{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "105fe78c-cb4e-4647-a733-bb8dc796a123",
   "metadata": {},
   "source": [
    "# Simple Time Series Decomposition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b99a4a24-bdfb-4852-a2e8-debd696752f1",
   "metadata": {},
   "source": [
    "In this notebook we will see how time series decomposition work. This is not the original breakdown of the `statsmodels` seasonal decomposition instead this post will help to understand each and every component of the decomposition process."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a05c27b2-86d5-44c8-96f2-495a78d8cb69",
   "metadata": {},
   "source": [
    "## Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b7c3e3bc-21ec-4234-bb54-6b539b97656a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels as stm\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1029a303-4985-43cd-bcdf-c9dfaa7c55a2",
   "metadata": {},
   "source": [
    "## Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c3068f27-0fb4-448b-aba6-894a7fc660d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"TS/data.csv\", index_col='Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a59c22a-c888-4a36-9106-b69ad2e965e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "56fbf380-ff6a-47c8-8444-47b5b31c3287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08</th>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09</th>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10</th>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11</th>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Passengers\n",
       "Month              \n",
       "1949-01         112\n",
       "1949-02         118\n",
       "1949-03         132\n",
       "1949-04         129\n",
       "1949-05         121\n",
       "...             ...\n",
       "1960-08         606\n",
       "1960-09         508\n",
       "1960-10         461\n",
       "1960-11         390\n",
       "1960-12         432\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed99a662-91b3-4a48-9dc9-4f128c3923c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 504x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "\n",
    "series = df\n",
    "result = seasonal_decompose(series, model='multiplicative', period=12)\n",
    "\n",
    "fig = result.plot()\n",
    "\n",
    "fig.set_size_inches((30, 9))\n",
    "fig.autofmt_xdate(rotation=90)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d137fd1e-c4c6-49b9-97b6-ac3d31b70657",
   "metadata": {},
   "source": [
    "## Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4aee6bd8-e824-45f1-82e3-137628e9fa88",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"TS/data.csv\", index_col= 'Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "546c3cb8-1570-4252-a1bb-dcb5a40fde32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Passengers\n",
       "Month              \n",
       "1949-01         112\n",
       "1949-02         118\n",
       "1949-03         132\n",
       "1949-04         129\n",
       "1949-05         121"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "286f56f7-e531-457a-822f-becc4eafa4e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e01d9374-5c24-4ae4-b36c-e8c2837b3041",
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardize(df, ts:str, data:str):\n",
    "    df.sort_index(inplace=True)\n",
    "    df.reset_index(inplace=True)\n",
    "    df.rename(columns={ts:\"ts\", data:\"data\"}, inplace=True)\n",
    "    df.reset_index(inplace=True)\n",
    "    return df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2aa34a1-0bc0-4cf5-9078-cf65fe2c6262",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_r = standardize(df, ts='Month', data = 'Passengers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eee5e243-ab27-46f9-b064-dd67b0f32d42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ts</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1949-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1949-02</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>139</td>\n",
       "      <td>1960-08</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>140</td>\n",
       "      <td>1960-09</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>141</td>\n",
       "      <td>1960-10</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>142</td>\n",
       "      <td>1960-11</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>143</td>\n",
       "      <td>1960-12</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index       ts  data\n",
       "0        0  1949-01   112\n",
       "1        1  1949-02   118\n",
       "2        2  1949-03   132\n",
       "3        3  1949-04   129\n",
       "4        4  1949-05   121\n",
       "..     ...      ...   ...\n",
       "139    139  1960-08   606\n",
       "140    140  1960-09   508\n",
       "141    141  1960-10   461\n",
       "142    142  1960-11   390\n",
       "143    143  1960-12   432\n",
       "\n",
       "[144 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "194c289f-f1e7-4df9-96f3-682c3a3b444b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'ts', 'data'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_r.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2b9d6f39-2593-4160-b00d-af7585d32675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bPeX7vcZmsJY+b2RR124S3IUQU4TDmvb+UWqKJ7bJLc3NtDUt0+/xRwJeNDuO2guHtZGWSWLmHgxr+pLMg7f0ecl3OSmYdLJTMorNN7I+jx+3x4/XH5rVm0QyJLgLIaboGvbhD4XnNC0TCmsGRqceXA1GWibVnHuvZ4xgWCfcaFQeKYdMLjXT0uelrmR2Adn6Wfs9/pTSO8mQ4C6EmKK936pxnzhzL8nNwOMPMepPfSFwcDSA1lAcYwac70p95t41aLwJxesrY4nsUk3yTStWuipZGU4HeZlO+rz+lNI7yZDgLoSYwkq9TM4Hl9m4kclaVJx8tinYc47qud4RAKqLpp7AFC2ySzXGzF1rzekLw5Gvg6Ewbf2js6pxtxSZG5la3EZwr5nFZqhkSHAXQkxhtRgozZ0Y3K2e5HYEd6vWO2bO3YaZ+8Hz/WRnpLG+Im/ax1knNMXqL/PLY53c/JUX2HfODUBjzwj+UJgNldPfczqR4N7npTwvk6yMqYeI2EGCuxCL1H8f6+T/PH3a1ha8lsiselI+vCTH2qWaesVMf5zXAKMzZKrVMgea+9leV5iw/0umM43C7HS6Ju1S1Vrz9efPAvBacx8Ar7cNArC1umDW4yoxO0O2pJDeSYYEdyEWqZ/sb+Hfn2vkF4fbbb93n8dPnss5YecoQGmefWmZ6WbueS4n/lCYseDscvtDvgCnLgyxa2VxUo+vyHNNmbm/cKaXk51DKAVHWgcAeKN9kOyMNBpKc2c1LjCbh3kCKeXukyHBXYhFqqnXA8CXHj9Om9kqwC69I2NTUjJgzDqt76eqz9ylGa/OHWbeGTJk1qofbhkgrGFXfVFSzyvPz6Rr0oLqN54/S2W+i9svWcGR1gG01rzePsjmFfmkJXEgdjzFOen0jIzROeRLKXefSNLBXSmVppQ6rJR60vy6WCn1jFLqjPmxKOqx9yulGpVSp5VSN8/FwIVYzsaCIToGR3nvjho08PnHjtl6/7449eeudKNPix1tf/u9flzpjpg559l0hvzuS01c+8+/Y8gX4GBzHw4F2+uSC+4V+S4uDI4fXP162yB7z7n52Fsa2F1fRO+IkUY50TnElhRSMmDk3P3BMFrPXaUMzGzm/ingZNTXXwCe1VqvBZ41v0YptQm4C9gM3AI8qJSamxUDIZap1j4vWsM1a0v56NX1vNzotrVPSbzgDkZqpsemapniGLN2gPwsq8FW8i0ITnYO0T4wyoO/O8trzf1srMqPvEkkUl2YRffwGH7zUO6953oB+IOdNWyrNd4gHjvYhi8QTinfDkz4mWdbL5+MpIK7UqoGeAfw7ajLdwB7zM/3AHdGXX9Yaz2mtW4CGoHdtoxWCAFAU6+RhllZks3KkhxgvHe5HXpH/JEUzGRl5nb9VPV7/DHLIAFWFBq15x0DozG/H4vVFuG7LzdxqKWfy+qTy7eDUc+vtdGLBozmYPkuJ0U5GWyoyiPT6eAnr7UCqS2mwsTSz4Uwc/8K8FdAdBPiCq11J4D5sdy8Xg20Rj2uzbw2gVLqXqXUAaXUgZ6enpmOW4hl7bzbyLc3lOZQZQbCzhkEwumEw5p+b+yGXmDUhc+k0VY8/d74vx1Ytd/WZqpkuEfG2FCZhwLGgmF2rkwuJRP9em3m67X2j/drT09zsKW6gJ7hMbIz0lhVNvvFVBhfQM50Oib0sbdbwuCulLod6NZaH0zynrFWGqbUammtH9Ja79Ja7yorK0vy1kIIMBZTC7LSKczOYEWBsUmnw6aZ+5AvQCisI+1pJyvPc82oRW48/d5AzMVUgIKsdPJczhktFPeO+Nm0Ip8/vXYVTodid8PMZu5A5PUmn7S0rbYQgE1VqS2mwvgCcm1xNo4U7zWdZBJSVwPvUkrdBriAfKXUD4EupVSV1rpTKVUFdJuPbwNqo55fA3TYOWghlrtmt4f6UiMdY3U9tGvmbqU34qVlyvMz8fpDjIwFk85pxzJdXh+MPHhbkjN3rXWkwufTb1vH+3bVJmw7EK2ywIVDGb8phMOatv5R3raxIvJ9K7inupgK4/9d5zIlA0nM3LXW92uta7TW9RgLpc9prT8EPAHcYz7sHuBx8/MngLuUUplKqQZgLbDf9pELsYw193qpNxfjXOlpFOdk2DZztzYoTZeWgdjb9ZMVDIUZHA1QOE1nxZqibNqTfMPy+EOMBcOU5mbgcKgZlximpzmoKjDeTHpGjIXV2qi2Bbsbisl0OrhqdcmM7htLQVY6Toea8+A++7dd+DLwiFLqY0AL8D4ArfVxpdQjwAkgCNyntZ6740aEWGZ8AaMMsr6kJnKtqmBiKV8q+jxGyiXerLo8b7zR1mzzzwOjgWlfA4xUyavn3Gitpz1JCYh0qiyJk0pKhvWbgtXQqyYq+Fbkuzj4P28ix4ZWAQ6H4j8+sJ3NK1L/LWA6MwruWuvngefNz93AjXEe9wDwQIpjE0LE0NZvlEE2mGkZwJx12rORaTwtEyfnbvViSSHvPl3rAUtNURYjY0Fzhh//cQBu8w0p3m8bybDeTCJnx05q6JVKCmqyW7ZU2XaveGSHqhCLTHQZpGVFoWtGZYPTsdIy1pFwk9mRlrHq1xPN3IGk8u7xGp3NRE1RFheGfJzr8Ux4/cVKgrsQi0xz73gZpKWqIIshXxBPip0UYbyvTKYzdgqiICudDKcjpVr3eI3Jok0uT5yO25bgnk1Yw/6mPiryM3GlL+69lxLchVhkmt3jZZAWa9NPpw15d7cn/gYmAKUUZbmZqaVlpmkaZplcnjgdq9fNdPdLxOr7frh1YEpKZjGS4C7EItPs9kQqZSzWOaEdA6lXzPR5xhIGyfL8TLqHZ/9a1sx9umqZgqx0cjOdSc7cxyK/UcyW9WbiD4bntKHXxSLBXYhFpnPAN+X0nhWFRmCyZeY+4qckQXrD2KWa2oJqdkbatKkPpRTVhVlJlUP2euLvqE1WVUEWVlFO7SLPt4MEdyEWne7hsSnH31Xku1DKnpl7orQMpL5Ltc/rnzbfbqkpSm4jU+/wGKUplEGCcb5phVnmKTN3IURMc3E6EoBnLMjIWHDK7ssMp4PS3EwupLiRKRzW9CfYOQrGzH1wNDDrTpQD3kBS+XEjuCfOubs9/sgRgKmwUjMS3IUQU3QOjnLJ3z0dOZrNTtZsuTxv6ix1RYGLjhTTMkO+AMGwTirnDiRdMfNm1zDB0HjfQfc0HSGj1RRlM+wzat2n4x4ZS2kD0/jrSXAXQsRxqnOYYV+QRw+02X7vLrO2PFbflKqCrJTb/lobmBKVFI7vUk38en0eP7d+9cXIeaSD3gAnO4dYk8TuVquCJVZ3yFfO9nK4pZ9AKEy/N5Byzh1gQ1U+RdnpkQXqxUyCuxA2s2bPT524QCAUTvDomYnM3POnBt+qQhedA6MppYSsKpZkZ+7JLKo29XoIhTU/2d9CKKz55bEO/MEw794+pRP4FPHKIbXWfPanR/nCY69HdrumUuNu+ejVDfz2s9el3PlxIZDgLoTNOs1FzQFvgH3n7E3NWLtCrYW/aFUFLjz+EEMzOHf068+f5d0PvhzJnVtplsQ59/H+MolYgblj0McLZ3p47FAb6yvy2FKdn/C541VAE39DaO0b5cKQj9NdwxxqGQCg1IaZe4bTkbBSaLGQ4C6EzToGRynNzSQ7I41fvdFp6727h8fIcDrIz5ra56SqYOblkK8193G4ZYD/7zen8IwF+epvz1CYnR5pJxxPSU4GaQ6VVFrGqnYpyk7nfz91msMtA7x3Z3XCZmDW62Q4HVNaK+xrckc+/8n+FuOxSyQo20WCuxA26xzwsbIkmxs2lPPUGxcIhe2rnOka8lGRnxkzMEZ2qc6gHLJryIdS8L2Xm/ngt/dxpnuYf3//9oRNshwORWluRlJpmbZ+L6W5GfzRZXUc7xjCoeDObYlTMmDUulcVuKa0M97f1EdhdjrrK/J44YxxkpsdaZmlRIK7EDbrGBylqsDFbVurcHv8E2aZqeoeGoukRCYbn7nPJLiPcee2alaX5XCkdYDP37KBa9YmdzJasrXurX2jVBdlc9dlxhk+164ro3wGC5ZVBa4pB5Hsb+7jsvpibt5cgbXEYMeC6lIiwV0IG2mt6Rz0UV2YxXXrylAKW/PuXcPGzD2W8rxMHCr5tEwgFMbtGaOuOJvv3HMZ//Serdx77aqkx1KR70rqtdr6vdQWZVFfmsO//uGlfPG2jUm/BsCKSVVAFwZ9nHd7ubyhmJs2VQKQkeYgz8aWvEuBBHchbOT2+PEHw1QVuMjJdFJTlMXZnhHb7t8zzczdmeagIt+V9C7V3pExtDaCdH1pDu/fXZdUHtxSV5xNa9/01TmhsKZ9YDTSLuE9O2pYW5GX9GuAUQV0YcgXSW/tN/cP7G4oZkt1PlUFLkpzM2Y09uVAgrsQNrLy3VVmlceaslwau+0J7l5/kOGxYMwySEtVQXKzaTBSMkDc3wQSqSvOYjQQivRSB+M3lx/sbeYPv7GXfo+f7mEfgZBOqTd6VUEWobCOVPLsb3KTk5HGpqp8lFL8+VvX8O4dyeXwlxP5PUYIG1k17ivM/Pea8lxePusmFNYp105bi5fxZu5gBMITnUNJ3W+6DVHJqDM7U7b0eSkz2xF89qdHePZUNwAvnOmJrAOksuOz2nyj7BgcpbLAxf6mPnbWF+NMM+amd1+xctb3Xspk5i6EjayFvyqzcmVNeS7+YNiWI/DGg/H0M/eOJDcyWTXz0/0mMB3rgGfrzNE9rzTz3Olu/sc7NpLncvLqOXfk505p5l5otTMeZdgX4Ez3CDvrimZ9v+VCgrsQNuoc9BkbYcxNQGvKjS32duTdrcqU6WbaVYVZjAXDkWPsptM1NEaaQ826J4uVR28xg/vxjkEaSnP4+DWruLyhmL1n3bT2GW921ux7NiJVQAM+Xm8fRGu4tHZuD5deCiS4C2Gj9oFRVhS4Iot7q83+KXbk3a2Ze6ymYZYVBcmfyNQ15KM0N2PW6SJXehoV+ZmR4H7qwjAbK41dp1esKqHZ7eXA+T7K81I7si7f5SQnI42OwVGOtQ0CcElN4azvt1xIcBfCRp2DvshME6AwO4PS3AxbgnuPuTu1ICv+6UXWQm4yG5m6hsdmnW+31BVn09LnZWQsyHm3lw2VRiXMlatLAHipsTflDotKKaoKs+gc8HGsbYDa4qyUjtNbLiS4C2GjzoHRSI7YstqmipmuIR/lebF3p1pmMnPvHvJNuzibjNribFr7vJy+MAwYXRUBNlbmU5idjtap5dstVhXQ0dZBLqkuTPl+y4EEdyFsEgpruobHIpUyljXlRnBP9QCP7iRm2iW5mTgdasp2/VisVgapqCvO5sKQMaMGIjN3h0NxeUMxYE9wry7M4kz3CO0Do1xSI/n2ZEhwF8Im3cPGRptYM/chX3BCPXgyog+30FpzYdA3bb4dIM2hjJ2jCc4dHQuG6PcGbEnLaA3Pnuwm19y0ZblilZGaqS1K/eCLqoIsvH6jc6Xk25MjwV0Im1g7Q2PN3GFmi6oXBn1s+dun+MxPjzA4GuB//fdJzvV62FKdeNa6onBqoy3LqQtDdA/7IjXzdszcAV4952ZDZd6ElNHbNlZQVeBix8rUyxatN0ylYKvM3JMim5iEsEnHpBp3SyS494xEFhoTOdE5iC8Q5heH23nq+AW8/hB/fFU9n7hudcLnVhVkcbi1f8r1tn4v7/7aK+xuKOYvblwDMKMGXrFYwT0Y1myomthWoLY4m73335jS/S3WG+bqstyEHSuFQWbuQtik3QzuNZPSEFUFLnIy0jg7g5l7c69RXvjQ3TtZVZbDX9+2gS+9cxOOJMoWqwpdXBj0EY5qNay15kuPH2c0EOKFMz0cOj8AxD70YybK8jLJdBphZENl4sM3Zst6w5R8e/LkLVAIm7T1eynMTp8ys1RK0VCWQ1OvJ+l7nXd7yM10ctOmCt6+uXJG41hRkEUgpHF7/JSZOfqnjnfx7KluPnJ1PXteaeabL5wDUk/LKKWoK87mTPcIG6tm1hBsJqoLs6gqcHHDhvI5e42lJuHMXSnlUkrtV0odVUodV0r9nXm9WCn1jFLqjPmxKOo59yulGpVSp5VSN8/lDyDEQtHWPxq3MqShNHdmwb3Py8qS7Fl1OqwqGN+uDxAOa/7+l8fZUJnHX9+2kbeuL6d3ZIz0NEVRdur14lZqZt0Muz3OhCs9jb3338jtl6yYs9dYapJJy4wBN2itLwW2Abcopa4AvgA8q7VeCzxrfo1SahNwF7AZuAV4UCk1++1pQtioqdfDma7hObl3W/8oNYWxK0MaSnNo6/cyFgwlda/zbi/1JdMfdRfPSvN5zW7jzaR9YJSOQR93X7mS9DQH799dBxgNyJJJ8yRy/foy3raxnDxX/M1V4uJLGNy1wUoWppt/NHAHsMe8vge40/z8DuBhrfWY1roJaAR22zloIWbr/p8f448eepV+z8zKEhPRWtPW7407c19VmkNYjzfZmk4wFKa1zxvpujhT9aXZONR4dU6j2ddmbbkxs75+fRmV+S4qC1LLt1vuvrKeb99zmS33EvZJakFVKZWmlDoCdAPPaK33ARVa604A86OVDKsGWqOe3mZem3zPe5VSB5RSB3p6elL4EYRIXmP3CH0eP1/+9Slb79vn8eMLhKmOm5YxZtPnehKnZjoHfQTDmvpZBvdMZxorS3Iiwd1ayLWqdpxpDr5x907+5vZNs7q/WBySCu5a65DWehtQA+xWSm2Z5uGxfs+bsjVPa/2Q1nqX1npXWVlyZzYKkYohX4DeET+luZn89EArrzXbd/xdW3/sShlLvRXck8i7W+mUlbNMy4BRMmh1omzsHqE4J2NCP5ZttYVcWls46/uLhW9GpZBa6wHgeYxcepdSqgrA/NhtPqwNqI16Wg3QkepAhUhVsxlY/8c7NlJdmMU/PHnCtnuPB/fYM/eCrHRKczNoSmLm3uw2UjcrZzlzB2OW3tTrIRgK09g9whqzO6VYPpKplilTShWan2cBbwNOAU8A95gPuwd43Pz8CeAupVSmUqoBWAvst3ncQsyYVa2yaUU+791Zwxvtg/gCyS1wJmIdShEvLQNGaiaZipkWt4dMpyOlGvTVZTkEQpqWPi9ne0ZYXS7BfblJps69CthjVrw4gEe01k8qpfYCjyilPga0AO8D0FofV0o9ApwAgsB9Wmt7/gUJkYKmXg9KGaV76ypyCWsjB75pReqbb9oHRsl3OcmfpmKkoTSH504lXl9qdhtlkKlUslj59dea++j3BlhdNvsUj1icEgZ3rfUxYHuM624g5t5irfUDwAMpj04IGzX1eqguzMKVnhapHDnTPWxLcDdq3KdPozSU5tI70saQLzDlTaBjYJTfv9nDH+2q5bzbQ11xasHYmqn/5o0LwHiwF8uH7FAVy0ZzrydStVJfmk2aQ9nSZx2MtEyiunTrtZt7PRM6G3YP+3j/t17lvNtLx8AoLX1erl2bWpFBviudivxMXm50AxLclyPpLSOWBa0156KCu1EumM2ZrtSDu1HjPjptvh2IpEai8+6D3gAf/s5+eobHuH59Gf/+XCO+QJiVpamnUdaU5+IPhclKT5vSqVIsfRLcxbLg9vgZ9gUnzK7XlufyZnfqu1UHvAG8/lDCtExdSTZKTax1/8YLZznTPcI3797JNz60M9IYa2WKR9MBkQqZ1eU5tuxEFYuLBHexLFhlkA1l0cE9j/Pu5FsCxJOoDNKS6Uyjpihrwsz9TNcwa8pyuWZtGa70NL55907+5JoGdpunGKXCyruvljLIZUmCu1hQmns93PSvv+dwy9R+5KmwNg81RM/cK3IJhXWkve5sWZuOkjlObnIDsfPuiW0Gqgqy+OI7NuFKT70dkzVzlxr35UkWVMWCEQyF+fRPj3Cme4RXzrrZXpf6CT6Wpl4PToeaEICtRcYz3cOsr0y+o2E4rNnX1MfjR9p5qbGXtv5RHCr+7tRoq0pzePR8f+Q81ZY+L9eum5sd2ltqCti8Ip/r1ssO8OVIgrtYML72u7McaR0gPc2+KhZLc6+HupJsnGnjv6yuLstFKWa8qPr3T57g+680k5ORxnXry3j/7jp2NxRTkJW4K2JDaQ4jY0F6RsbQGsaC4ZR2ok4n35XOf//FNXNyb7HwSXAXC0Jj9wj/9twZ7ty2gt4Rv+3BvanXMyElA0aP8Lri7Bm/1gtv9nD1mhK+/eHLyMqYWfrEqtZp6vFEerXX2bB4KsRkknMXC8LTJy4QCmv++raNrCk3ml5ZqYtU+YNhzvaMsDbGYRJry3M5M4OKmbFgiGa3hx11RTMO7BAV3Hs9nLehQZgQ8UhwFwvCy429bKjMozzfxeryXLz+EJ2DPlvu3dg9QiCkY+5EXVOeR1Ovh0AonNS9mno9hPXsNwWtKMwiw+mgqddDS58XhzKOkBPCbhLcxbzzBUK81tzPW9aUAuPVHXalZk52DgGwKcYZnxsq8wiEdKQ9biJWft5qXzBTaQ5FfUk258zgbgV7Iewmf6vEvDvQ3I8/GObqtUZwX11upCnsCu4nOodwpTtoKJ0627Zm89YbQCJnukdwKFiVQiMuqzvkebdX8u1izkhwF/PupcZe0tMUu+uNjTtluZnku5yR4+FSdbJziPUVeaTF2KW5qjSHDKeDEx3JBffG7mHqirNTqkNfVZbLebeHZrdnziplhJDgLubdy429bK8rIifTKN5SShmLqjbM3LXWnOgcitv50ZnmYH1FHieSnbl3jbBmlikZS0Op0Wt9wBtIufujEPFIcBfzqt/j542OwUi+3WJVzKTqwpCPAW+AjVXx2/puqsrnZOdwwuqcQChMU6+HtRWp7fhcFdUUTGbuYq5IcBdJe6N9kH3n3Lbe86XGXrSGq2ME994RPwNef0r3t9Itm6YJ7hur8ujz+OkaGov5/V+/3sl5t1G6GAxr1qbYPrchKrhLzl3MFQnuIinBUJhP/Ogg/++jx2y972OH2qjIz+RSsxuixSo1THVR1Voo3TDdzH1FwYTHRmvq9fCJHx3iEz88xMlOox5+tpUyluKcDPJdRgpKZu5irkhwF0n51RsXaO0zDpLw+oMzfv6QL0Brn3dC6qM96vSh6LYAMN7JMNXgfqJziJUl2eRmxt+MvcEskYyVd//B3maUMr73L0+dNsZWnlqeXClFQ1kuxTkZ5E1zLJ8QqZD2AyIhrTXfeP4saQ5FKKw50zXCpbWFM7rHn/7gIHvPucnLdHLzlkr+6T1b+elrrQD84WW1Ux5fU5RNdkZa0iWKYJxo9LlHjvKld26OzPxPdAyxsXL6Y/TyXenUFmdNqZgZGQvyswNtvOvSFQyNBvjd6R5qirLIzkj9n83tW6toHxhN+T5CxCPBXST04pleTnQO8WfXreYbvz/Lm13DMw7uJy8McVl9EStLcnj0YBv+YJj9TX1ct64sZjfFNIdia3UBR1oHkn6NQ+f7efFML5/+6WF+/omr+dXrnTS7vXzoipUJn2ssqk4M7o8dbGNkLMhHrm6gJCeDm/7v71kfo4XBbPzJtatsuY8Q8UhwFwl94/dnqcjP5FM3ruW7LzdxZoapkgGvnwFvgJs3V/Lxa1axqiyHf/6NkeL4uzs2x33etrpCvvtSE75AKKm68vYBo13BG+1D/OXPjvL0iQtc3lDMPVfVJ3zuxqp8nj7RhdcfJDvDSTis2fNKM9tqC9lmvpH96ONXUJQtaRSxOEjOXUzrWNsAr5x187G3NJCVkcbqslze7JrZ0XTn3cZhGFZlyCeuW81f3LiW3fXF3LChPO7zttcWEgjppGvQ2/tHyc5I4w921vDE0Q6KsjP42gd3kJ6W+K/55hUFaA3HzdTMme4RzvV6uCsqZbRzZRGr5OALsUjIzF1M6xu/P0uey8n7d9cBsK4il9ea+mZ0D+ukonqzBFApxWdvWgc3Tf+8bbXGYR1HWgbYkcTBHR0Do1QXZvG379pMptPBBy6vozQ3M6kxXlprVMwcbR3gsvpijrQaJ0FdZsNxd0LMB5m5i7iaej38+o0L3H3FykhVx7qKPDoGfQz7Aknfp2XSzD1ZlQUuKvNdHG0bSOrxHYOjrCjMIjfTyQPv3srmFQWJn2Qqz3NRXZjFYTPHf6R1gHyXc0oPeCEWCwnuIq6HXjhHepqDj1zdELm2zlxQnEnevdntparANat+LNtqC5NeVG3vN4L7bG2rLeRIi/Fah1sGuLS2EEeMfjRCLAYS3EVM4bDm8SPt3LltBWV546mNdebW+zcvJJ93P+/2zHon5ra6Qs67vfR5pt+p6guEcHv8SR1SHfe1agtpHxilxe3lza5hts+wIkiIhUSCu4ipfWAUrz805ZDq2qJsXOkO3pzBuaPn+7zUzzK9YVWqHE0we7dqxlcUumb1OmC8kQD856vNhPX410IsRhLcRUzWztDJJw45HEbHxmSPpvOMBekZHmNl6exm7lurC3AoIrnweDqs4F4w+5n7lhUFpDlUZHPVpTWFs76XEPNNgruIKRLcY5T+ravI49SFxF0UYbwMcuUsW9vmZDpZU57LiY7BaR9nBffqFNIyWRlpbKjMY8gXpK44m5IkK22EWIgSBnelVK1S6ndKqZNKqeNKqU+Z14uVUs8opc6YH4uinnO/UqpRKXVaKXXzXP4AYm6c7RmhJCeDopyMKd+7rL6YnuExTiWRdx8/BHr2DbI2mi15p9PeP4pDQUX+7NMyMJ4G2ib5drHIJTNzDwKf01pvBK4A7lNKbQK+ADyrtV4LPGt+jfm9u4DNwC3Ag0qp2R9bI+ZFY/dIpHnXZDduLEcpePp4V8L7nO8zZ+4pBPcNlfm0D4wyODpefnn6wjAf3/Mat3zlBfzBMO0DPirzXUltWJqOFdRn2l5BiIUm4b8ErXWn1vqQ+fkwcBKoBu4A9pgP2wPcaX5+B/Cw1npMa90ENAK7bR63MA16A/SOjDE0g7rzRLTWNPaMsDpO3/LyPBfbawt55uSFhPc67/ZQkmL3Q6tr42nzN4Uf7TvPLV99gRfe7OXUhWFeeLOHjoHUyiAt160vY0ddITdtrEj5XkLMpxlNc5RS9cB2YB9QobXuBOMNALD2kVcDrVFPazOvTb7XvUqpA0qpAz09PbMYunjuVBeX/v3T7Ppfv+WSv32a/TPcORqP22P0gpm8mBrt7ZsreaN9KJLrjqe515tyz3Krq+OpC0ZrgP/ce54tKwp46QtvpSg7ncePdtBuU3Avz3Px8z+/mjrpsy4WuaSDu1IqF3gM+LTWerpmH7F2fUxZedNaP6S13qW13lVWVpbsMESUZ050kZfp5B/u2Eyey8nD+1tsuW+8SploN20yZra/PTkxNXOiY4i//+UJrvqnZ9n6pad4tck96zJIS0V+JoXZ6ZzsHI7k+m/dWkl5notbt1bx2xNddA6OprSYKsRSk1RwV0qlYwT2H2mtf25e7lJKVZnfrwK6zettQHSD7hqgw57himgvNfZy5eoS7r6yntsvWcGv37jAyNjMDtI42zPCx/ccoD9qk5B1dunqsvhBeXVZLqvKcibk3c/2jPCu/3iJH756ni3VBbxvVy0fvbqBj1+TWntbpRQbKvM4dWGIV872AkTOXH3XpSsYDYQIhLQtM3chlopkqmUU8B3gpNb6X6O+9QRwj/n5PcDjUdfvUkplKqUagLXAfvuGLMDIZbf2jfKWtUaQ+4Od1YwGQvzq9c4Z3edrv2vktye7+N4rzZFrjd0jZKWnJawZf/umSl49547k+39/uodgWPPUZ67loQ/v4m/euYn/efsmNq2Y/rCMZGyozOf0hWFePNNLQVZ6pG/M7vpiKs0KmeoUNjAJsdQkM3O/GrgbuEEpdcT8cxvwZeAmpdQZjP5+XwbQWh8HHgFOAL8B7tNah+Zk9MvYS43GDNY6WHpHXRENpTk8drAt6Xv0DI/x5NFO0hyKH+xtjhyf19g9wurynIR9Vd62sZxgWPPim8ZY9p5zU1ecPeEAaLtsrMrD6zfevK5aXUKaOTaHQ/HOS6sAZOYuRJRkqmVe0lorrfUlWutt5p9faa3dWusbtdZrzY99Uc95QGu9Wmu9Xmv967n9EZanlxt7qSpwsSqqje57d1Szr6mPVrP8MJGf7G/BHwrz5fdsZcAb4OH9xjr42e6RmJuXJtteV0RhdjrPneomFNbsO+fmylUls/+hprHBXFT1+kORNzTLn1y7is/dtI51KR5cLcRSIjtUF6FQWPPKWTdXrynFyJoZ7txuFCX9+o3EqRl/MMwPXz3PdevKeN+uWi6rL+JbL57j848eo2PQN+1iqiXNobhuXRnPn+7meMcgQ74gV66em+C+riIP60d9y6TgXp7n4pM3rpUOjkJEkeC+gGmtY27xP9ExxIA3MCXI1RRls6osh71n3Qnv/eSxDrqHx/hj8wi6P79+DZ2DPp481sF7dlRz9xX1SY3xhg3luD1+vvnCOYA5C+5ZGWk0lORQXZiVcmmlEMuBnMS0gL33669waW0hX3rn+DmjWmu+by5+XrVmaiC9clUJjx/pIBgK44yzW3PIF+DLvz7F5hX5XLfOKEN964ZynvzkW1hVlkN2RvJ/La5bV4ZDwX8f62RVaU7K2/+n87m3r8ehmPDbihAiNpm5L1BDvgCHWgZ45LXWyEInwL88dZrHDrVx31tXU543NZBeubqEkbEgr7fHb7T1f546Tc/IGP/47q0TUhlbqgtmFNgBCrMzIkfgXTFHs3bLOy6p4tatVXP6GkIsFRLc54jWmkAoTCAUTqp74mTH2419Yh5/iKeOG9v8f7C3mQefP8sHLq/jL9++PubzrjAXNPeei52aOdI6wA9ePc+Hr1hpW/+Ut5qHXM/VYqoQYuYkLTNH7nzwlcgBE7dfUsV/fGDHjJ7/hjnzLs3N5LGD7exaWcw//uok168v4x/u2BI3NVGam8m6ilz2nnXz59evmfC9wy39fGzPASryXHzu5thvDrPxvp01tPZ5I0FeCDH/ZOY+B1rcXo62DnDb1kpu3lzBk8c6I02vkvV6+yBVBS4+eHkdL5/t5TM/PYJC8cC7t0ZqvOO5clUJB5r78QfDkWsvnenlA9/aR26mk4fvvYL8FBp5TVae7+LL772E3EyZKwixUEhwnwMvm1vkP3vTer78nkvIzkjjm78/O6N7vNE+yJbqAt67owat4cD5fj5z01qqk9ioc+XqEkYDIY61DUSu/eOvTlJV4OKxT1xF/RxsMhJCLCwS3OfAS429VOa7WF2WQ1FOBnddVscTZufCZAz7Apzr9bC1uoC6kmyuWVvKlup8PnJ1Q1LPv7yhBKXg5UYj79495ONE5xB/sKtmwmHXQoilS4K7zcJhzSuNvRM2GH38GiMof/vFc0nd43iHsZi6tdron/KtD+/i0T+7KumDKIpyMthZVxTpM/PCGeM3iWvXSvdNIZYLCe42O9E5RL83wFvWjleOrCjM4tatVfzX4fakKmesxdQtZnB3pafhSp/ZYVbv2raC013DnDYPsyjNzWBTVeoNvIQQi4MEd5tZLWmvXj1x9+hVq0vo9wZodifu+2ItpqaSQrltaxVpDsV/HWnnxTM9XLu2TLbnC7GMSHC32UuNbtZV5FI+aaemtdHn0Pn+hPd43VxMTUVpbiZXrS7h+y830+8NcO06SckIsZwsu+B+tmeEv3r0KJ975Ch/+8TxCbs/U+UPhtnf5OaqSbN2gLXlueRlOjnUEj+4a63559+c4lyPh8sbilMej3WQhVJwzdqpYxJCLF3LLrj/69Nv8l+HO3j1nJvvv9LMj/fZczQdGDNuXyDMFaumBmaHQ7GtrpBDLQMxnxsIhfnLnx3jwefP8v7ddZGGXqm4eUslGU4HW1YUUJIrVTJCLCfLKrh3DIzym+MX+MjV9bz8hRu4vKGY77zUNGGzTyoOnjda2u9cGXvWvb2uiNMXhqYchecZC/LxPQd47FAbn3nbOv7x3VviNv2aiXxXOg/cuYXP37Ih5XsJIRaXZRXcf/jqebTWfOiKlQD82fWr6Rz08cTRmR3x+uKZHm75ygu82TVx1+lrzf3Ul2THXQjdUVdIWMMxsy0BGIH9A996lRfP9PDl92zlU29ba2vXw/ftqo0cxSeEWD6WTXD3BUL8ZH8LN22qoLbY6Ad+/boyNlTm8c3fnyUcTr6510MvnOPUhWHu/s6+yKlHWmsOnu9nV338XPn2WmNR9WDUouqzp7o52jbIV+7azl2762bzowkhxBTLJrg/caSDfm+Ae6Jy2Uop/uy61ZzpHuFF80zSRDoHR3mpsZfbL6nCFwjzoe/sY8Dr51yvhz6Pn10ri+I+tyA7nTXluRMWVY+0DJDpdHDrlspZ/2xCCDHZkgvuv369k30x2t3+aN951pbnTmlLe+vWSvJcTp5MMjXzi8PtaA1/+fb1fPePd9Ha5+Xfnm3kYLMRsKebuYORmjncOhD5TeFIaz9bqwuS3n0qhBDJWFIRpbXPy5//+BB/9NCr/PH39nOuZwQwdnwebRvkA5fXTclnZzrTuGljBU+f6CIQmn5hVWvNYwfbuKy+iPrSHHauLOYPd9Xyn6828/PDbRRlp7O6bPqmXFetLmXAG+BY+yD+YJg3OobYZlNfdSGEsCyp4P7T11pRwCdvWMOh8/3c/Z39DPsCPPxaC5lOB+82D5Ce7NatVQyOBhKePXq0bZCzPR7eu6Mmcu2zN60jPc3Bq+f62LmyOOFiqHUs3XOnujl1YQh/MMy2usKZ/qhCCDGtJRPcA6Ewjxxo5a3ry/nc29fzvY/spnNwlPt//jr/dbiDd2ytojA7I+Zzr1lbSk5GGr9+o3Pa1/jPvedxpTu47ZLxo97K81386bWrAdhVHz/fbinKyWB7XRG/O9XNEbNqRmbuQgi7LZng/typbrqHx3i/WXGyc2UR9711DU8e62RkLMj7L49fieJKT+PGjRU8dbyLYJzUTFOvh18cbuODl6+cctDFvdeu4t5rV8X9zWCyGzaU83r7IE8f76I0NzOpHu1CCDETSya4/2R/C5X5Lq5fP95D5S9uXMv2ukK2VhdMW8UCcNvWSvo8fvY19UWuecaCjPpDAPzbs2fIcDr4s+tWT3luVkYaf33bRirypx5YHcsN5nF0LzX2sq220Na6diGEgCVyhuozJ7r4/Zs9fPKGtRN2dqanOfjZn16JPxROIhdeTk5GGo8faefqNaVorbnroVdp7ffywcvrePxIOx+/ZpUth11sqMyjqsBF56CPbbWpNQgTQohYFv3Mfe9ZN/f9+BCXVBdw77WrpnzfmeYgOyPxe1hWRhq3ba3iV69fYNQf4lDLAK+3D1KcncHXfncWV3oafxrj/rOhlIocJr2tNnGeXgghZmpRz9zfaB/kT35wgJXF2Xz/I7tTPqD5vTtr+NnBNp46foGXGnvJyUjjl598S+TwDDubb31gdx1t/aPsWFlo2z2FEMKyqIN7VYGLa9aW8qV3bqYoJ3YlzEzsri+mpiiLPXubOdk5xHt21JCT6eTySRuf7LCluoAffHS37fcVQghIIi2jlPquUqpbKfVG1LVipdQzSqkz5seiqO/dr5RqVEqdVkrdPFcDB2Mm/fUP7aSyILmFzEQcDsV7dtRwuGUAXyDMB6TXixBikUom5/594JZJ174APKu1Xgs8a36NUmoTcBew2XzOg0qpmR3+Oc/eu8MoZ9xaXZDyaUhCCDFfEqZltNYvKKXqJ12+A7je/HwP8DzwefP6w1rrMaBJKdUI7Ab22jTeObeyJIcv3raRS2oksAshFq/Z5twrtNadAFrrTqVUuXm9Gng16nFt5rUplFL3AvcC1NUtrPTHn9hUFSOEEPPF7lLIWMXkMRula60f0lrv0lrvKiuTw5uFEMJOsw3uXUqpKgDzY7d5vQ2ojXpcDTCzY46EEEKkbLbB/QngHvPze4DHo67fpZTKVEo1AGuB/akNUQghxEwlzLkrpX6CsXhaqpRqA74EfBl4RCn1MaAFeB+A1vq4UuoR4AQQBO7TWofmaOxCCCHiSKZa5v1xvnVjnMc/ADyQyqCEEEKkZtH3lhFCCDGVBHchhFiCJLgLIcQSpLSOWYZ+cQehVA9wPoVblAK9Ng1nLi2WccLiGetiGScsnrEulnGCjHWl1jrmRqEFEdxTpZQ6oLXeNd/jSGSxjBMWz1gXyzhh8Yx1sYwTZKzTkbSMEEIsQRLchRBiCVoqwf2h+R5AkhbLOGHxjHWxjBMWz1gXyzhBxhrXksi5CyGEmGipzNyFEEJEkeAuhBBL0KIO7kqpW8yzWhuVUl+Y7/FYlFK1SqnfKaVOKqWOK6U+ZV6Pe/bsfFNKpSmlDiulnjS/XpBjVUoVKqUeVUqdMv/7XrkQx6qU+oz5//4NpdRPlFKuhTLOhXwucpJj/Rfz//8xpdQvlFKF8z3WWOOM+t5fKqW0Uqr0Yo5z0QZ382zWrwG3ApuA95tnuC4EQeBzWuuNwBXAfebYYp49u0B8CjgZ9fVCHetXgd9orTcAl2KMeUGNVSlVDfwFsEtrvQVIwzhbeKGM8/ssnnORv8/UsT4DbNFaXwK8CdwP8z7WWONEKVUL3ITRPde6dlHGuWiDO8bZrI1a63Naaz/wMMYZrvNOa92ptT5kfj6MEYCqMca3x3zYHuDOeRngJEqpGuAdwLejLi+4sSql8oFrge8AaK39WusBFuBYMTquZimlnEA2xqE1C2KcWusXgL5Jl+ONLXIusta6CbDORb4oYo1Va/201jpofvkqxqFA8zrWOP9NAf4v8FdMPJHuooxzMQf3aqA16uu457XOJ/Nw8e3APiadPQuUT/PUi+krGH8Bw1HXFuJYVwE9wPfMFNK3lVI5LLCxaq3bgf+NMVvrBAa11k+zwMY5SbyxLfR/Zx8Ffm1+vqDGqpR6F9CutT466VsXZZyLObgnfV7rfFFK5QKPAZ/WWg/N93hiUUrdDnRrrQ/O91iS4AR2AF/XWm8HPCycdFGEma++A2gAVgA5SqkPze+oZm3B/jtTSn0RIwX6I+tSjIfNy1iVUtnAF4G/ifXtGNdsH+diDu4L+rxWpVQ6RmD/kdb65+bleGfPzqergXcppZoxUls3KKV+yMIcaxvQprXeZ379KEawX2hjfRvQpLXu0VoHgJ8DV7HwxhltUZ2LrJS6B7gd+KAe36yzkMa6GuPN/aj5b6sGOKSUquQijXMxB/fXgLVKqQalVAbGAsUT8zwmAJRSCiMvfFJr/a9R34p39uy80Vrfr7Wu0VrXY/w3fE5r/SEW5lgvAK1KqfXmpRsxjnRcaGNtAa5QSmWbfxduxFh3WWjjjLZozkVWSt0CfB54l9baG/WtBTNWrfXrWutyrXW9+W+rDdhh/h2+OOPUWi/aP8BtGKvlZ4Evzvd4osb1Foxfs44BR8w/twElGJUIZ8yPxfM91knjvh540vx8QY4V2AYcMP/b/hdQtBDHCvwdcAp4A/hPIHOhjBP4CcZaQAAj6HxsurFhpBfOAqeBWxfAWBsxctbWv61vzPdYY41z0vebgdKLOU5pPyCEEEvQYk7LCCGEiEOCuxBCLEES3IUQYgmS4C6EEEuQBHchhFiCJLgLIcQSJMFdCCGWoP8f97b3OE0jbnAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_r['data'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b5852d07-fe56-4162-8163-7c09c24ef2c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.00819832e-03, 1.65501155e+00, 1.14028041e+02])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array(df_r['index'])\n",
    "y = np.array(df_r['data'])\n",
    "\n",
    "order = 2\n",
    "coef = np.polyfit(X,\n",
    "                  y.ravel(),\n",
    "                  order)\n",
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d454210-f164-494c-a17e-7f8c29da291d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poly1d([7.00819832e-03, 1.65501155e+00, 1.14028041e+02])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_1d = np.poly1d(coef)\n",
    "poly_1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "974d460d-4923-4d9c-9d30-8fb002801f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_r['approx_trend'] = poly_1d(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "30ec7ea2-3007-4346-893a-fe1b61d4788f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ts</th>\n",
       "      <th>data</th>\n",
       "      <th>approx_trend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1949-01</td>\n",
       "      <td>112</td>\n",
       "      <td>114.028041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1949-02</td>\n",
       "      <td>118</td>\n",
       "      <td>115.690061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "      <td>117.366097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "      <td>119.056149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "      <td>120.760218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>139</td>\n",
       "      <td>1960-08</td>\n",
       "      <td>606</td>\n",
       "      <td>479.480046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>140</td>\n",
       "      <td>1960-09</td>\n",
       "      <td>508</td>\n",
       "      <td>483.090345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>141</td>\n",
       "      <td>1960-10</td>\n",
       "      <td>461</td>\n",
       "      <td>486.714660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>142</td>\n",
       "      <td>1960-11</td>\n",
       "      <td>390</td>\n",
       "      <td>490.352992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>143</td>\n",
       "      <td>1960-12</td>\n",
       "      <td>432</td>\n",
       "      <td>494.005340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index       ts  data  approx_trend\n",
       "0        0  1949-01   112    114.028041\n",
       "1        1  1949-02   118    115.690061\n",
       "2        2  1949-03   132    117.366097\n",
       "3        3  1949-04   129    119.056149\n",
       "4        4  1949-05   121    120.760218\n",
       "..     ...      ...   ...           ...\n",
       "139    139  1960-08   606    479.480046\n",
       "140    140  1960-09   508    483.090345\n",
       "141    141  1960-10   461    486.714660\n",
       "142    142  1960-11   390    490.352992\n",
       "143    143  1960-12   432    494.005340\n",
       "\n",
       "[144 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f0eb98eb-3279-42e5-b563-5d63af6ac5be",
   "metadata": {},
   "outputs": [],
   "source": [
    "orig = approx_trend = pd.Series(data = df_r['data'].values,\n",
    "                  index = df_r['ts'])\n",
    "approx_trend = pd.Series(data = df_r['approx_trend'].values,\n",
    "                  index = df_r['ts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c09691ca-e9cb-48cf-b8e3-00b4d948ad86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ts'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "orig.plot()\n",
    "approx_trend.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "712180ed-b949-43a1-b56d-095253d2ba15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ts</th>\n",
       "      <th>data</th>\n",
       "      <th>approx_trend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1949-01</td>\n",
       "      <td>112</td>\n",
       "      <td>114.028041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1949-02</td>\n",
       "      <td>118</td>\n",
       "      <td>115.690061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "      <td>117.366097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "      <td>119.056149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "      <td>120.760218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>139</td>\n",
       "      <td>1960-08</td>\n",
       "      <td>606</td>\n",
       "      <td>479.480046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>140</td>\n",
       "      <td>1960-09</td>\n",
       "      <td>508</td>\n",
       "      <td>483.090345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>141</td>\n",
       "      <td>1960-10</td>\n",
       "      <td>461</td>\n",
       "      <td>486.714660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>142</td>\n",
       "      <td>1960-11</td>\n",
       "      <td>390</td>\n",
       "      <td>490.352992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>143</td>\n",
       "      <td>1960-12</td>\n",
       "      <td>432</td>\n",
       "      <td>494.005340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index       ts  data  approx_trend\n",
       "0        0  1949-01   112    114.028041\n",
       "1        1  1949-02   118    115.690061\n",
       "2        2  1949-03   132    117.366097\n",
       "3        3  1949-04   129    119.056149\n",
       "4        4  1949-05   121    120.760218\n",
       "..     ...      ...   ...           ...\n",
       "139    139  1960-08   606    479.480046\n",
       "140    140  1960-09   508    483.090345\n",
       "141    141  1960-10   461    486.714660\n",
       "142    142  1960-11   390    490.352992\n",
       "143    143  1960-12   432    494.005340\n",
       "\n",
       "[144 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ea340913-1b6f-4e1a-80e6-69130735aca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_r['detrended'] = df_r['data'] - df_r['approx_trend']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fe0694c2-595e-4433-b0b5-dd00da5b9083",
   "metadata": {},
   "outputs": [],
   "source": [
    "detrended = pd.Series(data = df_r['detrended'].values,\n",
    "                  index = df_r['ts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "28ca4a0d-73c5-4760-80dc-caf663b2388e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_r[\"detrended\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bf8b352e-0812-439c-bf88-aab8fb837e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ts\n",
       "1949-01     -2.028041\n",
       "1949-02      2.309939\n",
       "1949-03     14.633903\n",
       "1949-04      9.943851\n",
       "1949-05      0.239782\n",
       "              ...    \n",
       "1960-08    126.519954\n",
       "1960-09     24.909655\n",
       "1960-10    -25.714660\n",
       "1960-11   -100.352992\n",
       "1960-12    -62.005340\n",
       "Length: 144, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detrended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f10de067-c844-4061-93ce-ea2247ce42cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7d1f643a-71e0-458d-a820-ee5908e24e65",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_r['date'] = pd.to_datetime(df_r.ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "98714b75-942c-4431-aeea-234b7023977e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ts</th>\n",
       "      <th>data</th>\n",
       "      <th>approx_trend</th>\n",
       "      <th>detrended</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1949-01</td>\n",
       "      <td>112</td>\n",
       "      <td>114.028041</td>\n",
       "      <td>-2.028041</td>\n",
       "      <td>1949-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1949-02</td>\n",
       "      <td>118</td>\n",
       "      <td>115.690061</td>\n",
       "      <td>2.309939</td>\n",
       "      <td>1949-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "      <td>117.366097</td>\n",
       "      <td>14.633903</td>\n",
       "      <td>1949-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "      <td>119.056149</td>\n",
       "      <td>9.943851</td>\n",
       "      <td>1949-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "      <td>120.760218</td>\n",
       "      <td>0.239782</td>\n",
       "      <td>1949-05-01</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>139</td>\n",
       "      <td>1960-08</td>\n",
       "      <td>606</td>\n",
       "      <td>479.480046</td>\n",
       "      <td>126.519954</td>\n",
       "      <td>1960-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>140</td>\n",
       "      <td>1960-09</td>\n",
       "      <td>508</td>\n",
       "      <td>483.090345</td>\n",
       "      <td>24.909655</td>\n",
       "      <td>1960-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>141</td>\n",
       "      <td>1960-10</td>\n",
       "      <td>461</td>\n",
       "      <td>486.714660</td>\n",
       "      <td>-25.714660</td>\n",
       "      <td>1960-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>142</td>\n",
       "      <td>1960-11</td>\n",
       "      <td>390</td>\n",
       "      <td>490.352992</td>\n",
       "      <td>-100.352992</td>\n",
       "      <td>1960-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>143</td>\n",
       "      <td>1960-12</td>\n",
       "      <td>432</td>\n",
       "      <td>494.005340</td>\n",
       "      <td>-62.005340</td>\n",
       "      <td>1960-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index       ts  data  approx_trend   detrended       date\n",
       "0        0  1949-01   112    114.028041   -2.028041 1949-01-01\n",
       "1        1  1949-02   118    115.690061    2.309939 1949-02-01\n",
       "2        2  1949-03   132    117.366097   14.633903 1949-03-01\n",
       "3        3  1949-04   129    119.056149    9.943851 1949-04-01\n",
       "4        4  1949-05   121    120.760218    0.239782 1949-05-01\n",
       "..     ...      ...   ...           ...         ...        ...\n",
       "139    139  1960-08   606    479.480046  126.519954 1960-08-01\n",
       "140    140  1960-09   508    483.090345   24.909655 1960-09-01\n",
       "141    141  1960-10   461    486.714660  -25.714660 1960-10-01\n",
       "142    142  1960-11   390    490.352992 -100.352992 1960-11-01\n",
       "143    143  1960-12   432    494.005340  -62.005340 1960-12-01\n",
       "\n",
       "[144 rows x 6 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3ba79682-789d-4583-ba2d-95b7c3bd78a0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_r['month'] = df_r['date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b2f2ba47-9357-4cb2-bd99-954b7d7ec01f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ts</th>\n",
       "      <th>data</th>\n",
       "      <th>approx_trend</th>\n",
       "      <th>detrended</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1949-01</td>\n",
       "      <td>112</td>\n",
       "      <td>114.028041</td>\n",
       "      <td>-2.028041</td>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1949-02</td>\n",
       "      <td>118</td>\n",
       "      <td>115.690061</td>\n",
       "      <td>2.309939</td>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "      <td>117.366097</td>\n",
       "      <td>14.633903</td>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "      <td>119.056149</td>\n",
       "      <td>9.943851</td>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "      <td>120.760218</td>\n",
       "      <td>0.239782</td>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>139</td>\n",
       "      <td>1960-08</td>\n",
       "      <td>606</td>\n",
       "      <td>479.480046</td>\n",
       "      <td>126.519954</td>\n",
       "      <td>1960-08-01</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>140</td>\n",
       "      <td>1960-09</td>\n",
       "      <td>508</td>\n",
       "      <td>483.090345</td>\n",
       "      <td>24.909655</td>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>141</td>\n",
       "      <td>1960-10</td>\n",
       "      <td>461</td>\n",
       "      <td>486.714660</td>\n",
       "      <td>-25.714660</td>\n",
       "      <td>1960-10-01</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>142</td>\n",
       "      <td>1960-11</td>\n",
       "      <td>390</td>\n",
       "      <td>490.352992</td>\n",
       "      <td>-100.352992</td>\n",
       "      <td>1960-11-01</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>143</td>\n",
       "      <td>1960-12</td>\n",
       "      <td>432</td>\n",
       "      <td>494.005340</td>\n",
       "      <td>-62.005340</td>\n",
       "      <td>1960-12-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index       ts  data  approx_trend   detrended       date  month\n",
       "0        0  1949-01   112    114.028041   -2.028041 1949-01-01      1\n",
       "1        1  1949-02   118    115.690061    2.309939 1949-02-01      2\n",
       "2        2  1949-03   132    117.366097   14.633903 1949-03-01      3\n",
       "3        3  1949-04   129    119.056149    9.943851 1949-04-01      4\n",
       "4        4  1949-05   121    120.760218    0.239782 1949-05-01      5\n",
       "..     ...      ...   ...           ...         ...        ...    ...\n",
       "139    139  1960-08   606    479.480046  126.519954 1960-08-01      8\n",
       "140    140  1960-09   508    483.090345   24.909655 1960-09-01      9\n",
       "141    141  1960-10   461    486.714660  -25.714660 1960-10-01     10\n",
       "142    142  1960-11   390    490.352992 -100.352992 1960-11-01     11\n",
       "143    143  1960-12   432    494.005340  -62.005340 1960-12-01     12\n",
       "\n",
       "[144 rows x 7 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_r"
   ]
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  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6cb90e07-3f66-4be7-bfce-f3a7f63fd7ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "detrended = pd.Series(data = df_r['detrended'].values,\n",
    "                  index = df_r['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2198e622-6e47-41c6-909f-1254826d895f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1949-01-01     -2.028041\n",
       "1949-02-01      2.309939\n",
       "1949-03-01     14.633903\n",
       "1949-04-01      9.943851\n",
       "1949-05-01      0.239782\n",
       "                 ...    \n",
       "1960-08-01    126.519954\n",
       "1960-09-01     24.909655\n",
       "1960-10-01    -25.714660\n",
       "1960-11-01   -100.352992\n",
       "1960-12-01    -62.005340\n",
       "Length: 144, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detrended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9b48b04a-2765-4193-8150-c6e7a747aeb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "seasonal = detrended.groupby(by = detrended.index.month).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d983c2b9-af21-4c28-98aa-776534fe79b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1    -24.062583\n",
       "2    -33.399685\n",
       "3     -0.834137\n",
       "4     -6.532605\n",
       "5     -4.411756\n",
       "6     32.778410\n",
       "7     69.787893\n",
       "8     66.866692\n",
       "9     15.514809\n",
       "10   -23.017758\n",
       "11   -59.481007\n",
       "12   -33.208273\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0c4fd974-4fb2-4e2c-9f14-9975e5b7f49e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-24.06258325268634"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasonal.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "565429fb-1fb0-40a2-8fc7-284d12e07084",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in seasonal.index:\n",
    "    df_r.loc[df_r.month == i, \"seasonality\"] = seasonal.loc[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "721ec26f-08c0-43de-8535-853805515d65",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>1949-01</td>\n",
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       "      <td>1949-01-01</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>115.690061</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "      <td>117.366097</td>\n",
       "      <td>14.633903</td>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.834137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "      <td>119.056149</td>\n",
       "      <td>9.943851</td>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.532605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "      <td>120.760218</td>\n",
       "      <td>0.239782</td>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>5</td>\n",
       "      <td>-4.411756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>139</td>\n",
       "      <td>1960-08</td>\n",
       "      <td>606</td>\n",
       "      <td>479.480046</td>\n",
       "      <td>126.519954</td>\n",
       "      <td>1960-08-01</td>\n",
       "      <td>8</td>\n",
       "      <td>66.866692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>140</td>\n",
       "      <td>1960-09</td>\n",
       "      <td>508</td>\n",
       "      <td>483.090345</td>\n",
       "      <td>24.909655</td>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>9</td>\n",
       "      <td>15.514809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>141</td>\n",
       "      <td>1960-10</td>\n",
       "      <td>461</td>\n",
       "      <td>486.714660</td>\n",
       "      <td>-25.714660</td>\n",
       "      <td>1960-10-01</td>\n",
       "      <td>10</td>\n",
       "      <td>-23.017758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>142</td>\n",
       "      <td>1960-11</td>\n",
       "      <td>390</td>\n",
       "      <td>490.352992</td>\n",
       "      <td>-100.352992</td>\n",
       "      <td>1960-11-01</td>\n",
       "      <td>11</td>\n",
       "      <td>-59.481007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>143</td>\n",
       "      <td>1960-12</td>\n",
       "      <td>432</td>\n",
       "      <td>494.005340</td>\n",
       "      <td>-62.005340</td>\n",
       "      <td>1960-12-01</td>\n",
       "      <td>12</td>\n",
       "      <td>-33.208273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index       ts  data  approx_trend   detrended       date  month  \\\n",
       "0        0  1949-01   112    114.028041   -2.028041 1949-01-01      1   \n",
       "1        1  1949-02   118    115.690061    2.309939 1949-02-01      2   \n",
       "2        2  1949-03   132    117.366097   14.633903 1949-03-01      3   \n",
       "3        3  1949-04   129    119.056149    9.943851 1949-04-01      4   \n",
       "4        4  1949-05   121    120.760218    0.239782 1949-05-01      5   \n",
       "..     ...      ...   ...           ...         ...        ...    ...   \n",
       "139    139  1960-08   606    479.480046  126.519954 1960-08-01      8   \n",
       "140    140  1960-09   508    483.090345   24.909655 1960-09-01      9   \n",
       "141    141  1960-10   461    486.714660  -25.714660 1960-10-01     10   \n",
       "142    142  1960-11   390    490.352992 -100.352992 1960-11-01     11   \n",
       "143    143  1960-12   432    494.005340  -62.005340 1960-12-01     12   \n",
       "\n",
       "     seasonality  \n",
       "0     -24.062583  \n",
       "1     -33.399685  \n",
       "2      -0.834137  \n",
       "3      -6.532605  \n",
       "4      -4.411756  \n",
       "..           ...  \n",
       "139    66.866692  \n",
       "140    15.514809  \n",
       "141   -23.017758  \n",
       "142   -59.481007  \n",
       "143   -33.208273  \n",
       "\n",
       "[144 rows x 8 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "49d5ab96-4135-47af-af74-b56d23c82d56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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10pKG0eDLho8UxNEswGCJ5xcKqpMvBJMvCkYtILmfldasko+CHlxCHFEjYCU6axcjZAHXSn7ckwO/s1ZClrqsLN3+gF6ge9Pwcd2NGn5nUhDHYlR+ybEsw+7Ngg1Wsyqwx2Jq/l2PAolK7kt0omehtavkQwoL3G7a8LAlkOmsjfJ+wLEsnQhZhKKfqQiD5fKdhSEhw0dCFq97c5QrgZVadlhgnXwYRnNeEBE2vt7/3csyGM7GgtKTL5zC3o85iC4Xuh3h/cpgv4MhJAAyl5OEhy15fNxfghDOL0iMgQ1XV4CMLIv+pEtTJy2xLuZC+qUdr26hp+CF9CB3AU4rlCuTuDQm7MlLXU7SDkUlwctJiqa1qeQ7/SUHA9wudKTHKNRyPh3ysMGxLDHQk0j9ujBc0wpcTDHi4/7+1VgYzaXBCuHLho9U96aJeJtSEEdE4lW8ukY0Kok4l2fAm1+TSr7VHYQ2kx+yOfUYfRwzjP0LJHiiEq8uIY7IxKtQgi9sSEA+KpGqxw6H6yCfK5GSxVxIDzKyDEIX0hs+EvNxYLSXhwURju9fXZJfKJV8sRSLYwt48uJ1vx35/EK4exPkZJGuFBqV0I2XnUpU14QTb1BQhCXRvSmd3I8xvq4hjiH0JFxdV0QRQVYqlTyySn5J+CnQdFGULEsGYQmUt4knkUPdm4aP9ERFCYgjKr8glUQWhzdjDBa4Xf92d3Qh/ZCP4+jHXEjfFH5nWWnNKfnwTBmQSfBFJl5FEkmhzeQfDJelZ4XBAksmKsrJMiVcW750OqQviwCMJp/cH8fKDcThdF16S2WRqnoLnn3wHBaXZaftCFlKT75AMt2bkXjZCgyll0QlBSWRJSCOJSGuSH4hxpMvoH4dpMp0ZSGOVggSBPfGxJyXcOQL7s9lWBbXYy3iSo5hhWPySqmqUuoepdRn/a83K6W+oJR61P//Jle8bMh4P2FMDtwrxmD3psdHAOKIaGuHlVldE675FjG+BeUXiuhEDk+HBKkqngjv13H0ExctgmvsfxDhyTs2WAXJkpVcevLvAB4KfP0u4Ita68uAL/pfn3GKSyKC++qaJd6P44MR170JjqGnqPBTaHaNeHVNQRBHOMKSGGsRhcmLQByRnrzbsRaJ59KxwxJtsNxFpUmynIlxw06UvFJqO/BG4E8D374RuNX/963AW1zwsqWiPAYPx4wKC4U3kwDEsdjpU1GjpjEQ6kYMD44rqntXAuKIMvKOjUlc4hXcG8awYnQOcSTJ4tRhGe8rMXxEii6ES2izkitP/veBdwJBCc7VWj8N4P9/W9QvKqVuVkrtUUrtmZ2ddfQ48ZS4mRx3vAbDaMNHIixsCkMc4VtuwP3B6PUHdPqDcaxcsHu3CLx8KqxMVirEESWLc4MVHS1CAZ68lCyrpU5eKfUm4LDW+u5Jfl9rfYvWerfWevfWrVttHyeVwnO+QfCQ12ThmlYn4mAI4djiB8NAQgHDKAJxdMa7N0GwiqMhC3Eklh06Xv9o6NFdVBpdpiszu0gaRh0l94PRosyMnCxUc/A3XgG8WSn1BmAKOFsp9RHgWaXUeVrrp5VS5wGHHfCypmHiNWJGivPNtOSQu512OPJKZRNvkUrehzi01mMe/qQUBT2ATPQT7N40PMBdeaPp3gwbefcQR7yRd112Grkujo0vRBusImRZWOg54xGeWW94gFtZspK1J6+1frfWervWegfwVuBLWuufBm4DbvJ/7CbgM7a8XFDc2FyQCHFlD0ZkVCIQ4oaxcnBvTKK8UpDwsiJkcdwnMezejHhnEpj8WKVYgQ6L6woeKOJcRlfXOE0iR3XvrgJMPop+G3idUupR4HX+12ecCsvix2wmaUxWAuKIwpddH8DwCFhDziGO0OAoj4dbWaLwZZCp4gheSA/uja/p3pQ2vpHQkwDEEb4wBuRq/qVhtKzkAq4Zktb6y8CX/X8fBa53+fddUFxbO7jveJ0+uzn2vUZ1dDmJC4gjKvEKMh5jVKUIuNu0ixHTIcE9xNGKxModyxKBLxs+7hWWrCzhC+mDfFxCHNG3jxWTK3N9/+poOmhE1dtKhGtWGkU2wwiVUEZ58uAQFojw5EHAy4rB5MEhXBMni4DBikqIgzuIIxZ6EsovBMl1hBUFCYJ7iCN8Ib3hAe5kMSNNIqEnkfyCbJNiVlpzSn7oMQQOR7WiUK4hjqgGEsfGZOT9LPWyXFdxhBWW64sjWjHer3ehh9sqjvAhrwsZ36XvzO3lJOEL6T0ejtclouQURlGpK4oq03R9mY8XRUeti0xps3THc1Zag0reDKgaia6UEtq0xcACUYlEp4nXXnRjD0hAHNJ5jKWyuL5/NTbCqlULSe6D+4R4ZIRVEPTkCuKIujDG8HEtS70aHmlSKvnCqNX1ujcb1aXebxFZfCgArhFIJEVFC+AO4ohKiIN7jzFJFueJ1whZXI8CiGq4AolosXhI0PX9q1FVLyDT8RqG0aoVRbWinF9OkoXWnJI30EM48dl0CHH0B5pOP3p2DRTl/Tqs4uglJF6FIQ7nxjdq2JZUVBJVJ7/SEuJJSeSVJksEVg7euewN3I21CF9IH+RTevIFUFTHG7hdgDh82bnHaMazRpQESreCu4Y44mEBxxBHTFs7CCSRo0r1CkruO4M4kqJFx/mFqCoxEIA3C3BYwmff8CmVfAEUBaOA6UYVxspdQxw+Vh6OSkw3qgsaDDTtmPGs4DKUTqgtd9glHNfWDhJJ5KguYdnkvnOII2EvO+1E7SwdHOYa4oi6MAbcj7WIWhdwH/1kpTWo5OWtbBL0AG43k7QswzHD0rXlMVGJ+8mdCd27Bax/YXkfaUjQ8eTOKEjQ8JGG0dw7LPGyrNhRwyuJorL44NbKxh1yiSqOaI/B3eUko4MRAz05HAUQ7t40fFx2byZ1vK40iCNuOiQU0L0r4rDEnEtXDktCQhzcOixh6AncN/ZlpTWn5KMy3+Aak085GA49k8iDIZBfkD4YUVi54eO8ezMcrjuGOOK6d4tIVrqGOKIupAf3EEes9+vwnSUlkcEt9BgrS6nk5SmqGQbcLkDaZnKFZUbVSYNbiCOpggfcJiulD0bU/a6GB7hbl6juTXALcURdSB/m44JaCXXy4NYwRnm/npF3I0tSzT84NFgJMOqKnEK50ig+8eoQ4ijoYEThy4aP+HTIImWRbh4TwOTjDjm4USZRF9IH+RSRRAZ56NFl2WlsnbxAaXNsfqFU8vIU5/3KlFDKz3uJVCZOIY7og+Ec4vAnKobJba4kGkYbQhxOlXy0wgI36x/XpARux1pEXUhveIBjJd+IWX9XUalJ7seVnTqVpYRrzhh5HsNSsYuEOJxuJmGIIwlfBre1xZEHwyHEEdcMY/iIy+JQMcb1YoD7/FLcHgM3628upI/MlUlUihWQK2sKy5KH1pySj8UxHXqMsVl8ic1UEI4tDz3FJMRder8xw7YMH6cJ8ZjkPrhV8tIQR+wek5AlrkvUIfQYvpAe3CeRoy7ZgbKEsjCK9X5F6nFDFQmOIY52gpclXZHgHuKIPhhOIY6Y/AK4hThi29odGsa4aNHwcRWVRl1Ib3iAm6g07l4Ew8c17r+0edDduYy6kH7Ip2yGkqfBIPqWGyg4wSddkeIS4khSJi69rAToCdwqxiKMfBQk6HL945L7ho94EtEl9BRxIX2Qj1NZpGG0YfNgOdbgjNDolpszW6rnsulCuoqjnRRKO8X++0tqscE1LBANo4HjKo6E5D64laUI4yueRI5J7oNbiCMJKwec9Bakwmilkpel0QIsFdvlxQGL3T6NiO7NakU5u39Va53YPg2OPMYET77u8AC2Y2RxeQlGXFu74eMK4oiDBOsOIY6kxGvdcaVY3NqDo3VJSIjXHefKogy8y8tJ4ip4PD4lXCNOaTimK4ijFTOgyPBx4mH3vFtu4nBMcLVpfY8xorzRpWeS1NYOjg2WcCgd6/0KGCz53oJ4eBPkYTSX96/GlhwLVD1JQ4J5aE0p+aQsvusa5qjNBO4Wuh1T8w2uMUbvlptaNUYxOnhfXlSSUqrn5J2lJCsd1uNLG6w4SNDwcRZhxeUXqu4hjqISr2EyBRFuIqxiIOE8ZK3klVIXKqXuUEo9pJR6QCn1Dv/7m5VSX1BKPer/f5P949pRWhIR3HmM8Z581WlFQlypFrgLP6Pel+HjAuLo9jX9gU5Mijl5Z0nJSpcQR8KoWSjIk19BCfFE79exLEVFi3GyuLycJCu58OR7wK9orZ8LvBR4u1LqKuBdwBe11pcBX/S/PqMU14kK7jdtnGJ0BXHElTaC48RrDFZu+LiUJarj1SXE0epFd2+CBFwjDQvEe4xOIY40GE04Ie606qkQWaIvjBnjUzAub63ktdZPa62/7f/7NPAQcAFwI3Cr/2O3Am+x5WVLcW3t4HZIVZzHYPi4DNfFPcYkT95VfiHFKwVH69KJhlEMHxfRguneTEqIu5AlsePVMcQRbUj82nIHg9DSHBaXHa9R62J6PlydfSC6iqfqtuM9KznF5JVSO4AXAHcC52qtnwbPEADbYn7nZqXUHqXUntnZWZePs4TSwnVwZ81jvd9qhW4BzTBQAPRUrdB1MCEwLcQFdxBHkpJ3qXyl67FN92b4QnrDx92k0/jBceC2IkUa4oi7sQncRQxZ8n5FT6J0puSVUuuBvwH+k9b6VNbf01rforXerbXevXXrVlePE0mjtnb5UDo28erYk5eu4kiTxcXkzrRkFbir+Y+TpemovM3IklT15OadRV9ID+4UlrmQPnHchCMYDeTX37vEPeFcOi1tlX1neciJkldK1fEU/Ee11p/yv/2sUuo8//PzgMMueNlQ3NhccN+mHWXJDZ8iklVQjPdbRCcquJNFOr+Q2AzjsH0+VRanUclSNeHycpK4C+nBcUFEpx85bgLcvbO0qARWoJJXnivxIeAhrfXvBj66DbjJ//dNwGdsedlSJivr0MuKIldeVtx0SHBfdphssOwP+aiBRL6GWdr4ZkqIO4uwkqAne4gjCRIEhxBHL/pCegiUNlvyGV5IHxGVgLvO2rgL6T0eJo+xwpQ88ArgZ4DXKKW+4//3BuC3gdcppR4FXud/fUYpsa3dMSafmOArKvHqKokcczCcQRxJbe0OIY4kWZxjsoVUcMVDD2C//klOkeHjLrmfIosln7gL6Q05q3pLikrOkCdfs/0DWut/BpaaYI+ut/37LiluOiS4TyQVVXYY2b3pGuKQhp46CckqpxDHgA3T9cjPigjXXUIciesSgDjiFHQWSjJY4PCdpUBPYL/+SZCg4ePK+EZdSG94wAqvrlnu1Oomd2+C/WYy3ZvJCR53XYJF1DCLJ6tM4k04wdfu9iMNvOHjAuIw4XpU4hXcRgyxEZajd5aUEAeHEEcivOkG4kgqOQW3eYyk9wUrEJNfSZSWRAR7Kzvs3oxrhnJ4MCAF4nBhTFI6Xt10oiY0wziUJS0qAXtlkuTJGz5FJPfBfi+neb9NR7KkwZswGuMxKWXJL9jyMHzSohIXezkPrSkln7SZzMIY5Tkxj4RyMICpRnXoIdnQYrdPNaZ7cySLGy87btNON6q0un20dpTgi/BMqxVFo1pxI0vCAXS2/ike43S9as3D4xMPxUw5kiVpOqTh03YkS5zBGq6LpWJMqqwDfy87ibAzyOJgL+ehNabkozveIHAwLBc66fYh8BSZs4MhfMi7/QHdvk5UJgPtRS82NFSMEfkF8KpuXCjGpO7dKUcHMKkZxuPjzmBJK/m0xKsrWeLGQBgewWeZlOIupDfUrLlzvqKSrjCSpfTkBSkpIWq8SNuFTgtxp+oVJx5DEvRUr1aoVpS9wcoQroMDw9iN794E32N05GUlKSzzLDaUvv5uPHlvL8e/L3C4l2MNlktZZI1vEiTo8ak4cr7iYTRXxjcvrSkln9Tx1nR0yNOSVVP1Kt2+pucgkRQnC3gVRGZjT87DyJKiTDr2Sj6ue9PwX7TkMezejJPFN/KLwuvfrFeteYDZy3EOiyPvN8NediVLXKLarJf9usRDgh4fR7IkJMTNe7Tdy3lpTSn5pHC9WauglENPLgZ6GGGM9ko+zvvx+NtjjGnhuiuMMSlZZfg4g1ESciXBn5uUkiZqevwdJfgSvN9px7Ik5TGc5BcyefLy59JJVJLgybs6+3lpTSn5JBxTKcVUzX6hs+CYwZ+blJIMFhiM0RFckxZ+WifFkuu5p+ruDFasLI7gOhNhxUcl9rIMuzelIY6UJHLTVX4hZjqkx9v7vi2UkjQd0uPjKlcSL0vTUYSVl9aYko9fAHCz0GmlWk1HnkkWWZyVnSUcDO9Z7CuSkqEne4OVWRYHxiRxXRzIknQhveFhnsWGskAcTkpbEzpeXRmspHHWhk+r56BSLOZCeoBKRdGouTEmeWhNKfmkUArc4HKtlDppl+Fn3LAlw8dWlsWETlTDI/hzk1Krk7wuzXqFRUdwTdw7c5VfSIJRPD4V+3UZwijJVRwu+MR1b4Kn/G3XXmudCNeZ/IK7dxa//lrb9xakG3k3lWJ5aE0p+bRkpQtcLq1O3hWO3Uro3jR87GXJmHi1PRi9+GQVeLLYhuvDhGgsVu6ohDYBRgHTWyA7U8ZVfqGdUI0EHr5tu/btlA7hWrVCvaoKKYgA+6arJEgYRr0lRdKaUvJJZYfgbTRnpVrCmHxSqZbHxwHEkVbz7zC/IC5L1vyC9frL50pSZfENpnXHa1pUUqvSH2irSzDSEuKGjwsYteGXFkfycADXpUUlHp9SyYtSmpWdqlesMcbUKg6HSbEk79dFfiGtgcQd9DSITYh5fOxlSV8Xd8Y3Mb9QrzrxFs3fiqJ6VVFxVCmWJkvweSahpMmwhpoOktVJWDm4yWN0+5qBTpbFhcHKS2tGyff87s00j8EWYxxm8Quox049GOKevCtYQD5XklUWF/XYybJ4g7D6FoPQ0pS8Usp7Z476F+LIBfafVsFjPrPNlSRdSO/xsF//tPJZj499TiYvrRklnzTM35Cr2nKlEuqkG+46KxOxP5cQRyF18rL5hdSqp2F5m3zNv8fHQpmkQILmM2vvN4PxBTscO22gm/nMhSefJMvoXDqAnoShx7y0dpR8Vo/BSUI0vnuz6SAsHAw0nYRxxmDGJ8iOmnULcSRHJe3ewKq8bdS9Gf3OPO/XvrW91Y3v3vT4u4A4suxle1ig3Y2/ScnwCD7PJGSUd9o7sz+XKbI4OJdpJadgSjVLuEaE0sJ1cFePncX7sVnopJuUhnwcJvhiBy45nPeTVnYIdonEtKjE4+OigSy9fwHs1j+LLC6GumXeyzbebwZPfsqRLEklxy76V9IS4uBuRk4eWjNKPkso5QbHznjILTDGtPp1j4+HY9t4vyaJGFcnXfHHANtgjFprkgaHQSCPYfPOshzAmgPsP0N1BbiRpRCHpSBMPu2duVmX5MgXbGG0YmTJS2tIyZs66TQc277mOzGL7yTEzSBLw2vusLkEIw1GAXsvK617EwKzWCxw2SyhtKsadumKlHYGJe9KlqS97CK/kAYJms9cdLym5bCCzzMJmd9Ne2clJi9EWUMp63A9pba4Xq1QsxwDPISekqISB4nEtDppsG9tH3k/WbwsO4ijkdC9Cd47s1n/rHXSYDdTvLC9nFEWJxBHCh8Xs2ukZclU8+/AYOWlNaPksyarerbNHQkjYIN8XGTxkzpeXQx2SuveNHysZEnpEAY3STEviZi83W2TYp3+AK1TkogOjG9a9673mYtKsbTBcfb5hUzn0sEogHRZXCbEZXMleWnNKPlMOKYjXC7d+7XDsbOWaoElXprSvenxsZv1njW/APayJPHw+NjVY7cylDa6wuTjLqQP8rHhkSUqGVaK2bwzcy6l+yQy1/wLJ/drXqWY7YXxeUhcySulblBKPaKU2quUepc0vzjKEkq5wOUWUzwGsK/iyLKZ3GCMyckqw8cKespkfN3AAmnG15UsyfXY9vmFbMbXLsIaXkhfgCyQoX/FNr+QYUQDyFfXmM9cXICelUSVvFKqCvwh8MPAVcBPKqWukuQZR9lCKTdJsaTQ0zyDTQNJtmSVfVSSJfFqW5GURxa7d5ZVMQrXrzsoO21nggTtRnQM69czQIK2dfJxF9IP+dQq1mOA08ZZD8cAWyX30wsiXN3alYekPfkXA3u11vu01h3g48CNwjwjKWt5E9gnxbLANS48huXg/brKLxSR4JP2frMlEYuCBC3XJQuM5iS5n1xyDJ4jYVMplmWkCXjy2DoSQOyF9BDsk1k9Sv4C4MnA1wf97w1JKXWzUmqPUmrP7OysM8ZfePBZnjy2MPy6laFUbzi7OuPdqMfnO3z6noNjHkYmjzFnPfbXHp3le8+eHuMB6d27kB3HXuj0+KtvHRjDCrPJks9gfWv/Me47eGKMR/B5I3nkxOR7/QF/eeeBsQS6NzY3LcLKlyv57lMnuevxY8OvjSxJide80aLWmo/fdYCFTi/Ax70sew+f5qvfG52/LF5pza8Uy8PnU98+yImFzohPiocNAcWY8Vw+eWyBzz/wTIBH+tk3n+fJY/zD/U/zzMnWiE/XG2kSdyF98BmKvOdVWslH1auNxVxa61u01ru11ru3bt3qjPE7Pn4Pv/eF7w2/Ni81KfzMizF+9r5D/PJf3csjAQWc1iVo+ORRjO/+1P289+8fGn6dz/vNdjDueHiWX/+b+/nmvqMBPun5hbyy/PfPPsh7Pv3d4dcS+YVv7T/Of/n0/Xzuu6ODnj0qyS7L//P5R/jlv/rOGI/g80ZR3tryRw/P8a5P3c9f7zk4xidLfiHPGOAPfnkfb/vI3cML5rPgy4ZPVlmOzLX5z5+4lz/7+v7h91oZ8gt5Z/3/xTef4D9+5G5OtbpAtpJjyDe7qtcf8Pa//DYfuOPR4fdMhBU30gTcdAnnJWklfxC4MPD1duCQME/6A81Cp8+dIS8rqXsT8sMCp9ued3XnPo9Plu5NMDPFsy/yXLvHnv3HRgcwR0VKVuhpru0diG8G3plnsFK8rJyynG73eODQyaUH0CHEMWfW5fGRwcpifPPmSubaPZ46sTiMGLMY39EY4Gx8TreiZcnilQafKY3m2l3mO30eOHRqyAOSZYF8dzDMGVn2hdYlA7wJOc5lq8dAw937j4/9Xjpck91gLXT7aD06+5BTllUE13wLuEwptVMp1QDeCtwmzHO4QZ86scjB46MDmAVG8X4226Y1CsqE7O2Um5SGfOr5EjwLnT7znT4PPn1q7PlcDo9aGMoS9OSTZ9Z7fPLJstjpewfwCf8AZoHRcnpyBtoIQympstSqucYAL4TWP0sS2YwBzrouwT1mYMG07k0IwkLZ9vJSWQz0lL6Xs/ZiGB73PHli6HxkTYh7P5v1XBrDOC5LpnOZ8+w/eniOo3Pt4fO5Nr4uSFTJa617wC8A/wQ8BHxCa/2AJE9gDL80ljaPlc2OY3s/d+fjR7264gzJXY9PNXNtcd+fOAnjsqR1b47yCzkP4AHvAA7rpDN4v3nwxeE782XJkuAbdu9m5GOe53vPznFs3sN/W93kAVWQ32NcDKx/8Oss658nVwJwZK7DY7PzHp8sezlnFccSWbJ6vzlkWex6snR6A+598uSQj+S5DP5elqgkLw/w8kyQPtICVqGSB9Ba/4PW+nKt9SVa6/dK84NxxRb0slxbWbPQR+Y67Dsyn6l7E0w9dlYPK2CwAp5JWvfmKL+QzzNp9wbcd/DksHszyzvLMwZ4MRQxZOneNWOAs7+zmPVP8eSH7yx39OPzGK5/ht6CrB5jN24vZ1z/CWQZDHSm+TiQD5MfXxez/oMM0UI+Wcw7u//gSRY6vUwRFuS7Szj2XGbNL6wiTP6MkNlMjWpl6GUfPL7AugxeKWQfBbDY6Q0z6XfuO8bB44sAGfhkr0hZDMjyrf3eATx4fJGZZi2ZR87mjoVOn5ofGdy572gOWbI3d/T6Azr9AY1qhfv8A3jw+CKNWiWxe9PwyXvIzfofmWuz0Okx08wI1+UwwI1qhf1HF3j2VGv4ztKin2YOiCu8l+faPY7Pd1iXef2zG5NGtcKpVo+Hnzk9lGWmkcInB8QxLssxOr0Bz55qZeCRfy83qhV6A823nzgxhGyznct8TlGjWuHOfd65fOrEYqb3BavMkz8TZDbTS3ZtZv/RBf74K/v49oET/PiLtif+Xt5BWAudPju3zLBlfZOvP3aE3/jMA2xZ3+QHL0+uEjIKK4v3G5Tl5GKXP/nqPm5/6NlUWcwY4OyHvMfGdQ2uOPcs7nz8GP/tb7/L+maN1z/vOSmyZN+0C92RLL2B5s+/vp+/vvvJVFkgZ1Ks06Oi4EUXb+Kux4/xG7c9QK1S4c3XnZ/4e828cE23z0t2bQbgY3cd4EP//Dg3PO85nDVVT/y9qVoej3H0zu7cd4z3fe5h5jo9fuyFFyT+3iR5DCPL3913iD/44qO8eOdmLtw8nconK4/FgCx3P3Gc//GlR3n6ZCv9XOY0WAudPi+8eCPViuL2h57ldz73MFc+5yyed/6GZD45ZAmuy0PPnOKDX3mMh585nUHHlJ68EzKbySjb3/ncw+y+eBM3vWxH4u/lrcc2mPVLdm3m7+97mgefPsV/f8vVbFzXSOUzyNjcsRAhy2Xb1vMLr7k09XfzDENa6PRZ58vytUeP8I3HjvJf3vBczt+Yfsgh2zsz6/L9l26houD9//QI5549xbt/+MrU3/XqvrMf8nWNGi/ZtZkHDp3i7+97ml+6/lIuO/esFB7Za5i7foPNCy/axPpmjd+//VGm61V+8y3PyyhL9mgR4FVXbOOZUy3+5788wc++fAcvunhzKg/v97Ov/yVb17N90zQf/PJjdPsD3vdj1yaWA4JXKZY37/OqK7ax0OnzP760lxuffz6vvercxN/LnyvpsfWsKa4+/2w+/I39HJnr8P4fv45G2oC6CWXR2tvLP3DZFn5idzaDVeRM+VWp5A1etnvHZtY3azRqFX7nx69NTFRCYAxwTsX40p3egXvjtedxw9XJni/ks+YmWXXptvVcsHGaioL3/fi1w+FQSZQXL13XqPKSnecA8PJLzuEnX3xhJh6QTRZzMLae1eTqCzyv6v/60WtSPV/ICdf4w8iMLFeddzY//4OXpP7e9BB6yhCV+LKcNVVj945NAPzGj1zFtrOm0vnkmMWy0PHa/r//0i0AXLh5ml97/RWpv5cbx/bf2Yv9vfyrP3QFO7bMpP7edKOaeQ6LOZfGYTlnpsFv/Ei6UZwkv7CuPpLl5lfu4prtyV684ZM9ivNkefkl59CoVphpVPmtH70m1ShOObrjOQ8lA0grlIyVXN+s8es3XMHZ03Uu2bo+0+/maQdf6PTZtK7OG645jwefPsWv/lD64fN4mFksfZhOVnBGmaxr1PiVH7qcxW6fF1y0KSOf/IrxVVds5SdffBFvf/UlqRvW45EDrvEP+bpGlV949aU8fmSeV1+xLdPz5ZHFGKwXXbyJn3npxfybl12cOBslyAMyGt9AVdDNP7CLa7dv5H95QTKEMuRTq3JioZvpZ43Cuvzc9fzc9+/kTdedz7oU3BeCcE26LJ3egN5As65e5aaX7eCcmQb/9hU7Mz1fno5n884u2ryOX7r+Ml6yczObZ5KjXo/HZAbrX33fRbR7A95x/WWZfq85QXJ/07oGv/b6K7hw8zq2b1qX+nuNagWl7EaA56VVqeRHirHKz6RANGHKU/e92Okx3ahxzvomv/Wj12bnkQNjDMryoy9Mx67H+ORKivVY16gy06zxWz96TWYeedr0R4qxlpq3CJNXj539AE7XqzRqFf7Pt1ydiwfkN1gvv3QLL/c97Wx88htfpRT/9U3ZZ/vlkSVosK67cCPXXbgxB598DUQ1fxDYf37d5bl4QDaDpbVmoesZ+Uu3rec3b8yx/rUqHX8McFrUH3xn/+GVuzLzUEr5s/5LTN6KgooxLzVr2WvYjZeVlybBsdMqNuL45Kn7na7nt/l5MEabdclbjz0pD+/3s8syyTtr5jG+vsLKS3ngmoWuMVgTrH+u/EJ630UUNXP0fJhmNqt3lgOum4yP3R0MeWlVKvksl2rEUZ75FVmahaJ55PCyunaKMauX1ZpQmQznY2fKL2RrSonkk9P7nURh5ckvtCzWJa8s01ayZHckJpclW5/EYmeyPVapKO9qxgzncnhxy0TvLHt1nbm4JQsMuJRPsfe8rkolv9DpUfVLCPNS3lrZiZRvDoxx6DFM4mXnarqa1GPMDwtMbLBylLdN5DHmgmuKMb62UUkeSHCyd5a9T8KLSiZDiLPOFRpFJbLRT5Zxz0l8SrjGkgyMkiVxGKas9djDZJXFwciy0KaEbiK4ppZ9rsikofSogSiPYpwMFsjT2CMOcVjBaF6CL2ufxCSyeBdxZKsUs40WIWMk1+lZKMZsCV5b4wvZczKTGizbi3by0qpU8pMqLMiO/QaTiPl5ZMcYTSdqWo1vNJ9ssgSTVZPwgGwz+BcsDFbuXIlwhLVog2PXzBjgbBDHxIox470FtvgyZM9jTMLD45PzXE6UKytKlmIv816VSt52M2UKcS3Cwrz12JMarKzY3yhZtYyx3zy5kgmTyPWqolpRuaue8lKeewus9nLGevxhtDjBO8uz/kXsZbtoMV8JrbQsrmjVKvlJPGzIPjq1qLBwUtzf45MN4rDxfoY4dhaFZZGsmqpVh5dLJ5HWelgOmpe88rZsXpZN1VOestMVs5czrL/NXs46t94mWswzu6oIR9IVrUolP2myCrInxezCwhxJMctklTSO2ax5zR2ZvR8LTBbSFWO7N2CgJzvkHp9sEcMoIT5ZrgSy49gT7+VaTlmsEu/Zot+J93JO4yteQtm1M76lJ29JtnhZPhzTApPPtGknT1Y1/THAgxTv1yaJqJRf3iacrMpaw25zyA2fbPkFb9Jh2vTMOB6QLotNrsTwyZL3serFMH0SGflYGd+i8gsZ1n+x05vIwEP2XIkrWpVK3sZjzDrr2yoszFlCOekhH2H/yfIsWhgsw6coWdL4mEmXVhBXJuihZ4XJQraoRFtEJdn3soXDkje/IH0ubXpkcsJo0rkSV7Qqlbw1XtZLHwNs4zFW/GqZrAdwcu8nG8QRbNGfjE92iGtSWZoZYYFRyalNPbY8JgvpsthAQpB9bv1Ct0ejVqGa0sofRcZhSXtn5qaxIowvWCZeM+YXJj6XOUZNu6BVq+RtDrnOMAbYJiyEfBijtTJJ2bQ23o/hk9Vg2SvGNINlpxizV1cVaXxtcjLZciU2yhfSDZbXFWtnfHP1L0yUK8nXQCZtsFzRqlTyVjj28D7RFCVv0aIPOTZt166BBNLxUpskMnjvLFMeo5t+PVoc5VXyNgo4az22zdpD9vxC2t20SXwyV/AIyzKEN1Ou+kvikxX3nzQqydrxPBjoTHfTxtFU3asU62W4T8IFrTolb5usylrDbNOJan4vO8QxOVYOOWABcVkmrxTJKotNEtHwyZ5EXt5RyXTGKg7bmm/IIYuNJ59ldIJNmeawUixZFqMbrM9lQaMNVp2St01WZU2KWm/amjzE0cwI19gaLA9jzCrL5GVnkGddbOqxZY3v6D4BYUiwUIOVYnytIcHKcAxwEtkkd4djgIX3WNH3vFopeaXU+5VSDyul7lNKfVoptTHw2buVUnuVUo8opV5v/aQZycXBgGwe46RhoccnHZezTlYVZbAyJ8XsSuggQ37B4NgTdG9CTuNrCz2lGd+upfHNkyuZ8H0N4U1xxZixUqw7edWTxye9IMI2WszTDOeCbD35LwBXa62vBb4HvBtAKXUV8FbgecANwB8ppSZ/8znIvlIkWw27jYcN3kKnYYyjZJV0gs8+v5B1Do9NbTFkyC848BjFS+jq2WSxjxa9/EJqpZhFEnlUKZYRk7coVICs53Lyu5CyzMhx50iuACWvtf681rrnf/lNwFxddCPwca11W2v9OLAXeLENr6w0miVvi2OnK5NJFZbhk4bJDacDTorJNrJFJa1un6ZFVDKdoUvUOioxM/gz1/zLYvItm+qaWtZyUEtl0sjo/Vo6LFne2Wj+/oTnspHxXFpEi5BNFpupnYYHZKvicUEuMfl/B/yj/+8LgCcDnx30vydO9rXFxWymLHNFrEvocsA1tlFJlhI6sCuhg/R6bBdRiXSCr1ateGOAU+Eay6hkWMOejv1LQxxFeb82jgRk28sLFgPdoHhMPvUplVK3A8+J+Og9WuvP+D/zHqAHfNT8WsTPR8aMSqmbgZsBLrroogyPnEzukiLpC20bFmYxJGCHyUK26ho7WbKH69L5hcVun6l6JfWOzlg+9Yo/BngQO0htWEJn884KSfCNsP8NxF8Yb6sYs8z7sTW+zYw17AudPts3WTpfqYUKxeT9XFHqLtVavzbpc6XUTcCbgOv1CPw7CFwY+LHtwKGYv38LcAvA7t270wdsp5A7xZi+aa28nwzzK1wZrNR6bOtkVXaFNSmfelVRUdnqsW2NL5jr3aKVvFFo9h5jtndmDFxeytonYf3Oauk5GXvFmHEvTzhmesgngyz2jZDZegtckW11zQ3ArwNv1lovBD66DXirUqqplNoJXAbcZcMrK9kmq3Jh8jbYX4b5FfaNPcXANdP19DHAtjimUirT/BKbxh7I9s5sDzl49/xmGdEwXa9OHJVMBzz5OBoMNK3uwO6dNdIhLlfnMgvEaX0uM3ryk/fILDO4JoU+ADSBL/hX7X1Ta/0ftdYPKKU+ATyIB+O8XWtdiEQu5rBANsVoExY2M0AcNrcPwai5IwuObacYR5t2phn9rC4UY1aIy5YHJOPYth3CkB2ucSFLkjGxNb6QbUTHYqeHUqO9kptH5hJa23eWJb9gey6Lra6xUvJa60sTPnsv8F6bv5+Hun6LsIsSOu/vpFck2IaFZgxwnKdmqxjNGOAsIe6W9Y2JeMA4xBGv5O2SVYaPdGlrFljA1iv1+KTLYpvcb2aAa1wZ3+MLncSfMY7EJPcuGx6QPAa4P9C0ewN7GDUNrrGedLqCSiiXC939xHGe+98+x12PH3OGl0UtwO994Xv84sfuAdyEhbC0vO3kYpdXvf8OvrX/mHWyyvxulGdy6zf28zMfuhNwh2OH31mnN+CG3/8qn3/gGWtM1uNTifSwP/Odp3jzB/7ZS4g6KKGDpbJorfnxD36DT3zrSeto0fCJ2mNf/d4sr/3dr7DY6TuB0SDa+/25W/fwJ195zOqu4iCfKFnuO3iCH3jflzg617YaNWJ4QPS5/LW/vpff+seH3EQljWpk4nX/kXle8dtf4sDRBRY7fZQaNYLlpaylza5oVSj5CzZO0xto9h2Zt05WVSrxl2B8+ZHDfOmhZ/0r5txs2rA39+ChU+w/usA39h51ohinYzzGLz9ymK89eoTFTl9MMe4/Os/Dz5zm63uPOMKxo2X56veOcN/BkzxzqmV1+xAE1iXkzR2d77DnieN89dFZa0wWPGUSFS3+894j7D08x2Ozc1a3DwWfL3wBeqc34I5HDvOV781a3VUc5BO1Lt947ChPHlvku4dO2ZccN+IjrNsfepavPDIbaLiyW/8oT/6u/cd46sQidx84NmzqmzgqydjY5YpWhZI/9+wm0/Uq+2bnrJNVEL1ptdbsm51nvtPnmVMt67AwTsnvOzIHwONH5tzAAjEHcN+RecBTxK68rHAovW92bsjLhWKMO4CjdzZvX/XUiFmX2fkxHmBrfCtLlK/HJ/jOJr99yOMRLcuBYwv0B3pMFtvqqigYxcjy+OycFy1aQHVxjsTx+Q7HF7o8fmSe+bZdj4zhE9UlPFz/WbPHJpelVq3QqGabduqCVoWSV0qxc8vMcNPaHD6IDj9n59qcbnuewoOHTgG2JXTRGXazmcwhh8mTVeDJEk68tnt9njy2MORn3wwTDQs8ZmSZnbdOVhk+YR7G+Hp85rzEq2VCFJaG0kOFdWSeeVdwTQQsEJRFKvFqZHn6ZIujcx6WbqsYo5L7wb3sbI/FOEXt3mAol21UMtDQ7YeVvPe3HzPG11LHZCm8cEWrQskD7NzqKfnFzuQzyw15SbHwwZgf/vu7T3lK3hYrh6WwwMj7mbdOVkF0gu/A0QVMtePew3N0enYldKYkbKks3js7dHKR4wtd72ctyxvDPI7Ndzi56P1tF8okrn3eRD4LnT77jywMn2dSipKl2x9wwDe+w73sQDEujRZHe9k4LLYdr0nRopHFZu3r1Qq1ilrC57GIc2nzzpoxUMpQlsC5tKGs4zNc0KpR8ru2zPDksQVOLnatrWzUAQwq+QcOnQQssb8UZXK63ePAsQUnUUlYlsciZHHhMcZBT1rDQ0+fsiqhg+iDEVRYQ2UiAaP5xheC78xtdc2BYwv0fOs7jEodOBJLo8UoWez49PwuYUMnFjocm+/4/NxF2EshwYi9bOWwLH1nvf6AJ46Ow3U2e8zwyTLUzwWtHiW/dYaBhoefOe1gMy0NpfbNzjFVr3D5uet5wAFcE6VMDIxy3YUbAXjg0CnrzRQZlfjK97rtG4ay2CarYPxgGBhlTBbLqCQqiWwU1nUXbuTRZ+fo9AdOsN8oIx+UBexhgTio7roLN/oeox0sUK8qqhUVKcu12zcAuFn/iDzGYwFZDp1c5MRCx34vR+SX9s3OccnWGWYa1cC6uF3/g8cX6fY1123fwGK3z+NH5t0YrNKTz0c7t6wH4KkTi06sbJTHuOOcGS7dtp6nTiwOf25SisJLDYzy2iu3AZ4s1pupsRQv3Tc7z7azmlyzfcNQFiscO0LJGxjleseyLMWX52nUKvzApVtGsrjIlQTwcgOjvOKSc5iqV3jqxKJVCR2MuoSDV8AZg/XaK7dxut3jVKtnpXy9SzAiHJYj8zzv/LM5f8OU2PoHZdEaDp1sOYiwlw7123dknku2rmfn1hkn5zKq7NQ4Ra997rmAm72cZRCaK1o9Sv6cmeG/bSw5RM+V8TyG9ezcEuDj4GBEeT/ff9kWGv7cFJtDDqOZ4kHaNzvHrq0zQ8MIjqKSTvBgeLJcs30D285qej/nIFkVhcnuOGcdl24byWLVQGSugAvIYmCUS7auZ4e/z2xK6CDQJdwLKvl5tqxvDCMGsFsXWFopZmCUXVs8xRj8uUlpqOQDUMq+I/PUq4pXXr51+D3bcxn2fg2Msmvremd7OWrWv4mwrveVPNify+mMdwm7oFWj5Desq3POjNe16Tos7PQGPHl8MUIxuq1hNh7DpdvWc/E56zwetgmeqBD3iHcwdm1xc8hH4fpSr/SSLSPDaAOjgHfIO73B2IycfUfmPIUVNL4WspgZOYvdpYd819YZdvmK0f6QRxlGt7LAUuz/sYAshk/FQVQChN7ZHBdtdmd8DZ8gDwOjBGUB9/mlx2bn2bSuzpXPOWsoq/W5LBOvk9FImThYgDFPbp7+YOlmcp3gMzDKWVP1kSyOE6/H5jucWOiya0v4YEyutKIqEgyMcsGm6YBitJcFRrBAtz/gwNEFdm2dYYdDxbhUyXsGa1cgknOhfCEMccyza+sM52+cplEzkZxbZTIui6eA1zVqdrmSiEYlT5b1zDRrnHu2F8nZnstwQYRxii7ZOuPcYQm/s11b11OpqOE+KxOvZ4hcKsZgGD30fraMe7+u27QNjAIMQ2kXidd24ALkoYe9dT3bN01T85vGXHi/7ZD3s+OcdVQryt26hA7gkz6MsmvrejZM14fzd6whrhBeamCUDdP1gGJ0K8vJhS5H5zvs2jpDtaLYYSI5x3kMA6NcuGl6uJdd7DEYydIfaJ7wjS+MzqWTXFkI3gLGop9qRQ2hzol4ROTK9h2ZH76rXQ6NfJZ7kV3QqlLyu7Z6B9A6lA5Z2WC4vmmmwcZ13gUMVgm+KO/Xh1HA3WYKz8gJylKrVrjIVyb2B70SksWDHgBnijEcSgdl8fi4OoBJsriNSgyfx3yvdAkfS4grPHDLwCi1asWpUwQjWQ4eX6DTH3DJkvW3lyUYYRsYZdNMY+gU2eZKwjDa6VaX2dPt4bl0GcklDVtzSatKyTtbgNAFyPtm59jqwyiAEw/IzMgxByMIo8DIYNkfjHFj8tiRORrVCts3ecp9lyvPNBBKB2EUwBmOHfYYh+H6FmMY1w+fxYamG+PKxMAo4EED4NBgdaIN1mj93eaXDIwCsH3TNPWqsn5fo8RrtCyu3lk4v2RgFICzp+psWd904qxAuiPhIicTjOI7vUHqheuT0qpS8rtcHcCQ97v/6PwYfr1zy3rrsBDGlcnjR+I2kxtP3mza/UfmuciHUYL8bJOiQWXy1PFFegM9lOHCTR4/F7kSGM3IefzIAptnGmzwI6udrpRJAJM/1fJgFCPLxnUNNq2r23vY9aXrUq0oLtzsGV93XvaohHJgYBT/b9eqFS7avM65J2/2spHB1V4OJ5HD53LXlhlnZ9+8s/1HDSQ0DqO62mNGsb/x//0av+BPuHVNtpeGLCvatWWGn3jRdr7/si1WfycYsk3Vqxyb73Dlc84efv5jL7yATevqVmGh4WM27XG/O3DLei9Jdc5Mg3/zsouHdeaTUthjPD7fHZsd/yPXns/pVpezptx5Jsf82eJb/NLJRq3Cz79yF9+3Y7M1D2DsnQVlef3znsNDT58aRimT0lS9ypw/pyi8LgA3v/ISLtg0bcUjnJM5ttBh07r68MrBV12+lTdeex5Xnnd27N/Iyse8r/lOj05/MCbLz75iJxaz/DweIUfi+EIHpWCzX+32fTs388Zrz+OFF22y4xMqiPD28kiWn3rpRTxzsmXNA0bn5Vho/a8672ze8vzzeemuc+z4NKr+XcKaRk0x17YbRpdEq0rJ16oV3v8T11n/nXBDxHy7z0xztAAvv3QLL7/UzpDAeILPDL0yl24opfjNG6+25hFWJnPtHudvnBp+fs32DfzW9mut+YzJ4ivI9YELRN55w5XWPMLXps13emOXlOzcMsMfvPUF1nym6lWO+IO7jLIP8nnbqy6x5hGeKT7fHpdl29lT/OG/fqE1n+BtWmZKY5DPz7z0Yic8YCTLXLvH+kDFztlTdUeyVIZnst3r0+kPWB84lzc+/wJrHmZGzujsj6//VL3K7zvYYyYn1+r1adQq3juzdLTiaFXBNa4o7P3OtXusb8bfdm/Dx3g/p1veZjor5mYlGx4wruTXO+YB4x7jXGupkndBzdAFyKdbcrK0QrLYRjphCmO/c0KyBMsO59reIDfXymQqNFF1riWjsEyXcLc/GBosuXfmGazT7R6NWmVY0uqKgn0yWmuxcwmlko+kIF46GGjmO70xj8EVBfHSsMfgjEcolA57jK5oXJnIKPlw2eG8pMHqjKIFEFiXCEdCYl2CJZRzQ8Xodi83qhUqirF3JiULeOsvdV5g3PmS3GPgncuFTh+tZQwWlEo+koKbaaHrL4CEZ9IYV4xK2Sd0lvAIl4QJhYXBeT9iSj4ywhJal1CE5ZpPOPE61+45j+LA7xLue13CowjLbVQa7hKWirCC7+y0UIQFHizYEo6wgkpe0mBBqeQjKdgQIeox1EYNEWEc0xmPYX5hQMf/b71l+Vckn1oBUUkE9CSxLs2xCEsGFjAzctriEdYIShnlFwSi0kZVPMIye7ndHYhFWBCC69rxl9PbUDCPYS4jkjBYUCr5SJoK3HIv5cmBX3bYkcUxh4e8M/IYxKKS7ihakMAxR56cV1M83+7JeHKBLmEpHNubEFkd8+SlcGzwPEaj5M8SyC81w7IIe79SeR8Yh2vm2l2RCCuY94sqVHBJTk6hUupXlVJaKbUl8L13K6X2KqUeUUq93gWfoigqlJJL8I2qa6S8Ehg/5FJ8pHHMYJfwYrfPQMvJAl7lw5yDe0Nj+TTkFWOUMpH35GW83+ANZFKQIIznl8KVda4oCAkbgyUF11j/VaXUhcDrgAOB710FvBV4HnA+cLtS6nKtdTHDGixpKkIxSlekiFWKNKI8OZmD0fI9bCkcs1JRXhmdsCc3fGed/lAWm4vhY/n4VRzd/oBWdyAqSxCukYoYgqMAJCKsyHMpJMuJhVEJbXD4nUse4MnS8e8UWM6e/O8B7wSCPbk3Ah/XWre11o8De4EXO+BVCA3nvQQSPFJQShBfFjkYtWIO+RAv7Q1Ey8EMXiqJYw7fWW/AXLsrJoup+5aMFoMXk59u9WhUK8NSVJc0HTTywlGJuJEPRNhySeRRrmRZwzVKqTcDT2mt7w19dAHwZODrg/73ov7GzUqpPUqpPbOzszaP44yKhGtMa/N8u8eMQEI0OCNHFq4ZD6UllfwY9CCRRG4EIQ6ZcB1GYy1Eo8XG+F6WksWMtWh1B/IwWnAvC6x/EEbzoEcZowjyUQlkgGuUUrcDz4n46D3AfwF+KOrXIr4XOX1Ha30LcAvA7t27ZSb05KQRjjlgriabeNXa936FEq/gQymdkfcjUqoXgoW2nTWV8huTkUmKzQlGWEFl4pWcuk9UwugGsiIiLMNHbI/VKhzu9jktlKiGpYpxplEVgdHMFNJef8Bity/TCBlwJKThmtS/qrV+bdT3lVLXADuBe/2yv+3At5VSL8bz3C8M/Ph24JD10xZEZvjYYrdPre1toiI8E1Hvt4AGEjAeY5+ZLYIGq4BcCYy8XwlPDjzDONfuia5LsLdgTihahJH3Oy/UcGV4gOd8SZWcwsgpmu+YMRBynnzLb4aq+RG3BE38V7XW92utt2mtd2itd+Ap9hdqrZ8BbgPeqpRqKqV2ApcBdzl54oJomOBr96hXZRZgiZclpeQbVRa7g2I8Rr/sVFYW6eqKAPQkKIup4hAt0w3Wybdk8j4wgtGkGq5glF9YHEZYsk7RnGDep16tUK2oUVTSdN8jY0jkLWmtH1BKfQJ4EOgBb18plTWGTEnYQGuxBTDW/Ph8VwzHhKXerwiOOUy8Cnu/BUYlrcABlCBTjy85h2VsFECnN5wM6ZqW7DGB9Z9qjCcrJSPf3kAPK2wk17/VlS1UAIdK3vfmg1+/F3ivq79fNBnst9OTKW0zPABm59qAXOIlWHa4rlEdzpJ3SUaZnG71xHBM8GQ5Nt8Rra4J5xckchjgY7+dvljDFSxtILpos90Y5jiaCigskGm4MjNyzF6WPpdmEqloJCccYUHZ8RpLJvw8LQyjAMye9pW8pPfbkfdKYXQwxKo4jMfY6lEVwjGX4NjCuZIhXCNRKVQPQRyCsnT6A04uegZLYv2HM3KE12VqybmUOv8VH/uXkwVKJR9LUwFYQMyS+wrqiPHkhbzfIMYo6ZXCSBZR7DewLhIwmlGMJxa79AdaLsIKJSslFKOZkWPGWkgqLAjsZcFKMem9PHJYhGWpjTx5SbimVPIxNF2v0vbDTzFPzvcYjvgeg2QNszS+PAxxh7LIvbNWAV4pjGQR9X57A061ukzVK9Qsr5OMIjMjZ77jVXGIR3LC7ywICxUli2RFkvRehlLJx1KwikMyiw8B71fQkzcTNcUV45y8YpSOsJpLIizZd3Z0ri0WxYG3l48KR1hTgfWvqJFsrsk7l17Zqdi5bBQTlRaBFkCp5GPJNETMtSQhjuISrwb7lTsY47JIHYymb7AkZTEzcmaFlXxw/SUTb9P1aqGySMFo4MlycrFLt68FYdTxvSybk/EaIUtM/gyQqWGWbroAOHJaNlk5HAXQkcwvhGWR9n47ogdjql4dyiIPC3XE1h68+fhFrcuR0x1Rr3QqIIvYXm6MZGnWKsPL1Z3zqVdYaPeY7/TFHBYolXwsTderLHT8BZDG5IuAa3qyTUqViqJRqxQAcYxCaakIy+NTlU+8BdZfUjEWIUtwL0sqrKmgLAVAj9IR1rF5z2BJ7uVSycfQdL3KCb8cTLq65thCh4oaVag45+PPyDm52BX1fqfrVY4tCHu/vjI5tiDr/QZlkfZ+jy3Ier9FrMtUPbguxchSxLqIytKQlwVKJR9LU3VPMYLgdLhqhUa1MrzEVwrHNFCK1nJYOXiHw7wzaYjLe2dyycrg+kuXnUpe4gyeMhnu5VUki3QSWVqWInQMlEo+lozHCLJWtukfDumDYWjG8UXhQTIHXRbHHD2/VPOYx2f0/NIeoyQPYGx+vDTEIckDRg4LrPx1KWovl0o+hoILII39gqwlHzuAQmNzYfTOpKMFQ6LvzDeGSsE6IcM4VbAsIA+jSfIoio+ZkQPFnH2QjUpLJR9DRVlzs2lXg8dQhCxFRVhD49sQLAcMKiyhhhsYJathtXm/MnzMjBwoZo95fEpPvnCaDlhz6aSYOI9GMR5DIbIUBQsUHmHJ82nUKjSEZpaPRb4r/J2ZGTmSPGBUXQVylXVQKvlYCmJ/kpu2WQDEMRU42NLlbSCtfAOh9IqXpViDJQk9mBk5UMz6KwXrhLpqPT7y7yx4LktP/gzQVGGwgLcEUvMxIOzJy5YdejyKUYyi76xeAPRUsJKXlMXMyJHmM4QEGzWRq/8MFfHOxs5lWV1TPBWFlxWeeBUuO4RVAnE05COsZkERlpFF0pAE+az0CAuKeWdDGK1aGauAck2lko+hohagiM00VZTB8vMYhXk/RXi/gtGCmZEDBSWRpRVjAe9sFGHJ7eMgnyKUvLQspZKPoSK8Uhhh/6sB4jCyyOKYRSl5v39Bev2LwH4LkqVZAJ/RuZSLSD0+8rI0C9IxpZKPoaKs7FSBZYczjaoojllECaWZkSPNp3Dvd4Vj8lCs9yuZW4KCMPkCIh8Qush7NZBpiJDEsGG00EVU1xTllRahGLXWIlf/BXlAcUq+iDxGYbKIOizyHeJQbGmz5NmHUsnHUlEeQxHW3MzIWQ2enOGjFGJNSlBMVALFYP+jvI/wXi6wsU98LxeYeJWWpVTyMVR4Fr8AvFQS94XiZPGuypNT8B6P4mRZ16hSlYTRhntZNio1BQpFNENJ7+VhrmwVRFjW8a5S6heVUo8opR5QSr0v8P13K6X2+p+93pZP0VSvVqhVlHiCx0AORXi/RSgskJdlql4thAfIK5PpRnGySK//dKNKraJEYbQiZQHZvWwS1csarlFKvRq4EbhWa91WSm3zv38V8FbgecD5wO1Kqcu11n3bBy6SpuvVwkLcIiIG6QRPUeHndKNKVRCqgQJlKdBgyUOPHiQoCqMVtC4jGE3unZku4eWeeH0b8Nta6zaA1vqw//0bgY/7339cKbUXeDHwL5b8CqV3/vCVXH3+2aI8Xvvcc3n6RIuLNq8T5fOO6y/j3LOnRHm8/NIt/Pwrd3H1BbLv7OdfeQmC6AYA127fwM2v3MVLd20W5XPTy3dwdK4jymPnlhn+11ddwquv3CbK519930Vcd+FGUR4b19X5ldddzhuvOU+Uz1tecD7nzDSoCY3MBi+n9F/feBUv23WOGA8Apc3U+kl+WanvAJ8BbgBawK9qrb+llPoA8E2t9Uf8n/sQ8I9a609G/I2bgZsBLrroohc98cQTEz9PSSWVVNJaJKXU3Vrr3VGfpXrySqnbgedEfPQe//c3AS8Fvg/4hFJqFxDlZ0VaE631LcAtALt3757c4pRUUkkllbSEUpW81vq1cZ8ppd4GfEp74cBdSqkBsAU4CFwY+NHtwCHLZy2ppJJKKikn2QJOfwu8BkApdTnQAI4AtwFvVUo1lVI7gcuAuyx5lVRSSSWVlJNsE69/BvyZUuq7QAe4yffqH1BKfQJ4EOgBb19plTUllVRSSauBrJS81roD/HTMZ+8F3mvz90sqqaSSSrKjckBZSSWVVNIqplLJl1RSSSWtYiqVfEkllVTSKiarZijXpJSaBWy6obbgVfcsd1opzwkr51lXynPCynnWlfKcUD7rxVrrrVEfLCslb0tKqT1xXV/LiVbKc8LKedaV8pywcp51pTwnlM+aRCVcU1JJJZW0iqlU8iWVVFJJq5hWm5K/5Uw/QEZaKc8JK+dZV8pzwsp51pXynFA+ayytKky+pJJKKqmkcVptnnxJJZVUUkkBKpV8SSWVVNIqplWh5JVSN/h3ye5VSr3rTD+PIaXUhUqpO5RSD/l34L7D//5mpdQXlFKP+v/fdKaf1ZBSqqqUukcp9Vn/62X5rEqpjUqpTyqlHvbf78uW47MqpX7ZX/vvKqU+ppSaWi7PqZT6M6XUYX/AoPle7LOdyXubY571/f7636eU+rRSauOZftao5wx89qtKKa2U2lLkc654Ja+UqgJ/CPwwcBXwk/4ds8uBesCvaK2fi3exytv9Z3sX8EWt9WXAF/2vlwu9A3go8PVyfdY/AD6ntb4SuA7vmZfVsyqlLgB+Cdittb4aqOLdfbxcnvPDeLe6BSny2UL3Nt8A/JF/9oqiD7P0Wb8AXK21vhb4HvBuOOPPGvWcKKUuBF4HHAh8r5DnXPFKHu/u2L1a633+VMyP490xe8ZJa/201vrb/r9P4ymiC/Ce71b/x24F3nJGHjBESqntwBuBPw18e9k9q1LqbOCVwIfAm4aqtT7BMnxWvEmv00qpGrAO7/KcZfGcWuuvAsdC3457tuG9zVrrxwFzb3MhFPWsWuvPa617/pffxLuc6Iw+a8w7Bfg94J2M35BXyHOuBiV/AfBk4OuD/veWFSmldgAvAO4EztVaPw2eIQBkb1jOTr+PtxEHge8tx2fdBcwCf+5DS3+qlJphmT2r1vop4P/G896eBk5qrT/PMnvOEMU923I/Z/8O+Ef/38vqWZVSbwae0lrfG/qokOdcDUo+832yZ4qUUuuBvwH+k9b61Jl+nihSSr0JOKy1vvtMP0sGqgEvBD6otX4BMM/ygZGG5OPZNwI7gfOBGaVU5P0LK4CW7TlTSr0HDxr9qPlWxI+dkWdVSq3Duw/7f4v6OOJ7zp9zNSj5ZX2frFKqjqfgP6q1/pT/7WeVUuf5n58HHD5TzxegVwBvVkrtx4O8XqOU+gjL81kPAge11nf6X38ST+kvt2d9LfC41npWa90FPgW8nOX3nEGKe7Zlec6UUjcBbwJ+So+afpbTs16CZ+Tv9c/WduDbSqnnUNBzrgYl/y3gMqXUTqVUAy+RcdsZfiYAlFIKDzd+SGv9u4GPbgNu8v99E/CZop8tTFrrd2utt2utd+C9wy9prX+a5fmszwBPKqWu8L91Pd5Vk8vtWQ8AL1VKrfP3wvV4eZnl9pxBinu2ZXdvs1LqBuDXgTdrrRcCHy2bZ9Va36+13qa13uGfrYPAC/09XMxzaq1X/H/AG/Cy648B7znTzxN4ru/HC7/uA77j//cG4By8yoVH/f9vPtPPGnruVwGf9f+9LJ8VeD6wx3+3fwtsWo7PCvwfwMPAd4G/AJrL5TmBj+HlCrp4yuffJz0bHuzwGPAI8MPL4Fn34mHa5mz98Zl+1qjnDH2+H9hS5HOWYw1KKqmkklYxrQa4pqSSSiqppBgqlXxJJZVU0iqmUsmXVFJJJa1iKpV8SSWVVNIqplLJl1RSSSWtYiqVfEkllVTSKqZSyZdUUkklrWL6/wEc5OA/SSaPSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_r['seasonality'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2bed6449-5619-4be6-9286-d9b1101bc9c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_r['deseasonal'] = df_r['data'] - df_r['seasonality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d089ef12-17f8-47e0-9d27-fa24c98c98c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ts</th>\n",
       "      <th>data</th>\n",
       "      <th>approx_trend</th>\n",
       "      <th>detrended</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>deseasonal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1949-01</td>\n",
       "      <td>112</td>\n",
       "      <td>114.028041</td>\n",
       "      <td>-2.028041</td>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>-24.062583</td>\n",
       "      <td>136.062583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1949-02</td>\n",
       "      <td>118</td>\n",
       "      <td>115.690061</td>\n",
       "      <td>2.309939</td>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>2</td>\n",
       "      <td>-33.399685</td>\n",
       "      <td>151.399685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "      <td>117.366097</td>\n",
       "      <td>14.633903</td>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.834137</td>\n",
       "      <td>132.834137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "      <td>119.056149</td>\n",
       "      <td>9.943851</td>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.532605</td>\n",
       "      <td>135.532605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "      <td>120.760218</td>\n",
       "      <td>0.239782</td>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>5</td>\n",
       "      <td>-4.411756</td>\n",
       "      <td>125.411756</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>139</td>\n",
       "      <td>1960-08</td>\n",
       "      <td>606</td>\n",
       "      <td>479.480046</td>\n",
       "      <td>126.519954</td>\n",
       "      <td>1960-08-01</td>\n",
       "      <td>8</td>\n",
       "      <td>66.866692</td>\n",
       "      <td>539.133308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>140</td>\n",
       "      <td>1960-09</td>\n",
       "      <td>508</td>\n",
       "      <td>483.090345</td>\n",
       "      <td>24.909655</td>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>9</td>\n",
       "      <td>15.514809</td>\n",
       "      <td>492.485191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>141</td>\n",
       "      <td>1960-10</td>\n",
       "      <td>461</td>\n",
       "      <td>486.714660</td>\n",
       "      <td>-25.714660</td>\n",
       "      <td>1960-10-01</td>\n",
       "      <td>10</td>\n",
       "      <td>-23.017758</td>\n",
       "      <td>484.017758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>142</td>\n",
       "      <td>1960-11</td>\n",
       "      <td>390</td>\n",
       "      <td>490.352992</td>\n",
       "      <td>-100.352992</td>\n",
       "      <td>1960-11-01</td>\n",
       "      <td>11</td>\n",
       "      <td>-59.481007</td>\n",
       "      <td>449.481007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>143</td>\n",
       "      <td>1960-12</td>\n",
       "      <td>432</td>\n",
       "      <td>494.005340</td>\n",
       "      <td>-62.005340</td>\n",
       "      <td>1960-12-01</td>\n",
       "      <td>12</td>\n",
       "      <td>-33.208273</td>\n",
       "      <td>465.208273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index       ts  data  approx_trend   detrended       date  month  \\\n",
       "0        0  1949-01   112    114.028041   -2.028041 1949-01-01      1   \n",
       "1        1  1949-02   118    115.690061    2.309939 1949-02-01      2   \n",
       "2        2  1949-03   132    117.366097   14.633903 1949-03-01      3   \n",
       "3        3  1949-04   129    119.056149    9.943851 1949-04-01      4   \n",
       "4        4  1949-05   121    120.760218    0.239782 1949-05-01      5   \n",
       "..     ...      ...   ...           ...         ...        ...    ...   \n",
       "139    139  1960-08   606    479.480046  126.519954 1960-08-01      8   \n",
       "140    140  1960-09   508    483.090345   24.909655 1960-09-01      9   \n",
       "141    141  1960-10   461    486.714660  -25.714660 1960-10-01     10   \n",
       "142    142  1960-11   390    490.352992 -100.352992 1960-11-01     11   \n",
       "143    143  1960-12   432    494.005340  -62.005340 1960-12-01     12   \n",
       "\n",
       "     seasonality  deseasonal  \n",
       "0     -24.062583  136.062583  \n",
       "1     -33.399685  151.399685  \n",
       "2      -0.834137  132.834137  \n",
       "3      -6.532605  135.532605  \n",
       "4      -4.411756  125.411756  \n",
       "..           ...         ...  \n",
       "139    66.866692  539.133308  \n",
       "140    15.514809  492.485191  \n",
       "141   -23.017758  484.017758  \n",
       "142   -59.481007  449.481007  \n",
       "143   -33.208273  465.208273  \n",
       "\n",
       "[144 rows x 9 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ee9dedf4-0da2-42c3-845d-34df6504ccfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_r['deseasonal'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e580d54f-f74f-4766-bf46-44a02858d374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.13671707e-03, 1.63975702e+00, 1.14239650e+02])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array(df_r['index'])\n",
    "y = np.array(df_r['deseasonal'])\n",
    "\n",
    "order = 2\n",
    "coef = np.polyfit(X,\n",
    "                  y.ravel(),\n",
    "                  order)\n",
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ecdba04d-2eaf-496e-a0d7-b7b4807f412d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poly1d([7.13671707e-03, 1.63975702e+00, 1.14239650e+02])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_1d = np.poly1d(coef)\n",
    "poly_1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f377c5f8-a099-47fc-8332-bf5dc77eb531",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_r['trend'] = poly_1d(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8f01f672-869b-49c0-b4cb-b5bd84fa33a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ts</th>\n",
       "      <th>data</th>\n",
       "      <th>approx_trend</th>\n",
       "      <th>detrended</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>deseasonal</th>\n",
       "      <th>trend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1949-01</td>\n",
       "      <td>112</td>\n",
       "      <td>114.028041</td>\n",
       "      <td>-2.028041</td>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>-24.062583</td>\n",
       "      <td>136.062583</td>\n",
       "      <td>114.239650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1949-02</td>\n",
       "      <td>118</td>\n",
       "      <td>115.690061</td>\n",
       "      <td>2.309939</td>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>2</td>\n",
       "      <td>-33.399685</td>\n",
       "      <td>151.399685</td>\n",
       "      <td>115.886544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "      <td>117.366097</td>\n",
       "      <td>14.633903</td>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.834137</td>\n",
       "      <td>132.834137</td>\n",
       "      <td>117.547711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "      <td>119.056149</td>\n",
       "      <td>9.943851</td>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.532605</td>\n",
       "      <td>135.532605</td>\n",
       "      <td>119.223151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "      <td>120.760218</td>\n",
       "      <td>0.239782</td>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>5</td>\n",
       "      <td>-4.411756</td>\n",
       "      <td>125.411756</td>\n",
       "      <td>120.912865</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>139</th>\n",
       "      <td>139</td>\n",
       "      <td>1960-08</td>\n",
       "      <td>606</td>\n",
       "      <td>479.480046</td>\n",
       "      <td>126.519954</td>\n",
       "      <td>1960-08-01</td>\n",
       "      <td>8</td>\n",
       "      <td>66.866692</td>\n",
       "      <td>539.133308</td>\n",
       "      <td>480.054386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>140</td>\n",
       "      <td>1960-09</td>\n",
       "      <td>508</td>\n",
       "      <td>483.090345</td>\n",
       "      <td>24.909655</td>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>9</td>\n",
       "      <td>15.514809</td>\n",
       "      <td>492.485191</td>\n",
       "      <td>483.685287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>141</td>\n",
       "      <td>1960-10</td>\n",
       "      <td>461</td>\n",
       "      <td>486.714660</td>\n",
       "      <td>-25.714660</td>\n",
       "      <td>1960-10-01</td>\n",
       "      <td>10</td>\n",
       "      <td>-23.017758</td>\n",
       "      <td>484.017758</td>\n",
       "      <td>487.330462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>142</td>\n",
       "      <td>1960-11</td>\n",
       "      <td>390</td>\n",
       "      <td>490.352992</td>\n",
       "      <td>-100.352992</td>\n",
       "      <td>1960-11-01</td>\n",
       "      <td>11</td>\n",
       "      <td>-59.481007</td>\n",
       "      <td>449.481007</td>\n",
       "      <td>490.989910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>143</td>\n",
       "      <td>1960-12</td>\n",
       "      <td>432</td>\n",
       "      <td>494.005340</td>\n",
       "      <td>-62.005340</td>\n",
       "      <td>1960-12-01</td>\n",
       "      <td>12</td>\n",
       "      <td>-33.208273</td>\n",
       "      <td>465.208273</td>\n",
       "      <td>494.663631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index       ts  data  approx_trend   detrended       date  month  \\\n",
       "0        0  1949-01   112    114.028041   -2.028041 1949-01-01      1   \n",
       "1        1  1949-02   118    115.690061    2.309939 1949-02-01      2   \n",
       "2        2  1949-03   132    117.366097   14.633903 1949-03-01      3   \n",
       "3        3  1949-04   129    119.056149    9.943851 1949-04-01      4   \n",
       "4        4  1949-05   121    120.760218    0.239782 1949-05-01      5   \n",
       "..     ...      ...   ...           ...         ...        ...    ...   \n",
       "139    139  1960-08   606    479.480046  126.519954 1960-08-01      8   \n",
       "140    140  1960-09   508    483.090345   24.909655 1960-09-01      9   \n",
       "141    141  1960-10   461    486.714660  -25.714660 1960-10-01     10   \n",
       "142    142  1960-11   390    490.352992 -100.352992 1960-11-01     11   \n",
       "143    143  1960-12   432    494.005340  -62.005340 1960-12-01     12   \n",
       "\n",
       "     seasonality  deseasonal       trend  \n",
       "0     -24.062583  136.062583  114.239650  \n",
       "1     -33.399685  151.399685  115.886544  \n",
       "2      -0.834137  132.834137  117.547711  \n",
       "3      -6.532605  135.532605  119.223151  \n",
       "4      -4.411756  125.411756  120.912865  \n",
       "..           ...         ...         ...  \n",
       "139    66.866692  539.133308  480.054386  \n",
       "140    15.514809  492.485191  483.685287  \n",
       "141   -23.017758  484.017758  487.330462  \n",
       "142   -59.481007  449.481007  490.989910  \n",
       "143   -33.208273  465.208273  494.663631  \n",
       "\n",
       "[144 rows x 10 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "10d0a473-0f7b-4e12-9e9a-a74425e0abb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_r['trend'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b0dc48e3-4d7b-4f89-a282-cbd674f0102e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_r['residual'] = df_r[\"data\"] - df_r['trend'] - df_r['seasonality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a18730f4-5c4c-40fe-8851-20b1bab1d77d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_r['residual'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf9c5d34-26b4-4a4b-ba49-e641adfecd14",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}