CRYPTO_Training_and_Testing.ipynb

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      "name": "CRYPTO - Training and Testing.ipynb",
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        "<a href=\"https://colab.research.google.com/github/ArpanSM/Machine_Learning_Hackathons/blob/master/CRYPTO_Training_and_Testing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n",
        "##Notebook Outline\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "###Library Loading\n",
        "\n",
        "---\n",
        "###Data Retrival\n",
        "\n",
        "* Downloading NASDAQ listed comapanies data and filtering them according to the need. \n",
        "* Downloading historical stock data from 1990 of top 150 NASDAQ US companies.\n",
        "\n",
        "\n",
        "---\n",
        "###Feature engineering and Data preprocessing\n",
        "\n",
        "* Adding various Technical Indicators such as MACD.\n",
        "* Adding the prediction values, percentage change and target direction.\n",
        "* Feature Selection to minimize overfitting.\n",
        "\n",
        "\n",
        "---\n",
        "###Clustering\n",
        "\n",
        "* Clustering the 100 companies into similar groups based on selected features.\n",
        "* Elbow graph usage for number of cluster.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "###Data Spliting\n",
        "\n",
        "* Splitting data into various time series.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "###Machine Learning Classification Modelling\n",
        "\n",
        "* Building ML models of all the clusters separately.\n",
        "* Performing hyperparameter tuning on those models.\n",
        "* Analyzing confusion metrics and precision scores.\n",
        "\n",
        "---\n",
        "\n",
        "###Validation Testing\n",
        "\n",
        "* Predicting the target probability and sorting them on decreasing order.\n",
        "* Analyzing the model prediction using  SHAP.\n",
        "* Explaining model interpretability using SHAP\n",
        "---\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v5ed9x8wPINE"
      },
      "source": [
        "#Machine learning modelling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ca-LSFvrPoIr"
      },
      "source": [
        "###Libraries"
      ]
    },
    {
      "cell_type": "code",
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        "id": "9XcoMEFrHIpe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49bd8d4e-295a-4ee4-dd1f-f3725e6b3694"
      },
      "source": [
        "!pip install shap\n",
        "!pip install pycaret[full]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/98/30/b1e70bc55962239c4c3c9660e892be2d8247a882135a3035c10ff7f02cde/visions-0.6.0-py3-none-any.whl (75kB)\n",
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            "  Stored in directory: /root/.cache/pip/wheels/5b/24/f3/34d8e3964dac4ba849d844273c49a679111b00d5799ebb934a\n",
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            "  Stored in directory: /root/.cache/pip/wheels/ba/52/4e/4c28d04d144a28f89e2575fb63628df6e6d49b56c5ddd0c74e\n",
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            "  Stored in directory: /root/.cache/pip/wheels/48/b4/d5/fb5b7f1d040f2ff20687e3bad6867d63155dbde5a7c10f4293\n",
            "  Building wheel for pyperclip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyperclip: filename=pyperclip-1.8.2-cp37-none-any.whl size=11107 sha256=c79f579418af1dc9ac4310618cecc3f460234414c989ab917ea670851db0fee0\n",
            "  Stored in directory: /root/.cache/pip/wheels/25/af/b8/3407109267803f4015e1ee2ff23be0c8c19ce4008665931ee1\n",
            "Successfully built umap-learn pyod htmlmin phik prometheus-flask-exporter alembic gunicorn databricks-cli pynndescent gpustat pyperclip\n",
            "\u001b[31mERROR: pandas-profiling 2.11.0 has requirement requests>=2.24.0, but you'll have requests 2.23.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: pandas-profiling 2.11.0 has requirement tqdm>=4.48.2, but you'll have tqdm 4.41.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: imbalanced-learn 0.8.0 has requirement scikit-learn>=0.24, but you'll have scikit-learn 0.23.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: phik 0.11.2 has requirement scipy>=1.5.2, but you'll have scipy 1.4.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: pyldavis 3.3.1 has requirement numpy>=1.20.0, but you'll have numpy 1.19.5 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: pyldavis 3.3.1 has requirement pandas>=1.2.0, but you'll have pandas 1.1.5 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: botocore 1.20.44 has requirement urllib3<1.27,>=1.25.4, but you'll have urllib3 1.24.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: threadpoolctl, scikit-learn, imbalanced-learn, scikit-plot, yellowbrick, htmlmin, confuse, tangled-up-in-unicode, phik, imagehash, visions, pandas-profiling, kmodes, websocket-client, docker, querystring-parser, prometheus-flask-exporter, smmap, gitdb, gitpython, Mako, python-editor, alembic, gunicorn, databricks-cli, mlflow, pynndescent, umap-learn, pyod, Boruta, funcy, pyLDAvis, lightgbm, colorama, jmespath, botocore, s3transfer, docutils, awscli, xgboost, catboost, blessings, gpustat, py-spy, multidict, async-timeout, yarl, aiohttp, colorful, redis, opencensus-context, opencensus, hiredis, aioredis, aiohttp-cors, tensorboardX, ray, tune-sklearn, pyaml, scikit-optimize, azure-core, cryptography, isodate, msrest, azure-storage-blob, colorlog, pbr, stevedore, pyperclip, cmd2, cliff, cmaes, optuna, pycaret\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "  Found existing installation: imbalanced-learn 0.4.3\n",
            "    Uninstalling imbalanced-learn-0.4.3:\n",
            "      Successfully uninstalled imbalanced-learn-0.4.3\n",
            "  Found existing installation: yellowbrick 0.9.1\n",
            "    Uninstalling yellowbrick-0.9.1:\n",
            "      Successfully uninstalled yellowbrick-0.9.1\n",
            "  Found existing installation: pandas-profiling 1.4.1\n",
            "    Uninstalling pandas-profiling-1.4.1:\n",
            "      Successfully uninstalled pandas-profiling-1.4.1\n",
            "  Found existing installation: lightgbm 2.2.3\n",
            "    Uninstalling lightgbm-2.2.3:\n",
            "      Successfully uninstalled lightgbm-2.2.3\n",
            "  Found existing installation: docutils 0.16\n",
            "    Uninstalling docutils-0.16:\n",
            "      Successfully uninstalled docutils-0.16\n",
            "  Found existing installation: xgboost 0.90\n",
            "    Uninstalling xgboost-0.90:\n",
            "      Successfully uninstalled xgboost-0.90\n",
            "Successfully installed Boruta-0.3 Mako-1.1.4 aiohttp-3.7.4.post0 aiohttp-cors-0.7.0 aioredis-1.3.1 alembic-1.4.1 async-timeout-3.0.1 awscli-1.19.44 azure-core-1.13.0 azure-storage-blob-12.8.0 blessings-1.7 botocore-1.20.44 catboost-0.25 cliff-3.7.0 cmaes-0.8.2 cmd2-1.5.0 colorama-0.4.3 colorful-0.5.4 colorlog-4.8.0 confuse-1.4.0 cryptography-3.4.7 databricks-cli-0.14.3 docker-4.4.4 docutils-0.15.2 funcy-1.15 gitdb-4.0.7 gitpython-3.1.14 gpustat-0.6.0 gunicorn-20.1.0 hiredis-2.0.0 htmlmin-0.1.12 imagehash-4.2.0 imbalanced-learn-0.8.0 isodate-0.6.0 jmespath-0.10.0 kmodes-0.11.0 lightgbm-3.2.0 mlflow-1.15.0 msrest-0.6.21 multidict-5.1.0 opencensus-0.7.12 opencensus-context-0.1.2 optuna-2.7.0 pandas-profiling-2.11.0 pbr-5.5.1 phik-0.11.2 prometheus-flask-exporter-0.18.1 py-spy-0.3.5 pyLDAvis-3.3.1 pyaml-20.4.0 pycaret-2.3.0 pynndescent-0.5.2 pyod-0.8.7 pyperclip-1.8.2 python-editor-1.0.4 querystring-parser-1.2.4 ray-1.2.0 redis-3.5.3 s3transfer-0.3.6 scikit-learn-0.23.2 scikit-optimize-0.8.1 scikit-plot-0.3.7 smmap-4.0.0 stevedore-3.3.0 tangled-up-in-unicode-0.0.7 tensorboardX-2.2 threadpoolctl-2.1.0 tune-sklearn-0.2.1 umap-learn-0.5.1 visions-0.6.0 websocket-client-0.58.0 xgboost-1.3.3 yarl-1.6.3 yellowbrick-1.3.post1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80hlwAZn-Km2"
      },
      "source": [
        "#importing packages\n",
        "import os\n",
        "import pickle\n",
        "import shap\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import datetime as dt\n",
        "import pandas_datareader as pdr\n",
        "import matplotlib.pyplot as plt\n",
        "pd.set_option('display.max_columns', None)\n",
        "sns.set()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pg99EU29aMLw"
      },
      "source": [
        "import datetime as dt\n",
        "import pandas_datareader.data as web\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t67R2XIYYCyb"
      },
      "source": [
        "from IPython.display import clear_output\n",
        "from scipy.stats import mstats\n",
        "from sklearn import metrics\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.mixture import GaussianMixture\n",
        "from sklearn.model_selection import GridSearchCV, validation_curve, TimeSeriesSplit, RandomizedSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JQQzaq-0E6rP"
      },
      "source": [
        "###Get Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VYoArwphaVbx"
      },
      "source": [
        "Get NASDAQ data from [here](https://www.nasdaq.com/market-activity/stocks/screener)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8wTMul_SeXAf"
      },
      "source": [
        "Here we downloaded the data of all NASDAQ listed companies.\n",
        "The companies where filtered by US country and  then were sorted by Market Capital as it shows the stability of stocks through the time. \n",
        "Top 100 companies where chosen for analysis."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36ULao_kaJRs",
        "outputId": "1351c3b4-16ff-4b8f-c199-2e3548dfbf4a"
      },
      "source": [
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/nasdaq_screener_1616425528797.csv' '/content/'\n",
        "nasdaq_tickers = pd.read_csv(\"/content/nasdaq_screener_1616425528797.csv\")\n",
        "nasdaq_tickers.sort_values(by=['Market Cap'], inplace=True,ascending=False)\n",
        "nasdaq_tickers = nasdaq_tickers[nasdaq_tickers['Country']==\"United States\"]\n",
        "nasdaq_tickers = nasdaq_tickers.head(150)\n",
        "nasdaq_tickers['Symbol'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['AAPL', 'MSFT', 'AMZN', 'GOOG', 'GOOGL', 'FB', 'TSLA', 'JPM', 'V',\n",
              "       'JNJ', 'WMT', 'MA', 'DIS', 'UNH', 'BAC', 'NVDA', 'PG', 'HD',\n",
              "       'PYPL', 'INTC', 'CMCSA', 'XOM', 'VZ', 'NFLX', 'KO', 'ADBE', 'NKE',\n",
              "       'ABT', 'T', 'CSCO', 'CVX', 'PFE', 'CRM', 'MRK', 'AVGO', 'ORCL',\n",
              "       'SHW', 'PEP', 'ABBV', 'TMO', 'LLY', 'MCD', 'TXN', 'WFC', 'DHR',\n",
              "       'MDT', 'TMUS', 'MS', 'QCOM', 'C', 'BA', 'COST', 'HON', 'AMGN',\n",
              "       'NEE', 'BMY', 'PM', 'UPS', 'UNP', 'LOW', 'CHTR', 'SBUX', 'SCHW',\n",
              "       'CAT', 'ABNB', 'RTX', 'DE', 'IBM', 'GS', 'GE', 'AXP', 'SE', 'BLK',\n",
              "       'MMM', 'AMAT', 'INTU', 'EL', 'SQ', 'MU', 'AMT', 'LMT', 'ZM', 'AMD',\n",
              "       'CVS', 'MO', 'BKNG', 'TGT', 'NOW', 'FIS', 'SYK', 'ANTM', 'ISRG',\n",
              "       'CI', 'SPGI', 'SNAP', 'MDLZ', 'GM', 'FISV', 'GILD', 'USB', 'LRCX',\n",
              "       'TJX', 'ADP', 'TFC', 'PLD', 'ZTS', 'CCI', 'PNC', 'FDX', 'CME',\n",
              "       'DUK', 'CSX', 'COP', 'ATVI', 'BDX', 'ITW', 'NSC', 'CL', 'HCA',\n",
              "       'SO', 'ICE', 'WDAY', 'ILMN', 'APD', 'VIAC', 'VIACA', 'VMW', 'GPN',\n",
              "       'TWLO', 'MMC', 'ECL', 'D', 'EQIX', 'ADSK', 'MRNA', 'COF', 'ADI',\n",
              "       'VRTX', 'SCCO', 'MCO', 'BSX', 'MET', 'PGR', 'EMR', 'NOC', 'HUM',\n",
              "       'WM', 'REGN', 'FCX', 'BX'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAvgUT4Hd4iz"
      },
      "source": [
        "tickers = nasdaq_tickers['Symbol'].unique()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LXF36NZ1fDc6",
        "outputId": "bca7c19a-0eea-47f6-86af-406fe7a9fa9f"
      },
      "source": [
        "tickers.size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "150"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLW0QC9ZfUNn"
      },
      "source": [
        "We use Yahoo Finance to get the historical data of all the listed companies in tickers and store them in Pandas Dataframe `all_data`. We download data from 1990-01-01 to the latest day. Open, close, high, low, adjusted close and volume of the listed are downloaded."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6biPE991_2ZJ"
      },
      "source": [
        "def get_latest_data():\n",
        "  all_data = pd.DataFrame()\n",
        "  test_data = pd.DataFrame()\n",
        "  no_data = []\n",
        "\n",
        "  #Extract data from Yahoo Finance\n",
        "  for i in tickers:\n",
        "      try:\n",
        "          print(i)\n",
        "          test_data = pdr.get_data_yahoo(i, start = dt.datetime(1990,1,1), end = dt.date.today())\n",
        "          test_data['symbol'] = i\n",
        "          all_data = all_data.append(test_data)\n",
        "          clear_output(wait = True)\n",
        "      except:\n",
        "          no_data.append(i)\n",
        "\n",
        "      clear_output(wait = True)\n",
        "  return all_data,test_data,no_data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KFBhhWDrJ4_i",
        "outputId": "316cc0c5-66fd-45b0-d237-5b5c3838aa0f"
      },
      "source": [
        "all_data, test_data, no_data = get_latest_data()\n",
        "all_data.to_csv(\"all_data_orig.csv\",index_label=\"Date\")\n",
        "test_data.to_csv(\"test_data_orig.csv\",index_label=\"Date\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BX\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlgOWtQ7fa_w"
      },
      "source": [
        "!rm -rf '/content/drive/MyDrive/Work/Projects/CRYPTO/all_data_orig.csv'\n",
        "!rm -rf '/content/drive/MyDrive/Work/Projects/CRYPTO/test_data_orig.csv'\n",
        "!cp 'all_data_orig.csv' '/content/drive/MyDrive/Work/Projects/CRYPTO/'\n",
        "!cp 'test_data_orig.csv' '/content/drive/MyDrive/Work/Projects/CRYPTO/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XSgpzFGqzYtx",
        "outputId": "5f46b514-78f5-48ce-a165-d5c7f6f6fd13"
      },
      "source": [
        "#original data\n",
        "all_data = pd.read_csv(\"/content/drive/MyDrive/Work/Projects/CRYPTO/all_data_orig.csv\",index_col=\"Date\")\n",
        "test_data = pd.read_csv(\"/content/drive/MyDrive/Work/Projects/CRYPTO/test_data_orig.csv\",index_col=\"Date\")\n",
        "#shape of all_data and test_data\n",
        "all_data.shape, test_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((955754, 7), (3463, 7))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IR36C9tnxkpy",
        "outputId": "adad63bf-53ca-471b-d869-573ce8a624c1"
      },
      "source": [
        "all_data.tail(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>symbol</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>2021-03-25</th>\n",
              "      <td>72.199997</td>\n",
              "      <td>71.410004</td>\n",
              "      <td>72.0</td>\n",
              "      <td>71.989998</td>\n",
              "      <td>926755.0</td>\n",
              "      <td>71.989998</td>\n",
              "      <td>BX</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 High        Low  Open      Close    Volume  Adj Close symbol\n",
              "Date                                                                         \n",
              "2021-03-25  72.199997  71.410004  72.0  71.989998  926755.0  71.989998     BX"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TkhGzKk_gonZ",
        "outputId": "07c9f645-926b-450a-affa-b14ad51b8186"
      },
      "source": [
        "test_data.tail(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>symbol</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>2021-03-25</th>\n",
              "      <td>72.199997</td>\n",
              "      <td>71.410004</td>\n",
              "      <td>72.0</td>\n",
              "      <td>71.989998</td>\n",
              "      <td>926755.0</td>\n",
              "      <td>71.989998</td>\n",
              "      <td>BX</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 High        Low  Open      Close    Volume  Adj Close symbol\n",
              "Date                                                                         \n",
              "2021-03-25  72.199997  71.410004  72.0  71.989998  926755.0  71.989998     BX"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iejl3m_GYNpa",
        "outputId": "8f9288d3-5833-446c-f0be-1a524d45b923"
      },
      "source": [
        "#downloaded data tickers\n",
        "all_data['symbol'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['AAPL', 'MSFT', 'AMZN', 'GOOG', 'GOOGL', 'FB', 'TSLA', 'JPM', 'V',\n",
              "       'JNJ', 'WMT', 'MA', 'DIS', 'UNH', 'BAC', 'NVDA', 'PG', 'HD',\n",
              "       'PYPL', 'INTC', 'CMCSA', 'XOM', 'VZ', 'NFLX', 'KO', 'ADBE', 'NKE',\n",
              "       'ABT', 'T', 'CSCO', 'CVX', 'PFE', 'CRM', 'MRK', 'AVGO', 'ORCL',\n",
              "       'SHW', 'PEP', 'ABBV', 'TMO', 'LLY', 'MCD', 'TXN', 'WFC', 'DHR',\n",
              "       'MDT', 'TMUS', 'MS', 'QCOM', 'C', 'BA', 'COST', 'HON', 'AMGN',\n",
              "       'NEE', 'BMY', 'PM', 'UPS', 'UNP', 'LOW', 'CHTR', 'SBUX', 'SCHW',\n",
              "       'CAT', 'ABNB', 'RTX', 'DE', 'IBM', 'GS', 'GE', 'AXP', 'SE', 'BLK',\n",
              "       'MMM', 'AMAT', 'INTU', 'EL', 'SQ', 'MU', 'AMT', 'LMT', 'ZM', 'AMD',\n",
              "       'CVS', 'MO', 'BKNG', 'TGT', 'NOW', 'FIS', 'SYK', 'ANTM', 'ISRG',\n",
              "       'CI', 'SPGI', 'SNAP', 'MDLZ', 'GM', 'FISV', 'GILD', 'USB', 'LRCX',\n",
              "       'TJX', 'ADP', 'TFC', 'PLD', 'ZTS', 'CCI', 'PNC', 'FDX', 'CME',\n",
              "       'DUK', 'CSX', 'COP', 'ATVI', 'BDX', 'ITW', 'NSC', 'CL', 'HCA',\n",
              "       'SO', 'ICE', 'WDAY', 'ILMN', 'APD', 'VIAC', 'VIACA', 'VMW', 'GPN',\n",
              "       'TWLO', 'MMC', 'ECL', 'D', 'EQIX', 'ADSK', 'MRNA', 'COF', 'ADI',\n",
              "       'VRTX', 'SCCO', 'MCO', 'BSX', 'MET', 'PGR', 'EMR', 'NOC', 'HUM',\n",
              "       'WM', 'REGN', 'FCX', 'BX'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQq4C6H-hDsV",
        "outputId": "0a9f399b-f861-4f09-c5d9-27607b890214"
      },
      "source": [
        "#if we were not able to find data for any ticker, the ticker would be shown here.\n",
        "no_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-a7t7YqFCJY"
      },
      "source": [
        "###Feature Enginerring and Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVlM8iDsgq6A"
      },
      "source": [
        "For feature engineering techniques, we use **Technical Indicators**.\n",
        "\n",
        "Technical Indicators were primarily used by traders who would look up at these indicators on their trading screen to make a buy/sell decision.\n",
        "\n",
        "Technical indicators work best in short term, I will use 5 days and 15 days as my fast and slow signal respectively.\n",
        "\n",
        "Technical Indicators used here are: \n",
        "\n",
        "1. Simple Moving Average (Fast and Slow)\n",
        "  * SMA calculates the average of prices over a given interval of time and is used to determine the trend of the stock.\n",
        "2. Average True Range\n",
        "  * Average True Range is a common technical indicator used to measure volatility in the market, measured as a moving average of True Ranges. A higher ATR of a company implied higher volatility of the stock. \n",
        "3. Average Directional Index (Fast and Slow)\n",
        "  * Two of its main components, +DI and -DI helps in identifying the direction of the trend. In general, an ADX of 25 or above indicates a strong trend and an ADX of less than 20 indicates a weak trend.\n",
        "4. Stochastic Oscillators (Fast and Slow)\n",
        "  * Stochastic oscillator is a momentum indicator aiming at identifying overbought and oversold securities and is commonly used in technical analysis.\n",
        "5. Relative Strength Index (Fast and Slow)\n",
        "  * RSI is one of the most common momentum indicator aimed at quantifies price changes and the speed of such change.\n",
        "6. Moving Average Convergence Divergence\n",
        "  * MACD uses two exponentially moving averages and creates a trend analysis based on their convergence or divergence. \n",
        "7. Bollinger Bands\n",
        "  * Bollinger bands capture the volatility of a stock and are used to identify overbought and oversold stocks. \n",
        "8. Rate of Change\n",
        "  * Rate of change is a momentum indicator that explains a price momentum relative to a price fixed period before.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJsrA17vDPRA"
      },
      "source": [
        "def get_technical_indicators(all_data = all_data):\n",
        "  #Creating Return column\n",
        "  all_data['return'] = all_data.groupby('symbol')['Close'].pct_change()\n",
        "\n",
        "  all_data['SMA_5'] = all_data.groupby('symbol')['Close'].transform(lambda x: x.rolling(window = 5).mean())\n",
        "  all_data['SMA_15'] = all_data.groupby('symbol')['Close'].transform(lambda x: x.rolling(window = 15).mean())\n",
        "  all_data['SMA_ratio'] = all_data['SMA_15'] / all_data['SMA_5']\n",
        "\n",
        "  all_data['EMA_5'] = all_data.groupby('symbol')['Close'].transform(lambda x: x.ewm(span = 5).mean())\n",
        "  all_data['EMA_15'] = all_data.groupby('symbol')['Close'].transform(lambda x: x.ewm(span = 5).mean())\n",
        "  all_data['EMA_ratio'] = all_data['EMA_15'] / all_data['EMA_5']\n",
        "\n",
        "  all_data['SMA5_Volume'] = all_data.groupby('symbol')['Volume'].transform(lambda x: x.rolling(window = 5).mean())\n",
        "  all_data['SMA15_Volume'] = all_data.groupby('symbol')['Volume'].transform(lambda x: x.rolling(window = 15).mean())\n",
        "  all_data['SMA_Volume_Ratio'] = all_data['SMA5_Volume']/all_data['SMA15_Volume']\n",
        "\n",
        "  all_data['EMA_5_Volume'] = all_data.groupby('symbol')['Volume'].transform(lambda x: x.ewm(span = 5).mean())\n",
        "  all_data['EMA_15_Volume'] = all_data.groupby('symbol')['Volume'].transform(lambda x: x.ewm(span = 5).mean())\n",
        "  all_data['EMA_Volume_ratio'] = all_data['EMA_15_Volume'] / all_data['EMA_5_Volume']\n",
        "\n",
        "  def Wilder(data, periods):\n",
        "      start = np.where(~np.isnan(data))[0][0] #Check if nans present in beginning\n",
        "      Wilder = np.array([np.nan]*len(data))\n",
        "      Wilder[start+periods-1] = data[start:(start+periods)].mean() #Simple Moving Average\n",
        "      for i in range(start+periods,len(data)):\n",
        "          Wilder[i] = (Wilder[i-1]*(periods-1) + data[i])/periods #Wilder Smoothing\n",
        "      return(Wilder)\n",
        "\n",
        "  all_data['prev_close'] = all_data.groupby('symbol')['Close'].shift(1)\n",
        "  all_data['TR'] = np.maximum((all_data['High'] - all_data['Low']), \n",
        "                      np.maximum(abs(all_data['High'] - all_data['prev_close']), \n",
        "                      abs(all_data['prev_close'] - all_data['Low'])))\n",
        "  for i in all_data['symbol'].unique():\n",
        "      TR_data = all_data[all_data.symbol == i].copy()\n",
        "      all_data.loc[all_data.symbol==i,'ATR_5'] = Wilder(TR_data['TR'], 5)\n",
        "      all_data.loc[all_data.symbol==i,'ATR_15'] = Wilder(TR_data['TR'], 15)\n",
        "\n",
        "  all_data['ATR_Ratio'] = all_data['ATR_5'] / all_data['ATR_15']\n",
        "\n",
        "  all_data['prev_high'] = all_data.groupby('symbol')['High'].shift(1)\n",
        "  all_data['prev_low'] = all_data.groupby('symbol')['Low'].shift(1)\n",
        "\n",
        "  all_data['+DM'] = np.where(~np.isnan(all_data.prev_high),\n",
        "                            np.where((all_data['High'] > all_data['prev_high']) & \n",
        "          (((all_data['High'] - all_data['prev_high']) > (all_data['prev_low'] - all_data['Low']))), \n",
        "                                                                    all_data['High'] - all_data['prev_high'], \n",
        "                                                                    0),np.nan)\n",
        "\n",
        "  all_data['-DM'] = np.where(~np.isnan(all_data.prev_low),\n",
        "                            np.where((all_data['prev_low'] > all_data['Low']) & \n",
        "          (((all_data['prev_low'] - all_data['Low']) > (all_data['High'] - all_data['prev_high']))), \n",
        "                                      all_data['prev_low'] - all_data['Low'], \n",
        "                                      0),np.nan)\n",
        "\n",
        "  for i in all_data['symbol'].unique():\n",
        "      ADX_data = all_data[all_data.symbol == i].copy()\n",
        "      all_data.loc[all_data.symbol==i,'+DM_5'] = Wilder(ADX_data['+DM'], 5)\n",
        "      all_data.loc[all_data.symbol==i,'-DM_5'] = Wilder(ADX_data['-DM'], 5)\n",
        "      all_data.loc[all_data.symbol==i,'+DM_15'] = Wilder(ADX_data['+DM'], 15)\n",
        "      all_data.loc[all_data.symbol==i,'-DM_15'] = Wilder(ADX_data['-DM'], 15)\n",
        "\n",
        "  all_data['+DI_5'] = (all_data['+DM_5']/all_data['ATR_5'])*100\n",
        "  all_data['-DI_5'] = (all_data['-DM_5']/all_data['ATR_5'])*100\n",
        "  all_data['+DI_15'] = (all_data['+DM_15']/all_data['ATR_15'])*100\n",
        "  all_data['-DI_15'] = (all_data['-DM_15']/all_data['ATR_15'])*100\n",
        "\n",
        "  all_data['DX_5'] = (np.round(abs(all_data['+DI_5'] - all_data['-DI_5'])/(all_data['+DI_5'] + all_data['-DI_5']) * 100))\n",
        "\n",
        "  all_data['DX_15'] = (np.round(abs(all_data['+DI_15'] - all_data['-DI_15'])/(all_data['+DI_15'] + all_data['-DI_15']) * 100))\n",
        "\n",
        "  for i in all_data['symbol'].unique():\n",
        "      ADX_data = all_data[all_data.symbol == i].copy()\n",
        "      all_data.loc[all_data.symbol==i,'ADX_5'] = Wilder(ADX_data['DX_5'], 5)\n",
        "      all_data.loc[all_data.symbol==i,'ADX_15'] = Wilder(ADX_data['DX_15'], 15)\n",
        "\n",
        "  all_data['Lowest_5D'] = all_data.groupby('symbol')['Low'].transform(lambda x: x.rolling(window = 5).min())\n",
        "  all_data['High_5D'] = all_data.groupby('symbol')['High'].transform(lambda x: x.rolling(window = 5).max())\n",
        "  all_data['Lowest_15D'] = all_data.groupby('symbol')['Low'].transform(lambda x: x.rolling(window = 15).min())\n",
        "  all_data['High_15D'] = all_data.groupby('symbol')['High'].transform(lambda x: x.rolling(window = 15).max())\n",
        "\n",
        "  all_data['Stochastic_5'] = ((all_data['Close'] - all_data['Lowest_5D'])/(all_data['High_5D'] - all_data['Lowest_5D']))*100\n",
        "  all_data['Stochastic_15'] = ((all_data['Close'] - all_data['Lowest_15D'])/(all_data['High_15D'] - all_data['Lowest_15D']))*100\n",
        "\n",
        "  all_data['Stochastic_%D_5'] = all_data['Stochastic_5'].rolling(window = 5).mean()\n",
        "  all_data['Stochastic_%D_15'] = all_data['Stochastic_5'].rolling(window = 15).mean()\n",
        "\n",
        "  all_data['Stochastic_Ratio'] = all_data['Stochastic_%D_5']/all_data['Stochastic_%D_15']\n",
        "\n",
        "  all_data['Diff'] = all_data.groupby('symbol')['Close'].transform(lambda x: x.diff())\n",
        "  all_data['Up'] = all_data['Diff']\n",
        "  all_data.loc[(all_data['Up']<0), 'Up'] = 0\n",
        "\n",
        "  all_data['Down'] = all_data['Diff']\n",
        "  all_data.loc[(all_data['Down']>0), 'Down'] = 0 \n",
        "  all_data['Down'] = abs(all_data['Down'])\n",
        "\n",
        "  all_data['avg_5up'] = all_data.groupby('symbol')['Up'].transform(lambda x: x.rolling(window=5).mean())\n",
        "  all_data['avg_5down'] = all_data.groupby('symbol')['Down'].transform(lambda x: x.rolling(window=5).mean())\n",
        "\n",
        "  all_data['avg_15up'] = all_data.groupby('symbol')['Up'].transform(lambda x: x.rolling(window=15).mean())\n",
        "  all_data['avg_15down'] = all_data.groupby('symbol')['Down'].transform(lambda x: x.rolling(window=15).mean())\n",
        "\n",
        "  all_data['RS_5'] = all_data['avg_5up'] / all_data['avg_5down']\n",
        "  all_data['RS_15'] = all_data['avg_15up'] / all_data['avg_15down']\n",
        "\n",
        "  all_data['RSI_5'] = 100 - (100/(1+all_data['RS_5']))\n",
        "  all_data['RSI_15'] = 100 - (100/(1+all_data['RS_15']))\n",
        "\n",
        "  all_data['RSI_ratio'] = all_data['RSI_5']/all_data['RSI_15']\n",
        "\n",
        "  all_data['5Ewm'] = all_data.groupby('symbol')['Close'].transform(lambda x: x.ewm(span=5, adjust=False).mean())\n",
        "  all_data['15Ewm'] = all_data.groupby('symbol')['Close'].transform(lambda x: x.ewm(span=15, adjust=False).mean())\n",
        "  all_data['MACD'] = all_data['15Ewm'] - all_data['5Ewm']\n",
        "\n",
        "  all_data['15MA'] = all_data.groupby('symbol')['Close'].transform(lambda x: x.rolling(window=15).mean())\n",
        "  all_data['SD'] = all_data.groupby('symbol')['Close'].transform(lambda x: x.rolling(window=15).std())\n",
        "  all_data['upperband'] = all_data['15MA'] + 2*all_data['SD']\n",
        "  all_data['lowerband'] = all_data['15MA'] - 2*all_data['SD']\n",
        "\n",
        "  all_data['RC'] = all_data.groupby('symbol')['Close'].transform(lambda x: x.pct_change(periods = 15)) \n",
        "\n",
        "  return all_data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wK58ie7bPKes"
      },
      "source": [
        "all_data_orig = all_data.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "N_3ystHSKpi4"
      },
      "source": [
        "all_data = get_technical_indicators()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5QZ9ttQKPnbv"
      },
      "source": [
        "all_data.to_csv(\"all_data_ti.csv\",index_label=\"Date\")\n",
        "!rm -rf '/content/drive/MyDrive/Work/Projects/CRYPTO/all_data_ti.csv'\n",
        "!cp 'all_data_ti.csv' '/content/drive/MyDrive/Work/Projects/CRYPTO/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_JVYQA7PP4zy"
      },
      "source": [
        "all_data = pd.read_csv(\"/content/drive/MyDrive/Work/Projects/CRYPTO/all_data_ti.csv\",index_col=\"Date\")\n",
        "all_data.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GUMyZrlPQCo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "outputId": "b6fe7082-da84-42fa-f419-8665372bf701"
      },
      "source": [
        "all_data.tail(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>symbol</th>\n",
              "      <th>return</th>\n",
              "      <th>SMA_5</th>\n",
              "      <th>SMA_15</th>\n",
              "      <th>SMA_ratio</th>\n",
              "      <th>EMA_5</th>\n",
              "      <th>EMA_15</th>\n",
              "      <th>EMA_ratio</th>\n",
              "      <th>SMA5_Volume</th>\n",
              "      <th>SMA15_Volume</th>\n",
              "      <th>SMA_Volume_Ratio</th>\n",
              "      <th>EMA_5_Volume</th>\n",
              "      <th>EMA_15_Volume</th>\n",
              "      <th>EMA_Volume_ratio</th>\n",
              "      <th>prev_close</th>\n",
              "      <th>TR</th>\n",
              "      <th>ATR_5</th>\n",
              "      <th>ATR_15</th>\n",
              "      <th>ATR_Ratio</th>\n",
              "      <th>prev_high</th>\n",
              "      <th>prev_low</th>\n",
              "      <th>+DM</th>\n",
              "      <th>-DM</th>\n",
              "      <th>+DM_5</th>\n",
              "      <th>-DM_5</th>\n",
              "      <th>+DM_15</th>\n",
              "      <th>-DM_15</th>\n",
              "      <th>+DI_5</th>\n",
              "      <th>-DI_5</th>\n",
              "      <th>+DI_15</th>\n",
              "      <th>-DI_15</th>\n",
              "      <th>DX_5</th>\n",
              "      <th>DX_15</th>\n",
              "      <th>ADX_5</th>\n",
              "      <th>ADX_15</th>\n",
              "      <th>Lowest_5D</th>\n",
              "      <th>High_5D</th>\n",
              "      <th>Lowest_15D</th>\n",
              "      <th>High_15D</th>\n",
              "      <th>Stochastic_5</th>\n",
              "      <th>Stochastic_15</th>\n",
              "      <th>Stochastic_%D_5</th>\n",
              "      <th>Stochastic_%D_15</th>\n",
              "      <th>Stochastic_Ratio</th>\n",
              "      <th>Diff</th>\n",
              "      <th>Up</th>\n",
              "      <th>Down</th>\n",
              "      <th>avg_5up</th>\n",
              "      <th>avg_5down</th>\n",
              "      <th>avg_15up</th>\n",
              "      <th>avg_15down</th>\n",
              "      <th>RS_5</th>\n",
              "      <th>RS_15</th>\n",
              "      <th>RSI_5</th>\n",
              "      <th>RSI_15</th>\n",
              "      <th>RSI_ratio</th>\n",
              "      <th>5Ewm</th>\n",
              "      <th>15Ewm</th>\n",
              "      <th>MACD</th>\n",
              "      <th>15MA</th>\n",
              "      <th>SD</th>\n",
              "      <th>upperband</th>\n",
              "      <th>lowerband</th>\n",
              "      <th>RC</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>2021-03-25</th>\n",
              "      <td>72.199997</td>\n",
              "      <td>71.410004</td>\n",
              "      <td>72.0</td>\n",
              "      <td>71.989998</td>\n",
              "      <td>926755.0</td>\n",
              "      <td>71.989998</td>\n",
              "      <td>BX</td>\n",
              "      <td>-0.016396</td>\n",
              "      <td>72.846001</td>\n",
              "      <td>71.980667</td>\n",
              "      <td>0.988121</td>\n",
              "      <td>72.930159</td>\n",
              "      <td>72.930159</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2083151.0</td>\n",
              "      <td>3.458284e+06</td>\n",
              "      <td>0.602366</td>\n",
              "      <td>1.962044e+06</td>\n",
              "      <td>1.962044e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>73.190002</td>\n",
              "      <td>1.779999</td>\n",
              "      <td>2.045895</td>\n",
              "      <td>2.059285</td>\n",
              "      <td>0.993497</td>\n",
              "      <td>74.120003</td>\n",
              "      <td>72.769997</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.359993</td>\n",
              "      <td>0.394475</td>\n",
              "      <td>0.588812</td>\n",
              "      <td>0.475876</td>\n",
              "      <td>0.47125</td>\n",
              "      <td>19.281303</td>\n",
              "      <td>28.780176</td>\n",
              "      <td>23.108804</td>\n",
              "      <td>22.88416</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.212373</td>\n",
              "      <td>18.383998</td>\n",
              "      <td>71.339996</td>\n",
              "      <td>75.989998</td>\n",
              "      <td>63.709999</td>\n",
              "      <td>76.25</td>\n",
              "      <td>13.978523</td>\n",
              "      <td>66.028694</td>\n",
              "      <td>24.949864</td>\n",
              "      <td>56.832531</td>\n",
              "      <td>0.439007</td>\n",
              "      <td>-1.200005</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.200005</td>\n",
              "      <td>0.216</td>\n",
              "      <td>1.036</td>\n",
              "      <td>0.747333</td>\n",
              "      <td>0.587333</td>\n",
              "      <td>0.208495</td>\n",
              "      <td>1.272418</td>\n",
              "      <td>17.25242</td>\n",
              "      <td>55.994011</td>\n",
              "      <td>0.308112</td>\n",
              "      <td>72.930159</td>\n",
              "      <td>72.373351</td>\n",
              "      <td>-0.556808</td>\n",
              "      <td>71.980667</td>\n",
              "      <td>3.15389</td>\n",
              "      <td>78.288447</td>\n",
              "      <td>65.672886</td>\n",
              "      <td>0.034488</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 High        Low  Open      Close    Volume  Adj Close symbol  \\\n",
              "Date                                                                            \n",
              "2021-03-25  72.199997  71.410004  72.0  71.989998  926755.0  71.989998     BX   \n",
              "\n",
              "              return      SMA_5     SMA_15  SMA_ratio      EMA_5     EMA_15  \\\n",
              "Date                                                                          \n",
              "2021-03-25 -0.016396  72.846001  71.980667   0.988121  72.930159  72.930159   \n",
              "\n",
              "            EMA_ratio  SMA5_Volume  SMA15_Volume  SMA_Volume_Ratio  \\\n",
              "Date                                                                 \n",
              "2021-03-25        1.0    2083151.0  3.458284e+06          0.602366   \n",
              "\n",
              "            EMA_5_Volume  EMA_15_Volume  EMA_Volume_ratio  prev_close  \\\n",
              "Date                                                                    \n",
              "2021-03-25  1.962044e+06   1.962044e+06               1.0   73.190002   \n",
              "\n",
              "                  TR     ATR_5    ATR_15  ATR_Ratio  prev_high   prev_low  \\\n",
              "Date                                                                        \n",
              "2021-03-25  1.779999  2.045895  2.059285   0.993497  74.120003  72.769997   \n",
              "\n",
              "            +DM       -DM     +DM_5     -DM_5    +DM_15   -DM_15      +DI_5  \\\n",
              "Date                                                                          \n",
              "2021-03-25  0.0  1.359993  0.394475  0.588812  0.475876  0.47125  19.281303   \n",
              "\n",
              "                -DI_5     +DI_15    -DI_15  DX_5  DX_15      ADX_5     ADX_15  \\\n",
              "Date                                                                            \n",
              "2021-03-25  28.780176  23.108804  22.88416  20.0    0.0  24.212373  18.383998   \n",
              "\n",
              "            Lowest_5D    High_5D  Lowest_15D  High_15D  Stochastic_5  \\\n",
              "Date                                                                   \n",
              "2021-03-25  71.339996  75.989998   63.709999     76.25     13.978523   \n",
              "\n",
              "            Stochastic_15  Stochastic_%D_5  Stochastic_%D_15  \\\n",
              "Date                                                           \n",
              "2021-03-25      66.028694        24.949864         56.832531   \n",
              "\n",
              "            Stochastic_Ratio      Diff   Up      Down  avg_5up  avg_5down  \\\n",
              "Date                                                                        \n",
              "2021-03-25          0.439007 -1.200005  0.0  1.200005    0.216      1.036   \n",
              "\n",
              "            avg_15up  avg_15down      RS_5     RS_15     RSI_5     RSI_15  \\\n",
              "Date                                                                        \n",
              "2021-03-25  0.747333    0.587333  0.208495  1.272418  17.25242  55.994011   \n",
              "\n",
              "            RSI_ratio       5Ewm      15Ewm      MACD       15MA       SD  \\\n",
              "Date                                                                        \n",
              "2021-03-25   0.308112  72.930159  72.373351 -0.556808  71.980667  3.15389   \n",
              "\n",
              "            upperband  lowerband        RC  \n",
              "Date                                        \n",
              "2021-03-25  78.288447  65.672886  0.034488  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIDWA6Ii5c_1"
      },
      "source": [
        "all_data_orig.tail(1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1x0rdRMvlq2E"
      },
      "source": [
        "** *italicized text*Creating Prediction variable**\n",
        "\n",
        "Here we observe the technical indicators for today and use it to predict the direction of movement of the stocks 7 days later. If the stock went up in 7 days, we denote it by 1 and if it went down we denote it by 0 (`“Target_Direction”`)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-jZV3cfPWBv"
      },
      "source": [
        "all_data['Close_Shifted'] = all_data.groupby('symbol')['Close'].transform(lambda x: x.shift(-6))\n",
        "all_data['Target'] = ((all_data['Close_Shifted'] - all_data['Open'])/(all_data['Open']) * 100).shift(-1)\n",
        "all_data['Target_Direction'] = np.where(all_data['Target']>0,1,0)\n",
        "all_data = all_data.dropna().copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p00PrUHPx4m1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "outputId": "41d9b52b-7c20-4337-de80-ca2149c7493c"
      },
      "source": [
        "all_data.tail(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>symbol</th>\n",
              "      <th>return</th>\n",
              "      <th>SMA_5</th>\n",
              "      <th>SMA_15</th>\n",
              "      <th>SMA_ratio</th>\n",
              "      <th>EMA_5</th>\n",
              "      <th>EMA_15</th>\n",
              "      <th>EMA_ratio</th>\n",
              "      <th>SMA5_Volume</th>\n",
              "      <th>SMA15_Volume</th>\n",
              "      <th>SMA_Volume_Ratio</th>\n",
              "      <th>EMA_5_Volume</th>\n",
              "      <th>EMA_15_Volume</th>\n",
              "      <th>EMA_Volume_ratio</th>\n",
              "      <th>prev_close</th>\n",
              "      <th>TR</th>\n",
              "      <th>ATR_5</th>\n",
              "      <th>ATR_15</th>\n",
              "      <th>ATR_Ratio</th>\n",
              "      <th>prev_high</th>\n",
              "      <th>prev_low</th>\n",
              "      <th>+DM</th>\n",
              "      <th>-DM</th>\n",
              "      <th>+DM_5</th>\n",
              "      <th>-DM_5</th>\n",
              "      <th>+DM_15</th>\n",
              "      <th>-DM_15</th>\n",
              "      <th>+DI_5</th>\n",
              "      <th>-DI_5</th>\n",
              "      <th>+DI_15</th>\n",
              "      <th>-DI_15</th>\n",
              "      <th>DX_5</th>\n",
              "      <th>DX_15</th>\n",
              "      <th>ADX_5</th>\n",
              "      <th>ADX_15</th>\n",
              "      <th>Lowest_5D</th>\n",
              "      <th>High_5D</th>\n",
              "      <th>Lowest_15D</th>\n",
              "      <th>High_15D</th>\n",
              "      <th>Stochastic_5</th>\n",
              "      <th>Stochastic_15</th>\n",
              "      <th>Stochastic_%D_5</th>\n",
              "      <th>Stochastic_%D_15</th>\n",
              "      <th>Stochastic_Ratio</th>\n",
              "      <th>Diff</th>\n",
              "      <th>Up</th>\n",
              "      <th>Down</th>\n",
              "      <th>avg_5up</th>\n",
              "      <th>avg_5down</th>\n",
              "      <th>avg_15up</th>\n",
              "      <th>avg_15down</th>\n",
              "      <th>RS_5</th>\n",
              "      <th>RS_15</th>\n",
              "      <th>RSI_5</th>\n",
              "      <th>RSI_15</th>\n",
              "      <th>RSI_ratio</th>\n",
              "      <th>5Ewm</th>\n",
              "      <th>15Ewm</th>\n",
              "      <th>MACD</th>\n",
              "      <th>15MA</th>\n",
              "      <th>SD</th>\n",
              "      <th>upperband</th>\n",
              "      <th>lowerband</th>\n",
              "      <th>RC</th>\n",
              "      <th>Close_Shifted</th>\n",
              "      <th>Target</th>\n",
              "      <th>Target_Direction</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>2021-03-15</th>\n",
              "      <td>75.559998</td>\n",
              "      <td>72.919998</td>\n",
              "      <td>75.139999</td>\n",
              "      <td>74.93</td>\n",
              "      <td>3717700.0</td>\n",
              "      <td>74.93</td>\n",
              "      <td>BX</td>\n",
              "      <td>0.001203</td>\n",
              "      <td>72.778</td>\n",
              "      <td>70.106667</td>\n",
              "      <td>0.963295</td>\n",
              "      <td>73.053493</td>\n",
              "      <td>73.053493</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3778740.0</td>\n",
              "      <td>3.402307e+06</td>\n",
              "      <td>1.110641</td>\n",
              "      <td>3.821809e+06</td>\n",
              "      <td>3.821809e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>74.839996</td>\n",
              "      <td>2.639999</td>\n",
              "      <td>2.600467</td>\n",
              "      <td>2.100825</td>\n",
              "      <td>1.237832</td>\n",
              "      <td>75.269997</td>\n",
              "      <td>73.050003</td>\n",
              "      <td>0.290001</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.931828</td>\n",
              "      <td>0.316932</td>\n",
              "      <td>0.602426</td>\n",
              "      <td>0.387816</td>\n",
              "      <td>35.833103</td>\n",
              "      <td>12.187521</td>\n",
              "      <td>28.675682</td>\n",
              "      <td>18.460177</td>\n",
              "      <td>49.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>36.352028</td>\n",
              "      <td>22.536914</td>\n",
              "      <td>67.800003</td>\n",
              "      <td>75.559998</td>\n",
              "      <td>63.709999</td>\n",
              "      <td>75.559998</td>\n",
              "      <td>91.881473</td>\n",
              "      <td>94.683567</td>\n",
              "      <td>91.671472</td>\n",
              "      <td>64.515444</td>\n",
              "      <td>1.420923</td>\n",
              "      <td>0.090004</td>\n",
              "      <td>0.090004</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.48</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.786</td>\n",
              "      <td>0.425333</td>\n",
              "      <td>inf</td>\n",
              "      <td>1.847963</td>\n",
              "      <td>100.0</td>\n",
              "      <td>64.887188</td>\n",
              "      <td>1.541136</td>\n",
              "      <td>73.053493</td>\n",
              "      <td>70.874565</td>\n",
              "      <td>-2.178929</td>\n",
              "      <td>70.106667</td>\n",
              "      <td>2.665131</td>\n",
              "      <td>75.436929</td>\n",
              "      <td>64.776405</td>\n",
              "      <td>0.077819</td>\n",
              "      <td>73.190002</td>\n",
              "      <td>-4.166673</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 High        Low       Open  Close     Volume  Adj Close  \\\n",
              "Date                                                                       \n",
              "2021-03-15  75.559998  72.919998  75.139999  74.93  3717700.0      74.93   \n",
              "\n",
              "           symbol    return   SMA_5     SMA_15  SMA_ratio      EMA_5  \\\n",
              "Date                                                                   \n",
              "2021-03-15     BX  0.001203  72.778  70.106667   0.963295  73.053493   \n",
              "\n",
              "               EMA_15  EMA_ratio  SMA5_Volume  SMA15_Volume  SMA_Volume_Ratio  \\\n",
              "Date                                                                            \n",
              "2021-03-15  73.053493        1.0    3778740.0  3.402307e+06          1.110641   \n",
              "\n",
              "            EMA_5_Volume  EMA_15_Volume  EMA_Volume_ratio  prev_close  \\\n",
              "Date                                                                    \n",
              "2021-03-15  3.821809e+06   3.821809e+06               1.0   74.839996   \n",
              "\n",
              "                  TR     ATR_5    ATR_15  ATR_Ratio  prev_high   prev_low  \\\n",
              "Date                                                                        \n",
              "2021-03-15  2.639999  2.600467  2.100825   1.237832  75.269997  73.050003   \n",
              "\n",
              "                 +DM  -DM     +DM_5     -DM_5    +DM_15    -DM_15      +DI_5  \\\n",
              "Date                                                                           \n",
              "2021-03-15  0.290001  0.0  0.931828  0.316932  0.602426  0.387816  35.833103   \n",
              "\n",
              "                -DI_5     +DI_15     -DI_15  DX_5  DX_15      ADX_5  \\\n",
              "Date                                                                  \n",
              "2021-03-15  12.187521  28.675682  18.460177  49.0   22.0  36.352028   \n",
              "\n",
              "               ADX_15  Lowest_5D    High_5D  Lowest_15D   High_15D  \\\n",
              "Date                                                                 \n",
              "2021-03-15  22.536914  67.800003  75.559998   63.709999  75.559998   \n",
              "\n",
              "            Stochastic_5  Stochastic_15  Stochastic_%D_5  Stochastic_%D_15  \\\n",
              "Date                                                                         \n",
              "2021-03-15     91.881473      94.683567        91.671472         64.515444   \n",
              "\n",
              "            Stochastic_Ratio      Diff        Up  Down  avg_5up  avg_5down  \\\n",
              "Date                                                                         \n",
              "2021-03-15          1.420923  0.090004  0.090004   0.0     1.48        0.0   \n",
              "\n",
              "            avg_15up  avg_15down  RS_5     RS_15  RSI_5     RSI_15  RSI_ratio  \\\n",
              "Date                                                                            \n",
              "2021-03-15     0.786    0.425333   inf  1.847963  100.0  64.887188   1.541136   \n",
              "\n",
              "                 5Ewm      15Ewm      MACD       15MA        SD  upperband  \\\n",
              "Date                                                                         \n",
              "2021-03-15  73.053493  70.874565 -2.178929  70.106667  2.665131  75.436929   \n",
              "\n",
              "            lowerband        RC  Close_Shifted    Target  Target_Direction  \n",
              "Date                                                                        \n",
              "2021-03-15  64.776405  0.077819      73.190002 -4.166673                 0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGdYru6vRBY2"
      },
      "source": [
        "all_data.to_csv(\"all_data_train.csv\",index_label=\"Date\")\n",
        "!rm -rf '/content/drive/MyDrive/Work/Projects/CRYPTO/all_data_train.csv'\n",
        "!cp 'all_data_train.csv' '/content/drive/MyDrive/Work/Projects/CRYPTO/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Ahw06AlRKWU"
      },
      "source": [
        "all_data = pd.read_csv(\"/content/drive/MyDrive/Work/Projects/CRYPTO/all_data_train.csv\",index_col=\"Date\")\n",
        "all_data.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jN66X9uemLUl"
      },
      "source": [
        "**Outlier Removal**\n",
        "\n",
        "To deal with the outliers that exist in Technical Indicators we winsorize them. The idea of winsorizing is to bring extreme outliers to the closest value that is not considered an outlier. Advantages of winsorizing is that the information contained in the extreme outliers is not lost, only the absolute values of those are sensitized.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEWErVuZPeG_"
      },
      "source": [
        "Target_variables = ['SMA_ratio','EMA_ratio','ATR_5','ATR_15','ATR_Ratio',\n",
        "                       'ADX_5','ADX_15','SMA_Volume_Ratio','EMA_Volume_ratio','Stochastic_5','Stochastic_15','Stochastic_Ratio',\n",
        "                      'RSI_5','RSI_15','RSI_ratio','MACD']\n",
        "for variable in Target_variables:\n",
        "    all_data.loc[:,variable] = mstats.winsorize(all_data.loc[:,variable], limits = [0.1,0.1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3WSVNoQxEdDN"
      },
      "source": [
        "all_data.loc[:,Target_variables].head(1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n1GKr88CFKJO"
      },
      "source": [
        "**bold text**###Clustering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TbMWYN7FoRyO"
      },
      "source": [
        "Different companies/industries react differently to a set of Technical Indicator. One way to solve this problem is to create different ML model for each cluster of companies that are expected to behave similarly perhaps belonging to the same industry.\n",
        "\n",
        "Thus, to make our model even more sophisticated, we will create different ML models for each cluster.\n",
        "\n",
        "The idea is to use returns of these companies and create an elbow curve to determine the number of clusters that would create a balance between low sum of squares within a cluster vs the total number of clusters.\n",
        "\n",
        "We use K-means clustering to create an elbow curve. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L2wVPIwBZJMj"
      },
      "source": [
        "#Extract the returns\n",
        "returns = all_data[['symbol','return']].copy()\n",
        "returns['Date'] = returns.index.copy()\n",
        "#Pivot the returns to create series of returns for each stock \n",
        "transposed = returns.pivot(index = 'Date', columns = 'symbol', values = 'return')\n",
        "#Transpose the data to get companies on the index level and dates on the column level since clusters takes place on index level\n",
        "X = transposed.dropna().transpose()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8yiMX55wboF"
      },
      "source": [
        "from pycaret.clustering import *\n",
        "exp_clu = setup(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0DSMj37wkHq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "referenced_widgets": [
            "2d7af129122e4b7aa4769897a80f1197"
          ]
        },
        "outputId": "d55aa02f-e4ec-473f-aeab-31c203e71ed0"
      },
      "source": [
        "kmeans = create_model('kmeans')\n",
        "kmeans_df = assign_model(kmeans)\n",
        "kmeans_df['Cluster'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Cluster 2', 'Cluster 0', 'Cluster 1', 'Cluster 3'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7VARot-cw5ZQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617,
          "referenced_widgets": [
            "f7ff30f70a98465bbe326b0089ff1ead"
          ]
        },
        "outputId": "15ef4f06-a7bd-410c-8456-6233580cd16c"
      },
      "source": [
        "plot_model(kmeans, plot = 'cluster')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bm1_RjR5w_Tz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "97d164ca-1653-4f35-8aaf-f62570bc890b"
      },
      "source": [
        "plot_model(kmeans, plot = 'distance'\t) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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kCMuWLSMjI6PN/UaMGMGKFSuYMWMGRqMRm83GE088Eax/7ty5PP744/z4xz/m7rvvJj4+nrlz53LDDTfwyCOP8Pe///2sNaSmpvLLX/6SW265BZ1OR15eHvPnz8dkMvGrX/2Ku+++G6/XS0xMDA899FC7z0ej0TBt2jScTudp58GvuuoqVqxYwbRp08jMzOS+++7jzjvv5MknnyQmJoaxY8fy2GOPceDAAdLT09uMWD9lyJAh/O53v0NRlDaDnB577DF+9KMfnXZ/q9XKf/3XfzFnzhzsdjt33nknU6dOZcGCBXz44YdnfW9Pnz6dX/3qV2264r9u1apVbN++HZ/PRyAQYPbs2dx9990ApKen8/zzz7NkyRIef/xxFEXBYrHw4IMPBkdM19bWUl1dfdr5a9F1aJRv9ssIcY4eeOABsrKyuOuuu9QuRQjg5CVKFRUV5/TlZdasWfzyl79kxIgRIaisc7zxxhusX7++zehw0bVId7QQolu68847efHFF9Uu44J5vV5eeeWV0045iK5FQlgI0S195zvfwWw2X9B593Dwpz/9iWnTpn3rADER3qQ7WgghhFCJtISFEEIIlYR0dHQgEKClpQWDwRAckSiEEEJEslOTDMXExJw22j+kIdzS0nLa8l5CCCFEd5CTkxOcGfCUkIawwWAIFnK+0/4JcS7+d802+qTaiY8xq11Kt1RUVNRmCslId6ismh72OMYPyFa7FBHGPB4Phw8fDmbg14U0hE91QRuNxuC8skJ0pAqHkyG9zKDVqV1Kt+T1B7rVa280GqlqdsnnmTgnZzoNKwOzRMRwe334Awp6nbytRWhEGQ04Wr99fWshzkY+rUTEaHZ5MBv1MuhPhEyUyYCj1a12GaILkxAWEaPJ6cas7z5doUJ9UUYDDqeEsLhwEsIiYjS7PBgNsiaJCB2DTos/oOD2+tQuRXRREsIiYjS7PBilJSxCSKPRYDboaXZ51C5FdFHSbBARo7HFhUlawudtz44vef3lv1BTWUGvfrnc/t8/Iyk1Te2yugyTQUeT04099vyX6hRCWsIiYjicbswSwufF2drKc797HKs1njv/52FKjx/jr889o3ZZXYpRr6PFLS1hcWEkhEXE8PkD6LQyMvp87P9qJ60tLUyacQWXjBzN8DHj2ffVTlpbmtUurcvQak+eFxbiQkgIi4gRUAJo5fKk81JVcQKAeJsdgDibDSUQoKq8XM2yuhQNEJAQFhdIQlhEDH9AkWuEz5PHffLyGr1e3+Zvj1smoDhXGg0EZEVYcYEkhEXECCgKksHnx/jv6RZ9Pl+bv01mmXv7XGk4uUqOEBdCQlhEDK1Gg3wWnp+U9AwA6mqqAaitqkSr1ZKclqFmWV2KwpnnBBbiXMhQUhExdFqttEjO06Chw7DEWlm/agXRMTHs+uJfXDJiNFHRcrnNuVIUZCyCuGDSEhYRQ6fVyLm582QyR3H3zx+hyeHg+ad+TVbvPtx8571ql9WlKIqCVkbliwskLWERMQx6HU63V+0yupyBQ4fxxLP/q3YZXZY/oGCQlbvEBZJ3jogYcVFmXDKHrwgxj8+HxSzrCYsLIyEsIkZstEkm0hch5/L6sJiNapchuigJYRExrFFGPD6/2mWIbkRRFNxeP5YoCWFxYSSERcSwmE24vRLCInQ8Pj8mgw6DTlbvEhdGQlhEDIvZiNsn3dEidFrdXmKj5HywuHASwiJiWKKMuDw+uVZYhIzT48UaLbOLiQsnISwihkGnw2TQyXlhETJOj4+4KAlhceEkhEVEscdG43C61S5DdBNNLjfJcTK7mLhwEsIiomQlxlPX1Kp2GaKbaGr1kJZgVbsM0YVJCIuIkpkYR2OrtIRF51MUBUerizRbrNqliC5MQlhElNR4C03SHS1CoMXtxWjQESsTdYiLIHNHi4iSHGeh1e3F5w+gl/l8RScIKArNTjdHKuow6LRsOVyCo9VFfYsLl8dHQFGCC4noNBr0Wi2x0Sbiok3ERp38YzGbSIgxEyMB3u1JCIuIotdpSY23UN/iJMkao3Y5oosLKAqNLS7qmp1UN7ZQ5WimtsmJXqfF0eoi2RrDrqJyzAY9UUY91igTGs1/1hcOKAqBgEKzy0ONowW314fb58fj89Pi8hBtMpJpt5KVFE+6zUp6QqwEczcjISwiTlbSycFZEsLiQnh9fsrqHBRXN3C8uhGNBqJNBqJNBhJjo+mVnIBep2VvcRWjczLJTIy7oOMoyslwrm1qZX9JFV8UlNLY6iI5Loa8rFT690giNd4SDHQRmSSERcTJTIzjWFW92mWILsTj83Osqp7Cyjoq65uJMRuIj4licFYyZuPpH5OKAi0uD/bYC788SaPRBLuneyYnACdbztWNLRwsq2bjgWPodVryslMYkp1GVmKcBHIEkhAWEadXcgLvbd1PQFHQyoeWaEd9s5MDpdUUlNdiMRtJtEaT3yf9W8cTOJwuLGYj0SZDh9aj1WhIibeQEm85eZxWF6W1Dl5ZtwNrlInxA3qSl52CySAf3ZFCfpMi4iRYokiwRFErXdLiDAKKwvGqBvaVVFLb7CTZGsPQnucXbDWOVnqn2DqxypOs0WYGRpsZ0COJyoZmNhw4xvJtBxneN4OxuVkX1RIX4UFCWESkIdmpFJTXSgiLIEVRKK118EVBKR6fn3RbLL1TEi6oi7ehxcWonMxOqPLMNBoNqQmxpCbE0ur2cqSilj9++Dn5fTKYnNcbqywi0WVJCIuIlJuRxOeHitUuQ4SJyoZmvjhycuBTdmIc9tjoCz6/6nR7AbBb1GmFRpsMDMlOJTc9kQOl1Sx+fxPjBmQxYUBPoowd2z0uOp+EsIhIGbZYNECT0y1LzXVjLW4PWw4Wc6K+iR52K30usOX7ddVNrfRMjkerVXe8gcmg55JeafRLt3OgpIoth0qYNSyHEX0zZABXFyKzGYiIpNFoGJSVQlmtQ+1ShAoURaGgvIZ3t+zD6w+Q3zudtITYDgmnhmYXWUnxHVBlx4gxGRnetwej+2WyZnchL326nfpmp9pliXMkISwi1sAeyVQ2NqtdhgixFreH1V8d4cuCMvr3SOrQVqvXF8Dp8Yblog1xMWYuG9wLnVbLH1ds4YuCUllbuwuQ7mgRsXqnJuDy+Gh2ebDILETdwtHKOjYfOE6iNYahvVI7/BK1ysZmshLjMITplKhajYYBPZLIsMWyZnchu49XcMO4PDklE8bC850kRAcw6HSM6NeDI+W1apciOllAUdh2pIxNB46Tm5F4svXbwQGsKCcHeA3MTO7Q/XYGa/TJVrGiwJ8+/hcn6uS0TLiSEBYRbVS/Hpyoc+APBNQuRXQSj8/Pp7sLOVJRyyU9Uzut1dfQ4sJk0Acn0gh3Wo2GvOwU+qTaeOGTL9lbXKl2SeIMpDtaRDR7bDQ9kxMorm6gVwgmVxCh5Wh18clXRzDodORlp3TqDGnl9Q4GZyZ3uZHHWYnxxJpNvPX5Xsrrm7g8r4/qI7vFf0hLWES8MblZHKtuULsM0cHqmlpZ/uVB4qOjyEm3d2oAu71+mpxeeqd2zS9yCZYoJg/uxbbCMt7esld6hsKIhLCIeLnpiSgK1MllGxGjxtHCRzsO0yMxjgx7bKcf70R9E/3SbF16zmaz0cDEAT05Vt3AG5v34PNLEIcDCWER8bRaDeP6Z3G0QgZoRYIaRysf7ywgOymO5LjOn5Y0oEB1YwsDeiR1+rE6m06nZVz/LE7UOXhj8x5pEYcBCWHRLeT3yaDa0UqL26N2KeIi1DU7WbnzMNlJcSSGaF7wivomEq3R2CJksQSdVsuY3JNB/PaWfQQCci2xmiSERbdgMRsZNyCL/SVVapciLlCLy8PKHYfJTLSGbGGOQEChrM7BiD4ZITleqOi0WsbmZlFUWc9HOw+pXU63JiEsuo0JA3pS1+yksdWldiniPHn9ftZ8VYg9NprkuNBdIlRa6yA9IZbkLnJZ0vnQ6bSMzc1k+5ET7CgsU7ucbktCWHQbUUYDl+f1YW+xtIa7EkVR2HTgOH4lQGZi6KaL9PkDlNc3kR9hreCvMxr0jMnN5P0vD1IsVxCoQkJYdCuj+vXA7fVS42hVuxRxjvYUV1Je10ROuj2k1+gW1zTSO9VGgiUqZMdUgzXazNCeqby2fheNLdJLFGoSwqJbMeh1zLikH/uKK2Vy+y7gRJ2DnUfLGZCZhE4buo8rt9dPdWMLl/ZOC9kx1ZRus5Jus/Lq+p1y6VKISQiLbmdYr3T0Oi3l9U1qlyLa4fH6WL+viL5pNswhvj73eHUDAzOTsZi7z8IH/TMScXn9bNh/TO1SuhUJYdHtaLUavjOiP7uPV+L1+9UuR5zFF0fKiDYZsYW4O7ih1UWT082QnikhPa7aNBoN+b3TWb+viAr5ghoyEsKiW+qXnkheVgq7j8mk9uGorNbB0co6eqckhPS4gYDCkfJaJgzoidlgCOmxw0G0yUBuRiJvfr5XuqVDREJYdFtX5OfQ2OqksqFZ7VLE13i8PjbsP0bfVBv6EK/bW1RVRw97HNnJ8SE9bjjplZyA2yfd0qEiISy6rSijgevGDGZnUbl0S4eRnUXlRJv0IR+V3NDqoqHFzZiczJAeN9yc6pb+bO9RmW89BCSERbeWI93SYaXZ6eZAWTU9k1XshjZ2v27ob4o2GchKimf1V0fULiXiSQiLbk+6pcPHjqITJFljMOp1IT2udEOfLjcjkX3FVTJIq5NJCItuL8po4Poxg9lZdAKXx6t2Od1WfbOTosoGshLjQnrc2iYnjdINfRqDTkffVBsrdxaoXUpEkxAWgpOjpScM6MmWwyWyvJtKthWWkZYQG9LBWC1uL4UVdUwd2le6oc+gT5qNY9UNHKuqV7uUiCUhLMS/TcnrTaY9jh2FJ2Q2rRCrb3Zyoq6JDFtsyI7p9Qc4UFrFmNxMUiJwgYaOoNNq6ZdmZ+2eo2qXErEkhIX4N41Gw/VjBxNQFApO1KpdTrdyoKyapLhotNrQzA0dUOBAaTV90+zkpCeG5JhdVc+keI5V1VPbJPOtdwYJYSG+xmTQM++yYRyvbpABKSHi8fkpOFFLekLoWsFFlXXERpkY1bdHyI7ZVel0WtJtVr4oKFW7lIgkISzEN9gsUdw06RJ2HC2nyelWu5yId7SiDovZiClE80NX1DfR4vYyJa8X2hAuCtGV9U218UVBKV6fXE/f0eQdKMQZ9EpJ4KrhuXx+sBinjJjuNIqisK+kkrSE0JyTrWtyUlrrYPrQvt1yWsoLZYkyYTEb2Vsia3F3NAlhIc5iZL8eTBjYk437j+Hy+tQuJyLVNrXS6vERH2Pu9GPVN7s4UlHHtEv6RvwawZ0hOzmeLwpK1C4j4kgIC9GOyYN7MTo3k437j+GRIO5wxTWNJMSY0Wg6d0BWY6uLgvJapl/Sl9T40J17jiRp8bGU1DTS6vaoXUpEkRAWoh0ajYZpQ/qS3yeDDfuP45Yg7lBFlfUkxkZ36jEaWl0cKqtl6pDepIVw8Fek0em02CzRHCmvU7uUiCIhLMS30Gg0zBrWj5H9Mtiw/5jMqtVBmp1umlxurNGmTjtGfbOLghO1TB3ahwx7aGfiikQp8Rb2lsg86x1JQliIc6DRaJg2tC/j+mezYf8xWqRL7qKV1DqIj+68rujaplaOVNQyfWhfMmzWTjlGd5Nhi+VgWY2sOtaBJISFOEcajYYpeb2ZOqQvG/Ydo9rRonZJXdqx6vpOGSClKFBc4+BYVQOzhuWQKl3QHcZsNBBtNFBS06h2KRFDQliI8zS2fxY3TRzKjqMnOFoh58cuhKIoVDW2dPio6EBA4dCJalrdHuaMGkBSXEyH7l+ANdpEaa2EcEeREBbiAvRLT+SumaMor29i59ETBGSu6fPS5PKggQ5dstDt9fPV8Qrios1cNbw/FnPnnWvuzmyWKIqrJYQ7ioSwEBcoyRrDnTNHYTbq2XTguFzCdB7qmlqJMXXcZBmOVje7jlUwoEcykwf3xhDClZi6G3tsNMXSHd1h5J0qxEWINhm45bJLGZyVwtq9RdQ3O9UuqUuocbQSZTRe9H4UBcrrmzh0ooYpg3sxrFdap19z3N1ZzEZa3V5aXDI4sSOEZrJWISKYVqvhiktzyLDF8t4XB0i3WWxdvX8AACAASURBVBnUIzlkKwJ1RdWOFqzRFxfCbq+fgvJadFoNs4f3l1mwQkSj0RAXbaK8oYm+qXa1y+nypCUsRAcZ2jONn1w1FoNOy6d7CqVV3I76FucFd0cryslFGHYVVdAnNYGrRwyQAA6xaJORWocsbdgRpCUsRAeyRpu55bJh7CwqZ/m2g9IqPoOAouD0+DDqz//jp23rNwe7VUY/q8Fk0OGQFcY6hISwEB1Mo9Fwae90+qbaeHfrftbuLSS/d4a01v7N7fWh02jO64uJokBlQxPFNQ7yspMZmp2GTgZfqSbKaKChxaV2GRFBQliITvL1VvGKbYewRpsYlJWCxXzxA5K6spOt4HO/NKm+2cXx6gbMRj1X5Yd/63fle2+zevl7NDc10qtfLrfedR+pGT3ULqtDRRn11Mnplg4hXyWF6ESnWsUL50xgcFYKG/cfY+fRE916aUSnx4vhHEK4yelhz/FKSmoaGJXTgzkjB4R9AO/duZ1/vPRnLhk5ijt+9iDHCwv4v6V/ULusDhdlNNDYKi3hjiAtYSFCwGzUc/mQPozKyWTd3qOs3V1IZlI8/TMSMeg6bsKKrsDp9rZ7HW+r28uxqnpcHh/5fdPpl5rYZbqejUYj1998O1OumE10jIX0zGyOHy1Qu6wOF2XU0+yUS5Q6goSwECFkMRuZPbw/Y3OzWLO7kNW7CumZHE+fVBsmQ/f47+gPKGe8lrfF5aWs1kGj08XQ7DQGZiadU4s5nOQMyiNnUB4A5aUlHC8sICO7l8pVdTydTiuLOHSQ7vG/XogwY4+N5oZxeVQMbGLTweN8uruQRGs0fVLt2Dt5fV21KSicyuBAAGocLVQ0NOHzBxiYmcyAHn0xGztuNi011NVU8/Qvf4HP5+O7N96sdjkdTqvREJCZWjuEhLAQKkpNiOW6MYOZNSyHXUXlbDp4HH9AITspnp5J8V2mG/Z8KIqC1xfgaGU9NY5W7LHRjMrJJNMeFxHPt7G+nicf/h+qKsu5+c57uWTkaLVL6nAajYaApHCHkBAWIgzEmI2MG5DNmNwsCitq+fxQCSt3FZAabyHDZiU5ztLlrzX2eH2cqG9if0k1x6vrGdM/i7G5WRF16VbA72fpbxZRU1XJPT9/hOFjJ6hdUqdQFKXLvx/DhYSwEGFEq9XQLz2RfumJ1Dc72VNcwZ5jlWwrPEGiNZrUeAvpCbEYu8j542anm5JaB9WOFpqcbvql2RnWK40ediujcjLVLq/DbV63hoID+xg7eSrxNjtHDu4HILtPXwyGyLk0LaAoSAZ3jK7xP1mIbijBEsXEgb2YOLAXTU43BeW17C2uZPXuQmLNJpLiYkiyRpMQExU23bhef4ATdQ6qHC3UNLaioDA4K4WxuVn0TknAoNexq6icDfuPqV1qpzi45ysAPl+3hs/XrQne/vv/XUZSSqpaZXU4vz/Q7Ub1dxYJYSG6gNgoE5f2TufS3ul4fX6OVtZzsKyawoo6qhpbiDYZsEabsEaZscdGhSSY3V4ftU2t1DU7aXK6cTjdVFXVMTQ6nn6pdq7K70+GLfa0kdAxZiMeX2SOrP3hT+7nhz+5X+0yOp3T4yM2StZr7ggSwkJ0MQa9jtyMRHIzEgHw+v1UNbZQUd9ESU1jMJjNBj0mox6TQY9Rp8Vs1BNlNJz8YzJg0GnRajRoNBq0Gg0KCkpAIQAoAQWX14fT48XpOfm32+vH4/Ph8flxenwEFIUedivZifFk2K2kJcRSdGg/w4cPb7d+a5QJdzeerCQSOD1e4qIlhDuChLAQXZxBpyPDZiXDZiW/TwZwMpjrm500uzw0Oz00udw0trppaHFSXt9EY6sLj9ePXwkQUCAQCACg1WqD8zpHmwzERZuJizaTHBeDNcqEJcpIrNlEbJSJ+Bjzaa3cY+ewlq/FbOzWM4ZFAqfHhzXarHYZEUFCWIgIZNDpSI6zkByndiWnizYZ8PkD+AMBdNrwOJctzo/T4yU5LrynEO0q5H+AECKkNBoNsVFGnB5pDXdVbq8Pq5wT7hASwkKIkEuJs9AoS+F1WS1uD4lhvphGVyEhLIQIuaykeOqaW9UuQ1wARVFobHGTlhCrdikRQUJYCBFyGTYrTbIKT5fU5HQTG2Ui2tS15/cOFxLCQoiQS0uIlfVou6i6Zic9EsNwxF8XJSEshAi5+BgzWo0Gp8erdiniPNU3O8mWEO4wEsJCiJDTaDRkJcVT3diidiniPDW2uuhhlxDuKBLCQghVDM5Kpry+Se0yxHlwery4vD4ypSXcYSSEhRCqyElLpMbRSkCRdWm7irJaB/0zktCHyYIhkUBeSSGEKuJizCTFRVPjkC7prqKysZmBmclqlxFRJISFEKrJy0qlrNahdhniHPj9AeqbnfRNtatdSkSREBZCqCY3I5EqRwuKdEmHvbJ6B1lJ8XJ9cAeTEBZCqCYtIZb4aDMVDc1qlyK+xfGqBkb3y1S7jIgjISyEUI1Go2H8gGyKKuvVLkW0w+F04/L4GJCZpHYpEUeWMhRCqCovO5UPvjxIi9tDjMmodjniDAorahmV2wODTqd2KR3K5w9Q2dhMVWMLbq8Pnz8QXGZTr9P++48Oa5SR1PjYM66hfbEkhIUQqjLqdYzol8GR8jqG9kxVuxzxDT5/gBN1TcwdN0TtUi7KqcCtqG+ipKaR49UNVDW2EG0yYDEbMeh1aDUatFoNGjQElACBgEJAObl0Y2OrC61GQw+7leykBDLssR0SzBLCQgjVjeqXydLD/2JwZjI6uQY1rBRV1dM31U6CJUrtUs6boigU1zTyr0PF7C6uJNpoIDbKRFy0md6pNob3yTiv95vT46WuyUlRVR1fHSvH4XRjMugYm5vNpX3SL2iNZQlhIYTqkqwx5GYkcuhEjVyHGkb8/gBHymv5r6nD1S7lvLi9PnYfr2DzgeM4nB6yk+KYMbQvRsPFRV6U0UCG3UCG3Rq8rb7Zyd7iSj7dU8iAHsmMzc0kOyn+nFvHEsJCiLAw/ZK+/Omjf9E31XbRH5aiYxwur6Fvmr3LTFNZ1djM1oISth05QVyMmd4pCaTEWTr8PO7XJViiGN43A6/PT1FlPa+t30WMycj4AVkM7ZmG6Vvey/JOF0KEhSRrDJf0SuNAWTVDe6apXU635/H5Kaps4J4rRqldyrfy+vys2V3Ivw6XkGG3MmlQr5Bfz2zQ68jJSKRfup2qxhY2Hyxmze5Crh8zmCx77FkfJyEshAgbl+f15vcfbCYnLZEomRRCVQdLqxnaK5XkOIvapbSrpKaRNzfvQavVMDmvN2aVe1E0Gg0p8RZS4i1UNjTzt427GZaVRI+zDPyXERBCiLBhjTYzLjeLPcUVapfSrbW4PJTWObg8r7fapZyV1+fn4x2HeXH1NjKT4hiTm6V6AH9TSryFy4f0prqd+dHDq2IhRLd32eBe7DpWzok6B+k267c/QHQoRVHYVljG1Lw+xMeE54jor7d+pwxRv/XbHoNOx+DsVGipPeN2aQkLIcKKyaDn+rGD+epYBR6fX+1yup3CijosZiPjB2SrXcoZbS8s43/XhG/r93xJCAshwk7vFBv5fdLZVVSudindSrPLw+HyWq4fOxittvNGFF+ozQeO88GXBxk3IJusxHi1y+kQEsJCiLA045J+OD1eTtTJUoehoCgK2//dDR1ug7EUReHT3YV8uqeQiYN6XtCkGOFKQlgIEZZMBj3fG5vHrqIKml0etcuJePtKqoiLNodlN/TG/cfZfLCYiQN7Rtz84hLCQoiw1SslgZnD+rHlUDFev5wf7iwlNY1UO1r4wcShYdcN/eWRUtbuLWT8gGzMxsi7bE1CWAgR1sbkZjIoM5kvCkpRFEXtciLOqWkXb75sGLFh1s17qKyGD7cdYnz/7JBPvhEqEsJCiLCm0WiYPaI/0SYje0uq1C4nori8PrYWlHLtmEFkhNnlYK1uD29v2cvwPhlYwuzLQUeSEBZChD2DTsdNE4dS42ihqLJO7XIigtfv5/ODxYztn8mQ7PBbQvLDbYewWaJIiotRu5ROJSEshOgSYqNM/NfU4RRW1HG8ukHtcro0vz/A5weLGZCRyLQhfdUu5zQHSqvZX1pNXhh+OehoEsJCiC4jyRrDD6eN4PCJGooliC+Izx9g88Hj9EpO4OqRAzt1haEL0er28M6WveT3TkffDdaWjvxnKISIKCnxFn44dTiHTtRwrLJe7XK6FK/fz6YDx+idauO6MeE5IceH2w5hj42O+G7oUySEhRBdTmpCLHdMH8HRqjoOlFXLqOlz0Or2smHfMXIzkrhm1KCwDODDJ2q6TTf0KRLCQoguKTnOwl0zR9HsdPNFQSl+f0DtksJWjaOVz/YVMWFAT+aMHBCWAQywds9RBvZI6hbd0Kd0n2cqhIg48TFR3DF9JMlxFtbvL8Lp9qpdUtgpqqxjW2EZP5gwlImDeobdOeBTKhuaKa9vokdinNqlhJSEsBCiSzPqdcwdn8e43Gw+21fU7tqt3Yk/EGBX0QlKax3cNXMkuRmJapfUrq0FJfSwx6EN0y8JnaVrrwElhBCcnNBj0uBepCRYeGvzXpLiYsjLTkGn7Z7tjLpmJ9sLy8hKjOfmyy4N+9mm3F4f2wtPMGlQL7VLCTmNEsIRDW63m71794bqcEKIbsjp9bH5aDVlDa30S4olLiqyJvxvTyCgcLy+hZoWN+N6JdE3KTZsu5+/7kBFA3tONDAoLTKWJ/wmg05LTrKVwYMHYzK1nf1LlZbwmQoRQnR927dvJz8/X+0yGDdKYV9JFf/8134MhhgGZSVj0OnULqtT1TW1sv3oCXpmZfGT0QO7zHJ/iqKwfvnnjB06kJT48FpCscME/NBSe8ZN0h0thIg4Go2GwVkp9ExOYMX2Q6z5qpC+qTb6pNrDdmTwhWpyutlbXEmLy8NV+blc0iutS7R+Tymrc9Di8pDcTa4L/iYJYSFExLKYjdwwLo8TdQ5W7ixg9e4j5KYnkp0U36WC6kycHi/7S6qodrQweVBvxvTPwqjveq39kppGbLFRXf73caEkhIUQES/dZuW2y/M5WlnHxzsOc6Sijt4pCWQnxqPrYtekOpxuCstrKW9oZkxOJvOnXEp0F17ovri6kfiYKLXLUI2EsBCi2+idYuOumaM4fKKWzw8dZ9WuAtJtVvqm2sJ6ubyAolBW6+BYVT0uj49RuT34/oShxMWY1S7tohXXNJCXnaJ2GaqREBZCdCsajYbcjERyMxKpbWrli4JSNh8sJtpsIMNmJd1mxWxQ/6NRURTqmp2U1joor3eQEm9hxiX9GJCZFDGDzFweHw0tLqzRXf/LxIVS/50mhBAqscdGM+vSHKYO6cOB0mp2F1eydnchMWYjSdYYetitIQ0Ivz9ARWMz5XVNVDtaiI0yMSQ7he+M6E9aQmzI6giVioYmYqOM3W6Cjq+74BBeuHAhTz31VEfWIoQQqjDodQzpmcqQnql4/X6OVTVwoLSKbYVluL1+rFEmrNEmbJZobLFRRBkvfvKLQEChsdVFbVMrDS0umlxuWlweMhPjGdY7jf4ZSdhjozvg2YWv8vomYqPCqxVcV1vDX/7wJAd27+L6m2/nqutv7NTjtRvCgcDZJ0SvqKjo8GKEEEJtBp2Ofml2+qXZmT28Pw0tLsrrmyirc1Bc3cCe4koCgQBRJgNGvQ6TXodRryfKZMCg06LVaNBoNCgoKMrJz1GX14fL48Pj8+P2+XF7fLi8PmyWKDIT4xneN4O0hFhS4y2YwqArPFRKahqJD6Pz2nU11Txy7x3ExoVu0pB2f9sDB555wWdFUbrtcHIhRPeh0WhIsESRYIliYGYycPLzr8nlocnpptnpodnlpsnppqHFhdPjwxvw4w8oaDUatFoNOq2WlPhY4qJNxJpNxEaZsEQZiYs2d8lLijpSo9NNYhi19j1uN9fdfBupGT148qH/Cckx2w3hefPmMWDAAK655pozbhNCiO5Go9Gc7J4O49HUXYXP50cXRpOnpGb0IDWjBwf27ArZMdu9QO7+++9n3759tLScvipJfHxkzvEphBAiNLz+QLddZOOUdlvCBoOBRx555Izbli5d2ikFCSGE6B58/kC3HhkN39ISDgQCPPfcc/j9/uBthYWFPP/8851emBBCiMim02oI2TJ+YardEH722WfZt28fHo8neFtKSgoHDx7k1Vdf7fTihBBCRC6DTovff/arcELN0djAl5s3cHDvbgBKi4/x5eYN1FRVdtox2w3hdevW8fTTTxMV9Z95PS0WC7/97W/56KOPOq0oIYQQkU+v1+Fv51LYUCsrPsafnvwl7/39ZCNzy2ef8qcnf8mB3Z03UKvdc8Jmsxmj8fSJwc1mM9pufjJdCCHExYk2GXF5fWqXETQg7xJeWb4mpMdsN0lbW1tpbW097fbGxsYzjpgWQgghzlWm3UpDi0vtMlTVbghfffXV3HPPPRw7dix428GDB1mwYAHz58/v7NqEEEJEsLSEWJqcbrXLUFW73dHz58/HaDRy66230tTURCAQwG63c8cddzBnzpxQ1SiEECICpSXE4mh1d+tZGNsN4ebmZkpLS+nfvz95eXnccsstWCyWUNUmhBAigsVGmTAb9bS4PGG9nnNnarc7etGiRWg0Gm644QZKS0t56aWXQlWXEEKIbiAzMY66ZqfaZaim3ZZwWVkZv//97wGYOHEit956ayhqEkII0U1kJ8VzoLSarKTuORVyuy1hvf4/Ga3Tde/VPoQQQnS8dJuVhpbu2xJuN4S/eaK8u544F0II0Tl6JSfg8wdo7KaXKrXbHb1z504uu+yy4M+1tbVcdtllwZFsn332WSeXJ4QQIpLpdVrG5GZxsKya/D4ZapcTcu2G8MqVK0NVhxBCiG5qeJ8MPttXhNfvx9DNTn22G8IZGd3vW4kQQojQiosxk5OeyLGqBvql2dUuJ6RkAmghhBCqG5ubRXF1A4rSvRY3lBAWQgihut4pCZgMemqaTl+vIJJJCAshhFCdRqNh4sBs9pdUdavWsISwEEKIsDC8Tw9io0wUVtSpXUrISAgLIYQIC1qthuvHDqagvJZml0ftckJCQlgIIUTYSLLGMHVIH7YXlnWLbmkJYSGEEGFlXP/sbtMtLSEshBAirHSnbmkJYSGEEGEnyRrDtKF92Xq4BK/fr3Y5nUZCWAghRFga1z+LgZnJfH6wGH8goHY5nUJCWAghRFjSaDRcPWIAWYnx/OtwCYEIHKglISyEECJsnTo/nBgbE5EtYglhIYQQYU2v0/KDiUPJsFnZdOB4RJ0jlhAWQggR9vQ6LTeMyyMnPZEN+47hcLrVLqlDSAgLIYToErRaDXNGDmBKXm82HTjOobLqLj+hh4SwEEKILkOj0TAmN4sfXzkGl9fHZ/uKwrpVrCgKpbWNZ90uISyEEKLLscdG88OpI7hsUK+wbRU7PV4+P1RMjaPlrPeREBZCCNElabWnt4pP1DlUD2OP18fB0mrW7SliaM9Ubp407Kz31YewLiGEEKLDnWoVf3W8go37jrGnuJKsxDh6p9gwGUIXc3XNTgoraqlqbGFwVgp3zRxJakIsbvfZu8slhIUQQnR5Wq2GYb3SuKRnKqW1DrYWlLBmdyFJ1mj6pNqxx0Z3ynH9/gDHaxo4Xt0ACoztn0V+nwwsZuM5PV5CWAghRMTQaDRkJsaRmRjHrGE57Cw6weaDxXj9AeKjzcTFmLFbooiLMaPTnv8ZWbfXR12zk7qmVhxON3XNTnqnJHDNqEH0S7Oj1WrOa38SwkIIISJSjNnI+AE9GZubTVmdgxP1TZRUN3CgrJraJicWsxFrlAlrtAmjXodOq0Wn1aDVavAHFPyBAH6/Qqvbg8PpprHVTUBRyLBZyU6KY4TNSlZiHPExURdco4SwEEKIiKbV/qd1PKpfDwA8Pj+VDc2cqG/iRJ2DVrcXt9eLx+8nEAig1+kw6LQY9Doy7FZG2uJIt8Vis0Sh0Zxfa7c9EsJCCCG6HaNeFwxmNcklSkIIIYRKJISFEEIIlUgId4AHHniA5557rlP2XVpaysCBA8+4bdmyZTzzzDOdclwhhBCdT84Jd2E33XST2iUIIYS4CBHdEi4tLWX8+PG8+OKLzJgxgxkzZrBr1y5+9KMfMWHCBB588EEA1qxZw+zZs7n88su57bbbqKurA2Dp0qU8+uij3HHHHYwfP56FCxeybt06rrnmGsaPH8+6deuCx6qsrOSmm25i8uTJ3H333bS2tgJw5MgRbrrpJmbMmMHs2bPZs2cPAFu3bmXu3Ln8+Mc/5mc/+xk+n4+HH36YGTNmMG3aNO655x6am5uD+3/77beZPXs2kyZN4sMPPwzW9/DDDwMwZcoUXnzxRa655hpGjx4tLWQhhOgCIjqEAerr60lKSmLVqlXk5ubyk5/8hCeffJIPPviADz/8kOLiYu6//34WL17Mp59+yqhRo1i0aFHw8Z999hlPPPEEy5cvZ+XKlWzYsIF3332XBQsW8OKLLwbvt3HjRpYsWcKaNWtobGzkrbfeIhAIcPfdd3P11VezatUqFi1axF133YXP5wNg//79zJ07l8WLF7Np0yZKS0tZuXIln3zyCX379mXnzp0ABAIBvF4vy5cv58EHHzxrwO7atYu33nqLFStW8Pe//52DBw923gsrhBDiokV8CPt8PmbOnAlATk4OeXl52Gw2EhISSEpK4oMPPmDkyJHk5OQAMHfuXNauXYvf7wdg2LBh2O324P0nTpwY3FdVVVXwOBMnTsRms6HT6Zg2bRq7du3i6NGj1NbWct111wGQn5+PzWYLhqvZbGbMmDEA2Gw2CgsLWb16NU6nk/vuu48JEyYAJ5fCmjNnDgADBw6koqLijM91zpw56HQ67HY7+fn57Nixo0NfSyGEEB0r4s8J63Q6zGYzAFqtlujo6Dbb9Ho927ZtCwY1gMVioaGhAYCYmJg29z/1eK1WSyAQCG6z2WzBf8fGxuJwOHA4HLhcLmbNmhXc1tzcTENDA1arlbi4/1yfNmTIEH7xi1/w2muv8fOf/5wpU6bw6KOPBo8bFRV1xuN+3df3FxcXh8PhONeXSQghhAoiPoS/TXJyMmPHjmXJkiUXtZ/Gxv8s2uxwOIiLiyM5OZmYmBhWrlx52v23bt162m0zZ85k5syZNDQ08NBDD/HSSy9x/fXXn3MN9fX1wX83NDS0CWUhhBDhJ+K7o7+N0Whk27ZtlJSUALB7924ef/zx897Phg0baGxsxO/3s3r1avLz88nIyCA1NTUYwnV1dfz0pz8NDtr6unfeeYdnn30WgPj4eHr37n3eNXz00UcEAgFqamrYsWMHw4cPP+99CCGECJ1u3xJOSkriV7/6FXfffTder5eYmBgeeuih897P5MmT+e///m9KS0sZPHgw1157LRqNhj/84Q8sWrSIZ555Bq1Wy/z589t0iZ9y+eWX89BDDzF9+nR0Oh3Z2dk8+eSTbUZIf5t+/fpx3XXXUVZWxrx58+jXr995Pw8hhBCho1EURQnVwdxuN3v37mXw4MGYTKZQHbZbmDJlCr/73e+k9StUtX37dvLz89UuQ4iw0l72dfvuaCGEEEItEsJCCCGESrr9OeGz8fh8rNtTxMpdBRRXNzBxYC/SbbF4/X42HygGDYzNzeTK/P7Ex5jVLpe1a9eqXYIQQojzJCH8Dc+t3Mqy9btocXvb3H6koo7hfTLw+v18dezkZBmbDhznd+9tQqvV8Oo915DXM41AIIBWKx0MQgghvl23D+F/HSrhjys+56oR/TlR6yDGbAQgJc5CXLSJsblZTBnShwEZiRgNejw+Hy0uD8u3H2LdnqMcKa/D4XRz05J3uGfWaN791z4CisL8KZfyvbGDJZCFEEKcVbccHR0IBPjL6i/5x8Y9NLS6ANBqwGQw8Nq919IvPfG89lfjaOHdrfsZ2SeDpz7YzN7iSgAMeh1TBvdi0femEP3vcBciksnoaCFO1172dbuWcCAQYNbjr1LR0IxWo8FiNtLs8jBuQDb/7/rJJMdZznufidYYfjRtBAB/u+967vu/FWw5VIJfCbBq1xGqHa28fM81Hf1UhBBCdHHdLoS1Wi09kxLonZJAs9PN0aoGHr9xKlcNz0Wj0XTIMW6/fDhFVfWUVDcw45K+/GT2OAB2H6ugb6pNWsVCCCGAbhLCRZX1/OjP73H9mMH8aPoIXrjzagCOVdXj8vron5HUocfLy07htXuv4+4Xl7NmdyFX5Odij43i9uf+CcDjN05lxjCZzUoIIbq7iB81tLe4kut//w+qGls4WlWP0+Pljc178PkD9ExO6PAAPsUabeb5O66mf0YSj7+1Dp9f4ZbJw/D5A9z/2ipe/WxnpxxXCCFE1xHRLeEvCkpZ8ML7+AMKD14zkbnjh/DU+xtZtv4rBmQkMaRnaqce32I28qcfzsbn9xNtMnDPrNFMHdKHeX98m8UfbMbhdHPPrNGdWoMQQojwFbEt4aLKeha88D6BgMJvfjCNueOHsOPoCf624StuGJfX6QF8is0SRXKchUBAYX9JFf0zknh34Y1EGfUsW/8VPt+Z1wYWQggR+SK2JZxpj2Ngj2Tmjs/jivxcFEXhiXfWk5YQy31XjQl5PS+u2cZfVn/Jx7+4mcykeN5ZeCNoNOj1Efs9SAghxLeIyASocbSg12tZdt/1XDW8PwA7jp6goLyWH00bQbQp9KOTr7g0B58/wLv/2g9Ahj2ODJuVsjoHP3jmLWqbTl9jWAghRGSLuBD+w/LNTH3sZTbuP9bmdoNex+TBvZip0qjkzMQ4xvXP4u0t+/D6/cHbN+4/xt7iShb8+X1V6hJCCKGeiArhosp6Xv1sJya9nuF909tsG5KdyjO3XUmU0aBSdfC9cXlUO1rYdOB48La544eQk27ncHktf1v/lWq1CSGECL2ICuEFL7yPosDj359KlPE/Xc7VjhZKaxsJ4QydZzSufxZGvY4dR0+0/nB/fwAAFH5JREFUuf3Pd1yNXqtl8fJN0i0thBDdSMSE8F/X7qCioZmRfXswbWjfNtve2bKPq554jdZvrIwUagadjjd+dgP/fUXbgWH22Gh+dvU4/AGFn778sUrVCSGECLWIGR29+qsjaDTw9G1XnLbtQGk1PZMSgiskqal3iu2Mt39/wlD2l1Qxc1hOiCsSQgihlogJ4b/95HsUlNdiOUPQltQ20jM5QYWqTrfneCVrdhfyo2nDT/tS8Pj3p6lUlRBCCDVERHe00+MBoF+a/Yzb3V4fUcbw+L5RUF7LX9ftwOF0n3H7u1v2ccuSd0JclRBCCDV0+RBudXkY99CL/PTlj856Hw0dszpSRzpbResPHGPXsXJW7jwc0nqEEEKEXpcP4ZfW7sAfUMhJP3MrGOBnV4/jhnF5Iazq7Jyek4PDjIYzt8zvv3o8AC+v3RGymoQQQqgjPPpoL8KXR0oBmDt+6FnvM3lw71CV862OVtYRG2UiIcZ8xu0Z9jgsZiPFNY0hrkwIIUSodfmW8PGqBkx6HfFnCTWAhhYna3YX0ur2hLCyMyurczCgRxIazdm7yHvYrbS6vbg8vhBWJoQQItS6dAgHAgEaW12kxFvavd/e4ip+9teP2VdSFaLKzu7Pd1zNH2+7st37DMlORafVcPhETYiqEuL/t3fn0VWV9xrHv2dIcjKcMIQpEMIQBkkgyiDKsMQZ5DqgRe21ILQiKmBbcdHaWvDWWi2ly2uLttRSoNXicNWKiCBIGbRqKRIZjBCQoQRCQgiZc4Z99r5/BCKYQAIkZ5+E57NWFuScd+/9O2StPLzvfvf7iogdmvVwtC9gMLBnZ67o3eWs7QakdiTK5eQf2/dyea+UMFVXm2VZOBwO4mLOvnTmT+64isfHXx2eokRExDbNuicc54lm8Yw7eHD0FWdt1yreww2X9uKdf++0ddWsHy56j+dXflpvO6ezWf9YRESkgZr1b/vC0gp25x3DMMx62949YgDlvgDvbdkVhspq219wnPVf7CPG3bDBh5t+8Rfu/d0bTVyViIjYqVmH8NNvbmD8vFfIO15ab9tLu3eiX0p7th3ID0Nlp7Msi+fe/YRot4vbr+jXoGMKyyo5dKz+zyUiIs1Xs74nHDCq9+WNa8Ca0A6Hgxfuv4W2CbFNXVYtK7fksG7HXmbeMpx2ifENOsbpcGCY9ffwRUSk+WrWPWG3q7p8X6Bh93mTvHE4HA7yjpexdX9eU5ZWIxgK8dyKT8js1pEJoy5r8HGWZeHSvWERkRatWfeEPdHVs4xLqvycfX701yzL4rGX3mfPkSIWThtHv5QOTVcg1dsXLnxoHMA5hWrIsohyKYRFRFqyZv1b/uTQ8v6C4gYf43A4mDtxNN7YGB5YsIztTXSPuKi8iqUfbsWyLFLbtya1fetzOr5DYny9zz+fq1WrVjFmzJjTvvr27Ut5eXmjXkdERBqmWfeExw7qw8HCYrp3OLeA69TGy8Jp43hgwTLu/8PbzLx1BHcOyzjrKlbn4rOvDjH7lbUUllYw8pJu5xzAACtnT2qUWk51MnhPeu+991i5ciUJCY0b9iIi0jDNuifcP7Uj86fcQvp5DCmnJLViycPfIrNbR9Zu+6pR6qn0B5n7941874W/43DAn6aNO68ANsMwIcvv9/Pb3/6WWbNmNfm1RESkbs26JwzV+/NuysnlO6POvIHDmbRPjOePD95GuS+Aw+Fgf8FxFn6wmbtHZNI/tcM594yn/P7vfHGwgP8emcn3/2tYvStjncnk+W/x5aGjfPL0A7jd9f8/yTQtfMEgVQGDqkCQkko/7RPj6NDqzD3cN954g0GDBpGamnpeNYqIyIVr9iE855UPyM49yh3D+hEbXf+jSt/kcDjwxsYA1YG+dvtelm/eRd8u7RjSswv9urZn9GW9iXa7MEImAcOguMLHzkOFZOcWsC//OL+acCNRbhffu3YQSYnxDOyRXOs6lmURMEJUBarDsrwqQHFlFcUVPkorffiCBn4jRCAY4stDBYRMi1+9vZFA0CBghAgYIfwn25z4e/WfIQKGQcAwCRoG/mCIkGXxvWsH8YObh9f5mU3TZNGiRSxYsOCc/71ERKTxNPsQvqxHMtm5R1m2aSffHpl5Qee64dJeDO+byorPdrFiSw5v/usLrE8sbhrYB4DZr3zAe1tyTjsmwRPN1AVv44mKwgiZmJaJZYFhmjWh7Q+G8BshgqcEaDBU/5Dz/32847w/i9N55l58VlYWcXFx9O7d+7zPLyIiF67Zh/B91w1m6Yfb+Ov6zy84hAHiPdHcNWIAd40YQMg0yTteVvM88uiBvenbuR3xnmj6dE6iT+d2RLlcJ3q3QSr9QYorfJRUVn9V+II1vVR/TY/WwBc0CARNAiEDfyCE3zjRqzUMDhaWUFYVwBsbTZTLRcAI4QsaGA0I7YZav349o0aNarTziYjI+Wn2IdwuMZ4+yUnk5B1jb34RPTu2bbRzu5xOUpJa1Xx/dUYPyOhRq503NqZmSPtCmKbJsJ+8SJTbxYYn78MwrVOGr/0UV/oorqiipNKPL/B1cAeCpwxXnxie7naWCWE7d+5k7NixF1yviIhcmGYfwgAzxl7J9/+8gpc3bmXOndfYXc4FuXvkAKLdLlwuFy4XxES5aR0PtPE22jWOHDlCu3btGu18IiJyflpECI/K6MGfp9/OkLSGrpsVmZxOJzNvGdHk11m+fHmTX0NEROrXrJ8TPtXJAD5w9LjNlZyff+zYyz3/+zrFFT67SxERkTBpMSEM8NjLq7n1mb+FbXOGxuILGPzkpdVkHyygqLzS7nJERCRMWlQIjx+WAcDDC98Ny6pTjeWHi1bgCxp856rLGnVimYiIRLYWFcJD0row5rLelFT6mfPqWrvLaZC1277ik5yDdGyVwKxxI+0uR0REwqhFhTDAL++5AW9sNMs37+LD7P12l1Ovn7/+DxzA76feYncpIiISZi0uhN1uJ/Pvuxmnw8Hmrw7ZXU69nrjrWqaNuYJeyUl2lyIiImHWIh5R+qaBPTuz7sn7aB3vAaDcFyDBc+7rSjeVcl+AZ9/5iJ+Nv5rrMtO4LjPN7pJERMQGLa4nfNLJAP7dik+46md/4p87D9hcUbXC0grGPvUX3vw0m+Wbd9ldjoiI2KjFhvBJvZKTMC2L6S8u5zfLPrK1lvezdjP2qb9SUunnW1emc9vQfrbWIyIi9mrxITx2UB/+MPVWYqLcvLThc258cgl784vCXsejS97jRy+9TyAU4qHRlzPnrmvDdu25c+eSmZlJ//79GTRoEGvWrAFg2rRp9O/fn4yMDEaPHk1FRUXYahIRkYsghAGG9U1lwy+mcGWfruQXl/Pyxq1hr6FHx7Ykt0lg2WPf4cHRV4TtullZWSxevJjFixezY8cOhg0bxqxZs3j99ddZv34977//Ptu3b6eqqoqZM2eGrS4REWmhE7Pq4ol288cHb+Pfe3IZ3LMzAD/922oSYqL5/s3DG3XilmmavPLhdv6yIYtendry+6m3MuOmK5lx05WNdo2GiomJYebMmQwePBiAcePG8cEHH/Dqq6/Sv39/unSpXu7zrrvuYuHChWGvT0TkYnbRhPBJl/dKAaqDct32fVQGgrz+yQ4yUzvxyC3DGXgioM9HfnE5v1n2Eet27CMYCgHQqVUCpmnidDbtoIMvYOCJrv3jTE9PJz09vbqNz8eCBQtISUnhyJEjjBz59eIgmZmZVFVVNWmNIiJyuosuhE9yOp18+NT9LFm/hVc+3MbWA0eY/PxbXJXenflTbqa00seqrN1cl5lGkjeu1vG+gMGHX+7nn18e4KfjRxHtdjPtxXfYc6QIT5Sbmwf344e3jKiZpX2qgGFwvNxHYWkFRWWVHDxWQt7xMo6WVuILGPgMA3/AwG9U7xMcCIYIhqq/jJCJYZoYIZOQaWGaFiYWDuDjpx+oM4gBpk+fztq1a/F4PCxdupRJkybh8Xxdm9fbeFsliohIw1y0IQzVC3tMuX4IU64fwtb9ebyw8l+MuCQVgNVb9/DLNzfwyzc34HCA0+GoOS5kWqed57O9h/HGxlDm89POG0dMlIuPd/2HDdn7MUIhQiELw7QwLRPTsrAsi2+c4oI56nn/hRdewDRNnnrqKcaPH09CQgI+39c7NpWUlDRuQSIiUq+LOoRPdWn3ZF58aFzN9yMu6caU64ewZe9hiiuqCBohQqaFw+nA6QC300mXNl46tUnEE+WiqMJHh8T48+rFWqZFI2dyjXXr1rFnzx7uv/9+nE4nc+bMYenSpTgcDvbt21fT7vPPPyc+Pr6JqhARkboohM8guY2Xh8eGZyJVpS/A0bIKisqqKCipIPdYCfnF5RRX+qjyG/iNIP5gCH/QIGCYBA2DQMisCfaQaREyTeq67Zybm8uzzz7L8OHDycjIYOnSpViWxYwZM3j66afJyckhNTWVpUuXnnaPWEREmp5COALEeaLp5ommW/s2jX7uiRMn8tlnn3H33XcD1ffCH374YSZOnMimTZu4/fbbAejevTu//vWvG/36IiJyZgrhi8Bzzz1X5+vz588PcyUiInKqi2KxDhERkUikEBYREbGJQlhERMQmCmERERGbKIRFRERsohAWERGxiUJYRETEJgphERERmyiERUREbKIQFhERsYlCWERExCYKYREREZsohEVERGyiEBYREbFJs93KsNIf5GhpBQEjRLTbRfvEeOJiouwuS0REpMGaVQhX+AJs2XuYT3MOUlzhIzEuBpfTiREKUVoVoG1CLMP6dGVgz2TiYqLtLldEROSsmkUIW5ZF1r48lm/eSZt4D/1SOpDkjcXhcNS0MS2LY2WVfLb3MGu3f8W4oekM6NbxtDYiIiKRJOJD2DQtlm3KZsfBAob2SqFNQmyd7ZwOB+0T42mfGE9ReRXL/v0l+wqOc+vllyiIRUQkIkX8xKwVW3ax63AhV/fvccYA/qa2CbFcndGD7NwCVmblNHGFIiIi5yeiQ3h33jE27znEiEu6EeVyndOxUW4XI/qmsiknl735RU1UoYiIyPmL2BA2TYu3Pv2CgT2SiXLXDuCNa1Yya+q9TL3zZn7xox+wb/euWm2io9xkdu/EW59mY1lWOMoWERFpsIgN4d15xzAti05tvLXe27Mzm0Xzn6XfgEuZNutn5B/K5flfPVnneZLbePEHDfYcUW9YREQiS8SG8Nb9R+jSNrHO98xQiNu+PYE7753CZUOvpF/mZRQW5FNVWVmrrcPhICWpFdsPHGnqkkVERM5JxM6O/k9hMRldO9T5Xp+MAfTJGIBpmhQcOcxXu76kR+++xMbF1dm+XWIcOYcLm7JcERGRcxaRIWydeOY3MS7mrO1e+fMCVr/zFl2792T6j2efsV2rOA+FpbV7ySIiInaK2OFo07Jw1vN875hx43n0f54mGAwwb86PCQQCdbZzOhyYmpglIiIRJiJD2OFwEBcdhS9g1Pl+9tYslr32Mt5WrckcPJShI0eRf/gQhw7sq7N9VTCodaVFRCTiRORwNEBKUiLHyitJiWlV6738vEO89fISyktLuXTIFWz++COiYzx0SO5c57mKyqromlT7PCIiInaK2BDundyOrQfySKkjPEfdOJbjhYWse/9d1q16l+QuXZnx2GziE2o/zgSQd7yMwWl1B7SIiIhdIjaEB6V1Zs22PfiDBjFRp5fpdDq5Y8Jk7pgwud7z+AJBCssqGNgjuYkqFREROT8ReU8YIMETzbC+qWTty7ug82Tty2NE327a2lBERCJOxIYwwPWZaYRCJl/lHTuv43cfLsSy4NrMno1cmYiIyIWL6BCOdruYfO0gDhQWszP3aIPXf7Ysiy8PFpBbVMqkawae8+YPIiIi4RDRIQyQ5I3jwdFXUBkIsjF7P8fKzr7oRmFpJRu+2I/fCPHgjUNJ8ta9ipaIiIjdInZi1qnaJsTy4I1D2bT7IOu/2I+FRev4WFrHeYhyOwkaJsUVVRyvqMLldHLtgB4MSUvB6Tz7Yh8iIiJ2ahYhDOB0OriybypDe3dl/9Hj5B4r4VBRGeW+ADFRbjJSO9A1qTXd2rdW+IqISLPQbEL4JKfTQc+ObenZsa3dpYiIiFyQiL8nLCIi0lKFtSd8cnbzmTZaEJHmz+/3212CSEQ5mXl1PeET1hAOBoMA5OTkhPOyIhJGO3bssLsEkYgUDAbxeDynveawGvrwbSMwTZOKigqioqJw1LNNoYiISEtgWRbBYJD4+HicztPvAoc1hEVERORrmpglIiJiE4WwiIiITRTCIiIiNlEIi4iI2EQhLCIiYhOFsIiIiE0UwiIiIjZRCIuIiNik2e2iJCK15ebmMmbMGAYOHAhUL4/XpUsXnnjiCbxeL0uWLOHtt98mNjYWv9/PNddcw/Tp03G5XKxatYqFCxcSFRWF1+tl3rx5tGrVyuZPJHJx0IpZIi1Abm4u99xzDxs3bqx5be7cuQCkpKSwZs0ann/+eRISEvD5fMycOZP09HQmTJjA2LFjWbFiBW3atOGZZ57B6/UyY8YMuz6KyEVFPWGRFuryyy/ntddeY8WKFSxevJiEhAQAPB4P8+bNIzo6GrfbzerVq2veS0pKIj8/386yRS4quics0gKFQiHWrFnDgAEDKCsrIy0t7bT34+PjazZSORnAJSUlvPHGG9x22212lCxyUVJPWKSFKCoqYuLEiUD1jmVDhgxh0qRJLFy4sN5j8/PzmTp1KlOnTiUzM7OpSxWRExTCIi1E27Zteemll+p8PTs7m/T09JrXysrKKCgoIC0tjaNHj/Ld736XRx55hBtuuCGcJYtc9DQcLdLCPfTQQzz55JMUFxcD4PP5ePzxx1m1ahUAjz76KLNmzVIAi9hAPWGRFu7OO+/E7XZz7733EhcXh2VZ3HTTTUyePJlt27aRlZWFZVksWrQIgD59+jB79mybqxa5OOgRJREREZtoOFpERMQmCmERERGbKIRFRERsohAWERGxiUJYRETEJgphERERmyiERUREbKIQFhERscn/AxzUuSIgg8nsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wV4REaSbxDZ_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "084e4deb-1a00-40b0-a965-b15ba1cf123c"
      },
      "source": [
        "plot_model(kmeans, plot = 'elbow'\t) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e_VnxVyno4P-"
      },
      "source": [
        "Thus, we can see from the above curves and the table that 10 clusters best serve our purpose. The decrease in sum of squares after 10 is not large enough. Hence, for our analysis, we select 10 clusters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5tNF4DtwpZdK"
      },
      "source": [
        "We now use the Guassian Mixture clustering algorithm to assign the companies to 10 clusters based on their returns. Gaussian Mixture is an uses a probabilistic method of determining the appropriate cluster for a series of observation, assuming the universe is formed out of different Gaussian distributions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "YnSI7BY8Poud",
        "outputId": "7732231d-b557-4914-b8e2-c805e7fb2af7"
      },
      "source": [
        "#Get 7 clusters\n",
        "gmm = GaussianMixture(n_components = 5)\n",
        "gmm.fit(transposed.dropna().transpose())\n",
        "\n",
        "#Predict for each company\n",
        "clusters = gmm.predict(transposed.dropna().transpose())\n",
        "clusters_df = pd.DataFrame({'Cluster':clusters,\n",
        "                           'Companies':transposed.columns})\n",
        "\n",
        "#Sort by Clusters\n",
        "clusters_df = clusters_df.sort_values(['Cluster']).reset_index(drop = True)\n",
        "\n",
        "#Save as csv\n",
        "clusters_df.to_csv(\"clusters.csv\")\n",
        "clusters_df = pd.read_csv(\"clusters.csv\", index_col = 0)\n",
        "clusters_df.head()"
      ],
      "execution_count": null,
      "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>Cluster</th>\n",
              "      <th>Companies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>JNJ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>NFLX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>NEE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>MSFT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>MRK</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cluster Companies\n",
              "0        0       JNJ\n",
              "1        0      NFLX\n",
              "2        0       NEE\n",
              "3        0      MSFT\n",
              "4        0       MRK"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqLTU3YpX6G4"
      },
      "source": [
        "!rm -rf '/content/drive/MyDrive/Work/Projects/CRYPTO/clusters.csv'\n",
        "!cp 'clusters.csv' '/content/drive/MyDrive/Work/Projects/CRYPTO/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jAR7hd_gpglT",
        "outputId": "54b56c6e-c8b9-4a90-ddf6-c083bc1dd804"
      },
      "source": [
        "clusters_df = pd.read_csv(\"/content/drive/MyDrive/Work/Projects/CRYPTO/clusters.csv\", index_col = 0)\n",
        "for c in clusters_df['Cluster'].unique():\n",
        "  print(f\"Cluster number - {c}\")\n",
        "  print(clusters_df.loc[clusters_df['Cluster']==c,\"Companies\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cluster number - 0\n",
            "0      JNJ\n",
            "1     NFLX\n",
            "2      NEE\n",
            "3     MSFT\n",
            "4      MRK\n",
            "      ... \n",
            "80     ADP\n",
            "81     ECL\n",
            "82     DHR\n",
            "83     ABT\n",
            "84      FB\n",
            "Name: Companies, Length: 85, dtype: object\n",
            "Cluster number - 1\n",
            "85    MRNA\n",
            "86    AAPL\n",
            "87    TSLA\n",
            "88    ILMN\n",
            "89     AMD\n",
            "90    ABNB\n",
            "91    QCOM\n",
            "Name: Companies, dtype: object\n",
            "Cluster number - 2\n",
            "92      PNC\n",
            "93     BKNG\n",
            "94      XOM\n",
            "95      TFC\n",
            "96      BAC\n",
            "97       BA\n",
            "98     SCCO\n",
            "99     SCHW\n",
            "100     USB\n",
            "101     UNP\n",
            "102     AXP\n",
            "103     WFC\n",
            "104     RTX\n",
            "105    FISV\n",
            "106     DIS\n",
            "107      GE\n",
            "108     FCX\n",
            "109      GM\n",
            "110     GPN\n",
            "111      GS\n",
            "112     HCA\n",
            "113     EMR\n",
            "114     HON\n",
            "115      EL\n",
            "116     BSX\n",
            "117     JPM\n",
            "118      DE\n",
            "119     CVX\n",
            "120       C\n",
            "121      MS\n",
            "122     NSC\n",
            "123      MA\n",
            "124     CAT\n",
            "125     COF\n",
            "126     MET\n",
            "127     CME\n",
            "128     COP\n",
            "Name: Companies, dtype: object\n",
            "Cluster number - 3\n",
            "129     VIAC\n",
            "130    VIACA\n",
            "Name: Companies, dtype: object\n",
            "Cluster number - 4\n",
            "131     ADI\n",
            "132    LRCX\n",
            "133    WDAY\n",
            "134    PYPL\n",
            "135     NOW\n",
            "136    ADBE\n",
            "137      MU\n",
            "138    ADSK\n",
            "139    AMAT\n",
            "140     TXN\n",
            "141    TWLO\n",
            "142    INTC\n",
            "143    INTU\n",
            "144      ZM\n",
            "145      SE\n",
            "146    SNAP\n",
            "147      SQ\n",
            "148    AVGO\n",
            "149    NVDA\n",
            "Name: Companies, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IX5MJePZFSg_"
      },
      "source": [
        "###Data Preparation and Spliting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LNKKBSX4twE8"
      },
      "source": [
        "**Training period**\n",
        "\n",
        "Since our data is a time series data and we want to split the Train-Test as a timeline rather than randomly selecting observations as train or test.\n",
        "We first convert our index into a date time index and split the data to before and after 31st December 2018.\n",
        "Train period: 15th October 1990 – 31st December 2018\n",
        "Test period: 1st January 2019 — Present Working Day"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yF3sSGtnRe3y"
      },
      "source": [
        "all_data.index = pd.to_datetime(all_data.index)\n",
        "all_data = all_data.sort_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HmxasUnCP0n9"
      },
      "source": [
        "#all_data.index = pd.to_datetime(all_data.index)\n",
        "train_data = all_data.loc[:'2018-12-31',]\n",
        "test_data = all_data.loc['2019-01-01':] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        },
        "id": "xFMc-k9xcFjH",
        "outputId": "73d41053-229d-4eb0-ca96-cd24a7796104"
      },
      "source": [
        "#Separate between X and Y\n",
        "X_train = train_data.loc[:,Target_variables]\n",
        "X_train.tail(1)"
      ],
      "execution_count": null,
      "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>SMA_ratio</th>\n",
              "      <th>EMA_ratio</th>\n",
              "      <th>ATR_5</th>\n",
              "      <th>ATR_15</th>\n",
              "      <th>ATR_Ratio</th>\n",
              "      <th>ADX_5</th>\n",
              "      <th>ADX_15</th>\n",
              "      <th>SMA_Volume_Ratio</th>\n",
              "      <th>EMA_Volume_ratio</th>\n",
              "      <th>Stochastic_5</th>\n",
              "      <th>Stochastic_15</th>\n",
              "      <th>Stochastic_Ratio</th>\n",
              "      <th>RSI_5</th>\n",
              "      <th>RSI_15</th>\n",
              "      <th>RSI_ratio</th>\n",
              "      <th>MACD</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",
              "      <th></th>\n",
              "      <th></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>2018-12-31</th>\n",
              "      <td>1.01252</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.101983</td>\n",
              "      <td>3.078092</td>\n",
              "      <td>0.99431</td>\n",
              "      <td>31.467857</td>\n",
              "      <td>17.199879</td>\n",
              "      <td>0.733927</td>\n",
              "      <td>1.0</td>\n",
              "      <td>87.664491</td>\n",
              "      <td>62.495631</td>\n",
              "      <td>1.463322</td>\n",
              "      <td>87.266337</td>\n",
              "      <td>49.262827</td>\n",
              "      <td>1.605192</td>\n",
              "      <td>-1.273608</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            SMA_ratio  EMA_ratio     ATR_5  ...     RSI_15  RSI_ratio      MACD\n",
              "Date                                        ...                                \n",
              "2018-12-31    1.01252        1.0  3.101983  ...  49.262827   1.605192 -1.273608\n",
              "\n",
              "[1 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "TZrztfsmcKoJ",
        "outputId": "7b3ea4ef-ef30-4b57-ad9f-9c448161175c"
      },
      "source": [
        "Y_train = train_data.loc[:,['Target_Direction']]\n",
        "Y_train.tail(1)"
      ],
      "execution_count": null,
      "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>Target_Direction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Target_Direction\n",
              "Date                        \n",
              "2018-12-31                 1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "smlDeQapFhj9"
      },
      "source": [
        "###Modelling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bl0DfCyCt5uD"
      },
      "source": [
        "\n",
        "####Random Forest Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "g5vUL0XSQFW9",
        "outputId": "f7da3e28-18d1-4515-abb4-41f0f9062bcf"
      },
      "source": [
        "#Run the loop for every unique cluster - 17 loops\n",
        "for cluster_selected in clusters_df.Cluster.unique():\n",
        "    \n",
        "    print(f'The current cluster running is : {cluster_selected}')\n",
        "    \n",
        "    #Get data for that cluster\n",
        "    co_data = all_data[all_data.symbol.isin(clusters_df.loc[clusters_df.Cluster==cluster_selected,'Companies'].tolist())].copy()\n",
        "    co_train = co_data[:'2018-12-31']\n",
        "    co_train = co_train.dropna().copy()\n",
        "    \n",
        "    X_train = co_train.loc[:,Target_variables]\n",
        "\n",
        "    Y_train = co_train.loc[:,['Target_Direction']]\n",
        "\n",
        "    #Define paramters from Validation Curve\n",
        "    params = {'max_depth': [5, 7],\n",
        "          'max_features': ['sqrt'],\n",
        "          'min_samples_leaf': [10, 15, 20],\n",
        "          'n_estimators': [5, 7, 9],\n",
        "          'min_samples_split':[20, 25, 30]}\n",
        "\n",
        "    rf = RandomForestClassifier()\n",
        "\n",
        "    #Perform a TimeSeriesSplit on the dataset\n",
        "    time_series_split = TimeSeriesSplit(n_splits = 3)\n",
        "\n",
        "    \n",
        "    rf_cv = RandomizedSearchCV(rf, params, cv = time_series_split, n_jobs = -1, verbose = 1)\n",
        "\n",
        "    #Fit the random forest with our X_train and Y_train\n",
        "    rf_cv.fit(X_train, Y_train)      \n",
        "    #Save the fited variable into a Pickle file\n",
        "    file_loc = f'RF_Cluster_{cluster_selected}'\n",
        "    pickle.dump(rf_cv, open(file_loc,'wb'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-92a76051c647>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Run the loop for every unique cluster - 17 loops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mcluster_selected\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclusters_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCluster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'The current cluster running is : {cluster_selected}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'clusters_df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_IgSLGyPgkR"
      },
      "source": [
        "!rm -rf '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_0'\n",
        "!rm -rf '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_1'\n",
        "!rm -rf '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_2'\n",
        "!rm -rf '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_3'\n",
        "!rm -rf '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_4'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wObJ9Ov-UWJY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efac980c-ee60-44a9-bd0c-ecc212126788"
      },
      "source": [
        "!cp '/content/RF_Cluster_0' '/content/drive/MyDrive/Work/Projects/CRYPTO/'\n",
        "!cp '/content/RF_Cluster_1' '/content/drive/MyDrive/Work/Projects/CRYPTO/'\n",
        "!cp '/content/RF_Cluster_2' '/content/drive/MyDrive/Work/Projects/CRYPTO/'\n",
        "!cp '/content/RF_Cluster_3' '/content/drive/MyDrive/Work/Projects/CRYPTO/'\n",
        "!cp '/content/RF_Cluster_4' '/content/drive/MyDrive/Work/Projects/CRYPTO/'\n",
        "!cp '/content/RF_Cluster_5' '/content/drive/MyDrive/Work/Projects/CRYPTO/'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cp: cannot stat '/content/RF_Cluster_0': No such file or directory\n",
            "cp: cannot stat '/content/RF_Cluster_1': No such file or directory\n",
            "cp: cannot stat '/content/RF_Cluster_2': No such file or directory\n",
            "cp: cannot stat '/content/RF_Cluster_3': No such file or directory\n",
            "cp: cannot stat '/content/RF_Cluster_4': No such file or directory\n",
            "cp: cannot stat '/content/RF_Cluster_5': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qY9dc82OUsE-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c34e53d0-0582-4a41-ca64-781d2f516f61"
      },
      "source": [
        "!rm -rf '/content/RF_Cluster_0'\n",
        "!rm -rf '/content/RF_Cluster_1'\n",
        "!rm -rf '/content/RF_Cluster_2'\n",
        "!rm -rf '/content/RF_Cluster_3'\n",
        "!rm -rf '/content/RF_Cluster_4'\n",
        "!rm -rf '/content/RF_Cluster_5'\n",
        "\n",
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_0' '/content/'\n",
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_1' '/content/'\n",
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_2' '/content/'\n",
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_3' '/content/'\n",
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_4' '/content/'\n",
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_5' '/content/'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cp: cannot stat '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_0': No such file or directory\n",
            "cp: cannot stat '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_1': No such file or directory\n",
            "cp: cannot stat '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_2': No such file or directory\n",
            "cp: cannot stat '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_3': No such file or directory\n",
            "cp: cannot stat '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_4': No such file or directory\n",
            "cp: cannot stat '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_5': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DquUgj_PtJRi"
      },
      "source": [
        "def get_model_evaluation(date):\n",
        "  #Use 2nd January Data\n",
        "  day_data = test_data.loc[date]\n",
        "  #print(day_data)\n",
        "\n",
        "  pred_for_tomorrow = pd.DataFrame({'Date':[],\n",
        "                                    'company':[],\n",
        "                                    'prediction':[]})\n",
        "\n",
        "  #Predict each stock using the 2nd January Data\n",
        "  for cluster_selected in clusters_df.Cluster.unique():\n",
        "      rf_cv =  pickle.load(open(f'RF_Cluster_{cluster_selected}', 'rb'))\n",
        "      best_rf = rf_cv.best_estimator_\n",
        "      cluster_data = day_data.loc[day_data.symbol.isin(clusters_df.loc[clusters_df.Cluster==cluster_selected,'Companies'].tolist())].copy()\n",
        "      cluster_data = cluster_data.dropna()\n",
        "      if (cluster_data.shape[0]>0):\n",
        "          X_test = cluster_data.loc[:,Target_variables]\n",
        "\n",
        "          pred_for_tomorrow = pred_for_tomorrow.append(pd.DataFrame({'Date':cluster_data.index,\n",
        "                                                                    'company':cluster_data['symbol'],\n",
        "                                                                    'prediction':best_rf.predict_proba(X_test)[:,1],\n",
        "                                                                    'prediction_round':best_rf.predict(X_test)}),\n",
        "                                                      ignore_index = True)\n",
        "  #print(pred_for_tomorrow.head())\n",
        "  top_10_pred = pred_for_tomorrow.sort_values(by = ['prediction'], ascending = False)#.head(30)\n",
        "\n",
        "  for selected_company in top_10_pred['company']:\n",
        "      actual = all_data[all_data.symbol == selected_company].loc[date,'Target_Direction']\n",
        "      pct_change = all_data[all_data.symbol == selected_company].loc[date,'Target']\n",
        "      top_10_pred.loc[top_10_pred.company == selected_company,'actual'] = actual\n",
        "      top_10_pred.loc[top_10_pred.company == selected_company,'pct_change'] = pct_change\n",
        "\n",
        "  print(\"Accuracy for Random Forest: \",metrics.accuracy_score(top_10_pred['actual'],top_10_pred['prediction_round']))\n",
        "\n",
        "  print(\"Confusion Matrix\")\n",
        "  print(metrics.confusion_matrix(top_10_pred['actual'],top_10_pred['prediction_round']))\n",
        "\n",
        "  print(\"ROC-AUC\")\n",
        "  print(metrics.roc_auc_score(top_10_pred['actual'], top_10_pred['prediction']))\n",
        "\n",
        "  # Building ROC curve\n",
        "  rf_roc_auc = metrics.roc_auc_score(top_10_pred['actual'], top_10_pred['prediction_round'])\n",
        "  fpr, tpr, thresholds = metrics.roc_curve(top_10_pred['actual'], top_10_pred['prediction'])\n",
        "  plt.figure()\n",
        "  plt.plot(fpr, tpr, label='Random Forest (area = %0.3f)' % rf_roc_auc)\n",
        "  plt.plot([0, 1], [0, 1],'r--')\n",
        "  plt.xlim([0.0, 1.0])\n",
        "  plt.ylim([0.0, 1.05])\n",
        "  plt.xlabel('False Positive Rate', fontsize=14)\n",
        "  plt.ylabel('True Positive Rate', fontsize=14)\n",
        "  plt.title('Receiver Operating Characteristic (ROC)', fontsize=16)\n",
        "  plt.legend(loc=\"lower right\", fontsize=14)\n",
        "  plt.savefig('RF_ROC', fontsize=14)\n",
        "  plt.show();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "iPpffAFgY6BS",
        "outputId": "6950d7b4-c596-4e52-a93b-f0bec8b8c2ae"
      },
      "source": [
        "get_model_evaluation(date='2019-02-04')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-b6625beca63b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_model_evaluation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2019-02-04'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-18-8249add8401e>\u001b[0m in \u001b[0;36mget_model_evaluation\u001b[0;34m(date)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_model_evaluation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m   \u001b[0;31m#Use 2nd January Data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m   \u001b[0mday_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m   \u001b[0;31m#print(day_data)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'test_data' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gMCY5_TPPH2"
      },
      "source": [
        "#Predictions and Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vWRG1HOWG-xR"
      },
      "source": [
        "###Predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvqEfll0P4_i"
      },
      "source": [
        "This showcases the final validation output of our Model along with SHAP model explainability. Here we have the predictions for which stock should we buy on `2019-02-04`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9FtgK0we98U"
      },
      "source": [
        "Target_variables = ['SMA_ratio','EMA_ratio','ATR_5','ATR_15','ATR_Ratio',\n",
        "                       'ADX_5','ADX_15','SMA_Volume_Ratio','EMA_Volume_ratio','Stochastic_5','Stochastic_15','Stochastic_Ratio',\n",
        "                      'RSI_5','RSI_15','RSI_ratio','MACD']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2DWHsdcdxVR"
      },
      "source": [
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/all_data_ti.csv' '/content/'\n",
        "!rm -rf '/content/RF_Cluster_0'\n",
        "!rm -rf '/content/RF_Cluster_1'\n",
        "!rm -rf '/content/RF_Cluster_2'\n",
        "!rm -rf '/content/RF_Cluster_3'\n",
        "!rm -rf '/content/RF_Cluster_4'\n",
        "!rm -rf '/content/RF_Cluster_5'\n",
        "\n",
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_0' '/content/'\n",
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_1' '/content/'\n",
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_2' '/content/'\n",
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_3' '/content/'\n",
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_4' '/content/'\n",
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/RF_Cluster_5' '/content/'\n",
        "\n",
        "!cp '/content/drive/MyDrive/Work/Projects/CRYPTO/clusters.csv' '/content/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lv_gqdZWbKqO"
      },
      "source": [
        "dftx = pd.read_csv(\"all_data_ti.csv\",index_col=\"Date\")\n",
        "dftx['Close_Shifted'] = dftx.groupby('symbol')['Close'].transform(lambda x: x.shift(-6))\n",
        "dftx['Target'] = ((dftx['Close_Shifted'] - dftx['Open'])/(dftx['Open']) * 100).shift(-1)\n",
        "dftx['Target_Direction'] = np.where(dftx['Target']>0,1,0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "TXWzF6pVbRJ_",
        "outputId": "af40cfa3-03d1-4496-bdbe-1b5037bce002"
      },
      "source": [
        "dftx.tail(8)"
      ],
      "execution_count": null,
      "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>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>symbol</th>\n",
              "      <th>return</th>\n",
              "      <th>SMA_5</th>\n",
              "      <th>SMA_15</th>\n",
              "      <th>SMA_ratio</th>\n",
              "      <th>EMA_5</th>\n",
              "      <th>EMA_15</th>\n",
              "      <th>EMA_ratio</th>\n",
              "      <th>SMA5_Volume</th>\n",
              "      <th>SMA15_Volume</th>\n",
              "      <th>SMA_Volume_Ratio</th>\n",
              "      <th>EMA_5_Volume</th>\n",
              "      <th>EMA_15_Volume</th>\n",
              "      <th>EMA_Volume_ratio</th>\n",
              "      <th>prev_close</th>\n",
              "      <th>TR</th>\n",
              "      <th>ATR_5</th>\n",
              "      <th>ATR_15</th>\n",
              "      <th>ATR_Ratio</th>\n",
              "      <th>prev_high</th>\n",
              "      <th>prev_low</th>\n",
              "      <th>+DM</th>\n",
              "      <th>-DM</th>\n",
              "      <th>+DM_5</th>\n",
              "      <th>-DM_5</th>\n",
              "      <th>+DM_15</th>\n",
              "      <th>-DM_15</th>\n",
              "      <th>+DI_5</th>\n",
              "      <th>-DI_5</th>\n",
              "      <th>+DI_15</th>\n",
              "      <th>-DI_15</th>\n",
              "      <th>DX_5</th>\n",
              "      <th>DX_15</th>\n",
              "      <th>ADX_5</th>\n",
              "      <th>ADX_15</th>\n",
              "      <th>Lowest_5D</th>\n",
              "      <th>High_5D</th>\n",
              "      <th>Lowest_15D</th>\n",
              "      <th>High_15D</th>\n",
              "      <th>Stochastic_5</th>\n",
              "      <th>Stochastic_15</th>\n",
              "      <th>Stochastic_%D_5</th>\n",
              "      <th>Stochastic_%D_15</th>\n",
              "      <th>Stochastic_Ratio</th>\n",
              "      <th>Diff</th>\n",
              "      <th>Up</th>\n",
              "      <th>Down</th>\n",
              "      <th>avg_5up</th>\n",
              "      <th>avg_5down</th>\n",
              "      <th>avg_15up</th>\n",
              "      <th>avg_15down</th>\n",
              "      <th>RS_5</th>\n",
              "      <th>RS_15</th>\n",
              "      <th>RSI_5</th>\n",
              "      <th>RSI_15</th>\n",
              "      <th>RSI_ratio</th>\n",
              "      <th>5Ewm</th>\n",
              "      <th>15Ewm</th>\n",
              "      <th>MACD</th>\n",
              "      <th>15MA</th>\n",
              "      <th>SD</th>\n",
              "      <th>upperband</th>\n",
              "      <th>lowerband</th>\n",
              "      <th>RC</th>\n",
              "      <th>Close_Shifted</th>\n",
              "      <th>Target</th>\n",
              "      <th>Target_Direction</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>2021-03-15</th>\n",
              "      <td>75.559998</td>\n",
              "      <td>72.919998</td>\n",
              "      <td>75.139999</td>\n",
              "      <td>74.930000</td>\n",
              "      <td>3717700.0</td>\n",
              "      <td>74.930000</td>\n",
              "      <td>BX</td>\n",
              "      <td>0.001203</td>\n",
              "      <td>72.778000</td>\n",
              "      <td>70.106667</td>\n",
              "      <td>0.963295</td>\n",
              "      <td>73.053493</td>\n",
              "      <td>73.053493</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3778740.0</td>\n",
              "      <td>3.402307e+06</td>\n",
              "      <td>1.110641</td>\n",
              "      <td>3.821809e+06</td>\n",
              "      <td>3.821809e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>74.839996</td>\n",
              "      <td>2.639999</td>\n",
              "      <td>2.600467</td>\n",
              "      <td>2.100825</td>\n",
              "      <td>1.237832</td>\n",
              "      <td>75.269997</td>\n",
              "      <td>73.050003</td>\n",
              "      <td>0.290001</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.931828</td>\n",
              "      <td>0.316932</td>\n",
              "      <td>0.602426</td>\n",
              "      <td>0.387816</td>\n",
              "      <td>35.833103</td>\n",
              "      <td>12.187521</td>\n",
              "      <td>28.675682</td>\n",
              "      <td>18.460177</td>\n",
              "      <td>49.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>36.352028</td>\n",
              "      <td>22.536914</td>\n",
              "      <td>67.800003</td>\n",
              "      <td>75.559998</td>\n",
              "      <td>63.709999</td>\n",
              "      <td>75.559998</td>\n",
              "      <td>91.881473</td>\n",
              "      <td>94.683567</td>\n",
              "      <td>91.671472</td>\n",
              "      <td>64.515444</td>\n",
              "      <td>1.420923</td>\n",
              "      <td>0.090004</td>\n",
              "      <td>0.090004</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.480000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.786000</td>\n",
              "      <td>0.425333</td>\n",
              "      <td>inf</td>\n",
              "      <td>1.847963</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>64.887188</td>\n",
              "      <td>1.541136</td>\n",
              "      <td>73.053493</td>\n",
              "      <td>70.874565</td>\n",
              "      <td>-2.178929</td>\n",
              "      <td>70.106667</td>\n",
              "      <td>2.665131</td>\n",
              "      <td>75.436929</td>\n",
              "      <td>64.776405</td>\n",
              "      <td>0.077819</td>\n",
              "      <td>73.190002</td>\n",
              "      <td>-4.166673</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-16</th>\n",
              "      <td>76.250000</td>\n",
              "      <td>74.029999</td>\n",
              "      <td>75.120003</td>\n",
              "      <td>74.870003</td>\n",
              "      <td>4418000.0</td>\n",
              "      <td>74.870003</td>\n",
              "      <td>BX</td>\n",
              "      <td>-0.000801</td>\n",
              "      <td>73.882001</td>\n",
              "      <td>70.478000</td>\n",
              "      <td>0.953927</td>\n",
              "      <td>73.658996</td>\n",
              "      <td>73.658996</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3866000.0</td>\n",
              "      <td>3.540213e+06</td>\n",
              "      <td>1.092025</td>\n",
              "      <td>4.020539e+06</td>\n",
              "      <td>4.020539e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>74.930000</td>\n",
              "      <td>2.220001</td>\n",
              "      <td>2.524374</td>\n",
              "      <td>2.108770</td>\n",
              "      <td>1.197084</td>\n",
              "      <td>75.559998</td>\n",
              "      <td>72.919998</td>\n",
              "      <td>0.690002</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.883463</td>\n",
              "      <td>0.253546</td>\n",
              "      <td>0.608264</td>\n",
              "      <td>0.361962</td>\n",
              "      <td>34.997309</td>\n",
              "      <td>10.043916</td>\n",
              "      <td>28.844507</td>\n",
              "      <td>17.164584</td>\n",
              "      <td>55.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>40.081623</td>\n",
              "      <td>22.701120</td>\n",
              "      <td>69.779999</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>63.709999</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>78.670835</td>\n",
              "      <td>88.995238</td>\n",
              "      <td>91.889831</td>\n",
              "      <td>66.342792</td>\n",
              "      <td>1.385076</td>\n",
              "      <td>-0.059998</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.059998</td>\n",
              "      <td>1.116000</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>0.786000</td>\n",
              "      <td>0.414667</td>\n",
              "      <td>93.003815</td>\n",
              "      <td>1.895498</td>\n",
              "      <td>98.936213</td>\n",
              "      <td>65.463629</td>\n",
              "      <td>1.511316</td>\n",
              "      <td>73.658996</td>\n",
              "      <td>71.373994</td>\n",
              "      <td>-2.285002</td>\n",
              "      <td>70.478000</td>\n",
              "      <td>2.920510</td>\n",
              "      <td>76.319019</td>\n",
              "      <td>64.636981</td>\n",
              "      <td>0.080375</td>\n",
              "      <td>71.989998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-17</th>\n",
              "      <td>76.099998</td>\n",
              "      <td>73.910004</td>\n",
              "      <td>74.699997</td>\n",
              "      <td>76.089996</td>\n",
              "      <td>2877100.0</td>\n",
              "      <td>76.089996</td>\n",
              "      <td>BX</td>\n",
              "      <td>0.016295</td>\n",
              "      <td>74.948000</td>\n",
              "      <td>70.893333</td>\n",
              "      <td>0.945900</td>\n",
              "      <td>74.469330</td>\n",
              "      <td>74.469330</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3816660.0</td>\n",
              "      <td>3.599487e+06</td>\n",
              "      <td>1.060335</td>\n",
              "      <td>3.639393e+06</td>\n",
              "      <td>3.639393e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>74.870003</td>\n",
              "      <td>2.189995</td>\n",
              "      <td>2.457498</td>\n",
              "      <td>2.114185</td>\n",
              "      <td>1.162386</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>74.029999</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.119995</td>\n",
              "      <td>0.706770</td>\n",
              "      <td>0.226836</td>\n",
              "      <td>0.567713</td>\n",
              "      <td>0.345830</td>\n",
              "      <td>28.759752</td>\n",
              "      <td>9.230356</td>\n",
              "      <td>26.852587</td>\n",
              "      <td>16.357627</td>\n",
              "      <td>51.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>42.265298</td>\n",
              "      <td>22.787712</td>\n",
              "      <td>70.900002</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>63.709999</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>97.009276</td>\n",
              "      <td>98.724054</td>\n",
              "      <td>92.134071</td>\n",
              "      <td>67.341566</td>\n",
              "      <td>1.368161</td>\n",
              "      <td>1.219994</td>\n",
              "      <td>1.219994</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.077998</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>0.830000</td>\n",
              "      <td>0.414667</td>\n",
              "      <td>89.836851</td>\n",
              "      <td>2.001607</td>\n",
              "      <td>98.899125</td>\n",
              "      <td>66.684511</td>\n",
              "      <td>1.483090</td>\n",
              "      <td>74.469330</td>\n",
              "      <td>71.963495</td>\n",
              "      <td>-2.505835</td>\n",
              "      <td>70.893333</td>\n",
              "      <td>3.250674</td>\n",
              "      <td>77.394681</td>\n",
              "      <td>64.391986</td>\n",
              "      <td>0.089178</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-18</th>\n",
              "      <td>75.989998</td>\n",
              "      <td>72.900002</td>\n",
              "      <td>75.879997</td>\n",
              "      <td>73.029999</td>\n",
              "      <td>2533500.0</td>\n",
              "      <td>73.029999</td>\n",
              "      <td>BX</td>\n",
              "      <td>-0.040216</td>\n",
              "      <td>74.751999</td>\n",
              "      <td>71.201333</td>\n",
              "      <td>0.952501</td>\n",
              "      <td>73.989553</td>\n",
              "      <td>73.989553</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3398140.0</td>\n",
              "      <td>3.621980e+06</td>\n",
              "      <td>0.938200</td>\n",
              "      <td>3.270762e+06</td>\n",
              "      <td>3.270762e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>76.089996</td>\n",
              "      <td>3.189995</td>\n",
              "      <td>2.603997</td>\n",
              "      <td>2.185905</td>\n",
              "      <td>1.191267</td>\n",
              "      <td>76.099998</td>\n",
              "      <td>73.910004</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.010002</td>\n",
              "      <td>0.565416</td>\n",
              "      <td>0.383469</td>\n",
              "      <td>0.529866</td>\n",
              "      <td>0.390109</td>\n",
              "      <td>21.713396</td>\n",
              "      <td>14.726169</td>\n",
              "      <td>24.240104</td>\n",
              "      <td>17.846544</td>\n",
              "      <td>19.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>37.612239</td>\n",
              "      <td>22.268531</td>\n",
              "      <td>72.900002</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>63.709999</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>3.880517</td>\n",
              "      <td>74.322161</td>\n",
              "      <td>73.384108</td>\n",
              "      <td>66.586436</td>\n",
              "      <td>1.102088</td>\n",
              "      <td>-3.059998</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.059998</td>\n",
              "      <td>0.427998</td>\n",
              "      <td>0.623999</td>\n",
              "      <td>0.830000</td>\n",
              "      <td>0.522000</td>\n",
              "      <td>0.685896</td>\n",
              "      <td>1.590038</td>\n",
              "      <td>40.684355</td>\n",
              "      <td>61.390521</td>\n",
              "      <td>0.662714</td>\n",
              "      <td>73.989553</td>\n",
              "      <td>72.096808</td>\n",
              "      <td>-1.892745</td>\n",
              "      <td>71.201333</td>\n",
              "      <td>3.217272</td>\n",
              "      <td>77.635878</td>\n",
              "      <td>64.766789</td>\n",
              "      <td>0.067534</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-19</th>\n",
              "      <td>72.849998</td>\n",
              "      <td>71.339996</td>\n",
              "      <td>71.849998</td>\n",
              "      <td>72.470001</td>\n",
              "      <td>2712800.0</td>\n",
              "      <td>72.470001</td>\n",
              "      <td>BX</td>\n",
              "      <td>-0.007668</td>\n",
              "      <td>74.278000</td>\n",
              "      <td>71.417333</td>\n",
              "      <td>0.961487</td>\n",
              "      <td>73.483036</td>\n",
              "      <td>73.483036</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3251820.0</td>\n",
              "      <td>3.574853e+06</td>\n",
              "      <td>0.909637</td>\n",
              "      <td>3.084775e+06</td>\n",
              "      <td>3.084775e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>73.029999</td>\n",
              "      <td>1.690002</td>\n",
              "      <td>2.421198</td>\n",
              "      <td>2.152845</td>\n",
              "      <td>1.124651</td>\n",
              "      <td>75.989998</td>\n",
              "      <td>72.900002</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.560005</td>\n",
              "      <td>0.452333</td>\n",
              "      <td>0.618776</td>\n",
              "      <td>0.494541</td>\n",
              "      <td>0.468102</td>\n",
              "      <td>18.682195</td>\n",
              "      <td>25.556612</td>\n",
              "      <td>22.971524</td>\n",
              "      <td>21.743397</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>33.289791</td>\n",
              "      <td>20.983962</td>\n",
              "      <td>71.339996</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>63.709999</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>23.014339</td>\n",
              "      <td>69.856471</td>\n",
              "      <td>58.891288</td>\n",
              "      <td>64.370715</td>\n",
              "      <td>0.914877</td>\n",
              "      <td>-0.559998</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.559998</td>\n",
              "      <td>0.262000</td>\n",
              "      <td>0.735999</td>\n",
              "      <td>0.775333</td>\n",
              "      <td>0.559333</td>\n",
              "      <td>0.355978</td>\n",
              "      <td>1.386174</td>\n",
              "      <td>26.252507</td>\n",
              "      <td>58.091904</td>\n",
              "      <td>0.451913</td>\n",
              "      <td>73.483036</td>\n",
              "      <td>72.143457</td>\n",
              "      <td>-1.339579</td>\n",
              "      <td>71.417333</td>\n",
              "      <td>3.184060</td>\n",
              "      <td>77.785452</td>\n",
              "      <td>65.049213</td>\n",
              "      <td>0.046800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-22</th>\n",
              "      <td>74.110001</td>\n",
              "      <td>72.279999</td>\n",
              "      <td>72.589996</td>\n",
              "      <td>73.550003</td>\n",
              "      <td>2750000.0</td>\n",
              "      <td>73.550003</td>\n",
              "      <td>BX</td>\n",
              "      <td>0.014903</td>\n",
              "      <td>74.002000</td>\n",
              "      <td>71.605333</td>\n",
              "      <td>0.967613</td>\n",
              "      <td>73.505358</td>\n",
              "      <td>73.505358</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3058280.0</td>\n",
              "      <td>3.583993e+06</td>\n",
              "      <td>0.853316</td>\n",
              "      <td>2.973183e+06</td>\n",
              "      <td>2.973183e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>72.470001</td>\n",
              "      <td>1.830002</td>\n",
              "      <td>2.302959</td>\n",
              "      <td>2.131322</td>\n",
              "      <td>1.080531</td>\n",
              "      <td>72.849998</td>\n",
              "      <td>71.339996</td>\n",
              "      <td>1.260002</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.613867</td>\n",
              "      <td>0.495021</td>\n",
              "      <td>0.545572</td>\n",
              "      <td>0.436895</td>\n",
              "      <td>26.655569</td>\n",
              "      <td>21.494999</td>\n",
              "      <td>25.597821</td>\n",
              "      <td>20.498772</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>28.831833</td>\n",
              "      <td>20.318365</td>\n",
              "      <td>71.339996</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>63.709999</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>45.010286</td>\n",
              "      <td>78.468925</td>\n",
              "      <td>49.517051</td>\n",
              "      <td>61.278674</td>\n",
              "      <td>0.808063</td>\n",
              "      <td>1.080002</td>\n",
              "      <td>1.080002</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.459999</td>\n",
              "      <td>0.735999</td>\n",
              "      <td>0.747333</td>\n",
              "      <td>0.559333</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>1.336114</td>\n",
              "      <td>38.461538</td>\n",
              "      <td>57.193877</td>\n",
              "      <td>0.672477</td>\n",
              "      <td>73.505358</td>\n",
              "      <td>72.319275</td>\n",
              "      <td>-1.186083</td>\n",
              "      <td>71.605333</td>\n",
              "      <td>3.223585</td>\n",
              "      <td>78.052503</td>\n",
              "      <td>65.158163</td>\n",
              "      <td>0.039870</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-23</th>\n",
              "      <td>74.120003</td>\n",
              "      <td>72.769997</td>\n",
              "      <td>73.510002</td>\n",
              "      <td>73.190002</td>\n",
              "      <td>1492700.0</td>\n",
              "      <td>73.190002</td>\n",
              "      <td>BX</td>\n",
              "      <td>-0.004895</td>\n",
              "      <td>73.666000</td>\n",
              "      <td>71.820667</td>\n",
              "      <td>0.974950</td>\n",
              "      <td>73.400240</td>\n",
              "      <td>73.400240</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2473220.0</td>\n",
              "      <td>3.529567e+06</td>\n",
              "      <td>0.700715</td>\n",
              "      <td>2.479689e+06</td>\n",
              "      <td>2.479689e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>73.550003</td>\n",
              "      <td>1.350006</td>\n",
              "      <td>2.112369</td>\n",
              "      <td>2.079235</td>\n",
              "      <td>1.015936</td>\n",
              "      <td>74.110001</td>\n",
              "      <td>72.279999</td>\n",
              "      <td>0.010002</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.493094</td>\n",
              "      <td>0.396017</td>\n",
              "      <td>0.509867</td>\n",
              "      <td>0.407769</td>\n",
              "      <td>23.343176</td>\n",
              "      <td>18.747526</td>\n",
              "      <td>24.521880</td>\n",
              "      <td>19.611475</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>25.265466</td>\n",
              "      <td>19.697141</td>\n",
              "      <td>71.339996</td>\n",
              "      <td>76.099998</td>\n",
              "      <td>63.709999</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>38.865657</td>\n",
              "      <td>75.598107</td>\n",
              "      <td>41.556015</td>\n",
              "      <td>59.476782</td>\n",
              "      <td>0.698693</td>\n",
              "      <td>-0.360001</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.360001</td>\n",
              "      <td>0.459999</td>\n",
              "      <td>0.795999</td>\n",
              "      <td>0.747333</td>\n",
              "      <td>0.532000</td>\n",
              "      <td>0.577889</td>\n",
              "      <td>1.404763</td>\n",
              "      <td>36.624183</td>\n",
              "      <td>58.415853</td>\n",
              "      <td>0.626956</td>\n",
              "      <td>73.400240</td>\n",
              "      <td>72.428116</td>\n",
              "      <td>-0.972124</td>\n",
              "      <td>71.820667</td>\n",
              "      <td>3.213693</td>\n",
              "      <td>78.248053</td>\n",
              "      <td>65.393280</td>\n",
              "      <td>0.046169</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-25</th>\n",
              "      <td>72.199997</td>\n",
              "      <td>71.410004</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>71.989998</td>\n",
              "      <td>926755.0</td>\n",
              "      <td>71.989998</td>\n",
              "      <td>BX</td>\n",
              "      <td>-0.016396</td>\n",
              "      <td>72.846001</td>\n",
              "      <td>71.980667</td>\n",
              "      <td>0.988121</td>\n",
              "      <td>72.930159</td>\n",
              "      <td>72.930159</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2083151.0</td>\n",
              "      <td>3.458284e+06</td>\n",
              "      <td>0.602366</td>\n",
              "      <td>1.962044e+06</td>\n",
              "      <td>1.962044e+06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>73.190002</td>\n",
              "      <td>1.779999</td>\n",
              "      <td>2.045895</td>\n",
              "      <td>2.059285</td>\n",
              "      <td>0.993497</td>\n",
              "      <td>74.120003</td>\n",
              "      <td>72.769997</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.359993</td>\n",
              "      <td>0.394475</td>\n",
              "      <td>0.588812</td>\n",
              "      <td>0.475876</td>\n",
              "      <td>0.471250</td>\n",
              "      <td>19.281303</td>\n",
              "      <td>28.780176</td>\n",
              "      <td>23.108804</td>\n",
              "      <td>22.884160</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.212373</td>\n",
              "      <td>18.383998</td>\n",
              "      <td>71.339996</td>\n",
              "      <td>75.989998</td>\n",
              "      <td>63.709999</td>\n",
              "      <td>76.250000</td>\n",
              "      <td>13.978523</td>\n",
              "      <td>66.028694</td>\n",
              "      <td>24.949864</td>\n",
              "      <td>56.832531</td>\n",
              "      <td>0.439007</td>\n",
              "      <td>-1.200005</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.200005</td>\n",
              "      <td>0.216000</td>\n",
              "      <td>1.036000</td>\n",
              "      <td>0.747333</td>\n",
              "      <td>0.587333</td>\n",
              "      <td>0.208495</td>\n",
              "      <td>1.272418</td>\n",
              "      <td>17.252420</td>\n",
              "      <td>55.994011</td>\n",
              "      <td>0.308112</td>\n",
              "      <td>72.930159</td>\n",
              "      <td>72.373351</td>\n",
              "      <td>-0.556808</td>\n",
              "      <td>71.980667</td>\n",
              "      <td>3.153890</td>\n",
              "      <td>78.288447</td>\n",
              "      <td>65.672886</td>\n",
              "      <td>0.034488</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 High        Low       Open      Close     Volume  Adj Close  \\\n",
              "Date                                                                           \n",
              "2021-03-15  75.559998  72.919998  75.139999  74.930000  3717700.0  74.930000   \n",
              "2021-03-16  76.250000  74.029999  75.120003  74.870003  4418000.0  74.870003   \n",
              "2021-03-17  76.099998  73.910004  74.699997  76.089996  2877100.0  76.089996   \n",
              "2021-03-18  75.989998  72.900002  75.879997  73.029999  2533500.0  73.029999   \n",
              "2021-03-19  72.849998  71.339996  71.849998  72.470001  2712800.0  72.470001   \n",
              "2021-03-22  74.110001  72.279999  72.589996  73.550003  2750000.0  73.550003   \n",
              "2021-03-23  74.120003  72.769997  73.510002  73.190002  1492700.0  73.190002   \n",
              "2021-03-25  72.199997  71.410004  72.000000  71.989998   926755.0  71.989998   \n",
              "\n",
              "           symbol    return      SMA_5     SMA_15  SMA_ratio      EMA_5  \\\n",
              "Date                                                                      \n",
              "2021-03-15     BX  0.001203  72.778000  70.106667   0.963295  73.053493   \n",
              "2021-03-16     BX -0.000801  73.882001  70.478000   0.953927  73.658996   \n",
              "2021-03-17     BX  0.016295  74.948000  70.893333   0.945900  74.469330   \n",
              "2021-03-18     BX -0.040216  74.751999  71.201333   0.952501  73.989553   \n",
              "2021-03-19     BX -0.007668  74.278000  71.417333   0.961487  73.483036   \n",
              "2021-03-22     BX  0.014903  74.002000  71.605333   0.967613  73.505358   \n",
              "2021-03-23     BX -0.004895  73.666000  71.820667   0.974950  73.400240   \n",
              "2021-03-25     BX -0.016396  72.846001  71.980667   0.988121  72.930159   \n",
              "\n",
              "               EMA_15  EMA_ratio  SMA5_Volume  SMA15_Volume  SMA_Volume_Ratio  \\\n",
              "Date                                                                            \n",
              "2021-03-15  73.053493        1.0    3778740.0  3.402307e+06          1.110641   \n",
              "2021-03-16  73.658996        1.0    3866000.0  3.540213e+06          1.092025   \n",
              "2021-03-17  74.469330        1.0    3816660.0  3.599487e+06          1.060335   \n",
              "2021-03-18  73.989553        1.0    3398140.0  3.621980e+06          0.938200   \n",
              "2021-03-19  73.483036        1.0    3251820.0  3.574853e+06          0.909637   \n",
              "2021-03-22  73.505358        1.0    3058280.0  3.583993e+06          0.853316   \n",
              "2021-03-23  73.400240        1.0    2473220.0  3.529567e+06          0.700715   \n",
              "2021-03-25  72.930159        1.0    2083151.0  3.458284e+06          0.602366   \n",
              "\n",
              "            EMA_5_Volume  EMA_15_Volume  EMA_Volume_ratio  prev_close  \\\n",
              "Date                                                                    \n",
              "2021-03-15  3.821809e+06   3.821809e+06               1.0   74.839996   \n",
              "2021-03-16  4.020539e+06   4.020539e+06               1.0   74.930000   \n",
              "2021-03-17  3.639393e+06   3.639393e+06               1.0   74.870003   \n",
              "2021-03-18  3.270762e+06   3.270762e+06               1.0   76.089996   \n",
              "2021-03-19  3.084775e+06   3.084775e+06               1.0   73.029999   \n",
              "2021-03-22  2.973183e+06   2.973183e+06               1.0   72.470001   \n",
              "2021-03-23  2.479689e+06   2.479689e+06               1.0   73.550003   \n",
              "2021-03-25  1.962044e+06   1.962044e+06               1.0   73.190002   \n",
              "\n",
              "                  TR     ATR_5    ATR_15  ATR_Ratio  prev_high   prev_low  \\\n",
              "Date                                                                        \n",
              "2021-03-15  2.639999  2.600467  2.100825   1.237832  75.269997  73.050003   \n",
              "2021-03-16  2.220001  2.524374  2.108770   1.197084  75.559998  72.919998   \n",
              "2021-03-17  2.189995  2.457498  2.114185   1.162386  76.250000  74.029999   \n",
              "2021-03-18  3.189995  2.603997  2.185905   1.191267  76.099998  73.910004   \n",
              "2021-03-19  1.690002  2.421198  2.152845   1.124651  75.989998  72.900002   \n",
              "2021-03-22  1.830002  2.302959  2.131322   1.080531  72.849998  71.339996   \n",
              "2021-03-23  1.350006  2.112369  2.079235   1.015936  74.110001  72.279999   \n",
              "2021-03-25  1.779999  2.045895  2.059285   0.993497  74.120003  72.769997   \n",
              "\n",
              "                 +DM       -DM     +DM_5     -DM_5    +DM_15    -DM_15  \\\n",
              "Date                                                                     \n",
              "2021-03-15  0.290001  0.000000  0.931828  0.316932  0.602426  0.387816   \n",
              "2021-03-16  0.690002  0.000000  0.883463  0.253546  0.608264  0.361962   \n",
              "2021-03-17  0.000000  0.119995  0.706770  0.226836  0.567713  0.345830   \n",
              "2021-03-18  0.000000  1.010002  0.565416  0.383469  0.529866  0.390109   \n",
              "2021-03-19  0.000000  1.560005  0.452333  0.618776  0.494541  0.468102   \n",
              "2021-03-22  1.260002  0.000000  0.613867  0.495021  0.545572  0.436895   \n",
              "2021-03-23  0.010002  0.000000  0.493094  0.396017  0.509867  0.407769   \n",
              "2021-03-25  0.000000  1.359993  0.394475  0.588812  0.475876  0.471250   \n",
              "\n",
              "                +DI_5      -DI_5     +DI_15     -DI_15  DX_5  DX_15  \\\n",
              "Date                                                                  \n",
              "2021-03-15  35.833103  12.187521  28.675682  18.460177  49.0   22.0   \n",
              "2021-03-16  34.997309  10.043916  28.844507  17.164584  55.0   25.0   \n",
              "2021-03-17  28.759752   9.230356  26.852587  16.357627  51.0   24.0   \n",
              "2021-03-18  21.713396  14.726169  24.240104  17.846544  19.0   15.0   \n",
              "2021-03-19  18.682195  25.556612  22.971524  21.743397  16.0    3.0   \n",
              "2021-03-22  26.655569  21.494999  25.597821  20.498772  11.0   11.0   \n",
              "2021-03-23  23.343176  18.747526  24.521880  19.611475  11.0   11.0   \n",
              "2021-03-25  19.281303  28.780176  23.108804  22.884160  20.0    0.0   \n",
              "\n",
              "                ADX_5     ADX_15  Lowest_5D    High_5D  Lowest_15D   High_15D  \\\n",
              "Date                                                                            \n",
              "2021-03-15  36.352028  22.536914  67.800003  75.559998   63.709999  75.559998   \n",
              "2021-03-16  40.081623  22.701120  69.779999  76.250000   63.709999  76.250000   \n",
              "2021-03-17  42.265298  22.787712  70.900002  76.250000   63.709999  76.250000   \n",
              "2021-03-18  37.612239  22.268531  72.900002  76.250000   63.709999  76.250000   \n",
              "2021-03-19  33.289791  20.983962  71.339996  76.250000   63.709999  76.250000   \n",
              "2021-03-22  28.831833  20.318365  71.339996  76.250000   63.709999  76.250000   \n",
              "2021-03-23  25.265466  19.697141  71.339996  76.099998   63.709999  76.250000   \n",
              "2021-03-25  24.212373  18.383998  71.339996  75.989998   63.709999  76.250000   \n",
              "\n",
              "            Stochastic_5  Stochastic_15  Stochastic_%D_5  Stochastic_%D_15  \\\n",
              "Date                                                                         \n",
              "2021-03-15     91.881473      94.683567        91.671472         64.515444   \n",
              "2021-03-16     78.670835      88.995238        91.889831         66.342792   \n",
              "2021-03-17     97.009276      98.724054        92.134071         67.341566   \n",
              "2021-03-18      3.880517      74.322161        73.384108         66.586436   \n",
              "2021-03-19     23.014339      69.856471        58.891288         64.370715   \n",
              "2021-03-22     45.010286      78.468925        49.517051         61.278674   \n",
              "2021-03-23     38.865657      75.598107        41.556015         59.476782   \n",
              "2021-03-25     13.978523      66.028694        24.949864         56.832531   \n",
              "\n",
              "            Stochastic_Ratio      Diff        Up      Down   avg_5up  \\\n",
              "Date                                                                   \n",
              "2021-03-15          1.420923  0.090004  0.090004  0.000000  1.480000   \n",
              "2021-03-16          1.385076 -0.059998  0.000000  0.059998  1.116000   \n",
              "2021-03-17          1.368161  1.219994  1.219994  0.000000  1.077998   \n",
              "2021-03-18          1.102088 -3.059998  0.000000  3.059998  0.427998   \n",
              "2021-03-19          0.914877 -0.559998  0.000000  0.559998  0.262000   \n",
              "2021-03-22          0.808063  1.080002  1.080002  0.000000  0.459999   \n",
              "2021-03-23          0.698693 -0.360001  0.000000  0.360001  0.459999   \n",
              "2021-03-25          0.439007 -1.200005  0.000000  1.200005  0.216000   \n",
              "\n",
              "            avg_5down  avg_15up  avg_15down       RS_5     RS_15       RSI_5  \\\n",
              "Date                                                                           \n",
              "2021-03-15   0.000000  0.786000    0.425333        inf  1.847963  100.000000   \n",
              "2021-03-16   0.012000  0.786000    0.414667  93.003815  1.895498   98.936213   \n",
              "2021-03-17   0.012000  0.830000    0.414667  89.836851  2.001607   98.899125   \n",
              "2021-03-18   0.623999  0.830000    0.522000   0.685896  1.590038   40.684355   \n",
              "2021-03-19   0.735999  0.775333    0.559333   0.355978  1.386174   26.252507   \n",
              "2021-03-22   0.735999  0.747333    0.559333   0.625000  1.336114   38.461538   \n",
              "2021-03-23   0.795999  0.747333    0.532000   0.577889  1.404763   36.624183   \n",
              "2021-03-25   1.036000  0.747333    0.587333   0.208495  1.272418   17.252420   \n",
              "\n",
              "               RSI_15  RSI_ratio       5Ewm      15Ewm      MACD       15MA  \\\n",
              "Date                                                                          \n",
              "2021-03-15  64.887188   1.541136  73.053493  70.874565 -2.178929  70.106667   \n",
              "2021-03-16  65.463629   1.511316  73.658996  71.373994 -2.285002  70.478000   \n",
              "2021-03-17  66.684511   1.483090  74.469330  71.963495 -2.505835  70.893333   \n",
              "2021-03-18  61.390521   0.662714  73.989553  72.096808 -1.892745  71.201333   \n",
              "2021-03-19  58.091904   0.451913  73.483036  72.143457 -1.339579  71.417333   \n",
              "2021-03-22  57.193877   0.672477  73.505358  72.319275 -1.186083  71.605333   \n",
              "2021-03-23  58.415853   0.626956  73.400240  72.428116 -0.972124  71.820667   \n",
              "2021-03-25  55.994011   0.308112  72.930159  72.373351 -0.556808  71.980667   \n",
              "\n",
              "                  SD  upperband  lowerband        RC  Close_Shifted    Target  \\\n",
              "Date                                                                            \n",
              "2021-03-15  2.665131  75.436929  64.776405  0.077819      73.190002 -4.166673   \n",
              "2021-03-16  2.920510  76.319019  64.636981  0.080375      71.989998       NaN   \n",
              "2021-03-17  3.250674  77.394681  64.391986  0.089178            NaN       NaN   \n",
              "2021-03-18  3.217272  77.635878  64.766789  0.067534            NaN       NaN   \n",
              "2021-03-19  3.184060  77.785452  65.049213  0.046800            NaN       NaN   \n",
              "2021-03-22  3.223585  78.052503  65.158163  0.039870            NaN       NaN   \n",
              "2021-03-23  3.213693  78.248053  65.393280  0.046169            NaN       NaN   \n",
              "2021-03-25  3.153890  78.288447  65.672886  0.034488            NaN       NaN   \n",
              "\n",
              "            Target_Direction  \n",
              "Date                          \n",
              "2021-03-15                 0  \n",
              "2021-03-16                 0  \n",
              "2021-03-17                 0  \n",
              "2021-03-18                 0  \n",
              "2021-03-19                 0  \n",
              "2021-03-22                 0  \n",
              "2021-03-23                 0  \n",
              "2021-03-25                 0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9hXDfkmOzQs"
      },
      "source": [
        "clusters_df = pd.read_csv(\"clusters.csv\", index_col = 0)\n",
        "def get_pridiction(date):\n",
        "  #Use 2nd January Data\n",
        "  day_data = dftx.loc[date]\n",
        "  #print(day_data)\n",
        "\n",
        "  pred_for_tomorrow = pd.DataFrame({'Date':[],\n",
        "                                    'company':[],\n",
        "                                    'prediction':[]})\n",
        "\n",
        "  #Predict each stock using the 2nd January Data\n",
        "  for cluster_selected in clusters_df.Cluster.unique():\n",
        "      rf_cv =  pickle.load(open(f'RF_Cluster_{cluster_selected}', 'rb'))\n",
        "      best_rf = rf_cv.best_estimator_\n",
        "      cluster_data = day_data.loc[day_data.symbol.isin(clusters_df.loc[clusters_df.Cluster==cluster_selected,'Companies'].tolist())].copy()\n",
        "      cluster_data = cluster_data.dropna()\n",
        "      if (cluster_data.shape[0]>0):\n",
        "          X_test = cluster_data.loc[:,Target_variables]\n",
        "\n",
        "          pred_for_tomorrow = pred_for_tomorrow.append(pd.DataFrame({'Date':cluster_data.index,\n",
        "                                                                    'company':cluster_data['symbol'],\n",
        "                                                                    'prediction':best_rf.predict_proba(X_test)[:,1],\n",
        "                                                                    'prediction_round':best_rf.predict(X_test)}),\n",
        "                                                      ignore_index = True)\n",
        "  #print(pred_for_tomorrow.head())\n",
        "  top_10_pred = pred_for_tomorrow.sort_values(by = ['prediction'], ascending = False)#.head(30)\n",
        "\n",
        "  for selected_company in top_10_pred['company']:\n",
        "      actual = dftx[dftx.symbol == selected_company].loc[date,'Target_Direction']\n",
        "      pct_change = dftx[dftx.symbol == selected_company].loc[date,'Target']\n",
        "      top_10_pred.loc[top_10_pred.company == selected_company,'actual'] = actual\n",
        "      top_10_pred.loc[top_10_pred.company == selected_company,'pct_change'] = pct_change\n",
        "\n",
        "  return top_10_pred.head(10)\n",
        "\n",
        "def get_force_shap(company,date):\n",
        "  shap_df = dftx.loc[date]\n",
        "  shap_df = shap_df[shap_df['symbol']==company]\n",
        "  shap_df = shap_df.loc[:,Target_variables]\n",
        "  #print(shap_df)\n",
        "  cluster_number = clusters_df[clusters_df['Companies']==company].iat[0,0]\n",
        "  #print(cluster_number)\n",
        "  rf_shap =  pickle.load(open(f'RF_Cluster_{cluster_number}', 'rb'))\n",
        "  best_rf = rf_shap.best_estimator_\n",
        "  explainer = shap.TreeExplainer(best_rf)\n",
        "  choosen_instance = shap_df\n",
        "  shap_values = explainer.shap_values(choosen_instance)\n",
        "  shap.initjs()\n",
        "  return shap.force_plot(explainer.expected_value[1], shap_values[1], choosen_instance)\n",
        "  \n",
        "def make_shap_waterfall_plot(shap_values, features, num_display=20):\n",
        "    column_list = features.columns\n",
        "    feature_ratio = (np.abs(shap_values).sum(0) / np.abs(shap_values).sum()) * 100\n",
        "    column_list = column_list[np.argsort(feature_ratio)[::-1]]\n",
        "    feature_ratio_order = np.sort(feature_ratio)[::-1]\n",
        "    cum_sum = np.cumsum(feature_ratio_order)\n",
        "    column_list = column_list[:num_display]\n",
        "    feature_ratio_order = feature_ratio_order[:num_display]\n",
        "    cum_sum = cum_sum[:num_display]\n",
        "    \n",
        "    num_height = 0\n",
        "    if (num_display >= 20) & (len(column_list) >= 20):\n",
        "        num_height = (len(column_list) - 20) * 0.4\n",
        "        \n",
        "    fig, ax1 = plt.subplots(figsize=(8, 8 + num_height))\n",
        "    ax1.plot(cum_sum[::-1], column_list[::-1], c='blue', marker='o')\n",
        "    ax2 = ax1.twiny()\n",
        "    ax2.barh(column_list[::-1], feature_ratio_order[::-1], alpha=0.6)\n",
        "    \n",
        "    ax1.grid(True)\n",
        "    ax2.grid(False)\n",
        "    ax1.set_xticks(np.arange(0, round(cum_sum.max(), -1)+1, 10))\n",
        "    ax2.set_xticks(np.arange(0, round(feature_ratio_order.max(), -1)+1, 10))\n",
        "    ax1.set_xlabel('Cumulative Ratio')\n",
        "    ax2.set_xlabel('Composition Ratio')\n",
        "    ax1.tick_params(axis=\"y\", labelsize=13)\n",
        "    plt.ylim(-1, len(column_list))\n",
        "    \n",
        "def get_waterfall_shap(company):\n",
        "  samples = dftx[dftx['symbol']==company].loc[:,Target_variables]\n",
        "  cluster_number = clusters_df[clusters_df['Companies']==company].iat[0,0]\n",
        "  rf_shap =  pickle.load(open(f'RF_Cluster_{cluster_number}', 'rb'))\n",
        "  best_rf = rf_shap.best_estimator_\n",
        "  explainer = shap.TreeExplainer(best_rf)\n",
        "  shap_values = explainer.shap_values(samples, approximate=False, check_additivity=False)\n",
        "  # Shap waterfall plot\n",
        "  return make_shap_waterfall_plot(shap_values[1], samples)\n",
        "\n",
        "def get_dependence_shap(features, company):\n",
        "  samples = dftx[dftx['symbol']==company].loc[:,Target_variables]\n",
        "  cluster_number = clusters_df[clusters_df['Companies']==company].iat[0,0]\n",
        "  rf_shap =  pickle.load(open(f'RF_Cluster_{cluster_number}', 'rb'))\n",
        "  best_rf = rf_shap.best_estimator_\n",
        "  explainer = shap.TreeExplainer(best_rf)\n",
        "  shap_values = explainer.shap_values(samples, approximate=False, check_additivity=False)\n",
        "  # Visualizing top features\n",
        "  for feature in top_features:\n",
        "    shap.dependence_plot(feature, shap_values[1], samples, interaction_index=None)\n",
        "\n",
        "def get_summary_shap(company):\n",
        "  samples = dftx[dftx['symbol']==company].loc[:,Target_variables]\n",
        "  cluster_number = clusters_df[clusters_df['Companies']==company].iat[0,0]\n",
        "  rf_shap =  pickle.load(open(f'RF_Cluster_{cluster_number}', 'rb'))\n",
        "  best_rf = rf_shap.best_estimator_\n",
        "  explainer = shap.TreeExplainer(best_rf)\n",
        "  shap_values = explainer.shap_values(samples, approximate=False, check_additivity=False)\n",
        "  return shap.summary_plot(shap_values[1], samples)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tl1kpS0zQQAE"
      },
      "source": [
        "In the given dataframe, we have the probabiltiy prediction sorted on decreasing order whether we should buy the stock, rounded predictions, actual predictions and percentage change. \n",
        "\n",
        "You can see that we get 7/10 predictions correctly."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "hUPmRAyUPp40",
        "outputId": "68c6f7a2-fdaf-416b-a3cc-83e128111ec1"
      },
      "source": [
        "get_pridiction(date='2021-01-15')"
      ],
      "execution_count": null,
      "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>company</th>\n",
              "      <th>prediction</th>\n",
              "      <th>prediction_round</th>\n",
              "      <th>actual</th>\n",
              "      <th>pct_change</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>2021-01-15</td>\n",
              "      <td>DIS</td>\n",
              "      <td>0.616232</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-5.270196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2021-01-15</td>\n",
              "      <td>CRM</td>\n",
              "      <td>0.593029</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.378915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2021-01-15</td>\n",
              "      <td>KO</td>\n",
              "      <td>0.591517</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.430864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>2021-01-15</td>\n",
              "      <td>AMD</td>\n",
              "      <td>0.574855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.803932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>2021-01-15</td>\n",
              "      <td>MMM</td>\n",
              "      <td>0.573475</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.592730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2021-01-15</td>\n",
              "      <td>NKE</td>\n",
              "      <td>0.573473</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-7.071424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2021-01-15</td>\n",
              "      <td>ORCL</td>\n",
              "      <td>0.573362</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.724639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>2021-01-15</td>\n",
              "      <td>ICE</td>\n",
              "      <td>0.572133</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-5.275471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2021-01-15</td>\n",
              "      <td>MDT</td>\n",
              "      <td>0.567974</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-5.227371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>2021-01-15</td>\n",
              "      <td>MU</td>\n",
              "      <td>0.566190</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-7.615653</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date company  prediction  prediction_round  actual  pct_change\n",
              "93   2021-01-15     DIS    0.616232               1.0     0.0   -5.270196\n",
              "20   2021-01-15     CRM    0.593029               1.0     1.0    4.378915\n",
              "14   2021-01-15      KO    0.591517               1.0     0.0   -0.430864\n",
              "88   2021-01-15     AMD    0.574855               1.0     0.0   -0.803932\n",
              "43   2021-01-15     MMM    0.573475               1.0     1.0   11.592730\n",
              "15   2021-01-15     NKE    0.573473               1.0     0.0   -7.071424\n",
              "22   2021-01-15    ORCL    0.573362               1.0     1.0    0.724639\n",
              "70   2021-01-15     ICE    0.572133               1.0     0.0   -5.275471\n",
              "30   2021-01-15     MDT    0.567974               1.0     0.0   -5.227371\n",
              "140  2021-01-15      MU    0.566190               1.0     0.0   -7.615653"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BafEJEaTv1Y8"
      },
      "source": [
        "####SHAP Plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_035LGV4FS7n"
      },
      "source": [
        "**SHAP (SHapley Additive exPlanations)** is a method to explain individual predictions. The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction. The SHAP explanation method computes Shapley values from coalitional game theory. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lIKtnDWSRa0Z"
      },
      "source": [
        "Let me describe this **force_shap** plot in detail:\n",
        "\n",
        "* The output value is the prediction for that observation (the prediction of the model for a given company on the given date (here it is 0.56).\n",
        "\n",
        "* The base value :The base value is “the value that would be predicted if we did not know any features for the current output.” \n",
        "\n",
        "* Red/blue : Features that push the prediction higher (to the right) are shown in red, and those pushing the prediction lower are in blue.\n",
        "\n",
        "* MACD: has a positive impact on the prediction rating. \n",
        "\n",
        "* SMA_Volume_Ratio: is negatively related to the prediction rating."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "eyPyjcPg5GGC",
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      },
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        "get_force_shap(company=\"GOOGL\",date ='2021-03-15')"
      ],
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
              "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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              " <script>\n",
              "   if (window.SHAP) SHAP.ReactDom.render(\n",
              "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 0.5400807687331022, \"outValue\": 0.5540462125974467, \"link\": \"identity\", \"featureNames\": [\"SMA_ratio\", \"EMA_ratio\", \"ATR_5\", \"ATR_15\", \"ATR_Ratio\", \"ADX_5\", \"ADX_15\", \"SMA_Volume_Ratio\", \"EMA_Volume_ratio\", \"Stochastic_5\", \"Stochastic_15\", \"Stochastic_Ratio\", \"RSI_5\", \"RSI_15\", \"RSI_ratio\", \"MACD\"], \"features\": {\"0\": {\"effect\": 0.0009906868522647693, \"value\": 0.9968480824232244}, \"2\": {\"effect\": -0.00025411206207026795, \"value\": 58.77368512436722}, \"3\": {\"effect\": -0.0004942884822652001, \"value\": 58.245242640737864}, \"4\": {\"effect\": 0.0005891072666637132, \"value\": 1.0090727149492509}, \"5\": {\"effect\": 0.0031565125575063607, \"value\": 19.64870484788272}, \"6\": {\"effect\": 5.558379055169594e-06, \"value\": 16.976790694555753}, \"7\": {\"effect\": 0.0021986051683412656, \"value\": 0.870800235995577}, \"9\": {\"effect\": 0.007396028583584615, \"value\": 38.1879063641421}, \"10\": {\"effect\": -0.0003570684118173195, \"value\": 51.98765756665883}, \"11\": {\"effect\": 0.003126434537279202, \"value\": 0.9780377704745976}, \"12\": {\"effect\": -0.0036676447758305034, \"value\": 65.010203747047}, \"13\": {\"effect\": -0.0007946707315884363, \"value\": 50.01663076497457}, \"14\": {\"effect\": -0.0006555001084938077, \"value\": 1.2997717509707205}, \"15\": {\"effect\": 0.002725795091714988, \"value\": -5.083569239337977}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
              "    document.getElementById('iGKNCBZTN3GOCCS8XRK1N')\n",
              "  );\n",
              "</script>"
            ],
            "text/plain": [
              "<shap.plots._force.AdditiveForceVisualizer at 0x7fc3888cf150>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2j17e0T_FnUD"
      },
      "source": [
        "Based on the **SHAP summary plot**, we can see the top 14 features and their indications of the relationship between the value of a feature and the impact on the classification model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90BAUFH6SoUA"
      },
      "source": [
        "This plot is made of all the dots in the train data. It demonstrates the following information:\n",
        "\n",
        "Feature importance: Variables are ranked in descending order.\n",
        "\n",
        "Impact: The horizontal location shows whether the effect of that value is associated with a higher or lower prediction .\n",
        "\n",
        "Original value: Color shows whether that variable is high (in red) or low (in blue) for that observation.\n",
        "\n",
        "Correlation: A high level of the ADX_% content has a low and negative impact on the prediction rating. The “low” comes from the blue color, and the “negative” impact is shown on the X-axis. Similarly, we will say the “RSI_15” is positively correlated with the target variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "Z6yAgbMHg3h6",
        "outputId": "8c48d192-631d-4ae6-8817-e11f8f3b2cec"
      },
      "source": [
        "get_summary_shap(company=\"GOOGL\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FTTPvMry8B4Fm/YFR5OQ0d5+9lplOWG6mlMEONa1iFr8ZzNWgNUiIPReEQCSK0JJMFAtkowihNTOeS9XzqGYynDIyRiQETcsicC1cpSjEMQKwE4XV+S0/vmwxdpKwcnIKJQRtKbCVwkoUw9kMW3tT75yTJJwxPkUiJUrKBUIJQAmQGkIpadoWiS35jakZVrUCAE5stHioqzR78a+4Lj/qdZECTqtUefXwBK5SrGjHxEKkQknMiejzqnX6WyGPl/Ns68qDI1NxdLibE4XwLHpG6jixIrEEkz15EgfGPQ8QJBqsfIaBkRpRRhIi0FJA1gEhaFuS8WIOlGKgHrCyI4oGpGBzdw4Va2hEICT4MhVKUs4d5qyk1k6oTcfsbJGKOXRa1pEQa/ZVJB+5R4ItQCn+/VE7/RuR1qN1KsgyqTAUSqFr8eFugJzVmQPDTzv8sIc0VliVAATEWReRSM4am2JyVZk9gT13DkWda7gkFZMCSiSUPc25S+BvLwLvSe7Q1Ta853bJtknBG05SvPss4+84Fpig83GEbdv87u/+LkmSsG3bNiqVCvv27ePyyy8nn88jhGBgYIArrrgC131hvCo9va3Klht2Ut/fZPiucbZ+fg/NyZCf/f0uqgdajG6q8uC/7FuwzUN/uYnqthrVrVUe/tAjC9bt+eo+Dt46RGN/k11f3MvInWPPuM2tqZAH/3UfkbCoeVm2Z/vJRC28Rp3cn3x2zs4PbaZVidAImjMxUdZDSwlCUB+LuO9T20nefhPFg5P0BjU2Th1kRC+nJXIABEmeaXpR5HAAC0lXPANATzSNmpd9BBAzJyjbIoOyBKozIKQWguFiGqIDRT+76WY3O61VDEcrGQkW4UYxPg08AroYooc9gIVA4VLHp0qeSQpU8AjoY4g801goFrGVbvYSd266oSWpZTwclRB7NrFjM1POMtVXoCiq7M73s2HqIBlClCNx44TidMi0KjBsFdnb3Qs6FVF9I+OUJyos3jfG4MQMjXyWtusQ+B5KSvwk6uwXTOczxK4DUiC1RlmCnukG2SBkspSjNNnAb6beN4SgVvRnhUIr49HI+iSuPXucALQlceOY3lqdJZUZFs9UWTIz56kMtabi+7RsGy0EbcuiLSUasLVGiNRDBRBbFqOFPNMZn/5qg956i75miynXZmW1zsmTFXqDNiMZn3y7TbbdRsQJTSu9lNel5N6uAg/0FRnLetRdhzXNNjk9F07MKkU4z42hhOCxUo4tpTy3DfayI5eh5TtEIu0jS2tQmoHpJlJpfrqijwcHy7x4vEIxjNKutWXqKunEw/xmhBOnbVqJJtMMwXMWnI+JFAitsZM52xa4V7SGRkipmYr8g0WfTUvKKN+BUHU8WwISUlXhzBNLolOXZaWCZ/bEV+knUodPibSuVjInlA7jWuk+HT7OsiMKDxcLVbpsHRZY6ceeaCLbMTKIsadb6Fhzf3cXe5rzJG00zybVOT2FYEZJ9s3AVx6Dzz3Mk/J390m+s0uysyK45qcWD4w8eblnF/GEz3Mf41k6joiiiJtuugmAFStWUC6XWb16Nddccw2XXXYZJ510EqtWrZq9sD4d3v/+9xPHMUuXLuXKK6/kggsueKbMf0rK5QyO86ufbuGuYMGyFUEx6zPv2o8OF7qTk0Y87+9kwbr9auFgh37ndfZn0h3dEO0Fy3re8RJKz7YlFQt5wnFNZhRyZM7bIwCXeOEmSCzSxN50z2wgzQPSWFiEJLjYBMTkCaXLgUI/E/YAF07dzY8yZzLlpVOQWEKRo8pSNiM7d5IT2Myd+VchlWCnXkpX1MQnootpJKBROIRI1Gzr8+llCEkbSYLwQg70lkBrvLZKw2BZRWUwS6UrB1pjRwlt22UgrFC3Mqw5MEFd5RCdN+4Ugkoxg99KKOsA7YNOBPldFZQL2rLQUhL6qTBceXAMC00969BwXRLLnvW82LHCDSKaGZdICEQjYbBZQQPDK7poFDyk1thRjJN0+rjjBZJq3s1Oa9w4oRTMnavFdhuhNfkwpBjFTD+hX7w4ptwKmM7leCK20nhJMvsk6yWKgXaI32ky22ixpVxkLOOTWJLAssiFIfko5rtdRSIpEcBLZUKhY+e069IfpCLwgOeyz5asacckluBgZs5DVg4i9uWzDGExVc6xqh2yYrRKK9REZX9WXO8v5xieqOIc7ocnXJISa+FzuHLEE85vjay1KSUKFcY0/VR8iiBCH3anBOm5XnUscmgOlbNzbWVsqHVEbTLvWAhA6SO/O4wlZ4UJVmfbjqfwSW/qC0zulD2MPW/l/H2bJ/6EXniegFhYx1M0GFsuffM85oeJZAid3zyA8LP09ZlBXI82RiwdB3z+85/ni1/8Is1mE9u2+dCHPsS6desA+OxnP8uXvvQlvvzlL7Nr1y4KhQK///u/z1VXXfUri6Ybb7yR005LQ0B33HEHf/VXf4Xv+5x33nnP+D49kUql9bS2c1Z6LHp5H8N3juP3eiy7fAlxRrHutwbY8Z1RnJzFib83uCDnaP2716dhOGD9u09YsK7/Nf3kvr2fxv4m3aeVKbyoBDzzOVUnvaaPrd8ZJRu1OWVoF6FwwJLUP/kWkk5bG967nomr7yWJNG7WQvqKepjeVASaDX+0jPi8VxC99Us4WjGSLaNVjWI8RY0izaxHELh00pA6pBfiUAja2sEiJsEi23mdHyWZ9vrZ0ruODZX9vHroXvZnB4gTn96gSg97ZoUSwKbiGfhhTKER0ibPMHkEGo86LgkKFz0vvAcQY2GToBFYNJCk4vH+8tlpqAVBkBEoDUHeJd9sUC14dE02cMKEh3pWsqg1RWkypKqc2TwqAUz0+nhBejPqrrfwpiOa2ifyJEoKZKRY89ghJheXKNWarN99iMC2mM5mkRpiK/UEoTV2rJGJolLKsW9RD9lqONtOphEy051LIyTBgg4GINMK0EIQ2zaWSlIPjFKp96FzFE4dGcOPY7SULJ2pUXNdGq5DOWizcrqKG7ZpeB6hbUPHgyOVYmBmhpl5IkoDuSgm6eQoCa3ZODaBnSTs7SpxsFSk4XlUtCaa1/6IZVFQcfpWoYZDnkfTsRm1JFceGiWjFNsKWR4vD4CUnD5V48ROqG51O+TWdYNMOkWsKOaKzft5MNPDRL4TStWaHb7HpOcsFA0AQhB6FtWCh9+OCT2bZv4JN/0woRimQ5Z2xwnT7QgtBTLQ1MJkQdER16bVOWazomS+3jisy1Saq4SeCxlikXqfDuuXJ7tkehY0VFqP6pQ5nBweKZAC1xGE8wWyI1Jv1qxNc2G4pOhhV9NzPnHTY2ZpBb5DknTClQ5PGoajs+9ruuD314WMjx957l21AW7dZjFUF/zmKsVpxSbj40+yX78ERy1n6Xn4NpxJ8H6Wefvb384555zD1VdfTbVa5aMf/Si+7/PRj370iLJBEPD973+fa665hg9+8IO87nWv+5USvJ/INddcQ7vdftK2nml+XTHSroQ4eXtB8nO7FmP7EutJXv2OGzEIsLNHPg/oRBNWI9yygxDiqCV4R9WI1rYpsht7SAKFW3KOmAdNxYqoHuN1RqCOWgmV7TVKa3K4eQeAcNMI7Y98j0IhQXzycqYnob6rjhiaYfDLP6DnsZ0IDWEmR8sRBG0otjUuM9jEBJTxmfN2PV5ewX8PnsYV2+6goFs08dOHbAQFDs56qgC+2Xsp5ckwTclg7h61ir0sZpgId/btOIGiTp4tnEaBGdayE4eQKYo0KPDAwEaCTvhYaE2x3mS6kOW0id0M6wGqpQy1vEfop8es1Ghy4t4xqhwWDpqCaHIo101/u0p3lCYljzgFRrpLszb3VBuUWiEKaODS8hzCjD1rf9uTaCmwEkW9kGGqv4Tdjlm1fWz2on5wZQ+17ixOEGGFMVEmzYNKpCS0LbwoIpGSxLZTZ8ZhCy2Ltm0xVCqSjWIarsOSmdpsQrfu+MgyzRaxbRHZVvqGnFJ4UcxQuUQhisi1WiRSIjsCKnRdAttGAm4Q0NURNRr44arl1D0Xv9XijvLcRMUnBW0GEkUuTkfMqluSGcdmTa3G4sbcw8uXVi9hNOPz6qEJeuK5Y/+t9YtouDZnDE3xyr1jNG2L21cNcDDrE7QiSnHCjOtA1gVPzhMyGuJOmCxMwLWQSiPCmMR3cOKErlqb8Z4cyyYb9NXa1IVgwrZoOZKm76TCR2tod+yxrdSb5Nmp56YepjsvgYKb/n/Yo2xbs3fnLmJK3TZ7G1Zqk9KpmOokpzsOvHwwplsomjGojE09EpRczZKCZm0JXr86QUr4+g7Bf+1UjLYtQkviu5LLT7B4cbnFhh74/kHJ/eOSlw4qsiRsnRYcakvKluaSlZquDGyeFExWFftqacj1gqWak3s00+10PuqcA74DXX7qBPt5xApm2tCT+fllfhmOllhS4i0LlqX+t6PSzrHEeJaOI4rFIh/60Ie49NJL+fGPf8wrX/nKBet93+e1r30tX/nKV9i+ffuv3d6vE8471nhPMp2FV/j5p6+d+/nrhCXwuo5+zpdTdHBeNJDa83MuatKWC/bNyVj0nVZeUMY9bRD361cC6f2h3A/lDSVgCbz9JCpPqDP3qR8iP/49JBqLNhlGSSgjgUhYHMgPcPrQTgo6vWFmCWjgs9tbysntUZqUaVLm0YGVTBYKFCrTyHhhzDBPKi5tIgJyBJTZXhhgc3kddqIZHHfJRgEr2YODjabEmqlxdvYMkEjBokqV7qDKmfVJvDhmzB4ArYnmjd0zk8uSFQFaC2pkyBDi64h80qTcEUoAjrXQExFbaUgiwEYhUfNeIReA0Gk/eo0Y/zW9ZBsJbZUhe+kpeAeqTB4I6OnNcspvDDDib+ayK17HyIEZtt9dYfeuhLyXUPJgxYtLFBf7hC2FtiRRLeInXxzhwJYG5UaTSjaLGyfEjkAlUGgGFKt1pNaEjs10VwmpFZFM32wbLRZJpMBVCu15eHGMFjDj+9Rtl1BA03NZHkV0zdsfP44RUrA4ilnXDpm2LMpJQinR5OO55G9BqinieYJdk749BzDsu/TU03Oi4lg0nXRLR6XHPhsnvHbHED/s62JzT5GZsgcShFZkw4hs3qYRS5xWG9uzCIQkUop8O+bMxfDStRa/t6ZNn68ZqwnKmRaubfHjLQ7bDsxwcnkni5Yup+ov5t4hgW8LptqS4amY/9xt03ZtaCcUVUyhLLBR9Gbgw68MOXsQ7tyr2T4juGiVZt3c8FqkYem58HWl89xQdFOHzkLiJ34xyxtP1LzxxMPSOO2T9EErXf/qZYpXL5tzYb1oMczJ6JTzFmlYdLjRuXW9mfTzy2LLX18oGX41jFg6ziiVSrzpTW/ixhtv5PTTT+eLX/wiF198MStXrgTS8NmuXbt4y1ve8ivVu3PnToIgYP369Qgh+MlPfsKtt97Kxz72sWd+JwzPKtGfXYA6aynyb27B2rQVEYVIWhzoWcuOzCB7u5aydvruBdscLPWwur4Fn4Aai2jQS7YpaPfZ7F3ZxeLhGXoaNTK0KDFNgRohPgF5Qlwca4JN3SekIRgLxruz9IwWOcRKYjKpZyURnDCS5hDtWtLH7q4++uu9vGjfdgbjSeJqH42cS5BNxWO21aahMzTx0hxiYiZlHjtIRZ+nU5FUbAfYSUJsWQilyT4hbGZHitjpeD60xm3HOJGi+8we1n7ytKfsy7G7QEqBk7U4+cIeTr7wyDKO1xF4BY/Xvm/FgnVJrLFsgVKae3/WZORre4maCmdjL2dtyHLeBUV2b21x/f9uIBPwwwg3CMj6sL1cxhWapdmEk9dnaAWax/YkVFWW/mYTW2lqvktBKfpaATVLsrzVpuy5JELQG4W4WpBImSaAJwmWJTmUyRBLQTGMeGigi9i1yMcx43mXXR64Gsa6PM4J6wzhcDDvMe469IUR010+f3RlH11OyAXLIrwFqTLzw7EJ8/Nq5r4DEAwU51TKq05xeNUpvUDv7Hdn9s8PeFh88knrW+h6uXCN4EkOzxGUj0wDMjzjPHcexH9ZjFg6DnnDG97Af/zHf/Bf//VfTE1N8b73vY+JiQksy2LRokW8973vnR2H6ZelUqnwiU98gqGhIRzHYenSpXz4wx/mFa94xVHaC8OzSfKKE6i/4mhE+ywAACAASURBVM+ZPxhADli3r47zVw+RjGRQoQYtmVoyiG7YHMivwKvvoCs5RJscAzXFK3dWwGsyGDTwidCAJVqgFTW6gTSZuEnfgiTXNK9JEZFFotMpU0Qbl4Shch97lvQDMJPNU2w1WT06QhB4LNkzQats0bB8JkWJQ/kSdqzA0iRRlkyYAJo6GSRNLNKwytrJCQ4VC/TVGowWSwxnPJYOT5EgcSJNEsLEQJFlF/fRtTRLbn2R8isGjvpxsDr5PFIKzj0vB+edfESZNSdl+fQnMxya1mSloqtk/UKvb9jsp1WNKfa7hApu/knII6PwO+sEffUQ2W+z2+6jrBJ6srDUiQkrMQOrPGxX0g7zvO9um207JaV2xEfOirjiZS5pMs18UpkKiwkDhetL+vpcxsfbRxplMDyPMTlLhmPC8TDo48/jeBmU8tnEun8v9atuZjKw6KtO0JXMkKNJerNMGOJELCDwJdN5n5Mm9lBhblLaWAgeXLScKaeAl8ScNbaT3nCCKr1INB4tDuT66G602Nm7lEdXz2278dBeVo6MoRMXi5j0HTvFo10riCwbK0mIHItytUUumAuT2FZEPkm9GVOlPIPvPJHiH5+M6OQnjf/tZqr/uJUwhslTBlj70VMZPGNhiPMXcdddt3PZZZe+4M+P+ZjfyxzP9b44WjlLiXjrgmVL/++j0s6xxHiWDAYDydkryWz6c5YEMb0Zl4N/8xMmP3U7/XoYRysyIqBhF9mcXcmS9hgtKXBUg4gciRDcs2IdQ8UuXnboPk6e3EONPmJylKjQIkeCx+JGFVAsnprkQH8X1XyWfNBk5fgwFVmkoAI8HRJjgS9Zm6uwx++nGttkWgH1rIcXJViJJslJlv7xKlpND72mzAlvXot4QkZs33tPoe+9pzw7HWowvKAxYTjDccz555//pN+fccYZXHfddcfYGsNzEeHbyN4cmfedB+87j1oUIyfqqJ48nmuz/jMPcuCfIrbpDaxpPI4nK9y/+FT29/ahpWR1dYgG3USdaU2CzkS8h2kIny5V4cLHHuagM0gubuHrmH6mGaObVl8Pfd9/Lc7i9A24vnm2BeMBtcerlE8r45TSvCaT42owGI4FRiw9j7jrrruebRMMzzccG7VoLnRVfs+ZlN91Omho/WAfzT/+PwwVB7FJWDQ5hhVpknmJtwJBKKx0zjNAvHEj8pqXklQSVkiNOtDAWlvE7fFZ/gtM8ft8/D7/F5QyGAzPNs/H3B4jlgwGw69GJ9zlXbwKd/M7uXBnhUpd0fOlSeJ9BTLxDBE+Gguxvkx+Y5loOES/+zwKF6wCYHbu5sVHjmBtMBgMxxtGLBkMhqeNKHo4Zw6k4bKXX0r7ny+lDegoSSc6LfudyVeej1kMBoPhyXn+/dqNWDIYDM84wrGgbOarMhheiDxxupPnA08xoLrBYDAYDAaDwXiWDAbDC46wmRBsmiDrQLyhl8aBFnrrFALofWQ71qPDJFe/GEw+ucHwNHj+eZaMWDIYDC8o9n7kPg59e4RTDuxhNO/xvbPOJhuFXHbPnXhRTI0CBWrYP9pN+UMb4bKjb5NWmrGRmB8flEy7Nr+1RrPs6IwXaDAYngZGLBkMhhcE8WSbxtd20vyPHbx4+gBtXLLVmDfeeTsZEeLH6WjgiiYzlOlimmWfGGLn+l3ofpvyGd00qzH7vjnET9s+u1f38RsnWrxy8dyEqN/+8DaGfjbBqWMHOMOt4125Ed557uz61n3D3P6J3dRim5rv0Th5kLf9WT9fvG6ciX0hdcfi6+uX8vG7Jf9x5gznvTh/xH4ATFY1+8YVAyrk4KM1lp6UZ8kJ2aPbgQbDL8nzMWfJTHdiOCYcz1MCPNenLHjahDFyuILqLeD+YBNqcRfxi9Y9p/tDx4rhf9pKOBky4DfYvV/xaFii2dT4Uy3WPzyE1gJft7F1ekm3iNjALgQQ4BMwN5zBwVKZTNBg56LFVK44A3HzLoaKPh9+8wUASDTfvqTN2X2aHfdW+Pt/GOVrG9YggD/96b388UMP8ZHLL6fhefSqFmds38v+/kEArCQmtG2kUrQdd3Zuvc29RR7tynPm/lGWXtDH8sU2/9dpkHXS9btGFG/7XMhIxmUy41Foh1yydR9vvLKHl7zk6LujnsvnxzPNc70vjtZ0J6H4vxcsu/qfjko7xxLjWTIYXiBordMJWqMY+/ZHKbzr/yOpamxmsIjRSDQusXQpKYkgwSYCKVCOg4gUWkVIGROfsJjK195L2Hawul2s/BMnYD06TH/kXqrfOUDG1/jnLyH/gbMQeWd24tnxi/8PfY/sx6LNt190Lge6+rE6z4NhKUcsJJbWBMJjQE+TTu3rEGPjEBPhLmhv3cx+vrfxTH50yplwCOyz1zEhwtn1CsFDb/shZ917H/bKFXzjt9OYnQY+9ZIX8dL9B1k+Ps53li/iijvvZePYBJl1EVuXLaPtOCAEiZRIrVGdffiNR3bzJ7uGCFybb9TXckdfD/9wm8efnRhx62OSx12fiXwW7aeX72rG4z9PWUvteyNHiKV6CJMtzfKi4BfMzWswGJ4CI5YMhucj7Qjn7m2oviJqqMGh927CGZ5kFbtxaCNQSMACFC5tyggSBAmJyiCJUdhoEqRSyHbYKW2jlMTaegD/lA/TppsduY0s/9qryJ7d84zvxqzA05qJTzzMo18bYVGziVIJk/+6D/2v++nXE0yLMqI7w9LJ/QC0fIddA0vxwnnCRgqkUAgNWggytMkQMkWBGBeFjZg39rBA4RAzXZwTILFt86Jd+/nWGScS2Ra5IOSsHSPIWLFq5x5y7TYVP80KX1ats2lwCdlWxFu27GSibxE/7B1ksqtI03XwkmS23sCSaCEoVpus3jMMgB/GnH5gnDyC4sE232oupppx6EkUTrPNkD93+ZZRwjbh8wfXVtnt+FRsm3wQc9pUjVLQpqvd5M8+uZJccW6bfVX47h5JI4bTexRn+hGlfhspBVpzhLh6su8Mhifj+RiGM2LJYHgOIx8bJvOBm3C270Gt7CH4nXPwvnofzuN70FoisBhhEZo+TmA76VsqNhoFxABoLCQtwELj4BDMigaNRCHQiM7lT6ORSCQFahSo4TbaHPx0P9nPvQRxcBq9rBu8p3dpUZMBqhERPDzF7v95H5EWdEcNsnGLSsZndRLTH6RhjxjJXgYYpQ+0gMmYaXL4Vou6yuOFIZFl4cYxQmsu+ukmclGIAhwd0MckGkkbSYUCBepIEmLAJSJDkykvj9dozNpnxQndU03+/e9vZuvyfs7cPczSyToAAs2bNz3KP5xzJqV2yHvvfphMnEC9RZhxaRSy7FqyiMSyQGsspbB1eiSG8lkatk13o805SrN/SS9bVy9hMudTtW3K0zMcKmbZV05DhPkggiAGR0KkULFmZ6HATgBH0l9rMWTbDPeWeUmlTtVz+chfHCQbhWw9cRC1PM9/TztE7fQ4W1pgC5u2YyOyNhK4cNcQZ85U+beTV9JIJE3X5kTavH3ROK89zyLffWy8iQbD8YARSwbDcYz7+Z9g37uH9pUvITl3LfY37yd77W0gITpxEf53HsTSaWIyU9O4D+5EIVDkkCRYxEgSNnI/MD9ZeP6TX4LAAiKYJ4sOl9Ody4TuSCyb2oISZerkvv89xPLbaAJtfIobCrS/+07IuiS1iObdY3hOiHdGP7qURQxV4Ya7SSwL8ZevpPLenxLdugfZjEiAg/kuZAg9YQOPBIVLrqnJyyCdYw6wUSxinGEG8AgpEKCx2O8sxokUL/7ZdnatXoQVx5y1azteK7VXAi6685emmzoCQYJDhSwQMcQAYyvgzZf+Hk3X5RX79/PKQxWKMzX+7lVns72/m5fsOsTFD+7EJUQDSgje9dDPuG/lMrSWqVDqoBXsWbyIRHaGthOChuMQq4SRYoHIslg7NsHamQqPnLCUel+ZvErI1RrsLhWY8VwmM3MhwrrvQCuCSKcxP9k5Io4ERzKeddFx6gp6qJDlXdt248cJCgj2Vbkz9IhKTuouAhIpSPI+WAItBAnw/bVLOOG/ZxiYqDNUytC0XHp2D/EXeiU3/GuTJVEDZ22W910oWZTVNCNYVpwz5TBhAgfqgv6MpjAvyhnEcLAuCBIYaQnOHVAco2iuwfArY8TS02BoaIjf+Z3f4Tvf+Q4DAwPHpM3Xv/71XH311bz61a8+Ju0ZniGUQj42hLNpL/GKfuxDE8jpKuFFpwGQrBqAJMG5/g7sHzyOyHu0//hlCMD/f76FtWMUgcC9+QEEbdLbvIVG4uycRhLP8/k4tCmQ4OJRB2wSJP0MYRGTEKDx0UCCi+rUk5L+LwjQP9eJLo5Yo4GYQudbQY6QXg4RPp7FPvnDeNQ5FPSyJXc6g9E4p7W2ApoqBXwCPOq0//k77HM2Mi1LuK7C0wHlVotsEiFRKKkJbRs3TIiVQxMHicInoIsaNXL4qNkpVXrbdRrap1RtcebDu9HAUHeZVa1pDotEierYL2f7TwBZAsbookWGT7/4DJpuene/Y/lyLtmyj3uXD7JtIA03/nTtUr596hpe9+jjjOR9bKX5+ob1fOJ7t3HNhRcxk/EptQLQmvUTB8k8GrF1+TKmy52wnpTYiaIQhKw/NMri8SlkkqBLBeqd/o2loGlb1It5Tp2qcV9/mciSCKXRUiK0pisIsZSm6ju0c6na0I6EIAENVpLgd4SbBPJRyCtHp9iX9YjU4bCjgHYCWTsVUKGCWLF5cR+ujhkvpDbfuXYdv/vQQ3zjlNOIphq0xgQXfauTnK5T4XZBa4aXFUPWnZGhe8cUf7a/i23lImhwVMKp/RpXwD0jEqVJt5UgrbQOnWgW5eEvTot52RLF57faPDAmKLrw3tNjMjbcMyq5aGlC3oEP3+ewtSI4vTvh3AHFncMWKwqa/3FKgiVhbw1+eFBywRLFymI6TAOkjwi2TGfl+fZeSX9G86ql6bq484KjbYZtflqYMNxxzMGDB7n++ut5+OGHaTabFItFNmzYwLXXXsttt93G5z73Ob7xjW8822b+Qn6eEPvqV7/6a9d9yy238Nd//df4/txIe+effz4f+9jHfu26n23EwWn8f/4xOucRvOtCyHuz6+S2Udy//QHyYIXoko1Ef3L+7GSws9uP1fBv/CFYkuBdF6AzLu6NdyIm6kRXnYda03dEm9buEfx//QG6p0Dzna8Bf+6xWYzOkL3snxA7JpEobAIkEaBQOGQ/fAsQd27xDnMeHIFzx1Y0CoVHjN/JL1JIIgQSRTqNiEWD1BskAEmCTYKLQwsxK4LSPCNNjEWApk2Ci0Wak6NQaOa/ci5RxB2bEtIgXYDGRmNjEZDgY9FGIwhZmFB8OHzn0sRuhEhgOfvJV0NsJBqBTY0yASFlQspsYw25KEDTpI1HngivI2YCW1JKmohQM+qX8YL0e4VFhINLm1UcYoRBmBWNgrZtk4njTg8k9DabZAho4xJiUyVDhhZux5t2eNsZP8OhUoGVMyO4c+lOOHHCS/buY2reeQUw2lcGR7FuahqAP737Hq55xctYPDHOJ19yGv/y7Vs5aXKYnqDOqWP7cesh+5YMsnfFIBrY1FWgZjuctXU3Tph6CLumZpjqKhJmfCZ8H9XxRvmJ5sRKnc19xdmbUXcrJNcRQn6jzcGih5YiFSCWgFhTyfpMeQ7d7bT+PeU8g62E6HB+lm+nvwetoRmnCkFpFtcDAilJnjCHfKndxooT9gtBknHSrotU2o1Jwg9bLj+rSf781sdxlGbXxctTewQMVgOs/Q3uXtybllcKbAG2lQonBdgWQy3Nn97d+T3ptAmN5vsHvVlRlg8SpBBUc+lt7KEJyecfp5NcBR97wE7dXPFhyzV9zQYiUkwXc0SW7DwjiNmELCk0KiLtu85PqMvV5G2YbmpiLVhd0rx4ID0P9zUitk24XLQ04eMvTZ54WTE8j3jeiKX3vOc9nHvuudx8883kcjnGxsa46667MCMjLGTJkiXPCdH4K6EUhctuwNo7AYC1Y5TG5/4oXVcPyL7uH5FTzXTdA/sQzZDw/b+xoIr8mz6LvelAWubh/cQrB3C/cC8Azi2PUL/3A5CZFyNohZQu/TjWSAUAeWiS+qfeOlffFf+I2lHtSJsQi06obNaTkb55lnpDNOmtIELhIjr/EqyO5yi9mSuyCEJsKh3hNP/cFtiEWLSIyZKKnU734HWStRUONSQxkgBI71cRERqHVBi1kAQoCp02ok65CI2HwErFCL0cFhmCBI2FIMalMtuunG1B0c2hzg3e7nzaQIWQblaxnzKTJFhsYSPS1ugYAhy64ipWRzj1BjXCeUNqR1iM00Uf05SYYYYSoHF1TFYJElxkJ+uqECTUyTJJniwBy5lGY9PupLhrLIazJXaVB1BSsrV7GZdv3k3NyzCd9bh88w6ySnPZpq08sHyAu1ctY3W1yRJUKhgac/uciWLuWrqcfLPN8voEPUHqJ3JVjKUVmWaLmuNwVzHHsJ1egnfls/RU67P7Vmi1aAkY6SoumJOq6dngWZBo0Bq7MT8hHdwooe11vEOJZuXUDLl2yLdOWMbFu4fpb7Q4aWyaW1cvnRNIh1WKJVOR4KYtjmufQ8UsfhSyfnSMQ+USv/XYNmyRY1kjYE9PPm00UVyybT/99Rb/vmY5Wgi6g5BCGNNwbOJ5CiK0LM4Ynebege6OgBJpu4czx605D9UcOo0WWp0yHfFTdz2yKp4rJkQa7zx82h2OtDqdOhPBtOcTF+y5WKHSnT5IN1KHHSLzYonToWA60OmzA/DYpOCxqc6tU2vQgn/farOqCH9y2lzo9YWN8Swdl1QqFfbt28cnP/lJ8vk0L2NgYIArrriCRx55hGuvvZYoijj//PMB+PSnP83ZZ5/NAw88wHXXXcfevXvp7e3lD/7gD7j88stn692xYwfXXXcdjz/+OEopTjzxRG688cbZ9ffffz//9m//xujoKKeccgof+chH6O3tBeDLX/4yN910E+Pj4xQKBV7zmtfwjne8A8uy0Fpz4403csstt9BsNimVSrzpTW/iDW94A2984xsBuPzyyxFCcOWVV3L11Vfz2te+lne84x1ccsklv5RtxxvlcgbHOTqnm640STpCCcB9dIhsZ/wQNVEn6gilw2S2jVKaN76I1hp7y6HZZWfzIaymnr1ey9EavYlCzB+TZO8YjMwJg8xjB8l01usoJt422hE3T3bx1E/595yf4zBi9n+LJhYhR6I7JXTHgzXnqUpDbQJBQEweOc8mAbTRuISdutudBO7Dj/lz5VTHRyaJcJlCEqGwUXiAwqb1BIsO+z/mbEuzniTgIYmJkexkDauBHibpZop7VpzC+t0jKG09QQ7+/+y9d5xcVf3//zy3TJ+dne092U3vpJAQINQIJJAgCSp8FLAiH9FgFxCFn6J8hC8KfABFiojS/EhTQjGiUiQESCAJqbspm832Njt95pbz+2MmmywJGDCkeZ+Pxzx255Zzzj1z7zmv8z7v8742clA4Aii4MRkgRBaNpOam2IoQlClK7QEG8NNLmF12iTBxQiQGJy5VDFQsksLFTlHGzmDhoBXHFgoViRhLXl2dy0nYKFoWT0wyvruXs9u6ifpzztarRo6mdCBCwMjy3NixvDBlGn2qwridXdw1/Vh+/PdlqFLyQsNout1+VjfU0O1x06ntfh6WjqhjYncfLtPCcukkvG6ibhcTIzE6/V76PG4yikJ2sDMHxaUQsg1SqAggqavM3NFFoWnyl7pyTmjaySfWNALQWFJIyJULVeAzLaa3d7O2LASmmbMKAbjJDQjywiXr0SBjE85YnLepE0EnhqLw8PjhtIb8u29dVWFaRy9ew0S3bbKqSo/HTUzXCBomH9u4g2Vj63CZFqdtaacl6B26rM4eooz2ZtdUnRB7HevOmCR3WXT3NTDelY8QoObsq3s5Vdl7/L9LZO2VznuUbdf0I9CS1SktdQKDHq0cFWKpsLCQhoYGrr/+ehYtWsT48eOpr69HCMHkyZO56qqr9pqGa21tZcmSJVx55ZXMmzePDRs2cMUVVxAKhZg7dy49PT1ceumlXHzxxdx4441omsaqVauG5Lts2TLuvvtuNE1jyZIl/OpXv+Kaa64BoKysjNtuu42qqio2bdrEkiVLqKysZPHixaxYsYKlS5dy//33U1FRQV9fH11dXUBOZC1cuJDHHnvsPf2h9qds70VnZydnnnkmmqYxefJkvvrVr1JdXf1hqv0DEYmk/vVB/waB08ehv7ABgNTCY0jvChQX8uKfXI26JieGJILEvAkM7BFIrrQ0SObcqbgfXwlA5txjMOvL8azMLUM3jx1G1KvDnsHnPG4KZ41GX7EZgPg5M0jtsd9/zmSUpzeikMj7CQkEMj/ttks8WNioe6w8U7FRkHlvIrDy59nsaq0Fe4yk85i4UckMtucShSxBVMw9ZJaBioHEj0BBYiCALB4UgpiAiQ+FjsESSlzkJFIW8jIlJ7ysvCAjb6Xau0wAWz3DaEi3kPMO2cXu//ooZzsjMHARJUQBMQQe4l4PXYUFlPQniBIgRByBxETL15CJnu/hTAR+BljnHkFKdVOX7BhMv4AEGTQsVIKk0bBJagqPTBiDz8rwufVv0K+F6HIVkjQ0vDGTeGFuWmlUtJW6ZDebC6pJSC9Tottx2bkJqeJslLrOFO+MyIkldzbLxsJSfn3aHFqKixFAmS1Jhfw8XDuFxqpaAkaWLcWlVKcyaIqCz7YZmcmy2e1CkZK+gI/rzjyRiEvDa1oIl8LnN7egSqhKpChJpnmhvIhOTScUy+C3bSZ09hNIpPlTfTW18RQn9ETQbJuxPf38vbac43bsrotRPRG6KopBzU3hmkLJWac0ZbeDjrVv0VKWTA/+atsLAzmhtAuZs6R5TBMF+NTWFv7YUIsKvF5XhWabHL+xmY+93US0qBAhBE+OrB4ah+Dd4kWyd5wCW5LX/EM4vrGFhp4Ijx43nq7APqKdvysd1ZbYto3c5Wi/T2W0K789MtvjMCH2+JoXSpqQfGZElu7u9L7TO0z5qIJSOj5LhzG//vWvefDBB3n44YfZsmULwWCQT33qU3zhC1/Y5/HPP/88Y8aMYcGCBQBMmjSJRYsW8eSTTzJ37lyWLl1KbW0tn/vc5wbPmTVr1pA0vvSlL1FYWAjAWWedNUSMnX766YP/jx07lvnz5/PGG2+wePFiNE0jk8mwdetWwuEwRUVFFBUV7fe17k/Z9sXUqVN55JFHqK2tpa+vj9tvv53LL7+chx9+GK/Xu9/5H47EH/gi+l/WIQNuzJPH7N6hqySe+m+0P69BaY1gnjYGe2rtXucn7/w0xnlTkaqCefq4XLDAqbWInjjm3HF7+TihKkT+79u4lq1GlhRgzB4zZHfi7kvw3Pgc3P0yrkQXSCtnmdE9pMJV6H19WMfUY6s+tJXbUEwTGx2BjaqlUc0UGhIDPxaevM9TBgV70PJko2Phw8KNhYVGDIlKlgJ2+TApWChk0Yhh5yNTy/xUGmSx2XP5kYpFQT4Pgcz7RklUVOJoJJAk8+m+PxIoMCwG1CIKrAhg5PN1oZBbMbeZcfk8JEEiRChBASbubGFdZQ3BeAYMSOPeQwjCnvYmLV8bKTXnS5RS3PjtXIclgGq6yKINRuX+7MKzWV6bGxy8VFfHJa9vzyXkAXfMoLwjTpXsoURG0DFQvVCV6sdlW4Npzt24k7K+BGX9UQxNZXh3B26ZpXagn5bifKwpKcGlo0pJZ6iQTkCVEk3ZXXMTYwk6VQWPolBgWTT6cmVM6RqIXMDLXdfqz2Q5eftONpdNxLZszm1sw9IEfx5djTttc1ZbD3q+4+4K+om7ddpCAWojOQE/4HGxtKGGOW3dxHWNJ0fVgksDFzmxlDRy01xZO7eiDnLL2NIW27weUqqC17LxGu8WxpJA1iCrqnhNi6pUBuHV0FwaLxV5mNDSz4phFZz7diPB1g6aysK8UxrK/3h5v6pdz5Z8lyLJ40tkybpy6zpRxK7gYIxp76Eokeal0XV0BfNTglIgLBspBH43ZPLTay7DpGggyaffWEf75CpaZ9SgKZK3Wmy6VH3QulTgguPKTNYPqESygqyEaq9kbq1NbwrOqLFY0CB5o1PQmxaUh92saM5w/gibyn2/meY/FEcsHbYUFhZy+eWXc/nll5NOp1m2bBnXX389paWlKMreTXtnZ+deFpWamhpefPFFANrb26mrq3vfPHdNuQF4vV6Syd3TPc899xwPPfQQra2tmKaJaZpMnDgRgBkzZnD55Zdz7733ctVVVzFx4kQuv/xyxo8fv1/Xuj9l2xc1NTVDyn7NNddw8skns3btWmbOnPmB0zus0FWMsyfve5/fhXnBjPc/X1Ewzpw4ZJM1u+H9z/G4yC44dt/7VIX0VfNJX5WbNsW0IGuCL9ep7zX+TGZQ17Zij6lAFuZN+VmT0Bk3oK5vzfs+5abITHTAPejzpCAx8WDhRQIu+pG48k7ZNu2UM4xI3rdoVxgABYPQu6xCcg//KXVwqxhiocrlIDHz02K7m0UblV3uwAIIW61kfWUkkmUkhR8pJFqBBlVBMqMqGPPXt4gnvNjodFJJgDQaUBWJUDEwQFJqqCh7Nbs2yuBUosx/91ppUqqHze5qGjLtFNjJwdVxHgzS2BhCGRRKAK/U7CGWgJCSoMHuREViobOtpJLHZ55MfUcbn3rtRQQ5i4yWd5Su6Mv7qyFBSCa2dNFUUk7C5eL4jc1sLS6graqEXo+byqyBtG0Uy8LOW3f8yRT+ggAIyZhkmqagDyl21/QrFWEWrd9GOBIFKbErSjixrYcR/VFq4zlL7bzGVryGhb6HzvBmDTTL4uEZ4xjwuPBmTf42uo6OggDvFIU4YWcnAdsaXHGHpoBbyYkW084Jpl3bVUHUVLlnRB0jjDQt4T0VgaRkIIEuJX+aMIyRGJw43c3LM2zaTZuJxRKfXkBbJMAVAPaeTAAAIABJREFUD4RJR7IUzyiic77N95cL7l4LKDk7Z7Vf8s3JBiq5omQkJEwYX2gTdsOIQhNNwPp+gVeT1AUgbQZ5+h0vZyg2Z0xJE8sFmyduQE9aMD4s32W0cgFT8/8bg1vTVs4q61b3PPb9fY+Or8w9J6WlGjMLP1qrucPhwVEjlvbE4/GwYMECHn30UTZv3sy4ceP2Oqa8vJx//vOfQ7a1trYOTn1VVlbywgsvfKj8Ozo6+OEPf8iNN97ICSecgK7r3HLLLaxfv37wmEWLFrFo0SLS6TR33XUX3/nOd1i6dOk+hd27+XfK9m6EEI4T/MFAU3Of98Lnxpr1LnHm0hj4xw8gYyAGkiivb0Fr6sWcMwoxkEJduhrv799ASIlKBhMdlTRg5if3TEAhg49GjqGOZgQaILHRIC86XMSxUVExEGQRpBAE807fDBFOuS5Cx6QAnUjeMiWQRT6ImtimhQhqWJPqSH5rAcaccRDPoORXke2601yA5Dy8SRM7biKXdyECCvLW10i81Uubr4CsoVMVj+bX+O0SdQJrMMyBzE9behiX2cZ2rYYCohTaMXL+XWbeTwoM3EgpmLmzjddrqgA4vmUnmm1hKiqKtBlwu1FTklzk7iyjelo57Z2VZBQ32wMVbBheS3NZOaOadmKKXsLJJEmXzoDXT8hOI6TCeavWAVAejTCuxcuzdeWMS6SpN02QkpquXlK6hjuT5dXSItKaise2Cdg20wfirAv68Fg2BdkMKUVQ0ts/KBYrIjGme70E0hk2FQRoDPoJGSZTkjEQSm5qSUoqevrRLZuUS+eJY0bn/XwEwpaEY0kKUxk0Y7cYcJsWc1r7GHC7WBfykdQ1XKZFVuacn/1uQbw4wAbTi4oJAjTbZnZPL5fUZ2jw24w5vRjdu/s+qd3D+ldVqPB/S/yAf/AO+Mlsi2uOtdCVD7Y8f0LR7nQDLrhg2u4urCDvuhTQocK3/22a530eS4cPhzMNd5gSjUZ54IEHOOussxg+fDgAL774Ilu2bOGzn/0sgUCA/v5+4vH4oAP4mWeeyb333svTTz/NWWedxcaNG3n88ce58sorAZg/fz733Xcf999/PxdccAGqqrJq1ar9mu5KpVLYtk04HEbTNNauXcszzzwzWLZ33nkHwzCYMGECuq7j9/tR86PNwsJCFEWhpaXlPX2WPmzZXnnlFUaNGkVZWRnRaJTbb7+dwsJCJk2atD/V7HCocOvIshDWOdOGjHfN08aR/e9TcT3/DsbxI5DlIVxPrsSc2YC6die+/+9JtGSMUawjQjFZ3OjYg47fu/yRctapXDABQW65v0IMGxcWPgwKBh27c+JIR5DN26EUVNWgZ+XPwO/ZR+EZEsbh3Sg+DcWnUXBu3lJ6+vl4gWBLko6frMFevg1XNEoy4cItsrhrdIwWYzBvgCyCBMVUmBFUDFx5B/hcSE6NGAGsfFP3yz+9wBMTGtAtyaJ1jbhklg0l1Qz4vICLrkyQ6lTvYFM/u2kDOwtK+dOM4+kuyk0f9U0NEl/XiFAkPzv1OM7d3s60LU1Drqs0GUf3GRRlTRoMI2cxEoLuwgJKunvYWRDA7fUyN5ogqQi2ul2MSKapT6YJ9g8QKSrEbZpDuhxh2yAEnR43r5TmVpN1eiHh0ZnT1QemRUlPP75shtO2trJ0zLDciZYE20ZKiT4syKr6AKcMV2mOWTRGBB0plddrixndG6ehP8ZAdYBYgU7AkmhSUOAVzBtp8KXxFlakle07uwm5Shg/pRpF+fA+L96jovdx+E/hqLhddV2nr6+P7373u/T09KCqKpWVlXz7299m7ty5mKbJzJkzOffcc7Esi5tvvpnp06dz6623ctttt3HTTTdRXFzMZZddxsc+lltSXlpayl133cVtt93GAw88AMD48eP3SyzV19fz5S9/mW9961sYhsGMGTM488wz2bRpE5ATU7fccgstLS0oisLIkSMHYx15PB4uu+wyvv/975PJZLjooov28rv6sGVbuXIl119/PfF4HL/fz5QpU7jjjjvw+ZwVHEcqcmQ5mZG7RXXmK3MBsGY0kP3cSRDP4P3Zn/G/sRXRGwEUjIm16EvX51ej+fIr33Ju3dqQlXJZciEIfFj4sSAvriSCJHbQhxxRTv8NF723UPqQuGt9DPvVccBxABTusS962h+x3+kBJBk0+vzFlH68FO20YdjNAzTe/DaubJZiI5F//13OWmQjcBnwybebiBIgQghFWER8u1dnNQXKUNK59+aVyT4UJOXRfuw95nNsRZDRNdaVh9kcDtEZS7GseAZnvboCIaEwlaS+r5sWtZSzmtsxQ0Hi+frxpzIkvR56isJU5+Mq+WyJ37IYsXkbIcOkvbqCiBBkXC62VpZR396FqSr0lIQB2F4QGOLT0xj0M+DSqdEznD7Zg2naXDa/lDvqs/zwsTQv7BBUahZ3fMpFffUuH7Xdv3PWgne6YUTYS2hQ1+7baR9/NcccM5buPRc7ODj8ByCkMwfjcBA4nBvX0tLgYV2+jwL17R34/mcpMppCbu1H9KXQieXCEvjcmLVF2BVhEt8/H/2+V9Fe2Yo5pQb38s0o2RTpL55C6uqPH5Kyy3iWzAPrMVZ3oy4eg++Mof57xs44b138T0Zs2ErAymAIgS4NBgjm4zTZJPYIxNla7iedj6FV0p5EzesEn0jyysRSdGlj6B4GKmrIahpTmproVuB78+ZSPxBjcTZGMWnOOcdP8w9WcMy6JpJuN0/NOo4dwRCnvL6BFZNHolk27Qo8NX0sU/vjFO0xFdarKkzqiwz6M6VUhQG3i5SmUJKJ87mzvbz0mkljD6wuDtGqu9BMG1sIYn4X5fEUDyyQjDnmox/4/Cc+L+/FkV4XH9VquIS4Ysh3v7z1I8nnYOKIJYeDwuHcoBzpDd4BJWNQWlN0VNSHTJu0/OQtYtsTBIIqrmXbiaUU/Nk08cH35EkUt0FnYRDNtNH7GbTa2IrgpdPHU7V1B5NvmMLoVzaTvuVNhJT8cdIEkuUhLrphPMFhu52e+3oMfvetLRhaLl5RRU8XqaIgw1q6GTErxN/mT+T613VO7B7AlgKfZZFWFCrMBP6EhSYlhqJS2dOLpesM/0odC47373Vtc/+gsKFL4LYsRmRSfOc0hTNmvPd054HEeV52c6TXhSOW9h9HLB1FPPvss+/56pKrr76aefPmHeQS7eZwblCO9AbvQHM014fZkeCdE56mJJ7IxW2SFh5MLFQSeIgrLkyRs+70lgVpnFjNdX+ZOVgf0rKR3UlImigNhfvMo7Mpzpr/t4FKkWTilZNhWHjI/vY4NO0weHFZAl0VXPbZABkTfnBbnGBXjGG9ERRFsHlSBTd/twjt3XGI8hgW6IfAOflovj8+KEd6XXx0YunrQ7775S0fST4HE0csORwUDucG5Uhv8A40R3t9xN7sZvWX36Q9ECReEqCyJ0I4lsDTnaDIiNPvC9BUXUVPRQE1M/q55P+dc1DrI5Ky6U5AQ5FAfQ+hdCg52u+PD8KRXheOWNp/jgoHbwcHB4f9JTijlBNXzsO2ZO5N93lk/p1k1VmLUe1pXGUeXl314kEvX6FXofDIjhHr8B+OEzrAwcHB4ShhT6EEuZhjCBAeDW+9E47ZweHDcjSKpQ8QDszBwcHBwcHB4T8Px7Lk4ODgcADZ1if56yYbXIJPThCEPEffKNvB4T8NRyw5ODg4HCBOu9uiKaFSG8swubWb39QVc8NnfJxcpxDpMbjnqu0EBpKYHp3P/u9oAiGnCXZwOBJwnlQHBweHfxPLhjvelDRGBfO2NvPM+AYaiwpAwieeUxCq4MSNXSza2YPl0iBr8Jslm/nab/fv5dkODkcSjs+Sg4ODg8MQpIRjf6fwy38Y/PRPL5NUVLKqAqbMvZctZSElnNC4g4L+GN6BBGlNoUNz0bwmRiz6/m+4d3A48hDv+hz5OJYlBwcHhw+JbUmG3QEZy+aCNVspTaRYNapq7wNNiSf/LjhPOsuy4TWsrirh0b8JLlm7Bb9lcfLnypl+8r4DXTo4OBxaHMuSg4ODw4fg8X90MeeGDIZUQFNZW1tGpDjIl19ZQ3k6PfRgy2bpxJEsr63gKwtPZXlJEbXRNKVJg/XFIQpSGV67ayd3/aiJtuYUG1claN2W3nfGDg6HOTL/uu1dn6MBx7Lk4ODg8C66/9bBW79tZqvmZ5xrJ7FaP2se2snWf/SyvKYU26UggyE6i9zYSm7Mua6qlJatLdRGE5y7polfz5gIksFZiDeGVbKqKIyVP75VU5jb3U9aWpiKgiqhu83FAz9qR0+nyeiCAbeL4plF/NckSemYAO6Q6xDViIPDfzaOWHJwcNgn9vqdgIpZXoSr+L1f0pp5px/rnztwTy5CnV07ZJ+VsTAiBq4iHaEpucCPu9IfyGAnLdwvrCf6j2YeO3Y6VWcMZ2499CclOwYkIcWiMqziEvD8JpuvLZVYimDh5m18+Xt1jBu5d7ks0yYeMQmEddR3BZ5ESrBs0jGLdSujPPJCnPr1O+l1BzA8cPaKJtJC4426Sm49ayYZofDjly0+/dYmzlyzjd/Pn01nQf7FtqpCVtv9cjYhJZ6MgbBtFNNGAHLX60qkRLMsLCEImhYhy0ZIqIon8WcymHquKfZmsyTdbkrj3cxueoN+fzEbu0fw8sMZ9KzB7J9NJjgqhDeoorudiQGHw5OjxZq0J45YcnA4WskYiB29SAFmRxaZsfG/8AYimiQ9qgE+eQyiMojY0Y14eyd9GQ8v3r+OioEuatvjIDVK41lUAe2FBbhOrUetDOHqiuKzY6SWbaVfuggl4wyztpNUPbxWeCwDnhDhTB9eM4mRCOI3wBIKliIocbeQQeOhaTNp9xczu2kHfxk5nIQ6jeA/U4z4y7PQ2cpVpy+mIpKiKJ0l4XORQGFlbQVouVeSPDG8jlfvzZB0WYzt6GFWWw9liSRl/XHC6STl6T4eH1nLlK4OspkArQVBnjhmNNtLCyhOZTB1F2N7Bjhlwza8KQOIkXbpvNNQzdM1NfxzdA3YApAYKBgujS31pbuFEmDbubIgJaotOW99ExXxJBJ4taacQDRJxpcTc8FUmhHRNBsDXkpMa7ArWVtYwOzO7sE0VdMk7fOytayW1qKqnJ+EbaMWWYSjcTZesQpD19ncUMHps3Uq55RQNMnxc3I43HDEksP7cO+99/LLX/6S6667jnPOOWdw+4IFC+jt7UXTNIQQlJSUMG3aNC666CLq6uoAiEajXHDBBVx88cVccMEFg+fefPPNvP322/zmN79B097759q8eTO33347mzZtore3l3vuuYdjjjlmyDEzZszA7XajKLtHpM8++yyBgPNqh0ON95anUZvaMGaPwThxPMaARH1mPd5NTbhefhtSBhnLj4VC1FtKcEYJrgoF4VUQyQzJYyeReC2O/tc3CA604yaFgk2KMFm8DCgB6u2tKGSx8OHmLayfPIqbFCZu4lQSEhrniBhtdj1ZygiSIoUXIS1C/VHanmmnKr0JL2l0MgRJUIFFQnVjCoXnK04lpudezJlRFCb0NoFhAwJNWvQJLxFC3HTmHFZWjwbgrdparvnjS6hSkPKpRH1uPFLw4789wR9Gn8LpjduJeDz8z9yZABQk0kRdOiEJts+NB3hrVA1rJtTxy3ueQbUlKc1Nj6uQU5s38teK6dT2x6kYyOAzbDZXlOYqXEqG90UIJeNkRU7QeLIGvx07gsv+vo5JO/v41elTAChLJJnV0Y0NDIvGaS7IPS9SzT9HNixes4GTd3QBuW6iLp5ik8/P7LYeVoaC9GoavYUB3HsIJYCsqpDVNHTLIuHSSWgawrYpyBpIIfCm0oTiCap6+imLRMGCUF+Ckv4oayKV9N+/mdaiAlZXlTKsL86ULe14dQNR7WFDSSnvBAtRZJZPN64i0CVRIgkePW4cjaOGcXHPFmZv2sJfzzyRSKdg1op1ePsiqLE0lq1AQyFiYiWuFzYhBWhBgdAEbeOGIStcKLpJbHkf2toOpA24VEStD//PT0U5rv4jelIcHA4Njlg6QNi2zZNPPkkoFOLxxx8fIpYArrnmGubPn4+UkubmZh588EH+67/+i1/+8pdMmjSJgoICrr32Wr71rW8xa9Ys6uvree2113jyySf53e9+975CCUDXdU499VQuu+wyLr744vc87o477thLRDkcWkKLfobrlQ2AxPuHf2K7dWKZCtwYaEQwKQACuMjioodAog/7xRZ04ggkANk/NOHGTRk7ctM/QJIKVFS82FTYMQQZTAqRuBCAhkAlxgDDkegoEqQMEScEQAY342nEQieFm9p0HyoCCxcpXEgsvPSTssqJiSIMPAgpqUz24rcSBJMQ20MalJhxsoqb9SXDBrfFPB7ibjceYWJrCoGswYBSwzfnn8y3XlnO+RvXEtd1/jypnlXVlUTdLjQpccnd9eczbRA2ipQkvBqdAR9RdxF3zTmbXr+foniKnz3+Ev2+PabshGBjWQEN0U42hnIDlm3hIM1FBfz+uDH84Ok3+NVpk0EIsqqKTW41zKXvbGZtOES/W+fPM8fhiWeZt2EdP33hef7WMBVD1TBVwfTufjYVBFkf9BHfY6ouoyr401kSqooiJTWpNEIIun0eGovCCAkTe/tzFiUpsXSNcCzB9M3bBmtyTf0wqlp6qNjajztmEJJZqootzli+EVXmKmaL4qWtKEh5xCSh6bygjeATO9dx95xj+cXJswH4U0k9r/xlIxU3raYIN90IvPjR8OImQ+HGDnwbm1GQZPEg+sBCgS1rUFDpw0cx3XRTTRYXZCShpl7MhQ/ie/J8zOPH/ruPhsMRivzXhxxxOGLpALF8+XK6urq4+eab+frXv05TUxMjR47c6zghBMOHD+f73/8+ra2t/OIXv+C+++4DYNasWZx33nlcc8013HrrrVx33XVcccUVDB8+/F/mX19fT329M5o7EtFfbxzyXckY+OjHIoBEB3KdrcSFjRuNNBG8FBIbPMdDDD+9gx1qLrrJbqGiIrHxIIc88mq+Udu3ydxGQcVGI4OXODYqCXZP+Vi48BCliGY65VjG9bTiVRJUp3oBSOImQQgbFZC4MfBnbc5/ay2/nzUdKQRjd/ZQ3R+lv8i7u1S2pK5/gEveXgNAwDD4zkuvcuEFi/jhiy9xYnMLq6uruf3EE5BCkNFVIoVeNlUV8c/qcp4fVYeQEunKXWtfwMvfx9SyvSQ0mIdm2Vz82jqGxzuxVMl3TjmLlTVlWIqC27SwNHj69/fwfMMENtWPJiMUfKaJ17SYvaMNFAU1oOFXffjdId5qGMW5G5eTcLl5cfRU4lJybCzJCo+Lflti5n2XNE2hNmtiYKICilCQwKaiMIaa+501295dx4qCoSpDfiFF2PT5g5R2537/muY++ov8g0IJwJMx0fPf/abFqM4+ADZUlg4ek3bpvF08nGEDmcG7xkBDI0saDy5MXBhk8A7mr2KTxYNOFp0kJp6cUMqfn8JPBj+hPyx3xJLDUYUjlg4QTzzxBMcffzwnnngio0aN4vHHH+e73/3u+57zsY99jBtuuIF0Oo3H4wHga1/7GhdddBEXXnghEyZM4Pzzzz+g5fze976HaZrU1NRwySWXcNpppx3Q9N+LwkIvun743m6lpcFDl3ltCWzpGLLJwJOP6zF0jCawSeND5Jfk7rIs2ahINDTMwbMsxOADbqHQSQMVdKDkU1bIdZIBuolRlt+WRmAhUaimc49OWqCTze/Ldepe+gHQSQFQkumlkMTgGV5S1LGNbkryIi9X1jlbtlPZnibpdtPQ2Y+OTdVAlOaiMFLAO+UlJNwuTCHQ8h2+qah8/s23uezNVQBM7O5mfWUZ/zd1MnG3ii9t8PKoGpZVV+auXwgwLchbdR6bNYasmquNioE4n1++hsKUyauVYxkx0MEJHdvZUF5ETSTBJ9du4ZT03wknY8xrX8FDHWfTWFJNYSzDyupymkoKmdDVy/i+AVpLfGRcbp4+5jjGtzXzZnkFK+qGUZlK0+p2U5syCGctOnUNj21Tk04zor2HpMdNezhERhFsCgUHhRJAh89LTSIJgCeTJRbw0xvwURxPktVUtlaVU5npwz+QHTzHVgR9hX6KIgksIWgr3y1qJdBYWcIxze3MX9vIXyY0YCsK1b1Rjm3dTg/l2Pl7ImdDy52lYRLHiwub3YIdXMQBNxINP10IypH58zWyqEDg1EkEDuUzdRA5pG3HYYrj4O2wT7q7u3n55Zf52c9+BsC5557LXXfdxZIlSwZF0L4oKyvDtm2i0ejgcS6Xi8mTJ/PEE09w7rnnHtBy3nnnnUyZkvPDePHFF/nBD36Ax+Ph+OOPP6D57ItIJPWR5/FhKS0N0t0d+9cHfkSIZ75P8LK7ULd3YY2qJHP2DBItGp6n38LT04baE8XEQxIfGYaTwkW1p4dsfT2KaqK09aENLyU6Zgw8/Hc0TAw03PSRoQQLnS6KCRKnizCVtOY7xVxnq5GkmG2ABUhCdGPjw8SLjY7AQsVEAD4iPDx2NvN2vIo/2QNAgmIkEomJwMhbw3ICLYmHMANYaMQpxBKCvzfUUj/QzoyWNFJqFGQNVCQTWttp9YWZGWvjzuMmcvU5c/nqK68Tc7v5xZkncsrmzUPqzSsN4gEXn37xLR6ZOZGVVSX4sgZJl56v2HyDLeCYlm4uXLWBAY+LsniSHm/OktXuL6IxXMbclu1UZHPnFStxwnbuflCQ9BVbPDx9HCdvauae43LPz99H1nH1m68PKc85n/4C20JBJvdGqEkZWC4dVUr8tqQhY4CUeFEQAsa3daAbWdZUluExLUKpNAPeXBuQcLnwdPfiNkz0/Aq6x045jsr+AQb8PmxFYNULwn0JvCmDntIAvcUB/l46nqrOXlAExUYMc6CH5sIibGkyu28naSw+vuE1pvxyDSvKx3FyRzvVepy3JozC7leY2roVPWtg4aKQKAnhpcddgj+dxE8KF1mEYpKpLMcVUijpaiXV46WSJgYoR8FCcUHhFxvoPvc4OITP1MHiULcd/y6O0Nt/HLF0AHjqqacIhUKceOKJAMybN4/bbruNZcuWsWDBgvc8r6urC0VRKCgoGNz22muv8fzzz3PBBRfw85//nGOPPfaAOWDPnDlz8P8zzjiD119/nWefffagiCWH90aGg0Qf/faQbRpgfnc28Xcd68p/3r19F7Fbc4sDlJYe7M09GA9vwL16C+X+LJlwFf7t7cion+zkSswZw0hPH0MsXIbymzcIPPEaqkyTwEMZCTQSSCRdlLOmZgTpsE5TQw0FO2M8VD+Xqa1bkKpNZTqGMG3qM33EVYWoLCejuFlRXs+s7h0EsgZSSFJC8vLw0YztjvGxlrcpk+0YaLwUOpW1tTWsrinl0r+9SUMyxWMPPcmViz/GCVddBsBp6xopi/djKhaarbKtuJBHp01mREcPCSm48/4/kiyU/LVhAm9UVuWcpd1edGkzvaUdRdp4slm8hkm/103GrbHBX4ypKFT2x2l2VeJKZlhdXcqGMWUs3qbisnNC5aX6cWwpDeOXu0fLUgiKos2kvAF6A0VsDnjZFsp1PG7DZHnIT7+uU5I1GJcxUKXEJXPj7YQ7N21VnkgxKpEbRBQmoijdKVZX1OGybTaUlTCuo5u0qtLv15EunbaSMLppEYwnCaViiMkGTK1l3EUTOWWUn/dn8uB/4/IfABuY/z5n7avlqcwLBBvyshiK99h/NPqrOHxQHMuSw7uwbZunnnqKWCzG2WefPbjdsiwef/zx9xVLy5YtY+LEiYNWpUgkwnXXXcc3vvENPv7xj7NlyxZuvPFGfvSjH30kZd8z5o3D0YVdWwK1Jeinjx20IQkgk//sSQBg1kLsXy3EBqpLg3T84S20Hz+POsyH+6cfZ0ZlGIATh5yZm8LtXd5F5gfPkIzWIL90MgVfOhZp2JytCuyeFNnHG1FKPZScM5JF7tx0TrrjdBr/tBlZVsC9nnKeaYJRke2sqGjm1K98ljLKOHdthrOa1jKlUOWUn9TR1+xHmft30qkQSkrlin8+zoJbTsBdNI50egzlRoLj3+rmMd1gTVeSk2oSfO74YhSrjExrArXyVN5al2BDq4v0+iiRxn58sSwim6bb46EkkqA40M/qqcfx7ZM/y5iBnawrH0baDqJbFg3xFGsLC7AUgcuy2Vo6jFE9q9lcOInldVPxJTIM643R7PfSr+dkRI9LZ7UmmJrKohs2o5tbGb2zA6kI2gp3+1BF/AXMbtvI67UNtOenqw0kqYxJhSIJWTZ6OkNDtcKo88qYuHDKAb1fHBwOJM40nMNevPrqq3R2dvLb3/6W0tLdzpONjY187Wtfo6mpaa9zduzYwYMPPsjq1au58847B7f/5Cc/YcKECZx33nkAXHvttVx44YW88MILnH766e9bDikl2exuHwbDMMhkMmiahqqqNDU1kU6nGTNmDEIIXnnlFZ555hl++tOf/rtV4HAUop46EnnqyLwH1PtTPLsM/vrZIduEnveBKfPhvWzvjt1T4cVzaW77rcD32lrZuTNCTc0XqKqqBuArwz3AbkFRMqoImv6H/nc205vqY+Hw+sFj8SpAkPozgwy10QGKimd4zno7c1qImdOABV6gfK9yxTOST1lw5Usncl+LSlk8wb3nuLi2zOa33+/nEztsut0u6roHaArXc++s2aRdGoptI1wKG4pDYNk5f6l8h1EWS/LPqiIaemMs2JnzTRO2pLhvgJ7inG+RhcSVieHPpkm48qsKuwdIhAMELJuCQsFX7xyLohx9nZCDw5GAkFI6VtN/g29+85uoqspNN920177Pf/7zjBkzhldeeWVInKXi4mKmTZvGZz7zmcGVbk899RR33nknjzzyCOFweDCN5557jptuuolHH32UkpKS9yxHW1sbCxcu3Gv7tddey4IFC3jzzTe58cYbaWtrQ9d1ampq+MxnPsN93SlrAAAgAElEQVSZZ57571fCfnA4z+sf6X4HBxqnPnbz8ssvsGjRxwfrI52yUVTQohn+5/JNrBzZQFfQzebyEDJrQdzICyXQLYux3REUn87q8mJKkmmuf+aVwbT7CwKsHzWMhKYR1TRWFQUYFk0yu7uLYeUKs+YWMe6EAsRhJpCc+2M3R3pdfFQ+Sz3i+0O+l8iffCT5HEwcseRwUDicG5QjvcE70Dj1sZt3i6U9sWNZ7vjqen47ahw7agvBtKEnObhfkZKZPb28NqwSkbGQPhdnrt/KWRu2EXe7aKmroiUUxFYUkrbNx4qjfH5JNS7P4W3wd+6P3RzpdeGIpf3n8H4qHRwcHA5TlKCLS28ZT9Gvd3Jt0kvU5wZFgJ0P5yAEr5WXQtqkLpmmOeDm+YkjeH7iCMZ29XHB2k3M9g1QNjHEqMXVFIbC/yJHBweHQ4Ujlo4Qnn322ff0L7r66quZN2/eQS6Rg4ODO+zi099r4NPYLP1HDz9vNNju9RJ3aShCoFg2IdPishVvcfcJ09haHMJjmFz66ht4DYN5j37M8UNyOOpwHLwdDhnz5s1zBJGDw2HM2af4+fJ6ZTD4pWVYaDpcOS3LtBPH4dvQT+Py7dT39mFpOlu/PscRSg4ORwiOWHJwcHA4QKy82GLhH2wiKcklk+B7cwSqogJFTDwZuKyKdFYiJXjdjlByODpxLEsODg4ODu9JeUCw4vPwfkH5PK6jryNxcDjaccSSg4ODg4ODwwHk6BsQOGLJwcHB4RBhGTZSguZSDnVRHBwOGEdjPCJHLDk4ODgcAu68qQ17zQAAr1YWcc8Pi/EFnCbZweFwxBnOODg4OBxkbrqsEXNtFIkgo2lM6o3x2R/3HepiOTgcECRiyOdowBnGODg4OBwk1v6tl1f+1Me6cCmT011oEnRbYikKEZ8fI22je5wxrIPD4YYjlhwcHA5rZMoEWyL8eu67LembdT8DzTYFDOAOKrj/8F/ox5RgbB0gfctbJMMFdG9I4h7uZ/gPJ+MK6Yeu/LbkqQe6eGt5nBfLy8kU1VJgWoxWVQL598h5TIthiQzb4j5Gew5ZUR0cDghHizVpTxyx5ODgcFiSveoF4k9uI9FrUkkfXpEh+8RnMT/5AErWT4mu0K8XYCcsrHmPYSAJ0oeLFB5NYmnlNK+uZ+dfOjj+H3PxFLsPyXX87KoWClrjrK8qJ6PlmtyoppKwbbYW+Gn1+5neG6Gup4/ff7cX78klXLiwkOHho6/DcfhP4ei7dx2x5ODgcEgwNvWR/vlKZHscNZMl/NZqdAxESKXNqKAvGaSGZiqwaaeKVunD//GldBTW0VdYgtfMMK2/CUVCjAI0FNqpR6gmxXY/5ekMwm5ho2c42+5Yx7gfTvvIruWV5Umu+RukbEG3z0XS52JkPMoxzX00DCQI9UdJV5WjSIktBLUDMeoHYoyORHmrrIj7xgznB8vfJuP1sOX1BL98LcqU0ToXfr0SIY6+jsfB4UjDEUsODg4Hnf6vv4T7oRWoqEh0fETQUJGo2APgJcN4NtLPcPrR8ZGkkAGa1Sq6PWUAjE00E5JRABRM+iihvdhPV1EAISuZ3LmTytgA4yNNvPN/JqrbRnP58Y8toHxe9QG5jlTC4q6ftfFa2sNZnf0goDfgo8WlUZ1Is7aijM0lJVSE+2nzeujRNGqTaWZ2dKHL3ALrqV19LBtWRWN1BV4pGR0ZwG1axF+He6/K8MX/qT8gZXVwOFg403BHAG1tbSxcuJClS5dSXl5+UPL85Cc/yRe/+EXOOOOMg5LfB+GnP/0pqqryve9971AXxcEBgN6Zv6dq+zsYuIiRe0aThHHTi4aZb2YVojSgoaAhEdj4STKMFprJiaXSTP9gml5SREROKAFIodBSUEBDrJm2bC3hWJrI7S1gQ6tHYaO6CsWyEUFJ0QnFNPxgBp4K7z7LaxkWf7y/k2VrLNqCXtymRdTjRpqSuu5+/jpxOGeu3c6to4djKQpz2zr5+NpGHj52CuStQu3hQlq8bhCCzQV+NgcDlCbTABhCYCHwAoqUuPN+TADplhTnX9PDnd8KUxZWD+wP4eDgsN8cFLG0c+dO/vd//5e3336bZDJJQUEB48aN44YbbuC5557j3nvv5cknnzwYRfm3eC8h9oc//OHfTvuuu+7ivvvuw+VyIYQgHA4zf/58Lr300v02wy9YsID//u//Zv78+YPbrr766n+7bA4Og3TFoC0Kx1RjmzZmJIsWTSESGYzCIKk3e7D6UhQOdJOOC5hdx0BjFN/9r+JJZNgeCWMJQQmQpJjd4esEdj6SyfqCGlqtCgoTGSroo5A4EpUUfnTLojbew05/EVHVT5EVAyCJD0WahCJpVBPQDc7peQ43BmF2siE5g05KSHp1dAsU08JQFboKCmjfYLN14UsgJF7LxBZQlYiyuaqSAZefxb9bT0s4SKwsTFkqS3fIz8ayAqQQdIYClPfH+dOwamxFQNjLX8saqEwn0eTu0HxCykHhlLtasIE2v5dldVUct70Vw+VCF4KMquK2coIpkEgyd3Mb938zQtqlYdf42VpVQGW9i0vG2QxYCg1hKPfn0o5lJF4dtA/5gt60mXtvnUcFI2niChw6x3iHIxcnKOWH5IorruC4447jsccew+/309XVxcsvv4yUR2OVfnimT5/OnXfeiZSSt99+m69+9atUVVWxYMGCQ100BwfUh1fiv+JRBJK+kgqerppOWX83p7SsQUWygZGYuJBABIsSYmx5qI2OYAgh6xidaCFt+ADIEkYi0DAQgAV0U8TvZk/l4enTELbkM/9Yy0nrBQUkULCRKKTwURvvpzbej4KgmyJMdHoJE9W9eFN2rrCGyoAIU0YXKiYVtJHFR9YuwFYFCIEqJXo6i6YIMl4X3lSGpOaiIJ1k9bDhCCFwI5k6EKVM1YiYkqBlwUCCSZ0D/HVYKf0enYzPDVkTVAVUAarC70+YxKdW76BNCKSAmliSHgE9Lp3x/QPM6O5FAf5WUUpjaYjGEWVMbOni4283kdVUXKYFAhTLwtY03FLizhi8mlL5ux2EjTa/aRSM74qwXnNx3RkanV0WD620Cfvg3gs0JlV9sBAEf15v8fU/WRi25IL2ncx6p4XyY8Mc/5NJKLoTzsBh/zkap+E+8icgEonQ3NzM4sWLCQQCCCEoLy/n/PPPZ+PGjdxwww20trYyZ84c5syZw5tvvgnAypUrueSSSzj55JNZvHgxjz322JB0Gxsb+drXvsbcuXM57bTT+MpXvjJk/5tvvsknPvEJTjrpJC6//HJ6enoG9z388MMsXryYk046ibPPPpvbb78dKz+Sk1Jyxx13cNZZZ3HSSSexYMECHnnkEQAuvPBCABYvXsycOXO45557gJxF55lnntnvsv0rhBBMnTqVESNGsH79+v0q9ze+8Q06Ojq4/vrrmTNnDpdffjkA1113HT/+8Y8H02hvb+eb3/wmp59+OmeffTY333wz6XT6A5XP4T8T1w1/QeTHjEU9HXiSCSb0NqMiMdAwcQG7rCYqKaHTEQwBIIWgXSsdTMvAg4qVb1JtAvTh1Tt4ZNrU3PGKYOmMkQgkJWwlTAtgYQNZNGwEKQL0eoO0e4ox0LHfZYHNiN1r8LP40LCG7FckiPx4Tc8YmEquOYz4/EOsuZ6sgaoqhLIGa4uD/H5MNc/VlzPg0ckIJTeM1jXImNCTBMNCCoFbSsZH40yIxChPpTi7rZMr39nE+S3tCFWlx++jSFEwinwoUnLRig0UpTKUx5LopoluWqhAIJHYXW+6CpoCXg0sm6ayEHXJNDe+aPPQypxQ7E/CrS8Nvdb94Ud/tchYYEvBo2XV2EDnG/20v9r7gdNycDja+MgtS4WFhTQ0NHD99dezaNEixo8fT319PUIIJk+ezFVXXbXXNFxraytLlizhyiuvZN68eWzYsIErrriCUCjE3Llz6enp4dJLL+Xiiy/mxhtvRNM0Vq1aNSTfZcuWcffdd6NpGkuWLOFXv/oV11xzDQBlZWXcdtttVFVVsWnTJpYsWUJlZSWLFy9mxYoVLF26lPvvv5+Kigr6+vro6uoCcmJl4cKFPPbYY+/pD7U/ZftX2LbNqlWr2LJly5Aptfcr9y9+8Yt9TsPtiWmaXHHFFUyZMoWnn36aWCzGt7/9bW699daP3KepsNCLrh++LnKlpcFDXYTDin3VR6I4CB25KNOmUMiqOikttxw/J6Ikey4ZVqWNaltYSs7XRtEtbCFQJMTxUEwfGhIFiYssBUYXhekE/d6c31E4mWYM7+AiA4BFik5Ri5ACiaS3wEtXOFfOWe0bGGMM8KY2gaziwtQEipKlXyklaxcQJ0y/5sVnZokrOd+hpFfNWZkYutBZWDaqaWFpuXJH/TlrmG1avFEeBiDr1sCWYNl7npmbastYBEyTkoEopqYjsCmJJ9lcEMBl59WZEEi3zoh0mhcB1bbRbZuMqrCqtoxp7b2oecu7yP+N6yorq4v3yE4QyJpkFQWPS0GmLHYlXxzUP/A9HfJFaI/lRJbLsgfrpLgysFdazvOyG6cu9sXRZ1k6KL3Xr3/9ax588EEefvhhtmzZQjAY5FOf+hRf+MIX/n/27jw+qupu/PjnbrNkJslksi8sAYSwCUhERFERlEURnggubUUtovCjhbaoFftobWu1j1oXlD6o+IhWpbYVEEVAi5Xihigg+x6W7Psy+9zl98eEkIBKUFmC5/16zSszdzn33JOZO997zvfe+crlV65cSY8ePZqHn/r27UtBQQFLlixhxIgRLFu2jA4dOnDrrbc2r3PBBRe0KmPKlCl4PB4ARo0a1SoYGz58ePPzvLw8xowZw7p167j22mtRVZVwOMy+fftISkrC6/Xi9XrbvK9tqdvX+eKLL7jssssIh8NEo1EmTJjAhAkT2lTvtti6dSuHDh3ipZdewul04nQ6mTZtGnfeeSd33333Sb1Eua4ueNLK/q5SU+OprGw83dU4Y3xteyz4Mdr4/0Ou8lF9ST9ccjybjK7EhaO4rCh2l0y4NhY82AljSDJdKiuoTbRhMwx61+2nWO2AX7LhiZQjozR3bUdIwEaY59+az6MXXoOiK/xszRqcWJjI1JPEfqkHmhXL9dFRSGiIIuk+wgkmHSOxE5qLAhspjMvgc885hKN2nFYd25I7UBGXSEZZlLiwjs9mo0fDISK6SqGSQlRS6FxTzT5vKhFNoS7OjhUME06IR5c06uLjSSmv55DaoiPeAlQJ9Kbnh4Om+FggltDgI7WuHsWy2JvqRTJNvI1+tFCEqF3DkiQsScITjnLhgUrW5STzr7yObMpKpTDFw9biSq7bsBObYRK12ShXZXxuF3rTZ1QyTNKDQdLqguz0unl7ImwvVpj/iUFWosQvh1on/J5+/CqJX78jEYpa3FhehjvLQc5laTi6O1uVJT4vR7T3thCBXtudkmDJ4/Ewffp0pk+fTigU4r333uPBBx8kNTUVWT52JLC8vJzs7NaX9ubk5LB69WogNpTUsWPHb9xmSkpK83On00kgEGh+vWLFCl577TWKi4vRdR1d1+nTpw8A+fn5TJ8+nRdeeIHZs2fTp08fpk+fTq9evdq0r22p29c5nLMUjUZ55ZVXeOeddwiFQrjd7uPWuy3Ky8tJSkrC6Txy1U9OTg7hcJja2toTCgqFH6BOSUQ3zALADVzePCPWk5lz+GXUgGAUEhyxa91CUeS9FVjbq0j65ce4wyHshAmQgptAyy3Qr+Igr7z5DCV0w0SlkVTCxFGNGwMZDRMDmcNnrvGBCKbzyDFEwUSzdLyhOjr7DyADQyo/5YFLJ7GvXw4efxC/TWXy6iAZoQZyq6pRLYPSRDdeosQ5Quw5L5ONuEkvrie5rJ7u20tRTZ3SS7ojSWBZoIZ13OEwdS4HeaXV7HfEEYp3NCdxlyS4KY130bHehy5ZuOr9JBoGFfFxhCQZ2WnHUGI9V/nFVVy2vwy/qvBmSuwE78vsVL7MSCahysethUWsykrDlazwUq8GGuwaY/orOFQNOJKA3S9d4Ybzvv0Vc+dmyiyffLgtuzQ9BOHEnY05S6d8XMThcDB27Fhef/11du3aRc+ePY9ZJj09nY8++qjVtOLi4uahr8zMTFatWvWttl9WVsb999/PI488wkUXXYSmaTz55JOtcoMKCgooKCggFArx7LPPctddd7Fs2bKvDOyO9l3qdpimadx666188sknPPvss8yaNatN9T5e/dLT06mtrSUUCuFwxPI5iouLsdvtJCUlfac6C0IzTYk9DnNomL2zoXc2CRP64XtuE3X//QERHKjoaERx4EMjAoCBjNniYBvBFRuusyQCmoI9ahDL5gELCzUSpURJItnwEZTtHLKyiRpO3k+6ArsZwhY2uH31xyzv2Q9d0uh3sIgGp40dnTpiOzeF7j/Po1f3hObt5QJXND1fs2YVBQXXU1beiOP9GjxvbmOr20VSMITl0Pg40YsV0PnD+g3MP78nO1NjJxx2XScpGBs+DKsaqmHw6uBefNItG8Uw+cna7eTWNVIbZ6fR5cKpGzh1A28gTE1c053GdRO3blDfL5l3J8eR6BRJ1kL7cDZeunXSP30NDQ0888wz7Nmzp7k3ZNWqVezdu5cBAwaQkpJCbW0tPp+veZ2RI0eyY8cO3n77bXRdZ8uWLSxatIhx48YBMGbMGA4cOMCCBQsIhUJEo1HWrl3bpvoEg0FM0yQpKQlVVdm8eXOr5OwtW7awYcMGIpEImqbhcrlQms4APR4Psixz6NChry3/u9TtaNOmTeOf//wnpaWlx603QHJy8jfWrXfv3nTo0IEnnniCUChEZWUl//u//8vYsWPFXYKFU8Z9+7kkbpxECoeoIYky0mnEjY9EAriIoKKgNy+vESKbUlKoIDHaQCL1OAkBFjoKSqODA2YnPoo7j/WOWM4SQJ2WRLk9k5DNiZsKrn+4Gze8czF5a8ZzwbbruHj1GAY9PQhPi0Dp6ygynDfCy6+f7srLD2fw1JOdmfOnbD6f7eRnP/XyYW42w/eV4IxEAUj2h6iNc1DtsBN1uihOiueTbrHeckOReb9HB5LrGihKSKDQk8h2bxIyMGpPCYmNIRyNIRLrgzx+o41HfpYoAiVBOM1Oes+SpmnU1NRw9913U1VVhaIoZGZmcueddzJixAh0XWfQoEGMGzcOwzD485//zMCBA3nqqaeYM2cOjz76KMnJyUydOpUrroid76WmpvLss88yZ84cXn75ZQB69erVptyg3Nxc7rjjDmbNmkU0GiU/P5+RI0eyc+dOIBZMPfnkkxw6dAhZlunWrRsPPfQQEOsVmzp1Kr/5zW8Ih8PcdNNNx+RdfZe6HW3AgAEMGDCAZ599lgceeOAb6w3w05/+lEcffZS//e1v9O3blzlz5rQqT1VVnnjiCR577DGuuuoq7HY7w4YN4+c///kJ100QvpMsD1L578n6aCfKngpCmReiz/wH1AfxdcrDm1aP+WkZJhIOgkiYdGYPBjYOkEM6YXaT3dzDhNU0PiZJRDQJQ5GQrNjkPv4vUH95OZEhXZAAKe77vXfQ9T0g/ecZPPfPAPn1dcSn1uJ0eYi7OoeytQ0EFYW9OVloukG0KWk8MRjGZ7NRFxcbEm+w29idlEhQlsmsbsSuSSyd7cKpiZMYof05G4fhJEvc7Eg4Bc7kJMj2nqT5fTtj2qPWj3KgAvuv/oG8rRLLbSew/OcUz9lFh9ffpIjOVJEBgJt6kuKq2W92oM7txlRkvJFqzm38Ej3Di+2Le1vdFLKtYsNw479Te/x2uc6GzyPUOG34bDLJjQEKNu5hX0oyO9KSadAUPBEdt2FgyhYP3mTjnO5ffTfxM8EZ8/44A7T3tjhZCd4HpIdave5ktf+bI5+513ILgvDDluTCSMol8P7drSZnPZ1O5cQuRKe8jzsQxnVpBqnzxyM5VPoDwe21BItDRPb7CQ37L+K6nt4rfn43WuWTfioL/xXg430mIw6UELTb8KkyX3jdmJKEYloUFBbxzJPih3OF9u9s7FkSwdIpsnz58ubhvKPde++9jB49+hTXSBDaL+8l2Xh33vSV85w9k3Aee93IaXVhFlw4KY5yn0VhcS7/fLqC7WnJzTfSNGSJ3YnxIlASzhJn3/tYBEunyOjRo0VAJAg/cOluifQeGsF8iaKtPnYkxHH4fgTZ4RAg7nsjCGciESwJgiCcYsNuyeIS3eKuvwVYewA6WWHuvzP5+CsKQjtwNiZCi2BJEAThNFBUicd/cvj36xzfuKwgCKeXCJYEQRAEQfjeiARvQRAEQRCEbyCCJUEQhLPItjW1vDevlJCmcijRwZ/+mI3b/e1/X00QhLOTCJYEQTirBVbuwXHPUiwkzF8P50CXTjT8cg2mabKuY2ci8W46VFWRFLRz5/0Sv71SxtPXgzPbdbqrLgjtkuhZEgRBaA/KGtn3QSllW+q49IWllDi9FKXmsOe1RhzBdVyz93M+69iNqKZx03urSAr6MYGu3XqwensiFgfIy5XgxtO9I4IgnAnErzMKgnBWCf7qbZb9aBUPbUjlBb07D4z5EUlWLdWJcdS6Ein1pvNlRncqbdmcc6CYpKAfiB0M80pLaLRpmJLEFwdkat4EM2Kc3h0ShHbGOupxNhDBkiAI7Z768W4Set+NJ+0OEhf+m105HbEkCQPYlZZNYXIq1298ix479pK7u4g6Iw5PfT1qRCeiHMlR8ttt3Dd0IL8fOhA5ZNC4w8ULfd5i99r607dzgtDuSEc92j8xDCcIQrskb9qPa9qLGD4LrbQMBQkTJ3GGwaADO/gyqwvucIA7Vy8kp76S3UkdKUlPw9UYxBfvJOy0AfC+sw9d6yoJ2mzUqG4ee+tTVvTuxNbOmfSvrCDs0qi6bSWRlQXkZago4hRTEH5wRLAkCEK7omwtQhs1F3e4FAmrqatfxsKOih8JiWu2r6He5iQtVM35RTsByKSGf+dFKElKw1NzpKcoaLOxKSOHhGofmYWNpNBI9z0VaFl1VDt1nu8zlMTOHelx736W6Dr5t2Yycnjiadp7QTjznY0J3uIc6SuUlJSQn59PeXn5Kdvmddddx7vvvnvKticIZzqpxoeybh9U+bD2VqFc/xJq9/9BGjYPe7gOqSkbInZYllEwkJABGRmLm758j4EH97Qq06ZHQZZaJVLIpok9FCbOrzdPU0yLXgf3cuOOD3lx5bMs65xJhSYju5zsfmInv/31/pO670U+uPNDhdtXQN/50G+uxVMfG9SGT+pmBUH4Gu2mZ6moqIinn36ajRs3EggESEhIoGfPnjz88MOsWLGCF154gSVLlpzuah5XSUkJ11xzDcuWLSM9Pb15+t///vfvXPauXbt45pln2LlzJ9XV1cyfP5/+/fu3WiY/Px+73Y4sH4mTly9fjtvt/s7bF4QTYlkQjCDvr8bsnk7SwLuQS+uxkJAxMZEwUTBxICMjYxBFJUQCfjKwqMCOD5CQv+JMVsbEEwwSljV0ReHD3EGYkoxlmBSnekmvqUU2QYlGQZJIkHx0MqvwEUet7CbJqgGgU2MNKYFG3slMw5BlfrFzL9MXfELNwgAreg3k7wP6sTU3g18PMhnaAV7ZLrF4h0R9RCIuGqWzwyDosBGoNIjIEg2SRUTT0DU5drpqgR2IShKmpsTaJWJC1AJDQtZNZAke2ergjzsViJpIhomiSMTbwaFZmFETSZVptGRkA3p5TEZ0NsnzwhXZBiDRGI21S6L9VP2DhR+qs7Fnqd0ESzNnzmTw4MG88cYbuFwuKioqWLNmDZZ1tuTaf3eapjFs2DCmTp3KpEmTvna5uXPnHhNECcLRjIYoB+7ZSHivj9Rbckm5sTMAu1bXsG1FNQnpNi66LRu7u+2HkQPr6vlyUTkXvf0W3cv3ARImOgohJA4nWpuAgQLI6BjYUIkA4MOLiYqMSQAvNnzNPUyxPiaLlgmlGmFClsT8wTdRkZACQJw/QKavHlNV6Lt7H+t7dCOjppr+1fsIqXbSorUcSPDgqo1dJbcvMRsFG6VxTiZ8vocLttVRSgekkMmY9Xs5d28ZE39yLff+ywmSDDYFVBkUCNs0atVYT5acYJLaGMK0a+hRC8J6LDCKGoQBXBrYVDAt0A0wYkGTCZjupnkAdgUraKEjURsCQhLIMhxuP8tibaXE2pqm5S0goINdOdKjplhISNglC5dqUR2WQJXAklrl5MoSnJtksuLqCFEL7v1QYWtthDGdZGYMMAGYvlrlzUKFRBssGh2mR1Kb3w6tWBb8cb3KByUKF2UY/DZfRz6B79zaMNz1icZBn8xtPXWu6yquYjxdzsZv5XYRLNXV1XHgwAEeffTR5h6Q9PR0JkyYwKZNm3j44YeJRqMMHToUgCeeeIL8/Hy++OIL5syZw/79+0lJSeFHP/oR1157bXO5u3fvZs6cOWzfvh3TNMnLy+Mvf/lL8/zPP/+cBQsWUF5eTt++ffnd735HSkrsgLtw4UL++c9/UllZSXx8PKNHj2batGkoioJlWfzlL3/hrbfeIhAIkJiYyI9//GNuuOEGbrwxduOWa6+9FkmSuPnmm7ntttsYO3Ys06ZNY8yYMW2q21fJzc0lNzf3+2t44Qet+H+2Ubu4CIADszbgGuglEm9nzf8WYVlQtS+IzaVw8ZScNpUXatD54In9eGur6FG+r2mqiUKwRaAEsW9qGTCbnkU5/O2tESKBOiQgisyRAAmOBEktAyaJOkdWc6AEELHZsFfVY8lQmphE/o5CqpOdrEnvgyErxEcCZPpq+TBhKJoZYVtqN+yRKFdvK2RoYXEsnrBieVIRVKaNvZJqzQYRA2QTJCkWLKlKbDckCcIGpmFRHmePRQVG05Cf3uIL3R+NBUy14VjARNO6Lf8epkhHdjXWjLF1ZCm2rNw07TCH0vq1IWFJFiETQtGmsgxAa7Edy8JEYmOtwm8/U0m1m/x1uwJYrC9T6Z8axQEhMpsAACAASURBVJTgH3tiXyOVQZj0LxtrJ0b4NhYXKszZogGwqUYmz2Nx4zltD3j+8IXG0gOxusz8SGNQmknn+LPxa1s4HdpFsOTxeOjSpQsPPvggBQUF9OrVi9zcXCRJ4txzz2X27NnHDMMVFxczY8YM7rnnHkaPHs327duZOXMmiYmJjBgxgqqqKm6//XYmTZrEI488gqqqrF+/vtV233vvPZ5//nlUVWXGjBnMmzeP//7v/wYgLS2NOXPmkJWVxc6dO5kxYwaZmZlce+21rF27lmXLlrFgwQIyMjKoqamhoqICiAVZ11xzDW+88UarYbiW2lK37+LXv/41uq6Tk5PDzTffzOWXX/69lf11PB4nmnbmvt1SU+NPdxXOKKmp8RQ3tviissBtqgRVOy07c81Q29uuLhrEiFqY0lelSrbuEWqZThlL4paQAAf1SICBSggvFgoqPpxUAjoSDkyOdI4Y2IgPhfH66qlxx5KyE+p8ZO+vBcDvUrEHI8RXQbUrth+NtjhUu0lQcQKg6BZ3rd5ISmMQgIYkjcSaKCo6DiIUelokex8Ocg7/tY6aDrFgpjnGaxHsSRwJeo4WiMYCHkkCw4wFS0bLDbRsSuvYyUf30HxVj8039NI3ouGSoGmjAEQ0J0ZTKzdX05C/9WcpdNCkZUQXVBykprY9rbbBNDi844YlgdNFaurJHw4Sx45jiWG40+i5557j1VdfZeHChezdu5f4+Hiuv/56Jk+e/JXLr1y5kh49ejB27FgA+vbtS0FBAUuWLGHEiBEsW7aMDh06cOuttzavc8EFF7QqY8qUKXg8HgBGjRrVKhgbPnx48/O8vDzGjBnDunXruPbaa1FVlXA4zL59+0hKSsLr9eL1etu8r22p27f1l7/8hX79+gGwevVq7rvvPhwOB0OGDPleyv86dXXBk1r+d5GaGk9lZePprsYZ43B7eH6aS+V7JRh1URKvyEA/x4kmWXQ6P4ED6xqwuRTyRia1ve006D7cy65V8GGn87jw4EYkCyQ0JKJYR2UexQ64alMvkq1FL5NFBBdWU2+UjhuDBlTCRJBRCSIhYaEQRUWxTG7+ZBlf5pyDEZUI1To5HC3EBaLYwzoNmqPFhi1SfAHKXSphVSNiV/HWBI7MlyWeGd2faq+dAaVlJBk65WrTofTwfQW0w0NiNPX4cCSmkIgNiUVN0LSmIbemHilfONY7dDhgsjUN60lAYwS0pt40TY71AkWbAiNFakpct0A3wZSO9D4BhI1YOS17l46myrH1JalV4OSUTe7uEyZswN82a5T4JQammwzxBnCq0DHexsHG2P9u9sAIlZWhb9jI1xuVBj0S7eysl+maYHJ1ZpDKyravf3t3mQ8O2miISlzdyaCjcmLrfxvt/dghAr22azfBksfjYfr06UyfPp1QKMR7773Hgw8+SGpqaqtk5cPKy8vJzs5uNS0nJ4fVq1cDUFpaSseOHb9xm4eH3ACcTieBwJED5ooVK3jttdcoLi5G13V0XadPnz5ALIl6+vTpvPDCC8yePZs+ffowffp0evXq1aZ9bUvdvq1BgwY1P7/yyiv57LPPWL58+UkPloT2x9U/ib6fj0KviWDLcSI1DQWNmNUJX1UUR7yC5jixH529eGpH+k/IwGZ1x/9iDsaeGmyVtZCoYn/ncxSMppxnCz8pBPDioJY4GgEFAxsW0WPOXA/nLckYyE0RgYSBjoRMBFckQud95dSTTFmcA6MpGcalR0iwQlRrLpIb/UQ1haRAAJceRdEsyrISCTrtOH0RHOHY0JkhS9R54yhNTsBTHyYjECJoV7FkE7cJDboNvy6DpIJpxgIPTYmN3elNQYhTA4d1ZHgtFIWoAaGmwEltCpLitFgukUlTHpQEfh1ZNzAdamyaacYCpqhxZNhPimWDeewwtqNOdUhicGaEEr+MU4Erc00aw+C1WyTYYh1Xbx9U6JZoUNggk59qkeK0sCzo7jkS+316YxTD6cYR9jXHhZ9fF2FHrURmnPWdkse9Dnj/mjAl/lhZthP8PeNBaSYbJ4aoDUvkuKxjRi6FU0f0LJ0hHA4HY8eO5fXXX2fXrl307NnzmGXS09P56KOPWk0rLi5uHvrKzMxk1apV32r7ZWVl3H///TzyyCNcdNFFaJrGk08+ybZt25qXKSgooKCggFAoxLPPPstdd93FsmXLvjKwO9p3qduJksQRRfgGiktFcbU+TEiSRHyq7VuX6U6xATYid48E4HCfY5DbY+VXN2I1hnDOeBXHjjrk1ESCuV3R3tuOZVko2FAJY6I1XTnnRyaKgQMDV1OyeEyUOBz4qJfslFvp2NFJDvpptNmxESU1EusVUIwwk6+7mnJXHDd9sZVffPgFlckJRBwalqpQlZFIUnkdqmnR4FCwyxE++e84oAsAJSXFFBUdICenE1lZ2cS6kVrm2+i09LcdOvsa4PruFl1jndeEDagKKZiGzLVLFMqqDaJRA0OPJV939ViMSY5yYSeJSzqDTfk2uUFfnwPUM/nwvK/vfnKokOqRjumxyUv6fnKDNBk6fYc8I7cGbk3kKQnfv3YRLDU0NPDyyy8zatQoOnfuDMSGkPbu3cstt9yC2+2mtrYWn8/XnAA+cuRIXnjhBd5++21GjRrFjh07WLRoEffccw8AY8aM4f/+7/9YsGABN9xwA4qisH79+jYNdwWDQUzTJCkpCVVV2bx5M++8805z3bZs2UI0GqV3795omobL5UJp+kkFj8eDLMscOnToa3OWvm3dLMsiEjlyAI1Go4TDYVRVRVEU9uzZQygUokePHkiSxIcffsg777zDQw89dNx9FoRTxUqOh+R4gkt/0Wp6CLAMA63rH7EHfESwoRG7Ys3ETRQXIBHFC4TxkwxI2Aij2WDlRf1oSHTR6VA5gzftwGVF0KxYYPDE0EHsT4rlHj13YX9yAwEcqkLQbkfGont9KRtyswmFJA7kuJk43teqbllZ2U1BUtvckHfsF7pdgWwXgMRnkw5nXh0d3IiTG+HMdzaGq+0iWNI0jZqaGu6++26qqqpQFIXMzEzuvPNORowYga7rDBo0iHHjxmEYBn/+858ZOHAgTz31FHPmzOHRRx8lOTmZqVOncsUVVwCQmprKs88+y5w5c3j55ZcB6NWrV5uCpdzcXO644w5mzZpFNBolPz+fkSNHsnNn7E7BwWCQJ598kkOHDiHLMt26dWsOSBwOB1OnTuU3v/kN4XCYm2666Zi8q29bt9LSUq655prm19OmTQPgt7/9LWPHjqWuro5HHnmEkpISNE0jJyeH+++/n0svvbQt/wZBOO0kRUHffz96lQ/7Zc8QrQCZAHbCqAQxsGOiYWLDTQMSFhIma7qfT70ndiK1v2MGfYoOkVrmR8JAwUQ9qudne24HEi0wVAV7JMKwDZs40KM7va/byYieXTj33AGnY/cFoV04G4fhJEvcqEg4Bc7kJMj2nqT5fWtv7WGGDGqHvkL6wS/xWcn4SKIxDeL1CB1rDhJHDW/3HsGqHpc0r3PNR5/RqbwSXZKIkwMcTEhk8rhr2J/k4fK9h7isvJqi7HRU0+CyDZupl+G8BZeR1jPhNO7pmaG9vT9OpvbeFicrwXuL9FSr132smSdlO6dSu+hZEgRB+DqyQyF53c3ogM2wyOw1m0SfxeLuV9MtPokrDvyby3d9SGFSJ4o8WZxTXELH8ljSjYGEYdjIrg2yYsHrrO3alSp37Aukx54DFKUl0u+vw4nLc53GPRSE9uVs7FkSwVI7snz58q/NL7r33nsZPXr0Ka6RIJxZZEUiuv0hEm/+C+M+fZddCbkUxuWRHGjg2g8/pTrBTaXipdyVSFiVOdAhg0u2bUUxLcKqSp1mQ4noSEB9fBwDbqui09CL2nXvgSAI350IltqR0aNHi4BIEI5Hlgn89WfYgb5A+Mq/4t8oY5cbiXbw4jchdXc9Nt1GuTeedwbl4230UZGYQJ9te/FneKm54hzG3p7D9l3/Od17IwjtztmY2yOCJUEQzmr2d28CYgfwjk0PAP+eejrf9iH70nNoiIuj274iui8dQ1xei7ykXae6toLQ/olhOEEQhLOEq1siwz+4iksCURq3NJDQpydqnDgkCoJwLHFkEAThB02L0/AOSj7d1RCEs8bZ2LPU9l8pFARBEARB+AESPUuCIAhtsO+vB9n1l11EVZm0YSkMmNkdzWNHUs6+s2hB+C5EgrcgCMIP0LZXDtL4wAaSIxaqqbM13s1nM/eTXVLNmBf74+gk7sMkCIeJYThBEIQfoOIndyJHLJSwCVGZ7l+WouomBzuls+QnGyhZX3+6qygIwkkkgiVBEITjMHWQjNj5ctQm4/OoZBRVIOsG3vowO6/7iPfHrybSGD3dVRWEM4B01KP9E8GSIAjCcVR74lAkA0uC6nQHIZeKPRImvaSC1PI6XIEQ2vZGvhiwnM96LsWMGqe7yoIgfI9EsCQIgvANPnttH4lSlC2DcjnYLRmrRUJ3cl0DeVYx55jlOKKxXiU9Chv7L0b3R05XlQXhtLKQWj3OBiLBWxAE4SvUVYZZv9TOvh2FBLJSMRSF+ngX6UUVuHxBwCK3tgoAhxklOdrIgYRkQk6FWtnB9onrkRSJq+b2IbGjSAAXfjjE1XCCIAjtwMZ/7GHL+gYGF3Sk2/kpx13esix2z9+Eb8VmKqqj7MzoTCJ2qlK7YqbUIhsyimWBJFGdnoSpSqTVNKDpJiaxLvqgXSPkVECSkC0Ld4OfqqwUVkz5kjHP9iWugxtF3GZAENolESyd5ZYvX87LL7/MwoULT3dVhB84w4RAFOLt37ycL2zxr10GvqjMqJ4yHjuoRyUM6KaFKkv4ouAPWMimiTdRZkdRhKce2cPSfv0xc0z4xGTAG6X8aNt6lvfqTs/qIlIb6jlv/yHOLy4ngIuPMruRGPExuvRjFHRKXGm4Im7eHXgx3UuKGL/uEz7PPYfCtEwMSaLvngMETZWwprE5qwNptXWkhP0cSkgGzOY6yrqJu7qe1Eo/X1y9htKMREqzU1EMgyiQVOuja65GxjAvH79WRVDWSKmqJ3tMOhffdc733v6CcKqcLUNvLYlg6ShFRUU8/fTTbNy4kUAgQEJCAj179uThhx9mxYoV/O53v2PIkCHMmTOn1XoTJ06ksLCQefPmkZ+f3zw9FAoxatQoEhMTWbJkCZJ08t5EDzzwAIqicN999zVPGz16NKNHjz5p2/yhMkx4cSOs2AudEmFUV/ikCHKT4Cd94ST+m1uxgjqR5zdjBaLYftoHOS3ueylXXbePL5YW8kxmP75MzqR7ElzTxeDDzQEWlTiIaHawLIgaoCmgSmAZECHWzXK4ARRAkcGwjkQ8hgkhA0wTFAVMCzQ5Nt+yQDdBtoFdZdaXJtd99Cm1HhfxgTqWdz4X1aaQ1uhDDUGDI45ah8ZFRXu57+Ol1DnsvHfNHZjVoeZ9OZiYzNyhV/LSPx/lksIdADw16Eq84QZuG30zO5IzqJozHY0wADn+UtZLsXyjgs8+wm93UJiWGdsdy+JQWjLOhhCNnjgyK+rIrPVjyhKKBXIAdIeFbFiE3RrpZY3YorEAquPBGoqz0wjEu4nzBajN8PJ5SMK2yEd9WipIEjUpHho+qaLwqk9BkpAsC0cgjCsQJpJox9E7iYF3dKbskxrUOIVz/iubaEOEjdM+p7osguVW6H5DR7rfnPu9vA8EQYgRwdJRZs6cyeDBg3njjTdwuVxUVFSwZs0aLCs2CpuamsqWLVsoKysjIyMDgI0bN2IYBoqiHFPeypUrASgrK2Pt2rUMHjz4hOtkWRaGYaCq4t91pvifj+DJtUdev7LpyDh9TRBmXnBq6hH8xQdEF+8BILqsEPd/rvvOAbm1v5KDd/yd0T++G90RByGJ/aXwbrECURVsUmxnJQlsKshNrw0pdpWwSSw4gliw5CAWCB2uV0MEQnrsuWrEyjAMcMuxwEs78jmyZJnXL77wyLpNQ2ENSQkkN4SodduRTJNfvLmMSw5sZlWHnviN1p/DlLpKlr75NN1rSgD4ILcX9439MY0OJwR1Lt+yHm/I32qdQ6leArKMZFkohtlqnt9uJ+pRwDLpeqgCCVBMiw61tSwcNYRRy79E1XXqUtwoRqB5PQlwBiOE4hxYLdrDVJQj+ydJWBaoloUJmKpCMCEOTbdwlfupDxm8P6MeyYy1b90eH8ayImqQCTtViMDmF/ejuhS6TOh4Qv93Qfi+iJyls1xdXR0HDhzg0Ucfxe12A5Cens6ECROal7Hb7Vx66aW8+eab3HHHHQAsXryY8ePHM3fu3GPKXLRoEaNHj6asrIxFixa1OVjKz89n1qxZvPPOO+zbt4958+YRDAaZO3cuBw8eRFEUzj//fO666y68Xi8vvfQSy5cvB+Ddd98F4IMPPuCdd97hhRdeYMmSJUCsp+uZZ57h3//+N6FQiP79+3PXXXc1B34ni8fjRNPO3Ldbamr8CS2/qSoAHLk8vOXBYXO1Smqq8/up2HHs31DZ/NzcWUuy3Y6SeJxxruOwPt/HhqQsdFWFljk2ErFeIPmoYKwpgAEpdkQJtQguDEC3YgHW4WUPB0oQ60WyHd6BrznEtgz+WjyvjrfHXssK91z9E656fCN1jqN61mwKE3eupXtNCfU2B0gyE398ZyxQAnCqjN2/udWgQY0zkc869aPaYeMfgy9m4sdruHbj29gIsju5K3vs3TEVmXqPA1OWkZuCqaiqEHbY2JmXgaYYqHoUX7yNhPowEhC2KdQmubEsCy2iE3HGgjpbJNrchpJloVhmU3MfaY+oXUW2YkGZ2aKd6nf5iK8IEelw5P1ryRJ16+tJnXZi7+kTcaKfl7OZaItjmWIY7uzm8Xjo0qULDz74IAUFBfTq1Yvc3NxjztTHjx/PrFmzmDJlCn6/n9WrVzNz5sxjgqVdu3axdetWZs+eTWlpKbNnz6a6uprk5Lb9wvmbb77JY489RmZmJoZhsG3bNu6++2569OhBXV0ds2fP5rHHHuOhhx7i5ptvprCw8JhhuKP9+c9/ZteuXbz44ovEx8fz2GOP8ctf/pJXXnnlK3vGvi91dcGTVvZ3lZoaT2Vl4wmtM6ITvL/3yGtVjn3vAwzvoJ9wed+WPLIjPLsZAOXiLGoiEaj8bpespwzuxsX6YjwBP3VurXn4TMbClIgNs7XsbJGk2Je9ZUHEigVYh3uWVCn28OsQp8aWadlYhwMvWYoNxR1mWiBBfDBEo9NxTM+SZJpYLT6XhhRb9+p9X3L5wW2837FXrEyXxs82/RuAxEiIPw35LyKKdmQ7lkV8JPberHa4eW7wdWzK6YUlyzTIMvtT0vgiL4uCLSsA6FhfjNuuU6WkU5mRyP4UL+kNjYTsNt6/oA9YFpJlklbcQFkHDyWd3TTU+bFFovjcdvRQiIBh0r28iqo0L7qiYMgSabW1RFQNxTSRZLmpo+7IPjv9YQxZQteUWDs1De2lD/YSqA0RVxLE3xQkS4ZFytCUk/Ye/Dafl7NVe28LEei1nQiWjvLcc8/x6quvsnDhQvbu3Ut8fDzXX389kydPbl4mLy8Pr9fLxx9/THFxMRdccAFer/eYshYtWkT37t3Jy8ujW7duJCQksHTpUm699dY21eUnP/kJOTk5ACiKQv/+/ZvnpaSkMGnSJH7/+9+3ed9M02TZsmU8/vjjpKWlATBr1iwuv/xytm7dyrnnntvmsn7objsPzkmG9/dBh0QY1hnWFkNnDwzpcOrq4fj9ENQLs7B8UbRrunwvZUpJLjyL7uCD93ezNK4jnzuS6Z8KYzqZrNse4PF1UGhPBMlq6lKTUFSw2SAYJBbQ2A8P1TUFUTLgi8R6pxLsEIwAEjiaDkGqBEE9FiTZmqZZFlZdkEv27mdbx2yiEvTfvYveDWVoUYklfS+kKM2DZVqYjTprM7qQEvbTK1TJ+14nSOCKRLCaArf/dOrLP/sMJ6uqEUORiagy161fx8CSAwAkh3x8mZxOmcNGSJbpVlJEsi6R3lDTqn2cViy4cjcEOZCbQk2NnWCcG3dDkKSqBnpuK0G2wF0f5LOLz8FuCxFyajTYNJZ2yuKifUWouk5GaSWGLHMwJx1Fl7CZJlgWWjRKRJaxR6LomkWcrqOm2tE9bnL6e+l3aycqPq9FjVPIGpKMdXsXtv3PNso+rUHyaOTd2pnsYSe3p1gQvolI8P4B8Hg8TJ8+nenTpxMKhXjvvfd48MEHSU1NRZaPnPmOHz+exYsXU1JSwsyZM48pJxgMsnz5cqZNmwaAqqpcddVVLFmyhFtuuaVNeSVZWVmtXm/fvp25c+eye/duQqEQlmURCAS+Zu1j1dbWEolEyM7Obp4WFxeH1+ulvLy8zeUIMZd2ij0O63psvHzSSZKENub7T+a1kt1kTTyXqUDL4cYug51cPxhimdzflsmRn0DQv2K+QThqYdckwAl0bjGvd9MDbi0ppqhoAw6Hk9C5QRz/7zYa3VlYj26gZ1kx25PS8YctRt94NzcbxXxZ7+TGTe+z25tDbZmLjPpKrtq1lvc69CchHCTDX8eDK17j3S751DkSyK0LsqNrLpsyejHw0Jc4rDA+yU2ZEvtc1iS66FhVS+/CIkwkym0J+OIczXumRQ0iqkZZqhdXdT3rUpMZfKCUEQdLkTu4MYMGHSZ24NLzEvnspUOU7dWxKSbn/79O5I1O/8YW7HTFkfmSAn3u7U2fE/4/CILQViJY+gYOh4OxY8fy+uuvs2vXLvLy8prnjRo1ijlz5uDxeLjggmOzeVeuXInf7+f555/nxRdfBCASidDY2Minn37KhRdeeNzttwzOAO69916GDx/On/70J9xuN2vWrOGXv/xl8/zjBWBJSUnYbDZKSkro0CHW/REIBKipqSE9/ZsPzoJwKsUCpW+WlZVNVlb2MdN/+4fz+C1wqNgHYYMOXToAHfgxAP1aLJkHDGUAAAX4G0MEd/vp8r+fcf47b/Kfc86jW1mEYndHXjjvJ3Q/VEQ0Ykfyg4RJTXo8Qz/eBYCCRUrER1hVwYrVPWKT6bLvACndXVz61mCmNW+3+zF1HvtQr7Y1jCC0AyLB+yzX0NDAyy+/zKhRo+jcuTMAq1evZu/evdxyyy0Eg0fyblwuF/PmzcNut39lkHI4sfvoXqf777+fRYsWtSlYOprf78ftduNyuSgrK2PBggWt5qekpLB582ZM0zwm0IJY8HXVVVcxb948unTpQnx8PE888QSdO3emd+/eJ1wfQTiTdcg+wdsoxDvIOM9BxvOjMRnNxcAHS1cSnaujI7MjqwOJ5SFUzWJ/1zQCR90wykKi0RNHjccJRuzCwY6ZMhc8ff73t1OC0A6cjcNw4rfhWtA0jZqaGu6++24uv/xyrrjiCl544QXuvPNORowYcczyPXv2pEuXY/NEdu7cybZt25g0aRIpKSmtHpMmTeI///kPVVVVJ1y/e++9lyVLlnDJJZdw1113HVOncePGEQwGGT58OJdddhmGceyPef7qV7+iZ8+eTJo0iauuuoqqqioef/zxk5rcLQjtlZKkMu2P55BVXIMkSTRkOKnJdpJcX03f3fupcTsxkYgiE0Sje0kJffcWU3NeFhNXXsxFr12M6hSfLUFo7yTr8A2EBOEkOpOvGGnvV7R830R7HLFmzSoKCsbzj5xX8Tmc1CS6sekG9amJWLIMpklaSSU9C8uJi0QIOGzsKujHuMfP3mE18f44or23xcm6Gu5DaX6r1xdbt52U7ZxKYhhOEAThOOL0MJGIDVs4SijeGQuUAGQZvzuO1QN70C1bYsQzA+h5eqsqCMJJIIKl02TGjBls2LDhK+etWbPmFNdGEIRvortViFh4GwIcTHC1uBEnkGDj1gX9sdtFVoMggEjwFr5HR/+2nCAIZ668p4fw6d0baJRcJPh9aHW1BFwuXBqMf/PEL9YQhLPZ2ZjgLYIlQRCE4/DmpzP4iUH864mDaKEoQ6Z3In1o2umuliAIp4gIlgRBENrA28/LdQtOw51HBaGdORt7lsQguyAIgiAIwjcQPUuCIAjfghk1+eKx3RwMagy+LZvsjvbjryQIPwAiwVsQBEHgzZ99SZ+3NqFpKlt7dOftchs33xDPZcMST3fVBOG0E8NwgiAIP2CBYj9/u/h9eq7YQkrQj99lJ041+TgrhUfePRvPpwVBANGzJAiC0Ca1ZRH+dcNanBETb6OfLZlp3P1fozBlmSQg+BW/xygIP0Rn42mDCJYEQRCOIxoxeeVXu+kc1kmoDjP/sovYkptFimFSKUkgSTgUmUafQbxb/BacIJxtxKmQIAjCcRzcHqDrjiJ25+awr1MqG8/piK6qOC2LBNNEsizSI1F+f2/F6a6qIJx2FlKrx9lA9CwJgiB8BcOEJ7fk8fP/VNChsoEhPTqS4fNRlZHQarmkiE43fwA0jbCq8LPb99N3dCp3/JfrNNVcEE6vsyVAakkES4Ig/OAt2Gyx+JMgA1Isfn6JjQpUCl6KcvMnFfy4dAfuYIjq9CQimobb0Emv91Ge4CIhEsVlWqBp6JZFWm09aTX1vPRpAtZzm8moC2Al2Bnyp96kD/Cc7t0UBOFb+kEFS/n5+cyfP5/+/fuf7qqcMjNmzGDgwIHcfPPNp7sqgnBKyJ/uwv7PjwlXG9h2lRGedCnWHRcfs1xUh+l/D1H1YTn9SsvJczvZnZnGlM8U1GiU/pbFmryOGApcv3Yb1RleTFmm32cHGBTay76cZNZc0qu5PFWSyCmuQLHgji+3859OOawe1YkO1Q2E/t96Vl3ehwcmu8jp4jyVzSEIp5x5uitwErQpWCoqKuLpp59m48aNBAIBEhIS6NmzJw8//DArVqzgd7/7HUOGDDnmx2EnTpxIYWEh8+bNIz8/v3l6KBRi1KhRJCYmsmTJEiTp+F12N954I5deeilTp05tNd2yLMaPH8+ECRO46aab2rI7Z6QHHniA5cuXY7PZkGWZ1NRUcKv5qwAAIABJREFUrr/+eiZOnNjmMr4qGBQ/2NtOmSbqJ3ux3HaMfh1PeHWp3o/6xT7MdA+2JV+g7CvDOCcV9dPdRHt1ITooD+2DTWi7SwhPOB/pYA3qJ3vBY8eqqSNeN1D3VkKcRnTgOchbipBLapEJA2BhAyR0HIRwYCeIme6GgIwa8hGOc6H7bWiGH0vWAAXMMNgkoriRlSgORxiVMGZNAAsNExuKqqPqfgwUgqoXXXciE8XmDKNnZGI1RsCtQMd4rE/LkPQQepwdl68eFQMTDQkd0FHQkDDR7vsb9fe9TwQnUUXlr/kD2J2axKV796H37c/GvFw2d8rAr2kM3VOCpZh0La9k5I5CtnZMY8C+YrSITqWikFIXxB7SAfiyVwcMWUIxY9f+WJaFIxrFGdKp0FwsPb8HAPUuBysHdGWHrvDLeUG8oRoaHHbCskKiZJBimSSnKOR2Usnv76DrOeLmloJwpmlTsDRz5kwGDx7MG2+8gcvloqKigjVr1mBZsYNEamoqW7ZsoaysjIyMDAA2btyIYRgoyrFXhqxcuRKAsrIy1q5dy+DBg49bh4KCAl588UWmTJnSqsy1a9dSUVHB2LFj27IrZ7Srr76a++67D9M0+fe//80999xDbm5uq0BT+GFw37EA+5sbAPD/9zWEZlzR5nWl6kaSRv4O5WAlFmDhAhQk/EgY2D/ejj7/A1T8SIB93Tak5ot9ZUDFTjQ2LQjqu5+3Kj9WpgLYsAEyYQLEk1hejIWCjIGtvo4aOtNABvFGIyABCuGwnSgOwMIZOIiGDwONIEnYqMOmNwKgYGDoJhpRAKJBN1JhAwoB4qqqqN/fqSkwsmH5ZHw4SKIIG2EsIEgcMgoWFjJBXDRgYOPZC4bwx5HDAOheVccDK99l05ZMfn7DOB782xqGbjsEgIaOkyg9y6qpczh5ffC5dG70o9sk/G4bmq5z8aZd7M9K4ctenQHIrK7DZpjIpkWd005OTQM3binEDtSrCllI6KpCnAmKbrLH7SQ5GCJsypSUWgT2+9i22sfo6z0MHirynYT2y5LPrJyljz76iGXLllFTU8O8efPYvHkzPp+PCy+8sM1lHPdquLq6Og4cOMC1116L2+1GkiTS09OZMGECNpsNALvdzpVXXsmbb77ZvN7ixYsZP378V5a5aNEiRo8ezZAhQ1i0aFGbKjp69GgaGxv56KOPWk1fvHgxw4YNw+PxsHv3bqZOncqwYcMYN24c8+fPxzCMryzvrbfeOqZ+DzzwAH/4wx8AKCkpIT8/n7fffpuJEydy8cUXM2PGDBoaGnj66ae54oorGDlyJH//+99blbFhwwYmT57M5Zdfzrhx43jllVeag8q2kmWZ4cOHk5iYyPbt25unz507l3HjxjF06FDGjRvHa6+91jzvxhtvBGD69OkMHTq0eT9uv/125s+f37zcibSRcHpIDcHmQAnA8dePT2h923+2ohysjJUFSEQBCQuteZpCsDkFU2p1VxQTkJCOm6Bpa36mYhDCjQTIGM3bcFGN0lTe4S3JzduSCBK727VCFAsJlUBzmbGraI5sQ0Enih0ndeg4sdCa66hgYqISJL6p5CguqnBQjoMq7ARwU4OTRjbkZAEwbPde7vj0M3LqGxizbSfTP/ikOVCCWL/UYZsyk+nc6I/tq2VR2NlD1KkQF47Sq7CUHntL8PiD9CgsQpdl1vfqyqp+uUz5bBuOpr336AZJwRA0fYmkRaJ4dJ1yhx3NNDFlmYiiIAGffXSkHQShPbKk1o/T6a9//SsPPPAAnTt3Zt26dQA4HA6eeuqpEyrnuD1LHo+HLl268OCDD1JQUECvXr3Izc09Zuhs/PjxzJo1iylTpuD3+1m9ejUzZ85k7ty5rZbbtWsXW7duZfbs2ZSWljJ79myqq6tJTk7+xnq43W6uuOIKFi9ezCWXXAJATU0Nq1ev5plnnsHn8zF9+nSuu+465syZQ3FxMb/4xS+w2WxMmjTphBqlpVWrVjF//nwsy2LKlCnccsst3HTTTSxfvpy1a9fyq1/9iksuuYSMjAz27dvHzJkz+f3vf8/QoUM5dOgQM2bMwOPxcPXVV7d5m4ZhsGrVKurq6ujUqVPz9NzcXObPn09KSgqff/45v/jFL8jNzeXCCy9k4cKF5OfnM3fu3K/NyTpZbdQWHo8TTTtzU+RSU+NPdxWaWV4XVo4XimoAUPpkn1j98ruALIMZyxywms6JpBaZBLGeocPzaREaSc1LfDOTw+daFmAjeExZURwYKK2mGS2CEI1Q0zqxuTpObPiaamEhEWkOmEwUwMJAQ2nqbTqyLzEyBhZWU3B4eJtHtm4jyMgdu3indw/ioq3LSG9sRJWi6FYsoPTbVeLD4Nc0tqcl0zUcQms66XFFIq3Wdfr9LO/am3/kdaBHrZ86Cw44bHStTyIvGDrSYrJMRZydzg2xYEiXZGymidl0LFWayu/cNe6Mej9+lTO9fqeSaIsz20svvcSCBQvIycnh+eefB6BLly4UFhaeUDlt+vZ67rnnePXVV1m4cCF79+4lPj6e66+/nsmTJzcvk5eXh9fr5eOPP6a4uJgLLrgAr9d7TFmLFi2ie/fu5OXl0a1bNxISEli6dCm33nrrcetRUFDA5MmTqaioIC0tjaVLl5KdnU1+fj4rVqxA0zQmT56MJEnk/n/27js8qip//Pj7lumZSZ0USOhdigiogIAURSmLit3vqghWFHeVxZ9llXV13bUsi9hXbCu6igIqgspio6gUBSlSpKRB+iQzmUy75ffHhEAEJKGYoOf1PDxk7tz7uWVm7v3cc849p21brr76aubMmXNMicCkSZNITIzfAZ911lmsWLGCCy+8EICBAwfi8XjYsmULmZmZzJ07l+HDh3P22WcD0KZNGy699FIWLVrUoGRp0aJFLF26lFAohGEY3HTTTXWJIcCoUaPq/u7Xrx8DBw5k1apVDS5KXL58+Qk5Rg1RWRk6ofGPhdfrprQ00NSbUY/89i04Zn6CmWAnNG0UZmO2Lycd6+zJ2D5YjWnKWP63DTkUxpRNTE3GsDoJd2iPffsupFgEPc2DEgwihSJgKkgYGGY8OZExapMRGdAPSKrCmNgwUQhjJZFCNFQ0HKhEieKgmlSshGqr2qyoRJAJEcGJhWoclGMAOlYshIiRgAlYqEYCdDSieJAwMZCwUEMlLXDiw0IlMfZfpOwEsVFJFDu2AxI9s7b8yQRqSKRdhY8/ffol29NS2ZmcRDtfJVU2O7kpyVxufs9eNZl53bryypndufy7LVy1+gcmfLOZHzOTWdUxmyqblaFbt+GKaQQtdmx6jNysZCpsKmGLyuqUBAjF2zTNb9uSW3/Ygdsw0YD5p7SiY2mAalnGb1FRDYOscCRe9qZpGA6Zjt0cjL7I2ey+jwdqjr+XpnKyH4sTleg1p2q4YDBIVlYWQF0hj6ZpWCyWRsVpULKUlJTE5MmTmTx5MuFwmCVLlvDQQw/h9XqRD+ji/4ILLmD+/Pns2bOH22+//aA4oVCIxYsXc/PNN8dXrqqMHj2aBQsWcO211x6xoXf37t1p374977//PhMnTuS9995j/PjxAHXtpQ6MkZ2dTXFxcUN28bDS0tLq/rbb7fVe75tWUxO/U9yzZw9r1qzhs88+q3vfNE0yMjIatK5Ro0bx5z//mXA4zMyZM1m1ahXXXnstqhr/mP773/8yf/58SkpKME2TSCTCeeed1+B9OVHHSDj+jA4ZBGcd/QML0dF9iY7++bZuh0tfvV43FUe4AEglVSird6K1y4CuLQ4Za9/JJXiI9yK1/xojQryM6FDrigJRw0R5bilMf4ewbEVHxWWEQbZQM6Q32mMXcuqnu0nq3IIdicnMNwIs9rZir9tNcrCG260ryImWc+uGFXzTqyVn7SxkX6lUhyIfr5/ZgxaYrOnRjcuWf0XbilJeHH42mf4ody35ltn9u1Lg2v+kmybLLM9Kp6UWY3NGMh1LA3S1VzNlejYuKw16sEUQhGPTr18/Xnjhhbq8A+C1117jjDPOaFScRteL2O12xo4dy1tvvcW2bdvo0qVL3XvnnXceTz75JElJSYfckI8//phgMMi///1vXn75ZQCi0SiBQICvv/66QSUkF110Ea+++irdu3enuLi4rsQmMzOToqIiTNOsOwkVFhYeNlFxOp2EQvVPu6WlpXUN1I9GVlYWv/vd77jrrruOOgbEj/Edd9zBpZdeyty5c7niiitYt24ds2bN4plnnqF79+4oisK0adPqtYc60sm3scdIEA7HTE9EG927qTejPllCv2UEgVtG1E06MOVTAfXaXnQGOgOjnj+NO014fq3Oywtl/jJ8KD1KSljfIQeprYdyh43WtRE0WaJHOIxc+7t5ZeggXJEoLQt9ZJfEq0v/X3klT59/BttUK7ok4Y5EQZEpl638aaDGBQOSgeRf5FAIQlMym9HYIPfddx833XQTc+fOJRgMMnLkSFwuF88//3yj4hxxl/x+P0899RQ//vgjmqahaRpLly5lx44d9O5d/2Tpcrl47rnnmDFjxiEv3Psadr/11lvMmTOHOXPmMHfuXE4//fQGN/Q+77zzqKys5JFHHmHYsGEkJcU7ejvrrLOIRqO89NJLxGIxdu/ezauvvsq4ceMOGadz5874fD6WLVtW9/TZd999d8h5G+riiy/mk08+4csvv6w7Vjt37mTt2rWNjmWxWJg0aRKzZ88mGAwSDAaRZZnk5GQkSWL58uWsXFm/4W9qaip5eXmHjdnYYyQIv3ayBDf3VVg1PZHHX+3JNYtH8M9ZnXnj1iQ6LxjOqnYZ5HncBGVLvZNlgqaj6iapVfvTsfSqIG5/mIyYxoDiMj67towPH/Iw729JXDBA9K0kCE0hPT2dd999lxkzZvD444/zj3/8g3feeQev19uoOEdMliwWCxUVFUybNo1hw4ZxzjnnMHv2bKZOncqIESMOmr9r1660a9fuoOlbt25l8+bNXH311aSlpdX7d/XVV/Pll19SVlZ2xA1OSEhg5MiRFBYW1rUd2jf9qaeeYtWqVZx77rncdtttjB49mquuuuqQcbKzs7nzzjt5+OGHGTZsGCtXrmTYsGFHXP/P6dChAzNmzODNN99k5MiRnHvuuUyfPh2fz3dU8c477zw8Hg+vv/46/fv3Z/To0VxzzTWMGDGCpUuXMnTo0Hrz33LLLTz//PMMHTqUhx9++KB4jT1GgvBb1qWVlfTHFC6o+j0ZH5xD6t6KeGso08QTidI6v4hKj7Nu/qLURNoFgiycILPgiXRa5WSLqjbhN8lUpHr/mpokSfTq1Yvzzz+fU089tV7zoQbHMBv7XLsgHIXm3AjyZG+kebyJ47HfsmVLueiiCyjeW8WS/ksoczjwZyahyhI1ukFOQQlWzcDndvFNz4784SobHfr9uoc1Ed+P/U72Y3GiGnh/4J5T7/XYQNPdkA8ZMuSwNy2ff/55g+M032e5BUEQmglZlen82GnYbvuGjKpqXh1xOoppkr23LN4+KRwmsTpIh36i/Z8gNCePPfZYvdelpaW89tpr9Z4ub4hmlSxNmTLlsO2Gli1b9gtvzfH10ksv1TVq/6knn3zyoPZfgiA0L23O9qK/OIhdN6zkwuXr2Z2Vilo71IkEZPhO3hIGQTiemlMD79NPP/2Q0yZNmtSoMVObVbL0ax7H7LrrruO6665r6s0QBOEYtD8jkcS3B7DoD1tJ1mN1XV6aQGma6JxQEE4GVquVgoKCRi3TrJIlQRCE5i6tcyJXze/Dprfz+XCJB0c4gi/dw11PtD7ywoLwG9CcOqX86bAm4XCYL774ol6Hzw0hkiVBEIRGUuwKPa9uQ88T2/G9IJyUmno8uAMVFRXVe+1wOJgwYUKju8wRyZIgCIIgCL9KjzzyyHGJI5IlQRB+U8yIjmRTjjyjIAhHpamr4b766qsGzdfQcVVBJEuCIPwGaBVhdp8yD1k344P0Kiae32fj/fsQpGbUvkIQhGN37733HnEeSZJYunRpg2OKZEkQhF+93P4L6xIlAEWXiM3ZQOIrcyiydKQ0lo6JRKRFAq1eHoKjd3zAbLlSZ/Gd31L4vR9vGwf97u+MxR4vlTJNk3//pYDdxRAD9mS7GNTbxpUDbXgSxalV+O0ymvj+49NPPz3uMcUvWhCEX72IL4YNA+mAEZ46xLZjI0qCVoFFNpEMicI9JvkjP0YfmEnnOUP498KO+FKTONsusbMIfpjwAyP+0ILV09ZjiWpomWk43AkM3LKLqh0J/Lg7iynLE/nBZaf/zgLcF7cmFoUerVSu6i2q/oTfhqauhjsRRLIkCMKvW3WYJKqISVYcpkYEKx65iDxbNqrRAm8kQomZSjmJ2IiQSA36il18dVqAkpH9GVxUXhfK77Cz4p7NJIWimECGr5KU6iAhh4Uf27bkszaZbE9OwBsMI9kdfPSDSrVV5a1tGm8uD7PwNnvTHQdB+A2qrq5m1qxZrF69Gp/Px4EjvInhTgRBEACqwzhOvY+AfCpVVhcDw+sxUClXPOyy5xCVLWSZZfijHgCyqCCZagC0Gokn317CxlPasKtVJgCypmOLxQDYm+Lmoy5tCTht/NA2nYhFxTQAAwoTXXzUJYdyjyO+nMtkfVGA/AKNnGxx2hV+3ZpT1wHTp0+nuLiYW265hT/96U889thjzJ49m5EjRzYqjvjVCoLwq6X8bwOb/KdQgxNbWCOGHRmTlFiQDtWFbPa0oVqJJzQqWl2iBJAZ9bE+oQN9Nv6IOxiiNDEBX4KDaqeNt07pwLrsDCodNki0glpbvSebYMT/1A8Y2dyQJdymwS3/ruGem1LonyXGLxeEX8KKFStYtGgRycnJKIrCiBEj6NGjBzfddBPXXnttg+M0oxFcBEEQjq+KxUVUkQiAgoHM/iTFZmgARGQLNjmChkKU/e2KTEMh3V8NBnTbUUDrvGLKbHbmdO/Ety1rEyWAn+Y9pklWoIYB+aXItWPH2UMxVKvCprRkbnglxORbdlJdrZ24HReEJmRKUr1/TckwDNzu+FBETqeTQCCA1+slNze3UXFEydKv2N/+9jcUReGuu+5q6k0RhOPHMCCqgSQj7yjBsnE3tpe/wLCoaLl+9LREoo9dTOHln5JZmY9MVwxUYlgIYMdNGBMoURJRwzqYCn6Hk7x2yVQEHfTO34k1ZlBJEjIgIVHhsvPlmd3BZqODptOt1MfizBR8NisEY+CxgiwhaQYpueVcvbOAQLKbKzfkErSqbHM62JSdAkDQZWONrvH+BV9RfFpr+p7tJruzHcWmkJ2pIqviHlY4uTX103AH6tKlC6tXr6Z///707duX6dOn43K5aNOmTaPiSOaBrZ2Ew7rhhhvYsGEDqqoiyzItW7bkuuuuY8SIEQAUFBQwa9Ys1q1bR01NDR6Ph65du/LII49gsVj44IMPmD17NgsWLDgh2zd27FhuvvlmRo0adULiH6vS0uY7IrvX627W2/dLay7HQ/LXQFU1yq4y9Ow01I25mDe8TaJRiYQGGPjxUEkKHjQsGJioqEQpw4tfSsIjFVBheCkjHSsROpKPBOwhkxhWNEmm0u7AkCTy2qdgyhLJlUE65ZbWdTOwslMmsRQPhiQRRUaXZYozUomqKp9nJrPD68Gw1953+iNIYY1LfthFmnN/Y+7vvR6W53gBOH3XXs7ZkouzJsybfbpR5HIQtKhMWLOZHvlFGIpM9oT26OkerLEYpm7Q9lQ3oWqTnWGJd/fY+CFqJVOKcINWhmRCq3Z2kru6CasKaVYTRT2+VyvTBMMERT6674eumyjKkbcpGjaIhA0cCTKqKmPoJrIiYejxy1QoEGXvphAZXVy4klUMwyQcNJAlE1MzcSZbwIwX9skHPJFl1JbwybKErpkggSzH+9o5Fs3lt3K0vN4TM/jzf1u8Ve/15XsuOyHraYj8/HxM06RVq1aUl5fzz3/+k2AwyK233kqHDh0aHEeULDXCxIkTmTRpEpqm8fbbb3PvvffSuXNncnJyuP322znzzDN59913cblclJSUsGzZMo41F9U0DVUVH5Pw2+K54l9Yl24EIIoHAxsSQaz40VGQMVEAD35smFiJIQExnETwUC152JWYRsuaMIGoFxmTkGJnK61I1/3EsAKgmgYOLUyFx40pS6i6xhmF24ioFnIT0jAlcNpUfIpCgi+ALRQFINXnZ0OnVuRnejAsajybCMYgomNKEl/mZHBJcQW6qhBUZPY47WCapFcFOXtbAanBMKph0K6sknKXkyt2F6OnprDDaqXDjjx2/ScXu2ZgDUfZ1TKVra/oRCwqhsVCD6CTIrO6pZfr2rUEYMwbebQtzac40UOLYJic7k7G/KElqvXYS6lWFslM+MxKIAr39dF4YGjDly3Mi/H68xUEAwaDz01gxJjDX5x3baxh3qwigrIFTVFwWQy8u4rweVPQJInkKj8W3QDTxATyvWmYsoRD04F4W7GMFJPysIqum4y8MpVeAxNYvzbE/DcqAWjvjOLLi6ApMrEUB5fdlEabDrZjO0DCQZpT1wEtWrRAUeLV66mpqTz88MNHFUeU9x4FVVW58MIL0XWdrVu3UllZSW5uLuPHjychIQFJksjIyODiiy/GarU2KvaaNWs444wz+PDDDxk3bhzDhg0D4M0332T8+PEMHjyY0aNH89RTT6HrOgB//OMfKSoq4qGHHmLQoEFMnjwZiD8F8Ne//rUu9t69e7njjjsYPnw4o0eP5oknniAcDh+noyIIx4ecV4Z16UYkQAJUwkjo2KhCxsSCVtf2SAJsBJAJIxHGgh/QMBWdmKJSKmVgAgpgMUyCipNSKbne+joYW/FUhuiwrYQBW7bh1ML8kNKCKocTv92JszIEgBrZ38bIEYpQLRmELbU3MpIEB5ScpFaHyCipQI3FKHJY2e1yYglrnFFcRSg1mR1tsgk67CRGovSoDGCpvakKuBMIuhzIhonbX4NF00mOxpBNE1NV646JVTf4skMLAnYrAbuV909pjVM3yQ7UAJC/sYatX/mPy+dx/2oLvoiEZkr8ZY2KL9zwG8Al7/up9huYJnzxcTW+ssO301o2v4KoKaPVXtiCMZkibyqaJGOLxuKJEoAkIZmQ6g/UJkoQLyqS2FspE42Y6Bp8/EY5hmHy/ttVxGKgBGP48iIAqLqBWRXl43erjuqYCCePgQMHMn36dNasWXNMcUSRxVGIxWK88847ALRu3ZqkpCTatWvHQw89xEUXXUS3bt1o27btURfx6rrOihUrmDNnTl2pUnp6Ok8++SQtWrRg69atTJkyhaysLMaPH8+MGTOOWA2naRq33347vXr1YuHChQQCAaZOncrMmTN/kTZNSUkOLJbm+3U7UcXRJ6smPR5VwYMmSegc+GsykZAOalkNoGEhTGt9CzuNTBR9/3yKaSKZJmFZppW+mxAJpFBKur6HH5RuKCGDoMWOR6ohpuz/rtoiMVzhMBGHFWcwfnMRsVloXeHHounE1PjFvUtFkC0OG+5wlL6xGBXeJFTgFF+AEqedwgQnbQLxxEsGKhLdLGudSYvYAfthmiiaTpk3idTKYHzL951HTKg7CFL8Cbt9DFlCqt2/fZJTnMflc3TZ9ic4qiyhypDcwLhOpx+Il8ZJEnjTE0hOtRxyXk+SlcL8+smUZJogHfxJS5joklzvkPyUapFJT3djsZQSDukHzyBJOJzqMR8jce44WHPqOuCll15i4cKFTJ06FVmWGT16NGPGjKFz586NitN8r17N0Msvv8zrr79OTU0Nqqpy33330bFjRwBeeOEF5syZw5tvvsmOHTtwu91cdtllTJw48aiSpilTppCQkFD3evjw4XV/d+nShVGjRrF69WrGjx/foHibNm0iPz+fV199FYfDgcPh4Oabb2bq1KlMmzbtmOvuj6Sy9u68OTrZ2x0cb01+PBJdJFx0BvZ53wCgY8VARkdFIX4xNQEfKcSw4KW43uJ2ytBNhTOr1rFTakMYV90yLZVcusfWAyCjADJFSjroCgbgj7mQbCZpNdWUOeO/P08wRMrWQsIWC7lZyUStFtRIlOxiH7d+/h3fZ6cTVq04Ijpun59R2/LQHTZqEvf/fm26QY2qoEug1F75E6pDTFm6lq056TgtFqJ2G/ZIlILWLXHWhIhZFCwxHU3TkVUFJRZDs8VLqjVJ5tS8Mr5tnYZkwuhNuwkk2ynxJJAdCNGlj4vMUyzH5XN8uK/E5C+t+KIS9/eJ4bY6Gxx3+Bgn5WUR/FUGZ49MQDPClJYeujR76JXJ+GZGKCzV0BSFVLdJWkUlZZ5EdLeVqBbFEo6BaRJTFHwtktAjJq5oDNkwUSWDzj3t5BeZ6JrJuVekUFZWzcX/l8iCt6qQTJlst0Lx9hARU8adbefci47tu97kv5VjdKISvaZ+Au5A3bp1o1u3bkybNo1Vq1axcOFCrrnmGrxeLx988EGD44hkqREmTJjApEmT8Pv9/PWvf2Xt2rVccMEFACQlJTF58mQmT55MOBxmyZIlPPTQQ3i9XsaNG9eo9ciyTEZGRr1pH330EW+88QaFhYVomoamaXTv3r3BMYuLi0lOTsbhcNRNy87OJhKJ4PP5SElJadQ2CsKJVP3c9VQ/OyleHFFbuqD8UIA55R3k9bm48WMniJ+WBPHgIl7lJBFPikIkYxrQiw3k0Yp8pQVhu8yg4Pq6KrwoCrvkLuzRW9atNz8nhXw5lW6FBSTWlBIzFWRNwoKJmyhJ/hDft/OCJFPkcfFJq2y8hoGsQ9Q0uPy7rSQHajBkiZWndibJMKiwWtiU6KLaqbK4bSa9SqqQDJ1OxRWgytjsdraluSlIsNF/dzGnZeoMv709FVUe9uTmcm7XVmRlteDD+e+z9YcAia5WtBnYj2GpKpJZTZZXxn5pBnZ3y0McyWPXPcXkiwsiR7VsarrKzdO8DZo3MdXChAdzfjI1+6D5TNNs1M1dhy42pj6Q3uD5hV/eq7NTAAAgAElEQVSvdu3a0b59e1q0aMHu3bsbtaxIlo6Cx+Phvvvu44ILLuDzzz/n7LPPrve+3W5n7NixvPXWW2zbtq3R8SVJqncyKCoq4v777+fRRx9l4MCBWCwW/vWvf7F58+a6eWT555ufZWRk4PP5CIfD2O3xp3QKCwux2WwkJyf/7LKC0CT2/QZq/9e7ZsOSP2AAvtpZnLpOaMUutIXfwpAOoOnIX+cRzUwi8ZF3qTFcVFqdhBQLhiph1qVTEMNKpZGKXDdNIqM8gKprVCoubJF41U0C+xMFi25gjersTvfw2IBTqbFYsBgmfYMhBvyYS2KwtprNMPk0KZEau5WILCPVaCiJsMvrZldaAoqm80mLFG7sWc2dgz0YMXC5ZGD/nb7bm0PrDvuThzEXjWPMCTjMJ5sTXQouHLvm1HWA3+/n448/ZuHChaxfv56BAwcyadKkerU1DSGSpaOUmJjIVVddxTPPPMOpp57K66+/znnnnVfXd8MXX3zBjh07GtVD6OGEQiEMwyA5ORlVVdmwYQOLFi2q109Eamoq+fn5h41xyimnkJOTw4wZM/jjH/9IIBDg2WefZezYseLkI5y8FAUGdyA2+IBHgMf1A6Dq9uEYr6/FdedyJNlAicp8a+9HK20nLq0GOzWczqfk04Ef6YIBuGs0ohaFqCqjKRKqbqAhodQmWD6XDU2VefnULtRY4m1vYrJEYaKDD0/tQAyTU3Pj1YKnlPtYnpMFkkTEpmLUaPxux05alFWyeUA6T1wao3V2bWlQ454DEQShgQYNGkTv3r0ZM2YMs2bNwuPxHFUckSwdg8svv5w33niDTz75hIqKCqZNm0ZZWRmKopCVlcXUqVPr+mE6Fm3btuXGG2/kzjvvJBaL0bdvX0aOHMnWrVvr5rnuuut47LHH+O9//0uPHj148skn68VQVZUZM2bw+OOPM3r0aGw2G0OHDuW222475u0ThOZK6enFa5ZSLVkoIZNcdw471DY4tBrOLV+Caui04kd204YITiR0rDEdyQRNlbAbGlbTwCSeFOV6PQQTnJxW5iMvMV4KlKIY5KW5gQS2ZyVz6q69OKIaX3fMpnt+KbbqCN+0zgLTpH1FJTf8oz2pLR0/u92CcDJrTm2WlixZQnr6sVfDik4phV9Ec24EebI30jzefm3HQz77n+za4UBRw2z17H8C5lT/OroEt2ICS20jMSI2LBgomBgSBJ0WEvQornCsbpmPBnTFUx3CcFjY6Ung+5aZ5GcmsjN1f/VZ39wS1rSOn5yvWb6B5S0z2ZHiIb2ymo33O3+p3T5hfm3fj2Nxsh+LE9XA+9W279R7fc2ui0/Ien5Jop8lQRB+1YzP76BNZBcpekW96aYhEZZsbLD3IiI7AAkHYVwEUeUYrhwLUdv+U2RZcgI1ThsVHhdyVKOdvxq7LNPKF0Q24n0AJYYipFTXkFNcyYjvd9J6TznecIgWwSCzrxGdHwrCyUpUwzWBQYMGHXJ67969D6o+EwTh2CVQRVo4TCCQQLE1g4hs4wdHd7ar3VBMg9Y1JSiYBHEABm1W/g5bWzdr+r3Lt61aoasWStI8tPVX4hiUxdptHnRZRtF1HJLEFd/tRJdNvP4QezwuRuzIp41bJ/nuXlyTZmN8l+ZTLSEIJ5rRjKrhjheRLDWBZcuWNfUmCMJvStVZp2FbvpJTqjeTo+SzKmEAzmiAzFAJpZZ0fkzMJIkQ6eNak3xfH1RPvMV16J9JXJDWh1UvbqNjKys9pwxAkiRG1sZd9V2Ip+aG2J3splUHhY5ntuLPHY2m21FBEE4IkSwJgvCrJ79zPeX9fbh35aLqNjpXF6H29KK+fj2pXtfPLps+OJMzuh56ntN7O3it94GNtUWiJAjNqQdv0zSZO3cuCxcuxOfz8cEHH7B69WpKS0sbNfC8SJYEQfj1k2XMb/4f+0ZLE8+iCcKJ05yehps5cyYrV67kmmuu4YEHHgAgMzOTRx55pFHJkmjgLQiCIAjCr9L8+fN57rnnGD16dF2fgtnZ2T/bL+GhiJIlQRAEQRCOm+ZUsqTrOi5XvBp9X7IUDAZxOhvXjYdIlgRB+M0zTZMvXyvgi5VB9jrc5FSUsDWjJZ1Oz+Sipt44QRCO2uDBg3nkkUe45557gPhvfebMmQwdOrRRcUQ1nCAIv3nf3r2KNYt8VLlSccpWognJPPfOcwQW2QhERKNtQWgMU6r/ryndc889lJWV0adPHwKBAL1792bPnj1MnTq1UXFEyZIgCL9NusHbFy7hg+z2qHo6WSkWJNNEk+D7TC+Lu/fgim++YlVBD3oe3XBSgvCbZMrNoxpO13U++ugjnnjiCaqrqyksLCQrKwuv19voWCJZEgThN2nq3wt5bfj5YEJ2MExamQ8ZE1OWWdsiieUXX8mlK9dzaY0OIlkShJOOoij8/e9/5+KLL8Zms5GamnrUsUQ1nCAIv0lvObNRTJNLduZyVkkJ++6FbbpBRnUEgE96diZS5D98EEEQDmJKUr1/TWno0KF8+umnxxxHlCwJgvCrE9IgUK2xbH0EPdHOmFNknJb9J+1XvzdoGwhhWHS+bplIvpyAK6YxwufHo+v4bBYwTTyRKPfPizDwFBuxGoNw2CAjw9KEeyYIQmNEIhGmTJlC7969yczMrHsiDuDRRx9tcByRLAmCcNLbtifKv2ZsxQxGyQlV8UqPISiBKOV2O8gS/+9L+GqiSYZLwlets2nmJmq6tiMvOT0eQDcI+kKsSnBgJDqpsqjYwhrjN+7ig+wsOs4CQ1LwhHUyfBW06pHAM5dZqR0VRRCEAzSXNksAnTp1olOnTsccRyRLgiCc1P77jcYz8/1sS+9O17JiWpdU06Mgn/VpLelYWkWp00al3U3P2WCqMgPlME6vk4LEFFpWVXPetjxisszCjHRKHVa0BDsAI7d9xwUbPyKtuh9LOp5GjR7BkCS8hkZws59Oz6XhioRJT7Ew5yKFr3ZoFKwt57wWMfaUKVSnORg/KhFZaT4XDkH4RTSjfpZuvfXW4xJHJEuCIDRre4vD7Pnfj7Tq5cXbPYNQDN7cAnsCEu9/FaLV7nJahWJctXE1U1YvZ0Hn7jzXbyA1Fgv+FqkM25TL93aVMocNWyDMSq8bs1N3+uUVc+72fDyRGAApgRr+1bc7AKO3rOW91x8HYNDujWxK9bKgXTduW/8DnmiM13p1xLBZCVgtBMMGo1+JUZ7gpF1ZFOuiLUQTHVgMH5eu1FiXlULfknLu9+6kw+R+WFWZ9SvL+eabEAUJbrp3t7K90qDgsz2EwiZmyxTumZBCmgNS3YduVqrrJsovmIStW1zCFd8l4bdb6J5ezVsXQ5L9F1u9IBy1r7766rDv9e/fv8FxRLJ0gsyePZtnn32W6dOn07dvXy655JK696LRKJIkYbHE2z5kZWXx9ttvM3bsWMrLy1EUBVVVadu2LTfffDP9+vU74vpKSkr4+9//zrZt2ygqKuLBBx88aNybA+Pv8/LLL9OhQ4fjtNfCr9HeINy3UqUyIjG1j0b/LLPBy9ZETf7yP50txQYOA9xWuHGwSo5X5tolFrb6JHqkGbwyQmPJl2E+/DpGMQobkl1YVYmkGj8bZk6jUyxCwGbn3Al3sy6nPcQMCOugOLhp03ZSqeL/Nn7Fc30H8MfzLkSO6fTx+fHENH5on0F2dQ1lFgtRu0pKIIIlqtOm3I+7NlECSIuE43+ENc7cvR2AXFc2RfZ0uhUW8HHrTiRFY5Q6bGxNS6pbzhIzKFcsqMEYlS4Hr444Db9VJShJmIoEIY3PU72sjqbS8+5itrbwUubKol2sGn/IwoJvTJJqotgcyVy0ayv/c2dw63MRNnmcaCag66Cb2AwDrAoRpw0kcMR0WqeCxSZTXKHTZdM2qhUbPUvz6BKsQfOkEMhqwUbFzhqPGznFytPnmbyfq7K1UuaKjjqXd9QP+9k9+43OS9+aWEqquG7zD5zZuht+qw3TJ3HJ3xXWez24rbBpgoZdhTe2Kby1XaFLssFfTo9Pa4jyMNy7UqW4RuK2XhrDchr+/RKap+ZUDXfvvffWe+3z+YjFYmRkZLB06dIGxxHJ0glgGAYLFiwgMTGRefPmMWbMGJYtW1b3/i233EKvXr248cYbD1r2vvvuY9SoUUSjUZ555hmmTp3Khx9+SEJCws+uU5ZlzjzzTK6++uqDvhyHii8IDfXHLy18mh8v4VhXamHj76M4GnjmeGKZzpzvDNyGyb7mPZv3xmjT08XaknjMr/Yq3PFeFG19FAAvGlnBKHkuOy8unk9yOAiAvaaa+754j4uvmQqKDIYJEZ25g0/nqu++ZvbpQ1mb6cUZi9DTV8NplQEAOgRDfNg+iwFl1SBJlKkKYVlmTVoarSqr2eVNZK/HyRWrVvLQlwu4b9QlLG/bmbw1LfikRbyXX285TP56CaVJbUmIatg0nYiqgAkROb4fmizjCkbo56umSFX5Jt2NjgSaiQEEJIkV2S1JMg2QJHamuOlUEWDUjr1IgN+i8mmnHpiyjFUz0AwzXp1hApJERFFAB6T465BFoaA0RrXLghIxWZ/WGoAdyZk8uHwuO7xd8FVLLGqVHH8iqdLksvcUYrVZzNdFEj1SDU5JOTg5+XSHwQP/i3fG+fTKJcztN4qNiS7OqKgGTBI1g8qaCLmSnTs/l7npVJM/LlMxkfiqSCbFDnedpjXoO3L3CpUFO+M3cN+WWPjuyigpotRKOE5++iScrus8++yzdUOgNJRIlk6Ar776ipKSEp544gn+8Ic/8OOPPza69MZqtTJ27Fhef/118vLy6Nat28/On5aWxqWXXgrEE6fmJinJgcXSfL9uXq+7qTehWTnweJRGYsSv2BCISdg9CXhdDbtzrIwGgGi9PkqqI1AWUerN5w/CgSM1WY34+qpt9T8Xi3FASYgsYTHBFYsybdR5aIpM96I8ugaKSNMc+5cxTboXV6EpCpgmGTGNXJsVXZZ5pXcXylPja16bPY45/5pLi17l+MKJzOswGFcoHkMCRm7fwvyedmwxibs+X8grfQYgmzZ2H3DSTYpECeg6K5PcUFObLFiUem04JHN/ctKxIlDXZYEnpuHUdAJWmZgs7V/mp7mMSV3CZNQufWBMgBrVimwaBBWl3qPbMc08IIxE2OLE6z34fBHcEQHiSWpWTRV7HTbsRv112HUdJCiLqIQsFkz2fzY+3YLX66AhyqL7v18hXUJ2JeBNaT4lE0cizh0Ha+ruAn6OoijcdNNNDBkyhAkTJjR4ueZ79TqJzZ8/nwEDBnDWWWfRsWNH5s2bx7Rp0xoVIxwOs2DBAqxWK1lZWcdt22bMmMHjjz9OZmYm48ePZ/z48cct9s+prAz9Ius5Gl6vm9LSQFNvRrPx0+NxS3eZ2z5T0UyJ33fRkWoilNY0LNaVPQ0WbYJQGNy119rxvWUGnxrlio8s6KaEIplMGqKwpFKmtMwgJEsUOG0AvNJvOBGlhhtW/Y+CxBTuHnlFPIhhIoc1EnSdPR4nmhK/4G/MbIU7HOaOt96n+94S1rdpywe9exKT5bqkpO5/0yRq3Z+0RS0qE68fhyMcY0h+Ea6QpS4vUY0YXSt3kbmugieGTqRNRSG7ZtzO/f2v5etO3VnrTSY5GmPw3nKWJf+kB0uztoRIkkiOxnDpNWgWiU7llZQ5rHTy1a5flshLsJMU1VEAhXhBErIEerw0CkXavwO6gSpL8V7HVYXESJAqm4s2VSVIsoIzHCJLVsiuCVPgrG203lHi63KTqqjEGRkG3Z1hSksP/twGtzTpmArby+Httj0498d1PNu7P+2qw7g1nbAikeeyg2nyx94xerqi9E23sqZEJslqclXbCKWlDatOu6GbzOq9KhFd4sL2Oola5JDb1Byd7OeOE5XomVLzu2E/0IoVK+p1IdAQIlk6zkpLS1m2bBn/+Mc/ABg3bhzPP/88U6ZMwW4/ctny3/72Nx599FGCwSAJCQn84x//IDk5+bhs2/Tp0+natStWq5U1a9bUVdf9UgmTcHIa38HgrBZRAlGJDkmNa0/Sp6XMyskWSqvBbTWJxKBNmgyYbPl9lPVlEt1STNIccO7tLrbv0dlZDtc4dDp6TT7e5eZ96whe7HMOez0u2uU4aV1ZQ7giSv895eS5XfgS9pdgSKbJHz9dztCtPwIw4vv1pFZv4dOuvVjbshemBFtSXJRLMpmBMJ5gmGqXNd7GwjDx22z4bTZe69+Dv77/JWGblUT8jNv6EUmxAMnlfoZvW8kHPUZQkpBKq6oyTnv/a1ad3gnTGj+dpsX2Vz9ZNZ2Yw4KpStgNg/G7N/Oj6cKvVFOT4KTMbkVO8ZDtD5LnclBiUShWJNpX+GlPlNI0D21dGha7zJi2Bjt9sKkgiC0cZfxQD6e1seCxRimqBkuVjS+WFNK9ZYiug7rg79cRj25wvQlbYiapCRK90nUqIzrFNRLtE03Uw1zTEu0SSyepbC2FstgAnn2xgPaVJazIyMAZiZEqa5yWZTLzXI1Wtbnhe6Oj7KiSyHKaJNoa/h05r7XB2sujVEYkOiaZzelBKuFXYMiQIfUSo1AoRDQa5f77729UHJEsHWfvvfceiYmJnHXWWQCcf/75PPnkkyxZsoSxY8cecfl77rmHUaNGUVZWxrRp0/j+++8ZNGjQcdm2Pn361P195plncuWVV7J48WKRLAlHlOGEDOfRNbxNdkgkO2B/kUhcoi1egrGPokh0yVHpkrN/ni4pQJ/0n0SUABvQAsOEax4oJz0cISrFOH/rarzV9bdz+M7NLOvQiS86eLFoOgO25fF5h9Y4wn5WzvwXO71prMpoy52/G01MjZ8S9dqq7PJENzEpTFJsf+mBJqu4IkGKnS4+y+nK3i6JbGznpW95AF2SWJ/mYdD2YqptFsaNS+SWYbC/7qzHIY6QDfjpDdGBN1Y/7czpwE4x4/vawgVkOOnRqW3dOxkHzNXigL+TbJBkO/JnaVcletUWag9/MJuYZlJRHaNbew/l5dX8tH7QIkOX5KP7jqQ7If0ov19C89OcGng/9thj9V47HA7atm17xHbAPyWSpePIMAzee+89AoEAo0ePrpuu6zrz5s1rULK0T1paGg888ACXX345w4cPp0uXLsd9e2VZxjTFCUo4eckSvPxACrfdW0amr4ZKe0c25eicvW0XqTUhfC6ZF/qMZWPmqSzuXU5GqoXg7zK54eliNmdncPqdU+lbkMf3mVlcv/Z/PHP6SCTTpPOeUub17IjkcYOeQ7eCrQzJ30BuchZLW/dij83gtpvvYPZ1SQc8Qn9gcUp2ExyNE8uiSmQkScjN6EIoCEeyYcMGJk6ceND0l19+WbRZaiorV66kuLiYV199td6oxtu3b+e2225rdEPv1q1bc/755/P0008za9asI84ficTHszJNE03TiEQidd0Q7N27l8LCQnr06IGqqqxbt4433njjkF8iQTiZqIrEs3/f/3vbXKRx66mXkfF9MbpixWpR+cv1TnI672+IndDRjbMiwu60VHanxQfXvGYV/OuDL9Fkidd6dWZFu2z6FFXQdU8xC/80kW8/3o59j5+MDn7+7/IcurROOmhbBEFoXg28n3766UNe55599lmRLDWVefPmMWTIELp27VpvelpaGj179uTdd9/lrrvualTMiRMnMn78eNauXVuvGu1QBg4cWPf3gw8+yIMPPsj111/PjTfeSCgU4p///CcFBQVIkkRGRgaTJk3isssua9T2CEJz1y1TZe5tKUDKYedZ+H8ql9ydxxeeTiBJ2KMxEkLxtk8Ww2TMtly+z0glu6KKMVM7MKyXBS7p9QvtgSAIx2pfZ5SGYfD111/Xq0UpKChodNcBkinqYYRfQHN+YuRkf6LlePutHI8r/18pRS6djZk52DSdBxd/jV2LP/7ut0rIAY0tLTN46j+nEDGjTby1zcdv5fvRECf7sThRT8PN6vtxvde3rRl5Qtbzc4YNGwbA3r176z1RLkkSXq+X66+/nuHDhzc4nihZEgThN+mSa1LY/Ng6Vj3zZ+4efQUvDTyTset3ELGoeEsqGblhN713l2BL6k3EJ5IlQWio5lANt68zymnTpvHoo48eczyRLJ0kvvvuO6ZMmXLI9yZMmMB11133C2+RIJzcLuyqcP7zp/Ho2w/S642PeXLg+Tw+8nQckRhvzJgHgEXXsSpHCCQIQrN1PBIlEMnSSaN37971hkwRBOHY2S0St1yVBRddwWM3vM2PjmS2O9uTGqghaFVZOK4DI5rBXbIgnEyaU9cB1dXVzJo1i9WrV+Pz+eq1Xfr8888bHEckS4IgCA4bV/9nHACbKyR233gx/dvKDF79WRNvmCAIx2L69OkUFxdzyy238Kc//YnHHnuM2bNnM3Jk49pRiWRJEAThAN1STLqliFOjIByt5tBmaZ8VK1awaNEikpOTURSFESNG0KNHD2666SauvfbaBscRZwRBEARBEI6b5pQsGYaB2x1/6s/pdBIIBPB6veTm5jYqjkiWBEEQDkOp1igc9TYl35Ridkoh8+URWFKPPMajIAjNQ5cuXVi9ejX9+/enb9++TJ8+HZfLRZs2bRoVp3kPDSwIgtCEuk7aQeXiAswK4OsKqk55GXSjqTdLEJo1U5Lq/WtKDz30EC1btgTg3nvvxW634/f7G/2UnChZEgRB+AlTM9jx+g4SY1YiWNk3CHDIcKH/51uUa/s26fYJgtAwOTn7R+ZOTU3l4YcfPqo4omRJEAThAFpRDUXZ/6bNtAWoxDABDQkdMDExpr3Hrn5vEfmuuKk3VRCapeZUsmSaJm+//TZXX3113WD2q1evZtGiRY2KI5IlQRB+NfLKDZ5bqbGuUD/ovVhI54sX8vjgT5vwb6465PJlxRGevPZ7XEoEGVCJEUHFQMJERkelhLY4cwPERv6Xsn6vn+A9EoSTT3NKlmbOnMk777zDZZddxt69ewHIzMzkxRdfbFQcUQ0nCMKvwgvzAzxQkoJuU2Ar3Fyey1+mZgCgRQxG/LWKatPDhdtK2Xj3j6iZLkIZCQzuYUGviNJndCpzbtjIvB4duWbzWoiBUXs/eeBdZRQrBgZRLLTI3cGe1i8CKtKotmQ9O+SX33FBEA5r/vz5zJ8/n5SUFKZPnw5AdnY2+fn5jYojkiVBEE5qofwgs96sZH2RTEePSURVcBg6Sx3JvPSihfZ7fVy6YjOnJboZtisfixFvoG3ml5Loj1KS6mblGZ1ZvqaIYIKTPW4XX7buSN9d+YSidnRkFPTaVksmMiYSBgYyARJRQzpRVOR3t1H6v514t09oysMhCE2uqUuTDqTrOi6XC4gPogsQDAZxOp2NiiOSJUEQTlqb/7mF6XsSOWdnMcMxkTWDxV2ysVhsfNI6MT5PKy+bc1O57PPNBJMsdcsaqoQv0UYg3cUp+XuRYjqegkqCmoIWlPBHPew75WvouIkgAQo6yZQTxs4eMgAJDzXEsECVTl7myzjbG6Qtvw6a0UVDEH6LhgwZwiOPPMI999wDxNswzZw5k6FDhzYqjmiz1ARuuOEG+vbty5IlS+pN37hxI3379q1rhLbP4sWL6du3Ly+88MIh4y1ZsoSJEycyePBghg0bxpVXXsl//vMfYrEYAGPHjmXAgAEMHjyYIUOGMH78eB5++GHy8vJOzA4KwglUtjPIJ3/ZwuLxK1j/3yL6F5RT404g4HZT7bSzKScTXa4/+q1mUYi41Z9MU/GnuTHU+LymRcEhwaVbttOjuJQD0xwrOjLxZ+IMFAxU0vCRRQkmEjWSDRsaICEbMqEfFQrbvIi+x48662OslzyH+enWE3pcBKG5MGWp3r+mdPfdd1NaWkqfPn0IBAL07t2bPXv2MHXq1EbFESVLTaRt27bMnz+fc845p27a/Pnzadu2LeFwuN688+bNIzExkffee4+JEyeiKPsvBC+88AJvvPEGd9xxB2effTYej4cdO3bwyiuvUFZWRlZWFgD33Xcfo0aNwjRNcnNzmTNnDldeeSXPPvssPXr0+GV2Wjhuojr8Z7tCMCbx+04aybbGx/CtLKX03TysikTKBTlUbPGj2BVSbupWb77CoMTsTTKf50KSQ+LB/jrdU/cPRmnEDIrfzkMPxEge5MX/SRFKuo2oCUZYJ+uS1pR862PNUzugJIynPIRmkahOsROzyHjsCi2qQsR8MYIOC6psohuQm56EFNORDAibMot7taNTQQnjvvmRFsEYi8/sQAtnAIuioNeW4Gh2OyVuBznbi2jjqyYv0UV6MMzwjbsAUHUTX4oL3aJgDUWB/fuhRjQSK8O4ozXYYzo1B5weDQU4uM04VqIAlKUk4CgPYyOMgkmCWYMZkijq9x+6xnYgYaIs28FD/cawLqsNMZtCp5JKTs8tIr1DAtl9kkg6Lwtn18Qjfm6RBTswXvkWfU0xZlTHRhQdC37VTUpqgARvjMhL10Cb9IZ+FQThuGoO1XClpaV4vV4SEhJ4+umnKS8vp7CwkKysLLxeb6PjiWSpiQwdOpR3332XgoICsrOzCQaDfPrpp0yYMIG5c+fWzbdr1y6+++47ZsyYwdSpU1m5ciWDBg0CYM+ePcyePZs///nPjBkzpm6Z9u3b89e//vWQ65UkiTZt2nDvvfdSWFjIjBkzeOmll07szgrH3dSvLPx3R/znO3+XwqdjI42q8alYVsLGG1fFX5gmuQv3oFniBc3hrdV0/Et3AIIxGLvYSkGpDk4L1MDID1XWXRLG64gvvnP6BkrmxkspC/61BbVKI5KgoNniSf2ehXvIrZGxRHSyd1fVldhIpknUruCqjBCKmpiyhLVaQ43p5LdLjd+V2lRcVTXIVgvjV2zEWxPBGYyxsktLbFqM7JIqVEWhKCd+8pMMAySJxZ2yGLVtL1bdINFfjTOixffHamF9Tga7vEnYAyHO2Z6HozoCpkm7zaXIhknEtGInhopW+wSczI6cFvTdvZ0oVuyEcRNAl9J5GhwAACAASURBVGR2u7LIT05BcuiklfsxACshHIQxkGkVCxIiBTCRjRgbWnj5uGsbkCQ+7dCKIqeDq1b9QMFnRRQ99yOnLB2GLfvwbSnCszcRvHsFVmJISEio6ICKgVsLklvspW/xZzjPuh/fzqfAKk7xwm/TyJEj+fbbb+teP/DAAzz11FNHHU/8kpqI1Wrl/PPP57333mPy5Ml8/PHHnHbaaaSlpdWbb968eXTs2JFBgwYxcOBA5s2bV5csff3115im2ejRk/c555xzeOSRRwiHw9jtJ3YIh6QkBxZL8/26eb3upt6ERvmmTKv7e5NPxpaYQKKt4dlS8ead+19IEsYBReWl35QyoPZ4lJSZFAQ0UPbX2MdMiWJcdPPGp63/1lf3Xm3baXR1//yBvBrwurHXxOpVbVmiBlG7QsipYtH274+mSFQnOepeh102nIEIqq7tv2O1wqm74/0cZe2tIOSyE3TbkUNhzlm/k1XtW2JoMQzFQoE3haLkRM5Yt5UHLjiLMk+8sWfrUh/nb9uNGg6Ts7MKaV/H3JKEapq4iACQl5VMarQKlSh2YoBJOSms7NSNwow0bJEo52xYFz82yCQRLxlWMJCQiYeVMFB5bvECWnXrXrdv37TJ4so1WzAVMKo1LPlRvL0zDvu5FX5bhoRZ7ziayICBio6BTBgX7mglXlMHb/JhYx2Lk+33ciKJY3Gw5lCyZJpmvderVq06pnjN9+r1G3DBBRdw6623cuONNzJ//nxuvPFG/H5/3fuRSIRFixYxceJEAMaNG8e0adMoLi4mIyMDn89HUlISFovlcKv4Wenp6RiGgd/vP+HJUmVl6ITGPxZer5vS0kBTb0ajnJ1pYVdV/Ofbx2sQ9YcobcTytt4eaq+xYJooBhhK/ASXdXZm3fFwa9A+ycaOIgOIlxQ5FZMWUpDS2hV6BqYR3BGfX1bjMZSYgVZbXZzSxU1lqUHYZcEk3u7H5P+zd99xVlR348c/M3N727u9Arv0jiDSpKko0sSCEY2axwaxYUwMPj8lioom+GhMrI8mmIcodlaQGIOIGomAIkoTpMNStrd79/Ypvz/ucmGVqmzlvH3dlzv9zDB37nfO+c4ZiFrjAZU9oCJrOnp9QGZWDazBKBGHJT4cUVFUnZgugRlqku3klR/+ngAk1frYnWJna2YOE77YzoQvt7OrR3sOOq2osgxmM2t6d+bWD7/GGwzz/oAueMIhXDVh0spCSJqBgYQESIaOtb7NzUKUQcVbkIEYCiVSKhZDI4id7ltL6byjHEVTSSJIBDMBrKRTedRjnvj5MIxE4nfn8hok3UDSwJRsQS2wHvdc1IdnYby9tT4MMzAAub6stTixEMWBn7AzGb/JDI1wXrfG70tjae3Hoi0HetJpDthEsNSMOnfuTHZ2NvPmzaOqqoqhQ4eydOnSxPSPPvqIYDDI+PHjATj33HPxer0sWrSI6dOnk5ycTE1NDbFY7EcFTGVlZciyjMfjOW37JDSN3w+OcU6GTl0MpnQ8SjLNCXgHpXHWgnOpfG8/ZpNM8iW5VG+O5yz1/kVXKqsCANhN8I9xEd7ZIbPqQJRUB/x6gE7KEbF1/n29cPXzovlVvOem41tegjnTRlQ30EIamRNz6VYWYc3vt3DAJuEsDaGZJNR0G3qqFVOyBXcgSqQsRKXJhMWkkKlIHFSBkIbNH8GnmPno7AL6lFThyjYwx2IoJbXYIzGiFoXvspN5YXR//vrUEjqVVLNxQB6uUAg1+XAOUMBqIVNXiSTZufaLzSjBCB5fFEu0visBDPZluMEwyCz3I2NgJ5R4CsaMRkQyUSUnkaTFa49MWrwLgWrcKGgEcVCFFy+1aEhYqMPAFe9sQNKZ330YF2/YRXGqmzxfHZds2U3eVe1w5brwjsnCknW4Ru1obFd3Q850wMIN6Eu3E65TsOoRopIVJctKrjdCTdq5aH+7BkkRz+8IzaMl1CxpmpZofQFQVbXBMMDQoUNPen0iWGpml112GY888gg333xzg8RtiCd867rOVVddlRjn9/t57733uPnmmxkyZAiSJPHhhx8yYcKEU972smXL6N27d6PXKgmnnyz9uCDpSJ5+yXj6HW6mcfbyxtf9vR/ZVBtM760zvTdHJckS6ZPyEsO2Gzv9YB5XvonzXvxp71P7OQD5hKMGi5bWsfuLCpxfHsARjbErIxkkiTRfCF2W0EwK6bU+vF4PNfV9rGTW+KlOi98YhOwWsotKkI6ooJKAXVlJvHVeHx5/eRnZdT50iwNnJF4ragBePYRmNxGJSlg1g/h/EiGsJFOJjJUSMqnASzt5F+qG2ehRldiqYpS+GUzvlsr0xBbTgPxTPg6W89vB+fH3XR365loAZ/3fOtD8P1XCmawlBEupqamJ7gIAvF5vg2FJkli+fPlJr08ES81s7NixZGZm0qNHjwbjd+3axbp16/jjH/9Iz56Hn06qrq7m2muv5fPPP2fkyJHcdNNNPPnkkxiGwahRo3C73ezZs4f58+czbdq0xNNwRyoqKmLBggWsX7+e559/vtH3URBOJ5tFYuokN0xy88HfLRwoLKFDdQBF13llTF/ueO9L0kt8lGd56LN3P2pUIqkyRDjVjj+1PqSQJTCgJsWKJaphihlEbDIbclPZ7bBx1W8uY8yWXcwpfJ9yyYPbqMOHlzA2kkJhtmen4Q0E6OgrIYwdCR0zBqnU4qYMJc+N+sVsMJuQAeuV3uY8ZIJwxvn4449P6/pEsNTMrFYrgwcP/sH4wsJCunfvzsiRIxuMT0tLY8yYMRQWFjJy5EimTZtGfn4+b7zxBnPnzsVsNpOVlcX48eMbJIvPmTOHP/zhD0iSRGpqKgMGDGDBggXk5+c39i4KQqMZd30u6lVZvPj4Qa5fvo6teWksurA3edV+fDYbJt0gq7wCWdHxmxxImo6hyEgxjff7dmTMt3spznbi9EXZmpvKuJ17+MXGb+hftgeLHiNFrWWnkk+pll2fSA1RJHRZxqH4cBAEJAI4MBMDlwVj16Ooxy+2ILRpLaFm6XSTjO+njAtCI2jJSZCtPUnzdGuNx8NQdWZN38ZexYFdMnCqEa4/38GAn8ebB2MhlV/MLCXXHyDTF+Tr7DTy801oXdP4bVoV/h1Bci/P47Mxywkj0e/AHjLUSvwkgW4ihhkNBR0ZQzIIZAbpX7KTKG7CyJiSbWjnd8f87Lg2nyvUGs+PxtLaj0VjJXg/OmZVg+H7Pzr53KCWStQsCYLQ6kkmmUfndSdSGaFsewBvhwzc2YeTpc12E689k8vr/6qjtirK/MlJmJ2HcgQzSDs3/tfoZeex+px/UmzKoNiUSXaoCjcRzEQwgAA28oyteEvK2U8XkqxhrEWzQJJQflAqQTgztcWaJREsCYLQZlhTrbRLPXZ35ldf7Dru8iaPhbP/fSE7B71HTDJjMw73DSUBdW4T66Q+5I1KIXVcLrHLe5/2R5QFobUz2uBXQgRLgiAIR7BnOelddDX6phJKLvgHZkNPBEylHjcjv5mcmLcN/iYIgnAUIlgSBEE4Crl3FgcGe7B/p4EkUeOwY7KKxjZBOJG22AzXtjMRBUEQfoLwb1PpdXdfbC4TeeckM2z1xBMvJAhCmyNqlgRBEI4j44EhSLf2au5iCEKr0RZrlkSwJAiCIAjCaaOLYEkQBKGNK6nBv8PHjs99+KoNyrr7kNLb3sVfEISTJ3KWBEEQ6pkGPYqt33088mKQvYv2Y10a4u2JH7LzfzY1d9EEodUwkBp82gIRLAmCIABs3I9nz3ZeGPZfDD9YSs+DlWTVBkgNqez9+w4MXbzsQBDOVKIZThCEM171rhre+3+7GJ0yCGvMYMim7Sj1b4LyhMKUue3UravCPSC1mUsqCC2fSPAWBEFoI9SohkmGz0a8xT+69mdiWQ1rMruTUVqZCJQA7JqKo07l22v+TUpXG10WjxO9dgvCcYhgSRAEoZXb+Fklnzy3n3WpSWg6XCJlMPK7IkodbmTAb7cSsFhwRqMYgIaETQNXVZDomjAbCl6n355rmns3BEFoQiJYEgThjGAYBn9/Yi9FG6MoKOQHY3TbX4JuM6OFDGTdIGC3ErZa+KZ9Ll13VqIDmg28oRgAFk2nwuog/Ml+bOflNe8OCUILJWqWBEEQWpn9S4p49//KiDldqIZEt+Jy/G4HjhofkiRhKBKGTaLM6WZNry4YskRGeS05e75Gy43wQe8+jF2zm7yyagBikszmX3/OgE8uAa+9mfdOEISmIIIlQRDarG8eXMsHRS7cDheKJOMIhalx2lnfIZdR327j0KtwZQyyOIAhdwWgLD2J2u4aSwZcAMBbF6Uz8OvvSKvw86tpF2NXVSbevpnrftsRS56T/DTxzjhBOMRoexVLIlhqbNOmTWPjxo2YTCZkWSY3N5cbb7yRMWPGALB//36eeeYZ1q1bRzAYxOPx0KNHD37/+99jNptZsmQJ8+bNY9GiRSfc1iOPPMLGjRvZu3cvEydO5He/+12D6bNnz+aDDz7AYrEkxs2YMYMrr7zy9O600ObVRWFTOXT0Qobz5JfzR+HbCuicDGk/slLGMAzWLK2h5B+7yDi4j6ruHQlsriQqyZS5MnBEoiSF/KgWC2HZjNsOSBKyYRCzmNnULpugzUqSVoq3LkJUttCzeisHU5IS27CoUepcDiwxFQBVlul5oIz02gDz/vc97r1uDB+e1ZXrL3kLwjI7ZInvMtNwhmN81DufV0f2IWS3YNI0jJiBrijIFkjTY6iqQSxq4EtygCSBqoHB4Y9uxGM4zQCTHP9oOvGR9YnnuoFJMsj2gE9TQNfJMGuMzpPYUQtVmsy52TpWWeL2vhqKDPevUnh3p0IoVL89xSDDLdPbrTKsvcyBkMy3pTpbamSSTTpBVSbLYXBBvsG+kExdTOKyApUuQZ3aSshxQ3lYJqgZtHeB22SwuxbMJolN1TID0nSsCgRUMMvxTfZJafzuF3bXQkVIYkCGgSI6x2kWogdv4Ue56aabuPnmm1FVlbfeeov777+fbt260a5dO+666y6GDBnCwoULcTqdlJWVsWLFCgzj1C8qXbp0YcyYMRQWFh5znqMFUYJwKqpCMP5NiV01Eh6LwaIrDXqnn3i5siCMf1uhyC+RbDVYcoVG15RT27ZhGDz3QDFl1WBWU7mi+EtWenvgy+mMpy6AQ9OQJImIYcZZEUCxmIiYreimeM1P1GomYjYD0LdiMz1K97LTW4BFi3Lu7m/4LqUHJe40Rny3lYhDoYO3BoCkSh/ptQEA2lf4eO+pV1nZM4evs3IZtqcYRTfoWF7LSyPP4o2B3dBVCepiqHYTmCTQDbQYlNpsgAEmA2J6PDBymOHQ912SIKqBaoAigUUGuT5ggni8FFIhoqMC+2QTWBVAoTaksH2zHh+WJdbXGCBJPLNeQ9J1YlGA+m0qEtgslKkSH1eZ+LhaOnSAwTDwB+PRTUUENlUb8XVKEh/uV0DSQbWCLMU/AJqOLawSVon33ucyx/fle+7oHeOBs9VT+0c/BYu2S9z6kYJmSFzQXmfBBC1RREH4KUTc3YRMJhOXXXYZmqaxdetWampq2Lt3L1dccQUulwtJksjMzGTKlCkNan9O1tSpUxk6dChO5ync6gvCKVq+B3bVxH+BfFGJNzef3K/R0t0SRf74vNURiXe2nvrlp6JUpT51iJjJwvu9LiBgtaNLEo5YPAnbHI7i9gWQDQNrJEbW/hI6bt1L3t6DTNzwKe1rakgK+uhTspMlBWP5JG8ESztcwJqMQUxes5HpH39Cz4MHSasOJbZr1bQG5ZAwuHjzdt4Y2p2tGfGIT9ENqhy2w3fVBhDR4oEJgA6oev0EKf6R5XhwJEnxj26AxuFh9Sg3TUd2jmk54hgmgqv67UkSGAaqLNUHShweXx/8JIaPnIaUqMBKjPv+vKYjtlO/b2H18N/E9B+WG/jrlsa9P395k4xW3wa0vEhmZ02jbk44BkOSGnzaAlGz1IRisRjvvPMOAB06dMDr9dKxY0fmzJnD5ZdfTs+ePSkoKGjUPlw+/vhjPvnkE7xeL6NGjeKWW27B4XA02vYO8XrtmM0t93RLT3c3dxFalOMdj7OCGhBODPfJs5Kebj7hOvu114DDv9rx5U7tnLBaVCTKEq9QsMfCRMw2FCCiKFgNA8lo+ENtjmkomo49EEHxwy8/+xeF/c+l1JlKqTMzMd/6zJ60L/060cdSleNwO2Glx0Wt1YpNi5GkBnESBKDGYWNHhpcuZVX4rVYqXLaGBa4PWID4ramqg3IK+U2HghbDOByoHBmkqAaYjwjG6muGGgQ3+lECF82Ao/2TGQYn9XaKI7dxNMeozunolRr1u9Y9M8bq4nhg67JA93YuUuyN+2Mtrh1nhpb769WG/O1vf+PVV18lGAxiMpmYNWsWXbp0AeCll15iwYIFvP766+zcuRO3281VV13FTTfddNqDpquuuoo777yT5ORkdu/ezUMPPURpaSmPPfbYad3O0dTUhE48UzNJT3dTXu5v7mK0GCc6Hl0c8MxF8P4OiX6ZBlM6hikvDx9z/kP6uuGP50l8uEdiYJbBxDyV8vJTL9+VNySz5C9lZFcVM+a7z/h312EUpeQSsNsw1QWJWi1ErGYs0RiSHg+WDlFlhYLK/fz2o9ewUY03XEONzQtAyGbjqy6dGLx9K0XuJG69aAxn++sAMGkB+tkd+AyD7akeCmrNvNK/HztSUtmWm8Z3eRkc8DjZkpsaD4r0eCzRodJHRJYpTnZhjmpgU3CEwtTajwiqTEcEVBKAUV/5VB/oRKmvnTLitUpmCSQlHiipWnx7h3KdohooOpgVkMGKwXXdVTxWiT9+VV9rpNTXWEXVeA2RamAySRgyaJoRr+0yS/GAyqC+/SFePqfJoItXQgnHSHFI+JGpiUr0T9VIlXQ2VUhUazIVOvRNUbGboDYqIWHgscC9Z6mUlzde3tLvBoKkypQEJG7tp6PVxSiva7TNtfprR2MFem2lNulIIlhqAjfccAM333wzPp+PRx55hLVr13LppZcC4PV6uf3227n99tsJh8MsW7aMOXPmkJ6ezuTJk09rOXr06JH4u1OnTvz6179m+vTpRKPRH9XsJ5y5ruoJV/U89R+9a3sZXNvrp/1Y9j7bQe+z84F8YCg/B2KqwVfLq1G3quRkmglbUtkxfy9SZQBVkTGrGpmhWtbn5qPI0Kt6PSZVJ2qPEZRNlKR6UTCwRr1UOdyUuxwUeVwUeVwA/GrbOsx1KrIBqVGdshQHpoDOff/8nF0dM3BjcJ6vmDscMQqu60CuR0I3DCTJhbrdR503TJXZgi24j+/2HmT21o7URTXGuPbTvufZjMiF99ft49UDGYTMNlIUSLEYpDjgvA4a/bNhUJrB0Stnj5YD9MNx/31O/P+GAb5oPK3oxyRAxwOEYwXHBvXtjae+4tPAbYG5I4/eBCg0HZHgLfwkHo+HWbNmcemll/Lpp58yevToBtNtNhuTJk3izTffZNu2bY1eHlmOXyl/TDK5ILQkZpPE0LEpMPZwxnivy9oRCmn8/dov2Zmexd0f/YsdRhbrszri1MvpV1lNnZZCxGJGtZi45dNlJIcChBQzgwM+/rx0Kf/s3JnOJTWM3bIPjHjzmRKVMVfJzPx7J5x9ko9ZJqW+PcvUzYMNSANIyaV9Xi4XnXtorgwO1dqcPTaPB4B4sNF4JAmSrI26CUFoc0SCdxNLSkri5z//Oc8//zw1NTU8++yz7NixA1VVUVWV5cuXs3PnTvr373/K647FYkQiETRNQ9d1IpEIsfqkV4ClS5fi98erjIuKivjTn/7EyJEjsVrFlVNom+x2hekLh3L7zBzmX3Qxsfo8nTWZAyixp+MJhbBFYpxVtIfkUAAfTiq0ZEqkZK7cvJn//ccHPPTle3SI7mmw3vYP9TtuoCQIZzJDavhpC0TNUjOYOnUqr732Gh9++CFVVVXMnDmTiooKFEUhOzube+65J9EP06m4/fbb+frrrxPDS5YsYcCAAbz00ksALFy4kLlz5xKNRklJSWH06NFMmzbttO2XILRUHTpa+e/nCwiGO/DOtC/I31TMXRfdQH5ZKT0O+lEsOmEsHCSNQ0+DaSaJfLWEAOk4DY0ko44QVjxSHUnjxatOBOFMIhmiDUZoAi05CbK1J2mebmfC8fj4nTI2Li1jm2wj1V/G2N0bsQbScJUczrWpSHNSoB0kv7oMPzYUJLanevFP7MXg/xnSjKVvXmfC+XGyWvuxaKwE719fvqnB8B8LezfKdpqSqFkSBOGMc/6UDM6fkgHArn+a+fzFEPvsWVzk24YrGEWTJSrTXMjRDPKry1AxU3L9AHo+MaKZSy4IQnMQwVIrMmLE0S/U/fv35+mnn27i0ghC29BxfAc6ju9A1aoDLKlx4w6Z0SwKqlkheb+fKsnB6szOXCgCJUE4KeJpOKFZrVixormLIAhtVsrQXIZkrSO6bj+SopISqaagfB/rXWehiGQFQThpbbGfJfE0nCAIQr1u/zeBtNsGUiVlUKFlsjppCCUpmQz59NQfuBAEoe0QNUuCIAhHyJ7Wm+xp8YTUFSuWc+Xll7bqJF5BaGqiZkkQBEEQBOEMI4IlQRCEkxCqi514JkEQ0KWGn7ZANMMJgiAcRyig8eyUDeSWVhG1mNiTncK9r/Zs7mIJQoslmuEEQRDOME/dsI5Oe0vwpboJpLjI9gV498V9zV0sQRCakAiWBEEQjqPYD7UpLjRT/HKpWs1s/I9I+BaEY9GRGnzaAtEMJwiCcBxVZhlrXQhnbR1Rq5naNC81ZvHyaUE4k4hgSRAEoV5Mg5t+XYQ9ECGnk53zh8PgHfsxaRoRswlTTMVR4SPTakHXDGSlbdw1C8Lp1BZzlkSwJAiCAOgGDH60jv0d88EwUCRwPb8Pj1VnT/ts9mWnY1JVRn++mVH/2cnfLwly/XsDRcAkCN/TVp6AO5LIWRIEQQB2Vers97hA1UGR0YC5o4fzj3N6sC87HQDVZGJz13aYYxppZXW8evFqtJjevAUXBKHRiZqlNuzll19m48aNPPXUU81dFEFoVtK+CgyTAtnJx5xn4YOboGs/cJlBjt8ae2sD7Exyk1Phxx5TUQwdS1QFwOdysKtzNs/+bAPp/joMRUZKtnDx3F6kdLA3yX4JQkskXqR7hpg3bx4vvPACs2fPZuDAgVx55ZWJadFoFEmSMJvNAGRnZ/PWW28xadIkKisrURQFk8lEQUEBt956K+ecc84Jt7dkyRIefvhhbDYbAB6Ph9GjRzNjxgys1pNLJJ02bRqDBg3i5ptvToy78cYbT2W3hVbGMOCkr0mGgYF00vMbhoEkSegG1EXAYwNDN2B3OaG8dKxmUCSgNkhwaznmlUXo/TOxnNsZyTDAYiK6rYzPyxQ6bttJ0lnZWHK9OFIcGGW1kJOMBBjBGMWlYYzaKHqOB6vLjEXTcPuCBJGwZ9r59rsgWXk2Mt0KRjiK8sBCapfsR4pB7NI+VN8xgqyHF6J8tAe/TUbKdrDp6oms3GUw5D8bmbBnDQ6qUZEJkEEUC7XDerOtYyqpa4rY2TWb5FUHqDrrbDAriUAJRaIm1QUGREtryA1HAIgl2SjPdFKe68FQFD7rksfwg2V4QmGkoMZf7i/CWRciyR+g1uPAkBUUs0R6jkTfCRm42tlR63TSujmxWmVMJlHBLwgtnQiWvkfXdRYtWkRSUhKFhYVMnDiRFStWJKbfdttt9OvXj+nTp/9g2VmzZjF+/Hii0SjPP/8899xzD++//z4ul+uE283NzWXRokUA7Ny5k9tuuw2Px3PU7Qitg++fByi++yuQIOfP5+Aem/ODefSQxv4bVxJYUYZzZAZ5Lw9DtilHXZ9hGNy9FBZ+a+AKhqi2O+hmDvP6jXZy3McoRDgKwx/Ec6CMf+f3YMrP7kJXTPxmCLy908TuWhjd3uDLgxKGAdf31HjhCx9/+mA+KWqImy6bTsBsQYmq3P35Cu797GMyjXKsRoB97hRGXH8/Vs3B229/Rn5lFBtBNMng2iuu5d0eA+lVWszbr39KQFFY2Kk7XSv3c/meVRgYBExmVrYfwIi9G/Fb7JQruXzUPZdfTfkFyC5yayoolxSieEGWOLtqDx++8D8k67V4UdjEQGpeC5L82mvszg2gZJqZPHkG1VYHPYtLmVi6lzF7dhAiCRUFEwZmZBRUWLkV6/p8tICFDlsr2Zifw6vD+8UTlzS9YdAkQbnHRkEwjKzrSJLBuoGdUWIxvsxKZtjeYuyajmo2E3S5UMwmwlYTMZNCbUYy8agQqnxRNr8ZIqPyIBkV1Wzt3J6Y1UJWSQ3u7k4qKgy6D3Fx4Q0ZSG3wzlw4c7TFBG9xS/M9q1atoqysjIceeogNGzawY8eOU16HxWJh0qRJBAIBioqKTnn5Tp06cdZZZ7Fly5bEuKVLl3L11VczatQoxo4dy6OPPkooFAJg7ty5rFu3jnnz5jFixAguv/xyAF588UVuu+22xDpqamp44IEHGDt2LGPHjuXBBx+ktrb2lMsnnJziu79Cq46iVUU5ePdXR52n5rXd1C0vwYjq1H1UQs1ru4+5vo93w2sbIaJLVNoc6MAW3cFTK9RjLuN89B3Siw5i1VQu2rmRW77+hIAu88hqhS1VEmFN4l+7ZKrCEtURiae/Vhi2ewu3rv+EOyf+FwGrDZDIqKvjd58ux6kHsBl1SBi091cy46tlbE/N4nfnT8ROHRLwftc+vNtjIADfZmbzwuARdK6pZsbalbzaewgaChKwul0fLtz1JXYtSEaoknTtAB/3GACKDJLEgeR0onL9/ZxusDazI8l6LRKgoNGVDQBUk0r/A5U8OeBiqm1OkCQ2u7O4bO16olhQsRMinSjxiFIGFFTsgfjlTwLyS/3EFAUiGgRifL9rGHMkCoaBjBGfpMgEnA76VPoxG2AoMrpJwaqpiSq/ULIrHnBJErH6+cN2jR6dtAAAIABJREFUK0V5WQScdtzBMEgSJdnJaF9VoqkG3/7Hz56NweOdVoLQ4onXnZwB3n33XYYNG8bw4cPp0qULhYWFzJw585TWEQ6HWbRoERaLhezs7FMuw7Zt2/j6668ZP358YpzL5WLOnDkUFBRw4MABfv3rXzNv3jzuuOMO7r33Xnbu3PmDZrjv+93vfofJZOLtt99ODD/wwAP8+c9/PuUyniqv147Z3HJPt/T0Y1XN/Hjb5MNXCVmRj7qNqLthM6vbYztmWbxVKvDDH1K300x6+jFqLz22BoMGEhjHKfQRk826dpwZ4w7lJhx5PbSqDYO3+HC8esUVjWDU36MlhUJIRxTGpkaOf2H93jSFQ+UzUIhh0xq+u81vtwGBxLDG4Ro7C0EUVLT6S+COzGSc0Si2QIhKhyPeh4C1fn7doM/eErLqwlRkpf6gOAGblaT6JjoJkAwDQ5KOeyeqKTKyfjgxXDIOH4ekJHujnI+nU0svX1MSx+LM0HJ/vZpBeXk5K1asYO7cuQBMnjyZF198kRkzZiTyiY7nscce4/HHHycQCOByuZg7dy7JycdOKD3SwYMHGT16NLFYjEgkwnnnnce0adMS088999zE3+3atWPKlCm8//77p7Rvq1atorCwEI/HA8Ddd9/NlClTqKioIC0t7aTX9WPU1IQadf0/RXq6m/Ly098jc/afBsZrlCTIeurso27DdEkOriXZ9c1wmSiTso9ZlgHJBtf1hYXf6iTVBQmYzfSyxrj1nOOU/65LoPBr7EVlfJbfnXlnjybJpPObofD2DoldtXBee4Mv6pvhbu6j8azWg2f7X8AL781j+uRbKHW4KXG7mTVmLPd9+hFoIaxGHfvcaTw/4AJ6lh/gkeXvE8aOlTAX7/yWO7/4kDd7DaFfcTF3rfoMCZ1VOe0YvWsbClEMoF/Jd5TYs8gKlaAjEdXcXLh5Df/o1QckiQx/LWUmF1JUxYLOZZu+pNLsJTVWA0Ap7XDhJ5Ni5p81gP/atJKdSZlsTclkdPF33D7lcj58bh42VQcMrPiQkJCJYsZPumU/gWgqB91J/GHiYEwhlWvWbOGZUWeDPwYhDWQDiyJBkgfJF8RbWUtNigdNkgjVB//VFhPZxAMlRVXJqanDZ7dS6k1C1nR0WcKi6aiSjmo2kVzrw1UXpDzVixLTyCmpxjw0HXO5TrfBbpLbS41yPp4ujfV9aY1a+7ForEDPaCO9dh9JBEtHWLx4MUlJSQwfPhyAcePG8fTTT7Ns2TImTZp0wuXvu+8+xo8fT0VFBTNnzmTDhg2MGDHipLadk5PDokWL0DSNDz74gGeffRa/34/T6QRg9erV/PWvf2XPnj3EYjE0TSMlJeWk9620tDSxnUPy8vIAKCkpafRg6UzkHptDt82XHHce2abQ/pXhJ7U+SZJ4ciw8OVYBDl3kThDEW0ywag4h4BzgyEblX5599Jqj30/wUH7X9QCsNwC0eGL4jGEQGojPZkGWwAF8CcTWF2EdeCHRnu2IdM4E4HeyzMxQDP+yCvbldqW4cz7tR/Wlr0ehgp9xsLiE/aX7ycvrgFInx2tyspO52mriknAtNpcJNQqVaghvVQ22DinIpoEYzwxk2+2vYCzfhUlWyDpbovSXk8j5yxfU1KWy4N032XVRF1aNLmDU5+v480XD+eXH79MhXIaVIDHMxLCwu+dwivoW4PqulAqHnXuWrWBx356sLcgBi5JoShuzpQif1wGyTEp1mLSKEqqTbfhddrZ06UDEbMYmSai6jjMUoc5spTzZi2SAo8qHIktE7VayY366DXGSne/AnJZEWv8CFJsJWZaADif17y8IQvMRwVI9XddZvHgxfr+fCRMmJMZrmkZhYeFJBUuHpKWl8eCDDzJ16lQuuOACunfvftLLKorCxIkTWbNmDU888QRPPPEEsViMe+65hxkzZnDJJZdgs9l48803efXVVxPLyfLx088yM+M/YsXFxbRr1w6AAwcOAJCVlXXS5RPOLD/I07RbftC8ZO7XHr1f+x8sa7WbsV4yhLRLhvxgWk6H9uR0+OEyAPX3ByhWEzkAyQ0D+ZTnrmsw3A5od27fxPDZ9R+uPTRmEL6wSrA2gj3DiSxBGvEPQFegLmLwyZMHKC07lNQt4YjEuHjdThaO7MVem5XzK2oxqQbp5SHSykN81bsL5qhK9+IyNMNAV3U6esKc91gBrlznUfdNEM4EouuANmzlypWUlpYyf/580tPTE+O3b9/OnXfeyY4dO+jcufNJr69Dhw6MGzeO5557jmeeeeaUy3PLLbcwZcoUNm7cSKdOnYjFYng8Hmw2G7t27eKtt95qMH9qair79+8/5vrS09MZMmQITz31FA899BCGYfCnP/2JYcOGiVoloc2TbCactmNf7lxWid/flUvvl2TGr9+BgcSkb3bwz7M64jNbcYQDbM1Kpff+cgCqvE4sMY2UUJCbCvs31W4IQqvQFoMl8TRcvcLCQkaNGkWPHj1IS0tLfIYOHUrfvn1ZuHDhKa/zpptuYs2aNaxdu/aUl83Ly2PChAk8++yzOBwO/vu//5unn36aESNGMHfuXC6++OIG819zzTVs3ryZ0aNH87Of/eyo63zkkUdwOp1cccUVTJkyBZfLxcMPP3zKZROEtijDGU9CX9qrgFK3gyfGDaIsSaFPaRVTP1/PrpxkNuRnUZbsYkPXbPJ81dz7cpfmLrYgCE1AMgzjeM/GCMJp0ZKTIFt7kubpdiYfD1/EoPeLBjEV7lOKOavvFt5b0ImcYJiqVC8Abn+AvKKDXPPxyGYubfM4k8+P72vtx6KxEryv+UXDLlBem1/QKNtpSqIZThAEoZ7HKlE041ATQi4rVnxHlcuJ64huL/wuB2VukZMkCGcSESw1gW+++YYZM2YcddoNN9wgXksiCC2Yqmkk1dRRmRavWfLW1KFaTu41RIJwJtJF1wHCj9G/f/8Gr0wRBKH1CKQ4SN+yh5RqP7osETMrZPVyNHexBKHFaouvOxHBkiAIwnH86e4MHq7w0yUYRpclCMW4eU635i6WIAhNSARLgiAIx9G/i4WXnstD1436TiQFQTietvI+uCOJrgMEQRBOggiUBOHMJWqWBEEQBEE4bdpip5QiWBIEQQB+9kQ1a6NOFMPgv/rGuG+Sq7mLJAitkngaThAEoQ1auayc/+gpqJZ4ZsLLG+Czf+9n8H4rF4+LNnPpBEFobiJnSRCEM97/W21CPeJl1BGTwp7MDD7r2o3xN29qxpIJQuujSQ0/bYEIlgRBOOMFdRmHqiaGIyYTVU4n32bnIsviMikIZzrRDCcIwhmvzmHHpEsgSdg0nXbhCFokyj6PA5sqXp8pCKdCJHgLgiC0Qaps4HPbkUIq3XxhHGqMgpISUlLSMevNXTpBaF3aYj9LIlgSBOGM51J1ahUJI8nCASXKf16YQ7fKA9RYnVx56V3NXTxBEJqZaIwXBKFVKt4b5Kq7t3H+fQe5ambRj1pHRIWYBumBGCjxZrgxOzfQrfIAAN5IgJkrl/LnJf7TWXRBaNN0pAaftkDULAmC0Crd9/gOPunUFwxQDJ2fPXCAtx7OTUxXdTAdcTv49kc+lr9ehI7EsPOcrFsWYvrnX3HXFRdzIDUlMV+RN7XBdjTMXPubP/Crd6/gvge6k9Le0WC9giC0fSJYaibz5s3jhRdeYPbs2QwcOJArr7wyMS0ajSJJEmazGYDs7GzeeustJk2aRGVlJYqiYDKZKCgo4NZbb+Wcc8454fa++uorfvnLX2K32xPjunTpwssvv3z6d04QGlFpEIbOh2kHfHyREaDC6UAzYEVyDiMfCZIVCPNZbhqGIqNIoFkUDFni6i/W4zVgTft2bNtlkJlp48nLxtPBkDDV1OHVQuxIT2Vl+25Mm3gTk7auZe6oy/kqrwBXOIQjFKb3R17MuspVKz/j6279+C4nnRyHQcwAXYfamIQE3NMvRnFQZl9A4qbuKufn6hjvbcZ2XyGGyUT4xZ/z73VV/O2gh47JcM3P2jH/b7WoYY3zJiZx8WBrcx9mQfjRNJHgLZwOuq6zaNEikpKSKCwsZOLEiaxYsSIx/bbbbqNfv35Mnz79B8vOmjWL8ePHE41Gef7557nnnnt4//33cblO3NuwoigNtiMIrdGV/7IwfNMOOpbFAyUAJAnNkPgu04v1YBW6SQGTjAqgAwq8PngwRA9nayfrVcSiGgABl43tGUmJaX8ZeD5/OecCsCgARKwWKq0OJH+UWJKVV4eMBnv8ZmZ/8Ic/DI9+Y4b65ofPDsp8eVmI7tNewqzHO7g0pj7PtXc8Siw5fgmufOogOb4IAKvnRxjSOxevU1RfCa2TSPAWTotVq1ZRVlbGk08+ya9+9St27NhB586dT2kdFouFSZMm8eqrr1JUVETPnj0bqbSnh9drx2xuuadberq7uYvQorTk41EVjXFWOExBTS29/CFsBpRaTOx3WECCSquFk0mTsGiHAyfte+1ql2zbSElSe6Imme2pbgJWE8gyBjqoBpzwpbqHp0d0ibBuSQRKAOZImJhy+PtgPSKIs2o6umQjPb3l1i615POjqYljcWZoub9ebdi7777LsGHDGD58OF26dKGwsJCZM2ee0jrC4TCLFi3CYrGQnZ19UstomsaECRNQVZXu3btz++2307Vr1x+zC6espibUJNv5MdLT3ZSXiwTeQ1r68bi5h8Lfd+diRSGtPuBxhaNErQqVYZVhZVW85bbHAxpJij/GIkkoqoYtqhKwxC97xS4bBbXx89IRimKPRghZrLSrrsBvz8KiG1iiGl0q/exJdlKjGiiGgSZLUBtGMUloigIYfD86c5sMono8UBqRpdHOFcPXuzueTd9hAMGR/Ti3bBefZ3TEpkZJ6W5FXxdDBkJ5DlIcUcrLW+ZrVlr6+dGUWvuxaKxAT2sjSd1HEsFSEysvL2fFihXMnTsXgMmTJ/Piiy8yY8YMbDbbCZd/7LHHePzxxwkEArhcLubOnUtycvIJl8vPz+e1116jY8eOhEIh5s+fz6233sobb7xBenr6T94vQWgqv+qncV6OgweeTMFyxPicimqsSS4s56XTtTxGUW0Ms26QrEZwdnTSa9VGqpOyMBk6xOrQrUnUyCZKXXassRghk5lBO4q4aela/j5+eOJy74xqpNYGKPCH2ZThwagL062uhtE9XWgpDn7eRWVrrYzXCgfqIBCTuKWnRnEQSoISfVMNTDJEP55BxQdbMJxWpJEdeas2xOavi8jsmkJWbipb93io8ukM6WM52m4LgtCMRLDUxBYvXkxSUhLDhw8HYNy4cTz99NMsW7aMSZMmnXD5++67j/Hjx1NRUcHMmTPZsGEDI0aMOOFyaWlppKWlAeB2u7njjjtYvnw5n3/+OZdeeulP2ylBaGL90iUGle1hTU4XzIARi3LfLV5Gdz7aJc0BGPCz3gRXFyFpGvZzO/L8ZzFeXamRXVmFEomxLduLPWzw0vmDKE5yUuALogNaKEjUbmPJbA8Wm1K/zkNPz8VfkdIj5Yc9V+Y4Icf5vd6/x/VIBGHmJDv9zstLTOqWb/7xB0QQWpC28j64I4lgqQnpus7ixYvx+/1MmDAhMV7TNAoLC08qWDokLS2NBx98kKlTp3LBBRfQvXv3Uy6P1AafWBDOHPe/3J9vPjzIl98EuOSWArJTTnw5cwxpn/j7tpFmbhtpBrK46IFq+u2r5KtOWSTVhjnoslHsjOcMTVl9gAWzOx8RKAmCcKYRwVITWrlyJaWlpcyfP79B09f27du58847TznRu0OHDowbN47nnnuOZ5555rjzrlmzhqysLHJzcwmHw7zyyitUVVUxZMiQH70/gtDc+l+UQ/+Lfvp6QmYL6ArtKkNUWE1gGBiShCcQwa1FSXKJQEkQTpZ4N5zwkxQWFjJq1Ch69OjRYHxaWhp9+/Zl4cKF3Hvvvae0zptuuokrrriCtWvXcvbZZx9zvm3btvHQQw9RU1OD3W6ne/fuPPfcc2RlZf2ofRGEtiSgKNgNiSqPFVWCrJoAsgFp/jrWtc9s7uIJQqvSFvtZkgzDEK/UFhpdS35ipLU/0XK6nYnHo/OTYSJOFxFr/P5RVnXkiI5qkrHEYuy/U1wmDzkTz49jae3HorGehhtyW2mD4dXPt/4bDlGzJAjCGc+k6fgsh5vadEVCr+97yaSLQEkQToXa3AVoBCJYaiO++eYbZsyYcdRpN9xwAzfeeGMTl0gQWo9sh0HVkX3DHAqQDINf69uApumPTBCElkkES21E//79xatMBOFH+sd0JwUva2BSwDBIqgzySo8S/MZmrrn5ilbd1CIITa0t5iyJlw8JgnDGc5ph140xLk0O8POUAFtnmhhySR62FPEUnCCcKlVq+GkLRM2SIAgC4DLDS5eL4EgQhB8SwZIgCIIgCKeN2gbfDSea4QRBEI7h4X/1ZcKl63ho6pfUHAw2d3EEQWgmomZJEAThKCbOOMA9n27CHYygyRLvXBfjhg+HoijiHlMQjifW9iqWRM2SIAjC9xmGwdhdB9nrcbCyIN7LfcfiWrYsKmrmkglCyxeTpAaftkAES4IgCN8T0yXe7ZbPqwO6ocR0/t0pl6DZRGB7bXMXTRCEZiCa4QRBEL7HosBul4O/vfYpjli8P+LN7dLIn9yleQsmCK1ArLkL0AhEzZIgCML3VL2/m1x/PKF7c1YytTYzmXUhnGXVzVwyQRCag6hZEgRBqBf4aDd7f/4FKiamd0rilmvPp9ZmwRmJccu3u7lzVQbqGp13Lo0xsLO1uYsrCC1SsI3kKR1J1CwJgiDU23vtKnTDhGzAV+3aU2uzABCwmvl3fjJENYIGXLHY0swlFYSWKyQ1/LQFIlgSBOGMo0c0ol8Vo23clxj3r6tWU+P2ABBwWukUqyElEk1M16wuJlZV0t0fxJAkKnxag3X+/YF1PH79f5jzQiXhmN40OyIIQpMQzXBNbN68ebzwwgvMnj2biRMnJsZPmjSJyspKTCYTkiSRlpbGgAEDuO6662jfvj0APp+PqVOncv311zN16tTEsk8++STr1q3jb3/7GybTsf9JlyxZwsMPP4zNZkuMGzFiBI899lgj7KlwSGinn9qPSnD08uIZnt7cxTkhLaRS9spuJEUidUp7ZEvDe6pYWZjK9/ZjybaTMiGXyvf2EyuPkHpZHuYUK7WflBLa7sc7NhtbB+fJbTOosv0Xq9BqY+Q/NQBnL+9x56/9tJTy1/cQ3OLDNTCFjn88OzEtuLKE4utXoPujaEYUKyBLOhaTD1m1k2YUk8JBzMQDIZ+czDeec0hxOMkM11DqSearwZ2QFZnbt+9mky3G4INb2VowCL/Dg2FWMEXCDHlBwhGLMvfD91if25mVnTtQ0ceDrSzCoqdifDmxGsunO9F7ZfHpWj9/L00lZFipUiz4rCbe+JWDz1eFeX9VFCmiIyFx0Kzwr4fdKHIbuR0XzkjRNtiDtwiWmpCu6yxatIikpCQKCwsbBEsAs2bNYvz48RiGwd69e1mwYAHXXHMNL7zwAn369MHj8fDggw/ym9/8hsGDB1NQUMDq1atZtGgRr7zyynEDpUNyc3NZtGhRY+2i8D2RA0G2XPJvNF/8iapO/3sOKRNym7lUx/f1Vf+m/MODANR+Vkbn/x2UmKaFVLZc9hmRonjyc9nQ3fhXVQBQ/spusqZ3Zs/MdQAUP7eN3h+djzndxolsGLQUrTr+DM2Wiz+l/+YJKG7zUeetfHcfu2asTQxHdgeIFAXo8c5IAPZf+gkyYCBjwoKNKBgyxLyYCJLJHmpsLpLD8WApSa+mKs1KsSOVTmUlrO2WgiTF+1qSJBnN5uEX367knq4j4tvTdOo8bpAlfMC8s4bx7KKlSPYkqv0asq6jhwJIF76OORbjq7w8pt5xB2qqAoaBszSAKaZx4xN19AxHsckyYZMJA8jSDMbM8vHJY0kn+a8lCEJTEM1wTWjVqlWUlZXx0EMPsWHDBnbs2HHU+SRJIj8/n/vvv5++ffvy1FNPJaYNHjyYyy67jFmzZlFRUcHs2bO56667yM/Pb6K9EE5F4JvqRKAE4FtR3oylOTHDMKhYXpwY9n1W1mB6ZE8gESgBBDbWJP4O76yj+sOSxLBaFSWw6eT6JToUKAEYOtRtOPZTZ7XfKxNAcEN8O4HPixMXNQ0ZK2qD+Uz1b6066M5sML5DdQW6SWHp2QMo86YgGwaKrqMDX2dkccvkO9lrt/GNx8E+ixmOqPnZkZ7CxvwOVHvcAOiyTMdAHY5YfJ8+69wZVal/Qa8kodnif0eN+nLKR1yGJYl2qmjCE1o56XufNkDULDWhd999l2HDhjF8+HC6dOlCYWEhM2fOPO4yF154Ib///e8Jh8OJ5rM777yT6667jquvvppevXoxZcqUky5DaWkpY8eOxWQy0bdvX+644w5ycxu/psPrtWM2t9zTLT3d3SjrdY7OZY/rG7S6+I927ti8RtvW6ZIyMpPKT+JBT9rorAblVe02drZzEt4XACCpbzLVK+MBoKOjm7xL2lP7UXxZc7KFdiNysKU7TrhNU5IZtTYeXEgSdBjdDrP36EnUkYvaUfnOvgbjPH2TSU93kzbZxUE+RQIUdKIomIgHHwYQxI2PFD4qGEK38gPIxAiTjM8Sz1Xyu+yJdUqGwUdpKRxw2sHUnv3u+mmaHv/Uv/bkvG27cYbDDcpTa3MQMpmxqzGG7tkTr22SZTAMlEg818ltxMtl0bR4L8eShKwblJjkFn+OtPTyNSVxLI6iDT4N13J/vdqY8vJyVqxYwdy5cwGYPHkyL774IjNmzGiQQ/R9GRkZ6LqOz+dLzGexWOjbty/vvvsukydPPuky9O/fnzfeeIN27dpRVVXFs88+y+23387rr7+O3W4/8Qp+gpqaUKOu/6dIT3dTXu5vnJW7oPu7I6lZVoyjVxLm89Iab1unydnvjGbL05uQFIn0q/N/UN5uC4dTWbgPS46d5Mm5VC3cT6w8TNqU9pgzbHS2DyK0o47kcdn4TRr+k9jfPl9cyNafr0LzxSj40wBqYhEojxx1Xuu4DDrPG0zZq7sJb/Pj7J9Mp+fPSZQz+7URlNy8EiOoEiGGAliI4KIKH8nspQudSsp4u8d4ztm7h6DJTKmSTG5RJSGHjYg93iWANRTFFNXoURdij/uI74ciY41ESa+rI7PWzzVfb6AqJQVXXQCf00FMgn8U5PKr927A9J+dDOiRyf9t/IL51ZnoupmILhGwmHnmt24+Wh7h03URnGEVJIlyk8zC3zXi+XgaNOr3pZVp7cdCBHonTwRLTWTx4sUkJSUxfPhwAMaNG8fTTz/NsmXLmDRp0jGXKysrQ5ZlPB5PYtzq1atZunQpU6dO5Y9//CPnnHMOLpfrhGXIy8tL/J2WlsasWbMYNWoUGzduZNCgQcdZUvgpHN09OLp7TjxjC2Fymsm6ufMxp1uy7WTf3jUxnHZl+wbTky/OIfkUt6m4LfR8b9RJz598UTbJF2UfdZprTB6d9/zsqNM8wMGDB5D276UHyZi/SCbrlqFkWU1UP7ka01+L2NUxCwlwltWRkZVEqZGMM6YRTNSMGmREY7z/KwdZLif8zzUAmO7/ktUHFUI9svhkhhXZloc6MP6du3gsXHyU8twyxcEtU05c8yYIQvMSwVIT0HWdxYsX4/f7mTBhQmK8pmkUFhYeN1hatmwZvXv3TtQq1dTUMHv2bO6++24uvfRSdu7cyeOPP87DDz/8o8omSRKGYfyoZQWhNcrJySUnp77peVCPxPiUe4aS9/hfcPhCVHrdfNarPf/Izydqiidm59WFcQdDyJEYu7KTyXI1bGq44NFBXNCUOyIIQpMRwVITWLlyJaWlpcyfP5/09MOPjm/fvp0777zzqIneRUVFLFiwgPXr1/P8888nxj/66KP06tWLyy67DIAHH3yQq6++muXLl3PBBce/VP/nP/+hS5cuZGRk4PP5ePbZZ/F6vfTp0+c07akgtG7pTw7GN3MjfruFnWnJ8UAJQJJIqfYjKQYb26Uxf0QIED14C8JRiZwl4ccoLCxk1KhR9OjRo8H4tLQ0+vbty8KFCwGYM2cOf/jDH5AkidTUVAYMGMCCBQsST7otXryYDRs28MYbbyTWkZWVxb333stjjz1Gv379SEtLO2Y51q5dy5w5c6irq8PpdNKvXz+ee+45HA7RDCAIALbr+tLrur7kH/Dz2f8rxqTpqPWJ3J0rqrg2q5bhd6QgAiVBOI62FyshGaINRmgCLTkJsrUnaZ5u4njE/faG7ehWK5tTk+hYVUvPfaUMvSKVATd0bO6iNStxfhzW2o9FYyV4S/fUNBg2njh+J7OtgehnSRAE4ShGlu3GK8sMq/aTJcms6NEReX/L7idLEFqGttfRkmiGa0M++OCDY7665L7/3969h9WYrg8c/9aqFDWUQ3Jo2IMQjVhKOUTlkIaWGDTbYYzTjIY9GMfZZocZ25DNGDY72rMbDObQYYhNiOk3hMZhnP0cxpCSUUJTk1bv7w8/ayzVKlkd3Z/rWtfVe1jPez93i3X3PM9619y5+Pn5lXNEQlRddX/Tv6Fmag1zajvJnbWFeBFJsVSN+Pn5SUEkhJG0SvmZa3Ub8XPdxjS8m0KedUtefr11RYclROVXPQaT9Mg0nBBCFKLW/mCcr5ym28l4TlpYsXP2S8j32wpRAtVvFk5GloQQojBWDWuS+WkLAgM1dK/Ci3iFEM9PiiUhhBBCGFE1GU56gkzDCSFEIYbEwOArfpiE5lV0KEKICibFkhBCPKVVBHx/1xJ4dAfvBhHmFRuQEFVJNVyzJMWSEEI85S4W6P8vr6qoUISogqpftSTFkhBCCCGEAbLAWwghhBDGUz0Gk/TIyJIQQgghhAEysiSEEEII46mGI0tSLAkhhBDCiKpftfRCTsMlJSXh7u5e0WGUuZ07dxIUFFTRYQghhBBV2jMXSxMmTMAnJXVhAAAgAElEQVTDw4Pu3bvrPS5dukRISAhqtZqIiAi959y+fRt3d3fUanWB9k6cOIFarWb+/Pklun56ejpdunTh6NGjBY7dvHmTzp07c/bs2WftVpUXEhLCwoUL9fb5+fmxefPmCopICCHEC6n63TmgdCNLY8eOJSEhQe/RokULAJo3b05MTIze+TExMTg6OhbaVmRkJLVr1yYuLo4HDx4Ue207Ozt69uxJVFRUgWNRUVE4OTnRtm3bUvSqclIUhbw8uYOwEEIIUVGMPg3n4uKCSqUiKSkJePRm/91336HRaAqce+/ePfbu3cuMGTOoUaMGsbGxJbpGYGAg+/fv5+7du7p9eXl5bNu2jUGDBgGwd+9egoKC8PLyIigoiPj4+CLbK2xUZsCAAezYsQOAbdu2odFo2LRpE/3796dHjx6sWLGCu3fvMmPGDLy8vBg8eDAnTpzQayMqKoqhQ4fi5eXFG2+8QWJiYon6p1ar2bx5MyNHjqRbt26cO3eOI0eOMHr0aHr16oWvry9z5swhPT0dgIiICHbu3Mn27dt1I31arVYX92M5OTmEhobi7++Pj48P06dPJzU1tUQxCSGEECViYqL/qAbKZIG3RqMhOjoatVrN4cOHsba2xtnZucB5sbGxWFlZ4ePjw+nTp4mKimLYsGHFtt+5c2fs7e3Zvn07I0aMACAhIYHffvuNfv36cfLkSebNm8fSpUtxd3cnMTGRmTNnEhYWRrt27UrVp5SUFB48eEBMTAxXr15l9OjRHDt2jBkzZrB48WLWrl3L/PnzdSNeUVFRREREsGTJElq0aMHBgweZMWMGX375JU2bNi32ejExMYSGhuLg4IBWq+Xs2bPMnDkTJycn7t69y5w5cwgNDWXRokWMHj2aq1evolKpmDdvXpFtLlu2jIsXL/L5559jY2NDaGgoU6dOZePGjahUZXuH4jp1rDA3r7yfJ6hf36aiQ6hUJB9Pj+aaSE6eILn4g+TixVCqd6/PP/+cjRs36u3bv3+/7md/f3/WrVtHZmYmkZGRhY4qwaOCws/PD3NzcwICAtiyZQs//fQTLi4uBq9vYmKCRqMhJiZGVyxFRUXRt29fatWqxfbt2/H29qZr164AdOvWjZ49exITE1PqYsnS0pLx48djampKq1ataNmyJc7OzrRv3x54tD7o888/58GDB1hbW7N582bGjRtHq1atdDGo1Wp27drFuHHjir3eiBEjaNKkCQAqlYoOHTrojtWrV49Ro0axYMGCEsefn59PbGws//jHP2jQoAEA06dPx9vbmzNnzhSb8+d19252mbb/POrXt+H27fsVHUalIfkAsED/K04Uycn/k9fHH6p6LqTQK7lSFUtjxowx+IZfp04dPD092bBhA0eOHGHevHlcvnxZ75zjx49z5coVPv74YwBatmxJ27ZtiYyMLNEb98CBA1m7di3Hjx+nYcOGJCYm6haW37p1i9atW+ud36RJE86fP/+sXdWxtbXF1PSPWUtLS0vq1q2rtw2QlZWFtbU1N2/eZMmSJYSGhurO0Wq1ukKlOI0aNdLbPnfuHKtXr+Z///d/ycnJQVEUfvvttxLHn5GRQW5uLo0bN9btq1mzJnZ2dty6davE7QghhBAGVY+ZNz1lNi8SGBjIO++8g7+/PzY2BavXyMhIAN59913dvqysLC5fvsz06dMLfc6TbG1tdQu9HRwccHJyok2bNgDY29uTkpKid35ycjL29vaFtlWzZs0C658erwcqLQcHByZOnIivr2+pnv9kYQYwd+5cfHx8WLx4MdbW1iQkJDB16lTdcZNi5oVtbW2xsLDg5s2bumnA3377jfT09CLzIoQQQogyvM9Sp06dWL16tV4x9FhmZib79u1j1qxZbNq0Sff4+uuvn3mh9969e4mOjiYwMFC339/fn3379nHo0CG0Wi0//PAD8fHxDBw4sNB22rRpw9GjR0lOTiY3N5d//vOfz/0JtDfeeIOwsDAuXLiAoijk5ORw4sQJfv7551K193jEqlatWqSmpvKf//xH73i9evVITk4mPz+/0Oebmpri7+/P2rVruX37Njk5OSxfvpxmzZoVup5MCCGEKJ3qd++AUo0shYeHF7iX0qJFi/S2TUxMcHNzK/T527dvx8bGBo1Gg7m5ud6xwYMHExkZyfDhw4uNQ61WY29vz507d+jbt69uf4cOHQgJCWHFihWkpqbSsGFDFixYoFtf9DQ/Pz9OnDjBiBEjsLKy4s033yzxdFlRBg0ahLm5OfPnz+fmzZuYmZnRunVr3nvvvVK1N3fuXFasWEF4eDjNmjWjf//+nDx5Unc8ICCAI0eO4OPjg6Io7N27t0Ab06ZN47PPPmPUqFHk5ubi4uLCP/7xjzJf3C2EEEJUZSaKoigVHYSo/irzIsiqvkjT2CQf0CCi4ALvtNE5FRVOpSKvjz9U9VyU1QJvk3n662mVhTXL5DrlqfJ+llsIIYQQVU/1mHnTU2mLpe7duxe639XVlZUrV5ZzNMY1ZcoUjh8/XuixhISEco5GCCGEEIZU2mKpOhcNVb3YE6L6k9UJQpRe9RtaKrNPwwkhRFWVNvohkM/joqlTDVmvJMSLrNKOLAkhREVKG/07CQl7CQzUcPt2RUcjRBVS/QaWZGRJCCGEEMIQKZaEEKIImb/Lf5FCCJmGE0KIAtLvQesoS8CPt0LzABPSRsuibyFKRKbhhBCi+msdZYH+VzXUqMBohBAVTUaWhBBCCGE8xXyxe1UkI0tCCCGEEAZIsSSEEEIIYYBMwwkhhBDCeKrfLJyMLAkhhBBCGCLFUjW2aNEiPvnkk4oOQwghxAvF5KlH1SfF0hMmTJiAh4cH3bt313tcunSJkJAQ1Go1ERERes+5ffs27u7uqNXqAu2dOHECtVrN/Pnzyzz2AQMGsGPHDr19c+fOZdasWWV+bSGEEEKn+tVKUiw9bezYsSQkJOg9WrRoAUDz5s2JiYnROz8mJgZHR8dC24qMjKR27drExcXx4MGDUsWTl5dXqucJIYQQwjikWHoGLi4uqFQqkpKSAFAUhe+++w6NRlPg3Hv37rF3715mzJhBjRo1iI2NLdE1kpKScHd3JzY2loCAALy9vQHYvHkzgwcPpkePHvj7+7Nq1Sq0Wi0AU6dOJTU1lY8++oju3bsTHBwMQEhICAsXLtS1nZKSwrRp0/Dx8cHf359ly5aRkyPfpi6EEEIYIp+Ge0YajYbo6GjUajWHDx/G2toaZ2fnAufFxsZiZWWFj48Pp0+fJioqimHDhpXoGlqtlh9++IFNmzZhZvboV9SgQQNWrlxJo0aNuHDhAlOmTMHBwYHBgwezfPlyBgwYwDvvvEP//v0LbTMvL4+//OUvvPrqq2zfvp379+/z/vvv8+mnn5bLVF2dOlaYm1fel1v9+jYVHUKlIvl4ekTXRHLyBMnFHyQXL4bK++5VQT7//HM2btyot2///v26n/39/Vm3bh2ZmZlERkYWOqoEEBUVhZ+fH+bm5gQEBLBlyxZ++uknXFxcShTHlClTsLa21m37+Pjofm7dujX9+/fn6NGjDB48uETtnTlzhuvXrxMREYGVlRVWVla88847vP/++8ycOROTMr7j6t272WXa/vOoX9+G27fvV3QYlYbkA8ACUD2xrUhO/p+8Pv5Q1XNRZoVeNVmn9CQplp4yZswYxo0bV+TxOnXq4OnpyYYNGzhy5Ajz5s3j8uXLeuccP36cK1eu8PHHHwPQsmVL2rZtS2RkZImKJVNTU+zt7fX2/fe//+XLL78kOTmZvLw88vLyaNeuXYn7devWLWxtbbGystLta9KkCb///jsZGRnY2dmVuC0hhBDiRSLFUikEBgbyzjvv4O/vj41Nwco8MjISgHfffVe3Lysri8uXLzN9+vRCn/MkExMTvZGe1NRUPvzwQ5YsWULXrl0xNzdnxYoVnD17VneOqanh5Wf29vZkZGSQk5ODpaUlAMnJydSoUQNbW9viOy2EEEK8oKRYKoVOnTqxevVq/vSnPxU4lpmZyb59+5g1axa9evXS7X/48CF//vOfiY2NZfjw4c90vezsbPLz87G1tcXMzIxTp06xY8cOmjVrpjunbt26XL9+vcg2nJ2dadq0KcuXL2fq1Kncv3+fNWvWMGDAgDKfghNCCPECqYbvKVIsPSU8PLzAvZQWLVqkt21iYoKbm1uhz9++fTs2NjZoNBrMzc31jg0ePJjIyMhnLpaaN2/OxIkTmT59Og8fPkStVtO3b18uXLigO+ett95i6dKlbNmyhfbt27Ny5Uq9NszMzFi+fDmhoaH4+/tTo0YNevXqxeTJk58pFiGEEOJFY6IoilLRQYjqrzIvgqzqizSNTfIBDSIKLvBOGy232QB5fTypqueirBZ4myzK1dtW5lqUyXXKk9xnSQghhBDCAJmGqwDdu3cvdL+rq2uB6TMhhBBCVCwplipAQkJCRYcghBBClI3qt75bpuGEEOJpm7rkAk8u53xYUaEIISoBKZaEEOIpvZ3g1qgc1KaXufwmpI3WVnRIQlQhJk89qj4ploQQohAmJjCn2Tn+VE9WKwjxTKpfrSTFkhBCCCGEIVIsCSGEEEIYIMWSEEIIIYQBMhkvhBBCCOOpJuuUniQjS0IIIYQQBkixJIQQQghhgEzDCSGEEMJ4ZBpOCCGEEOLFIsWSEEIIIYQBUiwJIYQQwnhMTPQfhfD29ubixYvlHFjpSbEkhBBCCGGALPAW5cLUtHKv+Kvs8ZU3yccjNWvWBCQfT5N8/EFyUYhSpiQ6Oprw8HAAHB0dWbBgAXXr1mXYsGF88MEHuLi4EBISwtGjR4mNjSUvL4+uXbsSHx+v+7daVqRYEuWibl3rig7BoMoeX3mTfDzSr18fQPLxNMnHHyQXBSnvP3tpcfHiRUJDQ4mMjKRBgwasWLGChQsXsmLFCrp06UJiYiIuLi78+OOP1KhRg7S0NJKTk3nllVfKvFACmYYTL7hbt24xYMAAbt26VdGhVAqSD32SD32Sjz9ILozr8OHDeHl50aBBAwCGDx/OoUOHAPDw8ODgwYOkpKRQp04devXqxaFDhzh48CBdunQpl/ikWBIvNK1WS0pKClqttqJDqRQkH/okH/okH3+QXJSfjh07cvbsWfbv34+HhwceHh4kJiaSmJiIh4dHucQgxZIQQgghKpS7uzsHDhzg9u3bAHz11Vd4enoCYGFhQdu2bVm3bh2enp68+uqrHDt2jAsXLvDqq6+WS3yyZkkIIYQQ5W7MmDGoVCrd9vTp03nrrbcAaNq0KQsWLNAd8/Dw4NSpU7Rv3x6VSoWjoyNNmjTBwsKiXGKVYkkIIYQQ5Wrfvn2F7h80aFCh+ydOnMjEiRN12+vWrSuTuIqiCgkJCSnXKwpRyVhYWKBWq6lRo0ZFh1IpSD70ST70ST7+ILl4cZgoiqJUdBBCCCGEEJWVLPAWQgghhDBAiiUhhBBCCANkgbeo9nJycpg/fz7nzp1DpVLx3nvv0b1790LPjYqKIiIiAkVR8PT0ZMaMGZiamrJ//37Wr19Pbm4uAAMHDmTEiBHl2Q2jMUY+0tLSmDdvHufPn8fR0ZENGzaUcy+ez7Vr1wgJCSEzM5PatWszf/58HB0d9c7RarWEhoZy8OBBTExMePPNN9FoNMUeq4qeNx+JiYmsXr2aS5cuMWzYMN57772K6IbRPG8+1q9fz+7duzE1NcXMzIzg4OByux+QKCOKENVcWFiYsnDhQkVRFOXatWtKnz59lKysrALn3bhxQ/Hz81PS09MVrVarBAcHK9u2bVMURVFOnTqlpKWlKYqiKPfv31cCAgKUY8eOlV8njMgY+bh//75y7NgxJSEhQRkxYkS5xm8MEydOVGJjYxVFUZTY2Fhl4sSJBc7Ztm2bEhwcrGi1WiU9PV3x8/NTkpOTiz1WFT1vPn755Rfl/PnzyurVq5Xly5eXa+xl4XnzcfDgQSU7O1tRFEW5cOGC4uXlpdsWVZNMw4lqLy4ujsDAQODRlzO2adOGgwcPFjhv7969eHl5YWtri6mpKRqNhri4OADatWtH/fr1AbC2tqZ58+akpKSUXyeMyBj5sLa2xtXVFUtLy3KN3RjS09M5f/48ffv2BaBv376cP3+ejIwMvfPi4uLQaDSYmppia2uLl5cXe/bsKfZYVWOMfDRt2hQnJye9e+ZUVcbIh4eHh+7fRsuWLVEUhczMzPLtiDAqKZZEtZeamoqDg4Nuu2HDhqSmppbovMK+9+nnn3/m1KlTdO7cuWwCLmPGzkdVc+vWLRo0aKB7Y1epVNSvX79A3wz1vzrlxhj5qE6MnY/Y2FiaNGmCvb192QYuypSsWRJV3p///OdC3+wBdu/ebdRr/frrr0ybNo3Zs2frRpoqm/LMhxCiaD/++CNr1qxh9erVFR2KeE5SLIkqb9OmTQaPN2zYkJSUFGxtbYFHfxGq1eoiz3ssNTVV76/B9PR0Jk2axKhRo/D19TVS9MZXXvmoquzt7UlLS0Or1aJSqdBqtdy+fbtA3x7339nZGdAfSTB0rKoxRj6qE2Pl46effuLDDz9k2bJlNGvWrDy7IMqATMOJas/Hx4fIyEgAfvnlF86ePVvoJ1O8vb05cOAAGRkZ5OfnEx0dTe/evQG4e/cuwcHBDB06tEp/6gmMk4+qzM7OjlatWrFr1y4Adu3ahZOTk654fMzX15fo6Gjy8/PJyMjgwIED+Pj4FHusqjFGPqoTY+TjzJkzzJkzh08++YTWrVuXex+E8ckdvEW1l52dTUhICBcuXMDU1JQpU6bQs2dPANauXUu9evUYMmQIAN9++y1ffPEFAF26dGHmzJmoVCo+/fRTvvrqK15++WVdu8OHD2fgwIHl3p/nZYx8aLVaBgwYQG5uLg8ePMDOzo6AgAC9726qzH7++Wf+9re/cf/+fWxsbJg/fz7NmjVjypQpvP3227Rt2xatVsuSJUtITEwEYPTo0bqF8YaOVUXPm48TJ04wd+5csrKyUBQFa2tr5s2bV2U/Lv+8+Rg1ahQ3b96kQYMGujYXLFhAixYtKqQ/4vlJsSSEEEIIYYBMwwkhhBBCGCDFkhBCCCGEAVIsCSGEEEIYIMWSEEIIIYQBUiwJIYQQQhggxZIQwqAbN27g5ORU5F3BjWXz5s3MmDFDtz1u3DjWrVtXptcUhevdu7fuXlzFKa/XR3nIzc2ld+/eXL58uaJDEZWMFEtCGMn169eZMmUKXbt2xdXVFS8vL4KDg8nNzQUgMjKy0Js6FrX/u+++w8nJiVWrVhU4NnLkSNq1a4erqyudOnVCo9HobqJXFf3222+sXLmSyZMn6/atX7+e8ePHV2BUhjk5OZGUlFTRYbwQyiLXhw8fpm3btnr7LCwsGDt2LEuXLjXqtUTVJ8WSEEYyfvx4GjRowH//+1+OHTvG1q1b6datW6nb27p1K3Xq1OGbb75Bq9UWOD5p0iSOHz/O4cOH8ff3Z+rUqVy9evV5ulBhvvvuO1q1aoWjo2NFhyJecK+99hqJiYlcu3atokMRlYgUS0IYQUZGBlevXmX48OHY2NhgYmJCw4YNCQoKwsLC4pnbu3z5MklJSSxevJjbt2/z/fffF3mumZkZb7zxBlqtlosXLxY4vmnTJgICAvT2Xb9+nTZt2nDjxg0A5syZg5eXF66urvTv359t27YVeb3PPvuMN998U2/fyJEj+ec//6nbvnjxImPHjqVLly707NmTZcuW8fDhwyLb3LNnD127di2yzcdTPVFRUfTv358OHTowfvx4MjMzCQ0NxcPDg65du+p9L97jEbuwsDC6deuGh4cHixcv1oujuH6fP39e1w83Nzddvx/fuX3s2LG4urrywQcfFNqv7OxsPvroI7y8vHB3d2fSpEncvHlTr4+LFy9m8uTJuLq64uvry549e4rM0+M+/ec//6FHjx64urryySefkJGRweTJk+nYsSP9+vXTG4XJy8tj1apV+Pj40LlzZ0aPHq33Onn48CF///vfdTkMCwsrcN2kpCSCgoJwc3PD19eXf//73zzL/Yx37drFwIED6dSpEwMHDiQuLq5An540e/ZsXU6LyrW3tzerVq0iKCgIV1dXAgMD+emnnwpt4zFvb29iYmK4desW48ePR6vV4urqiqurK1FRUQBYW1vTvn179u3bV+L+iepPiiUhjMDW1paWLVvy17/+lejoaC5duvRMbyZP27p1K05OTvTq1YsePXqwdevWIs/Nzc1l06ZNmJubF/o9VK+99hpXrlzh3Llzun1RUVG4ubnRpEkTADp27Eh0dDRJSUkEBwczZ84cLl26VKrY79y5w8iRI+nduzfff/89W7du5YcffuBf//pXkc85e/Ysr7zySrFt79q1iy+//JL4+HiSk5MZOnQojo6OJCQksGjRIhYtWqRXjNy8eZOUlBT27NnD1q1biY+PJzw8XHfcUL/T0tIYOXIkbm5u7Nu3j//5n/9hwoQJwKORMIDw8HCOHz/Oxx9/XGi8f//73zl58iRfffUV8fHx2Nra8vbbb+uNFEZFRTFmzBh+/PFHRowYwezZs8nOzi4yBzdv3uTevXvs2bOHzZs3s2HDBsaPH8/YsWM5evQoffr0Ye7cubrzw8PDiYmJISwsjB9++AG1Ws1bb73FgwcPAAgLC2P//v1s2bKFvXv3kpycrJfDS5cu6do/dOgQ//rXv9i4cSMxMTHF/r4Ajh07xvvvv8/06dM5fPgw06ZNY9q0aZw8ebJEzzeU6y1btvDBBx9w+PBh+vXrx4QJE3T9MsTe3p5169ahUqk4fvw4x48fZ9CgQbrjrVq14syZMyWKT7wYpFgSwki++OIL3NzciIiIQKPR4OnpyerVq/WKphs3bqBWq/Ue8+fP12vn999/JyYmRvc9U0OGDOH7778vsIB27dq1qNVqvLy82LdvHytXrtT77rrHateujY+PD99++y0AiqIQHR3N4MGDdee8/vrr2NraolKp8Pf3p1WrVhw+fLhUeYiOjsbJyYnhw4djYWGBvb09EydONPjmeu/ePaytrYtte9KkSdSpUwdbW1t69uyJmZkZQ4cOxczMDC8vL1566SXOnj2rO9/ExISZM2diaWmJo6Mj48aN040gFNfvmJgYHB0dmThxIjVr1sTCwgJPT88S5yE/P5+oqCjee+897O3tqVmzJnPnzuXKlSt6IyD9+/enY8eOmJqaMnToUO7fv29wCqhGjRq8++67WFhY0Lp1a1q3bk379u3p0KEDKpWKgQMHcu3aNe7fvw88GrkZP348r7zyChYWFgQHB2Nqasr+/ft1/Rw3bhwvv/wylpaWzJo1CxMTE931vvzyS/r164evry8qlYpXXnmFESNGEB0dXaI8REVF0adPH7y8vDAzM6Nnz5707t1b93p8HkOGDKFdu3ZYWFgwfvx4LC0tiY+Pf+52ra2tyczMfO52RPVhVtEBCFFd2NnZ6f5qzs7OZufOncybNw97e3vdF9M2adJEbwoCHr2ZrVmzRre9c+dOsrKydNMPXl5e2NnZ8fXXX+stgH777beZNGlSiWILDAxk5syZzJo1i6SkJO7du0efPn2AR2/qn332GTt27ODXX3/FxMSE7OxsMjIySpWHGzducOzYMdRqtW6foijk5+cX+ZyXXnqpRCMCT34xqZWVFfXr19c7bmVlRVZWlm67bt26WFlZ6bYbN26sKzqL63dycjLNmjUrNqaipKenk5ubqxu9A6hVqxZ2dnakpKTg6uoKoNeHmjVrAhjMRd26dTE1/ePv3KfzYGlpCUBWVhY2NjakpKToxWBqakrjxo1JSUkBIDU1Ve94zZo1sbOz023fuHGDxMREvddtfn4+Dg4OJcpDSkoKzs7OevuaNm2qV9SWVuPGjXU/m5iY4ODgYJRP5T148IDatWs/dzui+pBiSYgyYGVlRWBgIBs3buT8+fPP9NyvvvqK/Px8BgwYoNt37949vvnmGyZNmoRKpXrmeLp27YqFhQXx8fHExcXRv39/3Zvq9u3b+frrr/n3v/9NixYtMDU1JTAwsMhpxFq1ahWYJkpLS9P93KhRIzw9PQtd+1KUNm3acPnyZXx8fJ65b4bcuXOH7OxsXcGUnJxMw4YNgeL73bhxY4OfMHxy9KUwdnZ2WFhYkJycrBvxy8rKIj09vcSFhjE4ODiQnJys287Pzyc5OVkXg729vW7tGjz6ZGJ6erpuu1GjRgwePJi//e1vRrk+PCrAHl+/qNfTkzkqKtdPtqsoCikpKbrfb61atfQK/ry8PO7cuaPbfrLgfNrFixfp1atXcV0TLxCZhhPCCDIzM1m2bBkXL17k4cOH5OXlsWvXLi5evEinTp1K3M6lS5f48ccfWbVqFdHR0brH119/za+//sqBAwdKFZ9KpUKj0bBhwwbi4uJ0I13w6K9oMzMz7OzsyM/P55tvvuHChQtFtuXs7MyZM2c4ffo0eXl5bNy4Ue/NVqPRcPr0ab755ht+//138vPzuX79usFF6r6+vhw8eLBUfTNEURRCQ0PJycnh+vXrhIeHo9FogOL7PXDgQK5evUpYWBjZ2dnk5ubqxVivXj2D02WmpqZoNBo+/fRTbt26RXZ2Np988gnNmzfHxcXF6H0tyqBBg1i/fj1Xr14lNzeXNWvWoNVq6dmzJwABAQGEh4fzyy+/kJOTw9KlS/UK5TfeeIMdO3awb98+3Wv70qVLHDlypETX12g07N69m4SEBLRaLQcOHGD37t26aeY2bdpw584d4uPjyc/PJy4ujqNHj+q1UVSuv/32W86cOcPDhw9Zv3492dnZun45Oztz6NAhrl+/Tm5uLsuXLycvL0+vTa1Wy/Xr1/XafPDgAadOncLb27tE/RMvBimWhDACc3Nz7ty5w+TJk3Fzc8PDw4M1a9bw17/+FT8/vxK3s2XLFpydnfH29qZ+/fq6R+vWrenXr5/Bhd7FCQwM5MiRIzRp0kTvzXrQoI7E75MAAAIGSURBVEG4uLjQu3dvevToweXLlw0WeO7u7owZM4Zx48bRrVs3fv31Vzp27Kg7Xr9+fb744gv27NmDt7c3nTt3Jjg4uMCb0pMCAgI4f/68wXNKo1GjRtjb2+Pj48Prr79O9+7dGTduHFB8v+3t7dmwYQMHDx7Ey8uLbt266S0Onzp1KitXrqRz5858+OGHhV5/zpw5tGvXjiFDhtCzZ0/S0tJYs2ZNqUYHS2vs2LH4+/szduxYunbtSmJiIuHh4bo1YhMmTKBbt24MHToUHx8fHBwcaNSoke75rVq1Yu3atURERNCtWzc8PT2ZPXu23uiTIZ06dWLx4sUsWbKEzp07ExoaytKlS+nQoQMAjo6OfPDBB8ybNw83NzcSEhJ0U8SPFZXrYcOG8dFHH+Hm5sbOnTsJCwvDxsYGgAEDBuDt7U1gYCC+vr6618JjzZs3JygoiNdffx21Wq1bgxUbG4u7u/tzTcGK6sdEeZ6P7AghhJFs3ryZY8eOGe2GgI/Xgj29RkxUD97e3vzlL38pcFuM55Gbm8trr73GmjVrSvTpTPHikDVLQohKISgoiKCgoIoOQ7zALCws2L17d0WHISohmYYTQgghhDBApuGEEEIIIQyQkSUhhBBCCAOkWBJCCCGEMECKJSGEEEIIA6RYEkIIIYQwQIolIYQQQggDpFgSQgghhDDg/wBTsmXqFrEwSgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x568.8 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6EMw27CBF3lJ"
      },
      "source": [
        "Based on the **SHAP waterfall plot**, we can say that `Stochastic_5`, `Stochastic_Ratio`, `Stochastic_15`  is the most important feature in the model, which has more than 70% of the model’s explainability."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "BbpHb9lGh3Au",
        "outputId": "8d6ff547-d15e-40bf-d819-fb8a965ba949"
      },
      "source": [
        "get_waterfall_shap(company=\"GOOGL\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7U1vr0EYULDH"
      },
      "source": [
        "The partial dependence plot shows the marginal effect one or two features have on the predicted outcome of a machine learning model. It tells whether the relationship between the target and a feature is linear, monotonic or more complex"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DkHoKeXqi7K1",
        "outputId": "bbfb8622-2d26-43ea-cc3c-21cf36c7bd0d"
      },
      "source": [
        "# Let's have a look on the top features and their dependence plots\n",
        "top_features = ['SMA_Volume_Ratio', 'ATR_5', 'Stochastic_Ratio', 'RSI_15']\n",
        "get_dependence_shap(features=top_features, company=\"GOOGL\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OAH/5Vz3+mC53Qx80W2Bvux27Azho/hETEvrJ5trTMBoje+z99+R7BwJ3/57EMSSELT4+HtXV1WBZFgzDgGVZ1NTUID4+XnacyWRCRUUFxowZA0BubXna169fP3GMO+64A/Hx8bh06RJSU1PF8wTLsLKyEhMnTnSZY2RkJCIjQ/evNGUkJOA+qAPgXXOHTjXg1A83ZOJj6kOhTxgjE7b0ITqXoI6tp51rZAKHSnl3ohDswa+pucJygLnWIVpaavlzMb0opJp0eHR3i5sMOXU+L2XxUtENJMfSYlJ6qkkHGlYxNYFULiEQgktIrLHFxMRg5MiR2L9/PwBg//79SEpKkrkhAWDatGkoKCiAw+FAfX09jhw5gvT0dK/7qqurxTFKSkpQUVGBwYMHAwDS09ORn58PACgrK8O5c+cwadKkoN9zIBHWfwpKrO1aB0qJY5Bq4kWo8KLVJahDer1Fu1vwzin5GhkAfFfPykQrKYaCuZZFlIEC07aWJY1oVFJ40Squ9ynHFmAooLbVIY4hjY4USI5lsK3Y4pOoCeww2/CHoxZsOG7Fot0tuFTPimkL7tIXCARC4AiZPx1zcnKwZs0abN68GZGRkcjNzQUArFixAo8//jhGjx6NjIwMnDlzBrNnzwYAPPbYY0hISAAAj/tef/11MZxfr9cjNzdXtOIWLFiAtWvXioEnOTk56N27d0ffvt8UlFhdEpX9rb0oz+1yCpAyqOJEpd1lfUxg7yVWJiYldRxK6uygYUfOFAOO/mDH56WehJfymr82uC+F8ib5DBotnEww/3DU4uEa2nEA2PSNq8injQnI8AQCQYWQEbYhQ4Zg69atLtv/8pe/iK8ZhsHq1atVz/e0TxBJNXr16oWXX37Zx9l2DYqrWbx41NUq0RK2rhbar8ztAvhAjNWT5dXypa45KcqgESkO8Plthz2KGpAcS2NYNOM2fw0Avmtw3XG5weFWbLWQMYxBhJ7CR2a7y8+z6rpzCymiTCAEH/Ib1gW474NmOHrLF1OPPxr89bgTla4PYcD7w1dZWV8ociy4C6WC4uBcw/WFqiGHrgKtrTZxPeq7ehZ7LrkXru8bHF5dgw0WTlzv+/2/W1VFTI26G5oOc8ueSyyen2LAzwZzHi1KaSoBgUAIDkTYejDSJGMKwCNj9egTRnmtfK8M5BBcmRSAOxJofHnVafuoiWRxNYvCi1b06hUmlqEqrmbx+nGr7LhhURQuSYTpRKVnm4puK4m15bQFUQYKpY3+rJB5xsAAFje6pcV9+Xkpiy/KWrAjKgKDXMtbEgiEAECErQvwyX/3QXx8x0dMeqvpWFBiFV2V0nU3ZdUNMY8LkIkaAESHA7/6rAWRYcBNBhoT+zPYdtrW5vaz491vgL+3zUEpQzofQ5scXODWxtzhTtR8geWA/LMtWDmeWG8EQjAgwtbDcVfTUbn+Jl13S4ljsHqyQXV9DpBHLV5r5f+vvwEADpdIRUdbPcY+KtZLSV3gLS4CgRD6hES4PyHwFF60yUSLgtyl2GDh3K53PTJWj1uM2r9aR0pt+MjsmpcGAEOjum98fLgHg2xMnGuCOYFACAxE2AhukMvWzwbzT2khzy3VpIOa5Nw1mMGTE8ORHKv9q1XTCjRZ1fcN6dt9v6I3PLgtD3/fzmgVAoHgFuKK7AJ0VlSkJ5SNPKcM1LlEQiqhACxIMbQleFOqidS3GCmcqdHmYrxrMIPJA3Vi12uBcMazaHQHvquzAyDRIwRCMCDCRnDLnGQdAEq1T9nBy3YXV+VzU3ixcwogEKEDmiRexssaw+8BYHBfGuZaVwXr7qIGAENjyK8egRAsOuW3q6GhwacCwqFOR0ZFaumZJs9TAzKH62WRkDQFJMXSOFbOiqkCz00xICspDFtOW2QC2KRYOmtWX0pTZYtKPchQYeltkQCCG8FJIPRUOkzYLBYL/vznP2PXrl2wWq0ICwvDrFmzsHLlShgMrm4tQuBRJlarlc0qrmbx1imLSwmohWMNeDhFjy2nbXC0FSEeb6IxNIpG5vAwTV2rCTzdNxyGQOgedJiwbdiwAWfOnMH69esxcOBA/PDDD9i4cSM2bNiAVatWddQ0ejRKd6IQvi9YcVEGSuwULSAkWPOFkp0WFAfgZKUDJysdqG3lENuLRuZwPtJvcF9Kc8WPnggHksdGIASTDhO2zz//HFu2bBGLBw8cOBDDhg3DwoULibB1EM1WudhEGSiZFaeEgrPO4/8cdO0/JsCXkGLFkH0iad759poNABE2AiEYdJiw3bhxw6UfWWRkJCwWss7QERRXs9hWLF+zKrhgQ6vNvduQg7PO4+VG7yWCiaBpJxAVTAgEgjpek4Tsdjt+/vOft1uAxo0bh9dee00cR1hzS0lJade4BO8UV7N49esbLpXzz9Q4ZLUYBYQ1IGmdx+6cT9YVmUQKRRIIQcOrxabT6dDc3AyqnR0Sn3nmGaxcuRJpaWmIiopCQ0MDBg4ciD/96U/tGpegjnTdLO+oxaeWLM9NMYjdn4WgELV8MoL/NFmJfUsgBAtNrsgHH3wQb7zxBp588knodP55L00mE7Zv344zZ86guroa8fHxGDNmDBiGrDMEGqFDtQPq3aZTTTROVqq3gHH354uy9QyBQCB0VTSpVH5+PsrLy7Fjxw7069cPNE3L9mmFYRiMGzfO91kSfKLworOJp1KOaAo45UbUhOOF4sYUrBhjpJE1Uo8oAwlSDyR8rcj2tDYlEAju0CRsixYt8mvwP/7xj3jmmWcAAC+++KLb45577jm/xg8VAl9SSy5CaYMZxPSiUNfKobqFc6mwr4ST/H+mxoEzNRbcNTg4lvWAPkB5c1CG7tKcrbZhWgLxVhAIwUCTsM2cOdOvwe12u+prQnDJHK7HzhIbXyUEQEwvGsmxtEuOmhQKwMgYym2rmJoWzqU7thpJHsZQo6IHihqBQAgumhfMzp49i127dqGqqgrx8fGYNWsWxowZ4/Gc1atXi6/XrFnj/yxDnECX1BIaiBZetGJniR07zDbQFFyiIoVtQvdsTyWsskbqsep2RhxTKXB99EBKHO3SaFSNfr2cfdoCtXIXqXct39WVuW5xgOSxEQjBQVMM9+HDh7FkyRI0NzcjKSkJ169fx5IlS/D5559rvtDy5ctVt69cuVLzGATtpMQxMPWhZV2ulatkc5J0eHJiGP4xM8IleVsgJhyiG/JEpR3JsQxmJ+lw12AGtGTAZptr92x3NAUhdbE7iRoAfFPRzSZMIHQjNFlsb731Fl5++WX85Cc/EbcdPXoUGzZswF133aXpQqdPn1bdfubMGU3nE3xHWreRoYCHU/TYeppvIMoXNw4TS2rlm9VdxXU3+MoinytC/RkKmJrIuGzXgoXETODW/qTRKIEQLDQJW0VFBSZPnizbNmnSJE1BH/v27QPAr7EJrwXKyspcqpEQAofgkpRW85+aqHep7i+NotQKy/HrbmrpBID3NbuezkgjETYCIVhoErb+/fvj2LFjuOOOO8Rtx44dg8lk8nruG2+8AQCw2WziawCgKAqxsbFi1CShfUjb0VyqZ3Hwsh3pQ3TISgqTVfBPiWNEK+2lohsAOHzf4J8JdabGAYYCfprIILYtQIXvn0aJBZEf3d3i1zqasW0dLlRlMSqcBgn3JxCCg+Zw/1WrViEtLQ0JCQkoLy/HoUOHsHbtWq/n7tq1CwDw9NNP47XXXmvXZAnqSAsZU7CKYvDlVd5NmJUU5vZ4X1Emd7McENuLBsDBXOuQtbABvAeluKOmFcgYxmDPpdCsdkLC/QmE4KFJ2NLT09GvXz/s3r0b586dQ3x8PF5//XWfkq2DLWqlpaVYu3YtGhsb0bdvX+Tm5iIxMVF2DMuyWL9+PYqKikBRFBYuXIisrCyv+zZv3oxPP/0UNE1Dp9Nh2bJlmDRpEgBg7dq1OHbsmNg4NT093e+8P3+RtqNRatXBy3YXYZMe7wsMBdwcxeBEpdzS+MhsE6+7s8Qm9nkrKLFiq0LUfLFT9l5iQ1rcCARCcHArbM888wz++Mc/AuCtrlmzZrWragjHcSgoKMCxY8fQ0NAAjnM+WTdu3Oj3uALr1q3DvHnzkJGRgT179iAvL89l3L179+LKlSvYuXMnGhsbMX/+fNx2220YMGCAx31jxozBQw89hPDwcFy4cAFLlizBvn37EB4eDgB45JFH8POf/7zd9+Av0iAR5ZpX+hDXj1jZDJQGsGCsM7DEHRwHJMfSLuIkPUfo8wYAeUUW2b6YcGBcPIPBfWlNVhwHYGg0g1QT5yKmUu4azOB4BYsmq3ObngJsXdSPSQGYMyYCpIM2gRAc3Ib7/+c//xFfv/rqq+2+0BtvvIGNGzfCZDKhuLgYo0aNwvfff4+RI0e2e+y6ujqYzWZMnz4dADB9+nSYzWbU19fLjjtw4ACysrJA0zSio6MxdepUfPbZZ173TZo0SRSxESNGgOM4NDY2+jTHpqYmlJeXy/5VVVW199YBOINEhND956cYMCmBwfNTDKpuyMKLVvw0kUHaYAZzk3X4+8wIPDkxHP+YGYG5yTq39SIdAMy1LEYb3WeJUOD7vql1ExAiLJVWnDto8D3jbo6iPXad/qJMLmpA1xU1AJhgojEhgVT3JxCChVuLbejQocjJycHw4cNhs9nwj3/8Q/W4Rx99VNOF9u3bh7/+9a8YOXIkCgoKxEr/27Zt82/mEqqqqhAXFycWVGYYBkajEVVVVYiOjhaPq6ysRP/+/cX3JpNJFBdP+6QUFhZi4MCBiI+PF7dt374d+fn5GDhwIJ588kncfPPNLudt374dmzZtUp2/Wkmt0t8M0HLrImlGIK0tXz4NwOKfuB7z3jfXsXp/k2hFMRSwYz7f+HXHJQuiwvXo1QsA1EP/aQAfme2erTrAqzWmVXPuGRGOl768AbuDv7a78/xxq3YmTXZepo3Gnh0R3JPvvyffOxD8+3crbL///e+xZcsWHD9+HCzL4uuvv3Y5hqIozcL2448/itYZTdNgWRYpKSk4fvy4n1PveE6cOIG//e1veP3118VtTzzxhFgYevfu3Vi+fDk+/vhjl64F2dnZuO+++2TbqqqqsHjxYtVr1dQ0qW73l+JqFjmftri4Dd893qBaSUTKwrF6NFs5r6IWCISyXQwFWKw22Ns8kA64ryvpLuWgq/LtNRYnr1oxKKznuiKNxsiAf8e7Cz353oHA3b8ncXQrbAkJCWKeWnZ2Nt588812TsKIyspKmEwmJCQkoKioCFFRUX63wZESHx+P6upqsCwLhmHAsixqampkVhXAW2EVFRViKTCpleZpH8AnmP/ud7/Dq6++iiFDhojb4+LixNczZ87Ea6+9hurqatm5AN8t3F3OXqBLaqlxotLu4hpkKACg3IoaTQE5k3l35pbTlg4Rj4dT9OgTRiHKQGFdkfPBTwN4dJwBfzjqKgaPjOXPqWx2YIck0TwmnHd/tpdACycH4OsrFgwaFsBBCQSCiKaSWtu3b2/3hebOnYvz588D4IXy17/+NRYtWhSQoIuYmBiMHDkS+/fvBwDs378fSUlJMjckAEybNg0FBQVwOByor6/HkSNHkJ6e7nXf2bNnsXr1arz88stITk6WjVldXS2+/vLLL0HTNIxGY7vvqb0UV7PYctqC4mo+olDZdibVRGNzZgQyh+vbBM4JBb4jQM5kA8y1DrxU1IooAyUeRwFIvMm3+fTWmI9cUuvAwrH8daWCK6zrKecKAH3CKCwca0ByLCNbi1MTNQr8GpcvjPGwpugvtw8yBHxMAoHA035zSSMPPvig+HrGjBkYP348WltbZdZPe8jJycGaNWuwefNmREZGIjc3FwCwYsUKPP744xg9ejQyMjJw5swZzJ49GwDw2GOPISEhAQA87nv55ZdhsViQl5cnXu+FF17A8OHDsXbtWtTW1oKmafTu3Rv/+7//GxArtD1I89QYyorNmREujUJvjqLFfDOhOkmzlRMjIw+XsjhcxopWHg07ZrSF3nMAyn50jiUUUW62AjvMzvW1+AigqoV/fV1jKlv6EB02HL8hGwfgk8HPXbOoWp2pJh0KSqwuUZhq3BxFYWgU47EnnZKJ/RmcrZEfPzdZD1MfCmdrXMuNEQiEzoXipHH3HUxlZSXefEOm9A0AACAASURBVPPNHlv5v7y8HLNmzcKWLe8hPt57FRetbDltwYbjzjDBJyeGIdWkE7tqA7wgrJ5sQIOFE8trKc/zBaEWpT/J2MZeQKSBgp4GmqxAebP2ryQF4LkpBrE5qjeEVAWhYkpZowOXGjyf+eTEMDRbOdm9LRyrx5MTw1FQYlV1j3rj4fERWDm+5yZo9+R1pp5870Anr7EFkt27d+P8+fMYMmQI5syZA4vFgo0bN2LHjh0YP358R0yhR6EsfiwI15xkvWgJsZyzU7Zg1aWadLLKJb7AcsD757yLWnxvCgmRFE5K8tKutQLXWjm/rssB2Fasbc7S4BOW461SLeelmnRibp7A1tM2DIykkeeHqBEIhOASdGF76623sG3bNgwdOhQff/wxvvvuO5w4cQJRUVF44403cOuttwZ7Cj0OteLHgLwBqTQgQkiqXjjWgAF9KFyVWEy9dECrxh6xWo6rus6h+rpcTtrrMij70TmCp0APZUSlL9dVrlFy4Ku6+FvtkSRoEwjBQ5OwsSyLt99+G4WFhairq8ORI0fw5Zdf4urVq5g7d67Hc/fs2YMNGzbg1ltvxfHjx/HLX/4Sy5Ytw8KFCwMx/5BALY/t+KPti5IUih0rt62ebEDBBRvO1jgfyUIi9EtFrTJRA4DEmyhcrOfanStmYABL21JUMH3fPxus3kpnaBSF79y4HAW3rLmWRXE169KR4NWvb8h+XsI56UN0+Poq67O4ZQxjMCEhDDU1RNgIhGCgKdxr48aN+OKLL7B8+XJQFP+Xa2JiIvLz872eW19fL1plqamp0Ov1eOihh9oxZYI/CNX8845acEYRCOEAsK7IIguVF7hQx+Gnie1fC7J0UHwFBfXISXeiNsFEY3aSHsOiGTw7uRfezeqDjGHy+1X+vABgXDyNrKQw5EwxYGiUp7oormi1gAkEgn9ostj27duHt99+G/369cMf/vAHAMCAAQNQUVHh08UoikJ4eHinRw32NLRU83e3jwPQaHHfdw3wrbBxsGm0cC6Rk544WenAyUoH8ktsmJOkQ3Isg6HRDDKGwWPx5ZOVDmw4fgPvFNt8tmY/L2Xx3jfXMS3Bt/MIBII2NCnMjRs3XHLCbDYbwsK817u7ceMGli1bJr5vaWmRvQcgq+RBCDy+VPOnAPTvQ8kiE4XQeJoCZgxlsO87ZxpAxjAGE0w6HLxsR7gOnR767qlYsiccHNosVu3mlD8RoAL7LrRiWgLJZSMQgoEmYUtOTsYnn3witnEBeCtOqNLhCWULl/Z0CCA4kTYWVa6lKUk16UCD75JNg092PlOjLgAU5SpswisHB9TfgMwi2nOJFYVOq0NOqPuoLSKRRmWzA1dVSmkBwADFXLsLM0b2QtexcwmE0EKTsK1cuRJLly7Fp59+ihs3buDpp5/GuXPnNJXZWrJkiebJ7Nu3DzNmzNB8fE9FLQHbm7hRbb5EDnxAyJka9eMcHPBDk/sgi/QhOnx1VR4mLwidN3nJGMa7+SqbOVkCtqfADm8W2KPjwrCuyKLZIk28SZ5c3hncNZjB/7u1d4/OZSIQgokmYRs+fDh27NiBwsJCDB48GP3798fzzz+P2NjYgE5m3bp1PVLYfK0VKXUtCqH6noRN2YjUW+POquuuKjEpgcGS8QakxDEo+sGOQz66HFNNNF6YGgGAF2Yh7YChgMF9aXzX4LsLU2jLMyyawTvFFk1zGhbNoNHCojFAAYk0xQu7V0sYaMsZBBakEBckgRBMNEdxREdHBz2asROLoHQqvob7qyVgeyLVpANNWWUuRGUwiJrVJH0YC6IGAA+nGHCkjLcYaYpvQOrpk6Mp4MmJ4eJ7Ic/u0FXghzoLDvuxLkeBFykBrWOUNjoCJmoU+CLRQvWWS/UszrgJ4Z9gojFpoE6T65hAILQPTcLmrhcboL0fmxaEVAKCZ9wlYHs6PmeyQVJphC9/ta3YBkebOE4wMfiuwRk4IVT2l5bccnd9gLcKowwU9lyyyaqKAPw4yjleqmfx7jeutR+1wgEovGhDShzDdy7QeF5UuGt8p79RnRx4cU2JY1BczSKvSC5q0iudqHTg3mEUETUCoQPQJGzKXmw1NTUoLy/HrbfeGlBhI2hHLQHbE8OiGTyQrAfAIXN4GFLiGExN1MvESejLJlgiyu7bnq4vvM5KCkNBiRUFF2wwRlBYkGIQH/zSa+UV+S5q0eF88IqAkFAtDY7xxMK2Qs1S4U010XhyYjjeOmXBl1d9txwFN7BaWyA9DVglk9p+1ir+oZDW+Q0gCISQRZOwqQWJvP/++2hoaAj4hHoiwe7HJg82ATKH84KlFKfNmREovGgDwMncfN7GVlqOWUlhMlGUXp+CFT8bzKiK2gQTjaFRDJJjaZhrWZeE8YYbcivoTI0Dj+5uwSNj9ZiTrENtK4cfLRwqmh2qDUmbrRwASjbGN1W88kSHux6v5BYjhTM18vJdglArS24BclED+CTxDcetYCgrdkRFYJD3bBkCgeAHfmdKz507F/feey+WLl0asMn01DW2YONLsIkQ1LGzxO412lIpWM9NcVp5UsFTBq8cLmVV3X9Do2g8OzkcxdUszLUOl2N4WZLDwZlPJgRyqEEBqp3CWY53aRb9oG6tJcVQSInToU+YPG8tKYbCs5N7ISWOQUGJFS/6UAyZ5UijUQIhmPgtbN9++23AhSgnJyeg43UXglErUorWYBOlABZetOJEJe2yjmaudaC21YGaFk4mWHlFFgyLZnCpnhXX82hYkTPFILOSOPAh7/U2GqfKpUnOlNcqKZ6+cZ5cm9Eeumnnm21u3Zh3D9Uj1aTDs4daZNv1DCVbW/P1N+GbCiuSIn1zJxMIBG1oErYnnnhCFtjR2tqKkpISZGdna76QUPj4v/7rv8Rtx44dwz//+U/85S9/AYAeGerfEXgLNhGsK6FLNtsWvu5cc+N7tHl7eDs4uITdOwAc/cHuEoX5eSmLX94ejtMVNomLVO9TlRQlniw2d6ImzFFAWqwZ4N2XakIbF8H/PqitrUkbr7oTzX0XLDjwLTTlIBIIBN/QJGzK1jIRERFYtmwZUlNTNV/IbDa79F6bMGECVq9erXkMgv+4CzZRrr8JzUelSdS+6IxaSa2aFs7l4c4B+NvX1/HcFNfIS8G61MKkBAbpQ3Q4+oMdNS0cbCznUp3fF5TFmo9XsC5zoSlg8kAdXipqRV0rJ7pMKfDdBYSAGQBIjqVx8LIdSbE0tp2Wi5yWHEQCgeA7moTNl+oh7qBpGna7XVYA2W4nZc6DiZayW0r3Y4OFw8KxBlkStSeETtSCoAnrYMJpNAVkjdTj/DXX6iAceGvuj+kR4jbButSScE2Dz6+7VM8GrUalMUJuxU4dzIACZB27aQqYm6QTo00FiqtZsSrKl1dZl/VBLTmIBALBd9z+VtXUuKm5pMBo1Ba3PGrUKLz//vt45JFHxG0ffPABkpKSNJ0fygQjKlKt7BYAF6Fzt/4mCEzhRSvyzc48MUG0pNYJAHxRJrf6zLUsAAqZw/VIiWNUoxwBPpCkuJp1Ed4jZa5ClXgTJWsqOieZH/utU/LAjVuMNIwRVLvFjqaA2F4UHk7Ro6TWgaRYWrWaP++KdOaoFZRYcfCyHXWtDtmx0tN+dnMYHhlDkrUJhGDgVtgyMjI8JkxzHAeKonDs2DFNF1q+fDmWLl2KQ4cOITExEVeuXEFpaammepMEdTxZZGqBIMKambS+pKf1N/51GHaW2AEOLgEgY4xO9+bqyQYcvGxH+hAdhkUzLu7FzOFhqlGJHFzdcWrrbDSA4dE0fviRb+wprMkBfP1KaQ5a1kh9m7C6MsFE4/+rcrh0EVeSaqJxotIhE2NljUwpO8w29AkDBkbS+IOXCEmGAlZOuQmDwkijUQIhGLgVtl27dgX0QiNGjMAHH3yAPXv2oKKiAmlpacjIyEC/fv0Cep2egrdCyEpLDKDchvx7SvZWhupL4fPC5C63Y+UsOA5t4mMV1+xSTTqsllQ/EaDhtBLVglgEV+cXZSwOlfLuvLsk61jF1Wyb+5S3qgRhzXMjLkOjaAyNogFQqG11uLXq1Iove1u523LahmEemo5SAB5I1iNzuJ500CYQgohbYevfv3/AL9avXz8sWLAg4OP2RLTkps1O0kFwBwKQFR7WurYjiJcaW0/bxOol0rkIsJyzwghDWTE7SeciDoJTwF0Qi5AHJ13DO1LGYkGK6zmCuL9U1KoaiSiN9GQoYGoAOoMrcffTEiIl+4S5Fz5va6K+tCoiEHoymleuL1++jOPHj6OhoUGWv7Z48WK353z22WeYNm0aAL4ljTtImL/veMpNc600ove5vqQwzlYPzTSFeo2Zw/WqkYwUnOH3bNs6lGCJCQii7DxGHsTiHMsq6wundo5T3OXikTaYwWijvF0OywExvSiPKQJS+uiBZg19RbPH8AnqBy/bER0OlP3IIS6CQmJfGltP29pqdbpWHvFmgfvTqsgXlKJJRJTQndEkbPv27UNubi5GjBiBb7/9FiNGjMCFCxcwYcIEj+e99dZborC98cYbqsdQFEWEzQ88CZU7a87X+pInKu1ytyEF3BpPK4occ0iJY2SRkQAfwJE1Ui+6KAWBzRyuV6z3AZXNDiTHMjL3o1SoL9XL17ak+6XiXtnsQHE1i8zhepl1+nCb27KgxCprMwNQyJlsQMEFm9t2MwJzR+nFwBF3a3Npgxmx8srJSju+KGOREElh8kC9LImb5YA/H/1RFjzizQL3tVVRcTWLwotWSAN4hO3K74xSNFdPNkg+t8CLKIEQbDRX93/hhRdw991346677sK2bdvw8ccf4/Llyx7P++CDD8TXgV6zI7hfG/O1rY07pOMIhZGHRTNYtLtF7MYt1J2M7UUDcApbciwj9kpTPkhT4nohcziLQ1eBd0+1YIfZDoayy9yP0oeuslzV7CTnfqG+Zb7Zhh1mu1gKTCn6wjqgaJ1RfMCH4PY8d83i0tZngomGhQUm9mfQJ4wS5xdloFyam9LgBRQAfnekRex5V1LHqQaTHP7ein9ddoqGt89MWYtSrTalQHE1K35GAO+CFqJi1aw+pWgevOy+Ag0ROEJ3QNMTr6qqSrS8BDIzM5GZmYmVK1dqutDf//53LFq0yGX722+/jV/84heaxvBEaWkp1q5di8bGRvTt2xe5ublITEyUHcOyLNavX4+ioiJQFIWFCxciKysraPs6C3/cjr6M8/eZrtuUVpKwrudOfFPiGJQ0Oat+KN2PgmVR2exwsRoFMRXGkbatEayZhWMNbi0eQO4iPXjZjtH9nAWOhZY9WUlhbtfxhkUz2FbM95Lj4FwrBIAvVFIV1Kw8pTXt6TNrsMjPNte6tzCVbXzcuXuFaytFNX2IDsfKWZcKNMR6I3QXNAlbnz590NzcjMjISMTExOD7779H3759ceOGhzpFCrZt26YqbO+8805AhG3dunWYN28eMjIysGfPHuTl5WHjxo2yY/bu3YsrV65g586daGxsxPz583HbbbdhwIABQdnXmfjqdvRlHHfbfBXT2wcZwFBNLlaKUkyklT3UertpsVClx9DghUh4yCvb1cwY6nQpenIBCqIm7Hun2IKYXrTqWtwjY/XiGpsAQ/GW15bTFvFn5u7nJp0/wJfqkroYlcdK2/i4c90qcxaln51gaSvXJUmlFEJ3QJOw3Xbbbfj8888xa9Ys3H333XjiiSeg0+kwefJkr+cKid4OhwPXrl2TBZ6UlZVBr9f7OXUndXV1MJvNeP311wEA06dPxyuvvIL6+npER0eLxx04cABZWVmgaRrR0dGYOnUqPvvsMyxYsCAo+3oavorphIQwVTFUisncZD1MfSi3gqlFVNWao7769Q3VtTXpNqVoCkKktCQBtFVKkYtkXwPwp7v5RHfp8eP765A5lNG8lpUSx2B2kk7Mq3PAvcikxDHImWJw6YkHwGPOotp7aQUaUimF0F3Q9C1ds2aN+Hrp0qUYMmQImpubMXPmTK/nShO9MzIyxO0cx4Gmafzyl7/0dc4uVFVVIS4uDgzTVmuQYWA0GlFVVSUTtsrKSlkag8lkQlVVVdD2SWlqakJTk7yCv9pxPQ01MYwyOKMVpVGdvo7j7ZjkWFpV2NKGyH8tZifxDVqTYxlZMIwWfrTwwS/KSM1bTGFosMgF/K1TFiwZ72qRCkiT3D2JjDSvkKGABSnOfb7+8REotzaB0JFoErbKykqYTCYAvkcx7tq1CxzHITs7G++99564naIoREdHw2AweDg7dNi+fTs2bdqkui8mpjeMxuA1Gu3KKO/75FUrXvqyiY9cpIAX7+mLtDG9232dk1et+PqKBbcPMmBCAu9mnD/RgJ0l10RxGRBJI2t0L/zmZ33FcxbvuQa7A9DRwP8bFyYTohkjwrHvW8/ueA58Xclf3t4bDOWMqjz5gxU2jpP1nPvyKov/VLTgw+x+4hylpBmBHVERLveh5NCpBtk8S5oYpI3x//uVZgTSxvh9ult66nce6Nn3DgT//jUJ2/3334/U1FTcf//9uOuuuxAWpr31r2DNHD582K8JaiE+Ph7V1dVgWRYMw4BlWdTU1CA+Pl52nMlkQkVFBcaM4X9LpdZWMPZJyc7Oxn333SfbVlVVhcWLF6Ou7jr0+iaXc0IdozESNTXy+z5otsDe9qR3cMAPta2o8RKK7w35ml2T6PIbFKbumhPmJJ2L3QG0tlplKQkT4zhYrYzXYs0c+E4G0nW2szXOUl1Doyh818CJ1zlo/hGDwtT/4BsUhrYGpRaXyiVCiH9+iXNsGkBSJOvyc/aHQOa2qX32PYWefO9A4O7fkzjSWgbIz8/HuHHj8Prrr2PGjBl46aWXcO7cOZ8m8cEHH+DChQsAgPPnz2PmzJm4//77fR5HjZiYGIwcORL79+8HAOzfvx9JSUkyNyQATJs2DQUFBXA4HKivr8eRI0eQnp4etH1SIiMjMWDAANk/pfD2RIqrWWw5bUFxNS8O/JoWvy9QazpqASACKXGMSwSlgHIumcP5NcEnJ4aJuV6HSlkwFDA3WYeFY/WYlMDg+SkGPN/WXFWAA1BS67ouBwAReqrd9yyI9w6zvD+cUCi6vQjjbzhuxWOFLeLnRSB0RTT9BiUkJGDp0qVYunQpjh07ht27d+Pxxx9HQkKCzL3oiXfffRf33HMPAD5Z+5577kFERAT+9Kc/4a233vL/DtrIycnBmjVrsHnzZkRGRiI3NxcAsGLFCjz++OMYPXo0MjIycObMGcyePRsA8NhjjyEhIQEAgrKP4JmTV62qeVWBXtPxJ69PsE7UcutS4hhsOW2RiaWpDy2rlCIg5LDR4MPo1QopZ43UY9Xtrvl+yrkoO5lL56VWOFqaduHtPr1d1110KKlQQuiK+PynYWpqKq5fv46qqiqcOnVK83kNDQ2IioqC1WrF6dOnsX79euh0Omzfvt3XKagyZMgQbN261WW70J0b4INK3DU2DcY+gme+vmJRfVgGKlVBwFexdJe7JkWLWA6LdlZTETTnuSnyQtALx+rF1AKAL1FWeNEq9naTzoWGVZamADj/IEg16UBTVpm1Jk1k936f8qhMtWokysowwS7zRSD4i2Zh+/bbb7Fr1y7s378fer0emZmZeP755zVfKCIiAjU1Nbh48SJGjBgBg8EAm80GliUujZ6Kuzy2YOCLWHorX+XJmpPur2zmZJ0RXjxqwSNj9aKoUQCmJurFcwSRAIB8s11MhBe2OYSBJPCVQWx4dnI4ciTdEwTXqb/3qdaAVvnHgdJqJTluhK6CpidJdnY2SktL8dOf/hS5ubm4/fbbQdOaludEZs6ciYULF8JqtWLZsmUAgLNnz2LQoEG+z5oQErjLY+tsfCkw7alYMd8cR97DboukqDQHvlxVSlwv18oocDaFdZdYDtlIcFvCTMt90gAqmzmx6avaz0D5x4HyGKFWZ1f5HAk9F03CNnv2bMyYMQORkf6HaC5btgypqanQ6/VITU0FAOj1ejz11FN+j0no/gTa7RgI/CkwLVB40SazsJJiKFyo48Su40pNOlnJdxBXVhah4BQTYS5RBgrmWha1rRyOlLJiNZbkWPeJ1lruUyhKvcNsw84Sm2iJurNIXc93rdXZ1T5TQs9Ck7DNmzcvIBe74447ZO+FEHkCoavhf4FpuXSV1PHvhaLKygam3zVweKywBZszI2SNWGlJSKUwD9ESpIAZwxjsvcQHoqwrsmBYNCNbH9PSgkbYrmxCKwS8uFtfVP6c1Gp1EmEjdCYdVh+H4zgUFBTg2LFjLj3dlDUdCYSuirdAlMzhYcg320VrSuqGvMlNRX5pCoK09mThRd5tKRSDFi1BDqKoSc8XRMzZfcGKnCnqLWiULlNlnzzluJ4IVDcJAiFQdNg38I033sCuXbuQkZGBf/3rX5g3bx727NmD6dOnd9QUCISA4MndlxLHiEEf0vY2nkpwUeDLiPFRlPJCx/lmGxzgz1cKpfBeKiaFF53Fjx0ACi7YZNaY0IJGJpQA5ibpcLWJkxWEFtyhWn4egVgrJakDhEDRYcK2b98+/PWvf8XIkSNRUFCAlStXIi0tDdu2beuoKRAIHYJU+KTBHLwF5hoFLLgTN2dGuBQ6FmA5vpHp520dBYQ+cq5rYHIFjYtwdi2XtqCRBrZIIyiPlTvdnWqdFLTcsz+Q1AFCIPEqbFeuXBFD9AcOHOj3hX788UeMHDkSAEDTNFiWRUpKCo4fP+73mARCV0f6wOeLIasjuP2khY4p8P8Ei+3hFAMeTuGtrrpWDuZaVsx3E1DrHv5wCsT0A6EFjSCaFICHU5zVSQIZpeqLBeZrh/D2Xo8Q2ngUtkOHDmH16tVwOBzQ6/V45ZVX8JOf/MSvCxmNRrGYckJCAoqKihAVFQWdjvjjCaGJ8kFrrvWcs/ldPeuS+8YBuMVIY2J/RnRvCsIHADvMdjw/xSAmebtzCwrraoJrU4ADsPW0TcynC6So+WKB+bJOpyZgxOIjSPGoKn//+9+xbNkyzJs3D++//z7efvttv4Vt7ty5OH/+PEwmE7Kzs/HrX/8aHMdhyZIlfo1HIHRl1B60Sjehkj2XXPu5AXx/OKG9jlrKQF5bVCTgFCa18l5AW6dvxQB8Pp1NYum1Xxh8tcC0rtOp/1z5lj8kWZwg4FHYysvL8dBDD4GmaTz00EOa60Kq8eCDD4qvZ8yYgfHjx6O1tRVDhgzxe0wCoaui9mDvo70phlvUiig72oJCnL3a1IVJmQQuXWOrbXUEVBj8Sd7Wsk6n/LlKBVmARGYSPJYPcTgcYoURnU4Hm02l572fxMfHE1EjhCxqXQqOVwSnfBx/HcpFmLzN6bkpBrFTwRFJ6x2pMCi7L2hFsMDmJuvBcbzL1JeuAO6uq7wHgJOJ2qQEhrghCZ4tNpvNhn/84x/ie4vFInsPAI8++qimC9XW1mLjxo04f/48WlpaZPvy8/O1zpdA6BaoudaMERrbbvvIwyl6TE10Bo0I6QPKtSi1ORVXs3jrlEW27vbTRH4971I965IDB2hfh/M3edvTepnyHgDIuop76kBO6Dl4FLZbbrkFX3/9tdv3FEVpFra1a9eipaUF999/P3r16uXndAmE7oPStTa4Lw21NbT2UlLrwJMTGayebEBekQUODsg7ahHrSkrFQTonZeFlgSNlLD4vZUFTELsFsBrdnUrUgkKKq1kcOtWA1larmGYgFUtv63PKn2tXrDdK6Fw8Clsg+qQJnD59Gnv37kVERETAxiQQuhPBckUmxdLYctqCymaHKETSTgBKcXB2H3C4iBrgFDMHJ1+HU3N3arHalBaWVEw/MttBKwTYnRi6E6/25NCRFIHQxK8VVo7jcPToUXz00Ud47bXXNJ0THx8Pu93V708g9BRsairSDvroebfhO8XOvDUaaCunBYnFJl8zk3YnEI4XUL5/ZKwefcIoidvP5jKmN6TCI211A/CiqRTLhWMNbsXQn4hNT7UySYpAaOKTsNXU1KCgoAAff/wxrl27hrvvvtvr8QILFy7EmjVrsHTpUsTGxsqOMxqNvkyDQOiW6Bm1YH3/abYJKQI8LAekmmhMGqiTdduWPtCVbr65yXqY+lD4rp7FmRoH+kVQOCkp1NwnjBJTBwpKrBjVj4YxgsKCFP/WstS6GNAqAuxODH2N2PQkXoFICid0TbwKm2Cd5efni0nVTU1N+Oc//4nhw4d7PDcjIwMURYnjAMC///1v2TaKonDs2LH23geB0OVJvInCmRrvx7WHE5UO3DuMkgVbSFG6+ZJjaRz9wY7P26Iiy36UC2+zlX9fUGIVq/4DwJSBrqH7Wtx6gmvy0FW4XWNT0p4iy57EixRvDl08fpKbN2/Gxx9/jJqaGkyZMgWvvPIKJk+ejMzMTMTExHgdfNeuXQGbKIHgja68XlJczWLvpY7pFl9wwea2j5qyv1veUXlEpJKtp20YGEljW7FVtv3gZbtY7QTw3a2XcBODpIQwtwLsbs6+fraexCtQxZsJXQ+Pwvbmm2+ib9++WL9+vV8VR/r37+/3xAgEX+jq6yUnKu0BdEJ6hq9UYnX7cxDcfC8VtXoUNYB3nAqRllLSh8gfHVrdet46kLvD3wARb+LV3uLNhK6JxwTtF154AUOHDsWvfvUrZGdn4//+7//Q2NgouhJ9YdmyZfjPf/4j23bs2DGsWLHC57EIBCVqD9auRJSbXmyBggIwNIqSFe0SQvTdUdcqV6uYcGDhWL2sySkAF1GjKYglvATUEtLVKLxo7fDPKSWOwcKx2tcEpcnh/iaoEzoXjxbbvffei3vvvRfff/898vPzsWnTJvz1r38Fy7I4d+6cT1ac2WzG+PHjZdvGjx+P1atX+zdzAkFCV18vabAE117jO3Qz+K5BLhQ7S+zIHK5eyiqml1zB0obo0CeMchEyJY62UlbK3DJPllFxNYvCi1Z8ZJbP72wN67XUlnB+sFyG0rEBSadyD4XzbQAAIABJREFUWFVzAQldH02//TfffDNWrVqF5cuX48CBAygoKMDTTz+NUaNGae6nRtM07Ha7rJo/y5K/ggiBoauvl/APTffWU3sRSiQrO2F7cgtKu30Lrpsog+sYauSbbcgcrncRN2/uRyWfl7L4oqzFo2gE082sHHt2kk7WgNVdLiCha+PRFakkLCwMmZmZ2LRpE95//32MGzdO87mjRo3C+++/L9v2wQcfICkpyZcpEAhu8dXl1NEE0xkpRDhynOt2oQCxkpQ4BjlTDLjFSIMDX89xXZEFD6fovc7VAacb0Zu7Tll8WYnSZaocL5huZuXYwh8HAP9w1OJeJXQ9/P6khg4dilWrVmk+fvny5Vi6dCkOHTqExMREXLlyBaWlpXjzzTf9nQKB0G0IVvCIngaMEYCpD42CC/Jea/G9KdRc57DDbMfOErtLx+3iamctSAGW40t0eZsrQ/HW3UtFN8Qeb+6sKWXumhr5bS5TwDUZW2unAH/clcqxM4frkTlcL3NNdlUvgCe6coRwR+BR2GbPnu01UERrAeMRI0bggw8+QGFhISorK5GWloaMjAz069dP+2wJhG5KsFyRNgdQ3gyUN7vGN1ZddyoJywEvHrW0lceyim5bpdgwFB/xeKycVRWi56fw4ths5VyiJVkO2FZswRgj4/JAnZ2kQ10rh8OlLBwAdDQwLo7GibZkcAcH/OHfrRhv0rlYZ0IlkneKLThUyopCLRXR4moWi3a3tLlVrciZYnCb8iDFU2NW6THdia4eIdwReBS2RYsWia85jsPLL7+MZ5991u+L9evXD4888ojf56tx48YN5Obm4vz582AYBk899RTuvPNO1WN37tyJrVu3guM4TJ48Gc8884zYlsfdvsOHD2Pz5s2wWvmH0qxZs/DQQw8BAD755BO8+uqrGDBgAABgwIABWL9+fUDvjxAapMQxmGCiZVU9OhJlQeO3TlmQPkQnrqcxFF+eK7YtoGR2kg7fNzhE4aEpIGcy36m7oMSKDcfVRfrzUr6AsrQbgHR9LWMYg6HRDNKTb8Jf/lUvO/dSA4dLDfLWWEKXgsKLNjGJXLiHwotWnKikkWrSYVuxMx/PAYjJ5DRlFeftjlAL+ScVVbwI28yZM2XvX3vtNZdtvnDw4EF8/PHHqKyshMlkwv3334/09HS/xwOAd955B71790ZBQQHKysqwePFi7Ny506XY8tWrV7Fp0ya8++676Nu3L1asWIE9e/Zg5syZHvf169cPr732GoxGI5qbm/HQQw9hzJgxYoTnbbfdhldeeaVd90DoGfQNcsi/O+4azGDKQJ0sGfvLqyy+LmexIIWvBRlloCRuSfkallTUiqtZ5BVZlJdwQboWJrX89lxi8fwU/rFzpMx78Ji51tVdKpBfYoejzSoZ3Ff9Z+tos1SHRYeWeHmiq0cIdwQ+BY+0h/z8fOTl5SEpKQnZ2dlISkpCXl4ePvroo3aNe+DAAcyZMwcAkJiYiFGjRqGoqMjluIMHD2Lq1KmIjo4GTdPIysrCgQMHvO675ZZbxFqWffr0wc0334yKigqf59nU1ITy8nLZv6qqKn9vm9ANie3VYb9uMijwAqHUBgcHbDltQ5SBQoOFc7sG5uD4dAWxd5uGxUIa/ANV7aF68LId+WdbvI6j7CigNi+AF06+JZA6HPwLOOmuOWyCe/XJiWE90g0JtCN4xFfee+89/PnPf8Ytt9wibps6dSrWrl2LBx54wO9xKysrZRVOTCYTKisrNR0nCIunfVIuX76M4uJi5OTkiNtOnjyJ7Oxs9O7dG4888ojb3L7t27dj06ZNqvtiYnrDaIz0cqehSaDu++RVK76+YsHtgwyYkODe7dSZTBxMY4e5scOve6jU84M5r8iCvHv6egzwcDB6PFZ43WsagMD88RFIGxOFk1etGBNnwVmJOPTtrcO312wezgYeHh+BOWN4r0vBhWuwO+TuVCXTk/tglMmOfRduYMbIcCRG6ZDzaSMcHL+el558E65Yofk7cvKqFYv38NfV0Vb84e6+aLjhCNj3S/m9D/T3N80IpI0J3vjtJdjPuw4TtpqaGowePVq2bdSoUaitrfV43vz581WFCgA+/fTTgM3PG9euXcOvfvUrPPvss6IFd+edd+Luu+9GeHg4zGYzVq5ciY0bN+Lmm292OT87Oxv33XefbFtVVRUWL16Murrr0OubOuQ+uhJGYyRqatp/3/LF8qYu+1fqp+YW7wd1Ag4O+OfJJkxNZNyK4L4SeR7asCgK3zVwMitQ2rstLQE4dLbBJX+NArDvgsVjOkFkGDA4gsVB849INemwKSNC7B+3w6xueR0vvS42QX3z2HVszozA3yVBIQ0NLZLE6ybMSda75OFJOWi2wN7mt7U7IIpkIL5fwvdeiFyUuoF9GV9r5GNX+/0I1O+9J3H0KSry+vXrottPQGtU5JAhQ7B3715kZmaK2/bv34/ExESP57377rse95tMJlRUVCA6OhoAb31NnDjR7XEClZWViI+P97oPAOrq6vDEE09gwYIFmDZtmrg9KipKfJ2cnIyxY8fi7NmzqsIWGRmJyMieaZUFm+6yWG6u7bourTM1vgW19NJTkMoaBXnvNr4W5Q0XC4+T/O+uiU+T1Rn8IQShLBxrQEGJFRRc0yZoii8PJv0ObCu24I/pEWJaw6tfO+fiALDDbMPOEpvbh7x0nYqC3O2p1rTVXbUVT/sEsZH+HLR+f32JfOwuvx+BRHNUZHtZvnw5VqxYgZ07d2LAgAGoqKjA+fPn8ec//7ld46anpyM/Px+jR49GWVkZzp07hxdffNHluLS0NCxZsgRLlixB3759UVBQgBkzZnjd19DQgGXLluG///u/kZWVJRuzuroacXFxAICKigqcOXMmoD8zgja6y2J5uC6w/dg6CgpA9pgwMfiEBpA1Uo/z15xBHRyAd4qdQlFczeIjs81lHKH3mo4Gnp1kgLnWgY/MNrc/FWkE57oii+pxC1L0YnsdgcOlrLg25q7qidpDXipGQhpAs5XD1tM20RpVb9pqdUk/8CQ8UrHh4HSzav3++iJW3eX3I5D4FBXZHlJTU/Hhhx9i//79qKqqwuTJk5GbmyuGyvvLggULsHbtWmRlZYGmaeTk5KB3794AgI0bN6Jfv36YO3cuBg4ciEWLFmHhwoUAgDvuuAP33nsvAHjct3XrVpSVlSE/P1+0Th988EHMmjULH374IY4cOQKG4b9Qy5YtQ3Jycrvuh+A7Xb2clkD2mDBZT7PuAAXguSl8ROSwaEaWuDw7SQ9zLStae9IHbOFFq0yEhkZR+O1PegHgj0lPvgmDwizYclpdrKQIEZzS9bWhURS+b3OFvlNsw+rJBpk1Jw0YcbcuqHzIq4lRqkmHxwpbRPFZPdlZ2caTuHgTHqXYKJPnveGLWPnz+9HdE7wpjlMW4XFit/NfDGl9x08++QQXLlzA+PHjkZaWpvlCJ0+exIQJE1y2nzp1yqU4ck+hvLwcs2bNwpYt7yE+3tTZ0+lwAuVr705sOH4DW057DpzoKkxKYLBkvEG0wNQLBQOQWBuCZfJS0Q3skFhsc5PlFT3SxkSJ60zuLKr4CKBKZVmSoXhRlY7/5MQwvr9cW9K4MBcAeHR3iyh4UpepEA0q/F/ZzLmMCUCWs/fkxDCxo7inFjye9inX2PwVj2CJj7+thbTS6Wtsq1evxqRJk8R1tc2bN2Pz5s0YMWIE8vPz8eOPP7q459zx9NNP48iRIy7bV61ahUOHDmkag0Do7jw5MRwltSy+vNr5idpKkmIoXKjjRJebVNS0FAoWqv4DfN1KwfFKt9WxlI6xIyoCg8Lk1sR39XwzVmF61QpRoylgTpJO7Lq9s8Qms1hS4hiZVSnMXQoHiOuASkEVakMqrSB/GpVqsZLamxgerMTyUFiT8yhs58+fx//8z/+I7z/44AM8//zzmDlzJg4ePIi3335bs7CpGYbXr1/3q7cbgdBd2XD8RqeJmtTl1WzlXCzH3mEU/jGzlxipV3jRhsKLVnzf4JA96OpaOdCAS5NSDnxQRr7ZBlDO1UQHBxz9Qf6w/PPRH/HIGJ34cBYenBNMVtHqUsu7M/WhxWPdlcKSPoQLL9pcIjcrmx0ovGhzsRIdAOYm6WDqQ8vG1NKoVMh5E6w/4Vi1IJM0o+tnI1RXAThkDg/rVCEJhTU5jzNuamoSQ9u///57NDc34+677wYA/OxnP0NeXp7XCwiRlRaLxSWisq6uDrfffru/cycQuhXF1Sy2dpIbUqg+In3oHvjOjqvNzqe7UO5LzZqRwgEYbaTdRlJKrTiBz0tZmRge/t6Kf112BlUUlFhx8LIdvXTuLUpA3rBVi8VS2yqfIy++dpl1JsAXQXYVFW/XUXOnSgNGlBavYK26O/8js11c1+wMusuatSc8Clt4eDhaWloQERGBc+fOYdiwYTAYeP8yx3HiGpwnFi1aBI7j8NJLL+HRRx8Vt1MUhdjYWNXQfAIhFAlWhX8tfF7K4nCp4OazItVEY1w8javNclfd7//digmSQsRqOMfhEepMflHGiutuHFwtrjnJelxtcuDLq/w1BTfXpXpWFlTjyV26rkh7eaziahY1LeoDOQDclchgjJFBs5VDSa0D6UP8e4irFZOWuvCUrr38sy1obeWts+RYBgcvy8/n0PllwLp7/UyPwjZ+/Hj87W9/wwMPPID8/HxMmjRJ3FdaWqqpMv+MGTPAcRyGDBmClJQUAHwASklJCcaPHy8LTCEQ/KU7RHFpad8STKSXPeGmGPN3DRy+b/BsVSpde6sldSSFz+BImU3m6qTAt4S5VM/iq6tOYYwyUDh4Wf4H8pC+FK7b5N0JBPzJ8xKgKYCTuDi/KGMxZaAOfzvBfybHylmYa1mfXYFqn6tQUky5nwbw7jfScmLqxoEQ1dlVv8tdHY/F65YvX44vv/wS8+bNw/Xr1zF//nxx3759+3Drrbd6vcDq1avxySefiKK2efNmvPjii/jmm2/w29/+FgUFBe28BUJPR3iIbThuxWOFLV22tl9KHIPpQxn06uLPKl90lwNgrnXgpaJWFF60iX9YKPPKAOBSPYu8o/Lw/ryjFkSHy4+73Pj/t3fmYVGd59//zooIBhAGUBCTYAUXbFXeulVF0RgFlLgkqb4xGmvcoqZJbRvbq2LjL2lrvGLSpNWouTRWYxsXasQkEpKavjEalySSKDTSX0AHBkFBMcDAzDzvH8dzOOfMmQWYhRnuz3XlCjNne56Z4/nOfT/3whRFjUfp3GL4mpZioRmToMGurJ6Yk6oT3rMySKwlK+PclPJ7yFXNSN51NzdVC/VdT6k4dEBcu3F2qs6hNZoc2XZQoK5tdRWcfnIJCQk4ePAgbt26hYiICMm2hQsXQqfTOTiyjUuXLnksAIUglAiUKK7XzjXjeJn04TjUoMLXNYGXtC1GHCJ/pJTLKTtSKrVEGGDXCBXgXILviT6TkfFqh9Ykz1vFrZiYpFwOS3m9qy3Ckx8jHxih1HtOfA/J18cc5ZtxLke1wwol4iATpYR0jQr4rSjPryt7HgIBt34SyEUNgNvloe7cudPpABSCcEagRHG9V2bvdvr2pn9FTa8GWjwYpCm3gsRcqrEpltESv65rZsIam6M6LTYG/PrjJkxP1kpKeAHK613ipGqlwIjkKA0KrrSKOoG33UPyH01teXL21UTcuQ/TYjV4cVoEnvvglpAKwacwKDU5JTqG158AnghAIQhnBEoUV0IvlZ2Lzexnr6knRY0nJVotWEFicbKBi868VGNVTLwGuDU+MY4CSaq/b0tXEFtSkSEqO0H89JpFEmEoD4zgX4sTyPntI+O1UKMFNtjXjCy40irZ39378Kc/CkOcrrXD92sgrCf7G68LmycCUAjCFYEQxbU6vQcWH+uaFf49yd7iVjyWpkPpDRuiekDifj1ZbsXCYTq3qq8wcMEerpBaUkCfcKDyTtt2PjJSXE3fkTtR6R5S3VVKca1LjQoiC6/NenP3Puzo/dqe4sftOWewCaXXhW316tVYu3YtDhw4gOTk5A4FoBBEMJAWq8Fvx4Xgfz51XR8xkOGq67cK7kQxNgDlt6RmoiOXo7Owf/nxYksqJVqDSlEaQ+5AnctcM0eIXZHiBG5xCx1frut6ej3ZG0LZnmt7S1C9LmyeCEAhiGCBd4kV/K8NlfWtDl1ygYxYaJR06WNZzze5hcWfY2GaTqiq7+g6c1K5dTaxBTguUYtxiVoUfWdB5r1aJEdp7KIkgTZhABwHbMjXzVKjNag3c/lnGpXF5+u6nl5P9lfglbcF1Wer7J0JQCGIYKH4ulVoKhmsDHEQ6akCkNFfYydsPbQqqFVMYp0xcGH9k+7ur/RxMXAltuTwbsaSG1Z8es3i8PPWqLg8uiXHGu+242nBruyedu5Jft1M2hC0/RX5PYF8PLwwd/T6/gq88ragds3wMYIIUpSi9oINJVGb1F+DhWlc0NjJ8kZJ2P+EJC2+k625qQGhIzZf2YSHr3DCC1PJDZtQrksuVnLGJGiQeW9babGCKy3CfjYABVdakBbLhd2LXWWLhoVg90Wz5GFc9J1FkkbgK/jrecLi8VfglbcFlYSNIHxIZIhK0lRSLnKB2YrUNf8qt2JhGvcgXS9aZ1QBOFdllYjQmAQNEnqphfw4KwNOVliFz0wcASm2oOamaIUSVUqixuetia2sgivyVUDutZKrTFynEuD6xH1e2eiXAA5PWjz+CLzytqCSsBGEjyi+bhW6UKsAPJamw50WgK8ZWHLDhhtNNrtajHwB41PXLA7dcr6ilx5oaHG9nxwGzho6b1LDdIdJGoKKiynzydRAWyK1PDiEt5TkD/ebTUzR7agGV6cyNVotEkJOrLIG6CQJ21kDuDV/JeFQwl8BHIGSu+kMbwpq4H0aBBGgiN1eDBACIzgrRCM8YMUsGqbDU+lczSm+HuOWM80OK+u7Q9I9KhgbmFC7MF4heEMJFYC1/8e9qE41gMR7gIrbbe8dKrEIVpqSZSpvbPpQihaACuH6tihLoM1SmpCkkZxHSfQn99fgsTTunHJX4nmTBYuGhShaDkrCUVZnn3SoJCq+sL4CJXfTX5CwEYTPkLqy+Iews2od5bds2H3RLHl41TV1Lqu64jZDcqQKSRFqjEvU4q3iFrjjAOVHf1+kyi6RWowhFKhpkooaRFdQOlKFtrJX+aUtgniqweWRycP+rcw+ulLpvIMNGqdiBShbDkrCIbfaxELM40vrKxByN/0FCRtB+Aix20t919TgAx7khYB5Pi634uNyq1Bdw1MRlWX1DGX1VmHtyh1saEuEdkZNU/vHwwDBInrhVJtFKO/t5moNUl57U96/jRerOy0MW840w9BThYVpbeIkt7aclcxSChxx1/qSX4esL89CwkYQPkL8AMtMvQf19Y3Cw2zLmWanxzqz6jqDu6LW0f3bw6ZPzRgRr3Z6DQbg/kgV/reeKQqcPCKz5IbUuk2L1aCszorXzrUtFP6rvBFvZvfkug+IakHKw/ndESB3rK8LxhZFq44EzXOQsBGED+EfYAaDHjV6s7CedKnWuXuRj+gT9zJTQqcGWr1Q/9FXXHBR2R/g6knKYxnFiK26wyWtyBog7QSQ/x9pagEX2NIqqbrvqOCxKwFyR/zOXJWu9b3xhdkvaQPBjNN+bARBeJ/zJovESokJlW6f1J97WOam6PH4MOeVegJZ1NoDH4QiR6MChse3PdZsaItoLL5uxbqiRrvAG9XdMzLZe/IWNO6SFqvBomGOhWpUvxBoRIP/zGjt0n0EAxGy2AjCh/BrK5mpIeh3t+C8vANzbRP3YB1iUCN3oE4ow1V83YpzVfTwAzgB4wst82WzxNVBxESGqJBf2oJNn5rtztM3HPifjJ4A2hLCVQAeH6bD3uJWtwI62hsFOSJBj51ZPfHGF2Z8ZuS+z67cRzAQIWEjCB8hjpjbdqEWO2b0lKzdiB90DEBGf61E1ORFfDuKTgW0BnAWuArAtPs1QrrEGaMVs1N1QtsZ+WfkLD2h+nvu/0ouxIlJ9m1s5HS05mFaLBdR+Xllo0PxDMaq+76ChI0gfIT4oWux2XdYfnJ4CM4Y+bqF0gedJ0txBbKoAZzoi1vh2MB18T5Y0ooZyfYC4Gy6YkvJUZ82Z3SmAoiz9Th/Vt0PBgJe2Jqbm7Fx40ZcvnwZGo0GTz/9NMaPH6+475EjR7Bnzx4wxjB27FisW7cOarXa6bZz585h7dq16N+/PwBAp9Nhz549wjl37tyJd999FwCQk5ODn/3sZ16eMRGoiF2OWrWye0vo/aVyfGww4ekSYmLBc4fOVu0QNyKV/xhxB0fi6a+q+8FCwAvb3r17ERYWhvz8fFRUVGDp0qU4cuQIevbsKdnPaDRix44d2LdvHyIiIrBmzRocP34c2dnZTrcBwP3334+9e/faXfvChQv48MMP8fe//x0AsGjRIowYMQIjRozw/sSJgEMp3P8Pp5oBMGQN0Ns9zAqutEosOnFVd740F2GPqz5uKgC/Gee5yvyOfox0hmAomeVPAj4qsrCwELNnzwYAJCUlYdCgQTh16pTdfkVFRZg4cSKioqKgVquRm5uLwsJCl9tcXTsrKws9evRAjx49kJWV5fC4hoYGVFZWSv6rrq7uxMyJQISPmAO46uycC82CJccaERmikkTLHS5plUTK8cfmpuixflyIJCqQL1Plb8JFQZsqAAnhzvd3Zq3NSNZgZHz7HlF8H7e5qTq7z2dSfw3mpmrdFrXi61bsvmh2Gq3obk3J9sL/kHkqXU9uyA4Q8D8DTCYT+vTpI7yOj4+HyWRyaz9eWJxtA4CKigosWLAAWq0W8+bNEyw5k8mEkSNHSo67cOGC4jj379+PHTt2KG7r3TsMBkP37E3niXlfMLbgzFUzRvULwYgEvQdG5X0Onm6QuBVtACxaPeb/SIO9XzQK75U2aDB5iPQzumBswf+rlAZE/N/hnIeCP9Zf3BGliDEAYSEa4I577sEhBi1umxmgYhgSp8PSH/fCiAQ9fv7uTRReMSMpQoOJ94fgL2e+d3gOBmBvcSsOLohBev9W/P3i94gL12LZqHCMSNDjgrEF8/bXwmIDtOoWvDM/RvGeuWBswdLj9vvJ77XM1BD89XytYFllpt4Dg8H5PcidAy7v18kGYPIQtz66gMPbz7suL2wLFixQFCoAOHHihNevn5qaioKCAoSHh8NoNGLlypUwGAwYNWpUu84zf/585OTkSN6rrq7G0qVLcfPm99DpGjw57IDAYOiFmprOzVu6yN4QML9uuVymNnFTA0jpZUVKL2D/lxAelCm9rJLPSCk6UqMCJidwf/PHtnftSqmFDg+XeqDcPNQVieHAtzfcG8ulGouw39VbZpz4jxkLh+lw+BKnlt/UWPDQQA1+Oy5E6I4NcBVZWqwM5+8md1sZ8Oq/64S+bWpYEK6xor6ei3K03PXhWmxAUclt9NOH2I2lqMRst199vdbuXgMAdnfQjAH19Y2o0dunFfAUX7di6fFGWGyBdb96Ek/8u+fP44guL2z79u1zuj0+Ph5VVVWIiooCwFlR6enpDvfjMZlMiIuLc7ktPLzNl5KQkICMjAx89dVXGDVqlOJx8fHxiuPs1asXdQz3AoG6yM7nMhVcaQW/xsaP21nlCnl0pLwQr2Qdzo26jjzOglIYlJuHusPYRC0YpAWL79EDtxVa38ivYAPwXpnUtfdWcQs2TggV0iAAruvBH041C8IGADWNTJgTHzV5pLQVz40NEURcvHYlD61XWuNy5HYUNyrl33P2/fGCGUj3a6AR8GtsmZmZOHz4MADOZXjp0iWMGTPGbr/Jkyfj5MmTqKurg81mQ35+PqZOnepyW21tLdjdn2S3bt3C6dOnMXDgQOHaBQUFaG5uRnNzMwoKCjBlyhRfTJu4C/cA4v4OtEX2tFgNfj22B349NtRhEd780has/qAR+aWcEsjnK27zsvsiZyksGhaCT69ZvFrX0V3qzQzRodLHjJKozUjWSNYXeaq/l06i4jZTrNKRNUAnHK8C15pHfjor48azM6sn5qZq8VAKtyCYX9qCJ4414rVzLcK5lda45I1GI0NUdt9HZIgKPyuQnkvMyHgttOq2/QPpfg0kAv5TXbhwIfLy8pCbmwu1Wo3169cjLCwMALBt2zbExMRg7ty5SExMxJIlS7Bo0SIAwOjRozF9+nQAcLqtqKgIhw4dglarhcViQXZ2NjIyMgAA6enpmDRpEh555BEAwIwZMyRrboT3CabK6PLcpcfSdNh9kXPD8YnbuSl6u/nKj3tubAj+Vd41KpQ46mMmp66Z64xdcsOGU9da7frD9e4B3LxbJ9rKgC1nmpEarRYs3bRYDZ4b29YrThz2z7tlVWir9s9XGTlSyvV5E9eIdJTXVm+Wimy9mdndf648CGmxGrwzPwZFJbcD/n7tyqgYb44QPqeyshIzZ87E7t1vIy5O2YUZzHjK1x6IKM1990WzpOp8XJhKYrEMNaixOyfM7lzy44Ya1J1qROpJZiRr8P5/3WuNwzVcVW7N42jNUA1gV3ZPoZGo+HNQOl6jAh5K0eFgSavD/d7MVl73kv6AgOL6mDv7GAy98NE39UHxY6wj0BobQXQj5EnYcjfcNzU2wU0mP45PEub36yo4S5jm62HyImxlXOV9pfU+R7rIr2vJ18UcHc9tY5J1Nt5i4/PbxG5h+RqoK++AO/s4altDeA4SNoLoIqTFavBQihYHS9oCJu4XdatmcBxsMFgkEL5ywUSEANGhzrtpK8FbT2oVkN5HI7EuXVma6rsn4C1AcbUPeSNRvpak/PisAXohIZ4vmsw3f02OahM1cfTp4RKLYBm6EiFX+8jb1lAAiechYSOILkTWAL2w/qNRAfOH6IUHLx9swFkSLQBUCNcDbxW3Slx9asAnVUlumYFb5vaJmnyt7KPv3E9oDtcBT/84BMlRGsVoUkDlubYAAAAYdklEQVQqKhOTdJLC0gAwO7WtNxvvvlQSGXn0qdgy7CziVA8KIPEO9IkSRBdCyZXFt2ThH4BLjjU6FK6ke1SYfK8W5bdskhB7f6ACMD1ZI3FHGnqqUNfc1vus4jaT7M+/UiqLdacVePGUGTuzeuLXY3u4vH5arAaZ92olwnajSerOdVS6Su7W7EgdSEfwqR7ddY3NF5CwEUQXw1mV+d0XndeIrLjNhEhKfzOpvwb3R2mwaJga56qs+LrGhtKbji088RZHwSbuuu74tAnTHemJPi634pOKRklHbCWR4d93ZBl2FndcmkTHIWEjiABCHijSVdGogH+VW/FRuRVqAPdGeqaSpbOkah7x+pga9lVV5OLoSGTE7/N5gmRhBQYBn6BNEN2JtFgN1o+zLwHV1egfoZJU5GhvgIkSKgATkqTBHUqJ0OL1MdvdY8SyKhdHV4WOnV2L6JqQxUYQAYY8Ubgr4krIhhi0iO3JJOuAYxLUCNWq8HG5Vcg5E1tafGmuTyoa8VCKzmFkoXzdLDpUDYa26zyUopzY7ijsPlDLtnVnSNgIwofw7rPM1BD062AjgpHxWqjQ4rOw/s4Q1xOoabJfMxuRqMfa4VzJML6gMV//sW19zCZJfeBRykUTB3bI180ArsIIv2/WAO467goW9UYLPOgbIggfIbYQtl2oxY4ZPSVrOO5GyaXFanBfpDR/LCaUC6O3daCyP398bZPj7R3tdF3dyK13iI9XAZg9pCcAM3JT9JKCxuLPAWgrfSUnNVqDnVl6h5+ZfN1MKUDEXcHqaNm29nynhGchYSMIHyG2ECy2NgvBXZeYmHsj1PhvfZt7LUSjgo21JXK3lxAvPnflgS5DDMpL+0qfAxeZ2IJDJRbJvPg6je4KhtK+7RGs9kYxduQ7JTwHBY8QhI8QV4LXqrmCvLsvmlFwpcXOJeaM4utWO/Ey3mFQi6rbtxfjHefbxddLukd6BbWLC2pU0gfN1zU2zNtfaxeE4cg1GB+ullxf7UF3IN+V3NOi463O2oR7kMVGED5CbCEkRofiNyduCW4wvlqIqzUcpUajPPw6Fl/30BtrcBoVsDBNWg3lubEhqDczHPimxc6dObm/Bo+lcVGc4iogYouVx51kaRWA9WM9L0Sehtbl/At92gThQ3iX1sEym+QX/dxUHeLDVZJWNEouMnmpJ0cwAMmRKpR5IMwe4Kvia5EarUG9mQliJh7fNzVWu2onTRYI258cHoLPKzlR1qrtH/aukqUDab0qEMccTJCwEYQfkNcLzBrQVsPQ2fqM2BJQA8jor0HvUBVSozV44dO2qiQaFfBTUZ3J9rJomA7hehUiQ1SCgAFw2pJlYVoIPi5vlJwn815ptOJzY0NQ9J0Fs4aGIy3WPs3cnWTpQCEQxxwskLARhB9wVi/QWRi6M0sgOUqDt4rNqGlkyB2oQ26KHslRGrtCwEMNaqRGa5A1QIeyOiuKvrOghxYSa+tOC0O4XoXkKGk5L1dNNBcNa2uOqkJbtXyAE2xeaD+vvIVzKVqPl6oiCICEjSD8hqNf9K7WZ5xZAp9UWGFlwOVasyBKYhegRgU8O4orIMyLY26KHsXXucRn3hJs6zDQZjG6GlfxdSveKm6rUylvsyMX7IMlFhwptQjnV8pp8xcUqh/YkLARRBejo+szziy9h1K0AFTIGqADAEVXJ39N0x0mdJgWn8fVuM6bLHaJ2GLxU2oEyp+/rM6KTZ+aAUCwLv0lbhSqH/hQuD9B+BmleoV8GDoAl7UMecTpBOLebT8raLxrHXFi5SgUnb9m1gCd3Xnk43JkaYoj/+VpALwwzk21P3+RrC+b/LUvoVD9wIcsNoLwI86sg/ZaDkoWldK6mDuuTk9E9NkcrMOlxWqwID0CRSW3hfPL+6aJg058DYXqBz70jRGEH1FKzna0JuVO8V35+pvSQ9od4epIRN95k8XtROoRCXr003MWafF1K+rNDIuG6VB6w+b3NTYK1Q98SNgIwoeIiyDX11txpLTNzSXv0uwJy8FZbpi7zTrdfbh3JJFaapXapxD4CwrVD2xI2AjCR8iLIOcO1EoCKWan6uzcdvIq9R1pdtmRh3RHAig6YulQSxjCG5CwEYSPkBdBBlSS1it8xKIYXpS8GamnZJn5SnBoPYvwBnQXEYSPED/EtWpOyLIG6NyycLwlNI4EsyOCU3zdiiXHGmEDoEYLdmUri2/xdStKyxqQ0stK61mEVwh4YWtubsbGjRtx+fJlaDQaPP300xg/frzivkeOHMGePXvAGMPYsWOxbt06qNVqp9sOHDiAo0ePCucwGo2YNWsWnnnmGZw7dw5r165F//79AQA6nQ579uzx/qSJgET8EM9MvQf99GbhfVd4y7JxJJjuCI7c0iu40iKU9LIBeKvYjCEGjeR4R2tqJGiEJwl4Ydu7dy/CwsKQn5+PiooKLF26FEeOHEHPnj0l+xmNRuzYsQP79u1DREQE1qxZg+PHjyM7O9vptkcffRSPPvooAMBisWD69Ol48MEHhfPef//92Lt3r0/nTAQu/EPcYNCjpsbcruO8Ydk4E0xngqNk6ckb5vyrnCuKLLYEaU2N8AUBn6BdWFiI2bNnAwCSkpIwaNAgnDp1ym6/oqIiTJw4EVFRUVCr1cjNzUVhYaHLbWI++eQTxMTEYPDgwd6dFEEo0NneYY4SwXdm9cRT6fp2rdsVXGmVCFTBlVZJYre4bY44yVkpiZwgPE3A31Umkwl9+vQRXsfHx8NkMrm1X3V1tcttYo4ePYqcnBzJexUVFViwYAG0Wi3mzZuH7OxsxXE2NDSgoaFB8p7SNQjCHeRuQP61uBq/WKScBZ90zBUobxnAJFZlZIhK0rONFzB+n9IGjbDGRhCepssL24IFCxSFCgBOnDjhs3HU1tbi7NmzyMvLE95LTU1FQUEBwsPDYTQasXLlShgMBowaNcru+P3792PHjh2K5+7dOwwGQy9vDb1L013nDXR87heMLVh6vBYWG6BVt2DT1Aj8trDhbqQlh1bdgnfmx2BEApfoXFrWILGwShs0mDyk45/9gvQQHCmtFYRrQXokDAY9JhuAyUO4fUbe14IzV80Y1S9EGAcAbp8OXzk46M73PeD9+Xd5Ydu3b5/T7fHx8aiqqkJUVBQAzvpKT093uB+PyWRCXFycy208x44dw7hx4xAZGSm8Fx4eLvydkJCAjIwMfPXVV4rCNn/+fDtrr7q6GkuXLsXNm99Dp2uwOybYMRh6oaam+80b6Nzci0rMgohZbMDfLkhFjX+/qOS2UN0jpZdVklqQ0svaqc++nx6SNb9+erPdmmE/PdAvGQDst9F33z3nDnhu/s7EMeDX2DIzM3H48GEAnFvw0qVLGDNmjN1+kydPxsmTJ1FXVwebzYb8/HxMnTrV5Taeo0ePYubMmZL3amtrwRj3M/jWrVs4ffo0Bg4cqDjOXr16oW/fvpL/5OJJEO4gXqcCgEs1Nrt/yEqBIB1ZS3NGZ9f8CMJbdHmLzRULFy5EXl4ecnNzoVarsX79eoSFhQEAtm3bhpiYGMydOxeJiYlYsmQJFi1aBAAYPXo0pk+fDgBOtwHAl19+iaamJjvBLCoqwqFDh6DVamGxWJCdnY2MjAyvz5no3qTFavBQihYHS7iADBuAuak6xIerHK6x8ceRCBHdARXjTQ7C51RWVmLmzJnYvfttxMXF+3s4Pqc7u2Q6O/euWmPRXei7755zB3zjigx4i40guiPyCERxTzV/Q92nCX9DwkYQAQovGl2p2zN1nya6AgEfPEIQ3Rl3uj0rJWb7czwE4W3IYiOIAMZVDUlfW1BUrZ/oCtBdRxA+RNxotJ8HmkS7qiHp69qMVK2f6AqQsBGEj5A3Gt0xw3P5ZI7O4w8LitIKCH9DwkYQPkLeaNQXle3JgiK6IyRsBOEj5I1GfbX+RBYU0d0gYSMIH+Go0ShBEJ6FhI0gfEhHG416GkqiJoIZEjaC6Gb4I4mahJTwJSRsBNHN8HUKgJKQTjZ47XIEQZVHCKK7IW5744sUAKpGQvgastgIopvh6xQAqkZC+Bq6wwiiG+LLFADKpSN8DQkbQRBeh3LpCF9Ca2wEQRBEUEHCRhAEQQQVJGwEQRBEUEHCRhAEQQQVJGwEQRBEUEHCRhAEQQQVJGwEQRBEUEF5bH7EarUCAGpqavw8Ev/Q2tqAmze/9/cw/EJ3njvQveffnecOeG7+ra0NiI2NhVZrL2MkbH6ktrYWALBu3Ro/j4QgCCLwOHr0KPr27Wv3vooxxvwwHgJAc3MzLl++jOjoaMVfHcFMdXU1li5dih07diAuLs7fw/Ep3XnuQPeef3eeO+D5+ZPF1gXp0aMHhg8f7u9h+JW4uDjFX1zdge48d6B7z787zx3w/vwpeIQgCIIIKkjYCIIgiKCChI0gCIIIKjR5eXl5/h4E0T3R6/VIT09HSEiIv4fic7rz3IHuPf/uPHfAN/OnqEiCIAgiqCBXJEEQBBFUkLARBEEQQQUJG0EQBBFUUII24VXKy8uRl5eHW7duISIiAhs3bkRSUpJkn+3bt+PgwYMwGAwAgB/+8If41a9+5Y/hepStW7fio48+QmVlJQ4cOIABAwbY7WO1WvHSSy/h1KlTUKlUWLRoEXJzc/0wWs/iztyD9Xuvr6/H7373O1y7dg06nQ5JSUlYv349oqKiJPs1Nzdj48aNuHz5MjQaDZ5++mmMHz/eT6P2HO7OPy8vD59//jkiIyMBAJmZmViyZIlnBsEIwossW7aMFRQUMMYYKygoYMuWLbPbZ9u2bezll1/29dC8zhdffMGqqqpYdnY2+/bbbxX3effdd9mqVauY1WplN2/eZNOnT2dGo9HHI/U87sw9WL/3+vp6dvbsWeH11q1b2caNG+32e+ONN9jzzz/PGGOsvLycPfDAA+z777/32Ti9hbvz37BhAztw4IBXxkCuSMJr3Lx5EyUlJZg2bRoAYNq0aSgpKUFdXZ2fR+YbfvSjHyE+Pt7pPoWFhcjNzYVarUZUVBQmTpyIDz/80Ecj9B7uzD1YiYiIQHp6uvB66NChMJlMdvsVFhZi9uzZAICkpCQMGjQIp06d8tk4vYW78/cm5IokvEZ1dTViY2Oh0WgAABqNBgaDAdXV1XZuiRMnTuD06dOIjo7GsmXLMGzYMH8M2eeYTCb06dNHeB0fH4/q6mo/jsi3BPv3brPZcOjQIUyYMMFum9J372sB8DbO5g8A+/fvx+HDh5GYmIinnnoK9913n0euS8JG+J05c+ZgyZIl0Gq1OH36NJ599lm88847gu+dCE66w/e+efNmhIaG4uGHH/b3UPyCs/mvXLkSMTExUKvVOHbsGFavXo1//vOfwg/hzkCuSMJrxMXF4fr160JDVavVipqaGrt2FTExMULridGjRyMuLg5lZWU+H68/iI+PR1VVlfDaZDJ1m3Ymwf69b926FRUVFXjxxRehVts/apW++2By37qaf2xsrPB+dnY2mpqacP36dY9cm4SN8Bq9e/fGwIED8cEHHwAAPvjgA6SkpNi5IcU3c2lpKaqqqtC/f3+fjtVfTJkyBfn5+bDZbKirq8PJkyeRmZnp72H5hGD+3l9//XVcvnwZW7ZsgV6vV9wnMzMThw8fBgBUVFTg0qVLGDNmjC+H6TXcmb/4+//ss8+gVquFCNnOQiW1CK/y3XffYcOGDWhoaECvXr2wceNG3HvvvVizZg2WL1+OwYMHY8OGDULIs06nw5NPPomf/OQn/h56p9m8eTM+/vhj3LhxA5GRkYiIiMA//vEPydytViv+9Kc/4fTp0wCAxx9/XAgoCGTcmXuwfu9lZWV45JFHkJSUhB49egAA+vbti5deegnz58/HK6+8AoPBgKamJuTl5aG0tBRqtRpr1qxBRkaGfwfvAdyd/8qVK3Hjxg2o1WqEhYVh7dq1SEtL88gYSNgIgiCIoIJckQRBEERQQcJGEARBBBUkbARBEERQQcJGEARBBBUkbARBEERQQcJGEARBBBUkbAQRoFRWViI9Pd2ntSUffvhhnDhxwmfXaw8vvPAC/vjHP/p7GEQXgPLYCMKDXLt2DX/+85/x5ZdforGxEffccw8GDRqEF198Ee+//z527dqF/Px8j1yrsrISM2fOREFBgcfLcHnz3Nu3b8ebb74JvV4PlUqFqKgozJgxA08++SRUKpVb58jJycGKFSswY8YMj46NCA6oCDJBeJC1a9di9OjROHToEMLCwnD9+nX8+9//Bv1+lDJy5Ej85S9/AWMMX375JZ566in07dsXOTk5/h4aEQSQK5IgPER9fT3Ky8sxZ84chIeHQ6VSIS4uDnPnzkVJSQlefPFFGI1GjB8/HuPHj8e5c+cAAOfPn8fjjz+OiRMnYs6cOTh06JDkvN9++y1Wr16NKVOmYPLkyVi5cqVk+7lz5zBv3jxMmDABq1atQm1trbDt7bffxpw5czBhwgRkZWXhtddeE4pSM8bw+uuv48EHH8SECROQk5ODAwcOAAB++tOfAuAq8I8fPx47d+4EwFlKx48fd3tsrlCpVBg+fDiSk5Nx6dIlt8b985//HCaTCZs2bcL48eOxatUqAFxH5ueff144R1VVFZ555hlkZmYiKysLW7ZsQXNzc7vGRwQoXmlfShDdlHnz5rHFixezd999l5WVlTGbzSZsO3r0KJs1a5Zk/2vXrrGxY8eyo0ePstbWVnbx4kU2adIkVlhYyBhjrKamhmVkZLA333yTNTY2spaWFnb69GnGGGNGo5GNHDmSrV27ltXV1bGGhga2ePFioSszY4x9+OGH7Nq1a8xms7HLly+zqVOnsoMHDzLGGPvss8/Y9OnTWVVVFWOMsRs3brDLly9Lzm0ymSTjzc7OFjqiOxubM7Zt28ZWrFjBGGPMarWys2fPsrFjx7K3337brXHLx8GzYcMG9vvf/54xxlhrayubN28e27RpE2tsbGTV1dXsscceY3/4wx9cjo8IfMhiIwgP8sYbb2DkyJF4++23MX/+fDzwwAPYuXOnQ1ck3/EgJycHWq0WaWlpmD17trAOV1BQgH79+mHx4sUIDQ2FTqfDqFGjJOdYunQpIiMjER4ejgcffFBi+WRmZiIhIQEqlQqpqamYMWMGzp49CwDQarUwm83473//C7PZjN69eyM1NdXtubozNkecP38eGRkZGDduHJYvX46cnBzMnTvXrXG7wzfffIOrV6/imWeeQWhoKGJjY7FixQocPXqU3MLdAFpjIwgPEhkZiVWrVmHVqlVobm5GYWEhNm3aBIPBoNiTqrq6GgkJCZL3EhMTcfLkSQCcOy0pKcnpNWNiYoS/Q0ND0djYKLx+//33sX//fhiNRlgsFlgsFgwdOhQAkJ6ejlWrVmHXrl147rnnMHToUKxatQqDBw92a67ujM0R/Bpba2sr/va3v+H48eNobm5GeHi4y3G7A9+lPTQ0VHgvMTERZrMZdXV16N27d4fGTQQGZLERhJfo0aMHcnJy8IMf/AD/+c9/FCP+4uLiUFlZKXnPaDQKkYh9+vRBRUVFh65vMpnwu9/9Dk888QTef/99nDx5EvPmzZNYLLNnz8auXbvwwQcfYODAgVi3bh0AKIqwnM6MjUen02Hx4sWIiorC9u3b3R63q/HFxcWhrq5OsqZmNBoREhJi1w+QCD5I2AjCQ9y+fRuvvfYarly5IlgZRUVFKCsrw/DhwxETE4O6ujrcuXNHOGbatGkoKSnBsWPHYLFY8PXXX+Pw4cOYNWsWAGDGjBkoLy/H7t270dzcjNbWVpw5c8at8TQ1NcFmsyEqKgparRbFxcWSwI+vv/4aX3zxBVpaWqDT6RAWFgaNRgOAszzVajWuXr3q8PydGZucFStW4ODBg6iqqnI5bgCIjo52OrYhQ4agX79+ePnll9Hc3Iyamhr89a9/RU5OjtspBUTgQq5IgvAQOp0ON2/exC9/+UvU1tZCo9GgT58++MUvfoEpU6bAYrHgxz/+MWbNmgWr1YotW7Zg5MiReOWVV/Dqq69i8+bNiI6OxvLlyzF16lQAgMFgwPbt2/Hqq6/irbfeAgAMHjzYrbWs++67D8uWLcOzzz6L1tZWpKenY9q0aSgtLQXACd/WrVtx9epVqNVqDBgwAC+88AIAztpcvnw5fvOb38BsNuOxxx7DkiVLJOfvzNjkDB8+HMOHD8f27duRl5fndNwA8MQTT2Dz5s04cOAA0tLS8Oqrr0rOp9Vq8fLLL+Oll15CVlYWQkJCMGnSJKxevbrdYyMCD0rQJgiCIIIKckUSBEEQQQW5IgmC8Cjvvfee4NKUs379ekyfPt3HIyK6G+SKJAiCIIIKckUSBEEQQQUJG0EQBBFUkLARBEEQQQUJG0EQBBFU/H9R4yv4a5YXegAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ObzZyRoG4KGD"
      },
      "source": [
        "###Testing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sJE7N9phUUlU"
      },
      "source": [
        "Check the last date of available data. Taking dates after the given below date or of any Saturday and Sunday for result in a error.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0jcIhzX4Mdv"
      },
      "source": [
        "dft = pd.read_csv(\"all_data_ti.csv\",index_col=\"Date\")\n",
        "\n",
        "def get_future_predictions(date):\n",
        "  day_data = dft.loc[date]\n",
        "  #print(day_data)\n",
        "\n",
        "  pred_for_tomorrow = pd.DataFrame({'Date':[],\n",
        "                                    'company':[],\n",
        "                                    'prediction':[]})\n",
        "\n",
        "  #Predict each stock using the 2nd January Data\n",
        "  for cluster_selected in clusters_df.Cluster.unique():\n",
        "      rf_cv =  pickle.load(open(f'RF_Cluster_{cluster_selected}', 'rb'))\n",
        "      best_rf = rf_cv.best_estimator_\n",
        "      cluster_data = day_data.loc[day_data.symbol.isin(clusters_df.loc[clusters_df.Cluster==cluster_selected,'Companies'].tolist())].copy()\n",
        "      cluster_data = cluster_data.dropna()\n",
        "      if (cluster_data.shape[0]>0):\n",
        "          X_test = cluster_data.loc[:,Target_variables]\n",
        "\n",
        "          pred_for_tomorrow = pred_for_tomorrow.append(pd.DataFrame({'Date':cluster_data.index,\n",
        "                                                                    'company':cluster_data['symbol'],\n",
        "                                                                    'prediction':best_rf.predict_proba(X_test)[:,1],\n",
        "                                                                    'prediction_round':best_rf.predict(X_test)}),\n",
        "                                                      ignore_index = True)\n",
        "  #print(pred_for_tomorrow.head())\n",
        "  top_10_pred = pred_for_tomorrow.sort_values(by = ['prediction'], ascending = False).head(10)\n",
        "  return top_10_pred\n",
        "\n",
        "def get_force_shap(company,date):\n",
        "  shap_df = dft.loc[date]\n",
        "  shap_df = shap_df[shap_df['symbol']==company]\n",
        "  shap_df = shap_df.loc[:,Target_variables]\n",
        "  #print(shap_df)\n",
        "  cluster_number = clusters_df[clusters_df['Companies']==company].iat[0,0]\n",
        "  print(cluster_number)\n",
        "  rf_shap =  pickle.load(open(f'RF_Cluster_{cluster_number}', 'rb'))\n",
        "  best_rf = rf_shap.best_estimator_\n",
        "  explainer = shap.TreeExplainer(best_rf)\n",
        "  choosen_instance = shap_df\n",
        "  shap_values = explainer.shap_values(choosen_instance)\n",
        "  shap.initjs()\n",
        "  return shap.force_plot(explainer.expected_value[1], shap_values[1], choosen_instance)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VIYZR5SLUnlX"
      },
      "source": [
        "These are predictions for the next week from the curent latest date."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "iCRquCJL8XBq",
        "outputId": "6da8f389-b99a-4f0f-ce48-1cdf41ba93da"
      },
      "source": [
        "get_future_predictions(date='2021-03-25')"
      ],
      "execution_count": null,
      "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>company</th>\n",
              "      <th>prediction</th>\n",
              "      <th>prediction_round</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>2021-03-25</td>\n",
              "      <td>TSLA</td>\n",
              "      <td>0.624461</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>2021-03-25</td>\n",
              "      <td>INTC</td>\n",
              "      <td>0.596035</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>2021-03-25</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.595975</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>2021-03-25</td>\n",
              "      <td>SQ</td>\n",
              "      <td>0.595321</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>2021-03-25</td>\n",
              "      <td>PYPL</td>\n",
              "      <td>0.595321</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>2021-03-25</td>\n",
              "      <td>SE</td>\n",
              "      <td>0.590266</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>2021-03-25</td>\n",
              "      <td>MU</td>\n",
              "      <td>0.586692</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>2021-03-25</td>\n",
              "      <td>WDAY</td>\n",
              "      <td>0.583657</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>2021-03-25</td>\n",
              "      <td>SNAP</td>\n",
              "      <td>0.579800</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>2021-03-25</td>\n",
              "      <td>ABNB</td>\n",
              "      <td>0.574623</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date company  prediction  prediction_round\n",
              "86   2021-03-25    TSLA    0.624461               1.0\n",
              "133  2021-03-25    INTC    0.596035               1.0\n",
              "85   2021-03-25    AAPL    0.595975               1.0\n",
              "140  2021-03-25      SQ    0.595321               1.0\n",
              "132  2021-03-25    PYPL    0.595321               1.0\n",
              "137  2021-03-25      SE    0.590266               1.0\n",
              "141  2021-03-25      MU    0.586692               1.0\n",
              "146  2021-03-25    WDAY    0.583657               1.0\n",
              "144  2021-03-25    SNAP    0.579800               1.0\n",
              "88   2021-03-25    ABNB    0.574623               1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "oL46unSTZ9SX",
        "outputId": "d2c88754-3e60-49e8-af3a-2deaff593d05"
      },
      "source": [
        "get_force_shap(company=\"TSLA\", date = '2021-03-25')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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p=this.xlabel.selectAll(\"option\").data(f);p.enter().append(\"option\").merge(p).attr(\"value\",function(t){return t}).text(function(t){return t}),p.exit().remove();var h=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";f=(0,y.map)(this.usedFeatures,function(e){return[t.props.featureNames[e],t.props.featureNames[e]+\" effects\"]}),f.unshift([\"model output value\",h]);var d=this.ylabel.selectAll(\"option\").data(f);d.enter().append(\"option\").merge(d).attr(\"value\",function(t){return t[0]}).text(function(t){return t[1]}),d.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var t=this,e=!0,n=!1,r=void 0;try{for(var i,o=this.props.explanations[Symbol.iterator]();!(e=(i=o.next()).done);e=!0){var a=i.value,c=!0,s=!1,l=void 0;try{for(var 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f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
              "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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              "    document.getElementById('iI9TH2V1GHG2UJS0AD3PM')\n",
              "  );\n",
              "</script>"
            ],
            "text/plain": [
              "<shap.plots._force.AdditiveForceVisualizer at 0x7f2e34fd7cd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    }
  ]
}