notebooks/ArimaFD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline for the anomaly detection on the SKAB using ARIMA fault detection algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Details regarding the SKAB one can find in the [SKAB repository](https://github.com/waico/SKAB)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea behind this algorithm is to use ARIMA weights as features for the anomaly detection algorithm. Using discrete differences of weight coefficients for different heuristic methods for obtaining function, which characterized the state (anomaly, not anomaly) using a threshold. \n",
    "\n",
    "Links at [PyPi](https://pypi.org/project/arimafd/), [GitHub](https://github.com/waico/arimafd) and [paper](https://waico.ru)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# libraries importing\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import linalg\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from itertools import product\n",
    "from sympy import diff, symbols, sympify, Symbol, poly\n",
    "from time import time\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "# additional modules\n",
    "import sys\n",
    "sys.path.append('../algorithms')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Put the `data` folder from the SKAB repository in the root of this repository. So the path will be following: `../data/`. Or select your own path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_data = '../data/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# benchmark files checking\n",
    "all_files=[]\n",
    "import os\n",
    "for root, dirs, files in os.walk(path_to_data):\n",
    "    for file in files:\n",
    "        if file.endswith(\".csv\"):\n",
    "             all_files.append(os.path.join(root, file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# datasets with anomalies loading\n",
    "list_of_df = [pd.read_csv(file, \n",
    "                          sep=';', \n",
    "                          index_col='datetime', \n",
    "                          parse_dates=True) for file in all_files if 'anomaly-free' not in file]\n",
    "# anomaly-free df loading\n",
    "anomaly_free_df = pd.read_csv([file for file in all_files if 'anomaly-free' in file][0], \n",
    "                            sep=';', \n",
    "                            index_col='datetime', \n",
    "                            parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SKAB v0.9\n",
      "- A number of datasets: 34\n",
      "- Shape of the random dataset: (1147, 10)\n",
      "- A number of changepoints: 129\n",
      "- A number of outliers (point anomalies): 13067\n",
      "\n",
      "Head of the random dataset:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accelerometer1RMS</th>\n",
       "      <th>Accelerometer2RMS</th>\n",
       "      <th>Current</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Thermocouple</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Volume Flow RateRMS</th>\n",
       "      <th>anomaly</th>\n",
       "      <th>changepoint</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-01 15:44:06</th>\n",
       "      <td>0.082065</td>\n",
       "      <td>0.133521</td>\n",
       "      <td>1.27794</td>\n",
       "      <td>0.054711</td>\n",
       "      <td>92.2562</td>\n",
       "      <td>22.2577</td>\n",
       "      <td>209.639</td>\n",
       "      <td>76.0197</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accelerometer1RMS  Accelerometer2RMS  Current  Pressure  \\\n",
       "datetime                                                                       \n",
       "2020-03-01 15:44:06           0.082065           0.133521  1.27794  0.054711   \n",
       "\n",
       "                     Temperature  Thermocouple  Voltage  Volume Flow RateRMS  \\\n",
       "datetime                                                                       \n",
       "2020-03-01 15:44:06      92.2562       22.2577  209.639              76.0197   \n",
       "\n",
       "                     anomaly  changepoint  \n",
       "datetime                                   \n",
       "2020-03-01 15:44:06      0.0          0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dataset characteristics printing\n",
    "print('SKAB v0.9')\n",
    "print(f'- A number of datasets: {len(list_of_df)}')\n",
    "print(f'- Shape of the random dataset: {list_of_df[10].shape}')\n",
    "n_cp = sum([len(df[df.changepoint==1.]) for df in list_of_df])\n",
    "n_outlier = sum([len(df[df.anomaly==1.]) for df in list_of_df])\n",
    "print(f'- A number of changepoints: {n_cp}')\n",
    "print(f'- A number of outliers (point anomalies): {n_outlier}\\n')\n",
    "print(f'Head of the random dataset:')\n",
    "display(list_of_df[0].head(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# random dataset visualizing\n",
    "list_of_df[1].plot(figsize=(12, 3))\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Value')\n",
    "plt.title('Signals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labels visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the labels both for outlier and changepoint detection problems\n",
    "list_of_df[1].anomaly.plot(figsize=(12, 3))\n",
    "list_of_df[1].changepoint.plot()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method applying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arimafd import *\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.exists(r'tensors.pickle'):\n",
    "    with open(r'tensors.pickle', 'rb') as f:\n",
    "        tensors = pickle.load(f)\n",
    "else:\n",
    "    tensors = []\n",
    "    for df in list_of_df:\n",
    "        a = Anomaly_detection(df.iloc[:,:-2])\n",
    "        tensors.append(a.generate_tensor(ar_order=100))\n",
    "    with open(r'tensors.pickle', 'wb') as handle:\n",
    "        pickle.dump(tensors, handle, protocol=pickle.HIGHEST_PROTOCOL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XXX 1 20 20\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 18:20:42'), Timestamp('2020-02-08 18:20:16'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 18:51:37'), Timestamp('2020-02-08 18:51:25'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 19:26:28'), Timestamp('2020-02-08 19:26:07'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 18:51:37'), Timestamp('2020-02-08 18:51:25'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 19:26:28'), Timestamp('2020-02-08 19:26:07'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 17:17:12'), Timestamp('2020-02-08 17:16:43'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 17:24:11'), Timestamp('2020-02-08 17:23:44'))\n",
      "Standart  -  0.09\n",
      "LowFP  -  -1.53\n",
      "LowFN  -  0.94\n",
      "[[1, 20, 20, 0.09, -1.53, 0.94]]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "metrics = range(1,6)\n",
    "windows= [20,50,100,150]\n",
    "window_insensitivitys = [20,50,100,150]\n",
    "history = []\n",
    "from tsad.evaluating.evaluating import evaluating \n",
    "\n",
    "for No_metric,window,window_insensitivity in product(metrics,windows,window_insensitivitys):\n",
    "    print('XXX',No_metric,window,window_insensitivity)\n",
    "    predicted_cp=[]\n",
    "    predicted_cp1 = []\n",
    "    for i,df in enumerate(list_of_df):\n",
    "        acci = df.changepoint\n",
    "        a = Anomaly_detection(df)\n",
    "        a.tensor = tensors[i]\n",
    "        a.proc_tensor(No_metric=No_metric, window=window, window_insensitivity=window_insensitivity)\n",
    "        predicted_cp.append(a.bin_metric)\n",
    "    nab = evaluating(true_cp, predicted_cp, metric='nab', numenta_time='30 sec')\n",
    "    history.append([No_metric, window, window_insensitivity, nab['Standart'], nab['LowFP'], nab['LowFN']])\n",
    "    print(history)\n",
    "    print()\n",
    "    print()\n",
    "history = pd.DataFrame(history, columns=['No_metric','window','window_insensitivity','Standart','LowFP','LowFN'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(16,4))\n",
    "grid = gridspec.GridSpec(1, len(metrics),wspace =0.7)\n",
    "for i in metrics:\n",
    "    globals()['ax'+str(i)] = f.add_subplot(grid[i-1])\n",
    "    history[history.No_metric==i].plot.scatter(x='window',y='window_insensitivity', c='Standart', colormap='viridis',ax=globals()['ax'+str(i)])\n",
    "    globals()['ax'+str(i)].set_title(f\"Metric {i}\")\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "No_metric= 5\n",
    "window= 150\n",
    "window_insensitivity = 20\n",
    "\n",
    "predicted_cp=[]\n",
    "for i,df in enumerate(list_of_df):\n",
    "    acci = df.changepoint\n",
    "    a = Anomaly_detection(df)\n",
    "    a.tensor = tensors[i]\n",
    "    a.proc_tensor(No_metric=No_metric,window=window, window_insensitivity=window_insensitivity)\n",
    "    predicted_cp.append(a.bin_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ozdu4X4/eWktjOht711//eZu1+qcMvdCjiXreGNi7QIGiVlAeeilrfN0GAAAAGMF5SWdgM9xPXQhOE1WqbVSqcQ0gqQYAAAC8uvT2bxNkqYXgjtgE8dKnGteCoJun2irWrl1rdAgAAAC4Eo9KtTXqUebqU537TKUawcwaLQMAAABgBI9k0ARZaiG4QraZIF5XDOTUCGYk1QAAAIA3lqxU5z6bolJ94ZRZYGxkwG/WaBkAAAAAQ1ivT7UrZjOE665UGxwHEEgk1QAAAIA3VqxUXwjZZoKs2hWCw0FajeBljZYBAAAAMIIV+1SbqVJto1KN4EdSDb+0b99eI0eONDoMAACAwLJipdrdp9r4rPri6N/GxgEEkjVaBgAAAMAQ1utTfXH0b+O5YmCgMgQzkmoAAADAm0uTQYtUqp3ueaqNT6tdMZBTI5hZo2UIUitXrtQtt9yismXLqkKFCrrrrru0d+9eSdL+/ftls9m0dOlS3XrrrYqKitL111+vTZs2eezjgw8+UMOGDRUeHq6aNWtq2rRpHq/XrFlTzz77rJKSkhQdHa34+Hh99NFHOnLkiO6++25FR0ercePG2rJli3ubY8eO6YEHHlD16tUVFRWlxo0ba9GiRV7fx5QpU9S4ceM8yxMTEzVx4sSrOUUAAADGsmSf6lwmyKndp8xJr2oEseBLqp1OKfuUf48fl0r/GZP77M/2Pn4Fd+rUKY0ePVqbN2/W6tWrZbfbdc8998jhuNh3Z/z48RozZoxSUlJUp04dPfDAAzp//rwkaevWrerZs6fuv/9+bd++XZMmTdKECRM0b948j+O8+OKLat26tbZt26auXbuqb9++SkpK0oMPPqjvvvtOtWrVUlJSkvtbzbNnzyoxMVGffPKJfvzxRw0aNEh9+/bVN998k+/7eOSRR5SamqrNmze7l/3www/atm2b+vXr59M5AQAAMBWPPtVmyFKvzOF0DVRmfLyuSjV9qhHMQo0OoMidOy39X9Wr28fmN/zb7v87KIWVLPTqPXr08Pj3W2+9pUqVKik1NVXR0dGSpDFjxqhr166SpMmTJ6thw4bas2eP6tWrp+nTp+u2227ThAkTJEl16tRRamqqpk6d6pHM3nnnnRo8eLAkaeLEiZo1a5ZuvPFG/e1vf5MkjR07Vi1bttThw4dVuXJlVatWTWPGjHFvP3z4cK1cuVJLlixRixYt8ryP6tWrq3Pnzpo7d65uvPFGSdLcuXPVrl07XXfddYU+HwAAAOZDn+qrcbFPtaFhAAEVfJVqC9m7d6969+6t6667TqVLl1ZCQoIkKS0tzb1OkyZN3D9XqVJFkpSRkSFJ2rlzp1q3bu2xz9atW2v37t3KycnJdx+xsbGS5HG7tmuZa785OTn6f//v/6lJkyaqUKGCoqOj9fnnn3vEdbmBAwdq0aJFOnv2rM6dO6eFCxfqkUce8eFsAAAAmJCrUm2R/tTSxUq13QRZ9cVKNVk1glfwVapLROVWjH216zPp/YclW4jkzJHumyvV6ez7sX3QrVs3xcXF6Y033lDVqlXlcDjUqFEjZWdnX9xliRLun1238LhuD3c6nXlu68lvZMX89lHQfqdNm6YXX3xRM2bMUOPGjVWyZEmNHDnSI6783kt4eLg+/PBDhYeHKysrK08lHgAAwHLc11YmyFAL6WKfauNjNkEIQMAFX1Jts/l0C7Zbo3ul0Ahp/1dSzVukencWfWyXOHbsmHbu3KnXX39dbdq0kSR99dVXPu2jQYMGebbZuHGj6tSpo5CQEL9jW79+ve6++249+OCDknKT7d27d6t+/fpetwkNDdVDDz2kuXPnKjw8XPfff7+ionz7kgEAAMB0LFipdpqoUn2xeEOlGsHLr9Zh5syZSkhIUEREhBITE7V+/foC11+4cKGuv/56RUVFqUqVKnr44Yd17NgxvwIOqHp3Snf8X8ATakkqV66cKlSooNmzZ2vPnj1as2aNRo8e7dM+Hn/8ca1evVrPPPOMdu3apfnz5+vVV1/16A/tj1q1amnVqlXauHGjdu7cqcGDB+vQoUNX3G7AgAFas2aNPv30U279BgAAQcLVQdkEGWohOU00/LfNPfo3ELx8TqoXL16skSNHavz48dq2bZvatGmjLl26eO1v+9VXXykpKUn9+/fXjh07tGTJEm3evFkDBgy46uCtzG63691339XWrVvVqFEjjRo1SlOnTvVpH82aNdN7772nd999V40aNdLEiRM1ZcqUqx5xe8KECWrWrJk6d+6s9u3bq3LlyurevfsVt6tdu7ZatWqlunXr5jugGQAAgOVYsFJtrj7Vuc/0qUYw8/n27+nTp6t///7upHjGjBn67LPPNGvWLCUnJ+dZ/+uvv1bNmjU1YsQISVJCQoIGDx6sF1544SpDt76OHTsqNTXVY9mlfaIv7x9dtmzZPMt69OhRYN/l/fv351l2+T5q1qzpsax8+fJatmxZgbGvXbs23/0ePnzYPdI4AACA5VmxT7V79G/jY3bFQE6NYObTV27Z2dnaunWrOnXq5LG8U6dO2rhxY77btGrVSr/++qtWrFjhTrref/999zRR+cnKytKJEyc8HjC3jIwMTZ8+Xb/99psefvhho8MBAAAoGpasVOc+m6lSnd9gukCw8KlSffToUeXk5LinYHKJjY312ue2VatWWrhwoXr16qWzZ8/q/Pnz+utf/6pXXnnF63GSk5M1efJkX0KDwWJjY1WxYkXNnj1b5cqVMzocAACAImK9PtWumM0QsmugMlJqBDO/vnLLbxonb0P2p6amasSIEZo4caK2bt2qlStXat++fRoyZIjX/Y8bN06ZmZnux4EDB/wJE8XI6XTqyJEj6t27t9GhAAAAFB33vdTWq1SbaUot+lQjmPlUqa5YsaJCQkLyVKUzMjLyVK9dkpOT1bp1az3xxBOSpCZNmqhkyZJq06aNnn32WVWpUiXPNuHh4QoPD/clNAAAAKDoWbJP9YVKtcFxSPSpxrXBp6/cwsLClJiYqFWrVnksX7VqlVq1apXvNqdPn5bd7nkY1xzK9K0AAACAqbn7VJshRS2ci32qjY/54ujfxsYBBJLP97GMHj1ab775pubMmaOdO3dq1KhRSktLc9/OPW7cOCUlJbnX79atm5YuXapZs2bpl19+0YYNGzRixAjddNNNqlq1apG9EYfDUWT7gjXwOwcAAIFnvT7VJpqm+pJ5qsmqEbx8nlKrV69eOnbsmKZMmaL09HQ1atRIK1asUHx8vCQpPT3dY87qfv366eTJk3r11Vf1+OOPq2zZsurQoYOef/75InkDYWFhstvtOnjwoGJiYhQWFmaK/iMIHKfTqezsbB05ckR2u11hYWFGhwQAAIKVBUf/drrnqTb+mtgVAzeoIpj5nFRL0tChQzV06NB8X5s3b16eZcOHD9fw4cP9OdQV2e12JSQkKD09XQcPHgzIMWBOUVFRqlGjRp7uBQAAAEXGkn2qc59NkFO7TxvdPhHM/EqqzSYsLEw1atTQ+fPnlZOTY3Q4KAYhISEKDQ3lrgQAABBYFqxUu0baNsN1kqtSTZ9qBLOgSKql3EajRIkSKlGihNGhAAAAIGiYqexbOO5KtbFhSLoYA4VqBDPrfOUGAAAAFDcLV6rN1KeaeaoRzKzTOgAAAADFzYp9qi88myCnNkUMQKCRVAMAAADeWLBSfXH0b4MD0cV+3VSqEcys0zoAAAAAxc66farNUF2nTzWuBSTVAAAAgDemmp+qcBwmqlTTpxrXApJqAAAAwBtL96k2PmZXCKTUCGYk1QAAAIA3FuxT7ZoT2hyV6txnJ5VqBDHrtA4AAABAsbPe7d+u6roZQnZVy8mpEcxIqgEAAABvLFypNtPt3/SpRjCzTusAAAAAFDdL9qm+UKk2OA5JsolKNYIfSTUAAADgjRUr1RdCtpugUm13V6qNjQMIJOu0DgAAAECxs16f6oujfxsahkcMTsb/RhAjqQYAAAC8sWCl2umep9r4rNrOQGW4BlindQAAAACKmxX7VLuK68aG4YEptRDMSKoBAAAAbyxYqXa4p9QyPq12VarpU41gZp3WAQAAACh2Jiz7XoE5+1QDwYukGgAAAPDGfS+1dS6bHSbsU8081Qhm1mkdAAAAgOJmwT7VZhqw3B0DOTWCGEk1AAAA4I2F+1TbTZFUU6lG8LNO6wAAAAAUOxOVfQvpYvpqfMzuQjU5NYIYSTUAAADgDZXqq0KfalwLrNM6AAAAAMXNgn2q3WOrmaC6zujfuBaQVAMAAADeWLBS7UqqzVGpzn12UqlGELNO6wAAAAAUOwv2qb6QwNpMUF13xUBOjWDmV1I9c+ZMJSQkKCIiQomJiVq/fn2B62dlZWn8+PGKj49XeHi4/vKXv2jOnDl+BQwAAAAUGwtWqh0m+h7AFQN9qhHMQn3dYPHixRo5cqRmzpyp1q1b6/XXX1eXLl2UmpqqGjVq5LtNz549dfjwYb311luqVauWMjIydP78+asOHgAAAAgoK/apvlBdN0dSfaFSbXAcQCD5nFRPnz5d/fv314ABAyRJM2bM0GeffaZZs2YpOTk5z/orV67UunXr9Msvv6h8+fKSpJo1a15d1AAAAEBxcFeqTZChFpLD3afa+Jjt7kq1sXEAgeTTfSzZ2dnaunWrOnXq5LG8U6dO2rhxY77bLF++XM2bN9cLL7ygatWqqU6dOhozZozOnDnj9ThZWVk6ceKExwMAAAAwjAkS1MJymvD2bwYqQzDzqVJ99OhR5eTkKDY21mN5bGysDh06lO82v/zyi7766itFREToww8/1NGjRzV06FAdP37ca7/q5ORkTZ482ZfQAAAAgKJnwT7VTvc81cZn1a4YyKkRzPxqHS6f887pdHqdB8/hcMhms2nhwoW66aabdOedd2r69OmaN2+e12r1uHHjlJmZ6X4cOHDAnzABAACAq2PJPtW5zBSxk17VCGI+VaorVqyokJCQPFXpjIyMPNVrlypVqqhatWoqU6aMe1n9+vXldDr166+/qnbt2nm2CQ8PV3h4uC+hAQAAAEXPgpVq10jb3opexclVqXY4DA4ECCCfWoewsDAlJiZq1apVHstXrVqlVq1a5btN69atdfDgQf3555/uZbt27ZLdblf16tX9CBkAAAAoLibqoFxIpuxTTaUaQcznr9xGjx6tN998U3PmzNHOnTs1atQopaWlaciQIZJyb91OSkpyr9+7d29VqFBBDz/8sFJTU/Xll1/qiSee0COPPKLIyMiieycAAABAUbNwpdpMfaoZ/RvBzOcptXr16qVjx45pypQpSk9PV6NGjbRixQrFx8dLktLT05WWluZePzo6WqtWrdLw4cPVvHlzVahQQT179tSzzz5bdO8CAAAACAQL9ql2F9eNjULSJTGQVCOI+ZxUS9LQoUM1dOjQfF+bN29enmX16tXLc8s4AAAAYHpUqq+KzV2pJqtG8LJO6wAAAAAUOxN1UC4kd23dBCFf7FMNBC+SagAAAMAbC1eqzZBU26lU4xpgndYBAAAAKG4WTAYvjv5tfFbtisCCpxEoNJJqAAAAwBsLVqpdCazd+Jxa9gunzUlWjSBmndYBAAAAMIoJqr6F5ZoT2maC8b9dMZBSI5iRVAMAAADeWLBS7TBRpdr1XQR9qhHMrNM6AAAAAMXNgvNUu2+1NkHIrn7d5NQIZiTVAAAAgDeWrlQbn1Xb3ZVqY+MAAsk6rQMAAABQ7Kw1T/WlA4KZIWJ3n2pK1QhiJNUAAACANxarVF+au5qpUk1OjWBmjdYBAAAAMILF+lRfmruaIKd2nzYn438jiJFUAwAAAN5YrFJ96SjbNhNk1a5qOX2qEcys0ToAAAAAhrBan+qLP5shZFcI9KlGMCOpBgAAALxxVaotcvu3w2wDlTGlFq4BJNUAAACAN05rVaovZaqByowNAwgokmoAAADAG4sl1Z59qg0M5LIYHJSqEcRIqgEAAACvXEm1NS6bzTalFrd/41pgjdYBAAAAMIKF+1SbgeusmS0uoCiRVAMAAADeOC1Wqb7kZzNUqu1UqnENsEbrAAAAABjBPU+18QlqYbgL6zJHyK4YmFILwYykGgAAAPDKapXqi8mrqSrVBscBBJI1WgcAAADACJbrU33xZzNFTJ9qBDOSagAAAMAbq/WpNtmUWvSpxrXAGq0DAAAAYASL9an2qFSbIOaL81QbGwcQSCTVAAAAgFcWq1RfiNdufD4t6dJ+3WTVCF7WaB0AAAAAI1w6nLYFuO9WN0GVWqJSjWuDX0n1zJkzlZCQoIiICCUmJmr9+vWF2m7Dhg0KDQ3VDTfc4M9hAQAAgOJluT7Vuc/mqVTnPjOlFoKZz63D4sWLNXLkSI0fP17btm1TmzZt1KVLF6WlpRW4XWZmppKSknTbbbf5HSwAAABQrC6Wfo2No5Bco2zbTDP2d24cVKoRzHxOqqdPn67+/ftrwIABql+/vmbMmKG4uDjNmjWrwO0GDx6s3r17q2XLln4HCwAAABQvi1WqLzyb5TsAKtW4FvjUOmRnZ2vr1q3q1KmTx/JOnTpp48aNXrebO3eu9u7dq6effrpQx8nKytKJEyc8HgAAAECxs9o81RdKwmZJqm1MqYVrgE9J9dGjR5WTk6PY2FiP5bGxsTp06FC+2+zevVv/+Mc/tHDhQoWGhhbqOMnJySpTpoz7ERcX50uYAAAAQNGwWJ9qF7tJsmp3pdrYMICA8qt1uHw0QafTme8Igzk5Oerdu7cmT56sOnXqFHr/48aNU2Zmpvtx4MABf8IEAAAAro7l5ql29ak2B5u7TzVpNYJX4UrHF1SsWFEhISF5qtIZGRl5qteSdPLkSW3ZskXbtm3To48+KklyOBxyOp0KDQ3V559/rg4dOuTZLjw8XOHh4b6EBgAAAASAtSrVF0f/NkdabXP3qTY2DiCQfGodwsLClJiYqFWrVnksX7VqlVq1apVn/dKlS2v79u1KSUlxP4YMGaK6desqJSVFLVq0uLroAQAAgECyWp9q9+3qxsbhcnGearJqBC+fKtWSNHr0aPXt21fNmzdXy5YtNXv2bKWlpWnIkCGScm/d/u2337RgwQLZ7XY1atTIY/tKlSopIiIiz3IAAADAdCzWp9qVupqlUu2Kg5QawcznpLpXr146duyYpkyZovT0dDVq1EgrVqxQfHy8JCk9Pf2Kc1YDAAAA1mCtearNNq22Ow6yagQxn5NqSRo6dKiGDh2a72vz5s0rcNtJkyZp0qRJ/hwWAAAAKF4WG6jMNR+02SrV3P6NYGaN+1gAAAAAI7iTQXMkqVditmgpVONaQFINAAAAeGOxPtXuKbVMUqm2UanGNcAarQMAAABgCJN1Ur4Cs/apJqdGMCOpBgAAALxx96m2xmWzw92n2uBALri0b7eTzBpByhqtAwAAAGAEq/Wpdk9TbY54L42CnBrBiqQaAAAA8MZilWpX4mrGSjX9qhGsrNE6AAAAAIYwWSflK3DKXAOVXVqqJqVGsCKpBgAAALyxWKXaYbLvAC6tmFOpRrCyRusAAAAAGMFyfapdlWqDA7nA5jFQmYGBAAFEUg0AAAB4Y9FKtd0kWfWllWqSagQra7QOAAAAgCFMdj/1FV2oVBschculo5A76VWNIEVSDQAAAHjjrlSbJU0tmNkq1TaPPtXGxQEEEkk1AAAA4I3l+lRf+MEk4do8bv8mq0ZwIqkGAAAAvHElgpbpU50br1kq1Z7zVBsYCBBA1mgdAAAAAENYq0+1+zsAY8Nw84iDpBpBiqQaAAAA8MZio387TV2pJqtGcLJG6wAAAAAYwWydlK/AHa1JwvXoU21cGEBAkVQDAAAA3lisUu2qBttMklXbqFTjGmCN1gEAAAAwBH2qr5br1JFTI1iRVAMAAADeWLRSbTdRuK5+1UyphWBlov9uAAAAgMlYtU+1ieJ1RUJKjWBFUg0AAAB4Y7FK9cXRvw0O5BKuSjV9qhGsrNE6AAAAAIawZp9qU8VLn2oEOZJqAAAAwBuntZJqx4VwzVWpzn2mUo1gRVINAAAAeGO1PtWuKbUMjuNSrv7d5NQIViTVAAAAgDcW61N9sVJtnrTazu3fCHJ+tQ4zZ85UQkKCIiIilJiYqPXr13tdd+nSpbr99tsVExOj0qVLq2XLlvrss8/8DhgAAAAoPta6/dsVr5nCtbmm1GL8bwQpn5PqxYsXa+TIkRo/fry2bdumNm3aqEuXLkpLS8t3/S+//FK33367VqxYoa1bt+rWW29Vt27dtG3btqsOHgAAAAgoi1aqbSbKqm3uPtXGxgEEis+tw/Tp09W/f38NGDBA9evX14wZMxQXF6dZs2blu/6MGTP05JNP6sYbb1Tt2rX1f//3f6pdu7Y+/vjjqw4eAAAACCjL9anOfTZTtO55qrn/G0HKp6Q6OztbW7duVadOnTyWd+rUSRs3bizUPhwOh06ePKny5ct7XScrK0snTpzweAAAAADFznKVatc81eZJq+121zzVBgcCBIhPrcPRo0eVk5Oj2NhYj+WxsbE6dOhQofYxbdo0nTp1Sj179vS6TnJyssqUKeN+xMXF+RImAAAAUESs1afaxNNUS/SpRpDy6yu3y/toOJ3OQvXbWLRokSZNmqTFixerUqVKXtcbN26cMjMz3Y8DBw74EyYAAABwdSxWqXaasVJto1KN4Bbqy8oVK1ZUSEhInqp0RkZGnur15RYvXqz+/ftryZIl6tixY4HrhoeHKzw83JfQAAAAgKJn1T7VJgrXxpRaCHI+feUWFhamxMRErVq1ymP5qlWr1KpVK6/bLVq0SP369dM777yjrl27+hcpAAAAUNwsVql29ak21+jfrko1WTWCk0+VakkaPXq0+vbtq+bNm6tly5aaPXu20tLSNGTIEEm5t27/9ttvWrBggaTchDopKUkvvfSSbr75ZneVOzIyUmXKlCnCtwIAAAAUNRMOp10Ac4/+bWgYQMD4nFT36tVLx44d05QpU5Senq5GjRppxYoVio+PlySlp6d7zFn9+uuv6/z58xo2bJiGDRvmXv7QQw9p3rx5V/8OAAAAgEBxZ6nWqlTbTZRV26lUI8j5nFRL0tChQzV06NB8X7s8UV67dq0/hwAAAACMZ7U+1ReezXX7t9ERAIFlja/cAAAAACNYrE+1k0o1UOys0ToAAAAAhjDhcNoFuJi3mi9ecmoEK5JqAAAAwBuLVapdc0GbqlJ94dRRqUawskbrAAAAABjBcn2qXVNqGRzIJWwXzh0pNYIVSTUAAADgjWUr1ebJql1VcyeVagQpa7QOAAAAgCGs1afaVVk3U7iukcjJqRGsSKoBAAAAbyxaqTbjlFoOkmoEKWu0DgAAAIARrNan2lWpNjiOS7li4fZvBCuSagAAAMAbVyJosUq1ufpUu+apNjgQIECs0ToAAAAAhnAl1cZGUVjuurqJ4nXF4mT8bwQpkmoAAADAG4v1qXbdYm3GSjV3fyNYWaN1AAAAAIxguT7Vuc9mjJakGsGKpBoAAADwxmKVaod7Si3zpNUX+1STVSM4WaN1AAAAAAxhrXmqzd2nGghOJNUAAACANxatVNtNlFRTqUaws0brAAAAABjBsn2qzROvu2pOTo0gRVINAAAAeGOxSrV79G8ThWujUo0gZ6L/bgAAAIDZWKxPtTtvNU+87kI1OTWCFEk1AAAA4I3FKtWOC4mrufpU5z5TqUawskbrAAAAABjBhJXfgjjlmlLL4EAu4br9m5QawYqkGgAAAPDGXak2UZZagIuVavPE66pUO6lUI0iRVAMAAABeWatPtavjspmidY1ETk6NYEVSDQAAAHhj0T7VNhN9CWBz96k2Ng4gUKzROgAAAABGsNo81absU5377KRXNYIUSTUAAADgjUUr1ebqU+2ap9rgQIAAsUbrAAAAABjCWn2qXYV1M0VrY6AyBLlQfzaaOXOmpk6dqvT0dDVs2FAzZsxQmzZtvK6/bt06jR49Wjt27FDVqlX15JNPasiQIX4HbUb7jp7S7C/3asOeY/r9dLYcDqdC7DZFh4cqMixEZ7JzdDLrvBwXvqLz9lpB23hz+Tanz+UoqkRIgfvw9TghdpvKRYWpcfUykqQffs10v09fYitMLIWJv7iPYZbjBPJzczLrvGxSgfvx9zi+fnbMdJxL1zmfk3vjWokQm+l/n4FoB1zbFNV59ufc+HqurfaZdq1XNipMpSNDlXnmnDJPn7uqz1ogznOg37+V/r8VNk6znWt/9+/LMQL5+/S2TiD+vqfYclTCJv31Xxt1KrxSwI7jTxuWn3M5uZX1DXuOat/RU0qoWLLA9QNt39FT2pPxpyRpysc79PLq3Vd9zqz898Oq1+xF9fm89O9c5TIRqlu5lHo2jzP8c3q1bE4fvzJavHix+vbtq5kzZ6p169Z6/fXX9eabbyo1NVU1atTIs/6+ffvUqFEjDRw4UIMHD9aGDRs0dOhQLVq0SD169CjUMU+cOKEyZcooMzNTpUuX9iXcYvHelgMa+/4P9BIBAAAIMnvCH1SozaGbzv5LGSpndDg+sUl64b4m+lvzOEOOzzUyrsQ13drzPYz7nBaksHmoz0l1ixYt1KxZM82aNcu9rH79+urevbuSk5PzrD927FgtX75cO3fudC8bMmSIvv/+e23atKlQxzRzUr3v6Cl1+OdaOSV1tG9Vr5AvFKM/3K8fUVn95IhTPfsBj+UFvVbQNt5cvk1h9uHrcY6orBbn3CpJed6nL7EVVfzFfQyzHCeQn5vC7Mff4/j62THTcS5dR5Jlfp+BaAdc2xTVefbn3Ph6HKt9pl3rRdmyddoZViSftUCc50C/fyv9fytsnGY71/7u35djBPL36W2dov67e739F9ls0uPZQ/SBo22h9mcmdpu05vH2qlnMlcBLr5GBKzHqc3olAUmqs7OzFRUVpSVLluiee+5xL3/ssceUkpKidevW5dmmbdu2atq0qV566SX3sg8//FA9e/bU6dOnVaJEiTzbZGVlKSsry+PNxMXFmTKpfn7lT3pt7V7dZt+qN8OmGR0OACBIOJ2W6cIJXDMGZD+u/zoSjQ7DJzZJQ9r/RWPvqFesx3VdI5NUozBC7DYNantdsX9Or6SwSbVPA5UdPXpUOTk5io2N9VgeGxurQ4cO5bvNoUOH8l3//PnzOnr0aL7bJCcnq0yZMu5HXJz5bgVw+fX3M3JKamlPZUJ7AECRIaEGzMXhtOlme6rRYfjMqdzr1eLmukYGCsPpdBryOS0qfo3+fflk8k6ns8AJ5vNbP7/lLuPGjVNmZqb7ceDAAX/CLBbVy0XKJmmTowEXQACAIsPUM4C52G1Ofe1oYHQYPrMp93q1uLmukYHCsNlshnxOi4pPo39XrFhRISEhearSGRkZearRLpUrV853/dDQUFWoUCHfbcLDwxUeHu5LaIbp2TxOr63dq/86EjUg+3H1DPlCMcp0v35EZfWzo7rq2g94LC/otYK28ebybQqzD1+Pc0Rl9V5O+9z3fdn79CW2ooq/uI9hluME8nNTmP34exxfPztmOs6l60g2y/w+A9EOuLYpqvPsz7nx9ThW+0y71ou0ZeuMM6xIPmuBOM+Bfv9W+v9W2DjNdq793b8vxwjk79PbOoH4++46T1a79VvKveullwEDQLmukYHCcDqdhnxOi4pfA5UlJiZq5syZ7mUNGjTQ3Xff7XWgso8//lipqRdvl/n73/+ulJSUoBioTJKWbDmgJxnZEAAAACZi9OjfXCPjSq7Z0b9dU2q99tpratmypWbPnq033nhDO3bsUHx8vMaNG6fffvtNCxYskHRxSq3Bgwdr4MCB2rRpk4YMGRJUU2pJ0v6jpzT7y1/01Z6j+v10tpwOp+wX5mGLCgvV6ezzOpl1Xs4L9/N5e62gbby5fBvXnHcF7cPX49gvzHnX5MKcd99fmPPO19gKE0th4i/uY5jlOIH83Fw636C3/fh7HF8/O2Y6zqXrnMtxyCkpLMRu+t9nINoB1zZFdZ79OTe+nmurfaZd67nmqT5x5pz+uDBPtb+ftUCc50C/fyv9fytsnGY71/7u35djBPL36W0dq/599/b6ldowb8cpFxWm1rUqalDb6wwfTdl1jfzt/uPKPu8oks+Alf9+WPWavSg/n5fPU92reZzhn1NvCpuH+nT7tyT16tVLx44d05QpU5Senq5GjRppxYoVio+PlySlp6crLS3NvX5CQoJWrFihUaNG6V//+peqVq2ql19+udAJtVXUrFhS/3dvY6PDAAAAAEyDa2RcC3yuVBvBCpVqAAAAAEDwCMiUWgAAAAAA4CKfb/82gquYfuLECYMjAQAAAABcC1z555Vu7rZEUn3y5ElJUlyc+UaEAwAAAAAEr5MnT6pMmTJeX7dEn2qHw6GDBw+qVKlSstmYRt4MTpw4obi4OB04cIB+7gCuebSJAHARbSKChdPp1MmTJ1W1alXZ7d57TluiUm2321W9enWjw0A+SpcuTWMJABfQJgLARbSJCAYFVahdGKgMAAAAAAA/kVQDAAAAAOAnkmr4JTw8XE8//bTCw8ONDgUADEebCAAX0SbiWmOJgcoAAAAAADAjKtUAAAAAAPiJpBoAAAAAAD+RVAMAAAAA4CeSagAAAAAA/ERSDQBAARjPEwAAFISkGh7OnDmjmTNn6tChQ0aHAgCGy8nJ0enTp40OAwBMg2tFIC+SarhNnTpV0dHRWrJkiaKioowOBwAMNW3aNN14443q1q2bkpOT9euvv0qicg3g2sW1IpA/5qmG1q1bp4ceekiSNH36dN17770GRwQAxnrssce0bNkyTZkyRVu2bNGGDRsUEhKijRs3qkSJEkaHBwDFimtFoGAk1dC9996r//znPzp9+rRCQkJ0+PBhHT58WJUqVVJMTIxCQkLkdDpls9mMDhUAAsrpdOrw4cPq3LmzRo0apX79+kmSfvzxR3Xs2FG9evXStGnTFBoaamygAFCMuFYECkZSfQ3LyclRSEiIduzYoRYtWuiVV17R7t27tXDhQlWoUEEHDx7Uww8/rOTkZKNDBYCAOnz4sGJjY90/V6lSRVu2bFGzZs3cF4rvvvuuHnroIa1du1YtW7Y0OGIACKxDhw6pcuXKksS1InAF9Km+xsyfP18NGzbUoUOHFBISIofDoYYNG2rQoEHq37+/tm3bpldeeUUzZ87Uo48+qnfffVdPPPGEJMnhcBgcPQAUrVdeeUUxMTFat26de9nZs2d100036YMPPpAkd+Xl/vvvV/PmzTV9+nRJtIkAgpOrXfzyyy/dY0hwrQgUjPvXrjFz5szRzp07NWHCBL3xxhvu5ePHj1eJEiX04IMPqnHjxpKkxMRERUZGKjk5WU8++aRiYmKMChsAitSGDRv0yCOP6OTJk3ruuefUs2dP92vVqlVT/fr19f333ys1NVUNGjTQ+fPnFRoaqlGjRunhhx/WkSNHaBMBBJWC2kWJa0WgIFSqryGHDx+W0+nUyy+/rDlz5ujrr7+W3W5XTk6OKlSooKeeesrdSEpSiRIlVLlyZUVGRio9Pd3AyAGg6MyePVtt2rRRx44ddfDgQfXv31/nzp1zvx4aGqru3bvr4MGDeu+999zLJKlkyZKKiYnRb7/9ZkjsABAIV2oXJal8+fJcKwJeUKm+hoSHh8tms6lDhw7q2rWrRowYoW+//VYhISGSpFKlSrnXdfUh/Pnnn5WQkKA6deoYFTYAFKkuXbpIkm677TZJuVPEpKamKjIyUm3bttX999+vu+++W6tXr9ann36q2rVrq0+fPpJyv5yMjo7WX/7yF8PiB4CiVlC72K5dO/Xq1Us2m03R0dHubbhWBC4iqb6G/Pzzzzp+/LgaNGigsWPH6rbbbtNHH32kihUrym63uwfeOXHihLKzszVnzhzNnz9fkyZNUkREBKM6ArC8nJwcxcXFadSoUerVq5fq1q2r8PBw3X777VqzZo2WLFmi1NRUTZkyRSNHjtSLL76opKQkrVq1SmXLltX8+fP12GOPKSoqijYRQFAoTLv4008/6emnn3a3eVwrAp4Y/TtIuUb2djVuTqdT3377rZ555hl98sknkqQ+ffpo8eLFKleunN5//321a9dO33//vd5//329++67cjqdeuWVV9zfXgKAVbnaRFffaElq0KCBunbtqqefflrR0dE6d+6c3njjDT322GPat2+fqlevLqfTqVmzZunHH3/Unj17NHz4cHXr1s3gdwMAV8/XdnH//v2qVq2atm3bpqVLl3KtCFyCpDoIZGZmKiUlRe3atfP4hvD06dP6/fffVa1aNUnSO++8o9WrV2v27Nnq27evlixZooiICPXt21czZ86UJJ06dUrLli2T0+nUgw8+aNh7AgB/FbZNTE1NVVhYmGrVquXeNiMjQy1atNDw4cM1evRoQ+IHgKJWlO0i14pAXgxUZnFTpkxRuXLlNH/+fEkXp37JyMjQ3Xffrb/97W/udQ8fPqxPP/1UUVFR+vXXX7Vu3Tq98MILmjNnjvbt2ycpdxCePn360EgCsCRf2sQGDRq4Lxxd08CkpaXp+PHjqlGjRjFHDgCBUdTtIteKQF70qbaoFStW6JFHHlHp0qW1fPly3XXXXR6vx8TEqHv37kpPT9epU6dUsmRJNWrUSA0aNNCQIUPUvXt3hYaGqnbt2po+fboWLFigp59+2qB3AwBXx5c28ezZs4qIiPB43TUTwkcffaSbb75Zbdu2Lc7wAaDI0S4CxYfbvy1ow4YN6tGjh+rVq6e1a9dKko4eParo6GiFhoa6+8WcOXNGkZGRHtuePn1aUVFRHssyMjJUqVKlYokdAIra1bSJWVlZ+uabb/Tdd99p3rx5OnHihN5880116NChuN8GABQZ2kWgeHH7t4W4vv+oU6eOHnjgAZ0/f1779u3T+PHj1alTJ7Vr107du3fXzp07JcmjkXRte/m3kJJIqAFYUlG0iWfOnNHu3bu1ePFi3Xvvvfrll1+4cARgWbSLgDFIqi3g4MGDkuQexTsmJkZ//etflZWVpbp16+q7777T448/rj59+mj//v0aPny41q9f795+x44d6tWrlw4ePCi73S5uTgBgZUXVJv72228qW7as7r33Xq1Zs0YTJ0406i0BwFWhXQSMRVJtYhs2bFCTJk00duxYd2PpGjSidevWeuCBBzRx4kS9/fbb6tOnj0aMGKG3335bmZmZWrlypXJyciTlNrTr16/XO++8I0nMHwjAkgLVJpYrVy7P7Y8AYAW0i4A50KfahJxOpz744ANNnjxZ4eHhyszM1LPPPqtevXpJym0s7Xa7fvvtN4WFhSkmJsZj+y5duig6OlpLliyRlNs3Zu/evWrQoEGxvxcAuFq0iQDgiXYRMBcq1SbkcDgUGhqqO++8U6tWrVJ8fLzeeecd7dq1S9LFSnO1atU8GkmHw6Hff/9dx44dU3x8vHt5eHg4jSQAy6JNBABPtIuAuZBUm1BISIjatWun8ePHq1y5cpoyZYq2bNmizz77TOfOncv39m2n06kzZ85oxowZOnv2LHMHAggatIkA4Il2ETAXbv82uZycHIWEhKh///5KTU3VjBkz1KJFC491li1bppUrV+q///2vQkNDNWfOHLVq1cqgiAEgcGgTAcAT7SJgPCrVJuf6pvGZZ57RoUOH9NFHHykzM1OS3INL3HDDDTp16pTGjRunn376iUYSQNCiTQQAT7SLgPGoVBtg6dKl6tChg8qWLVuo9V3fQCYnJ2vBggV6+eWXlZCQoAULFmjYsGGKjY2V0+lkVG8AlkSbCACeaBcBa6FSXYy+/PJLNWzYUPfdd597yoLCsNtzf03jxo1TRESE/v73v6tevXrasGGDQkNDaSQBWBJtIgB4ol0ErIlKdTHZtWuXJk+erDJlyshms2nZsmX69ttvVa1atUJtf+bMGX388ccaNGiQqlatqueff17dunULcNQAEBi0iQDgiXYRsC6S6mJy5MgRLV++XDfffLMSEhJUv3593XPPPZoxY0ahtv/xxx/VrFkzjRs3TpMnTw5ssAAQYLSJAOCJdhGwLpLqAPnqq69Uo0YN1ahRw73s/PnzCg0NlST9+9//1oABA7Rp0yY1a9asUPvMyspSeHh4QOIFgECiTQQAT7SLQPAgqS5ia9as0YABA+RwOJSdna2OHTtq1KhRatq0aZ7+LLfccovKlCmjZcuWqUSJEgZGDQCBQZsIAJ5oF4Hgw0BlRejXX3/VU089pd69e+vLL7/U7NmztX37dj3xxBPas2ePbDabe2oDSZo6dapWrlypTz75RJLkcDh07Ngxo8IHgCJFmwgAnmgXgeBEUl2Edu7cqW3btumhhx5SjRo1dNddd+n555+Xw+HQU089JUkKCQlxr9+yZUslJSVp0qRJWr16tbp27app06YpKyvLqLcAAEWGNhEAPNEuAsGJpLoIHT9+XPXq1ZPD4XAv69ixo+677z598803+vzzzyXJ4xvIv//979q+fbtuv/125eTkaMyYMfSFARAUaBMBwBPtIhCcSKqLUMOGDbVz50799NNP7mV2u1233XabbrjhBi1atEhS7jeQOTk5euedd9S2bVslJibq22+/1eeff67y5csbFT4AFCnaRADwRLsIBCeS6iLUqFEjdejQQS+++KL+/PNP9/K6deuqRo0aOnTokM6cOSNJys7O1rFjxzRjxgxt3rxZzZs3NypsAAgI2kQA8ES7CAQnkuoilpycrA0bNujtt9/26O8SExOj1NRU98iNkZGRGj58uIYMGWJUqAAQcLSJAOCJdhEIPqFGBxBsrr/+eo0dO1ZTpkxRSEiIHnjgATkcDn377bfq06ePe+5BALgW0CYCgCfaRSD4ME91gAwbNkwffPCBatSooYyMDEVFRem9995To0aNjA4NAIodbSIAeKJdBIIHSXWAZGVlKTU1VSkpKQoLC1OfPn2MDgkADEObCACeaBeB4EFSDQAAAACAnxioDAAAAAAAP5FUAwAAAADgJ5JqAAAAAAD8RFINAAAAAICfSKoBAAAAAPATSTUAAAAAAH4iqQYAAAAAwE8k1QAAAAAA+ImkGgAAAAAAP5FUAwBgoPbt22vkyJHX3LEBAAgWJNUAAFjE2rVrZbPZ9McffxTJdkuXLtUzzzxTdAECAHANCjU6AAAAYIzy5csbHQIAAJZHpRoAgGJy6tQpJSUlKTo6WlWqVNG0adM8Xn/77bfVvHlzlSpVSpUrV1bv3r2VkZEhSdq/f79uvfVWSVK5cuVks9nUr18/SZLT6dQLL7yg6667TpGRkbr++uv1/vvvX3G7y2//rlmzpp599ll3jPHx8froo4905MgR3X333YqOjlbjxo21ZcsWj7g3btyotm3bKjIyUnFxcRoxYoROnTpV1KcPAABTIqkGAKCYPPHEE/riiy/04Ycf6vPPP9fatWu1detW9+vZ2dl65pln9P3332vZsmXat2+fOwGOi4vTBx98IEn6+eeflZ6erpdeekmS9NRTT2nu3LmaNWuWduzYoVGjRunBBx/UunXrCtwuPy+++KJat26tbdu2qWvXrurbt6+SkpL04IMP6rvvvlOtWrWUlJQkp9MpSdq+fbs6d+6se++9Vz/88IMWL16sr776So8++mggTiEAAKZjc7r+KgIAgID5888/VaFCBS1YsEC9evWSJB0/flzVq1fXoEGDNGPGjDzbbN68WTfddJNOnjyp6OhorV27Vrfeeqt+//13lS1bVlJu9btixYpas2aNWrZs6d52wIABOn36tN555518t5NyK9U33HCD+9g1a9ZUmzZt9O9//1uSdOjQIVWpUkUTJkzQlClTJElff/21WrZsqfT0dFWuXFlJSUmKjIzU66+/7t7vV199pXbt2unUqVOKiIgowrMIAID50KcaAIBisHfvXmVnZ3skvuXLl1fdunXd/962bZsmTZqklJQUHT9+XA6HQ5KUlpamBg0a5Lvf1NRUnT17VrfffrvH8uzsbDVt2tTnOJs0aeL+OTY2VpLUuHHjPMsyMjJUuXJlbd26VXv27NHChQvd6zidTjkcDu3bt0/169f3OQYAAKyEpBoAgGJwpRvDTp06pU6dOqlTp056++23FRMTo7S0NHXu3FnZ2dlet3Ml3v/5z39UrVo1j9fCw8N9jrNEiRLun202m9dlruM6HA4NHjxYI0aMyLOvGjVq+Hx8AACshqQaAIBiUKtWLZUoUUJff/21O9n8/ffftWvXLrVr104//fSTjh49queee05xcXGSlGdAsLCwMElSTk6Oe1mDBg0UHh6utLQ0tWvXLt9j57ddUWnWrJl27NihWrVqFfm+AQCwAgYqAwCgGERHR6t///564okntHr1av3444/q16+f7PbcP8U1atRQWFiYXnnlFf3yyy9avnx5njmk4+PjZbPZ9Mknn+jIkSP6888/VapUKY0ZM0ajRo3S/PnztXfvXm3btk3/+te/NH/+fK/bFZWxY8dq06ZNGjZsmFJSUrR7924tX75cw4cPL7JjAABgZiTVAAAUk6lTp6pt27b661//qo4dO+qWW25RYmKiJCkmJkbz5s3TkiVL1KBBAz333HP65z//6bF9tWrVNHnyZP3jH/9QbGyse4TtZ555RhMnTlRycrLq16+vzp076+OPP1ZCQkKB2xWFJk2aaN26ddq9e7fatGmjpk2basKECapSpUqRHQMAADNj9G8AAAAAAPxEpRoAAAAAAD+RVAMAAAAA4CeSagAAAAAA/ERSDQAAAACAn0iqAQAAAADwE0k1AAAAAAB+IqkGAAAAAMBPJNUAAAAAAPiJpBoAAAAAAD+RVAMAAAAA4CeSagAAAAAA/PT/AxtpqnTSEa4qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# true outlier indices selection\n",
    "true_outlier = [df.anomaly for df in list_of_df]\n",
    "\n",
    "predicted_outlier[0].plot(figsize=(12,3), label='predictions', marker='o', markersize=5)\n",
    "true_outlier[0].plot(marker='o', markersize=2)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YMVNtdfztNYfXwJvZvwmrEZwIqgEAAABXzFhTbXSm2qEfDzLVRb+JqRGkCKoBAAAAV8yYqbY5/vaaw2vg+eXf1FQbY+/evbJYLEpPT/f3UMrNqlWrZLFY9Pvvv/t7KCUiqAYAAABcMmGm2uiFqr2uqS7KnAOeadeunbKyshQXF1fmNv369VPPnj19N6iLsE41AAAA4IpDQGmWoNrg2ba9rqku+k1YDc9EREQoMTHR38NwiUw1AAAA4IpDTbU5Zv82OlHtfU010397wmq1asqUKWrYsKEiIyNVt25d/fOf/7Q/vnv3bt14442KiYnRVVddpQ0bNtgfO3bsmO655x7VqVNHMTExatGihRYuXOjQ/w033KBhw4bpiSeeUNWqVZWYmKgJEyY47PPLL7+oQ4cOioqKUtOmTfXFF1/IYrFoyZIl9n0OHDig1NRUValSRdWqVdMdd9yhvXv32h8vyhhPnDhRNWrUUKVKlfTQQw8pPz/fvk9eXp6GDRumGjVqKCoqSh06dNB3331nf7z45d9z5sxR5cqV9fnnnys5OVmxsbG65ZZblJWVJUmaMGGC5s6dq48//lgWi0UWi0WrVq3y8F/i0giqAQAAAFdMWVMdYJnqotnIAyVTbbNJ+ac9+/npQ+m/owp/e9Lejddg7NixmjJlisaPH6+MjAwtWLBACQkJ9sfHjRunUaNGKT09XVdeeaXuuecenT9/XpJ09uxZtW7dWp9++ql++uknDRo0SH369NHGjRsdjjF37lxVqFBBGzdu1PPPP69JkyYpLS1NUmFQ37NnT8XExGjjxo164403NG7cOIf2Z86c0Y033qjY2FitWbNG69atswe4FwfNX375pbZt26avvvpKCxcu1EcffaSJEyfaH3/iiSf0wQcfaO7cufr+++/VsGFDde/eXcePH3f5+pw5c0Yvvvii3n77ba1Zs0aZmZkaNWqUJGnUqFHq1auXPdDOyspSu3btyvzau4vLvwEAAACXzFtTbVgM62VNtQFNjXXujPR/tbzr47v/eNbu/x2UIipccreTJ0/qlVde0Wuvvab7779fknTFFVeoQ4cO9izwqFGjdPvtt0uSJk6cqGbNmmnnzp1q0qSJateubQ8wJWno0KFavny5Fi9erDZt2ti3t2zZUk8//bQkqVGjRnrttdf05ZdfqmvXrlqxYoV27dqlVatW2S+9/uc//6muXbva27/77rsKCQnRm2++KYvFIkl66623VLlyZa1atUrdunWTVHj59uzZsxUTE6NmzZpp0qRJGj16tJ555hnl5uZq5syZmjNnjm699VZJ0n/+8x+lpaVp1qxZGj16dImv0blz5/Tvf/9bV1xxhSTp0Ucf1aRJkyRJsbGxio6OVl5eXrlcNk5QDQAAALhi4ky1YetCG7ROtWGzkV8Gtm3bpry8PN10000u92nZsqX9ds2aNSVJ2dnZatKkiQoKCvTcc89p0aJFOnDggPLy8pSXl6cKFSq47KOon+zsbEnS9u3blZSU5BCUXnfddQ77b968WTt37lTFihUdtp89e1a7du2y37/qqqsUExNjv9+2bVudOnVK+/btU05Ojs6dO6f27dvbHw8PD9d1112nbdu2uXz+MTEx9oC6+NjLG0E1AAAA4Iop16l2/G1cj571as+cB0pRdXhMYcbYXb9+Lr3/gGQJLfyC5e63pCu7u3/sMoiOjr50V+Hh9ttFWWLrhWvtp06dqpdfflnTpk1TixYtVKFCBQ0fPtzhkuzifRT1U9SHzWaz9+uK1WpV69atNX/+fKfH4uPjL/kcLBaL/cuf4se61PFLGrthXyS5iaAaAAAAcMXLLK0/GD/798WXf3ueqQ6Yy78tljJdgu2k+Z1SWJS0d51Uv4PU5Dbjx3ZBo0aNFB0drS+//FIDBgxwu/3atWt1xx136L777pNUGPzu2LFDycnJZe6jSZMmyszM1OHDh+213BdPHiZJrVq10qJFi+wTkLmydetW5ebm2r8s+OabbxQbG6s6deqoWrVqioiI0Lp163TvvfdKKry0e9OmTRo+fLg7T9tBRESECgrK54swjyYqmzFjhho0aKCoqCi1bt1aa9euLXX/+fPn21P+NWvW1AMPPKBjx455NGAAAACg/JgwUx1gNdX2zHnARNVeaHKbdMv/+TSglqSoqCiNGTNGTzzxhObNm6ddu3bpm2++0axZs8rUvmHDhkpLS9PXX3+tbdu26aGHHtKhQ4fcGkPXrl11xRVX6P7779cPP/yg9evX2ycqK8og9+7dW9WrV9cdd9yhtWvXas+ePVq9erUee+wx7d+/395Xfn6++vfvr4yMDH322Wd6+umn9eijjyokJEQVKlTQww8/rNGjR2v58uXKyMjQwIEDdebMGfXv39+tMV+sfv36+uGHH7R9+3YdPXpU586d87ivS3E7qF60aJGGDx+ucePGacuWLerYsaNuvfVWZWZmlrj/unXr1LdvX/Xv318///yzFi9erO+++86jb1wAAACAcmXimmrDapi9zNbbqKn2yPjx4/X444/rqaeeUnJyslJTU8tcMzx+/Hi1atVK3bt31w033KDExET17NnTreOHhoZqyZIlOnXqlK699loNGDBATz75pKTCoF8qrGtes2aN6tatqzvvvFPJycl68MEHlZub65C5vummm9SoUSN16tRJvXr1Uo8ePRyW73ruued01113qU+fPmrVqpV27typzz//XFWqVHFrzBcbOHCgGjdurJSUFMXHx2v9+vUe93UpFpubXxm1adNGrVq10syZM+3bkpOT1bNnT02ePNlp/xdffFEzZ850KFSfPn26nn/+ee3bt69Mxzxx4oTi4uKUk5NT6mUFAAAAgKF++5/0yoXJnFL6S39+yb/jKYNB8zZpRcZhNUmsqOXDO3nf4als6cVGhbdvnypd615yrPUzaTp2Ol/3XV9Xz/Zs4f143HD27Fnt2bPHfpUtvLN+/Xp16NBBO3fudJgkrDT9+vXT77//7rC2dSAp7T1S1jjUrUx1fn6+Nm/ebJ8avUi3bt309ddfl9imXbt22r9/v5YtWyabzabDhw/r/ffft0//XpK8vDydOHHC4QcAAAAod6bMVBf+Nu7y74tfA/c7ZfZv8/roo4+UlpamvXv36osvvtCgQYPUvn37MgfUlwu3guqjR4+qoKDAYdFxSUpISHB5jX67du00f/58paamKiIiQomJiapcubKmT5/u8jiTJ09WXFyc/ScpKcmdYQIAAAAGMV9NddGYDZtt27CaamOGg/Jz8uRJDRkyRE2aNFG/fv107bXX6uOPP/b3sAKORxOVuTPdeUZGhoYNG6annnpKmzdv1vLly7Vnzx4NHjzYZf9jx45VTk6O/aesl4kDAAAAhvJy5mt/KMoIB0pNtdVq8LrZKDd9+/bVjh07dPbsWe3fv19z5sxRtWrV3Opjzpw5AXvpt1HcWlKrevXqCg0NdcpKZ2dnO2Wvi0yePFnt27fX6NGjJRUuMF6hQgV17NhRzz77rH2h8otFRkYqMjLSnaEBAAAAxjPjOtU2o4NYL9epLvpNTI0g5VamOiIiQq1bt1ZaWprD9rS0NLVr167ENmfOnFFIiONhQkNDJfFtFQAAAAIcNdUGzP5d+NuwdbOBAOP25d8jR47Um2++qdmzZ2vbtm0aMWKEMjMz7Zdzjx07Vn379rXv36NHD3344YeaOXOmdu/erfXr12vYsGG67rrrVKtWLeOeCQAAAGA4E2aqi/32vkMva6qLMudGjccDVqs5Lt1H+TPiveHW5d+SlJqaqmPHjmnSpEnKyspS8+bNtWzZMtWrV0+SlJWV5bBmdb9+/XTy5Em99tprevzxx1W5cmV16dJFU6ZM8XrwAAAAgE+ZMFP9x7rQRk1U5mVNtR8z1REREQoJCdHBgwcVHx+viIgIl3NB4fJis9mUn5+vI0eOKCQkRBERER735XZQLUlDhgzRkCFDSnxszpw5TtuGDh2qoUOHenIoAAAAwH9MWVPt+NuAHl3cLmvrogEZMxp3hISEqEGDBsrKytLBgwfLfwAIeDExMapbt65TybI7PAqqAQAAgMuCl1laf7Aanqn2bgZ0f2aqpcJsdd26dXX+/HkVFJjjixGUj9DQUIWFhXl99QJBNQAAAOASmWpva6r9mKi2s1gsCg8PV3h4uB9HgWDleY4bAAAACHYmrKm2Gr2kltc11UWZc2OGAwQagmoAAADAFTPWVBf7bVyPnvX6xzrVRNUITgTVAAAAgCsmzFT7dvZv9/v8I3NuzHCAQENQDQAAALh0cabaHBOVBVpNtX08fq2qBnyHoBoAAABwxSGgNEem2vAaZi9qqi++5Nsk30kAbiOoBgAAAFwxcU21cVXVntdUO3wnQaYaQYqgGgAAAHDFhDXVf6wLbVCHXmSqL67rZvZvBCuCagAAAMAl82WqZfiSWp7XVDvkuAmqEaQIqgEAAABXyFQblqlmSS0EK4JqAAAAwBWHmmpzzLRVVLtsXBBrVE01EJwIqgEAAABXzJipvjBk3yyp5e7s33/cNmzdbCDAEFQDAAAALpmvptpW7Lf3HXpTU33x5d9GDQgILATVAAAAgCsmzFTb7OtUGzVRmTc11RffJqpGcCKoBgAAAFwx4zrVNsffBvToeUsCaVwGCKoBAAAAV7zI0vqLlUw1UK4IqgEAAACXTJipLvbb+w49r6mWF00BsyCoBgAAAFxxCCjNEVQXZYQNu/TaoHWqyVQjWBFUAwAAAK6YsKZaPq2pdnf274tuE1MjSBFUAwAAAK6YcPbvwKqpZkktBD+CagAAAMClizPV5pioLJBqqh2aGjciIKAQVAMAAACumDhTbVhm2ItMtc2hptqg8QABhqAaAAAAcMWENdWOiWUjIlmjaqqJqhGcCKoBAAAAV0yYqXb4HsCQmPriKN2b2b8NGAsQgAiqAQAAAJfMmKm2lXjbiw5Lvu1uU+9HAgQkj4LqGTNmqEGDBoqKilLr1q21du3aUvfPy8vTuHHjVK9ePUVGRuqKK67Q7NmzPRowAAAAUG5MmKm2Gp6pvvg1cK9Dq9EBPhCAwtxtsGjRIg0fPlwzZsxQ+/bt9frrr+vWW29VRkaG6tatW2KbXr166fDhw5o1a5YaNmyo7OxsnT9/3uvBAwAAAD5VPBC0WqWQwL7Y8+JZto2ZcduLmmrPk9yAabgdVL/00kvq37+/BgwYIEmaNm2aPv/8c82cOVOTJ0922n/58uVavXq1du/erapVq0qS6tev792oAQAAgPJQvIbYVqBAr6C0Gh3IejX79x+3DVs3Gwgwbp0R8vPztXnzZnXr1s1he7du3fT111+X2Gbp0qVKSUnR888/r9q1a+vKK6/UqFGjlJub6/I4eXl5OnHihMMPAAAA4HcmqKs2PDvsTU31xVlzYmoEKbcy1UePHlVBQYESEhIctickJOjQoUMlttm9e7fWrVunqKgoffTRRzp69KiGDBmi48ePu6yrnjx5siZOnOjO0AAAAADjlZipDmyOa0MbMVGZ55lqK5lqXAY8unbFYrE43LfZbE7bilitVlksFs2fP1/XXXedbrvtNr300kuaM2eOy2z12LFjlZOTY//Zt2+fJ8MEAAAAvONUU22CoNrFbX/0yORkuBy4lamuXr26QkNDnbLS2dnZTtnrIjVr1lTt2rUVFxdn35acnCybzab9+/erUaNGTm0iIyMVGRnpztAAAAAA45kwU20lUw2UK7cy1REREWrdurXS0tIctqelpaldu3Yltmnfvr0OHjyoU6dO2bf9+uuvCgkJUZ06dTwYMgAAAFBeSpj9O8AFUk21qKnGZcDty79HjhypN998U7Nnz9a2bds0YsQIZWZmavDgwZIKL93u27evff97771X1apV0wMPPKCMjAytWbNGo0eP1oMPPqjo6GjjngkAAABgNJNnqg25/JpMNVAqt5fUSk1N1bFjxzRp0iRlZWWpefPmWrZsmerVqydJysrKUmZmpn3/2NhYpaWlaejQoUpJSVG1atXUq1cvPfvss8Y9CwAAAMAXTFhTLaMz1a46L8veF4/F4JEAgcLtoFqShgwZoiFDhpT42Jw5c5y2NWnSxOmScQAAACDgmTxT7f+aai7/RvAL7JXrAQAAAL8yX6ba8Nm/vVmn2qEpUTWCE0E1AAAA4AqZ6mKZavf6cxyL90MBAhFBNQAAAOCKU021uWb/NihVbUiHNqqqEaQIqgEAAABXTJiptjnMuG1Eh8bUVJvg+wjAIwTVAAAAQFmZoqbaVuJtzzs0pqYaCFYE1QAAAIArJsxUWwM1U02EjSBFUA0AAAC4YsJ1qm0Oy1gZElW7uO1mS2JqBCmCagAAAMAVk2eqjYmpPc9U28hU4zJAUA0AAAC4ZK7Zv4tnpo0Jqg1ap9qAoQCBiKAaAAAAcMVkmWqnq9UNX6fa3Zrqi5qSqUaQIqgGAAAAXDFZTXXxsNX4MNbdTPXF9d1GjwUIDATVAAAAgCsmy1QXz0wbn6l2rz/HmciJqhGcCKoBAAAAl0yWqbaVft/rTt2tqXZYMxsITgTVAAAAgCsmz1QbUsfs1ezff9y2GrJoNhB4CKoBAAAAV5xqqgN79u/ijAljvVinmtm/cRkgqAYAAABccbqe2lyZav/P/s1EZQh+BNUAAACAS9RUe1dTfXFTomoEJ4JqAAAAwBWT11QHUqaakmoEK4JqAAAAwBWzr1NtSCDreU21HGqqiaoRnAiqAQAAAFecMtWBPVGZ03ANufybTDVQGoJqAAAAwCWzZaptpd73rFMvaqq9SHIDZkFQDQAAALhiuprq0u97xCGo9iZTTVSN4ERQDQAAALhitprqYuM1ZsZtL9ap9rglYB4E1QAAAIArZKqL1VS7e/k3mWoEP4JqAAAAwCWTZaqd5/82oFNjaqqJqRGsCKoBAAAAV8w2+7fPM9Xu1lQX64rIGkHIo6B6xowZatCggaKiotS6dWutXbu2TO3Wr1+vsLAwXX311Z4cFgAAAChfpqupLv2+h726uF2WlsVrvA0YDhBg3A6qFy1apOHDh2vcuHHasmWLOnbsqFtvvVWZmZmltsvJyVHfvn110003eTxYAAAAoFw5RamBHVQXr1s2pI7ZwEw1ddUIRm4H1S+99JL69++vAQMGKDk5WdOmTVNSUpJmzpxZaruHHnpI9957r9q2bevxYAEAAIDyZbJMdfH7hi+p5flEZRIzgCM4uRVU5+fna/PmzerWrZvD9m7duunrr7922e6tt97Srl279PTTT5fpOHl5eTpx4oTDDwAAAFDuzDb7t7X45db+zVQ713gTViP4uBVUHz16VAUFBUpISHDYnpCQoEOHDpXYZseOHfrHP/6h+fPnKywsrEzHmTx5suLi4uw/SUlJ7gwTAAAAMIbJaqqLMyaEpaYaKI1HE5VZLBaH+zabzWmbJBUUFOjee+/VxIkTdeWVV5a5/7FjxyonJ8f+s2/fPk+GCQAAAHjHZLN/B1xNdfGXj6AaQahsqeMLqlevrtDQUKesdHZ2tlP2WpJOnjypTZs2acuWLXr00UclSVarVTabTWFhYVqxYoW6dOni1C4yMlKRkZHuDA0AAADwAXNlqn0y+7c3NdVO94mqEXzcylRHRESodevWSktLc9ielpamdu3aOe1fqVIl/fjjj0pPT7f/DB48WI0bN1Z6erratGnj3egBAAAAXyJT7VVQ7Twe74cDBBq3MtWSNHLkSPXp00cpKSlq27at3njjDWVmZmrw4MGSCi/dPnDggObNm6eQkBA1b97coX2NGjUUFRXltB0AAAAIOCZbUss5M2x0r2726JQ5J6pG8HE7qE5NTdWxY8c0adIkZWVlqXnz5lq2bJnq1asnScrKyrrkmtUAAACAOZj78m9DomqvLv9mSS0EP7eDakkaMmSIhgwZUuJjc+bMKbXthAkTNGHCBE8OCwAAAJQvky2pVTwT7PeJyopnqgP76nnAIx7N/g0AAABcFpyW1ArsqNApUW1IatiLJbWKB9XkqhGECKoBAAAAV4qiQkvohfuBnakOuCW1mKgMlwGCagAAAMClC1FgaHjhb5PVVBuTqDZwSS0mKkMQIqgGAAAAXCnKzIZcmIrIZJlqQ4JYLzLVzjXe3g8HCDQE1QAAAIArRUFhUVBttkw1NdWAzxFUAwAAAK6YLFPtNK+aIUtqGVdTzdXfCEYE1QAAAIBLxWuqA3327+LrQhtx+bcXNdU+yZwDgYWgGgAAAHDFZJnq4plpYzLVngfVPpmNHAgwBNUAAACAK6arqfbBRGVe1FSX1hMQLAiqAQAAAFdMnqk2JqY2cJ1qpv9GECKoBgAAAFwy1zrVxXPBgVZTDQQjgmoAAADAFXumOtTxfoByqqk2YrheZaqL3yfKRvAhqAYAAABcMV1NdbH7xvTqcY9Os5ETUyMIEVQDAAAArtiD6guXfwd8TbUPZtv2IlPtvG42UTWCD0E1AAAA4JK5M9WGpKq9qqkuXuMNBB+CagAAAMCVosxsqDlm/y4exPo7U+08GzlhNYIPQTUAAADgitlqqi9x3/tevZv9m5gawYigGgAAAHDFPvt3uOP9ABVoNdXO4/F+OECgIagGAAAAXDJZptoXmWEvOnXOnBNVI/gQVAMAAACumKymunhm2JAaZm+C6uKZ6sBO9AMeIagGAAAAXKGm2qtendfNJlON4ENQDQAAALjiVFMd4EG1U2bY4Jrqku6Xwjlz7v1wgEBDUA0AAAC4VDxTHdjXLztnhn3RqRc11QTVCEIE1QAAAIArRVGgaWqqS7/vEQMz1YbMRg4EGIJqAAAAwBWz1VT7YqIybyq1fZE5BwIMQTUAAADgislqqotnpo2JqclUA6XxKKieMWOGGjRooKioKLVu3Vpr1651ue+HH36orl27Kj4+XpUqVVLbtm31+eefezxgAAAAoPwUZapDC38HeKa6eC7YkNm2vVpSy+OmgGm4HVQvWrRIw4cP17hx47RlyxZ17NhRt956qzIzM0vcf82aNeratauWLVumzZs368Ybb1SPHj20ZcsWrwcPAAAA+JR9nWpzZqr9X1NdrClRNYKQ20H1Sy+9pP79+2vAgAFKTk7WtGnTlJSUpJkzZ5a4/7Rp0/TEE0/o2muvVaNGjfR///d/atSokT755BOvBw8AAAD4lFNNtclm/zZm+u9L3C+tZfHMORB83Aqq8/PztXnzZnXr1s1he7du3fT111+XqQ+r1aqTJ0+qatWqLvfJy8vTiRMnHH4AAACAcme6mmof1DA7Repl/2KheFND1s0GAoxbQfXRo0dVUFCghIQEh+0JCQk6dOhQmfqYOnWqTp8+rV69erncZ/LkyYqLi7P/JCUluTNMAAAAwCDmqqn2ScjqVU01mWoEP48mKrNYLA73bTab07aSLFy4UBMmTNCiRYtUo0YNl/uNHTtWOTk59p99+/Z5MkwAAADAOyarqS4exBqSGXaqqS57n8413oTVCD5h7uxcvXp1hYaGOmWls7OznbLXxS1atEj9+/fX4sWLdfPNN5e6b2RkpCIjI90ZGgAAAGA8061TXey+Mb1e4n5pLVmoGsHPrUx1RESEWrdurbS0NIftaWlpateunct2CxcuVL9+/bRgwQLdfvvtno0UAAAAKG9ONdWBPVGZb2qqjZv9m5JqBCO3MtWSNHLkSPXp00cpKSlq27at3njjDWVmZmrw4MGSCi/dPnDggObNmyepMKDu27evXnnlFV1//fX2LHd0dLTi4uIMfCoAAACA0UxWU+2L2b+NXKeaVDWCkNtBdWpqqo4dO6ZJkyYpKytLzZs317Jly1SvXj1JUlZWlsOa1a+//rrOnz+vRx55RI888oh9+/333685c+Z4/wwAAAAAXymKCk1SU108M23IutBeZKqdaryJqRGE3A6qJWnIkCEaMmRIiY8VD5RXrVrlySEAAAAA/zNbTfUl7pd3r85JbqJqBB+PZv8GAAAALgsmW6faOTPs75rq4plz74cDBBqCagAAAMClYjXVNmtAR4YBV1PtdD9wXzvAUwTVAAAAgCvF16m+eFsA8sls2wZmqq2B+9IBHiOoBgAAAFwpXlMtBXRddfFMsDGZYS8qtVmmGpcBgmoAAADAleI11VJA11UXz0z75vJvLzLVAXzpPOApgmoAAADApWI11VJAZ6qLB8DGLKllYE01MTWCEEE1AAAA4EqJNdWBG1SXT0112Tt1zpwTVSP4EFQDAAAArpitptonS1h5s0518RpvIPgQVAMAAACulBRUm2r2b/+uU1388NRUIxgRVAMAAAAuXQgCLRZJlsLbgZyp9kmn3tRU+yJzDgQWgmoAAADAlaKsrCXkj8nKArimuvjl1v7OVBdfl5pMNYIRQTUAAADgij0ItEiWC0F1IGeqfbGkljc11VRR4zJAUA0AAAC4YrJMtU/WhfYmU01NNS4DBNUAAACASxfVVJshU32J+5516kVNtU8y50BgIagGAAAAXHHIVIc4bgtAxTPBhqwL7RQZuzP7d/HMuffDAQINQTUAAADgCjXV8q6muth9UtUIQgTVAAAAgCsmq6kOuNm/nTLn3g8HCDQE1QAAAIBLJqup9kWm2qlTb5oSVSP4EFQDAAAArpgsU+0827YBnRqYqaamGsGIoBoAAABwxR4EXpypDtyJyopngo3JDBs3pziXfyMYEVQDAAAArtgz1ZaLZv82T6bamMu/jcxUE1Uj+BBUAwAAAC6Zq6a6eBTtmyW1vFin2vvRAAGHoBoAAABwhZpqg2f/JqxG8CGoBgAAAFwx2zrVF3LBFkvRfWN6LWQpdr8MLS9K9F98HwgmBNUAAACAKybNVIdeiGINXafaEuJ4vyxNfTEeIMAQVAMAAAAuXVxTfeGjcyDP/n1huCEhBqaqi/qwf6ngRqb6QuOi8RBTIxhZbB4UNsyYMUMvvPCCsrKy1KxZM02bNk0dO3Z0uf/q1as1cuRI/fzzz6pVq5aeeOIJDR48uMzHO3HihOLi4pSTk6NKlSq5O9xysefoab2xZpfW7zym387ky2q1KTTEotjIMEVHhCo3v0An887LeuHrQ1ePldbGleJtzpwrUEx4aKl9uHuc0BCLqsREqEWdOEnSD/tz7M/TnbGVZSxlGX95HyNQjuPL983JvPOySKX24+lx3H3vBNJxLt7nfEHhR4PwUEvA/3v64jxQ1Mao19mT18bd19ps7+mi/SrHRKhSdJhycs8p58w5r95rvnidff38zfT/razjDLTX2tP+3TmGL/89Xe3ji7/v6ZZ7FW4p0F8i/qPnz09RE+tODbX8Q5sjrjP0OJ6cw0pyrsCq/AKbQiRZJUWHhygyPLTU1+VSY9to6adYS65ybRGKtuSrv3Wcvgu5qkyvfe65AllthReO2yQlVIxQaEhIwJ3Xyuvvh1k/sxv1/rz471xiXJQaJ1ZUr5QkNaheodR2/lLWONTtoHrRokXq06ePZsyYofbt2+v111/Xm2++qYyMDNWtW9dp/z179qh58+YaOHCgHnroIa1fv15DhgzRwoULdddddxn6ZPzlvU37NOb9H5jNEAAAIMjsjLxPYRarrjv7L70e8bKuCdmpAfmP6wtra38Prdz8FPmgYi1ndcoWpVjLWd2XP1brrC38PSwEgaILKqbc1VJ/S0ny72BK4LOguk2bNmrVqpVmzpxp35acnKyePXtq8uTJTvuPGTNGS5cu1bZt2+zbBg8erK1bt2rDhg1lOmYgB9V7jp5WlxdXySbp5pDNSg39SvH63f74EVXWL9YkNQnZ57C9tMdKa+NK8TZl6cPd4xxRZS0quFGSnJ6nO2MzavzlfYxAOY4v3zdl6cfT47j73gmk41y8jyTT/Hv64jxQ1Mao19mT18bd45jtPV20X4wlX2dsEYa813zxOvv6+Zvp/1tZxxlor7Wn/btzDF/+e7rax+i/u1eF7JbFIj2eP1iDw5aqUchB7S5I0C7VNvz5eHIOK+txvDkXtgjZrVCLlGcLU6TlvHYV1NRJRbv92hv5b2Pmvx9m/cxu9PszxpKvDdam+sLaWiEWaeXjN6h+gGWsfRJU5+fnKyYmRosXL9Zf//pX+/bHHntM6enpWr16tVObTp066ZprrtErr7xi3/bRRx+pV69eOnPmjMLDw53a5OXlKS8vz+HJJCUlBWRQPWX5L/r3ql26KWSz3oyY6u/hAACChM32x2y5AAAEG6utMFM9IP9xfaUUDer0J425pYm/h+WgrEG1WxOVHT16VAUFBUpISHDYnpCQoEOHDpXY5tChQyXuf/78eR09erTENpMnT1ZcXJz9Jykp8C4FKLL/t1zZJLUNyWDiBQCAYQiogcBiszHJ1uX+/GGsEIt03hai60MyZLPZtP+3XH8PyWMezf5tKfaX3mazOW271P4lbS8yduxY5eTk2H/27dvnyTDLRZ0q0bJI2mBtygcgAIBhLjHXC4ByZrHwZdfl/vxhrAKbRWEWq76xNpXFYlGdKtH+HpLHwtzZuXr16goNDXXKSmdnZztlo4skJiaWuH9YWJiqVatWYpvIyEhFRka6MzS/6ZWSpH+v2qUvrK01IP9x9Qr9SvHKsT9+RJW13VpHjUP2OWwv7bHS2rhSvE1Z+nD3OEdUWe8V3FD4vIs9T3fGZtT4y/sYgXIcX75vytKPp8dx970TSMe5eB/JYpp/T1+cB4raGPU6e/LauHscs72ni/aLtuQr1xZhyHvNF6+zr5+/mf6/lXWcgfZae9q/O8fw5b+nq3188fe9pNfJV8dx9xxW1uN4ey4sS5++Ot8G298Ps35mN/r9GW3J1zf2mmqbUgNworKy8miistatW2vGjBn2bU2bNtUdd9zhcqKyTz75RBkZGfZtDz/8sNLT04NiojJJWrxpn55g9m8AAAAAKLNgmf3brUy1JI0cOVJ9+vRRSkqK2rZtqzfeeEOZmZn2dafHjh2rAwcOaN68eZIKZ/p+7bXXNHLkSA0cOFAbNmzQrFmztHDhQg+fWuD5W0qSrq1fVW+s2a11O4/qtzP5slltCrmwDltMRJjO5J/Xybzzsl24ns/VY6W1caV4m6I170rrw93jhFxY867lhTXvtl5Y887dsZVlLGUZf3kfI1CO48v3zcXrDbrqx9PjuPveCaTjXLzPuQKrbJIiQkMC/t/TF+eBojZGvc6evDbuvtZme08X7Ve0TvWJ3HP6/cI61Z6+13zxOvv6+Zvp/1tZxxlor7Wn/btzDF/+e7rax6x/3109fqlzmKvjFP93PXH2XKnj9eXfHbOc18rr74dZP7Mb+f4svk51akpSwM367S63g+rU1FQdO3ZMkyZNUlZWlpo3b65ly5apXr16kqSsrCxlZmba92/QoIGWLVumESNG6F//+pdq1aqlV199tcxrVJtF/eoV9H93sl4fAAAAAFxO3L782x8C/fJvAAAAAEBw8cmSWgAAAAAA4A9uX/7tD0XJ9BMnTvh5JAAAAACAy0FR/Hmpi7tNEVSfPHlSkpSUFHgzwgEAAAAAgtfJkycVFxfn8nFT1FRbrVYdPHhQFStWlIVV5wPCiRMnlJSUpH379lHnDuCyxzkRAP7AORHBwmaz6eTJk6pVq5ZCQlxXTpsiUx0SEqI6der4exgoQaVKlThZAsAFnBMB4A+cExEMSstQF2GiMgAAAAAAPERQDQAAAACAhwiq4ZHIyEg9/fTTioyM9PdQAMDvOCcCwB84J+JyY4qJygAAAAAACERkqgEAAAAA8BBBNQAAAAAAHiKoBgAAAADAQwTVAAAAAAB4iKAaAIBSMJ8nAAAoDUE1HOTm5mrGjBk6dOiQv4cCAH5XUFCgM2fO+HsYABAw+KwIOCOoht0LL7yg2NhYLV68WDExMf4eDgD41dSpU3XttdeqR48emjx5svbv3y+JzDWAyxefFYGSsU41tHr1at1///2SpJdeekl33nmnn0cEAP712GOPacmSJZo0aZI2bdqk9evXKzQ0VF9//bXCw8P9PTwAKFd8VgRKR1AN3Xnnnfrvf/+rM2fOKDQ0VIcPH9bhw4dVo0YNxcfHKzQ0VDabTRaLxd9DBQCfstlsOnz4sLp3764RI0aoX79+kqSffvpJN998s1JTUzV16lSFhYX5d6AAUI74rAiUjqD6MlZQUKDQ0FD9/PPPatOmjaZPn64dO3Zo/vz5qlatmg4ePKgHHnhAkydP9vdQAcCnDh8+rISEBPvtmjVratOmTWrVqpX9g+K7776r+++/X6tWrVLbtm39PGIA8K1Dhw4pMTFRkvisCFwCNdWXmblz56pZs2Y6dOiQQkNDZbVa1axZMw0aNEj9+/fXli1bNH36dM2YMUOPPvqo3n33XY0ePVqSZLVa/Tx6ADDW9OnTFR8fr9WrV9u3nT17Vtddd50++OADSbJnXv7+978rJSVFL730kiTOiQCCU9F5cc2aNfY5JPisCJSO69cuM7Nnz9a2bds0fvx4/ec//7FvHzdunMLDw3XfffepRYsWkqTWrVsrOjpakydP1hNPPKH4+Hh/DRsADLV+/Xo9+OCDOnnypJ577jn16tXL/ljt2rWVnJysrVu3KiMjQ02bNtX58+cVFhamESNG6IEHHtCRI0c4JwIIKqWdFyU+KwKlIVN9GTl8+LBsNpteffVVzZ49W998841CQkJUUFCgatWq6cknn7SfJCUpPDxciYmJio6OVlZWlh9HDgDGeeONN9SxY0fdfPPNOnjwoPr3769z587ZHw8LC1PPnj118OBBvffee/ZtklShQgXFx8frwIEDfhk7APjCpc6LklS1alU+KwIukKm+jERGRspisahLly66/fbbNWzYMH377bcKDQ2VJFWsWNG+b1EN4fbt29WgQQNdeeWV/ho2ABjq1ltvlSTddNNNkgqXiMnIyFB0dLQ6deqkv//977rjjjv05Zdf6rPPPlOjRo3Uu3dvSYVfTsbGxuqKK67w2/gBwGilnRc7d+6s1NRUWSwWxcbG2tvwWRH4A0H1ZWT79u06fvy4mjZtqjFjxuimm27Sxx9/rOrVqyskJMQ+8c6JEyeUn5+v2bNna+7cuZowYYKioqKY1RGA6RUUFCgpKUkjRoxQamqqGjdurMjISHXt2lUrV67U4sWLlZGRoUmTJmn48OF6+eWX1bdvX6Wlpaly5cqaO3euHnvsMcXExHBOBBAUynJe/OWXX/T000/bz3l8VgQcMft3kCqa2bvo5Gaz2fTtt9/qmWee0aeffipJ6t27txYtWqQqVaro/fffV+fOnbV161a9//77evfdd2Wz2TR9+nT7t5cAYFZF58Si2mhJatq0qW6//XY9/fTTio2N1blz5/Sf//xHjz32mPbs2aM6derIZrNp5syZ+umnn7Rz504NHTpUPXr08POzAQDvuXte3Lt3r2rXrq0tW7boww8/5LMicBGC6iCQk5Oj9PR0de7c2eEbwjNnzui3335T7dq1JUkLFizQl19+qTfeeEN9+vTR4sWLFRUVpT59+mjGjBmSpNOnT2vJkiWy2Wy67777/PacAMBTZT0nZmRkKCIiQg0bNrS3zc7OVps2bTR06FCNHDnSL+MHAKMZeV7ksyLgjInKTG7SpEmqUqWK5s6dK+mPpV+ys7N1xx136G9/+5t938OHD+uzzz5TTEyM9u/fr9WrV+v555/X7NmztWfPHkmFk/D07t2bkyQAU3LnnNi0aVP7B8eiZWAyMzN1/Phx1a1bt5xHDgC+YfR5kc+KgDNqqk1q2bJlevDBB1WpUiUtXbpUf/7znx0ej4+PV8+ePZWVlaXTp0+rQoUKat68uZo2barBgwerZ8+eCgsLU6NGjfTSSy9p3rx5evrpp/30bADAO+6cE8+ePauoqCiHx4tWQvj44491/fXXq1OnTuU5fAAwHOdFoPxw+bcJrV+/XnfddZeaNGmiVatWSZKOHj2q2NhYhYWF2eticnNzFR0d7dD2zJkziomJcdiWnZ2tGjVqlMvYAcBo3pwT8/LytHHjRn3//feaM2eOTpw4oTfffFNdunQp76cBAIbhvAiULy7/NpGi7z+uvPJK3XPPPTp//rz27NmjcePGqVu3burcubN69uypbdu2SZLDSbKobfFvISURUAMwJSPOibm5udqxY4cWLVqkO++8U7t37+aDIwDT4rwI+AdBtQkcPHhQkuyzeMfHx+svf/mL8vLy1LhxY33//fd6/PHH1bt3b+3du1dDhw7V2rVr7e1//vlnpaam6uDBgwoJCREXJwAwM6POiQcOHFDlypV15513auXKlXrqqaf89ZQAwCucFwH/IqgOYOvXr1fLli01ZswY+8myaNKI9u3b65577tFTTz2ld955R71799awYcP0zjvvKCcnR8uXL1dBQYGkwhPt2rVrtWDBAkli/UAApuSrc2KVKlWcLn8EADPgvAgEBmqqA5DNZtMHH3ygiRMnKjIyUjk5OXr22WeVmpoqqfBkGRISogMHDigiIkLx8fEO7W+99VbFxsZq8eLFkgprY3bt2qWmTZuW+3MBAG9xTgQAR5wXgcBCpjoAWa1WhYWF6bbbblNaWprq1aunBQsW6Ndff5X0R6a5du3aDidJq9Wq3377TceOHVO9evXs2yMjIzlJAjAtzokA4IjzIhBYCKoDUGhoqDp37qxx48apSpUqmjRpkjZt2qTPP/9c586dK/HybZvNptzcXE2bNk1nz55l7UAAQYNzIgA44rwIBBYu/w5wBQUFCg0NVf/+/ZWRkaFp06apTZs2DvssWbJEy5cv1xdffKGwsDDNnj1b7dq189OIAcB3OCcCgCPOi4D/kakOcEXfND7zzDM6dOiQPv74Y+Xk5EiSfXKJq6++WqdPn9bYsWP1yy+/cJIEELQ4JwKAI86LgP+RqfaDDz/8UF26dFHlypXLtH/RN5CTJ0/WvHnz9Oqrr6pBgwaaN2+eHnnkESUkJMhmszGrNwBT4pwIAI44LwLmQqa6HK1Zs0bNmjXT3XffbV+yoCxCQgr/mcaOHauoqCg9/PDDatKkidavX6+wsDBOkgBMiXMiADjivAiYE5nqcvLrr79q4sSJiouLk8Vi0ZIlS/Ttt9+qdu3aZWqfm5urTz75RIMGDVKtWrU0ZcoU9ejRw8ejBgDf4JwIAI44LwLmRVBdTo4cOaKlS5fq+uuvV4MGDZScnKy//vWvmjZtWpna//TTT2rVqpXGjh2riRMn+nawAOBjnBMBwBHnRcC8CKp9ZN26dapbt67q1q1r33b+/HmFhYVJkt5++20NGDBAGzZsUKtWrcrUZ15eniIjI30yXgDwJc6JAOCI8yIQPAiqDbZy5UoNGDBAVqtV+fn5uvnmmzVixAhdc801TvUsHTp0UFxcnJYsWaLw8HA/jhoAfINzIgA44rwIBB8mKjPQ/v379eSTT+ree+/VmjVr9MYbb+jHH3/U6NGjtXPnTlksFvvSBpL0wgsvaPny5fr0008lSVarVceOHfPX8AHAUJwTAcAR50UgOBFUG2jbtm3asmWL7r//ftWtW1d//vOfNWXKFFmtVj355JOSpNDQUPv+bdu2Vd++fTVhwgR9+eWXuv322zV16lTl5eX56ykAgGE4JwKAI86LQHAiqDbQ8ePH1aRJE1mtVvu2m2++WXfffbc2btyoFStWSJLDN5APP/ywfvzxR3Xt2lUFBQUaNWoUtTAAggLnRABwxHkRCE4E1QZq1qyZtm3bpl9++cW+LSQkRDfddJOuvvpqLVy4UFLhN5AFBQVasGCBOnXqpNatW+vbb7/VihUrVLVqVX8NHwAMxTkRABxxXgSCE0G1gZo3b64uXbro5Zdf1qlTp+zbGzdurLp16+rQoUPKzc2VJOXn5+vYsWOaNm2avvvuO6WkpPhr2ADgE5wTAcAR50UgOBFUG2zy5Mlav3693nnnHYd6l/j4eGVkZNhnboyOjtbQoUM1ePBgfw0VAHyOcyIAOOK8CASfMH8PINhcddVVGjNmjCZNmqTQ0FDdc889slqt+vbbb9W7d2/72oMAcDngnAgAjjgvAsGHdap95JFHHtEHH3ygunXrKjs7WzExMXrvvffUvHlzfw8NAMod50QAcMR5EQgeBNU+kpeXp4yMDKWnpysiIkK9e/f295AAwG84JwKAI86LQPAgqAYAAAAAwENMVAYAAAAAgIcIqgEAAAAA8BBBNQAAAAAAHiKoBgAAAADAQwTVAAAAAAB4iKAaAAAAAAAPEVQDAAAAAOAhgmoAAAAAADxEUA0AAAAAgIcIqgEA8KMbbrhBw4cPv+yODQBAsCCoBgDAJFatWiWLxaLff//dkHYffvihnnnmGeMGCADAZSjM3wMAAAD+UbVqVX8PAQAA0yNTDQBAOTl9+rT69u2r2NhY1axZU1OnTnV4/J133lFKSooqVqyoxMRE3XvvvcrOzpYk7d27VzfeeKMkqUqVKrJYLOrXr58kyWaz6fnnn9ef/vQnRUdH66qrrtL7779/yXbFL/+uX7++nn32WfsY69Wrp48//lhHjhzRHXfcodjYWLVo0UKbNm1yGPfXX3+tTp06KTo6WklJSRo2bJhOnz5t9MsHAEBAIqgGAKCcjB49Wl999ZU++ugjrVixQqtWrdLmzZvtj+fn5+uZZ57R1q1btWTJEu3Zs8ceACclJemDDz6QJG3fvl1ZWVl65ZVXJElPPvmk3nrrLc2cOVM///yzRowYofvuu0+rV68utV1JXn75ZbVv315btmzR7bffrj59+qhv376677779P3336thw4bq27evbDabJOnHH39U9+7ddeedd+qHH37QokWLtG7dOj366KO+eAkBAAg4FlvRX0UAAOAzp06dUrVq1TRv3jylpqZKko4fP646depo0KBBmjZtmlOb7777Ttddd51Onjyp2NhYrVq1SjfeeKN+++03Va5cWVJh9rt69epauXKl2rZta287YMAAnTlzRgsWLCixnVSYqb766qvtx65fv746duyot99+W5J06NAh1axZU+PHj9ekSZMkSd98843atm2rrKwsJSYmqm/fvoqOjtbrr79u73fdunXq3LmzTp8+raioKANfRQAAAg811QAAlINdu3YpPz/fIfCtWrWqGjdubL+/ZcsWTZgwQenp6Tp+/LisVqskKTMzU02bNi2x34yMDJ09e1Zdu3Z12J6fn69rrrnG7XG2bNnSfjshIUGS1KJFC6dt2dnZSkxM1ObNm7Vz507Nnz/fvo/NZpPVatWePXuUnJzs9hgAADATgmoAAMrBpS4MO336tLp166Zu3brpnXfeUXx8vDIzM9W9e3fl5+e7bFcUeP/3v/9V7dq1HR6LjIx0e5zh4eH22xaLxeW2ouNarVY99NBDGjZsmFNfdevWdfv4AACYDUE1AADloGHDhgoPD9c333xjDzZ/++03/frrr+rcubN++eUXHT16VM8995ySkpIkyWlCsIiICElSQUGBfVvTpk0VGRmpzMxMde7cucRjl9TOKK1atdLPP/+shg0bGt43AABmwERlAACUg9jYWPXv31+jR4/Wl19+qZ9++kn9+vVTSEjhn+K6desqIiJC06dP1+7du7V06VKnNaTr1asni8WiTz/9VEeOHNGpU6dUsWJFjRo1SiNGjNDcuXO1a9cubdmyRf/61780d+5cl+2MMmbMGG3YsEGPPPKI0tPTtWPHDi1dulRDhw417BgAAAQygmoAAMrJCy+8oE6dOukvf/mLbr75ZnXo0EGtW7eWJMXHx2vOnDlavHixmjZtqueee04vvviiQ/vatWtr4sSJ+sc//qGEhAT7DNvPPPOMnnrqKU2ePFnJycnq3r27PvnkEzVo0KDUdkZo2bKlVq9erR07dqhjx4665pprNH78eNWsWdOwYwAAEMiY/RsAAAAAAA+RqQYAAAAAwEME1QAAAAAAeIigGgAAAAAADxFUAwAAAADgIYJqAAAAAAA8RFANAAAAAICHCKoBAAAAAPAQQTUAAAAAAB4iqAYAAAAAwEME1QAAAAAAeIigGgAAAAAAD/1/jLWqEJu2rDYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# true changepoint indices selection\n",
    "true_cp = [df.changepoint for df in list_of_df]\n",
    "\n",
    "predicted_cp[0].plot(figsize=(12,3), label='predictions', marker='o', markersize=5)\n",
    "true_cp[0].plot(marker='o', markersize=2)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation (metrics calculation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 'pip install tsad' if needed\n",
    "from tsad.evaluating.evaluating import evaluating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary classification (outlier detection) metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Since you not choose numenta_time and portion, then portion will be 0.1\n",
      "False Alarm Rate 4.66 %\n",
      "Missing Alarm Rate 37.02 %\n",
      "F1 metric 0.73\n"
     ]
    }
   ],
   "source": [
    "# binary classification metrics calculation\n",
    "binary = evaluating(\n",
    "    true_outlier, \n",
    "    predicted_outlier, \n",
    "    metric='binary'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changepoint detection metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intersection of scoring windows(Timestamp('2020-03-01 15:58:10'), Timestamp('2020-03-01 15:54:50'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 18:02:23.600000'), Timestamp('2020-02-08 17:58:38'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 18:09:01.600000'), Timestamp('2020-02-08 18:08:03'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 18:24:52.800000'), Timestamp('2020-02-08 18:20:46'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 18:31:06.800000'), Timestamp('2020-02-08 18:28:37'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 18:48:51.600000'), Timestamp('2020-02-08 18:46:33'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 18:55:37.600000'), Timestamp('2020-02-08 18:51:55'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 19:01:15.800000'), Timestamp('2020-02-08 18:58:30'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 19:06:15.800000'), Timestamp('2020-02-08 19:03:36'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 19:30:25.750000'), Timestamp('2020-02-08 19:26:37'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-01 16:37:42.600000'), Timestamp('2020-03-01 16:34:52'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-01 16:43:24.600000'), Timestamp('2020-03-01 16:40:53'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-01 16:57:52.600000'), Timestamp('2020-03-01 16:54:53'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-01 17:03:52.600000'), Timestamp('2020-03-01 17:00:53'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-01 17:19:07'), Timestamp('2020-03-01 17:14:53'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-01 17:20:07'), Timestamp('2020-03-01 17:19:53'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 16:41:09'), Timestamp('2020-02-08 16:38:12'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 16:47:08'), Timestamp('2020-02-08 16:44:10'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 17:00:59.200000'), Timestamp('2020-02-08 16:59:15'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 17:03:03.200000'), Timestamp('2020-02-08 17:02:12'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 17:06:00.200000'), Timestamp('2020-02-08 17:03:14'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 17:21:12.400000'), Timestamp('2020-02-08 17:17:13'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 17:28:11.400000'), Timestamp('2020-02-08 17:24:14'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 17:41:17.800000'), Timestamp('2020-02-08 17:38:17'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 17:47:18.800000'), Timestamp('2020-02-08 17:44:19'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 10:28:32.800000'), Timestamp('2020-03-09 10:25:33'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 10:34:32.800000'), Timestamp('2020-03-09 10:31:33'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 10:48:33'), Timestamp('2020-03-09 10:45:33'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 10:54:33'), Timestamp('2020-03-09 10:51:34'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 13:48:38.800000'), Timestamp('2020-03-09 13:45:39'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 13:54:38.800000'), Timestamp('2020-03-09 13:51:39'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 14:08:39'), Timestamp('2020-03-09 14:05:40'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 14:14:40'), Timestamp('2020-03-09 14:11:41'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 14:28:40'), Timestamp('2020-03-09 14:25:40'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 14:34:40'), Timestamp('2020-03-09 14:31:41'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 14:48:40.800000'), Timestamp('2020-03-09 14:45:41'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 14:54:40.800000'), Timestamp('2020-03-09 14:51:41'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 15:08:40.800000'), Timestamp('2020-03-09 15:05:41'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 15:14:40.800000'), Timestamp('2020-03-09 15:11:41'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 15:28:41'), Timestamp('2020-03-09 15:25:42'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 15:34:42'), Timestamp('2020-03-09 15:31:42'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 11:10:39.750000'), Timestamp('2020-03-09 11:10:34'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 11:15:33.750000'), Timestamp('2020-03-09 11:11:34'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 11:28:34.200000'), Timestamp('2020-03-09 11:25:35'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 11:34:35.200000'), Timestamp('2020-03-09 11:31:36'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 11:48:35.800000'), Timestamp('2020-03-09 11:45:36'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 11:54:35.800000'), Timestamp('2020-03-09 11:51:36'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 12:08:35.800000'), Timestamp('2020-03-09 12:05:36'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 12:14:36.800000'), Timestamp('2020-03-09 12:11:36'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 12:28:35.800000'), Timestamp('2020-03-09 12:25:36'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 12:34:35.800000'), Timestamp('2020-03-09 12:31:37'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 12:48:37'), Timestamp('2020-03-09 12:45:37'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 12:54:37'), Timestamp('2020-03-09 12:51:38'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 13:08:37.600000'), Timestamp('2020-03-09 13:05:38'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 13:14:37.600000'), Timestamp('2020-03-09 13:11:38'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 13:28:38'), Timestamp('2020-03-09 13:25:38'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 13:34:38'), Timestamp('2020-03-09 13:31:39'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 16:10:29.800000'), Timestamp('2020-03-09 16:07:30'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 16:16:29.800000'), Timestamp('2020-03-09 16:13:30'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 16:30:30'), Timestamp('2020-03-09 16:27:30'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 16:36:32'), Timestamp('2020-03-09 16:33:31'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 16:50:30.800000'), Timestamp('2020-03-09 16:47:31'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 16:56:30.800000'), Timestamp('2020-03-09 16:53:31'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 17:10:02.600000'), Timestamp('2020-03-09 17:07:32'))\n",
      "Intersection of scoring windows(Timestamp('2020-03-09 17:16:03.600000'), Timestamp('2020-03-09 17:13:32'))\n",
      "Amount of true anomalies 129\n",
      "A number of missed CPs = 84\n",
      "A number of FPs = 14\n",
      "Average time 0 days 00:01:11.694444444\n"
     ]
    }
   ],
   "source": [
    "# average detection delay metric calculation\n",
    "add = evaluating(\n",
    "    true_cp, \n",
    "    predicted_cp, \n",
    "    metric='average_time',\n",
    "    anomaly_window_destenation='righter', \n",
    "    portion=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intersection of scoring windows(Timestamp('2020-02-08 18:20:42'), Timestamp('2020-02-08 18:20:16'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 18:51:37'), Timestamp('2020-02-08 18:51:25'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 19:26:28'), Timestamp('2020-02-08 19:26:07'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 17:17:12'), Timestamp('2020-02-08 17:16:43'))\n",
      "Intersection of scoring windows(Timestamp('2020-02-08 17:24:11'), Timestamp('2020-02-08 17:23:44'))\n",
      "Standart  -  4.62\n",
      "LowFP  -  2.01\n",
      "LowFN  -  6.18\n"
     ]
    }
   ],
   "source": [
    "# nab metric calculation\n",
    "nab = evaluating(\n",
    "    true_cp, \n",
    "    predicted_cp, \n",
    "    metric='nab', \n",
    "    numenta_time='30S',\n",
    "    anomaly_window_destenation='center', \n",
    ")"
   ]
  }
 ],
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