neuronal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers import Dropout\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# importar datos xls de generacion de energia PPC\n",
    "\n",
    "df = pd.read_excel('gen_2022.xlsx', sheet_name='Datos_Potencia_2022')\n",
    "# join Date and Time into one column and convert to datetime using strptime\n",
    "df['DateTime'] = pd.to_datetime(df['Date'].astype(str) + ' ' + df['Time'].astype(str))\n",
    "# drop Date and Time columns\n",
    "df.drop(['Date', 'Time'], axis=1, inplace=True)\n",
    "# set Date column as index\n",
    "df.set_index('DateTime', inplace=True)\n",
    "# calcular energia en Kw/h\n",
    "df['Energia Kwh'] = df['Valle - PPC - TOTAL AC ACTIVE POWER (kW)'] / 60\n",
    "# agrupar ene rgia cada 5 minutos\n",
    "df = df.resample('5T').sum()\n",
    "# dividir la columna de Valle - PPC - ACTIVE POWER SETPOINT (kW) entre 5\n",
    "df[\"Valle - PPC - ACTIVE POWER SETPOINT (kW)\"] = df[\"Valle - PPC - ACTIVE POWER SETPOINT (kW)\"] / 5\n",
    "# Eliminar columna Valle - PPC - TOTAL AC ACTIVE POWER (kW)\t\n",
    "df.drop(['Valle - PPC - TOTAL AC ACTIVE POWER (kW)'], axis=1, inplace=True)\n",
    "\n",
    "# importar datos xls de radiacion solar\n",
    "df2 = pd.read_excel('gen_2022.xlsx', sheet_name='Datos_Recurso_Solar_2022')\n",
    "# join Date and Time into one column and convert to datetime using strptime\n",
    "df2['DateTime'] = pd.to_datetime(df2['Date'].astype(str) + ' ' + df2['Time'].astype(str))\n",
    "# drop Date and Time columns\n",
    "df2.drop(['Date', 'Time'], axis=1, inplace=True)\n",
    "# set Date column as index\n",
    "df2.set_index('DateTime', inplace=True)\n",
    "\n",
    "# importar datos xls de temperatura\n",
    "df3 = pd.read_excel('gen_2022.xlsx', sheet_name='temperatura')\n",
    "# join Date and Time into one column and convert to datetime using strptime\n",
    "df3['DateTime'] = pd.to_datetime(df3['Date'].astype(str) + ' ' + df3['Time'].astype(str))\n",
    "# drop Date and Time columns\n",
    "df3.drop(['Date', 'Time'], axis=1, inplace=True)\n",
    "# set Date column as index\n",
    "df3.set_index('DateTime', inplace=True)\n",
    "\n",
    "# agregar columan de Irradiancia media (W/m2) de df2 a df\n",
    "df['Irradiancia media (W/m2)'] = df2['Irradiancia media (W/m2)']\n",
    "# agregar columan de Temperatura media (°C) de df3 a df\n",
    "df['Temperatura media (°C)'] = df3['Temperatura panel promedio - (ºC)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Valle - PPC - ACTIVE POWER SETPOINT (kW)</th>\n",
       "      <th>Energia Kwh</th>\n",
       "      <th>Irradiancia media (W/m2)</th>\n",
       "      <th>Temperatura media (°C)</th>\n",
       "      <th>Hour</th>\n",
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       "      <th>2022-01-01 00:00:00</th>\n",
       "      <td>50000.0</td>\n",
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       "      <th>2022-01-01 00:05:00</th>\n",
       "      <td>50000.0</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-01 00:10:00</th>\n",
       "      <td>50000.0</td>\n",
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       "      <th>2022-01-01 00:15:00</th>\n",
       "      <td>50000.0</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-01 00:20:00</th>\n",
       "      <td>50000.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "                     Valle - PPC - ACTIVE POWER SETPOINT (kW)  Energia Kwh  \\\n",
       "DateTime                                                                     \n",
       "2022-01-01 00:00:00                                   50000.0   -10.544850   \n",
       "2022-01-01 00:05:00                                   50000.0   -10.526300   \n",
       "2022-01-01 00:10:00                                   50000.0   -10.530717   \n",
       "2022-01-01 00:15:00                                   50000.0   -10.671283   \n",
       "2022-01-01 00:20:00                                   50000.0   -10.486217   \n",
       "\n",
       "                     Irradiancia media (W/m2)  Temperatura media (°C)  Hour  \\\n",
       "DateTime                                                                      \n",
       "2022-01-01 00:00:00                       NaN                     NaN     0   \n",
       "2022-01-01 00:05:00                 -3.127231               19.325083     0   \n",
       "2022-01-01 00:10:00                 -3.089154               19.282083     0   \n",
       "2022-01-01 00:15:00                 -2.865615               19.229000     0   \n",
       "2022-01-01 00:20:00                 -2.904385               19.199667     0   \n",
       "\n",
       "                     Month  Day  \n",
       "DateTime                         \n",
       "2022-01-01 00:00:00      1    1  \n",
       "2022-01-01 00:05:00      1    1  \n",
       "2022-01-01 00:10:00      1    1  \n",
       "2022-01-01 00:15:00      1    1  \n",
       "2022-01-01 00:20:00      1    1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create from datetime index a column of hours,monts,day\n",
    "df['Hour'] = df.index.hour\n",
    "df['Month'] = df.index.month\n",
    "df['Day'] = df.index.day\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funciones\n",
    "# Funcion de imputacion de datos faltantes\n",
    "def imputacion_lineal(df):\n",
    "    df = df.interpolate(method='linear', limit_direction='forward', axis=0)\n",
    "    df.fillna(0, inplace=True)\n",
    "    return df\n",
    "\n",
    "# Funcion para reemplazar valores NaN por 0\n",
    "def reemplazar_nan(df):\n",
    "    df.fillna(0, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valle - PPC - ACTIVE POWER SETPOINT (kW)    0\n",
      "Energia Kwh                                 0\n",
      "Irradiancia media (W/m2)                    0\n",
      "Temperatura media (°C)                      0\n",
      "Hour                                        0\n",
      "Month                                       0\n",
      "Day                                         0\n",
      "dtype: int64\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Valle - PPC - ACTIVE POWER SETPOINT (kW)</th>\n",
       "      <th>Energia Kwh</th>\n",
       "      <th>Irradiancia media (W/m2)</th>\n",
       "      <th>Temperatura media (°C)</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01 06:30:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>90.330383</td>\n",
       "      <td>33.933769</td>\n",
       "      <td>16.942273</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-01 06:35:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>152.545200</td>\n",
       "      <td>57.114308</td>\n",
       "      <td>16.980250</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 06:40:00</th>\n",
       "      <td>50000.0</td>\n",
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       "      <th>2022-01-01 06:45:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>296.787717</td>\n",
       "      <td>109.117077</td>\n",
       "      <td>17.669417</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 06:50:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>416.128133</td>\n",
       "      <td>132.461231</td>\n",
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       "      <td>6</td>\n",
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      "text/plain": [
       "                     Valle - PPC - ACTIVE POWER SETPOINT (kW)  Energia Kwh  \\\n",
       "DateTime                                                                     \n",
       "2022-01-01 06:30:00                                   50000.0    90.330383   \n",
       "2022-01-01 06:35:00                                   50000.0   152.545200   \n",
       "2022-01-01 06:40:00                                   50000.0   218.782100   \n",
       "2022-01-01 06:45:00                                   50000.0   296.787717   \n",
       "2022-01-01 06:50:00                                   50000.0   416.128133   \n",
       "\n",
       "                     Irradiancia media (W/m2)  Temperatura media (°C)  Hour  \\\n",
       "DateTime                                                                      \n",
       "2022-01-01 06:30:00                 33.933769               16.942273     6   \n",
       "2022-01-01 06:35:00                 57.114308               16.980250     6   \n",
       "2022-01-01 06:40:00                 81.989923               17.259667     6   \n",
       "2022-01-01 06:45:00                109.117077               17.669417     6   \n",
       "2022-01-01 06:50:00                132.461231               18.132000     6   \n",
       "\n",
       "                     Month  Day  \n",
       "DateTime                         \n",
       "2022-01-01 06:30:00      1    1  \n",
       "2022-01-01 06:35:00      1    1  \n",
       "2022-01-01 06:40:00      1    1  \n",
       "2022-01-01 06:45:00      1    1  \n",
       "2022-01-01 06:50:00      1    1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imputacion de NaNs usando interpolacion lineal\n",
    "df = imputacion_lineal(df)\n",
    "# replace nan values with 0\n",
    "df.fillna(0, inplace=True)\n",
    "# count number of NaN values in dataframe\n",
    "print(df.isnull().sum())\n",
    "\n",
    "# Copiar datos de generacion de energia\n",
    "df_gen = df.copy()\n",
    "# Eliminar registros donde Valle - PPC - ACTIVE POWER SETPOINT (kW) < 50000\n",
    "df_gen = df_gen[df_gen['Valle - PPC - ACTIVE POWER SETPOINT (kW)'] >= 50000]\n",
    "# Eliminar registros donde Energia Kwh <= 0\n",
    "df_gen = df_gen[df_gen['Energia Kwh'] > 0]\n",
    "# Eliminar registros donde Energia Kwh > 4000\n",
    "df_gen = df_gen[df_gen['Energia Kwh'] <= 4150]\n",
    "# Eliminar registros donde Irradiancia media (W/m2) <= 20\n",
    "df_gen = df_gen[df_gen['Irradiancia media (W/m2)'] > 20]\n",
    "df_gen.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to split data into train, test and validation sets\n",
    "def train_test_val_split(df):\n",
    "    # split data into train and test sets\n",
    "    train, test = train_test_split(df, test_size=0.2, random_state=42)\n",
    "    # split train data into train and validation sets\n",
    "    train, val = train_test_split(train, test_size=0.2, random_state=42)\n",
    "    return train, test, val\n",
    "\n",
    "# split data into train, test and validation sets\n",
    "train, test, val = train_test_val_split(df_gen)\n",
    "\n",
    "# split data into X and y , x is Irradiancia media (W/m2)\tand Temperatura media (°C)\t, y is Energia Kwh\n",
    "X_train = train[['Irradiancia media (W/m2)', 'Temperatura media (°C)', 'Month', 'Day', 'Hour']]\n",
    "y_train = train['Energia Kwh']\n",
    "X_test = test[['Irradiancia media (W/m2)', 'Temperatura media (°C)', 'Month', 'Day', 'Hour']]\n",
    "y_test = test['Energia Kwh']\n",
    "X_val = val[['Irradiancia media (W/m2)', 'Temperatura media (°C)', 'Month', 'Day', 'Hour']]\n",
    "y_val = val['Energia Kwh']\n",
    "\n",
    "# normalizar datos usando StandardScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "X_val = scaler.transform(X_val)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "333/333 [==============================] - 1s 1ms/step - loss: 650.4269 - mae: 650.4269 - val_loss: 183.1315 - val_mae: 183.1315\n",
      "Epoch 2/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 154.2558 - mae: 154.2558 - val_loss: 140.0919 - val_mae: 140.0919\n",
      "Epoch 3/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 135.0119 - mae: 135.0119 - val_loss: 132.2567 - val_mae: 132.2567\n",
      "Epoch 4/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 131.5095 - mae: 131.5095 - val_loss: 128.8524 - val_mae: 128.8524\n",
      "Epoch 5/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 128.6380 - mae: 128.6380 - val_loss: 139.4494 - val_mae: 139.4494\n",
      "Epoch 6/100\n",
      "333/333 [==============================] - 0s 990us/step - loss: 128.1213 - mae: 128.1213 - val_loss: 126.7750 - val_mae: 126.7750\n",
      "Epoch 7/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 127.4545 - mae: 127.4545 - val_loss: 123.9262 - val_mae: 123.9262\n",
      "Epoch 8/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 126.4295 - mae: 126.4295 - val_loss: 125.1371 - val_mae: 125.1371\n",
      "Epoch 9/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 126.1288 - mae: 126.1288 - val_loss: 133.5008 - val_mae: 133.5008\n",
      "Epoch 10/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 125.1113 - mae: 125.1113 - val_loss: 121.7739 - val_mae: 121.7739\n",
      "Epoch 11/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 124.3078 - mae: 124.3078 - val_loss: 125.1843 - val_mae: 125.1843\n",
      "Epoch 12/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 124.1502 - mae: 124.1502 - val_loss: 121.0684 - val_mae: 121.0684\n",
      "Epoch 13/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 123.8297 - mae: 123.8297 - val_loss: 122.6372 - val_mae: 122.6372\n",
      "Epoch 14/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 122.7163 - mae: 122.7163 - val_loss: 122.3630 - val_mae: 122.3630\n",
      "Epoch 15/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 123.4163 - mae: 123.4163 - val_loss: 120.3623 - val_mae: 120.3623\n",
      "Epoch 16/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 123.3844 - mae: 123.3844 - val_loss: 120.6033 - val_mae: 120.6033\n",
      "Epoch 17/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 122.3459 - mae: 122.3459 - val_loss: 126.9417 - val_mae: 126.9417\n",
      "Epoch 18/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 122.4661 - mae: 122.4661 - val_loss: 119.9103 - val_mae: 119.9103\n",
      "Epoch 19/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 122.7044 - mae: 122.7044 - val_loss: 121.1411 - val_mae: 121.1411\n",
      "Epoch 20/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 123.9161 - mae: 123.9161 - val_loss: 121.0929 - val_mae: 121.0929\n",
      "Epoch 21/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 121.0644 - mae: 121.0644 - val_loss: 119.7645 - val_mae: 119.7645\n",
      "Epoch 22/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 121.0549 - mae: 121.0549 - val_loss: 122.5850 - val_mae: 122.5850\n",
      "Epoch 23/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 121.8135 - mae: 121.8135 - val_loss: 129.6494 - val_mae: 129.6494\n",
      "Epoch 24/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 120.3448 - mae: 120.3448 - val_loss: 122.7493 - val_mae: 122.7493\n",
      "Epoch 25/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 120.0798 - mae: 120.0798 - val_loss: 117.9288 - val_mae: 117.9288\n",
      "Epoch 26/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 122.2811 - mae: 122.2811 - val_loss: 126.1601 - val_mae: 126.1601\n",
      "Epoch 27/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 120.0447 - mae: 120.0447 - val_loss: 118.1123 - val_mae: 118.1123\n",
      "Epoch 28/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 119.3984 - mae: 119.3984 - val_loss: 118.2066 - val_mae: 118.2066\n",
      "Epoch 29/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 119.3860 - mae: 119.3860 - val_loss: 125.3100 - val_mae: 125.3100\n",
      "Epoch 30/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 119.6552 - mae: 119.6552 - val_loss: 117.2148 - val_mae: 117.2148\n",
      "Epoch 31/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 118.8555 - mae: 118.8555 - val_loss: 117.4941 - val_mae: 117.4941\n",
      "Epoch 32/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 119.2885 - mae: 119.2885 - val_loss: 120.4565 - val_mae: 120.4565\n",
      "Epoch 33/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 119.6505 - mae: 119.6505 - val_loss: 124.5039 - val_mae: 124.5039\n",
      "Epoch 34/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 119.7254 - mae: 119.7254 - val_loss: 116.7012 - val_mae: 116.7012\n",
      "Epoch 35/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 118.5127 - mae: 118.5127 - val_loss: 123.5271 - val_mae: 123.5271\n",
      "Epoch 36/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 117.9328 - mae: 117.9328 - val_loss: 115.4749 - val_mae: 115.4749\n",
      "Epoch 37/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 119.0549 - mae: 119.0549 - val_loss: 116.7852 - val_mae: 116.7852\n",
      "Epoch 38/100\n",
      "333/333 [==============================] - 0s 863us/step - loss: 118.4103 - mae: 118.4103 - val_loss: 115.8216 - val_mae: 115.8216\n",
      "Epoch 39/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 118.2978 - mae: 118.2978 - val_loss: 126.4645 - val_mae: 126.4645\n",
      "Epoch 40/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 119.5394 - mae: 119.5394 - val_loss: 122.2328 - val_mae: 122.2328\n",
      "Epoch 41/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 117.1377 - mae: 117.1377 - val_loss: 115.9013 - val_mae: 115.9013\n",
      "Epoch 42/100\n",
      "333/333 [==============================] - 0s 921us/step - loss: 116.3604 - mae: 116.3604 - val_loss: 116.4279 - val_mae: 116.4279\n",
      "Epoch 43/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 118.5969 - mae: 118.5969 - val_loss: 117.8672 - val_mae: 117.8672\n",
      "Epoch 44/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 116.3480 - mae: 116.3480 - val_loss: 116.3577 - val_mae: 116.3577\n",
      "Epoch 45/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 117.1139 - mae: 117.1139 - val_loss: 117.6231 - val_mae: 117.6231\n",
      "Epoch 46/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 115.9115 - mae: 115.9115 - val_loss: 119.6692 - val_mae: 119.6692\n",
      "Epoch 47/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 117.2838 - mae: 117.2838 - val_loss: 129.7202 - val_mae: 129.7202\n",
      "Epoch 48/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 117.7402 - mae: 117.7402 - val_loss: 115.8252 - val_mae: 115.8252\n",
      "Epoch 49/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 115.9559 - mae: 115.9559 - val_loss: 126.2562 - val_mae: 126.2562\n",
      "Epoch 50/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 116.2929 - mae: 116.2929 - val_loss: 114.9808 - val_mae: 114.9808\n",
      "Epoch 51/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 115.6517 - mae: 115.6517 - val_loss: 119.0117 - val_mae: 119.0117\n",
      "Epoch 52/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 119.1813 - mae: 119.1813 - val_loss: 114.7982 - val_mae: 114.7982\n",
      "Epoch 53/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 115.9030 - mae: 115.9030 - val_loss: 113.8419 - val_mae: 113.8419\n",
      "Epoch 54/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 116.2350 - mae: 116.2350 - val_loss: 117.1576 - val_mae: 117.1576\n",
      "Epoch 55/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 114.9451 - mae: 114.9451 - val_loss: 120.6667 - val_mae: 120.6667\n",
      "Epoch 56/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 115.9869 - mae: 115.9869 - val_loss: 112.9625 - val_mae: 112.9625\n",
      "Epoch 57/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 114.9392 - mae: 114.9392 - val_loss: 113.2348 - val_mae: 113.2348\n",
      "Epoch 58/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 114.9482 - mae: 114.9482 - val_loss: 117.3886 - val_mae: 117.3886\n",
      "Epoch 59/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 116.3496 - mae: 116.3496 - val_loss: 114.5041 - val_mae: 114.5041\n",
      "Epoch 60/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 116.8650 - mae: 116.8650 - val_loss: 117.1016 - val_mae: 117.1016\n",
      "Epoch 61/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 115.5894 - mae: 115.5894 - val_loss: 116.1512 - val_mae: 116.1512\n",
      "Epoch 62/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 114.0900 - mae: 114.0900 - val_loss: 112.8699 - val_mae: 112.8699\n",
      "Epoch 63/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 114.4669 - mae: 114.4669 - val_loss: 113.2838 - val_mae: 113.2838\n",
      "Epoch 64/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 114.7958 - mae: 114.7958 - val_loss: 113.2440 - val_mae: 113.2440\n",
      "Epoch 65/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 114.8549 - mae: 114.8549 - val_loss: 112.8035 - val_mae: 112.8035\n",
      "Epoch 66/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 114.8616 - mae: 114.8616 - val_loss: 121.4827 - val_mae: 121.4827\n",
      "Epoch 67/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 113.9214 - mae: 113.9214 - val_loss: 112.8757 - val_mae: 112.8757\n",
      "Epoch 68/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 114.4809 - mae: 114.4809 - val_loss: 116.6061 - val_mae: 116.6061\n",
      "Epoch 69/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 114.2751 - mae: 114.2751 - val_loss: 114.0470 - val_mae: 114.0470\n",
      "Epoch 70/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 114.7083 - mae: 114.7083 - val_loss: 129.5161 - val_mae: 129.5161\n",
      "Epoch 71/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 115.4952 - mae: 115.4952 - val_loss: 114.6648 - val_mae: 114.6648\n",
      "Epoch 72/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 113.7144 - mae: 113.7144 - val_loss: 125.2658 - val_mae: 125.2658\n",
      "Epoch 73/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 115.2726 - mae: 115.2726 - val_loss: 114.5944 - val_mae: 114.5944\n",
      "Epoch 74/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 115.0112 - mae: 115.0112 - val_loss: 112.7717 - val_mae: 112.7717\n",
      "Epoch 75/100\n",
      "333/333 [==============================] - 0s 995us/step - loss: 114.5756 - mae: 114.5756 - val_loss: 119.1839 - val_mae: 119.1839\n",
      "Epoch 76/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 115.4764 - mae: 115.4764 - val_loss: 119.7131 - val_mae: 119.7131\n",
      "Epoch 77/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 113.3602 - mae: 113.3602 - val_loss: 115.8988 - val_mae: 115.8988\n",
      "Epoch 78/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 113.2823 - mae: 113.2823 - val_loss: 113.0911 - val_mae: 113.0911\n",
      "Epoch 79/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 113.9959 - mae: 113.9959 - val_loss: 114.7198 - val_mae: 114.7198\n",
      "Epoch 80/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 114.1615 - mae: 114.1615 - val_loss: 112.8897 - val_mae: 112.8897\n",
      "Epoch 81/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 112.9992 - mae: 112.9992 - val_loss: 118.3497 - val_mae: 118.3497\n",
      "Epoch 82/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 113.2609 - mae: 113.2609 - val_loss: 112.5326 - val_mae: 112.5326\n",
      "Epoch 83/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 115.6105 - mae: 115.6105 - val_loss: 115.6633 - val_mae: 115.6633\n",
      "Epoch 84/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 113.4190 - mae: 113.4190 - val_loss: 119.8947 - val_mae: 119.8947\n",
      "Epoch 85/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 113.8128 - mae: 113.8128 - val_loss: 113.9920 - val_mae: 113.9920\n",
      "Epoch 86/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 112.3585 - mae: 112.3585 - val_loss: 114.8671 - val_mae: 114.8671\n",
      "Epoch 87/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 114.5649 - mae: 114.5649 - val_loss: 112.3020 - val_mae: 112.3020\n",
      "Epoch 88/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 113.5179 - mae: 113.5179 - val_loss: 115.2027 - val_mae: 115.2027\n",
      "Epoch 89/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 114.0187 - mae: 114.0187 - val_loss: 135.3296 - val_mae: 135.3296\n",
      "Epoch 90/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 113.9771 - mae: 113.9771 - val_loss: 114.2068 - val_mae: 114.2068\n",
      "Epoch 91/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 112.5586 - mae: 112.5586 - val_loss: 112.4609 - val_mae: 112.4609\n",
      "Epoch 92/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 113.3553 - mae: 113.3553 - val_loss: 119.1673 - val_mae: 119.1673\n",
      "Epoch 93/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 112.6469 - mae: 112.6469 - val_loss: 116.0122 - val_mae: 116.0122\n",
      "Epoch 94/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 113.3830 - mae: 113.3830 - val_loss: 113.0918 - val_mae: 113.0918\n",
      "Epoch 95/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 113.9150 - mae: 113.9150 - val_loss: 115.1783 - val_mae: 115.1783\n",
      "Epoch 96/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 113.9449 - mae: 113.9449 - val_loss: 114.9208 - val_mae: 114.9208\n",
      "Epoch 97/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 112.7691 - mae: 112.7691 - val_loss: 113.5097 - val_mae: 113.5097\n",
      "Epoch 98/100\n",
      "333/333 [==============================] - 0s 1ms/step - loss: 113.8097 - mae: 113.8097 - val_loss: 112.6939 - val_mae: 112.6939\n",
      "Epoch 99/100\n",
      "333/333 [==============================] - 0s 2ms/step - loss: 112.5144 - mae: 112.5144 - val_loss: 113.1284 - val_mae: 113.1284\n",
      "Epoch 100/100\n",
      "333/333 [==============================] - 1s 2ms/step - loss: 112.8986 - mae: 112.8986 - val_loss: 112.1461 - val_mae: 112.1461\n",
      "208/208 [==============================] - 0s 408us/step\n",
      "Root Mean Squared Error: 239.56646295274194\n",
      "R2 score: 0.9672845699655358\n"
     ]
    }
   ],
   "source": [
    "# Crear un modelo de deep learning más profundo\n",
    "neurons = 64\n",
    "model_nn = keras.Sequential([\n",
    "    layers.Dense(neurons, activation='relu', input_shape=[X_train.shape[1]]),\n",
    "    layers.Dense(neurons, activation='relu'),\n",
    "    layers.Dense(neurons, activation='relu'),\n",
    "    layers.Dense(neurons, activation='relu'),\n",
    "    layers.Dense(neurons, activation='relu'),\n",
    "    layers.Dense(neurons, activation='relu'),\n",
    "    layers.Dense(1)\n",
    "])\n",
    "\n",
    "# compile model\n",
    "model_nn.compile(\n",
    "    optimizer='adam',\n",
    "    loss='mae',\n",
    "    metrics=['mae'])\n",
    "\n",
    "# early stopping\n",
    "early_stopping = keras.callbacks.EarlyStopping(\n",
    "    patience=50,\n",
    "    min_delta=0.001,\n",
    "    restore_best_weights=True)\n",
    "\n",
    "# train model\n",
    "history = model_nn.fit(\n",
    "    X_train, y_train,\n",
    "    validation_data=(X_val, y_val),\n",
    "    batch_size=64,\n",
    "    epochs=100,\n",
    "    verbose=1)\n",
    "\n",
    "# print rsme and r2\n",
    "y_pred = model_nn.predict(X_test)\n",
    "y_pred = y_pred.reshape(y_pred.shape[0],)\n",
    "print('Root Mean Squared Error:', np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "print('R2 score:', r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot loss\n",
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='validation')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cardenas/anaconda3/envs/gpu/lib/python3.11/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "# save model\n",
    "model_nn.save('model_nn.h5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1/208 [..............................] - ETA: 1s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "208/208 [==============================] - 0s 416us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot actual and predicted values for test data\n",
    "y_pred_nn = model_nn.predict(X_test)\n",
    "a = 0\n",
    "b = 100 + a\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(y_test.values[a:b], label='actual')\n",
    "plt.plot(y_pred_nn[a:b], label='predicted')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1/3285 [..............................] - ETA: 28s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3285/3285 [==============================] - 2s 465us/step\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>Valle - PPC - ACTIVE POWER SETPOINT (kW)</th>\n",
       "      <th>Energia Kwh</th>\n",
       "      <th>Irradiancia media (W/m2)</th>\n",
       "      <th>Temperatura media (°C)</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Energia Kwh predicha (nn)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</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>2022-01-01 00:00:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>-10.544850</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>57.053604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 00:05:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>-10.526300</td>\n",
       "      <td>-3.127231</td>\n",
       "      <td>19.325083</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.002754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 00:10:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>-10.530717</td>\n",
       "      <td>-3.089154</td>\n",
       "      <td>19.282083</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.011566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 00:15:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>-10.671283</td>\n",
       "      <td>-2.865615</td>\n",
       "      <td>19.229000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.069817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 00:20:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>-10.486217</td>\n",
       "      <td>-2.904385</td>\n",
       "      <td>19.199667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.058434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Valle - PPC - ACTIVE POWER SETPOINT (kW)  Energia Kwh  \\\n",
       "DateTime                                                                     \n",
       "2022-01-01 00:00:00                                   50000.0   -10.544850   \n",
       "2022-01-01 00:05:00                                   50000.0   -10.526300   \n",
       "2022-01-01 00:10:00                                   50000.0   -10.530717   \n",
       "2022-01-01 00:15:00                                   50000.0   -10.671283   \n",
       "2022-01-01 00:20:00                                   50000.0   -10.486217   \n",
       "\n",
       "                     Irradiancia media (W/m2)  Temperatura media (°C)  Hour  \\\n",
       "DateTime                                                                      \n",
       "2022-01-01 00:00:00                  0.000000                0.000000     0   \n",
       "2022-01-01 00:05:00                 -3.127231               19.325083     0   \n",
       "2022-01-01 00:10:00                 -3.089154               19.282083     0   \n",
       "2022-01-01 00:15:00                 -2.865615               19.229000     0   \n",
       "2022-01-01 00:20:00                 -2.904385               19.199667     0   \n",
       "\n",
       "                     Month  Day  Energia Kwh predicha (nn)  \n",
       "DateTime                                                    \n",
       "2022-01-01 00:00:00      1    1                  57.053604  \n",
       "2022-01-01 00:05:00      1    1                  60.002754  \n",
       "2022-01-01 00:10:00      1    1                  60.011566  \n",
       "2022-01-01 00:15:00      1    1                  60.069817  \n",
       "2022-01-01 00:20:00      1    1                  60.058434  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  agregar columna de generacion de energia predicha a df\n",
    "# agregar columna de generacion de energia predicha a df\n",
    "df_final = df.copy()\n",
    "df_final['Energia Kwh predicha (nn)'] = model_nn.predict(scaler.transform(df[['Irradiancia media (W/m2)', 'Temperatura media (°C)', 'Month', 'Day', 'Hour']]))\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot Energia Kwh vs Energia Kwh Pred\n",
    "plt.figure(figsize=(15,5))\n",
    "a = 2400\n",
    "b = a+400\n",
    "# graficar primeros 100 registros con index\n",
    "plt.plot(df_final.index[a:b], df_final['Energia Kwh'][a:b], color='black', label='Real')\n",
    "plt.plot(df_final.index[a:b], df_final['Energia Kwh predicha (nn)'][a:b], color='red', label='Predicha')\n",
    "plt.title('Generacion de energia')\n",
    "plt.xlabel('Tiempo')\n",
    "plt.ylabel('Energia Kwh')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Valle - PPC - ACTIVE POWER SETPOINT (kW)</th>\n",
       "      <th>Energia Kwh</th>\n",
       "      <th>Irradiancia media (W/m2)</th>\n",
       "      <th>Temperatura media (°C)</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Energia Kwh predicha (nn)</th>\n",
       "      <th>Energia perdida</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</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>2022-01-01 00:00:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>-10.544850</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>57.053604</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 00:05:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>-10.526300</td>\n",
       "      <td>-3.127231</td>\n",
       "      <td>19.325083</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.002754</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 00:10:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>-10.530717</td>\n",
       "      <td>-3.089154</td>\n",
       "      <td>19.282083</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.011566</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 00:15:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>-10.671283</td>\n",
       "      <td>-2.865615</td>\n",
       "      <td>19.229000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.069817</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 00:20:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>-10.486217</td>\n",
       "      <td>-2.904385</td>\n",
       "      <td>19.199667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.058434</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 07:55:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1371.942800</td>\n",
       "      <td>290.474615</td>\n",
       "      <td>29.505917</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1386.848511</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 08:00:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1949.658400</td>\n",
       "      <td>344.506154</td>\n",
       "      <td>29.652167</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1668.952759</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 08:05:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>2336.487583</td>\n",
       "      <td>449.102615</td>\n",
       "      <td>30.454000</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2140.954834</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 08:10:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>2345.565850</td>\n",
       "      <td>501.667077</td>\n",
       "      <td>32.192750</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2379.934326</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01 08:15:00</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>2439.689883</td>\n",
       "      <td>511.490769</td>\n",
       "      <td>34.276583</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2426.921875</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Valle - PPC - ACTIVE POWER SETPOINT (kW)  Energia Kwh  \\\n",
       "DateTime                                                                     \n",
       "2022-01-01 00:00:00                                   50000.0   -10.544850   \n",
       "2022-01-01 00:05:00                                   50000.0   -10.526300   \n",
       "2022-01-01 00:10:00                                   50000.0   -10.530717   \n",
       "2022-01-01 00:15:00                                   50000.0   -10.671283   \n",
       "2022-01-01 00:20:00                                   50000.0   -10.486217   \n",
       "...                                                       ...          ...   \n",
       "2022-01-01 07:55:00                                   50000.0  1371.942800   \n",
       "2022-01-01 08:00:00                                   50000.0  1949.658400   \n",
       "2022-01-01 08:05:00                                   50000.0  2336.487583   \n",
       "2022-01-01 08:10:00                                   50000.0  2345.565850   \n",
       "2022-01-01 08:15:00                                   50000.0  2439.689883   \n",
       "\n",
       "                     Irradiancia media (W/m2)  Temperatura media (°C)  Hour  \\\n",
       "DateTime                                                                      \n",
       "2022-01-01 00:00:00                  0.000000                0.000000     0   \n",
       "2022-01-01 00:05:00                 -3.127231               19.325083     0   \n",
       "2022-01-01 00:10:00                 -3.089154               19.282083     0   \n",
       "2022-01-01 00:15:00                 -2.865615               19.229000     0   \n",
       "2022-01-01 00:20:00                 -2.904385               19.199667     0   \n",
       "...                                       ...                     ...   ...   \n",
       "2022-01-01 07:55:00                290.474615               29.505917     7   \n",
       "2022-01-01 08:00:00                344.506154               29.652167     8   \n",
       "2022-01-01 08:05:00                449.102615               30.454000     8   \n",
       "2022-01-01 08:10:00                501.667077               32.192750     8   \n",
       "2022-01-01 08:15:00                511.490769               34.276583     8   \n",
       "\n",
       "                     Month  Day  Energia Kwh predicha (nn)  Energia perdida  \n",
       "DateTime                                                                     \n",
       "2022-01-01 00:00:00      1    1                  57.053604              0.0  \n",
       "2022-01-01 00:05:00      1    1                  60.002754              0.0  \n",
       "2022-01-01 00:10:00      1    1                  60.011566              0.0  \n",
       "2022-01-01 00:15:00      1    1                  60.069817              0.0  \n",
       "2022-01-01 00:20:00      1    1                  60.058434              0.0  \n",
       "...                    ...  ...                        ...              ...  \n",
       "2022-01-01 07:55:00      1    1                1386.848511              0.0  \n",
       "2022-01-01 08:00:00      1    1                1668.952759              0.0  \n",
       "2022-01-01 08:05:00      1    1                2140.954834              0.0  \n",
       "2022-01-01 08:10:00      1    1                2379.934326              0.0  \n",
       "2022-01-01 08:15:00      1    1                2426.921875              0.0  \n",
       "\n",
       "[100 rows x 9 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columna de energia perdida con las siguientes condiciones \n",
    "# si Valle - PPC - ACTIVE POWER SETPOINT (kW) >= 50000 , Energia perdida = 0\n",
    "# si Valle - PPC - ACTIVE POWER SETPOINT (kW) < 50000 y energía predicha > Energia Kwh , Energia perdida = (Energia predicha - Energia Kwh)\n",
    "df_final['Energia perdida'] = np.where((df_final['Valle - PPC - ACTIVE POWER SETPOINT (kW)'] < 50000) & (df_final['Energia Kwh predicha (nn)'] > df_final['Energia Kwh']), df_final['Energia Kwh predicha (nn)'] - df_final['Energia Kwh'], 0)\n",
    "# si Energia Kwh <0 y Valle - PPC - ACTIVE POWER SETPOINT (kW) < 50000 , Energia perdida = 0\n",
    "df_final['Energia perdida'] = np.where((df_final['Energia Kwh'] < 0) & (df_final['Valle - PPC - ACTIVE POWER SETPOINT (kW)'] < 50000), 0, df_final['Energia perdida'])\n",
    "df_final.head(100)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
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LdN555+nxxx/XI488IklKSUlRcnKyUlNTA39uk0hr5WouFzO5mov9HhlX18vFXC7+jJbc3VsurpeLmVzO5eLecnWtyBXfmSQ397vk5nq5ulbkCp+L51mSm2vlai4XM7mcy8U9z1oF2d/WdCSGhQsXWvXq1e2cc86x888/3zp16mTZ2dn23HPPmZnZ119/bSeccIIdeeSRds8999iUKVPs+uuvt1q1atmyZcuikunXX3+1rKwsu+iii+zOO++0AQMGWP369e3OO++0LVu22KJFi2z48OHWpEkTu/rqq+1f//qXXXPNNZaVlWW//vprVDKRKzJLly61GjVq2IgRI+wf//iHnX/++damTRu74IILbOnSpbZ161a74447LDMz084880wbO3asXX755VatWjVbsGBBVDKZublWruZyMZOrudjvkXF1vVzM5eLPaDN395aL6+ViJpdzubi3XF0rcsV3JjM397uZm+vl6lqRK3wunmeZublWruZyMZPLuVzc86xVKArmKJHf77crrrjCzjjjjJDjV155pdWvX9/+9re/mZnZ3Llzbdy4cVa/fn07/PDDrVOnTvbjjz9GLdctt9xiQ4YMCTl2zz33WIcOHWzUqFG2fft2W7t2rU2aNMnatWtnXbt2tV69etmcOXOilolckXnkkUesX79+Iceee+4569Gjh51xxhm2YsUKMzObPn269e3b1/r162cnn3yy/fTTT1HLZObmWrmay8VMruZiv0fG1fVyLZerP6PN3NxbLq6Xi5lczmXm3t5yda3IFd+ZPK7tdzN318vFtSJXZFw7z/K4uFau5nIxk8u5XNzzrFUoCuYo1QUXXGDnnXeemZnl5uYGjl9//fVWs2ZNmzJlSuDYzp07bcuWLbZt27aoZho9erT16dPHcnJyLD8/P3D84YcftlatWtkzzzwTOJafn2+5ubm2a9euqGYiV2Qeeugha9++vW3dujXk+Msvv2zdunWzMWPG2M6dO83MrKCgwMz+vHhDNLm4Vq7mcjGTq7nY75Fxdb1czOXiz2gzd/eWi+vlYiaXc7m4t1xdK3LFdyYzN/e7mZvr5epakSt8Lp5nmbm5Vq7mcjGTy7lc3POsVSgK5ijVddddZ23atAm8H7zphg4daq1atQpcHbeiPPzww9awYUPbtGlTkUwjR460+vXrF/kfiVxu5Xr11VetcePG9sMPP5iZWV5eXuC2Bx980GrVqmXLly83s32dLMH/jSYX18rVXC5mcjUX+z0yrq6Xi7lc/Blt5u7ecnG9XMzkci4X95ara0Wu+M5k5uZ+N3NzvVxdK3KFz8XzLDM318rVXC5mcjmXi3uetQpFwRylWr16tbVp08aGDRsWOLZ7924zM1uwYIE1aNDApk+fXuG5OnbsaD169Ai8v2fPHjMz27Fjh9WrV89ef/31Cs9Ersj07dvXOnToEHgyDn7Sa9SokT3xxBMVnsnMzbVyNZeLmVzNxX6PjKvr5VouV39Gm7m5t1xcLxczuZzLzL295epakSu+M3lc2+9m7q6Xi2tFrsi4dp7lcXGtXM3lYiaXc7m451mrPyWV51VLUTnk5+cH/l2rVi3ddtttmjdvni666CJJUtWqVSXtu/JsRkaG0tPTKyyT/f8r2z755JP6448/1LdvX0kKZNi1a5dq166tmjVrRj0TufYvU0FBgSRp0qRJSk5O1vHHH68//vhDVapUkSTt2LFDDRo0UP369aOeKTiXS2vlai4XM7mai/2+f7lcXS+Xcrn4Mzo4l6t7S3JnvVzMFA+5XNpbrq8VueIzU3Aul/Z7cC7JnfVyfa3IFX4ml86zgnO5tFau5nIxUzzkcmnPs1alKPcSPCqFgoKCwNVkt2zZYk899ZS1bdvW+vTpY/Pnz7e5c+faXXfdZc2bN7dVq1ZVWCavWyEnJ8c++ugja926tR166KH20Ucf2fTp0+2OO+6wBg0a2G+//VYhmcgVeaY33ngj8FvKefPmWefOna158+b29NNP2zvvvGO33HKL1a5d25YuXVohmbxcrq2Vq7lczORqLvZ75LlcXS/Xcrn4M9rL5erecm29XMzkei7X9pbLa0Wu+M3k5XJtv3u5XFsvl9eKXOFncu08y8vl2lq5msvFTK7ncm3Ps1bFo2COIgoKCuyWW24xn88XOCnatWuXffbZZ9a1a1erXbu2tW7d2lq2bGmzZ8+usEyPPPKI+Xw+++qrr8xs30UGlixZYkOGDLHmzZtbixYt7NBDD62wTOSKjN/vt0mTJpnP57O33347cHzv3r12ySWX2FFHHWUtW7a0rl272v/+978KyWTm5lq5msvFTK7mYr9HxtX1cjGXiz+jvVwu7i0X18vFTK7ncm1vubxW5IrfTF4u1/a7l8u19XJ5rcgVHhfPs8zcXCtXc7mYyeVcLu551qpkPrP/33ePhLR27VotWrRIVapUUdOmTdWsWTNJ0uzZs/Xee+9p5MiRqlu3bsjHfPfdd8rKylKdOnXUoEGDcs/0xx9/6Pvvv1eVKlXUpEkTderUSZK0YcMGTZ48Weecc06RTIsWLVJaWpqqVaumOnXqlHsmckVmxYoVmjp1qpKTk9WkSRMNHDgwcNsrr7yi/v37q169eiEfs27dOvl8PqWlpSk7O7vcM0lurpWruVzM5Gou9ntkXF0vF3O5+DNacndvubheLmZyOZeLe8vVtSJXfGeS3Nzvkpvr5epakSt8Lp5nSW6ulau5XMzkci4X9zxrFaGolOERF+bOnWuNGze2jh07WkZGhnXp0sUeffTRwO25ubnFflw0r8w7d+5cq1+/vnXq1Mnq1KljTZs2tVtuuSVqn49c0clUu3Zt69atm7Vq1cqqVatmF110UYl/mhl85eVo53JtrVzN5WImV3Ox3yPP5ep6uZbLxZ/RXi5X95Zr6+ViJtdzuba3XF4rcsVvJi+Xa/vdy+Xaerm8VuQKP5Nr51leLtfWytVcLmZyPZdre561ihwF8wS1ceNGa9OmjV1//fW2YcMG+/rrr+22226ztLQ0u+mmmwL3805+xowZYzNmzIhqpq1bt1rHjh3tuuuus71799r8+fPt2WeftapVq9r5558fmFvkZRo9erRNnjw5qpnIFZkdO3bYMcccY9dee62Zma1du9amTp1qderUsRNOOMEWL14ccv9Ro0bZAw88EPUnPRfXytVcLmZyNRf7PTKurpeLuVz8GW3m7t5ycb1czORyLhf3lqtrRa74zmTm5n43c3O9XF0rcoXPxfMsMzfXytVcLmZyOZeLe5612j8UzBPU4sWL7ZBDDrFffvklcGzbtm32z3/+01JSUuyuu+4KHF+zZo316tXLGjVqZHv37o1aB8GGDRvssMMOC1xswPPZZ59ZVlaWXX755YFjO3futPPOO8/q1atn27dvj0oeckVuz549dtRRRxV5cl28eLHVq1fPTj75ZMvPzw8cv/XWW6127dq2adOmqGUyc3OtXM3lYiZXc7HfI+PqermYy8Wf0Wbu7i0X18vFTC7ncnFvubpW5IrvTGZu7nczN9fL1bUiV/hcPM8yc3OtXM3lYiaXc7m451mr/UPBPEEtXbrUUlNT7a233go5vmfPHnvyySetdu3aIYP1Fy5caCtXroxqpg0bNli1atXsmWeeKXLb+++/b+np6SG3bdmyxdasWRPVTOSKzM6dO61JkyY2duzYwDHvTzfnzp1rmZmZNm7cuJCP2bBhQ1QzeZ/DtbVyNZeLmVzNxX6PjKvr5WIuF39Gm7m7t1xcLxczuZzLxb3l6lqRK74zmbm5383cXC9X14pc4XPxPMv7HK6tlau5XMzkci4X9zxrtX8omCeo3bt329lnn22nnXaazZ8/P+S2NWvW2EknnWSjR4+u8FyjRo2yLl262DfffBM45vf7be/evXbFFVfY0KFDA38uQi43cz322GPWpEkTe//99wPHvCe9+++/37p27WobN260goKCCs3l4lq5msvFTK7mYr9HxtX1ci2Xqz+jzdzcWy6ul4uZXM5l5t7ecnWtyBXfmTyu7Xczd9fLxbUiV2RcO8/yuLhWruZyMZPLuVzc86xV5JKicylRuGbLli1auXKlVqxYIUmqWrWqzjjjDM2bN0/PP/+8li1bFrhvgwYN1KRJE82cOVMFBQVRy7RhwwbNnz9fs2fPDhw744wzVLVqVf3973/XrFmzJClw5duGDRtqyZIlSk5OjlomckVmzZo1+v777zV16tTAXjnttNPUvXt3Pfzww5o6daokKSUlRZJUu3Ztbd++Xenp6UpKit7Tj4tr5WouFzO5mov9HhlX18vFXC7+jJbc3VsurpeLmVzO5eLecnWtyBXfmSQ397vk5nq5ulbkCp+L51mSm2vlai4XM7mcy8U9z1qVkwov0aPC/fTTT3bkkUda8+bNrU2bNjZkyJDAn1dMnDjRmjZtatdee63Nnj078DGXXnqpXXjhhZaXlxe1TG3btrUWLVpY/fr17eijj7bvv//ezPb9SUjnzp3t9NNPt08++cTMzAoKCmzkyJF28skn2+7du6OSiVyRZ2revLm1bdvWatSoYW3btrXXXnvNcnJybNasWXbiiSdaly5dAvOocnNzbcyYMdazZ8+ozsJyca1czeViJldzsd8jz+XqermWy8Wf0V4uV/eWa+vlYibXc7m2t1xeK3LFbyYvl2v73cvl2nq5vFbkCj+Ta+dZXi7X1srVXC5mcj2Xa3uetSo/FMwrud9//90aNmxoN998s3366af22muvWceOHe2ggw6yr776yszM/vWvf1nXrl2tbdu2dvLJJ9tpp51mWVlZNnfu3KhkWr16tR100EF222232ezZs23GjBnWs2dPa9Sokb300ktmZvbxxx/bKaecYtnZ2dajRw/r16+fZWdn25w5c6KSiVyRWb9+vbVr185uu+02W7p0qa1evdr+8pe/WNu2bW3s2LG2Z88e++mnn+zKK6+0KlWqWMeOHa1bt25Ws2ZN+/HHH6OSyczNtXI1l4uZXM3Ffo+Mq+vlYi4Xf0abubu3XFwvFzO5nMvFveXqWpErvjOZubnfzdxcL1fXilzhc/E8y8zNtXI1l4uZXM7l4p5nrcoXBfNK7pNPPrFDDjnE1q5dGzi2d+9e69u3rzVq1CjQNfDdd9/Zc889Z8OGDbObb77Z5s2bF7VM33//vbVu3doWL14ccvzcc8+1Zs2aBS4ms3TpUvvggw/smmuusQcffNAWLlwYtUzkisy8efPsoIMOslmzZoUcv/nmm+2QQw6xv/71r+b3+23Xrl02c+ZMu++++2zixIlFvoby5uJauZrLxUyu5mK/R8bV9XIxl4s/o83c3VsurpeLmVzO5eLecnWtyBXfmczc3O9mbq6Xq2tFrvC5eJ5l5uZauZrLxUwu53Jxz7NW5YuCeSU3efJkq1WrVuBP53JycgK3HXfccXbooYdWeKZPP/3U6tataytWrDAzs127dgVuO+OMM6xx48a2ZcsWcjmc66effrImTZrYl19+aWYW8qc71113nTVv3jyqv6EsiYtr5WouFzO5mov9HhlX18vFXC7+jDZzd2+5uF4uZnI5l4t7y9W1Ild8ZzJzc7+bublerq4VucLn4nmWmZtr5WouFzO5nMvFPc9alS8K5pXcli1brGHDhnbDDTcEjnknRb/99ps1a9bMHnvsMTPbd4XcipCXl2dt27a1YcOGBY7t3bs38O82bdrYqFGjKiQLufbf0Ucfbb179y42U5cuXeyss86q8EyurpWLuVzM5HIu9ntkXFwvM/dyufgz2szdveXiermYyeVcLu4tV9eKXPGdyczN/W7m5nq5ulbkioxr51lm7q6Vi7lczORyLjP39jxrVb5icJlRVJSCggJVq1ZNo0eP1rRp0zRhwgRJUmpqqvx+v+rVq6cmTZpo7dq1kvZdITfa8vPzVaVKFT300EOaOXOmbrrpJklSWlqacnNzJUlHHHGEtm7dGvUs5No/3tWMn332Wc2fP19nn312IFN+fr4kqWfPntq1a1eFZZLcXCtXc7mYydVc7PfIuLpeLuZy8We05Pbecm29XMzkci4X95ara0Wu+M4kubnfJTfXy9W1Ilf4XDzPktxcK1dzuZjJ5Vwu7nnWqvxRMK+EvA2ZnJysKlWq6NRTT1XXrl316quv6sEHH5QkJSUlKT09XXXq1FGVKlUkSWYW9Uze5zrmmGN01VVX6f/+7/907bXXStp3oibtOzFLTU2V7fsLiKhlIldk/H6/pH37SpLat2+vJ554Qp999pnOOOMM5ebmKilp31PKunXrVK1aNeXn5yfkWrmay8VMruZycb+bmZNrJbm5Xq7mcvFndHAu1/aWi+vlYqZ4yOXS3nJ9rcgVn5mCc7m034NzubRerq8Vucrm4nmW5OZauZrLxUwu53Jtz/P6MMrKrVcdMRU8A8hs35/PXX755bZy5UozM1u2bJmNGjXKWrRoYUOGDLFHHnnELr30UqtWrVpUB/wH/xmf3++3U045xZYuXWpm+66U+8QTT1iDBg2sa9eudu2119oFF1xgmZmZUb8IT+GM5Ao/06BBg+yTTz4xs3377v3337cmTZpYu3bt7JRTTrFhw4ZZZmam/fzzzxWSycvlylq5uOfz8/Ody+RlcSlXQUFBkXwu7PedO3cWyRXrtSqOK+sVD7lc+RldXC5X95Zr6+ViJtdzuba3XF4rV3IF/1x0JZc3d9ulTIXHlriy33l9eGAZyRV+JtfOs7xcrqyVi3ue14fhc/E1Iq8PKwYF80pg0aJFNnjwYJs/f37g2PTp061p06Z27bXXBp4MN27caFOmTLE+ffpY7969bciQIfbTTz9FJdPOnTstJyfHNm/eHDg2f/58a9mypQ0dOjSQae/evbZo0SI799xz7fTTT7dzzz23wv9HmTdvXsxzFX4SdiWXWegPrY0bN1rPnj3tlFNOsdzc3MDxHTt22M0332wjRoywa665pkKfhM3c2Fve7MfgdYl1rjVr1gReLHmff8GCBTFfqwULFtivv/5a5Fgscy1evNieffZZW716deCYC/t94cKFdthhh9m3334bOBbrtTIz27Ztm23ZssV+//33wLH169fHfL2Key5dv369HXfccU49b33++ecx/RldEhf2VnGmTZsW0/Uqbl998cUXTn4P2Vvhi/W+MnN3b23cuDEwW9TLEOvXFosWLbLt27eHHIt1JrN9Ref33nsv5CJqCxcujPl+5/XhgXHhdRivDw+MC3uL14fhc/H1oZmbrxF5fVhxKJjHuR9//NGqVatmPp/P3n///cBxv99vX331VcgJSWHBV0QvT/PmzbMhQ4ZYp06drH///jZlypTAbYsWLSo1U3DXSHlbuHChjR071s455xx7/vnnbebMmWa2b63mz58fs1zz58+3a6+91oYMGWL333+/TZs2LZBrwYIFMcm1atUq+/777wPvB58Ubdq0ybZt2xZ4v7jfuEbL4sWLbfz48Xb++efbyy+/HPgNqlls99aCBQvskksusd69e9u1115r//3vf2Oea+nSpZaSkmIDBw60HTt2hNy2aNGiUq+OHc21mjNnjvl8vsDFpIItXLgwJmv1008/Wc2aNe3GG28scqK2YcOGmO33H3/80bKysszn89nTTz8dclss9/svv/xivXv3tsMPP9zatm1rr732WuC2WD4/zJ8/30aOHGlDhgyxu+66yz7++OPAbRs3brStW7dWeK5FixbZ3Xffbeeff7698sorgau/FxQU2PTp02PyM9rMbN26dUX2uieWe2vZsmX2zDPP2KhRo+yLL74I5IjlOc2iRYts9OjRdtJJJ9mjjz5qixcvjnkmLxd7Kzwu7iszd/fW4sWLrWHDhnbFFVeEdCjHMpd37vD3v/895His1+qnn36y2rVr25gxY2zFihUht8XqnMaM14eR4PVh+Hh9GBleH4bPxdeHZm6+RuT1YcWiYB7H5syZY1WrVrX77rvPhg8fbh07drQ9e/aUeH+vK8P7jVM0Nua8efOsZs2adsMNN9j48eNt6NCh9pe//KXEXN6Tsvc/b7T+Z/n555+tRo0aduGFF9rpp59uvXv3tlatWtmbb74Z01wLFiyw7OxsO/fcc+2MM86wPn36WJ06dYo8+VVkrgULFlj9+vWtX79+9tVXXwWOF9flYLbvhWhwlmh+D+vXr2+nnnqq9ejRw1q1amU33XRTzPfW3LlzrVatWjZixAi78sorrWfPnnb99deH/AY1Frm+/fZba9SokfXt29dOOumkIidFscg0Z84cy8jIsJtvvjms+1dErlWrVlnr1q1t9OjRIcdLWq+K2u/e8/tDDz1kY8aMscaNG4cUfAurqO/h/PnzrVatWjZmzBh74YUX7JprrrGuXbuWmK2i1sv72XPFFVfYFVdcYWeddZZVrVrVnnnmmZjl8jKddNJJ1rNnT+vQoYMdddRRISeQwSriZ7TZvu9hw4YNbfjw4SF/ch/8p7nBKvK5tHHjxta3b1875JBDLDs7u0hxLFhFrNfcuXOtTp06du6559ppp51m7dq1swkTJsQ0kxl7KxIu7isvl4t7y8zsjTfesKysLBs0aJCNHDmyyFiPis71448/RnTuUFFrtXLlSmvWrJnddNNNIcdLKjhX5LkWrw/Dw+vDyDLx+jB8vD4Mn4uvD83cfI3I68OKR8E8Ts2ZM8fS0tLs1ltvNTOz//u//7MWLVoEOuoK//B6/PHHrX379rZu3bqoZdq9e7edeuqpNnLkyMCxZ5991oYNG2a7du0q8rn/9re/WWpqqv3xxx9Ry2S270lt4MCBNmbMmMCx7777zmrWrGk+n88mTZoUk1xmZtdee62dccYZgfeXLl1qY8eONZ/PV+SFU0XkWrt2rfXs2dOOPfZYO+aYY+zMM8+0L7/8MnB74SezF154wXw+n3333XdRy2S270VJ+/bt7ZZbbglkmDhxotWpU6fY9aio7+Hy5cutZcuWdvvttweOjR071i644ALLzc0tcrJWkXtr9uzZ1rp1a3v88cetW7dudvLJJwd+WG7atKnCMy1atMh8Pp/dc889Zrbvhdm7775rDzzwgP373/8u8udhFZVr6tSp1r17d/P7/Zafn2833HCDDRo0yHr37m3/+Mc/Qu5bUft9zpw5VqVKlcDz+48//mht27YNFHkK/39YUWuVm5trF154oV166aWBY//5z3/s5JNPtk2bNoV09JhV3Hrt2bPHzjzzTLvhhhsCx5YsWWKtW7c2n89nDz30UIXnysvLs/PPP9+GDx8e+H599913duWVV1qtWrXslVdeCbl/RfyMNjNbvXq1devWzY466ig76KCD7Kqrrip1Tm1F7a0VK1ZY69at7bbbbgu8mBw7dqw1aNAgpCPFUxHrtXTpUmvWrFnI8/uIESPsqquuKrYAXFHfQ/ZW+FzcV2bu7i3PF198YYceeqiNGTPGjj76aBs5cmSxL2orIteiRYssOTnZHnjgATPbd+7w2Wef2bPPPmszZ84M+bPvisrkeeONN6xPnz5mtu//y7vvvtuGDh1qw4cPt3feeSfkvhX1XMrrw/Dx+jB8vD6MDK8Pw+fq60Mz914j8vowNpJifdFRRG7z5s0aNmyYbrzxRj3wwAOSpP79+yszM1OTJk2SpMDVZj316tVTw4YNtXv37qjl8vv9WrZsmZo3bx44tmLFCs2ePVudO3dWnz599NRTTwVu69y5s3r37q1du3ZFLZMk5eXladWqVTryyCMDObt3767+/fvrhBNO0I033qhPPvkkcP+jjz66QnL5/X6tWLFCGRkZgWMtW7YMfF9Hjx6t1157LXBbly5dop5r1apVys7O1oQJEzRu3DitWrVKTz75pL766itJ+66qbEFXLT7++ON13nnnqVatWlHL5Pf79dlnn+mQQw7RlVdeGfj8F1xwgerUqaPly5cX+ZiKWCsz0/fff68TTjhBI0eODBzftm2b5s2bp86dO2vYsGGaPHlyhebysh100EHq2LGjzjvvPF177bXasGGDzjvvPJ155pl66qmnlJOTI6li9ruZafr06ZKkjh07SpL69eune+65R88++6xuvPFGXXrppZoyZUrgYypqrX7//XelpqbK5/Opb9++WrhwoTp27KjDDjtMl19+ue64447Affv16xf1/b59+3bdcMMNuvnmmwPP7+3bt9dBBx2kd955R9K+/w+DVeRz1qJFi1S7du3AsR9++EEzZsxQjx49dMwxx4SsV0U8P0j79teSJUvUsmVLSVJBQYFatWqlvn376tRTT9Vtt92mN954I3D/E044Ieq5zExLly5VrVq1At+v7t27a/To0TrvvPN06623huz3ivgZLUkLFixQvXr19Oqrr+qhhx7S//3f/+mJJ57QokWLir1/RfycLigo0DvvvKPOnTvr+uuvV3JysiTpoosuUtWqVbV27doiH1O3bt2orld+fr6mTJmiIUOG6MYbbwz83KlSpYoWL16sY489VldffbU+/vjjCsvkYW+Fx8V9Jbm9t6R9z/ONGjVSq1atdOedd+qkk07SDz/8oDvuuENDhw7Vs88+K7/fLyn6eys/P1+TJ0+W3+9Xnz59JEmDBg3Sddddp9tvv10nnXSSbrjhBn3//feBj6mo/S5Jv/76qzIzMyVJvXv31owZM1StWjXt3r1bw4YNC3nNUxHnNLw+jAyvD8PH68Pw8fowsjyuvj6U3HqNyOvDGIpBkR7lIHgGlvdbwbffftsaNmxon332WZH7+/3+IhfKKU9+v992795t55xzjvXq1ctefvllGzNmjGVkZNiLL75o77zzjj388MOWlpZmH330UeDjSvsTwfLK9fvvv9uRRx5pf/vb3wJ/JrlkyRJr3ry5vfDCC3bCCSfYFVdcEfJx0c7lefDBB+2QQw4JzK30bNy40a6++mrr0aOHrVmzpsJy5efn29y5cwPvT5061bp162ZnnnmmTZ8+PXA8eP5VNGdheT788EN7/PHHQ47t2rXLmjRpYm+99VaxH1MR38M1a9bYggULAu+PGzfOMjMzbcKECfbkk0/addddZw0bNgyZ91dRe8vM7LjjjrOpU6eamdk777xj9evXt6SkpJCukIrKtH37drv//vvN5/NZs2bN7Iwzzgis3TfffGNDhw61E044IaTTqCJyTZ8+3WrUqGGPP/64DR48OOSCLq+99pr5fL7AVb3NSh4tUJ6CL9Dlfb4ffvjBqlWrZpMnTy72YypqX40ePdoOOugg++tf/2ojR460jIwMe+ONN+zbb7+1119/3ZKSkuzll18ukj+atm/fboMHD7ZRo0YFumOWLVtm9erVszfffNMuvPBCGzx4cMgaVUSu6667zgYPHlyke27evHl2xhln2Nlnn227du0ys+j/jPZs3LgxpKPj9ddft8aNG9tVV10V8lwWvD4Vsbdefvllu++++0KObd682erUqRPys8dTEeu1cOHCwFxwM7M777zTqlatavfff7/deeedNnToUDv66KNt0aJFFZbJw94Kz8svv2z3339/yLFY7yuzfXsr+KKKLu0tz7HHHmvz58+3/Px8e/jhh61BgwZWpUoV+/rrrwP3qYhcixYtsmuvvdaysrKsTZs2dvrppwf+v3z77betZ8+edvHFFwfGxlTkWr377rvWsmVL+9vf/mb9+/cPnK/v2LHDHnzwQatTp4798MMPgftXxHMprw/Dz8Xrw/Dx+jAyvD4Mn6uvD83ce43I68PYoGAexwpvtF9//dUOPvhgGzt2rJn9+Wd3FTkT6P3337dzzjnHTj31VDv44IPtX//6V+C2DRs22KGHHlrkz+MrwvXXX29169a1ESNG2P3332+ZmZl29dVXm5nZY489Zi1btrScnJwKWavgP4ecNm2ade7c2W677baQJ2Ezs48++siys7OjdqX6kjIVPvbJJ58EToq8H6S33HKLffjhh1HPVdyTqfc9KigosI4dO4ZczOj111+3//3vfzHJZWZ2+eWX23/+85/A+95cvTfeeCPqmYK/h96/Tz/99MCf55977rlWo0YNO+yww2zo0KEV9oIyeK127dpl48ePt2OPPbbI9+mNN96wjIyMCrlCdvBabdq0yc455xw76qij7Kijjgoc9/v9tm3bNjv88MNLnINd3kqaa2i27wrjffr0scsuu8zMSp4ZGQ3Bn+vHH3+0G2+80c455xzr0qWL/e1vfwu5b48ePUL+7Lqicj366KN28MEH26BBg+zGG2+0jIwMu/LKK83MbPLkydagQYNSL2QUDS+99JK1aNHCnn322UDx0vPCCy9YVlZWkXECFSH4OdRs3/97XmHTG6Hx8MMPF3nhVNH5duzYYa1atQpcgM3MbMqUKUUKCBWRJTc314YOHRry8+azzz6z7Oxs++KLLyosj+fFF190cm95e8qlvVV4lIgL+8rszzVybW/l5OTY3r177dhjjw0UMM855xyrXr26dezY0caMGVNkz0Xb4sWL7eqrr7ZevXoVOUd44oknrHr16rZq1aoKzWS272Jw/fv3t+OOO84GDhwYctuSJUusRYsW9u6771Z4Ll4fho/Xh+FnKnyM14fh5TLj9WFxXHx9aObma0ReH8ZWlVh3uCM8v/32mz7//HP5/X4ddNBB6tevX+DPSz1t2rTR8OHDNX78eJ133nlq1aqVpKJ/nhGNTE2bNtWAAQN00kkn6YQTTtDevXvVrVs3ZWVlBe5fvXp11ahRQ9WqVYtKnuJyNWnSRAMHDtTjjz+uWrVq6dtvv9WSJUt0991366abbpIkZWRkqE6dOkpJSYnaWknSli1bVLNmTSUlJamgoEDJycnq3bu3zjzzTE2cOFHp6em64IILAn+yePjhh6tx48bas2dPhWbyeGtxwgknyOfz6a677tKTTz6pRx55RB9//LHOPvvsqOdKTk5Wfn6+qlT586nKy5WUlKTMzEylpaVJkm699VZNnDhRs2fPrpBcwetlZvL5fJo4caKkfX+alJSUpOzsbDVr1kx169aNeqakpKTA5/X+5Pb444/XunXrdP755+uLL77Q1KlTtWzZMo0bN05XXHGFXnnllajt+eLWKiMjQ1deeaUGDhyoDh06SPpzrRo3bqyDDjoo8OfN0cwUvFa1atXSiSeeqO+//17Lli3Tl19+qV69esnn8ykrK0s1a9YM7LFo50pJSQnkKqxu3bo677zzdMUVV+jKK6/UEUccEdVMwbmSkpIC/x8eccQROuKII7Rnzx4dc8wxqlq1auD+fr9f6enpatCgQYXlysvLU0pKikaNGqUaNWro66+/1rJlyzR+/PjAn8KmpqaqQYMGSk1NjVqm1atXa+7cudq5c6fatWunQw89VOeff75++OEH3XjjjUpLS9Mpp5yiGjVqSNr3Z5LRfn4vKZf3p9Pe//vDhg2Tz+fTqFGjZGbatGmT/vOf/2jWrFkVkuvggw/WYYcdJunP5wMzU1JSkqpWrRr4/++WW27RpEmToparpLUqKChQSkqK3njjjcD7ycnJatSokVq0aBH4nkZL8DlNs2bN1L9/f11wwQWaNWtWTPdWcK7mzZvrhBNOKPLcVdF7q7jzUp/PF9N9VThX8Fr5/X5n9paXy3uePOGEE7R7925dfPHFmj59uj755BNNmzZNL774opKTk3X//fdH5fyhuP3eunVr3XDDDVqzZo3atGkjSYG1atasmZo2bar09PRyz1JSLm+tOnbsqIEDB2rMmDGqVq2afvrpJx1++OGSpCZNmqhBgwbF/jyPVi5eH4afi9eHkWfy8Pqw9Fy8Pgwvl0uvD4NzufQakdeHjohhsR5h+vnnn6127dp23HHHWaNGjaxZs2Z21lln2d69ewP38X6rOm/ePDvyyCPtscceq/BMQ4cODfxJW35+vp1yyil2zz332G+//WY5OTl2xx13WNOmTQNXxa2oXGeccUZgffLy8op0xowYMcLOPPPMqHYQzJ8/3zp27Gh33HFH4Fjwbwvvvvtua9eunZ1//vk2ffp0W7FihY0ZM8aaNm0a8id30c5U+LeSwesxZcoUS09Ptxo1aoT8mXoscpmZ7d2719q2bWvvv/++3XvvvVa1atWQP32NRa7C++fWW2+1jh07xux7+Oyzz5rP57ODDjrIZs+ebWb7/h94/fXXbfny5VHJVFKusv70avTo0XbssceWeqXv8s4U/P/gK6+8Yu3atbNmzZrZ5MmT7ZtvvrFbb73VGjVqVOFrVdK+2rJli/Xt29euvPLKkOf/ispV+M9rL7vsMrvwwgtt7ty5tnXrVrvjjjusUaNGUe3ULC6X93OnpJxXXnml9e/fP2pdkXPnzrUWLVpY9+7drWrVqta3b1977bXXArdfdtllVqdOHbvrrrvsxx9/tC1bttjo0aOtTZs2tnHjxqhkKilX4T9RDn7O8v68NDs7O6qdWOHkMjPbtm2bNWnSxGbOnGl33323ZWRkhIwbqOhMhZ/fb775ZuvSpYtt2LAhKpnMSj6n8cRqb4VzXhr8PFYReyucTGYVu6/CzVX4OT9Weys417333ms+n8+aNm1qs2bNMrN9f9r90EMPRe1nYkmvLbyf1cWdo99www3Wr18/27Hj/7V371FRFWofx5/BCwQqjpCiomQqiYkiXqBMkdTQorLMVnq8plamJz2CZh6ztLJTecuzrGV1EpeXlnW6eepoZVommfcLpOINMVTEC4o43GSe94/e2QfSlEFm5nn2/L5ruRbsGeTDsBn2ftiz9yWXmP7M9dhjjxmeuXPncnBwsHGag4yMDJ42bRo3b97cpa/0wP7hzbmwf1h5E/YPb86F/cM/d3l6//DPXJ7eR8T+oZwwMBdeQUEBx8bGGi8PO3HiBK9evZqbNGnCPXv25NzcXGau+APUp08f7tq1q8vOHVZZ08yZM/mOO+7g2267jePi4rhJkyYu3Qm/nqtHjx6Gy/HE/Ouvv/Jzzz3HgYGBV12BuTrLysriqKgobtasGXfr1s24CjRzxUHP+++/zw8++CBbLBZu3749N2/e3GWP1/VM13oyLi4u5vHjx7PVauX09HSXmJx1lZSUcGxsLLdr187lG0POuJh/f/lrUlISW61Wl208Xs9UfuPjtddeMx4bd7yktLIuRwcPHuRJkyax1WqtcH5Ed5nKbxB98803/OSTT3Lt2rW5Xbt2HBkZ6dLnLGfXK2bmkSNHcrt27YzztXrS9eGHH/Ldd9/NgYGBHBMTw2FhYR57vK71+27Hjh08duxYl750+fDhw9ysWTOeOnUq5+Xl8Z49e/jBBx/kMWPGVLjfrFmz+O6772ZfX1/u1KkTN2rUyKWPVWVdzP97jp84cSJbrVaXvuzVGVd+fj63b9+ee/Towb6+vsbQzpMm5t/Xw+eff57r16/v0pfEX2+b5p577jFeNj1jxgy3rlvObJe6a92qrInZfeuVsy5mGetWXFwc5+XlcWFhIScnJxvbD65+mfeNTNd6rKZMmcJWq9Wl2/A3+jl0DHE++OADTkxMZIvFwpGRkdyyZUsxP4eOsH+I/cObMWH/8OZczNg/lLZ/eCOXp/YRsX8oKwzMhZefn88dO3bkzz//vMLyX3/9lVu0aMEJCQnGMscPdVZWlkv/gnMjU+/evY1ln376Kb/11lu8aNEilx45UBlX+cfqzJkzvHTpUo6JieFdu3a51DV37lzu06cPb9iwgf/2t79xTEzMn24UFRQU8K5du3jv3r2ck5PjMdMff3FlZmZyYGBghYuTeNpVWFjIXbt25eDgYJefx88Zl2NDOzY21qVHWjizXrkzZx6rtLQ0fuqppzgqKsqlP4fOPlZZWVmck5PD58+fd5mpMq7yj5VjY7aoqMjlz6U3cpXfgNyyZQunpKTwRx99xMePH/eoq/zjZbPZ+D//+Q/HxcW57OewuLiYk5OT+YknnuDCwkLje/TRRx9xUFAQ5+bmVtiAzMrK4nXr1vH69es5OzvbJabKuv7Yzp07uV69ei59jnfWdfbsWW7UqBEHBQW57DneWdOuXbt46NChLn/OYr7xNk2vXr2MZUePHnXLulUZV/ltLbvd7pZ1yxnT6dOnXb5eVcW1fft2MevWfffd59LPXxVT+cdq69atPHDgQI6IiPD4Y1X+5/Dy5cuclpbGBw4cuObzrDtd2D+svAv7h5U3Yf+w6i7sH8rbP6yMyxP7iNg/lBUG5sIrKSnh5s2b85QpU4xljh+M7du3c3BwME+bNk2cqfxtklzlH6vz58+75SJw586d4xUrVhifc+LEidd9gnFHlTH98S+Y7rjIk7Ou999/37iQmCTXzp07K1zN21Om8r9Q3XU1amcfqy1btrjsJYnOmMofcSX1sXLXRq7E56zKuso/XjabzaUv07fZbPzKK69wSkpKheXbt2/nkJAQY6fWnRfgccb1x1z5ctequt544w2XHrVWFdOPP/7olgsMmmVbi9n165azpjlz5rh0vaqqKzU1Vcy6JXHforxpw4YNbrmwbWVczz//vMsdVXFJ/B5qeM7C/iH2D13lwv6hrP3DyrrcvY+I/UNZYWAuOMcPwptvvskdOnTgL774osJtZWVlPH36dO7Vq5dLhwJVNeXn5xsbJK5+qY+zLnd1ra/7zJkzPHHiRO7atSu/8sorxvLy57yVaHL199AZl+PJ2h1JdElcr5x14bHS71q5cqXXu8ofNeH4PXTy5Em+4447KuwUufIcyWZ0bd682atNzm7/uePl1FVxSTO5enBfVdfFixdFuvA9lPdYSXVh/9C5JG4DYv9Qv0vieuWsS+r3UPrPoSSXO/fD3BEG5gpKT0/n+++/nx944AFeu3Zthdvee+89Dg8Pd+sGpFSTZJcjx5NNbm5uhZeyjB8/ni0Wi1uOltFgqozr+PHjbhsSSHfhe1h9JqmPFVzOudz98r/y63FGRgYHBQUZhhkzZnDLli1degE/M7pyc3Pd+vxQWZM7k7pNI9El0QSXfhNc+k2SXY4kbmtJNFXGhf3DypvwPXTOhZ9D+S5XhIG5kjZu3MhxcXGckJDAS5YsYebfz1WUlJTE8fHxbj2yQbJJssuR42gHx1/l/Pz82Gq1Glephgkus5ikuiSa4DKPKz09nQMDA/ns2bP8yiuvcO3atV16YUG4zG2Suk0j0SXRBJd+E1z6TZJdjiRu00g0waXfBJd+k2RXdYeBuaI2b97MI0aM4KCgIA4PD+d77rmHrVaryy+GoM0k2eXI8Ve5J598kuvVq+eWc2neKIkmZricSaKJWaZLookZLmeT6MrOzuZOnTrxsGHD2NfX1+PDX0dw6TVJ3aaR6JJogku/CS79JskuRxK3aSSamOFyJokmZricSaKJWa6rOrMwMxPyaBcvXqSCggLy8/OjgIAA8vPzI2Ymi8Vy1X3PnDlDv/32G61evZpCQ0MpLi6OWrdu7RUms7gcLV68mJ599lnavn07dezY0StMcOk3SXVJNMFlDpezpoyMDIqIiCB/f3/atGkTRUVFVbsJLv0mIqJz587RyZMnqU6dOmS1Wql+/foitmkkuiSa4NJvgku/ySwuR67eppFogku/CS79Jskuj+bKaTy6cXv37uX27dtzdHQ0h4WF8eDBgzk1NZWZXX8RDU0mM7oKCwv5yJEjXmOCS79JqkuiCS5zuKpiOnXqFA8bNowPHDjgEhNc+k3MzHv27OHWrVtzeHg433rrrXzffffxunXrrutyRxJdEk1w6TfBpd9kRpcrt2kkmuDSb4JLv0myy9NhYO7Bjh49yo0aNeLk5GTev38/L1myhHv06MFWq5XXr1/PzO7/RS/RZEbXlStXvMoEl36TVJdEE1zmcN3M753CwkK4BLgkmph/H8iHhobypEmT+MCBA7xixQoePHgw165dm1etWnVdlyuT6JJogku/CS79JjO6XLlNI9EEl34TXPpNkl0SwsDcg82dO5f79+9fYdmrr77KFouFfX19+bvvvoMJLlOY4NJvkuqSaILLHC6JJrj0m5iZt27dyh06dOATJ04Yy06ePMlJSUlssVj4iy++YGb3D3okuiSa4NJvgku/CS79Jrj0m+DSb5LsklBNT58SxpvLy8uj06dPU0FBAdWpU4eIiNq1a0cDBw6kGjVq0IwZM6hNmzYUGhrq1Sa49Jvg0m+S6pJogsscLokmuPSbiIgKCwtp7969dO7cOWrSpAkRETVu3JimTZtGJSUl9Mwzz1BYWJjLzp2uySXRBJd+E1z6TXDpN8Gl3wSXfpNkl4R8PA3wxux2OxERNWrUiHJycig1NZVyc3MpMzOTRo8eTTExMTRmzBjKzs6mc+fOea0JLv0muPSbpLokmuAyh0uiCS79pvJ16NCB4uPj6Z133qHc3FxjeYMGDeiZZ56hVq1a0aZNm4iIiJm92iXRBJd+E1z6TXDpN8Gl3wSXfpNkl4jcfES7V2ez2bioqIhtNpuxrH///ty4cWMODw/nunXr8tixY43bQkJC+N133/U6E1z6TXDpN0l1STTBZQ6XRBNc+k3lKysrM95+8803+c477+T58+fz+fPnK9yvT58+/Pjjj3u1S6IJLv0muPSb4NJvgku/CS79JskuSeGULG4qPT2d/v73v1NWVha1aNGCunXrRsnJyfT555/TZ599RqWlpVSnTh164IEHiJkpKyuLQkJCKCIiwqtMcOk3waXfJNUl0QSXOVwSTXDpNxERZWZm0qZNm2jo0KHk4+NDpaWlVKtWLZo8eTIdO3aMFi1aREVFRTRy5Ehq1KgREREFBwdTaGgoMTNZLBavcUk0waXfBJd+E1z6TXDpN8Gl3yTZJTbXzuMRM/OhQ4fYarXy+PHj+bXXXuNx48ZxQEAADx48mEtKSq66f1FREb/44ovcqlUrzs7O9hoTXPpNcOk3SXVJNMFlDpdEE1z6TczMGRkZHBwczM2aNeNFixYZy4uLi423k5OTOTo6mrt06cJJSUk8ZMgQrlu3Lqenp3uVS6IJLv0muPSb4NJvgku/CS79JskuyWFg7obmz5/PPXr04NLSUmb+fYX85ptvuEGDBjxw4EDjfna7ndPS0njUqFFcv3593rlzp1eZ4NJvgku/SapLogkuc7gkmuDSbzp79iz37duXExMTediwYRwbG8v//Oc/jdvL75x8+eWXnJSUxPHx8Tx8+HDeu3evV7kkmuDSb4JLvwku/Sa49Jvg0m+S7JIeBuZuKDk5mTt16mS8b7fbmZn5p59+4rp16/KECROM244cOcKLFi3i/fv3e50JLv0muPSbpLokmuAyh0uiCS79ptzcXP7LX/7C//3vfzkzM5PHjBlz1c7JH49+Ly0trXA+SW9xSTTBpd8El34TXPpNcOk3waXfJNklPQzM3dC6des4MDCQV69ebSxz7MwtXbqUmzVrxj///LNxmztWSokmuPSb4NJvkuqSaILLHC6JJrh0mxyf+8yZM8ayI0eO8OjRozk2NpYXLlxoLC9/RI+rk+iSaIJLvwku/Sa49Jvg0m+CS79JsktDGJi7od9++40HDhzIDz30UIUdNmbmw4cPc0hICK9atcrrTXDpN8Gl3yTVJdEElzlcEk1w6TeVz3GqmKNHjxo7J44jekaNGsWvv/46XIJNcOk3waXfBJd+E1z6TXDpN0l2SQwDczf19ddfc2xsLA8YMIDXr19vLL9y5QrHxMTwihUrYILLFCa49JukuiSa4DKHS6IJLv2m8jmOas/MzOTRo0dzt27dODY2lmvWrMlbtmyBS7gJLv0muPSb4NJvgku/CS79JskuaWFg7uLKnwfoxx9/5J49e3Lnzp15wYIFvGHDBp40aRIHBQVxZmamV5vg0m+CS79JqkuiCS5zuCSa4NJvYv59UM/8v6N47Ha78ZLYjIwMbt68OVutVrdfSEmiS6IJLv0muPSb4NJvgku/CS79Jsku6WFg7sIcK2VmZiYPGjSIi4uLefPmzTx16lQODAzkyMhI7tChA+/atcurTXDpN8Gl3yTVJdEElzlcEk1w6TeVd2VlZfETTzzBOTk5xm3FxcU8ceJEDggI8NjOkiSXRBNc+k1w6TfBpd8El34TXPpNkl0awsC8GnP8taZ8x44d4yZNmvDYsWMrLD9//jyfPXuWL1y44HUmuPSb4NJvkuqSaILLHC6JJrj0m27kGj9+vHEEDzNzfn4+33vvvbxt2zavdEk0waXfBJd+E1z6TXDpN8Gl3yTZpTEMzG+y9PR0HjBgABcWFjJzxZXz0qVL3KpVK3766acrrJSO8wV5kwku/Sa49JukuiSa4DKHS6IJLv2mqrocbzuO9PEWl0QTXPpNcOk3waXfBJd+E1z6TZJd2sPA/CY6evQoh4WFscVi4XvuucdYOcuvcBs3bqywUnqjCS79Jrj0m6S6JJrgModLogku/abqcLnKK9El0QSXfhNc+k1w6TfBpd8El36TZJcZ8iFUpWw2G82dO5c6d+5My5cvp8uXL1PPnj2pqKiIatSoQcXFxURE1L17d7JYLF5rgku/CS79JqkuiSa4zOGSaIJLv6m6XK7wSnRJNMGl3wSXfhNc+k1w6TfBpd8k2WWWMDCvYv7+/tSmTRt67LHH6IknnqB58+ZRSUmJsXL6+vpSWVmZ15vg0m+CS79JqkuiCS5zuCSa4NJvgku/CS79Jrj0m+DSb4JLvwku/SbJLtPk6UPcNXatlyyUlJTw999/zx07duSuXbsaL4Ow2WyclZXl8vNpSjTBpd8El36TVJdEE1zmcEk0waXfBJd+E1z6TXDpN8Gl3wSXfhNc+k2SXWYKA3MnKigoYJvNZqx0jhwn1L9y5QqvW7eOO3bsyDExMXzhwgUeN24c9+rVi202m9eY4NJvgku/SapLogkuc7gkmuDSb4JLvwku/Sa49Jvg0m+CS78JLv0myS4zhoF5JUtLS+O77rqLo6OjOTQ0lOfNm8f79u0zbnecUL+srIy///577ty5M/v5+bG/vz9v2bLFa0xw6TfBpd8k1SXRBJc5XBJNcOk3waXfBJd+E1z6TXDpN8Gl3wSXfpNkl1nDwLwSZWZmclBQEI8bN44//vhjnjZtGoeHh/Ojjz7KGzZsMO7neHnD5cuXuV+/ftygQQNOT0/3GhNc+k1w6TdJdUk0wWUOl0QTXPpNcOk3waXfBJd+E1z6TXDpN8Gl3yTZZeYwMK9Eixcv5m7dulVY9vnnn3Pv3r25b9++/PPPPxvLS0tLefbs2VyrVi3etWuXV5ng0m+CS79JqkuiCS5zuCSa4NJvgku/CS79Jrj0m+DSb4JLvwku/SbJLjPn4+mLjmrIx8eHTp48STk5Ocay/v3706RJk6iwsJCWLl1KeXl5RERUs2ZNslqttHv3boqKivIqE1z6TXDpN0l1STTBZQ6XRBNc+k1w6TfBpd8El34TXPpNcOk3waXfJNll6jw9sdfQmjVrODg4mNesWcPM/zsvEDPz0qVL2d/fn7dt2+b1Jrj0m+DSb5LqkmiCyxwuiSa49Jvg0m+CS78JLv0muPSb4NJvgku/SbLLzGFgXsmGDBnCISEhfPDgQWb+3xVomZkjIiJ45syZMMFlChNc+k1SXRJNcJnDJdEEl34TXPpNcOk3waXfBJd+E1z6TXDpN0l2mTWckuUG2e12IiJauHAhtWvXjuLj42nPnj1Us2ZNIiIqLi6mwMBAaty4sVeb4NJvgku/SapLogkuc7gkmuDSb4JLvwku/Sa49Jvg0m+CS78JLv0myS6zV9PTAOnZ7Xby8fGhunXr0gcffEB//etfqWfPnpScnEwNGzakQ4cOUUZGBsXHx3u1CS79Jrj0m6S6JJrgModLogku/Sa49Jvg0m+CS78JLv0muPSb4NJvkuwyfZ4+xF1qdrvdeHnDkSNHeNCgQXzixAlmZn7xxRe5e/fu3KZNG+7Zs6fbrjor0QSXfhNc+k1SXRJNcJnDJdEEl34TXPpNcOk3waXfBJd+E1z6TXDpN0l2eUsYmDPz6dOnefv27bxx40Y+e/ZshdsyMzO5adOmPHToUC4rKzOW5+Xl8aVLlzg/P99rTHDpN8Gl3yTVJdEElzlcEk1w6TfBpd8El34TXPpNcOk3waXfBJd+k2SXN+f1A/O9e/dyREQEd+zYkS0WCz/++OP8yy+/MDOzzWbj/v3788iRI9lutzMzGyun431vMcGl3wSXfpNUl0QTXOZwSTTBpd8El34TXPpNcOk3waXfBJd+E1z6TZJd3p5XD8wPHjzIjRs35hdeeIEzMzN5w4YNfMcdd/DLL7/MzMzFxcWcnp7u1pVQogku/Sa49JukuiSa4DKHS6IJLv0muPSb4NJvgku/CS79Jrj0m+DSb5LsQl48MLfZbDxmzBgePnw4l5aWGivfnDlzODw8nAsKCmCCyxQmuPSbpLokmuAyh0uiCS79Jrj0m+DSb4JLvwku/Sa49Jvg0m+S7EK/V9PTFx31VMXFxVSzZk269957qWbNmsTMRETUsmVLKi0tJYvFctXHMPM1l5vZBJd+E1z6TVJdEk1wmcMl0QSXfhNc+k1w6TfBpd8El34TXPpNcOk3SXah/6/qs3b97du3z3j7ypUrzMy8Y8cOjoqK4pKSEuO2/fv3e7UJLv0muPSbpLokmuAyh0uiCS79Jrj0m+DSb4JLvwku/Sa49Jvg0m+S7ELMPp4e2Luz/Px8ys7OpuPHj5PdbqeIiAgiIrLb7VSjRg0iIrp8+TLl5ORQYWEhERG99NJLNHbsWLp48aLXmODSb4JLv0mqS6IJLnO4JJrg0m+CS78JLv0muPSb4NJvgku/CS79JskudI08PbF3V2lpaRwXF8etWrXiyMhIfvbZZ7m0tPSq+23YsIGDg4O5tLSUX375Za5RowZv377da0xw6TfBpd8k1SXRBJc5XBJNcOk3waXfBJd+E1z6TXDpN8Gl3wSXfpNkF7p2XjEw379/PwcFBXFSUhKvWbOGZ8+ezTExMbxy5cqr7rtjxw6+++67edKkSezr6+uylVKiCS79Jrj0m6S6JJrgModLogku/Sa49Jvg0m+CS78JLv0muPSb4NJvkuxCf57pB+YXLlzgxMREHjdunLGspKSE4+PjeeTIkVfdf9OmTWyxWLhu3bq8Y8cOrzHBpd8El36TVJdEE1zmcEk0waXfBJd+E1z6TXDpN8Gl3wSXfhNc+k2SXej6mf4c5vn5+dSwYUPq3bs3ERGVlZVRrVq16JFHHqG8vDxjmSOr1Urdu3enrVu3UnR0tNeY4NJvgku/SapLogkuc7gkmuDSb4JLvwku/Sa49Jvg0m+CS78JLv0myS50gzw9sXd1BQUFvH79euN9u93OzMwLFy7knj17VljmuCLtpUuXvM4El34TXPpNUl0STXCZwyXRBJd+E1z6TXDpN8Gl3wSXfhNc+k1w6TdJdqHrZ8ojzAsKCqi4uJguXLhAAQEBFB8fT0REV65cIYvFYrxdXFxMREQWi4WmT59OgwcPJiKigIAArzDBpd8El36TVJdEE1zmcEk0waXfBJd+E1z6TXDpN8Gl3wSXfhNc+k2SXciJPD2xr+727t3L3bp14+joaA4LC+MFCxbwkSNHjNvLysqYmXnp0qUcHx/PzMwvvPAC33LLLbxt2zavMcGl3wSXfpNUl0QTXOZwSTTBpd8El34TXPpNcOk3waXfBJd+E1z6TZJdyLlMNTDPzMzkoKAgnjBhAqekpPCsWbPYarXy4MGD+ccff6xw3w8//JATExP5xRdfdOlVZyWa4NJvgku/SapLogkuc7gkmuDSb4JLvwku/Sa49Jvg0m+CS78JLv0myS7kfKYamL/77rscExNTYdnatWu5U6dOPGDAAN6yZYuxfOHChcZVZ125Uko0waXfBJd+k1SXRBNc5nBJNMGl3wSXfhNc+k1w6TfBpd8El34TXPpNkl3I+Ux1DnOLxUL5+fmUl5dHzEx2u50SEhLojTfeoF9//ZWWLVtGJSUlRETUpk0b6tKlC23evJk6derkVSa49Jvg0m+S6pJogsscLokmuPSb4NJvgku/CS79Jrj0m+DSb4JLv0myC1Uh187j3dvatWu5Vq1a/N133zEzc0lJiXHbJ598wj4+PvzTTz8x8+9XnD1z5oxXmuDSb4JLv0mqS6IJLnO4JJrg0m+CS78JLv0muPSb4NJvgku/CS79Jsku5HymGpgzM48aNYqtVivv27ePmZmLi4uN29q3b8+vv/46THCZwgSXfpNUl0QTXOZwSTTBpd8El34TXPpNcOk3waXfBJd+E1z6TZJdyLlqevoI96p2+PBhWrx4MR07dozatm1Lzz77LDVq1IimTp1Kp06dori4OPruu++oQ4cORERUVlZGfn5+VL9+fa8ywaXfBJd+k1SXRBNc5nBJNMGl3wSXfhNc+k1w6TfBpd8El34TXPpNkl2omvL0xL4qpaWlcUhICD/yyCM8ZMgQrlu3Lg8aNKjC7Y888gjXrl2b33jjDX7vvfd48uTJbLVa+dChQ15jgku/CS79JqkuiSa4zOGSaIJLvwku/Sa49Jvg0m+CS78JLv0muPSbJLtQ9aVuYJ6dnc2RkZGclJRkLNuzZw8HBATw+vXrjWUFBQX8+uuvc1RUFEdGRnL37t15165dXmOCS78JLv0mqS6JJrjM4ZJogku/CS79Jrj0m+DSb4JLvwku/Sa49Jsku1D1pm5gnpKSwr169eLs7GxmZi4tLeULFy5wREQEr1279qr75+bmss1m4/z8fK8ywaXfBJd+k1SXRBNc5nBJNMGl3wSXfhNc+k1w6TfBpd8El34TXPpNkl2oelN3DvMePXrQkSNHqGnTpkREVKNGDQoMDCR/f386ffr0Vfe/9dZbvdIEl34TXPpNUl0STXCZwyXRBJd+E1z6TXDpN8Gl3wSXfhNc+k1w6TdJdqHqzcfTAGdr0aIFzZo1i4iImJksFotx2+XLl423V61aRdu2bfNaE1z6TXDpN0l1STTBZQ6XRBNc+k1w6TfBpd8El34TXPpNcOk3waXfJNmFqjd1A/PyWSwWunLlChER3XLLLRQYGEhERNOnT6dBgwZRUFAQTHCZwgSXfpNUl0QTXOZwSTTBpd8El34TXPpNcOk3waXfBJd+E1z6TZJd6OZTPTAnIuMvOXa7nXx9fWn27Nk0f/582rp1K91+++0wwWUaE1z6TVJdEk1wmcMl0QSXfhNc+k1w6TfBpd8El34TXPpNcOk3SXahm8y1p0h3X7169eImTZqwr68vb9u2zdMcZpZpYobLmSSamOFyJokmZpkuiSZmuJxNokuiiRkuZ5JoYobLmSSamOFyJokmZricSaKJGS5nkmhihsuZJJqY4XImiSZmuS5UtdRd9POPMTMVFxfT+fPn6dSpU5SWlkZ33nknTHCZzgSXfpNUl0QTXOZwSTTBpd8El34TXPpNcOk3waXfBJd+E1z6TZJd6OayMDN7GlEd7d+/n5iZ2rZt62mKkUQTEVzOJNFEBJczSTQRyXRJNBHB5WwSXRJNRHA5k0QTEVzOJNFEBJczSTQRweVMEk1EcDmTRBMRXM4k0UQElzNJNBHJdaGqZZqBOUIIIYQQQgghhBBCCCF0M6m/6CdCCCGEEEIIIYQQQgghVB1hYI4QQgghhBBCCCGEEEIIEQbmCCGEEEIIIYQQQgghhBARYWCOEEIIIYQQQgghhBBCCBERBuYIIYQQQgghhBBCCCGEEBFhYI4QQgghhBBCCCGEEEIIEREG5gghhBBCCCGEEEIIIYQQEWFgjhBCCCGEkIhSUlKofv36nmYghBBCCCHk1WFgjhBCCCGEUCUbMWIEWSyWq/4dPnzY07Rq7bbbbqMFCxZctfzll1+mqKgot3sQQgghhBByVzU9DUAIIYQQQkhTffv2pSVLllRYduutt3pIo6/S0lKqVauWpxkIIYQQQghdMxxhjhBCCCGEkBP5+vpSSEhIhX81atQgIqIvv/ySoqOjyc/Pj26//XaaOXMmXblyxfjYCxcu0NNPP02NGjUiPz8/ateuHX311VcV/v9vvvmGIiIiqE6dOtS3b186deqUcdu2bduoT58+FBwcTIGBgRQXF0c7d+78U+vGjRupVq1alJOTU2H5xIkTqXv37jf9WNjtdpo1axaFhoaSr68vRUVF0dq1a43bjx07RhaLhVatWkVxcXHk5+dHK1asoHPnztGgQYOoadOm5O/vT5GRkfTRRx/dtAchhBBCCKGbDQNzhBBCCCGEqqGffvqJhg0bRhMmTKB9+/bR4sWLKSUlhV577TUi+n243K9fP0pNTaXly5fTvn376B//+IcxbCcistlsNGfOHFq2bBlt3LiRjh8/TsnJycbtly5douHDh9OmTZvol19+odatW9P9999Ply5duqapR48edPvtt9OyZcuMZaWlpbRixQp68sknb/prfvvtt2nu3Lk0Z84c2rt3LyUkJNBDDz1Ehw4dqnC/qVOn0oQJE2j//v2UkJBARUVF1KlTJ/r6668pPT2dnnrqKRo6dCht3br1pk0IIYQQQgjdTBZmZk8jEEIIIYQQ0tCIESNo+fLl5OfnZyzr168fffLJJ9S7d2/q1asXvfDCC8Zty5cvpylTptDJkyfp22+/pX79+tH+/fspPDz8qv87JSWFRo4cSYcPH6aWLVsSEdE777xDs2bNuuoIcUd2u53q169PK1eupMTExGve580336SUlBTat28fERF99tlnNHz4cMrJyaGAgIBrfsxtt91Gp06duurUKSUlJdS2bVvavXs3ERE1bdqUxo0bR9OmTTPu07VrV+rSpQstWrSIjh07Ri1atKAFCxbQhAkTrvm5HCUmJlKbNm1ozpw5170fQgghhBBCrgznMEcIIYQQQsiJ4uPj6d133zXedwyd9+zZQ6mpqcYR5UREZWVlVFRURDabjXbv3k2hoaHXHJY78vf3N4blRESNGzem3Nxc4/3Tp0/T9OnT6YcffqDc3FwqKysjm81Gx48f/9P/c8SIETR9+nT65ZdfKDY2llJSUujxxx//02G5o8mTJ9OIESMqLFu4cCFt3LiRiIjy8/Pp5MmT1K1btwr36datG+3Zs6fCss6dO1d4v6ysjGbPnk0ff/wxnThxgkpKSqi4uJj8/f2va0IIIYQQQsjVYWCOEEIIIYSQEwUEBFCrVq2uWl5QUEAzZ86kRx999Krb/Pz86JZbbrnh//3HI7otFguVf0Ho8OHD6dy5c/T2229TWFgY+fr60l133UUlJSV/+n82bNiQHnzwQVqyZAm1aNGC1qxZQz/88MMNLcHBwVd9nQ0aNLjhx12rPw7n33rrLXr77bdpwYIFFBkZSQEBATRx4sTrfh0IIYQQQgi5IwzMEUIIIYQQqoaio6MpIyPjmsN0IqL27dtTdnY2HTx48LpHmV+v1NRUeuedd+j+++8nIqLffvuNzp49e8OPGz16NA0aNIhCQ0OpZcuWVx0VXpXq1atHTZo0odTUVIqLi6tg7Nq163U/NjU1lR5++GEaMmQIEf1+apmDBw9S27Ztb9qFEEIIIYTQzYSBOUIIIYQQQtXQjBkzKDExkZo3b06PPfYY+fj40J49eyg9PZ1effVViouLox49etCAAQNo3rx51KpVKzpw4ABZLBbq27dvpT5H69atadmyZdS5c2fKz8+nyZMnV+rI9YSEBKpXrx69+uqrNGvWrJv9Uo0mT55ML730ErVs2ZKioqJoyZIltHv3blqxYsV1P65169b073//m37++WeyWq00b948On36NAbmCCGEEELI4/l4GoAQQgghhJAZSkhIoK+++oq+/fZb6tKlC8XGxtL8+fMpLCzMuM+nn35KXbp0oUGDBlHbtm1pypQpVFZWVunP8a9//Yvy8vIoOjqahg4dSs899xw1bNjwhh/n4+NDI0aMoLKyMho2bFiVvr5r9dxzz9GkSZMoKSmJIiMjae3atbR69Wpq3br1dT9u+vTpFB0dTQkJCdSzZ08KCQmh/v37V5sLIYQQQgihqmbh8idFRAghhBBCCJmyUaNG0ZkzZ2j16tWepiCEEEIIISQ2nJIFIYQQQgghE3fx4kVKS0ujlStXYliOEEIIIYTQDcLAHCGEEEIIIRP38MMP09atW+mZZ56hPn36eJqDEEIIIYSQ6HBKFoQQQgghhBBCCCGEEEKIcNFPhBBCCCGEEEIIIYQQQoiIMDBHCCGEEEIIIYQQQgghhIgIA3OEEEIIIYQQQgghhBBCiIgwMEcIIYQQQgghhBBCCCGEiAgDc4QQQgghhBBCCCGEEEKIiDAwRwghhBBCCCGEEEIIIYSICANzhBBCCCGEEEIIIYQQQoiIMDBHCCGEEEIIIYQQQgghhIgIA3OEEEIIIYQQQgghhBBCiIiI/g9EIA8vk0G7FQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates  # Importa el módulo de fechas\n",
    "\n",
    "# Supongamos que tienes una serie de fechas en formato datetime\n",
    "# Asegúrate de que tus fechas estén en un formato adecuado, si no, puedes usar pd.to_datetime\n",
    "# df_final['Fecha y Hora'] = pd.to_datetime(df_final['Fecha y Hora'])\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "a = 2400\n",
    "b = a + 400\n",
    "\n",
    "# Grafica primeros 100 registros con index\n",
    "plt.plot(df_final.index[a:b], df_final['Energia perdida'][a:b], color='black', label='Energia perdida')\n",
    "plt.title('Energia perdida')\n",
    "plt.xlabel('Fecha y Hora')\n",
    "plt.ylabel('Energia Kwh')\n",
    "\n",
    "# Formatea las fechas en el eje x para mostrar una fecha cada hora\n",
    "ax = plt.gca()  # Obtiene el objeto de ejes actual\n",
    "ax.xaxis.set_major_locator(mdates.HourLocator(interval=1))  # Una fecha cada hora\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S'))  # Formato de fecha y hora\n",
    "\n",
    "plt.xticks(rotation=45)  # Rota las etiquetas para una mejor legibilidad\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save df_final to excel\n",
    "df_final.to_excel('df_final.xlsx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19767537.560694613"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#suma de energia perdida total\n",
    "df_final['Energia perdida'].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gpu",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}