anomaly_AWS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.core.pylabtools import figsize\n",
    "figsize(16, 7)\n",
    "\n",
    "import mxnet as mx\n",
    "from mxnet import nd, autograd, gluon\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.svm import OneClassSVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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>timestamp</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-02-14 14:30:00</td>\n",
       "      <td>6.456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-02-14 14:35:00</td>\n",
       "      <td>5.816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-02-14 14:40:00</td>\n",
       "      <td>6.268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-02-14 14:45:00</td>\n",
       "      <td>5.816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-02-14 14:50:00</td>\n",
       "      <td>5.862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp  value\n",
       "0  2014-02-14 14:30:00  6.456\n",
       "1  2014-02-14 14:35:00  5.816\n",
       "2  2014-02-14 14:40:00  6.268\n",
       "3  2014-02-14 14:45:00  5.816\n",
       "4  2014-02-14 14:50:00  5.862"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path1 = './data/realAWSCloudwatch/realAWSCloudwatch/rds_cpu_utilization_cc0c53.csv'\n",
    "path2 = './data/realAWSCloudwatch/realAWSCloudwatch/rds_cpu_utilization_e47b3b.csv'\n",
    "\n",
    "df1 = pd.read_csv(path1)\n",
    "df2 = pd.read_csv(path2)\n",
    "\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make timestamp conversions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['timestamp'] = pd.to_datetime(df1['timestamp'])\n",
    "df2['timestamp'] = pd.to_datetime(df2['timestamp'])\n",
    "\n",
    "df1['time_epoch'] = (df1['timestamp'].astype(np.int64)/100000000000).astype(np.int64)\n",
    "df2['time_epoch'] = (df2['timestamp'].astype(np.int64)/100000000000).astype(np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a4be02550>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(x='time_epoch', y='value', kind='scatter')\n",
    "df2.plot(x='time_epoch', y='value', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract additional features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['hours'] = df1['timestamp'].dt.hour\n",
    "df1['week_day'] = df1['timestamp'].dt.dayofweek\n",
    "\n",
    "df2['hours'] = df2['timestamp'].dt.hour\n",
    "df2['week_day'] = df2['timestamp'].dt.dayofweek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot hours and day of week histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_histogram(df, category_count, category_name):\n",
    "    values = []\n",
    "\n",
    "    for index in range(category_count):\n",
    "        values.append(df.loc[df[category_name] == index, 'value'])\n",
    "    fig, ax = plt.subplots()\n",
    "    heights, bins = np.histogram([values[0]])\n",
    "    histogram_tuples = [[heights, bins]]\n",
    "\n",
    "    for index in range(1, category_count):\n",
    "        heights, bins = np.histogram(values[index], bins=histogram_tuples[index - 1][1])\n",
    "        histogram_tuples.append([heights, bins])\n",
    "\n",
    "    width = (histogram_tuples[0][1][1] - histogram_tuples[0][1][0]) / 30\n",
    "\n",
    "    for index in range(category_count):\n",
    "        ax.bar(histogram_tuples[index][1][:-1]+width*index, histogram_tuples[index][0]*100/values[index].count(), width=width, facecolor='blue', label=category_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(df1, 24, 'hours')\n",
    "plot_histogram(df2, 24, 'hours')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6IAAAGfCAYAAABFg2/dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFaJJREFUeJzt3W2Mpfd51/HfhTehVVuwjdfGsg1rKhcSIerAYkUKD63TokCr2C8S1KqgRViyKA9KWkriNhKoiBcJRU1AQiCridiiUMfkobaqFGIch1KJOF0ncZPUjZxGJjU23m0TK7EQqZxevJhj7WS7szO7M3Odh/18pNWcc+aenUvnPw/nO/d97lPdHQAAAJjyh5Y9AAAAAJcXIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoIQoAAMCoI5Of7Jprruljx45NfkoAAACGPPbYY7/T3Ud32240RI8dO5ZTp05NfkoAAACGVNX/2st2Ds0FAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABglBAFAABg1JFlDwBVZy93L28OAABghj2iAAAAjNrTHtGqeirJ15J8I8mL3X28qq5O8r4kx5I8leRvdvdXDmdMAAAANsXF7BH93u6+tbuPL67fk+Th7r4lycOL6wAAAHBB+zk0944kJxeXTya5c//jAAAAsOn2GqKd5CNV9VhV3b247brufjZJFm+vPd8HVtXdVXWqqk6dOXNm/xMDAACw1vZ61tzXdPczVXVtkoeq6jf3+gm6+94k9ybJ8ePHnRMVAADgMrenPaLd/czi7ekkH0pyW5Lnqur6JFm8PX1YQwIAALA5dg3Rqvq2qvqOly4n+WtJPpvkwSQnFpudSPLAYQ0JAADA5tjLobnXJflQVb20/X/q7v9SVb+W5P6quivJl5K88fDGBAAAYFPsGqLd/cUk332e2383yWsPYygAAAA2135evgUAAAAumhAFAABglBAFAABglBAFAABglBAFAABglBAFAABg1F5eR/Syt/USqlu6lzcHAADAJrBHFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFCFAAAgFFHlj3Aqqpa9gQAAACbyR5RAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARh1Z9gCrpGrZEwAAAGw+e0QBAAAYJUQBAAAYtecQraorqupTVfVLi+s3V9WjVfVkVb2vql5+eGMCAACwKS5mj+ibkjyx7fo7kryzu29J8pUkdx3kYAAAAGymPYVoVd2Y5AeS/NzieiW5Pcn7F5ucTHLnYQwIAADAZtnrHtF3JXlLkt9fXP9jSZ7v7hcX159OcsMBzwYAAMAG2jVEq+oHk5zu7se233yeTXuHj7+7qk5V1akzZ85c4pgAAABsir3sEX1NktdX1VNJ7svWIbnvSnJlVb30OqQ3JnnmfB/c3fd29/HuPn706NEDGBkAAIB1tmuIdvdPdveN3X0syQ8l+Wh3/0iSR5K8YbHZiSQPHNqUAAAAbIz9vI7oW5P8eFV9IVvPGX33wYwEAADAJjuy+yZndffHknxscfmLSW47+JEAAADYZPvZIwoAAAAXTYgCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAwSogCAAAw6siyBwAAgJ1Unb3cvbw5gINljygAAACj7BEFAGCj2asKq0eIsrL80gAAgM3k0FwAAABGCVEAAABGCVEAAABGeY4oAAAbZ/u5JoDVY48oAAAAo4QoAAAAo4QoAAAAo3YN0ar6lqr6RFU9XlWfq6qfXtx+c1U9WlVPVtX7qurlhz8uAAAA624ve0S/nuT27v7uJLcmeV1VvTrJO5K8s7tvSfKVJHcd3pgAAABsil1DtLe8sLj6ssW/TnJ7kvcvbj+Z5M5DmZDLStXZfwAAwGba03NEq+qKqvp0ktNJHkryW0me7+4XF5s8neSGHT727qo6VVWnzpw5cxAzAwAAsMb2FKLd/Y3uvjXJjUluS/KK8222w8fe293Hu/v40aNHL31SAAAANsJFnTW3u59P8rEkr05yZVUdWbzrxiTPHOxoAAAAbKK9nDX3aFVdubj8rUm+L8kTSR5J8obFZieSPHBYQwIAALA5juy+Sa5PcrKqrshWuN7f3b9UVb+R5L6q+hdJPpXk3Yc4JwAAABti1xDt7l9P8qrz3P7FbD1fFC6as+ICAMDl66KeIwoAAAD7JUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYJUQBAAAYdWTZA2yiqrOXu5c3BwAAwCqyRxQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRu4ZoVd1UVY9U1RNV9bmqetPi9qur6qGqenLx9qrDHxcAAIB1t5c9oi8m+cfd/Yokr07yD6rqlUnuSfJwd9+S5OHFdQAAALigXUO0u5/t7k8uLn8tyRNJbkhyR5KTi81OJrnzsIYEAABgc1zUc0Sr6liSVyV5NMl13f1sshWrSa496OEAAADYPHsO0ar69iQfSPLm7v7qRXzc3VV1qqpOnTlz5lJmBAAAYIPsKUSr6mXZitD3dvcHFzc/V1XXL95/fZLT5/vY7r63u4939/GjR48exMwAAACssb2cNbeSvDvJE939s9ve9WCSE4vLJ5I8cPDjAQAAsGmO7GGb1yT520k+U1WfXtz2U0nenuT+qroryZeSvPFwRlwtVWcvdy9vDgAAgHW1a4h2968mqR3e/dqDHQcAAIBNd1FnzQUAAID9EqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMEqIAAACMOrLsAdZZ1dnL3cubAwAAYJ3YIwoAAMAoIQoAAMAoIQoAAMAoIQoAAMAoJysCAAAumRN4cinsEQUAAGCUEAUAAGCUEAUAAGCUEAUAAGCUEAUAAGCUEAUAAGCUl28BAIDLlJdeYVnsEQUAAGCUEAUAAGCUQ3MBAFgLDiOFzWGPKAAAAKOEKAAAAKOEKAAAAKOEKAAAAKOEKAAAAKOEKAAAAKOEKAAAAKOEKAAAAKOEKAAAAKOEKAAAAKOEKAAAAKOEKAAAAKOEKAAAAKOEKAAAAKOEKAAAAKOOLHsAAABgvVQtewLWnT2iAAAAjBKiAAAAjBKiAAAAjBKiAAAAjBKiAAAAjBKiAAAAjBKiAAAAjBKiAAAAjBKiAAAAjBKiAAAAjBKiAAAAjBKiAAAAjBKiAAAAjBKiAAAAjNo1RKvqPVV1uqo+u+22q6vqoap6cvH2qsMdEwAAgE2xlz2i/yHJ68657Z4kD3f3LUkeXlwHAACAXe0aot39K0m+fM7NdyQ5ubh8MsmdBzwXAAAAG+pSnyN6XXc/mySLt9futGFV3V1Vp6rq1JkzZy7x0wEAALApDv1kRd19b3cf7+7jR48ePexPBwAAwIq71BB9rqquT5LF29MHNxIAAACb7FJD9MEkJxaXTyR54GDGAQAAYNPt5eVbfiHJ/0zyp6vq6aq6K8nbk3x/VT2Z5PsX1wEAAGBXR3bboLt/eId3vfaAZwEAAOAycOgnKwIAAIDthCgAAACjhCgAAACjdn2OKByUqmVPAAAArAJ7RAEAABglRAEAABglRAEAABjlOaKsne3PNe1e3hwAAMClsUcUAACAUUIUAACAUUIUAACAUUIUAACAUUIUAACAUUIUAACAUUIUAACAUUIUAACAUUIUAACAUUIUAACAUUIUAACAUUIUAABI1dY/mCBEAQAAGCVEAQAAGHVk2QMAAFyOth8C2b28OQCWwR5RAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARglRAAAARh1Z9gAAAOus6uzl7uXNAbBO7BEFAABglBAFAABglENzAQAOiMN0AfbGHlEAAABGCVEAAABGCVEAAABGCVEAAABGOVkRAMCSOckRcLmxRxQAAIBRQhQAAIBRQhQAAIBRQhQAAIBRTlYEG84JMAAAWDX72iNaVa+rqs9X1Req6p6DGgo4HFVn/wHApvF7jk20qV/XlxyiVXVFkn+b5K8neWWSH66qVx7UYOtmU79AWE97+Xr0NQsAXAyPHS7e9vvsYu67y+G+3s8e0duSfKG7v9jdv5fkviR3HMxYAAAAbKr9hOgNSX572/WnF7cBS3A5/OVsVZzvr5v+wskm8XV6uNy3APs7WdH5foT+gVOhVNXdSe5eXH2hqj6/j8+5dg7oF801SX7nQP6nNbXT/biCv8jXcq1W8H6ccOBrdbH342V6v1+qtfze2gSX8HVqrRYu9Xt88GfDWq/VGty/B21svfZzH63x/XuQLrhWl8H9+yf3stF+QvTpJDdtu35jkmfO3ai7701y7z4+z2Wvqk519/Flz8HurNX6sFbrxXqtD2u1PqzVerFe68Na7c1+Ds39tSS3VNXNVfXyJD+U5MGDGQsAAIBNdcl7RLv7xar6h0n+a5Irkrynuz93YJMBAACwkfZzaG66+8NJPnxAs7AzhzavD2u1PqzVerFe68NarQ9rtV6s1/qwVntQ3X/g/EIAAABwaPbzHFEAAAC4aEJ0hVTVe6rqdFV99pzb/1FVfb6qPldV/3JZ8/HNzrdeVXVrVX28qj5dVaeq6rZlzsiWqrqpqh6pqicW30dvWtx+dVU9VFVPLt5etexZL3cXWKufqarfrKpfr6oPVdWVy571crfTWm17/09UVVfVNcuakbMutF4eZ6yWC/wc9BhjxVTVt1TVJ6rq8cVa/fTi9pur6tHF44v3LU7syjkcmrtCquqvJHkhyc93959d3Pa9Sd6W5Ae6++tVdW13n17mnGzZYb0+kuSd3f3LVfU3krylu79niWOSpKquT3J9d3+yqr4jyWNJ7kzyd5J8ubvfXlX3JLmqu9+6xFEvexdYqxuTfHRxorx3JIm1Wq6d1qq7f6Oqbkryc0n+TJK/0N1r+1qVm+IC31vXxeOMlXKBtXpXPMZYKVVVSb6tu1+oqpcl+dUkb0ry40k+2N33VdW/T/J4d/+7Zc66iuwRXSHd/StJvnzOzT+a5O3d/fXFNn45rIgd1quT/JHF5T+a87y2LvO6+9nu/uTi8teSPJHkhiR3JDm52Oxktn7Rs0Q7rVV3f6S7X1xs9vFshSlLdIHvqyR5Z5K3ZOtnIivgAuvlccaKucBaeYyxYnrLC4urL1v86yS3J3n/4naPL3YgRFffdyX5y4vd+/+9qv7isgfigt6c5Geq6reT/KskP7nkeThHVR1L8qokjya5rrufTbZ+8Se5dnmTca5z1mq7v5vkl6fnYWfb16qqXp/kf3f340sdih2d873lccYKO2etPMZYQVV1RVV9OsnpJA8l+a0kz2/74+nTOftHOrYRoqvvSJKrkrw6yT9Jcv/iMABW048m+bHuvinJjyV595LnYZuq+vYkH0jy5u7+6rLnYWc7rVVVvS3Ji0neu6zZ+Gbb1ypba/O2JP90qUOxo/N8b3mcsaLOs1YeY6yg7v5Gd9+arSN1bkvyivNtNjvVehCiq+/pbB1j3t39iSS/n8SJH1bXiSQfXFz+z9n6gcQKWDx34wNJ3tvdL63Rc4vn4rz0nByHpK2AHdYqVXUiyQ8m+ZF2goOVcJ61+s4kNyd5vKqeytYDs09W1R9f3pS8ZIfvLY8zVtAOa+Uxxgrr7ueTfCxbf9S5sqqOLN51YxxGfV5CdPX9YraOM09VfVeSlydx0ofV9UySv7q4fHuSJ5c4CwuLv+6/O8kT3f2z2971YLZ+sWfx9oHp2fhmO61VVb0uyVuTvL67/++y5uOs861Vd3+mu6/t7mPdfSxbkfPnu/v/LHFUcsGfgx5nrJgLrJXHGCumqo6+dBb3qvrWJN+Xref0PpLkDYvNPL7YgbPmrpCq+oUk35Otv0Q+l+SfJfmPSd6T5NYkv5fkJ7r7o8uakbN2WK/PJ/nX2TrU6f8l+fvd/diyZmRLVf2lJP8jyWey9df+JPmpbD3n5v4kfyLJl5K8sbvPPQEVgy6wVv8myR9O8ruL2z7e3X9vfkJestNadfeHt23zVJLjzpq7fBf43vpv8ThjpVxgrb4ajzFWSlX9uWydjOiKbO3gu7+7/3lV/akk9yW5Osmnkvytl04IxllCFAAAgFEOzQUAAGCUEAUAAGCUEAUAAGCUEAUAAGCUEAUAAGCUEAUAAGCUEAUAAGCUEAUAAGDU/wcZIE6urwpD/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(df1, 7, 'week_day')\n",
    "plot_histogram(df2, 7, 'week_day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers_fraction = 0.01\n",
    "\n",
    "\n",
    "def prepare_data_for_k_means(df):\n",
    "    data = df[['value', 'hours', 'week_day']]\n",
    "    min_max_scaler = preprocessing.StandardScaler()\n",
    "    np_scaled = min_max_scaler.fit_transform(data)\n",
    "    data = pd.DataFrame(np_scaled)\n",
    "\n",
    "    pca = PCA(n_components=2)\n",
    "    data = pca.fit_transform(data)\n",
    "\n",
    "    min_max_scaler = preprocessing.StandardScaler()\n",
    "    np_scaled = min_max_scaler.fit_transform(data)\n",
    "\n",
    "    return pd.DataFrame(np_scaled)\n",
    "\n",
    "\n",
    "data1 = prepare_data_for_k_means(df1)\n",
    "data2 = prepare_data_for_k_means(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scores(n_cluster, scores):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(n_cluster, scores)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "n_cluster = range(1, 20)\n",
    "\n",
    "kmeans = [KMeans(n_clusters=i).fit(data1) for i in n_cluster]\n",
    "scores = [kmeans[i].score(data1) for i in range(len(kmeans))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(n_cluster, scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_k_means_features(df, kmeans):\n",
    "    df['cluster'] = kmeans[14].predict(data1)\n",
    "    df['feature1'] = data1[0]\n",
    "    df['feature2'] = data1[1]\n",
    "    return df\n",
    "\n",
    "\n",
    "df1 = set_k_means_features(df1, kmeans)\n",
    "df2 = set_k_means_features(df2, kmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getDistanceByPoint(data, model):\n",
    "    distance = pd.Series()\n",
    "    for i in range(len(data)):\n",
    "        Xa = np.array(data.loc[i])\n",
    "        Xb = model.cluster_centers_[model.labels_[i] - 1]\n",
    "        distance.at[i] = np.linalg.norm(Xa - Xb)\n",
    "    return distance\n",
    "\n",
    "\n",
    "distance = getDistanceByPoint(data1, kmeans[14])\n",
    "number_of_outliers = int(outliers_fraction * len(distance))\n",
    "threshold = distance.nlargest(number_of_outliers).min()\n",
    "\n",
    "df1['anomaly_k_means'] = (distance >= threshold).astype(int)\n",
    "df2['anomaly_k_means'] = (distance >= threshold).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_anomaly_k_means(df):\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    a = df.loc[df['anomaly_k_means'] == 1, ['time_epoch', 'value']]\n",
    "\n",
    "    ax.scatter(df['time_epoch'], df['value'], color='blue')\n",
    "    ax.scatter(a['time_epoch'], a['value'], color='red')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_anomaly_k_means(df1)\n",
    "plot_anomaly_k_means(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hist_k_means(df):\n",
    "    a = df.loc[df['anomaly_k_means'] == 0, 'value']\n",
    "    b = df.loc[df['anomaly_k_means'] == 1, 'value']\n",
    "\n",
    "    fig, axs = plt.subplots()\n",
    "    axs.hist([a, b], bins=32, stacked=True, color=['blue', 'red'], label=['normal', 'anomaly'])\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist_k_means(df1)\n",
    "plot_hist_k_means(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Isolation Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers_fraction = 0.01\n",
    "\n",
    "\n",
    "def prepare_data_for_i_f(df):\n",
    "    data = df[['value', 'hours', 'week_day']]\n",
    "    min_max_scaler = preprocessing.StandardScaler()\n",
    "    np_scaled = min_max_scaler.fit_transform(data)\n",
    "    return pd.DataFrame(np_scaled)\n",
    "\n",
    "\n",
    "data1 = prepare_data_for_i_f(df1)\n",
    "data2 = prepare_data_for_i_f(df2)\n",
    "\n",
    "model = IsolationForest(contamination=outliers_fraction)\n",
    "\n",
    "model.fit(data1)\n",
    "\n",
    "\n",
    "def add_i_f_anomaly(df, model, data):\n",
    "    df['anomaly_i_f'] = pd.Series(model.predict(data))\n",
    "    df['anomaly_i_f'] = df['anomaly_i_f'].map({1: 0, -1: 1})\n",
    "    return df\n",
    "\n",
    "\n",
    "df1 = add_i_f_anomaly(df1, model, data1)\n",
    "df2 = add_i_f_anomaly(df2, model, data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_anomaly_i_f(df):\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    a = df.loc[df['anomaly_i_f'] == 1, ['time_epoch', 'value']]\n",
    "\n",
    "    ax.scatter(df['time_epoch'], df['value'], color='blue')\n",
    "    ax.scatter(a['time_epoch'], a['value'], color='red')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_anomaly_i_f(df1)\n",
    "plot_anomaly_i_f(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_hist_i_f(df):\n",
    "    a = df.loc[df['anomaly_i_f'] == 0, 'value']\n",
    "    b = df.loc[df['anomaly_i_f'] == 1, 'value']\n",
    "\n",
    "    fig, axs = plt.subplots()\n",
    "    axs.hist([a, b], bins=32, stacked=True, color=['blue', 'red'], label=['normal', 'anomaly'])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_hist_i_f(df1)\n",
    "plot_hist_i_f(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OCSVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers_fraction = 0.01\n",
    "\n",
    "\n",
    "def prepare_data_for_ocsvm(df):\n",
    "    data = df1[['value', 'hours', 'week_day']]\n",
    "    min_max_scaler = preprocessing.StandardScaler()\n",
    "    np_scaled = min_max_scaler.fit_transform(data)\n",
    "    return pd.DataFrame(np_scaled)\n",
    "\n",
    "\n",
    "data1 = prepare_data_for_ocsvm(df1)\n",
    "data2 = prepare_data_for_ocsvm(df2)\n",
    "\n",
    "model = OneClassSVM(nu=0.95 * outliers_fraction + 0.05)\n",
    "\n",
    "model.fit(data1)\n",
    "\n",
    "\n",
    "def add_ocsvm_anomaly(df, model, data):\n",
    "    df['anomaly_ocsvm'] = pd.Series(model.predict(data))\n",
    "    df['anomaly_ocsvm'] = df['anomaly_ocsvm'].map({1: 0, -1: 1})\n",
    "    return df\n",
    "\n",
    "\n",
    "df1 = add_ocsvm_anomaly(df1, model, data1)\n",
    "df2 = add_ocsvm_anomaly(df2, model, data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_anomaly_ocsvm(df):\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    a = df.loc[df['anomaly_ocsvm'] == 1, ['time_epoch', 'value']]\n",
    "\n",
    "    ax.scatter(df['time_epoch'], df['value'], color='blue')\n",
    "    ax.scatter(a['time_epoch'], a['value'], color='red')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_anomaly_ocsvm(df1)\n",
    "plot_anomaly_ocsvm(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_hist_ocsvm(df):\n",
    "    a = df.loc[df['anomaly_ocsvm'] == 0, 'value']\n",
    "    b = df.loc[df['anomaly_ocsvm'] == 1, 'value']\n",
    "\n",
    "    fig, axs = plt.subplots()\n",
    "    axs.hist([a, b], bins=32, stacked=True, color=['blue', 'red'], label=['normal', 'anomaly'])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_hist_ocsvm(df1)\n",
    "plot_hist_ocsvm(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_data_for_mlp(df):\n",
    "    data = df1[['value', 'hours', 'week_day']]\n",
    "    min_max_scaler = preprocessing.StandardScaler()\n",
    "    np_scaled = min_max_scaler.fit_transform(data)\n",
    "    return pd.DataFrame(np_scaled).values.astype(np.float32)\n",
    "\n",
    "\n",
    "data1 = prepare_data_for_mlp(df1)\n",
    "data2 = prepare_data_for_mlp(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = len(data1)\n",
    "split_factor = 0.2\n",
    "\n",
    "test = data1[0:int(rows * split_factor)]\n",
    "train = data1[int(rows * split_factor):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "train_data = mx.gluon.data.DataLoader(train, batch_size, last_batch='keep', shuffle=False)\n",
    "test_data = mx.gluon.data.DataLoader(test, batch_size, last_batch='keep', shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctx = mx.cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = data1.shape[1]\n",
    "\n",
    "model = gluon.nn.Sequential()\n",
    "with model.name_scope():\n",
    "    model.add(gluon.nn.Dense(16, activation='tanh'))\n",
    "    model.add(gluon.nn.Dropout(0.25))\n",
    "\n",
    "    model.add(gluon.nn.Dense(8, activation='tanh'))\n",
    "    model.add(gluon.nn.Dropout(0.25))\n",
    "\n",
    "    model.add(gluon.nn.Dense(16, activation='tanh'))\n",
    "    model.add(gluon.nn.Dropout(0.25))\n",
    "\n",
    "    model.add(gluon.nn.Dense(features))\n",
    "\n",
    "model.collect_params().initialize(mx.init.Xavier(), ctx=ctx)\n",
    "\n",
    "trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate': 0.001})\n",
    "\n",
    "L = gluon.loss.L2Loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_accuracy(data_iterator, model, L):\n",
    "    loss_avg = 0.\n",
    "    for i, data in enumerate(data_iterator):\n",
    "        data = data.as_in_context(ctx)\n",
    "        label = data\n",
    "        output = model(data)\n",
    "        loss = L(output, label)\n",
    "        loss_avg = loss_avg * i / (i + 1) + nd.mean(loss).asscalar() / (i + 1)\n",
    "    return loss_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 50\n",
    "\n",
    "all_train_mse = []\n",
    "all_test_mse = []\n",
    "\n",
    "for e in range(epochs):\n",
    "    for i, data in enumerate(train_data):\n",
    "        data = data.as_in_context(ctx)\n",
    "        label = data\n",
    "        with autograd.record():\n",
    "            output = model(data)\n",
    "            loss = L(output, label)\n",
    "            loss.backward()\n",
    "        trainer.step(data.shape[0])\n",
    "\n",
    "    train_mse = evaluate_accuracy(train_data, model, L)\n",
    "    test_mse = evaluate_accuracy(test_data, model, L)\n",
    "    all_train_mse.append(train_mse)\n",
    "    all_test_mse.append(test_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_errors(train_mse, test_mse):\n",
    "    epochs = range(len(train_mse))\n",
    "    plt.plot(epochs, train_mse, label='Train')\n",
    "    plt.plot(epochs, test_mse, label='Test')\n",
    "    plt.ylabel('MSE')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_errors(all_train_mse, all_test_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(to_predict, L):\n",
    "    predictions = []\n",
    "    for i, data in enumerate(to_predict):\n",
    "        input = data.as_in_context(ctx)\n",
    "        out = model(input)\n",
    "        prediction = L(out, input).asnumpy().flatten()\n",
    "        predictions = np.append(predictions, prediction)\n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_mlp_anomaly(df, predict, threshold, L, data):\n",
    "    predictions = predict(data, L)\n",
    "\n",
    "    df['anomaly_mlp'] = pd.Series(predictions)\n",
    "    df['anomaly_mlp'] = df['anomaly_mlp'].apply(lambda v: int(v > threshold))\n",
    "    return df\n",
    "\n",
    "\n",
    "train_predictions = predict(train_data, L)\n",
    "threshold = np.mean(train_predictions) + 3 * np.std(train_predictions)\n",
    "\n",
    "plot_data1 = mx.gluon.data.DataLoader(data1, batch_size, shuffle=False)\n",
    "df1 = add_mlp_anomaly(df1, predict, threshold, L, plot_data1)\n",
    "\n",
    "plot_data2 = mx.gluon.data.DataLoader(data2, batch_size, shuffle=False)\n",
    "df2 = add_mlp_anomaly(df2, predict, threshold, L, plot_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_anomaly_mlp(df):\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    a = df.loc[df['anomaly_mlp'] == 1, ['time_epoch', 'value']]\n",
    "\n",
    "    ax.scatter(df['time_epoch'], df['value'], color='blue')\n",
    "    ax.scatter(a['time_epoch'], a['value'], color='red')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_anomaly_mlp(df1)\n",
    "plot_anomaly_mlp(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA64AAAGfCAYAAABfv49TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAH/1JREFUeJzt3X2wXlV9L/DvL4R3LYkQuV6CN8FSxTQgcEZScSya1oLl7Q9tsV4JSoeRi1eLdlqsfzC2Y6eOTmmZ6bWDAmLHl3K1vTCO1lJQ1BYoB8VgACXKS06hkAKBVkol7bp/nA0e4JCEc5Jz1jnn85l55tl77bX3XvusJyf5Zq29n2qtBQAAAHq1aLYbAAAAANsiuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALq2eLYbsC0HHHBAW7FixWw3AwAAgF3gpptu+pfW2rLt1es6uK5YsSKjo6Oz3QwAAAB2gaq6e0fqmSoMAABA1wRXAAAAuia4AgAA0LWu73EFAADoyRNPPJGxsbE8/vjjs92UOWWvvfbK8uXLs/vuu09pf8EVAABgB42NjeWFL3xhVqxYkaqa7ebMCa21PPjggxkbG8vKlSundAxThQEAAHbQ448/nv33319ofR6qKvvvv/+0RqkFVwAAgOdBaH3+pvszE1wBAADomuAKAAAwRVU79zUXHHfccRkdHZ3RcwquAAAAC8TWrVtnuwlT4qnCAAAAc8hdd92VE044Ia997WvzD//wDznooINyxRVX5Pvf/37e9a535bHHHsvLXvayXHLJJVm6dGmOO+64vOY1r8nf//3f5+STT84tt9ySvffeO7fffnvuvvvuXHrppbnsssty3XXX5ZhjjsmnPvWpJMnZZ5+dG2+8Mf/+7/+eN7/5zfnQhz40a9dsxBUAAGCOueOOO3LOOedkw4YNWbJkSb74xS/m9NNPz0c+8pGsX78+q1evflrQ3LJlS6699tq8//3vT5I8/PDDueaaa3LBBRfkpJNOyrnnnpsNGzbklltuyc0335wk+fCHP5zR0dGsX78+1157bdavXz8r15oIrgAAAHPOypUr86pXvSpJcvTRR+eHP/xhtmzZkl/8xV9Mkqxbty7f+MY3nqr/67/+60/b/6STTkpVZfXq1TnwwAOzevXqLFq0KKtWrcpdd92VJLn88stz1FFH5cgjj8yGDRty6623zszFTcJUYQAAgDlmzz33fGp5t912y5YtW7ZZf9999510/0WLFj3tWIsWLcrWrVtz55135mMf+1huvPHGLF26NGeccca0vod1uoy4AgAAzHH77bdfli5dmm9+85tJkr/4i794avR1Kh599NHsu+++2W+//XL//ffnK1/5ys5q6pQYcQUAAJii1ma7BT912WWXPfVwpkMOOSSXXnrplI91xBFH5Mgjj8yqVatyyCGH5Nhjj92JLX3+qvX0k36GkZGRNtPfD7SgzcQXR3X8eQMAgO257bbbcthhh812M+akyX52VXVTa21ke/tud6pwVV1SVQ9U1fcm2fbbVdWq6oBhvarqwqraWFXrq+qoCXXXVdUdw2vdDl0ZAAAAC96O3OP6qSTHP7Owqg5O8stJ7plQfEKSQ4fXWUk+PtR9UZLzkxyT5NVJzq+qpdNpODtfpe3yFwAAwPO13eDaWvtGkocm2XRBkt9JnpZGTkny6Tbu+iRLquolSX4lyVWttYdaaw8nuSqThGEAAAB4pik9VbiqTk7yT6217z5j00FJNk1YHxvKnqscAAAAtul5P1W4qvZJ8sEkb5xs8yRlbRvlkx3/rIxPM85LX/rS59s8AAAA5pmpjLi+LMnKJN+tqruSLE/y7ar6bxkfST14Qt3lSe7dRvmztNYuaq2NtNZGli1bNoXmAQAAMJ887+DaWrultfbi1tqK1tqKjIfSo1pr/5zkyiSnD08XXpPkkdbafUm+muSNVbV0eCjTG4cyAACAuatq577mkOOOOy4z9fWlO/J1OJ9Lcl2Sl1fVWFWduY3qX07yoyQbk3wiyf9KktbaQ0n+IMmNw+v3hzIAAADYph15qvBbW2svaa3t3lpb3lq7+BnbV7TW/mVYbq21c1prL2utrW6tjU6od0lr7WeH16U7/1IAAAAWhlNPPTVHH310Vq1alYsuuihJ8oIXvCAf/OAHc8QRR2TNmjW5//77kyR333131q5dm8MPPzxr167NPfeMf6PpGWeckbPPPjuvf/3rc8ghh+Taa6/NO9/5zhx22GE544wznjrX2WefnZGRkaxatSrnn3/+s9py8cUX59xzz31q/ROf+ETe97737dTrndJThQEAAJg9l1xySW666aaMjo7mwgsvzIMPPpgf//jHWbNmTb773e/mda97XT7xiU8kSd797nfn9NNPz/r16/O2t70t73nPe546zsMPP5xrrrkmF1xwQU466aSce+652bBhQ2655ZbcfPPNSZIPf/jDGR0dzfr163Pttddm/fr1T2vLaaedliuvvDJPPPFEkuTSSy/NO97xjp16vYIrAADAHHPhhRc+NbK6adOm3HHHHdljjz1y4oknJkmOPvro3HXXXUmS6667Lr/xG7+RJHn729+eb33rW08d56STTkpVZfXq1TnwwAOzevXqLFq0KKtWrXpq/8svvzxHHXVUjjzyyGzYsCG33nrr09qy77775g1veEO+9KUv5fbbb88TTzyR1atX79Trfd5fhwMAAMDs+frXv56/+7u/y3XXXZd99tknxx13XB5//PHsvvvuqeEBT7vttlu2bt066f414SFQe+65Z5Jk0aJFTy0/ub5169bceeed+djHPpYbb7wxS5cuzRlnnJHHH3/8Wcf8zd/8zfzhH/5hXvGKV+z00dbEiCsAAMCc8sgjj2Tp0qXZZ599cvvtt+f666/fZv3XvOY1+fznP58k+cxnPpPXvva1O3yuRx99NPvuu2/222+/3H///fnKV74yab1jjjkmmzZtymc/+9m89a1v3fGL2UFGXAEAAKaqtRk/5fHHH58///M/z+GHH56Xv/zlWbNmzTbrX3jhhXnnO9+Zj370o1m2bFkuvXTHn5V7xBFH5Mgjj8yqVatyyCGH5Nhjj33Our/2a7+Wm2++OUuXLt3h4++oarPwg95RIyMjbaa+F4iZ+dqojj9uAACwXbfddlsOO+yw2W5Gl0488cSce+65Wbt27aTbJ/vZVdVNrbWR7R3bVGEAAACmbMuWLfm5n/u57L333s8ZWqfLVGEAAACmbMmSJfnBD36wS89hxBUAAOB56Pl2y15N92cmuAIAAOygvfbaKw8++KDw+jy01vLggw9mr732mvIxTBUGAADYQcuXL8/Y2Fg2b948202ZU/baa68sX758yvsLrgAAADto9913z8qVK2e7GQuOqcIAAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALq23eBaVZdU1QNV9b0JZR+tqturan1V/XVVLZmw7QNVtbGqvl9VvzKh/PihbGNVnbfzLwUAAID5aEdGXD+V5PhnlF2V5Odba4cn+UGSDyRJVb0yyWlJVg37/J+q2q2qdkvyZ0lOSPLKJG8d6gIAAMA2bTe4tta+keShZ5T9bWtt67B6fZLlw/IpST7fWvuP1tqdSTYmefXw2tha+1Fr7SdJPj/UBQAAgG3aGfe4vjPJV4blg5JsmrBtbCh7rnIAAADYpmkF16r6YJKtST7zZNEk1do2yic75llVNVpVo5s3b55O8wAAAJgHphxcq2pdkhOTvK219mQIHUty8IRqy5Pcu43yZ2mtXdRaG2mtjSxbtmyqzQMAAGCemFJwrarjk/xukpNba49N2HRlktOqas+qWpnk0CT/mOTGJIdW1cqq2iPjD3C6cnpNBwAAYCFYvL0KVfW5JMclOaCqxpKcn/GnCO+Z5KqqSpLrW2vvaq1tqKrLk9ya8SnE57TW/nM4zruTfDXJbkkuaa1t2AXXAwAAwDxTP53l25+RkZE2Ojo6281YMGqyO5F3so4/bgAAwAyrqptaayPbq7cznioMAAAAu4zgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNe2G1yr6pKqeqCqvjeh7EVVdVVV3TG8Lx3Kq6ourKqNVbW+qo6asM+6of4dVbVu11wOAAAA882OjLh+Ksnxzyg7L8nVrbVDk1w9rCfJCUkOHV5nJfl4Mh50k5yf5Jgkr05y/pNhFwAAALZlu8G1tfaNJA89o/iUJJcNy5clOXVC+afbuOuTLKmqlyT5lSRXtdYeaq09nOSqPDsMAwAAwLNM9R7XA1tr9yXJ8P7iofygJJsm1Bsbyp6rHAAAALZpZz+cqSYpa9sof/YBqs6qqtGqGt28efNObRwAAABzz1SD6/3DFOAM7w8M5WNJDp5Qb3mSe7dR/iyttYtaayOttZFly5ZNsXkAAADMF1MNrlcmefLJwOuSXDGh/PTh6cJrkjwyTCX+apI3VtXS4aFMbxzKAAAAYJsWb69CVX0uyXFJDqiqsYw/HfiPklxeVWcmuSfJW4bqX07ypiQbkzyW5B1J0lp7qKr+IMmNQ73fb60984FPAAAA8CzV2qS3mnZhZGSkjY6OznYzFoya7E7knazjjxsAADDDquqm1trI9urt7IczAQAAwE4luAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuLZ7tBsx1Vbv+HK3t+nMAAAD0yogrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0bVrBtarOraoNVfW9qvpcVe1VVSur6oaquqOq/rKq9hjq7jmsbxy2r9gZFwAAAMD8NuXgWlUHJXlPkpHW2s8n2S3JaUk+kuSC1tqhSR5Ocuawy5lJHm6t/WySC4Z6AAAAsE3TnSq8OMneVbU4yT5J7kvyhiRfGLZfluTUYfmUYT3D9rVVVdM8PwAAAPPclINra+2fknwsyT0ZD6yPJLkpyZbW2tah2liSg4blg5JsGvbdOtTf/5nHraqzqmq0qkY3b9481eYBAAAwT0xnqvDSjI+irkzy35Psm+SESaq2J3fZxrafFrR2UWttpLU2smzZsqk2DwAAgHliOlOFfynJna21za21J5L8VZLXJFkyTB1OkuVJ7h2Wx5IcnCTD9v2SPDSN8wMAALAATCe43pNkTVXtM9yrujbJrUm+luTNQ511Sa4Ylq8c1jNsv6a19qwRVwAAAJhoOve43pDxhyx9O8ktw7EuSvK7Sd5XVRszfg/rxcMuFyfZfyh/X5LzptFuAAAAFojqedBzZGSkjY6OznYztmkmnos8U100n64FAADoX1Xd1Fob2V696X4dDgAAAOxSgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1xbPdgOADlTt+nO0tuvPAQDAvGTEFQAAgK4ZcQVS2fWjocZbAQCYKiOuAAAAdM2I61wwE/cfJjEmBgAA9EhwnQNmYhonAABAr0wVBgAAoGuCKwAAAF0TXAEAAOia4AoAAEDXBFcAAAC6JrgCAADQNcEVAACArgmuAAAAdE1wBQAAoGuCKwAAAF0TXAEAAOia4AoAAEDXBFcAAAC6JrgCAADQNcEVAACArgmuAAAAdG1awbWqllTVF6rq9qq6rap+oapeVFVXVdUdw/vSoW5V1YVVtbGq1lfVUTvnEgAAAJjPpjvi+qdJ/qa19ookRyS5Lcl5Sa5urR2a5OphPUlOSHLo8DoryceneW4AAAAWgCkH16r6mSSvS3JxkrTWftJa25LklCSXDdUuS3LqsHxKkk+3cdcnWVJVL5lyywEAAFgQpjPiekiSzUkurarvVNUnq2rfJAe21u5LkuH9xUP9g5JsmrD/2FD2NFV1VlWNVtXo5s2bp9E8AAAA5oPpBNfFSY5K8vHW2pFJfpyfTgueTE1S1p5V0NpFrbWR1trIsmXLptE8AAAA5oPpBNexJGOttRuG9S9kPMje/+QU4OH9gQn1D56w//Ik907j/AAAACwAUw6urbV/TrKpql4+FK1NcmuSK5OsG8rWJbliWL4yyenD04XXJHnkySnFAAAA8FwWT3P//53kM1W1R5IfJXlHxsPw5VV1ZpJ7krxlqPvlJG9KsjHJY0NdAAAA2KZpBdfW2s1JRibZtHaSui3JOdM5HwAAAAvPdL/HFQAAAHYpwRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuTTu4VtVuVfWdqvrSsL6yqm6oqjuq6i+rao+hfM9hfeOwfcV0zw0AAMD8tzNGXN+b5LYJ6x9JckFr7dAkDyc5cyg/M8nDrbWfTXLBUA8AAAC2aVrBtaqWJ/nVJJ8c1ivJG5J8YahyWZJTh+VThvUM29cO9QEAAOA5TXfE9U+S/E6S/xrW90+ypbW2dVgfS3LQsHxQkk1JMmx/ZKj/NFV1VlWNVtXo5s2bp9k8AAAA5ropB9eqOjHJA621myYWT1K17cC2nxa0dlFrbaS1NrJs2bKpNg8AAIB5YvE09j02yclV9aYkeyX5mYyPwC6pqsXDqOryJPcO9ceSHJxkrKoWJ9kvyUPTOD8AAAALwJRHXFtrH2itLW+trUhyWpJrWmtvS/K1JG8eqq1LcsWwfOWwnmH7Na21Z424AgAAwES74ntcfzfJ+6pqY8bvYb14KL84yf5D+fuSnLcLzg0AAMA8M52pwk9prX09ydeH5R8lefUkdR5P8padcT4AAAAWjl0x4goAAAA7jeAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADo2uLZbgCwQFTt+nO0tuvPAQDAjBNcgRlR2fWhUmwFAJifTBUGAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuTTm4VtXBVfW1qrqtqjZU1XuH8hdV1VVVdcfwvnQor6q6sKo2VtX6qjpqZ10EAAAA89d0Rly3Jnl/a+2wJGuSnFNVr0xyXpKrW2uHJrl6WE+SE5IcOrzOSvLxaZwbAACABWLKwbW1dl9r7dvD8r8muS3JQUlOSXLZUO2yJKcOy6ck+XQbd32SJVX1kim3HAAAgAVhp9zjWlUrkhyZ5IYkB7bW7kvGw22SFw/VDkqyacJuY0MZAAAAPKdpB9eqekGSLyb5rdbao9uqOklZm+R4Z1XVaFWNbt68ebrNAwAAYI6bVnCtqt0zHlo/01r7q6H4/ienAA/vDwzlY0kOnrD78iT3PvOYrbWLWmsjrbWRZcuWTad5AAAAzAPTeapwJbk4yW2ttT+esOnKJOuG5XVJrphQfvrwdOE1SR55ckoxAAAAPJfF09j32CRvT3JLVd08lP1ekj9KcnlVnZnkniRvGbZ9OcmbkmxM8liSd0zj3AAAACwQUw6urbVvZfL7VpNk7ST1W5Jzpno+AAAAFqad8lRhAAAA2FUEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6Nri2W4A8NyqZrsFAAAw+4y4AgAA0LUZD65VdXxVfb+qNlbVeTN9fgAAAOaWGZ0qXFW7JfmzJL+cZCzJjVV1ZWvt1plsB7NnJqa+trbrzwEAAMycmR5xfXWSja21H7XWfpLk80lOmeE2AAAAMIfM9MOZDkqyacL6WJJjZrgNzHMz9UAjI7v90fcAAPPTTAfXyf5Z+bR/AlbVWUnOGlb/raq+/xzHOiDJv+zEtjG3zHr/e+LvrJrV/tf3s2rW/+wzq/T/wqb/Fy59P7/9jx2pNNPBdSzJwRPWlye5d2KF1tpFSS7a3oGqarS1NrJzm8dcof8XNv2/cOn7hU3/L2z6f+HS9yQzf4/rjUkOraqVVbVHktOSXDnDbQAAAGAOmdER19ba1qp6d5KvJtktySWttQ0z2QYAAADmlpmeKpzW2peTfHknHGq704mZ1/T/wqb/Fy59v7Dp/4VN/y9c+p5U83hMAAAAOjbT97gCAADA8zIngmtVXVJVD1TV9yaUvaiqrqqqO4b3pbPZRnadqjq4qr5WVbdV1Yaqeu9Q7jMwz1XVXlX1j1X13aHvPzSUr6yqG4a+/8vhYW/MU1W1W1V9p6q+NKzr/wWgqu6qqluq6uaqGh3K/N5fIKpqSVV9oapuH/7+/wX9vzBU1cuHP/dPvh6tqt/S/8yJ4JrkU0mOf0bZeUmubq0dmuTqYZ35aWuS97fWDkuyJsk5VfXK+AwsBP+R5A2ttSOSvCrJ8VW1JslHklww9P3DSc6cxTay6703yW0T1vX/wvH61tqrJnwNht/7C8efJvmb1torkhyR8d8B+n8BaK19f/hz/6okRyd5LMlfR/8veHMiuLbWvpHkoWcUn5LksmH5siSnzmijmDGttftaa98elv814395HRSfgXmvjfu3YXX34dWSvCHJF4ZyfT+PVdXyJL+a5JPDekX/L2R+7y8AVfUzSV6X5OIkaa39pLW2Jfp/IVqb5Iettbuj/xe8ORFcn8OBrbX7kvFgk+TFs9weZkBVrUhyZJIb4jOwIAzTRG9O8kCSq5L8MMmW1trWocpYxv8jg/npT5L8TpL/Gtb3j/5fKFqSv62qm6rqrKHM7/2F4ZAkm5NcOtwm8Mmq2jf6fyE6LcnnhmX9v8DN5eDKAlNVL0jyxSS/1Vp7dLbbw8xorf3nMF1oeZJXJzlssmoz2ypmQlWdmOSB1tpNE4snqar/56djW2tHJTkh47eIvG62G8SMWZzkqCQfb60dmeTHMS10wRmeX3Bykv87222hD3M5uN5fVS9JkuH9gVluD7tQVe2e8dD6mdbaXw3FPgMLyDBN7OsZv895SVU9+T3Uy5PcO1vtYpc6NsnJVXVXks9nfIrwn0T/LwittXuH9wcyfn/bq+P3/kIxlmSstXbDsP6FjAdZ/b+wnJDk2621+4d1/b/AzeXgemWSdcPyuiRXzGJb2IWGe9ouTnJba+2PJ2zyGZjnqmpZVS0ZlvdO8ksZv8f5a0nePFTT9/NUa+0DrbXlrbUVGZ8udk1r7W3R//NeVe1bVS98cjnJG5N8L37vLwittX9OsqmqXj4UrU1ya/T/QvPW/HSacKL/F7xqrf8ZVlX1uSTHJTkgyf1Jzk/y/5JcnuSlSe5J8pbW2jMf4MQ8UFWvTfLNJLfkp/e5/V7G73P1GZjHqurwjD+AYbeM/0fb5a2136+qQzI+AveiJN9J8j9ba/8xey1lV6uq45L8dmvtRP0//w19/NfD6uIkn22tfbiq9o/f+wtCVb0q4w9l2yPJj5K8I8PfA9H/815V7ZNkU5JDWmuPDGX+/C9wcyK4AgAAsHDN5anCAAAALACCKwAAAF0TXAEAAOia4AoAAEDXBFcAAAC6JrgCAADQNcEVAACArgmuAAAAdO3/AxiroAyMu1peAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_hist_mlp(df):\n",
    "    a = df.loc[df['anomaly_mlp'] == 0, 'value']\n",
    "    b = df.loc[df['anomaly_mlp'] == 1, 'value']\n",
    "\n",
    "    fig, axs = plt.subplots()\n",
    "    axs.hist([a, b], bins=32, stacked=True, color=['blue', 'red'], label=['normal', 'anomaly'])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_hist_mlp(df1)\n",
    "plot_hist_mlp(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "\n",
    "\n",
    "def prepare_data_for_lstm(df):\n",
    "    data = df[['value', 'hours', 'week_day']]\n",
    "    min_max_scaler = preprocessing.StandardScaler()\n",
    "    np_scaled = min_max_scaler.fit_transform(data)\n",
    "    return pd.DataFrame(np_scaled).values.astype(np.float32)\n",
    "\n",
    "\n",
    "data1 = prepare_data_for_lstm(df1)\n",
    "data2 = prepare_data_for_lstm(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = len(data1)\n",
    "split_factor = 0.2\n",
    "\n",
    "test = data1[0:int(rows * split_factor)]\n",
    "train = data1[int(rows * split_factor):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = mx.gluon.data.DataLoader(train, batch_size, last_batch='keep', shuffle=False)\n",
    "test_data = mx.gluon.data.DataLoader(test, batch_size, last_batch='keep', shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctx = mx.cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = data1.shape[1]\n",
    "\n",
    "model = mx.gluon.nn.Sequential()\n",
    "with model.name_scope():\n",
    "    model.add(mx.gluon.rnn.LSTM(features, dropout=0.35))\n",
    "    model.add(mx.gluon.rnn.LSTM(features))\n",
    "\n",
    "model.collect_params().initialize(mx.init.Xavier(), ctx=ctx)\n",
    "\n",
    "trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate': 0.01})\n",
    "\n",
    "L = gluon.loss.L2Loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_accuracy(data_iterator, model, L):\n",
    "    loss_avg = 0.\n",
    "    for i, data in enumerate(data_iterator):\n",
    "        data = data.as_in_context(ctx).reshape((-1, 1, features))\n",
    "        label = data\n",
    "        output = model(data)\n",
    "        loss = L(output, label)\n",
    "        loss_avg = loss_avg * i / (i + 1) + nd.mean(loss).asscalar() / (i + 1)\n",
    "    return loss_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 40\n",
    "\n",
    "all_train_mse = []\n",
    "all_test_mse = []\n",
    "\n",
    "for e in range(epochs):\n",
    "    for i, data in enumerate(train_data):\n",
    "        data = data.as_in_context(ctx).reshape((-1, 1, features))\n",
    "        label = data\n",
    "        with autograd.record():\n",
    "            output = model(data)\n",
    "            loss = L(output, label)\n",
    "        loss.backward()\n",
    "        trainer.step(batch_size)\n",
    "\n",
    "    train_mse = evaluate_accuracy(train_data, model, L)\n",
    "    test_mse = evaluate_accuracy(test_data, model, L)\n",
    "    all_train_mse.append(train_mse)\n",
    "    all_test_mse.append(test_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ie/sC7p26NGVjSpIkSVJLZchNp9JxULsNqt9K2ZCF+bl85YT+vDB/Dcs27EjZuJIkSZLUEhly06l0XOPvJVNSOuxlJw4gNwQeeH1ZSseVJEmSpJbGkJtO7btD72NT+lwuQK9OhZw7vA8PzljB9t2pWwotSZIkSS2NITfdysZB1ZtQm9qlxVeOLWHb7joemVmV0nElSZIkqSUx5KZb2Xior4Xlr6d02JH9uzKiXxfun7aUhgZfJyRJkiTp8GTITbf+J0FOfsqXLEPjbO6S9TuY8v66lI8tSZIkSS2BITfdCtpDvxNSvvkUwDnD+nBExzbc5+uEJEmSJB2mDLmZUDYeVr8DOzakdNiCvBwuO3EAUxauY9Ha7SkdW5IkSZJaAkNuJpQ1vUpo6SspH/orJ/SnIDeHB15fmvKxJUmSJCnbGXIzoWgUFHRM5LncHh3aMKG8iIdnVrFl156Ujy9JkiRJ2cyQmwm5eVDyuURCLjRuQLWztp6HKlckMr4kSZIkZStDbqaUjYdNSxt/UmxYcWdGl3Tl/teXUu/rhCRJkiQdRgy5mfLhc7kJ7LIMcOXYUlZs3MWL89cmMr4kSZIkZSNDbqb0HAwdesEHyYTcM4f0oqhzIfdO/SCR8SVJkiQpGxlyMyWExiXLS6ZAQ0PKh8/LzeFrJ5UwbfEG5q/emvLxJUmSJCkbGXIzqXQc7FwPa+cmMvylx/ejMD+H+6ctTWR8SZIkSco2htxM+ui53JcTGb5LuwIuHFnMo2+tZNOO2kRqSJIkSVI2MeRmUue+0P3IxEIuwBVjStld18CkGb5OSJIkSVLrZ8jNtLLxsGwa1CUz03p0746MGdid37++lLr61D/7K0mSJEnZxJCbaWXjYc8OWFmZWIkrx5ZSvaWGZ+euSayGJEmSJGUDQ26mlXwOQk5i78sFOHXwEfTr1tbXCUmSJElq9Qy5mda2K/QZkehzubk5gctPKmHG0k28t3JLYnUkSZIkKdMMudmgbFzjcuXd2xIr8aWKfrQryOXeqUsTqyFJkiRJmZZoyA0hnB1CWBBCWBRCuHEv318bQng3hDArhPBaCGFI0/mSEMKupvOzQgh3JtlnxpWNh4a6xg2oEtK5bT5fPK4vT8yuZv323YnVkSRJkqRMSizkhhBygduBc4AhwKUfhthm/hBjHB5jHAH8HLil2XeLY4wjmn6uTarPrNDvRMgrTHTJMsDlY0qorW/gD28sT7SOJEmSJGVKkjO5xwOLYoxLYoy1wCTgguYXxBi3NjtsD8QE+8le+YXQ74REN58CGNizA+MG9eT305dRW+frhCRJkiS1PkmG3GJgRbPjqqZzfyWE8J0QwmIaZ3K/1+yr0hDC2yGEKSGEk/dWIIRwTQihMoRQuW7dulT2nn5l42HtHNi+NtEyV44tYd223Tz93qpE60iSJElSJiQZcsNezn1ipjbGeHuMcSDwQ+Cfm06vAvrHGEcCNwB/CCF02su9d8cYK2KMFT179kxh6xlQNq7x9wevJFrmlKN6UtajPfe4AZUkSZKkVijJkFsF9Gt23Beo/ozrJwFfAIgx7o4xbmj6PBNYDAxKqM/s0GcEFHaGJS8lWiYnJ3DF2BJmr9jM28s3JVpLkiRJktItyZA7AzgqhFAaQigALgEmN78ghHBUs8PzgPebzvds2riKEEIZcBSwJMFeMy8nF0pPaXwuNyb7aPJFo/rSsU2erxOSJEmS1OokFnJjjHXAdcAzwDzgwRjjnBDCzSGEiU2XXRdCmBNCmEXjsuTLm86fArwTQpgNPAxcG2PcmFSvWaN0HGxZARuTzfMd2uRx8eh+PPXuKtZsrUm0liRJkiSlU16Sg8cYnwKe+ti5HzX7fP2n3PcI8EiSvWWlss83/l7yMnQfmGipy08q4Z6pH/Bf05fx/TOPTrSWJEmSJKVLksuVdaC6D4ROxfBBsq8SAujfvR2nDe7FH95YTs2e+sTrSZIkSVI6GHKzSQiNrxL64BVoSD54Xjm2hA07anli9mftByZJkiRJLYchN9uUjoNdm2D1O4mXGjOwO0f36si9U5cSE97sSpIkSZLSwZCbbT58X+6S5Jcsh9D4OqG5q7YyY6mvE5IkSZLU8hlys03H3tDzmMbNp9LgCyOK6dIun3unfpCWepIkSZKUJENuNiobB8unw57kX+/TtiCXS0b355k5q1m5eVfi9SRJkiQpSYbcbFQ2Hup2QdWbaSn3tZMGEELggdeXpqWeJEmSJCXFkJuNBoyFkJu2JcvFXdpy1tBeTHpzBbtqfZ2QJEmSpJbLkJuNCjtB8XFp2XzqQ1eMKWXLrj089vbKtNWUJEmSpFQz5GarsvFQ/Rbs2pyWcqNLujK0qBP3TfvA1wlJkiRJarEMudmqbBzEBlg2NS3lQghcMaaEhWu2M23xhrTUlCRJkqRUM+Rmq76jIb9d2p7LBZhQXkSnwjwenlmVtpqSJEmSlEqG3GyV1wb6n5TW53IL83M579gi/vLeanbsrktbXUmSJElKFUNuNisbD+sXwNbqtJW8aFQxu/bU8+zc1WmrKUmSJEmpYsjNZmXjG3+ncTb3uP5d6du1LY++5S7LkiRJkloeQ2426zUM2nWHD9IXcnNyAheOLGbqovWs3VqTtrqSJEmSlAqG3GyWkwOlpzRuPpXG1/p8YWQxDREmz07fMmlJkiRJSgVDbrYrHQfbVsH699NWcmDPDpT37eySZUmSJEktjiE325WNb/ydxlcJAVw4spi5q7ayYPW2tNaVJEmSpENhyM123UqhS/+0h9wJ5UXk5gQee9vZXEmSJEkthyG3JSgbD0tfg/r0vbu2e4c2jBvUk8dnraShIX3PA0uSJEnSoTDktgRl42H3Flg1K61lLxxZzKotNUz/YENa60qSJEnSwTLktgSl4xp/p3nJ8hlDetGhTR6PuQGVJEmSpBbCkNsStO8BvYanPeQW5udyzrDePP3eanbV1qe1tiRJkiQdDENuS1E2Dla8AbU701r2wlHFbN9dx/Pz1qS1riRJkiQdDENuS1E2HuprYcX0tJY9sbQ7fToXusuyJEmSpBbBkNtS9D8JcvLTvmQ5JydwwYhipixcx4btu9NaW5IkSZIOlCG3pWjTAfqOhiVT0l76olHF1DdEnphdnfbakiRJknQgDLktSdl4WDUbdm5Ma9lBvToypE8nlyxLkiRJynqG3JakbBwQYemraS990ahiZldtYfG67WmvLUmSJEn7y5DbkhQfBwUd0v5cLsDE8iJyAvzJ2VxJkiRJWcyQ25Lk5kPJ5zISco/oVMjYI3vw2NsraWiIaa8vSZIkSfvDkNvSlI6DjUtg8/K0l75oVDFVm3Yxc/mmtNeWJEmSpP2RaMgNIZwdQlgQQlgUQrhxL99fG0J4N4QwK4TwWghhSLPv/rHpvgUhhLOS7LNFKRvf+DsDuyyfOaQ3bfNzefQtlyxLkiRJyk6JhdwQQi5wO3AOMAS4tHmIbfKHGOPwGOMI4OfALU33DgEuAYYCZwN3NI2nI46B9kfAB+kPue3b5HH2sN78+Z1qdtfVp72+JEmSJO1LkjO5xwOLYoxLYoy1wCTgguYXxBi3NjtsD3z4sOcFwKQY4+4Y4wfAoqbxFELjLstLpkBM/7OxF44sZmtNHS/NX5v22pIkSZK0L0mG3GJgRbPjqqZzfyWE8J0QwmIaZ3K/dyD3HrZKx8GOtbB2XtpLjxnYnZ4d27hkWZIkSVJWSjLkhr2c+8TUY4zx9hjjQOCHwD8fyL0hhGtCCJUhhMp169YdUrMtStn4xt8Z2GU5LzeHC8qLeGnBWjbvrE17fUmSJEn6LEmG3CqgX7PjvkD1Z1w/CfjCgdwbY7w7xlgRY6zo2bPnIbbbgnTpB90GZiTkAlw4qpg99ZEn31mVkfqSJEmS9GmSDLkzgKNCCKUhhAIaN5Ka3PyCEMJRzQ7PA95v+jwZuCSE0CaEUAocBbyZYK8tT9k4WDYV6vekvfSQPp0Y1KsDf3rbJcuSJEmSsktiITfGWAdcBzwDzAMejDHOCSHcHEKY2HTZdSGEOSGEWcANwOVN984BHgTmAn8BvhNjdDvf5srGQ+12WDkz7aVDCFw4si+VyzaxfMPOtNeXJEmSpE+T6HtyY4xPxRgHxRgHxhh/2nTuRzHGyU2fr48xDo0xjogxfr4p3H5470+b7js6xvh0kn22SCUnAwEWPpOR8heMKCIEeMzZXEmSJElZJNGQqwS16waDz4Ppd2Rkl+WiLm05sbQ7f5q1kpiBVxlJkiRJ0t4Ycluy838JBR3g0b+FuvTvdHzhqGI+WL+DWSs2p722JEmSJO2NIbcl63AETPw1rH4XXvpp2sufM6w3bfJyXLIsSZIkKWsYclu6wefCqMth6q2w9LW0lu5YmM8ZQ3rxxOxq9tQ3pLW2JEmSJO2NIbc1OOt/Q7dSeOxa2JXepcMXjSpm0849TFmwLq11JUmSJGlvDLmtQZsOcNFvYWs1PPX3aS198lE96d6+gMdmuWRZkiRJUuYZcluLvhUw7h/g3Qfh3YfTVjY/N4cJ5UU8N3cNW2v2pK2uJEmSJO2NIbc1OfkHUFwBf74BtlSlrewXRhZTW9fA0++uSltNSZIkSdobQ25rkpsHF90N9XWNz+c2pGczqPK+nSnr0d5dliVJkiRlnCG3tek+EM7+P7D0VZh+R1pKhhC4cGQx05dsZOXmXWmpKUmSJEl7Y8htjUZ9HY4+D174Max+Ly0lvzCyGIDH3YBKkiRJUgYZclujEGDibVDYBR79W9hTk3jJft3aMbqkK4+9tZIYY+L1JEmSJGlvDLmtVfsecMHtsHYuvHBzWkpeOLIv76/dzpzqrWmpJ0mSJEkfZ8htzQadCaO/AdNvhyUvJ17uvOF9KMjNcQMqSZIkSRljyG3tzvgJdD8KHvsW7NyYaKnO7fI5dfARPD6rmrr69OzsLEmSJEnNGXJbu4J28De/hR1rG9+fm/Dzsl8YWcz67bt5bdH6ROtIkiRJ0t4Ycg8HRSNh/D/CnMfgnQcTLfX5wT3p3DafP7lkWZIkSVIGGHIPF5/7O+h3Ijz1A9i0LLEybfJyOf/YPjwzZw07dtclVkeSJEmS9saQe7jIyYWL7mpcrvzYtdBQn1ipC0cWs2tPPc/MWZ1YDUmSJEnaG0Pu4aRrCZz7c1g+DabemliZ4wZ0pX+3du6yLEmSJCntDLmHm/JLYcgF8NL/hupZiZQIIfCFkcVMXbSeNVtrEqkhSZIkSXtjyD3chADn/wra94BH/xZqdyZS5sKRxTREmDyrOpHxJUmSJGlvDLmHo3bd4At3wPqF8PxNiZQo7dGeEf268KhLliVJkiSlkSH3cDXwVDjhW/Dm3fD+84mUuHBkMfNWbWX+6q2JjC9JkiRJH2fIPZydfhP0PAYe/zbs2JDy4SeUF5GXE9yASpIkSVLaGHIPZ/lt4W9+Czs3whPfa3y9UAp1a1/A+KN78vjb1dQ3pHZsSZIkSdqbzwy5IYTLmn0e+7HvrkuqKaVR7+Fw2v8H85+Et/8r5cNfOLIvq7fW8MK8NSkfW5IkSZI+bl8zuTc0+/zrj313VYp7UaacdB2UnAx/uRE2Lknp0GcN7UVJ93bc8txCGpzNlSRJkpSwfYXc8Cmf93aslionF77wHxBy4dFvQn1dyobOy83hf5w+iPmrt/HUe6tSNq4kSZIk7c2+Qm78lM97O1ZL1qUfnPcLqHoTZt6b0qEnlBdx1BEd+OVzC302V5IkSVKi9hVyB4cQ3gkhvNvs84fHR6ehP6XTsV+CfifAtNtSOpubmxO44YxBLF63g8dnudOyJEmSpOTk7eP7Y9LShbLH2Oth0ldg7p9g+BdTNuxZQ3sztKgTv3r+fSaUF5Gf68bekiRJklLvM5NGjHFZ8x9gOzAK6NF0rNZm0DnQ/SiYemtKXymUkxP4/pmDWL5xJw/PrErZuJIkSZLU3L5eIfRkCGFY0+c+wHs07qr8+xDC/0hDf0q3nBwY811Y/Q4seTmlQ3/+6CMY2b8Lt73wPjV76lM6tiRJkiTBvp/JLY0xvtf0+UrguRjjBOAE9uMVQiGEs0MIC0IIi0IIN+7l+xtCCHObnvN9IYQwoNl39SGEWU0/kw/gb9KhOvbL0KFX42xuCoWna8uxAAAgAElEQVQQ+MGZR7NqSw2T3lye0rElSZIkCfYdcvc0+3wa8BRAjHEb0PBZN4YQcoHbgXOAIcClIYQhH7vsbaAixngs8DDw82bf7Yoxjmj6mbjPv0Spk18IJ1wLS16CVbNTOvSYgd05sawbv3lpMbtqnc2VJEmSlFr7CrkrQgjfDSFcSOOzuH8BCCG0BfL3ce/xwKIY45IYYy0wCbig+QUxxpdijDubDqcDfQ/0D1BCKq6Cgg4w7dcpHTaEwPfPPJr123fzwOtLUzq2JEmSJO0r5F4NDAWuAL4cY9zcdP5EYF8vUy0GVjQ7rmo691m1nm52XBhCqAwhTA8hfGEftZRqbbvAcVfAe4/CptTuMTa6pBvjBvXkzimL2VazZ983SJIkSdJ+2tfuymtjjNfGGC+IMT7b7PxLMcZ/38fYYW9D7vXCEC4DKoB/a3a6f4yxAvgK8KsQwsC93HdNUxCuXLdu3T7a0QE78dsQAky/I+VDf//MQWzauYd7py5N+diSJEmSDl+f+Z7cfW34tI9nZauAfs2O+wLVe6lxOvBPwLgY4+5mY1c3/V4SQngZGAks/lj9u4G7ASoqKlL3vhs16lwMw78Ebz0A434I7bqlbOhj+3bhzCG9+O0rS/j6SQPo0q4gZWNLkiRJOnzta7nySTSG01eBfwd+8bGfzzIDOCqEUBpCKAAuAf4qNIcQRgJ3ARNjjGubne8aQmjT9LkHMBaYu79/lFJozHdhz06Y8buUD33DmYPYXlvHb19dkvKxJUmSJB2e9hVyewP/ExgG3AqcAayPMU6JMU75rBtjjHXAdcAzwDzgwRjjnBDCzSGED2eA/w3oADz0sVcFHQNUhhBmAy8BP4sxGnIzoddQOOpMeOMu2LMrpUMP7t2J848t4t6pS1m/ffe+b5AkSZKkfQgx7t8q36aZ1UtpDKY3xxhTu+3uIaqoqIiVlZWZbqN1Wvoa3HcenHcLjL46pUMvXredM26ZwlVjS/nn8z/+hilJkiRJahRCmNm0b9Nn2tdMLiGENiGEi4D/Ar4D3AY8eugtqsUYMBaKj2t8nVBDat9tO7BnBy4a1ZffT1/G6i01KR1bkiRJ0uHnM0NuCOF+YBqN78j9cYxxdIzxJzHGlWnpTtkhBBh7PWz6AOY9kfLhrz/tKOobIre/tCjlY0uSJEk6vOxrJvdrwCDgemBaCGFr08+2EMLW5NtT1hh8PnQrg6m3wn4ucd9f/bq14+LR/Zg0YzlVm3amdGxJkiRJh5d9vSc3J8bYsemnU7OfjjHGTulqUlkgJxdOug6q32p8RjfFvnvqkYQQ+PULzuZKkiRJOnj7fCZX+siIr0C7Ho2zuSnWp3NbvnpCfx5+q4oP1u9I+fiSJEmSDg+GXO2//LZwwrWw6DlYMyflw39r/EAKcnO49fmFKR9bkiRJ0uHBkKsDM/pqyG/XuNNyih3RsZDLx5Tw+OxqFq7ZlvLxJUmSJLV+hlwdmHbdYNTl8O5DsKUq5cN/85Qy2hfk8cvnnM2VJEmSdOAMuTpwJ327cYfl6f+R8qG7ti/g6s+V8vR7q3lv5ZaUjy9JkiSpdTPk6sB16Q/DLoKZ98GuTSkf/uqTS+ncNp9bnM2VJEmSdIAMuTo4Y74Htduh8p6UD92pMJ9vjivjxflrmbks9SFakiRJUutlyNXB6XMsDDwV3rgL9tSkfPgrxpTQo0MBtzy3IOVjS5IkSWq9DLk6eGOvh+1r4J0/pnzodgV5fGv8kUxdtIFpi9enfHxJkiRJrZMhVwevdBz0KYdpt0FDQ8qH/+oJ/endqZBbnl1IjDHl40uSJElqfQy5OnghNM7mblgEC55K+fCF+blcd+qRVC7bxJSF61I+viRJkqTWx5CrQ3PMBY27LU/9VeNrhVLs4op+9O3all84mytJkiRpPxhydWhy8+Ck70LVDFg+PeXDF+TlcP1pR/Huyi08O3dNyseXJEmS1LoYcnXoRn4V2nZrfDY3AReOLKasR3tueXYhDQ3O5kqSJEn6dIZcHbqC9nD8NY3P5a5L/St/8nJzuP70o1iwZhtPvrsq5eNLkiRJaj0MuUqN4/8W8tomNps74dgiju7VkV89v5C6+tTv5CxJkiSpdTDkKjXa94CRl8HsP8LW1M+25uQE/u6MQSxZt4M/zapO+fiSJEmSWgdDrlLnpO9ArIc3/iOR4c8a2othxZ249YWF1NY5mytJkiTpkwy5Sp1upTDkAqi8F2q2pnz4EALfP/NoVmzcxUMzV6R8fEmSJEktnyFXqTXme7B7K8y8L5Hhxw/qyXEDunLbC++zfvvuRGpIkiRJarkMuUqt4lFQegpMvwPqalM+fAiBmyYMYcuuPVx13wy2765LeQ1JkiRJLZchV6k39nrYtgrefSiR4Y/t24XbvzKKOdVb+dZ/zfT5XEmSJEkfMeQq9QaeBr2GNb5OqCGZAHraMb34PxcN59X31/ODh2bT0BATqSNJkiSpZTHkKvVCaJzNXTcf3n82sTIXV/TjH84+msmzq/lff55HjAZdSZIk6XBnyFUyhl4InfrC1FsTLfOtcQO5YkwJ90z9gLteWZJoLUmSJEnZz5CrZOTmN743d/k0WDEjsTIhBH50/hDOP7YPP3t6Pg/PrEqsliRJkqTsZ8hVckZ9HQq7wLRkZ3NzcgK/uLiczx3Zgx8+8g4vzl+TaD1JkiRJ2cuQq+S06QCjvwHznoTpd0J9cq/7aZOXy51fO44hfTrx7f/7Fm8t35RYLUmSJEnZy5CrZI25DsrGw19+CHedAkunJlaqQ5s87r1yNL06FXLVfTNYtHZbYrUkSZIkZSdDrpLVtit87TG4+Peweyvcdy488g3YuiqRcj06tOGBq44nLyeHr//nm6zasiuROpIkSZKyU6IhN4RwdghhQQhhUQjhxr18f0MIYW4I4Z0QwgshhAHNvrs8hPB+08/lSfaphIUAQybCd96EU/4B5k6G31Q07rxcV5vycgO6t+e+K0eztaaOy+95ky0796S8hiRJkqTslFjIDSHkArcD5wBDgEtDCEM+dtnbQEWM8VjgYeDnTfd2A24CTgCOB24KIXRNqlelSUE7OPWf4DvToeRkeO5H8B9jYPGLKS81rLgzd3/tOJau38k3HphBzZ76lNeQJEmSlH2SnMk9HlgUY1wSY6wFJgEXNL8gxvhSjHFn0+F0oG/T57OA52KMG2OMm4DngLMT7FXp1K0MvjIJvvIgNNTB7y+EP14Gm5entMyYI3twy5fLqVy2iev+8DZ19Q0pHV+SJElS9kky5BYDK5odVzWd+zRXA08fyL0hhGtCCJUhhMp169YdYrtKu0Fnwbenw6n/H7z/PPzmeJjyc9hTk7IS5x9bxL9MGMrz89bwz396jxhjysaWJEmSlH2SDLlhL+f2mjBCCJcBFcC/Hci9Mca7Y4wVMcaKnj17HnSjyqD8QjjlB3DdjMbQ+9JP4Y4TYMHT+753P10+poTrPn8kk2as4JbnFqZsXEmSJEnZJ8mQWwX0a3bcF6j++EUhhNOBfwImxhh3H8i9akW69IOL74evPw65beC/L4H/ezFsWJyS4b9/5iC+XNGPX7+4iAdeX5qSMSVJkiRlnyRD7gzgqBBCaQihALgEmNz8ghDCSOAuGgPu2mZfPQOcGULo2rTh1JlN59TalY2Hb02FM38Ky6bBHSfCCzdD7Y5DGjaEwE8vHMbpx/Tipslz+PM7ybzCSJIkSVJmJRZyY4x1wHU0htN5wIMxxjkhhJtDCBObLvs3oAPwUAhhVghhctO9G4Gf0BiUZwA3N53T4SA3H8ZcB9+thKEXwau/gN+MhjmPwSE8U5uXm8NvvjKS4/p35e/+OItpi9ensGlJkiRJ2SC0lo14KioqYmVlZabbUBKWvQ5P/T2seRdKT4Fz/g2OGHzQw23eWcvFd71O9eYaJl1zIsOKO6ewWUmSJElJCCHMjDFW7Ou6JJcrS6kx4CT45hQ4999h1Wy4cyw8/y8HPavbpV0B9191PJ0K87ji3hks37Bz3zdJkiRJahEMuWoZcnLh+L+F774Fwy+G134Jb//XQQ/Xp3NbHrj6eOoaGvj6PW+wfvvufd8kSZIkKesZctWytO8BF9wOJSfDM/8TNq/Y9z2f4sgjOnLPFaNZvbWGK++dwfbddSlsVJIkSVImGHLV8uTkwAW/gYZ6mPzdQ9qMalT/rtzx1VHMXbWVr//nGyxauz2FjUqSJElKN0OuWqauJXDmT2DJSzDzvkMa6tTBvbjtkpEsWrudc259hX97Zj67autT0qYkSZKk9DLkquWquApKx8Gz/wyblx/SUOcd24cXfzCeCeVF3P7SYs745RRemLcmRY1KkiRJShdDrlquEBqXLQM8/h1oaDik4Xp0aMMtF49g0jUnUpify9X3V3LNA5Ws3LwrBc1KkiRJSgdDrlq2Lv3hrJ/CB6/AzHtSMuSJZd156nsn88OzB/PK++s4/RdTuGvKYvbUH1qIliRJkpQ8Q65avlGXw8BT4dkfwaalKRmyIC+Hb40fyPM3jGPskT34P0/P57zbXuXNDzamZHxJkiRJyTDkquULASb+uvFduo9fd8jLlpvr27Udv7u8gt9+vYIdu+u5+K7X+cFDs9nge3UlSZKkrGTIVevQuS+c9b9h6asw43cpH/6MIb147oZT+Nb4gfzp7ZWc+osp/Peby2loOPjXF0mSJElKPUOuWo+Rl8GRZ8DzN8GGxSkfvl1BHj88ezBPX38yg3t35B8ffZe/uXMac6q3pLyWJEmSpINjyFXrEQJMvA1y8lO+bLm5o3p1ZNI1J/KLL5WzfMNOJvz6NW5+Yi7bavYkUk+SJEnS/jPkqnXpVATn/AyWT4M370qsTAiBvzmuLy9+fzyXHt+fe6d9wOm3TOHJd6qJ0SXMkiRJUqYYctX6lF8Kg86G538M6xclWqpzu3x+euFwHv3WGHp0aMN1f3ibr9/zJkvX70i0riRJkqS9M+Sq9QkBzv8V5LWBx78NDfWJlxzZvyuPf2csN00YwtvLN3Pmr17hl88tpGZP8rUlSZIk/T+GXLVOnfrAOT+HFW/A9DvSUjIvN4crx5by4vfHcdbQ3tz6wvt87l9f4pfPLWTttpq09CBJkiQd7kJreX6woqIiVlZWZroNZZMYYdJXYfEL8M1XoeegtJZ/ffEG7n5lMS8tWEd+buC84X24cmwp5f26pLUPSZIkqTUIIcyMMVbs8zpDrlq1bWvgjhOg20C4+lnIyU17C0vWbeeB15fx8Mwqtu+uY2T/LlwxpoRzh/chP9fFFJIkSdL+MORKH3r3YXjkajj9x/C5/5GxNrbV7OHhmVXcP20pSzfspFenNlx2wgAuPaE/PTq0yVhfkiRJUktgyJU+FCM8+DVY+EzjsuUjBme0nYaGyMsL13Lv1KW8+v56CvJymFhexBVjShhW3DmjvUmSJEnZypArNbd9XeOy5S4D4OrnIDcv0x0BsGjtNu6ftoxH3qpiZ209o0u6cuXYUs4c0os8lzJLkiRJHzHkSh835zF46Ao47Udw8vcz3c1f2bJrDw9VruD+15eyYuMuijoXctlJA7h0dH+6ti/IdHuSJElSxhlypb156AqY9yR8cwr0Gprpbj6hviHywrw13DdtKdMWb6BNXg4XjizmirElDO7dKdPtSZIkSRljyJX2Zsd6uP0E6FwM33gBcvMz3dGnWrB6G/dNW8pjb1dRs6eBk8q6c8XYEk4dfIS7MkuSJOmwY8iVPs3cx+HBr8Pn/xnG/X2mu9mnzTtrmTRjBb9/fRkrN++iU2Ee444+gtMGH8H4o3vSpZ3LmSVJktT6GXKlz/Lw1Y1h95qXoPfwTHezX+rqG3h5wTqenbuaF+evY/323eQEqBjQjdOOOYLTjunFwJ7tCSFkulVJkiQp5Qy50mfZubFx2XLHXvCNFyGvZc2GNjRE3lm5hRfmreH5eWuZt2orAAO6t+O0wb047ZgjGF3SjYI8lzVLkiSpdTDkSvsy/88w6Ssw7kb4/D9muptDUr15Fy/MX8sL89YwbfEGausa6Ngmj1OO7slpg4/g80cf4S7NkiRJatEMudL+ePQaeO8R+NsXoU95prtJiZ21dbz2/npemLeWFxesZd22xmXNxw3oyqmDe3H6MUdw5BEdXNYsSZKkFsWQK+2PnRvhjpOgXXe45uUWt2x5XxoaIu+u3PLRLO+c6sZlzf27tePUwUdw+jG9OL7UZc2SJEnKfoZcaX8t+Av895dhzHfhjJ9AK57hXLVlFy/OX8sL89YyddF6dtc10DY/l/7d2tGnSyF9OrelqHMhfbq0pU/nQvp0LqSoS1sK83Mz3bokSZIOc4Zc6UBM/h68dT8M+xuYcCu06ZjpjhK3q7aeqYvW89qi9VRt2sXqrbtYtbmGDTtqP3Ft13b5jQG4KQg3BuIPQ3FbenVuQ5s8g7AkSZKSs78hNy/hJs4GbgVygd/FGH/2se9PAX4FHAtcEmN8uNl39cC7TYfLY4wTk+xVh7nzfwVdS+DFn8Cqd+DiB6DXkEx3lai2BbmcPqQXpw/p9Vfna/bUs3pLDdVbGkPv6q01VG/exaotNVRt2sWMpZvYsmvPJ8br0aFNUwgupF/Xdgzv25mR/brSr1tbn/+VJElS2iQ2kxtCyAUWAmcAVcAM4NIY49xm15QAnYAfAJM/FnK3xxg77G89Z3KVEh+8Cg9fBbu3wYRfQfklme4oK+2srWPVlhpWbf5/YXjVll1Ub6lh9ZZdLN+4k5o9DQB0a1/AiH5dPvop79eFzm3zM/wXSJIkqaXJhpnc44FFMcYlTQ1NAi4APgq5McalTd81JNiHtP9KT4ZrX4WHr4bHvgnLpsE5P4f8wkx3llXaFeQxsGcHBvbc+79D1dU3sGDNNmat2Mys5ZuZtWIzLy1Yy4f/plbWsz0j+nVhZL8ujOjXlcF9OpKf6+ZXkiRJOnRJhtxiYEWz4yrghAO4vzCEUAnUAT+LMf4plc1Jn6pjb/j64/DST+G1W6D6rcbly93KMt1Zi5GXm8PQos4MLerMV08YAMDWmj28W7WFWSs28/byzbyycD2PvrUSgDZ5OQwr7vxXM759u7rMWZIkSQcuyZC7t/87PZC10f1jjNUhhDLgxRDCuzHGxX9VIIRrgGsA+vfvf/CdSh+Xmwen3wT9T2x8l+5d4+ELd8Ax52e6sxarU2E+Y4/swdgjewAQY2Tl5l1/Ndv7X9OX8Z+vfQBAjw7Nlzl35dh+nelU6DJnSZIkfbYkQ24V0K/ZcV+gen9vjjFWN/1eEkJ4GRgJLP7YNXcDd0PjM7mH2K/0SYPOgm++Ag9dAX/8Kpx0HZz+L5Br2DpUIQT6dm1H367tOP/YIgD21DewYPU23l6+ibdXNAbf5+etbboeju7VkYqSrlQM6MZxA7o62ytJkqRPSHLjqTwaN546DVhJ48ZTX4kxztnLtfcBT3648VQIoSuwM8a4O4TQA3gduKD5plUf58ZTSlTdbnjmn2DGb6HfifCle6FTUaa7Oixs2bmH2VWbeWv5JmYu28TbyzezfXcdAL06taGipBsVAxqD7zF9OpLns72SJEmtUla8JzeEcC6NrwjKBe6JMf40hHAzUBljnBxCGA08BnQFaoDVMcahIYQxwF1AA5AD/CrG+J+fVcuQq7R475HGd+rmtYG/+R0MPDXTHR126hsi81dvZeayTcxYuomZSzdSvaUGgHYFuYzo1+Wj4Duyfxc6usRZkiSpVciKkJtOhlylzbqF8ODXYd18GH8jnPL3kJOb6a4Oa9Wbd1G5rDHwzli6ifmrt9IQISfA0b07Nc70lnSloqQbxV3aZrpdSZIkHQRDrpSk2h3w5A3wzqTG2dyLfgvte2S6KzXZVrOHWSs2U7n0wyXOm9hRWw9An86FHDegKxUDunLyoJ6f+hokSZIkZRdDrpS0GOGt++Gpf4B23eFL90H/A3lLltKlrr6B+au3Ubl0Y+OM77JNrGpa4jysuBMTy4uYUF5En87O8kqSJGUrQ66ULqtmw4OXw5YVcMbNcOK3G7cCVlZbsXEnz85dw+RZK5ldtYUQYHRJNy4YUcS5w/rQtX1BpluUJElSM4ZcKZ1qtsCfvg3zn4RjJsAFt0Nh50x3pf20dP0OJs+u5vFZK1m8bgd5OYFTBvVkYnkRZwzpRfs2Sb5tTZIkSfvDkCulW4zw+u3w/E3QuR9c/AD0OTbTXekAxBiZu2ork2dX88Ssaqq31FCYn8Ppx/TighHFnDKoB23y3GRMkiQpEwy5UqYsnw4PXQk7N8CY78LQC6HXUJcwtzANDZGZyzfx+KyVPPXuajbuqKVTYR7nDu/DxPIiTijrTm6O/00lSZLSxZArZdKO9Y3v013wFBChW1njMuZjLoDiUQbeFmZPfQOvLVrPE7OqeWbOanbU1nNExzacf2wRF4wo4ti+nQn+N5UkSUqUIVfKBtvXwvw/w7zJ8MEr0FAHnYqbAu8E6H+S79htYXbV1vPi/LU8PmslLy9YR219AyXd2zGxvIiJI4o48oiOmW5RkiSpVTLkStlm1yZY8BeY9wQsfgHqaqBdDxh8HgyZCCWnQJ47+rYkW3bt4Zn3VjN5djXTFq+nIUJR50LK+3Vp/OnbheF9O9PBjauk/7+9Ow+T467vPP7+dvU994xujW7Ljm1sy7ZsbAMCHMAmCWFzsNiQPLAL6yUkz7JHEuJsdjmSEJLdJCRPSJ6Hwxy7gGFJTBxCsI0NtoNtfCFjW8aHDkujY3TMffX52z9+1dM9o5mRZGmmeno+r+epp6p+Va3+TZdK6s/8jhIRETlrCrki9Sw3Ai/dA7vuhBfvhvyIn435/Lf6wLvlekjoma2LydHhCb77zBEe39fPUz0DvHxiDPA907euaOay7nYuXdfOtu52LljVQjIei7jGIiIiIouLQq7IYlGYgD3f9y28P/1nmBiARBNsfbPv0rz1LZBujbqWcob6R/M81TPAUwcGw/UAJ0bzACTjMS5e08pl3e1sC1t9N3ZlNa5XREREZA4KuSKLUakA+/7Vj+F97tswehSCpG/ZvfBtcMHPQbYz6lrKK+Cco6d/nJ/0+NC788AAT/cMMl4oAdCWSXBpdxvb1rVzaXc7l61rY0VLOuJai4iIiNQPhVyRxa5cggOP+hbe5+6EwQNgAVz+brj+f0Lz8qhrKGepWCrz0rERnjowwM4Dgzx1YIDne4cplf2/y2va0lza3c66zgwrW9Osakuzui3NytY0K1rS6vIsIiIiS4pCrkgjcQ4O74SdX4PHP++7M7/hw3D1LRAkoq6dnEPj+RK7Dg9Oht5nDg5yaHCciUL5pHOXNSdZ1ZZmVasPvqta06wMg3BluyUVVzdoERERaQgKuSKN6tgLcNet8NL3YNn5cMOfwNY3RV0rmUfOOQbHCxwZmuDI4AS9QxMcGcxxZGicI4MTHBnK0Ts0QV845rdWNhmwKmwFrgTflS0pOpqSdGT90p5N0NGUpCkZKBCLiIhI3VLIFWlkzsELd/mw27cHzr8RbvgEdG2JumYSoYlCiaNDOR+GhyboHZzgcCUU1wTkYnnmf/eTQcwH3krwzSbpaErQnk3SkfXrzillSdoyCYKYgrGIiIjMP4VckaWgmINH/g4e+F9++9oPwo7fgVRL1DWTOlUuO/rH8uFSoH80z8BYYXJ/YCxP3wxlswVjM2hNJ2hJx0kEMeIxIz65NhKxGPHACGI2eTwRxAimHZ98XU1ZUzJOZ1OSzuYkXU1JOpuSdDWlyCSDBf7UREREpB4o5IosJcO9cO/HYOdXoHklvOmjcOlNENPERHL2nHOM5IoMjBXoG/UBeXoIHpkoUig7iqUyhZKjWC5TKjsKpTLFkqNQdpTK4XapTLHsKIbnVcpKZTf5Z8ySqQHIJAIfeJt98PXhN0lnU2oyDNcG4+ZzNC65XHaUnKNUdhTLfu2cozWdIKbWbBERkXmnkCuyFPU8Af/yu3DwcVh7Jbz1z6D7lP8OiNSdctkxki/SN5LnxKhvXe4bzfntEb9fLc9zYjQ34+Rc4LthV8JwIrDJgFpZitO2y84H7Sn7Zcds/10mAmNVW5o1bRnWtmdYM7mkWdueYXV7huZUfB4/LRERkaVBIVdkqSqX4Sdfh+99BEZ64bKbfctuy6qoayYyr8byRU6M1Abfk4NxyTniMSNmFnajjhEYBDHflToIjMB89+p4zK8r27HJstjkPsDxkRyHBsbDxY9/Lk1rim7LJFjTnmFte7omBFf3V7SkNbZZRETkFBRyRZa63DA88L/hkb+FIAk7fhuu+SDEU/PzfqUi9O+Fo7tg7ARs3AHLzpuf9xKpY8VSmaPDYfAdnKgJwOMcHPD7g+OFKa8JYsaqVt/yu7ItTTYRkEkGpBIx0vGAdCIgk4iRTgThEiOVCMjU7Kfj/jXpuH9dKh7TbNkiItJQFHJFxDuxG+767/DCv0DnZj8L8/k3+hmDXgnnYOiQD7NHd0FvuD72PJRyU8/t3ALn3wBb3wIbXgPx5Nn/PCINYCRX5PDAOAfD1t9qCB6nd2iC8UKJiUKZ8UKJfHHmbtinYkYYkGNkk3Has4lwCWfLziSnzKZdO4u2Zs0WEZF6pJArIlO99D347q1w/AXYcj3c+ElYfsHcrxnrg6PPVQNtZXtisHpOy2pYcRGsuBBWXuzXqVbYfZ9/zNHeB3z4TTbDljfC1jD0tqyc359XpEGUy45cscxEocREscR43gfgiWLJl4WBuHY9XiiRK5SYCF83kisyGE4WNjBeYCCcMGy2Cb4qs2Z3ZBO0heG38sioyqOl2jIJWjPhOl3Zj5OKa/ZrERGZHwq5InKyUgEe/Sz84JNQGIWrb4HXf9h3Zz7+fLVVthJohw9XX5tqg5VhmF1xUTXYZjvnfs/8qA+6L9wFL94NQwd9+eptvkX5/LfA6ss1E7TIAiuXHcO5IgOVx0mN5SeDcP9YgcHa8vEwIBePLy8AACAASURBVI8WGM4V5/xz04nYtOBbCcLxyf3p4bgt64+fq5mwRUSkMSnkisjsRo7BfX8IT34ZEhkojAPhvwVByrfwVlplK4G2dc0r7+Jc4Rz0PgsvfNcH3p7HwJWhaQVsfbPv2rz5jZBuPesfUUTmR6FUZjBsDR6aKDA4XmAoXAbHCwxN+FbjyjFfVmBwzAfkub52BDGjPQy9HdnkSduVbtXtNd2t27MJhWMRkSVCIVdETu3QTnjiC9C8KmylvQg6NkGwQI87Gevz3ahf+K5fTwxCLAEbrvXdms+/UZNXiTSQSuvxZCAenxqGBye7UhcYGM9Xt8fyjOZLs/65lXA8GYIztWHYl7eF3a0nz8skaUnH9YxjEZFFRCFXRBaXUhF6Hq12az66y5d3bvaB95J3QPeV0dZRRCKTK5Z8EB4r0B8GXz++OAzDtdvh8f6xAuOF2cOxmX+8UyX8ttW2GIfdqtuntCKH3a3TCdIJjT0WEVloCrkisrgN7K8G3r0PQCkPb/0zuPo/RF0zEVlEasPxZEtxGIiHxgvVibjG/TjkwbBscLwwZ9fqZBCjJR0Pl8SU7daa/eq2X7dmqsc0SZeIyJlRyBWRxpEbhn+4BZ7/Drz6A/4xSDF9ORSR+VMuO4YnimHozU8JwkMTRYYnigxPFKashyb3i4ycYoIugGQ8RmttAJ4WllumHWudIVAn45q0T0SWjtMNuQs08E5E5CykWuCd/xfu/h/wyKehfx/8yuch1Rx1zUSkQcViRlvWT3y1nuwZv75UdozkagLweBiAc9UgPDT92ESB3qGJye25xiFXpOIx33qcqbQi+wDcnIrTlIrTlIyTTQV+nQxoSlXXTck4TamAbLjOJAJN4CUiDUEhV0QWh1gAN34CujbDd34XvnAj3Px1aFsbdc1ERE4SxGxyvO8rVSo7RsIwXNtKPFMLcm3r8uHBCUYmiozmi4zlS5RmeyDyNGaQTQRkU3Gakj78NqeqITmTDMgmAzLJamieLEtUArPfzobl2VScTCIg0ARfIrKAFHJFZHG56v3QvhH+33vhcz8LN98Oa7ZFXSsRkXMuqGlNfqWcc+SKZcbyJUZzPvSO5ouM5UqM5IqM5YuM5kuM5aat80VGc/41/aN5DvSNMZ4vMVYoMZYvkS+Wz6geqXgsDL7VAJxJBqQTAel4QDoRI50ISMXDdcKXpSrH4sGU4yefX91PBjG1SIsscRqTKyKLU++z8NV3wtgJ33X5Z34u6hqJiCwZxVKZsULJB98wFI+F2+P5Spiubo9XwnV+6msmCmVyxRIThTIThRIThRK5YpncGYbo6RKBEY/FSARGMh4jEfglHhjJoLJvJIIYyXiMeMxvJ8KQnAiMeFDdTsZ94E6Gx1MJv/blNcfC/ep5wZTzkkFMj60SOQsakysijW3lxfD+e+FrN8Ht74Ib/hiu+aDvbyciIvMqHsRoDWK0pl95K/NcymVHvlSeDL0+AE/fLzFRLJOrXRdKFEqOQqkcLlO386UyhaLfL5Yd+WKZ0Vxx1tfki34/Vyxxmr2+T6kSrg0wM8yY3I5ZWDbtWGzaeWb+v7tYzbnJIEZTyo+3bk7Fw+7m4fjrybKgOl57hjJNZCaNYl5DrpndCPwVEACfc859ctrxHcCngEuBm5xz36w59h7gD8LdP3LOfWk+6yoii1DLSnjvP8Mdt8Bdvw8ndvvHDAX6/Z2IyGIWixnpWFBXzyMulsrkw+CbD1ubc5Pbvgt3vlQmV5h+ng/mtccKxTIOcA4czq+dwwFlF+4Tljl/Xjk8PtO5ZecD+1i+xPBEkSODE4zm/Czfo2cwLjsZxCbHYDen4sSDSriuhmwmg3VN6J7c94W1+5VAXtluTSfoaErQkU3Snk3Ska1s+3VHNklLOq4Wbzkr8/ZN0MwC4NPAm4Ee4DEzu9M5t6vmtP3Ae4HfnvbaTuAjwHb8Pf5E+Nr++aqviCxSySy848tw70fhh3/lZ15+xxch3RpxxUREpJHEgxjxIEY2GXVNzkxlXPZozo+zrozFHslVx12P5othKA73w4BcKrtq2IYp4Rumh/SabcCVwVGe8pqSg4P94/SHz6SeLXsHMaM9k5gMvpNhuMmH4c6asrZsgnjMiJlfgpgRi/lW8cAq2xZu+z97yrlh67k0lvls7rgaeMk5twfAzG4H3g5Mhlzn3L7w2PSBFzcA9zjn+sLj9wA3Al+bx/qKyGIVi8GbPw6dm+Hb/xVuuwHe9XVoXx91zURERCJlZuFkXQFddfTkvXLZMTRRoH+sQP9YnoGxPP2jftsvhcmynv4xnjlYoG8sf8aTnp0OqwTiMAgngxht2QTtGR+qWzOJydDdlvHlbZXt8Ly2TIJ0QpOe1Yv5DLlrgQM1+z3Aq8/itSc9J8TMbgFuAVi/Xl9mRZa8K98L7RvgG++Bz/4svOt2WHtl1LUSERGRaWIxoz1skd1E02m9xjnHeKHkg/FonoEx/3itYtlRLjvKzlEK12VHdbvsW5Enz6mUlQnPrbzO7+cKJQbHCwyOFxgYL3Cwf3xye66u38l4jPZMNfy2heG3PZugKRWfnHysMhlasnaSsvjU/URw8kRmlePxmClMn8J8htyZPvnTHbJ/Wq91zn0G+Az42ZVPv2oi0rC2vBHedzd89R3whZ+HX/4MXPSLUddKREREzpKZhY+hirO2PbPg7++cYyRX9IF3rFANwmMFBsbzDI5N3T84MM6uQ4MMjBcYy5fOWT3MIBHEaEoGtKQTtGbitKQStKTjtKT9ujUdpzUztax6zK/racz7uTafIbcHWFez3w0cOoPXvmHaa39wTmolIo1vxc/A+++D22+Gb/w6vOlj8JoPaeZlERERecXMLAyKCbo7zuy1zrkpE5Kd0XZtWc3+aL7I8IRfhsYLvHxijOGJgi/LFU9Zp2QQC8NvfDIsv/e6Tbz5opWv8BOqH/MZch8DtprZJuAgcBPwrtN87V3AJ8ys8tfnLcCt576KItKwmpfDe/4JvvUb8L2PQN9u+Pm/gGB+HnchIiIiMhszIxUPSMUXpvW0XHaMTIbgAkPjxWoAnigwNFGctu2PlcrnfsxzFOYt5Drnimb2W/jAGgC3OeeeNbOPA4875+40s6uAO4AO4G1m9jHn3MXOuT4z+0N8UAb4eGUSKhGR05bIwK/c5iekevDPYWA/vONLkGmPumYiIiIi8yYWM1rTifBZ1gvftTtqVpkCfLHbvn27e/zxx6OuhojUqx9/Bf7pQz7wvvsb0LEx6hqJiIiIyBkwsyecc9tPdV5sISojIhK5y98Nv34HjPT6mZcPPBp1jURERERkHijkisjSsel18P7vQaoFvvgL8PDfwvhA1LUSERERkXNIIVdElpZlW+H998K6q+GuW+HPL4Bvvg923wcNMtmCiIiIyFI2n7Mri4jUp6YuP/Py4Z1+rO7T34Bnvgmt3bDtXX7p3BR1LUVERETkFdDEUyIihQl4/p994N19H+Bgw2v9ON6L3g7JpqhrKCIiIrLkne7EUwq5IiK1Bnvgqa/Bzq9C3x5INsPFvwSX/xqsezWYRV1DERERkSVJIVdE5Gw4B/sf9q27z94BhVHoOg+2vRsuuxlaV0ddQxEREZElRSFXRORcyY3Arm/5wLv/IbAYbPlZ37p7wVshnoq6hiIiIiINTyFXRGQ+nNjtuzLv/CoMH4JMB1zyb/343dWXRV07ERERkYalkCsiMp/KJdjzfd+6+9NvQykPKy+BC98Gm3bA2ishnoy6liIiIiINQyFXRGShjPXBM3/vJ6w6+CTgIJGF9df6wLvpdbB6G8SCqGsqIiIismgp5IqIRGGsD17+Iex9wC/HfurLU22w8TVh6N0Byy+EWCzauoqIiIgsIqcbcuMLURkRkSUj2+m7LF/4Nr8/3Av7HqyG3ue/E563zLfwbtoBG3dA1xY9nkhERETkHFDIFRGZTy0r4ZJf9QvAwH7YWxN6n70jPG9NtZV30w5oXxddnUVEREQWMXVXFhGJinPQtwf23h+G3gdh7Lg/1rHJt/RufB0s2wpt630rsVp7RUREZIlSd2URkXpn5rspd22B7f8eymU49ly1pffZf4Qnv1w9P5GFtm5oW+fX7et8+K1st6yBQP+si4iIyNKmb0MiIvUiFoOVF/vlmg/4xxQd3QX9+2CwBwYOwOB+v334qWqrb4XFfNBtX1cNw+3rwlAclqWaI/nRRERERBaKQq6ISL2KBbDqEr/MJD8GQwf9ON/BHhg8EAbhHjjwIz/et1yc+ppMB7Sv992hu7ZA5+bq0rxS3aFFRERk0VPIFRFZrJJZP1532daZj5dLMHzEh9/BnjAMH/DrI0/DT789NQQnmsLAO0MAblmtACwiIiKLgkKuiEijigXQttYvMykVfOjt2wN9e+HEbr999Dl4/l+gXKieG8/MEYDXTH3mr3NQzEFhDIoTUBj32zOua5dpx5LZqe/TuRmSTfP7mYmIiMiip5ArIrJUBYlqeJyuXKoJwDUh+PiL8OLdUMpXz42n/XN/awMtr2Dm/iAFiYyfYCuRhtwwjB6bek7zKl/frtrwu8WH71TLmb+niIiINByFXBEROVksgI6Nftly/dRj5RIMHYK+3dUQPHoiDKiVkFoTVif3px/L+BbiyrFYcHI9Joagv6aVuW+vf98X74GR3qnnNq2oaWXeFIbfMAinW+frkxIREZE6o+fkiojI4pQbqWlp3hOG7r1+e/jw1HOblvvJtlLNvju1KwMu3K7dL4fL9LLwvCn75eqf3dYdLmv9TNata/1+1OG6mPezcLuyr5PGVYuIyCKm5+SKiEhjSzXD6kv9Ml1+tBp4awPwxJB/1JKZXxOuY8HJZbPtm1XLXBlGjsLLP/St2640rY6tPuxWQm/t0rrWL/Hk6f/MhQkfWkePh+sT4fpYWHbCr0eP+e3cUPW12WXQvR3WXlldMu1n/rmLiIjUOYVcERFpPMkmWPUqvyyUUtF3oR7sgaGe8LFOPTB40I9vPvSkD55TGDSvqAnC66BpGUwMVkPs6LHqdn545veOxSHb5YNsUxesudy3MDct8+WuDAefhIOPwwvfrb6ua+vU4LvyVWcWukVEROqQQq6IiMi5EMRrZrN+9czn5Md8i2/lsU5DYQAePOhntX7pe37irlg8DKzLfWjt2BgG1mV+Xbud7YJ0+9QZrucyMVgNvD1P+Pd86mvhz5CC1Zf5wFsJvx0b1c1ZREQWFY3JFRERqRfO+ZCbyC5csHTOB+2ex+HgE359eKefLRt8iF4bBt7uSjfnjrn/vGLOdxkvjPpgP7keC8vHZi7PdsHKi2DFxdB1nv/FgYiISEhjckVERBYbs4V/FrAZtK/3y6t+2ZeVCnB019Tg++LdTD4aqus838V6tuBamZTr9CpQnW17vL86rjlIwrILYMWF1eC78iJNoCUiIqekllwRERE5tYlBOPTjavAdPQ7JLCSawnXWB/REdoby5tnPTWSqobUwAcdf8AG791nfhfvoLt+tuyLdBisuCpcLYeXFfluTaMlSNTHo70nw4/Hn6mmx0PKj0PMYvPyQXw4/Bcu2wqYdfll3jf83oR44BwMvw/5H/FwIG66D1dtmfrydROZ0W3IVckVERKS+jff7wFsbfHt3QW6wek7rWh92K62+Ky6E5Rf4FuFSwXe/LuZmWc91LAfF8ep+uQjNq6B9nZ8orH29b9WOp6L7fGTpKJf8PdDzmP+FU89j/hdD1Hyf79wydRb1VZf4Z5YvhPF+HxInQ+1Of89YzNdj9TY49ryfE6BchFgC1l1dDb1rty/c5Hflkv83Zf8jsP9hvx4+NPWcdBtsfB1sej1sfj0sOz/6niTDvf66H3rS/0Jj0w5Yecnpz8uwyCnkioiISONyzrfw9u7yobcSfI8/D6V8eFLly+hZftcJkhBP+yBrMd/KM71LdvPKMPCumxaAw/1Uy9nV4UwU8/6LeJBYuPdsFKUCDOyHEy9Vl5Gj/lp2bILOzdC5ye8vxOc7crQaZnse870p8iP+WKYTuq8KlysB8y26B5/065Ej/rxY3M+cPhl8r/Bh7Vy0UA4dhv0PwcsP+1B7dBfg/D2z9krfGrr+Oh9ka58bnhvxoXLv/bD3Ad/Ci/O9O9ZfUw2957IltTDuP5dKoD3waPUxa61rYf21/r3XX+sn9dv3r7DnB7Dnfhjc789rWV0NvJteH040OI+KOTj8k+r173m8WhcLqsM7Mh2w8bWwMfzcll8QfRifJwq5IiIisvSUCnBit/+yfex5H0bjqWpIjaenbc+xTmT8jNPTW0hKBR+wBw74SbsGDvgvnpX9wZ6aoB1Kt4fhd31NCA7XTct9K3F+xHfvzI/VbFcm8JphKUw7r7KUC/49s12+1bllZc06XFpWVdfzMQ7cOcgNT3ue8/Ga9QkY6/PPu25ZHdZlta9PZZ1qPvf1AiiXYfhwTZDd7dd9u6F/n29hrEi3+8d8Dfb4z7vCAn/9JoNvGH47N/sZyROZM69XMQdHnp4aaAZe9sdicd8SOhlqt/v3nivIDB0KQ29l+XH1MWTJZt+1ee0V1fB7qvHuzkH/3mqg3f+Qfw45+KEI61/tA+2G6/yfdyatx+P9sO+HPvDufQCOPefLU22w8TU+UG7a4XtonG54G+uraaV9GA7trN4bKy6qBtr11/hfWpzq595zfzWUVx4H17W1Gng3ve7suopXukvX/lLj8E+qdW5b569791W+xXv1pf5z2/sg7HsA9jxQDcBNK3x9Kr8sONXflUVEIVdEREQkCuUyjB49OfwOHPCthIMHqq1xp8uCcGxzU7hkq/uJ7MnHymXfkjfcW7PurX5hrpVsmRaEZ1k757/cTwmrM4XYcH960K+IZ3xLWabDh/Lhw1MD5GS9mqeG3pmC8Fwhfazv5CB7YrcPs7XvF89A15ZwOW/qku305zjnP7++vT7Y9YfryjIxOPW9W9aEwXdjGHxrWoHTbTWzmtd0Oz78VPUza11bDTTdV/lHe72S4FyrXIYTL1Zbeg8+4UN15e9E04qprb1rLofhI/DyD8NQ+7C/VuBbkTdc50Pihutg1aXndjb04V7Y92A19Pbv9eXZZdXgtmmH/0zNpo6nffkhvz7+vH9NkIQ1V8CGa319u6+qXtdXolyGo89WW3lffsj/Ispi/jpVWnrXXzv3NcsN1zzOLfw7MHrMH4tn/DWoDbWtq09dt/59PvRWPrdKa35rd83n9jo/xGKRqouQa2Y3An8FBMDnnHOfnHY8BXwZuBI4AbzTObfPzDYCzwHh304ecc59YK73UsgVERGRRcE53wJTCb5jJ6oBdUpgrdkOkmffElMu+/cdOeLDy0jv7OuZQudski3+ec7Z2mc4z7E/PZRWWn2Hj/gQNXykWsfKfmVdebRVrVRrNfBmOn0r+4mX/M9aYYFvYe067+RA27Lm7MczjvWFwbcSfmvC8Ejv1HOzXb4+o0f9fjzjA+VkqN0OrWvOrj6nq5iDI8/48Z2V4Hv8hZPPa1njw2xlWXbBwo4BHdhfE97ur4bt1m4/Dv/I09WydJuf0KrSUrvm8vkdk1zM+89t7/0++PY85nsDBCnfTXvz62HTG/yQhdpW+mPPVYc9dG2tXvvuq3xL89n+0sA5OP6ib+Xd+4D//Mb7/LHOzdXQu/F1vrfCIhF5yDWzAHgBeDPQAzwG3Oyc21VzzgeBS51zHzCzm4Bfcs69Mwy533bOvep0308hV0REROQcqITO6eHXYmFo7aqG12zXwk1q5JxvMZ0p/FZC8ehxHxCnt8h2bIhujHJuZGoA7t/rg9HaK3ygWXlxfY2frsykfminbz3fcC20b6if7q7O+Rb5StfhYz/1XbkroXb5hdFOwpQb8a3elZbe3qenHk+3+ZbZSiv92ivOrmX5dFVaoCu/LHj5h9UxycsvrLbybnxtfc3QPU09hNxrgY86524I928FcM79Sc05d4XnPGxmceAIsBzYgEKuiIiIiIgsZqPHfags5nxLbeeW+pgJuVSEI09Vuzbvf8T34LjhE3Dtb0Zdu1mdbsg9h53nT7IWOFCz3wO8erZznHNFMxsEusJjm8zsx8AQ8AfOuQfnsa4iIiIiIiLnVtMyeNUvR12LkwXx6hjs1/6Xarfrjg1R1+ycmM+QO1OfhunNxrOdcxhY75w7YWZXAt8ys4udc0NTXmx2C3ALwPr1c8yKJiIiIiIiIjOLJ33X9AYxn23lPcC6mv1u4NBs54TdlduAPudczjl3AsA59wSwGzh/+hs45z7jnNvunNu+fPnyefgRREREREREZDGZz5D7GLDVzDaZWRK4Cbhz2jl3Au8Jt38VuM8558xseThxFWa2GdgK7JnHuoqIiIiIiEgDmLfuyuEY298C7sI/Qug259yzZvZx4HHn3J3A54H/Y2YvAX34IAywA/i4mRWBEvAB51zffNVVREREREREGsO8Pid3IWl2ZRERERERkcZ1urMr18H81SIiIiIiIiLnhkKuiIiIiIiINAyFXBEREREREWkYCrkiIiIiIiLSMBRyRUREREREpGEo5IqIiIiIiEjDUMgVERERERGRhqGQKyIiIiIiIg1DIVdEREREREQahkKuiIiIiIiINAyFXBEREREREWkY5pyLug7nhJkdA16Ouh6nsAw4HnUlZFa6PvVN16e+6frUN12f+qbrU990feqbrk99O9fXZ4NzbvmpTmqYkLsYmNnjzrntUddDZqbrU990feqbrk990/Wpb7o+9U3Xp77p+tS3qK6PuiuLiIiIiIhIw1DIFRERERERkYahkLuwPhN1BWROuj71Tdenvun61Dddn/qm61PfdH3qm65PfYvk+mhMroiIiIiIiDQMteSKiIiIiIhIw1DIXSBmdqOZPW9mL5nZ70VdH5nKzPaZ2dNmttPMHo+6Pkudmd1mZkfN7Jmask4zu8fMXgzXHVHWcSmb5fp81MwOhvfQTjP7uSjruJSZ2Toz+76ZPWdmz5rZh8Jy3UN1YI7ro3uoDphZ2sweNbOnwuvzsbB8k5n9KLx/vm5myajruhTNcX2+aGZ7a+6fbVHXdSkzs8DMfmxm3w73F/z+UchdAGYWAJ8G3gpcBNxsZhdFWyuZwRudc9s0DX1d+CJw47Sy3wPudc5tBe4N9yUaX+Tk6wPwl+E9tM05950FrpNUFYH/5py7ELgG+M3w/xzdQ/VhtusDuofqQQ643jl3GbANuNHMrgH+FH99tgL9wPsirONSNtv1AfidmvtnZ3RVFOBDwHM1+wt+/yjkLoyrgZecc3ucc3ngduDtEddJpG455x4A+qYVvx34Urj9JeDfLGilZNIs10fqhHPusHPuyXB7GP9FYy26h+rCHNdH6oDzRsLdRLg44Hrgm2G57p+IzHF9pE6YWTfw88Dnwn0jgvtHIXdhrAUO1Oz3oP/Q6o0D7jazJ8zslqgrIzNa6Zw7DP5LIrAi4vrIyX7LzH4SdmdWV9g6YGYbgcuBH6F7qO5Muz6ge6guhF0tdwJHgXuA3cCAc64YnqLvcRGafn2cc5X754/D++cvzSwVYRWXuk8BvwuUw/0uIrh/FHIXhs1Qpt861ZfXOOeuwHcp/00z2xF1hUQWmb8DtuC7jx0G/jza6oiZNQN/D/xn59xQ1PWRqWa4PrqH6oRzruSc2wZ043vjXTjTaQtbK6mYfn3M7FXArcDPAFcBncCHI6zikmVmvwAcdc49UVs8w6nzfv8o5C6MHmBdzX43cCiiusgMnHOHwvVR4A78f2pSX3rNbDVAuD4acX2khnOuN/ziUQY+i+6hSJlZAh+gvuKc+4ewWPdQnZjp+ugeqj/OuQHgB/ix0+1mFg8P6XtcHai5PjeGwwCccy4HfAHdP1F5DfCLZrYPPzzzenzL7oLfPwq5C+MxYGs4s1gSuAm4M+I6ScjMmsyspbINvAV4Zu5XSQTuBN4Tbr8H+McI6yLTVMJT6JfQPRSZcPzT54HnnHN/UXNI91AdmO366B6qD2a23Mzaw+0M8Cb8uOnvA78anqb7JyKzXJ+f1vwCz/DjPXX/RMA5d6tzrts5txGfd+5zzr2bCO4fc069LRZC+CiATwEBcJtz7o8jrpKEzGwzvvUWIA58VdcnWmb2NeANwDKgF/gI8C3gG8B6YD/wDuecJj+KwCzX5w34bpYO2Af8x8r4T1lYZvZa4EHgaapjon4fP+5T91DE5rg+N6N7KHJmdil+YpwA3xj0Defcx8PvCrfju8L+GPi1sNVQFtAc1+c+YDm+a+xO4AM1E1RJBMzsDcBvO+d+IYr7RyFXREREREREGoa6K4uIiIiIiEjDUMgVERERERGRhqGQKyIiIiIiIg1DIVdEREREREQahkKuiIiIiIiINAyFXBERkYiZWcnMdtYsv3cO/+yNZqZnRoqIyJIRj7oCIiIiwrhzblvUlRAREWkEaskVERGpU2a2z8z+1MweDZfzwvINZnavmf0kXK8Py1ea2R1m9lS4XBf+UYGZfdbMnjWzu80sE9kPJSIiMs8UckVERKKXmdZd+Z01x4acc1cDfwN8Kiz7G+DLzrlLga8Afx2W/zVwv3PuMuAK4NmwfCvwaefcxcAA8Cvz/POIiIhExpxzUddBRERkSTOzEedc8wzl+4DrnXN7zCwBHHHOdZnZcWC1c64Qlh92zi0zs2NAt3MuV/NnbATucc5tDfc/DCScc380/z+ZiIjIwlNLroiISH1zs2zPds5McjXbJTQnh4iINDCFXBERkfr2zpr1w+H2Q8BN4fa7gX8Nt+8FfgPAzAIza12oSoqIiNQL/SZXREQkehkz21mz/13nXOUxQikz+xH+F9M3h2X/CbjNzH4HOAb8u7D8Q8BnzOx9+Bbb3wAOz3vtRURE6ojG5IqIiNSpcEzudufc8ajrIiIisliou7KIiIiIiIg0DLXkioiIiIiISMNQS66IiIiIiIg0DIVcERERERERaRgKuSIiIiIiItIwHGMMtgAAACVJREFUFHJFRERERESkYSjkioiIiIiISMNQyBUREREREZGG8f8BfKcvOGAmQm4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_errors(all_train_mse, all_test_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(to_predict, L):\n",
    "    predictions = []\n",
    "    for i, data in enumerate(to_predict):\n",
    "        input = data.as_in_context(ctx).reshape((-1, 1, features))\n",
    "        out = model(input)\n",
    "        prediction = L(out, input).asnumpy().flatten()\n",
    "        predictions = np.append(predictions, prediction)\n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_lstm_anomaly(df, predict, threshold, L, data):\n",
    "    predictions = predict(data, L)\n",
    "    df['anomaly_lstm'] = pd.Series(predictions)\n",
    "    df['anomaly_lstm'] = df['anomaly_lstm'].apply(lambda v: int(v > threshold))\n",
    "    return df\n",
    "\n",
    "\n",
    "train_predictions = predict(train_data, L)\n",
    "threshold = np.mean(train_predictions) + 3 * np.std(train_predictions)\n",
    "\n",
    "plot_data1 = mx.gluon.data.DataLoader(data1, batch_size, shuffle=False)\n",
    "df1 = add_lstm_anomaly(df1, predict, threshold, L, plot_data1)\n",
    "\n",
    "plot_data2 = mx.gluon.data.DataLoader(data2, batch_size, shuffle=False)\n",
    "df2 = add_lstm_anomaly(df2, predict, threshold, L, plot_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bP+0IgiZHtyutr3s1qs55r+vr3vyTjIAVAAAAU2NzdU3XdTRCu66WNldPZoRGEDRZul2v+e/+vvfKcZJmxp0AAAAAoCx3nu/qoqSl9bO6ZW9bzzQXtbm6pjvPn9wr/26XwAeTi4cuAQAAAABGhocuAQAAAKiFwcB7Am6j4b0yBizyIGAFAAAAJlTdg8HBQFpd9YZrMfNeV1fzp7PKfNa9DE86mgQDAAAAE8gPBnd3D+e1WvV6qFIZY4tWmc9JKMNplKdJMAErAAAAMIHKCAar1mh4NathznlPws2iynxOQhlOI/qwAgAAAFNuezvf/HFYjBn+Nm5+lCrzOQlleNIRsAIAAAATqIxgsGpra14T26BWy5ufVZX5nIQyPOkIWAEAAIAJVEYwWLVu1+sP2ul4zYA7nfz9Q6vM5ySU4UlHwAoAAABMoDKCwVHodr3+oPv73mve9FWZz0kpw5OMhy4BAAAAAEaGhy4BAADgRGNszXRllNHFMwPtzCxp3zW0M7Oki2co6FGb9nOdgBUAAABTxR9bc2vLG1Jla0u6917p5psPL+rPnKnmIj9v8JC0fJWBSFQZra7G78NPi3PSzIz32nUDveWhVd26t6WGTLfubentD90tc047M0u68L2DV9K/ujDQ5WZ0YDvOgGsw8M4L5w6nhYWj54qfnnBw/nvfcuaV/zfdkv6pG2hmxju3qkprsJzOnPHSeffd2Y/jJKJJMAAAAKZK3NiaSVqt4fsu+kHg7m627SYtL+XbVl55xh+NSqckXdKSlhRf0NfV0n3yMvMhrWpeu0fe+2xvXVtv71aazySDgXcj4+WXk5drtaQP/oOB/skTR/NgklxgOT+/j6qrXk86f77ctEYdgzh1H0c2T5NgAlYAAABMlUbDq23Ka9iL/DxBYNryUr5t5RVXRs55D0cKikvnnhpqKLmgN+VlJiqw3Wl2dOetm5XmM0meGxubWlInITg/XK6j27SpZlO6cWO49AXlvQkTdRzrhIAVAAAAJ1aRGlZp+Iv8PEFg0vJJygpE8gTXcelMq2GVpP2DOsiowHZfTjNuP1eZlSlP+WcJziUvT015CS8zzMp7rkxTDSt9WAEAADBVosbWzGJxcbj9xq2fd77kBWx518kjz/ijcft8UGuvBKRxtrWobUVv4JnmYqGyKUuefVyOyUOYn9dms0iK4uVJ67SNI0vACgAAgKkSHluz3Zbm5pLXKeMiP08QGLe8z+x40FpmIJJn/NG4dD6qrs7r/tig9bpaelBrelBruq7Wsfc2V9dyl1mZsu6j1ZKeWDmeh3CFp59fyetvWqasN2Ha7SkcR9bMajfdcccdBgAAAJSl3zfrdMyc8157vaP/9/vV7Cdtu/2+mReeRk9VpLEIP1+SWbN59PW++b5tNzq2J9nLatqeZJebHfvISv+V9B8u4+xys2NP9vrHtj2OfPZ60eW+sHA8PU/2+na5eZiH372998r/l9Sx0+pbs+ltswqjOodHQdKGZYwN6cMKAAAAjFHehzWhXIOBdPastL3tNb1dW/NqKOPmjzNN04I+rAAAADixiozrWeZYoHm3NapmsaMY7/T3vuWMbrgZmXO64Wb0e99S0aCkJep2vRsD+/veqx+sFhmntoyyPXNGeve74/cdNSbuqMevHamsVbGjnGgSDAAAgCL6fbNW62jzzlYrublk1DqSWbt9uF7WZqtF9p9n+3nXCTblde5oumZnvTwO26TU38cH1bP9UCHuS/aCFo40AQ6ny5+GbU5bVtPifv+wuXNUE+1g0+CratsVtY80C856zOP2HT5O4fMx6lwdZp/joBxNgscenEZNBKwAAAAoIhwElTHNz5vNzRVb97T6dkmHwc1V5wU3m4E+j34glCfYiAtcGo3DoC8puBmmLNptOxYAn1b/WLAanK6p9UowlzYtLBz2z4zqMxvVf7PXO55XPyAPr7uycjwgdc7bb1Rg7+fvWbVt/yAIj0v8VzV3LJ/z894UPEbhY97rxQfJRafgDZe6yROw0ocVAAAAU6PI2KZVOa2BPqRVzWs38v3rauk+retReZ0TW63sT3hNG2u215Mee6zYeLR5peXTt6mObtNmJWlwrrrjfloDXdC9erVezrT8FbX1Ol3NtGyr5R3LP/uzIRKYYG5OunChfv1f8/RhJWAFAADA1EgL5Ebpkpa0pOTEhIO4Tsfru5r2EKC0S/hm0+uTOYpL/Sz5lKR9OTW1X32CSpY1fz6T1Dg26M341PHhXQSsAAAAOJEGA+nee6WXs1WGVWpPjdTAJSqIa7Wk3d2j/99zj/TII0fnp+l0RhO8Z8mnVG0Na5Wy5s9Xt4DVOe/mRZ3wlGAAAACcSN2u9PVfP+5UeLa1mHuZZvN4ULq76zUVzhOsNpvlP2U4TpZ8mqQHVW2CnKtmu1nyF3RV7WoSUtBivuTXDgErAAAApsrzz5e/zdlZrz9gHg9qTdfVin3/ulpHgrhWS9rbi142bn6c1VUveG+PIHZKy6fkBax+X90qtFrS/fd7tcrOefnOe7ziPKg1fVWzke+F61G/qlk9oHPl7Lgko7pxURUCVgAAAEyVojVK8/NeoOMHPP7fnY708MPS177mPcwoa03eo+rqPq1rUx3ty+mK2rqitvbltKWO7tO6/m3TC+I6Ha8WtdOJ3lazGT2/0/HS5L/fbHr/nz/v/X/u3PExXsvm53NLndiGsNuKyZi8cu71DoNNP09+WQTzJh19319+fd3Lsz+e6tWr3sOGwtvIotOR+v3DYP9RdfUePawrasukV6Y9NbTxDSvaaXrHd1MdvUcP5w7Mq6oZlrxyqtsDl3LL+jjhUU4MawMAAICisoyrmnU7UeN69vuHw6XkmfxxMqO2mzReaqsVPWxL1nE3w/uL2lZ4mp09PpRP1LxwGp7s9e2ajm78mlrHxmEdh37fy0Pa8QkuHy4n/9jkHYt3dvbo0DbBfQaH2Wk2vf/949VuJw93025HD7lU5yFtzPINazP24DRqImAFAADAMPr96Av5YFBSVuCYZTocj1X2spq2J9mlg7FY08bSbLe9tMzPH47h2WwejrcazndUkB23XFQ+/X0E9+0HusFAPTie6EdW+nY1ME7pDTVsT7LLzU5qsBoVUCeNwRoOKqPWjbrJEJXf8JioSWmJOh/izqVgEOqc2cxM9LGNO5/88W79oDUY7PrrRY05HByHt87yBKypTwl2zl2Q9AOSnjWzbz2Y91OS7pN05WCxB83ssYh13yXpnKSmpA+b2c9kqfXlKcEAAAAYxvd+r/TEE+NOhfRBndEZPSQnKarl51c1W6gZaRGNRjVPi40bp9SPMrbU0f8+t6ZfUlcvvVTOPhcWvKbfw4xfmnXs1mbzsA/xaQ30AZ3VoraOHE+T03ndr/frfPEElajd9pqD17U5cKnD2jjnvlPSNUkfCwWs18zs3ySs15T0F5LeIWlH0qclnTaz1NOKgBUAAABFDQbS3XePOxVesPreg2A1yRW19TpdHUmaqpBlnNLrauk+rY8kMK/KaQ30Ia1qXtGPazZJv6BebYLWVsvr21vHoLX0cVidc0uSPpUzYP0Hkn7KzL7v4P+fkCQz+5dp+yNgBQAAQFFLS6MZfzTNy5rRjNIf72uq17ideU37OKy+LIH5DTU1qxsjSlG6Tsd7EFXdjGoc1vc55z7vnLvgnHttxPtvkHQ58P/OwbxIzrlV59yGc27jypUrcYsBAAAAiba3x50CTzNDsDoNso5TuqiaHJiCsqS/bse8Lp+FYRQNWB+S9HclvVnSlyX9bMQyUa0fYm+9mNm6mS2b2fKpU6cKJgsAAAAnXdFhbcq2p2xjqVzVCAZLrdCDWlOWrrFZA9u6ypL+rMd8VOryWRhGoYDVzL5iZntmti/pQ5LeGrHYjqQ3Bv6/VdIzRfYHAAAAZLW25j1gaNx+UaupDWVf1owe0LmRpKcqj6qr8+ol5vW6WnpQayNLUxUe1FpiHk3eMa+LVsv7LEy6Qh9l59zrA//+I0lfiFjs05K+yTl3m3NuTtIPS/pkkf0BAAAAWXW70sc+Js3Pjzcd79d5/YJ62tfxZoYm6QUt6B59dOwPImqWUCn4fp1XV31dUVsmL397amhfXt/VSX/gkuQF5nG14XV74FKnU98HLuWVGrA65x6V9IeSvtk5t+Oc+xFJ/8o596fOuc9L+m5J/+PBsrc45x6TJDO7Iel9kn5L0hcl/Vsze7qifAAAAACv6Hala9ekft+raQpqtbz5Zt6FfVHB7fhTeH/v13m9pmV6eKWvnWZH+3LaaXb08Epf39Z5MTGIC29/mLT6ms3jab5xI3oU2Lz7e1RdvU5X1XSmX+qbmran17VNt2mzULDqnDd8TZk6nWL584/Fn/fO6bqOnlDX1VJX/VeC1VbLG1amzPRGncdRy/rHdnNzOoJVSco0WOuopzvuuGOIYWgBAACAQ/2+Wadj5pz32u8ffa/VOhqqtVpmvd7x+bOzZu129Hay7i9q2fB+JG8/4fXypnVu7viySWnJmrakfTjnpSdpG86ZraxkK9+0NOSZwvnPuu3wMXyy17fLzY7tydnlZsc+stI/drzzpjvL8er3vfJJy9ckkLRhGWPDsQenURMBKwAAAKoSvvCfn48ORMOB58qKWbOZLbjMm55Ox9uWv/12+zCNwXnz88fT7QeB/vxG43gaFxYOA6kswXRSmoLr9nrHy+S0+rap6IAumK+oNPvb8l87HW8fflqC60RNwWMZTG9U2sP5DE+Nhrfv4LnSbh+mxzmz++b7tt04zOuTvX5iGUaVZdSxjrqxEi5DPy/B8olbv27yBKyZxmEdNcZhBQAAQFGDgfTAA9JzzxVbv92WvvpV6fr1ctN1WgN9QGe1qG1ta1EPak2PqqtXvcoLh156qdz9lW1hwWtm3WxKezGjt5zWQA/rPXqVDjNjkl7UvL6mV6ut54/kPav5eWl31yunNM55036WRxcP4YM6o/fqoSNDo3xNc7pXF/TJ+a5e/erkc7Ddll588fhxbzSk175Wev557ym/b3qT9Du/ky3vQXNz0oUL9WwanGccVgJWAAAATI3BQLr3Xunll8edkqNOa6APaVXz2n1l3nW1puJhREHP6madUvqdgknP+2kN1NfdkQ8EekHz+ju6NvI0RWm3patXx52K4whYAQAAcCItLUlbW+NOxXGXtKQlHU/YFbX1OtUwoihoX+5IjWOSTXV0mzarTE5l4o6n5NUod9WvTTBew3AvV8BagxGqAAAAgHJsb487BdEWFZ2wm/WcTmsw4tTUQ1yZTIKktDtJH9DZ0SVmyhGwAgAAYGosLo47BdG2FZ2waQtu4sYpjRJXJpMgLe11CcbLGl5nnAhYAQAAMDXW1qTZ2XGn4rgHtaa4lpl1CW7K8IDO6atKPwDX1dKDWhtBiqrxoNYS81mXYPzcuXGnYHgErAAAAJga3a708MPD1Sw55z2VtkyPqhtb+1hGcNMY4VV9sxn/3i+7rj6s/177oZ6s+5Je0IL25bSpTu4HLnU6Uq/nvTp3+P8oaxBf9arDv3+l0dV79LBe0PyxGxHX1dL/6sYfjLfb9XxCcF4ErAAAAJgq3a73ZNTwyJr9vtRqJa/bakkf/7g3fEtwvXCg5P/fbkcHt62Wt15wnw/onK7raAKCNY2tlrfttDRG7Wdv7zCtcUHc3Jz3flJ5+GmIqqX2179xI34k1P196Yeaj6kRCuMakp5XW03t6zZtHgtWZ2eTy3FzUzp/3nvd3z/8P3yc+/3otLuUJ0H5eYsrk37fG+rI38/envRL1tXX2zX9Qa+vnWZH+3LaaXb02d66+vvdyPMmrrzD82dnvTQV1WpNR+2qJGUarHXU0x133FHqwLQAAAA4Ofp9s07HzDnvtd+Pf6/X814ls2bTew2vU+Y+75vv23ajY3tytt3o2H3z/WPrxKXRObP5ebNG4zC9vV78Ou32YSjXbkfnKS7d/X629aPsyUXGs3tyqeWdVI5ZpaU96f1h8p01bXnKu9c7LKfwND/vTf7//nlRtNxGSdKGZYwNxx6cRk0ErAAAACii3zdrtaIv8IMX9MHAyUXHVzYzczQgCE7hICcchLXbx9f9oHr2spq2L9nLatrPqxdTTxkfaIXz1mp5eQnPd+7OQg4HAAAgAElEQVR4MBtMX1ye5+e9/QYDqqQAOio4f1btyI1fbnaOHatwusKvnY7ZysrRdC8sxAe04cDPn179am+ddvvw/XDAHHfu+GUSLrfT6tslHd58eLLXP5av4PLhADPLtLBwGLjGHbPgNDt7/PjVUZ6AlXFYAQAAMDXqOg7rB3VG79VDR3p2mqTP63a9WU+nrj8/L/3t33rNYevstAb6qP6Z5nQ0oSbp12/p6cdmz2t7W7rpJunFF6WXXhpPOsNaLenrvk567rn0ZU9roHN6QDfruSPH87paOnf7us7+WX06jrZa0vp6/fqy5hmHlYAVAAAAU6PR8Oqa6uZlzWhGe8fmm6RfUE/v1/nRJ6oCl7SkJUXfMdhUR7dpc7QJKtlpDfQhrWpeu5Hv1zGPnY7X57dOCFgBAABwItW1hnVfTnHP/bmhpmZ1Y6TpqcqeGsceuOTbl1NTNa8iTpEUkEv1zKNz9auZzxOw8pRgAAAATI21tXxP2R2VPcWPBdOMqHmdVElD9Dynm0aYkmqkjZlbl/FXgxbrl6RcCFgBAAAwNbpdr8/eKMfnzOIXtRpT75gczE6aB7VWs/rFciUFpPty+pTuGmFq0rVa3k2cSUbACgAAgKnij8OaNCbpqL1f5/V53X4saDV5wey0CI+vGtTW8yNMSXaNRvbz5EGtHRtL95XtyHSvHtFpDUpMXXZzc0fHCO506vnApbwIWAEAADCV/MDV7Hjw2m4fXtwX0WpJt9+effn5eenNelq/oJ5uqCmT13d1vdnT/+DOq9320uSc9zo/f3z9pPer0DiIFOL2t7DgvbpQ59zLii7Uv2osZirv8PaK8IO3Zkrl9dyc9LGPSefOSbOz6dt9VF3dp3VtqhNZYz6vXX1AZyPXXVjwzkP/fIwq07S8++dtOMBut6ULF6Tz570HLO3ve6+THqxKBKwAAAA4AYLBq5n3t39xHzeqZb9/WFsVDCj9mqunn/aWSeoz22p5y1y75m3zfXZeM3ZDzkwzdkM/euO89ve99Fy9qlf+9pf3p2vXkt+Pq01utw+DpKj8hdPup9dM2tvz/l5YkHZ3vXwHt/Xii97r/v7R7V7uHa+FvK6Wtn50TZub3jbigsNeT/r4x6PTFaw9DB+PcM2iH7w98sjR+VHLdbve9PDDyTc1/OD3/+109a96m7KYx2gtavvIueOX24svHgaQ3a53DMPLffzjx8+9YLn7523wXPbnT0NwGinrgK2jnO64445SB6YFAAAAqtLvm3U6Zs6Ztdve5Jw3r99PXj5umTLTFNxHeH6vF5+Wft+s1Toa5rZa2dL7ZK9vl5sd25Ozy82OPdk7ulK/75WTv912+/i+Ox3vvWbTe00rq6RtVlHml5udyHsdl5ud2PRVfdwnhaQNyxgbjj04jZoIWAEAAFBUFYFB2jaz7jMtCIzaTlTwFpwaDbOVlaNB89zc0WWc85bJE4D6+wxPzWb1wVY4+IxLb7BskibnigXeSZ7s9e2ajhboNbWOBedm3o2BuLT5gfVJCmjzBKyMwwoAAICpMRhIq6teE1ZfqyXdc4/02GPS9rY3zMfaWvYmlHHb9B9ok/Z+UNw4sZ2Ol6bwdmZnvWahL72ULa1FdDpe0+iwRsMLqaLE5a8MUeUZ1G57zZS3tryyKRrOxOU7j4tnBlpaP6tb9rb1THNRm6truvP80UIZDKS7707eTrPpTcHjXGUZj1uecVgJWAEAADA14gLCcGCTNRgYDLxgdy9iqFQ/4EkKQsMBUVwQ6JwXSEdtp2rOef1Qw+Ly5Ssj4AtLKu+yxeW7bGnlmKSKMq6DPAErD10CAADA1Njejp4fDhJ3d6Wz0Q9zfYVf0xcXPPn7ittn1PzFmGE8Fxfjt1O1uDTdlTKkaNnpTSvvssXlu2zDlNO4zok6IWAFAADA1MgThKQFA2fPxjdLDe4rKQgNW1uLfgLu2tpoAqjwsCn+vqM89ljytspOb1p5lykp32UbppxGFVTXGQErAAAApkZUQBg3tmVaMJAU0AYDnqQgNKzb9ZoiB4cs8ZsmR21ndtYbK7QMrZZ0//3R+46SNf9lyVKb2BgievHPg7R8l21tLX2M12bz+HEeZVBdZwSsAAAAmBpRAeH992cPKIPiAtpm82jAkxSExqVxc9PrP7m5mbydhx/2xgoNjwUa1GhIKyuHy0QF6O22t+3z5w9rc7e3vVrNwWC4/Jcl7QZCqyX96I/G35Bot48HfcEg1R/jNFjmoxA1xmtQu+2NF+sf5yzn0ImS9XHCo5wY1gYAAABlyjP+aHCdouOQjlNwqJfgcC7z896UNlRMcDtR+c9SdkXTHd6fn/7g+LZJY92OamiYkzQETRXEsDYAAABAtDzD0AwGXi1kkeFwxmUwkB54QHruuezrxD2NNpz/u+7yagOzlF0RUeUtZT9eo5Dn/EE0hrUBAAAAYuQZhmbSpI1hGifrEC/jKLss+wwH6e22dO6c93fZNxym+fwZFYa1AQAAAGLkGYZm0hR90u5NN3mBWKPhvcb1ax1H2cVte2vLS+dgIN1779Ea5eee88Zzfc97vOXMvNfV1cN1suQ3T3qi5g+zn6j1z5wZbnuTiBpWAAAAnCjTXEPWaBwfczbN7KxXw/rSS4fzopq4DgZeEBg1Tuo4alglL51f93X5mj+329Lf/m3xJr1Zz59hmw5nqS2f1KbI1LACAAAAMfIMQzNp8o7b2W5LX//1R4NVyQuSzp49/N8PnqKC1arLLup4+XZ38wWrkrd8OAgM5zdveqLKIKq2O89+stSW59nepCJgBQAAwImSdxiaSZIU3M3PewGqn+d+X7p6VXr++ejlg01c44Knqoa4CfKPV9WyNmvOev4M23y67OUmVWqTYOfcBUk/IOlZM/vWg3n/WtJ/LeklSf+fpHvN7D9FrLsp6UVJe5JuZK32pUkwAAAAUEzeJxtnaeIa19Q468OayhCXznZbeuEF6eWXj85vNr0p3NQ5rglx2c2ah216ntQUusj26qTsJsEflfSu0LzHJX2rmX2bpL+Q9BMJ63+3mb05a4IAAAAAFNftegHM/r73mlb7maWJa1xT47xNkIcRl85z56SHH/YCV1+77Q2/c+HC8ZrQc+dG0yR82KbnSbXlRbY3qWbSFjCz33fOLYXm/Xbg3z+S9N+WmywAAAAAo+AHtEm1smtr0Q8QGmWwlJbOuMA8bn7V4+tmKde86991l/TYY5M1LvCwMj0l+CBg/ZTfJDj03v8t6VfMrB/x3iVJfy3JJP0fZpap9TlNggEAAIB6ydvUGIgzsqcEO+fOSrohKW4EoLeb2bdL+n5J73XOfWfCtladcxvOuY0rV64MkywAAAAAJcvb1HjU/DFLnZNmZrzXkzJW6TQrHLA65+6R9zCmrsVU05rZMwevz0r6hKS3xm3PzNbNbNnMlk+dOlU0WQAAAEBp/CCo0TgMfqLmlbXtUSm678FAuvlmLxh0zvt71AFhVBrOnPGaLPsPKfKH39na8uaPM2gdxXEe57lUtUJNgp1z75L0c5L+KzOLrA51zs1LapjZiwd/Py7pp83sN9P2R5NgAAAAjJs/9miw3+bsrBckhZ88m3dol6htF9lOEUX3PRhI9957/Gm8c3Pew41GUeMal4Y043iS7mAgPfDA8ScSl32cx3kuFZWnSXCWYW0elfRdkm6W9BVJPynvqcCvkuQX/x+Z2f3OuVskfdjM7nLO/efyalUl7+FOv2RmmbplE7ACAABg3LIOKyLlD4iStt3pVNs/tOhwK2lpjlu3zL6veY5J0CiH35Gig8igYHkNWz7DDp8zDqUGrONAwAoAAIBxixt7NEregCht21XWkBUdUzUpzXHrll37l+eYBI06eEsLrP3yKqN86jBGbl4je+gSAAAAMK3yjDGadzzStOV3d71atyoUHVM16f24986ePV7LOEzektLgXPT8cYxVur2d/L6fjzLK56ab8s2fNASsAAAAQIS1NS/YCZqd9fpsBhUJiKK2HZYW9BQVte8seVhb8/IfNjcXv25cHormLSkN99/v1aRKUrPpvXY64+nLmRRYB8u67PKZRgSsAAAAQIRu1wt2Oh2v9q7TkR5+2HvAUHBekYAouO04eWtti+w7Tx66XS//7fbhvHY7+YFLRWtzi6Th/Hmv2a+ZdOOG9zqu4Xfibki020fLuozyef75fPMnDX1YAQAAgDGaxKe8ZjXNeUuT5WFKZZTPtD90iRpWAAAAYIyK1nhOgmnOW5pu1wsY9/fja3rLKJ+iTbwnBTWsAAAAQI2UOQwMToZJO2eoYQUAAAAKGAy8JpaNhvc6GIx+/6urXhNPM+91dbXadIw7z3lMUlpHKUtt7qSihhUAAABQPfpbjro/Yh3ynNUkpRXJ8tSwErACAAAAqsfDaxoNr2Y1zDmv9qxsdchzVpOUViSjSTAAAACQUx3GxCx7GJg0dchzVpOUVpSHgBUAAADQ6IPFKKN+4msd8pzVJKUV5SFgBQAAAFSP4UFGPQxMHfKc1SSlFeUhYAUAAABUnzFDR/nE17rkOYtJSivKw0OXAAAAAAAjw0OXAAAAAAATj4AVAAAAQO0NBt7QNo2G9zoYjDtFGIWZcScAAAAAAJIMBtLqqrS76/2/teX9L9GHddpRwwoAAACg1s6ePQxWfbu73nxMNwJWAAAAALW2vZ1vPqYHASsAAACAWltczDcf04OAFQAAAECtra1JrdbRea2WNx/TjYAVAAAAQK11u9L6utTpSM55r+vrPHDpJOApwQAAAABqr9slQD2JqGEFAAAAANQSASsAAAAAoJYIWAEAAAAAtUTACgAAAACoJQJWAAAAAEAtEbACAAAAAGqJgBUAAAAAUEsErAAAAACAWiJgBQAAAADUEgErAAAAAKCWCFgBAAAAALWUKWB1zl1wzj3rnPtCYN5NzrnHnXNfOnh9bcy69xws8yXn3D1lJRwAAAAAMN2y1rB+VNK7QvN+XNITZvZNkp44+P8I59xNkn5S0tskvVXST8YFtgAAAAAABGUKWM3s9yU9H5r9g5IeOfj7EUk/FLHq90l63MyeN7O/lvS4jge+AAAAAAAcM0wf1m80sy9L0sHr6yKWeYOky4H/dw7mAQAAAACQqOqHLrmIeRa5oHOrzrkN59zGlStXKk4WAAAAAKDuhglYv+Kce70kHbw+G7HMjqQ3Bv6/VdIzURszs3UzWzaz5VOnTg2RLAAAAADANBgmYP2kJP+pv/dI+vWIZX5L0judc689eNjSOw/mAQAAAACQKOuwNo9K+kNJ3+yc23HO/Yikn5H0DufclyS94+B/OeeWnXMfliQze17Sv5D06YPppw/mAQAAAACQyJlFdikdq+XlZdvY2Bh3MgAAAAAAJXPOPWVmy1mWrfqhSwAAAAAAFELACgAAAACoJQJWAAAAAEAtEbACAAAAAGqJgBUAAAAAUEsErAAAAACAWiJgBQAAAADUEgErAAAAAKCWCFgBAAAAALVEwAoAAAAAqCUCVgAAAABALRGwAgAAAABqiYAVAAAAAFBLBKwAAAAAgFoiYAUAAAAA1BIBKwAAAACglghYAQAAAAC1RMAKAAAAAKglAlYAAAAAQC0RsAIAAAAAaomAFQAAAABQSwSsAAAAAIBaImAFAAAAANQSASsAAAAAoJYIWAEAAAAAtUTACgAAAACoJQJWAAAAAEAtEbACAICJNxhIS0tSo+G9DgbjThEAoAwErDXEjy4mEectgHEZDKTVVWlrSzLzXldX+R4CgGngzGzcaThmeXnZNjY2xp2MsfB/dHd3D+e1WtL6utTtji9dQBLOWwDjtLTkBalhnY60uTnq1AAA0jjnnjKz5UzLErDWCz+6mERx563knbtrawSuAKrTaHg1q2HOSfv7o08PACBZnoCVJsE1s72dbz4my7Q2m006P2maB6Bqi4v55gMAJgcBa81M04/uNARnZeZhmvtYpZ2fu7vS2bOjSQuAk2dtzeuGENRqefMBAJONgLVmpuVHdxqCs7LzcPbs0T6e0vQEclHnbRitBABUpdv1+sx3Ol4z4E6HPvQAMC0KB6zOuW92zn0uML3gnPux0DLf5Zz7m8Ay/9vwSZ5u0/KjO0nBWVwtatE8RG1vMIjv41mnQK5ojXLwvI1Tp1YC01D7D+Cobtd71sP+vvc6ab+bAIBohQNWM/tzM3uzmb1Z0h2SdiV9ImLRJ/3lzOyni+7vJJmEH920C/64IGxrq14BQlItapH+xFHbu/de6T3viV+nLoHcsDXK/nnb79e7lcA01P6XJelzfJKD+pOcd2Dc+PwBOMbMhp4kvVPSH0TM/y5Jn8q7vTvuuMNQX/2+Watl5l3ue1Or5c33dTpH3/en0+rbJXVsT862Gx17steP3c8oxKWz00l+L+/24qZwuY1TkfzG6fe99ZzzXuuSR7Ny8znJkj7HWT7j0+ok5x0YNz5/wMkhacMyxoalDGvjnLsg6TNm9vOh+d8l6Vcl7Uh6RtL/ZGZPp23vJA9rEzQYeM1Pt7e9Wri6DA2SZeidqHE5T2ugD2lV8zqceV0tfba3rjvPjydjSUMhfPzj+ccWjdtenH6/HsdUOjnDQpyUfKZJ+hxLJ3d4LYYWwyjV9Xd+XPj8ASfHSIe1cc7NSfqHkv7PiLc/I6ljZn9f0gcl/V8J21l1zm045zauXLkybLImXp2bLWZpKhvVp/EDOnskWJWkee1qaX08HVsHAy94ibK4WKw/sd+897QGuqQl7amhS1rSaR0/cJ1OtguTUTWPKvsJ1XVt1lXVk7jrmt84Sc324/pbb215n4VJyF9cX/K0Y8TQYhiVcf7O1+X7KpyOtGc91CXdAEYsa1Vs3CTpByX9dsZlNyXdnLbcpDcJTmoOmbWp5KibLeZpwpk3bf7ye3KRK+7JjbzpaFSzozKaH/X7Znc3+nZNRzd+TS07rX7ufYyyeVSZ+6pzs660prBpn4OoZfLkt+g+hslv1LbiPscu+mM60ubsw+Y/6njMzprNzSXnod83azbr32S8zk3ukd24uifU5fs5Kh1x3z/+eV6HdAMoh3I0CS4jYP1lSffGvPefSa80O36rpG3//6RpkgPWsvqFxX1pOzfaNJe5/CV1IjN1SZ2R//jEXSg0m8Pvf7sRvfFNdXJfYNb5xkWSuvcTLRp0xi3TbmfL7zD7KPvGQZ6LxbipiuNZRv7z9CX381DVTayycdE+PUb5Ox9Ul+/nrDfN/PO7LukGUI6RBaySWpKek/R3AvPul3T/wd/vk/S0pD+R9EeS/oss253kgDUpEMp6UZu0HX/5Mmtc8qQrbhtp6en3ze6bT695zPPjE5eGLGlLu1CIC2j8ee22N0XtI6kmOa9xXdCE5T3edUl3HmkXQ0m1b3FTOL9ZLrjKvCjLkqfgcc2Tt6qOZ578x52XeQJvPw9V3sQqExft6epaA13Gb28Z6vL9nPQ5jfr9HeX3EIDqjbSGtYppEgPWpLt/WS+YwgFRuPlacCqzxiVpyhuUpi3/ZK9vl5veU4IvqXMsWA2WSdI+e73ou7C9Xrbah7hj5Qei4flRzQmj1u33zS43ozd+udnJVVZJ6RzlxWlSTVzwGPj5T0v3OC8mk/addBGX93MTd5yyXCgWvZiMylvWbRX9/irzplbe/MfVNPZ6+W8sxAUOWcp9VLIcI8R/VhcWsp2LVX0/ZW2mLpnNz8ffEC3DqH5X0soyazrSvn+5WQNMJgLWESt6MRu+iI/6MUu6kCqzxiXuQi18MVhWM+GsaYnaRtKFeNb+Z3kuHvJMrZbZR1aia5L9IXzy9nccd/O/POfM3Fxy89qsNxSqkFaWSRdPaWXQbmfLV9znud1OL++04LBoU+Wi319lfx/kzf+wfXCzTnW4GM5yjPwbKydd0d8Vs2q/b/PeJK3yO3IUvytldn9IOqY0hwcmFwHriBWtWQ1+2SZdpJXZfCfrxVzSgw/CkppK5r3IHuZCNS0/UfvP0jyryAVusCb5cvPoeLN5A5JxN28r2q8xKt3jrDHO0jw27uIpqQzyPLApS8Ba5s2fLIF0cN3gOMmX1LH75vvW6x0u43/O087DYY5z1vyXHZgmHdtxy/qdV4fgetyynhdRZVXl91PS73iW4zvq2s9h5ak9LdqFR6rH5xNAMQSsI5bnwqndztfnKunHrOoa1rj0BGUJOrM0v8qzzyIXqn4/tGBzwWbT+99X1kVuWp7r0n/ILP1iod83azSGO0eC0prdlnkBlad/ZrApvh9UBs+TtDxnTW/eJrpJ/bOz1Mz43znhPEV995xWcsuAPIY9x5POhWG6X5T5Oc6S1rLkudEYLqPwedzp2Cs3IdLSXEbeotKSd1v9/tFzPdj9IGo/Rb+nymyOHxaXtjzN1+O+ozods5WV+N+2YWTJW/j4ZPnNiDuG4eO8spL8vQtgchGwjljWpmnBu/VZa/fimgs7F/+DlHSxW0Ytor/NpJrVuHzHpXd2NvmHLUt5F516vfRatLzNhZPyPM5axqC0mqy041LkImKYmsA8+Yo6z5MuRoc51nnSW3bNY9YpaUgXP01xT/Guuu91eJtxwVSW76+sgV3WmxBFjkmZtbJFg/O8N/Xiul7E9V3PGnAmnbN+l5ekmxJ+gBZ1vPzuB2n7yXN8y2yOn6U8q56GDVqzNust8jsRdQzzbKcurR8AFEfAOmJxX+pJF155xwiMe8hQlh/FuL6Dw0x5L+qTfvDTLkLDAWvZP/zNZvJFoX8nOG/An9TEd9z9Us3SL86KXCgHL0CixOW9rKdlpp0b4c/QqPs7DnPsq6hVDN4QS3u6dZ60l3kRn/X7yw92s24v7dhnudiv8ubTqAOcvF0vhrlBk7S9PPku+l1VZh/WvDdn8j4QbJip2Uw+Pmmy5G3Y76Wix5BgFZh8BKxjkKfpVFKNbNwd56w/inmbHQWbKFf5w5nUpKrIumU3C0zKf5byzZvnqPNlFE0Lg9Kav2U5J+KeEpwkKp9lNZPO2hcsa7+xMs/zpPxnUdVn1D/3NlNqWPMGZ8M0kwxPWWtEg00m02pss2yv6DEpo3n/qJo9x6U5y/mWVkZF+r/nzXeWLiVJw5CF5f185j0Hksqk2Tye3mGP6zC/K0n7L+u6Ic/vTXACMPkIWCuUtfla0jppX97+j0Fw22k/2MNcgM/Pe9OwPzxJU6MRH5SlrRuuYQ0q66IurW+lL8+PalozsvB5lPfOflLfsGGbhQcftJOUt6SLoTJu4vjrRX3movKd9SLbT0uVtZZZjlOSrN0Ghp1mZ719PdlL7sNaJDhL+76sIj9ZPjtZjn2WoHPYGtaoPntpn72qpvn5fOUTzGvcZ3wc+YhKX/D7b9ibgnk+l35f0uA6STdfovqyD1Mj69xwrXlGcfyK1LAOW3MMoB4IWCuStfla+GI1rh9QGVPVTRzLnJrN482Is6Q3qZlp1iBlmCm4/6w/qnmH/IjLQ55mxf4U17y818vXR2hlJXr5tKFr0t6L0uvFpyFr88CkpsVxxycqnXkfMpV3+8HjlOccGXbYpaR1/ZtCH1np2+bBU4I31bGPrBwmsEgNa5ahWIYt67gpS/PspM9DlqBzmCbew/T9q6r8gs2gixy/LL9/45jKGlKr6OdyZiZbOtOeFp53Wlgofm6bZWs6X9Y5l+fzsLBAk2BgGhCwViTPHee0dcpqSlPl8lWnJ8+U9AM7ih/VYI1i2kWE/0TiYc8jv0yH3UYwXXmXj6oBSgvek5r1xR3HvE3Z46aohzdlOaZV1GRmad4Y13ogbr1gE/6op4UmTXE3BfwpLfjKG5xlPUer/OymiTvW4ZYVSYrW2hX5DIcfVJTUZ9cv1zyfoXDNVbB1QNabo+HPeNndN4pORYZey3rM/M/lsGnMMh5znmmYJuvDBMz+Tb+8zcqjfm9WVqK3w0OXgMlHwFqRrBdW/sVOVT/SVTenKzJVnZ4swwpU1WQymMdwoFAkrXn2GRf8jqomvYzPQ1TZBC/yyzxuwc9e2raL9DGrqozDwy2lrRPXJy/pc5hWzkk3DYo0qaz6BlbaMlmaDWbtu15EWlkV6d+Z1OUknPdgbWmefWXNT5Zthfu1F/mdGPdvS5Zj5m+jzLQM+/kpcuMwKG+5R20zeM4MU/5F8jHqZ0IAyI+AtSJZL6ydG67pXtIUrGGJu7CNm99oxDcRqvuU9mPYbldX5nHHOO69RuNoX71gcFEkOAvWqmQde7OMqWgNa9pxTKvpK2uKGw4jeJz8Mg324S6jSXCeqYz+48555Vq0bLOkYWEh38VfVYFGnj5vaX3A4z7HSf3m47YV7qccte35+WKf32BrgKjPY1rtd559ZhmvNU/fymbz6PdXnu9p/7yuMmj1H3SU1tc1Kc/+8Rn2CcDB826Y7/mkbhuzs9Hfd+HPdd6AOdi0N+qcSTqGwRssUevnrSkepqk+gNEhYK1AGf2Nhp2y9JVptZIH2h7nlDWQT+sX5R+POvSNiptOq2+XDvoCXlLHTqs/9jTlnW6/PX8f1qRpbm50wepJnIreEInqW542Zbn4q+IzmnfszTzDlwSnPAFr1d9Fwf7QcZ/HuON+33zfLjfL+x4qY4g0/wacf85mOa/LHpYtb/6SfnvDw8/F5SHP+d3vD3fjLNwXNnjzNCmoDualSA1r3ucahKeoZxYUGfZs2IehARgNAtYKjLoJbrPpfXmH79rHLRu8G1m35sLBH9F+P9uyaU156ppHyQtWo562OmlBa9I5518Q5WmKXVY/r7pNdX7QWdbjUmS9LM3x0s6jImkN7iNL2qPSmeU8TKphHNV3UbA5dt791Pl7KEv/7vDyo+j2Mczx8YWb96+s5As+85ZN1DTMsw+Kfk8nNRUPDtdTpEVJ1LMJkm6aDdN3F8Do5AlYnbd8vSwvL9vGxsa4k3FEo+F95VXltAb6gM5qUdva1qIe1JoeVbe6HdZcpyO96U3SE08cnb+wIL3tbcfn18klLWlJW8fmb6qj23vnaMAAABKtSURBVLQ5+gTViHPVfo4mxfy8dP36uFNRjkZD2t/3PrNra9If/IH00EPjTlW8Yc5B56Tv+R7pL/9S2tqSmk1pb6/c9IX5aXUu33rT9j3U70urq9Lu7rhTcpRz0uKi93v1O79Tn+83//PYDVxG5D2H6iT4uZ2fl179aum55w4/g+22997zz3vfSVGfy05H2twcWZIBpHDOPWVmy5mWJWDNZmnJu0CpwmkN9CGtal6Hv8TX1dJ9Wj/RQeuk2lNDDR3/XO3Lqan9MaSoHvwLu6o+R8C0cU76+Me9v9/97nzB0LR9Dy0sSNeujTsVk6XVktbXvaD1zJl630gKquIGfrAsANRDnoC1UXVipsXaWnXb/oDOHglWJWleu/qAzla3U1RmW4u55p8UZtV+jqp0WgNd0pL21NAlLem0BuNOEoYwKcfTTDp71pvy3luetu+hpGB1Uo7nsPLmc3fXO3ckL1ibBP4N/CVtqSHTkrb0Ia0WOqbNpnfTp9MhWAUmXta2w6Oc6tiH1Wy4PiVJ056iO1zsyVW2T6bqpjr3Hasir3keLjXKJzlzPJlO8vEkn+TTnybp2QGXFJ3YS+oU2h6A+hIPXapGmQ8PqfILmmn80zQ8JThLHk/ChSKfz+maTtLxPAnfQyfleJ6UfFZxA99/0jSAeskTsNKHNYeq+oDQhxWTaNoe6hJn2voCnnQcz+lyUo7nnlxkH659Sc2I/E+qKn9Xej3p/PmhNgGgRPRhrUhVX3SPqqv7tK5NdbQvp011CFZRe4vazjV/Uk1bX8CTjuM5XU7K8dxXM9f8SfWg1nRdrSPzrqulBzX8AxAmpR8vgOMIWHPyH51etkfV1W3aVFP7uk2bBKuovZNyoVjlBRRGj+M5XU7K8WwoevykuPmTqsob+FUPQQWgOgSsOZw54437BeDkXCjSAmK6cDyny0k5ntvq5Jo/yaq6gd+crspo4EShD2tGg4F0993jTkV15uell1+WXnpp3CnBJKlivDwAwFE862J49GEF6oU+rBU4O+VDol67Jl24MO5UVK/Xi27W3WxKKyveeG3IjqbsAFC9k1KTXCWCVWByUcOaUaPhPSB9GjWb0o0b3t9LS9LW8Qf0TYVOR9rcTF9umssAAICTJuvvP4DRoYa1AovT9RyZI1ZXD/9eW5NarfhlJ9laxq6Va2veDYppQt8dAMBJ9aY3jTsFAIYxZZfl1Vlbk2Znx52K8q2sHG0m0+16j37vdCTnvOazMzPjS19ZVla8vGXR7Uof+5jXr3fSzcxI/b70yCPTkZ86cG7cKZhM8/PS3Fz52202pdtvL3+7AKbH7/3euFMAYBgErBl1u9LDD1c3rE2cXu8weOx0vODDLH7Ko92W/t2/Oz6/2/WazuzvS1evSh/96GEayq6pa7W8PFUdBETlM0m36/XrNcuXtnY7+pgVzV+7ffSY51335Ze9vPj56feL99P105LGP0f813bbm9LKYJS1wM4dloV/nFZWjqZxYSH687a/7x3jcHqDn8/gtofl1/Y3m95+y/6s+McmnP4k/X6278Jg+V27Jn3ta8fXHfY79cYN6emns53bflrS8ld2X/ak7+1JUdV39LDfkXU06usEpGNIG2DCmVntpjvuuMPqrNNJChmPT81m/nUkb52q0tZqmfX7+bff73vrpm3fuej5s7Nm7bb3fqdzmIYi5ROc2u34bThXLK95yzTpeJWxjaTthMs7y/HNU+bBdDWb8WkInxvhdGQ5RsOeC1V9tvLo9719OBdfXlmmqOPabo+mHOKOQ3idrMslKXrMm83j24or7/CySenu9+O/w8pIY5b0jnJyLvm86nTyH6N2O9tvRdp57ZxZr5dtW3UoS//8yXpujDu9zeb40tFqZT+2ZeQTQL1I2jDLFhtmWmjUU90D1qxBmz/1evnXmZsrP6D0L8CCgWLR/PsX4+22l9aoHyH/Asf/MUzab97yiSqvXi/+InOYACVL2tICxH7fC9aH2UZcWoLlHb4RkHdbs7PRxzO4vV4vOv0LC+llnxQI+MtlKauk9Fb12cpq2HM5bfJv+Ay7nSznbNoNiDzLFS2zVstsZSX6vV7v+Lbizs/wsmnpjttOcJqZSV8mKo1Z0jvMlPf88H+joj53/uel3zdrNLJtb2bmcB3/dyAtTVEBrh+s+scry+/OKIKfLJ+puHM2WK7Dpjd8/iV9VpKOfRXnYNbJ/70q80ZcXD4B1AsB6wjE/RDPzR3+qDebR78ks9a6tNvlBJR+GoI/ClUI5muY/QS3Mz8ffXGUdMGUVAPgXLl5LBogBn+U5+eja5vzpqWsmw/+trJsv9c7PLf88zzugjRc9lmOUVxZRZ3T4QvZYJn2eke3M+xnK4u487DZzB5IxAX/4e+bqGl+Pvn9vOdslnOtjHMy7Xsr6pyLk3XZtHT3+9Hl6Z9HwTRHHe+sF8nhz8+rXnX8uyGqfNrt4+nz0xYsg+Cxf9Wr4j8P4c9d1PvB/TUaZrfffjTtCwvRxz+prPy05TmP4pYNz7/99vjPWNT3XfC7Pe33Ju13IOoYhMs1qVwWFg63vbJy/JxOKoOo8zbp+iSY1vB5klQOc3Pp3zlp30dx518ZU6NBsArUFQHrBMh6cV9UmUHNqKWlPemCPe5GQNEa1kkux1EbZRPScSp6fvoX5FnP0aRySivDSS/jk+Ikfr/U5dzMUvZZf6fLuKFZd2llUbTVR9xxT7oRVIfzB8DwCFgnQJU/2mU00RuXLGnP25eqzP66k1KO4zDKJqTjMsz5mdSnL285paVjksv4pDipx6gO+c6ahiy/02V0GZkERW+SFS2XpO4v4z5/AJSDgHUCVPmjXZc72EWUdYHgN78c5u72JJfjuIyyCek4FD0/kx4q02wWK6cszVknsYxPipP8/TLuczNr2Zd5A3XSj2uRm2TD/jZnbfbNdxswmfIErM5bvjjn3KakFyXtSbphZsuh952kc5LukrQr6b8zs88kbXN5edk2NjaGStckGAyks2el7W1pcdEb6zXrWKFJGg3vpyHMOW9IjjrLmna/7La2ordTRl4nuRxRjbznZ/Cz/e53cz7hEN8v45On7NN+p+O2lWXbkyatLILvx5XJNJQDgHI4554Kx42xy5YUsC6b2dWY9++S9H55AevbJJ0zs7clbfOkBKxVWVqKDuQ6HW981TrLm/Yq8zrJ5YhqDHNOcD4hiPNhfMos+7htlbHtScb5DSBNnoC1UXViJP2gpI8d1P7+kaRvcM69fgT7PbHW1qRW6+i8VsubX3d5015lXie5HFGNYc4JzicEcT6MT5llH7WtsJN4XDm/AZQqa9vhuEnSJUmfkfSUpNWI9z8l6c7A/0/Iq5E90X1YqzbJfTzypr3KvE5yOaIaw5wTnE8I4nwYnzLL/iQ8JbgIzm8ASTTiPqy3mNkzzrnXSXpc0vvN7PcD7/+GpH9pZhcP/n9C0v9sZk+FtrMqaVWSFhcX79jK0sYGAAAAADBRRtok2MyeOXh9VtInJL01tMiOpDcG/r9V0jMR21k3s2UzWz516tSwyQIAAAAATLihAlbn3Lxz7jX+35LeKekLocU+KemfOc93SPobM/vyMPsFAAAAAEy/mf+/vXsNtays4zj+/emoZF5xTMhMRRstxFsTRpRYVlqRF5pyLDNGC7wggRFGEULSCxMyyGQStSxyFM1bZZOR6Ug5xUiOd3PQtCHC1FS0Czr+e7HWqdO4Z5yzz95rr6PfDyxYe93285z9Y5/1X5e9Zrn+LsC1zZNrmAdcXlXLk5wCUFVLgRtpfiF4Dc1jbZbM8j0lSZIkSa8BsypYq+ph4IAB05dOGy/g9Nm8jyRJkiTptaeLx9pIkiRJkjRjFqySJEmSpF6yYJUkSZIk9ZIFqyRJkiSplyxYJUmSJEm9ZMEqSZIkSeolC1ZJkiRJUi9ZsEqSJEmSesmCVZIkSZLUS6mqSbfhZZL8DXh00u2YhfnAE5NuhHrHXGgQc6ENMRsaxFxoEHOhQfqci92raudNWbCXBetcl2RVVS2cdDvUL+ZCg5gLbYjZ0CDmQoOYCw3yasmFlwRLkiRJknrJglWSJEmS1EsWrONx0aQboF4yFxrEXGhDzIYGMRcaxFxokFdFLryHVZIkSZLUS55hlSRJkiT1kgXrAEkuTfJ4kns2MP/oJHcluTPJqiTvnjbv3CT3tMNx06b/KMmD7fRLk2yx3jbfkWRdkkXj65lmo+tcJDms3da9SW4db+80rC5zkWT7JD9JsrrNxZLx91DDGFMuLmk/+7uSXJ1km3b6VkmuTLImye+S7DHu/ml4HWfjzCT3tdN/lWT38fdQw+gyF9PmL0pSSeb8r8i+WnWdiySfaL8z7k1y+Xh7NwNV5bDeABwKHAzcs4H52/C/y6n3Bx5oxz8C/BKYB7weWAVs1877MJB2WAacOm17mwM3AzcCiybdf4fJ5wLYAbgPeHP7+g2T7r9DL3LxZeDcdnxn4Clgy0n/DRw6y8V209b/JvCldvw0YGk7vhi4ctL9d+hNNt4LbN2On2o2+jt0mYv29bbACmAlsHDS/XeYfC6AtwB/AHZsX/dm39MzrANU1QqaHcENzX+u2k+SJgRT428Dbq2qF6vqeWA1cGS7zo3VAn4PvGnaJs8Afgw8PtqeaJQ6zsUngWuq6rF2ObPRUx3nooBtk4Tmn9RTwIuj7pNmb0y5eBag/fxfN22do4HL2vGrgcPbZdRDXWajqn5dVf9o11/J/+97qEc6/s4AOAf4BvCvUfZDo9VxLj4HfKeq/t4u15t9TwvWISU5NskDwM+Ak9rJq4EPJdk6yXyaI5u7rbfeFsCngeXt612BY4GlXbVd4zOqXAALgB2T3JLkjiQndtMDjcMIc3EB8FbgL8DdwOer6qUOuqAxGCYXSb4H/BXYF/h2O3lX4M8AVfUi8AywUyed0FiMMBvTnQz8fKwN11iNKhdJDgJ2q6qfdtl+jccIvy8WAAuS/CbJyiRHdtaJV2DBOqSquraq9gWOoTlKRVXdRHNZ729pLuO7nZef/bgQWFFVt7WvvwWcVVXrOmm4xmqEuZgHvJ3mko4jgK8mWTD+HmgcRpiLI4A7gTcCBwIXJNlu/D3QOAyTi6paQvP53w9M3ZM06GyqjwCYw0aYDQCSnAAsBM7rov0aj1HkIslmwPnAF7ptvcZlhN8X82guCz4MOB64OMkO3fRi4yxYZ6k9Vb9Xe/SCqvp6VR1YVR+g2Yl4aGrZJGfT3Hd25rRNLASuSPInYBFwYZJjumq/xmMEuVgLLK+q56vqCZr7TA7orAMaixHkYgnNpeJVVWuAR2iOjmoOm0ku2vnrgCuBj7WT1tIeOU8yD9iejVxCprljBNkgyfuBrwBHVdW/O2u8xmaWudgW2A+4pd33fCdwgz+8NPeN6H/J9VX1QlU9AjxIU8BOnAXrEJLsPXV/UJKDgS2BJ5NsnmSndvr+NDc/39S+/izN2ZHjp1/CV1V7VtUeVbUHzb1Hp1XVdZ12SCMxylwA1wPvSTIvydbAITRHwTTHjDgXjwGHt8vsAuwDPNxVXzQ6M81FGnu30wN8FHig3dwNwGfa8UXAzdPuadIcM8pstJd+fpemWO3N/WiauVHloqqeqar50/Y9V9LkY9UEuqVZGvH/kutoLh2mLXoX0JN9jHmTbkAfJVlGczp8fpK1wNnAFgBVtZTmSMSJSV4A/gkcV1WV5n6z29rcPAuc0N5PBM09qo8Ct7fzr6mqr3XXK81Wl7moqvuTLAfuAl4CLq6qgT9prsnq+PviHOD7Se6mOVp6VnsGXj0z6ly0l/FdluYS8NDcn3Rq+3aXAD9MsobmzOrijrqpIXScjfNofqDtqna9x6rqqG56qpnoOBeaIzrOxS+ADya5D1gHfLGqnuyoqxs19TPIkiRJkiT1ipcES5IkSZJ6yYJVkiRJktRLFqySJEmSpF6yYJUkSZIk9ZIFqyRJkiRpkyS5NMnjSV7xCRZJzk9yZzv8McnTM34/fyVYkiRJkrQpkhwKPAf8oKr2m8F6ZwAHVdVJM3k/z7BKkiRJkjZJVa2gee73fyXZK8nyJHckuS3JvgNWPR5YNtP3mzdkOyVJkiRJArgIOKWqHkpyCHAh8L6pmUl2B/YEbp7phi1YJUmSJElDSbIN8C7gqiRTk7dab7HFwNVVtW6m27dglSRJkiQNazPg6ao6cCPLLAZOH3bjkiRJkiTNWFU9CzyS5OMAaRwwNT/JPsCOwO3DbN+CVZIkSZK0SZIsoyk+90myNsnJwKeAk5OsBu4Fjp62yvHAFTXk42l8rI0kSZIkqZc8wypJkiRJ6iULVkmSJElSL1mwSpIkSZJ6yYJVkiRJktRLFqySJEmSpF6yYJUkSZIk9ZIFqyRJkiSplyxYJUmSJEm99B/dQD6TWMx0xwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_anomaly_lstm(df):\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    a = df.loc[df['anomaly_lstm'] == 1, ['time_epoch', 'value']]\n",
    "\n",
    "    ax.scatter(df['time_epoch'], df['value'], color='blue')\n",
    "    ax.scatter(a['time_epoch'], a['value'], color='red')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_anomaly_lstm(df1)\n",
    "plot_anomaly_lstm(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ubK2N7K7enswqDAAAAPNOcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADo2m6Da1VdU1WPVtV3JpS9pqpuqaotg+tlg/KqqvVVtbWqNlXViROWWTeov6Wq1s3PywEAAGCxmcmI6+eTnLFL2UeSfL21dmySrw/uJ8mZSY4dXC5K8plkPOgmuTzJKUlOTnL582EXAAAAprPb4Npa+0aSx3cpPifJhsHtDUnOnVD+hTbujiSHVtXrk7wjyS2ttcdba08kuSUvDcMAAADwErM9x/Xw1tojSTK4ft2g/IgkD02oNzYom6r8JarqoqoararRxx57bJbNAwAAYLGY68mZapKyNk35Swtbu6q1NtJaG1m+fPmcNg4AAIC9z2yD6/bBIcAZXD86KB9LcuSEeiuSPDxNOQAAAExrtsH1xiTPzwy8LskNE8rPH8wuvCbJU4NDib+W5PSqWjaYlOn0QRkAAABMa//dVaiqLydZm+SwqhrL+OzAn0hyfVV9MMkPkvzcoPpNSd6ZZGuSZ5J8IElaa49X1W8m2Tio97HW2q4TPgEAAMBLVGuTnmrahZGRkTY6OjrsZgxdTXaG8Dzo+KMAAAAsQlV1Z2ttZHf15npyJgAAAJhTgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DXBFQAAgK4JrgAAAHRNcAUAAKBrgisAAABdE1wBAADo2h4F16raVlV3V9VdVTU6KHtNVd1SVVsG18sG5VVV66tqa1VtqqoT5+IFAAAAsLjNxYjr21prJ7TWRgb3P5Lk6621Y5N8fXA/Sc5McuzgclGSz8zBugEAAFjk5uNQ4XOSbBjc3pDk3AnlX2jj7khyaFW9fh7WDwAAwCKyp8G1Jfnjqrqzqi4alB3eWnskSQbXrxuUH5HkoQnLjg3KAAAAYEr77+Hyp7bWHq6q1yW5parum6ZuTVLWXlJpPABflCQ/9mM/tofNAwAAYG+3RyOurbWHB9ePJvlqkpOTbH/+EODB9aOD6mNJjpyw+IokD0/ynFe11kZaayPLly/fk+YBAACwCMw6uFbV0qp69fO3k5ye5DtJbkyyblBtXZIbBrdvTHL+YHbhNUmeev6QYgAAAJjKnhwqfHiSr1bV88/zpdbaH1XVxiTXV9UHk/wgyc8N6t+U5J1JtiZ5JskH9mDdAAAA7CNmHVxba99Pcvwk5TuSnDZJeUtyyWzXBwAAwL5pPn4OBwAAAOaM4AoAAEDXBFcAAAC6JrgCAADQNcEVAACArgmuAAAAdE1wBQAAoGuCKwAAAF0TXAEAAOia4AoAAEDXBFcAAAC6JrgCAADQNcEVAACArgmuAAAAdE1wBQAAoGv7D7sB9KNq/tfR2vyvAwAAWFyMuAIAANA1wRUAAICuCa4AAAB0TXAFAACga4IrAAAAXRNcAQAA6JrgCgAAQNcEVwAAALq2/7AbAAB7rGr+19Ha/K8DAJiUEVcAAAC6JrgCAADQNYcKA7DXq8z/YbwOFAaA4THiCgAAQNeMuALATCzEBFCJSaAAYBJGXAEAAOia4AoAAEDXBFcAAAC65hzXPbRQpzwBMFwLMXNxYvZiAJiMEVcAAAC6JrgCAADQNcEVAACArgmuAAAAdE1wBQAAoGuCKwAAAF0TXAEAAOia4AoAAEDXBFcAAAC6JrgCAADQNcEVAACArgmuAAAAdE1wBQAAoGuCKwAAAF0TXAEAAOja/sNuAACLW9WwW7CXWYg3rLX5XwcAzCEjrgAAAHTNiCsAdKQy/6OhxlsB2NsYcQUAAKBrgisAAABdE1wBAADomuAKAABA1wRXAAAAuia4AgAA0DU/h8OCqpr/dTS/8wAAAIuKEVcAAAC6JrgCAADQNcEVAACArgmuAAAAdE1wBQAAoGuCKwAAAF0TXAEAAOia33EFgH3NQvyoduKHtQGYMws+4lpVZ1TV/VW1tao+stDrB2Bc1cJc6E+lLcgFAObKgo64VtV+SX43yc8kGUuysapubK3ds5DtAOidwAcA8CMLPeJ6cpKtrbXvt9b+Mcl1Sc5Z4DYAAACwF1noc1yPSPLQhPtjSU5Z4DawyDl1i/lmNBRmZrFsK77vAYZvoYPrZLuwF+0OquqiJBcN7v6wqu6f91ZN77AkfzvkNvBSQ++XxfIH2Rwber8wKf3SJ/3Sp5f0i+/7Lthe+qRf+rM39smPz6TSQgfXsSRHTri/IsnDEyu01q5KctVCNmo6VTXaWhsZdjt4Mf3SJ/3SJ/3SJ/3SJ/3SJ/3SJ/3Sn8XcJwt9juvGJMdW1dFV9Yok5yW5cYHbAAAAwF5kQUdcW2s7q+rSJF9Lsl+Sa1prmxeyDQAAAOxdFvpQ4bTWbkpy00Kvdw90c9gyL6Jf+qRf+qRf+qRf+qRf+qRf+qRf+rNo+6SaqfIAAADo2EKf4woAAAAvi+A6UFXbquruqrqrqkYnebyqan1Vba2qTVV14jDauS+pqjcO+uP5y9NV9Su71FlbVU9NqPNfh9XexayqrqmqR6vqOxPKXlNVt1TVlsH1simWXTeos6Wq1i1cqxe/KfrlU1V13+B76qtVdegUy077ncfsTdEvv15VfzPhu+qdUyx7RlXdP9jXfGThWr34TdEvvz+hT7ZV1V1TLGt7mQdVdWRV/WlV3VtVm6vqlwfl9i9DNE2/2L8M0TT9ss/sXxwqPFBV25KMtNYm/d2jwYfgPyZ5Z5JTkvy31topC9fCfVtV7Zfkb5Kc0lp7cEL52iT/qbV21rDati+oqrcm+WGSL7TW/tWg7LeTPN5a+8TgC3BZa+3XdlnuNUlGk4xk/Deb70xyUmvtiQV9AYvUFP1yepJbB5PhfTJJdu2XQb1tmeY7j9mbol9+PckPW2tXTLPcfkm+m+RnMv7zcRuTvLe1ds+8N3ofMFm/7PL4p5M81Vr72CSPbYvtZc5V1euTvL619q2qenXG9xHnJrkg9i9DM02/rIj9y9BM0y/vyT6yfzHiOnPnZHxn11prdyQ5dPABYmGcluR7E0MrC6e19o0kj+9SfE6SDYPbGzL+5bmrdyS5pbX2+OCPiVuSnDFvDd3HTNYvrbU/bq3tHNy9I+N/aLCAptheZuLkJFtba99vrf1jkusyvp0xB6brl6qqjP/x9+UFbdQ+rrX2SGvtW4Pbf5fk3iRHxP5lqKbqF/uX4Zpme5mJRbF/EVx/pCX546q6s6oumuTxI5I8NOH+WGb+YWHPnZep/6D411X17aq6uapWLmSj9nGHt9YeSca/TJO8bpI6tpvhujDJzVM8trvvPObepYND7K6Z4tBH28vw/Jsk21trW6Z43PYyz6rqqCSrk/xV7F+6sUu/TGT/MkST9Ms+sX8RXH/k1NbaiUnOTHLJ4JCiiWqSZRxnvQCq6hVJ3pXkf0/y8LeS/Hhr7fgk/z3J/1nItrFbtpshqaqPJtmZ5ItTVNnddx5z6zNJ3pDkhCSPJPn0JHVsL8Pz3kw/2mp7mUdV9aokX0nyK621p2e62CRltpc5NFW/2L8M1yT9ss/sXwTXgdbaw4PrR5N8NeND6hONJTlywv0VSR5emNbt885M8q3W2vZdH2itPd1a++Hg9k1JDqiqwxa6gfuo7c8fLj+4fnSSOrabIRhMUnJWkve1KSYymMF3HnOotba9tfZPrbV/TnJ1Jn+/bS9DUFX7J/l3SX5/qjq2l/lTVQdk/I/wL7bW/mBQbP8yZFP0i/3LkE3WL/vS/kVwTVJVSwcnOaeqliY5Pcl3dql2Y5Lza9yajE/g8MgCN3VfNeV/wqvqXwzOTUpVnZzxz/SOBWzbvuzGJM/P4rguyQ2T1PlaktOratng0JXTB2XMk6o6I8mvJXlXa+2ZKerM5DuPObTLnAg/m8nf741Jjq2qowdHmpyX8e2M+fVvk9zXWhub7EHby/wZ7L8/l+Te1trvTHjI/mWIpuoX+5fhmqZf9pn9i+A67vAk36yqbyf56yT/t7X2R1X1oar60KDOTUm+n2Rrxv+b8R+G09R9S1UdnPEZ0Cb+t29iv7w7yXcGfbc+yXlT/QeQ2auqLye5Pckbq2qsqj6Y5BNJfqaqtmS8jz4xqDtSVf8rSVprjyf5zYx/YW5M8rFBGXNgin75H0leneSWGp8W/7ODuv+yqm4aLDrpd94QXsKiNEW//HaN/zzEpiRvS3LZoO4L/TKY9OTSjP/xfW+S61trm4fyIhahKfolmWQOBdvLgjk1yfuTvL1e/FMe9i/DNVW/2L8M11T9ss/sX/wcDgAAAF0z4goAAEDXBFcAAAC6JrgCAADQNcEVAACArgmuAAAAdE1wBQAAoGuCKwAAAF0TXAEAAOja/werm5Xcibi1XwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_hist_lstm(df):\n",
    "    a = df.loc[df['anomaly_lstm'] == 0, 'value']\n",
    "    b = df.loc[df['anomaly_lstm'] == 1, 'value']\n",
    "\n",
    "    fig, axs = plt.subplots()\n",
    "    axs.hist([a, b], bins=32, stacked=True, color=['blue', 'red'], label=['normal', 'anomaly'])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_hist_lstm(df1)\n",
    "plot_hist_lstm(df2)"
   ]
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