{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "from pprint import pprint\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [11, 5]\n",
    "\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "PROJECT_DIR = '..'\n",
    "if PROJECT_DIR not in sys.path:\n",
    "    sys.path.insert(0, PROJECT_DIR)\n",
    "\n",
    "from tadkit.base import TADLearner\n",
    "from tadkit.catalog.formalizers import PandasFormalizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthetic data simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tadkit.utils.synthetic_ornstein_uhlenbeck import synthetise_ornstein_uhlenbeck_data\n",
    "X, y = synthetise_ornstein_uhlenbeck_data(n_rows=1000, n_cols_x=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat((X, y), axis=1).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat([((X ** 2).sum(axis=1) ** .5), y], axis=1).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Formalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "formalizer = PandasFormalizer(\n",
    "        data_df=X,\n",
    "        dataframe_type=\"synchronous\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pandas', 'fixed_time_step']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formalizer.available_properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'resampling': {'default': False,\n",
      "                'description': 'Resampling of the target query',\n",
      "                'family': 'bool'},\n",
      " 'resampling_resolution': {'default': 120,\n",
      "                           'description': 'If resampling, resampling '\n",
      "                                          'resolution in seconds.',\n",
      "                           'family': 'time',\n",
      "                           'start': 60,\n",
      "                           'stop': 3600},\n",
      " 'target_period': {'default': (Timestamp('2021-01-01 00:00:00'),\n",
      "                               Timestamp('2021-02-11 15:00:00')),\n",
      "                   'description': 'Time period for your query.',\n",
      "                   'family': 'time_interval',\n",
      "                   'start': Timestamp('2021-01-01 00:00:00'),\n",
      "                   'stop': Timestamp('2021-02-11 15:00:00')},\n",
      " 'target_space': {'default': Index(['X0', 'X1', 'X2', 'X3', 'X4'], dtype='object'),\n",
      "                  'description': 'List of sensors used for your query.',\n",
      "                  'family': 'space',\n",
      "                  'set': Index(['X0', 'X1', 'X2', 'X3', 'X4'], dtype='object')}}\n"
     ]
    }
   ],
   "source": [
    "pprint(formalizer.query_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "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>X0</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-01 00:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 00:01:00</th>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>-0.000373</td>\n",
       "      <td>-0.000491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 00:02:00</th>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000824</td>\n",
       "      <td>0.000898</td>\n",
       "      <td>-0.000745</td>\n",
       "      <td>-0.000982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 00:03:00</th>\n",
       "      <td>0.000263</td>\n",
       "      <td>0.001236</td>\n",
       "      <td>0.001348</td>\n",
       "      <td>-0.001118</td>\n",
       "      <td>-0.001473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 00:04:00</th>\n",
       "      <td>0.000350</td>\n",
       "      <td>0.001649</td>\n",
       "      <td>0.001797</td>\n",
       "      <td>-0.001491</td>\n",
       "      <td>-0.001964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-11 14:56:00</th>\n",
       "      <td>-0.584958</td>\n",
       "      <td>0.942160</td>\n",
       "      <td>0.571343</td>\n",
       "      <td>-0.897471</td>\n",
       "      <td>0.755402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-11 14:57:00</th>\n",
       "      <td>-0.585516</td>\n",
       "      <td>0.941845</td>\n",
       "      <td>0.571196</td>\n",
       "      <td>-0.897901</td>\n",
       "      <td>0.755519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-11 14:58:00</th>\n",
       "      <td>-0.586074</td>\n",
       "      <td>0.941530</td>\n",
       "      <td>0.571048</td>\n",
       "      <td>-0.898330</td>\n",
       "      <td>0.755637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-11 14:59:00</th>\n",
       "      <td>-0.586633</td>\n",
       "      <td>0.941214</td>\n",
       "      <td>0.570901</td>\n",
       "      <td>-0.898760</td>\n",
       "      <td>0.755755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-11 15:00:00</th>\n",
       "      <td>-0.587191</td>\n",
       "      <td>0.940899</td>\n",
       "      <td>0.570754</td>\n",
       "      <td>-0.899189</td>\n",
       "      <td>0.755873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>59941 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           X0        X1        X2        X3        X4\n",
       "2021-01-01 00:00:00  0.000000  0.000000  0.000000  0.000000  0.000000\n",
       "2021-01-01 00:01:00  0.000088  0.000412  0.000449 -0.000373 -0.000491\n",
       "2021-01-01 00:02:00  0.000175  0.000824  0.000898 -0.000745 -0.000982\n",
       "2021-01-01 00:03:00  0.000263  0.001236  0.001348 -0.001118 -0.001473\n",
       "2021-01-01 00:04:00  0.000350  0.001649  0.001797 -0.001491 -0.001964\n",
       "...                       ...       ...       ...       ...       ...\n",
       "2021-02-11 14:56:00 -0.584958  0.942160  0.571343 -0.897471  0.755402\n",
       "2021-02-11 14:57:00 -0.585516  0.941845  0.571196 -0.897901  0.755519\n",
       "2021-02-11 14:58:00 -0.586074  0.941530  0.571048 -0.898330  0.755637\n",
       "2021-02-11 14:59:00 -0.586633  0.941214  0.570901 -0.898760  0.755755\n",
       "2021-02-11 15:00:00 -0.587191  0.940899  0.570754 -0.899189  0.755873\n",
       "\n",
       "[59941 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formalizer.formalize(data=X, columns_0=['X1', 'X2'],\n",
    "                     time_interval=['2021-01-01 02:00:00', '2021-02-02'],\n",
    "                     target_space=X.columns,\n",
    "                     resampling=True,\n",
    "                     resampling_resolution=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print(isinstance(IsolationForest, TADLearner))\n",
    "from tadkit.utils.tadlearner_factory import tadlearner_factory\n",
    "IsolationForestLearner = tadlearner_factory(IsolationForest, [], {})\n",
    "print(isinstance(IsolationForestLearner, TADLearner))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScalerIsolationForest()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScalerIsolationForest</div></div><div><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>StandardScalerIsolationForest()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "StandardScalerIsolationForest()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jiAAAAAAAhwAxiXUW+ro9kROBWkgAAGQXl0YAIUSACAAAAABwCBCTqGJVcr0JeYdRWQAAEtDcABBCBIgAAAAAAIcAEVnBHEQgv2n5g3PPPTdjj7906VI7/PDD3eK+VapUsQULFmTsuVBUtbRhw4axn6+66irr3LlzTrcJAJAdLHOBrCDFFMhvL7zwgtWoUSNjj//II4/YBx98YGPGjLHGjRtbgwYNLBdGjx5te++9t82fPz8ugMp3//rXv+yss87K9WYAALKAABEAsME22WSTjD7+Dz/8YDvssIO1b9++0o+xZs0aN/pYtSrJMxVVv3599wUAyH9cJZEVjCAChZVievfdd9s222xjtWvXtmbNmtkRRxwR+92KFSvs7LPPtqZNm7rf9+rVyz799NNSH/vWW2+1999/3wV4+lk0ite/f39r1KiR1a1b1w444AD7/vvvS6RJvvLKK9auXTurVauWTZ8+3T2/RsRatWpl9erVs+7du7uRQe/nn3+2gw8+2D2ufr/jjjva66+/btOmTXOjh6LfaVuOP/74pNv8+++/29FHH+2eQ9vWoUMHe/LJJ0u8Lu2HCy+80AXYzZs3d6mcQdreQw45xAVnSq/929/+ZrNnzy6R+vnQQw9ZmzZt3P1OP/10FwzffPPN7jG1n6+//vq4xx06dKjbJr2+1q1bu79ZvHhxyvcgWYrpAw884IJ2vYfbb7+9e8+9lStX2plnnmktWrRwv998881tyJAhKR8fABAejCAmQTCTAexSoHI0f3fV0tw8d426ZlUqXmbxs88+c4HP//73P9ttt93sjz/+cOmhngKi559/3qWNKnBQINO3b1+bOnVq0pFIpa9efPHFNmnSJPd9zZo13e0KzhQQKgBU8HTRRRfZgQceaN98800s3VVzF2+66SYXzGy66aYuWFLgovs89dRT1rJlS3vxxRdt//33t4kTJ7qg9owzznABjgJSBVC6rwIvBVLabs2FnDJlinvOOnXqJN0Hy5cvty5durht0v1ee+01O+6442yrrbaybt26xe6nfTBo0CD75JNPbOzYse419ezZ0/bbbz9bu3ZtLDh87733bPXq1W7bjjrqqLiAVqOrb7zxho0YMcJ9r2D8xx9/tG233db9ndJyTzzxROvdu7cLhkWjqP/5z39siy22cPdVgKj3JRjklebxxx+3K6+80u68807baaed7PPPP7eBAwe6/TVgwAD32HpfnnnmGRe4zpgxw30hAddGACFEgAgAYabg8IaWuXnuS381q1mvwn+mUS8FCn/5y19so402ckGggghZsmSJ3XPPPW50TyN+cv/999tbb71lDz74oF1wwQUlHk9Bo0bhFBhqREx8YPjRRx+5INQHLQriXnrpJTvyyCPdbatWrXJBT6dOnWLbNnz4cPevgkPRaKKCK91+ww03uN8pCNQIm2y55ZZx2yIKNEubg6iRQz2up/l7b775pguYggFix44dbfDgwe57BacKuEaNGuUCRP2roPWnn35yr0seffRRN6KpEddddtnF3aZAUiOI2tcaKdUopwJYjXoqENxuu+1ckPzuu+/GAsTgaG/btm3tuuuus1NPPbXcAaK2WaO6hx12mPtZgaYC6f/+978uQNQ+1OvR6LBGWvUZAABEAwFiEqyDmH6MygKFQ8GNAgIFVhqZ09ehhx7qgjyNcClo0yiZp9E+BU2TJ08u93PovtWrV48FPKIRQgVDwcdRUKkgzFPApfRLja4FKe1Ufy8a/TzttNNs5MiRbtRNwWLwMcpDz6FgUwHhzJkz3YiknkP7ICjxcZWSOWfOnNhrVGDog0NRAKjAVL/zAaICPAWHnlJ6q1WrFjfXUrf5x5W3337bpXx+++23tmjRIjc6qVFPjbgmbmMiBfl6H0866SQ3aujpMXzxII2E6nOg90PvvzoL+vTpU6F9WBBobgAIIQJEAAgzpXlqJC9Xz10JClYmTJjg0iAVZCkVUXPYSptnmClKAdUIlqd5dgqexo8f7/4N8kVYTj75ZJfyqrRQbb8CKY2WVaSK5y233GK33367DRs2LDbXT6N2ChSDEiu/als1IlgRyR6jtMfVXEoFbAqCNTdRo6IffvihC/i0fWUFiH6uokZ+gwG6+H268847u5FPpb4qGNXcSQXbzz33XIVeGwAg+wgQkRWMIAKVpOCmEmmeuabRPQUE+lI6oka93nnnHRd4aVRPqaE+7VAjigoeK7KOooqjaMRKc/d8iqkKwyi1UqNsqSjVVaN7Gk3bfffdU95Po3ZKudTXJZdc4oIhBYh+/qMeozR6fZo/+I9//MP9rODsu+++K3Xbkr1GP3fPjyIqjVNrQFbkcRIpONb2KOj1o4wa6SwvjUYqPVdzF4899tiU99PcS82X1JfmRWokUfNRM13xFgCwYQgQkRXrVGgDQEF49dVXXfCwxx57uGqfmgungETphhpJ08iV5hoqUFABExWpUWqjRrDKS/PbFIApxVHz3jRqqUI2mvun21NRaqmCGlU/VYCkgHHu3Lluvp/SPQ866CAXqGp+pO6rSqmau6dgTRTUajROr1EFcTRCmWz5B22fRstUIEb7QFVDVX20IoGdgmuNPmp7NRKpgFjFZPbcc0/r2rWrVdbWW2/tgvI77rjDVWtVMHvvvfdW6DGuvvpql4qrlFIFfkqfVXEi7S8V3dHrVbqs9q+C0GeffdbNHy2ktSMBIKpY5gIAkFYKAlRtdJ999nGBlYIPLfGg4ipy4403unl9quqpVERVL1UBFwVSFaGiMqoUqnTJHj16uI4oBaOJ6ZXJ/k4B4vnnn++C1n79+rkRTAWrfnRQ1UK17Qp+FCj64i0KQBUcKRjVSJoqoiZz+eWXu9emEVMtZ6HgSM9TEQpEX375ZbdfFGwrYNS8zqeffto2hAr2KIBT4RqtK6niPhVdgkJpuKoMq32pIFZBqwoPqViNKGBX4K9AVnMlldbqi+YAAMKtyroIDO1oAr16KRcuXOhSVjLtyo+utBenvui+nzhgYsafrxAsW73Muj1eVLnvfwf8zzo3jV9PC8ilDo8UVavs3ry7PdD3gYw9/kntT7Jzu5xb4vZzdj7HTu5wsvtehUI0d0sNba0fByBzcnW8+WO/Y5OO9viBj2fteVFk6PihNnzS8Mi1816e+rJd/tHlkdtuRC+eoisvCebLpV8E+iEAAMguLo0AQogAEQAAAADgECAmwTqIAAAg42huAAghAkQAAAAAgEOAiKxgXicAAAAQfgSIyAqK1AAAAADhR4AIAAAAAHAIEJEVpJgCAAAA4UeAmATBTPqxTwEASMClEUAIESACAAAAABwCxCRYBzH9KFIDAEACmhsAQogAEQAAAADgECACADbYo48+aptuuqmtWLEi7vZ+/frZcccdl7PtAgAAFVO9gvcHKoUUU6Dyx86y1cty8tx1qtexKlXKlwN35JFH2tlnn22vvPKK+17mzJljr732mo0cOTLDWwoAhXdtKO/5GagoAkQACDEFh92f6J6T5/7kmE+sbo265bpvnTp17JhjjrHhw4fHAsTHHnvM2rRpY3vttVeGtxQAAKQLKabICpa5APLfwIED3WjhzJkz3c8PP/ywHX/88fRyA0Ca0a5CJjGCmAQHXfqxT4HKp3lqJC9Xz10RO+20k3Xq1MnNR+zTp499/fXXLsUUQApcGgGEEAEiAISYRt/Km+YZBieffLINGzbMjSL27t3bWrdunetNAoD8rO1AcgYyhBTTJFgHMf0oUgMUBs1D/OWXX+z++++3E088MdebA4QbzQ1UEplZyCQCRABA2jRo0MAOP/xwq1+/vlviAgAARAsBIrLe00WvF5DflF567LHHWq1atXK9KQCQl2hLIZOYg4isI90UyE/z58+30aNHu6+7774715sDAAAqgQARAJAWqmKqIPGmm26y7bbbLtebAwD5i752ZBABIrI+akhaBJCfpk2blutNAICCQFsKmcQcRAAAAACAQ4CI7BepYQ4iAABApTGCiEwiQERWkGIKVAwdKUDmcZwhqvjsIpMIEAEgRGrUqOH+Xbp0aa43Bch7/jjzxx0AgCI1yBJGDYHyqVatmjVs2NDmzJnjfq5bt65VqVIl15sF5N3oi4JDHWc63nTcAVFCuwqZRICIrCMtAihd8+bN3b8+SASQGQoO/fEGAChCgAgAIaMRwxYtWljTpk1t1apVud4cIC8prZSRQ0QVne3IJAJEZAVFaoCKU+OVBiwAAMgmitQAAAAAABwCRGR/HURGEAEAACqNthQyiQAR2Q8QyZsHAACoNNpSyCQCRAAAAACAQ4CIrKBIDRCP3l8AQGXRlkImESAmwUGX4X3K7gU4zwDgegggfwLEu+66y9q2bWu1a9e27t2727hx40q9/7Bhw2y77bazOnXqWOvWre28886z5cuXV3abAQAAgIJFJyNCFSA+/fTTNmjQIBs8eLBNmDDBOnXqZH379rU5c+Ykvf8TTzxhF198sbv/5MmT7cEHH3SPcemll1pYVbEqud6E/BM3gMhJDSDFFADNDVQW1xCEKkAcOnSoDRw40E444QRr166d3XvvvVa3bl176KGHkt5/zJgx1rNnTzvmmGPcqGOfPn3s6KOPLnPUEQAAAAAQ4gBx5cqVNn78eOvdu/f6B6ha1f08duzYpH+z2267ub/xAeGPP/5or7/+uh144IEpn2fFihW2aNGiuC9EG+sgAvE4DgAAQBhVr8id582bZ2vWrLFmzZrF3a6fv/3226R/o5FD/V2vXr3ccPjq1avt1FNPLTXFdMiQIXb11VdXZNMQcqyDCMQjQAQAVBZtKUS6iuno0aPthhtusLvvvtvNWXzhhRfstddes2uvvTbl31xyySW2cOHC2NeMGTMyvZkAAAAAUPAqNILYuHFjq1atms2ePTvudv3cvHnzpH9zxRVX2HHHHWcnn3yy+7lDhw62ZMkSO+WUU+yyyy5zKaqJatWq5b6QP1gHEUjAYQAAqCTaUgjNCGLNmjWtS5cuNmrUqNhta9eudT/36NEj6d8sXbq0RBCoIDPMw+McdOnHPgXicUwA4DQAIPIjiKIlLgYMGGBdu3a1bt26uTUONSKoqqbSv39/a9WqlZtHKAcffLCrfLrTTju5NROnTp3qRhV1uw8UASBfhbUjDAAQXXQyIlQB4lFHHWVz5861K6+80mbNmmWdO3e2ESNGxArXTJ8+PW7E8PLLL7cqVaq4f2fOnGlNmjRxweH1119vYcU6iOlHkRoUqlQXcS7uAGhu5J7aJGqnRg1tKYQqQJQzzzzTfaUqShP3BNWr2+DBg90XAAAAAKCAq5gCTqCji5ETFJJUvbz0/gJA7kWpTcKa0sgWAkRkBSmmKFSkmAJAeEWpTRKlbUW0ESACAAAAABwCRGQF6yCiUKUcQaQnGAByLqptEq4hyCQCxDw6WYQZefMAACTgcphzUWqTRGlbEW0EiACQSVzPAQBpRrCITCJATIJ1EDN8IuOchgJCkRoAKdHcyL11EZ2uQ4opMogAEQAAAADgECAiKyhSg0JFkRoACK8otUmo54BsIUBE1nFSAzgOACAMonQujtK2ItoIEAEggxgpBAAAUUKAiOynRdBgRgGhSA0AhFeU2iQUqUG2ECAmQcMt/ZiDCMTj4g6Ay2Hu0SYBSiJABIAMIhAEAKQbgS0yiQAxCdZBTD8qbwEAkIDmRs5FqROPbCxkCwEiAAAAAMAhQMyjnqUwi+vpYpeigLAOIgAgHSj4h2whQCwDQ/hpEhcfsk8BjgMAyL0onYujtK2INgJEAMggenkBAOlGsIhMIkAsA4279CAtAoWKdRABILyi1CaJ29bobDYiiAAxCSpuph+Vt4B4LHgMgMth7kWpTRKlbUW0ESACQAZxQQcApBvXFmQSAWIZ6yByAKYHo7IoVKlGBzkmALAOYu5F9fwb1e1GNBAgAgAAAAAcAsSy0EGTFhSpAVLjmACA3IjS+Ze568gWAsQyMISfHpzIgHgUbgIAVATXCmQLASIAZBCdIwCAdCNYRCYRIJaBAzD92KcoJOVZB5FjAgByI0qdeGSeIFsIEJNgvlz6sU+BeCx4DIBjP/eiFGhFaVsRbQSIAJBBXNABAGnHpQUZRICYBOsgph9pEShUrIMIICXWQcy5qJ5/o7rdiAYCRAAAAACAQ4BYBubLpQdzEFGoyjNSSE8wAORGlNokrIOIbCFARFZwIgPicaEHgNyLUgddlLYV0UaACABZwsUdAEIgD07FXE+QSQSIZeAATA8KcqBQpVrOgmMCAHIjqudcrhvIFgLEJJgvl1nsU4DzDID8GMmKuiidf6O0rYg2AkQAyCB6fAEgXPIh0MqH14DwIkBMgnUQ0491EFGoUhWj4eIOgHUQcyOqHXdR2lZEGwEiAAAAAMAhQCwDvTWF3VsHZOqzzzEBALmRD3PAuW4gkwgQyxDVE0fY5MPJGPkpZ5/HYEVTjgkAyIkoBVpcK5AtBIgAkCVRaogAQL7Kh0ArH14DwosAEVnBiQyFGrSlLFJDiikAoAK4biBbCBCTIB0y/dinCKtcfR650APg0M+9KLVJorStiDYCRADIIII/AAiXfDgv58NrQHgRICbBOogZLsjBPkWI5OrzGNcTzCEBFCbWQcy5KLVJorStiDYCRADIIFJJASBc8iJVMw9eAsKLALEMNOjSg0YywooiNQBQWKJ6/o3qdiN6CBCToKBK+rFPEVo5WwaRYwIoRKSXh0ukzr8R2lREGwEiAGQQPb4AEC6RCgrz+DUgvAgQk0iVBobKY58irDL+eVxX9sgBxwRQOOKOd4rU5ERUO+6iut2IHgLEJDjo0o99irDKWRVTLvRAQSLFNGQi9B5wrUC2ECACQAYRCAIA0o3rCTKJALEM5HinH/sUYZKrz2Oq6qYA8hsppuESpUCLawWyhQARADIoSo0PACgE+RBo5cNrQHgRICZBQZX0Y58irMKwDiKAwsGxn3tRTf2P0rYi2ggQk2B9svRjnyKscpZiGtEGCoANFDzcOfRzLkptkihtK6KNABEAMohAEADCJR/OxfnwGhBelQoQ77rrLmvbtq3Vrl3bunfvbuPGjSv1/gsWLLAzzjjDWrRoYbVq1bJtt93WXn/9dQsrGnTpR2odClWq8wlFaoDCRJGa3MuHNgnXDWRS9Yr+wdNPP22DBg2ye++91wWHw4YNs759+9qUKVOsadOmJe6/cuVK22+//dzvnnvuOWvVqpX9/PPP1rBhQwsrDrr0i+oJGPkvDJ/NMGwDgBwc7xz6ORel82+UthUFFiAOHTrUBg4caCeccIL7WYHia6+9Zg899JBdfPHFJe6v2//44w8bM2aM1ahRw92m0UcAKAjBtiCdTwCANCBYRGhSTDUaOH78eOvdu/f6B6ha1f08duzYpH/zyiuvWI8ePVyKabNmzax9+/Z2ww032Jo1a1I+z4oVK2zRokVxX9lEQZX0Y58irHL1eVyzZnXs+6WLF+ZkGwDk+JxDimnuRahJQvsJoQwQ582b5wI7BXpB+nnWrFlJ/+bHH390qaX6O807vOKKK+zWW2+16667LuXzDBkyxBo0aBD7at26dUU2EwBC3cv73YTRtvq+fWM//zl3epa3CgAKVz6MvhEsItJVTNeuXevmH953333WpUsXO+qoo+yyyy5zqampXHLJJbZw4cLY14wZMyyr4qYHcACmBfsUIZXVdRCLn2v+mEesRpX1I4hrl87P6DYACA+ugbkX1SI1FFFEKOcgNm7c2KpVq2azZ8+Ou10/N2/ePOnfqHKp5h7q77wddtjBjTgqZbVmzZol/kaVTvWVKxyA6cc+RVjl4vNYffkfcc9adQUppkChiBv54XKYc1EaiaP9hFCOICqY0yjgqFGj4kYI9bPmGSbTs2dPmzp1qruf991337nAMVlwCAD5Pv+21qoFcfepszq786wBoJARaAFpTjHVEhf333+/PfLIIzZ58mQ77bTTbMmSJbGqpv3793cpop5+ryqm55xzjgsMVfFURWpUtCasKKiSfuxThFWmP4/JRs/rrF4Y1zypZ0ts7H1nZ3Q7AIQQRWpyIqopplTFRmiXudAcwrlz59qVV17p0kQ7d+5sI0aMiBWumT59uqts6qnAzJtvvmnnnXeedezY0a2DqGDxoosusrCK7IkjxNinwHr11yyy5dXWtwx1RHSZ+bitWzvMqgTOnwDyD+sghkuU2iRR2lYUWIAoZ555pvtKZvTo0SVuU/rpxx9/XJmnAoD86Rwp/r7BukU2L2HooGaV1Tb/99nWqEmLrG8jABSSfAi08uE1ILzoqk6C3r3040SGsMp2ms6yJX9a7Sqr4o6I5VY0H3v+bJa7APId6yCGS5RSNaO0rYg2AkRU2rq1a+2TO0+wj+8e6L4HUHZHycLfi9aMXRU4/a6oUtv9u3hulpf0AQBEEh3vyCQCxDJwAKY297efrfu8F2zXOc/Yz1MmlHpfitQgX4/xNWvXVejxF8+fU/Sv1Y3dtqpaUYC4Yv7MDdoWAOFHuyL3otoOoS2FbCFATIKCKmX4c7bZ6hW2YPbPsZtmffJC6X8Tl7XLPkV4bMjnceHSVdZjyCgb9PQX5f6bZQuLAsQlVevHbltdHCCuXvhrpbcFQDQwjSVcotQmidK2ItoIEFEhv71+iy2/tb19/X+3x6XDNfrl7ZxuF5ALL30x0+b8ucJe+HxmuYvULP1tivt+ebV662+vUcf9W3VxUfopACBz8iHQyofXgPAiQEyCIfzURv+8zGrbSmv6xR22YvZ3sds3X/WjrV2zJuXfsU+RjxkDy1at/8yvXJ18Hm7wMVcumGkdvhla9Ld1mq+/U42idNOay4pGFwHkL4rU5F5U2yRx2xqdzUYEESAWYorpBpwMP9qor01b28yaVFlkO/74QOx2VWWcM/PHwt2niKxkC9mX17KV6wPE35esKPP+fyzX2kKr7Svbxlruc2rs9qq1ikYTN1s62Rb+MbdC2wAgwrgc5hxtEqAkAsQCM33CSPvt1l626PfZlfr72YvX2DNr9nLfb2xL4343d9qktGwjEBWzFy2PfT/3zxVlNj5+X1vP/rHyUnum/X+tQeP1I4gNmm5mv1Rpbk1svn37xIUZ3moAKGxRGjVMhcAWmUSAmOYRhVBbs9pqvDHIWiyeZFPuOsLWrF5V4Yf4dcFyG7d2u6S/83OrCmqfoqAbDb8tXB53bJT4+zWr4kYI6s8db5+u2862aL5J3N2qVa9hf+x5g/t+u3kjbdXKskcjAUQTKabhEqU2SZS2NZfyoQMg16pblCxZYlatWsafpuayVVZnRdF8oipLlppVX2JRp9GNIa9Ptql/DLQna15nu1T50q695Gyr33Ognbdf8oAvWTn/RfMW2OK1rWyFVbdaVYoCzKnVtrSt1/xoVWd+U/QeJVFt6fLYPq2+bEXK+wFZt2xJ7LNZc/mqCn0258/5w+qsLAoSz3voI7uuX3s7vMtmsd/PfOd+WzH+ZqvTspH7eYuVs23uql9su/odrOqSZbHnrbFspbXruo/9PnIj29QW2cS3n7UOex6a5hcKIBSWrz/n1Fq+muthDqgdEtfOqx2N90DXith2L13KZyeJ58f/Yv8eOcXu/cfO1ql10bUXxSrweamyLgJh9qJFi6xBgwa20KU1AgAAAADKa5GmtGiJroULbeONS4+oSDEFAAAAAEQwxfTXX83KiHjT4fx3z7cPfv3Afb/kxzNt7cqmNvGqPnbly1/bNk3rW+c2DW2HFhtb7RqZT3dNlyPvHWOTZqrvwKxGtSo2/IRdbOAj412Z/vP7bGsn776ly9muUiX5hIhPfvzdjh/+qfu+a9tG9r+Tusf9/pA7P7TvZi+26w9tb5e9WFSs5qRebe3Vr36zf+6xpTVo8rVdNfYqd/vJHU62UzqekuFXDJTPnKVz7C8v/sV936lJJ7u/z/3l+rsf5vxpf7njo7jbdPhMvKqvvf/dHLv6/76x2YtWWNXav1i9tve53+/WfB+7uOtga96gjn0x+ws75e2i4+DALQ60q3YrOj4ue3GivTBhpp2+11Z21r7bpPnVAsil0x+fYKO//9Hqb3Oz+7n9pu3tof0fyvVmFZxLP7jU3p5etH7z8L7DbcfGO1oU3PLpLfbsd8+674fuNdR6teqV600KnS7XvmVLiyuMf311X6talYm+MYsWmbVsafkXINarV/SVYSvqVLdltYoGV5fVrGVrrbb93w8L7YmvfzfTl/1sZ+y9lV3Qd3uLioVVa9qymrXd98t0ALVrbf88cKUNeeNbGz9vpa0bP9v++/6P9tQp3W3rphuV+PtZaxbE/v68fjuVeB96dNzcvnzvB3vxu/X3u3OcFv2uYpeM/MnuGFg7tk9X1amZlfcRKI91Vjf22VxZu0a5P5u/rl4a+6wHnfrit/bWN6oSXMWsZm2rWrOmVS1+/BoNalvzlo3d92vqJT8m2rRuYssm/W7fLVnHcQLkke9m/2mv/7jIqtSsbdWKj/0VtatznOfAyjo1YufftXXrROY90DUqittdUb8uWGZ/LFlpO7bcOOXARSoNmjS03+cWzbX7eUUV26Jxfu6jSillvfJEpJiWVSWqStH35z39Zdx9nhw3Y4OfZ+nK1fbFjAUZq7bkH1f/zpyvsDBep9YN3b9fzlhg178+2eYtXmE3vvFt0sfyJfz/0rGF7dym5KTfFg2KGsqfTvsjrxalRf4rb4VdFWl6ZMw018gLVjDdfZvGdvexO1uDOjXcz0XBYTmeN7g2aOD7rZrUd//+MGdxhV8LgOwa//N8+2V+/JJPqbz/XdEap+1brc+EWr56dca2DdGsDKprjdqHUdjWTL3+w+8ZY3+540M7+ZHPKvz3K1YVFfGRvf892g76zwf2+fT5sdv+78tf7aEPf0rb9uYrAsRyqFuzWtIFsvUh3hDnPf2F9bvrI3tt4m+Wbne9O9V2uHKETZg+3+YvXWVLAgt6ex1aNTCNvP8aKNU/ZfafNmvhcnvww59s8m9FKakypzhAbLpRyRETaVSvpvt3eeDABPLJfe//aINf+dr+9t+x7uffFhQdN60a1rEDO7Sw3bbaNHZfpaI32ahWpS7uWzYp6u38ad4SW7uB5xgUtj+Xr7I5f64/vyO9fv59iWvI9rrpXVu5uuxr3yc/FXWg7r1909hti1XFFFlX3o5q3e+CZ7+0U/83vlzvcTqc9th42+W6t+23hcvK3LZ8NH/pylgH7Khv59jqNRXb74tXxB9TX/+6yM59+gv3vQLvQc98Yde8+k2Z+7fQESCWcdBpZGzv7dafzC8+YHurU6Oam7v307zUPfzqrQgGWMm8+XXRSMPd7/5g6TTjj6V2y5tTXLCmXhL9HNS/x+bu33q1qts2CemkM/5YZrsOGWXXvvqNDXhonAuEZU7xguBNNy7Z6JVN6hYFiKksDRywhdADhmgq7YL73PiirIEFS1e5wG3WoqKLS/Pi0XMFiZ7m6d56ZCf3/cGdWlRo5LL1JnXdPGGdY37lApYR85esdB1h+e6QOz+ybtePcqlaSL9vfl1/jd/28jfcXP1UdM7wGTY7tVYdwSJr8rSRHyWltUnUwf7s+F9sxNez7L/vpbetlsrIb2a7Tv3/vvejFSKfsZZsveHyXMOXJASI8vPvS91Iv7L2Vq0per9VIwCpESCWQaMDnQInc+Uyt2tZlB7ii74k+3AfevcYO+D2D2zF6uT5vsGRgeUp7lNZT3+6Pv116pzFNmVWUUpc59YN7cEBXe3SA3eI/T742hJp1PChj35KGEFMHiA2rFuUXucdGVgLTi5/uahwDRBFOo5/CaRpb3np6/bi5zPd9y0b1IkFiM2KO1D0/R7bNrExF+9jx+1a1CFT3h7fGtWq2vbNi84xr32V/uyCQqf3QJ1g+tIIW75avmqN/TivaB7OmB/m5Xpz8tLs4o5T7453psYFjUHT/1jqOpdqVa9q27dYn2K6oZlIqJzydlQH04cfHjPNMi14jfhhbunTDPK1s13TnYJmlDOFW1asXmurUxxT6qAZP219qukfSwgQ8zJA/HHuYlu4LDMX9+BB12SjmtZps6K5etJ203rWPhYgamXGkibOXBD7/qtfiu5z04hvXcC4cOmquIBL/kxzism3xQGhTxn9cGpR42CXto1s3x2axVVf7dp2k1If697RP7je9rJSTDcpTjH1jt11c1cpdf3cYuYgIpzi5gKmuOBO+HmBu/AE+XRqP4JYrWoVe+mMnjb8+F1s922auNtaNqxj1atVKfPxE28/rniUf/hH02hAppk68Px7+UNxIYN8NCfQO66CD0i/4PQM0bX2wP98kHQkUSnjvpO5eqCq4pq1TMvItdLaJMGOwd+XrCwxGq+/ffWrX9N2jAXTI9XBX9q25mtbKjFA/OWP8u/b4Ohh3x2b2fbNN7Kju7VxP3/y4x/26c/rA8TfF5NZkVcBog6IE4aPs31ufc/+eueHGQkSg421+rWqW/tW60fZ2mxS13Zs2SCW15xMcGRx3E9/uB7Fe0b/4FJOP5haNEl92u9L4hosPnBMh6lz1geIOn+88uWv7vvg6/D+2qn0crd/rlhtT4ybHhvyT5Vi2ighxbR1ozouNXe7ZiUror5XPFEfiEqRmo9LSR3brFHRCKK0aFAnbn5Rqc9byoX+kM4trV7NajZr0fKkjQRUnh9Vy/ceZH12knUapqLr1Eufz8zbRmcmzCwOCgbuvkXc7fd/8FPKz53mGAfPM2vZ36GWWIAocVTvmc9m2JlPfG7HPfjJBo/4K7NMo8zB1MoXP/+lIEYNg+b9GR+43fb2dyWCxlSWrCjKyNNUsP8e19VGnLuH7deu6Jo8espcmxAIEEm9z7MAUTnD706ZG8spvuXN5FU306V+7epurt5b5+1hI8/bw+rUrGY7Flcgm/TrwqQX04mBkUU1LFXwJbFXV5Pbg8aW0gAtL41oPvzRTzbt96WxEcMglQtOpNHE2//e2aWIXv3XHa35xutHCHdqUzRyOvLrWbFAPFWKqfZLshHFYO9bcP/4OY1AFPgAUb2RQY3q1nBZBaWpzAW9VnWdZ4o6dL76ZX1GAjbcj4FRw3yegxIXIP5WeoCo69hpj493hRxSdXyiJD9qlFjZ+8ck9QmU9SSJJfeZg5gb5e0ISawA799Hefub2XbR8xNj2QiJxVEqEoR2vmaknfnkBFegJeji5yfGRp8ThTVYXLVmrSvC+PSn0yv0dyMmzbKu170dm74RDJRVIK48/Hugdru321aNrXaNqu6cGHyPCBDzLEBM7M35LJBPnDaBY65+raLAZ5tmG9m2xaNhKuxSs1pVlxqqoi5BKuoSLKernOfg0g/+gvLTvPjXMbx4rl9l6UN/xL1j7Kr/+yZ2W48tN42rxLpF46Ly+YkO6dzKvriyjw3Yra0dtUvr2O39Ordy/35ZnCarns+GZRSj8fy6NT7IjNupti5WzQ1IvGhnu7JY3EV2XfKe3c9nFAVp/zl6J7vn2J1jv1Njr7KL8JY1cqkqw6WlsqNygsXF8rlQzezAa9ModGmVADU/Th2uwVExJKfO0ufH/+IqWvrreatAFoHvhEhs1K9PMa0fF5xQqTj3Sgu0fCd3zepV4zqYFi1fZSc/Gr8Ew6jJ5VviKNHHP/7hpiy8PnGWvfR5UcbXts3qW8+tN3Xp8Ne/Nrlc2+rpsznxl+QDGNmgTAQFeT54Lq9THxvvRgq/KS7wGFwORvPxyxOALyleHsS33f1ASM+titYfDlLKMPIkQNRcHD9ZtXH9Whm7mAUPwGAvhKcTxQ4tioLF/g99Ys+NX58CcOvIKTZv8UpXrEKjaDrodfH1fFXCKbOKDoAz9t7KLTWhgClxwntFA+fgEhNbNq5nWwfSO7ts3sjNkSqLX89Qem3TOG5uYe8dmpVrW1SB0bvh0A7WdfNGWjI8zkfF8yKBxKUkegx5x579bMPXGd3QOYhquGmkX2niuuBq2QotX3FAoFppsNhEuR4/1XzHJNfxjps1KJGRgA0XbLhvyDk3SiOIK9esTZrN4Y35YX0GC73qpdNyB+c/+6W71vu5+ZprfEHf7VIWiwsGFupUCh77a9YxBzEXyrM2s9JJtcyC7FE8r/z74pT/aUlG9c556gtXJbOifg+kT/rCgGp7+YKCai/5JTbKmjOv6UBXvDTJDr7zw9j0okzTeVQdqf58GjyvbkiQetQubeyIQMHDN8pYEk4V+4eO/C5p2/2Y7kXzEKVlcTuXc10eBYgaZfOTVbtvWVRcRaN45ZmHOP33pbb/sPddz0ZFbJQkQJSL9t/ejSIqnfNfz35pt7/9vbtg+IP76r+2t747Ni/xdzOL107zaTz7bN8slnLiq41Whu8J36h2dTugfXO74uB2tnXxgtuya2A0sayU2mCwePjORaOIsl+78gWIwQNTJfsfGNC1xH2e/mwGcxERRwHZkDeKUsYveO6rXG+O3fPeD7bnLaOt/0PjYiPyfmT85iM6ut7df/WJbxRWdo5jMqo67EfwWa+piHqQ1XkwppQOJhXV+m72n3bvez8kLXceDBCDQVSUvfn1LLv//R/jGmOJr620iojBADHYWEU87V8/HeS/bn8XNeQ3rVfTTt1zK5twxX527z+6uN8/9el0+/ebU1ythJe/mOneD/XRbtMsPpOHAcTcKE/gct2rRRlZmoJz/G5tYxWBVf3YT+UR/c63FVMthfHYxz/b0Le+S/q8yZZxaFinpu3QfGP3+dKSR4fc9ZFr58UFpgkPpXWvd7n+bde+kttHfW+ZpikQqgi9/RUjrPsNo+zdKXNiy0jI0iRrcCeTrOK/aln8+8hOsc6X/7zzvQtEk9E1co9b3o0dn4kBogo03nXMzm6g47S9t3a3MYKYRwGiRtl8L+i2TTeKjW4l5ognc/Ob37qJ+n6xzNIED+B6gWHqoN22buwqFu5bXJBCk2hV4tqf7NWYPLBDkgBx/jLXw6OeR7U1NafJp66qUbOhAaJG6+75RxdXIMYvuC3BSqyl2XyT9X9Tt2Z1u+SAHezef+xsw47qbLuUUfHUp8Qd1XV9mqo0qFPDqscdq+vchfWRLJSMRnR8/FP8PNzE9Tuz3ZP8woT44gDBTpa/dW1tj5+8a4nqvRV63jJ6gjfftJ5122ITlznxz/+NL/h5uwr2+t31kes8OP7hT5MuUTH+5z9sp2vfsj63vW83vvGtPT/hF7vzne9j5xrt8+BIWj7MQfQLeV//+mT7LFCAwaeY+swRP/KRrGE2NrAMRpQbTQpuUzUg0yFxSol0a7uJ6zjSftb5YN8dmrpsAxUbufPdqa6SuUaW/DVy49rxS0KtpYppzqXquPPHzC1HdHIdgmpTKVNL6Y4/FwdqGuG66q872lP/3NX9rBFHpZ8GKdPt8pcm2X9GfZ+0YFSyVPdG9Wq46Qt+fquKHCrIHB+YwpRY4EjZLomj1pozqds1t++BD9K/ruKjY3927Tnv5hFTKjXPL9k8Sy0zJyf0bOuy8nT8jZpcNKKb6K1vZsdtR7KEuYM6tnADFu2KM3/yuUhZwQWIP8xZbL8sWBqrHOg/POVJMw2mX1ZkWDk4opZI6yHqw+bTXT0VemlQt4b12rpxrJx19y2KgivlV/s1qTRy6BarT0eAWNx4bF68JpvPu1bPi4bWe2xVvhHEDps1sJsO72CPn9zd/awT1P7tW1i/ndaPJKby4PFd7ZYjOtp5+20bd7suno3qrG9I71a8LZReR9Dn0xeUqA6npWHU+M029dL6JRBUmEkT3PfcrijFqKI2pCz5Kbtv6f5VI/PW4tSZQqXed1/RVelW/nypAPqZT2fY/8ZOs5e/iE+penLcDPv3yO9cgQNlaCj9P7hcyaw8GJnVQt6LipdKej+QlTH7z+Vx51sFzPe9Hz+6Mf7n+bbd5SPcfvGimnaltLYeN75je97ybqXS/MpDoyOJ1ImTuI5p4jrAXo8k86AYQcyNsrI5NGfXj+wpsFc7xqc7vjbxt9gIYttN67p/FXRo3qDOTU9+El+c5cVAZ6MCvUS/FbffVPTMa1DcZlJGmPfGpN9iqaayaHn8sRqczuSp4v/f/jvWtT2vC8xlTJfglCL/GoLLuF364sRypfInK6S1ZXEWnAYrfIpvYlVvXavVVnivuHilV1pGXuP6RftWgzXpXEbqza9n2dlPfu7mf2bK97P/3KBYIW8DRH3oJhd/iOICxHIsohlMNUq2RlFQcE5AqhFETyeNSw/cPu42P6qg3701aE93sbjx8I6x+X2+N9H3YvilIL6bvf6Dr+Dp6Ps+dult5fkA+x6oYBVSOWPvrd08wPLMPwzmfffcuuSFrCxaI/HIrq3j1ln09g+c5PyyAPmS3oX0SOxF1Yi8LqbPjv8ls3PF4qYCrnMV2K4pTi1S43r0BXvZ+xfuHTvfVPzhk48Ulif1tHe7ZrHUpsnF85YL1eiExrmuBWrEnfLoZ3bh81/ZFS9/7Xqzg42WYGNM81t9Z+LGxR1/Cq6iGhCp+NnuN79jDwWqZPsAUR0RfnRUFfy8O0ZNjXuMd74tWVQjqmuDqbCFGs963YNfnlSioN2GUgNbI0CJkl0rtY6pOo7VuA828PfctqiRG1ekhjmIoZyDqOBQbS/VnPDV2/u0K3ovP/h+nstO8Jkevr03sLhD798jp9j/Beb/vTZxVtxyMon8aP/APYr+3hcWlEN3amXnF3e6u07LKuu3Nbgkhp9KJeqoV+ZXsqrz6c5ESQxKdf4IXq+1r5RmXZaR36zfRz7QDLZbtyjOiAsWGVP2w17/Hm0XPvdVbK6oV71a6vBG13ItI6WBo28TrqvKQLjoua/s9TLmOyajqWaa96n5n+VdlqOi1dT3u+19O+zuMUmnTxR0gCj+Yt62cb1YoPFVOYo4BD/EZz75uetxTlU9LFjtzR+kpdEB/NQpRekFEvehblzPbjmyk/v37mN3jhv21gikbFdcOl+NmQVLV7rtOvHhT10utS74+rr+tW/cYqz+RKb7aF6D0qd0wPtgK1hkJkx2aLlRiaBbJzdVfY065eCnCmD0WdLvS6sgiCL+M3xwkrU5U6WVpENioKbeyHe+nWO1qle1s/fdxvVeqvMjA09crpFFNTZ9z2mhVDzUxVW9pEqP6jD4Tbvqla9jPfZaI9L3EN89+ocSDQOdY+87ruS8Z60n9m7xfZXWv3lxz39iAyEqjrx3rEu5Uhqjp2uhrpEKfP1Iw1+L95df1zY4AuHXDJODiosvlZViquuN6gGoxL9K2W9oBe50CTZ8NW+3103vpq1IhzqNjntwnNs3GiXyU0v+ueeWset3kNZD/fSyfd01f8hhHdyc5fuO6xLL5ElcB5HrQ26pfTgsYa09XxBxs4Z1YpWqt2pSL3be8ILLlqgtqGlDmoM36JkvXLChNk5wxMdX6PT03s8pHu0/bKfN4kaiRc8dPIaDElNZf/5jSSxoVebXu//ay3rv0NQtGK95sn4twHSallCRX/sysT2kTpvg+tzeHaO+dwMhmk7y9jfx5/FNEzLzVHgxMRU1sRCUOv6eP20326HFxnbb3zqn3GYFj12Lp0xpGTq9T0NHTrFD7/7IFZ3UHM7TH59Q4WkuwdHNVEuTbAiNxopSeLNR3TxyAaL/EKhnxC9I/cKEmXbZixOTTnIV3e6rh4p6hdTj/PgnRb3NiVYHGmFVi4tSlEY9R5qfdMVf2rng8Kq/tkt6v53aNIrNNwyOIOqkow+0Up/2H/aBPTp2Wlye+gkPf+oW3tVirI9/Mt0VYdDJRyORSp8aNur72OhLs5AGiEHqkfOBtw4iVYqMasNXJ/6/3vmR9bzxnaS/v2f0D+73/3knvuceJfmLii5miRSwZcvwj4rmq124//blLu5UXpVZt2rzTeq6ETFN9ldhp3wv7qRzwTH3f2wH3v6Bnf3U5y6oeXjMNHferl+ruu1VnOqrwM730u9aXLRM1Aj3BX58gYl9tm/qUvl80QYtS+DXtPRZKZ466fT8qa4PYaA0o2TUz/Dh1Hmx64Eaheot/+GGA2Ojpn1uey/WEJ5b/K8CHRVZSZyX8/WvC12vuAIk78qXv7ar/+8bV+Jfpez1fWXXf0snv8Zw0GUvTEzLtUWdfOrAVSESFaG57e+d7ZUze9rF+8dnDyW2C/SlpaE0Z7lPkqJ13pI86CiNnMDHQm2uYW9/b7sNeSd2HfIpisElTPR+Hr5zURCn+YjqQAyuL63AQwGKKEhUUcR2g0fEZYEpQAx+JjVSqR81HUlzV/93UjfXSfqPXddX3UyVvRIs0Khj9NfiIog+iNUUpgcG7OIWjD+8OD328pcn2WF3f2RXvjxpg4+NYNv6o4v3cXU1VFDHL5kT9MqX8SNymhd561vfuYGQq//va1dpWdl0/tytZdeC/DJtajP6DlWNTgZ13Kyhq9j/xjm7uyr8pfGFLhU/7Hfbe66Npmkud7yzPkvgjsD3ZVGwrtdQ2vloQyxcuipuDd8vs7A+ciQDRPXY6UBVasfA3bdwtylwCi5IH6SiBPo8KSgZfsIusWpTN42Y4noIfO+dLppqdAQvhhVp0J3Ys61Nvf4A67J56mIuPiiUHVsWFXXRazm51xaxURS/lmFibreoMp8+zC8F5tpo2NkXXgjrCGLiCInfzr/c8YGrFHnQHR/G7feo8GtcqlMh2clWJ0BRapJGe9OZ7x42OnFrnpOOw8q8Tt+oVaGkk3pt4eZ9qAdevk/S+5guwWNcPYnqBaxTo5r9rWvyeUSZeN7SzjNqdGxZfHFUZ9GAh8a5BYXL9RwRXIRbAY7S7XVMTZoZ39uuzrTtm28cm5epIhLq3L/xsI7u/K737dpD2lujejVdYCgHdmhhp+9VFPx4yj7xj/NtQo/+Xe9OdVU9L3txkoVVslEAf73QHJgRk4oaY82Kpxyo49L3yGsk9uHiTpB5xXOF2rdsYJsWz8vRCKT/3Bz0nw9dATal5+r8ptH1EV+X/OyFYT1JP+cySJ0LZzwxIW4pqsrwDd4dW23s5kWp0Iwao76qcUUlHu/ZSBdDahNnFjW21cA/+ZHPXMVQdXz4SuxBZ+69tb1/wd729nl72qD9ti3xGVBQpmKBcvELE2OFU1S4UGmNypxSxU91bo394Xc7+v6P3e/bt2rgjtPdt2lidxy9k20UKGaka4CvZ1El8NlR0RotaaHPj9Iidd3VfPlkqaWn7bmVC56UQTBh+gKXjh8salUZSmn1bWstHVGvZuqaHb4QlpbsUPq3Rmy9t4szhLq0bWT39+/qOmGO7bY+QPZBr3a15lv7TMLEpUb80lDlsd8OzdxKBInFp4KFy975dk65g+jEYpnpnhaTOPL85QxGEJPauun6MtFaJ+ay4rViNFqTbN6BittI203rueqeXw7u4x5DvZ673/yuq2yqnhCl7GgEb9aiZZVqYPkew9L4fHVRb1EwNSFxHaVHT+zu0tx0XtBJSd8rEPQFBfoULzuhXgW9FjWI/DB8mGmfKgVH/LGn3tkNWeYjV2pVX5+CPK+Milga7Q3zqMSG0AVKadHqwLj21W/KHcB46qTxoxrNGtRyo/H/d1Yv61Tcm6g5uZkKroPHuM9C2KppvbgLdLoeP3G+Y3kllsbXPi7rwnXD65Ot2w2jcrLWn0qO+7SpiirtGNm66Ua2VZP6rrHki82ooa4pBxrRef2c3WOFDVQYTPfT6I16pX1jwJ+H/Vq2ietMBsvXK1sj+JpOfuRTl9qfK9f83zd2+D1jbPR3JUfUlVLm+YyF5oEOQ3+98OXwgyOImi/n585r5EO3B483pTMNHzPNzfVJprzzmtQLrk7B24o7ztLJN+x0HdWcLTWG5Y1Js9xSVOXtgNSoqTqOta06di9/aaINeubLElW+N0R8u2IdAWIOpDr/6nzwZaDIkS8w6Cnls82mdWNpp+U5X4vanH5US4HoWU9+7jIkfOe+UkFLc9peW7llzOJHxtbZ/z7+2XYc/Ka9ULyEm7JekrVD1Wmm4oPB6vqaZ6fCKpWdh+3Xs/ZFfPrt1NK1V4NtdE+jcxo1PPaBT1z6t1+OIkiPo3axalYk7l/VtmhTHKzrsdw6xQkpoGrjl5eKQ6q2wIunF434JjNv8Uqb9Gv5ArHEoovBQj26dmhfqZ1TmU5bTY3wHQnKovEDQ5nO3IhkgKg5QZ4+lCcWjzZoTcRTHxtf4g2YVDwp2KcC6IPn0wTk1a9+c6MePmdYlY0y5YRebW33bRrbVQfHp6Fqm1RQ5oy9t4qts6hUqTfP3cM+vGgf+1ff7eywwJqE5/Xe1u7r3zV2wPgJ8KVNzA2TYMMlVQ9JFARL7fv0Di/ZvJJspkpm0tKV8Scm9UYGc+5Vba0i1CD1aTaN663vOFHRJY2MqNGajaJGvhHZulF8r/GGqOw6iEF7FBe38FRsZWop69qJRn10LvtfceEWpU5mYwRbaU+qPnvInR/FzXcrD41E+d7kZHQOV4q6gkRvj+IGk4LH4HygoX/r7ObgKDjUeVGjP8H5dlq2R20QpfMHOxaD80h88KjGyN/v+9htmzoUczEyqw4UrbOryqMfTS3ZuNK145ID4lMe/QiinLLHltaveC6Tqnzqs+6vdWqUqQHmr5Hq4NH8z+A8ucQKqEpL9SMl5T02P/phnhsV1utI97QC3yDbpml9O2vfbaxnQuVuzeVPNQ0l2JDTqKk6jjtdM9Kuf22yPfbx+oqUCgwyIXEuGTIv8VysdtnlB+3ggrD2rTa2d87f0z64cG/7a5I58WUJnp/Uca9Ktzr+NDoYFGxr+ilTqZzfZzv74so+JQoRBqlgYrL5156O8buP7WL/Pa5orU6l7msJpb/854MKF3VSRV+f7aa1BUXZG99cs7+9PWhP6188d96nyCojRNckT6fQxNRZv1RaKnsVXwcV1D434Rd3fVH7QEvNqKiNlpipaDtUU798Ibhk1YlHl3POZuJqCsFOM81nVGC89WVv2BH3ji339UOvU515d49eP0Xp6G6tXbtf86GTFc1Kp2hEE8WUhqmDbUCP+DdTw/L6wOuDootPcJ0r+br4Iq8h/OCIXbAAjdZuSaayDbpUlJryv5O62/E9i1JKE52/33buxKTeIlHPeMvig+iSA3dww+xKIfDBYrDEthYCDavENd/8PKKgZOWfw07FIFKlGMxIsj7n+GnzXe90WQ2VMFMKXrsr34wr+e57A3Vc+R4vvx6Zejg7XzMyZelnNRR9cK0GbbDnUMe2v4j4Cm3pFjzGfUCTmFaUkeetwPIXf+m4fnTI+2xa6vSgxMa31gfsfM1bdsubyc9z6kFW0REVVND7pjndlR0pUyqR0qg0t0bBTEWoMqCCWK2d6mldVQV0Cn7+sWtRo2P74tE/2T0heA6megXfRzX+lIL0xMDu7ndKufRru2oNLdFoa7CjwweI6kX2aYZ6q/wSKJnqdNJ2aBTjs+IUdp0btY5ZkI4N7RNdD248rINLmf3nnlvZ8ON3id3HzzsUvV4Fzdq3ms+qwk/qVA1ms/iOU6WSBkdW3/x6dok1I7VGrg+Yyrue5LTfi/abntd/ny6+QeaDYr98lHfSI5+lHAH1ErNYHkiYtpJYoKSyEtsVmv+WyfUbURa1SZraybtvaV9e2cdeOaOXy0TQ+aMyacTBAFGP9cw/e7jH0nJhmg+dLLALTj9KRcd88LMTHA9QCuspe2zlOtDKosEEBcLerwuXxzoSy0PXq38Vj6rLPsWBmfaVr2J/1j7buGJOShnVGpLy9uT4qsnn9t4m1vmp7Q7W6Ehmv+Iqsqps7o9ltY8fPambfXDRPiUK25TX4IPb2SMndov9rKVL+nVulbR6dipjijvt/NQG32Gl9l5wCS9dE7W/yzPPXMG7KpYG25nqSPAdgbpmZ1KkAsRz99vW9aAk68XTh6Rd8Zw+nz6jeRiqfucXFlWvULDnQL0cicP6wV6PXFDj2KdIJQsuXz2rl43+196xhs/FB2zvvtT48VXoouAvHVu6PHtVJNS2pyr/nElKmypvpSn1tp/x+AQ3j1AnR43GPDVuelwPjhq3/iKvOS8qv59sTox6p3XgR5F6un2Qcc+7RSMKatBPKA4ENHdQcxFUdEG9XzLy61kuYHhiXPzaUBqR6HjVm7blpa+7tDlpvUnJyfj+s+6rymWSn2TeOlCYIAwjiMqaUNaBzmF+jTUVQEgVWAYrUVavVsXe/64oFei58cmrN2sei4qOaA05BQea060iWE8mvGflnUPoVbSgjk/tGhA4D6uK613H7uyCH9/48ZUjNZ88WJCmNJob/sqZveKWffCFQzQfSAGG5u8lniPUweE/n5V9Xb7TpKy5cHpPFch3v2GUmwd15H/Hur8784kJcfdTUKgGjPbJpKv62t8D83XUgPjnHlu60fjETkNdX3wnzllPFj2m9qkPJHU+1mOro7W03mlf4McHY+VNY/45UPEwnUUW9Jn2ozFNNy5qJCZrbCauk+nnMflsj7IKS6QrxTSRim0cenf8ZwyZFTx3KjXRjyLpGCktfbQ8lAZ6dLc2dvPhHePaq0oPfOj4XeJGrA7bqZUL7CoTiF5zyI6uSq4CsKFHdSr33ymIU0Ac9GNxW0gp/qMmz3aBzXWvfmO/J1muQdcXf41RJkEwIPbU6fTg8bvYfu2axZ1zvRN6tnXrSt7fv4vLiNPSHGUFtyosE8wSEbW59Hp86mVlaN/7KQei5dr2Kh7E0HxNxRGpMh6U9aI1eP3cbE1j8+fE1776zbX3Ek0sx7lP8+sTs1q0j7QvfQXWn35fUiKbK50qv0dDaKfWDV0DQw0b5fuqOpGvSKpjT5VCE4PKw3beLJbSpCFqpXK+/ng1W5GhEcQNpXlRwblRmjviq8+FWbJGsip16cuPHKrHWjnVG3Kgl4cafaq+p7RincTGXLxPrKR0Ip18dCL8ae4StziuvtSbk6wgktJH73xnqqsWpjkvpUl3qel0XjS1ILsuakofu/+DH93Ju2lxQzDYwPJpUUpT8e+bGo5/26W16xFXwRo13v26au9NmeMe318Ib3j92xLPv+/2zVIGiMkW0tVJe/HK1a7zJB2fzaXFOf2bpXEEMdVIYUUDR2Ud6Esjt+pBHTftD1dI4aq/7pg0Vc5btEyT+lfE5lRoKQS9T7qoap6g5rXpc+17PVX4wNNxooZOeakaXTAlT3Nc1FOt40LV+fyFLTEo0qjZcT3axhopXdpu4uah6vgLVgn09tq2qQ0d+Z0dunOrlMdueRzcsYVbQkiNAM3X9M7eZ2s3j0/z1/Tl6aOrt/CFCb+4omRlNer0+fxx3mI351oFhtxr27xRiUaO9+z4GXFpwHou/3eiObkP9O/qFnr2z51sWoGyTQb12TZujrR3fp9t3ZJJfi57k/q1Yo+lHvgTem7h5vMH52KKzgdq8Gme8dG7FH0mmlcwQAyOGqrIwqGBsv4bQucgXeuVFaRroqTax0HqxFJnnUapbzqiY5lpdps3TtN5IXC4169dzZYGlrlS1VNkXvCcu8e2jSu0VnRZdE5S4JaKgip9ZhOzwCpq4zo17IB2bSp0jvZU10Kdt+uKM2cUECrTxBfnUtqmrjGf/PSHqwcQ5KtpqtiXMgnK4pd38W47qlPs2Nc56pze25R7v6pCqUb6NZ/xhjcm2z/3SE/7V+dBXRcXLVtl/+i+uTWoW8NVVZ0y+0/3Xqnjza9j6um81+PGUbFCRAp4T959C3fd1O90nfB0Hft+9mLXCa1leIJzxpNJNnjhlwBTu1XnOU070L5QmqwV+ghiWXZqs74n+ZnPfolbrqLX1o3j5i56iTnLamgGRxqjWAUwaovS6iBUdUKlPWl9St9A0lD8mMBoRLooePMBnnqdlbqlamLJaGRl95veiUu3S1UtV7QmWbC3/4mTu9uHF+1t1/Zrb/snlDkPY0rR178ucq9B6Rv73PqePTluhl1dvGi8TA2U19fcra7XveUKpsgubRu5xupRu7R26d5KMVTu/e/FwYnSKnyvWKr1hTQ5PZE+H6J5S9q3akR5CmA7XT1yw1ItAh/HNevSPwexXJtQgfPMblttGku7ff/75B0Nwfmw85eutJ8Cjf1+d33kihpoMfFLX5jk5mAHqYJosDMlcd6iKveqTPqR945x5f89pU0H16VSUS2tiaURZ5U+VzXJRHovNadPi9yrc09L3ojOBxqN1rzsZEGY1pD9YnAfu/IvyZcUKi91fATnd6rI161HdrKTihe8TqTUHr0uHSefTptf5vumUfPeQ993lZoTi6Yl0v4rLW1YDbqXz+jpGgflGW1IFhyKOhiDc/AT1/E7JqGxqYrTSkvVer76V/Pj/ahIs+LROgXRwcIeqQTL3wc/O+Wl/a3iHjoP+B593faf4nL0F/bdLtZhoNEIpd4mCo4E+GI5WvdMfgnMI9Jx5ul4e+yk7hvUERX3OgInHe1fZV0kzn+NMn2WtUamRlaiwH+Os0UdRprfqBFADVJURLrapDruP7l0X/u/M4uCP52rg5UxFRz6jnsdc8FpMX5+cnk6YXymQTCFts0GjMSrY1MdZQP32NIVnFQNknTQOVWB8Nvn7+mCQxkcWLIuWVtU10f/dihgu65fe9usUV03t13VVv0avaoq+8CArrHOXHXABTtikwkuaeHPEz59V/yIZyZrd+RVgKheZZ/6IsGRKD93JdlFVKWKRRc+ZJ/SOdRrLde8+o1td/kbLrVNa5Ed88AnboQjXUtgqHEQXGPSF9XQyEciNYw1UqP2hP4tLzUeRSf/3bZu7E4Yx+26uVs3KVOl4XXyTsdiy8F1lTyNnnqJ83v9KIR6X4cc1jF28fvP33dy36vHzN9H3iseOQ2mIaoIgEZVju3eJuncP1Wh9AtTK5jx5cdlyBvfuhO00iPTRdUu0zXXKF0pponnrGdO7RGbl5nsfZ8VGEHU/MLEcuD6bD8y9ucSc0ISqdPGLySvY1A9zToeNeKmAEmLHKtRq0WQP/hunru/lpqYct3+bnHwIM1TU9q1RqC8j38saoT4YF/FiJTi2LK4ynFpdH5PR1Eupegf0L65q3z56tm9XAZAcA5kUPctNo2laKpwQJ/b3nepoKnOT74oVXABbo0oJqN5KqrMqobGhCv2c1Mggo0qX5UzHY7suplrxCiIUiXwoMRjcPQFe7k1zpI1BoP3TXYODVKWQbCYjc6TFT2vq5NJ6cA6D/jRZn2ulMKuc9DR3eODW6Xe+ikMyapNB+sQqOHt55Hrs6taAV6n1g3KXFdtQ2xVXPUx7AGi9pGm8JT1vilDRqMo6vhJPPeERTDQysX60Tp2+vdoW645g5kKFjVareNa5wKdu4N1BYJ0zCnVPXF0K1lqaSo6x3rpmsKRrg4bT51LwYGk3bZqbMOO6uy+19JHiYId3bf+rVMs1VVL8HnqUFShSbWLNFDlq2lf/tIkN50jlcTrRGLBN3WSSqraDumQVwGiov4R5+5h712wlxvq1YKlmvx7Ys8tYlWWkjlrn63dxVgjH8KoYfqVtU+DJw+N/F7ywsRYGXuNHF30fOnFBcpDefWa75ash1hBY2KOeUWK5qhnP1Gf4gnVXmLQkVgWubIe+vAn6zB4pPW48Z240bXKCDZkPb8+WjBATCxjrbm7wcq0mnegUUSlUwRHoFSeX6/bz2NUo/w/R+/kjtXrD02eklOnZjXX+3b73zu7FD81PNpe/FqJUvmV3Z+JQduYS1KnHKdTqnTT8mixsXoTq7pjJTFoF43eBi80yQJ/v6yBFxyUUmPbZ2SoqqNG2He9YZRte/kbseIlGlXRXNPeQ99zo2Q+SD+4UwsXxGqecaKR38x2qcd+XotfG0v8SKYWRN7QOUAVoeIu9/yji6t8GWwcqBqmLzqgfa39pXltfsFpNYI1Iq6RM80t1D5KzAoIVjn2fpiTvMGsKps+HUvPpWOsbSClsbxzLctDFV8fO7m7vXDabklL0vu1gkXvZaq0fwWwfg1fdRpoDquKKyTzSHFKneYZK+1Y5/eKLm0UrKzqRyz9Gqk6vyYbNVUF1qDZC9ef44I98kqv9pUINZKsY2DX4sW0/16cUpupTqNtmm5UYt6RKBirSHEunWsf/mh9RfZ0U/aMimaoo6e0isi+k1T+G6hcGSbBRc03TTgXhlm6pz0pQPXLjiUuPB+k851GzI578BN7pHhkOLHTuzR/La6grA5EnwYeBT2K24kqVKalb3Q8/vvNKa69Mb14/URVqA2mn/pUUN+x5zsBlHnx8aX7ugEDUWdrsoECtQ38CKIvaqR5kUFazqSiazUWdIAYXOPq30d2ckPomvx75cHtSs0v19CyLpLJ0nbCNgcxqsoaRdHcl2QnaeXB623RfNLypCTpwFIefbKy4api56mRojWBlBLg+R5pT7n3iTQvKri0iNeobk1X4EEO6tjCRp2/Z4mlPFo1rFtqWeTKUJ67Gty62Cld9tnPftmgCqnJ1kPyxR+0b/0cnfuOK5pY7u1QvOi4p5EdjZx6/vD79Kf5bnv1PMrJV9W48tCxeUjnVnGT+29PKKJR0bUXkwVqqhGn9zKtUqx9uCEjiwqg/JpPyRqDwc+WXwQ4mEalku4qeKVqyDoG9Lm+8+id4+ZzBwO8C5//Mq7wjeZsaf2oYCDh+b/TOVcLHmtxaHXSBWkEWfs9WYMkWcCSC7cd1dmlhb90ek+3XIb2lzorurRpFEvx9SNQ6tDSOlVaVD5ZSqUKMvhRyR+SLE/y/Phf7O7RRSOrfrQ8sYde65ilk3rHg5W9g+44pigDIHFt3mTH5WUH7RCr3KfXm3hc+tFDP3Ks84Zf0PqrCvZ+B9Ou1DBSkHjcg+Pcz9sWB1mJdN4IBriaT6SGmc6TwY4TFQbynS3+3KUKjHrfE5eZSfe85K2TjCBqdELB2B63vBs7D/d/aJz1ve39lEtxKaVTSw+o0ybdVAxjaHGn3LvFI4SpBItwfFPOdeSyLbj2pF+EPgrSnZFSWqqozt0PHV+0bMZTn85w5zids5evKgqud67A3De1y187u5fL0shmB+CGarZxbXd86pDVMnonPvKpm4bT66Z3bExxB2di1sXBHYvqayg7KrFGgDr/dJvOgTovaj54IqX76tykTva7j93Z7TdVfA3SAIeuv+rY0nUnEwNbeRkgIpqSLS6rggqHFpcb1vIK6ilRY0wX+GTUyDr8nrF2wvBP7f73f4xdBBIPHgV5atwo9Vhz50SBpafneTbJ8P9+OzRLurBqw3o1XCNbk69vP6pz0tSLxIZv4rqJlR09DPaEXv/6ZDs5EAhXlC8ooxz/f/XZNq5cs4rzaMTIF3jaefOGKecxSTCQVoNLP2tbX/my6ISok6Qa3BWhtaBSrZU08pvKBYhB1apWTWuxghIX8TSew33vbWLHRuJcr2BPpubXbdusvtv3eg+17IGOAc338ykrfi6oirD4z4APMnWsKOhTp5vm76lITOJzKPgMPs7X1+zv7u9T+UUdPprrqG1Xyk0wfTLZkh65oODp3uO6uKWG1MPulxtS4+bGwzu4UfOR5+0R17ga/tG02LlGqT/+2Dln323c8ho+W0EjqBrtVzVUpfCeHyhq5avgiQqQqePp3n+sD96zQRUOv7hyPzutHAXQdB5dFUh/SjaypHOplrbQyJyqr+7UulFsPmt5ffD9XBv8yvo0VhVWUnqvt1XTeikbeJ9d3ttlNfhq08pKUXGkZMtzqFPSd6ZovmaqIDqdkgWIwakQ+qxc+uJEN8qsohkKtnWN+uTH+FHrT4urSes9SHd1QwXkSkP0phSnnifS53/izEXl7ghVQUFf8TqbNDc76tIVICqLTpWM1Rnv5/z7oE4dSb7KsV8WR9QpVNFjY8eWDSqUlhoWuxWPImo5L3+M6jTnj9HElFm1a1SlX9lRyaopq43hC/Ilm0Oo6Ruitk7R+rRF/wYpW6J38TntjnemuqWI0o0AsYIFVbDhUu1TpRiqV0WjU8+e2sNuOryDq/B1avGakKo2+8CHP7r5ibrA+3RKVSHsdv3brpCM7+FUgRsFSyqSkmyURWkOiUP1WhRZ97v97e9dg00Hv3poNHqgCrhK8WrVqE6J1DyNGOp+SptQZa5U86I0oqjX5Zcj2dAUU+Wk+7mRpxfvI1EPn9azSzYaKEqHU6GeZCONfpRon+2a2rHdi9Ig9Di6rx891IRrnazaFy8rI8lOglpLyFPVxeDal6qI6NegqwiNBCid16+rJH6pGlU1q0yKbdy6UhWLV9N3HFTiNOMDE7/Oq6f3Ktmookb2lB458rw9k16kg4sWKz1aDX+t2xWkjAwFfVpHULTMgqo/K8BTMKnezlQFVM7edxt7+pRd3fcKjPoOe999r4BSI5JecAQtrLTg9TWHFBUkeHBA19iC8TomFbhoCZyD7/wwNgdFo4c6RhQgqddYiyb//b6PbcBD4+yvd64PcjRi6QskiPbz7X/fqcyKd5mg+Unl7ekf1Ge7EmnqSsfS/tD5w4+OqqCEHtM3uPxIcnmc+UT8EiSJfJpmMjpfJS4wrmwLX0DL03ulzozKLDmwIQ17n86sYMp3aqqaoqcloBQgeypwoeJOR933sSsS5tPUgoG6zofppKVPgmalWBJEc86DUxX0c6qCbLpO6VhQJdlg5eVsCC7fEKVMsUy0Sbtvual9cOE+9u75e7nzdPAao2NHywzp2FAnr36vEdfb/lY0N68Q7JZQhTVRsqyysvgOWXWWJi6b9GnxsbtLGRVulb3hi2reOnJK3Kh4IhWA0zJOqebA5/0yF4g2NVq1TpnnAwg1rNQAVdAXXBpBvfNasPr+D4qqimp5k0Q68JQ3/2hCNbVghdvT9trK5ddrbtXe/15fbVDUoNZJ8rWzernetcSGg6oBanJyeek1aXRHjchfN/CCqDmVanwpYDu397Zu3plP+znx4U/t459+t6v/uqObCO+pIeGX4Ji/ZKVbY04pldrH6vXyF81N6td0PYQa3fHpqyo440tb+5S3507t4XrDko0EtilOgXSPV6+WCxAfLV6Md0OWZlEDU1XQPrKiSeOD9tvOjUQp/UyFPlSOurLSPXqYqZQgP8pz17s/uPWXrl2x2gUTSrkLjqp4elnBkalkNE9Cc0HVqPdVKhUAacRRo336LPjR9mBQqTkV1cs58qqGyIX7b2c3jyiag+o7WJQGuGDZSnd8pKPwTDZp3drnTtvNzc1R50xiIKO5djpvKF3ov8d1cY364OiQLz5wxt5bxaVtR4nWrdR8Sy1vo95vpXuriqUaQZobqPOHgiDVBpCdN2/k5nXqds2z0xqVpVHAEUwHfXLgri7dzVPq+QEdSlZADlJnh4Km+rWru2vJZ8WjbaJ5lDpP1qtVrdKLbW8InUs1xUIddDqPddisQVxBn+fcGrtr3X00cq/58T4TQ2uW6kuZLcH0ZV3TEte62xB+pEPzMlVcKtXSJr4jUVUX9Z5p1FGdocnWdw6uhanX7efCZYoCVa3Ze897P7jtqh6dqYcZDxbVMaUvjbjrnKzOhtP33irWIfbxJfta3VrV3MjVeb23yXgnSpjsvk0T67RZA7c8haiDWrdpuoxGVzVwUFHBdRfVUagRR6WlqgPRFwvapYzzogYlhh21k0sp1/zljlePdNNDtm2+kVsLV+1DvwTGKY+Od9kHX/0UX7W8NNG6EmfJhhSPQGb2qSrt7dymYVzFL80lSVzAOtnk5/ve/9Ete6L2qwpvqCdMAZWnwhSp1hHy1agUlARPiJpkrAazgsuKatmwdlrmIKrgh6iqovaL5kz5UTqlQmiXX/ny167CpILBQc98YQMD1T5Vgvn44Z+6gMJXIPSjjo3rFa2z5hed1pyTt4qfL7imkda1S7UGj59c7Ue1lKqik+ze2zWJ+11lKLffjz6pEdp5s6J01y/KUWq/1BHETASIaVoHMZFGrdR5oYaOX8fw5jfXd6BoOQSN6ill89Wzdi9XOq86YhLXrFI1Ry0XoKImydZoU4OhIvvt9L22jqWUakRN76X+XqOTmVrPKRsu7Lu9S09Ug0GjVX5eXnD+5TbNNrJXzixZ0EqUehm14NjTdgcrhWuujpY2USCmHnLRunA+TUr/9ijO3FDDxS9vkozm+WgpouC5t/sWm7h97TsplDKdalkPT5+5N8/bw3WC+Pni/u8v19zyTetmLThMduz7Sqa+6E4ww8TvQ2W7HN0tvlhFsAiQ5i6t/5tf0ppmqlFM8efuOSkCRH9d0371admJ0yl0Hhwx6TdXqdxT4ZvRKapobggFqX+7d6wdePsHtv+w911ldHXkVKkSzXZeprdV14m7jtnZ7uvf1aU2Bjsx/DFWSMGhqPP15TN72ddX93WrHdxyRCfXya3ili+d0bPMc08ywYwdUQq5gkNlsmnwQx3x5amcrPR0fz5Tark6vp74ZLpbWunQu8fYO9/OdunoCg6TLZ9RGkYQkyDFNHz7VBf3F07v6Xrb+z/0ievB1EGQqO+OzVzgFJzE73ta1RAd9vedXInuxCqVwXlTQYkjJp6WsLjkwO2Trq1Z3hODGgDBheMrQge8TzkK9hIrZ109x0Hjfprv5jn5RkYymoB+4+EdYwGiT6NVL7AKN2g9SO+Acqa7KXhRARQtXaIKXGoU6iSbDgpiNHHbF2pRavILn5evkFGQGqY3jFg/n7VaFq97Gxog6nOj5Qo0Gqc5Xkd22SxuDpOOgTP3Kd8CxKVRj7KWC0inoX/r5Ja1OGX3rSIbFCXSqI9ffsRTIzox6NCo7C1HdHQLUqtTR2mOWt5DwWOUlVYJUun56kwKUlD3yyOfuZ5vpT7p/O47GnReVGNHPwfXC9O8T6X2iub3qEOkMgtlX35QO/vkxz/cKJ1Kz2ddMLu8+HqoEVaNcKqBp7XxklU31ILuB3ZoYa9++Zubc9lps4Z28QsT3e9e+qLoOqcOUB2zChZ1jSxvIbDS6HOsUVy9PSrYo0we7btk1y+/VIhGVdSw1jlp5oL4OdFKOfaVrD3Nb1eH5fjLe7vMoIosIK9quFoLU9dFZVEc37Ot63CS1yf+FlvTL6jjZg3t6/kRbOdFaFPzTb1a1eMGBTakoJqOG61tq9oJyirT/E51mKhwlw6pYIdaWTQNoWqVKkkLR930xhSXwVUZBIiIFI2UFVUDXX/Cf+u8PWy/24rmM6nUsFKJNFrWuXUjt35P4mhgsiUMVIVKa2gq9UvpFa8Vl9xPNmLiD+7KBIfiq5sqZcgVflmx2gWt6rlTumdZJwXd98h7x7q5HaqiGCzwoRFEV8wncBFRMYHyLCSvBomfO+IbtQowlP7gKWBU9dHy0qiClo/JxLIRwd5NFdURpYAoTVYBrt6jNyb+ZtN+X+pSr7SfNFKs/aPUY6WVXffaZPtlyTyrt2XmqtllKsXUpzir5LbS+pS+Gyy60ad4AnsYqfjBdf2SL2uST5QOmIw6TJRumU898clei9Yg1DzDYIGi4Gfg4RO72f63ve+O29MfH+/mEeoc9u+RU+LWyBQVjglWMdZ5VEWWKkOj6x9ctLcbXQrLEgfq5Hr8E3NBnbIDEucliYJDFc4JdkRondh9bh3tznOi85w6QxU4aj69lhf4btZiV9TKj+iVlw8APy6eE6WpCL5TTtcvLQaeuGaoH0HUc/lrpA8aRfPEtVB4Kl2KR4tVQdYXFiqNgsITho9za2R6CmA1cq/Op1GTS45Kal3PweNetKiL0sgnSvpX3+3clzrBtC6ir+p89j7buBTWilCHSrIAUSOH/ZJMOykPAsQsN+gKVTrTdn3ao7+oquf9hkM72LTfl9i+OzRzvc6acK10w2CAmJg6l2j4Cbu4BrZG4ZTH7YvXpJvSEdRA0QiPejvPeepzd6EVXRBVcEY97ipAs3rNOrcUgQoW6ASi3vaTd98ilkqp9T2Dabeay6M5YapGqjmVFz73lVtfLrhEgRptCowSqdiBtkP7z+9jBdxKaXpy3IzY/q5oozYbawoqzVSFcBQ0q4GhNChVmjytuEiRL86jxW79Z8IXcahaK7MpphUZRagojRRobqHShX2Fx8N2auXS1dRwRHjlU3DoDT9+F3t78mzXQaU0W41elTaCpWwKBcsqOqYqfPpSCflECg7HXrxvWsvj67yUq/XYkrUxNGr4f1/+VhRQP7O+qq3OwQoWlSGg4DCR9ok68vzc8oF7bGl7bNPEzUvUNaDb9aNi+1rnRDUky5MSpxRVTVFQdXHN7faZNurAVFCo4FrzEBMDRJ8aq+drWPy7qYG5ka9PnOVS6dQhq6JtqiZ7xcvrq9N6T386vVwB4uRZi+KCQ0+jhlqGQVVSE7VMWIIqSu28KG0rykedheo0UeeKCvFVZuqSOoU0Wq5pGkrBV4aO0lQf+7go006DCcoSu/7F8VayPn9yBIjJcPyF+qTWbKP1F3WfXnlM95IpcL4ynCjgCq7Ll4wmyPtJ8ulIyymNelcVIKpaqg8ORfP89KWFVzV30uemBw0pLtSjIPPmIzqWeOymG9V2X77cvA8O1dD4z993chPRFQg9/emMuGIZfh6bAtCNAw2RYHXAdi0yX/K9MtTgufqv7e2MJybESrL7hlFQsMNAlLt/ZI/N7LTi2kSahJ9J6VoHMUjpVAoQfaPsmn7tUy5sDmSSCkRVtEiUzt1aeLtalSpu5F+pi/ocK31L8eC64gZUlNZOq8y8ZAWs5/TexgWIQTpnT5gxv9Sqz/06t7QPv5/rzoN7bdvEdT5o3bTg2r9qfGrNyLP32dpVnVUFUa2fprmges+Gf/STy0LQiKoqVd404lsXyPngUHoVj2ooYFeAqMJoiRWsfwmkmNaoWtQ5+HXx/EVNi3imePkoBZsKeP/aqZX9e+R3rqNTU0h8MSIVxkqkUeYvpi+wl87s6a5xMmZqUbGyRBqJXbFqrZsLq8wXbWtc50xE23lMgco/tWtUc+ty3/D6ZFc4sLyppYnpr6oXEMyY0+dDI/4aPFGHsdKqe7Wpa5teXb7HpBWByGlSfGGQnqWMCuqAUcNC6YVKVwmTVg1r25czki9MLz44TEYXbVG6ZGknEt/A8ummKtXsS+if0HML96UUTE0+H/jIZ/bnitWud/mCQMl6CTYAgmvlhY1Sg5ts1MMVedBE7+CaXVs1qefmKl34/FfuZ71OTTZXOta3f6wv7JKJNuiGLmdRFs2hUkqaKrk+dPwuBIeIFJ2nNJKkpYJU6VLzyVQ5N1/mplaEMjSCHjmxmztnl1WBWPtK8+uDlEWhTkGVtd+6SX1XaEye/HSGnbfftjb0rSmuArimU6h4jwJCpY1qiZz+gWkFCiBVEXvTerVihYG2a76xC+BUrVbP4c35c3lsXVatDae/Ec2x+vrXhS5A9bZoXNSBq9enYh8q+qb5uJqfqGwPzaFURVTfsTtt3hI3+uhewyczXDCt53vq0+mx65syRETxnzJn3pg0K7YvPps236Xb6VqQLyNx+fAaUETr7aowUDqpIyRxsKMi51VaEknQQ5NZG7pPN66z/mNbVtXDfx9Z/iUosmlrNyo3Kza6+b+Tutk+t74XV1wnkYJdVZLz5caVolMaBY8qKOCL1qiKaKIDitdk/G//Lq6hoLlCiSeQbZvXj0SA6AM/fbVrsbFd9PxXbl7l0//sEVvPqVaNqq7anxaM17zTXB7j6RpB1EXgmX/2cMuRVKbnEcg1pQJG5RyTyWNfI4AKwpQeef2h7eOCr8qcFxRg6vymLJVr/u8be37CL+4a0+umd2NzBfWv5kApOFQHmdZL9YXdBh/cznUkak1LbaevhPyP7m1cNobWJF6xao1b6knPd+c7U90ooOaEa9kq3abRYD2Hnj9IcyI9ZcP4YFHX7O9Ujv+XhW700geIweyPJ8b9bG0b13WFbjRiqekFw47qbG9+Pct1aCobRqOhPotm3x2aurQ9LcOi+V0l3o8ItfOitK2INgJERE7PrRq7YgVqSATn30XJST23cIVjFOwppVOpNgd1aOGW7khG1UAVIL7yxa+xUbCyAkQZfPCONu6nD1zqVmkNLwWPyQJIUSqP1mdbu25didLMYaWOAy0I7yshelpvT1/5NqdZ6Xe1qxIcAlF357E7ucAoXUWmFKRpnqCCOK1VqSrfiUssaT03UWqpAigtjaPAzF8zfOaJp044zXP65Kc/XPVozZ/UYz9VPFddy+v4ObYaFdXz6b6pAsRE+hsFiJpDqbXhFBT5oFVUK8Cve6xFytXBqoJQxxWv+at1NhVHvfrVr7ZDi41dup2C72CHcT6MvhEsIpMIEJNgHcRw71M1hlUiPcp0wX3h9N1cT6nywuWCvtu5NdR237aJ/VS8Vo0PBo/t3sZdcFWR7oEPf3QLk6sKYFk0cjbq/KL0nQ0pwKJ0nigqz2vOdACXcu3DDKeeAgin0s4z6pDru2PzjDzvdYe2t85tGtqjY35229ChVUNXVEg0Cnds983d9bVPGc+va9ETA3e144ePc2sK/uPBT2K/U2dksLNRqfyaVxqcyyhNSikQVHRNnO7mG778xUwX3KlAm65jqor9v+KlTxTIXv3XHUssJaP5nNf2a+++8q2dF6VtRbQRICZBFdP0Y5+WpHRAHxz6OZMqHiC+IIHWutIaNr43Vn/z+tm7u8CnvFUQfeEdhCulnGMCKEzJitRkg4LP0/fa2s3HViro+J/nuwBRRWfuPGbnWKpneegadNY+27gAMUjVwoMdc5pb9fAJ3dxC9X7kUtkypV2/tL6jKNU2uIahlkBRqqhGEzWXUGml6ZirykgcUBIBIhBiWrYiUSEWb4gy5jQDCBMFcJpP2Gubxjbusn2tYZ2alZquoVTTe//RxaWXqiiY5iSetc/WJe6nFNfXz9ndBaUaBaxds2qZhYv83MWgAbu1dRXAP7l0X7cw+IZcC/OhUy4fXgPCiwAxCRp06cc+RWjFZXrmaASRYwIoSLlu5PvlIipr//bN4wK4VBLXSywrgH30pG522YsTbcYfy2ze4hUu8NRcQkl3Ma5cvwcVwbUC2UKAmEe56WHGSQ1hlelALWUqaY4CUwC5RXp52VQF9alTesTOy+WdUpHv7Tw6FpEt5KoBQCYFA0Eu6ABQIekODvNFlAJbRA8BYlk4/tKCHlOEVa56kjkmgMKUqyI1SCFCbwGfF2QLASIAZBDBHwCESz6cl/PhNSC8CBCToGc/s+gBQ5jk6hhnHUSgMNHGCJcovQdR2lYUYIB41113Wdu2ba127drWvXt3GzduXLn+7qmnnnK55P369bMwi+rk5TBjnyKsclWkhkYiUJhSFatC9kS1TUKRGoQ2QHz66adt0KBBNnjwYJswYYJ16tTJ+vbta3PmzCn176ZNm2b/+te/bPfdd9+Q7QWASGG+EQCES5SCQiASAeLQoUNt4MCBdsIJJ1i7du3s3nvvtbp169pDDz2U8m/WrFljxx57rF199dW25ZZbWtjRQ5N+7FOEFUVqAGQVS9yESqTaJBHaVBRQgLhy5UobP3689e7de/0DVK3qfh47dmzKv7vmmmusadOmdtJJJ5XreVasWGGLFi2K+8omGm7pF9V0DhSWTHw2U6aVMrIIFCTaGLkX1feAznaEMkCcN2+eGw1s1qxZ3O36edasWUn/5sMPP7QHH3zQ7r///nI/z5AhQ6xBgwaxr9atW1dkMwEgNKLaEAGAvJUHp2KuJ4hsFdM///zTjjvuOBccNm7cuNx/d8kll9jChQtjXzNmzLBsolcm/TiRIazC0CPL8QEUDrIHwiVK598obSuirXpF7qwgr1q1ajZ79uy42/Vz8+bNS9z/hx9+cMVpDj744Nhta9euLXri6tVtypQpttVWW5X4u1q1arkvAIi84PWcazsA5Fw+BFr58BqQJyOINWvWtC5dutioUaPiAj793KNHjxL333777W3ixIn2xRdfxL7++te/2t577+2+j0LqKL17+TNKA4S2SA3HBFAwSDsPmQi9BVwrEMoRRNESFwMGDLCuXbtat27dbNiwYbZkyRJX1VT69+9vrVq1cvMItU5i+/bt4/6+YcOG7t/E28OEgioZQNU2FOiaZOUpUgOgcHANzL2otvO4hiC0AeJRRx1lc+fOtSuvvNIVpuncubONGDEiVrhm+vTprrIpAID5RgAQNlEKCvP5NSCPAkQ588wz3Vcyo0ePLvVvH374YQs7emjSj5QahFUoUkw5JoCCQadRuETpPeBagWxhqC8JGm7pR9CNQj3eWQcRQCq0MXIjsu28CG0qoo0AEQAyKLINEQDIU/nQKZcPrwHhRYCYR5OXw4x9iihgHUQAmUanUbhE6T2I0rYi2ggQASCDSCUFAKQbwSIyiQCxLBx/kVhKAKgsitQAyCY6jcIlSu9BlLYV0UaAmAQNt/QjxRRhlavPY9yFnkMCKBi0MXIvqvudgn/IFgJEAMggGoMAEC75EFxxPUEmESAmwWhXZrFPESa56pElcAQKE9kD4RKl82+UthXRRoCYBEP46cc+RaF2CKWab8QxARQmOodyL7Ln3whtKqKNABEAMojGIACESz6ciyMV2CJyCBCTIMU0/dinCKtcBXAcEwA49nMvSu9BlLYV0UaACAAZRCAIAOGSD6NvXE+QSQSISZASln7sR4TWutw3Gjg+gMLBOojhEqX3gGsFsoUAsSwci4U9IRx5L5sdQikL1nBMAAWDTmhUFhkpyBYCRADIIC7iABAu+dAplw+vAeFFgJgEPTQZELfsE/sU4RH3ecziR5PjAChMHPvhEqX3I0rbimgjQEyCdMj0Y58irEKxDiIXfaBgkF6ee1FtkzCAgWwhQASADCIQBIBw4VwMlI4AMQkadOlHrxfCKhTrIEaoBxvAhqGNES68B0BJBIgAkEE0BgEgXPKhUy4fXgPCiwAxCUa70o9GMqIgmxdcjgmgQIVg7VVYJM+/UdpWRBsBYh5NXg6zXFWKBMJapCbuPhwUQMGgcyhcotTOYwAD2UKACAAAgIKRD8FVlAJbRA8BYjIcc2lHrxfCKlefR4rUAIWJEURUFp8XZAsBYhKcvDOLfYowyXSgxtqHAILizjOcBnIiqh10XEOQLQSIAAAAAACHADEJitSkH/sUhdojm+qzH9UebAAbhlGg3IvsexA3+Byh7UbkECACQAZFtiECAHkqLzrl8uAlILwIEJOgoEr6sU8RVrka3SZwBMCxn3tReg+itK2INgLEJEiHTD8awyjUlB3WQQQQRHp57kW1nUdnO7KFABEAMojOEQBAunE9QSYRICIrKOuNsMpVAMcoAlCY6DQKlyi9B1HaVkQbAWISDOGnHxdEhFXc5zGLH02OA6Aw0SGUe1F9D6KaGovoIUAEAAAAADgEiEnQQ5NZ7FMUUsZAyrUPGVUHClLi8c41Mfui2s4jww3ZQoAIABlEIAgA4ZIP5+IoBbaIHgLEJGjQpR+9XggritQAyOkIItfEnGL/AyURICZBMJN+BN0o1FSj8qSVckwABSThcKeDKPui2kHHtQLZQoAIABkU1bkuAJCv8iHQyofXgPAiQEyCBl3+LCUAlCnuo5mbDyfnGaBwkGIaLlHa/1wrkC0EiMlw/KUdabsIq6zOO0zR+cQxARQOAsQQiOguZwAD2UKACAAAAABwCBCToIcms+gtRZgkHuPpPubjHi8E6awAcqvEOYZTQdZFtZ1HcTNkCwEiAGQQF3QACJd8OBfnw2tAeBEgJkGDLv2i2luH/Jer+UBRLbMOYMMwBzFcorT/uVYgWwgQk6B4RPqxTxGZxlq6U0zLsfYhxwRQOAgQcy8v2iQR3WxEAwEiAGQQI4UAEC6RDQrz7DUgvAgQkyAdMv3Yp4hMkZpspZgygggUpsQaNVwTcypK+z9K24poI0AsAw239OCkhkI95lMGgsFvOT6AgkGKabhEaf/T2Y5sIUAEAAAAADgEiEnQK5N+pNMhrEp8HrP08eSYAApTptdeRdmius+5biBbCBABIIMoUgMA4ZIPwVU+vAaEFwFiEuR4Zxb7FGEShiI1XOeBwkHDPlyi1CaJ0rYi2ggQ83V9nJBhn6JQC0akXPuQYwIoSBSpyb18SNUkWEQmESACQAYRCAJAuBBcAaUjQEyCFNP0y4feOuSnXBWM4JgAChNFasIlSvufawWyhQAxCRpu6cd8K0RFRlNMUxSsiVIDBUB60c7Ivqi288hIQbYQIAIAAAAAHALEZOiUSTt6vRCZghFpHs0rT081xwRQOChSEwLrorn/mQKFUAeId911l7Vt29Zq165t3bt3t3HjxqW87/3332+77767NWrUyH317t271PsDQF4JXsO5ngNAzkUpKMzn14A8ChCffvppGzRokA0ePNgmTJhgnTp1sr59+9qcOXOS3n/06NF29NFH27vvvmtjx4611q1bW58+fWzmzJkWVvTQpB/7FGGVq88jF3egMFGkJmQitPv5rCC0AeLQoUNt4MCBdsIJJ1i7du3s3nvvtbp169pDDz2U9P6PP/64nX766da5c2fbfvvt7YEHHrC1a9faqFGjLKyiOnk51CKazoH8F4p1ELnoAwWDa2DuRbWdR2c7Qhkgrly50saPH+/SRGMPULWq+1mjg+WxdOlSW7VqlW2yySYp77NixQpbtGhR3BcARFFUGyIAkK/yIbjieoLQBIjz5s2zNWvWWLNmzeJu18+zZs0q12NcdNFF1rJly7ggM9GQIUOsQYMGsS+lpWYTBVXSj0YyCrVITXmel2MCKBykmIYL518gx1VMb7zxRnvqqafsxRdfdAVuUrnkkkts4cKFsa8ZM2ZkczMZws8A9iki01hLd4ppqrUPCRABcPznRFTbJFHaVkRb9YrcuXHjxlatWjWbPXt23O36uXnz5qX+7b///W8XIL799tvWsWPHUu9bq1Yt9wUAAAAACOkIYs2aNa1Lly5xBWZ8wZkePXqk/Lubb77Zrr32WhsxYoR17drVooSevfSg1wtRka0iNXHfcnwABYN1EMMlSvs/qiOfyPMRRNESFwMGDHCBXrdu3WzYsGG2ZMkSV9VU+vfvb61atXLzCOWmm26yK6+80p544gm3dqKfq1i/fn33BQD5jIs4AIRLPpyXoxTYogACxKOOOsrmzp3rgj4Fe1q+QiODvnDN9OnTXWVT75577nHVT4844oi4x9E6ildddZWFTT6cNMKI+VYIq1wVjOCYAAoTRWrCJUr7n2sFQhsgyplnnum+khk9enTcz9OmTbMoyVVFw0LCPkWhXnBTrn3IIQEUDFJMcy+q+5wq+8jLKqYAAAAAgPAiQMxyyftCRa8XCjVrIFUqKSmmQGHieM+9qLZJKFKDbCFATECKaWbQGEZYsQ4igGxiDmLuRTbQitCmItoIEAEAAAAADgFiAiaPZ7k4B5Dnx3zKtFKOCQC0M3IiqhkcUd1uRA8BIgBkEBd0AAiZPDgV07GITCJATJRwvNGgSw/2I6IiFxddjg+gcDAHMVyidP6N0rYi2ggQyzr4OBYLe0I48l7GKxcHHy5u6UOOA6AQMZUl9+L2eYR2f1SrryJ6CBABAAAAAA4BYgJ69jKEkROEVKY/jxSpARDENTD3ojo3nGwsZAsBYrbTzQpUVE/GyH+Zng9UnrUPOSaAwsEcxNzLhw46rhvIJAJEAAAAAIBDgFhWimlEe5bChrQIhFVW10FM0WvNMQEUDqay5F5UMzi4ViBbCBABIIOi2hABAIQXwSIyiQAxAXMQM4MTGaIiW59VAkcAwvGfW1Ha/1HaVkQbAWIZCGzSg8YwCrVTqDznEI4JoHBQpCa3orz/aUshWwgQAQAAAAAOAWICemQygyI1CKtsH/P+8x93HHBIAAWDIjW5FeX9nWrZJCDdCBATMAcxQ+LawuxTFE7l4lSNQVKFgMJU4hzD4Z9VUW7n0dmObCFABAAAAAA4BIgJWAcxM+j1Qlhlu0hNLMWUEUSgIJFimluRbueRjYUsIUAEgAyiMQgA4ZE35+A8eRkIJwLEBDTmMoOJ1QirXB3zccdElHqwAaQV18TcitL+j9K2ItoIEPNo8nKYsR8RFWkvUlOOcwrHB1A4orwOX15IrBEUof3P1ARkCwEiAAAAAMAhQCwLHTRpQZEaFGyRmhLd1Zl5HgDRwFSW3Iry/me6DrKFADEBKaYZQuUthFSqAC7Tz0eaGVCYohyg5IMo728625EtBIgAAAAAAIcAMZ/WxwkxJlYjrLK+DqIfQWQUAShIJdoVHPpZlS/ZG1wzkEkEiACQQXQ6AUB4EFgBZSNATECvfmaQN4+wCsM6iMl+BpCfaGeES5T2P9cJZAsBYgKK1GQGlbdQqCN8qRqDNBIBCI3+7IpyO4/OdmQLASIAAAAAwCFATMB8oSzsV3YpwqREvYgMF6lJcU6JUi82gMIcwcpHUWrnkY2FbCFARFZwUkNYhWUOIocFUBhIL8+tKO9vUkyRLQSIAAAAAACHADEBqR+Zxz5FqD+Paf54UqQGQBAVjHMryu081pRGthAgAgAAAAAcAsQEFKnJDPLmUai9yalGCxhBBAoTx3puRbqdF1fvL0LbjcghQMyj1IMwo0gNwipn6yCSZgYUJDqHcivK+5+K8MgWAkQAAAAAgEOAmE+pByEWpR46FJasp5hSpAYobIl1sWhnZFWUszcoUoNsIUAsAwdgejAHEVGR9gAxVacTjUSgINE5FC5R2v9M10G2ECACAAAAABwCxAT04mcIlbcQUrlKK+c4AAoTI4i5FeV2HtlYyBYCRAAAAACAQ4CYgCI1mcHEaoRVpo9x1kEEUOo5h0M/q/Klncc1A5lEgJiARlsWJlZH9GSM/JTpY551EAEE0c7Irajuf9bpRjYRIAIAAAAAHALEBPTQZAYppijUdKOUI4gR7cUGsGHIHsitqLbzSmxnNDYbEUWAmKe56aHer+xShEmJa+66nDQGo9JIAZBeHPvZFdV2XlQDW0QTASIAAAAAwCFATESHTOaL1LCTESIUqQGQTaSXh0tU9n9URz4RTQSIAAAAAACHADEBPXuZxz5FQa1JluLxOA6AwkT2QG5FdR1K2qfIJgLEBAzhZ6GKKfsUhZxiWvz5p5EIFCYa9rkV2UArwwXVgCACRAAAAACAQ4CYgDLCmUGRGoQV6yACyCaO/dyKajuPdRCRTQSICUgxzYyonIBReDLdWEiVSkojEShQie182hlZFdV2HtcMhD5AvOuuu6xt27ZWu3Zt6969u40bN67U+z/77LO2/fbbu/t36NDBXn/99cpuLwAAAAAgLAHi008/bYMGDbLBgwfbhAkTrFOnTta3b1+bM2dO0vuPGTPGjj76aDvppJPs888/t379+rmvSZMmWRjRQ5MZFKlBVGRrHURGEYDCRDsjXKKy/6OaGosCCRCHDh1qAwcOtBNOOMHatWtn9957r9WtW9ceeuihpPe//fbbbf/997cLLrjAdthhB7v22mtt5513tjvvvDMd2w8AAAAASJMq6yrQbb1y5UoXDD733HNuFNAbMGCALViwwF5++eUSf9OmTRs34njuuefGbtPo40svvWRffvll0udZsWKF+/IWLVpkrVu3tt6P9rbqdatbJq1au8pmL50d+7lu9bq2Se1NMvqchWDW0lm2eu3q2M+b1d8sp9sDeAtWLLDFqxbHfm5ap6nVrFYzbY//+/LfbdnqZbGfW9RrYdWqVLN5y+bZ8jXLS9wOoLDOOU3qNLFa1WrldJsKyZp1a+y3Jb/Ffq5TvY5tWntTCzuNGM5cPDP2c82qNa1p3aY53SZEy+qlq+3t/m/bwoULbeONNy71vhWKtubNm2dr1qyxZs2axd2un7/99tukfzNr1qyk99ftqQwZMsSuvvrqErfPXDLTqq3NbgNq6eqltnTx0qw+ZyH4ZfEvud4EIKk5y5Kny6dLsGFSntsB5Le5y+bmehMKmjrwotgmWbl2ZSS3G7mzZtmact83s8NxlXTJJZe4UcfEEcT7+9xv9Teqn5Vt2K7RdjZrySxbuHJhVp6vEDSv29yNzEz/c3quNwWIU79GfWu9UWv79o9vMzKvo1GtRta4TmP7fsH3cbcrO0FfUxdMTftzAgivetXr2eYbb+7OOWttba43pyBt03Abl8kxf8V8i5ItG2xpf678k44FVNjiPxdbL+uV/gCxcePGVq1aNZs9e30Kpujn5s2bJ/0b3V6R+0utWrXcV6IOjTuUOSSaTm0btM3acxWSRrUb5XoTgKQ6NumY0cfv1KRThW4HkN86NOmQ600oaG1qtDH9FzUb1dzIWtZvmevNQMQsqrUoM0VqatasaV26dLFRo0bFblu7dq37uUePHkn/RrcH7y9vvfVWyvsDAAAAAHKjwimmSv1UUZquXbtat27dbNiwYbZkyRJX1VT69+9vrVq1cvMI5ZxzzrE999zTbr31VjvooIPsqaeess8++8zuu+++9L8aAAAAAED2AsSjjjrK5s6da1deeaUrNNO5c2cbMWJErBDN9OnTrWrV9QOTu+22mz3xxBN2+eWX26WXXmrbbLONq2Davn37ym81AAAAACC3y1zkiorUNGjQoFxlWQEAAAAAlYunKjQHEQAAAACQvwgQAQAAAAAOASIAAAAAwCFABAAAAAA4BIgAAAAAAIcAEQAAAADgECACAAAAABwCRAAAAACAQ4AIAAAAAHAIEAEAAAAATnWLgHXr1rl/Fy1alOtNAQAAAIBI8XGUj6siHyD+/vvv7t/WrVvnelMAAAAAIJIUVzVo0CD6AeImm2zi/p0+fXqZL6i8dtllF/v000/T8liZeky2MZyPl4nHLMRtLMTXnInHZBvD+XiZeEy2MZyPl4nHLMRt1OiGBgJmzJhhG2+8cVoesxD3YyYesxC3cZc8fM0LFy60Nm3axOKqyAeIVasWTZVUcJiuk0a1atXS9liZeky2MZyPl4nHLMRtLMTXnInHZBvD+XiZeEy2MZyPl4nHLNRtFD0mbb1wPWYhbmO1PH7NPq4q9T5WoM4444zQPybbGM7Hy8RjFuI2FuJrzsRjso3hfLxMPCbbGM7Hy8RjFuo2pluh7ke2MXyPF5Vt9KqsK89MxRxT2oFGDzU0moneKgAAAOQObT0gPMdYJEYQa9WqZYMHD3b/AgAAIL/Q1gPCc4xFYgQRAAAAAJB5kRhBBAAAAABkHgEiAAAAAMAhQAyBu+66y9q2bWu1a9e27t2727hx40rcR5nABxxwgFWpUsVeeumlnGwnwuX999+3gw8+2Fq2bJn0c/HCCy9Ynz59bNNNN3W//+KLL3K2rYje52fx4sV25pln2mabbWZ16tSxdu3a2b333puz7UV4DBkyxK2/tdFGG1nTpk2tX79+NmXKlLj77LXXXu5zFfw69dRTc7bNAPK/zTxr1iw77rjjrHnz5lavXj3beeed7fnnn8/p9kYVAWKOPf300zZo0CA3aXTChAnWqVMn69u3r82ZMyfufsOGDXMXWMBbsmSJ+7zoZJnq97169bKbbrop69uG6H9+dF4aMWKEPfbYYzZ58mQ799xzXcD4yiuvZH1bES7vvfeeK6/+8ccf21tvvWWrVq1ynVH6TAUNHDjQfvvtt9jXzTffnLNtBpD/beb+/fu7zipdpyZOnGiHHXaY/e1vf7PPP/8815sePSpSg9zp1q3bujPOOCP285o1a9a1bNly3ZAhQ2K3ff755+tatWq17rffflNBoXUvvvhijrYWYVXa5+Knn35yv9fnCCjv52fHHXdcd80118TdtvPOO6+77LLLsrx1CLs5c+a4z9B7770Xu23PPfdcd8455+R0uwAUVpu5Xr166x599NG4v9lkk03W3X///Vnf1qhjBDGHVq5caePHj7fevXvHbqtatar7eezYse7npUuX2jHHHON6+TVkDgDZsNtuu7le2JkzZ7oU93fffde+++47N1IEBGlNLdlkk03ibn/88cetcePG1r59e7vkkkvc9QwAMtVm1nVLo4x//PGHrV271p566ilbvny5S3lHxVSv4P2RRvPmzbM1a9ZYs2bN4m7Xz99++637/rzzznMf+EMOOSRHWwmgEN1xxx12yimnuDmI1atXdxfi+++/3/bYY49cbxpCRI0wpR/37NnTBYKeOjY333xzN8f1q6++sosuusilfmluNABkos38zDPP2FFHHeVqL+i6VbduXXvxxRdt6623ztFWRxcBYoip9/6dd94hdxpATgJEzTHTeUgNfRW10bwzNfiDPbgobPpMTJo0yT788MO429W54HXo0MFatGhh++67r/3www+21VZb5WBLAeS7K664whYsWGBvv/22y15Q8TXNQfzggw/ceQjlR4CYQ/rwVqtWzWbPnh13u35WOqmCQ11MGzZsGPf7ww8/3HbffXcbPXp0lrcYQCFYtmyZXXrppa7n9aCDDnK3dezY0VXC/fe//02ACEdFi1599VXXeaCR5tKo2qBMnTqVABFA2tvMai/feeedrsNqxx13dL9TERsFh5qmRRXuimEOYg7VrFnTunTpYqNGjYpL19HPPXr0sIsvvtil5qhR5r/ktttus+HDh+dwywHkM1Wl1JfSSoN0cdY5CoVNc1IVHKoDQR2ZW2yxRZl/469fGkkEgHS3mf0cZ65b6cEIYo6pXO+AAQOsa9eu1q1bN7echUqFn3DCCS6vOllhmjZt2pTrgoz8pnXq1Bvv/fTTT64RpkIR+oxokvb06dPt119/db/365TpM0XBI5T1+dlzzz3tggsucGsgKsVUSxs8+uijNnTo0JxuN8KRVvrEE0/Yyy+/7NZC1Npj0qBBA/d5UU++fn/ggQe6uUDq6NR8es1f1Ug0AKS7zaxrl+Ya/vOf/3SZLjr3KMVUS/Eo0wEVlOsyqli37o477ljXpk2bdTVr1nQlfD/++OOU92WZC3jvvvuu+zwkfg0YMMD9fvjw4Ul/P3jw4FxvOiLw+dGyOscff7wrIV67du1122233bpbb7113dq1a3O96cixZJ8bfemcI9OnT1+3xx57uPLytWrVWrf11luvu+CCC9YtXLgw15sOII/bzN999926ww47bF3Tpk3X1a1bd13Hjh1LLHuB8qmi/1U0qAQAAAAA5B/mIAIAAAAAHAJEAAAAAIBDgAgAAAAAcAgQAQAAAAAOASIAAAAAwCFABAAAAAA4BIgAAAAAAIcAEQAAAADgECACAAAAABwCRAAAAACAQ4AIAAAAAHAIEAEAAAAADgEiAAAAAMAhQAQAAAAAOASIAAAAAACHABEAAAAA4BAgAgAAAAAcAkQAAAAAgEOACAAAAABwCBABAAAAAA4BIgAAAADAIUAEAAAAADgEiAAAAAidKlWq2EsvvZTrzQAKTs4DxOOPP9769euX680AAABABtp5CvQSv6ZOnZrrTQOQQvVUvwAAAAA21P7772/Dhw+Pu61JkyY52x4AIR9BDBoxYoT16tXLGjZsaJtuuqn95S9/sR9++CH2+2nTprlepxdeeMH23ntvq1u3rnXq1MnGjh2b0+0GAABAcrVq1bLmzZvHfVWrVs1efvll23nnna127dq25ZZb2tVXX22rV6+O+9vffvvNDjjgAKtTp467z3PPPZez1wEUilAFiEuWLLFBgwbZZ599ZqNGjbKqVavaoYceamvXro2732WXXWb/+te/7IsvvrBtt93Wjj766BInFAAAAITTBx98YP3797dzzjnHvvnmG/vvf/9rDz/8sF1//fVx97viiivs8MMPty+//NKOPfZY+/vf/26TJ0/O2XYDhaDKunXr1uU6N33BggVJJyHPmzfPpSBMnDjR2rdv70YQt9hiC3vggQfspJNOcvfRSWXHHXd0J4vtt98+B68AAAAAqdp5jz32mBsl9DQiOH/+fNt3333tkksuid2u+1144YX266+/up+VNXbqqafaPffcE7vPrrvu6kYd77777iy/EqBwhGoO4vfff29XXnmlffLJJy449COH06dPdwGi17Fjx9j3LVq0cP/OmTOHABEAACBkNC0oGOTVq1fPteU++uijuBHDNWvW2PLly23p0qVuGpH06NEj7rH0szLIABRIgHjwwQfb5ptvbvfff7+1bNnSBYgKDFeuXBl3vxo1asS+V++SJKahAgAAIPcUEG699dZxty1evNjNOTzssMNK3D842giggAPE33//3aZMmeKCw913393d9uGHH+Z6swAAAJBmShNVuy8xcEz08ccfu7mKwZ932mmnLGwhULhCEyA2atTIVS697777XNqo0kovvvjiXG8WAAAA0kxTilStvk2bNnbEEUe4woQqRDNp0iS77rrrYvd79tlnrWvXrq7K/eOPP27jxo2zBx98MKfbDuS7nFcxVWpo9erV3YnhqaeesvHjx7u00vPOO89uueWWXG8eAAAA0qxv37726quv2siRI22XXXZxxWduu+02N9UoSGmoah9qzuKjjz5qTz75pLVr1y5n2w0UgpxXMdXiqUovuPPOO3O5GQAAAABQ8HI2gqjyxuo5Gj16tPXu3TtXmwEAAAAAyPUcxBNPPNE+/fRTO//88+2QQw7J1WYAAAAAAMKSYgoAAAAACIecF6kBAAAAAIQDASIAAAAAIHsB4pAhQ1wJ44022siaNm1q/fr1c4ujBi1fvtzOOOMMtxZi/fr17fDDD7fZs2fHfq+1cY4++mhr3bq11alTx3bYYQe7/fbb4x7jt99+s2OOOca23XZbt2zGueeem42XBwAAAAB5ISsB4nvvveeCv48//tjeeustW7VqlfXp08eWLFkSu4/WPfy///s/tyCq7v/rr7/aYYcdFvu91kdUcPnYY4/Z119/bZdddpldcsklcctjrFixwpo0aWKXX365derUKRsvDQAAAADyRk6K1MydO9cFewoE99hjD1u4cKEL7J544gk74ogj3H2+/fZbN0o4duxYt3hqMgo6J0+ebO+8806J3+21117WuXNnGzZsWMZfDwAAAADkg5zMQVRAKJtssklsdFCjisH1ELfffntr06aNCxBLexz/GAAAAACAiK2DuHbtWjc3sGfPnta+fXt326xZs6xmzZrWsGHDuPs2a9bM/S6ZMWPG2NNPP22vvfZaVrYbAAAAAPJd1gNEpYVOmjTJPvzww0o/hv7+kEMOscGDB7u5jAAAAACAiKWYnnnmmfbqq6/au+++a5tttlns9ubNm9vKlSttwYIFcfdXFVP9Luibb76xfffd10455RRXjAYAAAAAEKEAUXVwFBy++OKLrqDMFltsEff7Ll26WI0aNWzUqFGx27QMxvTp061Hjx6x21S9dO+997YBAwbY9ddfn41NBwAAAICCUT1baaWqUPryyy+7tRD9vMIGDRq4NQ3170knnWSDBg1yRWc23nhjO+uss1xw6CuYKq10n332sb59+7r7+ceoVq2aq4DqffHFF+7fxYsXu2qp+lnzG9u1a5eNlwoAAAAAkZWVZS6qVKmS9Pbhw4fb8ccf775fvny5nX/++fbkk0+69QwVCN59992xFNOrrrrKrr766hKPsfnmm9u0adNKfa7E+wAAAAAAQrIOIgAAAAAgfHKyDiIAAAAAIHwIEAEAAAAADgEiAAAAAMAhQAQAAAAAOASIAAAAAACHABEAAAAA4BAgAgAAAAAcAkQAAAAAgEOACAAAAABwCBABAAAAAA4BIgAAAADAIUAEAAAAAJj8P5+DufkAxdkQAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tadkit.utils.decomposable_tadlearner import decomposable_tadlearner_factory\n",
    "\n",
    "StandardForestLearner = decomposable_tadlearner_factory(StandardScaler, IsolationForestLearner, [], {})\n",
    "learner = StandardForestLearner()\n",
    "learner.random_state = -1\n",
    "learner.fit(X)\n",
    "anom_score = -learner.score_samples(X)\n",
    "\n",
    "iso_score = pd.DataFrame(anom_score, index=X.index, columns=[\"iso forest anomaly score\"])\n",
    "\n",
    "ceil = .55\n",
    "\n",
    "iso_anomalies = iso_score[iso_score > ceil]\n",
    "iso_anomalies.rename(columns={\"iso forest anomaly score\": \"iso forest anomalies\"}, inplace=True)\n",
    "\n",
    "pd.concat([iso_score, iso_anomalies, y], axis=1).plot()\n",
    "plt.axhline(y=ceil, color='r', linestyle='-')\n",
    "learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ExponantialMean:\n",
    "    def __init__(self, lags, sub_X=False) -> None:\n",
    "        self.lags = np.array(lags)\n",
    "        self.sub_X = sub_X\n",
    "        assert sum(d != 1 for d in self.lags.shape) <= 1\n",
    "        self.lags = self.lags.reshape(-1)\n",
    "\n",
    "    def transform(self, X):\n",
    "        lags_len, = self.lags.shape\n",
    "        index = X.index\n",
    "        columns = sum(\n",
    "            ([f'{colname}_ema{i}' for i in range(lags_len)] for colname in X.columns),\n",
    "            start=[]\n",
    "        )\n",
    "        t = index.astype('int64') * 1e-9\n",
    "        X = X.values\n",
    "        n, d = X.shape\n",
    "        result = np.empty((n, d, lags_len))\n",
    "        result[0] = X[0, :, None]\n",
    "        for i in range(n-1):\n",
    "            weight = np.exp(-(t[i+1] - t[i]) / self.lags)[None, None]\n",
    "            result[i+1] = result[i] * weight + X[i+1, :, None] * (1 - weight)\n",
    "        if self.sub_X:\n",
    "            result -= X[:, :, None]\n",
    "        result = pd.DataFrame(\n",
    "            result.reshape((n, -1)),\n",
    "            index=index,\n",
    "            columns=columns\n",
    "        )\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X0_ema0</th>\n",
       "      <th>X0_ema1</th>\n",
       "      <th>X1_ema0</th>\n",
       "      <th>X1_ema1</th>\n",
       "      <th>X2_ema0</th>\n",
       "      <th>X2_ema1</th>\n",
       "      <th>X3_ema0</th>\n",
       "      <th>X3_ema1</th>\n",
       "      <th>X4_ema0</th>\n",
       "      <th>X4_ema1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-01 00:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 01:00:00</th>\n",
       "      <td>-0.005038</td>\n",
       "      <td>-0.005180</td>\n",
       "      <td>-0.023719</td>\n",
       "      <td>-0.024387</td>\n",
       "      <td>-0.025852</td>\n",
       "      <td>-0.026580</td>\n",
       "      <td>0.021447</td>\n",
       "      <td>0.022051</td>\n",
       "      <td>0.028259</td>\n",
       "      <td>0.029055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 02:00:00</th>\n",
       "      <td>-0.031711</td>\n",
       "      <td>-0.032745</td>\n",
       "      <td>-0.048762</td>\n",
       "      <td>-0.050794</td>\n",
       "      <td>-0.001294</td>\n",
       "      <td>-0.002048</td>\n",
       "      <td>0.037732</td>\n",
       "      <td>0.039390</td>\n",
       "      <td>0.037896</td>\n",
       "      <td>0.039748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 03:00:00</th>\n",
       "      <td>-0.015200</td>\n",
       "      <td>-0.016647</td>\n",
       "      <td>-0.069807</td>\n",
       "      <td>-0.073777</td>\n",
       "      <td>-0.007043</td>\n",
       "      <td>-0.007985</td>\n",
       "      <td>0.054845</td>\n",
       "      <td>0.058025</td>\n",
       "      <td>-0.014293</td>\n",
       "      <td>-0.012868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01 04:00:00</th>\n",
       "      <td>-0.067253</td>\n",
       "      <td>-0.070574</td>\n",
       "      <td>-0.048571</td>\n",
       "      <td>-0.053855</td>\n",
       "      <td>-0.062409</td>\n",
       "      <td>-0.065097</td>\n",
       "      <td>0.030707</td>\n",
       "      <td>0.034709</td>\n",
       "      <td>0.041473</td>\n",
       "      <td>0.044046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-11 11:00:00</th>\n",
       "      <td>-0.051206</td>\n",
       "      <td>-0.211574</td>\n",
       "      <td>0.117038</td>\n",
       "      <td>0.144954</td>\n",
       "      <td>-0.001470</td>\n",
       "      <td>0.090219</td>\n",
       "      <td>-0.091695</td>\n",
       "      <td>-0.115633</td>\n",
       "      <td>0.091504</td>\n",
       "      <td>0.073529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-11 12:00:00</th>\n",
       "      <td>-0.028717</td>\n",
       "      <td>-0.187681</td>\n",
       "      <td>0.081779</td>\n",
       "      <td>0.111612</td>\n",
       "      <td>0.058907</td>\n",
       "      <td>0.150990</td>\n",
       "      <td>-0.060468</td>\n",
       "      <td>-0.085780</td>\n",
       "      <td>0.081817</td>\n",
       "      <td>0.066394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-11 13:00:00</th>\n",
       "      <td>-0.007902</td>\n",
       "      <td>-0.164897</td>\n",
       "      <td>0.104148</td>\n",
       "      <td>0.136504</td>\n",
       "      <td>-0.004352</td>\n",
       "      <td>0.086338</td>\n",
       "      <td>-0.138787</td>\n",
       "      <td>-0.167659</td>\n",
       "      <td>0.096796</td>\n",
       "      <td>0.084312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-11 14:00:00</th>\n",
       "      <td>0.077915</td>\n",
       "      <td>-0.074720</td>\n",
       "      <td>0.158832</td>\n",
       "      <td>0.195215</td>\n",
       "      <td>0.010635</td>\n",
       "      <td>0.100374</td>\n",
       "      <td>-0.104210</td>\n",
       "      <td>-0.135619</td>\n",
       "      <td>0.077579</td>\n",
       "      <td>0.067452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-11 15:00:00</th>\n",
       "      <td>0.106862</td>\n",
       "      <td>-0.040657</td>\n",
       "      <td>0.170498</td>\n",
       "      <td>0.211182</td>\n",
       "      <td>0.018675</td>\n",
       "      <td>0.107702</td>\n",
       "      <td>-0.075242</td>\n",
       "      <td>-0.108337</td>\n",
       "      <td>0.067626</td>\n",
       "      <td>0.059545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      X0_ema0   X0_ema1   X1_ema0   X1_ema1   X2_ema0  \\\n",
       "2021-01-01 00:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2021-01-01 01:00:00 -0.005038 -0.005180 -0.023719 -0.024387 -0.025852   \n",
       "2021-01-01 02:00:00 -0.031711 -0.032745 -0.048762 -0.050794 -0.001294   \n",
       "2021-01-01 03:00:00 -0.015200 -0.016647 -0.069807 -0.073777 -0.007043   \n",
       "2021-01-01 04:00:00 -0.067253 -0.070574 -0.048571 -0.053855 -0.062409   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "2021-02-11 11:00:00 -0.051206 -0.211574  0.117038  0.144954 -0.001470   \n",
       "2021-02-11 12:00:00 -0.028717 -0.187681  0.081779  0.111612  0.058907   \n",
       "2021-02-11 13:00:00 -0.007902 -0.164897  0.104148  0.136504 -0.004352   \n",
       "2021-02-11 14:00:00  0.077915 -0.074720  0.158832  0.195215  0.010635   \n",
       "2021-02-11 15:00:00  0.106862 -0.040657  0.170498  0.211182  0.018675   \n",
       "\n",
       "                      X2_ema1   X3_ema0   X3_ema1   X4_ema0   X4_ema1  \n",
       "2021-01-01 00:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2021-01-01 01:00:00 -0.026580  0.021447  0.022051  0.028259  0.029055  \n",
       "2021-01-01 02:00:00 -0.002048  0.037732  0.039390  0.037896  0.039748  \n",
       "2021-01-01 03:00:00 -0.007985  0.054845  0.058025 -0.014293 -0.012868  \n",
       "2021-01-01 04:00:00 -0.065097  0.030707  0.034709  0.041473  0.044046  \n",
       "...                       ...       ...       ...       ...       ...  \n",
       "2021-02-11 11:00:00  0.090219 -0.091695 -0.115633  0.091504  0.073529  \n",
       "2021-02-11 12:00:00  0.150990 -0.060468 -0.085780  0.081817  0.066394  \n",
       "2021-02-11 13:00:00  0.086338 -0.138787 -0.167659  0.096796  0.084312  \n",
       "2021-02-11 14:00:00  0.100374 -0.104210 -0.135619  0.077579  0.067452  \n",
       "2021-02-11 15:00:00  0.107702 -0.075242 -0.108337  0.067626  0.059545  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MINUTE = 60\n",
    "HOUR = 60 * MINUTE\n",
    "DAY = 24 * HOUR\n",
    "exponential_mean = ExponantialMean([DAY, 3* DAY], sub_X=True)\n",
    "exponential_mean.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExponantialMeanIsolationForest()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ExponantialMeanIsolationForest</div></div><div><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>ExponantialMeanIsolationForest()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "ExponantialMeanIsolationForest()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EMAForestLearner = decomposable_tadlearner_factory(ExponantialMean, IsolationForestLearner, [], {\n",
    "    'lags': {\n",
    "        'description': 'Lags typical time of exponential mean',\n",
    "        'family': 'preprossing',\n",
    "        'value_type': 'set',\n",
    "        'start': 0,\n",
    "    },\n",
    "    'sub_X': {\n",
    "        'description': 'Value substract from to the lags',\n",
    "        'family': 'preprossing',\n",
    "        'value_type': 'bool',\n",
    "    },\n",
    "})\n",
    "learner = EMAForestLearner(lags=np.geomspace(.1 * DAY, 2 * DAY, 10), sub_X=True)\n",
    "learner.random_state = -1\n",
    "learner.fit(X)\n",
    "anom_score = -learner.score_samples(X)\n",
    "ema_score = pd.DataFrame(anom_score, index=X.index, columns=[\"ema iso forest anomaly score\"])\n",
    "\n",
    "ceil = .5\n",
    "\n",
    "ema_anomalies = ema_score[iso_score > ceil]\n",
    "ema_anomalies.rename(columns={\"ema iso forest anomaly score\": \"ema iso forest anomalies\"}, inplace=True)\n",
    "\n",
    "learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x29528e1e630>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat([ema_score, ema_anomalies, y], axis=1).plot()\n",
    "plt.axhline(y=ceil, color='r', linestyle='-')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tadkit-uv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}