CalculoQuemadoDraft1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a588656a",
   "metadata": {},
   "source": [
    "# Cálculo del quemado de un EE.CC. a partir de las cuentas neutrónicas del SICOM-NG-FA y otras variables de interés."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "76950399",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EECC</th>\n",
       "      <th>TipoEC</th>\n",
       "      <th>Muestra</th>\n",
       "      <th>Cnn</th>\n",
       "      <th>T(ms)</th>\n",
       "      <th>Cnn/s</th>\n",
       "      <th>Ciclos</th>\n",
       "      <th>Enf(mes)</th>\n",
       "      <th>Enr(%)</th>\n",
       "      <th>Mwd/TnU</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BL09</td>\n",
       "      <td>MAEF</td>\n",
       "      <td>1</td>\n",
       "      <td>32458</td>\n",
       "      <td>146916</td>\n",
       "      <td>220.94</td>\n",
       "      <td>3</td>\n",
       "      <td>127</td>\n",
       "      <td>4.697</td>\n",
       "      <td>44082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BL09</td>\n",
       "      <td>MAEF</td>\n",
       "      <td>2</td>\n",
       "      <td>39967</td>\n",
       "      <td>150917</td>\n",
       "      <td>264.84</td>\n",
       "      <td>3</td>\n",
       "      <td>127</td>\n",
       "      <td>4.697</td>\n",
       "      <td>46265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BL09</td>\n",
       "      <td>MAEF</td>\n",
       "      <td>3</td>\n",
       "      <td>39438</td>\n",
       "      <td>145916</td>\n",
       "      <td>270.29</td>\n",
       "      <td>3</td>\n",
       "      <td>127</td>\n",
       "      <td>4.697</td>\n",
       "      <td>46517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BL09</td>\n",
       "      <td>MAEF</td>\n",
       "      <td>4</td>\n",
       "      <td>30342</td>\n",
       "      <td>145930</td>\n",
       "      <td>207.93</td>\n",
       "      <td>3</td>\n",
       "      <td>127</td>\n",
       "      <td>4.697</td>\n",
       "      <td>43374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BL09</td>\n",
       "      <td>MAEF</td>\n",
       "      <td>5</td>\n",
       "      <td>30361</td>\n",
       "      <td>140921</td>\n",
       "      <td>215.46</td>\n",
       "      <td>3</td>\n",
       "      <td>127</td>\n",
       "      <td>4.697</td>\n",
       "      <td>43788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     EECC TipoEC  Muestra    Cnn   T(ms)   Cnn/s  Ciclos  Enf(mes)  Enr(%)  \\\n",
       "ind                                                                          \n",
       "1    BL09   MAEF        1  32458  146916  220.94       3       127   4.697   \n",
       "2    BL09   MAEF        2  39967  150917  264.84       3       127   4.697   \n",
       "3    BL09   MAEF        3  39438  145916  270.29       3       127   4.697   \n",
       "4    BL09   MAEF        4  30342  145930  207.93       3       127   4.697   \n",
       "5    BL09   MAEF        5  30361  140921  215.46       3       127   4.697   \n",
       "\n",
       "     Mwd/TnU  \n",
       "ind           \n",
       "1      44082  \n",
       "2      46265  \n",
       "3      46517  \n",
       "4      43374  \n",
       "5      43788  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"DataSet.csv\", sep=';', index_col=\"ind\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29768d9",
   "metadata": {},
   "source": [
    "### Codificación de la categoría TipoEC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9976550b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EECC</th>\n",
       "      <th>Muestra</th>\n",
       "      <th>Cnn</th>\n",
       "      <th>T(ms)</th>\n",
       "      <th>Cnn/s</th>\n",
       "      <th>Ciclos</th>\n",
       "      <th>Enf(mes)</th>\n",
       "      <th>Enr(%)</th>\n",
       "      <th>Mwd/TnU</th>\n",
       "      <th>AEF</th>\n",
       "      <th>MAEF</th>\n",
       "      <th>OFA</th>\n",
       "      <th>STD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <td>BL09</td>\n",
       "      <td>1</td>\n",
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       "      <td>220.94</td>\n",
       "      <td>3</td>\n",
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       "      <td>4.697</td>\n",
       "      <td>46265</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>BL09</td>\n",
       "      <td>3</td>\n",
       "      <td>39438</td>\n",
       "      <td>145916</td>\n",
       "      <td>270.29</td>\n",
       "      <td>3</td>\n",
       "      <td>127</td>\n",
       "      <td>4.697</td>\n",
       "      <td>46517</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BL09</td>\n",
       "      <td>4</td>\n",
       "      <td>30342</td>\n",
       "      <td>145930</td>\n",
       "      <td>207.93</td>\n",
       "      <td>3</td>\n",
       "      <td>127</td>\n",
       "      <td>4.697</td>\n",
       "      <td>43374</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BL09</td>\n",
       "      <td>5</td>\n",
       "      <td>30361</td>\n",
       "      <td>140921</td>\n",
       "      <td>215.46</td>\n",
       "      <td>3</td>\n",
       "      <td>127</td>\n",
       "      <td>4.697</td>\n",
       "      <td>43788</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     EECC  Muestra    Cnn   T(ms)   Cnn/s  Ciclos  Enf(mes)  Enr(%)  Mwd/TnU  \\\n",
       "ind                                                                            \n",
       "1    BL09        1  32458  146916  220.94       3       127   4.697    44082   \n",
       "2    BL09        2  39967  150917  264.84       3       127   4.697    46265   \n",
       "3    BL09        3  39438  145916  270.29       3       127   4.697    46517   \n",
       "4    BL09        4  30342  145930  207.93       3       127   4.697    43374   \n",
       "5    BL09        5  30361  140921  215.46       3       127   4.697    43788   \n",
       "\n",
       "     AEF  MAEF  OFA  STD  \n",
       "ind                       \n",
       "1      0     1    0    0  \n",
       "2      0     1    0    0  \n",
       "3      0     1    0    0  \n",
       "4      0     1    0    0  \n",
       "5      0     1    0    0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.TipoEC.value_counts()\n",
    "\n",
    "dummies = pd.get_dummies(df['TipoEC'])\n",
    "df = pd.concat([df, dummies], axis = 1)\n",
    "df.drop('TipoEC', axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f13d4e",
   "metadata": {},
   "source": [
    "### Reordeno los datos y elimino varias columnas [Cnn y T(ms), porque son combinación lineal de Cnn/s; AEF, porque es la ausencia de MAEF, OFA y STD simultáneamente]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4e5fbc55",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EECC</th>\n",
       "      <th>Muestra</th>\n",
       "      <th>MAEF</th>\n",
       "      <th>OFA</th>\n",
       "      <th>STD</th>\n",
       "      <th>Cnn/s</th>\n",
       "      <th>Ciclos</th>\n",
       "      <th>Enf(mes)</th>\n",
       "      <th>Enr(%)</th>\n",
       "      <th>Mwd/TnU</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BL09</td>\n",
       "      <td>1</td>\n",
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       "      <td>BL09</td>\n",
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       "      <td>BL09</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>270.29</td>\n",
       "      <td>3</td>\n",
       "      <td>127</td>\n",
       "      <td>4.697</td>\n",
       "      <td>46517</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BL09</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>207.93</td>\n",
       "      <td>3</td>\n",
       "      <td>127</td>\n",
       "      <td>4.697</td>\n",
       "      <td>43374</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BL09</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>215.46</td>\n",
       "      <td>3</td>\n",
       "      <td>127</td>\n",
       "      <td>4.697</td>\n",
       "      <td>43788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     EECC  Muestra  MAEF  OFA  STD   Cnn/s  Ciclos  Enf(mes)  Enr(%)  Mwd/TnU\n",
       "ind                                                                          \n",
       "1    BL09        1     1    0    0  220.94       3       127   4.697    44082\n",
       "2    BL09        2     1    0    0  264.84       3       127   4.697    46265\n",
       "3    BL09        3     1    0    0  270.29       3       127   4.697    46517\n",
       "4    BL09        4     1    0    0  207.93       3       127   4.697    43374\n",
       "5    BL09        5     1    0    0  215.46       3       127   4.697    43788"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df [['EECC', 'Muestra', 'MAEF', 'OFA', 'STD', 'Cnn/s', 'Ciclos', 'Enf(mes)', 'Enr(%)', 'Mwd/TnU']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d05e467",
   "metadata": {},
   "source": [
    "### Tipificación y discretización de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "af618be1",
   "metadata": {},
   "outputs": [
    {
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       "      <td>BL09</td>\n",
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       "      <th>5</th>\n",
       "      <td>BL09</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.144162</td>\n",
       "      <td>0.536875</td>\n",
       "      <td>-0.602062</td>\n",
       "      <td>0.871258</td>\n",
       "      <td>0.238809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     EECC  Muestra  MAEF  OFA  STD     Cnn/s    Ciclos  Enf(mes)    Enr(%)  \\\n",
       "ind                                                                          \n",
       "1    BL09        1     1    0    0 -0.103873  0.536875 -0.602062  0.871258   \n",
       "2    BL09        2     1    0    0  0.218885  0.536875 -0.602062  0.871258   \n",
       "3    BL09        3     1    0    0  0.258954  0.536875 -0.602062  0.871258   \n",
       "4    BL09        4     1    0    0 -0.199524  0.536875 -0.602062  0.871258   \n",
       "5    BL09        5     1    0    0 -0.144162  0.536875 -0.602062  0.871258   \n",
       "\n",
       "      Mwd/TnU  \n",
       "ind            \n",
       "1    0.273655  \n",
       "2    0.532389  \n",
       "3    0.562256  \n",
       "4    0.189741  \n",
       "5    0.238809  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "from sklearn.preprocessing import KBinsDiscretizer\n",
    "\n",
    "\n",
    "# Creo una función para tipificar las columnas del df\n",
    "def TipificarColumnas(dataframe, column):\n",
    "    dataframe[column] = dataframe[column].astype(float)\n",
    "    dataframe[column] = stats.zscore(dataframe[column])\n",
    "    return dataframe\n",
    "\n",
    "# Copio el df para no modificar el original y tipifico las cinco últimas columnas\n",
    "data = df.copy()\n",
    "for column in data.columns[-5:]:\n",
    "    data = TipificarColumnas(data, column)\n",
    "\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6246c5de",
   "metadata": {},
   "source": [
    "### Cálculo de estadísticos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ede1c173",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medias:  [ 2.623e-17  1.779e-16 -6.692e-17 -4.395e-16  1.582e-16]\n",
      "\n",
      "Varianzas:  [1. 1. 1. 1. 1.]\n",
      "\n",
      "Medianas:  [-0.01   0.537 -0.247  0.619  0.249]\n",
      "\n",
      "Distancia intercuartil:  [1.84  1.4   1.858 1.348 1.592]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import iqr\n",
    "\n",
    "# Por algún motivo asigna por defecto el tipo object a los estadísticos, así que\n",
    "# especifico que los quiero de tipo float para que les aplique set_printoptions.\n",
    "medias = np.mean(data.values[:,5:], axis=0).astype(float)\n",
    "varianzas = np.var(data.values[:,5:], axis=0).astype(float)\n",
    "medianas = np.median(data.values[:,5:], axis=0).astype(float)\n",
    "intercuartil = iqr(data.values[:,5:], axis=0).astype(float)\n",
    "# print(medias.dtype, varianzas.dtype, medianas.dtype, intercuartil.dtype)\n",
    "\n",
    "np.set_printoptions(precision = 3)\n",
    "\n",
    "# Compruebo que las variables están tipificadas (media = 0, varianza = 1)\n",
    "print('Medias: ', medias)\n",
    "print()\n",
    "print('Varianzas: ', varianzas)\n",
    "print()\n",
    "print('Medianas: ', medianas)\n",
    "print()\n",
    "print('Distancia intercuartil: ', intercuartil)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba68bdb7",
   "metadata": {},
   "source": [
    "### Diagramas de cajas e histogramas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20eed45b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(16,8))\n",
    "ax1.set_title('Box Plot datos tipificados')\n",
    "ax1.boxplot(data.values[:,5:], labels=data.columns.tolist()[5:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf23c2bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Diagramas de las variables tipificadas\n",
    "fig2,ax2 = plt.subplots(1,5,figsize=(24,6))\n",
    "variables = data.columns.tolist()[5:]\n",
    "for a,var in enumerate(variables):\n",
    "    ax2[a].hist(data[var].values)\n",
    "    ax2[a].set_title(var)\n",
    "    a+1\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "360f6c8a",
   "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>EECC</th>\n",
       "      <th>Muestra</th>\n",
       "      <th>MAEF</th>\n",
       "      <th>OFA</th>\n",
       "      <th>STD</th>\n",
       "      <th>Cnn/s</th>\n",
       "      <th>Ciclos</th>\n",
       "      <th>Enf(mes)</th>\n",
       "      <th>Enr(%)</th>\n",
       "      <th>Mwd/TnU</th>\n",
       "      <th>Ciclos_D</th>\n",
       "      <th>Enf(mes)_D</th>\n",
       "      <th>Enr(%)_D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BL09</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.103873</td>\n",
       "      <td>0.536875</td>\n",
       "      <td>-0.602062</td>\n",
       "      <td>0.871258</td>\n",
       "      <td>0.273655</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BL09</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.218885</td>\n",
       "      <td>0.536875</td>\n",
       "      <td>-0.602062</td>\n",
       "      <td>0.871258</td>\n",
       "      <td>0.532389</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BL09</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.258954</td>\n",
       "      <td>0.536875</td>\n",
       "      <td>-0.602062</td>\n",
       "      <td>0.871258</td>\n",
       "      <td>0.562256</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BL09</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199524</td>\n",
       "      <td>0.536875</td>\n",
       "      <td>-0.602062</td>\n",
       "      <td>0.871258</td>\n",
       "      <td>0.189741</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BL09</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.144162</td>\n",
       "      <td>0.536875</td>\n",
       "      <td>-0.602062</td>\n",
       "      <td>0.871258</td>\n",
       "      <td>0.238809</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     EECC  Muestra  MAEF  OFA  STD     Cnn/s    Ciclos  Enf(mes)    Enr(%)  \\\n",
       "ind                                                                          \n",
       "1    BL09        1     1    0    0 -0.103873  0.536875 -0.602062  0.871258   \n",
       "2    BL09        2     1    0    0  0.218885  0.536875 -0.602062  0.871258   \n",
       "3    BL09        3     1    0    0  0.258954  0.536875 -0.602062  0.871258   \n",
       "4    BL09        4     1    0    0 -0.199524  0.536875 -0.602062  0.871258   \n",
       "5    BL09        5     1    0    0 -0.144162  0.536875 -0.602062  0.871258   \n",
       "\n",
       "      Mwd/TnU  Ciclos_D  Enf(mes)_D  Enr(%)_D  \n",
       "ind                                            \n",
       "1    0.273655       3.0         0.0       2.0  \n",
       "2    0.532389       3.0         0.0       2.0  \n",
       "3    0.562256       3.0         0.0       2.0  \n",
       "4    0.189741       3.0         0.0       2.0  \n",
       "5    0.238809       3.0         0.0       2.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Discretizo para visualizar después los datos según rangos de estas variables.\n",
    "# Elijo el número de intervalos según las modas en la distribución.\n",
    "enfriDis = KBinsDiscretizer(n_bins=2, encode='ordinal', strategy='kmeans').fit_transform(df[['Enf(mes)']])\n",
    "enriqDis = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='kmeans').fit_transform(df[['Enr(%)']])\n",
    "\n",
    "# Añado las variables discretas al dataframe\n",
    "data['Ciclos_D'] = df['Ciclos'].astype(float)\n",
    "data['Enf(mes)_D'] = enfriDis\n",
    "data['Enr(%)_D'] = enriqDis\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0870bc37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Diagramas de las variables tipificadas\n",
    "fig3,ax3 = plt.subplots(1,5,figsize=(24,6))\n",
    "variables = data.columns.tolist()[5:10]\n",
    "for a,var in enumerate(variables):\n",
    "    ax3[a].hist(data[var].values)\n",
    "    ax3[a].set_title(var)\n",
    "    a+1\n",
    "\n",
    "# Diagramas de las variables discretizadas\n",
    "fig4,ax4 = plt.subplots(1,5,figsize=(24,6))\n",
    "variables = data.columns.tolist()[10:]\n",
    "for a,var in enumerate(variables):\n",
    "    ax4[a+1].hist(data[var].values)\n",
    "    ax4[a+1].set_title(var)\n",
    "    a+1\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91f4f050",
   "metadata": {},
   "source": [
    "### Coeficiente de correlación entre Cnn/s y Mwd/TnU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3e5e9971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/1z/HI2b2OxPadKN5VYmD/vl9tGi/QkzydjM7CDyK953oa+YlK+7Cu6nwRfOqXL5YdM7/y8x+6V+PPzOzNWa23X+dB83/ol54r4pec6WZfce/Rv1Fx1xjZo/6y06Y2dfMrHUm12cWrsW7IfI551zaTyIZ8LIS17zJ3/4u/2/zSeDbeDdapj2Wc26Pc+5LwFNzfA5SR5SgEJm5jUAU7275ZD4IvAi4DLgUuAr4UNH6c/G+0K4GCt0AbsL7z78V+BpeNjoLXAhcDvw6MFmZ4c/911oMfB34lplF/XV/CLweeCXeF+i3AadLHONevLv3F+DdyX8L8NtF668G9gBtwKeAL5mZTXENpvJiYB1eZcKHzWy9v/z3gZv9118OnAL+smi/h4CLgGXAL/Cu07TM7MVmNjDFz2QltZcATxY9fxJoN7MlEzd0zvUB/wj8th/MbcR7fwt9fL8BXGhma82rtrgN2DpJe9uBteiDXURkIVIcMbs4YtJrYWab/Pa9wj/Pl07Yd8RvRyteFcDvmdnNE7Z5KbAeuL54oXPug8BPgXf7XVneXbR6E95NhBcB7wU2A28EVgJdeNdrHD859M/AAbyqzE682AC8L/d/jhf7rPeP89HJLoiZ7ZwipvmrSXa7BNjpnCvucrHTX/68l5jwu/C46wyOJXJmnHP60Y9+ZvCD9wF0dJpt9gGvLHp+PfCs//haYBSIFq3/KLCt6Hk7kAZiRcteD/yr//iteHfjJ3v9U8Cl/uM9wE2TbOfwPtCD/utdXLTuHcBPil5vb9G6Rf6+505y3GeBVxSd2wP+4/P8/VYUbfufwK3+427g5UXrOvDKBEMlXqPVP1bcf/4V4BNz/F7vAzYVPQ/7r3neJNu/GujDCwizwO8WrWsAPu/vn8WrkDi/xDHCwL8Af1vpv3X96Ec/+tHP3P8ojph1HDHVtfgy8OdF6y4stGmS434OuMd/XIhJLihaX1gW8p//BPidEuf8q0XPHwPeV/T8M3iVBYX36rD/eCNwnBIxTYl23gw8Psd/d38KfGPCsq8BH51k+5/hJZ2iwAuAk8Ce2Ryr8H7Mx78j/Sz8n4r1UxOpQf1Am5mFnHPZSbZZjpchLzjgLys47pxLTdjnUNHj1XhfVHuLbi4EJmwzxsz+CO+uyHK8D84WvDsU4GXh9011Qv62DSXa3Fn0/GjhgXPutN+upmmOO5mjRY9PFx1nNfBdM8sXrc/hVS0cxRt463XAUqCwTRuQOMN2TGcY71oWFB4PTdzQzH4F+Cbwm8AjeJUe/2xmR5xzW4CPAFfivR9HgTcBj5rZJc650/4xAnjjUowC7574GiIisiAojphdHDHVtVgO7ChaN+78zOxq4G68O/8NQAT41oTjl7wm0+grepws8fzcEvusBA6Ues/NbBnwBbwuJc1479WpM2jXVCbGNPjPnxfT+N6IV8V6CHgGLwFR6LY622OJzJq6eIjM3HYghZfdnswRvOCgYJW/rKDUiMbFyw7h3Yloc861+j8tzrnnlc75/UTfB/wWcI5zrhXvC7sVHWvN1KfECbxKhYlt7plmv7l2CLih6JxbnXNR51wP8Aa88tVX4JWQnufvM215qHnTfA1P8fOSSXZ9Cq+ctOBSoM85119i2y68Ows/ct44EnuALcANRft+0zl32DmXdc59BW9gqYv9NhrwJby7Xv/TOZeZ7rxERKQmKY6YnamuRS+womjdygn7fh34AbDSORcH/obnxw1TzTIxlzNQHAJWWekBTP/cf60NzrkWvJsYk8Y3ZvbUFDHN30yy21PAhgndajYwSXdS59wB59yrnHNLnXNXA0vwql5nfSyRM6EEhcgMOecSwIeBvzSzm81skZmFzewGM/uUv9k/Ah8ys6Vm1uZv/8AsXqMXeBj4jJm1mDd45Bozm9i3ErxMexa/bNDMPsz4rPbfA39mZheZZ8PEMRScN2jjg8BdZtZsZqvx+nTOuM1z5G/8NqwG8K9fYUTzZrxgqx+vNPSTMz2o86b5apri56eT7PpV4O1mdrGZnYPX5/Urk2z7OHCReVONmpmtAV7Fc2NY/Bx4nZm1++/nm/Hubu311/81Xr/TVzvnkjM9NxERqS2KI2ZtqmvxIN7YT+vNbJG/buK5nXTOpczsKrybHbPRhzemxlz4T7yEyt1m1mhmUTP71aJ2DgMDZtYJ/PFUB3LOXTJFTPPOSXb7CV5V6u+bN/BooVLz0VIb+9e02cwazOxNeGOYfHYmx/L/TqJ4VSv45zrpNO0ipShBITILzrnP4n3wfgjvA/0QXkn+9/xNPoFXcrgT+C+8AR0/McuXeQvef+y78cr8vo03JsNEP8IbPPJpvLLHFOPLFT+L9wH+MDCId5c+VuI4d+ANJvUMXr/Dr+P17Synz+Pd6XjYzIaAf8cbVAu8ZMEBvLsxu/1188o5txVvIK9/9V/7AF5XDQDMm7P+T/xt9+ENHPYFvOv8b8D/xbveAH+Bl6x4AhgA7sSrlBjwA7l34A0AdrToLsgb5/scRUSk/BRHzMqk18I59xDe5+6/4iX8t/v7pP3f/wv4uB9TfNg/j9n4PPBa82YeOaupU/0kzqvxxmU4CBzGm5IcvCnMX4BXubIF+M7ZvNYkrz+KV7XzFrw45G3Azf5yzOyNZlZcAXE93nt5Cngn3phcx2dyLLyKlyTPVVQk8cYyEZkxc24uK5hERERERETKx7xZwXYBkSnG9xCRGqAKChERERERqSlm9pt+N4Rz8KoV/0nJCZHapwSFiIiIiIjUmnfgdZPZhzcuwu9VtjkiMhfUxUNEREREREREKk4VFCIiIiIiIiJScaXm4615bW1t7rzzzqt0M0RERKrKY489dsI5t7TS7agHikVERERKmyoeWZAJivPOO48dO3ZUuhkiIiJVxcwOVLoN9UKxiIiISGlTxSPq4iEiIiIiIiIiFacEhYiIiIiIiIhUnBIUIiIiIiIiIlJxSlCIiIiIiIiISMUpQSEiIiIiIiIiFacEhYiIiNQ0M3vWzP7LzJ4wsx3+so+aWY+/7Akze2XR9h8ws71mtsfMri9a/kL/OHvN7AtmZv7yiJl901/+H2Z2XrnPUUREpB4oQSEiIiILwa855y5zzl1RtOwef9llzrkfApjZxcCtwCXAJuCvzCzob//XwO3ARf7PJn/524FTzrkLgXuAv5j/0xEREak/SlCIiIhIPbkJ+IZzLu2c2w/sBa4ysw6gxTm33TnngK8CNxftc5//+NvAywvVFSIiIjJ3lKAQERGRWueAh83sMTO7vWj5u81sp5l92czO8Zd1AoeKtjnsL+v0H09cPm4f51wWSABLJjbCzG43sx1mtuP48eNzcV4iIiJ1RQkKERERqXW/6px7AXAD8C4zuwavu8Ya4DKgF/iMv22pygc3xfKp9hm/wLnNzrkrnHNXLF26dJanICIiIkpQiIiISE1zzh3xfx8Dvgtc5Zzrc87lnHN54O+Aq/zNDwMri3ZfARzxl68osXzcPmYWAuLAyfk5GxERkfqlBIWIiIjULDNrNLPmwmPg14Fd/pgSBb8J7PIf/wC41Z+Z43y8wTD/0znXCwyZ2Yv88SXeAny/aJ/b/MevBR71x6kQERGRORSqdANEREREzkI78F1/zMoQ8HXn3FYzu9/MLsPrivEs8A4A59xTZvYgsBvIAu9yzuX8Y/0e8BUgBjzk/wB8CbjfzPbiVU7cWobzEhERqTsVTVCY2ZeBVwHHnHNdJdZfi3f3Yr+/6DvOuY+Xr4UiIiJSzZxzzwCXllj+5in2uQu4q8TyHcDz4hHnXAp43dm1VERERKZT6QqKrwBfxJvKazI/dc69qjzNEREREREREZFKqGiCwjm3zczOq2QbREREqkl3b4Ktu/roGUjS2RpjU1c76zvilW6WiIiI1JFKxSO1MEjmRjN70sweMrNLJttIc4+LiEit6+5NsHnbfhLJDB3xKIlkhs3b9tPdm6h000RERKROVDIeqfYExS+A1c65S4F7ge9NtqHmHhcRkVq3dVcf8ViYeCxMwGzs8dZdfZVumoiIiNSJSsYjVZ2gcM4NOueG/cc/BMJm1lbhZomIiMyLnoEkzdHxvS+boyF6BpIVapGIiIjUm0rGI1WdoDCzc/25yDGzq/Da21/ZVomIiMyPztYYQ6nsuGVDqSydrbEKtUhERETqTSXjkYomKMzsH4HtwDozO2xmbzezd5rZO/1NXgvsMrMngS8AtzrnXKXaKyIiMp82dbWTSGZIJDPknRt7vKmrvdJNExERkTpRyXik0rN4vH6a9V/Em4ZURERkwVvfEef2a84fN2r2LVeu0CweIiIiUjaVjEcqmqAQERGR8dZ3xJWQEBERkYqqVDxS1WNQiIiIiIiIiEh9UAWFiIjIGeruTYwrf9zU1a7qBxEREakJ1RjHqIJCRETkDHT3Jti8bT+JZIaOeJREMsPmbfvp7k1UumkiIiIiU6rWOEYJChERkTOwdVcf8ViYeCxMwGzs8dZdfZVumoiIiMiUqjWOURcPERGRaZQqgewZSNIRj47brjkaomcgWaFWioiIiEyvuzfBI7v7yLs88VgDFy5rpK0pWhVxjCooREREpjBZCWQkaAylsuO2HUpl6WyNVailIiIiIlMrxDXhoBEJBkhlcjx2YIATw6mqiGOUoBAREZnCZCWQDkgkMySSGfLOjT3e1NVe6SaLiIiIlFSIay5Z3kI65wBoCBq7egarIo5RgkJERGQKPQNJmqPje0Q2R0OM5hy3X3M+8ViY3kSKeCzM7decX/HRr0VEREQmU4hrljZHecGqViLhIJmcYzSXr4o4RmNQiIiUQTVO4yQz09kaI5HMEI+Fx5YVSiDXd8T1PoqIiNSZWo7riuOapc1RljZHx55XwzmogkJEZJ5V6zROMjObutrVlUNERESA2o/rqj2uUQWFiNS9+c6CF49hAIz93rqrryoy1TK19R1xbr/m/HF/I7dcuULvnYiIyAIw2ziw1uO6ao9rlKAQkbpWyILHY+FxWfCz7YNX/GH31JEEl66Ij+siUA3TOMnMqSuHiIjIwjPbOLC7N8HDu4+Cg+ZYmAuXNrK0uTqm55yNao5r1MVDROraZDM0bN3Vd8bHnFj61xAM8PP9pzg+lBrbphqmcRIRERGpZ7OJAwvxXUMwQDhopDM5fnFwgOND1TE950KhBIWI1J3u3gT3PPI07/nWkzy8+yipTHbc+rPNgk/8sOvqbMEBTx0ZrMq+fiIiIiL1plAN8R/P9LP9mf6xG0mTxYGF+K6rs4VRf3rOSNB46kh1TM+5UKiLh4jUje7eBA9sP8BP9/ZzzqIw6zuax6obrr7AWNocBc6+uqFnIElHPDr2vK0pytUXnMOThwbpTaSqrq/fQlTLo2uLiIjI/Nqys4d7f7yPvqEU4aCRzef5RTLDC1a10hAKlowDC/FdwMK8cHUre4+NkEiOErBAVUzPWayW4yBVUIhIXSiU5e06MkhrzMvNPnEoQWdrdM6rGzpbYwylxldlREIhrru4nU+/7lLuvG5tzXxI1KJaH11bRERE5k93b4J7H90HBp2tUUazjuPDo+RyuSmrIYrju7amKC+6YAkvuqCN6y6uri//tR4HqYJCROpCoSxvNJenORLCzAA4MZKZUXXDbDLRm7ra2bxtP+CVCQ6lsiSSGW65csX8nqQAtT+6toiIiMyfrbv6yOTyLGlswMxYudg4Npji5OkM4VCI2685H4B7Hnl6XNxXK/FdrcdBSlCISF0olOW1RMOkMjmi4SCRUIDhVHasuuHO69aW3LdQBpjNOxY3hhnN5Ni87fSk5XzVPn3TQjexiw1o1hQRERHx9AwkWdLYQDqbJxoOsqghxOoljZwcyXDdxe08c3x40rivFuK7Wo+DlKAQkQWnVLVDZ2uMRDLDhcsaeezAgLehc4SDNmX2u7gMMBo2DvSP8N99Q7Q3R3hg+wHues2GkvtV8/RNC13hvS6e1lWja4uIiNS3Qny4+8gguXyedCYHixqIhAIMprKEAsba9sZp475qj+9qPQ7SGBQisqBM1u9ubXsjiWSGcDDI5au8D5ZTySyXLG+ZcmCjrbv6GEln6B9O83TfMMPpHCHzkho/3dtfM/356smmrvaxsUQ0a4qIiIgUx5oHgckAACAASURBVIeXrmwhk3Nk85DN5ekfGQXgjpev4em+ETK5PNlcjn3HR0gks942w6M1E/fVehykBIWILCiTzWf9dN8It19zPvFYmEwOrl23jL998wv45DSZ8N29CUbSOYbTWcJBb9yK06NZUpk85ywqPU+2VFahi008FqY3kSIeC1fd6NoiIiJSPsXx4bLmGBvXLGZJUwN54FUblnPPLZdy44ZOegaSxEIBDp3yphwNBQ0cDKezxMKBmoj7aj0OUhcPEVlQpup3dybdLhLJLPFYmJHRHGYQ8D6nyOTyrO9orpn+fPVGXWxERESkoNQU8NesjdCbSI0bg6yzNcbOQwM457CAd2PK4TAzQgGrmbivluMgVVCIyIJSaorPs+l31xINEQkFaWwIgvMSEwEzWmMhouFQzfTnExEREalXM40PN3W1k8rmaYqExuI+MDrjEVLZvOK+MlCCQkQWlLnud3fJ8jhr25tY3hojHArQGmtgRWuUZS2xmurPJyIiIlKvZhofru+I8+ILl9DWFBmL+y5oa6QhHCIcDCjuKwMlKERkQZnrfnebutoJBgNccd5ibug6l3PjUdI56JpmcE0RERERqQ6ziQ/fvHE1l3TGeelFS1m9ZBHprAMHd7xsjeK+MtAYFCKy4Mxlv7vCB9rWXX0Mp7Ncu24Zm7raq/oDqtQ0q9XcXhEREZH5NtP4sDj2awgH2bhmbmIpxWczowSFiNSNM/1gqIWBhgrn9tSRBIdPJVnX3sSqJY1j06yq2kNERETEM11MONex35adPdz76D4yuTxLGhvIZHNs3nZa8VkJ6uIhInVhy84e7vzGk2zZ2cvB/hH2Hx9m87b9NTGf9XSK5/YeTGYA2NM3zMmR9NiUWrUwLZaIiIjIfCt3TNjdm+DeH+8DYEljA+lsnj19w+TzecVnJShBISILXndvgnsf3QcGixvDpLN5nj42TC5X2x8M3b0J7nnkaf7owZ08c3yY0WyO4XTOn3kkwN5jI8Bz06yKiIiI1LNyx4TdvQk+8oPd9AwkSSRHOT2aIxoOEgkF6E2kFJ+VoASFiCx4W3f1kcnlaYmGMLOxD4ajg7X7wVBcNZF3eXCOXxwcIGCQzuaJhAIMprxqirOZZlVERERkoShnTFiI1fqH0zRHg2Syjt5EipF0lkgoQP/IqOKzEpSgEJEFr2cgOVZSVxAJBTg5kqnZD4atu/rGum/EYw1gRiQUwOElKAZTWZojobOeZlVERERkoShnTFiI1dqaIjRGQjjAgP6RNIOprKYtnYQGyRSRBa+zNUYmm2NP3zCAX12QJRSwmvpgKB7Q6akjCS5dESceC3PhskYeOzBAQ9DI5x1rlzXxdN8w8UVeAuOWK1doACYRERGpe+WKCbt7Ezy8+yg4CAWNTDbP4sYwI+ksQ6kc8Sjc8XJNW1qKEhQisuBt6mpn87bTrGtvojeRon9klHAwUFMfDIUywXgsTEc8yi/7hvj5/lNcfYGxtDnKC1e3sqtnEMxx/tImfu/XaufcRERERMqhHDFhIWZrCAZwzmFmmBmRUJC8gxWLG/nYb1ysOG0SSlCIyIJXPJ91OBRk45q2mpt7urhLB0BXZwvb953kqSODXLM2QjgY5IKlTZquSkRERGQS5YgJCzFbV2cLjx0YIBIyWqIhMGPDilbFatNQgkJE6sJcz2ddbj0DSTri0bHnbU1Rrr7gHJ48NEhvIkVna0xdOURERESmMd8xYSFmC1iYF65uZe+xERLJUQIWUHJiBpSgEBGpAZ2tMRLJzFgFBUAkFOK6i9u587q1FWyZiIiIiBQUx2xtTVHamqJjz5WcmJ5m8RARqQGbutrHZuTIO6fZOURERESqkGK2s6MKChGpKcUzWXS2xmpuLIkzVdxnsnDu6tIhIiIi8nyVjBcVs50dJShEpGZs2dnDvT/eRzbvWNwYZjSTY/O203XTn6/Wx9EQERERmW/VEC8qZjtzSlCIyLyaqwx2d2+Cex/dBwaLG8Oks3mePjbM2mVNbN3VV1MfAvVaBSIiIiIyW7OJm8odLyqmm3sag0JE5k1hHuhEMkNH3BsgaPO2/XT3JmZ9rK27+sjk8rREQ5gZ0XCQSCjA0cEUPQPJeWj9/JjLayIiIiKykM02bipnvKiYbn6ogkJE5k1hHujCzBOF3zPJYHf3Jnhg+wEeP5TA4UiN5ljUECSdzRMNBwGIhAKcHMmwcU1sfk9kDp3NNRERERGpJzONm7bs7OG+7Qd56kgCw5HJ5ulo9eLD+YoXFdPNDyUoRGTeFOaBLtYcDU2bwe7uTfDpHz3N/hMjNEWCGEZvMsNIOsOiiPeffyQUYDCVJRSwqh4VeWLp3+7eBL9ybsu4bWZyTURERETqzUxiyS07e7j7oT00RkK0REIMp7P0JlIAnBuPznm8WIjtvvdED+3NES5qb6KtKVqybTJ76uIhIvOmszXGUCo7btlQKktn69QZ7K27+jgxnKY5GiLWECLaEGJZU4TRHESCASKhAP0jowDc8fI1VZulLlX6d6g/ycH+kXHbzeSaiIiIiNSbmcSS920/SGMkRDwWZklThIZQkGg4wMnT6TmPF4tju/bmCIOpLI8dGODEcKpk22T2lKAQkXlzpvNA9wwkSWdzRELP/RfVuijMOY1hAkFj1ZJGXrVhOffccik3buic79M4Y8WlfwEz4rEwa9ub2NM3rLmxRURERKYxk1iybzBFc8Tr/tsYCdERj9IUCeGwOY8Xi2O7C5c1jS3/pR/bKaY7e+riISJzYrJRjM9kHujO1hi/7BsaN95EOpunJRrm2nXLuPO6tWfdrnIoVZa4uq2R05kc8VhYc2OLiIiITDAxdnvF+qU83TcyadzU3hJlMJkhHvNubDVGQmTzjpVLGp8XM55tXFgc2y1tjvKCVa3sPTZM31CajbGwYro5oASFiJy1QrlbPBYeN4pxYb7p2f5HvamrnV09CfafGME5hwHD6RyrliyaVVZ6unbNt87WGIlkZmzQJPBK/y5ZHp9VkkVERESkHpSK3f6l+/iUsdttG1dx90N7AGiOBBlK5xhJZ7njZWumPfZs48KJsd3S5igNoSAbY2HFdnNEXTxE5KyV6soQj4XZuqvvjI63viPOe65fy8YLFpPNw2jecfUFi3nvpnWzSizMdbtm60y7uIiIiIjUozOJ3W7c0Mn7b1hHSyzMseFRWmJh3n/Duud165iLuFCx3fyraAWFmX0ZeBVwzDnXVWK9AZ8HXgmcBt7qnPtFeVspItM509k6prK+I85dr9lQde2ajTPt4iIiIiJSj840drtxQ+e040zMRVyo2G7+VbqLx1eALwJfnWT9DcBF/s/VwF/7v0WkikzWlaHSoxhXQ7vOpIuLiIiISD2az9htro6t2G5+VbSLh3NuG3Byik1uAr7qPP8OtJpZR3laJyIzVa3lbtXaLhERERF5vvmM3RQX1oZqH4OiEzhU9Pywv+x5zOx2M9thZjuOHz9elsaJiKdQ7haPhelNpIjHwmUbiLK7N8E9jzzNe771JPc88jTdvYmqaJeIlI+ZPWtm/2VmT5jZDn/ZYjN7xMx+6f8+p2j7D5jZXjPbY2bXFy1/oX+cvWb2Bb+rKWYWMbNv+sv/w8zOK/c5iojUg7mM3SbGiIDiwhpQ6S4e07ESy1ypDZ1zm4HNAFdccUXJbURk/lSi3G0mozGrDE+kbvyac+5E0fP3Az92zt1tZu/3n7/PzC4GbgUuAZYD/2Jma51zObyupLcD/w78ENgEPAS8HTjlnLvQzG4F/gK4pVwnJiJST+YidpsqRtRsG9Wt2isoDgMri56vAI5UqC0iUmUqPUuHiFS1m4D7/Mf3ATcXLf+Gcy7tnNsP7AWu8ruQtjjntjvnHN74WDeXONa3gZcXqitERKT6KEasXdWeoPgB8BbzvAhIOOd6K90oEakOPQNJmqPjC8HKOUuHiFQNBzxsZo+Z2e3+svZCzOD/XuYvn6z7aKf/eOLycfs457JAAlgysRHqbioiUh0UI9auSk8z+o/AtUCbmR0GPgKEAZxzf4NXXvlKvLsbp4HfrkxLRepLd29i3PRJm7raq7KbRDXM0iEiVeFXnXNHzGwZ8IiZ/fcU207WfXSqbqUz6nKq7qYiIlMrV4ypGLF2VTRB4Zx7/TTrHfCuMjVHRIAtO3u499F9ZHJ5ljQ2kMnm2LztdFUOIrSpq53N2/YDXlZ8KJUlkcxwy5UrKtwyESkn59wR//cxM/sucBXQZ2Ydzrlev/vGMX/zybqPHvYfT1xevM9hMwsBcaaehUxERCbo7k3w6R89zYnhNOlsjl/2DbGrJ8F7rl875zGmYsTaVe1dPESkjLp7E9z7430ALGlsIJ3Ns6dvmHw+X5V99jRLh4iYWaOZNRceA78O7MLrJnqbv9ltwPf9xz8AbvVn5jgfuAj4T78byJCZvcgfX+ItE/YpHOu1wKP+TRQREZmhB7YfYP+JEQBaol5lw/4TIzyw/cCcv5ZixNpV7bN4iEgZbd3VRzbvWNwYxsyIhoMA9CZShEPBCreuNM3SIVL32oHv+mNWhoCvO+e2mtnPgQfN7O3AQeB1AM65p8zsQWA3kAXe5c/gAfB7wFeAGN7sHQ/5y78E3G9me/EqJ24tx4mJiCwkjx9K0BQJjsWX0XAQ5xyPH0pMs+eZUYxYm5SgEJExPQNJFjeGSWfzYx8ekVCA/pFRNq5pq3DrRESezzn3DHBpieX9wMsn2ecu4K4Sy3cAXSWWp/ATHCIicmYcDpswpI/5y0UK1MVDRMZ0tsY4tyVKOpsnlcnhnGMwlSUcDLCpq73SzRMRERGRGnX5ylaG07mxGDOVyTGcznH5ytZKN02qiBIUIjJmU1c7wWCAtcuaiIQCnBzJgIM7XrZGJXIiIiIicsbevHE1q5YsArwZNQBWLVnEmzeurmSzpMqoi4eIjCkMKLR1Vx8N4SAb15R/itFameJURERERGZufUec925aN6dxnuLGhUcJChEZp5IDCnX3Jti8bT/xWJiOeJREMsPmbfs16rKIiIjIAjCXcabixoVJCQoRqYhSGe+tu/qIx8LEY97UU4XfW3f16YOmyukOhoiIiJRDIeZ4ePdRGoIBujpbCFh4QcaN9RhfaQwKESm7QsY7kcyMy3jv7k3QHB2fN22OhugZSFaopTITk72f3b3zM22YiIiI1KfimAMHzjkeOzDAieEUsLDixnqNr1RBISJlM13Gu2cgyVAqO/YcvEGUOltjlWqyzIAqX0RERGS+dfcm+MgPdtM/nKatKUIoaJgZkZCx99gIbU3RBRU31mt8pQoKESmLmWS8W6IhEskMiWSGvHNjjzXFaXXrGUiq8kVERETmTSGOPDk8yuJFYVKZHEPJDIPJDDhHIjm64OLGeo2vlKAQkbIoZIFHszkSqQyHTiU5NTLKk4e8MrWhVJZLlnuziMRjYXoTKeKxsAY6qgGdrbGx6cIKFtIdDBEREamsrbv6yOXynM5k2Xt8hBPDacKhAI2REJgRsMCCixvrNb5SFw8RKYuegSShADxxKEFTQ4jUaI5cPs/BkyPsPz5MMBjglitXVHQWETkzm7ra2bxtP+Bl9odSWRLJDLdcuaLCLRMREZGF4KkjCQ6fTI7FkOlMnlQmRywc5MrzlyyoxERBvcZXqqAQkbLobI3R3TtEJBTgnMYGlrfGCAYCBAMBjg6lF+QHS71Y36HKFxEREZk/g6ksGGMxZEM4QCbnyMOCjTnqNb5SBYWIlMWmrna++3gPrbEQzjmCAeOcxgYuXxUnk2PB/2e70KnyRUREROZLPBYicXqUVCbHooYgwUCE5kiIVUsWLej4ox7jKyUoRKQs1nfEecmFS9h1ZJChdJaWaJiuzhbCwSDLmsPTH0Dqci5sERERkYs74iwKBzk6mGYwlaElGua8JYs4r62p0k0rSTHbmVMXDxEpmzdtXM0FS5u4+vwlXHX+YsLB4IIabXk+1etc2CIiIiKbutoJBAKs72jhFevbWd/RQiAQqMoYUjHb2VGCQkTKpl770s2F4rmwA2Zjj7fu6qt000RERETmVS3FkIrZzo66eIhIWdVjX7q50DOQpCMeHbesHubCFhEREYHaiSEVs50dJShERKpId2+C+7cf4PFDAxjG5SvjvGnjajpbYySSGeKx58brqIe5sEVERERqwZadPdy3/SB7jg4SDQV54epWLmpvARSzzYYSFCIiVaK7N8Gntu7hYP9pmiJBHLD9mZMcHUzzmhcs51+6jwOzmwtbgzSJiIiIzK8tO3u4+6E9NEZCdLZGOXwyxbZfniDvHOfGF43FbIrLpqcxKEREqsTWXX2cHBmlKRoi2hAi1hCiORrixHCap/tGZt33UoM0iYiIiMy/+7YfpDESIh4L0xxtYNWSRURDQXYcGBiL2QDFZTOgCgoRkSrRM5BkNJunOfrcf81DyVF6B9PsPT7M9mf6uW3jKu68bu2Mjlc8SBMw9nvrrj5l60VERETmSN9gimVNDQCcGEpxbCjNaM4BjrXtjazviHPPI08rLpsBJShERKpEZ2uMX/YNkc7miYaDnBhKcWggRcCgNRJmMJnh7of2AHDjhs5pj6dBmkRERETmX3tLlMFkhkx2lEOnkgQDgAMLMBa7KS6bGXXxEBGpEpu62lnc2MBwKktqNEvvYAqcIxYO0tYcIR4L0xgJcd/2gzM6XmdrjKFUdtwyDdIkIiIiMrdu27iKkXSWI4kUgQCA4YAVrYvGYjfFZTOjBIWISJVY3xHnvZvWcfUFixnNOzI5RzwWZuXiRSxq8AremiNB+gZTMzrepq52EskMiWSGvHNjjzd1tc/naYiIiIjUlRs3dPL+G9aRd5DLOYIBY9XiRSxpiozFborLZkYJChGRKrK+I84nX7OBh/7gGi5fdQ5tzdGx5ATAUDpHe0t0iiOMP9ZsB9YUERERkdm7cUMnl65sZc2yZi5ZHmdJUwR4LnZTXDYzGoNCRKqSpmHyygUL/RabI0GG0jlG0lnueNmaGR9jfUe87q6biIiI1JdqiRuni90Ul01PCQoRqTpbdvZw74/3kc07FjeGGc3k2LztdN1lmQsDYd63/SB9gynaW6Lc8bI1MxogU0RERKQeVFPcqNjt7ClBISJVpbs3wb2P7gODxY1h0tk8Tx8bZu2yprqchunGDZ36UBMREREpoRrjRsVuZ0cJChGpKlt39ZHJ5VnS2ICZEQ0HATg6mKLBfywiIiIiorhx4VGCQkQqamKfwd29CZY0NpDO5sc+ZCKhACdHMmxco2mYREREROpdIX783hM9jGZzBM04p7EBUNxY65SgEKlzW3b2jOsnd9vGVWUrS+vuTbB5237isTAd8SiJZIZD/UnOjUcY9OeJjoQCDKayhAKmaZhEREREasxcx5rF8WN7c4Tjw2mO+lOwty4KK26scZpmVKSObdnZw90P7WEwmWFZUwODyQx3P7SHLTt7yvL6W3f1EY+FicfCBMyIx8KsbW/i6GCade1NREIB+kdGAbjj5WvqbvwJERERkVo2H7Fmcfx44bImIqEgS5oaGEpnFDcuAKqgEKlj920/SGMkRDwWBiAeC4wtL0cVRc9Ako54dNyy1W2NnM7kOK+tiXAoyMY1bVUzxWi1TGElIiIiUgvmI9Ysjh+XNkd5wapW9h4bpm8ozas2LJ+z+ExxX2UoQSFSx/oGUyxrahi3rDkSpM8vk5tvna0xEsnM2IcWwFAqyyXL49x53dqytGGmSnVH2bxtf91NfSoiIiIyU/MRa06MH5c2R2kIBdkYC89Z/Ki4r3LUxUOkjrW3RBlK58YtG0rnaG+JTrLH3NrU1U4imSGRzJB3buxxNfYZLNUdJR4Ls3VXX6WbJiIiIlKV5iPWLEf8qLivcpSgEKljt21cxUg66/0Hn8+TSGYYSWe5beOqsrz++o44t19zPvFYmN5EingsXLWZ6Z6BJM3R8UVnzdEQPQPJCrVIREREpLrNR6xZjvhRcV/lqIuHSB0r9P0rHln5jpetmZPxJ2bab299R7wqExITFcoJR7M59h4fYTiVJRw0LlneUummiYiIiFSls401J4sn5zt+7GyNsf/4MEeH0gynsjRFQ5zbHOH8pU3z9priMedcpdsw56644gq3Y8eOSjdDpG4V99trjoYYSnmZ82qtjpiJ7t4En9q6h4P9p2mKBHHAcDrH+W2NvOf6tWPnpQGVpJqZ2WPOuSsq3Y56oFhEROTsVDKeLMw+0hgJ0RwJMpTOMXB6lMtXttIYDSvGO0tTxSPq4iEic26h9dsrJB0O9I8wnM6SSGWINYTYuGYxKxcvGjuvwgdpIpkZN6BSd2+iwmcgIiIiUlsqFU929ya4b/tBAIZSGY4PpwkHjGDAOJJIKcabZ+riISJzrtT0oRP77dVKpUFx9r4hFKA5EiKdc6xZ2khbU5S8c2PnVfxBCoz93rqrryrPTURERKRaTRZP7u5NcM8jT89LDFmI+/qH05zbEmE050hn8zhgSWMDmZwbS5aAYrz5oASFSB0odzJgsulDO1tjY+2plambipMO8VgDqUyOSMjYe3yEpc3Rcec1k8SMiIiIyEI1lzFnqXjyYP8Ih/qTdLYumpcYshD3tTVFSGVyRMNBwIvxOuNRmooGzlSMNz/UxUNkgatEt4Pppn8qR8let59df8+3nuSeR54+4/MtHsX5wmWNXhbdOYb8cyo+r87WGEOp7Lj9ixMYIiIiIgvVXMecpeLJPX3DrG1vOqMYciaxYSHuK8R8qUyOhqCRyzuG0zkuXNo4tq1ivPmhBIXIAleJ/nvTTf80n1M3dfcm+JPv7OQd9/+Cn+w5RjjIWX1AFicd2pqivHB1K2YGxvPOqxzzcouIiIhUo7mOOUvFkyvOibG6rXHcdjOJIbfs7OHObz7JP+88wsH+EZ49MVwyNizEfYWYLxoOcvJ0hhWtMVYtWURDKKgYb56pi4fIAlRcXrf7yCCXrmwBniuPK0dJWuFLe6EdhQ+n9R3xabuAnKlC5v6Z48OcE/P+e3v8YIIXrm4d+4Ccbfnfpq52Nm/bD3jXLRwMcsHSppKlhIUP0uLSxluuXFF13VZERERE5koh7vzeEz20N0e4qL2Jtiavy+tcx5zLmiMMpbKziiG7exPc++N9YN44Eulsnj19w6xrb3pebFgc9y1ujLC+Izg2cwigGK8MlKAQWWC27Ozh3h/vI5t3LG4Mk8nl+I9nTrFxjY19WJSjJK0wLefJkVFGs3l+2TfEzsMDvHfTuud96S9MG3XLlSvO6jULmftMztEUCXqVDsDeYyNcdf7iM/qAnG3SYb7n5RYRERGpFsVxZyab59hgisFUlheubqWtKXpWMWepWDISCtAYCbF6SeOMY8itu/rG4mIzGxtXojeRIhwKjtt2urhPMd78U4JCZAHZsrOHD39/N5lcnsZIkIBBJufIZvPs6hnkmrWROUsGTOf+7Qc42H+apmiI5miIdDbPwf7T3L/9AJ98zYZ5qTQoDFLZFA2R9gc2ioQCDKYy4z4gt+zs4b7tB+kbTNHeEuW2jau4cUPnpMdV0kFERERkvIlxZ0MwwMnTGRYDv+wbJhwMnlXMWSqW7B8epa2xgXgsPOMYsmcgyeLGMOlsfiw5EQkFONA/wuGBJC/9P/86Lh5U3FdZSlCILBCF8rVMLk9TJEguD/0joyxpbKAxEmQ0l6c3kSpbSdrjhwZoigTHPgii4SA4x+OHBoD5+dJf6Dpy4dJGfnHQex3nHA3BwNgH5JadPdz90B4aIyGWNTUwmMxw90N7AMYlKWplGlQRERGRcisVdw5nc5yzKMxoztE3lGZjLHxWMedkseT+k6f5qzdfMePjdLbGGM3kePrYMOAlJw6fOs3JkQydrVGWFsWDh0+d5vSoU/xXQRUdJNPMNpnZHjPba2bvL7H+WjNLmNkT/s+HK9FOkVpQKF8rfEiEgkYoYAylM6Qzjl+/+Fw+/bpLufO6tWX5j9Yw3IRlzl8+XwqDVDaEgly20jvHgWSWruUtY2NG3Lf9II2RkDeAUyBAPBamMRLivu0Hx45TiZlPRERERGrFZHHnaC7PooYQN1/WedYx51zFkpu62gkGA6xd1kQkFODkSIZTpzO0NYVpj8fG4sFQ0Ni87VnFfxVWsQSFmQWBvwRuAC4GXm9mF5fY9KfOucv8n4+XtZEiNaAwZdL3nujh9GiWcDBANu/I5hzBAIykc4QCVvZRhi9fGWc4nSOVyeGcI5XJMZzOcfnK+UuOFI/2nM3DteuW8bdvfgF3vWbD2Adk32CK5sj4/obNkSB9g6mx55WY+URERESkFnT3Jnhkdx+nTqdJZ/Kksrl5iTvnKpYsxIfnL21i1ZJGbtzQ4c8IsmjcdqnRHKPZvOK/CqtkF4+rgL3OuWcAzOwbwE3A7gq2SaSmFO70x2Nh2psjHBtMcfJ0hnMWhcnk8gync4SDAe54+Zqyl6e9aeNqjg6mOTGcZjCVIRIKcn5bI2/auHpeX3e6riPtLVEGkxnisefys0PpHO0t0bHnhbEsipVj5hMRERGRalaIPcNBY/GiMMdHMuTzjlw+TzLj5jTunMtYcmJ8uP2Z/ufFgyOjOZqj478eK/4rv0p28egEDhU9P+wvm2ijmT1pZg+Z2SWTHczMbjezHWa24/jx43PdVpGqVHyn/8JlTUQbQixe5M1isaghzPJ4jI/fdPGUA0DOl/Udcd5z/VquXbeMrs5Wrl23jPdcX57uJVO5beMqRtLeQKH5fJ5EMsNIOsttG1eNbVOYA7tYOWY+EREREalmhdjzkuUtBINBljY1EGsIknPMedw5n7FkqXgQ4KJl/z979x8l913f9/75nh+7M9KuZpElrdcryxb+FRlhQxAUJa1D+BEETiHhhuL2Am7LuUo5hKScplxozrktuZdekksvaWhKUJOcCHJbQnNCoTGIYgioyVEgTgB5zWJbWLbs1XotS97ZXWl2d3587h87K9ZingwEpAAAIABJREFU9Xt3v/vj+Thnz8585vsdvecfzUtvfX6sf9515r+ll+UMivkWD527zOhvgRtSShMR8UbgvwG3zPdmKaV9wD6AXbt2nfs+0qo093/6N3eX+PFtPRx5ZoKR8SnuvqMv8419luMuyLNfmnNP8Xjvq2963pfpYh2DKkmStJLNZs9cFGdy54nTFHI5CPh//8GdC577FitLzpcHf/6lfXz/6dNUa3XzX4aybFA8BVw/5/lW4PjcC1JKY3MefzEi/mNEbEopPbtENUrL2uypFZVyEZhpUnQU8uwuF3nf627NuLrl6+47+i96rOhiHIMqSZK0ks3Nnpu7S2zuLp19vtJy0nx58NxT3Mx/Sy/LBsVfA7dExHZgCLgH+EdzL4iIa4GRlFKKiFcwsyTl5JJXKi1Tq+1/+pfT0Z7LcfaHJElSlpZb9lzo7Gj+y15mDYqUUiMifgn4MpAH/iCl9FBE/LP2678L/ALw7ohoADXgnpSSyze0plzoL97V9D/9czf8nHu00+zxoJIkSVo658ugyyV7mh1Xp1iN/97ftWtXeuCBB7IuQ7pqc//indulXo1/8X7sK488b7kKcPa5y1WkhRERf5NS2pV1HWuBWUTSSrYSMqjZceW6UB7J8hQPSRcx95SO1X4e89BozaOdJEmSloGVkEHNjquTDQppGVtLf/F6tKckSdLysBIyqNlxdcpyk0xJF3HuKR2wuH/xZrlJ5XLbdEmSJGmtWowMutA50+y4OjmDQlrG9uzspVqrU63VaaV09vGenb0L/mfNrjWs1urP22hocLi64H/WfGY3XaqUiwxXJ6mUi8tqnaMkSdJasdAZdDFyptlxdXIGhbSMLeVOyXPXGgJnfx8YGLnqP+9SO+Ye7STpSkVEHngAGEop/WxE/BvgfwNOtC/5VymlL7av/SDwLqAJ/HJK6cvt8ZcBfwiUgS8Cv9I+6rwT+BTwMmaOO39bSunxJfpokrTkFjqDXm3OPF+WNDuuPjYopGVuqf7iHRqt0VcpPW9sIdYaegSUpCXyK8AgsGHO2MdSSh+de1FE3A7cA7wIuA64PyJuTSk1gU8Ae4G/YqZBsQf4EjPNjOdSSjdHxD3AbwBvW+TPI0mZWsgMejU50yy5trjEQxKweBsNrYRdoCWtbBGxFbgb+L1LuPzNwGdSSlMppaPAEeAVEdEHbEgpHUozZ7B/Cvi5Offsbz/+E+A1EREL+iEkaRW7mpxpllxbbFBIAhZvv4uVsAu0pBXvt4D3A61zxn8pIg5HxB9ExAvaY/3Ak3Oueao91t9+fO748+5JKTWAKnDNuUVExN6IeCAiHjhx4sS5L0vSmnU1OdMsubbYoJAELN5GQx4BJWkxRcTPAs+klP7mnJc+AdwEvAQYBv7d7C3zvE26wPiF7nn+QEr7Ukq7Ukq7Nm/efCnlS9KacDU50yy5trgHhaSzFmO/C4+AkrTIfhJ4U0S8ESgBGyLij1JKb5+9ICL+E/Bn7adPAdfPuX8rcLw9vnWe8bn3PBURBaACnFqEzyJJq9aV5kyz5NriDApJi8ojoCQtppTSB1NKW1NKNzKz+eXXUkpvb+8pMevngYH24y8A90REZ0RsB24BvpVSGgbGI+KV7f0l3gl8fs4997Yf/0L7z/iRGRSSpIVnllxbnEEhadF5BJSkDPxmRLyEmaUYjwO/CJBSeigiPgt8D2gA72mf4AHwbn54zOiX2j8Avw98OiKOMDNz4p4l+gySJMySa4kNCkmStCqklL4OfL39+B0XuO7DwIfnGX8A2DnP+CTw1oWqU5Ikzc8lHpIkSZIkKXM2KCRJkiRJUuZsUEiSJEmSpMy5B4W0CAaHqxwYGGFotEZ/T5k9O3vd2EeSJElLxjyqlcgGhbTA7js8xMe/9gPqzRbXrO+g3miy7+AZj0OSJEnSkjCPaqVyiYe0gAaHq3z8qz8A4Jr1HUw1Wjw8MkGr1eLAwEjG1UmSJGm1M49qJXMGhXQBlzs17sDACI1WYuP6IhFBqZgHYLg6SbGQX6qyJUmStIpcTiY1j2olcwaFdB6Dw1X2HTxKtVanr1KiWquz7+BRBoer571naLTGxvVFphqts2OdhRwnT0/T31NeirIlSZK0ilxuJjWPaiVzBoV0HgcGRqiUi1TKRYCzvw8MjJztWJ/bze7IB9duKPHIMxPAzJfB2GSDYj7Hnp292XyQBeRmS5IkSUvrcjPpsVNnKBdnMigs/zxqvtRczqCQzmNotEZ36fk9vO5SgaHRGjB/N3tkbIrxqQa3bumis5Dj1Ok6JHjvq29a8X/RXsmMEkmSJF2dy82kfRs6eezZM/R2dyz7PGq+1LmcQSGdR39PmWqtfrZLDTA+2Tg7NW7ebvbGddQbTTZ1l+go5tl90+rpAl9K916SJEkL63Iz6Y2bugAYHpti2zXrl3UeNV/qXDYotOadb1rZnp297Dt4FJjpUo9PNqjW6rzt5VuBmW52X6X0vPfqLhUYrjZ43+tuXfLPsdjO93lnu/eSJEm6cguZSbdds55iIc9H33rnkn+Oy2G+1Llc4qE17ULTynb0Vdh713Yq5SLD1Ukq5SJ779oOwMe+8gjfOz7GwUdOcGJ88uz7ze1mrzb9PWXG22sZZ63mzytJkrRU1momNV/qXM6g0Jp2sWllsz+zZr88KuUid16/gW8+9hzffOwUL9/+AkrFwvO62avNxbr3kiRJujJrNZOaL3UuGxRa0y5nWtngcJV//YXvcXJiik1dndy8ZT27b9rIwNAY332qys/cfi1ve/nWFb9e7nzTC2e793NfWw2fV5IkKWtrJZPOlzPNl5rLBoXWtIttOjRrtkt9amKajeuKTNab/M0To7zshh7uunUzw9XJVbHvxNxu/NzphXvv2j5v916SJElXby1k0gvlzOVas5aee1BoTduzs5dqrU61VqeV0tnHs2dEDw5X+dhXHuFffPYwj52YoFTMMd1MlIp5Ogs5jjxzelWtk5s7vTAXcfbxgYGRrEuTJElatS6WSeGHMycefKrKmXqDaq2xojKpOVOXwhkUWjPOt3ThfNPK5nZ5W6kFKcfEZB0iAOjIB89OTK2qdXLupCxJkrS4LjeTzt6z7+BRTk5MsXFdkaglnh6b2RSzUi6siExqztSlsEGhNeFSli6ca26Xt1LuYLLepLKug0Yr0VnMc2pimmu6Os++x2pwqdMLJUmSdPmuJJPCD3Pppq5OJutNNq7vBGBiukErsSIyqTlTl8IlHloTrmRK2dBoje7STA/v5i3rmWq0SCnRaiVu79vAi7dW+NCbbl/WXwSX61KmF0qSJOnKXOkyh9lcOptJJ+tNespF1hULKyaTmjN1KWxQaE2Y22yYdbEpZXPPZd7UVeJlN/QQERCcPX96uX8RXK7znbO92j6nJElSFq4kk8IPc+lsJi0V85w6U2djV8eKyWrmTF0Kl3hoTbiSKWXnnstczOd54eauVf8XqSd1SJIkLY4rXeYwN5duXN/Jjr481Vp9xeVSc6YuxgaFVqVzNx+6tXc99w+eAGaaDeOTjYtuJHSxzYokSZKkC1mITArmUq0dNii06sy3+dD9gyd47Y7NPDJy+rL+UrfLK0mSpCuxkJkUzKVaG2xQaNWZu/kQcPb3IyOned/rbs2yNEmSJK0RZlLp8rlJpladK918SJIkSVooZlLp8tmg0Koz9/SNWZ6xLEmSpKVkJpUunw0KrTqesSxJkqSsmUmly2eDQquOZyxLkiQpa2ZS6fK5SaZWJXc5liRJUtbMpNLlcQaFJEmSJEnKnA0KSZIkSZKUORsUkiRJkiQpczYoJEmSJElS5mxQSJIkSZKkzGXaoIiIPRHxcEQciYgPzPN6RMRvt18/HBE/nkWdkiRJkiRpcWXWoIiIPPA7wBuA24F/GBG3n3PZG4Bb2j97gU8saZGSJEmSJGlJZDmD4hXAkZTSYymlaeAzwJvPuebNwKfSjL8CeiKib6kLlSRJkiRJiyvLBkU/8OSc50+1xy73GgAiYm9EPBARD5w4cWJBC5UkSZIkSYsrywZFzDOWruCamcGU9qWUdqWUdm3evPmqi5MkSZIkSUsnywbFU8D1c55vBY5fwTWSJEmSJGmFy7JB8dfALRGxPSI6gHuAL5xzzReAd7ZP83glUE0pDS91oZIkSZIkaXEVsvqDU0qNiPgl4MtAHviDlNJDEfHP2q//LvBF4I3AEeAM8E+yqleSJEmSJC2ezBoUACmlLzLThJg79rtzHifgPUtdlyRJkiRJWlpZLvGQJEmSJEkCLnEGRUS8c77xlNKnFrYcSZK0lpk5JElauy51icfL5zwuAa8B/hYwLEiSpIVk5pAkaY26pAZFSum9c59HRAX49KJUJEmS1iwzhyRJa9eV7kFxBrhlIQuRJEmah5lDkqQ14lL3oPjvQGo/zQG3A59drKIkSdLaZOaQJGntutQ9KD4653EDeCKl9NQi1CNJktY2M4ckSWvUpe5B8Y3FLkSSJMnMIUnS2nVJe1BExFsi4tGIqEbEWESMR8TYYhcnSZLWFjOHJElr16Uu8fhN4O+nlAYXsxhJkrTmmTkkSVqjLrVBMWJQ0LkGh6scGBhhaLRGf0+ZPTt72dFXybosSdLKZuaQZM6U1qhLbVA8EBF/DPw3YGp2MKX0p4tSlZa9+w4P8fGv/oBGK7FxfZHpepN9B8+w967tfnlIkq6GmUNa48yZ0tp1qQ2KDcycQ/4zc8YSYFhYg+47PMSvfW6AWr1FLuDURI4T41O8uL/CgYERvzgkSVfDzCGtYZ/8xqP81v1HaLQSnYWg3uxkbLLBrVu6zJnSGnDBBkVE/FJK6T+klP7JUhWk5W1wuMpHv/wIE5MNioUgIjhdbzE9PsVjz07QUcxnXaIkaQUyc0i67/AQv/O1x2g0W3QUcjRbMFydpK9S4umxSXOmtAZc7BSPf7okVWjFODAwwmitTkchRy5y5CMo5KCVEkOjk/T3lLMuUZK0Mpk5pDVu/6FjENBZzANBIRfkc8HJ01OcOl03Z0prwCUdMyrNGhqtkQsoFfO0UqKVZsabKdFsJfbs7M22QEmSJK1II2OTrO/I05HPnc2ZuYCpRqKQC3OmtAZcrEFxR/sM8nN/PJN8jervKXPN+k5SgnIxTwTUmwkIfvKma1wXKEm6UleVOSIiHxHfjog/az/fGBFfiYhH279fMOfaD0bEkYh4OCJeP2f8ZRHxYPu1346IaI93RsQft8e/GRE3LvzHl9S7oUSpmCcizubM6UaLQi5472tuMmdKa8DFGhQPppQ2zPPTnVLasCQValnZs7OX6zeuY11nnkIuKOZyrOss8OL+Dfzya2/JujxJ0sp1tZnjV4C5x5N+APhqSukW4Kvt50TE7cA9wIuAPcB/jIjZhe2fAPYCt7R/9rTH3wU8l1K6GfgY8BtX9Uklzeve3dtotBIbSgU6CzlyEZSLBf75a2/m7jv6sy5P0hJwiYcuy46+Cr/6+lt51a2b2dRd4tqeEm/ceS3/58/ttKstScpERGwF7gZ+b87wm4H97cf7gZ+bM/6ZlNJUSukocAR4RUT0ARtSSodSSgn41Dn3zL7XnwCvmZ1dIWnh3H1HPx94w21s3lCio5jnx/o28H//Lzv5xZ/yP8GkteJix4z+14i4JqV0ckmq0Yqwo6/Ch99yR9ZlSJJWl6vJHL8FvB/onjPWm1IaBkgpDUfElvZ4P/BXc657qj1Wbz8+d3z2nifb79WIiCpwDfDs3CIiYi8zMzDYtm3bFXwMSXff0e9sCWkNu9gMihYzgeF/RsS/iYi/4/8YSJKkRXBFmSMifhZ4JqX0N5f458z3nukC4xe65/kDKe1LKe1KKe3avHnzJZYjSZJmXbBBkVL6SErp1cAbge8ycwTY30bEf46Id0aEW+lKkqSrdhWZ4yeBN0XE48BngFdHxB8BI+1lG7R/P9O+/ing+jn3bwWOt8e3zjP+vHsiogBUgFNX8XElSdI8LmkPipTSeErpcymlX0wpvRT4v4DNzKzPlCRJWhCXmzlSSh9MKW1NKd3IzOaXX0spvR34AnBv+7J7gc+3H38BuKd9Msd2ZjbD/FZ7Och4RLyyPXPjnefcM/tev9D+M35kBoUkSbo6F9yDIiJ+/AIv/3lK6d8tcD2SJGkNWoTM8RHgsxHxLuAY8FaAlNJDEfFZ4HtAA3hPSqnZvufdwB8CZeBL7R+A3wc+HRFHmJk5cc9l1iJJki7BxTbJnA0DJWAXM1MuA7gD+CbwdxevNEmStIZcdeZIKX0d+Hr78UngNee57sPAh+cZfwDYOc/4JO0GhyRJWjwX24Pip1NKPw08Afx4e+OnlwEvZeZYLkmSpKtm5pAkSZe0BwXwYymlB2efpJQGgJcsTkmSJGkNM3NIkrRGXWyJx6zBiPg94I+YOVbr7cDgolUlSZLWKjOHJElr1KU2KP4JMxtH/Ur7+UHgE4tSkZbcfYeH+MTXH+PJ585QzOd4xQ0v4L2vvYUdfZWsS5MkrT1mDmkF+eQ3HmX/oWNUa3Uq5SL37t7GL/7ULVmXJWmFutQGxU8An0wpfWwxi9HSu+/wEL/+Z4PUppqUikEzJQ4eeZZnT0/zoTe/yCaFJGmpmTmkFeKT33iUf//VI3Tk82zozHNmqsm//+rMljE2KSRdiUvdg+IfA9+JiEMR8ZsR8fcj4gWLWJeWyP5Dx2g2E+WOPMVCnlIxT6mQ54mTZzgwMJJ1eZKktecfY+aQVoT9h47Rkc+zvjNPLpdjfWeejnye/YeOZV2apBXqkmZQpJTeCRAR1wG/APwOcN2l3q/la2RskpQS+dwPe1XFfHCm3mRotJZhZZKktcjMIa0c1VqdDZ35542Vi0G1Vs+oIkkr3SV92UfE24G/B7wYeBb4D8D/XMS6tAQGh6vUphqcOlMH6uQCOgs5Ogo5SoU8/T3lrEuUJK0xZg5p+RocrnJgYISh0Rqd+aDRbPH0WJN8DjryObpKBaYbiUq5mHWpklaoS/3fiN8CfgD8LvDnKaXHF60iLYnB4Sof/fIj1KYbpPZYM8GZeovJRosd15bZs7M30xolSWuSmUNahgaHq+w7eJRKucjpyWm+fOQkU42ZFNlsQa3VYroxTaGQ492v2p5xtZJWqkvagyKltAn4p0AJ+HBEfCsiPr2olWlRHRgY4cgz45ypt37ktVIhuPP6HjfIlCQtOTOHtDwdGBihUi7yzFiNrz/yLKenm+Rjzj8mAiLgZdf3uEGmpCt2qUs8NgDbgBuAG4EK8KP/stWK8b3hKsdHaySgmAsg0UqQzwXljgLTzXSxt5AkacGZOaTlaWi0RjEP3zz6HM1WIgckZpoSPeUipWKeDeUCfS9Yl3WpklawS13i8Rdzfv5DSumpxStJS6FaaxAR5NoLPCKCSIlWmmlUuP+EJCkjZg5pGervKfP1h5+hlRIdhRxT9RakRARMN5okYHN3pxlS0lW51FM87ljsQrS0NpQKlDvynJlqzHTBY6YLnlozXXD3n5AkZcHMIS1Pe3b28rlvD1HMB0Ew3Ug0ExQDppuJQh42dXWaISVdlQs2KCLiCxd6PaX0poUtR0vlRddVqDeafPepKmemmzRbiQC6y0V+9fW3uv+EJGlJmTmk5W1HX4W/e/M1PPD4c4yMT9JdKtJotqjVWwTwEy/cyHtfe4sZUtJVudgMit3Ak8B/Ab4JxKJXpCWxZ2cvx06d4a5bOnh6bJJTp+sUcsF7X3MTd9/Rn3V5kqS1x8whLXPv2H0DtXqLH2t2mx8lLYqLNSiuBV4H/EPgHwH3Af8lpfTQYhemxbWjr8Leu7ZzYGCEjmKe3TfNHCtq11uSlBEzh7TMmR8lLbYLNihSSk3gAHAgIjqZCQ1fj4hfTyl9fCkK1OLZ0VfxC0WStCyYOaSVwfwoaTFddJPMdki4m5mgcCPw28CfLm5ZkiRprTFzSJK0tl1sk8z9wE7gS8CHUkoDS1KVFtR9h4fYf+gYI2OT9G4oce/ubWfXCV7oNUmSloqZQ8rGlWZBM6SkxXCxGRTvAE4DtwK/HHF2v6oAUkppwyLWpgVw3+EhPvKlh1nfWWBLVwdjtTof+dLDZ18/32t+wUiSlpiZQ1piF8qJF8qCV3qfJF3MxfagyC1VIVp4g8NVPvzF73N6qkErJQq5oFIuArD/0DEA1ncWzo5Vyrmzr/nlIklaSmYOaWldLCdeKAvuP3TMDClpURgGVqnB4Sr7Dh5lvFZnXTFHo9liuDrJ6akG3Z15RsYmGRmbpLsz/7z7Zl+TJEnS6nQpOfFCzJCSFosNilXqwMAIlXKR7nKR6SYU8jkKOXjuzDTjU016N5To3VBifKr5vPtmX5MkSdLqdCk58ULMkJIWiw2KVWpotEZ3qcBLtlaoN1tM1lvkAiamGpyeanDv7m3cu3sbp6caVGt1Wq0W1Vr97GuSJElanS4lJ16IGVLSYrnoMaOLISI2An/MzBFijwP/IKX03DzXPQ6MA02gkVLatXRVrlyDw1WOnTrDt489x6auTu7YuoHHnj3DeK1Od7nIB95w2/PWB87dgfm9r77JtYOSJEmryOBwlQMDIwyN1ujvKdOZD8YnG9zSO7P37Heeqp43J85n9nUzpKSFlkmDAvgA8NWU0kci4gPt5//7ea796ZTSs0tX2so2u6bw2u5Oxs7UqdbqpJR4+Q0vIJfLsfeu7ezoq5y9/u47+v0ykSRJWqVms2GlXKSvUqJaq3O8OkkuAjau46Yt3WzZUKZaq/9ITrwQM6SkxZDVEo83A/vbj/cDP5dRHavO7JrC7Zu7+PEbethQLtJswfDY1GV96UiSJGnlm82GlXKRXMyc1HHDNevp3dBJpVxkuDpJpVw0J0paFrKaQdGbUhoGSCkNR8SW81yXgP8REQn4ZEpp3/neMCL2AnsBtm1bu+vfhkZr9FVmNija3F1ic3eJVkoMVyf90pEkSVpj5mbDWd2lAsPVBu973a0ZVSVJ81u0BkVE3A9cO89Lv3YZb/OTKaXj7QbGVyLi+ymlg/Nd2G5e7APYtWtXuuyCV4n+npkperPnUgOMTzbo7ylnWJUkSZKyYDaUtJIs2hKPlNJrU0o75/n5PDASEX0A7d/PnOc9jrd/PwN8DnjFYtW7WuzZ2Uu1NrP3RCuls4/37OzNujRJkiQtMbOhpJUkqz0ovgDc2358L/D5cy+IiPUR0T37GPgZYGDJKlyhdvRV2HvX9uetKXztjs0cGBjhV//rd/nYVx5hcLiadZmSJElaAvNlw3P3mxgcrvKxrzxiVpSUuaz2oPgI8NmIeBdwDHgrQERcB/xeSumNQC/wuYiYrfM/p5QOZFTvirKjr3L2S2e+nZv3HTzqRkiSJElrxNxseC6zoqTlJJMGRUrpJPCaecaPA29sP34MuHOJS1t15u7cDJz9fWBgxC8dSZKkNc6sKGk5yWqJh5bI0GiN7tLz+1DdpQJDo7WMKpIkSdJyYVaUtJxktcRDS2BwuMqxU2f49rHn2NTVyc1b1rOpq+TOzZIkSWvU4HCVAwMjDI3W6O8p05kPxicbnvIhaVmwQbFKffIbj7Lv4OOcmapDBLXpBqNnpvmxa7vJ5XK87eVbsy5RkiRJS2RwuMpv3/8of/nYSfIR9FdK1BtNxiYb5CJg4zq6SwXGJxtUa3WzoqRM2KBYhe47PMTv/Plj5CJ4wfoOJqaaPDsxTSvB8NgUH3rT7a4plCRJWiMGh6v85oGH+faxUTpyQS4XPDU6yenpJndsrbC+s0ClXDw7q+JtL99qVpSUCRsUq9D+Q8eIgHUdOSKC7lKBYj5HBGzbuM4vHEmSpDXkwMAIp05PEwEdhZl8GCTOTDcZrk6y7Zr1vO91t2ZdpiS5SeZqNDI2yfqOPM1WOjvWWQgmXE8oSZK05gyN1phutCgXf5gP87mg0Wpx8vS0+VDSsmGDYhXq3VCiI5+j0YJGs0VKiTPTLToKefbs7M26PEmSJC2h/p4yHYUc6zvzZ/Nho9kiCIr5nPlQ0rJhg2IVunf3NpoJujvz5HPB6ekmrZTYe9eNLu+QJElaY/bs7GXj+g5aLdi4rkACztRbdJcKvPfVN5kPJS0b7kGxCt19Rz8wsxfFyNgkt21cz727t50dlyRJ0tqxo6/C+/fcxqcPPcG3nxxlU1eBl15f4e27b7A5IWlZsUGxzJx7NvWenb1X9MVx9x39NiQkSZKW0ELluMWwo6/Cv33LHVmXIUkX5BKPZWRwuMq+g0ep1ur0VUpUa3X2HTzK4HA169IkSZJ0AeY4Sbp6NiiWkQMDI1TKRSrlIrmIs48PDIxkXZokSZIuwBwnSVfPJR7LyEPHq4zV6kxMNekqFbh583qu6epkaLSWdWmSJEk6x9wlHQ8dr3Ln1gqVcvHs692lgjlOki6DDYplYnC4yg+emWB8skEicXIiODE2yYv7K2zf3JV1eZIkSZrjvsNDfPxrP6DebHHN+g5azcRfH32Ov/PCYHN3CYDxyQb9PeWMK5WklcMlHsvEx7/6KM9OTDE2WWey3qTebPHsxDQPDlU9m1qSJGmZGByu8mt/epgP/ukAz45PUSrmmGq0mGq2mGo0eej4GK2UqNbqVGt1c5wkXQYbFMvA4HCVv/zBSYr5HJVykQScnm5RyEEEy2b3Z0mSpLVsdiPMgeNj5HNBLuDp6hTNVjq750S9mRiuTlIpF9l713ZznCRdBpd4LAMHBkYIgkI+KOZzdBQ6aDQTiURnMZ91eZIkSeKHG2FON1uUi3marRaFCE6dmWZrT5mJqQY/e8d1vO91t2ZdqiStSM6gWAZmzsouMd1MNJotUpppTtSmm7z0+p6sy5MkSRIzma27VGBDqcj6zjyNFiQSU/UmY5MNivmcSzok6SrYoFgG+nvK3Lyli55SAQimm4lmC65Z38E7dt+QdXmSJEliJrONTza4ect68rkcG9cXCaCVgATvffVNLumQpKtgg2IZ2LOzl1wux52S8qebAAAgAElEQVTX93DDNeuolIts7u7kX+65zS85SZKkZWLPzl6qtTrFfJ6XbquwrqNAZ7HAG3Zey8fuuZO77+jPukRJWtHcg2IZ2NFXYe9d2zkwMEKxkGf3TZvYs7N33ubE3PO2+3vK571OkiRJC2tuZhsabfCq27YsWhYz80lai2xQLBM7+ioX/dKZ3Tm6Ui7SVylRrdXZd/CoO0RLkiQtkUvJbFfLzCdprXKJxwoyu3N0pVwkF3H28YGBkaxLkyRJ0gIx80laq2xQrCCzO0fP1V0qMDRay6giSZIkLTQzn6S1yiUeK0hHPjj4yAnqzURXqcDNm9fTUcjT31POujRJkiRdhbl7Thw7dYbpepPtm7vOvj4+2TDzSVr1nEGxQgwOVxkZm2J8skEhB1PTDb752CmeOHna87YlSZJWsNk9J6q1On2VEn0bOvn2k6McPTFBKyWqtTrVWt3MJ2nVs0GxQhwYGOH6jevYfdNGyh0Fplszsyiuq5TcLEmStGZFRCkivhUR342IhyLiQ+3xfxMRQxHxnfbPG+fc88GIOBIRD0fE6+eMvywiHmy/9tsREe3xzoj44/b4NyPixqX+nFrdzt1z4sZNXfz4th6eHp9iuDpJpVx0g0xJa4JLPFaIodEafZUSuSiyqasEQCslhquTGVcmSVKmpoBXp5QmIqII/EVEfKn92sdSSh+de3FE3A7cA7wIuA64PyJuTSk1gU8Ae4G/Ar4I7AG+BLwLeC6ldHNE3AP8BvC2JfhsWiNmc95c265ZT7GQ56NvvTOjqiRp6TmDYoXo7ykzPtl43phrESVJa12aMdF+Wmz/pAvc8mbgMymlqZTSUeAI8IqI6AM2pJQOpZQS8Cng5+bcs7/9+E+A18zOrpAWgjlPkmbYoFgh9uzsPbv+0LWIkiT9UETkI+I7wDPAV1JK32y/9EsRcTgi/iAiXtAe6weenHP7U+2x/vbjc8efd09KqQFUgWvmqWNvRDwQEQ+cOHFigT6d1gJzniTNsEGxQuzoq7D3ru1UykXXIkqSNEdKqZlSegmwlZnZEDuZWa5xE/ASYBj4d+3L55v5kC4wfqF7zq1jX0ppV0pp1+bNmy/zU2gtM+dJ0gz3oFhBdvRV/KKSJOk8UkqjEfF1YM/cvSci4j8Bf9Z++hRw/ZzbtgLH2+Nb5xmfe89TEVEAKsCpxfgMWrvMeZLkDApJkrSCRcTmiOhpPy4DrwW+395TYtbPAwPtx18A7mmfzLEduAX4VkppGBiPiFe295d4J/D5Offc2378C8DX2vtUSJKkBeQMCkmStJL1AfsjIs/Mf7x8NqX0ZxHx6Yh4CTNLMR4HfhEgpfRQRHwW+B7QAN7TPsED4N3AHwJlZk7vmD0N5PeBT0fEEWZmTtyzFB9MkqS1xgaFJElasVJKh4GXzjP+jgvc82Hgw/OMPwDsnGd8Enjr1VUqSZIuxgbFIhscrnJgYISh0Rr9PWX27Ox1faEkSdICMGdJ0upig2IRDQ5X+c0DD3Pq9DTTjRaPjoxz+KlR3r/nNr88JUmSrtDgcJU/OvQE//PISV6wrsiOvm6qtTr7Dh719AtJWsHcJHMRffrQEzw6MsHT1RrPjE/ydLXGoyMTfPrQE1mXJkmStCINDlf56Jcf4SuDI4zVpnm6WuObj51iutGkUi5yYGAk6xIlSVfIGRSL6OsPP8PJ09MEQSEfpFww1Whx6LGTWZcmSZK04gwOV3n/fz3M0ZNnaDRbdHXmgOC5M3UeHKryqtu2MDRay7pMSdIVcgbFIhkcrnJyYpqUoJCDlBK1RotWKzE+2ci6PEmSpBVlcLjKvoNHeWq0RrkY5HIwPtWilVp05IPh6iTjkw36e8pZlypJukI2KBbJgYEROgo5IoJEMHOkOkw1E90lJ65IkiRdjgMDI1TKRfK5IBfB+o4CkDgz3YSARjNRrdXZs7M361IlSVfIBsUiGRqtsW3jOjryORKJZisBiXwu2P3Ca7IuT5IkaUUZGq3RXSpw7YYS041ELoLuzgLNFtSmm/S/oOwGmZK0wvlf+Yukv6dMvdHk9HST2nSz3aCArlKed+y+IePqJEmSVpb+njLVWp07r68wMdVo5yuolIvcft0GT0mTpFXAGRSLZM/OXnK5HHdurXDDNeuorCuyqbuTf/l6vzwlSZIu156dvVRrdYr5PLtv2si1lRLd5SKv2bHF5oQkrRLOoFgkO/oq7L1rOwcGRigW8uy+aRN7dvb65SlJknQF5marodEGr7pti9lKklYZGxSLaEdfxS9NSZKkBWK2kqTVzSUekiRJkiQpczYoJEmSJElS5mxQSJIkSZKkzGXSoIiIt0bEQxHRiohdF7huT0Q8HBFHIuIDS1mjJEmSJElaOlnNoBgA3gIcPN8FEZEHfgd4A3A78A8j4valKU+SJEmSJC2lTE7xSCkNAkTEhS57BXAkpfRY+9rPAG8GvrfoBUqSJEmSpCW1nPeg6AeenPP8qfbYvCJib0Q8EBEPnDhxYtGLkyRJkiRJC2fRZlBExP3AtfO89Gsppc9fylvMM5bOd3FKaR+wD2DXrl3nvU6SJEmSJC0/i9agSCm99irf4ing+jnPtwLHr/I9pWVhcLjKpw89wbefHCUIXnp9hbfvvoEdfZWsS5MkSdIqZxbVcpXJHhSX6K+BWyJiOzAE3AP8o2xLkq7O7JfBV7//DFONFlu6OigV8xx67BRPj03xq6+/1S8GSZIkLZr7Dg/x/xx4mGdPT7OuI09PuWgW1bKRSYMiIn4e+DiwGbgvIr6TUnp9RFwH/F5K6Y0ppUZE/BLwZSAP/EFK6aEs6pWu1uBwld++/1H+8gcnmW60AOgs5Dh5us51PXm6SwWenZjiwMCIXwqSJElacLN59Kvff4ZGK9GRDxrNmTy6qavDLKplIatTPD4HfG6e8ePAG+c8/yLwxSUsTVpQg8NV/ujQE9z//WcYPVOnVAhSStRbiVZKdOWCU6en6e8pMzZZZ2i0lnXJkiRJWkXOzaMpJQKYbiQSTdZ35BmfbBCBWVSZW85LPKQVbXC4yr6DR3nsxAQpJVJKTDchnwuaKdFqJaabLXKNYKrRorOQp7+nnHXZkiRJWiXmy6OzpwnkAlqtFtPNIAGbuzvNosrccj5mVFrRDgyMUCkXmW62aCUo5n94ME0uoJVgutEiB4xPNtjU1cmenb3ZFSxJkqRVZb48mo8gpZnjEZstmG40SQmzqJYFZ1BIC+S+w0PsP3SMkbFJejeUyJF4+fZr2FAqcmpims5CnjPTTRLQ1VlgYqoBQFepwO4XbnTnZEmSJF2V2eUc336ySiIxOd3klTdtfF4ebbaaFPIQkag3ISLHT9y0kfe+5hazqDJng0JaAPcdHuIjX3qY9Z0FtnR1MFarc/y5GqVCjpu3rGekWmOq2aKzkKPebDHdSPT3rONXX38rd9/Rn3X5kiRJWuEGh6t89MuPcPTZ03R15gmC4Vqdgw8/y53XVxip5p6XR3OR47pKp3lUy4oNCmkB7D90jPWdBSrlIgCVco6pepMHj49z4+Zudt98DQ88/hwjY1NcWymx+4XXOGNCkiRJC+bAwAjPTkzRXSpQKuYB2NLVyTPjUwyNTppHtSLYoJAWwMjYJFu6Op43tqmrg8lGi0q5yNBogze++Dr27Oz1S0CSJEkLbmi0xlSjyYZS8exYz7oi9VaL6WaLehPzqJY9GxTSAujdUGKsVqdS/uG+s+NTTa7fuI73ve7WDCuTJEnSWtDfU+bRkXGmGq2zMyimGi02lIq86rYtZlKtCJ7iIS2Ae3dv4/RUg2qtTqvVolqrc3qqwb27t2VdmiRJktaAPTt72dTVyfhkg9p0g8npBhOTDTau7/B0Dq0YNiikBXD3Hf184A23saFc5JmJaTaUi3zgDbe54ZAkSZKWxI6+Cr/6+lvZ/cKNNFow3Ur8nRdu5P17bnNJh1YMl3hIC+TuO/ptSEiSJCkzO/oqfPgtd2RdhnTFnEEhSZIkSZIyZ4NCkiRJkiRlzgaFJEmSJEnKnA0KSZIkSZKUORsUkiRJkiQpczYoJEmSJElS5mxQSJIkSZKkzNmgkCRJkiRJmbNBIUmSJEmSMmeDQpIkSZIkZc4GhSRJkiRJypwNCkmSJEmSlDkbFJIkSZIkKXM2KCRJkiRJUuZsUEiSJEmSpMzZoJAkSZIkSZmzQSFJkiRJkjJng0KSJEmSJGXOBoUkSZIkScqcDQpJkiRJkpQ5GxSSJEmSJClzNigkSZIkSVLmbFBIkiRJkqTM2aCQJEmSJEmZs0EhSZIkSZIyZ4NCkiRJkiRlzgaFJEmSJEnKXCHrArQ4BoerHBgYYWi0Rn9PmT07e9nRV8m6LEmSFlRElICDQCczueZPUkr/OiI2An8M3Ag8DvyDlNJz7Xs+CLwLaAK/nFL6cnv8ZcAfAmXgi8CvpJRSRHQCnwJeBpwE3pZSenyJPqK0IMyGklYCZ1CsQoPDVfYdPEq1VqevUqJaq7Pv4FEGh6tZlyZJ0kKbAl6dUroTeAmwJyJeCXwA+GpK6Rbgq+3nRMTtwD3Ai4A9wH+MiHz7vT4B7AVuaf/saY+/C3gupXQz8DHgN5big0kLxWwoaaWwQbHKDA5X+ddf+B4PPlXle8NjnJyYolIuUikXOTAwknV5kiQtqDRjov202P5JwJuB/e3x/cDPtR+/GfhMSmkqpXQUOAK8IiL6gA0ppUMppcTMjIm598y+158Ar4mIWMzPJS2EweEqH/vKI/yLzx7msRMTTDea5CLMhpKWLRsUq8hsd/zkxBQvWFdgqt7kb4+NcmJ8ku5SgaHRWtYlSpK04CIiHxHfAZ4BvpJS+ibQm1IaBmj/3tK+vB94cs7tT7XH+tuPzx1/3j0ppQZQBa6Zp469EfFARDxw4sSJhfp40hWZO2uilVqQ0tlcCJgNJS1LNihWkQMDI1TKRTZ1dTLdTJSKeToLOY6cOM34ZIP+nnLWJUqStOBSSs2U0kuArczMhth5gcvnm/mQLjB+oXvOrWNfSmlXSmnX5s2bL1a2tKhmc+HMTwdEnM2FgNlQ0rLkJpmrxH2Hh/j/vvkE9WaLznyOfC7YsqFERz44NTFNtVbnbS/fmnWZkiQtmpTSaER8nZm9I0Yioi+lNNxevvFM+7KngOvn3LYVON4e3zrP+Nx7noqIAlABTi3aB5GuwH2Hh9h/6BgjY5P0biiRI/Hy7TMTfW7esp6/eWKUjnwwXqtTbf+YDSUtN86gWAV+/b8/yPv++LucnJhmYrJBbbrJ2GSDick6p87U2djVwd67trtTsyRp1YmIzRHR035cBl4LfB/4AnBv+7J7gc+3H38BuCciOiNiOzObYX6rvQxkPCJe2d5f4p3n3DP7Xr8AfK29T4WUucHhKv/rvkP88898l+8ce47Tk3VOjE3yvePjPPjkcwBs6irxsht6iAgIqJSLZkNJy5IzKFa4X//vD/IHf3kMmJl/2kxwerrJuo4cE1NNfuLmTX4BSZJWsz5gf/skjhzw2ZTSn0XEIeCzEfEu4BjwVoCU0kMR8Vnge0ADeE9Kqdl+r3fzw2NGv9T+Afh94NMRcYSZmRP3LMknky5gcLjKx+9/lG88eoLT0y1yQDEP41NNGq1EVynPg8fHuXFzN92lAsV8nhdu7jIXSlrWbFCsYPcdHuKP/urJHxlvAbV6i3y+6ZeQJGlVSykdBl46z/hJ4DXnuefDwIfnGX8A+JH9K1JKk7QbHNJyMDhc5TcPPMy3nxyl2Z7M0wIaLSjmE/Vmi5TydBRyVMpFhkZr9PeUedvLt5oLJS1rNihWsP2HjpFSIkd7d6+YeRBALuC2azf4JSRJkrTKHBgY4dTpaQIIghyJBLQSNFszmfD0dJMf69vA+153a9blStIlcw+KFWxkbJKOQo58bqZBkdLM7xYQEdy7e1vGFUqSJGmhDY3WmG60KBdz5CIo5GMmCwKtlGi1Q6FZUNJKY4NiBevdUKJSLhIRFHPPPwPt7a+8nrvv6D/vvZIkSVqZ+nvKdBRydJeKFHJxNgvCzCyKcjHPe179QrOgpBUnkwZFRLw1Ih6KiFZE7LrAdY9HxIMR8Z2IeGApa1wJ7t29jXwux6aujrMzKTrywT/9yW38H3//xVmXJ0mSpEWwZ2cvG9d30Ggmejd0sq7dnejqzPOGF/XyX/a+kl/8qVsyrlKSLl9We1AMAG8BPnkJ1/50SunZRa5nRZrtiu8/dIxi+8zre3dvs1suSZK0iu3oq/D+Pbfx6UNP8O0nR+nrWcdLr6/w9t03uP+YpBUtkwZFSmkQZvZJ0NW5+45+GxKSJElrzI6+Cv/2LXdkXYYkLajlvgdFAv5HRPxNROy90IURsTciHoiIB06cOLFE5UmSJEmSpIWwaDMoIuJ+4Np5Xvq1lNLnL/FtfjKldDwitgBfiYjvp5QOzndhSmkfsA9g165d6YqKXmbuOzzE/kPHGHH5hiRJ0oo0OFzlwMAIDx2vMjbZoFIucHtfhT07e12OIUnnWLQGRUrptQvwHsfbv5+JiM8BrwDmbVCsNp/8xqP8zp8/RgSs78jzTLXGR770MIBNCkmSpBXgvsNDfPyrP+D0dIMz0026O/NUz+RZV8yz7+AZ9t613SaFJM2xbJd4RMT6iOiefQz8DDOba6569x0e4rfuP8JkvUmz1WJyusn4VJN8zGyIKUmSpOVrcLjKr/3pYf7V5wY4MTHFmek6AYxPNWmlxNNjU1TKRQ4MjGRdqiQtK1kdM/rzEfEUsBu4LyK+3B6/LiK+2L6sF/iLiPgu8C3gvpTSgSzqXUqDw1U+/rUf0Gi2KOaDlKDWaJFaLaabLUbGJrMuUZIkSecxOFxl38GjDBwfI4B8DiYmm0CikIPTU03GJut0lwoMjdayLleSlpWsTvH4HPC5ecaPA29sP34MuHOJS8vcx+9/lMefPU2jBY3pFh15KORzTLdaNKfhto3rsy5RkiRJ55jdO+zhp8coFfIUcrCuo0Czlcjng1q9SaVcpFZvcn1pHeOTDfp7ylmXLUnLyrJd4rEW3Xd4iK8/+iyQKOZmjjCZasJ0o8V0I5ES3Lt7W9ZlSpIkaY77Dg/xkS89zFitTiGCRqvFiYlpWqlFo5XozOdotODMdItcwLUbOqnW6uzZ2Zt16ZK0rNigWEb2HzrGumKedR0F8vkcnfkAoN6CfC54z0+/0A0yJUmSlpn9h46xvrNApVyksyNPIZejXMxTrTXY1NVBsZCjoxDkc8EtW7q4cVOXG2RK0jwyWeKh+Y2MTbJlQwcjY9OUi3mmmy2gRQv46FvvsDkhSZK0DI2MTbKlqwOAjes6GK5O0lkImikoFfOUigVeu6OXd+y+waaEJF2ADYplpHdDibFanb5KiVNnpsk1glIhz7WVks0JSZKkZWo2w1XKOdZ3FuirlHi6OkmpmOdVt21hz85eGxOSdAlc4rGM3Lt7G6enGjRaif5KiU1dnZQ78rz7VS/MujRJkiSdx2yGq9bqtFoz+06UO/J86E23877X3WpzQpIukTMolpHZWRL7Dx1jZGyS3g0l3vvqm5w9IUmStIyZ4SRpYdigWGbuvqPfLzNJkqQVxgwnSVfPBkWGBoerHBgYYWi0Rn9P2fWJkiRJy4AZTZKy4R4UGRkcrrLv4FGq7U0xq7U6+w4eZXC4mnVpkiRJa5YZTZKy4wyKjHz60BM8dmKCejPRVSpw8+b1VMpFDgyM2KGXJElaQnNnTBw7dYZruzuplIsAZ3+b0SRp8TmDIgODw1X+4shJSImuzjxT9SZ/e2yUyXqDodFa1uVJkiStGefOmDg5McUjIxOcGJ88e013qWBGk6QlYIMiAwcGRnjBuiJEEBGUink6CzkGh8fp7ylnXZ4kSdKacWBghEq5SKVcJBfBpq5OCDhy4vTZa8YnG2Y0SVoCNigyMDRa4/bruplqtJisN0kpkVLiuTN19uzszbo8SZKkNWNotEZ36Yernm/esp6UEqcmpmmlRLVWp1ozo0nSUrBBkYH+njKdhQIvu6GHUjHP+FSDiODv3XyNaxslSZKWUH9PmfHJxtnnm7pK/Ni13Wzs6mC4OkmlXGTvXdvNaJK0BNwkMwN7dvay7+BRKuUir9i+kfHJBtVanbfvviHr0iRJktaU2VwGM3tNjE82yOVyfOhNt9uUkKQl5gyKDOzoq7D3ru1UykU785IkSRkyl0nS8uEMiozs6Kv4xSdJkrQMmMskaXlwBoUkSZIkScqcDQpJkiRJkpQ5GxSSJEmSJClzNigkSZIkSVLmbFBIkiRJkqTM2aCQJEmSJEmZs0EhSZIkSZIyZ4NCkiRJkiRlzgaFJEmSJEnKnA2K/7+9ew+ytK7vPP7+9GWGYYDhKgwDcolDFhgViWIsokHFMKAbYlYrJJFYbnZZU9HSVKyNxjWJVXHXVIxZJZYR0YCRxLW8bCi5KAQJ0RUR2FEGG3UAFWYmgOA0jHPt7u/+cZ5Zepo+3adnevr06X6/qrr6nN/ze57zu50+v/6e3/M8kiRJkiSp6wxQSJIkSZKkrjNAIUmSJEmSus4AhSRJkiRJ6joDFJIkSZIkqesMUEiSJEmSpK4zQCFJkiRJkrpuoNsF6BVDm4e5cf0jbNyynVWHL2PtmmM5feWKbhdLkiTpGZy3SJJ6kQGKDlz3nY1c/s/3MzJWHLl8kF27R7nitm1c9rJT/LCXJEnzxtDmYT79jR/xrxse54iDBzl95aEMb9/NFbc96LxFkjTvGaCYxtDmYf78S0Ns2b6bsSo2DYeHntjGi04+khvXP+IHvSRJ6rqhzcN8+OYf8PX7H2fXyBgHD/azdCCse2iYs599OCuWDTpvkSTNewYopnH5zT/g357cSX9ztY7RseLRJ3eyftMWlgz2d7dwkiRp0bvuOxv5yy9/j01bdtDfF0ZGx/gZxehT8KxDl7DhsZ/x4lOOZOOW7d0uqiRJU/IimdP4+gOPkwAFofVTgYeeaJ3TKUmS1C1Dm4e5/Jb72bpjtDVPaSYrVcXu0VF+tnOUrTtGeGrHiPMWSdK8Z4BiGjt2jzLQRysyQVqf/AWjBWvXHNvl0kmSpMXsxvWPsHt0DIDBgT5CGOgPYwWjo8X23aMM9ofh7budt0iS5j1P8ZjGkv5+tu4caVZRtJZRFHDIkgHP45QkSXNu/B06vrvpSZYN9LG1Lyzp72P77lH6CH2B9LVOTT3z+MO49CUnOW+RJM17BiimMLR5mCUDwM5WbAJav5cOhJeuPqqrZZMkSYvLZHfoGOwPP9m6i/6+sGsElg32s23XCFWw8rBlvOOC03j181Z1u+iSJHXEAMUUPnzzD9g1UvSldUoHQF/g0KX9vPWVq7tbOEmStCgMbR7m77/xI7624XF27B5hxUGDAKx7aJhTjjqYrTtG6O8LRxw8yKbhHSwdHODcU4/kreevdtWEJKmnGKBoY2jzMF+//3GWDvRx0GA/23aNMjI2xvIlAxy6bNAPfEmSdMANbR7mitse5IHHtnLEsgF+vGM3T2zbzdLBfpYO9PH4tt286JQj+PbDw5x+/ArOP+M41q451nmKJKknGaBo48b1j9DfF/oSBgf6WDLQx8hoUVUsG7TZJEnSgXfj+kdYsWyQ3aPFIUv7OXhJPzt2j/HEtl2ccPgytu4Y4aDBAX7ljOP4g1ed1u3iSpK0X7yLRxsbt2xn1eEHsWu0msAEFMW2XaO84ES/lZAkSQfexi3bOfSgAQ45aICdI2McuXwJUGzfNcpO79AhSVpgDFC0serwZZx69CEccfAgUOwcGWVsDI45dClveMlJ3S6eJElaBFYdvoyndozwnGOWs3NkjL6Eo5YvYbA//HT7CGcefxiXvewUT+mQJC0IBijaWLvmWPr7+3juqhWcdNRyjjh4KUcfspR3XHCakwBJkjQn1q45luHtu1ky0M9ZzQrOHSPFq04/lo9dejb//def57xEkrRgGKBo4/SVK7jsZadwyjGH8OyjlvPq563kry95vrfqkiRpHklyYpKvJhlKcm+StzXpf5ZkY5J1zc9F4/Z5V5INSb6X5IJx6b+Q5J5m24eTpElfmuR/NenfTHLyXNVvz3xkxbJBRsbgvJ9/Fh+79GzeZ2BCkrQAdeVqj0n+Evj3wC7gfuBNVbVlknxrgQ8B/cCVVfX+uSzn6StX+OEvSdL8NgL8YVXdneRQ4K4kNzXb/rqqPjA+c5IzgEuAM4HjgZuTnFZVo8BHgcuA24HrgbXADcDvAj+tquckuQT4C+A35qBugPMRSdLi0a0VFDcBa6rqecD3gXdNzJCkH/gIcCFwBvCbzaRCkiQJgKraXFV3N4+fAoaAqZY7Xgx8pqp2VtWDwAbgnCQrgcOq6htVVcCngF8bt8/VzePPAa/cs7pCkiTNnq4EKKrqK1U10jy9HThhkmznABuq6oGq2gV8htYEQZIk6RmaUy9eAHyzSXpLku8k+WSSI5q0VcBD43Z7uElb1TyemL7XPs38ZRg4apLXvyzJnUnufOyxx2alTpIkLSbz4RoU/5HW8smJ2k0gJuWkQJKkxSvJIcDngbdX1ZO0Ttf4OeAsYDPwV3uyTrJ7TZE+1T57J1RdUVUvrKoXHnPMMTOsgSRJOmABiiQ3J1k/yc/F4/K8m9a5o9dMdohJ0p4xGfj/G5wUSJK0KCUZpBWcuKaqvgBQVY9U1WhVjQEfp7UyE1pfeJw4bvcTgE1N+gmTpO+1T5IBYAXwxIGpjSRJi9cBu0hmVZ0/1fYkbwReA7yyOddzonYTCEmSJACaa0F8Ahiqqg+OS19ZVZubp68F1jePrwX+IckHaV0kczVwR1WNJnkqyS/SOkXkd4DLx+3zRuAbwOuAW9rMXSRJ0n7o1l081gJ/BPxyVW1rk+1bwOokpwAbaV1x+7fmqIiSJKk3nAtcCtyTZF2T9se0Lq59Fq3Vlz8E/gtAVd2b5LPAd2mt4vz95g4eAL8HXAUso3X66Z5TUD8B/H2SDbqij2AAAAtLSURBVLRWTlxygOskSdKi1JUABfA3wFLgpuYi2LdX1ZuTHE/rdqIXVdVIkrcAX6Z1m9FPVtW9XSqvJEmah6rqa0x+Wuj1U+zzPuB9k6TfCayZJH0H8Pr9KKYkSepAVwIUVfWcNumbgIvGPb+eKSYYkiRJkiRpYZgPd/GQJEmSJEmLnAEKSZIkSZLUdQYoJEmSJElS1xmgkCRJkiRJXWeAQpIkSZIkdZ0BCkmSJEmS1HUGKCRJkiRJUtelqrpdhlmX5DHgR/txiKOBn8xSceYD6zO/LbT6wMKrk/WZ3xZafeDA1emkqjrmABxXE0wyF1mI4/RAsa1mxvaaGdurc7bVzNhenWs7H1mQAYr9leTOqnpht8sxW6zP/LbQ6gMLr07WZ35baPWBhVmnxc4+7ZxtNTO218zYXp2zrWbG9podnuIhSZIkSZK6zgCFJEmSJEnqOgMUk7ui2wWYZdZnflto9YGFVyfrM78ttPrAwqzTYmefds62mhnba2Zsr87ZVjNje80Cr0EhSZIkSZK6zhUUkiRJkiSp6wxQSJIkSZKkrjNAASR5fZJ7k4wlaXtrmCQ/THJPknVJ7pzLMs7EDOqzNsn3kmxI8s65LONMJDkyyU1JftD8PqJNvnndP9O1d1o+3Gz/TpKzu1HOTnVQn/OSDDf9sS7Jn3SjnJ1K8skkjyZZ32Z7r/XPdPXptf45MclXkww1f9/eNkmenumjDuvTU3202CU5KMkdSb7d9Ol7J8ljn06QpD/J/03ypUm29cx7ei5M01aOrXGmmxM6tvbWQXs5vsZJcniSzyW5r/kcf8mE7Y6v/TDQ7QLME+uBXwc+1kHel1fVTw5wefbXtPVJ0g98BHgV8DDwrSTXVtV356aIM/JO4J+r6v3NP8LvBP6oTd552T8dtveFwOrm58XAR5vf884Mxs+/VtVr5ryA++Yq4G+AT7XZ3jP907iKqesDvdU/I8AfVtXdSQ4F7kpyU6++h+isPtBbfbTY7QReUVVbkwwCX0tyQ1XdPiGffbq3twFDwGGTbOul9/RcmKqtwLE10VRzQsfWM003h3Z8Pe1DwI1V9bokS4CDJ2x3fO0HV1AAVTVUVd/rdjlmS4f1OQfYUFUPVNUu4DPAxQe+dPvkYuDq5vHVwK91sSz7qpP2vhj4VLXcDhyeZOVcF7RDvTR+OlJVtwFPTJGll/qnk/r0lKraXFV3N4+fojVJXzUhW8/0UYf1UQ9pxt3W5ulg8+OVyKeQ5ATg1cCVbbL0zHv6QOugrTQzji3tkySHAS8DPgFQVbuqasuEbI6v/WCAYmYK+EqSu5Jc1u3C7KdVwEPjnj/M/J0cH1tVm6E1qQee1SbffO6fTtq7l/qk07K+pFnufEOSM+emaAdML/VPp3qyf5KcDLwA+OaETT3ZR1PUB3q0jxarZgn+OuBR4Kaqsk+n9j+B/wqMtdnek+/pA2S6tgLH1njTzQkdW3vrZA7t+Go5FXgM+LvmlKsrkyyfkMfxtR8WzSkeSW4Gjptk07ur6p86PMy5VbUpybOAm5Lc13xLOedmoT6ZJK1r3/RMVZ8ZHGbe9M8kOmnvedUn0+ikrHcDJzXLnS8C/jetpW69qpf6pxM92T9JDgE+D7y9qp6cuHmSXeZ1H01Tn57so8WsqkaBs5IcDnwxyZqqGn8dGPu0keQ1wKNVdVeS89plmyRtXr+nD4QO28qxtbfp5oSOrb1N116Or6cNAGcDb62qbyb5EK3Tz98zLo/jaz8smhUUVXV+Va2Z5KfT4ARVtan5/SjwRVrL3LtiFurzMHDiuOcnAJtmv6SdmaY+j+xZFtX8frTNMeZN/0yik/aeV30yjWnLWlVP7lnuXFXXA4NJjp67Is66XuqfafVi/zTn9X8euKaqvjBJlp7qo+nq04t9pJZmue+twNoJ6fbp084FfjXJD2mdJviKJJ+ekKen3tMH0LRt5djaWwdzQsfWONO1l+NrLw8DD49bIfc5WgGLiXkcX/to0QQo9leS5c2FzGiW8fwKrYtR9qpvAauTnNJc3OUS4Noul6mda4E3No/fCDwjCNMD/dNJe18L/E5z5d9fBIb3nNoyD01bnyTHJUnz+Bxaf28en/OSzp5e6p9p9Vr/NGX9BDBUVR9sk61n+qiT+vRaHy12SY5pVk6QZBlwPnDfhDz2aaOq3lVVJ1TVybQ+Q26pqjdMyNYz7+kDqZO2cmw9rcM5oWOr0Ul7Ob6eVlX/BjyU5OebpFcCEy9w7fjaD4vmFI+pJHktcDlwDHBdknVVdUGS44Erq+oi4FhayzWh1W7/UFU3dq3QU+ikPlU1kuQtwJeBfuCTVXVvF4s9lfcDn03yu8CPgdcD9FL/tGvvJG9utv8tcD1wEbAB2Aa8qVvlnU6H9Xkd8HtJRoDtwCVVNW+XtyX5R+A84OgkDwN/Susidz3XP9BRfXqqf2h9g3gpcE9a5/gD/DHwbOjJPuqkPr3WR4vdSuDqtO5y1Ad8tqq+1Mt/F7uhVz8Xu8Gx1dakc0LHVludtJfja29vBa5pvqR7AHiT42v2ZHGPLUmSJEmSNB94iockSZIkSeo6AxSSJEmSJKnrDFBIkiRJkqSuM0AhSZIkSZK6zgCFJEmSJEnqOgMUkjrW3Af7M0nuT/LdJNcnOW0fjzWY5K7ZLqMkSepdSbZ2mO9zSU5tHh+S5GPN/OTeJLclefF+luOu5jaSk237QJJX7M/xJU1uoNsFkNQb0rpB9heBq6vqkibtLFr3z/7+Phzyl4D/M3sllCRJi0GSM4H+qnqgSboSeBBYXVVjTeDi9P04/snAxqra1SbL5cDHgVv29TUkTc4VFJI69XJgd1X97Z6EqloH9Ce5tfkm474k1zTBDJL8MMl7k9yd5J4k/27c8dYCNyRZnuS6JN9Osj7Jb8xttSRJ0nyT5Lx28wvgt4F/avL9HPBi4L9V1RhAVT1QVdclOTnJUJKPNysrvpJkWbPfrUn+IskdSb6f5KXjXv5C4MYk/UmuauYn9yT5g+b4PwKOSnLcHDWHtGgYoJDUqTVAu1MyXgC8HTgDOBU4d9y2n1TV2cBHgXeMS385cCutQMWmqnp+Va0BbpzlckuSpN7Ubn5xLk/PSc4E1lXVaJtjrAY+UlVnAluA/zBu20BVndO8xp+OS19Laz5yFrCqqtZU1XOBvxuX5272nu9ImgUGKCTNhjuq6uHmm4t1wMnjtn2h+X3XnvQkxwNPVNU24B7g/OZbjJdW1fDcFVuSJM1j7eYXK4HHOjzGg82KTxg3F2lMNkdZApzQnD7yAHBqksuTrAWeHLfvo8DxM6qNpGkZoJDUqXuBX2izbee4x6PsfX2bnZOkXwh8GaCqvt8c9x7gfyT5k9kqsCRJ6mnt5hfbgYOax/cCz0/S7v+amc5RXgp8DaCqfgo8n9aKz9+nda2LPQ5qyiFpFhmgkNSpW4ClSf7znoQkLwJ+eR+OtRa4oTnG8cC2qvo08AHg7FkoqyRJWriGgOcAVNX9wJ3Ae8ddA2t1kov38djj5yhHA31V9XngPew9RzkNWL+PryGpDQMUkjpSVQW8FnjVntt4AX8GbJrJcZL007rK9n1N0nOBO5KsA94N/PnslVqSJC1A1wHnjXv+n4DjgA1J7qF1h40ZzU/GOQ/4l+bxKuDWZo5yFfAuaN0qnVaA5M59fA1JbaT1P4ckzY0kvwS8oare3O2ySJKk3tPcieOrwLlTXBxzX457AvDxqrpwmnyvBc6uqvfM1mtLajFAIUmSJKmnJLkAGKqqH3fhtV8P3FRVW+b6taWFzgCFJEmSJEnqOq9BIUmSJEmSus4AhSRJkiRJ6joDFJIkSZIkqesMUEiSJEmSpK4zQCFJkiRJkrru/wESwNN8GiP8GwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "indep = data['Cnn/s'].values\n",
    "dep = data['Mwd/TnU'].values\n",
    "indepL = np.log(df['Cnn/s'].values)\n",
    "depL = df['Mwd/TnU'].values\n",
    "\n",
    "corrcoef = np.corrcoef(indep, dep)\n",
    "corrcoefL = np.corrcoef(indepL, depL)\n",
    "\n",
    "\n",
    "fig,ax = plt.subplots(1, 2, figsize=(18,8))\n",
    "\n",
    "ax[0].plot(indep, dep, 'o', alpha=0.5)\n",
    "ax[0].set_title('Correlación lineal = {:.3f}'.format(corrcoef[0,1]))\n",
    "ax[0].set_xlabel('Cnn/s')\n",
    "ax[0].set_ylabel('Mwd/Tnu')\n",
    "\n",
    "ax[1].plot(indepL, depL, 'o', alpha=0.5)\n",
    "ax[1].set_title('Correlación logarítmica = {:.3f}'.format(corrcoefL[0,1]))\n",
    "ax[1].set_xlabel('ln(Cnn/s)')\n",
    "ax[1].set_ylabel('Mwd/Tnu')\n",
    "\n",
    "\n",
    "# Para visualizar los datos por EECC en lugar de por caras:\n",
    "dataEECC = data.groupby(['EECC']).mean().drop(['Muestra'], axis=1)\n",
    "dfEECC = df.groupby(['EECC']).mean().drop(['Muestra'], axis=1)\n",
    "# dfEECC.info()\n",
    "\n",
    "indepEECC = dataEECC['Cnn/s'].values\n",
    "depEECC = dataEECC['Mwd/TnU'].values\n",
    "indepEECCL = np.log(dfEECC['Cnn/s'].values)\n",
    "depEECCL = dfEECC['Mwd/TnU'].values\n",
    "\n",
    "corrcoef = np.corrcoef(indepEECC,depEECC)\n",
    "corrcoefL = np.corrcoef(indepEECCL,depEECCL)\n",
    "\n",
    "\n",
    "figEECC,axEECC = plt.subplots(1, 2, figsize=(18,8))\n",
    "\n",
    "axEECC[0].plot(indepEECC, depEECC, 'o')\n",
    "axEECC[0].set_title('Correlación lineal = {:.3f}'.format(corrcoef[0,1]))\n",
    "axEECC[0].set_xlabel('Cnn/s')\n",
    "axEECC[0].set_ylabel('Mwd/Tnu')\n",
    "\n",
    "axEECC[1].plot(indepEECCL, depEECCL, 'o')\n",
    "axEECC[1].set_title('Correlación logarítmica = {:.3f}'.format(corrcoefL[0,1]))\n",
    "axEECC[1].set_xlabel('ln(Cnn/s)')\n",
    "axEECC[1].set_ylabel('Mwd/Tnu')\n",
    "\n",
    "fig.suptitle('Correlación cuentas/segundo vs quemado')\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57f93a2d",
   "metadata": {},
   "source": [
    "### Mínimos cuadrados:  $\\beta = (X^{T}X)^{-1}X^{T}Y$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a584d73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Incluyo término independiente\n",
    "X = np.array([np.ones(data['Cnn/s'].shape[0]), data['Cnn/s']]).T\n",
    "B = np.linalg.inv(X.T @ X) @ X.T @ data['Mwd/TnU']\n",
    "XL = np.array([np.ones(df['Cnn/s'].shape[0]), np.log(df['Cnn/s'])]).T\n",
    "BL = np.linalg.inv(XL.T @ XL) @ XL.T @ df['Mwd/TnU']\n",
    "# print(B)\n",
    "# print(BL)\n",
    "\n",
    "\n",
    "# Gráficas incluyendo la línea de regresión\n",
    "fig,ax = plt.subplots(1, 2, figsize=(18,8))\n",
    "\n",
    "ax[0].plot(indep, dep, 'o', alpha=0.5)\n",
    "ax[0].set_title('Regresión lineal')\n",
    "ax[0].set_xlabel('Cnn/s')\n",
    "ax[0].set_ylabel('Mwd/Tnu')\n",
    "ax[0].plot([-1.5, 2.5], [B[0]+B[1]*(-1.5), B[0]+B[1]*2.5], c='red')\n",
    "\n",
    "ax[1].plot(indepL, depL, 'o', alpha=0.5)\n",
    "ax[1].set_title('Regresión logarítmica')\n",
    "ax[1].set_xlabel('ln(Cnn/s)')\n",
    "ax[1].set_ylabel('Mwd/Tnu')\n",
    "ax[1].plot([3.6, 6.4], [BL[0]+BL[1]*3.6, BL[0]+BL[1]*6.4], c='red')\n",
    "\n",
    "figEECC,axEECC = plt.subplots(1, 2, figsize=(18,8))\n",
    "\n",
    "axEECC[0].plot(indepEECC, depEECC, 'o')\n",
    "axEECC[0].set_title('Correlación lineal = {:.3f}'.format(corrcoef[0,1]))\n",
    "axEECC[0].set_xlabel('Cnn/s')\n",
    "axEECC[0].set_ylabel('Mwd/Tnu')\n",
    "axEECC[0].plot([-1.5, 2.5], [B[0]+B[1]*(-1.5), B[0]+B[1]*2.5], c='red')\n",
    "\n",
    "axEECC[1].plot(indepEECCL, depEECCL, 'o')\n",
    "axEECC[1].set_title('Correlación logarítmica = {:.3f}'.format(corrcoefL[0,1]))\n",
    "axEECC[1].set_xlabel('ln(Cnn/s)')\n",
    "axEECC[1].set_ylabel('Mwd/Tnu')\n",
    "axEECC[1].plot([3.6, 6.4], [BL[0]+BL[1]*3.6, BL[0]+BL[1]*6.4], c='red')\n",
    "\n",
    "fig.suptitle('Cuentas/segundo vs quemado')\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44095474",
   "metadata": {},
   "source": [
    "### Correlación separando por colores según las demás variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "67353648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "cmap = matplotlib.cm.get_cmap('hsv')\n",
    "\n",
    "fig,ax = plt.subplots(1, 2, figsize=(18,8))\n",
    "colores = int(np.amax(data['Ciclos_D'].values)+1)\n",
    "\n",
    "for i in range(colores):\n",
    "    indep = data['Cnn/s'].loc[data['Ciclos_D']==i].values\n",
    "    dep = data['Mwd/TnU'].loc[data['Ciclos_D']==i].values\n",
    "    ax[0].plot(indep,dep, 'o', alpha=0.5, c=cmap(i/colores))\n",
    "    \n",
    "    indepL = np.log(df['Cnn/s']).loc[data['Ciclos_D']==i].values\n",
    "    depL = df['Mwd/TnU'].loc[data['Ciclos_D']==i].values\n",
    "    ax[1].plot(indepL,depL, 'o', alpha=0.5, c=cmap(i/colores))\n",
    "    \n",
    "ax[0].set_title('Regresión lineal')\n",
    "ax[0].set_xlabel('Cnn/s')\n",
    "ax[0].set_ylabel('Mwd/TnU')\n",
    "\n",
    "ax[1].set_title('Regresión logarítmica')\n",
    "ax[1].set_xlabel('ln(Cnn/s)')\n",
    "ax[1].set_ylabel('Mwd/Tnu')\n",
    "\n",
    "fig.suptitle('Cuentas/segundo vs quemado (según ciclos)')\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d4f89915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4peyxMyLiweJYvxgRXyi1KSLaIuJfImJbsc9/iYglZc+9OyKujoj/Al4EXgOsAC4u9nETcHbkoarby475hoi4q1j+XxGxMCI+Vuzje5F7kZT28WREvLm4PTkiPhIRjxftvT8ijike++uIeDoidhTL/8dYXhupEZigkJpMcTK8AHisuL8Y+FfgT4AjgMuBf4qI+cVTPgt8BzgSWA1cMsRm3wicApwfEe8APgJ0AvOB/wA+V6z3s8AbgJOAucC7gOeGaOP/BP4UuAg4GngK+Pyg1d4G/CRwWrHe+WN+EQb6JeBXgaOAaeTjH8vrsrVow+zi+X8VEWccYhskSWoKrRxHRMRJRVt+u2jbncA/R8S0iJgGfAm4pXgdPgf8QtnTJwH/CBwLtAO7gOsH7eIS4H3ArKLNAKSUNgAfAO5NKR2eUppb9pyLgI8C84A9wL3AA8X9NcBfDnM4vwP8IvBWcizzXnJiBOC75ATUEeT374sRMWPEF0dqECYopObx5YjYCTxN/nL9R8XyXwbuTCndmVI6kFL6OrAWeGtEtJNP3n+YUnoppfSfwB1DbHt1SunHKaVdwPuBP00pbUgp7QOuAZYVv37sJZ90XwtEsc4zQ2zv3cDNKaUHijGjHyb/anBc2Tp/llLaXtSV+P8o+yXnIP1jSunRou23l21n2NcFIKX0rymlx1P2TeBrgL8wSJImKuOInBD515TS11NKe4HrgJnAOcBZ5Pp8H08p7U0pdZETMwCklJ5LKf1TSunFlNJO4GpyYqbcLSmlh1NK+4rtj8WXUkr3p5R2kxMku1NKt6aU9gNfIA/HGcqvAR9NKX2/iGUeSik9V7T100V796WU/gKYDpw8xvZIdWWCQmoe70gpzQLOJZ/YS0MZjgXeWXRV3F50G/xp8i8Oi4DnB43dfHqIbZcvOxb467JtPQ8EsDil9A3yrwV/C2yJiE9ExOwhtreIgb8cvED+hWRx2To/Krv9Iode/HK47Yz0uhARF0TEfUXX0e3kxMU8JEmamIwjXrndA0XbFxePbUoppaGOKyIOi4i/i4inImIH8C1gbgycWWyo12Y0W8pu7xri/nDHdQzw+FAPRMSHiiE2fcV7MAdjHDUJExRSkyl+7b+FnPWHfDK8LaU0t+zyqpTSnwHPAEdExGFlmzhmqM2W3X4aeP+g7c1MKd1T7P/jKaWfAJaSu2j+7hDb20wOUACIiFeRu4ZuOpRjPkTDvi6Rpyr7J/JruKDoanknOYCSJGnCavE4YvB2ozieTeRjXTyolkX5sX6I3Avh9Sml2eShKjAwdih/HQYb6bFD8TRw/OCFRb2J3yMPHWkrYpw+jHHUJExQSM3pY8DPRMQy4NPA2yPi/KJg0oyIODcilqSUniJ301xdjK88G3j7KNu+CfhwRCwFiIg5EfHO4vZPRsTrI2Iq8GNgN3nKssE+C/xqRCwrkgHXAN9OKT05/kMfs2FfF3KtiunANmBfRFxAHhcrSVIraNU44nbg5yLiTUUbPkSu+3APufbDfmBVREyJiAuBM8ueO4vco2F7RBxB/xCZsdoCLClqXVTCPwBXRcSJkXVExJFFO/eRY5wpEfGH5BoVUlMwQSE1oZTSNuBW4MqU0tPAheSCVNvIGfXfpf//+93A2eSukX9CHs847FziKaUvAX8OfL7owrieXEwL8gnu74FechfJ5+j/BaZ8G/8XuJLcS+EZcob/4kM+4EMw0utSjB39IDlQ6SUX2hxqTK0kSRNOq8YRKaXvk2tu/A3wLDnZ8vaivsZL5MKeK4HtxXr/UnasHyPXq3gWuA/4t4Pc/TeAh4EfRcSz4zwUyMUzbyfX0NoBfLJo31eBu4BHya/xbg5t6IlUFzFwmJWkiS4ivgB8L6V0sJl/SZLU4lopjoiIbwM3pZT+sd5tkVqFPSikCa7oTnl8REyKiLeQfyX5cr3bJUmSGl8rxRER8caIWFgM8bgU6ODge0pIGocp9W6ApKpbCHSRi0ttBC5LKT1Y3yZJkqQm0UpxxMnkYROHk2fIWDHMNKiSqsQhHpIkSZIkqe4c4iFJkiRJkupuQg7xmDdvXjruuOPq3QxJkhrK/fff/2xKaX6929EKjEUkSRraSPHIhExQHHfccaxdu7bezZAkqaFExFP1bkOrMBaRJGloI8UjDvGQJEmSJEl1Z4JCkiRJkiTVnQkKSZIkSZJUdyYoJEmSJElS3ZmgkCRJkiRJdWeCQpIkNbWIeDIi/jsi1kXE2mLZ6ojYVCxbFxFvLVv/wxHxWER8PyLOL1v+E8V2HouIj0dEFMunR8QXiuXfjojjan2MkiS1AhMUkiRpIjgvpbQspbS8bNlfFcuWpZTuBIiI1wEXA0uBtwA3RMTkYv0bgfcBJxaXtxTLVwK9KaUTgL8C/rz6hyNJUusxQSFJklrJhcDnU0p7UkpPAI8BZ0bE0cDslNK9KaUE3Aq8o+w5nypurwHeVOpdIUmSKscEhSRJanYJ+FpE3B8R7ytbvioiuiPi5ohoK5YtBp4uW2djsWxxcXvw8gHPSSntA/qAIwc3IiLeFxFrI2Lttm3bKnFckiS1FBMUkiSp2f1USukM4ALgNyPiDeThGscDy4BngL8o1h2q50MaYflIzxm4IKVPpJSWp5SWz58//yAPQZIkmaCQJElNLaW0ubjeCnwJODOltCWltD+ldAD4e+DMYvWNwDFlT18CbC6WLxli+YDnRMQUYA7wfHWORpKk1mWCQpIkNa2IeFVEzCrdBn4WWF/UlCj5BWB9cfsO4OJiZo5Xk4thfiel9AywMyLOKupL/ArwlbLnXFrcXgF8o6hTIUmSKmhKvRsgSZI0DguALxU1K6cAn00p/VtE3BYRy8hDMZ4E3g+QUno4Im4HHgH2Ab+ZUtpfbOsy4BZgJnBXcQH4JHBbRDxG7jlxcQ2OS5KkllPXBEVE3Ay8DdiaUjp1iMfPJf968USxqCul9Me1a6EkSWpkKaUfAqcNsfySEZ5zNXD1EMvXAq+IR1JKu4F3jq+lkiRpNPXuQXELcD15Kq/h/EdK6W21aY4kSZIkSaqHuiYoUkrfiojj6tkGSZIaSnc3dHVBTw+0t0NnJ3R01LtVkiSpldQpHmmGIplnR8RDEXFXRCwdbiXnHpckNb3ubrjuOujthSVL8vV11+XlkiRJtVDHeKTRExQPAMemlE4D/gb48nArOve4JKnpdXVBW1u+TJrUf7urq94tkyRJraKO8UhDJyhSSjtSSi8Ut+8EpkbEvDo3S5Kk6ujpgTlzBi6bMycvlyRJqoU6xiMNnaCIiIXFXORExJnk9j5X31ZJklQl7e3Q1zdwWV9fXi5JklQLdYxH6pqgiIjPAfcCJ0fExohYGREfiIgPFKusANZHxEPAx4GLU0qpXu2VJKmqOjvzOM/eXjhwoP92Z2e9WyZJklpFHeORes/i8YujPH49eRpSSZImvo4OuPzygVWzV650Fg9JklQ7dYxH6pqgkCRJg3R0mJCQJEn1Vad4pKFrUEiSJEmSpNZgDwpJkg5Vd/fA7o+dnfZ+kCRJzaEB4xh7UEiSdCi6u+G663LRqCVL8vV11+XlkiRJjaxB4xgTFJIkHYquLmhry5dJk/pvd3XVu2WSJEkja9A4xiEekiSNZqgukD09+ReHcnPm5OWSJEmNqrsbvvIVSAnmzoVTToEFCxoijrEHhSRJIxmuC+T06dDXN3Ddvr6cwJAkSWpEpbhm2rR82bUL7rkHtmxpiDjGBIUkSSMZrgtkSjlZ0dsLBw703+7srHeLJUmShlaKa04/HfbsycumT4cHHmiIOMYEhSRJI+npyV0ey82ZAy+9BJdfnk/yGzfm68svr3v1a0mSpGGV4pqFC+Hss2HmzBzT7NnTEHGMNSgkqRYacBonjVF7e/5Foa2tf1mpC2RHh++jJEmtppnjuvK4ZuHCfCndb4BjsAeFJFVbg07jpDHq7HQohyRJypo9rmvwuMYeFJJU7Sx4eQ0D6L/u6mqITLVG0dGRuzyW/42sXOl7J0nSRHCwcWCzx3UNHteYoJDU2kpZ8La2gVnw8Y7BKz/ZPfAAvP71Ax9vgGmcdBAcyiFJ0sRzsHFgdzd8+cv59ty58NrX5iESzRbXNXBc4xAPSa1tuBkauroOfZuDu/5Nnw7f+hb86Ef96zTANE6SJEkt7WDiwFJ8N316//Sc996b4zvjuooxQSGp9XR3w+rV8N735iz47t0DHx9vFnzwye6MM/LyBx9syLF+kiRJLafUG+Kb34S77+7/IWm4OLAU351xxsDpOR980LiughziIal1dHfDjTfC178ORx4Jy5b192544xtzFz0Yfxa8pyf3nChZsADe8Ab4znfydJQNNtZvQmrm6tqSJKm61qyBq66CzZtzb4h9+3JviLPPzrHhUHFgKb6bNAnOOQc2bIDt2yGiIabnHKCJ4yB7UEhqDaVueQ88AEcckZfdd1//CaiSvRva23OSo9yMGXDhhXDzzbn3RpOcJJpSs1fXliRJ1dPdnZMTETlm27Mn957Yt2/k3hDl8d2CBXDuufkHrgsvbKy4rsnjIBMUklpDqVveSy/BzJn5MmMGbNuWeze89FLu3dDWNnQWvHxYyOrVI3/IN/j0TRNeNeqKSJKkiaGrC/buzUM5Zs2C447LvSaeey7Hg5dfntcbHPc1S3zX5HGQCQpJraGnJ5+I5szprzkxY0bOhI/Wu2HNGrjkErj9dnj8cXj00ZEz0aXpm9raRk56qDpK73W5ZquuLUmSqqOnB+bP748HDz8cTjgh97C98MIc5w0V90FzxHdNHgdZg0LSxDPUuLv29pzlPuUUuOeevF5Kedxhb2+uCTHctkrdAGfOhMceg//+b1i0KNezuPHGoZ/XwNM3TXil97o0LzlYXVuSpFZXig8ffDAP5ygVuiz9YDV1Kpx66uhxX6PHd00eB9mDQtLEMty4u1NPzbenTYOzzsrrPv88nH76yNnvri7YuRO2bIFHHoEdO2Dy5Lytr3+9acbztZRm6YIpSZJqozw+PPPMnKDYuzdfb9uWf7S68kpYvz4vf+kl+N738vp798LWrc0T9zV5HGSCQtLEMty4u/Xr+7vl7d0LF1yQ173pppEz4evWwQsv5MTElKLT2Y9/DC++mGcCaZLxfC3FITaSJKlceXx49NG5wOVRR8H+/XDRRXDbbbBiRe59O3MmPPlk7kUxdWpOXuzYkZc3Q9zX5HGQQzwkTSyDp/iE/nF3hzLsYvv2/MG+c2dOeEya1J91X7asacbztRyH2EiSpJKhpoA///z8BX716v7l7e3w3e/mxMXkyXlZSjn+mzateeK+Jo6D7EEhaWIZaorP8Yy7mzs3V3aeNSufoPbuzSesI4/MYxabZDyfJElSyxprfNjZCbt2wezZ/XFfBBx7bF5u3Fd1JigkTSyVHne3bFmuX9HenjPnbW35JLVwYVON55MkSWpZY40POzrgzW/OcV4p7jv55Pxj1dSpxn01YIJC0sRS6XF3nZ259sRP/RS88525e+CePXDGGU01nk+SJKllHUx8+Bu/kYuov+UtefrRXbv6i2ga91VdpJTq3YaKW758eVq7dm29myFpohhq2tJGPkE1W3tVMxFxf0ppeb3b0QqMRSSpiVUjljI+e9lI8YhFMiW1jkM9MTRDoaHSsa1bB088kYelHH98/zSr9vaQJEnKRosJKx37rVkDV12Va1rMn5974xqfDckhHpJaw5o1cMklcPvt8Pjj8Oij+cTQDPNZj6Z8bu/e3lzMaf36PK93aUqtZpgWS5IkqdpqHRN2d+fkREROTuzeneO0/fuNz4ZggkLSxDfUieHhh/N0oc18YujuzlNjXXopfP/7ORu/Y0eeVnXGDNiwIa9XmmZVkiSpldU6Juzuhg9+EJ56Kv+I9OMfw8yZOU7buNH4bAgmKCRNfF1duUvdnDn5hFQ6MWza1LwnhvJeEynly7335ilQd+/Ox1eaTms806xKkiRNFLWMCUux2tateX+7d8PTT8MLL+R9bttmfDYEExSSJr6env4seUmznxi6uvqHb8ydm0+yM2bkx3bvzkmJ2bPHP82qJEnSRFHLmLAUqx11FMyalWM1yPvq63Pa0mFYJFPSxNfenoc/rF+f75d6FzTbiaG8oND3fVkAACAASURBVNMDD8DrX5+Xn3IK3HNPnqN73z5YujR3VywlMFautACTJElSrWLC7m748pfz7SlT4KWXYN482Lkz72/uXKctHYY9KCRNfJ2deejDqaf2Z8mbbT7r8iEdS5bkZMS3vgU/+hEsWADnnNOfmT/pJLjttnxiXL26eY5RkiSpmmoRE5ZitunTYdq0vD/I+3vVq2DZshynrVhRmf1NMPagkDTxdXTkaZy6uvLJ4rzzmm/u6fIhHQBnnAF33w0PPgjnn59PgCef7HRVkiRJw6lFTFiK2c44I/dwnTGjfzju8uXGaqMwQSGpNVR6Puta6+nJPSdKFiyAN7wBvvOdXAW6vd2hHJIkSaOpdkxYitkmTco9XDdsgO3bc4LC5MSoTFBIUjNob8/DO0o9KCBn5C+8MA/jkCRJUv2Vx2wLFuRL6b7JiVFZg0KSmkFnZ/+MHAcOODuHJElSIzJmGxd7UEhqLuUzWbS3N18tiUNVPmaydOwO6ZAkSXqlesaLxmzjYoJCUvNYswauugr27u2fw/q661pnPF+z19GQJEmqtkaIF43ZDpkJCklV1c1+ujhAD4l2gk4m0cHkQ9hQdz7ZRPSfbB5+GJYuzRnqZjoJtGovEEmSpIN0ULFkreNFY7qKswaFpKrpZj/XsY9eEkuAXhLXsY9u9h/8xrq6ciZ8zpx80pk5MxeJ3LQpnxSaRWlu7N7eXOG5tzff7+6ud8skSZIaykHHkrWMF43pqsIEhaSq6eIAbQRtBJOK6zaCLg6M/uTubrjsMjj9dFi2DD73uXyS2b27f50ZM2DbtpyxbhalubHb2vL0U6XbXV31bpkkSVJDGXMsuWYNnHsu/OVf5mTEpk39j1UrXjSmqwqHeEiqmp4i211uTrF8RN3d8Ad/AI89BrNm5WUbN8LOnXD44fn+jBnQ1wdTpzZ2VeTBXf/WrXtl1785c5qrF4gkSVINjCmWXLMGrrgCZs+GuXNhx47+uGrx4srHi6XY7jOfgUWL4HWvy1OJgjFdBdiDQlLVtBP0DVrWVywfUVcXbN2aTzSHHZYvRx8NL70E06f3Z8JTgiuvbNyxfkN1/XviCXj88YHr9fU1Vy8QSZKkGhhTLHn99f3JiaOOyrHiYYflWLHS8WJ5bLdoUY7h7rkHtmwpGmdMN14mKCRVTSeT6CXRS+JAcd1LonO0j56eHtizJyciSo44Ao48EiZPhuOPh4sugttugxUrqnsQ4zFU17+lS2H9eufGliRJGsWYYslNm3KCAnJP22OOyfdTqny8WB7bnXJK3kcEPPKIMV2FOMRDUkUMV2H5chiwfCWTR5/Fo709V1zevTvXnYB8e+5cuOACWL36IBpWx+rKPT2550S5E06AF1/MJzbnxpYkSRpgcEz580xiPQwfSy5eDNu35zgRcpJi3z54zWteGTOONy4sj+0WLoSzz4YNG2DzZjjvPGO6CjBBIWncShWW24gBFZYvBzrGkpAYrLMT7r8/16BIxRjDnTtzz4mDyUqXuuG1tQ2srlyrebDb2/M+29r6l/X15aKfB5NkkSRJagFDxZR3kLicKcPHk6tW5RoUkHtO7NiRLx/96KCNVyAuHBzbLVyYh5Scd56xXYU4xEPSuI1rto6hdHTA1Vfnasx79+baE298I1xzzcElFupdXbmzs7+7n8M5JEmSRnRIMeWKFXDttbkHxTPP5Otrr33lsI5KxIXGdlVX1x4UEXEz8DZga0rp1CEeD+CvgbcCLwLvSSk9UNtWShrNIc/WMZKODrjxxvE0a+ghFrWsrtzRkbPy5V0J7fonSZI0pEOOKVesGL3ORCXiQmO7qqv3EI9bgOuBW4d5/ALgxOLyeuDG4lpSA2kn6CVRNpBhbLN1VNtwQyxqWV25o8OTliRJ0hhUNaasVFxobFdVdR3ikVL6FvD8CKtcCNyasvuAuRFxdG1aJ2msDnm2jqo3zG54kiRJzaKqMaVxYVNo9BoUi4Gny+5vLJa9QkS8LyLWRsTabdu21aRxkrI8W8cU2gg2Am3EyMWMKqm7Oxcleu9783V3d1nDim54bW2wcWO+rlWBTEk1ExFPRsR/R8S6iFhbLDsiIr4eET8ortvK1v9wRDwWEd+PiPPLlv9EsZ3HIuLjxVBTImJ6RHyhWP7tiDiu1scoSa2gojHl4BgRjAubQL2HeIxmqL48Qw5ASil9AvgEwPLly8cx8F3SoTik2TrGayzVmO2GJ7WK81JKz5bd/33g/6aU/iwifr+4/3sR8TrgYmApsAj494g4KaW0nzyU9H3AfcCdwFuAu4CVQG9K6YSIuBj4c+BdtTowSWolFYkpR4oRnW2joTV6D4qNwDFl95cAm+vUFkmNpt6zdEhqZBcCnypufwp4R9nyz6eU9qSUngAeA84shpDOTindm1JK5PpY7xhiW2uAN5V6V0iSGpAxYtNq9ATFHcCvRHYW0JdSeqbejZLUIHp6cvXlcrWcpUNSo0jA1yLi/oh4X7FsQSlmKK6PKpYPN3x0cXF78PIBz0kp7SPXbDtycCMcbipJDcIYsWnVe5rRzwHnAvMiYiPwR8BUgJTSTeTulW8l/7rxIvCr9Wmp1Fq62U8XB+gh0U7QyaTaD98Yi0aYpUNSI/iplNLmiDgK+HpEfG+EdYcbPjrSsNIxDTl1uKkkjaxmMaYxYtOqa4IipfSLozyegN+sUXMkAWvYy1XsZy+J+cAegus4wOXQeEmKzs48nhByVryvL5+MVq6sb7sk1VRKaXNxvTUivgScCWyJiKNTSs8Uwze2FqsPN3x0Y3F78PLy52yMiCnAHEaehUySNEg3+/kD9rKVxB7gYeB+gqupQoxpjNi0Gn2Ih6Qa6mY/V7GPIDGfYDfBehL7SXRxoN7NeyVn6ZBaXkS8KiJmlW4DPwusJw8TvbRY7VLgK8XtO4CLi5k5Xg2cCHynGAayMyLOKupL/Mqg55S2tQL4RvEjiiRpjG5kH48VHdbmEEDwGIkb2Vf5nRkjNq1Gn8VDUg11cYC9BPNJBDATgGAjielDT6BTf87SIbW6BcCXipqVU4DPppT+LSK+C9weESuBHuCdACmlhyPiduARYB/wm8UMHgCXAbeQP/7uKi4AnwRui4jHyD0nLq7FgUnSRHIfiVmU4st8nYrlVWGM2JRMUEh6WU8xrGM3/SePGcA24Lwhh2BLUn2llH4InDbE8ueANw3znKuBq4dYvhY4dYjluykSHJKkQzNcGqJBfwJTnTjEQ9LL2gkWA7sJdpFIJPpITC2KGEmSJEmH4iyCnfByjLmLxM5iuVTiNw5JL+tkElMIlgIzCLYRJOBKJjdegUxJkiQ1jd9gCscDEPQVNSiOL5ZLJf41SHpZB5O5nFyLYgaJ8+oxxWh3N3R15Xmq29tzFWbHD0qSJDW1DiZzDdMqO82oceOEY4JC0gAd9ewt0d2dp4Rqa4MlS/J0UNddZ9VlSZKkCaCicaZx44RkgkJSfQyV8e7qyieZtra8Tum6q8sTTaPzFwxJklQLpZjjy1+G6dPhjDNg0qSJGTe2YHxlDQpJtVfKePf2Dsx4r1sHc+YMXHfOnPyhrMY13PvZ3V3vlkmSpImkPOYASAnuuQe2bMn3J1Lc2KLxlT0oJNXOaBnvnh7o6+u/D/l+e3t92quxseeLJEmqtu5u+OAHYetWOOoomDIFImDGDNiwARYsmFhxY4vGV/agkFQbY8l4z52bH+/thQMH+m93dtav3RpdT489XyRJUvWU4sitW2HePNi1C3bsyHFiSrB9+8SLG1s0vjJBIak2SlngPXvySeSJJ+DZZ+G7382P9/XBsmW5sFFbG2zcmK8tdNT42tvz+1duIv2CIUmS6qurC/btgxdfzL0ltmyBadNg9uzciyJi4sWNLRpfOcRDUm309MDUqXDffTBrVj7B7NsHjz8Ojz6au+mtXJlPKhPlxNIqOjvzrxqQM/t9ffkXjJUr69suSZI0MaxbBz/8YX8MuWtXvhx2GPyP/zGxEhMlLRpf2YNCUm20t+eTy4wZcOSR+f6UKfmyefPEPLG0io4Oe75IkqTq2b491y0rxZAzZ8LevXl4x0SNOVo0vrIHhaTa6OyET38ajjgin0wmT85jCM86K59gJviH7YRnzxdJklQtc+fC88/nXhOvelWOI2fPhte8ZmLHHy0YX5mgkFQbHR3wMz8DDzyQixrNmZNn8Zg2DY4+ut6taw4tOBe2JEkSy5blxMSmTXmow5w5cMIJcOKJ9W7Z0IzZDplDPCTVzmWXwcknwxvekC/Tpk2sasvV1KJzYUuSJNHZmXtNnHYavP3t+Xry5MaMIY3ZxsUEhaTaadGxdBVRPhf2pEn9t7u66t0ySZKk6mqmGNKYbVwc4iGptlpwLF1F9PTkLHy5FpgLW5IkCWieGNKYbVxMUEhSI+nuhhtuyNOxRuQiopddlscv9vbmDHxJC8yFLUmS1BTWrIHrr8+x3MyZcM45sHRpfsyYbcxMUEhSo+juho98BB5/PM/zDXD33bkr46WXwh135GUHMxe2RZokSZKqa80auOKKPLPIccfBE0/A176WZ65bvLg/ZjMuG5U1KCSpUXR1wbZt+eR22GH5Mns2bN0K69cf/NhLizRJkiRV3/XX55ht7tx8ffzxMGMG/Od/9sdsYFw2BvagkKRG0dMDe/bkHhIlO3bA00/Dhg25N8WqVbB69di2V16kCfqvu7rM1kuSJFXKpk1w9NH59tatsHlzjukATj01x12rVxuXjYEJCklqFO3t8PDDsHt3Hru4dSv88Ie5AvQRR8D27bn7IMCKFaNvzyJNkiRJ1bd4cY7TXnopx26TJ+daYhH9sZtx2Zg4xEOSGkVnJ8yfn3tNvPhi7jkBeajHUUf1dxu8/vqxba+9PdeqKGeRJkmSpMpatSrHbz09/cmJAwdyPYpS7GZcNiYmKCSpUXR0wDXXwBvfmDPwL72Uu/+9+tVw+OF5ndmzczfCsejszOMbe3vzSbJ0u7OzescgSZLUalasgGuvhf37Yd8+mDIl16GYP78/djMuGxMTFJLUSDo64KabYN06OPtsWLiwPzkBOTu/ePHYt3WwhTUlSZJ08FasgNe/Hk45BZYty8kJ6I/djMvGxBoUkhqT0zDl7oKlcYuzZ+cT3I4d8NGPjn0bHR2t97pJkqTW0ihx42ixm3HZqOxBIanxrFkDl1wCt98Ojz8Ojz7amtMwlboLzp0LzzyTr6+9dmwFMiVJklpBI8WNxm7jZg8KSY2luxuuuioXF5o/P89o8fDDsHRpa07DtGKFJzVJkqShNGLcaOw2LiYoJDWWri7YuzefZCLydJuQiwvNmFHftkmSJKlxGDdOOCYoJNXX4DGD69b1Z8BLJ5kZM2DbNjjvvPq2VZIkSfVXih8/85k869nkyXDkkfkx48amZg0KqdWtWQPnngsnnpiv16yp3b67u/MYwd5eWLIkXz/xRJ61Yvdu2LULUspzRE+d6jRMkiRJzabSsWZ5/LhoUU5IbNwIzz1n3DgBmKCQWtmaNbnS8PbtcPTR+fqKK2qXpOjqylMstbXBpEn5eunSfJI59dT+DHhKcOWVrVd/QpIkqZlVI9Ysjx9POSXHi0cdlWfLMG5seg7xkFrZ9dfnKZDmzs33S9fXX1+b4j49PbnnRLkTToAXX8xZ9unTc/e8RplitFGmsJIkSWoG1Yg1y+PHhQvh7LNhwwbYvBkuuqhy8ZlxX12YoJBa2aZNOZtdbvbsvLwW2ttz97y2tv5lfX2wbBmsXl2bNoxVqTthW1v/cJTrroPLL/dkJUmSNJRqxJqD48eFC/t/1KpU/GjcVzcO8ZBa2eLFuTtcuR078vJa6OzMH/i9vXDgQP/tRhwzONRwlLa2vFySJEmvVI1Ysxbxo3Ff3ZigkFrZqlX5JLF9e/6A374931+1qjb77+jImei2tlx3oq2tcTPTPT0wZ87AZXPm5OWSJEl6pWrEmrWIH4376sYhHlIrK439u/763NVu8WL46EcrU39irOP2OjoaMyExWKk74Z498L3v5aEo06bB6afXu2WSJEmNabyx5nDxZLXjx/Z2ePTRXNeiry8nJxYtgpNOqt4+BUCklOrdhopbvnx5Wrt2bb2bIbWu8nF7c+bkD/be3sbtHTEW3d3wkY/A44/DrFl52c6duajn1Vf3H5cFldTAIuL+lNLyerejFRiLSNI41TOeLM0+Mnt2vuzYAc8+mwtyzppljDdOI8UjDvGQVHkTbdxeKenw2GP5BNXXB4cdlufyfs1r+o+rfF7u8oJK3d11bb4kSVLTqVc82d2de3xE5CEpW7bAlCkwdWr/DCLGeFXjEA9JlTfU9KGDx+01S0+D8uz9jBk5i75nD7z2tbBgQR5PWTqu8hMp9F93dTXmsUmSJDWq4eLJdevybB3ViCFLcd/WrXk4yp49sHt3fmz+fHjppf5kCRjjVYE9KKRW0N2dP8jf+958Xe1sb3t77mVQrq8vLy+1p1l6GpQnHebOzdn0GTNyHQoYeFwWVJIkSa2skjHnUPHk44/DE09UL4YsxX1HHZWTEzNn5rhv48b8eHmcZ4xXFSYopImuHsmA0aZ/qkWXvUqdIMuTDqeckrPoKeUuf4OPa7TEjCRJ0kRV6ZhzqHhy/XpYuvTQYsixxIaluK8U8+3aBdOnw/79ufbYa1/bv64xXlWYoJAmunqM3xtt+qdq9jTo7oYPfCCf1O66K48XHM8JsjzpsGABnHNO7kUBrzyuWszLLUmS1IgqHXMOFU+++tW5QHm5scSQa9bAJZfA7bfnXhg/+MHQsWEp7ivFfDNn5uKYxx4Lxx+fkxXGeFVlgkKaiMozxF/5Sv/YuZJadEnr6Mgf2u3teV9dXf0ngWr1NChl7h98EI44Ii+77748XvBQT5CDkw7TpsHJJ8Ott+bXuHzcYS3m5ZYkSWokpbjzM5/J9SG2bOl/rNIx58KFBx9DdnfDVVflH5jmz89x8fr1uVfE4NiwPO6bPx9OOw2WL4ebb4ZrrjHGqwGLZEoTzZo1+UN47978wbp3L3zrW3nGiQUL8jq16JJWmpZz27Y8hu/hh2Ht2vzh3tmZEwkwcNqolSvHt89S5v6ll3Ixy1JPhw0b4A1vOLQTZCnpUF7Qc+XK4U9I1Z6XW5IkqVGUx51798Izz+RhsOeck+PO8cScQ8WS06fnGO/448ceQ3Z19cfFEblXBOREw/TpA9cdLe4zxqs6ExTSRLJmDaxalb+gz5oFkyfn23v3wgMPwPnnVy4ZMJobbshd6GbPzieQ3bvz/RtugJtuOrgv/WNVqvY8Z04eM1gqbNTXN/AEuWZNnj5q06ZcoXnVKlixYvjtmnSQJEkaaHDcOXVqTibMnw+PPJJ7nY4n5hwqlty2LSc+2trGHkP29PT3nCglJ2bMyNPHP/UUnHjiwHjQuK+uTFBIE0Wp+1rpJLFvX54i6aij8v09e3KmuFLJgNHcd1/eb+lEMHNmLi553335fjU+/Nvb84nwta+Fe+/Ny1IaeIJcswauuCKf7I4+Omf5r7gir1uepGiWaVAlSZJqbai4c/duOPLIvGzzZjjvvPHFnMPFkj/4AXzxi2PfTnt7btvDD+f7M2bkxMS2bXDccXnYSCkefOqpXAzT+K9u6lqDIiLeEhHfj4jHIuL3h3j83Ijoi4h1xeUP69FOqSmUuq+VThJTp8KUKbBjR+5N8I535PFzg+smVEtpeMVYl1dCadzg9Olw1ll52fPPwxln9I8TvP76nJyYOzcXcJo7N9+//vr+7TTTNKiSJEm1NlzcuXcvHHYYvPvd4485KxVLdnbmti1dmpMT27blwpcLF8KiRf3x4NSpcO21xn91VrcERURMBv4WuAB4HfCLEfG6IVb9j5TSsuLyxzVtpNQMygsTvfhi7i2wb18+QUyZkrPAU6fWvsrwWWflfe/albPdu3bl+6XEQTWUF6ncuxcuuCCfQG+8sf8EuWlTTkiUmz07Ly+px8wnkiRJzaC7Oxdhf/75HN/t3l2duLNSsWQpPjzppFy74qKLclx33HED19u1K/c4Nv6rq3oO8TgTeCyl9EOAiPg8cCHwSB3bJDWX0i/9bW05A/zMMzkrPG9e7l63c2dOWFx5Ze27p112WR5SsnVrrv8wfXqeFuqyy6q739GGjixenLvxzZ3bv2zHjry8pFTLolwtZj6RJElqZKXYc9q0PJxjy5Y8G8aBA5WPOysZSw6OD++++5Xx4AsvDLwPxn91UM8hHouBp8vubyyWDXZ2RDwUEXdFxNLhNhYR74uItRGxdtu2bZVuq9SYyn/pP+WUPDavNHPHq14FxxyThy6MVACyWjo64Oqrcy+GM87I11dfXf9xfKtW5YTE9u35ZLp9e76/alX/OtWaBlWSJKmZlWLP00/PPSYWLswx5759lY87qxlLDhUPppTj6XLGfzVXzx4UQw0eSoPuPwAcm1J6ISLeCnwZOHGojaWUPgF8AmD58uWDtyNNTOW/9C9cCGefnafU3Lw5d1+rd2GfRqyCXDppls/i8dGPDjyZVmsaVEmSpGZWij0nTcpx5/e+lxMVALfeWvm4r1qx5FDx4CWXwEMP5ZjP+K9u6pmg2AgcU3Z/CbC5fIWU0o6y23dGxA0RMS+l9GyN2ig1ttKsFW1t+f7Chbn723nn5boUGtqKFaNPK1qNaVAlSZKaWXnsuXBhvpTuN1ucNFQ8OHgWN+O/mqtnguK7wIkR8WpgE3Ax8EvlK0TEQmBLSilFxJnkISnP1bylUqOaaL/0N9LUno3Y+0OSJKmeGi32rHTsaPxXd3VLUKSU9kXEKuCrwGTg5pTSwxHxgeLxm4AVwGURsQ/YBVycUnL4hlrLSB+8E+mX/vKCn+VTO5WmB5UkSVLtDBeDNkrsaew4IcVE/L6/fPnytHbt2no3Qxq/8g/e8iz1RPzgXb164HAV6L/vcBWpIiLi/pTS8nq3oxUYi0hqas0Qgxo7Nq2R4pF6zuIhaTTls3RM9PmYe3ryCbCcUztJkiTVXjPEoMaOE5IJCqmRtdIHr1N7SpIkNYZmiEGNHSekehbJlDSawbN0QHU/eOtZpLLRii5JkiS1qmrEoJWOM40dJyR7UEiNrLMzf9D29sKBA/23Ozsrv6/SWMPe3oGFhrq7K7+voZSKLrW1wcaN+bqRxjlKkiS1ikrHoNWIM40dJyR7UEiNrJaVksvHGkL/dVfX+Pc31oy5UztJOkQRMRlYC2xKKb0tIlYDvw5sK1b5SErpzmLdDwMrgf3AB1NKXy2W/wRwCzATuBP4rWKq8+nArcBPkKc7f1dK6ckaHZok1V6lY9DxxpnDxZLGjhOOCQqp0dXqg7enJ2e0y1VirKFTQEmqjd8CNgCzy5b9VUrpuvKVIuJ1wMXAUmAR8O8RcVJKaT9wI/A+4D5yguItwF3kZEZvSumEiLgY+HPgXVU+Hkmqr0rGoOOJM40lW4pDPCRl1So01AxVoCU1tYhYAvwc8A9jWP1C4PMppT0ppSeAx4AzI+JoYHZK6d6U52C/FXhH2XM+VdxeA7wpIqKiByFJE9l44kxjyZZigkJSVq16F81QBVpSs/sYcAVwYNDyVRHRHRE3R0Sp0tti4OmydTYWyxYXtwcvH/CclNI+oA84cnAjIuJ9EbE2ItZu27Zt8MOS1LrGE2caS7YUExSSsmoVGnIKKElVFBFvA7amlO4f9NCNwPHAMuAZ4C9KTxliM2mE5SM9Z+CClD6RUlqeUlo+f/78sTRfklrDeOJMY8mWYg0KSf2qUe/CKaAkVddPAT8fEW8FZgCzI+LTKaVfLq0QEX8P/EtxdyNwTNnzlwCbi+VLhlhe/pyNETEFmAM8X4VjkaSJ61DjTGPJlmIPCknV5RRQkqoopfThlNKSlNJx5OKX30gp/XJRU6LkF4D1xe07gIsjYnpEvBo4EfhOSukZYGdEnFXUl/gV4Ctlz7m0uL2i2McrelBIkqrAWLKl2INCUvU5BZSk2rs2IpaRh2I8CbwfIKX0cETcDjwC7AN+s5jBA+Ay+qcZvau4AHwSuC0iHiP3nLi4RscgSQJjyRZigkKSJE0IKaW7gbuL25eMsN7VwNVDLF8LnDrE8t3AOyvVTkmSNDSHeEiSJEmSpLozQSFJkiRJkurOBIUkSZIkSao7a1BI1dDdDV1d0NOT52ju7LSwjyRJkmrHeFRNyB4UUqWtWQOXXAK33w6PPw4/+EGeu7m7u94tkyRJUiswHlWTMkEhVVJ3N1x1FUTA/PmwezesXw/79+cMtiRJklRNxqNqYg7xkEbQzX66OEAPiXaCTibRweThn9DVBXv35pNBBMycmZdv3AjTp9em0ZIkSZpQDiomNR5VE7MHhTSMbvZzHfvoJbEE6CVxHfvoZv/wT+rp6c9Ul8yYAdu25bF/kiRJ0kE46JjUeFRNzB4U0jC6OEAbQRsBQFvZ8pcz1oOLD02bBosXw8MP58dnzIC+Ppg6NRcmanYWW5IkSaqpg45Jf/hDOOww2L49P9bo8ajxpcrYg0IaRg+JOYOWzSmWA/nD9LrroLcXlizJ15s2wY4dsHRpf6Y6Jbjyyub/oB3qeC22JEmSVFUHHZMuXgzf/z4sWtT48ajxpQaxB4U0jHaCXtLLWWqAvmI5kDO9bW35Avn6Na+Bl16CBQvyCeG88yZOFnio4y0tnwjHJ0mS1IAOOiY98cR8vWkTHH98Y8ejxpcaxASFWt5wRYc6mcR17ANylrqPPOZvZakrXU9PzvSWmzMnFyBavbqWh1Abwx1vT0992iNJkjSBVDQmPf74XBDz5ptregwHzfhSgzjEQy1tpKJDHUzmcqbQRrARaCO4nCl0dD+cExAPPghf/Sr86Ef9G+zrm7jFh9rb8/GVm8jHK0mSVCMtG5MaX2oQExRqaeVFhyYV120EXRwAoIPJrGYqNzON1UzNJ4LSOLkzz8z1Jr75TXjmmbyst7cxiw9VQmdn/zEeODDxj1eSJKlGqqdcBQAAIABJREFUWjYmNb7UICYo1NJGLTpUrrsbPvhBWLsWHnoIJk2Cc8+F2bPh29/OY+Yuv7z5x8t1d+ds/Hvfm69LRYo6OvLxtbXlYSwT5XglSZLqrGVi0sFxJhhfagBrUKiljVp0qKRUYXjrVpg3D3btgnvugXPOgfPPnzh1J0rH2dY2sJJy6URRukiSJKliWiImHSnObNQ2q+bsQaGW1skkekn0kjhQXPeS6Cz9a5SyvJdemqdrOuww2LMHZs7Ms3Rs2DCxxsmVV1KeNKn/dldXvVsmSZI0YY0ak8LAnhMvvpi/4DdTTGqcqTGwB4VaxnCVkS+HActXMpkOJg/M8qaULzt29G9w+vScve7thZUr63ZcFWUlZUmSpKo66JgUXtlz4vnnc28JyLFqM8SkxpkaAxMUagmlyshtxIDKyJeTiw69/OFfrjzLO3du7kLX1gb79+ds9datcNRRE2ucXHt7Prm1lXUwbPRsvCRJUpM4pJgU+uPSo47KMem8eXn5zp05Nm2GmNQ4U2PgEA+1hNEqIw+ppydndQFOOQV27869KPbtg9NOg+XL4eMfb+wTwcGykrIkSVLVHFJMCv1xaSkm3bULjjgiDz9ulpjUOFNjYIJCLeGgKiOXlM/LvGBBLj4URaGiiVph2Jk6JEmSquaQYlLoj0tLMenMmfDss83Rc6LEOFNj4BAPtYQxV0Yu19mZx/pBzlhPmwYnnzzxP0idqUOSJKkqDikmhYFx6fz5OS7t7W2+uNQ4U6MwQaEJaXDxoVOBO4rM9BzyiaCXxMrhxvlBf5a3qyt3q2tvz4WH/FCVJEnSGFQkJgXjUrUMExSacIYqPnQHiZ9nEuth6MrIwzHLK0mSpENQ0ZgUjEvVEkxQaMIpLz4EvNyFbj2wmql1a5ckSZJahzGpdPAskqkJ55CLD0mSJEkVYkwqHTwTFJpw2gn6Bi0bU/EhSZIkqUKMSaWDZ4JCE04nk+gl0UviQHHdS6LTP3dJkiTViDGpdPD879CE08FkLmcKbQQbgTaC/5+9ew+zsy4Pfv+9Z3LkkGSQcDBhxANW0UbUqLS+raJWgVaxeUNL3Sqvcm1alL7trmxfj7u4KV7WN/agUSxVt2gPyk7HV6qggkqtuwKNiIMIaEQNSVCC5AQkk5m17v3H7xkyjJOZSebwrDXr++Fa16z1W8+zuJ/1rJnnzr1+h0uYN7nJhyRJkqRpYE4qHTonydSctGqysyFLkiRJM8ScVDo09qCQJEmSJEm1s0AhSZIkSZJqZ4FCkiRJkiTVzgKFJEmSJEmqnQUKSZIkSZJUu1oLFBFxZkTcHRGbIuJtYzwfEfHB6vn+iHhOHXFKkiRJkqSZVVuBIiK6gQ8DZwGnAn8QEaeO2uws4JTqdiFwxawGKUmSJEmSZkWdPSieD2zKzHsycz/wGeCcUducA3wqi5uAZRFx4mwHKkmSJEmSZladBYoVwL0jHm+p2g51GwAi4sKI2BgRG7dv3z6tgUqSJEmSpJlVZ4EixmjLw9imNGZemZmrM3P18uXLpxycJEmSJEmaPXUWKLYAJ414vBLYdhjbSJIkSZKkNldngeI/gVMi4okRsQA4D7hm1DbXAK+vVvM4HdiVmffNdqCSJEmSJGlmzavrf5yZQxFxMfBloBv4RGbeERF/VD3/UeBa4GxgE/AI8Ia64pUkSZIkSTOntgIFQGZeSylCjGz76Ij7Cbx5tuOSJEmSJEmza9wCRUSsGdWUwAPAbZm5Z8aikiRJkiRJHWWiHhSvHKPtGGBVRFyQmV+bgZgkSVKHiojXj9WemZ+a7VgkSdLsGrdAkZljzvkQEU8ArgZeMBNBSZKkjvW8EfcXAS8FbgUsUEiSNMcd1hwUmfnTiJg/3cFIkqTOlpl/PPJxRCwFPl1TOJIkaRYd1jKjEfErwMA0xyJJkjTaI8ApdQchSZJm3kSTZP4rZWLMkY4BTgReO1NBSZKkzjQq9+gCTqUMK5UkSXPcREM81o16nMAvgB9m5v6ZCUmSJHWwkbnHEPDTzNxSVzCSJGn2TDRJ5r/NViCSJEnmHpIkda5JzUEREWsi4ocRsSsidkfEnojYPdPBSZKkzmLOIUlS55rsKh7vB16ZmXfOZDCSJKnjmXNIktShJlug+LmJgkbrp0EfTTaT9BKsoYtVdNcdliSpvZlzSDLPlDrUZAsUGyPis8D/YsTyopnZNyNRqeVtYJDLGGKQYDmwD1hHk0vAi4ckaSrMOaQOZ54pda7JFiiWUNYhf/mItgRMFjrQBgb5Q4Z4BOgi2Q7cB6wG+mh64ZAkTYU5h9TBPsAAf06TQWARyX5gJ8EzSPNMqQOMW6CIiIszc31mvmG2AlJr66fBOxhiD7AACOBhYD9wN8miR5eulyRp8sw5JG1gkL+oihMLKesMbwFWkmwlzDOlDjDRKh5vnJUo1Db6aLKDctEIoJtS5WoCm4FeosboJEltzJxD6nDraRDAIkq3qfmUXHN7dTPPlOa+SS0zKg3bTNIFLKYUJRpV+1B1W+NHSpIkSYdhK3AU5Yuw4TyzmzIHxXzSPFPqABP9lq+q1iAffXNN8g7VS3Ac5aJxJOUDtJ/Sm+JlOHGRJOmwTSnniIjuiPhORHyhenxMRFwfET+sfvaM2PbtEbEpIu6OiFeMaH9uRNxePffBiIiqfWFEfLZqvzkiTp7+w5e0gvIlWHAgzxyg9KR4N/PMM6UOMFGB4vbMXDLG7ejMXDIrEaqlrKGLkwmOpgztWECpdD8XeDcLao1NktTWpppz/AkwcnnStwFfzcxTgK9Wj4mIU4HzgGcAZwIfiYjhf/VcAVwInFLdzqzaLwB2ZOZTgL8G/nJKRyppTBfTzSCwjDLMoxs4AngPXaxlfq2xSZod9pPSIVlFN5cznzPp4niCFQRr6eLDLLCqLUmqRUSsBH4b+NiI5nOAq6r7VwGvHtH+mcwcyMwfA5uA50fEicCSzPxWZibwqVH7DL/WBuClw70rJE2ftczn/czjRIKFBKsI/p55vIWFdYcmaZZMtMzo/xsRj8vMX8xKNGoLq+jmCosRkqTpNZWc42+AtwJHj2g7PjPvA8jM+yLiuKp9BXDTiO22VG2D1f3R7cP73Fu91lBE7AIeBzwwMoiIuJDSA4Pe3t7DOAxJa5lvbwmpg03Ug6JJSRj+PSIujYgX+I2BJEmaAYeVc0TE7wD3Z+a3J/n/Ges1c5z28fZ5bEPmlZm5OjNXL1++fJLhSJKkYeMWKDLzfZn5EuBs4LuUJcBujYh/iojXR8TxsxGkJEma26aQc7wQeFVE/AT4DPCSiPgH4OfVsA2qn/dX228BThqx/0pgW9W+coz2x+wTEfOApcCDUzhcSZI0hknNQZGZezLzc5n5h5n5bOAvgOWU8ZmSJEnT4lBzjsx8e2auzMyTKZNffi0zXwtcA5xfbXY+8Pnq/jXAedXKHE+kTIZ5SzUcZE9EnF713Hj9qH2GX2tt9f/4pR4UkiRpasadgyIinjPO01/PzA9MczySJKkDzUDO8T7g6oi4ANgMnAuQmXdExNXA94Eh4M2Z2aj2uQj4JGWlw+uqG8DHgU9HxCZKz4nzDjEWSZI0CRNNkjmcDCwCVlO6XAawCrgZ+C8zF5okSeogU845MvNG4Mbq/i+Alx5ku8uBy8do3wg8c4z2fVQFDkmSNHMmmoPijMw8A/gp8Jxq4qfnAs+mLMslSZI0ZeYckiRpUnNQAE/LzNuHH2Tm94DTZiYkSZLUwcw5JEnqUBMN8Rh2Z0R8DPgHyrJarwXunLGoJElSpzLnkCSpQ022B8UbgDuAPwH+lDKx1BtmKijNrg0Mspq9HMNeTmAv57KXfhoT7yhJ0vQz55DayAcY4GT2soS9nMxePsBA3SFJamOT7UHx68DfZeZfz2Qwmn0bGORPGeJhypTlDeArwM/Zz3oWsIruegOUJHUacw6pTXyAAf6cJouBZcDDwJ/TBAZ4CwvrDU5SW5psD4r/BtwWEd+KiPdHxCsjomcG49IsWU+DQeAIYAGlSLEI+BHQR7PO0CRJnem/Yc4htYUPVcWJIyn/qDiSkkt+yBxS0mGaVA+KzHw9QEQ8HlgLfBh4/GT3V+vaWv0ceSIXAg8Bm8nZD0iS1NHMOaT28SCl58RIi6t2STock7rYR8Rrgd8AfhV4AFgP/PsMxqVZ0E+Dh0keoMxC1kW5qAz3pOgl6gxPktSBzDmk1tVPgz6abCZZSDJE+bKrm/IF19HAIHBMnUFKamuT/Tbibyi9/j8KfD0zfzJjEWlW9NPgnQzyCDzaCa9B6TnRDZwMrJn0CCBJkqaNOYfUgvppsI4hegj20KAP2Fs9l5Q8coDyj4u3mUNKOkyT+uuRmccCb6RMT3B5RNwSEZ+e0cg0o/pocifJw2M8txh4Hl1OkClJmnXmHFJr6qNJD8F9NLmO8qXWPMo/Jkb2uX0hOEGmpMM22SEeS4Be4AmUL9eXgrPftLPbaHIvpeK9oPqZlN4TRwL7Hd4hSaqBOYfUmjaTzKfJv5E0OFCU6AJ6KBOuLwNO8gsuSVMw2SEe3xxxW5+ZW2YuJM2GnSRBuagMzz/RoGSADZx/QpJUG3MOqQX1ElxH0qR8udWg5JBBGdqRwPGYQ0qamsmu4rFqpgPR7FpG6SnxEAcuMMNrdvTg/BOSpHqYc0itaQ1d/AOlOAGwj5JDLqQUKOYDxxHmkJKmZNwCRURcM97zmfmq6Q1Hs+U0uhlgiFuAh+HRrnpLgPcyz/knJEmzypxDam2r6OZldPH/0WQrZexVAx6dz+wM4N3MN4eUNCUT9aD4NeBe4J+Bm8E+W3PFGrq4hy5eQbIV2A7MJ3k381jL/LrDkyR1HnMOqcW9iXk8whC/av4oaYZMVKA4Afgt4A+A1wBfBP45M++Y6cA0s1bRzSWUGZkXkZxRdcmz6i1Jqok5h9TizB8lzbRxCxSZ2QC+BHwpIhZSkoYbI+L/zswPzUaAmjmr6PaCIklqCeYcUnswf5Q0kyacJLNKEn6bkiicDHwQ6JvZsCRJUqcx55AkqbONO81uRFwF/AfwHOA9mfm8zLwsM7fOSnSaFhsY5MXs4xT28WL2sYHBST0nSdJsMeeQ6nG4uaA5pKSZMFEPitdRJud9KvDfIx6dryqAzMwlMxibpsEGBnkrQywBTgR2Am9l6NHnD/acEx1JkmaZOYc0y8bLE8fLBQ93P0mayERzULiQcRvrp8ElDLEbaFJO9rKS57GeBlCWFV1WTZS+DB59zouLJGk2mXNIs2uiPHG8XHA9DXNISTPCZGCO6qfBOobYARwFDAJbgIdIlgBbq9vor6OGn5MkSdLcNJk8cTzmkJJmigWKOaqPJj0EPcB+YD7QDTwA7AZWVLfdo/Ybfk6SJElz02TyxPGYQ0qaKRYo5qjNJEuB5xMMAPsoF57d1e1iurmYbnYDO0maJDvJR5+TJEnS3DSZPHE85pCSZsqEy4zOhIg4BvgsZQmxnwC/l5k7xtjuJ8AeoAEMZebq2YuyffXT4B6a3ERyHMHzgB8AO4Ae4P3Me8z4wPU02Eqper+LbscOSpIkzSH9NOijyWaSXoKFJLsITqUbaHALedA8cSzDz5tDSpputRQogLcBX83M90XE26rH/+Mg256RmQ/MXmjtbXhM4eOBBxmubMMLCboJLmEeq0ZUt9cy34uJJEnSHDWcG/YQrAR2kNxLkyB4El08jS5OrNpH54njMYeUNBPqGuJxDnBVdf8q4NU1xTHnDI8pfCrd/DpdLCMYItgKh3TRkSRJUvs7MN9E0FX9fDLdrKjubwF6xvgSS5LqUFcPiuMz8z6AzLwvIo47yHYJfCUiEvi7zLzyYC8YERcCFwL09vZOd7xtYzPJyur+CXRxAtAk2QJedCRJkjrMyNxw2FJgC8Gl9oCQ1GJmrEARETcAJ4zx1DsP4WVemJnbqgLG9RFxV2Z+Y6wNq+LFlQCrV6/OQw54jugl2EHSM6JtV9UuSZKkzmJuKKmdzNgQj8x8WWY+c4zb54GfR8SJANXP+w/yGtuqn/cDnwOeP1PxzhVr6GIHyY5qVuXh+2tcsEWSJKnjmBtKaid1/WW6Bji/un8+8PnRG0TEkRFx9PB94OXA92Ytwja1im4uYd5jxhS+ii76aPJG9nMpg/TTqDtMSZIkzYKxcsPR80300+BSBs0VJdWurjko3gdcHREXAJuBcwEi4vHAxzLzbOB44HMRMRznP2Xml2qKt62sovvRi85YMzevY4hLcE4KSZKkTjAyNxzNXFFSK6mlQJGZvwBeOkb7NuDs6v49wLNmObQ5Z+TMzcCj4w/7aHrRkSRJ6nDmipJaiYPP5rjNJEtHtS2t2iVJktTZzBUltZK6hnhoFvTT4B6a3ERyHMHTCY6ny5mbJUmSOlQ/Dfpospmkl2AhyS7CVT4ktQQLFHPUBxjgf9JkT/X4EZIHSX6VpJvgArvsSZIkdYx+GlzGfm6g/AOgFxgg2EUSBE+ii6WU4sQO0lxRUi0sUMxBGxjkL2gyDzgWeAj4GdAAtgIfHDVzsyRJkuaufhq8g/3cBCyg/APgp8AekucRHF3NQTHcq+KCcSbVlKSZZIFiDlpPgwCOAAI4GphPmXDkSXR5wZEkSeogfTTZThAki+DRwRsPA1tInkwXlzK/xgglqXCSzDloK6UoMTSibSGwE8cTSpIkdZrNJAPAkRzID+dRetdux/xQUuuwQDEHraAUJBrAIJDAI8BiYI2nXJIkqaOUyTDhKA7kh8M54nzC/FBSy/Cv0Rx0Md0MAUso1fE9lGr5/+nwDkmSpI6zhi6WkzSAx1Vtj1CWE323801IaiHOQTEHra3GEK6nwVZgFaVosdaxhZIkSR1nFd28lwV8hKFq+Xk4neAiJ06X1GIsULSY0WtTrznMXg9rmW9BQpIkaRZNVx43E1bRzUdbJBZJOhiHeLSQfhqsY4gdJCspa1CvY4h+GnWHJkmSpHGYx0nS1FmgaCF9NOmp1qHuqn72EPTRrDs0SZIkjcM8TpKmziEeLeQ2GuwAdlMmLXoawXEEm8maI5MkSdJoI4d03EqTFwCMGEaxFMzjJOkQWKBoEf00+D7Jrurx/cA2ktUET3W8oCRJUkvZwCCX0WCQZDll+c5vAC+iyQlVJ+VdlCU+JUmT4xCPFvEXDHI/5UL2CLAf2A5sJF2bWpIkqUX00+AiBvjfGeJnJIuBfQQDwD7gOyRNkh3VzTxOkibPv5gtoJ8GN5AsBHqqtoco3VsCWmb2Z0mSpE42PBHmrTSZRxnMsQVokPQQHEP5kmkL0ENwict4StIhcYhHC+ijSTflZMwHFgKD1XOL7RYoSZLUEoYnwtxPcCTJICV/ewB4AqX36+/RzaUu9S5Jh8UeFC2grJUNA5TCxPBUSg8Bp1ugkCRJagmbSZZSJr88Ch5dQHQvsItkPuGQDkmaAv+CtoBegqdV3QKhFCoawPHAm+zkIkmS1BJ6CXYBTyfoBo6t2puUL5jeTbdDOiRpCixQtIA1dNFN8DyCpwDHAMcTvNdxi5IkSS1jDV3sIFlAcDrBkcBi4L/SxadZwFqHdkjSlPj1fAtYRTeXUMY1LiQ5o+oeOFZxYuR6273jbCdJkqTpNTJn20wXZ81gLmbOJ6kTWaBoEasm0SVweOboHoKVwA6SdQxxCa70IUmSNBsmk7NNlTmfpE7lEI82MjxzdA9BV/Wzh6CPZt2hSZIkaZqY80nqVBYo2sjwzNEjLa3aJUmSNDeY80nqVA7xaCMLSL5Msp9ykXoawUKCXpcilSRJamsj55y4hyb7gKeOGM6xC8z5JM159qBoE/002Eqym2Q+ySMk/0aTH9FwvW1JkqQ2NjznxA6SlcAK4CaSH9CgSbKjupnzSZrr/CvXJvpo8iS6eDFdHEEwSLAEOMkZnSVJHSwiFkXELRHx3Yi4IyLeU7VfGhFbI+K26nb2iH3eHhGbIuLuiHjFiPbnRsTt1XMfjIio2hdGxGer9psj4uTZPk7NbaPnnDiFbn6NYBuwBeghuMTl5yV1AId4tInNVUW9iy6Or9qaJFvqDEqSpPoNAC/JzIciYj7wzYi4rnrurzNz3ciNI+JU4DzgGcDjgRsi4qmZ2QCuAC4EbgKuBc4ErgMuAHZk5lMi4jzgL4Hfn4VjU4cYzvNGejJdLAQ+wYI6QpKkWtiDok30Euwa1eZYRElSp8vioerh/Oo23kyC5wCfycyBzPwxsAl4fkScCCzJzG9lZgKfAl49Yp+rqvsbgJcO966QpoN5niQVFijaxBq6Hh1/6FhESZIOiIjuiLgNuB+4PjNvrp66OCL6I+ITEdFTta0A7h2x+5aqbUV1f3T7Y/bJzCHKvx0fN0YcF0bExojYuH379mk6OnUC8zxJKvyr1yZW0c0lzKOHcCyiJEkjZGYjM08DVlJ6QzyTMlzjycBpwH3AB6rNx/pKOsdpH2+f0XFcmZmrM3P18uXLD/Eo1MnM8ySpcA6KNrKKbi9UkiQdRGbujIgbgTNHzj0REX8PfKF6uAU4acRuK+HRuQhXjtE+cp8tETGPstr3gzNxDOpc5nmSZA8KSZLUxiJieUQsq+4vBl4G3FXNKTHsd4HvVfevAc6rVuZ4InAKcEtm3gfsiYjTq/klXg98fsQ+51f31wJfq+apkCRJ08geFJIkqZ2dCFwVEd2UL16uzswvRMSnI+I0ylCMnwB/CJCZd0TE1cD3gSHgzdUKHgAXAZ8EFlNW7xheDeTjwKcjYhOl58R5s3FgkiR1GgsUkiSpbWVmP/DsMdpfN84+lwOXj9G+EXjmGO37gHOnFqkkSZqIBYoZ1k+DPppsJuklWEOX4wslSZKmgXmWJM0tzkExg/pp8A72cx0NbqXJddXjfhoT7yxJkqQx9dPgIgZYU+VZ82myg2QdQ+ZZktTGLFDMoI8wxB3AFpL7SLaQ3FG1S5Ik6dD10+CdDHINTXZS8qyvkwyQ9BD00aw7REnSYbJAMYO+RJOfAzuAfcB+YA9woxdOSZKkQ9ZPgzeyn38n2cGBscoPAhtpshTYjAusSFK7skAxQ/pp8DPK1OHzq597gSaws87AJEmS2lA/DdYxxE+BIyjFid2U3GoBsAXYBfQS9QUpSZoSCxQzpI8mi4GgXDiDUqTYByyrMzBJkqQ21EeTHoJuSl51FCW3eqh63AB2kKwxvZWktuVf8BmymeRJlIo+lIXWA+gGXuzbLkmSdEg2kywFVgIDlLxqKaUw8RDwBOAS5rmKhyS1MZcZnSG9BAMEe0geplw8k3IhfZNvuyRJ0iHpJdhB8jy62E3z0fyqB3g28F4WWJyQpDbnV/kzZA1ddBM8j+ApwDHACQTvtbIvSZJ0yNbQxQ6SBQRnEKykfPHzSrosTkjSHOFX+TNkFd1cQhkvuZDkDII1dHnxlCRJOgwjc6vNdHGWuZUkzTkWKGbQKrq9aEqSJE0TcytJmtsc4iFJkiRJkmpngUKSJEmSJNXOAoUkSZIkSapdLQWKiDg3Iu6IiGZErB5nuzMj4u6I2BQRb5vNGCVJkiRJ0uypqwfF94A1wDcOtkFEdAMfBs4CTgX+ICJOnZ3wJEmSJEnSbKplFY/MvBMgIsbb7PnApsy8p9r2M8A5wPdnPEBJkiRJkjSrWnkOihXAvSMeb6naxhQRF0bExojYuH379hkPTpIkSZIkTZ8Z60ERETcAJ4zx1Dsz8/OTeYkx2vJgG2fmlcCVAKtXrz7odpIkSZIkqfXMWIEiM182xZfYApw04vFKYNsUX1NqDf398JGPwE03QQScfjpcdBGsWlV3ZJIkSZrrzEXVomqZg2KS/hM4JSKeCGwFzgNeU29I0hQNXwy++EXYuxdOOAGOOAJuvBG2bIHLL/fCIEmSpJmzYQO84x3w85/DUUdBT4+5qFpGXcuM/m5EbAF+DfhiRHy5an98RFwLkJlDwMXAl4E7gasz84464pWmrL8fzj0XXvQi+NSn4Be/gEzYvh2aTViyBO6/H/r66o5UkiRJc9FwPvra18I998DgYLlt3w7z5pmLqiXUtYrH54DPjdG+DTh7xONrgWtnMTRpevX3wxVXwBe+UIoSixaVwsTg4IHCxAMPwBOeALt2webNdUcsSZKkuWR0PppZhnUMDJT7Rx8Nu3dDd7e5qGrXykM8pPbW3w/r1sHdd5diRLNZLgTd3TA0VB7v3w9dXbBvHyxcCL29dUctSZKkuWKsfDSr9QS6ug7ko5ll6LG5qGrWysuMSu2tr6+M6du/HxqNUoCAUrHu7i5t+/aVi8Pu3XDccbBmTb0xS5Ikae4YKx/t7i4FicwD+Wimuahagj0opOmyYQOsXw9bt8KKFaUQ8Ru/AUuXljF9CxeWnhONRulKt2dP2W/JEnjxi505WZIkSVMzPJzjpptK0WHv3pJnjs5H580rX5INDJSc9SUvgXe9y1xUtbNAIU2HDRvgrW8txYYTT4SdO+GnP4XFi+HpT4dt2+DBB8scFAMDpYrd2wvvfS+sXVt39JIkSWp3/f3wznfCpk3lyzAoK3N85SvwvOf9cj7a3Q0rV5qPqqU4xEOaDuvXl+LEsmWlGr1sWekm9+1vw4IFcMYZZVxfs1l6V7zmNaWo4cVAkiRJ06Gvr/SSWLKkLGN/xBHli7M9e8oXZ+ajagP2oJCmw9at5QIw0vHHl251PT1lRuS1a8u4PrvOSZIkabpt3lx6RixdeqDtmGNKz92BgbKKnPmoWpwFCmk6rFhRhnUsW3agbfdueNKT4NJLawtLkiRJHaK3F+64o0x6uXhxadu3r+SnZ51lTqq24BAPaTpcfHEpSOzcWbrN7dxZHl98cd2RSZIkqROsWVOGGO/eDY88Um67d8Py5a7OobZhgUKaDmvXwvvfXyrU991Xfr7//Y7pkyRJ0uxYtQouv7ys2jE4WIZ2vOhFZRJMh3SoTTjEQ5oua9dakJAkSVJ9Vq0qy4xKbcoeFJIkSZIkqXYWKCRJkiRJUu0sUEgAflZYAAAct0lEQVSSJEmSpNpZoJAkSZIkSbWzQCFJkiRJkmpngUKSJEmSJNXOAoUkSZIkSaqdBQpJkiRJklQ7CxSSJEmSJKl2FigkSZIkSVLtLFBIkiRJkqTaWaCQJEmSJEm1s0AhSZIkSZJqZ4FCkiRJkiTVzgKFJEmSJEmqnQUKSZIkSZJUOwsUkiRJkiSpdhYoJEmSJElS7SxQSJIkSZKk2lmgkCRJkiRJtbNAIUmSJEmSameBQpIkSZIk1c4ChSRJkiRJqp0FCkmSJEmSVDsLFJIkSZIkqXYWKCRJkiRJUu0sUEiSJEmSpNpZoJij+mlwKYO8kf1cyiD9NOoOSZKkaRcRiyLiloj4bkTcERHvqdqPiYjrI+KH1c+eEfu8PSI2RcTdEfGKEe3PjYjbq+c+GBFRtS+MiM9W7TdHxMmzfZzSVJkbSmoHFijmoH4arGOIHSQrgR0k6xjyQiRJmosGgJdk5rOA04AzI+J04G3AVzPzFOCr1WMi4lTgPOAZwJnARyKiu3qtK4ALgVOq25lV+wXAjsx8CvDXwF/OxoFJ08XcUFK7sEAxx/TT4L8zyEaS79LkfpIegh6CPpp1hydJ0rTK4qHq4fzqlsA5wFVV+1XAq6v75wCfycyBzPwxsAl4fkScCCzJzG9lZgKfGrXP8GttAF463LtCamXDvSbOZ5C7SQZIuqq80NxQUiuyQDGHDFfH7yc5lmQvybdIfkaTpcBmsu4QJUmadhHRHRG3AfcD12fmzcDxmXkfQPXzuGrzFcC9I3bfUrWtqO6Pbn/MPpk5BOwCHjdGHBdGxMaI2Lh9+/bpOjzpsIzsNTH833BeCJgbSmpJFijmkD6a9BAcRzBAsJhgEcldJLuAXvyyR5I092RmIzNPA1ZSekM8c5zNx7oY5jjt4+0zOo4rM3N1Zq5evnz5RGFLM2o4L+whWEYQI/JCwNxQUkuaV3cAmh4bGOSjDLEfWEQ5sScSLKR8nbSD5AK6x30NSZLaWWbujIgbKXNH/DwiTszM+6rhG/dXm20BThqx20pgW9W+coz2kftsiYh5lC+fH5yxA5EOwwYGWU+DrZQuP0HyG1Xu93SC/6DJQmAnJS80N5TUiuxBMQe8hb28liG2A3uAhykXnz0kD1D6tF7CPFZ5EZIkzTERsTwillX3FwMvA+4CrgHOrzY7H/h8df8a4LxqZY4nUibDvKUaBrInIk6v5pd4/ah9hl9rLfC1ap4KqXb9NPgt9vIahriJZA/JfSS3ARurSTCPp4tfp+vR/hI9hLmhpJZkD4o29xb28lfV/QCGgIeAIyld915KlxcgSdJcdiJwVbUSRxdwdWZ+ISK+BVwdERcAm4FzATLzjoi4Gvg+5bL55swcXsrgIuCTwGLguuoG8HHg0xGxidJz4rxZOTJpHP00uIz9fJmS+wUlsd8NDAJLgG8Dp5AsBRYQ/Ip5oaQWZ4GijW1gkA9X90eOIExgL+XkehGSJM1lmdkPPHuM9l8ALz3IPpcDl4/RvhH4pfkrMnMfVYFDagX9NHgH+7kJaFByvwT2AwuqnwkspPSW2EzSS3AB3eaFklqaBYo2tp4GSfm6aPhnk1Ks6AJWEV6EJEmS5pg+mmwnCPLRvG+4SNGg5IIPUXLBS5lfY6SSdGicg6KNbeXAhJjAoytZDxcpLrY4IUmSNOdsJhkAjqDkfMMliOaIW2IuKKn9WKBoYyuAHg5cmIIDa569GVhrxVySJGnO6a1WaltCGdLRxYFcsEkpXLyLLnNBSW2nlgJFRJwbEXdERDMiVo+z3U8i4vaIuC0iNs5mjO3gYrrpAk7gQE+KhcCfAR9gcZ2hSZIkaYasoYvlJEOUWWKPrNqPAtYAX2cBb2FhbfFJ0uGqaw6K71H+fv7dJLY9IzMfmOF42tJwVXw9DeZTelRcTLfVckmSpDlsFd28lwV8pFpadCVwOsFFTo4uqc3VUqDIzDsByjLjmoq1zLcgIUmS1GFW0c1HLUZImmNafQ6KBL4SEd+OiAvH2zAiLoyIjRGxcfv27bMUniRJkiRJmg4z1oMiIm6gTI8w2jsz8/OTfJkXZua2iDgOuD4i7srMb4y1YWZeCVwJsHr16hxrm3azgUHW02ArDt+QJElqR/006KPJbTTYCSwjOI0u1tDlcAxJGmXGChSZ+bJpeI1t1c/7I+JzwPOBMQsUc80HGOAvaBLA0cA24K0MAa7OIUmS1A42MMhlDLEHeBhYCjxIciTJOppcAhYpJGmElh3iERFHRsTRw/eBl1Mm15zzNjDIn9NkL9AAHgF2U6pJ62nUGpskSZLG10+DixjgQob4OSWXg5LPNYGtQA9BH83aYpSkVlTXMqO/GxFbgF8DvhgRX67aHx8R11abHQ98MyK+C9wCfDEzv1RHvLOpnwaX0WA/ZV3rhEcLFQOUC5okSZJaUz8N1jHErTTpArqBnUBU9x8CdlF6U2xmToxKlqRpU9cqHp8DPjdG+zbg7Or+PcCzZjm02l3Gfn4IDFG6Ai6knKT9lCLFqhpjkyRJ0tiG5w7rJ1kMzAeOpOR0Cyh53THVz5MpRYpeXNFOkkZq2SEenWgDg3yJUmFfQOkCuJdSnBig9Ka42HGKkiRJLWUDg7yVIXaSzKcUJX5GyeWGKF84DVGGenRRJj/fQbLGVFySHsO/ii1kPQ2OoFTbu4FFlGLFAKUXxbvocoJMSZKkFrOeBksoK3QspuRtRwIPAsdRvnhaRMnvTgVOoZtLmOcEmZI0Si1DPDS24eVEt1IuagNVexP4JPMsTkiSJLWgrcCJ1f1jgXs50GviCEqP2FfSxZssSkjSuCxQtJAVlEmUTgIeoHRvWVQ9tjghSZLUmoZzuGXAUQQnkdwLLAbOYh5r6LIwIUmT4BCPFnIx3eymVNt7gRMoPSneZh1JkiSpZQ3ncDtJmiRDlBxuPfO4lPkWJyRpkvyXbwsZ7iWxnsajwz3eRbe9JyRJklqYOZwkTQ8LFC1mLfO9mEmSJLUZczhJmjoLFDXqp0EfTTaT9BKOT5QkSWoB5miSVA/noKhJPw3WMcQOkpWUtbDXMUQ/jbpDkyRJ6ljmaJJUH3tQ1OQjDHE3yX6SpcDTCHoI+mhaoZckSZpFI3tM3EOTxwM91fd4PdU25miSNPPsQVGDfhrcQJMkWUKyl+RbJPuqC6MkSZJmx+geE/eTfI/kZzQf3WYpmKNJ0iywQFGDPpo8Dojqv8UEi0huA3qJusOTJEnqGH006al6snYRHEfQBdw1oiCxC3M0SZoNFihqsJnkNIJ9JHuBrG6/IFnjKZEkSZo1m6vhtsOeTtAE7geaJDuqmzmaJM08/9LWoJdgEV38Ol0sBnYDAfyWM0RLkiTNql6CXSMeH08Xv0pwHLAF6CG4hHnmaJI0C5wkswZr6GIdQ/QQ/GZ1UdxBcpGnQ5IkaVYN52VQ5prYBXQTfNCihCTNOntQ1GAV3VzCPHoIK/OSJEk1Mi+TpNbhV/Y1WUW3Fz5JkqQWYF4mSa3BHhSSJEmSJKl2FigkSZIkSVLtLFBIkiRJkqTaWaCQJEmSJEm1s0AhSZIkSZJqZ4FCkiRJkiTVzgKFJEmSJEmqnQUKSZIkSZJUOwsUkiRJkiSpdhYoJEmSJElS7SxQSJIkSZKk2lmgkCRJkiRJtbNAIUmSJEmSameBQpIkSZIk1c4ChSRJkiRJqp0FCkmSJEmSVLt5dQfQNvr7oa8PNm+G3l5YswZWrao7KkmSpF9m3iJJakP2oJiMDRvgda+Dq6+GH/0IfvADWLeuXPwlSZJaRX8/XHRRKUhcdx3Mnw87dpi3SJLaggWKifT3w5/9GWzaBD/+Mdx6K3zzmzA0VL6ZkCRJqlt/P5x7LrzoRXDVVbB7Nzz8MNx0EwwMQE+PeYskqeVZoJjIZZfBtm2wf38pSuzbB/fdB9/5Tuk2KUmSVKcNG2DtWvjXfz2Qr+zZU/KVRgPuuguWLjVvkSS1PAsUE/na1w7c7+oqt0y4554yplOSJKku/f3ly5RduyCi5CnDBgfhoYfKc7t2mbdIklqeBYqJPPIIzKvmEs080D40VMZ3SpIk1aWvrxQiImDBgtI2f37JUwYHyzCPBQvKPBTmLZKkFucqHhNZuLB0k4yAZrP8zISjj3Y2bEmSNPtGrtDxne/A4sXQ3V1ylocfLrlKd3fpTTE0BM9+NrzpTeYtkqSWZ4FiPP39sGhRmWhquPdEZkkAXv7yemOTJEmdpb8frrgCrr8eHvc4OO200jvi/vtLb8+BATjyyFKkyIQVK+C97y3zU0iS1AYsUIznssvKpJhdXaX3BJT7S5fCu95Vb2ySJKkz9PfDRz4CN9wAe/eWFTmgrNDx1KeWL1LmzYNjjy29KhYuhLPPhne/214TkqS2YoHiYPr7SyKwcGHpOvnQQ6Wb5FFHwbJlXvAlSdLM6++Hdevg7rvhmGPKkufbt5cenosWlfu/+Ztw883wrGfBK19Z5powT5EktSELFAfT11e+jejuLt0nFy4sk01lloKFJEnSTOvrKz0m9u+HJUvKEI59++CBB+AJTyircyxaBK9+NVx6ad3RSpI0Ja7icTCbN5fluPbvP1CYgDKu8/TT641NkiR1hs2by9DSpUtLYeLYY0tO8vDDZbiHK3RIkuYQCxQH09sLv/IrpTsllKRgaAiOPx4uuqje2CRJUmfo7S29JJ72tJKLdHfD8uWlMPHgg2WFjksucUiHJGlOsEBxMGvWlCEeq1fDU55SZss+4YQyG7ZJgCRJmg1r1pQeEgsXHujBuW8fvOpVZfjHRz9qXiJJmjMsUBzMqlXlG4mnPhWe/GT4vd+DT3/apbokSWohEXFSRHw9Iu6MiDsi4k+q9ksjYmtE3Fbdzh6xz9sjYlNE3B0RrxjR/tyIuL167oMREVX7woj4bNV+c0ScPGsHOJyP9PSUIadnnVUKE1dcYWFCkjTn1DJJZkT8T+CVwH7gR8AbMnPnGNudCfwt0A18LDPfN6uBrlrlxV+SpNY2BLwlM2+NiKOBb0fE9dVzf52Z60ZuHBGnAucBzwAeD9wQEU/NzAZwBXAhcBNwLXAmcB1wAbAjM58SEecBfwn8/iwcW2E+IknqEHX1oLgeeGZmrgJ+ALx99AYR0Q18GDgLOBX4gyqpkCRJAiAz78vMW6v7e4A7gRXj7HIO8JnMHMjMHwObgOdHxInAksz8VmYm8Cng1SP2uaq6vwF46XDvCkmSNH1qKVBk5lcyc6h6eBOwcozNng9sysx7MnM/8BlKgiBJkvRLqqEXzwZurpoujoj+iPhERPRUbSuAe0fstqVqW1HdH93+mH2q/GUX8Lgx/v8XRsTGiNi4ffv2aTkmSZI6SSvMQfFGSvfJ0Q6WQIzJpECSpM4VEUcB/wL8aWbupgzXeDJwGnAf8IHhTcfYPcdpH2+fxzZkXpmZqzNz9fLlyw/xCCRJ0owVKCLihoj43hi3c0Zs807K2NF/HOslxmj7pWTg0SdMCiRJ6kgRMZ9SnPjHzOwDyMyfZ2YjM5vA31N6ZkL5wuOkEbuvBLZV7SvHaH/MPhExD1gKPDgzRyNJUueasUkyM/Nl4z0fEecDvwO8tBrrOdrBEghJkiQAqrkgPg7cmZl/NaL9xMy8r3r4u8D3qvvXAP8UEX9FmSTzFOCWzGxExJ6IOJ0yROT1wIdG7HM+8C1gLfC1g+QukiRpCupaxeNM4H8AL8rMRw6y2X8Cp0TEE4GtlBm3XzNLIUqSpPbwQuB1wO0RcVvV9g7K5NqnUXpf/gT4Q4DMvCMirga+T+nF+eZqBQ+Ai4BPAospw0+Hh6B+HPh0RGyi9Jw4b4aPSZKkjlRLgQJYDywErq8mwb4pM/8oIh5PWU707MwcioiLgS9Tlhn9RGbeUVO8kiSpBWXmNxl7WOi14+xzOXD5GO0bgWeO0b4POHcKYUqSpEmopUCRmU85SPs24OwRj69lnARDkiRJkiTNDa2wiockSZIkSepwFigkSZIkSVLtLFBIkiRJkqTaWaCQJEmSJEm1s0AhSZIkSZJqZ4FCkiRJkiTVzgKFJEmSJEmqXWRm3TFMu4jYDvx0Ci9xLPDANIXTCjye1jbXjgfm3jF5PK1trh0PzNwxPSEzl8/A62qUMXKRufg5nSm+V4fG9+vQ+H5Nnu/VofH9mryD5iNzskAxVRGxMTNX1x3HdPF4WttcOx6Ye8fk8bS2uXY8MDePqdN5TifP9+rQ+H4dGt+vyfO9OjS+X9PDIR6SJEmSJKl2FigkSZIkSVLtLFCM7cq6A5hmHk9rm2vHA3PvmDye1jbXjgfm5jF1Os/p5PleHRrfr0Pj+zV5vleHxvdrGjgHhSRJkiRJqp09KCRJkiRJUu0sUEiSJEmSpNpZoAAi4tyIuCMimhFx0KVhIuInEXF7RNwWERtnM8ZDcQjHc2ZE3B0RmyLibbMZ46GIiGMi4vqI+GH1s+cg27X0+Zno/Y7ig9Xz/RHxnDrinKxJHM+LI2JXdT5ui4j/q444JysiPhER90fE9w7yfLudn4mOp93Oz0kR8fWIuLP6+/YnY2zTNudoksfTVueo00XEooi4JSK+W53T94yxjed0lIjojojvRMQXxniubX6nZ8ME75WfrREmygn9bD3WJN4vP18jRMSyiNgQEXdV1/FfG/W8n68pmFd3AC3ie8Aa4O8mse0ZmfnADMczVRMeT0R0Ax8GfgvYAvxnRFyTmd+fnRAPyduAr2bm+6p/CL8N+B8H2bYlz88k3++zgFOq2wuAK6qfLecQPj//npm/M+sBHp5PAuuBTx3k+bY5P5VPMv7xQHudnyHgLZl5a0QcDXw7Iq5v198hJnc80F7nqNMNAC/JzIciYj7wzYi4LjNvGrWd5/Sx/gS4E1gyxnPt9Ds9G8Z7r8DP1mjj5YR+tn7ZRDm0n68D/hb4UmaujYgFwBGjnvfzNQX2oAAy887MvLvuOKbLJI/n+cCmzLwnM/cDnwHOmfnoDss5wFXV/auAV9cYy+GazPt9DvCpLG4ClkXEibMd6CS10+dnUjLzG8CD42zSTudnMsfTVjLzvsy8tbq/h5Kkrxi1Wduco0kej9pI9bl7qHo4v7o5E/k4ImIl8NvAxw6ySdv8Ts+0SbxXOjR+tnRYImIJ8JvAxwEyc39m7hy1mZ+vKbBAcWgS+EpEfDsiLqw7mClaAdw74vEWWjc5Pj4z74OS1APHHWS7Vj4/k3m/2+mcTDbWX6u6O18XEc+YndBmTDudn8lqy/MTEScDzwZuHvVUW56jcY4H2vQcdaqqC/5twP3A9ZnpOR3f3wBvBZoHeb4tf6dnyETvFfjZGmminNDP1mNNJof281U8CdgO/D/VkKuPRcSRo7bx8zUFHTPEIyJuAE4Y46l3ZubnJ/kyL8zMbRFxHHB9RNxVfUs566bheGKMttq+6RnveA7hZVrm/IxhMu93S52TCUwm1luBJ1Tdnc8G/helq1u7aqfzMxlteX4i4ijgX4A/zczdo58eY5eWPkcTHE9bnqNOlpkN4LSIWAZ8LiKemZkj54HxnFYi4neA+zPz2xHx4oNtNkZbS/9Oz4RJvld+th5ropzQz9ZjTfR++fk6YB7wHOCPM/PmiPhbyvDzd4/Yxs/XFHRMD4rMfFlmPnOM22SLE2Tmturn/cDnKN3cazENx7MFOGnE45XAtumPdHImOJ6fD3eLqn7ef5DXaJnzM4bJvN8tdU4mMGGsmbl7uLtzZl4LzI+IY2cvxGnXTudnQu14fqpx/f8C/GNm9o2xSVudo4mOpx3PkYqqu++NwJmj2j2nB7wQeFVE/IQyTPAlEfEPo7Zpq9/pGTThe+Vn67EmkRP62RphovfLz9djbAG2jOght4FSsBi9jZ+vw9QxBYqpiogjq4nMqLrxvJwyGWW7+k/glIh4YjW5y3nANTXHdDDXAOdX988HfqkI0wbnZzLv9zXA66uZf08Hdg0PbWlBEx5PRJwQEVHdfz7l780vZj3S6dNO52dC7XZ+qlg/DtyZmX91kM3a5hxN5nja7Rx1uohYXvWcICIWAy8D7hq1jee0kplvz8yVmXky5Rrytcx87ajN2uZ3eiZN5r3ys3XAJHNCP1uVybxffr4OyMyfAfdGxK9UTS8FRk9w7edrCjpmiMd4IuJ3gQ8By4EvRsRtmfmKiHg88LHMPBs4ntJdE8r79k+Z+aXagh7HZI4nM4ci4mLgy0A38InMvKPGsMfzPuDqiLgA2AycC9BO5+dg73dE/FH1/EeBa4GzgU3AI8Ab6op3IpM8nrXARRExBOwFzsvMlu3eFhH/DLwYODYitgB/Tpnkru3OD0zqeNrq/FC+QXwdcHuUMf4A7wB6oS3P0WSOp93OUac7EbgqyipHXcDVmfmFdv67WId2vS7Wwc/WQY2ZE/rZOqjJvF9+vh7rj4F/rL6kuwd4g5+v6ROd/dmSJEmSJEmtwCEekiRJkiSpdhYoJEmSJElS7SxQSJIkSZKk2lmgkCRJkiRJtbNAIUmSJEmSameBQtKkVetgfyYifhQR34+IayPiqYf5WvMj4tvTHaMkSWpfEfHQJLfbEBFPqu4fFRF/V+Und0TENyLiBVOM49vVMpJjPbcuIl4yldeXNLZ5dQcgqT1EWSD7c8BVmXle1XYaZf3sHxzGS/4X4D+mL0JJktQJIuIZQHdm3lM1fQz4MXBKZjarwsXTp/D6JwNbM3P/QTb5EPD3wNcO9/8haWz2oJA0WWcAg5n50eGGzLwN6I6IG6tvMu6KiH+sihlExE8i4j0RcWtE3B4RTxvxemcC10XEkRHxxYj4bkR8LyJ+f3YPS5IktZqIePHB8gvgfwM+X233ZOAFwLsyswmQmfdk5hcj4uSIuDMi/r7qWfGViFhc7XdjRPxlRNwSET+IiN8Y8b8/C/hSRHRHxCer/OT2iPg/qtf/KfC4iDhhlt4OqWNYoJA0Wc8EDjYk49nAnwKnAk8CXjjiuQcy8znAFcAlI9rPAG6kFCq2ZeazMvOZwJemOW5JktSeDpZfvJADOckzgNsys3GQ1zgF+HBmPgPYCfzXEc/9/+3dO2sUURTA8f9R0XwAC92kEFErQySijQZTKCaVpLCzVBD8BCLxAYKNnaWFFrbaiYjgA6xClEAMEfEBIhYKvopICjkWMwsTyJrdZcmY8P81e/fOzLl3uzNnZu/dlJkHyzEuVfrHKPKRfUB/Zu7NzEHgVuWclyzNdyT1gAUKSb0wlZmfyicXM8COyrF75eeLZn9ENIBvmbkAzAJHy6cYI5n5c/WmLUmS/mOt8ovtwNc2Y3wo3/iESi5SWi5H2QwMlH8feQ/sjIgbETEG/Kpc+wVodPRrJK3IAoWkds0B+1scW6y0/7B0fZvFZfrHgYcAmfmmjDsLXIuIi72asCRJWtNa5Re/gb6yPQcMRUSr+5pOc5QR4DlAZn4Hhije+DxHsdZFU185D0k9ZIFCUrseA1si4kyzIyIOAEe6iDUGPChjNICFzLwDXAeGezBXSZK0fs0DuwAy8x0wDVyprIG1OyJOdBm7mqNsBTZk5l1gkqU5yh7gVZdjSGrBAoWktmRmAhPAseY2XsBl4HMncSJiI8Uq26/LrkFgKiJmgAvA1d7NWpIkrUP3gdHK99PANuBtRMxS7LDRUX5SMQo8K9v9wNMyR7kNnIdiq3SKAsl0l2NIaiGKew5JWh0RcRg4lZln656LJElae8qdOJ4Ah/6xOGY3cQeAm5k5vsJ5E8BwZk72amxJBQsUkiRJktaUiDgOzGfmxxrGPgk8yswfqz22tN5ZoJAkSZIkSbVzDQpJkiRJklQ7CxSSJEmSJKl2FigkSZIkSVLtLFBIkiRJkqTaWaCQJEmSJEm1+wspGrR/EcW2CAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1, 2, figsize=(18,8))\n",
    "colores = int(np.amax(data['Enf(mes)_D'].values)+1)\n",
    "\n",
    "for i in range(colores):\n",
    "    indep = data['Cnn/s'].loc[data['Enf(mes)_D']==i].values\n",
    "    dep = data['Mwd/TnU'].loc[data['Enf(mes)_D']==i].values\n",
    "    ax[0].plot(indep,dep, 'o', alpha=0.5, c=cmap(i/colores))\n",
    "    \n",
    "    indepL = np.log(df['Cnn/s']).loc[data['Enf(mes)_D']==i].values\n",
    "    depL = df['Mwd/TnU'].loc[data['Enf(mes)_D']==i].values\n",
    "    ax[1].plot(indepL,depL, 'o', alpha=0.5, c=cmap(i/colores))\n",
    "    \n",
    "ax[0].set_title('Regresión lineal')\n",
    "ax[0].set_xlabel('Cnn/s')\n",
    "ax[0].set_ylabel('Mwd/TnU')\n",
    "\n",
    "ax[1].set_title('Regresión logarítmica')\n",
    "ax[1].set_xlabel('ln(Cnn/s)')\n",
    "ax[1].set_ylabel('Mwd/Tnu')\n",
    "\n",
    "fig.suptitle('Cuentas/segundo vs quemado (según enfriamiento)')\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9e0fd531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1, 2, figsize=(18,8))\n",
    "colores = int(np.amax(data['Enr(%)_D'].values)+1)\n",
    "\n",
    "for i in range(colores):\n",
    "    indep = data['Cnn/s'].loc[data['Enr(%)_D']==i].values\n",
    "    dep = data['Mwd/TnU'].loc[data['Enr(%)_D']==i].values\n",
    "    ax[0].plot(indep,dep, 'o', alpha=0.5, c=cmap(i/colores))\n",
    "\n",
    "    indepL = np.log(df['Cnn/s']).loc[data['Enr(%)_D']==i].values\n",
    "    depL = df['Mwd/TnU'].loc[data['Enr(%)_D']==i].values\n",
    "    ax[1].plot(indepL,depL, 'o', alpha=0.5, c=cmap(i/colores))\n",
    "\n",
    "ax[0].set_title('Regresión lineal')\n",
    "ax[0].set_xlabel('Cnn/s')\n",
    "ax[0].set_ylabel('Mwd/TnU')\n",
    "\n",
    "ax[1].set_title('Regresión logarítmica')\n",
    "ax[1].set_xlabel('ln(Cnn/s)')\n",
    "ax[1].set_ylabel('Mwd/Tnu')\n",
    "\n",
    "fig.suptitle('Cuentas/segundo vs quemado (según enriquecimiento)')\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca88a1b",
   "metadata": {},
   "source": [
    "### Mínimos cuadrados con todas las variables: $\\beta = (X^{T}X)^{-1}X^{T}Y$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce149c36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.345e-16 8.662e-01 1.011e-01 4.736e-01 5.258e-01]\n",
      "\n",
      "[-48849.678   9076.753   1238.675   4287.401  15351.33 ]\n"
     ]
    }
   ],
   "source": [
    "X = np.array([np.ones(data['Cnn/s'].shape[0]), data['Cnn/s'], data['Ciclos'], data['Enf(mes)'], data['Enr(%)']]).T\n",
    "B = np.linalg.inv(X.T @ X) @ X.T @ data['Mwd/TnU']\n",
    "XL = np.array([np.ones(df['Cnn/s'].shape[0]), np.log(df['Cnn/s']), np.log(df['Ciclos']), \\\n",
    "               np.log(df['Enf(mes)']), np.log(df['Enr(%)'])]).T\n",
    "BL = np.linalg.inv(XL.T @ XL) @ XL.T @ df['Mwd/TnU']\n",
    "\n",
    "print(B)\n",
    "print()\n",
    "print(BL)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af2042ac",
   "metadata": {},
   "source": [
    "Entonces:\n",
    "\n",
    "$Mwd/Tnu \\; = \\; B[0] + B[1] \\cdot Cnn/s + B[2] \\cdot Ciclos + B[3] \\cdot Enf(mes) + B[4] \\cdot Enr(\\%)$\n",
    "\n",
    "$Mwd/Tnu \\; = \\; BL[0] + BL[1] \\cdot \\ln(Cnn/s) + BL[2] \\cdot \\ln(Ciclos) + BL[3] \\cdot \\ln(Enf(mes)) + BL[4] \\cdot \\ln(Enr(\\%))$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83100af1",
   "metadata": {},
   "source": [
    "# Método LOOCV (Leave One Out Cross Validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e70caaf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dd659b9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESIÓN LINEAL\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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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>Excluido</th>\n",
       "      <th>Predicción</th>\n",
       "      <th>Validación</th>\n",
       "      <th>Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BL09</td>\n",
       "      <td>0.214449</td>\n",
       "      <td>0.371791</td>\n",
       "      <td>-42.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BT18</td>\n",
       "      <td>0.783182</td>\n",
       "      <td>0.676038</td>\n",
       "      <td>15.85%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BR03</td>\n",
       "      <td>0.890665</td>\n",
       "      <td>0.734825</td>\n",
       "      <td>21.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BP08</td>\n",
       "      <td>2.099059</td>\n",
       "      <td>1.391645</td>\n",
       "      <td>50.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BM09</td>\n",
       "      <td>1.317191</td>\n",
       "      <td>1.141534</td>\n",
       "      <td>15.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BK49</td>\n",
       "      <td>0.875361</td>\n",
       "      <td>0.925823</td>\n",
       "      <td>-5.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BJ12</td>\n",
       "      <td>1.327539</td>\n",
       "      <td>1.295909</td>\n",
       "      <td>2.44%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BH08</td>\n",
       "      <td>1.033677</td>\n",
       "      <td>1.154497</td>\n",
       "      <td>-10.47%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BS40</td>\n",
       "      <td>-1.181718</td>\n",
       "      <td>-1.898310</td>\n",
       "      <td>-37.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BS19</td>\n",
       "      <td>0.444042</td>\n",
       "      <td>0.511959</td>\n",
       "      <td>-13.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>L16</td>\n",
       "      <td>-1.511240</td>\n",
       "      <td>-1.064683</td>\n",
       "      <td>41.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BG14</td>\n",
       "      <td>1.220369</td>\n",
       "      <td>1.296976</td>\n",
       "      <td>-5.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BG31</td>\n",
       "      <td>0.645525</td>\n",
       "      <td>0.993114</td>\n",
       "      <td>-35.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BC05</td>\n",
       "      <td>0.006992</td>\n",
       "      <td>0.186156</td>\n",
       "      <td>-96.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BB40</td>\n",
       "      <td>-0.507012</td>\n",
       "      <td>-0.592980</td>\n",
       "      <td>-14.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BA20</td>\n",
       "      <td>-0.533706</td>\n",
       "      <td>-0.412441</td>\n",
       "      <td>29.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>L32</td>\n",
       "      <td>-1.408192</td>\n",
       "      <td>-1.439762</td>\n",
       "      <td>-2.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>L42</td>\n",
       "      <td>-1.431311</td>\n",
       "      <td>-1.577885</td>\n",
       "      <td>-9.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>T06</td>\n",
       "      <td>-0.595460</td>\n",
       "      <td>-0.787075</td>\n",
       "      <td>-24.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>N16</td>\n",
       "      <td>-0.153110</td>\n",
       "      <td>-0.247843</td>\n",
       "      <td>-38.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BR47</td>\n",
       "      <td>0.459560</td>\n",
       "      <td>0.478313</td>\n",
       "      <td>-3.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>BP45</td>\n",
       "      <td>0.326976</td>\n",
       "      <td>0.507677</td>\n",
       "      <td>-35.59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>BN64</td>\n",
       "      <td>0.492350</td>\n",
       "      <td>0.655030</td>\n",
       "      <td>-24.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>BL15</td>\n",
       "      <td>0.220515</td>\n",
       "      <td>0.362487</td>\n",
       "      <td>-39.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BL21</td>\n",
       "      <td>0.538256</td>\n",
       "      <td>0.749166</td>\n",
       "      <td>-28.15%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Excluido  Predicción  Validación    Error\n",
       "0      BL09    0.214449    0.371791  -42.32%\n",
       "1      BT18    0.783182    0.676038   15.85%\n",
       "2      BR03    0.890665    0.734825   21.21%\n",
       "3      BP08    2.099059    1.391645   50.83%\n",
       "4      BM09    1.317191    1.141534   15.39%\n",
       "5      BK49    0.875361    0.925823   -5.45%\n",
       "6      BJ12    1.327539    1.295909    2.44%\n",
       "7      BH08    1.033677    1.154497  -10.47%\n",
       "8      BS40   -1.181718   -1.898310  -37.75%\n",
       "9      BS19    0.444042    0.511959  -13.27%\n",
       "10      L16   -1.511240   -1.064683   41.94%\n",
       "11     BG14    1.220369    1.296976   -5.91%\n",
       "12     BG31    0.645525    0.993114  -35.00%\n",
       "13     BC05    0.006992    0.186156  -96.24%\n",
       "14     BB40   -0.507012   -0.592980  -14.50%\n",
       "15     BA20   -0.533706   -0.412441   29.40%\n",
       "16      L32   -1.408192   -1.439762   -2.19%\n",
       "17      L42   -1.431311   -1.577885   -9.29%\n",
       "18      T06   -0.595460   -0.787075  -24.35%\n",
       "19      N16   -0.153110   -0.247843  -38.22%\n",
       "20     BR47    0.459560    0.478313   -3.92%\n",
       "21     BP45    0.326976    0.507677  -35.59%\n",
       "22     BN64    0.492350    0.655030  -24.84%\n",
       "23     BL15    0.220515    0.362487  -39.17%\n",
       "24     BL21    0.538256    0.749166  -28.15%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RMSE = 0.26\n",
      "\n",
      "MAE = 0.19\n",
      "\n",
      "\n",
      "\n",
      "REGRESIÓN LOGARÍTMICA\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Excluido</th>\n",
       "      <th>Predicción</th>\n",
       "      <th>Validación</th>\n",
       "      <th>Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BL09</td>\n",
       "      <td>46924.602336</td>\n",
       "      <td>44910.000</td>\n",
       "      <td>4.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BT18</td>\n",
       "      <td>45219.993301</td>\n",
       "      <td>47477.000</td>\n",
       "      <td>-4.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BR03</td>\n",
       "      <td>47103.763166</td>\n",
       "      <td>47973.000</td>\n",
       "      <td>-1.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BP08</td>\n",
       "      <td>51617.575243</td>\n",
       "      <td>53514.750</td>\n",
       "      <td>-3.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BM09</td>\n",
       "      <td>51063.353396</td>\n",
       "      <td>51404.500</td>\n",
       "      <td>-0.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BK49</td>\n",
       "      <td>50052.614033</td>\n",
       "      <td>49584.500</td>\n",
       "      <td>0.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BJ12</td>\n",
       "      <td>51980.306452</td>\n",
       "      <td>52707.000</td>\n",
       "      <td>-1.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BH08</td>\n",
       "      <td>50747.005998</td>\n",
       "      <td>51513.875</td>\n",
       "      <td>-1.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BS40</td>\n",
       "      <td>25659.830289</td>\n",
       "      <td>25756.625</td>\n",
       "      <td>-0.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BS19</td>\n",
       "      <td>45671.601801</td>\n",
       "      <td>46092.625</td>\n",
       "      <td>-0.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>L16</td>\n",
       "      <td>36173.243589</td>\n",
       "      <td>32790.125</td>\n",
       "      <td>10.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BG14</td>\n",
       "      <td>51621.643355</td>\n",
       "      <td>52716.000</td>\n",
       "      <td>-2.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BG31</td>\n",
       "      <td>49408.320590</td>\n",
       "      <td>50152.250</td>\n",
       "      <td>-1.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BC05</td>\n",
       "      <td>44038.557134</td>\n",
       "      <td>43343.750</td>\n",
       "      <td>1.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BB40</td>\n",
       "      <td>38473.622566</td>\n",
       "      <td>36770.000</td>\n",
       "      <td>4.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BA20</td>\n",
       "      <td>38188.796888</td>\n",
       "      <td>38293.250</td>\n",
       "      <td>-0.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>L32</td>\n",
       "      <td>29094.691918</td>\n",
       "      <td>29625.500</td>\n",
       "      <td>-1.79%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>L42</td>\n",
       "      <td>26998.177295</td>\n",
       "      <td>28460.125</td>\n",
       "      <td>-5.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>T06</td>\n",
       "      <td>33807.643581</td>\n",
       "      <td>35132.375</td>\n",
       "      <td>-3.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>N16</td>\n",
       "      <td>38861.410863</td>\n",
       "      <td>39682.000</td>\n",
       "      <td>-2.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BR47</td>\n",
       "      <td>46578.549497</td>\n",
       "      <td>45808.750</td>\n",
       "      <td>1.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>BP45</td>\n",
       "      <td>46917.351733</td>\n",
       "      <td>46056.500</td>\n",
       "      <td>1.87%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>BN64</td>\n",
       "      <td>48050.641260</td>\n",
       "      <td>47299.750</td>\n",
       "      <td>1.59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>BL15</td>\n",
       "      <td>46740.223461</td>\n",
       "      <td>44831.500</td>\n",
       "      <td>4.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BL21</td>\n",
       "      <td>48909.281209</td>\n",
       "      <td>48094.000</td>\n",
       "      <td>1.70%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Excluido    Predicción  Validación   Error\n",
       "0      BL09  46924.602336   44910.000   4.49%\n",
       "1      BT18  45219.993301   47477.000  -4.75%\n",
       "2      BR03  47103.763166   47973.000  -1.81%\n",
       "3      BP08  51617.575243   53514.750  -3.55%\n",
       "4      BM09  51063.353396   51404.500  -0.66%\n",
       "5      BK49  50052.614033   49584.500   0.94%\n",
       "6      BJ12  51980.306452   52707.000  -1.38%\n",
       "7      BH08  50747.005998   51513.875  -1.49%\n",
       "8      BS40  25659.830289   25756.625  -0.38%\n",
       "9      BS19  45671.601801   46092.625  -0.91%\n",
       "10      L16  36173.243589   32790.125  10.32%\n",
       "11     BG14  51621.643355   52716.000  -2.08%\n",
       "12     BG31  49408.320590   50152.250  -1.48%\n",
       "13     BC05  44038.557134   43343.750   1.60%\n",
       "14     BB40  38473.622566   36770.000   4.63%\n",
       "15     BA20  38188.796888   38293.250  -0.27%\n",
       "16      L32  29094.691918   29625.500  -1.79%\n",
       "17      L42  26998.177295   28460.125  -5.14%\n",
       "18      T06  33807.643581   35132.375  -3.77%\n",
       "19      N16  38861.410863   39682.000  -2.07%\n",
       "20     BR47  46578.549497   45808.750   1.68%\n",
       "21     BP45  46917.351733   46056.500   1.87%\n",
       "22     BN64  48050.641260   47299.750   1.59%\n",
       "23     BL15  46740.223461   44831.500   4.26%\n",
       "24     BL21  48909.281209   48094.000   1.70%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RMSE = 1308.30\n",
      "\n",
      "MAE = 1073.06\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "\n",
    "# Creo nuevos dataframes vacíos, donde iré guardando los resultados de cada iteración.\n",
    "LOOCV = pd.DataFrame(columns=['Excluido', 'Predicción', 'Validación', 'Error'])\n",
    "LOOCVL = pd.DataFrame(columns=['Excluido', 'Predicción', 'Validación', 'Error'])\n",
    "\n",
    "# Hago una lista con la identificación de cada elemento\n",
    "lista = data['EECC'].unique().tolist()\n",
    "# print(lista)\n",
    "\n",
    "# En cada bucle separo las 4 u 8 medidas de un elemento en \"valid\" y el resto de medidas las guardo en \"entren\"\n",
    "# para hacer las regresiones con ellas, y voy almacenando los resultados en los nuevos dataframes.\n",
    "agrupa = data.groupby(data.EECC)\n",
    "agrupaL = df.groupby(df.EECC)\n",
    "\n",
    "for excluido in lista:\n",
    "    # print(excluido)\n",
    "    \n",
    "    valid = agrupa.get_group(excluido)\n",
    "    valid = np.mean(valid.values[:,5:10], axis=0)\n",
    "    validL = agrupaL.get_group(excluido)\n",
    "    validL = np.mean(validL.values[:,5:], axis=0)\n",
    "    # print(valid)\n",
    "    # print(validL)\n",
    "    \n",
    "    entren = data[data.EECC != excluido]\n",
    "    entrenL = df[df.EECC != excluido]\n",
    "    # print(entren)\n",
    "    # print (entrenL)\n",
    "    \n",
    "    X = np.array([np.ones(entren['Cnn/s'].shape[0]), entren['Cnn/s'], \\\n",
    "                  entren['Ciclos'], entren['Enf(mes)'], entren['Enr(%)']]).T\n",
    "    B = np.linalg.inv(X.T @ X) @ X.T @ entren['Mwd/TnU']\n",
    "    XL = np.array([np.ones(entrenL['Cnn/s'].shape[0]), np.log(entrenL['Cnn/s']), \\\n",
    "                   np.log(entrenL['Ciclos']), np.log(entrenL['Enf(mes)']), \\\n",
    "                   np.log(entrenL['Enr(%)'])]).T\n",
    "    BL = np.linalg.inv(XL.T @ XL) @ XL.T @ entrenL['Mwd/TnU']\n",
    "    \n",
    "    sol = valid[4]\n",
    "    calc = B[0]+B[1]*valid[0]+B[2]*valid[1]+B[3]*valid[2]+B[4]*valid[3]\n",
    "    solL = validL[4]\n",
    "    calcL = BL[0]+BL[1]*np.log(validL[0])+BL[2]*np.log(validL[1])+BL[3]*np.log(validL[2])+BL[4]*np.log(validL[3])\n",
    "    error = ((calc - sol)/sol)*100\n",
    "    errorL = ((calcL - solL)/solL)*100\n",
    "    # print('{0:.2f} %'.format(error))\n",
    "    # print('{0:.2f} %'.format(errorL))\n",
    "    \n",
    "    LOOCV = LOOCV.append({'Excluido': excluido, 'Predicción': calc, 'Validación': sol, \\\n",
    "                          'Error': '{0:.2f}%'.format(error)}, ignore_index = True)\n",
    "    LOOCVL = LOOCVL.append({'Excluido': excluido, 'Predicción': calcL, 'Validación': solL, \\\n",
    "                                'Error': '{0:.2f}%'.format(errorL)}, ignore_index = True)\n",
    "\n",
    "\n",
    "print('REGRESIÓN LINEAL')\n",
    "display(LOOCV)\n",
    "print()\n",
    "rmse = mean_squared_error(LOOCV.values[:,2], LOOCV.values[:,1], squared = False)\n",
    "print('RMSE =', '{0:.2f}'.format(rmse))\n",
    "print()\n",
    "mae = mean_absolute_error(LOOCV.values[:,2], LOOCV.values[:,1])\n",
    "print('MAE =', '{0:.2f}'.format(mae))\n",
    "print()\n",
    "print()\n",
    "print()\n",
    "\n",
    "print('REGRESIÓN LOGARÍTMICA')\n",
    "display(LOOCVL)\n",
    "print()\n",
    "rmse = mean_squared_error(LOOCVL.values[:,2], LOOCVL.values[:,1], squared = False)\n",
    "print('RMSE =', '{0:.2f}'.format(rmse))\n",
    "print()\n",
    "mae = mean_absolute_error(LOOCVL.values[:,2], LOOCVL.values[:,1])\n",
    "print('MAE =', '{0:.2f}'.format(mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4642c28d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE = 0.17\n",
      "\n",
      "MAE = 0.14\n"
     ]
    }
   ],
   "source": [
    "# Calculo el error cuadrático medio y el error absoluto medio con los resultados tipificados.\n",
    "\n",
    "calcLT = stats.zscore(LOOCVL['Predicción'])\n",
    "solLT = stats.zscore(LOOCVL['Validación'])\n",
    "\n",
    "rmse = mean_squared_error(calcLT, solLT, squared = False)\n",
    "mae = mean_absolute_error(calcLT, solLT)\n",
    "print('RMSE =', '{0:.2f}'.format(rmse))\n",
    "print()\n",
    "print('MAE =', '{0:.2f}'.format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e712cf",
   "metadata": {},
   "source": [
    "# Regresión con Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f9303d88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a4676635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>Cnn/s_val</th>\n",
       "      <th>Mwd/TnU_val</th>\n",
       "      <th>Mwd/TnU_pred</th>\n",
       "      <th>Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.451293</td>\n",
       "      <td>-1.925407</td>\n",
       "      <td>-1.907614</td>\n",
       "      <td>-0.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.441000</td>\n",
       "      <td>-1.895776</td>\n",
       "      <td>-1.870437</td>\n",
       "      <td>-1.34%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.429678</td>\n",
       "      <td>-1.864131</td>\n",
       "      <td>-1.866085</td>\n",
       "      <td>0.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.292855</td>\n",
       "      <td>-0.882885</td>\n",
       "      <td>-0.881307</td>\n",
       "      <td>-0.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158018</td>\n",
       "      <td>-1.545069</td>\n",
       "      <td>-1.558642</td>\n",
       "      <td>0.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.149636</td>\n",
       "      <td>-1.533691</td>\n",
       "      <td>-1.558145</td>\n",
       "      <td>1.59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.054353</td>\n",
       "      <td>-1.448592</td>\n",
       "      <td>-1.449994</td>\n",
       "      <td>0.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.924588</td>\n",
       "      <td>-0.349772</td>\n",
       "      <td>-0.428070</td>\n",
       "      <td>22.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.923265</td>\n",
       "      <td>-0.371817</td>\n",
       "      <td>-0.434276</td>\n",
       "      <td>16.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.913266</td>\n",
       "      <td>-0.358898</td>\n",
       "      <td>-0.434276</td>\n",
       "      <td>21.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.899444</td>\n",
       "      <td>-0.317416</td>\n",
       "      <td>-0.338560</td>\n",
       "      <td>6.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.897827</td>\n",
       "      <td>-0.315401</td>\n",
       "      <td>-0.338560</td>\n",
       "      <td>7.34%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.777032</td>\n",
       "      <td>-0.170092</td>\n",
       "      <td>-0.183477</td>\n",
       "      <td>7.87%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.227756</td>\n",
       "      <td>0.258958</td>\n",
       "      <td>0.168805</td>\n",
       "      <td>-34.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.199524</td>\n",
       "      <td>0.189741</td>\n",
       "      <td>0.195107</td>\n",
       "      <td>2.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.118209</td>\n",
       "      <td>-1.098003</td>\n",
       "      <td>-1.083522</td>\n",
       "      <td>-1.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.103873</td>\n",
       "      <td>0.273655</td>\n",
       "      <td>0.212558</td>\n",
       "      <td>-22.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.094021</td>\n",
       "      <td>0.282307</td>\n",
       "      <td>0.211104</td>\n",
       "      <td>-25.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.166170</td>\n",
       "      <td>0.492328</td>\n",
       "      <td>0.514815</td>\n",
       "      <td>4.57%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.326005</td>\n",
       "      <td>0.646289</td>\n",
       "      <td>0.687690</td>\n",
       "      <td>6.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.555758</td>\n",
       "      <td>1.074865</td>\n",
       "      <td>0.974912</td>\n",
       "      <td>-9.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.580976</td>\n",
       "      <td>0.844221</td>\n",
       "      <td>0.812264</td>\n",
       "      <td>-3.79%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.691552</td>\n",
       "      <td>1.063724</td>\n",
       "      <td>1.057478</td>\n",
       "      <td>-0.59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.777571</td>\n",
       "      <td>0.971869</td>\n",
       "      <td>0.923479</td>\n",
       "      <td>-4.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.831609</td>\n",
       "      <td>1.258930</td>\n",
       "      <td>1.188245</td>\n",
       "      <td>-5.61%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.858371</td>\n",
       "      <td>1.022241</td>\n",
       "      <td>1.001323</td>\n",
       "      <td>-2.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.142898</td>\n",
       "      <td>1.322814</td>\n",
       "      <td>1.292685</td>\n",
       "      <td>-2.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.343022</td>\n",
       "      <td>1.151075</td>\n",
       "      <td>1.119200</td>\n",
       "      <td>-2.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.346698</td>\n",
       "      <td>0.790412</td>\n",
       "      <td>0.790596</td>\n",
       "      <td>0.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.467126</td>\n",
       "      <td>0.749048</td>\n",
       "      <td>0.874299</td>\n",
       "      <td>16.72%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cnn/s_val  Mwd/TnU_val  Mwd/TnU_pred    Error\n",
       "0   -1.451293    -1.925407     -1.907614   -0.92%\n",
       "1   -1.441000    -1.895776     -1.870437   -1.34%\n",
       "2   -1.429678    -1.864131     -1.866085    0.10%\n",
       "3   -1.292855    -0.882885     -0.881307   -0.18%\n",
       "4   -1.158018    -1.545069     -1.558642    0.88%\n",
       "5   -1.149636    -1.533691     -1.558145    1.59%\n",
       "6   -1.054353    -1.448592     -1.449994    0.10%\n",
       "7   -0.924588    -0.349772     -0.428070   22.39%\n",
       "8   -0.923265    -0.371817     -0.434276   16.80%\n",
       "9   -0.913266    -0.358898     -0.434276   21.00%\n",
       "10  -0.899444    -0.317416     -0.338560    6.66%\n",
       "11  -0.897827    -0.315401     -0.338560    7.34%\n",
       "12  -0.777032    -0.170092     -0.183477    7.87%\n",
       "13  -0.227756     0.258958      0.168805  -34.81%\n",
       "14  -0.199524     0.189741      0.195107    2.83%\n",
       "15  -0.118209    -1.098003     -1.083522   -1.32%\n",
       "16  -0.103873     0.273655      0.212558  -22.33%\n",
       "17  -0.094021     0.282307      0.211104  -25.22%\n",
       "18   0.166170     0.492328      0.514815    4.57%\n",
       "19   0.326005     0.646289      0.687690    6.41%\n",
       "20   0.555758     1.074865      0.974912   -9.30%\n",
       "21   0.580976     0.844221      0.812264   -3.79%\n",
       "22   0.691552     1.063724      1.057478   -0.59%\n",
       "23   0.777571     0.971869      0.923479   -4.98%\n",
       "24   0.831609     1.258930      1.188245   -5.61%\n",
       "25   0.858371     1.022241      1.001323   -2.05%\n",
       "26   1.142898     1.322814      1.292685   -2.28%\n",
       "27   1.343022     1.151075      1.119200   -2.77%\n",
       "28   1.346698     0.790412      0.790596    0.02%\n",
       "29   1.467126     0.749048      0.874299   16.72%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión del entrenamiento = 99.93%\n",
      "Precisión de la validación = 99.77%\n",
      "\n",
      "RMSE = 0.05\n",
      "\n",
      "MAE = 0.04\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "\n",
    "# X almacena las variables independientes\n",
    "# Y almacena la variable dependiente\n",
    "X = data.iloc[:, 5:9].values\n",
    "Y = data.iloc[:, 9].values\n",
    "# print(X)\n",
    "# print(Y)\n",
    "\n",
    "# División de los datos en conjuntos de entrenamiento y validación\n",
    "Xent, Xval, Yent, Yval = train_test_split(X, Y, test_size = 0.2)\n",
    "# print(len(Xent), len(Yent))\n",
    "# print(len(Xval), len(Yval))\n",
    "\n",
    "# Aplicar el algoritmo y entrenar\n",
    "bosque = RandomForestRegressor(n_estimators = 2000, max_depth = 6)\n",
    "bosque.fit(Xent, Yent)\n",
    "\n",
    "# Predicción\n",
    "Ypred = bosque.predict(Xval)\n",
    "\n",
    "# Error relativo\n",
    "Error = pd.DataFrame(columns=['Error'])\n",
    "for n in range(len(Ypred)):\n",
    "    error = ((Ypred[n] - Yval[n])/Yval[n])*100\n",
    "    Error = Error.append({'Error': '{0:.2f}%'.format(error)}, ignore_index = True)\n",
    "\n",
    "\n",
    "# Ordeno los valores de menor a mayor abscisa para que la representación se vea mejor\n",
    "Pred = pd.DataFrame({'Cnn/s_val': Xval[:, 0], 'Mwd/TnU_val': Yval, 'Mwd/TnU_pred': Ypred, 'Error': Error['Error']})\n",
    "Pred = Pred.sort_values('Cnn/s_val')\n",
    "Pred = Pred.reset_index(drop=True)\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(10,10))\n",
    "ax.plot(Xent[:, 0], Yent, 'o', alpha=0.25, label='Observaciones')\n",
    "ax.plot(Pred['Cnn/s_val'], Pred['Mwd/TnU_val'], 'x', color='red', label='Validación')\n",
    "ax.plot(Pred['Cnn/s_val'], Pred['Mwd/TnU_pred'], alpha=0.75, label='Predicción')\n",
    "ax.set_title('Cuentas/segundo vs quemado')\n",
    "ax.set_xlabel('Cnn/s')\n",
    "ax.set_ylabel('Mwd/Tnu')\n",
    "ax.legend(fontsize=10)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "\n",
    "display(Pred)\n",
    "\n",
    "precEnt = bosque.score(Xent, Yent)\n",
    "precVal = bosque.score(Xval, Yval)\n",
    "print('Precisión del entrenamiento =', '{0:.2f}%'.format(100*precEnt))\n",
    "print('Precisión de la validación =', '{0:.2f}%'.format(100*precVal))\n",
    "print()\n",
    "rmse = mean_squared_error(Yval, Ypred, squared = False)\n",
    "print('RMSE =', '{0:.2f}'.format(rmse))\n",
    "print()\n",
    "mae = mean_absolute_error(Yval, Ypred)\n",
    "print('MAE =', '{0:.2f}'.format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00ebf35",
   "metadata": {},
   "source": [
    "Los resultados son tan extraordinariamente buenos porque he hecho completamente aleatoria la división de los conjuntos de entrenamiento y valicación, y hay datos de un mismo elemento en los dos conjuntos que son prácticamente idénticos.\n",
    "\n",
    "Estoy validando prácticamente con los mimos datos con los que entreno."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca7a90c1",
   "metadata": {},
   "source": [
    "### Repito la regresión separando en el conjunto de validación los datos completos de 5 elementos aleatorios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "62014181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>Cnn/s_val</th>\n",
       "      <th>Mwd/TnU_val</th>\n",
       "      <th>Mwd/TnU_pred</th>\n",
       "      <th>Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.303516</td>\n",
       "      <td>-0.905760</td>\n",
       "      <td>-0.704404</td>\n",
       "      <td>-22.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.298075</td>\n",
       "      <td>-0.893908</td>\n",
       "      <td>-0.704404</td>\n",
       "      <td>-21.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.296826</td>\n",
       "      <td>-0.891300</td>\n",
       "      <td>-0.704404</td>\n",
       "      <td>-20.97%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.292855</td>\n",
       "      <td>-0.882885</td>\n",
       "      <td>-0.704404</td>\n",
       "      <td>-20.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.199557</td>\n",
       "      <td>-0.699294</td>\n",
       "      <td>-0.704112</td>\n",
       "      <td>0.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.191396</td>\n",
       "      <td>-0.684479</td>\n",
       "      <td>-0.704112</td>\n",
       "      <td>2.87%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.190000</td>\n",
       "      <td>-0.681990</td>\n",
       "      <td>-0.704112</td>\n",
       "      <td>3.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.175957</td>\n",
       "      <td>-0.656982</td>\n",
       "      <td>-0.704111</td>\n",
       "      <td>7.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.357104</td>\n",
       "      <td>0.930387</td>\n",
       "      <td>-1.044221</td>\n",
       "      <td>-212.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.368059</td>\n",
       "      <td>0.938565</td>\n",
       "      <td>-1.044221</td>\n",
       "      <td>-211.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.490766</td>\n",
       "      <td>1.028641</td>\n",
       "      <td>-1.042990</td>\n",
       "      <td>-201.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.555758</td>\n",
       "      <td>1.074865</td>\n",
       "      <td>-1.042980</td>\n",
       "      <td>-197.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.557670</td>\n",
       "      <td>0.382103</td>\n",
       "      <td>0.215733</td>\n",
       "      <td>-43.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.661114</td>\n",
       "      <td>0.445394</td>\n",
       "      <td>0.219600</td>\n",
       "      <td>-50.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.818302</td>\n",
       "      <td>0.537841</td>\n",
       "      <td>0.222188</td>\n",
       "      <td>-58.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.835727</td>\n",
       "      <td>0.547915</td>\n",
       "      <td>0.222188</td>\n",
       "      <td>-59.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.992032</td>\n",
       "      <td>0.509633</td>\n",
       "      <td>0.788452</td>\n",
       "      <td>54.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.022764</td>\n",
       "      <td>1.251819</td>\n",
       "      <td>1.233347</td>\n",
       "      <td>-1.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.039454</td>\n",
       "      <td>1.261775</td>\n",
       "      <td>1.233655</td>\n",
       "      <td>-2.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.127017</td>\n",
       "      <td>0.580627</td>\n",
       "      <td>0.797895</td>\n",
       "      <td>37.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.142898</td>\n",
       "      <td>1.322814</td>\n",
       "      <td>1.217620</td>\n",
       "      <td>-7.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.185025</td>\n",
       "      <td>1.347229</td>\n",
       "      <td>1.217346</td>\n",
       "      <td>-9.64%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.467126</td>\n",
       "      <td>0.749048</td>\n",
       "      <td>0.903587</td>\n",
       "      <td>20.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.717318</td>\n",
       "      <td>0.864844</td>\n",
       "      <td>0.905642</td>\n",
       "      <td>4.72%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cnn/s_val  Mwd/TnU_val  Mwd/TnU_pred     Error\n",
       "0   -1.303516    -0.905760     -0.704404   -22.23%\n",
       "1   -1.298075    -0.893908     -0.704404   -21.20%\n",
       "2   -1.296826    -0.891300     -0.704404   -20.97%\n",
       "3   -1.292855    -0.882885     -0.704404   -20.22%\n",
       "4   -1.199557    -0.699294     -0.704112     0.69%\n",
       "5   -1.191396    -0.684479     -0.704112     2.87%\n",
       "6   -1.190000    -0.681990     -0.704112     3.24%\n",
       "7   -1.175957    -0.656982     -0.704111     7.17%\n",
       "8    0.357104     0.930387     -1.044221  -212.24%\n",
       "9    0.368059     0.938565     -1.044221  -211.26%\n",
       "10   0.490766     1.028641     -1.042990  -201.39%\n",
       "11   0.555758     1.074865     -1.042980  -197.03%\n",
       "12   0.557670     0.382103      0.215733   -43.54%\n",
       "13   0.661114     0.445394      0.219600   -50.70%\n",
       "14   0.818302     0.537841      0.222188   -58.69%\n",
       "15   0.835727     0.547915      0.222188   -59.45%\n",
       "16   0.992032     0.509633      0.788452    54.71%\n",
       "17   1.022764     1.251819      1.233347    -1.48%\n",
       "18   1.039454     1.261775      1.233655    -2.23%\n",
       "19   1.127017     0.580627      0.797895    37.42%\n",
       "20   1.142898     1.322814      1.217620    -7.95%\n",
       "21   1.185025     1.347229      1.217346    -9.64%\n",
       "22   1.467126     0.749048      0.903587    20.63%\n",
       "23   1.717318     0.864844      0.905642     4.72%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión del entrenamiento = 99.97%\n",
      "Precisión de la validación = -6.01%\n",
      "\n",
      "RMSE = 0.85\n",
      "\n",
      "MAE = 0.46\n"
     ]
    }
   ],
   "source": [
    "# Hago una lista con la identificación de cada elemento\n",
    "lista = data['EECC'].unique().tolist()\n",
    "# print(lista)\n",
    "\n",
    "# Selecciono 5 elementos aleatorios y los separo, para generar un data frame de validación y otro de entrenamiento\n",
    "random = np.random.choice(a=len(lista), size=5, replace=False)\n",
    "# print(random)\n",
    "valid = pd.DataFrame()\n",
    "agrupa = data.groupby(data.EECC)\n",
    "for excluidos in random:\n",
    "    val = agrupa.get_group(lista[excluidos])\n",
    "    valid = valid.append(val)\n",
    "entren = data.drop(valid.index)\n",
    "# print(valid)\n",
    "# print(entren)\n",
    "\n",
    "\n",
    "# Repito el proceso anterior\n",
    "Xent = entren.iloc[:, 5:9].values\n",
    "Xval = valid.iloc[:, 5:9].values\n",
    "Yent = entren.iloc[:, 9].values\n",
    "Yval = valid.iloc[:, 9].values\n",
    "\n",
    "# Aplicar el algoritmo y entrenar\n",
    "bosque = RandomForestRegressor(n_estimators = 4000, max_depth = 9)\n",
    "bosque.fit(Xent, Yent)\n",
    "'''\n",
    "EN PRINCIPIO CUANTOS MÁS ÁRBOLES MEJOR, PERO NO HE VISTO DIFERENCIA SIGNITICATIVA EN UN RANGO DE ENTRE 200 A 50000.\n",
    "TAMPOCO APRECIO DIFERENCIAS ENTRE PROFUNDIADES DE VALOR ENTRE 4 Y 12.\n",
    "¿HAY ALGÚN MODO DE ELEGIR EL NÚMERO DE ESTIMADORES Y DE PROFUNDIDAD MÁS ÓPTIMOS?\n",
    "'''\n",
    "\n",
    "# Predicción\n",
    "Ypred = bosque.predict(Xval)\n",
    "\n",
    "# Error relativo\n",
    "Error = pd.DataFrame(columns=['Error'])\n",
    "for n in range(len(Ypred)):\n",
    "    error = ((Ypred[n] - Yval[n])/Yval[n])*100\n",
    "    Error = Error.append({'Error': '{0:.2f}%'.format(error)}, ignore_index = True)\n",
    "\n",
    "\n",
    "# Ordeno los valores de menor a mayor abscisa para que la representación se vea mejor\n",
    "Pred = pd.DataFrame({'Cnn/s_val': Xval[:, 0], 'Mwd/TnU_val': Yval, 'Mwd/TnU_pred': Ypred, 'Error': Error['Error']})\n",
    "Pred = Pred.sort_values('Cnn/s_val')\n",
    "Pred = Pred.reset_index(drop=True)\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(10,10))\n",
    "ax.plot(Xent[:, 0], Yent, 'o', alpha=0.25, label='Observaciones')\n",
    "ax.plot(Pred['Cnn/s_val'], Pred['Mwd/TnU_val'], 'x', color='red', label='Validación')\n",
    "ax.plot(Pred['Cnn/s_val'], Pred['Mwd/TnU_pred'], alpha=0.75, label='Predicción')\n",
    "ax.set_title('Cuentas/segundo vs quemado')\n",
    "ax.set_xlabel('Cnn/s')\n",
    "ax.set_ylabel('Mwd/Tnu')\n",
    "ax.legend(fontsize=10)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "\n",
    "display(Pred)\n",
    "\n",
    "precEnt = bosque.score(Xent, Yent)\n",
    "precVal = bosque.score(Xval, Yval)\n",
    "print('Precisión del entrenamiento =', '{0:.2f}%'.format(100*precEnt))\n",
    "print('Precisión de la validación =', '{0:.2f}%'.format(100*precVal))\n",
    "print()\n",
    "rmse = mean_squared_error(Yval, Ypred, squared = False)\n",
    "print('RMSE =', '{0:.2f}'.format(rmse))\n",
    "print()\n",
    "mae = mean_absolute_error(Yval, Ypred)\n",
    "print('MAE =', '{0:.2f}'.format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e274a63e",
   "metadata": {},
   "source": [
    "#### Algunas veces hace buenas predicciones y otras tiene una precisión malísima"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f308457",
   "metadata": {},
   "source": [
    "### Repito la regresión separando en el conjunto de validación los datos completos de 1 solo elemento aleatorio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e464d9ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>Cnn/s_val</th>\n",
       "      <th>Mwd/TnU_val</th>\n",
       "      <th>Mwd/TnU_pred</th>\n",
       "      <th>Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.062867</td>\n",
       "      <td>1.296502</td>\n",
       "      <td>1.004075</td>\n",
       "      <td>-22.56%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.254095</td>\n",
       "      <td>1.378993</td>\n",
       "      <td>1.004075</td>\n",
       "      <td>-27.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.311956</td>\n",
       "      <td>1.403409</td>\n",
       "      <td>1.004075</td>\n",
       "      <td>-28.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.516418</td>\n",
       "      <td>1.487678</td>\n",
       "      <td>1.004075</td>\n",
       "      <td>-32.51%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cnn/s_val  Mwd/TnU_val  Mwd/TnU_pred    Error\n",
       "0   2.062867     1.296502      1.004075  -22.56%\n",
       "1   2.254095     1.378993      1.004075  -27.19%\n",
       "2   2.311956     1.403409      1.004075  -28.45%\n",
       "3   2.516418     1.487678      1.004075  -32.51%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión del entrenamiento = 99.98%\n",
      "Precisión de la validación = -3235.03%\n",
      "\n",
      "RMSE = 0.39\n",
      "\n",
      "MAE = 0.39\n"
     ]
    }
   ],
   "source": [
    "# Hago una lista con la identificación de cada elemento\n",
    "lista = data['EECC'].unique().tolist()\n",
    "# print(lista)\n",
    "\n",
    "# Selecciono 1 elemento aleatorio y lo separo, para generar un data frame de validación y otro de entrenamiento.\n",
    "excluido = np.random.choice(a=len(lista), size=1)\n",
    "agrupa = data.groupby(data.EECC)\n",
    "valid = agrupa.get_group(lista[int(excluido)])\n",
    "valid = pd.DataFrame(valid)\n",
    "entren = data.drop(valid.index)\n",
    "# print(valid)\n",
    "# print(entren)\n",
    "\n",
    "\n",
    "# Repito el proceso anterior\n",
    "Xent = entren.iloc[:, 5:9].values\n",
    "Xval = valid.iloc[:, 5:9].values\n",
    "Yent = entren.iloc[:, 9].values\n",
    "Yval = valid.iloc[:, 9].values\n",
    "\n",
    "# Aplicar el algoritmo y entrenar\n",
    "bosque = RandomForestRegressor(n_estimators = 4000, max_depth = 10)\n",
    "bosque.fit(Xent, Yent)\n",
    "\n",
    "# Predicción\n",
    "Ypred = bosque.predict(Xval)\n",
    "\n",
    "# Error relativo\n",
    "Error = pd.DataFrame(columns=['Error'])\n",
    "for n in range(len(Ypred)):\n",
    "    error = ((Ypred[n] - Yval[n])/Yval[n])*100\n",
    "    Error = Error.append({'Error': '{0:.2f}%'.format(error)}, ignore_index = True)\n",
    "\n",
    "\n",
    "# Ordeno los valores de menor a mayor abscisa para que la representación se vea mejor\n",
    "Pred = pd.DataFrame({'Cnn/s_val': Xval[:, 0], 'Mwd/TnU_val': Yval, 'Mwd/TnU_pred': Ypred, 'Error': Error['Error']})\n",
    "Pred = Pred.sort_values('Cnn/s_val')\n",
    "Pred = Pred.reset_index(drop=True)\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(10,10))\n",
    "ax.plot(Xent[:, 0], Yent, 'o', alpha=0.25, label='Observaciones')\n",
    "ax.plot(Pred['Cnn/s_val'], Pred['Mwd/TnU_val'], 'x', color='red', label='Validación')\n",
    "ax.plot(Pred['Cnn/s_val'], Pred['Mwd/TnU_pred'], alpha=0.75, label='Predicción')\n",
    "ax.set_title('Cuentas/segundo vs quemado')\n",
    "ax.set_xlabel('Cnn/s')\n",
    "ax.set_ylabel('Mwd/Tnu')\n",
    "ax.legend(fontsize=10)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "\n",
    "display(Pred)\n",
    "\n",
    "precEnt = bosque.score(Xent, Yent)\n",
    "precVal = bosque.score(Xval, Yval)\n",
    "print('Precisión del entrenamiento =', '{0:.2f}%'.format(100*precEnt))\n",
    "print('Precisión de la validación =', '{0:.2f}%'.format(100*precVal))\n",
    "print()\n",
    "rmse = mean_squared_error(Yval, Ypred, squared = False)\n",
    "print('RMSE =', '{0:.2f}'.format(rmse))\n",
    "print()\n",
    "mae = mean_absolute_error(Yval, Ypred)\n",
    "print('MAE =', '{0:.2f}'.format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3609013f",
   "metadata": {},
   "source": [
    "#### La precisión también es muy variable, pero en general es mucho peor."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7322c687",
   "metadata": {},
   "source": [
    "### Repito la regresión utilizando los datos de los EECC completos just for fun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dd50f035",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataEECC.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e492dca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>Cnn/s_val</th>\n",
       "      <th>Mwd/TnU_val</th>\n",
       "      <th>Mwd/TnU_pred</th>\n",
       "      <th>Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.441800</td>\n",
       "      <td>-1.898310</td>\n",
       "      <td>0.010418</td>\n",
       "      <td>-100.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.181131</td>\n",
       "      <td>-1.577885</td>\n",
       "      <td>-1.176394</td>\n",
       "      <td>-25.44%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.475087</td>\n",
       "      <td>0.749166</td>\n",
       "      <td>0.817940</td>\n",
       "      <td>9.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.891198</td>\n",
       "      <td>1.296976</td>\n",
       "      <td>0.994823</td>\n",
       "      <td>-23.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.097535</td>\n",
       "      <td>1.295909</td>\n",
       "      <td>0.999201</td>\n",
       "      <td>-22.90%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cnn/s_val  Mwd/TnU_val  Mwd/TnU_pred     Error\n",
       "0  -1.441800    -1.898310      0.010418  -100.55%\n",
       "1  -1.181131    -1.577885     -1.176394   -25.44%\n",
       "2   0.475087     0.749166      0.817940     9.18%\n",
       "3   0.891198     1.296976      0.994823   -23.30%\n",
       "4   1.097535     1.295909      0.999201   -22.90%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión del entrenamiento = 95.51%\n",
      "Precisión de la validación = 60.16%\n",
      "\n",
      "RMSE = 0.89\n",
      "\n",
      "MAE = 0.60\n"
     ]
    }
   ],
   "source": [
    "# X almacena las variables independientes\n",
    "# Y almacena la variable dependiente\n",
    "X = dataEECC.iloc[:, 3:7].values\n",
    "Y = dataEECC.iloc[:, 7].values\n",
    "# print(X)\n",
    "# print(Y)\n",
    "\n",
    "# División de los datos en conjuntos de entrenamiento y validación\n",
    "Xent, Xval, Yent, Yval = train_test_split(X, Y, test_size = 0.2)\n",
    "# print(len(Xent), len(Yent))\n",
    "# print(len(Xval), len(Yval))\n",
    "\n",
    "# Aplicar el algoritmo y entrenar\n",
    "bosque = RandomForestRegressor(n_estimators = 5000, max_depth = 4)\n",
    "bosque.fit(Xent, Yent)\n",
    "\n",
    "# Predicción\n",
    "Ypred = bosque.predict(Xval)\n",
    "\n",
    "# Error relativo\n",
    "Error = pd.DataFrame(columns=['Error'])\n",
    "for n in range(len(Ypred)):\n",
    "    error = ((Ypred[n] - Yval[n])/Yval[n])*100\n",
    "    Error = Error.append({'Error': '{0:.2f}%'.format(error)}, ignore_index = True)\n",
    "\n",
    "\n",
    "# Ordeno los valores de menor a mayor abscisa para que la representación se vea mejor\n",
    "Pred = pd.DataFrame({'Cnn/s_val': Xval[:, 0], 'Mwd/TnU_val': Yval, 'Mwd/TnU_pred': Ypred, 'Error': Error['Error']})\n",
    "Pred = Pred.sort_values('Cnn/s_val')\n",
    "Pred = Pred.reset_index(drop=True)\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(10,10))\n",
    "ax.plot(Xent[:, 0], Yent, 'o', alpha=0.25, label='Observaciones')\n",
    "ax.plot(Pred['Cnn/s_val'], Pred['Mwd/TnU_val'], 'x', color='red', label='Validación')\n",
    "ax.plot(Pred['Cnn/s_val'], Pred['Mwd/TnU_pred'], alpha=0.75, label='Predicción')\n",
    "ax.set_title('Cuentas/segundo vs quemado')\n",
    "ax.set_xlabel('Cnn/s')\n",
    "ax.set_ylabel('Mwd/Tnu')\n",
    "ax.legend(fontsize=10)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "\n",
    "display(Pred)\n",
    "\n",
    "precEnt = bosque.score(Xent, Yent)\n",
    "precVal = bosque.score(Xval, Yval)\n",
    "print('Precisión del entrenamiento =', '{0:.2f}%'.format(100*precEnt))\n",
    "print('Precisión de la validación =', '{0:.2f}%'.format(100*precVal))\n",
    "print()\n",
    "rmse = mean_squared_error(Yval, Ypred, squared = False)\n",
    "print('RMSE =', '{0:.2f}'.format(rmse))\n",
    "print()\n",
    "mae = mean_absolute_error(Yval, Ypred)\n",
    "print('MAE =', '{0:.2f}'.format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "547e225f",
   "metadata": {},
   "source": [
    "Parece que la muestra es demasiado pequeña para que puedan hacerse buenas predicciones, al menos con estos métodos."
   ]
  }
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