mean-median-imputation.ipynb

{
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   "cell_type": "code",
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   "id": "a8dba1fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.XWYDX2IKJW2NMTWSFYNGFUWKQU3LYTCZ.gfortran-win_amd64.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a49b7f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db667658",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"https://raw.githubusercontent.com/campusx-official/100-days-of-machine-learning/main/day36-imputing-numerical-data/titanic_toy.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9629b89",
   "metadata": {},
   "outputs": [
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       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
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       "    Age     Fare  Family  Survived\n",
       "0  22.0   7.2500       1         0\n",
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9572126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age         0.198653\n",
       "Fare        0.050505\n",
       "Family      0.000000\n",
       "Survived    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.isna().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56c2641c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f73d4a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "713a80e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.20,random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5bb2777c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(712, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84c17d55",
   "metadata": {},
   "outputs": [
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       "(179, 3)"
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     "execution_count": 10,
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   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "924c024e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age       0.184358\n",
       "Fare      0.044693\n",
       "Family    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.isna().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0f5b8a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age       0.202247\n",
       "Fare      0.051966\n",
       "Family    0.000000\n",
       "dtype: float64"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.isna().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "85894032",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_age=x_train[\"Age\"].mean()\n",
    "median_age=x_train[\"Age\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dae053a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_Fare=x_train[\"Fare\"].mean()\n",
    "median_Fare=x_train[\"Fare\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5270d42c",
   "metadata": {},
   "outputs": [
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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    "mean_Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26d71f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train[\"mean_age\"]=x_train[\"Age\"].fillna(mean_age)\n",
    "x_train[\"median_age\"]=x_train[\"Age\"].fillna(median_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5735d1c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train[\"mean_fare\"]=x_train[\"Fare\"].fillna(mean_Fare)\n",
    "x_train[\"median_fare\"]=x_train[\"Fare\"].fillna(median_Fare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "26971757",
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      "text/plain": [
       "      Age      Fare  Family   mean_age  median_age   mean_fare  median_fare\n",
       "146  27.0    7.7958       0  27.000000        27.0    7.795800       7.7958\n",
       "195  58.0  146.5208       0  58.000000        58.0  146.520800     146.5208\n",
       "786  18.0    7.4958       0  18.000000        18.0    7.495800       7.4958\n",
       "290  26.0   78.8500       0  26.000000        26.0   78.850000      78.8500\n",
       "521  22.0       NaN       0  22.000000        22.0   31.944715      14.4000\n",
       "..    ...       ...     ...        ...         ...         ...          ...\n",
       "256   NaN   79.2000       0  30.303715        29.0   79.200000      79.2000\n",
       "643   NaN   56.4958       0  30.303715        29.0   56.495800      56.4958\n",
       "249  54.0   26.0000       1  54.000000        54.0   26.000000      26.0000\n",
       "664  20.0    7.9250       1  20.000000        20.0    7.925000       7.9250\n",
       "874  28.0   24.0000       1  28.000000        28.0   24.000000      24.0000\n",
       "\n",
       "[712 rows x 7 columns]"
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1e34a210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(219.4128798268002, 174.97482821630902, 175.24944458331322)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[\"Age\"].var(),x_train[\"mean_age\"].var(),x_train[\"median_age\"].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ed353d65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2567.927686728423, 2434.2943190646374, 2449.4804988255223)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "x_train[\"Fare\"].var(),x_train[\"mean_fare\"].var(),x_train[\"median_fare\"].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e94a05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 21,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_train[\"Age\"].plot(kind=\"kde\",color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b77e033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21b2bf8ea60>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15,16))\n",
    "x_train[\"Age\"].plot.density(color=\"red\",label=\"age\")\n",
    "plt.legend()\n",
    "x_train[\"mean_age\"].plot.density(color=\"green\")\n",
    "plt.legend(\"mean_age\")\n",
    "x_train[\"median_age\"].plot.density(color=\"blue\")\n",
    "plt.legend(\"median_age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "222b110a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21b2c4fe880>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iDDSb461PkiTpdGdYlDSRBp3F5VqducL0sQeEwFxhCorpUqiuiCpJkjRchkVJE6nRgBAiR6ZazJVnj3vMbHkGimmqdEVUSZKk4TIsSppI9TpUyz2OVI7zjMW+mfIclOwsSpIkjYJhUdJEajSgWuqyVIKZ6vxxj5mtzdtZlCRJGhHDoqSJVK9DrdhmqQTTtfnjHjM1tc05i5IkSSNiWJQ0kRoNqBQSkgJM17Yd95jc3Dy1kC6DamdRkiRpuAyLkiZSvQ6VfBoEZ2Z2HP+g2Vlmop1FSZKkUTAsSppIjQYUc2lYnJ7ZfvyD5uaYjkuAnUVJkqRhMyxKmkj1OpRyaQKcPsGjM5ibY7ZrZ1GSJGkUDIuSJlKjAfmQJsCZ0szxD5qdZb7raqiSJEmjYFiUNJHqdSiEdIjpdGn6+AfNzT0QFu0sSpIkDZdhUdJEajQg1+8snjAszs4y1+5CvmVnUZIkacgMi5ImUr0OudwicPKwONuCUFq2syhJkjRkhkVJE6nRAAZzFssnmLM4M8NsC2KhzvJyHF9xkiRJW4BhUdLEibG/YE3uCHCSzuL0NLMtoLTMkaXu2OqTJEnaCgyLkiZOkqSBsZdLF7ipFWvHP3AQFot1jix1xlegJEnSFmBYlDRxBovV9PKLTMciuXCCr6rpaWYSoLjMop1FSZKkoTIsSpo4jUa67eaXmKZ04gOnph7oLC45Z1GSJGmoDIuSJs6gs9gp1pkK5RMfWCgwG0tQbNBoGBYlSZKGybAoaeI0m+m2W25Qy50kLAKz+RoUGg90IyVJkjQchawLkKSjDcJiu9SkVqie9NiZQg3yTZrNMIbKJEmStg47i5ImTquVbtulFrVC5aTHzhZnoNig1fTrTJIkaZjsLEqaOIPOYlJunvixGX2zlVmIDZJmfgyVSZIkbR3+U7ykiTPoLCblJrXS1EmPLddmyRWadBL/7UuSJGmYDIuSJs6gs9gqt6iVpk9+8PQ0lVyTXjdPpzP62iRJkrYKw6KkiTPoLDYrTWqVmZMfPD1NJaRLoboiqiRJ0vAYFiVNnId0FlcRFqukJxgWJUmShsewKGniDMJio9qkVp09+cHT09R6dhYlSZKGzbAoaeIMhqFSaFGtnrqzWOstA4ZFSZKkYTIsSpo4g84ihSa16tzJD56eZiraWZQkSRo2w6KkifNAZzHfOuWjM1aGxQdCpiRJkjbMsChp4jSbUMx1IBepFWsnP3h6mmk7i5IkSUNnWJQ0cZpNKOUTgFWFxZmuYVGSJGnYDIuSJk6rBcXcGsJifzXUpeXuqEuTJEnaMgyLkiZOswnFfDpxcTVhcbYfFg8vtU5+rCRJklbNsChp4rRakM+tISx20pVtFpaSUZcmSZK0ZRgWJU2cZhMKuTQAriYsbuukncUjS+1RlyZJkrRlGBYlTZy0s5iGxWqxevKDp6eZ7y9ws7hsZ1GSJGlYRhoWQwjPCyHcGEK4KYTwpuO8H0IIb+u/f30I4dIV7707hHBvCOGrR53zoRDCdf2f20II1/X3XxBCaKx478pRfjZJo9NsQj6sobPYTo9drLvAjSRJ0rAURnXhEEIeeDvwHGAf8IUQwlUxxq+tOOz5wEX9nycD7+xvAf4I+B3gvSuvG2N88Yp7/AawsOLtm2OMlwz1g0gau2YTcrm0W3jKsFguM9PNQaHhaqiSJElDNMrO4pOAm2KMt8QYE+CDwOVHHXM58N6Y+hwwH0I4CyDG+PfAoRNdPIQQgH8HfGAk1UvKTKsFDIahFk4xDDUEpoo1KDRYtrMoSZI0NKMMi+cAd6z4fV9/31qPOZGnA/tjjN9asW9vCOFLIYRPhxCefryTQgivDiFcE0K45r777lvlrSSNU7MJIdegHPPkc/lTHj9VnIJig+V6HEN1kiRJW8Mow2I4zr6j/5JbzTEn8hIe2lW8GzgvxvgE4CeB94cQZo+5eIzvijFeFmO8bNeuXau8laRxarWAfJ0axVUdP12ehkKTesOwKEmSNCyjDIv7gHNX/L4HuGsdxxwjhFAA/hXwocG+GGMrxniw//pa4GbgEeuqXFKmms0I+Ra1UFrV8VPlGSg0aDRGXJgkSdIWMsqw+AXgohDC3hBCCbgCuOqoY64CXt5fFfUpwEKM8e5VXPvZwDdijPsGO0IIu/qL6hBCuJB00ZxbhvFBJI1XqxmJheaqw2KtOgvFBs3m8QYrSJIkaT1GFhZjjB3g9cDHga8DH44x3hBCeG0I4bX9w64mDXQ3Ab8H/Pjg/BDCB4DPAheHEPaFEH50xeWv4NiFbZ4BXB9C+DLwEeC1McYTLpAjaXI1m9ArtqjlK6s6Pjc1TS7foGVYlCRJGpqRPToDIMZ4NWkgXLnvyhWvI/C6E5z7kpNc95XH2fdR4KPrrVXS5Gi2Ar1ik2quvLoTajXyhSZJc6SPjpUkSdpS/MtK0kSJEZIk0C22qJ3qsRkDtRqFXIOkdeqVUyVJkrQ6hkVJE6XVSredUnNNYbGYa9BujXSwhCRJ0pZiWJQ0UR4SFku11Z1Uq1EKTTrJ6h61IUmSpFMzLEqaKM1mum2XWtRKU6s7qVajHOp0WoZFSZKkYTEsSpooD+0sTq/upFqNMg167VUuiCNJkqRTMixKmiiDzmJSalGrrD4sVkODXnt1z2WUJEnSqRkWJU2UB8JipUmtMrO6k2o1KrEBvQLt9uhqkyRJ2koMi5ImymAYKvkW1TWExRoNABqN0dQlSZK01RgWJU2UQWeRQpNabW51J9VqTMU0JdbrcTSFSZIkbTGGRUkT5YHOYqFJrTa7upNWhMWFpWQ0hUmSJG0xhkVJE+XBzmKLanH1j86YjumJh444DlWSJGkYDIuSJsrKYajVYnV1J01NMdNNQ+KhRcOiJEnSMBgWJU2UlQvcVAqV1Z1UqzHTGwxDbZ3iYEmSJK2GYVHSRFnZWVxLWJzrdxbvX2qe4mBJkiSthmFR0kRZucDNesLikSUftChJkjQMhkVJE2XlAjdrCYvznfTEI8uuhipJkjQMhkVJE2VdncVKhbluGhaXG53RFCZJkrTFGBYlTZQHOov5ZPVhMZdjLh8BWKobFiVJkobBsChpojSbUMg1IUC1sMpHZwDzhQBAvdEbVWmSJElbimFR0kRptaCQS+cdrrqzCMyX068zw6IkSdJwGBYlTZQHOousLSxuKxcAqDcNi5IkScNgWJQ0UVotyPfDYrlQXvV51VoFQodGI46qNEmSpC3FsChpojSbkMs1KcU8ubCGr6haDQqtBxfIkSRJ0oYYFiVNlFYrksu1qFBY24m1GiHfpNUMoylMkiRpizEsSpooSbNHLt+iEoprO7FWI1doPvicRkmSJG2IYVHSREmaPcgl6wuL+Ratll9rkiRJw+BfVZImStKMkE+o5NYeFgu5JolhUZIkaSj8q0rSRElavX5YLK3txKkp8rkm7cSvNUmSpGHwrypJEyVp9TuL+dU/NgOAWo1irkGnlR9NYZIkSVuMYVHSREkSiIX1hcVCaNJJ1riKqiRJko7LsChpoiQJxHxCJV9Z24m1GqXQpNNe41xHSZIkHZdhUdJESRLoFRIqxfWFxV5iWJQkSRoGw6KkiZK0QxoWC2sPi+XQpNte48I4kiRJOi7DoqSJ8kBYLFbXdmKtRokWsWNYlCRJGgbDoqSJ0koC3WJCpVRb24m1GhWa0K7Q7rZHU5wkSdIWYliUNFGSTo5uYX1hsRqb0KlQb9dHU5wkSdIWYliUNFGSTo5OMaFSmlrbibUaVQyLkiRJw2JYlDQxul3oxVw6DLW8xrBYrVKLTeiVWGwaFiVJkjbKsChpYiRJ/0U+oVJZZ1gEDi81hluYJEnSFmRYlDQxHhoWp9d2crXKVC8Ni0fqreEWJkmStAUZFiVNjIeExeIaF7ipVpnqdxYXlg2LkiRJG2VYlDQxHgiLhRaVQmVtJ1erzPQ7iwtLhkVJkqSNMixKmhgP6SyuNSxWKkx3+8NQl5NTHCxJkqRTMSxKmhgbCou5HLOhA8BivT3cwiRJkrYgw6KkibGhsAjM5nsALC13hliVJEnS1mRYlDQxNh4W062dRUmSpI0zLEqaGBsNi3PFAMByvTvEqiRJkrYmw6KkibHRsDhd6YfFhmFRkiRpowyLkibGRsNipWpYlCRJGhbDoqSJMayw2GjEIVYlSZK0NRkWJU2MDYfFWvqV1mz1hliVJEnS1mRYlDQxNhwWp9LlUBuNIRYlSZK0RRkWJU2MYYXFZnOIRUmSJG1RhkVJE2NlWCwXyms+vzRVBKDVCkOsSpIkaWsyLEqaGIOwWMx1yYW1fz2FWpVcvmFYlCRJGgLDoqSJMQiLlfw6F6ipVsnnmrRbfrVJkiRtlH9RSZoYSTN9PmKlsJGw2CJp5YdYlSRJ0tZkWJQ0MZL6ICyu8zmJlQqFXJO2YVGSJGnDDIuSJkZS7wBQya/zq6lapZhr0kkKQ6xKkiRpazIsSpoYrXqXXK5FJV9c3wWqVYqhSbdtWJQkSdoow6KkiZE0uoRcQjlXWt8FqlVKoUlsl2l328MtTpIkaYsxLEqaGEkzDYulDXQWyzShU6HRaQy3OEmSpC3GsChpYiTNHiGfUM6X13eBapVyaEK3TL1dH25xkiRJW4xhUdLESJqRkE8o5dc/DLXS7ywaFiVJkjbGsChpYiStHuQSysXK+i5gWJQkSRoaw6KkiZG0IuQTSoX1D0OtRsOiJEnSMBgWJU2MpAUUWpQK6+8s1vphsdF2gRtJkqSN8GFkkiZGkqSdxY0MQ63FJnTtLEqSJG2UnUVJEyNJIBYSShsKiy2HoUqSJA2BYVHSxEiSQMwnlEu19V2gWmWq14ROleXEsChJkrQRhkVJEyNpB3qFhFJp/Z3FmV4TgCP15hArkyRJ2noMi5ImRtJJw2K5vM7OYqnEdD8sLtbbQ6xMkiRp6zEsSpoYSTuXPjqjWF3fBUJgJtcFYHHZsChJkrQRhkVJE6PVDf2wuM5hqMBUsQfAYj0ZVlmSJElbkmFR0sRIOmlnsZwvr/sa1VIEYKneGVZZkiRJW5JhUdLEaHXzaWcxX1r3NSplw6IkSdIwGBYlTYykHxbLhfV3Fiv9Eaz1Rm9IVUmSJG1NhkVJE6M9jM6iYVGSJGkoDIuSJkbSK2x4zuIgLDaacUhVSZIkbU2GRUkTIUbo9Iob7yzW0q81O4uSJEkbY1iUNBHag8cibjAslqvp11qzOYSiJEmStjDDoqSJkAwei7jRBW6m0q+1VisMoSpJkqSty7AoaSKsDIsbG4aaB6DVNCxKkiRthGFR0kR4SGdxIwvcTBcAw6IkSdJGGRYlTYShdRb7YTFJ/HqTJEnaCP+akjQRWq3+iw3OWSxPpWGx3fLrTZIkaSP8a0rSRBhWZzE/VSEXEtpJnhh91qIkSdJ6GRYlTYRhhUWqVYq5JnTKJN3k1MdLkiTpuEYaFkMIzwsh3BhCuCmE8KbjvB9CCG/rv399COHSFe+9O4Rwbwjhq0ed8/MhhDtDCNf1f16w4r039691YwjhuaP8bJKGa1gL3FCtUgxNaFdpdBpDqU2SJGkrGllYDCHkgbcDzwceDbwkhPDoow57PnBR/+fVwDtXvPdHwPNOcPnfijFe0v+5un+/RwNXAI/pn/eOfg2SNoGhdhZDC7plGm3DoiRJ0nqNsrP4JOCmGOMtMcYE+CBw+VHHXA68N6Y+B8yHEM4CiDH+PXBoDfe7HPhgjLEVY7wVuKlfg6RN4CGdxQ0scEO1Sik0oVOxsyhJkrQBowyL5wB3rPh9X3/fWo85ntf3h62+O4SwbYPXkjQBhtlZTMOinUVJkqSNGGVYPN4TsY9emnA1xxztncDDgEuAu4HfWMu1QgivDiFcE0K45r777jvFrSSNS9LsARDyCYVcYf0XqlYp04JOhXq7PqTqJEmStp5RhsV9wLkrft8D3LWOYx4ixrg/xtiNMfaA3+PBoaarulaM8V0xxstijJft2rVrVR9E0ugljS4AxUJ3YxeqVqngMFRJkqSNGmVY/AJwUQhhbwihRLr4zFVHHXMV8PL+qqhPARZijHef7KKDOY19LwIGq6VeBVwRQiiHEPaSLprzz8P4IJJGL6m3ASjmhxQWXeBGkiRpQzYw1uvkYoydEMLrgY8DeeDdMcYbQgiv7b9/JXA18ALSxWjqwKsG54cQPgA8E9gZQtgH/FyM8Q+A/xlCuIR0iOltwGv617shhPBh4GtAB3hdjHGDf3VKGpeknv7nWi70NnahapUKLejM0egcHEJlkiRJW9PIwiJA/7EWVx+178oVryPwuhOc+5IT7P/hk9zvrcBb11WspEwljQ4ApSGExWpsOmdRkiRpg0Y5DFWSVi1ppCGxXDzVGlenUK1S64dFh6FKkiStn2FR0kQYLHBTGkJYnOr1H53hAjeSJEnrZliUNBEGYbFSPN5TcNagUqEWW3YWJUmSNsiwKGkiDJ6zWN7oTOpikenocxYlSZI2yrAoaSIkzR4htCkXixu+VrXQTR+d4TBUSZKkdTMsSpoISatHyCeU8+UNX6tc7EG3TL3VHEJlkiRJW5NhUdJESJqRkEsoFUobvlallM5/XGq0N3wtSZKkrcqwKGkiJEkcWmexUkrnPy71n90oSZKktTMsSpoISStCPqFUrGz4WoNnNS4bFiVJktbNsChpIiQJkEsoF4bQWSz3w2K9u+FrSZIkbVWGRUkTIWkBheF0Fgdhsd7obfhakiRJW5VhUdJESNoQhzQMtVINADSahkVJkqT1MixKmghJEiCfUC5VN3ytcn8ka6MRN3wtSZKkrcqwKGkitNqBXiGhVNx4WKzU0q+2po9ZlCRJWjfDoqSJkLQDMZ9QLtU2fK3BMNRmy86iJEnSehkWJU2EVicMbYGbcr+z2GqFDV9LkiRpqzIsSpoIrXYunbM4jEdn1PLpNZuGRUmSpPUyLEqaCK1uusBNKV/a8LUqU2lYbLdyxOhQVEmSpPUwLEqaCEknP7SwWJ4qpC+6ZZodV7mRJElaD8OipImQdPvDUPNDGIY6U0xfdCo0Oo0NX0+SJGkrMixKmghJtwD51nCGoU73O4udCvV2fcPXkyRJ2ooMi5ImQtLLD22Bm/JMP3B2yjTadhYlSZLWw7AoaSK0u4WhzVksTJXJ0XEYqiRJ0gYYFiVNhHavMLQ5i1SrlEIzDYt2FiVJktbFsCgpczFCu1ccWmfxgbDYLdtZlCRJWifDoqTMdbsQyQ0vLFYqlGm5wI0kSdIGGBYlZS5J+i+GtMAN1Splh6FKkiRtiGFRUuZWhsVhdRYrNF3gRpIkaQMMi5Iy95DO4pAWuKnElo/OkCRJ2gDDoqTMDb2zWK1S7XcWnbMoSZK0PoZFSZkb+pzFSoVadBiqJEnSRhgWJWVuJJ3F2EofneEwVEmSpHUxLErK3KgWuAmdqp1FSZKkdTIsSsrc0Be4yeWo5BKCcxYlSZLWzbAoKXNJK6YvhtVZBCr5NsHnLEqSJK2bYVFS5pJ6B4BcvkM+lx/KNcuFbvroDIehSpIkrYthUVLmBmGxkO8M7ZqVQpfoaqiSJEnrZliUlLlkuQ1AqdAb2jUrxS6x42qokiRJ62VYlJS5pNEFoFToDu2a5VIkdissJy5wI0mStB6GRUmZay2nw0+Lw+wsltLgWW8Ob2irJEnSVmJYlJS5QWexXIxDu2allF6r3hheAJUkSdpKDIuSMjdY4KY0xLBY7j+usdE0LEqSJK2HYVFS5pJ+oCsXh3fNSiXd2lmUJElaH8OipMw9OAx1eNcchMVGY3jdSkmSpK3EsCgpc0kr7f5VysP7ShqExWZraJeUJEnaUgyLkjKX9IeKlkthaNcsV9Ovt06So9sb3iM5JEmStgrDoqTMJa0IoUullB/aNStT/a+3ToVGpzG060qSJG0VhkVJmUtakZBLKBcqQ7tmpboiLLYNi5IkSWtlWJSUuaQVCfmEUrE8tGuWpwrpi07ZzqIkSdI6GBYlZS5pRcgPubM41R/SamdRkiRpXQyLkjKXJEAuoVQcYlicHnQWK9Tb9aFdV5IkaaswLErKXJIAhYRyqTq0a5an+w9t7DoMVZIkaT0Mi5IylyQQ8wml4vDCYmWmHxYdhipJkrQuhkVJmUvaAfIJpdIQh6GuDIt2FiVJktbMsCgpc0kbYiGhPMTOYnmmlL6wsyhJkrQuhkVJmWsNOouF4T06ozhTIdCDTtkFbiRJktbBsCgpc81OGhbL+eGFxVCrUqbpMFRJkqR1MixKylyrHxZL+dLwLlqpUBmERYehSpIkrZlhUVLmWp3c8MNitUqFlo/OkCRJWifDoqTMtbr5dBjqEOcsruwsOmdRkiRp7QyLkjKXdEfVWWyS79QchipJkrQOhkVJmUu6haEvcEOlQpkW+U7VYaiSJEnrYFiUlLl2Lz+yBW5yroYqSZK0LoZFSZlLeoXhz1kMgUpICM5ZlCRJWhfDoqTMtXvF4XcWgXK+TfDRGZIkSetiWJSUuXZ3NGGxku9Ax0dnSJIkrYdhUVLm2rE4/AVugEqhDW07i5IkSethWJSUqW4XerEwms5ioUu0syhJkrQuhkVJmWq3+y+GvcANUC52id2SC9xIkiStg2FRUqaSpP9iFJ3FYpdep+wwVEmSpHUwLErK1MqwOPQ5i6VeGhYdhipJkrRmhkVJmRplZ7FcinS6FeqJYVGSJGmtDIuSMvVgWGwNfxhqOQI56q3klMdKkiTpoQyLkjL1kGGoQ17gptK/XK9dpN1tn/xgSZIkPYRhUVKmknonfTGKYaiD7NmpOG9RkiRpjQyLkjKVLKcdv1y+Qy4M9yupUg3pC1dElSRJWjPDoqRMDcJiMd8d+rUr1f6LTsVnLUqSJK2RYVFSph4Ii4VRhMX+V5zDUCVJktbMsCgpU0kjnbM4irBYruXTF12HoUqSJK2VYVFSppLlflgs9oZ+7UrNzqIkSdJ6GRYlZSpppB3Fcj4O/dqV6UL6wjmLkiRJa2ZYlJSpwaMzSqXhh8UHhqF2Kg5DlSRJWiPDoqRMJc10+Gm5OPxrV2b6F+2UHYYqSZK0RoZFSZkaDEMtlYZ/7ZXDUO0sSpIkrY1hUVKmBp3FygjCYnmmf1EXuJEkSVozw6KkTD2wwE1p+F9Hldl+WOyWXeBGkiRpjQyLkjKVtNKFbcrlMPRrPzhn0WGokiRJa2VYlJSpwTDUaiU/9GtX5isA5Do1h6FKkiSt0UjDYgjheSGEG0MIN4UQ3nSc90MI4W39968PIVy64r13hxDuDSF89ahzfi2E8I3+8X8aQpjv778ghNAIIVzX/7lylJ9N0nA82FkcflgszZQBKHSm7CxKkiSt0cjCYgghD7wdeD7waOAlIYRHH3XY84GL+j+vBt654r0/Ap53nEt/AnhsjPHxwDeBN6947+YY4yX9n9cO5YNIGqmkFSGXUClXh37tUKtSpkmhW3XOoiRJ0hqNsrP4JOCmGOMtMcYE+CBw+VHHXA68N6Y+B8yHEM4CiDH+PXDo6IvGGP8mxtjp//o5YM/IPoGkkWu1IOQTSsXK8C9erVKhSb5TdRiqJEnSGo0yLJ4D3LHi9339fWs95mR+BPirFb/vDSF8KYTw6RDC09dSrKRsJAmQTygXh99ZpFKhTIt827AoSZK0VoURXvt4SxvGdRxz/IuH8BagA7yvv+tu4LwY48EQwhOBPwshPCbGeOSo815NOuSV8847bzW3kjRCg7BYKo0gLJZKVGjS7boaqiRJ0lqNsrO4Dzh3xe97gLvWccwxQgivAL4feGmMMQLEGFsxxoP919cCNwOPOPrcGOO7YoyXxRgv27Vr1xo+jqRRSNoQRzUMNQQqoUVo+5xFSZKktRplWPwCcFEIYW8IoQRcAVx11DFXAS/vr4r6FGAhxnj3yS4aQnge8NPAD8QY6yv27+ovqkMI4ULSRXNuGd7HkTQKSRLSYaiFEYRFoJxrE9oVh6FKkiSt0ciGocYYOyGE1wMfB/LAu2OMN4QQXtt//0rgauAFwE1AHXjV4PwQwgeAZwI7Qwj7gJ+LMf4B8DtAGfhECAHgc/2VT58B/EIIoQN0gdfGGI9ZIEfSZGm2SYeh5ksjuX4l1yZ0yg5DlSRJWqNRzlkkxng1aSBcue/KFa8j8LoTnPuSE+x/+An2fxT46LqLlZSJZod+Z7E8kutX8gm0y3YWJUmS1miUw1Al6ZRanQCF1sg6i+V8l+icRUmSpDUzLErKVKuTSzuL+RF1FgsdosNQJUmS1sywKClTzW5utHMWix1ip+gwVEmSpDUyLErKVNIZdVjs0u13FvtP2pEkSdIqGBYlZSrp5Ue6wE25GOl1ykQiSTcZyT0kSZJOR4ZFSZlKuoXRdhZLPbq99NouciNJkrR6hkVJmRqExZEtcFPq0emm13beoiRJ0uoZFiVlqt0bbWexXIZ2twLgiqiSJElrYFiUlKlBWBzVnMVKBXoUoJu3syhJkrQGhkVJmWr3iqOds1jpv+iWnbMoSZK0BoZFSZlqx9GGxXI1pC86FYehSpIkrYFhUVKmOv3O4sgWuKmsCIsOQ5UkSVo1w6KkzMQInVge7TDUWv9rrlO2syhJkrQGhkVJmel0+i9GucDNA2Gx4pxFSZKkNTAsSspMkvRfjHLO4lQhfeEwVEmSpDUxLErKzMqwOLI5i1P59EXXYaiSJElrYViUlJmk0U1fjHLO4rSdRUmSpPUwLErKTLLcTl/kE4r54kjuUZ7uX9dHZ0iSJK2JYVFSZgZhMZ/vkAuj+ToadBYL3WkXuJEkSVoDw6KkzLQW00mLhXznFEeuX2U2Hd5a6k45DFWSJGkNDIuSMpPU05BYLPRGdo/ybLpwTrFTcxiqJEnSGhgWJWVmMAy1UOiO7B6DzmLRzqIkSdKaGBYlZWbQWSyNsLNYmUs7i4VOxTmLkiRJa2BYlJSZcQxDfSAstmt2FiVJktbAsCgpM4PnLJZG84hFAMrzVQDybR+dIUmStBaGRUmZGXQWy8U4snvkykWKJOTaZTuLkiRJa2BYlJSZpJkOPy2VRhcWASo0ybWdsyhJkrQWhkVJmRkMQy2PcBgqQDn0O4sOQ5UkSVo1w6KkzDwQFsthpPep5BJIHIYqSZK0FoZFSZkZDEOtlEf7VVTJJYROyc6iJEnSGhgWJWVmEBbL5fxI71POdSApO2dRkiRpDQyLkjKTtNKFbSqVEXcW821iu0Sr26IXR/dMR0mSpNOJYVFSZgZhsVotjvQ+lUKH2ElX0Wl2miO9lyRJ0unCsCgpM4OwWK4URnqfcqFDt52GRectSpIkrY5hUVJmkiRC6FCtVkZ6n0qxS6/fWXRFVEmSpNUxLErKTJIEyCeUiqMPi91uGhZd5EaSJGl1DIuSMpMkEfIJ5WJ1pPeplHp0OmXAYaiSJEmrZViUlJnWoLOYL430PuUSdLoOQ5UkSVoLw6KkzDTbcSxhsVLu0e7ZWZQkSVoLw6KkzDQ7aWexnC+P9D6VMg+ERecsSpIkrY5hUVJmmm3GMwy1DElMF9FxGKokSdLqGBYlZabVyaWdxcKIO4vVQIcS9ILDUCVJklbJsCgpM83ueBa4qVRD+qJbtrMoSZK0SoZFSZlpdfJjmbNYHoTFTsU5i5IkSatkWJSUmVY3P57OYi2fvuhUHIYqSZK0SoZFSZlJeuMKi/2vuk7VYaiSJEmrZFiUlJlBZ3HkC9xMpZ3Fcm/GzqIkSdIqGRYlZabdK4ynszhdAKDcnbGzKEmStEqGRUmZSfphcdQL3FRn+mGxM+sCN5IkSatkWJSUmXavOJ7O4kwRgFJ3ys6iJEnSKhkWJWVmEBZHPWexOpuGxWJ72jmLkiRJq2RYlJSZzpg6i9W59PqFds3OoiRJ0ioZFiVlphOLUGiNfhjqbHr9YrvqnEVJkqRVMixKykwnlsazwM18ev18UnMYqiRJ0ioZFiVlptMrjWeBm7l+WGyXHYYqSZK0SoZFSZnotntE8uNZ4Kb/6IxcUrGzKEmStEqGRUmZSJbb6YtxdBYr6TaXVJyzKEmStEqGRUmZSBZb6Yt8QjFXHOm98nkokhASh6FKkiStlmFRUiYGncV8vkMIYeT3q4YmtMoOQ5UkSVolw6KkTCRLCQCFfHcs96vkEkjKtHttOr3OWO4pSZK0mRkWJWUiqaeBrVAYT1is5hJiO11Ix+6iJEnSqRkWJWViMAy1OKawWMm3iUm6kI7zFiVJkk7NsCgpE4POYrHQG8v9qoWEbrsfFu0sSpIknZJhUVImHugsFscTFiuFDr22nUVJkqTVMixKykTSSIeflopxLPerFjt0nLMoSZK0aoZFSZkYDEMda1jspGGx3q6P5Z6SJEmbmWFRUiYGncViaTxhsVLq0ek4DFWSJGm1DIuSMjEIi+VSGMv9qqUeSbcCOAxVkiRpNVYVFkMIHw0hfF8IwXApaSiSZj8slscTFivlSLtrZ1GSJGm1Vhv+3gn8EPCtEMKvhhAeOcKaJG0BSSNdBbVcHs+/QVUrkVasAs5ZlCRJWo1V/ZUWY/xkjPGlwKXAbcAnQgj/FEJ4VQihOMoCJZ2eWs10ruLYOovVQCs6DFWSJGm1Vv1P+iGEHcArgR8DvgT8L9Lw+ImRVCbptJY0085ipZIfy/2qVWhSg+gwVEmSpNUorOagEML/BR4J/B/ghTHGu/tvfSiEcM2oipN0+kpaaWdxbGFxqt/B7JTtLEqSJK3CqsIi8PsxxqtX7gghlGOMrRjjZSOoS9Jp7oHOYnW1X0MbU6mloTR0a85ZlCRJWoXVDkP9pePs++wwC5G0tSRJuq3WSmO5X3Uq/bqr9rY5DFWSJGkVTvpP+iGEM4FzgGoI4QnAYCWKWaA24tokncbGPQy1Mp1+3VW60w5DlSRJWoVTjf96LumiNnuA31yxfxH4ryOqSdIW8EBnsTymzuJ0GkrL3Vk7i5IkSatw0rAYY3wP8J4Qwr+OMX50TDVJ2gJaCZBvUi6MJyxWZtKn/JQ6U4ZFSZKkVTjVMNSXxRj/GLgghPCTR78fY/zN45wmSafUaEfIJ5QL5bHcrzqXhtJSe9oFbiRJklbhVMNQp/rb6VEXImlrabaBfEIpP6ZhqLNpZ7HYnqLRPjyWe0qSJG1mpxqG+rv97X8fTzmStopmh7GGxcpc2sEstqsOQ5UkSVqFVT06I4TwP0MIsyGEYgjhUyGEAyGEl426OEmnr0YnpMNQ82Mahjqf3iefVF0NVZIkaRVW+5zF740xHgG+H9gHPAL4LyOrStJpr9XJZdJZLCQV5yxKkiStwmrDYrG/fQHwgRjjoRHVI2mLaHVz413gZiYddZ9vVRyGKkmStAqnWuBm4GMhhG8ADeDHQwi7gOboypJ0uhuExbF1FivpNpeUHYYqSZK0CqvqLMYY3wR8F3BZjLENLAOXj7IwSae3Vjc/3jmL1XQbWmU7i5IkSauw2s4iwKNIn7e48pz3DrkeSVtE0g+L4+4s0ipTb9eJMRJCGMu9JUmSNqNVhcUQwv8BHgZcB3T7uyOGRUnrlPQKkF8aW1jM5aAUEkJSohd7tHvtsd1bkiRpM1rtAjeXAU+NMf54jPE/9H/+46lOCiE8L4RwYwjhphDCm47zfgghvK3//vUhhEtXvPfuEMK9IYSvHnXO9hDCJ0II3+pvt6147839a90YQnjuKj+bpAykYXF8C9wAVHMtaKUB0XmLkiRJJ7fasPhV4My1XDiEkAfeDjwfeDTwkhDCo4867PnARf2fVwPvXPHeHwHPO86l3wR8KsZ4EfCp/u/0r30F8Jj+ee/o1yBpAg3C4ji7e5Vcm5j0w6LzFiVJkk5qtWFxJ/C1EMLHQwhXDX5Occ6TgJtijLfEGBPggxy7KM7lwHtj6nPAfAjhLIAY498Dx3tEx+XAe/qv3wP84Ir9H4wxtmKMtwI39WuQNIHaveJYF7gBqBYSev2w6LMWJUmSTm61C9z8/DqufQ5wx4rf9wFPXsUx5wB3n+S6Z8QY7waIMd4dQti94lqfO861JE2gdsygs1jo0EvSx8Y6DFWSJOnkVhUWY4yfDiGcD1wUY/xkCKEGnGqI5/GWGYzrOGa1VnWtEMKrSYe8ct55563zVpI2qtMrjX/OYqFDt53ez2GokiRJJ7eqYaghhP8P+Ajwu/1d5wB/dorT9gHnrvh9D3DXOo452v7BUNX+9t61XCvG+K4Y42Uxxst27dp1iltJGpVOLI69s1gtdei0XeBGkiRpNVY7Z/F1wFOBIwAxxm8Bu096BnwBuCiEsDeEUCJdfOboeY5XAS/vr4r6FGBhMMT0JK4CXtF//Qrgz1fsvyKEUA4h7CVdNOefT/3RJI1djA90Fsc6DLXYo9NxgRtJkqTVWO2cxVaMMRk8wDqEUOAUw0VjjJ0QwuuBj5MOWX13jPGGEMJr++9fCVwNvIB0MZo68KrB+SGEDwDPBHaGEPYBPxdj/APgV4EPhxB+FLgd+Lf9690QQvgw8DWgA7wuxjh4JqSkSdLp0Iml8S9wU+7R7qT3c4EbSZKkk1ttWPx0COG/AtUQwnOAHwc+dqqTYoxXkwbClfuuXPE6knYtj3fuS06w/yDwrBO891bgraeqS1LGWi06sTz+zmI50u5VAIehSpIkncpqh6G+CbgP+ArwGtIA+DOjKkrS6a3XTOjFIuTbFHKr/TerjatWIkmvCjgMVZIk6VRWuxpqL4TwZ8CfxRjvG21Jkk537aUWAPl8m8Hw9nGoVCHBzqIkSdJqnLSz2F945udDCAeAbwA3hhDuCyH8t/GUJ+l0lCy3ASgUxjutuFqFVkw7i85ZlCRJOrlTDUN9I+kqqN8ZY9wRY9wOPBl4agjhJ0ZdnKTTU7KUABmExVqOBg5DlSRJWo1ThcWXAy+JMd462BFjvAV4Wf89SVqzBzuLnbHet1LL0aJCgYLDUCVJkk7hVGGxGGM8cPTO/rzF4mhKknS6S+ppSCwWTvoEnqGrTqVfedXenJ1FSZKkUzhVWEzW+Z4kndCgs1gsjrmzOJ1Pt70Z5yxKkiSdwqlWQ/2OEMKR4+wP0F9SUJLW6IHOYnHMncXp9Cuv0pm1syhJknQKJw2LMcb8uAqRtHU8EBZL4w2LlZl09Hy5M+WcRUmSpFM41TBUSRq6pJGugloe88zn6mx6w1IyZWdRkiTpFAyLksZuEBaLpfHedxAWi207i5IkSadiWJQ0dg90FsccFitzZQCKrYoL3EiSJJ2CYVHS2D0QFivj/QqqzqXptJjUHIYqSZJ0CoZFSWOXNHsAlMphrPetVNP7FVplh6FKkiSdgmFR0tgNwmKlMt4Fl6vVdJtvVewsSpIknYJhUdLYJa30kRnljMJiLik7Z1GSJOkUDIuSxm7QWaxWT/qo16GrVNJtvmVYlCRJOhXDoqSxS5J0WxlzWBx0FkOzTLPTpBd7Y72/JEnSZmJYlDR2g2GolXJGw1Bb6aqoLnIjSZJ0YoZFSWM3CIvVMc9ZLJch0INm+rzF5fbyWO8vSZK0mRgWJY1dM+mHxTF3FkOAWr5FbPXDYmJYlCRJOhHDoqSxa7QHYXG8cxYBqoU2vf4wVDuLkiRJJ2ZYlDR2jXaEXJtKsTT2e9cKCb1WuiyqK6JKkiSdmGFR0tg1kgD5hHKhPPZ7V4sdukm/s+gwVEmSpBMyLEoau0YnQL5FKZ9BZ7HUodN2gRtJkqRTMSxKGrtmJwf5JJOwWC11HwyLdhYlSZJOyLAoaeya3TQslvPjH4Zaq/RIejXAOYuSJEknY1iUNHbNTh4KGQ1DrfRIelXAYaiSJEknY1iUNHatQWcxiwVuKtCKaWfRYaiSJEknZliUNHatXj67BW5qkQYOQ5UkSToVw6KksWt1i5nNWaxWAw2q1PIVh6FKkiSdhGFR0tglvUJ2cxanA3Vq1HIVh6FKkiSdhGFR0tglvWJ2j86YytOgRi2U7SxKkiSdhGFR0ti1ewXItzJZ4KY2m0+33RnnLEqSJJ2EYVHS2CWxlFlnsTZbBKDamrWzKEmSdBKGRUlj1+5luMBNPyyW21POWZQkSToJw6Kk8er1aMdydgvczKf3LLWmHIYqSZJ0EoZFSePVatHpD0PNYs5idT69Z7lVcxiqJEnSSRgWJY3XA2Exo87ibAGAQrPqMFRJkqSTMCxKGq8koRPL2S1wMxUAKDaqdhYlSZJOwrAoabxaLbq9EhQSCrnC2G9frabbQrPsnEVJkqSTMCxKGqvYbNGNZfL5Tib3r9XSba5Rodlp0u11M6lDkiRp0hkWJY1VezkBoFDIJiwOOou5RjoE1u6iJEnS8RkWJY1VazENi8Vitp3F0DQsSpIknYxhUdJYJfU0JBYKvUzuP+gshn5n0UVuJEmSjs+wKGmskuU2AIViNnMFB51F+p1FH58hSZJ0fIZFSWPVWkrDYqmYTWexWIRC6NBr2lmUJEk6GcOipLEaDEMtFmNmNVQLbXrNIuCcRUmSpBMxLEoaq9ZyGhZLpezCYq3YpttyGKokSdLJGBYljVXSTOcqlkrZ1VAtdum20s6iw1AlSZKOz7Aoaaxa9XSuYrmSXQ21cod2N13pxs6iJEnS8RkWJY1V0hh0FkNmNVTLPdq99BkazlmUJEk6PsOipLEahMVKJbuvn1olksR+Z9FhqJIkScdlWJQ0Vq3GYBhqPrMaatVIM9YIBIehSpIknYBhUdJYJa10FdRKLbuwWK1Bgyq1fMVhqJIkSSdgWJQ0Vq1mGharWXYWpwJ1akzlKg5DlSRJOgHDoqSxSlrpMNQs5yxWa7m0sxhKhkVJkqQTMCxKGqtWM91m2lmcyaedxVB0zqIkSdIJFLIuQNLW0kr6w1DLWYfFKlOx5JxFSZKkE7CzKGmsmkm6rVUzDItzBRLKVDtlh6FKkiSdgGFR0lgt9zuLtXJ2Axum5osAlJs1h6FKkiSdgGFR0lg12v2wWM0wLM4NwuKUw1AlSZJOwLAoaawanQC5hHKhlFkNUzPpV1+pUXMYqiRJ0gkYFiWNVaOTg0KLcr6cWQ21Wrot1B2GKkmSdCKGRUlj1egEyCeUC9mFxampdFtoVOwsSpIknYBhUdJYtbq5NCxm2FkchMV8vULSTej0OpnVIkmSNKkMi5LGqtHNQ76VaWdxMAw1V09rcJEbSZKkYxkWJY1Vq5ufmM5iqKeL7DhvUZIk6ViGRUljlfTyUGhRyme4Gmo/LNKwsyhJknQihkVJY9XsFjNf4GYwDJVBZ9FFbiRJko5hWJQ0VkmvkM5ZnIBhqL2Gw1AlSZJOxLAoaaySmH1nsVyGXOjRa6Y12FmUJEk6lmFR0lglvWLmC9yEALVim24/LC4lS5nVIkmSNKkMi5LGKumVMl/gBmCq1KbbrgCw2FrMtBZJkqRJZFiUND6dDh1KmQ9DBZgqd2h308mLdhYlSZKOZViUND6tFu1YynyBG4BauUtiWJQkSTohw6Kk8Wm16MTyZHQWqz2a0bAoSZJ0IoZFSePTatGJEzJnsRZpUGMqXzUsSpIkHYdhUdL4JAmdXplcvk0uZPv1MzUFy0wxk6+xmLjAjSRJ0tEMi5LGp9WiG0vk8p2sK6E2FVhmiulc2c6iJEnScRgWJY1Pq0W3V6ZQzD4sTk3nqFNjGsOiJEnS8RgWJY1Nr9GiF4sUCt2sS2FqNp92FikaFiVJko7DsChpbNr1NsBEdBZrM/2w2DMsSpIkHY9hUdLYtJbSsFgs9DKuBKbmC7QpMdWuuMCNJEnScRgWJY1NsjzoLE5CWEwf3VFp1OwsSpIkHYdhUdLYtJbT4aelYsy4knQYKkCp7nMWJUmSjsewKGlskno/LJYnoLM4lW5L9QpLyRIxZh9gJUmSJolhUdLYtOrpKqjFUsaF8GBYLC5V6PQ6JN0k24IkSZImzEjDYgjheSGEG0MIN4UQ3nSc90MI4W39968PIVx6qnNDCB8KIVzX/7kthHBdf/8FIYTGiveuHOVnk7R2g85iuRQyrgRqtXRbWE6Tq4vcSJIkPVRhVBcOIeSBtwPPAfYBXwghXBVj/NqKw54PXNT/eTLwTuDJJzs3xvjiFff4DWBhxfVujjFeMqrPJGljWo10+Gm5kn1YHHQW80tpWFxKlthZ25lhRZIkSZNllJ3FJwE3xRhviTEmwAeBy4865nLgvTH1OWA+hHDWas4NIQTg3wEfGOFnkDRESSMdhloqT05YzC0XAVzkRpIk6SijDIvnAHes+H1ff99qjlnNuU8H9scYv7Vi394QwpdCCJ8OITx9I8VLGr5BWKxUs58uPQiLLBkWJUmSjmdkw1CB47UOjl5u8ETHrObcl/DQruLdwHkxxoMhhCcCfxZCeEyM8chDbhjCq4FXA5x33nknKV/SsLWa6X/G5Uo+40oenLPI8oPDUCVJkvSgUf7z/j7g3BW/7wHuWuUxJz03hFAA/hXwocG+GGMrxniw//pa4GbgEUcXFWN8V4zxshjjZbt27VrHx5K0XkkznbNYnaTO4nL6b2aLLRe4kSRJWmmUf7F9AbgohLA3hFACrgCuOuqYq4CX91dFfQqwEGO8exXnPhv4Roxx32BHCGFXf2EcQggXki6ac8uoPpyktRt0Fivl7DuLg7DYqdtZlCRJOp6RDUONMXZCCK8HPg7kgXfHGG8IIby2//6VwNXAC4CbgDrwqpOdu+LyV3DswjbPAH4hhNABusBrY4yHRvX5JK1d0uqHxUr2ncVSCQq5Lp12FTAsSpIkHW2UcxaJMV5NGghX7rtyxesIvG61565475XH2fdR4KMbKFfSiDX6YbFazj4shgDT5Tbt1jRgWJQkSTpa9n+xSdoyGskgLGY/DBVgutKl2TMsSpIkHY9hUdLY1NvpAjdTtZEOali16VqXZaaZLtRYTFzgRpIkaSXDoqSxqbfTba08KWExssQ007mqnUVJkqSjGBYljU1jEBarkzEMdWZmEBbLhkVJkqSjGBYljU29HQColosZV5KanglpWMSwKEmSdDTDoqSxaXZzkG9RKZSzLgWA6dk8i8wwHYuGRUmSpKMYFiWNTaOThsXypITF+TxLTDPTK7jAjSRJ0lEMi5LGJu0sJpTzkxIWi+kw1G7ezqIkSdJRDIuSxqbVy0NhgjqLc3mWmWIqyRkWJUmSjmJYlDQ2zW5hsjqLM4FIjkq9YliUJEk6imFR0tgk3TzkW5TypaxLAWBmJt2WlqosthaJMWZbkCRJ0gQxLEoam1avmHYWJ2UY6nS6LS5V6MYurW4r24IkSZImiGFR0tgkvUI6Z3FShqH2w2JhsQLgUFRJkqQVDIuSxmZSO4thybAoSZJ0NMOipPHodGhTmqwFbvphMbeYzqE0LEqSJD3IsChpPJpNklieqAVuBmGRpSIAi63F7IqRJEmaMIZFSePRbNIZdBYnbBhqXLKzKEmSdDTDoqTxaLVox/JELXAzeHRGr57WY1iUJEl6kGFR0ng0m7R76TDUQq6QdTXAg53FbsOwKEmSdDTDoqTxaLXoxAr5YkIIIetqACiXIZ/r0e6kqdGwKEmS9CDDoqTxaDbp9MrkC+2sK3lACDBdbtPupmFxMXGBG0mSpAHDoqTxaLXo9srkC52sK3mI6WqXZm+aQHA1VEmSpBUMi5LGo9mk06tQKE1OZxFguhZZYobpfNVhqJIkSSsYFiWNRa/eJMYihUI361IeYno6ssQ0s7kqR1pHsi5HkiRpYhgWJY1FayntKBZLkxUWZ2ZDGhZDhSOJYVGSJGnAsChpLJqLkxkWp2dzaViMJTuLkiRJKxgWJY1Fazld2KZYjhlX8lDTc4U0LHYLhkVJkqQVDIuSxmIQFkulCQuL84OwmDcsSpIkrWBYlDQWzeV0+GmpMmFhcaY/ZzHJGRYlSZJWMCxKGotWPQ2L5UrIuJKHmp6GJaaZaeJzFiVJklYwLEoai0kNi7Oz0CNPdbHMkdYRYpyszqckSVJWDIuSxqLV6AFQqU7W187sbLotLVSJRJbby9kWJEmSNCEm6682SaetZj0Ni9UJ7CwC5BcqAM5blCRJ6jMsShqLVjMd3lmtTNbXzoNhsQwYFiVJkgYm6682SaetB8PiZHUWZ2bSbTAsSpIkPYRhUdJYNJvptlbNZ1vIUQadRY4YFiVJklYyLEoai3ornbNYq0xmWIxLJcCwKEmSNGBYlDQWy0k6DHWqWsi4kocahMVuO31hWJQkSUoZFiWNxVKSzlWcqk1WZ3EwZ7FtWJQkSXoIw6KksVjupNvp2mR1FstlKBe7tLqGRUmSpJUMi5LGotFOv26mq6WMKznWbK3LcpylkisZFiVJkvoMi5LGYrmbg1ybamkCw+J0jyPMMpurGRYlSZL6DIuSxqLezUO+RaVQybqUY8zOxjQshophUZIkqc+wKGksGt08FCY0LM7l0rCIw1AlSZIGDIuSxqLZLUKhOZlhcVs+DYu9omFRkiSpz7AoaSwavQLkW5QL5axLOcYDYbGTZzFZzLocSZKkiWBYlDQWrV5xgoehBo6EOWaTYGdRkiSpz7AoafRipNkrTe4w1Fk4EmeZbfqcRUmSpAHDoqTRa7dpUZ7g1VChRZnaYt6wKEmS1GdYlDR6rRZJLEOhRTk/eXMWZ2bSbflwmaSb0Ow0sy1IkiRpAhgWJY1es0ky4Z1FgNLhNMjaXZQkSTIsShqHVoskViZ6ziJA4f4SAIebh7MrRpIkaUIYFiWNXrNJezAMdRIfndEPi/nDRcCwKEmSBIZFSePQbNKJZUI+IRcm72tnEBZzC2mQNSxKkiQZFiWNQ6tFu1ehUGxnXclxDcIirfTFQnMhu2IkSZImhGFR0ug1m3R7ZfITHha77fSFnUVJkiTDoqRxaLXo9MoUCt2sKzmuQVjsGBYlSZIeYFiUNHrNJt1ehWKpk3Ulx1WrQS4XaXTnyJMzLEqSJGFYlDQGvUaLGIsUS5PZWQwB5qa7HGGO+fwUCy3nLEqSJBkWJY1caymdq1gsxowrObFt83CYeeYo21mUJEnCsChpDJpHEgBKlV7GlZzY/PbAYeaZ7xkWJUmSwLAoaQwGncVSeXI7i/Pbc9zPdua7BcOiJEkShkVJYzAIi+VKyLiSE5ufDxzOb2c+yTlnUZIkCcOipDFoLqcL25SrkxwW4TDbmGv56AxJkiQwLEoag9Zy+siMSe4sbtsGh+Ms8/VoWJQkScKwKGkMWo10YZtKeXLD4vw8LPdqzCzCUrJEpzeZz4SUJEkaF8OipJEbDEOtTvgwVIDygSoAR1pHsitGkiRpAhgWJY3coLNYrUzuV84gLObvrwDOW5QkSZrcv9wknTY2VVg8VAYMi5IkSZP7l5uk00ajmYbFWiWfcSUntm1bug2L04BhUZIkybAoaeQWWxGAqdrkhsVBZ7HbSlPjQtNnLUqSpK3NsChp5JaSflisTu5XziAsdpL0hZ1FSZK01U3uX26SThuL7fSrZjN0Ftvt9IVhUZIkbXWGRUkjt5Skj8yYqRUzruTEajUoFno0O/MEgmFRkiRteYZFSSO33Ek7ijNThYwrObEQYH4ucphtzIaKYVGSJG15hkVJI7fczUPoUquUsi7lpOa3BQ4zz7ZY4f7m/VmXI0mSlCnDoqSRq3cKUGhQLVayLuWk5rflOJzbzvZuiUONQ1mXI0mSlCnDoqSRq/eKUGhSKUx4WJyHw/mdbE/yhkVJkrTlGRYljVyjW9oUYXHbNrg/t53tTQyLkiRpyzMsShqtTocGFSg2KOfLWVdzUvPzcDjOsX2pZ1iUJElbnmFR0mg1GjSpbIrO4vw8HO5Ms/1Im0ONQ8QYsy5JkiQpM4ZFSaPVbNKMlf4CN9Wsqzmp+Xlo9UrMHIRu7LKYLGZdkiRJUmYMi5JGq9GgSXVTdBa3b0+35f1pnQ5FlSRJW5lhUdJoNZskMR2GWi1Mdmdxx450m19KU+PB+sEMq5EkScqWYVHSaDUatGIVipM/DHUQFmmkL+wsSpKkrcywKGm0mk3asQKFFsVcMetqTmoQFrtNw6IkSZJhUdJoNRq0exUKxRYhhKyrOalBWGy3DIuSJEmGRUmj1WzS7lUpFDtZV3JKg7DYTAyLkiRJIw2LIYTnhRBuDCHcFEJ403HeDyGEt/Xfvz6EcOmpzg0h/HwI4c4QwnX9nxeseO/N/eNvDCE8d5SfTdIqNRp0ehUKpckPi5UKTNUih+NOpigZFiVJ0pZWGNWFQwh54O3Ac4B9wBdCCFfFGL+24rDnAxf1f54MvBN48irO/a0Y468fdb9HA1cAjwHOBj4ZQnhEjLE7qs8oaRWaTbrdKsVyO+tKVmXHTjh4+w62xwqHmoZFSZK0dY2ys/gk4KYY4y0xxgT4IHD5UcdcDrw3pj4HzIcQzlrluUe7HPhgjLEVY7wVuKl/HUkZ6i436cUSpdLm+HebHTsCB4tnsr1btLMoSZK2tFGGxXOAO1b8vq+/bzXHnOrc1/eHrb47hLBtDfeTNGatpbSjWKr0Mq5kdXbsgIP5M9jeyhsWJUnSljbKsHi8ZQ/jKo852bnvBB4GXALcDfzGGu5HCOHVIYRrQgjX3Hfffcc5RdIwNY6kYbFcybiQVdqxAw6GHWyv9wyLkiRpSxtlWNwHnLvi9z3AXas85oTnxhj3xxi7McYe8Hs8ONR0NfcjxviuGONlMcbLdu3ateYPJWltmkvpwjbl6mQ/NmNgxw442J1n+5GOYVGSJG1powyLXwAuCiHsDSGUSBefueqoY64CXt5fFfUpwEKM8e6Tnduf0zjwIuCrK651RQihHELYS7pozj+P6sNJWp3GUjpXsVrNuJBV2rED7k+mmL8/4VDjEDEeM0BBkiRpSxjZaqgxxk4I4fXAx4E88O4Y4w0hhNf2378SuBp4AeliNHXgVSc7t3/p/xlCuIR0iOltwGv659wQQvgw8DWgA7zOlVCl7DWX0/8MK5soLEZyVO+tkHQPUW/XmSpNZV2WJEnS2I0sLALEGK8mDYQr91254nUEXrfac/v7f/gk93sr8Nb11itp+Jr1dGGbzdRZBCgt7gQOcaB+wLAoSZK2pFEOQ5UkGstpWKxVNsfXzc6d6bawnKbG++ouhCVJkramzfHXm6RNq9lI5/zVapvj62bQWQz1flhcNixKkqStaXP89SZp06r3w+LUJguLsWFnUZIkbW2b4683SZvWYisNi9O1kU6RHppBWGy30hcH6gcyrEaSJCk7hkVJI3UkGYTFfMaVrM7sLBQKkeXuLoox5zBUSZK0ZRkWJY3UYpJ+zcxMFTOuZHVCgB07Ageq57KzV3EYqiRJ2rIMi5JGarGTbjfLMFSAM86AewvnsKtVMCxKkqQty7AoaaSWOmlInNsknUWA3bthfziDXXVXQ5UkSVuXYVHSSC13BsNQyxlXsnpnnAH3dnewa7FrZ1GSJG1ZhkVJI1XvFiDfZKpUzbqUVTvjDNjfnGPXoZadRUmStGUZFiWN1HK3CIUm1eLmCYu7d0O9U2b2UJmF1gJJN8m6JEmSpLEzLEoanV6PRq8fFgubJyyecUa6LR/ZDfisRUmStDUZFiWNTrNJnSoUG5uqszgIi/kj6QuHokqSpK3IsChpdOp1mlSg0KRSqGRdzartThuKxHo/LLrIjSRJ2oIMi5JG54Gw2NiUw1A7jTQ12lmUJElbkWFR0ujU67RidVMucAPQ7JwD2FmUJElbUyHrAiSdxup1kljZdAvclEqwbRsc6e4lF4OdRUmStCXZWZQ0OoPOYrFJMV/Mupo12b0b7ivtYUe7YGdRkiRtSYZFSaPTaNCOFfLFzfecwjPOgP3hTM6oB+5ZuifrciRJksbOsChpdOp12r0qhWI760rW7IwzYH9nB2cd7hgWJUnSlmRYlDQ6g7BY3nydxd274d7mLGcd7nH3kbuyLkeSJGnsDIuSRqdep9OtUSxvzs7i/Y0qu48UuWf5HmKMWZckSZI0VoZFSaPTD4ulSifrStbszDPT7dShM0h6bQ41DmVbkCRJ0pgZFiWNTHuxSeyVKFW7WZeyZmefnW4LC+mzFu9eujvDaiRJksbPsChpZBpH0uGn5Wov40rW7pw0IxIX0xcuciNJkrYaw6KkkRmExUpt84XFPXvSbdK5EIC7F+0sSpKkrcWwKGlk6ovp8NPqJuws7tgB5TIsFi4GHIYqSZK2HsOipJEZhMVKdfOtJBpCOm/xvuLDmOrk7CxKkqQtx7AoaWTqy2lInK6FjCtZn3POgTs5h7OW7CxKkqStx7AoaWQGYXFqanN+1ZxzDuxr7eKshR73LNyZdTmSJEljtTn/gpO0KSzX0+3MVD7bQtZpzx64c3GGM4/A3Yf3ZV2OJEnSWBkWJY3MkWa6nZnenGHxnHOg2S6w7f5t3F3fn3U5kiRJY2VYlDQy9yfpdnaqmG0h6zR41mL1/nNY7DVYTpazLUiSJGmMDIuSRmYhSRe2mZvZ3GExf+R8AO5avCvDaiRJksbLsChpZBY6BQDmZ0oZV7I+g7AIjwRg3xHnLUqSpK3DsChpZI5s8rB49tnpthMuBuD2hdszrEaSJGm8DIuSRmaxW4LQYa5Wy7qUdSmVYPduWMxdBMAdR+7IuCJJkqTxMSxKGo1ul6VYhmKdqdLmDIsA558Pd3TOZfcS3H7/rVmXI0mSNDaGRUmj0WiwFGpQrFMrbt6wuHcv3La8i/MW4Pb938q6HEmSpLExLEoajUaDBtV+Z3Eq62rWbe9e+PbBac69P8ft99+WdTmSJEljY1iUNBr1OnVOj85iu5Nj231nc0fzXmKMWZckSZI0FoZFSaNRr9Poh8Wp4ubuLAJUFi5miRaHm4czrUeSJGlcDIuSRqNepxlPj84iQOAJgI/PkCRJW4dhUdJo1Os0ezUoNinlN+dzFgHOOw9CgCaPB3x8hiRJ2joMi5JGo14niTUKpSYhhKyrWbdyGc4+G47ERwJw+8FbMq5IkiRpPAyLkkajXifp1SiUkqwr2bC9e2F/8zyKXbj99q9kXY4kSdJYGBYljUa9Trtbo1hpZ13Jhu3dC7cenmfPEbj9nhuzLkeSJGksDIuSRqNep9OtUSqfHmFx370lLjhQ5JYj3866HEmSpLEwLEoaibi4RLdbo1ztZV3Khl14IcQYOOPAI7ipvT/rciRJksbCsChpJNpHGsRYoFzb/GHxEY9It9XmkzmYb3F/4/5sC5IkSRoDw6KkkagfThe2qW7eRyw+4OKL022v910A3HzopgyrkSRJGg/DoqSRqC+kcxWrtZhxJRu3fTvs3g2LnUsAuOlbn8+2IEmSpDEwLEoaicZC2lmsVTMuZEge+Ui4e+nhgGFRkiRtDYZFSSNRX+wCMDUVMq5kOB75SPjmnbOccwRuuudrWZcjSZI0coZFSSNRX0oXtpmqnT5h8eChHOcfOJublm7PuhxJkqSRMyxKGonFpbSzODOVz7iS4XjkI9PttqUnc1PucKa1SJIkjYNhUdJI3N9MO4vTp1lYLLeewv5Kh8XFg9kWJEmSNGKGRUkjcbiVbudmitkWMiTnnQeVCrSTJwLwrS9+IuOKJEmSRsuwKGkkFpJ0ruLsdCHjSoYjn0+ft3j/0uMB+Np1hkVJknR6MyxKGonD7TQkbpspZVzJ8FxyCdx0606KXfjK7V/IuhxJkqSRMixKGr4YWeikIXH73OkTFp/wBLjnnsDDDlzIV5dvzbocSZKkkTIsShq+RoOFMA302DZbybqaobnkknR75sJz+Wp1CRYXM61HkiRplAyLkoZvaYkjYRpKy0yXa1lXMzSDsFiqfw+3z8ORz/99luVIkiSNlGFR0vAtLbEUpqG0SK14+oTFuTm48EJYOvKdANzwhb/MuCJJkqTRMSxKGr7lZZbiNJSWTquwCOm8xTtvPRuAr9z82YyrkSRJGh3DoqThW1pimRkoLTFTmsm6mqF6whPg27cWmWps46sHvw7dbtYlSZIkjYRhUdLwLS2x3O8sTpems65mqC69NN2ed/D7uG5bC667LtN6JEmSRsWwKGn4lpZo9qahvEylcPqshgrwlKdACDC7+K+49izofPJvsi5JkiRpJAyLkoavHxaL5SYhhKyrGapt2+Cxj4X67U+mXoIbPvexrEuSJEkaCcOipOFbXqbVnaZYbWVdyUg87Wlwy/VnQjfP5++5Blqn5+eUJElbm2FR0vAtLdHuTlOutrOuZCSe9jRYXs4xd+ApfH53Gz7zmaxLkiRJGjrDoqShi4tLdDrTVKY6WZcyEk97Wro9e/ElfP7cAH/2Z5nWI0mSNAqGRUlDlyw0iLFIdaqXdSkjcd55cO65EO54Jl/bGTnysY9A7/T8rJIkaesyLEoauqXDaUexdpqGRYBnPQtuv+4RxF6ez+fuhmuuybokSZKkoTIsShq6pYX0QfXTp9cjFh/iBS+ApYUi+bueyicfHuBP/zTrkiRJkobKsChp6JaOpB3F6amMCxmh5zwH8nk4e/+P8YnHT8MHP+hQVEmSdFoxLEoauqXFCMDcbD7jSkZnfh6e+lRof+M5fGl6kfvuvQ3+7u+yLkuSJGloDIuShm5huR8WZ07fsAjpUNR7bjoTjpzNpx5bg3e/O+uSJEmShsawKGno7muk2/nZQraFjNgP/EC6rX7zh/nEvzwfPvpRuP/+bIuSJEkaEsOipKE7mKRfLdvmihlXMlqPehRccglUv/5j/MX8fjrtFvzBH2RdliRJ0lAYFiUN3aF2Ovx053w540pG72Uvg0M3P5x779zBpy//Dvhf/wva7azLkiRJ2jDDoqShO9ROO4o75k7/sHjFFRBCpHjDq/jQs8+EffvgQx/KuixJkqQNMyxKGq5Wi8OxBsDO+VrGxYzeOefAs54VKH7l/+MjC1+k/ZhHwf/4Hz5GQ5IkbXqGRUnDtbDAkdw0FJeZr85kXc1YvP71UD+wk/u/9C/4+Bu/H7761fS5i5IkSZuYYVHScB05whGmobTEdGk662rG4vu/H/ZeGCl+4b/wzupX4Tu+A/7bf3PuoiRJ2tQMi5KGa2GBpX5YnClvjc5iPg9vfEOgfduTuPrvDnPzz74ebr7Z5y5KkqRNzbAoabiOHGF5EBZLWyMsAvzIj8COnV3C3/0i75j+Gjz1qfALvwD1etalSZIkrYthUdJwHTlCPaZhsVY8/Re4GZiehp95S554y7N454e/xaFfeDPcdRe87W1ZlyZJkrQuIw2LIYTnhRBuDCHcFEJ403HeDyGEt/Xfvz6EcOmpzg0h/FoI4Rv94/80hDDf339BCKERQriu/3PlKD+bpBNYWKDZmyZfbhBCyLqasXrta+HMcxIaf/Xz/Frns/DCF8Kv/iocPJh1aZIkSWs2srAYQsgDbweeDzwaeEkI4dFHHfZ84KL+z6uBd67i3E8Aj40xPh74JvDmFde7OcZ4Sf/ntaP5ZJJO6sgRmr1pipVW1pWMXaUCv/lrJbj7ifzm79Q58N9/GhYX4a1vzbo0SZKkNRtlZ/FJwE0xxltijAnwQeDyo465HHhvTH0OmA8hnHWyc2OMfxNj7PTP/xywZ4SfQdJaLSyQdKcp1ZKsK8nEFVfAdz1jieRvfo6f/tpfwateBW9/O9x2W9alSZIkrckow+I5wB0rft/X37eaY1ZzLsCPAH+14ve9IYQvhRA+HUJ4+noLl7QBR47Q7sxQrm7Nx0aEAO/5/WnyvRp/+KuP5atveEm6XOrP/EzWpUmSJK3JKMPi8SYrxVUec8pzQwhvATrA+/q77gbOizE+AfhJ4P0hhNljigrh1SGEa0II19x3332n+AiS1ioeXqDdnqM6szXDIsBFF8F/+qk28atX8LI/+iTxjW+A970PvvSlrEuTJElatVGGxX3AuSt+3wPctcpjTnpuCOEVwPcDL40xRoAYYyvGeLD/+lrgZuARRxcVY3xXjPGyGONlu3btWudHk3Qi9ftbEAvUprduWAT4hZ+tccb5h/jyH7yWDz7/EtixA950zDpfkiRJE2uUYfELwEUhhL0hhBJwBXDVUcdcBby8vyrqU4CFGOPdJzs3hPA84KeBH4gxPvAAsxDCrv7COIQQLiRdNOeWEX4+ScexcKgLwPRMN+NKslUuwwffOwsL5/Pan12m+ab/DH/zN/C5z2VdmiRJ0qqMLCz2F6F5PfBx4OvAh2OMN4QQXhtCGKxUejVpoLsJ+D3gx092bv+c3wFmgE8c9YiMZwDXhxC+DHwEeG2M8dCoPp+k4zt8fzpifGa2l3El2XvmMwq88KV3cOTTr+Q/13bC9u3wK7+SdVmSJEmrUhjlxWOMV5MGwpX7rlzxOgKvW+25/f0PP8HxHwU+upF6JW3cwkK63b4tn20hE+L/vP1czrj6IO/8xafwlte9mrN+8VfhK1+Bxz0u69IkSZJOapTDUCVtQfctp+tT7dg20n+L2jTm5uDXfrNB757H8pJ7Hg9TU/Bbv5V1WZIkSadkWJQ0VHe30o7iru3FjCuZHK9/xR7Ofvw3+PT7nsM1L3kJvP/94GrMkiRpwhkWJQ1PjOzvlAHYvaOccTGTIwR4zzt3QmM7L7vjudBqwe//ftZlSZIknZRhUdLw1OvcF2YAOGtHLeNiJsuzv3snj33Wl7nxUy/gM895PrzjHdDd2ivGSpKkyWZYlDQ8R45wMDcHoctZO2ayrmbivOt/ng/dMq/pvgz27YNPfCLrkiRJkk7IsChpeBYWOJSbg/IRtlXns65m4nzXE7bzyGdexw3/+AN86bwL4d3vzrokSZKkEzIsShqeI0dYYA7KC8xV5rKuZiL9718+B5Jp3rj3jfDnfw4HD2ZdkiRJ0nEZFiUNz8ICR5iDygLzlfmsq5lIz37Kmex67PX843UvYrHbTVdGlSRJmkCGRUnDc+QIS705qBxhqjiVdTUT643/MUdvYQ8/fcmPwgc/mHU5kiRJx2VYlDQ8CwvUe3MUq8uEELKuZmL91KseQ3HHPv744Cvgn/4J7rgj65IkSZKOYViUNDwLCzQ6c5RrzawrmWiFQuA5/+bbLN723fz19r3w4Q9nXZIkSdIxDIuShuf++0k6c1RmkqwrmXi/9BMXQ+jxC+f8OHzoQ1mXI0mSdAzDoqShiQcP0W7PMTXTybqUifeEi3ey67HX8/nb/y3JNdfArbdmXZIkSdJDGBYlDc3yvcsQC0zPdrMuZVP44Ve06S2cz2+f9QyHokqSpIljWJQ0NAv3pcNP52ZjxpVsDm/5scdDsc4fzr4C/uRPsi5HkiTpIQyLkoZm4WA6/HRuzpVQV2P7XJnzvvMr3HjHC2h+8Utw551ZlyRJkvQAw6KkoTl4fzr8dPu2fMaVbB5X/LsccfkM3nbWM+Av/iLrciRJkh5gWJQ0NHfV0+GnO7cVM65k8/jpVz0Oisv84dRL4aqrsi5HkiTpAYZFScPR7XJPUgBg945yxsVsHttnK+lQ1LteSONv/w6WlrIuSZIkCTAsShqWhQXuLcwBcObOSsbFbC4PDEXd9hT4xCeyLkeSJAkwLEoalkOHuC83D8BZO6vZ1rLJ/PSrHg/FZd5b/XfwsY9lXY4kSRJgWJQ0LIcOcW/YCfkWZ22fzbqaTWX7bIWzvuMGbjzwffQ+9jHo+pxKSZKUPcOipOE4dIiD7IDqQbZV57OuZtN5/ve16R45lw9xNnz+81mXI0mSZFiUNCSHDnE/O6B2gPnKfNbVbDo/+fKLgR7vmrncVVElSdJEMCxKGo5Dh1jo7SDUDjFbdhjqWj3mwp3MXPg1/rlhWJQkSZPBsChpOA4eZLGzk/LMEiGErKvZlL77WQep3/NErr39MNx8c9blSJKkLc6wKGk4Dh2i0d5Bba6edSWb1r9/2R4AfmvH98Ff/EXG1UiSpK3OsChpKHoH7ydJtjMzn2Rdyqb1wqddSGH7HXyCf+UjNCRJUuYMi5KGYuHeFsQC89t97MN65XKBxz79Fu6965kc+MfPw8JC1iVJkqQtzLAoaSgO3tcDYMeOjAvZ5F7yr2egU+W3dv5L+PjHsy5HkiRtYYZFSUOx/1AEYPeufMaVbG6v+zePg/ICHym9yHmLkiQpU4ZFSUNx+3IaEs/eXcq4ks1tqlrk3CfewE33Pp/OX14NXYf1SpKkbBgWJW1ct8u3kwoAe86sZlzM5vfCF0Z6y2fynvzD4LOfzbocSZK0RRkWJW3cgQPcmd8OwAVnz2RczOb3n1/+GMh1ePfUD7oqqiRJyoxhUdLG7d/PPfkdEDqcf8Z81tVsenvPnmf+4q9w7dLlzluUJEmZMSxK2rj9+7k37IDqIXZP78y6mtPCM5+7QOvAo/m7fU245Zasy5EkSVuQYVHSxt1zD4fYCbWD7KwZFofhP/7wXgDeNv9Ch6JKkqRMGBYlbdz+/Sz0dpCbup9KoZJ1NaeFf3np+ZTP/iaf7rzIsChJkjJhWJS0cfv3s9TdQXlmKetKTiuX/os7uf/up3LbZ6+DhYWsy5EkSVuMYVHSxu3fT729i6m5RtaVnFZ+5IqdEAv8+vbnwl//ddblSJKkLcawKGnD4t33kLS2M7MtybqU08orXvBoctP38rHci+AjH8m6HEmStMUYFiVt2MLddWK3wvZdhsVhKhbyPPy7vsHt93wvy1d/HJYc5itJksbHsChpw+65J93u3h2zLeQ09G9eVIRklt+ZeQr85V9mXY4kSdpCDIuSNqbb5dYjRQD2nF3IuJjTz0/80OOhtMgf1X4IPvzhrMuRJElbiGFR0sYcOMCNpd0A7N1TzbiY08/OuSnOf8p13Hj35Sz/1d/A4mLWJUmSpC3CsChpY/bv55bimQA84vzZjIs5Pb38pUVicxu/Nv9M+Iu/yLocSZK0RRgWJW3M/v3cXjgTcgmP2LMr62pOS//lZU8gVA/x3tLL4AMfyLocSZK0RRgWJW3MPfdwN2fC9H7Onjkr62pOSzO1Mhc9/Xpuvev7OPTXf/vgikKSJEkjZFiUtDH793NfPJMwfS/bq9uzrua09aM/PAXtaX551wvgj/8463IkSdIWYFiUtDF33sn9nT2Utx0khJB1NaetN7z4CeRm7uH9xVfBH/4hRB9TIkmSRsuwKGlj9u1juXk2s7uOZF3Jaa1cLHDp82/g7tufw/W33g+f/WzWJUmSpNOcYVHShjS+fS/t1g52ntnKupTT3i/+5PkQC/zUWf8f/M7vZF2OJEk6zRkWJW3Ivm+3ATjn7IwL2QKe9+SHM/eoa/nbQ6+i++E/gbvvzrokSZJ0GjMsSlq/dpsb7y8BsPe8UsbFbA0vfeUy7cMX8Ku7vheuvDLrciRJ0mnMsChp/e6+m69WzwHg4r1TGRezNfyP//BkcnN38pvFN6dDURcXsy5JkiSdpgyLktbvjjv4ZjENi4+7yMdmjMN0tcz3/tDXOXTH0/hwPB/e+c6sS5IkSacpw6Kk9bvtNm7JnQ+VQ1x8tpMWx+UdP3splI/w0zt+Dn7912F5OeuSJEnSaciwKGn9br2Vfb0LYNttnDV9VtbVbBl7z9rOk170eW676YX8eftM+I3fyLokSZJ0GjIsSlq/W2/lQGcvtV33UswXs65mS/njX3sCVBb499t/A/7H/4B9+7IuSZIknWYMi5LWLd5yK4vL57P9LBdZGbeL9uzke152LXff8hzeMftd8FM/lXVJkiTpNGNYlLRuB28+TK8zxZ5zO1mXsiW9/38+mfz22/nJzjuof/AjcNVVWZckSZJOI4ZFSevTbvONewoAPGxvIeNitqYzts3w02+9g9aBR/CiR/0SvOY1cOBA1mVJkqTThGFR0vrceiufq1wIwOMeWcu4mK3rl17z3Zz9lH/kb77xE/xxby+89KXQ7WZdliRJOg0YFiWtzze/yRdLjwDgKY/blXExW1cIgf/3J48kP7efH2l/gDv+7vPwlrdkXZYkSToNGBYlrc83v8mNuYtg9nYev+eirKvZ0i7as5Pf/r17aR85iyec/ZfU/+dvwv/+31mXJUmSNjnDoqT1+eY3uaP7CIq7b2NbdVvW1Wx5r//Xl/LKn/0MB7/9VB530Ydp/8c3wpVXZl2WJEnaxAyLktbnxhu5f/kR7NhzKOtK1PeHP/cv+Z4f+wS3fPMHufiij9L89/8B/vt/hxizLk2SJG1ChkVJaxcj9375bjqtbZx/YSvrarTCJ9/1bJ796k9y67d+kD3n/y23/sr/gle+EpaXsy5NkiRtMoZFSWt3zz18ZvlsAB7/+JBxMVophMAnfvfZvPLn/x8H9z2Ji2vX8Gcf+yJcdhl8+ctZlydJkjYRw6KktfvKV/jb6ccC8D1P3p1xMTqeP/y5Z3LlR75BtzPFi45cw/d3/w3JZd8JP/3TsLSUdXmSJGkTMCxKWruvfIVr84+F6kH+xWMemXU1OoHX/OB38OUvB8564rX85bd+kR27PssfvvvT8KhHwTvfCc1m1iVKkqQJZliUtHZf+hI39b6D4pk3cub0GVlXo5N47N7d7Pvsd/HaX/4H6kt7+JEDn+PC8m/xV29+GzzsYekCOLfcknWZkiRpAhkWJa1Z5wtf4sDC4znzEXcRgnMWJ10uF3jnm5/O7bdU+a4f+ltuvf0FvGDh65xd/n1++Xc/S+PhF8N3fif81E/BVVfBt74F7XbWZUuSpIwVsi5A0iazsMCXbs4Tu1UuubSTdTVag3N2zvJP7/sebvile/n3P/85/vEjT+It9b/mZ2oHePiRT/KMD32SH3zXm3nq4q1sCwnMz8O2bTA3BzMzMDUF09Ppdvt2uPBCuOgiePKT0/clSdJpxbAoaW2uvZY/m70M7ofnPn1n1tVoHR6zdzd//57v4fDbG/zKH/0T739/5KYvP4dv1a/gD/rH5Gr3kesuk7+/Se5IixC6hNAh0CPQpRDqzBfv4szc53hq7x380DkLPOFfPR9e8QrYtSvTzydJkoYjxC38sObLLrssXnPNNVmXIW0uv/ALfPfbz+CzCy/mjv119sydnXVFGoJur8dffvYm/vLT93DzrR3uvCNPs5mj3SzQTvLEXkh/YrpNmmUa98/RXTgDekUAqrv/me+dfS+/eekyF/7yz6RzIiVJ0kQLIVwbY7zsuO8ZFg2L0po861nMfvEdtM/eR+OGZ2VdjTJ2ZDnhTz55M3/61wf5u6t2U7/rEVA6wnfv+S3e/5yDnP8/fjEdxipJkibSycKiC9xIWr0k4Z5/+haLhy/mMZcdyLoaTYDZqRI/evmj+It3Po3lOx/BB/7mRvZ8x1f5p1t+jr3v/Vle+13/hd7Vf5V1mZIkaR0Mi5JW7x/+gf9TfgoAz3/WdMbFaBJd8ZyLueOfv5v3/fWNTO2+i9/9+rs449Vdrv+Rn4BWK+vyJEnSGhgWJa3e1Vfzocr3QfUgP/bC78i6Gk2wH3ruxRy66XH86zf8PQf2fw9P+NCb+NWnvQZuvTXr0iRJ0ioZFiWtToz0/vxjXL/4fLY99p85f9uerCvShCsWcnzkt5/Bn/3tnRSnF3jzNe/mqc99P60//bOsS5MkSatgWJS0Ol/8In9211m067v5nuctZ12NNpHLn34Rd9y4h0c84+/5p2+9hTNeN8OX3/AW6HazLk2SJJ2Ez1mUtDrvex+/NvtS6CzxX3/0cVlXo01m13yNGz/9TF79c//A7/3yd3PpHzyKX//yq/mJD/8K7N6ddXnj1+vR+8pX+bs/+QQf+PwyN9y7jfvqc8SYo1asc+78YZ79mMgrXvpd7Hjm0yGErCuWJG1BPjrDR2dIp1avs3jOI5hv3MC2Sz7Dgc+9IOuKtIn96f+7mSv+TSA5dB7PeeTPc/XvPY/CU5+WdVmjFyPx2mt53//+CL/12TO57u5/TW/p3PS90CE/dS8h16HbmiW25tP9pSNceO5VvOa7b+c//8oryZ3jc00lScPlozMkbcx73sNb8j9ArzXHa17by7oabXIveubDuO0bZ3DepZ/jE1//Jc6+4iDf+sVfg95p+v+tb36Tz/zkz/Gcx/8CleeV+OH3/ipfvPl17D73Vl76Hz7On3/6VpIkR2fxbNoL59FrznPzHUf4ud/6By6+5B+5Zd8L+en/81+ZvuQe3vD8/07yjW9m/YkkSVuEnUU7i9LJNRo0L3oM8wufprd9Pws3PZZqsZJ1VToN9HqRH/qJT/Oh3/lu8rX9/OJjf4U3//F/goc9bLT3vfMuvvr3X+Dar9zFzXc0WGx26LVzTFd7nLujwqPOmeayS/cw86hHwp49kFvjv6v2evCVr/DND/01v/iXh/nYfc9m4e5/CeSYPfc6XvhvD/Pff+IJPGzP3Koud3ixxU/+3N/yx++5iPahh1PadgM//qQ/4dfe9lIKj7ho7f8DSJK0wsk6i4ZFw6J0cv/tv/GyK9u8775f4T/8zlW87XU/kHVFOs28769u5EdeCcm9F7P3vD/m//7At7jkl/8zzMwM5fqdb97MB678BO/9+y5f3H8R9x+8lNjYefKTQof83G3MTN3EGdO3s3fbfTzmjDqXnV/gaY/exTlnn0+oVCCfh4UFkv0HuP7L3+avv36ET91xFl9cfipH9j8NyFHccTNP/95b+fn/8hie/oSz1v052p0eP/XLf8c7//ceWgcuprL9et7wpI/wy297ObmLHr7u60qj1qs3+MY/XMNn/ukmvnFHg6WlDkkvx86ZwPlnlfmuJ1/IE555Gbm52axLlbYkw+IJGBalU/jMZ/jUc1/Ps5ufZe7x/8h9X/gXFPPFrKvSaejwUoMX/sjf84//95kQejzlgnfw+y9q8pj/9GNwxhlru9jiIt/6yN/wvz54C1ffuJfb9v9LYnMH0KOy+2ucf94tPPaRCY965CwPf8wutm2bJ1+MLBxu8e3b7uXmm5e46Zs9br+9yn337mb50AXE5Kg/YktHyJcPQwzEXoleczv0+v9thC7Vs77OE59+O//+x87liu95LLnc8BaoSdpdfuIXP8Xvvf1C2oceTnXHF/mpJ/8p/+1/vZLcw0fblZVWpd3mGx/7W97xvq/w1189i5vvesaD83NPINTuZfv/3969B2lVnwcc/z677y7LQgyIYJS7I16IkmpQjEZjjVbUNImdpGOiBqOtYxNz6XQmiUkm05n+Udt0ktLJTUYTTQ0m1ppGnSReUNPJGEADFEWCoigCIihyvyy7+/SP9ygve3YXFndZLt/PzDt7zu9y3t+yD7/zPu+5DVvAlOOWcPUFzXziby+jcsx79tOApcNbvyWLETEVmA7UA7dm5s0d6qOovxTYClyTmfO66xsRRwK/AMYBLwF/nZlvFnU3AdcBbcAXM/PB7sZnsih1Y8ECFl48jTO23c9OKjz6xAbOP+Xk/h6VDnGz5r3AdZ9bxstzLoC6NkYf+z9cfdyT3HDZWEZfcl71FNXm5l0ddu6EVat4Y84C/vOBRfxyYTPzVp7N5tcnA3XEoNWMOXEuUy9q58YbpnDKuJ4f2cuEV17dzu/nr+apBa+z+NmNrF67nc2bG0jaaRgAQ4+EsRPqOfN9R/HJi8Yx6qghvfZv0pXtLa3c+K1Z3DHjBFrfHM+g4XP5yhn3c9O3r6Rh4kl9/v7S2zLZ/Mxibr3lUf7riXoWrJjM1rXvB+qIpjcZOX4ukyeuY9Kpgzn1vUcz9OghNDVWWLliHS88t5Z5/7eehc8189LyE2l5oxq70byGscc8ztSTX+KLV5zAyZf/xe7/9yX1mn5JFiOiHngOuAhYATwJfCozn61pcynwBarJ4hRgemZO6a5vRPwrsC4zb46IrwFDM/OrETERuAs4EzgWeAQ4ITO7fJCXyaLUibY28ie3891vPsBXNv2AtvYmvjvzab58+Xn9PTIdRn49eylf/6clLHzsLHLbMIg2BgxZzNCmZQwduJpKBjuoZ/3297B+23haNhwPWQHaGTRqHpMnL2PateO58pI/o7FyaD8lauv2ndzwjVnMvG0ibRvGUP+ul/nguF/yhUsbuPxzH6VuTPdHdKR90fLiS9x7xyx+8fgmnlh2ImtWnws7B0O0csTIPzL5tOVcdeV4rvyr02hsqN/r7c5/diW33DKXh37XyEvPn0FuHQG0M2j4U5x+7GyuOnM70645jwFTzqieBi7pHeuvZPEDwD9m5sXF+k0AmfnPNW1uAR7PzLuK9SXA+VSPGnba9602mflqRBxT9D+x4/Yj4sFiG3/oaowmi1KhrY18filPz/wtt967jJlrPs4ba8+nfsQSZvx0PddePKW/R6jD1OZtLdzyq/ncd+86Fj8zkI3rhtOyaThEEtFO47vWcOSwtYwfv4ULLx7GNZdPYtyxh+d1T5u27uCb336cO+8cyLql1esl6we/wtgRT/D+Mas4f9IAzv7QiZx6ziTqRxzlsxu1Z5m0vfEmS+YvYf6cpfx+/jrmvzSIF9aM5fW1Z0DxiJfKkBc46aSF/OVlA/nc9R9g1Ii9u3nTnrS2tTPz/oXcccdSnnxqLJtWng5ZD42bePeR8zn+6OeZPH4Tp793COd8cAInTz6JumFHGttSD/VXsvgJYGpm/k2xfjUwJTNvrGnzAHBzZv6+WJ8FfJVqsthp34hYn5lDarbxZmYOjYjvAbMz886i/DbgN5l5T1djPBCTxV/c+iAzZq4qlSdRPReqE139BTM7nyy7+pN39R5JvL209+8RXYwsivqOdbu20/Xv0/m2qn1qKrOLbXXov2s1OpR3vq3dx1JbnjVL0Vnx7uW1lV1tv9PSorynfTprn0m2tbO9pYHN245g67Z3s2Xz8bRvGw5A3ZCXufDTi7jrX87lyMG9c5MRSfvP/OdWMf37c3n4kWZWLz+F9s01z2esbKNh8Cs0DthAU+NGmps20dSwg0olqdS3U6lrp1Jpp6G+nfq6rH7urvnw3fFzeJC7TaOdzVARXX/W6Dg/dpyzOs79HbfUcT7ufr2rbcZulaUxdVio1tfM/T0Zc0Jpv9OxfYd9aGkfkuV989vrmV306f4929pge0sj21qa2bGzmZaWgezYfhStW0ZC24BdDet30DRsCWPHLuPPz02mXXM6Z506hv3hxVVvMv3Wp3jskS0se3Ekm187BVoH7mpQ2UZ90+s0Nr3BwKY3aWrcQkOllcaGnTRU2hhQaaWx0kYEb794a7mu+leJyE5/viN9kruaEB+sHrj/UzQ1H1h3le8uWezLc3M6i+KuM4Td2+xN3315PyLieuB6gDFj9s/k1hMPPraSRx+7th9H0MVzzrrZ0Xf5p+myTy+1P2Teo0N51Gymj9+7rrKNxoHraBq8kePG/YFTT08uvmQ0135kEg31Y7vYlqQD3WknHMvt0z8OVB9RMnvRch556E88M28tLy6vsGbdILZubWb7jiPY+Ppo2lsHku0NZFsD2d5QvVlPW0Nxau/BoMO+qzQXdjIH7qnNgV5Pkaj31jYjqW/YTKVxCw0NW2lq3sJRw1/j6GGzGf2eNo47bhDnXHA8F517Ms1Nk4BJpfH0teOOHcr0b10E36qub9newu/mPMvc/13K089s5OVXK2zYOICNmwexdesQtmwYQXvbgLdf2doEbY1AFMlzx58+flx9b/vW9QdcstidvtwLrABqL5QYBXQ8ZNZVm8Zu+r4WEcfUnIa6pgfvR2bOAGZA9chiT36h/WHG7Z/hR7e1dFpX/ear84msrovyqAuiR6djOFEefg68L00k9Z66uuDsU8dwdh8c/Wlvz90OUbWXD5FV29T2yaQudj8K1vFusXVRnOlStKurC8qHNGsPZ7rv6h0j+nsAPTKoqZFLPzSRSz808Z1vrL2dt750aG9P2ovYLcV0T/XFGXyH8ZMMDgWNAw6uSyX6Mll8EpgQEeOBlcAVwKc7tLkPuDEifk71BjcbiiRwbTd97wOmATcXP39VUz4zIr5D9QY3E4C5ffXL9ZVKQwV8MoEk6SBQTfJ2JW2mbDpo1XzpXldnLEtv6bNkMTNbI+JG4EGqj7/4cWYuiogbivofAb+meifUpVQfnfHZ7voWm74ZuDsirgOWA58s+iyKiLuBZ4FW4PPd3QlVkiRJktS1Pn3O4oHuQLzBjSRJkiTtL93d4Maj7JIkSZKkEpNFSZIkSVKJyaIkSZIkqcRkUZIkSZJUYrIoSZIkSSoxWZQkSZIklZgsSpIkSZJKTBYlSZIkSSUmi5IkSZKkEpNFSZIkSVKJyaIkSZIkqcRkUZIkSZJUYrIoSZIkSSoxWZQkSZIklZgsSpIkSZJKTBYlSZIkSSUmi5IkSZKkEpNFSZIkSVKJyaIkSZIkqcRkUZIkSZJUYrIoSZIkSSoxWZQkSZIklZgsSpIkSZJKTBYlSZIkSSUmi5IkSZKkEpNFSZIkSVKJyaIkSZIkqcRkUZIkSZJUYrIoSZIkSSoxWZQkSZIklZgsSpIkSZJKTBYlSZIkSSUmi5IkSZKkEpNFSZIkSVKJyaIkSZIkqSQys7/H0G8iYi3wcn+P4yB2FPB6fw9ChxRjSr3JeFJvM6bUm4wn9bZ9jamxmTm8s4rDOlnUOxMRT2Xm5P4ehw4dxpR6k/Gk3mZMqTcZT+ptfRFTnoYqSZIkSSoxWZQkSZIklZgs6p2Y0d8D0CHHmFJvMp7U24wp9SbjSb2t12PKaxYlSZIkSSUeWZQkSZIklZgsao8i4tsR8aeIWBgRv4yIITV1N0XE0ohYEhEX15S/PyKeLur+IyKiXwavg0JETC1iaGlEfK2/x6MDX0SMjojHImJxRCyKiC8V5UdGxMMR8Xzxc2hNn07nK6lWRNRHxPyIeKBYN6a0TyJiSETcU3yGWhwRHzCe9E5ExN8X+7xnIuKuiGjq65gyWdTeeBg4JTMnAc8BNwFExETgCuC9wFTgBxFRX/T5IXA9MKF4Td3fg9bBoYiZ7wOXABOBTxWxJXWnFfiHzDwZOAv4fBE3XwNmZeYEYFaxvqf5Sqr1JWBxzboxpX01HfhtZp4EvI9qXBlP2icRMRL4IjA5M08B6qnGTJ/GlMmi9igzH8rM1mJ1NjCqWP4Y8PPM3JGZy4ClwJkRcQxwRGb+IasXxf4U+Pj+HrcOGmcCSzPzxcxsAX5ONbakLmXmq5k5r1jeRPVD2EiqsXNH0ewOds09nc5X+3XQOuBFxCjgMuDWmmJjSj0WEUcA5wG3AWRmS2aux3jSO1MBBkZEBWgGVtHHMWWyqJ66FvhNsTwSeKWmbkVRNrJY7lgudaarOJL2SkSMA04D5gBHZ+arUE0ogRFFM+NMe+Pfga8A7TVlxpT2xXHAWuAnxWnNt0bEIIwn7aPMXAn8G7AceBXYkJkP0ccxZbIoACLikeL8546vj9W0+QbVU79+9lZRJ5vKbsqlzhgv2mcRMRj4b+DLmbmxu6adlBlneltEfARYk5l/3NsunZQZU3pLBTgd+GFmngZsoTg9sAvGk7pVXIv4MWA8cCwwKCKu6q5LJ2U9jqlKTzvo0JSZF3ZXHxHTgI8AH85dz1tZAYyuaTaK6uHwFew6VbW2XOpMV3EkdSsiGqgmij/LzHuL4tci4pjMfLU4JX5NUW6caU/OAT4aEZcCTcAREXEnxpT2zQpgRWbOKdbvoZosGk/aVxcCyzJzLUBE3AucTR/HlEcWtUcRMRX4KvDRzNxaU3UfcEVEDIiI8VRvZDO3OAS+KSLOKu6C+hngV/t94DpYPAlMiIjxEdFI9WLs+/p5TDrAFXPLbcDizPxOTdV9wLRieRq75p5O56v9NV4d+DLzpswclZnjqM5Dj2bmVRhT2geZuRp4JSJOLIo+DDyL8aR9txw4KyKai33gh6ler9+nMeWRRe2N7wEDgIerscnszLwhMxdFxN1UJ79W4POZ2Vb0+TvgdmAg1Wscf1PaqgRkZmtE3Ag8SPXOXj/OzEX9PCwd+M4BrgaejogFRdnXgZuBuyPiOqo71k8C7GG+krpjTGlffQH4WfFF6IvAZ6keqDGe1GOZOSci7gHmUY2R+cAMYDB9GFOx64xCSZIkSZKqPA1VkiRJklRisihJkiRJKjFZlCRJkiSVmCxKkiRJkkpMFiVJkiRJJSaLkiRJkqQSk0VJkiRJUonJoiRJkiSp5P8BKUz2kS+nV+EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15,16))\n",
    "x_train[\"Fare\"].plot.density(color=\"red\",label=\"fare\")\n",
    "plt.legend([\"Age\"],)\n",
    "x_train[\"mean_fare\"].plot.density(color=\"green\")\n",
    "plt.legend(\"mean_fare\")\n",
    "x_train[\"median_fare\"].plot.density(color=\"blue\")\n",
    "plt.legend(\"median_fare\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "27a77b36",
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Family</th>\n",
       "      <th>mean_age</th>\n",
       "      <th>median_age</th>\n",
       "      <th>mean_fare</th>\n",
       "      <th>median_fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>219.412880</td>\n",
       "      <td>95.204389</td>\n",
       "      <td>-6.023340</td>\n",
       "      <td>219.412880</td>\n",
       "      <td>219.412880</td>\n",
       "      <td>89.800987</td>\n",
       "      <td>88.679151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>95.204389</td>\n",
       "      <td>2567.927687</td>\n",
       "      <td>16.621812</td>\n",
       "      <td>75.544747</td>\n",
       "      <td>78.764841</td>\n",
       "      <td>2567.927687</td>\n",
       "      <td>2567.927687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>-6.023340</td>\n",
       "      <td>16.621812</td>\n",
       "      <td>2.266874</td>\n",
       "      <td>-4.803423</td>\n",
       "      <td>-4.713678</td>\n",
       "      <td>15.756823</td>\n",
       "      <td>15.758695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_age</th>\n",
       "      <td>219.412880</td>\n",
       "      <td>75.544747</td>\n",
       "      <td>-4.803423</td>\n",
       "      <td>174.974828</td>\n",
       "      <td>174.974828</td>\n",
       "      <td>71.613445</td>\n",
       "      <td>70.718817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_age</th>\n",
       "      <td>219.412880</td>\n",
       "      <td>78.764841</td>\n",
       "      <td>-4.713678</td>\n",
       "      <td>174.974828</td>\n",
       "      <td>175.249445</td>\n",
       "      <td>74.665968</td>\n",
       "      <td>73.723626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_fare</th>\n",
       "      <td>89.800987</td>\n",
       "      <td>2567.927687</td>\n",
       "      <td>15.756823</td>\n",
       "      <td>71.613445</td>\n",
       "      <td>74.665968</td>\n",
       "      <td>2434.294319</td>\n",
       "      <td>2434.294319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_fare</th>\n",
       "      <td>88.679151</td>\n",
       "      <td>2567.927687</td>\n",
       "      <td>15.758695</td>\n",
       "      <td>70.718817</td>\n",
       "      <td>73.723626</td>\n",
       "      <td>2434.294319</td>\n",
       "      <td>2449.480499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Age         Fare     Family    mean_age  median_age  \\\n",
       "Age          219.412880    95.204389  -6.023340  219.412880  219.412880   \n",
       "Fare          95.204389  2567.927687  16.621812   75.544747   78.764841   \n",
       "Family        -6.023340    16.621812   2.266874   -4.803423   -4.713678   \n",
       "mean_age     219.412880    75.544747  -4.803423  174.974828  174.974828   \n",
       "median_age   219.412880    78.764841  -4.713678  174.974828  175.249445   \n",
       "mean_fare     89.800987  2567.927687  15.756823   71.613445   74.665968   \n",
       "median_fare   88.679151  2567.927687  15.758695   70.718817   73.723626   \n",
       "\n",
       "               mean_fare  median_fare  \n",
       "Age            89.800987    88.679151  \n",
       "Fare         2567.927687  2567.927687  \n",
       "Family         15.756823    15.758695  \n",
       "mean_age       71.613445    70.718817  \n",
       "median_age     74.665968    73.723626  \n",
       "mean_fare    2434.294319  2434.294319  \n",
       "median_fare  2434.294319  2449.480499  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c0c36e78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Family</th>\n",
       "      <th>mean_age</th>\n",
       "      <th>median_age</th>\n",
       "      <th>mean_fare</th>\n",
       "      <th>median_fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.115737</td>\n",
       "      <td>-0.275539</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.113281</td>\n",
       "      <td>0.111448</td>\n",
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       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.115737</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.215003</td>\n",
       "      <td>0.111851</td>\n",
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       "      <td>-0.241185</td>\n",
       "      <td>-0.236493</td>\n",
       "      <td>0.212114</td>\n",
       "      <td>0.211480</td>\n",
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       "    <tr>\n",
       "      <th>mean_age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.111851</td>\n",
       "      <td>-0.241185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999216</td>\n",
       "      <td>0.109729</td>\n",
       "      <td>0.108022</td>\n",
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       "    <tr>\n",
       "      <th>median_age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116537</td>\n",
       "      <td>-0.236493</td>\n",
       "      <td>0.999216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.114316</td>\n",
       "      <td>0.112523</td>\n",
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       "    <tr>\n",
       "      <th>mean_fare</th>\n",
       "      <td>0.113281</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Age      Fare    Family  mean_age  median_age  mean_fare  \\\n",
       "Age          1.000000  0.115737 -0.275539  1.000000    1.000000   0.113281   \n",
       "Fare         0.115737  1.000000  0.215003  0.111851    0.116537   1.000000   \n",
       "Family      -0.275539  0.215003  1.000000 -0.241185   -0.236493   0.212114   \n",
       "mean_age     1.000000  0.111851 -0.241185  1.000000    0.999216   0.109729   \n",
       "median_age   1.000000  0.116537 -0.236493  0.999216    1.000000   0.114316   \n",
       "mean_fare    0.113281  1.000000  0.212114  0.109729    0.114316   1.000000   \n",
       "median_fare  0.111448  1.000000  0.211480  0.108022    0.112523   0.996895   \n",
       "\n",
       "             median_fare  \n",
       "Age             0.111448  \n",
       "Fare            1.000000  \n",
       "Family          0.211480  \n",
       "mean_age        0.108022  \n",
       "median_age      0.112523  \n",
       "mean_fare       0.996895  \n",
       "median_fare     1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "724dfa70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "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": [
    "x_train[[\"Age\",\"mean_age\",\"median_age\"]].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "64b11976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "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": [
    "x_train[[\"Fare\",\"mean_fare\",\"median_fare\"]].boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deed9bfb",
   "metadata": {},
   "source": [
    "# Use sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c41b89b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.20,random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "68497708",
   "metadata": {},
   "outputs": [],
   "source": [
    "Imputer1=SimpleImputer(strategy=\"mean\")\n",
    "Imputer2=SimpleImputer(strategy=\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "157410fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "trf=ColumnTransformer([\n",
    "    (\"imputer1\",Imputer1,[0]),\n",
    "    (\"imputer2\",Imputer2,[1])\n",
    "    \n",
    "],remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "22bc6a14",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=trf.fit_transform(x_train)\n",
    "x_test=trf.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6c9b12ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30.30371479])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trf.named_transformers_['imputer1'].statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "89da7b08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14.4])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trf.named_transformers_['imputer2'].statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d749db9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 27.    ,   7.7958,   0.    ],\n",
       "       [ 58.    , 146.5208,   0.    ],\n",
       "       [ 18.    ,   7.4958,   0.    ],\n",
       "       ...,\n",
       "       [ 54.    ,  26.    ,   1.    ],\n",
       "       [ 20.    ,   7.925 ,   1.    ],\n",
       "       [ 28.    ,  24.    ,   1.    ]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a183f4e5",
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Family</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare  Family  Survived\n",
       "0  22.0   7.2500       1         0\n",
       "1  38.0  71.2833       1         1\n",
       "2  26.0   7.9250       0         1\n",
       "3  35.0  53.1000       1         1\n",
       "4  35.0   8.0500       0         0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8734b3a5",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Can only use .str accessor with string values!",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\ABDURR~1\\AppData\\Local\\Temp/ipykernel_9232/852873003.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSurvived\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misdigit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5485\u001b[0m         ):\n\u001b[0;32m   5486\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5487\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5488\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5489\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\accessor.py\u001b[0m in \u001b[0;36m__get__\u001b[1;34m(self, obj, cls)\u001b[0m\n\u001b[0;32m    179\u001b[0m             \u001b[1;31m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 181\u001b[1;33m         \u001b[0maccessor_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    182\u001b[0m         \u001b[1;31m# Replace the property with the accessor object. Inspired by:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    183\u001b[0m         \u001b[1;31m# https://www.pydanny.com/cached-property.html\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\strings\\accessor.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    166\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mStringDtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 168\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_inferred_dtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    169\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_categorical\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mis_categorical_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    170\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_string\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mStringDtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\strings\\accessor.py\u001b[0m in \u001b[0;36m_validate\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m    223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minferred_dtype\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mallowed_types\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 225\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Can only use .str accessor with string values!\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    226\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0minferred_dtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: Can only use .str accessor with string values!"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f07f377d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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