Temas/Módulo 06 - Redes recurrentes/Cuadernos/rnr_accion_Apple_Prediccion_un_dia-dropout_new.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "<img src=\"../Imagenes/logo-final-ap.png\"  width=\"80\" height=\"80\" align=\"left\"/> \n",
    "</figure>\n",
    "\n",
    "# <span style=\"color:#4361EE\"><left>Aprendizaje Profundo</left></span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:red\"><center>Diplomado en Inteligencia Artificial y Aprendizaje Profundo</center></span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:green\"><center>Predicción del valor de una acción a un día</center></span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<center>Aplicaciones de Redes LSTM</center>**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Profesores</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Alvaro  Montenegro, PhD, ammontenegrod@unal.edu.co\n",
    "1. Camilo José Torres Jiménez, Msc, cjtorresj@unal.edu.co\n",
    "1. Daniel  Montenegro, Msc, dextronomo@gmail.com "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Asesora Medios y Marketing digital</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Maria del Pilar Montenegro, pmontenegro88@gmail.com\n",
    "5. Jessica López Mejía, jelopezme@unal.edu.co\n",
    "6. Venus Puertas, vpuertasg@unal.edu.co"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Jefe Jurídica</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Paula Andrea Guzmán, guzmancruz.paula@gmail.com"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Coordinador Jurídico</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. David Fuentes, fuentesd065@gmail.com"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Desarrolladores Principales</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. Dairo Moreno, damoralesj@unal.edu.co\n",
    "10. Joan Castro, jocastroc@unal.edu.co\n",
    "11. Bryan Riveros, briveros@unal.edu.co\n",
    "12. Rosmer Vargas, rovargasc@unal.edu.co"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Expertos en Bases de Datos</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "13. Giovvani Barrera, udgiovanni@gmail.com\n",
    "14. Camilo Chitivo, cchitivo@unal.edu.co"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Referencias</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [Introducción a Redes LSTM](Intro_LSTM.ipynb)\n",
    "1. [Time Series Forecasting with LSTMs using TensorFlow 2 and Keras in Python](https://towardsdatascience.com/time-series-forecasting-with-lstms-using-tensorflow-2-and-keras-in-python-6ceee9c6c651/)\n",
    "1. [Dive into Deep Learnig](https://d2l.ai/)\n",
    "1. [Understanding LSTM Networks](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)\n",
    "1. Ralf C. Staudemeyer and Eric Rothstein Morris,[*Understanding LSTM a tutorial into Long Short-Term Memory Recurrent Neural Networks*](https://arxiv.org/pdf/1909.09586.pdf), arxiv, September 2019\n",
    "1. Karpathy, [*The Unreasonable Effectiveness of Recurrent Neural Networks*](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)\n",
    "1. Anton Lucanus, [Making Automation More Efficient by Learning from Historical Trade Data](https://datafloq.com/read/making-automation-more-efficient-by-learning-from-/7444), 8:43 AM, January 7, 2020\n",
    "1. https://www.analyticsvidhya.com/blog/2018/10/predicting-stock-price-machine-learningnd-deep-learning-techniques-python/\n",
    "1. https://www.youtube.com/watch?v=2BrpKpWwT2A&list=PLQVvvaa0QuDcOdF96TBtRtuQksErCEBYZ&index=1\n",
    "1. https://towardsdatascience.com/using-lstms-for-stock-market-predictions-tensorflow-9e83999d4653\n",
    "1. https://github.com/llSourcell/Reinforcement_Learning_for_Stock_Prediction/blob/master/README.md\n",
    "1. [Python: How to Get Live Market Data (Less Than 0.1-Second Lag](https://towardsdatascience.com/python-how-to-get-live-market-data-less-than-0-1-second-lag-c85ee280ed93)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Contenido</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Introducción](#Introducción)\n",
    "* [Importar las librería requeridas](#Importar-las-librería-requeridas)\n",
    "* [Funciones-para-graficar-predicciones](#Funciones-para-graficar-predicciones)\n",
    "* [Lectura de los datos](#Lectura-de-los-datos)\n",
    "* [Extrae datos para la serie que se desea predecir-close](#Extrae-datos-para-la-serie-que-se-desea-predecir-close)\n",
    "* [Visualización de la serie precio al cierre](#Visualización-de-la-serie-precio-al-cierre)\n",
    "* [Preparación de los datos para el entrenamiento](#Preparación-de-los-datos-para-el-entrenamiento)\n",
    "* [Crea el modelo LSTM](#Crea-el-modelo-LSTM)\n",
    "* [Predicciones](#Predicciones)\n",
    "* [Guarda el modelo entrenado](#Guarda-el-modelo-entrenado)\n",
    "* [Intervalos de confianza. TO DO](#Intervalos-de-confianza.-TO-DO)"
   ]
  },
  {
   "attachments": {},
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   "metadata": {
    "colab_type": "text",
    "id": "lYpNoQSeTSo1"
   },
   "source": [
    "## <span style=\"color:#4361EE\">Importar las librería requeridas</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "I-vGZMQzvnGw",
    "outputId": "329ee962-21a2-4b88-ab18-7e83330f90ed"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Versión de Tensorflow:  2.9.2\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib widget\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#setting figure size\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 20,10\n",
    "\n",
    "\n",
    "# importa objetos de keras\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Dense, Dropout, LSTM, Bidirectional\n",
    "print(\"Versión de Tensorflow: \", tf.__version__)\n",
    "\n",
    "# optimizador\n",
    "from tensorflow.keras.optimizers import Adam, Adamax\n",
    "\n",
    "# Callbacks\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Lectura de  los datos</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estos datos corresponden a la empresa *Apple*. Son  3019 datos que corresponden a observaciones del precio de la acción, el número de transacciones de la acción (compra-venta). Los datos son diarios (días hábiles o comerciales). Están entre el 3 de enero de 2006 hasta el 1 de enero de 2018.\n",
    "\n",
    "la columna *Date* es la fecha, *Open* es el valor de acción a la apertura del mercado, *High* el valor más alto alcanzado en el día, *Low* el valor más bajo del día, *Close* el valor al cierre, *Volume* es el volúmenes de acciones transadas en el día  y *Name* es el código de identificación de la empresa, Apple en este caso.\n",
    "\n",
    "\n",
    "Los datos pueden ser bajados directamente de [Kaggle](https://www.kaggle.com/szrlee/stock-time-series-20050101-to-20171231)\n"
   ]
  },
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       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "LWqiDRF4tmhb",
    "outputId": "cbaa88a3-85ce-4eb7-e0fe-0ccacd5bbcbc"
   },
   "outputs": [],
   "source": [
    "#reading from a local file\n",
    "data_raw = pd.read_csv('../Datos/AAPL_2006-01-01_to_2018-01-01.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-01-03</td>\n",
       "      <td>10.34</td>\n",
       "      <td>10.68</td>\n",
       "      <td>10.32</td>\n",
       "      <td>10.68</td>\n",
       "      <td>201853036</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-01-04</td>\n",
       "      <td>10.73</td>\n",
       "      <td>10.85</td>\n",
       "      <td>10.64</td>\n",
       "      <td>10.71</td>\n",
       "      <td>155225609</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-01-05</td>\n",
       "      <td>10.69</td>\n",
       "      <td>10.70</td>\n",
       "      <td>10.54</td>\n",
       "      <td>10.63</td>\n",
       "      <td>112396081</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-01-06</td>\n",
       "      <td>10.75</td>\n",
       "      <td>10.96</td>\n",
       "      <td>10.65</td>\n",
       "      <td>10.90</td>\n",
       "      <td>176139334</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006-01-09</td>\n",
       "      <td>10.96</td>\n",
       "      <td>11.03</td>\n",
       "      <td>10.82</td>\n",
       "      <td>10.86</td>\n",
       "      <td>168861224</td>\n",
       "      <td>AAPL</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3014</th>\n",
       "      <td>2017-12-22</td>\n",
       "      <td>174.68</td>\n",
       "      <td>175.42</td>\n",
       "      <td>174.50</td>\n",
       "      <td>175.01</td>\n",
       "      <td>16349444</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3015</th>\n",
       "      <td>2017-12-26</td>\n",
       "      <td>170.80</td>\n",
       "      <td>171.47</td>\n",
       "      <td>169.68</td>\n",
       "      <td>170.57</td>\n",
       "      <td>33185536</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3016</th>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>170.10</td>\n",
       "      <td>170.78</td>\n",
       "      <td>169.71</td>\n",
       "      <td>170.60</td>\n",
       "      <td>21498213</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3017</th>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>171.00</td>\n",
       "      <td>171.85</td>\n",
       "      <td>170.48</td>\n",
       "      <td>171.08</td>\n",
       "      <td>16480187</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3018</th>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>170.52</td>\n",
       "      <td>170.59</td>\n",
       "      <td>169.22</td>\n",
       "      <td>169.23</td>\n",
       "      <td>25999922</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3019 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date    Open    High     Low   Close     Volume  Name\n",
       "0     2006-01-03   10.34   10.68   10.32   10.68  201853036  AAPL\n",
       "1     2006-01-04   10.73   10.85   10.64   10.71  155225609  AAPL\n",
       "2     2006-01-05   10.69   10.70   10.54   10.63  112396081  AAPL\n",
       "3     2006-01-06   10.75   10.96   10.65   10.90  176139334  AAPL\n",
       "4     2006-01-09   10.96   11.03   10.82   10.86  168861224  AAPL\n",
       "...          ...     ...     ...     ...     ...        ...   ...\n",
       "3014  2017-12-22  174.68  175.42  174.50  175.01   16349444  AAPL\n",
       "3015  2017-12-26  170.80  171.47  169.68  170.57   33185536  AAPL\n",
       "3016  2017-12-27  170.10  170.78  169.71  170.60   21498213  AAPL\n",
       "3017  2017-12-28  171.00  171.85  170.48  171.08   16480187  AAPL\n",
       "3018  2017-12-29  170.52  170.59  169.22  169.23   25999922  AAPL\n",
       "\n",
       "[3019 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a cambiar el índice de los datos. Tomaremos la fecha como índice: *df.index*. Los datos se reordenan para invertir la tabla, debido a que los datos contienen las observaciones más recientes en la parte superior de la tabla."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Extrae datos para la serie que se desea predecir-close</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-03</th>\n",
       "      <td>10.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-04</th>\n",
       "      <td>10.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-05</th>\n",
       "      <td>10.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-06</th>\n",
       "      <td>10.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-09</th>\n",
       "      <td>10.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close\n",
       "Date             \n",
       "2006-01-03  10.68\n",
       "2006-01-04  10.71\n",
       "2006-01-05  10.63\n",
       "2006-01-06  10.90\n",
       "2006-01-09  10.86"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating dataframe with date and the target variable\n",
    "data_raw['Date'] = pd.to_datetime(data_raw.Date,format='%Y-%m-%d')\n",
    "# Change index for datetime index\n",
    "data_raw.index = data_raw['Date']\n",
    "#Order if neccesary\n",
    "data_raw.sort_index(ascending=True, inplace=True)\n",
    "# Get only what you want\n",
    "data = pd.DataFrame(data_raw[['Date', 'Close']])\n",
    "\n",
    "#setting index\n",
    "data.index = data.Date\n",
    "# Drop Date\n",
    "data.drop('Date', axis=1, inplace=True)\n",
    "#Show Data\n",
    "data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Desde aquí podríamos avanzar. El problema es que tenemos datos desactualizados.\n",
    "\n",
    "Usemos alguna API para obtener datos en tiempo real."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Yahoo Finance API</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como las acciones van cambiando de valor todos los días, será mejor utilizar una API que nos dé información actualizada de la bolsa.\n",
    "\n",
    "Para esto, usaremos la API de Yahoo Finance de la bolsa de valores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install yfinance\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "338007712d3145cc98ef49a0d2b2a690",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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Ysmz7mWeeUUhIiK699loVFBTo4osv1quvviqbjXmqAQAAvCmd6epEdw/sVK3jj18/BMGDBETSgAEDKvwFkKTPP/+8ynNEREToX//6l/71r3/5MjQAAIBGJ75JeLlt3qb1WbH1kK7tmVz3AcGnSBsBAAAQEK4KvgAO8bIKemGxs9w2p6uyTlyor0hAAAAAUKd++z1HN87+QWt2ec4Yuu1ArtfyB46tgn68wmJXuW0htoqXO0D9RQICAACAOjXx7XX6ZushjXxxpcf2t1d7X4Rwq5fExFtrSThjQIISPzUAAADUqU37s6ssM3lwF/fzIakJ5fYnxkaU23b7BR1PLjAEBAkIAAAA6lRFHaVK1/oY1aOtx7S73sZ2tPAyMD25eZRP4oN/kYAAAACgTnlbw0MqG8PRPDpM8TFlCUZ1Fxa08kk2KPFjAwAAQJ2yVtAE4jrW0mGxSKHHzXyV2ia2Wue1VZDYoH4jAQEAAECdslTQCcvuKJnZKtxmVXR4zZens5KABCUWIgQAAECdcbqMipzlp9CVpDy7Q5IUHR6i809pqaGpCTotoWm1z22tqGkF9RoJCAAAAHxm8YZ0bdqfrUmDu8hiseixTzdXWDb3uATEZrXoxf/p4a8wEUAkIAAAAPCZO+evlST1aN9cA09tpdkr0sqVKShyymIp64IVedwMWGj4GAMCAAAAnzuYY/e6fc+RfJ358Gfq/shS2R1OSVJoNRcU/HJSf5/Fh8AhAQEAAIDPVTQ64/NNGXIZKb/IqTx7SQISZqveWI6O8U18FB0CiQQEAAAAPlfR2h+PLiobE7JhX5Ykzyl4q+usttWbqhf1DwkIAAAAfK4m81PlFDpqfH7GjQQvEhAAAADUGVs1psrNsdc8Afkh7UhtwkE9QAICAACAgOrfOb7Gx4TVotsW6gd+cgAAAPC50iEgTpepsmyrpuE1Pv+gM1rV+BjUDyQgAAAA8LkKxqB7VZtB6LU5BvUDPzkAAAD4nKUGw9CrM06k3DE1yXBQr5CAAAAAIOickdQ00CGglkICHQAAAAAaBlc1xnucKCk2okblP5lwvpZvPaib+7av8XuhfiABAQAAgE98vinD/by6PaTaNI+s0XuktolVahsWIQxmdMGStHz5cl1xxRVKSkqSxWLRwoUL3fuKi4v15z//WV27dlV0dLSSkpJ00003af/+/R7nGDBggCwWi8dj9OjRfr4SAACAwNlzNL/Gx2QVFNdBJKjPSEAk5eXlqVu3bpo5c2a5ffn5+Vq7dq0eeughrV27Vu+//75+++03XXnlleXKjhs3Tunp6e7HSy+95I/wAQAA6oXdR8oSkPCQ6n3M/O333LoKB/UUXbAkDR06VEOHDvW6LzY2VkuXLvXY9q9//Uvnnnuudu/erXbt2rm3R0VFKSEhoU5jBQAAqK/SDuW5n0eFhciYmo8JQcNHC0gtZGVlyWKxqFmzZh7b58+fr5YtW+rMM8/U5MmTlZOTU+l57Ha7srOzPR4AAADBqnVM2YDym175UXNX7qzymOvPTa7DiFAf0QJSQ4WFhfrLX/6iG264QU2blk3/NmbMGHXo0EEJCQnauHGjpkyZovXr15drPTne9OnTNW3aNH+EDQAAUOfCQ20er6d+/EuVx+QUOuoqHNRTJCA1UFxcrNGjR8vlcmnWrFke+8aNG+d+npqaqs6dO6tnz55au3atunfv7vV8U6ZM0cSJE92vs7OzlZzMtwAAACA4DTq9ld78cbfXfV1aN/E63qOms2Ah+JGAVFNxcbGuvfZapaWl6csvv/Ro/fCme/fuCg0N1datWytMQMLDwxUeHl4X4QIAAPidtZIVzUOs3nv+3zPwlLoKB/UUY0CqoTT52Lp1q7744gu1aNGiymM2bdqk4uJiJSYm+iFCAACAwKts0HlKi6hy2yZcdIpiIkLrMiTUQ7SASMrNzdW2bdvcr9PS0rRu3TrFxcUpKSlJ11xzjdauXatPPvlETqdTGRkli+zExcUpLCxM27dv1/z583XZZZepZcuW+uWXXzRp0iSdc8456tevX6AuCwAAwK9cror33TuosxZvLFuocOfjw/wQEeojEhBJq1ev1sCBA92vS8dl3HzzzZo6dao++ugjSdLZZ5/tcdxXX32lAQMGKCwsTP/973/13HPPKTc3V8nJyRo2bJgefvhh2Wyeg7EAAAAaKlclLSDNo8L8GAnqMxIQlaxiXlmTYVVzWCcnJ+vrr7/2dVgAAABBpbIEJL4J415RgjEgAAAA8AlXBflHv1NaVDpAHY0LCQgAAAB8oqIWkNMTPGcPnXARM181ZnTBAgAAgE9U1AKyZvdRSdKb43rrQE6hrjq7jR+jQn1DAgIAAACfqGjcbOixNUD6dKp6KQM0fHTBAgAAgE9kFRR73d4sirU+UIYEBAAAAD7x9w83ed3eghmwcBwSEAAAANSp2EhaQFCGBAQAAAB1ysYnThyH2wEAAAB16uzk5oEOAfUICQgAAADq1KDTWwU6BNQjJCAAAACoM+1bRMliYRV0lCEBAQAAQJ1xVrA2CBovEhAAAADUGaeTBASeSEAAAABQZxwuEhB4IgEBAACAT4SHlP9oGRlmC0AkqM9IQAAAAOATxU6Xx+vYyFC9cEP3AEWD+iok0AEAAAAg+BljdGJvq5V/uUjR4XzchCdaQAAAAHDSvA31sFmZfhflkYAAAADgpLm8TLdrZf0PeEECAgAAgJPmLQGhBQTekIAAAADgpHlbb5D8A96QgAAAAOCkndgC0jwqVBa6YMELpiUAAADASTt+EPrqvw1Ss8jQwAWDeo0WEEnLly/XFVdcoaSkJFksFi1cuNBjvzFGU6dOVVJSkiIjIzVgwABt2rTJo4zdbteECRPUsmVLRUdH68orr9TevXv9eBUAAACBc3wLSNOIUIXY+JgJ77gzJOXl5albt26aOXOm1/1PPvmkZsyYoZkzZ2rVqlVKSEjQJZdcopycHHeZ++67Tx988IEWLFigFStWKDc3V5dffrmcTqe/LgMAACBgzHFrEDL2A5WhC5akoUOHaujQoV73GWP07LPP6q9//atGjBghSZo7d65at26tN954Q3/4wx+UlZWl2bNn6/XXX9egQYMkSfPmzVNycrK++OILXXrppX67FgAAgEA4vgWE6XdRGVpAqpCWlqaMjAwNHjzYvS08PFz9+/fXypUrJUlr1qxRcXGxR5mkpCSlpqa6y3hjt9uVnZ3t8QAAAAhGxycg5B+oDAlIFTIyMiRJrVu39tjeunVr976MjAyFhYWpefPmFZbxZvr06YqNjXU/kpOTfRw9AACAf2zJKOuazuxXqAwJSDWd+ItkjKnyl6uqMlOmTFFWVpb7sWfPHp/ECgAA4G83vPxDoENAkCABqUJCQoIklWvJOHDggLtVJCEhQUVFRTp69GiFZbwJDw9X06ZNPR4AAABAQ0YCUoUOHTooISFBS5cudW8rKirS119/rb59+0qSevToodDQUI8y6enp2rhxo7sMAAAAAGbBkiTl5uZq27Zt7tdpaWlat26d4uLi1K5dO91333167LHH1LlzZ3Xu3FmPPfaYoqKidMMNN0iSYmNjddttt2nSpElq0aKF4uLiNHnyZHXt2tU9KxYAAAAAEhBJ0urVqzVw4ED364kTJ0qSbr75Zr366qt64IEHVFBQoLvuuktHjx7VeeedpyVLligmJsZ9zDPPPKOQkBBde+21Kigo0MUXX6xXX31VNpvN79cDAAAA1FcWY46bMw0BlZ2drdjYWGVlZTEeBAAABJX2f1nkfr7z8WEBjKRu8Xnt5DEGBAAAAIDfkIAAAADgpNChBjVBAgIAAICT4nSRgKD6SEAAAABwUrIKigMdAoIICQgAAABOyrItBwMdAoIICQgAAABOyiOLfgl0CAgiJCAAAAA4KZn5ZV2wlv9pYCUlARIQAAAA+FC7FlGBDgH1HAkIAAAAAL8hAQEAAADgNyQgAAAA8IlhZyUGOgQEARIQAAAA+ETL6LBAh4AgQAICAAAAwG9IQAAAAFBrxhj3c5eppCBwDAkIAAAAau34pMNlyEBQNRIQAAAA1JrD5XI/jwi1BTASBAsSEAAAANRasbOs1eMPF3YMYCQIFiQgAAAAqDa7w6lVO48ov8ghScopLJYkhdosio8JD2RoCBIhgQ4AAAAAwePUv33mfr7z8WH6estBSSUtIRaLJVBhIYjQAgIAAIBqMV4Gmf/l/Q0BiATBjAQEAAAA1fJj2hGP1w6nq4KSQMVIQAAAAFAt1/37e4/Xu47kBygSBDMSEAAAANSK87hFQK4/t10AI0EwIQGppvbt28tisZR73H333ZKksWPHltvXu3fvAEcNAADgOyO6t/F4ffzCg/27xPs7HAQpZsGqplWrVsnpdLpfb9y4UZdccolGjRrl3jZkyBDNmTPH/TosLMyvMQIAANSlE8eg24vLxoCE2pgBC9VDAlJN8fGeWf3jjz+uTp06qX///u5t4eHhSkhI8HdoAAAAflHk8Bx0/uaPu93PbVYSEFQPXbBqoaioSPPmzdOtt97qMd/1smXL1KpVK3Xp0kXjxo3TgQMHKj2P3W5Xdna2xwMAAKC+sp+QgCxYtcf9PMTKx0pUD3dKLSxcuFCZmZkaO3ase9vQoUM1f/58ffnll3r66ae1atUqXXTRRbLb7RWeZ/r06YqNjXU/kpOT/RA9AABA7Xyx+fcK9/Xq0NyPkSCYWYy3FWVQqUsvvVRhYWH6+OOPKyyTnp6ulJQULViwQCNGjPBaxm63eyQo2dnZSk5OVlZWlpo2berzuAEAAE5G+78s8rq9W3IzfXh3Pz9HExjZ2dmKjY3l89pJYAxIDe3atUtffPGF3n///UrLJSYmKiUlRVu3bq2wTHh4uMLDw30dIgAAgM8dP+XuifLsDj9GgmBHF6wamjNnjlq1aqVhw4ZVWu7w4cPas2ePEhMT/RQZAABA3dl2ILdW+4ATkYDUgMvl0pw5c3TzzTcrJKSs8Sg3N1eTJ0/Wd999p507d2rZsmW64oor1LJlSw0fPjyAEQMAAPiGw+WquhBQDXTBqoEvvvhCu3fv1q233uqx3WazacOGDXrttdeUmZmpxMREDRw4UG+99ZZiYmICFC0AAIDvbP2dVg74BglIDQwePFjexuxHRkbq888/D0BEAAAA/nHfW+sq3Hdtz7b+CwRBjy5YAAAAqNJVZydVuC8qjO+0UX0kIAAAAKhSq5iSmTv/cGHHcvusFlZBR/WRgAAAAKBK76zZK0myWcsnG7ee397P0SCYkYAAAACgUp9uSFdmfrEk6b21e8vtb9s8yt8hIYiRgAAAAKBSd81f635++/nlu2ABNUECAgAAgGrre0oLj9cDTo0PUCQIViQgAAAAqLbwEJvHawago6ZIQAAAAFAhp8tzDbSkZhEer7cdYIFC1AwJCAAAACqUXVDsfv7NAwMVFRai689Ndm/bfSQ/EGEhiJGAAAAAoEKH8+zu58lxJbNd/U/vlECFgwaABAQAAAAVmrH0t3LbvK0FAlQXCQgAAAAq9NPuzHLbcgod/g8EDQYJCAAAACo0NDWx3LZQGx8hUXvcPQAAAKjQnqMlg8zvvbize1t8TLj7eZPwEL/HhOBGAgIAAIAKLf3ld0nS1gM57m1tmkW6nz9y9Zl+jwnBjQQEAACgEdiXWaD31uxVsdNVq+Nz7U6v28NsNq/bgYrQZgYAANDAvfnjbk15f4MkKbuwWLf061Djc/z1stO9bjcyXrcDFaEFBAAAoIErTT4k6fXvd1X7uC9//d39vNVx4z6OlxIXXfvA0CjRAgIAANCI7DiYV61yBUVO3frqavfrZlGhHvvfu7Ov9h7NV9e2sT6NDw0fLSAAAAANmDHlu0htycjxUtJTrt1zrQ+LxXPxwR4pzXXV2W1OLjg0SiQgAAAADdjijRnltv3nmx2VHpN2KM/rCuiAL9AFCwAAoAG7+4215bYdzSuq9JirZq5Q9nGrnY/q0dbncaHxogWkmqZOnSqLxeLxSEhIcO83xmjq1KlKSkpSZGSkBgwYoE2bNgUwYgAAAOmsNuXHaPRsH1du24Ifd2vax5tkjPFIPiTpqVHd6iw+ND4kIDVw5plnKj093f3YsKFsRoknn3xSM2bM0MyZM7Vq1SolJCTokksuUU5O1X0sAQAA6sr6vVmSpJQWUe5tT3z2q0cZY4z+8v4Gzfl2p77ddtiv8aHxIQGpgZCQECUkJLgf8fHxkkp+aZ999ln99a9/1YgRI5Samqq5c+cqPz9fb7zxRoCjBgAAjZXLVTYAfdfh/ArLfbXlgPv5wdzCOo0JIAGpga1btyopKUkdOnTQ6NGjtWNHyQCutLQ0ZWRkaPDgwe6y4eHh6t+/v1auXBmocAEAQCPnOC4B+XJS/wrL/ZB2xP38/rfW12lMAAlINZ133nl67bXX9Pnnn+s///mPMjIy1LdvXx0+fFgZGSWzS7Ru3drjmNatW7v3eWO325Wdne3xAAAA8BWHy+V+nhAbobbNI72We+nrimfF+vGvF/s8LjRuJCDVNHToUI0cOVJdu3bVoEGDtGjRIknS3Llz3WVOnB/bGFNu2/GmT5+u2NhY9yM5ObluggcAAI2O02VU7ChrAQmxWvXY8K6SpDbNyhKR/CJHuWOP1yomom4CRKNFAlJL0dHR6tq1q7Zu3eqeDevE1o4DBw6UaxU53pQpU5SVleV+7Nmzp05jBgAAjYPd4VSnBz9Vt38scW8LtVkUFx0mSSp2lrWMTP/013LHl7JW/D0qUGskILVkt9u1efNmJSYmqkOHDkpISNDSpUvd+4uKivT111+rb9++FZ4jPDxcTZs29XgAAACcrCcWbym3zWKxqGlEqCTPVc5f/36X13P86dJT9dl9F9ZNgGjUWIiwmiZPnqwrrrhC7dq104EDB/Too48qOztbN998sywWi+677z499thj6ty5szp37qzHHntMUVFRuuGGGwIdOgAAaESO5BXplW/TvO5rElHy0S+/yCmH06UQm/fvopNiI3T3wFPqLEY0biQg1bR3715df/31OnTokOLj49W7d299//33SklJkSQ98MADKigo0F133aWjR4/qvPPO05IlSxQTExPgyAEAQGORVVCsLRnl1yCbM7aXJCkmouyj37fbD6t/l3gNODVey7Yc9Ch/Y5/2dRonGjcSkGpasGBBpfstFoumTp2qqVOn+icgAACAY5wuox0Hc3XJM8vd4zyO179Lydploce1eNz8yo/a+fgwmWPj1M9p10w/7c6UJEWE0ksfdYcEBAAAIIjtOpyn/k8tc78+klckqaQb1f6sQj0w5FRZKxlNnl1YLEnq0a65OwEJYfQ56hAJCAAAQBD6aP1+/fHNnyrcbyTtfHxYpef46tcD7qTjgi7xenlFydiRI3nFvgoTKIf2NQAAgCBTWOysNPmQpDx75et7SNItr65yP28eFaqUFlGSpD6dWpxcgEAlaAEBAAAIMhlZhVWWCa1ghquKWC0WLb73Am1Oz1H3ds1qGRlQNVpAAAAAgkhOYbHyi5xVlis6brHB43Vp3cTr9lMTYhQVFqIeKc1lsTAGBHWHBAQAACBILNtyQF2nLtF1//6uyrI5hd67YF11dpty27b979Aat5gAtcWdBgAAEARcLqOxc0rGbJQmF8lxkUqbflmNznNrvw4er3u1b17hgoRAXeBuAwAAqMeO5BXJGKNBz3xdbt+eIwXluks9cnWqJGnOLb28ni8yzObxmu5W8DcGoQMAANRTn2/K0B9eX1OjY27snaIbe6dUu/yPaUdqGhZwUmgBAQAAqKeqSj5m39xTkjTo9FaSpKvPTqrxe7DqOfyNFhAAAIAgdfHprSVJs8b00IZ9WerWNrbG5/jnqG6+DguoFAkIAABAPXQ41+51e++OcdqfWahHj431kKSwEKt6pDSv1fsMOTOhVscBtUUCAgAAUM8cyrWr56NfeN335rjeJz1w/LwOcfrh2NgPZsCCv3HHAQAA1CMul6kw+Ti3Q5xPZq16YuRZSoqN0IOXnXbS5wJqihYQAACAemT+j7s9Xk+6pIuu6JaktEN56tOphU/eo33LaK2ccrFPzgXUFAkIAABAFVwuox/Sjii1TVNN/egXOV0u/ePqVOXZHSoocqpjfBOfvddDCze6n790Yw9demyMRvuW0T57DyCQSEAAAACqMP/H3Xpo4UZFh9mUV+SUJC1ct9+9f/3fBys2KtSn7zng1Hh38gE0JIwBAQAAqEJpq0Rp8nGigxXMWFVThcVOWY8N8Xhy5Fk+OSdQ35CAAAAAVKFjFd2fSseFG2NkjKn1++w4mCeXkZpGhCg+JrzW5wHqMxIQAACAKrRoElbp/qkfbZLLZdT9kaUaPmtlrZOQjOwCSVJ2ocMns10B9REJCAAAgBfr9mTqvgU/ac+RfK3aedRj350DOunugZ3cr7f+nquf92XpaH6x1u3JVIcpn8rlql4SsuDH3Vr+20FJ0q2vrvbdBQD1FIPQAQAAvLj6hW8leQ42n3blmbqxd4qsVouKnS698NV2SVJGdqG7fKkXvtqmCRd3rvD8xhjNWPqb/vXlNklSk3A+lqFxoAUEAADgBM4KWi9u7tte1mOjxENtVkWH2So8x64j+e7n+zMLtC+zwGP/55sy3MmHJOXaHe7nWx4dUqu4gWBAAlJN06dPV69evRQTE6NWrVrp6quv1pYtWzzKjB07VhaLxePRu3fvAEUMAABqa/dxyUOpEee0Kbet3yktKzzHu2v2SpJ+3pupvo9/qX6Pf6nC4pJZtAqKnLr7jZ+8Hvfh3f0UHlJxYgMEOxKQavr6669199136/vvv9fSpUvlcDg0ePBg5eXleZQbMmSI0tPT3Y9PP/00QBEDAIDamrtyZ7XKPX/9OZXuL3K4dOXMsq5ZGVmFkqTr/v1dha0s3ZKbVeu9gWBFZ8Nq+uyzzzxez5kzR61atdKaNWt04YUXureHh4crIYFFgwAACEZZBcXaezRfr3pJQHp3alFuW0Ro+ZaKJ0eepQfe+1mS9PbqPR77ftpzVG2bR+rnvVle3/9Pl55ai6iB4EILSC1lZZX84YiLi/PYvmzZMrVq1UpdunTRuHHjdODAgUCEBwBAg7L9YK5+zy6s0/dwOF3qNm2Jhj2/wr1t8BmtJUlXdkvSqB5tKz0+tU1TbX/sMo3qWVbub8cWMCx1INuuTfuzyx3bLbmZ7h/URXcPPOVkLgEICrSA1IIxRhMnTtT555+v1NRU9/ahQ4dq1KhRSklJUVpamh566CFddNFFWrNmjcLDyy8mZLfbZbeXrZyanV3+DxIAAI3dml1HNPLF79Q8KlQ//X1wnbxHQZFTp//9s3LbZ1x3drVnp2oeFSabtfK1O6Yv/tXjdULTCE298kwNSaX3BBoPEpBauOeee/Tzzz9rxYoVHtuvu+469/PU1FT17NlTKSkpWrRokUaMGFHuPNOnT9e0adPqPF4AAIJVnt2hkS9+J0k6ml+sXzOydVpCU5++x/aDubr46a+97qtslqsT1Wbtwe8fvLjmBwFBji5YNTRhwgR99NFH+uqrr9S2beVNsYmJiUpJSdHWrVu97p8yZYqysrLcjz179ngtBwBAY7HjYK6O5hW5X7+1yvP/xiv+teLEQ06KMabC5ENSjVYjPzOpLDG6tV+HKstf2S2p2ucGGhJaQKrJGKMJEybogw8+0LJly9ShQ9V/WA4fPqw9e/YoMTHR6/7w8HCvXbMAAGiMNuzN0hUzV8hmtWj7Y5fJ7nBqwz7PwdrFTiOny1TZ1am63lm9t9y2jdMu1c97M9W+RXS1zvHJhPP1+aYM3TmgbGX0hy4/Xa98m+Z+/ejVqeXGg1Q1gxbQUFmMqU2DYeNz11136Y033tCHH36oU08tm6EiNjZWkZGRys3N1dSpUzVy5EglJiZq586devDBB7V7925t3rxZMTExVb5Hdna2YmNjlZWVpaZNfdu8DABAfdf+L4vcz2fecI7uOW6dDIulrIvTSzf20KVnnvyYCWOMOkwpmy5/RPc2mnHt2Sd93uPPvz+rUEmxEZLkfq95t52ns9s1Y+XzIMXntZNHAlJNFTXBzpkzR2PHjlVBQYGuvvpq/fTTT8rMzFRiYqIGDhyoRx55RMnJydV6D25oAEBjtedIvi548qsK988Z20u3vLrK/Xps3/aaeuWZNXoPY4zH/+dTP9rknm53ZPe2euqas9yrnNeFgzl2FTtdSmoWWWfvgbrH57WTR+pdTVXlaZGRkfr888/9FA0AAA3L5vTKZ4K8sEu8Qm0WFTtL/j9+deVOPXzFGdUeo3HeY1/o92y7IkKt+umhwYoMs3ms9XHXwE51mnxIUnwM3a4BiUHoAACgHvhqS8XrZo3t2142q0X3DOzssf2vJ4ypqMivGdn6Pbtk2vvCYpdO//tnHt29JKlTfJMaRgygtkhAAABAwL35o/eZIHc+Pszd1SqzoMhj3xs/7K6yh8L7a/dqyLPfVFrmmeu61SBSACeLBAQAAATUzkN5Hq8fuepMnZHYVP+d1N9j+70Xe7aASNK/l+/wes7pn25W+78s0sS311f5/snNo2oQLYCTRQICAAAC6vkvy9bL+n7KxbqxT3t9eu8F5bpFNYsK0xcTPZOS6Yt/lcvl2QpijNFLXhKTV2/pVW5bu7go9UhpfjLhA6ghBqEDAICAyi10uJ8nHJuytiKntGqiHY9dpo4Plk2f2/HBT9Ujpbl2Hc7TlKGn6/TE8jMTfTLhfKW2idW/b+yhxRsz9I+rzlRMRKjvLgJAtZGAAACAgDHGaMkvv0uSBp/RulrHWK0WXdC5pb7Zesi9bc2uo5KkSe9473KV2ia25D3OTNBgH6whAqD26IIFAAD85rffc3TlzBW67dVVOpRr9xh8fl2v6q2bJUlnJsVWq1yozaJv/3JRjeMEUHdoAQEAAHVu+qeby43L6PnoFx6vL+wSX+3z3Teos7IKiiqcPUuSvnlgoBJjIxRi4/tWoD7hNxIAANQ5b4PCjzfnll4KrUGiEBFq0/QRZ+mLif3V30vi0ik+WslxUSQfQD1ECwgAAPAJl8vovrfW6aP1+yVJTSNCNKpnsoamVj7m4qLTWmngqa1q9Z6ntGqiR69O1f/M/kG39G2vuCbh+nxThh4f0bVW5wNQ9yymqhV84DfZ2dmKjY1VVlaWmjYtP4MHAAD12Ymri1fmjMSm+iU9W5K0+R9DFBlmq6uwAJ/i89rJowUEAADUijFG5zyyVJn5xdU+5tnrztZVZyfJYrHUYWQA6jMSEAAAUCuPL/7Va/JxausYNYkIcU+NW+q7KRcpMTbSX+EBqKdIQAAAQKUO59r194826dqeyXpt5U7den4HdW/XvNzA8sdHdNXoc9uVO37jvixJIvkAIIkxIPUKfQoBAPVRVWM7ZlzbTZd1TVREKOM40PDxee3k0QICAAAqNLmClcVL3dG/k0Z0b+unaAA0BCQgAABAxhj9vDdLWzJytGDVbq3dnVmt4+4a2KluAwPQ4JCAAABQTxU5XLrs+W+07UCue9sPD16s1k0jJEl/fvdnvbV6jx65OlU3nNtONmvtZpY6klek7o8srbTMq7f0UkZWoUZ0b6uwEKuO5hXJarGoaURord4TQOPFGJB6hD6FAIDjXfTPZdpxKM9j25lJTbXojxdo6S+/a9xrq8sd0yQ8RKv+Oqha62oYY3TPGz9p0Yb0Ssu9duu5utDLauNAY8TntZNnDXQAAACgzJ4j+fr38u16e/WecsmHJG3an63dh/O9Jh+SlGt36MqZKyp9D5fLaOW2Qzr/ia/KJR89UportU1TfTLhfLWLi9InE84n+QDgU3TBAgCgHsgpLFaf6V8q1+7wun/ImQn6bFOGJOnCp75yb3/mum66/y3PgeJbD+TK5TKyntAlyxijy55foc3HViA/3jntmumDu/p5bFv+wMBaXQsAVIYEBACAAHC6jHvMxsZ9Wbr8X95bLd4c11t9OrWQ5H063OHntNVlXRP1zNKtyswv0oJVeyRJHR/8VIv+eL7OTIpVnt2h9KwCDZqxvNzxPVOa6907+/rqsgCgSowBqUfoUwgAwaXY6dI7q/fqtMQYdW/XvNKy+UUO/f3DTXp3zd5qn//568/Rld2S3K//+fkWzfxqm/v1zseHlTumqjU7SsVFh+mDu/oquXlUuZYSABXj89rJowXEx2bNmqWnnnpK6enpOvPMM/Xss8/qggsuCHRYAAAf2pdZoElvr9P3O4543R8eYtVfhp6m7AKHnvniN1ktkqsaX/f9echpuqN/RxkjOY1RqM1zqObkS0/V5EtP9dq9qia8JS4A4C+0gPjQW2+9pRtvvFGzZs1Sv3799NJLL+nll1/WL7/8onbt2lV5PBk1AARG6X+Fe48W6Ntth3Qkv0gRITad1zFOdodLBUVObdiXpXW7M93jMHzl3A5xOiOxqe7o30kJsREnfb5dh/PU/6llXvf1at9cb47rrRAbc9AAtcXntZNHAuJD5513nrp3764XX3zRve3000/X1VdfrenTp1d5PDc06qPCYqeyC4qVXVis5LgohYdUPrVnZn6Rcu0OWS0WhdqsiokIkd3hUkx4iNcBsXaHSxaLFGK11noNA1TN5TI6mGvX0fwiHc0r1rItBzR7RZq6to1ViNWiVTuPusteemZr2R0uSVKL6HAt2rBfibGRWnL/heW+kQ8WDqdLFotFOYXFyil0yGa1KCO7UDsO5mnltkNa+svvyqlg8HdlrunRVsPOStQfXlujIqerwnITL+mii05rpS6tYxQWEpx1CKAEn9dOHgmIjxQVFSkqKkrvvPOOhg8f7t5+7733at26dfr666+rPIe/b+iXv9mho/lFMkYKsVkVZrPIZUoGRrqMkTFyfyA0kmSMXEYyKtlX+lxG7vJGZc+l47cfO9ZITpdLVotFTpdRocOlwmKnQm0WRYTaFBVmU1RYiCJCbYoMtR13jpLjXaUxmJIYQ6xWnXgDH/8R1mI5frvnh1vPfZ6cxsjhNCp2ulTkdMnlMrJYLLJaLCr9jOw0Ri6XkdMYZRc4lFNYrIJip4qdRnaHU4XFJddWWOyUvdjl/kCX0iJK0eEhyioolsUiWS0W2Y4FU+xyqdjpktNpFB5qU+ixn0mIteS9i50uOY1RVFhJ/dgdLuUUOlRY7FRkqE0hNosKip0ypqQLSHiITWEhVlktJT9LY6RCh9N9bc5j8Ucfq/OYiBBZLBZl5RfpUG5JIpFVUOxRN82jQt2x5Bc5FRZSkjgUO0uusaK/KBGhVjUJD1GRwyWHq+T9i52ehS0WKdRqVViIVRGhVoXarGoWFabmUaFqGhGqsBCrnMao2FFSTw6Xkb3YJYfLpVCb1V2fFkvJz9vhKomp9HpL39fhNAqxlSygZrWo5BvuYmfJeV1GYTarmkaGKjLUKqfLyEiKjQxV+LFrtcjifi+V/DvufY/fXnK/lMZjtR67E4+VKyh2yuUyigyzyWa1KMRqVcixG8zhKrmPCoqcyi9yquDYvZR/7LXLGLVsEl5yflmUXVisYqdLTcJLfpY5hQ4dzS9ZKM7ucCqroLhcfddUWIhVZyQ2VUxEiDLzi5Vnd6iguCQ2h9MoLjpMkaE2Wa0WRR77+RU5S1oPCo/9bhhj5HAZ971ntVgUGxmqyFCbIsNsigi1KiLEJoul5G+RxeL5m2u1WpSVXyy70yWHs+Q+KH1/p6uky1KIzaLCYqdcRsq3O5RX5KzRdTaLCpXLZZRdWJKQWC1SVFiITk2I0SVntFaI1aIm4SG6tmdypd2gjDHKzC9WbGQoYyyABoQE5OQxBsRHDh06JKfTqdatW3tsb926tTIyvDfX2+122e129+vs7PLTItal177bpd1H8v36niiZHjPYlH7YdrqMjuZ7JiQOLx/uwkOsMkYe3wiXJGRFlb5P6TFFTpdyj/1qpGcVnvwF1MK+zIKAvG9N7Dpcu9/flk3C1L5FtDILimWRdEqrJjqnXTMVFrs0Y+lvuqxrgtbsOqp+nVoqLjpMc7/bqWKnUZHDpXV7Mis8b0XTx1blSF7l90VdSYqNUNu4KJ3VJlbdU5rrlFZNFN8kXM2jwySVJBDFTqNQm0UWS80TCIvF4j4XAKAMCYiPnfiflDGmwv+4pk+frmnTpvkjLK9Gdm+ro/kl//E7XCXfClssFtmsks1S8h+uw1Xyjbb7m2WVXKPlxG93vW079lzHWg1KvzUu+Sa+5L1KWzocrpJv0wuOfdObX+RQYbHL/W2y1apjLRDHXpe2GDhd7ufSsRaZ0ufHfdl74ve+nt/Sex5Tej1hNqtCbSXdiKwWi7sVx3WsqcdqLWm5sFotig6zqVlUqCJCbQqxWhUVZlN4qFWRoTZFhNoUHmJVRKhNv2cX6mCOXS5TMgPN8S07xqjk/UJKvgUvcrhU5CjpNlLsdMl1bECqzWpxf6McFlLSqhAVFqK8IoecLqOI0JJvj0uPtztKjnUdGwEbEWZT6LHuTiG2krqzF7uUa3cov8ghl5GaRoSoRZMwxUSEqlVMuKLDQ2SzWHT0WMuIVPJzjA63uVs0wmxWhYdYFRMR6l6B2ekyyiksVkSoTelZhbI7nAq1WRVqtSo0pOQb/4jQklYsh9O470O7wyW7w6kih0tH8oqUmV+sHLtDRQ6XQo7FHXrs5xNms7lbYEp/mua4FrzwEKv7W/EQa8kxITarHE6Xu3Un1FbyMytpLSo5V/axlqUQa0krVE5hsYqOtZDImGPv49n6Z0xZi5+3FsHSn/fxZUJtpfdyyTf4xS6XZEpiCg+xKjKspGUg8lgLYUSoTaE2q+yOshYF17EWr5LtJXUXHmJTfEy4zLH7pkWTMMU3CS9pwankw/QfL+5cbttfh52ubQdydSi3SNnHujA1iwxVTETJvRcZZpXFYtGRvCJ3K1JBkVMOV8nPq6SM7djvktytPVaL3C2Ipa07hcdaVKSSv0NGxt16aWTkdEmRYVY1CQ899vO3KjzU6r4PipwuOV0uhYeUvA61WdU0MsT99ygmIrSkRbEaLRIWi0VhIbRcAICv0QXLR2rTBctbC0hycjJNegAAAPUUXbBOHiPhfCQsLEw9evTQ0qVLPbYvXbpUfft6X+ApPDxcTZs29XgAAAAADRldsHxo4sSJuvHGG9WzZ0/16dNH//73v7V7927dcccdgQ4NAAAAqBdIQHzouuuu0+HDh/WPf/xD6enpSk1N1aeffqqUlJRAhwYAAADUC4wBqUfoUwgAAFC/8Xnt5DEGBAAAAIDfkIAAAAAA8BsSEAAAAAB+QwICAAAAwG9IQAAAAAD4DdPw1iOlE5JlZ2cHOBIAAAB4U/o5jYlka48EpB7JycmRJCUnJwc4EgAAAFQmJydHsbGxgQ4jKLEOSD3icrm0f/9+xcTEyGKx1OjY7OxsJScna8+ePcxJLerjeNSFJ+qjDHXhifooQ114oj7KUBclLR85OTlKSkqS1cpohtqgBaQesVqtatu27Umdo2nTpo32D4I31EcZ6sIT9VGGuvBEfZShLjxRH2Uae13Q8nFySNsAAAAA+A0JCAAAAAC/IQFpIMLDw/Xwww8rPDw80KHUC9RHGerCE/VRhrrwRH2UoS48UR9lqAv4AoPQAQAAAPgNLSAAAAAA/IYEBAAAAIDfkIAAAAAA8BsSEAAAAAB+QwICAA0Uc4ygItwbqAj3BvyBBCQINfY/Dunp6Tpy5Eigw6iXuDe4N0odOHBAOTk57teN+d7YtGmTHnjgAf3222+BDqVe4N4ow73hiXsD/kICUs8VFRXpiSee0MyZM/X1119LkiwWS4CjCoyioiKNGTNG/fr105YtWwIdTsBxb5Th3ijjcDh022236dxzz9WgQYM0ZswYHTp0qFHeG0VFRbrlllvUtWtXFRYWqn379oEOKaC4N8pwb3ji3oC/kYDUY4sXL1ZSUpIWLlyoOXPmaPjw4frb3/6mwsLCQIfmd88//7yaNWumnTt36v3331efPn0CHVJAcW+U4d4o43A4NHbsWP3yyy+aO3eurr/+ev38888aMWKENm/eHOjw/OqVV15Ry5Yt9dtvv2n9+vV6/vnnFRYWJqlxfqvLvVGGe8MT9wYCwqDeGjVqlPnDH/5gjDHmyJEj5p133jHh4eHmmWeeMfn5+QGOzn9uuOEGY7FYzIsvvujelp2dHcCIAo97owT3hqfdu3ebzp07m9dff929LT093bRp08ZMmDDBZGRkBDA6/+rbt685/fTTzdGjR40xxqxZs8Z8+umnZsuWLaagoMAYY4zL5QpghP7FvVGGe8MT9wYCgRaQemrHjh1auXKlLrjgAklS8+bNdc0112j8+PGaN2+eVqxYEeAI/WfQoEHq2LGjunbtqj179uiOO+7QrbfeqnHjxundd98NdHh+l5aW1ujvDYfDIYl740SHDx/W3r171bt3b0mS3W5XQkKCpkyZoiVLlmj58uUBjrDumWPfYP/zn/+U3W7X888/r6uuukqjRo3Sn/70J1144YW65ZZbJDWuLovcG2V/N7g3PHFvIBBIQOqJJUuWaP369XI6nZKkDh06qLi4WNnZ2ZKkgoICSdLDDz+svLw8LV68WLm5uQGLty6dWBe33HKLUlJSNGbMGJ133nk6fPiwOnfurLS0NF177bX6+OOPAxxx3dq+fbtHt4CUlJRGe2+U1kVISIikxn1vPPbYY3r44Ye1YMEC97bTTz9drVq10rx58yRJVmvJn/i7775bMTExWrx4sex2e0DirUvH10XpB8c+ffrowgsv1PTp0xUXF6f3339fb775pl5++WUtXLhQjzzySICjrjuffvqpJM/uRKeeeqoSEhIa3b1xfF2EhITIGKM+ffqof//+jfLe+Pe//63//Oc/HklF586dG+W9gQALZPMLjJkzZ45JSEgwXbt2NTExMeauu+4y+/btM8YY84c//MGcddZZ7rJFRUXGGGMef/xxk5yc7G4+bii81cWuXbuMMcZ899135pxzzjFvv/22cTgc7mPGjRtnOnfu7K6bhmT27NmmXbt2pkePHua8884zr7/+uvvax48f36jujRPrYt68ecZutxtjjFm5cmWjujd++OEH065dO9O9e3czdOhQExMTY0aOHGm2b99ujDFm8uTJpkuXLub33383xhh3l5K5c+eaZs2auV83BN7q4pprrjGbN282xhiTkZFh/va3v7n/ppb65z//aVq2bNng7o1PPvnEtGnTxlgsFvPtt98aY4xxOp3GGGPy8/PNAw880GjuDW914XK53H8jDhw40KjujTfeeMO0atXK9OnTx5x99tkmPj7e/O///q8xxpisrKxGdW+gfiABCaCXX37ZnHLKKebNN980Bw8eNPPnzzfR0dFm3bp1xhhj3nvvPXPaaaeZZ5991hhjTGFhoTHGmIMHD5rIyEjzzTffBCx2X/NWF02aNDE//fSTu8zKlSvL9e/fvHmzCQsLMytXrvRzxHXr2WefddfHihUrzN///ndjsVjMrFmzjMvlMh9//LHp0qVLo7g3vNWF1Wo1L7zwgvu6V6xY0WjujYkTJ5phw4YZY0o+XG7YsMGkpKSYO+64w2RmZprvv//edO/e3dx1113GmLK+7F999ZVp1aqVWb9+fcBi97WK6uLOO+90f7D0NibozTffNM2bNzcbNmzwa7x16ZtvvjFDhgwx99xzjxk6dKjp2bNnuTJffPGF6dWrV4O/N6qqi9LrzsvLK3dsQ7w35s+fb7p162b+7//+zxhjzL59+8zMmTNNdHS0ycrKMsYYs3Tp0kZxb6D+IAEJgNJvYW644QZz4403euzr0qWLWbt2rTGm5Nu7P/7xjyY5OdnjW5olS5aYdu3auROVYFZVXVR0jaXf6r388sumdevWDeo/i7y8PHPJJZeYhx9+2BhT9h/BBRdcYNq2bWs+++wzU1hYaCZMmNCg7w1jKq+LlJQU8/7775c7pqHeGy6Xy2RmZprzzz/fTJ482RhTdq2zZs0y55xzjvsDxjPPPGOioqLM+++/724pevTRR82AAQMaxODaquqiR48e5rnnnqvw+DvvvNOMGDHCL7HWtdKf52+//WZmzJhhduzYYVavXm2ioqLMyy+/bIwxpri42BhT8q32M888Y6KjoxvkvVGduii9TyrSEO+NV1991YwfP95jgpIVK1aYLl26mO+++84Y0/DvDdQ/JCABdPbZZ5vbb7/dPcPEhAkTzKmnnmqmTp3q/tZ2+/bt7ibTefPmma1bt5rRo0ebQYMGef32JlhVVhffffed1+bfffv2mZEjR5o77rijQf1xtNvtJi4uzrzxxhvGmLKm8JEjR5qkpCRz4403mpycHPPbb7+Zfv36Neh7o6q6uOmmm8zBgwfLHddQ7o01a9aYzMxMj209e/Z0z4BW2gJUVFRkRowYYa688kqzb98+U1RUZP70pz+ZmJgY079/fzNq1CgTGRlpXnjhBWNMcM7wU9O6GD58uNmxY4e7bFpamtm2bZu57bbbTLt27czChQuNMcFZF8Z4r4/S7kXFxcVm0qRJJj4+3l0vpfuys7PNAw880ODvjcrq4kQN8d44vhtuZmamR/dUY4xZt26dSUhIMEeOHHFva4j3BuovEhA/ePvtt83tt99unn32WfPzzz+7ty9YsMCkpKSYwYMHmxYtWpjTTjvN/OMf/zADBw40Z511lnn88ceNMSUtIUOGDDGnn366adOmjenXr59JS0sL0NWcnNrURbdu3dx9VY8ePWrefPNNc//995sWLVqYSy+9tFwf3mBSUX1cf/315rTTTjN79+41xhgzb948M3DgQHP77bebU045xd0c3hjujcrq4vgWw4Z0b7z77rumbdu2plOnTqZdu3bm73//u/v6n3vuOdOkSRN3kln6TeV7771n2rZt6+7vbowx77zzjnn44YfNHXfc4R4XEWxqWxfJycnuuti8ebO5++67TatWrcyAAQPMli1bAnMxPuCtPtLT040xJR8QSz8k7tixwyQnJ5tJkyYZY8p/8//22283yHujOnVx/AfpX3/9tcHeGw899JDHFLrH3wMzZsww/fr1M8aU/d6Uagh/N1D/kYDUoUOHDplrrrnGJCQkmDvuuMOcf/75JikpycyZM8dd5sCBA+app54y/fv39+irPG7cODN8+HCPAWHp6elB253EF3WRmZlpDh065C7z0UcfBeBKfMNbfSQmJprXXnvNGFPShaBjx46mY8eOJikpyURFRZn33nvPGGNMSEiIWbRokftchYWFDe7eqE1dpKenm3/+859Bf2+sWrXKPfZr/fr1ZtasWSY+Pt7ceeedJjMz0+zatct06tTJ/c3/8YNlW7RoYWbPnh2o0H3uZOuitNtNbm6uWbp0qVm+fHlArsNXKquPw4cPG2PKvvl3uVxm1qxZJiQkxN0SZLfb3X3+g93J1kVhYaGx2+3G4XCYzz//vFHcG06n090db/jw4ebuu+8OZMho5EhA6tA777xjzj33XPe3dcYYc9VVV5kOHTq4+64XFxeb0aNHm0cffdQYU/ZNxMSJE02nTp1Mbm6uMSb4mz59URel/VdzcnL8HL3vVVQf7du3Nx988IExxpg9e/aYzz//3MydO9f9werAgQOmY8eO5p133glE2HXiZOvi7bffdh8XzPdG6e/4iy++aNq2bevxQXHmzJnm3HPPNdOnTzfGGPPCCy8Ym81mvv76a3eZ7du3m06dOrmTs2Dmq7p49913/Rt4HamqPnr37m0eeeSRcscdPnzY9O3b11x11VVmzZo1ZvDgweb1118P6v9PfFUXl1xySdDXhTE1rw+n02lcLpfp1KmT+eSTT4wxxmzZssWMHj3a7N6927/Bo1FjHZA69MYbb6ht27Zq06aNe12G4cOHa+fOnXrhhRd04MABhYSE6PDhw1q9erUkKSwsTL///rt+++03jR49WtHR0ZKCf1EkX9RFZGSkJKlJkyYBuw5fqag+du3apZkzZ+rgwYNq27atBg0apJtuukmhoaGSpK+++kphYWE6//zzAxm+T51sXZQuyCgF971R+juelpamLl26uNc6kaSxY8eqV69e+vDDD/Xbb7/pzjvv1OjRo3XdddfpH//4h9atW6cnn3xSUVFR7sXEgpmv6qJPnz6BugSfqqo+evToocWLF2vTpk2S5F5DKS4uTuPGjdNHH32kXr16KSwsTCNHjgzq/098VRfh4eEaMWJEUNeFVPP6sFqtWrVqlaKiotS9e3fdd999Ouuss3T48GG1atUqINeAxokExEeWL1+uzz//3L3SqlSyuE/pL33pB6Nff/1VF110kQoLC7Vw4UJJ0pQpU7Ro0SL169dPd911l3r27Kns7GyNHz/e79fhC9SFp5OpD6vVqoMHD+rXX3/VzJkzdf/992vEiBFq2bKlxyJjwYK6KLN06VL98Y9/1HPPPacff/zRvb1fv35auXKlMjIyJJV8gIqOjtZVV10lq9WqRYsWyWKxaN68eRo1apQ++OADjRo1SqtWrdL8+fOVlJQUqEuqNerCU23qw2KxaMmSJZIkm82moqIizZo1S7fddpsuvPBC/fzzz/r444/dX+QEi7qsi6ioqIBc08k42fqQShZn3Lhxo0499VQtXbpU3377rZYsWaLw8HC/Xw8asUA3wQS7gwcPmptuuslYLBbTrVs3jwHA27dvN/Hx8aZ///7miSeeMH369DEdOnQw//3vf023bt3M3/72N3fZDz74wPz5z382N9xwg0eXkmBCXXg6mfp46KGH3GXXrFljrr76atOhQwfz+uuvB+BKTh51UWb//v3m8ssvN61atTJjxowxXbt2NbGxseaHH34wxpSM9zrttNPM+PHjjTGeA0cvuOACc+edd7pfO51Ok5eXZ3799Vf/XoSPUBeeTrY+StdwMKZkgop7773XzJ07178X4SPUhSdf1sejjz5q4uPjG0R3TQQvEpCTUFxcbGbNmmUuvfRSs2DBAhMVFWWmT5/uMdXfihUrzLhx40z37t3NPffc454y9MYbbzQjR44MVOg+R1148nV9lM70FIyoizJ5eXnm5ptvNtddd53HFLG9evUyY8eONcaUDJx97bXXjNVq9ZjRyhhjxowZYwYOHOh+Hcz916kLT76uj2BGXXjyRX0MGDDA/frAgQP+CRyoBAnISfr+++/Nxx9/bIwxZtq0aSY+Pt5j9e5Sx09z9/vvv5vU1FT3YOuqFkYKFtSFJ1/UR+mMJcGOuigzfvx4s3jxYmNM2TVNmzbNnHfeee4yhYWFZvjw4eb00083y5YtMy6Xy6Snp5tzzz3XPbNTQ0BdeKI+ylAXnqgPNDQkICfpxG/dkpKSzPjx493TyB6/v6CgwBQVFblXLT5+rYOGgLrwRH2UoS7KHD9VbOl1/8///I8ZN26cx7aCggIzYMAA06pVKzN48GCTlJRkevfu3aBmqqEuPFEfZagLT9QHGhoSEB8p/eb27bffNiEhIWbJkiUe+/fu3WtmzZplevbs6bGyc0NEXXiiPspQF95dcMEF7jVxXC6Xe/2CjIwMs2TJEvO///u/Zv78+QGM0H+oC0/URxnqwhP1gWBmMSYIp4+p5/r27avo6GjNnz9frVq10sGDBxUfH68333xT+/fv16RJkwIdot9QF56ojzLURYkdO3aob9++WrRokXr06CFJKioqUlhYWIAj8z/qwhP1UYa68ER9INiFVF0E1eVwOBQSEqL//Oc/6tatmxYsWKDt27drxYoVmjt3rq6//vpAh+g31IUn6qMMdVHCGCOLxaIVK1aoSZMm7g8R06ZNU0ZGhqZNm9Zo5uWnLjxRH2WoC0/UBxoKWkDqyLnnnqvVq1erXbt2eumll3TppZcGOqSAoS48UR9lqAvpnnvuUXR0tAYNGqTx48crPz9fr7/+ugYPHhzo0PyOuvBEfZShLjxRHwh6Aev81UBt27bNpKammqioqEY/6wR14Yn6KENdlCgoKDCnnHKKsVgsJjw83Dz++OOBDilgqAtP1EcZ6sIT9YGGgC5YPmaz2TRy5Ej9+c9/DroVZ32NuvBEfZShLkpERESoffv2uuSSSzRjxgxFREQEOqSAoS48UR9lqAtP1AcaArpgAUAAOZ1O2Wy2QIdRL1AXnqiPMtSFJ+oDwY4EBAAAAIDfWAMdAAAAAIDGgwQEAAAAgN+QgAAAAADwGxIQAAAAAH5DAgIAAADAb0hAAAAAAPgNCQgAAAAAvyEBAQBUaezYsbJYLLJYLAoNDVXr1q11ySWX6JVXXpHL5ar2eV599VU1a9as7gIFANR7JCAAgGoZMmSI0tPTtXPnTi1evFgDBw7Uvffeq8svv1wOhyPQ4QEAggQJCACgWsLDw5WQkKA2bdqoe/fuevDBB/Xhhx9q8eLFevXVVyVJM2bMUNeuXRUdHa3k5GTdddddys3NlSQtW7ZMt9xyi7KystytKVOnTpUkFRUV6YEHHlCbNm0UHR2t8847T8uWLQvMhQIA6hQJCACg1i666CJ169ZN77//viTJarXq+eef18aNGzV37lx9+eWXeuCBByRJffv21bPPPqumTZsqPT1d6enpmjx5siTplltu0bfffqsFCxbo559/1qhRozRkyBBt3bo1YNcGAKgbFmOMCXQQAID6bezYscrMzNTChQvL7Rs9erR+/vln/fLLL+X2vfPOO7rzzjt16NAhSSVjQO677z5lZma6y2zfvl2dO3fW3r17lZSU5N4+aNAgnXvuuXrsscd8fj0AgMAJCXQAAIDgZoyRxWKRJH311Vd67LHH9Msvvyg7O1sOh0OFhYXKy8tTdHS01+PXrl0rY4y6dOnisd1ut6tFixZ1Hj8AwL9IQAAAJ2Xz5s3q0KGDdu3apcsuu0x33HGHHnnkEcXFxWnFihW67bbbVFxcXOHxLpdLNptNa9askc1m89jXpEmTug4fAOBnJCAAgFr78ssvtWHDBt1///1avXq1HA6Hnn76aVmtJUMM3377bY/yYWFhcjqdHtvOOeccOZ1OHThwQBdccIHfYgcABAYJCACgWux2uzIyMuR0OvX777/rs88+0/Tp03X55Zfrpptu0oYNG+RwOPSvf/1LV1xxhb799lv93//9n8c52rdvr9zcXP33v/9Vt27dFBUVpS5dumjMmDG66aab9PTTT+ucc87RoUOH9OWXX6pr16667LLLAnTFAIC6wCxYAIBq+eyzz5SYmKj27dtryJAh+uqrr/T888/rww8/lM1m09lnn60ZM2boiSeeUGpqqubPn6/p06d7nKNv37664447dN111yk+Pl5PPvmkJGnOnDm66aabNGnSJJ166qm68sor9cMPPyg5OTkQlwoAqEPMggUAAADAb2gBAQAAAOA3JCAAAAAA/IYEBAAAAIDfkIAAAAAA8BsSEAAAAAB+QwICAAAAwG9IQAAAAAD4DQkIAAAAAL8hAQEAAADgNyQgAAAAAPyGBAQAAACA35CAAAAAAPAbEhAAAAAAfkMCAgAAAMBvSEAAAAAA+A0JCAAAAAC/IQEBAAAA4DckIAAAAAD8hgQEAAAAgN+QgAAAAADwGxIQAAAAAH5DAgIAAADAb0hAAAAAAPjN/wMtEfPs0AYl1gAAAABJRU5ErkJggg==",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=800.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define Stock\n",
    "stock = 'AAPL'\n",
    "\n",
    "# Get data\n",
    "data = yf.download(tickers=stock, period='max', interval='1d', progress=False)\n",
    "\n",
    "#Print data\n",
    "#display(data)\n",
    "\n",
    "#Plot data\n",
    "plt.close('all')\n",
    "plt.figure(figsize=(8,4))\n",
    "data['Open'].plot(title=stock);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Forma de los datos: (10533, 6)\n",
      "\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>1980-12-12</th>\n",
       "      <td>0.128348</td>\n",
       "      <td>0.128906</td>\n",
       "      <td>0.128348</td>\n",
       "      <td>0.128348</td>\n",
       "      <td>0.100039</td>\n",
       "      <td>469033600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15</th>\n",
       "      <td>0.122210</td>\n",
       "      <td>0.122210</td>\n",
       "      <td>0.121652</td>\n",
       "      <td>0.121652</td>\n",
       "      <td>0.094820</td>\n",
       "      <td>175884800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16</th>\n",
       "      <td>0.113281</td>\n",
       "      <td>0.113281</td>\n",
       "      <td>0.112723</td>\n",
       "      <td>0.112723</td>\n",
       "      <td>0.087861</td>\n",
       "      <td>105728000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17</th>\n",
       "      <td>0.115513</td>\n",
       "      <td>0.116071</td>\n",
       "      <td>0.115513</td>\n",
       "      <td>0.115513</td>\n",
       "      <td>0.090035</td>\n",
       "      <td>86441600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18</th>\n",
       "      <td>0.118862</td>\n",
       "      <td>0.119420</td>\n",
       "      <td>0.118862</td>\n",
       "      <td>0.118862</td>\n",
       "      <td>0.092646</td>\n",
       "      <td>73449600</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15</th>\n",
       "      <td>154.649994</td>\n",
       "      <td>155.240005</td>\n",
       "      <td>151.380005</td>\n",
       "      <td>152.369995</td>\n",
       "      <td>152.369995</td>\n",
       "      <td>90481100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-16</th>\n",
       "      <td>151.210007</td>\n",
       "      <td>151.350006</td>\n",
       "      <td>148.369995</td>\n",
       "      <td>150.699997</td>\n",
       "      <td>150.699997</td>\n",
       "      <td>162157000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-19</th>\n",
       "      <td>149.309998</td>\n",
       "      <td>154.559998</td>\n",
       "      <td>149.100006</td>\n",
       "      <td>154.479996</td>\n",
       "      <td>154.479996</td>\n",
       "      <td>81474200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-20</th>\n",
       "      <td>153.399994</td>\n",
       "      <td>158.080002</td>\n",
       "      <td>153.080002</td>\n",
       "      <td>156.899994</td>\n",
       "      <td>156.899994</td>\n",
       "      <td>107689800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-21</th>\n",
       "      <td>157.339996</td>\n",
       "      <td>158.740005</td>\n",
       "      <td>153.600006</td>\n",
       "      <td>153.720001</td>\n",
       "      <td>153.720001</td>\n",
       "      <td>101407100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10533 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "1980-12-12    0.128348    0.128906    0.128348    0.128348    0.100039   \n",
       "1980-12-15    0.122210    0.122210    0.121652    0.121652    0.094820   \n",
       "1980-12-16    0.113281    0.113281    0.112723    0.112723    0.087861   \n",
       "1980-12-17    0.115513    0.116071    0.115513    0.115513    0.090035   \n",
       "1980-12-18    0.118862    0.119420    0.118862    0.118862    0.092646   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2022-09-15  154.649994  155.240005  151.380005  152.369995  152.369995   \n",
       "2022-09-16  151.210007  151.350006  148.369995  150.699997  150.699997   \n",
       "2022-09-19  149.309998  154.559998  149.100006  154.479996  154.479996   \n",
       "2022-09-20  153.399994  158.080002  153.080002  156.899994  156.899994   \n",
       "2022-09-21  157.339996  158.740005  153.600006  153.720001  153.720001   \n",
       "\n",
       "               Volume  \n",
       "Date                   \n",
       "1980-12-12  469033600  \n",
       "1980-12-15  175884800  \n",
       "1980-12-16  105728000  \n",
       "1980-12-17   86441600  \n",
       "1980-12-18   73449600  \n",
       "...               ...  \n",
       "2022-09-15   90481100  \n",
       "2022-09-16  162157000  \n",
       "2022-09-19   81474200  \n",
       "2022-09-20  107689800  \n",
       "2022-09-21  101407100  \n",
       "\n",
       "[10533 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the first five rows of the data\n",
    "print(f'\\n Forma de los datos: {data.shape}\\n')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-12</th>\n",
       "      <td>0.128348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15</th>\n",
       "      <td>0.121652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16</th>\n",
       "      <td>0.112723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17</th>\n",
       "      <td>0.115513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18</th>\n",
       "      <td>0.118862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Close\n",
       "Date                \n",
       "1980-12-12  0.128348\n",
       "1980-12-15  0.121652\n",
       "1980-12-16  0.112723\n",
       "1980-12-17  0.115513\n",
       "1980-12-18  0.118862"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_close = data[['Close']]\n",
    "data_close.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note cómo la API ya devuelve la información de manera amena, pero no tiene por qué ser así."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3Vqv_JU4Td2F"
   },
   "source": [
    "## <span style=\"color:#4361EE\">Visualización de la serie precio al cierre</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10533 = 9479 + 1054\n"
     ]
    }
   ],
   "source": [
    "# plot\n",
    "len_data = len(data_close)\n",
    "len_train = int(len_data*0.9) # 90%  = 9479 -> Depende de la fecha y acción elegida \n",
    "len_test = len_data - len_train # 10% = 1054 -> Depende de la fecha y acción elegida \n",
    "print (len_data, '=', len_train, '+',len_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abe64c5c903144309b6ddaf88269fac7",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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width=1200.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(data_close[:len_train], label='Conjunto de entrenamiento (Training set): {} puntos (90%)'.format(len_train))\n",
    "plt.plot(data_close['Close'][len_train:], label='Conjunto de Test (Test set): {} puntos (10%)'.format(len_test), color='r') \n",
    "plt.title(\"Apple: Historia del precio la acción al cierre (Close)\", size = 20)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Preparación de los datos para el entrenamiento de la red LSTM</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para evitar problemas con las tendencias y para mejorar la estimación (entrenamiento) los datos se van a transformar a la escala $[0,1]$. Para las predicciones se utiliza la transformación inversa."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Primero extrae los valores y se crea el  objeto MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating train and test sets\n",
    "dataset = data_close.values\n",
    "\n",
    "# create the scaler object and scale the data\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "# Scaled data\n",
    "dataset = np.squeeze(np.array(scaler.fit_transform(dataset)),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10533,)\n",
      "\n",
      "Min: 0.0, Max: 1.0\n"
     ]
    }
   ],
   "source": [
    "# Check ok\n",
    "print(dataset.shape)\n",
    "# Check ok in scaler\n",
    "print(f'\\nMin: {dataset.min()}, Max: {dataset.max()}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crea datos de entrenamiento "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " La red LSTM tendrá como entrada \"**time_step**\" datos consecutivos, y como salida **1 dato** (la predicción a\n",
    " partir de esos \"time_step\" datos se hace para el siguiente día). \n",
    " \n",
    " Se conformará de esta forma el set de entrenamiento\n",
    "\n",
    "1. Número de datos consecutivos para entrenamiento: time_step = 60. \n",
    "2. Días a predecir: days = 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Función para crear los datos entrenamiento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def univariate_data(dataset, start_index, end_index, history_size, target_size):\n",
    "    ''' dataset: conjunto de datos en forma de array\n",
    "        start_index: índice inicial de donde empezar a tomar los datos\n",
    "        end_index: índice final para tomar los datos. None para tomarlos todos\n",
    "        history_size: tamaño de la ventana para crear las secuencias\n",
    "        target_size: dentro de cuántas observaciones futuras desea pronosticar\n",
    "    '''  \n",
    "    data = []\n",
    "    labels = []\n",
    "    \n",
    "    # Empezamos desfasados para tomar desde el incio de la serie\n",
    "    start_index = start_index + history_size\n",
    "    if end_index is None:\n",
    "        # El último índice a tomar debe dejar espacio para el tamaño de la ventana\n",
    "        end_index = len(dataset)\n",
    "\n",
    "    for i in range(start_index, end_index-target_size+1):\n",
    "        indices = range(i-history_size, i)\n",
    "        # Reshape data from (history_size,) to (history_size, 1)\n",
    "        data.append(np.reshape(dataset[indices], (history_size, 1)))\n",
    "        # Etiquetas (Datos a predecir)\n",
    "        labels.append(dataset[i+target_size-1])\n",
    "\n",
    "    return np.array(data), np.array(labels)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Se coloca una semilla para garantizar reproductibidad dentro de tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Semilla\n",
    "tf.random.set_seed(500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9479\n",
      "(9419, 60, 1)\n",
      "(9419,)\n",
      "(1054, 60, 1)\n",
      "(1054,)\n"
     ]
    }
   ],
   "source": [
    "# hiperparámetros para crear las secuencias\n",
    "past_history = 60 # tamaño secuencias de entrada (Ventana de tiempo)\n",
    "future_target = 1 # días adelante\n",
    "TRAIN_SPLIT = len_train # nuḿero de datos entrenamiento\n",
    "\n",
    "# secuencias de entrenamiento\n",
    "X_train, y_train = univariate_data(dataset, start_index=0, \n",
    "                                            end_index=TRAIN_SPLIT,\n",
    "                                            history_size=past_history,\n",
    "                                            target_size=future_target)\n",
    "#secuencias de test\n",
    "# No se usará ningún dato que el modelo haya visto\n",
    "X_test, y_test = univariate_data(dataset, TRAIN_SPLIT-past_history-future_target+1, None,\n",
    "                                          past_history,\n",
    "                                          future_target)\n",
    "\n",
    "print(TRAIN_SPLIT)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.000383</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.000380</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000380</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000350</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.000592</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.000592</td>\n",
       "      <td>0.000567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>0.000297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.000592</td>\n",
       "      <td>0.000567</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.000325</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9414</th>\n",
       "      <td>0.239795</td>\n",
       "      <td>0.241293</td>\n",
       "      <td>0.244618</td>\n",
       "      <td>0.244068</td>\n",
       "      <td>0.237144</td>\n",
       "      <td>0.227416</td>\n",
       "      <td>0.226757</td>\n",
       "      <td>0.223597</td>\n",
       "      <td>0.224572</td>\n",
       "      <td>0.225356</td>\n",
       "      <td>...</td>\n",
       "      <td>0.253122</td>\n",
       "      <td>0.252752</td>\n",
       "      <td>0.254593</td>\n",
       "      <td>0.254057</td>\n",
       "      <td>0.256901</td>\n",
       "      <td>0.252422</td>\n",
       "      <td>0.254455</td>\n",
       "      <td>0.257986</td>\n",
       "      <td>0.261572</td>\n",
       "      <td>0.261256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9415</th>\n",
       "      <td>0.241293</td>\n",
       "      <td>0.244618</td>\n",
       "      <td>0.244068</td>\n",
       "      <td>0.237144</td>\n",
       "      <td>0.227416</td>\n",
       "      <td>0.226757</td>\n",
       "      <td>0.223597</td>\n",
       "      <td>0.224572</td>\n",
       "      <td>0.225356</td>\n",
       "      <td>0.222745</td>\n",
       "      <td>...</td>\n",
       "      <td>0.252752</td>\n",
       "      <td>0.254593</td>\n",
       "      <td>0.254057</td>\n",
       "      <td>0.256901</td>\n",
       "      <td>0.252422</td>\n",
       "      <td>0.254455</td>\n",
       "      <td>0.257986</td>\n",
       "      <td>0.261572</td>\n",
       "      <td>0.261256</td>\n",
       "      <td>0.257863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9416</th>\n",
       "      <td>0.244618</td>\n",
       "      <td>0.244068</td>\n",
       "      <td>0.237144</td>\n",
       "      <td>0.227416</td>\n",
       "      <td>0.226757</td>\n",
       "      <td>0.223597</td>\n",
       "      <td>0.224572</td>\n",
       "      <td>0.225356</td>\n",
       "      <td>0.222745</td>\n",
       "      <td>0.226784</td>\n",
       "      <td>...</td>\n",
       "      <td>0.254593</td>\n",
       "      <td>0.254057</td>\n",
       "      <td>0.256901</td>\n",
       "      <td>0.252422</td>\n",
       "      <td>0.254455</td>\n",
       "      <td>0.257986</td>\n",
       "      <td>0.261572</td>\n",
       "      <td>0.261256</td>\n",
       "      <td>0.257863</td>\n",
       "      <td>0.262190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9417</th>\n",
       "      <td>0.244068</td>\n",
       "      <td>0.237144</td>\n",
       "      <td>0.227416</td>\n",
       "      <td>0.226757</td>\n",
       "      <td>0.223597</td>\n",
       "      <td>0.224572</td>\n",
       "      <td>0.225356</td>\n",
       "      <td>0.222745</td>\n",
       "      <td>0.226784</td>\n",
       "      <td>0.232060</td>\n",
       "      <td>...</td>\n",
       "      <td>0.254057</td>\n",
       "      <td>0.256901</td>\n",
       "      <td>0.252422</td>\n",
       "      <td>0.254455</td>\n",
       "      <td>0.257986</td>\n",
       "      <td>0.261572</td>\n",
       "      <td>0.261256</td>\n",
       "      <td>0.257863</td>\n",
       "      <td>0.262190</td>\n",
       "      <td>0.262603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9418</th>\n",
       "      <td>0.237144</td>\n",
       "      <td>0.227416</td>\n",
       "      <td>0.226757</td>\n",
       "      <td>0.223597</td>\n",
       "      <td>0.224572</td>\n",
       "      <td>0.225356</td>\n",
       "      <td>0.222745</td>\n",
       "      <td>0.226784</td>\n",
       "      <td>0.232060</td>\n",
       "      <td>0.242323</td>\n",
       "      <td>...</td>\n",
       "      <td>0.256901</td>\n",
       "      <td>0.252422</td>\n",
       "      <td>0.254455</td>\n",
       "      <td>0.257986</td>\n",
       "      <td>0.261572</td>\n",
       "      <td>0.261256</td>\n",
       "      <td>0.257863</td>\n",
       "      <td>0.262190</td>\n",
       "      <td>0.262603</td>\n",
       "      <td>0.262026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9419 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           h_0       h_1       h_2       h_3       h_4       h_5       h_6  \\\n",
       "0     0.000435  0.000399  0.000350  0.000365  0.000383  0.000423  0.000457   \n",
       "1     0.000399  0.000350  0.000365  0.000383  0.000423  0.000457  0.000488   \n",
       "2     0.000350  0.000365  0.000383  0.000423  0.000457  0.000488  0.000527   \n",
       "3     0.000365  0.000383  0.000423  0.000457  0.000488  0.000527  0.000601   \n",
       "4     0.000383  0.000423  0.000457  0.000488  0.000527  0.000601  0.000613   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "9414  0.239795  0.241293  0.244618  0.244068  0.237144  0.227416  0.226757   \n",
       "9415  0.241293  0.244618  0.244068  0.237144  0.227416  0.226757  0.223597   \n",
       "9416  0.244618  0.244068  0.237144  0.227416  0.226757  0.223597  0.224572   \n",
       "9417  0.244068  0.237144  0.227416  0.226757  0.223597  0.224572  0.225356   \n",
       "9418  0.237144  0.227416  0.226757  0.223597  0.224572  0.225356  0.222745   \n",
       "\n",
       "           h_7       h_8       h_9  ...      h_51      h_52      h_53  \\\n",
       "0     0.000488  0.000527  0.000601  ...  0.000359  0.000380  0.000383   \n",
       "1     0.000527  0.000601  0.000613  ...  0.000380  0.000383  0.000374   \n",
       "2     0.000601  0.000613  0.000592  ...  0.000383  0.000374  0.000368   \n",
       "3     0.000613  0.000592  0.000567  ...  0.000374  0.000368  0.000365   \n",
       "4     0.000592  0.000567  0.000577  ...  0.000368  0.000365  0.000359   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "9414  0.223597  0.224572  0.225356  ...  0.253122  0.252752  0.254593   \n",
       "9415  0.224572  0.225356  0.222745  ...  0.252752  0.254593  0.254057   \n",
       "9416  0.225356  0.222745  0.226784  ...  0.254593  0.254057  0.256901   \n",
       "9417  0.222745  0.226784  0.232060  ...  0.254057  0.256901  0.252422   \n",
       "9418  0.226784  0.232060  0.242323  ...  0.256901  0.252422  0.254455   \n",
       "\n",
       "          h_54      h_55      h_56      h_57      h_58      h_59    target  \n",
       "0     0.000374  0.000368  0.000365  0.000359  0.000310  0.000282  0.000261  \n",
       "1     0.000368  0.000365  0.000359  0.000310  0.000282  0.000261  0.000282  \n",
       "2     0.000365  0.000359  0.000310  0.000282  0.000261  0.000282  0.000276  \n",
       "3     0.000359  0.000310  0.000282  0.000261  0.000282  0.000276  0.000297  \n",
       "4     0.000310  0.000282  0.000261  0.000282  0.000276  0.000297  0.000325  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "9414  0.254057  0.256901  0.252422  0.254455  0.257986  0.261572  0.261256  \n",
       "9415  0.256901  0.252422  0.254455  0.257986  0.261572  0.261256  0.257863  \n",
       "9416  0.252422  0.254455  0.257986  0.261572  0.261256  0.257863  0.262190  \n",
       "9417  0.254455  0.257986  0.261572  0.261256  0.257863  0.262190  0.262603  \n",
       "9418  0.257986  0.261572  0.261256  0.257863  0.262190  0.262603  0.262026  \n",
       "\n",
       "[9419 rows x 61 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize\n",
    "train_df = pd.DataFrame(X_train[...,0], columns=[f'h_{i}' for i in range(past_history)])\n",
    "pd.concat((train_df,pd.DataFrame(y_train, columns=['target'])),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(61,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.00043548, 0.00039868, 0.00034961, 0.00036495, 0.00038335,\n",
       "       0.00042322, 0.00045695, 0.00048762, 0.00052749, 0.00060109,\n",
       "       0.00061336, 0.00059189, 0.00056736, 0.00057656, 0.00055816,\n",
       "       0.00052135, 0.00048762, 0.00047229, 0.00051215, 0.00050602,\n",
       "       0.00047842, 0.00048149, 0.00049682, 0.00049069, 0.00053669,\n",
       "       0.00051215, 0.00052749, 0.00053669, 0.00053362, 0.00052135,\n",
       "       0.00051522, 0.00049069, 0.00046309, 0.00042322, 0.00038335,\n",
       "       0.00040788, 0.00043242, 0.00043242, 0.00043548, 0.00039868,\n",
       "       0.00039868, 0.00037722, 0.00037108, 0.00035575, 0.00037108,\n",
       "       0.00039868, 0.00035881, 0.00032508, 0.00033428, 0.00031281,\n",
       "       0.00034961, 0.00035881, 0.00038029, 0.00038335, 0.00037415,\n",
       "       0.00036801, 0.00036495, 0.00035881, 0.00030975, 0.00028214,\n",
       "       0.00026068])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dataset[:61].shape)\n",
    "dataset[:61]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>h_0</th>\n",
       "      <th>h_1</th>\n",
       "      <th>h_2</th>\n",
       "      <th>h_3</th>\n",
       "      <th>h_4</th>\n",
       "      <th>h_5</th>\n",
       "      <th>h_6</th>\n",
       "      <th>h_7</th>\n",
       "      <th>h_8</th>\n",
       "      <th>h_9</th>\n",
       "      <th>...</th>\n",
       "      <th>h_51</th>\n",
       "      <th>h_52</th>\n",
       "      <th>h_53</th>\n",
       "      <th>h_54</th>\n",
       "      <th>h_55</th>\n",
       "      <th>h_56</th>\n",
       "      <th>h_57</th>\n",
       "      <th>h_58</th>\n",
       "      <th>h_59</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.227416</td>\n",
       "      <td>0.226757</td>\n",
       "      <td>0.223597</td>\n",
       "      <td>0.224572</td>\n",
       "      <td>0.225356</td>\n",
       "      <td>0.222745</td>\n",
       "      <td>0.226784</td>\n",
       "      <td>0.232060</td>\n",
       "      <td>0.242323</td>\n",
       "      <td>0.242763</td>\n",
       "      <td>...</td>\n",
       "      <td>0.252422</td>\n",
       "      <td>0.254455</td>\n",
       "      <td>0.257986</td>\n",
       "      <td>0.261572</td>\n",
       "      <td>0.261256</td>\n",
       "      <td>0.257863</td>\n",
       "      <td>0.262190</td>\n",
       "      <td>0.262603</td>\n",
       "      <td>0.262026</td>\n",
       "      <td>0.262767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.226757</td>\n",
       "      <td>0.223597</td>\n",
       "      <td>0.224572</td>\n",
       "      <td>0.225356</td>\n",
       "      <td>0.222745</td>\n",
       "      <td>0.226784</td>\n",
       "      <td>0.232060</td>\n",
       "      <td>0.242323</td>\n",
       "      <td>0.242763</td>\n",
       "      <td>0.252298</td>\n",
       "      <td>...</td>\n",
       "      <td>0.254455</td>\n",
       "      <td>0.257986</td>\n",
       "      <td>0.261572</td>\n",
       "      <td>0.261256</td>\n",
       "      <td>0.257863</td>\n",
       "      <td>0.262190</td>\n",
       "      <td>0.262603</td>\n",
       "      <td>0.262026</td>\n",
       "      <td>0.262767</td>\n",
       "      <td>0.261325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.223597</td>\n",
       "      <td>0.224572</td>\n",
       "      <td>0.225356</td>\n",
       "      <td>0.222745</td>\n",
       "      <td>0.226784</td>\n",
       "      <td>0.232060</td>\n",
       "      <td>0.242323</td>\n",
       "      <td>0.242763</td>\n",
       "      <td>0.252298</td>\n",
       "      <td>0.254125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.257986</td>\n",
       "      <td>0.261572</td>\n",
       "      <td>0.261256</td>\n",
       "      <td>0.257863</td>\n",
       "      <td>0.262190</td>\n",
       "      <td>0.262603</td>\n",
       "      <td>0.262026</td>\n",
       "      <td>0.262767</td>\n",
       "      <td>0.261325</td>\n",
       "      <td>0.263358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.224572</td>\n",
       "      <td>0.225356</td>\n",
       "      <td>0.222745</td>\n",
       "      <td>0.226784</td>\n",
       "      <td>0.232060</td>\n",
       "      <td>0.242323</td>\n",
       "      <td>0.242763</td>\n",
       "      <td>0.252298</td>\n",
       "      <td>0.254125</td>\n",
       "      <td>0.255348</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261572</td>\n",
       "      <td>0.261256</td>\n",
       "      <td>0.257863</td>\n",
       "      <td>0.262190</td>\n",
       "      <td>0.262603</td>\n",
       "      <td>0.262026</td>\n",
       "      <td>0.262767</td>\n",
       "      <td>0.261325</td>\n",
       "      <td>0.263358</td>\n",
       "      <td>0.262754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.225356</td>\n",
       "      <td>0.222745</td>\n",
       "      <td>0.226784</td>\n",
       "      <td>0.232060</td>\n",
       "      <td>0.242323</td>\n",
       "      <td>0.242763</td>\n",
       "      <td>0.252298</td>\n",
       "      <td>0.254125</td>\n",
       "      <td>0.255348</td>\n",
       "      <td>0.257148</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261256</td>\n",
       "      <td>0.257863</td>\n",
       "      <td>0.262190</td>\n",
       "      <td>0.262603</td>\n",
       "      <td>0.262026</td>\n",
       "      <td>0.262767</td>\n",
       "      <td>0.261325</td>\n",
       "      <td>0.263358</td>\n",
       "      <td>0.262754</td>\n",
       "      <td>0.262987</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>0.746429</td>\n",
       "      <td>0.743571</td>\n",
       "      <td>0.759619</td>\n",
       "      <td>0.778249</td>\n",
       "      <td>0.778249</td>\n",
       "      <td>0.755057</td>\n",
       "      <td>0.764895</td>\n",
       "      <td>0.751100</td>\n",
       "      <td>0.763246</td>\n",
       "      <td>0.777699</td>\n",
       "      <td>...</td>\n",
       "      <td>0.867829</td>\n",
       "      <td>0.856013</td>\n",
       "      <td>0.848979</td>\n",
       "      <td>0.856837</td>\n",
       "      <td>0.848594</td>\n",
       "      <td>0.864586</td>\n",
       "      <td>0.897890</td>\n",
       "      <td>0.845187</td>\n",
       "      <td>0.853265</td>\n",
       "      <td>0.837108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>0.743571</td>\n",
       "      <td>0.759619</td>\n",
       "      <td>0.778249</td>\n",
       "      <td>0.778249</td>\n",
       "      <td>0.755057</td>\n",
       "      <td>0.764895</td>\n",
       "      <td>0.751100</td>\n",
       "      <td>0.763246</td>\n",
       "      <td>0.777699</td>\n",
       "      <td>0.785174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.856013</td>\n",
       "      <td>0.848979</td>\n",
       "      <td>0.856837</td>\n",
       "      <td>0.848594</td>\n",
       "      <td>0.864586</td>\n",
       "      <td>0.897890</td>\n",
       "      <td>0.845187</td>\n",
       "      <td>0.853265</td>\n",
       "      <td>0.837108</td>\n",
       "      <td>0.827930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>0.759619</td>\n",
       "      <td>0.778249</td>\n",
       "      <td>0.778249</td>\n",
       "      <td>0.755057</td>\n",
       "      <td>0.764895</td>\n",
       "      <td>0.751100</td>\n",
       "      <td>0.763246</td>\n",
       "      <td>0.777699</td>\n",
       "      <td>0.785174</td>\n",
       "      <td>0.804024</td>\n",
       "      <td>...</td>\n",
       "      <td>0.848979</td>\n",
       "      <td>0.856837</td>\n",
       "      <td>0.848594</td>\n",
       "      <td>0.864586</td>\n",
       "      <td>0.897890</td>\n",
       "      <td>0.845187</td>\n",
       "      <td>0.853265</td>\n",
       "      <td>0.837108</td>\n",
       "      <td>0.827930</td>\n",
       "      <td>0.848704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>0.778249</td>\n",
       "      <td>0.778249</td>\n",
       "      <td>0.755057</td>\n",
       "      <td>0.764895</td>\n",
       "      <td>0.751100</td>\n",
       "      <td>0.763246</td>\n",
       "      <td>0.777699</td>\n",
       "      <td>0.785174</td>\n",
       "      <td>0.804024</td>\n",
       "      <td>0.807816</td>\n",
       "      <td>...</td>\n",
       "      <td>0.856837</td>\n",
       "      <td>0.848594</td>\n",
       "      <td>0.864586</td>\n",
       "      <td>0.897890</td>\n",
       "      <td>0.845187</td>\n",
       "      <td>0.853265</td>\n",
       "      <td>0.837108</td>\n",
       "      <td>0.827930</td>\n",
       "      <td>0.848704</td>\n",
       "      <td>0.862003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>0.778249</td>\n",
       "      <td>0.755057</td>\n",
       "      <td>0.764895</td>\n",
       "      <td>0.751100</td>\n",
       "      <td>0.763246</td>\n",
       "      <td>0.777699</td>\n",
       "      <td>0.785174</td>\n",
       "      <td>0.804024</td>\n",
       "      <td>0.807816</td>\n",
       "      <td>0.795890</td>\n",
       "      <td>...</td>\n",
       "      <td>0.848594</td>\n",
       "      <td>0.864586</td>\n",
       "      <td>0.897890</td>\n",
       "      <td>0.845187</td>\n",
       "      <td>0.853265</td>\n",
       "      <td>0.837108</td>\n",
       "      <td>0.827930</td>\n",
       "      <td>0.848704</td>\n",
       "      <td>0.862003</td>\n",
       "      <td>0.844527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1054 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           h_0       h_1       h_2       h_3       h_4       h_5       h_6  \\\n",
       "0     0.227416  0.226757  0.223597  0.224572  0.225356  0.222745  0.226784   \n",
       "1     0.226757  0.223597  0.224572  0.225356  0.222745  0.226784  0.232060   \n",
       "2     0.223597  0.224572  0.225356  0.222745  0.226784  0.232060  0.242323   \n",
       "3     0.224572  0.225356  0.222745  0.226784  0.232060  0.242323  0.242763   \n",
       "4     0.225356  0.222745  0.226784  0.232060  0.242323  0.242763  0.252298   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1049  0.746429  0.743571  0.759619  0.778249  0.778249  0.755057  0.764895   \n",
       "1050  0.743571  0.759619  0.778249  0.778249  0.755057  0.764895  0.751100   \n",
       "1051  0.759619  0.778249  0.778249  0.755057  0.764895  0.751100  0.763246   \n",
       "1052  0.778249  0.778249  0.755057  0.764895  0.751100  0.763246  0.777699   \n",
       "1053  0.778249  0.755057  0.764895  0.751100  0.763246  0.777699  0.785174   \n",
       "\n",
       "           h_7       h_8       h_9  ...      h_51      h_52      h_53  \\\n",
       "0     0.232060  0.242323  0.242763  ...  0.252422  0.254455  0.257986   \n",
       "1     0.242323  0.242763  0.252298  ...  0.254455  0.257986  0.261572   \n",
       "2     0.242763  0.252298  0.254125  ...  0.257986  0.261572  0.261256   \n",
       "3     0.252298  0.254125  0.255348  ...  0.261572  0.261256  0.257863   \n",
       "4     0.254125  0.255348  0.257148  ...  0.261256  0.257863  0.262190   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "1049  0.751100  0.763246  0.777699  ...  0.867829  0.856013  0.848979   \n",
       "1050  0.763246  0.777699  0.785174  ...  0.856013  0.848979  0.856837   \n",
       "1051  0.777699  0.785174  0.804024  ...  0.848979  0.856837  0.848594   \n",
       "1052  0.785174  0.804024  0.807816  ...  0.856837  0.848594  0.864586   \n",
       "1053  0.804024  0.807816  0.795890  ...  0.848594  0.864586  0.897890   \n",
       "\n",
       "          h_54      h_55      h_56      h_57      h_58      h_59    target  \n",
       "0     0.261572  0.261256  0.257863  0.262190  0.262603  0.262026  0.262767  \n",
       "1     0.261256  0.257863  0.262190  0.262603  0.262026  0.262767  0.261325  \n",
       "2     0.257863  0.262190  0.262603  0.262026  0.262767  0.261325  0.263358  \n",
       "3     0.262190  0.262603  0.262026  0.262767  0.261325  0.263358  0.262754  \n",
       "4     0.262603  0.262026  0.262767  0.261325  0.263358  0.262754  0.262987  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "1049  0.856837  0.848594  0.864586  0.897890  0.845187  0.853265  0.837108  \n",
       "1050  0.848594  0.864586  0.897890  0.845187  0.853265  0.837108  0.827930  \n",
       "1051  0.864586  0.897890  0.845187  0.853265  0.837108  0.827930  0.848704  \n",
       "1052  0.897890  0.845187  0.853265  0.837108  0.827930  0.848704  0.862003  \n",
       "1053  0.845187  0.853265  0.837108  0.827930  0.848704  0.862003  0.844527  \n",
       "\n",
       "[1054 rows x 61 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.DataFrame(X_test[...,0], columns=[f'h_{i}' for i in range(past_history)])\n",
    "pd.concat((test_df,pd.DataFrame(y_test, columns=['target'])),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(61,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.7782491 , 0.75505729, 0.76489454, 0.75110039, 0.76324581,\n",
       "       0.7776995 , 0.78517363, 0.80402388, 0.80781584, 0.79589021,\n",
       "       0.80133097, 0.79929759, 0.81567471, 0.82501736, 0.80798078,\n",
       "       0.82957879, 0.84078995, 0.85348506, 0.84656044, 0.84029536,\n",
       "       0.83287624, 0.86139878, 0.86447643, 0.89283411, 0.88733843,\n",
       "       0.8790949 , 0.91272855, 0.91096989, 0.90844193, 0.90580394,\n",
       "       0.90607874, 0.92982014, 0.92569838, 0.94553781, 0.95152809,\n",
       "       0.95064876, 0.95900223, 0.9568039 , 0.9423503 , 0.92064236,\n",
       "       0.91877376, 0.92042248, 0.9341617 , 0.89893433, 0.88662404,\n",
       "       0.87304969, 0.86376197, 0.86782881, 0.85601303, 0.84897856,\n",
       "       0.85683743, 0.8485939 , 0.86458629, 0.89789013, 0.84518652,\n",
       "       0.85326519, 0.83710785, 0.82793007, 0.84870376, 0.86200331,\n",
       "       0.84452706])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dataset[-61:].shape)\n",
    "dataset[-61:]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Crea el modelo LSTM</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"series_LSTM_model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_3 (InputLayer)        [(None, 60, 1)]           0         \n",
      "                                                                 \n",
      " Dropout_01 (Dropout)        (None, 60, 1)             0         \n",
      "                                                                 \n",
      " LSTM_layer (LSTM)           (None, 50)                10400     \n",
      "                                                                 \n",
      " Dropout_02 (Dropout)        (None, 50)                0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 10,451\n",
      "Trainable params: 10,451\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# shapes\n",
    "input_shape = (X_train.shape[1], X_train.shape[2])\n",
    "units = 50\n",
    "\n",
    "# layers\n",
    "inputs = Input(input_shape)\n",
    "x = Dropout(0.2, name= 'Dropout_01')(inputs)\n",
    "x = LSTM(units=units, name='LSTM_layer')(x)\n",
    "#x = LSTM(units=units, return_sequences=True,name='LSTM_layer')(inputs)\n",
    "#x = Dropout(0.4)\n",
    "#x = LSTM(units=units//2, name='LSTM_layer_2')(x)\n",
    "#x = Dropout(0.4)\n",
    "x = Dropout(0.2, name= 'Dropout_02')(x)\n",
    "outputs = Dense(1)(x)\n",
    "\n",
    "# model\n",
    "model_01 = Model(inputs=inputs, outputs=outputs, name='series_LSTM_model')\n",
    "model_01.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.makedirs(f'../Checkpoints/{future_target}_dia', exist_ok=True)\n",
    "\n",
    "checkpoint_filepath = f'../Checkpoints/{future_target}_dia'\n",
    "checkpoint_callback = ModelCheckpoint(\n",
    " filepath=checkpoint_filepath,\n",
    " save_weights_only=False,\n",
    " monitor=\"val_loss\",\n",
    " mode=\"min\",\n",
    " save_best_only=True)\n",
    "\n",
    "early_stopping_callback = EarlyStopping(\n",
    " monitor=\"val_loss\",\n",
    " min_delta=0.005,\n",
    " patience=20,\n",
    " mode=\"min\",\n",
    " restore_best_weights=False\n",
    ")\n",
    "\n",
    "rlrop_callback = ReduceLROnPlateau(monitor='val_loss', factor=0.2, mode='min', patience=5, min_lr=0.0001)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Compila"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se usará el optimizador Adam y la función de pérdida MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_01.compile(loss='mean_squared_error', optimizer=Adam())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entrena el modelo\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-22 03:31:42.228971: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-09-22 03:31:42.402668: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-09-22 03:31:42.556231: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "265/265 [==============================] - ETA: 0s - loss: 8.0324e-05"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-22 03:31:48.576014: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-09-22 03:31:48.648584: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "WARNING:absl:Found untraced functions such as lstm_cell_4_layer_call_fn, lstm_cell_4_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ../Checkpoints/1_dia/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ../Checkpoints/1_dia/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "265/265 [==============================] - 13s 43ms/step - loss: 8.0324e-05 - val_loss: 3.8038e-05 - lr: 0.0010\n",
      "Epoch 2/30\n",
      "265/265 [==============================] - 6s 22ms/step - loss: 1.1363e-05 - val_loss: 1.6657e-04 - lr: 0.0010\n",
      "Epoch 3/30\n",
      "265/265 [==============================] - 6s 22ms/step - loss: 4.9584e-06 - val_loss: 9.5919e-05 - lr: 0.0010\n",
      "Epoch 4/30\n",
      "265/265 [==============================] - 6s 22ms/step - loss: 4.0872e-06 - val_loss: 1.1824e-04 - lr: 0.0010\n",
      "Epoch 5/30\n",
      "265/265 [==============================] - 6s 22ms/step - loss: 4.3980e-06 - val_loss: 2.3721e-04 - lr: 0.0010\n",
      "Epoch 6/30\n",
      "265/265 [==============================] - 6s 21ms/step - loss: 3.6751e-06 - val_loss: 8.0710e-05 - lr: 0.0010\n",
      "Epoch 7/30\n",
      "265/265 [==============================] - 6s 22ms/step - loss: 2.1899e-06 - val_loss: 1.5520e-04 - lr: 2.0000e-04\n",
      "Epoch 8/30\n",
      "265/265 [==============================] - 6s 24ms/step - loss: 2.0555e-06 - val_loss: 1.9853e-04 - lr: 2.0000e-04\n",
      "Epoch 9/30\n",
      "265/265 [==============================] - 6s 23ms/step - loss: 1.9546e-06 - val_loss: 2.2560e-04 - lr: 2.0000e-04\n",
      "Epoch 10/30\n",
      "265/265 [==============================] - 6s 22ms/step - loss: 1.9435e-06 - val_loss: 2.3230e-04 - lr: 2.0000e-04\n",
      "Epoch 11/30\n",
      "265/265 [==============================] - 6s 22ms/step - loss: 1.9758e-06 - val_loss: 1.7928e-04 - lr: 2.0000e-04\n",
      "Epoch 12/30\n",
      "265/265 [==============================] - 6s 23ms/step - loss: 1.6780e-06 - val_loss: 1.4921e-04 - lr: 1.0000e-04\n",
      "Epoch 13/30\n",
      "265/265 [==============================] - 6s 21ms/step - loss: 1.8259e-06 - val_loss: 2.3276e-04 - lr: 1.0000e-04\n",
      "Epoch 14/30\n",
      "265/265 [==============================] - 6s 22ms/step - loss: 1.7323e-06 - val_loss: 1.4777e-04 - lr: 1.0000e-04\n",
      "Epoch 15/30\n",
      "265/265 [==============================] - 6s 21ms/step - loss: 1.8317e-06 - val_loss: 1.9188e-04 - lr: 1.0000e-04\n",
      "Epoch 16/30\n",
      "265/265 [==============================] - 6s 21ms/step - loss: 1.7384e-06 - val_loss: 2.0532e-04 - lr: 1.0000e-04\n",
      "Epoch 17/30\n",
      "265/265 [==============================] - 6s 21ms/step - loss: 1.7815e-06 - val_loss: 1.1286e-04 - lr: 1.0000e-04\n",
      "Epoch 18/30\n",
      "265/265 [==============================] - 6s 21ms/step - loss: 1.7233e-06 - val_loss: 1.3547e-04 - lr: 1.0000e-04\n",
      "Epoch 19/30\n",
      "265/265 [==============================] - 6s 21ms/step - loss: 1.7968e-06 - val_loss: 1.4855e-04 - lr: 1.0000e-04\n",
      "Epoch 20/30\n",
      "265/265 [==============================] - 6s 22ms/step - loss: 1.7501e-06 - val_loss: 1.8890e-04 - lr: 1.0000e-04\n",
      "Epoch 21/30\n",
      "265/265 [==============================] - 6s 22ms/step - loss: 1.7173e-06 - val_loss: 2.1192e-04 - lr: 1.0000e-04\n"
     ]
    }
   ],
   "source": [
    "epochs=30\n",
    "\n",
    "history = model_01.fit(\n",
    "    X_train, y_train,\n",
    "    epochs=epochs,\n",
    "    batch_size=32,\n",
    "    validation_split=0.1,\n",
    "    verbose=1,\n",
    "    shuffle=True,\n",
    "    callbacks=[early_stopping_callback, checkpoint_callback, rlrop_callback]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99d1bec5315f41f3a8b26b32d823f792",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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3ZPNLbFPbWx/YZOYFwQY3LQ3+i+9di81F5davzGObHfpPhdG/g1an+Ldsoe7QNvjxXXPxUnnhAubX9v5TTRjx1b+Rqxw+uc10nAfTPOzse/zbB2Pn9/DWVNN/q01v0wfFW0M/b//GTLC4e5l57EyA034Lw2/0be3PyXC74b3psOGjipGx5kOLTv4uVfArLTT/Xxb9o2LySMznP/ImGHY9RCb4t3x+oADSdAogASQkTmi32//tiQPRnJtg1esQ3wF+9a3nfgF96wrY9CkMuhomPeOZfQaaHd+Z4FH5K7vNboY3Pf325tEBOpRYlgmKq9+CdbOhJLf6uQ7DYMDl0Odi7/2SWloA7//c/PpvCzMzmw+7zjvHaqz96+D1SyAvExI6mlnTW3Xz3P53r4CvHoBtX5vH4VEw4kbTp6I5/XJdWgAvj4N9P2pkLG8rzoXlL8H36VBwwKyLa2vOmSHTA6cppR+ExPWalymABJCgP6G/fQq+ecxcCPeZ7O/SBI61s+D9awEbzPjEsxO67VoCL48BuwNu+RHi23pu3/5kWaaj/oJHzD2YUVcGToPRt2u+gOagrMiE49Vvw0//M/0gwJyr3ceZWpFu53muWVRBNrw5FfYsh/BIuPRl0x8lkBzeCf+5CA5tNb8wT3uv6UMBZ22Arx40nYXBNF8aeq1pbhWX3PQy+0POHvj32Wa+I42M5XmFh2DJC7DkeTMaI5gO5affZr5jA7WJng8F/fWaDyiABJCgPqHdLniil/mDYXeYJgZpZ/i7VP53JMMMuVuSYy4Izv2z54/x0lgzEdRpt8J593t+/75kWeYX3AWPwK5FZl1YhBl15fTbgnrUlaCWtx/WvGfCyP411eujW5r+OwMuh7YDT76Z1KHtpnbh0FZTu3jFO9BxuEeK7nH5B+CNSyFzlenYe/nrZpLHxjq0Hb5+2AyNjGVqfPpfbjqYB0Ozpd3L4ZUJZiJMjYzlGflZZnjoZS9Bab5Z17Kbacba79JmM0mgLwT19ZqPKIAEEF+e0MXFcMklZvmDDyAy0quHg+0L4dULqh874uDaT0N7/gW3C2ZeYC6k2w+Fn3/unS/4jZ/C21eAMx5uW9s82+taFmz9nwkelePM2x0w+BoTPBI6+Ld84jn71pgg8uO7UJBVvb51LxNE+k9pXP+IvavgjcvMvhI6mh8/Ar0/VEkevHOVCdthEXDR8+YCsCHy9pn/Jz+8Wj05ZK8LTT+XNj29VmS/0MhYnpGzG777hzlnyovNuuR+cMbvzLmj2qVjKIA0nQJIAAnqUbA+vt20Je13GeRmws5vITbZtN8N1eYy3zxqmkY4Ys2Qu0ldvHMctxueHQHZm8yEZ6fd4p3jeINlwZZ5po/HnuVmnd0JQ2bA6bd6r6Ou+J+rHLbNh1VvwsZPzC/dQKOG9P3pf/DuNebX3OR+pklTc2mGWF4Cs280w51ig/GPwPBf1L994SH47ilY8i8zpwlA13PhnD9B+8G+KLF/fPWg+S61O8zIWB1H+LtEzcfBrWZEq9VvmyHbwfwYdsbvofvYwJgcM0ApgDSdAkgACdoA4iqHJ3qaTmxXfWC+4F6ZYIZ/TOpiJtvzxcRfgSRjGbw81rR7n/y86TTtTStfN8PyxrWFW1Y3jza8lmUuwH582zwOj4KhPzcj9oTAOPNSQ9ERM7nh6rdg1/fV6483pO+qt+Cjm00tQNqZMPV1iGxmFwpuN3x+Fyz9l3l8xp1w9h9rXxiW5MHi52DRP6s79acON7OXdz7d92X2tVojY7WCG74KjiZm3pS1wczhsfYDMwcOQOfRZo6ktDMVPBpAAaTpFEACSNAGkG1fm9m9o5Lgjs2mmVFuppnzImeXmX14+seBPwSkpxTnmiF3D+8wE+Nd8pL3v/DLS+DpAWaEnQufMX0mAt2SF+CzO03n8uE3mpqb2Db+LpX4W71D+naEAVNNP4cNH5kJPcHUuk56FsIdfiluk1mW+YV//t/M4yEz4PwnzOzly182F5KF2ea55H6mH1m3MaF1EVlrZKw+cN1cjYxVl70rzUAwlQMSgDlXRt8RuH2iApQCSNMpgASQoA0g/70FVsyEwdPhwn9Ur8/eYkJI0SHTyfKKd5rvRUJjzL7RXDwldDRNr6ISfXPc7/4B8/4MrbrDr5cE9nDIu5ebCwp3GYx72ExyJVLT8Yb0rTTqt/Cz+wP7XG+o5S+bpqxYZgCPg1ur52RI6grn3AO9LwqO93oycnbDv842fX26jzeTr4Z634XiXDO88/61Zujpn76seMIGvSaazuXtBvqzhM2WAkjTKYAEkKAMIK5yeKybCRlXz4GuZ9d+fvdyeHUilBVCvylw0QvB/Qe0stOkLQxmfOrbmXyLc+HJPuZC7fK3oOcE3x27MQoPwfOjIXe3aV5z2auh9WuuNN4xQ/q6YdxDwRdc138IH1wPrlLzOL49nHmXGRrVHu7fsgWCmiNjnXaL6fMWCiwLjuwyQWPfWlMTtH9t7RpCMHMk9bvUzJEUbAMS+JgCSNPpG0u8a/sCEz6iW5k2pkfrMBSmvAZvXQ5r3jVNbMb+zffl9IXDO+Hj28zyGb/3bfgA0/596M9NR9XvngrMAOJ2w6xfmPCR1NU0F1P4kBOJiDLNGfteYoYSLckLzkkoe1c0ZV34uJkjZeh1EOHtIQybkQ5DYVI6zLoevnsaWvc0gxUEk7JiOLChImisqQ4dJTl1bx/fHpL7mhEnB17pvcFORBpJAUS8a91sc9/7wvp/oet2nrnQnHOjGYM8LgVG/cZ3ZfQFV7m5sC7JhQ6nms6k/jDiV7D4WTOU7a7FgTdizLdPwE/zzERxU15rfp2Gxf9i2wR3X6G00eYmdet/GRzYCAsfM81/k7o23/4N+VkmZNQMGtmbqyftrCkswgSulL6Q0s+EjpR+zWuWewkpCiAhKibG1Np6VXkpbPivWe5z0fG3HXiFmaTwy3vhiz9BTBvToTRYLHzcTAboiINL/u2/5hJxKWYuhR9eM78QBlIA2f5NdUfbCY+ZP6QiIo119j0mhGz8GN6+En4xP7AnKXWVw8GfKkLGjyZo7F9r/ibWJSrJfD8m9zMhI6UvtOoRGn0oJWgogIj3bF8AxUdMmOh02om3P+0W84W7+Fn48NcQ0xJO+ZnXi+l1u5aYeSwAzn/c//OejPot/PAf02Y+a2NgtAXO2wfvX2fa7g+c1jxG6RKRwBQWBhf/ywx1vm8NvHl5YI2Mlf0TbP2quq9G1obqCQBrsZmmhMl9K2o2+pvl+HZqmirNngKIeE9V86tJDRuNxGaDMX8z1c5r34d3roEZ/4X2Q7xbTm8qzjHtkS2X6WQfCLU6rbqZCdw2fmzmDpic7t/yuMpN+CjIMkNoTnjMv+URkebPEQNXvG1GxspaZ5rATn3Df4OcFB4y826sfrt6UtWaImIguU+NJlT9ILn38SfaFGnGFEBCVHExXF3xI/N//gORnu7HWF4CGyrGGj9R86uawsJg8nNQeNDMgvzGZWaiwlaneLiAPvLJHWZ0ksSOcH4AXVifdqsJID++Y4bv9OeM4vP/Bju/NZPKTXkVHNH+K4uIBI+EDnD5mzDzfFPj+7/74bz7fXf88hLY8oUJHZvnVs82brOboZTbD6loQtUPWqQF9wiQIkdRAAlRLhe8/75ZnjnTCwfYOt+MyhGbAh0bOdpTuAOm/gdmXgCZq+D1i+C6ec1v9uvV75iRvWx2uPhFiEzwd4mqpQ6DjqNg1yLT5G3Mg/4px+a5puM5wIX/NLUzIiKekjqsxshYT1WMjHWF945nWWY44NVvwbpZUHS4+rmU/jDgCjMUbjAPlCDSAIrb4h2Vza/6TD65X3WccTDtffOr0JFd8PqlpjlTc3FoO3zyO7N85l2BOQrL6bea++UzoeiI749/eKdpFgFw6i+h78W+L4OIBL/+l5lJ9wD++1vTL8/TDu+EBY/CP4fASz+D5S+Z8BHX1vS7+9UiM/HsyF8rfIigACLeUFYMGz8xy41pfnW02NZw9SyIaQ3718Db00yVdqCrHHK3NA9SR1T/4Qs0p5wHrXuZcq54xbfHLi+B92aYQQraD/FfDYyIhIaz/wQ9LzCTOL59pflhq6mKc8yIgq9MgKf7w/wH4dBWiIiG/lPh6tlw2zoY81fTv0NEqiiAiOdt/Z+5qI1vb+a8aIqkLqYmxBELOxaaC3t3HWOgB5JvHoHdS8EZb0ZiCdQZisPCzMhjAIufM8HRV774E+z9ASIT4bKZGj5SRLwrLAwuesF07i7MhreugJL8xu/HVQ6bv4D3fw6PdYePfgM7vwNskHYmTH4e7thivvu7ntOwAVhEQpACiHje2lnmvvdkz3SqazcQLn/DTLS0fg58dpcPJjE5STu/h28eNcsXPAktOvm3PCfS9xITFPP3mw7pvrD2A1j6L7N88b8Ce3x+EQkezli44i0zNPz+tRU/aLlP/DrLgswf4fM/whO94M3LzPdYebGZf+Nn95majukfmf4lzlivvxWR5k4BRDyrrAg2fWaWPdmmv8tZcPELgA2W/dvMchtILAu2L4RZN5i5LCo7Gga6cAeM+LVZXvSPhv0xborsLfDRb83y6bdD97HePZ6ISE2JqWZkLLsTNn0CXz1Q/7a5mWbC1udOgxdGw+J0M1x4dCsYfiP84mu4aQmcfhsktPfZWxAJBgHaNkSarS3zoKwAElI9P39H30sg/wB8fhd89SDEJsPgazx7jMYqyYcf34alL8KBDWZdi84w4VG/FqtRhkw3zcYO/mT+IPea6J3jlBbCu9dAaT50Ot3MViwi4mupw2DSM+YHo2+fNCNjDbjcPFdaYPowrn4Ltn1tflACE1h6jDc/Lp1yLtgj/FZ8kWCgABKioqMhP7962WPWVTS/6jPZOzO1jrgR8veZPxr/vcV0UO8x3vPHOZHsLbD03+aPVEmuWVfZ8XD07wJnxt2GcMbBsOth4ePw7VOmo6Y3/u0+vQOy1pvmD5e+FLh9Y0Qk+PWfYmYg//YJ04+jrAgylsKGj8yPJJU6jjThpPdkiEr0V2lFgo6uAEKUzQYxnp5gtbTAzOsA0MeLQ6qee6+ZLX3VG2YkpWs+8s0wt24XbP7c9F/Y9nX1+qSucOoN5pex5voHaviNsOgZM0Pvru+h0yjP7v+H/5h/L1sYXPpy85vTRUSCzzl/huzNZlLWj2+tXt8izYSO/lPMQCgi4nEKIOI5W76AskLTBKndIO8dx2aDiU9DwQFzzDenwM/nQpue3jleQbYZanH5y5CTUVkI6D7OBI8uZzf/GWxj28DAK81wvN8+5dkAsm+Nqf0AOOdPkDbac/sWETlZlSNj/eciE0T6XGR+SEo91Tu1wCJSRQHECwoLC+nVqxeXXXYZjz0WYJ2lK5SUwC9/aZZfeAGcTg/stHL0qz4Xef/L2x5hhm99bRLsXgavXwzXfQEJHTx3jN0rTIf3tbPAVTH/SFQSDL4ahv7cBK1gMuo3sGImbJkL+9dDcu+m77M4x/T7KC+GbmPgtNuavk8REU9xxpq/HQocIj7VzH+2DUx/+9vfGD48AGe+rqG8HF591dzKyz2ww5J8UxsBTZt8sDEcMXDlu9CqO+TugdcvgcJDTdtnWTGsehP+dTa8eI7p4+EqMTU6k5+D29fDeQ8EX/gAaNm1ugP6on82fX+WBR/eDIe2mUEJLnqh+dcUiUjwUfgQ8TldDXjYli1b2LhxIxMmTPB3UXxr8+fmV+6kLpDS33fHjU6Cq2ZBXFs4sNFMLlVW1Pj9HNkF8+41Y7zP+ZWZJM/ugP6Xw/VfmeEWB14JEVEefwsB5fRbzf2adyFnd9P2teR506EzLAIue9X8W4mIiEjIC6kA8s033zBx4kTatWuHzWZjzpw5x2zz7LPPkpaWRmRkJEOGDGHhwoWNOsYdd9zBQw895KESNyPrZpv7Phf7/tekxFS46gNwJkDGYjNDrasB1TpuN2z9yoSWpwfAd09B0SGI7wDn/gVuW2/mHung4eGEA1n7IdB5NLjLzezoJytjmZntHGDs30LrMxQREZHjCqkAUlBQwIABA3jmmWfqfP6dd97h1ltv5Z577mHlypWMHj2a8ePHs2vXrqpthgwZQt++fY+57d27lw8//JDu3bvTvXt3X72lwFCca+b/AN81vzpach8zw63dCZs+hU9uq3+29OIcWPw8pA8znQ83fWrGeu9yFkx9A25ZbYbSjW3t07cQME67xdyvmAlFhxv/+sJDZnQyd7kZuvLUX3iwcCIiItLchVQn9PHjxzN+fP1zRjzxxBNcd911XH/99QA89dRTzJ07l+eee66qVmPFihX1vn7x4sW8/fbbvPfee+Tn51NWVkZ8fDx/+ctf6ty+pKSEkpKSqse5ubkn87b8b9Nnpp9Ey24mCPhL59PMEK/vXm1GrYpNgXNqTHa3f73pVL76HTNZIoAjzjStGnY9tA6x4FifU34GbfpA1jpY9hKccUfDX+t2w6xfQO5uMzzxhf9U+2oRERGpJaRqQI6ntLSUFStWMGbMmFrrx4wZw6JFixq0j4ceeoiMjAx27NjBY489xg033FBv+KjcPiEhoeqWmprapPfgN5XNr/r6ofnV0XpdAOc/YZa/eQSWvADr5sAr58NzI81QumUFZubbCY/B7zbAhEcUPmqy2aprQZY8bzrmN9S3j8NP8yA8Eqa8BpHx3imjiIiINFsKIBWys7NxuVwkJyfXWp+cnMy+ffu8csw//OEP5OTkVN0yMjJO/KJAU3QEtv7PLPur+dXRhl4LZ/3BLH92J7w3HXZ+CzY79LoQpn8Mv15s5vBoTjOW+1Lfi83IVQUHzEhgDbH9G5j/f2b5/Ccgpa/3yiciIiLNVkg1wWoI21G/4FuWdcy6hpgxY8YJt3E6nTg9MgFH40VHQ1ZW9fJJ2/QpuEqhdS9o08sjZfOIM+8ys6UvfwliWsOQGTDkWkho7++SNQ/2CBh5E3x+Nyz6Bwy+BsLs9W+ftw/ev870pRl0FQya5ruyioiISLOiAFKhVatW2O32Y2o7srKyjqkVCQY2G7T2RB/rqtGvAqT2o5LNBuc/bvp2tOwK4f4Jes3aoKvh64fNPB4bP4bek+rezlVuRh4ryILkvqZpm4iIiEg91ASrgsPhYMiQIcybN6/W+nnz5jFq1Cg/lSrAFR4yw9hC4AUQMCEkubfCx8lyxppmagDfPlX/qGLzH4Sd35kO/VNeC/65UkRERKRJQiqA5Ofns2rVKlatWgXA9u3bWbVqVdUwu7fffjsvvvgiL7/8Mhs2bOC2225j165d3HjjjX4stXeUlMBNN5lbjYG4GmfjJ2ao1eS+6sQdrE79pelQvvcH2PHtsc9v+hy+fdIsT3rG1DaJiIiIHEdINcFavnw5Z599dtXj22+/HYDp06czc+ZMpk6dysGDB3nggQfIzMykb9++fPrpp3Tq1Mmr5UpPTyc9PR2Xy+XV49RUXg7PPmuWH3kETqorSlXzq8meKpYEmtjWMHCa6Uvz3dOQNrr6ucM7YfYvzfLwG3UeiIiISIPYLKu+dhXia7m5uSQkJJCTk0N8vHeHLy0ogNhYs5yfDzExjd3BQXisG1gu+M0P+uU7mB3aBv8cYjqY3/idGd2qvAReHgt7V0L7oXDtZxDu8HdJRUREvM6X12vBKqSaYIkHbfyvCR8p/RU+gl1Sl+oO6Iv+Ye7n3mPCR1QLuGymwoeIiIg0mAKInJxAHf1KvGPUb839mvdNU6xl/zaPL/43JDbTCTRFRETELxRApPHyD5hJ50ABJFS0HwxpZ5har3l/MetG3wHdzvNvuURERKTZUQAJUWUud9Xy4YLSxr14w0emP0C7QZCU5uGSScA67dbq5c6j4ew/+q0oIiIi0nwpgASA9PR0evfuzbBhw3x2zAh79T/9su2HGvdiNb8KTV3Pge7jzaz3l7x0/JnRRUREROqhUbACiC9HVXC74bYXN/HByj1cO6YNf72ob8NemLcfHu8BWHDrGkjs6NVyioiIiAQSjYLVdKoBCVFhYTB+VDzhCUUs3n6w4S/c8BFgmaFXFT5EREREpJEUQELY8LSW2GywJSufA3kNnA597Sxz3/di7xVMRERERIKWAkiIKi2F/7vPAUsGYLlsLN7WgFqQ3L2w63uzXDkvhIiIiIhIIyiAhKiyMnjsMdgxvwOWK4xFWxsQQNZXNL9KHQ4JHbxeRhEREREJPgogAtCwGpB1Fc2v+qj5lYiIiIicHAUQIcwG27ML2JdTXP9GObshYwlgU/MrERERETlpCiABwB/zgNTUu50ZQu77bdn1b7RujrnvNAri23q/UCIiIiISlBRAAsBNN93E+vXrWbZsmV+OP7xLEgCLfjpOMyxNPigiIiIiHqAAIpza2QSQ7+vrB3J4J+xZDrYw6HWhD0smIiIiIsFGAUQY0imJ8DAbuw8XkXGo8NgN1s8x951Og7hkn5ZNRERERIKLAkiIioqCtWvNrVViOP07JAD11IKo+ZWIiIiIeIgCSIgKC4M+fcwtLAxGdm0JwPdHzwdyaDvsXanmVyIiIiLiEQogAsCorq0AE0Asy6p+orL2I+0MiG3th5KJiIiISDBRAAlRpaVw333mVloKQzq1wGEPY19uMTsO1ugHouZXIiIiIuJBCiAhqqwM7r/f3MrKIDLCzsCOiUCNZlgHt8K+H8FmV/MrEREREfEIBZAA4O+JCCuN7GL6gSzaWjEh4bpZ5r7LWRCd5J9CiYiIiEhQUQAJAP6eiLDSqIqO6Iu3HTL9QCpnP1fzKxERERHxEAUQqTKwYyLO8DCy80vYuWkV7F8LYRHQ6wJ/F01EREREgoQCiFRxhtsZ2rkFAEeWvWNWdj0bolr4sVQiIiIiEkwUQKSWyn4grXd9Zlb0udiPpRERERGRYKMAIrWM7NqKbrbdtC/bgWV3QI/x/i6SiIiIiASRcH8XQPwjMhKWLq1ertS/QwIXOZYAkN/+DOKiEn1fOBEREREJWgogIcpuh7pG/Y0IszE5YimUw4q4sznL5yUTERERkWCmJlhS2/51tCvPoMSK4P38fv4ujYiIiIgEGdWAhKjSUnj6abN8yy3gcFQ8sW42AF+7B7BgZzEut4U9zOafQoqIiIhI0FENSADwx0zoZWVw553mVlZWsdKyqmY//9J+GnnF5azbm+OzMomIiIhI8FMACQCBMhM6+36EQ9sgPIrCTj8D4PutB/1bJhEREREJKgogUq2i+RXdxzDolA4AfL9NAUREREREPEd9QMSwLFhrml/R5yJGtWgFwNLthyhzuYmwK6uKiIiISNPpqlKMvSvhyE6IiIZuY+iZEkeL6AgKS138uFv9QERERETEMxRAxKjofE73ceCIISzMxvC0lgAsVjMsEREREfEQBRCpGP1qjlnuc1HV6pFdTQBRR3QRERER8RT1AQlRkZEwf37F8sEVkJMBjljodl7VNpUBZNmOQ5SUu3CG2/1RVBEREREJIqoBCVF2O5x1lrnZN1Q0v+oxHiKiqrbp1iaWVrEOSsrdrNp1xB/FFBEREZEgowAS6txuWD/HLNdofgVgs9kY0aWiGZb6gYiIiIiIByiAhKiyMkhPh/S/7aLs8H5wxkPXc4/ZTv1ARERERMST1AckRJWWws03A3Rmxh8cRPSYABGRx2w3sqIGZOWuIxSXuYiMUD8QERERETl5qgER46jmV5XSWsWQEh9JqcvNip2HfVwoEREREQk2CiABID09nd69ezNs2DD/FMAZD13PqfMpm82mZlgiIiIi4jEKIAHgpptuYv369Sxbtsw/BegxHsId9T49Uh3RRURERMRDFEBCldtVvdzrwuNuWlkDsjrjCAUl5d4slYiIiIgEOQWQUFUzgKSNPu6mqUnRdGgRRbnbYtmOQ14umIiIiIgEMwWQUFWzyZU94oSbqxmWiIiIiHiCAkiIcjrh44/Nzek88faVzbAWqyO6iIiIiDSB5gEJUeHhcP75Dd++MoCs2ZNDbnEZ8ZEnrjURERERETmaakCkQdomRJHWKga3BUu3qR+IiIiIiJwcBZAQVVYGM2eaW1lZw14zQv1ARERERKSJFEBCVGkpXHutuZWWNuw1mpBQRERERJpKAUQabESXJAA27MvlcEEDU4uIiIiISA0KINJgbeIi6dYmFsuCJdtVCyIiIiIijacAIo2iZlgiIiIi0hQKINIompBQRERERJpCAUQaZXhFANm8P58DeSV+Lo2IiIiINDeaiFAaJSnGQa+28WzIzGXxtoNMHNDO30USERHxO5fLRVlDx7WXgBYREYHdbvd3MYKaAkgASE9PJz09HZfL5bNjOp3w7rvVy40xsktLNmTm8r0CiIiIhDjLsti3bx9Hjhzxd1HEgxITE0lJScFms/m7KEHJZlmW5e9CiJGbm0tCQgI5OTnEx8f7uzj1mrd+Pze8tpwurWL46o6z/F0cERERv8nMzOTIkSO0adOG6OhoXbA2c5ZlUVhYSFZWFomJibRt2/aYbZrL9VogUw2INNqpaUmE2WBbdgH7copJSYj0d5FERER8zuVyVYWPli1b+rs44iFRUVEAZGVl0aZNGzXH8gJ1Qg9R5eXw3nvmVl7euNcmREXQp10CAN9vy/ZC6URERAJfZZ+P6OhoP5dEPK3y31T9erxDASRElZTAlCnmVnISg1mN0nwgIiIiAGp2FYT0b+pdCiByUkZ01XwgIiIiItJ4CiByUoZ1TsIeZiPjUBEZhwr9XRwRERHxk86dO/PUU0/5uxjSjCiAyEmJdYbTv0NlPxDVgoiIiDQnZ511FrfeeqtH9rVs2TJ+8YtfeGRfEhoUQOSkVfYDWax+ICIiIkHFsizKGzhKTevWrdURXxpFAURO2sgurQBTA6LpZERERJqHGTNmsGDBAp5++mlsNhs2m42ZM2dis9mYO3cuQ4cOxel0snDhQrZu3cqkSZNITk4mNjaWYcOG8eWXX9ba39FNsGw2Gy+++CIXXXQR0dHRdOvWjY8++sjH71ICmQKInLQhnVoQYbeRmVPMzoPqByIiImJZFoWl5X65NfTHwKeffpqRI0dyww03kJmZSWZmJqmpqQDceeedPPTQQ2zYsIH+/fuTn5/PhAkT+PLLL1m5ciVjx45l4sSJ7Nq167jHuP/++5kyZQo//vgjEyZMYNq0aRw6dKjJn68EB01EGKIcDnjllerlkxHlsDMotQVLdxzi+20H6dwqxnMFFBERaYaKylz0/stcvxx7/QNjiXac+NIuISEBh8NBdHQ0KSkpAGzcuBGABx54gPPOO69q25YtWzJgwICqxw8++CCzZ8/mo48+4uabb673GDNmzOCKK64A4P/+7//45z//ydKlSxk3btxJvTcJLqoBCVERETBjhrlFRJz8fkZW9ANZpH4gIiIizd7QoUNrPS4oKODOO++kd+/eJCYmEhsby8aNG09YA9K/f/+q5ZiYGOLi4sjKyvJKmaX5UQ2INMnIri15+n9b+H6r6QeiiXtERCSURUXYWf/AWL8du6liYmq3Zvj973/P3LlzeeyxxzjllFOIiori0ksvpbS09Lj7iTjq102bzYbb7W5y+SQ4KICEqPJymFtRQzx2LISf5JkwqGMizvAwsvNL2Hogn1PaxHmukCIiIs2MzWZrUDMof3M4HLhcrhNut3DhQmbMmMFFF10EQH5+Pjt27PBy6STYqQlWiCopgQsuMLeSkpPfjzPczpBOLQD4Xs2wREREmoXOnTuzZMkSduzYQXZ2dr21E6eccgqzZs1i1apVrF69miuvvFI1GdJkCiDSZKPUD0RERKRZueOOO7Db7fTu3ZvWrVvX26fjySefpEWLFowaNYqJEycyduxYBg8e7OPSSrCxWZrAIWDk5uaSkJBATk4O8fHxXj1WQQHExprl/HyIacIAVit2HuKS576nRXQEK/50HmFh6gciIiLBr7i4mO3bt5OWlkZkZKS/iyMedLx/W19erwUr1YBIk/XvkEi0w87hwjI27c/zd3FEREREJIApgEiTRdjDGNo5CVA/EBERERE5PgUQ8Qj1AxERERGRhlAAEY8Y2cUEkCXbD+Jyq1uRiIiIiNQt8AeqFq9wOOCZZ6qXm6pPu3jinOHkFZezfm8u/TokNH2nIiIiIhJ0VAMSANLT0+nduzfDhg3z2TEjIuCmm8ztqMlKT0q4PYxT0yr6gWzLbvoORURERCQoKYAEgJtuuon169ezbNkyfxelSUaqH4iIiIiInICaYIUolwsWLjTLo0eD3d70fVYGkGXbD1HmchNhV74VERERkdp0hRiiiovh7LPNrbjYM/vslRJPYnQEBaUu1uzJ8cxORUREJOB07tyZp556quqxzWZjzpw59W6/Y8cObDYbq1atatJxPbUf8S8FEPGYsDAbw9M0H4iIiEioyczMZPz48R7d54wZM5g8eXKtdampqWRmZtK3b1+PHkt8SwFEPKpyOF4FEBERkdCRkpKC0+n0+nHsdjspKSmEh6sXQXOmACIeNeqUVgAs33mIknKXn0sjIiIiR3vhhRdo3749bre71voLL7yQ6dOns3XrViZNmkRycjKxsbEMGzaML7/88rj7PLoJ1tKlSxk0aBCRkZEMHTqUlStX1tre5XJx3XXXkZaWRlRUFD169ODpp5+uev6+++7j1Vdf5cMPP8Rms2Gz2fj666/rbIK1YMECTj31VJxOJ23btuXuu++mvLy86vmzzjqL3/72t9x5550kJSWRkpLCfffd1/gPTjxGAUQ8qlubWFrFOiguc7M6Q/1AREREAs1ll11GdnY28+fPr1p3+PBh5s6dy7Rp08jPz2fChAl8+eWXrFy5krFjxzJx4kR27drVoP0XFBRwwQUX0KNHD1asWMF9993HHXfcUWsbt9tNhw4dePfdd1m/fj1/+ctf+OMf/8i7774LwB133MGUKVMYN24cmZmZZGZmMmrUqGOOtWfPHiZMmMCwYcNYvXo1zz33HC+99BIPPvhgre1effVVYmJiWLJkCY888ggPPPAA8+bNa+xHJx6i+ivxKJvNxvAuLfnkx0y+33qwam4QERGRkGBZUFbon2NHRIPNdsLNkpKSGDduHG+++SbnnnsuAO+99x5JSUmce+652O12BgwYULX9gw8+yOzZs/noo4+4+eabT7j/N954A5fLxcsvv0x0dDR9+vRh9+7d/OpXv6ouakQE999/f9XjtLQ0Fi1axLvvvsuUKVOIjY0lKiqKkpISUlJS6j3Ws88+S2pqKs888ww2m42ePXuyd+9e7rrrLv7yl78QFmZ+a+/fvz/33nsvAN26deOZZ57hf//7H+edd94J3494ngKIeNzIygCyLZtb6Obv4oiIiPhOWSH8Xzv/HPuPe8ER06BNp02bxi9+8QueffZZnE4nb7zxBpdffjl2u52CggLuv/9+Pv74Y/bu3Ut5eTlFRUUNrgHZsGEDAwYMIDo6umrdyJEjj9nu+eef58UXX2Tnzp0UFRVRWlrKwIEDG3SMmscaOXIkthrB67TTTiM/P5/du3fTsWNHwASQmtq2bUtWVlajjiWeowASoiIi4JFHqpc9aVTFfCA/7DxCcZmLyAgPTDIiIiIiHjNx4kTcbjeffPIJw4YNY+HChTzxxBMA/P73v2fu3Lk89thjnHLKKURFRXHppZdSWlraoH1blnXCbd59911uu+02Hn/8cUaOHElcXByPPvooS5YsadT7sCyrVvioefya6yOOutix2WzH9IER31EACVEOB/z+997Zd1qrGJLjnezPLeGHnYerOqaLiIgEvYhoUxPhr2M3UFRUFBdffDFvvPEGP/30E927d2fIkCEALFy4kBkzZnDRRRcBkJ+fz44dOxq87969e/Of//yHoqIioqKiAFi8eHGtbRYuXMioUaP49a9/XbVu69attbZxOBy4XMcf0KZ379588MEHtYLIokWLiIuLo3379g0us/iWOqGLx9lsturheLdpOF4REQkhNptpBuWPWwP6f9Q0bdo0PvnkE15++WWuuuqqqvWnnHIKs2bNYtWqVaxevZorr7yyUbUFV155JWFhYVx33XWsX7+eTz/9lMcee6zWNqeccgrLly9n7ty5bN68mT//+c8sW7as1jadO3fmxx9/ZNOmTWRnZ1NWVnbMsX7961+TkZHBb37zGzZu3MiHH37Ivffey+23317V/0MCj/5lQpTLBcuWmdsJflw4KSO7aj4QERGRQHbOOeeQlJTEpk2buPLKK6vWP/nkk7Ro0YJRo0YxceJExo4dy+DBgxu839jYWP773/+yfv16Bg0axD333MPf//73WtvceOONXHzxxUydOpXhw4dz8ODBWrUhADfccAM9evRg6NChtG7dmu++++6YY7Vv355PP/2UpUuXMmDAAG688Uauu+46/vSnPzXy0xBfslkNaagnPpGbm0tCQgI5OTnEx8d79VgFBRAba5bz8yGmYX3WGizjUCGjH5lPeJiN1feOIcap1n4iIhJciouL2b59O2lpaURGRvq7OOJBx/u39eX1WrBSDYh4RWpSNO0Toyh3WyzfedjfxRERERGRAKEAIl6jZlgiIiIicjQFEPEadUQXERERkaMpgIjXVNaArNl9hNziY0euEBEREZHQowAiXtMuMYrOLaNxW7Bs+yF/F0dEREREAoACiHiV+oGIiEiw04CiwUf/pt6lsVFDVEQE3Htv9bK3jOjSkreWZqgfiIiIBJ2Iij+ghYWFVTN+S3AoLCwEqv+NxbMUQEKUwwH33ef941R2RF+fmcuRwlISox3eP6iIiIgP2O12EhMTycrKAiA6OhpbI2cjl8BiWRaFhYVkZWWRmJiI3W73d5GCkgKIeFWb+EhOaRPLT1n5LN52iHF9U/xdJBEREY9JSTF/1ypDiASHxMTEqn9b8TwFkBDldsOGDWa5Vy8I82JvoJFdWlYEkIMKICIiElRsNhtt27alTZs2lJVpxMdgEBERoZoPL1MACVFFRdC3r1nOz4eYGO8da2TXlvxn8U51RBcRkaBlt9t10SrSQBoFS7xuREU/kE3788jOL/FzaURERETEnxRAxOuSYhz0TIkDYLFGwxIREREJaQog4hOaD0REREREQAFEfKRyOF7NByIiIiIS2hRAxCeGp7XEZoNtBwrYn1vs7+KIiIiIiJ8ogIhPJERH0LddAgBfrNvn59KIiIiIiL8ogISoiAi44w5zi4jwzTEnD2oPwJNfbiGnUGOli4iIiIQiBRAPCw8PZ+DAgQwcOJDrr7/e38Wpl8MBjz5qbg6Hb455zchOdGsTy6GCUh77YpNvDioiIiIiAUUTEXpYYmIiq1at8ncxAlKEPYwHJvXlin8v5o0lO5k6LJW+7RP8XSwRERER8SHVgIQotxt27DA3t9t3xx3ZtSUXDmiH24I/f7gWt9vy3cFFRERExO9CKoB88803TJw4kXbt2mGz2ZgzZ84x2zz77LOkpaURGRnJkCFDWLhwYaOOkZuby5AhQzj99NNZsGCBh0rueUVFkJZmbkVFvj32Pef3IsZhZ+WuI7y/YrdvDy4iIiIifhVSAaSgoIABAwbwzDPP1Pn8O++8w6233so999zDypUrGT16NOPHj2fXrl1V2wwZMoS+ffsec9u7dy8AO3bsYMWKFTz//PNcc8015Obm+uS9NSfJ8ZHcdl53AB7+fCNHCkv9XCIRERER8RWbZVkh2QbGZrMxe/ZsJk+eXLVu+PDhDB48mOeee65qXa9evZg8eTIPPfRQo48xfvx4/vrXvzJ06NA6ny8pKaGkpKTqcW5uLqmpqeTk5BAfH9/o4zVGQQHExprl/HyIifHq4Y5R5nJz/j8Wsnl/PleN6MiDk/v5tgAiIiIiJyE3N5eEhASfXK8Fq5CqATme0tJSVqxYwZgxY2qtHzNmDIsWLWrQPg4fPlwVKHbv3s369evp0qVLvds/9NBDJCQkVN1SU1NP/g00M5Ud0gHeWLKLNbtz/FwiEREREfEFBZAK2dnZuFwukpOTa61PTk5m376GTZy3YcMGhg4dyoABA7jgggt4+umnSUpKqnf7P/zhD+Tk5FTdMjIymvQempsRXVoyaWA7LHVIFxEREQkZGob3KDabrdZjy7KOWVefUaNGsWbNmgYfy+l04nQ6G1W+YPPHCb3434YsVmUc4b0VGUwd1tHfRRIRERERL1INSIVWrVpht9uPqe3Iyso6plZEPCc5PpJbf9YNgIc/U4d0ERERkWCnAFLB4XAwZMgQ5s2bV2v9vHnzGDVqlJ9K5T3h4fDrX5tbuJ/rwaaP6kyP5DgOF5bx6FzNkC4iIiISzEKqCVZ+fj4//fRT1ePt27ezatUqkpKS6NixI7fffjtXX301Q4cOZeTIkfzrX/9i165d3HjjjX4stXc4nZCe7u9SGKZDeh+m/msxby7dxdRhqfTvkOjvYomIiIiIF4RUAFm+fDlnn3121ePbb78dgOnTpzNz5kymTp3KwYMHeeCBB8jMzKRv3758+umndOrUyavlSk9PJz09HZfL5dXjBLLhXVpy0aD2zF65hz9/uI7ZvxpFWFjD+t6IiIiISPMRsvOABCJfjittWZCdbZZbtYIG9rP3qqzcYs55fAH5JeU8fHE/Lj9VHdJFREQksGgekKZTH5AQVVgIbdqYW2Ghv0tjtKkxQ/rfP9/I4QJ1SBcREREJNgogElCmj+xEz5SKDulfqEO6iIiISLBRAJGAEl5jhvS3lu5idcYR/xZIRERERDxKAUQCzqlpSVw8qH3VDOkuzZAuIiIiEjQUQCQg3T2hJ3HOcH7cncM7yzL8XRwRERER8RAFkACQnp5O7969GTZsmL+LEjDaxFV3SH9krjqki4iIiAQLBZAAcNNNN7F+/XqWLVvm76IElGsqOqQfKSzjEc2QLiIiIhIUFEBCVHg4TJ9ubuEBOh1lzQ7pby/bxSp1SBcRERFp9hRAQpTTCTNnmpvT6e/S1O/UtCQuHmw6pP9FHdJFREREmj0FEAl4fxjfq6pD+tvLdvm7OCIiIiLSBAogIcqyoKDA3KwAr1RoHefk9jEVHdI/38QhdUgXERERabYUQEJUYSHExppbYaG/S3NiV48wHdJzisp4dO5GfxdHRERERE6SAkgA0DC8JxZuD+Ovkys7pGewctdhP5dIRERERE6GAkgA0DC8DTOscxKXDO5Q0SF9nTqki4iIiDRDCiDSrNw9vidxkeGs2ZPDW0vVIV1ERESkuVEAkWaldZyT31XMkP7o3E0czC/xc4lEREREpDEUQKTZuWpEJ3q1jSenqIxHPtcM6SIiIiLNiQKINDvh9jD+OqkPAO8sz+AHdUgXERERaTYUQEKU3Q6XXmpudru/S9N4QzsncemQDoBmSBcRERFpThRAQlRkJLz3nrlFRvq7NCenskP62j25vKkO6SIiIiLNggKINFutYp38fmwPAB79fKM6pIuIiIg0AwogAUATEZ68acM70bttPLnF5fz9c82QLiIiIhLobJZlqfF8gMjNzSUhIYGcnBzi4+O9eqyCAoiNNcv5+RAT49XDedWKnYe45LnvAZj161EM7tjCzyUSERGRYOXL67VgpRoQafaGdErisooO6X+eow7pIiIiIoFMAUSCwl3jexIfGc66vbm8uWSnv4sjIiIiIvVQAJGgUKtD+txNZKtDuoiIiEhAUgCRoHHl8E70aVfRIf0zdUgXERERCUQKIBI07GE2HpjUF4D3VuxmxU7NkC4iIiISaBRAJKgM6dSCKUPVIV1EREQkUCmAhCi7HSZMMDe73d+l8ay7xpkO6eszc3lDHdJFREREAooCSIiKjIRPPjG3yEh/l8azWsY6+f24noDpkL4/t9jPJRIRERGRSgogAUAzoXvelad2pG/7ePKKyznz0fnc/s4qFm87iObdFBEREfEvzYQeQDSzpmdt2Z/HzW+uZNP+vKp1nVtGM2VYKpcO7kCb+CCr+hERERGv0/Va0ymABBBfntAFBdCmjVnOyoKYGK8ezm8sy2JVxhHeWZbBf1fvpaDUBZgRs87u0Zqpwzpydo/WhNtVGSgiIiInpgDSdAogAcTXASQ21izn5wdvAKmpoKScT9Zk8s6yjFpD9LaOc3LJ4A5MHZZKWqsQ+CBERETkpCmANJ0CSABRAPGdn7LyeHf5bj5YsZuDBaVV609NS2Lq0FQm9GtLlCPIhgcTERGRJlMAaToFkACiAOJ7peVuvtq4n3eWZbBg8wEqpw2Jc4YzcWA7Lh+WSr/2CdhsNv8WVERERAKCAkjTKYAEEAUQ/8rMKeKDFbt5d/ludh0qrFrfMyWOqcNSuWhQexKjHX4soVFQUs727AJ2HCxg58FC2sQ5Gds3hfjICH8XTUREJOgpgDSdAkgAUQAJDG63xeJtB3lneQafrd1HabkbAIc9jLF9U5g6NJVRXVsSFua9WpGSchcZhwrZdsAEje3ZBVXL+3NLjtneER7Geb2SmTyoPWd2b40jXJ3qRUREvEEBpOkUQAKIAkjgySksY86qPbyzLIP1mblV6zu0iGLK0FQuHdKBdolRJ7Vvl9ti75EitmUXsP1APjsOFprl7Hz2HC6qag5Wl6QYB51bRtOpZQxr9uTwU1Z+1XMtoiO4oH87Jg9qz+COiWo+JiIi4kEKIE2nABJAfHlCFxXB+PFm+bPPIOrkrqFDyto9Oby9bBcfrtpLXnE5ADYbnNGtNVOHpfKzXsnH1DxYlkVWXkmdNRm7DhZS6nLXe7wYh5201jGktYolrWU0aa1j6NwyhrRWMbWaglmWxbq9ucxeuYcPV+0lO7+6hqRTy2gmD2zP5EHtNcKXiIiIByiANJ0CSADRCd08FJW6+HydGc538bZDVeuTYhxMHtieGKe9olbDBI3CirlH6uIID6NTUjRprWJM2KgIGGmtY2gd62x07UW5y82irQeZs3IPn6/bV+vYA1MTuWhQey7o35aWsc7Gv3ERERHR9ZoHKIAEEJ3Qzc+O7ALeXZ7B+yt2k5V3bN8MgDAbpFaEjM4tY+hSoyajXWIUdi/1JSksLeeLdfuZvXIPC7dUj/AVHmbjzO6tmTyoPef1TiYyQsMNi4iINJSu15pOASSA6IRuvspdbr7edIBP12YSGWGvVZOR2iLa753Cs/KK+Xh1JrNX7mHNnpyq9bHOcMb1TeHiQe0Z3qWl18KQiIhIsND1WtMpgASA9PR00tPTcblcbN682Wed0Dt3Nss7dqgTeij5KSuPOSv3MnvlHvYcKapanxIfyaSB7bhocHt6pugLVUREpC4KIE2nABJANAqW+JLbbbFi12Fm/bCHT37cS25Fx3owc59cNKg9Fw5sR9sEjVAgIiJSSQGk6RRAAogCiPhLSbmL+RsPMGflHr7amFU1OpfNBiO7tOSiQe0Z1zeFOE12KCIiIU4BpOkUQAKIAogEgiOFpXy6Zh9zVu5h6Y7qUb6c4WGc1zuZacM7MbJrSz+WUERExH8UQJpOASSAKIBIoMk4VMhHq/cy64fdbD1QULX+kUv7M2Voqh9LJiIi4h8KIE2nABJAFEAkUFmWxdo9ubz07TbmrNqLzQZPTR3IpIHt/V00ERERn1IAaTr/jg0qIs2CzWajX4cEnpw6kGnDO2JZcPu7q/l87T5/F01ERESaGQWQEBUWBkOHmluYzgJpIJvNxl8n9eWSwR1wuS1+89YPzN+Y5e9iiYiISDOiS88QFRUFy5aZW5RGWZVGCAuz8cil/bmgf1vKXBa/fH0F3/2U7e9iiYiISDOhACIijWYPs/Hk1IGc1zuZ0nI317+6nGU1RswSERERqY8CiIiclAh7GM9cOYgzu7emqMzFta8sY3XGEX8XS0RERAKcAkiIKiyEzp3NrbDQ36WR5soZbueFq4cwsktL8kvKueblpazfm+vvYomIiEgAUwAJUZYFO3eamwZilqaIjLDz4vShDO6YSE5RGVe9tIQt+/P8XSwREREJUAogItJkMc5wZv78VPq1T+BQQSnTXlzCjuyCE79QREREQo4CiIh4RHxkBK/9/FR6psSRlVfCtBeXsPuw2veJiIhIbQogIuIxLWIc/Oe64XRtHcOeI0Vc+e8l7Msp9nexREREJIAogASA9PR0evfuzbBhw/xdFJEmax3n5I3rR9AxKZpdhwqZ9uJisvNL/F0sERERCRA2y1IX5ECRm5tLQkICOTk5xMfHe/VYBQUQG2uW8/MhJsarh5MQtPtwIVOe/569OcX0TInj7V+MIDHa4e9iiYiINIkvr9eClWpAQpTNBr17m5vN5u/SSDDq0CKaN28YQZs4Jxv35XH1S0vJLS7zd7FERETEzxRAQlR0NKxbZ27R0f4ujQSrzq1ieOP64STFOFizJ4drX1lGQUm5v4slIiIifqQAIiJe1S05jtevG058ZDgrdh7m+leXU1zm8nexRERExE8UQETE63q3i+e164YT6wzn+20H+eV/VlBSrhAiIiISihRAQlRhIfTpY26FmqpBfGBgaiKvXDuMqAg7CzYf4OY3V1Lmcvu7WCIiIuJjCiAhyrJg/Xpz0zho4ivDOifx4vShOMLDmLd+P7e9swqXWyegiIhIKFEAERGfOu2UVrxw1RAi7DY+/jGTuz74EbdCiIiISMhQABERnzu7Zxv+ecUg7GE23l+xm798tBZNSSQiIhIaFEBExC/G9W3LE1MGYLPB64t38eAnGxRCREREQoACiIj4zaSB7fn7xf0BeOnb7Tz+xWY/l0hERES8TQFERPxqyrBUHpjUB4Bn5v/EM19t8XOJRERExJvC/V0A8Q+bDTp1ql4W8adrRnampMzN3z7dwGNfbCYyws71o7v4u1giIiLiBQogISo6Gnbs8HcpRKrdcEYXispcPDFvMw9+sgFnhJ2rR3Tyd7FERETEw9QES0QCxm/OOYVfn9UVgD/PWct7yzP8XCIRERHxNAUQEQkYNpuN34/twbWndQbgrg9+5MNVezQ6loiISBCxWfrLHjByc3NJSEggJyeH+Ph4rx6rqAjOOMMsf/MNREV59XAijWJZFn+cvZa3lu4CINphJ7VFNB1aRJGaZO471HicEBXh5xKLiEio8OX1WrBSH5AQ5XbD8uXVyyKBxGaz8bfJfQkPs/H6kp0UlrrYtD+PTfvz6tw+LjL8mICS2iKaDknmPsaprzoREZFAoRqQAOLLRF1QALGxZjk/H2JivHo4kZNWXOZi75EiMg4XsftwIRmHKu4PF7HncCHZ+aUn3EeL6IjawaRFFB2SokmtqEmJjLD74J2IiEgwUA1I0+lnQREJaJERdrq0jqVL69g6ny8sLWfP4SIyDhey+3ARGYcq7iseHyks43BhGYcLc/hxd06d+2gV6yQ1qbpZV0p8JMnxTtrER5IcH0nrWCeOcHWZExER8QQFEBFp1qId4XRLjqNbclydz+cWl5mAclQwqXycX1JOdn4J2fklrNx1pN7jtIxxVAQSJ8lxkbSpDChxTpIrgkqrWAfhdgUVERGR41EAEZGgFh8ZQXzbCHq1Pbaa3LIscorKajTrKmTP4SL255awP6+YrNwSsvKKKXNZHCwo5WBBKRsy6z+WzWZqU2qFlLjIioBigkqbeCctY5zYw4JjBlDLsigpd1NS5qa43EVJmZuSchfFNe5TEpx0bR2LTbOeiogICiABIT09nfT0dFwul7+LIhJSbDYbidEOEqMd9OuQUOc2brfFkaIy9ucWsz/XhJL9ucVk5Zn7/XklZFU8drktDuSVcCCvhLXk1ntce5iN1hVBpXWcE2e4nbAwG3YbFfc27GG22ss2G/YwCLNVr69+njq2Nc+H2ThqWxtlLrcJDeVuistcFQHCVevx0fdHh4qSMhfF5W5Kyxs2ikWX1jFM6NuW8f1S6N02XmFERCSEqRN6APF1J/TOnc3yjh3qhC7SVG63qSXZn1vMgcpwUlWTUrGcW0x2fgnuIP3WDbOZPjvO8DCc4XYiI8JwhIexI7uQUld1UOnUMprxfdsyoV8K/donKIyISLOiTuhNpwASQHRCiwQ/l9viYH5JVSDJyiuhzOXG5bZwWxYut4XLsnC7LVxuqpcr7s021LFtjWWLGtvW3K9ZH2634QwPqwoLR4eGmvfOGveRR99Xvc4sh4fZ6gwTecVlfLUxi0/XZPL1pgOU1Kg1aZ8Yxfi+KYzv15ZBqYmEBUnTNBEJXrpeazoFkACiE1pEgl1BSTnzN2Xx2Zp9fLUxi6Ky6qanKfGRjOubwoR+bRnSqUXQ9JMRkeCi67WmUwAJIDqhRSSUFJW6WLD5AJ+tzeR/G7LILymveq51nJNxfVIY3y+FUzsnBczoYiXlLrZmFbAlK4/N+/PYtC+frLxiWsY4SEkwAw6kxEeSnGDuU+IjSYyOCNhmZpZlmg7uyylm75Ei9uUWs/dIMZk5RWTmmPv9uSVEO+wVw1NHVg1TXfkek+MjSUmIJCnaoRosCQm6Xms6BZAA4ssTuqgIxo83y599BlFRXj2ciMhxFZe5+HZLNp+uzWTe+v3kFVeHkZYxDsb0SWFCvxRGdGlJhA/CSJnLzY7sAjbtz2Pz/nw278tjc1YeOw8W4mpkJx5neNhRwcRZddFeeQGfHB/p8blmLMvicGGZCRO1QkV12MjMKW7wQAInEmG3VYz65qwVxo5e9vXEny63RWFpOQUlLgpKyykoqVguKa947MLCIiEq4phbXGREwNbEudwWRwrN6HwH80s5VFDKwYISDuab+0MFpWRXrM8tKiPKYSfGEU5sZDixzopbjeUYZzhxFfdmvZ1YZ4RZdoQT47QHzA8B/qYA0nQKIAFEM6GLiEBpuZvvtmbz2ZpMvli/nyOFZVXPJUZHcF6vZCb0a8tpp7Rq8kW7y22x82CBCRn786pu27MLKHPV/ecxPjKc7slxdE+Jo3ubWNolRnGooJR9FSOl7cspZl9FH59DBaUNLktlLcrRNSg1l+OjwrHZbFVDSO89Usy+3KLatRZHiivCRRHFZScOF5XDR7dLMAGhbUIU7RIjSUmIol1FeCgoLTf9lnLMvvflFlct788t4WBBCQ29moiPDK83oCTHR5Kc4CQiLKwqHOSXlFcEiJohomaAqCNc1NimZjO/xrLZINYZXmc4SYiKIL7iPjG66eGlcsS9QwUlVcHBhAsTJiqDRWXYOFxY6vMBLSIjwkwocdqJjQwnxhFOXGR1gKkMK7GR4URF2Ily2ImMMLeoypvD9CuLcpjHkRH2gA159VEAaToFkACiACIiUluZy82SbYf4dG0mc9fu42CNC/q4yHDO65XM+H5tGd2t1XF/WXe7LXYfLjIBIyvP1Gjsz+enA/n11gDEOOx0S46jR3Ic3ZJj6Z4cR4+UONrEORvcpKqk3EVWbom5aM+pGVAqlnOL2Z9TUmuUsOOJjAijZYyTQwWlDb6wbhXroG1CFG0TIs0tsXLZ3Hui9qXM5SYrr6TqPe6vI6TsyyluUhhoKnuYjRiHnVhnONEVF8wxDjsxTjMjQU5RGblFZeRU3ApLm17WuMi6w4sjPKwqVFTWXBwuLGt07RqYUJ4U46BVjJOkGActYx20jHHQMrbicYyD+KgIistMmMsvMUEtr7gy4JVVrHeRX1xGQYmLvIptKrf3VC1ZfRz2MCIjwqoCS1REzfuweoKMGQSjMsgkRkdwTs9kr5azkgJI0ymABBAFEBGR+rncFku3H+KztZl8tnYfB/JKqp6Lcdg5t1cyE/ql0LttAluz89lS2Xxqfx5b9ufXe/EbGRFGtzYmZPRIjqN7ReBonxjlk74blU2lqgJKRdOo/UcFlZo1QZUqa02qAkZiJO0SokhJMPfJCWaemUBgWRZ5JeXVtSg13m/lqHD7cqqHqo6MCCPGYYJCdI3gEOu0E+2o/NW9ern+bcxzzvCwRv17lpa7q8LI0eGkrpunwkt8ZDitYqvDRFKMk1axjorHzopwYR63iHb4pEliSbmrqkYpr9jUPOUXl9cRaMzjvJJySspcFJW5KCp1UVQxd1BRjXUlHg41qUlRLLzzHI/usz4KIE2nABJAFEBERBrG7bZYseswn63Zx2drM8nMKT7haxz2MLq0jqFHigkZ5hZLhxbRzaIJSHGZi/25xRwsKCUp2uGX/hS+UF5RG9Sc+xucKLyUlrtr1FaYsNEq1kGLGN8EikDgdluUlLspKnNRXCOYFJeZyU4rw0px5fOllY/dVY+Ly6vXt4518sTUgT4puwJI0ymABBAFEBGRxnO7LVbvPsJna/fx6ZpM9uUUk9YqplbI6J4SR6ek6GZ9USsigUEBpOnC/V0AERGRpggLszGoYwsGdWzBHyf0wu22NBysiEgAUwAJYdHR/i6BiIjnKXyIiAQ2BZAQFRNjmmGJiIiIiPiSGsOKiIiIiIjPKICIiIiIiIjPKICEqOJiOP98cys+8eiVIiIiIiIeoT4gIcrlgk8/rV4WEREREfEF1YCIiIiIiIjPKICIiIiIiIjPKICIiIiIiIjPKICIiIiIiIjPKICIiIiIiIjPaBSsAGJZFgC5ubleP1bNWdBzczUSloiIiEhDVF6nVV63SeMpgASQgwcPApCamurT47Zr59PDiYiIiDR7eXl5JCQk+LsYzZICSABJSkoCYNeuXTqha8jNzSU1NZWMjAzi4+P9XZyAos+mbvpc6qfPpm76XOqnz6Zu+lzqF+yfjWVZ5OXl0U6/4J40BZAAEhZmuuQkJCQE5X/YpoqPj9fnUg99NnXT51I/fTZ10+dSP302ddPnUr9g/mz0Q3HTqBO6iIiIiIj4jAKIiIiIiIj4jAJIAHE6ndx77704nU5/FyWg6HOpnz6buulzqZ8+m7rpc6mfPpu66XOpnz4bORGbpTHERERERETER1QDIiIiIiIiPqMAIiIiIiIiPqMAIiIiIiIiPqMAIiIiIiIiPqMA4mPPPvssaWlpREZGMmTIEBYuXHjc7RcsWMCQIUOIjIykS5cuPP/88z4qqW889NBDDBs2jLi4ONq0acPkyZPZtGnTcV/z9ddfY7PZjrlt3LjRR6X2jfvuu++Y95iSknLc1wT7+QLQuXPnOv/9b7rppjq3D+bz5ZtvvmHixIm0a9cOm83GnDlzaj1vWRb33Xcf7dq1IyoqirPOOot169adcL8ffPABvXv3xul00rt3b2bPnu2ld+Adx/tcysrKuOuuu+jXrx8xMTG0a9eOa665hr179x53nzNnzqzzPCouLvbyu/GsE50zM2bMOOY9jhgx4oT7DeZzBqjz395ms/Hoo4/Wu89gOGca8jc6VL9npGkUQHzonXfe4dZbb+Wee+5h5cqVjB49mvHjx7Nr1646t9++fTsTJkxg9OjRrFy5kj/+8Y/89re/5YMPPvBxyb1nwYIF3HTTTSxevJh58+ZRXl7OmDFjKCgoOOFrN23aRGZmZtWtW7duPiixb/Xp06fWe1yzZk2924bC+QKwbNmyWp/JvHnzALjsssuO+7pgPF8KCgoYMGAAzzzzTJ3PP/LIIzzxxBM888wzLFu2jJSUFM477zzy8vLq3ef333/P1KlTufrqq1m9ejVXX301U6ZMYcmSJd56Gx53vM+lsLCQH374gT//+c/88MMPzJo1i82bN3PhhReecL/x8fG1zqHMzEwiIyO98Ra85kTnDMC4ceNqvcdPP/30uPsM9nMGOObf/eWXX8Zms3HJJZccd7/N/ZxpyN/oUP2ekSayxGdOPfVU68Ybb6y1rmfPntbdd99d5/Z33nmn1bNnz1rrfvnLX1ojRozwWhn9LSsrywKsBQsW1LvN/PnzLcA6fPiw7wrmB/fee681YMCABm8fiueLZVnWLbfcYnXt2tVyu911Ph8q5wtgzZ49u+qx2+22UlJSrIcffrhqXXFxsZWQkGA9//zz9e5nypQp1rhx42qtGzt2rHX55Zd7vMy+cPTnUpelS5dagLVz5856t3nllVeshIQEzxbOz+r6bKZPn25NmjSpUfsJxXNm0qRJ1jnnnHPcbYLxnDn6b7S+Z+RkqQbER0pLS1mxYgVjxoyptX7MmDEsWrSoztd8//33x2w/duxYli9fTllZmdfK6k85OTkAJCUlnXDbQYMG0bZtW84991zmz5/v7aL5xZYtW2jXrh1paWlcfvnlbNu2rd5tQ/F8KS0t5fXXX+fnP/85NpvtuNuGwvlS0/bt29m3b1+tc8LpdHLmmWfW+50D9Z9Hx3tNc5eTk4PNZiMxMfG42+Xn59OpUyc6dOjABRdcwMqVK31TQB/7+uuvadOmDd27d+eGG24gKyvruNuH2jmzf/9+PvnkE6677roTbhts58zRf6P1PSMnSwHER7Kzs3G5XCQnJ9dan5yczL59++p8zb59++rcvry8nOzsbK+V1V8sy+L222/n9NNPp2/fvvVu17ZtW/71r3/xwQcfMGvWLHr06MG5557LN99848PSet/w4cN57bXXmDt3Lv/+97/Zt28fo0aN4uDBg3VuH2rnC8CcOXM4cuQIM2bMqHebUDlfjlb5vdKY75zK1zX2Nc1ZcXExd999N1deeSXx8fH1btezZ09mzpzJRx99xFtvvUVkZCSnnXYaW7Zs8WFpvW/8+PG88cYbfPXVVzz++OMsW7aMc845h5KSknpfE2rnzKuvvkpcXBwXX3zxcbcLtnOmrr/R+p6RkxXu7wKEmqN/pbUs67i/3Na1fV3rg8HNN9/Mjz/+yLfffnvc7Xr06EGPHj2qHo8cOZKMjAwee+wxzjjjDG8X02fGjx9ftdyvXz9GjhxJ165defXVV7n99tvrfE0onS8AL730EuPHj6ddu3b1bhMq50t9Gvudc7KvaY7Kysq4/PLLcbvdPPvss8fddsSIEbU6Y5922mkMHjyYf/7zn/zjH//wdlF9ZurUqVXLffv2ZejQoXTq1IlPPvnkuBfcoXLOALz88stMmzbthH05gu2cOd7faH3PSGOpBsRHWrVqhd1uPybdZ2VlHfMrQKWUlJQ6tw8PD6dly5ZeK6s//OY3v+Gjjz5i/vz5dOjQodGvHzFiRLP9VamhYmJi6NevX73vM5TOF4CdO3fy5Zdfcv311zf6taFwvlSOmNaY75zK1zX2Nc1RWVkZU6ZMYfv27cybN++4tR91CQsLY9iwYUF/HrVt25ZOnTod932GyjkDsHDhQjZt2nRS3zvN+Zyp72+0vmfkZCmA+IjD4WDIkCFVI/ZUmjdvHqNGjarzNSNHjjxm+y+++IKhQ4cSERHhtbL6kmVZ3HzzzcyaNYuvvvqKtLS0k9rPypUradu2rYdLF1hKSkrYsGFDve8zFM6Xml555RXatGnD+eef3+jXhsL5kpaWRkpKSq1zorS0lAULFtT7nQP1n0fHe01zUxk+tmzZwpdffnlSAd2yLFatWhX059HBgwfJyMg47vsMhXOm0ksvvcSQIUMYMGBAo1/bHM+ZE/2N1veMnDR/9HwPVW+//bYVERFhvfTSS9b69eutW2+91YqJibF27NhhWZZl3X333dbVV19dtf22bdus6Oho67bbbrPWr19vvfTSS1ZERIT1/vvv++steNyvfvUrKyEhwfr666+tzMzMqlthYWHVNkd/Lk8++aQ1e/Zsa/PmzdbatWutu+++2wKsDz74wB9vwWt+97vfWV9//bW1bds2a/HixdYFF1xgxcXFhfT5UsnlclkdO3a07rrrrmOeC6XzJS8vz1q5cqW1cuVKC7CeeOIJa+XKlVWjOT388MNWQkKCNWvWLGvNmjXWFVdcYbVt29bKzc2t2sfVV19dayS+7777zrLb7dbDDz9sbdiwwXr44Yet8PBwa/HixT5/fyfreJ9LWVmZdeGFF1odOnSwVq1aVet7p6SkpGofR38u9913n/X5559bW7dutVauXGlde+21Vnh4uLVkyRJ/vMWTdrzPJi8vz/rd735nLVq0yNq+fbs1f/58a+TIkVb79u1D+pyplJOTY0VHR1vPPfdcnfsIxnOmIX+jQ/V7RppGAcTH0tPTrU6dOlkOh8MaPHhwreFmp0+fbp155pm1tv/666+tQYMGWQ6Hw+rcuXO9X3zNFVDn7ZVXXqna5ujP5e9//7vVtWtXKzIy0mrRooV1+umnW5988onvC+9lU6dOtdq2bWtFRERY7dq1sy6++GJr3bp1Vc+H4vlSae7cuRZgbdq06ZjnQul8qRxi+Ojb9OnTLcsyQ2Tee++9VkpKiuV0Oq0zzjjDWrNmTa19nHnmmVXbV3rvvfesHj16WBEREVbPnj2bXVg73ueyffv2er935s+fX7WPoz+XW2+91erYsaPlcDis1q1bW2PGjLEWLVrk+zfXRMf7bAoLC60xY8ZYrVu3tiIiIqyOHTta06dPt3bt2lVrH6F2zlR64YUXrKioKOvIkSN17iMYz5mG/I0O1e8ZaRqbZVX0UhUREREREfEy9QERERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGf+X85xmknbJ+PKgAAAABJRU5ErkJggg==",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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wDSpo1ZzspSAPG7knx4YbQZKaT3ZPNLbFPbWx/YZOYFwQY3LQ3+i+9di81F5davzGObHfpPhdG/g1an+Ldsoe7QNvjxXXPxUnnhAubX9v5TTRjx1b+Rqxw+uc10nAfTPOzse/zbB2Pn9/DWVNN/q01v0wfFW0M/b//GTLC4e5l57EyA034Lw2/0be3PyXC74b3psOGjipGx5kOLTv4uVfArLTT/Xxb9o2LySMznP/ImGHY9RCb4t3x+oADSdAogASQkTmi32//tiQPRnJtg1esQ3wF+9a3nfgF96wrY9CkMuhomPeOZfQaaHd+Z4FH5K7vNboY3Pf325tEBOpRYlgmKq9+CdbOhJLf6uQ7DYMDl0Odi7/2SWloA7//c/PpvCzMzmw+7zjvHaqz96+D1SyAvExI6mlnTW3Xz3P53r4CvHoBtX5vH4VEw4kbTp6I5/XJdWgAvj4N9P2pkLG8rzoXlL8H36VBwwKyLa2vOmSHTA6cppR+ExPWalymABJCgP6G/fQq+ecxcCPeZ7O/SBI61s+D9awEbzPjEsxO67VoCL48BuwNu+RHi23pu3/5kWaaj/oJHzD2YUVcGToPRt2u+gOagrMiE49Vvw0//M/0gwJyr3ceZWpFu53muWVRBNrw5FfYsh/BIuPRl0x8lkBzeCf+5CA5tNb8wT3uv6UMBZ22Arx40nYXBNF8aeq1pbhWX3PQy+0POHvj32Wa+I42M5XmFh2DJC7DkeTMaI5gO5affZr5jA7WJng8F/fWaDyiABJCgPqHdLniil/mDYXeYJgZpZ/i7VP53JMMMuVuSYy4Izv2z54/x0lgzEdRpt8J593t+/75kWeYX3AWPwK5FZl1YhBl15fTbgnrUlaCWtx/WvGfCyP411eujW5r+OwMuh7YDT76Z1KHtpnbh0FZTu3jFO9BxuEeK7nH5B+CNSyFzlenYe/nrZpLHxjq0Hb5+2AyNjGVqfPpfbjqYB0Ozpd3L4ZUJZiJMjYzlGflZZnjoZS9Bab5Z17Kbacba79JmM0mgLwT19ZqPKIAEEF+e0MXFcMklZvmDDyAy0quHg+0L4dULqh874uDaT0N7/gW3C2ZeYC6k2w+Fn3/unS/4jZ/C21eAMx5uW9s82+taFmz9nwkelePM2x0w+BoTPBI6+Ld84jn71pgg8uO7UJBVvb51LxNE+k9pXP+IvavgjcvMvhI6mh8/Ar0/VEkevHOVCdthEXDR8+YCsCHy9pn/Jz+8Wj05ZK8LTT+XNj29VmS/0MhYnpGzG777hzlnyovNuuR+cMbvzLmj2qVjKIA0nQJIAAnqUbA+vt20Je13GeRmws5vITbZtN8N1eYy3zxqmkY4Ys2Qu0ldvHMctxueHQHZm8yEZ6fd4p3jeINlwZZ5po/HnuVmnd0JQ2bA6bd6r6Ou+J+rHLbNh1VvwsZPzC/dQKOG9P3pf/DuNebX3OR+pklTc2mGWF4Cs280w51ig/GPwPBf1L994SH47ilY8i8zpwlA13PhnD9B+8G+KLF/fPWg+S61O8zIWB1H+LtEzcfBrWZEq9VvmyHbwfwYdsbvofvYwJgcM0ApgDSdAkgACdoA4iqHJ3qaTmxXfWC+4F6ZYIZ/TOpiJtvzxcRfgSRjGbw81rR7n/y86TTtTStfN8PyxrWFW1Y3jza8lmUuwH582zwOj4KhPzcj9oTAOPNSQ9ERM7nh6rdg1/fV6483pO+qt+Cjm00tQNqZMPV1iGxmFwpuN3x+Fyz9l3l8xp1w9h9rXxiW5MHi52DRP6s79acON7OXdz7d92X2tVojY7WCG74KjiZm3pS1wczhsfYDMwcOQOfRZo6ktDMVPBpAAaTpFEACSNAGkG1fm9m9o5Lgjs2mmVFuppnzImeXmX14+seBPwSkpxTnmiF3D+8wE+Nd8pL3v/DLS+DpAWaEnQufMX0mAt2SF+CzO03n8uE3mpqb2Db+LpX4W71D+naEAVNNP4cNH5kJPcHUuk56FsIdfiluk1mW+YV//t/M4yEz4PwnzOzly182F5KF2ea55H6mH1m3MaF1EVlrZKw+cN1cjYxVl70rzUAwlQMSgDlXRt8RuH2iApQCSNMpgASQoA0g/70FVsyEwdPhwn9Ur8/eYkJI0SHTyfKKd5rvRUJjzL7RXDwldDRNr6ISfXPc7/4B8/4MrbrDr5cE9nDIu5ebCwp3GYx72ExyJVLT8Yb0rTTqt/Cz+wP7XG+o5S+bpqxYZgCPg1ur52RI6grn3AO9LwqO93oycnbDv842fX26jzeTr4Z634XiXDO88/61Zujpn76seMIGvSaazuXtBvqzhM2WAkjTKYAEkKAMIK5yeKybCRlXz4GuZ9d+fvdyeHUilBVCvylw0QvB/Qe0stOkLQxmfOrbmXyLc+HJPuZC7fK3oOcE3x27MQoPwfOjIXe3aV5z2auh9WuuNN4xQ/q6YdxDwRdc138IH1wPrlLzOL49nHmXGRrVHu7fsgWCmiNjnXaL6fMWCiwLjuwyQWPfWlMTtH9t7RpCMHMk9bvUzJEUbAMS+JgCSNPpG0u8a/sCEz6iW5k2pkfrMBSmvAZvXQ5r3jVNbMb+zffl9IXDO+Hj28zyGb/3bfgA0/596M9NR9XvngrMAOJ2w6xfmPCR1NU0F1P4kBOJiDLNGfteYoYSLckLzkkoe1c0ZV34uJkjZeh1EOHtIQybkQ5DYVI6zLoevnsaWvc0gxUEk7JiOLChImisqQ4dJTl1bx/fHpL7mhEnB17pvcFORBpJAUS8a91sc9/7wvp/oet2nrnQnHOjGYM8LgVG/cZ3ZfQFV7m5sC7JhQ6nms6k/jDiV7D4WTOU7a7FgTdizLdPwE/zzERxU15rfp2Gxf9i2wR3X6G00eYmdet/GRzYCAsfM81/k7o23/4N+VkmZNQMGtmbqyftrCkswgSulL6Q0s+EjpR+zWuWewkpCiAhKibG1Np6VXkpbPivWe5z0fG3HXiFmaTwy3vhiz9BTBvToTRYLHzcTAboiINL/u2/5hJxKWYuhR9eM78QBlIA2f5NdUfbCY+ZP6QiIo119j0mhGz8GN6+En4xP7AnKXWVw8GfKkLGjyZo7F9r/ibWJSrJfD8m9zMhI6UvtOoRGn0oJWgogIj3bF8AxUdMmOh02om3P+0W84W7+Fn48NcQ0xJO+ZnXi+l1u5aYeSwAzn/c//OejPot/PAf02Y+a2NgtAXO2wfvX2fa7g+c1jxG6RKRwBQWBhf/ywx1vm8NvHl5YI2Mlf0TbP2quq9G1obqCQBrsZmmhMl9K2o2+pvl+HZqmirNngKIeE9V86tJDRuNxGaDMX8z1c5r34d3roEZ/4X2Q7xbTm8qzjHtkS2X6WQfCLU6rbqZCdw2fmzmDpic7t/yuMpN+CjIMkNoTnjMv+URkebPEQNXvG1GxspaZ5rATn3Df4OcFB4y826sfrt6UtWaImIguU+NJlT9ILn38SfaFGnGFEBCVHExXF3xI/N//gORnu7HWF4CGyrGGj9R86uawsJg8nNQeNDMgvzGZWaiwlaneLiAPvLJHWZ0ksSOcH4AXVifdqsJID++Y4bv9OeM4vP/Bju/NZPKTXkVHNH+K4uIBI+EDnD5mzDzfFPj+7/74bz7fXf88hLY8oUJHZvnVs82brOboZTbD6loQtUPWqQF9wiQIkdRAAlRLhe8/75ZnjnTCwfYOt+MyhGbAh0bOdpTuAOm/gdmXgCZq+D1i+C6ec1v9uvV75iRvWx2uPhFiEzwd4mqpQ6DjqNg1yLT5G3Mg/4px+a5puM5wIX/NLUzIiKekjqsxshYT1WMjHWF945nWWY44NVvwbpZUHS4+rmU/jDgCjMUbjAPlCDSAIrb4h2Vza/6TD65X3WccTDtffOr0JFd8PqlpjlTc3FoO3zyO7N85l2BOQrL6bea++UzoeiI749/eKdpFgFw6i+h78W+L4OIBL/+l5lJ9wD++1vTL8/TDu+EBY/CP4fASz+D5S+Z8BHX1vS7+9UiM/HsyF8rfIigACLeUFYMGz8xy41pfnW02NZw9SyIaQ3718Db00yVdqCrHHK3NA9SR1T/4Qs0p5wHrXuZcq54xbfHLi+B92aYQQraD/FfDYyIhIaz/wQ9LzCTOL59pflhq6mKc8yIgq9MgKf7w/wH4dBWiIiG/lPh6tlw2zoY81fTv0NEqiiAiOdt/Z+5qI1vb+a8aIqkLqYmxBELOxaaC3t3HWOgB5JvHoHdS8EZb0ZiCdQZisPCzMhjAIufM8HRV774E+z9ASIT4bKZGj5SRLwrLAwuesF07i7MhreugJL8xu/HVQ6bv4D3fw6PdYePfgM7vwNskHYmTH4e7thivvu7ntOwAVhEQpACiHje2lnmvvdkz3SqazcQLn/DTLS0fg58dpcPJjE5STu/h28eNcsXPAktOvm3PCfS9xITFPP3mw7pvrD2A1j6L7N88b8Ce3x+EQkezli44i0zNPz+tRU/aLlP/DrLgswf4fM/whO94M3LzPdYebGZf+Nn95majukfmf4lzlivvxWR5k4BRDyrrAg2fWaWPdmmv8tZcPELgA2W/dvMchtILAu2L4RZN5i5LCo7Gga6cAeM+LVZXvSPhv0xborsLfDRb83y6bdD97HePZ6ISE2JqWZkLLsTNn0CXz1Q/7a5mWbC1udOgxdGw+J0M1x4dCsYfiP84mu4aQmcfhsktPfZWxAJBgHaNkSarS3zoKwAElI9P39H30sg/wB8fhd89SDEJsPgazx7jMYqyYcf34alL8KBDWZdi84w4VG/FqtRhkw3zcYO/mT+IPea6J3jlBbCu9dAaT50Ot3MViwi4mupw2DSM+YHo2+fNCNjDbjcPFdaYPowrn4Ltn1tflACE1h6jDc/Lp1yLtgj/FZ8kWCgABKioqMhP7962WPWVTS/6jPZOzO1jrgR8veZPxr/vcV0UO8x3vPHOZHsLbD03+aPVEmuWVfZ8XD07wJnxt2GcMbBsOth4ePw7VOmo6Y3/u0+vQOy1pvmD5e+FLh9Y0Qk+PWfYmYg//YJ04+jrAgylsKGj8yPJJU6jjThpPdkiEr0V2lFgo6uAEKUzQYxnp5gtbTAzOsA0MeLQ6qee6+ZLX3VG2YkpWs+8s0wt24XbP7c9F/Y9nX1+qSucOoN5pex5voHaviNsOgZM0Pvru+h0yjP7v+H/5h/L1sYXPpy85vTRUSCzzl/huzNZlLWj2+tXt8izYSO/lPMQCgi4nEKIOI5W76AskLTBKndIO8dx2aDiU9DwQFzzDenwM/nQpue3jleQbYZanH5y5CTUVkI6D7OBI8uZzf/GWxj28DAK81wvN8+5dkAsm+Nqf0AOOdPkDbac/sWETlZlSNj/eciE0T6XGR+SEo91Tu1wCJSRQHECwoLC+nVqxeXXXYZjz0WYJ2lK5SUwC9/aZZfeAGcTg/stHL0qz4Xef/L2x5hhm99bRLsXgavXwzXfQEJHTx3jN0rTIf3tbPAVTH/SFQSDL4ahv7cBK1gMuo3sGImbJkL+9dDcu+m77M4x/T7KC+GbmPgtNuavk8REU9xxpq/HQocIj7VzH+2DUx/+9vfGD48AGe+rqG8HF591dzKyz2ww5J8UxsBTZt8sDEcMXDlu9CqO+TugdcvgcJDTdtnWTGsehP+dTa8eI7p4+EqMTU6k5+D29fDeQ8EX/gAaNm1ugP6on82fX+WBR/eDIe2mUEJLnqh+dcUiUjwUfgQ8TldDXjYli1b2LhxIxMmTPB3UXxr8+fmV+6kLpDS33fHjU6Cq2ZBXFs4sNFMLlVW1Pj9HNkF8+41Y7zP+ZWZJM/ugP6Xw/VfmeEWB14JEVEefwsB5fRbzf2adyFnd9P2teR506EzLAIue9X8W4mIiEjIC6kA8s033zBx4kTatWuHzWZjzpw5x2zz7LPPkpaWRmRkJEOGDGHhwoWNOsYdd9zBQw895KESNyPrZpv7Phf7/tekxFS46gNwJkDGYjNDrasB1TpuN2z9yoSWpwfAd09B0SGI7wDn/gVuW2/mHung4eGEA1n7IdB5NLjLzezoJytjmZntHGDs30LrMxQREZHjCqkAUlBQwIABA3jmmWfqfP6dd97h1ltv5Z577mHlypWMHj2a8ePHs2vXrqpthgwZQt++fY+57d27lw8//JDu3bvTvXt3X72lwFCca+b/AN81vzpach8zw63dCZs+hU9uq3+29OIcWPw8pA8znQ83fWrGeu9yFkx9A25ZbYbSjW3t07cQME67xdyvmAlFhxv/+sJDZnQyd7kZuvLUX3iwcCIiItLchVQn9PHjxzN+fP1zRjzxxBNcd911XH/99QA89dRTzJ07l+eee66qVmPFihX1vn7x4sW8/fbbvPfee+Tn51NWVkZ8fDx/+ctf6ty+pKSEkpKSqse5ubkn87b8b9Nnpp9Ey24mCPhL59PMEK/vXm1GrYpNgXNqTHa3f73pVL76HTNZIoAjzjStGnY9tA6x4FifU34GbfpA1jpY9hKccUfDX+t2w6xfQO5uMzzxhf9U+2oRERGpJaRqQI6ntLSUFStWMGbMmFrrx4wZw6JFixq0j4ceeoiMjAx27NjBY489xg033FBv+KjcPiEhoeqWmprapPfgN5XNr/r6ofnV0XpdAOc/YZa/eQSWvADr5sAr58NzI81QumUFZubbCY/B7zbAhEcUPmqy2aprQZY8bzrmN9S3j8NP8yA8Eqa8BpHx3imjiIiINFsKIBWys7NxuVwkJyfXWp+cnMy+ffu8csw//OEP5OTkVN0yMjJO/KJAU3QEtv7PLPur+dXRhl4LZ/3BLH92J7w3HXZ+CzY79LoQpn8Mv15s5vBoTjOW+1Lfi83IVQUHzEhgDbH9G5j/f2b5/Ccgpa/3yiciIiLNVkg1wWoI21G/4FuWdcy6hpgxY8YJt3E6nTg9MgFH40VHQ1ZW9fJJ2/QpuEqhdS9o08sjZfOIM+8ys6UvfwliWsOQGTDkWkho7++SNQ/2CBh5E3x+Nyz6Bwy+BsLs9W+ftw/ev870pRl0FQya5ruyioiISLOiAFKhVatW2O32Y2o7srKyjqkVCQY2G7T2RB/rqtGvAqT2o5LNBuc/bvp2tOwK4f4Jes3aoKvh64fNPB4bP4bek+rezlVuRh4ryILkvqZpm4iIiEg91ASrgsPhYMiQIcybN6/W+nnz5jFq1Cg/lSrAFR4yw9hC4AUQMCEkubfCx8lyxppmagDfPlX/qGLzH4Sd35kO/VNeC/65UkRERKRJQiqA5Ofns2rVKlatWgXA9u3bWbVqVdUwu7fffjsvvvgiL7/8Mhs2bOC2225j165d3HjjjX4stXeUlMBNN5lbjYG4GmfjJ2ao1eS+6sQdrE79pelQvvcH2PHtsc9v+hy+fdIsT3rG1DaJiIiIHEdINcFavnw5Z599dtXj22+/HYDp06czc+ZMpk6dysGDB3nggQfIzMykb9++fPrpp3Tq1Mmr5UpPTyc9PR2Xy+XV49RUXg7PPmuWH3kETqorSlXzq8meKpYEmtjWMHCa6Uvz3dOQNrr6ucM7YfYvzfLwG3UeiIiISIPYLKu+dhXia7m5uSQkJJCTk0N8vHeHLy0ogNhYs5yfDzExjd3BQXisG1gu+M0P+uU7mB3aBv8cYjqY3/idGd2qvAReHgt7V0L7oXDtZxDu8HdJRUREvM6X12vBKqSaYIkHbfyvCR8p/RU+gl1Sl+oO6Iv+Ye7n3mPCR1QLuGymwoeIiIg0mAKInJxAHf1KvGPUb839mvdNU6xl/zaPL/43JDbTCTRFRETELxRApPHyD5hJ50ABJFS0HwxpZ5har3l/MetG3wHdzvNvuURERKTZUQAJUWUud9Xy4YLSxr14w0emP0C7QZCU5uGSScA67dbq5c6j4ew/+q0oIiIi0nwpgASA9PR0evfuzbBhw3x2zAh79T/9su2HGvdiNb8KTV3Pge7jzaz3l7x0/JnRRUREROqhUbACiC9HVXC74bYXN/HByj1cO6YNf72ob8NemLcfHu8BWHDrGkjs6NVyioiIiAQSjYLVdKoBCVFhYTB+VDzhCUUs3n6w4S/c8BFgmaFXFT5EREREpJEUQELY8LSW2GywJSufA3kNnA597Sxz3/di7xVMRERERIKWAkiIKi2F/7vPAUsGYLlsLN7WgFqQ3L2w63uzXDkvhIiIiIhIIyiAhKiyMnjsMdgxvwOWK4xFWxsQQNZXNL9KHQ4JHbxeRhEREREJPgogAtCwGpB1Fc2v+qj5lYiIiIicHAUQIcwG27ML2JdTXP9GObshYwlgU/MrERERETlpCiABwB/zgNTUu50ZQu77bdn1b7RujrnvNAri23q/UCIiIiISlBRAAsBNN93E+vXrWbZsmV+OP7xLEgCLfjpOMyxNPigiIiIiHqAAIpza2QSQ7+vrB3J4J+xZDrYw6HWhD0smIiIiIsFGAUQY0imJ8DAbuw8XkXGo8NgN1s8x951Og7hkn5ZNRERERIKLAkiIioqCtWvNrVViOP07JAD11IKo+ZWIiIiIeIgCSIgKC4M+fcwtLAxGdm0JwPdHzwdyaDvsXanmVyIiIiLiEQogAsCorq0AE0Asy6p+orL2I+0MiG3th5KJiIiISDBRAAlRpaVw333mVloKQzq1wGEPY19uMTsO1ugHouZXIiIiIuJBCiAhqqwM7r/f3MrKIDLCzsCOiUCNZlgHt8K+H8FmV/MrEREREfEIBZAA4O+JCCuN7GL6gSzaWjEh4bpZ5r7LWRCd5J9CiYiIiEhQUQAJAP6eiLDSqIqO6Iu3HTL9QCpnP1fzKxERERHxEAUQqTKwYyLO8DCy80vYuWkV7F8LYRHQ6wJ/F01EREREgoQCiFRxhtsZ2rkFAEeWvWNWdj0bolr4sVQiIiIiEkwUQKSWyn4grXd9Zlb0udiPpRERERGRYKMAIrWM7NqKbrbdtC/bgWV3QI/x/i6SiIiIiASRcH8XQPwjMhKWLq1ertS/QwIXOZYAkN/+DOKiEn1fOBEREREJWgogIcpuh7pG/Y0IszE5YimUw4q4sznL5yUTERERkWCmJlhS2/51tCvPoMSK4P38fv4ujYiIiIgEGdWAhKjSUnj6abN8yy3gcFQ8sW42AF+7B7BgZzEut4U9zOafQoqIiIhI0FENSADwx0zoZWVw553mVlZWsdKyqmY//9J+GnnF5azbm+OzMomIiIhI8FMACQCBMhM6+36EQ9sgPIrCTj8D4PutB/1bJhEREREJKgogUq2i+RXdxzDolA4AfL9NAUREREREPEd9QMSwLFhrml/R5yJGtWgFwNLthyhzuYmwK6uKiIiISNPpqlKMvSvhyE6IiIZuY+iZEkeL6AgKS138uFv9QERERETEMxRAxKjofE73ceCIISzMxvC0lgAsVjMsEREREfEQBRCpGP1qjlnuc1HV6pFdTQBRR3QRERER8RT1AQlRkZEwf37F8sEVkJMBjljodl7VNpUBZNmOQ5SUu3CG2/1RVBEREREJIqoBCVF2O5x1lrnZN1Q0v+oxHiKiqrbp1iaWVrEOSsrdrNp1xB/FFBEREZEgowAS6txuWD/HLNdofgVgs9kY0aWiGZb6gYiIiIiIByiAhKiyMkhPh/S/7aLs8H5wxkPXc4/ZTv1ARERERMST1AckRJWWws03A3Rmxh8cRPSYABGRx2w3sqIGZOWuIxSXuYiMUD8QERERETl5qgER46jmV5XSWsWQEh9JqcvNip2HfVwoEREREQk2CiABID09nd69ezNs2DD/FMAZD13PqfMpm82mZlgiIiIi4jEKIAHgpptuYv369Sxbtsw/BegxHsId9T49Uh3RRURERMRDFEBCldtVvdzrwuNuWlkDsjrjCAUl5d4slYiIiIgEOQWQUFUzgKSNPu6mqUnRdGgRRbnbYtmOQ14umIiIiIgEMwWQUFWzyZU94oSbqxmWiIiIiHiCAkiIcjrh44/Nzek88faVzbAWqyO6iIiIiDSB5gEJUeHhcP75Dd++MoCs2ZNDbnEZ8ZEnrjURERERETmaakCkQdomRJHWKga3BUu3qR+IiIiIiJwcBZAQVVYGM2eaW1lZw14zQv1ARERERKSJFEBCVGkpXHutuZWWNuw1mpBQRERERJpKAUQabESXJAA27MvlcEEDU4uIiIiISA0KINJgbeIi6dYmFsuCJdtVCyIiIiIijacAIo2iZlgiIiIi0hQKINIompBQRERERJpCAUQaZXhFANm8P58DeSV+Lo2IiIiINDeaiFAaJSnGQa+28WzIzGXxtoNMHNDO30USERHxO5fLRVlDx7WXgBYREYHdbvd3MYKaAkgASE9PJz09HZfL5bNjOp3w7rvVy40xsktLNmTm8r0CiIiIhDjLsti3bx9Hjhzxd1HEgxITE0lJScFms/m7KEHJZlmW5e9CiJGbm0tCQgI5OTnEx8f7uzj1mrd+Pze8tpwurWL46o6z/F0cERERv8nMzOTIkSO0adOG6OhoXbA2c5ZlUVhYSFZWFomJibRt2/aYbZrL9VogUw2INNqpaUmE2WBbdgH7copJSYj0d5FERER8zuVyVYWPli1b+rs44iFRUVEAZGVl0aZNGzXH8gJ1Qg9R5eXw3nvmVl7euNcmREXQp10CAN9vy/ZC6URERAJfZZ+P6OhoP5dEPK3y31T9erxDASRElZTAlCnmVnISg1mN0nwgIiIiAGp2FYT0b+pdCiByUkZ01XwgIiIiItJ4CiByUoZ1TsIeZiPjUBEZhwr9XRwRERHxk86dO/PUU0/5uxjSjCiAyEmJdYbTv0NlPxDVgoiIiDQnZ511FrfeeqtH9rVs2TJ+8YtfeGRfEhoUQOSkVfYDWax+ICIiIkHFsizKGzhKTevWrdURXxpFAURO2sgurQBTA6LpZERERJqHGTNmsGDBAp5++mlsNhs2m42ZM2dis9mYO3cuQ4cOxel0snDhQrZu3cqkSZNITk4mNjaWYcOG8eWXX9ba39FNsGw2Gy+++CIXXXQR0dHRdOvWjY8++sjH71ICmQKInLQhnVoQYbeRmVPMzoPqByIiImJZFoWl5X65NfTHwKeffpqRI0dyww03kJmZSWZmJqmpqQDceeedPPTQQ2zYsIH+/fuTn5/PhAkT+PLLL1m5ciVjx45l4sSJ7Nq167jHuP/++5kyZQo//vgjEyZMYNq0aRw6dKjJn68EB01EGKIcDnjllerlkxHlsDMotQVLdxzi+20H6dwqxnMFFBERaYaKylz0/stcvxx7/QNjiXac+NIuISEBh8NBdHQ0KSkpAGzcuBGABx54gPPOO69q25YtWzJgwICqxw8++CCzZ8/mo48+4uabb673GDNmzOCKK64A4P/+7//45z//ydKlSxk3btxJvTcJLqoBCVERETBjhrlFRJz8fkZW9ANZpH4gIiIizd7QoUNrPS4oKODOO++kd+/eJCYmEhsby8aNG09YA9K/f/+q5ZiYGOLi4sjKyvJKmaX5UQ2INMnIri15+n9b+H6r6QeiiXtERCSURUXYWf/AWL8du6liYmq3Zvj973/P3LlzeeyxxzjllFOIiori0ksvpbS09Lj7iTjq102bzYbb7W5y+SQ4KICEqPJymFtRQzx2LISf5JkwqGMizvAwsvNL2Hogn1PaxHmukCIiIs2MzWZrUDMof3M4HLhcrhNut3DhQmbMmMFFF10EQH5+Pjt27PBy6STYqQlWiCopgQsuMLeSkpPfjzPczpBOLQD4Xs2wREREmoXOnTuzZMkSduzYQXZ2dr21E6eccgqzZs1i1apVrF69miuvvFI1GdJkCiDSZKPUD0RERKRZueOOO7Db7fTu3ZvWrVvX26fjySefpEWLFowaNYqJEycyduxYBg8e7OPSSrCxWZrAIWDk5uaSkJBATk4O8fHxXj1WQQHExprl/HyIacIAVit2HuKS576nRXQEK/50HmFh6gciIiLBr7i4mO3bt5OWlkZkZKS/iyMedLx/W19erwUr1YBIk/XvkEi0w87hwjI27c/zd3FEREREJIApgEiTRdjDGNo5CVA/EBERERE5PgUQ8Qj1AxERERGRhlAAEY8Y2cUEkCXbD+Jyq1uRiIiIiNQt8AeqFq9wOOCZZ6qXm6pPu3jinOHkFZezfm8u/TokNH2nIiIiIhJ0VAMSANLT0+nduzfDhg3z2TEjIuCmm8ztqMlKT0q4PYxT0yr6gWzLbvoORURERCQoKYAEgJtuuon169ezbNkyfxelSUaqH4iIiIiInICaYIUolwsWLjTLo0eD3d70fVYGkGXbD1HmchNhV74VERERkdp0hRiiiovh7LPNrbjYM/vslRJPYnQEBaUu1uzJ8cxORUREJOB07tyZp556quqxzWZjzpw59W6/Y8cObDYbq1atatJxPbUf8S8FEPGYsDAbw9M0H4iIiEioyczMZPz48R7d54wZM5g8eXKtdampqWRmZtK3b1+PHkt8SwFEPKpyOF4FEBERkdCRkpKC0+n0+nHsdjspKSmEh6sXQXOmACIeNeqUVgAs33mIknKXn0sjIiIiR3vhhRdo3749bre71voLL7yQ6dOns3XrViZNmkRycjKxsbEMGzaML7/88rj7PLoJ1tKlSxk0aBCRkZEMHTqUlStX1tre5XJx3XXXkZaWRlRUFD169ODpp5+uev6+++7j1Vdf5cMPP8Rms2Gz2fj666/rbIK1YMECTj31VJxOJ23btuXuu++mvLy86vmzzjqL3/72t9x5550kJSWRkpLCfffd1/gPTjxGAUQ8qlubWFrFOiguc7M6Q/1AREREAs1ll11GdnY28+fPr1p3+PBh5s6dy7Rp08jPz2fChAl8+eWXrFy5krFjxzJx4kR27drVoP0XFBRwwQUX0KNHD1asWMF9993HHXfcUWsbt9tNhw4dePfdd1m/fj1/+ctf+OMf/8i7774LwB133MGUKVMYN24cmZmZZGZmMmrUqGOOtWfPHiZMmMCwYcNYvXo1zz33HC+99BIPPvhgre1effVVYmJiWLJkCY888ggPPPAA8+bNa+xHJx6i+ivxKJvNxvAuLfnkx0y+33qwam4QERGRkGBZUFbon2NHRIPNdsLNkpKSGDduHG+++SbnnnsuAO+99x5JSUmce+652O12BgwYULX9gw8+yOzZs/noo4+4+eabT7j/N954A5fLxcsvv0x0dDR9+vRh9+7d/OpXv6ouakQE999/f9XjtLQ0Fi1axLvvvsuUKVOIjY0lKiqKkpISUlJS6j3Ws88+S2pqKs888ww2m42ePXuyd+9e7rrrLv7yl78QFmZ+a+/fvz/33nsvAN26deOZZ57hf//7H+edd94J3494ngKIeNzIygCyLZtb6Obv4oiIiPhOWSH8Xzv/HPuPe8ER06BNp02bxi9+8QueffZZnE4nb7zxBpdffjl2u52CggLuv/9+Pv74Y/bu3Ut5eTlFRUUNrgHZsGEDAwYMIDo6umrdyJEjj9nu+eef58UXX2Tnzp0UFRVRWlrKwIEDG3SMmscaOXIkthrB67TTTiM/P5/du3fTsWNHwASQmtq2bUtWVlajjiWeowASoiIi4JFHqpc9aVTFfCA/7DxCcZmLyAgPTDIiIiIiHjNx4kTcbjeffPIJw4YNY+HChTzxxBMA/P73v2fu3Lk89thjnHLKKURFRXHppZdSWlraoH1blnXCbd59911uu+02Hn/8cUaOHElcXByPPvooS5YsadT7sCyrVvioefya6yOOutix2WzH9IER31EACVEOB/z+997Zd1qrGJLjnezPLeGHnYerOqaLiIgEvYhoUxPhr2M3UFRUFBdffDFvvPEGP/30E927d2fIkCEALFy4kBkzZnDRRRcBkJ+fz44dOxq87969e/Of//yHoqIioqKiAFi8eHGtbRYuXMioUaP49a9/XbVu69attbZxOBy4XMcf0KZ379588MEHtYLIokWLiIuLo3379g0us/iWOqGLx9lsturheLdpOF4REQkhNptpBuWPWwP6f9Q0bdo0PvnkE15++WWuuuqqqvWnnHIKs2bNYtWqVaxevZorr7yyUbUFV155JWFhYVx33XWsX7+eTz/9lMcee6zWNqeccgrLly9n7ty5bN68mT//+c8sW7as1jadO3fmxx9/ZNOmTWRnZ1NWVnbMsX7961+TkZHBb37zGzZu3MiHH37Ivffey+23317V/0MCj/5lQpTLBcuWmdsJflw4KSO7aj4QERGRQHbOOeeQlJTEpk2buPLKK6vWP/nkk7Ro0YJRo0YxceJExo4dy+DBgxu839jYWP773/+yfv16Bg0axD333MPf//73WtvceOONXHzxxUydOpXhw4dz8ODBWrUhADfccAM9evRg6NChtG7dmu++++6YY7Vv355PP/2UpUuXMmDAAG688Uauu+46/vSnPzXy0xBfslkNaagnPpGbm0tCQgI5OTnEx8d79VgFBRAba5bz8yGmYX3WGizjUCGjH5lPeJiN1feOIcap1n4iIhJciouL2b59O2lpaURGRvq7OOJBx/u39eX1WrBSDYh4RWpSNO0Toyh3WyzfedjfxRERERGRAKEAIl6jZlgiIiIicjQFEPEadUQXERERkaMpgIjXVNaArNl9hNziY0euEBEREZHQowAiXtMuMYrOLaNxW7Bs+yF/F0dEREREAoACiHiV+oGIiEiw04CiwUf/pt6lsVFDVEQE3Htv9bK3jOjSkreWZqgfiIiIBJ2Iij+ghYWFVTN+S3AoLCwEqv+NxbMUQEKUwwH33ef941R2RF+fmcuRwlISox3eP6iIiIgP2O12EhMTycrKAiA6OhpbI2cjl8BiWRaFhYVkZWWRmJiI3W73d5GCkgKIeFWb+EhOaRPLT1n5LN52iHF9U/xdJBEREY9JSTF/1ypDiASHxMTEqn9b8TwFkBDldsOGDWa5Vy8I82JvoJFdWlYEkIMKICIiElRsNhtt27alTZs2lJVpxMdgEBERoZoPL1MACVFFRdC3r1nOz4eYGO8da2TXlvxn8U51RBcRkaBlt9t10SrSQBoFS7xuREU/kE3788jOL/FzaURERETEnxRAxOuSYhz0TIkDYLFGwxIREREJaQog4hOaD0REREREQAFEfKRyOF7NByIiIiIS2hRAxCeGp7XEZoNtBwrYn1vs7+KIiIiIiJ8ogIhPJERH0LddAgBfrNvn59KIiIiIiL8ogISoiAi44w5zi4jwzTEnD2oPwJNfbiGnUGOli4iIiIQiBRAPCw8PZ+DAgQwcOJDrr7/e38Wpl8MBjz5qbg6Hb455zchOdGsTy6GCUh77YpNvDioiIiIiAUUTEXpYYmIiq1at8ncxAlKEPYwHJvXlin8v5o0lO5k6LJW+7RP8XSwRERER8SHVgIQotxt27DA3t9t3xx3ZtSUXDmiH24I/f7gWt9vy3cFFRERExO9CKoB88803TJw4kXbt2mGz2ZgzZ84x2zz77LOkpaURGRnJkCFDWLhwYaOOkZuby5AhQzj99NNZsGCBh0rueUVFkJZmbkVFvj32Pef3IsZhZ+WuI7y/YrdvDy4iIiIifhVSAaSgoIABAwbwzDPP1Pn8O++8w6233so999zDypUrGT16NOPHj2fXrl1V2wwZMoS+ffsec9u7dy8AO3bsYMWKFTz//PNcc8015Obm+uS9NSfJ8ZHcdl53AB7+fCNHCkv9XCIRERER8RWbZVkh2QbGZrMxe/ZsJk+eXLVu+PDhDB48mOeee65qXa9evZg8eTIPPfRQo48xfvx4/vrXvzJ06NA6ny8pKaGkpKTqcW5uLqmpqeTk5BAfH9/o4zVGQQHExprl/HyIifHq4Y5R5nJz/j8Wsnl/PleN6MiDk/v5tgAiIiIiJyE3N5eEhASfXK8Fq5CqATme0tJSVqxYwZgxY2qtHzNmDIsWLWrQPg4fPlwVKHbv3s369evp0qVLvds/9NBDJCQkVN1SU1NP/g00M5Ud0gHeWLKLNbtz/FwiEREREfEFBZAK2dnZuFwukpOTa61PTk5m376GTZy3YcMGhg4dyoABA7jgggt4+umnSUpKqnf7P/zhD+Tk5FTdMjIymvQempsRXVoyaWA7LHVIFxEREQkZGob3KDabrdZjy7KOWVefUaNGsWbNmgYfy+l04nQ6G1W+YPPHCb3434YsVmUc4b0VGUwd1tHfRRIRERERL1INSIVWrVpht9uPqe3Iyso6plZEPCc5PpJbf9YNgIc/U4d0ERERkWCnAFLB4XAwZMgQ5s2bV2v9vHnzGDVqlJ9K5T3h4fDrX5tbuJ/rwaaP6kyP5DgOF5bx6FzNkC4iIiISzEKqCVZ+fj4//fRT1ePt27ezatUqkpKS6NixI7fffjtXX301Q4cOZeTIkfzrX/9i165d3HjjjX4stXc4nZCe7u9SGKZDeh+m/msxby7dxdRhqfTvkOjvYomIiIiIF4RUAFm+fDlnn3121ePbb78dgOnTpzNz5kymTp3KwYMHeeCBB8jMzKRv3758+umndOrUyavlSk9PJz09HZfL5dXjBLLhXVpy0aD2zF65hz9/uI7ZvxpFWFjD+t6IiIiISPMRsvOABCJfjittWZCdbZZbtYIG9rP3qqzcYs55fAH5JeU8fHE/Lj9VHdJFREQksGgekKZTH5AQVVgIbdqYW2Ghv0tjtKkxQ/rfP9/I4QJ1SBcREREJNgogElCmj+xEz5SKDulfqEO6iIiISLBRAJGAEl5jhvS3lu5idcYR/xZIRERERDxKAUQCzqlpSVw8qH3VDOkuzZAuIiIiEjQUQCQg3T2hJ3HOcH7cncM7yzL8XRwRERER8RAFkACQnp5O7969GTZsmL+LEjDaxFV3SH9krjqki4iIiAQLBZAAcNNNN7F+/XqWLVvm76IElGsqOqQfKSzjEc2QLiIiIhIUFEBCVHg4TJ9ubuEBOh1lzQ7pby/bxSp1SBcRERFp9hRAQpTTCTNnmpvT6e/S1O/UtCQuHmw6pP9FHdJFREREmj0FEAl4fxjfq6pD+tvLdvm7OCIiIiLSBAogIcqyoKDA3KwAr1RoHefk9jEVHdI/38QhdUgXERERabYUQEJUYSHExppbYaG/S3NiV48wHdJzisp4dO5GfxdHRERERE6SAkgA0DC8JxZuD+Ovkys7pGewctdhP5dIRERERE6GAkgA0DC8DTOscxKXDO5Q0SF9nTqki4iIiDRDCiDSrNw9vidxkeGs2ZPDW0vVIV1ERESkuVEAkWaldZyT31XMkP7o3E0czC/xc4lEREREpDEUQKTZuWpEJ3q1jSenqIxHPtcM6SIiIiLNiQKINDvh9jD+OqkPAO8sz+AHdUgXERERaTYUQEKU3Q6XXmpudru/S9N4QzsncemQDoBmSBcRERFpThRAQlRkJLz3nrlFRvq7NCenskP62j25vKkO6SIiIiLNggKINFutYp38fmwPAB79fKM6pIuIiIg0AwogAUATEZ68acM70bttPLnF5fz9c82QLiIiIhLobJZlqfF8gMjNzSUhIYGcnBzi4+O9eqyCAoiNNcv5+RAT49XDedWKnYe45LnvAZj161EM7tjCzyUSERGRYOXL67VgpRoQafaGdErisooO6X+eow7pIiIiIoFMAUSCwl3jexIfGc66vbm8uWSnv4sjIiIiIvVQAJGgUKtD+txNZKtDuoiIiEhAUgCRoHHl8E70aVfRIf0zdUgXERERCUQKIBI07GE2HpjUF4D3VuxmxU7NkC4iIiISaBRAJKgM6dSCKUPVIV1EREQkUCmAhCi7HSZMMDe73d+l8ay7xpkO6eszc3lDHdJFREREAooCSIiKjIRPPjG3yEh/l8azWsY6+f24noDpkL4/t9jPJRIRERGRSgogAUAzoXvelad2pG/7ePKKyznz0fnc/s4qFm87iObdFBEREfEvzYQeQDSzpmdt2Z/HzW+uZNP+vKp1nVtGM2VYKpcO7kCb+CCr+hERERGv0/Va0ymABBBfntAFBdCmjVnOyoKYGK8ezm8sy2JVxhHeWZbBf1fvpaDUBZgRs87u0Zqpwzpydo/WhNtVGSgiIiInpgDSdAogAcTXASQ21izn5wdvAKmpoKScT9Zk8s6yjFpD9LaOc3LJ4A5MHZZKWqsQ+CBERETkpCmANJ0CSABRAPGdn7LyeHf5bj5YsZuDBaVV609NS2Lq0FQm9GtLlCPIhgcTERGRJlMAaToFkACiAOJ7peVuvtq4n3eWZbBg8wEqpw2Jc4YzcWA7Lh+WSr/2CdhsNv8WVERERAKCAkjTKYAEEAUQ/8rMKeKDFbt5d/ludh0qrFrfMyWOqcNSuWhQexKjHX4soVFQUs727AJ2HCxg58FC2sQ5Gds3hfjICH8XTUREJOgpgDSdAkgAUQAJDG63xeJtB3lneQafrd1HabkbAIc9jLF9U5g6NJVRXVsSFua9WpGSchcZhwrZdsAEje3ZBVXL+3NLjtneER7Geb2SmTyoPWd2b40jXJ3qRUREvEEBpOkUQAKIAkjgySksY86qPbyzLIP1mblV6zu0iGLK0FQuHdKBdolRJ7Vvl9ti75EitmUXsP1APjsOFprl7Hz2HC6qag5Wl6QYB51bRtOpZQxr9uTwU1Z+1XMtoiO4oH87Jg9qz+COiWo+JiIi4kEKIE2nABJAfHlCFxXB+PFm+bPPIOrkrqFDyto9Oby9bBcfrtpLXnE5ADYbnNGtNVOHpfKzXsnH1DxYlkVWXkmdNRm7DhZS6nLXe7wYh5201jGktYolrWU0aa1j6NwyhrRWMbWaglmWxbq9ucxeuYcPV+0lO7+6hqRTy2gmD2zP5EHtNcKXiIiIByiANJ0CSADRCd08FJW6+HydGc538bZDVeuTYhxMHtieGKe9olbDBI3CirlH6uIID6NTUjRprWJM2KgIGGmtY2gd62x07UW5y82irQeZs3IPn6/bV+vYA1MTuWhQey7o35aWsc7Gv3ERERHR9ZoHKIAEEJ3Qzc+O7ALeXZ7B+yt2k5V3bN8MgDAbpFaEjM4tY+hSoyajXWIUdi/1JSksLeeLdfuZvXIPC7dUj/AVHmbjzO6tmTyoPef1TiYyQsMNi4iINJSu15pOASSA6IRuvspdbr7edIBP12YSGWGvVZOR2iLa753Cs/KK+Xh1JrNX7mHNnpyq9bHOcMb1TeHiQe0Z3qWl18KQiIhIsND1WtMpgASA9PR00tPTcblcbN682Wed0Dt3Nss7dqgTeij5KSuPOSv3MnvlHvYcKapanxIfyaSB7bhocHt6pugLVUREpC4KIE2nABJANAqW+JLbbbFi12Fm/bCHT37cS25Fx3owc59cNKg9Fw5sR9sEjVAgIiJSSQGk6RRAAogCiPhLSbmL+RsPMGflHr7amFU1OpfNBiO7tOSiQe0Z1zeFOE12KCIiIU4BpOkUQAKIAogEgiOFpXy6Zh9zVu5h6Y7qUb6c4WGc1zuZacM7MbJrSz+WUERExH8UQJpOASSAKIBIoMk4VMhHq/cy64fdbD1QULX+kUv7M2Voqh9LJiIi4h8KIE2nABJAFEAkUFmWxdo9ubz07TbmrNqLzQZPTR3IpIHt/V00ERERn1IAaTr/jg0qIs2CzWajX4cEnpw6kGnDO2JZcPu7q/l87T5/F01ERESaGQWQEBUWBkOHmluYzgJpIJvNxl8n9eWSwR1wuS1+89YPzN+Y5e9iiYiISDOiS88QFRUFy5aZW5RGWZVGCAuz8cil/bmgf1vKXBa/fH0F3/2U7e9iiYiISDOhACIijWYPs/Hk1IGc1zuZ0nI317+6nGU1RswSERERqY8CiIiclAh7GM9cOYgzu7emqMzFta8sY3XGEX8XS0RERAKcAkiIKiyEzp3NrbDQ36WR5soZbueFq4cwsktL8kvKueblpazfm+vvYomIiEgAUwAJUZYFO3eamwZilqaIjLDz4vShDO6YSE5RGVe9tIQt+/P8XSwREREJUAogItJkMc5wZv78VPq1T+BQQSnTXlzCjuyCE79QREREQo4CiIh4RHxkBK/9/FR6psSRlVfCtBeXsPuw2veJiIhIbQogIuIxLWIc/Oe64XRtHcOeI0Vc+e8l7Msp9nexREREJIAogASA9PR0evfuzbBhw/xdFJEmax3n5I3rR9AxKZpdhwqZ9uJisvNL/F0sERERCRA2y1IX5ECRm5tLQkICOTk5xMfHe/VYBQUQG2uW8/MhJsarh5MQtPtwIVOe/569OcX0TInj7V+MIDHa4e9iiYiINIkvr9eClWpAQpTNBr17m5vN5u/SSDDq0CKaN28YQZs4Jxv35XH1S0vJLS7zd7FERETEzxRAQlR0NKxbZ27R0f4ujQSrzq1ieOP64STFOFizJ4drX1lGQUm5v4slIiIifqQAIiJe1S05jtevG058ZDgrdh7m+leXU1zm8nexRERExE8UQETE63q3i+e164YT6wzn+20H+eV/VlBSrhAiIiISihRAQlRhIfTpY26FmqpBfGBgaiKvXDuMqAg7CzYf4OY3V1Lmcvu7WCIiIuJjCiAhyrJg/Xpz0zho4ivDOifx4vShOMLDmLd+P7e9swqXWyegiIhIKFEAERGfOu2UVrxw1RAi7DY+/jGTuz74EbdCiIiISMhQABERnzu7Zxv+ecUg7GE23l+xm798tBZNSSQiIhIaFEBExC/G9W3LE1MGYLPB64t38eAnGxRCREREQoACiIj4zaSB7fn7xf0BeOnb7Tz+xWY/l0hERES8TQFERPxqyrBUHpjUB4Bn5v/EM19t8XOJRERExJvC/V0A8Q+bDTp1ql4W8adrRnampMzN3z7dwGNfbCYyws71o7v4u1giIiLiBQogISo6Gnbs8HcpRKrdcEYXispcPDFvMw9+sgFnhJ2rR3Tyd7FERETEw9QES0QCxm/OOYVfn9UVgD/PWct7yzP8XCIRERHxNAUQEQkYNpuN34/twbWndQbgrg9+5MNVezQ6loiISBCxWfrLHjByc3NJSEggJyeH+Ph4rx6rqAjOOMMsf/MNREV59XAijWJZFn+cvZa3lu4CINphJ7VFNB1aRJGaZO471HicEBXh5xKLiEio8OX1WrBSH5AQ5XbD8uXVyyKBxGaz8bfJfQkPs/H6kp0UlrrYtD+PTfvz6tw+LjL8mICS2iKaDknmPsaprzoREZFAoRqQAOLLRF1QALGxZjk/H2JivHo4kZNWXOZi75EiMg4XsftwIRmHKu4PF7HncCHZ+aUn3EeL6IjawaRFFB2SokmtqEmJjLD74J2IiEgwUA1I0+lnQREJaJERdrq0jqVL69g6ny8sLWfP4SIyDhey+3ARGYcq7iseHyks43BhGYcLc/hxd06d+2gV6yQ1qbpZV0p8JMnxTtrER5IcH0nrWCeOcHWZExER8QQFEBFp1qId4XRLjqNbclydz+cWl5mAclQwqXycX1JOdn4J2fklrNx1pN7jtIxxVAQSJ8lxkbSpDChxTpIrgkqrWAfhdgUVERGR41EAEZGgFh8ZQXzbCHq1Pbaa3LIscorKajTrKmTP4SL255awP6+YrNwSsvKKKXNZHCwo5WBBKRsy6z+WzWZqU2qFlLjIioBigkqbeCctY5zYw4JjBlDLsigpd1NS5qa43EVJmZuSchfFNe5TEpx0bR2LTbOeiogICiABIT09nfT0dFwul7+LIhJSbDYbidEOEqMd9OuQUOc2brfFkaIy9ucWsz/XhJL9ucVk5Zn7/XklZFU8drktDuSVcCCvhLXk1ntce5iN1hVBpXWcE2e4nbAwG3YbFfc27GG22ss2G/YwCLNVr69+njq2Nc+H2ThqWxtlLrcJDeVuistcFQHCVevx0fdHh4qSMhfF5W5Kyxs2ikWX1jFM6NuW8f1S6N02XmFERCSEqRN6APF1J/TOnc3yjh3qhC7SVG63qSXZn1vMgcpwUlWTUrGcW0x2fgnuIP3WDbOZPjvO8DCc4XYiI8JwhIexI7uQUld1UOnUMprxfdsyoV8K/donKIyISLOiTuhNpwASQHRCiwQ/l9viYH5JVSDJyiuhzOXG5bZwWxYut4XLsnC7LVxuqpcr7s021LFtjWWLGtvW3K9ZH2634QwPqwoLR4eGmvfOGveRR99Xvc4sh4fZ6gwTecVlfLUxi0/XZPL1pgOU1Kg1aZ8Yxfi+KYzv15ZBqYmEBUnTNBEJXrpeazoFkACiE1pEgl1BSTnzN2Xx2Zp9fLUxi6Ky6qanKfGRjOubwoR+bRnSqUXQ9JMRkeCi67WmUwAJIDqhRSSUFJW6WLD5AJ+tzeR/G7LILymveq51nJNxfVIY3y+FUzsnBczoYiXlLrZmFbAlK4/N+/PYtC+frLxiWsY4SEkwAw6kxEeSnGDuU+IjSYyOCNhmZpZlmg7uyylm75Ei9uUWs/dIMZk5RWTmmPv9uSVEO+wVw1NHVg1TXfkek+MjSUmIJCnaoRosCQm6Xms6BZAA4ssTuqgIxo83y599BlFRXj2ciMhxFZe5+HZLNp+uzWTe+v3kFVeHkZYxDsb0SWFCvxRGdGlJhA/CSJnLzY7sAjbtz2Pz/nw278tjc1YeOw8W4mpkJx5neNhRwcRZddFeeQGfHB/p8blmLMvicGGZCRO1QkV12MjMKW7wQAInEmG3VYz65qwVxo5e9vXEny63RWFpOQUlLgpKyykoqVguKa947MLCIiEq4phbXGREwNbEudwWRwrN6HwH80s5VFDKwYISDuab+0MFpWRXrM8tKiPKYSfGEU5sZDixzopbjeUYZzhxFfdmvZ1YZ4RZdoQT47QHzA8B/qYA0nQKIAFEM6GLiEBpuZvvtmbz2ZpMvli/nyOFZVXPJUZHcF6vZCb0a8tpp7Rq8kW7y22x82CBCRn786pu27MLKHPV/ecxPjKc7slxdE+Jo3ubWNolRnGooJR9FSOl7cspZl9FH59DBaUNLktlLcrRNSg1l+OjwrHZbFVDSO89Usy+3KLatRZHiivCRRHFZScOF5XDR7dLMAGhbUIU7RIjSUmIol1FeCgoLTf9lnLMvvflFlct788t4WBBCQ29moiPDK83oCTHR5Kc4CQiLKwqHOSXlFcEiJohomaAqCNc1NimZjO/xrLZINYZXmc4SYiKIL7iPjG66eGlcsS9QwUlVcHBhAsTJiqDRWXYOFxY6vMBLSIjwkwocdqJjQwnxhFOXGR1gKkMK7GR4URF2Ily2ImMMLeoypvD9CuLcpjHkRH2gA159VEAaToFkACiACIiUluZy82SbYf4dG0mc9fu42CNC/q4yHDO65XM+H5tGd2t1XF/WXe7LXYfLjIBIyvP1Gjsz+enA/n11gDEOOx0S46jR3Ic3ZJj6Z4cR4+UONrEORvcpKqk3EVWbom5aM+pGVAqlnOL2Z9TUmuUsOOJjAijZYyTQwWlDb6wbhXroG1CFG0TIs0tsXLZ3Hui9qXM5SYrr6TqPe6vI6TsyyluUhhoKnuYjRiHnVhnONEVF8wxDjsxTjMjQU5RGblFZeRU3ApLm17WuMi6w4sjPKwqVFTWXBwuLGt07RqYUJ4U46BVjJOkGActYx20jHHQMrbicYyD+KgIistMmMsvMUEtr7gy4JVVrHeRX1xGQYmLvIptKrf3VC1ZfRz2MCIjwqoCS1REzfuweoKMGQSjMsgkRkdwTs9kr5azkgJI0ymABBAFEBGR+rncFku3H+KztZl8tnYfB/JKqp6Lcdg5t1cyE/ql0LttAluz89lS2Xxqfx5b9ufXe/EbGRFGtzYmZPRIjqN7ReBonxjlk74blU2lqgJKRdOo/UcFlZo1QZUqa02qAkZiJO0SokhJMPfJCWaemUBgWRZ5JeXVtSg13m/lqHD7cqqHqo6MCCPGYYJCdI3gEOu0E+2o/NW9ern+bcxzzvCwRv17lpa7q8LI0eGkrpunwkt8ZDitYqvDRFKMk1axjorHzopwYR63iHb4pEliSbmrqkYpr9jUPOUXl9cRaMzjvJJySspcFJW5KCp1UVQxd1BRjXUlHg41qUlRLLzzHI/usz4KIE2nABJAFEBERBrG7bZYseswn63Zx2drM8nMKT7haxz2MLq0jqFHigkZ5hZLhxbRzaIJSHGZi/25xRwsKCUp2uGX/hS+UF5RG9Sc+xucKLyUlrtr1FaYsNEq1kGLGN8EikDgdluUlLspKnNRXCOYFJeZyU4rw0px5fOllY/dVY+Ly6vXt4518sTUgT4puwJI0ymABBAFEBGRxnO7LVbvPsJna/fx6ZpM9uUUk9YqplbI6J4SR6ek6GZ9USsigUEBpOnC/V0AERGRpggLszGoYwsGdWzBHyf0wu22NBysiEgAUwAJYdHR/i6BiIjnKXyIiAQ2BZAQFRNjmmGJiIiIiPiSGsOKiIiIiIjPKICIiIiIiIjPKICEqOJiOP98cys+8eiVIiIiIiIeoT4gIcrlgk8/rV4WEREREfEF1YCIiIiIiIjPKICIiIiIiIjPKICIiIiIiIjPKICIiIiIiIjPKICIiIiIiIjPaBSsAGJZFgC5ubleP1bNWdBzczUSloiIiEhDVF6nVV63SeMpgASQgwcPApCamurT47Zr59PDiYiIiDR7eXl5JCQk+LsYzZICSABJSkoCYNeuXTqha8jNzSU1NZWMjAzi4+P9XZyAos+mbvpc6qfPpm76XOqnz6Zu+lzqF+yfjWVZ5OXl0U6/4J40BZAAEhZmuuQkJCQE5X/YpoqPj9fnUg99NnXT51I/fTZ10+dSP302ddPnUr9g/mz0Q3HTqBO6iIiIiIj4jAKIiIiIiIj4jAJIAHE6ndx77704nU5/FyWg6HOpnz6buulzqZ8+m7rpc6mfPpu66XOpnz4bORGbpTHERERERETER1QDIiIiIiIiPqMAIiIiIiIiPqMAIiIiIiIiPqMAIiIiIiIiPqMA4mPPPvssaWlpREZGMmTIEBYuXHjc7RcsWMCQIUOIjIykS5cuPP/88z4qqW889NBDDBs2jLi4ONq0acPkyZPZtGnTcV/z9ddfY7PZjrlt3LjRR6X2jfvuu++Y95iSknLc1wT7+QLQuXPnOv/9b7rppjq3D+bz5ZtvvmHixIm0a9cOm83GnDlzaj1vWRb33Xcf7dq1IyoqirPOOot169adcL8ffPABvXv3xul00rt3b2bPnu2ld+Adx/tcysrKuOuuu+jXrx8xMTG0a9eOa665hr179x53nzNnzqzzPCouLvbyu/GsE50zM2bMOOY9jhgx4oT7DeZzBqjz395ms/Hoo4/Wu89gOGca8jc6VL9npGkUQHzonXfe4dZbb+Wee+5h5cqVjB49mvHjx7Nr1646t9++fTsTJkxg9OjRrFy5kj/+8Y/89re/5YMPPvBxyb1nwYIF3HTTTSxevJh58+ZRXl7OmDFjKCgoOOFrN23aRGZmZtWtW7duPiixb/Xp06fWe1yzZk2924bC+QKwbNmyWp/JvHnzALjsssuO+7pgPF8KCgoYMGAAzzzzTJ3PP/LIIzzxxBM888wzLFu2jJSUFM477zzy8vLq3ef333/P1KlTufrqq1m9ejVXX301U6ZMYcmSJd56Gx53vM+lsLCQH374gT//+c/88MMPzJo1i82bN3PhhReecL/x8fG1zqHMzEwiIyO98Ra85kTnDMC4ceNqvcdPP/30uPsM9nMGOObf/eWXX8Zms3HJJZccd7/N/ZxpyN/oUP2ekSayxGdOPfVU68Ybb6y1rmfPntbdd99d5/Z33nmn1bNnz1rrfvnLX1ojRozwWhn9LSsrywKsBQsW1LvN/PnzLcA6fPiw7wrmB/fee681YMCABm8fiueLZVnWLbfcYnXt2tVyu911Ph8q5wtgzZ49u+qx2+22UlJSrIcffrhqXXFxsZWQkGA9//zz9e5nypQp1rhx42qtGzt2rHX55Zd7vMy+cPTnUpelS5dagLVz5856t3nllVeshIQEzxbOz+r6bKZPn25NmjSpUfsJxXNm0qRJ1jnnnHPcbYLxnDn6b7S+Z+RkqQbER0pLS1mxYgVjxoyptX7MmDEsWrSoztd8//33x2w/duxYli9fTllZmdfK6k85OTkAJCUlnXDbQYMG0bZtW84991zmz5/v7aL5xZYtW2jXrh1paWlcfvnlbNu2rd5tQ/F8KS0t5fXXX+fnP/85NpvtuNuGwvlS0/bt29m3b1+tc8LpdHLmmWfW+50D9Z9Hx3tNc5eTk4PNZiMxMfG42+Xn59OpUyc6dOjABRdcwMqVK31TQB/7+uuvadOmDd27d+eGG24gKyvruNuH2jmzf/9+PvnkE6677roTbhts58zRf6P1PSMnSwHER7Kzs3G5XCQnJ9dan5yczL59++p8zb59++rcvry8nOzsbK+V1V8sy+L222/n9NNPp2/fvvVu17ZtW/71r3/xwQcfMGvWLHr06MG5557LN99848PSet/w4cN57bXXmDt3Lv/+97/Zt28fo0aN4uDBg3VuH2rnC8CcOXM4cuQIM2bMqHebUDlfjlb5vdKY75zK1zX2Nc1ZcXExd999N1deeSXx8fH1btezZ09mzpzJRx99xFtvvUVkZCSnnXYaW7Zs8WFpvW/8+PG88cYbfPXVVzz++OMsW7aMc845h5KSknpfE2rnzKuvvkpcXBwXX3zxcbcLtnOmrr/R+p6RkxXu7wKEmqN/pbUs67i/3Na1fV3rg8HNN9/Mjz/+yLfffnvc7Xr06EGPHj2qHo8cOZKMjAwee+wxzjjjDG8X02fGjx9ftdyvXz9GjhxJ165defXVV7n99tvrfE0onS8AL730EuPHj6ddu3b1bhMq50t9Gvudc7KvaY7Kysq4/PLLcbvdPPvss8fddsSIEbU6Y5922mkMHjyYf/7zn/zjH//wdlF9ZurUqVXLffv2ZejQoXTq1IlPPvnkuBfcoXLOALz88stMmzbthH05gu2cOd7faH3PSGOpBsRHWrVqhd1uPybdZ2VlHfMrQKWUlJQ6tw8PD6dly5ZeK6s//OY3v+Gjjz5i/vz5dOjQodGvHzFiRLP9VamhYmJi6NevX73vM5TOF4CdO3fy5Zdfcv311zf6taFwvlSOmNaY75zK1zX2Nc1RWVkZU6ZMYfv27cybN++4tR91CQsLY9iwYUF/HrVt25ZOnTod932GyjkDsHDhQjZt2nRS3zvN+Zyp72+0vmfkZCmA+IjD4WDIkCFVI/ZUmjdvHqNGjarzNSNHjjxm+y+++IKhQ4cSERHhtbL6kmVZ3HzzzcyaNYuvvvqKtLS0k9rPypUradu2rYdLF1hKSkrYsGFDve8zFM6Xml555RXatGnD+eef3+jXhsL5kpaWRkpKSq1zorS0lAULFtT7nQP1n0fHe01zUxk+tmzZwpdffnlSAd2yLFatWhX059HBgwfJyMg47vsMhXOm0ksvvcSQIUMYMGBAo1/bHM+ZE/2N1veMnDR/9HwPVW+//bYVERFhvfTSS9b69eutW2+91YqJibF27NhhWZZl3X333dbVV19dtf22bdus6Oho67bbbrPWr19vvfTSS1ZERIT1/vvv++steNyvfvUrKyEhwfr666+tzMzMqlthYWHVNkd/Lk8++aQ1e/Zsa/PmzdbatWutu+++2wKsDz74wB9vwWt+97vfWV9//bW1bds2a/HixdYFF1xgxcXFhfT5UsnlclkdO3a07rrrrmOeC6XzJS8vz1q5cqW1cuVKC7CeeOIJa+XKlVWjOT388MNWQkKCNWvWLGvNmjXWFVdcYbVt29bKzc2t2sfVV19dayS+7777zrLb7dbDDz9sbdiwwXr44Yet8PBwa/HixT5/fyfreJ9LWVmZdeGFF1odOnSwVq1aVet7p6SkpGofR38u9913n/X5559bW7dutVauXGlde+21Vnh4uLVkyRJ/vMWTdrzPJi8vz/rd735nLVq0yNq+fbs1f/58a+TIkVb79u1D+pyplJOTY0VHR1vPPfdcnfsIxnOmIX+jQ/V7RppGAcTH0tPTrU6dOlkOh8MaPHhwreFmp0+fbp155pm1tv/666+tQYMGWQ6Hw+rcuXO9X3zNFVDn7ZVXXqna5ujP5e9//7vVtWtXKzIy0mrRooV1+umnW5988onvC+9lU6dOtdq2bWtFRERY7dq1sy6++GJr3bp1Vc+H4vlSae7cuRZgbdq06ZjnQul8qRxi+Ojb9OnTLcsyQ2Tee++9VkpKiuV0Oq0zzjjDWrNmTa19nHnmmVXbV3rvvfesHj16WBEREVbPnj2bXVg73ueyffv2er935s+fX7WPoz+XW2+91erYsaPlcDis1q1bW2PGjLEWLVrk+zfXRMf7bAoLC60xY8ZYrVu3tiIiIqyOHTta06dPt3bt2lVrH6F2zlR64YUXrKioKOvIkSN17iMYz5mG/I0O1e8ZaRqbZVX0UhUREREREfEy9QERERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGfUQARERERERGf+X85xmknbJ+PKgAAAABJRU5ErkJggg==' width=800.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot data\n",
    "plt.close('all')\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.semilogy(range(1, history.epoch[-1]+2),history.history['loss'], label='train')\n",
    "plt.semilogy(range(1, history.epoch[-1]+2),history.history['val_loss'], label='validation')\n",
    "plt.axvline(x = 1, linestyle='--', color = 'b')\n",
    "plt.legend();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Predicciones</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepara los datos de validación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1054, 60, 1)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcula predicciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_01 = tf.keras.models.load_model(checkpoint_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'verbose': 1, 'epochs': 1, 'steps': 33}"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_01.history.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1/33 [..............................] - ETA: 12s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-22 03:34:22.781596: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-09-22 03:34:22.846869: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33/33 [==============================] - 1s 13ms/step\n"
     ]
    }
   ],
   "source": [
    "# predictions\n",
    "prediction1 = model_01.predict(X_test)\n",
    "#prediction = scaler.inverse_transform(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1054, 1)\n",
      "(1054,)\n"
     ]
    }
   ],
   "source": [
    "print(prediction1.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elimina dimensiones sobrante para los gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_p1 = y_train #np.squeeze(y_train, axis=-1)\n",
    "y_test_p1 = y_test #np.squeeze(y_test, axis=-1)\n",
    "y_pred_p1 = np.squeeze(prediction1, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9419,)\n",
      "(1054,)\n",
      "(1054,)\n",
      "|-------test------|---pred--|----------dif---------|\n",
      "0.2627674160750257 0.25993785 0.0028295634539471926\n",
      "0.26132479416579846 0.26042247 0.0009023259803736527\n",
      "0.26335821327939013 0.26082775 0.0025304633118150566\n",
      "0.2627536844058564 0.26130116 0.0014525245471527781\n",
      "0.2629872485680162 0.26173598 0.0012512728256761085\n",
      "0.2648969988015629 0.26215425 0.00274274746451153\n",
      "0.26739754623947415 0.2626432 0.004754358001330228\n",
      "0.26655945320378066 0.2632573 0.0033021583105158903\n",
      "0.2621216712296038 0.26384345 -0.001721775740382181\n",
      "0.2606515859820381 0.26417783 -0.0035262430451378224\n"
     ]
    }
   ],
   "source": [
    "print(y_train_p1.shape)\n",
    "print(y_test_p1.shape)\n",
    "print(y_pred_p1.shape)\n",
    "\n",
    "k=0\n",
    "print('|-------test------|---pred--|----------dif---------|')\n",
    "for i,j in zip(y_test_p1, y_pred_p1):\n",
    "    print (i,j, i-j)\n",
    "    k+=1\n",
    "    if k==10:\n",
    "        break"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica de las predicciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f945711016e4c42b852fa52d6252352",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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Hx+PSpUvYuXOn3udWUFCA119/HefPnwcAdOnSBVOnToWfnx+io6Oxbt067N27FyEhIejRoweuX78Oa2trjfUNHjwYISEhmD59Ovr16wdHR0fcu3cPX3/9NUJCQrB//36sXbsW7777rt6xAoprERAQgP79+6Nly5bw9PSEIAh48uQJ9uzZgz///BOhoaEYOHAgrl+/DgsLixIdpzhldT0Awz7zZs+ejZs3b6J///4YN24cfHx88PTpU6SmppaobGV6HpfW33//jaVLlwIAmjRpgilTpsDf3x92dnaQyWS4e/cuDh8+LP5uvKg090BBQQHatGmD119/Hc2bN4ebmxtyc3MRGhqKzZs3499//8W1a9cwYsQInDhxQu9zKygogJmZGXr16oVXX30VDRs2hKOjI5KSknD//n38/PPPuHPnDjZv3oxatWphwYIFeh8DACIjI8W/P7QNcJ6eno7IyEgAQIMGDbTWWXh7SEhIieIytFatWmHfvn04ffp0RYdCVHlVdAaNiIq6efOm+O3NmjVrVLb9+uuv4rabN2/qVN9PP/0k7nP//n2VbW+++aYAKFqpaPt2TJcWWMnJySrf9p06dUple2VrgeXp6SncunWr2J///vtP6zecgqD5G8pHjx6J2wq3sHqRXC4XkpKSiqxXfpNXuOWTOnFxcYKVlZV4XuHh4UXKXL16VZBKpQIAoUaNGkJubq7K9sKtUAAIY8aMEeRyucZjFtcC6+HDh1r3v3nzpmBtbS0AED7//HOt56dJbGyseN4+Pj5qv0U+evSoyrf46q7hpUuXxG+3NcVSUFAgjBo1SgAg2NjYCMnJySrbDdkCC4BgamoqnDx5skiZqKgooWbNmgIAwcrKSnj69GmRMoWvY48ePYTs7Gy1x0tPTxfc3NwEAELPnj2FjIwMteXWrFkj1nfkyBGVbZmZmeK3xlZWVlp/N9Xdl9paYBnivtZFcdcuPj6+yPUuLCcnR3j11VfF88jPz9c7BkEQhMjISI3XQBAEISUlRWjSpIkAQOjYsaPaMjNmzBDP5f3339f4O5iTkyPExsaqrNu7d6+4b7t27bSec0RERJF1mp6DgiAIK1euLPbZUri14CeffFJke+Fnjqmpqdp7LTExUbynmzRpojH+4rz4f+WLDh8+LD4zfvvtN7VlStsCqyyvhyGeeS+2TPziiy80xqdPWUPEZiiF454yZYpOfzO8+AxStmb28fHR2oo1MTGxyLrS3gPF3cfr1q3T+GxX0nYfpaenC9HR0Rrrl8vlwrhx4wQAglQqFVJSUrTGo8n27dvFOE6fPq2xXEhIiMrzT5v4+Hix7IgRI0oUlzqlaYG1YMECcV91/7cTkSAwgUVUCX300UcCoOgW8+IfK8nJyYK5ubkAQPjoo490qi8wMFAAILRu3brItgMHDoj/We7fv19jHdoSWMnJycLevXuFRo0aqfyh9aLKlsAqyY++CayzZ8+K227cuKF3rLomsL7//nvxONu2bdNYbuHChWK5P//8U2Vb4Q/x9vb2QmpqqtZjFpfA0oXyw3bjxo1LtH/h8965c6fGclOmTNF6DYcMGSIAEAIDA7Um3Qr//q1du1Zlm6ETWFOnTtVYrvAf8//3f/9XZLtym5GRkdbftRUrVggABAsLi2L/WG7durUAQHjrrbdU1v/yyy/i8ZYuXaq1DnW0JbAMcV/rwhDX7vr162IdV65cKVEduij8gTYhIUFlW1JSkpjwa9Gihd6JtLZt24qJyMjISL1j0/ZB19/fXwAgODs7a3y25OfnCw0aNBAACA4ODkUSr4WfOTNnztQYx6effiqWK+kHZl0MHDhQACC8/vrrareXNoFVltfDEM+8wsmdevXqab3f9ClriNgMRdfuw4V/XnzmKpPbgwYN0vv4pb0HdNG8eXOt/+dou490kZiYKBgbGxf7/7Q2P/74oxjHvXv3NJa7dOmSWG727Nla68zMzBTLavodLonSJLBWr14t7nv16lWDxURUnXAQd6JKpqCgAFu3bgUAvPbaa7C3t1fZbm9vL3aR2bp1KwoKCrTWFxwcjKCgIADqZ23p2bOn2HVr06ZNOsXYrVs3lcHEHRwcMHDgQNy5cweAYoBN5WCbhrRhwwYIisS7ymDilZmHh4e4rBwYvSwcOXIEgOL+UA4Eqs7EiROL7KNOv379YGNjY7gAoRhM9tGjR7hz5w5u376N27dvi/d3cHAw8vLy9K5TeQ4ODg4YMGCAxnJvv/22xm15eXliF9uhQ4dqHJQeULy/ysFjNXX3MBRt3dAGDRokvnfarmOHDh20Do6uHCi2S5cuxXbh7Ny5M4Ci533gwAEAikFx33nnHa116MvQ97Wh5OTkIDw8HMHBweK9LAiCuP3GjRsGOU5GRgbCwsJUfmdMTU01Huf48eNiV9np06fD2NhY52MlJibi4sWLABQz1daoUcMAZ6AQHR0tdtEZNmyYxmeLsbGxeN8nJydrHaS7cHf4FwUGBorLoaGhJQm5iPj4eDx48EC8Drdv34aLiwsAw13vwsryepTFM2/48OE632/aylbW53FpKP8OOHXqFB49eqTzfoa+BwRBQGxsLO7fv69yHyuHezDEfZyXl4fIyEiEhISI9UdHR8PJyalUxyg8QL1y9kB1srOzxeUXh1l4kbm5ubisbUKL8uTo6CguqxuUn4gAjoFFVMkcOnQIMTExADRPEzxq1Cjs2bMHMTExOHLkCHr16qWxPuVYWiYmJhgxYkSR7SYmJhg+fDhWrFiBffv2QSaTwc7OTu+4jYyM0LBhQ7z11luYPn06rKys9K6jvPn4+Og0plNYWBj8/PxKdAw/Pz906tQJp0+fxtKlS/Hff/9hyJAh6Nq1K9q2bWuw9+n27dsAgObNm6t8wH2Rm5sbfH19ERYWJu6jTpMmTQwS161bt7B06VL8888/iI2N1VhOLpcjOTlZ73GwlOP8NG/eHCYmmv9La9asGczMzNTOthQcHCx+6J8zZw7mzJmj07G1nU9pmZmZab0GpqamaN68OY4fP16q63jlyhUAwH///af1g2JhL573tWvXAAAtW7Y0+O+9oe/r0sjIyMDy5cvxv//9D3fu3NH65UFpZtdMSEjAkiVLsGvXLjx48EAlMVbccZTXAniecNTV9evXxWPpu29xCl+TNm3aaC1bePvt27fRrl07teW0jW9T+ENgWlqarmEWcfbsWSxfvhxHjhwRZ/FVx5CzqSqV5fUoi2eePv9naCtbGZ/HSvPmzSt2Rkd1xowZg40bNyIxMRGNGzfGgAED0KtXL3Tq1Al16tTRuJ+h7oEDBw5g9erVOHXqlNbfh5Lex3l5eVizZg02bdqEa9euaZ3VsKTHKPz7py2BVXgsuuJmV8zJyRGXLS0tSxSXoRU+t8TExAqMhKjyYgKLqJLZuHEjAMU3i6+99praMsqWWSkpKdi4caPGBJZcLseWLVsAKFpaKb8tftGoUaOwYsUKZGdn488//8SkSZO0xrhu3TpxEE2JRAKpVApXV9cqkbSqCNu2bcMbb7yB8+fPIzg4GMHBwfj6669hamqKdu3a4c0338S4ceNKNQiw8o87Nze3Ysu6u7sjLCxM6wcybX8g6ur333/H5MmTdZ7OviTfgCYnJwNAsYkvExMTODo6qv2QExcXp/dxARQ7KHxpODo6ak3IAc+vdUmvY15eHlJSUvSO7cXzVn4gKdza0FAMfV+XVFhYGLp3765za56SfpsfFBSEXr166fzB5cXjFP5wqO/1KM2+xSl8TYq7lu7u7mr3e5G2/2+MjJ53MCiulbIm8+fP13nA6bJovVGW16Msnnn6/J+hrWxlfB6XVo8ePbBy5Up8/PHHyMrKwvbt28VJFGrUqIHXX38dU6ZMQdOmTVX2K+09IAgCJk2ahN9//12n8iW5j5OSktCzZ0+xpX9ZHANQTUxlZWVpbMVZeH16errWOjMyMsRlbRNGlKfC709lSaoRVTZMYBFVIqmpqWKXnpSUFJXmzZrs3bsXaWlpav8zP3r0KKKiogAABw8e1KmFxcaNG4tNYPn5+aFx48bF1kUKNWrUwLlz53D06FHs3r0bJ0+eFLvMnTp1CqdOncIPP/yAgwcPol69eqU6li7XWFuLDiV9uh6pc/fuXTF55erqio8//hjdu3eHr68vbGxsxNY069atw4QJE3SOS5PSnHfhD7iLFy9G7969dTqmVCrVLbgSKI/rWPi8hw0bhi+++EK34DTQtQVXWdVdmvunOKNHj0ZoaCgkEgnGjx+PESNGwN/fHy4uLuJzWi6Xi+93SWLJzc3FsGHDkJiYCFNTU0ybNg0DBgxAvXr14ODgIB7n8ePHqF27domPo4uKvJZleR11dfToUTF5VatWLcyaNQsdO3aEt7c3rK2txetceKa8smTo61EWzzx9/s/Q9blUWZ7HhvD+++/jjTfewNatW3H48GGcPXsWMpkMUVFR+PXXX7FmzRrMnTsXCxcuVLt/Se6BdevWicmrZs2aYcaMGWjTpg1q1KgBKysr8TqMGTMGmzZtKtHv3gcffCAmrwYOHIi3334bTZo0gaurKywsLMS4vb29ERERUeLf78JfwCYlJWlMYNWsWVNcVs5GqElERIS47OXlVaK4DK1w0l7Tl85ELzsmsIgqkT///FPvb6cyMzOxc+dOtePlKLsP6uPs2bN4/PgxatWqpfe+pF2PHj3Qo0cPAIqm4UeOHMGaNWtw7NgxPHr0CMOHD1fpAqQPR0dHxMTE6NSN4unTp+I+ZWXDhg3Iz8+HsbExTpw4AX9/f7XllC2oSsrBwQGxsbHiOWmSn5+v8VjKsTkARaukypCcTUxMREFBgdYPesqWCiW9jhYWFrCyskJmZiZSUlJKfN7Ozs6IjIxEdHR0ifbXpjLc13fv3sWZM2cAKLo0ffPNN2rLlfZePnbsGB4/fgwA+PnnnzV+kaDtOM7OzuJyTEyMXl2fC+9r6GtZ+JoUdy0L/y6X5TNKm7Vr1wJQtIQ+f/68xhaepb3m2pTl9aiMzzylyhxbabm6umLGjBmYMWMG5HI5rl+/jt27d+Pnn39GSkoKvvnmG7Rq1Uocz7G094DyPq5duzbOnTunsUVPSe/j1NRUsSXZyJEjxRb/hjyGUuFkTnJyMnx8fNSWs7a2hpeXFyIiInD37l2tdRberulvlPJW+H1iAotIPQ7iTlSJKLsPenh4YNu2bcX+eHt7q+xXWHp6Ovbs2QNAkTgprq7ffvsNgOLbb10Hc6eSc3JywvDhw3H06FH0798fgGK8iwcPHqiU0/VbV+Uf+deuXdM6GHpcXByePHmisk9ZUA7o37RpU61/GCrHYCop5QC+169f19pV8caNGxrHw2jUqJE42OuhQ4dKFY+h5Obmah3sNj8/H9evXwdQuuvYvHlzAIrEdUm74LRo0QKA4loauhtPZbivlfcyALXjCCqV9l42xHGU1wJQDBitj+bNm4vPG333LU7ha6IclFqTS5cuqd2vPCmvRffu3bV2Ty7tNdemLK9HZXzmKVXm2AzJyMgILVq0wMKFC3H06FFxfeEJcEp7Dyjv4wEDBmhMXgmCoHWyBG0ePHggPpe1PbPu3btXbHe+4ij/rweA+/fvay3bsWNH8bjaEuYnT54Ulzt06FCq+AxFeW5SqZRfJBNpwAQWUSURGhoqfss/ZMgQjBgxotifN954A4DiP+Hw8HCV+nbu3Cl+mJwyZUqxdU2YMEGcuYkJrPKlbJUFFB3gVDnuQ+HBRtV55ZVXACi6nu7atUtjud9//11swq/cpywok0naEhqxsbFil9mSUp5DUlIS9u/fr7HcunXrNG6zsrISr8GJEydUPkBXJG0tKPfs2SN+U1ua66hMnmZkZODnn38uUR39+vUDoLjWa9asKXEs6lSG+7pwYlTb/fzLL7+U+XHkcrnW97hbt25iV6oVK1boNf6To6Mj2rdvD0DxIdqQrX48PT3FRPaOHTs0DiRdUFAgztbq4OCgkpArT7o8v65fv44LFy6UWQxleT0q6zMPqNyxlZUWLVqI44IV/hugtPeALvfxvn37SnxvldezEVBMEqJMwl2+fFlr2YEDB4rLmmZ/zszMFJOFDRs2LPXwDYaiPLe2bdsWOw4m0cuKCSyiSqLw+ANDhw7VaR9lOXWtppStsqysrNCnTx+96nv06BHOnj2r0z7lady4cZBIJJBIJDhx4kRFh6OT69eviy1l1BEEAUeOHAGgaG3l6+ursl05cOvjx4+1jh0xfvx4cVDjjz76SGVsB6UbN27g22+/BaAYl6vwH3mGVrduXQCKbxPVfcjLzMzEyJEjSz348dixY8U/amfOnKm2K+HJkyeLTax89tln4jfdI0aM0DrVeUFBAbZu3Vrs+BqltXr1ajGpXVhsbCxmzZoFQPH7PXbs2BIfY/LkyWI3lS+++EKcvl6Ts2fPFmkJMGrUKHF6988++0zlW+0X6fueVYb7WnkvA5qTiqtXr8bevXvL/Dhz5szR2lrC3t4e7777LgDFgPAzZszQ+NzIy8srMmD27NmzASh+P9944w3IZDKNx9L3Wr7//vsAFFPDT5s2TW1cCxYsQHBwMABg0qRJOo0DWRaU1+LMmTNit87C4uPjNc4SbEhleT0q4zPP0LF17dpV/JtBlxmHy8r27du1/l935coV8QuJF7v9luYeUN7H+/fvV9uF79GjR3jvvfd0Owk16tSpI14ndT0BAODvv//GihUrSnwMJTMzM7Ru3RoAik1qDho0SBwncNGiRWrvn48//lh8Tz7++GO19Zw4cUK8f8aNG1eK6HWTk5ODmzdvAgA6depU5scjqqqY2iWqJJQJKFdXV53/42rTpg1q1qyJyMhIbNq0CZ999hkAIDw8XEzw9OnTR+fZAYcMGSJOWb1x48YybVK9c+dOlfEdNCmPPxrK0vXr1zF+/Hi0atUK/fr1Q4sWLeDu7o68vDyEhoZi/fr1OHz4MABFM/8XZxpq37491q9fj7i4OMycOROjRo2CnZ0dAMDU1FQcB8LFxQWLFy/G+++/j+joaLRs2RKffvop2rdvj4KCAhw5cgSLFy9Geno6JBIJ1qxZIw6kXhZGjx6NFStWQC6Xo2/fvvjkk0/Qvn17WFhYICgoCEuXLsWDBw/QoUOHUiVL3dzc8PXXX2PWrFkICwtDYGAg5syZg9atWyM7OxsHDx7E0qVLUaNGDWRmZiI+Pl5tPR06dMCXX36JBQsWIDQ0FM2aNcOECRPQs2dPeHh4ICcnB2FhYTh//jx27tyJ6Oho3Lp1S2XAWENycXGBlZUVXn31VXz44Yfo27cvzM3NcenSJXz77bfiN+Zff/11sTMwamNra4tt27ahT58+yMnJweuvv44hQ4ZgyJAh4geAmJgYBAUFYc+ePbh58yZWrFihMqW7hYUFNm3ahJ49eyIzMxM9evTA6NGjMWjQINSsWRM5OTm4d+8eDh48iL/++qvY1oQvvg8VfV83b94cjRs3xu3bt7F69WqkpKTgrbfegoeHByIiIrB582bs3Lmz1Pdyr1694Orqiri4OHz22Wd48uQJ+vfvD2dnZzx8+BBr167F0aNHiz3O119/jcOHD+PWrVtYuXIlzp8/j3fffRcBAQEwMzNDZGQkzpw5g61bt2LhwoUqz9h+/fphwoQJ+P3333Hu3Dk0bNgQU6dORYcOHWBra4uEhARcuXIFf/75J5o0aaKxdYM6kydPxpYtW3D+/Hn88ccfePLkCd5//33UqlULMTExWLduHXbv3g1AMWZPaScVKI0xY8Zg//79SE9PR5cuXTB79mwEBgZCEAScO3cOS5YsQWxsLNq1a4fz58+XWRxleT0q2zOvKsQWFxeH27dvF1vO0tJSfH4CiiTU5MmTMWDAAHTu3Bn16tWDVCpFYmIizpw5IyZ4jI2Ni4x9V5p7YMyYMfj4448RFRWF9u3b45NPPkGjRo2QnZ2NY8eOYdmyZcjJyUGLFi1K1I3QyckJffv2xYEDB3Dw4EH07t0b7777Lry9vREXF4ddu3Zhw4YNqFWrFlJSUjT+/6ur1157DSdPnsSlS5c0Tl4EKP42Wr58Ofr164fU1FR06NABn3/+OVq3bo3k5GSsXbtWbNHbsWNHjB49usQxxcbG4t9//y2yTunF38mOHTuiTp06aus6deqU2CVT0yzkRARAIKIKd+bMGQGAAEB499139dp3+vTp4r4XLlwQBEEQFi5cKK7btm2bXvU1adJEACDY29sL2dnZ4vp58+aJdR4/flyvOpXGjh0r1qHrj6b9SxqDIAiCj4+PAEDw8fHRqXxoaKh43LFjx6oto9w+b948lfXr16/X6Tw7duwoJCYmFqk3LS1NqFWrltp91MX/zTffCEZGRhqPY25uLvzxxx/Fnuf69euLfV8K3xPqLFiwQOs5f/TRRyrvT2hoaLHH1KTw78GLP87OzsLly5fF667pGgqCICxdulQwNzcv9nqZmZkJDx48UNlX3/dPHeU97uPjI1y+fFlwdnbWGMP06dM11qPpftTk6NGjgru7u073qqb7599//xUcHBz0+p1+8Zw1Kc19rYvirt21a9e0nltAQIAQHR2t9/v+on///VewsLDQeJyuXbsKt2/fLvY+i4+PFzp37lzstVC3f35+vjB16lRBIpFo3Vfd71Fx55+YmCh06NBBa73+/v5CWFiY2v2Le+YoHT9+XCxX0v8rxo8frzFGY2NjYdmyZcXGo8szpzhleT0EoXTPPH3e55Jck9LEJgiC0KVLF7FMSf9/KRy3rj9NmzZVqUN5H2j7sbCw0PgMK+k9kJubK/Ts2VNjeUtLS+HPP/8s9hms7T4KDw8XvL29NR7D29tbuHPnjkF+FyIjIwVjY2MB0Pz/UGFr1qwRzMzMNMbWunVrIT4+XuP+Bw8eFMvOnDlTbRl97w9tfxuMGzdOACDUr1+/2HMjepmxCyFRJVC46fWQIUP02rdweWU9ytZc5ubmen+Lo6wvJSUF+/bt02tfKmrkyJE4fvw45s6di06dOsHPzw9WVlYwMzNDzZo10b9/f2zduhUnT55UO+OWtbU1zp07hw8++AD+/v7FtqabO3curl27hkmTJqF27dqwtLSEVCqFv78/PvjgA9y9exdjxowpq9NV8eWXX+LAgQPo2bMnHBwcxHMePHgwDh06hB9++MFgx/rpp59w4MAB9OrVC46OjrCwsECdOnUwffp0XLt2DS1bttSpnhkzZuDRo0f44osv0LZtWzg7O8PExARSqRT16tXDkCFD8MsvvyAqKkrjt6iG0rJlS1y9ehXTp09H7dq1YWFhAScnJ/Tu3RsHDx7ETz/9ZLBjde/eHY8ePcLKlSvRu3dveHh4wMzMDBYWFvDy8kLPnj3xzTffaL1/evXqhcePH+Pbb79F+/bt4eTkBFNTU9SoUQNt2rTB3LlzcevWrRLFV9H3dbNmzXD9+nVMnjwZPj4+MDU1haOjI1q3bo0ffvgBly5dKtJ6siR69eqFK1euYNSoUfD09ISpqSlcXFzQpUsXrFmzBkePHhXHuNLG2dkZJ0+exO7duzF06FDUrFkT5ubmcHBwQOPGjfHWW2/hr7/+wsiRI4vsa2xsjBUrVuDKlSt45513xNYiVlZWqFu3Lvr27Yu1a9di6dKlep+fo6MjTp06hU2bNqF3795wc3ODqakpnJyc0LVrV6xcuRLXr1/XOMNYeVq3bh02bdqETp06wcbGBubm5vDx8cHo0aPFZ3J5KMvrAVSuZ15Vik0fp06dwm+//Ybhw4cjICAALi4uMDExga2tLVq0aIGPP/4YwcHBGp9hJb0HTE1NceDAASxfvhwtW7aElZUVLC0tUadOHUyePBlXr14Vx1ItKS8vL1y9ehUff/wx6tWrB3Nzc9jZ2aFp06aYN28erl+/joYNG5bqGEo1atQQZ2jUNuOh0qRJkxAUFIRJkyahVq1a4v+hHTt2xOrVq3H27FmtPQGUrStNTEwwdepUg5yDJtnZ2eLES6Xp1kn0MpAIgpZBVYiIiKjcjBs3Dn/88Qd8fHwqdMwWIiKiyubChQto164djI2N8fDhwyLjhhpS165dcfLkSYwfP17rRDCGsHnzZowePRqOjo4ICwvT2D2SiDiIOxEREREREVVybdu2RZ8+fVBQUIBFixaV2XFycnJw8eJFGBsbY+7cuWV2HEAxu6xyIpJZs2YxeUVUDCawiIiIiIiIqNL7/vvvYWxsjPXr1yM8PLxMjnHp0iVkZ2dj5MiRZd49dceOHQgJCYGXlxdmzJhRpsciqg44CyERERERERFVegEBAdiwYQMePnyI8PBweHt7G/wYnTp1QnmNslNQUIB58+ahe/fusLS0LJdjElVlTGARERERERFRlTBq1KiKDsFg1E2mQUSasQshERERERERERFVapyFkIiIiIiIiIiIKjV2ISSDk8vliI6Oho2NDSQSSUWHQ0RERERERNWcIAhIS0uDp6cnjIzY2aw6YgKLDC46OhpeXl4VHQYRERERERG9ZCIiIlCzZs2KDoPKABNYZHA2NjYAFA8OW1vbCo6GiIiIiIiIqrvU1FR4eXmJn0ep+mECiwxO2W3Q1taWCSwiIiIiIiIqNxzGpvpix1AiIiIiIiIiIqrUmMAiIiIiIiIiIqJKjQksIiIiIiIiIiKq1DgGFlWYgoIC5OXlVXQYZGCmpqYwNjau6DCIiIiIiIioGmECi8qdIAiIjY1FSkpKRYdCZcTe3h7u7u4cQJGIiIiIiIgMggksKnfK5JWrqyusrKyY5KhGBEFAZmYm4uLiAAAeHh4VHBERERERERFVB0xgVXOnTp3C4sWLERQUhJiYGOzZswcDBw7Uus/Jkycxc+ZM3LlzB56envjkk08wefJkg8RTUFAgJq+cnJwMUidVLpaWlgCAuLg4uLq6sjshERERERERlRoHca/mMjIy0LRpU6xcuVKn8qGhoejbty86deqEa9euYe7cuZg+fTp27dplkHiUY15ZWVkZpD6qnJTXl2OcERERERERkSGwBVY116dPH/Tp00fn8r/88gu8vb2xbNkyAIC/vz+uXLmCH374AUOGDDFYXOw2WL3x+hIRERERVR4xsiyEJmTAz1kKDzvLig6HqESYwCIV58+fR8+ePVXW9erVC7///jvy8vJgampaQZERERERERGRvrZfDsec3bcgFwAjCbBocACGt/Ku6LCI9MYuhKQiNjYWbm5uKuvc3NyQn5+PhIQEtfvk5OQgNTVV5ac66tq1K2bMmKFxu0Qiwd69e8stHqUTJ05AIpFwVkciIiIiIlIRI8sSk1cAIBeAubtvI0aWVbGBEZUAE1hUxIvdvwRBULteadGiRbCzsxN/vLy8yjzGyigmJkbn7pqGTHa1b98eMTExsLOzM0h9RERERERUPYQmZIjJK6UCQUBYQmbFBERUCkxgkQp3d3fExsaqrIuLi4OJiYnGWQPnzJkDmUwm/kRERJRHqJWOu7s7zM3Ny/WYeXl5MDMzg7u7O8edIiIiIiIiFX7OUrz4KUEiAXydOakWVT1MYJGKdu3a4fDhwyrrDh06hJYtW2oc/8rc3By2trYqP9WVXC7HJ598AkdHR7i7u2P+/PnitsKtqnJzczF16lR4eHjAwsICvr6+WLRoEQDA19cXADBo0CBIJBLxNQCsXr0atWvXhpmZGerXr49NmzapHF8ikeCXX37BgAEDIJVKsXDhwiJdCBMTE/Hmm2+iZs2asLKyQkBAALZt21ZWbwkREREREVVSX+0PxgsNsPCsgw1uRCRj7elHuBGRXO5xEZUEB3Gv5tLT0/Hw4UPxdWhoKK5fvw5HR0d4e3tjzpw5iIqKwsaNGwEAkydPxsqVKzFz5kxMmjQJ58+fx++//16mCRBBEJCZV/5NWK1MrfRutfTHH39g5syZuHjxIs6fP49x48ahQ4cOePXVV1XKLV++HPv27cOff/4Jb29vREREiC3TLl++DFdXV6xfvx69e/eGsbExAGDPnj344IMPsGzZMrzyyiv4+++/MX78eNSsWRPdunUT6543bx4WLVqEpUuXwtjYGKGhoSrHzs7ORmBgIGbPng1bW1scOHAAo0ePRq1atdCmTZuSvFVERERERFTF3IhIxj+3Y9Vum7jhCu7EPB+7eEiLGvhxWLNyioyoZJjAquauXLmikvyYOXMmAGDs2LHYsGEDYmJiEB4eLm738/PDwYMH8eGHH+Lnn3+Gp6cnli9fjiFDhpRZjJl5mbBeZF1m9WuSPicdUjOpXvs0adIE8+bNAwDUrVsXK1euxNGjR4sksMLDw1G3bl107NgREokEPj4+4jYXFxcAgL29Pdzd3cX1P/zwA8aNG4f33nsPgOJaXbhwAT/88IPKNRw5ciTefvtt8fWLCawaNWpg1qxZ4utp06bh33//xY4dO5jAIiIiIiJ6SVwKS9K4rXDyCgB2XY3CmHY+aOrlUNZhEZUYE1jVXNeuXcVB2NXZsGFDkXVdunTB1atXyzCqqqtJkyYqrz08PBAXF1ek3Lhx4/Dqq6+ifv366N27N15//XX07NlTa90hISF45513VNZ16NABP/30k8q6li1baq2noKAA3333HbZv346oqCjk5OQgJycHUql+yToiIiIiIqq6Wvs66lX+SlgyE1hUqTGBRRXOytQK6XPSK+S4+npxHDCJRAK5XF6kXIsWLRAaGop//vkHR44cwbBhw/DKK69g586dWutXNwPki+uKS0T9+OOPWLp0KZYtW4aAgABIpVLMmDEDubm5WvcjIiIiIqLqo6mXA5p52eF6hEyn8hzYnSo7JrCowkkkEr278lUFtra2GD58OIYPH46hQ4eid+/eSEpKgqOjI0xNTVFQUKBS3t/fH2fOnMGYMWPEdefOnYO/v79exz19+jQGDBiAUaNGAVAMPP/gwQO96yEiIiIioqptSIuaOiewrMzUT9pFVFkwgUVUBpYuXQoPDw80a9YMRkZG2LFjB9zd3WFvbw9AMRPh0aNH0aFDB5ibm8PBwQEff/wxhg0bhhYtWqBHjx7Yv38/du/ejSNHjuh17Dp16mDXrl04d+4cHBwcsGTJEsTGxjKBRURERET0krn/NE2nchJJ0RZYMbIshCZkwM9ZCg87y7IIj0gvRhUdAFF1ZG1tje+//x4tW7ZEq1atEBYWhoMHD8LISPEr9+OPP+Lw4cPw8vJC8+bNAQADBw7ETz/9hMWLF6NRo0b49ddfsX79enTt2lWvY3/xxRdo0aIFevXqha5du8Ld3R0DBw408BkSEREREVFlFiPLwqYL4cUXBPDisMnbL4ej/aJjGLn2ItovOobtl3Wrh6gsSQRtI3wTlUBqairs7Owgk8lga2ursi07OxuhoaHw8/ODhYVFBUVIZY3XmYiIiIioYm06H4Yv/rqjc/mPe9XD+93qIkaWhfaLjqFwokAC4Nyc7pW6JZa2z6FUPbAFFhEREREREVE18zhev4myroQmKf4NS8KLrVwEAEFhyYYJjKiEmMAiIiIiIiIiqmZquVjrVd7VVtFz4sVZ0JU0rCYqN0xgEREREREREVUzrzR006u8i405AMDSVH2awELDeqLywjuQiIiIiIiI6CW3+sRjxMiycCNSpnb7TQ3ricoLE1hERERERERE1UxoQoZe5QsEAWEJmTA1Vt9X0NSY6QOqWCYVHQARERERERERGVZWbr7e+/g6WyExI0ftNj9naWlDIioVplCJiIiIiIiIqpnHerbAAoC41Gx4OViq3VZTw3qi8sIWWERERERERETVTK0StJi6EpaMp6nZardl5spLGxJRqbAFFlElM3/+fDRr1qzU9UgkEuzdu7fU9RARERERUdVjaaZ/exVfZyusOR1aZL2RRLGNqCIxgUVERERERERUzfg5S6F+OHb1XG3MkZlboHabj5MUHnbsQkgViwksogqSl5dX0SFolZubW9EhEBERERFRCXnYWWJUW2+dy8el5SA8MVPtttCEDNyISDZUaEQlwgQWkQ5+/fVX1KhRA3K5ar/v/v37Y+zYsQCA/fv3IzAwEBYWFqhVqxYWLFiA/PznM39IJBL88ssvGDBgAKRSKRYuXAgA+O677+Dm5gYbGxtMmDAB2dmqfc4vX76MV199Fc7OzrCzs0OXLl1w9epVlTIPHjxA586dYWFhgYYNG+Lw4cNFziEqKgrDhw+Hg4MDnJycMGDAAISFhYnbx40bh4EDB2LRokXw9PREvXr1AAC3bt1C9+7dYWlpCScnJ7zzzjtIT08v+ZtJRERERETlok0tJwCAq7WZTuUT0zV/iX00JM4gMRGVFBNYVGXFyLJw7lECYmRZZX6sN954AwkJCTh+/Li4Ljk5Gf/99x/eeust/Pfffxg1ahSmT5+O4OBg/Prrr9iwYQO++eYblXrmzZuHAQMG4NatW3j77bfx559/Yt68efjmm29w5coVeHh4YNWqVSr7pKWlYezYsTh9+jQuXLiAunXrom/fvkhLSwMAyOVyDB48GMbGxrhw4QJ++eUXzJ49W6WOzMxMdOvWDdbW1jh16hTOnDkDa2tr9O7dW6Wl1dGjRxESEoLDhw/j77//RmZmJnr37g0HBwdcvnwZO3bswJEjRzB16lRDv8VERERERGRgiek5AIA4LYmpwh4laP6i2tXW3CAxEZUUZyGkCicIArLy1Pe11mRXUCTm7bsDuaAYUHBB/0YYElhTrzosTY0hkejWK9zR0RG9e/fG1q1b0aNHDwDAjh074OjoiB49eqBbt2749NNPxdZYtWrVwtdff41PPvkE8+bNE+sZOXIk3n77bfH1m2++ibfffhsTJ04EACxcuBBHjhxRaYXVvXt3lVh+/fVXODg44OTJk3j99ddx5MgRhISEICwsDDVrKt6Db7/9Fn369BH3+d///gcjIyP89ttv4jmvX78e9vb2OHHiBHr27AkAkEql+O2332BmpviGZu3atcjKysLGjRshlSpmMVm5ciX69euH77//Hm5ubjq9f0REREREVL62Xw7HvH3Beu1z8n6Cxm09/Pm3P1UsJrCowmXlFaDhl/+VeH+5AHzx1x188dcdvfYL/qoXrPSYmeOtt97CO++8g1WrVsHc3BxbtmzBiBEjYGxsjKCgIFy+fFmlxVVBQQGys7ORmZkJKyvFjB0tW7ZUqTMkJASTJ09WWdeuXTuVll5xcXH48ssvcezYMTx9+hQFBQXIzMxEeHi4WIe3t7eYvFLWUVhQUBAePnwIGxsblfXZ2dl49OiR+DogIEBMXinrbtq0qZi8AoAOHTpALpfj3r17TGAREREREVVCMbIszN51y2D1dannzEHcqcIxgUWko379+kEul+PAgQNo1aoVTp8+jSVLlgBQdONbsGABBg8eXGQ/CwsLcblwIkhX48aNQ3x8PJYtWwYfHx+Ym5ujXbt2Ytc/QRCK7PNiyzK5XI7AwEBs2bKlSFkXFxeN8QmCoLGVmq6t14iIiIiIqHxdCUsyaH3KrohEFYkJLKpwlqbGCP6ql87lY2XZeGXJScgL5W2MJMCRmV3gbmeheUc1x9WHpaUlBg8ejC1btuDhw4eoV68eAgMDAQAtWrTAvXv3UKdOHb3q9Pf3x4ULFzBmzBhx3YULF1TKnD59GqtWrULfvn0BABEREUhIeN60t2HDhggPD0d0dDQ8PT0BAOfPn1epo0WLFti+fTtcXV1ha2urc3wNGzbEH3/8gYyMDDG5dfbsWRgZGYmDvBMRERERUeWSkmXYGc9vR6fh11OP8G7n2gatl0gfHMSdKpxEIoGVmYnOP7VcrLFocACMn7UAMpZIsGhwAGq5WOtVT0laEL311ls4cOAA1q1bh1GjRonrv/zyS2zcuBHz58/HnTt3EBISgu3bt+Pzzz/XWt8HH3yAdevWYd26dbh//z7mzZuHO3dUu0LWqVMHmzZtQkhICC5evIi33noLlpbPm+++8sorqF+/PsaMGYMbN27g9OnT+Oyzz4rE7ezsjAEDBuD06dMIDQ3FyZMn8cEHHyAyMlLr+VpYWGDs2LG4ffs2jh8/jmnTpmH06NHsPkhEREREVEk5WOk266A6thbq27l8/8/dcplAi0gTJrCoShreyhtnPu2GbZPa4syn3TC8lXe5HLd79+5wdHTEvXv3MHLkSHF9r1698Pfff+Pw4cNo1aoV2rZtiyVLlsDHx0drfcOHD8eXX36J2bNnIzAwEE+ePMGUKVNUyqxbtw7Jyclo3rw5Ro8ejenTp8PV1VXcbmRkhD179iAnJwetW7fGxIkTi8x+aGVlhVOnTsHb2xuDBw+Gv78/3n77bWRlZWltkWVlZYX//vsPSUlJaNWqFYYOHYoePXpg5cqV+rxtRERERERUjgJ9HEq0nwRAana+2m1yAQhLyCxFVESlIxHUDaBDVAqpqamws7ODTCYrkhzJzs5GaGgo/Pz8VMaGouqF15mIiIiIqOLEyLLQbtExvfcb2MwTe69Hq91mLJHgzKfdKu1g7to+h1L1wBZYRERERERERNXIkeCnJdpPW8utT/rUr7TJK3o5MIFFREREREREVI3cipLpvY8EgINU89hZTWrYlzwgIgNgAouIiIiIiIioGrEy02/GdaWQ6FSN23ydrUoaDpFBMIFFREREREREVI0Mal5D730EAKtPPNK4/dT9+FJERFR6TGARERERERERVSNNvRzQ2FP/gczlUHQlVOfTXbcQI8sqVVxEpcEEFhEREREREVE1ECPLwrlHCbgRkYzgGM3dAbXxc5aqXS8ACApLLkV0RKVjUtEBEBEREREREVHpbL8cjjm7b0EuKFpRCSWs53FChsZt+29G4/WmniWsmah02AKLiIiIiIiIqAqLkWXh02fJK0Bz8urjnvVKdZxDd56yGyFVGCawiIiIiIiIiKqw0IQMCDo0ufJxkmoc40oXAoCwhMxS1EBUckxgEZHo4cOH+Pbbb5GVxW9ViIiIiIiqCqmZcbFljCUSeDlalrhroZKvs1UpayAqGSawiCohX19fLFu2THwtkUiwd+/eMqlbKTs7G2+88QY8PT1haWlpkGMREREREVHZO3AzRut2Iwnw7eDGiEjmF9VUdXEQd6IqICYmBg4ODgap6/Lly5BKi84sMmPGDAwcOBDjxo0zyHGIiIiIiKjsxciysOZ0qNYyyu6Fgi79DIsRlpAJDzt+4U3ljwksojKSm5sLMzMzg9Tl7u5ukHoAwMXFRe36X375xWDHICIiIiKi8nEk+GmxZVpF3Ib30Llw/H1VqWYoBAArM3bkoorBO49IR127dsXUqVMxdepU2Nvbw8nJCZ9//rn4LYavry8WLlyIcePGwc7ODpMmTQIAnDt3Dp07d4alpSW8vLwwffp0ZGQ8n5o2Li4O/fr1g6WlJfz8/LBly5Yix36xC2FkZCRGjBgBR0dHSKVStGzZEhcvXhS379u3Dy1btoSFhQWcnZ0xePBgcduLXQjDw8MxYMAAWFtbw9bWFsOGDcPTp8//E5w/fz6aNWuGTZs2wdfXF3Z2dhgxYgTS0tJK/Z4SEREREVHpPI5PL7bM/x38Ce3Cb6JO/55YOLBxqY6XmSsv1f5EJcUEFlU8QQAyMsr/pwTNZ//44w+YmJjg4sWLWL58OZYuXYrffvtN3L548WI0btwYQUFB+OKLL3Dr1i306tULgwcPxs2bN7F9+3acOXMGU6dOFfcZN24cwsLCcOzYMezcuROrVq1CXFycxhjS09PRpUsXREdHY9++fbhx4wY++eQTyOWK/0gOHDiAwYMH47XXXsO1a9dw9OhRtGzZUsNbL2DgwIFISkrCyZMncfjwYTx69AjDhw9XKffo0SPs3bsXf//9N/7++2+cPHkS3333nd7vHxERERERGZaTtXmxZTxT4wEAxlmZGOH/fGgSfWckNJJwEHeqOOxCSBUvMxOwti7/46anA2rGgtLGy8sLS5cuhUQiQf369XHr1i0sXbpUbG3VvXt3zJo1Syw/ZswYjBw5EjNmzAAA1K1bF8uXL0eXLl2wevVqhIeH459//sGFCxfQpk0bAMDvv/8Of39/jTFs3boV8fHxuHz5MhwdHQEAderUEbd/8803GDFiBBYsWCCua9q0qdq6jhw5gps3byI0NBReXl4AgE2bNqFRo0a4fPkyWrVqBQCQy+XYsGEDbGxsAACjR4/G0aNH8c033+j1/hERERERkWFFp2gfmN1IXgChUKbq0IEL4rK+X+lP7FiL419RhWELLCI9tG3bFhLJ86d/u3bt8ODBAxQUFABAkZZOQUFB2LBhA6ytrcWfXr16QS6XIzQ0FCEhITAxMVHZr0GDBrC3t9cYw/Xr19G8eXMxeaVue48ePXQ6n5CQEHh5eYnJKwBo2LAh7O3tERISIq7z9fUVk1cA4OHhobWVGBERERERlb0YWRa2XorQWsZL9hTmBfni6237LgEArHKzMO/IrwiMDNb5eK81MdzYvET6YgssqnhWVorWUBVxXAN7cXY/uVyOd999F9OnTy9S1tvbG/fu3QMAlaRYcSwttX/jUdz2wgRBUHvsF9ebmpqqbJdIJGKXRSIiIiIiqhhXwpKKLVM7MVLl9ao932Jur6loHn0X44P2Y3zQfvjO/lun43H8K6pITGBRxZNI9O7KV1EuXLhQ5HXdunVhbGystnyLFi1w584dlS5+hfn7+yM/Px9XrlxB69atAQD37t1DSkqKxhiaNGmC3377DUlJSWpbYTVp0gRHjx7F+PHjiz2fhg0bIjw8HBEREWIrrODgYMhkMq3dGImIiIiIqOIt/vdesWXqJKq20LLOzcKSv3/E1RoNxHXSnExkmGv/gt9YIuH4V1Sh2IWQSA8RERGYOXMm7t27h23btmHFihX44IMPNJafPXs2zp8/j/fffx/Xr1/HgwcPsG/fPkybNg0AUL9+ffTu3RuTJk3CxYsXERQUhIkTJ2ptRfXmm2/C3d0dAwcOxNmzZ/H48WPs2rUL58+fBwDMmzcP27Ztw7x58xASEoJbt27h//7v/9TW9corr6BJkyZ46623cPXqVVy6dAljxoxBly5dNA78TkREREREFe9oSCzCk7WPfwUAtZKiiqwzEeRwzkgRX9ePf1KkjF1WGl55cBESQQ5jiQTfDm7M8a+oQjGBRaSHMWPGICsrC61bt8b777+PadOm4Z133tFYvkmTJjh58iQePHiATp06oXnz5vjiiy/g4eEhllm/fj28vLzQpUsXDB48GO+88w5cXV011mlmZoZDhw7B1dUVffv2RUBAAL777juxFVjXrl2xY8cO7Nu3D82aNUP37t1x8eJFtXVJJBLs3bsXDg4O6Ny5M1555RXUqlUL27dvL+E7RERERERE5eHvmzE6lXPJSAYAJLjWVFlfKzlaXG6QEFZkvyUHluC33V/jL8v7OPNpNwxv5V3yYIkMQCIIgr4TD1AVs2rVKixevBgxMTFo1KgRli1bhk6dOmksv2XLFvzf//0fHjx4ADs7O/Tu3Rs//PADnJycdDpeamoq7OzsIJPJYGtrq7ItOzsboaGh8PPzg4WFRanOq7x17doVzZo1w7Jlyyo6lEqvKl9nIiIiIqKqYOXxB/jhv/vFltux+RO0igpGim8d2Ic9VFvmt5YDsLDHJJV1Yd+/DgBIa94KNlcvlT7gMqbtcyhVD2yBVc1t374dM2bMwGeffYZr166hU6dO6NOnD8LDw9WWP3PmDMaMGYMJEybgzp072LFjBy5fvoyJEyeWc+RERERERESkib+7TfGFANhlKybMihw5HjBRPwy267NWWkoSodBg7VnFd1MkKg9MYFVzS5YswYQJEzBx4kT4+/tj2bJl8PLywurVq9WWv3DhAnx9fTF9+nT4+fmhY8eOePfdd3HlypVyjpyIiIiIiIg0+f1MmE7l7HIUCSy3nl2BhARg27YiZdzSVWcztM3OEJfzc3JKHCORITGBVY3l5uYiKCgIPXv2VFnfs2dPnDt3Tu0+7du3R2RkJA4ePAhBEPD06VPs3LkTr732msbj5OTkIDU1VeWnOjpx4gS7DxIRERERUYWLkWXh3KNEncoqk1Eu3h6AnR2gZrZx13TVupwzU8RlS1kyiCoDJrCqsYSEBBQUFMDNzU1lvZubG2JjY9Xu0759e2zZsgXDhw+HmZkZ3N3dYW9vjxUrVmg8zqJFi2BnZyf+eHl5GfQ8iIiIiIiI6LmgJ7ollczy82CZ/6wFlYOD4l/vooOxe6QlqnQbdMqUicsWSQlAXl7JgyUyECawXgISiUTltSAIRdYpBQcHY/r06fjyyy8RFBSEf//9F6GhoZg8ebLG+ufMmQOZTCb+REREGDR+IiIiIiKil82NiGSsPf0INyKKJqvCEjPU7FGUfZaid4xgbAwoBza3t1cpk2NsAov8XNSUxYnrnDJSVCuK0W3GQ6KypH4EN6oWnJ2dYWxsXKS1VVxcXJFWWUqLFi1Chw4d8PHHHwMAmjRpAqlUik6dOmHhwoXw8PAoso+5uTnMzc31ik0ulxdfiKosXl8iIiIiopL76M/r2HU1Snw9pEUN/Dismfg6J69Ap3qcn7Wkynd0hqnRs/YrLzRmiLRzQ+2kKNRIjUOEvTsA1RZYAIDoaLUtt4jKExNY1ZiZmRkCAwNx+PBhDBo0SFx/+PBhDBgwQO0+mZmZMHlhZgpjY2MAipZbhojJyMgI0dHRcHFxgZmZmcbWYFT1CIKA3NxcxMfHw8jICGZmZhUdEhERERFRlXIjIlkleQUAu65GYUw7HzT1UnQDbOZlr1NdTs/GsspycIRp4Q1SKZCRAZmjC6JtXBQJLFm8uLnwGFgAFAksogrGBFY1N3PmTIwePRotW7ZEu3btsGbNGoSHh4tdAufMmYOoqChs3LgRANCvXz9MmjQJq1evRq9evRATE4MZM2agdevW8PT0LHU8RkZG8PPzQ0xMDKL5EKy2rKys4O3tDSMj9lImIiIiItLHkZCnatcfC4kTE1iWZrp9lHd+1hUwx8FZdcPixRBmzsSsru+i972zirKZz7sqFmmBFaWaUCOqCExgVXPDhw9HYmIivvrqK8TExKBx48Y4ePAgfHx8AAAxMTEIDw8Xy48bNw5paWlYuXIlPvroI9jb26N79+74/vvvDRaTmZkZvL29kZ+fj4IC3Zq+UtVhbGwMExMTtqwjIiIiIioBVxsLtetdbJ8P2yI1M9apLmULLBOPF4aQmTIF53sMxuF1V9A2/BYAwCEr7fl+zxJfgpERJHI5W2BRpcAE1kvgvffew3vvvad224YNG4qsmzZtGqZNm1amMUkkEpiamsLU1LT4wkRERERERC+JVxq64Yu/7qiskwDo4f88CZWRq98YWMnWDnB4YZufmy0kAJItbQAA9oUTWM/2i6rVEDUf3gZCQ/U7CaIywP49RERERERERJWEh50lvurfSHwtAfDdkAB42FmK6/ycpdClv4NThiIRlevoXGSbh50lBjTzQIqlYnZCh2czFgLPE1gFnTsrVty5U2R/ovLGFlhERERERERElUhBoQm0SjOVlrILoXtd9TMIejtJ8ciiaAssZTLLZ8wwoHt7ICCgFFEQGQZbYBERERERERFVEjGyLHz9d7DKurm7byNGliW+Dk3I0CmxpZxN0N63ptrtPRq4il0IlWNgGckLYJ+dDgC4Y+MOvPUW0KSJnmdBZHhMYBERERERERFVEqEJGZC/kJ0qEASEJWSKr/2cpTB6oQ+hug/3yi6EcHVVe6ymXg6wqaEYW8shW9HqyjYnA8aCHABw5Gme/idAVEaYwCIiIiIiIiKqJPycpWrXb7oQJi572FmigbutyvaGnrawKMiFeX6uYoUgiC2w4PbCLITPxMiycCtLMbKQfVYaIAhwzFQkslLNpXC0ty75iRAZGBNYRERERERERJXEpvNP1K4/eCsWNyKSAQA3IpIRHJOqsj38cTSCfpuE/X/MgFl+HuzysmFe8KwFlYuL2jqDniQj6VkXQlN5Aaxzs8Txr5IsbdGkpp0hTonIIJjAIiIiIiIiIqoEYmRZWHXikcbtR0PiFP/ejSuyrVn0PUhTElEvIRx7AwpweHQDxQZra8DKSm19giAg29QC2SZmAAD7rFQ4PktgJVvaIiIpS+1+RBWBsxASERERERERVQKhCRlat5+8F4djd+NQ096iyLaGcaHPlzOeAsfCFC9qqh/AHQBa+joCAJItbOCRngj77HQ4POtCmGRli5SsXD3PgKjsMIFFREREREREVM5iZFkITciAn7MUHnaWADSPf6V0I0qRXLodnVpkW0Dsg+cv7t4FDh1SLHfooLE+DztLzOnTACnrFAkshxdaYAm6THVIVE6YwCIiIiIiIiIqR9svh+PT3bcgCICRBFg0OADDW3mXuD67rDR0fRz0fMXx48C9e4rlJUu07htQ0w7JlooB4R2y0lTGwKohNStxTESGxjGwiIiIiIiIiMpJjCwLc54lrwBALgBzd99GjCwLV8KSSlTnwkOrIM3Lfr7i9m3Fv7VrA7a26nd6xs9ZCpmFYrbBAcEnxFkIk61s0cLHoUTxEJUFJrCIiIiIiIiIykloQgbkL3TNKxAEhCVkQiKR6F1f7cQI9Lt7GgAQNe9b1Y0BAcXu72FnCXcnRQKrx6PLqJ0UAQDo3sFf7NpIVBkwgUVERERERERUTvycpTB6IU9lLJHA19kKO4Mi9a5vbNDfAIA4qQMiho0FfH2fb2zcuNj9Y2RZOGDhJb5u9FQxC2Jtfx+9YyEqS0xgEREREREREZUTDztL9GnsobJuYHNPxKVm48S9eL3ra/z0IQBgYY+J8KnhADRq9HyjDi2wgp4k44/A18XX5gX5AIC7+eZ6x0JUlpjAIiIiIiIiIionMbIs/HM7RmXd3mvROHo3Tu+6JIIc9eOfAABqdW+v6PJXuNWVDgmspIwc5Bmb4qRfC5X1T6WOesdDVJaYwCIiIiIiIiIqJ5rGwDIz1v/jeY3UeEjzspFjbIIm3VsqVr75JmBiAtSpA9SvX2wdjlJFS6tEKzuV9RZ+JZ8VkagsMIFFREREREREVE40j4El1bsul/RkAEC81BHXYzIUK5s2Ba5dA06eBIyK/8jv5aAYqD3J8vlshfFSe3i62esdD1FZYgKLiIiIiIiIqJx42FmiZ0M3lXUDm3si0MdB77ocs2QAFK2nzE0Lfbxv3Bjw9NSpjozcAgBAUqEWWNE2LsjMlesdD1FZYgKLiIiIiIiIqJzEyLJwKPipyrq916IRl5qtd11OGYoEVpKVLXwc9W/BBTxvEVa4C2GsrTN8na1KVB9RWWECi4iIiIiIiKicaBoDa8uFcL3rchJbYNkj0Ff/FlyAokXYosEBSCmUwPJr3kAxIDxRJcIEFhEREREREVE58XOW4oUhsCCRAKnZeXrX5ZSRAkDReqokLbiUhrfyxtefDxdf1+vWpsR1EZUVJrCIiIiIiIiIypHw4msBJRwDKxWAIoF1JSy5VDG5Nm8M7N4NzJgBjBlTqrqIygITWERERERERETlJOiJ+kRTek6+6gpBwHvn/8S3/66EWb761lnOz1pgJVnZGWbMqkGDgKVLAUt2H6TKx6SiAyAiIiIiIiJ6WQjCi+2vFEyNVduXdAm9ik9ObQQAnPFthoMNOhbZR9kCK8HKDlZmpgaOlKhyYQssIiIiIiIionJy8m682vU/HLqv8nrQnWPicq2kSLX7KMfASjZUCyyiSowJLCIiIiIiIqJyECPLws5rUTqV9UhLFJdryOLEZfusVMWgWYIAp0zFLISe9X04ayBVe0xgEREREREREZWD0IQMncs6ZzwfK6tGqqLV1sjr/+D68pGYcHkvbHMyYCZXjJvVNLCeYQMlqoSYwCIiIiIiIiIqB37OUhhJdCvr8qx7oGJZkcz69r+fAQBzTqwX16WaWcHLw9GgcRJVRkxgEREREREREZUDDztLLBocgOJyWB6p8bDNed5ayyUjWdFt8BkTQY5aSYquiE9tnNDCx6EswiWqVJjAIiIiIiIiIionw1t5w8POQnMBQcBfG2eqrHLOlIktrpS6Pr4CADCrV4fjX9FLgQksIiIiIiIionJyIyIZ0bJsjds90+Lh+ixZdahuW+QZGQMAGj19pFKux8NLAACfVgFlFClR5cIEFhEREREREVE5uRSWpHW7e6pi9sGn1o54Z9BnSLCyBwAExD5ULZf+rJ5atQweI1FlxAQWERERERERUTlp7at9wHX3dEUCK9zOHZBIEGetKN/4hRZYIj8/g8ZHVFkxgUVERERERERUTu7Gpmnd7vYsgRVn7aDyr7IL4SPHmqo7NGpk4AiJKicmsIiIiIiIiIjKQYwsC3N239JaxjVdMf5VnLUj2vg5Il6qSGDVTI0HAPxbrx3ipfbPd2AXQnpJMIFFREREREREVA5CEzIgF7SXcU1XjoHlhNFtfRAvVe1ymGjtiEefzAMkEuDTTxX/Er0ETCo6ACIiIiIiIqKXgZ+zFBIA2nJYz1tgOeBubCqSn3UhVJo2sgMcxo8GvviAySt6qbAFFhEREREREVE58LCzRB1XKQCgfdh1/N/BZbDKzVIp4/ZsdsGn1k74+fgjcRB3Jbmzi2KBySt6ybAFFhEREREREVE5uBGRjAdxGQCArds/BwCkmkuxsMcksYxrhiKBFSd1gADgrouvSh0RJjZwKpdoiSoXtsAiIiIiIiIiKgd7r0UXWdc24ra4bJ6fC/vsdACKQdwlACLs3XGwXnuxjFuLhmUeJ1FlxAQWERERERERUTnIyMkrsq5wF0Lv5BgAQJqZJWQW1niztTeMJRJ8030iQh088bj/cHi4ORSpg+hlwC6EREREREREROXAx0laZJ1VXra4XD/hCQDggbM3IJFgeKuamNajDsISMmHx3Qh42FmWW6xElQ0TWERERERERETlwMZS8RHcLP95SyyrvBxxuW5CBADgvrMPACAzVw4PO0smrojALoRERERERERE5UICxcyB1rmZ4jrLvGxAEAAAtZMiAQCPHGvCWCKBr7NV+QdJVEkxgfUSWLVqFfz8/GBhYYHAwECcPn1aa/mcnBx89tln8PHxgbm5OWrXro1169aVU7RERERERETVk72VKQDAOud5AstUXoDl+xdj4X8/4/W7is9qj5298O3gxmx5RVQIuxBWc9u3b8eMGTOwatUqdOjQAb/++iv69OmD4OBgeHt7q91n2LBhePr0KX7//XfUqVMHcXFxyM/PL+fIiYiIiIiIqhdvR0WLKpucDJX1/UNOqbxu81oHDG+l/vMa0cuKCaxqbsmSJZgwYQImTpwIAFi2bBn+++8/rF69GosWLSpS/t9//8XJkyfx+PFjODo6AgB8fX3LM2QiIiIiIqJq6YdD9wEANoW6EL4o1VyK7+7lop8siy2wiAphF8JqLDc3F0FBQejZs6fK+p49e+LcuXNq99m3bx9atmyJ//u//0ONGjVQr149zJo1C1lZWWrLExERERERUfFuRCTj9IMEAIB1jubPVxOGfAG5AIQlaE5yEb2M2AKrGktISEBBQQHc3NxU1ru5uSE2NlbtPo8fP8aZM2dgYWGBPXv2ICEhAe+99x6SkpI0joOVk5ODnJznM2ekpqYa7iSIiIiIiIiqgaN348TlF7sQFna1hj8HcCdSgy2wXgISiUTltSAIRdYpyeVySCQSbNmyBa1bt0bfvn2xZMkSbNiwQWMrrEWLFsHOzk788fLyMvg5EBERERERVWXO1mbisrWaLoQFEiPMeP0jFBgZY0rXWuw+SPQCJrCqMWdnZxgbGxdpbRUXF1ekVZaSh4cHatSoATs7O3Gdv78/BEFAZGSk2n3mzJkDmUwm/kRERBjuJIiIiIiIiKqBVxu6i8s2OaoJrExTczSe8Sf2NuoGAHCwMgMRqWICqxozMzNDYGAgDh8+rLL+8OHDaN++vdp9OnTogOjoaKSnp4vr7t+/DyMjI9SsWVPtPubm5rC1tVX5ISIiIiIiouc87Czx/ZAAAM9bYG1r0hPfdH0bA0YvQZaZhVi2pa9DhcRIVJkxgVXNzZw5E7/99hvWrVuHkJAQfPjhhwgPD8fkyZMBKFpPjRkzRiw/cuRIODk5Yfz48QgODsapU6fw8ccf4+2334alJZuwEhERERERlZayBdZTGyesbTMYD1x8xG1DWtRAUy8msIhexEHcq7nhw4cjMTERX331FWJiYtC4cWMcPHgQPj6KB2RMTAzCw8PF8tbW1jh8+DCmTZuGli1bwsnJCcOGDcPChQsr6hSIiIiIiIiqvBhZFubsvgUAsH6WwEozUx2ofVbPepjavW65x0ZUFTCB9RJ477338N5776ndtmHDhiLrGjRoUKTbIREREREREZVcaEIG5IJiWdmFMN38eQJLAmBIoPphW4iIXQiJiIiIiIiIypyfsxRGzyaDV3YhTH/WAssIwHdDAjjzIJEWbIFFREREREREVMY87CyxaHAAZu+6JSaw+nasj1GD28LX2YrJK6JisAUWERERERERUTkY3sobtV2kYhfC1zo2QLvaTkxeEemACSwiIiIiIiKicmJsJBEHcYetbcUGQ1SFMIFFREREREREVE7kckHsQgg7u4oNhqgKYQKLiIiIiIiIqJwY5+XCTJ6veMEWWEQ6YwKLiIiIiIiIqJxYZWc8f2FtXXGBEFUxTGARERERERERlRPLLEUCq8DaGjDiR3IiXfG3hYiIiIiIiKgcxMiyYJwmAwAU2LD7IJE+mMAiIiIiIiIiKmPbL4ejw3fHkJ+cCgBIMbGs4IiIqhYmsIiIiIiIiIjKUIwsC5/uugW5AFjnKmYgjCwwwa+nHlVwZERVBxNYRERERERERGUo6EkyhGfLNjmKMbDSzazw3cG7iJFlVVxgRFUIE1hEREREREREZSg5M1dcts5RtMBKNZdCAHD1SXIFRUVUtTCBRURERERERFSGIpMzxWXrXEWLq3RzKwBAUkau2n2ISBUTWERERERERERl6EZEirhs86wFVrqZYhB3R6lZRYREVOUwgUVERERERERUhixMjMVlcQysZy2wLEz5sZxIF/xNISIiIiIiIipDo9v5iMt22ekAgBQLGwBAWEKm2n2ISBUTWERERERERERlqKGnnbgsJrAsFQmslr4OFRITUVXDBBYRERERERFRGboSlgQAcMpIQeewawAAmYV1RYZEVOUwgUVERERERERUhs4/SgQAdAkNEtfddqsDADgaElchMRFVNUxgEREREREREZWRGFkWtl6KAADUSVT8u7tRN8RbK7oOutqaV1hsRFUJE1hEREREREREZeRwcKy47JkaDwAIcfET1/Xwdyv3mIiqIiawiIiIiIiIiMpIaKFZBpUJrGhbFwDA6wEe8LCzrJC4iKoaJrCIiIiIiIiIyoi5sURcViawouxcAQCTOvup3YeIimICi4iIiIiIiKiMPIhLBwAYywvgnqYYzD3K1gX+7jZo6uVQkaERVSlMYBERERERERGVgRhZFo7eVbS6cktPhIkgR66RCRKk9pjVq14FR0dUtTCBRURERERERFQGQhMyxGVl98EYW2cIEiPcjU2rqLCIqiQmsIiIiIiIiIjKgJ+zVFx+cQD3W5GyComJqKpiAouIiIiIiIioDHjYWcLfwwYAUOOFBFZATbsKi4uoKmICi4iIiIiIiKgMxMiyEBKj6CoozkBoo0hgdazjXGFxEVVFTGARERERERERlQF1Y2ApW2Bl5sorJCaiqooJLCIiIiIiIqIyIDUzFpcLJ7CMJICvs1VFhUVUJTGBRURERERERFQGIpKzxOUasjgAQJStK95s7Q0PO8uKCouoSmICi4iIiIiIiMgAYmRZOPcoATEyReIqOTMXAGCTkwHb3EwAihZYyoHdiUh3JhUdABEREREREVFVN3/fbWw490R8/f2QAHFZ2X0wydIWWWYW5R4bUXXAFlhEREREREREpfD68tMqySsAmL3rlrisTGDF2ChmHrS3NCu/4IiqCSawiIiIiIiIiEroaEgsbkenqt0WmaToNuiRlgAAiLZVJLC8HDn+FZG+mMAiIiIiIiIiKqGVxx5q3Pa/SxEAAI9URQIrxsYFAHDmYULZB0ZUzTCBRURERERERFQCMbIsXIuQia/N8vPgnJEsvpZl5wN43gIr1sYJAPA4Lr0coySqHpjAIiIiIiIiIiqBXVcjxWWTgnwc2DAd51aNR+3ECJVyNVLjAChmIASABh625RckUTXBBBYRERERERFRCdyKfN76qktoEOomRsBMno/AyBCVcnWeJbQeO9YAABhJJOUXJFE1wQQWERERERERUQm4WD+fTbDv3TPismdavLhsn5UKl4wUAMBDJy8AQEtfh/IJkKgaYQKLiIiIiIiIqARy8wUAiu6Drzy8JK53TX8+DlbdhHAAQKStCzLNLFHTwQJNvZjAItIXE1hEREREREREJSA1NwYAtIq8A7ucDHG9S0aSuFzvWQLrgbM3AKC5l335BUhUjTCBRURERERERFQCHes6A3g+xpUcirGtCrfAUm677+wDAHCxsSjPEImqDSawXgKrVq2Cn58fLCwsEBgYiNOnT+u039mzZ2FiYoJmzZqVbYBERERERERV0MXHipZWNWSKWQav1mgAAHBNf94CS9mFUDn+1YBmnuUZIlG1wQRWNbd9+3bMmDEDn332Ga5du4ZOnTqhT58+CA8P17qfTCbDmDFj0KNHj3KKlIiIiIiIqOqIkWVhzelQAECNVMWg7dc86wMAnDNTIBHkAICaz5JbYQ4eaOPnyPGviEqICaxqbsmSJZgwYQImTpwIf39/LFu2DF5eXli9erXW/d59912MHDkS7dq1K6dIiYiIiIiIqo7QhOdjXtVIVSSpbnjUgxwSmMoL4JiZCggCPNISAAAxNs6Y27dBhcRKVB0wgVWN5ebmIigoCD179lRZ37NnT5w7d07jfuvXr8ejR48wb968sg6RiIiIiIioSpKaGYvLyhZYT+w9kGRlCwBwzUiCfXYazAvyAABx1k64FSUr/0CJqgmTig6Ayk5CQgIKCgrg5uamst7NzQ2xsbFq93nw4AE+/fRTnD59GiYmut0eOTk5yMnJEV+npqaWPGgiIiIiIqIqICI5CwBglp8Ht2djXkXZuSJe6gDnTJnKQO4JVnbINTFFfGqO2rqIqHhsgfUSkEgkKq8FQSiyDgAKCgowcuRILFiwAPXq1dO5/kWLFsHOzk788fLyKnXMRERERERElVnYsy6E7s+6CGaZmCPJ0hZx1o4AFAO5u6UlAgBibRSzFdZ0tKyASImqByawqjFnZ2cYGxsXaW0VFxdXpFUWAKSlpeHKlSuYOnUqTExMYGJigq+++go3btyAiYkJjh07pvY4c+bMgUwmE38iIiLK5HyIiIiIiIgqi5z8AgCAl+wpACDSzhWQSBAnVSSwXDKS4aFMYD1LaqVm5VdApETVA7sQVmNmZmYIDAzE4cOHMWjQIHH94cOHMWDAgCLlbW1tcevWLZV1q1atwrFjx7Bz5074+fmpPY65uTnMzc0NGzwREREREVEl5u1oBQDwSlE0GIiwUzQSSHw2BpZDVios8hRdBpUtsGwt+RGcqKT421PNzZw5E6NHj0bLli3Rrl07rFmzBuHh4Zg8eTIAReupqKgobNy4EUZGRmjcuLHK/q6urrCwsCiynoiIiIiI6GV28r5i4HZlC6wIe0UCK9XCGgBgl50Ol4Jk1W1sgUVUYkxgVXPDhw9HYmIivvrqK8TExKBx48Y4ePAgfHx8AAAxMTEIDw+v4CiJiIiIiIiqluvhiuSU9wstsGTPElj22elweTaQ+xN7DwCAr7NVeYdJVG0wgfUSeO+99/Dee++p3bZhwwat+86fPx/z5883fFBERERERERVVIwsC5Epiu6BzxNY7gCAFAsbAIoWWN4pMQCA8GcJrBhZdnmHSlRtcBB3IiIiIiIiIj2EPpuB0FhegPoJTwAA91wUvVyULbBqyOLglJUKAHhir0huxafmlHeoRNUGE1hEREREREREeth9JRIA4JsUDYv8XGSYWuCJg6KVlTKBVTM1DgCQYGWHDHNF18Hu/q4VEC1R9cAEFhEREREREZGOYmRZ2HktCgDQKO4xAOCuiy8EieLjdYqljUp5ZdfC9rUd0dTLoRwjJapemMAiIiIiIiIi0tHKYw/E5V73zwEA7rjVFtcpW2ApRdkpWl218nUsh+iIqi8msIiIiIiIiIh09NfVaABAi6gQvHbvLABgT6Nu4vY0cyvIIRFfx9g4AQDMTPjxm6g0+BtEREREREREpIMYWRbS8woAAENvHQEAHKvVEtdqNAAASAAIEiOkWkjFfWJtnAEAtham5RssUTXDBBYRERERERGRDnYHRYrL7cJvAgD+COwnrhvQzBOAajfCmGcJLEepWXmESFRtMYFFREREREREpINbUTIAgHl+LrxTngIAgl39AChaX9VxUySuUiyeD+Qea+MECYAWPhzAnag0mMAiIiIiIiIi0kGjGrYAAJ/kaBgLcqSaSxEvVSSmvhsSgMS0XABAuL27uE+0jQs61HGCh51l+QdMVI0wgUVERERERESkg4YeigRW7URFV8JHjjUBiQSLhwZgeCtvJGfmAACueTYQ94m1cUKvRu5FKyMivZhUdABEREREREREVcGNSEUXwjqJEQCAR041AQCRyVmIkWVh7/UYAMDOgB7o/ugSDtVtC0gkuBmZUiHxElUnbIFFREREREREpIPEDEULqzrPWmA9dPIS14cmZIjlUi2sMWrEN9j4bID3HUFRiJFllXO0RNULE1hEREREREREOjgeHA8AqJfwBADwwPlZAis9F1IzY637BoUll21wRNUcE1hERERERERExbgRkYzo1GyYFuSJXQhDns1A2MjTDhm5BVr3l0jKPESiao1jYBEREREREVG1FCPLQmhCBvycpaWeBXDrxXAAigHcTeUFSDWXItrGBQAwJFAxFpYEgKBmXwmAFj4OpTo+0cuOCSwiIiIiIiKqVmJkWVh3JhRrT4cCUCSQvhuimCmwpO4/TQMANIgPAwCEuPgCEgm8HSzF5Nh3QwIwe9ctlf2Uxy5tAo3oZccuhERERERERFRtbL8cjvaLjonJK0DRKmr2rlulGkjdztIUwPME1j0XXwBALVepWGZ4K2+86u+qst+nfRqUKnFGRApMYBEREREREVG1ECPLwqe7bqntxgcAR0Oelrhuo2eDWDWICwMA3HX1BQC8FuChcvwjIXEq+/3fv/c4AyGRATCBRURERERERNVC0JNkleRV86i72LptLro+ugIA2HLhSYnqnbI5CMfuKWYgbBCvaNl191kLrJx8uVguNCGjSPKsQBAQlpBZouMS0XNMYBEREREREVG1EJaQIS6/ffkv7Nk8C+3Db2L13kVwyJQhJDYdv556pFedNyKS8c/tWACAR2o83NOTIIcE95x9AACP4p4fU2pmrLYOKzN+9CYqLf4WERERERERUbWQk18AAPBJjsbc47+L6y3zczDr9CYAwKKDd/Xq0rf3WpS4PPj2MQBAUA1/ZJhbAQBqFxoDKyO3QG0dmblyteuJSHdMYBEREREREVG1YGuhGGi9w5MbMBEUSaNFXccBAIbeOgLL3GwAwNUnyTrXGZ+eAwCwycnAO5d2AwC2Nustbu/h7yYu+zlLYSRR3d9YIoGvs5V+J0JERTCBRURERERERNXCpvNhAICm0fcBACvbDcOvbYYiwcoO5gX5qJMYAQBIzsxVu/+NiGSsPf0INyIUCa7tl8Px901F98Euj4Ngl5OBxw6e+KthFwCAo9QEHnaW4v4edpZYNDgAxs8GfDeWSPDt4MYqZYioZEwqOgAiIiIiIiKi0oqRZSE8WdHCqknsAwDALfc6AIAHzt5wDr+FuonhuOVRFymZeUX2f3v9JXGgdgDo09hdHPsKAAKjQgAAJ2sFQm6kGOtqSpc6ReoZ3sobneu5ICwhE77OVkxeERkIW2ARERERERFRlXck+CkAwDw/F3UTwgE8T2Ddd/YGANR7tj4pQ7UF1rgXklcAxOSVWX4ezPLz0DIyGIBi/CsAMDOWYFLn2mpj8bCzRLvaTkxeERkQW2ARERERERFRlbf14hMAQL34JzAR5EiytEW0jQsA4IGTIoFVOzESAGBm8nygqhsRyTjxLHnVKPYh6ic8wZ5G3SBIjGCel4MDGz5AnSTFfnJIcNGrMQDg4171y+fEiAgAE1hERERERERUDUSlKGYWbBj3GABwx7UW8GwsqhhbZwCAW3oiACAnTxD323JR0SrLPD8XG3bMh0tmCjJNLfBv/Q4YcfOQmLwCgF2NeyDe2hEAkJadX8ZnRESFsQshERERERERVXm9G7oDeJ7ACnarJW6LkyqSTq7pSQAAZxszcdvREEXXwz73zsIlMwWAYrwrs/w8TL6wUyx3pYY/Puk7XXztamteBmdBRJqwBRYRERERERFVeR/2qo8/r0ah0dNnCSxXP3Hb02etppwzUmAkL4CdpSkARffBxAzFgO49758Xy9dOjESv++fgkZ6IWGtH9B+zFCmWthAkz9uA9PB3K/NzIqLnmMAiIiIiIiKiKu+TnTchEeTwjwsFANxxez7AeqLUHnJIYCLI4ZiVCntLRQus5UcVsxVKBDnah98Uy9dLeIK+984CAHYGvII4GyeVY70RWIMDtBOVMyawiIiIiIiIqMqKSo3Cd8e24fQDf/gmx0Cal41sEzOEOtYQyxQYGSNRageXjBS4pidj99VIBPo64OhdxeDtDeNCYZ+dLpavmRqPmqmKbf/Ua69yPKmZERa/0azsT4yIVHAMLCIiIiIiIqqSCuQFGLpjKLZdTAYADAw+AQC46+KLAiNj2Fs+b7Px1FrRiso9LQHH7sXjSPBTcVvbJ4rWV8dqtUS81F5cH29lr9KSCwCWv9m8LE6FiIrBBBYRERERERFVOQXyAtRZUQcXIi/ARPCFZ2ocJl7eCwD4u0FHAEBK1vOZAqNtXQAAnmkJAICEtBxxW7tn3QfPezfBI8ea4vqLXo3FmQyVrMxMDX8yRFQsJrCIiIiIiIioygmXhSMsJQwAICAJ/YNPwTo3C5drNMTvrQYWKR/1LIFVQxYHALj8RDEjoZG8AK0j7gAAzvs0weNCCazLXo1U6jCWSODrbGXoUyEiHXAMLCIiIiIiIqpyHic/FpdTzXZgddv5CHPwwH1nH3G2QCMJ0L+pB/Zej0GYgycAoHZSJADg3CNFAqt+whPY5mYizcwSwa5+CH1WDgCu1GwoLhtLJPh2cGMO3k5UQZjAIiIiIiIioirhXsI9jPtrHOZ2nIukrCRxfbZxEHIkIfinfntI8LzL36LBAcgtkGPv9Rjcc/EBAHGWQqWWkcEAgGueDSA3MsZZ32aQQ4IwBw+EuPgCAD7oUQcjWnszeUVUgZjAIiIiIiIiokorOi0aA/43AOOajsPUf6YCAPr/rz/qO9UHAAzxH4LRTUZj4PaBeLvBEtihK2q7WmNwi5rwsLPEyuMPACgGdgcAL9lTSHMykWGu6ArY6lkC6/Kz1lbBbrXQ5d21yDCzhNzIGADgYmPO5BVRBWMCi4iIiIiIiCoNQRAw69AsJGcnQ2oqxcrLKwEAV6KvqJS7l3gPACA1k2JAgwGQfymH5IUB1wEgIjETAJBiaYs4qQNcM5JRNzEC1z3rQyLI0Tb8lqL+Qt0FI+zdVeqwtzQz3AkSUYkwgUVERERERESVxrXYa1hyYYnO5aWmUgBQm7wCgIyc5zMR3nf2ViSwEp7gumd99As5DdeMZKSbWSKoRkO1+wNAoK+DzvEQUdngLIRERERERERUaTxKeqRXeWUCS+N28+ftNh44ewMA6iWEA4KAdy/uAgD83nIAck1M1e7fsY4Tuw8SVQJMYBEREREREVGlserKKr3KS820J7Aa1bAVl5UJrLoJEWj89BEaxT1Glok51rUcoHH/j3vV1yseIiobTGARERERERFRhZILcgiCAAA4EXZCr32tzay1bn+14fPxrO6LCaxw9Lx/HgBwrHZLyCxt1O47pEUNNPVi90GiyoBjYBEREREREVGFkQtydNnQBeGycNyacgu25rZIzUnFiMYjsH7Aepgbm6Puirp4lKy+a2FaTprW+j3sLNHAzRp3n6bjvrMPAKBGWjw6PrkOADjt21ws2zfAHe92roUrYclo6evA5BVRJcIEFhEREREREVWIuIw4uP3gJr5u+ktTpOakAgAGNxgMCxMLAEBmXqZY5o+Bf+By1GVxdsI8eV6xx3m9mSfu/ncfqRbWeGrtCLf0JLSIVsxieNfVDwDQpa4zVr0VqIiDiSuiSocJLCIiIiIiIqoQI3eNVHkdlhImLtuYP+/W9zTjqbjc3a87xjQdg7pOdbEzeCe+6PxFscfxdXo+TtZ9J2+4pSeJrx84eQEAvhvaRO/4iaj8cAwsIiIiIiIiKnMp2SmQfiuFZIEEkgUSZOVl4WjoUY3lC49t5SZ93krLxkyR2JreZjpOjT8FS9PiZwgM9Hneoko5kDsAhNl7IMPcCvXcrDnTIFElxwTWS2DVqlXw8/ODhYUFAgMDcfr0aY1ld+/ejVdffRUuLi6wtbVFu3bt8N9//5VjtEREREREVB3NPzFfpSvgppubtJZv5dlKXN41bJe4bGtuq664Vh52lqhpbw4AOOXXQlx/wTsAAHD/aTpiZFl610tE5YcJrGpu+/btmDFjBj777DNcu3YNnTp1Qp8+fRAeHq62/KlTp/Dqq6/i4MGDCAoKQrdu3dCvXz9cu3atnCMnIiIiIqLq5ObTmyqv5xydIy5/2/1blW3yL+UwNzEXX7fzaodjY47hxuQbkEgkJTp+K18nAMCJWoFY3m44tjTrjUVdx4vbwxIyNe1KRJWARFDOVUrVUps2bdCiRQusXr1aXOfv74+BAwdi0aJFOtXRqFEjDB8+HF9++aVO5VNTU2FnZweZTAZbW/2/HSEiIiIiourH/jt7yHJkarf91u83nI88j9+v/Y5T406hk08ngx//4x3XsSMoSu02CYBzc7qzG2EVxs+h1R9bYFVjubm5CAoKQs+ePVXW9+zZE+fOndOpDrlcjrS0NDg6Omosk5OTg9TUVJUfIiIiIiIiAHiY9BB+P/lpTF4BwIjGI/Dr678iemZ0mSSvAEACzS23Pu3bgMkrokqOCaxqLCEhAQUFBXBzc1NZ7+bmhtjYWJ3q+PHHH5GRkYFhw4ZpLLNo0SLY2dmJP15eXqWKm4iIiIiIqgdBEFB3RV2V2QVflDI7BVIzKYyNjOFh41FmsXg5qU9QdanrjHc71y6z4xKRYTCB9RJ4sY+4IAg69Rvftm0b5s+fj+3bt8PV1VVjuTlz5kAmk4k/ERERpY6ZiIiIiIiqtsTMRBh9VfQj518j/lJ5bWdhVy7x+Dlbq10/rBW/gCeqCkwqOgAqO87OzjA2Ni7S2iouLq5Iq6wXbd++HRMmTMCOHTvwyiuvaC1rbm4Oc3NzrWWIiIiIiOjlMmj7oCLr/hv1H3rW7onDow/D08YTfvZ+5RZPoI8DJAAKDwItkQAtfBzKLQYiKjm2wKrGzMzMEBgYiMOHD6usP3z4MNq3b69xv23btmHcuHHYunUrXnvttbIOk4iIiIiISiA9N72iQ9AotyAXp8NPi68vTLgAYZ6AnrUV4/O+UusVNHRpCEvT8ht3ysPOEt8NCYDRs84oRhLgu8EBHPuKqIpgC6xqbubMmRg9ejRatmyJdu3aYc2aNQgPD8fkyZMBKLr/RUVFYePGjQAUyasxY8bgp59+Qtu2bcXWW5aWlrCzK5+mvUREREREpN2Rx0fw6qZXsaDrAnzZ5UtM+XsKfr/2O/LkeahhUwNhM8Iwaf8ktK/ZHpMCJ5V7fH/dfd5N8NH0R6jlUKvcY1BneCtvdK7ngrCETPg6WzF5RVSFSARBEIovRlXZqlWr8H//93+IiYlB48aNsXTpUnTu3BkAMG7cOISFheHEiRMAgK5du+LkyZNF6hg7diw2bNig0/E4fSkRERERUdmqt6IeHiQ9AAAEvxeMhqsaqmzv5tsNx8OOAwCEeeX/ka/Dug44F3EOliaWyPwss9yPTy8ffg6t/pjAIoPjg4OIiIiIqGxJFhQ/KZNSzuc5MDM2K8NoilLG9233bzGn05xyPTa9nPg5tPrjGFhERERERERVjIe1h85l4zPiVV4XyAuQL883dEhqKce8IiIqLSawiIiIiIiIqpB8eT5i0mM0bv+629cqrxefWywup+akwuRrE7Re2xpl1RknM+95l0FrM+syOQYRvXyYwCIiIiIiIqrE5IIctotsMeB/AwAAsw7NKlJm25BtkH8phzBPwOedP1fZ9tPFn1AgLwAAtFrbCgBwLfYajL4ywsYbG0sVW0ZuBlZdXoWwlDAxVum3UnG7s5VzqeonIlLiLIRERERERESV2Fcnv0Jabhr23duHxMxE/HTxJ3GbpvGtVvRZgWn/TBNfN/mlCXILcvEw6aFKubF7x2JM0zF6x5SRm4F/H/6LoTuGiutSZqfA/nt78fWrtV6Fk5WT3nUTEanDBBYREREREVEl9v3Z78Vl58XPWzRNaD5B4+DsU1tPRVpOGuYemwsACI4P1lh/vjwfJkaqHw1zC3JhamQKiUT9YPGfH/scyy4uU1m39MJScdnCxAKHRh/SeEwiIn2xCyEREREREVEllp2frXZ9r9q9tO73ZsCbOtV/MfKiyuuZ/82E+UJzdFrfSW15QRCKJK8AYMHJBeLykxlPdDo2EZGumMAiIiIiIiKqpPbe3atxW3puutZ9fe19Mb/LfLXbAlwDxOWO6zuKy3kFeWJLqrMRZyEX5Cr7/Xb1Nxh9pf1j5KWJl+AqddVahohIX0xgERERERERVVKDtg/SuK1//f7F7t/Co0WRdX3q9MHNKTdV1qXlpAEApv8zXWX99djr4rIgCJi0f5LK9lV9V6G5e3OVdU3dmxYbFxGRvpjAIiIiIiIiqkQWn12MifsmqrS+mtdlnkqrqQDXAJ0GSBcgiMuPpz9G7ue5OPjWQQDA5MDJ4rawlDBk5mXil6BfVPY/FnpMXN5/f3+R+guEAmwevFl8nft5rsZxuYiISkMiCIJQfDEi3aWmpsLOzg4ymQy2trYVHQ4RERERkYrI1Ej8cf0PTGgxAe7W7hUdjop5x+fhq1NfFVkv/1LRlS8xKxGnn5xGj1o9YGte/N/aocmhqLW8FgBAmKf60S+3IBfmC83F173r9Ma/D/8FAHjaeCI6LRrtvdqjmVszfNjuQ+y7tw8fHfpIpY7zE86jbc22+Pfhv/Cw9mDrK6ow/Bxa/TGBRQbHBwcRERERVVaCIKDpL01xK+4WfOx8EDYjrKJDAqAYe8psoeaWSy8mn/RxPPQ4PG08Ud+5fpFtkgXqZxlc/OpifHz4Y4113n3/Lh4mPcRr9V4rcVxEhsTPodWfSfFFiIiIiIiIqj5BEFQGIH8ie4L7ifdRz6lehcWUW5CLx8mP4f+zf5kdo5tfN73Kr+q7CiZG2j8q1neurzYhRkRUVjgGFhERERERvRT+uvdXkXX1V1ZsEqbhzw3VJq9GNxmN4Y2GAwB+7vtzmR1/+9DtRda9Vu81yHJkGvfZPWx3mcVDRKQJE1hERERERFRtjN4zGpIFEkSlRhXZFhQdpHafgw8OlnVYaj1MeohHyY+KrP9v1H/YOGgj/jf0f8j5PAfvtXqvzGIY1mgYfuv3m/i6T50+8LbzxsAGA9WWj54ZjUH+mmdGJCIqKxwDiwyOfY+JiIiIqCKEy8Lhs8xHfJ3/RT6MjYyxK3gXhu4YKq7vV68fOvt0VhnjqTRjTJXUh/9+iGUXl4mv17y+BhNbTIREon5cqrIiCAJSslNga24LYyNjcf3A/w3EX/f+wpyOc2BtZg2pqRQftP2gXGMj0hU/h1Z/TGCRwfHBQUREREQV4cUByT/v9Dm+6PKFykx7AHBm/Bm092qvMh7Wij4r8H6r98steVR3RV08THoIAGju3hzLei9DZ5/O5XJsXWXlZeFM+Bl08ukECxOLig6HSCt+Dq3+2IWQiIiIiIiqvOSs5CLrFp5eWCR5BQAdvDtAIpFgdofZ4rpp/0zDojOLDBaPLFuG4PhgRMgiimx7nPxYTF4BwJ7heypd8goALE0t8WrtV5m8IqJKgQksIiIiIiKq8u4l3tOp3Kgmo8TlD9qodof7+tTXSM9NL3UsGbkZsP/eHo1WNYL3Mm9IFkgQEh8ibn+a/lRc7lu3L3zsfdRVQ0REhWifG5WIiIiIiKgKmPHvDABAbYfaagdGd5O6YW6nuRjbdKy4zsPGQ6VMdn42PH70wL2p9+Bp41miOOIz4rH++voi6xuuagh3a3fEpseK65q5N8OBkQdKdBwiopcNE1hERERERFSlyQU5LkZdBAAkZSUh/uN41F5eG6k5qYrtX8p1HtsqPTcdrde2RuTMSL3j+N/t/+HNXW9q3F44eQUA12Ov630MIqKXFbsQEhERERFRlVZ3RV1xeW2/tXC2csbh0YdRw6YGfnntF63Jq8WvLi6yLiotCpeiLhV73Hx5Pky/NoVkgQSvb30dnx37TGX78EbD9TgLIiLShrMQksFx9gciIiIiKk+FZx8U5un/8eZx8mPMPzEfm25uEte19GyJy5Mua93vbPhZdFzfUeP2yA8jITWTwuF7B3HdvhH7sP76euy5uwfJs5Nhb2Gvd7xEVBQ/h1Z/TGCRwfHBQURERETlJa8gD2YLzQAoZvMb2GBgieu69fQWmvzSRHy9os8KuEnd0MmnE+4n3se9hHuYFDhJ3N7u93a4EHmhSD05n+fAzNhMfL3+2nokZCZgVvtZOndlJCL98HNo9ccxsIiIiIiIqMr66uRXAAAHCwcMqD+gVHUFuAWovJ72zzQAQGPXxrgddxsA8M7f7+DQqENo7tFcbfLqux7fqSSvAGB88/GliouIiJjAIiIiIiKiKiItJw1WplYwNjIGAOTk52Dh6YUAFONNlVXrJmXySqnn5p4qr71svRCRGoHhjYZjdsfZZRIDEdHLjoO4ExERERFRpZNXkIe8gjwAwM7gneiwrgNsv7OFydcmiE6LBgBYfGMhli9N18HCDo48qPc+Ie+H4MDIA/hj4B8GiYGIiIriGFhkcOx7TERERESloZzdDwC2D92O4TuLn80vc24mLE0tDRbD2qC1eOfvd4qsb1uzrUrXwQsTLqBNzTYGOy4RlQw/h1Z/TGCRwfHBQURERESlcS7iHDqs66BTWRcrFzyd9bRMug8KggABAoy/UnRZ3DBgA7r7dYf3Mm8AwJMZT+Bt523w4xKR/vg5tPrjGFhERERERFRuZNky2H9vDwCwMLHAn0P/hL2FPZKzk/F6vdcBAN3+6KZzfSfGnSizsa8kEgkkkGDHGzsQEh+CMU3HQCKR4PT408iX5zN5RURUjtgCiwyOmW8iIiIiUsf/Z3/cTbhbon07+3TGqSencHvKbTRe3RgA8GDaA9RxrGPIEImoiuLn0OqPLbCIiIiIiMjgEjIT8N/D/7Dl1hb88/CfEtXRv35/pOakYvOgzahhW0Ncn/t5LoyNjGEk4ZxUREQvCyawiIiIiIjIoARBQK/NvXA15qrGMrPazcIP53/QuH1s07HYMHCD2m2mxqalDZGIiKoYdiEkg2PTTSIiIqKXV4G8ALWW10K4LLzItjkd52Ba62lwlbrC2MhYXJ+TnwNzE3MIgoBXN70KWY4MlyddLs+wiaiK4+fQ6o8tsIiIiIiISGf/PPgHfbf2BQAMqD8Af937CwDwQZsPkC/Ph5WplUryalCDQWjs2hgLui7QONi6uYk5AMWg6UfGHCnjMyAioqqILbDI4Jj5JiIiIqqe5IIcxl8ZF1/wmWNjjqGbn+4zChIRlRQ/h1Z/bIFFRERERETFuhh5EW1/b6tz+azPsmBhYlGGERER0cuECSwiIiIiIkJOfg7CZeEIjg+Gk5UTfg36FZtvbgYA7BuxD/3/11/tfm80fANZ+Vnwd/bH4nOLAQBbB29l8oqIiAyKXQjJ4Nh0k4iIiKiov+//jY03NqJ3nd6YsG8CzIzNcGzMMXTw7qBSThAEfPDvB3iY9BBvN38bQxsOLdO4BEHAsdBjeGXTKzqVn9pqKn7q8xNSc1Jhb2FfprEREemKn0OrPyawyOD44CAiIiJS9euVXzH5wGS12/K+yIOJ0fOOEV02dMGpJ6eKlGvp2RJXoq9gXpd5mN91fqljSstJw7mIc+i9pbfO+1yZdAWBnoGlPjYRkaHxc2j1Z1TRARARERERVWdbb23VmLwCgPkn5gMACuQFkCyQqE1eAcCV6CsAgAUnF+Buwt1SxbT37l7YfmerNnnVq3YvNHFrAgAInxEOewt7uEndEPFhBJNXRERUYdgCiwyOmW8iIiJ6WeUV5KHX5l44HnZca7njY4+j2x+K2flq2tZE6AehMP3aVKXMlsFb8NbutzTWIf9SDolEolNct57ewvXY6xizd4za7R28OuDAyAOws7DTqT4iosqGn0OrPyawyOD44CAiIqKXTVB0EFqubam1zOnxp7EnZA86+XTCwAYD8dvV3zBp/yS1ZR9Pfww/Bz/x9WtbX8PBBwexss9KTP1nKgBgZMBIbBy4EcZGxhqPufnmZozeM1rttpaeLTG11VSMDBgJU2NTtWWIiKoKfg6t/pjAIoPjg4OIiIiqG0EQsO32NrSu0Rp1HOsAAO4m3IWZsRlWXFyBZReXad0/5P0QNHBuoLJOli2D/ff2RY81T/Of5/ny/CIttdyt3VHfqT7mdpqL9l7tsfzicjxKeoQLURcQHB+stp4B9Qdgy+AtkJpJtcZNRFRV8HNo9ccEFhkcHxxERERUkbLzs5GWk4blF5eji28X9PDroXNXOwAITQ5FUlYSjoYe/f/27j06ivrsA/h3LnvfZHMjCSEJdwImWCAoRamIl4giyivHgkXAcmybVjRcRPFyKvoW0dMej68esVQEPWoBLdSjlGNJlDsptIEgN7GUkEBICIHcN3ub+b1/pDtmSVDEZDck3885c3Z35jczv5l9ZrL75JlZFFUU4cPDH36v9b875V38bPjPMGPDDNw+4HY8POrhS7b1Bry4/b3bsaNsB4DLuyxwccFivLzr5e/Vp4z4DIzsPRIv3/Yy0l3p32teIqKrAb+Hdn9MYFGH44mDiIiIwk0XOgpOFGD9kfV498C78GrekOlZiVk4VHUIACBBwuiU0bCqViwcuxDprnS8vOtl7D61G6fqT33vddtUG5beshTzfjzveyXKrpSma1D/V/3uhv91ev5p9Inu04k9IiKKPH4P7f6YwOoBli9fjt///veoqKhAZmYmXn31VfzkJz+5ZPtt27ZhwYIFOHz4MFJSUvDEE08gN/fSv5xzMZ44iIiI6EoIIVB4uhDvHXgPg+MHI7NXJgbGDcTA2IE433weVtUKWZKx9eRWHKs+hvKGcuws24k95Xs6rU/3ZtyLCf0m4ONjH2PP6T2Y/aPZ2HVqF67pdQ2W3rIUsbZYyJKMGGtMp/WhPdXualQ2VqKXvRdGrhiJvDF5qPfWY+O/N2JKxhTE2+Oxt3wv/m/i/yHeHh/WvhERRQK/h3Z/TGB1c+vWrcPMmTOxfPly3HjjjVixYgVWrlyJI0eOID29bfl4SUkJsrKy8Itf/AK/+tWvsGvXLvzmN7/BmjVrMHXq1MtaJ08cRETUFehCh6ZrMCkmNHgb8OXZL1FwogBOsxN7z+xFWnQazrnPYWfZTpyoOWHMN/PamfjZ8J/hq+qvUO2uhkWxoE90H6woWoHDVYcx9ZqpWH3vasiSHMGtu3oJIVDrqcXe8r3YdWoX3vvyPTT7mxFni8PR6qM/ePk5A3OQNyYPqdGpGBQ3COPfGY8j547g4ZEP44ODH+B883ncOehOxNvj8f6X7xvzxdviMTljMuyqHQtvWIjU6FSYZFNYKqqIiOiH4/fQ7o8JrG5uzJgxGDVqFN58801j3LBhwzBlyhQsW7asTfsnn3wSn3zyCY4e/eYDZG5uLg4cOIDCwsLLWufVcuI413QObr8bPs2HgB4AAEiSBAlSu4+yJF9ymoT/Tv+OaUDLB/eAHoBf98Ov+eHTfG0Gv94yXhc6FEmBQMs83oAXmtAghIBZMcNussOiWmBRLG0egZYvb62HIEmSoOkamgPNEELApJigSErIh/TgqUFAGK9bPw8uxySbYFJMIftHkRSoshqyH4Lz60KHEKJN31oPmtCML57BfSGEML4sBvthkltuYhvQA8Y+NZ5rfjT5m9DgbYAn4DH2YZOvCQE9AE1oRtvWg6ZrCIgAfJoPwxKGwW6ywxPwwK99s2xNaC3bD8nYD8HXrZ+3jqn2nrc3zw+dX5Zk+DQfvJoX3oAXXs1r7Cev5m2JL80Pp9kJi2qBw+SAw+xArDUWutDR6GtEk78Jjb5G1HnqUN1cjVpPLeo8daj11OKc+xzsJjsGxg5E35i+sKk2mBUz3H433H43Gn2N8Gk+aEJr2Zet9nUw5jwBj/HaaXbCZXVBkRQ0+hoRY41BlCUKZsUMk2yCWTFDlVUICFgVK9wBNy40X0CDtwG60NEcaAYAWFUroi3R8Gk+xFhjYFEs8AQ88AQ86OXohfTodLisLiQ7kyGEMI4Tv+5HTXONEbeSJMGv+dHoa4Tb74YudJiUln5YFEvLo2qBSTbBq3nR5GtCc6AZmt4Ss17NiwZvA8yKGVbVGnJMBd87AFBlFT7NhwZfA7wBr7EMi9qyDlmSYVbMiLa0nEOD8ec0O+EwOyBBQpQlythHsiRDlmRjv1tVKxRZgSIpUGTlm2NL1+DVvCFxFFyPWTG3Oe4BQJEUyJJsLEfTNVS7q0Pi0apaYVWtUGXV6EfrYzjY/+Ax6tf88Ot+eANeVDVVGbEnSRJUWTXOIYqsGMdyk68JMdYY2Ew2WBSLMU9lY6VxTjMpJpysPQlPwIMzDWdQUluCgB6ABAl+3Y+ONCJ5BKZnTocqq4iyREGChLNNZ+H2u2FWzPBpPgDAmYYzcPvdiLHGINoSjXpvPcyKGTHWGDjNTlhVKyyKBXXeOngDLcdoc6AZAT0AXeio99a3xLPmMc6jqqwax8/Fg91kb3kP/9v24nN363HBR03X0OBrgCIp8Ot+uP1u4+9irafW6Esw3ixqy/4P/v0UQqDOW4dGXyMafY3GMdPsb8aZhjPGsRgcLr6071KSHEmoaqoKicegZGcyhiUMQ6wtFv1j+sPtd+P4heNYec/K731/JyEE9lXsw+D4wcYxR0REV5+r5XsoXbnLv3ierjo+nw9FRUVYvHhxyPicnBzs3r273XkKCwuRk5MTMu6OO+7A22+/Db/fD5Op7U8se71eeL3ffBitr6/vgN53vslrJnfqJQdE3VlZXVmku0D0vfV29oYmNJhkE8obynFzv5uRFp2GghMFyBmYg51lO/Gfmv9gaMJQ1Hvr4bK4YFWt6BPdB37Nj1pPrfF3o7iyGMWVxZHdoKvcgNgBuDHtRmT2ykRzoBmJjkQk2BPwP0P/Byal5fOGLnScqDkBVVYRa401kvQx1hgostIh/ZAkCdkp2R2yLCIiIuo8TGB1Y9XV1dA0DUlJSSHjk5KSUFlZ2e48lZWV7bYPBAKorq5G796928yzbNkyPP/88x3X8TCxmWywqlajwgO49H+ng1VDF0/Thd7mP9yXS0JL5YNZMRv/rTZet6qqCFb7qLIKi2IxKpu8Aa9RQda62ib4GKzGCQ4XV1fJkgy7yQ5Zko2qiPb6CFy62kcTmlFZ0Xo/BV9/27a3rngJ6Scko3IkWIViVsyQJAm60EP6FKxwUGUVJtkEVVZbnistz+0mO6It0bCqVqOdw+QIaRscFFkxnp9tPItaby0USYHb74bNZINNtRnTW1821Lpi5buq1i6uYLt4nsue/1um60I3qoWCFUNezQtFUoyqI0VuqXbyBDxw+91o8jfhQvMFyJKMKHMUHGYHHCYHXBYX4u3xiLfFw2V1IdoSDafZiXpvPSobK1FaW2pU0TjMDthUG5xmJ8yKOWR/tq6msSgWI+5UWUWjrxEXmi9AQCDKHIULzRfg9ruNyrtghYcECZ6ABzaTDXG2OESZoyBJEhymlp9/D1aOmWQTaj218Gk+4/gurStFTXMNqpurWyqH/luN4wl4IEsyEuwJEEIYVUMm2WRUsiiSgoAIGMeVT/MZVTLBCjabyWZsp0k2IcoSBb/WsnwjTlodD0II+HU/zIoZDpMDdpPdqF4KHsMCAs3+ZjT5m4w+mRSTUR0nhECDr8GoZApWWAUrrjwBDzRdMyqygsdX8D1oHTc+zYcaT41Rpdn6GAvul2CFWXAZMdYYqLJqHJNN/iajuk+g5fwSPIZNisnof/DYCx6jZsWMREciVKnleJUkCU2+Jng0DyyKxUg22U12WBQLmvwtFW/egNeI1wR7gnF8+zQfekf1Row1BsnOZKRFp+Fs01mYZBMGxQ1CL0evS56XLle9tx4r/rUCZXVlaPQ3whvwosZTA1VWkeRIgt1kh19reX8BIM4Wh2hLdEtVo7cONtWGisYK4/32BDzwBrxwWV2wKlaYlJbtVWUVQgi4rK6W2Fdtxvvo1/1QJMWIh0ZfIxp8DWj0NaLZ32y8fxdXBAff3/amRVuioemaUd0bXH+MNcY4BwfjP7j+4PsoQYLNZDMSfj7NB0mSYFNtSHImGZVmwcGm2mAztZwvvossyRgUN+gHv29ERER09WMCqwe4+N4NQohvvZ9De+3bGx/01FNPYcGCBcbr+vp6pKWlXWl3w2bL7C2dstz2Elshl+9BMi65667aS6xcfCklEVE4ZCRkdOjyoi3RWHTjog5dJhERERF9NyawurGEhAQoitKm2qqqqqpNlVVQcnJyu+1VVUV8fPu/YGOxWGCxWDqm091AsLKoJ2t9bxwwX0VEREREREQ/UPctASGYzWZkZ2cjPz8/ZHx+fj5uuOGGducZO3Zsm/abN2/G6NGj273/FRERERERERFRZ2MCq5tbsGABVq5ciVWrVuHo0aOYP38+ysrKkJubC6Dl8r9Zs2YZ7XNzc1FaWooFCxbg6NGjWLVqFd5++208/vjjkdoEIiIiIiIiIurheAlhNzdt2jScP38eL7zwAioqKpCVlYVNmzahb9++AICKigqUlX3za2L9+/fHpk2bMH/+fLzxxhtISUnBa6+9hqlTp0ZqE4iIiIiIiIioh5NE8A7dRB2kvr4eLpcLdXV1iI6OjnR3iIiIiIiIqJvj99Duj5cQEhERERERERFRl8YEFhERERERERERdWlMYBERERERERERUZfGBBYREREREREREXVpTGAREREREREREVGXxgQWERERERERERF1aWqkO0DdjxACQMvPmBIRERERERF1tuD3z+D3Uep+mMCiDtfQ0AAASEtLi3BPiIiIiIiIqCdpaGiAy+WKdDeoE0iC6UnqYLqu48yZM4iKioIkSZHuziXV19cjLS0Np06dQnR0dKS7Qz0U45AijTFIXQHjkCKNMUhdAePwhxFCoKGhASkpKZBl3i2pO2IFFnU4WZaRmpoa6W5ctujoaP6BoIhjHFKkMQapK2AcUqQxBqkrYBxeOVZedW9MSxIRERERERERUZfGBBYREREREREREXVpTGBRj2WxWPDcc8/BYrFEuivUgzEOKdIYg9QVMA4p0hiD1BUwDom+HW/iTkREREREREREXRorsIiIiIiIiIiIqEtjAouIiIiIiIiIiLo0JrCIiIiIiIiIiKhLYwKLiIiIiIiIiIi6NCawqMdavnw5+vfvD6vViuzsbOzYsSPSXaKr0LJly3DdddchKioKiYmJmDJlCo4dOxbSRgiBJUuWICUlBTabDTfffDMOHz4c0sbr9eLRRx9FQkICHA4H7rnnHpw+fTqkTU1NDWbOnAmXywWXy4WZM2eitra2szeRrjLLli2DJEmYN2+eMY4xSOFQXl6OBx98EPHx8bDb7RgxYgSKioqM6YxD6kyBQADPPvss+vfvD5vNhgEDBuCFF16ArutGG8YgdbTt27dj8uTJSElJgSRJ+Pjjj0OmhzPmysrKMHnyZDgcDiQkJOCxxx6Dz+frjM0mihxB1AOtXbtWmEwm8dZbb4kjR46IvLw84XA4RGlpaaS7RleZO+64Q6xevVocOnRIFBcXi0mTJon09HTR2NhotHnppZdEVFSUWL9+vTh48KCYNm2a6N27t6ivrzfa5Obmij59+oj8/Hyxb98+MWHCBPGjH/1IBAIBo83EiRNFVlaW2L17t9i9e7fIysoSd999d1i3l7q2vXv3in79+olrr71W5OXlGeMZg9TZLly4IPr27SseeughsWfPHlFSUiIKCgrE8ePHjTaMQ+pMv/vd70R8fLzYuHGjKCkpER999JFwOp3i1VdfNdowBqmjbdq0STzzzDNi/fr1AoD461//GjI9XDEXCAREVlaWmDBhgti3b5/Iz88XKSkpYu7cuZ2+D4jCiQks6pGuv/56kZubGzJu6NChYvHixRHqEXUXVVVVAoDYtm2bEEIIXddFcnKyeOmll4w2Ho9HuFwu8cc//lEIIURtba0wmUxi7dq1Rpvy8nIhy7L47LPPhBBCHDlyRAAQ//jHP4w2hYWFAoD46quvwrFp1MU1NDSIwYMHi/z8fDF+/HgjgcUYpHB48sknxbhx4y45nXFInW3SpElizpw5IePuu+8+8eCDDwohGIPU+S5OYIUz5jZt2iRkWRbl5eVGmzVr1giLxSLq6uo6ZXuJIoGXEFKP4/P5UFRUhJycnJDxOTk52L17d4R6Rd1FXV0dACAuLg4AUFJSgsrKypB4s1gsGD9+vBFvRUVF8Pv9IW1SUlKQlZVltCksLITL5cKYMWOMNj/+8Y/hcrkYtwQAeOSRRzBp0iTcdtttIeMZgxQOn3zyCUaPHo37778fiYmJGDlyJN566y1jOuOQOtu4cePw+eef4+uvvwYAHDhwADt37sRdd90FgDFI4RfOmCssLERWVhZSUlKMNnfccQe8Xm/IpdxEVzs10h0gCrfq6mpomoakpKSQ8UlJSaisrIxQr6g7EEJgwYIFGDduHLKysgDAiKn24q20tNRoYzabERsb26ZNcP7KykokJia2WWdiYiLjlrB27Vrs27cP//znP9tMYwxSOJw4cQJvvvkmFixYgKeffhp79+7FY489BovFglmzZjEOqdM9+eSTqKurw9ChQ6EoCjRNw9KlS/HAAw8A4LmQwi+cMVdZWdlmPbGxsTCbzYxL6laYwKIeS5KkkNdCiDbjiL6PuXPn4ssvv8TOnTvbTLuSeLu4TXvtGbd06tQp5OXlYfPmzbBarZdsxxikzqTrOkaPHo0XX3wRADBy5EgcPnwYb775JmbNmmW0YxxSZ1m3bh3ef/99/PnPf0ZmZiaKi4sxb948pKSkYPbs2UY7xiCFW7hijnFJPQEvIaQeJyEhAYqitPlvRFVVVZv/XBBdrkcffRSffPIJtmzZgtTUVGN8cnIyAHxrvCUnJ8Pn86GmpuZb25w9e7bNes+dO8e47eGKiopQVVWF7OxsqKoKVVWxbds2vPbaa1BV1YgPxiB1pt69e+Oaa64JGTds2DCUlZUB4LmQOt+iRYuwePFiTJ8+HcOHD8fMmTMxf/58LFu2DABjkMIvnDGXnJzcZj01NTXw+/2MS+pWmMCiHsdsNiM7Oxv5+fkh4/Pz83HDDTdEqFd0tRJCYO7cudiwYQO++OIL9O/fP2R6//79kZycHBJvPp8P27ZtM+ItOzsbJpMppE1FRQUOHTpktBk7dizq6uqwd+9eo82ePXtQV1fHuO3hbr31Vhw8eBDFxcXGMHr0aMyYMQPFxcUYMGAAY5A63Y033ohjx46FjPv666/Rt29fADwXUudzu92Q5dCvNoqiQNd1AIxBCr9wxtzYsWNx6NAhVFRUGG02b94Mi8WC7OzsTt1OorAK803jibqEtWvXCpPJJN5++21x5MgRMW/ePOFwOMTJkycj3TW6yvz6178WLpdLbN26VVRUVBiD2+022rz00kvC5XKJDRs2iIMHD4oHHnig3Z9QTk1NFQUFBWLfvn3illtuafcnlK+99lpRWFgoCgsLxfDhw/mz3dSu1r9CKARjkDrf3r17haqqYunSpeLf//63+OCDD4Tdbhfvv/++0YZxSJ1p9uzZok+fPmLjxo2ipKREbNiwQSQkJIgnnnjCaMMYpI7W0NAg9u/fL/bv3y8AiFdeeUXs379flJaWCiHCF3OBQEBkZWWJW2+9Vezbt08UFBSI1NRUMXfu3PDtDKIwYAKLeqw33nhD9O3bV5jNZjFq1Cixbdu2SHeJrkIA2h1Wr15ttNF1XTz33HMiOTlZWCwWcdNNN4mDBw+GLKe5uVnMnTtXxMXFCZvNJu6++25RVlYW0ub8+fNixowZIioqSkRFRYkZM2aImpqaMGwlXW0uTmAxBikcPv30U5GVlSUsFosYOnSo+NOf/hQynXFInam+vl7k5eWJ9PR0YbVaxYABA8QzzzwjvF6v0YYxSB1ty5Yt7X4OnD17thAivDFXWloqJk2aJGw2m4iLixNz584VHo+nMzefKOwkIYSITO0XERERERERERHRd+M9sIiIiIiIiIiIqEtjAouIiIiIiIiIiLo0JrCIiIiIiIiIiKhLYwKLiIiIiIiIiIi6NCawiIiIiIiIiIioS2MCi4iIiIiIiIiIujQmsIiIiIiIiIiIqEtjAouIiIjoMixZsgQjRoyIdDeIiIiIeiQmsIiIiKjHkyTpW4eHHnoIjz/+OD7//POI9G/9+vUYM2YMXC4XoqKikJmZiYULFxrTmVwjIiKi7k6NdAeIiIiIIq2iosJ4vm7dOvz2t7/FsWPHjHE2mw1OpxNOpzPsfSsoKMD06dPx4osv4p577oEkSThy5EjEkmlEREREkcAKLCIiIurxkpOTjcHlckGSpDbjLq5yeuihhzBlyhS8+OKLSEpKQkxMDJ5//nkEAgEsWrQIcXFxSE1NxapVq0LWVV5ejmnTpiE2Nhbx8fG49957cfLkyUv2bePGjRg3bhwWLVqEjIwMDBkyBFOmTMHrr78OAHjnnXfw/PPP48CBA0bF2DvvvAMAqKurwy9/+UskJiYiOjoat9xyCw4cOGAsO7hNK1asQFpaGux2O+6//37U1tZ21K4lIiIi6hBMYBERERFdoS+++AJnzpzB9u3b8corr2DJkiW4++67ERsbiz179iA3Nxe5ubk4deoUAMDtdmPChAlwOp3Yvn07du7cCafTiYkTJ8Ln87W7juTkZBw+fBiHDh1qd/q0adOwcOFCZGZmoqKiAhUVFZg2bRqEEJg0aRIqKyuxadMmFBUVYdSoUbj11ltx4cIFY/7jx4/jww8/xKefforPPvsMxcXFeOSRRzp+ZxERERH9AExgEREREV2huLg4vPbaa8jIyMCcOXOQkZEBt9uNp59+GoMHD8ZTTz0Fs9mMXbt2AQDWrl0LWZaxcuVKDB8+HMOGDcPq1atRVlaGrVu3truORx99FNdddx2GDx+Ofv36Yfr06Vi1ahW8Xi+Aby5vVFXVqBiz2WzYsmULDh48iI8++gijR4/G4MGD8Yc//AExMTH4y1/+Yizf4/Hg3XffxYgRI3DTTTfh9ddfx9q1a1FZWdnp+4+IiIjocjGBRURERHSFMjMzIcvffJxKSkrC8OHDjdeKoiA+Ph5VVVUAgKKiIhw/fhxRUVHGPbXi4uLg8Xjwn//8p911OBwO/O1vf8Px48fx7LPPwul0YuHChbj++uvhdrsv2beioiI0NjYiPj7eWJfT6URJSUnIutLT05Gammq8Hjt2LHRdD7kHGBEREVGk8SbuRERERFfIZDKFvJYkqd1xuq4DAHRdR3Z2Nj744IM2y+rVq9e3rmvgwIEYOHAgHn74YTzzzDMYMmQI1q1bh5///Oftttd1Hb179263sismJuaS65EkKeSRiIiIqCtgAouIiIgoTEaNGoV169YZN1W/Uv369YPdbkdTUxMAwGw2Q9O0NuuqrKyEqqro16/fJZdVVlaGM2fOICUlBQBQWFgIWZYxZMiQK+4fERERUUfjJYREREREYTJjxgwkJCTg3nvvxY4dO1BSUoJt27YhLy8Pp0+fbneeJUuW4IknnsDWrVtRUlKC/fv3Y86cOfD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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=1200.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close('all')\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.plot(np.arange(0, len(y_train_p1)), y_train_p1, 'g', label=\"historia\")\n",
    "plt.plot(np.arange(len(y_train_p1), len(y_train_p1) + len(y_test_p1)), y_test_p1, marker='.', label=\"verdadero\")\n",
    "plt.plot(np.arange(len(y_train_p1), len(y_train_p1) + len(y_test_p1)), y_pred_p1, 'r', label=\"predicción\")\n",
    "#plt.ylabel('Valor')\n",
    "plt.xlabel('Time Step')\n",
    "plt.title(f\"{stock}: Historia del precio la acción al cierre. Escala (0,1)\", size = 20)\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regreso a la escala original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_or1 = scaler.inverse_transform(y_pred_p1.reshape(-1,1))\n",
    "y_test_or1 = scaler.inverse_transform(y_test_p1.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[47.86249924] [47.347633] [0.51486588]\n",
      "[47.59999847] [47.435814] [0.16418457]\n",
      "[47.97000122] [47.50956] [0.46044159]\n",
      "[47.86000061] [47.5957] [0.2643013]\n",
      "[47.90250015] [47.67482] [0.22768021]\n",
      "[48.25] [47.75093] [0.49906921]\n",
      "[48.70500183] [47.839897] [0.86510468]\n",
      "[48.55250168] [47.95164] [0.6008606]\n",
      "[47.74499893] [48.058296] [-0.31329727]\n",
      "[47.47750092] [48.11914] [-0.64163971]\n"
     ]
    }
   ],
   "source": [
    "k=0\n",
    "for i,j in zip(y_test_or1, y_pred_or1):\n",
    "    print (i,j, i-j)\n",
    "    k+=1\n",
    "    if k==10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.01165819652469\n"
     ]
    }
   ],
   "source": [
    "rmsLSTM = np.sqrt(np.mean(np.power(y_pred_or1-y_test_or1,2)))\n",
    "print(rmsLSTM )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1054, 1)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_or1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3f32e2683d14427a64d2917d27d83c7",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=1200.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close('all')\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.plot(np.arange(0, len(y_test_or1)), y_test_or1, marker='.', label=\"verdadero\")\n",
    "plt.plot(np.arange(0, len(y_pred_or1)), y_pred_or1, marker='+', label=\"predicho\")\n",
    "plt.xlabel('Time Step')\n",
    "\n",
    "plt.annotate(\"modelo = LSTM(50), timestep=60\" , xy=(100, 146), size = 15)\n",
    "plt.annotate(f\"epochs = {history.epoch[-1]}\" , xy=(100, 126), size = 15)\n",
    "plt.annotate(\"rms = \"+str(round(rmsLSTM,2)) , xy=(100, 106), size = 15)\n",
    "plt.title(\"Apple: Intervalo de predicción a un día. Escala original\", size = 20)\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Guarda el modelo entrenado</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_01.save(f'../Modelos/modelo_{stock}_{future_target}_dia.h5')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Intervalos de predicción. TO DO</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recupera la  configuración del modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'series_LSTM_model', 'layers': [{'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 60, 1), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_1'}, 'name': 'input_1', 'inbound_nodes': []}, {'class_name': 'Dropout', 'config': {'name': 'Dropout_01', 'trainable': True, 'dtype': 'float32', 'rate': 0.2, 'noise_shape': None, 'seed': None}, 'name': 'Dropout_01', 'inbound_nodes': [[['input_1', 0, 0, {}]]]}, {'class_name': 'LSTM', 'config': {'name': 'LSTM_layer', 'trainable': True, 'dtype': 'float32', 'return_sequences': False, 'return_state': False, 'go_backwards': False, 'stateful': False, 'unroll': False, 'time_major': False, 'units': 50, 'activation': 'tanh', 'recurrent_activation': 'sigmoid', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}, 'shared_object_id': 2}, 'recurrent_initializer': {'class_name': 'Orthogonal', 'config': {'gain': 1.0, 'seed': None}, 'shared_object_id': 3}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}, 'shared_object_id': 4}, 'unit_forget_bias': True, 'kernel_regularizer': None, 'recurrent_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'recurrent_constraint': None, 'bias_constraint': None, 'dropout': 0.0, 'recurrent_dropout': 0.0, 'implementation': 2}, 'name': 'LSTM_layer', 'inbound_nodes': [[['Dropout_01', 0, 0, {}]]]}, {'class_name': 'Dropout', 'config': {'name': 'Dropout_02', 'trainable': True, 'dtype': 'float32', 'rate': 0.2, 'noise_shape': None, 'seed': None}, 'name': 'Dropout_02', 'inbound_nodes': [[['LSTM_layer', 0, 0, {}]]]}, {'class_name': 'Dense', 'config': {'name': 'dense', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'name': 'dense', 'inbound_nodes': [[['Dropout_02', 0, 0, {}]]]}], 'input_layers': [['input_1', 0, 0]], 'output_layers': [['dense', 0, 0]]}\n"
     ]
    }
   ],
   "source": [
    "print(model_01.get_config())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Predicción para Mañana</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hoy:  2022-09-21 00:00:00\n"
     ]
    }
   ],
   "source": [
    "from datetime import date\n",
    "hoy = data_close.index[-past_history:][-1]\n",
    "print(\"Hoy: \", hoy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mañana:  2022-09-22 00:00:00\n"
     ]
    }
   ],
   "source": [
    "mañana = data_close.index[-past_history:][-1]+pd.DateOffset(1)\n",
    "print(\"Mañana: \", mañana) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 154ms/step\n"
     ]
    }
   ],
   "source": [
    "ultima_ventana = dataset[-past_history:]\n",
    "\n",
    "ultima_ventana_tensor = np.reshape(dataset[-past_history:], (1,past_history, 1))\n",
    "\n",
    "# predictions\n",
    "prediction1 = model_01.predict(ultima_ventana_tensor)\n",
    "\n",
    "# Escalas originales\n",
    "ultima_ventana_or = scaler.inverse_transform(ultima_ventana.reshape(-1,1))\n",
    "prediction_or = scaler.inverse_transform(prediction1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec3be26b297f4c3cabf9ebd93b74cbc2",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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PPPEEAGDLli2q7aIoqhZ7qEnz9kd/3jxq5syZ8PHxAQDs3bu3ythWrlyp6kFV2jvvvKP6/9mzZ8vtt7KyqvQ1rauri6+//hqAondfVRVy8+fPV9uzzcLCAjNnzgSgqOBUV1HZpk0btdeg5O3tjZdffhlA1Y8JERG1PExgERFRo1FSUqJaxv7pp58u96bLzMwMo0aNAqCY5lNSUlLpeOHh4ao31cpkVWnDhg2DjY0NgMpXHyttyJAhqmbGgiDA3Nwc48ePx/Xr1wEo3rTu2LGjWmPVxIYNGyCKIkRRrLLhd3Xcu3evzHVoa2vDzc0NS5cuVTVVnzt3Ll599VW15+vq6pZLMJa2b98+AIqkkHKKWkWUyY74+HjExsaqth8/flz1f+UbY3WeeeaZSt+gVyY1NRUBAQEAFAnSVq1a1ej8Q4cOAVBM7xs3btxjxVAR5fV7enqWWSDgUbNnzy53jjqTJ0+uMHnQrVs31f/v3r1b01Dr7LlSvm4GDRqk+t6siPJ18zgJt4ooE1R+fn7IyckBoEgKKRuql56GXBOiKCIxMRG3b99GWFiY6ks5Pbeq6Z/PPfecKnn/qHbt2qmm7VbnuSsoKEBMTAzCw8NVcYiiqNpfVSyVPQalX0dRUVFVxpKeno7IyEhcv35dFYvy9REeHo6ioqIqxyAiopZDW9MBEBERKR07dgwJCQkA1CeclNv37NmDhIQEHD9+vNLVwJRVWtra2mr7zGhra2PixIlYuXIl9u/fD5lMBqlUWuO4tbS04OnpiSlTpmDBggUVVo00BcbGxhgwYADmz5+PkSNHVnicu7s79PX1K9x/5coVAEBubi60tav/50ZiYiKcnJwAAKGhoQAUybJOnTpVeI6Ojg58fHxw8uTJat+PUnBwsOrNu7qqocoUFRUhLCwMgKJfW2WVJTVVUFCAiIgIAECvXr0qPdbHxwc6Ojpl4lGnffv2Fe6zsLBQ/T8rK6uG0dbdc6V83Rw9erTaj2diYmINo62Yr68v5s6di+zsbOzduxdTpkxRVXEOHDhQ1S+qug4dOoRVq1bhzJkzlT6uVa28V9lzBwDm5ubIzs6u8D5ycnLw008/Ydu2bbh+/Xqlyf/axFKd11FoaCi+//57/P3335U+d3K5HOnp6VUmMomIqOVgAouIiBoN5RtFMzOzCqt2lJVZGRkZ2LRpU4UJLLlcrpr2M2zYMFhbW6s97sUXX8TKlSuRn5+PHTt2lKlmUWfdunXo0aMHAMXUKyMjI9jY2DS5pJWDgwOOHj2quq2trQ1TU1PY2dlBS6vqAm1zc/NK9z9uA2bldDxAUZ0BKN4UV5UEs7W1faz7K/1m3d7evkbnpqWlqZJfNT23KsprB6q+Nh0dHVhaWiIxMRFpaWkVHlfZa7T0c15VZaM6dfFcFRUVISMjo8b3Xfo1U1uGhobw9fXFpk2bsHnzZjz77LPYtWsXgJpNHxRFEbNnz8batWurdXxeXl6VcVVG+fype+6io6MxdOjQalVE1TaWql5Ha9euxZw5c1BcXFwnsRARUcvCBBYRETUKmZmZqulDGRkZFU6XKW3v3r3IysqCiYlJuX3//vsv7t+/DwA4fPhwtao5Nm3aVGUCy83NrdI+R02Fjo5Ora6jqhX1lG9e3dzcsH///mqP6+bmpvq/MjlUneeu9BSox1WbCqq6rL56nLHr4vproy6eq9IJjwkTJuCDDz6om+BqaOrUqdi0aROOHz+O3377DTKZDPr6+njuueeqPca6detUyasuXbrgzTffRK9evdCqVSsYGhqqvn+mTZuGzZs31+vzN3XqVERFRUEQBMycORMvvPACOnToAGtra9XPWblcroqpvmK5efOmKnllY2ODd999F0OHDoWrqytMTEygo6MDQPHYzZo1q15jISKipokJLCIiahR27NhR40/bc3NzsWvXLrU9d6rb5L20c+fO4e7duxU2HKfqs7S0BAAkJSWhffv2NZpGqKScjpSamoqSkpJKk2aPW/FlZWWl+n98fHyN49PS0oJcLq/xuVUpXeFW1RS54uJiVeVV6SlcDakunit9fX0YGhoiNzcXGRkZGksUDx06FK1atcL9+/exePFiAMDYsWNrNL14zZo1ABRNy8+fPw8DAwO1x5WutKsPN2/ehL+/PwBgyZIl+PzzzzUSB6Do41dcXAyJRIJTp06hQ4cOGouFiIiaJiawiIioUVBOH7S3t8d3331X5fGLFi1CTEwMNm3aVC6BlZ2djT179gAAnnjiCdWqVhXJycnByy+/DFEUsXnzZnz44YePeRWk5OPjg8OHDyM3Nxfnzp3DoEGDajyGt7c3tm3bhsLCQly7dg1du3ZVe1xxcXGVK6dVFqcgCBBFEWfOnKm0AfmjlFVsISEhOHv2LERRrLNKLD09Pbi7uyMiIkLVZL4iV69eVTW71lTSp66eKx8fH5w7dw7nzp1Dbm6uRqbmamlpYcqUKfjqq69UCxrUZPogANWiDuPGjasweSWKIoKCgmoXbDXjAKC2D6CSsvdYQ8TSuXPnCpNXDRULERE1TVyFkIiINC4qKkpVJfDss8/ihRdeqPJLuQLe6dOnERMTU2a8Xbt2qfrizJ07t8qxZs2apVo9q7qrEVLlSq/I99VXXz3WGE8++aTq/5VV1O3Zs+exqzYsLCzQt29fAIoqwJpWUo0ZMwaA4jWsnAJbV5TXHx4ejosXL1Z43B9//FHunIZWV8/V2LFjASiSyr/88kvdBVhDU6dOhZ6eHvT09NCqVSuMGDGiRucrezxV1p9r//79dV65V1EcVcWyevXqeo2jdCyVxZGYmFjn30dERNR8MIFFREQaV7oHTHX7zCiPU1ZNlaas5jI0NKx0JT1140VGRuLcuXPVOqchzZgxA4IgQBAEnDp1StPhVKlHjx4YNmwYAEUPsqqq2qKjo/HXX3+V2dazZ09VJc+qVatUSc7SEhIS8M4779Qq1kWLFgFQvLF+/vnnIZPJKjw2Li6uzO3XX38dRkZGAIBXX3210lUAHz23KnPnzlU1xX7llVfUxnXs2DFVr6WePXuqFhhoaHX1XM2ZM0c1rfODDz7A33//Xenx586dw5kzZx4z6op5eXkhPz8f+fn5iIuLq/EUWHd3dwDAgQMH1CbsIiMjMW/evDqJtTpxABUnFletWoW9e/c2WCy3b99Wm5DNzc3F5MmT2bidiIgqxCmERESkccoElI2NDQYMGFCtc3r16gVHR0fExcVh8+bNeP/99wEAMTExqgTPyJEjqz0F6dlnn8WSJUsAKBJg/fr1q+FVVN+uXbvK9F6qyIwZM+othoawfv16dO/eHQkJCfjkk09w9OhRvPTSS/D29oa+vj5SU1MREhKCI0eO4MSJExg/fjwmTZpUZoxff/0V/fv3R1FREZ566im89dZbGDVqFPT09BAQEIAvvvgCKSkp6Ny5M65du/ZYcY4ZMwazZs3C2rVrcf78eXh6euL1119Hv379YGpqipSUFFy5cgU7duxAp06dsGHDBtW5dnZ2WLVqFaZNm4bk5GT07NkTs2fPxsiRI2FnZ4fs7GyEhYVh//79uHXrFiIjI6sdl7e3NxYuXIivv/4aoaGh6Nq1KxYtWgQfHx/k5ubiwIED+Omnn1BSUgJdXV389ttvj3X9daUunitTU1P89ddfGDlyJAoKCjB69Gg8++yzePbZZ9GmTRsAikRYYGAg9uzZg5CQEKxcuRIDBw5syEut0rRp0/Duu+/i/v376Nu3L9577z107NgR+fn5OHHiBH744QcUFBSga9eu9TqN0MfHB15eXggLC8OqVauQkZGBKVOmwN7eHrGxsdiyZQt27dqFfv361XvifurUqVi5ciXkcjlGjRqF9957D3379oW+vj4CAwPx/fffIyIiokFiISKiJkokIiLSIH9/fxGACEB89dVXa3TuggULVOdevHhRFEVR/Oyzz1Tb/vrrrxqN16lTJxGAaGZmJubn56u2f/jhh6oxT548WaMxlaZPn64ao7pfFZ3/uDGIoii6uLiIAEQXF5fHOn/QoEEiAHHQoEHVOj46Olrs0aNHta535syZasfYunWrqKurq/YcbW1tcc2aNarHp6LrUh7/4Ycfqt1fXFwsvv7666IgCJXGOH36dLXnb9iwQTQwMKj0XHWxVfV4lpSUiPPmzat0XKlUKh49elTt+SdPnqz266aqx6g66uK5EkVR/Pfff0U7O7tqvW42btz4WLEqvxeq+1p+VGWvicLCQnHYsGEVxmxgYCDu2LGj0sciKipKdfz69eurdS3qYrl69apobm5eYSze3t5ifHx8pc9/6Z+Blanq9fbxxx9X+lwuXLhQXL9+vep2VFRUpfdHREQtC6cQEhGRRimn+wGKKqiaKH28chxlNZeenh6efvrpxxovIyMD+/fvr9G5pJ6LiwsCAgKwZ88evPDCC3Bzc4OhoSF0dHRgbW2Nvn37YuHChTh9+rRqKtyjJk2ahKtXr2Lq1KlwcHCArq4uWrVqhQkTJsDf37/KJv3VIZFIsHLlSly5cgWvvPIKPDw8YGRkBENDQ7i7u2PUqFFYs2YNvv/+e7XnT58+HZGRkXj//ffRrVs3mJmZQVdXF87Ozujfvz8+//xznDx5ssZxaWlp4ZdffsGZM2cwZcoUODs7Q09PD6ampujSpQuWLl2KiIgI1XRNTaur52ro0KGIjIzEzz//jBEjRsDe3h66urrQ19eHk5MThg0bhs8//xw3b97EtGnT6vmqak5HRweHDh3CTz/9hO7du8PQ0BAGBgZo27Yt5syZg6CgIFUfv/rWpUsXBAcHY86cOXBxcYGOjg4sLCzQs2dPfPPNN7h06RLs7e0bJJZly5bh0KFDGDZsGMzNzaGrqwtHR0f4+vri2LFj+OabbxokDiIiapoEUXzYdISIiIiIiIiIiKgRYgUWERERERERERE1akxgERERERERERFRo8YEFhERERERERERNWpMYBERERERERERUaPGBBYRERERERERETVqTGAREREREREREVGjpq3pAKj5kcvliI+Ph4mJCQRB0HQ4RERERERE1MyJooisrCw4ODhAS4u1Os0RE1hU5+Lj4+Hk5KTpMIiIiIiIiKiFiY2NhaOjo6bDoHrABBbVORMTEwCKHxympqYajoaIiIiIiIiau8zMTDg5Oanej1LzwwQW1TnltEFTU1MmsIiIiIiIiKjBsI1N88WJoURERERERERE1KgxgUVERERERERERI0aE1hERERERERERNSosQcWERERERFRI1FSUoKioiJNh0HU6Ojo6EAikWg6DNIgJrCIiIiIiIg0TBRFJCYmIiMjQ9OhEDVaZmZmsLOzY6P2FooJLCIiIiIiIg1TJq9sbGxgaGjIN+hEpYiiiNzcXCQnJwMA7O3tNRwRaQITWERERERERBpUUlKiSl5ZWlpqOhyiRsnAwAAAkJycDBsbG04nbIHYxJ2IiIiIiEiDlD2vDA0NNRwJUeOm/B5hn7iWiQksIiIiImq0EmR5OB+ZggRZnqZDIap3nDZIVDl+j7RsTGARERERUaO0/XIM+q04gclrAtBvxQlsvxyj6ZCIiNQ6deoUBEGo1yb8DXEfRI0ZE1hERERE1OgkyPKw2C8UclFxWy4Ci/1CcSEyFaIoqj2elVpEDWvMmDF48skn1e67cOECBEFAUFBQre8nOjoagiAgODi41mM1VoMHD4YgCBV+ubq6om/fvkhISIBUKtV0uEQawSbuRERERNTo7AuOx6N5KlEEJq25CCtjPfRqbYHerS3Rp7UFrkSnY+keRbJLSwCW+3pjYg9nzQRO1ILMmjULvr6+uHfvHlxcXMrsW7duHbp06YKuXbtqKLqmxc/PD4WFhQCA2NhY9OzZE8ePH0fHjh0BABKJBLq6urCzs9NkmEQaxQosIiIiImpUjoQl4tujt9Tu05UISMkuwKGQBHywNwxPfnemXKXWUr8wVmIRNYDRo0fDxsYGGzZsKLM9NzcX27dvx6xZswAA58+fx8CBA2FgYAAnJycsWLAAOTk5quNdXV3xxRdf4KWXXoKJiQmcnZ3x+++/q/a7ubkBAHx8fCAIAgYPHgwAuHz5Mp566ilYWVlBKpVi0KBB5Sq+BEHAH3/8gWeeeQaGhoZwd3fH/v37VftLSkowa9YsuLm5wcDAAO3atcOPP/5Y5bUfPnwYHh4eMDAwwJAhQxAdHV3umKquuzQLCwvY2dnBzs4O1tbWAABLS8sy29RNIazOY/vZZ59h2rRpMDY2houLC/bt24cHDx5g3LhxMDY2hre3N65cuaI6Z8OGDTAzM8PevXvh4eEBfX19PPXUU4iNjS0T86pVq9CmTRvo6uqiXbt22Lx5c5WPG1FtMIFFRERERI2GX1AcXtsahCK5CO9WUmg97NcrEQR8+aw3Qj8ejh2v9sHbT3mgbxtL6GiVb+hbIoqITslt4MiJGo+GmlKrra2NadOmYcOGDWWm9u7cuROFhYWYMmUKQkNDMXz4cPj6+iIkJATbt2+Hv78/Xn/99TJjffvtt+jevTuuXr2KefPmYe7cubh58yYA4NKlSwCA48ePIyEhAX5+fgCArKwsTJ8+HWfPnsXFixfh7u6OUaNGISsrq8zYH3/8MSZMmICQkBCMGjUKU6ZMQVpaGgBALpfD0dERO3bsQHh4OJYtW4alS5dix44dFV53bGwsfH19MWrUKAQHB+Pll1/G4sWLyxxT3euujerex/fff49+/frh6tWrePrppzF16lRMmzYNL774IoKCgtC2bVtMmzatzHOYm5uLzz//HBs3bsS5c+eQmZmJF154QbV/z549eOONN7Bw4UKEhYXh1VdfxcyZM3Hy5Mk6uz6ickSiOiaTyUQAokwm03QoRERE1IRsuhAtuiw6KLosOii+syNYLC6Ri/EZueL5OylifEau2nOiU7JF18UHVee5LDootl58qMLjiRqjvLw8MTw8XMzLy1Ntk8vlYk5BUY2/Np2PEt0efk+4LT4objofVeMx5HJ5tWO/ceOGCEA8ceKEatvAgQPFSZMmiaIoilOnThVfeeWVMuecPXtW1NLSUl2vi4uL+OKLL5a5dhsbG3HVqlWiKIpiVFSUCEC8evVqpbEUFxeLJiYm4oEDB1TbAIj/+9//VLezs7NFQRDEv//+u8Jx5s2bJz777LMV7l+yZInYoUOHMo/TokWLRABienp6ta+7IhVd78mTJ2t8H48+tgkJCSIA8YMPPlBtu3DhgghATEhIEEVRFNevXy8CEC9evKg6Rvk8BwQEiKIoin379hVnz55d5r6ff/55cdSoUZVeW22p+15R4vvQ5o89sIiIiIhI41adisSXRxTVFjP6umLZaE9oaQmwlxrAXmpQ4XkulkZY4euNJaWmEb71lHul5xA1BXlFJfBcdrRWY8hF4IN91/HBvus1Oi/8k+Ew1K3eW8X27dujb9++WLduHYYMGYLIyEicPXsWx44dAwAEBgbizp07+PPPP1XniKIIuVyOqKgodOjQAQDQqVMn1X5BEGBnZ4fk5ORK7zs5ORnLli3DiRMnkJSUhJKSEuTm5iImpuyKpaXHNjIygomJSZmxV69ejT/++AP37t1DXl4eCgsL0aVLlwrv98aNG+jduzcE4b8K0D59+pQ5prrXXRuP89ja2toCALy9vcttS05OVvXY0tbWRvfu3VXHtG/fHmZmZrhx4wZ69uyJGzdu4JVXXikTT79+/ao1/ZLocTGBRURERER1JkGWh6iUHLhZGVUriSSKIr45dgu/nIwEALw+pC0WDvMo88awKhN7OGOAuxXm/hmEa7EyXLybhteGiDUag4ge36xZs/D666/jl19+wfr16+Hi4oInnngCgGKK3quvvooFCxaUO8/Z+b/FFnR0dMrsEwQBcrm80vudMWMGHjx4gB9++AEuLi7Q09NDnz59VM3QqzP2jh078NZbb+Hbb79Fnz59YGJigq+//hoBAQEV3q+oZiXUR1X3umvjcR5b5c9FddsefbzV/Qwtve3R/aLIn7tUv5jAIiIiIqI6sf1yjKoSqqrVABNkebibnIO9wfexMzAOALB4ZHvMGdTmse7bwcwQP73gg6e+PwP/Oyk4GJKAMZ0dHvtaiDTNQEeC8E+G1+icRFk+nvzutKoaEVB8Lx5/exDspPo1uu+amDBhAt544w1s3boVGzduxOzZs1WJjK5du+L69eto27ZtjcYsTVdXF4Ci4XppZ8+exa+//opRo0YBUPSmSklJqdHYZ8+eRd++fTFv3jzVtsjIyErP8fT0xN69e8tsu3jxYpnbdXHdVanP+yguLsaVK1fQs2dPAMCtW7eQkZGB9u3bAwA6dOgAf39/TJs2TXXO+fPn66SyjKgibOJORERERLUiiiIC7qZi8e6yqwEu3h2K1aciceJmEsLuy5CclY8SuYjtl2PQb8UJTFkboEpefTre67GTV0oulkZ4bbDijdynB8ORlV9Uq/GINEkQBBjqatfoq7W1MZb7ekPyMHkkEQQs9/VGa2vjGo1T0yoaY2NjTJw4EUuXLkV8fDxmzJih2rdo0SJcuHABr732GoKDgxEREYH9+/dj/vz51R7fxsYGBgYGOHLkCJKSkiCTyQAAbdu2xebNm3Hjxg0EBARgypQpMDCo2fThtm3b4sqVKzh69Chu376NDz74AJcvX670nDlz5iAyMhJvv/02bt26ha1bt5ZbibEurrsq9XkfOjo6mD9/PgICAhAUFISZM2eid+/eqoTWu+++iw0bNmD16tWIiIjAd999Bz8/P7zzzju1vm+iirACi4iIiIhqJK+wBNfiMhAUk46ge+kIvJeO9NzyySIRwIqHfa2UtASUqQ4BAEEAnuxgUyexvTqoNfYG30dUSg6+++c2PhzTsU7GJWoqJvZwxkAPa0Sn5MLVyrDB+sHNmjULa9euxbBhw8pMX+vUqRNOnz6N999/HwMGDIAoimjTpg0mTpxY7bG1tbXx008/4ZNPPsGyZcswYMAAnDp1CuvWrcMrr7wCHx8fODs744svvqhxAmXOnDkIDg7GxIkTIQgCJk2ahHnz5uHvv/+u8BxnZ2fs3r0bb731Fn799Vf07NkTX3zxBV566aU6ve6q1Od9GBoaYtGiRZg8eTLi4uLQv39/rFu3TrV//Pjx+PHHH/H1119jwYIFcHNzw/r16zF48OBa3zdRRQSxOhN4iWogMzMTUqkUMpkMpqammg6HiIiIaik+Iw+BDxNVQTHpCI/PRPEjWSgdiYCikrLbBAB92lgiM78ISZkFSM0uKJe8Uvprdm/0aWNZJ/GejXiAqWsvQUsADszvj44O0joZl6i+5OfnIyoqCm5ubtDXr/5UP6L6sGHDBrz55pvIyMjQdCjlVPa9wvehzR8rsIiIiIhIpahEjvD4TEXC6mGFVYIsv9xxtqZ66OZijq7O5ujmYo6ODlLsuRqHpX5hKBFFSAQBX/h6lemBVVwiR3hCJsb9cg6lP0KVCAJcrQzr7BoGuFtjdCd7HAxJwP/2hmH3nL7Q0mJjYSIioqaMCSwiIiKiFiw1uwBBMRmq6qqQuAzkF5VdiUqiJcDT3lSRsHIxR1dnM7QyMyjXJ6eqqUvaEi10cjTDCl/vcomuup7m9MFoT5y69QBXYzKw/UosJvWsm1W/iIiISDM4hZDqHEs3iYiIGocEWR6iUnLgZmUEe6kB5HIREcnZZaYDRqXklDtPaqCDbi7mqgqrzk5SGOrW7eeeCbK8eu/Rs9Y/Cp8eDIfUQAcnFg6CpbFevdwPUW1xCiFR9XAKYcvGCiwiIiKiZmj75Rgs8VOsCigAaGtrjERZPrLyi8sd625jrJoK2NXFHK2tjOp9yp291KDem0tP7+OCXYFxuJGQiRV/38TXz3eu1/sjIiKi+sMEFhEREVEzkyDLw2K/UFWfKRFARFI2AMBQV4IuTmaqZJWPkxnMDHU1F2w90pZo4bPxXnh21XnsDIzDhB5O6OFqoemwiIiI6DEwgUVERETUzESl5EBdk4gvnvHChO5O0JZoNXxQGtLNxRyTejrhr0ux+N+eMBxc0B86Lej6iYiImgv+9iYiIiJqZgSUn/4nEQQMaW/TopJXSu8Nbw9zQx3cSsrCR/uvI0GWp+mQiIiIqIZa3l8wRERERM3cgZD4Mrfra6W/psLcSBdPtLcFAPwZEIN+K05g++UYDUdFRERENcEphERERETNSFpOIXYHxgEAfp3sA3MjvXpd6a8pSJDlwe9qnOq2XASW+IVioId1i35ciKh6RFHE999/j0GDBqFbt26aDoeoxWIFFhEREVEz8ufFeygolsO7lRQjve3Rp41li0/SRKXkQP5ITzC5CGy7FKuZgIjosXz00Ufo0qWL6vaMGTMwfvz4ehm7tBUrVuDIkSPo1KlTndzXowRBwN69e+tl7Nq4du0arK2tMXnyZNy6dQuDBw/WdEjUwjGBRURERNRMFBSXYOOFewCAlwe4QRDK98JqidysjKCl5qH48d8IfHfsFuSPZreIqNpmzJgBQRAgCAJ0dHTQunVrvPPOO8jJyan3+/7xxx+xYcOGOhnrnXfewb///ltu+7lz57Br1y7s2rULOjo6dXJfdS06OhqCIEBbWxv3798vsy8hIQHa2toQBAHR0dE1GvfgwYP4+OOP4e7ujmHDhmHOnDl1GDVRzTGBRURERNRM7A+OR0p2AexM9THK217T4TQa9lIDLPf1huRhQk9LAAa4WwEAfjpxB3O2BCK7oFiTIRI1aSNGjEBCQgLu3r2Lzz77DL/++iveeecdtccWFRXV2f1KpVKYmZnVyVjGxsawtLQst71fv34IDAyEqalpndxPfXJwcMCmTZvKbNu4cSNatWr1WOO9//77mDdvHj7++GPcu3cPL7zwQl2ESfTYmMAiIiIiagZEUcRa/ygAwIx+rtBpgasNVmZiD2f4Lx6Cv2b3xrnFQ7F5Vi9883xn6Eq0cCw8Cc/+eh6xabmaDpOobly5Agwdqvi3Aejp6cHOzg5OTk6YPHkypkyZopoSp5yat27dOrRu3Rp6enoQRREymQyvvPIKbGxsYGpqiqFDh+LatWtlxl2xYgVsbW1hYmKCWbNmIT8/v8z+R6cQyuVyfPnll2jbti309PTg7OyMzz//XLU/Li4OL7zwAiwsLGBkZITu3bsjICCgTJylx/rkk0/g6OgIPT09dOnSBUeOHFHtV1Y9+fn5YciQITA0NETnzp1x4cKFSh+riIgIDBw4EPr6+vD09MQ///xT7pj79+9j4sSJMDc3h6WlJcaNG1et6qnp06dj/fr1ZbZt2LAB06dPL7OtpKQEs2bNgpubGwwMDNCuXTv8+OOPZY5RPrbffPMN7O3tYWlpiddee61MAnLLli3o3r07TExMYGdnh8mTJyM5OVm1/9SpUxAEAf/++y+6d+8OQ0ND9O3bF7du3VIdExkZiXHjxsHW1hbGxsbo0aMHjh8/XuW1UsvEv2yakTNnzmDMmDFwcHBQO49aWdr76NfXX3+tOqagoADz58+HlZUVjIyMMHbsWMTFxYGIiIgat3N3UnEzMQuGuhJM6uGs6XAaJXupQZmeYM91c8S2V3vD2kQPt5KyMPZnf1yITNVwlER1YNMm4ORJYPNmjdy9gYFBmUTHnTt3sGPHDuzevRvBwcEAgKeffhqJiYk4fPgwAgMD0bVrVzzxxBNIS0sDAOzYsQMffvghPv/8c1y5cgX29vb49ddfK73fJUuW4Msvv8QHH3yA8PBwbN26Fba2ihVIs7OzMWjQIMTHx2P//v24du0a3nvvPcjlcrVj/fjjj/j222/xzTffICQkBMOHD8fYsWMRERFR5rj3338f77zzDoKDg+Hh4YFJkyahuFh9RadcLoevry8kEgkuXryI1atXY9GiRWWOyc3NxZAhQ2BsbIwzZ87A398fxsbGGDFiBAoLCyu9/rFjxyI9PR3+/v4AAH9/f6SlpWHMmDHl4nB0dMSOHTsQHh6OZcuWYenSpdixY0eZ406ePInIyEicPHkSGzduxIYNG8pM2SwsLMSnn36Ka9euYe/evYiKisKMGTPKxfX+++/j22+/xZUrV6CtrY2XXnpJtS87OxujRo3C8ePHcfXqVQwfPhxjxoxBTAxXiiU1RGo2Dh8+LL7//vvi7t27RQDinj17yuxPSEgo87Vu3TpREAQxMjJSdcycOXPEVq1aif/8848YFBQkDhkyROzcubNYXFxc7ThkMpkIQJTJZHV1aURERFSF6esCRJdFB8UP94VpOpQmJyEjTxyz8qzosuig2GbJIXHThWhNh0QtTF5enhgeHi7m5eX9t1EuF8Xs7Op/hYeL4tmzoujvL4rW1qIIKP7191dsDw+v/lhyebVjnz59ujhu3DjV7YCAANHS0lKcMGGCKIqi+OGHH4o6OjpicnKy6ph///1XNDU1FfPz88uM1aZNG/G3334TRVEU+/TpI86ZM6fM/l69eomdO3dWe9+ZmZminp6euGbNGrVx/vbbb6KJiYmYmpqqdv+HH35YZmwHBwfx888/L3NMjx49xHnz5omiKIpRUVEiAPGPP/5Q7b9+/boIQLxx44ba+zh69KgokUjE2NhY1ba///67zHu3tWvXiu3atRPlpZ6DgoIC0cDAQDx69KjacZWxXL16VXzzzTfFmTNniqIoijNnzhTfeust8erVqyIAMSoqSu35oiiK8+bNE5999lnV7enTp4suLi5l3gc+//zz4sSJEysc49KlSyIAMSsrSxRFUTx58qQIQDx+/LjqmEOHDokAyr7WH+Hp6SmuXLlS7T613ysP8X1o86etobwZ1YORI0di5MiRFe63s7Mrc3vfvn0YMmQIWrduDQCQyWRYu3YtNm/ejCeffBKAoizUyckJx48fx/Dhw+sveCIiInpsEUlZOHXrAQQBmNnPVdPhNDl2Un3seLUPFu0Owb7geHywNww3EzLx4ZiO0NVumhMWEmR5iErJgZuVUYtfhbLJys0FjI1rN8aDB0D//jU/LzsbMDKq9uEHDx6EsbExiouLUVRUhHHjxmHlypWq/S4uLrC2tlbdDgwMRHZ2drmeU3l5eYiMjAQA3Lhxo1zT8D59+uDkyZNqY7hx4wYKCgrwxBNPqN0fHBwMHx8fWFhYVHk9mZmZiI+PR79+/cps79evX7lpjqVXJrS3V/QeTE5ORvv27dXG6OzsDEdHxzLXVFpgYCDu3LkDExOTMtvz8/NVj01lZs2ahT59+uCLL77Azp07ceHCBbUVYatXr8Yff/yBe/fuIS8vD4WFheVWYezYsSMkEkmZ6wsNDVXdvnr1Kj766CMEBwcjLS1NVc0WExMDT09P1XEVPUbOzs7IycnBxx9/jIMHDyI+Ph7FxcXIy8tjBRapxQRWC5WUlIRDhw5h48aNqm2BgYEoKirCsGHDVNscHBzg5eWF8+fPM4FFRETUSK07p+h9NczTFi6W1X/TSf/R15Hgh4ld0N7OFF8dvYk/A2JwJzkbv07pCktjPU2HVyPbL8dgiV8o5KKiYf1yX29M5LRSqkdDhgzBqlWroKOjAwcHh3Kr9Rk9kgyTy+Wwt7fHqVOnyo31uE3ZDQwqT9RWtV+dR1dyFUWx3LbS16rcV9G0RFEsv+Lpo+PJ5XJ069YNf/75Z7ljSycBK+Ll5YX27dtj0qRJ6NChA7y8vFTTNpV27NiBt956C99++y369OkDExMTfP3116p+YOquTRmr8tpycnIwbNgwDBs2DFu2bIG1tTViYmIwfPjwclMdK3uM3n33XRw9ehTffPMN2rZtCwMDAzz33HNVTpeklokJrBZq48aNMDExga+vr2pbYmIidHV1YW5uXuZYW1tbJCYmVjhWQUEBCgoKVLczMzPrPmAiIiJSKzW7ALuDFMumvzygtYajadoEQcDcwW3gYWuMN7YFIyAqDWN/Poc/pndHB/vGvwIZoKi8UiavAEAuAkv9wjDQw5qVWE2NoaGiEqomgoPVV1z5+wOPVNdUed81YGRkhLZt21b7+K5duyIxMRHa2tpwdXVVe0yHDh1w8eJFTJs2TbXt4sWLFY7p7u4OAwMD/Pvvv3j55ZfL7e/UqRP++OMPpKWlVVmFZWpqCgcHB/j7+2PgwIGq7efPn0fPnj2ruLqKeXp6IiYmBvHx8XBwcACAck3fu3btiu3bt6ua2z+Ol156CfPmzcOqVavU7j979iz69u2LefPmqbZVp7qrtJs3byIlJQUrVqyAk5MTAODKYywacPbsWcyYMQPPPPMMAEVPrOo0rKeWqWnWRFOtrVu3DlOmTIG+vn6Vx6r7pKG05cuXQyqVqr6UP8CIiIio/m25GIPCYjk6O0rR3cW86hOoSk90sMWeeX3hYmmI+xl5eHbVeRwJS9B0WNUSlZKjSl4plYgiolO4wmKTIwiKaXw1+VJWGWlplf3XwKBm41Tyt39dePLJJ9GnTx+MHz8eR48eRXR0NM6fP4///e9/qiTIG2+8gXXr1mHdunW4ffs2PvzwQ1y/fr3CMfX19bFo0SK899572LRpEyIjI3Hx4kWsXbsWADBp0iTY2dlh/PjxOHfuHO7evYvdu3dXuGrgu+++iy+//BLbt2/HrVu3sHjxYgQHB+ONN96o1XW3a9cO06ZNw7Vr13D27Fm8//77ZY6ZMmUKrKysMG7cOJw9exZRUVE4ffo03njjjWovrjV79mw8ePBAbSIPANq2bYsrV67g6NGjuH37Nj744ANcvny5Rtfi7OwMXV1drFy5Enfv3sX+/fvx6aef1mgMZSx+fn4IDg7GtWvXMHny5Aor2IiYwGqBzp49i1u3bpX7gWZnZ4fCwkKkp6eX2Z6cnKxavUOdJUuWQCaTqb5iY2PrJW4iIqLmLkGWh/ORKUiQ5VXr+PyiEmy+GA0AmDWgdaUfOFHNuNuaYN9r/dC/rRVyC0swZ0sQfjweAfmj2aFGxkrNdEctAXC1qllFDTVRNjaAnR3QrRuwerXiXzs7xfZGRBAEHD58GAMHDsRLL70EDw8PvPDCC4iOjla975g4cSKWLVuGRYsWoVu3brh37x7mzp1b6bgffPABFi5ciGXLlqFDhw6YOHEikpOTAQC6uro4duwYbGxsMGrUKHh7e2PFihVlejyVtmDBAixcuBALFy6Et7c3jhw5gv3798Pd3f2xr1tLSwt79uxBQUEBevbsiZdffhmff/55mWMMDQ1x5swZODs7w9fXFx06dMBLL72EvLy8aldkaWtrw8rKCtra6idczZkzB76+vpg4cSJ69eqF1NTUMtVY1WFtbY0NGzZg586d8PT0xIoVK/DNN9/UaAwA+P7772Fubo6+fftizJgxGD58OLp27VrjcahlEER1E3GpyRMEAXv27MH48ePL7ZsxYwbCwsLKlXjKZDJYW1tjy5YtmDBhAgAgISEBjo6OOHz4cLV7YGVmZkIqlUImkz122SsREVFL8zh9i3ZcjsV7u0PgINXH6feGQEfCzybrWnGJHJ8fvoH156IBACO97PDthM4w1G2cnTiW+IXgr0tlP0x8ZWBrLB3VQUMRUXXk5+cjKioKbm5u1ZohUamCAkBXV1FFJYpAYSGg17T6uBFVpLLvFb4Pbf74V04zkp2djeDgYFWTvqioKAQHB5dZwSEzMxM7d+5UW04qlUoxa9YsLFy4EP/++y+uXr2KF198Ed7e3qpVCYmIiKjuVdS3qLJKLFEU8Yf/XQDAjH6uTF7VE22JFj4c0xFfPusNHYmAv8MS8eyqC4hLb3xT8i5EpqqSV79O9kHfNooV3gx11VeYUDOlp/ffFEBBYPKKiJoN/qXTjFy5cgU+Pj7w8fEBALz99tvw8fHBsmXLVMds27YNoihi0qRJasf4/vvvMX78eEyYMAH9+vWDoaEhDhw4UGFpLREREdVeRX2Lwu7LKjznbEQKbidlw0hXwhXmGsDEHs74a3ZvWBnr4kZCJsb9fA6XotI0HZZKflEJlviFAAAm93LGqE4OGN1J0ST64t1UTYZGRERUJ5jAakYGDx4MURTLfW3YsEF1zCuvvILc3FxIpVK1Y+jr62PlypVITU1Fbm4uDhw4wKbsRERE9czNygjqulfN/+sqPj0YjviM8pVYf/hHAQAm9HCC1ECn3H6qe91dLbDv9f7o6GCK1JxCTF5zEX9diqn6xAbww/EIRKfmwtZUD4tHtgcA9G6tWGktKCYD+UUlmgyPiIio1pjAIiIiItIwO1N9WBjpqm4LAmAv1Ud+kRxr/aMw8KuTeGfnNdxJzgIA+Eek4MztBxAAzOzrpqGoW6ZWZgbYNacvnu5kj2K5iCV+ofhwXxiKSjS3albYfRnWnFVMJ/1svDdM9RUJTTcrI9iY6KGwWI6rMRkai4+IiKguNM7uk0REREQtSOC9dKTmFMJARwurXuyGdnYmsDPVx+nbD7D6dCQu3k3DrsA47AqMg6e9KcITMgEAIoALd1PgbMkphA3JQFeCnyf5oIOdCb45dhsbL9zD7aRs/DqlK8xLJSIbQnGJHIv9QlAiF/G0tz2e8vxv5WhBENC7tSX2X4vHxbup6POwJxYREVFTxAosIiIiIg3bFRgHAHi6kwMGt7OBvdQAgiBgcDsbbHulD/bM64vhHRWJCWXySqmqZu9UPwRBwOtD3fH71G4w0pXgwt1UjP3FH7cSsxo0jrX+UQi7nwmpgQ4+Gtux3P7erRVJK/bBahq4QDxR5fg90rIxgUVERESkQflFJTgUkgAAeLaro9pjfJzN8dvU7vjmuU7l9pWIIqJTGt+KeC3FsI522D2vL5wsDBCblgffX8/hn/CkBrnv6JQcfPfPbQDA+093gLVJ+dXmlFVXV2PZB6sx09FRTPvMzeX3MlFllN8jyu8Zalk4hZCIiIhIg45eT0RWQTFamRmgl5tFpcf2c7eCloAyKxZKBAGuVob1HCVVpr2dKfa91h+v/RmEC3dT8crmK1j4lAdeG9IWgqCuPX/tiaKIpXtCUVAsR7+2lni+m/rkp6ulIWxN9ZCUWYCrMRmcRthISSQSmJmZITk5GQBgaGhYb68doqZIFEXk5uYiOTkZZmZmkEgkmg6JNIAJLCIiIiIN2h10HwDwbDdHaGlV/obVXmqA5b7eWOoXhhJRhEQQ8IWvF+ylBg0RKlXCwkgXm2b1xKcHw7Hpwj18c+w2biZm4evnOsNAt+7faO28EofzkanQ19HC8mc6VZjsUPbB2hccjwvsg9Wo2dnZAYAqiUVE5ZmZmam+V6jlYQKLiIiISEMSZfnwj3gAAHi2a6tqnTOxhzMGelgjOiUXrlaGTF41IjoSLXwyzgvt7UyxbF8YDoYkIDo1B5+N80JuUQncrIzq5PkKjcvAR/vDAAALn2oHZ8vKK/CUCSz2wWrcBEGAvb09bGxsUFRUpOlwiBodHR0dVl61cExgEREREWnInqv3IReBHq7mcLE0qvZ59lIDJq4ascm9nNHG2ghz/wxC2P1MjP/1PABASwCW+3pjYo/HXzVy++UYLNodqrptpFf1mzllI/fgGEUfLH0dvgFszCQSCd+kExGpwQQWERERkQaIoojdQYrVBytq3k5NV6/WllgzrRueXXVBtU0uAot2h+LcnRT0aWOFzo5m8LA1hrZEsa5SgiwPUSk5qkotuVzEvbRc3EjIRHh8JoJi0nE+smwV1Qd7r2NIe5tKE5ql+2AFxaSjbxur+rloIiKiesQEFhEREZEGhMTJcCc5G3raWhjVyV7T4VA9KCiWq92+/1oC9l9TrDxpoCOBdysp9HS04B+RAmV/fhcLQ6RkFyCnsPKVA5WrUFaWwBIEAX1aW2JvcDwu3k1jAouIiJokJrCIiIiINEBZfTXCyw6m+lwOvDlyszIqt2qklgC82MsFdx5kIyROhuyCYlyKTit37r00xVLxetpaaG9nAk8HUziYGeC7f25DfIxVKHurEljsg0VERE0TE1hEREREDayguAT7r8UD4PTB5qyiVSOVPbDkchF3U7KxK/A+Vp+OLHf+N891wnifVqophgBgY6L3WKtQsg8WERE1dUxgERERETWwEzeSkZFbBDtTffRry+lczVllq0ZqaQloa2OC6X1d8PuZyDKVWhJBQD93qzLJq6rGq4yLpSHsTPWRmJmPoHvp6MvXHRERNTFaVR9CRERERHVJOX1wvE8rSLQEDUdD9c1eaoA+bSwrTDYpK7UkguK1UFVlVVXjqSMIAnq3tgAATiMkIqImiRVYRERERA0oJbsAp249AAA8162VhqOhxuJxK6tqonepRu5ERERNDRNYREREpBEJsjxEpeTAzcqoXt6sN1b7guNRLBfR2ckMbW1MNB0ONSL2UoN6/V7o0+ZhH6zYDOQVlsBAl32wiIio6WACi4iIiBrc9ssxWOwXClEEBAH44GlPvNTfTe2xzS3RtTtQMX3wua6svqKG5WxhCHupPhJk+bgawz5YRETUtDCBRURERA0qQZaHJQ+TVwAgisAnB8Px+5m76OwkhXcrKbxaKf49fiMJS/xCIRcBLQFY7uutWsGtKQqPz0R4QiZ0JVoY09lB0+FQC6Pog2WJPVfv4+LdVCawiIioSWECi4iIiBpUVEpOmdXWlBIz85F4PR9HryepPU8uAkv9QjHQw7rJVmIpm7c/0cEGZoa6Go6GWqLerS2w5+p9XGAjdyIiamK4CiERERE1KDcrIzy67p6WAPw8yQfvj+qAsZ0d0NrKSO25JSLw/p5QHAlLRFZ+UZl9CbI8nI9MQYIsr54ir52iEjn2Bd8HADzXzVHD0VBL1bt12T5YRERETQUrsIiIiKhB2UsN0NHBFGHxmQAAiSDgC18vjH5kSl1EUhaG/XBGNdVQ6cTNBzhx8wG0tQT0cLXA4HbWyC8qwY//RjTqqYZnbj9ASnYhrIx1MdDDWtPhUAtVug9WUEw6+nEaIRERNRFMYBEREVGDKpGLiEnLBQB8Oq4jnvS0VTsl0N3WBCt8vbHULwwloggtAZje1xWiCJy+/QBRKTm4cDe13FQouQgs3h2KjNwieNiZwMZED7am+rAw1IWW1qO1Xw1HOX1wXJdW0JGwCJ40QxAE9GltCb+HfbCYwCIioqaCCSwiIiJqUNfjZcjML4aJnjYm9XSGdiXJnIk9nDHQwxrRKblwtTIsk+iKTsnBqVvJ2BN8H9diZWXOEwEs//tmmW3aWgKsTfRgY6IHG1N9VWJL+a/1w38tjaqf6KruCom3EjNx7GFvr2e7cvogaVbvUgksIiKipoIJLCIiImpQ5+4o3jT3am1ZafJKyV5qoDY55GplhBlWbhjuZYd+K06UaQwvAOjV2gKZecVIzspHak4hiuUiEmT5SJDlA5CVG09JoiXA2lgPNqZ6sDHRh42pHmyV/5ba9u+NZLy/p+IVEkVRREGxHH9disEnB8KhDC/0fgY8HUyr81AR1YtH+2AZ6Eo0HBEREVHVmMAiIiKiBnU+MgUA0K+tZZ2MZy81wPJSUw2VPbVKJ5OKSuRIyS5AcmYBkjLzkZxVgOSH/yap/i1Aak4BSuSiYkXEzMoTXaXJRWDR7lCs+PsWiuVyFBTLUVgsV3vsUr+wJr2SIjV9ThYGcJDqI16Wj8B76ejvzmmERETU+DGBRURERA0mv6gEl6PTAKBOe+9UNtUQAHQkWhVWcpVWXCJHSnYhkrPykZRZoPr3wSO3U7IKIKo5Pz23sMpYS0QR0Sm5TGCRxgiCUGYaIRNYRETUFDCBRURERA0mKCYd+UVyWJvowd3GuE7Hrk6CqiraEi3YSfVhJ9Wv9LjYtBwM+vpUmWmLWgKwYWZPOJobQE9HAj1tLaTnFGL4D2fKHCcRBLhaGdYqTqLaYh8sIiJqargEDhERETWY8w/7X/VrYwlB0NyKgLXlZGGE5b7ekDy8BokgYLmvNwZ6WKO1tTFamRnAylgP7rYm5Y77wteL1VekcX3aKKbwXovLQG5hsYajISIiqhorsIiIiKjBnHvY/6pvHU4f1JSqpi3W9DiihuRoboBWZga4n5GHoHsZnEZIRESNHiuwiIiIqEFk5RchJE7RFL0u+19pkr3UAH3aWFaZlKrucUQNRRAE9GptAQCcRkhERE0CE1hERETUIALupqFELsLV0hCtzJjIIdK03q0V0wiPhSciQZan4WiIiIgqxwQWERERNQj/O4rpg82l+oqoqUvNLgAA3E7KRr8VJ7D9coyGIyL6T4IsD+cjU5hcJSIV9sAiIiKiBnE+kgksosYiQZaHr4/eUt2Wi8ASv1AMcLeCgxlXySTN2n45Bkv8QiEXFSu8Lvf1xsQezpoOi4g0jBVYREREVO+Ss/JxOykbggD0eThtiYg0JyolB3Kx7Da5CExfdwnHridC/uhOogaSIMtTJa8AxetyqV8YK7GIiAksIiIiqn8XIhVNoj3tTWFupKvhaIjIzcoIWkL57RHJOXhlcyCG/XAGO67EorBY3vDBUYumLrlaIoqITsnVTEBE1GgwgUVERET17hz7XxE1KvZSAyz39YZEUGSxJIKA95/ugLmD28BEXxt3krPx3q4QDPzqJNacuYvsgmINR0wthbaazKpEEOBqxamtRC0de2ARERFRvRJFEefuKCqwmMAiajwm9nDGQA9rRKfkwtXKEPZSxeqg8wa3wdaAGKz1j0JiZj4+P3wDK09EYGofF8zo6wZrEz0NR07NVU5BMT7Ye73c9i98vVSvTyJquViBRURE1AQ1pdWZ7qXm4n5GHnQkAnq4mms6HCIqxV5qgD5tLMskB0z0dfDqoDY4u2gIvnzWG62tjZCZX4xfTkai35cnsHRPKKJTcjQYNTVHoijivd0huJWUBWsTPWx9uZdq3yhvew1GRkSNBSuwiIiImpimtjrTuYerD/o4m8NQl396EDUVetoSTOzhjOe7OeFYeBJWn45EcGwGtgbEYNulGIz0ssecQW3g7SjVdKjUDPx+5i4OhSRAW0vAqild0d3VAramekjKLEBEcja6OvMDEKKWjn9FEhERNSHqVmdatDsUR64nopebJTo5SuHdSgoTfR3NBlrKeeX0wTacPkjUFGlpCRjhZYfhHW0REJWG1acjcerWAxwKTcCh0AT0a2uJOYPaoH9bKwiCms7wRFXwj0jBl0duAgA+HOOJ7q4WAAAPWxNFAispiwksImICi4iIqClRtzoTAJy8+QAnbz4AAAgC0MbaGJ0cpejsaIZOjlJ0sDeFvo6kgaMF5HIR5yOVDdwtG/z+iajuCIKA3q0t0bu1JW4kZOK305E4EJKAc3dSce5OKrxameLVgW0w0ssO2hJ2KqHqiU3Lxet/BUEuAs93c8SLvV1U+9raGONsRApuJ2VrMEIiaiyYwCIiImpC3KyMym3TEoBXB7VBdEoOQuJkuJ+RhzvJ2biTnA2/oPsAAB2JgHZ2JujkaIbOjlJ0cjSDu41xvb/JvJGYifTcIhjpStDZyaxe74uIGk4He1P88IIPFg5rh7X+Udh2OQZh9zMx/6+rcLYwxOyBrfF8N0eNJM6p6cgrLMGrmwORkVuETo5SfDreq0wVn4etCQDgdlKWpkIkokaECSwiIqImxNpYDwY6EuQVlQBQLC3+ha9XmR5YKdkFCInLwLVYmeLfOBnScgoRdj8TYfczsTVAcZyBjgQdHUzR2clMVa3lYmlYp1OAzt1RVF/1am0JHVZkEDU7ThaG+GhsRyx4wh0bz0dj44VoxKTl4oO9Yfjx+G3M6OuKqb1dITVsPNOaqXEQRRFL/EIQnpAJSyNdrH6xW7mEp4etMQAgghVYRAQmsIiIiJqUS9FpyCsqgZmBDn6Z0hWtrY3KLS1uZayHoe1tMbS9LQDFm4S49DyExCkTWhkIu5+J7IJiXLmXjiv30lXnSg100MlR+vDLDJ0dzWAn1VftT5DlISolB25W5e9XnXMP+1/1bcPpg0TNmYWRLt56ygOvDmqN7Zdj8cfZKNzPyMM3x25j1alITOrpjFkD3Kr1c4NahvXnorE3OB4SLQE/T+4KB7Pyr422NooKrMTMfMjyiiA1YCKUqCVjAouIiKgJ+Sc8CQDwlKct+rWtXlN0QRDgZGEIJwtDPN1JsRS5XC7ibko2gktVad2Iz4QsrwhnI1JwNiJFdb6NiR46OZpBIgDHbiRBrObqh4XFclyKSgOAasdKRE2boa42ZvZzw4u9XXAoJAGrT0fiZmIW/vCPwsYL0RjXpRXmDGoNIz3tGiXDqXm5eDcVnx++AQBYOqoD+lTwIYfUQAd2pvpIzMzHneRsdHNhI3eilowJLCIioiZCFEUcu65IYA3raFersbS0BLS1MUFbGxM8180RgCLhdCsxC9fiMnAtNgMhcTJEJGchOasAx28klTlfLgJL/cIw0MO6wjefwbEZyCsqgaWRLto97GNCRC2DjkQL431aYVwXB5y6/QCrT0UiICoNuwLjsCswTnVcdZLh1LzEZ+ThtT+DUCIXMb6LA17q51rp8e62xkjMzEdEUhYTWEQtHBNYRERETUR4QibuZ+RBX0cL/euhoklXWwvejlJ4O0pVq0DlFBTjenwm9gffx5aAmDLHl4giolNyK0xgKftf9W1rBS2tuuurRURNhyAIGNLOBkPa2eBqTDp+OH4bp2//V+FZnWQ4NR/5RSWYuyUQqTmF8LQ3xXLfTlX2XfSwNeFKhEQEAGA3VSIioiZCWX010N0aBroNs7KXkZ42erpZ4LWhbaEuB2VWSWNmZQKrH/tfEREAH2dzvDqoTbntymQ4NW+iKOKDvWG4FieDmaEOfpvarVq/y1SN3JO5EiFRS8cEFhERUROh7H9V2+mDj8NeaoDlvt6QPPJJ+fy/riJRll/u+JyCYgTHZgBg/ysi+o+blVG5ZLiWALhaGWomIGowWwJisDMwDloCsHKSD5wsqvecuz+cgn47iQksopaOCSwiIqImIDYtF+EJmdASgCfa22gkhok9nOG/eAj+mt0bf83uBXupPu4kZ+PZVecRnZJT5thLUWkolotwsjCo9psUImr+1CXDTfV1YKrP1eWasyvRafjkwHUAwHsj2mOAu3W1z21ro6jASsosgCyvqF7iI6KmgQksIiKiJkBZfdXD1QLmRroai8NeaoA+bSzRp40Vds7pA1dLQ9zPyMNzqy/gRkKm6rj/pg+y+oqIylImw9fP6AE7U31k5BXh66O3NB0W1ZOkzHzM/TMIRSUinva2x6sDW9fofFN9HdhL9QEAEazCImrRmMBqRs6cOYMxY8bAwcEBgiBg79695Y65ceMGxo4dC6lUChMTE/Tu3RsxMf815S0oKMD8+fNhZWUFIyMjjB07FnFxceXGISKihqXJ6YMVcTQ3xM45fdHB3hQp2QWY+NsFBN5LBwCci0wFwOmDRKSevdQAQ9rb4KvnOgEANl6IxpXoNA1HRXWtsFiOuVsC8SCrAO1sTfDVc1U3bVfnv2mEbORO1JIxgdWM5OTkoHPnzvj555/V7o+MjET//v3Rvn17nDp1CteuXcMHH3wAfX191TFvvvkm9uzZg23btsHf3x/Z2dkYPXo0SkpKGuoyiIjoEek5hbj08I3dME9bDUdTlrWJHra90hvdXMyRmV+MF/8IwMbzUapqrL5s4E5ElRjoYY3nuzlCFIFFu0OQX8S/OZuTjw9cR1BMBkz1tfHb1G4w0tN+rHE8Hk4jZB8sopbt8X6CUKM0cuRIjBw5ssL977//PkaNGoWvvvpKta116/9KeGUyGdauXYvNmzfjySefBABs2bIFTk5OOH78OIYPH15/wRMRUYVO3ExGiVxEezuTRtlPSmqgg82zemLOliCcuf0AH+4PV+07fiMJE3s4azA6Imrs/ve0J07dfoDIBzlYeSIC7w5vr+mQqA5svxyDPwNiIAjAjy/4wNXK6LHH8nhYgXUnmRVYRC0ZK7BaCLlcjkOHDsHDwwPDhw+HjY0NevXqVWaaYWBgIIqKijBs2DDVNgcHB3h5eeH8+fMVjl1QUIDMzMwyX0REVHeOhScCaFzTBx9lqKuNz8Z1LLd9qV8YEmR5GoiIiJoKqaEOPh3nBQBYffoursfLNBwR1VZwbAY+2Kto2v72kx4YUsvFR9xtWYFFRExgtRjJycnIzs7GihUrMGLECBw7dgzPPPMMfH19cfr0aQBAYmIidHV1YW5uXuZcW1tbJCYmVjj28uXLIZVKVV9OTk71ei1ERC1JflEJztxWNERvbNMHHxWXUT5RVSKKiE7J1UA0RNSUjPCywyhvO5TIRby3KwRFJXJNh0SP6UFWAeZsDkRhiRxPedritSFtaz2msgdWclYBZLlciZCopWICq4WQyxV/BIwbNw5vvfUWunTpgsWLF2P06NFYvXp1peeKolhps8UlS5ZAJpOpvmJjY+s0diKilsw/IgV5RSVoZWaAjg6mmg6nUm5WRtB65NeFRBDgatX4pj0SUePz8VgvSA10cD0+E2vO3tV0OPQYikrkeG1rEBIz89Ha2gjfTegMrUd/MTwGYz1ttDIzAADcTmYVFlFLxQRWC2FlZQVtbW14enqW2d6hQwfVKoR2dnYoLCxEenp6mWOSk5Nha1vxp/56enowNTUt80VERHVDOX3wKU/bx1q5qSHZSw2w3NcbkodxSgQBX/h6wV5qoOHIiKgpsDbRw7LRir9VfzgegcgH7HfU1Hx+6AYuRaXBWE8bv0/tDhN9nTobuy0buRO1eExgtRC6urro0aMHbt26VWb77du34eLiAgDo1q0bdHR08M8//6j2JyQkICwsDH379m3QeImICCiRi/j3RjKAxj99UGliD2f4Lx6Cv2b3hv/iIWzgTkQ14tu1FQZ5WKOwWI5Fu0Igl4uaDomqyS8oDhvORwMAvpvQWZVwqiseD/tgRSQxsUnUUnEVwmYkOzsbd+7cUd2OiopCcHAwLCws4OzsjHfffRcTJ07EwIEDMWTIEBw5cgQHDhzAqVOnAABSqRSzZs3CwoULYWlpCQsLC7zzzjvw9vZWrUpIREQNJygmHak5hZAa6KCHm4Wmw6k2e6kBq66I6LEIgoDPn/HC8O/P4Mq9dPx88g66u5rDzcqIP1casbD7MizxCwUALBjatl4WHVH2wWIFFlHLxQRWM3LlyhUMGTJEdfvtt98GAEyfPh0bNmzAM888g9WrV2P58uVYsGAB2rVrh927d6N///6qc77//ntoa2tjwoQJyMvLwxNPPIENGzZAIpE0+PUQEbV0x64rpg8ObW8DHQmLpomoZXA0N8Sike2xbN91fPfPbQCAlgAs9/VmVWcjlJZTiFc3B6KgWI4h7azx5pMe9XI/HqoEFiuwiFoqQRRF1uVSncrMzIRUKoVMJmM/LCKixySKIgZ/cwr3UnOxakpXjPS213RIREQN5n56Lvp9ebLMNokgwH/xEFZiNSLFJXJMX38J5+6kwtXSEPte7w+pQd31vSotp6AYHT88CgC4+sFTMDfSrZf7oaaL70ObP36cS0RE1AhFJGfjXmoudLW1MNDDWtPhEBE1qHtpueW2lYgiolPKbyfN+eroLZy7kwpDXQl+m9q93pJXAGBUaiXCiGRWYRG1RExgERERNULK6YP921rBSI8z/omoZXGzMoKWmoVX7aX6DR8MqbX/Wjx+P3MXAPD1c53Rzs6k3u9T2cidfbCIWiYmsIiIiBqhY+FJAJrO6oNERHXJXmqA5b7ekAhls1ifHbqBohK5hqIipRsJmVi0KwQAMGdQGzzdqWGmuSv7YEUwgUXUIvEjXSIiokYmQZaHkDgZBAF4ogMTWETUMk3s4YyBHtaITslFSnY+3tkZguM3krBwxzV8P7ELJOpKtCqRIMtDVEoOVzSspYxcRdP2vKISDHC3wrvD2zXYfbuzkTtRi8YEFhERUSNz/GH1VTdnc1ib6Gk4GiIizbGXGqiSTUZ62nhlUyD2X4uHoa4Ey329IQjVS2JtuxSDJXtCIYpc0bA2SuQi3tgWjJi0XDiaG+CnF3xqnEisDXcbxRTCiGRWYBG1RJxCSERE1Mgopw8+xemDREQqQ9vb4scXfKAlANsux+KTg+GoakH14hI5Np6PwmI/RfIKAOQisNQvDAmyvAaIunn57p9bOH37AfR1tPDb1G4NvhJg24cJrJTsQqTlFDbofROR5rECi4iIqBG5nZSFc3dSAADDOtppOBoiosbl6U72yC3shHd3hWD9uWgY62lj4bDyU9hyC4ux43Is/vCPQlx6+USVckVDTiWsngRZHnZcjsUvJyMBAF8+2wkdHaQNHoeRnjYczQ0Ql56H20lZ6N3assFjICLNYQKLiIiokdh+OQaLd4dCWU9wKSoVblZGGo2JiKixeb67E/KKSrBs33WsPHEHJXIR/d2t4GZlBF2JFjZeuIdNF6KRkVsEADAz0IEsrwiP1mrpajfc1LembPvlGCzxC4X84QPYv60VxnVppbF4PGxNEJeehwgmsIhaHCawiIiIGoG49FzFFJdS25b6hWGghzUrBIiIHjGtjyuyC4rx1ZFb+PVUJH49pagM0tYSUPww0+JsYYjZA1vjua6O2H/tPpb6haGk1JTDTw7ewM5X+0BXm11VKpIgyyuTvAKA85EpSJDlaex3k7utMU7cTGYjd6IWiAksIiIiDRFFEcGxGdh/LR57gu7j0VYunOJCRFSxZ3xa4esjt8ok/ovlItrbmWD+UHeM8LJTNRgvvaKhrraAmesv41psBr46chP/G+2pmQtoAqJScsokrwBFDzFN/m7ysFGuRMhG7hXhipvUXDGBRURE1ABK/zGZlV+M/cHx2H8tHjFpuRWeIxEEuFoZNmCURERNR1RKTrlpgQDw4RhP9GljVW576RUNv36+M17dHIg//KPQq7UlF82ogJmBTrltmv7d5GGrSGDdSWYFljqlp3xyxU1qbpjAIiIiqmeP9g8pzUBHgqc8bTGuiwMSZflYtu86SkQREkHAF75e/OSUiKgCblZG0BJQ5merIrlSde/A4R3t8FI/N6w7F4V3dl7DoQX94WjODwwetf1ybJnbjeF3U1sbYwgCkJpTiNTsAlga62kslsbm0SmfyhU32Y6AmgsmsIiIiOqRuv4hgKIJ7oQeTniygw0Mdf/7dTy0gw2iU3LhamXIPzaJiCphLzXAcl9vVW+rmiZXFo9sj8B7abgWJ8P8v65ix6t9oCNhPyyluw+y8WdADABg5aQusDLWbxS/mwx0JXAyN0RMWi5uJ2WjDxNYKuqmfLIdATUnTGARERHVI3V/TALAa0Paok+b8qsnlZ7iQkRElSvd26qmyRVdbS38PLkrRv10FldjMvDN0VtYMqpDPUbbtHx15BaK5SKGtLPGmM6aW3VQHXcbY8Sk5SIiOUvt79KWqrBYXm6blgC2I6Bmgx8xEBER1SM3KyMIj6zUrun+IUREzYm91AB92lg+VvLfycIQXz/XGQDw25m7OHEzqa7Da5IuR6fhyPVEaAlolEk9d1s2cn9UYbEcK/6+WW77E+1t+cEYNRtMYBEREdUje6kBerhaqG43hv4hRET0nxFedpjR1xUA8PaOa4jPyNNsQHUgQZaH85EpSJDV/FpEUcQXh28AACb2cFI1TW9MPGyNAQC3k9jIXemXk3dwMzELFka6OLygP+YPbQsAuBiVCllekYajI6obnEJIRERUj+RyEVEpOQAU/VbGdXFg8oqIqJFZMqo9Au+lI/S+DHM2B+LdEe3Q1sa4Sf68ru0qdIdDE3E1JgOGuhK89aRHPUb6+JRJtYikLIiiCOHRUucW5nq8DL+cvAMA+HScFzwdpGhnZ4ojYYmISM7GOv8ovPVU43wuiWqCFVhERET1KDguAw+yCmCip42X+rk1yTdDRETNnZ62BL9M7go9bS2E3Jdh6tpL6LfiBLZditF0aDWifhW60GpXYhUUl+DLI4ppaK8MbA0bU/36CrVW2lgrViJMzy1CSnahpsPRqKISOd7ZGYJiuYhR3nZ4upM9AECiJeDNhwnIdf5RkOWyCouaPiawiIhIY2ozxaGp+Cdc0U9lUDtr6Grz1y4RUWOloy2UaYItF4HFfqGY9PsFfHfsFo5dT0SCLA+iqGZljkbiWHiSmlXogKgHOdU6f8vFGMSk5cLaRA+zB7SuhwjrhoGuBM4Wil6SES28D9avJyNxIyET5oY6+GScV5l9I73s0N7OBFkFxfjD/66GIiSqO5xCSEREGlHbKQ5NhTKB9ZSnrYYjISKiykSl5EBdaurC3TRcuJumum1lrIuODlJ4t5LCq5UU3o5SOEj1NT6N7Xh4Er44FK5236YL99DDzQI6koo/SJHlFmHliQgAwNtPecBIr3G/VXS3McG91FxEJGejb1urKo9PkOUhKiUHblZGzaYaOjw+U/WcfTLOC1bGemX2a2kJePNJd8zZEoT156LxUj83mBvpaiJUojrRuH8qERFRs6R+ikMYBnpYN5s/KgHg7oNs3EnOhraWgMHtbDQdDhERVcLNyghaAspUMGkJwFtPeSAmNReh92WISM5GSnYhTt9+gNO3H6iOszTSRcdWUni3MlUltlqZGTRYUmtrQAz+t1fxe7WdrTEikrMhF6FaBffI9UTM3RKInyd3hb6ORO0Yv5y6g4zcInjYGuP5bo4NEndteNga4/iNpGqtRNgcPzQrKpHj3V3XUCwXMaKjHUY/nDr4qGGeduhgb4obCZlYc/Yu3hvRvoEjJao7TGAREVGDi0rJUTPFQUR0Sm6zSmApq6/6tLGE1EBHw9EQEVFl7KUGWO7rjaV+YSgRRdWqsaUTHflFJbiRkImw+zKE3pch9H4mIpKykJpTiDO3H+BMqaSWhZEuOjooElrKpJajed0mtURRxPf/3MZPJxQNvCd0d8Tnz3gjJbsA0Sm5cLUyRHh8Jub9GYTjN5IxY/0l/DG9B4wfqa6KTcvFhnPRAIAlIztAu5JKrcbC/eFKhBFVrESYIMvDYr9QiM3sQ7PVpyJxPT4TZoY6+HS8V4WvK2UV1qubA7HxfDReHtAaFqzCoiaKCSwiImpwblZGareHxGWgTxvLBo6m/nD6IBFR0zKxhzMGelirkj+PJjj0dSTwcTaHj7O5alt+UQluJmYh9L4MYXGKxNbtpCyk5RTibEQKzkakqI41N9SBl3Lq4cOvx01qFZXI8f6eUOy4EgcAWPCEO9560h2CIMBeaqCK3V5qgI0v9cTLG6/g4t00TF5zERtm9iyTxPjm2C0UlsjRt40lBrezrnEsmuBuo1iJ8HZy5SsRbr8Ui0fbljX1D81uJmbip4dTBz8e2xHWJnqVHj/M0xYdHUxxPT4Tv5+5i8UjWYVFTRMTWERE1OBuJqov91/+901Ep+bgo7EdoaetfopDU5GSXYDAmHQAwJMdmMAiImoqSid/qkNfR4IuTmbo4mSm2pZfVIJbyqTW/f+SWum5ReWSWmaGOvByKJvUcrKoPKmVU1CM17YG4dStB9ASgM/Ge2Nyr4qnxPVubYmts3th+rpLCImTYeJvF7B5Vi/YSfUREpeBfcHxEARg6agOGu/lVV1tbYyhJQAZuUV4kF0AG5PyKyZuDYjBD/9GlNuuJQCuVoYNEWadU6w6eA1FJSKGedpibGeHKs8RBAFvPemBlzddeViF5VauXxZRU8AEFhERNai0nEK8tysEgGKqwzM+jnC2MMCeq/fx7T+38delWITHZ2LVi93gYNY0PxkFgBM3kiGKgFcr0yZ9HUREVHP6OhJ0djJD51JJrYLi8kmtW4lZyMgtgv+dFPjf+S+pJTXQgVcr0zJJLWcLQyRm5uNabAZ+OB6Bm4lZ0NfRws+TuuLJalT6dnI0w45X+2Dq2kuISM7Gc6vP4/uJnfHB3usAgGe6tIJXK2mdPxb1RV9HsRJhdGouIpKyyySwRFHELyfv4JtjtwEAPd0scCU6TdW+wNHcAHam5RNejV2CLA8//BOBsPuZkBro4LNnKp46+KgnOtigk6MUIXEy/H7mLpaO6lDP0RLVPSawiIiowYiiiKV+oXiQVYC2Nsb4ZJyXqpns60Pd4e1ohje2XcW1OBlGr/THykk+6FeNlYUao2PhiQAUzVOJiIj0tCXo5GiGTo5mqm0FxSW4nZj9sJ+WIrF1KzELsrwinLuTinN3UlXH6mtrIb9YrrptqCvBlpd7oWup6YxVcbc1wc45fTB1bQCiU3Px/OqLqn0edia1u0ANcLc1QXRqLm4nZan+XpDLRXxyMBwbzkcDAOYPbYu3n/JAYmY+rsZk4O3twYhJy8PR64kY4aW+8XljVLoRPQCM9LJTW3VWEWUV1swNl7HpQjRmD2hd5dRDosam8XfnIyKiZsMv6D6OXE+EtpaAHyZ2KbcS0iAPaxx4vT86OpgiLacQU9cGYNWpSMRn5OJ8ZAoSZHkairxmcguLVdND2P+KiIgqoqctgbejFJN7OWO5rzcOzO+PsI+H4+D8/ljuq5gW2MlRCh0toUzyClBMU7SX1ryKyMnCECsn+5Tb/vWRW03m96ySx8NG7rcfNnIvLJbjze3BquTVh2M8sXBYO1VfsFHe9nhlYGsAwJdHbqGoRK523MZG2Yi+9AI4O6/E1vj5GtzOGl2czJBfJMfq05F1HCVR/WMCi4iIGkRsWi4+3K+YpvDWUx4VTlNwsjDE7rl98Vw3R8hF4MsjN9F3xUlMXhOAfitOYPvlmIYM+7GcjUhBQbEcjuYGaN8EP9EmIiLN0dXWglcrKSb1dMYXz3hj/+v9sXZGj3LHyUUgOiX3se4jK7+43DZlY/OmxMNW8Ts2IikLuYXFeHnTFey/Fg9tLQE/vtAFM/u5lTvnlUFtYGmki6iUHGy71Pj/pgCATRfuqWlEX/PnXxAUKxICwJaL95CcmV9XIRI1CCawiIio3pXIRSzceQ3ZBcXo5mKOVx9++lkRfR0Jvn6uE94d3q7MdrkILPELRdh9WX2GW2ulVx9sKs1wiYio8XK3VTQsL00iCI/diNzNyqhOx9MU5UqE1+NleG7VBZy5/QAGOhL8Mb07xnVppfYcYz1tVRLnh+MRyC4on8xrLAqL5Vi2LwyrTpWvlnrc52uQhzW6OpuhoFiOb47dalIV7kRMYBERUb1b638Xl6LSYKgrwXcTOkNbUvWvH0EQ4ONsVm67XARGr/THkG9O4d2d17DjSiyiUnIgPvrRpIYUl8jx743/ElhERES1ZS81wHJfb0gefigiEQR84etVo9US63M8TQl6uNpvXpEc4QmZMNCVYOvsXhjczqbS817o6Qw3KyOk5hTi90Y6lS4pMx+T1lzEpgv3ACiasCuTjrV5vgRBwFtPeQAAdlyJa1IV7kRs4k5ERPXqRkImvjmqWAVo2WhPuFgaVftc5SfEcjW5qaiUHESl5GBnYBwAwNpEDz1czdHdxQI93SzQ3s6kWomyuhZ4Lx3puUWQGuigp6tFg98/ERE1TxN7OGOghzWiU3LhamVY62RTXY/X0BJkeVi2L6zMtoKiEthVoy+YjkQLi0a0w5wtQVhzNgov9naBTSNalTDgbipe23oVKdkFMNHXxg8Tu+CJDrZIkOXVyfPVxrrs32JyEVjqF4aBHtZN7nVALQsTWEREVG8Kikvw1vZgFJbI8WQHG0zs4VSj85WfEC/1C0OJKKo+cRzhZY+ge+m4FJ2GK9FpuBYrw4OsAhwOTcThUMXqf0a6EnR1MUcPVwv0cLVAFyczGOhKqrjH2lNOH3yivY1GEmhERNR82UsN6jTBUNfjNaSolJxyH3Ap+4JV55qGd7RDV2czBMVk4PvjEVju611PkVafKIpY6x+F5X/fRIlcRHs7E6x+sRtcrRQJp7p6vqJTy/fOUvZAa6qvB2oZmMAiIqJ6892x27iZmAVLI10s9+30WP2gKvqEeEh7Gwxpr5gikF9UgtD7MlyKUiS0rtxLR1a+YiVA5WqAOhIBXq2kqoRWdxdzmBvp1t3FQvGH5z+cPkhERFTv1FVp16QvlCAIWDKqA55ffQHbL8dgVn9XtLXR3MIrOQXFWLQ7BAdDEgAA47s4YLlvp3r58K22jx2RpjCBRURE9eJQSDx+O3MXALDc1xvWJnqPPVZVnzjq60hUiSlA0TT+VmIWrtxLw6WoNFyOTkNSZgGuxmTgakwGfn8Yl7uNMXq4WaCHq6JSq5WZQZkkW4IsD1EpOXCzMqrWJ5IRydm4l5oLXW0tDPSwfuzrJSIiospVVKVdkwqiHq4WGOZpi2PhSVjx9y38Mb17PUZcsbsPsjFnSyBuJ2VDW0vA/57ugOl9XettIZi6eOyINIEJLCIiqnMbzkfho/3hqtvpuYUNev8SLQGeDqbwdDDFtD6uEEURcel5igqth0mtyAc5iEjORkRyNrYGKBqX2kv1HybCzJGaU4if/o2AXAS0BEUSbmIP50rvVzl9sH9bKxjp8VcsERFRfaqLPl7vjWiPf28m4/iNJATcTUWv1pb1EGnFjl1PxMId15BVUAxrEz38OqWr6gO5+tTUe6BRyySIjWXZJmo2MjMzIZVKIZPJYGpqqulwiKiBxWfkou+Kk2W2SQQB/ouHNKo/jlKzC3DlXjouR6Xh8r10XL8vQ7G6bvEPVecaxv3sj2txMiz39caknpUnu4iIiKhxeH9PKP4MiEFnJzPsnde33iqfSiuRi/jun1v45aRiFcQerub4ZXLXRtVMvqnh+9Dmjx8PExFRnVp54k65bY2xMailsR6Gd7TD8I52AIDcwmIEx2TgUnQajocnISw+s8zxVV1DUmY+rsXJIAiKpa6JiIioaXjjSXfsuXof12IzcDg0EU93sq/X+0vPKcSCbVdVfTpn9nPF0lEdoMPFX4gqxe8QIiKqM+fupGDbpdhy25tCY1BDXW30bWuFN5/0wJrp3aGl5sNXI72KG6kqpw92cTKDjQk/PSUiImoqbEz08crA1gCA5YfDceb2AyTI8urlvkLjZBi90h9nI1JgoCPBjy90wYdjOjJ5RVQN/C4hIqI6cT8jD/P/ugoRQDcXc0geJoCaYmNQZXNTySNTCF7ZFIiw+zK15ygTWMM87eo9PiIiIqpbswe0hrGeNuIy8jFt3SX0W3EC2y/H1Ol97Lgci2dXn8f9jDy4Whpiz2t9Ma5Lqzq9D6LmjFMIiYio1vKLSjB3SyDScgrh1coUf77cC+m5hU26MWjp5qY6EgGL/UJxJzkbz6++gO8ndsYIr/+mF2TlF+FCZCoA4ClPW02FTERERI8pM78IOQXFqttyEVjqF4aBHta1/jumoLgEH+0Px1+XFAmxJzvY4NsJXSA10KnVuEQtDSuwiIioVkRRxLJ9YQiJk8HcUAerX+wGfR0J7KUG6NPGskkmr5SU19Dd1QJ+8/pigLsV8opKMGdLEH45eQfKdVBO336AwhI5WlsZoa2NsYajJiIiopqKSsnBo0u5KPtf1kZ8Rh4mrL6Avy7FQBCAhU954Pep3Zm8InoMTGAREVGt/HUpFjuuxEFLAH6a5ANH88bd6+pxmerrYP2MHpjexwUA8PXRW1i48xoKikuwPzgeANCnTcMuvU1ERER1w83KSG3/y9osSHj+TgpGr1SsUCw1UPwdMf8Jd2ipuyMiqhITWERE9NiuxqTjo/3XAQDvDG+HAe7WGo6ofmlLtPDxOC98Oq4jJFoC/ILuY9h3Z3DsYf+rrZdi6rxfBhEREdW/ivpfvrktGDGpNavCEkURq09H4sW1AUjLKURHB1McnN8fg9txlWKi2hBE5fwHojqSmZkJqVQKmUwGU1NTTYdDRPUkJbsAo3/yR2JmPoZ3tMXqF7tBqM3HlE3MmdsPMPfPQOQUlJTZLhEE+C8e0qSnThIREbVUCbI8RKfkQmqgjTe2BSMiORuO5gbY8WofOJhV/bs9K78I7+4MwZHriQCA57o54rPxXtDXqXglY6obfB/a/LECi4iIaqy4RI7XtwYhMTMfbayN8M3znVtU8goABnpY4+MxHcttr4t+GURERKQZyv6Xng5S/PlyL7haGiIuPQ9T/ghAclZ+pefeSc7C+F/O4cj1ROhIBHw23gtfP9eJySuiOsIEFhER1UiCLA9vbLuKi3fTYKQrwW9Tu8FEv2U2Iu3nblWuX4ZEEOBq1Tz7gBEREbUkNqb6+HN2b7QyM0BUSg5e/EMxJVCdw6EJGPfzOUQ+yIGdqT52vNoHL/Z2aXEf8BHVJyawiIio2rZfjkHf5SdwKFRRFu/btRXa2phoOCrNebRfhkQQ8IWvF6cPEhERNROtzAywdXYv2Jrq4XZSNqauDYAst0i1v7hEjuWHb2Den0HIKSxB79YWOLigP3yczTUYNVHzxB5YVOc495ioeUqQ5aHfihOQl/qtwX5PCsp+Ga5Whi3+sSAiImqO7iRn44XfLyAluxBdnMzwzfOdcSc5C7+fuYugmAwAwCsDW+O94e2gLWGdiCbwfWjzp63pAIiIqGmISskpk7wC/uv31NKTNvZSgxb/GBARETVnbW2MsXlWL0xacxHBsRl48rvTqn26EgE/vOCDUd72GoyQqPljapiIiKrF3rR8gob9noiIiKil6GBviu8mdC63vVguwsfZrOEDImphmMBqRs6cOYMxY8bAwcEBgiBg7969ZfbPmDEDgiCU+erdu3eZYwoKCjB//nxYWVnByMgIY8eORVxcXANeBRElyPJwPjIFCbI8TYdSxuXotDK32e+JiIiIWhp1KwrKRXAFYqIGwCmEzUhOTg46d+6MmTNn4tlnn1V7zIgRI7B+/XrVbV1d3TL733zzTRw4cADbtm2DpaUlFi5ciNGjRyMwMBASCZd/Japv2y/HYIlfKOQioCUAy329MbGHs6bDAgD8GXAPADBvcBsMcLdmvyciIiJqcdysjKAloFxPUFakE9U/JrCakZEjR2LkyJGVHqOnpwc7Ozu1+2QyGdauXYvNmzfjySefBABs2bIFTk5OOH78OIYPH17nMRPRfxJkearkFaD4w2ipXygGelhrPFEUGifDtTgZdCVamNXfDZbGehqNh4iIiEgTlCsQL/ULQ4kosiKdqAExgdXCnDp1CjY2NjAzM8OgQYPw+eefw8bGBgAQGBiIoqIiDBs2THW8g4MDvLy8cP78+QoTWAUFBSgoKFDdzszMrN+LIGqm1DdJB4LuZeDpTpr9o2jLRUX11UhvOyaviIiIqEWb2MMZAz2suQIxUQNjD6wWZOTIkfjzzz9x4sQJfPvtt7h8+TKGDh2qSj4lJiZCV1cX5ubmZc6ztbVFYmJiheMuX74cUqlU9eXk5FSv10HUXMVnqO959d6ua9hy8R7kj2a3Gogsrwj7r8UDAF7s7aKRGIiIiIgaE3upAfq0sWTyiqgBMYHVgkycOBFPP/00vLy8MGbMGPz999+4ffs2Dh06VOl5oihCEIQK9y9ZsgQymUz1FRsbW9ehEzV75yNTsHRPGABA+d2mJQDOFgbIKSzB//aG4YU1FxH5ILvBY9sTFIe8ohJ42Bqju4t51ScQERERERHVMU4hbMHs7e3h4uKCiIgIAICdnR0KCwuRnp5epgorOTkZffv2rXAcPT096OlxShHR47oak47ZG6+gsFiOJzvYYtmYDrifng9XK0PYmOhj04VofH30Fi5FpWHkj2fxxhPueGVga+hI6v8zCFEUsSUgBoCi+qqyZDYREREREVF9YQVWC5aamorY2FjY29sDALp16wYdHR38888/qmMSEhIQFhZWaQKLiB7fjYRMzFh/GTmFJejX1hI/T/aBs4WRqiRdoiVgZj83HHtrIAZ5WKOwWI6vj97C2J/PISQuo97juxSVhjvJ2TDUleAZn1b1fn9ERERERETqMIHVjGRnZyM4OBjBwcEAgKioKAQHByMmJgbZ2dl45513cOHCBURHR+PUqVMYM2YMrKys8MwzzwAApFIpZs2ahYULF+Lff//F1atX8eKLL8Lb21u1KiER1Z27D7Ixde0lyPKK0NXZDL9P7Q59HYnaYx3NDbFhZg98P7EzzA11cCMhE+N/OYfPD4Ujr7Ck3mJUVl+N6+IAE32dersfIiIiIiKiyjCB1YxcuXIFPj4+8PHxAQC8/fbb8PHxwbJlyyCRSBAaGopx48bBw8MD06dPh4eHBy5cuAATExPVGN9//z3Gjx+PCRMmoF+/fjA0NMSBAwcgkah/U01Ej+d+Rh5e/CMAKdkF8LQ3xfqZPWGkV/msbkEQ8IyPI46/PQjjujhALgJrzkZh+A9ncO5OSp3H+CCrAEfCEgAAU3qxeTsREREREWmOIIqiZpa1omYrMzMTUqkUMpkMpqammg6HqNFJzsrHhNUXEJ2ai9bWRtjxah9YGde8j9yJm0n4354wxMvyAQDPd3PE/572hNSwbiqlfj11B18duYUuTmbY+1q/OhmTiIiIiKg+8H1o88cKLCKiBnQrMRPP/noe0am5aGVmgD9f7vVYySsAGNreFsfeHoTpfVwgCMDOwDg88d1pHA5NQG0/myiRi9j6cPrglF7OtRqLiIiIiIiotpjAIiJqIBvPR2P4D2cRm54HQJEYspca1GpMYz1tfDzOC7vm9EFbG2OkZBdg3p9BeGVzIBIfVmY9jjO3HyAuPQ+m+toY09mhVjESERERERHVFhNYREQNIEGWh4/2Xy+z7dtjt5Egy6uT8bu5WODQgv5Y8IQ7dCQC/glPwlPfncbWgBjI5TWvxvoz4B4A4LluThU2liciIiIiImooTGARETWAgLtpeDSNVCKKiE7JrbP70NOW4O2nPHBw/gB0cTJDVkExlu4JxaQ1F3H3QXa1x7mfkYcTN5MBAFN6c/ogERERERFpHhNYREQN4HBoQrltEkGAq5Vhnd9XOzsT7J7bF8tGe8JAR4KAqDSM+PEsfj11B0Ul8irP/ysgBnIR6NvGEm2sjes8PiIiIiIioppiAouIqJ6F3ZfhWHgSAEBLUGyTCAK+8PWqdQ+siki0BLzU3w3H3hqIAe5WKCyW46sjtzDu53MIjZNVeF5RiRzbLscCAKb0cqmX2IiIiIiIiGpKW9MBEBE1d18euQkAGNfFAYtHtkd0Si5crQzrLXlVmpOFITa91BN7rt7HJwfDEZ6QiXG/+GP2gNZ480kPGOiW7W917HoSUrILYG2ih2Edbes9PiIiIiIioupgBRYRUT3yj0jB2YgU6EgELHyqHeylBujTxrJBkldKgiDAt6sjjr89CGM7O0AuAr+duYsRP57B+TspZY7dclHRvP2FHk7QkfBXBBERERERNQ58d0JEGpcgy8P5yJQ6W5GvsZDLRVX11ZReLnC2rPt+VzVhZayHnyb5YO307rCX6uNeai4m/xGARbtCIMstwoXIVFy4mwoBwAs92bydiIiIiIgaD04hJCKN2n45Bkv8QiEXFf2hlvt6Y2KP5pE8ORSagND7MhjraWP+0LaaDkfliQ626Olmga+O3MLmi/ew/UosDoUmILugGAAgAvCPeNBsngciIiIiImr6WIFFRBoTm5aDxbsVySsAkIvAUr+wZlGJVVgsxzfHbgEAXhnYGpbGehqOqCwTfR18Ot4LO+f0gbOFgSp5pdRcngciIiIiImoemMAiogaXnlOIVaciMf6XcxAf2VciigirZJW8pmLb5RjcS82FlbEeZvV303Q4FerhaoFPxnmV214iiohOydVAREREREREROVxCiERNZgbCZnYeD4ae67eR0GxvMLjXv8rCBO6O2NWfze4Whk1YIR1I7ugGD/9GwEAeONJdxjpNe4fte3sTKAlQFUJBwASQYCrlWZ7dhERERERESk17ndVRNSkJcjycCc5G3Fpedh37T4u3k1T7evoYIoZfV1RUCzHh/uuo0QUIQiAnak+EmT52HzxHrYE3MMwT1vMHtAa3VzMIQiCBq+m+v44excp2YVwtTTECz2cNB1OleylBlju642lfmEoEUVIBAFf+Ho16EqJRERERERElRFEUXx0Bg9RrWRmZkIqlUImk8HU1FTT4ZCGbLukaM5e+geMREvAiI52mNHPFd1LJaQSZHmITsmFq5Uh7Ez1cSEyFWvO3sXJWw9U53ZxMsMrA1tjeEc7SLQabyLrQVYBBn99EjmFJfhlclc83cle0yFVW+nngckrIiIiImpK+D60+WMCi+ocf3BQgiwPfZefKJO8EgDsmdcXXZzNqz1ORFIW/jgbhT1X76OwRDHl0MnCAC/1c8OE7k6Ncmreh/vCsPHCPXR2lGLva/2aTNUYEREREVFTxvehzR+buBNRnbubnFOuObsIIK+o4r5X6rjbmuDL5zrh3OKhWDC0LcwNdRCbloePD4Sjz/J/8eWRm0jKzK+zuNVJkOXhfGRKtVbku5eagz8DYgAAi0a2Z/KKiIiIiIiojjS+8gUiavLiMsone2rTFNzaRA9vD2uHuYPbYldQHNb5RyEqJQerTkXij7N3MaazA2YPaI0O9nX7Sctfl2Kw9OE0SC0B+OIZb7zQ07nC4785dhvFchGDPKzRt41VncZCRERERETUknEKIdU5lm62bKIownfVeVyNyYAAReWVsin4xB4VJ39qQi4XcfxGEtacvYvL0emq7QPcrfDygNYY6G5V6+qn07eSMX395XLbB3tYo7+7Fbq5mKOjgxS62opC1pM3kzFzg+L4wwsGwNOBr30iIiIioobC96HNHxNYVOf4g6NlC7ibiom/X4SuthZ2z+2D7PySem0KHhybgTVn7+Lv0ATIH/40a29ngln93TC2iwP0tCU1Gi+7oBjfHbuN9eeiyk2DfJS+jhY6OZrBUFeCU6Uazn/5rHedJeuIiIiIiKhqfB/a/DGBRXWOPzhathnrL+HUrQeY0ssZnz/j3WD3G5uWi3XnorD9cixyC0sAKKYezujriim9nGFmqFvp+aIo4khYIj4+EI7ECvpqaQnAq4Na43ZiNgJj0pGRW6T2OIkgwH/xEK7kR0RERETUQPg+tPljAovqHH9wtFw3EjIx8sez0BKAk+8MhoulUYPHIMsrwl+XYrDhXLQqEWWgI8GE7o54qb+b2phiUnOxbH+YqorKxdIQn47zQoIsD0v9wlAiiuWmQcrlIu6mZGP75VisORtVbsy/ZvdGnzaW9XilRERERESkxPehzR8TWFTn+IOj5Xpj21XsC47H6E72+HlyV43GUlgsx8GQeKw5G4UbCZkAFBVUwzva4eUBreFgpo+IpGycj0zB+nPRKCiWQ1eihTmD22De4DbQ11FMPUyQ5SE6JbfCaZAJsjz0W3FCNX0RYAUWEREREVFD4/vQ5o8JLKpz/MHRMsWk5mLwNychF4GD8/vDq5VU0yEBUEwNPB+ZijVn75bpU/Wovm0s8el4L7SxNq7xfWy/HFNhpRYREREREdU/vg9t/rQ1HQARNQ9rzt6FXAQGelg3muQVAAiCgH5trdCvrRVuJ2Xhp39v42BI4iPHAN883wkOZoaPdR8TezhjoId1pZVaRERERERE9Pi0NB0AETV9D7IKsONKLABg7qA2Go6mYh62Jpjcy6XcdlEE7qXm1Wpse6kB+rSxZPKKiIiIiIioHjCBRUS1tuF8FAqK5ejiZIberS00HU6l3KyMoCWU3SYRBLhaPV71FREREREREdU/JrCIqFay8ouw6cI9AMDcwW0gCEIVZ2iWvdQAy329IXkYp7JnFSuniIiIiIiIGi/2wCKiWtkaEIOs/GK0sTbCUx1sNR1OtbBnFRERERERUdPCBBYRPbaC4hKs9Y8CAMwZ1AZaj87Na8TspQZMXBERERERETURnEJIRI9tT9B9JGcVwF6qj3FdWmk6HCIiIiIiImqmmMAiosdSIhfx25m7AICXB7SGrjZ/nBAREREREVH94DtOInosR68nIiolB1IDHbzQw0nT4RAREREREVEzxgQWEdWYKIpYdSoSADC9ryuM9NhOj4iIiIiIiOoPE1hEVGP7r8Uj9L4MetoCZvR11XQ4RERERERE1MwxgUVENbLtUgze2BYMACgsFvFPeKJmAyIiIiIiIqJmjwksIqq2mLQcLPYLVd0WASz1C0OCLE9zQREREREREVGzxwQWEVWLLLcIc7cEldteIoqITsnVQERERERERETUUrDzMhFVKTolBy9tuIy7KTnl9kkEAa5WhhqIioiIiIiIiFoKVmARUaUC7qZi/K/ncDclBw5Sfbz5hDskggBAkbz6wtcL9lIDDUdJREREREREzRkrsIioQrsC47DELwRFJSI6O5lhzbRusDHRx8SeToh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      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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width=1200.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close('all')\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.plot(data_close.index[-past_history:], ultima_ventana_or, marker='.', label=\"Ventana de Tiempo\")\n",
    "plt.plot(mañana, prediction_or, marker='*', label=\"Predicción de Mañana\", color='r')\n",
    "plt.xlabel('Tiempo')\n",
    "\n",
    "plt.title(f\"{stock}: Predicción de Mañana\", size = 20)\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([128.79514], dtype=float32)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_or"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Crea el modelo LSTM bidirecional</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"series_LSTM_model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_7 (InputLayer)        [(None, 60, 1)]           0         \n",
      "                                                                 \n",
      " Dropout_01 (Dropout)        (None, 60, 1)             0         \n",
      "                                                                 \n",
      " bidirectional (Bidirectiona  (None, 60, 100)          20800     \n",
      " l)                                                              \n",
      "                                                                 \n",
      " bidirectional_1 (Bidirectio  (None, 24)               10848     \n",
      " nal)                                                            \n",
      "                                                                 \n",
      " Dropout_02 (Dropout)        (None, 24)                0         \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 1)                 25        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 31,673\n",
      "Trainable params: 31,673\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# shapes\n",
    "input_shape = (X_train.shape[1], X_train.shape[2])\n",
    "units_2 = 64\n",
    "dropout = 0.2\n",
    "\n",
    "# layers\n",
    "inputs = Input(input_shape)\n",
    "x = Dropout(dropout, name= 'Dropout_01')(inputs)\n",
    "x = Bidirectional(LSTM(units, return_sequences=True,dropout=dropout,\n",
    "    recurrent_dropout=dropout,))(x)\n",
    "x = Bidirectional(LSTM(units//4, dropout=dropout,\n",
    "    recurrent_dropout=dropout,))(x)\n",
    "x = Dropout(dropout, name= 'Dropout_02')(x)\n",
    "outputs = Dense(1)(x)\n",
    "\n",
    "# model\n",
    "model_02 = Model(inputs=inputs, outputs=outputs, name='series_LSTM_model')\n",
    "model_02.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Compila"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se usará el optimizador Adam y la función de pérdida MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_02.compile(loss='mean_squared_error',\n",
    "  optimizer=Adam(0.001),metrics=[\"mae\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-21 21:23:25.162948: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [233], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m tf\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mset_seed(\u001b[38;5;241m500\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_02\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m64\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_split\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[1;32m     10\u001b[0m \u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniforge3/envs/ocupaciones/lib/python3.10/site-packages/keras/utils/traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 64\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m     66\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/miniforge3/envs/ocupaciones/lib/python3.10/site-packages/keras/engine/training.py:1409\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1402\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m   1403\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m   1404\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[1;32m   1405\u001b[0m     step_num\u001b[38;5;241m=\u001b[39mstep,\n\u001b[1;32m   1406\u001b[0m     batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m   1407\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m   1408\u001b[0m   callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1409\u001b[0m   tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1410\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m   1411\u001b[0m     context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m~/miniforge3/envs/ocupaciones/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/miniforge3/envs/ocupaciones/lib/python3.10/site-packages/tensorflow/python/eager/def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    912\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    914\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 915\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    917\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    918\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m~/miniforge3/envs/ocupaciones/lib/python3.10/site-packages/tensorflow/python/eager/def_function.py:980\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    976\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m  \u001b[38;5;66;03m# Fall through to cond-based initialization.\u001b[39;00m\n\u001b[1;32m    977\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    978\u001b[0m     \u001b[38;5;66;03m# Lifting succeeded, so variables are initialized and we can run the\u001b[39;00m\n\u001b[1;32m    979\u001b[0m     \u001b[38;5;66;03m# stateless function.\u001b[39;00m\n\u001b[0;32m--> 980\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stateless_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    981\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    982\u001b[0m   _, _, filtered_flat_args \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    983\u001b[0m       \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn\u001b[38;5;241m.\u001b[39m_function_spec\u001b[38;5;241m.\u001b[39mcanonicalize_function_inputs(  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m    984\u001b[0m           \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds))\n",
      "File \u001b[0;32m~/miniforge3/envs/ocupaciones/lib/python3.10/site-packages/tensorflow/python/eager/function.py:2453\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2450\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m   2451\u001b[0m   (graph_function,\n\u001b[1;32m   2452\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2454\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniforge3/envs/ocupaciones/lib/python3.10/site-packages/tensorflow/python/eager/function.py:1860\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1856\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1857\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1858\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1859\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1860\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1861\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   1862\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1863\u001b[0m     args,\n\u001b[1;32m   1864\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1865\u001b[0m     executing_eagerly)\n\u001b[1;32m   1866\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m~/miniforge3/envs/ocupaciones/lib/python3.10/site-packages/tensorflow/python/eager/function.py:497\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    495\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    496\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 497\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    499\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    503\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    504\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m    505\u001b[0m         \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m    506\u001b[0m         num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    509\u001b[0m         ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[1;32m    510\u001b[0m         cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n",
      "File \u001b[0;32m~/miniforge3/envs/ocupaciones/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 54\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     57\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "tf.random.set_seed(500)\n",
    "\n",
    "history = model_02.fit(\n",
    "    X_train, y_train,\n",
    "    epochs=10,\n",
    "    batch_size=64,\n",
    "    validation_split=0.1,\n",
    "    verbose=1,\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['loss'][:], label='train')\n",
    "plt.plot(history.history['val_loss'][:], label='test')\n",
    "plt.plot(history.history['mae'][:], label='mae_train')\n",
    "plt.plot(history.history['val_mae'][:], label='mae_loss')\n",
    "plt.legend();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Predicciones</span>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepara los datos de validación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcula predicciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predictions\n",
    "prediction2 = model_02.predict(X_test)\n",
    "#prediction = scaler.inverse_transform(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(prediction2.shape)\n",
    "print(y_test.shape)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elimina dimensiones sobrante para los gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_p2 = y_train #np.squeeze(y_train, axis=-1)\n",
    "y_test_p2 = y_test#np.squeeze(y_test, axis=-1)\n",
    "y_pred_p2 = np.squeeze(prediction2, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(y_train_p2.shape)\n",
    "print(y_test_p2.shape)\n",
    "print(y_pred_p2.shape)\n",
    "\n",
    "k=0\n",
    "for i,j in zip(y_test_p2, y_pred_p2):\n",
    "    print (i,j, i-j)\n",
    "    k+=1\n",
    "    if k==10:\n",
    "        break"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica de las predicciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(np.arange(0, len(y_train_p2)), y_train_p2, 'g', label=\"historia\")\n",
    "plt.plot(np.arange(len(y_train_p2), len(y_train_p2) + len(y_test_p2)), y_test_p2, marker='.', label=\"verdadero\")\n",
    "plt.plot(np.arange(len(y_train_p2), len(y_train_p2) + len(y_test_p2)), y_pred_p2, 'r', label=\"predicción\")\n",
    "#plt.ylabel('Valor')\n",
    "plt.xlabel('Time Step')\n",
    "plt.title(\"Apple: Historia del precio la acción al cierre. Escala (0,1)\", size = 20)\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regreso a la escala original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_or2 = scaler.inverse_transform(y_pred_p2.reshape(-1,1))\n",
    "y_test_or2 = scaler.inverse_transform(y_test_p2.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k=0\n",
    "for i,j in zip(y_test_or2, y_pred_or2):\n",
    "    print (i,j, i-j)\n",
    "    k+=1\n",
    "    if k==10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmsLSTM = np.sqrt(np.mean(np.power(y_pred_or2-y_test_or2,2)))\n",
    "print(rmsLSTM )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(np.arange(0, len(y_test_or)), y_test_or, marker='.', label=\"verdadero\")\n",
    "plt.plot(np.arange(0, len(y_test_or)), y_pred_or, marker='+', label=\"predicho\")\n",
    "plt.xlabel('Time Step')\n",
    "plt.annotate(\"rms = \"+str(round(rmsLSTM,2)) , xy=(100, 140), size = 15)\n",
    "plt.annotate(\"modelo = LSTM(50), timestep=60\" , xy=(100, 146), size = 15)\n",
    "plt.annotate(\"epochs=40\" , xy=(100, 143), size = 15)\n",
    "plt.title(\"Apple: Intervalo de predicción a un día. Escala original\", size = 20)\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  }
 ],
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