2.DatosFaltantes-Tratamiento.ipynb

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   "cell_type": "markdown",
   "id": "9cba75f6",
   "metadata": {},
   "source": [
    "# Datos Ausentes\n",
    "\n",
    "## Tratamiento de Datos Faltantes\n",
    "\n",
    "Ya hemos aprendido a cómo identificar y visualizar la presencia de datos ausente en un dataset. Sin embargo, ahora deben¡mos tomar una decisión acerca de su tratamiento. Las alternativas ante esto son las siguientes:\n",
    "\n",
    "- No hacer nada: Este es el método más simple y rápido. Esta decisión conlleva a dejar a criterio de las funciones la imputación de los mismos. Sin embargo, no todos los métodos y funciones trabajan con datos perdidos, por lo tanto es necesario tratarlos.\n",
    "\n",
    "- Eliminar Datos Faltantes: Para la eliminación de datos faltantes existen dos tipos: \n",
    "\n",
    "    - Eliminanción por pareja: Los valores faltantes se omiten durante los cálculos, tal como es el caso de cálculo de la media.\n",
    "\n",
    "    - Eliminación por lista: Se elimina la fila completa. Esta eliminación se utiliza cuando tenemos datos completamente perdidos al azar, es decir, cuando no es posible la imputación de la data.\n",
    "\n",
    "    Es importante señalar que este método se ocupa cuando no podemos realizar imputación de valores perdidos o bien cuando son pocos los datos ausentes dentro de la data. \n",
    "\n",
    "A continuación, abordarenos las técnicas de tratamiento de datos perdidos señalas."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5c82c843",
   "metadata": {},
   "source": [
    "### Eliminación de datos\n",
    "\n",
    "#### Eliminación por parejas:\n",
    "\n",
    "A continuación importaremos nuevamente nuestro dataset de diabetes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6a84c16e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importar pandas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import missingno as msno\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "045452d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
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       "      <td>26.6</td>\n",
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       "      <td>23.3</td>\n",
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       "      <td>28.1</td>\n",
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       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Archivo csv\n",
    "file = \"datos/diabetes.csv\"\n",
    "\n",
    "# Leer csv\n",
    "diabetes = pd.read_csv(file, sep=\",\")\n",
    "\n",
    "# Visualizar primeros registros\n",
    "diabetes.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "6a2794b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selección de columnas a modificar\n",
    "columnas_cambiar = list(diabetes.columns[1:6])\n",
    "\n",
    "# Bucle for\n",
    "for columna_cambiar in columnas_cambiar:\n",
    "    # seleccionar df.loc[filas == 0 , columna]\n",
    "    diabetes.loc[diabetes[columna_cambiar] == 0 , columna_cambiar] = np.nan"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "680b044e",
   "metadata": {},
   "source": [
    "A las columnas con datos faltantes aplicaremos la eliminación por pareja, es decir, procederemos a omitir dichos datos en los cálculos de nuestros estadísgrafos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "065cd312",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Media Glucose = 121.6867627785059\n"
     ]
    }
   ],
   "source": [
    "# Cálculo de media de variable glucosa por medio de eliminación por parejas\n",
    "media = diabetes['Glucose'].mean()\n",
    "print(f'Media Glucose = {media}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c09cbeb0",
   "metadata": {},
   "source": [
    "Ahora comprobaremos que el cálculo de la media se haya realizado de forma correcta, es decir, omitiendo las filas nulas en el cálculo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e2c91e52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Número de filas DataFrame = 768 \n",
      "\n",
      "- Número de filas no nulas variable Glucose = 763\n",
      "- Suma total variable Glucose = 92847.0\n"
     ]
    }
   ],
   "source": [
    "# Cálculo número de filas Dataframe\n",
    "filas = len(diabetes)\n",
    "print(f'- Número de filas DataFrame = {filas} \\n')\n",
    "\n",
    "# Cálculo número de filas no nulas varaibles glucosa\n",
    "filas_no_nulas_glucosa = diabetes['Glucose'].count()\n",
    "print(f'- Número de filas no nulas variable Glucose = {filas_no_nulas_glucosa}')\n",
    "\n",
    "# Suma total variable Glucose\n",
    "total_glucosa = diabetes['Glucose'].sum()\n",
    "print(f'- Suma total variable Glucose = {total_glucosa}' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "addc20ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Media Glucose cálculo manual = 121.6867627785059\n"
     ]
    }
   ],
   "source": [
    "# Cálculo de media de variable glucosa de forma manual\n",
    "print(f'Media Glucose cálculo manual = {total_glucosa/filas_no_nulas_glucosa}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "80138e05",
   "metadata": {},
   "source": [
    "Hemos comprobado que la función `mean()` realiza la eliminación parejas. Sin embargo, no todos los algoritmos de machine learning cuentan con este tipo de eliminación, por lo tanto debemos buscar la forma de tratar estos datos."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e386abc1",
   "metadata": {},
   "source": [
    "#### Eliminación por lista:\n",
    "\n",
    "Con este método se procede a eliminar observar que variable presenta valores perdidos completamente al azar. Usaremos la biblioteca missingno."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "30241ccc",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficar DataFrame\n",
    "msno.matrix(diabetes, color=(0.4,0.4,1),fontsize= 12,figsize= (8,4))\n",
    "plt.title('Matriz datos faltantes')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "00d7b1d7",
   "metadata": {},
   "source": [
    "Podemos observar que la variable *Glucose* presenta un bajo número de datos perdidos, los cuales aparentemente son completamente al azar. Estos datos faltantes cumplen con ser datos perdidos completamente al azar ya que no cumplen con un patrón de ausencia de datos, junto con presentar un bajo número de valores faltantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4065b416",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficar DataFrame\n",
    "diabetes_sort = diabetes.sort_values('Glucose')\n",
    "msno.matrix(diabetes_sort, color=(0.4,0.4,1),fontsize= 12,figsize= (8,4))\n",
    "plt.title('Matriz datos faltantes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "e57db101",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Cantidad datos faltantes de la variable Glucose ascienden a 5\n",
      "- Los datos faltantes de la variable Glucose corresponden al 0.65% del total de la data\n"
     ]
    }
   ],
   "source": [
    "# Visualizar valores perdidos en cantidad y porcentaje\n",
    "total_na = diabetes['Glucose'].isnull().sum()\n",
    "print(f'- Cantidad datos faltantes de la variable Glucose ascienden a {total_na}')\n",
    "mean_glucose =round(diabetes['Glucose'].isnull().mean() *100, 2)\n",
    "print(f'- Los datos faltantes de la variable Glucose corresponden al {mean_glucose}% del total de la data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3de09dcd",
   "metadata": {},
   "source": [
    "Al cumplirse los dos criterios, se procede a eliminar la lista de datos faltante para la variable _Glucose_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "d7671427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Eliminar la fila de valores donde la variable Glucose presente valores nulos.\n",
    "diabetes.dropna(subset=['Glucose'], how='any', inplace=True)\n",
    "\n",
    "# Graficar DataFrame luego de la eliminación de datos faltantes.\n",
    "msno.matrix(diabetes, color=(0.4,0.4,1),fontsize= 12,figsize= (8,5))\n",
    "plt.title('Matriz datos faltantes')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "78353ece",
   "metadata": {},
   "source": [
    "Se observa, al igual que la variable _Glucose_, la variable _BMI_ presenta datos perdidos completamente al azar, por tanto cumple una de nuestras condiciones. La segunda condición es que presente un bajo número de valores faltantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3752890c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XA9cAD+fVbvkqBXaROZS2qSxZiQiSu7n7i8BSwCTgX6Uh7ryVtRfgLcDN7My0R/Y+QE/gt0SSlDQ5M1sWOIjIZi/pTtR8/7j0gLsfBTwI/MXMtnP3t9z9olL2e10bLe3Sf4TIHEjD79PMrKuZrU2cCFdIz11KrOXdKPWCdkprgHOTpgSmpemCP5jZKsD/iOphXYG3gTXd/VDgWWDJHJsrdWBm3YhpmF+7+7tmtoiZ9U9TMvcC3zWzpTM/cjswBjgrFaopzcdrVUWDUGAXmU2lk1kafh8K/NTdHwReNbPXgG+5+6ruPsXMfgPsT2ySkVd7LZMo9xxxAdLi7mOJZUuHE3Ps08zsYGAQrdMK0oTMbGvgQmARd//MzOYFziCCdl/gX0Av4EAzWyv92HeJZLr/An8rXdzm0HxphwK7yGzKJJ7tAjzt7r9MT91E9Gj+z8yWMbOjgCOB/d3903xaO1My3PXAq2m64PXUG1s1PbeCmZ1A5Ads5e5v17+lUkcfAVsBR5rZIsB44mJuPuA4IvN9CDFE/5CZPQRs5u7XAZcA44hpG2kgKlDTpIq2lrpIUo/X0+c7mDgBjjGzxd39Q+BmYpvKfYALgAnEyfClnJo8Q5pf/wS4O23icSEx3L6emf0JOJ7YfnMjBfXmlkacXkgZ7bcTo0knEQlxk4GdiZ758e5+T5pamkqMTgFsTszDl+dsSM5UoKZJZHZc6p+GVqVOzOwIogrX+cAt7j4m85wB3dy9Jae2faVISArg3wVeJ8p/7kJs4nEwcbKerIvC5pcpTNSDGFE6Avg7kTQ5HtiNCO5vA6e5++fp55YH9gQOBTZx9+dzaL50QEPxTSAT1FcFHjazLfJuU7MzszvSWnXc/UzgbmAvYDsz61c6zkOuQT0lyu1tZoPNbKWUGLc70Tv/Tio4sjjwJXGxr6A+F0hBfWXgc6Infg3xvTgbmBe4jtjcZW0ikJe0APMDGyqoNyb12JuEma0EPEr8Up7n7l/m3KSmUj61YWYbEMOX/3D3A9NjpwFrAf8Ari8VeslTmjZ4FhhOnKw/IaYGDkxJfcsDOxG9tc3d/bncGit1Z2YnA73c/Tfp/irERep/iB75OGAL4P5s1ruZdc/rglVmTT32JpDmTY8Ghrj7acAkM/u5me1vZgNzbl7hZYN6qcyquz9BJB3tZmZ/TY8dDbwGfJ/G+d06Dhju7ju4++bAYrRu1AFxIbI1sKmC+lxpAPDN0h13H0bkjfwE+BMwr7vfm1Z/dMkcp6DewBrl5COzIRNkphEBZWEz2xl4BtgOOBz4U5pDk9mUCeqnAmdmSm4+Q8xJ75Wew90PBvZz9y/yam+ZbsCdAGZ2JTGE+kOgr5mt7u7XAz9w9xdybKPUQTsFZK4GupjZjpnH3gDuAnoTPXYgKivWtoVSLQrsBZSpHJYN2E8Qy062B+509x8Q65DnIRKkpJOyFdpSEY/PgWWBYzMnyeeIIfmjzOxMAHcfVe+2pja2lZ3cC/ixmQ0BVgTWT72tQ4Gd05DqmDo2U3KQqbkw0MxONbNjzGw3d3+UqDz4IzM7ymLDlxOAl4Dd3d1LHQgpDs2xF0wqBjE9Jb0cTyxL+Qw4xd1HmVkPd5+Sjr2SGHrdUglRnZPJGO5CFJa5hQjsPwO2JEZFTk3/F78h/g/+z91fz6m9pUQ5A9YnBhqeNLP+wK3psfncfZKZHUB8dzZz91fyaK/UT2kqycxWJNao30JUmvsdcDpwLTF99HNgNGBEUmWLls0WkwJ7AZnZN4D/I5ZXvQZsQyxLWZpIjFqXWLq0IrBW+gXtoqG0ymROhF2JxLNXgGPc/b1UjWsv4jPvRuyKdjCwirt/kFN7Sxd7XYiCItOIYdRH3H1fM1uH6IUtQfxbVgP21Jx6c2rrdz19N84FPnH3k9NjbwF3ufuv0v2eRCGaEen7383dp9a39VINCuwNLs19PZKtWGZmhwCrpZN2V2Jbxdfd/Sepx7YG8D3g7NTr1C/obDCzW4Gx7r53ur8YUYJ1lJltCOwH9CF67rnPUZvZ74hplyOA1YkKeI+6+x7p+e2Bd4HPUyEdaTKZkZvexK59fYBb3f0TM7uEuBC9lCgy86q775Yu/LqlhNDS66gjUGCqPNf4BhMn42wp0umZ+88Sv6A/MbMliGSuq0q9sfSLrqBegbLs9/7p4XPS/UuJEZEFzexmdz+JKBk7Y+oj5/YeSaw1/q27Twaesqgo9pCZXe/uu7r7bXm0U+ojE9T7EXXchwPrAXub2T7EUsfViXycYe5eWpv+K2Lkb0ZgV1AvNiXPNahMxvv33P05M1vWzEpJcJ8DPzWzl4l53d3S46cD62TnxFybM1QkjWq4hd4e1fs+AJ6w2Mp0ZeAwos76cmbWHSCPoJ7a2CUT1LsQc/wtxEm8R2rbW0RFue+b2RX1bqfUVwrq8xIX+7e4+1ZEueDliaTaS4i59PmJLVoxs6uJfQLOzKXRUhMaim9glqlJbmZ3EFfbq3vswnQWsZxtzXT4Ienva6mH3jmZnk4XYq/pu4A/pgIu2wKjgKFpWuM4YB1g59Qzrmc7BwDT3f3TTKby34G/ED20XYliM28AJ7r7hPRzSwPd3f3NerZX6it9J84EDnf3LpnHLwDec/ezzeybxFD8BMCJRLntUh7OV8oPSzEpsDeoTLBZ0N0/S489QBSU+E46uZ9KzKd/SdR2/rl+QWdPCupDgVeBweXr0M1sSWBv4mJq03rPqaekvXOJ6bPfpnn+vsAdwN7uPjwtyduTqGHwJrFSIvfqd1I/abXM6URhme+Y2abADcC27v50OmY+oB+xecvwlHipPJxZKJv6aujVAgrsDSiT5bwaMfT7O3e/OT33KLGl4ndSz30+IrC3lDK5FdQ7z8x+Bvw4DV9iZj8nPuf3gIeJmgBHEsVnns+pjXsR1e5GA6cRIwn/BbZ294/TMaXgvieRVHlCI5+ApDqyyW5p1cxfgOWIZMqt0tLHUmGlr2TMa069Y9kLn7R6oFtmRKzhgrwCe4Mys2WIJW1nEVt/TisFbDN7mAg63yud0NPjDfcFa1Tln5WZDSLWdpfWfK9EJBStAuzl7kPNbAFPO1zl1VYz+zGxtHEEUfLzfKLHPiZz/DzAtsCTeS3Bk/rJjO4tASzi7s9a7AFwOrCSu6+YjlN999lgMy8nvR7oC3wBPO7uf863dW1TYG9QFkVE1nb3fTOPZa/KXwWecvef5NXGoiofdjSzXsS+0ocTyUbjgUPTL/NdwIXufns+rW2zvTsQu3BBBPmziOS+CUTBoo+BI3WR1/wyQWd14GZiuuZqdx9jZisQF37zEL32ieqdz56Uv3A/8D/gQmK/hVuAndz9Xzk2rU1a7ta4ugNfM7Oe7j45c1W+lLsPd/cVre0SotKBdGIrVZS7jlgZ0pdY839C2bG/JIq5vFj3hra2oWumvaX93m9JFyM7Ej33z4h51PmJhKh7FNTnDimoLwvcC5yR7UG6+xtmth9wMfCsma2RxyqOJrEq0NXd9wEws4uBF4DbzKx/WkXTMBTYG0A2KztzNf0hUQ72G8Q69dK8+Rlmdru7/z39TN3n1MtGDkpV2goxDZBOhEaU1nwP+CswELjXzLZ09/ssSm/uBhxA9HSG17udpc84k63/DJHY92b6d1xvZi1EfsVCwF9Kc34y11md6KWfk+Z//0zsIzHZ3QdbFLQaTFQklNkzjdizvlSq+1vEiOo0i500b22kVSdax56zFBCnpWByhpmdZmZfc/d/EhuM3Gpm+5jZlmb2N6JM7A2ln88hqHcrBce0/OprqR0NHdTLRjdWJpIN93T3x4l59JeBB9JxHxNDbhu5+7N1bueSZjZP2XDpIcAH7r5H9iIjfUduJ04yJ5hZN2t7By9pIunCNKsLcJDFUswniJUzjwDbm9n33P1Nd/9NqSNQ7/YWTUpALf299HmNApa1KMP7DWJZ8RQzO4xYYtoouzkC6rHnqizp5VngMuC7wGpmdpa7/9TMfk8MufYgvlxrp6HZvHrqpWHhu1ObupvZS+6+fz3b0hmZi6fuxN7jbwFd03D2xcSSwbXTBcsxwCXufmkO7ewDnEJcuN2ReWohovogFsVnphJD7vO7+z/NbBLwvJYrNb/MOWN+IoH2Y3e/yaLa3ELABe7+t3TsD4iiRTNoxUzH0rliagro5wN9LCpN3mZmPySmPG4BNjSz9YjyzVu4+8j8Wv1VCuw5Sr+gywJrA0e4+wXpxH0hcEQKpCelQNrL3b+EryZT1bG9pV7kbcBHwG+IzUbeM7OXGzFDtOyzugd4HhhGtPuh9Pi30v/Fr4ls8svq3MySScTSxv+lXlkPjyI4HxA9sqU81quXtuDcx8yec/c7On5ZaQaZC9TSMtgx6eFbgL951DawdOyVxKY/D+fV3iLKjDzeRVxADwOuMbPj3P1cM/s2cDKxy2MLsex4WD6t7YC765bjjagCNR04M/NYTyK43Er01rtnnrOc27s48ADQJ93/C5FE0hVYsAE+zzY/H+A44KzM/R8SGeS/AjYlysV+BqyR978hte80YlSkN1FM5Coi2W8VolrYgcSUwTfybqtudf1eLEpUFvx1uv9zYCSwfbq/QvrePFU6bxBJX7m3vZFvxLr00t8XBM7J3N8BeJ+o6DfjWOLCO/e2t3XTfFydtTE/dgBwBbCXRbEZPHppBxAJG+t6Zu2pp29UvbQxJzcptWtRM7sc+DbRxmnAYWa2UT3bl5VN4DOzjc1sIzMbYGbrA4cSFdkA8Fii8mNiGP6XRMDc1PMrPpMtAVrasW8icFH680/EMrwniXn1g4Ft3P2d+rdWai17nig7Z/QDXnT3c9P9nxNbr95mZou7+xvEReEGHlUou7mG3zuUmWLsarGnwnHAlhb1IHD3W4g8l4PM7Gii4wVl0xyNROvY68hm3n2pJ/BFCuKY2W1EkFnDUxGUlMQx3XNad1oaxk5BZ3eijvonxHrZNYnM/Y3SCeQQ4iSztbu/n0Nbs5n6NxE7sX0T+BdR4OdrRK/3DHc/JfNzvdx9kqVlhfVud2rDjMIhlimCY1E057fETn6DPZY9bkDkWoz3THEiaT4pJ2SAu3+Y8nB6EL3JfxGVEP9O1IDfNeWLHANc6u7/Sz+vKpSzUHbeuJn4fB8hAvnF7v6bzLG7Ab8Dvu3uo/Nob6XUY6+T9AUqzY/dSXx5/mpRSQx3356oVf6smS2UHpvqrRWP6t3ebBLJs8Tcc5cUgPYhhv/eAvY3sxOIL/xeeQR1aJ3/T/ONA4CNiS1sNwQGpavunwE/M7NjMz86Nf18vTd0WcbM/mlm/dKFUVczuxO438wuMLN13f1hovjMQsAlZtbH3Z9w97fyDOrtZVa3MRolsyl9xpcAe5jZVsCjxLTL08C/iXPF/9x91/QjlxBbtM6oNKigPmveusLnVOBDdx/k7r8nkph/bmZ/zBx7HbB+owd1UGCvm/QF+iZwHzF3vidR8/sQi72ScfcdiF7wxeU/W9fGMtOQ/+3E0N9u7v6+mS1KtPvbRCLaMsSc7yae9oDPi5kdT8w1buLuE939v8A/iatwiH/LEcBPzOwUiIunOrexFPw+IJIm/2mxmcuVxHa8xxLV7w4ysy1ScD8T+DoxHJ+rzAVqFzM7yswOtFiqiXvUM8i7jc0gBeU7gF2I7/DV7n5fevpmIhG0t5ntb2Y3EEs4t8urI1BwGwJbAjtarEwhnTu2Js4VQzLHFmJTJQ3F10kaVjuWSGT5fXpsGJHA9Slwpbtfmx5viLKPFvu/X0kkjbxqZhcRazi7A9e4+2XpuNyK05jZ14Al3f2pFGDuJyqv7ZMuQp4Ejk5X26X/h52JDV02Bz6vV9vLcgB2IoZUXyFGP54B/uDuX1iUAj0S6EVkO9+fchf+l9eISArYpaBuRLbwR8QFxwvAre7+99KxeX0fmkHZ9+QxIrv9DOBOT3UMzGwgkTOyKJH0eXYaYdMubbNQPkVhsRJpY6K2/kvELpmlfTkGAVcTWzV/UpTvtQJ7HaWT8wdEhuX/ESfHo4jgMxE43lt3cat7cG9rTs6idOKORCb8UkTS1l7E3P/B6ZhcTuQWVbZOJsq+nujuj1tsfvEoMVT5TWKe7PSyn+tOLB8cV8e2Zk/WtwBLufu3UlueJIrMrOOpIE4K7ocTuQF/dPeH6tXWsnbPtPFNCuqbESMjh5jZgsR3eGngNne/unRcUU6CjaT89z5Ny61HdAruAK5191JNg96elsCm+5pTnwVrzXMy4sJoOpGn8LKZbU7ktHxABPfS72svd5+UX6s7T4G9RjJfoN5EEMmeHLcDDnT3bdL9a4g590vz6qlba6KcARsQdcfvIYbZNyMy4R9K88EnEWVY9/Ccd4sys02I4cqFieIcj6SgeCPQ192XTcf18JzqZJcF9euBRd39O5nnuxFVBj8GfuhpD/U0AnEAcLq7f5RDuxcEriWmL4al7/PZxNLAa4GfpMcWJBK3FgPu9xyK+zSDzDljILAF0Rv/m7u/a1Fs5ghifv0a4qJvjLufoIuozrHWEs2fEKMhnxHf2xPN7LtE4tx4YLfCfq7eAGvumu1G6wXTt4hlS88CJwH90+O7E731tYgqY7cTw5yU/syp3V2I/b0fAF4ngs0PSOtgiUpXxxBz7Ks2wOdc+szWB/5IzEWumR5bnhjivrb8+Dy+C+nv1xM9hMGZx3qmP7sT28TeR1yQlJ7vXo92ttP2vkTxnvLHryMuQhbPPLYAMAS4HOiX93ejqDdirvwTYpe2fxEX18em53YjKp/9lxgyzu27UeQbcA5wQ/r74sD3iemkX6XHdkjn5a/l3dbZvanHXmXWuo3ifMTQ2d3ECfss4mr7OGAsMRQ/gdhScROPnnCuc+tmdgFRZGa3dP8sovd+JHFxsjsxFP9Tzz9RbsbmM0QPpg+wDfGZn+0xLL8CcSIc5u7b5thcLDLeBxCFh3YBbnL3C9JzpR38uhNTCJOIila5bOpiZksSPcZb3f3z9BlfBAx391PTMfcSUzObe5r3tyhz2sMbrLxmUaTP+W/Am5nP+Qvi+3xiur8s0B94wXPaBKpo2pjeOJc02pHu9wR+QiTQ7UZ0cLrl9ftXDcqerLIU1Jckis486u4nufuNwFbEkPapRJW29Ykv0YbeWkii3nPq2aIo3Yjh7HtLj7n7b4ndxE71mGO6haiLnGtQh5my9u8h6qbvRvxyjgV+bWYbeBTr2Ar4hpktnk9Lwcx+Q4zWrE0E9n8Bu5vZARBL7VJwbyFGcZzWTP48bEsMt++c5nh7ErXqtzGzgwHc/bvAcOBuM1sqPTZaQX2OGLEi4kkAM3uGSJg70cxWNLOl3f1tdx+qoF4Zm3nTqhUyT61T+ovHUtdXiETQed19cpGDOiiwV0262i4ZQFQy+3HpAXd/jcjG/jaRaT6/u49Mvc4uXv9lV9kvfO/0/sOJHaGyQeUvwGSLIiqfu/un9WxnR1IPdwqxZelEj8z3S4je+xFmtpG7vwqs5u4f5tjU/7j7RgAe8/zXptteZcG9l7tPcfd1PRUZyYO7X0R8jnsCPyIuRP9CTCXsVhbcJwA3WWZHLKmMldUDSBf2zwG7mNmLxMY+pXXqJxEXXNnjFdQ7YDNvWvUwcaHaFzgBWNfMLrVUXY7IGZpIqmtRdArsVZAZFl7YzFb3yGzeHXAzu6p0nLu/AuxBzLOOyTxe95565gv/CHCUxZKPu4kyiQdZVLoC+A5pF7d6trEtZSMMRgTwrxOjH6X/h8eJ+bINiWI0vcnplzVdeODuL6X73dL9UcQcXim4D06PT0rH5bYWPH0PcPfzic1+dgf2JkYRriTavJuZHZSOWxfYqd4XpkVlZkunKQtSr3spiy2Zt0iB/iaiUzCV2GSJdA5ZmshhkAqVOi7EZk8fpOmNye4+BtiImG56zMzuJ6ZID3X3htp+dXbpKnsOZTJZFyGuBOc3s+M91lXvDlxoZle6+08A3P0FIrjntl4984V/GHgHODf1JB9Ic3ibAc+Z2T1EBaYtPbOsJg+WWZ9rZvMSiUOfW+xBfbmZDfe0VBAYQQSgU/Jqd/petKTPeSdiKPsdIvEQj524biBWGxxiZi2ettvMTDPUncce0908qh6endq/e3r6amKKaRrwq9TmIXmOLhSJxSqHvxEJhheb2XLAY8DbxEUqxNTRGcD2wDAze5KYItvAc9quuWhKHa10dyAwzt1L3+Gp6fk30v/HJkQH9xVPNQKagZLn5kCmp74asfFCDyLZ7Hpi6dXzZrYuMYw50t236+Dl6sqi8MIJ7r5puj/jhGFRmGYzYpj1ZU/rZvNirQmJXYD/EMPv6xKf+X3p7xcC/0g/shVRw/71Bmjvi6m9ixE93n+kEZ3SsYsQJ/F73f29PNpbzsz+QWQEb5zu/5Zo47VEcO9CJADm2mYzW9Pdh+b1/rPDzP5ATMddC2xK5OEMMbP+RP7FGsBK6QJrHWJlxwfp+1TX4jOZTkt3YkXJ5MxzDbnEzszmcfeJ6e/zEbvdPQCs7LHlcWlZ7wBgvpSH03Q0FD8HUlBfkFiudh+RVbkXMce+v5mt4u5PETuLjbIcSz22MQc6DehjZv3TEKBblAldCJjo7je4+7/zDuow01TFPcRSoJ8ARxMXH3sTSX2bEz3it4ja8HUN6haZtaUTXqm92wNPuvuawL5EYtS+ZrZ26edSstllOQfI8qH/Y4DFLArp4O5nEWWQfwz8gvjuXJpzm7cHbjSzHfNqQ2eUfvfd/XhiM6XdiQJKb6bHx7r7zkRxlDPTY0+7+/9KF4l1DuqlKoOrEhch95nZ7yzWeec6qtQeMzsT2M7M+prZf4je+FBipczeFvsylD7DPYD9LDbPaToK7HOuOzGUdpmHfxFrUDcBjjSzVd39MXf/qedYx7k0p55GFyDqki9HbP05zd1Lu8gdApyU5zxvWyx2xJtCzIN96lEE5Vyi9/Ntd3/U3Y9z9xPc/eU6t21x4HozW4BINCOdWP5C1CvA3e8gEtIWJE4yG5R+Po/pmKzSSTrlI+DubxEXSmtY7DqIu59N9Hy2IJa05X1if4aYFji8CMG91ONOfz+ZCDZfAzZL35uS+4ns+K/8fF0amnm/NC1X2tv9D8SF6SlmtlI929IJnxOjeC8BX7r7bSmQPwysDpxjZjuY2VHA8URZ7EJVlKuUAnsntRHwpgOrEkOTpSvdB4jCNKsQyVFfLx2c80n8F8DzZraJRyLf4cDfzexQM/uBxdarBwKX533izn7O6e89iSS57UqPeWyK8TyZ1Qc5mR+40GPVQKlHcAQR5AeVDnL3B4kpg+WBnRqpt2BmJwPnpvwFUm98E2AdM7s8PXYSsKfnuLuVmZ1tZnsRW9cOIQLPEY0c3K01+33G7767/5H4LmwC7Gpmy6Sn1gB617WB7duAqGVwVvpd2wR4yt1fScPcDSFzwXQ6MX24MHBZ5hzyV6KoEkRC4lrEqN6L9W5rvSh5rhMyc04LEfPpPT3KPf6WSCYa47FmHaJ3eT+RVf4kUNcEo/Sl7pJNtHH3i8xsaeBOM9vW3S83szFE0ZlJRCbuZnl/4duYS+zjkWx2KhEQh7v7/em5z9ItN+4+jEh0MuA8YkOfZ81sC+C/Znaeux+Sjn3YzKYB7+bZW2gjCWs4MR95nJmd5JEd/D6xk9gB6f9kL1LyXx7M7HvE+vo7ie/rLbRmih9hZnhrAmVDyJwzViRGFyYB77v7Ge5+Sgr6vwAOM7P/I3rxu3b0mjVsa3ky7zeJioKY2VBil8eDLDZe2tjMbi/NZ+fJZ9785pfEGvU/AfOZ2a0ee0L8M93aOr80H2+A8ndFuNGaaLga8AZRRW4E8UVaH/g1kehyC7G8Ylg6/sL0WF3KmRK9x/7ZdgNrlx1zJnFlOyjd70+M3vRpgM95RmldIhnuRmLY9fvEcNqZwNPEVfhpRM9tlRzb2y3z9yVSm+8l1s6Xvi8TiI1ccv8eZ9ucPuMjiBGELkS+wk3pM+6fjjmUKP6zXAO0e/H0u/cGccHxY6JzMgD4PXEBvUPe7cy0t3TOWIXYwfFSYlnV8Oz3gbiwHkHUDSiVb+6WU1uXBDZNf1+JyNofAVyUOfZ6Yuox988406ZLgNsz948l8m12Tfd3JaYZu5b+rc18y70BRboRQzyvA4el+zsRw2ubpftrErsDHUBs/AJwPnH1WPMvUwrqV6WTx3zpscFEgs53y479K5GoM6gBPtduZfeNmMq4mpjrPYqYP/sZsFD63K8ntlmse1DP/N9apr1nEEuW1kyf7QOZ4L5q+p6c3gCfdanNXYhpjBuAr5f+H1JwuTkFyXOIzTCWzrvdmfbvDJxNlLh9hCigUwruxxLlm7fLsX2Dsr9rxEXzI8TWwaXHXk7fh8szj+1LTvtF0HoxMQA4MZ0vNkqP/T619wBiy+Zr0vcm1zr12fNp+i5vSJTpvjHz+NHpfP1PYnvs3DoAdf988m5Ao99KX/r096WIvb4hrvweBv6e7g8o+7nuKSCNrucXisho/hcxl9SH6OWckgLllpnjtiV6u+8C8+T4+S6W2vyNzGMrAg+WHbcLMfy6fs7fh4WJxKdBmce2IpZ+le6vkQLPA6TNcojNPb6ZZ9vL/h3XAVdl7i9LbCUL0VM7i1hznetmP8QmRKtm7m+QTtQLp9+vbHBflLiwXjqntvYDLgBWKHt8b2CRdM54nljW9m0iuF9XdmzXerU3vV92JPIFIgP+Y2KjmY3S57orMWp2dbp1z6OtbX1GRAnY0vl4ndTubHDfjrhYXaGebcz7lnsDGvmW+dKvRWRRfp1Yl7wBMRx8bXq+F9FLWybd70HsYX4HabexWrcx/X0AcfV6AFE17HAiEWcRYtj6cSILHmB/Yog11x2M0kn5ASLrdqn02DeAD4HvpfulnsyNwDE5fxeWT//XDxJ1/iGmCe4rO+5bRFb8szRYT4G44Lsp8/lekQLkUKL+Qum4ug4Ht9HOk1Lwe4aY0iqNLJyY+d07nZj62DOd3PMKNqX/977pzyVJF9KpXUZcYP8j8z26jsgXyG1Hx9SW/kRQPzjd34C4sHuC1p57+ahaLp9zWRv+kL4LC5TamM7VLwJD8m5fnjclz2WUF11wd7co/3g5MQT1MXHyux4Y6q3VjIYQSS/D089NsdjN6z6vYYnCbHvN7HziwuMZ4gTSm8hihQhE5xLDUf80s4eJ4hhrufuIWrWvkna7+41pCeCeQFczu9Td30nLxQaZ2bveWkRiIlD3SnJl34vSDlw7A2eY2f7EHOQEaF065u7PmdllRBLluHq3Oas8WcjdJ5jZy0QS5U3EqMlPiWmPTSxq1k/y/BOMXiAuOF4jRkEOtyh1/A9geTNb2N2PMrMLifn2UqJU3bSRcFYqvfxbYPGUPHdXOtaITYogRqnecvfft/M69TSZGL17BsDdnzCz0UTy3Plm9huPFR0zVqt4DtXvzGw/YqTmG8RF/uJEqeP9zOwyd//MzF4hvjc/T9/7fevdzkagynNJJnu1FzFPOpZYYrUnsW73oHTcZsDviPXJE4hguhKwrue09apFIZF5iZNzL49yid2IHvt6RE/9onRC3xRYBnjEY71yXZVdjGxM1Mx/nbjQ+BUxVHk2MXT9B2KqYDgR0I8ktritW/GZsvZeQ/S+TiN6kvsCaxM7Qy1D9B67EZnERvTW3/Mo15sLm7kK3t5E4LncI5P428T3/NV0/2giuG/vOZYQNrONgC+IJLlNid/Bd4mpr2WI7/UKwI/cvZTpPMDdP6lzO0vnjD5EIF+NOB/cTpwftkqH3u7ut1nsCfAzYoQPIqm1pbxDUeM29wH2IYbUJ6VOyABidPEGjzLCpe/MiUTdggnESNnT9WhjO+2+jRh5fJUYDZmPKEg1NbXvNeAKd//UYp36J8D93kRlYjtDgZ2ZfkH7EcPC3YkT91ii1/sxMaT+cTp+beIEsyaxJOhin3nJRT3bfiaRvLd25rFlie1hL0ltXJ/Ibr3S3XNbGlYWJG8kTs5fEr3abYm69D8lhtJOI+Z9tyJOLh8Bf/KotV93ZnYz0Vv4GVHic6KZDSROkrvSutf6EkQS42TgkHpehJTLfK+NVLQDmAfoS+yj/k46bini33EIkRGdy2ec2tLW92IrYtroVWJ4vj9RXOl1YHQePd2yc8YTxNTcW8TSy+OIi5DniFrlXYhRv0eJRMqvE9uxTqvnOSN9D4YA+xFTMaOAc9II2ebElMZPiXn/KWY2hPi9Wwp4w91Pr+dFSKbd5xO5NetmHvsxMX++ADFt14f4vXuNqEy5jjdA1czc5DkP0Eg3osf7OrEOuScxT3078HPil/UiOshsJ4c5J+LK/0bSvHl6bAlizfxwoqc+iOhN3E/0iLt09O+oU7tvJk5yPYkT3a20zu/tROQHnAwskh7rQo5ZuETg/m/ZY6UTyffS5/soMWoD0VvPdQ6S1gQnAw4ik5FPZMK/Dyyf7m+fvitrNPD3YufM92LJPNuZae+8RCC5oOzxxYnCOdenz/ZS4O/A98uOy+OcsQ3Ry72S6LV/SYyQbUjkiownlvIOBV5KP3M8KYckh/b2Tf/vG6f7vTLPbU90xAYT5bzPTt/t1fL+buR9U+U5ZtRxvh/42N0PcffJ7v5XYu7xWSIhY3cigadNns+OS72INbLZSlVrAme6+1LEXNOlxDDbjcAtHqVjcxumMbNLiezVzdPn/BJxxb2KmR1JjI7cR/TajjKzpVKbW/JqMxHA3wawqK2/ITEf+RCRvLMQcRH4NzPbwENuO3CZ2WnAjy3q1+9L5FzMyKdx912IXubdZvZNd7+NuDh8Po/2QkXfixG0fi8OM7Ml82orzHTOGOmt03RmZt3d/UOiV7wpcYFyLDFSskb2NXL6jtxHBPWPid75ocQFysPEKMgZxDnjeOK8B3FxODJ9n+qtG/F/vki6Pzkzz38bsTTvR+5+j7v/hqiM2LQV5SqlwM6MMq9HAAua2Q4AZrYdcUKf6lFecy1gFzO7rr3XycE0Ysh3jdIDHvWRL0h/P5DouXd394vc/f1cWjmzUi9hc5ixk9weRBLM6kRi1PvEVfoi5JAs14ZhwPZmdh7RY/wHcbF0PDF8uSEx1H0rEYDy9iAxHLwVMXx9E7CjRfUzANz9x8TQ8U0pGI1t85Xqp7Pfi1xrfGfOGQuY2fZm1idd0LWkz/MDYjpsB4+Nfg4nLrBy5ZHv8RYxAtLN3YcQU45jiBUqexJLOW8HeliUGv41cIZndnero2lE0uyaMCM51Sx2nIP4vZtx0Z9zB6Bx5D1k0Eg3Ytj6VWKpx+dE7wEieQ4iSe4ecl6eUtbm3YiT3O5tPDeY2MBhkbzb2c7nfABx4XFs5rm/ESMLkNaoNsKN1rW8ZwA7Zx7fgshfmLfBvheDiKS+HxAnxQuIYcvVy45bIu+2Fvx7MYhI8tuRKDENrcszjyctb8scn9t3hJmXxr6U2ndI+qxLuQADaS1YsxaxJO9bOX/GuxIdmLbOcWcSuURzRUW5ij+zvBvQaDeicMSXwEllj3cvu98QJ3FiOP4U4qr7cCIxZzmi6tLo8hN5o9yIpMQJwPVlj59G5DM0xOdb1rYebTz2qxTY58u7fW207TspUP6A2LP+PGIodq2829Zk34tscO+Tefz67MVJI9xKwY+ouf9pavcG7R1buljJuc3Zc9xvgKWJ5cWHp8dWzruNjXbLvQGNeAM2Jno7O5NJ1mjUG5FgclAK5G8RvfTHyDkZqoJ2b5QCz47pfilRsaEKurTT9iWIGt+jG/lzTsG91HNfh8jcv70RTtjN9L3IBPft0v0riAS0XIv8dNDerxN7W5yR7jd0bzed436Zft/eIVYhDCXn0YRGvWm5WzvMbBDRQziZGE5r+LkbM1uMKKs5FvjM3cfk26JZS5/zn4n169sQ1bqG5tqoWUhz1YcRgXIfzzHprBJm9h1iz4KTiCTADz3mfRtWQb8Xg4hqg18S1SfX8ZhzL99JryGY2WFEUvAOHjkBDc9iZ7mvEZ/xJ+7+ec5NakgK7B0wsy2Bn7l7Ltsozi1S0Zxrga08xzXUlUrFf1YCPnX3j/JuTyXM7LtEYN/C3cfn3Z5KFO17ATMuov5AfM4tedS2qFSqw3AEsJ8rEDQVBfZZyKMgw9zIUhnTvNvRzMyst+dYUW52FPF7UTpnNHJQL8m0Nc+StlJlCuwVUHAXkc7QOUPypMAuIiLSROpeoMbMljOzIWb2gplNM7OH6t0GERGRZpXHtq0rE1muTxKZoyIiIlIldR+KzyZpWOwFvZC7f6eujRAREWlSdR+KV+aliIhI7WgTGBERkSaiwC4iItJE8kiem22DB1OYtXmHHRZ//vGP+bajM9Tm+iham4vWXlCb66Fo7c0aMgSbw5foVCyaNAkOOaTjY847D3r1avOpTre1UIFdRESkKD75BKZOBXfYdlv43//i/oQJrbdJqa7i2LHtBvZOU2CXwhs4EIYMybsVnVfENotIZV55JXrhlerTp3rvrcAuhff668UaDizaEGapvSJSueWWg/XXh3feiV75xInQ0sEeoVOruKtA3QO7mfUmCtQALA70M7Od0/07i7ZJhcjsKOoog4hUplu3CO7zzRf3p0+H8eNh3Li4jR0Lo0e3Hj+tihv75tFjHwDcWPZY6f4ywHt1bY0UnoKkiDSal16Ca66Z9XFdusDqq8OCC1bvvese2N39PWYjy09ERKQoVlsN9tkHPvsseut33NH2cdOnw3PPwZgxrb37OaU5dhERkSozgw02aL3fXmAvqVZGPKhAjYiISE11lDRXUujkOZFqU1Z8bSkrXqTzRo2C3/2u8uOnV3EXFQV2KbyiJs8Vsc0iUpkpUzp3fDU3WlVgFxERqbLFF2+9eG9pgYMO+uox220XWfGLLgr9+1fvvRXYpfCKOBQ/cGDerRCRvJV69fPMU93XVWCXwivqULyIzB26d2/78Xvuaf3zz3+GHj2q834K7FJ46rGLSJEts0xUqqsWBXYpPPXYRaTI3n03SszOP391Xk+BXQqviD12KE6btdxNZPaU6sN//vmsj+3du3rvq8AuMylq71dtFpFG8tZbcNZZlR+voXipGfV+a69obVaPXaTzOtsDnzKletnxKikrIiJSZZMnd+54q+LWaOqxi4iIVNkyy8R02+TJsf/6scd2fPyUKdXbCEaBXUREpEZ69ozbfvvBY4+1Pj59egT9kSNh0iSYOBH69avOeyqwS+Ep4U9EGtn06fDgg/D22+0f014Rm9mhOXYREZEamjq146C+7LLVW8MO6rFLE1Amf20pK15kzsxqKdvbb8dwfLWy4hXYRXJQ1OkDEem8LnUeG1dgl8JTkBSRouvatXqvpTl2ERGRnE2bVr3XUo9dRESkyqZPh3vvhY8+ivnzWZk4UXPsIjMUMXlO27aKNLeXX4abb+74mB/9KPZgX2CBuFWLArtIDop0MaKseJHOW2UVOOAAmDAhkueuuOKrx9x4Y/y5yCKw/PJRyKYaFNil8IqaPFfENotIZcxgjTVa77cV2EtGjowLAAV2qYmiBkkRkUby/vtw8smVH+9evfdWYJeZFGmIGIpX7AWK12YNxYt0Xp8+nTteWfEiIiINbIEF2h/9vO46eOihmR+bVXW6zlBgFxERqbEjj4QxY9p/vpo9dhWoERERqbGOgjrAvPNW770U2EVERHLW0lK911JgFxERyVkl1ekqpcAuIiJSQ5UsZevVq3rvp+Q5kRyoXoDI3GPq1Fkfo6x4kQwFSREpOrPqvZYCuxReEYvqaBMYkblH9+6zPqaSXn2lFNhFclCkixFVnhOZc/vvD488AlOmwDvvxLauWV9+CX37Vue9FNhFcqDpA5G5S48e0Ls3dO06c1Dv1w+WXBLmn79676XALpID9dhF5i7nn9/24198EXu3axMYkYyi9n6L2GYR6bxqFp+phAK7FF6Rer+g3d1E5jaV9MZbWmK4vhoU2KXw1GMXkUZWnijXlmoFdVBglyZQxB67lruJzD169oStt4b334cJE+Ddd796TEtLZcviKqHALiIiUkNmMHo0DBvW/jHV7LGrVryIiEiNPflkx89XMlxfKQV2ERGRGppVVvyKK6pWvEjhFSkvQFnxIp03ciQcd1xlx776Knz+OSy0UHXeW4FdJAdFzeQXkcp0tuBMv37Ve28FdhERkSpbdNHWi/eWFjjooPaP7dULulRxYlyBXSQHGooXmXvMahnbpEkweXL15tkV2EVyoKF4kbnLmmvC0KHtPz9lCvTpU533UmAXyYF67CJzj5aWjoM6qPKcSOGpxy7S/IYNg1GjYpi9nhTYRUREquyll+CCCyo/Xj12ERGRBrbSSvCDH8CYMRG0e/SI0rJTpsA999T2vRXYRXKgOXaR5ta1K2yzTev9F1/suKzs2LEqUCMiItKwPv4Yjj++8uN79areeyuwi+RAyXMizc2sc8fPqp58Zyiwi+RAQ/EizW2RRdq/eL/qKnj88Zkfmzateu+twC4iIlJjF14IL7xQn/dSYJfCK+qwdhHbLCKzZ1ZBXXPsIhlFGtaG1qHtorRZQ/Eic6aSnd40FC+SoR67iDSyWVWeW3hhmHfe6r2fArsUnnrstaUeu8ic6dUL9tsPPvwQxo2Dxx6b+flRo2D8+Ortya7ALiIiUmPrrBM3gHffjSCf1dnlcR2p4tbuIiIi0paWlqguN2oU9O/f9vPVoh67FJ7m2EWkkbW0wEEHdXxM167Vez8Fdik8zbHXlubYReZMJUG7mru7aSheRESkhipZyqY5dhERkYLo3h322AP69Gn/mC5VjMYaihcREamyESPghBMqP3769Oq9twK7FJ6S50Sk0VSzB95ZCuwiIiJV1qcPDBoEY8ZEkH/uuY6PnzSpevXiFdil8IqYFT9wYN6tEJFaGjYMHn648uM7mn/vLAV2kRwU6WJEy91EOm/xxTt3/NixsNBC1XlvZcWLiIhUWc+enTu+kh3gKqUeuxSekudEpNEMGND6O95R5bm+faOnXq0NYECBXZpAkYa1QZXnROY23bu3/9z48dFb13I3kQz12EWkyCZMgIkTYZ55qvN6mmMXERGpoUpKynZ2Tr4jCuwiIiI1VEkdeJWUFcnQHHttaY5dZM5UErSrORSvwC4iIlJjF10EU6fC5Mnw0ENw++0zP6/kORERkYJwh+uvj4BeD5pjFxERqaGpU2cd1Pv2rd77KbCLiIjUUEfr2EumTKne+2koXiQHRV17LyKzZ5994L77Yo591KivPj91avXeS4FdCk9BUkQaWUsLXHFFx8f06FG999NQvIiISM4qGa6vlAK7iIhIDVUyzN6tiuPnCuwiIiI1VEm52EmTqvd+mmOXwlPludpS5TmROVNJ5bnJk6FPn+q8nwK7FF5Rk+eK2GYRqY3evav3WgrsUnjqsdeWeuwitTdlCvTqVZ3XUmCXwlOPXUSKTru7iWSox15b6rGL1N7EidUrK6vALoWnHruIFN0CC1TvtRTYpfDUY68t9dhF5tzee8MDD8RObx9++NXnJ0yAfv2q814K7CI5KOoog4hUZvx4OPtsGDGisuOnTaveeyuwS+EpSIpIoxk5svKgPt981eutgwK7iIhI1S27bHQ4pkyB0aPhuOPaP3bMmBiirxYFdpmJer8iInPunXfgjDMqP16BXWpGiWi1d9hhcQElIs1r4YXj1tbe622ZPLl6O7wpsEvhaZRBRBrNvPPCySe33h88uOPjtR+7iIhIE6lm5TkFdhERkZxNnly919JQvBReEfMCNMcuIlldu1bvtRTYpfA0xy4ijayS4jPKihfJUI9dRIpOPXaRgivSxYhqxYt03qefwrHHVn68WfXeW8lzIiIiVdbZZLixY6v33uqxi+RAeQEizW3xxdv/Hb/jDrjttpkfq+ZyNwV2kRxoKF5k7nL44bHjW3uUPCeSUdTebxHbLCKzp6OgDuqxi8ykSL1fKF59e/XYRWpvypTqvZaS50RERHJWrQ1gQD12kVwUdfpARGqjmpvAKLCLiIjU2DnnwMiRsQzummvgs89mfr6S6nSVUmAXyUGR8gI0xy4y5/79b3jwwfafV/KcSEZRh7WL2GYRqdz778O4cTB9esdBHRTYpYaKGiRFRBrJK6/AeedVfnw1S8oqsMtMijREDNpQRUQa0/LLw6BBMa/epUsE+o5MmgS9e1fnvRXYpfCKeDECxWmz5thFOq97d9h999b7gwd3fHxLS/XeW4FdCq+o0wdFbLOI1EbfvtV7LQV2KTz12GtLPXaR2hs/Hvr0qc5rKbBL4anHLiJFpx67SEYRe+xK+BORrK5dq/daCuwiOSjSxYiG4kVq78svoVev6ryWArtIDoo6fSAitaFa8SIFpx67iGSp8pyIiEiBHHccvPVW9MxvuQXGjJn5eRWoERERKZDFF48bwHrrwccfw9ixMHQoPPKICtSIiIgUysEHx5at7almVnwVR/VFRESkLR0FdYgStNWiwC4iIpKzadOq91oaihfJgZa7iUjWPPNU77UU2EVyoOVuInOXww6DJ5+M7PehQ7/6/IQJ1QvuCuwiIiI1NqsL+WpVnQMFdhERkaqbPh0efDDWq1dSVW7KlOq9twK7FF5R56uL2GYRqczLL8M//lH58drdTSSjSPPVoP3YReYGq6wCv/gFjBoVS91eegnef7/940ePhkUWqc57K7CLiIhUmRmstVbr/Tvv7Pj4/v2r995axy4iIlJjyy7b8fPjx1fvvdRjl8LTHLuINLq33+74+WrOsavHLiIikqP55tN+7CIzUfJcbSl5TmTOHXAAPP54/P3FF2d+bswYGDeuevPsCuwiOSjq9IGIzJ411ogbwDnnwBtvzPy8WfXeS4FdJAdFGmVQj11kzn36Kbz1VhSiKQ/qAO7Vey8Fdim8ovZ+i9hmEZk9xx7b8fPV3I9dgV0Kr0i9X9Acu8jc4rXX4JNPYuOXJZfsuEDNhAnVy4xXYJeZqPdbP0Vss4hUZtgw+POfKz++T5/qvbcCu8xEvd/aK1qb1WMX6bxvfhO23TYy3nv1ii1Zu3aNufSpU6GlJf786KM476rHLiIi0sC6dYPtt2/7OXf48stY4vb00xHYq/re1X05EalEUac8RGT23Hsv3HNP/P2LL776fPfu1XsvBXaRHBRpykND8SJz7qabOn5+nnmq914K7FJ4Re39FrHNIlIbWscuklGk3i8oeU5kbrTDDnD33fF399ijPWvcOOjduzrvpcAukoOijjKIyOzZeuu4AVx3HTz00MzP9+pVvfdSYBfJQZFGGdRjF6muN9/86mPTp1fv9RXYRXKgHrvI3OWkk+CDD9p/XiVlRQpOPXaRuUtHQR1ic5hqUWCXwitq77eIbRaR2tByN5GMIvV+QVnxIvJVU6ZUr158l+q8jIiIiDQC9dil8DQULyKNrKVl1sdUaw07KLBLE9BQfG1pKF5kznTrBt/7Hjz2WNyfMOGrx4wbBz17Vun9qvMyIiIi0hYz2HHHuAFcfjk8+WTt3k+BXUREpMY+/jhGFydPbjuoq/KcSIbm2EWk0UybBrffDp98EveffbZ+763ALoWnOfba0hy7SOe9+ircdVflx6tAjUiGeuwi0mhWWSUuir/4Iu5femnHx6ukrIiISIMbOLD177MK7N2qGI1VoEZERKSGKlnHXs3Arh67FJ7m2GtLc+wic6Z7d/jpT+GZZ+L+Sy999ZgJE7SOXWQGzbGLSKO7/PKOn1flOZEM9dhrSz12kTm3yy5www3tPz92bPXWsiuwS+Gpxy4ija6joA4qUCMyE/XYa0s9dpHaq9b8OiiwSxNQj11Eik7r2EUy1GOvLfXYRebM9OmzPmbatMierwatYxcREamhSgJ7NSmwi4iI1FC3brDBBh0fM3Zs9d5PgV1ERKTGnnii4+erNQwPmmMXyUVRE/5EZPZstBE8/nj7z5tV770U2EVyUKSEPyXPicy5vfeOG8RF/dChMz/vXr33UmAXyYF67CJzlw8/hGHDYt/18qAO6rGLzERBUkQa3Ykndvz8PPNU770U2KXwijSsDVrHLjI3mDABzj8fhg+v7Pjx41UrXkREpGGNGAHvvVf58X36VO+9FdhFcqDpA5Hmttxy8Ne/wtSpMa9++OEdH6/lbiIFV6TpAw3Fi3Te55/D0UdXfvy4cTD//NV5bxWoERERqbK33urc8b17V++9FdhFRESqbOmlO3f8uHHVe28NxYuIiFTZgAEz59EMHtzx8VrHLlJwSp4TaX6jR8PkyZUdO9981XtfBXaZSVEDThHbLCLN67XX4E9/qvz4KVOqV6RGgV1mUqRsbShesRcoXpuVFS/SeUsvDWuuCR98EPc/+aTj46u5Z7sCuxSeRhlEpNH06tU6r97SAgcd1PHxXaqYyq7ALoWnUYbaUo9dZM507w5rrAHPP9/+MePGVW8oXsvdREREaqyjoA7aBEak8Io6fSAis+fMM2HUKOjaFS6/HEaOnPn5lpbqvZcCu0gOijR9oKF4kTk3diy8+mpkvy+1lAK7SIeK2vstYptFpPPc4ZRTOj6mf//qvZ8CuxRekXq/ED3ggQPzboWI1MvUqbM+Zla7u5nZIcDPAQMucfdz2ztWgV1ERKTKPvsMjjmm8uOHDoW11mp72ZuZrUIE9XWBKcDdZnaHu7/Z1mvNMrCb2UPAoHae3tDdn0jHrQqcBmxMZNu/Chzg7s+m53cGDgMGAn2A4cDVwJnuPmVW7RBpJkUaZdAcu0jnTZzYueOvugoeewwOOaTN4L4i8KS7fwlgZg8DPwTObOu1KumxHwj0K3vsROBbwNPpTdYAHgVuBXZJx6wDZBP4FwQeBM4CxhBXHicAiwKzWLovIiKSDzN7DxgHTAOmunubx40YEaVkx42L25prRlLcxIkwZgyMHw+TJrX9HlOmwLvvwrBhsNpqX3l6GHCKmS0ITAS2AZ5pr72zDOzu/krZP7AHsDZwg7uXZg4uAm539z0zh95d9jrlqUIPmlk/4Jdm9itv75OSulIiWv0Urc1Fa69IlW3q7p+mv38lXk2fDhdfDB99VNmLDRjw1TKzkyfD++9/NbC7+6tmdgZwLzAeeAFod+Z+dubYtwLmB64DMLOVgPWAQ2fjtT4DeszGz0mNFGmIGIpXxQ2K1+ZSe5XwJ9K26dPhvPMqD+oA884bS+Cyu7/17AlLLtn28e5+GXAZgJmdCnzQ3mvPTmDfFfiQGHqHCOoA85vZC8DKxPz5qakhMzGzrkBPYE3gYOCv6q3LnNAog4jUmAP3mJkDQ8pD1rBhMYzell13hW7d4jZtWgzFT5wIgwbBZZfFz02eHEF9mWVglVXafh0zG+Dun5jZ14EdgQ3aa6x1JqaaWW9gJHCxux+eHjsaOJXofZ9JzLvvTMzNb+vud5a9xiQisANcBfzU3au4r42IiEj1mNli7v6RmQ0ghsN/5e6PlJ4fPJjfEzlj2bS36cDxQ4ZwcnuvO3gwXYGtgTWA54G7hgxhWjtteJTIVWsBDnP3+9ttbycD+y7A9cA67v5MeuxY4GTgDHc/KnPsA0B3d9+47DXWBHoTyXPHAde6+4EVN0JERCQnZnYCMN7dz867Le3p7CYwuwJvlYJ68nn688GyYx8AVip/AXcf6u6PufsfiaH4A8xs2U62Q0REpObMrI+ZzVv6O7AlkaXesCoO7GbWnxgyuK7sqVfb+xFiKKIjQ9Ofy1TaDhERkTpaBHgs5ZA9Bdzh7nfP4mdy1ZnkuR8Sc+Plgf3/gNHA5sB/Mo9vTqTkd2Sj9Gc7aQciIiL5cfd3gNXzbkdndCaw7wq84O4z9dDdfYqZnQicaWZjiOS5nYBNyFSsM7O7gfuAl4lF/hsBhxPr4d+ek3+EiIiIhIoCu5ktRPTAf9/W8+5+rpl1AX5FZAa+Duzs7o9mDnsa2AdYmlhY/w5wNFHcRkRERKqgU1nxIiIi0tg6mxUvIiIiDUyBXUREpIkosIuIiDQRBXYREZEmosAuIiLSRBTYRUREmogCu4iISBNRYBcREWki/w8rcIr5Ix0HOgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficar DataFrame\n",
    "diabetes_sort = diabetes.sort_values('BMI')\n",
    "msno.matrix(diabetes_sort, color=(0.4,0.4,1),fontsize= 12,figsize= (8,4))\n",
    "plt.title('Matriz datos faltantes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "eaeae985",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Cantidad datos faltantes de la variable BMI ascienden a 11\n",
      "- Los datos faltantes de la variable BMI corresponden al 1.44% del total de la data\n"
     ]
    }
   ],
   "source": [
    "# Visualizar valores perdidos en cantidad y porcentaje\n",
    "total_na = diabetes['BMI'].isnull().sum()\n",
    "print(f'- Cantidad datos faltantes de la variable BMI ascienden a {total_na}')\n",
    "mean_glucose =round(diabetes['BMI'].isnull().mean() *100, 2)\n",
    "print(f'- Los datos faltantes de la variable BMI corresponden al {mean_glucose}% del total de la data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "eef56db9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cantidad datos faltantes = 11\n",
      "Los datos faltantes corresponden al 1.44% del total de la data\n"
     ]
    }
   ],
   "source": [
    "# Visualizar valores perdidos en cantidad y porcentaje\n",
    "print('Cantidad datos faltantes = ' + str(diabetes['BMI'].isnull().sum()))\n",
    "print('Los datos faltantes corresponden al ' + str(round(diabetes['BMI'].isnull().mean() *100, 2))+ '% del total de la data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab55dc3",
   "metadata": {},
   "source": [
    "Al cumplirse los dos criterios, se procede a eliminar la lista de datos faltante para la variable _BMI_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "fab55f19",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Eliminar la fila de valores donde la variable BMI presente valores nulos.\n",
    "diabetes.dropna(subset=['BMI'], how='any', inplace=True)\n",
    "\n",
    "# Graficar DataFrame luego de la eliminación de datos faltantes.\n",
    "msno.matrix(diabetes, color=(0.4,0.4,1),fontsize= 12,figsize= (8,5))\n",
    "plt.title('Matriz datos faltantes')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "72ebb7ec",
   "metadata": {},
   "source": [
    "### Imputación de valores\n",
    "\n",
    "Existen distintas técnicas para el tratamiento de datos ausentes. Dentro de las técnicas básicas de imputación de datos destacan:\n",
    "\n",
    "- Contante (por ejemplo, imputar 0)\n",
    "- Media\n",
    "- Mediana\n",
    "- Moda\n",
    "\n",
    "Estas técnicas de imputación de datos las podemos encontrar en la función `SimpleImputer()` de la biblioteca sklearn.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "52c02314",
   "metadata": {},
   "source": [
    "#### SimpleImputer\n",
    "\n",
    "La función `SimpleImputer( )` cuenta con las siguientes estrategias (strategy) de imputación a los valores faltantes:\n",
    "\n",
    "- \"mean\": El valor faltante se reemplaza por la media. Sólo puede ser usada para datos numéricos.\n",
    "\n",
    "- \"median\": El valor faltante se reemplaza por la mediana. Sólo puede ser usada para datos numéricos.\n",
    "\n",
    "- \"most_frequent\": El valor faltante se reemplaza por la moda. Puede ser usada tanto para datos de textos y numéricos.\n",
    "\n",
    "- \"constant\": El valor faltante se reemplaza por cero. Puede ser usada tanto para datos de textos y numéricos. El valor por defecto es 0. También, la constante puede ser modificada. Por ejemlo, si se desea reemplazar por el valor 5 se debe indicar de la siguiente forma: `SimpleImputer(strategy='constant', fill_value= 5)`.\n",
    "\n",
    "A continuación imputaremos los datos ausentes usando esta función.\n",
    "\n",
    "- **Imputación por media**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "31fe1907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importar módulo de imputación\n",
    "from sklearn.impute import SimpleImputer \n",
    "\n",
    "# Generar copia de DataFrame con el nombre de media\n",
    "diabetes_media = diabetes.copy(deep=True)\n",
    "\n",
    "# Crear objeto imputador de media\n",
    "imputar_media = SimpleImputer(strategy='mean')\n",
    "\n",
    "# Imputar datos faltantes con la media en el dataframe \n",
    "diabetes_media.iloc[:, :] = imputar_media.fit_transform(diabetes_media)\n",
    "\n",
    "# Visualizar si existen datos nulos\n",
    "diabetes_media.isna().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6fdde2c6",
   "metadata": {},
   "source": [
    "A continuación, procederemos a generar distintas estrategias de imputación de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "a043fc66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_______________diabetes_media_______________\n",
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n",
      "   Pregnancies  Glucose  BloodPressure  SkinThickness     Insulin   BMI  \\\n",
      "0          6.0    148.0           72.0      35.000000  156.056122  33.6   \n",
      "1          1.0     85.0           66.0      29.000000  156.056122  26.6   \n",
      "2          8.0    183.0           64.0      29.172285  156.056122  23.3   \n",
      "\n",
      "   DiabetesPedigreeFunction   Age  Outcome  \n",
      "0                     0.627  50.0      1.0  \n",
      "1                     0.351  31.0      0.0  \n",
      "2                     0.672  32.0      1.0  \n",
      "\n",
      "\n",
      "_______________diabetes_mediana_______________\n",
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n",
      "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "0          6.0    148.0           72.0           35.0    125.5  33.6   \n",
      "1          1.0     85.0           66.0           29.0    125.5  26.6   \n",
      "2          8.0    183.0           64.0           29.0    125.5  23.3   \n",
      "\n",
      "   DiabetesPedigreeFunction   Age  Outcome  \n",
      "0                     0.627  50.0      1.0  \n",
      "1                     0.351  31.0      0.0  \n",
      "2                     0.672  32.0      1.0  \n",
      "\n",
      "\n",
      "_______________diabetes_moda_______________\n",
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n",
      "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "0          6.0    148.0           72.0           35.0    105.0  33.6   \n",
      "1          1.0     85.0           66.0           29.0    105.0  26.6   \n",
      "2          8.0    183.0           64.0           32.0    105.0  23.3   \n",
      "\n",
      "   DiabetesPedigreeFunction   Age  Outcome  \n",
      "0                     0.627  50.0      1.0  \n",
      "1                     0.351  31.0      0.0  \n",
      "2                     0.672  32.0      1.0  \n",
      "\n",
      "\n",
      "_______________diabetes_constante_______________\n",
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n",
      "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "0          6.0    148.0           72.0           35.0      0.0  33.6   \n",
      "1          1.0     85.0           66.0           29.0      0.0  26.6   \n",
      "2          8.0    183.0           64.0            0.0      0.0  23.3   \n",
      "\n",
      "   DiabetesPedigreeFunction   Age  Outcome  \n",
      "0                     0.627  50.0      1.0  \n",
      "1                     0.351  31.0      0.0  \n",
      "2                     0.672  32.0      1.0  \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Estrategias\n",
    "estrategias = {'diabetes_media': 'mean',\n",
    "              'diabetes_mediana': 'median',\n",
    "              'diabetes_moda': 'most_frequent',\n",
    "              'diabetes_constante': 'constant'\n",
    "              }\n",
    "\n",
    "for key, value in estrategias.items():\n",
    "    \n",
    "    # imprimir método imputación\n",
    "    print('_'*15 + key + '_'*15)\n",
    "    \n",
    "    # generar nuevo DataFrame por estrategia\n",
    "    key = diabetes.copy(deep=True)\n",
    "    \n",
    "    # Crear objeto imputador de media e imputar\n",
    "    imputar = SimpleImputer(strategy = value)\n",
    "    \n",
    "    key.iloc[:, :] = imputar.fit_transform(key)\n",
    "\n",
    "\n",
    "    # Visualizar si existen datos nulos\n",
    "    print(key.isna().sum())\n",
    "    print(key.head(3))\n",
    "    print() #imprimir salto de línea\n",
    "    print() #imprimir salto de línea"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "677ca134",
   "metadata": {},
   "source": [
    "Observemos ausencia de valor en la fila 3 para la variable _SkinThickness_ antes del proceso de imputación. Ahora los valores que toma, según el criterio de imputación son los siguientes: \n",
    "- media = 29.172285\n",
    "- mediana = 29\n",
    "- moda = 32\n",
    "\n",
    "A continuación verificaremos que los valores imputados sean los correctos. Para esto, usaremos las funciones de media, mediana y moda y compararemos con el valor imputado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "8d58006c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La media para la variable SkinThickness es de 29.172285\n",
      "La mediana para la variable SkinThickness es de 29.0\n",
      "La moda para la variable SkinThickness es de 32.0\n"
     ]
    }
   ],
   "source": [
    "media = round(diabetes['SkinThickness'].mean(),6)\n",
    "mediana = round(diabetes['SkinThickness'].median(),6)\n",
    "moda = round(diabetes['SkinThickness'].mode(),6)[0]\n",
    "\n",
    "print(f'La media para la variable SkinThickness es de {media}')\n",
    "print(f'La mediana para la variable SkinThickness es de {mediana}')\n",
    "print(f'La moda para la variable SkinThickness es de {moda}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "44822f15",
   "metadata": {},
   "source": [
    "Observamos que el valor calculado por medio de la función es el mismo al imputado por cada una de las técnicas."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0917b481",
   "metadata": {},
   "source": [
    "#### Imputación de datos faltantes para series temporales\n",
    "\n",
    "Cuando tenemos presencia de valores faltantes en dataset de series temporales, la técnica de imputación cambia, dado que los datos son medidos a través del tiempo, en determinados momentos (años, semestres, trimestres, meses, semanas, días, horas) y ordenados cronológicamente. Dada su naturaleza, estos datos presentan tendencias, ciclos, estacionalidad y aleatoriedad. \n",
    "\n",
    "A continuación, revisaremos algunos métodos utilizados para la imputación de datos faltantes en series temporales, usando un dataset de calidad del aire.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "bf64cd0c",
   "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>Ozone</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-05-01</th>\n",
       "      <td>41.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-02</th>\n",
       "      <td>36.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-03</th>\n",
       "      <td>12.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-04</th>\n",
       "      <td>18.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.3</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-06</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.9</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-07</th>\n",
       "      <td>23.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-08</th>\n",
       "      <td>19.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-09</th>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>194.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Ozone  Solar  Wind  Temp\n",
       "Date                                \n",
       "1976-05-01   41.0  190.0   7.4    67\n",
       "1976-05-02   36.0  118.0   8.0    72\n",
       "1976-05-03   12.0  149.0  12.6    74\n",
       "1976-05-04   18.0  313.0  11.5    62\n",
       "1976-05-05    NaN    NaN  14.3    56\n",
       "1976-05-06   28.0    NaN  14.9    66\n",
       "1976-05-07   23.0  299.0   8.6    65\n",
       "1976-05-08   19.0   99.0  13.8    59\n",
       "1976-05-09    8.0   19.0  20.1    61\n",
       "1976-05-10    NaN  194.0   8.6    69"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# Fijar la ruta donde se encuentra el archivo\n",
    "ruta = \"datos/\"\n",
    "csv_file = \"air-quality.csv\"\n",
    "\n",
    "# Leer csv indicando el nombre de archivo\n",
    "df = pd.read_csv(ruta + csv_file, parse_dates=['Date'], index_col='Date')\n",
    "\n",
    "# visualizar primeros registros\n",
    "df.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dd146e0d",
   "metadata": {},
   "source": [
    "Las variables que contiene este dataset son:\n",
    "\n",
    "- Ozone: Ozono medio en partículas por billón\n",
    "\n",
    "- Solar.R: Radiación solar.\n",
    "\n",
    "- Wind: Velocidad promedio del viento en millas por horas.\n",
    "\n",
    "- Temp: Temperatura máxima diaria en grados Fahrenheit.\n",
    "\n",
    "A simple vista, notamos presencia de valores faltantes. Procederemos a identificar los valores ausentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "a1a068c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 153 entries, 1976-05-01 to 1976-09-30\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Ozone   116 non-null    float64\n",
      " 1   Solar   146 non-null    float64\n",
      " 2   Wind    153 non-null    float64\n",
      " 3   Temp    153 non-null    int64  \n",
      "dtypes: float64(3), int64(1)\n",
      "memory usage: 6.0 KB\n"
     ]
    }
   ],
   "source": [
    "# Información de la data\n",
    "df.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "724cd2a1",
   "metadata": {},
   "source": [
    "Se observa que el esquema de la data ha sido inferido de manera correcta. Además, existen valores nulos en las columnas **Ozono** y **Solar**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "da701041",
   "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>Ozone</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>116.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.129310</td>\n",
       "      <td>185.931507</td>\n",
       "      <td>9.957516</td>\n",
       "      <td>77.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32.987885</td>\n",
       "      <td>90.058422</td>\n",
       "      <td>3.523001</td>\n",
       "      <td>9.465270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>115.750000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.500000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>63.250000</td>\n",
       "      <td>258.750000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>168.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>20.700000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Ozone       Solar        Wind        Temp\n",
       "count  116.000000  146.000000  153.000000  153.000000\n",
       "mean    42.129310  185.931507    9.957516   77.882353\n",
       "std     32.987885   90.058422    3.523001    9.465270\n",
       "min      1.000000    7.000000    1.700000   56.000000\n",
       "25%     18.000000  115.750000    7.400000   72.000000\n",
       "50%     31.500000  205.000000    9.700000   79.000000\n",
       "75%     63.250000  258.750000   11.500000   85.000000\n",
       "max    168.000000  334.000000   20.700000   97.000000"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descripción de la data\n",
    "df.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "65721a7b",
   "metadata": {},
   "source": [
    "Los valores mínimos y máximo de la data son concordante con los valores que pueden tomar cada una de las variables del dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "e42d0c1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cantidad de datos nulos\n",
      "Ozone    37\n",
      "Solar     7\n",
      "Wind      0\n",
      "Temp      0\n",
      "dtype: int64\n",
      "\n",
      "Porcentaje de datos nulos\n",
      "Ozone    24.183007\n",
      "Solar     4.575163\n",
      "Wind      0.000000\n",
      "Temp      0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Contar datos nulos\n",
    "print(\"Cantidad de datos nulos\")\n",
    "print(df.isna().sum())\n",
    "print()\n",
    "\n",
    "# Porcentaje datos nulos\n",
    "print(\"Porcentaje de datos nulos\")\n",
    "print(df.isna().mean()*100)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fa837657",
   "metadata": {},
   "source": [
    "Se observa que la variable **Ozone** tiene un alto porcentaje de valores nulos. En tanto, la cantidad de variable nulos **Solar** es baja. \n",
    "\n",
    "Además, de aplicar estas funciones, realizaremos un bucle para detectar los valores únicos por variable a fin de observar si hay valores nulos no detectados.\n",
    "\n",
    "*Nota: es dificil que existan, dado que cuando se ingresa información no nula de forma manual e importanmos nuestro dataset, la función de lectura no puede inferir de manera correcta el esquema de los datos.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "2bd99da3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ozone [  1.   4.   6.   7.   8.   9.  10.  11.  12.  13.  14.  16.  18.  19.\n",
      "  20.  21.  22.  23.  24.  27.  28.  29.  30.  31.  32.  34.  35.  36.\n",
      "  37.  39.  40.  41.  44.  45.  46.  47.  48.  49.  50.  52.  59.  61.\n",
      "  63.  64.  65.  66.  71.  73.  76.  77.  78.  79.  80.  82.  84.  85.\n",
      "  89.  91.  96.  97. 108. 110. 115. 118. 122. 135. 168.  nan] \n",
      "\n",
      "Solar [  7.   8.  13.  14.  19.  20.  24.  25.  27.  31.  36.  37.  44.  47.\n",
      "  48.  49.  51.  59.  64.  65.  66.  71.  77.  78.  81.  82.  83.  91.\n",
      "  92.  95.  98.  99. 101. 112. 115. 118. 120. 127. 131. 135. 137. 138.\n",
      " 139. 145. 148. 149. 150. 153. 157. 167. 175. 183. 186. 187. 188. 189.\n",
      " 190. 191. 192. 193. 194. 197. 201. 203. 207. 212. 213. 215. 220. 222.\n",
      " 223. 224. 225. 229. 230. 236. 237. 238. 242. 244. 248. 250. 252. 253.\n",
      " 254. 255. 256. 258. 259. 260. 264. 266. 267. 269. 272. 273. 274. 275.\n",
      " 276. 279. 284. 285. 286. 287. 290. 291. 294. 295. 299. 307. 313. 314.\n",
      " 320. 322. 323. 332. 334.  nan] \n",
      "\n",
      "Wind [ 1.7  2.3  2.8  3.4  4.   4.1  4.6  5.1  5.7  6.3  6.9  7.4  8.   8.6\n",
      "  9.2  9.7 10.3 10.9 11.5 12.  12.6 13.2 13.8 14.3 14.9 15.5 16.1 16.6\n",
      " 18.4 20.1 20.7] \n",
      "\n",
      "Temp [56 57 58 59 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80\n",
      " 81 82 83 84 85 86 87 88 89 90 91 92 93 94 96 97] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Iterar valores por columnas\n",
    "for columna in df.columns:\n",
    "    # Ordenar valores y seleccionar valores únicos\n",
    "    i = df[columna].sort_values().unique()\n",
    "    # Imprimir nombre de columnas y lista de sus valores únicos\n",
    "    print(columna, i, '\\n')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "205d524e",
   "metadata": {},
   "source": [
    "Tal como ya habíamos anticipados, no existe valores nulos ingresados con otra nomenclatura.\n",
    "\n",
    "**Métodos simples de imputación**\n",
    "\n",
    "A continuación procederemos a revisar los distintos métodos de imputación."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a37efc4b",
   "metadata": {},
   "source": [
    "- **Método ffill (forward fill): Imputación hacia adelante**\n",
    "\n",
    "    Se toma el valor anterior al dato nulo y se imputa dicho valor en los siguientes registros. \n",
    "    \n",
    "    Por ejemplo, para la columna *Ozone* el valor con fecha __1976-05-04 es de 18.0__, con este valor __se reemplaza el o los valores nulos siguientes__, hasta encontar un valor no faltante. En efecto, el valor faltante con fecha __1976-05-05 se reemplaza por 18.0__.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "a1dd2c7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ozone</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-05-01</th>\n",
       "      <td>41.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-02</th>\n",
       "      <td>36.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-03</th>\n",
       "      <td>12.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-04</th>\n",
       "      <td>18.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-05</th>\n",
       "      <td>18.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-06</th>\n",
       "      <td>28.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-07</th>\n",
       "      <td>23.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-08</th>\n",
       "      <td>19.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-09</th>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-10</th>\n",
       "      <td>8.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Ozone  Solar  Wind  Temp\n",
       "Date                                \n",
       "1976-05-01   41.0  190.0   7.4    67\n",
       "1976-05-02   36.0  118.0   8.0    72\n",
       "1976-05-03   12.0  149.0  12.6    74\n",
       "1976-05-04   18.0  313.0  11.5    62\n",
       "1976-05-05   18.0  313.0  14.3    56\n",
       "1976-05-06   28.0  313.0  14.9    66\n",
       "1976-05-07   23.0  299.0   8.6    65\n",
       "1976-05-08   19.0   99.0  13.8    59\n",
       "1976-05-09    8.0   19.0  20.1    61\n",
       "1976-05-10    8.0  194.0   8.6    69"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generar copia del df\n",
    "df_ffill = df.copy(deep=True)\n",
    "\n",
    "# Imputar NaNs usando forward fill, es decir, se toma el valor anterior y se reeemplzan por dicho valor los siguientes registros\n",
    "df_ffill.fillna(method='ffill', inplace=True)\n",
    "df_ffill.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e0f6d3b0",
   "metadata": {},
   "source": [
    "- **Método bfill (back fill): Imputación hacia atrás**\n",
    "\n",
    "    Se toma el valor siguiente al dato nulo y se imputa dicho valor en los valores faltantentes que lo preceden. \n",
    "    \n",
    "    Por ejemplo, para la columna ozono el valor con fecha __1976-05-06 es de 28.0__, con este valor __se reemplaza el o los valores nulos precedentes__, hasta encontar un valor no faltante. En efecto, __el valor faltante con fecha 1976-05-05 se reemplaza por 28.0__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6868ea7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Ozone</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Temp</th>\n",
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       "      <th>Date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-05-01</th>\n",
       "      <td>41.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-02</th>\n",
       "      <td>36.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-03</th>\n",
       "      <td>12.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-04</th>\n",
       "      <td>18.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-05</th>\n",
       "      <td>28.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-06</th>\n",
       "      <td>28.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-07</th>\n",
       "      <td>23.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-08</th>\n",
       "      <td>19.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-09</th>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-10</th>\n",
       "      <td>7.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Ozone  Solar  Wind  Temp\n",
       "Date                                \n",
       "1976-05-01   41.0  190.0   7.4    67\n",
       "1976-05-02   36.0  118.0   8.0    72\n",
       "1976-05-03   12.0  149.0  12.6    74\n",
       "1976-05-04   18.0  313.0  11.5    62\n",
       "1976-05-05   28.0  299.0  14.3    56\n",
       "1976-05-06   28.0  299.0  14.9    66\n",
       "1976-05-07   23.0  299.0   8.6    65\n",
       "1976-05-08   19.0   99.0  13.8    59\n",
       "1976-05-09    8.0   19.0  20.1    61\n",
       "1976-05-10    7.0  194.0   8.6    69"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generar copia del df\n",
    "df_bfill = df.copy(deep=True)\n",
    "\n",
    "# Imputar NaNs usando back fill\n",
    "df_bfill.fillna(method='bfill', inplace=True)\n",
    "df_bfill.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0b6e6379",
   "metadata": {},
   "source": [
    "**Métodos de interpolación:**\n",
    " \n",
    "Además de los métodos de imputación ya presentados, encontramos los métodos de interpolación dentro de la función `interpolate()`. Estos métodos usan estrategías más complejas, buscando imputar los valores faltantes siguiendo patrones que surgen desde los datos no nulos. \n",
    "    \n",
    "A continuacón revisaremos estos métodos de interpolación.\n",
    "\n",
    "- **Método lineal (linear):** \n",
    "\n",
    "    Tambien conocido como imputación con valores equidistantes. Este método imputa los valores nulos extrapolando una línea recta entre el último valor observado y el siguiente valor presente después del dato ausente.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "371d7a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Ozone</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Temp</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-05-01</th>\n",
       "      <td>41.0</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>7.4</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-02</th>\n",
       "      <td>36.0</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-03</th>\n",
       "      <td>12.0</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>12.6</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-04</th>\n",
       "      <td>18.0</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>11.5</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-05</th>\n",
       "      <td>23.0</td>\n",
       "      <td>308.333333</td>\n",
       "      <td>14.3</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-06</th>\n",
       "      <td>28.0</td>\n",
       "      <td>303.666667</td>\n",
       "      <td>14.9</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-07</th>\n",
       "      <td>23.0</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>8.6</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-08</th>\n",
       "      <td>19.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>13.8</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-09</th>\n",
       "      <td>8.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>20.1</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-10</th>\n",
       "      <td>7.5</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>8.6</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Ozone       Solar  Wind  Temp\n",
       "Date                                     \n",
       "1976-05-01   41.0  190.000000   7.4    67\n",
       "1976-05-02   36.0  118.000000   8.0    72\n",
       "1976-05-03   12.0  149.000000  12.6    74\n",
       "1976-05-04   18.0  313.000000  11.5    62\n",
       "1976-05-05   23.0  308.333333  14.3    56\n",
       "1976-05-06   28.0  303.666667  14.9    66\n",
       "1976-05-07   23.0  299.000000   8.6    65\n",
       "1976-05-08   19.0   99.000000  13.8    59\n",
       "1976-05-09    8.0   19.000000  20.1    61\n",
       "1976-05-10    7.5  194.000000   8.6    69"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generar copia del df\n",
    "df_linear = df.copy(deep=True)\n",
    "\n",
    "# Imputar NaNs usando interpolación lineal\n",
    "df_linear.interpolate(method='linear', inplace=True)\n",
    "\n",
    "# Visualizar imputación\n",
    "df_linear.head(10)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7f9a9203",
   "metadata": {},
   "source": [
    "- Método cuadrático (quadratic):\n",
    "\n",
    "    Tambien conocido como imputación de valores cuadráticos, es decir, sigue una parábola la unión de los puntos presentes para completar los faltantes. Este método busca suavizar la imputación de los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "7d4922c3",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ozone</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Temp</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-05-01</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>7.4</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-02</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-03</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>12.6</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-04</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>11.5</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-05</th>\n",
       "      <td>26.137476</td>\n",
       "      <td>413.213890</td>\n",
       "      <td>14.3</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-06</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>412.882252</td>\n",
       "      <td>14.9</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-07</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>8.6</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-08</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>13.8</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-09</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>20.1</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-10</th>\n",
       "      <td>3.089517</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>8.6</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Ozone       Solar  Wind  Temp\n",
       "Date                                         \n",
       "1976-05-01  41.000000  190.000000   7.4    67\n",
       "1976-05-02  36.000000  118.000000   8.0    72\n",
       "1976-05-03  12.000000  149.000000  12.6    74\n",
       "1976-05-04  18.000000  313.000000  11.5    62\n",
       "1976-05-05  26.137476  413.213890  14.3    56\n",
       "1976-05-06  28.000000  412.882252  14.9    66\n",
       "1976-05-07  23.000000  299.000000   8.6    65\n",
       "1976-05-08  19.000000   99.000000  13.8    59\n",
       "1976-05-09   8.000000   19.000000  20.1    61\n",
       "1976-05-10   3.089517  194.000000   8.6    69"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generar copia del df\n",
    "df_quadratic = df.copy(deep=True)\n",
    "\n",
    "# Imputar NaNs usando interpolación cuadrática\n",
    "df_quadratic.interpolate(method='quadratic', inplace=True)\n",
    "\n",
    "# Visualizar imputación\n",
    "df_quadratic.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "92272cf4",
   "metadata": {},
   "source": [
    "- Método valores cercanos (nearest): \n",
    "\n",
    "    Imputa con los valores faltantes con los valores cercanos observados. se considera una combinación de los métodos forward fill y backward fill."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "ddf6b58a",
   "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>Ozone</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-05-01</th>\n",
       "      <td>41.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-02</th>\n",
       "      <td>36.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-03</th>\n",
       "      <td>12.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-04</th>\n",
       "      <td>18.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-05</th>\n",
       "      <td>18.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-06</th>\n",
       "      <td>28.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-07</th>\n",
       "      <td>23.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-08</th>\n",
       "      <td>19.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-09</th>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-10</th>\n",
       "      <td>8.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Ozone  Solar  Wind  Temp\n",
       "Date                                \n",
       "1976-05-01   41.0  190.0   7.4    67\n",
       "1976-05-02   36.0  118.0   8.0    72\n",
       "1976-05-03   12.0  149.0  12.6    74\n",
       "1976-05-04   18.0  313.0  11.5    62\n",
       "1976-05-05   18.0  313.0  14.3    56\n",
       "1976-05-06   28.0  299.0  14.9    66\n",
       "1976-05-07   23.0  299.0   8.6    65\n",
       "1976-05-08   19.0   99.0  13.8    59\n",
       "1976-05-09    8.0   19.0  20.1    61\n",
       "1976-05-10    8.0  194.0   8.6    69"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generar copia del df\n",
    "df_nearest = df.copy(deep=True)\n",
    "\n",
    "# Imputar NaNs usando interpolación nearest\n",
    "df_nearest.interpolate(method='nearest', inplace=True)\n",
    "\n",
    "# Visualizar imputación\n",
    "df_nearest.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7d87ef7b",
   "metadata": {},
   "source": [
    "**Graficos de comparación**\n",
    "\n",
    "En Primer lugar graficaremos la data original para ver como se va imputando a posterioridad los valores ausentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "95f0003a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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Q56EiCz0nky8q5Ku/IqtkpWyh1OZV1Wd2JSNJGh8I6+hwOYzmaxgAugdBFgC4YKUSZFUDLQCAO2IhnxJdNJ8BALC7xFM5SdLIuoosj8fo6EhE1xeo5kBvyeSLCtVbkRUs369bDhTNVIKsAwMhDUb86gv5dGOJr2EA6BYEWQDggpVaRRZBFgC4qS/oUyLLezEAoDmLiXKQNbxuRpZUnpNFNQd6TTrXwIysgE+SlOqSA0UzK2n5PEajsaCMKYfRfA0DQPfwub0AAOhF1SArUekJboxxeUUA0JtiIZ8StHkFADRpKVkNsgL33D45EtH/+HBepZKVx9P8tf75i9N66ZXLur2c1sHBsM6dOcl8XXQla215Rla9QVYXVmTt7w/JW/l6PToc1bu3V1xeFQCgioosAHBBNcgqWSmZ644TaADQi6KVGVndMmgcALC7LFaCrNHYvUHW0ZGoMvmS7qxlmn7u8xen9fzLlzS9nJaVNL2c1vMvX9L5i9OtLBloi3zRqmSlkL++rcZosFKRleuOIGt2JaPxgVDt4yMjEU3H0yoUu2OGFwD0OoIsAHDByrrZWMzJAgD3xII+5Yu2awaNAwB2l6VEVtJmFVlRSdL1heZbk730ymWl8/ceekvni3rplctNPyfQLtV/q6E6K7IildaC3VSRdWBdkHV0OKJCyer2cvNhNADAOQRZAOCClXS+1rJgjZZWAOCavlB3baIAAHaXxWROxkiDkfsrsiKSpBuLyaaf+/ZyuqHbATdlKkFWOFBfkBWrVWS536HEWquZlfQ9QdaR6tfwUvNfwwAA5xBkAYALVtN5jfeXL5LXMlRkAYBbqpsozMkCADRjMZnTcCRQO6RWdXAwrIDXo+uLzVdkHRwMN3Q74KZqkBXy1VuR1T0zspZTeWXyJR0YuPu1Va2qvNHC1zAAwDkEWQDQYdZaraTzOjxcvkimIgsA3FMLsrpgEwWAu85fnNbTL76qY899SU+/+CpziFCXpURuQ1tBSfJ6jA4Ph1uqyDp35uSGgCzs9+rcmZNNPyfQLulmK7K64BpsZqXcPnB9RdZ4f0gBn0c3lwiyAKAbEGQBQIel80Xli1aHhsqtClapyAIA18RoLQhA5RDr+ZcvaXo5LStpejmt51++RJiFHS0lNw+ypHJFRysVWWdPTWhfLKCgzyMjaWIwrBeefUxnT000/ZxAu6QrLQLD9c7ICpbvl+yC1oKzq+V2nePrgiyPx+jwUGthNADAOT63FwAAvWYlXQ6uDg2VK7JWqcgCANfQWhCAJL30yuVaNUFVOl/US69cJjTAthaSWT083rfp546ORPXG1UVZa2WM2fQ+20lkC5pdy+p/fuZB/Z3veqjVpQJtlcmXJElBf31n5oM+r/xe0xWHiW4vlyuy7m/beXQkSmtBAOgSVGQBQIdVg6zDlYosZmQBgHtoLQhAkm4vpxu6HahaSuY0Eg1u+rnJ0YhSuaLmE9mmnvsbt5ZlrfTE0aFWlgh0RHVGVr0VWZIUCfi6orXg7EpGXo/RaOzer+UjwxHdXErJWuvSygAAVQRZANBhK6lycLWvPyi/1zAjCwBcVG0tuNYFmygA3HP/KfydbgckqVAsaTmV37K14NGRqCQ1XdHx9o24JOnxw4NNPR7opEZnZEnlA0WJrPutBWdWMtrfF9wwk+7oSDmMXkjkXFoZAKCKIAsAOqxakTUYDqgv5NdqmoosAHBLX9AvidaCQK87d+bkhiqCsN+rc2dOurQi7AZLqfLm9khsqxlZ5Q4M1xeam7Fz8dayHtgX00DY39wCgQ6qVmSFfPUHWdGgV6mc+9dgMytpHdjk4MLRytfwzSXmZAGA2wiyAKDDqkHWQNiv/pCPiiwAcFHI75HXY5SkIgvoaWdPTeiFZx+TtzLHaDDi1wvPPsZ8LGxrKVkOsraqyJoYDMvnMbq+2PgmuLVWF2/G9cSRwVaWCHRMMxVZkYCvK9o7z65kND4Q2nD7keHWqioBAM4hyAKADlsfZPWF/MzIAgAXGWMqbW3c30QB4K7v/Ph+FStzUP7Odz5EiIUdLVXajW01I8vn9ejQUFjXm9gEv7aQVDyV1xNHmI+F3SGdq1RkNTAjKxb0KZVzt7WgtVYzKxkd6N8YZB0eDssYgiwA6AYEWQDQYavpvIyR+kI+9VGRBQCuiwV5LwYgfXhnrfZ7wm3UYzG5fWtBqTwn60YTFVlv31yWJD1xlCALu0O2UJJUrnavVyTgdb0qfiWdVzpf3LS1YNDn1YH+kG4uEWQBgNsIsgCgw1bSefUFffJ4jPpCPq1SkQUAripXZPFeDPS6D+cStd8TZKEei4mspK1bC0rlOVk3FlKylWq/el28GVdf0KcHxmItrRHolHSuKI+RAt76txpjQZ+SLs/ImlnJSJIObNJaUJKOjESaCqMBAM4iyAKADltJ5zUQKQ9s7g/5qQIAAJfFQrQWBFCuyAr4PBoI+5Xg+gx1WErmZIw0FNm+ImstW6jN06rX2zeX9fiRQXk8ptVlAh2RzhcV9ntlTP3/ZiNBr5JZd1sLzqykJWnTGVmSdHQ4SkUWAHQBgiwA6LCVdF4D4XKQ1UeQBQCuiwV9bFoD0IdzCZ0Yi6k/TLiN+iwmcxqKBOTdJmw6NhqVpIbmZCWyBV2eXdUp5mNhF8nkiwoH6p+PJUnRoM/11oLViqyDAxtbC0rliqyFRI7vCwDgMoIsAOiwe4Os8kZJsdRYqxEAgHNiIZ/W2JwAet6HdxJ6cF9MsaCfDUvUZSmZ27atoCQdHYlIUkOtyd6ZWlbJSqeODLayPKCj0vmigr4Gg6yAT9lCSYViqU2r2tnsSkZej9FYX3DTz1e/hm82EEYDAJxHkAUAHXZ/kCWJSgAAcFFfF5wGBuCuRLag6eW0HtofUyzo5doMdVlM7BxkHRqKyGMaq8i6eHNZkvTEYSqysHs0W5ElScmce+0Fby9ntK8vuGVl5dHhclXlzSXmZAGAmwiyAKDDVtKFWpDVX/l1NZN3c0kA0NNoLQjgo7mEJOmBfX2KBX1K5nhPwM4Wk1mNxrYPsgI+jyaGwg1VZL19I64TY9HaXF1gN0jnyjOyGhGtBF9uHiiaXU3rwBbzsaRya0FJukFFFgC4iiALADrIWqvVdL4WYPVXKrKYkwUA7okGfUrmirR5BXrYh3fWJEkP7Y8pSriNOtXTWlCSJkeidVdkWWt18daynmA+FnaZTL6kkL+xbcZqRVbKxcMDMysZHdhiPpYkDYT9Goz4dWOJIAsA3ESQBQAdlMmXlCuW1rUWpCILANxWbfNKBQbQuz6cSyjg8+jIcKQ2wxTYTqFY0nI6r+Ho5nN11js6EtH1hfoqsm4sprSUzOkUQRZ2mXS+qFCjFVnB8v0TWXdaC1prNbOc0fg2FVmSdHQ4wowsAHAZQRYAdNBKuhxY1VoLVoIsKrIAwD2xIPMKgV734Z01HR+Nyuf1KBogyMLO4qm8rJVG6qzIWknntZzK7Xjft2/GJUlPHB1sdYlAR2XyzbQWrBwmcuk9dzVdUDpf3La1oCQdGYnqBjOyAMBVBFkA0EH3B1l9tdaCVGQBgFtilfdiNq6B3vXBnYQe2t8nqfyekKLdKHawlCyHUiM7zMiSpKMjUUmqq73g2zfjigV9enBfX2sLBDos01RFlrtB1sxqWpK2bS0olSuybi9nlC+WOrEsAMAmdgyyjDE/Z4yZM8a8u+62YWPM7xpjPqz8OrTuc88bYz4yxlw2xpxp18IBYDfaOshi8xQA3FKtyOK9GOhNyWxB08tpPbgvJunuewLtRrGdxWRWkuqckRWRJN1Y3Lmi4+LNZX3b4QF5Paa1BQIdlm6mIsvl99uZ5Ywk7dha8MhIRMWS1XQ83YllAQA2UU9F1s9L+uJ9tz0n6SvW2gclfaXysYwxH5f0w5IeqTzmXxljGvsuBgB72MYgqzIjK01FFgC4pTYji4osoCd9NJeQJD1YrchyuUIAu8NiolKRVceMrMPDERkjXV/YviIrlSvo/dk1PcF8LOxC6VxR4UBzM7KSLs3ImlkpB1kHB3eekSVJN5aYkwUAbtkxyLLW/oGkpftu/j5Jv1D5/S9IOrvu9l+21mattdckfSTpU84sFQB2v/uDrIDPo5DfozU2SgDANbFg+T2Z1oJAb/qwFmRVKrJCzM3DzqqtBeupyAr5vTrQH9qxIusbt1ZULFmCLOxKmUJJQX9jE0zcnpE1u5KWx0hjse0D6Wp70Jt1VFUCANqj2RlZ+621M5JU+XVf5fYJSbfW3W+qctsGxpifMMZcMMZcmJ+fb3IZALC73B9kSeWqLGZkAYB7qqeB2bQGetOHd9YU8HpqJ+6rra4It7GdxWROxkhDEf/Od5Y0ORrV9R02wS/eikuSHj882OrygI4qlqxyhVLDrQXDfq+MkZI5dyqybq9ktL8/JJ93++3RfX1BBX0e3ahjzh0AoD2aDbK2slkT500n5Fprf8Zae9pae3psbMzhZQBAd6oGWdWWguXf+7TK5ikAuKavUpFFdSzQmz64s6bjY9HaRmYfQRbqsJTMajDs33EDvOroSHTHTfC3byzr+GhUQ3VUeQHdJJMvB1GNBlkej1HE73WxIiuz43wsqbzOI8MRWgsCgIuaDbLuGGMOSFLl17nK7VOSDq+73yFJt5tfHgDsLavpvPpCvnuGN/eF/MzIAgAXUZEF9LYP5xK1+VjS3YosZmRhO4uJXF1tBasmRyJaTOa0ukUnBmutLt6M6xRtBbELVYOsUINBllR+z03l3Hm/vb2S1oE6gixJOjoS0U0qsgDANc0GWb8p6Ucrv/9RSf913e0/bIwJGmOOSXpQ0h+3tkQA2DtW0vl72gpKUn/IpzU2TwHANT6vR2G/V4kshwqAXpPMFjQVT+vBfbHabbFKkMX1GbazmMxpZIe5OuvdnbGz+Ub4zaWUFpM5PXF00InlAR2VbrIiSyoHWYls51sLWms1u5LRgYFwXfc/OhLVzaWUrN208RQAoM18O93BGPNLkr5D0qgxZkrST0l6UdKvGmN+XNJNST8gSdba94wxvyrpm5IKkv6WtdadRrcA0IU2D7L8ur2cdmlFAABJioXc2UTZDc5fnNZLr1zW7eW0Dg6Gde7MSZ09tekY3IbuC3SDK/MJSdJD+zcGWbQWxHaWkrl7AtCdTI6WZ7BdX0zq0YmBDZ+/eHNZknTqMBVZ2H1qFVmBZoIsr1IuvN+uZgpK5YoNVWSl80XNr2W1r7++xwAAnLNjkGWt/ZEtPvWFLe7/05J+upVFAcBetVmQxYwsAHBfX9DHpvUmzl+c1vMvX6qdtJ5eTuv5ly9J0oaAqpH7At3igzvlIIvWgmjUUrKx1oJHhitB1kJy08+/fTOuaMCrk+N9m34e6GaZfEmSFPI13vgpEnDnGmxmpXyYtJ4ZWdLdr+EbSymCLABwwY5BFgDAOSvp/IaTm30hn9a26JUPAOiMaNCnBO/FG7z0yuVaMFWVzhf1D37jkt66Eb/n9pffntr0vi+9cpkgC13rw7k1BbweHa1sUEpSwOdRwOfRGkEWtlAsWcVTOY00EGRFAj7t7w/q+hatBd++Gde3HR68Z5YusFvUWgs2UZEVC/o0v5Z1ekk7mlnJSFJDrQUl6cZiSk9ODrdtXQCAzRFkAUAHbdVaMJMvKV8sye9tdnQhAKAVMSqyNrVV69tkrqgvXZrZcFsjzwF0gw/vJHR8LCrffddgfUEfFVnYUjyVk7VqaEaWVN4Iv7G4sSIrnSvqWzNr+ht/8oRTSwQ6Kp1rfkZWJOB15f12thZk1VddNTEYlsdINzf5GnYSbZoBYHMEWQDQQVu1FpTKA8UbaU8CAHBOLOTTraXNT8n3soODYU1vEkRNDIb12nPP3HPb0y++uul9Dw7Wd9IZcMOHc2v6tkODG24vV2kSZGFzS8mcJDV87T45EtFXL89vuP2dqWUVS1anjgw6sTyg42ozspoIsmJBn5I5F1oLLqflMdK+vvoC6YDPo4ODYd1o4/UibZoBYGsc/QeADsnki8oVSurfEGSVP15N09IKANzCjKzNnTtzcsO8i7Dfq3NnTm563/tPYof8nk3vC3SDVK6gW0tpPbR/40yicpXm5lWGwEKi3AatkdaCUrkia34tu6H65O2by5KkU0eGHFkf0GnpFoKsSMCnpAvvtzMrGe3rC22oyN3O0ZGIbmzRHtQJW7V0fumVy217TQDYLQiyAKBDVipB1YbWgpWP1zj1CwCuiYUIsjZz9tSEfvhThyVJRuVKrBeefWzTU8FnT03ohWcf08S6CqwffvIwJ4jRta7MldtD3T+/VKoGWRwywuZqFVmxRiuy7s7YWe/tm3EdG43SnQG7VqalGVleJXMFWWudXta2ZlczGq+zrWDVkeGobraxImurdsy0aQYAWgsCQMdsFWTdbS3IZgkAuCVWmYdjrZUxxu3ldBVjjEJ+j975qTMK+LY/B3f21ITOnppQsWT1J/7pq7q2QLtGdK8P7qxJkh7crCIr5NP8WrbTS8IuUQ2yRqKNzciaHI1Ikm4sJvXxg/2SJGutLt5c1uceHHV2kUAHtTIjKxr0ydpy5VEk0LltytvLm1fkbufoSERLyZxWM3n1h/w7P6BBW7V0pk0zAFCRBQAds1OQtUpFFgC4JhbyKV+0yhZKbi+l67x1I65vOzS4Y4i1ntdj9AOfPKQ/+HB+0w0ZoBt8OJeQ32t0dCSy4XNR2o1iG4uJcpA1FGlsI/topSLr+rqKrKl4WguJrE4dpa0gdq9M5fop5G98mzESLP883Mn3XGutZlYyOjDQWEB0dLj8/eJmm9oL/tjTkxtu26qlMwD0GoIsAOiQldQWrQWrM7KoyAIA18Rc2ETZDZLZgt67vaonJ4cbfuwPnD4sa6VfuzDVhpUBrfvwzpqOj8bk32Q+SowgC9tYTGY1GPE3NFtHKv+7Go0FdWMxWbvt7ZtxSdITRwadXCLQUdWKrJCvudaCkpTq4Jys1UxBqVxRBxptLThSrapsT5A1u5KRkTTeX15X0OfZsqUzAPQagiwA6JAtZ2SFmJEFAG6rBVm8F9/jG7eWVSxZfXKy8UqBw8MRPf3AiP7LW7dUKnV27gVQjw/m1vTA/o3zsaTyxirvB9jKUjLX9DyryZGIrq8Lsi7eXFYk4NXJBlucAd0kky8q6PPI42m8PXO1nWAnDw/MrmQkqeEZWdWqyhtLyR3u2bhUrqBfvXBLf+YTB/S1v/8F/bXPHpOV9MVHxx1/LQDYjQiyAKBDtgqyYszIAgDXUZG1uTevx2WM9MSR5lpe/dCTRzQVT+v1K4sOrwxoTSpX0FQ8rYf2bR4exIJ+pfNFFQlhsYnFRE6jDc7Hqjo6EtX1dfMD374Z1ycODTRc3QV0k0y+qFAT87Gku9dgqVznKrJmVsptjw8ONhZkxYI+jUQDbWkt+F+/flurmYL+6lOTkqSnTowqVyjp7Rtxx18LAHYjrpQAoEOqQVb/fUGW12MUDXipyAIAF909VMB78XoXbizp5P6+DYcw6vWnPr5fA2G/fvnNmw6vDGjNlbmkrJUe3KoiK0S4ja21WpE1u5pROldUJl/UN2+vNn1YAOgW6XxR4SaDrEig/LhkB99vZ2oVWY3NyJLK7QWdbi1ordUvvH5dHzvQr9OVeXlPHhuW12M4DAQAFQRZANAhK+m8+oI+eTdpt9AX8ms1TUUWALilL1gOati0vqtYsrp4c1mnm2grWBXye/X9pyb0O+/dUTyZc3B1QGs+nFuTJD20TWtBifcEbG4xmdNwrLkg6+houTXZzaWULk2vqFCyBFnY9dL5ksKB1iqykrnOBlnGSPv6Gq+sPDoc0c0lZ4OsP762pPdn1/SjnzkqY8r7BbGgT992aECvX1lw9LUAYLciyAKADllN5zdUY1X1h31UAQCAi6rVF508Ddzt3p9dVSJb0Omjwy09zw+ePqxcsaTzX592aGVA6z64k5Dfa2rzTu4Xq4TbvCfgfsWSVTyV02gLFVmSdH0xWWsZ9viRQaeWB7iiOiOrGZFg56/BZlfS2tcXlL+Jlp5HRqK6vZJWtuBcK8RffOOGBsJ+fd/jE/fc/tSJUX1jaqXjhyrOX5zW0y++qmPPfUlPv/iqzl/kGg6A+wiyAKBDVtL5LVsz9YX8WstSkQUAbolWqi/W2LSuuXC9vMHaSkWWJH38YL8emxjQr7x5S9Yybwjd4aO5NR0bjW65iVl7T+CgEe6znMrJWjXdWvDocDk8vbGY1Ns34zo6EtForLl5W0C3yOSLzVdkBapBVidnZGWaaisolSuyrJWm4mlH1jK7ktFvvzerH3ry8Ia/w6dOjKhYsnrz2pIjr1WP8xen9fzLlzS9nJaVNL2c1vMvXyLMAuA6giwA6JDtgywqsgDATbXWgrwX17x5fUkHBkKaGGxuo2e9H3rysN6fXdM7UysOrAxo3Qd3Enpwf9+Wn++jShNbWKq0SR1uMnwaiPg1FPHr2kJKb99cpq0g9oR0roUZWUF3ZmQd6A819dijlarKmw7NyfpPf3RDJWv1l7796IbPPXF0SAGfR6991Ln2gi+9clnp/L2hYjpf1EuvXO7YGgBgMwRZANAhO1VkMSMLANwT8nvk9RglqI6VVB46fuF6XJ88OlSb1dCK7338oEJ+j37lwi0HVge0Jp0r6lY8pQf3bT4fS5KilVZXzMjC/RYS5SBrpMmKLEk6OhLVG1cWNL+W1RO0FcQekCkUFWoyyPJ7PQr4PErmOleRNbuS0YHB5oKsI5Ug68ZisuV1ZAtF/dIf39QXHt5Xe971Qn6vTh8d0utXFlt+rXrdXt680myr2wGgUwiyAKBDtguy+qnIAgBXGWMUC/qoyKqYXk5rdjWjJydbm49V1R/y6888ekC/9fXbSndwowrYzJX5hKyVHtqmIitGkIUtVCuyRmLNB1nHRqO6XqnmOEVFFvaAViqyJCka8HasIms1k1ciW9CBgeaCrLFYUJGAVzeWWq/I+m+XZrWQyOmvfGZyy/s8dWJE35xZVbzy3tNuB7eoxN/qdgDoFIIsAOiQlXReA5FtZmRlCswOAQAXxYI+ZmRVvHWjPB/rk0ed22D9wScPay1b0JcvzTj2nEAzPpxbk6RtK7JqQRbhNu6zlMxKan5GliSlc3f/Xf3Ef7jA7Bnsepl8qemKLKlcBZvMdeb9dnYlI0k60OSMLGOMjgxHHGkt+AtvXNfx0aj+xAOjW97nMyfKn/va1c5UZZ07c1J+773V+GG/V+fOnOzI6wPAVgiyAKADMvmisoXStjOycsWSsoVSh1cGAKjqC/mYh1Px5vUlxYI+PTy+dcVKo7792LAmRyK0F4TrPriTkM9jNDka3fI+1daCvCfgftXWgkOR5oKs8xen9ZX352of317O6PmXLxFmYVfL5IsK+ZvfYowGOncNNlMLspqryJKkI8ORliuy3pla1sWby/rLnzkqj2frNs6fODSgaMDbsfaCZ09N6PS6g0wHBkJ64dnHdPbUREdeHwC2QpAFAB2wUpl/1b9Va8HK7asZZrMAgFuiQR9txCouXI/r1JFB+bzO/bhgjNEPPnlYf3xtSVfnE449L9CoD+8kdGw0Kv82/779Xo+CPg/vCdhgKZnTQNi/7b+f7bz0ymXli/d2YUjni3rplctOLA9wRTrfYmvBoFfJbGdaD89UZj2NtxBkHR2J6OZSSqVS8x1VfuH1G4oGvPrznzy07f38Xo8+dWxYr11ZaPq1GnV7JaOAr/we9+9/7ElCLABdgSALADqgGmRtNyNLEnOyAMBFzMgqW0nndfnOmk4fdWY+1np//olD8nqMfvXClOPPDdTrw7m1bedjVfWFCLex0VIy19J8rNuVTfR6bwe6nbW2HGQFdkdrwZmVjIyR9ve3UJE1ElWuUNKdtUxTj19MZPVb79zWs08cUl9o8z2C9Z46Maqr88laW8R2moqndGMxpe/62H5J6shrAkA9CLIAoAN2CrL6CLIAwHWxEDOyJOnizbislU5POjcfq2pff0ifPzmmX397SoUi7XTReZl8UTeXUnpgm/lYVVRpYjOLyaxGWpiPdXBw87k8W90OdLtcsSRr1dqMrI62FkxrLBZsuqpSko4ORyRJN5qck/XLb95SrlDSX/nM0bru/9QDI5KkN662vyqr2sLw+ytVWHdWCbIAdAeCLADogJXUTkFWpbVgmtaCAOCWPiqyJJXbCno9Ro8fHmzL8//Qk0c0v5bVVy/Pt+X5ge18NJeQtaqrIisWZG4eNlpM5DTcQpB17szJDS3Ywn6vzp052erSAFdkcuWDKS0FWUFf51oLrmRamo8lSZMj5RmLN5sIsgrFkv7T127o6QdG9GAd34sk6WPj/RqM+PX6R+2fk/XGlUWNRAP6Ew+OSpJmV7Jtf00AqAdBFgB0wM6tBcu3U5EFAO6JUX0hSbpwY0mPHOxXNOhry/N//uSYxvqC+pU3b7bl+YHtfDi3Jkl6cH99FVlcm+F+S8mchqPBph9/9tSEXnj2MU0MhmUkTQyG9cKzjzGDBrtWOl8OoFqekdWh1oKzKxkdGGitAvLgYEg+j9GNpWTDj/3v35rT7ZWM/spnJut+jMdj9JnjI3r9yqKsbX4u106stXr9yoI+c2JEIb9Xw9GAZqnIAtAl2vPTKQDgHvW3FqQiCwDcEgv5lMoVVSxZeT3G7eW4Ilco6eu3lvUjnzrSttfweT36c08c0r/7H1c1t5rRvhZmVACN+vBOQj6PqZ2m305f0Nf0/BPsTaWSVTyV02gLM7KkcphFcIW9ohZkBZo/Kx8N+pTqYEXW0w+MtvQcPq9HE0PhploL/uIb1zUxGNYXHt7X0OOeOjGi//burG4tpXVkJNLw69bj6kJSd1aztb+f/f0hWgsC6BpUZAFAB1SDrP7Q5ucHmJEFAO6LVSqQOnUiuBu9d3tFmXxJT04Ot/V1fvD0IRVLVr/29lRbXwe43wd3Ejo2GlXAt/OPwlHajeI+y+m8SlYttRYE9ppMJcgK+VqZkeVVrlhSrtDe+ZlrmbwS2ULLrQUl6chwRDeXGguyPrizptevLOovfvqIfA3O6PrMiXK49NqV9s3Jqs7HeupEeSbXgYGQZlcIsgB0B4IsAOiAlXResaBvy4vVaMAnY6RVKrIAwDXVIKuXN67fuhGXJJ0+OtTW1zk+FtOnjg3rv1yYamuLHOB+H82t1dVWUCpXadJuFOstJsqzYgiygLuqFVmhQGszsiQp1ebDRNVQ5sBga60FJenoSKThiqxffOO6Aj6PfvjJxivfT4xFta8vWAub2uGNKwuaGAzryHC54ouKLADdhCALADpgNZ3fsq2gVO553cccBgBwVaxSHdvLG9dvXl/SkeFIR9r9PTAW1bWFpI4//2U9/eKrOn9xuu2vid6WyRd1YymlB/f11XX/Pubm4T6LyZwkaTTW/IwsYK/J5ByYkRXozDXY7WqQ5UBF1tHhqFbSea2kdj6Mev7itD7zwlf0H792Uz6P0R98MN/w6xlj9NSJEb1xZaEth4BKJas3rizqMydGZEy5xfZ4f0iLyZyyhc60fQSA7RBkAUAHrKTz6t8myJKkvpCfiiwAcFG1IqtXDxVYa/XWjXjbq7Gk8obOy5XgykqaXk7r+ZcvEWahra7MJ2St6q7IigZ9yuRLKhTb2+oKu8dSJciiIgu4K1MJOUKtBFm1iqz2BiazK2lJ5YCmVdU5VTeWktve7/zFaT3/8iXNVEK0VK7Y9DXPUw+MaiGR04dzicYXvINvza4qnsrX2gpK0vhAObSfW806/noA0CiCLADogJV0XgPhzedjVfWFqMgCADf19XhF1vXFlBYSOZ1u83wsSXrplcvK5O8NB9L5ol565XLbXxu968M75Y2/h/bXV5FVm5uX5SQ6yqoVWSMEWUBNOlf+ft5KRVYkWH5su6/BZlYyMqbcMq9VV+bL31O+91++tqGyPJUr6I+uLurf/v4VPffyO7X2i1XNXvNUQ6bXP3J+TtYbtflYo7Xbqn9PtBcE0A2231UFADhiJZ3X8bHotvfpD/m1mqYiCwDcEguWK2d7dUbWhetLkqTTk+2vyLq9nG7odsAJH86tyecxmhzZ/pqsqlalmc1rILJ9ZT16Q3VG1hBBFlBTDWlaCbKq77epNh8cmFnOaDQWVMDX2rn+8xen9S++8mHt4+nltM792jf0K2/e1HK6oA/urKlY2r79XzPXPIeGIjoyHNFrVxb1V58+1vDjt/P6lUUdH4tqfF3bxervq9VkAOAmKrIAoANWdpiRJUn9YSqyAMBNd2dk9eahggvX4xoI+/XAWH1t11pxcIsh61vdDjjhgzsJTY5G697ArL4nUJGFqqVkTgNhv/xetlKAqky+2lqw+a+LSKBDFVmrGUfmY21WWZ4vWn3t6pJGYwH9re84oZ/7q6f11v/jOzXh8DXPUydG9LWrizsGZY3IF0v6o6uL97QVlO62YKQiC0A34OoLADqgniCrL+TXWo9ungJAN4jVBo335qb1hRtLOn10SB6PaftrnTtzcsPJ7bDfq3NnTrb9tdG7PppL6MF99Qe11ZktvRpuY6PFZI62gsB9akFWwIGKrFybg6zltCNB1nbVVP/hx79df/dPndQzD+/XSCzo+DXPZ06MaC1T0Hu3V5p6/GYuTa8omSve01ZQkgbCfgV9Hs1SkQWgCxBkAUCbZfJFZQulOoIsKrIAwE3R6nyGHnwvXkrmdGU+qU92oK2gJJ09NaEXnn1ME4NhGUkTg2G98OxjOntqoiOvj96TyRd1YzGpB+ucjyXd3Vjt1XAbGy0mshomyALukc613lowEqhWwLb3Gmx2JaMDA61XfzdSWe70Nc9nqnOyKjOtnFCdufXp4/dWZBljND4Q0iwVWQC6ADOyAKDNqnOvBiLb/9DbH/JrLVOQtVbGtP80PADgXj6vR2G/tyerL966EZckPTk53LHXPHtqguAKHXN1PqmSVUMVWbUgq4Ph9vmL03rplcu6vZzWwcGwzp05yddJF1lK5uqesQb0ikyhKK/HtNRysxMHB9Yyea1lC/fMgGrWuTMn9fzLl2rzwaTtq6ycvObZ1xfSg/tiev3Kon7yT55w5Dlfv7Kojx3o3zSo398forUggK5ARRYAtNlKNciqoyKrWLJK5Tj1CwBuiYV8bZ/P0I0uXF9SwOvRYxMDbi8FaIsP59YkSQ81UpEV6kyFQNX5i9N6/uVLml5Oy0qaXk7r+Zcv6fzF6Y68Pna2lMxpJBZ0exlAV0nnSi1VY0nl+Voe097WgtX2eE60FnS7svzpB0b15rUl5Qqlne+8g0y+qAs34hvmY1WN91ORBaA7UJEFAG1Wf5BV/vxaplCbyQAA6Ky+YG+2eX3z+pIeOzSgUIsbUUC3+vBOQl6P0eRopO7HVOfmrXUoyHrplcv3nO6XpHS+qJdeuUxVVhcolWw5yKK1IHCPdL7Y8vWDMUbRQHsPE83UgqzWWwtK7laWf+bEiH7+9ev6+q1lfepYa9X0b9+MK1co6ekHtgiyBkK6816WzjEAXEdFFgC0WSMVWZK0mum9llYA0C16sSIrky/q0vSKTh/tzHwswA0f3FnT5EhEQV/9m63VuXmdqsi6vZxu6HZ01nI6r5IVM7KA+2TzRYX8rW8vRoM+pdrYWtDJiiy3ffrYiIyRXr+y0PJzvXFlUV6P2bK99P7+kHKFkuIp9ikAuIsgCwDarN4gqz9crcjiAhEA3BIL+jq2ad0t3plaUb5odbqD87GATjl/cVpPv/iqfuebdzS9nG6oTZ/P61HI7+lYuH1wcPMqga1uR2ctJbOSpJEYQRawXjpfbLm1oCRFgl4l2tha8PZK+VDA/v7dH2QNRPx69OCAXr+y2PJzvX5lUZ84NFDrEHO/8crfVzUIBAC3EGQBQJs1XpHVWxuoANBNoj3YWvDCjSVJ0iepyMIes37mlCRl8qWGZ07Fgv6OBVnnzpzcsBkc9nt17szJjrw+treYyEmSRqLMyALWS+eLCgdaD7JiQZ9SbXy/nV3JaDQWVMC3N7ZCnzoxoos340q3MGM7kS3oG7eWt5yPJUnjA+X3vDvMyQLgsr3x7g0AXawaZPWHtp97Vf18r22gAkA36Qv2XmvBC9fjOjEWpV0W9pztZk7VKxb0KtGha7Ozpyb0wrOPyespzyDxe41eePYx5mN1iaVkOcjivRK4V8aBGVmSFAl4lWxja8GZlYwODu7+aqyqz5wYUb5oaweSmvHmtSUVSlZPnRjd8j7jlZliswRZAFxGkAUAbbaSzisW9Mnn3f4tt1rKv5qmtSAAuKXXZmSVSlZv3YhvORcBvaHafu/Yc1/S0y++2lDFUjdzYuZUp98TvvjouErWyhgp5PPq+x4/2LHXxvYWKkEWrQWBe6XzJUeCrFjQp2QbWwvOrKRrbfL2gk8dG5bPY/TaR823F3z9yoICPs+2Vfn7+oIyhtaCANxHkAUAbbaSzu/YVlCS+kPVGVm9s4EKAN0mFvQpkSnIWuv2Ujrio/mEVtJ52gr2sPXt96yk6eV0w+33upUTM6diHa7SvLaQlLXSE0eGtJYtaKHSzg7uW6r8vxiKEGQB62VyRYX9rW8vRgLtnVM6s5LRgYG9E2RFAj6dOjKoN64sNP0cr19Z1CePDG0bRPq9Ho1Eg7QWBOA6giwAaLPVdF79dQRZIb9HPo/RWoaKLABwSyzkU6FklS2U3F5KR1y4HpckKrJ6mBPt97qVEzOnquF2p1yZT0iSvvCxfZKk64vJjr32btaJqsKlZFb9Id+ema8DOCWdL254r21GNOhTsoV5T9tJZAtayxR0oIGDDLvBcCSgb0ytNPXeF0/m9M2Z1W3nY1WNDwRpLQjAdVyBAUCblSuytp+PJUnGGPWFfFRkAYCL+oK9Na/wwvUljcYCOjoScXspcIkT7fe6VXXm1MRgWEbSxGC44ZlT7W51db8rc0kZI33+ZDnIurZAkLWTTlUVLiZzGokFHX1OYC9wakZWNOBtW0XW7Er5e9peqsg6f3FaX/1gXpKaeu/7o2uLslZ66oE6gqz+EK0FAbhu551VAEBLVtJ5HRuN1nXfvpBfq1RkAYBropUgK5ktaKxv729YXrgR1+mjwzLGuL0UuOTgYFjTm4RWjbTf62ZnT000FFzdL+pCRdbEYFgP7IvJ5zG6TpC1o+2qClv5f3+/xUROw1HaCgL3SzsVZAV9SuWKKpWsPB5nr0tmKiHMXpqR9dIrl5W7r4NAI+99r320qEjAq08cGtzxvvv7Q3rrRrzZpQKAI6jIAoA2q3dGliT1h6nIAgA3xSpBVidn4rjh/MVpffp/+4puLqX0xtWFPTEPCc05d+akgve1Sgv7PQ2139vLYqHOzsi6upDQibGY/F6PDg9HaC1Yh05VFS4lcxohyAI2yOSLCgecCLLKz5HKO99ecGa5HGTtlUMaUuvvfa9fWdCnjg3L7915a3i8P6R4Kq9MG/7fAEC9CLIAoM0aCbL6gn5mZAGAi2Khvd9asNqGqzrrYCVdaEsbLuwOZ09N6Nkn7j25/ROfO+5oJctuFgv4lC2UlC+2f25eqWR1ZS6pE2MxSdLkSERX5wmydrLVxrTTG9bl1oIEWcB6hWJJ+aJVyOdMRZYkpdpweKBakbWvf+9U27fy3ndnNaMr88m65mNJ0v5KS8Y7zMkC4CKCLABoo2yhqEy+VH+QxYwsAHBVX7D8fr2XK7K2a8OF3pTNlzQSDejr//C75PUYFUrW7SV1jWq43a65LevNrmaUzhd1fKzcknpyNKobiylZy/+P7Zw7c1J+771tyMJ+r6NVhaWSVTxFa0HgfplKa7twoPXtxXZVxZ+/OK1/+wdXJEnP/L9+f88c3Dl35qTCm7R0/IHTh3Z87BtXFiVJT50Yreu1qi0ZmZMFwE0EWQDQRivpcnVV/a0F/VpNU5EFAG6pblonsnv3vbhTbbiwO1hr9fqVRX36xIgGIwF98siQvvr+vNvL6hrVCoFOHDSqVl9VK7KOjUaVzhd1ZzXb9tfezc6emtCf/cSB2scTg2G98OxjjlYVrqTzKpashqN7p5oDcEI6Vz4Ys1mg0qhIoHpwwLn2ddUq9FRlndPL6T1ThX721IReePYxTQyGZVQOm0aifv37167r8uzato99/cqCBsJ+ffxAf12vNV6pyJqlIguAi3zNPtAYc1LSr6y76bikfyhpUNJfk1T96efvW2u/3OzrAMBuVg2l+qnIAoBdoXYaeA+/Fx8cDGt6k9BqL82NQP2uLiQ1u5rR05VT2Z9/eJ/+6W+/r9mVTG3jqpf1Vd4Tkrn2vydcmU9Ikk7sK1dkHRst/3ptIcn/ix0cGopIkh6bGNBv/U9/wvHnX0zmJEmjtBYE7lGdmRR0IMiqzshy8v12uyr0vdBC9+ypiXv+HLeWUvrz/+Z1/eWf/SP92k8+pSMjkU0f9/qVRX3m+Ig8HrPp5++3v5/WggDc13RFlrX2srX2cWvt45I+KSkl6Tcqn/7n1c8RYgHoZY1WZPWF/ErkCirR0gcAXNFXnZG1h1sLbtaKxuk2XNg9Xq+1FyrPyfj8w2OSpN+7POfamrpJtIPh9pX5hPqCPo3FylU/kyPlIOv6InOydhJPla+5Z9rU9mqpEmTRWhC4VzXIcqIiKxpwvpVrr1WhHx6O6D/8+LcrVyzpL/3sH2luk+Dp5mJKU/G0nnqgvvlYktQf8ins92p2hQphAO5xqrXgFyRdsdbecOj5AGBPaLi1YMgna/f2BioAdLOgzyOvx3RkHo5bzp6a0D/8sx+rfdyONlzYPd64sqCDAyEdrZzaPrm/TwcGQvoqQZak9e1GO9Na8Pi+mIwpn5A/OBhWwOvRtQWCrJ3EU+WgaSGRVa4ys8dJi4ny5i1BFnCvtJNBVq0C1rnWgltVm+/lKvSH9vfp53/sU1pIZPWXf/aPtVx5f6x6/cqCpLsHWOphjNH4QIiKLACucirI+mFJv7Tu479tjHnHGPNzxpihzR5gjPkJY8wFY8yF+Xl6sAPYmxoPssr3W8vs3dksANDNjDGKBX17urWgJD1+uHyJ/n/8hSf02nPPEGL1qFLJ6o0ri/rMidFaeGKM0ecf3qc//HBB2YJzm4m7Va3daAeCrCvzCZ0Yi9Y+9nqMjoxECLLqsJy6e+3cjo3Wu60FmZEFrJfJl4PjkJOtBR18v+3VKvTHDw/q3/2V07q2kNSP/fyb9/ydvn5lUWN9wdo8xnrt7w8yIwuAq1oOsowxAUnfK+m/VG7615JOSHpc0oykf7bZ46y1P2OtPW2tPT02NtbqMgCgK62kGm0t2LmB4gCAzcWCvj1fGTsVL7fUOTS0d08kY2fvz64pnspvOJX9+ZP7lMwVdeF63KWVdY9qkNXuKs1EtqCZlcyGjcVjo1FdJ8jaUTyVU9BX3t5oR5BVbS04FKEiC1ivVpEVaP2cfLQN77dnT03ohWcf08RgWEa9VYX+9AOj+hc/ckrfuLWsn/yPbylbKMpaq9evLOqpEyO1Ayz1OjAQ1myb2rcCQD18DjzHn5b0trX2jiRVf5UkY8y/k/T/c+A1AGBXWkmXL8L7G5iRJRFkAYCb+kJ7vyJrKp6SRJDV66rthT5zX5D19AMjCng9evX9OT39wKgbS+sa1Y3Vdl+bXZsvh1XrK7KkcpD1+x/Mq1Sy8nga23TsJfFkTifH+/TO1Epb5mQtJXPqC/kU8DnV1AbYG9KVNoBOVGT99/dmJUn/5Evf0r9/7brOnTnpSOB09tRETwRXm/nio+P6p3/uEzr3a+/oB/7N65pdyWohkdXvX57X+YvTDf297O8PaW4tw/cjAK5x4irsR7SuraAx5sC6z32/pHcdeA0A2JVW0nlFA175vfW93VYrslbTtBYEALfEgr6OtBFz062ltMJ+L/NeetzrVxZ1bDS6YVZIJODTtx8fZk6WOtda8OpCQpI2VGRNjkSVK5R0eyXd1tff7eKpvD423i9JbakYWEhkNcL7JbBBtQVtq0HW+YvT+gfn36t9PL2c1vMvX9L5i9MtPS+kHzh9WGcfP6h3plY1t1ae97eczjf89zveH1S+aLV038wtAOiUloIsY0xE0ndJenndzf+7MeaSMeYdSZ+X9HdaeQ0A2M1W0vm62wpKdyu31rIEWQDgllho7wdZU/GUDg2FG24rg70jXyzpj64ubjns/ZmH9+nqfFI3Fnu7rZ3XYxT2e9veWvDKXEIeIx0Zidxz++Ro+WPmZG0tky8qnS/qyEhEkYC3LTNclpI5jTAfC9igWpF1/xyqRr30yuVam8Lac+eLeumVyy09L8re3KRVcKN/v+MDIUntOSwAAPVoKciy1qastSPW2pV1t/1la+1j1tpPWGu/11o70/oyAWB3Wknn624rKDEjCwC6QbQHKrKm4mnaCva4S9MrSuaKeurE5q0DP39ynyTpq+9TldWJcPvKfFJHhiMK+u7dDD42Wm41yJysrS1XZtIORvwaHwi1ZZN1KZmjghXYRG1GVotB1u3lzatOt7odjXHi73d/fznIasccQgCoBw2eAaCNVhusyCLIAgD39QV7Y0bW4eHIznfEnvXGlUVJ0qePD2/6+cnRqI6NRvXVy/OdXFZX6gv6lMgWd75jC67MJza0FZSk/X0hhf1eXVtItfX1d7OlZLnN1XAkoPH+kGba0IZxIZGjtSCwiUy+JEkKB1oLsu5vcbvT7WiME3+/tYosgiwALiHIAoA2arS1YNDnVcDnYUZWlzp/cVpPv/iqjj33JT394qv0bAf2qL0+I2slnddqpkBFVo97/cqCHh7v27Zd2udP7tMbVxeVyu3dr4d6RIM+JTLtuzYrlqyuLSR1Yt/GIMvjMTo6EtH1Hm/xuJ3lyryWwUhA4wMh3VnNOvr8pZJVPEVFFrCZakVW0Nfa9uK5Myc3VHWF/V6dO3OypedFmRN/v2OxoDxGukNrQQAuIcgCgDZqNMiSpP6QX6t7vBJgNzp/cVrPv3xJ08tpWTGAGNjLYiGfUrmiiiXr9lLaYjperlY4NERFVq/K5Iu6cD2+ZVvBqs8/PKZcoVSr3upVsaBPyTZWZN1eTitbKOl4pY3g/Y6NRmktuI14pbXgUNSvAwMh3VnNOPr+vZrJq1iyzMgCNpHJFxXye1qeuXn21IReePYxTQyGZSRNDIb1wrOP6eypCWcW2uOc+Pv1eT0ajQWpyALgGp/bCwCAvay5IMuntTae+kVzthtAzA9YwN4SC5YvkRPZQsPv4bvBrXi5RRkVWb3r4s1lZQslPXViZNv7ferYsCIBr756eU5f+Nj+Dq2u+0SDPk23cU7LR/MJSdq0Iksqt3n83W/eUaFYks/LWdT7xSsVWUORgMYHwiqUrBYTWe2rzHNp1WKldSGtBYGN0rliy/Oxqs6emuDnqjZy4u93fCCkGSqyALiEq2AAaJNcoaR0vtjwJmhfyMeMrC7EAGKgd1TnFe7V9oJTVGT1vNevLMjrMfr2LeZjVQV9Xj39wKi++v68rN2bFYr16Av5lGzj+8HV+XK11WYzsqRyRVahZGtfu7jX3daCfo33Oz/DZTFRmcFFkAVsUK7IcibIQvfb31+uegUANxBkAUCbrFTmXA1EGg2y/FqlIqvrMIAY6B3RakXWHj1UMBVPKRrwaqjB70/YO16/sqjHJgbUF9r538AzD+/T9HJaH84lOrCy7hQNetsabF+ZT2gw4t8yKDlWaTl4jTlZm1pK5hUNeBX0eXVgoBxkOVkxsJQsz9wiyAI2Suedq8hC9xvvD2mWiiwALiHIAoA2qQVZjbYWDFOR1Y0YQAz0jvWtBfeiqXhah4YiLc+zwO6UyBb0jVvLO7YVrPqOk2OSpFffn2vnsrpaLOhvb5A1l9iyGkuSJkfKQRZzsja3nMppMFIOmcYrQZaTG63V1oKjzMgCNqAiq7eMD4S0mikonWvf3EgA2ApBFgC0STXI6m+0tWDQz4ysLlQdkDtYqWDY3x9kADGwR/VCa0HmY/WuN68vqVCyeurEaF33PzAQ1sPjffpqTwdZXuUKJeUKpbY8/9WFpE6MRbf8/GgsoFjQR5C1hXgqp6Fo+fpsOBKQ32ucrciqtBasvgaAuzL5kkJ+thZ7xf42tG8FgHrx3QYA2mS1yYosZmR1r7OnJvSzP3pakgixgD0sFiy/b+/F1oLWWk0tpQiyetgbVxYV8Hr0yaNDdT/mmYf36cKNeO2QTq+pVmm2Y07WSjqv+bWsjm9TkWWM0eRoRFcJsjYVT+U1VKnI8niM4zNcFpM59QV9CvqoOgHul84XFQ7wtdEranMIaS8IwAUEWQDQJs23FvQrlSsqX2zPqV+0ptre59pCyuWVAGiXWK0ia+9t2q+mC1rLFnRoKOL2UuCS168s6NSRwYY2Hj//8D4VS1Z/+OFCG1fWvaJtbDd6db48e2y71oKSdGw0puvMyNrUcipXC7Ik6cBASDMraceefzGZ00iM+VjAZtI5ZmT1kvGBcotVJw8LAEC9CLIAoE2aDbJqLa32YCXAXjAcDagvRHsfYC+rVl/sxerYW/FyCH94mIqsXrScyum926t1txWsOnV4UANhv756uTfbC7az3ejV+fL1xHatBSXp2EhE0/F029ob7mZLyZyGInevt/f3hxytFlhKZjUcJcgCNpMpFBUkyOoZ4wPl60daCwJwA0EWALRJ80FW+f57cQN1LzDG6NholFPRwB4Wa2P1hdum4uUqBSqyetPXri7KWunpB0YaepzP69HnHhrT712eU6lk27S67tXOiqwr8wn5PEaHh7f/mpwcjapkpZtLVISvVyiWtJopaPC+iqzZ1Yysdebf6mIip+Fo0JHnAvaaDBVZPSUW9CkW9NFaEIArCLIAoE1W0nlFAl75vY291VZP/a5m9l5Lq73i2GhU16jIAvYsr8co7PfuycrYqUpFFjOyetPrVxYVCXj1iUODDT/2mYfHtJDI6d3bK84vrMvVwu02vCdcmU/o6Ehkx+vFydFqa2OuP9arHhxbX5E1PhBWJl9ybKbbYjKnESqygE2l8wRZvWZ/f5DWggBcQZAFAG2yks43XI0lSf2ViiyCrO41ORLV9HJamXzR7aUAaJNYyKdkbi8GWWnFgr6mvj9h93v9yqKenBxWwNf4j4Gfe3BMxkivvt977QXbWaV5ZT6543wsSTpeCbJobXyveKoSZK0Lmsb7Q5KkGQcqBn7j7SnNr2X1Kxdu6ekXX9X5i9MtPyewl2TyJYX8bC32kvFK1SsAdBrfbQCgTZoNsqoVWbQW7F7HRqOyVrpFex9gz+oL+vbk+/BUPKVDQ2EZY9xeCjpsbjWjj+YSeupEY20Fq0ZiQX3boUF99fK8wyvrfrE2zcgqFEu6sZjUiX07B1mDkYAGI35do7XxPZZTOUnS0LrWguMD5SCr1Y3W8xen9fxvXKp9PL2c1vMvXyLMAiqstVRk9aD9/SHdobUgABcQZAFAm6yk8+pvoSJrL26g7hW09wH2vljIt2dnZNFWsDe9cXVRkvTUidGmn+OZh/fpnallLSSyTi1rV6hWZCUdfk+4FU8rX7S1aqudTI5Eqci6z1JyY5B1oBpktbjR+tIrl5XJl+65LZ0v6qVXLrf0vLjr/MVpPf3iqzr23JeoeNuFsoXy10coQJDVS8b7Q5pby/bkzEwA7iLIAoA2WW2xImvVob7+cN6xkUp7H05FA3tWLOjbczOyrLWVICvi9lLggtc+WlB/yKePH+xv+jk+f3KfrJV+v8eqsqKB9lTLX5lLSFJdFVlSuSKcIOtey5XWgoPrZmSN9QXlMa23Fry9nG7odjTm/MVpPf/yJU0vp2VFxdtuVG2zHvIRZPWS8YGQCiWrhWRvHWoB4D6CLABoE1oL7l0DEb+GowEqsoA9LBbsjoosJ0+rr6TzSmQLVGT1qNevLOozJ0bk9TTfVvKRg/0a6wvqq5d7a06Wx2MUDXgdr8i6ulAJskbrC7ImR6K6vZJROtfcjM69WP0Sr7YWXDcjy+/1aDQWbLn11cHBzd8rt7odjXnplctK3zdvloq33aX6/y9MRVZP2V+ZQ3hnhSALQGcRZAFAmzQbZPm8HkUCXq1lqMjqZpMjEYIsYA+LdcGMLKdPq0/Fy1UEVGT1nltLKU3F0y21FZTKgc53PDSmP/hgXoViaecH7CHRNoTbV+aSGo0FNBCp73rx2Fi5IvzGUuPXH3u1+iWeysvvLQeN6x0YCGmmxRlZ586c3BD8hv1enTtzsqXnRRkVb7tfNVRnRlZvGe93Zg4hADSKIAsA2iBfLCmVKzYVZEnlqiy3N1CxvcnRqK4vpNxeBoA2iYV8SubcfR92+rT6raXyexYVWb3n9SsLkqSnToy0/FzPPLxPq5mC3r653PJz7SbtmJt3ZT6h42P1VWNJ61obN3GQZq9Wv8STOQ1FAjLm3sBpfCCk2ZXWApGzpyZ0dDiigNcjI2liMKwXnn1MZ09NtPS8KKPibferzpALEWT1lPHaHEJCZwCdRZAFAG2wUplv1XyQ5dcqFVld7dhIVLOrzbf3AdDdqjOyrHVvkLXTp9WrFVmHqcjqOa9fWdRoLKgH6pzFtJ3ldLmV2w/+2zf2THu6erSj3ejVhaRONBBkTY6Wv3avNXGQZq9Wv8RT5SDrfuP9oZZnZEnSWrag73v8oK69+N167blnCLEcdO7MSfm9VLztZtVwPORna7GXjMaC8noMFVkAOo7vNgDQBq0GWf1UZHW9anuf64u0FwT2oljIp0LJKltwr32a06fVp+Ip9YV8dbcxw95grdXrVxb11ImRDVUrjTp/cVr/+Le+Vft4r7Snq0cs6HN0RtZSMqelZE4nKtcT9egL+TUaCzRVkbVXq1+WU3kNbvKeNj4Q1lqm0NL/s7VMXvNr2do1H5x19tSEnjm5T5KoeNulMnlaC/Yir8doLBbULDOyAHQYQRYAtIETFVnMyOpuk5X2PszJAvamvqBPklw9VHDuzMkNm0OtnFafiqeZj9WDPppLaH4t60hbwb3anq4eUYfn5l2dT0hSQxVZknRsNNrUtcf/9MyJDbd5PUZ/70891PBzdZOtKrIODLQ+w6X693x8tPVKRmwuGvLp4ECIirddqhZkBQiyes3+gZDuUJEFoMMIsgCgDapBVj8zsvasyVGCLGAvi4XKQZbTrcQacfbUhF549jEdHCxvyEaD3pZOq5eDrN1dfYHGvX5lUZL01InRlp9rr7anq0df0Nm5eVeaDLImR6K61kQ1eDhQfk8bjQVkVK4wK5asrszv7uuYeCqvoejGIGt/f3WGiwNBFhVZbcMBi93tbmtBgqxeM94fpLUggI7zub0AANiLVlttLRhmRla3iwV9GusLNtXeB0D3iwXL798Jlw8VnD01obOnJvTn/vXr8hrTdIhlrdWteEpPPdB6VQ52h/MXp/XSK5c1vZyW12P09s24joy0tmF8cDCs6U1Cq93enq4e0crcPKdcnU8q4PNoosFweXI0qvm3ppTIFhQL1v/j/JfemdF4f0ivP/eMPB4ja63+/m9c0r/86kcaCPv11z53vNE/guustVpO5TS0SWvBWkVWC0HWlfmkjJGOtvh1g61Nx9P69mPDbi8DTarOCqa1YO85MBDW6x8tur0MAD2GiiwAaIPWWwv6tEpFVtc7NhJlRhawR0WD5U2ZtWx3HCp45GC/3ru9olLJNvX4eCqvVK7Iyfcecf7itJ5/+VItdCqWrCOzrJxud7mbxEI+JbPFne9YpyvzCR0bicrraWxu2bFKRXgjB2kS2YJ+74N5ffHRcXkqr2eM0T85+5i++7ED+ukvf0u/+uathtbRDdayBRVKdtPWguMOtRY8NBRW0McmfTvkiyXNrFApvJtlqMjqWfv7Q1rLtjaHEAAaRZAFAG2wkmqxIivkV65QUrbg3IYJnNfsnAoA3a+vUpHl5MZ1Kx49OKBkrqgbS6mmHj8VLz/uMBuGPaFds6zub3fZF/S11O5yN4kFfcoVnbs2uzKf1Il9jbesq87obOQgzVe+dUe5Qknf/YkD99zu9Rj98x96XJ99cFTPvfyO/tulmYbX46blZPl6e3CTiqyQ36uhiF8zK823vbw6n2A+VhvNLGdUsuKAxS6WyZckSSE/W4u9ZnwgKKm1wwIA0Ci+2wBAG6yk8wr7vQr4mnub7avMZmFOVnebHI1qIZHTGm0ggT3n7oys7vj6fmSiX5L07vRKU4+fipc3c9kw7A3tnGV19tSEXn/uCxrrC+rPPHagJ0IsSbU2fk60F8wVSrq5lGp4PpYkTY6Wv4avNTDb6suXZrS/P6hPHhna8LmAz6N/+5c/qVNHhvR/+eWv6398ON/wmtwST+UkadOKLKlcMTC7km3qua21uraQrFXAwXnVAxaHhjlgsVsxI6t3VecQ3mmhfSsANIogCwDaYCWdb7oaSypXZEl3Z22hOx2rbCZdX2iuQgJA93Jy09oJD+7rk99r9O7tZoOs8vtUo/N4sDttNbPKyVlW+/uDurPWOxtY0cp7ghNVmjeXkiqWrI6PNR6SRAI+jfeHdK3OiqxEtqDfuzyvP/3ogVpbwc2e8+d+9EkdH4vqr/+Ht/T2zXjD63JDLciKbh5kHRgIaXa1ufB2bi2rVK7Y1P8j1Kd6wOIwByx2rXS+KL/XyO9la7HXjPe33r4VABrFdxsAaINWgywqsnaHycop3Xo3kwDsHrX34S7p/R/weXRyvE/fvL3a1OOn4mn1h3wtfW/C7tGJWVb7+kKaW22u2mU3qobbTszN+2iufN3QTEWWVK7KqndG1le+dUfZQkl/5rED295vIOLXL/74pzTWF9SP/fs39a9/7yM9/eKrOvbcl/T0i6+2PF+tHe5WZG3+vjY+ENZsk9UCV+YTkkRrwTaaiqfkMXfnmbnl/MXprv+33q0y+aJCzJDrSU7MIQSARhFkAUAbtB5klR9LkNXdanMqmJMF7DlBn0c+j+maiixJeuTAgN6dXpG1tuHH3lpK0Vawh5w9NaGfPvto7eOJwbDjs6z29QU1t9Z7QZYTFVm1kKTJIOvYaFTXF+urBv/ypRnt6wvq9NGNbQXvt68vpP/4498ua0v6p799WdPLaVlJ08tpPf/ypa7b4I9XZmRt1VpwvD+khUROuUKp4eeuzkA9RkVW20zF0zowEHa1muf8xWk9//Klrv+33q0y+aJCAYKsXhQJ+NQX8tFaEEBHEWQBQBuspPPqd6Aia5XZS10t5Pfq4ECottkBYO8wxiga9CnRJRVZkvToRL/iqbxuN7FpMBVP6xBtBXvK40cGJUn/+5//hF577hnHZ1nt6w9pMZlVodh4SLAbOTk37+p8Uvv7g7VwrFHHRqNaSua0ktp+LclaW8HxLdsK3u/wcEQh/8Z1pfNFvfTK5abW2y7LqZyM0ZbX3AcqFQN3mqgYuDqfVMjv0YF+d6uF9rKpeNr1drcvvXK5Nuepqhv/rXerdK64ofoXvWO8P0RFFoCOIsgCgDZYbXVGVrhakUWQ1e0mR6MEWcAeFQv6uqsia2JAkvTedGNzsqy1moqndXiYiqxe8v7smiTpY+P9bXn+fX1BWSstJHJtef5uU5ub51BFVrNtBaW7FeE7tTb+yvtzdbUVvN/8FpV2t5ebmzfVLvFU+Xrbu0VI10rrq2sLSU2OROsOANG4W/GU6wcstvo33W3/1rtVJl9SyM+2Yq8aHwhptodaDANwH99xAKANVtJ5DW7Rr78ezMjaPSZHo7rOjCxgT+oLdVdF1sfG++Ux0rsNzslaSuaUzhdd3zBEZ70/syqPkR7c354ZP/v6gpKkubXeOI1dC7JavDaz1rYcZB0bra+18ZffqbQVnBxu6PkPDm7+XrHV7W6Jp3Ia3qKtoHQ3yJppoor1aov/j7C9XKGk2dWMDrvc8na3/FvvVuk8FVm9bH9/iNaCADqKIAsAHJYvlpTMFVuqyIoFfDJGWiXI6nrHRqJaTuUVT/bGiXSgl8S6rLVgOODVibGYvnm7sYqsqXj5ZDkzsnrL+7NrOjYaVahNm4z7+6tt23rjNHa1tWCyxfeEhUROa5mCjrcwe+nwcETGaNuK8GS2oK9entOffnR8y4qlrZw7c3LD5nTY79W5MyebWm+7xFO5bQ+OVYOsRjdac4WSbsXTtcAQzptZSctauX7A4tyZkwp47/366MZ/690qnS+27XsMut94f0jzid5pMQzAfQRZAOCw1XS5HWArQZbHYxQL+GrPhe5V3eTYqb0PgN0n1mUVWZL06MSA3p1urCLrVjwlyf0NQ3TW+7NrevhAe9oKStK+/t6qyIpUNmvXWnxPuDKfkKSWqn3KMzrD21aEv9pkW0FJOntqQi88+5j6K+Hd+EBILzz7mONz1loVT+Y1tE1FVl/Qp2jA23BF1q14SsWSJchqo245YHH21MQ9XyMTg+Gu/LferbL5osIBgqxetX8gpGLJ9kyLYQDuI8gCAIetOBBkSeU5WbQW7H6Tdbb3AbD7dNuMLEl65GC/ZlczWkjUXwVzd8OQIKtXJLIF3VxK6eH9fW17jdFYUMZIcz1SkeXxGMWCvpYrsmpB1r7W2tYdH4tue+3x5UszGmuirWDV2VMT+tm/+qQk6R9/7yNdubG/nMppcJsgyxhTmeHS2Lyjq/Plv9dWquawvakuOmAxFL37b+jL//Nnu/LferdK54sK+QiyetV4f/NzCAGgGQRZAOAwp4KsvpBPaxkqsrrdkeGIPIYgC9iL+kK+lqsvnPbIwQFJ0nsNzMmaiqc0GPGrL9Ta9yXsHpdn1ySprRVZfq9HI9FAz1RkSVI06G053L46n1TI79GBygZgsyZHorq6kJS1dsPnUrnm2wqu99jEgAJejy7ciLey1LaJp/Ia2mEm7fhASLMNVmRdWyiHjcdHmZHVLreW0vJ6jA4MtPZ14ITqYQ/pbgUz6pOmIqun1YIs5mQB6BCCLABwWDXI6nckyOquDVRsFPB5NDEU1rVFfvAF9ppooPsqsj5+sBxMvDtd/5ysqXi6K069o3Peny0HnQ+Pt68iS5LG+kI9U5ElVao0c61XZB0fjcnTQsAklSvC1zIFLW0yo/PV9+eUyTfXVnC9kN+rxw4N6ML1pZaepx0y+aLS+eI91TSbGe8PN7zJenU+qZFoQAM7hGRo3lQ8pQMDIfm87m9J3VpK1QK1W0tczzciky8xI6uH7R8otxi+Q0UWgA5x/6oBAPYYx1oLhvxapSJrVzg2GqMiC9iDYiGf0vmiiqWNFQ9uGQj7dWQ4ovduNxhkDbo7hwSddXl2TbGgr+0B5r6+oObWeizIajHcvjKfaLmtoCQdGy1/TW82J+vLl2Y0GgvqySbbCq53enJIl6ZXlMkXW34uJ8VT5QBvuxlZknRgIKQ7a9mG3sevLiSZj9Vm3XLAwlqr6Xhanzk+IomKrEZlckWF/Gwr9qrRaFA+j6G1IICO4TsOADhs1dHWgt1VCYDNHRuJ6NoW7X0A7F6xoE9Sed5QN3l0or/u1oLWWk3FU12xYYjOeX9mTQ+P98mY1qp+drK/P9hTJ7FjodZmZGXyRU3F0zrhwOylY5W2d9cW7t14T+UKevX91tsKVp0+Oqx80eqdqfrD806IJ8vX2zu1Ftw/EFKxZLXYwFzBawtJ5mO1WTnIcv+AxWq6oLVsQR870K+BsF+3lhqbp9br0vmiwlRk9SyPx2h/f0h3aC0IoEMIsgDAYc7NyPIzI2uXmByNKpEtaCGxsb0PgN2rL9SdQdYjBwd0YzFVV9XuQiKnTL5EkNVDrLX61uyqTra5raAk7esLaSHRWLXLbhYN+Fp6P7i+mJS10vGx1iuyDg2F5fWY2jynKqfaClZ98uiQJOnNLmsvuFypyBrcqSKrMsNlps6N1rVMXvNr2VpQCOdlC0XdWct0xfelagXWoaGwDg+HqchqQL5YUqFkCbJ63P7+IBVZADqGIAsAHLaSzivs9yrga+0ttlqRRZVP95ustJ/ZrL0PgN0rFiwfSOi2OVmPVOZkfbOOqqypyqbc4WH3T76jM26vZLSWKejhA/1tf619/UGVrLSY7I32grEWq+WvzJWvE5yoyPJ7PTo8FNb1+yqyqm0FP3Ws9baCkjQcDejEWLTr5mTFU+Ugf3inGVkDjQVZ1yqtomkt2D4zyxlZq66oyJqKlyuwDg1FdHgowoysBlTbjTIjq7eND4QIsgB0DEEWADhsJZ1vuRpLkvrDfhVKVukum0mAjY6NlDc7rjEnC9hTYrWKrO6qjn3k4IAk6d3pnVt9rd+kQ294f6YccH6sQxVZkjS32iNBVtCnZK6FIGu+XD113KFqn8nR6D3XHtW2gl98dL8jbQWrnpwc1ls34ip1UeXdUm1G1vbX3NUgq94WmFfnnQsbsbn1VVBuu3vYI6zDwxFNxdMcIqxT9WfUUIAgq5fRWhBAJxFkAYDDnAqyqi2tmJPV/Q4NheXzGF0nyAL2lFiwvDnTbe/DY31B7e8P1jUnqxpkTXTBhiE64/3ZNUnSQ50IsvqDkqS5td7YxIoFfUq0UC1/dT6hicGwwg5t/B4bjVbaFZbX89X35x1tK1j1yaNDWs0U9OFcYuc7d8hysr7WgsORgAJeT90VWVcXkvIY6cgI4X+7VL8vdUOl8FQ8rVjQp4GwX4eHwsoWSppf641gvlWZXEmSaC3Y48b7Q0rmioxEANARBFkA4DDngqzyc3BR2P18Xo+ODEeoyAL2mFprwS6bkSVJjx4c0Hu366nISmko4lcs6OvAqtAN3p9d06GhsPpDrV+L7GRfXyXI6pGKrGjQp0LJKlsoNfX4K/NJHXew0ufYaFSpXLG28V5uKxjQtx8bcew1pHJFliRduNE97QXjqbyigZ1beXs8RvsHgppdSdf1vFfnEzo0FFHQx+Z8u0zFU/J5jPZX3j/cXsuhobCMMTpUCdZu0l6wLplCtbUg24q9rNGqVwBoBd9xAMBhK+mC+h2syFpJd98GKja6v70PgN2v2low2YVB1iMH+/XRXELp3PbtZ2/F07QV7DHvz6zq4Q5UY0nl6kBJutMjQVZfrd1oY+8J5y9O6+kXv6JL0yt6+2Zc5y9OO7KeyUpr46sLSaVzRb36/pzOPDLuaFtBSTo6EtFoLKAL1+OOPm8rllO5Hauxqg70hxuakcV8rPaaiqd1YDAkn9f97aipeLrW4vBw5XtltfUhtle9/qAiq7ft7y8HWbMrvXEdAMBd7l85AMAes+rUjCwqsnaVyZGobiym6KsP7CHVKqZuay0oSY9MDKhkpW/Nbt9esHraHL0hky/q6kJSD4/3d+T1gj6vhiL+nmktGA00Hm6fvzit51++pOnlTOWxRT3/8iVHwqxq4HJ9IamvXp5TOl/UdzvcVlCSjDE6fXS4yyqychqO1hdk7R8I1VUtYK3VtQVnq+aw0VQ8rUOD7h+wsNZWgqzyWqrfK28t1Ve91+uqM7IIsnrbeDXIoiILQAcQZAGAw5xqLdjPjKxd5dhYVOl8sWdOpQO9oBpkdWVrwYkBSdp2Tpa1VtPxdFfMIUFnfDSXULFk9fCBzlRkSdK+vlDPfO+LNXFt9tIrl2sbvlXpfFEvvXK55fUcHAwr4PXo2mJSX7o0o5FoQJ86Ntzy827m9OSQbi2lu6Z91FIqr8FIfdfbBwZCmlnJ7HjY6M5qVqlcUcepyGqrW0vdccBiJZ1XIluorSXk92pfX1C3aC1Yl0zlfS1IkNXTaC0IoJMIsgDAQYViSYlsweEZWd23gYqNjtXa+3TPIHQArfF6jCIBrxJd+D58cCCkwYhf701vPSdrPpFVtlDqig1DdMbl2TVJ6lhFliTt6w9qvkcqsvqCjVdk3V7evLpjq9sb4fUYHRmJ6P2ZNb36rTmdeXS8be3aTlfnZHVJe8HlVE5DdbYWHO8PKVsoaTm1fZeD6jXcsdFYy+vD5jL5oubWsl1xwKJaebW+/e7h4QitBeuUoSILKgfAA2G/Zuts3woArWjpKtcYc90Yc8kY83VjzIXKbcPGmN81xnxY+XXImaUCQPdbrWx2DoR9LT9XdQ7DKq0Fd4XJ0fIPwdcX+OEX2EuiQV9XVmQZY/TowYFtK7Km4tVNOoKsXvH+7KqCPo8mRzq3SbyvL6S5td6oyIo2UaV5cHDzr7+tbm/E+YvTmlpK6fc/mFc6X9SgAweptvLIwX6F/B69eb072gvGkzkN1VmRVa0Y2Kn11dX58qxTWgu2TzXA7YbvS1OVwGr9Wo4MR2gtWKdaa8EAQVavG+8P1T2HEABa4cRxrc9bax+31p6ufPycpK9Yax+U9JXKxwDQE1bS5dBpoM4frLcTCXjl9RhmZO0SBwfCCvg8ur6YdHspABzUF/RprQuDLEl6ZKJfl2fXlCuUNv18tT3S+tPm2Nven13Tg/tjbavK2cz+/qDm17Iqlfb+jMhqa8FGgqxzZ05uqFgI+706d+ZkS2upzt7KrPv6/7nXrjkye2szfq9Hjx8e1Fs33K/IKhRLWs0UNFhvRVY1yNpho/XaQlIhv6c28wXOu3vAwv3vS9W1HF5fkTUU1sxKWvni5t9XcVcmX/47Cvlp9NTr6p1DCACtasd3nO+T9AuV3/+CpLNteA0A6ErLqZwkOdJa0BijvpCP1oK7hMdjdHQ4omsLBFnAXhIL+bqytaAkPXJwQLliSR/OrW36+eom3YQDlR/YHb41s9bRtoKStK8vqELJaqlyDbSXNTM37+ypCb3w7GPyeYyk8tfjC88+prOnJlpay2aztzL5kiOzt7by5OSw3ru94nqVavXg2HC0viDrQCXI2qli4Op8QsdGY/JU/l/Bed1UKTwVT6kv6FP/uk4ah4YjKllpZplN+Z2kc7QWRNl4f3DHilcAcEKrQZaV9DvGmLeMMT9RuW2/tXZGkiq/7tvsgcaYnzDGXDDGXJifn29xGQDQHWoVWQ61diHI2l2OjUZ1nSAL2DPOX5zW5dk1/f4H83r6xVfbVunQrEcPlgOLrdoLTsXTGokGau3QsLctJLJaSGT18HhfR193X6V6ZW5177cXjDUxI0uSvucTByRJf+M7Tui1555pOcSS2jt7ayufPDqkkpW+fnO5ba9Rj3glNB2sswPCWCwoj9m5teC1haSOj9JWsJ2m4in5vUb7u6DqbSqe1qHhiIy5G1xWq7OYk7WzapAeIsjqeeP9IS0kslQyAmi7VoOsp621T0j605L+ljHmc/U+0Fr7M9ba09ba02NjYy0uAwC6g9NBVn/Ir9U0rQV3i2OjUd1YTKnYA+2VgL2u2rYrW2nbNb2c1vMvX+qqMGtyJKpowKv3plc2/fxUPNUVp97RGZdny5V5HzvQ2Yqs/f1BSdKdtb1/GjsS8MoYNVylOb2cVqFkdWzEuZCknbO3tvLE0SEZI1244e6crHiqfG08VGdrQZ/Xo7G+oGZXtg75coWSbsXTOkaQ1Va34mkdHAzL2wVVb7c2+R55eLj8cbU1L7aWzRdljBT00Vqw140PhGWtNN8j8zIBuKel7zjW2tuVX+ck/YakT0m6Y4w5IEmVX+daXSQA7BbV0KmfiqyeNDkaVa5YautpaACdsVnbrnS+2Na2XY3yeIw+frB/y4qs6Xi6K+aQYHvnL07r6Rdf1bHnvtRS5d+3Zsr/DjpekdVXrqyY74GKLGOMYoHG5+ZV2w5POhiStGv21nb6Q36d3N+nC9fdnZMVT5YrsuoNsqTyRut2rQVvLpUPIh0fI8hqp245YGGtLVdk3beWAwNh+TyGiqw6pPNFhXzeeyra0JvGB8oHWmgvCKDdmg6yjDFRY0xf9feS/pSkdyX9pqQfrdztRyX911YXCQC7hfOtBf1azVCRtVtMVk5aX1+kvSCw27nRtqsZjxwc0DdnVjdUgpZKVlPLGzfp0F2qlX/Ty2lZtVb59/7smkZjQY3Egs4vdBtjfeXXm+uBiixJigZ9DbcWvF4LspwLlquztyYGwzJybvbWTp6cHNbFm3EVXGwhtVytyIrWf719oD+kO9tsslbDRiqy2msqntahQfcPWMRTeaVyxQ2HPbweo4ODYd1a6q7v9d0onS8qHKCtIFRrFXpnhzmEANCqVhrm75f0G5XTFz5J/9la+9vGmDcl/aox5scl3ZT0A60vEwB2h5V0XiG/R0GfMxf1VGTtLtVTvNcXkvrsg7TNBXazg4NhTW8SWrWzbVczHjnYr1SuqGsLST2wL1a7fT6RVa5QIsjqcttV/jUaSLw/u6qPHehsNZZUno8yEPbrTg9UZElSLORToomKrFjQpzGHQ8azpybaHlzd7/TkkP7D127o/dk1PTox0NHXrlpKNVORFdJrVxa2/PzV+YQk6fhobMv7oDWZfFHza9mu+L40Vam42mwth4fDuklrwR1l8qUNVaHoTeOVIIuKLADt1nRFlrX2qrX22yr/PWKt/enK7YvW2i9Yax+s/OpuA20A6KCVdN6xaiypMiOLiqxdY19fUJGAV1cXqMgCdjs32nY1o7qR/N7te+dk1Tbpht0/+Y6tOVX5VyiW9MGdRMfbClbt6wv2VEVWIlvc+Y7rXFtMaXI0sidacJ2eHJYkXbju3o/58VROAa9HkQaqQcYHQlrLFLYMIa8tJDUSDWgg4tx1PO5VPRxyaLgbgqzyWg5v0n738FCk9j0UW0vniwr6mY8F6Q8+mJck/a+/9c2WWiQDwE74rgMADnI+yCqf+i3d1zIK3ckYo6Mj0VoLIQC71/q2XZJkjPTTZx/tePXDTh7YF1PA59kwJ+vuJp37G4bY2lYVfo1W/l1fTClXKOnh8X4nltWwff1BzfXIkPe+oE+JBg8ZXV9I1toP73YTg2EdGAjpzRvuzclaTuY1GPE3FAweGKhUDGzR+urqQpL5WG12a6laBeX+AYvqWiY2rciKaCGRUypHV4ztZHJFKrKg8xen9fd/493ax620SAaAnRBkAYCDnA6y+kJ+WSsl+UFq1zg2GtH1RU5xAnvB2VMTeu25Z/T/+eHHZa30wP7uaznl93r08Hif3p2+vyKrHGRNdMEsEmxts8q/kN/TcOXf+7PlIPNhF1oLStL+vpDmeqS1YDToVbKBiqxcoaSpeGpPzV46PTmsC9eXZK07B63iqVxDbQWldTNctmh9dXU+uaf+H3Wj7aqgOm0qnlZ/yLfpz23VdoPV9WJzmQJBFrZvkYy94/zFaT394qs69tyXqLqDqwiyAMBBK+mCw0FWeZQhc7J2j8mRqG4tpVwdgg7AWU8/MCpJ+h8fbj1fxU2PHBzQe7dX79lUnoqnNBoLMIi9y1Ur/3yeu5Ulf+FTRxqfjzWzJq/H3DMnrZPG+oOaX8u6Fmx0Uizob2hG1q14SiWrPVORJUlPTg7pzmrWtY3+5VReQ9HGrrerFVkzm1RkrWbyWkhkdYz5WG01FU/L7zXa1+fsrLjm1pLasjLscKUl7y3mZG0rnSsqRJDV85xqkYzudf7itJ5/+ZKml9OyouoO7iLIAgCHnL84rQ9m1/TfvzXn2CmV/kooxpys3ePYaFSFkuUUJ7CHjMaCeuRgf20GQLd55GC/VtL5e953bi2lNdEFp96xs7OnJhQL+fTDTx7WaCyw6Ub7Tt6fXdXx0aiCPnc2Fff1hZQrlrSc2vvXK7Ggt6Eg69p8ud3wsT3Utu6TR4ckSW+51F5wqYWKrNmVjddn1f9HtBZsr6l4ShODYXk87s+Km4qna5VX96tWjBFkbe38xWm9M7WiP/xogeqMHudUi2R0L6ru0E0IsgDAAdVTKsXKSWSnTqlQkbX7VNvSXGNOFrCnfPbBMb19M97QBnanPDoxIEn3zMkqnzZnE2E3SOeKWk7ldXg4ou/5xEF95VtzWkk3Fgi9P7umhw+4Mx9Lkvb3lyss7qw1HsLtNrHK/NJ6q8+uL1aCrD1UkfXweL9iQZ/evL7kyusvp3IabDDICvm9Gor4NbtJa8HqNdtxWgu2VTk8cv+AhbXlA2fVyqv7jcYCCvu9usWhtE1Vf+4tlJz9uRe702YtksN+b8MtktG9qLpDNyHIAgAHtOuUSl+oXJG1RkXWrjFJkAXsSZ97cFT5otUfXV10eykbPDzeJ6/H6L3b5TlZpZLV9HK6K+aQYGe3KxUiBwZC+v5TE8oVS/rtd2fqfvxqplyN9/C4O/OxpHJFlqSemJMVC/pVLFllC/W1EL62kNRA2K+haGPBSzfzeoxOHRnUheudr8iy1pZbC0Yab+U9PhDW7CYVj1fnE/IY6cgI75nttF0VVCctJXNK54tbrsUYo8PDYSqytkB1BtartkieWFeB9Y/+7McbbpGM7kXVHboJQRYAOKBdp1SqFVmr6e6rAMDmRqIB9QV9tRPYAPaGT04OKeT3dOWcrJDfqwfGYnp3uhxkza1llS/artgwxM5mlssb6wcGwvrEoQEdG43qNxo42f7B7Jok6WMH3AyyyhVZc2u9EGSVT57XWy1/fTFZO+Sylzw5OawP5ta00uF2kmvZggol23BrQakcFm/WuvPqQlKHhiKutebcDc5fnNbTL76qY899qalWculcUQuJ7JZVUJ1UrbTarjrs8FCkoxVZrf79dhLVGbjf2VMTeu25Z/RLf+3TkqSBJg4a7AW76eu4EeWqu3vjA6ru4BaCLABwQLtOqfRTkbXrGGN0bCxKRRawxwR9Xn36+Ij+4MMunZM10V9rLTgVL58iJ8jaHaoVWQcHQzLG6OzjE/qja0t1bwp+qxJknRx3r7XgvmprwU3atu01scoho2SdbUavzSd1bA9W+pw+OiRrpbdvdrYqazlZviZupsJtf39o03+j1xaSzMfaRrWV3PRyWlbNtZKbXu6e70v1fI88PBzR1FKq7hairXDi77eTqM7AVp6cHNJgxK/fee+O20vpuN32ddyIs6cm9PfWhVZ9IZ9eePYxqu7gCoIsAHDAuTMn5btvcLETp1RqFVnMyNpVJkcIsoC96LMPjunqfLK2CdZNHjk4oLm1rOZWM5qq47Q5ukc1sBofKLfnO3vqoKyVfvMbt+t6/Pszq+oL+XSw8ng3RAI+9QV9mu+BiqxooHxtVs+8vEy+qNsrGR0bjbV7WR33+JFBeT1GF250dk7WUionSU21FjwwENJCIqds4W5bNGutri0kazNOsZETreTuVkG5H3ZUv0dObLOWQ0NhrWULDc8rbMZua9XHTCRsxef16JmT+/Tq5TkVivW1390rdtvXcaNO7r97WOpzD40RYsE1BFkA4ICzpyZ0ZDgiv9fISJoYDDtySiXk9yrg9dTdvgbdYXI0qtvL6Xs2SgDsfp97cFSS9Idd2F7w0YPlHzDfu71am+vRDRuG2NnMckajsWCtrdnRkahOHRms+xTv5dk1fWy8X8aYne/cRmP9Qc2t9U5FVj1B1o3F8tfi5OjeC5UjAZ8eOdivNzs8JyteCbIGm2gtWA2L189yu7OaVSpX1HGCrC050Uqumw5YTMVTGgj7a50vNlNtgXhrqf3t8nZbq771M5Gc/LkXe8OfemS/llP5jn9vcNtu+zpuVHVswqMT/frwzprLq0EvI8gCAAcUiiXNrGT0lz59VNde/G699twzjl3M94d9WqW14K5ybDSikhVDooE95oF9MY33h7qyveDHa0HWiqbiaY31BRXyM+9lN7i9ktbBwXurqb7/1ITen13Tt2ZWt32stVbvz67pYRfnY1Xt7wvpzurer8iKBStBVh2HjKrV2Xu12uf00WF949aycoXOnbxfbrEiS5Jm17UXvDqfkCQdH9t7VXNOcaKV3FQ8pYDXo7FY0KllNW0qntbh4e3XfrgSuN3swLX8bmzVV52J5PTPvdj9PvvgmAI+j37nm7NuL6WjduPXcSNuLCYV9Hn0Jx4Y07WFpPI9VnGH7kGQBQAO+HAuoXS+qMcPDzr+3H0hPxVZu8zkSHnD6toCQRawlxhj9NkHR/WHHy6oWGr/3IxG9IX8mhyJ6N3pVU0tp6jG2kVmVjK1Dfaq737sgLweo/Nf374qayqeViJb0MMuzseq2tcrFVnB+iuyqieYJ/dqkDU5pGyhpHdvr3TsNeOVGVnDTczIGu8vf53NrKwLsvZ42OgEJ1rJTS2lNTEUlsfjbuWoVD5odmhw+8qwatB1qwOthP/W509suI1WfditokGfPvvAqH73m3c6MmOuW5w7c1J+r/OjJrrFtYWUjo5EdHI8pnzR6sYiYxTgDoIsAHDAN24tS5K+7dCg48/dF/JpjYqsXaW6GXKdOVnAnvPZh8a0minonallt5eywSMTA3pvplyR1Q3tm7Aza61mltM6MHBv8DgSC+pPPjSm3/z6bZW2CU3fny23dzk57n5F1r6+oOZWs3t+46qRIOvafFKjscC2Lcx2s9NHhyRJb3WwhVQ8lZPHqKm/02prwdmVu62ers4nFfZ7ayEXNqq2kqvO4YsGvA23kpuKd8cBC2tt5Xvk9mvpC/k1GPF3qLtCefN7MFz+Nz0c8dOqD7vad318v6biaX1rpnda0J09NaHTR4dUjbIiTbxPdrMbi0kdHYnqwX3l680P7iRcXhF6VVcEWZemV/T0i6/W3QceALrNN6aWNRjx6+iI8xuH5SCLiqzdZDAS0FDEXzvlC2Dv+BMPjMoY6X905ZysAd1aSte1SYfusJopKJkrbmgtKJU3RWZWMvqja0tbPv79SuvBbgiy9veHlC2UtJre29csjczIuraYrFVp70X7+kM6MhzRm9e3/jfqtHgqp4Gwv6nKnr6QX7Gg756KrGsLCU2ORruiUqibnT01odef/4K+82P7NRgJ6PseP9jQ47vlgMVCIqdsoVTX98jDQxHdird/vs35r0/r+FhUf/QPvqCA16MffPLIntn8Rm/6wsf2yxjpd795x+2ldNRiMqc/eXJM33FyTEeGI3vm67hUsrqxlNKx0ahOjP3/27vv8DivMm3g95kZaUbSSKM6sjSS1SUXucWdxIntQAwEiAkldFjasoRvK9lNgP2AZYEsXthvWdqykKUsSwkkJpCAE2Kn2Ylb5G5Lspqlkaw6I2mapp3vj5mRbXkkTW+6f9eVy9HUo1czbznPeZ5HCyGATgayKElSIpAFAEazHQ89dpbBLCJKS21XzFhXVRiXRusFmixM2ZmRlW5qS/OYkUWUgYrzsrHGoMOLKdgna7W/T5bHKxnIShND/syQuRlZAPC6leXIy1YueH10aXgay4tzZ7OEkqks39f7JtPLC+ZkKaEQgDWU0oJj1owtKxiwqbYIJ/tMCcvEM9lcKMoNv6xgQHmBGsNT1weyrKgvy+y/USztXqGH0WzH5ZHQJzFtTjfGrc6UOC4N+EsFhhJUqy7OwUCcM7IGTDYc65nAW9cboFYpsaIiH2eN5ri+J1G8leWrccvyIjxzcen0yTLbnOgYtmBTTRHWGnToGJ6G3elJ9rBi4uqUA063FzUlucjJVmJ5cS46RpZOth2llpQJZAGA3eXBvgPtyR4GEVFYbE43OoansS4O/bEAZmSlq7qSvNneGESUWXY0leLVK+aUK/t6ZeLaPucbT3dwgVgaGDL7JtSDZWTlZCuxp3UZnjo3BIcr+GTIpaEprEiBbCwA0Of7foeR6ZkkjyS+hBDIUy9+bmadcWNkeibjey9tri3GuNWJngQt3jHbnCjMjbxUY4UuZzYjy+n2ot9kR32G/41iaWdLGQDg4KWRkJ9j9Gc1pUYgyzeW6uIQAllFuRgw2Rcs7xqt354aBADcs96XudFq0OHswGTGl2ilzPe6VeU4Z5yC0Rz/rMZU8OoVX4ndTbXFWFNVCK8ELgwlrn9kPM32+/RnmDfptbjMjCxKkpQKZAHA4BLZyRFR5jhnnIJXAuurdXF5/XxNVspNltLi6krzMDTpyJiVWER0zY6mMni8Ei93jSd7KLP2txnx5Scvzf48bnWy2kEaGFwgIwsA3rrBgGmHG4eCTBo7XB70jFmxoqIgrmMMVXmBLyPr+myXTKVVqxbNyJo78ZOpAn2yTiSoT5bJ6kJxXuQZWct0Glz1B7KuTNjg8cqMDzbGUmVhDlYsy8eh9tADWQOzgazklxYMjMUQQlCtqjgXTo83bsF5KSX2txmxsaYIy/3l6dcadJhyuHElIb25iOLnrlXlAIA/LZHygsd7TVApBNZVFWJtlW9e6MxAZgSy+sZ9+6NAG42m8nx0j1ng8niTOSxaolIukBW4ACIiShen+80AgLVVhXF5/XyNClanB26eKKSVUYvvonfV//0j+0ASZZhblhchN1uZUn2y9h1oh31O1g6rHaS+IbMDCgHo84NfA72moRRl+Wo8HuQY0jlsgVcidTKyCpZGRhbgC2Qt1iMrkKGU6UGShjItcrIU+MLvzqPuwSfjfs5jsjlRGEVpwWUFGoxMz8Djlege9a0ory/Txmp4S8KuFXqc6DVhKsSFdv3+cn7VxcnPyOo32VCUmxVSOdbl/qytwPhj7cLQFDpHLDf00VmTYRPgtHTVl2nRUJaHpy8sjfKCJ3on0GrQISdbifICDcry1ThrzIzvce+4FdlKxeyiqya9Fi6PnA1wESVSygWyvFJiwupM9jCIiEJ2asCMqqIclGrjE4gv0PjKp4TSVJxSw/42I355vB8AIME+kESZJlulwPb6kpTqkzVfVQNWO0htg5N2lBdooFIGvyxTKgTesq4Sz7WPwmy78Rrp4tUpAKkTyNKqVcjNVmJkKvMDWXkhBLICfTJrS5OfhRJPT5wexIzbC5vTk5BzHpPNiaIoSgsu02ng8UqMWWaWTLAx1nav0MPtlXgpxMUcAyY71CoFyuJ0rRSOAZM95Mywan/WVn+csqN+e2oQKoXAm9ZUzN7WXJ6PbJUiYybAaWl73aplONo9gckM7/c94/bg9MDkbIYy4MuuPJshAem+MRuqi3OgVPj6wTeX+847O4fZJ4sSL2UCWYbCHHxqVwMm7W588JFjLKNFRGnjdL85bv2xAF9GFgD2yUoj+w60Y8Z9YwYdMyOIMsuOplL0jttwJUVWI1YWBl/pPt/tlBqGzA5U6G7uj3W9vesNcHq8eOrsjaua269OQ5OlQE0Kla4rL9BgeDrzSwvma0LJyLKhvECN3OzFMz/S2b4D7ZjbQihe5zwOlwcOlzeqjKzA921o0oHuUStKtdnQ5UQeGFuKNlQXokCjCrlP1oDJBkNRDoQQcR5ZaGMJtVeXb8xA/0TsF4R4vBK/PWXEzpYyFF1XKjNLqcDKigKcGTDH/D2JEu2u1eVweyWeC6MUaTo6Z5yE0+3Fptri2dvWVOlwedSyaBnidNA7br2hTHJDmRZCAJ0j7JNFiZcSgaw1Bh0OP7gbn96zAt993y24ODSFj/74xLxNjYmIUsWYZQYDJjvWx6msIODrkQUg5PIdlHzMjCDKfDuafQ3vX7ycGllZD+xpQU6W8obbcrKUeGBPS5JGRKEYmrSjYpFgY6uhAA1leTdluFy6OoWW8vzZFbKpoCxfjdGlkJGVHVqPrEzvjwUk9pzH5M9KLIqmtKA/kHV10oGeMSuzsSKgUipwe3MZnmsfhXduFDOIcLKg4klKCaPJjuri0MaiVilRnq+JS2nBV7rHMTw1c0NZwYC1Bp2vB3MI25Yola2vKkRZvhpPn8/sPlmBHpEbr8/IqtJBSuD84FSyhhUTUvpKCF6/aConW4nqolx0MCOLkiAlAlnX272iHF9/5zoc653Ap/73VTaPI6KUFlgtF8+MrAJmZKUdZkYQZb760jwYCnPwYkdq9Mnau8GAr967BobCHAj4qh189d41QSfJKDVIKTE06UDlIhlZQgi8dYMBx3onMOCfUJVS4uLQNFYsK0jEUEOmz1djZAlkZGk1KlgWOS/rXSJBkkSe85isvkVdxXlRlBYsCASy7Oges6K+lP2xIrF7hR5jlpmQJml9gazknwOPWmYw4/aGNZbq4py4lBbc32aEVq3Ca1eW33TfmiodLDNu9I5bY/6+RImkUAi8dqUez7WPYMaduYkKx3tNqCvNQ9l1/U5bDb5+d+leJnR0egZ2l+emMslNei0uMyOLkiDlAlkAcM96A/7pnlb86eII/uHXZ7gShYhS1qkrZigVAq2G+E0knbziW+Hzru+/EvcG2hQbzIyIzP42I259+GBCmsUTRUsIgR1NpTjcNQZ3iiy82rvBgMMP7kbPw3fj8IO7GcRKcRNWJ2bc3tnm2Qu5Z73vb/nbU4MAfBOyE1YnWlKkP1ZAeYEGw1MzkDKzr9+0i/TImrS7MG51LolAViLPeQIZWdGUFizOy0a2UoGOEQvGLDOoK8v8v1E83NFcBiGwaHlB64wbE1ZnSgSyAiUCwwpkFeXGPJDlcHnwh3NX8frWZdDM+e4AvkwOIP0nwIkA4K5Vy2B1enCkazzZQ4kLKSVO9k3ckI0FAPp8DZYVaHA2zcuE9vpLqM8tY91Uno/uUWvKXAPR0pGSgSwAeP+2Gnz6rmY81mbEP/3+QsZfDBFRejo1MInm8vy49T/Y32bEtw5env053g20KTaYGRG+/W1GPPTYWRjN9oQ0iyeKhR1NZZh2uHE6Q5o5U2INTfoylyoLF87IAoDq4lxsqinC/jYjpJS4NOQr57KiIrUCWfp8Newuz6L9o9JdIJA13zVq75gvk6J2CQSyAuc82Urf1EI8z3liUVpQCIFlOg1e8U+qLoVgYzyUaNVYV1WIQ4v0vhkw+YJH1SlQWjCQ0RpOmcOq4lwMTTngdMdusvbZiyOwzLjx1nm+I41lWmiyFDjDcwvKANsbSpCbrcQzFzKzvGDXqBUmmwuba4tuum9NlQ5n0jwgPXs+U3LjfrO5XAunxzsb6CJKlJQNZAHA/bsa8dHb6vCjI73492c7kz0cIqIbSClxut+M9dW6uL3HvgPtmJlz4RSvBtoUW8yMCM++A+2wz+mNyc86pbpbG0sgBPBiZ2r0yaL0EughFEpGFuA7rnSOWHBhaArtV/2BrBQrLVjuL9s2Mp3ZfbK0GhW8EnC4gk9uB0qCLZUgyd4NBrxtowHFedlxPecx2XylBYtyIy8tCPjKC3b7J+camJEVsV0tepweMGPcMv/3/VrwKPkZWYGgmiGMspfVRTmQMrY93x5vM6K8QI1t9SVB71cpFVhVUYCzDGRRBtBkKbGzpQx/ujCckdW2TvZNAAA21RbfdN9agw7do1ZMp3Gv895xK1QKcdN+s0nvW0h1eYR9siixUjqQJYTAZ+9eiXdsrML/+1Mn1n3xAMsNEVHK6Bu3YdLuwrqqwri9RyIbaBMlEz/rlI4Kc7OxtqoQL3amRp8sSi+BjKyKEDKyAODuNRVQKQT2txlx8eoUygvUKM6LPDMlHvT+/hDDU5ndJytP7e9fOhN8cqpnzAohgOXFyc9CSZT6Ui0mrE6YrM64vYfZGn1pQQBY5u9LpxC+bEeKzO4VekgJPN8x/2KOQPAonCyoeBkw2VGSlz37/Q1F4PPRb4pN1oHJ6sRz7SN4y7pKKBVi3setrSrE+cFJeDJw4p+WntetKsfI9AxOp3mZvWCO95pQnJeN+iALV9b4y4SeMy7eSzBV9Y3bUF2cC5XyxvBBo14LIYCOYfbJmg/bJsRHSgeyAF8wa3t9MRQCmLS7WW6IiFJG4ERsXXVh3N4jkQ20iZKJn3VKV7c3leJUvxmT9vRdbUnJMThpR5ZSoDRPvfiDARTlZWNnix5PnB7EhcGplMvGAgB9ge93Gc3wjKx8/0S4xRG8hGLPmBWVupyg/W8yVYPeN4nXPRa/SS2TzQWtWoVsVXTTGBX+QFZ1cS7UqqXzN4q11ZUFKNWqF+yTNWCyQa1SoFSb/KD7gMkWdmbYbCBrIjYLq548OwS3Vy6atbjGoIPV6UFPHL9PRImyq0UPpUJkZHnBE72+/lhC3ByYXmMIBLLSN7uyd9yKmpKbFyLkZCtRVZSDzhHuo4Jh24T4SflAFgB8/ZlOzF2IwnJDRJRsp/rNyMlSokmvjdt7JLKBNlEyPbCnBao5K1P5Wad0sKOpDB6vxMsZ2sSa4mfI7MAynQaKBVblz2Uo1GB4agaXrk7jZJ8p5S6I9YHSglOZHcgKZHRYZzxB7+8dsy6ZsoIBDWW+8+GuEWvc3sNkc6IwyrKCADDizxjsG7dxlXQUFAqBnS1leKFjFG5P8DKbAyY7qopygk7yJprRZA87M2xZgQZZShGzjKz9bUY0l2uxqmLhhQhr/Zkc7JNFmaAwNxtb64rxdIYFskanZ9A7bsOmmpv7YwG+XoKGwpy07ZMlpUTfuA21JcHPZ5r1+egcZmnBYNg2IX7SIpDFckNElIpO95uxxqC7Kc06lgINtA2FORCIbwNtomTau8EwO1kAALnZSn7WKS1sWF6IvGwl+2RR2IYm7SH3xwJ8E6C/PNE/+7Nlxp1yqzvz1SposhQZX1pQu0BpQSklesasqC1Nfim1RKoqykW2UoGu0XhmZDlRFGVZwf1tRjx5dmj2Z66Sjs7uFXpMOdxo6zcHvb/fZEuJ8o1er5wNqoVDqRCoLMxB/0T0gaz+CRtO9Jlwz3rDooG9+jItcrOVDGRRxnjdqnJcHrGgZyx+ix0SbaH+WAFrDDqcTdOSiuNWJywz7qAZWQDQWK5F96h13oUMSxnjGPGTFoEslhuiVMWap0uX0+3FucEprF9eGPf32rvBgMMP7kbPw3fHtYE2UTKNTDkwYLbjr1/bjLvXViBfo8I96yuTPSyiRWUpFdjeUMo+WRS2QbMDlbrQ+mMBvtWdDteNkwWptrpTCAF9vgYjGV5aULtARpbJ5sKUwz3vCuZMpVQI1JbmxjmQ5Yo6I2vfgXY4PTeWe0m171E6ua2pFCqFmLe8YCTBo3gYtczA6fFGNJbqolz0m6KffPztKd9cQSjnt0qFwOrKApxN00wOorlet6ocAPDMhatJHknsnOg1IVulQKth/gzLNVU69I7bMGlLvxLkfeO+oONCGVlOjxd9MQj0ZxrGMeInLQJZLK1FqYg1T5e29qvTcLq9WFdVmOyhEGWE5/yNwne16HF7UymGp2ZYc5vSxu3NpbgyYZu94CNajMcrcXXKEdYFbbqs7iwvUGNkOsMzsjT+HllBMrICq82XWmlBwFdesHs0fvtBs82J4rzoMrLS5XuULgo0WdhUW4RDQQJZ0w4XzDZX2OX84mHAXxqwKoLssOriXAxEOVErpcTjbUZsqS0OeXusMRTiwuAUsx0oI1QV5WJVRQGePp855QWP95mwvqpwwV6LgTKh5wbTLyjdO+bb782XkdVU7isp3DnMa/a5HtjTAiXbJsRFWgSyri+tBfhWp7DcECUba54ubaf86eHrqnXJHQhRhjh0aQTLCjRYWZGP25rKAAAvdLBUG6WH2wOfWWZlUYhGp2fg8UpUhBHISpfVnfp8zRLokeWbtLIEychayoGs+rI89E3Y4HTHZ+J9whp9acF0+R6lk10tely6On1TMNDo/zkVMrIG/BlV1ZFkZBXnYNzqhHXGHfH7nx+cQteoNaw5rLVVOthdHnTFMThMlEivW1WOk1dMGLOk/zmC3enBeeMkNtYG748V0Frpmy9Kx+zKvnErFALzBt8b9YFAFvtkzfX61mVQCV+7BIBtE2IpLQJZwLXSWg/saYHHK7FrhT7ZQ6Iljqv5lrbT/WaUarNnA+xEFDmXx4sXO8ewa0UZhBAwFOagoSyPpdoobdSU5KK6OAcvMvhKIRqc9J0vhlNaMF2qVJTlqzO+tGC+2lfezuK4eWK7d8wKpUKkRF+gRGso08LjlbgyEfuJd7fHi2mHO+rSgunyPUonu/1zM8+133gMHJgIBLKS/10IBLIMhRFkZPnHPxBFecHH24zIVipw95qKkJ/TavBNgJ9J0/46RHPdtbocUgLPXkz/rKxT/Wa4vRKbFwlkFeVlo7o4B2fTsN9d77gNhqIcZKuChw5ys1WoKsphFZUgDl4awYxH4vvv34Tt9SVoLs9nECtG0iaQFRA4mF8YnErySGip42q+pe10vxnrqgoXbdRLRIs70WuCZcaNnS3XFqnsaCrD0Z5xOFw3r3YnSjVCCOxoKsPLXeNwsQQQhWDI7Cu9V6EL/bzx+ioVAoChMCclV3eWF2hgmXFHlb2Q6jRZCigEgv6OPeNWVBXlIEuZdpfaUWso863OjkcGidnuK+MYbUZWunyP0kmjXgtDYc5NfbL6A+X8UiAjq3/ChlJtNnKy5y8BNp9AULo/wvKCHq/EE6cHsbOlDLowArH1pXnIy1amZSYHUTCrKgpgKMzBMxfSP5B1sm8CAHDL8oUDWQCw1lCIM0ZznEcUe33j1kX7fTaX56ODGVk3ebzNCH2+GtsbStCo16JrxAIp5eJPpEWpkj2AcK2u9DXROz84ie0NJUkeDS1lD+xpwd/+6hS81+2LuJpvcfvbjNh3oB2DZjsqC3PwwJ6WtLtwnHa4cHnUgjevW7xRLxEt7lD7CLKUArc2ls7edntzKX50pBcn+0w33E6UqtQqgekZN5o/+4e0Pb5R4gwFMrIKQ8/IAnyT8Kn+udLnqwEAI9MzqFOn3eVmSIQQ0KpVsAQJZPWOLT7xk6nqy3y/d9do7Fdnm21OAIg6IwtIj+9ROhFCYNeKMvzmpBEzbs9sv5gBkx05WUqURNnXLBYGTHYYIswMC5QjDATmwnWkawyj0zN4a5ifOYVCoNWgYyCLMoYQAvVlefjTxRHUPfhkWp8vH+81oblci8IQFlesqdLhybNDMFmdKEqB/WGoesdtePO6hbNIm/RavHR5DG6PF6oluIAnGLPNiefaR/DB7bVQKgQa9VpMz7gxMj2D8oLwzvvpZmn3KSvVqrGsQIPzzMiiJLt7bQWu793H1XyL299mxEOPnYXRbIeEr276Q4+dxf42Y7KHFpazxklICayrLkz2UIgywqFLI9haVwLtdROeW+tKkKUUeKGTpdoo9e1vM+Lnx/oBIK2Pb5Q4g2YHcrKU0OVEPymfavQF/kDWlCPJI4mvYIEsKSV6x6xLsj8WAORrsqDPV6NrJPYZWSabLyOrOI0mAZeS3Sv0sLs8ONo9MXvbgMmGqqKclKhgMWCyRdQfC/B95nKzlbgSYUbW421G5GtUEbXHWFulw4XBKWZ7U0bY32ac3Uek8/myxyvxap8Jm2qLQ3r8GkP69ckyWZ2YtLsWXZjTVJ4Pp9sb8f4xEz15dgguj5ydGw70ErvMEowxkXaBLMCXlXUujXYAlJnOD07B7QVqS3KRpRR4/oGdDGItYt+BdtjnlAmzuzzYd6A9SSOKzOl+3/5nXZUuySMhSn/9EzZ0jliws6Xshtvz1CpsrCnCix3sk0Wpb9+BdjhcN04ypePxjRJnaNKOikJNSkzwxlpgtWmm98nSalQ39cganZ6B1elZsoEswFdesHss9pM1E1ZfRla0pQUpPrbXl0KtUuBQ+7XyggMme0qUFfR6JYxme8S9uoQQqC7KRf9EeD2y9rcZ8ZqvPovHXjXC45X447mrYb93q0GHGbcXncOcAKX0t+9AO5ye9D9f7hiexvSMG5tqFi8rCACtlekXyOod9y1IWTSQ5Q/SsE/WNb9tG0STXjtbUY6BrNhKz0CWQYeuUQvsTvbNoOQ50etbSXLf5uVweSR6x7kCYTGD5uAn//PdnqpO9ZtQV5oXUho5ES3sOf+Ex+4gq1R3NJXhwtAURjN8MpTSX6Yc3yhxBicdqAyjP1Y6CZQWHF4CGVlW542BrJ4x/8TPUg5k6fPi0gsilqUFKfZyspXY3lCC59qvZdL7AlmRBY9iaWR6Bi6PjCqoVl2cg4EwSgsGKpEMTvr2gzanJ6LMk7VVhQCAs2nYX4dorkw5Xw7MBW4OMSNLl5uF2pJcnB1In0BWn39+s7Z04X14IEjTyT5ZAHyLdI/1TmDvBsPsYjV9vhr5ahUDWTGSloGs1soCeCVw8SrLC1LynOg1obo4B7f5e7dcHuGOezGVhcEvHua7PVWd7p9kNhZRjBy8NIKaktygq9dvb/JlaR2+zKwsSm2ZcnyjxBky21Ghy8w6+bqcLGSrFBm/CCFPrcL0nIyswArmuiXaIwsA6ku1mHK4MWZxxvR1A6UFmZGVuna16NEzZkXPmBVTDhcm7a6UyMgK9LaKZixVRbnon7CFHKCNVSWSmuJc5GtUOJNGE+BE88mU8+UTfSbo89Vh7VPWVBWmXUaWEFh0MUKeWgVDYQ46mDUKAHji9CAA4C3rKmdvE0KgQa9FJ+eMYyItA1mr/fVFz6fRToAyi5QSJ/omsLmmGI16LYRATHfc+9uMuPXhg6h78Enc+vDBtKsZPJ8H9rRApbixhE5OlhIP7GlJ0ojCd3XSgatTDvbHIooBh8uDI13j2NWiD1pea3VlAYpys9gni1LeA3takJOlvOG2dDu+UeI43V6MWmZQkWYTN6ESQkCfr8740oL5GhWsM3MzsmzIUgpUFmZmkDIUDf7V2d2jsZ3UMtmcyFYqkJutXPzBlBS7WnzZ9YcujWDAX4avujj5GVkDs4GsyMdSXZwLq9MzG1BdTKwyTxQKgTUGHVtrUEbIlPPlE70mbK4tDqs89FqDDkazHeOW9Dg36hu3oVKXA03W4sfc5nItSwvCN0/8eJsRm2uLbjr2Neq1uByH/qGLycS55bQMZFXqNCjKzcL5QWZkUXL0jdswZnFiY20RcrKVqCrKidmOO1CGwGi2p3UDzGD2bjCgSa+FSiEgABgKc/DVe9ekVW+x0wNmAGAgiygGXu4ex4zbO2/za4VC4LamMrzYORbzEkVEsbR3gwFfvXcNDIU5aXt8o8QZnnJASt81TabS56szvrRgXrYKlpsCWRZUF+dCpUzLy+yYaCjzZaN1jcZ2wsZsdaEoLysj+8pliuUluWgoy8Oh9pHrgkfJD9gHgmpRlRb0P7d/IrTygvMFsyPJPFlj0OHi0DScbu/iDyZKYdefLwf89Wub0up8edBsh9Fsx8YQ+2MFtBrSq09W77gVNSWhBf+byvPRNWqBx7u0r9fPD07h8ogl6Oe5Sa/FmGUGkyEuhoiFTJ1bTsszbCEEVlfqcG4wPXYAlHmOz6mJ26zPj1lN2FiVIUhVUw433rimAj0P343DD+5Oq5MWADjdb4ZKIbCqoiDZQ6EFZOLKk0x06NIIcrKU2Fo3f33xHU2lGJ2eQTvrblOK27vBgMMP7k7b4xslzpC/Z0qmZmQBgD5fk/EZWVqNCpa5pQXHbKhfwv2xAPhXcCvQFeOMrAmbk2UF08DuFXoc7Z5Ah/+8LRV6ZA2Y7CjLV4eUWTCfwOr6/hD7ZO1Zveym2yLNPFlTpYPT453dpkTpLHC+fOyzd0KlEBi3xrYMbbyd6DMBCL0/VkCrwTd/lC59svrGbagJsUxyk14Lp9uLKyEG+jPV/jYjspQCd6+puOm+QC+xy6OJ249n6txyWgayAGC1oQAdVy1clUJJcbLPhAKNCo1lvp1RY7kW3aNWuD3Rfx4zpQFmMNYZN4xmO5r8O/F0dHrAjJUVBVFdCFF8ZerKk0wjpcTBSyO4tbFkwe/TjiZfH8IXO9gni4gyw9Ck75wukzOyygvUGMnwjCytWgWL0z2bMez1SvSOW1G7hPtjAb5s6rpSbcxLC5ptThTmZsX0NSn2drXo4fR48ejJAeRmK1GUAn+zAbMt6syw2UDWxOLX5A6XB89cHMayAjUqCzVRZ2qvNRQCAPtkUUbR52uwe4Uej706AFcM5tES5WTvBHKzlVhZkR/W8/I1Wagvy8OZNMjImrS7MGF1ojaMjCwASzrY7vFKPHF6EDtb9CgMsuhmNpCVwBKMmTq3nLaBrNZK36oUNkujZDjeO4FNtcVQ+Ps9Nevz4fR40ReDFQiZ0gAzmMBOO3CgSzder8SZ/kmsq9Yleyi0gExdeZJpukYtGDDZsbMleFnBgApdDprLteyTRUQZY9C8BDKyCjSYcrjhmHM8ziRatQpSAjan73e8OuXAjNuL2iWekQX4ygvGurSgyeZiRlYa2FRbDK1ahb5xX/AoFUpBDpjsUWeGadUqFOdlh5SR9cOXetA/YcfX37keRx68M+pM7eriHOhystKmJBlRqO7bXI0xixPPXhxJ9lBCdrzXhA3LCyMqIbzWoEuLjKwr4779XDgZWUBigzSp5uWucYxMz+Ct8+znq4pyka1SJHQbZercctoGslZX+tIyzxtTs08Wy1plrgmrE12jVmyqvVYTt6nct+OORXnBB/a0QDHnfD8dG2AGE+gj1lyenhlZ3WNWTM+4sb46vHrIlFiZuvIk0xy65AtMzdcf63o7mspwrGcioydEiWjpGJq0I1+jglatSvZQ4qYsXw0AGJnK3PKCef6/n9XfJ6t3zBe4qWMgCw1lWvSbbDE9bpttThTlMZCV6rJVCtSX+oJGHcOWpM+FeLwSg2Z7THp1VRflLNoj6+qkA98+dBl7Vpfj1sbSqN8T8LXWWFulw1mjOSavR5Qq7mguQ3mBGr860Z/soYRk2uHCpatT2FgTXlnBgFaDDlenHBiZDi9jPdHzy73jvvOZ2tLQFgDkqVUwFOYs6Yysx9uMyFersHueuQ2lQqC+NC+hgawH9rQge07ANRPmltM2kFVbkoe8bCXOp2CfLJa1ymwn/TVxN1138AqkiXYOR79Tumd9JdQqxWwwq1SbnTEN4zuHp5GtVGB5cfJrpUfiVL8ZALCeGVkpLVNXnmSag5dG0FKef0Oz3/nsaCrFjNuLYz0TCRgZEVF8DZodqNRl9jGpvMBXNjHcyZp0kq/xBbKm/YGsbgayZtWX5UHKa5Nh0ZJS+jOykl+mjha2v82Ii1evTWYmey5keMoBl0eiOga9uqqKczFgWnhh3MN/uAi3V+Kzb1wV9ftdr9WgQ/vVaS7qooyiUirw9o1VeK59BFcnU/98oe2KGV4JbK6NbGHz2qpCAMC5MLIrkzG/3Oc/dtcUh34+01Sujcl8aDqyOz04cP4q3rBm2YItExr1WlyOcdnlhezdYMDG2kIIIOoSt6kkbQNZCoXA6kodzg2mXkYWy1plthO9E8hWKrC26lowIzdbhaqiHHTEILpuNNthd3nxd3e1QAjgA9tr035HE9AxPI36sryI0rBTwel+M7RqFepL0zOjbKl4YE8LcuacQGTCypNMMu1w4XjvREjZWACwta4E2UoFXmR5QSKKsWRUURiatKOiMHP7YwGA3p+RNZzJGVnZN2dkqVUKLCvI7L9tKBr8fYS7Y1RecMrhhscrWVowDew70A6XR95wWzLnQgKBp9hkZOXCaLLD45VB7z/ZN4H9pwbxsR11WB5ib5lQrTXo4PJItF9duhkPlJneuakaXgn8+mTqZ2Wd6J2AQgAblkcWyFpdWQAhwut3l4z55d5xG5YVaJCTHXpf+ObyfHSNWubdP2ayP10chmXGvei8baNeiwGTPWELEqSU6Buz4a7V5VGXuE0l6Tmb7LeqsgAXBqdS7ovCslaZ7USfCa2Ggpsi7c3l+TEpLXjOXy5ze0MJViwrwNGe8ahfM1V0jljStj8WAJweMGNtlW62Nxqlpr0bDPjqvWtgKMzJqJUnmeTw5TG4vRK7WspCenxOthKb64rwYudYnEdGREtJsqooDE06UJHhGVmBQFYmZ2Rp/RlZFoc/kDVuRW1JHs8T4cvIAoCuGJXQMducABC0gTqlllSbCxnw97SKSSCrOAdOjxfDUzfv17xeiS88cQHlBWp8cmdj1O811xr/Itoz7JNFGaamJA/b6ovxqxMD8KbY3O5cJ/pMWFlREHFp6Dy1Co1l2rD6ZCVjn9o7ZkVNmMH4Rr0WM27vouVXM9FvTxmxrECDbXUlCz6uUa+FlL5e4YnQPjyNwUkHdi3SkzzdpHUgq9Wgg93lQc9YbBvJRmu+8lXFrOmd9hwuD84OTGJz7c01cZvKtegetcLt8Ub1HhcGJ6EQwMplBdhaV4yTfSY43dG9ZiqwzrgxYLKjWZ+e2UwOlwcXh6awrrow2UOhEOzdYMDhB3dn1MqTTHLw0gjyNSrcUhP6arYdTWW4dHUaI0EmD4iIIpGMVa4OlwcTVicqdZmdtVOUm40spcDIdOZmZAUmsiz+jKyeMWvI/SQyXW62r19GrCZrTDYXALC0YBpItRLfgYysWLx/oDxhsInaX58cwFnjJB56w8rZ/nmxZCjMQXFeNs6FMQFOlC7etXk5rkzY8EoKL+J2ebxou2IOOhcYjjVVOpwxTkLK0IJ2gZ6jc8Vzn9o7bkNtSXhlkpv9C9aXWp+sCasTz7WP4p71lYsuZAq0pElUn6xAT/KdDGSljtWVBQCQcn2ygpW1EgKYtDvZ3yPNnTVOwunxYmOQydcmfT6cHi+uRLkC4dzgFBr1WuRkK7GtvhgOlxdnM2DlVeBCtqk8PQNZF4em4PJIrPPXNSaiyEgpcah9FLc3lyErjDKjO5p8DbOZlUVEsZKMVa5D/h4QFRnet1GhECjTqoNmLmSK6wNZHq/ElQkbatkfa1Z9Wd5s37BomfwZWUVcGJryUq3Ed/+EDfp89YJ9S0JV7e/z3D+nT9aUw4WvHbiEjTVFuGd9ZdTvE4wQAq0GHTOyKCO9vnUZ8jUq/PJ46pYXvDg0BbvLE3QuMBxrDDqMTs+EVHrZ45XIybr5ejme+1TLjBtjlhnUhLkwJxCk6UxQkCZVPHlmEG6vxD3rF184XVeaB4WIXbb6Yg5dGsGqigIsy7DFcxEHsoQQ1UKIQ0KIi0KI80KIv/Lf/gUhhFEIccr/3xtjN9wbNeq1yFYpcD7F+mQFK2v1pXtWY3lJHj7yo+NhNfaj1HK81xeIDHbwavYHaDqibHB4fnASqyt9pQMCqz0yobxgYLuka2nB0/1mAMB6ZmQRReX84BRGp2fCTnFfuawApdps9skiophJRubAkDmQHZBZF5XBlBVoMJrBGVmBrAvrjBtGkx0uj0Q9A1mzGsq06BqxhLzqfCEmqz+QxdKCKS/VSnwPmOwxKSsI+PbbQtyckfUfz3Zi3OrEF968GkLEr7ToWoMOHcPTCeuvQpQomiwl9q434A/nrmLSn4Gbao73mgAAm2qjC2St9ZcJDWWx+iMv9aBvwo73bVsOg//cVKUQcd2n9o37FqCEm5GlVfsysWPRbiWd7D81iJbyfKysWHyeU61SYnlxLi4noLTgpM2Fk1dM2LUitFYO6SSajCw3gL+TUq4EsA3A/UKIVf77/k1Kud7/31NRj3IeWUoFVi7LT8nA0NyyVu/bVouffmQr8jUqfPCRY+hOUE1Miq2TvSbUl+WhRHtzem+gqfHlkch33CPTDgxPzcxmG5Zo1WjSazMik69zZBrZSgVqitOz5MrpgUksK9Bk3GoGokQ7dGkEALAzxP5YAQqFwG2NpXjp8ljK108novTwwJ4WqFU3Xg7FO3PAGAhkZXiPLAAoz1djJIQVx+kq398ja3rGjZ4IJ34yWUNZHqxOT0irzhfD0oLpJZVKfA+YbbOZVNFSq5RYVqBBv+laIKtr1IL/PtyLd26snu1jFS9rqnTweCUuDKXWQm6iWLhvczWcbi9+ezq+fUojdbJvAobCnKh7nK6q0EEhgLMD5gUf1zVqwb8+3Y7XrSrHl+5pxeEHd+Mzb1wBt1fiNQ0L92KKRt+4b/8Wbo8swJdsEu3C/nRyZdyGk30m7N1gCHkRQ6Nei84EbKMXL4/C45XYvSKzygoCUQSypJRDUspX/f8/DeAigISfoayq1OFcGPVFk8lQmIOffnQrJID3//AYhiaT0/CUIuP1SpzoM2FzTfCauHlqFaqKcqLacQeyC1sN106Ct9YX40SvKereW8nWOWxBfVkeVGGUEkslp/rNWFcd34sToqXgYPsI1lXpUBpkQcBidjSVYczixMWrvIAnoujt3WDAnf4LvERlDgRKCy6FhTH6AjWGpzO3tKBapYBSIWCdcaPXX0KvjhlZs+r9i/xisYDTbHNCIYACDQNZFDq3x4shsyNmGVmAr0/WwMS1eZwv/f6CbwHE6+NfOjGQyZGKC7mJotVq0GF1ZUFKlheUUuJ4rwmbo8zGAoCcbCWay/MXLBPq8Uo88OhpaLKU+PLe1tkgydY6XwDrWG/8Frr3+hfm1ESwMKe5XIuuUQs8S2TR6f5TvqDrW8IoKdug16J33Br3+d2Dl0ZQmJuF9dXRf2ZTTUxmlIUQtQA2ADjqv+lTQogzQohHhBBx3WqthgJMOdyzTTxTXUOZFj/58BZM2l143w+OYsJfJoFSX9eoBZN214KpxE16bVQ1YS/4A1mr/BlZALClrgSWGXfar7zqHJmerZubbsw2J3rGrFjHsoJEUZmwOnGq34xdEa4MYp8sIoq1MYsTawy6hGUODE3aUZKXHZN+LalOn6+B2ebCjDszy2AJIaBVq2BxuNEzZkVetnLepuxLUaBaRVcMAlkmmxO6nKxFG6kTXW94egZur0RVUewqglQV58xmZB28NIzn2kfxV69timiBVriWFWhQqs3GmQEGsigz3be5GucHp1IqWLu/zYhtX30Wo9MzONQ+iv1t0WeMtRp0ODswf0LGfx/uwatXzPjCW1ZBX3Bt4dPqygLkZStxtDt+gay+MRtKterZPqDhaNLnY8btvan8aqzsbzPi1ocPou7BJ3Hrwwdj8reIlJQS+08ZsbWueLbsYygay7RweST64rSNAF8SxvPto7ijuQzKDDxvijqQJYTQAvgNgL+WUk4B+C6ABgDrAQwB+Po8z/u4EOKEEOLE6Gjk/S4CvYTOD6bOjm4xrQYdfvDBTRgw2fHBR45h2pGaNWDpRtdq4gbPyAKA5vL8qFYgnDNOoqYk94bVhtvqfO+XzuUFbU43+ifsaE7T/liBi4X1VYXJHQhRmnu+YwRSIuz+WAH6Ag1WLMtnnywiiolphwuvXjHNBskTYdDsQMUS6I8FAOUFvondTO6TpVWrYJnxoHfcipqSvLj2x0k35QVq5GUr0TVqjfq1TDYXivLYH4vCM+CfKIx1RtbVKQcsM2586fcXUV+Whw9sr43Z6y9ECIE1/glwokx0zzoDslWKlMnK2t9mxEOPnZ0tkTtpd+Ghx85GHUBZW6XDuNU5m6V/ve5RC/YdaMdrV+qxd/2Ni6tUSgU21hbjaM94VO+/kN5xK2ojKCsIAE3lvgUs0Szun0/gb2E02yHhK9Udi79FpM4aJ9E9ag17AVxgcf/lOGyjgDPGSYxbnRHPuaS6qAJZQogs+IJYP5NSPgYAUsphKaVHSukF8F8AtgR7rpTy+1LKTVLKTWVlkTcfW7EsH0qFwDljemWrbKsvwXfeewsuDk1h77cO4zVffTYloso0vxN9EyjVZi+4U2/Ua+F0e3Elwuj6ucHJ2f5YAfoCDepK8/BKHFddxFvXiO8CtikNM7L2txlx//++CgD4u0dP8/tJaSWVVi0BwKFLoyjVZmONIfIynTuaSnG8xwS7MzNX+BNR4rzSPQG3V2JHU+IaIQ9N2qPur5Au9Pm+gF0seiSlKl8gy4WeMSvqylhW8HpCCDTotbHJyLI6UZTLQBaFp99ftSeWGVkj0w5ICbR+/gB6xqx47Uo9slWJK52/pqoQnSPTsDndCXtPokTR5WbhDa3LsP+UEQ5XeNd68bju3XegHfY547C7PNh3oD2q1w1cC8/NrvR4Jf7+12egVinwlbeuCbo4ZmtdMTqGLXGr7tU3bouorCBwLUjTMTwdyyEBiN/fIlyBz9lbvnUYAMJuc5SIQNahSyMQArijOXHXN4kU8RFX+L5RPwRwUUr5jetur7juYW8FcC7y4S1Ok6VEk16bVhlZAXeuLMe7t1Sja8yKwUlHSkSVaX4nek3YWFO04ErLQMZRJDvuSZsL/RP22SzD622pLcbx3gl407TWbGB7NKVZRlZg1ce0w3ehMDTp4PeT0kaqrVryeCWe7xjFHc36qEoD7Wgqg9PjjetKNCJaGl7sHEVuthK31BQm7D2HzA5ULoH+WICvRxYAjGZwnyytRgWTzYUBkx11EU78ZLL60jx0xyojK5f9sSg8AyYbhAAqY5QFu7/NiN+8euN59E9fvpLQc+u1Bh288lpLAqJMc9/makw73PjjuashPyde171Gc/AWNoPz3B6qlRUFUCkEzhrNN9z+oyO9ONFnwhfesvqGkoLX21Yfv4pNdqcHV6ccEWdk5WuyUKnTxCVIM982j/ZvEY7rP2cBX/r9xbA+Z/maLCwr0KArnoGs9hFsqC7M2Ez2aJaO3Arg/QB2CyFO+f97I4CvCSHOCiHOANgF4G9iMdCFrKoswLk0PZAfvHRzeaRkRJVpYSNTDlyZsGHzAmUFgWvR9c4IAlnnh3zB2NYgmQpb64sxaXehPQ4rGxKhc8SCLKWI+ICYLKmy6oMoEqn2+W27YsKk3YVdK6JbGbSlrhjZKgX7ZBFR1F7sHMO2+hKoVYnpVzXtcGF6xo2KMGrpp7NARtZIBpcWzFOr0H51Gh6vRG0pA1lzNZRpYTTbo84eMducKGRGFoVpwGRHeb4mZvv4fQfa4XR7b7gt0efWa6p8cwVnU6iHEFEsbasrwfLiXPzi+JWQnxPr695JmwsPPHp63vsrozyP02Qp0VSef0NGVs+YFfsOXMKdK/R46wLl6tYYCqHJUsRlUWegslQ05zON5flxyciab5tH+7cIR6w+Z416LS7HIFs9mNHpGZwZmMzYsoJAFIEsKeVLUkohpVwrpVzv/+8pKeX7pZRr/Le/RUo5FMsBB9NaqcPo9AxGptJvtV8qRJVpcSf6fP2xNtYULfi4PLUKhsKciGrCBlZVzS0tCABb60sAAEe70zMDoXN4GvWlWqiUiSu7EAv8flI6S7XP76H2ESgVIuoSXposJbbWFbNPFhFFpX/Chp4xa0L7YwV6IVQskYyskrxsKBUCw2l4jRaqfLUKk3Zfv+O60vRasJUIDf5FftFmZZlsTmZkUVj2txnxxOlBXJ1yxKzMWCqcW5cXaKDPV7NPFmUshULgnZuq8Er3BPrGQzt2xOq7KaXEU2eHcOc3nsdjbUbcuVIPTdaNc1g5WUo8sKclrNcNZq1Bh7PGSUgp4fVK/P2vTyNbqcBX7g1eUjAgW6XALcuL4pKR1TPm2961UWSYN+u1uDxigSfG1aQe2NOCuUVdVAoRk79FqGL1OWvUa9E1Ygm7LGEonmsfAQDsWsFAVkoLTPyfT8OsrPmix+XzpJFSchzvnYAmSxG07N9cTeVadAyHH8g6Z5zEsgINSrXqm+4zFObAUJiDo3E4WCVC54hltvFjOkmFVR9EkUq1z+/BS6PYWFMEXU70E1E7mkrRMWzB1SANcomIQhHI6kxkf6zAhe5SOY9QKATKtGqMZHCPrDz1tUyPutL0O9eNt3p/37DuscgDWXanBw6XN2NL5FDsBco/BbKnYlVmLFXOrddW6XCGGVmUwd6+sRoKAfzqRP+ij32pc2zBsvVffvIC+kPoYT885cCf//QkPvmzV7FMp8Zv778VP/zgZjx871oYCnMg4JuX++q9a7B3gYypUK2p0sHsL03845d7cbzXhP/75tUhzQVvrSvBhaGp2YU0sRIIHC6PopJSU7kWM24vBkyLb/NwbFheCK8E8jUqCPgCilLKRZMNYilWx4AGvRZWp2d2gVssPdc+Cn2+OmiCRKbIiEDWKv8f6FwaHswf2NOCnKybU90dLjcuXU2/wFymOtlnwrqqwpAauTaX56NrNPwVCOcGp9BqmH9ns7W+GMd6JuIStY8nu9ODfpMNTfr06o8FBP9+xmoFDlG8pcrnd3+bEdu+8iwuDk3h0tBUTFbE3u5vXMqsLCKK1Iudo6jUadBQlrhycEstIwvw9cnK5NKCWrVvcUaBRsWMoSBqS/IgBKLqBWGy+RraF7G0IIUoXuW1U+XcWqVQ4PKIBXUPPhmzbDOiVLJMp8HOFj1+fXIAbo836GPMNic+/ehpvO+HR1Gcm3XTXJ1apcC6ah0eOdyL2/cdwsd+cgJHLo9BSon9bUbc+vBB/3foWfz9r0/jtd94Hs93jOLBN6zA/k/eOtvyY+8GAw4/uBs9D9+Nww/ujkkQCwAmrL5zox1fO4Qv/u4CVlXk4223hPbaW+qKISVwoje2C917x20ozsuOauFpU7lv3q8zgsX9C/mfV/qgUgj86W/vQM/Dd+Pgp+9AtkqJrzx1Mabvs5AH9rRAOSdoGskxoLHMt/Ap1r3EXB4vXugcxa4W/YJZfekuIwJZ+Zos1JXmpWVG1t4NBnz13jU3RPj/7q5mZKuUePt3X8bzHZykSzbrjBvnB6cW7Y8V0KjXwun2ztaXDYXN6Ub3qAWrFsj42lZXgnGrE11xqqUaL5dHLJASaE7DjKxg389YrcAhirdU+PwGVsRe9ZeVmnK4Y7IitqU8H2X5avbJIqKIuD1eHL48hh1NZQm90Bsy2yHE0qq8oM9XZ3RpQa0/I6uuNC+jJw0ipclSorooN6rrl2uBLAYKKTTxKgGYKufWBy/5SkdJxC7bjCjVvHNTNYanZm6aE5VS4skzQ3jtN17A421G3L+rAS/+w2587W03Zk79y9vWYv/9t+Glf9iF+3c24mSfCe/5wVFs/+qzeODXp2E02/3fIQd+dWIA+nw1/vjXt+MTdzTEvSXG/jYjvvNc1w23dY9a8dtTgyE9f8PyQmQrFTEvL9g3bkVNlH3tG/0lhTtGYtcny+Z045fH+7GnddnsOXSFLgef3NmAP5y7iiNdiZkT2FZfAikl8rKVUR0DAtso1oGsk30mTDvcUfckT3WqZA8gVlZVFuB0vznZw4jI3g2Gmz74b7ulCh/+0XF8+EfH8aV7WvGercuTNDo63W+GxyuxsTa0lNXm2RUI06gLsUnixaFpeCXQukD655Y6XyDtle4JNKZRdlOn/wCWjqUFgeDfT6J0kezP70IrYqMZlxACNcU5+P2ZQfzu9CAqC3PwwJ4WfleJKCRnjJOYcrixozlx/bEAYHDSAX2+Gllp1jM0GvoCDdqumJM9jLjY32bEj470AgA6hi3Y32bkcSiI+rK8qHpkmW2+0kmFzMiiEFUW5sAYJGgVixKAqXBu7ZyToRKLc2uiVHPnSj20aiXu/9mrmHF7UVmYg4/fXoeXLo/jmQvDaDUU4Mcf3jzb/mO+72aFLgef3tOCT+1uxO9OD+Izj5+Fy3NzlSO7yxPy/F209h1oh8N14/fY4faG/D3WZCmxvroQr8Q8kGWbnXeMVIEmCxU6DS7HMCPrt6cGMeVw44Pba2+4/WO31+OXJ/rxxScu4Mm/vC3uAchHDvcAAP7wV7dHVX6xVOvLersc4ySFQ+0jyFIK3NqY2OubWNrfZsS+A+3IXta4cb7HZMxVVGulDgMmO8z+FVvprrIwB7/+i9dgR1MpPvP4WXz1qYvwxrhZHoXmeK8JQgC3LA8tkBWIrneGEV2/MOgri7naMH9GVk1JLsoL1GnXJ6tj2IIspUBNFA0jiSg9xWtF7P42I04PTMIruRqViML3YscYhABuS/CF3tCkHRW6pdEfK0Cfr8a41QnXPKWB0lUg43jK4QbgmwDjcSi4hjItuscsEV/LBjKyitkji0KUKiUA4yFe59ZEqebJM0NwuLxwuL2z13uff+ICDl4cxkP+8n+h9LAP0GQp8Y5N1XAHCWIBwJA5cdnjsfgeb6krxjnjJCwz7piMyeHyYHDSHnVGFuArLxirjCwpJX58pBcrKwqweU5ygSZLic/dvRLtw9P432NXYvJ+85m0u/C/R6/g7rWVUQWxAN+i3Ea9NuYZWYcujWBzbTHyNemZwR44tw62EOV6GRPICjQyu5CG5QXno1Wr8IMPbML7ti3Hf77Qjfv/91U45qxsp/g70TeBlvL8kOvEatUqGApz0Dkc+o77nHEKRblZqFygZ4IQAlvrSnCsZzyt+mRdHvFlpi2l1cdE5BOvptj7DrTftJIuFr0PiGhpeLFzFGurChOe4TFkdqCycOmUFQQAfb7v9x3NsD5Z8erBk4kayrRwuLwYnIxsot00m5GVnhMzlHipUAIwXuJ1bk2UavYdaIc7yAKI0nw1/jyK8n+p8B2KxRi21hfD45V4tc8UkzENmGyQ0tfbMlpN/iBNLJIxjveacOnqND64vSZoCec9q5fhNQ0l+PrTHTBZ45fY8j+v9MEy48Yn7qiPyes1lmmj6h8614DJho5hC3a16GP2mokW7Nw6mIyZWQ4Ess75M1syhUqpwJfuacXn7l6JP56/irv+7Xls+8qzbOyZIB6vRNsVMzaFWFYwoKlci44wUmnPDU6i1aBbtLb+lrpiDE/NoG889P5bydYxbJlt+EhES0u8VsRyNSoRRWrK4UJbvxm3NyU2G0tKicElmJFVXqAGAIxkWCCLx6HQNZT5JsUiLS8YmJgqzGFGFoVu7wYDDj+4Gz0P343DD+7OiCAWkNnZZkTXm+94OjIV3flEKnyHYjGGjTVFUCkEjvaMx2RMvWO+OcZYZGQ1l/sWsAyYoj8n+vGRXhRoVLhnffB9uBACn3/zakw7XPjGMx1Rv18wDpcH/324F7c3l4WVBbiQRr0W41YnJmIUfDvU7uslt2tF+gayQj2HzphAVolWjUqdBuczKCMrQAiBj+6ox4deU4srE3ZcnXKwlFKCXLo6BcuMG5trw6sT26TXomvUAk8IKxCcbi86hqexaoH+WAHb6n3jiNXBKt7sTg/6TTY06dOzPxYRRSdeK2JTYSUdEaWnl7vG4fFK7GhKbCNks80Fh8uLigWy7zNRICNrZCpxJXsSgceh0NWX+a4DuiLsBWGyOaFVq5CtypipC6KIXX9uDQAKAfzz3tUZE6gjCojXcTYVMjZjMYbcbBVaDToc7Y5N65Hecd9ik1hkZDXqfQvZO8KoUhXM1UkH/nj+Ku7bXI2cbOW8j2tZlo/3bavBz4724dLV2McEfvPqAMYsMzHLxgKutaSJVXnB5y6NoLo4Z3bxULrweiUOXhrG+394FKHm76niOqIEW1WpwzljeBlZgUZig2Z7yjeLf/r88E23sbFnaCL9O5/o9aXpbqwJNyMrHzNuL/onbKhdpGFkx/A0XB6J1hAi+w1lWpRqs3G0ZwL3bV4e1piSoWvUAimBZmZkES1Z8WiK/cCeFjz02NkbUs81WQquRiWiRb3YOYq8bCU2LC9M6PsGyqottUCH3p+RNZxhGVnBjkPMigiuVJuNAo0q4kCW2eZiWUGi6wTOrf90YRgf/ckJaNO0HwrRQuJ5nI3H9WkyxrC1vhiPvNQDh8sDTdb8gZ5Q9I3bUKBRxeR42zHsCyZ99CcnYIhinv1/j/bBKyXev6120cf+7eua8cTpQXzxiQv4349tXbTaVag8Xon/eqEb66p02F5fEpPXBG4MZG2pCy9xYi6Hy4PDXWO4b1N1zH7vWAo2H3/nSj1+fXIAPz7Si95xG8oL1Hhj6zIcbB+Bw7VwX92MWtbUaihA95gV1hCb3V3fSCwdMpxYwiIy0fydT/SZUKHTzK54ClUgAymUFQiBvm6thsUDWUIIbKkrjtmqi3jr9Dd4ZEYWEcXS3FVsALBheWHSL0iIKPW90DGG7Q2lCe/dGWgivtQyskrysqEQwGiGZWSlworudCGEQINeG3lpQZsTxXksK0g0186WMujz1fjV8f5kD4Uo5nicXdy2uhK4PBKvXom+T1bvuBV1pXlRB0L2txnxT7+7OPtzpPPsM24P/vfYFexu0WN5COUOC3Oz8Xeva8bL3eP447mrYY97Pn88dxW94zZ84o6GmAaJDIU50GQpYpKR9Ur3OBwuL3amYFnBYPPxf/foaWz80jP44u8uoDgvG9989wa89A+78Z33bcTD965ddP49ozKyVlfqIKWvHNzGmsUjmgs16U3FnWNlYQ6MQYJWS21lZ7ii+Tuf6J3AxpqisHdYgZ5QnSMW3LV64ceeG5yEVq1CTXFotWi31BbjqbNXMWCyoaoo+vq18dQxbIFKIRbNSiMiCtf1q9i++Lvz+NGRXpwfnIxZ3Woiyjx941ZcmbDhozvqEv7eQ0s0I0ulVKBEq864HllAaqzoThf1pVq8dHk0ouearE4U5jKQRTSXSqnA2zdW4XvPd+HqpAPLlthCCcp8PM4ubGNtERQCONo9gdc0RNf7tW/chvXVhVGPKVbz7H84exVjFic+8JrakJ/z7i3L8bOjV/DPT17ErhX6qLPUpJT43vNdqCvNw12rl0X1WnMpFAL1pVpcjjBb/XrPtY9Ck6WIacZYrAT7PHi8EtlZSvz2E9uwbs5nLvCdFw9dPjnfa2ZcRhYAnDOGVhMz3TKcgjUEzFIKlrBYRKR/Z6PZjqFJR9j9sQBAq1ahUqdBZwgZWeeMk1hVUQCFIrRg2Vb/zulYT+pnZXUOW1BXmpfwVc9EtLT89WubUZSbjS8+cQFShlpdmYiWmhc6xwAg4f2xAGBw0oEspUCZVp3w9042fb4awxmWkUXhadDnYXhqBtMOV9jPNdlcKGJpQaKg3rmpGl7p6+FCREtLgSYLqyoLop4bdLq9GDDZUBtC5tNiYjXP/uOXe1FXmocdjaEH6FRKBT7/5tUwmu34/gvdYb1fMEe6xnHWOImP314PZYjzteFoKteiK8qMLCklDl4awWsaSqMO3MXDfH93h8tzUxArVBk1u7ysQIOSvOyQ+2SlW5Peuam1KoXAsgINVygs4Gj3ODDP/maxv/OJXt/BINz+WAFN5fnoXGSn5PFKXByaxmp/EDYULeX50OVkpUV5wc6RafbHIqK40+Vk4dN3teBY7wR+f2Yo2cMhohT1YscoqopyYnKhHq4hsx3lBZqQFy5lkvICTUZmZFHoGsp8ZcZ7xsIvL2iyOVHEjCyioGpL87C1rhi/OtEPr5eLuYiWmq11JXj1igkzbs/iD57HgMkGrwRqSqKvpBSLefYzA2a0XTHjA9trwj5v3t5QgjeuWYbvPHc56iSV7z3fhbJ8Nd4apzn3xjItjGZ7yO2Rguke81Wb2NWS+EV6odDlBF+IFE3cJaMCWUIIrKoswPnB0DKydjTdHNlN9Sa9ezcYcPjB3eh5+G7845tWod9kx6l+c7KHNa/9bUbc+vBB1D34JG59+GBC+4899uoA3vfDoyjTZkOtuvmjfnvzwpH9470T0KpVWLEsskBMk16LyyMWeBY4oewZs8Du8oRVCkuhENhcW4yjPeMRjStRHC4PrkzY0FTO/lhEFH/3ba7G6soCfPWpi7A7Iz+RJ6LM5PJ48XLXOHY0lSWlEfKg2YFKXWouloun/W1GvNI9jvODUwm/FqDUEQhkdYVZQuexk/2YdrjxoyO9/PwQzeNdW6rRN27D0TSo2EJEsbWlrhgzbi/ODISW0BFM37gNAFBbGv1Cr2CVxADgzpWh92/68ZE+5GYr8baNVRGN4TNvXAmX24s7v/58xHPR54yTeLFzDB++tS5umU6Net+5UaQ9RAHg0KURAMDOltTqj+X1SvzLHy/BbHdhbiwy2rhLRgWyAKDVoEPH8PSi0ehzxkk83mZEkz4PlYWatGweeO8tBuRlK/GTI73JHkpQwZq6RdLkL1xSSvzbMx3421+dxqaaYjzzNzvxL29bO5vJVqHToFmvxc+P9ePrT7fPW4bqRK8JG5YXQhVhWbzm8nzM+FN05xMog9kaRkYWAGyrL0bvuC2ly7RcHrFASqBJz4wsIoo/pULg829ejcFJB777fFeyh0NEKeZ0vxnTM27cHmQhWyIMTtpRUbi0+pcErgVs/sUFiboWoNSzvDgXSoVA10jokzX724z4zP5zsz/z80MU3BtaK5CvUeFXJ/qTPRQiSrAt/lYoR7sjX+jeO+47NsciI2tuJbFKnQb1pXn4+bEreK59ZNHnj1tm8Lszg7j3FgMKNJGVFT7Ra4IQAnaXJ+K56O8934V8tQrv3bY8ojGEIhDIujy6eEua+RxqH0GTXovq4sRXm5iPw+XB//lFG777XBfes3U59r392nx8LOIuqtgNNTWsriyA2yvROWxBqyF4lsukzYW/+NlJFOdl4xcf346SNK1Vn6/Jwts3VuHnx/rxmbtXojTFfo9YNfkLx4zbgwd/cxaPtxnx9o1V+Mpb1yBbpbipSaTL48XnHj+H/zh4GX3jNnzt7WtviLJP2l1oH57GG1orIh5Loz8TqWPYMu8B4fzgJLJVitlViqHaWufrk3W0ZwJvWVcZ8RjjqXPEtzNuZkYWESXIlrpivGltBf7z+S68c1MVqopS54SOiJLrhc4xKASiboYdCa9XYnjKgYollpGVjGsBSk3ZKgVqinPDysjad6AdDpf3htv4+SG6mSZLiXvWV+LREwP4wltWz1vKiYgyT1FeNlYsy8fRngl8KsLX6Bu3QatWoSQvNmV8586/TjlcePf3X8En/uck/ucjW7HJH3wL5pcn+uF0e/GB7bURv/++A+1wz6mMFc75Q9+4FU+dHcLHbq+POJgWipqSPCgVApcj7JNlmXHjWM8EPnxrXYxHFrlxyww+9pMTaOs34zNvXIGP7aiHEAJv21gds/fIvIwsf4m2+fpkeb0Sf/OrU7g66cC333tL2gaxAt6/vRZOjxe/OHYl2UO5Saya/IXKbHPi/T88hsfbjPj0Xc3Y9/a1yA5SUhAAspQKPPy2Nfj717fgidODeP8Pj2LC6py9v+2KCVICm2sj648F+EoLAtcCOsGcM05h5bJ8ZIWZ9bWyIh9atSqqVRfx1jlsgUohYrKqg4goVJ9540oIAXz1qUvJHgoRpZAXO0exrroQutzET/CNWWbg8khULrGMrERfC1Bqqy/ThlU+h58fotDdt2k5ZtxePHGKGYtES83WumKc7DPB5fEu/uAgesetqCnJjVvp7QJNFn784S2o1OXgz350HBfmaQfk9njxs1eu4DUNJWguj7yyU7TnD99/oRsqhQIfiXOAKFulQE1JbsSBrJc6x+DyyJQpK3h5xIK3fucIzg9O4TvvuQUfv70hLp+pjAtkLS/ORb5ahXODwQNZ33nuMg5eGsE/vmkVblkeeZAiVTTqtbitsRT/88oVuCPcacXLfM3bygtifxHfO2bFvd85glNXzPj3d63Hp3Y3LfqFEULgkzsb8a33bMDpgUnc+53D6PavEjzRa4JSIbB+eWHEY8rXZKFCp0HncPCdkpQS5wcnsXqezMGFqJQKbKotwrEUroPdMWxBXWnevMFEIqJ4qCzMwV/c0Ygnzw7h5a7UDfYTUeJM2lw43W/GjqbkNEIenPSVgl5qGVmxaPhNmaOhLA89Y9YF+wdfLx4NwokyVauhAKsqCvBLlhckWnK21JXA5vTMm9CxmL5xG2rjvAC9VKvGTz+6FVq1Ch945Ch6xm5e2PKniyMwmu1RZWMB858nKBUCZwbMCz53dHoGj54cwNs2GqCPw9z1XI1l2ogDWYcujSBfrcKmKBIwYuXlrnHc+53DsDnd+MXHt+ENayKvbraYjJthVigEVlYW4HyQCO+LnaP4+jMduGd9Jd6/rSYJo4uPD2yvwdUpB565MJzsodzggT0tNzV185Ew25zB7gjZ/jYjbn34IOoefBKb/vkZvPHfX4DJ5sTPPrYV96wPr9TEm9ZW4ucf24Yphxv3fvcI/vXAJXz/xW54vBKv+8YLUdVhbyrPnzcja8Bkx5TDjdWV4fXHCthaV4LOEQvGLTMRjy+eLo9Mo4llBYkoCf78jnoYCnPwxd+dT7lFHkSUeEe6xuCVSFp/rCH/CtAK3dLKyArW8DvaBs+UvhrKtHB6Fu4fHGA022F3umPeIJwoUwkhcN/mapwzTkU8mU1E6WlLna9UXyQL3d0eL/onbKgpiX9JfkNhDn76ka3wSuB9PziKockbM6R+8nIvKnUavHZldBlGwc4/s5UCudkK7P32YXz5yQuwOz1Bn/ujIz1webz42I76qMYQqka9Fn3jtrCy6Xzz4c/ilyf64fJ68eSZoTiOcKEx+Obk1//T03jvD15BeYEGj3/yVmyIc9JQxgWyAF95wYtDUzdMXhnNdvzlz9vQpNfiq/euiVvKZDLcubIchsIc/Pjl3mQP5QbVxTnwSqBAo5pt6vbJnQ2YsLrwof8+DuuMO6LXDTSONprtkADGLE7YXV58cmcjNi9Qa3UhG2uKsP+TtyJLKfCtQ11wun2fnWibCjfpfdF1b5CVh4ETzEA5zHBFc7CKN4fLg74JG5r0kacDExFFSpOlxGfvXolLV6fx8+NcmUq01L3QOYZ8tQrrqguT8v6BjKyllkkyt+F3LBo8U/pq0PtWey9WXlBKiYceOwulUoHP3r2Snx+iEO1db0C2SoFfMSuLaEkpy1ejoSwPRyOYGxw0O+D2yrhnZAU06rX48Z9twaTdhff/8Nhsi5fO4Wkc6RrHe7fVQBVm65W5gp1/fu3t6/DSg3fiXVuW479e7MGe//cCDl8eu+F5lhk3fvpyH16/ehnqyxKzKL9Rr4XbK9E3Hlrp5Wvz4b5rC4fLG9WcdSTmzsmbbS4AwJ/dVovq4vgHRFVxf4ckaDUUwOHyonvMiubyfMy4Pfjkz16FyyPxvfdtRG52Zv3aSoXA+7fX4OE/XMKlq1NYsSyyDJ9Y+9cDHSjVqvHC3++8YZuvqy7EX/zPSfz5T0/ihx/aBLVKucCr3CxY42gJ4EdHevGx2yOPmi8vyYVKcfMOM5qmws3lWjhcXgyY7Fg+Z4XD+cEpKBUCLcsiC/asrdIhJ0uJoz0TcU3bjETXqAVSghlZRJQ0b2hdhm31xfj60+1489oKFOaG1rx2f5sR+w60Y9BsR2VhDh7Y08JJM6I0JqXECx2jeE1jSdg9SWNlyGyHWqVAURL6cyXb3IbftHTVl/quC7pGLdi1Yv7V1o+eGMALHaP4p3tW4wPba/GR2xKzKpoo3elys/D61cuwv82Iz7xxJTRZ4c2zEFH62lJXgt+fHoTHK6EMXhrrJvvbjPjS7y8AAL524BKyVYqEnLOtqdLhBx/chA8+cgz3fOsluL0SQ/5FX7qc2MzXz3f++ZW3rsFb1lXiocfO4r0/OIp3bqrC+upCfPtQF4z+CgorKxK3IL9R7zs36hy2oDGERIBg8+HRzFlHItgYvBL49sEuvGdL/KvfZWRG1mp/hst5f5+sf/79RZzuN+Nf37E2YVHVRLtvUzXUKgV+8nJfsocCADhyeQwvd4/j/l0NNwUO96xehn9521q8dHkMf/XzU2GXfTLGsfHvVf/OM1avHdgRdQzfXF7w3OAkmvTaiE8ws5QKbKwpimjVRbwF+oJF06CRiCgaQgh8/s2rMWV34d+e6QjpOXNXF0WblUtEydc7boPRbE9afywAGJp0oLIwJ6MqQhCFqygvG8V52eganb8XxNCkHV/6/QVsrSvG+7ZmTisAokR51+ZqTDncOHD+arKHQkQJtK2+GNMzblwcurnNTjCB695xf0bUmMWZ0OvebfUleP/2GvSb7LNBLAD48pOX4j6GbfUl+MNf7cBf7GzAoycG8JnHz90wz/zd57oTth0a/DGKUPtkzTc3HYv58FAlewwZGcg6bzQDAP7ml6ex4Z+exk9f6cPHb6/H61tTK2sllorysnHP+ko8/qoRk3ZXUscipcS+p9tRodPg3VuWB33MOzZV4x/ftAp/PH8Vn3n8LKRcvOnv6PQM7v/Zq/PeH4tyLbFuSh3ISOoI0ifr/ODUbNA1UlvqinHp6hQmbcn9m8/VOTINlUIkLD2ZiCiYlRUFeM/W5fjJy33Y8uU/oe7BJ3HrwweDnpjOuD348pMX513hRETp6cXOUQDA7UkMZA1O2pdcfyyiYBrK8tA1T2lBKSUe/M1ZuL0SX3v7WihCXFFORNdsqy9BdXEOfsnS2kRLSqD1SKgL3RfK7EmUP5y9OeCeqDFospT4h9evQGm+OmljAIA8tQqVOg0uL7DI53rzXU8ksnx5rOfNw5Vxgaz9bUZ8dv/52Z9NNhcUAlixBEqcfWB7LewuD359ciCp4zjUPoK2K2b8n91NC2YbfeS2Ovzl7kb86sQAvvLUxXmDWVJK/PrkAF77jefxzIVhvLF1GTRZN350Y9X4N9ZNqQs0WVhWoMHl4Rt3SiNTDoxOz2B1ZXRlILfWFUNK4FhvamVldQxbUFuah2xVxu1iiCjNrFxWAAlgZHrmuiyrM/ivF3wrrb7wxHnc8+3DWPP5pzFqmQn6Golc4UREsfVCxxhqSnJvKvGcSENmByp0S6s/FlEwDWVadM8zWfPoyQE83zGKf3h9C2q4GI4oIgqFwDs3VuNI13jIPVeIKP1V6HKwvDgXR7vHQ3p8srNqUmUMY9PJv/5v0GtDzshaEaTsYazmw0MV63nzcGXcLPN8tRq//kxnkkaUOK0GHTbWFOGnL/fC6108wykevF6Jrz/dgeXFuXjHpqpFH/83r2vGB7fX4L9e7MF3nuu66f7+CRs+8MgxfPrR02jSa/HUX+3Ad963EQ/fuzYujX/j0ZS6qVx7U0bWOX/Zy1ZDdBlZ66oLka1S4FhPaAerRLk8YkGTPvODx0SU+oIdW+wuL7781EX89S9P4ZfH+6FWKfChW2tRPE//mkSucCKi2HF5vHi5aww7mkqTNga3x4uRaQcqC5mRRVRflocxi/OmahKBkoJb6orxge21yRkcUYZ4+6YqKISv3xwRLR1b64pxvHdi0flg64wb6nkWnS+lzJ5UGUOjXouuUcuif7e2KyY81z6KbXXFcZkPD1U85s3DEZsuaikkFSK6yfSB7TX4q1+cwvOdo9jVMn8T3Xj54/mrOD84hW+8c11IDbUDPUwm7S7sO9CO77/QjSm7CxWFGmypLcaB88NQCOBL96zGe7fWzJaYiGfj6Fi/dnN5Pn52tA9er5wd/3mjr27tqigzsjRZSlQV5uBHR3rxgxd7UFmYgwf2tMRk/PvbjNh3oB2DZntYr+twedA3bsWb11VGPQYiomgtdPx/6i93oLlcC5X/eLWqogAPPXb2hgUxiV7hRESx82qfCVanJ6n9sYanZ+CVYEYWEa71gugas+CW5UUAfNU3PvPYWbg8XuxjSUGiqFXocnBHcxl+fXIAf/O6Zij5nSJaErbWl+DRkwPoHLGgZVnwfvXDUw58+EfH4XB7kaUUcHmuBU+SkdmT7GvvVBhDo14Lh8sLo9mO6uLgFSQcLg8e+PUZlBdo8P0PbkKBJvgC3ESJ55z8YjIuIysVoqnJ9IbWCpTlq/GTI70Jf2+PV+Ibz3SgUa/FPetD/0ArFAI7mkqhEMCk3QUJYNDswP5Tg6gtycXTf3sH3r+9Nm0vapr8O6UB07XJ1HODk6grzYNWHV0seX+bEVcmbHB55HUls6Jv0Bho/Gg028N+3e5RK7wSaF4C5TyJKPXNd/w3FOZgVWXBbBALSP7qIiKKrRc7x6BUCGxvKEnaGIb8wfQKZmQRXQtkXVdC5zevGnGofRT/8PoVLClIFCP3ba7G1SkHXugYTfZQiChBts72yQpesenC4BT2fvsweses+O8Pbca+t69b0pk9qTKGJr0v6LhQn6x/f7YTl0csePhta5MexEq2jMvISoVoajJlqxR4z5bl+ObBTvSOWVFbmriLgSdOG3F5xILvvPeWsFf9fOOZTgTLopxyuGBI8yBkU7lvp9Q5Mj3bn+GccQoblhdG/dr7DrTDPWfDBRoTRrPjXajx42Kv2+kvoxjYGRMRJVO45wXJXF1ERLERyCo3mu3IVipw8OJI0r7Xg5MOAEAlM7KIUFWUgyylQNeor3fP1UkHvvi789hSW4wPsqQgUczsXlGOkrxs/OL4FexakfhKPUSUeFVFOajUaXC0Z+KmMr2H2kfwqZ+9inxNFh79xGtmq0Ml+7o3Fa69kz2GRv21RT7BKqud7jfjP5/vwn2bqnFHc/KqTKSKjMvISoVoarK9Z+tyKIXAT1/pS9h7ujxe/NsznVhVUYDXr14W9vPnLwnpiHZoSRfYKXUM+6LrZpsTRrM96v5YQPxKaUbzup3DFigVAnUJDKISEc2H5wVES8v1WeUA4PR4Y5KtHilmZBFdo1IqUFuSh65Ri6+k4OO+koL/wpKCRDGVrVLg3lsMePbiCEanZ5I9HCJKACEEttaX4Gj3BKS8tuD9p6/04SM/Oo7a0jzsv//WqFucUGwV52WjOC8bl0duzsiacXvw6UdPo7xAg8++aWUSRpd6Mi4jC0h+NDXZygs0eH3rMvzqRD/+7q5m5GbH/8/865MDuDJhwyMf2hTRRUhlYc7shMPc29OdLicLywo0s5lK5wd9/bFWx+DgEa/tVq7T4OrkzUHEUF63Y3gatSW5yJ6neSQRUaIt9fMCoqUkmqzyeBiadECrVi35MiBEATlZChy6NIK6h54CAOxdX8kFcERxcN/mavzXiz248+vPYdrhjmk/bSJKTVlKgTHLDOofegoVhRo067V4rmMMd67Q45vv3oC8KNubUHw0lmmDBrK++WwnOkcs+O8/28xrCT/ONGeoD72mFtMON/a3Dcb9vRwuD775bCc2LC8MmgYZigf2tCAnS3nDbZlUErKpXItOf0bW+cFJAMDqyugzsuK13dYFyRbLVoqQXrdzxILmcpYVJCIiosSLV7Z6pAbNdlTomI1FBPgyJi8MTd9QGv3A+atJy5gkymTnjFMQAphyuGPaT5uIUtP+NiN+e8o3Byzhq3D1XMcYdjSV4vsf2MQgVgpr0Gtx2Z+tHnBmwIzvPd+Nd2ysiniuPRMxkJWhNtYUYVVFAX7ycu8NX4R4+PmxKxiadODTd7VAiMhKQmR66acmfT4uj1jg9UqcM06hUqdBcV521K97/XYL+OvXNUW13WxON472TmB1ZcHs3yNLKZClVOD2ReqxOlwe9I1b0eQvp0hERESUSPNljycry39o0pERFQaIYiF4f18v9h1oT9KIiDLXvgPtmDsVFMhQJqLMs+9AO2bc3ptu7x61QsnyvSmtUa+F2ebCuNUJ4FpJwVJtNj73plVJHl1qYTg2QwkhsKaqAL88PoD6h56KWxq5zenGtw91YXt9CW5tLI3qtTK59FNTuRZ2lwdGsx3nBiexOgb9sQIC223MMoNbHz6I7hFrVK/3i2P9MNtc+OEHN2FjTTEA4NLVKbz5P17C5584j/9494Z5n9s9aoVXAk3MyCIiIqIkeGBPCx567OwN5QWTmeU/NGlHq4G9CIiA1MuYJMpk/L4RLS38zqevRn8ywOURC0q1avzHs5fRMWzBIx/aBF0OSwpejxlZGWpuSmm80sh/fKQPY5YZ/N1dzTF93UzTXO7bKbX1m9EzZkVrDMoKzlWqVeMdm6rweJsRw1M397cKhcvjxQ9f6sGW2uLZIBYArFhWgL/c3YTfnR7EH88Nzfv8QB+wpnJmZBEREVHipVKW/4zbgzGLExU6ZmQRAamXMUmUyfh9I1pa+J1PX4FAVueIBWcHJvHd57vwtluqsHtFeZJHlnoYyMpQ+w60w+G6MaU0Vmnk+9uMuPXhg6h78El87cAlrFyWj021xYs/cQlr1PsylJ44NQgpgdWV8VmZ+/EdDXB7vXjkpZ6Inv+704Mwmu34xM76m+77xM4GrK4swOf2n4PJn+46V+ewBUqFYMNmIiIiSpq9Gww4/OBu9Dx8Nw4/uDspQaz9bUbc/rVDAIBHDvewJwkRMr8vMlEq4feNaGnhdz59HesehwDwj/vP4a3fOYy8bAX+L0sKBsVAVoaKV0rp/jYjHnrsLIxmOyQAKYHuMSsvzhehy8lCeYEaz3eMAABaY1ha8HrLS3LxxjUV+NnRK5i0u8J6rtcr8b3nu9BSnh+0kWCWUoF/fcc6mG0ufOF354O+RufINGpKcqFWKYPeT0RERJTpAufLw1MzAACzzRWXyghE6SaVMiaJMh2/b0RLC7/z6Wl/mxGfefwcAi0N3V4Jh0viUPtIUseVqtgjK0NVFubAGCRoFW1K6b4D7Tf0HACAGbevQS93jgtr0ufjpctjKNVmo7xAHbf3+cQdDfj9mSH87GgfPrmzMeTnHWofQcewBd945zoIEbwR5MqKAvyf3U34tz914I1rKrBn9bIb7u8ctqCZ/bGIiIhoCQt2vhyojMDzZVrqMrkvMlGq4feNaGnhdz79BLtucHo4zz4fZmRlqHillLJ5YOQU/tjQmMWJ2/7lUNxW5bYadNjRVIpHXuqFY87OcCHfe74LhsIcvHld5YKP++SuBqyqKMBnHz8Hs+1aicEZtwe941b2xyIiIqIljefLRERERES0GF43hIeBrAwVr5RSNg+MzP42I17uHp/92Wi2x7XEzCfuaMCYZQaPvRra65/sm8DxXhM+clsdspQL7xaylArse8damG1OfOGJayUGu0et8EqgiRlZREREtITxfJmIiIiIiBbD64bwMJCVweLR6JrNAyOz70A7XB55w22BEjPx8JqGEqwx6PD9F7rg8cpFH//d57pRmJuFd22pDun1V1fqcP+uRuw/NYinz18FAHSOWAAAzczIIiIioiWM58tERERERLQYXjeEh4EsCgubB0Ym0amiQgj8xc4G9I7bcMAfaJpP5/A0/nRxGB/YXovc7NDb5t2/qxErluXjs/t9JQY7h6ehVAjUleZFO3wiIiKitMXzZSIiIiIiWgyvG8IjpFw8WyPeNm3aJE+cOJHsYRDFza0PH4QxSNDKUJiDww/ujst7erwSr/3G88jXqPDb+2+FECLo4z796Gn8/swgjjx4J4rzssN6j3PGSdzz7cPYUK3DucEpOFxeGApz8MCeFu50iYiIiIiIiIiIiCgkQoiTUspNwe5jRhZRAiQjVVSpEPjYjnqcGZjEka7xoI8ZmrTjt6eMuG9TddhBLABoNejw2hVlONFnhsPlBRD//l9EREREREREREREtHQwkEWUAMlKFb33FgNKtWp87/muoPf/8MUeeCXw0R31Eb/HWePUTbfFs/8XERERERERERERES0doTfEIaKo7N1gSHi5PU2WEh++rRZf+2M7zhkn0WrQzd43aXPh58eu4M1rK1BdnBvxewxNOoLeHq/+X0RERERERERERES0dDAjiyjDvXdrDbRq1U1ZWT99pRdWpwd/fkdDVK9fWZgT1u1ERERERERERERERKFiIIsow+lysvDercvx1Nkh9I1bAQAOlwf/fbgXO1vKsLKiIKrXT0b/LyIiIiIiIiIiIiJaGhjIIloCPnxbHVQKBf7rxW4AwKMnBzBudeITUWZjAcnr/0VEREREREREREREmY89soiWgPICDd66wYCfH72CP10YwdUpB7KUAkMx6mOVjP5fRERERERERERERJT5mJFFtETU6/PgkcDVKQcAwOWR+Mzj57C/zZjkkRERERERERERERERBcdAFtES8ZMjfTfdZnd5sO9AexJGQ0RERERERERERES0uLgFsoQQrxdCtAshLgshHozX+xBRaAbnKSM43+1ERERERERERERERMkWl0CWEEIJ4NsA3gBgFYB3CyFWxeO9iCg0lYU5Yd1ORERERERERERERJRs8crI2gLgspSyW0rpBPALAPfE6b2IKAQP7GlBTpbyhttyspR4YE9LkkZERERERERERERERLSweAWyDAD6r/t5wH/bLCHEx4UQJ4QQJ0ZHR+M0DCIK2LvBgK/euwaGwhwIAIbCHHz13jXYu8Gw6HOJiIiIiIiIiIiIiJJBFafXFUFukzf8IOX3AXwfADZt2iSDPJ6IYmzvBgMDV0RERERERERERESUNuKVkTUAoPq6n6sADMbpvYiIiIiIiIiIiIiIiCgDxSuQdRxAkxCiTgiRDeBdAJ6I03sRERERERERERERERFRBopLaUEppVsI8SkABwAoATwipTwfj/ciIiIiIiIiIiIiIiKizBSvHlmQUj4F4Kl4vT4RERERERERERERERFltniVFiQiIiIiIiIiIiIiIiKKCgNZRERERERERERERERElJIYyCIiIiIiIiIiIiIiIqKUxEAWERERERERERERERERpSQGsoiIiIiIiIiIiIiIiCglMZBFREREREREREREREREKYmBLCIiIiIiIiIiIiIiIkpJDGQRERERERERERERERFRSmIgi4iIiIiIiIiIiIiIiFISA1lERERERERERERERESUkhjIIiIiIiIiIiIiIiIiopQkpJTJHgOEENMA2uPw0joAk3F43UzGbRYZbrfwcZuFL5xtVgpgLI5jSRf8nIWP2yx83GbhS4Vtlm77yVTYZumG2yx83Gbh4za7Jpz9Krdb+LjNwsdtFj5us/DFa5ul27lqOPg5Cx+3Wfi4zcKXCtusRUqZH+wOVaJHMo92KeWmWL+oEOL7UsqPx/p1Mxm3WWS43cLHbRa+cLaZEOJEPPar6Yafs/Bxm4WP2yx8qbDN0m0/mQrbLN1wm4WP2yx83GbXhLNf5XYLH7dZ+LjNwsdtFr54bbN0O1cNBz9n4eM2Cx+3WfhSYZsJIU7Md1+mlxb8XbIHkIa4zSLD7RY+brPwcZuFj9ssfNxm4eM2Cx+3Wfi4zcLHbRY+brPwcZtFhtstfNxm4eM2Cx+3Wfi4zcLHbRY+brPwcZuFL6W3WaqUFszYVQZERMnA/SoR0cK4nyQiii3uV4mIYof7VCJaihba96VKRtb3kz0AIqIMw/0qEdHCuJ8kIoot7leJiGKH+1QiWorm3felREYWERERERERERERERER0VypkpFFREREREREREREREREdAMGsoiIiIiIiIiIiIiIiCglxT2QJYSQQoifXvezSggxKoT4fbzfm4gokwkhLMkeAxFRulhsnymEeE4IwYbaRETzEEK81X99vyLZYyEiSndCiM8KIc4LIc4IIU4JIbYme0xERKksERlZVgCtQogc/8+vA2BMwPsSERERERERUWy8G8BLAN6V7IEQEaUzIcR2AG8CcIuUci2A1wLoT+6oiIhSW6JKC/4BwN3+/383gJ8H7hBCbBFCHBFCtPn/bfHf/qIQYv11jzsshFiboPESEaUFIcTO6zNchRDfEkJ8yP//vUKILwohXhVCnOXqWSJa6hbaZxIR0fyEEFoAtwL4CPyBrEXOQ98ohLgkhHhJCPFNVmQhIrpBBYAxKeUMAEgpx6SUg0KIjUKI54UQJ4UQB4QQFcBs5YD/5583PSeE2JLU0RMRJUGiAlm/APAuIYQGwFoAR6+77xKA26WUGwD8XwBf8d/+AwAfAgAhRDMAtZTyTILGS0SUKcaklLcA+C6ATyd7MERERESUlvYC+KOUsgPAhBDilvke6L/u/08Ab5BS3gagLDFDJCJKG08DqBZCdAghviOEuEMIkQXgPwC8XUq5EcAjAL583XPypJSvAfBJ/31EREtKQgJZ/gBULXzZWE/NuVsH4FEhxDkA/wZgtf/2RwG8yb8j/zCAHyVirEREGeYx/78n4dsPExERERGF693wLVCF/993L/DYFQC6pZQ9/p9/vsBjiYiWHCmlBcBGAB8HMArglwD+HEArgGeEEKcAfA5A1XVP+7n/uS8AKBBCFCZwyERESadK4Hs9AeBfAewEUHLd7V8CcEhK+VYhRC2A5wBASmkTQjwD4B4A7wTA5ttERDdz48ZFCZo598/4//Ugsft8IqJUtNg+k4iI5hBClADYDV/vawlACUDCd40fbJ8qEjtCIqL0I6X0wDcH+pwQ4iyA+wGcl1Jun+8pi/xMRJTRElVaEPClvf6TlPLsnNt1AIz+///QnPt+AOCbAI5LKSfiOzwiorTUB2CVEEIthNABuDPZAyIiSmHcZxIRhe/tAH4ipayRUtZKKasBBLKtgu1TLwGo9y9UBYD7EjtcIqLUJoRoEUI0XXfTegAXAZQJIbb7H5MlhFh93WPu899+G4BJKeVkosZLRJQKErY6X0o5AODfg9z1NQA/FkL8LYCDc55zUggxBeC/EzBEIqK0IYRQAZiRUvYLIX4F4AyATgBtyR0ZEVHq4T6TiCgq7wbw8JzbfgPgPQBu2qdKKe1CiE8C+KMQYgzAsQSOlYgoHWgB/Ie/PKAbwGX4ygx+H8A3/YsDVAD+H4Dz/ueYhBBHABTA14KFiGhJEVKmbiaqEKISvjTbFVJKb5KHQ0SUMoQQ6wD8l5RyS7LHQkSU6rjPJCJKLCGEVkppEUIIAN8G0Cml/Ldkj4uIKB0JIZ4D8Gkp5Ylkj4WIKFkSWVowLEKIDwA4CuCzDGIREV0jhPgEfI1eP5fssRARpTruM4mIkuJjQohT8GUS6AD8Z3KHQ0RERETpLKUzsoiIiIiIiIiIiIiIiGjpStmMLCIiIiIiIiIiIiIiIlra4hbIEkI8IoQYEUKcu+62dUKIl4UQZ4UQvxNCFPhvf68Q4tR1/3mFEOv992ULIb4vhOgQQlwSQrwtXmMmIiIiIiIiIiIiIiKi1BG30oJCiNsBWAD8RErZ6r/tOHzNCZ8XQnwYQJ2U8h/nPG8NgN9KKev9P38RgFJK+TkhhAJAsZRyLC6DJiIiIiIiIiIiIiIiopQR1x5ZQohaAL+/LpA1BUAnpZRCiGoAB6SUq+Y85ysApJTys/6f+wGskFJa4zZQIiIiIiIiIiIiIiIiSjmJ7pF1DsBb/P//DgDVQR5zH4CfA4AQotB/25eEEK8KIR4VQpTHfZRERERERERERERERESUdIkOZH0YwP1CiJMA8gE4r79TCLEVgE1KGeirpQJQBeCwlPIWAC8D+NcEjpeIiIiIiIiIiIiIiIiSRJXIN5NSXgJwFwAIIZoB3D3nIe+CPxvLbxyADcDj/p8fBfCROA+TiIiIiIiIiIiIiIiIUkBCM7KEEHr/vwoAnwPwvevuU8BXbvAXgdukr4HX7wDs9N90J4ALCRouERERERERERERERERJZHwxYri8MJC/By+AFQpgGEAnwegBXC//yGPAXjIH6yCEGIngIellNvmvE4NgJ8CKAQwCuDPpJRX4jJoIiIiIiIiIiIiIiIiShlxC2QRERERERERERERERERRSOhpQWJiIiIiIiIiIiIiIiIQsVAFhEREREREREREREREaUkBrKIiIiIiIiIiIiIiIgoJTGQRURERERERERERERERCmJgSwiIiIiIiIiIiIiIiJKSQxkERERERERxYEQwiOEOCWEOC+EOC2E+FshxILXYEKIWiHEexI1RiIiIiIiolTHQBYREREREVF82KWU66WUqwG8DsAbAXx+kefUAmAgi4iIiIiIyE9IKZM9BiIiIiIioowjhLBIKbXX/VwP4DiAUgA1AH4KIM9/96eklEeEEK8AWAmgB8CPAXwTwMMAdgJQA/i2lPI/E/ZLEBERERERJRkDWURERERERHEwN5Dlv80EYAWAaQBeKaVDCNEE4OdSyk1CiJ0APi2lfJP/8R8HoJdS/rMQQg3gMIB3SCl7Evm7EBERERERJYsq2QMgIiIiIiJaQoT/3ywA3xJCrAfgAdA8z+PvArBWCPF2/886AE3wZWwRERERERFlPAayiIiIiIiIEsBfWtADYAS+XlnDANbB17vYMd/TAPwfKeWBhAySiIiIiIgoxSiSPQAiIiIiIqJMJ4QoA/A9AN+SvvruOgBDUkovgPcDUPofOg0g/7qnHgDwF0KILP/rNAsh8kBERERERLREMCOLiIiIiIgoPnKEEKfgKyPoBvBTAN/w3/cdAL8RQrwDwCEAVv/tZwC4hRCnAfwIwL8DqAXwqhBCABgFsDcxwyciIiIiIko+4VsMSERERERERERERERERJRaWFqQiIiIiIiIiIiIiIiIUhIDWURERERERERERERERJSSGMgiIiIiIiIiIiIiIiKilMRAFhEREREREREREREREaUkBrKIiIiIiIiIiIiIiIgoJTGQRURERERERERERERERCmJgSwiIiIiIiIiIiIiIiJKSf8fZYtaDCGuU18AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2160x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Ozone'].plot(title= 'Data Original', marker='o', figsize=(30, 8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd3dfba",
   "metadata": {},
   "source": [
    "A continuación se generaliza la gráfica para todos los métodos de imputación. Utilizaremos la columna _Ozone_ para visualizar los resultados de la imputación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "334dcede",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x2880 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crear diccionarios métodos de imputación\n",
    "metodos = {'Imputación Forward Fill':'ffill',\n",
    "          'Imputación Back fill': 'bfill',}\n",
    "\n",
    "metodos2 = {'Imputación por Interpolación Lineal': 'linear', \n",
    "           'Imputación por Interpolación Cuadrática': 'quadratic', \n",
    "           'Imputación por Interpolación por Valores Cercanos': 'nearest'}\n",
    "\n",
    "# Crear diccionario resultado de imputación\n",
    "interpolaciones = {key:df.fillna(method=value) for key, value in metodos.items()}\n",
    "\n",
    "# Actualizar diccionario resultado de imputación (interpolación)\n",
    "interpolaciones.update({key:df.interpolate(method=value) for key, value in metodos2.items()})\n",
    "\n",
    "# Configurar número de filas a 5 y número de columnas a 1\n",
    "fig, axes = plt.subplots(5, 1, figsize=(20, 40))\n",
    "\n",
    "# Loop for sobre axes e interpolaciones\n",
    "for ax, key in zip(axes, interpolaciones):\n",
    "  # Generar gráfica de data imputada\n",
    "  interpolaciones[key]['Ozone'].plot(color='red', marker='o', linestyle='dotted', ax=ax)\n",
    "  # Generar gráfica de data original\n",
    "  df['Ozone'].plot(title=key + ' - Ozone', marker='o', ax=ax)\n",
    "  \n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1b3b9fa1",
   "metadata": {},
   "source": [
    "### Métodos avanzados de imputación\n",
    "\n",
    "- Imputación de datos por medio de KNN (K-Vecinos Cercanos). Biblioteca KKNImputer().\n",
    "\n",
    "En el caso de los K-Vecinos más cercanos por ejemplo, el objetivo fundamental es encontrar vecinos cercanos al valor faltante por por medio de métricas de distancias. La más utilizada es la euclidiana.\n",
    "\n",
    "Este método es más preciso que los métoodos imputación simples tales como media, mediana, moda o valor constante. Sin embargo, estos modelos pueden ser costosos computacionalmente hablando, pueden generar multicolinealidad y son sensibles a valores atípicos.\n",
    "\n",
    "A continuación, realizaremos un ejemplo usando lel módulo `KNNImputer()` de la bibliteca sklearn.impute,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "id": "a3799f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Dataframe\n",
    "data = pd.DataFrame({'ranking': [1, 2, 1, 3, 1, 2, 3, 2, 3],\n",
    "                     'area': [1, 2, 3, 1, 2, 3, 1, 2, 3],\n",
    "                     'edad': [32, 30, np.nan, 23, 27, 44, 67, 23, np.nan],\n",
    "                     'monto': [102, 121, 343, np.nan, 121, np.nan, 155, 149, 221]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "id": "b32bebf6",
   "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>ranking</th>\n",
       "      <th>area</th>\n",
       "      <th>edad</th>\n",
       "      <th>monto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ranking  area  edad  monto\n",
       "0      1.0   1.0  32.0  102.0\n",
       "1      2.0   2.0  30.0  121.0\n",
       "2      1.0   3.0  44.0  343.0\n",
       "3      3.0   1.0  23.0  149.0\n",
       "4      1.0   2.0  27.0  121.0\n",
       "5      2.0   3.0  44.0  343.0\n",
       "6      3.0   1.0  67.0  155.0\n",
       "7      2.0   2.0  23.0  149.0\n",
       "8      3.0   3.0  44.0  221.0"
      ]
     },
     "execution_count": 559,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importar módulo KNNImputer\n",
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "# Generar copia del DataFrame diabetes\n",
    "data_imputer = data.copy(deep = True)\n",
    "\n",
    "# Iniciar KNNImputer con 2 vecino cercano\n",
    "imputacion_knn = KNNImputer(n_neighbors=1)\n",
    "\n",
    "# Imputar usando fit_tranform en data_imputer\n",
    "data_imputer.iloc[:, :] = imputacion_knn.fit_transform(data_imputer)\n",
    "\n",
    "data_imputer\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b9c8704b",
   "metadata": {},
   "source": [
    "Observamos que los valores ausentes de la variable *edad* han tomado los valores 44 en la fila 3 y 44 en la fila 9. En tanto, para la variable *monto* ha tomado el valor 149.0 en la fila 4 y 343.0 en la fila 6\n",
    "A continuación realizaremos el calculo manual de este tipo de imputación a fin de ver si  el algoritmo realiza de manera correcta la imputación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "id": "044952aa",
   "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>ranking</th>\n",
       "      <th>area</th>\n",
       "      <th>edad</th>\n",
       "      <th>monto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ranking  area  edad  monto\n",
       "0        1     1  32.0  102.0\n",
       "1        2     2  30.0  121.0\n",
       "2        1     3   NaN  343.0\n",
       "3        3     1  23.0    NaN\n",
       "4        1     2  27.0  121.0\n",
       "5        2     3  44.0    NaN\n",
       "6        3     1  67.0  155.0\n",
       "7        2     2  23.0  149.0\n",
       "8        3     3   NaN  221.0"
      ]
     },
     "execution_count": 560,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "03d196e9",
   "metadata": {},
   "source": [
    "La formula de calculo es la siguiente.\n",
    "\n",
    "$\\text{Distancia Euclidiana nan = } \\sqrt{peso * ((x_2 - x_1)^2)+(y_2 - y_1)^2)+ ...+ (z_2 -z_1)^2} $\n",
    "\n",
    "$\\text{donde peso} = \\dfrac{\\text{num columnas predictoras}}{\\text{num columnas predictoras}- \\text{columnas nan}}$\n",
    "\n",
    "\n",
    "nota: las columnas predictoras son aquellas que buscan predecir el valor nulo. \n",
    "\n",
    "El siguiente ejemplo busca predecir el valor faltante tanto de la fila 3 y 9 de la columna edad. En este caso:\n",
    "\n",
    "\n",
    "\n",
    "|ranking  |area\t|edad |monto|\n",
    "|-----|-----|-----|-----|\n",
    "|1    |3\t|NaN  |343.0|\n",
    "|3    |3\t|NaN  |221.0|\n",
    "\n",
    "\n",
    "\n",
    "- columnas predictoras= 3, es decir: _ranking_, _area_, _monto_.\n",
    "- columnas predictoras- columnas nan = número de columnas válidas de la fila que se usa para predecir. En el caso de las fila 4 son 2, , es decir: _id_, _area_. _Monto_ es NaN.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "00cbbdfe",
   "metadata": {},
   "source": [
    "Para la fila 3, el valor a imputar es 44 dado que tiene la menor distancia.\n",
    "\n",
    "A continuación, se genera una función ad-hoc a los al dataset que tenemos a fin de obtener las distancias euclideanas para las filas 3 y 9 para así predecir el valor de la columna _age_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "id": "2715aa21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sqrt(peso * ((x2 - x1)^2 + (y2 - y1)^2 + (z2 - z1)^2 )\n",
    "import math\n",
    "def fn_knn_manual(x1,y1,z1):\n",
    "    valor = pd.DataFrame({'numero': [data.iloc[0,2], data.iloc[1,2], data.iloc[3,2],data.iloc[4,2], data.iloc[5,2], data.iloc[6,2], data.iloc[7,2]],\n",
    "              'distancia':[math.sqrt(3/3 * (( int(data.iloc[0,0]) - x1 )**2 + ( int(data.iloc[0,1]) - y1 )**2 + (int(data.iloc[0,3]) - z1 )**2)),\n",
    "                           math.sqrt(3/3 * (( int(data.iloc[1,0]) - x1 )**2 + ( int(data.iloc[1,1]) - y1 )**2 + (int(data.iloc[1,3]) - z1 )**2)),\n",
    "                           math.sqrt(3/2 * (( int(data.iloc[3,0]) - x1 )**2 + ( int(data.iloc[3,1]) - y1 )**2)),\n",
    "                           math.sqrt(3/3 * (( int(data.iloc[4,0]) - x1 )**2 + ( int(data.iloc[4,1]) - y1 )**2 + (int(data.iloc[4,3]) - z1 )**2)),\n",
    "                           math.sqrt(3/2 * (( int(data.iloc[5,0]) - x1 )**2 + ( int(data.iloc[5,1]) - y1 )**2)),\n",
    "                           math.sqrt(3/3 * (( int(data.iloc[6,0]) - x1 )**2 + ( int(data.iloc[6,1]) - y1 )**2 + (int(data.iloc[6,3]) - z1 )**2)),\n",
    "                           math.sqrt(3/3 * (( int(data.iloc[7,0]) - x1 )**2 + ( int(data.iloc[7,1]) - y1 )**2 + (int(data.iloc[7,3]) - z1 )**2))]})\n",
    "    return(valor)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3e31ab2c",
   "metadata": {},
   "source": [
    "A continuación calcularemos el valor a imputar con 1 vecino cercano para la fila 3 de nuestro dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "id": "ce05b6b2",
   "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>numero</th>\n",
       "      <th>distancia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1.224745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.0</td>\n",
       "      <td>3.464102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "      <td>188.021275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.0</td>\n",
       "      <td>194.005155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.0</td>\n",
       "      <td>222.002252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30.0</td>\n",
       "      <td>222.004504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32.0</td>\n",
       "      <td>241.008299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numero   distancia\n",
       "0    44.0    1.224745\n",
       "1    23.0    3.464102\n",
       "2    67.0  188.021275\n",
       "3    23.0  194.005155\n",
       "4    27.0  222.002252\n",
       "5    30.0  222.004504\n",
       "6    32.0  241.008299"
      ]
     },
     "execution_count": 578,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encontrar valor ausente para fila 3\n",
    "distancias_f3 = fn_knn_manual(x1 = 1, y1 = 3, z1 = 343.0)\n",
    "#distancias = distancias.sort_values('distincia').reset_index(drop=True)\n",
    "distancias_f3 = distancias_f3.sort_values('distancia').reset_index(drop=True)\n",
    "distancias_f3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4de5cde4",
   "metadata": {},
   "source": [
    "Observamos que el valor a imputar es de 44 años dado que presenta la menor distancia.\n",
    "\n",
    "A continuación, realizaremos el cálculo para imputar la data faltante con hasta 5 vecinos cercanos y comprobaremos los resultados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "id": "5aeaf33d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neighbors = 1\n",
      "   ranking  area  edad  monto\n",
      "0      1.0   1.0  32.0  102.0\n",
      "1      2.0   2.0  30.0  121.0\n",
      "2      1.0   3.0  44.0  343.0\n",
      "3      3.0   1.0  23.0  149.0\n",
      "4      1.0   2.0  27.0  121.0\n",
      "5      2.0   3.0  44.0  343.0\n",
      "6      3.0   1.0  67.0  155.0\n",
      "7      2.0   2.0  23.0  149.0\n",
      "8      3.0   3.0  44.0  221.0\n",
      "_____________________________________________\n",
      "\n",
      "neighbors = 2\n",
      "   ranking  area  edad  monto\n",
      "0      1.0   1.0  32.0  102.0\n",
      "1      2.0   2.0  30.0  121.0\n",
      "2      1.0   3.0  33.5  343.0\n",
      "3      3.0   1.0  23.0  185.0\n",
      "4      1.0   2.0  27.0  121.0\n",
      "5      2.0   3.0  44.0  282.0\n",
      "6      3.0   1.0  67.0  155.0\n",
      "7      2.0   2.0  23.0  149.0\n",
      "8      3.0   3.0  33.5  221.0\n",
      "_____________________________________________\n",
      "\n",
      "neighbors = 3\n",
      "   ranking  area       edad       monto\n",
      "0      1.0   1.0  32.000000  102.000000\n",
      "1      2.0   2.0  30.000000  121.000000\n",
      "2      1.0   3.0  44.666667  343.000000\n",
      "3      3.0   1.0  23.000000  237.666667\n",
      "4      1.0   2.0  27.000000  121.000000\n",
      "5      2.0   3.0  44.000000  222.000000\n",
      "6      3.0   1.0  67.000000  155.000000\n",
      "7      2.0   2.0  23.000000  149.000000\n",
      "8      3.0   3.0  44.666667  221.000000\n",
      "_____________________________________________\n",
      "\n",
      "neighbors = 4\n",
      "   ranking  area   edad   monto\n",
      "0      1.0   1.0  32.00  102.00\n",
      "1      2.0   2.0  30.00  121.00\n",
      "2      1.0   3.0  39.25  343.00\n",
      "3      3.0   1.0  23.00  208.50\n",
      "4      1.0   2.0  27.00  121.00\n",
      "5      2.0   3.0  44.00  196.75\n",
      "6      3.0   1.0  67.00  155.00\n",
      "7      2.0   2.0  23.00  149.00\n",
      "8      3.0   3.0  39.25  221.00\n",
      "_____________________________________________\n",
      "\n",
      "neighbors = 5\n",
      "   ranking  area  edad  monto\n",
      "0      1.0   1.0  32.0  102.0\n",
      "1      2.0   2.0  30.0  121.0\n",
      "2      1.0   3.0  36.8  343.0\n",
      "3      3.0   1.0  23.0  191.0\n",
      "4      1.0   2.0  27.0  121.0\n",
      "5      2.0   3.0  44.0  181.6\n",
      "6      3.0   1.0  67.0  155.0\n",
      "7      2.0   2.0  23.0  149.0\n",
      "8      3.0   3.0  37.4  221.0\n",
      "_____________________________________________\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# n_neighbors cantidad de vecinos. Si se escoge un valor mayor de uno se obtiene el promedio delos vecinos cercanos detectados.\n",
    "for i in range(1,6):\n",
    "\n",
    "    # Generar copia del DataFrame diabetes\n",
    "    data_imputer = data.copy(deep = True)\n",
    "\n",
    "    # Cantidad de vecinos\n",
    "    knn = KNNImputer(n_neighbors=i)\n",
    "\n",
    "    # Generar copia del DataFrame diabetes\n",
    "    data_imputer = data.copy(deep = True)\n",
    "\n",
    "    # se aplica la imputación por knn entrenada a la data.\n",
    "    data_imputer.iloc[:, :]  = knn.fit_transform(data_imputer)\n",
    "\n",
    "\n",
    "    # Modificamos el nombre de las columnas por el nombre original \n",
    "    #neighbors.columns = columnas\n",
    "    print('neighbors = ' + str(i))\n",
    "    print(data_imputer)\n",
    "    print('_'*45) # imprimir linea divisora de df\n",
    "    print() #imprimir salto de línea\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c5092cc8",
   "metadata": {},
   "source": [
    "Ahora bien, realizaremos una función que nos ayudará a calcular el valor a imputar para el dato ausente de la variable edad para distintas cantidades de vecinos cercanos. Luego calcularenos el valor usando el método manual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "id": "1321430d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn_imputar_cant_vecinos(n_neighbors, df):\n",
    "    # Cantidad de vecinos cercarnos\n",
    "    cantidad = n_neighbors\n",
    "    # Valor inicial para calcular el valor a imputar\n",
    "    total_sum = 0\n",
    "    for i in range(cantidad):\n",
    "        total_sum += df.loc[i, 'numero']\n",
    "        valor_a_imputar = total_sum / (i+1)\n",
    "        print(f'* Cantidad de vecinos cercanos = {i+1}, el valor a imputar es {valor_a_imputar}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "id": "684eaa0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Cantidad de vecinos cercanos = 1, el valor a imputar es 44.0\n",
      "* Cantidad de vecinos cercanos = 2, el valor a imputar es 33.5\n",
      "* Cantidad de vecinos cercanos = 3, el valor a imputar es 44.666666666666664\n",
      "* Cantidad de vecinos cercanos = 4, el valor a imputar es 39.25\n",
      "* Cantidad de vecinos cercanos = 5, el valor a imputar es 36.8\n"
     ]
    }
   ],
   "source": [
    "# Fila 3\n",
    "fn_imputar_cant_vecinos(5, distancias_f3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "66dc5505",
   "metadata": {},
   "source": [
    "Ahora comprobaremos los valores de imputación para la fila 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "id": "4bee31d4",
   "metadata": {
    "scrolled": true
   },
   "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>numero</th>\n",
       "      <th>distancia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1.224745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.0</td>\n",
       "      <td>2.449490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "      <td>66.030296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.0</td>\n",
       "      <td>72.013888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.0</td>\n",
       "      <td>100.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27.0</td>\n",
       "      <td>100.024997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32.0</td>\n",
       "      <td>119.033609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numero   distancia\n",
       "0    44.0    1.224745\n",
       "1    23.0    2.449490\n",
       "2    67.0   66.030296\n",
       "3    23.0   72.013888\n",
       "4    30.0  100.010000\n",
       "5    27.0  100.024997\n",
       "6    32.0  119.033609"
      ]
     },
     "execution_count": 586,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encontrar valor ausente para fila 8\n",
    "distancias_f8 = fn_knn_manual(x1=3 , y1 =3 , z1 = 221.0)\n",
    "#distancias = distancias.sort_values('distincia').reset_index(drop=True)\n",
    "distancias_f8 = distancias_f8.sort_values('distancia').reset_index(drop=True)\n",
    "distancias_f8"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b6be110e",
   "metadata": {},
   "source": [
    "Vemos que el valor a imputar es el mismo que nos muestra la función KNNImputer().\n",
    "\n",
    "También, aplicaremos la función para calcular el valor a imputar con distinta cantidad de vecinos cercanos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "id": "b2b169ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Cantidad de vecinos cercanos = 1, el valor a imputar es 44.0\n",
      "* Cantidad de vecinos cercanos = 2, el valor a imputar es 33.5\n",
      "* Cantidad de vecinos cercanos = 3, el valor a imputar es 44.666666666666664\n",
      "* Cantidad de vecinos cercanos = 4, el valor a imputar es 39.25\n",
      "* Cantidad de vecinos cercanos = 5, el valor a imputar es 37.4\n"
     ]
    }
   ],
   "source": [
    "# Fila 8\n",
    "fn_imputar_cant_vecinos(5, distancias_f8)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "824eb638",
   "metadata": {},
   "source": [
    "Hemos verificado que los valores a imputar de la variable edad son los mismo tanto usando la función KNNImputer() y el método manual."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c912c326",
   "metadata": {},
   "source": [
    "- Imputación de datos por medio de KNN (K-Vecinos Cercanos). Biblioteca fancyimpute()\n",
    "\n",
    "A diferencia del paquete anterior, funcyimpute asume que cada característica ha sido centrada y reescalada para tener media cero y varianza 1. Dado esto, puede presentar diferencias con el paquete anterior para k-Neighbor > 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "id": "ec409c8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputing row 1/9 with 0 missing, elapsed time: 0.006\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ranking</th>\n",
       "      <th>area</th>\n",
       "      <th>edad</th>\n",
       "      <th>monto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>121.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>41.666567</td>\n",
       "      <td>343.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>181.162812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>121.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>280.087899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>39.802423</td>\n",
       "      <td>221.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ranking  area       edad       monto\n",
       "0      1.0   1.0  32.000000  102.000000\n",
       "1      2.0   2.0  30.000000  121.000000\n",
       "2      1.0   3.0  41.666567  343.000000\n",
       "3      3.0   1.0  23.000000  181.162812\n",
       "4      1.0   2.0  27.000000  121.000000\n",
       "5      2.0   3.0  44.000000  280.087899\n",
       "6      3.0   1.0  67.000000  155.000000\n",
       "7      2.0   2.0  23.000000  149.000000\n",
       "8      3.0   3.0  39.802423  221.000000"
      ]
     },
     "execution_count": 604,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importar KNN desde fancyimpute\n",
    "from fancyimpute import KNN\n",
    "\n",
    "# Generar copia de dataset\n",
    "data_imputer = data.copy(deep = True)\n",
    "\n",
    "# Inicializar KNN\n",
    "imputacion_knn = KNN(k=5)\n",
    "\n",
    "# Imputar usando fit_tranform \n",
    "data_imputer.iloc[:, :] = imputacion_knn.fit_transform(data_imputer)\n",
    "\n",
    "data_imputer"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a814ebb6",
   "metadata": {},
   "source": [
    "Ahora realizaremos imputación a la data diabetes usando fancyimputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "id": "2eb8aca9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputing row 1/752 with 1 missing, elapsed time: 0.073\n",
      "Imputing row 101/752 with 0 missing, elapsed time: 0.075\n",
      "Imputing row 201/752 with 2 missing, elapsed time: 0.077\n",
      "Imputing row 301/752 with 0 missing, elapsed time: 0.078\n",
      "Imputing row 401/752 with 1 missing, elapsed time: 0.080\n",
      "Imputing row 501/752 with 2 missing, elapsed time: 0.081\n",
      "Imputing row 601/752 with 0 missing, elapsed time: 0.083\n",
      "Imputing row 701/752 with 0 missing, elapsed time: 0.084\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>162.743255</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>62.914770</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>26.464588</td>\n",
       "      <td>180.726394</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>10.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>122.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>169.595752</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>32.886413</td>\n",
       "      <td>130.228539</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>72.651916</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
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       "<p>752 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness     Insulin   BMI  \\\n",
       "0            6.0    148.0           72.0      35.000000  162.743255  33.6   \n",
       "1            1.0     85.0           66.0      29.000000   62.914770  26.6   \n",
       "2            8.0    183.0           64.0      26.464588  180.726394  23.3   \n",
       "3            1.0     89.0           66.0      23.000000   94.000000  28.1   \n",
       "4            0.0    137.0           40.0      35.000000  168.000000  43.1   \n",
       "..           ...      ...            ...            ...         ...   ...   \n",
       "763         10.0    101.0           76.0      48.000000  180.000000  32.9   \n",
       "764          2.0    122.0           70.0      27.000000  169.595752  36.8   \n",
       "765          5.0    121.0           72.0      23.000000  112.000000  26.2   \n",
       "766          1.0    126.0           60.0      32.886413  130.228539  30.1   \n",
       "767          1.0     93.0           70.0      31.000000   72.651916  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction   Age  Outcome  \n",
       "0                       0.627  50.0      1.0  \n",
       "1                       0.351  31.0      0.0  \n",
       "2                       0.672  32.0      1.0  \n",
       "3                       0.167  21.0      0.0  \n",
       "4                       2.288  33.0      1.0  \n",
       "..                        ...   ...      ...  \n",
       "763                     0.171  63.0      0.0  \n",
       "764                     0.340  27.0      0.0  \n",
       "765                     0.245  30.0      0.0  \n",
       "766                     0.349  47.0      1.0  \n",
       "767                     0.315  23.0      0.0  \n",
       "\n",
       "[752 rows x 9 columns]"
      ]
     },
     "execution_count": 605,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importar KNN desde fancyimpute\n",
    "from fancyimpute import KNN\n",
    "\n",
    "# Generar copia de dataset diabetes\n",
    "diabetes_imputacion_knn = diabetes.copy(deep = True)\n",
    "\n",
    "# Inicializar KNN\n",
    "imputacion_knn = KNN(k=5)\n",
    "\n",
    "# Imputar usando fit_tranform en dataset diabetes_knn_imputed\n",
    "diabetes_imputacion_knn.iloc[:, :] = imputacion_knn.fit_transform(diabetes_imputacion_knn)\n",
    "\n",
    "# Imprimir resultado\n",
    "diabetes_imputacion_knn"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f77e9aa4",
   "metadata": {},
   "source": [
    "- Imputaciones de Valores para datos \n",
    "categóricos: \n",
    "\n",
    "Hemos imputado valores para variables numéricas, pero ¿cómo actuamos en presencia de variables categóricas?. Esta biblioteca nos ofrece la forma de cómo debemos imputar la data\n",
    "\n",
    "Este algoritmo en primer lugar, transforma las varaibles categóricas a variables numéricas. Para esto, exiten dos técnicas para transformar los datos: One-Hot Encoder y Ordinal Encoder\n",
    "\n",
    "**One-Hot Encoder**: \n",
    "\n",
    "Crear una columna para cada valor distinto que exista en la variable que estamos codificando y coloca el valor 1 a la columna que pertenece el registro y las demás se coloca el vaor 0.\n",
    "\n",
    "|Pais|Pais_Chile|Pais_Argentina|Pais_Perú|Pais_Bolivia|\n",
    "|--|--|--|--|--|\n",
    "|Chile|1|0|0|0|\n",
    "|Argentina|0|1|0|0|\n",
    "|Perú|0|0|1|0|\n",
    "|Bolivia|0|0|0|1|\n",
    "\n",
    "**Ordinal Encoder**\n",
    "\n",
    "Se crea una columna que va tomando distintos valores por categoría de la variable que estamos codificando. \n",
    "    \n",
    "|Pais|Vaue|\n",
    "|--|--|\n",
    "|Chile|0|\n",
    "|Argentina|1|\n",
    "|Perú|2|\n",
    "|Bolivia|3|\n",
    "\n",
    "A continuación, revializaremos un ejemplo usando un dataset descargado desde el sitio https://archive.ics.uci.edu/ml/datasets/Restaurant%2B%26%2Bconsumer%2Bdata. Además, se han seleccionado algunas columnas para el ejercicio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "id": "662054a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>student</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>informal</td>\n",
       "      <td>family</td>\n",
       "      <td>independent</td>\n",
       "      <td>professional</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>no preference</td>\n",
       "      <td>family</td>\n",
       "      <td>independent</td>\n",
       "      <td>student</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>True</td>\n",
       "      <td>social drinker</td>\n",
       "      <td>no preference</td>\n",
       "      <td>friends</td>\n",
       "      <td>independent</td>\n",
       "      <td>student</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>casual drinker</td>\n",
       "      <td>informal</td>\n",
       "      <td>solitary</td>\n",
       "      <td>independent</td>\n",
       "      <td>student</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>social drinker</td>\n",
       "      <td>formal</td>\n",
       "      <td>solitary</td>\n",
       "      <td>independent</td>\n",
       "      <td>student</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>formal</td>\n",
       "      <td>family</td>\n",
       "      <td>kids</td>\n",
       "      <td>student</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>social drinker</td>\n",
       "      <td>no preference</td>\n",
       "      <td>friends</td>\n",
       "      <td>kids</td>\n",
       "      <td>student</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  smoker     drink_level dress_preference  ambience        hijos  \\\n",
       "0  False      abstemious         informal    family  independent   \n",
       "1  False      abstemious         informal    family  independent   \n",
       "2  False  social drinker           formal    family  independent   \n",
       "3  False      abstemious         informal    family  independent   \n",
       "4  False      abstemious    no preference    family  independent   \n",
       "5   True  social drinker    no preference   friends  independent   \n",
       "6  False  casual drinker         informal  solitary  independent   \n",
       "7  False  social drinker           formal  solitary  independent   \n",
       "8  False      abstemious           formal    family         kids   \n",
       "9  False  social drinker    no preference   friends         kids   \n",
       "\n",
       "       activity  budget  \n",
       "0       student  medium  \n",
       "1       student     low  \n",
       "2       student     low  \n",
       "3  professional  medium  \n",
       "4       student  medium  \n",
       "5       student  medium  \n",
       "6       student     low  \n",
       "7       student     low  \n",
       "8       student  medium  \n",
       "9       student  medium  "
      ]
     },
     "execution_count": 607,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importar data\n",
    "users = pd.read_csv(ruta + 'userprofile.csv', sep = ';', na_values= '?')\n",
    "users.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "id": "ef1b3c20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoker               3\n",
       "drink_level          0\n",
       "dress_preference     5\n",
       "ambience             6\n",
       "hijos               11\n",
       "activity             7\n",
       "budget               7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 609,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determinar nulos\n",
    "users.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "id": "29480aca",
   "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>smoker</th>\n",
       "      <th>drink_level</th>\n",
       "      <th>dress_preference</th>\n",
       "      <th>ambience</th>\n",
       "      <th>hijos</th>\n",
       "      <th>activity</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>False</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>low</td>\n",
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       "      <th>2</th>\n",
       "      <td>False</td>\n",
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       "      <td>formal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>independent</td>\n",
       "      <td>student</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>informal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>independent</td>\n",
       "      <td>professional</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>no preference</td>\n",
       "      <td>0.0</td>\n",
       "      <td>independent</td>\n",
       "      <td>student</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>True</td>\n",
       "      <td>social drinker</td>\n",
       "      <td>no preference</td>\n",
       "      <td>1.0</td>\n",
       "      <td>independent</td>\n",
       "      <td>student</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>casual drinker</td>\n",
       "      <td>informal</td>\n",
       "      <td>2.0</td>\n",
       "      <td>independent</td>\n",
       "      <td>student</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  smoker     drink_level dress_preference ambience        hijos      activity  \\\n",
       "0  False      abstemious         informal      0.0  independent       student   \n",
       "1  False      abstemious         informal      0.0  independent       student   \n",
       "2  False  social drinker           formal      0.0  independent       student   \n",
       "3  False      abstemious         informal      0.0  independent  professional   \n",
       "4  False      abstemious    no preference      0.0  independent       student   \n",
       "5   True  social drinker    no preference      1.0  independent       student   \n",
       "6  False  casual drinker         informal      2.0  independent       student   \n",
       "\n",
       "   budget  \n",
       "0  medium  \n",
       "1     low  \n",
       "2     low  \n",
       "3  medium  \n",
       "4  medium  \n",
       "5  medium  \n",
       "6     low  "
      ]
     },
     "execution_count": 627,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizar encoder\n",
    "users.head(7)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f55f27d5",
   "metadata": {},
   "source": [
    "Realizar función que captura valores únicos a fin de revisar si los valores nulos han sido ingresados en un formato incorrecto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "id": "69a3cbb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "smoker [False True nan] \n",
      "\n",
      "drink_level ['abstemious' 'casual drinker' 'social drinker'] \n",
      "\n",
      "dress_preference ['elegant' 'formal' 'informal' 'no preference' nan] \n",
      "\n",
      "ambience ['family' 'friends' 'solitary' nan] \n",
      "\n",
      "hijos ['dependent' 'independent' 'kids' nan] \n",
      "\n",
      "activity ['professional' 'student' 'unemployed' 'working-class' nan] \n",
      "\n",
      "budget ['high' 'low' 'medium' nan] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Iterar valores por columnas\n",
    "for columna in users.columns:\n",
    "    # Ordenar valores y seleccionar valores únicos\n",
    "    i = users[columna].sort_values().unique()\n",
    "    # Imprimir nombre de columnas y lista de sus valores únicos\n",
    "    print(columna, i, '\\n')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "531007b8",
   "metadata": {},
   "source": [
    "Ahora procederemos a pasar los valores categóricos a numéricos. Usaremos la técnica OrdinalEncoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "id": "5b2ebb11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importar librería\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "# Crear Objeto Encoder Nominal\n",
    "ambience_ord_enc = OrdinalEncoder()\n",
    "\n",
    "# Seleccionar los valores no nulos de la columna ambiece del DataFrame users.\n",
    "ambience = users['ambience']\n",
    "ambience_not_null = ambience[ambience.notnull()]\n",
    "\n",
    "# Cambiar forma  a data ambience_not_null a forma (-1, 1)\n",
    "reshaped_vals = ambience_not_null.values.reshape(-1, 1)\n",
    "\n",
    "# Aplicar Ordinal Encode a reshaped_vals\n",
    "encoded_vals = ambience_ord_enc.fit_transform(reshaped_vals)\n",
    "\n",
    "# Asignar valores codificados a valores no nulos de la variable ambience en el DataFrame users\n",
    "users.loc[ambience.notnull(), 'ambience'] = np.squeeze(encoded_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f1d2024",
   "metadata": {},
   "source": [
    "A continuación se procede a generalizar lo realizado anteriormente por medio de un loop for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "id": "8cde4921",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crear diccionario vacio \"ordinal_enc_dict\"\n",
    "ordinal_enc_dict = {}\n",
    "\n",
    "for col_name in users:\n",
    "    # Crear Ordinal encoder para col\n",
    "    ordinal_enc_dict[col_name] = OrdinalEncoder()\n",
    "    col = users[col_name]\n",
    "    \n",
    "    # Seleccionar valores non-null de col\n",
    "    col_not_null = col[col.notnull()]\n",
    "    reshaped_vals = col_not_null.values.reshape(-1, 1)\n",
    "    encoded_vals = ordinal_enc_dict[col_name].fit_transform(reshaped_vals)\n",
    "    \n",
    "    # Almacenar los valores a los valores de la columna de DataFrame users\n",
    "    users.loc[col.notnull(), col_name] = np.squeeze(encoded_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "id": "e151680b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smoker</th>\n",
       "      <th>drink_level</th>\n",
       "      <th>dress_preference</th>\n",
       "      <th>ambience</th>\n",
       "      <th>hijos</th>\n",
       "      <th>activity</th>\n",
       "      <th>budget</th>\n",
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       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  smoker drink_level dress_preference ambience hijos activity budget\n",
       "0    0.0         0.0              2.0      0.0   1.0      1.0    2.0\n",
       "1    0.0         0.0              2.0      0.0   1.0      1.0    1.0\n",
       "2    0.0         2.0              1.0      0.0   1.0      1.0    1.0\n",
       "3    0.0         0.0              2.0      0.0   1.0      0.0    2.0\n",
       "4    0.0         0.0              3.0      0.0   1.0      1.0    2.0\n",
       "5    1.0         2.0              3.0      1.0   1.0      1.0    2.0\n",
       "6    0.0         1.0              2.0      2.0   1.0      1.0    1.0"
      ]
     },
     "execution_count": 629,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Primeros registros data Encoder\n",
    "users.head(7)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4821c3f5",
   "metadata": {},
   "source": [
    "Una vez codificada la data, se procede a realizar la imputación por medio de `KNN()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "id": "f8ee7291",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputing row 1/138 with 0 missing, elapsed time: 0.007\n",
      "Imputing row 101/138 with 0 missing, elapsed time: 0.010\n"
     ]
    }
   ],
   "source": [
    "# Crear imputador KNN\n",
    "KNN_imputer = KNN()\n",
    "\n",
    "# Imputar y redondear los valores en el DataFrame users\n",
    "users.iloc[:, :] = np.round(KNN_imputer.fit_transform(users))\n",
    "\n",
    "# Aplicar loop for a las columnas del DataFrame users\n",
    "for col_name in users:\n",
    "    \n",
    "    # Transformar la data\n",
    "    reshaped = users[col_name].values.reshape(-1, 1)\n",
    "    \n",
    "    # Realizar transformación inversa de las columnas codificadas ordinalmente\n",
    "    users[col_name] = ordinal_enc_dict[col_name].inverse_transform(reshaped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "id": "2fea9b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoker              0\n",
       "drink_level         0\n",
       "dress_preference    0\n",
       "ambience            0\n",
       "hijos               0\n",
       "activity            0\n",
       "budget              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 631,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revisar presencia de datos faltantes.\n",
    "users.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "id": "564adc57",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>informal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>independent</td>\n",
       "      <td>student</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>social drinker</td>\n",
       "      <td>formal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>independent</td>\n",
       "      <td>student</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>informal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>independent</td>\n",
       "      <td>professional</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>abstemious</td>\n",
       "      <td>no preference</td>\n",
       "      <td>0.0</td>\n",
       "      <td>independent</td>\n",
       "      <td>student</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  smoker     drink_level dress_preference ambience        hijos      activity  \\\n",
       "0  False      abstemious         informal      0.0  independent       student   \n",
       "1  False      abstemious         informal      0.0  independent       student   \n",
       "2  False  social drinker           formal      0.0  independent       student   \n",
       "3  False      abstemious         informal      0.0  independent  professional   \n",
       "4  False      abstemious    no preference      0.0  independent       student   \n",
       "\n",
       "   budget  \n",
       "0  medium  \n",
       "1     low  \n",
       "2     low  \n",
       "3  medium  \n",
       "4  medium  "
      ]
     },
     "execution_count": 633,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizar data imputada\n",
    "users.head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8fb5dc55",
   "metadata": {},
   "source": [
    "- Imputaciones múltiples por ecuaciones encadenadas (MICE)\n",
    "\n",
    "Método robusto para imputación de data faltante. Este algoritmo utiliza regresiones múltiples sobre los datos y toma un valor promedio para completar el valor faltante para el conjunto de datos.\n",
    "\n",
    "A continuación imputaremos los valores faltantes del dataset diabetes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "id": "43d4390f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
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       "      <td>8.0</td>\n",
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       "      <td>64.0</td>\n",
       "      <td>21.532888</td>\n",
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       "      <td>32.0</td>\n",
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       "    <tr>\n",
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       "      <td>40.0</td>\n",
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       "      <td>180.000000</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
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       "    <tr>\n",
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       "      <td>2.0</td>\n",
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       "      <td>70.0</td>\n",
       "      <td>27.000000</td>\n",
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       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>72.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
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      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness     Insulin   BMI  \\\n",
       "0            6.0    148.0           72.0      35.000000  218.666626  33.6   \n",
       "1            1.0     85.0           66.0      29.000000   70.738265  26.6   \n",
       "2            8.0    183.0           64.0      21.532888  268.732425  23.3   \n",
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       "4            0.0    137.0           40.0      35.000000  168.000000  43.1   \n",
       "..           ...      ...            ...            ...         ...   ...   \n",
       "763         10.0    101.0           76.0      48.000000  180.000000  32.9   \n",
       "764          2.0    122.0           70.0      27.000000  159.038930  36.8   \n",
       "765          5.0    121.0           72.0      23.000000  112.000000  26.2   \n",
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       "767          1.0     93.0           70.0      31.000000   87.825351  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction   Age  Outcome  \n",
       "0                       0.627  50.0      1.0  \n",
       "1                       0.351  31.0      0.0  \n",
       "2                       0.672  32.0      1.0  \n",
       "3                       0.167  21.0      0.0  \n",
       "4                       2.288  33.0      1.0  \n",
       "..                        ...   ...      ...  \n",
       "763                     0.171  63.0      0.0  \n",
       "764                     0.340  27.0      0.0  \n",
       "765                     0.245  30.0      0.0  \n",
       "766                     0.349  47.0      1.0  \n",
       "767                     0.315  23.0      0.0  \n",
       "\n",
       "[752 rows x 9 columns]"
      ]
     },
     "execution_count": 594,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import IterativeImputer from fancyimpute\n",
    "from fancyimpute import IterativeImputer\n",
    "\n",
    "# Copy diabetes to diabetes_mice_imputed\n",
    "diabetes_mice_imputed = diabetes.copy(deep= True)\n",
    "\n",
    "# Initialize IterativeImputer\n",
    "mice_imputer = IterativeImputer()\n",
    "\n",
    "# Impute using fit_tranform on diabetes\n",
    "diabetes_mice_imputed.iloc[:, :] = mice_imputer.fit_transform(diabetes_mice_imputed)\n",
    "diabetes_mice_imputed"
   ]
  }
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