{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting Stocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{image} img/portada.png\n",
    ":alt: portada\n",
    ":class: bg-primary mb-1\n",
    ":width: 425px\n",
    ":align: center\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Te has planteado alguna vez invertir en el mercado de valores? Este juego te va a ayudar a tomar la decisión porque se trata, precisamente, de predecir la evolución del precio de la acción de empresas reales. El juego consiste en los siguiente:\n",
    "\n",
    "1. El jugador empieza seleccionando la empresa con la que quiere jugar\n",
    "2. A continuanción elige un nivel de dificultad que se va a traducir en la cantidad de días que tendrá que predecir. Cuanto más largo sea el plazo de predicción, más complicado.\n",
    "3. Por último, el juego le muestra la evolución del percio de la acción seleccionada y el jugador tiene que dibujar sobre la gráfica la tendencia que ha previsto\n",
    "\n",
    "La puntuación se calcula en función de cómo de acertado ha estado el jugador marcando los valores predichos respecto a los valores reales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descarga de datos bursátiles\n",
    "\n",
    "El primer paso es indicar el nombre de la empresa con la que vamos a jugar. Para acceder a su información bursatil, vamos a utilizar el paquete de Python **pandas_datareader**.\n",
    "\n",
    "Por el momento jugaremos con el precio de la acción de **AMAZON** cuyo código es **AMXN**, pero el juego puede hacerse mucho más entretenido si contamos con una lista de empresas y el juego elige aleatoriamente una de ellas para cada jugador."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{seealso}\n",
    "[Aquí](https://pandas-datareader.readthedocs.io/en/latest/) puedes encontrar más información sobre la instalación y los métodos que importa la librería **pandas_datareader**.\n",
    "\n",
    "Los códigos de las empresas los puedes obtener de la web de [Yahoo Finance](https://finance.yahoo.com/)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{warning}\n",
    "En el momento de escribir este documento Yahoo ha cambiado las condiciones de su servicio por lo que el acceso a su API a través de la librería **pandas_datareader** no coincide exactamente con lo que aparece en su documentación. Por el momento te vamos a proporcionar un workaround funcional. Pero seguramente, cuando este libro llegue a tí, los desarrolladores de **pandas_datareader** ya hayan actualizado su documentación.\n",
    "\n",
    "Este tipo de problemas son muy habituales en el mundo de la programación. Y esta no será la última vez que tendrás que pegarte con las librerías, sus versiones y los cambios en las condiciones del servicio.\n",
    "::::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solución:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\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>2018-01-02</th>\n",
       "      <td>58.599998</td>\n",
       "      <td>59.500000</td>\n",
       "      <td>58.525501</td>\n",
       "      <td>59.450500</td>\n",
       "      <td>59.450500</td>\n",
       "      <td>53890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>59.415001</td>\n",
       "      <td>60.274502</td>\n",
       "      <td>59.415001</td>\n",
       "      <td>60.209999</td>\n",
       "      <td>60.209999</td>\n",
       "      <td>62176000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>60.250000</td>\n",
       "      <td>60.793499</td>\n",
       "      <td>60.233002</td>\n",
       "      <td>60.479500</td>\n",
       "      <td>60.479500</td>\n",
       "      <td>60442000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>60.875500</td>\n",
       "      <td>61.457001</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>61.457001</td>\n",
       "      <td>61.457001</td>\n",
       "      <td>70894000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>61.799999</td>\n",
       "      <td>62.653999</td>\n",
       "      <td>61.601501</td>\n",
       "      <td>62.343498</td>\n",
       "      <td>62.343498</td>\n",
       "      <td>85590000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                       \n",
       "2018-01-02  58.599998  59.500000  58.525501  59.450500  59.450500  53890000\n",
       "2018-01-03  59.415001  60.274502  59.415001  60.209999  60.209999  62176000\n",
       "2018-01-04  60.250000  60.793499  60.233002  60.479500  60.479500  60442000\n",
       "2018-01-05  60.875500  61.457001  60.500000  61.457001  61.457001  70894000\n",
       "2018-01-08  61.799999  62.653999  61.601501  62.343498  62.343498  85590000"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas_datareader import data\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "yf.pdr_override()\n",
    "\n",
    "# Define the instruments to download. We would like to see Apple and Microsoft\n",
    "tickers = ['AMZN']\n",
    "\n",
    "# We would like all available data from 01/01/2000 until 12/31/2016.\n",
    "start_date = '2018-01-01'\n",
    "end_date =   '2022-01-01'\n",
    "\n",
    "# User pandas_reader.data.DataReader to load the desired data. As simple as that.\n",
    "df = data.get_data_yahoo(tickers, start_date, end_date)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{note}\n",
    "La librería **pandas_datareader** nos da acceso a múltiples APIs de datos. En este caso estamos leyendo la información de *Yahoo Finance*, por eso la sintaxis de la llamada responde a esta instrucción:\n",
    "\n",
    ":::{code}\n",
    "df = data.get_data_yahoo(tickers, start_date, end_date)\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a revisar sus columnas y los índices de las filas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05',\n",
       "               '2018-01-08', '2018-01-09', '2018-01-10', '2018-01-11',\n",
       "               '2018-01-12', '2018-01-16',\n",
       "               ...\n",
       "               '2021-12-17', '2021-12-20', '2021-12-21', '2021-12-22',\n",
       "               '2021-12-23', '2021-12-27', '2021-12-28', '2021-12-29',\n",
       "               '2021-12-30', '2021-12-31'],\n",
       "              dtype='datetime64[ns]', name='Date', length=1008, freq=None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limpieza de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para nuestro juego vamos a utilizar el precio de la acción al cierre del mercado. Para simplificar el *DataFrame*, tendrás que quedarte exclusivamente con el índice **Date** y la columna **Close**. Utiliza la sintaxis de **pandas** para generar una variable llamada **close** que sólo contenga el índice **Date** y la columna **Close**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solución:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2018-01-02    59.450500\n",
       "2018-01-03    60.209999\n",
       "2018-01-04    60.479500\n",
       "2018-01-05    61.457001\n",
       "2018-01-08    62.343498\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting just the adjusted closing prices.\n",
    "close = df['Close']\n",
    "close.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{note}\n",
    "Si notas que al invocar la función **head** el aspecto del dataframe ha cambiado, eso se debe a que nuestra variable **close** ya no es un *DataFrame* de Pandas, sino un objeto de tipo *Series*. Esto ocurre siempre que trabajamos con una sóla columna.\n",
    "::::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(close)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos convertir la variable **Close** en un *DataFrame*, pero no es necesario:\n",
    "\n",
    ":::{code}\n",
    "pd.DataFrame(close)\n",
    "::::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualización del precio de la acción\n",
    "\n",
    "Antes de seguir programando el juego, deberíamos asegurarnos que los datos están bien descargados. Para ello, lo más sencillo, es hacer una visualización rápida.\n",
    "\n",
    "Utiliza el paquete **pyplot** de la librería **matplotlib** para visualizar el precio de la acción a cierre del mercado, frente a la fecha. Coloca un título representativo a la figura y a sus ejes. Y asegúrate que las fechas se representan de manera correcta."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solución:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(close)\n",
    "plt.xlabel(\"Dates\")\n",
    "plt.ylabel(\"Dollars\")\n",
    "plt.title(\"Evolution AMAZON stock values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trabajar con **pandas** no sólo es cómodo desde el punto de vista del procesado de los datos, sino que también posée funciones que simplifican otras tareas, como por ejemplo, la visualización.\n",
    "\n",
    "Puedes invocar directamente al comando **plot** desde una variable tipo DATAFRAME o SERIES de **pandas**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "close.plot(kind='line', title=\"Evolution AMAZON stock values\", \n",
    "           xlabel = \"Dates\",\n",
    "           ylabel = \"Dollars\",\n",
    "           figsize=(10,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otra de la ventaja de trabajar con objetos de **pandas** es que dispone de funcionalidades específicas para series temporales. Como por ejemplo calcular medias móviles mediante la función **rolling**, medias de ventana expandida a través de **expanding** o medias de ventana ponderada con **ewm**.\n",
    "\n",
    "\n",
    "```{seealso}\n",
    "Puedes investigar sobre la sintaxis de estas funciones en estos enlaces: [rolling](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html), [expanding](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.expanding.html) y (ewm)[https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ewm.html]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "close.plot(kind='line', title=\"Evolution AMAZON stock values\", figsize=(10,5),legend=True)\n",
    "close.rolling(30).mean().plot(label= 'Rolling 30 days',legend=True)\n",
    "close.expanding().mean().plot(label= 'Expanding',legend=True)\n",
    "close.ewm(span=60).mean().plot(xlabel = \"Dates\",ylabel = \"Dollars\", label= 'Weighted 60 days',legend=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selección del nivel de dificultad\n",
    "\n",
    "Predecir el precio de la acción de un día para otro puede ser relativamente facil si el sector de la empresa es estable. Pero predecir el precio de la acción a un mes vista es otra historia. Vamos a definir el nivel de dificultad de la siguiente manera:\n",
    "\n",
    "* **Easy**. Predecir el precio de la acción al día siguiente. Para jugar en este nivel necesitaremos al menos un mes de información\n",
    "* **Medium**. Predecir el precio de la acción durante el mes siguiente. Para jugar en este nivel necesitaremos varios meses de información\n",
    "* **Hard**. Predecir el precio de la acción durante el año siguiente. Para jugar en este nivel necesitaremos varios años de información\n",
    "\n",
    "El juego consiste en mostrar una gráfica con la evolución del precio de la acción permitiendo al usuario \"pintar\" sobre la gráfica la posible tendencia futura de ese valor.\n",
    "\n",
    "Para ello necesitamos un código que realice la siguientes tareas:\n",
    "\n",
    "1. En función del nivel de dificultad, mostrar una gráfica con 1 mes, 12 meses o varios años de la evolución del precio de la acción\n",
    "2. La gráfica debe \"esconder\" la información del último día, el último mes o el último año que posteriormente se utilizará para validar la predicción del jugador"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solución:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El primer paso es preguntar el nivel de difictultad. Para simplificar la respuesta hemos asignado un número a cada nivel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chose difficulty: \n",
      "   - [1] Easy (1 day forecast)\n",
      "   - [2] Medium (1 month forecast)\n",
      "   - [3] Hard (1 year forecast)\n",
      "Enter difficulty: 2\n"
     ]
    }
   ],
   "source": [
    "print(\"Chose difficulty: \")\n",
    "print(\"   - [1] Easy (1 day forecast)\")\n",
    "print(\"   - [2] Medium (1 month forecast)\")\n",
    "print(\"   - [3] Hard (1 year forecast)\")\n",
    "difficulty = int(input(\"Enter difficulty: \"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez seleccionada la dificultad, tienes que filtrar la cantidad de fechas. Para el nivel fácil nos vamos a quedar con 30 días, para el nivel intermedio 365 y para el nivel más dificil preservaremos todo el histórico. Estos valores los almacenaremos en la variable **dates**. \n",
    "\n",
    "También en función del nivel de dificultad tendrás que esconder 1 día, 30 días o 365 días. Estos valores los utilizaremos para validar la predicción del jugador. Empieza construyendo un condicional para crear una variable **hide** de tipo INT que tomará diferentes valores dependiendo del nivel seleccionado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available days: 1008\n",
      "Playable days: 365\n"
     ]
    }
   ],
   "source": [
    "if difficulty == 1:\n",
    "    dates = close.index[-30:]\n",
    "    hide = 1\n",
    "elif difficulty ==2:\n",
    "    dates=close.index[-365:]\n",
    "    hide = 30\n",
    "elif difficulty ==3:\n",
    "    dates==close.index\n",
    "    hide = 365\n",
    "else:\n",
    "    print(\"Unknown value\")\n",
    "    \n",
    "print(\"Available days: {}\".format(len(close.index)))\n",
    "print(\"Playable days: {}\".format(len(dates)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{note}\n",
    "Nota que en el código anterior jugaríamos con 365 días porque con anterioridad habíamos seleccionado un nivel de dificultad intermedio.            \n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación, tienes que filtrar los datos para que sólo contengan las fechas que hemos seleccionado. Esto puedes hacerlo con la función **loc** de **pandas**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Historical data: 365 days\n"
     ]
    }
   ],
   "source": [
    "historical=close.loc[dates]\n",
    "print(\"Historical data: {} days\".format(len(historical)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No olvides ocultar el rango de fechas que vamos a predecir. Para ello tienes que utilizar la variable **hide**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2021-12-27    169.669495\n",
       "2021-12-28    170.660995\n",
       "2021-12-29    169.201004\n",
       "2021-12-30    168.644501\n",
       "2021-12-31    166.716995\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2021-11-11    173.625000\n",
       "2021-11-12    176.257507\n",
       "2021-11-15    177.283997\n",
       "2021-11-16    177.035004\n",
       "2021-11-17    177.449997\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hidden_data=historical[:-hide]\n",
    "hidden_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Realización de la predicción"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La predicción queremos hacerla dibujando sobre la gráfica. Esto es posible en Python y en Jupyter Notebook con un poco de ayuda. En primer lugar tenemos que utilizar lo que Jupyter llama *magic commands* que dan funcionalidades extra a los notebooks. En este caso, vamos a invocar el siguiente comando:\n",
    "\n",
    ":::{code}\n",
    "%matplotlib notebook\n",
    ":::\n",
    "\n",
    "Este comando permite representar las mismas visualaciones pero en una ventana mucho más potente con algunas acciones adicionales como hacer scroll, zoom o clickar en la propia imagen.\n",
    "\n",
    "También necesitamos almacenar las posiciones de aquellos puntos en los que vayamos a clickar. Para esto necesitamos la librería **mpl_point_clicker**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{seealso}\n",
    "Puedes encontrar más información sobre la instalación de la librería **mpl_point_clicker** y ejemplos de utilización [aquí](https://mpl-point-clicker.readthedocs.io/en/latest/)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{tip}\n",
    "La función **clicker** importada desde la librería **mpl_point_clicker** te va a permitir capturar los clicks del jugador al mismo tiempo que va dibujas una linea para facitar su seguimiento.\n",
    "\n",
    ":::{code}\n",
    "klicker = clicker(ax, [\"Predicción\"], markers=[\"x\"], **{\"linestyle\": \":\"})\n",
    ":::\n",
    "::::         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solución:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute('tabindex', '0');\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;' +\n",
       "            'z-index: 2;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: relative;' +\n",
       "            'z-index: 0;'\n",
       "    );\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'left: 0;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: absolute;' +\n",
       "            'top: 0;' +\n",
       "            'z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            /* This rescales the canvas back to display pixels, so that it\n",
       "             * appears correct on HiDPI screens. */\n",
       "            canvas.style.width = width + 'px';\n",
       "            canvas.style.height = height + 'px';\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        /* User Agent sniffing is bad, but WebKit is busted:\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=144526\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=181818\n",
       "         * The worst that happens here is that they get an extra browser\n",
       "         * selection when dragging, if this check fails to catch them.\n",
       "         */\n",
       "        var UA = navigator.userAgent;\n",
       "        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n",
       "        if(isWebKit) {\n",
       "            return function (event) {\n",
       "                /* This prevents the web browser from automatically changing to\n",
       "                 * the text insertion cursor when the button is pressed. We\n",
       "                 * want to control all of the cursor setting manually through\n",
       "                 * the 'cursor' event from matplotlib */\n",
       "                event.preventDefault()\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        } else {\n",
       "            return function (event) {\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        }\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    canvas_div.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    canvas_div.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.canvas_div.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "function getModifiers(event) {\n",
       "    var mods = [];\n",
       "    if (event.ctrlKey) {\n",
       "        mods.push('ctrl');\n",
       "    }\n",
       "    if (event.altKey) {\n",
       "        mods.push('alt');\n",
       "    }\n",
       "    if (event.shiftKey) {\n",
       "        mods.push('shift');\n",
       "    }\n",
       "    if (event.metaKey) {\n",
       "        mods.push('meta');\n",
       "    }\n",
       "    return mods;\n",
       "}\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        modifiers: getModifiers(event),\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"935.4545251791146\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_point_clicker import clicker\n",
    "\n",
    "fig, ax = plt.subplots(constrained_layout=True)\n",
    "hidden_data.plot(kind='line', title=\"Evolution AMAZON stock values\", \n",
    "           xlabel = \"Dates\",\n",
    "           ylabel = \"Dollars\",\n",
    "           figsize=(13,4),\n",
    "           ax=ax)\n",
    "plt.axvspan(dates[-hide], dates[len(dates)-1], facecolor='0.2', alpha=0.1)\n",
    "klicker = clicker(ax, [\"Prediction\"], markers=[\"x\"], **{\"linestyle\": \":\"})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{note}\n",
    "Seguimos haciendo uso de la función plot de **pandas**, pero hemos introducido el parámetro **ax** para poder disponer de una mayor capacidad de personalización de las propiedades gráficas.\n",
    "\n",
    "Tambien hemos utilizado la función **plt.axvspan** que nos permite colorear el fondo de la figura y así poder resaltar mejor la zona en la que realizar la predicción.\n",
    ":::::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez capturados los puntos, podemos deshabilitar el efecto del *magic command* con la siguiente instrucción que devuelve a las visualizaciones realizadas en jupyter el aspecto original."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el siguiente GIF podemos apreciar el efecto de hacer una predicción sobre los datos:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![prediccion.gif](img/prediccion.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez realizada la predicción podemos acceder a las coordenadas de la curva naraja."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Prediction': array([[18949.34740493,   178.05912833],\n",
      "       [18953.8678076 ,   179.83593644],\n",
      "       [18956.88140938,   181.02047518],\n",
      "       [18961.40181205,   181.61274455],\n",
      "       [18964.41541383,   181.02047518],\n",
      "       [18966.67561517,   179.63851332],\n",
      "       [18968.93581651,   178.25655145],\n",
      "       [18972.70281873,   177.07201271],\n",
      "       [18976.46982096,   177.26943583],\n",
      "       [18980.23682319,   178.84882082],\n",
      "       [18983.25042497,   180.82305206],\n",
      "       [18988.52422809,   182.0075908 ]])}\n"
     ]
    }
   ],
   "source": [
    "print(klicker.get_positions())\n",
    "x = klicker.get_positions()[\"Prediction\"][:,0]\n",
    "y = klicker.get_positions()[\"Prediction\"][:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{note}\n",
    "Fíjate que a pesar de haber haber realizado la predicción en una gráfico cuyo eje X son fechas, la función **get_positions()** nos devuelve unos valores numéricos. Esto tiene que ver con la correspondencia del tipo de datos que usa **pandas** para las fechas y su conversión a segundos.\n",
    "\n",
    "Si queremos ver la fecha con la que se corresponde uno de esos valores tenemos que utilizar el siguiente código:\n",
    "\n",
    ":::{code}\n",
    "import matplotlib\n",
    "\n",
    "matplotlib.dates.num2date(x[0]).strftime('%Y-%m-%d %H:%M:%S')\n",
    ":::\n",
    "\n",
    "Si por el contrario queremos ver el valor numérico con el que se corresponde una determinada fecha, podemos utilizar la conversión dual:\n",
    "\n",
    ":::{code}\n",
    "import matplotlib\n",
    "\n",
    "matplotlib.dates.date2num(fechas[-1])\n",
    ":::\n",
    "::::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez que tenemos las coordenadas de la predicción del jugador, podemos representar los valores reales del intervalo oculto y los valores predichos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(constrained_layout=True)\n",
    "hidden_data.plot(kind='line', title=\"Evolution AMAZON stock values\", \n",
    "           xlabel = \"Dates\",\n",
    "           ylabel = \"Dollars\",\n",
    "           figsize=(13,4),\n",
    "           ax=ax)\n",
    "plt.axvspan(dates[-hide], dates[len(dates)-1], facecolor='0.2', alpha=0.1)\n",
    "plt.plot(historical[-hide:], label=\"Actual\", color=\"g\")\n",
    "plt.plot(x,y, label=\"Prediction\", color=\"orange\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como ves, la predicción de valores vursátiles no es lo nuestro. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cálcular la puntuación"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este apartado es, posiblemente, el más complicado. Tenemos que idear una manera de medir como de buena ha sido la predicción del jugador en relación a los valores reales.\n",
    "\n",
    "Conceptualmente es sencillo, podríamos ir cogiendo cada una de las predicciones del jugador y calcular la diferencia respecto de los valores reales de ese día. Sin embargo, el jugador puede haber hecho click sólo 5 veces en un intervalo de 30 días. Además, puede haber hecho click en cualquier intanste entre dos días consecutivos para los que no tenemos lectura. \n",
    "\n",
    "La solución pasa por interpolar. Interpolar es estimar el valor intermedio entre dos valores conocidos. Existen muchas técnicas de interpolación. En este caso, vamos a utilizar una interpolación basada en [B-spline](https://en.wikipedia.org/wiki/B-spline).\n",
    "\n",
    "Pero no sólo tendremos que interpolar los clicks del jugador, sino también los valores bursátiles reales para poder calcular la diferencia de predicción en muchos puntos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{seealso}\n",
    "Puedes encontrar más información sobre las función de interpolación **splrep** y **splev** de la librería **scipy** [aquí](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.splrep.html) y [aquí](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.splev.html).\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solución:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a empezar interpolando los clicks del jugador. Para ello invocaremos a la funcion **splrep** para calcular el modelo y a la función **splev** para evaluarlo. La evaluación la vamos a hacer diariamente en todo el intervalo que habíamos ocultado. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import interpolate\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "\n",
    "tck = interpolate.splrep(x, y, s=0.25)\n",
    "dates_start = dates[-hide]\n",
    "dates_end = dates[-1]\n",
    "                                        \n",
    "x_new = np.arange(matplotlib.dates.date2num(dates_start), matplotlib.dates.date2num(dates_end)+1, 1)\n",
    "y_new = interpolate.splev(x_new, tck, der=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,\"x\", color=\"orange\", label=\"Clicks\")\n",
    "plt.plot(x_new,y_new, \":\", color=\"blue\", alpha=0.5, label=\"Clicks Interpolated\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si contamos el número de puntos que tiene la predicción interpolada obtenemos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Que no coincide con el número de puntos que tienen los datos reales, debido precisamente, a que no hay valores los fines de semana y otros festivos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(historical[-hide:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para resolverlo vamos a amplicar otra interpolación a los datos reales. Para que la interpolación funcione, tenemos que convertir el eje con fechas en un eje numérico. Para aprovechar el espacio vamos a utilizar una *comprehension list*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import interpolate\n",
    "import matplotlib\n",
    "\n",
    "x=[matplotlib.dates.date2num(d) for d in list(historical[dates[-hide:]].index)]\n",
    "y=historical[dates[-hide:]].values\n",
    "tck = interpolate.splrep(x, y, s=0.25)\n",
    "\n",
    "x_real = np.arange(matplotlib.dates.date2num(dates_start), matplotlib.dates.date2num(dates_end)+1, 1)\n",
    "y_real = interpolate.splev(x_real, tck, der=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,\"x\", color=\"green\", label=\"Actual\")\n",
    "plt.plot(x_real,y_real, \":\", color=\"blue\", alpha=0.5, label=\"Actual Interpolated\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a representar las dos curvas interpoladas para comprobar que el resultado es prácticamente idéntico al original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(constrained_layout=True)\n",
    "hidden_data.plot(kind='line', title=\"Evolution AMAZON stock values\", \n",
    "           xlabel = \"Dates\",\n",
    "           ylabel = \"Dollars\",\n",
    "           figsize=(20,5),\n",
    "             ax=ax)\n",
    "plt.axvspan(dates[-hide], dates[len(dates)-1], facecolor='0.2', alpha=0.1)\n",
    "plt.plot(x_real,y_real, label=\"Actual Interpolated\", color=\"g\")\n",
    "plt.plot(x_new,y_new, label=\"Prediction Interpolated\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora sí podemos calcular la diferencia entre ambas curvas ya que ambas tienen el mismo número de puntos. Para visualizarlo mejor, vamos a utilizar la función **fill_between** de matplotlib que invocaremos desde el objeto **ax**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lfm4y91+6kO2VzfxtzV6sDjd2lwe7y+v/3UObw0Ndm4N/XbGYU2aOi/SyhRAiZGprayO9BNGHjIyMSC8hYpRS67XWPWZvSQe1EEIIIYQQQohRr77NQUVTO1ce47vTdFZWEvdfurDHfVvtLub8/C12VbdKgVoIIYSIMMmgFkIIIYQQQggx6m3x50/PzUnud9+EaBPZyTHsqmoN86qEEEII0Z+wFaiVUg8rpWqUUts6bZuvlPpUKbVJKbVOKbXUv/0EpVSzf/smpdRPw7UuIYQQQgghhBBjz+byJpSC2dlJQe0/fXyCFKiFEEKIESCcHdSPAKcftu33wJ1a6/nAT/1/D/hQaz3f/+sXYVyXEEIIIYQQQogxZkt5M5Mz4om3BJdkWTg+gb21bTjd3jCvTAghhBB9CVuBWmv9AdBw+GYg0f/nJKAyXOcXQgghhBBCCHFk0FqzpbyZuTnBdU+Dr0Dt9mr211nDuLK+tTs97Ktti9j5hRBCiJFguDOobwPuVkodAP4A/LDTY8uUUpuVUq8rpWb1dgCl1PX+eJB1MplUCCGEEEIIIcTBZjt1bQ7mBZE/HVA4PgGAoqqWMK2qfz98YQvn3v8RWuuIrUEIMbbU2GoivQQhBmy4C9Q3AbdrrScCtwMP+bdvAPK01vOA+4AXezuA1vpBrfVirfXijIyMcK930F588UWUUhQVFfW777333ovNZhv0uR555BG++c1vBr29s5KSEp588slBn3sgTjjhBNatW9fnPoP5XKxZs4azzjprKEsTQgghhBBCjGJflPhu3p03MTno50xKjyfKoCKWQ72npo2XNlfSYnfT0u6OyBqEEGPLT979Ccc8eQztrsHXmISIhOEuUF8JvOD/87PAUgCtdYvWus3/59cAk1IqfZjXFlJPPfUUy5cv56mnnup336EWqIdiMAVqtzt8L54i+bkQQgghhBBCjE7/23KQcYkW5gQ5IBHAHGVgUkYcxdWRKVDf9+5uAo3TNa32iKxBCDG2HJ9/PC3OFt7a+0L/Owsxggx3gboSON7/55OA3QBKqfFKKeX/81L/uuqHeW0h09bWxtq1a3nooYd4+umnO7Z7PB6+853vMHv2bObOnct9993HX/7yFyorKznxxBM58cQTAYiPj+94znPPPcdVV10FwCuvvMJRRx3FggULOOWUU6iurg56TVdddRW33norxxxzDJMmTeK5554D4Ac/+AEffvgh8+fP55577sHj8fDd736XJUuWMHfuXP7xj38Avi7lFStWcM455zBz5kxKSkqYPn06X/va15gxYwYXXHBBR2F59erVLFiwgDlz5nD11VfjcDi6reemm25i8eLFzJo1i5/97GcAPX4u3nrrLZYtW8bChQu58MILaWvz5bO98cYbTJ8+nYULF/LCC/KNVwghhBBCiCNVc7uLNbtq+dKcLIwGNaDnFo5PpCgCHdR7atp4eXMlC3OTAahp7f6eSQghBurE/BPJis3gmaKn+99ZiBEkuPHGg6CUego4AUhXSpUDPwOuA/6slIoC7MD1/t0vAG5SSrmBduBiHYIQrtveuI1NVZuGepgu5o+fz72n39vnPi+99BKnn34606ZNIy0tjfXr17No0SIefPBBSkpK2LRpE1FRUTQ0NJCamsqf/vQn3nvvPdLT+24aX758OZ9++ilKKf71r3/x+9//nj/+8Y9Br/3gwYOsXbuWoqIizjnnHC644AJ++9vf8oc//IFXX30VgAcffJCkpCS++OILHA4Hxx57LKeddhoAGzZsYNu2bRQUFFBSUsKuXbt46KGHOPbYY7n66qv529/+xje/+U2uuuoqVq9ezbRp07jiiiv4+9//zm233dZlLb/61a9ITU3F4/Fw8skns2XLFm699dYun4u6ujruuusu3nnnHeLi4vjd737Hn/70J773ve9x3XXX8e677zJlyhS++tWvBv05EEIIIYQQQowtb22vwunxcva8CQN+7vTxCbyyuZI2h5t4S9jeHndz37u7iTEZ+dGZM7jggU+kg1oIERJGg5GLCk7hvh2rqG47wLj4iZFekhBBCVsHtdb6Eq31BK21SWudo7V+SGu9Vmu9SGs9T2t9lNZ6vX/fv2qtZ/m3H621/jhc6xoOTz31FBdffDEAF198cUfMxzvvvMMNN9xAVJTvhU9qauqAjlteXs7KlSuZM2cOd999N9u3bx/Q888991wMBgMzZ87stfv6rbfe4rHHHmP+/PkcddRR1NfXs3v3bgCWLl1KQUFBx74TJ07k2GOPBeCyyy5j7dq17Nq1i4KCAqZNmwbAlVdeyQcffNDtPM888wwLFy5kwYIFbN++nR07dnTb59NPP2XHjh0ce+yxzJ8/n0cffZTS0lKKioooKChg6tSpKKW47LLLBvR5EEIIIYQQQowdr2w5yMTUGOYPIH86YNo436DEXVWtuDxe6trC38kc6J6+Ylk+0yckAlDTIh3UQojQ+OqkU/FoL/8tGp55Y0KEwvBdIo6A/jqdw6GhoYF3332XrVu3opTC4/GglOLuu+8O+hj+tBMA7PZDV9JvueUWvv3tb3POOeewZs0afv7znw9obRaLpePPvTWoa6257777WLlyZZfta9asIS4urtd19vT33uzfv58//OEPfPHFF6SkpHDVVVd1+Tg7r+XUU0/tluO9adOmoM4jhBBCCCGEGNvq2xx8tKeOG46bFPT7kc6mj/cVqJ/54gDff34LB5va+ez/TglrN3Wge/q6FQXEW6KINRsl4kMIETLTknJZlDqFVcXPc+Pi70d6OUIEZbgzqMe85557jssvv5zS0lJKSko4cOAABQUFfPjhh5x66qn84x//6Bgy2NDgmzSdkJBAa+uh3LNx48axc+dOvF4v//3vfzu2Nzc3k52dDcCjjz4akvUefu6VK1fy97//HZfLBUBxcTFWq7XH55aVlfHJJ58A8OSTT7J8+XIKCwspKSlhz549ADz++OMcf/zxXZ7X0tJCXFwcSUlJVFdX8/rrr/e4nqOPPpqPPvqo41hWq5Xi4mKmT59OSUkJe/fuBQhqEKUQQgghhBBi7HltWxUer+bseVmDen52cgxxZiOr1h2gsqkdq9PD/tqe3/+EQqB7+vJleaTF+xqIMhMsUqAWQoTUxZNOZntTCVurPo/0UoQIihSoQ+ypp57ivPPO67LtK1/5Ck899RTXXnstubm5zJ07l3nz5vHkk77bLa6//npOP/30jsGAv/3tbznrrLM45phjmDDhUI7az3/+cy688EIWLVrUb151sObOnYvRaGTevHncc889XHvttcycOZOFCxcye/Zsbrjhho6C+uEKCwu5//77mTFjBo2Njdx0001ER0fz73//mwsvvJA5c+ZgMBi48cYbuzxv3rx5LFiwgOnTp3PppZd2xIQc/rnIyMjgkUce4ZJLLmHu3LksW7aMoqIioqOjefDBB/nSl77EwoULyczMDMnnQgghhBBCCDG6vL2jmkkZcR2d0ANlMChuP3Ua3zltGk9edzQAJfXhK1D/9d3dREcZuX7FpI5tmQnR1LRIBrUQInTOzV0OwJqS1/vZU4iRQYVgFmHELF68WK9bt67Ltp07dzJjxowIrejIUVJSwllnncW2bdsivZRhIV9XQgghhBBCjDxLfvUOx03N4I8XzRvysWxONzN/+ibfOW0a3zxpaghW19WemjZOu+d9rjtuEj8849B7i288uYGdlS28+50TQn5OIcSRp2HvapTHypyXrmFZxhz+9qVVkV6S6CQjIyPSS4gYpdR6rfXinh6TDmohhBBCCCGEEKNOg9VJbatj0N3Th4s1RzEu0cL+OltIjne4v767G8th3dPgi/iolg5qIUSIzUguYGfTPhjFjaniyCEFajEo+fn5R0z3tBBCCCGEEGLk2VXlm10zLUQFaoC8tDhKwxDxEcievuKYQ9nTAZkJ0VidHqyOnqMVhRBiMGYmF1DcWo7L1RbppQjRLylQCyGEEEIIIYQYdXZVtQCErIMaoCAtLiwZ1L11T4OvgxqQQYlCiJCamVyAy+thX2NRpJciRL+kQC2EEEIIIYQQYtTZVd1Gcqypo8AbCnnpsdS1OWm1u0J2zI7u6WXdu6cBMhP9BWqJ+RBChNDMlAIAiurl7ncx8kmBWgghhBBCCCHEqLOrqoVp4xJQSoXsmAVpcQCU1ocuh/pv7+3BEmXkuuO6d0+DL+IDpINaCBFaUxMnYlQGdtZvj/RShOiXFKiFEEIIIYQQQowqWmuKq9tCGu8BvgxqIGQxH41WJ69uOchFi3NI76F7GiTiQwgRHhajmckJ2exs2BXppQjRLylQh4HRaGT+/PnMnj2bCy+8EJtt8Fffr7rqKp577jkArr32Wnbs2NHrvmvWrOHjjz/u+PsDDzzAY489NuhzB5SUlDB79ux+9/v1r3895HMF4+c//zl/+MMf+tznxRdf7PNz1Zv4+PjBLksIIYQQQggxTCqa2mlzuCkMcYE6Pz0WgJK60BSoX9pUgdPj5atLcnvdJznWhNlooKZVIj6EEKE1M3kSO5tKQHsjvRQh+iQF6jCIiYlh06ZNbNu2DbPZzAMPPNDlcbd7cNOZ//WvfzFz5sxeHz+8QH3jjTdyxRVXDOpcgzGYArXH4wnDSgZfoBZCCCGEEEKMfLuqWgEoHBfaAnWsOYrMBAslIYj40Fqzal05c7KTmJmV2Ot+SikyEizUtkgHtRAitGamFFBirabNUR/ppQjRJylQh9mKFSvYs2cPa9asYcWKFZxzzjnMnDkTj8fDd7/7XZYsWcLcuXP5xz/+AfhexHzzm9+ksLCQU045hZqamo5jnXDCCaxbtw6AN954g4ULFzJv3jxOPvlkSkpKeOCBB7jnnnuYP38+H374YZdO402bNnH00Uczd+5czjvvPBobGzuO+f3vf5+lS5cybdo0Pvzwwz4/nkceeYTzzz+f008/nalTp/K9730PgB/84Ae0t7czf/58vva1rwHwn//8h6VLlzJ//nxuuOGGjmJ0fHw8d9xxB/PmzeOTTz4hPz+f733ve8yZM4elS5eyZ88ewNe5fdJJJzF37lxOPvlkysrKuq3nn//8J0uWLGHevHl85StfwWaz8fHHH/Pyyy/z3e9+l/nz57N371727t3L6aefzqJFi1ixYgVFRb4ptvv372fZsmXMmTOHH//4x4P7RxZCCCGEEEIMqyJ/gXpaiDuoAfLT4ygNQcTHtooWdh5s4aLFOf3um5FgkYgPIUTIzUjOB6C4TgYlhpqxcR8Jb32bpFeuJuGtbxOz4UHwDq4hVUBUpBcQVq//AKq2hvaY4+fAGb8Nale3283rr7/O6aefDsCGDRvYtm0bBQUFPPjggyQlJfHFF1/gcDg49thjOe2009i4cSO7du1ix44dVFdXM3PmTK6++uoux62treW6667jgw8+oKCggIaGBlJTU7nxxhuJj4/nO9/5DgCrV6/ueM4VV1zBfffdx/HHH89Pf/pT7rzzTu69996OdX7++ee89tpr3Hnnnbzzzjt9flybNm1i48aNWCwWCgsLueWWW/jtb3/LX//6VzZt2gTAzp07WbVqFR999BEmk4mbb76ZJ554giuuuAKr1cpRRx3FH//4x45jJiUlsXXrVh577DFuu+02Xn31VW655RauvPJKrrzySh5++GFuvfVWXnzxxS5rOf/887nuuusA+PGPf8xDDz3ELbfcwjnnnMNZZ53FBRdcAMDJJ5/MAw88wNSpU/nss8+4+eabeffdd/nWt77FTTfdxBVXXMH9998f1L+rEEIIIYQQIrKKq1vJTo4hMdoU8mPnp8XyblHtkI+zal0ZligD58zP7ndfX9d2aGJFhBAiYGZyAQBF9dtZmHNihFczRrhsxH32J2K2/gdtNOONz8BYv4Po3S9jLl9Ly+n3o82hv3g61o3tAnWEBDqJwddBfc011/Dxxx+zdOlSCgp83xzeeusttmzZ0pEv3dzczO7du/nggw+45JJLMBqNZGVlcdJJJ3U7/qeffspxxx3XcazU1NQ+19Pc3ExTUxPHH388AFdeeSUXXnhhx+Pnn38+AIsWLaKkpKTfj+/kk08mKSkJgJkzZ1JaWsrEiRO77LN69WrWr1/PkiVLOj4nmZmZgC+j+ytf+UqX/S+55JKO32+//XYAPvnkE1544QUALr/88o5u7c62bdvGj3/8Y5qammhra2PlypXd9mlra+Pjjz/u8jE7HL7uhI8++ojnn3++4xzf//73+/34hRBCCCGEEJG1q6qVaePCMz8mPz2OurZy2hxu4i2De8tsd3l4aVMlZ8weT1JM/0X0cYnRfF7SMKhzCSFEb/LixxNrtLCzXjqoQ8LjIOm1mzCVf4xz0grsc87Bm+CbMWAufpPYL/5N8rPn03Tuk+i4jAgvdnQZ2wXqIDudQy2QQX24uLi4jj9rrbnvvvu6FVRfe+21cC+vG4vFNzXaaDQGlY8d2L+v52itufLKK/nNb37T7bHo6GiMRmOXbUqpHv/cn6uuuooXX3yRefPm8cgjj7BmzZpu+3i9XpKTk3v8Nxno+YQQQgghhBCR5fJ42VvbxgmFmWE5fn6a731bSZ2V2dlJgzrGtopmWu1uzpwzIaj9MxMsNNlcONweLFHG/p8ghBBBMCgD05Py2NmwO2THNDaVYC55F/OBteBxoS0JeJLzaZ99Gd6ErJCdZ8Txekh8+w7M5R9hXXoFzqlngMHc8bCz8Ey8CROIX/M7Ej74CS1nPNDHwcThJIM6QlauXMnf//53XC4XAMXFxVitVo477jhWrVqFx+Ph4MGDvPfee92ee/TRR/PBBx+wf/9+ABoafFfaExISaG1t7bZ/UlISKSkpHfnSjz/+eEc3dSiZTKaOj+fkk0/mueee68jQbmhooLS0tNfnrlq1quP3ZcuWAXDMMcfw9NNPA/DEE0+wYsWKbs9rbW1lwoQJuFwunnjiiY7tnT8XiYmJFBQU8OyzzwK+4vnmzZsBOPbYY7ucQwghhBBCCDGy7a+z4vJopochfxoOFahLhzAoMfDcyZnBdXlnJvqagGolh1oIEWIzUiaxs7lsyPnIhpYDJL52I6lPnEL8R7/G2LwPg6OGqPrtxGx8iNTHTyTh7W+jrEOPSBqJ4tfehWXvG7TPv7BbcTrAnbUA+4yzsex7h6jKzyKwytFrbHdQj2DXXnstJSUlLFy4EK01GRkZvPjii5x33nm8++67zJw5k9zc3I5ibWcZGRk8+OCDnH/++Xi9XjIzM3n77bc5++yzueCCC3jppZe47777ujzn0Ucf5cYbb8RmszFp0iT+/e9/h/xjuv7665k7dy4LFy7kiSee4K677uK0007D6/ViMpm4//77ycvL6/G5jY2NzJ07F4vFwlNPPQXAfffdx9e//nXuvvtuMjIyelzzL3/5S4466igyMjI46qijOorSF198Mddddx1/+ctfeO6553jiiSe46aabuOuuu3C5XFx88cXMmzePP//5z1x66aX87ne/48tf/nLIPydCCCGEEEKI0OoYkDguPAXqvLRYgCFlQpc12FAKclJigto/MyEagFc2H+TN7VVMzYzn7gvnDfr8QggRMCM5nyf2vkGttZyMhPyBH0BrYjb+k7jP/wxK0T77HFx5R+NJygVDNKBRbVVE73gRy97XMVV8SvOXHsKTMSPUH0rEmEveI2br49innYp95rk9FqcD7LPPx7JnNfEf/oKmi14FuWs/KEprHek1DNrixYv1unXrumzbuXMnM2aMnf8ER4L8/HzWrVtHenp6pJfSK/m6EkIIIYQQYmS4+80iHnh/Hzt+sTJscRgLf/k2K2eN5zfnzxnU829ftYnP9tXz8Q9PDmr/bRXNnHXfWgAMCsxRBrb+fCUmo9z0LIQYmIa9q1EeKxh9F9veO7ier6z+Af89898cm/+lgR3MbSfh3R8SvfsVnDmLaV9wAd6kSaB6/t5rrNtN/JrfoFwOWlb+BWd+97lqo41qbyD1qTPxWmJpO/X/0NH9167Me1YT98nfaDn51zimX9TlsYyMIzebWim1Xmu9uKfH5KedEEIIIYQQQohRY1dVG5PS48Ka1ZyTEkN541AiPqzk+juxgzElM55TZ47j/86cwR8vmofd5WV7Zcugzy+EEAEzkvIBKBrgoERlbyT5xcuI3v0K7XPPw7riVrzJU3stTgN40qfScsbdeOIzSHzjG0RVfD6UpUee1iS8/1OUownb0suDKk4DOCefiDs5l7h198MobgweTlKgFhFXUlIyorunhRBCCCGEECPHruoWpoUpfzogJyWGisb2QT+/rMFGXmpc0PtHm4z884rFXHfcJI6d7Htv9MX+hkGfXwghAsbFpJJiTqCovijo5yhHC0kvf52ouu1Yj7ke+5yLICq472k6Lo22U3+BNzaFpNeux9iwZ7BLjzjz/new7H0D+6yz8WTMDf6JyoCj8AyMzRVEVa3rf38hBWohhBBCCCGEEKNDm8PNgYZ2pocpfzogJyWWiqZ2BhOJ2eZwU9fmHFAHdWeZidHkpcXyRYkUqIUQQ6eUYmZyPjubgisUK2cbSa9cTVR9EdZl1+GcdEqfmcs90dGJtJ38M1CKpFeuQrWPwu9nbjvxa3+FO2kijplngmFgY/xcecvQBiPRu54P0wLHljFZoB7Nudpi5JGvJyGEEEIIIUaG3dW+AYmFw9BB7XB7qW1zDPi5Bxp80SB5gyxQAyzJT2VdaaO8FxFChMSM5AJ2NpWhPc6+d/S4SHz9ZqJqtmI7+hpcecf3GenRF2/CeNpO/CEGWx2Jb9826qIuYjc8iLG1HPuCr6DNqQN+vrYk4Mqaj2XfO+Bxh2GFY8uYK1BHR0dTX18vP8hFSGitqa+vJzo6OtJLEUIIIYQQ4oi3q2r4CtQA5YOI+Sit9xeoBxDxcbgl+Sk0WJ3srbUO+hhCCBEwIzmfVnc7B1v3976T1sS//1PM5R9jW/I1nAUnDbhr+HCejELaF16G+cDHxGz8+5CONZwMLeXEbvgHztyluLKXDvo4zkknYGhvwnzgvRCubmwa2ldaP5RSDwNnATVa69n+bfOBB4BowA3crLX+XCmlgD8DZwI24Cqt9YaBnjMnJ4fy8nJqa2tD9FGII110dDQ5OTmRXoYQQgghhBBHvKKqVmLNRiamDL47ORg5/uOXN7azMDdlQM8ta/AVlXNTh9ZBDbCupIEpmfGDPo4QQoCvgxpgZ902spILe9wnZuODxOx8FvvML+GcevqQi9MBjulnEVW1jbhP/4xrwlLcExaH5LjhFP/xbwFon3ceGCyDPo4rexE6KhrLrhdw5p8aquWNSWEtUAOPAH8FHuu07ffAnVrr15VSZ/r/fgJwBjDV/+so4O/+3wfEZDJRUFAwtFULIYQQQgghhBhxiqtbmTouAYNBhfU82cmBDmrbgJ9bWm8jKcZEUqxp0OcvSI8jLc7M5yUNXLw0d9DHEUIIgOlJeQAU1W/j5Clf6fa4ef9q4j+5G2fuUbTPvWhIRdlulMJ2zC0kvno7iW/fTsMlb4IpvBcZhyLq4AbfYMSZZ+FNmjzEg1lw5h6NpXQtrS4rmAZ/Z81YF9aID631B8DhSegaSPT/OQmo9P/5y8Bj2udTIFkpNSGc6xNCCCGEEEIIMXrsqmqlcFz4O4rjLFGkxpkHFfFR1mAbUv40+IaaLc5PYV1J45COI4QQAMmWBCbEpLGrYVe3x4yN+0h45w7cqZOwLf06mEL/PVZb4rEecyvG1oPEr/1FyI8fMloT/9Gv8cakYp955qDztztz5i9Hudoxl60Z+vrGsEhkUN8G3K2UOgD8Afihf3s2cKDTfuX+bUIIIYQQQgghjnC1rQ7qrU4Kxyf2v3MI5KTEDLpAPZR4j4Al+amUNdiobrEP+VhCCDEjOZ8djXu7bFPOVhJfvwkMRqzHXIuOyQjb+d0T5mAvPJ2YHc9hKl0TtvMMhWXPa5iqN2Gf9SV0dGZIjunOnIFWRkwVH4fkeGNVJArUNwG3a60nArcDDw3kyUqp65VS65RS6yRnWgghhBBCCCGODMXVvgGJ08M8IDHAV6AeWMSH2+OlorF9yB3UAPlpvlvBpUAthAiFGckF7Gopx+P2f1/TXhLe+R7GphJsR1+NN3lq2NfQvvAKPAnjSHj3ByhHS9jPNyBuB3Gf3o07OQ/n5JNAhShKyhSNJ20y5oPrQ3O8MSoSBeorgRf8f34WCIzDrAAmdtovx7+tC631g1rrxVrrxRkZ4buyI4QQQgghhBBi5Ciq8hWop40brgJ1LBWN7Witg35OZZMdt1eTlzr0nFFzlO/tusvjHfKxhBBiRnI+Dq+LkkZfzEfMhn9g2f829nnn48o+GtQwlAijLFiPvQ1Dez1xH94Z/vMNQMyWRzG2lGOfdy7aHNo7dVzjZ2Os3weOw1OQRUAkCtSVwPH+P58E7Pb/+WXgCuVzNNCstT4YgfUJIYQQQgghhBhhiqtaSYszk5EQwuFdfchJicHh9lLb5gj6OaUNVgAmhiDiI1CgdrilQC2EGLrF6TMAWHPgXUwH1hL32T04c4/CPuNsMEQN2zo8GdNwTD+LmF0vYSp5d9jO2xdlqyN23f04sxfgyl7a/xMGyD1uNkp7MVd8EvJjjxVhLVArpZ4CPgEKlVLlSqlrgOuAPyqlNgO/Bq737/4asA/YA/wTuDmcaxNCCCGEEEIIMXoUVbcOW/c0+ArUwIByqMsafLfOhyLiw2T0vV13SoFaCBEC05JymZGUx+fFL5L45m14knKwLb0SooZ+x8dAtS+4FE/iBBLe+9GIiPqI++xelNuOfe55YIwO+fHdGYW+HOpyyaHuTVgL1FrrS7TWE7TWJq11jtb6Ia31Wq31Iq31PK31UVrr9f59tdb6G1rryVrrOVrrdeFcmxBCCCGEEEKI0cHr1eyubqVwmPKnwRfxAQMrUO+ubsMcZWB84tALHJYoKVALIULrookr+Fl9OdrrxLbsWnTMuMgsxGjGesytGNrrif/gp5FZQ2ApdUVE73wG55QT8aQWhuckkkPdr0hEfAghhBBCCCGEEEErb2zH5vQM24BEgOzkQAd1cIMSyxttPP1FGafMyMRgGPpwrUMZ1MFnYAshRK+05pb6A8zBwHM5s/CkzcDucbLm4AY+q93O3pbyAWXuD5UnYxr2mecQXfwq5r2vD9t5u9BeEt7/Kdocj33WWWGNOunIoW6vD9s5RjMpUAshhBBCCCGEGNGKqny3gE8bxgJ1nCWK1Dhz0B3Ud726E4D/+9LMkJzfHIj48HhCcjwhxJEteutzpFVu5M8xsfyptQyX9nLJez/h/NXf54w3b2PJy1/n0eIXh3VN9nmX4EmeSMKaH6NsdcN6boDo7U9jqtpA+7zz8MZnh/VcgRxqSj4I63lGKylQCyGEEEIIIYQY0XZVtQIMawY1+HKoK4IoUL9fXMsb26u45aSpHZ3XQ2WSiA8hxqxWu4vLH/qMsvrg7tAYKvO+D4jZ/DTOvKOpm/01Pq/fzdc/uJP3qzZw14JreWblA6RHp/BFzbZhWU8Ho4m25bejnFYSV98Bw9jBbbDWEPfJ3bjGzcI5+WRQ4S2RBnKo2fdeWM8zWkmBWgghhBBCCCHEiLarupWJqTHEW8J3+3VPJqbEsuNgC802V6/7eL2aO1/ZTkF6HNeuKAjZuc0yJFGIMauoqpUPd9fxeUlD2M8VVb2D2E/ux50xlZaT7uacaRcB8Fr5p3xv1kVcv/QnnDD5fGZnzGV7c1nY13M4b0oe7QsuxVz2ETEbHxiek2pN/Id3ojx22hd+dXgGRZqicUw/HVLGh/9co5AUqIUQQgghhBBCjGi7qlopHObuaYArluXRZHNy3ePrsLt6jtrYWtHMvlor3zhxCpYoY8jOHcigdkiBWogxp77NCUCTzRnW8xjrdhP/3q/xxqXRsvIedOwEJiVP4tzJZ3HDtLP4zjG/AaPvro+ZabPY1VKB2x38YNhQccw4B2fOYuI+vZeoys/Dfr7o7U9j2fsm9lln40mbHvbzBbQvvAymnjBs5xtNpEAthBBCCCGEEGLEcrg97KuzUjiM+dMBR01K448Xzefz/Q3cvmoTHm/328/f2VmNQcHJ0zNDem6LDEkUYsxqsDq7/B4Oxvp9xK/+JV5zHC2n34sn6VAh9sGVD/PLkx4E06HvqzNSZ+DwutjXXBK2NfVKKWzH3oo3NpWkN29BWWvCdipj7Q7i1/4S14S52GeeAyp0FxbF4EmBWgghhBBCCCHEiLWv1orHqykcnxiR858zL4ufnDWT17dVcecr29GHZaS+vaOaxfmppMSZQ3pek0R8CDFmNVgdADT2ER80FFHVO4l/5+cQZaHljHtwZy7pvpOha2TSzHTfgNcdTfvCsqb+aHMc1hO+h3K0kvzyFShna8jPoRwtJL55K9ocj3XplV0K9CKypEAthBBCCCGEEGLECgxIjETER8A1ywu4/rhJPPZJKX9bs7dj+4EGG0VVrZw6Y1zIz2k0KIwGhdPTc7SIEGL0qvd3TjeGoYPaUvwW8e/+Dm1JoPmMe3BnHh3U86alTMOojGxvKu133/V1Rax49QZeLn1/qMvtwpM6ibbjv4OxcR+Jr14DHkfoDu5qJ+l/12NsLcd69FXohLzQHVsMmRSohRBCCCGEEEKMWEVVrZiMikkZwzDEqg8/OH06587P4u43d/HsugMArN5ZDcApM0NfoAbfoETpoBZi7AlEezSGOoP6/buJ++wB3OMKaT7337jHHwtKBfVUi9HClORJ7OhnUOKTe9/krLe+zfamfbxX/lEoVt2FO3sRtmU3Yz64gaRXrwWXbegH9ThJeuMbRFVtwLb0KtxZRwX9eRHDY3hHIAshhBBCCCGEEANQXN3K5Iz4jsiLSDEYFL+/YB71Vic/eGEr6fEWVhfVMDkjjoL08BTPzVEGyaAWYgwKDEkMeYG68Axs9fuxLf0W2pI64KfPSJvJhoMfg/aC6v4997UDH/PNT/7AcZmzqHa2U2KtDcWqu3FOPhHcdmK/eIjkFy6i+exH0LHpgzqWcrSS8NZtmMs+wLb4MpyTT+kWbyIiTzqohRBCCCGEEEKMWLuqWpkWwXiPzsxRBv5+2SJmTEjg5ic28Mne+rB1T4Mvh9ohHdRCjDkdER8hyqDuyMYfPxvrijsHVZwGmJk2izJrLS2Oph4ff+/gOhKiYnj6rCeYmT6PUmutr5gdBs7CM7Ae9x2iGveS8uyXMZV/MuBjGBv3kvzc+ZgPrMW26FIchV8CgykMqxVDJQVqIYQQQgghhBAjUovdRUVTO4XjR0aBGiDeEsXDVy0hPcGM26vDkj8dYImSiA8hxqKOIYlWZ7fBqwPl8Wpu+s8Gnvys72iOYMxImwFAUePuHh/f1LCbeSkFRJkzyE3MpdxWh9tjH/J5e+PKPZrWU+8EvCS/dDnx7/0I1d4QxBNtxH7xV1KeOQ+DvYG2E76FY/qXwRDaYbYidKRALYQQQgghhBBiRNpd7RuQOH0EFagBMhOiefLao7nr3NkszE0J23nMUQacHilQCzGWaK1psDoxRxlwezVtDveAnv/y5kr+82kpXq+vsH3X/3bwxvYqXCH4XjEzbSYAO5r2d3vM5XWzrWEv89OmgcFEbmIubu3hYFvVkM/bF09GIS1n/xl74WlE73iWtEeXk/DOdzGVf4JytBzaUXuJqt5M7Gf3kPrEqcR9fi+u8TNoPfUHuLOPkViPEU7+dYQQQgghhBBCjEhFVb4C9UiJ+OhsYmoslx2dF9ZzmI0GXNJBLcSY0upw4/Jopo+Pp6iqlUari4To4GIn/vnBPn712k4AXt1SydKCNP79UQnXLC/gymPyh7y2nPgcEkzxbG860O2xoqYSHF4Xc9PnAZCX6Pv+V9pWwcTkSUM+d5+iLLQvvQHH1FOJ3vkKlr2vEb3rvwB4YjNQXjfK2YryutHKgDtzOrajLsc9YSEYLOFdmwgJKVALIYQQQgghhBiRdlW1Em+JIiclJtJLiQhTlJIOaiHGmAb/gMTJGf4Ctc1Jblpsn8/RWvPn1bu5953dfGnOBI6bls4vX93Jp/saWDlrHD86c0ZI1qaUYkbaDHY0d48L2VhfDMD88UsAyE3IBaCkrYrlITl7/7wpk7Ad8y1si1oxVW/C2FCKobUKjGa0KQZPUhburLl4Y8dJnMcoIwVqIYQQQgghhBAjkm9AYjxKqUgvJSLMRsmgFmKsCQxInJwZD0CDzdnn/lprfv3aTv754X4uWJTD774yF6NBcczkdF7bepArluVjNITue+Ts9Dk8U/QUXo8Tg/FQkXdzw24STbHkJxcCkB2fjVEZfYMSh5slAVfuCly5K4b/3CIsJINaCCGEEEIIIcSIo7VmV3XriBqQONzMMiRRiDGnIVCgzogDoKmPArXXq/nxi9v454f7uWJZHr/3F6fBFzN0w/GTiTEbQ7q++ZnzaXO3s6e5aw71pvpi5qcUQJRv3Sajiez4CZRaa0J6fnFkkgK1EENwoMHG5Q99RnO7K9JLEUIIIYQQYkypaXXQZHNROALzp4eLOcooER9CjDENVgcAUwId1Nae6wluj5fvPLuZJz4r48bjJ3PnObMwhLBTujfzMnwZ0xvrdnRsc3icbGvax/zUwi7DBvMS86VALUJCCtRCDMEb26r4cHddx3RxIYQQQgghRGjs8g9ILByfGOGVRI7ZqKSDWogxJhDxkZ8Wh0H13EHtdHv55pMbeWFjBd85bRrfP71w2KKOpqVMIzYqhk0N+zq27WwqweV1My9zXpd9cxPzfBEf2hOSczs9Ljb5s67FkUUK1EIMwYayRgBsztB8MxZCCCGEEEL4HCpQH8kd1AbpoB4BXtt6kNue3hjpZYgxoqHNSYzJSJwliuRYc0fkR2ffenojb2yv4idnzeSbJ00d1hx+o8HInPQ5bGjc27FtU8NuAOZlLumyb15iHjX2JqzOliGft83Vzlff+zEnvf4NttXvHPLxxOgiBWohBklrLQVqIYQQQgghwqSoqpWMBAupceb+dx6jZEhi5Hm8vgF1L26q7LGQKMRANVidHd/XUmJNNNm6RnzYXR5e31bF1ccWcM3ygkgskfnjFrC1qRS32w748qeTzXHkpkzrsl9uYi4AB9oqhnS+ensz577zPd6v2gDA9vqiIR1PjD5SoBZikA4226lu8WVH2ZzuCK9GCCGEEEKIsaW4upXpR3D3NPg6qF3SQR1Rb22voryxHTjU1S9EX55Zd4DXth7s9fE6q5O0+ECBunsH9YEGGwBzc5LCt8h+zM+Yj93jpKixGK01n9RsZUHKJDDGdtkvN8FXoC5trRzS+b7/xV/Z3riXR1b8mCgVxZ7W3j9/YmySArUQgxTongbpoBZCCCGEECKUPF5NcXUr047gAYkAJumgjriH1u4nzd/tWiyzh0QQ/vDmLm57ehM7KnuOvWiwOg51UMeZaTwsg7q03legzk2L7fbc4bIgcwEAG+t38U7lF+xuOcAFk07rMiARfBEfAKVtQxuUuK+1guPGzebMWTeTn5TH7tahdWSL0UcK1EIM0obSJqL8E3TbpUAthBBCCCFEyJQ12HC4vUd0/jT4M6ilQB0xmw40sa60kZtPnEJSjIki6aAW/Wi0OqlpdeD0eLn16Y091goa2rpGfHQrUPs7qPNSI1egzk/KJ9GcwMaGvfx1x7NkxaRy7vQru+2XHpNObFQMpdahFajrHc2kWZLAEMWU5CkUt1SC1kM6phhdpEAtxCBtKGtk/sRkQDqohRBCCCGECKWOAYlHeAe1OcqAQyI+IuahtftJsETx1SUTKRyfIB3Uol+Br5HrVhSwp6aNu/63o8vjWmvqrU7S4y2Av4Pa6kJ3KsaW1VuJt0RFNH/foAzMy5jHy+Wf82H1Jm4s/DKm6HHd9lNKkZeYS6m1dkjna3C0kBqdAsCUlKnsa6vC47EP6ZhidAlbgVop9bBSqkYpta3TtlVKqU3+XyVKqU3+7flKqfZOjz0QrnUJEQp2l4ftlc0syk/BHGWQDGohhBBCCHFE0VrT5gjfa+CqZl/mb05KTNjOMRpYjL4Mai2dhMPu2XUHeGVzJZcenUu8JYrCcQkUV7XKv4XoU6BAffXyAm44bhJPfFbGm9urOh63OT043N5OHdRmnB5vl6a30gYbuamxKKWGd/GHmZ+5gAZnKwlRMVw2+3roZT25CXmUWGsG3fHc7nZgddtJ8xeopyZPxel1c6C1fNBrF6NPODuoHwFO77xBa/1VrfV8rfV84HnghU4P7w08prW+MYzrEmLItlc24/JoFuamEGc2Sge1EEIIIYQ4ojy/oYLZP3uT5b97l289vZHSemtIj1/b5sBoUKTERq6DcCQwGQ1oDW6vFEWH01vbq/jBC1tZMTWdO04tBKBwfAKtDjeVzdLVKXq3q7qVhOgoxidGc8dphczOTuT7z2+hyv91ExiIGChQp/q/x3UelFhWbyMvgvnTAfMz5wNw9dSVxCfk97pfbmIeZdYatNfZ6z59aXD4srpTotMAmJw8GYDi5tJBHU+MTmErUGutPwAaenpM+S4DXQQ8Fa7zCxFOG0qbAFiYm0KsOUoK1EIIIYQQ4oiypbyJGJOReTnJvLy5kmfXhbbTra7VSVqcGYMhsh2EkWaO8r1llxzq4fPpvnq++dRG5mQn8cBlizr+DQJ56MWSQy36UFzdRuG4BJRSmKMM/OXiBThcXm5ftQmP1xfvAXQM3kyONQHQZHMBvgGxBxptER2QGHBS7kl8c+413LzwDlDGXvfLTsimzW2nxdk8qPM0OHzPS4tOB2BKyhQAdrdIB/WRJFIZ1CuAaq317k7bCpRSG5VS7yulVvT2RKXU9UqpdUqpdbW1Q8u4EWKwNh1oYmJqDBkJFmLMRtpdEvEhhBBCCCGOHKX1NqZkxnP/1xaSnRzDgUZbSI9f2+YgI8ES0mOORlKgHl7bKpq57tF15KbG8u+rlhBniep4bJo/D10GJYreaK0prm5laqfs/EkZ8dx5ziw+2VfPgx/so8HqADp1UPt/b/APSqxqsePyaPJS44Z59d3FmeL46fLfkJJU2Od+2fHZAFS0VQ/qPPX+DurUmAzf79GppEensbu1clDHE6NTpArUl9C1e/ogkKu1XgB8G3hSKZXY0xO11g9qrRdrrRdnZGQMw1KF6G53TSvTx/u+RGPNRqwO6aAWQgghhBBHjtJ6a0eHX25qLAcaQlygbnV0DBE7kgUK1C4ZlBh2++usXPXvz0mMMfH4NUtJOWxAXVKMiQlJ0TIoUfSqttVBk81F4bj4LtsvXJzDl+ZM4I9v7WLNLl+jZVqc7/tbsj/io8lfoA7EJY2EiI9gBQrU5daqfvbsWb2/gzo1Nq1j25SUKexuPTj0xYlRY9gL1EqpKOB8YFVgm9baobWu9/95PbAXmDbcaxMiGB6vpqTexqR03xXNWLORdon4EEIIIYQQRwi3x0t5Yzv5/gLKxJRYyhraQ3qOOumgBnwZ1AAO6aAOq+oWO5c/9BleDY9ds5QJST0P5ywcnyAd1KJXu/wXL6aNT+iyXSnFr8+bQ2aChcc+8eUqp8Yf1kHtj/4oq/dd7MtNHT0F6pyEHADKbXWDen4g4iM1+lAT6pTkqb4Oai21liNFJDqoTwGKtNYdYTJKqQylfIE2SqlJwFRgXwTWJkS/Kpvacbq9FHQUqKOwScSHEEIIIYQYxbZXNvPDF7by2taD2Jx9v7atbLLj9mry0nyvh3PTYqlrc/T7vGBpraVA7WcJRHxIB3XYNNtcXPHQ5zRanTzy9SVMzojvdd/CcQnsrWnDPYR/D49X8/HeOrSWwZdjzS7/xYvCcQndHkuKNXHvxQswKN+dEXFmX6ZzUowJpaDRn0Fd2mDDZFRkJfd8kWQkyojJIEpFUWGrH9Tz6+0tKBTJ0akd26akTKHO0UJD++CK3mL0CVuBWin1FPAJUKiUKldKXeN/6GK6D0c8DtiilNoEPAfcqLXuccCiEJG2v853y02gQB1jNsqQRCGEEEIIMaq9tKmSpz4v4+YnNrDol+/w1vbeb9Uu8d+Cnu8vUOek+Aop5Y2h6aJubnfh8miJ+ADMRsmgDieb083Vj37B/jor/7xiMXNzkvvcf9q4BJweb8f/gcF4eO1+Lv3nZ7xfLDO1xpri6lbS482k9fK9a2lBKj86cwanzxqPUr4BsEaDIinGRGOnDuqclFiMo2hArNFgJCt+/JA6qJPNcRijDn3epiZPBWBPc0kolihGgbAVqLXWl2itJ2itTVrrHK31Q/7tV2mtHzhs3+e11rO01vO11gu11q+Ea11CDFWgQD3Jf2U91mTEJhnUQgghhBBiFKttdZCdHMOT1x1FfnocP3xhK/Vtjh73PTwjNXAreuDW9FCsBZAOaiSDOpxcHi83P7GBjWWN/OWS+RwzJb3f5xT6oxs2H2ge1Dnr2hz8ZfVugI4sYjF2FFe3dQzT7M21Kybxl0sWdNmWEmumMZBB3WAdVfEeAdnxOZQPtoPa0UKaJQHUoaGkU1KmALCnuby3p4kxJlJDEoUYtfbVtpFgiSLdnxkVZ4kK2e2MQgghhBBCREJNq51xiRaOmZzOPV+dR4vdxZ2v7Ohx35J6G9EmA5n+AnKgmHKgMUQFan9hPPB6+0gWKFBLB/Xgebyaiqau3f1er+Y7z25mza5afnXeHE6fPSGoY82YkEhBehz//nj/oCI6/vjWLtpdHgrHJfCBdFCPKV6vZnd1a78F6p5MSIpmfWkjTTYnpfW2UTUgMSArPpuK9oZBZUY3OJpJNXctUOcm5GI2mNjVWhHKZYoRTArUQgzQvjorBRlxHbfkxJiNtLukg1oIIYQQQoxeNS0OMhOiAZg+PpFvnDiFlzdX8vaO6m77ltbbyE879Ho4Nc5MrNlIWUNoO6gzpYO6Y0iiFKgHx+vV3LZqE8f9/j2Kqlo6tv/1vT28tKmS764s5JKluUEfz2hQ3HT8ZLZVtAw4omN7ZTNPf3GAK4/J5+KlE9lXZ+VAiP7PiMiraGrH6vQMqkD9vdOnU9fm4Kb/bKDV7h6VHdQ5CTlU2urxeHq+86Yv9Y5m0iyJ4BtNB/hiQwpTC9neVBrKZYoRTArUQgzQ/jprR/40+CI+XB4tLxqFEEIIIcSoVdPadSjhzSdMYfr4BP7vv1tpbnd12be03tqlw08pRW5qbMiKbYECtWRQH+qgdkjEx6D8/s1dvLK5Eq01//xgP+DLOP/nB/tYOWscN58wecDHPHdBNllJ0fztvb0Det59q/eQHGPi1pOncty0DADJoR5DAneQ5KcPvLg8f2IyPzxjBp/s80VkjMYCdXZ8Nm7toXoQOdT19mZSLUndts/LmM/Gxn1or9yxfiSQArUQA2B3eahoau9SoI7xT99tl0GJQgghhDgCfFHSwC9e2UF5iOIcROTZXR6a211dOpbNUQbuvmAe9VYnv/rfoagPr1dT2mAjLy2uyzFyUmI50BCaIYl1bU5MRt/gsCNdYEiiS5phBuypz8t44P29XHZ0LpcfncfLmyuoarbz2McltDrc3Hry1I67AAbCHGXg+uMm8XlJA5/vbwjqOU63lw9313LmnAkkxZiYlB5HdnKMFKjHkEar70JeWtzgLqx9/dh8Tps5DoBJGXH97D3yZMdnA1Bp7X3Abk+01jQ4WkiLTun22LzMeTQ62yhrKQvJGsXIJgVqIQagrMGG1nQpUMdZfDlJNpdc1RNCCCHE2OZwe7jjmc08/NF+TvrD+/zqfztwuOUi/WjXEamR2LWwMicnietWTOKZdeV8uNtXSKtqseN0e7tlpOamxvpfKw88l7en9WTEWwZVPBxrLIEMaumgHrAH3t/L4rwUfn72LK5ZPgmPV/O3NXt837+mZzIrq3vHZrC+uiSXtDgzP3xhC5/u638w3IayRqxOT0fntFKK4wsz+GRvvdyJO0Y0+IccpsQN7sKaUop7vjqfBy9fxJTMgceERFp2gq9AXW6rGdDzrG47Dq+L1B4K1PMz5gOwqX7nkNcnRj4pUAsxAPtq2wCYlB7fsS3W30Ftkw5qIYQQQoxxj31cSlmDjd9fMJdz5mfxzw/389Rn0tk02gWGEgYyqDu77ZSpTMqI4wfPb8XqcFNa77+N/bAO6tzUGNpdHuranENeT12bg3TJnwYkg3qwnG4vBxpsLJucRpTRQG5aLGfMnsBjn5TSaHPxjROnDOn4MWYjf/rqfNqdHi5+8FOufXQde/3vFXvyQXEtUQbFMZPTOrYdNzWDNoebDWWNQ1qLGBkarb7vfckxgx/uGmeJ4rRZ40O1pGGVE58DQLl1YBEfDY5mAFJj0ro9Nj1tOmaDiY0NA4vTEaOTFKiFGIB9dVYACjrdchNjkogPIYQQQox9DVYnf3l3NycUZnDR4on84cJ55KfF8uHugedNipGlpsVXoM7ooSgcbTLy+6/MpbK5nd+/UURpve/18OEd1BP9makHQhD9EuigFocyqKVAPTAHGm14ddcLKdeuKABg2aQ0FuV179YcqOOnZfDud07guysL+XRfPafd8wE/fWkb9W3dh8S9X1zLwtwUEqIPddceMyWNKIPiA4n5GBMabU4SLFEd/2ePNImWROJN8ZTb+r+joLP6jgJ1RrfHLEYLM9NmsKlx34COWdZWxQNFLwzoOSLyjsz/OUIM0v5aK5kJFuL9sR4AsWbfn60OifgQQgghxNj1l9W7sTrc/OjMGR3blk9N59N99bgkfmBUq221A90jPgIW56dy5bJ8Hv2klBc2VmAyKiYkxXTZJzDUKxSDEmvbHDIg0c8sER+Dsr+2e2PRgtwUfnXebO46b3bIzhNtMvKNE6ew5rsncOnSXJ74rIzj717D39bswe7yNTDVtjrYXtnC8YVdC3CJ0SZmZyexrlQ6qMeCRquTlLjBd0+PBTnxWVS0D7RA3QJAWg8FaoB5mQvY1Lgfryf4u3Oe3vc2P1r3d+qs1QNai4gsKVALMQD766xd8qcBYi3+iA+XdFALIYQQYmxye7w89XkZ5y/MYdq4Q9mYy6ekY3V62HSgKXKLE0NW0+rAoPoe7vW90wuZmBrD5/sbmJgai9HQNR86J8VXoC6rH1qB2uPVNFidPXZzH4mkg3pwSvyd/gWHRdF87ag8JmfE9/SUIUmPt/DLc2fz5m3HcfSkVH7/xi5W3vsBBxpsHfntx03tXoCbm5PE9opmvN6hZ7eLyGqwuY74AnV2wkTKbXUwgFkEDXZfB3VKTHqPj8/PmE+Ly8b+ltKgj1lj9130qWuXuxNGEylQCzEA++us3SbqBjKoJeJDCCGEEGNVSb0Nh9vL0ZO6ZkQum5SOUrBWYj5GtZoWB2nxlm5F585izVH89vy5AOSlxnZ7PMZsJCPBMuSIj0abE49XS4Haz2yUDurB2F9nJSnGNOwFwymZ8fzryiU8ce1RNNlcXPzgpzy3vpy0ODOzshK77T87Owmr09MRJSlGr0ark9TYwQ1IHCuy4rN8ER86+NrIoQ7qngvU8zLnAbC5vijoY9a0NwBQ6/9djA5SoBYiSA1WJ/VWZ/cOapMv4kOGJAohhBBirNpV1QrA9PEJXbYnxZqYm53ER3ukQD2a1bTayQyiIHzslHR+dd5srjtuUo+PT0yJoWyIER91/vxeifjwMcuQxEHp6c7X4XTslHSeuPYo2hxuPt5bz4qp6Rh6uAA0NycJgK0VTcO8QhFqDVYnKbFHdgd1TnwO9Y4W2l29Dww9XIOjGaMykGjpORe+MKUQi9HMpgEMSgx0UNf6u7PF6CAFaiGC9Pz6csD3YqOzGH8Htc0pGdRCCCGEGJt2VbVgUL7uwMMtn5rOxgNNtNpdEViZCIWaVkdQBWrwRSQcM7nnTrfc1NigIz6qW+x8+5lNXPrPT2np9LVT29r7wMYjkcGgiDIoKVAPUEmEC9Tg645+4tqjmDYungsXT+xxnykZ8USbDGwtbxnm1Y0uHq/mpU0V7KlpDds5Pt/f0JEbPhhNNsmgzk7IBqByANnP9Y5mUs3xKEPP3ecmo4nZabPYOIBBiTXt/gK1oyno54jIkwK1EEFwe7w88nEJRxWkMisrqctjcYEMaumgjohGq5PP9zfw6pZKPt03sIEMQgghhAhOUVUr+elxRJuM3R47dko6Hq/m8/1yK+1oVdvqIDMhesjHKUiPp7LZ3m/03aMfl3DSH9bw6paDfL6/gesfW4fD7XvOoQ7qI7vQ05k5yiCDSAfA7vJQ2WyPeIEafEXqt24/vluTU0CU0cCsrCTpoO7H01+U8a2nN3HKnz7g7PvWhvyuneoWO1998BOe8zelDZTd5cHq9JB6pBeo430F6t0tZUE/p87eTJolEVRUr/vMy1zA5sb9OFz9XwDVWndEfNRIB/WoIgVqccRqd3q48fH1QXV5vLWjmoqmdq5eXtDtsegoKVBH0pl/+ZCL/vEJ33xyI1c8/LkMGBFCCCHCoLi6tVu8R8DC3BSiTQY+lBzqUcnj1dS1OchMHHrHcoF/VktgQF1PNh9o4mcvb2dhXgpv334cf7hwHp/ua+Dbqzbj8WrpoO6ByWiQDuoBCHz95Y+AAnUw5mQnsa2iBY+8j+lRi93FH98qZkl+Cj85ayb1bQ5+9vL2kJ5jf50VraGiqX1Qz2+y+e4COdIjPuZmzGV8bCbfWf9PytuC66JucDSTaknos0B9Wv5ptLntvF72fr/Ha3O3Y/P4fo7UOeTOhNFECtTiiFVc3cob26t4a0dVv/s+vHY/uamxnDJjXLfHDAZFjMlIu0R8DLsmm5ODzXauOiafG4+fjNPtpdHmjPSyhBBCiDHF5nRT2mBj2rieC9TRJiNL8lMlh3qUqrc68GqCjvjoyyR/QXBfbe8F6nveKSYl1sTfL1tEXloc5y7I5v/OnMH/th7kB89vobrFgSXKQLyl92LFkcYcZZAhiQNQ4h84WJA2egrU7S4P+2qDz+09kty3ejeNNic/O3sW1ywv4LJleeypaeu42yIUAtn5NS2DO2aD1fceNOUIH5KYYE7g6bNW0eZ2cdG736cxiAJxfUeBuvfy5AkTT2BifDaP7n2z3+PVdBqMKB3Uo4sUqMURK1DILK7uO8dq84Em1pU2ctUx+b1ONo81G6WDOgJK/d3vx0xOY3a2byp2XZsUqIUQQohQ2l3dhtbdByR2tnxKOrtr2qhusQ/jykQoBAoyoehYDkQq7K/rudC2vrSRNbtqueH4yV0K0NcdN4lvnTyVZ9eX859PS8lIsKBUz6+7j0RmowGHdFAHbV9doIM6NsIrCU5gUOKWcimmHW5/nZVHPi7hokUTmZ3t+zwdVZAG0CVW6q3tVUHn3/fkgL9AXTvIoneTv7ZwpGdQA8xMn8WjZzzKvrYafvj5vf3u3+BoIa2XAYkBBmXgspmX837NdvY17+9z38CARLPBRK1D/k+NJlKgFkeswG04u6r7vlL974/2E2+J4sLFOb3uE2uRAnUklPpfSOSnx3VMeg/llXQhhBBCwK4q38X8wvGJve6zfKovX1W6qEefQ5EaQ8+gjrNEMT4xutcO6nvfKSYtzswVy/K6PXb7qdP41slTcbi9Ha/rhI8lyoDLI/EPwSqps5IebyEhenR0s07KiCfWbGRrhRTTDvfr13ZiNhq4Y+W0jm1zc5KIMRn5zD9/qKrZzg3/Wc+/P+67cNmXQx3Ug7vI2uAvUB/pGdQBx+as4IyC01lXvxt07xfXvNrrK1BHJ/d7zEtmXIpRGXms+JU+9wsMSJyWPJlaewtoqdOMFlKgFkesQAf17urWXnOLq5rtvLrlIBctntjnC5xYUxQ2ifgYdqX+7ojc1FgpUAshhBBhsqu6lWiTgdzU3rsRZ4xPJDXOzFopUI86Na2+gkwoIj4AJmXEsbeue4H68/0NfLi7jptOmEysuef4jttPncYvvuy7jV8c4sugliJLsErqbB1xM6OB0aCYlZUoBerDfLSnjrd3VPONk6Z0GeJqMhpYlJfCZ/4O6pc3V6A1NFoHfydtoEAduGA3UI0dER9SoA6YkjKNEmsNDnfvne0tTise7SU1OrXf442PG89peafwZMn7ON29/zvV2H1fF7PSplPraEZ7XQNfvIgIKVCLI1bgh4jN6el1GMLjn5bg1ZqvH5vf57FiJOIjIkobbIxLtBBtMpLhL1AP9kWFEEIIIXq2q6qVqZkJvUadgW8mxzGT01i7uw6tpdNzNAn1UMJJGXHsr23r9nVwz9vFZCRY+NpR3bunO7tiWT5nz8sKyVrGCnOUDEkciH111lET7xEwJzuZ7ZXNuAeRNb7zYAut9rFVhHN7vPzy1R1MTI3h6mO7X7A6elIqRVWtNFqd/HdjJQCNtsF/DgIRH/VWJ65B/Bs0WH3nTj7CM6g7m5I8Ba/2UtJS1us+tfYmAFKj04I65pWzv06do4WXS97pdZ+a9kaMysC05KnYPU7aXJLtPlpIgVocsTr/AAvcutpZu9PDk5+VcerMcUzso2MIfBnU7VKgHnZl9Tby/MNPEmOiMBsNkkEthBBChFhRVSuFfeRPByyfkk5Nq4M9NfJmcDSpaXWQFGMi2mQMyfEK0uNpsbup79TN+PHeOj7ZV8/NJ0wmxhya8xxJZEhi8FrtLuraHBSkx0d6KQMyJycRu8vLngEOSvx4Tx1n/uVDzr3/Iyp7aboajVatO0BRVSs/OmNGj9+bjprkK2g+9kkpOw/6BvE1tQ+uQG11uKlrc5KTEgMM7o7cRpuThOgoTEYpsQVMTZkKwO7m0l73qbTVApCV0HucamfH5xzP9JSp/H7bKtzenusv1e0NZFgSyYwfD0Bde/1Ali0iSP73iCNWo83Z0Smyq4dBiS9uqqDR5urxiu3hYs1RWKVAPexK6q3k+S8eKKVIizdLxIcQQggRQvVtDuraHH0OSAw4doovh1piPkaXmhZHyOI9wNdBDXTkUGutufft3YxLtHDJ0tyQnedIYjYacLnlzoRg7PV/3RWMwg5qgK0DGJRY02rn1qc3kZsaS02Lg6/8/WP21HR/XzvatNhd/PGtYpYWpHL67PE97jM3JwlLlIG/rdlDlEGxfEo6zbbBNSoF4j0W5/kG9Q3mjtxGm1Pypw8zKXkSALtbK3vdp9xWA0BWQnA/G4wGIz846v/Y03aQp3a/1OM+NfZGMqOTyYj1fe3UtDf0uF9/frflcbKfOouZz3+Vk167ic31xYM6jgieFKjFEavR5mRiSgzZyTEUH1ag1lrz8Nr9zM5OZGlB/3lIvg5qyaAeTu1ODzWtDvLSDr34TI+3SIFaCCGECKGdBwMDEvsvUE9MjSU/LVYGJY4yNa32kMV7AB3Zv/vrfJ2gH+2p5/OSBr554pSQdWkfaUxRBhzSQR2UD4t9HZkL/cXG0WJSehxxAxiU6PFqbn1qI1aHm39esZhVNyzD5dFc8MAnbCxrDPNqw+u+1btptDn56VkzUarnaClLlJGFuSk43F6On5bBpIy4QUd8BArUi/J97/trWgb+frLB6pT86cPEm+LJipvA7paKXvepsAY6qIO/eHlGwRksypzP77atwt5DFnVNe4OvQB0zDoA6f4zIQFS3N/CX7auYnZTLqVlLOGir59oPf0mba+zcpTASSYFaHLEarS5SYs0Ujk/oFvHx4e46dte0cfWxBb3+UOwsVjKoh13ghUQg4gMgXTqohRBCiJBpbnfxs5e3kRxrYm5OclDPOXZKOp/uaxhUhqeIjOoQd1DnpMRiNhrYV2tFa82f3t5FVlI0Fy2ZGLJzHGnMRsmgDtY7RTXMm5jcZajeaGAwKGZlJ7ElyA7qFzdW8Om+Bn7x5VlMG5fAzKxEnr9pGYnRJi7952e87y/Ujzb766w88nEJFy2ayOzspD73Pdof83HugmySY8202F14vAO/0yCQP70o13dRo0Y6qENmcvIU9rQe7PXxcmsNmdFJmE3BDzVVSvF/y35KZXsDDxc92+3xGnsjmTGpZMRl+v/eNOB1/3n7KpxeF/ef8Fv+uPJx/rHyYfa1VfOjz+8Z8LFE8KRALY5YTTYnybFmpo1LYF+ttcsbqYc/2k9GgoWz5gY3oCVGMqiHXUm97/a9bh3UrZJBLYQQQgyVy+Pl5ifWU9Zg4x+XLSIpJrjBT8unpNPmcLOlvCm8CxQh0WRzUtHUztRx/XfIB8toUOSlxbKvzsr7xbVsKGvilpOnYomS7unBskQZcLrlvUZ/alsdbD7QxCnTMyO9lEGZm53EzoMtQV3gW1faSFKMiQsWHcruzUuL47mblpGfHse1j37BS5t671wdqX792k7MRgN3rJzW774XLs7h6mMLOHXmOJJjTGgNLYPIoS5rsJEQHcWUTF9ueU2rfcDHaLS6ZEBiD6amTKW4tRLt6fnfpcJWS3ZMGqiBFfeXZy9nRfax/G3XK2jvof8vXu2l1t7EuJh00vyDF2vtLQM69kFbHf8ufoWL844jP/MoAI7JPpZvLfgm/9n/Hi/tX93vMT6p2YrLK3fYD1TYCtRKqYeVUjVKqW2dtq1SSm3y/ypRSm3q9NgPlVJ7lFK7lFIrw7UuIQIabE5S40wUjo/H6fFS6i947qlpY82uWq44Og9zVHD/ReLMUVidbplaP4zK6v0d1KmdOqgTLNRbHfLvIIQQQgzRb18v4qM99fz2/Lkdw6iCsWxyGkr57kYTI9/GA00ALMhNDulxC9Lj2Ffbxj1vFzMxNaZLEU0MnDnKgMsjr2/7816RL8/2pBmjs0A9JycJh9vL7ur+ByVuq2hmdnZit7t9MxOiWXXD0SzITeG2VZt45KP94VpuyH1R0sDbO6r5xklTguqAz0qO4adnzyTaZCQlzlccHsygxLIGG7mpsZijDKTGmQffQS0RH91MSZ5Ci8tGbXvPHf0V1lqyY9PBMPDi/gXTLqKyvYGt9ds7tjU523B53WTEZmIymkixJFPrGFiB+t7tT+PRXm5f9C0wHPo3/e7SHzAvfRY/2fBPnO7em+I+rNrEl976No8UvTDgj+lIF84O6keA0ztv0Fp/VWs9X2s9H3geeAFAKTUTuBiY5X/O35RScoldhI3d5cHu8nZ0UAPsqvK9EPj3R/sxRxm49Kjgc5BizEa8Ghxy692wKW2wkhRjIqnTler0eAsuj6Z5kBOchRBCCAEOt4dVXxzg/AXZfGWAhcXkWDNzspMkh3qU2FjaiNGgmBdkhEuwJmXEs7fWyubyZm45aSomo9y4OxQmo5KIjyC8s7OarKRoZk5IjPRSBmWOP9Jia0VTn/s53V52VbX2GoGRGG3isauXcsqMcfz8lR38e5QUqTf7L5hdOohhqskxvkJi4yAGJQYK1ACZCZYBD0m0uzzYnB5SJOKjmykpUwDY09z9a1BrTYWthuy4cYM69il5p6BQvFn+cce2av9AxEx/vEdGbAa1jqagj+n2enh8z+t8NW8FuRlLujxmMpr44dE/oby9nlV7Xu31GA/4C9OvlX8S9HmFT9heKWitPwB6HJepfJf5LgKe8m/6MvC01tqhtd4P7AGWhmttQgR+cKXEmpmcEY9BQVFVC9sqmnl+Qznnzc8mLT74LL5Ys+96isR8DJ/Sehv5aV2nc6fH+14USA61EEIIMXgf76mnzeHm7PnBRZ0dbvmUdDaWNdHmkNtbR7r1ZY1MH59AnCUqpMedlOG7wy0vLZbzF2SH9NhHInOUAafkuvfJ7vKwdk8dJ83IDGqG0EiUnxZHgiWq30GJxdWtOD3ejoJ2T6JNRv7+tYXMzk7k1S29ZwCPJM3tLpTyFdgHKhCv0TzAQYler6a8ob2jQJ2RYBlwB3WgtiAZ1N1NSfYVqHe3lHd7rNnZhtVtJyd+cD8jMmIzWDxuIa9Xru/YVuMvUGfETgAgPSZ9QBEfVe312D1OFo1f3KV7OuDEiScyP2MO9+x8Hren+2ucfa0VvFH+KcnmeD6q3UmLfXQPLB1ukbqUvQKo1lrv9v89GzjQ6fFy/zYhwqLR6vvBlRJrItpkJD89jvve3cNZ961Fa7hmRcGAjhcoUNtcUqAeLqX1NnLTug5TyPBfVKiVHGohhBCjnNPt5TvPbmbnwZ7fWL25vYr1pT32ggzZm9uriLdEcczk4KM9Ols+JR23V/P5/voQr0yEkser2VTWxEL/YLBQCnSw3n7KNKKke3rIzEajdFD349N99dicHk6ePrhuzJHAYFDMzk5iaz+DEgMF7NlZfQ8RjDIayE+Lo8E6Ot4bNbe7SIw2YTAM/AJDcuzgOqirW+04PV4mdipQ17YMLIP6UG1BCtSHy4rPIjYqht2tld0eK7f5InmyEgZf+ltZcAabGvdT0eorgNf4C8KZseMByIjJpNbRDNpDubWGLQ17+jxeha3Wv+6e7x5TSnH74u9QYq3hhf1vdHv8waIXiTIYuOfYn+HWHlZXfNzDUURvIvVq4RIOdU8PiFLqeqXUOqXUutra0TmZVkReRwe1/yrnjcdN5oJFOdx9wVze+84JHbEfwYo1+7pObNIpNCxcHi8VTe3kpR7WQe2fQC8d1EIIIUa794treW59Oat3Vnd7rNXu4tanNnL5Q5+zu7o1pOf1eDVv76jmxOmZgx5qtzAvBUuUgbW7pUA9khVXt2J1eliYlxzyY8/OTuL9757AudI9HRLSQd2/1TtriDEZWTbIC2sjxdycJHYebO3zgsTWimYSoqO6DIvvTWqceVQVqIMdyHu4FH8HddMAO6gDc40ORXxEU9s2sJlGh+7OliGJhzMoA5OSJrG7tXsXf4XVV8/Ljs8b9PFX5vvG17114CMAatr9Bep4fwd1bDo19hbQHm7++Pd86a3bqLR2f10VUGnzxZNlJUzs85wzUwv50/Zn8XgPNSi2OK08ufdNzp94DKdP/Srp0am8XvH5oD+2I9GwF6iVUlHA+cCqTpsrgM5fATn+bd1orR/UWi/WWi/OyMgI30LFmNY54gPgoiUT+cOF87hw8USykmMGfLyODmqJ+BgWlU3teLy624uy9HgpUAshhBgbXtrkeyl8sLl7J9eb26s75l7c8Ph6Wu2hm72wvrSRequT02YOvgsx2mRkaUEqa/dIM8lItr7U90Z+UW5qWI6fd9idbmLwzP4MahkE3jOtNat3VrN8ajrRptE9ymp2dhJOj5fiPi4+bq9oZnZWUlBRJqlxZprbXbhGwQWOliEUqBOiTSgFTQPsoC5rOLxA7Ztp1DiAQnfgAoBEfPRsaspUdrdUgu76NVhu9XVQZycOPHM8YFrKNPIT83i9ch3g66CONppIMPvuDMqIyaDFZWV7wx7WVm/G6nZw5/q/93q8Smugg7r3ArVBGbhjyfcobq3kN5se7Nj+5+1P0+Zu5/o5V2OMiubU/NN4++BmXB6pTQQrEh3UpwBFWuvOITQvAxcrpSxKqQJgKiCXGkTYBH7ghOoqZ4wUqIdVif9K9+FvfJJjTBgNSgrUQgghRjWrw807/s7p6h5uNX5pUwUTU2N4+KollDbYuOOZzXi9oSlcvbm9CrPRwAmFQ2sEOXZKOsXVbdQM8FZpMXw2lDWSHm9mYurAmzPE8DJH+d62uzxSoO5JUVUrlc12TpmRGemlDNncnMCgxJ5jPlweLzurWpmT03e8R0CgaDrQzuJIGEoHtdGgSIox0dQ+sI9zS3kzJqPqaFLLTPQ1PNW0Bv+z6/C7s0VXk5OnUGarxe5q67K9wlZLlDJ25EUPhlKKlfmn80HNNprtTdS0N5AZnQwG3x3uGbG+1zJ/2v4MUcrI5dPO49myj/iselOPx6uw1RIXZSExuu87Mc6efDaXT7+YP+14gVdL1/CX7au4Z/vTXJx3HHOyTwTgtPyVNLusfFa1vs9jiUPCVqBWSj0FfAIUKqXKlVLX+B+6mMPiPbTW24FngB3AG8A3tNZS6RNh0+i/ypkcopyoQMRHu0siPoZDWb0VoFsHtcGgSIszUycZ1EIIIUaxd3ZWY3d5SYszd+ugrmm189GeOr48L5ujJ6XxwzOm89aOav7+/t4hn1drzZvbqzh2ShoJgxhS1dnyKekAfLS3bsjrEuGxsayJBbkpo3ag3JEkUKCWmI+eBaKQTiwc/QXq3NRYEqOj2NJLDnVxtS/+Y3YfAxI7CxSoR0PMx1AK1OBrVhpI53Nzu4vnN5Rz9tysjv9jh2YaBd/wFMigTh7C2seywtRCvNpLcdO+LtsrrLVkxaZiiIoe0vHPm3oeLq+HU9+4lY0NxYyLTgbl+7dIj/G9FnnxwMecmb2IX6z4HRNix/HDdX/Dq7t/P6201ZIVkwaG/v8tf3383SzMmMN1H/2en2/8F+dPPIZ7TnkAjL4axfETj8diNPN6+adD+viOJEEVqJVSk5VSFv+fT1BK3aqUSu7rOVrrS7TWE7TWJq11jtb6If/2q7TWD/Sw/6+01pO11oVa69cH8bEIEbRGm5N4S1THD6KhivN3UFsdcl1lOJTW24g2Gcj0Z053lh5vkQ5qIYQQo9pLmyrJSorm1JnjunVQ/2/LQbwavjw/C4Brlhdw9rws/vjWLj7cPbRIjR0HWyhvbGflrPFDOg74huQlxZj4okQm2I8kJXVW3th2kLW769hfZ2VRXugHJIrQM/sHTbpkUGKP3tlZw7ycJDITh1boGgmUUszJSWJbLx3Uge1zgi1Qx46mArWbxKEUqGPNA4r4eHbdAWxOD18/tqBjW+BrqKZlAAVqm5PE6CgZCNuL+ZnzAVhfV9Rle7mthuyYNDAMrWlw4biFPHf209i8boqby7p2UMf4Oqg1misKv0qcJZkfL/spmxr3837FJ92OVWmrIysmNag1WYwWHj7jcdJjUrkwdzn3n/YvjJb0jsfjTfGckHM8zx/4GIdb6hPBCPZ/0POARyk1BXgQX170k2FblRBh1mRzkRzCIQaBiI92ifgYFiX1NvJS43rs+ElPkAK1EEKI0avR6uSD4lrOnpfFhKQY6tqcONyHXl+8tKmSmRMSmeof6KyU4ndfmcPUzARufWoj5Y22QZ/7ze3VGBScMoT86QCDQZGdHEN1DxnaIjLsLg+XPfQZN/5nA5c99BkAi6VAPSqYpIO6V7WtDjaXN3HyjKF/3xop5mQnU1TV0uV7f8DWimYSLFHdhsX3JjV+dBSotda0tLtIjIka9DGSY01BR5m4PV7+/VEJS/NTu8SlBBqgagbQQV3X5iAtvnvjlPDJTcglPTqV9Q17umyvsNaQE5cBaui58Ssmnsiai9dy/cxLuHjK2R3bAx3UeXEZrCjwbT8572QAdjTu7nacCmstWXEZoIIrlWbFZ7Huso3cf8YTXYrTAdfMvY4aezPP75Ue3GAEW6D2aq3dwHnAfVrr7wKDD4oRIsIabc6QDjEIRHzYnBLxMRzKGqzk9jK1Oj3eTF3byH4BJoQYGX7w/BZufkJy4cTIYHd5eGNbFbet2oTbqzl7Xhbjk/xvlP2dXGX1NjYdaOrong6INUfxwOWLcHs0N/1nA3bX4C6Yv7W9isV5qR1Dh4cqM9EyoDf5Irwe/GAf5Y3t/OmieTx81WIeuGyRdFCPEoEOaqd0UHfz3q4atIaTpo/+eI+AuTlJuDyaXVXdByVurWhhZlYiBkNw0TwdHdQDHB443OwuL06Pd0gRHymxZprag/s439lZTUVTO1cvL+iyPc4SRZzZOKAM6ppWR4939gofpRQLxy3yFaj9g149Xg+Vtjqy44Z+x1ZAcnQKd53wZ1bOurFj27i4cSSaE7h++nkok+9CRGp0Khkx6RQ1V3R5vtvrodreQFbswC52GY1RYOx5lsPxOcczK3U6f931kgy5DUKwBWqXUuoS4ErgVf82CdgRo1aj1Rmy/GmA2MCQxEG+IRTB83o1ZQ22XrsGMuIt1LY55AeAEKJf7xfXsvlAz7fQCjEcAkXpW5/ayKJfvs2N/1nPlvImvnHiZGZlJTI+yfeGJxDzsfGALy7jhB5yVgvS47jnq/PZWtHMT1/aNuCfg6X1VoqqWjltVui6EDMTLAPK8RThU9nUzt/W7OGM2eM5f2EOJ00fx+mzx0v+9CgRiCV0DKJAvaOyhZ+/vB1PiAapjjSrd1YzISmaWVmJkV5KyATiOw7PoXZ5vOw82BJ0vAccGtzXMMIbeJr9ww2HUqBOijHRZA2ug/rfH5WQkxLDqT3cMZSZGD2gi6s1LfYxES8TTgvHLaK4pZIWewMANfZG3NpDVnxWP88cmpioGDZdvoFrF/0QOv28m546nV0t5V32rW5vwKu9TAjhmpRS3Dj/GxS1lLO6fG3IjjtWBVug/jqwDPiV1nq/UqoAeDx8yxIivBptLlJCGPFhiTJgUGCTDOqwq2l1YHd5yUuP6/Hx9HgLTreXVod0swshelff5uBgs53qFjveMfqmXYxcW8ubuxSlP9xdyznzs3j8mqV88X+n8N2V01FKMd7/hjcwKHFvrRWDgvz0ni/SnjJzHLecNIVn1pXz1OcHBrSmN7dXAYQkfzogwx+7Jf/HIu83rxehNfzozBmRXooYBIu/QO0aRMTHA+/v5ZGPS3rsxh3tHG4PH+6u46TpmWPqYktOSgzJsaZuOdR7atpwur1dIin6YzIaSIiOonGEd1C32IdeoE6JNdPqcPf7/8Tr1Wwsa+LMORMw9tCJnhEf/MVVrTXVLQ7GSQd1nxaOW4hGs7FuOwDl1hoAsuNzw37ueEsSKqpr7WBaaiFFLRVoz6H/F5W2Wv+ackJ6/vOmnsf42Ez+tvOFkB53LOq3QK2UMgL/p7W+VWv9FIDWer/W+ndhX50QYdJoc5ISwg5qpRSx5ihskkEddqX1VoBeO6jTE3z/rnXSsSWE6MP2yhYA3F5NnVW+X4jh9ds3dvLOzuqOovTn/3cKvzl/LiumZnQZshQoUAc6qPfWtpGbGoslqve8xttOmcbRk1L509u7BtRF/eb2amZOSGRikLmmwchMiMbt1SP+1vKx7vP9DbyyuZIbjpsU0n9fMXxMg4z4aHd6eGdnNQDrSxtCvq5I+3RfAzanh1PGUP40+AclZid166De6i9Yzx5ABzVAWpyZ+hGeQR2KDurAjKnAsXpT3WrH6fGS28v3w8mZceyobMEaRMNTq8NNu8vDOOmg7tOCzAUArKvfBUBFoBicmBeR9RSmFNLmbqeirbJjW2BNWYmhLZqbjWaunXMda2q2sbdpX0iPPdb0W6DWWnuAPKVU6Kp5QkSQy+Ol1e4OaYEafDEfkkEdfqUNvuFPeb1mUPuuXksOtRCiL9sqD73pq26WArUYXlXNdo6fltFRlDYZe35JnhgTRYzJeKiDuqaNyRnxfR7baFCcPms8dW1OqlqCy9CsabWzoawxpN3T4OugBiTmI4I8Xs3PX97OhKRobjxhcqSXIwbJHOSQRJfHy0ubKjoK2Wt21WBzejAoWFfaGPZ1DrfVO6uJNhlYNjkt0ksJuTnZSRRXt3aZKbCtopl4SxQFaT3fSdqblDgzjSO9QG0LXYG6v0GJBxraAXq9YPeVhTm0Odz8b8vBfs9Z4/85m5koHdR9SbIkMTV5csegxI4O6oSJEVlPYWohALua93dsq7TVATAhLrQd1ACnFawEYH3tlpAfeywJNuJjH/CRUuonSqlvB36Fc2FChEvgB1ZKXGhj1FPjZDjfcCittxJlUGQn9zyI4FCBWt4MCyF6t72ypSOKLtginhChUtPqCKrbSinF+KRoqlrseLya/XVWJmf2XaCGQ9112ytaglrP2zuq0RpWzg5tF2JgaJQMSoycVV8cYMfBFn545oyOod5i9Al2SOLqndV86+lN/H3NXgBe3XKQ9Hgzp84cx7qSsVWg1lqzemcNy6dkEG3q/a6S0WpuThJur6aoUzTL1ormAQ1IDDhSOqgDDWhN/dy1c8Df8DQxpef3k4vyUpiSGc+Tn5f1e87AEGPpoO7fgsxFrKvfg8Pdzn9L1pBuSSQpOj0iawkUqIuaSzu2VdpqiTVaSI4J/ZqmJE8hxhjN5gbpoO5LsAXqvfiGIxqAhE6/hBh1Aj+wQjkkEXzDFGoHMO1XDE5pvY3slJgut0B3lpMSg9Gg2FEZ3JtyIcSRaXtFMwsmJgNQ1dwe2cWII0q700Or3d3RXdyf8YnRVDXbqWxqx+H2Mjmj/865GRMSUepQlA3A9Y+t4xev7Ohx/ze3V5OXFkvhuNC+vM9M8L1hr5GLQBHRbHPxh7d2sTQ/lbPnToj0csQQBNtBvck/+Pf+9/awraKZ1UXVnD57PEsL0qhoaufgGPp5t6u6lYqmdk6Z0X1o7FgwJycZgK3lTQC4/QMSZ2cNLN4DfIXbEd9B7S9QJ0YPQwd1ow2lILuXArVSikuW5rLpQBM7D/b9nrLa//4/UzKo+7Vw/ELqHC1c8+Gv2NhQzB+X3gpRA7sbIFRSo1NJj0ljV3NFx7YKay1ZsalgCH14RJQhilnps9jUuL//nY9gQRWotdZ39vQr3IsTIhwa/T+wUkNdoE6wSIfQMChrsPWaFwaQEG1iYW4ya4prhnFVQojRpNXuoqTexvHTMjEalHRQi2FVM8A3s+OTfAXqPbVtAP1GfADE+W8BD0TZtNhdvLOzmk/31Xfbt8Xu4pO9daycNT7kQ8Y6Ij7krqaIuHd1MY02Jz89e+aYGiB3JAo2g3pLeRP5abFYTAauePhz7C4vZ83NYkl+CsCY6qJevdP3Wv+k6WOzQJ2VFE1qnLkjh3pPbRt2l5c5OYkDPlZqvJkGq3NAcwmGW0eBOgQd1P0NhDzQ0M64hOg+5zmcvyAbc5SBp/vpoq72d1BnSgd1vxZlLgLgtYrP+GbhOZwx83pQwfbMht70lOkUtZR3/L3SVkdWTHgK1ABzM+axtakEr3dkXyyKpKC+GpRSGUqpu5VSryml3g38CvfihAiHBmuggzq0ER+ZCb5pvzKpPrxK62295k8HnFCYybaKlo4igBBCdBa4w2JuThKZCRaqJINaDKPAxexg38yOT4qmusXOnurgC9QAs7KTOr7WP9vXgFf7bms+vEDxXlENLo9m5azQDxmLMRtJsER13AIths/u6lYe+6SUi5fkDnigmhh5LFH9F6i9Xs3W8maOnZLO90+fToPVSUaChSX5qcyYkEiMycj6MZRD/c7Oat/P8TFaGAwMSlxX2ojXq9nmj2yaM4j/z6mxZpweL1anp/+dI6S53UWCJQrjAONLOksKckjigUYbE1N77p4OSIkzc8bs8bywsYL2Pj5v1S124i1RxFskQqk/M9NmkmCKZ1n6dH60/DdgjOz/3WmphRS1VKA9vvpQpa2WrNj0sBXN52XMo81tZ1+nWBHRVbCf+SeAIqAAuBMoAb4I05qECKtAxEdKXOg7qGVSfXg12Zw0t7vI72cwyAmFGQC8v6t2OJYlhBhlArEHs7ITGZfoK/4dbn1pIz9+cWu/3WpCDNShvMrgIz7cXs3nJQ2kxpmDfv0yKyuRiqZ2Gq1OPtrjG/zT6nB33EkW8Ob2KjISLCyYmDKAjyJ4Gf4L+GL4aK35xas7iDUb+c5p0yK9HBEC5iAK1PvrrbQ63MzLSebSpbmcMXs8160owGhQmIwG5k9MZl1pw3AtOazq2hxsOtDEydNDf2FtJDl/YTb766w8s+4A2yqaiTUbKUgP7iJlZ6n+nxsNI3heUku7a0jd00BHgbu/DuryBhsTU/pueAK4ZGkurXY3r23tfVhiTYtDBiQGyWQ08dYFb/DUWU8RZcmI9HIoTC2kzd1ORVslHq+HqvZ6suNCOyy6szkZcwDYUl8ctnOMdsEWqNO01g8BLq31+1rrq4GTwrguIcImbBEfiYGcRXkTFi6l9b6BFn1FfADMnJBIZoKFNVKgFkL0YFtlMxkJFjITopmQFN1jJuff1+zlP5+WcfebRRFYoRjLDkV8BN9BDfDp3vqg8qcDZmX5bgPfcbCFj/fWdXRgltZbO/axuzys2VXLqTPHDXjoVrCkQD08dh5s4ez71vKHN3fxn8/K+HB3HbefMo20eCmcjAXBZFBv8WcVz52YhMGg+Ptli7j+uMkdjy/OT2FHZQttDndY1zoc1uyqRWs4eYzmTwecMy+Lpfmp/O6NIj7dV8+srMRBdRh3FKhHcCNVi901pAGJ4Os6T44x9ZlB7XR7OdhiJ6ef95MARxWkMik9jqf6iPmobrEzLsif5wImp0wjNjY70ssAoDDFNyixuHk/1fZGPNrL+PissJ7PYjSzqVEGJfYm2AJ14H/4QaXUl5RSC4DUMK1JiLBqtDmxRBmIMYd22vOhSfUSKxEupf6Jy3n9dFArpTihMIMPd9fi7meYjBDiyLOjsoXZ/uKdr4O6a/Gsud3FB8W1JMea+OeH+3m3qDoSyxRjVHWLA5NRkRJk1Nh4/wXwVoc76HgPgFn+QVrvFdVQXN3GWXN9b7rK/D9LAT7aU4fN6WHlrPB1DGUmRstro2Hw6b56tlY0c/+aPfzkxW1MzYzn8mV5kV6WCJFABrWrj9e1mw80E2MyMqWX7xOL8lLwanhty0Ga+xkiN9KtK2kgKcbUcSFurFJKceeXZ9Hc7qKoqnXQcT0dBWrryL1Y2Nw+9AI1+GI8+ypQVza1ozVM7GVAYmdKKS5eOpF1pY0UV7f2uE9NqyPoO6LEyFKY6itQFzWXUWnzNbZlxeWE7Xwmo4lZaTPZ1CQRH70JtkB9l1IqCbgD+A7wL+D2sK1KiDDaV2tlYhBXTAeqY1K9dAmFTWmdr+urvw5q8OVQt9jdbDzQFOZVCSFGE7vLw+6ato43eeOTomlzuLt0lL29oxqnx8sDly1i5oRE7nhmM1XNUmAToVHTaicj3hL00LoJSYc6syYNoIM6Nc5MVlI0T39xAICLl04EoKz+UIH6ze1VJERHsWxSWtDHHaiMeOmgHg5VLXbMRgNrv38SPzpzOn+5ZEFHUVOMfsFEfGwpb2J2diJRvfy7L8xLwRJl4HvPb2HeL95iwS/e4tz7P+L2VZu4951iXtxYMWr+r2460MS8iclHxPDPGRMSuWJZPgCzs4ZaoB65FyZCV6A209Tee6f4gUbfz8Bg6wFfWZiDyah4+vMD3R7TWlPdYh+zOehjXVpMGunRaTyydzV/2PoEANmJ4b2wOydjHpubSvFqaaLrSVCvWrTWr2qtm7XW27TWJ2qtF2mtXw734oQIh50HW5gxIfRX2wPZU6Plhd1oVNpgY1yiJaju9+VT0zEaFO8V1QzDyoQQo8WemjY8Xs308b6fA4HiX+cC9KtbKslOjuGoglT+eukCHG4v33p6Ix4ZgitCoLbVMaA3s2nxlo5bugfSQQ0wMyuJNoeb5FgTi3JTyEywdNyN5PVq3tlZw0nTMzuKX+GQmWjB6vRgHQOxAiNZdbOdzEQL2ckxXH/c5LC81hWRY/YXnR29FKhdHi/bK1uYm5Pc6zESo02svuN4Hrx8ET86czpnzJlAnMXI5/sb+PPq3dy2ahO3rdoYjuWHlNXhpri6lfkTkyO9lGHz7dOmcfMJkzl1kMNsR0sHdWLM0AcNpsSaaOyjEH+gwRfrFmyBOi3ewmmzxvPCxnLsrq7DElva3Tjc3o47qcXoc+P8GzEZzXxYtZFEUyx5iflhPd+8jHm0umzsa6kM63lGqz6/Ayil7gN6fTemtb415CsSIoya211UNLXztaNzQ37saJORhOgoanoYtiVCo6zeRl5qcN1jidEmFuYm88m++jCvSggxmuytbQNgSqav0Dcu8VCBekpmPI1WJ2t313HN8gKUUkzKiOeuc2fz7Wc285fVu7n9VBk4JoampsVBXlrwd3IZDYpxCRYqm+0DLlDPykrknZ3VLJuUhsGgyEuL7Yj42FvbRoPVyYqp4R1UdCgCzUGBZejFB9GzqhZ7x/czMfYECtS9dVAXV7ficHuZm9N3h21OSiw5PQyHs7s8/PGtXTy0dj81rfagM/IjYWtFM14NC46gAnVitInvnT590M+Pt0RhMqojooM6KcbMzoM9x3GAr4PaZFQd8VnBuHRpLv/bcpA3t1fx5fmH8pOr/fFV8r139Lp14be4dcGtaK8Dt9uKyZwc1vPNzZgLwIbaXUyZEtZTjUr9tUusA9b38UuIUaXoYAtA2LpKxiVGS8RHGJXUWwf0pn5SejwVjd2Hnwkhjlx7a9owKMhP930vCbxBqfJfXHxzexVur+7I6wU4f2EOX1mYw33v7uaTvXLRSwxNTau9466rYI1LisZsNJATRGZmZ4Eom2OmpAOQmxrXEfGxvrQRgIW5yQM65kBlJMgdZsOhpsUxoIKLGF0MBkWUQfWaQb21vBmAeX10UPcl2mTkwsUT8Wp4Y1vVYJc5LDb74/v6K8aLQ5RSpMaZR2wHtcPtwe7yhqRAPT7JQlWLnUc/LkHr7r2WBxpsZCXHDGjY5LJJaeSmxvLkZ12HJVa3SIF6TFAKZYzGZEkDFdo5ZYebnjodk8HE+ro9YT3PaNVnG4PW+tHhWogQw2Gnv0A9M0wF6swEixSow6Td6aGmdWBdZ+OToqltc+DyeCWHUQgBwN5aK7mpsViifC9Ax/sjPgJvMl7dcpC8tFhmZ3f9OfGLL89i44FGvvX0Rl7/1grS4uV2TjFwDreHRptrwN2JheMSUNBrtmxvVkxN55aTpvDl+b4LLnlpsTy/wY7d5WF9aSMpsSYK0oPPtR6MQzM65A6zcNFaU9Vi54TCzEgvRYSROcrQ0UHt8njZX2dl58EWiqpaeWt7FUkxpgG9Tj7ctHEJTBsXz6ubD3ZkHo9Emw40kZsaKz+HBygl1jxiO6ib233rCkWB+trlk9hR2cLPXt7Ou0U13H3h3C4/cw80tjOxh7sI+mIw+IYl/v6NXeyrbWOS/26mwJBtGZIogmU2mrl69lXMHT8z0ksZkfqL+HiFviM+zgn5ioQIox0HW0iNM4ctJyozwcL6ssawHPtIF7glOTct+DfSE5Ki0dp3W3F28sC6zoQQY9Pe2rYuMQnRJiPJsSaqmu1UNrXz0d46vnnilG6Dl+IsUfz1koWc+7ePuOPZzTx85RIMA+i+GQib082vX9vJLSdNla6cMSbQRTzQN7M/P2cW7kFkoEebjNxxWmHH3wNDhssbbawva2RRXkrYh4wFOqhrWuQCfri0OtzYnB7GJ0mRZCwzRxl4a0c1n+yrZ3d1G05/N7XJqJicEc8tJ3X/2TVQZ83N4p53iqlqtndcwB1pNh1oYkl+aqSXMeqkxZtptPU+PDCSWvwF6sQQFKhT4sw8fNUS/vNpKXf9byen3/shvz1/DqfNGg9AeYON0waR5X3Bohz+9FYxT39xgB+dOQM4dOF1JEfiiJHnl8t/RUZGeOPVRqv+guD+MCyrEGKY7DzYyowJCWF7M5aZGE1NiwOt9RExVXo4ldRbAcgLcqAFHOqMrGpulwK1EGPMw2v3887Oap687uign+PxavbVWTl+WtcXheMToznYbOe59eVoDRctntjj82dmJfKTL83gJy9t519r93H9cZOH9DH05pXNlfzn0zLm5SRzYS9rEaNT4C6rgb6ZjTaF5pbTXH935aYDzeyrtXLBopyQHLcvKbEmTEZFbZsUqMOlulluMz8SzMpKpLi6jfz0OJZPSWf6hASmj09kckZ8yAadfmnuBP70djH/23qQa5YXhOSYoVTdYudgs515R1D+dKikxJrZXtkS6WX0qLndN0Q3FB3U4Is0uXxZPssmp3Hbqk1c//h6Ll4ykTtOK6Te6uwxh70/mQnRnDJjHM+tL+eO06ZhiTJS0+IgITqKGHN4YyGEOFL0F/HxfuDPSikzEJgMtEtrPTLvDxGiF26Pl13VrVy5LC9s58hMsOBwe2lpd5MUG5ofsMInkJmZP6AOal9R+mDz6L6t2OvV/OXd3XxlYU7QE6fF2KO15qG1+zl5xriw35I/Gry29SCbDjQN6IJgeaMNp9vbbdDcuMRoDja3s/NgC8unpPf5/+yyo/P4eG89v39jFycWZjJ1XMKQPo6ePL+hAkAKemNQoIs4I0x3cvUncJH3xY2+r7GFuSlhP6dSiox4i3RQh1EgQ18yqMe2J64N/oLsYE3OiGfGhET+t6VyRBaoN/nzp+dLgXrA0uLMNFhHdgd1qArUAVMyE3jhpmO5551iHnh/L+/srAYY9PupS47K5Y3tVby9o5qz5mZRLcNphQipoC61KqVOAHYD9wN/A4qVUseFb1lChN7+OitOtzdsAxKh022skrMYcqUNVpJiTAMq/HcMPxvlBep9dVbufWc3tz69Ec8gbvEWY8PBZjt3/W8nj39SGumlRJzT7WVLRTNur6bV4Q76eXtq2gCYnNm1wD8+MZrtlS1UNLVz0ZK+O5aVUtx17mwMSvHEYcNyQuFAg43P9zcAUNc6Mt9IisGrDdwOHKG8ytQ4M3FmIx/trcNoUIMeqDZQGQkWeW0URlXSQS1C6Ky5E9hQ1tQxm2Ek2XSgiSiDYlZW+N7PjVUpcWaa2129DtqMpOYQRnwczhxl4PunT+fp647umD9SMICGp85WTEknOzmmY1iir0At0UpChEqw9wL9EThNa3281vo4YCVwT/iWJUTo7fAPSAxngfrQICDpEgq10nrbgAe/JMZEEWMyjvoO6jp/F+XGsiYe/GBfhFcjIiVwW+a2iuYIryTytlc2dwyKahxAN9DeWn+B+rAO6kAcUHKsidNm9p9LmBZvYeXs8bywoRy7yxP0+YPxX39na2J0lHRQj0HVLQ6MBkVaXGTe0CqlyE2LQ2tfXMBw3ZackRDdkb8tQi/wunOkZgaL0SUQg/XpvvoIr6S7TWVNzJiQGLLYoyNJWpwZYETmUIdySGJvjpqUxuu3reDhqxZ3G4QdLINB8bWjc/l4bz0/eH4LlU12xkn+tBAhE2yB2qS13hX4i9a6GJD8AjHsWu0uPtxdO6jn7jzY2jFEJFwCHVHSJRR6vgL1wK52K6WYkBTdcevraFXf5nshOX18Ave8XcyuqtYIr0hEwg5/gXp7ZTPeI7yTfkNZU8efB3K76t4aK+nxZpJjzV22B4o65y3IDvpN7yVLJtJid/P6toNBn78/Wmte2FDO0ZNSmTYugTop6I05Na120uPNGMM0YDMYgZiP4Yj3CMhIsEiBOoyqmu0kxZikaCdCYsaERBKjo/hk78gqUFe32PmipIFlk9MivZRRaUqmL5Ls3Z01EV5Jd8NRoAZIjDZx0vRxQ5oVdf2KSXzjxMk8/cUBqlrsZMqdK0KETLAF6nVKqX8ppU7w//onsC6cCxOiJ8+vL+fyhz4fVAF4x8EWpmQmhGyISE8yZVJ9WLg8Xiqa2gc0IDFgfFL0qI/4CHRQ/+WSBSRER/Gr13ZGeEUiEnYc9HVOW50e9tVZI7yayNpQ1tjx5yZb8CMx9tS29XiRck52EgnRUXztqNygj3X0pDTy02J56rMDQT+nPxvKmiipt3H+whxfQU86qMecmlbHgAckhlrgbqSFecNXoB6XaKHe6gz5HQfCp6rFLvnTImSMBsXSgrQR10H9xGdleLQe0M9qccjRk1KZm5P0/+3dd3hjZ5k28PtVL5Z7Hdfx9D7JtLRJ7xBC6CQQCCXAB2GXtuyyLGVpuyxl6SFZINRAgBAgpPcySSaT6X3smXHvRZKtLr3fH+ccjT1uknxkWfL9u665MpGP5Nfj17L0nOfcD378TDMi8yzmw+0Pw2ExwmxM3/t0vZiMBnzmmpX45fu2YkmZE9sWF2d6SUQ5I9FngI8AOAzg4+qfw+ptUxJC/FwI0SuEOHjW7XcIIY4KIQ4JIb6p3tYghPALIfaqf+5M/kuhhWBILUSc7vcldT9/KIpDHW6sqtJ/mNVYeVYlUoIRH/rqHPYjGpOoSzLiA8idArVBKLEEV6wqx5Gu+TmBm9LrUKcHS8uV4mouxnzsaxuOD26bye6WIayvKQCQeAe1lBJNvSNYUj6xQL22ugD7v3h1vLsoEQaDwNu31GHn6cF4tvVsPXSgC1aTAdetrURpnjV+copyR68nGD+ZnSmrqvJhNgpsaZi7ArX23DX2Z6Vj2M8rgnTS4wmggvEepKPzl5Tg9IAPXW5/xtYQCEfjJ7WCkSh+90oLLl9RnvQVlaQQQuBjly1F66APf9/fmenljOP2h9PePa23S5aX4clPXYrLVpZneilEOSOhArWUMgjg1wA+JKV8k5Tyu+pt07kHwLVjbxBCXAbgRgAbpJRrAHxrzIebpZQb1T8fTvgroAVlRB2GdXog8e7BUCSGj/z2NQz6Qrhhw6J0LQ2A8ou/It/KArXOTg8oJyQaUnhBWlVgQ48nkNXDBftHgih2WmE0CNQUOdDnDbILbYFx+8NoH/Ljxg2LYDUZcCAHC9Q/fLoJn7xvL07P0B3e5fajyx3AFSuVrOhEsxQHR0Nw+8NTxjylcrnnWzbVwGQQ+MOr+gxLPNjhxppF+XDZzChzWTHsC8eztik39HozfznwGzYswnP/chmqCuxz9jlXVionf473nClI/8cDB/G2n74Uf21Hqet2B1DJQV2ko/Mala5QrYu61xPAay2Dc7qGL/z1IC78r6dwoN2Nhw50oX8khPdc0DCna8g1V66qwMpKF370dLOucXFSzu6xPFlYoCYi/U1boBaKLwkh+gEcA3BMCNEnhPjCTA8spXwOwNm/xT4C4L+04raUcv4FING8NhJQ3sS0DiTWQR2JxvDPf9iDZ4714es3rcNlK9J/hrPcZUNvlmcezzet6gmJZIckAkBlgR2RmMRAFnci9o+EUJqnZObWFisFhfah5K4ioOym5U+vry3Eqqr8nCxQN/WOICaBHz/TNO1xu1uGAQCXrCiDySASLlBrnZtLJ+mgTlWZy4orV1Xgz7s7EIzM7qSRlBJHujzxQb6leUqxaWA0e5+7aLxINIaB0VDGO6gNBjGnxWkAqC9xwmI0xDumpZTY1zYMtz+M37zcMqdryTWRaAz9I0FUMOKDdLSqMh8FdjNeah5AOBrDe37xKt5650tzGvux89QgBkZDeOfdL+N/nziBJWVObF9WOmefPxcZDAIfvWwpmnpH8Mihbl0e80t/O4S33PnSrArebn8Y+SxQEy14M3VQfwLAhQC2SCmLpZTFALYBuFAI8YkUPt9yANuFEK8IIZ4VQmwZ87HFQog96u3bU3hsWgC0LpuWwTPFuV5vAEe7J0YexGISn/3zATx0oBuff90qvHPr3OSVleVzEJDeWgZ8sJkNKb2pr1LfsHVlccxH/0gwXqyqLVKK9G1DmbvkkubeYTXWZXVVPtZVF+BwpyenBiUGwlG0DIzCYTHi/t0daBuc+gTM7tYhWE0GrK7KR6HDgsHRxDKom/uUE11LyvS9NPgdW2sxOBrC44d7ZvU4Xe4APIEIVqoF6jL1+Y6/T3JHlzsAKc8MVF5IzEYDlpTn4ZjaQd3pDmBgNASL0YD/e/4k/CFeFZSq/pEQYhIsUJOuDAaBbYuL8fLJQfzshVM40uVBocOCj9+7Z9zvJSU+y4vf72zFp+7bh6u/+yz+ujexuK7pjAYjaBn04ZZtdagqsKFlwIf3XNAwq+F2pLh+XRUWlzrx02ebZ935DAAvNvXjtZYhPJTi0OhoTKJt0IfiswZYE9HCM1OB+t0A3imlPKXdIKU8CeBdAG5N4fOZABQDOA/AZwDcJ5TfMl0A6qSU5wD4JIDfCSHyJ3sAIcTtQohdQohdfX19KSyBspk3qHVQn7kE/Ov/OIKbfrRjXM6wlBJf/vsh/Hl3Oz5x5XJ8YHvjnK2x3MWID72dHvChrtiR0ovSyoJcKVBrHdRKgbp9mgIe5Z7DnR6Uuawoc1mxrroAI8FIUlFH892p/lHEJPCJK5dDCOCnzzWP+7iUEiPBCFoHfHj55ADW1xTAYjKg2GnGUIIZ1E29I7CbjVikc+fo9mVlqC604/c7ZzcsUTvRukqNQtB+5plDnTseVbvVzmssyfBKMmNFRV68g/pAu3IVyKevWY7+kRB+t1OfmJyFqFu9ao9DEklv5zWWoHXQh+88fhxXr67Abz+wDW5/GB+/dw/ufLYZH/jlLpz7lcdx5Xeew7/efwBPH+tFy4APTx+d/UXSx3q8kBK4dEU5/vjh8/HVN67F27fU6vBVkdEg8L4LG7Cv3Y3XWoZmvsM0gpEzg7u/89jxlIYvPnqoG53uAG7cmN4oTiKa/0wzfNwspew/+0YpZZ8QIpVrMNoB3C+VU3U7hRAxAKVSyj4AWuzHa0KIZijd1rsm+dx3AbgLADZv3pw77WOUEG9AHZI4JuJjf7sb/nAU33z0KL7zto0AgP959Bh++VILPrh9MT5+xdI5XeOiAjtGghEMjoZQ7OSZYD20Do6mPBClSi1Qd2dwyMts9XtD8Q7qsjwrLCYDO6gXmEOdSjYxoAz0A4ADHW40TpGnnG20XNrty0txsr8W973ajpYBHwZHQxgcDWFgNDQui/mjly0BABQ6LAlHfDT3jaCxzAmDQd/uK6NB4O1bavGdx4+jdcCX0jBXADjSpfwbrFAL1Oygzj337+7AhpqCKXPQc92Kynw8sLcTbn8YBzvcMBoEbj2/AU8e6cVPn23GLdvqYDMbM73MrKM1aFRySCLpTDuZZjEa8OUb16CqwI4vv2EN/vX+A3jp5AAaS524clUFtjQUY1NDERpLnXjHXS+jXYfXqEfV34krK10odFjwrvPqZ/2YdMabN9Xg248fx93Pn8TmhuKUH6epdwTRmMQNGxbh7/s6cf+eDrxtc+InEqSU+OmzzWgoceDqNZUpr4OIcsNMBerp3vUl9o5wvAcAXAbgaSHEcgAWAP1CiDIAg1LKqBCiEcAyACdTeHzKcVoGtdsfhtsXhsEAnOwfRYnTgvt3d+A95zfghaZ+/PiZZty8rQ6fu37VnF8KtlotIh3scOPi5WVz+rlzUSwm0Trow8XLUvu3LHZaYDEa0O3JziKPLxSBPxxFqVqsMhgEaors00YgUG4JRqJo6h3B5eqU8GUVebCYDDjY4caNG6szvDp9NPWOwGgQWFzqxEcvW4IDHcPwBCKoyLdhVVU+SpwWFKt/SvOs8TfNxQ4LTvaPJPQ5mvtGcG5dUVrW/9bNNfjfJ47jD7ta8ZlrVqb0GEe6PKgttsNlU87/ayel+kdSeblF882xbi8Od3nwpRtWZ3opGbOiUinMH+/xYn+HG8vK82AzG/HhS5bgtntexY7mflyuDj+lxPWoHdSM+CC9rax04aKlpbjpnOp4bv07ttZh9aJ8LCq0x39PjVVT5MCLTRP625J2tNuDPKsJNUVzm5e/UDgsJtyyrQ4/fqYZLQOpNwJpV8V8/PKlaBkYxfeeOIEbNy6C1ZTYycaXTw5iX7sbX7tpLYw6NxAQUfaZqUC9QQgxMdwXEACmfRUkhLgXwKUASoUQ7QC+CODnAH4uhDgIpcD9HimlFEJcDOA/hRBhADEAH5ZSzu2YYMoKI8EICh1mDPvCaBkcRSCsdNR94YbV+MqDh3H7r3ehxxPEGzcuwldvXJuRnLK1i850N7JAPXu93iAC4VhKAxIBQAiBigLrnHVQ/31fJ1w2Ey7VaSBnv1cpTpWM6cavLXKgjUMSF4wTPSOIxCTWqM8tZqMBqypdOTUo8UTPCOpLHLCajKgpcuDBOxIbRVHkNGOwZeYMan8oio5hP966KT2XB1cV2HHZinLct0uJlTIZZ0pQm+hIlwcrK8+km9nMRrhsJnZQ54i/7OmA0SDw+g0L9xLmFer+PtrtxcEON65QT7ptXVwMgwD2tg6zQJ2Cbk8AJoMY9zqBSA8Gg8BvPrBtwu3rawqnvE9tsR093gCCkWjCRcrJHO3yYmWli5nTaXTr+Q2467mT+OlzJ/HVG9emdIXZsW4vLCYDFpc68emrV+DWn+/Efbva8e4EO95/+lwzSvMsePO5NUl/biLKPdO+g5JSGqWU+ZP8cUkpp434kFK+U0pZJaU0SylrpJQ/k1KGpJTvklKulVKeK6V8Sj32z1LKNVLKjertf9fzi6TcMRKIxC9zPz3gw0G1QHN+Ywk+ffUK9HiCuHp1Bb711g26X8adqAKHGXXFjvjaaHZ6vdqlq6l3UFTl2+csg/q7TxzHz144NfOBCepT82dLxwyIrC22o22QER8LxaFO5blEuzoDADbUFmJ/uxvhFLL+5qMTvV4sK08+9qDIYcGwLzTjkJ+T/SOQEliawudI1JvOrUGfN4jdrcNJ3zcQjuJU/2g8f1pTlmeNPwdQ9orFJP66twOXLC+btONwoVhUYIPLasIzR3sxOBrC+hrlpJvTasLyChf2tvN1Uyp6PAGUu6wZe91LNFZNkQNSAp3Dqb/ullLiSLcHK6tcMx9MKavIt+Gmc6rxu1dace5XH8eHfr0LHcPJvb842u3F0rI8mIwGbF9Wii0NRfjRU00IhGcefHuybwTPHOvDey9oYLwTEQGYeUgi0bwRi0mMhCJYXaUUaVoHRnGw040ylxXl+Ta8fUstfvfBbfjBzeek1L2mp3XVBTjYyTdaehhUB6AVO1OJvVdUFtjiQ4TSrd8bhEeNotHl8dTiVNmYokZtkQNufxiewMydo5T9dp4aQrHTgvriM1cRbF1cDF8omhMnwkKRGE4P+LCsPPk3osVOCyIxGR+gO5XmPmWAz5Ly1C5hTcT25aUwGQSeSmE41ImeEcQksKpq/HzoUpcV/eygznqvnBpElzuAN56TG5E8qRJCYHmlC88cV4aca3n6ALCxthD72oZnPNlEE7UP+VHB/GmaJ7RIjvZZXOnX6Q7AG4iMu6qI0uM/b1yLb791A65aVYFHD/XggT0dSd3/WLfS6Q4oz/GfvGoFuj0B3JvA4Ntdp5UBjdetq0p+4USUk1igpqzhC0chJVDusqHcZUXLgA+HOz1Yq3YVCiFwwZLSWV1Oppc11floG/RjOMHhXTS1YZ9ShC10pH7palWBDV3uQNrf+AYjUXgCEYzoWDgeUPNnx3bd1aqFSuZQ5z4pJV5q7sf5jSXjuuO2LVYymF85lf1pWKf6RxGNSSyrSL67WXteGBqd/rm2qXcEBgE0pJixmIh8mxlbGorx9JgC9V/3duAjv3kNv9/ZOm1Ux5EuJU1t5VkFanZQ54anjvbAajLgqlWMr1he4UI0JmE0iHEnZDbWFsLtD48bgk0zaxv04dXTg7hgSUmml0IEYGyBOvUr/Y6qvxNXsYM67WxmI968qQb/89YNWFqeh12nE39dOewLodsTGNfpfv6SEpzfWIIfPd0Mf2j6LupDnW44LUYsTuNrMyLKLixQU9bQBiTm2UyoL3HgeI8XJ3pH4rms88k6tSvoYMdkEe6UjCG1yF80iwJ1ZYENoUgMQ770dhxrxWRvGjqoi8/KoAbAmI8FoGXAh053AOefVXwoc1mxtDwPL58cyNDK9HOiVxmwk0r8hnZlxUw/2819I6gtdqT9EtIrVpXjWI8X7UM+BMJRfOXBw3jiSA/+9f4D2Pr1J3DTj1/Ej55uwvEe77gTZke6PbCbjagrHp+1X8YO6pzQMuBDXbEDdkvmT6BnmtZpt7zCNe7ncUNtIQBgb9tQJpaVtX7zSgsMQuCWbYnlvRKlW2W+DSaDmFUH9VF18N7yChao59Lm+iK81jKEWCyxhh7t+7TirE73T169HP0jQfzm5ZZp73+w04PVi/IZT0REcSxQU9bwql2pLpsJ9SVO7Gt3IxqTWFs9/y7/GjsokWZnyBeGEECBPfWID23y+OmBUb2WNSmtQ1LvAnWB3QyL6czTdW3x7C+fpOywo1kpQE/WHbdtcTF2nR5CJMtzqE/0KN3NS8pSy6AGZu6gbu4dSenxk3WZOvTt6aO9+PPudvSPhPDL923Fw/+0HZ+8cjliMYn/efQYrv7uc7jkf57Bf/79MHY09+NQhwcrKl0TJtiX5lngCUQSynKk+attyB+/8mWh0wpO68567ba8wgWHxYi9KWS4J2J/+zCiCRZdskUgHMUfXm3D1asrsKgw9TkdRHoyGQ2oKrTNqoniSJcHtcV2uGypv/an5G2qL4InEEFT30hCxx9TC9Qrz5qfsaWhGNuXleLOZ5sxOkUEWzQmcbjTMy8bzYgoc1igpqyhZYzmWU3jsljn4y+2IqcFNUX2hPJhm/tG8LV/HE74bPVCM+wLId9mnlC4ScbWxcUwGgSeOpJ8NmwytG5nfziq2/C6/pEgSvPGd48X2M1wWU2M+FgAdjT3ozLfhsWlEy9/PK+xBCPBCA51ZveVGk29I6hLsbtZK1APTlOgjsYkTvaPpnVAoqax1In6EgeeONKLu587iQ01BTi/sQSrqvJxxxXL8NePXYRXPncFvn7TOiwtz8NvXmnBzXe/gp2nBye9lLlMHY46MEMBnuYvKSXaB32oLWIBEVCGveZZTbhwaem4240GgXXVBWkZlPjc8T684Ycv4pGD3bo/dib9bV8nhn1h3Hp+Q6aXQjROTaFj1h3Uq5g/Pec2NxQDOJMNPZOj3V4UOswod00c/vuJq5ZjYDSEX750etL7nuofgT8cHTeLgIiIBWrKGlrEh8tmQr1arCmwm+NZZ/PNuuqChDqoHzvUg7ufP4VeXsY9qSFfGEWO2XVQFDstOK+xGA8d6EprDnX/mKzYEZ26qPtHQijJG//CTwiBmmIH2maR70fzXywm8VLzAC5YUgIhJp6g2daovJF45VR2x3yc6PViaQoDEgHlZCBwJgpoMh1DfoQiMSwpS3/GoRACl60ox7PH+3B6wIcPXbJkwveuIt+Gm7fV4efv3YK9X7gKP333Jrzn/PpJL9HXsueny6+m+c3tD8MbjLCDWlVgN+PVf78Sb9iwaMLHNtYW4kinB8GIvlcM3PXcSQDAvvZhXR/3SJcHf9nTrutjJkpKiV/uOI3lFXk4T/1dQDRf1BbbU86gDoSjONk3MmEmA6VfQ4kDJU4LdrUklkN9rNuDFRWuSV+jnltXhMtWlOGu507Gr4IeS2uumI9XQhNR5rBATVljJN5BbY53UK+tzp/0l+J8sLa6AK2DPrhnyEbVfmlP1wG4kA37QvEi1Gxct7YKJ/tHcazHq8OqJtc/cuZ7qFfMR/9IEGV5EzsTaorsjPjIccd7vRgYDU3In9aUu2xoLHPi5ZPZOygxEo3h1Cy6m/NtJhgNYtoCdVOf8jM/FxEfAHC5GvNRX+LANWsqpz3WYTHhmjWV+PKNayftItI6qJlDnb20y9xrilig1tgtxklfu22sLUQoGsORLv1+Tx/scOOFpn4AwGEdrzZ59FA33vTjHfjEH/ah1xvQ7XET1dw3ikOdHtyyrX7evg6mhaumyIFebzCleKrnT/QjJpU8ZJpbQghsUnOoZxKLSRzr9k6I9xjrk1etwLAvjF+8eHrCxw52uGExGebstRkRZQcWqClrjB2S2KBO+107D+M9NFqx4VDn9F3UHrVAPTxNgWUhGxwNzWpAouaaNZUwCOChA+m7xHdsl6Nnkm6BVPR7J0Z8AMqgxLZBf1o7wimzdjQpndFTFagBJebj1VODWZutOhKMIByV8UJssoQQKHKYMTg69c9bc6+SPT9Xb4K2NRZjbXU+PnX1illFEwFjOqhHWKDOVq1qFNPZAzBpovigxFb9BiX+3/Mn4bQYcd3aShzu8ujyO/MXL57Ch3/zWvzqrgNpiCWZSZt6gprdhzQfaVe3dgwn30X98IEuFNjN0772ofTZ3FCElgHfjFdudQz7MRqKTtvpvq6mAFetrsDdz5+E2z/+ddrBDg9WVbpgNrIcRURn8BmBssbYDOoChxk/vuVcvH/74gyvamrrqhMblKh12g6yQD2pYV8YhbOM+ACUTsSti4vx8IEuHVY1ubERH3p0UAcjUXgCkXiRaqzaYjv84SizaXPYjuYB1Jc4pu283La4GN5gJKG8+/nozJUxyedPa4oclmlP8DX1jqDEadHlSoxEWE1GPHjH9kkjDJJVop6cYgd19tIKidpwW5paVYEN5S4r9ulU8O0c9uPv+7vwjq11OK+xBIOjIXR7ZtftfLJvBF/9xxFcsbICf7/jIhgEsD8DBeoet/J1VBZwX9H8o71uSTbmIxSJ4fEjPbhqdQULlxmyqV6JDHpthpiPo+qAxBXTdFADwCeuXA5vIIKfPX8yfpuUEoc63VjD/GkiOguf+SlrxDuorSYAwPXrqlDusmVySdMqdlpQXWifsUDtUc8oD7HQOKkhnz4d1ICyZ070juBEmmI++rxB2NVBb5PlrSVLi305O4MaUDqoAXBQYo6KRGN45aSSPz2di5eVwWI04P7dmclBna3RoHL5r1N9Xk9FkdMybURSc99I1l5CajUZUWA3s4M6i7UN+lDoMMNlm/2J1lwnhMCG2kLsbRvW5fF+8eIpAMBtFzZgzSKly2+2MR/fe/IELEYDvvGmdSjJs2JpeR7265xtnYgudwBCYNLhZESZpp2QSzaK7sXmfngDEVy/bvp4LEqftdX5sJgMMw5KPNatPJcur5i+QL16UT6uX1eJn794Ov5et33ID08gMq+vhCaizGCBmrKGNxCG02Kc9SXTc2ltdf6MnY1ap+3QDFnVC1EwEoUvFJ31kETNNWsqIdIY89E/EsRidYCnHh3U/V7lhdykER/q5eIclJibDnV64A1GcP6S0mmPK3JacP26Sty/uwOjQX1yz+fSaEhZ86wK1A7ztBnUzX0jWJJixvV8UOayjrs6g7JL25A/fkKRZraxthCn+kdnHXvmCYRx7842vG5dFWqKHPHL0GdToD7e48Xf9nXiPRc0xGOJ1lUX4kCHe87jtrrdAZTmWdllSvNSucsGs1Ek3UH98IEuuKwmXLh0+tc+lD5WkxEbagqwa4Yc6qPdXtQW2+ONY9P5pyuWYzQUwd1qF7X23lg7cUhEpOGrGsoaI8EI8mypFzEyYV11AU4P+KbNI/ZwSOKUhtWifaFOHdQV+TZsri/CwwfTE/PRPxLC4jKlQD2iQ7FQK0qVTtIhpeX7sYM6N+1oVvOnG2fOYHzXefXwBiP4+77OdC9Ld6PB8VfGpKLYaZnyBN/ASBBDvjCWqD+X2aimyI5j3ekb7krp1T7oY7xHEs5Rc6hnG/Nx7yutGAlGcPvFjQCU55iGEgcOd00sUH/38ePYeWrmYbPfffw4nBYTPqQ+JgBsqC1A/0gIne65HZTY5QmgqmD+XkVIC5vRILCo0J7Ua9RwNIbHDvfgilXlsJpSj/2i2dtUX4xDne5ph1we7fZiRUViBeYVlS68fv0i3LPjNO57tQ33vtoGo0HMGA9CRAsPC9SUNbzByKyKGJmgZWsd6pi6Y+dMBzUL1GfT/k2KdcyOvX5dFY52e9HcN6LbYwJKbp7bH0ZjvIN69h3xWoG6bJKID6fVhBKnJenLJyk77Gjux4oKV0LDAzfVF2FFhQu/eaUl64ZmagVqp2U2HdQWDI2GJv3am/uUAYlLs7iD+rIV5WjuG9X9OYvSLxaTaGcHdVLW1RRACGBv63DKjxGKxPCLF0/jgiUl8YHVgHKp+aGzOqhfaxnE9548gXt3tk77mAc73Hj4YDfed9HicXn28Xkjcxzz0e32ozKfBWqav2qK7El1UL9ychDDvjCuW1eVxlVRIjbXFyEcldg3RdxSMBLFqf5RrKpKvMD8T1csQzASw7/8eT9eONGHa9dWwmbmiQgiGo8FasoaI4EI8rIsw1F74zJdzEc8g5oRHxMMjWod1Pp9369dq+Ta6T0scWBUKSZXFdhhNRn0ifgY0TKoJy/Q1xQ70DbIiI+5FIxE8Y/9XdN2lejxOV49PZjwBHshBN51Xh0Odnh0Gy42V0bUDOrZnHwsclgQicn4IN2xtKJutmZQA8BVqysAAI8f7snwSihZvd4gQtEYaopZoE6Uy2bG0rI87JtFwffB/Z3o9gTwwTGdzgCwZlEBWgfHX9V293NKTvWJ3umvUvju48dRYDfj/ReNH869qiofJoOY8+feLjc7qGl+qy1yJNVE8dDBLjgsRlyyvCyNq6JEbKovAoApYz6aekcQjcmkOqCXlufh0X/ejic+eQmOfuU6/Ojmc3VZKxHlFhaoKWuMBCNwZVkHdWmeFVUFtikHJUaiMYyGlAINhyROpHVQ6zUkEVAKyOfWFeqeQ93nVeM48ixw2czw6FCgHvaHYDEZ4Jiiu7S2yI42dlDPqYcPdOOjv9uNG37wQtoGY+1tHUYgHJtxQOJYbzynGg6LEb99uSUta0qXeAe1NfUuGq2bcXg0jLZBH3o8Zy61b+odgc1sQHVh9kYsLCq0Y111AQvUWUh7fq4tyt79lwnaoMRUrgiRUuKu505ieUUeLj2r0LVazaE+2qUUo0/3j+LRw92wmQ1o7h1FLDb559vTOoQnj/bi9osbUWAff8LcZjZiRaULB+awQD0ajMAbiKCygPuK5q+GUif6R0JwJ9CAE41JPHqwG5etLGdX7TxQ5LRgSZkTr01RoNZix1YmGdGxtNyFpeV5sJhYgiKiyfHZgbLGSCD7Ij4AYG11wZQd1GNziplBPVE6CtSAEvNxuMuD0/2juj3m2LzofJtJl4iPQCgKh2XqF+q1xQ50DvsRneJNNemvS80ZdfvDuOnHO3TvxAeU/GmDALYlkD+tcdnMuHFjNf6+vzOhN4PzxUhw9kMSi51KwegHT53AFd9+Fnf8bk/8Y819I2gszYMhi4brTuaq1RXY3TqEXu/c5tzS7Gj5q7XsoE7KxtpCDI6Gkh6wBgAvNPXjaLcXH9zeCCHG/9yvXqQNSlRek/3shVMwGwz48CVL4A9H0eme/PN95/HjKHZa8N4LGib9+PqaAuxvT62gHo1JhKOxpO7TrZ6EYwc1zWdad+3R7pkHk+48NYiB0RCuX8t4j/lic30xXmsZmvTE3bFuLywmAxpKsne+BxHNTyxQU9bIxiGJgBLzcbJ/dNKCpRYDUWA3z3pifS46MyRR32gXLd/uIR2HJfZ7le9fWZ4VLptJl4gPfzgK2zSDYmqLHAhHZfzNKqVfrzcAp8WIxz95CaoKbPjz7nbdP8dLzQNYW10woVNvJu86rw6BcAx/SsOa0mU0GIHRIGCdRTeNNkT1j6+1w2UzYU/bUDyCpal3BEuyOH9ac/WaCkgJPHmkN9NLoSS0qgXqbO7gz4SN6qDEPVPkn07nrudOotxlxRs2LprwsXKXFSVOCx7Y24m7nzuJP77Whjeesyg+jPZE78Sc952nBvH8iX585JIlU55IW19TCE8ggpaB5K9o+ve/HMCbf7Ijqft0qydKK5hBTfPYqkrlhNCxnpmH/D5ysAs2swGXrmC8x3yxqaEIbn940vkXR7q9WFqWB5ORpSQi0hefVShreAJhuLK0QA0AhzsndhC41fzp+hIHRkPRtObaZqOh0RDsZqPul/tVF9qxobYQD+sY89GnDTR0WeGymfXpoA7HYJ+2g1opeiQzJZ1mp88bRHm+DQV2M7YuLsbeNreugwl9oQj2tA0lnD891ppFBTinrhC/zaJhiaPBCJwW44ROx2Qsr3Dh4uVl+O83r8N/vXk9wlGJAx1u+ENRdAz7saQs+zt8VlS4UFfswGOH9I0movRqG1QG2fGS9eSsqHTBajJMOaBrKoc7PXj+RD/ee2EDrJOc3BVC4Oo1ldjXPoyvPXQE0ZjEB7c3YlmF0unZfFaBWkqJbz12DGUuK951Xv2Un1d7nffMseROILUN+vDH19pxsMONYCTx13/alTzsoKb5rCLfigK7GUe6pi9Qx2ISDx/sxqXLy2d1NRXpa/M0OdTHuj1Jx3sQESWCBWqalUiSlyWmSkqZlRnUALCmWukgmCyHWuuyrVMv/x3Ookvz58KQL4xip77xHprr11biQIdbt+Ju/0gQeVYTbGajvh3U0xQ2aouUfcMC9dzp8wZRlmcFAGyoKUT/SDBeLNDDrtNDCEclLlhSmtL9b9lWj5N9o3jp5IBua0qnkWB01tFNeVYTfvW+rXj7lrozg31OD+FU/yikVAbzZDshBK5eXYEXmwbGRUPR/NM+5MO/3X8ATx3tQevgaPxEIiXObDRgbXUB9iZZoP6/50/CYTHilq1TF5O/8aZ1aP7a9dj3havx6r9fiWUVLhQ7LSh2WtB0VoH6xaYB7Dw1iI9dtnTak8WrqvKxqb4IX/nHETy4vzPh9d79/ElEYxIxCbQm0X3drUaRVLJATfOYEAIrKl04NkPEhxJfFcR16yrnaGWUiMWlTpQ4Ldh1enyBetgXQo8niJVVLFATkf5YoKaUPX20F2u/9Ch65yBewBeKQkpkZcRHucuGinzrpDnU2iR5LcNriDEf4wz7QrrHe2iu12I+dMoQ7h8JoTRPKabnWfUpUAfCUdjMUz9NLyq0QwiklNNJqekbCaLMpRao1cvQ9RyWuKN5AGajwJaGopTu//r1VSiwm/Hbl1t1W1M6jQYjunZMFTstaFQH+zSpl6UuKcv+AjWg5FCHojE8d7wv00uhafzgySbcu7MV77tnF149PRQ/kUjJ2VhbiIMd7nH5zE8e6cFdzzVPenyX24+/7evE27fUomCG1w0Gg0CBwxyPBwKApWV54wrUUkp8+/FjWFRgwzu21k77eEaDwD23bcG5dYX4+L178KOnm/DyyQH0eYNTXs3SPxLEH15tw1q1iWGyy+in0uUOoMhhZmc+zXsrK1043jMy7VVdDx3ohsVowOUry+dwZTQTIQTOrS/Cay2D424/qg5IXKFGuBAR6YkFakrZb19pQSAcSyhbbLa0jrE8a3qKlem2rrpg+g7qEuUN7FCODErc2zaMD//6tVl32A/6QroPSNTUFjuwrroADx3U55L5Pm8ApWpnrctm1qXLMRCOwj7NG1CLyYDKfBvahnK3g7p/JIink7xsOp36PGcK1KuqXDAbBfa2TT4ENRU7mvtxTm0RHJbUirY2sxFv3VSDRw91z8nJw9kaDelboAaATXVF2N06hKbeEQihdAHlgk31RSh2WhjzMY8NjATxl70dePvmWvz4lnNxzZoK3DBJFjLNbGNtIYKRGI51n3mNeddzJ/HjZyYvUD9+uAeRmMR7zm9I6fMtrcjDid4zhbSnj/ViT+sw7rhi2aRxIWdz2cy457atOH9JCf7n0WN4x10vY8vXnsD6Lz+GG3/0Ij5531786OkmPHygC8d7vLj7+ZMIRWP4+k3rAADNfYkPbe7xBFBZwM58mv9WVLowEoxM2UghpcQjB7tw8fJSuGzZ+R4vl22uL8LpAR/6vMH4bdpzMiM+iCgdWKCmlAyOhvDMMaWLay66N7VCbjZ2UAPAWnVQ4tlFS4+WQa1GfAzmSAf100d78cih7nguc6qGfeG0dVADwHXrKrGvbRjtaoH3ZN9I0pmXmv6RULxw6bKZMBKMIDrJ5OtkzBTxASgxH+2DudtB/csdp3HbL15NaAp8uvlDUXiDkfj32WoyYmVlvm4d1G5fGAc73CnlT49187Y6RGIS9+1qm/CxwdEQbvjBC+OKPpk0GozMOuLjbJsbijA4GsITh3tQW+TImS5Dk9GAK1aW48mjveO6Smn++N0rrQhFYvjgxYtx/boq/PTdm3HZCnYFpuLsQYnRmJItP+wLTzqvo2PID4vJgPqS1DrWl5blwe0Po38kBCklvvP4cdQVO/CWTTUJP4bTasJv3r8NL3z2MvzqfVvxxRtW440bq+G0GLGjaQD/8+gxfOS3u3H1d5/DT589iWvXVGJ9TSEq8q04mUSBussdYP40ZQWtiDnVa4597W50ugO4dm3VXC6LErRZvZrvtTE51HvbhlGaZ0W5+lqYiEhPLFBTSv6xvxMRtfjWPgfdm1phNxszqAGlg1pK4EjX+CLbhA7qHMmg7lXPtA+Nzu7rGUpjBzUAXKe+IH7kYDcGR0N4590v40O/fi2lAXP9I8ExHdTKPp1tF3UgHJu2gxoAaortOd1BfbJfedP+yx2nM7sQKN9jAPECNQBsqC3AgXY3YrM8GQEAr5waQEwCF8yyQN1YlocLl5bg3p1tE06SvNjUjwMd7nFvNjJpNBiF06pvAVnLoT7c5cmJAYljXb2mEt5ABK+cHJz5YJpToUgMv3q5BZcsL8PScnaWzVZNkR0lTkv8pPHxHi98IaUwPbabT9Mx7Ed1oT3lgataVn1T7wgePdSDgx0e/NMVy2A2JvdWSQiBmiIHLl5ehtsuXIyvvHEtfvfB8/Dy567AwS9fg79/7CJ87x0b8amrluPfX7cKANBYmoeT/YlHfHS7A8yfpqywXB1AOtXVtg8f6ILJIHDVqoq5XBYlaG11ASwmw7iYj10tg9hcXzSr4dZERFNhgZpScv+eDqysdKG22D4nHdQjWd5BrU14P9A+PgrAEwjDaTGixKkUvHIl4kOLFphNpnY0JuH2h1GUxg7qxaVOrKrKxz8OdOEzf9yHHk8Q3Z4AOpMceheOxjDsC8cL1PnqZYpeNWP8tZahlL63/lAU1mkyqAGlg7rbE0AwMrGjLBdog6P+sqcj4z8fvV5lX4wtUK+vKYQ3GIkX0mdjR/MAbGYDNtYVzvqx3rWtHh3Dfjx9dHw8yu5WpTDdP8urG/QyonMGNaAUe7QrL3JhQOJY25eVwm424rHDjPmYb/5xoBN93iBuu7Ah00vJCUIIbKgtjA9KHDswUXsuHqtz2I9FhakXbZdVKM8VJ3q9+O7jx9FY5sQbz6lO+fEmk2c1YV1NAW7cWI07rliGGjWfvLHMiZN9owmdHA+EoxgYDaEynwVqmv9cNjNqiuwTGnQAJd7j4YPduHBp6Yy58ZQZVpMR66sLsEttauj1BNA26I93VhMR6S1tBWohxM+FEL1CiINn3X6HEOKoEOKQEOKbY27/NyFEkxDimBDimnSti2bvdP8o9rQO443nVKOm0DFHER9KoU/vS8HnSnm+DWWuiYMSvYEwXDYzLCYDXFYTBnOlQK11UM+iQO3xhyElUORMXwc1ALxuXSX2tA7jyaO9eKt6Ke/uJLtLB0aUr7PUpaxV66D2BiIIRqJ4510v454UOoCDkekzqAElS1tKoHN4/ucNJ0tKidMDo9jaUIxAOIY/TBJZMZe0rr2yvDMF6o06Dkp8qXkAWxqKE8o7ncmVqytQ7rLit6+0jLtd29vzpUA9GtI/4sNgEDi3TnnzlCsDEjU2sxHbl5Xi8cM9KV3pQekhpcTPXjiFJWVOXLysLNPLyRkbawvR3DcCTyCMva3D8dt7PROfvzqHA6iaRS5zZb4NeVYT7nruJI71ePHPVy6H0TA3HYKNarzIQAKvAbWvnR3UlC1WVromjfg41OlB66AP16+rzMCqKFGbGopwsMONQDgaL1RrV6oREektnR3U9wC4duwNQojLANwIYIOUcg2Ab6m3rwbwDgBr1Pv8WAiRG6GROeiBvR0QAnjDhkWoKbLPScSHV4v4yNIOamDyQYkefwT5duVrKnJaMJwjGdRad9NsIku04nY6Iz4A4Lp1SszHlasq8PU3rYPdbJw0/iAak9jTOoS/7u3Aj59pwr//5QDe+4uduPI7z+Kybz0DAKhwKW8YXfEO6ghaB3wIRWNw+5P/t/CHEsmgVt6Qtw3mXszHsC8MbyCCq9dU4PzGEvz6pZZZD96cDa1AXZ5/pkC9pCwPDosR+8dcHRGOxnCo043fvdKKz/5pP2780Yu4/NvP4PxvPIl7Xjw15WMf6/HOOn9aYzYa8I4ttXjmeF98bwTCURzqVLqY5k2BOg0d1MCZN09LcqyDGlBiPrrcARzsyHwuOyl2tQzhYIcHt124GIY5KmouBBtqCyGlcvXZ3rbh+NVoPWcNgA1HY+jxBrCoMPUCtRACS8qcaB/yY0WFC69fN3eZuI1qFJGWQ/3dx4/jE3/Yixeb+ifER3W5laYQZlBTtlhR6cLJ/tEJV/o9fLALRoPAVatZoJ7PNtcXIxyV2N+uxMNZTQasWVSQ6WURUY5KW7VPSvmcEKLhrJs/AuC/pJRB9Rjt2uMbAfxevf2UEKIJwFYAL6VrfZS650/045zaQiwqtKOmyIFebxDBSFSXrr+paBEfLmv2XgK2troAzxzrhS8UgcOidtgGw/FiZpHDjMEcyKCOxmS8kDc8i45wrbidziGJgFJg/MPt52FtdQHMRgPW1xRgT+vEAvVvXm7BF/92KP7/hQ7lssUlZU5csrwMDaVObF9eCmBsB3U4Xmj3h5KL4JBSIhCZOYO6Vh2wmYs51C1qYbWu2IHbLmzA7b9+DY8f7omfVJhrfd4gDALxSB4AMBoE1lUX4NnjffjKg4exr20YBzvdCISVQnqhw4w1i/JRU2THoQ43/u+FU3jPBQ0TsvtePjkAALhgSalu633H1jr88Okm/G5nKz577Ursb3cjEpMwGgT6vZk/GRaMRBGOyrRcGfOmc6vR7Q5gfU3uvYm6YmU5DAJ47HA31uXg15eNfv7CKRTYzXjTufpGQix0G2sKAQAvNPXjeK8Xd1y+DEe6PPGrtDTd7gCkBKpnEfEBKCe09rW78Ymrls/piYal6pUeJ/tGsLjUiR8+3YSYlPjLng5UF9rxlk01eMumGtQWK5FeAAvUlD1WVOYjGpNo7h3F6kX5ANR4jwPdOK+xGMVpvlKSZkc74b+rZRC7WoawoaYQFhNTYokoPea6HXU5gO1CiK8BCAD4tJTyVQDVAF4ec1y7ehvNQ13DfpyndvnVFNkhJdA1HEBDafqGUWnD5vQepjWX1lUXICaBw50ebG4oBqB0UJfmKS/MipyWeFRENhsYDUJr+Jmpg/pghxs2s3HSnFgtbzjdHdQAsK3xTNfqpvoi3PXcSQTC47uXnz3eh7piB/7vPZtRXWiftutzbMSH9mbSH06uQB2OSkRjEnbL9Hu+It8Gs1GgbTD9UTtzrWVA6SZrKHViSVkeaors+MWO0xkrUPd6gyh2Widc9r11cTF+8FQTfvNyC9ZVF+CWbfXYUFuIjTWFqC0+M7Trvl1t+Jc/7cf+djc2qNEgmh3NA3BZTVirvnnTw6JCOy5fWYH7Xm3DJ65cHr8yYGtD8YQOxEwYDSo/E84Z9ngqqgrs+Mob1+r+uPNBkdOCrYuL8dihHnzq6hWZXs6C1zbow6OHunH7xUviJ59JHwUOMxpLnbjv1TZICZxbV4gylxU9Z0V8dA4rv/9m00ENAG/dVIt8mxnXrJnbgW2LCu2wmAw42T+Kkb0diMYkHrzjIpzsH8Ufd7Xh+0+dwPeePIELlpTEX3tUziLOhGgura5SBiW+cmogXqA+0uXFyf5RvO+ixZlcGiWg2GlBY5kTLzb141CHGx+8uDHTSyKiHDbXp79MAIoBnAfgMwDuE0mOgBVC3C6E2CWE2NXX15eONdI0ojGJHm8w3rlRo8YLpDuHeiQYgd1shCnJaerziXZp6tgcai2DGgCKHZZZZTbPF2OzIaeLLJFS4oO/2oWvP3Rk3O2dw374QpE5i/g427l1RYjE5LjIhkg0hp2nBrF9WSmWV7hmjCTIG9NBfUq9ZNeXZAd1QL0U0jpDl4LRIFBdaM/KDuqZYk9aBs50UBsNAu85vwE7Tw3iUKd72vulS583OG5Aouajly3FE5+8GAe/fA3+9JEL8B+vX403bFiEuhLHuE7pa1ZXwmwUeHB/54TH2NHcj22Nxbo/x73rvDoMjIbwyKFuvNYyhMZSJ5ZX5KFvHkR8jKonHh1ZOlsgk65eXYljPd74SRzKnF+/3AIhBG49vz7TS8lJG2oL49nMG2sLUZ5vmzAksdOtT4H6/CUl+NIb1ky4wiXdjAaBxSVOnOwbwZ9ea8eG2kKsrS7AGzYswq/fvw0vfPZyfPKq5Wgf8uPxwz3It5mydiYLLTxLyvKwub4IP3mmOX414bcfOwanxYjr1jLeIxtsri/Ci00DiMQkNjN/mojSaK6rfe0A7peKnQBiAEoBdACoHXNcjXrbBFLKu6SUm6WUm8vKOIhmrg2MBBGNyfj08Bo1XiDdOdTeQCRe9MtWFflWlOZZcGBMbqgncCaDutBhiXcNZzPtjaPRIKYtuB/t9qLLHcDAmEKZlBLXf/95XP3d5/BCUz8AoNA5t7Eu59QVAgB2j4n5ONDhxkgwknD8Qr6WQR2M4FS/UkAKJNlBHVBfxM/UQQ0oMR/tWZRBHY7G8M1HjmLjfz6G7zx+fMrjWgZ8qMi3xjvZ37a5FnazEb9MYeCkHvpGgiifpECtXAXggnmG4nKBw4zty8rwj/1d4wbctQ/50DLg0zXeQ3PxsjLUFtvxm5dbsKd1COfWF6E0zwpvIJL0ntSbdmUMCy3Ju2q10uH5+OEeAMDgaAhv/+lLugzrpMSNBiO4d2crrltbOeviKE1OG0S7uNSJQocF5S7rhCGJ2pDgRVncVdxY5sSO5gEc7fbiLerAZk11oR0fv2IZnvn0pbj3g+fh7ls3Z2iVRMkTQuCz161ErzeIX+w4haeP9uLJo734+BXLUJI38TUVzT+b64vjf+eARCJKp7kuUD8A4DIAEEIsB2AB0A/gbwDeIYSwCiEWA1gGYOccr40S0OVW3gRolxZWuKwwGcScdFC7sryIIYTA2uqCeAe1lHJ8B7XTjNFQdMIQkWyjvXFsKHFMG/Hx1FElgn7sMZ5ABMO+MDqH/fjr3k6YDGLOv+8leVYsLnVi95hBiTualXzg8xqLp7rbOFaTAWajgDcQwcn+EQCALxRJah1aJIgtgWz3miIH2tL8M6iX0/2jeMtPduDHzzRjcakT33/yRLzIdraWgVHUl5yJDipwmPHmTdV4YG/nuBMbc2WqDupkvH59FTrdAexuHY7f9pK6vy5Yqs+AxLEMBoGbt9Zj56lBDIyGcG5dEUrVr2EwwyfERuPRTdn93J4JtcUOrKrKx2OHlJ+dO59txiunBvHIwe4Mr2xh+fPudngDEV6mnkZaHJJWqK7It6LnrA7qjmE/ip2WhE7ozldLyvLgC0VhMRpww/rJY6wMBoHzl5SMiyUjygZbGopxxcpy/OSZZnzp74fQWObEbRfyeTNbbGpQitJLy/NQOMdXthLRwpK2ArUQ4l4oQw5XCCHahRDvB/BzAI1CiIMAfg/gPWo39SEA9wE4DOARAB+VUmZ3lS5HaQVqLeLDZDSgqtA2Bx3U4azvoAaUmI8TvV74Q1EEwjGEozLebVukDgkZzvJBiVo25PIK17Qd1M8cUwrUY2NAtL9/4fWr8ZZNNbhyVcWcX2oLKF3Uu1uH4l2uLzUPYGWlK+FODyEEXDYzOof96Fdzxf3q0LxEaUP2EuugtmNwNBQv+M1HUkrct6sN13//eZwe8OEnt5yLhz6+HetrCvDJP+zFyb6RCfdpGfShXr1KQ/Oe8xsQisTw+1fb5mrpAICYOvxztgXqK1dXwGI04B/7u+K3vdQ8gBKnBcvLXbNd5qTetrkGFrW7e5PaQQ0A/RmO+RhVrxLIy+LZApl09eoK7GpRIm+0qwoOdGQm/mYhisUkfvHiaWysLcS5dewoS5fVVflYW52Pa9YoUQAVLhuGfeFxJ/O7hv1YNMsBiZnWWKacjL1qdQULQJSTPnPtCowEI2gZ8OGLN6zhoL0s0ljqRHWhHRct1f9KPyKisdL2m0FK+U4pZZWU0iylrJFS/kxKGZJSvktKuVZKea6U8qkxx39NSrlESrlCSvlwutZFs9Ot5vxVjpkeXlPoSH8HdSASHzyXzdZqgxK7PPAGlEK09nUVq29IMt3VOFu93gCKHGaUu6xTRpa4fWG81jIEm9kATyCCSFQpxmrd1HUlDnzrrRtw57s3zdm6xzq3rgj9IyE0940iGIliV8sgzl+SXMeSy2aK51g7LEb4U+2gNs/8NF1bpEXtzN8u6s8/cBD/8qf9WF9TgIf/aTuuW1cFm9mIn7xrE8wmAz7069fGFdh9oQj6vMEJw1eXVbhw0dJS3LuzdVxMRroN+8OIxCTKZnk5ar7NjEtWlOEfBzoRjsYgpcSO5gGct6QEBkN6TsaU5Fnx+vVVKHFasKw8Lz6YNeMFanZQz8rVayoQk8AHf7kLMSmxfVkp9re75/TnYiF75ngvTnHIV9pZTAY8eMd2XKtm1ZbnK8/BY2M+OocDWR3vAQDrawpgNAjcvK0u00shSouVlfm44/JluO3CBlyynDGd2UQIgQfvuAj/et3KTC+FiHIcT11SUro8AViMhngxFVAGJaazMCalxOBoCE5L9hcxtEGJhzrd8KgF6ny70kGtdcxkew51rzeIinwbCh2WccXnsZ490YeYVAZ9AUq0B4B4x3Wmu4cuXVEGh8WIT923Fy81DyAQjuH8JC+pddlM8fzpVVX58YJzogLxAnViGdQA0DZPc6ijMYnfv9qGGzcuwm8/cN64rNbqQjt+8M5z0Nw3gs/8aV+8uDZ2QOLZrl9XhfYhP070Tuy6Tpc+r1IM0Yojs3Hz1jr0eIL4zcstONU/im5PABckeQIkWV+9aS3+dsdFMBjEmQ5q7/jnGikl3nfPq7hvjrrTtQzqXHhuz4TVVfmoLrSj0x3AO7bU4bq1VXD7w2idp88DuebnL5xGZb6NQ77mWLk6A6XXO7ZA7c/6DPCl5S7s/+LVuJAdipTDPnnVcnzxhjWZXgaloMhpSeg9CRHRbLBATUnpcQdQUWAd1+lXU+RAjzeQtuzkfxzowsn+UVy0LPtftFcV2FDitOBAuztelI13UKsRH9PlNmeDXk8AZS4rihxK4d3tn/j1PHO0F4UOMy5doXRQaIVprThflOECdU2RA997xznY3+HGx+/dA4NA0pmPLqvy9RsEsKLSBV8ouZ8PfxIF6poi5Y15W5qjdlLVrw5X3dxQDOMkXcIXLi3FZ69diYcOdOOu504COFOgri+ZWKC+fGU5AODJI70Jff5oTCIam11XqVagnm0HNaCcANm+rBT/+8QJPHRAifpIx4DEsRwWE6rVAo4WU9J3Vgf1nrZhPHW0F/fM0RDKUQ5JnBUhBK5fVwmHxYiPXb4U62uUE6D72hnzkW7Hur14oakft15QP+NwVNKXNqi216NEznkCYXiDkfjzWzbj1SRERES0kPFVNSWlyx1AZf74nL+aIjukBLqGA1PcK3Vufxhf/vthrKsuwC3b6nV//LkmhMCa6gIc6HDDoxZuz2RQK/8dnCa3ORtoHdRFUxTcYzGJZ4734ZLlZfGivJa7rR2rFbcz6arVFfi361bCE4hgbXUBCuzJrUk78VBT5ECB3RzviE5UUD3enkCBusRpgd1sRNvg/Iz46NaGq+ZPnRF6+8WNeN26Kvz3I0fxYlM/WgeV7vP6YueEYysLbFhbnY8nj0w+XPFsX3nwMN70kx0prPyMXnUo12wzqAHleeDzr1sNbyCM7z5xAlUFNjRMUohPF5vZiDyraULEx9/2dgJQIojmohufER+z96mrV+DJT12CinwbVlS6YDEZsL9tONPLynm/ePEUbGYD3rmFcQxzreKsDurOYeX3XrZ3UBMREREtdCxQU1K6PQFUnpXzV612b6Yj5uObjxzFwEgQ33jTukk7L7PRuup8nOgdiXdk5quFzCKHBUIA/d7M5sIm6hsPH8H773l13G3aILlylzUe0zF8VsF9X/swBkdDuHxlebxTWjtm2BeCQZwp2mfaB7c34jPXrMDHLlua9H1d6tewuNQJh9mIcFQiPEncyVSS6aAWQqC22D5vO6jPHq46GSEEvvmW9VhSloeP/W43XmoeQKHDjIIpTlZcvrICu1uHZsxs94Ui+OOuNhzp8swqmzfeQa1DgRpQuupv3laHaEzi/CUlcz4MtDTPEh/gCQCRaAwP7u+KxxA9djix4v9sjASjsBgNHJQ0CzazEVXq72Sz0YDVVfnYz0GJaTUwEsT9ezrwpnNr4idiae4UOywwGQR61A7qMwXq7B6SSERERLTQ8V0hJUxKiS53YEKRqSZeoNa3OPZayxB++0orbrtwMdaqRZNcsK66ANGYxKunBwGcyaA2Gw0od1njb7bmMykl/rK7A08d6x0X4THoCyESkygfE/Fxdgf108f6YBDAxcvKUKgec6aDOoRChyVtw+KSJYTARy9biqvXJJ8xqnVQLy51wm5RiszJ5FAHwkoxO5EOakAZlDhfM6i1QkLFNB3UgNJJ+9N3b0IkKvH0sT7UT5I/rbliZTliEnj2+PQxH48e6sZoKIpQJAaPP7lBlWP1eYOwq53HevnElcuxuiofN26s1u0xE1WaZx13Muzlk4PoHwni/126BCsqXHjsUHfa1zAajMBpZZ6hnjbUFOBgh3vWkTY0tXt3tiIUieG2CxoyvZQFyWAQKHNZ0aMOSexQr97LhYgPIiIiooWMBWpK2LAvjFAkNuEy/cp8G4wGgT2tw7PqUBwrHI3hc/cfwKICGz551XJdHnO+0IrtO5oHAJwpZALKG6yOLChQH+8ZQa83CCmBXWqhHQB61TeMFfm2eHf00Fkd1M8c68U5dUUoclrODIbUMqh94XjROttp39fGsjEF6iRyqLVjEy5QFzvQPuTX7WdQT13uAMxGgZIEug0by/LwnbdvBADUl0yM99Csqy5Amcs6Yw71n15rj/+9x5t6DFHfSBBlLquunc4leVY89E/bMzLNvjTPOi7i4697O+CymnDZynJcvaYCr54exMDIzFdzdA774Q2klpuvFKgZ76Gn9TWF8IWiONk3dwNEF5JQJIZfvdSCi5eXYVmFK9PLWbDK823x2KXOYT/MxjPDX4mIiIgoO7FATfjWo8fwo6ebZjxuqsv0TUYD3nxuNf6wqw1fefAIYgl0bsVictrj7n7+JI71ePGfN67NuQJGdaEdRQ4z2of8MBnEuAJkdZEjLVEpenv+RB8AwGgQeOXUmQK1VgAsz7eeyaAeE8HQ6w1gf7sbl6nDEV1WEwxiTAf1aCjjAxL1Ei9Ql+bFv8dJFajVbmurObGn6ZoiO0aCkfi/5XzS4wmg3GVLuDP+qtUVuPvWzfj4FcumPMZgELh8RTmePd435YDWjmE/djQP4Hx1wKV2AiVZ/lAUBzvc8eFcuaDUZYkXqAPhKB451I2r11TCZjbimjWViEngyaMzD6F8650v4RsPH01pDSPBCAck6oyDEtProQNd6PUGcduFDZleyoJW7rLGn887h/2oLEj89wsRERERzU8sUBMePtiFHz7VNC6qYTLdHqVwWjFJjux/vWk93ntBA37+4il8/Pd7piwYad7205ew5ouP4g0/fAHffOTouGJ1y8AovvfECVy7phJXrq5I4Sua34QQ8S5ql800riOzutCOLrc/oSJ/Jj13oh9Ly/Nwbl0hXjk5EL+9T33DWO6ywWkxwmwU4yI+nj2mFLYvXVEOQCkyFjosGPaf6aCeDwMS9VBVYIfJILCsIg8OtYPal0SBOhiOQgjAmmA+b60ahzEfc6i73EoBIRlXra7A0vK8aY+5ek0FvIEINnz5MbzlJzvw1QcP4+/7OtE26FNjaNohJfDhS5cAODPoMBnRmMQ//X4PTvaP4oMXNyZ9//mqNM+KIV8Y4WgMzx3vgzcQwRs2LgIArFmUj+pC+4wxH8O+EDqG/djbOpzSGkZDkfjPBumjsSwPTosR+9uHM72UnPSLF0+hscyJS5bN/VUPdEZFvhW93gCklDjZN4pFBYz3ICIiIsp2LFATPIEI/OEo/rK7fdrjpht0ZjAIfPGG1fi361biwf1deO/PX4Vnisu+pZQ42OlGQ6kTVpMBP36mGX/f3xn/2OcfOAiz0YAvvWHNLL+y+UsrUGv505qaIjvCURmfTj8fBcJRvHJyANuXlWLb4hIc7PRgJKhk+2oFQC0KodBhGTck8ZljfSh3WbFmUX78tkK7OV7EHlYzqHPB9euq8OSnLkFFvi0+6DCZDGp/OAqbyZhwpERtkVqgHpx/Hfg9nmDSBepEXLFK6bS+eWs9JIBfv9yCO+7dg+3ffBqbv/oEfvrcSWxdXIxN9UUAkPTPlZQSX3nwMB473IMvvH41rkkhi3y+KlEvhx8cDeHxwz3It5lwwRKl01wIgatWV+C5E/0YDU6d293Uq8RInOj1IhRJfACoZiQYzbkrZDLNaBBYs6gAhzs9mV5KzvGHotjX7saNG6rZrZthFS4bhnxh/PzF0zjQ4cZVOdjMQERERLTQsEBN8Kid0795pXXa/NpudwAGAZRNkfMnhMCHLlmC7759A149PYi33flSfDjaWKOhKALhGN64cRH+cPv5WF2Vj28+cgyBcBR/29eJ50/04zPXrEhLQWu+WDemg3qsanXgZMfwzF2wUkq84YcvjMvYnQu7Tg8hGInh4mVl2NZYjGhMxnOoezxBFNjN8YJskcMcz5fWOjUvW1E+ruha6DDDPWZIYnECOcXZwGgQ8Qxlh0X5PicT8REIx+LZ1YmoLVb2znzroFaGq/onZNfr5arVFfjCDavx549cgINfvgYP3nERvvrGtbhsZTnqSxz4yCVLkGc1wWExJh3x8cyxPtyz4zTef9Fi3Hbh4rSsP1PK8pSfs15PEE8d7cWlK8phNp55SXDNmkqEIsrP7FSa1ZzjcFTGi9XJ8DHiIy0qCmwYGA3NfCAlpVt9PaMNhqbMKc9XXod+9R+HcdmKMrwvx56fiYiIiBYiFqgXuEA4imAkhsYyJ5p6R7BzTJ7w2brdSo6syTj9trnpnBr8/L1b0Dbow5t+vANNvd5xH+9XuxhL86wwGAT+/XWr0DHsx/efPIGvPHgYG2oL8a7z6mf/xc1jWoE633ZWB7U6hT6RHOq+kSD2t7uxu3VI/wVO4/kTfTAbBbY1Kp2ppjE51L3eACryz5zAKHRY4t3Rr7UMwRuM4LKV5eMeTzkmBL964iJXhiSOZU+5gzrxp2iXzYxChxltg/OrQO3xRxAIxya98kJvZqMBa6sL8K7z6vGtt27Ag3dsj++3cpc1qYiPWEzivx85irpiBz577cp0LTljtIFijx/pwcBoCFesGv9zuaWhCEUOMx6dJuZjbFH6cFfyHbsckpgehXbzuCtXSB9dbuX38lw8l9H0ytUTnpX5Nnz7bRvZ0U5ERESUA1igXuC0GI53bqlDvs2E37zSOuWx3Z5Awl3NFy8vwx8+dD6CkSjecudL44qoA6NKgbpE7eC7cGkpLltRhh8/04whXxjfuGkdjDn+ZqOmyI5Ch3lCgVrroE6kQK0VIvvmOA7kuRP92FxfDIfFBIfFhPU1BXjl5ADC0RhaB/0od53ZI0WOM4WSp4/2wmwUuGhZ6bjHK3SYMewLxzutc2VI4lj2eAb11HEJZwuEo7Almc9bOw+HbGpdhxVp6qBOVHm+LakO6r/v78TRbi8+dfVyWJI4UZAttAL1H3e1wWQQuHT5+AK1yWjAFasq8OTRXoSjk8d3NPWOYEWFCzazIaVICQ5JTI9Chxluf3jezzLINtoVYZPN4aC5tWZRPtbXFOBHt5ybM1ddERERES10ufeum5Li8SsFs/J8K968qQaPHOxC/8jkRZwudyCpy/TXVhfg/o9cCKfFhC//7VD89j6vUogsHRMV8m/Xr4LFaMAHtzdi9Zh84lwlhMA3blo3Yeiaw2JCsdOCjuGZi4wtA0qBeqrvVzr0egM40uXB9uVniszbGkuwv92Nq77zLI50ebBtcXH8Y0VjOqifPtaLrYuLJxSkitSc6jMF6hzsoFYLzYEkOqgDagZ1MmqL7fMu4mO+dB0m00EdisTw7ceOY1VVPm5YvyjNK8uMUpfy/NvlDmBLQzEKJvm5u2ZNJbyBCF4eMwh1rKa+ESyvdGFlZT4Od7mT+vxSSoyGonBaOSRRbwV2M2ISGEnwhNjTR3vxx11taV5V9tPmcKQrrogSV+6y4W8fuwjn1hVleilEREREpBMWqBc4rYM6327GLdvqEY5K3DfFG9Vud+Id1Jq6EgcuWVGGtjFdnVoH9dgC9fIKF17+3BX47LUrkv0SstZ166riw9vGqi60oyOBLtjWDHRQv9jUDwC4eFlZ/Lbty0oRiUnYzEb84r1b8LHLl8Y/pg1JbB/y4XjPCC5bUT7hMQvtZoyGovGvI1eGJI7lMGsd1OnLoAbOdFDPp87JnvnSQe2yJTwk8Q+vtqJ10Id/uXZFzl467rQYYTMrLwGunGLA2PZlpbCbjXjsUM+EjwXCUbQP+bG0LA+rF+XjcKdn2hkGZwtGYojGJCM+0kB7DtWy/acTicbwub8cwLceO5buZWW9HncALpuJe5aIiIiIKA1YoF7g3OqAxAK7GUvL83BeYzF+90rrhAKXNxDGSDCSUhdkZb4Ng6MhBCNKca5f7aA++7LMYqdl3PC8haq60I72BLpgW8d0UCdTGJqN54/3o9hpweqqM13uFywpxeOfuBj/+LiS9zv2e1jsNCMclXhwfxcA4NLJCtTqPjjVP6reJ/cK1FqhOdkMai27OlFlLitCkVjCnZNzQes6zHiBOt8KXyiKkeD0/za+UATfe7IJWxcX49LlZdMem82EEPGThFeumvhzCQA2sxGXLC/DY4e7J/xOONk3CimBJeVOrK7KhycQSejKD432fWDEh/4K7Uo3/HACBepnjvWhyx1AjyeY1BDXhSjZq8iIiIiIiChxLFAvcB61QK1lIb/rvHq0D/nx7Im+ccdp3bpVhclPr9fe0Gn5rwOjQRTYzTmZ66qH6iI7Oob9Mxadte9JIBybseimByklnm/qx0VLSyd0lS6rcE2aG6518t2/ux11xQ4sKXNOPEYtpmgF6lwckmg1GSAEkioABcLReIdrovLVf8uxnZOPHerGz184ldTjaEZ12Fc9ngBK8ywZ/3kvVyMtej3Tx3z84sXT6B8J4rPXrsj5E2aV+TYsK89DfcnEn0vN1Wsq0OMJYn/H+AiPpj5lQOLS8jysUk9YJZNDre0tp4UFar1pz6HD/pkHJf72lZb431vn2YBVQDmJnsoJ2L/u7cBdzzXrupaeJOZwEBERERFRclghXOA8AaVIkG9XigRXr65EaZ4Vv315/LDEl5qVDNLNk0RSzER7Q6cNS+sfCaI0L/e6ZPVSXWhHIBzD4Oj0xYWWQV+8w3YuYj6O9XjR5w1OGHI4HW3goRLvUTZpwU87Jl6gtufe3hBCwGE2JhXx4Q9HYUuyg7pAK1D7zxSo//BqG7756NGk8q+HfSF8+o/7sPZLj+LV04NJreFsXe5AxrunAcSHd04X8zE0GsKdzzTjylUV2FRfPOVxueKrN63FD28+d9pjrlhZAaNB4NFD3eNub+odgUEAi0udWFnpghDA4a7EC9TaSTXGJegvXqCeoYO6fciHZ4734dIVypUCpwdG0762ZPR6Atj6tSfw1NHepO/7/SdP4K7nTuq6HnZQExERERGlDwvUC9zZHdQWkwFv31KDp472jLtc+4WmfjSWObEolQ5qrUCtXurf7w2hZEz+NI1XU6T8G093ubwvFEGfN4gNtQUAgP6RmTvlZuv540r+9PakCtRnuqEvWzl5jIBWTDnVP4o8qynjnbbpYrcYk4r4CIRSL1B7xhSoh/1hBMIx7G4dmnB8MBJF26APu04P4h/7u/CzF07ha/84jCu/8ywe2NMBAWWA2mx0uwMZH5AIABX5ynNOzzQd1Hc+24yRUASfuWZhZOGvrMzHikrXtMcUOMw4r7EYj51VoG7uHUFdsQNWkxFOqwmLS5w4kkSBejSo/Cww4kN/BepJvmH/9AXq3+9sgwDw2WtXAgBa5lmB+lCXB8FILH7yMlFdbj+a+0bRPxKCNzBzzEkiwtEY+kaC8+K5jIiIiIgoF+VmJYgS5vGHYTUZxhXC3rm1DhLAH3YqXdShSAyvnBzERUsTL0yOpXVPxgvUo0GUsUA9pWq1QN0+zaDEtkHlY9qQxVQ7qAPhKN5250s4eNbl+5N57kQflpXnoaog8ZMUWsSHzWzAeY0lUxyjFFU7hv0ocuZevIfGbjEmF/ERiSWdQT1ZB/WwTzl5saNpIH7bv91/AOd+5XGs+Pwj2P7Np/GWO1/CR3+3G1958DB++VILGkvz8LePXYR1NYWz7qDu9syvDuqpfla63H7cs+M0bjqnesai7UJzzZpKNPeNoql3JH5bU+8Ilpbnxf9/1aL8pDqotYgPhzW5PU4ziz8P+KY+cRmOxvD7V9tw2YpyrKrKR5HDjJaB+RXx0dSj7DfPDIX2sz1/oj/+d72+pj5vEFICFSxQExERERGlBVuXFjhPIBzPrdXUFDlw2Ypy/P7VNtxxxTLsaR2CPxxNuUCdbzPBbjaeifjwBlG6NPdiHPRSU+gAAHRMU6DWOt20AnX/SGoF6s5hP3aeHsRrLUNYW10w5XGBcBQ7Tw3ilm31ST2+1kF9wZLSKbuBtSK2lGfiPnKR3ZxcgdofSj6DerICtfb3F5r68elrVuB0/yju3dmKi5aWYuviYlTkW1GRb0NlgQ0VLhsKHeZ4FMvWhiL8ckeLmoedfCExEI5i2BeeF12H+XalO3+qiI/vPXECMSnxiSuXz/HK5r+rVlfgC389hEcPdWNp+VJEYxKn+kdx6cozQyTXVRfgH/u70O1OLKeXQxLTx2IywGkxThvx8fjhHvSPBHHLeXUAgLoS57wrUJ/o9QIY/3yWiBdO9MNoEPF9Ot3vtkRpr1/mw3MZEREREVEuYgf1Auf2h+NFrbHedV4der1BPHG4By809cMggPOWTN4BOxMhBKoKbOj2BBCKxOAJRBjxMY18uwl5VtO0ER/aMKsNNYUwiNQ7qLXIiZmGLL56ehDBSAzblyd3kqLQYcHWxcW4eWvdlMc4LUaYjSJ+fK6yW0zwJRjxIaVEIBJNuoM6/6wCtZQSw74wLEYD9rcPw+0P475dbTAI4Ftv3YCPX7EMb99Sh0tXlGNlZT6KnJZxOeFbGooRisawv33mDvvJaHEa86GDWgiBcpd10iGJTb0juG9XG27ZVo/aYkcGVje/VRXYsaGmAI8d7gEAtA36EIrGsKTsTAf15WqEzxNHehJ6TF+IGdTpVOiwTBvx8btXWlFdaMcly5XvW0OJY95lUJ9QO/aTKVDHYhIvNvXjylXK16VXbIl2Bdh8eC4jIiIiIspFLFAvcB5/BPm2iQWCS5aXo7rQjt+80oIXmvqxobYwnlOdiop8G3rcAQyMKoXUUhaopySEQE2RfdqIj9ZBH1w2E4qdFpTkWVPuoNY6er2B6QvUz5/oh8VowLbFyQ2OMxoE7vvQ+bhydcWUxwgh4pmpYzOrc43dbEAgwQ7qYCQGKQFrkgVqp8UIo0HECzqjoSgiMYmLl5chJoEdTf3402vtuHRFeUJdrlsalO93qjEfXWpRJ5HPNRfKXdZJO6i/8/gx2M1GfOzypRlYVXa4ek0l9rUNo3PYj7ufV4bPrRwThbKsPA/1JQ48fnjmAvWu04O4Z0cLTAYx6QlSmr18u3nKDupT/aN4oakf79hSC6NBOSFVX+xA57AfoUhsLpc5JSllPOIjmQL1kW4PBkZDuGp1JSryrTjVr09XuFagTibiioiIiIiIEscC9QI3WcQHoBQWb95WhxebBrCvbTjleA9NpdpBPaAO8yvJy91OWT1UF9rR1OuNdxmerWXAh/oSB4QQKM2zptxB7QtpHdTTFwCeO96HzQ1FcFjS0+2oFaZzOeLDYTHBF57+RIAmGFaKRMl2UCvFfnO8oKPlT1+6ogx2sxH/89gx9HqDePuW2oQer8hpwbLyPOw8pRSonzvehzf+6MWEC0ZaB2R1CsNV06HcZZtQoN7fPoyHDnTjA9sbeeJsGtesUU4yveOul/HbV1rxgYsWY92Y6AQhBK5eXYGXmgemHEzXMezHHffuwVvufAlDoyH84J3nMOIjTQrtZrj9k2dQ37uzFUaDGPc8UF/iREwC7UPzI+ajxxOEV72yJ5kC9Qtq/vRFS0vRUOLUr4PaE4DFZMjpk6hERERERJnEAvUCN1XEBwC8dXMNTAaBmMSsC9QV+Tb0eALxQioLQdO7cnUFTg/4cNV3npu0I7Ft0Ic6NYqgzJV6B7UvgQ7qXk8AR7u92L6sbMpjZqtwARSok8mg1qJXUsl9Hl+gVv5b5rJiW2MxTvaNojTPGo9jSMSWxcXY3TIETyCMf/3zfuxtG8afX2tP6L5/29uBJWVOLC51Jv11pEN5/sSIj+8/2YQCuxkf2L44Q6vKDkvLXWgsc6J10IfPXrsS//66VePiYADgqtWVCEVjePZ437jbfaEIvvP4cVzx7Wfw2KFufPyKZXjq05fgunVVc/klLCiFjsk7qIORKP64qw1XrapA+Zi4ioZS5fdJOnKonz/Rh28/diyp+2j508VOS3IF6qZ+LCvPQ2WBDQ0lTpzW6evpdgdQmW+bsOeJiIiIiEgfLFAvcB5/eMrojnKXDdeurYTLasI5dUWz+jxVBTaEoxLHe5Q3naXsoJ7WO7fW4Y8fPh9OqxEf/NUufOCXu+KdbdGYRNuQD3XFStGvbBYd1H61o3dkmgL1C01KR9r2ZbM7STEdLXu6yJm73Wl2S+IF6oBaoLZbkn+Kzreb4QmM7zwstJvjJ5nefG41zMbEH3drQzG8wQg++tvd6HQHUF1ox29faYGUctr7tQ748OrpIbzp3Jp5U9SpyLfBE4jE/32PdHnwxJEe3HZhA1yziDBaKP7nLRvw8/duxkcuXTLp93RTfRGKnZb4STUpJR7Y04HLv/Usvv/kCVy1uhJPffpSfPKq5Wm7GoMUhQ7zpBnUjxzsxpAvHB+OqNF+n+jVcTzWH3e14wdPNcVjMhJxQo332FRfBLc/sStPvIEwdp4ajJ9MbSh1on8kOGVHfzK0AjUREREREaVH2grUQoifCyF6hRAHx9z2JSFEhxBir/rnevX2BiGEf8ztd6ZrXXSGlBKeQAT59qkLBV+7aR3+8tELYDHNbqtog4UOdnoAsIM6EVsaivGPj2/Hv123Ei829eOq7zyHO59tRvuQD+GojHdQl7os6B8JzVgwnEy8g3qaIYnPn+hHidOC1VX5qX0hCShUu/hzekii2RjvjJ6JdlyyER/A5B3UhQ4LrltXha0NxXjXefVJPd4WNXf8+RP9eOfWOvzzlcvQ3DeKV05Nn0v9lz0dAIA3nlOd7JeQNmUu5Xmn16Oc0PnR001wWox47wUNGVxV9thUX4TLV06dJ280CFy+shxPH+3FrtODeNNPduCf/7AXZS4r/vTh8/GDd54zb+Jecl2B3QK3Lzzh98JvX25FfYkDFy4Zf8KxNM8Cp8WoW8fxWG3qydWnjvYmfJ8TvSModJjRWOaExz/x65jM/z5xAqFoDDepzzkNJfp1hXd7AvMmS5+IiIiIKBels4P6HgDXTnL7d6WUG9U/D425vXnM7R9O47pINRqKIhqT0w6pKrCbsbTcNeXHE6W9sTvU6YbNbIDDknzhbSEyGw340CVL8MSnLsFFy0rxXw8fxZt/sgMAUK+++S7LsyIUjcGTYJfZWFpH71Qd1LGYxPMn+nHRslIYDOnrgi1y5v6QRIfFGD8hMBOtQJ3skERA+Zn1aAVqNYO20GFGdaEd9334fNSqJzYSVV1oR3WhHaV5FvzrtSvx+vWLkG8z4Tcvt0x5Hykl/rKnHec3lsyrgmS5WqB+7kQfmnq9+MeBLrz7/IacPjEy165aXQFPIIK33PkS2of8+J+3rMdfP3ohNjckN2CVZqfQYUYoGht3UuxEjxc7Tw/inVvrJjyfCyFQr2Nm81htg8rA3yePzDxAU9PU68Wy8jwU2JWvIxCefnjjsW4v7tlxGu/YUod1NUo2eoMaLXR6ll+TlJIFaiIiIiKiNEvbNbZSyueEEA3penyaPa2INVXEh560S2NP9Y+iutA+by75zxbVhXbcfetmPHG4B1/82yEYBNBYpkZ8qEW3vpEACpIs8J7poJ78Euij3V70jwTTmj8NIH6SJJczqG1mI4KRGGIxOWOxPzCrDmrThA7q6U5CJeJ779gIm9kY319v2VSLX798Gn3eYHz/jbW7dRinB3z4f5ctndXn1duqqnyU5lnx+QcOwmQQsBgNeP9FzJ7W08XLynDh0hJsqCnE/7tsKYcgZoh2VcqwLxyPU/ntK60wGwXeuqlm0vvUlzhwTI3h0os/FEX/SBAWkwEvNPXDH4rCPsMJaikljveM4Pp1VSi0K78T3P7wlPeTUuI//noQLpsJ/3LNivjt9Tp1UA/5wghFYoz4ICIiIiJKo0y8c/yYEOJWALsAfEpKOaTevlgIsQeAB8DnpZTPZ2BtC4pHzWXMn2XxKhFlLiuMBoFoTKKE8R4pu3J1BS5YWoLWQR+qCpTO1DL137PPG8LSxGffAThToJ6qg/r5E8qws3TmTwPAokIbDEIZYpertKsG/OEonDMU7QI6DEmUUsLtD8NmNqT0OGOd3f1687Y6/PzFU7jov5+Cy2aCw2KCw2KE06r8t9sdgM1swHVrK2f1efVWkW/Djn+9HE8d7cUDezqwZXHxpAV2Sp3dYsRvP3Beppex4GmDZ4d9YSwqtMMfiuLPu9tx3dqqKX8H15c48cSRHkRjEkadrpjRZifcsH4R/ry7HS+d7J82JgYA+kdCcPvD8Q5qQClQT9XB/Ne9ndh5ahDfeNO6+NU4AOCwmFCRb8Wp/tl1UGvZ2eygJiIiIiJKn7kuUP8EwFcASPW/3wbwPgBdAOqklANCiE0AHhBCrJFSes5+ACHE7QBuB4C6urqzP0xJ0CIhZttdmQijQaAsz4puTwBlHJA4Kw6LCSsrz+RBn+mgTn5Qoj+kDkkMRiClnNDZ/vyJfqyocMUzxNPl9esXYXmFC+Wu3C0A2JMqUCuXs6eaQR2NSYyGohj2heIdiHpaWp6Hb711A451ezAaisIXjCj/DUXgVU923H7xknk5eNBiMuDatZW4dp4Vz4n0VKD+3GsxP3/f3wlvIIKbt039uqmhxIFwVKJz2J90FNBUtPzpt2yqwSMHu/Dkkd4ZC9QnepUu7mUVeRBQfie5Jxn4CCiDEb/20BFsqCnA2zfXTvi4HrEl3R4loiTdvweJiIiIiBayOS1QSynjAYRCiLsBPKjeHgQQVP/+mhCiGcByKF3WZz/GXQDuAoDNmzcnPxWO4txzGPEBABUFNnR7AihxsmNRT9rAyX5v8gVqrYM6HJUIRmITOm0Pdbpx3bqq2S9yBmajAWsWFaT982SSVmz2J5BDrR1jMyc/JmBsx+GwLxzvpNTbW6aICSCizNN+7t1qzM/vd7ZiSZkT2xZPnQWuFaXbh3QsUKv500vKndi+rAxPHe2d9GToWE29IwCAZeUu9Km/16YqUP/vEyfQPxLE/926edLopMUlTjyZxHDGyXQMKx3UiwpZoCYiIiIiSpd0DkmcQAgxttJ1E4CD6u1lQgij+vdGAMsAnJzLtS1E8Qxq+9ycp6hU4xtKXeyg1lOB3QyzUaTUQe0bM0DLe1bMRygSw5AvzNxNnYztoJ5JIJJ6BrV2wsntC2PYH56TKySIaH6JR3z4w/CFItjbNozr11VNWxguUa9uGhwN6baOtkEfbGYDyvKsuHxVObrcAfzixdPwBiYvOAfCUfz25VZU5ttQkW8dd8LtbEe7Pbhnx2m8c2sdNtQWTvp49aUO9I8EMRJMfohw/PN0eeCymfi7kIiIiIgojdJWoBZC3AvgJQArhBDtQoj3A/imEOKAEGI/gMsAfEI9/GIA+4UQewH8CcCHpZSD6VobKbQM6rkqYGmZyeyg1pfBIFDitKbUQR0Y08179hv4frXgzYxefWgZ1L4kOqitKUZ8AEpBx53GDmoimr+0aJ9hXxhHujyISWB9TeG09yl2agXq5H+XTKVtyIeaIgeEELhmdSVWVrrwnw8exuavPoGP/W43njzSg3A0Fj/+fx49hmM9XnzjzesghIg/nw37xhfNpZT4wgOHkG8z4TNXr8BUGkqUYcKnZ5FDfbjLg1VV+RzuTERERESURmlrnZVSvnOSm382xbF/BvDndK2FJqd1JOXNkIerFy2/sZQFT92VuaypdVCPLVCf1UGtXVpdzu+XLuxm5ecskYiPYCT1DOr8sREf/hAK7YVJPwYRZTeb2QCLyYBhfwj7290AgHXV08coFTm0AvXk3c2paBv0o7ZIOTld4DDj4X/ajr1tw/jLng78fV8nHtzfhRKnBTdsWISl5Xn42QuncOv59bhshTLx12UzQYgzV3xpHtjbgZ2nJw5GPNvS8jwASq712hm+/snEYhLHur142yT51kREREREpJ+5HpJI84jHH0Ge1QSTcW6SXioL1IgPDknUXWmeJeWID6vJgGAkNuGS614vO6j1dCbiY+ZLzf2hKIwGAbMx+Y49rePQk+YMaiKav4QQKLSb4faF0ecNosxlRUX+9M/lZqMB+TaTrh3U7UM+bKovGreuc+qKcE5dET7/utV49ngfHtjTgd/tbEUoEsPS8jx87vpV8eMNBgGX1TQu4sMTCOPrDx3FhtrCSQcjjrW41AmzUeBotzel9bcM+uALRbGqypXS/YmIiIiIKDEsUC9gnsDc5tNeuKQU162tnLGLi5JX5rLiUKcn6fv5QxGU51vRNuiHNzhVBzVzN/WgRXz4Q7EZjlRyqm0mQ0qXlBeoBelebwDBSCz+/0S0sBQ6zBj2hdHcN4J11QUJPZ8UOy0Y9OnTQe32h+EJRFBbbJ/04xaTAVetrsBVqyvg9ofx9NFebKovmjCst8BhHleg/t/HlcGIP3vP5IMRxzIbDVhSlodjSRSoxw5xPKz+Xl1dxdctRERERETpNKdDEml+cfvDcNnm7hxFeb4NP3nXJrhsLJjprdxlw8BoCNGYTOp+vlAUFWoB+uyIj15vAMCZwVk0O1pchy80cwd1IByNd1wnK89igkEALQM+AGeyaIloYSm0W9Dp9scL1Ikodlp066BuG1Seg2qLHDMeW2A3443nVKO2eOKxBfYzBermvhH88iVlMOJMmdqalZWuhAvUjx7qxuavPoE9rUMAgCNdHhgNAssq8hK6PxERERERpYYF6gXM4w/H82opu1UU2BCNSQwkGfPhD0VRrl72ffaQxD5vEMVOC8xzFAGT67SCcyCcwJDEcBRWU2oFaoNBIN9uRotaHGLEB9HCVOAw42CHGzE5c/60RilQ69NB3T6kFqgnKTonY2yB+uWTA4jGJD588ZKE77+iMh9d7gDcM3SGv3xyAHfcuwcDoyH8fmcbAGVA4pIy54SubiIiIiIi0hcrTwuYJxBBPruZc0KFmhPd7QkkdT9fKBqP8Dg7g7rPG+SARB2d6aBOYEhiOJZyBzWgFHRa4x3U/BknWogK7WZoF9Wsq8lEB7UfQGId1NMZW6A+2TcKm9mAmqLJY0Mms7JSyY8+1jN1F/XhTg8++MtdqC2y48pVFXjoYBeCkSiOdHmwqip/VusnIiIiIqKZsUC9gHn8c5tBTelTWaAUmXs8iRcWYjEJfziKfLsZFpNhQgZ1rzpYi/ShFaj9CXZQ28ypPz0X2M3xkxXMoCZamLSrJ8pdVlTkJzZLoNhpxeBoCFImFxc1mbYhH1w206yfgwrsFrj9yu+nk30jWFyaN2P29FgrtAJ19+RzGtoGfXjPL3Yiz2bCr96/DbeeXw9vIIIH9nSgyx3AahaoiYiIiIjSjgXqBUyJ+OCczFygFR+S6aAORJRCqcNihMtqmpBB3ccCta4MBgGryQB/Ah3UgXA0XtBOxdgTT4UOZlATLUTaz/76BLunAaDYaUY4KidEPiWqbdCHf/3zfrQMjKJt0Dfr7mlAeT7z+MOQUuJk/ygay5xJ3b+qwAaXzYSjk+RQ948E8e6fvYJQJIZfvW8rqgvtuGBJCUrzLPju4ycAgB3URERERERzgAXqBSoak/AGGfGRK0rzrDAIoDeJArUWNeGwGOGymcYVJKSULFCngcNiTKKDOvUC9dhseUZ8EC1M2omqtQnmTwNKBzUADI6GUvqcjx/uwe9fbcPrvv8CdrcOJxXFMZUCuxmhaAwefwRtgz4sKU2uQC2EmHRQ4kgwgtt+8Sq6PQH8/L1bsKxC6bQ2GQ143bqq+AlfFqiJiIiIiNKPBeocJqXE0BRvMrW8YUZ85AajQaDMZUW3O/ECtdbJazMbkWczwTumg9rjjyAUjaEsjwVqPdnNxoQyqAPh2OwK1OqJJ7NRwDGLLGsiyl5axEeyHdRA6gVqj/raYmWlC25/GHWzHJAInHmdsr9jGDEJNJblJf0YKypdONbjjUeXBCNRfOjXu3C4y4Mf33IuNtUXjTv+DRurAQBlLitP1BIRERERzQEWqHPY/z1/Chf811MTht8BSgESGN9pSdmtMt+GHm/iGdRjO6jzzor46PUqhe7yBHNLKTH2BDuoA7PsoNYKOgV2M4RIPKuViHLHRUtL8YGLFuOCJaUJ32e2HdRufxh5VhN+f/t5+O7bN+AD2xtTepyxtOezPa3DAJB0xAcArKzMhzcQQac7gGhM4pP37cOLTQP45pvX4/KVFROOP7euEA0lDmyoKZzN0omIiIiIKEEMIM5RgXAUP32uGf5wFC0DvgmX+PaoBUh2UOeO8nwbWgd8CR/vCykFaaVAbUbHsD/+sT610M0Oan3ZLcYkMqhnNyRx7H+JaOEpdFjw+devTuo+xWpudcod1P4ICuxmmIwG3HROTUqPcbYzBeohAMDiJCM+AKWjGwB2twzh4YNdeOhANz53/Uq8edPkaxRC4N7bz4PFyD4OIiIiIqK5wFfeOepPr7Wjf0R5g9k2OLFo+fd9nbCYDNjSUDThY5SdlA7qJCI+1E5eu9mEfJsJI8Eznfa9aoG6PJ8Faj05zKaECtS+kD4d1ByQSETJKM6bXYHa7Q/DZdO39yFeoG4bRkW+Fa4UZmcsVwvUn7pvHx452I1/v34Vbr94ybT3qSqwo4QnaYmIiIiI5gQL1DkoGpO4+/mTWF6h5DS2nlWgDoSj+MueDly3tpIFrBxSkW/FsC+MQAIREsCZDGqHZWIGdbyDmtmburJZjPDN8P3xBMJw+8OoLEg9XiVeoGYHNRElwWkxwmI0YNCXega13lduaI837AujsTT5/GlAyeVfXOqEw2rEL9+3FR+8ePbRI0REREREpB8WqHPQwwe70DLgwyevWo5ChxltQ74JH/cGInj7ltoMrZDSoULNi+7xJNZFPVkGtTZAqm8kCKvJAJeVKUB6cpiNCMzQQd3UOwIAWFbuSvnzxCM+HCxQE1HihBAodlowOJJqxEdY99kWYwveqeRPa371vq14/BOXYPuyMj2WRUREREREOmKBOsdIKXHns81oLHXiqtWVqC1yoHXQP+6Ye3e2ob7EgfMWl2RolZQOZwrUiQ1K1Dqo7WoHdSQmEYzEAAC9ngDK860csKczu8UIXzgy7TFnCtSpdQoCYzuoeYUEESWnyGmZVcSH3h3ULpsJ2q+ixrLUnxdrix28KoiIiIiIaJ5igTrHvNDUj4MdHtx+cSOMBoG6Yse4DOqTfSPYeWoQb99SC4OBxcdcokVCdCfcQa0NSTTFMz21mI++kSAHJKaB3WKENxBBNCanPKapdwQWkwG1xY6UP8+ZDGp2UBNRckqcltQjPtJQoDYYRPxqntl0UBMRERER0fzFAnWOufPZZpS7rLjp3GoAQE2xHR1D/nhB7E+vtcNoEHjLuZNPrqfsVeFSCtS9iRaow2ciPrQ3/95AWH2MIMpdqWcg0+TOayzBsC+MX7x4aspjTvR4saQsD8ZZnEAqc1lR6DBjeUXqMSFEtDCl2kEdjsYwGooiP4UhhjPR5mUsSTGDmoiIiIiI5jcWqHPIgXY3XmwawPsvWgyryQgAqCt2IBSNxXOJXzo5gHNqC1Gez+Jjrsm3m2AzG9DtTqxA7Q9FIQRgNRmQpxaoR4JjOqh5KbTublhfhatWV+Cbjx5DU6930mNO9I7MKt4DUDq19/zHVbhmTcWsHoeIFp6SFAvU2hU4BXb9ZxcU2M2wmAyoLrLr/thERERERJR5LFDnkDufbYbLZsLN2+rit9UWKTEBbYM+hCIxHOr04Jy6wgytkNJJCIGKfBt6vIllUPtCUTjMRgghkGdTC9SBCIKRKIZ9YRao00AIga/ftA5OixGfvG8fwtHYuI/7QhG0D/lnXaDWPhczxIkoWcVOC7yBCEKR2MwHj+H2K1fg6D0kUVtTY6lzVleWEBERERHR/MUCdY441T+Khw524V3n1cfzhAGlgxoAWgd9ONLlQSgSw8baokwtk9KsIt+GngQ7qH2hKOwWpdPepRaoPYEI+keUzrlyFqjTosxlxdduWof97W785JnmcR9r7h0FACzVoUBNRJSKIqcSpzGcZA61VqDWO4MaAD7/ulX41ls36P64REREREQ0P7BAnSPueu4kzEYDbruwYdztiwrtEAJoG/JjT+sQALCDOocpHdSJRnxEzhSorUpBYSQYQZ/agc0O6vS5fl0V3rBhEb7/5Akc7HDHb2/qU2I/llWwQE1EmVGiFqgHkoz58KSxg3pZhQtrqwt0f1wiIiIiIpofWKDOAb3eAP68ux1vPrdmwmA7i8mARQV2tA36sLdtGOUuK6oKmD+dqyrzreh2ByClnPFYJeJD6Zw+E/ERxokepUhaVcCsz3T6zxvXoNhpwafu24dgRBlYeaJnBCaDQH2JM8OrI6KFqkgdSDiUZIE6nR3URERERESU21igzgG/ePE0wtEYbr+4cdKP1xQpBeo9bcM4p66QubQ5rCLfhmAkBo8/MuOx/vCZiI+xQxL/tq8TNUV2rKpypXWtC12hw4L/fvN6HOvx4n+fOAFAGZC4uNQJs5FPzUSUGSV5KXZQB1igJiIiIiKi1LAKkuU8gTB+81ILrl9bhcWlk3dd1hU7cLTbi5YBH/Onc1xFvtId3+2ZOebDF4rCoRaoLSYDrCYDTvaN4sWmftx0TjVPZMyBy1aW4x1bavHTZ5vxWssgmnpHGO9BRBmldVAPpthBnW9jgZqIiIiIiJKTtgK1EOLnQoheIcTBMbd9SQjRIYTYq/65fszH/k0I0SSEOCaEuCZd68o1977SCm8wgg9fsmTKY2qLHRgJKh21zJ/ObckUqP1jCtSAMijxHwe6EJPAjRur07ZGGu/fX7cKVQV2fPK+fWgZGMXScnauE1HmFDmUAnOyBWqPPwKL0QCbmb0PRERERESUnHS+i7gHwLWT3P5dKeVG9c9DACCEWA3gHQDWqPf5sRDCOMl96Sz37+7AloYirKuZenhQXbEDAGAQwDoOGcppWr5457B/xmOViA9T/P/zrCYEIzGsrynA0nJ28c4Vl82Mb711A1oGfIhJYBn/7Ykog0xGAwrs5pQ6qPPtJl59Q0RERERESUtbgVpK+RyAwQQPvxHA76WUQSnlKQBNALama2254mTfCI71eHH9uqppj6stVobdLa9wwWk1TXssZbeqAhtMBoG2Qd+Mx/pCETjMZ84DaYMSbzqH3dNz7fwlJbjtwgYAYPY3EWVcRb4V7UMz/x4Zy+MPI5/500RERERElIJMXIf5MSHEfjUCRAtErgbQNuaYdvU2msYjh7oBANesqZz2uFq1g/qcOuZP5zqT0YDqIjtaEypQnxmSCAAuqxlGg8Dr1y9K5xJpCp+7fhX+/JHzGfFBRBm3uaEYr54eQiQaAwBEojF89Le7saOpf8r7eAJhDkgkIiIiIqKUzHWB+icAlgDYCKALwLeTfQAhxO1CiF1CiF19fX06Ly+7PHqwGxtqCrCo0D7tcWV5Vtx+cSNu3lo3RyujTKordiRUoD47g/ryleW47YIGlLms6VweTcFsNGBTfXGml0FEhAuXlGIkGMG+djcAYFfLEP5xoAv/9chRSCknvY/bH+aARCIiIiIiSsmcFqillD1SyqiUMgbgbpyJ8egAUDvm0Br1tske4y4p5WYp5eaysrL0Lnge6xj2Y1+7G9eunT7eAwCEEPjc9aumzamm3JFIgToUiSESk+MK1B+8uBGff/3qdC+PiIjmufOXlABAvGP6UfWKrf3tbuxqGZr0Ph4/O6iJiIiIiCg1c1qgFkKMrabeBOCg+ve/AXiHEMIqhFgMYBmAnXO5tmzz6EEt3qMiwyuh+aau2IFhXxhuf3jKY/yhKADAZuYsUiIiGq/YacHqqny82NwPKSUeO9SDC5aUoNBhxs+ePzXpfbQhiURERERERMlKW4FaCHEvgJcArBBCtAsh3g/gm0KIA0KI/QAuA/AJAJBSHgJwH4DDAB4B8FEpZTRda8sFjxzqxooKFxrL8jK9FJpn6tTM8ekGJfrCEQCAw8JiAhERTXTh0hLsbhnG7tYhdAz78caN1bhlWx0ePdyNloHRccdKKeEJRNhBTUREREREKUlbgVpK+U4pZZWU0iylrJFS/kxK+W4p5Top5Xop5RuklF1jjv+alHKJlHKFlPLhdK0rF/R5g3j19CCuXTv9cERamGoTKVCrHdRjIz6IiIg0FywtRSgaw9cfOgqDAK5YVY5bz2+AySDwixdPjzt2NBRFNCaZQU1ERERERCmZ6yGJpAOXzYQf3Xwu3nRudaaXQvNQXYlSoJ4uh1qL+LCzQE1ERJPY2lAMk0HgtZYhbG4oRkmeFRX5NtywfhHu29U2LkZK+zs7qImIiIiIKBUsUGchm9mI69dVob7Ememl0DyUbzOjyGGetkDNDmoiIpqO02rCOXWFAICrV5+Zd/G+ixbDF4riD6+2xm/zsEBNRERERESzwAI1UQ6qK3bMUKDWMqhZoCYiosltX1YGIYBr1pyJFFtbXYDzG0twz4unEY7GAJzpoM5ngZqIiIiIiFLAAjVRDqqdoUAdj/gwc0giERFN7oPbG3H/Ry6IzzbQfGD7YnS6A3j4YDcAdlATEREREdHssEBNlIPqih3oGPIjona3nc0fZsQHERFNz24x4py6ogm3X7aiHI2lTvzf8ychpTzTQc0hiURERERElAIWqIlyUF2xA5GYRJc7MOnHmUFNRESpMhgEbrtoMfa3u7GrZYhDEomIiIiIaFZYoCbKQXXq5dhtU8R8xCM+WKAmIqIUvPncahQ6zPjZ86fgCUQgiNUpTAAAFPRJREFUBOCyMTaKiIiIiIiSxwI1UQ6qK1EK1JPlUAfC0fjtdjML1ERElDyHxYRbttXh0cPdONjhRp7VBINBZHpZRERERESUhdjqQpSDqgrsMBkEWgd9iERjONDhxo7mAexo7seu00MIRmKoKrDBZOQ5KiIiSs2t5zfgrudO4qmjvagpsmd6OURERERElKVYoCbKQUaDQE2RHX94tQ2/eqkFI8EIAGBlpQu3bKvHBUtKsK2xOMOrJCKibFaRb8MN6xfh/j0dHJBIREREREQpY4GaKEddsaoCTx/txXlLSnDBkhKc31iCkjxrppdFREQ55H0XLcb9ezo4IJGIiIiIiFLGAjVRjvqP16/Gf7x+daaXQUREOWxtdQHedE416kucmV4KERERERFlKRaoiYiIiChl33n7xkwvgYiIiIiIshgnpBERERERERERERFRRrBATUREREREREREREQZwQI1EREREREREREREWUEC9RERERERERERERElBEsUBMRERERERERERFRRrBATUREREREREREREQZwQI1EREREREREREREWUEC9RERERERERERERElBEsUBMRERERERERERFRRrBATUREREREREREREQZwQI1EREREREREREREWWEkFJmeg0pE0L0AWhJ8PBSAP1pXA4tPNxTpBfuJdIL9xKlgvuG9MK9RHrhXiK9cU+RXriXSC8LcS/VSynLJvtAVheokyGE2CWl3JzpdVDu4J4ivXAvkV64lygV3DekF+4l0gv3EumNe4r0wr1EeuFeGo8RH0RERERERERERESUESxQExEREREREREREVFGLKQC9V2ZXgDlHO4p0gv3EumFe4lSwX1DeuFeIr1wL5HeuKdIL9xLpBfupTEWTAY1EREREREREREREc0vC6mDmoiIiIiIiIiIiIjmERaoiaYhhBCZXgMREdFs8fcZERHlOv6uIyLKXjlToBZCvEEIsSTT6yAiIiKah0zaX/gGnmZDCLFCCJEz7yEoc4QQNwshNqh/5/MS6YHPTUREWSrrn8CFEFcKIV4C8DMAVZleD+UGIcQNQoh7AfyrEKI+0+uh7CWEeKMQ4iuZXgdlP+4lSoUQ4lohxKMAviWEuAkAJAeQUAqEEFcJIV4B8AHkwHsIyhz1/dvzAP4XwDkAn5dodoQQrxNCPAjgK0KICzO9Hspe6uvtHwghijO9FspufO+WPNPMh8w/6hl2J4B7AbgAfB7APwOoB/CCEMIgpYxlboWUzYQQVwL4DwBfALAFwB1CiKellP/g3qJEqd1l7wPwrwDqhRCPSSmfz/CyKMuov+8MAG4D9xIlSN03ZgBfB3A+gP8GUAPgrUKIg1LKE5lcH2UPdS+ZoLwueieAz0op7x/7cRYWKRHqXrIB+CWAcgBfBXAjAIf6caOUMpq5FVK2EkJsAvBFAF8CkA/gPUKIZVLKe/jejRKlPkfdBOBrUGpMzwgh/sL9Q8nge7fZycruB6kYAfAbKeWlUsonATwK5UUO+CRCs3QlgAellI8A+CmUX1DvE0I4ubcoUepeOQGlM+j/AeDZU0qa+vsuCqAJ3EuUIHXfhAA8AuASKeXfAOwAEAZwKqOLo6yi7qUwgBiAP2nFaSHEdiGEObOro2yi7iU/gN+q798ehfK89G714yxOU6quBPC8lPIhAH8F0A3g40KIAilljPExlAj1ZOtJABcB+CcA74Jycp8oYXzvNjtZVaAWQnxcCPFfQoi3AoCU8g/q7QYAQwDahBDWTK6Rss+YffU29aYdAC4UQtiklL0AAgCMULphiaYkhHiLEGLbmJt2SCm9Usq7ATiFEO9Xj8uq516ae+rz0t1CiA+oNz3LvUQzOXvfSCmfkFJGhBDXA7gfwAoAXxdCvF09nm/aaVJj9tLt6k13AqgSQvxCCHEAwL9Aidd7n3o89xJNasxe+iAASCn/qt5uhHLC7JAQojaTa6TscvaeAvA0gBuEEEXqSZAwADeAzwKMj6GpCSHeI4S4asxNB6WUA1LKP0PZR28SQlgytDzKInzvpo+s+McRik8AeDuAXQD+UwjxXiFEGRDvVDwF4HVSymAGl0pZZJJ99WUhxHsAHAXQCeA+IcTTUC4V+ysAF59QaDJCiHIhxLMAvg/g38bsk8iYv38BwCfVF8/sxKcpCSHeC+BmAH8G8G4hxL8BaBxzCPcSTTDJvvmcEGKp+uF+ANdKKc+D8kb+fUKIBr5pp8mctZduEUJ8HkAQwAMALADeCuAN6sffJISo416iyZy1l96lPi81AvGOaQ+ADQCGM7VGyi6T7Kl/B3AaytXUvxZKtnkjgP8CUCiEcGZoqTSPCSGKhBB/grJPvq2eMAOAsR333wNwA4C1Z92XJ2RpHL53009WFNvUF72XAfi8lPJPAD4B5cXMtWOO2QGgXQjxhsyskrLNJPvqkwA2QtlbH4CSZfYtKeVtAEIAFvMJhSajdtr/FcpzUheAD6kfEtqlhVLKhwEcAXC7EMKlXQlCNIkrAPy3GjP0KSiZnbdoH+ReoimcvW8sUPeNlHKnlPK4etwRAH0AIhlZJWWDs/eSFcCHpJQPALhdSnlUfQ21H0phMZyphdK8N9nz0ru0D0opD0C5UvEdmVkeZaHJXiPdKqW8A8ql9P+pvncLALBLKUczt1Sar6SUQwAeA7AKwGtQCojax6T63xcB7AVwnRBipXZFEU/I0iT43k0n875APab7cBeA7QCgfuOPA1gjhFipHpcPpfOVL5JpRlPsq4eh7KstAJZKKfdIKf+hHrcJwCtzvlCa98bspR8AOAzlxc7rhBBVanHagDPPtZ8F8A0o2dSVc75YmtfG7KU9AF4PAFLKXQBeAlAtxk+l514iANPum5cBLDpr3wDAe6EMJRuYqzVSdphmL70IYLEQ4sKzij3vAWCHErNHFDfD81K1EOIi9TgBpfPVxq5Ems4Mz0/LhRDbpZStUsrH1eNeB6B57ldK892Y55pfSSmHAfwYytVA9ep7N+OY/fa/AP4NwLNQhruyg5ri+N5Nf/OuQK1dXqH94I/pWG2CErGwTv3/ZwEUAMhTj/NACbGvmNMFU1ZIcl+51D8QQlwvhNgJoB7KJRu0wE21l6SUYSllBEqG+VEAH9c+LqWMCiGWAPgJlEukz5VS/iADy6d5Zmxs0JjnpRcBGIQQF6v/fxBKZ/4i9T5LobyYfgDcSwtSEvumE2f2za1CiIMAFgP4iJrTSQtcis9BbxZC7INy+epHpJSBOVwyzVNJPi9VqcdJKEWfUXYl0tmS3FOV6n0uVmP3lkHJzic6ey9pHdIB9b+vAngYwNfU/4+qheoKAD8E8BSAjVLKr469Py08Z5+c4Hs3/c2bArUQ4kIhxC8BfF4IUaz94IszE8J3Qrkc9WohhElKeRhANYDNYx7mHVLKe+Zy3TS/zWJfbVE/fgLAh6WUb1YvBaIFapq9ZDzrl1U/gL8BWCGEqBFClKpXePQD+JiU8k1Sys65/wpovhBCbBVCxE9gjLld+518AsAhAG8XQhillO1QTr42qB93g3tpwUlx31RCKUgDShzD7VLK90gpe+Zw6TTPzOI5SNtLx6G8NrqVe2lhm8XzUsOYh/m0lPLnc7Rkmud0eH46DeD/SSlvklL2z93Kab6ZZi8JMXGu1A8BLBVCrBFClAkhFkN573aHlPINUsquuVs5zTfqXrobwGeFOgdPvV3LLud7N53MiwK1UIZl/BjK4J56AF8RysR5SCnD6n+boMQxLAHwr+pdg1B+CUE9hh0cFKfHvpJSnpBS7p7bldN8M8NeikoppRDCKoSwqv//HJRfUgcBPA+gQkrpHpMBSwuUEOKfAfwFyomO69TbjMC4F89eKPvGCuBb6gm1IqiRDFLKPinliTleOmXQLPdNv3rcXqnM66AFTKe9dEBK+dIcL53mGT1+n6nHhuZw2TSP6fT81CqlPDTHS6d5Zoa9JNUOabsQQrsav1U9/gCU/VWkvqdrzcgXQPOC2oj2DQB3QemSPhfAF9Xuem3YL8D3brqZFwVqAFsBHFG7nz8NJYz+BiFEFQAIIb4qhPgZlAD77wPYKoR4DcAglLxXosnMZl89mpEV03w10176TwD/B/WSVSHEh6EMSvwpgPX8hURjnIKSUfYRqCfFxry4gRDiywB+B+VM+39AeXHzvPr/v5zrxdK8wX1DeuFeIr1wL5HeuKdILzPtpS8C+C2UqCoIId4JZcjmtwCsY4MajdEB4G1qHeATAM6DMn8DAJ+X9CYyEaEjhLgBShfiLinly2p34q8BvFNK2SqEWA3gVgA9AF6F8mTxBbXbFeqZLpNUQu2JAHBfkX502EtXAjit/T8tXJPsJe1SMDOA+wE8IqX8vnqp4Roog1j+Q0rZrN7fAMAppfRmYPmUIdw3pBfuJdIL9xLpjXuK9KLDXjoPQI+U8lQGlk/zyJi99IqU8lWhRHsOqldKB4UQDwD4qpRylxBiPZQTIHxe0smcFqjVbsO7ABRC6Xy+GcA/SykfFUJ8C0CXlPLb6hPKzVDOaP2vlNKt3t8wNj+ICOC+Iv3osJeMY8/O08I1w14SaizMFQC+A+CKs3MS+by0MHHfkF64l0gv3EukN+4p0osOe4nv3QjAlHvpn6SUj405xgXgBQDXybOypPm8pI+5jvjYDOB5KeV2KeVXAHwPwO3qx54HsE4IsU19kugAcDGLiJQA7ivSy2z3El/gkObsvfS/AD4MjJv+/TSAlwHcASgDONT/Cj4vLVjcN6QX7iXSC/cS6Y17ivQy273E926kmWwv/b+zjtkK4JCUslMIkSeEWAbweUlPaS9QCyFuFUJcKoSwAngSymXymgEok8AB4BUAewB8R41aWAOgRQjhAMZPXiXiviK9cC+RXmbYS4MAjqjHGYD4nvkqlInQbgDnat0ec7x0yiDuG9IL9xLphXuJ9MY9RXrhXiK9JLCXDqvHmdXbigC0CSFugxLzuREYdzKEZsmUjgcVQggAlVDCwmMAmgF8EEqLfJcQwiylDEMZKFYEAFLKbgDfE0LUA/g5lNyXW6WUvnSskbIP9xXphXuJ9JLiXoqp91sC4BdQpkL/s5TyQCa+Bpp73DekF+4l0gv3EumNe4r0wr1EeklxL4XVu98I4BYoww/fLqXcP9frz3W6d1ALJcdHAnAB6JBSXgFleuoglEwXQNkIAHAVgD+p9ytXb/sXAO+XUm6TUh7Te32UnbivSC/cS6SXFPbSn9X7Fav380AZrnkFXywvHNw3pBfuJdIL9xLpjXuK9MK9RHqZxV4qVW97CMDbpJS3sTidHrp1UAtlaNhXABiFEA8ByAcQBQApZVQI8U8AOoUQl0gpnxVCWAD0ATguhPgagNcLIS6VUg4B4MRLAsB9RfrhXiK96LSXLpNS9gLozdCXQXOM+4b0wr1EeuFeIr1xT5FeuJdILzrtpYullPdm6mtYKHTpoBZCXALgNSgt8E1QvvlhAJcJNYReKtk/XwLwZfVuNgDvhZL14gJwpVr4IQLAfUX64V4ivei4lwbndOGUUdw3pBfuJdIL9xLpjXuK9MK9RHrRcS+553ThC5ReHdQxAN+WUv4aAIQQ5wBYDOALAH4CYJNQQuofAHC5EKIGwCIAvwHwHSnlXp3WQbmF+4r0wr1EeuFeolRw35BeuJdIL9xLpDfuKdIL9xLphXspi+iVQf0agPvU1nlACaCvk1LeA6WN/g71rEQNgJiUsl1KuVNKeSu/4TQN7ivSC/cS6YV7iVLBfUN64V4ivXAvkd64p0gv3EukF+6lLKJLgVpK6ZNSBqWUUfWmq6BktgDAbQBWCSEeBHAvlA2iTc8kmhL3FemFe4n0wr1EqeC+Ib1wL5FeuJdIb9xTpBfuJdIL91J20W1IIhAPH5cAKgD8Tb3ZC+BzANYCOCWl7AAAKaXU83NT7uK+Ir1wL5FeuJcoFdw3pBfuJdIL9xLpjXuK9MK9RHrhXsoOekV8aGIAzAD6AaxXz0T8B5RW+Re0bzhRkrivSC/cS6QX7iVKBfcN6YV7ifTCvUR6454ivXAvkV64l7KA0PvkgBDiPAA71D+/kFL+TNdPQAsS9xXphXuJ9MK9RKngviG9cC+RXriXSG/cU6QX7iXSC/fS/JeOAnUNgHdDmXgZ1PXBacHiviK9cC+RXriXKBXcN6QX7iXSC/cS6Y17ivTCvUR64V6a/3QvUBMRERERERERERERJULvDGoiIiIiIiIiIiIiooSwQE1EREREREREREREGcECNRERERERERERERFlBAvURERERERERERERJQRLFATERERERERERERUUawQE1ERERElCZCiKgQYq8Q4pAQYp8Q4lNCiGlfgwshGoQQN8/VGomIiIiIMokFaiIiIiKi9PFLKTdKKdcAuArAdQC+OMN9GgCwQE1ERERECwIL1EREREREc0BK2QvgdgAfE4oGIcTzQojd6p8L1EP/C8B2tfP6E0IIoxDif4QQrwoh9gshPgQAQogqIcRz6nEHhRDbM/W1ERERERGlSkgpM70GIiIiIqKcJIQYkVLmnXXbMIAVALwAYlLKgBBiGYB7pZSbhRCXAvi0lPL16vG3AyiXUn5VCGEF8CKAtwJ4EwCblPJrQggjAIeU0jtXXxsRERERkR5MmV4AEREREdECZQbwQyHERgBRAMunOO5qAOuFEG9R/78AwDIArwL4uRDCDOABKeXe9C6XiIiIiEh/LFATEREREc0RIUQjlGJ0L5Qs6h4AG6BE7wWmuhuAO6SUj07yeBcDeB2Ae4QQ35FS/iotCyciIiIiShNmUBMRERERzQEhRBmAOwH8UCo5ewUAuqSUMQDvBmBUD/UCcI2566MAPqJ2SkMIsVwI4RRC1APokVLeDeD/AJw7R18KEREREZFu2EFNRERERJQ+diHEXihxHhEAvwbwHfVjPwbwZyHErQAeATCq3r4fQFQIsQ/APQC+B6ABwG4hhADQB+CNAC4F8BkhRBjACIBb0/7VEBERERHpjEMSiYiIiIiIiIiIiCgjGPFBRERERERERERERBnBAjURERERERERERERZQQL1ERERERERERERESUESxQExEREREREREREVFGsEBNRERERERERERERBnBAjURERERERERERERZQQL1ERERERERERERESUESxQExEREREREREREVFG/H+FLVlZ2nk6fQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(constrained_layout=True)\n",
    "hidden_data.plot(kind='line', title=\"Evolution AMAZON stock values\", \n",
    "           xlabel = \"Dates\",\n",
    "           ylabel = \"Dollars\",\n",
    "           figsize=(20,5),\n",
    "             ax=ax)\n",
    "plt.axvspan(dates[-hide], dates[len(dates)-1], facecolor='0.2', alpha=0.1)\n",
    "plt.plot(x_real,y_real, label=\"Actual Interpolated\", color=\"g\")\n",
    "plt.plot(x_new,y_new, label=\"Prediction Interpolated\")\n",
    "ax.fill_between(x_new, y_real, y_new, color=\"orange\", alpha=0.2)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuanto mayor sea el area anarajanda peor habrá sido la predicción. Puedes obtener un valor numérico de la bondad de la predicción realizada por el jugador restando punto a punto cada uno de los valores interpolados entre la curva real y la curva generada por los clicks del usuario. Cuanto menor sea el valor de la variable **score** mejor habrá sido la predicción."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The score of the prediction is 330.77263682114125\n"
     ]
    }
   ],
   "source": [
    "score=sum(abs(y_real-y_new))\n",
    "print(\"The score of the prediction is {}\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{warning}\n",
    "Recuerda que para calcular el **score** tienes que utilizar el valor absoluto de la diferencia para evitar que una situación en la que la mitad de los valores predichos esté por encima de los valores reales y la otra mitad esté por debajo compense los errores resultando en puntuaciones cercanas al cero.\n",
    "```  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extensiones del Juego\n",
    "\n",
    "El código realizado en este capítulo está centrado en un único jugador. También lo hemos programado centrándonos en la predicción mensual (dificultad 2). A continuación te proponemos algunas ideas para seguir practicando y conseguir un juego más entretenido:\n",
    "\n",
    "* Revisa el funcionamiento del código para la predicción diara (dificultad 1). Probablemente tendrás que pensar en una manera más sencilla de proporcionar un valor para la predicción diaria ya que será complicado hacer un único click en el día de la predicción.\n",
    "* Revisa el funcionamiento del código para la predicción anual (dificultad 3). Probablemente tendrás que modificar ligeramente la visualización.\n",
    "* En lugar de jugar siempre con la misma empresa, puedes definir una lista con los códigos de diferentes compañías de manera que el juego seleccione aleatoriamente una de ellas. El nivel de dificultad también podria elegirse aleatoriamente.\n",
    "* Pensando en una partida multi-jugador, el código podría proponer un determinado número de desafíos a cada jugador, con dificultades y empresas aleatorias, calculando una puntuación final para cada uno de ellos."
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