multiple_timeseries_anomaly_widgets_removed.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P6pO8Cz0igYd",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "<h1 align=center>Unsupervised Anomaly Sensor Detection - IMS Bearing NASA Acoustics and Vibration Database</h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWQnwn0sau9B",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "<h3 align=center>PyCaret Anomaly detection Models vs - BiLSTM Autoencoder</h3>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LNVq51-cau9B",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## 1. Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNX_v4qyau9B",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "In this project 4 unsupervised anomaly detection models from Pycaret will be used to detect anomalies in sensor bearing sensor signal. These models are: Decision Tree and Support Vector Machine. In addition, a customed LSTM model will be built using the PyTorch Framework to autoencode and decode the signal input. The data comprises three vibrational sensor readings from the NASA Acoustics and Vibration Database. The datasets contained text files with 1-second vibration signal snapshots (20,480 data points) recorded at 5 and 10 minute intervals at a sampling rate of 20 kHz."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HK7mRQWau9C",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "* Pycaret is a high-performance, open-source, low code library for ML modeling. It provides highly-efficient CPU/GPU implementations of ML models for  cluster analysis, classification,  time series and anomaly detection. Snap ML not only accelerates ML algorithms through system awareness, but also offers novel ML algorithms with best-in-class accuracy. Pycaret also automates ML workflows and aims to democratize ML. For more information, please visit [here](https://pycaret.org/).\n",
        "\n",
        "* PyTorch is a machine learning framework used for applications such as computer vision and natural language processing, originally developed by Meta AI and now part of the Linux Foundation umbrella. For more information, please visit [here](https://pytorch.org/)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nCQVn_uoau9C",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## 2. Tasks\n",
        "\n",
        "The objective of this piece of work is to identify the best model(s) than can anticipate potential sensor failures at the earliest stage in an unsupervised fashion using low code and deep learnig models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eImQKyHOau9D",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "1. Visualize the data an applied tranformations.\n",
        "2. Detect anomalies from sensor vibration signals using Pycaret anomaly detection models.\n",
        "3. Buid BiLSTM artificial neural networks to predict anomalies and train models.\n",
        "4. Visualize the results with plot\n",
        "5. Test the models with an idependent dataset\n",
        "6. Measure the goodness of the clusters generated using in usupervised clustering metrics\n",
        "7. Compare the performance of the model using non-parametric statistics\n",
        "8. Conclusion\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fA91YLQiau9D",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zoX62gnJau9E",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "The data comprises three datasets vibrational sensor readings from the NASA Acoustics and Vibration Database. The datasets contained text files with 1-second vibration signal snapshots (20,480 data points) recorded at 5 and 10 minute intervals at a sampling rate of 20 kHz. Each datset contains reading at from four sensors.\n",
        "\n",
        "The dataset is available on [kaggle](https://www.kaggle.com/datasets/vinayak123tyagi/bearing-dataset)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21pGDf43au9E",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Installing required libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PNj0sU4cau9E",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install git+https://github.com/pycaret/pycaret.git@master --upgrade"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T5bETHGFau9F",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install mlflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZgWXQIAWau9F",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install umap-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "70q2lHtMau9F",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install seaborn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3NdGcHA_ipQW"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install git+https://github.com/maximtrp/scikit-posthocs.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uGAFCjx2au9G",
        "outputId": "91721b39-7117-4ac2-9dfa-f810d7d1b5f1",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# Comment out if not using colab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "htyhmjMbau9G",
        "outputId": "558ee031-72dd-4fc6-d5b7-1c872165b271",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/anomaly\n"
          ]
        }
      ],
      "source": [
        "# comment out if not running in colab\n",
        "%cd '/content/drive/MyDrive/anomaly'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xx9zAYEBau9G",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Importing require libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-9pabcHHau9G",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "# Import libraries\n",
        "import os, gc\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "import time\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "np.random.seed(1234)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lRRc_NWqau9G",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Import supportig scripts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WoXyLem2au9G",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "from concat_datasets import concat_raw_data, average_signal_dataset, dir_list\n",
        "from plots import *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LigAA3Nlau9H",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "# Datasets processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dmxpbEytau9H",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "Data can be processed by changing the ```concat_data``` to True or running the script ```concat_datasets.py```. The ```txt``` dataset are concatenated and put together into one file per dataset test. In addition, data was transform (denoised) using the packag Scipy Fast Fourier Transforms (fft).\n",
        "\n",
        "For the purpose of this work we will use the concatenated data with average reading for each of the 204080 sample points."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M2e5L_odau9H",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "datasets = {'dataset_path1' : './ims_bearing/1st_test/1st_test',\n",
        "           'dataset_path2' : './ims_bearing/2nd_test/2nd_test',\n",
        "           'dataset_path3' : './ims_bearing/3rd_test/4th_test/txt'}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u-YAaz7gau9H",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "csv_data = os.path.join(os.getcwd(), 'csv_data')\n",
        "os.makedirs(csv_data, exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YS3WMNAuau9H",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "concat_data = False\n",
        "def generate_datasets(datasets_dict=datasets, csv_path=csv_data):\n",
        "    for i, (k, v) in enumerate(datasets_dict.items()):\n",
        "        dataset_num = i + 1\n",
        "        average_signal_dataset(v, dataset=dataset_num, csv_path=csv_path)\n",
        "        concat_raw_data(v, csv_path, dataset=dataset_num, fourier_tr=True, detrends=False)\n",
        "\n",
        "if concat_data:\n",
        "    generate_datasets(datasets)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DN0kpkotau9H",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "# Exploratory Data Analysis EDA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bm9yUgt_au9I",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "fft_dataset = [d for d in dir_list(csv_data) if 'fft' in d]\n",
        "avg_dataset = [d for d in dir_list(csv_data) if not 'fft' in d]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KGntwXa8au9I",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "def read_concat_data(path=None, fname=None):\n",
        "    df = pd.read_csv(os.path.join(path, fname), index_col=0)\n",
        "    df.index = pd.to_datetime(df.index)\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cpz7ZmMmau9I",
        "outputId": "b0b15c22-22cb-4884-e256-391ac5b1667f",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['avg_concat_dataset_1.csv',\n",
              " 'avg_concat_dataset_2.csv',\n",
              " 'avg_concat_dataset_3.csv']"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "avg_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "L6spTwHIau9I",
        "outputId": "5a5faf00-4015-48cd-cad3-341858dcd545",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset size: 2156\n"
          ]
        },
        {
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              "      <th></th>\n",
              "      <th>b1_ch1</th>\n",
              "      <th>b1_ch2</th>\n",
              "      <th>b2_ch3</th>\n",
              "      <th>b2_ch4</th>\n",
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              "      <th>count</th>\n",
              "      <td>2156.000000</td>\n",
              "      <td>2156.000000</td>\n",
              "      <td>2156.000000</td>\n",
              "      <td>2156.000000</td>\n",
              "      <td>2156.000000</td>\n",
              "      <td>2156.000000</td>\n",
              "      <td>2156.000000</td>\n",
              "      <td>2156.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.127485</td>\n",
              "      <td>0.124183</td>\n",
              "      <td>0.125529</td>\n",
              "      <td>0.120615</td>\n",
              "      <td>0.132830</td>\n",
              "      <td>0.134874</td>\n",
              "      <td>0.124395</td>\n",
              "      <td>0.125285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.006072</td>\n",
              "      <td>0.006244</td>\n",
              "      <td>0.005745</td>\n",
              "      <td>0.005540</td>\n",
              "      <td>0.014742</td>\n",
              "      <td>0.014387</td>\n",
              "      <td>0.014100</td>\n",
              "      <td>0.013660</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.103652</td>\n",
              "      <td>0.099853</td>\n",
              "      <td>0.106503</td>\n",
              "      <td>0.101036</td>\n",
              "      <td>0.106148</td>\n",
              "      <td>0.108152</td>\n",
              "      <td>0.048433</td>\n",
              "      <td>0.069658</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.127832</td>\n",
              "      <td>0.124815</td>\n",
              "      <td>0.125422</td>\n",
              "      <td>0.120932</td>\n",
              "      <td>0.131230</td>\n",
              "      <td>0.133210</td>\n",
              "      <td>0.118644</td>\n",
              "      <td>0.119841</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.128904</td>\n",
              "      <td>0.125841</td>\n",
              "      <td>0.126129</td>\n",
              "      <td>0.121639</td>\n",
              "      <td>0.132255</td>\n",
              "      <td>0.134239</td>\n",
              "      <td>0.120240</td>\n",
              "      <td>0.121462</td>\n",
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              "      <th>75%</th>\n",
              "      <td>0.129828</td>\n",
              "      <td>0.126581</td>\n",
              "      <td>0.127190</td>\n",
              "      <td>0.122351</td>\n",
              "      <td>0.133679</td>\n",
              "      <td>0.135751</td>\n",
              "      <td>0.129112</td>\n",
              "      <td>0.131733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.142146</td>\n",
              "      <td>0.132984</td>\n",
              "      <td>0.170624</td>\n",
              "      <td>0.162340</td>\n",
              "      <td>0.438412</td>\n",
              "      <td>0.364026</td>\n",
              "      <td>0.229181</td>\n",
              "      <td>0.221456</td>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-35730d22-6918-451d-b09a-64970fc35239 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ee777d61-d460-4cb2-8a1f-890696562f17 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ee777d61-d460-4cb2-8a1f-890696562f17');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            b1_ch1       b1_ch2       b2_ch3       b2_ch4       b3_ch5  \\\n",
              "count  2156.000000  2156.000000  2156.000000  2156.000000  2156.000000   \n",
              "mean      0.127485     0.124183     0.125529     0.120615     0.132830   \n",
              "std       0.006072     0.006244     0.005745     0.005540     0.014742   \n",
              "min       0.103652     0.099853     0.106503     0.101036     0.106148   \n",
              "25%       0.127832     0.124815     0.125422     0.120932     0.131230   \n",
              "50%       0.128904     0.125841     0.126129     0.121639     0.132255   \n",
              "75%       0.129828     0.126581     0.127190     0.122351     0.133679   \n",
              "max       0.142146     0.132984     0.170624     0.162340     0.438412   \n",
              "\n",
              "            b3_ch6       b4_ch7       b4_ch8  \n",
              "count  2156.000000  2156.000000  2156.000000  \n",
              "mean      0.134874     0.124395     0.125285  \n",
              "std       0.014387     0.014100     0.013660  \n",
              "min       0.108152     0.048433     0.069658  \n",
              "25%       0.133210     0.118644     0.119841  \n",
              "50%       0.134239     0.120240     0.121462  \n",
              "75%       0.135751     0.129112     0.131733  \n",
              "max       0.364026     0.229181     0.221456  "
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "avg_df1 = read_concat_data(csv_data, fname=avg_dataset[0])\n",
        "print(f'Dataset size: {len(avg_df1)}')\n",
        "avg_df1.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "3l9o3JCxau9J",
        "outputId": "49818ddb-824c-40f4-f3d0-17f01c765e3c",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset size: 984\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-0a54353f-6f19-413b-a6ce-890d29aead0b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>b1_ch1</th>\n",
              "      <th>b2_ch2</th>\n",
              "      <th>b3_ch3</th>\n",
              "      <th>b4_ch4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>984.000000</td>\n",
              "      <td>984.000000</td>\n",
              "      <td>984.000000</td>\n",
              "      <td>984.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.080905</td>\n",
              "      <td>0.078532</td>\n",
              "      <td>0.081356</td>\n",
              "      <td>0.047822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.040171</td>\n",
              "      <td>0.011779</td>\n",
              "      <td>0.011596</td>\n",
              "      <td>0.009541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.001168</td>\n",
              "      <td>0.000767</td>\n",
              "      <td>0.000716</td>\n",
              "      <td>0.001699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.060764</td>\n",
              "      <td>0.074232</td>\n",
              "      <td>0.076830</td>\n",
              "      <td>0.043949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.062005</td>\n",
              "      <td>0.075202</td>\n",
              "      <td>0.078197</td>\n",
              "      <td>0.044523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.083222</td>\n",
              "      <td>0.077462</td>\n",
              "      <td>0.080597</td>\n",
              "      <td>0.048110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.453318</td>\n",
              "      <td>0.161011</td>\n",
              "      <td>0.151296</td>\n",
              "      <td>0.119042</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a54353f-6f19-413b-a6ce-890d29aead0b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-261ecb4a-55f5-4f12-8595-243dbdb9aba3\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-261ecb4a-55f5-4f12-8595-243dbdb9aba3')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-261ecb4a-55f5-4f12-8595-243dbdb9aba3 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0a54353f-6f19-413b-a6ce-890d29aead0b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0a54353f-6f19-413b-a6ce-890d29aead0b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           b1_ch1      b2_ch2      b3_ch3      b4_ch4\n",
              "count  984.000000  984.000000  984.000000  984.000000\n",
              "mean     0.080905    0.078532    0.081356    0.047822\n",
              "std      0.040171    0.011779    0.011596    0.009541\n",
              "min      0.001168    0.000767    0.000716    0.001699\n",
              "25%      0.060764    0.074232    0.076830    0.043949\n",
              "50%      0.062005    0.075202    0.078197    0.044523\n",
              "75%      0.083222    0.077462    0.080597    0.048110\n",
              "max      0.453318    0.161011    0.151296    0.119042"
            ]
          },
          "execution_count": 107,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "avg_df2 = read_concat_data(csv_data, fname=avg_dataset[1])\n",
        "print(f'Dataset size: {len(avg_df2)}')\n",
        "avg_df2.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "bAT8dUMTau9J",
        "outputId": "ce0a38aa-3877-42f3-d67f-93b8e69c98b7",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset size: 6324\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-eb79605c-8ea0-4897-8106-2c603de86a8b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>b1_ch1</th>\n",
              "      <th>b2_ch2</th>\n",
              "      <th>b3_ch3</th>\n",
              "      <th>b4_ch4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6324.000000</td>\n",
              "      <td>6324.000000</td>\n",
              "      <td>6324.000000</td>\n",
              "      <td>6324.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.058445</td>\n",
              "      <td>0.064602</td>\n",
              "      <td>0.057390</td>\n",
              "      <td>0.044168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.005061</td>\n",
              "      <td>0.009270</td>\n",
              "      <td>0.023149</td>\n",
              "      <td>0.011082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.001533</td>\n",
              "      <td>0.002441</td>\n",
              "      <td>0.003663</td>\n",
              "      <td>0.001428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.056456</td>\n",
              "      <td>0.061596</td>\n",
              "      <td>0.052594</td>\n",
              "      <td>0.040506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.057657</td>\n",
              "      <td>0.062778</td>\n",
              "      <td>0.053941</td>\n",
              "      <td>0.043425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.059215</td>\n",
              "      <td>0.064786</td>\n",
              "      <td>0.055309</td>\n",
              "      <td>0.044282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.122481</td>\n",
              "      <td>0.224447</td>\n",
              "      <td>0.543762</td>\n",
              "      <td>0.244888</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb79605c-8ea0-4897-8106-2c603de86a8b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-79df34f2-1bc8-409a-830f-73b16a5cbd22\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79df34f2-1bc8-409a-830f-73b16a5cbd22')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-79df34f2-1bc8-409a-830f-73b16a5cbd22 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eb79605c-8ea0-4897-8106-2c603de86a8b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-eb79605c-8ea0-4897-8106-2c603de86a8b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            b1_ch1       b2_ch2       b3_ch3       b4_ch4\n",
              "count  6324.000000  6324.000000  6324.000000  6324.000000\n",
              "mean      0.058445     0.064602     0.057390     0.044168\n",
              "std       0.005061     0.009270     0.023149     0.011082\n",
              "min       0.001533     0.002441     0.003663     0.001428\n",
              "25%       0.056456     0.061596     0.052594     0.040506\n",
              "50%       0.057657     0.062778     0.053941     0.043425\n",
              "75%       0.059215     0.064786     0.055309     0.044282\n",
              "max       0.122481     0.224447     0.543762     0.244888"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "avg_df3 = read_concat_data(csv_data, fname=avg_dataset[2])\n",
        "print(f'Dataset size: {len(avg_df3)}')\n",
        "avg_df3.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xd465TSKau9J",
        "outputId": "62f0607d-d161-4975-ac01-8932fec8fb65",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "23"
            ]
          },
          "execution_count": 109,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Release resources del the daset not used\n",
        "del avg_df1\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4t4HtyB0au9K",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Visualise"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MssVWneVau9K",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "* For this piece of work only the average readings datasets 2 and 3 will be used.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "mCTF5Q-cau9K",
        "outputId": "dba27ee9-642f-4323-be63-af0f30d6d090",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x250 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "view_all(avg_df2, plot_name='avg_dataset2')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "_evy8FxMau9P",
        "outputId": "73a6609b-1693-4966-d922-135c3b9590ea",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x250 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "view_all(avg_df3, plot_name='avg_dataset3')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 950
        },
        "id": "8wG0711Tau9P",
        "outputId": "ec7f58ff-f67e-448a-fc20-2d5c4b1ba4a3",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "view_per_channel(avg_df2, plot_name='view_per_channel_avgdf2')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 950
        },
        "id": "ezgOL53Hau9P",
        "outputId": "b8c81f81-0f18-45ab-8aa8-2ecf32a3d463",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "view_per_channel(avg_df3, plot_name='view_per_channel_avgdf3')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yDs1Fww2au9P",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "# Unsupervised Anomaly detection using PyCaret"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ExQnWRxDau9P",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "The approach to unsupervised machine learning is:\n",
        "\n",
        "* Dataset `avg_df2` will be used to train the model and to obtain the anomaly labels.\n",
        "* The best models will be tested on unseen data , dataset `avg_df3`.\n",
        "    \n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-5kDlAEXau9P",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "# Create directoty to save anomalies for model comparison\n",
        "csv_files = os.path.join(os.getcwd(), 'csv_files')\n",
        "os.makedirs(csv_files, exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VS0hjtkUau9Q",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "from pycaret.anomaly import *"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "2SZnYqPxau9Q",
        "outputId": "ceca6bb6-78c7-4da9-946a-294eb2538084",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_b178b_row4_col1, #T_b178b_row9_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_b178b\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_b178b_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_b178b_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_b178b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_b178b_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_b178b_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b178b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_b178b_row1_col0\" class=\"data row1 col0\" >Original data shape</td>\n",
              "      <td id=\"T_b178b_row1_col1\" class=\"data row1 col1\" >(984, 4)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b178b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_b178b_row2_col0\" class=\"data row2 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_b178b_row2_col1\" class=\"data row2 col1\" >(984, 4)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b178b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_b178b_row3_col0\" class=\"data row3 col0\" >Numeric features</td>\n",
              "      <td id=\"T_b178b_row3_col1\" class=\"data row3 col1\" >4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b178b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_b178b_row4_col0\" class=\"data row4 col0\" >Preprocess</td>\n",
              "      <td id=\"T_b178b_row4_col1\" class=\"data row4 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b178b_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_b178b_row5_col0\" class=\"data row5 col0\" >Imputation type</td>\n",
              "      <td id=\"T_b178b_row5_col1\" class=\"data row5 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b178b_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_b178b_row6_col0\" class=\"data row6 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_b178b_row6_col1\" class=\"data row6 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b178b_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_b178b_row7_col0\" class=\"data row7 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_b178b_row7_col1\" class=\"data row7 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b178b_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_b178b_row8_col0\" class=\"data row8 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_b178b_row8_col1\" class=\"data row8 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b178b_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_b178b_row9_col0\" class=\"data row9 col0\" >Use GPU</td>\n",
              "      <td id=\"T_b178b_row9_col1\" class=\"data row9 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b178b_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_b178b_row10_col0\" class=\"data row10 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_b178b_row10_col1\" class=\"data row10 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b178b_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_b178b_row11_col0\" class=\"data row11 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_b178b_row11_col1\" class=\"data row11 col1\" >anomaly-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b178b_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_b178b_row12_col0\" class=\"data row12 col0\" >USI</td>\n",
              "      <td id=\"T_b178b_row12_col1\" class=\"data row12 col1\" >4013</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7e85910fa800>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# initiate setup\n",
        "exp = setup(avg_df2, session_id=123, log_experiment=False, use_gpu=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PqemcphshqYl",
        "outputId": "60f76994-8e76-46c7-e51e-de032ffdd025"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'USI',\n",
              " 'X',\n",
              " 'X_train',\n",
              " 'X_train_transformed',\n",
              " 'X_transformed',\n",
              " '_available_plots',\n",
              " '_ml_usecase',\n",
              " 'data',\n",
              " 'dataset',\n",
              " 'dataset_transformed',\n",
              " 'exp_id',\n",
              " 'exp_name_log',\n",
              " 'gpu_n_jobs_param',\n",
              " 'gpu_param',\n",
              " 'html_param',\n",
              " 'idx',\n",
              " 'is_multiclass',\n",
              " 'log_plots_param',\n",
              " 'logging_param',\n",
              " 'memory',\n",
              " 'n_jobs_param',\n",
              " 'pipeline',\n",
              " 'seed',\n",
              " 'train',\n",
              " 'train_transformed',\n",
              " 'variable_and_property_keys',\n",
              " 'variables'}"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "exp.get_config()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "Qcq0JCgHau9Q",
        "outputId": "f7eb1818-bd66-4469-ce3d-adba0c2db9c1",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-f64a452a-b20e-445b-82fb-d9d458547e2e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Name</th>\n",
              "      <th>Reference</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>abod</th>\n",
              "      <td>Angle-base Outlier Detection</td>\n",
              "      <td>pyod.models.abod.ABOD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster</th>\n",
              "      <td>Clustering-Based Local Outlier</td>\n",
              "      <td>pycaret.internal.patches.pyod.CBLOFForceToDouble</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cof</th>\n",
              "      <td>Connectivity-Based Local Outlier</td>\n",
              "      <td>pyod.models.cof.COF</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>iforest</th>\n",
              "      <td>Isolation Forest</td>\n",
              "      <td>pyod.models.iforest.IForest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>histogram</th>\n",
              "      <td>Histogram-based Outlier Detection</td>\n",
              "      <td>pyod.models.hbos.HBOS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K-Nearest Neighbors Detector</td>\n",
              "      <td>pyod.models.knn.KNN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lof</th>\n",
              "      <td>Local Outlier Factor</td>\n",
              "      <td>pyod.models.lof.LOF</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>One-class SVM detector</td>\n",
              "      <td>pyod.models.ocsvm.OCSVM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pca</th>\n",
              "      <td>Principal Component Analysis</td>\n",
              "      <td>pyod.models.pca.PCA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mcd</th>\n",
              "      <td>Minimum Covariance Determinant</td>\n",
              "      <td>pyod.models.mcd.MCD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sod</th>\n",
              "      <td>Subspace Outlier Detection</td>\n",
              "      <td>pyod.models.sod.SOD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sos</th>\n",
              "      <td>Stochastic Outlier Selection</td>\n",
              "      <td>pyod.models.sos.SOS</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f64a452a-b20e-445b-82fb-d9d458547e2e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-de422d7c-dec5-4c0b-9877-97025109cd50\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-de422d7c-dec5-4c0b-9877-97025109cd50')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-de422d7c-dec5-4c0b-9877-97025109cd50 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f64a452a-b20e-445b-82fb-d9d458547e2e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f64a452a-b20e-445b-82fb-d9d458547e2e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                        Name  \\\n",
              "ID                                             \n",
              "abod            Angle-base Outlier Detection   \n",
              "cluster       Clustering-Based Local Outlier   \n",
              "cof         Connectivity-Based Local Outlier   \n",
              "iforest                     Isolation Forest   \n",
              "histogram  Histogram-based Outlier Detection   \n",
              "knn             K-Nearest Neighbors Detector   \n",
              "lof                     Local Outlier Factor   \n",
              "svm                   One-class SVM detector   \n",
              "pca             Principal Component Analysis   \n",
              "mcd           Minimum Covariance Determinant   \n",
              "sod               Subspace Outlier Detection   \n",
              "sos             Stochastic Outlier Selection   \n",
              "\n",
              "                                                  Reference  \n",
              "ID                                                           \n",
              "abod                                  pyod.models.abod.ABOD  \n",
              "cluster    pycaret.internal.patches.pyod.CBLOFForceToDouble  \n",
              "cof                                     pyod.models.cof.COF  \n",
              "iforest                         pyod.models.iforest.IForest  \n",
              "histogram                             pyod.models.hbos.HBOS  \n",
              "knn                                     pyod.models.knn.KNN  \n",
              "lof                                     pyod.models.lof.LOF  \n",
              "svm                                 pyod.models.ocsvm.OCSVM  \n",
              "pca                                     pyod.models.pca.PCA  \n",
              "mcd                                     pyod.models.mcd.MCD  \n",
              "sod                                     pyod.models.sod.SOD  \n",
              "sos                                     pyod.models.sos.SOS  "
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Show avaialable models\n",
        "all_anomaly_models = models()\n",
        "all_anomaly_models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pl6K6D7Qau9Q",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Model Selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5v_Bxpcfau9Q",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "* Isolation Forest **iforest**\n",
        "* Minimum Covariance Determinant **mcd**\n",
        "* Clustering-Based Local outlier **cluster**\n",
        "* One Class SVM **svm**\n",
        "* Histogram-based Outlier Detection **histogram**\n",
        "* K-Nearest Neighbors Detector **knn**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CzB30AIoau9Q",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "selected_models = ['cluster', 'histogram', 'iforest', 'knn', 'mcd', 'svm']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QdJ-dC5au9R",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Build train and predict fuction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "SVr4rt86oXbD",
        "outputId": "458a1a33-53be-4996-bdb9-e2f558f1859e",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set(rc={'figure.figsize': (4, 3)})\n",
        "sns.set_style('whitegrid')\n",
        "sns.distplot(avg_df2.values, bins=30, kde=True)\n",
        "plt.grid(color ='k', ls= '--', lw=0.5)\n",
        "plt.xlabel('Avg signal')\n",
        "plt.title('Dataset distribution avg_df2')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NXxPfaG1u6xw",
        "outputId": "5c44669d-7589-4ccb-b262-c119d6892b31",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.06192544, 0.06287408, 0.06436785, 0.0646797 , 0.06500517,\n",
              "       0.0654315 , 0.06681847, 0.08007517, 0.10683016])"
            ]
          },
          "execution_count": 118,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.percentile(avg_df3.values, [80, 85, 90, 91, 92, 93, 95, 98, 99])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WDFqE7DUeMOW"
      },
      "source": [
        "__Note__: We will used the default contamination of 0.05, this to prevent over fitting and test the model on unseen data(avg_df3)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CJvFenMnau9R",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "def train_model(model_name=None, fraction=None):\n",
        "\n",
        "    print(f'Assigning labels: {model_name} model')\n",
        "    model = create_model(model_name, fraction=fraction)\n",
        "    model_anomalies = assign_model(model)\n",
        "    unique_labels = model_anomalies.Anomaly.unique()\n",
        "    print(f'Unique labels: {unique_labels}')\n",
        "    save_model(model, f'{model_name}_pipeline')\n",
        "    return model_anomalies\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tpUPp7JAau9R",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "def main(anomly_model_list=None, fraction=None):\n",
        "\n",
        "    results_dict = {}\n",
        "    anomaly_scores = {}\n",
        "    for m in anomly_model_list:\n",
        "        results = train_model(m, fraction=fraction)\n",
        "        results_dict[m] = results.Anomaly\n",
        "        anomaly_scores[m] = results.Anomaly_Score\n",
        "        results_filter = results[results.Anomaly == 1]\n",
        "        print(f'Anomalies detected by {m} model')\n",
        "        print(f'Anomalous readings: {len(results_filter)}')\n",
        "        print(' ')\n",
        "\n",
        "    df_anomalies = pd.DataFrame.from_dict(results_dict)\n",
        "    df_scores = pd.DataFrame.from_dict(anomaly_scores)\n",
        "\n",
        "    return df_anomalies, df_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JTPhHAzZau9R",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "##  Run the training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660,
          "referenced_widgets": [
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        },
        "id": "Vs62cIW0au9R",
        "outputId": "b78728cd-9d33-4793-b870-20800543ec44",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Assigning labels: cluster model\n"
          ]
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              "Processing:   0%|          | 0/3 [00:00<?, ?it/s]"
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unique labels: [0 1]\n",
            "Transformation Pipeline and Model Successfully Saved\n",
            "Anomalies detected by cluster model\n",
            "Anomalous readings: 50\n",
            " \n",
            "Assigning labels: histogram model\n"
          ]
        },
        {
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            "text/html": [],
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              "<IPython.core.display.HTML object>"
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              "version_minor": 0
            },
            "text/plain": [
              "Processing:   0%|          | 0/3 [00:00<?, ?it/s]"
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          },
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unique labels: [0 1]\n",
            "Transformation Pipeline and Model Successfully Saved\n",
            "Anomalies detected by histogram model\n",
            "Anomalous readings: 50\n",
            " \n",
            "Assigning labels: iforest model\n"
          ]
        },
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
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        },
        {
          "data": {
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              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Processing:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
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        },
        {
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unique labels: [0 1]\n",
            "Transformation Pipeline and Model Successfully Saved\n",
            "Anomalies detected by iforest model\n",
            "Anomalous readings: 50\n",
            " \n",
            "Assigning labels: knn model\n"
          ]
        },
        {
          "data": {
            "text/html": [],
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              "<IPython.core.display.HTML object>"
            ]
          },
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          },
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        },
        {
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            },
            "text/plain": [
              "Processing:   0%|          | 0/3 [00:00<?, ?it/s]"
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          },
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        {
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          },
          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unique labels: [0 1]\n",
            "Transformation Pipeline and Model Successfully Saved\n",
            "Anomalies detected by knn model\n",
            "Anomalous readings: 50\n",
            " \n",
            "Assigning labels: mcd model\n"
          ]
        },
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
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        },
        {
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
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        },
        {
          "data": {
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              "model_id": "679a8e52ba7544d2b4c67c0640d34454",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Processing:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
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              }
            }
          },
          "output_type": "display_data"
        },
        {
          "data": {
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unique labels: [0 1]\n",
            "Transformation Pipeline and Model Successfully Saved\n",
            "Anomalies detected by mcd model\n",
            "Anomalous readings: 50\n",
            " \n",
            "Assigning labels: svm model\n"
          ]
        },
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
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        },
        {
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "60e8696ccf024d5083d3ed800a85cd1c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Processing:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unique labels: [0 1]\n",
            "Transformation Pipeline and Model Successfully Saved\n",
            "Anomalies detected by svm model\n",
            "Anomalous readings: 50\n",
            " \n"
          ]
        }
      ],
      "source": [
        "anomaly_df, scores_df = main(selected_models, fraction=0.05)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b1RcRWDeau9R",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "# Save results for future comparison\n",
        "anomaly_df.to_csv(os.path.join(csv_files, 'unsupervised_anomaly_pred.csv'))\n",
        "scores_df.to_csv(os.path.join(csv_files, 'unsupervised_scores_pred.csv'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "El_FCioSau9S",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Visualize models results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xh66ihzkau9S",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "\n",
        "All models detected anomalies only at the end of the test on avg_df2 dataset. It is not apparent\n",
        "which model detectes earliest an anomalous signal.\n",
        "\n",
        "These models detected anomalies mainly atthe begining of the test and at the end.\n",
        "\n",
        "It can also be observed that anomalies were detected at the begining of the test in all models. KNN\n",
        "model detected anomalies all along the test meanwhile SVM detected anomalies for the first half\n",
        "of the test and at the end it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3y6g--e0au9S",
        "outputId": "60554e11-0f97-4b2d-aa12-f31f1c063522",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Anomalies detected by model\n",
            "cluster      50\n",
            "histogram    50\n",
            "iforest      50\n",
            "knn          50\n",
            "mcd          50\n",
            "svm          50\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print('Anomalies detected by model', anomaly_df.sum(axis=0), sep='\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "R8rXT_sHau9S",
        "outputId": "2a57f859-af9f-4dce-d027-ccfa646fc68d",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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9b02ePFmbNm2SdLXR+++/r6+//jrbU/XVqVNHK1as0DvvvKOvv/5a33//vfz9/VWyZEnVrl1bTZs2zXT7Rx55RFFRUVq8eLHmz5+vlJQUlS5d2jlM33HHHVq6dKkWLFigNWvW6NNPP1VaWpqKFy+uihUrqn379goJCXEez+Fw6P/+7/9Ut25dLV68WJ9//rmSk5NVokQJ1apVK9O7KxYpUkTTpk1TdHS0li1b5nwhY4sWLf5yz/Pf//53lS1bVu+8847WrFmjy5cv684771SXLl3UvXv3677pC4Bbn8OyrvE+swAAj/Hrr7/qscceU82aNfXBBx/YvRwA8BjsmQYALzB37lxZluV8lhcAYAbbPADAQ504cUIrV67U4cOH9fHHH+vee++9oVMGAgCyYpgGAA/166+/auLEiQoMDNTDDz+skSNH5tmLJgHAW7FnGgAAAHART1EAAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAXMUwDAAAALmKYBgAAAFzEMA0AAAC4iGEaAAAAcBHDNAAAAOAihmkAAADARQzTAAAAgIsYpgEAAAAX+dm9AG8UGxsry7Lk7+9v91IAAACQjZSUFDkcDtWoUeMvb8cwbQPLsmRZlt3LAAAAXuCPc4fD4ZDD4XBe7nA4Ml13veP88TbZ3e/P803G8f942z/e5npr+fNtr7dGk3I6q3n9MB0aGqrBgwerS5cuN+0xM56RDgsLu2mPeatasGCB2rdvb/cyPAItzaGlGXQ0h5ZmeFLHmJgYvfrqqzp27FiO7+NwOFSrVi2NGzdOlStXliQdPXpU7777rj766CPFxcXJz89Pt912m86ePau0tDRJUv78+VWxYkUdPnxYly5dypPP54/uvPNOvfLKK2rRokWeDte7du3K0e3YMw239sUXX9i9BI9BS3NoaQYdzaGlGZ7Q0bIsDRkyRF26dMnVIJ1x3++//16PPfaYli9fru+++06PPfaYFi9erHbt2qlXr15KT0/XqVOnlJaWpipVqujRRx9VUlKS/vOf/zgHaT+/vH2u9rffflPPnj3Vr18/t/hLv9c/Mw0AAOAp/v3vf2vBggWZLitTpozKlSunTZs2ydfX1/mMcoYePXronXfeUVJSkkqVKqUTJ06od+/eCgwMVM2aNTV37lwlJiaqdu3aSk9P15gxY3T27FlNnjxZZ8+eVf78+ZWUlCRJCggI0JUrV4x/Xtmte+nSpQoLC9M//vEP44+XG7l+Zjo2Nlbdu3dX3bp1FR4erpYtW2r58uXO67ds2aLQ0FB98803GjhwoGrUqKFHH31Us2fPznKsNWvWqGXLlgoLC1PdunU1duxY5xfjj8f66quv1K9fP9WoUUMNGjTQp59+Kkl677331KBBAz3wwAMaNmyYkpOTnfc9ffq0hg4dqkaNGqlatWpq0qSJJk2alOk2f/b++++revXqunjxYqbLDx48qNDQUG3cuDG3uXCD5s2bZ/cSPAYtzaGlGXQ0h5Zm3Ood09PTNWnSpEyXORwOTZ06VZs2bVJgYKBKlCiR5X5LlizRm2++KUkqWrSofHx8ZFmWkpOTNWvWLBUqVEjz589XUlKS2rVrp8jISA0cOFD33nuvTp8+7ZzdSpcufd1BumDBgi59bn8epDNMmzbtmtfdLLkepk+cOKGaNWtqzJgxevvtt9WkSRMNHz5cy5Yty3S7V199VeXKlVN0dLQeffRRTZgwQZs2bXJev27dOvXt21cVK1ZUdHS0unbtqoULF+qll17K8pgjR45UpUqVNGPGDFWvXl2DBw/Wm2++qa+//lqvvfaa+vbtq08++UTvvPOO8z5xcXEKDg7W0KFDNWfOHHXt2lXLli3Tq6++es3PrUWLFrIsSytXrsx0+ZIlS3T77berbt26uc2FG9S/f3+7l+AxaGkOLc2gozm0NONW7/jjjz/q1KlTmS575JFH9N1338nhcKhJkyY6efJklvudOXNGd9xxhwoVKqT//Oc/atCggaSrw3mhQoUkSR9++KEkqWvXrpKuDun58+d3HsPhcKhbt27XXeON7KkuWrRolsvOnTunLVu2uHxME3K9zaN58+bO/7YsSxERETp16pQWLVqk1q1bO69r0qSJ+vTpI0mqU6eONmzYoNWrV6tevXqSpBkzZig8PFwTJ06UJNWrV0+BgYEaMWKE9u/fr9DQUOexmjVrpt69e0uSqlWrprVr12rVqlVau3at88V8W7duVUxMjLp37y7p6gsLhwwZ4jxGzZo1FRgYqKioKI0YMUKBgYFZPrciRYqoadOmWrp0qZ599llJUmpqqlasWKG2bdvK19c3t7lwg+Li4uxegsegpTm0NIOO5tDSjFu945kzZ7JcVrVqVZ09e1aWZem222675n3Pnz+vUqVK6cCBA7rrrrskXR2mL126pMKFCys+Pl6SVKFCBed9UlJSnP9dqFAhlSpVytSnkq0CBQpk+zXK7vO+mXI9TMfHx2v69Olat26dcwO6JAUHB2e63R+fxXU4HKpQoYLzt6FLly5p7969mYZdSXriiSc0YsQIbd++PdMw/fDDDzv/OygoSMWKFdP999+f6TzN5cqVy/SbiWVZmj9/vhYvXqxjx45l2j7y66+/KiQkJNvPr127dmrfvr1++uknVapUSRs3btTvv/+uNm3a5DRRjiQmJioyMlLS1V8sxo0bp2PHjik0NFSdO3dWVFSUJOn5559XamqqFi9eLEmaNGmSoqOjdfDgQZUrV079+vVz/ibdpk0bBQYGOvdKvfHGG1qwYIH27NmjUqVK6ZVXXlGPHj0kSU899ZRKliypuXPnSrr67P/y5cu1Y8cOFS9eXOPHj3ee4aRp06aqUKGC3nrrLUnSyy+/rHXr1mnLli0KCgrS9OnT1bVrV6WmpqpBgwYKDw/XlClTJEkDBw7Utm3btGnTJuXLl0+zZs1Sr169dOnSJdWuXVv169fX+PHjJUm9evXS/v37nS8AmTdvnk6ePKnIyEjVqlVLzZs316hRoyRJ3bp10/Hjx7Vq1SpJ0syZMzVy5EidPHlSVatW1XPPPadhw4ZJkjp16qQLFy44/3oybdo0TZgwQUePHlWlSpXUrVs3519EMn6JWrhwoSTpzTff1KxZs/TTTz+pTJkyGjRokPr27StJat26tQoXLqz58+dLksaMGaMPP/xQu3fv1h133KGRI0c6f7lr3ry5SpcurVmzZkmSRowYoVWrVmn79u0qWrSoJk+e7Px+aNy4sUJDQxUdHS1JGjJkiDZu3KjNmzerYMGCio6OVrdu3ZScnKx69eopIiLC+Uvpiy++qB07dmjDhg3y8/PTnDlz1KdPHyUkJCg1NVUHDhzQG2+8IUnq2bOnDh48qNWrV0uS5s6dqyFDhujs2bMKDw9Xq1atNHLkSElSly5ddPr0aecWq7fffluvv/66Tpw4oSpVqqh9+/Z6+eWXJUnt27fX5cuXtXTpUknS5MmTNXXqVB0+fFgVKlRQr169NGDAAElX/735+fnpgw8+kCSNGzdO7777rvbv36+77rpLUVFRzl+kW7VqpaJFi+rdd9+VJL3++utasmSJfvzxR5UsWVJjxoxx7pt7/PHHVbZsWc2cOVOSNHz4cK1evVrbtm1TkSJFNHXqVL3wwgtKT09Xw4YNVbVqVU2bNk2SNGjQIH333Xf65ptvFBAQoJkzZ6p79+66cuWKHn74YdWpU0e//PKLIiMj1bdvX+3evVvr16+Xj4+P3nnnHfXr10/x8fGKiIhQ06ZNNXr0aElS9+7ddeTIEX3++eeSpNmzZ2vYsGE6ffq0qlevrrZt2+qVV16RJHXu3FlxcXHOLXSe+jPil19+0a5du27oZ0T//v0VFxfn9T8jUlNTFRkZeUM/Ix588EE1atTIq39G/PLLL+rXr98N/4yYMGGCJN30nxHZnX1i8+bNziE349jZmTlzpo4fPy7p6plAMrz11lvat2+f84V+HTp0UMGCBfXUU09l2tJx6dIl58+hvHL69OlsL589e7aWLVtm/GfEmTNnst0W82cOK5cvg+zRo4diY2PVq1cvVaxYUYUKFdKHH36ozz//XLGxsdqyZYs6duyoJUuWZDr1W8+ePZWQkKD3339fJ0+eVP369fXmm2+qRYsWmY4fFhamnj17qkePHtc8VsOGDdWgQQONGDHCedn06dP1zjvvKDY21hlw/Pjx6tq1qx588EEVLlxYu3bt0qhRozIdL7tT4zVr1kz169fX0KFDM63blIxvdk6Nd32HDh1S+fLl7V6GR6ClObQ0g47m0NKMW71jamqqqlWr5nwWWbp6Zo1FixapTZs2KlKkiHx8fLI8uxsUFKRp06apc+fOKl++vH799VelpKTI399f+/btU0BAgF555RW988476tatm3PLbLNmzTIN8KGhodq/f/9frrFAgQJKTEw09jkXLFhQu3btyrTlxJSczmu52jOdlJSkDRs2qEePHurQoYPq1KmjsLCwXJ+WJCgoSA6HQ+fOnct0eUJCgpKTk1WkSJFcHS87MTExatiwoQYOHKi6deuqWrVqKlCgQI7u+/TTT2vFihU6efKkNm7caPxZaeRcxm+QuHG0NIeWZtDRHFqacat39PPz0z//+c9Ml6WmpuqNN95Q9erVFR8fn+0LBNu1a+f8C5avr69z+4ZlWXrppZeUmpqqLl26yMfHR3PmzNGXX36pRYsWadeuXSpYsKBzkN2/f798fP56tHR1kL7W6fY6d+6cJ4N0buRqmE5OTlZ6enqm7RUXL17U+vXrc/WgBQsWVOXKlTP9GUH6358fatWqlavjZefKlStZ3q47489P19O6dWslJCRo0KBBCggIULNmzW54PQAAAHmtd+/eql+/fqbLtm/frh9//FGSdPny5Sz3mTt3rs6fPy9fX1/99NNPkqShQ4dq2rRpWrFihR577DFt2LBBffv2VXp6utq3b68BAwaoevXqatmyZaYzpaWnp+f4ycvcSE1NzXLZgw8+qEGDBhl/rNzK1Z7poKAghYWFafbs2SpWrJj8/Pycp0z587PM19O7d2/16tVLgwYNUosWLfTLL79o8uTJatq0aab90q566KGH9N5772nBggUqV66cVqxYoSNHjuTovsWKFVOjRo0UExOjZ555RgEBATe8HrgmJ68MRs7Q0hxamkFHc2hphid09PX11fvvv6/o6GhNnz49V88Ep6WlqVSpUhozZoyaNGkiSbrrrrs0c+ZMjRgxwvk6uYxzSf/444/OIf22227TuXPnZFmW0W0c2QkICFCPHj304osv5vkbxORErlcwceJEjRgxQlFRUQoODlaHDh2UmJiY6bR0OdGoUSNNnTpV0dHR6tmzp4KDg9WuXTsNHDgwt0vKVq9evRQXF+d8wUDTpk01fPhw54s9ruexxx5TTEyM2rZta2Q9cE3GiyFw42hpDi3NoKM5tDTDUzr6+vqqb9++6tOnjw4dOqQffvjB+eK9kiVLKiwsTFeuXFFcXJzy58+v06dPK1++fIqIiMjygrtatWpp9uzZOn/+vM6ePauCBQvqzjvvVFxcnP7zn//I19dXVatWVVBQkBITE3XkyBEdOnRI69evV9u2bVW8eHHt2bNHJ06cyHQ+aIfDIX9/fxUtWlRpaWnOQVySAgMDFRQUpMuXLyshIUGS5OPjo+LFi6tGjRqqUKFCnr6NeG7l+gWI3mLw4MHau3dvjreG5AYvQMy5yMjIW/4k+u6ClubQ0gw6mkNLM+hojie0zOm8Zv9z425m//792rt3rz777LO/fIMXAAAAgGem/6Rhw4Y6d+6cnnzySY0aNeq6r0p1Bc9M59yVK1fYs24ILc2hpRl0NIeWZtAxd44dO+bctvvnkzXcSMtjx45p2bJlateunW6//XYTS3VJnpwazxusX79eO3bs0OjRo/NkkEbuZLwhAG4cLc2hpRl0NIeWZrhjx+RzcToZs0bHli7TqXXrlXrR9bfjvplupOXx48c1Y8aMa75Ji7thmwfcWsa7ZuLG0dIcWppBR3NoaYY7dUxNTNShf83R2a++lmVZ8g0MVNp/L7ujWROV7dhePm5wJotrcaeWaWlpWU7tbJL7fhUASVWrVrV7CR6DlubQ0gw6mkNLM9ylY9qVK/rPiFG6fOK4ynXuqJKPPiq/QgWvPku9eo2OLflYV06d1r2DB8rh65vn64mNjdX06dO1Y8cOWZalihUr6sUXX1TZsmWz3DbjnaX/2HLevHkaO3as890RU1JSNGnSJH3++ec6e/asgoODVbVqVb355pvas2ePOnbsKEmZzqiWcd8LFy5o0qRJ+uKLL3T+/HmFhIRowIABqlu3rvO2HTp0UIECBdSsWTPNnDlTv/76qxYtWpRn22sZpuHWnnvuObuX4DFoaQ4tzaCjObQ0w106nlixUolHjihs3BgVqvC/tzfPV6yoyjz3jApVKK+9Y8bp7LebVeKRh/N0Ldu3b1enTp0UHh6u0aNHq3Dhwtq9e7dOnDiR7TCd4a9a/utf/9LChQs1aNAgVapUSXFxcfrmm2+UnJys++67TyNGjNCoUaM0duzYTG/vnpycrM6dO+v333/Xiy++qNtvv10rVqzQP//5T3388ceZ3qdk9+7dOn78uPr166fChQvrzjvvNBMkGwzTcGvDhg275U+t4y5oaQ4tzaCjObQ0wx06WmlpOhmzRiXq18s0SP9RsQciVLjqfTr5eUyeD9NvvvmmypYtq/nz58v3v8+CZzwLfOzYsWve769a7tq1S3Xr1tXf//5352VNmzZ1/nfFihUlSZUqVcr0bPKnn36qffv26ZNPPnHe5pFHHtGRI0f01ltvaerUqc7bxsfHa8mSJXk6RGfgFXYAAABu4srpM0r+/Xfd9lDtv7xd8Yfq6MLefbLS0/NsLZcvX9aPP/6oVq1aOQdpE6pUqaKNGzdq+vTp2rlzp9Jz+Dl88803CgkJUbly5ZSamur8v4ceesh55o0MISEhN2WQlnhmGm6uU6dOdi/BY9DSHFqaQUdzaGmGO3S0/vsugT758/3l7Xzy55PS06U8PMPxhQsXlJ6erpIlS+b6vn/VskePHvLx8dGyZcs0Y8YMFStWTH//+9/Vq1evv3xnw7i4OO3Zs0f33Xdfluv+POwXL14812t2FcM03NqFCxfsXoLHoKU5tDSDjubQ0gx36Ji/+G3yCQhQ/M7dKpLN0Jjh/I+7FFCqVJ6+ADEoKEg+Pj65OkVdvnz5lJKSkqnln7vmy5dPffr0UZ8+fXTkyBEtXbpU06dP11133aVWrVpd89hFihRRaGioxowZc9113My3G2ebB9zasmXL7F6Cx6ClObQ0g47m0NIMd+joGxCgkg3q6eTqNUq5kJDtbS6fOKHfv/1OdzR7LE/XUqBAAYWHh+uTTz5R2n+fMb+eO+64QwcPHszU8ttvv73m7cuWLasBAwYoODhYhw4dkiTnKeySkpIy3fahhx7Sr7/+qpIlSyosLCzL/9mFZ6YBAADcSOm/tdbZbzfrP6++pkr9eqtguXKSJMuydGHPHv00eZoCbi+p2xs3yvO1DBw4UJGRkYqMjNTzzz+vIkWK6D//+Y+KFi2q2rWz7utu2rSp5s+fr1KlSumrr77SihUrdOrUqUy36dmzp+677z5VqVJFgYGB+vLLLxUfH+88Xrly5eTr66ulS5fKz89Pvr6+CgsLU6tWrbRw4UJ17NhRL7zwgsqVK6eEhATt2bNHKSkpGjhwYJ73yA5vJ24D3k485y5cuKDChQvbvQyPQEtzaGkGHc2hpRnu1PHS4cPaO2a8kk6fVqFKFZW/+G26fPyEEo/+qoIVyqvyy1HKX/y2m7KWH374QVOmTNHOnTvl4+OjSpUq6cUXX9Tdd9+d5e3EExMT9frrr2vdunXy8fHRM888o+DgYI0bN855rug5c+bo888/15EjR5SWlqZ77rlHL7zwgp588knnYy5cuFBz5szRb7/9ptTUVOd9L168qOnTp2vNmjU6c+aMgoODVaVKFT3//PNq0KCBpP+dZ/pf//rXDX3eOZ3XGKZtwDCdcxnnmsSNo6U5tDSDjubQ0gx365iemqpzW7bp92+/U+qlS/IPDlbJBvVUpFqYHD7uvVPX3Vq6IqfzGts84NaOHj1q9xI8Bi3NoaUZdDSHlma4W0cfPz8Vf7iOij9cx+6l5Jq7tcxL7v1rDbxepUqV7F6Cx6ClObQ0g47m0NIMOprjTS3Z5mEDtnnk3JkzZ1SiRAm7l+ERaGkOLc2gozm0NIOO5nhCy5zOazwzDbf20ksv2b0Ej0FLc2hpBh3NoaUZdDTHm1oyTAMAAAAuYpiGW3v22WftXoLHoKU5tDSDjubQ0gw6muNNLRmmAQAAABcxTMOtLVy40O4leAxamkNLM+hoDi3NoKM53tSSYRoAAABwEafGswGnxss5Tzi1jrugpTm0NIOO5tDSDDqa4wktOTUePMKsWbPsXoLHoKU5tDSDjubQ0gw6muNNLRmm4dZ++uknu5fgMWhpDi3NoKM5tDSDjuZ4U0uGabi1MmXK2L0Ej0FLc2hpBh3NoaUZdDTHm1qyZ9oG7JnOuQsXLqhw4cJ2L8Mj0NIcWppBR3NoaQYdzfGEluyZhkfo27ev3UvwGLQ0h5Zm0NEcWppBR3O8qSXDNAAAAOAihmm4tdatW9u9BI9BS3NoaQYdzaGlGXQ0x5taMkzDrd3q+63cCS3NoaUZdDSHlmbQ0RxvaskwDbc2f/58u5fgMWhpDi3NoKM5tDSDjuZ4U0uGaQAAAMBFnBrPBpwaL+eOHz+u0qVL270Mj0BLc2hpBh3NoaUZdDTHE1pyajx4hA8//NDuJXgMWppDSzPoaA4tzaCjOd7UkmEabm337t12L8Fj0NIcWppBR3NoaQYdzfGmlgzTcGt33HGH3UvwGLQ0h5Zm0NEcWppBR3O8qSV7pm3Anumcu3LligICAuxehkegpTm0NIOO5tDSDDqa4wkt2TMNj9C9e3e7l+AxaGkOLc2gozm0NIOO5nhTS4ZpAAAAwEUM03BrzZs3t3sJHoOW5tDSDDqaQ0sz6GiON7VkmIZbu9XPUelOaGkOLc2gozm0NIOO5nhTS4ZpuLVZs2bZvQSPQUtzaGkGHc2hpRl0NMebWjJMAwAAAC7i1Hg24NR4OXfo0CGVL1/e7mV4BFqaQ0sz6GgOLc2gozme0JJT48EjrFq1yu4leAxamkNLM+hoDi3NoKM53tSSYRpubfv27XYvwWPQ0hxamkFHc2hpBh3N8aaWDNNwa0WLFrV7CR6DlubQ0gw6mkNLM+hojje1ZM+0DdgzDQAA4N7YMw2PEBkZafcSPAYtzaGlGXQ0h5Zm0NEcb2rJMA0AAAC4iGEabq1x48Z2L8Fj0NIcWppBR3NoaQYdzfGmlgzTcGuhoaF2L8Fj0NIcWppBR3NoaQYdzfGmlgzTcGvR0dF2L8Fj0NIcWppBR3NoaQYdzfGmlgzTAAAAgIs4NZ4NODVezu3du1eVK1e2exkegZbm0NIMOppDSzPoaI4ntOTUePAIGzdutHsJHoOW5tDSDDqaQ0sz6GiON7VkmIZb27x5s91L8Bi0NIeWZtDRHFqaQUdzvKklwzTcWsGCBe1egsegpTm0NIOO5tDSDDqa400t2TNtA/ZMAwAAuDf2TMMjdOvWze4leAxamkNLM+hoDi3NoKM53tSSYRpuLTk52e4leAxamkNLM+hoDi3NoKM53tSSYRpurV69enYvwWPQ0hxamkFHc2hpBh3N8aaWDNNwaxEREXYvwWPQ0hxamkFHc2hpBh3N8aaWDNNwaxMnTrR7CR6DlubQ0gw6mkNLM+hojje1ZJgGAAAAXMQwDbf24osv2r0Ej0FLc2hpBh3NoaUZdDTHm1oyTMOt7dixw+4leAxamkNLM+hoDi3NoKM53tSSYRpubcOGDXYvwWPQ0hxamkFHc2hpBh3N8aaWDNNwa35+fnYvwWPQ0hxamkFHc2hpBh3N8aaWvJ24DXg7cQAAAPfG24nDI/Tp08fuJXgMWppDSzPoaA4tzaCjOd7UkmEabi0hIcHuJXgMWppDSzPoaA4tzaCjOd7UkmEabu3BBx+0ewkeg5bm0NIMOppDSzPoaI43tWSYhltr1KiR3UvwGLQ0h5Zm0NEcWppBR3O8qSXDNNzaG2+8YfcSPAYtzaGlGXQ0h5Zm0NEcb2rJMA0AAAC4iGEabq1nz552L8Fj0NIcWppBR3NoaQYdzfGmlgzTcGsHDx60ewkeg5bm0NIMOppDSzPoaI43tWSYhltbvXq13UvwGLQ0h5Zm0NEcWppBR3O8qSXDNAAAAOAi3k7cBrydeM6lpaXJ19fX7mV4BFqaQ0sz6GgOLc2gozme0JK3E4dHGDJkiN1L8Bi0NIeWZtDRHFqaQUdzvKklwzTc2tmzZ+1egsegpTm0NIOO5tDSDDqa400tGabh1sLDw+1egsegpTm0NIOO5tDSDDqa400tGabh1lq1amX3EjwGLc2hpRl0NIeWZtDRHG9qyTANtzZy5Ei7l+AxaGkOLc2gozm0NIOO5nhTS4ZpAAAAwEUM03BrXbp0sXsJHoOW5tDSDDqaQ0sz6GiON7VkmIZbO336tN1L8Bi0NIeWZtDRHFqaQUdzvKklwzTc2qeffmr3EjwGLc2hpRl0NIeWZtDRHG9qyTANAAAAuIi3E7cBbyeec5cvX1ZgYKDdy/AItDSHlmbQ0RxamkFHczyhJW8nDo/w+uuv270Ej0FLc2hpBh3NoaUZdDTHm1oyTMOtnThxwu4leAxamkNLM+hoDi3NoKM53tSSYRpurUqVKnYvwWPQ0hxamkFHc2hpBh3N8aaW7Jm2AXumc+7EiRMqVaqU3cvwCLQ0h5Zm0NEcWppBR3M8oSV7puERXn75ZbuX4DFoaQ4tzaCjObQ0g47meFNLhmkAAADARQzTcGvt27e3ewkeg5bm0NIMOppDSzPoaI43tWSYhlu7fPmy3UvwGLQ0h5Zm0NEcWppBR3O8qSXDNNza0qVL7V6Cx6ClObQ0g47m0NIMOprjTS0ZpgEAAAAXcWo8G3BqvJyLi4tT0aJF7V6GR6ClObQ0g47m0NIMOprjCS05NR48wtSpU+1egsegpTm0NIOO5tDSDDqa400tGabh1g4fPmz3EjwGLc2hpRl0NIeWZtDRHG9qyTANt1ahQgW7l+AxaGkOLc2gozm0NIOO5nhTS/ZM24A90zl37tw5FStWzO5leARamkNLM+hoDi3NoKM5ntCSPdPwCAMGDLB7CR6DlubQ0gw6mkNLM+hojje15JlpG9ysZ6Yty1LcDz/o+Mef6NLhI0q7fFlySLKcN5Acjusf6I/3+ePHmS7/77H++LH+cOw/P1Z2H/s4sqwtJS1N/r6+116rw3H1OjdY63Vda63XWnN2a8vJWv+8tv9+nJqWJj8fn5u3Vlc65qSrG3zNnd+X17p9Ttea7eUufM1v0e/PlLQ0+fv53lI/k655e+da7Pn+TElP/9/3pBt/zd39+zNLx1vkZ9K1bu/w9ZVfgQIqFBqiMs88LTkc+nXRR7p4+LCUmiaHn5/8g4tIktIvX9GVM2eUnpz0vyX99z8cDh/J11fycchKt+RIT5fDz0++AQGyJKVdviwrNfXq4wUGKLB0ae3at0/3liolKyVFAXfeqdJtWqtI1fvk+O8601NSdG7rNl0+dlwOX18Vvq+Kgu4NVWpCgs5+851S4uPlV6CAij0YoYDbb8/a/SbI6bzGMH0dW7ZsUceOHbVkyRJjw+/NGKYv7N2nPaPHKu3ixTx7DAAAgJzyDw5WlZHDdfGngzq64AOlxMfLPzhY6SnJSruUKL/ChZWWmChZ1tX/vnRJ6Skpuq32A6rQs4f8Cwfd1PXmdF7zuxmLwc11Yd9+7Ro6PPNvygCM+fPzXXANHQH7BN5TTpd/OXxzHqtsGV357TelnD+vHwcMltLTVeLRBrrrb61UoMzdSk9J0e5hI5Sw/4Acfn6qMnK4gqtVU9qVKzqz6Ssdef8D7R4+QmHjxsivQIGbsubcuGX3TFuWpeTkZLuX4XYsy9JPU6YxSAN5iAHQDDoC9gi4q7RSzp3L08f44xRy+chRlf9HFylfPik9XX5FCivkxT4qUOZuSdK5LVuVsP+AQocMUoG779aR9z+QJPkGBOiOJo8pbMwoXTl1Wsc/Xp6na3ZVng/TUVFRevLJJ7Vlyxa1atVK4eHhatu2rXbv3u28TVJSksaOHau6desqLCxMLVu21Nq1a7M9zsaNG9WiRQuFhYVp/fr1mj59umrUqKE9e/bomWeeUbVq1dS6dWvt2bNHSUlJevXVVxUREaF69epp3rx5mY4ZGxur7t27q27dugoPD1fLli21fPnyvE6SpxL27deV307avQy4IX69MoeWZtARsMeVY8eVGn8hbw7+3z3Rf/xl2b9IYf3+zXcqHBoiSUqNv6DEo0ed1//2+WoVvq+Kij9UR3c/+7QuHvhJFw8ecl5foMzdKtmwgU6tXaf0lJS8WfcNuCnPTJ85c0ajR49Wly5dNGXKFCUlJal3795K+W+QQYMGadGiReratauio6NVsWJF9enTR+vWrct0nNOnT2v06NGKjIzU7NmzVblyZUlSSkqKhgwZonbt2mn69OlKTU1V7969NWzYMAUEBGjKlClq3Lixxo4dqx9++MF5vBMnTqhmzZoaM2aM3n77bTVp0kTDhw/XsmXLbkaWPHFh7z67lwA3xbOA5tDSDDoC7svh59pOYL/ChbNcZqVburBvv6zUVOdlF/btv3qdZSlh7z7dVqe2JKlYxP1y+PkpYV/meea2OrWVcv68rpw65dK68tJN2TMdHx+vBQsWqFKlSpKkwMBAdezYUT/++KMKFSqkNWvW6LXXXtOzzz4rSapXr56OHz+u6OhoNWrUKNNxZs+ererVq2c6fkpKigYNGqT69etLktLT09W9e3dVr15dQ4cOlSTVrl1bMTExiomJUc2aNSVJzZs3dx7DsixFRETo1KlTWrRokVq3bp13QSQlJiYqMjJSkjRjxgyNGzdOx44dU2hoqDp37qyoqChJ0vPPP6/U1FQtXrxYkjRp0iRFR0fr4MGDKleunPr166f+/ftLktq0aaOgAwfkp798/XKmjzOeGfqrj3N6rOw+zq0sx7Ms5yt//3y96bWa/FzzvIvBj/O64/W401r5mpv5OLdyu3Y7v+Y3spbcupW+5nn9756vuWtrVS4//uOxUlJTMw2JOf1c4y/Eq+Cfrk9KSpJverp+OnBAd/z3so1fbtDnH/xbQYUK6VnL0r8XLtR/ln6kBvXrq5JDWvD+Av3no8UaOHCgtm3bpn3rv1RLSUpPV69evXTp0iXVrl1b9evX1/jx4yVJvXr10v79+/XFF19IkubNm6f+/fsrLi5OtWrVUvPmzTVq1ChJUrdu3XT8+HGtWrVKkjRz5kyNHDlSJ0+eVNWqVfXcc8/pzJkzKlGiRDafZWY3ZZguWbKkc5CWpIoVK0qSTp06pf37r/5m0qxZs0z3efzxxzV27FglJiaqwH83mwcHB2cZpCXJx8dHderUcX5crlw5SdJDDz3kvMzX11dlypTRyZP/2wIRHx+v6dOna926dTp16pTS0tKcj5PXChQokGnbyejRozNd/+ctKU888YTzv1955ZVr3vbcHXdq73dbMl3/5292Rw6vM/FxbmU53p9OQ3Qz134jn2uedzH4sd3fA+601px+nN3/iPA1z72Mju609rxcS27dSl9zb/h378p9c8udvuZ/HhBz+rkW8vW7epq8P1wWUKCA8t12m0IKBOrCrqvbfB/v0F7PVLm6wyC2b389VaqUXop6SRf27NWu5Sv1j+EvKzj86swXFhamo4EFdXz5CuUvUULR0dGZHvOPc1BERITat2/v/Hjy5MnXvK0kPf30087/HjduXKbrzuVwX/lN2eZR+E9P+fv7+0u6+ptKfHy8/P39swywxYsXv/rUf0JCpsuyExAQoHz58mU5flBQ5lOo+Pv7KykpyflxVFSUVq5cqRdeeEFz587VkiVL1KZNm1v6hY1Fa9aQX9DNPXVMXmJPJdzRjf4PJq6iozn8rDTDWzrmv/MOOf4wN5mUsZXjjy1T4uNVsuGjSjxyRJLkV6iQgirf67z+jqZN9PuWrbr4y2EdW7JUAXfeoSLV/nc6upQLF3QyZo1KNqgn38DAPFn3jbD9bB5FihRRSkqK4uPjM11+9uxZORyOTAPxn5+lvBFJSUnasGGDevTooQ4dOqhOnToKCwvTrX7abYevr8p362L3Mozhf2wB4Pr4WWmGt3RMOnXK+ayvUX+Y0zL+y+Hrq4DSpXRi+SdKvXD1CdLUS5d0YsVKpf33Cc6SjR5VYKlS2jk4SnHbY1U2spMcPldH1ISfftbuV0ZKlqXSbVqbX7MBtp9nulatWpKkmJgYPfPMM87LY2JiVKVKFecWD9OSk5OVnp7ufBZbki5evKj169fnyePdTCXqPaLUS4k6NHOW3UsBAADuJt1S3NZt5o+bzROSVlqarhw7fvUDHx9V7NtbFw8c0OF35+vXRR8pKKSS0pOSdPn4cecwfvidd3V6zRolnf1diUeOKuDOO3Tf6yMVULKk+TUbYPswfe+996pJkyYaN26crly5onvuuUcrVqxQbGys3nrrrTx73KCgIIWFhWn27NkqVqyY/Pz8NGvWLBUqVCjHe2RclZKSIsuynO+skyfuKqXCr7+q1IsXlXb5iiwrPe8eCwAAuCmHHD4+V9/sxCGlJibKSkv/7zVyDrBX/zJv4q/zDjkcDqVb6fLJeKba4SPfwAD5FSyo0w6HVLy2gh6MUNrly0pKTZMcUsFWLeQbGKD01NSrb22enib5+KhQ/vzyyZ9fhy7ES3k5N2UjOTk5R7sibB+mJenNN9/UpEmTNHv2bJ0/f17ly5fXtGnT1LBhwzx93IkTJ2rEiBGKiopScHCwOnTooMTERL3zzjt5+rgmt6tc54HkFxTkUXuoAQCA63zd5B0EHb6+8itUKMvlPv7+8vnDrgE7ORyOHM1sDutW3yQMAAAA2MT2FyACAAAAtyqGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CI/uxfgjWJjY2VZlvz9/e1eCgAAALKRkpIih8OhGjVq/OXtGKZtYFmWLMuyexkAAABO6enpSk1NlcPhkMPhUHJysiQpICBAPj5XNzNYlqXLly8rOTlZ6enpkiQfHx8FBATI399fSUlJzvv5+V0dM1NTU523vRF+fn7Knz+/8ufPf8PHyomczmpeP0yHhoZq8ODB6tKly017zIxnpMPCwm7aY96qFixYoPbt29u9DI9AS3NoaQYdzaGlGd7acefOnZo+fbpWr16ttLS0bG9z7733ys/PT7t3777Jq8vK399fnTp10rBhw5QvX748e5xdu3bl6HbsmYZb++KLL+xegsegpTm0NIOO5tDSDG/suH79erVq1UoHDhzQ448/LklyOBwqWbKkfH19Va5cOd1zzz3at2+fWwzS0tXtF3PmzFHLli2VlJRk93IYpgEAALzRuXPn1L17d9WrV0/Tpk3TypUr5efnp08++USxsbFatGiRfvvtN50+fTrLfe+44w59++23N23LRXZ27typsWPH2vb4GXI9TMfGxqp79+6qW7euwsPD1bJlSy1fvtx5/ZYtWxQaGqpvvvlGAwcOVI0aNfToo49q9uzZWY61Zs0atWzZUmFhYapbt67Gjh2b6TeMjGN99dVX6tevn2rUqKEGDRro008/lSS99957atCggR544AENGzbMuUdHkk6fPq2hQ4eqUaNGqlatmpo0aaJJkyZlus2fvf/++6pevbouXryY6fKDBw8qNDRUGzduzG0u3KB58+bZvQSPQUtzaGkGHc2hpRne1nHhwoVKTU3VpEmTnJ973759VatWLUlSnTp19Nhjj+nSpUtZ7vv2229rw4YNtj8zvGDBAl2+fNnWNeR6mD5x4oRq1qypMWPG6O2331aTJk00fPhwLVu2LNPtXn31VZUrV07R0dF69NFHNWHCBG3atMl5/bp169S3b19VrFhR0dHR6tq1qxYuXKiXXnopy2OOHDlSlSpV0owZM1S9enUNHjxYb775pr7++mu99tpr6tu3rz755BO98847zvvExcUpODhYQ4cO1Zw5c9S1a1ctW7ZMr7766jU/txYtWsiyLK1cuTLT5UuWLNHtt9+uunXr5jYXblD//v3tXoLHoKU5tDSDjubQ0gxv6/j555+rSZMmKlasmD7//HNJ0t///vdMt8luD7W/v78iIiKyzH52uHz5sr777jtb15DrFyA2b97c+d+WZSkiIkKnTp3SokWL1Lp1a+d1TZo0UZ8+fSRd/c1mw4YNWr16terVqydJmjFjhsLDwzVx4kRJUr169RQYGKgRI0Zo//79Cg0NdR6rWbNm6t27tySpWrVqWrt2rVatWqW1a9c6X8y3detWxcTEqHv37pKuvrBwyJAhzmPUrFlTgYGBioqK0ogRIxQYGJjlcytSpIiaNm2qpUuX6tlnn5V09RWoK1asUNu2beXr65vbXNeUmJioyMhIZ4tx48bp2LFjCg0NVefOnRUVFSVJev7555WamqrFixdLkiZNmqTo6GgdPHhQ5cqVU79+/Zz/+Nu0aaPAwEAtWLBAkvTGG29owYIF2rNnj0qVKqVXXnlFPXr0kCQ99dRTKlmypObOnSvp6i8sy5cv144dO1S8eHGNHz/e+aLMpk2bqkKFCnrrrbckSS+//LLWrVunLVu2KCgoSNOnT1fXrl2VmpqqBg0aKDw8XFOmTJEkDRw4UNu2bdOmTZuUL18+zZo1S7169dKlS5dUu3Zt1a9fX+PHj5ck9erVS/v373fuWZs3b56+/vprRUZGqlatWmrevLlGjRolSerWrZuOHz+uVatWSZJmzpypkSNH6uTJk6pataqee+45DRs2TJLUqVMnXbhwwfmPftq0aZowYYKOHj2qSpUqqVu3bs5f4jK+7gsXLpQkvfnmm5o1a5Z++uknlSlTRoMGDVLfvn0lSa1bt1bhwoU1f/58SdKYMWP04Ycfavfu3brjjjs0cuRI5/dj8+bNVbp0ac2aNUuSNGLECK1atUrbt29X0aJFNXnyZOf3Q+PGjRUaGqro6GhJ0pAhQ7Rx40Zt3rxZBQsWVHR0tLp166bk5GTVq1dPERERzn9HL774onbs2KENGzbIz89Pc+bMUZ8+fZSQkKDDhw/rwIEDeuONNyRJPXv21MGDB7V69WpJ0ty5czVkyBCdPXtW4eHhatWqlUaOHClJ6tKli06fPu38q9Dbb7+t119/XSdOnFCVKlXUvn17vfzyy5Kk9u3b6/Lly1q6dKkkafLkyZo6daoOHz6sChUqqFevXhowYIAkqV27dvLz89MHH3wgSRo3bpzeffdd7d+/X3fddZeioqKc//ZbtWqlokWL6t1335Ukvf7661qyZIl+/PFHlSxZUmPGjNE//vEPSdLjjz+usmXLaubMmZKk4cOHa/Xq1dq2bZuKFCmiqVOn6oUXXlB6eroaNmyoqlWratq0aZKkQYMG6bvvvtM333yjgIAAzZw5U927d9eVK1f08MMPq06dOlq/fr0iIyPVt29f7d69W+vXr5ePj4/eeecd9evXT/Hx8YqIiFDTpk01evRoSVL37t115MgR5/9gzZ49W8OGDdPp06dVvXp1tW3bVq+88ookqXPnzoqLi3P+1c9Tf0bExsZq165dN/Qzon///oqLi/P6nxGxsbGKjIy8oZ8RDz74oBo1auTVPyPWr1+vfv363fDPiAkTJkiS2/+MOHTokKpUqaLIyEjnX+V37tzpvO8bb7yhPXv26M/S0tL08ccf6+TJk1mus8P//d//6b333jP+M+LMmTMqUaLE9Rdg5dL58+et119/3WrQoIFVuXJlKyQkxAoJCbEeeOABy7Isa/PmzVZISIj1zTffZLpf165drRdeeMGyLMu6ePGiFRoaar3zzjuZbnPhwgUrJCTE+ve///2Xx3r44YetgQMHZrps4sSJzjVYlmWlp6db7777rvX4449bYWFhznWGhIRY+/fvd94uJCTEmjNnjvPjrVu3WiEhIdaBAwcsy7KsL774wgoNDbWOHDmS21TXtHPnTmvnzp3GjufJpk2bZvcSPAYtzaGlGXQ0h5ZmeFvHdu3aWc8884xlWZZVvXp1q1SpUtbevXsz3aZbt25WqVKlMv3f3XffbSUnJ1vPP/98luvs+L/NmzfnSZ+czmu53uYRFRWllStX6oUXXtDcuXO1ZMkStWnTJste5KCgoEwf+/v7O2+TkJAgy7J02223ZblPvnz5FB8f/5fHypcvnwoXLnzN40vS/PnzNX78eDVq1EhvvfWWPvroI40YMUKS/nJ/T0REhO655x4tWbJEkrR06VJFRESoTJky17wP8s4f/xKCG0NLc2hpBh3NoaUZ3taxTZs2+uqrr/Tzzz/rueeek8Ph0L/+9a9Mt8luv3RaWpo+++wztziNYIkSJRQREWHrGnI1TCclJWnDhg3q0aOHOnTooDp16igsLCzXb0ASFBQkh8Ohc+fOZbo8ISFBycnJKlKkSK6Ol52YmBg1bNhQAwcOVN26dVWtWjUVKFAgR/d9+umntWLFCp08eVIbN25UmzZtbng9cE3Gn2Nw42hpDi3NoKM5tDTD2zq2aNFCZcuWVZcuXdSsWTPlz59fixcv1qxZs5Senq7Zs2fryy+/1N13353lvi+++KKKFi2qYsWK2bDy/+nfv7/zDWXskqtHz3i3mz++DfbFixe1fv36XD1owYIFVblyZcXExGS6PGOfUMarSG/ElStXsrxdd8Zerutp3bq1EhISNGjQIAUEBKhZs2Y3vB4AAAB3EhAQoPfff19XrlxRq1at9MADD8jX11evvfaaypYtq5EjR+ruu+/O9tR4ycnJatOmTZYnRm+mTp06qWPHjrY9foZcvQAxKChIYWFhmj17tooVKyY/Pz/NmjVLhQoVynXM3r17q1evXho0aJBatGihX375RZMnT1bTpk0zvfjQVQ899JDee+89LViwQOXKldOKFSt05MiRHN23WLFiatSokWJiYvTMM88oICDghtcD13Tr1s3uJXgMWppDSzPoaA4tzfDGjhUqVNDq1au1cOFCffjhh3I4HPLz83O+/fevv/6qIkWKqEOHDgoKCtLs2bOznEL4ZitfvrxGjRqlRx991NZ1ZMj12TwmTpyoESNGKCoqSsHBwerQoYMSExMznZYuJxo1aqSpU6cqOjpaPXv2VHBwsNq1a6eBAwfmdknZ6tWrl+Li4pyvvm3atKmGDx/ufOX09Tz22GOKiYlR27ZtjawHrjl+/LjdS/AYtDSHlmbQ0RxamuGtHYODg9W9e/cczUiDBg3K0TE/+ugjPf300ze6tFuCw8rthmcvMXjwYO3duzfHW0NyI+O93sPCwowf29NERkZ63Un08wotzaGlGXQ0h5Zm0NEcT2iZ03kt189Me7r9+/dr7969+uyzz/7yDV4AAAAAnpn+k4YNG+rcuXN68sknNWrUqDx5hSjPTOfclStX2LNuCC3NoaUZdDSHlmbcSh2joqK0e/fuLO/a/EehoaEaPHiw8w2Wrufjjz+Wv7+/nnrqqRte363U8lpyOq/Zey4RN7R+/Xrt2LFDo0ePtv1UK5Dz3bVw42hpDi3NoKM5tDQjNx0Tr6To8+8Oa8rCHzT5wx+0fONBXbiUfP073kSLFi3K1WC8bNmyvxzOc8ObvifZ5gG35i5vVeoJaGkOLc2gozm0NCOnHddtO6p/LdulpORUVbw7WD4OhzbFHtf7n+3R803v1d8erSiHw5HHq72+8PBw2x47py0ty1JKSory5cuXxyvKOzz1CrdWtWpVu5fgMWhpDi3NoKM5tDQjJx03bP9VUxbGqk7YnZozrIkm9quvN/vW07wRTfTEw/do3qo9+vjLn2/Caq/asmWLWrVqpfDwcLVt21a7d+92XhcaGqq5c+c6P96+fbv+/ve/q1atWqpRo4aeeuopLVu2TJLUoUMHbd26VRs2bFBoaKhCQ0M1ffp0530XLlyopk2bqmrVqmrYsKHeeust5+nzMnz//fdq1aqVwsLCtH//fn3zzTdq2bKloqKinLeJiorSk08+qY0bN6pFixYKCwvT+vXrlZiYqFGjRqlp06aqXr26GjZsqBEjRighISHTYzRs2FCjRo3SvHnzVL9+fdWoUUNRUVFKTk7W3r179eyzzzpb7N+/32jra+GZabi15557zu4leAxamkNLM+hoDi3NuF7HlNQ0zV3xH9WrUVovPlsj07PPRQrlV5cWVeVwOPTB6n167MGyKlwwb59tPXPmjEaPHq1u3bopKChIEydOVO/evbV27dosb1x38eJF/fOf/1StWrU0adIk5cuXTz///LMuXLggSXr11Vf10ksvKSAgQEOGDJEk3XHHHZKk999/X6NHj1aHDh3UoEEDxcbGasaMGUpISHDe9vTp0/rHP/6hKlWqaMqUKTp69KhGjhyphIQEVa5cOdNaTp8+rdGjR6tHjx668847VapUKV25ckVpaWnq37+/ihUrpt9++00zZ85Uz5499f7772e6/7p161SpUiWNGjVKv/76q8aNGyd/f3/t2LFDkZGRKl68uCZMmKB+/frps88+y/ttuxZuup07d1o7d+60exm3hE6dOtm9BI9BS3NoaQYdzaGlGdfruOmHY9aTA5ZbR09euOZtzidcsVq9tML6+MufDK8usyFDhlihoaHWgQMHnJdt3rzZCgkJsbZt22ZZlmWFhIRYc+bMsSzr6uwREhJi7du375rHbN++vdWtW7dMl6WmploPPvig1b9//0yXT5w40brvvvusc+fOWZZlWePHj7dq1aplJSQkWJZ1teW2bduskJAQa8iQIZnWHRISYu3YseMvP7+UlBTr+++/t0JCQqxDhw45L3/00UetevXqWUlJSc7L+vTpY4WEhFgbN250XrZ+/XorJCTE2rt3718+zl/J6bzGNg8AAIAc+OW3eJUsGqi7bw+65m2KFMqvSncH65cT8Xm+npIlS6pSpUrOjytWrChJOnXqVJbblilTRoUKFdLIkSP12Wef5fidqw8dOqS4uDg1a9Ys0+VPPPGEUlJStHPnTklXz3zx4IMPqlChQs7b3H///QoODs5yzODgYFWvXj3L5cuXL1erVq1Uo0YN3XfffXr++eclSYcPH850u4iIiEx7rMuVKycfHx/Vrl0702WS9Ntvv+Xo87wRDNNwa506dbJ7CR6DlubQ0gw6mkNLM67X0eFwKD39+mcUTk+3bsoLEAsXLpzp44ytHUlJSVluW6RIEb377rsqWLCgBg8erIcfflgdOnS47r7i+PirvxTcdtttmS7P+Djj+jNnzqhYsWLO6zNa/vGyDMWLF89y2dq1azVkyBBVq1ZNU6ZM0eLFixUdHZ3t55Pd5x0QEJBpwP6rFqYxTMOtZezlwo2jpTm0NIOO5tDSjOt1DLk7WGfjr+inX+OueZuz5y/rp1/jFFq2qOnl3bBq1appzpw5+v777zVz5kz9/vvv6tWr11/eJ+OZ5T8/k/37779LujqkS1KJEiUy3SajZXbPgGf3i0ZMTIwqV66sUaNGqX79+qpevXqWodldMUzDrWW8yhg3jpbm0NIMOppDSzOu1/H+KneoRNFA/Ttmn9KyeYbasix9sHqf8ufzVYOad+XVMm9YQECA6tevr+eee07Hjh1zPnvr7++f5Znce+65R8WKFVNMTEymyz///HP5+/urWrVqkq6+scnmzZt18eJFSVdbfv/99zp//nyO1nTlypUsL5r89NNPXfn0bjqGaQAAgBzw9XGoZ5vqij1wRq/P3ayffz3vvO7XUwma+O8ftHbrUXVtGaYCAf7XPpANNmzYoN69e2v58uXaunWrPvvsMy1YsEA1a9ZU/vz5JUnly5fX7t27tX79eu3atUunTp2Sr6+vevbsqZUrV2rMmDH6+uuvNWPGDM2ePVsdOnRQ0aJXn4GPjIxUenq6/vnPf2r9+vU6c+aMhg4dqqJFi+Zoy8tDDz2knTt3Kjo6Wt9++63Gjh2r7777Lk+bmMKp8eDWpk2bZvcSPAYtzaGlGXQ0h5Zm5KTj/ZVv1ysvPKjoJT+q/5SNKh4cKF8fh06dS1SRQvnU/7kaanh/mZuw2twpU6aMfHx8NGXKFP3+++8KDg5W3bp1NWDAAOdt/vGPf+jo0aMaMmSILly4oN69e6tPnz7q0KGD/Pz8NG/ePH344YcqUaKEevfure7duzvvW7JkSc2ePVujR49W3759ddddd2nYsGEaNWqUgoKu/YLNDM8++6yOHTumBQsWaO7cuapbt64mTpyodu3a5UkPkxyWZV1/Jz2Myul7vUMaMWKERo0aZfcyPAItzaGlGXQ0h5Zm5KZjWlq6vt97SvuPxik93dI9pYrooWp3yt/PN49XeWsYMWKEXnjhBT3++ON644031Lp1a7uXlGs5ndd4Zhpu7ejRo3YvwWPQ0hxamkFHc2hpRm46+vr66MGqd+rBqnfm4YpuLRMnTlRoaKhKliyp77//Xps3b1aJEiXUpEkTu5eWpxim4db+eP5M3BhamkNLM+hoDi3NoOONSUlJ0YQJE3T27FlJUr169TR48GAVLFjQ5pXlLbZ52IBtHjl35swZlShRwu5leARamkNLM+hoDi3NoKM5ntAyp/MaZ/OAW3vppZfsXoLHoKU5tDSDjubQ0gw6muNNLRmmAQAAABcxTMOtPfvss3YvwWPQ0hxamkFHc2hpBh3N8aaWDNMAAACAixim4dYWLlxo9xI8Bi3NoaUZdDSHlmbQ0RxvaskwDQAAALiIU+PZgFPj5ZwnnFrHXdDSHFqaQUdzaGkGHc3xhJacGg8eYdasWXYvwWPQ0hxamkFHc2hpBh3N8aaWDNNwaz/99JPdS/AYtDSHlmbQ0RxamkFHc7ypJcM03FqZMmXsXoLHoKU5tDSDjubQ0gw6muNNLdkzbQP2TOfchQsXVLhwYbuX4RFoaQ4tzaCjObQ0g47meEJL9kzDI/Tt29fuJXgMWppDSzPoaA4tzaCjOd7UkmEaAAAAcBHDNNxa69at7V6Cx6ClObQ0g47m0NIMOprjTS0ZpuHWbvX9Vu6ElubQ0gw6mkNLM+hojje1ZJiGW5s/f77dS/AYtDSHlmbQ0RxamkFHc7ypJcM0AAAA4CJOjWcDTo2Xc8ePH1fp0qXtXoZHoKU5tDSDjubQ0gw6muMJLTk1HjzChx9+aPcSPAYtzaGlGXQ0h5Zm0NEcb2rJMA23tnv3bruX4DFoaQ4tzaCjObQ0g47meFNLhmm4tTvuuMPuJXgMWppDSzPoaA4tzaCjOd7Ukj3TNmDPdM5duXJFAQEBdi/DI9DSHFqaQUdzaGkGHc3xhJbsmYZH6N69u91L8Bi0NIeWZtDRHFqaQUdzvKklwzQAAADgIoZpuLXmzZvbvQSPQUtzaGkGHc2hpRl0NMebWjJMw63d6ueodCe0NIeWZtDRHFqaQUdzvKklwzTc2qxZs+xegsegpTm0NIOO5tDSDDqa400tGaYBAAAAF3FqPBtwarycO3TokMqXL2/3MjwCLc2hpRl0NIeWZtDRHE9oyanx4BFWrVpl9xI8Bi3NoaUZdDSHlmbQ0RxvaskwDbe2fft2u5fgMWhpDi3NoKM5tDSDjuZ4U0uGabi1okWL2r0Ej0FLc2hpBh3NoaUZdDTHm1qyZ9oG7JkGAABwb+yZhkeIjIy0ewkeg5bm0NIMOppDSzPoaI43tWSYBgAAAFzEMA231rhxY7uX4DFoaQ4tzaCjObQ0g47meFNLhmm4tdDQULuX4DFoaQ4tzaCjObQ0g47meFNLhmm4tejoaLuX4DFoaQ4tzaCjObQ0g47meFNLhmkAAADARZwazwacGi/n9u7dq8qVK9u9DI9AS3NoaQYdzaGlGXQ0xxNacmo8eISNGzfavQSPQUtzaGkGHc2hpRl0NMebWjJMw61t3rzZ7iV4DFqaQ0sz6GgOLc2gozne1JJhGm6tYMGCdi/BY9DSHFqaQUdzaGkGHc3xppbsmbYBe6YBAADcG3um4RG6detm9xI8Bi3NoaUZdDSHlmbQ0RxvaskwDbeWnJxs9xI8Bi3NoaUZdDSHlmbQ0RxvaskwDbdWr149u5fgMWhpDi3NoKM5tDSDjuZ4U0uGabi1iIgIu5fgMWhpDi3NoKM5tDSDjuZ4U0uGabi1iRMn2r0Ej0FLc2hpBh3NoaUZdDTHm1oyTAMAAAAuYpiGW3vxxRftXoLHoKU5tDSDjubQ0gw6muNNLRmm4dZ27Nhh9xI8Bi3NoaUZdDSHlmbQ0RxvaskwDbe2YcMGu5fgMWhpDi3NoKM5tDSDjuZ4U0uGabg1Pz8/u5fgMWhpDi3NoKM5tDSDjuZ4U0veTtwGvJ04AACAe+PtxOER+vTpY/cSPAYtzaGlGXQ0h5Zm0NEcb2rJMA23lpCQYPcSPAYtzaGlGXQ0h5Zm0NEcb2rJMA239uCDD9q9BI9BS3NoaQYdzaGlGXQ0x5taMkzDrTVq1MjuJXgMWppDSzPoaA4tzaCjOd7UkmEabu2NN96wewkeg5bm0NIMOppDSzPoaI43tWSYBgAAAFzEMA231rNnT7uX4DFoaQ4tzaCjObQ0g47meFNLhmm4tYMHD9q9BI9BS3NoaQYdzaGlGXQ0x5taMkzDra1evdruJXgMWppDSzPoaA4tzaCjOd7UkmEaAAAAcBFvJ24D3k4859LS0uTr62v3MjwCLc2hpRl0NIeWZtDRHE9oyduJwyMMGTLE7iV4DFqaQ0sz6GgOLc2gozne1JJhGm7t7Nmzdi/BY9DSHFqaQUdzaGkGHc3xppYM03Br4eHhdi/BY9DSHFqaQUdzaGkGHc3xppYM03BrrVq1snsJHoOW5tDSDDqaQ0sz6GiON7VkmIZbGzlypN1L8Bi0NIeWZtDRHFqaQUdzvKklwzQAAADgIoZpuLUuXbrYvQSPQUtzaGkGHc2hpRl0NMebWjJMw62dPn3a7iV4DFqaQ0sz6GgOLc2gozne1JJhGm7t008/tXsJHoOW5tDSDDqaQ0sz6GiON7VkmAYAAABcxNuJ24C3E8+5y5cvKzAw0O5leARamkNLM+hoDi3NoKM5ntCStxOHR3j99dftXoLHoKU5tDSDjubQ0gw6muNNLRmm4dZOnDhh9xI8Bi3NoaUZdDSHlmbQ0RxvaskwDbdWpUoVu5fgMWhpDi3NoKM5tDSDjuZ4U0v2TNuAPdM5d+LECZUqVcruZXgEWppDSzPoaA4tzaCjOZ7Qkj3T8Agvv/yy3UvwGLQ0h5Zm0NEcWppBR3O8qSXDNAAAAOAihmm4tfbt29u9BI9BS3NoaQYdzaGlGXQ0x5taMkzDrV2+fNnuJXgMWppDSzPoaA4tzaCjOd7UkmEabm3p0qV2L8Fj0NIcWppBR3NoaQYdzfGmlgzTAAAAgIs4NZ4NODVezsXFxalo0aJ2L8Mj0NIcWppBR3NoaQYdzfGElpwaDx5h6tSpdi/BY9DSHFqaQUdzaGkGHc3xppYM03Brhw8ftnsJHoOW5tDSDDqaQ0sz6GiON7VkmIZbq1Chgt1L8Bi0NIeWZtDRHFqaQUdzvKkle6ZtwJ7pnDt37pyKFStm9zI8Ai3NoaUZdDSHlmbQ0RxPaMmeaXiEAQMG2L0Ej0FLc2hpBh3NoaUZdDTHm1ryzLQNbtYz04lXUrRo7X6t3XpUCYkpefpYecayJIfD7lV4BlqaQ0sz6GgOLc2gYyYZJf48KPr7+ahC6SLq/NR9unQ5RfNX7dGJMxeVli75+/uozB1BOvTzzypW8m7JId1TqrA6PF5Z5e4scrM/hRuS03mNYfo6tmzZoo4dO2rJkiXGht+bMUyv3nxYMz76Mc+ODwAA8Fd8fBzK7++jy0lpkqQaoSU0rPODyu/va/PKcoZtHl5sU+wxBmkAAGBUYL7sx0Y/X4cmvVhPFUpffeY54xnt9HRLoWWLaWK/errztoKK3X9Go+Zslqc9j3vLDtOWZSk5OdnuZbidtHRL/1q2y+5lmONh/+AAIE/wsxI3wZXkdN15W4Esl6emWdqw/VcdPB6vO4sX1NtRjaT0q89G7zhwRimp6ZoyoL6KBuXXzp/PavfB32/20vNUng/TUVFRevLJJ7Vlyxa1atVK4eHhatu2rXbv3u28TVJSksaOHau6desqLCxMLVu21Nq1a7M9zsaNG9WiRQuFhYVp/fr1mj59umrUqKE9e/bomWeeUbVq1dS6dWvt2bNHSUlJevXVVxUREaF69epp3rx5mY4ZGxur7t27q27dugoPD1fLli21fPnyvE6Sp37Yd0oXLnnQLxnsXYM7YnAxg47m8LMSeSif39Vx0ZIUWjb7dzVcvfmoJKl9s3tVukQh5U864rxuxaaDKhDgr3aNQyRJyzcdzNsF32Q35ZnpM2fOaPTo0erSpYumTJmipKQk9e7dWykpV18UN2jQIC1atEhdu3ZVdHS0KlasqD59+mjdunWZjnP69GmNHj1akZGRmj17tipXrixJSklJ0ZAhQ9SuXTtNnz5dqamp6t27t4YNG6aAgABNmTJFjRs31tixY/XDDz84j3fixAnVrFlTY8aM0dtvv60mTZpo+PDhWrZs2c3IkicO/3bB7iUAno/BxQw6AreE/Pn+t8c58UpqtrdJSsnYF11SkvSPvzdzXnfoeLwkqda9t0uSDp+Iz5N12sXvZjxIfHy8FixYoEqVKkmSAgMD1bFjR/34448qVKiQ1qxZo9dee03PPvusJKlevXo6fvy4oqOj1ahRo0zHmT17tqpXr57p+CkpKRo0aJDq168vSUpPT1f37t1VvXp1DR06VJJUu3ZtxcTEKCYmRjVr1pQkNW/e3HkMy7IUERGhU6dOadGiRWrdunXeBZGUmJioyMhISdKMGTM0btw4HTt2TKGhoercubOioqIkSc8//7xSU1O1ePFiSdKkSZMUHR2tgwcPqly5curXr5/69+8vSWrTpo0O/eab9dXIf/w445mgv/rYITl3PP3VsbL7WNb/7uuSzPe3LEuOaz6e9d+bO7K9b67XfiOf23Vva7bLDX0uOfoeuJlf8z/Lzeeax19zo98DuXWd7yHjX3P97/i57nAzv789+Wue5QFk7HO91f/d5+a2efk9cJP/NzDv/93n7FgJFxMkn/ySHPrhh+1SwF3ZrPWqXr36qORtQbIKlpVURpJ08dJFbd26VdP+9b4U3Fipqal6++23tWXLFgUFBWn69Onq2rWrUlNT1aBBA4WHh2vKlCmSpIEDB2rbtm3atGmT8uXLp1mzZqlXr166dOmSateurfr162v8+PH/fexe2r9/v7744gtJ0rx589S/f3/FxcWpVq1aat68uUaNGiVJ6tatm44fP65Vq1ZJkmbOnKmRI0fq5MmTqlq1qp577jmdOXNGJUqUyPpl+pM8P5tHVFSUvv32W23atMl52e+//66HHnpIkyZN0vnz5zVq1Cht2bJFwcHBztvMnz/f+UxygQIFFBUVpS+//FJbtmzJdPzp06frrbfe0o8//qh8+fJJkn755Rc1a9ZMo0eP1tNPP+287bPPPquSJUtq2rRpkq4O59OnT9e6det06tQppaVd/a0qODjY+Ti32tk8/nPod0VFf238uPAAnPLJHFqaQUfgluDrI6WlX/3v+jVKaWPsiSy3KRTor4uXU9T9b2Fq/nB5/a13tFLy3yVJavJgGfVpV0MfrtmnD1bvV6P779aLz9W8mZ+CS3I6r92UZ6YLFy6c6WN/f39JV/dKx8fHy9/fP9MgLUnFixeXZVlKSEhQgQIFnJdlJyAgwDlI//H4QUFBWR43KSnJ+XFUVJRiY2PVq1cvVaxYUYUKFdKHH36ozz//3LVP1A1UuaeY7ihWQCfPJdq9FLgbhhZzaGkGHYFbQsYg7e/rUOyBM9ne5m+PVtR7n+3VB6v3687iBZWSv7TzuhaPVNDZ85f18Yafr35cz7Peatz2s3kUKVJEKSkpio/PvH/m7NmzcjgcmQZih8EfvElJSdqwYYN69OihDh06qE6dOgoLC7vlT9ficDg04PmaRv/4BgAAkJpu6cKlrG8C5+PjUJGC/nqgyu26cClZr87arIwtIM1ql9Wug2fV8831upKUpqYPllX50rfWm7dcj+3DdK1atSRJMTExmS6PiYlRlSpVnM9Km5acnKz09HTns9iSdPHiRa1fvz5PHu9mqnzPbRrbs64KBtyUPzwAAAAvcK3nG9PTLU3/aKe27jmV5bqYzUf0r2W7lJScptYNKqrX09WzOcKtzfZp695771WTJk00btw4XblyRffcc49WrFih2NhYvfXWW3n2uEFBQQoLC9Ps2bNVrFgx+fn5adasWSpUqJDOnTuXZ48rXX3BpGVZzr04eWXYs2WUlJKmS5dTlJqWfmuehYo9lebQ0hxamkFHc2hpBh0l/e/1yI7/Pruc/qcBwtfHofz5fFUo0F+WJV28nKKklDRZliUfh0MB+fyUmJgo/3z5JUn5/H1VIMBfDkd6plMju7vk5OQc7YqwfZiWpDfffFOTJk3S7Nmzdf78eZUvX17Tpk1Tw4YN8/RxJ06cqBEjRigqKkrBwcHq0KGDEhMT9c477+Tp45rcrnI9+f19b5m37QQAALcWh0MqXDBflssLFbj1t3I4HI4czWx5fjYPAAAAwFPZvmcaAAAAuFUxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABcxTAMAAAAuYpgGAAAAXMQwDQAAALiIYRoAAABwEcM0AAAA4CKGaQAAAMBFDNMAAACAiximAQAAABf52b0AbxQbGyvLsuTv72/3UgAAAJCNlJQUORwO1ahR4y9vxzBtA8uyZFmW3csAAADIkStXrigtLU2SlJaWpsuXLystLU2WZcnHx0f58uWTw+FQcnKy83am+fr6KiAgQEFBQfL19c2Tx/ijnM5qXj9Mh4aGavDgwerSpctNe8yMZ6TDwsJu2mPeqhYsWKD27dvbvQyPQEtzaGkGHc2hpRl0zMyyLM2bN08zZszQyZMn7V5OJg888IDmzJmj2267Lc8eY9euXTm6HXum4da++OILu5fgMWhpDi3NoKM5tDSDjpmNGTNGw4cPV926dVWzZs0s1zscDjkcDhtWJm3dulWNGjXS77//bsvj/xHDNAAAADLZunWr3n77bY0cOVIPP/ywfvjhh0zXh4aGSsr5Voi8cObMGb3yyiu2PX6GXA/TsbGx6t69u+rWravw8HC1bNlSy5cvd16/ZcsWhYaG6ptvvtHAgQNVo0YNPfroo5o9e3aWY61Zs0YtW7ZUWFiY6tatq7FjxyopKSnLsb766iv169dPNWrUUIMGDfTpp59Kkt577z01aNBADzzwgIYNG6bk5GTnfU+fPq2hQ4eqUaNGqlatmpo0aaJJkyZlus2fvf/++6pevbouXryY6fKDBw8qNDRUGzduzG0u3KB58+bZvQSPQUtzaGkGHc2hpRl0/J958+apfPny6tKli95+++0s148bN84tXv+1atUqnTt3ztY15HqYPnHihGrWrKkxY8bo7bffVpMmTTR8+HAtW7Ys0+1effVVlStXTtHR0Xr00Uc1YcIEbdq0yXn9unXr1LdvX1WsWFHR0dHq2rWrFi5cqJdeeinLY44cOVKVKlXSjBkzVL16dQ0ePFhvvvmmvv76a7322mvq27evPvnkE73zzjvO+8TFxSk4OFhDhw7VnDlz1LVrVy1btkyvvvrqNT+3Fi1ayLIsrVy5MtPlS5Ys0e233666devmNhduUP/+/e1egsegpTm0NIOO5tDSDDr+z1dffaWWLVvq0qVLOnDgQKbr7rnnHrfYXiFJqamp+v77721dQ65fgNi8eXPnf1uWpYiICJ06dUqLFi1S69atndc1adJEffr0kSTVqVNHGzZs0OrVq1WvXj1J0owZMxQeHq6JEydKkurVq6fAwECNGDFC+/fvd/75QJKaNWum3r17S5KqVaumtWvXatWqVVq7dq3zxXxbt25VTEyMunfvLunqnx+GDBniPEbNmjUVGBioqKgojRgxQoGBgVk+tyJFiqhp06ZaunSpnn32WUlXv0grVqxQ27Ztjb5yNDExUZGRkc4W48aN07FjxxQaGqrOnTsrKipKkvT8888rNTVVixcvliRNmjRJ0dHROnjwoMqVK6d+/fo5//G3adNGgYGBWrBggSTpjTfe0IIFC7Rnzx6VKlVKr7zyinr06CFJeuqpp1SyZEnNnTtX0tVfWJYvX64dO3aoePHiGj9+vPNFmU2bNlWFChX01ltvSZJefvllrVu3Tlu2bFFQUJCmT5+url27KjU1VQ0aNFB4eLimTJkiSRo4cKC2bdumTZs2KV++fJo1a5Z69eqlS5cuqXbt2qpfv77Gjx8vSerVq5f279/v3LM2b948ff3114qMjFStWrXUvHlzjRo1SpLUrVs3HT9+XKtWrZIkzZw5UyNHjtTJkydVtWpVPffccxo2bJgkqVOnTrpw4YLzF75p06ZpwoQJOnr0qCpVqqRu3bo5f4nL+LovXLhQkvTmm29q1qxZ+umnn1SmTBkNGjRIffv2lSS1bt1ahQsX1vz58yVd3Vv24Ycfavfu3brjjjs0cuRI5/dj8+bNVbp0ac2aNUuSNGLECK1atUrbt29X0aJFNXnyZOf3Q+PGjRUaGqro6GhJ0pAhQ7Rx40Zt3rxZBQsWVHR0tLp166bk5GTVq1dPERERzn9HL774onbs2KENGzbIz89Pc+bMUZ8+fZSQkKDDhw/rwIEDeuONNyRJPXv21MGDB7V69WpJ0ty5czVkyBCdPXtW4eHhatWqlUaOHClJ6tKli06fPu38q9Dbb7+t119/XSdOnFCVKlXUvn17vfzyy5Kk9u3b6/Lly1q6dKkkafLkyZo6daoOHz6sChUqqFevXhowYIAkqV27dvLz89MHH3wg6eozHe+++67279+vu+66S1FRUc5/+61atVLRokX17rvvSpJef/11LVmyRD/++KNKliypMWPG6B//+Ick6fHHH1fZsmU1c+ZMSdLw4cO1evVqbdu2TUWKFNHUqVP1wgsvKD09XQ0bNlTVqlU1bdo0SdKgQYP03Xff6ZtvvlFAQIBmzpyp7t2768qVK3r44YdVp04drV+/XpGRkerbt692796t9evXy8fHR++884769eun+Ph4RUREqGnTpho9erQkqXv37jpy5Ig+//xzSdLs2bM1bNgwnT59WtWrV1fbtm2df67s3Lmz4uLinH/189SfEbGxsdq1a9cN/Yzo37+/4uLivP5nRGxsrCIjI2/oZ8SDDz6oRo0aefXPiPXr16tfv343/DNiwoQJknRL/4yIj49Xenq683vnj86ePauvvvoqy+V2mTx5sj744APjPyPOnDmjEiVKXPfxHVYun6OPj4/X9OnTtW7dOp06dcp5+pPg4GBt2bJFW7ZsUceOHfXuu+/qoYcect7vH//4h9LT0zV37lxdunRJtWrV0pAhQ9S5c2fnbRISEnT//ffr1Vdf1fPPP3/NY9WtW1e1a9d2frNKV785Fi1apC1btki6OujPnz9fixcv1rFjxzJtH/n0008VEhIiKevZPLZt26b27dtr5cqVqlSpktatW6devXppzZo1KlOmTG5SXVPGq0M5m8f1TZ8+3flLGW4MLc2hpRl0NIeWZtDxf5o0aaIyZcroX//6l+69914lJiY6r8ufP7/+/e9/q23btjau8H9Wr16tqlWrGj9uTue1XG/ziIqK0sqVK/XCCy9o7ty5WrJkidq0aZNlL3JQUFCmj/39/Z23SUhIkGVZWU5nEhQUpHz58ik+Pv4vj5UvXz4VLlz4mseXpPnz52v8+PFq1KiR3nrrLX300UcaMWKEJGUarP8sIiJC99xzj5YsWSJJWrp0qSIiIowN0sidP/4lBDeGlubQ0gw6mkNLM+j4P88//7zWrFmjw4cPO/8ikyEpKUlbt26Vn5/9Z1guX7687rvvPlvXkKthOikpSRs2bFCPHj3UoUMH1alTR2FhYbnegB4UFCSHw5Flw3hCQoKSk5NVpEiRXB0vOzExMWrYsKEGDhyounXrqlq1aipQoECO7vv0009rxYoVOnnypDZu3Kg2bdrc8Hrgmow/x+DG0dIcWppBR3NoaQYd/+fpp59W2bJl9fe//10NGjTIsj32//7v//LszVly49VXX7Xt9HwZcjVMJycnKz09PdPbYF+8eFHr16/P1YMWLFhQlStXVkxMTKbLM/YJ1apVK1fHy86VK1eyvF13xl6u62ndurUSEhI0aNAgBQQEqFmzZje8HgAAgFtFwYIFtXDhQhUoUEAdO3ZU2bJls7x2zM6zeTgcDv3f//2fGjdubNsaMuTq+fmgoCCFhYVp9uzZKlasmPz8/DRr1iwVKlQo16cl6d27t3r16qVBgwapRYsW+uWXXzR58mQ1bdo004sPXfXQQw/pvffe04IFC1SuXDmtWLFCR44cydF9ixUrpkaNGikmJkbPPPOMAgICbng9cE23bt3sXoLHoKU5tDSDjubQ0gw6Zla6dGmtXr1aa9as0UcffSRfX1/Fx8frzJkzf7llNi8FBAToiSee0PDhw3X77bfbsoY/y/Vml4kTJ2rEiBGKiopScHCwOnTooMTExEynpcuJRo0aaerUqYqOjlbPnj0VHBysdu3aaeDAgbldUrZ69eqluLg456tvmzZtquHDhztfOX09jz32mGJiYtxmc723On78uN1L8Bi0NIeWZtDRHFqaQces/P391bx581zvJ//oo4/09NNP59Gq3Euuz+bhLQYPHqy9e/fmeGtIbnA2j5yLjIzkJPqG0NIcWppBR3NoaQYdzfGElnl2Ng9Pt3//fi1fvlyfffaZOnbsaPdyAAAA3EJUVJSefPJJ58fffPONnnrqKVWtWlX333+/jSv7nwsXLmj69On6+eefb9pj8sz0nzRs2FDnzp3Tk08+qVGjRsnHx/zvGzwznXNXrlxhz7ohtDSHlmbQ0RxamkHHv3b06FElJibq3nvvlXT1DfdCQ0P1z3/+U/nz588019jV8tixY86txDd6AgmemXbR+vXrtWPHDo0ePTpPBmnkTsa7a+HG0dIcWppBR3NoaYa7dzx96Xd9sHO5Xl47XoNXj9Hkb+do58m9N+2sGmXKlHEO0pcuXdKpU6fUtGlT3X///VkGzty0tCwry/uV3EqYFuHWTp48afcSPAYtzaGlGXQ0h5ZmuHPHzw6sV59Vr2jNz5tUqvDtCrmtvI5d+E2jN07T6I3TlJhyOc/XkLHN4+OPP1bNmjUlScOGDVNoaKjz7cuTkpI0duxYrVy5UmFhYWrZsqXWrl2b7XE2btyoFi1aKCwszHma5djYWHXs2FHh4eGqVauWBg4cqN9//z3T/WfNmqXHHntMYWFhql27tiIjI/Xrr786n5WWpH79+ik0NFShoaE6duxYnnax/61rgL+QF28P6q1oaQ4tzaCjObQ0w107bjq8RfNiP9KTIY3ULuwpBfjll3T1Gd3Y3/6jqZvnavK3c/Ryvd435Q1MGjRooHfffVedO3dWjx491KBBAxUrVkySNGjQIH311VeqVauWunTpok8++UR9+vRRdHS0c9CVpNOnT2v06NHq0aOH7rzzTpUqVUqxsbHq0KGD6tevr8mTJ+vy5cuaMmWKevbsqUWLFkmSli9frqlTp6pv374KDw9XQkKCtm/frkuXLql8+fKaMWOGevfurQEDBujBBx+UJJUsWTJPezBMw60999xzdi/BY9DSHFqaQUdzaGmGO3ZMT0/Xot2fqvbdNdUhvE2mYdnhcKhmqarq/WCk3vx6pvafPah7S1TM8zUVK1bM+YtHmTJlFB4eLknat2+f1qxZo9dee02PPPKISpcurXr16un48eNZhun4+HjNnj1b1atXd142bNgwVa1aVTNmzHB+niEhIc5nsevXr6+dO3c692ln+OMbt1SuXFmSVLZsWee68hrbPODWhg0bZvcSPAYtzaGlGXQ0h5ZmuGPH3af368yl3/VUaONrPutcq1SY7ixUUl8c+vomry6z7du3S5KaNWuWqeXjjz+uPXv2KDEx0XlZcHBwpkH68uXL+uGHH9SsWTOlpaUpNTVVqampKleunO68807niwGrVKmiPXv2aOzYsfr++++VkpJykz67a+OZaQAAADd18uIZ+Th8VLFYuWvexsfho0rF79Gpi2dv3sKyER8fL39/fwUHB2e6vHjx4rIsSwkJCSpQoIDzsj+6cOGC0tLSNHbsWI0dOzbLsX/77TdJ0t/+9jddunRJixcv1rx58xQUFKRWrVpp0KBBtp2JhWEabq1Tp052L8Fj0NIcWppBR3NoaYY7dvT38VO6la6k1CQF+F97WExMuaJ8vvaOdUWKFFFKSori4+MztTx79qwcDoeCgoKcl/35WfagoCA5HA7985//zLRtI0PRokUlST4+PurUqZM6deqkU6dOadWqVZo4caKKFi2qXr165dFn9tcYpuHWLly4YPcSPAYtzaGlGXQ0h5ZmuGPHqreHyiGHvj76vRpXqJvtbRKSLurH3/6jp6s+me31N0utWrUkSTExMZmeJY6JiVGVKlWcz0pnp0CBAgoPD9ehQ4dy/D4ct99+u1544QWtXLlShw4dknT17c+lq2cVuVnYMw23tmzZMruX4DFoaQ4tzaCjObQ0wx07lih4m2qVrqal//lMvyfGZbk+3UrXezuWSpIa3vPQzV5eJvfee6+aNGmicePG6a233tKmTZs0aNAgxcbGqnfv3te9/+DBg7Vhwwa9+OKLWrt2rbZs2aJPPvlEQ4YM0ZYtWyRJI0aM0IQJE/TFF19o69atmjlzpvbt26fatWtLkkqUKKHChQtr1apV2r59u3bt2pXn57BmmAYAAHBjXWs+K4fDoZe/GK/PDqzXhSsJSk5N1o7f9mjMxmnadHiLukd0UOGAoOsfLI+9+eabevrpp3X8+HH17NlTBw4c0LRp09SwYcPr3rdmzZr64IMPlJiYqKFDh6pbt2566623FBAQoLJly0qSatSooe3bt2vYsGHq2rWrPv30Uw0dOlRPP/20pKvbQMaOHatjx44pMjJSbdu21enTp/P0c+btxG3A24nn3IULF1S4cGG7l+ERaGkOLc2gozm0NMOdO55LPK95Oz7StmM7lGalOy8vW6S0nqvWUjVLuddM4c4tc4q3E4dHmDBhgt1L8Bi0NIeWZtDRHFqa4c4dixUI1oCH/qHop8boxTpd1fvBSL3ReIj+r+kwtxukJfduaRovQIRbO3r0qN1L8Bi0NIeWZtDRHFqacSt0LBYYrIfK1LJ7Gdd1K7Q0hWem4dYqVapk9xI8Bi3NoaUZdDSHlmbQ0RxvasmeaRuwZzrnzpw5oxIlSti9DI9AS3NoaQYdzaGlGXQ0xxNasmcaHuGll16yewkeg5bm0NIMOppDSzPoaI43tWSYBgAAAFzEMA239uyzz9q9BI9BS3NoaQYdzaGlGXQ0x5taMkwDAAAALmKYhltbuHCh3UvwGLQ0h5Zm0NEcWppBR3O8qSXDNAAAAOAiTo1nA06Nl3OecGodd0FLc2hpBh3NoaUZdDTHE1pyajx4hFmzZtm9BI9BS3NoaQYdzaGlGXQ0x5taMkzDrf300092L8Fj0NIcWppBR3NoaQYdzfGmlgzTcGtlypSxewkeg5bm0NIMOppDSzPoaI43tWTPtA3YM51zFy5cUOHChe1ehkegpTm0NIOO5tDSDDqa4wkt2TMNj9C3b1+7l+AxaGkOLc2gozm0NIOO5nhTS4ZpAAAAwEUM03BrrVu3tnsJHoOW5tDSDDqaQ0sz6GiON7VkmIZbu9X3W7kTWppDSzPoaA4tzaCjOd7UkmEabm3+/Pl2L8Fj0NIcWppBR3NoaQYdzfGmlgzTAAAAgIs4NZ4NODVezh0/flylS5e2exkegZbm0NIMOppDSzPoaI4ntOTUePAIH374od1L8Bi0NIeWZtDRHFqaQUdzvKklwzTc2u7du+1egsegpTm0NIOO5tDSDDqa400tGabh1u644w67l+AxaGkOLc2gozm0NIOO5nhTS/ZM24A90zl35coVBQQE2L0Mj0BLc2hpBh3NoaUZdDTHE1qyZxoeoXv37nYvwWPQ0hxamkFHc2hpBh3N8aaWDNMAAACAixim4daaN29u9xI8Bi3NoaUZdDSHlmbQ0RxvaskwDbd2q5+j0p3Q0hxamkFHc2hpBh3N8aaWDNNwa7NmzbJ7CR6DlubQ0gw6mkNLM+hojje1ZJgGAAAAXMSp8WzAqfFy7tChQypfvrzdy/AItDSHlmbQ0RxamkFHczyhJafGg0dYtWqV3UvwGLQ0h5Zm0NEcWppBR3O8qSXDNNza9u3b7V6Cx6ClObQ0g47m0NIMOprjTS0ZpuHWihYtavcSPAYtzaGlGXQ0h5Zm0NEcb2rJnmkbsGcaAADAvbFnGh4hMjLS7iV4DFqaQ0sz6GgOLc2gozne1JJhGgAAAHARwzTcWuPGje1egsegpTm0NIOO5tDSDDqa400tGabh1kJDQ+1egsegpTm0NIOO5tDSDDqa400tGabh1qKjo+1egsegpTm0NIOO5tDSDDqa400tGaYBAAAAF3FqPBtwaryc27t3rypXrmz3MjwCLc2hpRl0NIeWZtDRHE9oyanx4BE2btxo9xI8Bi3NoaUZdDSHlmbQ0RxvaskwDbe2efNmu5fgMWhpDi3NoKM5tDSDjuZ4U0uGabi1ggUL2r0Ej0FLc2hpBh3NoaUZdDTHm1qyZ9oG7JkGAABwb+yZhkfo1q2b3UvwGLQ0h5Zm0NEcWppBR3O8qSXDNNxacnKy3UvwGLQ0h5Zm0NEcWppBR3O8qSXDNNxavXr17F6Cx6ClObQ0g47m0NIMOprjTS0ZpuHWIiIi7F6Cx6ClObQ0g47m0NIMOprjTS0ZpuHWJk6caPcSPAYtzaGlGXQ0h5Zm0NEcb2rJMA0AAAC4iGEabu3FF1+0ewkeg5bm0NIMOppDSzPoaI43tWSYhlvbsWOH3UvwGLQ0h5Zm0NEcWppBR3O8qSXDNNzahg0b7F6Cx6ClObQ0g47m0NIMOprjTS0ZpuHW/Pz87F6Cx6ClObQ0g47m0NIMOprjTS15O3Eb8HbiAAAA7o23E4dH6NOnj91L8Bi0NIeWZtDRHFqaQUdzvKklwzTcWkJCgt1L8Bi0NIeWZtDRHFqaQUdzvKklwzTc2oMPPmj3EjwGLc2hpRl0NIeWZtDRHG9qyTANt9aoUSO7l+AxaGkOLc2gozm0NIOO5nhTS4ZpuLU33njD7iV4DFqaQ0sz6GgOLc2gozne1JJhGgAAAHARwzTcWs+ePe1egsegpTm0NIOO5tDSDDqa400tGabh1g4ePGj3EjwGLc2hpRl0NIeWZtDRHG9qyTANt7Z69Wq7l+AxaGkOLc2gozm0NIOO5nhTS4ZpAAAAwEW8nbgNeDvxnEtLS5Ovr6/dy/AItDSHlmbQ0RxamkFHczyhJW8nDo8wZMgQu5fgMWhpDi3NoKM5tDSDjuZ4U0uGabi1s2fP2r0Ej0FLc2hpBh3NoaUZdDTHm1oyTMOthYeH270Ej0FLc2hpBh3NoaUZdDTHm1oyTMOttWrVyu4leAxamkNLM+hoDi3NoKM53tSSYRpubeTIkXYvwWPQ0hxamkFHc2hpBh3N8aaWDNMAAACAixim4da6dOli9xI8Bi3NoaUZdDSHlmbQ0RxvaskwDbd2+vRpu5fgMWhpDi3NoKM5tDSDjuZ4U0uGabi1Tz/91O4leAxamkNLM+hoDi3NoKM53tSSYRoAAABwEW8nbgPeTjznLl++rMDAQLuX4RFoaQ4tzaCjObQ0g47meEJL3k4cHuH111+3ewkeg5bm0NIMOppDSzPoaI43tWSYhls7ceKE3UvwGLQ0h5Zm0NEcWppBR3O8qSXDNNxalSpV7F6Cx6ClObQ0g47m0NIMOprjTS3ZM20D9kzn3IkTJ1SqVCm7l+ERaGkOLc2gozm0NIOO5nhCS/ZMwyO8/PLLdi/BY9DSHFqaQUdzaGkGHc3xppYM0wAAAICLGKbh1tq3b2/3EjwGLc2hpRl0NIeWZtDRHG9qyTANt3b58mW7l+AxaGkOLc2gozm0NIOO5nhTS4ZpuLWlS5favQSPQUtzaGkGHc2hpRl0NMebWjJMAwAAAC7i1Hg24NR4ORcXF6eiRYvavQyPQEtzaGkGHc2hpRl0NMcTWnJqPHiEqVOn2r0Ej0FLc2hpBh3NoaUZdDTHm1oyTMOtHT582O4leAxamkNLM+hoDi3NoKM53tSSYRpurUKFCnYvwWPQ0hxamkFHc2hpBh3N8aaW7Jm2AXumc+7cuXMqVqyY3cvwCLQ0h5Zm0NEcWppBR3M8oSV7puERBgwYYPcSPAYtzaGlGXQ0h5Zm0NEcb2rJM9PXsWXLFnXs2FFLliwx9kzyzXpm+uC5w5q7fZF+Pnc4Tx8nT1mW5HDYvQrPQEtzaGkGHc2hpRl0zDEfh0NlipRWqaDbFfvbf3Ql9YosSfl8/FWrdDXFzv9a7/1rnt3LvCE5ndf8bsZicHNZlqU52z/U2oNf2b2UG8cPNXNoaQ4tzaCjObQ0g445lm5ZOnz+mA6fPyZJCvDLLx+HjxJTLuu7X7dLDQO18ZfNqn9PbZtXmvdu2W0elmUpOTnZ7mW4pWV7YzxjkAYAALeEZhUbaN7fJulfT41V6cJ3SJKit87Xf04fsHlleS/Ph+moqCg9+eST2rJli1q1aqXw8HC1bdtWu3fvdt4mKSlJY8eOVd26dRUWFqaWLVtq7dq12R5n48aNatGihcLCwrR+/XpNnz5dNWrU0J49e/TMM8+oWrVqat26tfbs2aOkpCS9+uqrioiIUL169TRv3rxMx4yNjVX37t1Vt25dhYeHq2XLllq+fHleJ8lTyanJWvqfz+1ehjnsQgKA6+NnpRl0dNnKA18oMeWyihYI1huNh0jJV1v++8dlNq8s792UZ6bPnDmj0aNHq0uXLpoyZYqSkpLUu3dvpaSkSJIGDRqkRYsWqWvXroqOjlbFihXVp08frVu3LtNxTp8+rdGjRysyMlKzZ89W5cqVJUkpKSkaMmSI2rVrp+nTpys1NVW9e/fWsGHDFBAQoClTpqhx48YaO3asfvjhB+fxTpw4oZo1a2rMmDF6++231aRJEw0fPlzLlt26X/itx39USnqK3cswhz+5AcD18bPSDDrmWkH/QElSanqavju6XZIU6B8g3yOpkqSfzx3W6YtnbVvfzXBT9kzHx8drwYIFqlSpkiQpMDBQHTt21I8//qhChQppzZo1eu211/Tss89KkurVq6fjx48rOjpajRo1ynSc2bNnq3r16pmOn5KSokGDBql+/fqSpPT0dHXv3l3Vq1fX0KFDJUm1a9dWTEyMYmJiVLNmTUlS8+bNncewLEsRERE6deqUFi1apNatW+ddkDx0+pJnf8MCAAD3EegXoEsplyVJpy/97ry8a+tO+tfuD5yXlyxU3Jb13Qw3ZZguWbKkc5CWpIoVK0qSTp06pf3790uSmjVrluk+jz/+uMaOHavExEQVKFBAkhQcHJxlkJYkHx8f1alTx/lxuXLlJEkPPfSQ8zJfX1+VKVNGJ0+edF4WHx+v6dOna926dTp16pTS0tKcj5PXEhMTFRkZKUmaMWOGxo0bp2PHjik0NFSdO3dWVFSUJOn5559XamqqFi9eLEmaNGmSoqOjdfDgQZUrV079+vVT//79JUlt2rTRYR3L/Gpk67//z/nxf/+E9cffvq0/XX+t67L9WNIff5H/88e59af7W5Ylx189/l/cN/efSy4/11x8HjcsLz+3P39P5GWXnLje55qb297M7wG7u9j5NTf87/6vO1/ntvy7z/7jG/6ae9O/+z8fX3/x/XiL/bvPg6/52d9/lwpc3ejw/dbvtXrSMhUvXly6O790deu0Nq7foMQK8XrrrbckSS+//LLWrVunLVu2KCgoSNOnT1fXrl2VmpqqBg0aKDw8XFOmTJEkDRw4UNu2bdOmTZuUL18+zZo1S7169dKlS5dUu3Zt1a9fX+PHj5ck9erVS/v379cXX3whSZo3b5769++vuLg41apVS82bN9eoUaMkSd26ddPx48e16v/Zu/P4mO79j+PvySYhq1p6adWaoELQ2BtLFC0tStFeNCU09rXEUo2tpXYRNEFp9VpKqVqillpboRrbj6paSxqxRBaRdc7vjzRTkwSTySfOOPN+Ph7uNUvO+c4rkX4yOXNm2zYAwNKlSxESEoLY2FjUqlUL7777Lm7duoXSpUvnfdC5PJVh2tXV1eiyvb09gOxjpRMSEmBvb59ngC1VqhQURUFSUpJhmC5VKv+fahwdHeHg4JBn+y4uLnn2m5aWZrgcHByM6OhoDBo0CFWrVoWzszPWrFmDHTuK/pjj4sWLGx3DPW3aNKPbcx/f/cYbbxj+/vHHHz/yvtcT/saRyFP/3qgz/M8/l/P5l6B7zO1PvJx7W3k3XyB5dveE/T92LQV9LAV8rI8j/ZtCicf28DfGQn3OC9HFFE96rAW5b1F9DeT3H3q1uxTksvTn3NzHmtOxINt70n357z7/y2p2MYVF/bt/wv4K8rGFZVFfA49YY/F/jxge+HYgqgS+BADovqQ/AFs42Tmif6c+sLe1R4MGDQz39fT0xIABAwyXly1bZrTZh2cbb29v9OnTx3A5LCzskff19fVFz549DZfnzZv3yPsCwDvvvGP4+4wZM4xuu3v3Lkyh+tk83NzckJGRgYSEBKPrb9++DZ1OZzQQ5xmsCiEtLQ379u3DgAED0KtXLzRu3Bje3t541k+7/YLbf1C1ZEW1l0EWSfq7vDVjSxnsSKQVL7j+B1VKZg/Sh64eg1LSFgDwWlU/2Nvaq7m0Iqf6MF2/fn0AQGRkpNH1kZGRqFmzpuFZaWnp6enQ6/WGZ7EBIDk5GXv37i2S/T1Nwxv3haNtMbWXQZaGc4sctpTBjkSa8epLDXDs+klM3DULC4+sAJA9YHd7uf0TPvLZp/qbtlSvXh1t2rTBjBkzkJqaikqVKmHLli2Ijo42HFtTFFxcXODt7Y2IiAiULFkSdnZ2CA8Ph7Ozs8lP61uqMs6lMPf1SZh96AtcundN7eUQERGRxq05/b3h7zroYBOTgc+6BMPBzuExH6UNqg/TADBr1izMnTsXERERuHfvHipXroyFCxeiVatWRbrfOXPmYNKkSQgODoa7uzt69eqFlJQUrFixokj3m5GRAUVRDG9TWVT+W64D9P/R40FmKtIzM6BX9EW6v6KQlZUFW1tbtZehCWwphy1lsKMctpTBjo+m0+mgy/l1ki77bcMd7R1hq7NBWmYa0rLSoSiAg609HO2L4Xbp2/jj3Hl1F11I6enpJh1irFOe9YOEn0HR0dFQFMXoEBMiIiIishwZGRnQ6XSoW7fuY+/HYZqIiIiIyEyqvwCRiIiIiOhZxWGaiIiIiMhMHKaJiIiIiMzEYZqIiIiIyEwcpomIiIiIzMRhmoiIiIjITBymiYiIiIjMxGGaiIiIiMhMHKaJiIiIiMzEYZqIiIiIyEwcpomIiIiIzMRhmoiIiIjITBymiYiIiIjMxGGaiIiIiMhMHKaJiIiIiMzEYZqIiIiIyEwcpomIiIiIzMRhmoiIiIjITBymiYiIiIjMxGGaiIiIiMhMHKaJiIiIiMzEYZqIiIiIyEwcpomIiIiIzMRhmoiIiIjITBymiYiIiIjMxGGaiIiIiMhMHKaJiIiIiMzEYZqIiIiIyEwcpomIiIiIzMRhmoiIiIjITBymiYiIiIjMxGGaiIiIiMhMHKaJiIiIiMzEYZqIiIiIyEwcpomIiIiIzMRhmoiIiIjITBymiYiIiIjMxGGaiIiIiMhMHKaJiIiIiMzEYZqIiIiIyEwcpomIiIiIzMRhmoiIiIjITBymiYiIiIjMxGGaiIiIiMhMHKaJiIiIiMzEYZqIiIiIyEwcpomIiIiIzMRhmoiIiIjITBymiYiIiIjMxGGaiIiIiMhMHKaJiIiIiMzEYZqIiIiIyEwcpomIiIiIzMRhmoiIiIjITBymiYiIiIjMxGGaiIiIiMhMHKaJiIiIiMzEYZqIiIiIyEwcpomIiIiIzMRhmoiIiIjITBymiYiIiIjMxGGaiIiIiMhMHKaJiIiIiMzEYZqIiIiIyEwcpomIiIiIzGSn9gKsUXR0NBRFgb29vdpLISIiIqJ8ZGRkQKfToW7duo+9H4dpFSiKAkVR1F4GERERWRFFUZCYmIj79+9DURTY2NjA2dkZzs7OUBQF9+/fR2ZmJnQ6HZycnFCsWDEAQHJysuFjFEWBXq+HXq8HANjY2KB48eJwc3ODTqdDUlISkpOTkZWVJT7r2NjYwN7eHm5ubnB0dBTddn5MXb/VD9NeXl4YM2YM+vbt+9T2mfOMtLe391Pb57Nq9erV6Nmzp9rL0AS2lMOWMthRDlvK0GpHRVEQGhqKWbNmGYbgh+l0Otja2kKn06FChQpISkpCXFwcypcvj9u3byMtLU2FVT/ec889h7Vr16JmzZpFto/Tp0+bdD8eM00Wbffu3WovQTPYUg5bymBHOWwpQ6sd582bh5kzZ0Kv16NGjRqIjIzE9evXsWHDBsOz0pmZmRg/fjwOHDiA3377DWPHjsWNGzeQlpYGHx8fo+3Z2WU/F1u8eHFUqFBBhUcE3LlzBx06dMCff/6pyv4fxmGaiIiISKPi4uIwd+5cAMCrr76KH3/8Ed7e3tDpdHB2dkZycjLKli0LAJg5cyYePHgAvV6PZcuWAQBKly6NEydOGLb3n//8B2fOnMHbb7+NlJQU1K9f/6k/phxpaWkYP368avvPUeBhOjo6GkFBQWjWrBl8fHzQsWNHbN682XB7VFQUvLy8cPjwYYwaNQp169ZFy5YtERERkWdbP/74Izp27Ahvb280a9YMn332mdGvEnK2dfDgQQwbNgx169ZFixYt8MMPPwAAvvrqK7Ro0QINGjTAhAkTkJ6ebvjYuLg4jBs3Dv7+/qhduzbatGmDuXPnGt0nt6+//hp16tRBcnKy0fUXL16El5cX9u/fX9BcVEgrV65UewmawZZy2FIGO8phSxla7Lh69WrDsb+TJ0+Gjc2/o99XX32FcuXKGYbt1NRUfP/999i7dy/u3LmD6tWro0mTJkbbCw4OhouLCyZOnAidToctW7Y8vQeTj8OHD+P69euqrqHAw3RMTAzq1auH6dOnY8mSJWjTpg0mTpyITZs2Gd3vk08+QcWKFREWFoaWLVti9uzZOHDggOH2PXv2YOjQoahatSrCwsIQGBiItWvX4qOPPsqzz5CQEFSrVg2LFi1CnTp1MGbMGMyaNQuHDh3C5MmTMXToUHz//fdYsWKF4WPi4+Ph7u6OcePGYdmyZQgMDMSmTZvwySefPPKxvfXWW1AUBVu3bjW6fsOGDShbtiyaNWtW0FxUSCNGjFB7CZrBlnLYUgY7ymFLGVrsGBUVBQAoW7YsvLy8jG47ceIE/P390bx5czg4OAAAfv31V5w4cQI6nQ6dOnXCgwcPjD6mTZs2hu3VqFEDWVlZT+FRPN6pU6dU3X+BX4DYvn17w98VRYGvry9u3ryJdevWoXPnzobb2rRpgyFDhgAAGjdujH379mHnzp3w8/MDACxatAg+Pj6YM2cOAMDPzw9OTk6YNGkSzp8/b/QJb9euHQYPHgwAqF27Nnbt2oVt27Zh165dhhfzHT16FJGRkQgKCgKQ/cLCsWPHGrZRr149ODk5ITg4GJMmTYKTk1Oex+bm5oa2bdti48aN6NGjBwAgMzMTW7ZsQdeuXWFra1vQXI+UkpKCgIAAQ4sZM2bg+vXr8PLywgcffIDg4GAAwHvvvYfMzEysX78eADB37lyEhYXh4sWLqFixIoYNG2b4x9+lSxc4OTlh9erVAIBPP/0Uq1evxtmzZ1GuXDl8/PHHGDBgAADgzTffRJkyZbB8+XIA2T+wbN68GSdOnECpUqUwc+ZMw4sy27ZtiypVqmDx4sUAgPHjx2PPnj2IioqCi4sLQkNDERgYiMzMTLRo0QI+Pj6YP38+AGDUqFE4duwYDhw4AAcHB4SHh2PQoEG4f/8+GjVqhObNm2PmzJkAgEGDBuH8+fOGY9ZWrlyJQ4cOISAgAPXr10f79u0xZcoUAED//v1x48YNbNu2DQCwdOlShISEIDY2FrVq1cK7776LCRMmAADef/99JCYmGn7gW7hwIWbPno1r166hWrVq6N+/v+GHuJzP+9q1awEAs2bNQnh4OC5cuIAKFSpg9OjRGDp0KACgc+fOcHV1xapVqwAA06dPx5o1a3DmzBk8//zzCAkJMXw9tm/fHuXLl0d4eDgAYNKkSdi2bRuOHz8ODw8PzJs3z/D10Lp1a3h5eSEsLAwAMHbsWOzfvx9HjhxBiRIlEBYWhv79+yM9PR1+fn7w9fU1/DsaPnw4Tpw4gX379sHOzg7Lli3DkCFDkJSUhCtXruCPP/7Ap59+CgAYOHAgLl68iJ07dwIAli9fjrFjx+L27dvw8fFBp06dEBISAgDo27cv4uLiDL8VWrJkCaZOnYqYmBjUrFkTPXv2NPyqrWfPnnjw4AE2btwIIPtYvQULFuDKlSuoUqUKBg0ahJEjRwIAunXrBjs7O/zvf/8DAMyYMQNffvklzp8/jxdeeAHBwcGGf/udOnWCh4cHvvzySwDA1KlTsWHDBpw8eRJlypTB9OnT0a9fPwDA66+/jpdeeglLly4FAEycOBE7d+7EsWPH4ObmhgULFqBPnz7Q6/Vo1aoVatWqhYULFwIARo8ejV9++QWHDx+Go6Mjli5diqCgIKSmpqJp06Zo3Lgx9u7di4CAAAwdOhRnzpzB3r17YWNjgxUrVmDYsGFISEiAr68v2rZti2nTpgEAgoKCcPXqVezYsQMAEBERgQkTJiAuLg516tRB165d8fHHHwMAPvjgA8THxxt+66fV7xHR0dE4ffp0ob5HjBgxAvHx8Vb/PSI6OhoBAQGF+h7RsGFD+Pv7W/X3iL1792LYsGGF/h4xe/ZsALCI7xF37twBANy7dw8BAQFG3yNiY2ORkZGBgIAAZGZmAgBu3LiBI0eOQFEUJCcn48KFC3jYwIEDYWdnhzfffBMpKSmwBKGhoVi/fr3494hbt26hdOnST16AUkD37t1Tpk6dqrRo0UKpUaOG4unpqXh6eioNGjRQFEVRjhw5onh6eiqHDx82+rjAwEClT58+iqIoSnJysuLl5aWsWLHC6D6JiYmKp6en8s033zx2W02bNlVGjRpldN2cOXMMa1AURdHr9cqXX36pvP7664q3t7dhnZ6ensr58+cN9/P09FSWLVtmuHz06FHF09NT+eOPPxRFUZTdu3crXl5eytWrVwua6pFOnTqlnDp1Smx7WrZw4UK1l6AZbCmHLWWwoxy2lKHFjp9++qlSrlw5pVy5csqlS5eMbhsyZIjSpEkT5ZdffjHcZ+XKlcrWrVuVcuXKKe3atVOCgoIMt5UrV075/vvvFUVRlDt37igvvviiUr58eaPb1fiT+3FJMXVeK/BhHsHBwdi6dSv69OmD5cuXY8OGDejSpUueY5FdXFyMLtvb2xvuk5SUBEVR8Nxzz+X5GAcHByQkJDx2Ww4ODnB1dX3k9gFg1apVmDlzJvz9/bF48WJ8++23mDRpEgA89hQvvr6+qFSpEjZs2AAA2LhxI3x9fVV7taq1e/g3IVQ4bCmHLWWwoxy2lKHFjr179zb8ffr06UbnTu7duzeuXLmC0aNHA8ier95++220adMGbm5uOHXqFH799Vej7X3++edITU3F7NmzkZWVhddff/3pPJBHeOWVV1CpUiVV11CgYTotLQ379u3DgAED0KtXLzRu3Bje3t4FPim3i4sLdDod7t69a3R9UlIS0tPT4ebmVqDt5ScyMhKtWrXCqFGj0KxZM9SuXRvFixc36WPfeecdbNmyBbGxsdi/fz+6dOlS6PWQeXJ+HUOFx5Zy2FIGO8phSxla7Fi+fHkEBgYCAHbs2IFu3brhr7/+AgA4OzvDzc0Nly9fBpB9zLiLiwvs7e0NhyDFxMQYnRrv8uXL8Pb2xqpVq+Do6IgrV6481cfzMDs7O0yfPl21/RvWUZA7p6enQ6/XG70NdnJyMvbu3VugnZYoUcJwnsOcY8AAGI4TkjjNSmpqap636845lutJOnfujHnz5mH06NFwdHREu3btCr0eIiIiIjXkHNu+bNky/Pzzz2jUqBF0Op3Rk6E6nQ5hYWHYvXs3EhMTceHCBbi5uSE5Odno1HgADMdKp6am4uzZs0/rYRgpXrw4vvnmG9SqVUuV/T+sQM9Mu7i4wNvbGxEREYiMjMTu3bvRp08fODs7F3jHgwcPxokTJzB69GgcOHAAq1atwqeffoq2bdvmebWpOZo0aYLdu3dj9erVOHToEMaMGYOrV6+a9LElS5aEv78/jh07hvbt2z+Vt6yk/PXv31/tJWgGW8phSxnsKIctZWi1o06nw+TJk3Hy5En897//RZkyZVCiRAn85z//QVBQEM6dO4eoqCgEBQWhSpUqaNSoEZYvX45Tp07h/PnzGDNmDF5++WW8+OKLKFOmDJydnWFrawtbW1u4ubmhY8eOOHr0KM6ePYt+/fqhVKlSRfIY7O3tUblyZcydOxfnzp1DgwYNxPdjjgKfzWPOnDmYNGkSgoOD4e7ujl69eiElJcXotHSm8Pf3x4IFCxAWFoaBAwfC3d0d3bp1w6hRowq6pHwNGjQI8fHxhlfftm3bFhMnTjT82uJJXnvtNURGRqJr164i6yHz3LhxQ+0laAZbymFLGewohy1laL1jqVKl8Pnnn+d7m6ura76nBrSzs8OwYcMwbNgwk/YREhKCkJAQfPvtt3jnnXcKtd5nhU4p6AHPVmLMmDE4d+6cyYeGFETOe717e3uLb1trAgICNHkSfTWwpRy2lMGOcthSBjvK0UJLU+c1vp14LufPn8fmzZuxfft2o1fAEhEREZGc4OBgdOjQQe1lFBqfmc6lVatWuHv3Ljp06IApU6YYve2mFD4zbbrU1FQesy6ELeWwpQx2lMOWMqy5o6Io0Ol0YtszpWVwcDDOnDmT552nLQWfmTbT3r17ceLECUybNq1IBmkqmJxXIFPhsaUctpTBjnLYUoa1dYy/cx8/bjmLOSG7MPWjbfh84k5s/fYUbv6dWOhtW1NLTotk0WJjY9VegmawpRy2lMGOcthShjV1vHAuDktnH8DJY3/Bu155tO/ijVeavoQLZ28ifO5BnPz1eqG2b05LvV6PCRMmoGHDhjh9+jR69eqFDz/8EJGRkWjbti3q1q2L3r1749q1a4aPyXkL9e+//x5TpkyBr68vmjVrhpkzZxreIr2oFfhsHkRPkyWcP1Ir2FIOW8pgRzlsKcNaOt65lYxvV/2KytVK4+2edeFQ7N9xsHkbT2zfcBpb1p6Ax3PFUaFSSbP2UdCWmZmZGDNmDI4ePYqvv/4anp6eAIBz587h7t27GD16NLKysjBjxgx89NFHWLdundHHz58/H/7+/pg/fz6io6MRGhqKChUq4N133zVr/QXBYZos2tP4R2At2FIOW8pgRzlsKcNaOh49eBnFHO3RpXc92NvbGt1ma2uD9u/Uxo1r9/DLvotmD9MFaZmeno5hw4bh999/x+rVq1GxYkXDbUlJSdi8eTNKlsxeR0pKCsaNG4fY2Fg8//zzhvvVrl0bEydOBAA0bdoUUVFR2Llz51P5nPIwD7JoEyZMUHsJmsGWcthSBjvKYUsZ1tBRURScOn4DPg1ezDNI57Cx0aF+k5fwx//dROqDDLP2Y2rL1NRUfPjhh7h06RK++eYbo0EaAKpXr24YpAGgatWqAPIeRtKsWTOjy1WqVHlqh+1wmCYiIiKyEpmZeqSlZqJ02ce/e3Xpss5QFOB+clqRrufu3bs4duwYmjdvjnLlyuW53dXV1eiyvb09ACAtzXhdLi4uee6Xnp4uvNr8cZgmi/b++++rvQTNYEs5bCmDHeWwpQxr6GhnawMbWx2SElIfe7+c24s52pu1H1NblitXDrNnz8bq1auxZMkSs/alNh4zTRYtMbHwp+ehbGwphy1lsKMctpRhDR11NjpUr/U8Thz9C01aVnnkuaWjj/6FFyt6wNmlmFn7KUjLdu3aIT09HWPHjoWTkxMCAgLM2qda+Mw0WbRNmzapvQTNYEs5bCmDHeWwpQxr6djg1Uq4c+s+9mz7Hfm9d9/RQ5dx5c87aOhXyex9FLTlW2+9hSlTpmDmzJlYu3at2ftVA5+ZJiIiIrIiFSqVRJu3auLHLWfx1+W7eKXJSyhZ2hmJ9x7gt6hruPj7LTRqXhk1av/nqa7rnXfeQVpaGiZPnvxMvRMl305cBXw7cdMlJibmefEBmYct5bClDHaUw5YyrK3jhXM38fNPl3D14h3DdeVedENDv8qoVbdcod5eXAst+XbipAmzZ89WewmawZZy2FIGO8phSxnW1rFajbJ4f2BjDJ/kj34jXsXQCa0QOPxVeNcrX6hBGrCuljzMgyzaw28ZSoXDlnLYUgY7ymFLGdba0dXNCa5uTqLbtKaWfGaaLFq1atXUXoJmsKUctpTBjnLYUgY7yrGmljxmWgU8Ztp0t27dQunSpdVehiawpRy2lMGOcthSBjvK0UJLHjNNmvDRRx+pvQTNYEs5bCmDHeWwpQx2lGNNLTlMExERERGZicM0WbQePXqovQTNYEs5bCmDHeWwpQx2lGNNLTlMExERERGZicM0WbRn7S1FLRlbymFLGewohy1lsKMca2rJYZqIiIiIyEw8NZ4KeGo802nh1DqWgi3lsKUMdpTDljLYUY4WWvLUeKQJ4eHhai9BM9hSDlvKYEc5bCmDHeVYU0sO02TRLly4oPYSNIMt5bClDHaUw5Yy2FGONbXkME0WrUKFCmovQTPYUg5bymBHOWwpgx3lWFNLHjOtAh4zbbrExES4urqqvQxNYEs5bCmDHeWwpQx2lKOFljxmmjRh6NChai9BM9hSDlvKYEc5bCmDHeVYU0sO00REREREZuIwTRatc+fOai9BM9hSDlvKYEc5bCmDHeVYU0sO02TRnvXjrSwJW8phSxnsKIctZbCjHGtqyWGaLNqqVavUXoJmsKUctpTBjnLYUgY7yrGmlhymiYiIiIjMxFPjqYCnxjPdjRs3UL58ebWXoQlsKYctZbCjHLaUwY5ytNCSp8YjTVizZo3aS9AMtpTDljLYUQ5bymBHOdbUksM0WbQzZ86ovQTNYEs5bCmDHeWwpQx2lGNNLTlMk0V7/vnn1V6CZrClHLaUwY5y2FIGO8qxppY8ZloFPGbadKmpqXB0dFR7GZrAlnLYUgY7ymFLGewoRwstecw0aUJQUJDaS9AMtpTDljLYUQ5bymBHOdbUksM0EREREZGZOEyTRWvfvr3aS9AMtpTDljLYUQ5bymBHOdbUksM0WbRn/RyVloQt5bClDHaUw5Yy2FGONbXkME0WLTw8XO0laAZbymFLGewohy1lsKMca2rJYZqIiIiIyEw8NZ4KeGo80126dAmVK1dWexmawJZy2FIGO8phSxnsKEcLLXlqPNKEbdu2qb0EzWBLOWwpgx3lsKUMdpRjTS05TJNFO378uNpL0Ay2lMOWMthRDlvKYEc51tSSwzRZNA8PD7WXoBlsKYctZbCjHLaUwY5yrKklj5lWAY+ZJiIiIrJsPGaaNCEgIEDtJWgGW8phSxnsKIctZbCjHGtqyWGaiIiIiMhMHKbJorVu3VrtJWgGW8phSxnsKIctZbCjHGtqyWGaLJqXl5faS9AMtpTDljLYUQ5bymBHOdbUksM0WbSwsDC1l6AZbCmHLWWwoxy2lMGOcqypJYdpIiIiIiIz8dR4KuCp8Ux37tw51KhRQ+1laAJbymFLGewohy1lsKMcLbTkqfFIE/bv36/2EjSDLeWwpQx2lMOWMthRjjW15DBNFu3IkSNqL0Ez2FIOW8pgRzlsKYMd5VhTSw7TZNFKlCih9hI0gy3lsKUMdpTDljLYUY41teQx0yrgMdNERERElo3HTJMm9O/fX+0laAZbymFLGewohy1lsKMca2rJYZosWnp6utpL0Ay2lMOWMthRDlvKYEc51tSSwzRZND8/P7WXoBlsKYctZbCjHLaUwY5yrKklh2myaL6+vmovQTPYUg5bymBHOWwpgx3lWFNLDtNk0ebMmaP2EjSDLeWwpQx2lMOWMthRjjW15DBNRERERGQmDtNk0YYPH672EjSDLeWwpQx2lMOWMthRjjW15DBNFu3EiRNqL0Ez2FIOW8pgRzlsKYMd5VhTSw7TZNH27dun9hI0gy3lsKUMdpTDljLYUY41teQwTRbNzs5O7SVoBlvKYUsZ7CiHLWWwoxxrasm3E1cB306ciIiIyLLx7cRJE4YMGaL2EjSDLeWwpQx2lMOWMthRjjW15DBNFi0pKUntJWgGW8phSxnsKIctZbCjHGtqyWGaLFrDhg3VXoJmsKUctpTBjnLYUgY7yrGmlhymyaL5+/urvQTNYEs5bCmDHeWwpQx2lGNNLTlMk0X79NNP1V6CZrClHLaUwY5y2FIGO8qxppYcpomIiIiIzMRhmizawIED1V6CZrClHLaUwY5y2FIGO8qxppYcpsmiXbx4Ue0laAZbymFLGewohy1lsKMca2rJYZos2s6dO9VegmawpRy2lMGOcthSBjvKsaaWHKaJiIiIiMzEtxNXAd9O3HRZWVmwtbVVexmawJZy2FIGO8phSxnsKEcLLfl24qQJY8eOVXsJmsGWcthSBjvKYUsZ7CjHmlpymCaLdvv2bbWXoBlsKYctZbCjHLaUwY5yrKklh2myaD4+PmovQTPYUg5bymBHOWwpgx3lWFNLDtNk0Tp16qT2EjSDLeWwpQx2lMOWMthRjjW15DBNFi0kJETtJWgGW8phSxnsKIctZbCjHGtqyWGaiIiIiMhMHKbJovXt21ftJWgGW8phSxnsKIctZbCjHGtqyWGaLFpcXJzaS9AMtpTDljLYUQ5bymBHOdbUksM0WbQffvhB7SVoBlvKYUsZ7CiHLWWwoxxraslhmoiIiIjITHw7cRXw7cRN9+DBAzg5Oam9DE1gSzlsKYMd5bClDHaUo4WWfDtx0oSpU6eqvQTNYEs5bCmDHeWwpQx2lGNNLTlMk0WLiYlRewmawZZy2FIGO8phSxnsKMeaWnKYJotWs2ZNtZegGWwphy1lsKMctpTBjnKsqSWPmVYBj5k2XUxMDMqVK6f2MjSBLeWwpQx2lMOWMthRjhZa8php0oTx48ervQTNYEs5bCmDHeWwpQx2lGNNLTlMExERERGZicM0WbSePXuqvQTNYEs5bCmDHeWwpQx2lGNNLTlMk0V78OCB2kvQDLaUw5Yy2FEOW8pgRznW1JLDNFm0jRs3qr0EzWBLOWwpgx3lsKUMdpRjTS05TBMRERERmYmnxlMBT41nuvj4eHh4eKi9DE1gSzlsKYMd5bClDHaUo4WWPDUeacKCBQvUXoJmsKUctpTBjnLYUgY7yrGmlhymyaJduXJF7SVoBlvKYUsZ7CiHLWWwoxxraslhmixalSpV1F6CZrClHLaUwY5y2FIGO8qxppY8ZloFPGbadHfv3kXJkiXVXoYmsKUctpTBjnLYUgY7ytFCS1PnNbunsRhSl16vICMtEwoUQFGgANBBB+iAnJ+ldDodgFyXFUCBAl32FdmXdYBOUQCdDoqiQGdjA+j1QPa9ABud4bKC7NsVvT57f1AAGxtAn/PzW/ZlRa//Z38KFJ0OOuWfzSkKRo78CEsXLyn0WqEYr83ctUKvGNb2cMfHdv1nbSat9TFr+3ftD3V7xFpzd1QAjBw5GkuXLC14x1yXJTo+7nNuaV+f+X3OR44cjaWLl8isVehz/rivT8NaHvP1qcbnXLSjlX99jhz5EZYsXmza9yQVvj5N6mgBX58jR36EJUuWWPTnXOLr087WFrb2NsjMyEJWlt5orbZ2ttDr//mv1kNrs7WzhZ2tDTLSs2Bjq4O9Q/YImZmRhcyMTKO12trZYOTIkVi5ciWsAZ+ZfoKoqCj07t0bGzZsEHsm+Wk8M63XKzh68BIO/PgHUlOzimw/RERERLkpioLKnqXxRpdaeK60s9rLMQvP5mHFsjL1WLnoZ/y45RwHaSIiInrqdDodLl+4jcUz9+PP32+pvZwi9cwO04qiID09Xe1lWKTt353G9avxai+DiIiIrESdV15Ah6618lyvKArWrTiKe3dTVFjV01Hkw3RwcDA6dOiAqKgodOrUCT4+PujatSvOnDljuE9aWho+++wzNGvWDN7e3ujYsSN27dqV73b279+Pt956C97e3ti7dy9CQ0NRt25dnD17Ft27d0ft2rXRuXNnnD17Fmlpafjkk0/g6+sLPz+/PMfuREdHIygoCM2aNYOPjw86duyIzZs3F3WSIpVyPx0njv6l9jLE8CgkIiIiy+fsWgz1GldEvYYVABj/9zsrS8HRQ1dUWlnReyrPTN+6dQvTpk1D3759MX/+fKSlpWHw4MHIyMgAAIwePRrr1q1DYGAgwsLCULVqVQwZMgR79uwx2k5cXBymTZuGgIAAREREoEaNGgCAjIwMjB07Ft26dUNoaCgyMzMxePBgTJgwAY6Ojpg/fz5at26Nzz77DL/99pthezExMahXrx6mT5+OJUuWoE2bNpg4cSI2bdr0NLIUid9Px0JL82fOCyeIiIhIHcUcn3y+ipPHrgMAGjav9IjbtfNEX25P5WweCQkJWL16NapVqwYAcHJyQu/evXHy5Ek4Ozvjxx9/xOTJk9GjRw8AgJ+fH27cuIGwsDD4+/sbbSciIgJ16tQx2n5GRgZGjx6N5s2bAwD0ej2CgoJQp04djBs3DgDQqFEjREZGIjIyEvXq1QMAtG/f3rANRVHg6+uLmzdvYt26dejcuXPRBSlC95PT1F4CERERaYiNzZOf2HrwIPsJ0lL/vNgw95NhqQ8y5RdmIZ7KMF2mTBnDIA0AVatWBQDcvHkT58+fBwC0a9fO6GNef/11fPbZZ0hJSUHx4sUBAO7u7nkGaQCwsbFB48aNDZcrVqwIAGjSpInhOltbW1SoUAGxsbGG6xISEhAaGoo9e/bg5s2byMrKMuynqKWkpCAgIAAAsGjRIsyYMQPXr1+Hl5cXPvjgAwQHBwMA3nvvPWRmZmL9+vUAgLlz5yIsLAwXL15ExYoVMWzYMIwYMQIA0KVLF9yLs88+/Y7hi1hB9ll58jmNT849jO5vLPdtT7oMPHQqJbMYf/zj1pbnvlD+OZ2QeWt/4uVc2y/YYy3aLoW9bLSnp/45z7OCAmyvaLsU7rEKf86Fv76N9vRU/y3kuwKY/e/eoj7nj19rYT9e1c95UX+vz/M1VYCPtaivAcv6d2+05UJ0SE5Kgo1tscf+u7ez02H+/Pk4feo8XvJon+d2W1sY5p7x48djz549iIqKgouLC0JDQxEYGIjMzEy0aNECPj4+mD9/PgBg1KhROHbsGA4cOAAHBweEh4dj0KBBuH//Pho1aoTmzZtj5syZAIBBgwbh/Pnz2L17NwBg5cqVGDFiBOLj41G/fn20b98eU6ZMAQD0798fN27cwLZt2wAAS5cuRUhICGJjY1GrVi28++67uHXrFkqXLp1vz4c9lWHa1dXV6LK9vT2A7GOlExISYG9vn2eALVWqFBRFQVJSkmGYLlWqVL7bd3R0hIODQ57tu7i45NlvWtq/z9wGBwcjOjoagwYNQtWqVeHs7Iw1a9Zgx44d5j3QAihevLjRMdzTpk0zuj338d1vvPGG4e8ff/zxI++bmPAA888+fHiMDg//u8rvH9njDqXIfduTLhd+qHrS9h9z3yd8bKEv53lsBXmsRdulsJcL97GSg3RBt1fEn/NCPVbhz7nwYy3Utgr1byHfFTx2fwW5r7qf89ws69+97LaK9uu7QB9rUV8DlvXvvjAf+/BlW1vHJ661dv0X8Prbr2PPtnM4vPdintu967+A8d1WGi57enpiwIABhsvLli0zuv/Ds423tzf69OljuBwWFvbI+/r6+qJnz56Gy/PmzXvkfQHgnXfeMfx9xowZRrfdvXsXplD9bB5ubm7IyMhAQkKC0fW3b9+GTqczGoglj59NS0vDvn37MGDAAPTq1QuNGzeGt7f3M/+CN1c3J3i9XFbtZZAFynnjGCo8tpTBjkTaUczRHpcu3P5nkM5FBzTyq/z0F/WUqD5M169fHwAQGRlpdH1kZCRq1qxpeFZaWnp6OvR6veFZbABITk7G3r17i2R/T1PHd33g8ZyT2ssgC1OwZ37ocdhSBjsSacehPX9i9dIj+d7W4Z3aKP28S763aYHqbydevXp1tGnTBjNmzEBqaioqVaqELVu2IDo6GosXLy6y/bq4uMDb2xsREREoWbIk7OzsEB4eDmdnZ5Of1rdUjk72+HBUc/y45Sx+O3JN7eUQERGRlVEUBSVLlUCHbrVRqWr+h+lqherDNADMmjULc+fORUREBO7du4fKlStj4cKFaNWqVZHud86cOZg0aRKCg4Ph7u6OXr16ISUlBStWrCjS/WZkZEBRFMPbVBaVl6rr8FL1l5CVpUdWph56/RN+paoDjH7rmvOkkfLQbbn//1Ef+6Ttmvjb3fv376NEiRKmbaMo1laAtRZoGyqs1dDS1G0W5efclK6mbAMmri2/x1WIteVpaaGfc7O2+RTXmu/X5JO2iVxre8rfk0zexlP++nxsSwv6nD+Rymt94tfkk7YJ5P91aUFfnzqdDja2Otja2CArSw+9Xm84pa5O9+/rrBRFMbrexib7Nr0C6HSAnZ0NAB2yMrOQlaVkHyary96VjY0N7iXEo3TpUkh+8DdOn/67gA/AMqSnp5t0iLFOedYPEn4GRUdHQ1EUo0NMiIiIiMhyZGRkQKfToW7duo+9H4dpIiIiIiIzqf4CRCIiIiKiZxWHaSIiIiIiM3GYJiIiIiIyE4dpIiIiIiIzcZgmIiIiIjITh2kiIiIiIjNxmCYiIiIiMhOHaSIiIiIiM3GYJiIiIiIyE4dpIiIiIiIzcZgmIiIiIjITh2kiIiIiIjNxmCYiIiIiMhOHaSIiIiIiM3GYJiIiIiIyE4dpIiIiIiIzcZgmIiIiIjITh2kiIiIiIjNxmCYiIiIiMhOHaSIiIiIiM3GYJiIiIiIyE4dpIiIiIiIzcZgmIiIiIjITh2kiIiIiIjNxmCYiIiIiMhOHaSIiIiIiM3GYJiIiIiIyE4dpIiIiIiIzcZgmIiIiIjITh2kiIiIiIjNxmCYiIiIiMhOHaSIiIiIiM3GYJiIiIiIyE4dpIiIiIiIzcZgmIiIiIjITh2kiIiIiIjNxmCYiIiIiMhOHaSIiIiIiM3GYJiIiIiIyE4dpIiIiIiIzcZgmIiIiIjITh2kiIiIiIjNxmCYiIiIiMhOHaSIiIiIiM3GYJiIiIiIyE4dpIiIiIiIzcZgmIiIiIjITh2kiIiIiIjNxmCYiIiIiMhOHaSIiIiIiM3GYJiIiIiIyE4dpIiIiIiIzcZgmIiIiIjITh2kiIiIiIjNxmCYiIiIiMhOHaSIiIiIiM3GYJiIiIiIyE4dpIiIiIiIzcZgmIiIiIjITh2kiIiIiIjNxmCYiIiIiMhOHaSIiIiIiM3GYJiIiIiIyE4dpIiIiIiIzcZgmIiIiIjKTndoLsEbR0dFQFAX29vZqL4WIiIiI8pGRkQGdToe6des+9n5WP0x7eXlhzJgx6Nu371Pbp6IoUBTlqe2PiIiI6GGKouD+/ft48OABFEWBTqczXK/T6WBnZ4f09HRkZGQAAOzs7ODs7IyUlBSkpaXlmWN0Oh10Oh30en2RrtvGxgbOzs5wc3MzrLmomDqrWf0wrYacZ6S9vb1VXonlW716NXr27Kn2MjSBLeWwpQx2lMOWMqyl46FDhxAUFIR79+7B19cXV69exc2bNwEAHh4eyMjIQHJyMgDA2dkZjo6OuH37tppLzsPW1haLFy9Ghw4dimwfp0+fNul+PGaaLNru3bvVXoJmsKUctpTBjnLYUoY1dDx16hTef/991K5dG4cPH0aJEiVw//59LF68GF9++SUSExORnJyMSpUqwc/PD6mpqY88zOGDDz5A6dKln/IjyJaVlYWgoCD8/PPPquz/YQUepqOjoxEUFIRmzZrBx8cHHTt2xObNmw23R0VFwcvLC4cPH8aoUaNQt25dtGzZEhEREXm29eOPP6Jjx47w9vZGs2bN8NlnnyEtLS3Ptg4ePIhhw4ahbt26aNGiBX744QcAwFdffYUWLVqgQYMGmDBhAtLT0w0fGxcXh3HjxsHf3x+1a9dGmzZtMHfuXKP75Pb111+jTp06hp/Gcly8eBFeXl7Yv39/QXMRERERWYxZs2ahYsWKWLFiBa5evYqffvoJYWFh6NixI5ydnZGVlQUAGDZsGL7++mu8/PLL2LVrV57ttGnTBl26dMGtW7ee9kMwUBQFn3zyiWr7z1HgYTomJgb16tXD9OnTsWTJErRp0wYTJ07Epk2bjO73ySefoGLFiggLC0PLli0xe/ZsHDhwwHD7nj17MHToUFStWhVhYWEIDAzE2rVr8dFHH+XZZ0hICKpVq4ZFixahTp06GDNmDGbNmoVDhw5h8uTJGDp0KL7//nusWLHC8DHx8fFwd3fHuHHjsGzZMgQGBmLTpk2Pjf7WW29BURRs3brV6PoNGzagbNmyaNasWUFzUSGtXLlS7SVoBlvKYUsZ7CiHLWVoveONGzfw008/oV+/fnB0dMTq1atRvXp1+Pv7AwCWLVsGAKhYsSK+/fZb2NnZ4aWXXsp3W1evXjWau9Ry9uxZ/P7776quocDDdPv27dG/f380b94cjRo1wocffoi3334b69atM7pfmzZtMGTIEDRp0gQff/wxypcvj507dxpuX7RoEXx8fDBnzhz4+fkhICAA48ePx86dO3H+/HmjbbVr1w6DBw9G06ZNMW3aNOh0Omzbtg1hYWFo3rw5evbsiVatWiEyMtLwMV5eXhg7dixat26NBg0a4O2338aYMWOwZcsWPHjwIN/H5ubmhrZt22Ljxo2G6zIzM7FlyxZ07twZtra2Bc1FhTRixAi1l6AZbCmHLWWwoxy2lKH1jhcvXoSiKGjcuLHhcpMmTQwv5MsZSlu0aIE///wTAJCSkpLvtv766y+cO3fOcLlEiRJFufTHunDhgmr7Bsx4AWJCQgJCQ0OxZ88e3Lx50/DrAHd3d6P7Pfwsrk6nQ5UqVRAbGwsAuH//Ps6dO4exY8cafcwbb7yBSZMm4fjx4/Dy8jJc37RpU8PfXVxcULJkSbzyyitGp5arWLEioqKiDJcVRcGqVauwfv16XL9+3ejwkb/++guenp75Pr5u3bqhZ8+euHDhAqpVq4b9+/fjzp076NKli6mJTJKSkoKAgAAA2T9YzJgxA9evX4eXlxc++OADBAcHAwDee+89ZGZmYv369QCAuXPnIiwsDBcvXkTFihUxbNgwwz/+Ll26wMnJCatXrwYAfPrpp1i9ejXOnj2LcuXK4eOPP8aAAQMAAG+++SbKlCmD5cuXA8h+9n/z5s04ceIESpUqhZkzZxrOcNK2bVtUqVIFixcvBgCMHz8ee/bsQVRUFFxcXBAaGorAwEBkZmaiRYsW8PHxwfz58wEAo0aNwrFjx3DgwAE4ODggPDwcgwYNwv3799GoUSM0b94cM2fOBAAMGjQI58+fNxyztnLlShw6dAgBAQGoX78+2rdvjylTpgAA+vfvjxs3bmDbtm0AgKVLlyIkJASxsbGoVasW3n33XUyYMAEA8P777yMxMdHw25OFCxdi9uzZuHbtGqpVq4b+/fsbfiPSo0cPAMDatWsBZP86LDw8HBcuXECFChUwevRoDB06FADQuXNnuLq6YtWqVQCA6dOnY82aNThz5gyef/55hISEICgoCED2D6Hly5dHeHg4AGDSpEnYtm0bjh8/Dg8PD8ybN8/w9dC6dWt4eXkhLCwMADB27Fjs378fR44cQYkSJRAWFob+/fsjPT0dfn5+8PX1xZw5cwAAw4cPx4kTJ7Bv3z7Y2dlh2bJlGDJkCJKSknDlyhX88ccf+PTTTwEAAwcOxMWLFw0/5C5fvhxjx47F7du34ePjg06dOiEkJAQA0LdvX8TFxRkOsVqyZAmmTp2KmJgY1KxZEz179sT48eMBAD179sSDBw8MP5TOmzcPCxYswJUrV1ClShUMGjQII0eOBJD9783Ozg7/+9//AAAzZszAl19+ifPnz+OFF15AcHAwBg8eDADo1KkTPDw88OWXXwIApk6dig0bNuDkyZMoU6YMpk+fjn79+gEAXn/9dbz00ktYunQpAGDixInYuXMnjh07Bjc3NyxYsAB9+vSBXq9Hq1atUKtWLSxcuBAAMHr0aPzyyy84fPgwHB0dsXTpUgQFBSE1NRVNmzZF48aNsXfvXgQEBGDo0KE4c+YM9u7dCxsbG6xYsQLDhg1DQkICfH190bZtW0ybNg0AEBQUhKtXr2LHjh0AgIiICEyYMAFxcXGoU6cOunbtio8//hhA9nGI8fHxhkPotPo9Ijo6GqdPny7U94gRI0YgPj7e6r9HREdHIyAgoFDfIxo2bAh/f3+r/h6xd+9eDBs2rNDfI2bPng0AFvc94u7duwCA+fPnIz4+Hrdu3UJCQoLhaytnVtq5cycSEhIQExODixcvIj96vR6pqamGyzln/VDDkiVLsHHjRvHvEbdu3TLtmHClgIKCgpSGDRsqX331lfLzzz8rp06dUsaNG6f4+PgoiqIoR44cUTw9PZVTp04ZfdyAAQOUnj17KoqiKH///bfi6empfP/993m2X6tWLWXx4sWP3VbLli2VyZMnG123cOFCwxoURVG+/PJLpXr16srs2bOVgwcPKidPnlRWr16dZ3uenp7KsmXLjLbVtm1b5dNPP82zbimnTp3K85gofwsXLlR7CZrBlnLYUgY7ymFLGVrvmJiYqFStWlWZPXu2oiiK8vHHHyu1atVSUlNTFUVRlIkTJyrlypVTvLy8lMGDByuKkj33lStXLs+frl27Kp999lm+tz3NPy+88IISFxdXJL1MndcKdJhHWloa9u3bhwEDBqBXr15o3LgxvL29C3zOZBcXF+h0OsNPSDmSkpKQnp4ONze3Am0vP5GRkWjVqhVGjRqFZs2aoXbt2ihevLhJH/vOO+9gy5YtiI2Nxf79+8WflSbTtW/fXu0laAZbymFLGewohy1laL2ji4sLunTpghUrVuDKlSvo3bs37t69a/gtR58+fQBkz2P+/v64efMmjh49ChubvONiUlKSRZxG8LXXXlPtjCI5CjRMp6enQ6/XGx1ekZycjL179xZopyVKlECNGjWMjnEGYPjVRv369Qu0vfykpqbmeYfBnF8/PUnnzp2RlJSE0aNHw9HREe3atSv0esg8Ob+OocJjSzlsKYMd5bClDGvoOGbMGHh4eKBz5844cuQIhg8fjjlz5qBfv36Gw1OA7LN5NG/eHHq9HpMmTcqzndOnT6Ndu3aqvpuzq6ur4TAZNRXomGkXFxd4e3sjIiICJUuWhJ2dHcLDw+Hs7JznWeYnGTx4MAYNGoTRo0fjrbfewuXLlzFv3jy0bdvW6HhpczVp0gRfffUVVq9ejYoVK2LLli24evWqSR9bsmRJ+Pv7IzIyEt27d4ejo2Oh10NERESktpIlS2LTpk0YN24cxo0bBwBwcHDA9u3bDX/PyMhAZmYmkpKSkJSUZDg2Prf4+Pintew8PD09sXr1apQrV061NeQo8AsQ58yZg0mTJiE4OBju7u7o1asXUlJSCnx6FH9/fyxYsABhYWEYOHAg3N3d0a1bN4waNaqgS8rXoEGDEB8fb3jBQNu2bTFx4kTDiz2e5LXXXkNkZCS6du0qsh4yT//+/dVegmawpRy2lMGOcthShrV0LF26NJYtW4br16/jp59+QkpKCsqUKQNbW1v8/fffKFasGJo0aYLjx4/jp59+gl6vR4MGDfDf//4XBw8exIoVK/DHH3/g/v370Ol0cHFxwcsvv4zq1asjKioKN27cgI2NDTw9PREfH4/r16/jwYMH0Ov1Rm8Brjz0Nub5XQayT2KhKApsbGzg4eGBRo0aYfDgwahatepT6/UkOqWgBzxbiTFjxuDcuXMmHxpSEDlvT8m3E3+yb7/9Fu+8847ay9AEtpTDljLYUQ5bymBHOVpoaeq8xrcTz+X8+fPYvHkztm/fjt69e6u9HKuXc8oaKjy2lMOWMthRDlvKYEc51tSywId5aN2AAQNw9+5ddOrUiWfxICIiInoGDRw4EElJSfj666+LfF88zEMFPMzDdKmpqXwBqBC2lMOWMthRDlvKsIaON28Ct28D7u5A+fJFtx+1W0oM0zzMgzThUa8gpoJjSzlsKYMd5bClDC133LULaNMGeP55oFYt4IUXgCZNgG+/LZr9abllbhymyaLlvAU9FR5bymFLGewohy1laLVjaGj2IJ2YCHz5JXD4MLB2LVCiBNCtG/DPGfJE5W4ZHByMDh064Oeff8abb76J2rVro2fPnrh+/Tru3buHYcOGoV69emjdurXhNH059u3bhx49eqBOnTrw9fVFr169cPbsWcPtFy9eRM+ePeHt7Y3WrVtj06ZN8g/oMXjMNFm0WrVqqb0EzWBLOWwpgx3lsKUMLXY8cgQYOhQYNQqYNQt4+Mxz3bsD8+YBI0cCvr7A22/L7Te/lrdu3cKMGTMwYMAA2NnZYdq0aRg9ejScnJzwyiuvoFu3bli/fj0++ugj1KlTB+XLl8f27dsxcuRI+Pv7Y86cObC3t8dvv/2GmzdvombNmkhLS0OfPn3g5OSEzz//HACwcOFCJCcno2LFinIP6DE4TJNFe/fdd9VegmawpRy2lMGOcthShhY7LlgAVKsGfP658SCdY8QI4PvvgfnzZYfp/FomJCRg9erVqFatGgAgLi4OU6dORb9+/TBo0CAA2ccn79q1C7t370bv3r0xc+ZMNG3a1PCW5wDQvHlzw9+/++47xMXFYceOHYbhuWbNmmjXrt1TG6Z5mAdZtAkTJqi9BM1gSzlsKYMd5bClDK111OuB774D+vQBbB4z8QUGAgcPAnFxcvvOr2WZMmUMgzQAw7DbpEkTw3Wurq4oWbIkYmNjcenSJcTGxj727GqnTp1CtWrVjAbnl156CdWrVy/8gzARh2kiIiIiDUpLA9LTs19s+Dg5t9+7V7TrcXV1Nbpsb28PAHBxcTG63sHBAWlpabj3z4LKlCnzyG3GxcXhueeey3N9ftcVFQ7TZNHef/99tZegGWwphy1lsKMctpShtY6OjoCLC/D774+/3++/Zz9zXbq03L4lWrq7uwPIHpgfpUyZMrhz506e6/O7rqhwmCaLlpiYqPYSNIMt5bClDHaUw5YytNZRpwN69gSWLwdSU/O/j14PLFkCdOgAeHjI7VuiZeXKlfH888/ju+++e+R9vL29ceHCBVy9etVw3dWrV/H7k36CEMRhmiza0z69jZaxpRy2lMGOcthShhY7Dh2affjGe+8BDx4Y35aZCQwaBJw5k322D0kSLXU6HcaOHYtDhw5hyJAh2L17Nw4cOICFCxfip59+AgC8/fbbKFWqFD788EPs2LEDO3bsQFBQEEqVKlXo/ZuKZ/MgIiIi0qjq1bPfmOWdd4CKFYEPPgA8PYFr17LPOX39OhAeDvj5qb3S/L3xxhtwdHTE0qVLMXLkSBQrVgw1a9bEa6+9BgBwdHTEihUrEBISgo8++ghly5bFwIEDsWfPHiQlJT2VNfLtxFXAtxM3XWJiYp4XLJB52FIOW8pgRzlsKUPLHf/4AwgLA1avBu7eBZydswfsIUOAunXl96eFlnw7cdKE2bNnq70EzWBLOWwpgx3lsKUMLXf09Mw+5/SdO0BGBpCUBKxYUTSDNKDtlrlxmCaLdu3aNbWXoBlsKYctZbCjHLaUYS0d7Z7CQb7W0hLgME0W7uGTu1PhsKUctpTBjnLYUgY7yrGmljxmWgU8Ztp0t27dQmnJE19aMbaUw5Yy2FEOW8pgRzlaaMljpkkTPvroI7WXoBlsKYctZbCjHLaUwY5yrKklh2kiIiIiIjNxmCaL1qNHD7WXoBlsKYctZbCjHLaUwY5yrKklh2kiIiIiIjNxmCaLtnbtWrWXoBlsKYctZbCjHLaUwY5yrKklh2kiIiIiIjPx1Hgq4KnxTKeFU+tYCraUw5Yy2FEOW8pgRzlaaMlT45EmhIeHq70EzWBLOWwpgx3lsKUMdpRjTS05TJNFu3DhgtpL0Ay2lMOWMthRDlvKYEc51tSSwzRZtAoVKqi9BM1gSzlsKYMd5bClDHaUY00tecy0CnjMtOkSExPh6uqq9jI0gS3lsKUMdpTDljLYUY4WWvKYadKEoUOHqr0EzWBLOWwpgx3lsKUMdpRjTS05TBMRERERmYnDNFm0zp07q70EzWBLOWwpgx3lsKUMdpRjTS05TJNFe9aPt7IkbCmHLWWwoxy2lMGOcqypJYdpsmirVq1SewmawZZy2FIGO8phSxnsKMeaWnKYJiIiIiIyE0+NpwKeGs90N27cQPny5dVehiawpRy2lMGOcthSBjvK0UJLnhqPNGHNmjVqL0Ez2FIOW8pgRzlsKYMd5VhTSw7TZNHOnDmj9hI0gy3lsKUMdpTDljLYUY41teQwTRbt+eefV3sJmsGWcthSBjvKYUsZ7CjHmlrymGkV8Jhp06WmpsLR0VHtZWgCW8phSxnsKIctZbCjHC205DHTpAlBQUFqL0Ez2FIOW8pgRzlsKYMd5VhTSw7TRERERERm4jBNFq19+/ZqL0Ez2FIOW8pgRzlsKYMd5VhTSw7TZNGe9XNUWhK2lMOWMthRDlvKYEc51tSSwzRZtPDwcLWXoBlsKYctZbCjHLaUwY5yrKklh2kiIiIiIjPx1Hgq4KnxTHfp0iVUrlxZ7WVoAlvKYUsZ7CiHLWWwoxwttOSp8UgTtm3bpvYSNIMt5bClDHaUw5Yy2FGONbXkME0W7fjx42ovQTPYUg5bymBHOWwpgx3lWFNLDtNk0Tw8PNRegmawpRy2lMGOcthSBjvKsaaWPGZaBTxmmoiIiMiy8Zhp0oSAgAC1l6AZbCmHLWWwoxy2lMGOcqypJYdpIiIiIiIzcZgmi9a6dWu1l6AZbCmHLWWwoxy2lMGOcqypJYdpsmheXl5qL0Ez2FIOW8pgRzlsKYMd5VhTSw7TZNHCwsLUXoJmsKUctpTBjnLYUgY7yrGmlhymiYiIiIjMxFPjqYCnxjPduXPnUKNGDbWXoQlsKYctZbCjHLaUwY5ytNCSp8YjTdi/f7/aS9AMtpTDljLYUQ5bymBHOdbUksM0WbQjR46ovQTNYEs5bCmDHeWwpQx2lGNNLTlMk0UrUaKE2kvQDLaUw5Yy2FEOW8pgRznW1JLHTKuAx0wTERERWTYeM02a0L9/f7WXoBlsKYctZbCjHLaUwY5yrKklh2myaOnp6WovQTPYUg5bymBHOWwpgx3lWFNLDtNk0fz8/NRegmawpRy2lMGOcthSBjvKsaaWHKbJovn6+qq9BM1gSzlsKYMd5bClDHaUY00tOUyTRZszZ47aS9AMtpTDljLYUQ5bymBHOdbUksM0EREREZGZOEyTRRs+fLjaS9AMtpTDljLYUQ5bymBHOdbUksM0WbQTJ06ovQTNYEs5bCmDHeWwpQx2lGNNLTlMk0Xbt2+f2kvQDLaUw5Yy2FEOW8pgRznW1JLDNFk0Ozs7tZegGWwphy1lsKMctpTBjnKsqSXfTlwFfDtxIiIiIsvGtxMnTRgyZIjaS9AMtpTDljLYUQ5bymBHOdbUksM0WbSkpCS1l6AZbCmHLWWwoxy2lMGOcqypJYdpsmgNGzZUewmawZZy2FIGO8phSxnsKMeaWnKYJovm7++v9hI0gy3lsKUMdpTDljLYUY41teQwTRbt008/VXsJmsGWcthSBjvKYUsZ7CjHmlpymCYiIiIiMhOHabJoAwcOVHsJmsGWcthSBjvKYUsZ7CjHmlpymCaLdvHiRbWXoBlsKYctZbCjHLaUwY5yrKklh2myaDt37lR7CZrBlnLYUgY7ymFLGewox5pacpgmIiIiIjIT305cBXw7cdNlZWXB1tZW7WVoAlvKYUsZ7CiHLWWwoxwttOTbiZMmjB07Vu0laAZbymFLGewohy1lsKMca2rJYZos2u3bt9VegmawpRy2lMGOcthSBjvKsaaWHKbJovn4+Ki9BM1gSzlsKYMd5bClDHaUY00tOUyTRevUqZPaS9AMtpTDljLYUQ5bymBHOdbUksM0WbSQkBC1l6AZbCmHLWWwoxy2lMGOcqypJYdpIiIiIiIzcZgmi9a3b1+1l6AZbCmHLWWwoxy2lMGOcqypJYdpsmhxcXFqL0Ez2FIOW8pgRzlsKYMd5VhTSw7TZNF++OEHtZegGWwphy1lsKMctpTBjnKsqSWHaSIiIiIiM/HtxFXAtxM33YMHD+Dk5KT2MjSBLeWwpQx2lMOWMthRjhZa8u3ESROmTp2q9hI0gy3lsKUMdpTDljLYUY41teQwTRYtJiZG7SVoBlvKYUsZ7CiHLWWwoxxraslhmixazZo11V6CZrClHLaUwY5y2FIGO8qxppY8ZloFPGbadDExMShXrpzay9AEtpTDljLYUQ5bymBHOVpoyWOmSRPGjx+v9hI0gy3lsKUMdpTDljLYUY41teQwTURERERkJg7TZNF69uyp9hI0gy3lsKUMdpTDljLYUY41teQwTRbtwYMHai9BM9hSDlvKYEc5bCmDHeVYU0sO02TRNm7cqPYSNIMt5bClDHaUw5Yy2FGONbXkME1EREREZCaeGk8FPDWe6eLj4+Hh4aH2MjSBLeWwpQx2lMOWMthRjhZa8tR4pAkLFixQewmawZZy2FIGO8phSxnsKMeaWnKYJot25coVtZegGWwphy1lsKMctpTBjnKsqSWHabJoVapUUXsJmsGWcthSBjvKYUsZ7CjHmlrymGkV8Jhp0929exclS5ZUexmawJZy2FIGO8phSxnsKEcLLU2d1+yexmJIPWlpwMGD2X/i4gCdLvt6vT777zodoCjZf3JuA7IvA/9el3N77vs9fP/cH//wx9rY/LvPnMu5f4zLue3hj9m9+1f4+7cxut3UtT5u2/mtVafLXmN+a8v9WHPuk3vfufeXu9vD282vZ862c/5uSldTOioKsHv3b2jdurXhceZe96P29fD/P7y/h7vlt9ac/RT0cw6Y//X5uG3n1zln2wX9+ty79zf4+7fOs5bHrfXh++S31pz7Pqrr474+H/U5z/31knu/pqy1KD/nD3e0hO9Jj2rw8MfkXosaX5/5fR737j0Of//Xntr3pPwee36f84dvy++xPfy1nXvdanxP2rMnu2Puxy7x9WnKf4ce3q+53z8L8vWp0wElSwLFigEJCdm3eXgA6enAnTuAoyNQvTpgZwecP599HwcHoFYtoG1boEKF7G2dPw/s2gX89Vf2bc2bA337RuC118bi+eez71u2LDSLz0w/QVRUFHr37o0NGzaIPZP8NJ6ZTkgAgoKAtWuLbBdERERkxTw8gIwMIDn5yff18gJWrQIaNiz6dUnh2Tys2J072T8ZcpAmIiKiohIf/+8gXbly9jPc/8p+rrZBA8DePvvZ6yZNgC1bnvoyi9wzO0wrioL09HS1l2GRevYErl9XexVkefhLKDlsKYMdiZ41jo7Zf3J7/vnsQ0ttbID33weA7GNLTpwAzp3LHrb1eqBr1+zDTrWkyIfp4OBgdOjQAVFRUejUqRN8fHzQtWtXnDlzxnCftLQ0fPbZZ2jWrBm8vb3RsWNH7Nq1K9/t7N+/H2+99Ra8vb2xd+9ehIaGom7dujh79iy6d++O2rVro3Pnzjh79izS0tLwySefwNfXF35+fli5cqXRNqOjoxEUFIRmzZrBx8cHHTt2xObNm4s6SZG6ehWIjFR7FWSZ8jmwlMzEljLYkehZ8PAx16mpgJtb3vv8/HP2/w8ZAqxcCbi7XwOQffz1xo3Ad99l356RASxbVrTrfdqeyjPTt27dwrRp09C3b1/Mnz8faWlpGDx4MDIyMgAAo0ePxrp16xAYGIiwsDBUrVoVQ4YMwZ49e4y2ExcXh2nTpiEgIAARERGoUaMGACAjIwNjx45Ft27dEBoaiszMTAwePBgTJkyAo6Mj5s+fj9atW+Ozzz7Db7/9ZtheTEwM6tWrh+nTp2PJkiVo06YNJk6ciE2bNj2NLEXihx/UXoE0PnNFRESkJnd348s3bz76vkFB2f9fs+Zuw3WrVwN16gCentmXv/lGdn1qeypn80hISMDq1atRrVo1AICTkxN69+6NkydPwtnZGT/++CMmT56MHj16AAD8/Pxw48YNhIWFwd/f32g7ERERqFOnjtH2MzIyMHr0aDRv3hwAoNfrERQUhDp16mDcuHEAgEaNGiEyMhKRkZGoV68eAKB9+/aGbSiKAl9fX9y8eRPr1q1D586diy4IgJSUFAQEBAAAFi1ahBkzZuD69evw8vLCBx98gODgYADAe++9h8zMTKxfvx4AMHfuXISFheHixYuoWLEihg0bhhEjRgAAunTpguPHawCoDOOfkxQYPwP0pMsowH0Ls21z7l+UayvKxyrdRc21qrm2Z2mtaq/FktZa2P09zbXy69MyLlvyWh/H0r7Xy+zr/v1EAM4wZbaYMmUAypUrgaCgYMOz1bGxyTh69CwSE50B1MTduxlYsmQZoqKi4OLigtDQUAQGBiIzMxMtWrSAj48P5s+fDwAYNWoUjh07hgMHDsDBwQHh4eEYNGgQ7t+/j0aNGqF58+aYOXMmAGDQoEE4f/48du/OHuRXrlyJESNGID4+HvXr10f79u0xZcoUAED//v1x48YNbNu2DQCwdOlShISEIDY2FrVq1cK7776LW7duoXTp0o/o+fBDL2Jjx45VXn31VaPrbt++rXh6eipbt25VVq9erXh6eirx8fFG91m5cqXi5eWl3L9/37CdBg0a5Nn+woULlerVqytpaWmG6y5duqR4enoq69evN7pv9+7dlSFDhhgu37t3T5k6darSokULpUaNGoqnp6fi6elptJ8jR44onp6eyqlTp8xukNupU6dEt/ew5ctzToTDP/zDP/zDP/zDP/xT+D9ubsaXdbpH3/fw4ex5pF273YbrGjdWlKwsRXn++ezLvr5FMgKJM3VeeyqHebi6uhpdtre3B5B9rHRCQgLs7e3hnut3CKVKlYKiKEhKSjK6Lj+Ojo5wcHDIs30XF5c8+01LSzNcDg4OxtatW9GnTx8sX74cGzZsQJcuXZ7pFza+/Xb2+SC1Q1F7AURERFYtIcH48osv5r1PznHVs2dnHxe9b189w20DBgA7dgCxsdmXcw4F0QrVz+bh5uaGjIwMJOT6TN2+fRs6nc5oINbld2Z+M6WlpWHfvn0YMGAAevXqhcaNG8Pb2xuK8mwPb+7u2V+0RERkTZ7t/3ZZDnZ8kueeA65dM77O3h7458hVbNqU/UYvqanuALLfrKVateyzeABA6dLAu+8+teU+FaoP0/Xr1wcAROY6BUVkZCRq1qyJ4sWLF8l+09PTodfrDc9iA0BycjL27t1bJPt7mubOBdq0UXsVUvhqfyKiJ+P3Shns+CR37uS9LiMD+PprwNY2+/KlSwCgg41N9m2NG2efBcTVFThwAHByeporLnqqHxBQvXp1tGnTBjNmzEBqaioqVaqELVu2IDo6GosXLy6y/bq4uMDb2xsREREoWbIk7OzsEB4eDmdnZ9y9e7fI9vs02Nll/zrl66+BceOAv/9We0WFoYDf3KSwpRy2lMGOcthSBjuays4u+4m75GTg0CEgMzP3PRTo9TrcvZt9XuqAAGD69Oy3L9ca1YdpAJg1axbmzp2LiIgI3Lt3D5UrV8bChQvRqlWrIt3vnDlzMGnSJAQHB8Pd3R29evVCSkoKVqxYUaT7zcjIgKIohrepLCr16gE7dxbpLoqcya+kpSdiSzlsKYMd5bClDHaUk1/LGzey/zwr0tPTTTrEWKc86wcJP4Oio6OhKIrRISZEREREZDkyMjKg0+lQt27dx96PwzQRERERkZlUfwEiEREREdGzisM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDNBERERGRmThMExERERGZicM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDNBERERGRmThMExERERGZicM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDNBERERGRmThMExERERGZicM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDNBERERGRmThMExERERGZicM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDNBERERGRmThMExERERGZicM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDNBERERGRmThMExERERGZicM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDNBERERGRmThMExERERGZicM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDNBERERGRmThMExERERGZicM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDNBERERGRmThMExERERGZicM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDNBERERGRmThMExERERGZicM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmclO7QVYo+joaCiKAnt7e7WXQkRERET5yMjIgE6nQ926dR97P6sfpr28vDBmzBj07dv3qe1TURQoivLU9vcsS0lJQfHixdVehiawpRy2lMGOcthSBjs+3v3793Hv3j1kZWWpuo5ixYrBw8MDDg4ORbofU2c1qx+m1ZDzjLS3t7fKK7F8AQEBWLlypdrL0AS2lMOWMthRDlvKYMf83b9/H2+//TbOnDmj9lKMDB8+HB999FGRbf/06dMm3Y/HTBMRERHRIwUGBuY7SOt0OhVW86/58+dj48aNqq4BMGOYjo6ORlBQEJo1awYfHx907NgRmzdvNtweFRUFLy8vHD58GKNGjULdunXRsmVLRERE5NnWjz/+iI4dO8Lb2xvNmjXDZ599hrS0tDzbOnjwIIYNG4a6deuiRYsW+OGHHwAAX331FVq0aIEGDRpgwoQJSE9PN3xsXFwcxo0bB39/f9SuXRtt2rTB3Llzje6T29dff406deogOTnZ6PqLFy/Cy8sL+/fvL2guKiQ+QyCHLeWwpQx2lMOWMtgxr/Pnz+PAgQN5rndwcLCIQ1ZnzJih+joKPEzHxMSgXr16mD59OpYsWYI2bdpg4sSJ2LRpk9H9PvnkE1SsWBFhYWFo2bIlZs+ebfTJ2LNnD4YOHYqqVasiLCwMgYGBWLt2bb5P14eEhKBatWpYtGgR6tSpgzFjxmDWrFk4dOgQJk+ejKFDh+L777/HihUrDB8THx8Pd3d3jBs3DsuWLUNgYCA2bdqETz755JGP7a233oKiKNi6davR9Rs2bEDZsmXRrFmzguaiQhoxYoTaS9AMtpTDljLYUQ5bymDHvNasWZPv9U5OTk95JfmLiYnB8ePHVV1DgY+Zbt++veHviqLA19cXN2/exLp169C5c2fDbW3atMGQIUMAAI0bN8a+ffuwc+dO+Pn5AQAWLVoEHx8fzJkzBwDg5+cHJycnTJo0CefPn4eXl5dhW+3atcPgwYMBALVr18auXbuwbds27Nq1y3D88dGjRxEZGYmgoCAA2S8sHDt2rGEb9erVg5OTE4KDgzFp0qR8vwjc3NzQtm1bbNy4ET169AAAZGZmYsuWLejatStsbW0LmosKKT4+Xu0laAZbymFLGewohy1lsGNe165dy/f61NRUo8s6nU61Z4hjYmJU2W+OAg/TCQkJCA0NxZ49e3Dz5k3DKzrd3d2N7vfws7g6nQ5VqlRBbGwsgOwD2c+dO2c07ALAG2+8gUmTJuH48eNGw3TTpk0Nf3dxcUHJkiXxyiuvGJ1armLFioiKijJcVhQFq1atwvr163H9+nWjw0f++usveHp65vv4unXrhp49e+LChQuoVq0a9u/fjzt37qBLly6mJjJJSkoKAgICAGT/YDFjxgxcv34dXl5e+OCDDxAcHAwAeO+995CZmYn169cDAObOnYuwsDBcvHgRFStWxLBhwww/SXfp0gVOTk5YvXo1AODTTz/F6tWrcfbsWZQrVw4ff/wxBgwYAAB48803UaZMGSxfvhxA9rP/mzdvxokTJ1CqVCnMnDnTcIaTtm3bokqVKli8eDEAYPz48dizZw+ioqLg4uKC0NBQBAYGIjMzEy1atICPjw/mz58PABg1ahSOHTuGAwcOwMHBAeHh4Rg0aBDu37+PRo0aoXnz5pg5cyYAYNCgQTh//jx2794NIPvXbbGxsQgICED9+vXRvn17TJkyBQDQv39/3LhxA9u2bQMALF26FCEhIYiNjUWtWrXw7rvvYsKECQCA999/H4mJiYbfnixcuBCzZ8/GtWvXUK1aNfTv39/wG5GcH6LWrl0LAJg1axbCw8Nx4cIFVKhQAaNHj8bQoUMBAJ07d4arqytWrVoFAJg+fTrWrFmDM2fO4Pnnn0dISIjhh7v27dujfPnyCA8PBwBMmjQJ27Ztw/Hjx+Hh4YF58+YZvh5at24NLy8vhIWFAQDGjh2L/fv348iRIyhRogTCwsLQv39/pKenw8/PD76+voYfSocPH44TJ05g3759sLOzw7JlyzBkyBAkJSUhMzMTf/zxBz799FMAwMCBA3Hx4kXs3LkTALB8+XKMHTsWt2/fho+PDzp16oSQkBAAQN++fREXF2c4xGrJkiWYOnUqYmJiULNmTfTs2RPjx48HAPTs2RMPHjwwHMc2b948LFiwAFeuXEGVKlUwaNAgjBw5EkD2vzc7Ozv873//A5D967ovv/wS58+fxwsvvIDg4GDDD9KdOnWCh4cHvvzySwDA1KlTsWHDBpw8eRJlypTB9OnT0a9fPwDA66+/jpdeeglLly4FAEycOBE7d+7EsWPH4ObmhgULFqBPnz7Q6/Vo1aoVatWqhYULFwIARo8ejV9++QWHDx+Go6Mjli5diqCgIKSmpqJp06Zo3LgxLl++jICAAAwdOhRnzpzB3r17YWNjgxUrVmDYsGFISEiAr68v2rZti2nTpgEAgoKCcPXqVezYsQMAEBERgQkTJiAuLg516tRB165d8fHHHwMAPvjgA8THxxsOodPq94jLly/j9OnThfoeMWLECMTHx1v994jMzEwEBAQU6ntEw4YN4e/vb9XfIy5fvoxhw4YV+nvE7NmzAUAT3yOuXr2K/OQenNU81OKLL77Ad999J/494tatWyhduvQT969TCvjoBwwYgOjoaAwaNAhVq1aFs7Mz1qxZgx07diA6OhpRUVHo3bs3NmzYYHS2ioEDByIpKQlff/01YmNj0bx5c8yaNQtvvfWW0fa9vb0xcOBADBgw4JHbatWqFVq0aIFJkyYZrgsNDcWKFSsQHR1tCDhz5kwEBgaiYcOGcHV1xenTpzFlyhSj7eV3arx27dqhefPmGDdunNG6peS8OpRn83iyS5cuoXLlymovQxPYUg5bymBHOWwpgx3z2rFjBwIDA/Nc7+bmhoSEBBVWZKxYsWI4deoUnJ2dxbdt6rxWoGOm09LSsG/fPgwYMAC9evVC48aN4e3tXeCfRlxcXKDT6XD37l2j65OSkpCeng43N7cCbS8/kZGRaNWqFUaNGoVmzZqhdu3aJp878p133sGWLVsQGxuL/fv3iz8rTabL+QmSCo8t5bClDHaUw5Yy2DGv1157Dc8991ye6y1hkAayZ7aiGKQLokDDdHp6OvR6vdHhFcnJydi7d2+BdlqiRAnUqFEDkZGRRtfn/Gqjfv36BdpeflJTU/O8w2DOr5+epHPnzkhKSsLo0aPh6OiIdu3aFXo9RERERM8aOzs7rFq1CnZ2lvfWJFWqVHnsiSWelgIN0y4uLvD29kZERAQiIyOxe/du9OnTx6yfCAYPHowTJ05g9OjROHDgAFatWoVPP/0Ubdu2NTpe2lxNmjTB7t27sXr1ahw6dAhjxox55HE/uZUsWRL+/v44duwY2rdvD0dHx0Kvh8zTv39/tZegGWwphy1lsKMctpTBjvmrW7cufvzxR5H5TIKNjQ06deqEyMhIi3jHygL/mDFnzhxMmjQJwcHBcHd3R69evZCSkmJ0WjpT+Pv7Y8GCBQgLC8PAgQPh7u6Obt26YdSoUQVdUr4GDRqE+Ph4wwsG2rZti4kTJxpe7PEkr732GiIjI9G1a1eR9ZB5bty4ofYSNIMt5bClDHaUw5Yy2PHRvLy8sHfvXsTExGDLli349ddfDWc/sbe3h4eHBwAgMTER6enpSExMRLNmzaAoCi5dupTnPTxyzv6hKIrRm7/kHDqc+w1hdDodXnjhBTRv3hxt27a1mFPzAWa8ANFajBkzBufOnTP50JCC4AsQTce3dpXDlnLYUgY7ymFLGewoRwstTZ3XLO8AGJWdP38e586dw/bt2y3iOBwiIiIislx8ZjqXVq1a4e7du+jQoQOmTJkCG5sCv0nkE/GZadOlpqbymHUhbCmHLWWwoxy2lMGOcrTQskhOjWcN9u7dixMnTmDatGlFMkhTweS8IQAVHlvKYUsZ7CiHLWVYRUdFAe7+BlxdB/y1GUi9VSS7sYqW/+BhHmTRct41kwqPLeWwpQx2lMOWMjTfMWYHcHIiEP/bv9fZOAAVugF1ZwNOZcV2pfmWD+FTr2TRatWqpfYSNIMt5bClDHaUw5YyNN3xyv+Afe0BBzeg+VbgnQSg03WgzqdA7C5gVxPggdwA/KiWFy5cQL9+/dCwYUPUqVMHbdu2RUREBL777jvUrFkTt2/fNrr/vXv3UKtWLaxduxYAEBwcjA4dOuDnn3/Gm2++idq1a6Nnz564fv067t27h2HDhqFevXpo3bo1tm/fLvZ4HofHTKuAx0yb7saNGyhfvrzay9AEtpTDljLYUQ5bytBsx9Q4YHMF4KUeQKMVgC7Xc6nJV7KH6dKvAs3WiezyUS39/f1RqlQp9OvXD87Ozrh27RpiY2PxwQcfoEmTJhg7dix69uxpuP/69esxZcoUHDp0CO7u7ggODsZPP/2EsmXLIigoCHZ2dpg2bRrKlSsHJycnvPLKK/Dx8cH69euxe/du/Pjjj2Z/TnnMNGnChAkT1F6CZrClHLaUwY5y2FKGZjteXA7odEC9uXkHaQBwrgjUHAf89R2QEiOyy/xa3r17F9evX8eHH36I1q1bo1GjRujWrRuGDh0KFxcXNG/eHFu3bjX6mK1bt6Jp06Zwd3c3XJeQkIA5c+bgjTfeQJs2bdC/f39ER0fj5ZdfxqBBg9C0aVNMnToViqJg9+7dIo/ncThMExEREWnZ3zuBcm8AxUo++j6VegJKJnBzb5Etw8PDA+XLl8fcuXOxadOmPMdVt2/fHidOnEBMTPZAHxcXZ3g36oeVKVMG1apVM1yuWLEigOx3v87h6uqKkiVLPpVjtzlMk0V7//331V6CZrClHLaUwY5y2FKGZjtmpQL27o+/j53rP/d9ILLL/FrqdDosX74clStXxpQpU9C8eXO8/fbbOHbsGACgZcuWcHJywrZt2wAAO3bsQLFixdC6dWuj7bi6uhpdtre3BwC4uLgYXe/g4IC0tDSRx/M4HKbJoiUmJqq9BM1gSzlsKYMd5bClDM12dK4E3InKPi3eo9w5+s99q4js8lEtK1WqhIULF+Lo0aP4+uuv4eDggKCgINy/fx+Ojo5GLxzcvn07WrZsieLFi4usqahwmCaLtmnTJrWXoBlsKYctZbCjHLaUodmOVQKBhP8D/v4x/9sVBfh9LuBcGSjbQmSXT2ppb2+PBg0aoH///khOTkZcXBwAoEOHDjh79iwOHjyIEydO5DnEwxLxPNNEREREWla2FVCmBfDze0Czb4GyLbNfkAgAmQ+A058Af20AGn+V/wsUhfz++++YOXMm3njjDbz44otITk7GF198gfLly6NChQoAso97dnd3x/jx4+Hq6go/P78iW48UDtNk0RYuXKj2EjSDLeWwpQx2lMOWMjTbUacD/L4D9ncE9voDHvWA0s2AzGTg+iYg/R5Qdw5QqZfYLvNrWbp0aZQqVQpffPEFbt68CRcXF7zyyiuYNWsWbG1tAWQ/Y922bVusW7cOXbt2hYODg9iaigrPM60CnmfadJMmTcKUKVPUXoYmsKUctpTBjnLYUobmO+qzss/scTEcSPoz+90Py7YCqg0AXGSOlc6hhZamzmt8Zpos2rVr19RegmawpRy2lMGOcthShuY72tgC5d/I/lPENN/yIXwBIlm0h88jSYXDlnLYUgY7ymFLGewox5pa8jAPFfAwD9PdunULpUuXVnsZmsCWcthSBjvKYUsZ7ChHCy35duKkCR999JHaS9AMtpTDljLYUQ5bymBHOdbUksM0EREREZGZOEyTRevRo4faS9AMtpTDljLYUQ5bymBHOdbUksM0EREREZGZOEyTRVu7dq3aS9AMtpTDljLYUQ5bymBHOdbUksM0EREREZGZeGo8FfDUeKbTwql1LAVbymFLGewohy1lsKMcLbTkqfFIE8LDw9VegmawpRy2lMGOcthSBjvKsaaWHKbJol24cEHtJWgGW8phSxnsKIctZbCjHGtqyWGaLFqFChXUXoJmsKUctpTBjnLYUgY7yrGmljxmWgU8Ztp0iYmJcHV1VXsZmsCWcthSBjvKYUsZ7ChHCy15zDRpwtChQ9VegmawpRy2lMGOcthSBjvKsaaWHKaJiIiIiMzEYZosWufOndVegmawpRy2lMGOcthSBjvKsaaWHKbJoj3rx1tZEraUw5Yy2FEOW8pgRznW1JLDNFm0VatWqb0EzWBLOWwpgx3lsKUMdpRjTS05TBMRERERmYmnxlMBT41nuhs3bqB8+fJqL0MT2FIOW8pgRzlsKYMd5WihJU+NR5qwZs0atZegGWwphy1lsKMctpTBjnKsqSWHabJoZ86cUXsJmsGWcthSBjvKYUsZ7CjHmlpymCaL9vzzz6u9BM1gSzlsKYMd5bClDHaUY00tecy0CnjMtOlSU1Ph6Oio9jI0gS3lsKUMdpTDljLYUY4WWvKYadKEoKAgtZegGWwphy1lsKMctpTBjnKsqSWHaSIiIiIiM3GYJovWvn17tZegGWwphy1lsKMctpTBjnKsqSWHabJoz/o5Ki0JW8phSxnsKIctZbCjHGtqyWGaLFp4eLjaS9AMtpTDljLYUQ5bymBHOdbUksM0EREREZGZeGo8FfDUeKa7dOkSKleurPYyNIEt5bClDHaUw5Yy2FGOFlry1HikCdu2bVN7CZrBlnLYUgY7ymFLGewox5pacpgmi3b8+HG1l6AZbCmHLWWwoxy2lMGOcqypJYdpsmgeHh5qL0Ez2FIOW8pgRzlsKYMd5VhTSx4zrQIeM01ERERk2XjMNGlCQECA2kvQDLaUw5Yy2FEOW8pgRznW1JLDNBERERGRmThMk0Vr3bq12kvQDLaUw5Yy2FEOW8pgRznW1JLDNFk0Ly8vtZegGWwphy1lsKMctpTBjnKsqSWHabJoYWFhai9BM9hSDlvKYEc5bCmDHeVYU0sO00REREREZuKp8VTAU+OZ7ty5c6hRo4bay9AEtpTDljLYUQ5bymBHOVpoyVPjkSbs379f7SVoBlvKYUsZ7CiHLWWwoxxraslhmizakSNH1F6CZrClHLaUwY5y2FIGO8qxppYcpsmilShRQu0laAZbymFLGewohy1lsKMca2rJY6ZVwGOmiYiIiCwbj5kmTejfv7/aS9AMtpTDljLYUQ5bymBHOdbUksM0WbT09HS1l6AZbCmHLWWwoxy2lMGOcqypJYdpsmh+fn5qL0Ez2FIOW8pgRzlsKYMd5VhTSw7TZNF8fX3VXoJmsKUctpTBjnLYUgY7yrGmlhymyaLNmTNH7SVoBlvKYUsZ7CiHLWWwoxxraslhmoiIiIjITBymyaINHz5c7SVoBlvKYUsZ7CiHLWWwoxxraslhmizaiRMn1F6CZrClHLaUwY5y2FIGO8qxppYcpsmi7du3T+0laAZbymFLGewohy1lsKMca2rJYZosmp2dndpL0Ay2lMOWMthRDlvKYEc51tSSbyeuAr6dOBEREZFl49uJkyYMGTJE7SVoBlvKYUsZ7CiHLWWwoxxraslhmixaUlKS2kvQDLaUw5Yy2FEOW8pgRznW1JLDNFm0hg0bqr0EzWBLOWwpgx3lsKUMdpRjTS05TJNF8/f3V3sJmsGWcthSBjvKYUsZ7CjHmlpymCaL9umnn6q9BM1gSzlsKYMd5bClDHaUY00tOUwTEREREZmJwzRZtIEDB6q9BM1gSzlsKYMd5bClDHaUY00tOUyTRbt48aLaS9AMtpTDljLYUQ5bymBHOdbUksM0WbSdO3eqvQTNYEs5bCmDHeWwpQx2lGNNLTlMExERERGZiW8nrgK+nbjpsrKyYGtrq/YyNIEt5bClDHaUw5Yy2FGOFlry7cRJE8aOHav2EjSDLeWwpQx2lMOWMthRjjW15DBNFu327dtqL0Ez2FIOW8pgRzlsKYMd5VhTSw7TZNF8fHzUXoJmsKUctpTBjnLYUgY7yrGmlhymyaJ16tRJ7SVoBlvKYUsZ7CiHLWWwoxxraslhmixaSEiI2kvQDLaUw5Yy2FEOW8pgRznW1JLDNBERERGRmThMk0Xr27ev2kvQDLaUw5Yy2FEOW8pgRznW1JLDNFm0uLg4tZegGWwphy1lsKMctpTBjnKsqSWHabJoP/zwg9pL0Ay2lMOWMthRDlvKYEc51tSSwzQRERERkZn4duIq4NuJm+7BgwdwcnJSexmawJZy2FIGO8phSxnsKEcLLfl24qQJU6dOVXsJmsGWcthSBjvKYUsZ7CjHmlpymCaLFhMTo/YSNIMt5bClDHaUw5Yy2FGONbXkME0WrWbNmmovQTPYUg5bymBHOWwpgx3lWFNLHjOtAh4zbbqYmBiUK1dO7WVoAlvKYUsZ7CiHLWWwoxwttOQx06QJ48ePV3sJmsGWcthSBjvKYUsZ7CjHmlpymCYiIiIiMhOHabJoPXv2VHsJmsGWcthSBjvKYUsZ7CjHmlpymCaL9uDBA7WXoBlsKYctZbCjHLaUwY5yrKklh2myaBs3blR7CZrBlnLYUgY7ymFLGewox5pacpgmIiIiIjITT42nAp4az3Tx8fHw8PBQexmawJZy2FIGO8phSxnsKEcLLXlqPNKEBQsWqL0EzWBLOWwpgx3lsKUMdpRjTS05TJNFu3LlitpL0Ay2lMOWMthRDlvKYEc51tSSwzRZtCpVqqi9BM1gSzlsKYMd5bClDHaUY00tecy0CnjMtOnu3r2LkiVLqr0MTWBLOWwpgx3lsKUMdpSjhZY8Zpo0YeTIkWovQTPYUg5bymBHOWwpgx3lWFNLPjOtgqf6zHTSn8DvC4BbB4DUO4CiANADun9+jlJy/keXc8GYAkCXc/s/99HpHro957ICKA/dnrNdHbI/LmfThm3lfGzOhnT/rA3/bl8Bbt+7i1LuD/9k+9BaH9qU8e02//4937Xmelz5rvWhy/muNdf2FQDQ/7Pvhzs+bq0PbT/Pth7X8VFre+iy4aLOsLY78Ql4rqSHiZ/zXOt/ip9zQ0fD8p7W1+eTPuf/rvX23bso5VHy37X+u/h/11Gor8/HrO1RazVcFPz61D1qW0r29xBFMW1t+a41V8dn5HuS8ec811pzdzSs9Umf83/WWojvSbfj76KUu8dj1mrK1+dD23/U9ySjfRfme5IpX58PLT339yQg13oK+znPvvzIjlr8+jTcXsDvSbaOgKsXUKkXUOEdwMYOSLoI3P4FUPSAey3AvQ5Cg/0xpGv17O2Xex0o3/7fjs8IU+c1DtNPEBUVhd69e2PDhg1iw+9TGaYTLwD7OwJJ54puH0RERGS9dPaAc0Ug6UKuG2yQPdg/xN4VqDsHqBr4lBZXeDzMw5olnge219HEIM2f9IiInozfK2WwYwEpGdmDdNmWwDuJgNfwnBughw5o9BXQ8SpQ5UMg8wFwtB9wdqaaKy4Sz+wwrSgK0tPT1V6GZTrUA9A/UHsVIvL9LSSRyvj7PBnsKIffK2Wwo5lu/gRcXQ+cnw/YFgfan8WJuBeA34YBxUoBDZcC7X7Nfib7RHD2YSEaUuTDdHBwMDp06ICoqCh06tQJPj4+6Nq1K86cOWO4T1paGj777DM0a9YM3t7e6NixI3bt2pXvdvbv34+33noL3t7e2Lt3L0JDQ1G3bl2cPXsW3bt3R+3atdG5c2ecPXsWaWlp+OSTT+Dr6ws/Pz+sXLnSaJvR0dEICgpCs2bN4OPjg44dO2Lz5s1FnaRoxZ8E7p1QexVkgTi4EBFRkTn9Sfb/15oIuFXHmvO+QPo94Oqa7Os9agOegwHogPML1VplkXgqz0zfunUL06ZNQ9++fTF//nykpaVh8ODByMjIAACMHj0a69atQ2BgIMLCwlC1alUMGTIEe/bsMdpOXFwcpk2bhoCAAERERKBGjRoAgIyMDIwdOxbdunVDaGgoMjMzMXjwYEyYMAGOjo6YP38+Wrdujc8++wy//fabYXsxMTGoV68epk+fjiVLlqBNmzaYOHEiNm3a9DSyFI3YPU++zzOE858cHZ9yEcOWMtiRSEMe3Mj+/4r/BQCM+iQMKNUYiN39730qvw9AAWK2P/31FSG7p7GThIQErF69GtWqVQMAODk5oXfv3jh58iScnZ3x448/YvLkyejRowcAwM/PDzdu3EBYWBj8/f2NthMREYE6deoYbT8jIwOjR49G8+bNAQB6vR5BQUGoU6cOxo0bBwBo1KgRIiMjERkZiXr16gEA2rdvb9iGoijw9fXFzZs3sW7dOnTu3LnoggBISUlBQEAAAGDRokWYMWMGrl+/Di8vL3zwwQcIDg4GALz33nvIzMzE+vXrAQBz585FWFgYLl68iIoVK2LYsGEYMWIEAKBLly6ooT+Dygpg84gX5D7pckHua9JlPP7XZnle/J7r43PfIc8JOR7zsUV+Off+H3P5SffN7Wk+NjW7FPSxqv45L8DX99PsYs7lx65V+HOeZ3+57/8Mf86N/p1b+Of8Wfl3r2aXgj5W8f8G5r78DH7OBw4LRnH3cvDw8MCbLleRnnUdF66tQZUqVbD+y5mY/SqQkZ6CZUuWICoqCi4uLggNDUVgYCAyMzPRokUL+Pj4YP78+QCAUaNG4dixYzhw4AAcHBwQHh6OQYMG4f79+2jUqBGaN2+OmTOzj8MeNGgQzp8/j927swf4lStXYsSIEYiPj0f9+vXRvn17TJkyBQDQv39/3LhxA9u2bQMALF26FCEhIYiNjUWtWrXw7rvv4tatWyhdujSepMjP5hEcHIyff/4ZBw4cMFx3584dNGnSBHPnzsW9e/cwZcoUREVFwd3d3XCfVatWGZ5JLl68OIKDg/HTTz8hKirKaPuhoaFYvHgxTp48CQcHBwDA5cuX0a5dO0ybNg3vvPOO4b49evRAmTJlsHBh9q8XEhISEBoaij179uDmzZvIysoCALi7uxv288ydzePqeuBwd/ntEhERET2KjQOgTweabwXKt0e/Pr0Q0T4SqPwBUPfz7PtcXg380gso2wrwt/zfpFvU2TxcXV2NLtvb2wPIPlY6ISEB9vb2RoM0AJQqVQqKoiApKcnouvw4OjoaBumHt+/i4pJnv2lpaYbLwcHB2Lp1K/r06YPly5djw4YN6NKly7P9wsYXOgJ2zmqvQgyP8yUiejJ+r5TBjoXw4j9PXp6ZAih6tK95B0i7DVTpm329PgM4MzX7717D1FljEVH9bB5ubm7IyMhAQkKC0fW3b9+GTqczGoh1ggfYpaWlYd++fRgwYAB69eqFxo0bw9vbG8/8abdtiwHeU9RehRy5TzkRkWbx+HMZ7Ggmx/8A9ecDJV4C7hwFdtRHx/J7gErvZ7/By4NbwJ7WQNIfgFstoFz7J27yWaL6MF2/fn0AQGRkpNH1kZGRqFmzJooXL14k+01PT4derzc8iw0AycnJ2Lt3b5Hs76mqPhyoMUbtVYjg9zUiIiILl/UA+G0kUKoJoLPNPquYPh2IOwB8XwnYVDb7nZhdqwOt9wE2tmqvWNRTeQHi41SvXh1t2rTBjBkzkJqaikqVKmHLli2Ijo7G4sWLi2y/Li4u8Pb2RkREBEqWLAk7OzuEh4fD2dkZnzjMDgAAF0RJREFUd+/eLbL9AtkvmFQUxXAsTpGw6wm83B1Iv5N9onSeF4OIiIhE6LIHYnuP7LcXz0wCslIBKEC1QEBnh6y0BNjmPGWrswMcPAC7EsAfMQBiVFy76dLT0006KkL1YRoAZs2ahblz5yIiIgL37t1D5cqVsXDhQrRq1apI9ztnzhxMmjQJwcHBcHd3R69evZCSkoIVK1YU6X4lD1d5LBt7wPH5p7MvIiIisk4OJfNcZevgocJCZOl0OpNmtiI/mwcRERERkVapfsw0EREREdGzisM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDNBERERGRmThMExERERGZicM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDNBERERGRmThMExERERGZicM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDtBXasWMHBgwYAD8/P/j4+KBjx47YsGEDFEUxut+3336Ltm3bwtvbG2+99RZ++umnPNtKSkrC+PHj0aBBA9StWxdDhw5FXFzcI/cdGxuLunXrwsvLC3fv3jVpvRcvXsQHH3wAHx8fNG3aFJ9//jnS09MNtycnJyM0NBRdu3bFK6+8giZNmiAoKAjnz583afuHDx/GqFGj0Lp1a3h5eWHKlCn5Ps4hQ4agVatWqF27Nho1aoTAwEB88cUXbPkQU1rmNn36dHh5eaFv375s+RBTW3p5eeX54+3tzY7/KMjX5MWLFzFo0CD4+vrCx8cHrVq1Qvfu3dnyH6a0/O677/L9msz5umRH078mb9y4gZEjR6JZs2aoW7cuunTpghkzZvD75ENMbXnz5k0MHz4c9evXR926dREUFIS//vrLpH2YgsO0FVq5ciWcnJwQHByMJUuWwM/PDx9//DHCwsIM99m2bRs+/vhjvP7664iIiICPjw8GDx6MEydOGG1r+PDhOHz4MEJCQjB79mxcvnwZ/fr1Q2ZmZr77njFjBooXL27yWhMSEvD+++8jIyMDoaGhGDFiBNavX48ZM2YY7hMTE4N169ahadOmmD9/PqZOnYqkpCR0794dFy9efOI+Dh48iN9//x2+vr5wdXXN9z7p6elwcHDAgAED8MUXX2Dq1KlITU3F/PnzkZWVxZb/MKXlw86fP4+NGzfC2dkZv//+O78uH1KQlr169cK6deuwbt06VKtWDQ0aNGDHf5ja8cKFC+jevTvs7e0xa9YshIWFQa/Xw9bWli3/YUrLFi1aGL4Wc/5UqFABAPDGG2+wI0z/b05gYCDOnTuH8ePHY9GiRahSpQq+/PJLJCcn82vyH6a0zMrKQmBgIM6cOYOpU6di1qxZiI2Nxfvvv4/79++b/HgeSyGrc+fOnTzXTZw4UalXr56SlZWlKIqitGnTRhk5cqTRfbp3764EBgYaLv/222+Kp6encvDgQcN1Fy9eVLy8vJRt27bl2cfPP/+sNGjQQFm+fLni6emZ7zpyW7p0qeLj46PEx8cbrlu7dq1So0YNJTY2VlEURbl//76SkpJi9HHJyclKgwYNlClTpjxxHzmPWVEUpWXLlsrkyZOf+DE5+6hZs6ayZMkSo+vZMpspLf/73/8qCxYsUFq2bKmMGzcuz+1sme1xLT09PZVly5YZLvPftzFTO7777rvKsGHDjK5jS2Pmfq+cOXOmUqNGDSUuLs5wHTtme1TH6OhoxdPTUzly5IjRx/n5+eX5XsmW2R7VcuvWrYqnp6dy7tw5w3WxsbFKrVq1lC+//PKJ+zAFn5m2QiVLlsxzXY0aNZCcnIyUlBT89ddfuHLlCl5//XWj+7zxxhv45ZdfDL+iOXDgAFxdXdG0aVPDfSpXrowaNWrgwIEDRh+bkZGBqVOnYsiQIXB3dzd5rQcOHEDjxo2NPub111+HXq/H4cOHAQDFixeHk5OT0ceVKFECFSpUeOyvrHLY2Jj3z6B48eJwdHRERkaG0fVsaZotW7bg+vXr6NevHwDA0dExz33YsuD479uYKR0vXryI48ePo1evXkbXs6Uxc78m9+zZg0aNGqF06dKG69jx8XKeHXZxcTH6OFdX1zyHdLDl4509exalS5dG9erVDdeVLVsW1apVw969e018NE9Yh8hW6Jl3/PhxlC1bFs7Ozrh06RIAoFKlSkb3qVKlCjIyMgzHGV26dAmVKlWCTqczul/lypUN28jx1VdfwdbWFu+++26B1nXp0iVUrlzZ6DpXV1eULl06zz4elpiYiAsXLuT52MLS6/XIzMxEXFwcZsyYARsbG3Tq1MnoPmz5ZMnJyfj8888xZsyYPN9IH8aWpgkPD8fLL7+MV155BcOHD0dMTIzR7ez4eCdPngQApKSkoHPnzqhZsyZatGiB5cuX57kvWxbM6dOnceXKFXTo0MHoenZ8PB8fH1SrVg3z5s3DX3/9hcTERHz99de4cuUKunXrZnRftny8tLQ0ODg45LnewcHhsesoCA7ThF9//RXbt29Hnz59AGQf5wQgz/FHOZdzbk9MTDT6qTmHm5ub4T5A9oH/YWFhGD9+PGxtbQu0tsTExHyPg8q9j9xmzZoFnU5X4H/8T7JgwQK8/PLLePXVV/HDDz8gPDwcL774ouF2tjTNokWL8NJLL+GNN9545H3Y0jSdOnVCSEgIVq5ciREjRuDXX3/Fe++9Z1gHOz7Z7du3AQCjR49GmzZtsGLFCnTo0AGzZs3C2rVrDfdjy4LbunUrihUrhjZt2hiuY8cns7Ozw6pVq5CQkIDWrVvD19cXs2fPxrx581C3bl3D/djyySpWrIjY2FjcvHnTcN39+/fx559/4t69eyL7sBPZCj2zYmNjMWLECDRs2BC9e/cukn18/vnnaNq0KRo3bvzI+2RlZRn96srW1jbPT8ym2rhxo+FFDs8//7zh+twvqrCzK/iX/3vvvYfWrVvj1q1b+Pbbb9G/f3+sXLkSL7/8Mlua6MKFC/jmm2+wfv36R96HLU03c+ZMw999fX1Rv359vP3221i/fj3efPNNdjSBXq8HkP2DyYABAwAAjRo1QmxsLJYuXYoePXrwa9IMer0e27ZtQ4sWLeDs7AyA/7ZNlZqaiqFDh0JRFISFhaFEiRKIjIzEqFGjEBERgQYNGrCliTp06IAFCxZg/PjxCAkJgb29PWbOnImUlJQC/4DwKBymrVhiYiL69esHd3d3hIaGGo49cnNzA5B92pyHj3FLTEw0ut3V1RWxsbF5tpuQkGC4T3R0NHbu3In169cbPv7BgwcAsn8ydHJygpOTEwICAnD06FHDNr766is0bNgQrq6uSEpKeuw+HrZ//35MmjQJAwcOROfOnY1ue/nll40um3rqnYeVLVsWZcuWBZD9qvWuXbti4cKFmDVrFluaaMaMGWjXrh3Kly9veBx6vR4ZGRlITEyEXq9ny0KoXr06KlWqhBMnTmDLli3saIKcZ88aNWpkdH3jxo3xww8/4O+//0b//v3ZsoCioqJw69YtvPnmmwD435yCdNywYQNOnTqF/fv3G47db9y4Ma5du4a5c+ciPDycLU3k7u6OuXPnYvz48WjdujWA7CceOnXqhCNHjpi8ncfhMG2lUlNT8eGHHyIpKQnr1q0z+nVPznFKuY93unTpEuzt7Q2HNVSuXBm//PILFEUx+in08uXL8PT0NPw9IyMjzz8MAGjdujXeeOMNzJs3D5MnTzY6RU3O8V75Hb+VlJSEW7du5Tme6sSJExg2bBg6deqEYcOG5dnfhg0bTItjIhsbG9SoUQPHjh1jywK4fPkyDh06hC1bthhdv379eqxfvx41a9Zky0LS6/U4fvw4HB0d2dEE1apVe+ztQ4cO5dekGX744Qe4urqiefPm/G9OAf35558oW7ZsnhfB1qhRA5s2bWLLAnr11Vexb98+XLlyBQ4ODnjxxRfRv39/+Pj4FGq7OThMW6HMzEwMHz4cly5dwjfffGN4pjXHiy++iIoVKyIyMtLwUxwAbN++HY0bNzYcyO/n54fFixfjl19+QZMmTQBk/+M7e/YsAgMDAWR/AX/11VdG2z948CAiIiIQFhaGihUrAkCef1g5/Pz8sHTpUqNjryIjI2FjY2P0KuQ///wTH374IRo1aoTJkyfnuy1vb29TE5kkMzMTJ0+eRFJSEhISEtjSRHPnzkVaWprRdSNHjkTt2rVx9+5dXL58Gf/73//Y0kxnzpzBpUuX4OjoiNWrV7OjCXx8fODu7o6ff/4ZLVu2NFx/6NAhFCtWDNeuXeO/7wJKT0/Hrl278Nprr8HGxgZDhw7lf3MKoFy5coiNjcXdu3eNBuozZ84gIyODLc1ga2uLKlWqAMg+g8/PP/+MiIiIQm8X4DBtlSZPnoyffvoJwcHBSE5ONjqRe82aNeHg4IAhQ4Zg9OjRqFChAho2bIjt27fj1KlTWL16teG+devWRbNmzTB+/HiMHTsWxYoVw7x58+Dl5WV4sUnp0qWNftUEZL+rEwDUq1cv31NPPaxHjx74+uuvMWjQIHz44Ye4efMmPv/8c/To0cPwTeTOnTvo27cvihUrhvfffx9nzpwxfLyzszOqVq362H3cuHEDp0+fBpD9K6xr164hMjISANCuXTsAwLp163Dq1Ck0adIEpUuXxu3bt7F27Vr8+eefUBSFLQvQMr9nAnIGlj///JMtC9By+fLluHbtGho2bIiSJUviwoULmDVrFgAgKCiIHU3saG9vjyFDhuCzzz6Dm5sb6tWrh4MHD2L79u0AgBEjRrCliS1z7N+/H4mJiXjzzTf53xwzOr755pv44osv0K9fP/Tv399wzHRUVBQAsGUBWgLZL2r08fGBs7Mzzp8/jyVLlqBTp06PPRa8IHRK7hMWkua1atXK8A8itz179uCFF14AkP12pBEREYiJiUGlSpUwcuRIo2dtgOxf2Xz22WfYtWsXMjMz0axZM0ycODHPT8sP++677zBu3Dj88ssvT/zHCGT/BDl16lRER0ejRIkS6NixI0aMGGH4CTsqKuqRL75o0KABvv7668duP2c9+ck5Luv48eNYvHgxzp07h8TERJQuXRre3t6Ijo5+5Lkw2dLY445xa9WqFe7du/fId6NiS2M5Lffu3YsvvvgCly9fxv379+Hh4YH79+8jJSUl349jR2O5vya//vprrFq1CrGxsShfvjzu3bv3yFf7s6Wx3C2HDh2K3377DQcOHEDr1q3535x81pOfhzv+3//9H+bPn4//+7//Q2pqKipWrIiYmBjEx8fn+7FsaezhlqNGjcKRI0eQkJCAF154Ad27d0fv3r3FXoDIYZqIiIiIyEw8zzQRERERkZk4TBMRERERmYnDNBERERGRmThMExERERGZicM0EREREZGZOEwTEREREZmJwzQRERERkZk4TBMRERERmYnDNBHRM6hVq1YIDg5WexlERFaPwzQRkQX77bffEBoaisTERLWXYrIHDx4gNDQUUVFRai+FiKjIcZgmIrJg0dHRWLRoUZ5hOjIyElOnTlVpVY/34MEDLFq0CEePHlV7KURERc5O7QUQEVHBOTg4qL0EIiICn5kmIrJYoaGh+PzzzwEA/v7+8PLygpeXF65fv57nmOnvvvsOXl5e+PXXXzFt2jQ0atQIr7zyCiZNmoT09HQkJiZizJgx8PX1ha+vLz7//HMoimK0P71ej5UrV6J9+/bw9vZGkyZNMGnSJCQkJBjd7/Tp0+jbty8aNmyI2rVro1WrVhg3bhwA4Pr162jcuDEAYNGiRYY1h4aGAgB+//13BAcHw9/fH97e3mjatCnGjRuH+Pj4PI/dy8sLly9fxujRo1G/fn00atQI8+fPh6Io+PvvvzFgwADUq1cPTZs2xYoVK4w+PioqCl5eXti+fTvmzp2Lpk2bwsfHB0FBQfj7778FPjtERNn4zDQRkYV67bXXcOXKFWzduhXjxo2Dh4cHAKBkyZKP/Jhp06ahVKlSGDJkCE6ePIl169bBxcUF0dHR+M9//oMRI0bgwIEDWL58OTw9PdGpUyfDx06aNAmbNm3C22+/jV69euH69ev45ptvcPbsWaxZswb29va4c+cO+vbtCw8PD/Tv3x+urq64fv06du3aZVhbSEgIQkJC8Nprr+G1114DAHh5eQEAfv75Z/z11194++23Ubp0aVy4cAHr16/Hn3/+ifXr10On0xk9nhEjRqBKlSoYNWoU9u/fjyVLlsDd3R1r165Fo0aNMHr0aPzwww+YOXMmvL294evra/TxS5YsgU6nQ79+/XDnzh2sWrUKAQEB+P777+Ho6FjozxERERQiIrJYy5YtUzw9PZW//vrL6PqWLVsqY8eONVzeuHGj4unpqfTp00fR6/WG67t37654eXkpkyZNMlyXmZmp+Pn5KT179jRcd+zYMcXT01PZsmWL0X4OHDhgdP2uXbsUT09P5dSpU49c8507dxRPT09l4cKFeW578OBBnuu2bt2qeHp6KseOHTNct3DhQsXT01P5+OOP86zby8tL+eKLLwzXJyQkKLVr1zbqceTIEcXT01N59dVXlaSkJMP127dvVzw9PZVVq1Y9cv1ERAXBwzyIiDSka9euRs/u1q5dG4qioGvXrobrbG1tUatWLfz111+G6yIjI+Hi4oKmTZvi7t27hj8vv/wyihcvbjgzh4uLCwBg3759yMjIKPD6Hn42OC0tDXfv3kWdOnUAAP/3f/+X7+PJve7cj8fV1RWVKlUyejw5OnXqBGdnZ8Pldu3aoXTp0ti/f3+B105ElB8e5kFEpCHlypUzupwz/P7nP//Jc/3Dx0JfvXoVSUlJhuOdc7tz5w4AoEGDBmjbti0WLVqElStXokGDBmjdujXefPNNk14Uee/ePSxatAjbt283bDNHUlKSSY+nWLFieQ51cXFxwb179/J8/EsvvWR0WafT4aWXXsKNGzeeuFYiIlNwmCYi0hAbm/x/4fio63Po9Xo899xzmD17dr635wyvOp0OCxcuxIkTJ/DTTz/h4MGDGD9+PL788kusW7cOJUqUeOx+hg8fjujoaPTt2xc1atRA8eLFodfrERgYmOcFkY9at62tbb7bzu/jiYiKGodpIiILlvsFeUWlQoUK+OWXX1CvXj2TXpjn4+MDHx8fjBgxAj/88ANGjx6N7du345133nnkmhMSEvDLL79gyJAhGDx4sOH6K1euSD2MPK5evWp0WVEUXL161fCCSCKiwuIx00REFszJyQlA/odASHr99deRlZWFxYsX57ktMzPT8KYxCQkJeZ4BrlGjBgAgPT0dwL9rzv1GM496RnnVqlWFW/xjbN68GcnJyYbLkZGRuHXrFvz8/Ipsn0RkXfjMNBGRBXv55ZcBAPPmzcMbb7wBe3t7tGzZUnw/DRo0QPfu3fHFF1/g3LlzaNq0Kezt7XHlyhVERkZiwoQJaNeuHTZt2oQ1a9agdevWqFChAu7fv4/169fD2dnZMKA6OjqiatWq2LFjBypWrAh3d3dUq1YNnp6e8PX1xbJly5CRkYGyZcvi8OHDuH79uvjjyeHm5ob33nsPb7/9tuHUeC+99BK6detWZPskIuvCYZqIyILVrl0bw4YNw9q1a3Hw4EHo9Xrs2bOnSPY1ZcoU1KpVC2vXrsW8efNga2uL/2/fDm0UCMIwDH9XABVssMgVOELwqygBjdgeEEuCAYFlPSXQAxpFD1gUYk9cQgFzuVxCnqeAyYx78+efqqqyXC4znU6T/ET37XbL5XLJ4/HIaDRKXdfZ7/cZj8fvs7bbbbquy263y+v1Stu2mUwmORwO6bou5/M5wzBkPp+n7/ssFos/edN6vc79fs/pdMrz+cxsNstms3lPzwF+62vwYwOAD3O9XrNarXI8HtM0zX9fB/hgdqYBAKCQmAYAgEJiGgAACtmZBgCAQibTAABQSEwDAEAhMQ0AAIXENAAAFBLTAABQSEwDAEAhMQ0AAIXENAAAFPoGyiiX0y5ltsYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 800x1500 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "scatter_anomalies_plot(anomaly_df, 'anomalies')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NDDWI5vRau9S",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "train_anomalies = anomaly_df[(anomaly_df.index >= '2004-02-15') & (anomaly_df.sum(axis=1)>=1)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ia_cx2pAau9S",
        "outputId": "a37b1376-0f37-4efa-b181-ebb09067d3f7",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The first anomaly detected from 2004-02-15 was on 2004-02-18 15:42:39\n",
            "Model: cluster\n"
          ]
        }
      ],
      "source": [
        "print(f'The first anomaly detected from 2004-02-15 was on {train_anomalies.index[0]}')\n",
        "print(f'Model: {train_anomalies.columns[np.where(train_anomalies.iloc[0].values)[0]][0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KD0EvXE3au9S",
        "outputId": "fbfdcc72-01a6-404a-c469-0c2029b2b335",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          document.querySelector('#df-0c659363-d388-4964-a84d-82adca69f4d6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0c659363-d388-4964-a84d-82adca69f4d6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                     cluster  histogram  iforest  knn  mcd  svm\n",
              "2004-02-18 15:42:39        1          0        0    0    0    0\n",
              "2004-02-18 15:52:39        1          0        0    0    0    0\n",
              "2004-02-18 16:02:39        1          0        0    0    0    0\n",
              "2004-02-18 16:12:39        1          0        0    0    0    0\n",
              "2004-02-18 16:22:39        1          0        0    0    0    0"
            ]
          },
          "execution_count": 217,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_anomalies.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UG1WNd3Tau9T",
        "outputId": "bf405084-748e-4d12-ca44-8e5a0e97e7bc",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Anomalies detected by model from 2004-04-15:\n",
            "cluster      50\n",
            "histogram    50\n",
            "iforest      50\n",
            "knn          50\n",
            "mcd          50\n",
            "svm          50\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print('Anomalies detected by model from 2004-04-15:', train_anomalies.sum(axis=0), sep='\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KeuxSqjzau9T",
        "outputId": "f7c611a4-8ae8-4764-a165-d841b147fd01",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "One or more models detected an anomaly in 73 timestamps\n"
          ]
        }
      ],
      "source": [
        "# Get time stamps at which at leats one models detected an anomaly on each timestamp\n",
        "print(f'One or more models detected an anomaly in {len(train_anomalies)} timestamps')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "SwTTWpF6au9T",
        "outputId": "944109c2-1241-420a-afc7-511713652100",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "avg_df2.loc[train_anomalies.index].plot(xlabel='date', ylabel='average signal', figsize=(8,3))\n",
        "plt.grid(True, lw=0.5)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5Bg8J9fau9T",
        "outputId": "e192086d-df42-47bd-aa85-2a8ab8f60bbc",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "All models detected anomalies in 35 common timestamps\n"
          ]
        }
      ],
      "source": [
        "common_detected_anomaly = train_anomalies[train_anomalies.sum(axis=1) == 6].index\n",
        "print(f'All models detected anomalies in {len(common_detected_anomaly)} common timestamps')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ljh_ye6_au9T",
        "outputId": "b6963881-f334-47c3-fa8e-eb0d51b3fa6c",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x250 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "avg_df2.loc[common_detected_anomaly].plot(xlabel='date', ylabel='average signal', figsize=(8,2.5))\n",
        "plt.grid(True, lw=0.5)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "id": "G7zpSgufau9T",
        "outputId": "581b96e0-c123-4c38-b09c-6a1cdae231a3",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1019.38x600 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_scores_distribution(scores_df, plot_name='train_scores')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWSS1P14au9U",
        "outputId": "65420d43-d80b-44d4-c578-30dc457b892d",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The earliest that a failure could have been detected by more than one model was:\n",
            "cluster    1\n",
            "knn        1\n",
            "Name: 2004-02-18 17:22:39, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "models_ = train_anomalies[train_anomalies.sum(axis=1)>1].iloc[0]\n",
        "print('The earliest that a failure could have been detected by more than one model was:')\n",
        "print(f'{models_.iloc[np.where(models_.values ==1)]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M4ehyFXkau9U",
        "outputId": "fb9989b3-822f-4a78-f24c-d51cef592865",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The earliest that the failure could have been detected by all models was:\n",
            "2004-02-18 22:12:39\n"
          ]
        }
      ],
      "source": [
        "print('The earliest that the failure could have been detected by all models was:')\n",
        "print(f'{common_detected_anomaly[0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0t_KxdCau9U",
        "outputId": "931b8b90-ddbc-4fae-beed-9fd2587a28cf",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time for preventive maintenace before failure with at least one model detecting an anomaly:\n",
            "0 days 14:40:00\n",
            " \n",
            "Time for preventive maintenace before failure detected by all models:\n",
            "0 days 08:40:00\n"
          ]
        }
      ],
      "source": [
        "print(f'Time for preventive maintenace before failure with at least one model detecting an anomaly:')\n",
        "print(train_anomalies.index[-1] - train_anomalies.index[0])\n",
        "print(' ')\n",
        "print(f'Time for preventive maintenace before failure detected by all models:')\n",
        "print(train_anomalies.index[-1] - train_anomalies.nlargest(1, 'svm').index[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yMModCwlau9U",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "It is inconclusive which model performs better at detecting anomalies at early stage. We will further apply cluster metrics and non-paramentric test to compare the performace of the clusters formed. For the training dataset the model that detected anomlies the earliest was:\n",
        "\n",
        "---\n",
        "\n",
        "__Clustering-Based Local Outlier__\n",
        "\n",
        "---\n",
        "Next all models will be tested on the unseen dataset ```avg_df3```."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCvwH8nWsari",
        "outputId": "328ca9a8-46c1-4b37-c8ec-285dbf51e550",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "24300"
            ]
          },
          "execution_count": 145,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Release resources del the daset not used\n",
        "del anomaly_df, train_anomalies, scores_df, common_detected_anomaly\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xyx8AU77u6AQ",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Prediction on unseen data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ld4sa0h2f9hU",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "def predict_anomaly(unseen_data=None, anomly_model_list=None, thresh=0.10, plot_3d=False,\n",
        "                    fraction=0.05):\n",
        "\n",
        "    best_models = dict()\n",
        "    scores = dict()\n",
        "    best_models_list = list()\n",
        "\n",
        "    print(f'Unseen data size: {len(unseen_data)}')\n",
        "    print('-'*58)\n",
        "    for m in anomly_model_list:\n",
        "        path_to_pipeline = os.path.join(os.getcwd(), f'{m}_pipeline')\n",
        "        model = load_model(path_to_pipeline)\n",
        "        model.fraction = fraction\n",
        "        predictions = predict_model(model, data=unseen_data)\n",
        "        anomalies = predictions[predictions.Anomaly == 1]\n",
        "        print(f'{m} model detected {len(anomalies)} anomalies in the unseen data')\n",
        "        print('='*58)\n",
        "        if len(anomalies) >= (thresh * len(predictions)):\n",
        "            continue\n",
        "        else:\n",
        "            if plot_3d:\n",
        "                plot_model(model, plot='tsne')\n",
        "            best_models[m] = predictions.Anomaly\n",
        "            scores[m] = predictions.Anomaly_Score\n",
        "            best_models_list.append(m)\n",
        "\n",
        "    df_predictions = pd.DataFrame.from_dict(best_models)\n",
        "    df_scores = pd.DataFrame.from_dict(scores)\n",
        "\n",
        "    return  df_predictions, df_scores, best_models_list\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BTmVEQqHe6XH",
        "outputId": "2a542e2a-d298-4fad-f39b-3d0619364c6a",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unseen data size: 6324\n",
            "----------------------------------------------------------\n",
            "Transformation Pipeline and Model Successfully Loaded\n",
            "cluster model detected 6133 anomalies in the unseen data\n",
            "==========================================================\n",
            "Transformation Pipeline and Model Successfully Loaded\n",
            "histogram model detected 4843 anomalies in the unseen data\n",
            "==========================================================\n",
            "Transformation Pipeline and Model Successfully Loaded\n",
            "iforest model detected 187 anomalies in the unseen data\n",
            "==========================================================\n",
            "Transformation Pipeline and Model Successfully Loaded\n",
            "knn model detected 6304 anomalies in the unseen data\n",
            "==========================================================\n",
            "Transformation Pipeline and Model Successfully Loaded\n",
            "mcd model detected 78 anomalies in the unseen data\n",
            "==========================================================\n",
            "Transformation Pipeline and Model Successfully Loaded\n",
            "svm model detected 95 anomalies in the unseen data\n",
            "==========================================================\n"
          ]
        }
      ],
      "source": [
        "unseen_preds, unseen_scores, models_list  = predict_anomaly(avg_df3, selected_models)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ygvrk6gxB2_Z",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "unseen_preds.to_csv(os.path.join(csv_files, 'unsupervised_test_anomaly.csv'))\n",
        "unseen_scores.to_csv(os.path.join(csv_files, 'unsupervised_test_scores.csv'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rTZRjRe5wY2I",
        "outputId": "037ced9c-7958-47f6-da28-4ff4e6c0c84c",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "iforest    187\n",
              "mcd         78\n",
              "svm         95\n",
              "dtype: int64"
            ]
          },
          "execution_count": 258,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "unseen_preds.sum(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XbGA8M9by-3R",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "test_anomalies = unseen_preds[unseen_preds.sum(axis=1) >= 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "Xd-5BBo-igY1",
        "outputId": "bf89d863-0711-4e44-bd5b-001a3cf924cb",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 993.375x300 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_scores_distribution(unseen_scores, size=(3, 2), plot_name='test_scores')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "g8RDHZ9RH4L8",
        "outputId": "d0b9ac05-61bb-4d74-8220-1240c15c0442",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-46256208-26de-47e3-b824-7abad5cde645\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>iforest</th>\n",
              "      <th>mcd</th>\n",
              "      <th>svm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2004-04-17 01:12:55</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-04-17 01:22:55</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-04-17 01:32:55</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-04-17 01:42:55</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-04-17 01:52:55</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da35bf46-67fb-4d99-9528-048464575b45')\"\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-da35bf46-67fb-4d99-9528-048464575b45 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-46256208-26de-47e3-b824-7abad5cde645 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-46256208-26de-47e3-b824-7abad5cde645');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                     iforest  mcd  svm\n",
              "2004-04-17 01:12:55        1    0    0\n",
              "2004-04-17 01:22:55        1    0    0\n",
              "2004-04-17 01:32:55        1    0    0\n",
              "2004-04-17 01:42:55        1    0    0\n",
              "2004-04-17 01:52:55        1    0    0"
            ]
          },
          "execution_count": 152,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Error: Runtime no longer has a reference to this dataframe, please re-run this cell and try again.\n"
          ]
        }
      ],
      "source": [
        "test_anomalies[(test_anomalies.index >= '2004-04-15') & (test_anomalies.sum(axis=1)>=1)][0:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "99TG8M5K9yZ6",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "earliest_tail = test_anomalies[(test_anomalies.sum(axis=1)>=1) & (test_anomalies.index\n",
        "                                                                 >= '2004-04-15')]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clbpL5w8NM3O",
        "outputId": "0801123c-ad18-4783-d22c-5d61ee8d91e9",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The earliest that the failure could have been detected was on :\n",
            "2004-04-17 01:12:55\n",
            "Model: iforest\n"
          ]
        }
      ],
      "source": [
        "print(f'The earliest that the failure could have been detected was on :')\n",
        "print(f\"{test_anomalies[(test_anomalies.index >= '2004-04-15')].index[0]}\")\n",
        "print(f'Model: {test_anomalies.columns[np.where(test_anomalies.iloc[0].values)[0]][0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBYTmJlYO9ng",
        "outputId": "ea1988b3-94ec-43ca-c3f5-1074438c5ab4",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The earliest that a failure could have been detected by more than one model was:\n",
            "2004-04-17 05:22:55\n",
            "['iforest' 'svm']\n"
          ]
        }
      ],
      "source": [
        "print(f'The earliest that a failure could have been detected by more than one model was:')\n",
        "print(f'{earliest_tail[earliest_tail.sum(axis=1)>1].index[0]}')\n",
        "print(earliest_tail.columns[np.where(earliest_tail[\n",
        "    earliest_tail.sum(axis=1)>1].iloc[0]==1)].values)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLh3TTUIRNZ-",
        "outputId": "30b6040c-bc93-4f6a-d6c6-7349749f0e63",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The earliest the three models could have detected the failure was\n",
            "2004-04-17 12:22:55\n"
          ]
        }
      ],
      "source": [
        "print('The earliest the three models could have detected the failure was')\n",
        "print(f'{earliest_tail[earliest_tail.sum(axis=1)==3].index[0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qd84Qjz2STpj",
        "outputId": "0fbeacc5-7c33-4964-c724-785ff35ad62a",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time to prevent failure: 1 days 01:30:00\n"
          ]
        }
      ],
      "source": [
        "time_left = earliest_tail.index[-1] - earliest_tail.index[0]\n",
        "print(f'Time to prevent failure: {time_left}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "J5hsDyoyau9X",
        "outputId": "be298409-ce4a-4dfd-80c7-7c9dbf8d8862",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x700 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_predicted_anomalies(unseen_scores, test_anomalies, plot_name='test_anomaly_prediction')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6l4-oBMHDTyY",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "**The best performances on the test dataset ```avg_df3``` were:**\n",
        "\n",
        "1.   __iforest__\n",
        "2.   __mcd__\n",
        "3.   __svm__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1agNwptfTkPU",
        "outputId": "329aa357-3e70-4c64-be98-a3a28c7e1d20",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "27605"
            ]
          },
          "execution_count": 159,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "del unseen_preds\n",
        "del test_anomalies\n",
        "del unseen_scores\n",
        "del earliest_tail\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j8mEVbUJu6AR",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "# Multiseries Anomaly Prediction with Artificial Neural Networks -BiLSTM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZyPCyy5hu6AR",
        "outputId": "36a1300f-3378-464d-b337-7870da157435",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x78af6cbc5cd0>"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from collections import namedtuple\n",
        "import torch\n",
        "from torch import nn, optim\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data as data\n",
        "torch.manual_seed(1234)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hmmETfX_igY2",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "Approach to deep learning:\n",
        "\n",
        "* Built configurable Encoder - Decoder Model using PyTorch\n",
        "* Process data - scaling\n",
        "* Built training, evaluation and prediction functions\n",
        "* Build the data loaders for batch processing, seelect, optimizers loss function and hyperparameters\n",
        "* Experimental setup\n",
        "* Train and validate model on avg_df3 dataset\n",
        "* Visualise training metrics\n",
        "* Test model on avg_df2\n",
        "* Visualise tests metrics\n",
        "* Optimize model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CMTISd2GigY2",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Build configurable encoder-Decoder model - BiLSTM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OsFxZv3Nu6Ah",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "class AnomalyLSTM(nn.Module):\n",
        "\n",
        "    def __init__(self, input_size, hidden_dim=24, num_layers=2, p=0.1):\n",
        "        super(AnomalyLSTM, self).__init__()\n",
        "\n",
        "        self.input_size = input_size\n",
        "        self.hidden_dim = hidden_dim\n",
        "        self.num_layers = num_layers\n",
        "\n",
        "        self.lstm = nn.LSTM(\n",
        "          input_size=self.input_size,\n",
        "          hidden_size=self.hidden_dim,\n",
        "          num_layers=self.num_layers,\n",
        "          bidirectional=True,\n",
        "          batch_first=True)\n",
        "\n",
        "        self.linear1 = nn.Linear(self.hidden_dim * 2, 12)\n",
        "        self.linear2 = nn.Linear(12, self.input_size)\n",
        "        self.dropout = nn.Dropout1d(p)\n",
        "\n",
        "    def forward(self, x):\n",
        "\n",
        "        x= torch.tensor(x, dtype=torch.float32)\n",
        "        lstm_out, hidden = self.lstm(x.view(-1, 1, self.input_size))\n",
        "        out = torch.cat((hidden[0][-2, :, :], hidden[0][-1, :, :]), dim=1)\n",
        "        out = F.relu(self.linear1(out))\n",
        "        out = self.dropout(out)\n",
        "        out = self.linear2(out)\n",
        "\n",
        "        return out\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-KjBWoi9igY3",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Process data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GtIYyZmwau9Z",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "scaler = MinMaxScaler()\n",
        "train_data = torch.tensor(scaler.fit_transform(avg_df2.values), dtype=torch.float32)\n",
        "test_data = torch.tensor(scaler.transform(avg_df3.values), dtype=torch.float32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABrgtexTigY4",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Build training, evaluation and  prediction functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4FwLU3o3-EXF",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "def train_one_epoch(**kwargs):\n",
        "    \"\"\"\n",
        "    :param kwargs: hyperparameters dictionary\n",
        "    :return:training los for one epoch\n",
        "    \"\"\"\n",
        "\n",
        "    total_loss = 0\n",
        "    model = kwargs['model'].to(kwargs['device'])\n",
        "    model.train()\n",
        "\n",
        "    for true_seq in kwargs['train_loader']:\n",
        "        kwargs['optimizer'].zero_grad()\n",
        "        true_seq = true_seq.to(device)\n",
        "        pred_seq = model(true_seq)\n",
        "        loss = kwargs['loss_fn'](pred_seq, true_seq)\n",
        "        loss.backward()\n",
        "        kwargs['optimizer'].step()\n",
        "        total_loss += loss.item()\n",
        "\n",
        "    return total_loss / len(kwargs['train_loader'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "deHSaXi0VbHE",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "def evaluate(**kwargs):\n",
        "    \"\"\"\n",
        "    :param kwargs: a hyperparameters dictionary\n",
        "    :return: evalution loss\n",
        "    \"\"\"\n",
        "\n",
        "    total_loss = 0\n",
        "    model = kwargs['model'].eval()\n",
        "    with torch.no_grad():\n",
        "        for true_seq in kwargs['eval_loader']:\n",
        "            true_seq = true_seq.to(kwargs['device'])\n",
        "            pred_seq = model(true_seq)\n",
        "            loss = kwargs['loss_fn'](pred_seq, true_seq)\n",
        "            total_loss += loss.item()\n",
        "\n",
        "    return total_loss / len(kwargs['eval_loader'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IMXxjlwfk7T5",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "def time_minutes(s):\n",
        "    \"\"\"\n",
        "    :param s:  time in seconds\n",
        "    :return: time in hh:mm:ss format\n",
        "    \"\"\"\n",
        "    return time.strftime('%H:%M:%S', time.gmtime(s))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HWAPjv1o97vU",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "def train_lstm(**kwargs):\n",
        "    \"\"\"\n",
        "    :param kwargs: a hyperparameters dictionary\n",
        "    :return: namedtuple - statistics\n",
        "    \"\"\"\n",
        "\n",
        "    Stats = namedtuple('Stats', ['train_loss'])\n",
        "    train_loss_log = np.zeros(kwargs['epochs'])\n",
        "    best_loss = np.inf\n",
        "\n",
        "    epoch_start_time = time.time()\n",
        "    print('........Training Starting.......')\n",
        "    for epoch in tqdm(range(kwargs['epochs'])):\n",
        "        train_loss = train_one_epoch(**kwargs)\n",
        "        train_loss_log[epoch] = train_loss\n",
        "        if train_loss < best_loss:\n",
        "            best_loss = train_loss\n",
        "            torch.save({'epoch': epoch + 1, 'model': kwargs['model'].state_dict(),\n",
        "                'optim': kwargs['optimizer'].state_dict()},\n",
        "                os.path.join(kwargs['params'], f\"best_exp_{kwargs['exp']}.pt\"))\n",
        "\n",
        "        if (epoch + 1) % kwargs['logging'] == 0:\n",
        "            print(' ')\n",
        "            print('Epoch %d: | train loss: %.4f' % (epoch + 1, train_loss))\n",
        "\n",
        "    total_time = time.time() - epoch_start_time\n",
        "    print('Training time {}'.format(time_minutes(total_time)))\n",
        "    stats = Stats(train_loss=train_loss_log)\n",
        "\n",
        "    return stats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OnEG70Imu6Ai",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "def predict(model, data_loader, loss_fn=None):\n",
        "\n",
        "    preds_log, loss_log = [], []\n",
        "    with torch.no_grad():\n",
        "        model = model.eval()\n",
        "        for seqs_batch in data_loader:\n",
        "            for seq in seqs_batch:\n",
        "                seq = seq.to(device)\n",
        "                seq_pred = model(seq)\n",
        "                loss = loss_fn(seq_pred, seq)\n",
        "                preds_log.append(seq_pred.cpu().numpy().flatten())\n",
        "                loss_log.append(loss.item())\n",
        "    return preds_log, loss_log"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qfg5QdEnigY5",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "exps =[i for i in range(1, 5)]\n",
        "network = ['bilstm'] * 4\n",
        "exp_optim = sorted(['adam', 'adamw'] * 2)\n",
        "exp_loss = ['mae_loss', 'huber_loss'] * 2\n",
        "\n",
        "exp_setup = pd.DataFrame({'Exp':exps, 'Model':network, 'Loss': exp_loss, 'Optim':exp_optim },\n",
        "                         index=exps)\n",
        "exp_setup.set_index('Exp', drop=True, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qzxpGeVOau9a",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "| Exp | Model  | Loss        | Optim |\n",
        "|-----|--------|-------------|-------|\n",
        "| 1   | bilstm | mae\\_loss   | adam  |\n",
        "| 2   | bilstm | huber\\_loss | adam  |\n",
        "| 3   | bilstm | mae\\_loss   | adamw |\n",
        "| 4   | bilstm | huber\\_loss | adamw |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0aBvdC7igY6",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Hyperparameter selection and training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AaopeEeEu6Ai",
        "outputId": "ee77d378-c078-4396-c72c-fb380d7a5e98",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "AnomalyLSTM(\n",
            "  (lstm): LSTM(4, 32, batch_first=True, bidirectional=True)\n",
            "  (linear1): Linear(in_features=64, out_features=12, bias=True)\n",
            "  (linear2): Linear(in_features=12, out_features=4, bias=True)\n",
            "  (dropout): Dropout1d(p=0.1, inplace=False)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "batch = 32\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "bilstm_model = AnomalyLSTM(input_size=4, hidden_dim=32, num_layers=1, p=0.1).to(device)\n",
        "print(bilstm_model)\n",
        "learning_rate = 0.0002\n",
        "mae_loss = nn.L1Loss('mean')\n",
        "huber_loss = nn.SmoothL1Loss('mean')\n",
        "adam_optim = optim.Adam(bilstm_model.parameters(), lr=learning_rate)\n",
        "adamw_optim = optim.AdamW(bilstm_model.parameters(), lr=learning_rate)\n",
        "train_loader = data.DataLoader(train_data, shuffle=False, batch_size=batch)\n",
        "test_loader = data.DataLoader(test_data, shuffle=False, batch_size=batch)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jmjuIRdX6ewv"
      },
      "outputs": [],
      "source": [
        "exp = 4\n",
        "weights_dir = os.path.join(os.getcwd(), 'params')\n",
        "os.makedirs(weights_dir, exist_ok=True)\n",
        "\n",
        "config = {'exp': exp, 'epochs': 50,  'model':bilstm_model, 'loss_fn':mae_loss,\n",
        "          'optimizer': adamw_optim, 'train_loader': train_loader,'eval_loader': test_loader,\n",
        "          'device': device, 'logging': 10, 'params' : weights_dir}\n",
        "if exp in [1, 2]:\n",
        "    config['optimizer'] = adam_optim\n",
        "if exp in [2, 4]:\n",
        "    config['loss_fn'] = huber_loss\n",
        "elif exp > 4:\n",
        "    assert exp > 4, 'Experiment not in the experimental setup'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HxhyZwTKu6Ai",
        "outputId": "950c789d-b9b8-4da4-9a26-bcb3200faca6",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running experiment 4\n",
            "Model        bilstm\n",
            "Loss     huber_loss\n",
            "Optim         adamw\n",
            "Name: 4, dtype: object\n",
            "........Training Starting.......\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " 22%|██▏       | 11/50 [00:01<00:05,  7.70it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " \n",
            "Epoch 10: | train loss: 0.0189\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " 42%|████▏     | 21/50 [00:02<00:03,  8.39it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " \n",
            "Epoch 20: | train loss: 0.0120\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " 62%|██████▏   | 31/50 [00:03<00:02,  8.60it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " \n",
            "Epoch 30: | train loss: 0.0099\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " 82%|████████▏ | 41/50 [00:04<00:01,  8.61it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " \n",
            "Epoch 40: | train loss: 0.0116\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 50/50 [00:06<00:00,  8.31it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " \n",
            "Epoch 50: | train loss: 0.0100\n",
            "Training time 00:00:06\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Run experiments\n",
        "print(f'Running experiment {exp}', f'{exp_setup.loc[exp]}', sep='\\n')\n",
        "training_metrics = train_lstm(**config)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T7-HeNFIigY6",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "  ## Metrics visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "8Zug1lvki8_-",
        "outputId": "47ff9d4e-8fc7-49e8-fa42-1c880576a8a5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 600x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_loss(training_metrics, f'bilstm_exp{exp}', figs_dir=plots_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IggBsIWPiSWA",
        "outputId": "273c3980-69c8-4147-c7fe-1d1641e5ea2e",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "16252"
            ]
          },
          "execution_count": 207,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "del training_metrics\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i0kplyktigY7",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "best_params = torch.load(os.path.join(weights_dir, f'best_exp_{exp}.pt'))\n",
        "bilstm_model.load_state_dict(best_params['model'], strict=False)\n",
        "train_preds, train_losses = predict(bilstm_model, train_loader, config['loss_fn'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XcN3l1ftTff_",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Scores and threshold Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "rZqRZrjqigY7",
        "outputId": "487cea18-d421-463c-a8ce-d018980229bd",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set(rc={'figure.figsize': (4, 3)})\n",
        "sns.set_style('whitegrid')\n",
        "sns.distplot(train_losses, bins=30, kde=True)\n",
        "plt.grid(color ='k', ls= '--', lw=0.5)\n",
        "plt.xlabel('Loss')\n",
        "plt.title('Training dataset avg_df2')\n",
        "plt.xticks(rotation=90)\n",
        "save_plot(plots_dir, plot_name=f'train_dist_exp{exp}')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRbHJnAmau9c",
        "outputId": "188d7dac-7f60-4fb2-99e5-2b271272d80e",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.00027452, 0.00194301, 0.00222419, 0.00919591, 0.0128285 ,\n",
              "       0.0251641 , 0.04036013])"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.percentile(train_losses, [0.85, 90, 91, 95, 97, 98, 99])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B1_D1tJwau9c",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "threshold = np.round(np.percentile(train_losses, 90), 4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "96y29v0zau9c",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "train_scores = pd.DataFrame(index=avg_df2.index)\n",
        "loss_column = 'huber_loss' if config['loss_fn'] == huber_loss else 'mae_loss'\n",
        "train_scores[loss_column] = train_losses\n",
        "train_scores['Anomaly'] =  [0 if l < threshold else 1 for l in train_losses]\n",
        "train_scores.to_csv(os.path.join(csv_files, f'bilstm_train_exp{exp}.csv'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p_xSazBIKyZh",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "threshold_ = np.array([threshold]*len(train_scores))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "LUK99XRS_VDG",
        "outputId": "8277d7cd-8d89-432e-d8f2-32322568a453",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x250 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_anomaly_threshold(train_scores, plot_name=f'bilstm_thresh_exp{exp}', thresh=threshold_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "id": "xFViSaZpTNq0",
        "outputId": "155682a6-3837-43aa-938b-caa62e1d7e91",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x250 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "bilstm_predicted_anomalies(train_scores, size=(10, 2.5), plot_name=f'bilstm_val_pred_anom_exp{exp}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5bUgAExqaWDn"
      },
      "outputs": [],
      "source": [
        "preds_array = scaler.inverse_transform(np.array(train_preds))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 908
        },
        "id": "jPs7oZswjz_l",
        "outputId": "9788c4fe-c57b-41b5-ac5c-5609f6fe066d",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_reconstruction(avg_df2, preds_array, plot_name=f'bilstm_rec_exp{exp}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WcTLvP_UDJl",
        "outputId": "f76f3d45-ff30-4898-97ca-1bd2ca0403c5",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "20008"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "del train_preds,\n",
        "del train_losses\n",
        "del train_scores\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G5OqkHS1igY7",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Predict on unseen data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCZAeHU8j__f",
        "outputId": "580954bc-4407-4eb2-b15e-70712ba96e57",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bilstm_model.load_state_dict(best_params['model'], strict=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YdQ0j0VUigY7",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "test_preds, test_losses = predict(bilstm_model, test_loader, config['loss_fn'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "uw3CdYIDkZI2",
        "outputId": "827f2aa4-f095-4a50-af2d-fe425cbfa14d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set(rc={'figure.figsize': (4, 3)})\n",
        "sns.set_style('whitegrid')\n",
        "sns.distplot(avg_df3.values, bins=30, kde=True)\n",
        "plt.grid(color ='k', ls= '--', lw=0.5)\n",
        "plt.xlabel('Avg signal')\n",
        "plt.title('Dataset distribution avg_df3')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-YhMKyyoka2r",
        "outputId": "b2a5e44a-b691-4c42-a33a-50e7cf3dc654"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.06192544, 0.06287408, 0.06436785, 0.06681847, 0.06948128,\n",
              "       0.08007517, 0.10683016])"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.quantile(avg_df3.values, [0.8, 0.85, 0.90, 0.95, 0.97, 0.98, 0.99])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "pe_vI5qXwK2h",
        "outputId": "77285aac-c29e-4e1c-c75f-06cf525ff171",
        "pycharm": {
          "name": "#%%\n"
        },
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figsize = (5,5)\n",
        "sns.set_style('whitegrid', {\"grid.color\": 'k', 'grid.linestyle': '--'})\n",
        "sns.distplot(test_losses, bins=30, kde=True)\n",
        "plt.title('Test dataset avg_df3')\n",
        "plt.xlabel('Loss')\n",
        "plt.grid(lw=0.5, c='k')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lipbP43mau9h",
        "outputId": "712fda90-df36-4af7-d0ed-70b7ab3b3029",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.00214448, 0.0022975 , 0.003415  , 0.01104665, 0.05089649])"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.percentile(test_losses, [90, 95, 97, 98, 99])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLsRGQLbau9h",
        "outputId": "27d0dad3-4f13-41ec-d9f7-a7a9f82ff08c",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.0034"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_thresh = np.round(np.percentile(test_losses, 97), 4)\n",
        "test_thresh"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s5jLMTSbTxri",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "threshold_ = np.array([test_thresh]*len(test_losses))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pitk1xrUigY8",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "test_scores = pd.DataFrame(index=avg_df3.index)\n",
        "test_scores[loss_column] = test_losses\n",
        "test_scores['Anomaly'] =  [0 if l < test_thresh else 1 for l in test_losses]\n",
        "test_scores.to_csv(os.path.join(csv_files, f'bilstm_test_exp{exp}.csv'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "u441tAXkVYEz",
        "outputId": "e516c5f2-88dd-4006-9d98-7b761306554c",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x250 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_anomaly_threshold(test_scores, thresh=threshold_, plot_name=f'test_thres_bilstm_exp_{exp}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "RvcS4qMHZnmw",
        "outputId": "12bffdc4-3aea-49ac-8c32-fbf9c41f57af",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x250 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Loss scores with anomalies\n",
        "bilstm_predicted_anomalies(test_scores, size=(10, 2.5),\n",
        "                         plot_name=f'test_bilstm_anom_exp{exp}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "scKsy5clesw5",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "# Revert scaling for reconstuction\n",
        "test_preds_array = scaler.inverse_transform(np.array(test_preds))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 908
        },
        "id": "DFKZSxZLigY8",
        "outputId": "c1caca2e-f264-4e2f-8e8c-ab57132dec14",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_reconstruction(avg_df3, test_preds_array, f'bilstm_test_preds_exp{exp}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ATU88xZTau9i",
        "outputId": "a34c91d7-ffdc-420c-971e-546978691741",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "30210"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "del threshold_, test_preds, test_losses, test_scores, test_preds_array\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "09swpEf1au9j",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## BiLSTM Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "vwV43YMKau9j",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Train dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MwV8yDMOau9j",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "from models_comparison import merge_csv_files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ygbB1Vm5au9j",
        "outputId": "aa8a9d1d-7bdc-419f-858d-a3d2ce4aa079",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['bilstm_train_exp1.csv', 'bilstm_train_exp2.csv', 'bilstm_train_exp3.csv', 'bilstm_train_exp4.csv']\n"
          ]
        }
      ],
      "source": [
        "train_lstm_anomalies = [d for d in dir_list(csv_files) if 'bilstm_train' in d]\n",
        "print(train_lstm_anomalies)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mY62Dc8Rau9j",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "bilstm_train_anomalies = merge_csv_files(avg_df2.index, train_lstm_anomalies, path=csv_files)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IWCPXEpfau9j",
        "outputId": "927f76f2-b168-4591-d57a-714074dba0db",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Anomalies detected by model\n",
            "exp1     99\n",
            "exp2     98\n",
            "exp3     99\n",
            "exp4    104\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print('Anomalies detected by model')\n",
        "print(bilstm_train_anomalies.sum(axis=0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6p_WFZTaau9k",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "# Filter the timestamps where one or more models detected an anomaly\n",
        "train_earliest_detect = bilstm_train_anomalies[bilstm_train_anomalies.sum(axis=1) >=1]\n",
        "\n",
        "# Filter the timestamps where two or more models detected an anomaly\n",
        "anomalies_two_models = train_earliest_detect[train_earliest_detect.sum(axis=1) > 1]\n",
        "\n",
        "# Filter the timestamps where all models detected an anomaly on the training dataset\n",
        "common_training_anomalies = train_earliest_detect[train_earliest_detect.sum(axis=1) ==\n",
        "                                                len(train_earliest_detect.columns)].index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qHmKiAyau9k",
        "outputId": "a30fc143-ee56-44c6-a1d4-b2c864e8b21a",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "======================================================================================================\n",
            "The model that first detected an anomaly on the train dataset was:\n",
            "exp4 starting from 2004-02-18 08:22:39\n",
            " \n",
            "======================================================================================================\n",
            "Two or more models detected anomalies from 2004-02-18 08:22:39\n",
            "Model: ['exp2' 'exp4']\n",
            " \n",
            "======================================================================================================\n",
            "All models detected anomalies in the train dataset on 97 timestamps starting from 2004-02-18 14:02:39\n",
            "\n",
            "======================================================================================================\n"
          ]
        }
      ],
      "source": [
        "print('='*102)\n",
        "print('The model that first detected an anomaly on the train dataset was:')\n",
        "print(f'{train_earliest_detect.head().sum(axis=0).nlargest(n=1).keys()[0]} starting'\n",
        "      f' from {train_earliest_detect.index[0]}')\n",
        "print(' ')\n",
        "print('='*102)\n",
        "\n",
        "print(f'Two or more models detected anomalies from {anomalies_two_models.index[0]}')\n",
        "print(f'Model: {anomalies_two_models.columns[np.where(anomalies_two_models.iloc[0])].values}')\n",
        "print(' ')\n",
        "print('='*102)\n",
        "print(f'All models detected anomalies in the train dataset'\n",
        "      f' on {len(common_training_anomalies)} timestamps starting from {common_training_anomalies[0]}')\n",
        "print('')\n",
        "print('='*102)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0H_VpN-au9k",
        "outputId": "de010845-0af3-4b15-f01a-b4d5a062a7bc",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==================================================================================================\n",
            "The earliest that an anomaly could have been detected by one or more models before failure was:\n",
            "0 days 22:00:00\n",
            " \n",
            "======================================================================================================\n",
            "The earliest that an anomaly could have been detected by two or more models before failure was: 0 days 22:00:00\n",
            " \n",
            "======================================================================================================\n",
            "The earliest that an anomaly could have been detected by all models before failure was: 0 days 16:20:00\n",
            " \n",
            "======================================================================================================\n"
          ]
        }
      ],
      "source": [
        "print('='*98)\n",
        "print('The earliest that an anomaly could have been detected by one or more models before failure was:')\n",
        "print(f'{bilstm_train_anomalies.index[-1] - train_earliest_detect.index[0]}')\n",
        "print(' ')\n",
        "print('='*102)\n",
        "print('The earliest that an anomaly could have been detected by',\n",
        "        'two or more models before failure was:')\n",
        "print(f'{bilstm_train_anomalies.index[-1] - bilstm_train_anomalies[bilstm_train_anomalies.sum(axis=1) > 1].index[0]}')\n",
        "print(' ')\n",
        "print('='*102)\n",
        "print(f'The earliest that an anomaly could have been detected by all models before failure'\n",
        "      f' was: {bilstm_train_anomalies.index[-1] - common_training_anomalies[0]}')\n",
        "print(' ')\n",
        "print('='*102)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TkkQufHcau9k",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dug6lrF7au9k",
        "outputId": "b4afb516-5675-4fde-8e34-9f9f3b9912f0",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['bilstm_train_exp1.csv', 'bilstm_train_exp2.csv', 'bilstm_train_exp3.csv', 'bilstm_train_exp4.csv']\n"
          ]
        }
      ],
      "source": [
        "test_lstm_anomalies = [d for d in dir_list(csv_files) if 'bilstm_test' in d]\n",
        "print(train_lstm_anomalies)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9s2wk9kGau9k",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "bilstm_test_anomalies = merge_csv_files(avg_df3.index, test_lstm_anomalies, path=csv_files)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9Uu1_fnau9l",
        "outputId": "d6aa077c-101e-44e6-87fb-6781cae57ea8",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Anomalies detected by model:\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "exp1    190\n",
              "exp2    191\n",
              "exp3    190\n",
              "exp4    191\n",
              "dtype: int64"
            ]
          },
          "execution_count": 153,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print('Anomalies detected by model:')\n",
        "bilstm_test_anomalies.sum(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vmGJRFFyau9l",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "detect_test_anomalies = bilstm_test_anomalies[(bilstm_test_anomalies.sum(axis=1)>=1)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IOU98oK4au9l",
        "outputId": "e8c0caec-35e7-4cf9-be44-317519d4a34c",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Anomalies detected by model before 2004-04-15\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "exp1    27\n",
              "exp2    29\n",
              "exp3    25\n",
              "exp4    28\n",
              "dtype: int64"
            ]
          },
          "execution_count": 155,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print('Anomalies detected by model before 2004-04-15')\n",
        "detect_test_anomalies[detect_test_anomalies.index < '2004-04-15'].sum(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m7a12z-3au9l",
        "outputId": "409d10a9-9ce8-4ff1-e452-ef98e89adea4",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Anomalies detected by model from 2004-04-15\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "exp1    163\n",
              "exp2    162\n",
              "exp3    165\n",
              "exp4    163\n",
              "dtype: int64"
            ]
          },
          "execution_count": 156,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print('Anomalies detected by model from 2004-04-15')\n",
        "detect_test_anomalies[detect_test_anomalies.index >= '2004-04-15'].sum(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "esjvG9FDau9l",
        "outputId": "13d7dab4-86ad-4dca-d007-0ccee691e435",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Models that first detected an anomaly from 2004-04-15 :\n",
            "['exp3']\n",
            "Timestamp: 2004-04-16 23:22:55\n"
          ]
        }
      ],
      "source": [
        "test_tail_anomalies = detect_test_anomalies[detect_test_anomalies.index >= '2004-04-15']\n",
        "print('Models that first detected an anomaly from 2004-04-15 :')\n",
        "print(test_tail_anomalies.columns[np.where(test_tail_anomalies.iloc[0]==1)].values)\n",
        "print(f\"Timestamp: {test_tail_anomalies.index[0]}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JqQudF6U3CbG",
        "outputId": "fb186906-066b-45ab-9e09-1fd542a3602c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Lead time before breakdown from the fisrt detection:\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Timedelta('1 days 03:20:00')"
            ]
          },
          "execution_count": 116,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print('Lead time before breakdown from the fisrt detection:')\n",
        "test_tail_anomalies.index[-1] - test_tail_anomalies.index[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uqHF6YlB6F44",
        "outputId": "79656b06-38c6-442c-9171-e45b54f06562"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Lead time before breakdown detected by at leats two models:\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Timedelta('1 days 03:00:00')"
            ]
          },
          "execution_count": 159,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print('Lead time before breakdown detected by at leats two models:')\n",
        "test_tail_anomalies.index[-1] - test_tail_anomalies[test_tail_anomalies.sum(axis=1)>1].index[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dXAtO1Fuau9l",
        "outputId": "f481450e-279c-4f33-859c-16343c1ba28d",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "7828"
            ]
          },
          "execution_count": 160,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "del test_tail_anomalies, detect_test_anomalies\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qyDblZTGhRVF"
      },
      "source": [
        "\n",
        "\n",
        "## Cluster Metrics\n",
        "\n",
        "\n",
        "In this section, we will utilize three metrics, the silhouette coefficient, Calinski-Harabaz and Davies-Boulding indexes, to assess the cluster quality generated by each unsupervised model.\n",
        "\n",
        "Note: Adapted from scikit-learn\n",
        "https://scikit-learn.org/stable/modules/model_evaluation.html#clustering-metrics\n",
        "\n",
        "\n",
        "1. **Silhouette Score** is the mean Silhouette Coefficient for all clusters, which is calculated using the mean intra-cluster distance and the mean nearest-cluster distance. This score is between -1 and 1; the higher the score, the more well-defined and distinct the clusters are.\n",
        "\n",
        "1. **Calinski-Harabaz Index** is calculated using the between-cluster and within-cluster dispersion to measure the distinctiveness between groups. Like the Silhouette Score, the higher the score, the more well-defined the clusters are. This score has no bound, meaning no ‘acceptable’ or ‘good’ value exists.\n",
        "\n",
        "3. **Davies-Bouldin Index** is the average similarity of each cluster with its most similar cluster. Unlike the previous two metrics, this score measures the similarity of the clusters, meaning that the lower the score, the better the separation between the clusters. Zero is the lowest possible score. Values closer to zero indicate a better partition.The usage of centroid distance limits the distance metric to Euclidean space.\n",
        "\n",
        "The disadvantage of the three metrics is that they generally score higher for convex clusters than other concepts of clusters. Therefore further compare their performance with **non-parametric statistics**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7NpzLaI4oxA4"
      },
      "outputs": [],
      "source": [
        "from models_comparison import concat_dataframes, friedman_conover_comparison, compare_clusters_metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4QsJVCFQ9ach"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import pairwise_distances, silhouette_samples\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oJb7oLzQ9c35"
      },
      "outputs": [],
      "source": [
        "def compare_clusters_metrics(df_raw, df_cluster):\n",
        "    \"\"\"\n",
        "    :param df_raw: raw data dataframe\n",
        "    :param df_cluster: dataframe with cluster labels\n",
        "    :return: data frame with the cluster metrics scores\n",
        "    \"\"\"\n",
        "    model_scores = {}\n",
        "    for col in df_cluster.columns:\n",
        "        sil = metrics.silhouette_score(df_raw, df_cluster[col])\n",
        "        cal_har = metrics.calinski_harabasz_score(df_raw, df_cluster[col])\n",
        "        dav_boul = metrics.davies_bouldin_score(df_raw, df_cluster[col])\n",
        "        model_scores[col] = [sil, cal_har, dav_boul]\n",
        "\n",
        "    cluster_metrics = pd.DataFrame.from_dict(model_scores).T\n",
        "    cluster_metrics.columns = ['silhoutte', 'calinski_harabasz', 'davies_bouldin']\n",
        "\n",
        "    return  cluster_metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m7-dWPuBau9m",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Train dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ay3HrKtsau9m",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "# read pycaret anomalies csv file\n",
        "pc_train_anomalies = read_concat_data(csv_files, 'unsupervised_anomaly_pred.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "heojCYGZau9m",
        "outputId": "de100207-1bf5-4874-c100-459ef7bde6b0",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "cluster       50\n",
              "histogram     50\n",
              "iforest       50\n",
              "knn           50\n",
              "mcd           50\n",
              "svm           50\n",
              "exp1          99\n",
              "exp2          98\n",
              "exp3          99\n",
              "exp4         104\n",
              "dtype: int64"
            ]
          },
          "execution_count": 131,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Concatenate training results\n",
        "training_anomalies = concat_dataframes([pc_train_anomalies, bilstm_train_anomalies])\n",
        "training_anomalies.to_csv(os.path.join(csv_files, 'training_anomalies.csv'))\n",
        "training_anomalies.sum(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "id": "NkU7KdlS9oc-",
        "outputId": "b256eed9-4b48-4299-e5e0-b8d637d605b9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-c82eb904-b200-4d36-bdbc-12bd8aca6ef2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>silhoutte</th>\n",
              "      <th>calinski_harabasz</th>\n",
              "      <th>davies_bouldin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>cluster</th>\n",
              "      <td>0.7762</td>\n",
              "      <td>646.9312</td>\n",
              "      <td>0.8175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>histogram</th>\n",
              "      <td>0.8124</td>\n",
              "      <td>1001.4351</td>\n",
              "      <td>0.6754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>iforest</th>\n",
              "      <td>0.8124</td>\n",
              "      <td>992.9829</td>\n",
              "      <td>0.6791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>0.8017</td>\n",
              "      <td>897.6890</td>\n",
              "      <td>0.7167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mcd</th>\n",
              "      <td>0.8124</td>\n",
              "      <td>1004.5345</td>\n",
              "      <td>0.6739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>0.8140</td>\n",
              "      <td>1011.3661</td>\n",
              "      <td>0.6723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exp1</th>\n",
              "      <td>0.7442</td>\n",
              "      <td>879.5458</td>\n",
              "      <td>0.7615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exp2</th>\n",
              "      <td>0.7470</td>\n",
              "      <td>892.5695</td>\n",
              "      <td>0.7562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exp3</th>\n",
              "      <td>0.7442</td>\n",
              "      <td>879.5458</td>\n",
              "      <td>0.7615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exp4</th>\n",
              "      <td>0.7386</td>\n",
              "      <td>875.7697</td>\n",
              "      <td>0.7640</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c82eb904-b200-4d36-bdbc-12bd8aca6ef2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-0183ed57-c16e-4263-bca2-c17cbe85be38\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0183ed57-c16e-4263-bca2-c17cbe85be38')\"\n",
              "              title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
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              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "    domScope.querySelector('#df-0183ed57-c16e-4263-bca2-c17cbe85be38 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c82eb904-b200-4d36-bdbc-12bd8aca6ef2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c82eb904-b200-4d36-bdbc-12bd8aca6ef2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           silhoutte  calinski_harabasz  davies_bouldin\n",
              "cluster       0.7762           646.9312          0.8175\n",
              "histogram     0.8124          1001.4351          0.6754\n",
              "iforest       0.8124           992.9829          0.6791\n",
              "knn           0.8017           897.6890          0.7167\n",
              "mcd           0.8124          1004.5345          0.6739\n",
              "svm           0.8140          1011.3661          0.6723\n",
              "exp1          0.7442           879.5458          0.7615\n",
              "exp2          0.7470           892.5695          0.7562\n",
              "exp3          0.7442           879.5458          0.7615\n",
              "exp4          0.7386           875.7697          0.7640"
            ]
          },
          "execution_count": 132,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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              "<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
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          "output_type": "display_data"
        },
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-67a5d2fc-9d30-4cf0-bbd4-643c060043e2\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-67a5d2fc-9d30-4cf0-bbd4-643c060043e2\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_1714233288803544549 = autoviz.get_df('df_1714233288803544549')\n",
              "\n",
              "def value_plot(df, y, sort_ascending=False, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  if sort_ascending:\n",
              "    df = df.sort_values(y).reset_index(drop=True)\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  df[y].plot(kind='line')\n",
              "  plt.title(y)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(df_1714233288803544549, *['silhoutte'], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-d642ea05-0c43-49b9-8a64-04b733ce92d4\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-d642ea05-0c43-49b9-8a64-04b733ce92d4\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_1714233288803544549 = autoviz.get_df('df_1714233288803544549')\n",
              "\n",
              "def value_plot(df, y, sort_ascending=False, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  if sort_ascending:\n",
              "    df = df.sort_values(y).reset_index(drop=True)\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  df[y].plot(kind='line')\n",
              "  plt.title(y)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(df_1714233288803544549, *['calinski_harabasz'], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-b15d74ba-c1f7-4bc1-b3f5-a2ecbe93064c\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-b15d74ba-c1f7-4bc1-b3f5-a2ecbe93064c\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_1714233288803544549 = autoviz.get_df('df_1714233288803544549')\n",
              "\n",
              "def value_plot(df, y, sort_ascending=False, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  if sort_ascending:\n",
              "    df = df.sort_values(y).reset_index(drop=True)\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  df[y].plot(kind='line')\n",
              "  plt.title(y)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(df_1714233288803544549, *['davies_bouldin'], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ],
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x78aecbded360>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-3513593f-5d67-44ae-aa49-c7f02b5bc5ce\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-3513593f-5d67-44ae-aa49-c7f02b5bc5ce\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_1714233288803544549 = autoviz.get_df('df_1714233288803544549')\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  plt.hist(df[colname], bins=num_bins, histtype='stepfilled')\n",
              "  plt.ylabel('count')\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(df_1714233288803544549, *['silhoutte'], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-c4025f8b-710a-4e57-906b-b4992a5a9501\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-c4025f8b-710a-4e57-906b-b4992a5a9501\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_1714233288803544549 = autoviz.get_df('df_1714233288803544549')\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  plt.hist(df[colname], bins=num_bins, histtype='stepfilled')\n",
              "  plt.ylabel('count')\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(df_1714233288803544549, *['calinski_harabasz'], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-e8bd2a31-13f6-4f59-b401-2630565f96a0\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-e8bd2a31-13f6-4f59-b401-2630565f96a0\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_1714233288803544549 = autoviz.get_df('df_1714233288803544549')\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  plt.hist(df[colname], bins=num_bins, histtype='stepfilled')\n",
              "  plt.ylabel('count')\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(df_1714233288803544549, *['davies_bouldin'], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ],
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x78aecbc13580>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_1714233288803544549 = autoviz.get_df('df_1714233288803544549')\n",
              "\n",
              "def scatter_plots(df, colname_pairs, scatter_plot_size=2.5, size=8, alpha=.6):\n",
              "  from matplotlib import pyplot as plt\n",
              "  plt.figure(figsize=(len(colname_pairs) * scatter_plot_size, scatter_plot_size))\n",
              "  for plot_i, (x_colname, y_colname) in enumerate(colname_pairs, start=1):\n",
              "    ax = plt.subplot(1, len(colname_pairs), plot_i)\n",
              "    ax.scatter(df[x_colname], df[y_colname], s=size, alpha=alpha)\n",
              "    plt.xlabel(x_colname)\n",
              "    plt.ylabel(y_colname)\n",
              "    ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = scatter_plots(df_1714233288803544549, *[[['silhoutte', 'calinski_harabasz'], ['calinski_harabasz', 'davies_bouldin']]], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "train_clusters_scores = compare_clusters_metrics(avg_df2, training_anomalies).round(4)\n",
        "train_clusters_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HVzQuBIWqZHV"
      },
      "source": [
        "### __Training metrics resulst__\n",
        "\n",
        "Top Silhoutte scores by model\n",
        "\n",
        "*   Histogram, IForest, MCD\n",
        "\n",
        "Top Calinski-Harabasz indexes by model\n",
        "*   MCD, SVM, Histogram\n",
        "\n",
        "Top Davies-Bouldin indexes\n",
        "\n",
        "*   SVM, MCD, Histogram\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhU37bY7uTBi"
      },
      "source": [
        "### Test Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SenRiVo0au9n",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "pc_test_anomalies = read_concat_data(csv_files, 'unsupervised_test_anomaly.csv')\n",
        "test_anomalies = concat_dataframes([pc_test_anomalies, bilstm_test_anomalies])\n",
        "test_anomalies.to_csv(os.path.join(csv_files, 'test_anomalies.csv'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tfoPR0bHnF9J",
        "outputId": "2a7553c6-63f2-4e9d-b8a4-0cd194b5be44"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "iforest    187\n",
              "mcd         78\n",
              "svm         95\n",
              "exp1       190\n",
              "exp2       191\n",
              "exp3       190\n",
              "exp4       191\n",
              "dtype: int64"
            ]
          },
          "execution_count": 134,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_anomalies.sum(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "92GEaaMonhsx",
        "outputId": "2e84b0a2-fe88-412f-bfe7-352ddeda94e9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>silhoutte</th>\n",
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              "    </tr>\n",
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              "      <th>svm</th>\n",
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              "      <td>16564.1182</td>\n",
              "      <td>0.3939</td>\n",
              "    </tr>\n",
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              "      <th>exp1</th>\n",
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              "      <td>8389.7269</td>\n",
              "      <td>0.6920</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6aaa8273-0f86-4308-8596-3a9bcf0ee726 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6aaa8273-0f86-4308-8596-3a9bcf0ee726');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         silhoutte  calinski_harabasz  davies_bouldin\n",
              "iforest     0.9230          7629.4839          0.7218\n",
              "mcd         0.9557         15873.0144          0.3554\n",
              "svm         0.9533         16564.1182          0.3939\n",
              "exp1        0.9263          8549.1850          0.6857\n",
              "exp2        0.9256          8389.7269          0.6920\n",
              "exp3        0.9267          8656.1093          0.6818\n",
              "exp4        0.9256          8433.0381          0.6902"
            ]
          },
          "execution_count": 135,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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          "output_type": "display_data"
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              "RvL/AdrgORfwvOd1AAAAAElFTkSuQmCC\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-e935bf32-34ca-4a84-bdf2-d7dc0016ee0d\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-e935bf32-34ca-4a84-bdf2-d7dc0016ee0d\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_4074457464789776967 = autoviz.get_df('df_4074457464789776967')\n",
              "\n",
              "def value_plot(df, y, sort_ascending=False, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  if sort_ascending:\n",
              "    df = df.sort_values(y).reset_index(drop=True)\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  df[y].plot(kind='line')\n",
              "  plt.title(y)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(df_4074457464789776967, *['silhoutte'], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-117d54f1-fefb-4962-9f6e-0c22b3521d0a\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-117d54f1-fefb-4962-9f6e-0c22b3521d0a\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_4074457464789776967 = autoviz.get_df('df_4074457464789776967')\n",
              "\n",
              "def value_plot(df, y, sort_ascending=False, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  if sort_ascending:\n",
              "    df = df.sort_values(y).reset_index(drop=True)\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  df[y].plot(kind='line')\n",
              "  plt.title(y)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(df_4074457464789776967, *['calinski_harabasz'], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-ceb8bb8b-fbe7-4915-9dac-7a5b35fe154d\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-ceb8bb8b-fbe7-4915-9dac-7a5b35fe154d\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_4074457464789776967 = autoviz.get_df('df_4074457464789776967')\n",
              "\n",
              "def value_plot(df, y, sort_ascending=False, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  if sort_ascending:\n",
              "    df = df.sort_values(y).reset_index(drop=True)\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  df[y].plot(kind='line')\n",
              "  plt.title(y)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(df_4074457464789776967, *['davies_bouldin'], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ],
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x78aed0893880>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-ea27a8a4-3fa5-4e3a-8989-2e9587a7d23a\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-ea27a8a4-3fa5-4e3a-8989-2e9587a7d23a\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_4074457464789776967 = autoviz.get_df('df_4074457464789776967')\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  plt.hist(df[colname], bins=num_bins, histtype='stepfilled')\n",
              "  plt.ylabel('count')\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(df_4074457464789776967, *['silhoutte'], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-b76b666f-55a5-4037-a153-1c9106cc34c6\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-b76b666f-55a5-4037-a153-1c9106cc34c6\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_4074457464789776967 = autoviz.get_df('df_4074457464789776967')\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  plt.hist(df[colname], bins=num_bins, histtype='stepfilled')\n",
              "  plt.ylabel('count')\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(df_4074457464789776967, *['calinski_harabasz'], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-2fbce7e5-3028-4988-bb87-9d3e6a7c4142\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-2fbce7e5-3028-4988-bb87-9d3e6a7c4142\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_4074457464789776967 = autoviz.get_df('df_4074457464789776967')\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  plt.hist(df[colname], bins=num_bins, histtype='stepfilled')\n",
              "  plt.ylabel('count')\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(df_4074457464789776967, *['davies_bouldin'], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ],
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x78aed080c670>"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-e10c3f87-ab5a-4a5d-9fd2-1b000fbd71f9\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-e10c3f87-ab5a-4a5d-9fd2-1b000fbd71f9\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ],
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_4074457464789776967 = autoviz.get_df('df_4074457464789776967')\n",
              "\n",
              "def scatter_plots(df, colname_pairs, scatter_plot_size=2.5, size=8, alpha=.6):\n",
              "  from matplotlib import pyplot as plt\n",
              "  plt.figure(figsize=(len(colname_pairs) * scatter_plot_size, scatter_plot_size))\n",
              "  for plot_i, (x_colname, y_colname) in enumerate(colname_pairs, start=1):\n",
              "    ax = plt.subplot(1, len(colname_pairs), plot_i)\n",
              "    ax.scatter(df[x_colname], df[y_colname], s=size, alpha=alpha)\n",
              "    plt.xlabel(x_colname)\n",
              "    plt.ylabel(y_colname)\n",
              "    ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = scatter_plots(df_4074457464789776967, *[[['silhoutte', 'calinski_harabasz'], ['calinski_harabasz', 'davies_bouldin']]], **{})\n",
              "chart"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "test_clusters_scores = compare_clusters_metrics(avg_df3, test_anomalies).round(4)\n",
        "test_clusters_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYsY_FjTau9m",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "# Nonparametric  statistical comparison - PyCaret Models vs BiLSTM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7oGoBKInau9m",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "from models_comparison import concat_dataframes, friedman_conover_comparison"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4RCcoerau9m",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "---\n",
        "In this section the models will be compared by using nonparametrics statistics. The\n",
        "distributions are not normal and the data come from repeated measures of the same datasets.\n",
        "\n",
        "* __Nonparametric test - Friedmanchisquare__\n",
        "* __Posthoc test - Friedman-Conover__\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "o9GcYBUKau9m",
        "outputId": "1b004ef6-58cf-4f39-8866-0a030cc2c4de",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "p_value : 8.852114320734602e-75, we can reject the null hypothesis H0 with 95% certainty\n",
            " \n",
            "Post-hoc Conover-Friedman multiple comparison is applied\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " \n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x200 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "train_stats, tr_p, train_ranks = friedman_conover_comparison(training_anomalies,\n",
        "                                                             plot_name='train_models')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A27lLdjdau9m",
        "outputId": "bfed3570-2683-4e2b-e338-6a745df8cf0b",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "model\n",
              "exp4         0.5673\n",
              "exp1         0.5647\n",
              "exp3         0.5647\n",
              "exp2         0.5642\n",
              "cluster      0.5398\n",
              "histogram    0.5398\n",
              "iforest      0.5398\n",
              "knn          0.5398\n",
              "mcd          0.5398\n",
              "svm          0.5398\n",
              "Name: anomaly, dtype: float64"
            ]
          },
          "execution_count": 89,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_ranks.sort_values(axis=0, ascending=False).round(4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EgswrHNbau9n",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "## Test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "eaa7RfQDau9n",
        "outputId": "ca60b880-deb5-48f2-f7fe-d32632437d18",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "p_value : 6.509298258813597e-109, we can reject the null hypothesis H0 with 95% certainty\n",
            " \n",
            "Post-hoc Conover-Friedman multiple comparison is applied\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " \n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x200 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "test_statistic, test_p, test_ranks = friedman_conover_comparison(test_anomalies,\n",
        "                                                                 plot_name='test_models')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eXDnrnfDau9n",
        "outputId": "f5b0f495-6143-42a4-81f5-db036c1e2875",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "model\n",
            "exp2       0.573857\n",
            "exp4       0.573857\n",
            "exp1       0.573778\n",
            "exp3       0.573778\n",
            "iforest    0.573541\n",
            "svm        0.566267\n",
            "mcd        0.564923\n",
            "Name: anomaly, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(test_ranks.sort_values(axis=0, ascending=False))"
      ]
    },
    {
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      "execution_count": null,
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        "colab": {
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        "outputId": "2558171f-8cbd-45ff-837d-c425de1f7b4f",
        "pycharm": {
          "name": "#%%del train_preds,\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4602"
            ]
          },
          "execution_count": 100,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "del pc_train_anomalies, bilstm_train_anomalies, training_anomalies, avg_df2\n",
        "del pc_test_anomalies, bilstm_test_anomalies, test_anomalies, avg_df3\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "H1xg9feLau9o",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "# Statistical Summary\n",
        "---\n",
        "\n",
        "**Training**\n",
        "\n",
        "\n",
        "*Cluster Metrics*\n",
        "\n",
        "The models that obtained the highest Calinski-Harabasz and Davies-Bouldin indexes were SVM, MCV and Histogram. These models, in addition to IForest, showed the highest Silhouette scores.\n",
        "\n",
        "*Non-parametric Comparison*\n",
        "\n",
        "Statistically different\n",
        "\n",
        "* From the nonparametric statistical Conover-Friedman test, we found a significant difference in detecting anomalies among the models with 95 \\% certainty.\n",
        "\n",
        "* For the training dataset, there is no significant difference in the performance of each Pycaret model. However, all BiLSTM experiments were significantly different to all PyCaret models.\n",
        "* All BiLSTM experiments were not significantly different from each other, as shown in the Friedman-Conover and critical difference diagrams.\n",
        "* Exp-04 ranked the highest scored but was not significantly different to __Exp-02__ to __Exp-04__. The anomaly could have been detected with 22:00 lead time with Exp-02 and Exp-04, while for the PyCaret models, the Clustering-Based Local outlier **cluster** could have detected the anomalies 14:40 hr in advance.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "**Test**\n",
        "\n",
        "*Cluster Metrics*\n",
        "\n",
        "MCD and SVM obtained the highest Silhouette scores and Calinski-Harabasz and Davies-Bouldin indexes.\n",
        "\n",
        "\n",
        "* From the nonparametric test, we can reject the null hypothesis that the performance of all models at detecting anomalies is not significantly different with 95% certainty\n",
        "\n",
        "* The ranking of the models shows that the best model and the Conover-Friedman test and critical difference diagram showed no statistical difference amongst experiments Exp-01 to Exp-04 and iForest with 95% certainty.\n",
        "* Similarly, MCD and SVM showed no significant difference in detecting anomalies but significantly differed from all other models.\n",
        "\n",
        "* Experiments Exp-02 and Exp-04 minimised which objective function to minimised was the Huber-Loss ranked the highest of all models.\n",
        "* The Conover-Friedman test and critical difference diagram showed no statistical difference amongst experiments Exp-01 to Exp-04 and iForest.  \n",
        "* Any of the BiLSTM models could have detected the anomaly within a 27:00 hr lead time, specifically Exp-03 within 27:20:00, while iforest detected the anomalies within a lead time of 25:30:00.\n",
        "\n",
        "---\n"
      ]
    },
    {
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      "metadata": {
        "collapsed": false,
        "id": "9psK2h5_au9o",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "# Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "wfSZzB8bau9o",
        "pycharm": {
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      },
      "source": [
        "In summary, Exp-04 consistently obtained the best performance in both datasets. The test dataset was almost six times larger than the train set. It also presented spikes at the beginning and middle of the test. We can observe that the data's size and quality impact the model's choice to detect failures. Models Histogram, Cluster, and KNN were excluded in the test comparison since they accounted for more than 50% in the test dataset which is inacurate as per the data visualisations. These model showed to be less robust for unseen data.\n",
        "\n",
        "Conversely, the IForest model took the lead in detecting anomalies together with Exp-01 and Exp-04 with no significant difference in their performance for the unseen test dataset. It showed to be more robust to changes independently of datset size and the unknown contamination.\n",
        "\n",
        "We can conclude that the PyCaret Anomalies models selected in this work and the Bilstm (Bidirectional LSTM) Artificial Neural Networks can detect failures on the bearing sensors' signals at the same performance level. Whether these models can detect failures days or weeks in advance in other unseen data requires further testing and optimisation."
      ]
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