CalculoQuemadoRev0.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",
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       "      <td>220.94</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>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",
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       "    <tr>\n",
       "      <th>ind</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <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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I6AoAAAAAMCKhKwAAAADAiISuAAAAAAAj2jDrAlidtmy/bvRr7ttx8ejXBNYv/RSwXun/gJVOPwWrg/+rJ8dIVwAAAACAEQldAQAAAABGJHQFAAAAABiR0BUAAGBQVT9cVbdW1S1V9daqelBVnV5V11fVbcPytFnXCQCsbEJXAACAJFX1yCT/Mcl8dz8pySlJnp9ke5Ld3X1Wkt3DNgDAUQldAQAA7rUhyYOrakOSr0vy6SSXJNk17N+V5NIZ1QYArBJCVwAAgCTd/akkr0lye5L9Sf6mu9+TZGN37x+O2Z/kzNlVCQCsBkJXAACAJMNcrZckeUySb0zykKp6wXGcv62q9lTVnoWFheUqEwBYBYSuAAAAE89I8lfdvdDdX03yjiT/IsmBqtqUJMPy4JFO7u6d3T3f3fNzc3NTKxoAWHmErgAAABO3J3lKVX1dVVWSC5LsTXJtkq3DMVuTXDOj+gCAVWLDrAs4ki3br5t1CUuyb8fFsy4BAAAYSXffWFVvS3JTkruTfDjJziQPTXJVVV2eSTD73NlVCQCsBisydAUAAJiF7n5Vklcd1vyVTEa9AgAsiekFAAAAAABGJHQFpqqqHl9VNy96fKGqXl5Vp1fV9VV127A8bda1AgAAAJwIoSswVd39se4+u7vPTvLtSb6c5Ook25Ps7u6zkuwetgEAAABWHaErMEsXJPnL7v5EkkuS7BradyW5dGZVAQAAAJwEoSswS89P8tZhfWN370+SYXnmkU6oqm1Vtaeq9iwsLEypTAAAAIClE7oCM1FVD0zy7CT/43jO6+6d3T3f3fNzc3PLUxwAAADASRC6ArPyzCQ3dfeBYftAVW1KkmF5cGaVAQAAAJwEoSswK5fl3qkFkuTaJFuH9a1Jrpl6RQAAAAAjELoCU1dVX5fkwiTvWNS8I8mFVXXbsG/HLGoDAAAAOFkbZl0AsP5095eTfMNhbXcmuWA2FQEAAACMx0hXAAAAAIARCV0BAAAAAEYkdAUAAAAAGJE5XQEA1qgt268b/Zr7dlw8+jUBAGCtMdIVAAAAAGBEQlcAAAAAgBEJXQEAAAAARiR0BQAAAAAYkdAVAAAAAGBEQlcAAAAAgBEJXQEAAAAARiR0BQAAAAAYkdAVAAAAAGBEQlcAAAAAgBEJXQEAAAAARiR0BQAAAAAYkdAVAAAAAGBExwxdq+rRVfXeqtpbVbdW1cuG9ldX1aeq6ubh8azlLxcAAABg5ZOnwPq2YQnH3J3kFd19U1U9LMmHqur6Yd8vdvdrlq88AAAAgFVJngLr2DFHunb3/u6+aVi/K8neJI9c7sIAAACmqaoev2jk2c1V9YWqenlVnV5V11fVbcPytFnXCqx88hRY345rTteq2pLknCQ3Dk0vraqPVNUbj/bGo6q2VdWeqtqzsLBwUsUCAAAsl+7+WHef3d1nJ/n2JF9OcnWS7Ul2d/dZSXYP2wBLJk+B9WfJoWtVPTTJ25O8vLu/kOR1SR6X5Owk+5P8wpHO6+6d3T3f3fNzc3MjlAwAALDsLkjyl939iSSXJNk1tO9KcunMqgJWHXkKrE9LCl2r6tRMOogru/sdSdLdB7r7nu7+WpLXJzl3+coEAACYqucneeuwvrG79yeTnwsnOXNmVQGrijwF1q9jhq5VVUmuSLK3u1+7qH3TosOek+SW8csDAACYrqp6YJJnJ/kfx3menwID/0CeAuvbhiUcc16SFyb5s6q6eWh7ZZLLqursJJ1kX5IXL0uFAAAA0/XMJDd194Fh+0BVberu/UNYcvBIJ3X3ziQ7k2R+fr6nUyqwgslTYB07Zuja3R9IUkfY9a7xywEAAJi5y3Lv1AJJcm2SrUl2DMtrZlEUsLrIU2B9W/KNtAAAVpqqenRVvbeq9lbVrVX1sqH99Kq6vqpuG5ZHvCswwOGq6uuSXJjkHYuadyS5sKpuG/btmEVtAMDqIXQFAFazu5O8orufkOQpSV5SVU9Msj3J7u4+K8nuYRvgmLr7y939Dd39N4va7uzuC7r7rGH52VnWCACsfEJXAGDV6u793X3TsH5Xkr1JHpnkkiS7hsN2Jbl0NhUCAADr0VJupAUAsOJV1ZYk5yS5McnG7t6fTILZqjrzKOdsS7ItSTZv3jydQo9iy/brZvr8S7Ucde7bcfHo1wQAgFky0hUAWPWq6qFJ3p7k5d39haWe1907u3u+u+fn5uaWr0AAAGBdEboCAKtaVZ2aSeB6ZXcfuvHNgaraNOzflOTgrOoDAADWH6ErMHVV9YiqeltVfXS44/h3udM4cCKqqpJckWRvd7920a5rk2wd1rcmuWbatQEAAOuX0BWYhV9O8u7u/pYkT87kxjfuNA6ciPOSvDDJ06vq5uHxrCQ7klxYVbcluXDYBgAAmAo30gKmqqoenuSpSV6UJN3990n+vqouSXL+cNiuJDck+fHpVwisJt39gSR1lN0XTLMWAACAQ4x0BabtsUkWkvx6VX24qt5QVQ/JYXcaT3LEO40DAAAArHRCV2DaNiT5tiSv6+5zknwpxzGVQFVtq6o9VbVnYWFhuWoEAAAAOGFCV2Da7khyR3ffOGy/LZMQdkl3Gu/und09393zc3NzUykYAAAA4HgIXYGp6u6/TvLJqnr80HRBkj+PO40DAAAAa4QbaQGz8B+SXFlVD0zy8SQ/lMmXQFdV1eVJbk/y3BnWBwAAAHDChK7A1HX3zUnmj7DLncYBAACAVc/0AgAAAAAAIxK6AgAAAACMSOgKAAAAADAioSsAAAAAwIiErgAAAAAAIxK6AgAAAACMSOgKAAAAADAioSsAAAAAwIiErgAAAAAAIxK6AgAAAACMSOgKAAAAADAioSsAAAAAwIiErgAAAAAAIxK6AgAAAACMSOgKAAAAADAioSsAAAAAwIiErgAAAIOqekRVva2qPlpVe6vqu6rq9Kq6vqpuG5anzbpOAGBlE7oCAADc65eTvLu7vyXJk5PsTbI9ye7uPivJ7mEbAOCohK4AAABJqurhSZ6a5Iok6e6/7+7PJ7kkya7hsF1JLp1NhQDAaiF0BQAAmHhskoUkv15VH66qN1TVQ5Js7O79STIsz5xlkQDAyid0BQAAmNiQ5NuSvK67z0nypRzHVAJVta2q9lTVnoWFheWqEQBYBYSuAAAAE3ckuaO7bxy235ZJCHugqjYlybA8eKSTu3tnd8939/zc3NxUCgYAViahKwAAQJLu/uskn6yqxw9NFyT58yTXJtk6tG1Ncs0MygMAVpENsy4AAABgBfkPSa6sqgcm+XiSH8pksMpVVXV5ktuTPHeG9QEAq4DQFQAAYNDdNyeZP8KuC6ZdCwCwepleAAAAAABgREJXAAAAAIARCV0BAAAAAEYkdAUAAAAAGNExb6RVVY9O8htJ/mmSryXZ2d2/XFWnJ/ntJFuS7EvyvO7+3PKVCsDx2rL9utGvuW/HxaNfEwAA1hp5CqxvSxnpeneSV3T3E5I8JclLquqJSbYn2d3dZyXZPWwDAAAAIE+Bde2YoWt37+/um4b1u5LsTfLIJJck2TUctivJpctVJAAAAMBqIk+B9e245nStqi1JzklyY5KN3b0/mXQkSc4cuzgAAACA1U6eAuvPMed0PaSqHprk7Ule3t1fqKqlnrctybYk2bx584nUCKwxVbUvyV1J7klyd3fPm9cIAABYi+QpsLxW6r1MljTStapOzaSDuLK73zE0H6iqTcP+TUkOHunc7t7Z3fPdPT83N3fSBQNrxvd099ndPT9sm9cIAABYU+QpsH4dM3StyVcwVyTZ292vXbTr2iRbh/WtSa4ZvzxgHTGvEQAAsGbIU2B9W8pI1/OSvDDJ06vq5uHxrCQ7klxYVbcluXDYBliKTvKeqvrQ8JOZxLxGAADA2iJPgXXsmHO6dvcHkhxtwpELxi0HWCfO6+5PV9WZSa6vqo8u9UTzGgEAAKuBPAXWtyXN6Qowpu7+9LA8mOTqJOfGvEYAAADAGiF0Baaqqh5SVQ87tJ7ke5PcEvMaAQAAAGvEMacXABjZxiRXT+aUz4Ykb+nud1fVnyS5qqouT3J7kufOsEaA+7Vl+3WzLgGmajn+ze/bcfHo1wQAWCmErsBUdffHkzz5CO13xrxGAAAAwBpgegEAAAAAgBEJXQEAAAAARiR0BQAAAAAYkdAVAAAAAGBEQlcAAAAAgBEJXQEAAAAARiR0BQAAAAAYkdAVAFi1quqNVXWwqm5Z1PbqqvpUVd08PJ41yxoBAID1R+gKAKxmb0py0RHaf7G7zx4e75pyTQAAwDondAUAVq3ufn+Sz866DgAAgMWErgDAWvTSqvrIMP3AabMuBgAAWF+ErgDAWvO6JI9LcnaS/Ul+4WgHVtW2qtpTVXsWFhamVR8AALDGbZh1AXDIlu3XjX7NfTsuHv2aAKxs3X3g0HpVvT7JO+/n2J1JdibJ/Px8L391wEpXVfuS3JXkniR3d/d8VZ2e5LeTbEmyL8nzuvtzs6oRAFj5jHQFANaUqtq0aPM5SW6ZVS3AqvU9w4345oft7Ul2d/dZSXYP2wAAR2WkKwCwalXVW5Ocn+SMqrojyauSnF9VZyfpTEakvXhmBQJrxSWZ9DVJsivJDUl+fFbFAAArn9AVAFi1uvuyIzRfMfVCgLWkk7ynqjrJrw3TkGzs7v1J0t37q+rMI51YVduSbEuSzZs3T6teAGAFEroCAADc67zu/vQQrF5fVR9d6onmiQYADjGnKwAAwKC7Pz0sDya5Osm5SQ4cmi96WB6cXYUAwGogdAUAAEhSVQ+pqocdWk/yvZncjO/aJFuHw7YmuWY2FQIAq4XpBQAAACY2Jrm6qpLJZ6W3dPe7q+pPklxVVZcnuT3Jc2dYIwCwCghdAQAAknT3x5M8+Qjtdya5YPoVAQCrlekFAAAAAABGJHQFAAAAABiR6QUAAAAAgGW3Zft1sy5haox0BQAAAAAYkdAVAAAAAGBEQlcAAAAAgBEJXQEAAAAARiR0BQAAAAAYkdAVAAAAAGBEQlcAAAAAgBEJXQEAAAAARiR0BQAAAAAY0YZZF8B9bdl+3ejX3Lfj4tGvCSerqk5JsifJp7r7+6rq9CS/nWRLkn1Jntfdn5tdhQAAALA6LEeexMkx0hWYlZcl2btoe3uS3d19VpLdwzYAAADAqiN0Baauqh6V5OIkb1jUfEmSXcP6riSXTrsuAAAAgDEIXYFZ+KUkP5bka4vaNnb3/iQZlmfOojAAAACAkyV0Baaqqr4vycHu/tAJnr+tqvZU1Z6FhYWRqwMAAAA4eUJXYNrOS/LsqtqX5LeSPL2q3pzkQFVtSpJhefBIJ3f3zu6e7+75ubm5adUMAAAAsGRCV2CquvsnuvtR3b0lyfOT/GF3vyDJtUm2DodtTXLNjEoEAAAAOClCV2Cl2JHkwqq6LcmFwzYAAADAqnPM0LWq3lhVB6vqlkVtr66qT1XVzcPjWctbJrAWdfcN3f19w/qd3X1Bd581LD876/oAAABOhkwF1q+ljHR9U5KLjtD+i9199vB417hlAQAAAKx6b4pMBdalY4au3f3+JEacAQAAABwHmQqsXxtO4tyXVtW/SbInySu6+3NHOqiqtiXZliSbN28+iacDAAAAWBOOmanIU1aGLduvG/2a+3ZcPPo1WXlO9EZar0vyuCRnJ9mf5BeOdmB37+zu+e6en5ubO8GnAwAAAFgTlpSpyFNgdTuh0LW7D3T3Pd39tSSvT3LuuGUBAAAArD0yFVgfTih0rapNizafk+SWox0LAAAAwIRMBdaHY87pWlVvTXJ+kjOq6o4kr0pyflWdnaST7Evy4mWsEQAAAGDVkanA+nXM0LW7LztC8xXLUAsAAOuQG1QAsFbJVGD9OtEbaQEAAKxJVXVKVX24qt45bJ9eVddX1W3D8rRZ1wgArGxCVwAAgPt6WZK9i7a3J9nd3Wcl2T1sAwAcldAVAABgUFWPSnJxkjcsar4kya5hfVeSS6ddFwCwughdAQAA7vVLSX4sydcWtW3s7v1JMizPPNKJVbWtqvZU1Z6FhYXlrxQAWLGErgAAAEmq6vuSHOzuD53I+d29s7vnu3t+bm5u5OoAgNVkw6wLAAAAWCHOS/LsqnpWkgcleXhVvTnJgara1N37q2pTkvimRU4AABVkSURBVIMzrRJgyrZsv270a+7bcfHo14SVxEhXAACAJN39E939qO7ekuT5Sf6wu1+Q5NokW4fDtia5ZkYlAgCrhNAVAADg/u1IcmFV3ZbkwmEbAOCoTC8AAABwmO6+IckNw/qdSS6YZT0AwOpipCsAAAAAwIiErgAAAAAAIxK6AgAAAACMSOgKAAAAADAioSsAAAAAwIiErgAAAAAAIxK6AgAAAACMaMOsC2D5bdl+3axLAAAAAFhW8g9WEiNdAQAAAABGJHQFAAAAABiR0BUAAAAAYERCVwAAAACAEQldAQAAAABGJHQFAFatqnpjVR2sqlsWtZ1eVddX1W3D8rRZ1ggAAKw/QlcAYDV7U5KLDmvbnmR3d5+VZPewDQAAMDVCVwBg1eru9yf57GHNlyTZNazvSnLpVIsCAADWPaErMFVV9aCq+uOq+tOqurWqfmpo93NgYCwbu3t/kgzLM492YFVtq6o9VbVnYWFhagUCAABrm9AVmLavJHl6dz85ydlJLqqqp8TPgYEZ6O6d3T3f3fNzc3OzLgcAAFgjhK7AVPXEF4fNU4dHx8+BgfEcqKpNSTIsD864HgAAYJ0RugJTV1WnVNXNmQQh13f3jTmOnwMDHMO1SbYO61uTXDPDWgAAgHVI6ApMXXff091nJ3lUknOr6klLPdf8i8BiVfXWJB9M8viquqOqLk+yI8mFVXVbkguHbQAAgKnZMOsCgPWruz9fVTckuSjDz4G7e//9/Ry4u3cm2Zkk8/PzPbVigRWpuy87yq4LploIAADAIka6AlNVVXNV9Yhh/cFJnpHko/FzYAAAAGCNMNIVmLZNSXZV1SmZfPFzVXe/s6o+mOSq4afBtyd57iyLBAAAADhRQldgqrr7I0nOOUL7nfFzYAAAAGANML0AAAAAAMCIhK4AAAAAACMSugIAAAAAjEjoCgAAAAAwIqErAABAkqp6UFX9cVX9aVXdWlU/NbSfXlXXV9Vtw/K0WdcKAKxsQlcAAICJryR5enc/OcnZSS6qqqck2Z5kd3eflWT3sA0AcFRCVwAAgCQ98cVh89Th0UkuSbJraN+V5NIZlAcArCJCVwAAgEFVnVJVNyc5mOT67r4xycbu3p8kw/LMo5y7rar2VNWehYWF6RUNAKw4QlcAAIBBd9/T3WcneVSSc6vqScdx7s7unu/u+bm5ueUrEgBY8YSuAAAAh+nuzye5IclFSQ5U1aYkGZYHZ1gaALAKHDN0rao3VtXBqrplUZu7dwIAAGtKVc1V1SOG9QcneUaSjya5NsnW4bCtSa6ZTYXAaiNTgfVrKSNd35TJt7uLuXsnAACw1mxK8t6q+kiSP8lkTtd3JtmR5MKqui3JhcM2wFK8KTIVWJc2HOuA7n5/VW05rPmSJOcP67sy+dnNj49YFwAAwFR190eSnHOE9juTXDD9ioDVTqYC69eJzum6pLt3AgAAAHAfMhVYB4450vVkVdW2JNuSZPPmzcv9dAAAAACr3onmKVu2X7dcJQHH4URHui757p3dvbO757t7fm5u7gSfDgAAAGBNWFKmIk+B1e1EQ1d37wQAAAA4fjIVWAeOOb1AVb01kwmez6iqO5K8KpO7dV5VVZcnuT3Jc5ezSAAAWIv8BBRgbZOpwPp1zNC1uy87yi537wQAAAA4CpkKrF8nOr0AAAAAAABHIHQFAAAAABiR0BUAAAAAYERCVwAAAACAEQldAQAAAABGJHQFAAAAABiR0BUAAAAAYERCVwAAAACAEQldAQAAAABGJHQFAAAAABiR0BUAAAAAYEQbZl0AAADAerZl+3WjX3PfjotHvyYAsHRGugIAAAAAjEjoCgAAAAAwIqErMFVV9eiqem9V7a2qW6vqZUP76VV1fVXdNixPm3WtAAAAACdC6ApM291JXtHdT0jylCQvqaonJtmeZHd3n5Vk97ANAAAAsOq4kRYwVd29P8n+Yf2uqtqb5JFJLkly/nDYriQ3JPnxGZQIAADAMluOmwjCSmKkKzAzVbUlyTlJbkyycQhkDwWzZ86uMgAAAIATJ3QFZqKqHprk7Ule3t1fOI7ztlXVnqras7CwsHwFAgAAAJwgoSswdVV1aiaB65Xd/Y6h+UBVbRr2b0py8EjndvfO7p7v7vm5ubnpFAwAAABwHISuwFRVVSW5Isne7n7tol3XJtk6rG9Ncs20awMAAAAYgxtpAdN2XpIXJvmzqrp5aHtlkh1Jrqqqy5PcnuS5M6oPAAAA4KQIXYGp6u4PJKmj7L5gmrUAAAAALAfTCwAAACSpqkdX1Xuram9V3VpVLxvaT6+q66vqtmF52qxrBQBWNqErAADAxN1JXtHdT0jylCQvqaonJtmeZHd3n5Vk97ANAHBUQlcAAIAk3b2/u28a1u9KsjfJI5NckmTXcNiuJJfOpkIAYLUwpysAAMBhqmpLknOS3JhkY3fvTybBbFWdeZRztiXZliSbN2+eTqFMxZbt141+zX07Lh79mgCsHEJX1jRvjgAAOF5V9dAkb0/y8u7+QtXR7gF6X929M8nOJJmfn+/lqxAAWOlMLwAAADCoqlMzCVyv7O53DM0HqmrTsH9TkoOzqg8AWB2ErgAAAElqMqT1iiR7u/u1i3Zdm2TrsL41yTXTrg0AWF1MLwAAADBxXpIXJvmzqrp5aHtlkh1Jrqqqy5PcnuS5M6oPAFglhK4AAABJuvsDSY42gesF06wFAFjdTC8AAAAAADAioSsAAAAAwIhMLwAArFlVtS/JXUnuSXJ3d8/PtiIAAGA9ELoCAGvd93T3Z2ZdBAAAsH6YXgAAAAAAYERCVwBgLesk76mqD1XVtsN3VtW2qtpTVXsWFhZmUB4AALAWCV0BgLXsvO7+tiTPTPKSqnrq4p3dvbO757t7fm5ubjYVAgAAa47QFQBYs7r708PyYJKrk5w724oAAID1QOgKAKxJVfWQqnrYofUk35vkltlWBQAArAcbZl0AAMAy2Zjk6qpKJu953tLd755tSQAAwHogdAUA1qTu/niSJ8+6DgAAYP0xvQAAAAAAwIiErgAAAAAAIzqp6QWqal+Su5Lck+Tu7p4foygAAACAtUymAmvbGHO6fk93f2aE6wAAAACsJzIVWKNMLwAAAAAAMKKTDV07yXuq6kNVte1IB1TVtqraU1V7FhYWTvLpAAAAANaE+81U5Cmwup1s6Hped39bkmcmeUlVPfXwA7p7Z3fPd/f83NzcST4dAAAAwJpwv5mKPAVWt5Oa07W7Pz0sD1bV1UnOTfL+MQoDAIATtWX7dbMuAQDul0wF1rYTDl2r6iFJHtDddw3r35vkv4xWGQBwQpYjbNq34+LRrwkAsF7JVGDtO5mRrhuTXF1Vh67zlu5+9yhVAQAAAKxdMhVY4044dO3ujyd58oi1AAAAAKx5MhVY+072RloAAAAAACwidAUAAAAAGJHQFZiqqnpjVR2sqlsWtZ1eVddX1W3D8rRZ1ggAAABwMoSuwLS9KclFh7VtT7K7u89KsnvYBgAAAFiVhK7AVHX3+5N89rDmS5LsGtZ3Jbl0qkUBAAAAjGjDrAsASLKxu/cnSXfvr6ozZ10QAAAALIct26+bdQlMgZGuwKpSVduqak9V7VlYWJh1OQDAGmP+eQBgDEJXYCU4UFWbkmRYHjzagd29s7vnu3t+bm5uagUCAOvGm2L+eQDgJAldgZXg2iRbh/WtSa6ZYS0AwDpm/nkAYAxCV2CqquqtST6Y5PFVdUdVXZ5kR5ILq+q2JBcO2wAAK8V95p9PYv55AOB+uZEWMFXdfdlRdl0w1UIAAEZWVduSbEuSzZs3z7gaAGCWjHQFAAC4f0uaf97c8wDAIUJXAACA+2f+eQDguAhdAQAABuafBwDGYE5XYF3Ysv260a+5b8fFo18TAJgt888DAGMw0hUAAAAAYERGup6E5Rg5x/pkFCYAAADA2iF0BQAAYCYMZAFgrTK9AAAAAADAiISuAAAAAAAjEroCAAAAAIxI6AoAAAAAMCKhKwAAAADAiISuAAAAAAAjEroCAAAAAIxI6AoAAAAAMCKhKwAAAADAiISuAAAAAAAjEroCAAAAAIxI6AoAAAAAMCKhKwAAAADAiISuAAAAAAAjEroCAAAAAIxow6wLAAAAAFamLduvm3UJAKuSka4AAAAAACMSugIAAAAAjEjoCgAAAAAwInO6wnEypxEAAAAA98dIVwAAAACAEQldAQAAAABGJHQFAAAAABiR0BUAAAAAYERCVwAAAACAEQldAQAAAABGdFKha1VdVFUfq6q/qKrtYxUFrE/6FGBM+hRgTPoUYGz6FVjbTjh0rapTkvxqkmcmeWKSy6rqiWMVBqwv+hRgTPoUYEz6FGBs+hVY+05mpOu5Sf6iuz/e3X+f5LeSXDJOWcA6pE8BxqRPAcakTwHGpl+BNe5kQtdHJvnkou07hjaAE6FPAcakTwHGpE8BxqZfgTVuw0mcW0do6390UNW2JNuGzS9W1cdO4jnHckaSz8y6iJF5TavD1F5T/dxxHf5Ny1TG8ViJfcr9/n0d55/xLI36726Gr3vmfcJIr33mr+NEHOG139/rWGt9ykr5O1PHfanjvlZdHcfRp66FPmXqfz9L/PNdKf9uDrcS6xq9pjX+vmIl/5tfCX1KsoR+5Tg++6zUfwfLyWteP2byusd4n3IyoesdSR69aPtRST59+EHdvTPJzpN4ntFV1Z7unp91HWPymlaHtfiaRrTi+pS18vfldawsXsfUjNanrJTXqg51qGOmTqpPWal/LupaupVYU6Ku4zXUtWXWdQyO2a8s9bPPSv3zXk5e8/qxml/3yUwv8CdJzqqqx1TVA5M8P8m145QFrEP6FGBM+hRgTPoUYGz6FVjjTnika3ffXVUvTfL7SU5J8sbuvnW0yoB1RZ8CjEmfAoxJnwKMTb8Ca9/JTC+Q7n5XkneNVMs0rajpDkbiNa0Oa/E1jeb/b+/+Qicr6ziOfz7056YCszD/UgZ7kYVYiJh1oRiyLtFWENRNCwUiFBR0kSJ00a3gRaB1U6yC6I1pe6HkJoLdGJXsrspqriEou7iUYImQiV8vzln8OTvzm3NmnvM8zzzzfsHhN7P74zef55l5PnPO+f1mpsJOaeX+Yhx1YRyZJOyUWsZKjvcjx/uRY2Jrdkqt80Ku4WrMJJFrrKpyNbivkhNj3h4bO25HnPX+7wAAAAAAAACAFa3znq4AAAAAAAAAgBlbe9LV9ndsP2v7Hdsb+SloZ9jea/t52yds31I6z7ps/872advPlM6Siu1LbD9u+3j/uPtJ6UxYzvbttp+zfcz2g7bPKZ1pFZved610XAvdti1dNnTN2H7J9tO2j9j+W8Eck64R2+faPmz7hf7rxxd83yTzsWx87vyq//9jtr+U6rZH5rjW9uv9+I/Y/sUEGXbtkYxzsSzH5HNRu6H7ELmf42rptxUz5Z6rot0353aq6MIVcmXvg1q6MrehvdOSof3RgtwdWINla3kTbO1JV0nPSPq2pCdKB1mH7Q9IulPSjZIuk/Q925eVTbW2g5L2lg6R2NuSfhYRn5N0taQfNXA/bYPDkr4QEZdL+oekWwvnWdXG9l1jHXdQm99t29JlY9bMdRFxRURMsaO/NEemNXKLpMciYo+kx/rriySdj4Hju1HSnn67SdKvU9z2Cjkk6c/9+K+IiF+mzqHlPTL5XAzMIU0/F7Vbug9R6Dmuln4blanQXBXrvlm1dOGKuaT8fXBQdXRlbq0cu4yxscc5YzR2TDTGQW348dPWnnSNiOMR8XzpHAlcJelERPwzIt6SdL+k/YUzrSUinpD0WukcKUXEqYh4qr/8X0nHJV1UNhWWiYhHI+Lt/uqTki4umWdVG953zXRcC922LV1Wy5oZmCPHGtkv6e7+8t2Svpn45+9myPj2S7onOk9KOsf2BQVyTG5Aj+SYiyb6bGoD9yGyP65q6bedKuq6WSW7b1YtXbhKruxq6crcWjl2GaPGTptIlWttai3sb2ztSdeGXCTp5R3XX1GDB8Atsf0ZSV+U9JeySTDSDyQ9UjrEFqLjKkWXSZJC0qO2/277pkIZcqyRT0XEKak78S7pvAXfN8V8DBlfjjkYehtftn3U9iO2P584wxA1dWbpuajJon2Imu6vWTX0204l5qpk982qpQtXySXV1wc1r71UOHZpyzY8Zpv0wdIBpmT7T5LOn/Nft0XEH3LnmYjn/FtkT4FBbH9U0gOSfhoR/ymdB8N6wvZt6l5WfW/ObGM03Hd0XIVa6LJEa+YrEXHS9nmSDtt+rv+NfM4cSdbIbjlG/Ji152NetDn/Nju+HD0x5DaekvTpiHjD9j5JD6l76WpOtXRmDXMxuQT7EJPcX7X0W+JM2edqxI+Zovtm1dKFszalG2fV0pWjtXLsMkbDxzljbOxjdts1fdI1Ir5WOkMGr0i6ZMf1iyWdLJQFu7D9IXUnKe6NiN+XzoPOsp6wfUDS1yVdHxHVPrE13Hd0XGVa6bIUayYiTvZfT9t+UN1Lv0YdaCfIkWSN7JbD9qu2L4iIU/3LL08v+Blrz8ccQ8aXoyeW3sbOX0BExMO277L9yYj4V+Isu6miMyuZi8kl2IeY5P6qpd8SZ8o+V4W7b1YtXTg6V6V9UEVXrqKVY5cxGj7OGWNjH7PbjrcX2Hx/lbTH9qW2Pyzpu5IOFc6EGbYt6beSjkfEHaXzYBjbeyX9XNI3IuLN0nm2FB1XEbrsPbY/YvtjZy5LukHdhznklmONHJJ0oL98QNJZf1Uy4XwMGd8hSd9352pJr595SXBCS3PYPr9fI7J9lbr97H8nzrFMjrlYqpK5KGrgPkSVz3EV9dtOJeaqZPfNqqULR+eqtA+q6MrUOHZpWpXPFxggIrZyk/Qtdb8t+J+kVyX9sXSmNcayT92nE76o7k/si2daczz3STol6f/9ffTD0pkSjOmr6v78/5ikI/22r3QutqX32wl1751z5j77TelMK45jo/uulY5rodu2pcsWrRlJF0p6uL/8WUlH++3ZKR6bQ3L01yddI5I+oe6Tu1/ov56bcz7mjU/SzZJu7i9b3Sf6vijpaUlXTvS4WJbjx/3Yj6r7AJNrJshwVo8UmotlOSafi9o3LdiHyL1+5+Sqot/GZio0V0W7b06eKrpwhVzZ+6CWrsy9LeqdlrdF/dHilrsDa9jmreXSmcZu7gcCAAAAAAAAAEiAtxcAAAAAAAAAgIQ46QoAAAAAAAAACXHSFQAAAAAAAAAS4qQrAAAAAAAAACTESVcAAAAAAAAASIiTrgAAAAAAAACQECddAQAAAAAAACAhTroCAAAAAAAAQELvArmuUDtbW1FwAAAAAElFTkSuQmCC\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": "6d0419d4",
   "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",
       "    </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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.222986</td>\n",
       "      <td>0.575895</td>\n",
       "      <td>-0.344845</td>\n",
       "      <td>0.79487</td>\n",
       "      <td>0.273655</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>0.470416</td>\n",
       "      <td>0.575895</td>\n",
       "      <td>-0.344845</td>\n",
       "      <td>0.79487</td>\n",
       "      <td>0.532389</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>0.498225</td>\n",
       "      <td>0.575895</td>\n",
       "      <td>-0.344845</td>\n",
       "      <td>0.79487</td>\n",
       "      <td>0.562256</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>0.140130</td>\n",
       "      <td>0.575895</td>\n",
       "      <td>-0.344845</td>\n",
       "      <td>0.79487</td>\n",
       "      <td>0.189741</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>0.188697</td>\n",
       "      <td>0.575895</td>\n",
       "      <td>-0.344845</td>\n",
       "      <td>0.79487</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  0.222986  0.575895 -0.344845  0.79487   \n",
       "2    BL09        2     1    0    0  0.470416  0.575895 -0.344845  0.79487   \n",
       "3    BL09        3     1    0    0  0.498225  0.575895 -0.344845  0.79487   \n",
       "4    BL09        4     1    0    0  0.140130  0.575895 -0.344845  0.79487   \n",
       "5    BL09        5     1    0    0  0.188697  0.575895 -0.344845  0.79487   \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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creo otro data frame con los logaritmos de las variables explicativas y después lo tipifico\n",
    "dataL = df.copy()\n",
    "for column in dataL.columns[-5:-1]:\n",
    "    dataL[column] = np.log(dataL[column])\n",
    "    dataL[column] = stats.zscore(dataL[column])\n",
    "dataL['Mwd/TnU'] = stats.zscore(dataL['Mwd/TnU'])\n",
    "\n",
    "dataL.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8a7958e",
   "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": [
    "indep = data['Cnn/s'].values\n",
    "indepL = dataL['Cnn/s'].values\n",
    "dep = data['Mwd/TnU'].values\n",
    "\n",
    "\n",
    "corrcoef = np.corrcoef(indep, dep)\n",
    "corrcoefL = np.corrcoef(indepL, dep)\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, dep, '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",
    "dataLEECC = dataL.groupby(['EECC']).mean().drop(['Muestra'], axis=1)\n",
    "# dfEECC.info()\n",
    "\n",
    "indepEECC = dataEECC['Cnn/s'].values\n",
    "indepLEECC = dataLEECC['Cnn/s'].values\n",
    "depEECC = dataEECC['Mwd/TnU'].values\n",
    "\n",
    "corrcoef = np.corrcoef(indepEECC,depEECC)\n",
    "corrcoefL = np.corrcoef(indepLEECC,depEECC)\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(indepLEECC, depEECC, '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": "0c902e3c",
   "metadata": {},
   "source": [
    "### Tengo 3 data frames:\n",
    "\n",
    "df     -->  data frame original\n",
    "\n",
    "data   -->  data frame tipificado\n",
    "\n",
    "dataL  -->  data frame logarítmico tipificado"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57f93a2d",
   "metadata": {},
   "source": [
    "### Mínimos cuadrados:  $\\beta = (X^{T}X)^{-1}X^{T}Y$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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(dataL['Cnn/s'].shape[0]), dataL['Cnn/s']]).T\n",
    "BL = np.linalg.inv(XL.T @ XL) @ XL.T @ dataL['Mwd/TnU']\n",
    "# print(B)\n",
    "# print(BL)\n",
    "\n",
    "# print(data['Cnn/s'].min(), data['Cnn/s'].max())\n",
    "# print(dataL['Cnn/s'].min(), dataL['Cnn/s'].max())\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, 2.6], [B[0]+B[1]*(-1.5), B[0]+B[1]*2.6], c='red')\n",
    "\n",
    "ax[1].plot(indepL, dep, '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([-2.3, 1.6], [BL[0]+BL[1]*(-2.3), BL[0]+BL[1]*1.6], 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(indepLEECC, depEECC, '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([-2.3, 1.6], [BL[0]+BL[1]*(-2.3), BL[0]+BL[1]*1.6], 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": 13,
   "id": "67353648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qpfRPwB7ghojoIp+8fy2ldCKl9K/AZ6c49s6U0mBKaRj4eeC3UkoPppTGgA8AV5W+/Rgln3RfCERpnyenON6bgY+klO4tjRn9VfK3Bpsr9vntlFJ/aV6J/0XFNznn6M9SSg+V2v6piuNM+74ApJT+IaX0SMruBu4E/IZBkrRQmSNyQeQfUkr/lFIaBXYBrcArgJeT5+f7g5TSaEqpm1yYASCl9HRK6W9SSkMppaPA+8mFmUofTSk9kFIaKx1/Jv42pfSNlNIIuUAyklL6WErpJHA7eTjOVP4T8J6U0ndKWeb+lNLTpbb+Ram9Yyml/w9YDrxghu2RasoChdQ4XpdSWglcSz6xl4cyXAK8vtRVsb/UbfD7yd84dALPTBq7+cQUx67cdgnw+xXHegYIYGNK6V/I3xb8EXAgIv4kIlZNcbxOJn5zcIz8DcnGin2eqrg+xPlPfjndcc70vhAR10fEPaWuo/3kwsVaJElamMwRzz7uqVLbN5bu259SSlO9rohoi4g/jojHI+II8EVgdUxcWWyq9+ZsDlRcH57i9nSv62LgkanuiIh3lobYDJR+Bh2YcdQgLFBIDab0bf9HyVV/yCfDj6eUVlf8a08p/TbwJHBBRLRVHOLiqQ5bcf0J4OcnHa81pfSV0vP/QUrp3wFXkLto/vIUx+sjBxQAIqKd3DV0//m85vM07fsSeamyvyG/h+tLXS13kwOUJEkL1iLPEZOPG6XXs5/8WjdOmsui8rW+k9wL4WUppVXkoSowMTtUvg+Tnem+8/EEcOnkjaX5Jv4reehIoZRxBjDjqEFYoJAa0+8BPxQRVwF/Abw2Il5dmjCpJSKujYhNKaXHyd00d5bGV14DvPYsx/4w8KsRcQVARHRExOtL16+OiJdFRDMwCIyQlyyb7K+An42Iq0rFgA8AX0spPTb7lz5j074v5LkqlgOHgLGIuJ48LlaSpMVgseaITwE/EhE/WGrDO8nzPnyFPPfDSeCWiGiKiBuBl1Y8diW5R0N/RFzA6SEyM3UA2FSa66Ia/ifwvoh4XmRbI2JNqZ1j5IzTFBG/Rp6jQmoIFiikBpRSOgR8DHhvSukJ4EbyhFSHyBX1X+b0/+83A9eQu0b+Jnk847RriaeU/hb4f4BPlrow7iVPpgX5BPc/gCK5i+TTnP4GpvIY/wy8l9xL4Ulyhf+N5/2Cz8OZ3pfS2NF3kINKkTzR5lRjaiVJWnAWa45IKX2HPOfGHwKHycWW15bm1zhBntjzZqC/tN/fV7zW3yPPV3EYuAf4x3N8+n8BHgCeiojDs3wpkCfP/BR5Dq0jwJ+W2vd54HPAQ+T3eITzG3oi1URMHGYlaaGLiNuBb6eUzrXyL0mSFrnFlCMi4mvAh1NKf1brtkiLhT0opAWu1J3y0ohYEhE/TP6W5NO1bpckSap/iylHRMT2iNhQGuLx08BWzr2nhKRZaKp1AyTNuQ1AN3lyqX3A21NK36xtkyRJUoNYTDniBeRhEyvIK2TcNM0yqJLmiEM8JEmSJElSzTnEQ5IkSZIk1dyCHOKxdu3atHnz5lo3Q5KkuvKNb3zjcEppXa3bsRiYRSRJmtqZ8siCLFBs3ryZPXv21LoZkiTVlYh4vNZtWCzMIpIkTe1MecQhHpIkSZIkqeYsUEiSJEmSpJqzQCFJkiRJkmrOAoUkSZIkSao5CxSSJEmSJKnmLFBIkiRJkqSas0AhSZIkSZJqzgKFJEmSJEmqOQsUkiRJkiSp5ixQSJIkSZKkmrNAIUmSJEmSas4ChSRJkiRJqjkLFJIkSZIkqeZqWqCIiI9ExMGI2DvN/ddGxEBE3Ff692vz3UZJkrSwmUckSaoPTTV+/o8CtwEfO8M+X0opvWZ+miNJkhahj2IekSSp5mpaoEgpfTEiNteyDZIk1ZOePujeC71F6CrAji2wtbPWrVrYzCOSJE1UqzzSCHNQXBMR90fE5yLiiul2ioifi4g9EbHn0KFD89k+SZKqoqcPdt0NxWHYtDpf7ro7b1fNnTWPmEUkSQtBLfNIvRco7gUuSSldCfwh8Onpdkwp/UlKaVtKadu6devmrYGSJFVL914otEGhFZZEviy05e2qqRnlEbOIJGkhqGUeqesCRUrpSErpWOn6bqA5ItbWuFmSJM2J3iJ0tEzc1tGSt6t2zCOSpMWklnmkrgsUEbEhIqJ0/aXk9j5d21ZJkjQ3ugowMDJx28BI3q7aMY9IkhaTWuaRmk6SGRGfAK4F1kbEPuDXgWaAlNKHgZuAt0fEGDAMvDGllGrUXEmS5tSOLXmMJ+RvKgZGoDgEN19d23YtdOYRSZJOq2UeiYV4ft22bVvas2dPrZshSdI5m8tZsyPiGymlbdU5ms7ELCJJamS1yiM17UEhSZIm2trpsqKSJKm2apVH6noOCkmSJEmStDjYg0KSpPM0l90fJUmS5lI95hh7UEiSdB56+vIEUsVh2LQ6X+66O2+XJEmqZ/WaYyxQSJJ0Hrr3QqENCq2wJPJloS1vlyRJqmf1mmMc4iFJ0llM1QWyt5i/cajU0ZK3S5Ik1auePvjMA5BOweo2uPxCWL+yPnKMPSgkSTqD6bpALm/K64JXGhjJBQxJkqR6VM41y5bCsiYYHoWvPAYHjtZHjrFAIUnSGUzXBTIBxaFcsDiV8mVxKPeukCRJqkflXPPijXB8LG9b3gT37q+PHGOBQpKkM+gt5i6PlTpa4MQY3Lo9Fyz29efLW7fXfvZrSZKk6ZRzzYaVcM1maG2CEydzsaIecoxzUEjSPKjHZZw0M12F3Dui0Hp6W7kL5NZOf46SJC02jZzrKnPNhpX5X/l2PbwGe1BI0hyr12WcNDM7tjiUQ5IkZY2e6+o919iDQtKiN9dV8Mo5DOD0Zffe+qhU68y2duYuj5W/Izdf7c9OkqSF4FxzYKPnunrPNRYoJC1q5Sp4oW1iFXy2Y/AqT3b37oeXdQEVQwTqYRknzZxDOSRJWnjONQf29MGn9+brq1vghevzEIlGy3X1nGsc4iFpUZtuhYbuved/zMld/5Y3wRcfhaeOnt6nHpZxkiRJWszOJQeW893yprxE5/AYfPWxnO/MddVjgULSotPTBzvvhLfenqvgI6MT759tFXzyye4lG4FT8M399TnWT5IkabEp94a4+xH4wsOnv0iaLgeW891LJi3P+c06WZ5zoXCIh6RFo6cPPvRV+KeHYE07XNV5unfD9ufmLnow+yp4bzH3nChbvxJ+4FL4tyfycpT1NtZvIWrk2bUlSdLcuuN+eN9d0Hck94YYO5V7Q1yzOWfDqXJgOd8tCXjFZnjwIPQPQSypj+U5KzVyDrIHhaRFodwt7979cEFb3nbP49C1mqr3bugq5CJHpZZmuPEK+MhPwM5XNc5JohE1+uzakiRp7vT05eJERM6Bx8dy74mxU2fuDVGZ79avhGsvhe2X5XxXT7mu0XOQBQpJi0K5W96JMWhtzv9amuHQsdy74cTJ3Luh0Dp1FbxyWMjOO8/8R77el29a6OZiXhFJkrQwdO+F0ZN5KMfKFth8ASxfCk8P5jx46/a83+Tc1yj5rtFzkAUKSYtCbzGfiDpaYaQ0brClCQaOn713wx33w1s+AZ+6Hx55Gh46dOZKdHn5pkLrmYsemhvln3WlRptdW5IkzY3eIqxbcToPrlgOl62FC9pzHnzo0NS5Dxoj3zV6DnIOCkkLzlTj7roKudJ9+YXwlcfyfinlcYfFoTwnxHTHKncDbG2Chw/D/34KOlfl+Sw+9ONTP66el29a6Mo/60LFsq7Ori1J0uJWzoff7IOxk3B8FGgvfWE1As1LYMv6s+e+es93jZ6D7EEhaUGZbtzdlvW5ELGsCV5+Sd73mSF48cYzV7+798LR43DgKHzrABw5DkuX5GP900ONM55vMWmULpiSJGl+VObDl16cCxSjKV8eOpa/tHrvdbD3QB7+cWIMvn0o7z96Eg4ebZzc1+g5yAKFpAVlunF3ew+c7pY3ehKufyF0/zR8+CyV8Pv2w7HjuTDRtDRvGzwOQ6N5JZBGGc+3mDjERpIkVarMhxetgmsvgwtXwMkEb7gKPv4muOnK3Pu2tRkeK0IAzUtz8eLI8by9EXJfo+cgh3hIWlAmL/EJp8fdnc+wi/7hfEI72p8LHksCxshFjqs6G2c832LjEBtJklQ21RLwr35B/gC/81Wnt3cV4OtP5MLF0tJX+Ymc/5YtbZzc18g5yB4UkhaUqZb4nM24u9WteT3slctyBX30JCwNWNOaJ9dslPF8kiRJi9VM8+GOLTA8CquWn859AVyyOm839809CxSSFpRqj7u7aiNs2ZBPSMuacm+KSwqwoaOxxvNJkiQtVjPNh1s74brnw4aVp3PfC9bB8uY83MPcN/csUEhaUKo97m7HFmhaAt/3HHj9VtjUAcfH4CVnmVxTkiRJ9eFc8uEvXJMnUf/hF8Bla2B47PQkmua+ueccFJIWnGqOuyuf0Lr3Qu9Inlxzx5b6PkFNtcxqPbdXkiRprs00H1Zmv5YmeOVl1clS5rOZsUAhadE43xNDI0w0VH5t9+2HR5/Jw1IuXXt6mVV7e0iSJGVny4TVzn533A/vuyvPabFuRe6Naz6bmkM8JC0Kd9wPb/kEfOp+eORpeOhQPjE0wnrWZ1O5tndxGCJg71N5Xe/yMquNsCyWJEnSXJvvTNjTl4sTEbk4MTKWc9rJU+azqVigkLTgTTgxtOcTwwNPwViDnxh6+mDnnfDTt8N3DuVq/JHjeVnVlmZ48GDer7zMqiRJ0mI235mwpw/e8Wl4vD9Pyjl4Alqbc07bN2A+m4oFCkkLXvfe3KWuoyWfkMonhv0NfGKo7DWRTuXJm776WF4CdWQsj5kcGM77zmaZVUmSpIViPjNhOasdPAYdy2HkJDzRD8eO55x26Jj5bCoWKCQteL3F013qylqa4NBg454YuvfmoRuFVljdlk+yLc1AgpHRXJRY1TL7ZVYlSZIWivnMhOWsduFKWNkCARC5MDEw4rKl03GSTEkLXlchD3/Y+1S+3dJUOjEsaawTQ+WETvfuh5d1Aa1w+YXwlcdgeROMJbhiQ+6uWGjN/26+2gmYJEmS5isT9vTBp0tDRpqWwIlRWNsOR0dg4DisbnXZ0unYg0LSgrdjCyxdkle2KHepa7T1rCuHdGxanYsRX3wUnjoK61fCKzbnXhQAz18HH38TfPpnYeerGuc1SpIkzaX5yITlzLa8CZYtzc/Hkvx87cvgqotyTrvpyuo830JjDwpJC17letbLq7ie9XyqHNIB8JKN8IWH4Zv74dUvgGVN8IJ1LlclSZI0nfnIhOXM9pKNuYdrSzOsLs15se1is9rZWKCQtChUez3r+dZbzD0nytavhB+4FP7tCdjXn7ssOpRDkiTpzOY6E5Yz25LIPVwfPAj9QxBLLE7MhAUKSWoAXYU8vKPcgwJyRf7GK/IwDkmSJNVeZWZbvzL/K9+2OHF2zkEhSQ1gx5a8GkdxGE4lV+eQJEmqR2a22bEHhaSGUrmSRVeh8eaSOF+VYybLr90hHZIkSc9Wy7xoZpsdCxSSGsYd98P77oLRU7CuPa9hvevuxTOer9Hn0ZAkSZpr9ZAXzWznzwKFpDnVQy/d7KGXw3Sxlh1sYytd536cvnyyiTh9snngKbhiQ65QN9JJYLH2ApEkSTpX55Il5zsvmumqzzkoJM2ZHnrZxW6KDLKJNRQZZBe76aH3nI/VvRdGT0JHaZmm1uY8SeT+gXxSaBTltbGLw3mG5+Jwvt3TV+uWSZIk1ZdzzZLzmRfNdHPDHhSS5kw3eyjQToF2gPHLbvactRdFTx986KtwTy+kBMOjsGJZroS3Nud9Wprg0GBew7pRlNfGLq/GUb5stF4gkiRJc22mWfKO++G2L8O9fRDAiZOnl2efq7xoppsbFigkzZleDrOJNRO2ddBGL4fP+LiePvhv/wgPH4KVLXnbvgE4ejwXKWjLJ5uBEWheUt+zIk/u+nff/meftDpaGqsXiCRJ0nyYSZa84354125YtRxWt8CREejtz/dt7Kh+Xixnu7+8FzpXwYvW56VEwUxXDQ7xkDRnuljLAEMTtg0wRBdrz/i47r1w8BisaoW2ZfnfRStzNXx5U6kSfiz3rHjvdYV3upMAACAASURBVPVbpZ6q69+jz8Ajk+ozAyO5eCFJkqTTZpIlb/tyqTjRCheuyFmxrTn3mqh2XqzMdp2rcob7ymNw4GipbWa6WbNAIWnO7GAbRQYpMsgp0vj1HWw74+N6i3B8NBciyi5ogzVtsHQJXLoG3nAVfPxNcNOVc/wiZqGy69+SyJdXbIC9T7k2tiRJ0tnMJEvuP5ILFAArlsPFq2FVCySqnxcrs93l63PxIwK+dcBMVy0O8ZBUFdPNsHwrN0zYfjPbzzr/RFcBHjgwcb6JkbFcGb/+hbDzVefQrhrOrtxbPD3+seyytTB0Ip/YXBtbkiRposmZ8kd5MXvZP22W3LgK+kdyToRcpBg7Bc+94NmZcba5sDLbbVgJ12yGBw9A35E8x4WZbvYsUEiatfIMywXaJ8ywfCs3sJWuc15WdMcW+Ma+PAdFSnnb0RNw6QXnVpUud8MrtE2cXXm+1sHuKuTnLE+aBLnr31Ubz63IIkmStBhMlSk/yzfHM+VUbvm+PAcF5J4UR47nf+/5wUnHrkIunJztNqzMQ0peeZnZrloc4iFp1ipnWF5CjF/vZs95HW9rJ7z/h+Hay2D0VJ57Yvtz4QPXn1thYaohFoW2vH0+7NiSu/o5nEOSJOnszidT3nQl/M4NeYLMJ4/my9+54dnDOqqRC812c6+mPSgi4iPAa4CDKaVn/VgjIoDfB24AhoCfSSndO7+tlHQ257tax5ls7YQP/fgs2zXFEIv5nF15a2euyld2JbTrn1R/zCOSVB/ON1PedOXZ55moRi402829Wg/x+ChwG/Cxae6/Hnhe6d/LgA+VLiXVkS7WUmRwfG1qmNlqHXNtuiEW8zm78tZOT1pSA/go5hFJqrm5zJTVyoVmu7lV0yEeKaUvAs+cYZcbgY+l7B5gdURcND+tkzRT57tax5y3y254kmbAPCJJ9WEuM6W5sDHU+xwUG4EnKm7vK217loj4uYjYExF7Dh06NC+Nk5SVV+so0M4+nqZA+xknM6qmnj7YeSe89fZ82dNX0a5SN7xCK+zrz5fzNUGmpAVlRnnELCJJs1PNTDk5I4K5sBHUeojH2cQU29JUO6aU/gT4E4Bt27ZNuY+kuXM+q3XM1kxmY7YbnqQqmFEeMYtI0uxVI1OeKSO62kZ9q/ceFPuAiytubwL6ptlX0iJT61U6JC0a5hFJaiBmxMZV7wWKzwI/FdnLgYGU0pO1bpSk+tBbzLMvV5rPVTokLRrmEUlqIGbExlXrZUY/AVwLrI2IfcCvA80AKaUPA7vJS3o9TF7W62dr01Jpcemhl2720MthuljLDrbN+/CNmaiHVTokNT7ziCTNj/nKmGbExlXTAkVK6U1nuT8B/2WemiMJuIOv8T4+wygnWcdKjjPGLnbP26SX52LHljyeEHJVfGAkz8Z889W1bZekxmIekaS510Mv/42/5iBHOM4oD7Cfb/Ao7+f1Vc+YZsTGVe9DPCTNox56eR+fIYB1rGSEUfayj5Ocops9tW7es7hKhyRJUmP4EP/MwxwAoIM2AB7mAB/in6v+XGbExlXvq3hImkfd7BnvOREErSwDYB/PsLxO/1y4SockSVL9u4eHWUnLeL5sZRmJxD08PCfPZ0ZsTPagkDSul8PjPSfKWmjmEEfpYm0NWyZJkqRGNt3ay67JrEoWKCSN62ItGykwwijDnCCRGGCIZpayg221bp4kSZIa1Mu5lKOMjGfMYU5wlBFezqW1bprqiAUKSeN2sI0mlnIFG8d7TiTgvdxYdxNkSpIkqXH8AtdxKRcCMMAwAJdyIb/AdbVslupMfQ4ql1QTW+niVm6gmz200MwredG8LzHaKEucSpIkaea20sUHeENVc565ceGxQCFpgq101ewPew+97GI3BdrZxBqKDNbtEqeSJEk6N9XMmebGhckChaSa6OmD7r3QW4SuQl6vurtzDwXaKdAOMH7ZzR5PNHXObzAkSdJ8KGfIzxRPsaxwDS/ecoglnUcWZG5cjPnKOSgkzbuePth1NxSHYdPqfLnrbriv7+T4uthlHbTRy+EatVQzUf4Go8jghG8weuitddMkSdICUpkh0+qnScNtfPXu5/FU3ypgYeXGxZqvLFBImjc9fbDzTvipT8J3DsGJMVgSUGiFQhv0772CAYYmPGaAIZc4rXPdnO75soQYv97Nnlo3TZIkLRA9ffCOT8OeJ+D+Pmg6ViBah2hpG+XbezuBhZUbF2u+skAhaV5UVrwBUoKvPAYHjubbHS2wuthFkUGKDHKKNH7dJU7rWy+H7fkiSZLmTDlHHhyEte0wPApHHttE8WgTqWWQ/mLbgsuNizVfWaCQNC+69+ZeEidaB+hvOcijcYDDzc/w9YMjAAyMwFWFFdzKDRRoZx9PU6DdiY4aQBdr7fkiSZLmTPdeONl2hKH2gzx48gAHmg+xvDmx6uClxEg7FA4vuNy4WPOVk2RKmhe9RWhePcA9fJcV61cy/Nh6xppGeWToGN8dPsHSoVXcfHVtVxHR+dnBNnaxG8iV/QGGKDLIzWyvccskSdJCcF/xGN9b/dB4hhxmlKGlT7Pi6Fq+f+gybr0ati6QnhNlizVf2YNC0rzoKsB9I4dooZm1K09x8eZnaIolNC0J9rfu49btsLWz1q3U+dhKlz1fJEnSnOkv9LJkpH08Q7Y2JcaGWjm1on/BZsjFmq/sQSFpXuzYAn9x9ykuoI3UMsbSplOsXXeMl29/iNHOR9nKi2rdRM2CPV8kSdJcWb3lAZ65+2qGGaN9xXGWNp1i1VATz93+dbZ2vr7WzZszizFfWaCQNC+2dsJ123v55t51HCl20FEY4sVXP8byzqe4aIGPpauW8rrfvcXcI2XHloX5jYEkSVKlqzqX0rb9f9O391IGim10FIa49OoHeX7n0lo3bUpmtvNngULSvPmFzuezq3M3BdoX1Vi6aijPXl1og02r82oou+5mwXZrlCRJKtvBNnZ17ubKzv4JGXIHN9S6ac9iZpsd56CQNG8W61i6aiivglJohSWRLwttebskSdJC1kgZ0sw2O/agkDSvFuNYumroLeYqfKWOlrxdkiRpoWuUDGlmmx0LFJJUR3r64INfhXsehwh4eRe8/Zo8frE4nKvwZQMjebskSZJq64774bYvQ89T0NoMr7gErtiQ7zOzzZwFCkmqEz198O7PwSPPwMpledsXHoZ9A/DT/w4++628raMln+iKQ3Dz1Wc5Jr10s4deDtPFWnawrSG+fZAkSWoUd9wP79oNq5bD5gI8+gzc+RCkBBtXn85s5rKzcw4KSaoT3Xvh0GA+ubUty/9WtcLBY7D3QJ5cqdAK+/rz5dkmW+qhl13spsggm1hDkUF2sZseeufvRUmSJC1wt30557fVrbCqBS5dAy3N8K+Pnc5sdJrLZsIeFJJUJ3qLcHws95AoO9p0hMeHh3iw2MsXOv+JWzqvYycvm9HxutlDgXYKtAOMX3azx2q9JElSlew/AhetzNcPcYT9y4sMrx1jydHVbHnVw2zlZew0l82IBQpJqhNdBXjgAIyM5bGLhzjCI2NFljYHhcIw/QzyLm4H4KYZFCl6Ocwm1kzY1kEbvRyek/ZLkiQtRhtXQf8IjLYe4REO0sQSmo63Eav6x7ObuWxmHOIhSXVixxZY1w5HjsPQCXj8xFEYbqV1xQjrtnyH1bSzihZu464ZHa+LtQwwNGHbAEN0sXYumi9JkrQo3fJ9Ob89NjzEklNLSMNtnDzeQtf33Tue3cxlM2OBQpLqxNZO+MD1sP25cOIkjJ5KFC57nOf88BdY0XkIgFW0sp+ZrVO1g20UGaTIIKdI49d3sG0uX4YkSdKictOV8Ds3wKmWY6SjHTS3jPDcG+7iwisfGs9u5rKZcYiHJNWRrZ3w4R/P16/lz+lnkBWlMYoARxhmIzNbp2orXdzKDRNmi76Z7Y5zlCRJqrKbroTbrrydfgZZPUV2M5fNjAUKSXXJZZjgFq4bH7e4ilaOMMwRRngPN874GFvpWnTvmyRJWlzqJTeeLbuZy87OAoWkunMHX+N9fIZRTrKOlYwwyi52cys3LKo/6uWJMG/jLvZTZCMF3sONM5ogU5IkaTGop9xodps9CxSS6koPvbyPzxAwfpJ5gP1cwcZFuQzTTbzMk5okSdIU6jE3mt1mxwKFpLrSzZ7xCngQtLIMgP0UaaG5xq2TJElSvTA3LjwWKCTV1OQxg/fx+HgFvHySaaGZQxzllbyoxq2VJElSrZXz41/yFU4wylKCNawEzI2NzgKFtMjdwdcmjJO7hevmrVtaD73sYjcF2tnEGooM8iiH2ESB/tI60S00M8AQzSx1GSZJkqQGU+2sWZkfO1nNUwywr7QE+wWsMDc2uCW1boCk2rmDr/Eu8nJIF9FBP4O8i9u5g6/Ny/N3s4cC7RRoZwlBgXauYCP7KLKFTeMV8AS8lxsX3fwTkiRJjWwusmZlfrycTlpo5kJWcYRhc+MCYA8KaRG7jbtYRcv4Ws3ly9u4a156UfRymE2smbDtMjYwxHGexwaW08QreVHdLDHa0wfde6G3CF0F2LEFtnbWulWSJEn1aS6yZmV+3MBqruEyHqSPPvp5Ay+rWm4099WGBQppEdtPkYvomLBtFa3sL3WTm2tdrKXIIIXSyQpggCGuYjM72TEvbZipnj7YdTcU2mDTaigO59u3bvdkJUmSNJW5yJqT8+MGVrOcZl7Ji6qWH819teMQD2kR20iBIwxP2HaEYTZSmJfn38E2igxSZJBTpPHr9ThmsHtvPkkVWmFJ5MtCW94uSZKkZ5uLrDkf+dHcVzsWKKRF7Bau4wgj9DPIKU7RzyBHGOEWrpuX599KF7dyAwXa2cfTFGjnVm6oi+Eck/UWoaNl4raOlrxdkiRJzzYXWXM+8qO5r3Yc4iEtYuWxf5UzK7+HG6sy/8Tk5UOnGw+4la66LEhM1lXI3ftOtA7wIH0MMMSykQ5eUlgLrK518yRJkurObLPmdHlyrvNjVwG+O3yE/a37GWCIDtrYOLKR5xVWzdlzKouUUq3bUHXbtm1Le/bsqXUzpEWrcvmnDtoYYIgig3XbO2Imevrgv919jIfbvsfKlgQjbRwdWsKl27/BBzpfPv66ZlqYkWohIr6RUqq/MVQLkFlEkmanlnnyjr4nedfdR1jVdopVLYkjI8HhoVNcs/0hVnYeMuPN0pnyiEM8JFXdVMuHFminm8YM6z300t3ZzUPbP8KR1qcZ6G+lrXWU7dsf5dLO4+Ovq3wiLTLIJtZQZJBd7KaH3hq/AkmSpMZSqzzZQy+3dX6EtH03/a0HOdDfRFPrCM3bP09v531mvDnmEA9JVTfV8qEdtNHL4fHbjdLToLJ639r5FB2dhznOGC/kMjawmlMVr6vyRAqMX3azpy5fmyRJUr2aLk/ex+PspHtOMmQ59x3kCBd3nuJ4578wwiiwlHUEJzg5XiwBM95csAeFtAj09MHOO+Gtt+fLnr65fb4u1jLA0IRtAwzRxdrcngbqaVBZdFhNO0HQQjPf5klg4uvq5TAdtE14/OTCjCRJ0kJVzcw5VZ58hAM8yqE5y5Dl3HchqzjOGK0so4Vm9vEMwIScZ8abGxYopAWuvI5zcXjiOs5zWaQ42/JP89Flr1onyMqiw+V0MsIoiUR/aRxk5es6W2FGkiRpoap25pwqT+5lH1ew8bwy5EyyYTn3lTPfMCdYThMnOcVRRnghF43va8abGxYopAWuFus4n235p7nsadDTB2/7G9jx5/C5b0Pz0tmdICuLDuvp4BU8jyAAnvW65mNdbkmSpHpU7cw5VZ58Dmu5jA0T9ptJhrzjfnjLJ+BT98EjT8N3D02dDcu5r5z5WlnGYY5xCWu5lAtZTrMZb445B4W0APX05ZNBbxG+2QcvvRhoPX3/fKzjXP7QXp5nolzZ3koXXaylyOD4+D2oThW6XLn/ziG4oFT/uOdxeMXm0yfIrZ3ndswdbGMXu4F8AlxGEy/goilnkC6fSCvn1riZ7Y5NlCRJC1Y5d/7lvdC5Cl60HtavzPdVO3NuYDUDDJ1Thuzpg/fdBRGwbgWMjMHep2DLhmdnw8rct45VLKNpfOUQwIw3DyxQSAvMHffnP8Kjp2BdO4yehC8+AtdedvpkMTCS13eeSz308m4+xSGOcpwxHmA/e/geH+ANz/rQX1426ma2z+o5y5X7Eydh1fJ8IgJ48CD8wHPP7wR5rkWHuV6XW5IkqV5U5s7Rk/DkEegfzl8OrV85u8w5VZZcThOraOVS1s84Q3bvPZ2LI6C1OW/fNwDLJ30aPlvuM+PNPQsU0gJyx/1wy9/mD+grl8PSyNdHT8K9++HVL8gniuIQ3Hz13Lblg9zFIxxkFa100MoIozzCQT7IXXyYt85JT4PeYh7z2LEchsfyCailCQaGJ54g7+Br3MZd7KfIRgrcwnXcxMumPa5FB0mSpIkm587mJjg0mAsB3zoAy5pmlzmnypKHOMp6OijQPuMM2VvMbRoZO12caGmCh48N8fhlX+d5/N2EPGjuqy0LFNICUe6+Vj5JjCU4eAwuXJFvHx+Dff35Q/rNV5/7UIdzdQ+PsJIWWlkGQCvLSCTu4RFgbj70dxXyfBMvXA9ffSxvS2niCfIOvsa7uJ1VtHARHfQzyLu4HWBCkaJRlkGVJEmab1PlzpERWNOet/UdgVdeNrvMOV2W/C4H+GveMePjdBVyceKBp/LtliZ4fOQYh5YOsHnLfWyoyIOPc5ijHDf/1VBNJ8mMiB+OiO9ExMMR8StT3H9tRAxExH2lf79Wi3ZKjaDcfa18kmheAk1L4cgIDI/C67bAR34Cdr5q7osTQGkayZlvr4YdW3IhYnkTvPySvO2ZIXjJRrh1e37dt3EXq2hhNe0sYQmraWcVLdzGXePHaaRlUCXNnnlEks7NdLlzdAzalsGbXzL7zFmtLLljCzQtgSs25OLEoUE4nI6x4br/RWfnsfE82MxSfsf8V3M160EREUuBPwJ+CNgHfD0iPptS+takXb+UUnrNvDdQahCVExONnsy9BUZG8n1NAUePw+rW/Md5Pr2cy/gCDxIELTQzwihHGeFaLp+z59zamQsR3XuhdwSuf2F+3ZUnx/0UuYiOCY9bRSv7OT1BReUyqMD4ZTd7rKJLC4x5RJLOTU8ffOYBeGYQTgEnTwHN1c+d1cqSlfmwpSn37PjTLX/B5s5hKr+vH+YExxk1/9VYLYd4vBR4OKX0PYCI+CRwIzA5EEiaRnnVikJbnjX5ySO5Kry2HU6M5ZPEsqXw3uvmp9dEpbfzg+zjGQ5yhAGGWE4zl7Get/ODc/q8WzvP/Fo3UqCfQVZXzP58hGE2cnoGp14Os4k1Ex5XrWVQJdUd84gkzVA5ey5bmodzHDiWCxSnTsHRsermzmpmycn58Ascp5/hCXnwGCOspm3C48x/86+WQzw2Ak9U3N5X2jbZNRFxf0R8LiKumO5gEfFzEbEnIvYcOnSo2m2V6lLletOXr88T/5RX7mhfDhevhtt+DG66cv7btpUu3s/ruZ4reQmbuZ4reT+vr3kF+hau4wgj9DPIKU7RzyBHGOEWrhvfp7wGdqVqLIMqqS5VLY+YRSQtdOXs+eKNedjEhpXQvgzGTlU/d85llpwqDybgciZWVsx/86+WPSimGj6UJt2+F7gkpXQsIm4APg08b6qDpZT+BPgTgG3btk0+jrQglVetgHyCuGYzPHggT0z0hiufPbxhvtXjLMjliTArV/F4DzdOmCBzrpZBlVSXqpZHzCKSFrpy9lwSOXd++0AuVAB87I3Vz51zlSWnyoNv4fu4nycoMmj+q6FaFij2ARdX3N4E9FXukFI6UnF9d0R8MCLWppTsZyNxetWKQmu+vWFlniDylZfliYk0tZt42VmXFZ2LZVAl1SXziCTNUGX23LAy/yvfruWXYudjqjw4eRU389/8q2WB4uvA8yLiOcB+4I3AT1buEBEbgAMppRQRLyUPSXl63lsq1akdW/I4QICOFhgYmd1607VWnvCzt5hPgLXsAVKPvT8kzQnziCTNUL1lz2pnR/Nf7dWsQJFSGouIW4DPA0uBj6SUHoiIt5Xu/zBwE/D2iBgDhoE3ppTsMqlF5Ux/eCesWlG6fzbrTddS5YSfm1bnavyuu08vDypJc8E8IklTmy6D1kv2NDsuTLEQz6/btm1Le/bsqXUzpFmr/MNbWaVeiH94d945cbgKnL7tcBWpOiLiGymlbbVux2JgFpHUyBohg5odG9eZ8kgtV/GQdBaVq3QsiXxZaMvbF5reYj4BVupoydslSZI0fxohg5odFyYLFFIdW0x/eLsKuTpfaWAkb5ckSdL8aYQManZcmGo5Saaks5i8SgfM7R/eyTMX72DbvE0UVG+TLkmSJC1Wc5FBq50zzY4Lkz0opDq2Y0v+Q1schlMpXxaH8vZq66GXXeymyCCbWEORQXaxmx56q/9kUyhPulRohX39+bKexjlKkiQtFtXOoHORM82OC5M9KKQ6Np8zJXezhwLtFGgHGL/sZs+se1HMdAmorZ2eVCRJkmqt2hl0tjlzuixpdlx4LFBIdW6+/vD2cphNrJmwrYM2ejk8q+O6BJQkSVLjqWYGnU3ONEsuLg7xkARAF2sZYGjCtgGG6GLtrI7bCLNAS5Ikae7MJmeaJRcXCxSSANjBNooMUmSQU6Tx6zuYconiGWuEWaAlSZI0d2aTM82Si4sFCkkAbKWLW7mBAu3s42kKtHMrN8x6/gmXgJIkSVrcZpMzzZKLi3NQSBq3la6qLyvqElCSJEk635xpllxc7EEhaU65BJQkSZLOl1lycbEHhaQ55xJQkiRJOl9mycXDHhSSJEmSJKnmLFBIkiRJkqSas0AhSZIkSZJqzgKFJEmSJEmqOSfJlOZATx9074XeYl6jeccWJ/aRJEnS/DGPqhFZoJCq7I774X13wehJWLcCjo/ltZtdDkmSJEnzwTyqRuUQD6mKevryySAinwxGxmDvU3DyVK5gS5IkSXPJPKpGZg8K6QyO9MDBbhjphZYuuHAHrNo6/f7de2H0FKxrzyeF1ua8fd8ALPd/myRJks7DuWRS86gamT0opGkc6YHHd8FoEZZvypeP78rbp9NbzCeDkbHT21qa4NCxPPZPkiRJOhfnmknNo2pk1tCkaRzshqYCNJf+kJcvD3afrlhPnnxoWRNs7IAHnsr3tzTBwAg0L80TEzW6HnrpZg+9HKaLtexgG1vpqnWzJEmSFqxzzaTfexramqF/ON9X73nUfKlK9qCQpjHSC00dE7c1deTtkE8Eu+6G4jBsWp0v9w/AkRG4YkOpUj0IKcF7r2v8CYl66GUXuykyyCbWUGSQXeymh95aN02SJGnBOtdMurEDvnMYOlfVfx41X2oye1BI02jpyl3omiu6wo0N5O2Qq9SFNii05tuFVnjuGjgxButX5hPCKy9bOEs6dbOHAu0UaAcYv+xmj1VuSZKkOXKumfR56/Ll/gG4dE1951HzpSazQKFFb7pJhy7ckcf3Qa5Sjw3AWBE23py39RZzlbpSRwvs64edr5rf1zAfejnMJtZM2NZBG70crlGLJEmSFo5qZtJL1+YJMT/yE/P7Gs6V+VKTOcRDi9qZJh1atRUuuTVXq4/vy5eX3AqPrYWdd8I3++Dz34Gnjp4+3sDIwp18qIu1DDA0YdsAQ3SxtkYtkiRJWhgWayY1X2oye1BoUTvbpEPlf2XlMX6FNnjpxfDFR+Du78EPPAdamqE4BDdfPf+vYz7sYBu72A3kyvYAQxQZ5Ga217hlkiRJjW2xZlLzpSazQKFFbaQ3V6krVU46VKmnD97xaTh4DC5cCZdfCNdeBvfuh6/1wuu25BNBPY7vOxfTzaS8lS5u5YYJ993MdscHSpIkzdJiyaRT5UzzpSpZoNCidrZJh8rKVeqDg7C2HYZH4SuPwSs2w6tfsHDmnSjPpFygfcJMyrdyw3iRwhOGJElSdS2GTHqmnLmTHbVunuqEc1BoUbtwR55kaLQI6VS+HCvm7ZBPAjvvhJ++Hb5zKK8pffwktDbn7nMPHmycMX4zUTmT8hJi/Ho3e2rdNEmSpAXrbJkUTvec2LMPhk7kZUUbKZOaMzUT9qDQojHdzMiX3Dpx+8ab8/bKsX3pFKQlcGSY8bLe8qVw8GjjjPGbCWdSliRJmlvnmkmhoufEsdxz4pmAfQP5vkJrY2RSc6ZmwgKFFoXyzMhNhYkzI19y67MnHSqrXFN6dVvuQldoh5MnobUpd627cAXcur0+x/idjy7WUmRwfA1qcCZlSZKkajmfTAqnc+mFK3MmXVuKakePw8nUGJnUnKmZcIiHFoXKmZFjSb5sKuTt0+ktQkdLvn75hTAyCinBWIIrN8K2TfAHr6vvE8G52sE2igxSZJBTpPHrO9hW66ZJkiQ1vPPJpHA6l5Yz6fAoXNAKbcsaJ5OaMzUTFii0KIz05pmQK003M3JZVyGP5QNYvzJPPhSRbxda679KfT7KK3UUaGcfT1OgfXyCTEmSJM3O+WRSOJ1Ly5m0tRkOD8KF7Y2TSc2ZmgmHeGhRmOnMyJV2bMlj/SBXrJc1wQvWNc5J4Hy5UockSdLcOJ9MChNz6boVOZcWhxovl5ozdTYWKLQgTZ58qH0LHP5svq+pI58Ixop58qHpbO3Mf/S79+ZudV2F+l1TWpIkSfWnGpkUzKVaPCxQaMGZavKhw5+FtT8Kg3unnhl5Ols7/cMvSZKkc1fNTArmUi0OFii04FROPgSnLwf3wmU7a9UqSZIkLSZmUuncOUmmFpzznXxIkiRJqhYzqXTuLFBowWnp+v/bu/c4u+v63vevz5p7JslkQsidAREUEIPagFpr0UotsOulU+jtWK16Nru2nL17djmt28vWHlpPa+M+Z7dR0XZ3V9vuVpuG6qlRNF5w1wo2UhhCAQ0RhmQCSWAyk0zmvr77j98aWMSZzCRz+a3LkF50JgAAIABJREFU68ljHmut3/plzeeXX5Lfm898v99fNp+v3FwWH5IkSZIWiplUOnM2KFRz1nZniw2N90MqZo8T/dl2SZIkaSmYSaUzZ4NCNWflFjj/lmye3+iB7PH8W+a2+JAkSZK0EMyk0plzkUzVpJVb/MdfkiRJ+TKTSmfGERSSJEmSJCl3NigkSZIkSVLubFBIkiRJkqTc2aCQJEmSJEm5s0EhSZIkSZJyl2uDIiKujYiHI2JfRLx7mvcjIv6o9H5PRLwsjzolSVLtMo9IklQZcmtQREQD8FHgOuAy4Bcj4rJTdrsOuLj0dRPw8SUtUpIk1TTziCRJlSPPERRXAftSSvtTSmPA3wBvOmWfNwGfTpm7gFURsWGpC5UkSTXLPCJJUoXIs0GxCXi87PWB0rYz3QeAiLgpIvZExJ4jR44saKGSJKlmLVgeMYtIkjQ/eTYoYppt6Sz2yTam9MmU0taU0tZzzz133sVJkqS6sGB5xCwiSdL85NmgOACcV/Z6M9B3FvtIkiSdLfOIJEkVIs8GxT8DF0fE8yKiGfgF4POn7PN54K2l1bNfAQyklA4tdaGSJKlmmUckSaoQjXl945TSRETcDNwBNAB/llJ6ICJ+tfT+bcAu4HpgH3ASeHte9UqSpNpjHpEkqXLk1qAASCntIrvol2+7rex5An59qeuSJEn1wzwiSVJlOG2DIiK6T9mUgKPAvSml44tWlSRJkiRJqiuzjaB4wzTbVgNbIuKdKaWvLUJNkiRJPyQi3jrd9pTSp5e6FkmStPBO26BIKU07xzIizgc+C7x8MYqSJEmaxpVlz1uB1wH3ADYoJEmqAWe1BkVK6bGIaFroYiRJkmaSUvo/yl9HRAfwFzmVI0mSFthZ3WY0Il4IjC5wLZIkSWfiJHBx3kVIkqSFMdsimf8/2cKY5VYDG4C3LFZRkiRJpzollxSAy8imnEqSpBow2xSPbae8TsBTwPdTSmOLU5IkSdK0ynPJBPBYSulAXsVIkqSFNdsimXcuVSGSJEmnYy6RJKm2zWkNiojojojvR8RARAxGxPGIGFzs4iRJkqaYRyRJqm1zvYvHh4E3pJQeXMxiJEmSTsM8IklSDZtrg+JJw4BONdgDh3fCSC+0dsHabli5Je+qJEk1zDwi1QlzplSf5tqg2BMRnwH+nrLbi6aUdi5KVap4h3bAD26F4jg0nwuTI/DYNjj/Fi8ekqRFYx6R6oA5U6pfc21QrCS71/jry7YlwEBQhw7tgAf/HUyeBAowdgRGD8HKrVmn2wuHJGmRmEekGrf/I7D/A5DGIVqhOAYTx6D9ReZMqR6ctkERETenlLanlN6+VAWpsg32wL73wMRxiGaIgMmh7OIx9DA0tOZdoSSp1phHpPpwaAf84HezkROFFkgTMHoA2AyjB82ZUj2Y7S4e71iSKlQ1Du+E8f7sohEB0QDRCBSfnSMoSdICM49IdeDx7UBAoRVIUGgCGrLRumNHzJlSPZjTbUalKSO9QAEKbUAR0mS2PU0AE9kCRpIkSdKZGj0IDcuhoYVncmY0QBrJmhXmTKn2zbYGxZYZ7i8eQEoprVyEmlTBWrugZW225kRDO0yOQnEUCgXovMZ5gZKkRWEekepAy6YsY06MluXMUnPiee83Z0r1YLYRFPenlFZO87XCMFCf1nZD2wXQsAJohEIzNC2H5T8Cz39/3tVJkmqUeUSqA+fdDIxD46psgcxogIZlcOHvwIYb8q5O0lKY6108JCDrXF/0e/D4x2HgLiBBxyvgvF+zqy1JkqSzN9WEeHx7Nt1j2YVZ08LmhFQ/ZmtQ/G1EnJNSempJqlFVWLkFXvTxvKuQJNUR84hUJzbcYENCqmezNSiKZKGgCfgq8EXgOymltOiVSZIkZcwjkiTVgdOuQZFS+v2U0k8A1wP3kd3m656I+B8R8daIWLcURUqSpPplHpEkqT7MaQ2KlNJx4PbSFxFxGXAd8GngpxatOkmSpBLziCRJte20DYqIeNlp3v56SukjC1yPJEnSc5hHJEmqD7ONoJi64LcCW8mGVQawBbgb+LHFK02SJAkwj0iSVBdmW4PitSml1wKPAS9LKW1NKf0I8FJg31IUKEmS6pt5RJKk+nDaBkWZS1JK90+9SCntBV6yOCVJkiRNyzwiSVINm9MimcCDEfGnwF8CCXgL8OCiVSVJkvTDzCOSJNWwuY6geDvwAPAfgN8A/rW0TTXg0A749lb42mr4+nr4lxthsCfvqiRJ+iHmEanC7P8I3HkBfHVl9rjfJWslzcNcR1D8KPCJlNL/u5jFaOkd2gEP/wZMDkGhDZiEp78MY0/Cpdth5Za8K5Qk6RnmEamC7P8I7P9AliEbVmV5cv8Hsvcu/M18a5NUneY6guJXgHsj4tsR8eGIeENEdC5iXVoij2+HNA4Ny6DQnF1gCq0w/Agc3pl3dZIkPcevYB6RKsbjf1xqTrRDFLLHQlu2XZLOxpxGUKSU3goQERuBG4CPAhvn+utVuUYPZpN4C2VnMlqgeAJGenMrS5KkH2IekSrLxNPZyIlyhbZsuySdjTld0CPiLcCrgRcDR4HtwP9cxLq0BAZ7YHQYxvphImC0BRpboB1oboPWrrwrlCTpWeYRKV+DPdkI25FeGOiE4w0w+RSMN0NLI6xogcIoNK7Ou1JJ1WquP3H4/4BHgNuAr6eUHl20irQkBnvg3lvhoQ7YfBgaJqB1EkbHYQRovALWduddpSRJz2EekXIy2AOPbYPGTvhBI/zTXbC5Gc47kb0/UoQYhmUJLnh3vrVKql5zWoMipbQGeAfQCvxeRHwnIv5iUSvTojq8E/55AO5ZDd99Hpxoy7Y3TcK/Xgx7fsUFMiVJlcU8IuXn8M6sObFvFHZ9D54IeLgLHt4Mw02wbBRGmuAHv+QCmZLO3lyneKwEuoDzgQuADqC4eGVpsR16CB4ahpTg0DroWwuRYN0J+G9vhutdckySVGHMI1J+RnrhWAfcuR8mixABJ1vg6Ar4w7fBsiZY1Qov2wz/Lu9iJVWtuU7x+Meyr+0ppQOLV5KWwsNNsHwUBluyJkUhoG0EDq2AyQRdNigkSZXHPCLlpLUL9t0HxQTNjTA5no2aONqRTRFOCdatMENKmp+53sXDwf415oEXw7qHoDgKJ5qgbQyWj8CXLofOZdB9ed4VSpL0XOYRKT9ru2H0a7C6AfobofEEtI7AV7fA6CQ0NcDa5WZISfNz2gZFRHz+dO+nlN64sOVoqXS8BHonoemrsP4pONQOd7wYntoMn7gWtmzMu0JJkjLmESl/K7fAwC9B05eh83Ho74Sv/gg80pG9/9qL4P3XmCElzc9sIyheCTwO/DVwNxCLXpGWRPflsO0pmHg73DsAR4agtQCfuAZuuCLv6iRJeg7ziFQBrn0jbOuAiSIcHIChIXhhIWtMmB8lLYTZGhTrgZ8EfhH4JeALwF+nlB5Y7MK0uLZshFuuhp17obUx63p3X27XW5JUkcwjUgUwP0pabKdtUKSUJoEvAV+KiBayYPCNiPi/U0p/vBQFavFs2egFRZJU+cwjUuUwP0paTLMuklkKAv+GLAxcAPwRsHNxy5IkSXqWeUSSpNo32yKZnwIuB74I/E5Kae+SVKUFteM+2P4tODgIm1bCza96dp7g6d6TJKkSmEekxXO2WdAMKWkxzDaC4peBIeAFwL+PeGZNqgBSSmnlItamBbDjPvitXbCyBTasgGMj2espM73nBUaSVEHMI9IiOF1OPF0WPNtfJ0mzmW0NisJSFaKF19MHt/wDDI5CsQiNBVjVlr23/VvZ48qWZ7eVv+fFRZJUKcwj0sKbLSeeLgtu/5YZUtLi8IJfo3r6YNud0D8My5tgfBIODMCJ0eyCcnAw+1rZ8txfN/WeJEmSatNccuLpmCElLRYbFDVq517oXAadbTBWhKYGaAg4OpR1yjetzL4GR5/766bekyRJUm2aS048HTOkpMVig6JG9fZDRytcdR6MTsDIRHbhGRzNvm5+VfY1OArHhrOhfceGn31PkiRJtWkuOfF0zJCSFsustxldDBGxGvgM2W3CHgV+LqXUP81+jwLHgUlgIqW0demqrF49fbD/KbjrMVi7Aq7cDN87mg3j62yDD1//3PmB5Sswv+91zh2UJNUH84jqRU9fNmqitx+6OqGlEQZG4LL12fvfeXzmnDidqffNkJIWWqSUlv6bRnwYeDql9PsR8W6gM6X029Ps9yiwNaV09Ew+f+vWrWnPnj0LU2yVmZpTOFGEvU9AIbLO9os3QEMBbrkatmzMu0pJUh4i4rv+z/WzFjOP1HMWUWWZyoady7JREwMj8MhRiIALz3l2W/9Jc6KkpXG6PJLXFI83AZ8qPf8U8Oac6qg5U3MKX3Au/OgFsKoVJhIcHPCiI0nSKcwjqnnl600UInt8/hrY1JE9P3AsezQnSqoEuUzxANallA4BpJQORcTaGfZLwJcjIgGfSCl9cqYPjIibgJsAurq6FrreqtHbD5tXZc/Xr8i+iim7+HjRkSTpORY0j5hFVInKs+GUjtYsG37w9fnUJEkzWbQGRUTsBtZP89Z7z+BjXpVS6isFhq9ExEMppW9Ot2MpLHwSsmGVZ1xwjejqfHYO4ZSBkWy7JEn1ZinziFlElchsKKmaLFqDIqV0zUzvRcSTEbGh9NOKDcDhGT6jr/R4OCJuB64Cpm1QKNN9eTbPEJ47p/CdV+ZblyRJeTCPqN6ZDSVVk7zWoPg88LbS87cBnzt1h4hoj4gVU8+B1wN7l6zCKrVlYzaHsHxO4Rsvy+YfvuMz8MEvZ4slSZIk84hq33TZ8NT1Jnr6soxoVpSUt7zWoPh94LMR8U6gF7gRICI2An+aUroeWAfcHhFTdf6PlNKXcqq3qmzZ+OxFp3zl5s2rsiF+2+50ISRJkjCPqE6UZ8NTmRUlVZJcGhQppaeA102zvQ+4vvR8P+DdlOepfOVmePZx514vOpKk+mYekcyKkipLXlM8tER6+7P5huU6WrPtkiRJqm9mRUmVJK8pHloCPX2w/ym46zFYuwIuXQvrVrhysyRJUr3q6ctGR/T2Z3mwpTHLht7lQ1IlsEFRoz7ydfjDO+H4KBBwchyeHoIXb4CGgis3S5Ik1ZOePrh1N+zeB40BXatgdCJrRkTAhed4lw9J+bNBUYN23Ae/+zVoLMCadjgxBk8ch8kiHByAP3qzcwolSZLqRU8fvOeLcFcvNBeyjPhYPxwfgys3w4rWbATF1KiKd15pVpSUDxsUNWj7t7JO+LKm7HFFCzQ1QKHUHfeCI0mSVD927oUjQxBAa2OWDwkYGoMDA/D8Rvjg6/OuUpJcJLMmHRyEFc0wUXx2W0sDHHM+oSRJUt3p7c+mc7SX5cPGAkxOwpET5kNJlcMGRQ3atDJb8GgywfgkpJStQdHWCN2X512dJEmSltLUYpjLW57Nh+OTkCIbZWs+lFQpbFDUoJtfBRMJVrZk3fHjY1m3/P+62ukdkiRJ9ab7cji3PWtOnLMs23ZyLFsU8/3XmA8lVQ7XoKhBN1yRPW7/VjbdY8vqrGkxtV2SJEn1Y8tG+NB18LFvP3v7+Vd0wbteaXNCUmWxQVFhTr03dfflZ3fhuOEKGxKSJElLaaFy3GLYshFu+9m8q5Ck03OKRwXp6YNtd0L/MGxelT1uuzPbLkmSpMpljpOk+bNBUUF27oXOZdl9qAuRPXYuy7ZLkiSpcpnjJGn+nOJRQe49mHXbB0ehowUuWQdrl2fDBCVJklRZyqd03HMQXt4FtD37fkerOU6SzoQNigrR0wf/ehgGRoAEhwvQdxy2boYXnJt3dZIkSSq34z64dXd2u85zl8NkEb75A7j6Qli/IttnYCRbi0KSNDdO8agQv7sbDh+HgWE4OQ5jk3DkOOw54L2pJUmSKkVPH7zr7+Df7oAnTkBbM4xMwOgEjIzDvxyEYspGxfafNMdJ0pmwQVEBevpg9z5oaczmKwKcGIPGBoionNWfJUmS6tnUQpj3HITGAjQEHDiWjZ7oXAar27IfMh04lmW6W642x0nSmXCKRwXYuRcayC50TQ1Zo2K8CCRo8wxJkiRVhKmFMMcmoL05m97R2ABHh+D8TjgyAj/3Evjg6/OuVJKqkyMoKsDUvbJHJ7MLXUpAykZRvOL8vKuTJEkSZJmtoxU62mB5C0yWMtvweLbeRFODUzokaT5sUFSArk64ZC2sbgUia1RMJljXDr/2yryrkyRJEmSZbWAELl2bTe9Y055tL6bsB0zvv8YpHZI0HzYoKkD35dBQgCu74KJzsvmL65bDh673IidJklQpui/PFr5sbsxGubY3Q1sT/OwW+ItfhBuuyLtCSapurnBQAbZszBZR2rk3W3/itRdlF8DpmhPl99vu6px5P0mSJC2s8szWOwLXXbJ4WczMJ6ke2aCoEFs2zn7RmVo5unMZbF6V3b5q252uEC1JkrRU5pLZ5svMJ6leOcWjikytHN3ZBoXIHjuXZdslSZJUG8x8kuqVDYoqMrVydLmO1my7JEmSaoOZT1K9copHFWluhDsehrFJ6GiBS9Zla1Z0deZdmSRJkuajfM2J/U/ByAS84Nxn3x8YMfNJqn2OoKgSPX1wcAAGh6GpACfH4c798MhR77ctSZJUzabWnOgfztac2NQBd/XC945ktzDtH87uHmLmk1TrHEFRJXbuhQvPgc0d8OBhGBiGlS1w3ioXS5IkSapm5WtOAFxcGjlxcABaS6Nl33mlmU9S7bNBUSV6+7OOeiFg3YpsWzHBgWP51iVJkqT5mcp55Z6/JpvK+2c/n09NkpQHp3hUia7ObO5hOeciSpIkVT9zniRlbFBUie7Ls7mH/cPORZQkSaol5jxJytigqBJbNsItV2dzEw8cyx5vudq5iJIkSdXOnCdJGdegqCJbNnqhkiRJqkXmPElyBIUkSZIkSaoANigkSZIkSVLubFBIkiRJkqTcuQbFIuvpg517s/tbd3VmqzE7v1CSJGn+zFmSVFtsUCyinj54zxfhyBCMTsADT8Kex+FD13nxlCRJOls9ffDxb8NXvgfntMNLNma35tx2p3e/kKRq5hSPRfSxb2dNiQPH4NBg9vjAk9l2SZIknbmePnjvl+DzD8Cx4Sxfff2R7IdBncuyERWSpOpkg2IRfelBePI49I/AyASMTcLxEfjGvrwrkyRJqj49ffCOz8L/3J+NmGgsAAFPD2WjVDtas+kekqTqZINikfT0wRMnIAFNASnB8DgUExwbybs6SZKk6tLTl03heKwfljVlzYnBMSgWobkRDgzAwEi2FoUkqTrZoFgkO/dCWyMEUAQismbFyASsas25OEmSpCqzc282haOhkOWq5S3ZD4BOjGV5a3IS+k9mC2VKkqqTDYpF0tsPF56TdfQBJorZxbOhAK+5KNfSJEmSqk5vfzaFY3MHjE5mTYqOFpgsZk2K8ztdIFOSqp138VgkXZ3ZYk3HR2FoPLt4JrIL6a+9Mu/qJEmSqktXZ7buxJXnweBIKV+lbFTFSzd6lzRJqgWOoFgk3ZdnoyWuPA8uOgdWt8H65V48JUmSzkb35dkUjuZGeO1F2UiKjlZ4w2XmK0mqFY6gWCRbNmbDDHfuhZbShbT7ci+ekiRJZ6M8W/WOwHWXmK0kqdbYoFhEWzZ60ZQkSVooZitJqm1O8ZAkSZIkSbmzQSFJkiRJknJng0KSJEmSJOUulwZFRNwYEQ9ERDEitp5mv2sj4uGI2BcR717KGiVJUm0zj0iSVFnyGkGxF+gGvjnTDhHRAHwUuA64DPjFiLhsacqTJEl1wDwiSVIFyeUuHimlBwEi4nS7XQXsSyntL+37N8CbgH9d9AIlSVLNM49IklRZKnkNik3A42WvD5S2TSsiboqIPRGx58iRI4tenCRJqgtzziNmEUmS5mfRRlBExG5g/TRvvTel9Lm5fMQ029JMO6eUPgl8EmDr1q0z7idJkurHUuYRs4gkSfOzaA2KlNI18/yIA8B5Za83A33z/EypIvTQy8fYzV08QgCv4CLexevYQlfepUlSTTGPSNIPM4uqUuWyBsUc/TNwcUQ8DzgI/ALwS/mWJM3P1MXgC9zLMOOsp4NlNPMNHuQAT/N73OiFQZIqi3lEUk3Zwd28h7/lSQZZTgudtJtFVTHyus3oz0TEAeCVwBci4o7S9o0RsQsgpTQB3AzcATwIfDal9EAe9Urz1UMvN/JfuZrf49N8i6cYIpE4wnGKJFbSxmEG2cmevEuVpLphHpFUT6by6Fu4jf0cZpxJxpnkCMdppMEsqoqQ1108bgdun2Z7H3B92etdwK4lLE1aUD308nG+yj/wLzzFCVppIlFknEmKFFlJG0c5zvmsYYCT9HI075IlqW6YRyTVg1PzaCIRBKOMk0isoJVBhmkgzKLKXSVP8ZCqWg+9bGMXD3OIIokiiVEmaKDABEWKFBljggLBCOO00EQXa/IuW5IkSTViujyaSuv8Fohn8mgisZ4Os6hyV8m3GZWq2k720Ek7Y0wwSZGWUj8wCBooMElihHEKBIMMs5aVdLM156olSZJUK6bLow00lFoVMEmREcZIYBZVRXAEhbRAdnA329nNQfrZRCcBvJpL6GAZhxmkhSYmGGWSIito5TgjAKykjddwqSsnS5IkaV6mpnPcxT4SMMwor+GyU/JokUYaKQCjTBIU+Aku5X282Syq3NmgkBbADu7mt/gMK2llAx0cY4jHeIo2mrmUjfTRz9MM0UoTo4wzxgRdnMOHuJEbeHne5UuSJKnK9dDLe/lb9vEkK2gF4ABDfJkeruTCH8qjDRTYbB5VhbFBIS2A7exmJa2soh2AVbQzzBjf5VEuZj2v5TK+xfdKoytW8xNc5ogJSZIkLZid7OEwg6ykjTaaAdhAB4cY4DGeMo+qKtigkBbAQfrZQMdztq2jg2HG6aSdXo5yA1fRzVYvApIkSVpwvRxllHE6WPbMttUsZ4xJRplgnAnzqCqeDQppAWyik2MMPTOCAmCQYS5kLR+kO8fKJEmSVA+6WMMDHGSE8WdGUIwwziqWcR1XmElVFbyLh7QAbuYaBhnhGEMUKXKMIQYZ4Wauybs0SZIk1YFutrKWlQwyzElGOckogwxzLiu8O4eqhg0KaQHcwMv5MD/PKto5xACraOfD/LwLDkmSJGlJbKGL3+NGXsOljDPJGJNczSV8iJ9zSoeqhlM8pAVyAy+3ISFJkqTcbKGLj/P2vMuQzpojKCRJkiRJUu5sUEiSJEmSpNzZoJAkSZIkSbmzQSFJkiRJknJng0KSJEmSJOXOBoUkSZIkScqdDQpJkiRJkpQ7GxSSJEmSJCl3NigkSZIkSVLubFBIkiRJkqTc2aCQJEmSJEm5s0EhSZIkSZJyZ4NCkiRJkiTlzgaFJEmSJEnKnQ0KSZIkSZKUOxsUkiRJkiQpdzYoJEmSJElS7mxQSJIkSZKk3NmgkCRJkiRJubNBIUmSJEmScmeDQpIkSZIk5c4GhSRJkiRJyp0NCkmSJEmSlDsbFJIkSZIkKXc2KCRJkiRJUu5sUEiSJEmSpNzZoJAkSZIkSblrzLsALY4eetnJHno5Shdr6GYrW+jKuyxJkiTlwGwoqRo4gqIG9dDLNnbRzxCbOYd+htjGLnrozbs0SZIkLTGzoaRq4QiKGtNDL/+ev+Awg6xlJZewgfWsAmAne+yUS5Ik1YmpUROf47s008hLOZ8CQSftgNlQUuVxBEUNmeqOH2aQNSxnmDG+zT6e4BgdLKOXo3mXKEmSpCVQPmoilf6byoWA2VBSRbJBUUN2sodO2lnLSkaZoI1mWmniIQ4xwEm6WJN3iZIkSVoCU7mwk3ZW0U4Qz+RCwGwoqSI5xaNG7OBubuOrjDFBK0000sAGVtFCI4cZpJ8h3snVeZcpSZKkRbCDu9nObg7SzyY6CeDVXALApWzkn/g+LTRyjJP0M2Q2lFSRbFDUgN/kL/koX2OccQoUmKRIIrGcFo4TrGUlt3C9cwwlSZJqTA+9/CZ/xZ08TIFgFcs4BBzmOG00cxXPZx0d/CgXcw+PAtBJO+/karOhpIpjg6LK/SZ/yX/hDgACmKDICUZpp4UBhnkdL7I5IUmSVGN66OVW/p47uJ8TjBAEjTQwyDDjTLKSNr7Lo1zMejpYRjONvJAN5kJJFc0GRRXbwd18lK8BWXNiSiIxzCiNNHgRkiRJqjE99PIePstdPFIaOZvlvzGKNFNgjAkSiRYa6aSdXo7SxRpHTUiqeDYoqth2dpMoUiBIJAoERRJBUKDAFs7zIiRJklRjdrKHIxwnyH5INZUFE0UmS69OMMIWuvgg3XmXK0lz5l08qthB+ksLYmanMeufU2pSFLiZa/IsT5IkSYugl6OMMsEymgmCJhoAKAJFiqXVyDALSqo6Niiq2CY66SzdNqqJBoIotSjg1/kJbuDludYnSZKkhdfFGlpoZCVtNNNI4ZksmP2gahnNvI83mgUlVZ1cGhQRcWNEPBARxYjYepr9Ho2I+yPi3ojYs5Q1VoObuYYCBdbT8cxIihaa+I/8FB/hLXmXJ0lSRTOPqFp1s5VzWcEERTawinZaAFhOK91s5eu8h9/k3+RcpSSdubzWoNgLdAOfmMO+r00pHV3keqrSVFd8O7tpopFNdHIz19gtlyRpbswjqkpb6OJD/BwfYzd38QibWc0ruIh38TrXH5NU1XJpUKSUHgSIiNl21Sxu4OU2JCRJOgvmEVWzLXRxG+/IuwxJWlCVvgZFAr4cEd+NiJtOt2NE3BQReyJiz5EjR5aoPEmSVAfmlEfMIpIkzc+ijaCIiN3A+mneem9K6XNz/JhXpZT6ImIt8JWIeCil9M3pdkwpfRL4JMDWrVvTdPtUmx3czXZ2c5B+p29IknQWljKP1GIW0fz10MtO9nAvj3KMYVaxjJdwPt1sdTqGJJ1i0RoUKaV539copdRXejwcEbcDVwHTNihqzUf4Ar/L5wgTZNoPAAAUMUlEQVSCFbTSRz+/xWcAbFJIkjRH5hHlaQd3cyuf4zgjDDFKB208zQnaaWEbu7iF621SSFKZip3iERHtEbFi6jnwerLFrGreDu7mA+xkmHEmKXKSMQYZppEC29mdd3mSJNWNes4jOns99PIu/js38Wc8yQAnGQUSgwxTJHGQfjppZyfeFEaSyuV1m9GfiYgDwCuBL0TEHaXtGyNiV2m3dcA/RsR9wHeAL6SUvpRHvUuph15u5XOMMUkzDSQSw4wxSZFRxjlIf94lSpJUE8wjWgw99LKNXdzDoxQIGihwjGGi9PwEIwxwkg6W0Ys3hpGkcnndxeN24PZptvcB15ee7weuWOLScncrf8/3eYIJJhlikhYaaaSBMSaYpMgW1uZdoiRJNcE8ooU0tXZYD4/TRhNNFGinlQkmaabAEKOspp0hRrmAcxngJF2sybtsSaooFTvFox7t4G6+RA9B0EwDRWCYCcaYYJQJEombmfdUWkmSJC2gHdzNb/EZjjFEEwUmmOQJjlOkyARFWmhiojRtt0CwiU76GaKbrXmXLkkVxQZFBdnObpbRTDstNNBAK40EMMokjRR4H29ygUxJkqQKs53drKSVVbTTRjONNNBOM08zxFpW0kwjrTTSQIHL2MTFrHeBTEmaRi5TPDS97HaiqzlIP+20MMo4AEUSf85NNickSZIq0EH62UAHAGtYweM8TQuNTDDJMpoZppk38FJ+jWtsSkjSadigqCCb6OQYQ5zHao5ynAJBK02cxzk2JyRJkirUVIZbRTvLaeU8VvM4T9FGM9dxBd1stTEhSXPgFI8KcjPXMMgIE0zSxTmsp4N2Wnk3P513aZIkSZrBVIY7xlBp3YlJ2mllO2/lg3TbnJCkOXIERQWZGiWxnd2l6R6drjshSZJU4cxwkrQwbFBUmBt4uRczSZKkKmOGk6T5s0GRox562ckeejlKF2ucnyhJklQBzGiSlA/XoMhJD71sYxf9DLGZc+hniG3soofevEuTJEmqW2Y0ScqPIyhy8jF28zCHGGOCDpZxCRvopJ2d7LFDL0mStITKR0zs5wgbWUUn7QDPPJrRJGnxOYIiBz30spsHSCRW0sYwY3ybfYwwTi9H8y5PkiSpbpw6YuIwg+zlIE9w7Jl9OlhmRpOkJWCDIgc72cM5LCdK/7XRTCtN3MtjdLEm7/IkSZLqxk720Ek7nbRTIFjLSgrAQxx6Zp8BTprRJGkJ2KDIQS9HeQnnM8I4w4yRSv89xQm62Zp3eZIkSXWjl6N0sOyZ15eykSKJwwxSJNHPEP0MmdEkaQnYoMhBF2topYkf5WLaaGaQYYLgJ7ncuY2SJElLqIs1DHDymdfr6ODFnMdaVnKAp+iknVu43owmSUvARTJz0M1WtrGLTtr5cS5hgJP0M8S7eF3epUmSJNWVqVwG2VoTA5ykgQJ/xC/blJCkJeYIihxsoYtbuJ5O2u3MS5Ik5chcJkmVwxEUOdlClxc+SZKkCmAuk6TK4AgKSZIkSZKUOxsUkiRJkiQpdzYoJEmSJElS7mxQSJIkSZKk3NmgkCRJkiRJubNBIUmSJEmScmeDQpIkSZIk5c4GhSRJkiRJyp0NCkmSJEmSlDsbFJIkSZIkKXc2KCRJkiRJUu5sUEiSJEmSpNzZoJAkSZIkSbmzQSFJkiRJknJng0KSJEmSJOXOBoUkSZIkScpdY94FVIthehhgJ+P00kQXHXTTxpa8y5IkSfoh5hZJUjVyBMUc9LODXn6ZAT7LCI8wwvc4wjaG6cm7NEmSpGcM08MB3sVjdHOcL5JoYpJ+c4skqSrYoJjFMD0c4j8yxj5G+QEj3MNJ/pEiEwywM+/yJEmSGKaHH3Ajj3A1/XyKCQaZYIhh7qLIKA10mlskSRXPKR6zeJJbmaAPCAASE0xwiJP8Cw205lucJEmqe/3s4Anewzi9QAMwQeI4MAlsYJSHaOfHS+9LklS5HEExi+N8rexVofSVmGA/TXTlVJUkSVI2cuIwt1JkgOyHKc9GuyLjJE5QZIAiA+YWSVLFs0Exq5M8O9AklW2foIPuHOqRJEnKDLCTxDgQBM2l8Z5NwAQwTmKIoJlJ+s0tkqSK5xSPWQQtpWGSARRLj4kCK1wNW5IkLbnyO3QM8y8EbQQNpcwyRJZVGoACiQlaeSlr+DVziySp4tmgOI1stetWYJBnR08koIXlvD63uiRJUv0Zpoen+Dgn+AoNnEMLLyFoZoLDZJFulKC91KRINLOJdXyITm7IuXJJkubGBsVpPMGtJEbIZsIUS1sLFOhgHe/LsTJJklQvhunhKB9jiN1MMkyBThIwzF008wImGQQaaWAN4/RSoIXlXM863u+oCUlSVbFBMYNhehhiN0ELQRuJEyQmKLCcRlZ5wZckSYtumB6OsI1RHqbAaib4AUWO0EArQSuTHGEZP84Id9PGFTTxBjroNqdIkqqSDYoZDLCToBFoIGgGWkqLUCUKtOVcnSRJqgcD7KSBThJjBCsp0E5ihAmO0sT5FBmggVZW8mbW88G8y5UkaV68i8cMxumliS4SY1BqTAAUGWIZr8i1NkmSVB+yKRsdFOggMUIja0gkEkMkhr1DhySpptigmEETXTTzQhpZTQKKjJCYoIl1nMO78i5PkiTVgSa6KDJAC5eU1sVqoJFzgWaKPE0rL+VcbnFKhySpJtigmEEH3RRopJWtNHMRTZxDE+tZx4cMAZIkaUl00M0k/RRooY1XEACM0MEbOZ+dnMdt5hJJUs1wDYoZtLGFc7mlNPezlSZe66JTkiRpSZXnkSK9rOA684gkqWbl0qCIiD8E3gCMAY8Ab08pHZtmv2uB/wo0AH+aUvr9payzjS0GAEmSapR5RJKkypLXFI+vAJenlLYA3wP+06k7REQD8FHgOuAy4Bcj4rIlrVKSJNUy84gkSRUklwZFSunLKaWJ0su7gM3T7HYVsC+ltD+lNAb8DfCmpapRkiTVNvOIJEmVpRIWyXwH8MVptm8CHi97faC0bVoRcVNE7ImIPUeOHFngEiVJUo2bdx4xi0iSND+LtgZFROwG1k/z1ntTSp8r7fNeYAL4q+k+Ypptaabvl1L6JPBJgK1bt864nyRJqh9LmUfMIpIkzc+iNShSStec7v2IeBvw08DrUkrTXcQPAOeVvd4M9C1chZIkqdaZRyRJqh65TPEorYb928AbU0onZ9jtn4GLI+J5EdEM/ALw+aWqUZIk1TbziCRJlSWvNSi2AyuAr0TEvRFxG0BEbIyIXQClRatuBu4AHgQ+m1J6IKd6JUlS7TGPSJJUQRZtisfppJQummF7H3B92etdwK6lqkuSJNUP84gkSZWlEu7iIUmSJEmS6pwNCkmSJEmSlDsbFJIkSZIkKXc2KCRJkiRJUu5sUEiSJEmSpNzZoJAkSZIkSbmzQSFJkiRJknIXKaW8a1hwEXEEeGweH7EGOLpA5VQCj6ey1drxQO0dk8dT2WrteGDxjun8lNK5i/C5OsUCZJGlVot/j8rV8vHV8rFBbR9fLR8b1PbxeWzzM2MeqckGxXxFxJ6U0ta861goHk9lq7Xjgdo7Jo+nstXa8UBtHpMqW63/mavl46vlY4PaPr5aPjao7ePz2BaPUzwkSZIkSVLubFBIkiRJkqTc2aCY3ifzLmCBeTyVrdaOB2rvmDyeylZrxwO1eUyqbLX+Z66Wj6+Wjw1q+/hq+digto/PY1skrkEhSZIkSZJy5wgKSZIkSZKUOxsUkiRJkiQpdzYogIi4MSIeiIhiRMx4S5WIeDQi7o+IeyNiz1LWeCbO4HiujYiHI2JfRLx7KWs8ExGxOiK+EhHfLz12zrBfRZ+f2X6/I/NHpfd7IuJledQ5V3M4ntdExEDpfNwbEf85jzrnKiL+LCIOR8TeGd6vtvMz2/FU2/k5LyK+HhEPlv59+w/T7FM152iOx1NV50jVJSL+MCIeKv1duT0iVs2wX1VkhVPVWrYrV2s571S1kvvK1VoGLFdrebBcrWXDU1VsVkwp1f0XcCnwQuAbwNbT7PcosCbvehfieIAG4BHgQqAZuA+4LO/aZ6j1w8C7S8/fDfxBtZ2fufx+A9cDXwQCeAVwd951z/N4XgP8Q961nsEx/TjwMmDvDO9XzfmZ4/FU2/nZALys9HwF8L0q/zs0l+OpqnPkV3V9Aa8HGkvP/2C6a2s1ZYVpaq+pbHemx1bl567qc9+Znotqun6dxbFV7bWs1rLhWRxfLufOERRASunBlNLDedexUOZ4PFcB+1JK+1NKY8DfAG9a/OrOypuAT5Wefwp4c461nK25/H6/Cfh0ytwFrIqIDUtd6BxV05+fOUkpfRN4+jS7VNP5mcvxVJWU0qGU0j2l58eBB4FNp+xWNedojscjLZqU0pdTShOll3cBm6fZrWr/ra+1bFeuBnPeqWoh95WrtQxYrpr/nM2q1rLhqSo1K9qgODMJ+HJEfDcibsq7mHnaBDxe9voAlRuO16WUDkEW6oG1M+xXyednLr/f1XRO5lrrKyPivoj4YkS8aGlKWzTVdH7mqirPT0RcALwUuPuUt6ryHJ3meKBKz5GqzjvIfgp4qqr8O3WGKjk7zEc1n7tayH3lai0DlqvHPFiuWs/bmVjyc9e4FN+kEkTEbmD9NG+9N6X0uTl+zKtSSn0RsRb4SkQ8VOo8LbkFOJ6YZltu95w93fGcwcdUzPmZxlx+vyvqnMxiLrXeA5yfUjoREdcDfw9cvOiVLZ5qOj9zUZXnJyKWA38H/EZKafDUt6f5JRV9jmY5nqo8R6occ8kKEfFeYAL4q+k+YpptFfN3qtayXblay3mnqoPcV67WMmC5esyD5ar1vM1VLueubhoUKaVrFuAz+kqPhyPidrJhTbn8Q7gAx3MAOK/s9Wagb56fedZOdzwR8WREbEgpHSoNmzo8w2dUzPmZxlx+vyvqnMxi1lrL/2crpbQrIj4WEWtSSkeXqMaFVk3nZ1bVeH4ioonsf+b/KqW0c5pdquoczXY81XiOVFlmywoR8Tbgp4HXpZSmC9UV/Xeq1rJduVrLeaeqg9xXrtYyYLl6zIPlqvW8zUle584pHnMUEe0RsWLqOdniUtOueFol/hm4OCKeFxHNwC8An8+5ppl8Hnhb6fnbgB/6yUEVnJ+5/H5/HnhraUXgVwADU0McK9CsxxMR6yMiSs+vIvv35qklr3ThVNP5mVW1nZ9Srf8NeDCl9F9m2K1qztFcjqfazpGqS0RcC/w28MaU0skZdqumrHDGqiA7zEc1n7tayH3lai0DlqvHPFiuWs/bnOR27lIFrCCa9xfwM2QdsFHgSeCO0vaNwK7S8wvJVqa9D3iAbIhd7rWf7fGUXl9PtnL8IxV+POcAXwW+X3pcXY3nZ7rfb+BXgV8tPQ/go6X37+c0q45Xwtccjufm0rm4j2wBth/Nu+ZZjuevgUPAeOnvzzur/PzMdjzVdn5+jGzYZA9wb+nr+mo9R3M8nqo6R35V1xewj2zu9NSfv9tK26syK0xzfDWV7c702Kr83NVE7jvlmGoqA57hsVXttYway4ZncXy5nLsofXNJkiRJkqTcOMVDkiRJkiTlzgaFJEmSJEnKnQ0KSZIkSZKUOxsUkiRJkiQpdzYoJEmSJElS7mxQSJqz0v2Q/yYiHomIf42IXRHxgrP8rKaI+O5C1yhJkqpXRJyY4347IuLC0vPlEfGJUj55ICK+GREvn2cd342I5hne2xYRPzGfz5c0vca8C5BUHSIigNuBT6WUfqG07SXAOrL7X5+pHwP+aeEqlCRJ9SAiXgQ0pJT2lzb9KfAD4OKUUrHUuLh0Hp9/AXAwpTQ2wy5/DPwJ8LWz/R6SpucICklz9VpgPKV029SGlNK9QENEfKP0k4yHIuKvSs0MIuLRiPidiLgnIu6PiEvKPu9a4IsR0R4RX4iI+yJib0T8/NIeliRJqjQR8ZqZ8gXwvwGfK+33fODlwPtSSkWAlNL+lNIXIuKCiHgwIv6kNLLiyxHRVvp134iIP4iI70TE9yLi1WXf/jrgSxHREBF/Xson90fE/1n6/MeAcyJi/RL9dkh1wwaFpLm6HJhpSsZLgd8ALgMuBF5V9t7RlNLLgI8Dt5Rtfy3wDbJGRV9K6YqU0uXAlxa4bkmSVJ1myhev4tlM8iLg3pTS5AyfcTHw0ZTSi4BjwM+WvdeYUrqq9D0+ULb9WrI88hJgU0rp8pTSi4H/XrbPPTw370haADYoJC2E76SUDpR+cnEvcEHZeztLj9+d2h4RG4GnU0ongfuBa0o/xXh1Smlg6cqWJEkVbKZ8sQE4MsfP+EFpxCeUZZGS6TJKM7C5NH1kP3BhRPxxRFwLDJb92sPAxjM6GkmzskEhaa4eAH5khvdGy55P8tz1bUan2X4dcAdASul7pc+9H/h/IuI/L1TBkiSpqs2UL4aB1tLzB4ArImKm/68504zyauAfAVJK/cAVZCM+f51srYspraU6JC0gGxSS5uprQEtE/NupDRFxJXD1WXzWtcAXS5+xETiZUvpLYBvwsgWoVZIk1a4HgYsAUkqPAHuA3ylbA+viiHjTWX52eUZZAxRSSn8HvJ/nZpQXAHvP8ntImoENCklzklJKwM8APzl1Gy/gg0DfmXxORDSQrbL9UGnTi4HvRMS9wHuB3124qiVJUg36AvCastf/O7Ae2BcR95PdYeOM8kmZ1wB3lp5vAr5Ryih/DvwnyG6VTtYg2XOW30PSDCL7fw5JWhoR8WPAW1JKv5p3LZIkqfqU7sTxdeBVp1kc82w+dzPwJyml62bZ72eAl6WU3r9Q31tSxgaFJEmSpKoSET8FPJhS6s3he98IfCWldGypv7dU62xQSJIkSZKk3LkGhSRJkiRJyp0NCkmSJEmSlDsbFJIkSZIkKXc2KCRJkiRJUu5sUEiSJEmSpNz9L/UIPrl8jWoaAAAAAElFTkSuQmCC\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",
    "    indepL = dataL['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",
    "    ax[1].plot(indepL,dep, '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": 14,
   "id": "d4f89915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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c8pshSVKL6eQcUXzvzxRDJbYXOeL0qvvOjogHi2P9YkT8ZaVNEdEVEX8bEduK5/zbiDih6nvvjojrIuJfgF3Aa4A1wLuK57gFOC/yUNXtVcf8yYj4crH9XyJiRUR8rHiO70TuRVJ5jh9ExFuL67Mj4qqIeKJo7/0RcWJx3x9GxNMRsaPY/m8n89pIzcAChdRiipPhxcDjxe2VwN8BvwMcDVwB/FVELCu+5fPAN4FjgPXAZaPs9i3A6cBFEfFO4CqgF1gG/BPwheJx/x54M/A6YAnwc8ALo7Tx3wG/C1wKHAc8BfzFiIf9NPBjwBuLx1006RfhcL8AvA84FphHPv7JvC5bizYsKr7/f0TE2dNsgyRJLaGTc0REvK5oy68UbbsL+JuImBcR84AvAZ8uXocvAP+h6ttnAX8GnAR0A7uBm0Y8xWXAB4CjijYDkFLaBHwIuDeldGRKaUnV91wKXA0sBfYC9wIPFLc3AH8wxuH8KvDzwNvJWeb95MIIwLfIBaijye/fFyNiwbgvjtQkLFBIreOOiNgJPE3+5/q3iu2/CNyVUrorpXQopfQ1YCPw9ojoJp+8P5pS2pdS+mfgzlH2vT6l9KOU0m7gg8DvppQ2pZQOANcDZxaffuwnn3RfD0TxmGdH2d+7gVtTSg8UY0Z/g/ypwclVj/m9lNL2Yl6J/5+qT3Km6M9SSo8Vbb+9aj9jvi4AKaW/Syk9kbKvA18F/IRBktSuzBG5IPJ3KaWvpZT2AzcCC4HzgXPJ8/N9PKW0P6XURy7MAJBSeiGl9FcppV0ppZ3AdeTCTLVPp5QeTSkdKPY/GV9KKd2fUtpDLpDsSSndllI6CPwleTjOaP4TcHVK6btFlnk4pfRC0dbPFu09kFL6fWA+cNok2yOVygKF1DremVI6CriAfGKvDGU4CfjZoqvi9qLb4I+TP3E4HnhxxNjNp0fZd/W2k4A/rNrXi0AAK1NK/0j+tOATwHMR8amIWDTK/o7n8E8OXiJ/QrKy6jE/rLq+i+lPfjnWfsZ7XYiIiyPivqLr6HZy4WIpkiS1J3PEK/d7qGj7yuK+LSmlNNpxRcQREfHHEfFUROwAvgEsicNXFhvttZnIc1XXd49ye6zjOhF4YrQ7IuIjxRCboeI9WIwZRy3CAoXUYopP+z9NrvpDPhl+JqW0pOrrVSml3wOeBY6OiCOqdnHiaLutuv408MER+1uYUrqneP6Pp5T+b2AVuYvmr42yv2fIAQWAiHgVuWvolukc8zSN+bpEXqrsr8iv4fKiq+Vd5AAlSVLb6vAcMXK/URzPFvKxrhwxl0X1sX6E3AvhnJTSIvJQFTg8O1S/DiONd990PA2cMnJjMd/EfyMPHekqMs4QZhy1CAsUUmv6GPCTEXEm8FngHRFxUTFh0oKIuCAiTkgpPUXuprm+GF95HvCOCfZ9C/AbEbEKICIWR8TPFtd/LCLOiYi5wI+APeQly0b6PPC+iDizKAZcD/xrSukHMz/0SRvzdSHPVTEf2AYciIiLyeNiJUnqBJ2aI24HfioifqJow0fI8z7cQ5774SCwLiLmRMQlwJuqvvcoco+G7RFxNMNDZCbrOeCEYq6LWvifwLURcWpkPRFxTNHOA+SMMyciPkqeo0JqCRYopBaUUtoG3AZck1J6GriEPCHVNnJF/dcY/v1+N3AeuWvk75DHM465lnhK6UvA/wv8RdGF8RHyZFqQT3B/AgySu0i+wPAnMNX7+F/ANeReCs+SK/zvmvYBT8N4r0sxdvTD5KAySJ5oc7QxtZIktZ1OzREppe+S59z4I+B5crHlHcX8GvvIE3uuBbYXj/vbqmP9GHm+iueB+4C/n+LT/yPwKPDDiHh+hocCefLM28lzaO0A/rRo31eALwOPkV/jPUxv6IlUijh8mJWkdhcRfwl8J6U01cq/JEnqcJ2UIyLiX4FbUkp/VnZbpE5hDwqpzRXdKU+JiFkR8TbypyR3lN0uSZLU/DopR0TEWyJiRTHE4z1AD1PvKSFpBuaU3QBJdbcC6CNPLrUZuDyl9GC5TZIkSS2ik3LEaeRhE0eSV8hYM8YyqJLqxCEekiRJkiSpdA7xkCRJkiRJpWvLIR5Lly5NJ598ctnNkCSpqdx///3Pp5SWld2OTmAWkSRpdOPlkbYsUJx88sls3Lix7GZIktRUIuKpstvQKcwikiSNbrw84hAPSZIkSZJUOgsUkiRJkiSpdBYoJEmSJElS6SxQSJIkSZKk0lmgkCRJkiRJpbNAIUmSJEmSSmeBQpIkSZIklc4ChSRJkiRJKp0FCkmSJEmSVDoLFJIkSZIkqXQWKCRJkiRJUuksUEiSJEmSpNJZoJAkSZIkSaUrtUAREbdGxNaIeGSM+y+IiKGIeKj4+mij2yhJktqbeUSSpOYwp+Tn/zRwE3DbOI/5p5TSTzemOZIkqQN9GvOIJEmlK7VAkVL6RkScXGYbJElqKv390NcHAwPQ3Q29vdDTU3ar2pp5RJKkEUrKI60wB8V5EfFwRHw5IlaN9aCI+EBEbIyIjdu2bWtk+yRJqo3+frjxRhgchBNOyJc33pi3q2wT5hGziCSpLZSYR5q9QPEAcFJK6Y3AHwF3jPXAlNKnUkqrU0qrly1b1rAGSpJUM3190NWVv2bNGr7e11d2yzrdpPKIWUSS1BZKzCNNXaBIKe1IKb1UXL8LmBsRS0tuliRJ9TEwAIsXH75t8eK8XaUxj0iSOkqJeaSpCxQRsSIiorj+JnJ7Xyi3VZIk1Ul3NwwNHb5taChvV2nMI5KkjlJiHil7mdEvAPcCp0XE5ohYGxEfiogPFQ9ZAzwSEQ8DHwfelVJKZbVXkqS66u3N4zwHB+HQoeHrvb1lt6ytmUckSapSYh6Jdjy/rl69Om3cuLHsZkiSNHV1nDU7Iu5PKa2uyc40LrOIJKmllZRHSl1mVJIkjdDT47KikiSpXCXlkaaeg0KSJEmSJHUGe1BIkjRddez+KEmSVFdNmGPsQSFJ0nT098ONN+ZJo044IV/eeGPeLkmS1MyaNMdYoJAkaTr6+qCrK3/NmjV8va+v7JZJkiSNr0lzjEM8JEmayGhdIAcG8icO1RYvztslSZKaVX8//PVfQ0qwZAmcfjosX94UOcYeFJIkjWesLpDz58PQ0OGPHRrKBQxJkqRmVMk18+blr9274Z574LnnmiLHWKCQJGk8Y3WBTCkXKwYH4dCh4eu9vWW3WJIkaXSVXHPWWbB3b942fz488EBT5BgLFJIkjWdgIHd5rLZ4MezbB1dckU/ymzfnyyuuKH32a0mSpDFVcs2KFXDeebBwYc40e/c2RY5xDgpJaoQmXMZJk9TdnT9R6Ooa3lbpAtnT4/soSVKnaeVcV51rVqzIX5XbTXAM9qCQpHpr0mWcNEm9vQ7lkCRJWavnuibPNfagkKR6V8Gr5zCA4cu+vqaoVGsCPT25y2P1z8jatb53kiS1g6nmwFbPdU2eayxQSOpslSp4V9fhVfCZjsGrPtk98ACcc87h9zfBMk6aAodySJLUfqaaA/v74Y478vUlS+D1r89DJFot1zVxrnGIh6TONtYKDX1909/nyK5/8+fDN74BP/zh8GOaYBknSZKkjjaVHFjJd/PnDy/Pee+9Od+Z62rGAoWkztPfD+vXw/vfn6vge/Ycfv9Mq+AjT3Znn523P/hgU471kyRJ6jiV3hBf/zrcfffwB0lj5cBKvjv77MOX53zwQXNdDTnEQ1Ln6O+Hm2+Gr30NjjkGzjxzuHfDW96Su+jBzKvgAwO550TF8uXw5jfDN7+Zl6NssrF+bamVZ9eWJEn1tWEDXHstPPNM7g1x4EDuDXHeeTkbjpYDK/lu1iw4/3zYtAm2b4eIplie8zAtnIPsQSGpM1S65T3wABx9dN52333DJ6Ba9m7o7s5FjmoLFsAll8Ctt+beGy1ykmhJrT67tiRJqp/+/lyciMiZbe/e3HviwIHxe0NU57vly+GCC/IHXJdc0ly5rsVzkAUKSZ2h0i1v3z5YuDB/LVgA27bl3g379uXeDV1do1fBq4eFrF8//h/5Jl++qe3VY14RSZLUHvr6YP/+PJTjqKPg5JNzr4kXXsh58Ior8uNG5r5WyXctnoMsUEjqDAMD+US0ePHwnBMLFuRK+ES9GzZsgMsug9tvhyeegMceG78SXVm+qatr/KKH6qPyXldrtdm1JUlSfQwMwLJlw3nwyCPhta/NPWwvuSTnvNFyH7RGvmvxHOQcFJLaz2jj7rq7c5X79NPhnnvy41LK4w4HB/OcEGPtq9INcOFCePxx+N//G44/Ps9ncfPNo39fEy/f1PYq73VlXXJwdm1JkjpdJR8++GAezlGZ6LLygdXcuXDGGRPnvmbPdy2eg+xBIam9jDXu7owz8vV58+Dcc/NjX3wRzjpr/Op3Xx/s3AnPPQff/jbs2AGzZ+d9fe1rLTOer6O0ShdMSZLUGNX58E1vygWK/fvz5bZt+UOra66BRx7J2/ftg+98Jz9+/37YurV1cl+L5yALFJLay1jj7h55ZLhb3v79cPHF+bG33DJ+Jfyhh+Cll3JhYk7R6exHP4Jdu/JKIC0ynq+jOMRGkiRVq86Hxx2XJ7g89lg4eBAuvRQ+8xlYsyb3vl24EH7wg9yLYu7cXLzYsSNvb4Xc1+I5yCEektrLyCU+YXjc3XSGXWzfnv+w79yZCx6zZg1X3c88s2XG83Uch9hIkqSK0ZaAv+ii/A/8+vXD27u74VvfyoWL2bPztpRy/ps3r3VyXwvnIHtQSGovoy3xOZNxd0uW5Jmdjzoqn6D2788nrGOOyWMWW2Q8nyRJUseabD7s7YXdu2HRouHcFwEnnZS3m/vqzgKFpPZS63F3Z56Z56/o7s6V866ufJJasaKlxvNJkiR1rMnmw54eeOtbc86r5L7TTssfVs2da+5rAAsUktpLrcfd9fbmuSf+zb+Bn/3Z3D1w7144++yWGs8nSZLUsaaSD3/5l/Mk6m97W15+dPfu4Uk0zX11FymlsttQc6tXr04bN24suxmS2sVoy5Y28wmq1dqrhomI+1NKq8tuRycwi0hSC6tHljKfvWy8POIkmZI6x3RPDK0w0VDl2B56CJ58Mg9LOeWU4WVW7e0hSZKUTZQJa539NmyAa6/Nc1osW5Z745rPRuUQD0mdYcMGuOwyuP12eOIJeOyxfGJohfWsJ1K9tvfgYJ7M6ZFH8rrelSW1WmFZLEmSpHprdCbs78/FiYhcnNizJ+e0gwfNZ6OwQCGp/Y12Ynj00bxcaCufGPr789JY73kPfPe7uRq/Y0deVnXBAti0KT+ussyqJElSJ2t0Juzvhw9/GJ56Kn+I9KMfwcKFOadt3mw+G4UFCkntr68vd6lbvDifkConhi1bWvfEUN1rIqX8de+9eQnUPXvy8VWW05rJMquSJEntopGZsJLVtm7Nz7dnDzz9NLz0Un7ObdvMZ6OwQCGp/Q0MDFfJK1r9xNDXNzx8Y8mSfJJdsCDft2dPLkosWjTzZVYlSZLaRSMzYSWrHXssHHVUzmqQn2toyGVLx+AkmZLaX3d3Hv7wyCP5dqV3QaudGKondHrgATjnnLz99NPhnnvyGt0HDsCqVbm7YqWAsXatEzBJkiQ1KhP298Mdd+Trc+bAvn2wdCns3Jmfb8kSly0dgz0oJLW/3t489OGMM4ar5K22nnX1kI4TTsjFiG98A374Q1i+HM4/f7gy/7rXwWc+k0+M69e3zjFKkiTVUyMyYSWzzZ8P8+bl54P8fK96FZx5Zs5pa9bU5vnajD0oJLW/np68jFNfXz5ZXHhh6609XT2kA+Dss+Huu+HBB+Gii/IJ8LTTXK5KkiRpLI3IhJXMdvbZuYfrggXDw3FXrzarTcAChaTOUOv1rBttYCD3nKhYvhze/Gb45jfzLNDd3Q7lkCRJmki9M2Els82alXu4btoE27fnAoXFiQlZoJCkVtDdnYd3VHpQQK7IX3JJHsYhSZKk8lVntuXL81fltsWJCTkHhSS1gt7e4RU5Dh1ydQ5JkqRmZGabEXtQSGot1StZdHe33lwS01U9ZrJy7A7pkCRJeqUy86KZbUYsUEhqHRs2wLXXwv79w2tY33hj54zna/V5NCRJkuqtGfKimW3aLFBIqqt+Duk2+MoAACAASURBVNLHIQZIdBP0MoseZk9jR/35ZBMxfLJ59FFYtSpXqFvpJNCpvUAkSZKmaEpZstF50UxXc85BIalu+jnIjRxgkMQJwCCJGzlAPwenvrO+vlwJX7w4n3QWLsyTRG7Zkk8KraKyNvbgYJ7heXAw3+7vL7tlkiRJTWXKWbKRedFMVxcWKCTVTR+H6CLoIphVXHYR9HFo4m/u74fLL4ezzoIzz4QvfCGfZPbsGX7MggWwbVuuWLeKytrYXV15+anK9b6+slsmSZLUVCadJTdsgAsugD/4g1yM2LJl+L565UUzXV04xENS3QwU1e5qi4vt4+rvh9/8TXj8cTjqqLxt82bYuROOPDLfXrAAhoZg7tzmnhV5ZNe/hx56Zde/xYtbqxeIJElSA0wqS27YAFdeCYsWwZIlsGPHcK5aubL2ebGS7T73OTj+eHjDG/JSomCmqwF7UEiqm26CoRHbhort4+rrg61b84nmiCPy13HHwb59MH/+cCU8JbjmmuYd6zda178nn4Qnnjj8cUNDrdULRJIkqQEmlSVvumm4OHHssTkrHnFEzoq1zovV2e7443OGu+ceeO65onFmupmyQCGpbnqZxSCJQRKHistBEr0T/ekZGIC9e3MhouLoo+GYY2D2bDjlFLj0UvjMZ2DNmvoexEyM1vVv1Sp45BHXxpYkSZrApLLkli25QAG5p+2JJ+bbKdU+L1Znu9NPz88RAd/+tpmuRhziIakmxpph+Qo4bPtaZk+8ikd3d55xec+ePO8E5OtLlsDFF8P69VNoWImzKw8M5J4T1V77Wti1K5/YXBtbkiTpMCMz5c8wi0dg7Cy5ciVs355zIuQixYED8JrXvDIzzjQXVme7FSvgvPNg0yZ45hm48EIzXQ1YoJA0Y5UZlruIw2ZYvgLomUxBYqTeXrj//jwHRSrGGO7cmXtOTKUqXemG19V1+OzKjVoHu7s7P2dX1/C2oaE86edUiiySJEkdYLRMeSeJK5gzdp5cty7PQQG558SOHfnr6qtH7LwGuXBktluxIg8pufBCs12NOMRD0ozNaLWO0fT0wHXX5dmY9+/Pc0+85S1w/fVTKyyUPbtyb+9wdz+Hc0iSJI1rWplyzRq44Ybcg+LZZ/PlDTe8clhHLXKh2a7uSu1BERG3Aj8NbE0pnTHK/QH8IfB2YBfw3pTSA41tpaSJTHu1jvH09MDNN8+kWaMPsWjk7Mo9PbkqX92V0K5/UtMxj0hSc5h2plyzZuJ5JmqRC812dVf2EI9PAzcBt41x/8XAqcXXOcDNxaWkJtJNMEiiaiDD5FbrqLexhlg0cnblnh5PWlLz+zTmEUkqXV0zZa1yodmurkod4pFS+gbw4jgPuQS4LWX3AUsi4rjGtE7SZE17tY66N8xueJImZh6RpOZQ10xpLmwJZfegmMhK4Omq25uLbc+OfGBEfAD4AEC3a89KDTXt1TpqYbzZmO2GJ6k2JpVHzCKSNDM1zZSjZURzYdNr9gLFaH15Rh2AlFL6FPApgNWrV89g4Luk6ZjWah0zNZnZmO2GJ2nmJpVHzCKSNHM1yZTjZURX22hqzb6Kx2bgxKrbJwDPlNQWSc2m7FU6JHUK84gktRIzYstq9gLFncAvRXYuMJRSesXwDkkdamAgz75crZGrdEjqFOYRSWolZsSWVfYyo18ALgCWRsRm4LeAuQAppVuAu8hLej1OXtbrfeW0VOos/Rw8bOxfL7MaP3xjMpphlQ5JLc88IkmN0bCMaUZsWaUWKFJKPz/B/Qn4Lw1qjiRgA/u5loPsJ7EM2EtwI4e4ApqvSNHbm8cTQq6KDw3lk9HateW2S1JLMY9IUv31c5DfZD9bSewFHgXuJ7iOOmRMM2LLavYhHpIaqJ+DXMsBgsQygj0Ej5A4SKKPQ2U375Uqq3R0dcHmzfmyeoJMSZIkNYWbOcDjJCBYTADB4yRu5kDtn8yM2LKafRUPSQ3UxyH2EywjEcBCAILNJOaPvoBO+VylQ5IkqendR+IoKvkyX6Zie12YEVuSPSgkvWygGNaxp2rbAmAb0D3qKnuSJEnSxMYqQzTpR2AqiQUKSS/rJlgJ7CHYTSKRGCIxt5jESJIkSZqOcwl2wssZczeJncV2qcL/OCS9rJdZzCFYBSwg2EaQgGuY3XwTZEqSJKll/DJzOAWAYKiYg+KUYrtU4U+DpJf1MJsryHNRLCBxYRlLjPb3Q19fXqe6uzvPwuz4QUmSpJbWw2yuZ15tlxk1N7YdCxSSDtNTZm+J/v68JFRXF5xwQl4O6sYbnXVZkiSpDdQ0Z5ob25IFCknlGK3i3deXTzJdXfkxlcu+Pk80zc5PMCRJUiNUMscdd8D8+XD22TBrVnvmxg7MV85BIanxKhXvwcHDK94PPQSLFx/+2MWL8x9lNa+x3s/+/rJbJkmS2kl15gBICe65B557Lt9up9zYofnKHhSSGmeiivfAAAwNDd+GfLu7u5z2anLs+SJJkuqtvx8+/GHYuhWOPRbmzIEIWLAANm2C5cvbKzd2aL6yB4WkxphMxXvJknz/4CAcOjR8vbe3vHZrYgMD9nyRJEn1U8mRW7fC0qWwezfs2JFzYkqwfXv75cYOzVcWKCQ1RqUKvHdvPok8+SQ8/zx861v5/qEhOPPMPLFRVxds3pwvneio+XV35/evWjt9giFJksrV1wcHDsCuXbm3xHPPwbx5sGhR7kUR0X65sUPzlUM8JDXGwADMnQv33QdHHZVPMAcOwBNPwGOP5W56a9fmk0q7nFg6RW9v/lQDcmV/aCh/grF2bbntkiRJ7eGhh+D73x/OkLt3568jjoB/+2/bqzBR0aH5yh4UkhqjuzufXBYsgGOOybfnzMlfzzzTnieWTtHTY88XSZJUP9u353nLKhly4ULYvz8P72jXzNGh+coeFJIao7cXPvtZOProfDKZPTuPITz33HyCafM/tm3Pni+SJKleliyBF1/MvSZe9aqcIxctgte8pr3zRwfmKwsUkhqjpwd+8ifhgQfypEaLF+dVPObNg+OOK7t1raED18KWJEnizDNzYWLLljzUYfFieO1r4dRTy27Z6Mxs0+YQD0mNc/nlcNpp8OY3569589prtuV66tC1sCVJkujtzb0m3vhGeMc78uXs2c2ZIc1sM2KBQlLjdOhYupqoXgt71qzh6319ZbdMkiSpvlopQ5rZZsQhHpIaqwPH0tXEwECuwlfrgLWwJUmSgNbJkGa2GbFAIUnNpL8fPvnJvBxrRJ5E9PLL8/jFwcFcga/ogLWwJUmSWsKGDXDTTTnLLVwI558Pq1bl+8xsk2aBQpKaRX8/XHUVPPFEXucb4O67c1fG97wH7rwzb5vKWthO0iRJklRfGzbAlVfmlUVOPhmefBK++tW8ct3KlcOZzVw2IeegkKRm0dcH27blk9sRR+SvRYtg61Z45JGpj710kiZJkqT6u+mmnNmWLMmXp5wCCxbAP//zcGYDc9kk2INCkprFwADs3Zt7SFTs2AFPPw2bNuXeFOvWwfr1k9tf9SRNMHzZ12e1XpIkqVa2bIHjjsvXt26FZ57JmQ7gjDNy7lq/3lw2CRYoJKlZdHfDo4/Cnj157OLWrfD97+cZoI8+GrZvz90HAdasmXh/TtIkSZJUfytX5py2b1/ObrNn57nEIoazm7lsUhziIUnNorcXli3LvSZ27co9JyAP9Tj22OFugzfdNLn9dXfnuSqqOUmTJElSba1bl/PbwMBwceLQoTwfRSW7mcsmxQKFJDWLnh64/np4y1tyBX7fvtz979WvhiOPzI9ZtCh3I5yM3t48vnFwMJ8kK9d7e+t3DJIkSZ1mzRq44QY4eBAOHIA5c/I8FMuWDWc3c9mkWKCQpGbS0wO33AIPPQTnnQcrVgwXJyBX51eunPy+pjqxpiRJkqZuzRo45xw4/XQ488xcnIDh7GYumxTnoJDUnFyGKXcXrIxbXLQon+B27ICrr578Pnp6Ou91kyRJnaVZcuNE2c1cNiF7UEhqPhs2wGWXwe23wxNPwGOPdeYyTJXugkuWwLPP5ssbbpjcBJmSJEmdoJlyo9ltxuxBIam59PfDtdfmyYWWLcsrWjz6KKxa1ZnLMK1Z40lNkiRpNM2YG81uM2KBQlJz6euD/fvzSSYiL7cJeXKhBQvKbZskSZKah7mx7VigkFSukWMGH3pouAJeOcksWADbtsGFF5bbVkmSJJWvkh8/97m86tns2XDMMfk+c2NLcw4KqdNt2AAXXACnnpovN2xo3HP39+cxgoODcMIJ+fLJJ/OqFXv2wO7dkFJeI3ruXJdhkiRJajW1zprV+fH443NBYvNmeOEFc2MbsEAhdbING/JMw9u3w3HH5csrr2xckaKvLy+x1NUFs2bly1Wr8knmjDOGK+ApwTXXdN78E5IkSa2sHlmzOj+efnrOi8cem1fLMDe2PId4SJ3sppvyEkhLluTblcubbmrM5D4DA7nnRLXXvhZ27cpV9vnzc/e8ZllitFmWsJIkSWoF9cia1flxxQo47zzYtAmeeQYuvbR2+czcVwoLFFIn27IlV7OrLVqUtzdCd3funtfVNbxtaAjOPBPWr29MGyar0p2wq2t4OMqNN8IVV3iykiRJGk09subI/LhixfCHWrXKj+a+0jjEQ+pkK1fm7nDVduzI2xuhtzf/wR8chEOHhq8345jB0YajdHXl7ZIkSXqlemTNRuRHc19pLFBInWzdunyS2L49/4Hfvj3fXreuMc/f05Mr0V1ded6Jrq7mrUwPDMDixYdvW7w4b5ckSdIr1SNrNiI/mvtK4xAPqZNVxv7ddFPuardyJVx9dW3mn5jsuL2enuYsSIxU6U64dy985zt5KMq8eXDWWWW3TJIkqTnNNGuOlSfrnR+7u+Gxx/K8FkNDuThx/PHwutfV7zkFQKSUym5Dza1evTpt3Lix7GZInat63N7ixfkP++Bg8/aOmIz+frjqKnjiCTjqqLxt5848qed11w0flxMqqYlFxP0ppdVlt6MTmEUkaYbKzJOV1UcWLcpfO3bA88/nCTmPOsqMN0Pj5RGHeEiqvXYbt1cpOjz+eD5BDQ3BEUfktbxf85rh46pel7t6QqX+/lKbL0mS1HLKypP9/bnHR0QekvLcczBnDsydO7yCiBmvbhziIan2Rls+dOS4vVbpaVBdvV+wIFfR9+6F178eli/P4ykrx1V9IoXhy76+5jw2SZKkZjVWnnzoobxaRz0yZCX3bd2ah6Ps3Qt79uT7li2DffuGiyVgxqsDe1BInaC/P/8hf//782W9q73d3bmXQbWhoby90p5W6WlQXXRYsiRX0xcsyPNQwOHH5YRKkiSpk9Uyc46WJ594Ap58sn4ZspL7jj02FycWLsy5b/PmfH91zjPj1YUFCqndlVEMmGj5p0Z02avVCbK66HD66bmKnlLu8jfyuCYqzEiSJLWrWmfO0fLkI4/AqlXTy5CTyYaV3FfJfLt3w/z5cPBgnnvs9a8ffqwZry4sUEjtrozxexMt/1TPngb9/fChD+WT2pe/nMcLzuQEWV10WL4czj8/96KAVx5XI9blliRJaka1zpyj5clXvzpPUF5tMhlywwa47DK4/fbcC+N73xs9G1ZyXyXzLVyYJ8c86SQ45ZRcrDDj1ZUFCqkdVVeI//qvh8fOVTSiS1pPT/6j3d2dn6uvb/gkUK+eBpXK/YMPwtFH52333ZfHC073BDmy6DBvHpx2Gtx2W36Nq8cdNmJdbkmSpGZSyZ2f+1yeH+K554bvq3XmXLFi6hmyvx+uvTZ/wLRsWc7FjzySe0WMzIbVuW/ZMnjjG2H1arj1Vrj+ejNeAzhJptRuNmzIf4T3789/WPfvh298I684sXx5fkwjuqRVluXcti2P4Xv0Udi4Mf9x7+3NhQQ4fNmotWtn9pyVyv2+fXkyy0pPh02b4M1vnt4JslJ0qJ7Qc+3asU9I9V6XW5IkqVlU5879++HZZ/Mw2PPPz7lzJplztCw5f37OeKecMvkM2dc3nIsjcq8IyIWG+fMPf+xEuc+MV3cWKKR2smEDrFuX/0E/6iiYPTtf378fHngALrqodsWAiXzyk7kL3aJF+QSyZ0++/clPwi23TO2f/smqzPa8eHEeM1iZ2Gho6PAT5IYNefmoLVvyDM3r1sGaNWPv16KDJEnS4UbmzrlzczFh2TL49rdzr9OZZM7RsuS2bbnw0dU1+Qw5MDDcc6JSnFiwIC8f/9RTcOqph+dBc1+pLFBI7aLSfa1ykjhwIC+RdOyx+fbevblSXKtiwETuuy8/b+VEsHBhnlzyvvvy7Xr88e/uzifC178e7r03b0vp8BPkhg1w5ZX5ZHfccbnKf+WV+bHVRYpWWQZVkiSp0UbLnXv2wDHH5G3PPAMXXjizzDlWlvze9+CLX5z8frq7c9sefTTfXrAgFya2bYOTT87DRip58Kmn8mSY5r/SlDoHRUS8LSK+GxGPR8Svj3L/BRExFBEPFV8fLaOdUkuodF+rnCTmzoU5c2DHjtyb4J3vzOPnRs6bUC+V4RWT3V4LlXGD8+fDuefmbS++CGefPTxO8KabcnFiyZI8gdOSJfn2TTcN76eVlkGVNGPmEUmaorFy5/79cMQR8O53zzxz1ipL9vbmtq1alYsT27bliS9XrIDjjx/Og3Pnwg03mP9KVlqBIiJmA58ALgbeAPx8RLxhlIf+U0rpzOLrtxvaSKkVVE9MtGtX7i1w4EA+QcyZk6vAc+c2fpbhc8/Nz717d652796db1cKB/VQPUnl/v1w8cX5BHrzzcMnyC1bckGi2qJFeXtFGSufSCqFeUSSpqi/P0/C/uKLOd/t2VOf3FmrLFnJh697XZ674tJLc647+eTDH7d7d+5xbP4rVZlDPN4EPJ5S+j5ARPwFcAnw7RLbJLWWyif9XV25Avzss7kqvHRp7l63c2cuWFxzTeO7p11+eR5SsnVrnv9h/vy8LNTll9f3eScaOrJyZe7Gt2TJ8LYdO/L2ispcFtUasfKJpDKYRyRpsirZc968PJzjuefyahiHDtU+d9YyS47Mh3ff/co8+NJLh98G818JyhzisRJ4uur25mLbSOdFxMMR8eWIWDXWziLiAxGxMSI2btu2rdZtlZpT9Sf9p5+ex+ZVVu541avgxBPz0IXxJoCsl54euO663Ivh7LPz5XXXlT+Ob926XJDYvj2fTLdvz7fXrRt+TL2WQZXUjGqWR8wiktpeJXuedVbuMbFiRc6cBw7UPnfWM0uOlgdTynm6mvmv4crsQTHa4KE04vYDwEkppZci4u3AHcCpo+0spfQp4FMAq1evHrkfqT1Vf9K/YgWcd15eUvOZZ3L3tbIn9mnGWZArJ83qVTyuvvrwk2m9lkGV1IxqlkfMIpLaXiV7zpqVc+d3vpMLFQC33Vb73FevLDlaHrzsMnj44Zz5zH+lKbNAsRk4ser2CcAz1Q9IKe2oun5XRHwyIpamlJ5vUBul5lZZtaKrK99esSJ3f7vwwjwvhUa3Zs3Ey4rWYxlUSc3IPCJJk1WdPVesyF+V262Wk0bLgyNXcTP/NVyZBYpvAadGxKuBLcC7gF+ofkBErACeSymliHgTeUjKCw1vqdSs2u2T/mZa2rMZe39IqgfziCRNVrNlz1pnR/Nf6UorUKSUDkTEOuArwGzg1pTSoxHxoeL+W4A1wOURcQDYDbwrpWSXSXWW8f7wttMn/dUTflYv7VRZHlSS6sA8IkljGCuDNkv2NDu2pWjH8+vq1avTxo0by26GNHPVf3irq9Tt+Id3/frDh6vA8G2Hq0g1ERH3p5RWl92OTmAWkdTSWiGDmh1b1nh5pMxVPCRNpHqVjnZfj3lgIJ8Aq7m0kyRJUuO1QgY1O7YlCxRSM+ukP7wu7SlJktQcWiGDmh3bUpmTZEqayMhVOqC+f3jLnKSy2SZdkiRJ6lT1yKC1zplmx7ZkDwqpmfX25j+0g4Nw6NDw9d7e2j9XZazh4ODhEw3199f+uUZTmXSpqws2b86XzTTOUZIkqVPUOoPWI2eaHduSPSikZtbImZKrxxrC8GVf38yfb7IVc5d2kiRJKl+tM+hMc+ZYWdLs2HYsUEjNrlF/eAcGckW7Wi3GGroElCRJUuupZQadSc40S3YUh3hIyuo10VArzAItSZKk+plJzjRLdhQLFJKyes130QqzQEuSJKl+ZpIzzZIdxQKFpKxeEw25BJQkSVJnm0nONEt2FOegkDSsHvNduASUJEmSppszzZIdxR4UkurLJaAkSZI0XWbJjmIPCkn15xJQkiRJmi6zZMewB4UkSZIkSSqdBQpJkiRJklQ6CxSSJEmSJKl0FigkSZIkSVLpnCRTqof+fujrg4GBvEZzb68T+0iSJKlxzKNqQfagkGptwwa47DK4/XZ44gn43vfy2s39/WW3TJIkSZ3APKoWZYFCqqX+frj2WoiAZctgzx545BE4eDBXsCVJkqR6Mo+qhTnEQxpHPwfp4xADJLoJeplFD7PH/oa+Pti/P58MImDhwrx982aYP78xjZYkSVJbmVImNY+qhdmDQhpDPwe5kQMMkjgBGCRxIwfo5+DY3zQwMFyprliwALZty2P/JEmSpCmYciY1j6qF2YNCGkMfh+gi6CIA6Kra/nLFeuTkQ/PmwcqV8Oij+f4FC2BoCObOzRMTtTonW5IkSWqoKWfS738fjjgCtm/P9zV7HjVfqoo9KKQxDJBYPGLb4mI7kP+Y3ngjDA7CCSfkyy1bYMcOWLVquFKdElxzTev/oR3teJ1sSZIkqa6mnElXroTvfheOP77586j5UiPYg0IaQzfBIOnlKjXAULEdyJXerq78BfnyNa+Bfftg+fJ8QrjwwvapAo92vJXt7XB8kiRJTWjKmfTUU/Plli1wyinNnUfNlxrBAoU63liTDvUyixs5AOQq9RB5zN/aSle6gYFc6a22eHGegGj9+kYeQmOMdbwDA+W0R5IkqY3UNJOeckqeEPPWWxt6DFNmvtQIDvFQRxtv0qEeZnMFc+gi2Ax0EVzBHHr6H80FiAcfhK98BX74w+EdDg217+RD3d35+Kq18/FKkiQ1SMdmUvOlRrBAoY5WPenQrOKyi6CPQwD0MJv1zOVW5rGeuflEUBkn96Y35fkmvv51ePbZvG1wsDknH6qF3t7hYzx0qP2PV5IkqUE6NpOaLzWCBQp1tAknHarW3w8f/jBs3AgPPwyzZsEFF8CiRfCv/5rHzF1xReuPl+vvz9X4978/X1YmKerpycfX1ZWHsbTL8UqSJJWsYzLpyJwJ5ksdxjko1NEmnHSoojLD8NatsHQp7N4N99wD558PF13UPvNOVI6zq+vwmZQrJ4rKlyRJkmqmIzLpeDmzWdushrMHhTpaL7MYJDFI4lBxOUiit/KrUanyvuc9ebmmI46AvXth4cK8SsemTe01Tq56JuVZs4av9/WV3TJJkqS2NWEmhcN7Tuzalf/Bb6VMas7UJNiDQh1jrJmRr4DDtq9lNj3MPrzKm1L+2rFjeIfz5+fq9eAgrF1b2nHVlDMpS5Ik1dWUMym8sufEiy/m3hKQs2orZFJzpibBAoU6QmVm5C7isJmRryBPOvTyH/9q1VXeJUtyF7quLjh4MFert26FY49tr3Fy3d355NZV1cGw2avxkiRJLWJamRSGc+mxx+ZMunRp3r5zZ86mrZBJzZmaBId4qCNMNDPyqAYGclUX4PTTYc+e3IviwAF44xth9Wr4+Meb+0QwVc6kLEmSVDfTyqQwnEsrmXT3bjj66Dz8uFUyqTlTk2CBQh1hSjMjV1Svy7x8eZ58KIqJitp1hmFX6pAkSaqbaWVSGM6llUy6cCE8/3xr9JyoMGdqEhzioY4w6ZmRq/X25rF+kCvW8+bBaae1/x9SV+qQJEmqi2llUjg8ly5blnPp4GDr5VJzpiZggUJtaeTkQ2cAdxaV6cXkE8EgibVjjfOD4SpvX1/uVtfdnSce8o+qJEmSJqEmmRTMpeoYFijUdkabfOhOEj/DLB6B0WdGHotVXkmSJE1DTTMpmEvVESxQqO1UTz4EvNyF7hFgPXNLa5ckSZI6h5lUmjonyVTbmfbkQ5IkSVKNmEmlqbNAobbTTTA0YtukJh+SJEmSasRMKk2dBQq1nV5mMUhikMSh4nKQRK8/7pIkSWoQM6k0df52qO30MJsrmEMXwWagi+AK5kxu8iFJkiSpBsyk0tQ5SabaUs9kZ0OWJEmS6sRMKk2NPSgkSZIkSVLpLFBIkiRJkqTSWaCQJEmSJEmls0AhSZIkSZJKZ4FCkiRJkiSVrtQCRUS8LSK+GxGPR8Svj3J/RMTHi/v7I+LsMtopSZLal3lEkqTmUFqBIiJmA58ALgbeAPx8RLxhxMMuBk4tvj4A3NzQRkqSpLZmHpEkqXmU2YPiTcDjKaXvp5T2AX8BXDLiMZcAt6XsPmBJRBzX6IZKkqS2ZR6RJKlJlFmgWAk8XXV7c7Ftqo8BICI+EBEbI2Ljtm3batpQSZLUtmqWR8wikiTNTJkFihhlW5rGY/LGlD6VUlqdUlq9bNmyGTdOkiR1hJrlEbOIJEkzU2aBYjNwYtXtE4BnpvEYSZKk6TKPSJLUJMosUHwLODUiXh0R84B3AXeOeMydwC8Vs2efCwyllJ5tdEMlSVLbMo9IktQk5pT1xCmlAxGxDvgKMBu4NaX0aER8qLj/FuAu4O3A48Au4H1ltVeSJLUf84gkSc2jtAIFQErpLvJJv3rbLVXXE/BfGt0uSZLUOcwjkiQ1h3ELFBHRO2JTAp4HHkop7axbqyRJkiRJUkeZqAfFO0bZdjTQExFrU0r/WIc2SZIkvUJE/NJo21NKtzW6LZIkqfbGLVCklEYdYxkRJwG3A+fUo1GSJEmj+LGq6wuAnwAeACxQSJLUBqY1B0VK6amImFvrxkiSJI0lpfRfq29HxGLgMyU1R5Ik1di0lhmNiNOAvTVuiyRJ0lTs4VWh6gAAIABJREFUAk4tuxGSJKk2Jpok82/IE2NWOxo4DvjFejVKkiRppBG5ZBbwBvKQU0mS1AYmGuJx44jbCXgB+F5KaV99miRJkjSq6lxyAHgqpbS5rMZIkqTammiSzK83qiGSJEnjMZdIktTeJjUHRUT0RsT3ImIoInZExM6I2FHvxkmSJFWYRyRJam+TXcXjBuAdKaVN9WyMJEnSOMwjkiS1sckWKJ4zDGikfg7SxyEGSHQT9DKLHmaX3SxJUvsyj0gdwpwpdabJFig2RsRfAndQtbxoSqmvLq1S09vAfq7lAPsJlgF7gBs5xBXgyUOSVC/mEakDmDOlzjXZAsUi8lrj/75qWwIMBB1oA/v5IAfYBcwisQ14FlgN9HHIE4ckqV7MI1Kb+3328lscYj+wgMQ+YDvBKpI5U+oA4xYoImJdSummlNL7GtUgNbd+DnIVB9gJzAMC+BGwD/guiQUvL08vSVJtmEekzrCB/fxOUZyYT15LeDNwAokthDlT6gATreLx/oa0Qi2jj0MMkk8aAcwmV7kOAQNAN1Fi6yRJbco8InWAmzhIAAvIXaPmkrPmtuLLnCm1v0ktMypVDJCYBSwkFyUOFtsPFF+9/khJkiRpGrYAR5I/CKvkzNnkOSjmksyZUgeYaA6KnjHWFw8gpZQW1aFNamLdBMeS55x4FXmGsr3kStdbceIiSVJdmEekDrCSnDH3Mpwzc3ECrmGOOVPqABMVKP53SumshrRELaGXWdxP8BKJvQx3vzsduIZ55TZOktSuzCNSB1jHbK7kAEuA3eTeuUcAVzOLNcwtt3GSGmKyq3hIQO4hcR1wMwe4j0QCziX4ZavakiRJmoFKEeImDrIFeA25aGFxQuocExUovhgRx6SUXmhIa9QSepjNzRYjJEmNYx6ROsQa5lqQkDrYRAWKQ+RQMBf4X8CXgW+mlFzjR5IkNYp5RJKkDjDuVLgppd9LKf074O3Aw+Rlvh6IiM9HxC/9n/buP06uu77v/eszI2klbEtaYyzZEmvHwcQ4YQFXdhwIwU4IwU6BsJXbJJeUFloemPrRpkElNDaNUye+CRG59wZhHCdtQ5pccnmo60KDDbYbHMpNDDHGjO3YEFvBiySDZKwfli2tdme+/eN7Vhoru9JIuzPnzOzrqcc8ZubM0ez3u0fa897PfL/fExFretFISZK0eJlHJElaHDpagyKl9Cxwe3EjIi4GrgL+CPiprrVOkiSpYB6RJGmwHbdAERGXHOflL6SUPrLA7ZEkSXoB84gkSYvDiUZQzJzwlwMbyMMqAxgFvgz8aPeaJkmSBJhHJElaFE60BsWVKaUrgSeBS1JKG1JK/wB4DfB4LxooSZIWN/OIJEmLw3ELFG0uSik9NPMkpfQw8OruNEmSJGlW5hFJkgZYR4tkAo9GxB8Afwwk4B3Ao11rlSRJ0t9nHpEkaYB1OoLinwOPAP8G+EXgb4ptGgBbmWIDBzmTg6zlINdwkAbNspslSdKxzCNSxXyESc7nICs5yPkc5CNMlt0kSX2s0xEUrwV+L6X0f3WzMeq9rUzxi0zzHLACaAJ3Ad/lMFtYxij1chsoSdJR5hGpQj7CJL9KixXAauA54FdpAZO8n6FyGyepL3U6guKfAQ9GxF9FxIcj4i0RMdzFdqlHttBkCngRsIxcpFgOPAGM0yqzaZIkHeufYR6RKuOjRXHiNPIvFaeRs+RHzZCSTlFHIyhSSv8UICLOBTYCHwPO7fTvq7p2FPftB3IIOABMkHrfIEmS5mAekarlGfLIiXYriu2SdCo6OqFHxDuA1wOvBJ4GtgD/q4vtUg80aPIciafJK43VyCeVmZEUI0SZzZMk6QXMI1K5GjQZp8UEiSES0+QPu+rkD7jOAKaAM8tspKS+1uknDv83edT/rcAXUkrf6lqL1BMNmlzPFM/DkUF4TfLIiTpwPjDW8QwgSZJ6wjwilaRBk81MM0zwLE3GgYPFa4mcIyfJv1x80Awp6RR19NMjpXQW8C7y8gS/ERFfiYj/2tWWqavGafEoiedmeW0FcCk1F8iUJFWKeUQqzzgthgmeosWd5A+1lpB/mWgfc/s6cIFMSaes0ykeK4ER4Dzyh+urwNVv+tmDtPg2ueK9rLhP5NETpwGHnd4hSaoY84hUngkSS2nxFySaHC1K1IBh8oLrq4GX+gGXpHnodIrHl9puW1JK27vXJPXCXhJBPqnMrD/RJKe8Jq4/IUmqJPOIVJIRgjtJtMgfbjXJGTLIUzsSsAYzpKT56fQqHqPdboh6azV5pMQBjp5gZq7ZMYzrT0iSqsc8IpVnjBp/TC5OABwiZ8ghcoFiKXA2YYaUNC/HLVBExGeO93pK6a0L2xz1yqupM8k0XwGegyND9VYCN7PE9SckSZVhHpHKN0qdN1Lj/6fFDvL8qiYcWc/sSuBDLDVDSpqXE42g+BHg28AngS+DY7YGxRg1tlHjp0jsAHYDS0l8iCVsZGnZzZMkqZ15RKqA97GE55nmleZHSV1yogLFWuAngZ8Dfh74LPDJlNIj3W6YumuUOpvIKzIvJ3FlMSTPqrckqYLMI1IFmB8lddtxCxQppSbwOeBzETFEDgb3RsR/TCl9tBcNVPeMUveEIkmqPPOIVB3mR0nddMJFMosg8NPkMHA+8LvAeHebJUmSdJR5RJKkwXfcZXYj4hPAXwKXAL+WUro0pXRTSmlHT1qnBbGVKa7gEBdyiCs4xFamOnpNkqQqMI9I3XOqWdAMKakbTjSC4hfIi/O+HPjXEUfWpAogpZRWdrFtWgBbmeIDTLMSOAfYC3yA6SOvz/WaCx1JkirEPCJ1wfFy4vGy4Kn+PUk6kROtQeGFjPtYgyabmGY/0CIf7NU5y7GFJpAvK7q6WAx9NRx5zZOLJKkqzCPSwjtRTjxeFtxC0wwpqSs84Q+oBk02M80e4HRgCtgOHCCxEthR3I79yGnmNUmSJA2mTnLi8ZghJXWLBYoBNU6LYYJh4DCwFKgDTwP7gXXFbf8xf2/mNUmSJA2mTnLi8ZghJXWLBYoBNUFiFXAZwSRwiHzi2V/crqPOddTZD+wl0SKxl3TkNUmSJA2mTnLi8ZghJXXLCS8z2g0RcSbw/5EvE/Yt4B+nlPbMst+3gGeBJjCdUtrQu1b2rwZNttHiPhJnE1wKfBPYAwwDH2bJC+YHbqHJDnLV+wbqzh2UJC0K5hEtFg2ajNNigsQIwRCJfQQXUweafIU0Z06czczrZkhJC62UAgXwQeB/ppR+MyI+WDz/5Tn2vTKl9HTvmtbfZuYUngs8w0xlG15HUCfYxBJG26rbG1nqyUSStFiZRzTwZrLhMMF6YA+Jb9MiCC6gxkXUOKfYfmxOPB4zpKRuKGuKx9uATxSPPwH8TEntGDgzcwpfTp3XUmM1wTTBDjipk44kSYuAeUQD7+h6E0GtuP9+6qwrHm8Hhmf5EEuSylDWCIo1KaWnAFJKT0XE2XPsl4C7IiIBv5dSum2uN4yI9wDvARgZGVno9vaNCRLri8drqbEWaJHYDp50JEl6oQXNI2YRVVF7NpyxCthOcKMjICRVTNcKFBFxD7B2lpeuP4m3eV1KaWcRGO6OiMdSSl+cbcciLNwGsGHDhnTSDR4QIwR7SAy3bdtXbJckabHpZR4xi6iKzIaS+knXChQppTfO9VpEfDcizik+rTgH2DXHe+ws7ndFxO3AZcCsBQplY9TYzDSQq+P7yHMK3+3oCUnSImQe0WJnNpTUT8pag+IzwDuLx+8EPn3sDhFxWkScMfMYeBPwcM9a2KdGqbOJJS+YU/hWaozT4l0c5kamaNAsu5mSJFWBeUQDb7ZseOx6Ew2a3MiUWVFS6cpag+I3gU9FxLuBCeAagIg4F/iDlNLVwBrg9oiYaef/m1L6XEnt7Suj1I+cdGZbuXkz02zCNSkkSYueeUSLQns2PJZZUVKVlFKgSCl9D/iJWbbvBK4uHm8DXtXjpg2c9pWbgSPzD8dpedKRJC1q5hHJrCipWsqa4qEemSCx6phtq4rtkiRJWtzMipKqpKwpHuqBBk220eI+EmcTvIJgDTVXbpYkSVqkGjQZp8UEiRGCIRL7CK/yIakSLFAMqI8wyW/T4tni+fMkniHxShJ1wpWbJUmSFpEGTW7iMPeQfwEYASYJ9pEIgguoeZUPSaWzQDGAtjLFr9NiCXAWcAD4DtAEdgC/e8zKzZIkSRpcDZr8Coe5D1hG/gXgSeBZEpcSnFGsQTEzquLdx1lUU5K6yQLFANpCkwBeBARwBrCUvODIBdQ84UiSJC0i47TYTRAklsORyRvPAdtJfD81bmRpiS2UpMxFMgfQDnJRYrpt2xCwF+cTSpIkLTYTJCaB0ziaD5eQR9fuxnwoqTosUAygdeSCRBOYAhLwPLACGPOQS5IkLSp5MUw4naP5cCYjLiXMh5Iqw59GA+g66kwDK8nV8WfJ1fJ/5/QOSZKkRWeMGi8h0QReXGx7nnw50Q+53oSkCnENigG0sZhDuIUmO4BRctFio3MLJUmSFp1R6tzMMm5hurj8PFxOcK0Lp0uqGAsUFXPstanHTnHUw0aWWpCQJEnqoYXKcd0wSp1bK9IWSZqLUzwqpEGTzUyzh8R68jWoNzNNg2bZTZMkSdJxmOMkaf4sUFTIOC2Gi+tQ14r7YYJxWmU3TZIkScdhjpOk+XOKR4U8SJM9wH7yokUXEZxNMEEquWWSJEk6VvuUjgdo8cMAbdMoVoE5TpJOggWKimjQ5G9I7Cue7wJ2kthA8HLnC0qSJFXKVqa4iSZTJF5CvnznF4E30GJtMUh5H/kSn5KkzjjFoyJ+nSl2kU9kzwOHgd3A/SSvTS1JklQRDZpcyyT/kmm+Q2IFcIhgEjgEfI1Ei8Se4maOk6TO+ROzAho0uYfEEDBcbDtAHt4SUJnVnyVJkhazmYUwH6DFEvJkju1Ak8QwwZnkD5m2A8MEm7yMpySdFKd4VMA4Lerkg7EUGAKmitdWOCxQkiSpEmYWwjxMcBqJKXJ+exo4jzz69R9T50Yv9S5Jp8QRFBWQr5UNk+TCxMxSSgeAyy1QSJIkVcIEiVXkxS9PhyMXED0I7COxlHBKhyTNgz9BK2CE4KJiWCDkQkUTWAO8z0EukiRJlTBCsA94BUEdOKvY3iJ/wPQh6k7pkKR5sEBRAWPUqBNcSvAy4ExgDcHNzluUJEmqjDFq7CGxjOBygtOAFcA/osZ/ZRkbndohSfPix/MVMEqdTeR5jUMkriyGB85WnGi/3vbIcfaTJEnSwmrPbBPUuKqLWczMJ2kxskBREaMdDAmcWTl6mGA9sIfEZqbZhFf6kCRJ6oVOMtt8mfkkLVZO8egjMytHDxPUivthgnFaZTdNkiRJC8TMJ2mxskDRR2ZWjm63qtguSZKkwWDmk7RYOcWjjywj8XkSh8knqYsIhghGvBSpJElSX2tfc2IbLQ4BL2+bzrEPzHySBp4jKPpEgyY7SOwnsZTE8yT+ghZP0PR625IkSX1sZs2JPSTWA+uA+0h8kyYtEnuKm5lP0qBzBEWfGKfFBdRYT+JREvuAlSRe6orOkiRJfa19zQmAC6kDTXYAy8kjJ97dg8U5JalsFij6xERRUa9RY02xrUVie5mNkiRJ0rzN5Lx230+NIeA/s6yMJklSKRwn1idGCPYds825iJIkSf3PnCdJmQWKPjFG7cj8Q+ciSpIkDQ5zniRl/tTrE6PU2cQShgm2A8MEm1jiXERJkqQ+Z86TpMw1KPrIqIsjSZIkDSRzniQ5gkKSJEmSJFWABQpJkiRJklQ6CxSSJEmSJKl0rkHRZQ2ajNNigsQIwRg15xdKkiQtAHOWJA0WR1B0UYMmv8Jh7qTJA7S4s3jeoFl20yRJkvpWgybXMslYkbOW0mIPic1Mm7MkqY9ZoOiiW5jmEWA7iadIbCfxSLFdkiRJJ69Bk+uZ4jO02EvOWV8gMUlimGCcVtlNlCSdIgsUXfQ5WnwX2AMcAg4DzwL3euKUJEk6aQ2avIvD/C8Sezg6V/kZ4H5arAImSOU1UJI0LxYouqRBk+8ACVha3B8EWsDeMhsmSZLUhxo02cw0TwIvIhcn9pOz1TJgO7APGCHKa6QkaV4sUHTJOC1WAEE+cQa5SHEIWF1mwyRJkvrQOC2GCerkXHU6OVsdKJ43gT0kxoy3ktS3/AneJRMkLiBX9AGmySfPOnCF33ZJkqSTMkFiFbAemCTnqlXkwsQB4DxgE0u8iock9TEvM9olIwSTBM+SeI588kzkE+n7/LZLkiSdlBGCPSQupcZ+Wkfy1TDwGuBmllmckKQ+50f5XTJGjTrBpQQvA84E1hLcbGVfkiTppI1RYw+JZQRXEqwnf/DzFmoWJyRpQPhRfpeMUmcTeb7kEIkrCcaoefKUJEk6Be3ZaoIaV5mtJGngWKDoolHqnjQlSZIWiNlKkgabUzwkSZIkSVLpLFBIkiRJkqTSWaCQJEmSJEmlK6VAERHXRMQjEdGKiA3H2e/NEfGNiHg8Ij7YyzZKkqTBZh6RJKlayhpB8TAwBnxxrh0iog58DLgKuBj4uYi4uDfNkyRJi4B5RJKkCinlKh4ppUcBIuJ4u10GPJ5S2lbs+6fA24C/6XoDJUnSwDOPSJJULVVeg2Id8O2259uLbbOKiPdExP0Rcf/u3bu73jhJkrQodJxHzCKSJM1P10ZQRMQ9wNpZXro+pfTpTt5ilm1prp1TSrcBtwFs2LBhzv0kSdLi0cs8YhaRJGl+ulagSCm9cZ5vsR14advz9cDOeb6nVA2NBtxyC9x3H0TA5ZfDtdfC6GjZLZOkgWIekaRZmEVVUVWe4vHXwIUR8X0RsQz4WeAzJbdJmp9GA977Xvjpn4atW2F6GpYuhXvvheuvz69LkqrEPCJpsGzdChs3wic/Cbt3w9SUWVSVUdZlRt8eEduBHwE+GxGfL7afGxF3AKSUpoHrgM8DjwKfSik9UkZ7pXlrNOCaa+ANb4A/+iP43vcgpXxSaLVg5UrYtQvGx8tuqSQtGuYRSYvKTB59xztg27ZcmJiaynl0yRKzqCqhrKt43A7cPsv2ncDVbc/vAO7oYdOkhdVowMc/Dn/2Z7kosXx5LkxMTR0tTDz9NJx3HuzbBxMTZbdYkhYN84ikReHYPJpSntYxOZkfn3EG7N8P9bpZVKUrpUAhLQqNBmzeDN/4Ri5GtFr5RFCv56kdrRYcPgy1Ghw6BENDMDJSdqslSZI0KGbLo6lYw7dWO5pHU4K1a82iKl2V16CQ+tv4OAwP5x/6zWYuQECuWNfreduhQ/nksH8/nH02jI2V22ZJkiQNjtnyaL2eCxIpHc2jKZlFVQmOoJAWytatsGUL7NgB69blQsTrXw+rVuU5fUNDeeREs5mH0j37bP57K1fCFVe4crIkSZLmZ2Y6x3335aLDwYM5Zx6bR5csyR+STU7mzPrjPw433GAWVeksUEgLYetW+MAHcrHhnHNg71548klYsQJe8QrYuROeeSavQTE5mavYIyNw8815FWVJkiRpPhqNfCWOxx/PH4YBbN8Od90Fl1769/NovQ7r15tHVSlO8ZAWwpYtuTixenWuRq9enYfJffWrsGwZXHllntfXauXRFT//80cv8SRJkiTN1/h4HiWxciW86EX5ds45edTuk0+aR9UXHEEhLYQdO/IJoN2aNXlY3fBwXhF548Y8r8+hc5IkSVpoExN5ZMSqVUe3nXlmHrk7OZmvImceVcVZoJAWwrp1eVrH6tVHt+3fDxdcADfeWFqzJEmStEiMjMAjj+RFL1esyNsOHcr59KqrzKTqC07xkBbCddflgsTevXnY3N69+fl115XdMkmSJC0GY2N5ivH+/fD88/m2fz+85CVenUN9wwKFtBA2boQPfzhXqJ96Kt9/+MPO6ZMkSVJvjI7Cb/xGvmrH1FSe2vGGN+RFMJ3SoT7hFA9poWzcaEFCkiRJ5RkdzZcZlfqUIygkSZIkSVLpLFBIkiRJkqTSWaCQJEmSJEmls0AhSZIkSZJKZ4FCkiRJkiSVzgKFJEmSJEkqnQUKSZIkSZJUOgsUkiRJkiSpdBYoJEmSJElS6SxQSJIkSZKk0lmgkCRJkiRJpbNAIUmSJEmSSmeBQpIkSZIklc4ChSRJkiRJKp0FCkmSJEmSVDoLFJIkSZIkqXQWKCRJkiRJUuksUEiSJEmSpNJZoJAkSZIkSaWzQCFJkiRJkkpngUKSJEmSJJXOAoUkSZIkSSqdBQpJkiRJklQ6CxSSJEmSJKl0FigkSZIkSVLpLFBIkiRJkqTSWaCQJEmSJEmlW1J2A9QdDZqM02KCxAjBGDVGqZfdLEmSJJXAbCipHziCYgA1aLKZafaQWA/sIbGZaRo0y26aJEmSesxsKKlfOIJiwDRo8q+ZYhdwNomLCNYWdahxWlbKJUmSFomZUROfpsky4DVAjRrDxetmQ0lV4wiKATJTHd9F4iwSB0n8FYnv0GIVMEEqu4mSJEnqgfZREzN/ZnIhYDaUVEkWKAbIOC2GCc4mmCRYQbCcxGMk9gEjRNlNlCRJUg/M5MJhgtUE0ZYLAbOhpEpyiseA2MoUtzLNYWA5+cCeQzAE7CLPNXy3Q/gkSZIG0lam2EKTHcA6IEi8vsh+ryD4S1oMAXvJudBsKKmKLFAMgPdzkI8BU+QhMU0gAaeTeBY4G9jEEucYSpIkDZgGTd7PYf6CnANXA0+RP6BaQZPLWMIaarwWeKCY3jFM8G7qZkNJlWOBos+9n4P8TvE4gGngAHAaeejeT1CzOCFJkjRgGjS5icN8npz9ghzs95M/tFoJfBW4kMQqYBnBD5gLJVWcBYo+tpUpPlY8bp9BmICD5IPrSUiSJGmwNGjyKxzmPo6OnE3AYWBZcZ+AIfJoiQkSI46akNQHLFD0sS00SeThfDP3LXKxogaMEp6EJEmSBsw4LXYTBOlI7pspUjTJWfAAOQveyNISWypJJ8erePSxHRxdEBMoZhUeLVJcZ3FCkiRp4EyQmAReRM58MyWIVtstYRaU1H8sUPSxdcAwR09MAUeuZv2vgI1WzCVJkgbOSHGltpXkKR01jmbBFrlwcQM1s6CkvlNKgSIiromIRyKiFREbjrPftyLioYh4MCLu72Ub+8F11KkBazk6kmII+CXgI6wos2mSJFWeeUT9aowaLyExDZxDXhwd4HRgDPgCy3g/Q6W1T5JOVVlrUDxM/vn5ex3se2VK6ekut6cvzVTFt9BkKXlExXXUrZZLktQZ84j60ih1bmYZtzDNfSTWA5cTXOvi6JL6XCkFipTSowARcaJddQIbWWpBQpKkU2AeUT8bpc6tFiMkDZiqr0GRgLsi4qsR8Z7j7RgR74mI+yPi/t27d/eoeZIkaRHoKI+YRSRJmp+ujaCIiHvIyyMc6/qU0qc7fJvXpZR2RsTZwN0R8VhK6Yuz7ZhSug24DWDDhg1ptn36zVam2EKTHTh9Q5KkU9HLPDKIWUTz16DJOC0epMleYDXBq6kxRs3pGJJ0jK4VKFJKb1yA99hZ3O+KiNuBy4BZCxSD5iNM8uu0COAMYCfwAaYBr84hSVKnzCMq01amuIlpngWeA1YBz5A4jcRmWmwCixSS1KayUzwi4rSIOGPmMfAm8mJWA28rU/wqLQ4CTeB5YD+5mrSFZqltkyRpMVnMeUSnrkGTa5nkPUzzXXKWg5znWsAOYJhgnFZpbZSkKirrMqNvj4jtwI8An42Izxfbz42IO4rd1gBfioivA18BPptS+lwZ7e2lBk1uoslh8nWtExwpVEyST2iSJGn+zCPqhgZNNjPNA7SoAXVgLxDF4wPAPvJoigmcCSRJ7cq6isftwO2zbN8JXF083ga8qsdNK91NHOZvgWnyUMAh8kE6TC5SjJbYNkmSBol5RAtpZu2wBokVwFLgNHKmW0bOdWcW9+eTixQjeAUZSWpX2Skei9FWpvgcucK+jDwE8CC5ODFJHk1xnfMUJUmSKmUrU3yAafaSWEouSnyHnOWmyR84TZOnetTIi5/vITFmFJekF/CnYoVsocmLyNX2OrCcXKyYJI+iuIGaC2RKkiRVzBaarCRfoWMFObedBjwDnE3+4Gk5Od9dDFxInU0scYFMSTpGKVM8NLuZy4nuIJ/UJovtLeAPWWJxQpIkqYJ2AOcUj88Cvs3RURMvIo+IfQs13mdRQpKOywJFhawjL6L0UuBp8vCW5cVzixOSJEnVNJPhVgOnE7yUxLeBFcBVLGGMmoUJSeqAUzwq5Drq7CdX20eAteSRFB+0jiRJklRZMxluL4kWiWlyhtvCEm5kqcUJSeqQv/lWyMwoiS00j0z3uIG6oyckSZIqzAwnSQvDAkXFbGSpJzNJkqQ+Y4aTpPmzQFGiBk3GaTFBYoRwfqIkSVIFmNEkqRyuQVGSBk02M80eEuvJ18LezDQNmmU3TZIkadEyo0lSeRxBUZJbmOYbJA6TWAVcRDBMME7LCr0kSVIPtY+Y2EaLc4Hh4nO84WIfM5okdZ8jKErQoMk9tEgkVpI4SOKvSBwqToySJEnqjWNHTOwi8TCJ79A6ss8qMKNJUg9YoCjBOC1eDETxZwXBchIPAiNE2c2TJElaNMZpMVyMZK0RnE1QAx5rK0jsw4wmSb1ggaIEEyReTXCIxEEgFbfvkRjzkEiSJPXMRDHddsYrCFrALqBFYk9xM6NJUvf5k7YEIwTLqfFaaqwA9gMB/KQrREuSJPXUCMG+tudrqPFKgrOB7cAwwSaWmNEkqQdcJLMEY9TYzDTDBD9WnBT3kLjWwyFJktRTM7kM8loT+4A6we9alJCknnMERQlGqbOJJQwTVuYlSZJKZC6TpOrwI/uSjFL3xCdJklQB5jJJqgZHUEiSJEmSpNJZoJAkSZIkSaWzQCFJkiRJkkpngUKSJEmSJJXOAoUkSZIkSSqdBQpJkiRJklQ6CxSSJEmSJKl0FigkSZIkSVLpLFBIkiRJkqTSWaCQJEmFDNAIAAAPg0lEQVSSJEmls0AhSZIkSZJKZ4FCkiRJkiSVzgKFJEmSJEkqnQUKSZIkSZJUOgsUkiRJkiSpdBYoJEmSJElS6ZaU3YC+0WjA+DhMTMDICIyNweho2a2SJEn6+8wtkqQ+5AiKTmzdCr/wC/CpT8ETT8A3vwmbN+eTvyRJUlU0GnDttbkgceedsHQp7NljbpEk9QULFCfSaMAv/RI8/jj83d/BAw/Al74E09P5kwlJkqSyNRpwzTXwhjfAJz4B+/fDc8/BfffB5CQMD5tbJEmVZ4HiRG66CXbuhMOHc1Hi0CF46in42tfysElJkqQybd0KGzfC//gfR/PKs8/mvNJswmOPwapV5hZJUuVZoDiRP//zo49rtXxLCbZty3M6JUmSytJo5A9T9u2DiJxTZkxNwYED+bV9+8wtkqTKs0BxIs8/D0uKtURTOrp9ejrP75QkSSrL+HguRETAsmV529KlOadMTeVpHsuW5XUozC2SpIrzKh4nMjSUh0lGQKuV71OCM85wNWxJktR77Vfo+NrXYMUKqNdzZnnuuZxV6vU8mmJ6Gl7zGnjf+8wtkqTKs0BxPI0GLF+eF5qaGT2RUg4Ab3pTuW2TJEmLS6MBH/843H03vPjF8OpX59ERu3bl0Z6Tk3DaablIkRKsWwc335zXp5AkqQ9YoDiem27Ki2LWann0BOTHq1bBDTeU2zZJkrQ4NBpwyy1wzz1w8GC+IgfkK3S8/OX5g5QlS+Css/KoiqEhuPpq+NCHHDUhSeorFijm0mjkIDA0lIdOHjiQh0mefjqsXu0JX5IkdV+jAZs3wze+AWeemS95vnt3HuG5fHl+/GM/Bl/+MrzqVfCWt+S1JswpkqQ+ZIFiLuPj+dOIej0PnxwayotNpZQLFpIkSd02Pp5HTBw+DCtX5ikchw7B00/Deeflq3MsXw4/8zNw441lt1aSpHnxKh5zmZjIl+M6fPhoYQLyvM7LLy+3bZIkaXGYmMhTS1etyoWJs87KmeS55/J0D6/QIUkaIBYo5jIyAj/wA3k4JeRQMD0Na9bAtdeW2zZJkrQ4jIzkURIXXZSzSL0OL3lJLkw880y+QsemTU7pkCQNBAsUcxkby1M8NmyAl70sr5a9dm1eDdsQIEmSemFsLI+QGBo6OoLz0CF461vz9I9bbzWXSJIGhmtQzGV0NH8iMT6e53ZeeaWLTkmSpN5qzyMTE3DVVeYRSdLAKqVAERG/DbwFOAw8AfzzlNLeWfZ7M/D/AHXgD1JKv9nTho6OGgAkSRpQ5hFJkqqlrCkedwM/lFIaBb4J/Ptjd4iIOvAx4CrgYuDnIuLinrZSkiQNMvOIJEkVUkqBIqV0V0ppunh6H7B+lt0uAx5PKW1LKR0G/hR4W6/aKEmSBpt5RJKkaqnCIpnvAu6cZfs64Nttz7cX22YVEe+JiPsj4v7du3cvcBMlSdKAm3ceMYtIkjQ/XVuDIiLuAdbO8tL1KaVPF/tcD0wDfzLbW8yyLc319VJKtwG3AWzYsGHO/SRJ0uLRyzxiFpEkaX66VqBIKb3xeK9HxDuBfwj8REpptpP4duClbc/XAzsXroWSJGnQmUckSeofpUzxKFbD/mXgrSml5+fY7a+BCyPi+yJiGfCzwGd61UZJkjTYzCOSJFVLWWtQbAHOAO6OiAcj4laAiDg3Iu4AKBatug74PPAo8KmU0iMltVeSJA0e84gkSRXStSkex5NSetkc23cCV7c9vwO4o1ftkiRJi4d5RJKkaqnCVTwkSZIkSdIiZ4FCkiRJkiSVzgKFJEmSJEkqnQUKSZIkSZJUOgsUkiRJkiSpdBYoJEmSJElS6SxQSJIkSZKk0kVKqew2LLiI2A08OY+3OAt4eoGaUwX2p9oGrT8weH2yP9U2aP2B7vXpvJTSS7rwvjrGAmSRXhvE/0ftBrl/g9w3GOz+DXLfYLD7Z9/mZ848MpAFivmKiPtTShvKbsdCsT/VNmj9gcHrk/2ptkHrDwxmn1Rtg/5vbpD7N8h9g8Hu3yD3DQa7f/ate5ziIUmSJEmSSmeBQpIkSZIklc4CxexuK7sBC8z+VNug9QcGr0/2p9oGrT8wmH1StQ36v7lB7t8g9w0Gu3+D3DcY7P7Zty5xDQpJkiRJklQ6R1BIkiRJkqTSWaCQJEmSJEmls0ABRMQ1EfFIRLQiYs5LqkTEtyLioYh4MCLu72UbT8ZJ9OfNEfGNiHg8Ij7YyzaejIg4MyLujoi/Le6H59iv0sfnRN/vyH63eL0REZeU0c5OddCfKyJiX3E8HoyI/1BGOzsVEf85InZFxMNzvN5vx+dE/em34/PSiPhCRDxa/Hz7N7Ps0zfHqMP+9NUxUn+JiN+OiMeK/yu3R8TqOfbri6xwrEHLdu0GLecda1ByX7tBy4DtBi0Pthu0bHisymbFlNKivwGvAH4AuBfYcJz9vgWcVXZ7F6I/QB14ArgAWAZ8Hbi47LbP0dYPAx8sHn8Q+K1+Oz6dfL+Bq4E7gQAuB75cdrvn2Z8rgD8ru60n0acfAy4BHp7j9b45Ph32p9+OzznAJcXjM4Bv9vn/oU7601fHyFt/3YA3AUuKx78127m1n7LCLG0fqGx3sn3r82PX97nvZI9FP52/TqFvfXsuG7RseAr9K+XYOYICSCk9mlL6RtntWCgd9ucy4PGU0raU0mHgT4G3db91p+RtwCeKx58AfqbEtpyqTr7fbwP+KGX3Aasj4pxeN7RD/fTvpyMppS8Czxxnl346Pp30p6+klJ5KKT1QPH4WeBRYd8xufXOMOuyP1DUppbtSStPF0/uA9bPs1rc/6wct27UbwJx3rEHIfe0GLQO26+d/Zyc0aNnwWFXNihYoTk4C7oqIr0bEe8puzDytA77d9nw71Q3Ha1JKT0EO9cDZc+xX5ePTyfe7n45Jp239kYj4ekTcGRE/2JumdU0/HZ9O9eXxiYjzgdcAXz7mpb48RsfpD/TpMVLfeRf5U8Bj9eX/qZNU5ewwH/187AYh97UbtAzYbjHmwXb9etxORs+P3ZJefJEqiIh7gLWzvHR9SunTHb7N61JKOyPibODuiHisqDz13AL0J2bZVto1Z4/Xn5N4m8ocn1l08v2u1DE5gU7a+gBwXkrpQERcDfx34MKut6x7+un4dKIvj09EnA78N+AXU0r7j315lr9S6WN0gv705TFSdXSSFSLiemAa+JPZ3mKWbZX5PzVo2a7doOW8Yy2C3Ndu0DJgu8WYB9v163HrVCnHbtEUKFJKb1yA99hZ3O+KiNvJw5pK+UG4AP3ZDry07fl6YOc83/OUHa8/EfHdiDgnpfRUMWxq1xzvUZnjM4tOvt+VOiYncMK2tv+ylVK6IyJuiYizUkpP96iNC62fjs8J9ePxiYil5F/m/ySlND7LLn11jE7Un348RqqWE2WFiHgn8A+Bn0gpzRaqK/1/atCyXbtBy3nHWgS5r92gZcB2izEPtuvX49aRso6dUzw6FBGnRcQZM4/Ji0vNuuJpn/hr4MKI+L6IWAb8LPCZkts0l88A7ywevxP4e58c9MHx6eT7/RngnxYrAl8O7JsZ4lhBJ+xPRKyNiCgeX0b+efO9nrd04fTT8Tmhfjs+RVv/E/BoSul35titb45RJ/3pt2Ok/hIRbwZ+GXhrSun5OXbrp6xw0vogO8xHPx+7Qch97QYtA7ZbjHmwXb8et46UduxSBVYQLfsGvJ1cAZsEvgt8vth+LnBH8fgC8sq0XwceIQ+xK73tp9qf4vnV5JXjn6h4f14M/E/gb4v7M/vx+Mz2/QbeC7y3eBzAx4rXH+I4q45X4dZBf64rjsXXyQuwvbbsNp+gP58EngKmiv8/7+7z43Oi/vTb8flR8rDJBvBgcbu6X49Rh/3pq2Pkrb9uwOPkudMz//5uLbb3ZVaYpX8Dle1Otm99fuwGIvcd06eByoAn2be+PZcxYNnwFPpXyrGL4otLkiRJkiSVxikekiRJkiSpdBYoJEmSJElS6SxQSJIkSZKk0lmgkCRJkiRJpbNAIUmSJEmSSmeBQlLHiush/2lEPBERfxMRd0TEy0/xvZZGxFcXuo2SJKl/RcSBDvfbGhEXFI9Pj4jfK/LJIxHxxYj44Xm246sRsWyO1zZHxI/P5/0lzW5J2Q2Q1B8iIoDbgU+klH622PZqYA35+tcn60eBv1y4FkqSpMUgIn4QqKeUthWb/gD4O+DClFKrKFy8Yh7vfz6wI6V0eI5dPgr8PvDnp/o1JM3OERSSOnUlMJVSunVmQ0rpQaAeEfcWn2Q8FhF/UhQziIhvRcSvRcQDEfFQRFzU9n5vBu6MiNMi4rMR8fWIeDgi/klvuyVJkqomIq6YK18A/wfw6WK/7wd+GLghpdQCSCltSyl9NiLOj4hHI+L3i5EVd0XEiuLv3RsRvxURX4mIb0bE69u+/FXA5yKiHhF/WOSThyLi3xbv/yTw4ohY26Nvh7RoWKCQ1KkfAuaakvEa4BeBi4ELgNe1vfZ0SukS4OPAprbtVwL3kgsVO1NKr0op/RDwuQVutyRJ6k9z5YvXcTST/CDwYEqpOcd7XAh8LKX0g8Be4B+1vbYkpXRZ8TV+tW37m8l55NXAupTSD6WUXgn8l7Z9HuCFeUfSArBAIWkhfCWltL345OJB4Py218aL+6/ObI+Ic4FnUkrPAw8Bbyw+xXh9Smlf75otSZIqbK58cQ6wu8P3+LtixCe0ZZHCbBllGbC+mD6yDbggIj4aEW8G9rf93V3AuSfVG0knZIFCUqceAf7BHK9Ntj1u8sL1bSZn2X4V8HmAlNI3i/d9CPg/I+I/LFSDJUlSX5srXxwElhePHwFeFRFz/V5zshnl9cCXAFJKe4BXkUd8/ivyWhczlhftkLSALFBI6tSfA0MR8S9nNkTEpcAbTuG93gzcWbzHucDzKaU/BjYDlyxAWyVJ0uB6FHgZQErpCeB+4Nfa1sC6MCLedorv3Z5RzgJqKaX/BnyIF2aUlwMPn+LXkDQHCxSSOpJSSsDbgZ+cuYwXcCOw82TeJyLq5FW2Hys2vRL4SkQ8CFwP/PrCtVqSJA2gzwJXtD3/F8Ba4PGIeIh8hY2TyidtrgD+oni8Dri3yCh/CPx7yJdKJxdI7j/FryFpDpF/55Ck3oiIHwXekVJ6b9ltkSRJ/ae4EscXgNcdZ3HMU3nf9cDvp5SuOsF+bwcuSSl9aKG+tqTMAoUkSZKkvhIRPwU8mlKaKOFrXwPcnVLa2+uvLQ06CxSSJEmSJKl0rkEhSZIkSZJKZ4FCkiRJkiSVzgKFJEmSJEkqnQUKSZIkSZJUOgsUkiRJkiSpdP8bnYwa8qCMtvEAAAAASUVORK5CYII=\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",
    "    indepL = dataL['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",
    "    ax[1].plot(indepL,dep, '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": 15,
   "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",
    "    indepL = dataL['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",
    "    ax[1].plot(indepL,dep, '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": 16,
   "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",
      "[-4.207e-16  7.880e-01  4.613e-02  3.289e-01  5.219e-01]\n"
     ]
    }
   ],
   "source": [
    "X = np.array([np.ones(data['Cnn/s'].shape[0]), data['Cnn/s'], \\\n",
    "              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(dataL['Cnn/s'].shape[0]), dataL['Cnn/s'], \\\n",
    "               dataL['Ciclos'], dataL['Enf(mes)'], dataL['Enr(%)']]).T\n",
    "BL = np.linalg.inv(XL.T @ XL) @ XL.T @ dataL['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": 17,
   "id": "e70caaf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
       "        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>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",
       "<style scoped>\n",
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       "\n",
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       "        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>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>0.604096</td>\n",
       "      <td>0.371791</td>\n",
       "      <td>62.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BT18</td>\n",
       "      <td>0.403867</td>\n",
       "      <td>0.676038</td>\n",
       "      <td>-40.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BR03</td>\n",
       "      <td>0.630933</td>\n",
       "      <td>0.734825</td>\n",
       "      <td>-14.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BP08</td>\n",
       "      <td>1.165919</td>\n",
       "      <td>1.391645</td>\n",
       "      <td>-16.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BM09</td>\n",
       "      <td>1.100487</td>\n",
       "      <td>1.141534</td>\n",
       "      <td>-3.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BK49</td>\n",
       "      <td>0.980111</td>\n",
       "      <td>0.925823</td>\n",
       "      <td>5.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BJ12</td>\n",
       "      <td>1.209467</td>\n",
       "      <td>1.295909</td>\n",
       "      <td>-6.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BH08</td>\n",
       "      <td>1.062224</td>\n",
       "      <td>1.154497</td>\n",
       "      <td>-7.99%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BS40</td>\n",
       "      <td>-1.910217</td>\n",
       "      <td>-1.898310</td>\n",
       "      <td>0.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BS19</td>\n",
       "      <td>0.452957</td>\n",
       "      <td>0.511959</td>\n",
       "      <td>-11.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>L16</td>\n",
       "      <td>-0.664046</td>\n",
       "      <td>-1.064683</td>\n",
       "      <td>-37.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BG14</td>\n",
       "      <td>1.167152</td>\n",
       "      <td>1.296976</td>\n",
       "      <td>-10.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BG31</td>\n",
       "      <td>0.904157</td>\n",
       "      <td>0.993114</td>\n",
       "      <td>-8.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BC05</td>\n",
       "      <td>0.267430</td>\n",
       "      <td>0.186156</td>\n",
       "      <td>43.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BB40</td>\n",
       "      <td>-0.396228</td>\n",
       "      <td>-0.592980</td>\n",
       "      <td>-33.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BA20</td>\n",
       "      <td>-0.426368</td>\n",
       "      <td>-0.412441</td>\n",
       "      <td>3.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>L32</td>\n",
       "      <td>-1.503049</td>\n",
       "      <td>-1.439762</td>\n",
       "      <td>4.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>L42</td>\n",
       "      <td>-1.752910</td>\n",
       "      <td>-1.577885</td>\n",
       "      <td>11.09%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>T06</td>\n",
       "      <td>-0.951139</td>\n",
       "      <td>-0.787075</td>\n",
       "      <td>20.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>N16</td>\n",
       "      <td>-0.347970</td>\n",
       "      <td>-0.247843</td>\n",
       "      <td>40.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BR47</td>\n",
       "      <td>0.568356</td>\n",
       "      <td>0.478313</td>\n",
       "      <td>18.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>BP45</td>\n",
       "      <td>0.608702</td>\n",
       "      <td>0.507677</td>\n",
       "      <td>19.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>BN64</td>\n",
       "      <td>0.743695</td>\n",
       "      <td>0.655030</td>\n",
       "      <td>13.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>BL15</td>\n",
       "      <td>0.584593</td>\n",
       "      <td>0.362487</td>\n",
       "      <td>61.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BL21</td>\n",
       "      <td>0.843237</td>\n",
       "      <td>0.749166</td>\n",
       "      <td>12.56%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Excluido  Predicción  Validación    Error\n",
       "0      BL09    0.604096    0.371791   62.48%\n",
       "1      BT18    0.403867    0.676038  -40.26%\n",
       "2      BR03    0.630933    0.734825  -14.14%\n",
       "3      BP08    1.165919    1.391645  -16.22%\n",
       "4      BM09    1.100487    1.141534   -3.60%\n",
       "5      BK49    0.980111    0.925823    5.86%\n",
       "6      BJ12    1.209467    1.295909   -6.67%\n",
       "7      BH08    1.062224    1.154497   -7.99%\n",
       "8      BS40   -1.910217   -1.898310    0.63%\n",
       "9      BS19    0.452957    0.511959  -11.52%\n",
       "10      L16   -0.664046   -1.064683  -37.63%\n",
       "11     BG14    1.167152    1.296976  -10.01%\n",
       "12     BG31    0.904157    0.993114   -8.96%\n",
       "13     BC05    0.267430    0.186156   43.66%\n",
       "14     BB40   -0.396228   -0.592980  -33.18%\n",
       "15     BA20   -0.426368   -0.412441    3.38%\n",
       "16      L32   -1.503049   -1.439762    4.40%\n",
       "17      L42   -1.752910   -1.577885   11.09%\n",
       "18      T06   -0.951139   -0.787075   20.84%\n",
       "19      N16   -0.347970   -0.247843   40.40%\n",
       "20     BR47    0.568356    0.478313   18.83%\n",
       "21     BP45    0.608702    0.507677   19.90%\n",
       "22     BN64    0.743695    0.655030   13.54%\n",
       "23     BL15    0.584593    0.362487   61.27%\n",
       "24     BL21    0.843237    0.749166   12.56%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RMSE = 0.16\n",
      "\n",
      "MAE = 0.13\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 = dataL.groupby(dataL.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",
    "    entren = data[data.EECC != excluido]\n",
    "    # print(valid)\n",
    "    # print(entren)\n",
    "    validL = agrupaL.get_group(excluido)\n",
    "    validL = np.mean(validL.values[:,5:], axis=0)\n",
    "    entrenL = dataL[dataL.EECC != excluido]\n",
    "    # print(validL)\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",
    "    \n",
    "    XL = np.array([np.ones(entrenL['Cnn/s'].shape[0]), entrenL['Cnn/s'], \\\n",
    "                   entrenL['Ciclos'], entrenL['Enf(mes)'], entrenL['Enr(%)']]).T\n",
    "    BL = np.linalg.inv(XL.T @ XL) @ XL.T @ entrenL['Mwd/TnU']\n",
    "\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",
    "    error = ((calc - sol)/sol)*100\n",
    "    # print('{0:.2f} %'.format(error))\n",
    "    solL = validL[4]\n",
    "    calcL = BL[0]+BL[1]*validL[0]+BL[2]*validL[1]+BL[3]*validL[2]+BL[4]*validL[3]\n",
    "    errorL = ((calcL - solL)/solL)*100\n",
    "    # print('{0:.2f} %'.format(errorL))\n",
    "    \n",
    "    \n",
    "    LOOCV = LOOCV.append({'Excluido': excluido, 'Predicción': calc, 'Validación': sol, \\\n",
    "                          'Error': '{0:.2f}%'.format(error)}, ignore_index = True)\n",
    "    \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": "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>-1.441000</td>\n",
       "      <td>-1.895776</td>\n",
       "      <td>-1.881299</td>\n",
       "      <td>-0.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.434972</td>\n",
       "      <td>-1.878827</td>\n",
       "      <td>-1.876643</td>\n",
       "      <td>-0.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.237862</td>\n",
       "      <td>-1.659798</td>\n",
       "      <td>-1.613186</td>\n",
       "      <td>-2.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.175957</td>\n",
       "      <td>-0.656982</td>\n",
       "      <td>-0.695141</td>\n",
       "      <td>5.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.154856</td>\n",
       "      <td>-1.540683</td>\n",
       "      <td>-1.550345</td>\n",
       "      <td>0.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.118243</td>\n",
       "      <td>-0.783089</td>\n",
       "      <td>-0.705020</td>\n",
       "      <td>-9.97%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.063911</td>\n",
       "      <td>-1.459970</td>\n",
       "      <td>-1.450771</td>\n",
       "      <td>-0.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.056044</td>\n",
       "      <td>-1.450607</td>\n",
       "      <td>-1.447559</td>\n",
       "      <td>-0.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.009652</td>\n",
       "      <td>-0.596891</td>\n",
       "      <td>-0.540657</td>\n",
       "      <td>-9.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.006197</td>\n",
       "      <td>-0.591320</td>\n",
       "      <td>-0.542413</td>\n",
       "      <td>-8.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.994286</td>\n",
       "      <td>-0.466991</td>\n",
       "      <td>-0.484812</td>\n",
       "      <td>3.82%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.911796</td>\n",
       "      <td>-0.446131</td>\n",
       "      <td>-0.310645</td>\n",
       "      <td>-30.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.911281</td>\n",
       "      <td>-0.445420</td>\n",
       "      <td>-0.309924</td>\n",
       "      <td>-30.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.199524</td>\n",
       "      <td>0.189741</td>\n",
       "      <td>0.209584</td>\n",
       "      <td>10.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.144162</td>\n",
       "      <td>0.238809</td>\n",
       "      <td>0.219875</td>\n",
       "      <td>-7.93%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.094021</td>\n",
       "      <td>0.282307</td>\n",
       "      <td>0.239750</td>\n",
       "      <td>-15.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.218885</td>\n",
       "      <td>0.532389</td>\n",
       "      <td>0.513537</td>\n",
       "      <td>-3.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.357104</td>\n",
       "      <td>0.930387</td>\n",
       "      <td>0.849163</td>\n",
       "      <td>-8.73%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.490766</td>\n",
       "      <td>1.028641</td>\n",
       "      <td>0.923008</td>\n",
       "      <td>-10.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.540981</td>\n",
       "      <td>0.528833</td>\n",
       "      <td>0.520044</td>\n",
       "      <td>-1.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.580976</td>\n",
       "      <td>0.844221</td>\n",
       "      <td>0.805421</td>\n",
       "      <td>-4.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.612149</td>\n",
       "      <td>0.864962</td>\n",
       "      <td>0.806100</td>\n",
       "      <td>-6.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.691552</td>\n",
       "      <td>1.063724</td>\n",
       "      <td>1.034962</td>\n",
       "      <td>-2.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.835727</td>\n",
       "      <td>0.547915</td>\n",
       "      <td>0.727814</td>\n",
       "      <td>32.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.904616</td>\n",
       "      <td>0.517100</td>\n",
       "      <td>0.551724</td>\n",
       "      <td>6.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.904910</td>\n",
       "      <td>1.305865</td>\n",
       "      <td>1.265048</td>\n",
       "      <td>-3.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.127017</td>\n",
       "      <td>0.580627</td>\n",
       "      <td>0.669163</td>\n",
       "      <td>15.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.282661</td>\n",
       "      <td>0.758292</td>\n",
       "      <td>0.775694</td>\n",
       "      <td>2.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.717318</td>\n",
       "      <td>0.864844</td>\n",
       "      <td>0.811107</td>\n",
       "      <td>-6.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.062867</td>\n",
       "      <td>1.296502</td>\n",
       "      <td>1.368917</td>\n",
       "      <td>5.59%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cnn/s_val  Mwd/TnU_val  Mwd/TnU_pred    Error\n",
       "0   -1.441000    -1.895776     -1.881299   -0.76%\n",
       "1   -1.434972    -1.878827     -1.876643   -0.12%\n",
       "2   -1.237862    -1.659798     -1.613186   -2.81%\n",
       "3   -1.175957    -0.656982     -0.695141    5.81%\n",
       "4   -1.154856    -1.540683     -1.550345    0.63%\n",
       "5   -1.118243    -0.783089     -0.705020   -9.97%\n",
       "6   -1.063911    -1.459970     -1.450771   -0.63%\n",
       "7   -1.056044    -1.450607     -1.447559   -0.21%\n",
       "8   -1.009652    -0.596891     -0.540657   -9.42%\n",
       "9   -1.006197    -0.591320     -0.542413   -8.27%\n",
       "10  -0.994286    -0.466991     -0.484812    3.82%\n",
       "11  -0.911796    -0.446131     -0.310645  -30.37%\n",
       "12  -0.911281    -0.445420     -0.309924  -30.42%\n",
       "13  -0.199524     0.189741      0.209584   10.46%\n",
       "14  -0.144162     0.238809      0.219875   -7.93%\n",
       "15  -0.094021     0.282307      0.239750  -15.07%\n",
       "16   0.218885     0.532389      0.513537   -3.54%\n",
       "17   0.357104     0.930387      0.849163   -8.73%\n",
       "18   0.490766     1.028641      0.923008  -10.27%\n",
       "19   0.540981     0.528833      0.520044   -1.66%\n",
       "20   0.580976     0.844221      0.805421   -4.60%\n",
       "21   0.612149     0.864962      0.806100   -6.81%\n",
       "22   0.691552     1.063724      1.034962   -2.70%\n",
       "23   0.835727     0.547915      0.727814   32.83%\n",
       "24   0.904616     0.517100      0.551724    6.70%\n",
       "25   0.904910     1.305865      1.265048   -3.13%\n",
       "26   1.127017     0.580627      0.669163   15.25%\n",
       "27   1.282661     0.758292      0.775694    2.29%\n",
       "28   1.717318     0.864844      0.811107   -6.21%\n",
       "29   2.062867     1.296502      1.368917    5.59%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión del entrenamiento = 99.89%\n",
      "Precisión de la validación = 99.56%\n",
      "\n",
      "RMSE = 0.07\n",
      "\n",
      "MAE = 0.05\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": 28,
   "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>-0.547054</td>\n",
       "      <td>-39.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.298075</td>\n",
       "      <td>-0.893908</td>\n",
       "      <td>-0.547054</td>\n",
       "      <td>-38.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.296826</td>\n",
       "      <td>-0.891300</td>\n",
       "      <td>-0.547054</td>\n",
       "      <td>-38.62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.292855</td>\n",
       "      <td>-0.882885</td>\n",
       "      <td>-0.547054</td>\n",
       "      <td>-38.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.199557</td>\n",
       "      <td>-0.699294</td>\n",
       "      <td>-0.547054</td>\n",
       "      <td>-21.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.191396</td>\n",
       "      <td>-0.684479</td>\n",
       "      <td>-0.547054</td>\n",
       "      <td>-20.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.190000</td>\n",
       "      <td>-0.681990</td>\n",
       "      <td>-0.547054</td>\n",
       "      <td>-19.79%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.175957</td>\n",
       "      <td>-0.656982</td>\n",
       "      <td>-0.547054</td>\n",
       "      <td>-16.73%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.066705</td>\n",
       "      <td>-1.463288</td>\n",
       "      <td>-1.695853</td>\n",
       "      <td>15.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.063911</td>\n",
       "      <td>-1.459970</td>\n",
       "      <td>-1.695853</td>\n",
       "      <td>16.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-1.056044</td>\n",
       "      <td>-1.450607</td>\n",
       "      <td>-1.692988</td>\n",
       "      <td>16.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-1.054353</td>\n",
       "      <td>-1.448592</td>\n",
       "      <td>-1.692988</td>\n",
       "      <td>16.87%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-1.050089</td>\n",
       "      <td>-1.443614</td>\n",
       "      <td>-1.692988</td>\n",
       "      <td>17.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-1.046854</td>\n",
       "      <td>-1.439821</td>\n",
       "      <td>-1.692988</td>\n",
       "      <td>17.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-1.024062</td>\n",
       "      <td>-1.413509</td>\n",
       "      <td>-1.692988</td>\n",
       "      <td>19.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-1.011049</td>\n",
       "      <td>-1.398694</td>\n",
       "      <td>-1.692988</td>\n",
       "      <td>21.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.924588</td>\n",
       "      <td>-0.349772</td>\n",
       "      <td>-0.502635</td>\n",
       "      <td>43.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.899444</td>\n",
       "      <td>-0.317416</td>\n",
       "      <td>-0.502635</td>\n",
       "      <td>58.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.898635</td>\n",
       "      <td>-0.316349</td>\n",
       "      <td>-0.502635</td>\n",
       "      <td>58.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.897827</td>\n",
       "      <td>-0.315401</td>\n",
       "      <td>-0.502635</td>\n",
       "      <td>59.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.784016</td>\n",
       "      <td>-0.178152</td>\n",
       "      <td>-0.502635</td>\n",
       "      <td>182.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.781296</td>\n",
       "      <td>-0.174952</td>\n",
       "      <td>-0.502635</td>\n",
       "      <td>187.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.777032</td>\n",
       "      <td>-0.170092</td>\n",
       "      <td>-0.502635</td>\n",
       "      <td>195.51%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.768724</td>\n",
       "      <td>-0.160611</td>\n",
       "      <td>-0.502635</td>\n",
       "      <td>212.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.831609</td>\n",
       "      <td>1.258930</td>\n",
       "      <td>1.048551</td>\n",
       "      <td>-16.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.889176</td>\n",
       "      <td>1.295791</td>\n",
       "      <td>1.066598</td>\n",
       "      <td>-17.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.904910</td>\n",
       "      <td>1.305865</td>\n",
       "      <td>1.066598</td>\n",
       "      <td>-18.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.939097</td>\n",
       "      <td>1.327318</td>\n",
       "      <td>1.066598</td>\n",
       "      <td>-19.64%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.022764</td>\n",
       "      <td>1.251819</td>\n",
       "      <td>1.075738</td>\n",
       "      <td>-14.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.039454</td>\n",
       "      <td>1.261775</td>\n",
       "      <td>1.075738</td>\n",
       "      <td>-14.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.142898</td>\n",
       "      <td>1.322814</td>\n",
       "      <td>1.075738</td>\n",
       "      <td>-18.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1.185025</td>\n",
       "      <td>1.347229</td>\n",
       "      <td>1.078964</td>\n",
       "      <td>-19.91%</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     -0.547054  -39.60%\n",
       "1   -1.298075    -0.893908     -0.547054  -38.80%\n",
       "2   -1.296826    -0.891300     -0.547054  -38.62%\n",
       "3   -1.292855    -0.882885     -0.547054  -38.04%\n",
       "4   -1.199557    -0.699294     -0.547054  -21.77%\n",
       "5   -1.191396    -0.684479     -0.547054  -20.08%\n",
       "6   -1.190000    -0.681990     -0.547054  -19.79%\n",
       "7   -1.175957    -0.656982     -0.547054  -16.73%\n",
       "8   -1.066705    -1.463288     -1.695853   15.89%\n",
       "9   -1.063911    -1.459970     -1.695853   16.16%\n",
       "10  -1.056044    -1.450607     -1.692988   16.71%\n",
       "11  -1.054353    -1.448592     -1.692988   16.87%\n",
       "12  -1.050089    -1.443614     -1.692988   17.27%\n",
       "13  -1.046854    -1.439821     -1.692988   17.58%\n",
       "14  -1.024062    -1.413509     -1.692988   19.77%\n",
       "15  -1.011049    -1.398694     -1.692988   21.04%\n",
       "16  -0.924588    -0.349772     -0.502635   43.70%\n",
       "17  -0.899444    -0.317416     -0.502635   58.35%\n",
       "18  -0.898635    -0.316349     -0.502635   58.89%\n",
       "19  -0.897827    -0.315401     -0.502635   59.36%\n",
       "20  -0.784016    -0.178152     -0.502635  182.14%\n",
       "21  -0.781296    -0.174952     -0.502635  187.30%\n",
       "22  -0.777032    -0.170092     -0.502635  195.51%\n",
       "23  -0.768724    -0.160611     -0.502635  212.95%\n",
       "24   0.831609     1.258930      1.048551  -16.71%\n",
       "25   0.889176     1.295791      1.066598  -17.69%\n",
       "26   0.904910     1.305865      1.066598  -18.32%\n",
       "27   0.939097     1.327318      1.066598  -19.64%\n",
       "28   1.022764     1.251819      1.075738  -14.07%\n",
       "29   1.039454     1.261775      1.075738  -14.74%\n",
       "30   1.142898     1.322814      1.075738  -18.68%\n",
       "31   1.185025     1.347229      1.078964  -19.91%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión del entrenamiento = 98.51%\n",
      "Precisión de la validación = 93.73%\n",
      "\n",
      "RMSE = 0.25\n",
      "\n",
      "MAE = 0.24\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 = 100, max_depth = 3)\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": 29,
   "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>1.040262</td>\n",
       "      <td>0.631948</td>\n",
       "      <td>0.796743</td>\n",
       "      <td>26.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.282661</td>\n",
       "      <td>0.758292</td>\n",
       "      <td>0.957702</td>\n",
       "      <td>26.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.283396</td>\n",
       "      <td>0.758648</td>\n",
       "      <td>0.957702</td>\n",
       "      <td>26.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.346698</td>\n",
       "      <td>0.790412</td>\n",
       "      <td>0.994945</td>\n",
       "      <td>25.88%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cnn/s_val  Mwd/TnU_val  Mwd/TnU_pred   Error\n",
       "0   1.040262     0.631948      0.796743  26.08%\n",
       "1   1.282661     0.758292      0.957702  26.30%\n",
       "2   1.283396     0.758648      0.957702  26.24%\n",
       "3   1.346698     0.790412      0.994945  25.88%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión del entrenamiento = 99.97%\n",
      "Precisión de la validación = -903.11%\n",
      "\n",
      "RMSE = 0.19\n",
      "\n",
      "MAE = 0.19\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": 30,
   "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>-0.094605</td>\n",
       "      <td>-95.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.055652</td>\n",
       "      <td>-1.064683</td>\n",
       "      <td>0.231221</td>\n",
       "      <td>-121.72%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.630750</td>\n",
       "      <td>0.655030</td>\n",
       "      <td>0.626199</td>\n",
       "      <td>-4.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.707267</td>\n",
       "      <td>0.925823</td>\n",
       "      <td>0.762188</td>\n",
       "      <td>-17.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.833797</td>\n",
       "      <td>1.154497</td>\n",
       "      <td>1.047544</td>\n",
       "      <td>-9.26%</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     -0.094605   -95.02%\n",
       "1  -0.055652    -1.064683      0.231221  -121.72%\n",
       "2   0.630750     0.655030      0.626199    -4.40%\n",
       "3   0.707267     0.925823      0.762188   -17.67%\n",
       "4   0.833797     1.154497      1.047544    -9.26%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión del entrenamiento = 98.16%\n",
      "Precisión de la validación = 32.32%\n",
      "\n",
      "RMSE = 1.00\n",
      "\n",
      "MAE = 0.68\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 este método."
   ]
  }
 ],
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