPFxU2saD+A3216E2XrRzA8bOAHsy/ip4dPpKGohMzWUkYuGUJryNAaMqypOIKZ5Ufz0H1jmdZ4PVNffTX/fciRNBSVMHJFKcWNxTQWF9FQXMKm4lLG1F0A2VJ+MOV9/NN5h/D3EeMoaotMXZElBsiGQDYDryudxg8qT4fV/2Dxk59jxhkXAlCzJTJsS8dHbQgZflJ1JieVjIcQ+O2oNdzStoyxmSFM3FzC6JJKxpRVM760hmHFFRQF61FIUqH721PwrZvggdzMJi+fCMdPSQlwr5ud6ntdm5ua9k1HppMarzpo1wVm96dsNs12NGZoKkw7ptpkPUnaGwY4eldBBji2W74xDR8549B0duSqRensyP89ngIbnX3jTfDmo7pez+Zt8Iu70jR7naeBmzgcLp0HFSVpek4AHrsU7vlSNy0KcMGjUJybWmXF39Jt1ZOgavJe7xnEuPc7FVua4MMLU/HTicPg7cfBu07o3RqOj62EX96VipLXVHRk4JQWpSFGXzg7vcYhdGznoufT6IhDxqTslu/cnGpXjq6Gvy9OO4gNLek9PW5yqn25eE16rivvauOg0sWMK3mBykwDFWEbQzKNVGS2URka+OOW1/FEy0wA3jjsD5xfcxUlcRtDQm6ZTAPloYmmWM6xT9/F9FFw6sGBTzaeyvKwnrUlFcRMMSEUQ0kloXgIYfwcRky/gOlFw6BxJc3PXcejlTWE0qGE0uGUV09heHE1w0K544claRB7by3UP9H9/Ve/J2WjSpIGBgMcvaugAxzd2dKUDqKnj4JHXoBL/p5uX/heOHZyx3IPLIM/Pgx/eiQNNYF0FmV0VSpH8OFTukiv3LQY1j8MmdI0TrRsOJTWQEk1lFan1M9+eIqjPZuG8fTDpu2V7R+ZrrbnvqVptppDxsApM9L7f8OjsL6x43Gjq2DaKPjhBWnc9P/Wp+Xas3DIAXD0ZLjwmBR4mTYq9zxtjRCKU4HYgfJCSpJ6VWs7PLcuZYqu2ZJO0Fz7QDpBc/Zh8IML+rqFkqR8MsDRuwZkgKOzzdvgiP9Ol0+aBj//p1SbY10DvHlBR6bHxOHwtmPh3Sc4i9hAtaExvffdDclZuTn9Hzu099okSdL6hpRp+crpqTySJGngMMDRuwZ8gAPSgetrv5eqnHflk6enWVnKSnq3XZIkSZKkgWswBjisILifjR0KtRelWS+3CyENV/nJ2+GDrza4IUmSJEnSvjKDo5es2gw/uhWKM/CpMxyKIkmSJEnafwZjBocBDkmSJEmSBpjBGOBwiIokSZIkSSp4BjgkSZIkSVLBM8AhSZIkSZIKngEOSZIkSZJU8AxwSJIkSZKkgmeAQ5IkSZIkFTwDHJIkSZIkqeAV93UDJEmSJElS/zZn7rwzge8ARcBP6+tqv9rHTdqJGRySJEmSJKlbc+bOKwJ+AJwFzAIunDN33qy+bdXODHBIkiRJkqRdOQ5YXF9X+0x9XW0LcBVwXh+3aScGOCRJkiRJ0q6MB5Z2ur4sd1u/0mc1ODZv3kxbW1tfPb0kSZIkSQNWaVn5qDlz5y3ai4cuqK+rXZD3BvWCPgtwDB06tK+eWpIkSZKkAa2luWltfV3tMXla3XJgYqfrE3K39SvOoiJJkiRJknblbmDGnLnzppICGxcAb+vbJu3MGhySJEmSJKlb9XW1bcCHgBuAx4CF9XW1j/Rtq3YWYox99dx99sSSJEmSJA1kIYR7Yoz5GqJSEMzgkCRJkiRJBc8AhyRJkiRJKnh9VmR0ztx5NwCj+ur598IoYG1fN0Lqgn1T/ZH9Uv2VfVP9kf1S/ZV9s4AdfcprJ/d1G3pbX9bgKChz5s5blMcpdqS8sW+qP7Jfqr+yb6o/sl+qv7JvqtA4REWSJEmSJBU8AxySJEmSJKngGeCQJEmSJEkFzwCHJEmSJEkqeAY4JEmSJElSwTPAIUmSJEmSCp4BDkmSJEmSVPAMcEiSJEmSpIJngEOSJEmSJBU8AxySJEmSJKngGeCQJEmSJEkFzwCHJEmSJEkqeAY4em5BXzdA6oZ9U/2R/VL9lX1T/ZH9Uv2VfVMFJcQY+7oNkiRJkiRJ+8QMDkmSJEmSVPCK+7oBfWXO3HkTgSuAMUAEFtTX1X5nztx5I4CrgSnAc8D59XW1G+bMnTcT+DnwcuBz9XW13+i0rmHAT4HZuXW9u76u9o5e3BwNIPnqm3Pmzjskt/x204DP19fVfru3tkUDR56/My8G3ptbz0PAu+rrapt6cXM0gOS5b34UeB8QgJ/4fam9tRf98u3Ap0l9bwvwL/V1tQ/k1nUm8B2gCPhpfV3tV3t5czSA5Llv/gw4B1hdX1c7u7e3RerKYM7gaAM+UV9XOwt4BfDBOXPnzQI+A9TX19XOAOpz1wHWAx8BvtHFur4D/Lm+rnYmcATw2P5uvAa0vPTN+rraJ+rrao+sr6s9EjgaaAR+20vboIEnL/1yztx543O3H5PbGSoCLuidTdAAla++OZsU3DiO9Ft+zpy58w7qnU3QALSn/fJZ4NX1dbUvA/6LXN2DOXPnFQE/AM4CZgEX5tYj7a289M2cy4Aze6vhUk8M2gBHfV3tC/V1tffmLm8hBSXGA+cBl+cWuxx4Q26Z1fV1tXcDrZ3XM2fuvBrgZODS3HIt9XW1G3tlIzQg5atv7mAO8HR9Xe3z+63hGtDy3C+LgYo5c+cVA0OAFfu5+RrA8tg3DwXurK+rbayvq20D/ga8qRc2QQPQXvTL2+vrajfkbv8HMCF3+ThgcX1d7TP1dbUtwFW5dUh7JY99k/q62ltIQWOp3xi0Q1Q6mzN33hTgKOBOYEx9Xe0LubtWktK3dmUqsAb4+Zy5844A7gE+Wl9X27CfmqtBZB/7ZmcXAFfmt3UarPalX9bX1S6fM3feN4AlwDbgxvq62hv3Y3M1iOzjd+bDwJfnzJ03ktQ3zwYW7aemahDZi375HuBPucvjgaWd7lsGHL9/WqrBZh/7ptQvDdoMju3mzJ1XBfwG+Fh9Xe3mzvfV19VG0ti0XSkmjeP9UX1d7VFAAx0pXdJey0Pf3L6eUuBc4Nd5b6QGnX3tl3PmzhtOOks0FTgQqJwzd968/dRcDSL72jfr62ofA74G3Aj8GbgfaN8/rdVgsaf9cs7ceaeSDiI/3WuN1KBk39RANagDHHPmzishfbB/WV9Xe03u5lVz5s4bl7t/HLB6N6tZBiyrr6u9M3e9jhTwkPZanvrmdmcB99bX1a7Kf0s1mOSpX54OPFtfV7umvq62FbgGOHF/tVmDQ76+M+vrai+tr6s9ur6u9mRgA/Dk/mqzBr497Zdz5s47nFS0/rz6utp1uZuXAxM7rXZC7jZpr+Wpb0r90qANcMyZOy+Q6mY8Vl9X+81Od10HvDN3+Z3A73a1nvq62pXA0tyMFZBqHTya5+ZqEMlX3+zkQhyeon2Ux365BHjFnLnzhuTWOQcLM2sf5PM7c87ceQfk/k8i1d/4VX5bq8FiT/tlrs9dA7yjvq62c2DtbmDGnLnzpuYyMi/IrUPaK3nsm1K/FGLsUZb7gDNn7rxXAreSpijM5m7+LGkM2kJgEvA8aYqk9XPmzhtLGos7NLf8VmBWfV3t5jlz5x1JimqWAs+QpjzcgLQX8tw3K0kHlNPq62o39e6WaCDJc7/8T+CtpEru9wHvra+rbe7N7dHAkee+eSswklSA9OP1dbX1vboxGjD2ol/+FHhz7jaAtvq62mNy6zob+DZp1qmf1dfVfrnXNkQDTp775pXAKcAoYBXwhfq62kt7aVOkLg3aAIckSZIkSRo4Bu0QFUmSJEmSNHAY4JAkSZIkSQXPAIckSZIkSSp4BjgkSZIkSVLBM8AhSZIkSZIKngEOSZIkSZJU8AxwSJIkSZKkgmeAQ5IkSZIkFTwDHJIkSZIkqeAZ4JAkSZIkSQXPAIckSZIkSSp4BjgkSZIkSVLBM8AhSZIkSZIKngEOSZIkSZJU8Po0wBFC+FkIYXUI4eEeLDsphHBzCOG+EMKDIYSze6ONkiRJkiSp/+vrDI7LgDN7uOy/AQtjjEcBFwA/3F+NkiRJkiRJhaVPAxwxxluA9Z1vCyFMDyH8OYRwTwjh1hDCzO2LA0Nzl2uAFb3YVEmSJEmS1I8V93UDurAA+OcY41MhhONJmRqnAf8B3BhC+DBQCZzed02UJEmSJEn9Sb8KcIQQqoATgV+HELbfXJb7fyFwWYzxf0MIJwC/CCHMjjFm+6CpkiRJkiSpH+lXAQ7SkJmNMcYju7jvPeTqdcQY7wghlAOjgNW92D5JkiRJktQP9XWR0ZeIMW4Gng0hvAUgJEfk7l4CzMndfihQDqzpk4ZKkiRJkqR+JcQY++7JQ7gSOIWUibEK+AJwE/AjYBxQAlwVY/xiCGEW8BOgilRw9FMxxhv7ot2SJEmSJKl/6dMAhyRJkiRJUj70qyEqkiRJkiRJe8MAhyRJkiRJKnh9OYuKY2MkSZIkSdoPQgg3xBjP7Ot29CYzOCRJkiRJGnhG9XUDepsBDkmSJEmSVPAMcEiSJEmSpIJngEOSJEmSJBW83QY4QgjlIYS7QggPhBAeCSH8ZxfLlIUQrg4hLA4h3BlCmLI/GitJkiRJktSVnmRwNAOnxRiPAI4EzgwhvGKHZd4DbIgxHgR8C/hafpspSZIkSZLUvd1OExtjjMDW3NWS3N+OU7yeB/xH7nId8P0QQsg9VpIkSVIeLFy4sNv7zj///F5siST1Pz2qwRFCKAoh3A+sBv4SY7xzh0XGA0sBYoxtwCZgZD4bKkmSJEmS1J0eBThijO0xxiOBCcBxIYTZe/NkIYT5IYRFIYRFCxYs2JtVSJIkSZIk7WS3Q1Q6izFuDCHcDJwJPNzpruXARGBZCKEYqAHWdfH4BcD2yIbDVyRJkiRJUl70ZBaV0SGEYbnLFcAZwOM7LHYd8M7c5bnATdbfkCRJkiRJvaUnGRzjgMtDCEWkgMjCGOMfQghfBBbFGK8DLgV+EUJYDKwHLthvLZYkSZIkSdpBT2ZReRA4qovbP9/pchPwlvw2TZIkSZIkqWd6VGRUkiRJkiSpPzPAIUmSJEmSCp4BDkmSJEmSVPAMcEiSJEmSpIJngEOSJEmSJBU8AxySJEmSJKngGeCQJEmSJEkFzwCHJEmSJEkqeAY4JEmSJElSwTPAIUmSJEmSCp4BDkmSJEmSVPAMcEiSJEmSpIJngEOSJEmSJBU8AxySJEmSJKng7TbAEUKYGEK4OYTwaAjhkRDCR7tY5pQQwqYQwv25v8/vn+ZKkiRJkiTtrLgHy7QBn4gx3htCqAbuCSH8Jcb46A7L3RpjPCf/TZQkSZIkSdq13WZwxBhfiDHem7u8BXgMGL+/GyZJkiRJktRTe1SDI4QwBTgKuLOLu08IITwQQvhTCOGwPLRNkiRJkiSpR3oc4AghVAG/AT4WY9y8w933ApNjjEcA3wOu7WYd80MIi0IIixYsWLC3bZYkSZIkSXqJntTgIIRQQgpu/DLGeM2O93cOeMQYrw8h/DCEMCrGuHaH5RYA2yMbce+bLUmSJEmS1KEns6gE4FLgsRjjN7tZZmxuOUIIx+XWuy6fDZUkSZIkSepOTzI4TgLeATwUQrg/d9tngUkAMcYfA3OBfwkhtAHbgAtijGZoSJIkSZKkXrHbAEeM8e9A2M0y3we+n69GSZIkSZIk7Yk9mkVFkiRJkiSpPzLAIUmSJEmSCp4BDkmSJKkALFy4sK+bIEn9mgEOSZIkSZJU8AxwSJIkSZKkgmeAQ5IkSZIkFTwDHJIkSZIkqeAZ4JAkSZIkSQWvuK8bIEmSJEna/3Y1E8/555/fiy2R9g8zOCRJkiRJUsEzwCFJkiRJkgqeAQ5JkiRJklTwDHBIkiRJkqSCZ4BDkiRJkiQVvN0GOEIIE0MIN4cQHg0hPBJC+GgXy4QQwndDCItDCA+GEF6+f5orSZIkSZK0s55ME9sGfCLGeG8IoRq4J4Twlxjjo52WOQuYkfs7HvhR7r8kSZIkSdJ+t9sMjhjjCzHGe3OXtwCPAeN3WOw84IqY/AMYFkIYl/fWSpIkSZIkdWGPanCEEKYARwF37nDXeGBpp+vL2DkIIkmSJEmStF/0OMARQqgCfgN8LMa4eW+eLIQwP4SwKISwaMGCBXuzCkmSJEmSpJ30pAYHIYQSUnDjlzHGa7pYZDkwsdP1CbnbXiLGuADYHtmIe9ZUSZIkSZKkru02wBFCCMClwGMxxm92s9h1wIdCCFeRiotuijG+kL9mSpIkSdKuLVy4sNv7zj///F5siaS+0JMMjpOAdwAPhRDuz932WWASQIzxx8D1wNnAYqAReFf+mypJkiRJktS13QY4Yox/B8JulonAB/PVKEmSJEmSpD2xR7OoSJIkSZIk9UcGOCRJkiRJUsEzwCFJkiRJkgqeAQ5JkiRJklTwDHBIkiRJkqSCZ4BDkiRJkiQVPAMckiRJkiSp4BngkCRJkiRJBc8AhyRJkiRJKnjFfd0ASZJUWBYuXNjtfeeff34vtkSSJKmDGRySJEmSJKngGeCQJEmSJEkFzyEqkiRJkvaZw9ck9TUzOCRJkiRJUsHbbYAjhPCzEMLqEMLD3dx/SghhUwjh/tzf5/PfTEmSJEmSpO71ZIjKZcD3gSt2scytMcZz8tIiSZIkaZDY1bAOSdKe2W2AI8Z4Swhhyv5viiRJypfuDpocBy9JkgaqfBUZPSGE8ACwAvhkjPGRPK1XkiRJ0n6yJ8FQi4hK6u/yEeC4F5gcY9waQjgbuBaY0dWCIYT5wHyASy65hPnz5+fh6SVJkqQ9Y5aTJA08+xzgiDFu7nT5+hDCD0MIo2KMa7tYdgGwYPvVfX1uSZIkaU9Z90KSBqZ9niY2hDA2hBByl4/LrXPdvq5XkiRJkiSpp3abwRFCuBI4BRgVQlgGfAEoAYgx/hiYC/xLCKEN2AZcEGM0O0OSJEmSJPWansyicuFu7v8+aRpZSZIkSQPAwoULrUciqeDs8xAVSZIkSZKkvpavaWIlSZK65fSSkiRpfzODQ5IkSZIkFTwzOCRJkgYoM2cGlx3fb99jSYONGRySJEmSJKngmcEhSZIkDUC7yuABMzwkDTxmcEiSJEmSpIJngEOSpEFkd2d0JUmSCpVDVCRJ0qBlEU5p8Ojq8+7nXBpYDHBov3LHUZKk3dub30t/Y7WvzOjqGWuZSIXDAIckSQOEByuS+kp/+P7pD22Q1LcMcEiSJOWBGRUaaLb3afvvru342ff1kvqOAQ5JknpRfz8I7q59/aFtKowz1P29j2vPFUK/kyQwwCFJkiRJeWOgWOo7uw1whBB+BpwDrI4xzu7i/gB8BzgbaAQuijHem++GSpKk/m/hwoXuxEvqVfnIMDFLRRoYMj1Y5jLgzF3cfxYwI/c3H/jRvjdLkiRJkiSp53abwRFjvCWEMGUXi5wHXBFjjMA/QgjDQgjjYowv5KmNkiTtM+sC9B3PjEqSpN6Qjxoc44Glna4vy91mgEOSJKmP7C6wZNBPkjTQ9GqR0RDCfNIwFi655BLmz5/fm08vSYOSxc52r/Nr5OsiSZJUmPIR4FgOTOx0fULutp3EGBcAC7ZfzcNzS1LeeDZTkiTtLxZhlva/fAQ4rgM+FEK4Cjge2GT9DUmS+q8dg3n7c4fb+hsqNNv7rAei+dXVd4GvsaR868k0sVcCpwCjQgjLgC8AJQAxxh8D15OmiF1Mmib2XfursZIkFaI9Pcg3m2jg8cyt1HN+B0raWz2ZReXC3dwfgQ/mrUWSJElSAdtdUNODdEnaP3q1yKgkSRr4TPHvew4N6t98fySLoGv/MMAhacDyh1P51F9SpvfHUAeHT0gqRAaKJO3IAIck4U6SJGnX+kuQc7DbHpD1d1tSVwxwSJKk3fJgQpIk9XcGOAYQzyxIkiRJPWfwVhpYDHBIkiRJKhgGJTyxKXXHAIckSZJeopBnwrHAdOExYNG/GDxRITPAIalb7iRKUv509Z3q96kkSfljgKOXGRGVesbPijR4OE1tYRhs38tmFWh/2LFf7e/Pjv1Yg40BDkn71UDZIR6IB2AD5b3pCXfwJKn/GYi/rZL6lgGOQcIfECn/BlOAYDDx+zJ/DCxJkmDPfg8KuQaQ+p4BDikPrFVRWDq/X75H/c9gDhz1ZUDAYIQKlX1X6pqfDQ1GBjgkSZK6YFFQD5B6yswv9Sd+bjWYZfq6AZIkSdq1hQsXetCiAcl+re7YN7Q3epTBEUI4E/gOUAT8NMb41R3uvwj4OrA8d9P3Y4w/zWM7lQeeXVC+DNaxsszyeAAACjVJREFUkX6GpP5hT4cx9edhhO7AS+overKf476Q+rvdBjhCCEXAD4AzgGXA3SGE62KMj+6w6NUxxg/thzaqFwzmMe+FzvduZx4wSOptfu8kvg6SpL7UkwyO44DFMcZnAEIIVwHnATsGOCRJGhAK5QyVB5PqL+yLkqT+oCcBjvHA0k7XlwHHd7Hcm0MIJwNPAhfHGJd2sYwkaS+ZrSMNTgYP8sPXUZIGvnzNovJ74MoYY3MI4f3A5cBpOy4UQpgPzAe45JJLmD9/fp6eXtJg11s7ru4g715PXiMDMtqVfNTM8LM68PieaiDqbxmDhfg58wSQOutJgGM5MLHT9Ql0FBMFIMa4rtPVnwL/09WKYowLgAXbr/a8mYWvEL8spP1tb6dgHKxFTtV/9fZ0ov6mqFDZd6Wd7fi5cP9m/zIgMrD1JMBxNzAjhDCVFNi4AHhb5wVCCONijC/krp4LPJbXVipv9ubA0C8Bac/1tzMy6n0eyPWN3nzdfY/Vmf1Bg0V/2sfJ9+euP22b9s5uAxwxxrYQwoeAG0jTxP4sxvhICOGLwKIY43XAR0II5wJtwHrgov3YZkm9oC931NxJ3Df5eP32dB3uDKg3+N0gabDoz9Nb9yazW7SnelSDI8Z4PXD9Drd9vtPlfwX+Nb9NkwrfYP1xKqSDkP7a1v7arv7M16ywedZMkpJC+D3ry33cPXl9CuG1VH7lq8iodsEPVt8Z7MNrevLjsy/9s6/7dl8/f74MlO0YaDzg7n29/VnwsydJhaU3vret9VbYDHD0I4NtZ9ovDw10vfmZLrQDtUJJOR1s38vavUL7rElSb/I7Un3NAMce6K1sgMGedVAI9vXLuycHTYO1Hwy0H8bBcoCcj89EbzzP3hhofVKSVLj8Teo9nV/rwbAvN1AY4MiT3jobma+DJb8c+15fZrCYPdO7ensKUUmSJGkwMsCxn+xtAKEQAw+9kc2wP+zuefvze9Gf2yZJkiRJfcEAh/qFwTrbCBiskCRJkvqzwTLkeCAwwFGA+tMBcX9qi/ac71/f8YdSkiRJyi8DHOrX9jSzwwN2FRKDHJIkSVL+GOBQQcpXUVcPMNXXDMpJkiRJ+WGAQ4OeU0BJkiRJUuEzwKEBwelzJUmSJGlwy/R1AyRJkiRJkvaVAQ4NGGZfSJIkSdLgZYBDkiRJkiQVvB4FOEIIZ4YQngghLA4hfKaL+8tCCFfn7r8zhDAl3w2VJEmSJEnqzm4DHCGEIuAHwFnALODCEMKsHRZ7D7AhxngQ8C3ga/luqCRJkiRJUnd6ksFxHLA4xvhMjLEFuAo4b4dlzgMuz12uA+aEEEL+milJkiRJktS9ngQ4xgNLO11flruty2VijG3AJmBkPhooSZIkSZK0O8W9+WQhhPnAfIATXnvuIxVV1U29+fz50ty0bVRZecXavm6H1Jn9Uv2NfVL9jX1S/ZH9Uv2NfbJrlyy8rq+bsMeOPuW1k/u6Db2tJwGO5cDETtcn5G7rapllIYRioAZYt+OKYowLgAV719T+I4SwKMZ4TF+3Q+rMfqn+xj6p/sY+qf7Ifqn+xj6pQtaTISp3AzNCCFNDCKXABcCO4avrgHfmLs8Fbooxxvw1U5IkSZIkqXu7zeCIMbaFED4E3AAUAT+LMT4SQvgisCjGeB1wKfCLEMJiYD0pCCJJkiRJktQrelSDI8Z4PXD9Drd9vtPlJuAt+W1av1bww2w0INkv1d/YJ9Xf2CfVH9kv1d/YJ1WwgiNJJEmSJElSoetJDQ5JkiRJkqR+zQAHEEKYGEK4OYTwaAjhkRDCR3O3jwgh/CWE8FTu//Dc7TNDCHeEEJpDCJ/cYV3DQgh1IYTHQwiPhRBO6IttUuHLV78MIRwSQri/09/mEMLH+mq7VLjy/F15cW4dD4cQrgwhlPfFNqnw5blffjTXJx/xe1J7ay/65NtDCA+GEB4KIdweQjii07rODCE8EUJYHEL4TF9tkwpfnvvlz0IIq0MID/fV9uj/t3c/oXEWcRjHvz8aCyb+QSrWmlqtIKIUsSqtFA1K9NAejChoC0oVPXgULwUv6kFQKOLNS6pG0ErVgB5UCh40IoZqKigGpK20TU3TYmNrVUyjj4d3Amuhjbs7+zbv7vM5vZl3M8zAs++yP3Zm7Ey8RAWIiGXAMkljEXEh8A1wH/AocEzSi+lD5RJJWyLiMuCq9JppSVtr+hoCRiQNplNnuiX9WvacrPpy5rKmz0UUxzqvlbS/rLlYe8iVyYjoBb4AbpD0Z0TsAD6S9Eb5s7Kqy5jLVcA7wBpgBvgEeFLSntInZZXWQCbXAeOSpiNiPfCcpLXpM/tH4B5gguJkw02SfjgX87Jqy5XL1FcfcBJ4U9KqczIhszPwLzgASZOSxtL1b8A40AsMAEPpZUMUDwEkHZG0CzhV209EXAz0UZwqg6QZFzesUblyeZp+YK+LG9aIzJnsAs6PiC6gG/i5xcO3NpUxl9cDo5L+kDQLfAbcX8IUrM00kMkvJU2n9q+A5el6DbBH0j5JMxQFuIFyZmHtJmMukfQ5xcmZZguOCxyniYirgdXAKLBU0mS6dRhYOs+/rwSOAq9HxO6IGIyInlaN1TpHk7mstRHYnnVw1pGayaSkQ8BW4AAwCRyXtLNlg7WO0eSz8nvgjohYEhHdwAbgyhYN1TpEA5l8HPg4XfcCB2vuTaQ2s6Y0mUuzBc0FjhoRcQHwPvCUpBO191Ss5ZlvPU8XcDPwqqTVwO+A10taUzLkcq6fxcC9wLvZB2kdpdlMpvW9AxRF4SuAnoh4uEXDtQ7RbC4ljQMvATsplqd8C/zdmtFaJ6g3kxFxF8UXyS2lDdI6jnNp7c4FjiQizqN4s78laTg1T6X1anPr1o7M080EMCFpNP39HkXBw6whmXI5Zz0wJmkq/0itU2TK5N3AT5KOSjoFDAPrWjVma3+5npWStkm6RVIfME2x/4FZ3erNZETcCAwCA5J+Sc2H+O+viJanNrOGZMql2YLmAgcQEUGxb8a4pJdrbn0IbE7Xm4EPztaPpMPAwYi4LjX1A94IyhqSK5c1NuHlKdaEjJk8ANwWEd2pz36KtcBmdcv5rEwbkBIRKyj233g772itE9SbyZS3YeARSbVFtV3AtRGxMv0Kc2Pqw6xuGXNptqD5FBUgIm4HRoDvgH9S8zMU69J2ACuA/cCDko5FxOXA18BF6fUnKU4DOBERN1FUOhcD+4DHajboMfvfMueyh+JL5TWSjpc7E2sXmTP5PPAQMAvsBp6Q9FeZ87H2kDmXI8ASig1In5b0aamTsbbQQCYHgQdSG8CspFtTXxuAV4BFwGuSXihtItZWMudyO3AncCkwBTwraVtJUzE7Kxc4zMzMzMzMzKzyvETFzMzMzMzMzCrPBQ4zMzMzMzMzqzwXOMzMzMzMzMys8lzgMDMzMzMzM7PKc4HDzMzMzMzMzCrPBQ4zMzMzMzMzqzwXOMzMzMzMzMys8lzgMDMzMzMzM7PK+xfC4/clUV07eQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e2TSkYFIqr2v"
      },
      "source": [
        "## Data transforms and preprocessing <a name=\"data-transforms-and-preprocessing\"></a>\n",
        "\n",
        "Next we need to tidy-up our data and  transform it according to the needs of our\n",
        "project\n",
        "###  Data clean-up"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2gyDdYxSsmU"
      },
      "source": [
        "# extract the relevant data, i.e. `Open` values\n",
        "# the .values makes this a vector numpy array\n",
        "training_set = stock_history.iloc[:, 1:2].values "
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BxncqBArqgg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3827a014-1395-45de-933b-2bcb06499a24"
      },
      "source": [
        "training_set"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 46.14400101],\n",
              "       [ 45.27199936],\n",
              "       [ 44.        ],\n",
              "       ...,\n",
              "       [607.55999756],\n",
              "       [620.23999023],\n",
              "       [628.5       ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yND4Bs9JtgT1"
      },
      "source": [
        "### Feature Scaling\n",
        "\n",
        "Next we need to rescale our data to the range from 0.0 to 1.0. \n",
        "\n",
        "Feature scaling is essential as discussed if the Features lecture and needs to be applied to both the training and test sets.\n",
        "\n",
        "It is computed using the ScikitLearn library [MinMaxScaler()](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler) which transforms the selected feature by scaling it to a given range. If more than one, this estimator scales and translates each feature individually such that it is in the given range on the training set, i.e. between zero and one."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nue4vBM1tUI4"
      },
      "source": [
        "# import the MinMaxScaler\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZE8mnR8t46g"
      },
      "source": [
        "# create a scaler instance to rescale all data to the range of 0.0 to 1.0 \n",
        "sc = MinMaxScaler(feature_range = (0, 1))"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTtu_cbxt8Ac"
      },
      "source": [
        "# create the actual training set of scaled values\n",
        "training_set_scaled = sc.fit_transform(training_set)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWe9ghvIt_4-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da2377b8-8834-4d8c-b560-4af127754307"
      },
      "source": [
        "# confirm feature scaling\n",
        "training_set_scaled"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.02048867],\n",
              "       [0.01947814],\n",
              "       [0.01800407],\n",
              "       ...,\n",
              "       [0.67109196],\n",
              "       [0.68578632],\n",
              "       [0.69535852]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hg8Q4j1guJG0"
      },
      "source": [
        "### Splitting the training set to dependent and independent variables\n",
        "\n",
        "The stock prices are stored in a 2D numpy array containing a single column of data.\n",
        "\n",
        "From this array we need to define our feature vectors. Each feature vector starts with the stock price a day later compared to its predecessor. It is of fixed length, i.e it consists of a fixed number of consecutive stock prices. The stock price after the last one inserted in each feature vector can be considered as our dependent variable, i.e. the N stock prices before today can be used to predict today's price. N is the number of timesteps."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kytPJkBTuKuA"
      },
      "source": [
        "<img src=\"https://drive.google.com/uc?id=1bckuLGZCeLUzNA-xJCGOODzC-4n2U-If\"/>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTsXaCAmuEv0"
      },
      "source": [
        "# define the number of timesteps\n",
        "timesteps = 60 # 60 stock prices or 3 months worth of data assuming 20 working days a month\n",
        "\n",
        "# each row is to contain the last 60 stock prices before the reference date\n",
        "X_train = [] \n",
        "\n",
        "# each entry (this is a 1D vector) is to contain teh stock price at the reference date\n",
        "y_train = []"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kXPTJ8JJySvL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0b95fa5-d51b-4d45-984c-ac96bf2f1d32"
      },
      "source": [
        "training_set_scaled.shape"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1361, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zm_90YUWyXBG"
      },
      "source": [
        "# we start from day 60 because that is the first instance allowing us to \n",
        "# go back 60 days; first day is 0\n",
        "for i in range(timesteps, training_set_scaled.shape[0]): \n",
        "    # 0 is the column ID, the only column in this case.    \n",
        "    # put the last 60 days values in one row of X_train\n",
        "    X_train.append(training_set_scaled[i-60:i, 0]) \n",
        "    y_train.append(training_set_scaled[i, 0])"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LhPpPZjPyolg"
      },
      "source": [
        "# convert these to numpy arrays\n",
        "X_train, y_train = np.array(X_train), np.array(y_train)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjckpEXzyySa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15449315-cde7-4698-fbec-b99965ed8f46"
      },
      "source": [
        "# the X_train 2D numpy array has 60 rows less compared to the original dataset\n",
        "X_train.shape"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1301, 60)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bw7IIoQ30vPF"
      },
      "source": [
        "### Reshaping the Matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "POHKyNR-00yA"
      },
      "source": [
        "We need to add a new matrix dimension to accommodate the indicator (predictor). \n",
        "\n",
        "NumPy matrices are tensors (3D) and essentially we need to specify that our matrix consists of **60 days** (dimension x) times **total days in data set** (dimension y) times **1 value per matrix cell (scalar)** (dimension z). If we were to include the value of a different stock with the the past 60 days of Tesla, we would need to change the length of the 3d dimension to 2. RNN training tables are 3D!!! Read: [Reshaping NumPy Array | Numpy Array Reshape Examples](https://backtobazics.com/python/python-reshaping-numpy-array-examples/)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtCooPLO1_rX"
      },
      "source": [
        "# Reshaping the data matrix, we retain the 2 original dimensions and add a third of depth=1\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WEbnMrwKJ1z"
      },
      "source": [
        "## Build the RNN <a name=\"build-the-rnn\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_Jy9wfEKMV0"
      },
      "source": [
        "### RNN initialization\n",
        "\n",
        "- Import the sequential model from the Keras API;\n",
        "- Import the Dense layer template from the Keras API;\n",
        "- Import the LSTM model from the Keras API\n",
        "- Create an instance of the sequential model called regressor because we want to predict a continuous value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svBnHDIH2qO1"
      },
      "source": [
        "# Importing the Keras libraries and packages\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dropout"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKGJjzmIKR3f"
      },
      "source": [
        "# Initialising the RNN as a sequence of layers\n",
        "regressor = Sequential()"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wKebpAqcKXDo"
      },
      "source": [
        "### Add First Layer\n",
        "\n",
        "We first add an object of the LSTM class! \n",
        "\n",
        "- The first argument is the number of units or LSTM memory cells. Include many neurons to address the high dimensionality of the problem; say 50 neurons! \n",
        "- Second arg: return sequences = true; stacked LSTM !\n",
        "- Third arg: input 3D shape: observations vs time steps vs number of indicators"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPrMlppDKTXK"
      },
      "source": [
        "# Adding the input layer and the LSTM layer\n",
        "regressor.add(LSTM(units = 50, return_sequences = True, input_shape =  (X_train.shape[1], 1)))"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9PvyarKKauB"
      },
      "source": [
        "# the argument is the dropout rate to ignore in the layers (20%), \n",
        "# i.e. 50 units * 20% = 10 units will be dropped each time\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YwPyVM1UKgP9"
      },
      "source": [
        "### Add More Layers\n",
        "\n",
        "We can add more LSTM layers but along with Dropout regularization to make sure we avoid overfitting! \n",
        "\n",
        "We don’t need to add the shape of the layer again because it is recognized automatically from the number of input units.\n",
        "\n",
        "The last layer does not return a sequence but connected directly to a fully connected output layer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Y0olBBdKcDk"
      },
      "source": [
        "# Adding a second LSTM layer and some Dropout regularisation\n",
        "regressor.add(LSTM(units = 50, return_sequences = True))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9xORNaLKj1v"
      },
      "source": [
        "# Adding a third LSTM layer and some Dropout regularisation\n",
        "regressor.add(LSTM(units = 50, return_sequences = True))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xd676oPKKlMC"
      },
      "source": [
        "# Adding a fourth LSTM layer and some Dropout regularisation\n",
        "# we removed the return_sequences because we no longer return a \n",
        "# sequence but a value instead\n",
        "regressor.add(LSTM(units = 50))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tEbq5tG8KpDI"
      },
      "source": [
        "### Add Output Layer & Compile\n",
        "\n",
        "The output has 1 dimension , i.e. one value to be predicted thus or output fully connected layer has dimensionality = 1.\n",
        "\n",
        "- **Optimizer**: rmsprop is recommended in the Keras documentation. The Adam optimizer is also a powerful choice.\n",
        "- **Loss function**: regression problems take the mean square error as most common"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpiJ1X0AKmb9"
      },
      "source": [
        "# Adding the output layer\n",
        "regressor.add(Dense(units = 1))"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WjEnNuUmKtfq"
      },
      "source": [
        "# Compiling the RNN\n",
        "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "K_pjAjiw05jc",
        "outputId": "9e39f1e6-bb2f-4038-c2eb-489a7638d62d"
      },
      "source": [
        "# review the network architecture\n",
        "from keras.utils.vis_utils import plot_model\n",
        "plot_model(regressor, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D9hK_3Q-Kx4g"
      },
      "source": [
        "## Train the RNN <a name=\"train-the-rnn\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CBVpsF0EK4nB"
      },
      "source": [
        "### Fit the RNN to the Training set\n",
        "\n",
        "We now want to train our RNN using the data in our **Training Set X** and **predictors in y** (ground truth in this case). Parameters that can be specified are the:\n",
        "\n",
        "- **Batch size**:  update the cell weights not on every stock price on every batch_size values; \n",
        "- **Number of epochs**: how many iterations to be used, i.e. number of forward and backward propagations for the update of the weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RlW3HDbIK29t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d244a1f0-e7b2-48b6-9bec-3aa046acfefa"
      },
      "source": [
        "# Fitting the RNN to the Training set\n",
        "regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "41/41 [==============================] - 27s 116ms/step - loss: 0.0233\n",
            "Epoch 2/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0026\n",
            "Epoch 3/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0031\n",
            "Epoch 4/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0022\n",
            "Epoch 5/100\n",
            "41/41 [==============================] - 5s 115ms/step - loss: 0.0021\n",
            "Epoch 6/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0019\n",
            "Epoch 7/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 0.0019\n",
            "Epoch 8/100\n",
            "41/41 [==============================] - 5s 111ms/step - loss: 0.0020\n",
            "Epoch 9/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 0.0023\n",
            "Epoch 10/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 0.0020\n",
            "Epoch 11/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0017\n",
            "Epoch 12/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 0.0024\n",
            "Epoch 13/100\n",
            "41/41 [==============================] - 5s 117ms/step - loss: 0.0016\n",
            "Epoch 14/100\n",
            "41/41 [==============================] - 5s 116ms/step - loss: 0.0017\n",
            "Epoch 15/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 0.0015\n",
            "Epoch 16/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0019\n",
            "Epoch 17/100\n",
            "41/41 [==============================] - 5s 116ms/step - loss: 0.0012\n",
            "Epoch 18/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0013\n",
            "Epoch 19/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0016\n",
            "Epoch 20/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 0.0015\n",
            "Epoch 21/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 0.0011\n",
            "Epoch 22/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 0.0019\n",
            "Epoch 23/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 0.0013\n",
            "Epoch 24/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0011\n",
            "Epoch 25/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 0.0011\n",
            "Epoch 26/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 0.0013\n",
            "Epoch 27/100\n",
            "41/41 [==============================] - 5s 115ms/step - loss: 0.0025\n",
            "Epoch 28/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 0.0011\n",
            "Epoch 29/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 0.0013\n",
            "Epoch 30/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 0.0010\n",
            "Epoch 31/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 0.0012\n",
            "Epoch 32/100\n",
            "41/41 [==============================] - 5s 116ms/step - loss: 0.0011\n",
            "Epoch 33/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0010\n",
            "Epoch 34/100\n",
            "41/41 [==============================] - 5s 117ms/step - loss: 0.0015\n",
            "Epoch 35/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0012\n",
            "Epoch 36/100\n",
            "41/41 [==============================] - 5s 115ms/step - loss: 9.2190e-04\n",
            "Epoch 37/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0011\n",
            "Epoch 38/100\n",
            "41/41 [==============================] - 5s 116ms/step - loss: 0.0012\n",
            "Epoch 39/100\n",
            "41/41 [==============================] - 5s 115ms/step - loss: 0.0013\n",
            "Epoch 40/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 9.5715e-04\n",
            "Epoch 41/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 8.7462e-04\n",
            "Epoch 42/100\n",
            "41/41 [==============================] - 5s 111ms/step - loss: 0.0011\n",
            "Epoch 43/100\n",
            "41/41 [==============================] - 4s 110ms/step - loss: 0.0012\n",
            "Epoch 44/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 0.0015\n",
            "Epoch 45/100\n",
            "41/41 [==============================] - 5s 110ms/step - loss: 0.0011\n",
            "Epoch 46/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 0.0011\n",
            "Epoch 47/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0010\n",
            "Epoch 48/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 0.0014\n",
            "Epoch 49/100\n",
            "41/41 [==============================] - 5s 110ms/step - loss: 9.6839e-04\n",
            "Epoch 50/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0014\n",
            "Epoch 51/100\n",
            "41/41 [==============================] - 5s 111ms/step - loss: 0.0012\n",
            "Epoch 52/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 0.0012\n",
            "Epoch 53/100\n",
            "41/41 [==============================] - 5s 111ms/step - loss: 8.4890e-04\n",
            "Epoch 54/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 0.0010\n",
            "Epoch 55/100\n",
            "41/41 [==============================] - 5s 115ms/step - loss: 9.3364e-04\n",
            "Epoch 56/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 9.6090e-04\n",
            "Epoch 57/100\n",
            "41/41 [==============================] - 5s 117ms/step - loss: 9.8676e-04\n",
            "Epoch 58/100\n",
            "41/41 [==============================] - 5s 116ms/step - loss: 0.0010\n",
            "Epoch 59/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 9.5279e-04\n",
            "Epoch 60/100\n",
            "41/41 [==============================] - 5s 115ms/step - loss: 9.3703e-04\n",
            "Epoch 61/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0010\n",
            "Epoch 62/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 9.6734e-04\n",
            "Epoch 63/100\n",
            "41/41 [==============================] - 5s 115ms/step - loss: 0.0011\n",
            "Epoch 64/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0010\n",
            "Epoch 65/100\n",
            "41/41 [==============================] - 5s 116ms/step - loss: 8.4849e-04\n",
            "Epoch 66/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 8.8460e-04\n",
            "Epoch 67/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0011\n",
            "Epoch 68/100\n",
            "41/41 [==============================] - 5s 111ms/step - loss: 0.0012\n",
            "Epoch 69/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0011\n",
            "Epoch 70/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 0.0010\n",
            "Epoch 71/100\n",
            "41/41 [==============================] - 5s 111ms/step - loss: 8.6967e-04\n",
            "Epoch 72/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 0.0010\n",
            "Epoch 73/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 8.3388e-04\n",
            "Epoch 74/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 9.7975e-04\n",
            "Epoch 75/100\n",
            "41/41 [==============================] - 5s 111ms/step - loss: 8.6925e-04\n",
            "Epoch 76/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 9.8373e-04\n",
            "Epoch 77/100\n",
            "41/41 [==============================] - 5s 118ms/step - loss: 0.0010\n",
            "Epoch 78/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 0.0012\n",
            "Epoch 79/100\n",
            "41/41 [==============================] - 5s 118ms/step - loss: 8.7885e-04\n",
            "Epoch 80/100\n",
            "41/41 [==============================] - 5s 116ms/step - loss: 8.6162e-04\n",
            "Epoch 81/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 9.8668e-04\n",
            "Epoch 82/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 0.0010\n",
            "Epoch 83/100\n",
            "41/41 [==============================] - 5s 111ms/step - loss: 9.2127e-04\n",
            "Epoch 84/100\n",
            "41/41 [==============================] - 5s 115ms/step - loss: 8.2171e-04\n",
            "Epoch 85/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 0.0011\n",
            "Epoch 86/100\n",
            "41/41 [==============================] - 5s 116ms/step - loss: 0.0012\n",
            "Epoch 87/100\n",
            "41/41 [==============================] - 5s 116ms/step - loss: 9.9806e-04\n",
            "Epoch 88/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 7.8966e-04\n",
            "Epoch 89/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 8.4272e-04\n",
            "Epoch 90/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 8.1987e-04\n",
            "Epoch 91/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 7.3159e-04\n",
            "Epoch 92/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 8.2273e-04\n",
            "Epoch 93/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 9.3008e-04\n",
            "Epoch 94/100\n",
            "41/41 [==============================] - 5s 113ms/step - loss: 8.4505e-04\n",
            "Epoch 95/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 8.0245e-04\n",
            "Epoch 96/100\n",
            "41/41 [==============================] - 5s 115ms/step - loss: 8.2539e-04\n",
            "Epoch 97/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 9.2275e-04\n",
            "Epoch 98/100\n",
            "41/41 [==============================] - 5s 114ms/step - loss: 9.9234e-04\n",
            "Epoch 99/100\n",
            "41/41 [==============================] - 5s 115ms/step - loss: 8.2223e-04\n",
            "Epoch 100/100\n",
            "41/41 [==============================] - 5s 112ms/step - loss: 8.6725e-04\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fcf9d8dcc10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h8gZFgBAOW4P"
      },
      "source": [
        "## Deploy the RNN <a name=\"deploy-the-rnn\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eYueYBfdK_AB"
      },
      "source": [
        "### Computing Predictions\n",
        "\n",
        "Get the test set in a new dataframe. That will be one month of stock prices after the end of the preior used for training.\n",
        "\n",
        "There are 20 (19 to 21) financial days in one month, weekends excluded!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ob4psLLNK7yb"
      },
      "source": [
        "# Getting the real stock price\n",
        "\n",
        "# first set the date range (make sure it does not overlap with our training data period)\n",
        "start_date = getDateTime(2021,6,1)\n",
        "end_date = getDateTime(2021,6,30)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2wS-EywNSva",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e541da5d-b98f-4989-ea66-373512bd88d1"
      },
      "source": [
        "# get the relevant stock proce history\n",
        "dataset_test = getStockPriceHistory(df, start_date, end_date)\n",
        "dataset_test.reset_index(inplace=True)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 : TSLA,"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLHkDE4dLDcn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b57aa8ae-a348-427e-f388-dc901b413758"
      },
      "source": [
        "# extract the relevant columns \n",
        "real_stock_price = dataset_test.iloc[:, 1:2].values\n",
        "real_stock_price.size"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XZpRcm_TLFYD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77ba56d5-9af2-4f6a-d311-4c71e1675441"
      },
      "source": [
        "# verify your data\n",
        "real_stock_price"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[627.79998779],\n",
              "       [620.13000488],\n",
              "       [601.79998779],\n",
              "       [579.71002197],\n",
              "       [591.83001709],\n",
              "       [623.01000977],\n",
              "       [602.16998291],\n",
              "       [603.88000488],\n",
              "       [610.22998047],\n",
              "       [612.22998047],\n",
              "       [616.69000244],\n",
              "       [597.53997803],\n",
              "       [601.89001465],\n",
              "       [613.36999512],\n",
              "       [624.47998047],\n",
              "       [618.25      ],\n",
              "       [632.        ],\n",
              "       [674.98999023],\n",
              "       [689.58001709],\n",
              "       [671.64001465],\n",
              "       [684.65002441]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fSFS-8O8LHOY"
      },
      "source": [
        "To predict the stock price value for each day in this date range, we need the values in the last 60 days.\n",
        "\n",
        "To obtain this **history** we need to combine both the training and test sets in one.\n",
        "\n",
        "If we were to use the training_set and test_set we would need to use the scaler but that would change the actual test values. Thus concatenate the original data frames!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5Y88AHkLJj0"
      },
      "source": [
        "# axis = 0 means concatenate the lines (i.e. vertical axis)\n",
        "dataset_total = pd.concat((stock_history['Open'], dataset_test['Open']), axis = 0)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9TusBCXYLQxV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efe43327-142d-4995-998f-40015c76edc9"
      },
      "source": [
        "dataset_total.size"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1382"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJoti2VsLT5G"
      },
      "source": [
        "# the difference in the length of the first two gives us \n",
        "# the first day in the new date range, and we need to go back 60 days to get the necessary range\n",
        "inputs = dataset_total[len(dataset_total) - len(dataset_test) - timesteps:].values"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tw2NzN_6LUsk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91abadc5-0a2d-4f05-9b57-af675d9b0fd4"
      },
      "source": [
        "inputs.size"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "81"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOntslnMLWco"
      },
      "source": [
        "# we did not use iloc from panda so lets reshape the numpy array for \n",
        "# compatibility: i.e. all the values from input lines to be stacked in one \n",
        "# column. The -1 means that the numpy has no knowledge of how the \n",
        "# values were stored in lines. The 1 means we want to them in one \n",
        "# column.\n",
        "\n",
        "inputs = inputs.reshape(-1,1) \n",
        "\n",
        "# apply the feature scaler\n",
        "inputs = sc.transform(inputs)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Y4adPmSLZVj"
      },
      "source": [
        "1. For each price in test date range we need the **immediate 60 values** before it. \n",
        "2. We have 21 prices in June;\n",
        "3. We need a numpy 3D array of 60 prices (columns) times 21 days (rows) times 1 dependent variable \n",
        "4. We don’t need y_test. That is what we are trying to compute!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kdlX_S6NLbmE"
      },
      "source": [
        "# Getting the predicted stock price of 2017\n",
        "X_test = []"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vrLyeEzzLeV_"
      },
      "source": [
        "# the first 60 from inputs are from training set; start \n",
        "# from 60 and get the extra 21, i.e. up to 81\n",
        "for i in range(timesteps, inputs.size): \n",
        "    X_test.append(inputs[i-timesteps:i, 0])"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TEBuVmlZLf2g"
      },
      "source": [
        "X_test = np.array(X_test) # not 3D structure yet"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGi3PTs_Li_S"
      },
      "source": [
        "# create a 3D structure\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6rVxW66LjGK"
      },
      "source": [
        "# source this input to our regressor model\n",
        "predicted_stock_price = regressor.predict(X_test)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ft8H1ifSLmtm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2fdcd2a-9230-435f-c722-df49e70b3073"
      },
      "source": [
        "# need to inverse the scaling to get meaningful predicted stock price # outputs\n",
        "predicted_stock_price = sc.inverse_transform(predicted_stock_price) \n",
        "predicted_stock_price.size"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6CzDUW3CLoUK"
      },
      "source": [
        "### Result Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aE-WgHRPLsa_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "d3a616e7-de45-46ef-ed20-f63078601913"
      },
      "source": [
        "# Visualising the results\n",
        "\n",
        "plt.plot(real_stock_price, color = 'red', label = 'Real Tesla Stock Price')\n",
        "\n",
        "plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Tesla Stock Price')\n",
        "\n",
        "plt.title('Tesla Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Tesla Stock Price')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "527YCNE_LyMD"
      },
      "source": [
        "The blue line shows the trend of the stock for the month of January 2017.\n",
        "\n",
        "Some observations:\n",
        "\n",
        "- The prediction lags behind the actual price curve because the model cannot react to fast non-linear changes. Spikes are examples of fast non-linear changes\n",
        "- Model reacts pretty well to smooth changes\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tN3c8G77L48A"
      },
      "source": [
        "### Compute the RMSE\n",
        "\n",
        "If we need to compute the RMSE for our Stock Price Prediction problem, we use the real stock price and predicted stock price as shown.\n",
        "\n",
        "Then consider dividing this RMSE by the range of the Google Stock Price values of January 2017 to get a relative error, as opposed to an absolute error. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8SQnOtDgLv50"
      },
      "source": [
        "#import the libraries\n",
        "import math\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XoGDYOUUL8tb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "499baf80-7704-4431-d2ec-966ad543adb3"
      },
      "source": [
        "rmse = math.sqrt( mean_squared_error( real_stock_price[0:21,:], predicted_stock_price))\n",
        "rmse"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27.052240912898"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6srtrdLL-ui"
      },
      "source": [
        "## Improve the RNN <a name=\"improve-the-rnn\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JoRyqGZVMDh2"
      },
      "source": [
        "The new data need to be placed in the same order/format  as in the case of the training/test sets.\n",
        "\n",
        "1. Getting more training data: we trained our model on the past 5 years of the  Google Stock Price but it would be even better to train it on the past 10 years.\n",
        "\n",
        "2. Increasing the number of time steps: the model remembered the stock price from the 60 previous financial days to predict the stock price of the next day. That’s because we chose a number of 60 time steps (3 months). You could try to increase the number of time steps, by choosing for example 120 time steps (6 months).\n",
        "\n",
        "3. Adding some other indicators: if you have the financial instinct that the stock price of some other companies might be correlated to the one of Google, you could add this other stock price as a new indicator in the training data.\n",
        "\n",
        "4. Adding more LSTM layers: we built a RNN with four LSTM layers but you could try with even more.\n",
        "\n",
        "5. Adding more neurons in the LSTM layers: we highlighted the fact that we needed a high number of neurons in the LSTM layers to respond better to the complexity of the problem and we chose to include 50 neurons in each of our 4 LSTM layers. You could try an architecture with even more neurons in each of the 4 (or more) LSTM layers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_OJqZPvSMHup"
      },
      "source": [
        "## Fine tune the RNN  <a name=\"fine-tune-the-rnn\"></a>\n",
        "\n",
        "Parameter Tuning on the RNN model: we are dealing with a Regression problem because we predict a continuous outcome.\n",
        "\n",
        "Tip: replace: scoring = 'accuracy' by scoring = 'neg_mean_squared_error' in the GridSearchCV class parameters as we did in the ANN case."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jh4hCygu8Y-5"
      },
      "source": [
        ""
      ],
      "execution_count": 59,
      "outputs": []
    }
  ]
}