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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Paquetes\n",
      "\n",
      "Con \u00e9ste Notebook trataremos de dar a conocer `plotly`, un servicio web gratuito para generar gr\u00e1ficos interactivos y que permite la creaci\u00f3n de proyectos colaborativos.\n",
      "\n",
      "\u00c9ste Notebook emplea los siguentes paquetes:\n",
      "\n",
      "+ [`pandas`](http://pandas.pydata.org) 0.14.0, para importar y analizar estructuras de datos en Python.\n",
      "+ [`matplotlib`](http://matplotlib.org) 1.3.1, para generar diversisos tipos de gr\u00e1ficos con calidad de imprenta.\n",
      "+ [`plotly`](https://plot.ly/) 1.1.2, para generar gr\u00e1ficos interactivos para la web.\n",
      "\n",
      "que iremos cargando conforme los vayamos a utilizar.\n",
      "\n",
      "Empezaremos por importar los datos con `pandas`. Podeis encontrar una introducci\u00f3n a `pandas` en \u00e9ste post de [Pybonacci](http://pybonacci.wordpress.com/2014/05/30/pandas-i/).\n",
      "\n",
      "# Datos\n",
      "\n",
      "La estructura de datos con la que vamos a trajar es un `CSV` con datos de telemetr\u00eda correspondientes a un BMW Z4 GT3 compitiendo en el circuito Brands Hatch Indy.\n",
      "El paquete de datos nos lo ha proporcionado Steve Barker, un mec\u00e1nico que pasa la mayor parte de su tiempo en la GP2. Pero aqu\u00ed s\u00f3lo vamos a trabajar con los datos de la vuelta 27 de la primera carrera.\n",
      "\n",
      "El propio `pandas` incluye rutinas para importar datos desde diversas fuentes, ya sea un simple `CSV` o un Excel de multiples hojas."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Importamos pandas de la manera habitual\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pero antes de proceder a cargar los datos debemos conocer un poco la estructura del archivo en cuesti\u00f3n del que he recortado una muestra.\n",
      "\n",
      "    Format;MoTeC CSV File;;;Workbook;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    Venue;brandshatch indy;;;Worksheet;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    Vehicle;bmwz4gt3;;;Vehicle Desc;bmwz4gt3;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    Driver;Steve Barker;;;Engine ID;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    Device;ADL;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    Comment;;;;Session;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    Log Date;02/07/2014;;;Origin Time;1196.94;s;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    Log Time;23:05:43;;;Start Time;1196.95;s;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    Sample Rate;60;Hz;;End Time;1240.017;s;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    Duration;43.067;s;;Start Distance;49849;m;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    Range;Lap 27;;;End Distance;51765;m;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    Time;Distance;Gear;Engine RPM;Manifold Pres;Throttle Pos;Brake Pedal Pos;Clutch\n",
      "    s;m;;rpm;kpa;%;%;%;deg;N.m;;m;km/h;G;G;G;deg;deg;deg;deg/s;deg/s;deg/s;C;kPa;V;\n",
      "    ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n",
      "    0;0;5;8657;0.99752352;100;0;100;-5.724613;1.5780677;26;0.6267;225.407808;-0.296\n",
      "    0.017;1;5;8658;0.9975208;100;0;100;-6.10625;1.7365016;26;1.66957;225.514112;-0.\n",
      "    0.033;2;5;8654;0.99751824;100;0;100;-6.160785;1.929843;26;2.71285;225.58688;-0.\n",
      "    0.05;3;5;8670;0.99751552;100;0;100;-6.160785;2.5375902;26;3.7565;225.653952;-0.\n",
      "\n",
      "Como bien podemos apreciar, las primeras 11 l\u00edneas aportan informaci\u00f3n generada por MoTeC al exportar los datos.\n",
      "Las cabeceras de lo que realmente nos interesa son las l\u00edneas 14 y 15, siendo la primera la descripci\u00f3n del par\u00e1metro y la segunda las unidades de medida.\n",
      "Y ya en la l\u00ednea 18 empiezan los datos.\n",
      "\n",
      "Tomaremos como \u00edndice del `DataFrame` la variable tiempo (aunque tambi\u00e9n podr\u00edamos optar por la distancia)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Cargamos el archivo. Recordemos que pandas, y Python en general, es 0-indexed.\n",
      "data = pd.io.parsers.read_csv('brandshatch_lap27.csv', sep=';', header=13, index_col=0, skiprows=[14,15,16])\n",
      "# Otra forma alternativa de leer los datos de un archivo CSV ser\u00eda a trav\u00e9s del\n",
      "# propio DataFrame de pandas, pero \u00e9ste no nos permite saltarnos filas de datos:\n",
      "# data = pd.DataFrame.from_csv('brandshatch_lap27.csv', sep=';', header=13, index_col=0)\n",
      "data.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Distance</th>\n",
        "      <th>Gear</th>\n",
        "      <th>Engine RPM</th>\n",
        "      <th>Manifold Pres</th>\n",
        "      <th>Throttle Pos</th>\n",
        "      <th>Brake Pedal Pos</th>\n",
        "      <th>Clutch Pedal Pos</th>\n",
        "      <th>Steering Wheel Angle</th>\n",
        "      <th>Steering Wheel Torque</th>\n",
        "      <th>Lap Number</th>\n",
        "      <th>...</th>\n",
        "      <th>WaterTemp</th>\n",
        "      <th>Yaw</th>\n",
        "      <th>YawRate</th>\n",
        "      <th>dcABS</th>\n",
        "      <th>dcBrakeBias</th>\n",
        "      <th>dcFuelMixture</th>\n",
        "      <th>dcThrottleShape</th>\n",
        "      <th>dcTractionControl</th>\n",
        "      <th>dcTractionControlToggle</th>\n",
        "      <th>Fuel Density</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
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        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0.000</th>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> 8657</td>\n",
        "      <td> 0.997524</td>\n",
        "      <td> 100</td>\n",
        "      <td> 0</td>\n",
        "      <td> 100</td>\n",
        "      <td>-5.724613</td>\n",
        "      <td> 1.578068</td>\n",
        "      <td> 26</td>\n",
        "      <td>...</td>\n",
        "      <td> 88.536890</td>\n",
        "      <td>-0.790032</td>\n",
        "      <td>-0.064797</td>\n",
        "      <td> 9</td>\n",
        "      <td> 3570.19424</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0.017</th>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 8658</td>\n",
        "      <td> 0.997521</td>\n",
        "      <td> 100</td>\n",
        "      <td> 0</td>\n",
        "      <td> 100</td>\n",
        "      <td>-6.106250</td>\n",
        "      <td> 1.736502</td>\n",
        "      <td> 26</td>\n",
        "      <td>...</td>\n",
        "      <td> 88.540582</td>\n",
        "      <td>-0.791088</td>\n",
        "      <td>-0.063974</td>\n",
        "      <td> 9</td>\n",
        "      <td> 3570.19424</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0.033</th>\n",
        "      <td> 2</td>\n",
        "      <td> 5</td>\n",
        "      <td> 8654</td>\n",
        "      <td> 0.997518</td>\n",
        "      <td> 100</td>\n",
        "      <td> 0</td>\n",
        "      <td> 100</td>\n",
        "      <td>-6.160785</td>\n",
        "      <td> 1.929843</td>\n",
        "      <td> 26</td>\n",
        "      <td>...</td>\n",
        "      <td> 88.544275</td>\n",
        "      <td>-0.792130</td>\n",
        "      <td>-0.067104</td>\n",
        "      <td> 9</td>\n",
        "      <td> 3570.19424</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0.050</th>\n",
        "      <td> 3</td>\n",
        "      <td> 5</td>\n",
        "      <td> 8670</td>\n",
        "      <td> 0.997516</td>\n",
        "      <td> 100</td>\n",
        "      <td> 0</td>\n",
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        "      <td> 2.537590</td>\n",
        "      <td> 26</td>\n",
        "      <td>...</td>\n",
        "      <td> 88.547936</td>\n",
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        "    <tr>\n",
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        "      <td> 4</td>\n",
        "      <td> 5</td>\n",
        "      <td> 8695</td>\n",
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        "      <td> 100</td>\n",
        "      <td> 0</td>\n",
        "      <td> 100</td>\n",
        "      <td>-5.997180</td>\n",
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        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 215 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "       Distance  Gear  Engine RPM  Manifold Pres  Throttle Pos  \\\n",
        "Time                                                             \n",
        "0.000         0     5        8657       0.997524           100   \n",
        "0.017         1     5        8658       0.997521           100   \n",
        "0.033         2     5        8654       0.997518           100   \n",
        "0.050         3     5        8670       0.997516           100   \n",
        "0.067         4     5        8695       0.997512           100   \n",
        "\n",
        "       Brake Pedal Pos  Clutch Pedal Pos  Steering Wheel Angle  \\\n",
        "Time                                                             \n",
        "0.000                0               100             -5.724613   \n",
        "0.017                0               100             -6.106250   \n",
        "0.033                0               100             -6.160785   \n",
        "0.050                0               100             -6.160785   \n",
        "0.067                0               100             -5.997180   \n",
        "\n",
        "       Steering Wheel Torque  Lap Number     ...      WaterTemp       Yaw  \\\n",
        "Time                                         ...                            \n",
        "0.000               1.578068          26     ...      88.536890 -0.790032   \n",
        "0.017               1.736502          26     ...      88.540582 -0.791088   \n",
        "0.033               1.929843          26     ...      88.544275 -0.792130   \n",
        "0.050               2.537590          26     ...      88.547936 -0.793190   \n",
        "0.067               2.660200          26     ...      88.551744 -0.794294   \n",
        "\n",
        "        YawRate  dcABS  dcBrakeBias  dcFuelMixture  dcThrottleShape  \\\n",
        "Time                                                                  \n",
        "0.000 -0.064797      9   3570.19424              1               10   \n",
        "0.017 -0.063974      9   3570.19424              1               10   \n",
        "0.033 -0.067104      9   3570.19424              1               10   \n",
        "0.050 -0.067915      9   3570.19424              1               10   \n",
        "0.067 -0.072090      9   3570.19424              1               10   \n",
        "\n",
        "       dcTractionControl  dcTractionControlToggle  Fuel Density  \n",
        "Time                                                             \n",
        "0.000                  3                        0          0.75  \n",
        "0.017                  3                        0          0.75  \n",
        "0.033                  3                        0          0.75  \n",
        "0.050                  3                        0          0.75  \n",
        "0.067                  3                        0          0.75  \n",
        "\n",
        "[5 rows x 215 columns]"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Podemos ver que el archivo es inmenso. Contiene 215 con diferentes par\u00e1metros como revoluciones del motor, posici\u00f3n del pedal del acelarador, velocidad, etc. Un sinf\u00edn de datos que marear\u00edan al m\u00e1s experto de los ingenieros de pista.\n",
      "\n",
      "De todos modos, aqu\u00ed no vamos a analizar en detalle la vuelta realizada por Steve Barker, sino que nos centraremos exclusivamente en visualizar los datos.\n",
      "\n",
      "# Gr\u00e1ficas con `matplotlib`\n",
      "\n",
      "`matplotlib` es el paquete m\u00e1s empleado en Python para generar diversos tipos de gr\u00e1ficos de alta calidad.\n",
      "Y es precisamente lo que utiliza `pandas` para generar sus propias gr\u00e1ficas como veremos a continuaci\u00f3n.\n",
      "\n",
      "Empezamos por importar `matplotlib` y, de paso, crear un mapa de colores personalizado m\u00e1s vistoso que el que viene por defecto.\n",
      "La idea la he tomado de [Randal S. Olson](http://www.randalolson.com/2014/06/28/how-to-make-beautiful-data-visualizations-in-python-with-matplotlib/)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import matplotlib as mpl\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Color map\n",
      "tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),  \n",
      "             (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),  \n",
      "             (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),  \n",
      "             (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),  \n",
      "             (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]\n",
      "tableau10 = [tableau20[i*2] for i in range(10)]\n",
      "\n",
      "# Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts.  \n",
      "for i in range(len(tableau20)):  \n",
      "    r, g, b = tableau20[i]  \n",
      "    tableau20[i] = (r / 255., g / 255., b / 255.)\n",
      "for i in range(len(tableau10)):\n",
      "    r, g, b = tableau10[i]\n",
      "    tableau10[i] = (r / 255., g / 255., b / 255.)\n",
      "\n",
      "# Set the default color cycle\n",
      "mpl.rcParams['axes.color_cycle'] = tableau10"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Va de velocidades\n",
      "\n",
      "Una de las primeras cosas que podemos visualizar son la velocidad en funci\u00f3n del tiempo junto con otros par\u00e1metros relaciones como son la posici\u00f3n de los pedales y las revoluciones del motor."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(3, sharex=True, figsize=(10,6))\n",
      "\n",
      "# Revoluciones del motor\n",
      "data['Engine RPM'].plot(ax=ax[0], linewidth=2)\n",
      "ax[0].set_xlabel('')\n",
      "ax[0].set_ylabel('Engine RPM')\n",
      "ax[0].legend(loc='lower right')\n",
      "# Velocidad del veh\u00edculo\n",
      "data['Ground Speed'].plot(ax=ax[1], linewidth=2)\n",
      "ax[1].set_xlabel('')\n",
      "ax[1].set_ylabel('Speed (kph)')\n",
      "ax[1].legend(loc='lower right')\n",
      "# Pedales\n",
      "data['Throttle Pos'].plot(ax=ax[2], linewidth=2)\n",
      "data['Brake Pedal Pos'].plot(ax=ax[2], linewidth=2)\n",
      "ax[2].set_xlabel('Time (s)')\n",
      "ax[2].set_ylabel('Position (%)')\n",
      "ax[2].set_ylim(-10,105)\n",
      "ax[2].legend(loc='center right')\n",
      "\n",
      "for j in range(3):\n",
      "    ax[j].set_axisbelow(True)\n",
      "    # Quitamos los bordes superior y derecho\n",
      "    ax[j].spines[\"top\"].set_visible(False)  \n",
      "    ax[j].spines[\"right\"].set_visible(False)\n",
      "    # Dejamos s\u00f3lo los ticks abajo y a la izquierda\n",
      "    ax[j].get_xaxis().tick_bottom()  \n",
      "    ax[j].get_yaxis().tick_left()  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vC+YliKpTv1hgNg0+MLK5QT3D3QTdXU11ddp0wAZEYdzZCM3QU8CXduzbkvbo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0IAeGOEF4uGemqkKGZCRh7P/nyO8KRs+rW0YcOsnhjpN7zkZoGgIaJQqtjmd58VBwNJzy0s6Xey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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xafbbfd0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lo que hemos realizado en la gr\u00e1fica de arriba son tres subgr\u00e1ficas que comparten el mismo eje *x*. \n",
      "De ese modo podemos ver la relaci\u00f3n entre los diversos par\u00e1metros.\n",
      "\n",
      "## Relaciones de cambio\n",
      "\n",
      "Otro tipo de gr\u00e1fico que nos puede dar mucha informaci\u00f3n es la relaci\u00f3n entre la velocidad del veh\u00edculo y las revoluciones del motor.\n",
      "\n",
      "Para ello podemos utilizar en `pandas` el tipo `scatter` y colorear los valores en funci\u00f3n de la marcha engranada.\n",
      "Eso lo conseguimos con la herramientas `groupby` de `pandas`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig1, ax1 = plt.subplots(figsize=(10,6))\n",
      "\n",
      "for key, grp in data.groupby(['Gear']):\n",
      "    grp.plot('Ground Speed','Engine RPM',kind='scatter', ax=ax1, label=key, color=tableau10[key])\n",
      "\n",
      "ax1.set_xlabel('Ground Speed (kph)')\n",
      "ax1.set_axisbelow(True)\n",
      "ax1.legend(title='Gear', loc='upper left')\n",
      "# Quitamos los m\u00e1rgenes derecho y superior\n",
      "ax1.spines[\"top\"].set_visible(False)  \n",
      "ax1.spines[\"right\"].set_visible(False)\n",
      "# Dejamos s\u00f3lo los ticks abajo y a la izquierda\n",
      "ax1.get_xaxis().tick_bottom()  \n",
      "ax1.get_yaxis().tick_left()  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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g0aaTdDw39Dmf5yi0FXLlwivJLPXuQH9DtxuI9o+u8xpbDIriqlNakYhbb/WI\nAHXk55N2/wO1mj5o7NjyeTQNzWIh7aGHUcsCGXI/+wzNUq491SwWcj//vHb30MQo9CFBb3UERRi5\n8LouTHl+CK061E+QqOr7pCnmOlMVhZ0rf/FpbJfzh/k8r81cyq5Vy9i8aD6nUnwTEFsaLd6EqhSX\nIPubPEwWmqaRMfMF8hcsQJJljF270v7TT9CFhp7R6/fq1cvtWK/X06pVyytfc7ZpiT5wxfZij7ZJ\nnScRFeDdgbgymqYxbcU0ssxZXvsTQhL415B/1WuNjZGG2Cun3vKsnmDs1YvoJ/7p0X7i8X/4rnkr\no3jlSk9NniRhz8jE2DHBu7NS05MdfEZxqGhK/W6wONfK30uO4GfSERJVtca6pv1y6lghG75PdCvH\npdNLhLUOIKxV3c2y54rvnvfNf1Y2GJC9uAZ4w2Yu5csnH6IkLw9FcbBp/rdc+chTdBokAqMq0mI1\ncPb0dA5ffgWJQ4dyqP8A8r7/wa2/YMFCCpYsAYcDzWbDcugQ6c+7R35pqkrBokVkvvEG+T/9hKZ4\nmqd84YEHHiAwMJBevXrx7LPPMnDgwDrfl0DgDYvDQqAh0K3NKBuZ3NX3hK1vbHmDg3kHvfaFGcOY\nO25uk/TfORdYjx71bNu/n8MXj8RysPw11hwOSjdu9GlOOSjIvaGy0KeqGFq3QlMUgi+/HPzKS2VJ\nJhOR0+70/QaaGKn7c1DrKcABWEscrPvmECeT6+Y7V5BVysK3dpB5tBBV0ZAkMAbo6TY0homPDUSS\nm9b75+Cf68lIPuTT2J4jRvk87961qyjJzcVhs6IpCg6bjTWffljXZTZbGlqAexrYB+wBvgWMwAtA\nGrCj7Gd8pfFJwEGgoiftoLI5kgDvIVq1JG36DGwpKU4BzW4nc9YszLt2ufpLt21DM1fwR7DbMe/e\n7TbHyaeeIv2FF8md+xkZL79C2kMP1UkFPmfOHIqLi1m9ejXPPvssmzeLPDcNTUvzgZu9bTYWh7sJ\naWz8WHpF9ariDHeKbcV8c+Abr32t/FuxfNJyovx90+Q1Nc70XnFkZeHwlsNN03BkZZF65zQ0hwOA\n4o2bfNK+Gbp2Qa2q3qROh2Qy0fqZf2Heu5cj10wk8+V/g6Yhh4XhP2QIce+9S9CIEfW5rUbN+u8S\nz9hcDrvK8f1Vpx6pbr+k7M5BU8v/n5rmnG/ULd3x829aBrE961ax9N3/+DTWGBjE2Lt99680Fxfh\ncLi7GFhTxIexAAAgAElEQVTNIm1LZRpyx8QDdwM9ACvwA3AjTkX97LKfivQEbij73RZYDXQpG/8h\nMA3YDCwDxgF1dhTTNM35lFux9IeqYt61G/9+/QDwi49HMhrRTudlkmUMbcvLp9hPnKBo5a+ufs1s\npuTPjViTkjB19V7PrTokSWLkyJFcd911fPfddwz2EvIvENSVzRmbPdKHlNh9KzCtaRr3rbrPa+1U\ngJ8m/ESQX5DXvqpIzEskrSiNTmGd6BDSoVbnNnWK1q93+sBVgVpaiuPUKQyxsZz8V80m6dApU2jz\n9FMkDhnq/tBZhl/79gRddBGZL7/itBKUCYfg9H0LuvCCZi28/f5jIiV51efXq5ZK+TF0BhlTUN1q\noeoMsodlW9ZJrPvmIAc3pYMk0W90HEOv6eSmzU7elsm6bw5hszho0zGU8ff1wT/Ij3PJmk/m1DjG\nYPJn2LU3cf5VE2s1d3zfgWxdstCVxFdnMJDQT1imKtOQAlwhYAcCAKXs9wmcgp03PfHVwHdl56QA\nycAQ4BgQjFN4A/gSuIZ6CHCSJKELDUXJyytv1OvRt27tOoy47VaKVq7ElnIUJBlJryfm3y+5+pWS\nEtDrnKLp6Xn1+pprE9aA3W4nsoqi04IzR0vzgWsd0Joj+UdcQphBNhAXVHU9x4ocLzzOvtx9Xvv8\nZD/sqqczfnV8uPND5u6di17W41AdPD3kaSZ1mVSrOc4mtdkramkpkr9/tabkkgrRoN4nUZEDAzk+\nYwZqdna1Q9u8+CLhN1xf9vcLpD/xpMcY/0GDyPvxx/KH0QpoFgvmXbs92psL+/44we7f0moeWA2y\nXkK1O983kgyBYX70GO5ZIu40Ve0XTdMIax2AbJDBoYIGej+Z1vEhHPo7A8WhARq716YRHGGi98XO\n92fW8SLWfH4Ah92pcMg8Wsjyj/Yw6R+D6nVf9UFRHChegnBO89j3S+rkTqFpGtuXL2bPmpUEhoVh\nLi5CdSgkDBjEZfc9XJ8lN0sa0oSaC7wFpAIngXycWjWAB4FdwKdAWFlbLE7T6mnScGriKrefKGuv\nF7FvvIFkMiEHBiIFBBAwcCDBYy5x9csmE/Hff0fcB3No+9abdFq5AmNCgqvfGB+PLiTUGa4PIMtI\nRiPGWmjfsrKy+P777ykpKUFRFFauXMm8efO4+uqr63t7AoGLUnspx4uOu2nQQo2h3Nvv3hrP1TSN\nl/56CYfq8NofaAgkwhTh81qOFR7j072fYlEsFNuLsSgWXvnrFYpsRT7P0RixHDxI0kUXc+j8wSQO\nHkJJNX5rkp/Ra7scGIhkMhH96CMkjRtP8eo11V4zcsYMl/BW+OsqMp6fWe7XJkkgywQMHYp/36pT\nXqDTOf17n33OoypDc2Dd1775Z1WH6qhg8lTBYXXmbasNmqqx4r97WfrBLjRVQ6eTaN8rgkum9sRS\nYsdhK7cGOWwqR3eXC+4nDuWhVjCjq4pGxpGCcxqxmnbA+wMdQFBEVJ19YbctXcgf339JTloqBacy\nUR0K1z7zbyY89q8q66W2ZBpSA9cJeASnxq0AmAfcjNMcelqV9W+cQt60M3HB22+/nfj4eADCwsLo\n379/lWODRlxIxyWLMe/chS4inMBhwzzC9yWDgcChQ7yeL/n5Ef/N15z45xNYk5Pxi4+n7RuvIwf4\nHkUkSRIfffQR999/P5qm0bVrV7766ivOP/98r+PXrVvnero77Wchjut2/Pbbb9O/f/9Gs56GPP4p\n6SfSiyqVR7LBjk07ajw/slcku7J24Y1AOZD/jv0vOlnn83pM3UyguUei2Rzw85pltPNv3Sher8rH\nFX2avPVrNhuHb7kVudgZ5asWFZFyz70U3HUXwx5+CEmS3MaHX38dBStXINnKNBhGIyUXX4SjbRz9\nLh9P6l13oxYUeDVTnMYRGcn+3r1oBdgzM0l7/HGkChoR1WAg98knuPCmmzDv2IGiKG5P6xo4HzpV\nFcfx4+SdOEHOr7/SY/UqdCEhjer1r+uxqlRwkakl0ukMLCoe0bmlhTaWfriL+FESl187yuP63vbL\n4q/WcWKvhlbhOSgjNZerHuzPgY3pQHmEuCRDYWkuS39YS+YWPeZiT02XpIP169c37Otnt+OXm8mp\nI8lYDH5E9ehLQnQEf/88j8zDSV5eNSfjHni0ztc/vGoFjgqaYofNyr71a0jKOHXG768xHNeXhgx5\nuQEYC9xVdnwrMBSo6MkYDywB+gBPlbXNKvu9ApiJ04S6FqcvHcAU4GLgvkrX07w9kUiS1CRz61Sm\nudxHY2FdBWG4ufOfLf/hy/1furWF+oXyx5Q/ajx31t+z+Oage/CCjMzHl37Mea3Pq3XC3qzSLEb/\ncBnI5V9KmmLiiZ7fctvQLrWa62xR016xpaZy5OprPPzPJKOR4DFjiH3zPx4aifxFi8j+YA6oKhHT\n7iT8xhuRJIm0xx+naOmy6hckSSQsWYKpcyfAmZT3xCOPohaVazGlgAA6LvgJv7IH2iMTrsaaWO7I\nL0dGoOblu/kBSwEBxLz4AqFeaqY2RU4m57HwzR21OscYpOPmF4eRnpTPum8OYS6q2kzYd3QcI673\ntLh42y9bl6Xw95IjbsKg3iBz73sjycsoYf6srSiKhgTojTquerAfC9/a7qaZA6e/nCxLXHJ7DzoP\nak1DoSoKX/zjAXJPeibrro4J/3imVrneKvP54w+Qk+aecLn/uCu55I7KX/fNA6meYfsNaUI9iFNg\n88cpKI4B9gNtKoyZiDO6FGAxziAHPyABZwDDZiADpz/dkLJ5bgV+bsB1C1oALUV4s6t2fkv9za1N\nRmZwTM1BModyDzE/cb5He5R/G7YfCuebv49TYK6d/1t0QDT2jJvQVIPzRzFhO3EHVlvjrdxQ017R\nhYe7BQacRrNaKVy1iuI/3AXl0u07yHjxJZTsbBy5uRQsWIhmt1O0dm3NwpufHx2XLnUJbwCG2FhX\nLVMXioIuyplQWS0txXrEPRGqWljkJbpVcw/saqLYLA5Wzd3HL+971xxXx+R/nocpwEBueilWs3e3\nAXBaqPV+3r8+ve2XqLgg9Iby8ZIE4THOtD7hbQKZMnMoF17bmQuv78JNM4dQWmBDrpRSRNZJDBrf\ngUlPDGpQ4Q0gafOmWgtveqOxzsKbpmlkp6bQbfgIdIZyNwCDycSAy66s05wtgYY0oe7CGXCwFVCB\n7cD/gE+A/jifRY4Cpx1x9gM/lv12AA9Q/rzyAPA5TmFwGfUIYBAIWgoWh4Xpq6eTVuzuxK2i8sT5\nNSffPJB7wKuO/uTBm3lrZzJ6WWLO2sOseGQEYQG+R8SNjBvJ2sRu2LQiNEcQJoOBi7o23eoNuuBg\nov/xD069/rqnAGSzkfXmmwRdeKFLC3fy6afRSktdH27WpCTyf5zHqTffrPFace+87UzEW4YjK4vi\n337D1L8/5h07kP380BwO2rwwE12QU0Dwmp+ykgO6JsuUKDK37ZGZEHmEOy9IaJI5/czFNub+8486\nJSWO7RpGeOtAVn++n+Rtp9x839yQwGDS02uE767YHfpE0vuituxel4YsS5gCDYy7p7w+bVC40RW0\noGkaej8ZxYsJuP+Y9piL7Kz54gCWEjvxvSORdBKqohHfJ4qgcO/+lbUlabNvuQcrUtci85qmsfyD\n2ST9vRFZp0PWybTrOZDgyCgGXTGRiFjfgq1aIg2deOaNsp+K3FbN+FfLfiqzDaeZVSA4IzR3E+qG\ntA08tOYhFDy/vA2yAYNcfRoEVVNZlLwIq+IeuSipRsylEYCGQ9XIKbHyxcZjPDzGd/PnzAm92Ppe\nHtnFMhJw27B4urUJ9vn8s40ve0UOCEAyGLxGetqOpWLesYOAsgTdSk6OW79msZD7ww9upa28EXbH\n7QSPKk+Gas/I4Og1E50R8YqCZDAQed+9hFx2GX7t2rnGSTod/n37Yt67Fypr6gBNb2BzdFc+6j2B\njGKZxJVOU+u0CztWu57GQHZaEb9+up/iPAtRbYPIzSiuk/AWGRfIhAf7k5dRQvK2Uyj2SsKTDjIk\nlTy9hlWCqAFhBEd4d6r3tl8kSeKCa7swcFwHbGYHwREmZJ2nBi87rYgl7+7CUmJH05wRsJIkIUkw\nZEJHbGYHP766BZvFARqk7M5GkkGnk9m0IJnJT5xHRGygx7w1YS4qpLQgn5BWrTH4GWudc63jwMGc\nf1XdIsmTt2wiefMmV8oQgPzMdCb/66VqzhKAKKUlEDQ7lh1dxpMbPNNJnCY2KLbGyNHfUn9jb/Ze\ntzYJCUPubYAEsgXZeBJNX0RyoQ1V64Qs+eaR8cT83S7TqwZ8tekY43u3YUD7cJ/Ob4yUbtniVXgD\nZ3ohJb88atG/fz9K/vrbZXaVTCbsSVU7hQPoE+KJedL9f5rz6VyUoiJXTjnNaqVo5a9E3eV0O9Y0\njcx/v0zejz+CJKELDka129GK3CN+rQYjLwy5w3Vstit893dqoxfgLMV2Fr65HZvFef/ph2sXGVqR\ncff2ARn2bkjzFN6AA34qy/ysqGVKyYDkU4xLyuZiHzTHDpvC4R1Z2C0O4rpHENbae6Cboqgsensn\nlgpBC7Iscd74DsR1j6BNx1C2rzyGw6a4CamaCg5VBTv8MS+JCQ9XHbznja2/LOT3bz9HkmVknZ7r\nnn252iAFb5w4tI+UnduI71/71CZ56Sc9kvYWZje/iOiGoCF94ASCRktz1b6lFqby9Ianq+yXkHh9\nxOs1mseOFR7zyO8mI3Npwkj8g1IJ6vwqAR0+xtT2OzYUvcBdK+/yOR/c5qO52CuUNbIrKn8frTqz\n/bnGl72ij4wsTylUGU1zS+UR+5//YOrZE3Q60Ot9UhjFvviiR5tSkO+REFgpLHT9XbBoEfkLFzoF\nRbsdpagIU4/uSBXSMUhGIye6DfBILuunb/xfDcf2ZbuEt/pwwzPnExrlz9L3d7F3w0mvYxIlu0t4\nA2dakGM53hNhV9wvdqvCj69uYd23B/ljfjI/vLKZE4fyvJ5Xkmd1CmcV0OllotuH0Kajswa3qqjV\nBrOVFtYuYXHG4STWfz0XVVFQ7HbsFjPfPvsPzIW1E4atJSUsmv0q+acyah1sF90+Hr2+gi5JkoiI\nqXemsBZB43+XCgQCn3l3x7uoVO2Ifn+/+2ssn2VVrCxKXoSilX+ZSEh0Cu/Ev8Z3x9D2cySdDUly\nOmOrKOzK2uU14MEbISZ3861BJxNRCx+6xoY9I4PcefO8BgDoY2JoP/dT9FHlZcb04eEk/PgD3bZu\nIeb1WVCD6dT/vPMI9FKZJWTcOCT/CsKYyUROuy4snHwnv0y5l6ylKzzKAdqPpTr948LCkEwmgkaN\notsbrxJg0LncHU0Gmccv7Va7F+EcsG1FSr3nmDprOFHtgsk5UczJ5Hyvfm96g0xoiBEJMGgwqdiP\nB7L9KP76KFuXV7+G/X+cpDDHgsOqothVHDaVtV97rydsCjK4ldkCUFWNwLDy90bnQa3RVSFcS3qJ\nQ7KDf8zbxcGMQq9jKvPrx+95D2bxQuuEzlx4420YA72baBW7g08fupt3bpnIxvnf1njtwuxT/PLO\n6/y18EeiO3RE1ukwmEwEhoYx4fGaK5AIhAlV0EJpjj5wmSWZrD622mufv86fdy95l6ExQ2uc5/sD\n33Oi2D0CTS/reXvU27y3fi+qZPWIbbCpNo7mexZo98asyX2Y/u12NA10skR8ZCBXD4j16dxzQU17\nJX/+T+ClAoscFETsKy+7yvNVRtLrSf/XMzVe37xzJ6rNhuzn/CJXLRZOPv00xWuc0cVSmf9dVkJP\ngjauo7vqQAUsks6p2TgdIStJGGJjCbvmGsKuucbtGotmXMjcP49itStcd147hnZsfNVgrGYHmUcK\n0BlkQqL9yUv3LBtWGy6+qStBYU4BuOCUGcXuKbjIOghrHcAr07px89wtDDml0cEhIyOhKRrblqcQ\n0SaQjgPKTakV90tpodXDJGsp8a6p9jPpueD6Lmycl4wkO9NG9RgeS1RcuX9oWOsAJj4+kI0/JWMp\ndWD015NxtABV1dinc7C8sBRtWz7L9qSz8IELqvUttZlLyUo5UmV/ZcY/+BiRbdszZOL1WEtLmHP3\nzagVy7Kpzgc+xeFgy+KfiGzbjm7DvJdoMxcV8vVTj2ApKUZTVfR+RroOvZChk24krE0bdPq6lSpr\naQgBTiBoJuzP2Y9BNqBUMqsNiB7AF+O/8Cmq0Oww8/n+zz3qpob6hdIuuB1/JB2GIDyiU42ykd7R\nvfGFS3q0ZtH0C/nrSA5hAQbG9W6DUd9404jUhGaxeNW+aQ4H+latqjwvY9brUIXfXEUkSUItKXEJ\ncBkvv0Lxb2tdqUMknUzs67PY/9x/iCyrmCEDsqagqDr0/v5IOh3odMS88rLXa3RuFcSrExtvnFhB\nlpl5r21xmRjrWov0NCOmdKH3ReXRjdknir2Oa98zgsIcC7s+T+THCf1Z8+UBrNZyAcxhU0ndn+Mm\nwFUkrnsEu9emufK5yXqJtt2q9vXsc1EcsZ3CyE4rJiTKn5hOoR5jWnUI4ZrHyuuCaprG5e9s4EBG\nuUBrtil89udRZk3uW+W1Mo747ucm6/VEtm0PQMqu7RzZsYX4foM4tnsHkizhsNqoqLlzWK0c3bmt\nSgHuyPYtOGw2tLL3jcNm5dCm3xk/4zHkWuaWbMkIAU7QImlu2jdw+q1ZFHdznIzMR2M/8kl4O120\nPtvsXn9TRqZPtPPL3RbyM6edgU5bXiQJukV0Iz443ue1dmsT3KgjTytS014JHj+O3K+/do8i1esJ\nu+EGjJ07ez3HnJRE/jffeO1zQ5YxxLVFFxbmairZsMEtYEIzWyhevwGd4pm3TFYV9K1a0eqJfxIw\ncCD68KYZKLLykz1YS8vvryTfM5rWV+77YCS6ChGgKXuy2b7imMc4vUEm7VC+S/ha9oFnzVhZJ3mk\n7qi4X9r1iGD4pE5sXHAYxaES1zWcS27rQXVEtg0ism2Qz/cjSRKOSs8PGmCt3FiBk4kHWfpO5QQR\nVdM6wZl3cNfq5az74hMcNiuyXo8pMIjB11zHrl+XkZderrXX6fUER0RVNV2TTFHTGBE+cI2EpKQk\nTCYTt95667leiqAJomkac3bO8Wi/vtv1BBh8K+/2xpY32HHKM3O9UW/kpeEvoWkaOdIGJLkselJy\n+sbpJT0Hcw8y7ddpLEhaUL8baYL49+pFuw/nYOzRA32bNgRdMpr2n82lzdNPuY0z793L8fsfIPHS\ny0i5aoJPcxtiY2k/d67bF15FYc45yIA+KorcsROwS+7aCwmwpx7D1K1bkxXeALJTvWvIakv7XhFu\nwltpoY2V/9uLqribT/V+Mnqj7FEJ4TSS5KyYEBxhou+odl7HnKbPyHbc++5I7n9/FFc91B8//zOv\nN7l1aAf8DeX/e5NB5sbzva/r8La/+e65f1Ba4HugQufzna4Xv3/7hSvdh+pwYDOXojf4ccXDT2Aw\n+aM3GjEYTQSGRzDoymu8zpWXcRK/gEB0er2rfKXez0ivkWOE9q2WCA1cDVjNDhw2hYAQvwZ9apg+\nfTqDBw8WTyZniebmA2dRLB4524w6Y40BC6dZc2wNXx/42qNdQuKqjlcRZgpD0zRntGLF7zpJw6E5\nXG2v/PUKEztPbFb72Je9EjhsGB0XVi285v34o7PYfC1xZGbiyM3FEBMDQOnWrRh79cJ61OlvKOl0\n6MLDibjlZm4NDuGP3RuJPrDd3cKtgWavXcWMxkReZomnn30dGX+fu5k4L6MEWee+VyUJ+o1px9Gd\n2ViKvVdjkHUSo27pRkLfaAxGd6Gjqv0iyQ33nrh1WAdkCb7+OxU/vcyjY7sypAo/xsWzZ3ltr44O\nfQYAoFTKI6gqKnaLmdYJnbj9rTkc270DvZ8fnc4bgp/J32OeP77/km1Lf0an16MoCnE9eqM6HCQM\nOI/zr55c63W1dFq8AGezOND76TzKlmiaxrpvD3FwYzqSBBFtg5jwUH9MgWfeufL7778nPDycnj17\nkpycfMbnFzR/dp7a6TX6tGdkzxrPNTvMPLHBe2WGcFM4Dw96GHCaPSZ2nsjPyT97+MidxqE5sCpW\nTHrvSU5bEpqmoeTnoxYWkfFi3ZKSaoqCeds2/Hv1Im/+T2S+/DKa1YpkMCAHBxM1/QFCJ0xAFxSE\n5VAi3Qb1IO/gDvfIQp3OVRO1KeCwK+z/4yRFORZiOodRkF2/YIXTTHx8AHqDu7AVFG7CYa9cb1Sm\n/yXtadUuhFVz93n0A5iC/Oh6fhuP9nOFJEncMiyeW4bFVzmmKDebwqwsVEfthHk//wBaJXTCXFRI\nx0Hnc3jbFhS78/0v63QkDDgPgJCoaPqMvrTKeTKSE9m2dBEOmw1HmSCYeSSZGZ/90Kwe+M4mLVaA\nK8q1sOTdnRScMoMMI653d2o9uCmdxL8zXKr1nLRi1n190JnwsQxN1UjcnEF2WjHhMYF0HxbjIQjW\nRGFhITNnzmTt2rV8/PHHZ+bmBDXSnLRvAK9tfs2jrXdkb7qGexbbrswLf77gVSDzk/34avxXhPiF\nAJBvyWfHqR2oqEhIhBvDKbIVYdecXwg6SUfnsM7NTniry15x5OSQeuc0bEeOeC9l5QXJZPKsxqDT\noQt3Jl0+NWuWq1+z2VBLS5H9A9AFBZG/eDEZz890KkIlZwSjJslIskS7999vFF+QikMFDXSGqj13\nFEXlx1e3kJ9pRlM19q4/gayv/9ovu7s3sV3cTciKXWXnqlS0CuZTSYILruuEKdBAxwHRjL+/D/t+\nP8nxA7moDtVZHUGWGHlT1WlWGttni6aqLP9gNgc3bnAFDfiKKTCIa599hW+feZxTx46AphHWJhZz\nUSH+wcFccucDRMa192muvIyTLpPpaRw2K9bSEkyBvvv8CcppsQLc8g/3kH+qFE0FVPhzfjJR7YJp\nk+CM+klPLnDzf1AVjcwU99w6qz/fz5GdWThsKno/mZTd2Yy/r0+tPiyfe+457rrrLmJjYxvFh6yg\n6bEvex9HCzxTeLQKrDoC8jQ7Tu1gTeoaj3YZmTlj5tA+pPzD+eW/XialMAVHWaRjqaOUqzpdxdrj\naymwFdAjsgfvjHqnHnfSfDj5xBNYDx/2WuTeG4FjxhB5zz0cv/XW8gAFScLUvTsh4y4DnOlD3FAU\n1KIiNFUl47nn3QIbrLKeH7uO5vdOw3gstDN1K3J0ZlBVjbVfHeDQ35mARscBrRh7R0+v+cx2rkol\nL708JYvD7qwwUB/6X9KOzoPc3wuaprH4vZ2cTMx3a9fpZfyDyoMS2veMpH3PSOw2haQtmdjMzmoK\nUXFNR+DYu34NB/5c7yXfW0Ukelw8mtK8XPxDQhl27U2ERkWjMxhY/NarnDp2xJUypDD7FGPvnkHP\nEaOqmc+TyLj2HgKkMSAQY0DtS38JnLRIAU7TNLLTitz2s6ZC5tFClwAX2sofnUF25fCRJAiOLNcs\nFOaYObwjy9XvsKkcP5BL7skSnyOIdu7cyZo1a9ixY4drXYKzQ1P0gTuUe4gXN71IamEqccFxPDf0\nObpHdOeRtY94HT+hU/WO8pqm8d7297Cqnqksnhz8JENihri17c/d71ZtwaJYKHGUsOHGDWX+cc3z\nAaQue8W8e4/PwlvsB+8TesklAHRa9SuFS5ZgO55GwKCBzmS9BgOaquI/cCDm7dvL55VlAocPQzOb\n0SpdS5VkMgIjSTME886aJCYNPHcFwXf+mkrS1kxXktojO7P4e/Fhhk/yrJ+7d/0Jj7b60HFAFBdc\n53md/MxSTqV4JrtVHCr5pzxz+hn8dPS8wDNXoapqlBZYMQYYXL5wje2zJXXvrhqEN3js+8VVvn9P\nJh10y/fmsFpJO7DPZwEuPfkQqz5+n5KCfCJi48hOO4ZOb0CWZSY+ObPZfm6cDVqkACdJEsZAg3vN\nOR0EhZU/efUb3Y7D27PIzyxFkpx+EaNvLQ//tlsUZFlyKxUuyRJ2q++lXdavX09KSgrt2zu1HMXF\nxSiKwoEDB9i6dWvdb1DQ7MgoyeDW5bdidjj9gQpyCrh52c08OfhJssyedQODDcFc2PbCauf85cgv\n7Mre5dEeaYpkSvcpHu0JIQmcKDrh8rUz6ox0CXN+OYoPYXcMbdpgrVRz1CuBgS7hDcDQqhWR06a5\nDbGlnSB16lQc2dnO0lkGA/roaGJefBG/9u2xp6ejj43FceKEKx+djEZiuPNzRVHP7YNh6v4ctyS5\nmqKxa00au9akYTDqGHFDV7oNaYOmaZQW1T09SGUS+kcx/l7vedBUxfsDh6yXifSxGHx+ZimL3t6B\npdiOqmoMu6YT/cf6Zk48m+SdrF4o9g8Jrfb9GxIVTUleHqcjlXQGP8LbxFQ75+Ftm/nts4+wlpRg\ns5hdmjdrSTFtu/XkkjvvI6RVawx+xmrnEVRPixTgAMbe0ZPl/93j2rgxnUJJ6F+ejFHvp2Pyk4PK\nTKkKbTqGugUwhLUOwBigx2FX0NSysHI/Xa3y99xzzz1MmeL8otQ0jTfffJOUlBQ++uijM3SXgqpo\nTE/IvrD++HqsDndNmaIp/G/3/9xKXp3mnn73VDufQ3Uwd89cbIr7F6aExMeXfuzxga5pGmHGMJfw\nJiPTJbwLt/e+HYCTxSf5K/0v/PX+jGw3En+9ZwRaU6UueyVm1mukTr0dcEaAVlXoHqsVzWZD8qu6\nlFjaQw9hT093CWeSXk/sKy+ji4gg6eKRqFYrmt2OHBaGkp+PRdbz5oAbOREUjb9Bx81Dzq1QUTnK\nE3D5FltLHfz25QFCo/0xmHReS1nVhT6jYrnohu5V9vsHGTAG6rFXKgzfY1gb4vt6z1+WnZpC7sk0\nwmPjiG4fz9I5uynOt7rO/3vJEdp0Cm10ny1FOdnV9kfEetfOpuzcxtZli5BlHQaTEUmS0DQIj4ml\n/7grq5wv80gyv7z9uivdSEUUu520/XsJj2mLrBMpQ+pLixXg2veK5MbnhpB5tAD/ID/iuod7hHnr\ndMlksFkAACAASURBVDJxVWTN1ullJv1zEKvm7iP3ZAlhrQMYc0dPj5Dy6vD398ffv/yLLigoCH9/\nfyIjG18ZG8G5RS97f6t6075FmCKY2nNqtfN9tOsjUgpTPNp7RfbyGvgwP3E+q46tch3Lskyn0E4Y\ndUZ2Z+3m7l/vdplRowOi+eGKHwj0a7m+Lf69etFpxXLMO3dycuYLVQtwqkru198QeecdVc5lTUpy\nq/Sg2e1Y9u8n98uvUPLKC6NrpaXEf/0Vu0PbY12ZSA+7g8kD45h2YcIZu6+60GlQK44f8F7AHZzC\n3LYVKdjMvpmca6Jtt7BqhbeTyfn88t4uNDTQwOCvo01CKMMndyKqrffk0luWLGDjj98g63SoioNh\nk28iPzPCbYymQVZqkavw/LkiafNGNi/5CWtRMe379MdgrL7O8Og7PB/2ju7YyuLZr7mEML3Bj/Mn\nXkdMl+6069kHnb5q0eHIjq2uKFVvyDrZI5hBUDdarAAHEBrtT2h03TUFwREmJv1j0Blbz8yZtc8T\nJagbjc1PpSbGdBjDm1vfpNhefUJTnaTjkQGPVGsSOVF8grl75zrzt1VAL+t5bYRnNCvA+rT1mJXy\ndA4O1cH2U9sBeHHTi5Q6yv2GjhUeY9h3w7i267U8M+QZdE08Oacve0Wz2bAcOACyjKlHDyS9Hn1U\nFP4DB6Lm5FR9oqo6z6sGfVQUjvR017FkMKCLjsaRVUl4lyRsR48ydPJAfnqg6iz4Z5uEvtFsNCVj\ns1TtXnJ8fy7KGdC+GUw6rnl0YLVjln+4283VxW5R6Ds6rkrhrTg3hz9/+AqlQi69jfO/ISj6bqzm\ncr9oWZYIjjSds88WTdP44okHyUlNcbVVrI7gjZgu3WkV38mjfcuSBW4aNIfdRnriQYZN9nStqIzR\n3x9Zr3d7vU6jNxoZOvEG4XJxhhBisEDQBAg1hvLhmA8J8wurdpxBNjA4dnCV/ZqmcffKu92CEcBp\nOr20w6XEh8Z7jP815Vc2pG3wmCsm0OkHk2P2FFA0NJYcXsKnez6tdr3NAUdeHoevmsCxO6dxbOrt\nHL3+etSSEmxZWSSNGl3tuZLJhKlX9bn62s5+CzkwEDkoCMnfn8Dhwwi+/HLkYE93DV3bOAqzzdgs\nZ0abdSYICPFj0j8H0apDMAEhfgRFePo9nQnhTW+UufON6v0+FUXFUlLptdFg27KUKs8pzsv1KK6u\n0xsYfGUrDEYdfiYdej+Z+D6RdOh97qwn25YtchPeakSSuPqJZ712eQuo8zXIrufFl+AfHOp6zfR+\nfnQbfhH9L7uCKx78J0MmXu/7GgXV0qI1cIKWS1PSvoGzUsJTvz9VZQLd00zsMpG2QW2r7H9t82sc\nLz7u0W7SmXh00KNubUfzj3Lv6ntJL0n3GC8h8fyw5wEYEjOE1f/P3nmHR1Gtf/wzs33TQ0JCKCEB\n6b1XBRsoKIqCYsWC2K8dsV4btuu1YtdruYriRUV/VpCuUpVeA+mF9L595vfHpi0zu9lAQgLM53l4\nyJw5Z+bMZjL7nfe8JX25Ym52j53VWasb9cdr6zR2rxxe8CyunByosTg4Uw6SfsMN2LcqA0R8MJmw\njhhB9FVXBexmHTyYbr/8jG3HDgS9nsL33mf/wEFevzmDAdFs9lZamHoVi//nxO3cgCzJjL+sB33H\n+78XjiftOoYy+aZ+FGRUYKt0svrz/fW1dHWg4sYZNIIAIy9KZsi5iY1adgRBQKcXFIKxotjPEjde\nn68jxYssS/QY1YtuQw3kZ1RgCTMQ1zUcQRDq7hdbhZOULflIHpmkgTGEx7SsX+jm75cE3TcsJpZL\n5j9JSLjyhfDwoRQ69uxD7oG9dVY0vdHI0KkXB3Vsc0go1774BjtW/IK9qpKkwcPo1Cu4ijAaTUMT\ncBoabRxZlpm/br6iUL0aIXr/fmdL9i9h0d5Fina9oOeBEQ8QH1KfWT69LJ1Lvr9EYamrJcwYRmJ4\nIgCPjX6MMkcZv+f87tNHRCTWGqs2/KTCkZJSJ96gZjm1EfFmHjCAji88jyGxcdEB3mXUsIkTSb/u\nemx//w0eD7LNhmAyEXvXP7AMG8ai9/N9IuvXLT5AfLcI2iW0bs4yWZL58a3tpO1QX0qWm5ZbVsHc\n1yeo5pRriMctcfDvfP5cchCPR2lJimzvX1yZrCFc/MCjfPvi03jcLnR6PdPue6Qu+WxSpNKiWFli\n58tnNuFyeJBlmQ3fHWL6/UOI6aS+THuspO34uyZSNDiuf+Vd9AZlVaHlH7zJrtW/eWuSytCucyIh\nEZEMv/ASug4YHPTxzaGhDL9QK43V0mgCTuOU5ETygXN4HIo6p2roBB0DYtXTJvx46Eee+PMJ1X39\nY/sz/bT6VK9uyc3c5XP9ijeAad2m1f0cYgjh4VEPc8nSS+r95GSQZD2DQ69sdN5tncbuFXO/vjgP\nHkR2Bp8CI/Tcc4MqbyXZbDj270cMCcHYrRu2LVt8xaLHg2x3IHRKxlGd4zNWEAWKsiubLOC2ZpZy\n4HAFybGhDE1UD+JqCn/9mu5XvAG+tXWbyNhLuwcUbyV5Vfz6/i6KsitVU6GJegGDUccZASorAHTu\nO4DbPlyErbwcS3h4wKLrq1atgux4HNWuOnEquT38/lUK0+4OXgQFS2FmOkuefjTo/jqDQVW8Ze/b\nw+7Vv+FuEHBTlp/HtS+8rgUdtFE0Aaeh0cYx6810Cu1EZkWmN3LODzGWGCZ2USbX3Ji7kYfWPaQ6\nNkQfwhtnvoEo1D+gv9j7BbmVymXTWgbGDOTeYff6tP139399LYQCeBxRPPtdIUkRBZze4+S1xMXN\nm4djz14cKSnKUlhqmEyEjhzZaDdnejppV1yJ7HAgu92EjB+PEBLiIxQFgwFdVBRGix5RJ9Sl5wCv\n5bapy3av/3aAN1cdRBC8UZXXje3KA5P9R3QGw/aVWcc03h9RHSwMOtt/ihSn3c3X//rLxyp5JHFd\nwznv5v5YQgNHagKIoo6QyOAEbVWZU2FZbM4cd7XIssynD6on8vbH5NvuUW0vL8xHEHyFmuR247BV\na6Wu2iiarNY4JTlRrG+1vHX2WySEJqAX9ejQIeC77CYiMqnrJMW49PJ0bl5+s2quOAGBT877hHBT\neF2bLMss3re4Lt/bkf1n953NJ+d/oogsrXRVKgWi4MHukvhqs9Ln7kSisXtFFxpK18VfEnPbrY0e\nSzAa6fDUk1j692u0b/b9D+ApKUGqrES226lcsQJDXBwYjQhGI4LFgjGpK+FTpyCKApPm9ENvFDGa\ndej0Ar1Hx9dVlgmG/Ao7r69MwebyUO30YHN5+GBdKpnFysoETaG50oM0JKF7OLMeGxWwT8auYlyO\nwOdO7NcuKPHWFCZMmEC3wbHojfVfr3qDSPKg5n+J+fXdN5pUnP6ieY/Ta/R41X3tE5ORJN/nhCUs\nXCt11YbRBJyGxglAl/Au/DT9J1ZftprNV23mxv43Itb8+eoEHf1j+nP74NsV4+5bdZ/qUqiAwAun\nv0CPaN+cb7+k/UJWpdJi0iGkAxuv3Mi9w+71sdbVMiV5CmZdfUoFWTLgLhuMIIDVdAoY+gWB4s+V\n/oWKbkYjkRcGLnFWizM93Sf/Gx4Pjr17EfR62t14Ix2efIKuX3yBWJMEuGv/GC5/dATRHUORJJmd\na3P55f2deDzBOZkVVjgx6nx/t0adSH5F48v3gTBZm/f3H5Vg5eL7hgX0Hcw9WMZvH+32qQChQIBB\nZ7VMkuOeo+IZMikRg8kbodprdDzDp3Rt1nNIkoedK39t0hh/fmySxwMCjJlxJaJej95oxBoRySUP\nPaml/GjDnAJPVg0NJSeSD1wtgiAQbvRay+4ccie3D76djPIMBEGgS1gXxYN2Wdoy9pbsVR4HgTsH\n38nkpMmKfcszlisEn4jIp+d9illvVvSvZUzCGJ4a+xQvbHyF/IpKnKXDcBVNxGrUMWd88tFcbpsh\n0L0iSxIVy5ZTuWoVnlz/y851/YOsjwpgSk7Gtm2br4gDZLsdyWYj4oILFGP2/J5LYUZFzfKdTNq2\nQv76KZ3hUxtP5ts1xsoRucyRge7tj235zF7VvEuHDSvi+GPFx7txu/wLV51e4LThcegMzWvDsFe6\nWPTCWuzFAiarnnNv7EvX/s2Tk89eWcm6Lz+l7HAuXfoPpOvAIX5rnA6dNoMtS7/yaUvo0Us1AW91\neRlfPj6PiqJCZEmi64AhnHX9zYS2axfQ10+j9dEEnIbGCcC2gm28suUVKl2VTEmewrV9rkUUREXe\ntlo8koeH1j2kaBcQmNFjBjcOuFGxL60sjRUZKxTtPaN7EhcS1+gcJydNZnLSZLakF/O/LdmYeohc\nPTqRbrEnp/+Mbfdu0q+4AtkepIVKryf0zMB54RqS8OKLpF91Fe7Dh32/qCUJqVp9WTNrX4mPcHG7\nJLL2lQQl4KxGPZ/cMJIbP95EcZWTCIuB964ZRoSlccEUCE/wK3xBkZ9a4Q3OCFC2sKpc+TsRBAiP\ntSBLMl0HxDJmujKB7bHyw1vbqS4AZBlbuYtf3t3JjPnDiQ6yvqo/7JUVvHPr7LoAg7Ttf7P+m8Wq\nfZPPvYS7UzrQPXYcgws3YhIlEvv2Z+pd81T7L3v3dUoP53qtcEDGzm2kbFrPkPODsxRrtB6agGtl\nJkyYwIYNG9DXvBl16tSJPY1kZtc4dk4k69uBkgPc+OuN2N1eB/m0sjRsLhu3DLrF75giW5Fqzrih\ncUN5ZJR68s5vU77FLSktRAvGLWjSfIcmRjM0MbrxjicIaveKVF1N+tXXNCreQiacQdW6371F6CWJ\nypUrse3YgaV//0bPq49pR4cFz1Dy+edUrlkLNcELgtlMxAXqtSjDY8zkp5fXOdALOoHwGP+W0yMZ\n1DmSzY+cg83pwWwQ2+TymU4vUF3upJ2fFHe7f8/B7VBa32QZ3E6J2c+NbZF5yZLM4UNlPlG1MpBz\noOSYBdz3rzznEx2KLOOsqlLtuyAngZJqF5tC+7MptD9mg8h3N47zG4iQn5ZaJ94A3E4HeQf3H9N8\nNY4PmoBrBEd1FW6nE2tEZIs8zARBYOHChVx//fXNfmyNk4MfU3/0KWRv99hZvH+xXwEnyRJ3r7ob\n6YgwOJNo4tHRj6rex8X2Yj7f+7kiECHMEEb3qO7NcBUnPo5DqVStW4cYHoY+JhYaWw41GDB27EiV\nLHvVgywj22zkPf0MSV9+EXCou6iItJmX4SktBbyBEoLZjGi10v6ee7AOG6Y6buwlp5G9vxRXTdkq\no0XP6Iub/vuzGNvu0pksQ0wndTGSd6iMtV/s97eySHWZo65mb3OSm1LKb5/sUZxXFAVMQSz5BiJz\n9w4ydjSSFLoGa1QMpUeULNOJAntyy+kRp56Drl3HzlQUFSDXLNXrjUZiunQ9pjlrHB9OeQHntFWj\nN5kUa/2yLLPsvYXsWrUMQRSJ6ZzIpQ8/jTm0+ZeDgi1RotF8nEg+cHpBjyiIPpGkOsH/F+yBkgMc\nKD2gaL9t8G0kR6j7o32w4wNsbpui/eaBNx/FjE8uVq1YQbev/kflypX1jaKo8E07ko6vvEzFTz95\nrW8N8ASRcPXwgmdxHT5cJxIFl4vIqVOJf2h+wHEhkSau/OcosvaWgACdekVhNLfyY17gqHO9CQLE\nd4+gotBOZYkDc6iB8+b2wxKmHjmac6DUf9CGAJHx1mYXb+VFNr57bStup+959UaRyHjrMUeffvev\nZ4Lue9Z1c3n9hzJcDe45SYIu0Va/Y8656XYWPXo/jupKZEkmrlt3hk6Z5re/Rtuhpf+y5wNXARKw\nA7gOCAG+BBKBNGAmUNqg//WAB7gTqA2xGQp8BJiBH4F/HOvEygsLWLLgMUrzchAEgYnXzWXg2efV\n7d+1ajl71q30mpY9Hgoy0vj13de58J76B6gsSexZt4r89FTadepM3zPOOiqnz/nz5/Pggw/Ss2dP\nnnnmGc4444xjvTyNk4iLTruIT/d8SrWrGhkZs87MnP5z/PZ3S26F9c2sM3N6p9NV+6eVpfHZns8U\n7QICV/UJXObpaJGqqyn66COcaelYhw8n8tJL2uRyHYBl9Woq1xxRC7YR8Wbq35/ws85Cdrmo+G0F\nss0rjgWzmbCzzmr0nI5Dh3wsfLLTiTMlJaj5Gi16kge3nbx7sZ1DKciobPI4S5iBa54dg17vfaZ6\nPBI6nf+gg+LcKrb8nK5a2UFvFDFZDZx/s3qi62Mh50CpV2k2RIBxM0+j16gOjVaJaAx7VXCfnTk8\ngh4jR7MwMp9b//sXep2AyyNxxcguDO7iP39dWLsYrnvlbQrT09AZDMR26aol7j1BaEkB1xWYA/QG\nHHhF2+VAX2AZ8AIwD3iw5l8f4LKa/zsCy4HT8L67vQXcAGzEK+AmAz8fy+SW/utpSnKz68zGqz5+\nn/aJyXQ4zZuRO3vfbh+fA8ntJi9ln88xfnrzZVI2/oHL4UBvMnFw80am3fdwk76Inn/+efr27YvR\naGTRokVccMEFbN26leTkEztyr61zoljfPJKHjTkb8UgeREFEkiXOSzqPy3pdptpflmU+2PEBTk+9\n/5tO0NEprFNd6auG1C63quWJm9Z9mmrKkOpNm8h/6SWkahvh06bR7vrrmnTPy04nabOuwJmWiuxw\nUrFsGfYdO+jwpHqliNamk81OuadpxToT33sXgIjJk3Fn51D41lvIbjfhU6bQ/h7fmrP2ffvJvuce\nXDk5GLt0wTJsGLLHA3p9vQXObMYyuPmz+B8PJl7dm8XPbGrSmL7jOzD+8p7odCIel4St0okl3H++\nNo9HYunLf6vmnOvUK4qJV/UiNMqEGEAAHi1Gs54j735RFOgzJgHhyLDeJlKWnxd03+RBQwGY2LM9\nq+6fwN68CuLDzfSMb7x8l8Foqvvu0zhxaEkBVw64ACtei5oVyMFrZas1MX0MrMIr4KYBi2rGpAEp\nwEggHQjDK94APgEu4hgEnCzLFKQd8lm6lGWJ3JR9dTdxZHwCOoMRj6vGcVgQCI+tj8QrL8jnwPrf\ncdfsdzscpG//m6LM9Cb5D4wYMaLu52uuuYZFixbx448/cvvtypxeGqcWNpeNmd/PJK0izaf9p9Sf\nuKbPNaq+aRvzNrIuZ52PL5ssy3w46UP0ovLP/Ze0XzhYelDRLiDw6ChleR7brl1kzLmpruJA4Rtv\nIDudxN4S/FJr1aZNuDIzkR3evx3ZZqP066+Jm/cAYkjbSxrqL+LTH6a+fdFF1hcJb3fD9bS7Qd3H\n1ZWfT9rVVyOXlwPg2LcPx76aF0VBAL0eQRSxjhhOzNybju4CWpnYzmHojQJuZ/DrqONnesXbwb/z\nWf7hbgB0BpGptw8kPtk3ObHT7ubrF7dQXa6erqQkr7pFC8kn9mtHZJyVktwq3C4JvVFkxAVJxyze\nAEpystGbzLgdjVf4SOjVp+7nuHAzceHBB69onJi0pJ20GHgJyMAr3ErxWt7igMM1fQ7XbAMkAA0z\niGbhtcQd2Z5d037UCIKAOdT3rUTU6QiNble3PeT8C4np3AWD2YLRYsUUGsa5c++s2++0VSPodIpj\nOIMppaPR6qxataq1pxAQj+RhxvczFOINvFYzNR83gPzqfMXyqSiIGESlI3W1q5p//vFP1RJbdw25\nC6NOafEo//7/fMpFyTYbpV9+2djl+CA7nF4fsoYIAlITaokeL2xbt1KxelWTxnR64/VG+0h2Oxlz\nbiJlwsQ68aZAljHEx9Nt2TI6v/MOgrF5KwYcL7atyGySeItLDkdnEKkssbP8Q28+N7dLwlHt5vvX\nt+F2+VpDN/2QSulh/yLbEnpsQQSNodOLTL9/CGMv7c7Q8xLpOFpm8DlKa3dTKMrOYO2iT/jto7eV\n4s2PtTuua/OnRdFo27SkBa4bcBfepdQy4Cu8/nANkTmmUsZHz/m338vSfy+oqf0m07FnH7oPry/N\nYjCamPXUv8jeuxu3y0HCab19AhiiEjpiDgnF7XQgSxKCIKA3GoltgvWtrKyM9evXc8YZZ6DX6/ny\nyy9Zu3Ytr7/e+BeAxsnNT2k/kVmpXoLKLbvpHNZZ0e6RPHy1/yufwvcCAvEh8YQalcE3r/31GtVu\n5RdfrCWW2f1mq55bMBgUDvyCoWmPEevQIQh6fd1xBIMB8xFWq7ZA1R9/UPj+B+AOfvk0+rrZGDt0\naLRf/gsvUr1xY6O+dLLLhSGufdDnb4v8/j/1l40jEQTo0q8d517fF/D6tIk6wbsmU4MsyVSWOIhs\nb63rs+f3XDxu5deIIHitdqfPavmlQb1BR78zOgGwatWxlY7bvuIXlr3j/zvAGhFJSEQUBemH6toG\nnD2Z+G6nHdN5NU48WlLADQP+AIpqtr8GRgN5QHzN/x2A/Jr92UDDb6VOeC1v2TU/N2zPVjvh7Nmz\n6dq1KwCRkZEMGjTI7+S6DhrKtS8uJPfAXizhEST2G6hw3NTp9XTpp+70qtMbuPzJ5/nx9Zcoysog\nMj6B8++4F4M5eLO1y+Xi0UcfZe/eveh0Onr37s3SpUvp3l097L9h5GStBUnbPrrt2ra2Mp8jt9du\nX6uwpNXSN7ovhTsLWYXv/DdVbmJvmW/lBVEQeeecdxTHX7JsCZ/lKAMXAN448w3WrF6jOr8xM2dQ\n8vnneKqqEPD6ZsXcemuTrk8XEcHhu+8m/LP/ElJZhWXwYFLOPou01avbzOe/4dFHCVv6HYLTqfBv\nUkMGqidOoPe8eUEdv2DlSvQOh+IYDc8l63SY+/RBsttZs359i15vc26X5FXx48cbkCXo2KmDalBB\nQwQRYvpAbF+BiRMH1h3PUS4jeXw/fckjYw03smrVKtx2mdRfdKp+b4ZQaNdD4KxpI4iMsx7X658w\nYcJRjx83dizL3n1D9XOqRRAEOk++iLC0g3RsF0XXgYPZnZbZpp9n2rb69rHSkmFfA4HPgOGAHW8U\n6Ua80adFwPN4fd8iqQ9i+BwYQX0QQ3e8z7UNeKNSNwI/AK+h9IGT1dJxCIJwUqTpOFmuQ6NxZFnm\nyh+uZEfRDsW+hJAElly4RNWitmDDAhbt9a3HadQZ2XLVFsXxZ34/U7XM1tiEsbx9ztsB5+dITaXo\ngw+QKiuJuHAaYWdODOayThhkSWLfwEHIruBKCIRfdBExN1yP6bTgLSAZc2+mau3aegucToe5fz+s\nw4ZT9v13ePILQKdDMBoxxMeT9NXiNukfeCRZe4tY+kpwOctqSRoU4zc6dMP3h9j6awaiTsDjlkno\nEUFkeyvx3SIpzCxn+4osVetbl77RXHCH/xf4tkpRVgYf3XtrwD7JQ0Zw8bzHjtOMNFoS4RhD71vS\nArcNb8DBZrxpRP4C3sUbkLAYb1RpGt40IgC7a9p3A27gVuqXV2/FKwAteKNQjykCVUOj4dtqW2Nb\nwTZSypQpI/45+p9c0O0CVd+0PUV7WLJ/iU+bgECvqF6Kvmuz1qqKN72g59lxzzY6P1NSEglPP91o\nvxMV2eVqUs3S9nffhSGu8VJjAO6CAnIffQzHnj0giggmE4gi+uhourzzDrqICKrWrcNzOB/cbmS3\nG1dWFsWfLyJmjrL8WVvj1w92N9uxqsocWMOMDDirMzq9wF+/ZpC5u4TM3SXsWKW6CFNPK77rHsuz\n5Y//fd5onz5nNJ6GRuPUoKXzwL1Q868hxcDZfvovqPl3JFuAxmvPaGicBJQ5yhSJeo2ikXEdx6mK\nN4APdn6gKJ1lEA28NOElnzZZlrl/zf2qx7ht8G1EWfznizpVKHjlFb9Fwo/EmJwctHiTXS7Srroa\nV3a2Nz2ITkQ0mYh/4p+Enn46osUbKekuLPQd53Tizstt2kW0Eg6V5czGkNzKNdbyQhuLF2zy1nWV\nZTweOWhRpjeK9J/QqfGObYii7Cw+f+RenNXq5bFqiUroRI+RY47TrDTaOqd8JQaNU5O2an0DMOlN\nVLrqk3cKCLS3tifWqp6cNb08XbUIfXJEMvEh8T5tH+z8QDVwIcocxdV9rj7GmZ/4SA4HxR99HFxn\nQaD9/epiWA1nWhrugoL6BL0eyRuk0KFDnXgDsI4YQeXy5ci1tU8tFqyjRqkdss0RGmmivLBpkfj2\nKqXo2/DdIa8YbIIlzRyqJzzGwtBJXek6IKZJc2hOmvpsKczM4OP7Ai+bhrWLpeeY8Zx+xew2m/Ba\n4/ijCTgNjTaELMvMWzNP0f74mMdVk+oCzFszD5fk669lEA1c0fsKn7ZqVzUL/16oGC8g8My4ZzDp\nTMcw8xMfWZZJv3Z20NY3Q5cuhE4IvmqKYDYrymrJkoRg8g186vDkk2SVlFC9YQOIIu3mzCH8nHOC\nPk9rUVZQTVWpo/GOR2C0KKvXVJc7m7wMOvri7vQZm9Dk87c23/4rsDtC/7Mmce5NdzT7eR3V1axd\n9DGFGWnEJXdn3GVXNykIT6P10QScxilJW/SBK7YX89Lmlyi2F/u0Ww1WSh2lqmM25m5kV9EuRfuQ\n9kO4+LSLfdqeXv80bllp7RgeN5zxHccfw8xPDmxbt2Lfvl3RfmR0aC2RM2cEbQ1xl5RQ+tX/0EVH\ne5dIXS5vdYVBgzCd5ht1rgsNIfE/H3otcDWJfE8ESg/bVAMKGiN7v++9XZxbRVlB05In6/QCstQ2\ngrya8myRZZmyvJyAfYZfeGkzzMoXyePhi8cfoCQnG4/bRd7B/eSm7GPWEy+cMPebhibgNDTaBGWO\nMqZ9M41Sp1KouSQXHUOUuatdHhd3rFC+mZt0JqZ19y1GXWgr5MfUHxV9RUHk8TGPH8PMTx48JaWq\n1jc1iSaGhRF9tXfJ2VNRQe6jj2H76y/0HTqQ8PRTPhGpnrIyUqddhLu42Lt8ajBg7t+f8CnnE33l\nlX6/ME+0xL15qSVHNU7y1H/mhdmVLH5mo//UIwK0TwzDXumivMheZ6UTBIH4bhF+BrVNZFnm6+cC\nlI8TRabPe5yo+MbzCjaVgvRUyvLz8Li9lnuPy0VBWioleblEJxxTnnyN44gm4DROSdqa9e2FId6J\nHgAAIABJREFUTS+oijeA7hHd6R+rjOF5duOzqv5sg9sPZmryVJ+2x39/XLXe6YXdLqRLeJejnLU6\nkiTz+ooDLN2aQ4hJz8NTejMquV3jA1sZT3VVcMungkCnt95ErBFYmXNvxrZjB7hcuPPzOXThNDAY\nCBk1io4v/Yvyn37GU15e7/vmcuFMTaXd7NktdzHHmT0bc9n8Q0aj/cyhBuyVvsv9IRHez1GWZf7v\n9a0B88YJeEtj6XQCUXEhlByuwmjScea1vWmXoEyt0xoE+2z5eeG/Sdu6WXXf6BlXMObSK1T3NQeq\nKakEaNXwXY0mc9ILuKioqJPC6TMqSosOPFnJq8rjh0M/+N1/VqIybcDW/K18l/Kdot0kmpg/Yr7P\nPb+7aDfrstcp+hpFIw8Mf+AoZ+2fF3/dx0e/p2GrKXk0+z8bWXLLGPomtF0Libu4mNz7g/gsBIFO\nb79FyLBhgNf6Ztu+vV6cgVcEOp1U//kn2ffeR8iY0chHVFzwl2NOdrlwZmahCwtFH6setHI8kWUZ\nSZLRBSgC//0bW8nYWex3P0CPEXGMvaQ7lnAj37z0F7kpZQAYzTqm3z8UWZZZ9sEuqkoDlFMTvB+t\ny+7BLUB4rJVbFk5EbIaao8cDWZb5ffFn/PXTUlx2B36VqiAy+pJZLTqX2MQkImLjKMnNweN2oTMY\niOmcSFT8iedDeCpz0gu44uLADxaNU5O25AOXUpqCiIgHpYXMqrdyaQ9fHxiX5OKp9U/hkJQO43MG\nzCE5Mtmn7dUtryKh/LK4qs9VhBnDFO3HylebM+vEG4DDJfHD9tw2K+A8ZWUcPO/8oKxv3X77DWNC\n/ZKWYDT6HSe7XFSvX491+HCfPoLJRNikcxX9nVlZpF99DZ6yMnC7iZw5g7iHH261F9CULYdZ8cle\nXE4P7TqGMvW2AYRG+Tq5f/H0BoqyAqe+mHh1T/qMrV+Wm37fUNwuDy6nB0uI1/qWur2QA1vy/R2C\n+ORw8g7V14yVZSjLr26T4s3fs+W7fz9LysY/Gh1/2ojRLf471+n1XP7kC6z57D8UpKcR3+00xs26\nRvN/O8E46QWchkZbZ/HexbhkpUVmSOwQ/j3x30Sbo33a39v2HodKDyn6R5oiuWnATT5tu4p2sT53\nvaKvVW/l9sG3H+PM1TEcYa0RRQGTvu1+MRS+/z5SWVmj/bp8/JGPeKtav4Hs++/zVlMQBFUhJ7vd\nFL75preSiiigi25H+Lnn0P7BBxV9s++6G/fhw3XVGUqXfI115MhWiUAtyq7kt4/2ePOwAcXZlfzf\nG9u4/NGRdX3WLt7fqHgzhxnqxJssy5TkVeO0u2mXEFon3iqK7fzy3g7V1TudQeDie4dQethGYdZe\n3E7vfARRoF3HtrFkGgxrF30clHgzWqycf/u9x2FGYLKGcM6clnkGaBwfNAGncUrSVqxvTreTVVmr\nFO1X9r6SB0cov+QPlR7i/Z3vK6JJRUTePvttxZv721vfVrW+PTTyIQyi4dgm74d7zunBY0t3YXN5\nEAUIMeqYMaxz4wNbCceBxoutx86bR8jIevHiys0l85ZbkG02b4MgIIaGIhsMyNXV3nQhsgyyXN8H\nMCQkEP+Yehkkx8GDPsXtZbsdx7590AoCLu9QmU/0hixDcU4VHo+ETidSVeZg+4qsRo/ToSawQJZk\nfnlvJ+k7ixB0AnqDyPT7hhIZZ2XHyiw8LnUr5rXPjsUSaqR9YjhZe4s5sOkwok7EHGrg7Ov6NMu1\nNjdHPlt++887bP35+0bHmUPDuOXd/yLqlGlVjhZZltmTW0GpzUnfDhFEWA3YqyrZs3YlTrudpEFD\nad81ufEDabRJNAGnodGKLNq7CPkI04NRNDIoVlnHUZZl5i6fq8j5JiBwRqcz6BvT16d9X/E+Vd83\nq96qiFJtTmYM60y7UCPfbc0h3GJgzvhkEiItjQ88zsiyTNa991G1anXAfhGXX0bMdbN92mzbtiGI\nYv1vTpaRHQ66/d/3VK5di1RWRtXGTVStWeMzzlNU5Pc8ho4dcR48WGfJE8xmjIldm3hVzYM13Kh4\nGdAbdXVLlss+VKauORKjRceEK3rh8Uj88XUKqdsLvRGnLnA5PPz6wS4uuGMgWfv9uLkIYLIakCUZ\nQRQ469o+jLwwGZfDQ3isJaBfXlth7x+rgxJvOoOBmxb+p9nF252L/mb5nnz0OgEB+PjK/mx6+SFs\n5eV4PB7WL/mCafc+RNdBQ5vtvBrHD03AaZyStAUfOFmWeW/He6r7xnRUlst57I/HyKvKU7SHGkJ5\nbIzSqvPO9ndU877NHzn/KGbbNM7sFceZvYIrMdVapF87G9vGjQH7GLt1Y/8ZZ3Cka7cuMkoRyScj\no4+OJmrGDFyH83EcSAG9vi7AQTCZCBk3TnEO29atZN35D2+VBkHwJvwFQsePJ3zK+Ud/gcdAYv+Y\nOr8zWfaWsZp4dS8EQcBpd5O9Tz1iuiFul0RRdgXrvz1EYValT7oQZCjIqODD+5UvGOBdkY5LCufr\nf20hP7UcvUnHhCt60mNEvGr/tkTtsyV12xZ+ePXFRvuLBgO3fbAIg6l5k+j+vDOP3/bme/1Ra975\n3njrE/qVlSLV3JNup4ffPnybG15Tfw5ptG00Aaeh0Ur8mfsnFc4KRfvpnU4n3Bju07ajYAffHVRG\nnRpEA4+MeoQYi2/poIzyDFZmrFT0N+vMTOvWcta3EwWP09moeEOnI/GTj8ncscOn2bZjJ/kvv+xd\n7tTrvaJLp6P93XchGI04MzJIvfRSJJu9fklUFAk94wzi5vsui3tKS8m4cQ5SZU3pNFlG0Ovp8uEH\nmPv1a7UABlEUmHrHINJ3FFJd7iQ+OYKI9haW/WcX+zccDuoYkkdm7/o8inKqfMVbEHTuE429yk1B\nZkVd5OnKT/cSFR9CbJfmD7xpCX5e+HLA/ZHxCXTq048zZ8/FYGr+KihpRdU4j6gz66yqrBNvtThs\nTUuarNF20AScxilJa1vfALIrs9GLekUR+psH3eyzXWov5eblNyOppB0YmzCW85LOU7S/v0PpJwde\n69vJkFbnWDm8YEGjfTq/8zb6du187hVnVhbp117r9XMDb1Lenj2Ie+ghrEOGAFDw+utIlVX14k0Q\nCBk3jk6vvao4h33/fq+5qSEeD2JISKv/nkRRIGmgN5WJ2+Xhowd/x6FSt9QvMoiiiMcVILGbCiar\nngvuGMSbt67wybQhyTI5B0rbvIAbN2Y0Xy14lOoy/1bKc2/+B/0ntqxvY68OYRh0Im7JGxFulWyE\nhociVhqQahL46g1GkocMb9F5aLQcmoDT0GglekT2UIiyeGs8PaN6+rT9d89/qXRWciQRxgieP/15\nxRd9oa1Q1Vpn0Vk4P6l1luTaCrIsk/PgfMqXLvXfSRDo8vlnhAwerNhVuXKVbz1TlwvH/gN14g3A\nU1TsE4yALGPfvw9ZlpV+Ze3aKXLCyS4XusjIJl1XS7P8P7uaJt4Aa7iB9F0FTT5XrbXOaNH7nFPU\nCVjCWibwprlwOR28cd3ldQJJjRtfe5+IuJZfCp7Ysz3Xjknkw3WpxLqLmZr5DVajiCyAIOrQGfSc\nNnw0Z91wS4vPRaNlaPteoBoaLcCqVataewp8nfK1z7aAwAtnvODTtqNgBx/u/FA1kvTT8z/FarAq\n2l/Z8opq1YV7ht2DWX9qF6uuXLmS8u8DO5VHXXutj3hreK8IRqPSYqavfw8u++UXHOnpimN6Coso\nXfxV3bYsSRS8/TaZc29GMBjAaEQwmRAsFtrddBP66GjFMVqLzD1FHPyrMPgBNR9PdbkLW3nTRJ8g\nQEiUib+XZTDs/K7oDSI6g4jepKNdQijdhrZv0vGON+/dfkNA8Tb3nU+Oi3ir5cHzevPn/LO4zrMB\no+TAbbfhcbnQ6XWMnzWb8++4D4Ox+ZdvNY4PmgVOQ6MVkGSJpSlLfYSWSWcivTydwe3rxcPLf72s\niDoVBZGLu19MUkSS4rgl9hJV65tJZ1Jdaj1VcKSlkXXnP3Du3x+4o05HxHmTVXe5srOx/f2X17om\niiBJCBYLMTd7l7zLly0jd96DyHa7crDbTdnSpURdNhOAovffp+idd+tSjAhGI5GXXkr45EnexL9t\nhLyDpXz36ramDRI4qopMguitaVpRZGP9twcRRYExl3QHwBJmJGlQTKtGnrqdTnavWUFZwWEEUaR9\nYjKJAwZjsnpfov5c8gW2AMumlvAIQiOPvzBvF2rCWeob/ex2OinLzz3uc9FoXjQBp3FK0hZ84I5E\nEASEBsm3NudtZkveFkW/eGs8j4x6RPUY//zjn4q0JAC3DryVCFPbrITQ0lRv3kz61dcEVWkhZOxY\nLAMH+rRNmDAB1+HDHLp4ujfYoCZ4wdS7N+1uuJ6I873L0iWffqou3mrQhdX7bpUu+donP5zsdCLZ\nqtuUeLNVOlny4l9NHxiEy5vJqsdpd9f5uAkCWMNN2CqddTnhJI/Mlp/Tmf3c2KbPoZlxu1x8/si9\nFGVlInl8rYoGs5ne489k+28/BzzG+Cuva8kpBiS+ew/St/+NVLP8bzCZSOjRu9Xmo9E8aEuoGhqt\ngCiIXNLjEsw675KmiIhZZ+aMTmfU9Zm/dr5i6dQgGrhzyJ3oReW7V15VHisyVyjaTToT00+b3sxX\ncGLgzM4m/drZwRWp1+mIve1W1V3l33+PVF1d79vmduPOza0TbwCy6CeHlyB4LXV33lHXJJqPWMoW\nRUSLcjm8tSjIKOfD+9RTfBwLA8/pjN4o4qh2+wQoyDLYKpyKaFWnrWlLsC1FyqY/KcnNUYg3AJfd\nzvZlP/r6PTZEEBgxbQb9J5zdwrP0z+Rb76Zd50R0egOiTseAs8+jxyhlShuNEwvNAqdxStIW8sA9\nPPJhOod2Zk3WGuJD4rlzyJ1Emr3O67uKdpFXrcz5Nr7jeKYkT1E93q3L1cXHLQNvqTvuqYQjLY1D\nU6b6Bh0EIGbuXIX1Dbz3Sl+XSyECPcXFHLzgQsImTsRdVKRMS2I0En7eZAxx8URMuxBTt244s7Ip\nXLjQ6/dWmyNOFBEtFqKvveaor7UhVaUOUrd5c8p1GxyLJczYpPG5B0v5uomWN0u4AVu5f98vgAET\nO1Jd6qwrh3Uk5hADjmoXHrf3cxb1Ap16RzVpHi2Fo6oS2V/x+QAMmzqdM66+vgVm1DSs4RFc/dyr\n2CrKMRhNGI58gdA4IdEEnIZGK7E+dz3LMpbhcDuYnDSZOKs38a3dbWfur3MV/c06M1f1uUr1WMvT\nlnOgVFkSKsQQwsyeM5t34m0Q+8FD5P/rXzj27UMXGYl1zGhKPvgwOMsbEHnZZcTcoV4XUiwswpGS\nomphcR44QFFKinej9lyiiCExkYRnnvaJTnXl55M6vX4ZVjCZMPXujXXoUKKumIWxS5emXbQKJXlV\n/O/5zd7UHYLAhqUHmfnwCMKig/vCliWZpa9ubfJ5A4k3QRTo0C2C8Zf15Me3tvvt56h20T4xnKLc\nSjxOiU69ozl7dtsol9W57wB8aosFgajXc/qVs1tkPkeDIAhYw09NN4qTFU3AaZyStIb1zS25+XTX\np2w6vAmL3sLqrNU4PA4AXtz0Ih7Zw6xes8iqyFLkhgOY1n0aw+OVPlJljjLuX3u/ol1A4M7BdxJm\nbNt5s5qKbecucubNw5maWhO2GALl5XX73Tk5OHbvDu5gERF0efNNQoYOUd3tyssj7oXnqShXJlyu\n40iRKEnorFYf8QZQ/uOPXr+3GiEoOxw409JI+mpxcHMNglWf7cVpq7U4ynhcEhu+O+RXCNkqnOzf\nfBidTqBr/1iWfbgTjx8L2dEiSzIFmd7Pr8/YBDJ3F+NWyQ3ncXv7XfiPwXU1VNsK0QmdGHf51az+\n9IOgx5x9w60IoualpNFyaAJOQ+M4cc+qe1iXvQ6X5EJA8Ak2sHvsLNq7iFm9ZvHV/q+wuW0+Y42i\nkav7XK163Jt+vQm3pPTNibXEMqPnjOa9iFZClmVsf/9N9V9/UfDvl32tYQ3EW1OIe/xxomddHrBP\n6dffeJPyNgVRxJDQwafJvn8/tr+3Ih+5nOvPb+oo8Hgkcg6UKdpL89Uz7R/6O5+f3tlZt72aRiJ0\njwGj2ftV03VADBOv6cXmH9LwuCXKi+w+EauCKFBV6mixeRwL+akHg+5rDAmh/5nntuBsNDQ0Aadx\ninK8feBWZaxiZWZ9aSu1SFG9oGfL4S18k/KNYt+s3rNIDE9UtC/Zv4TdxUprU21OOYPYthOfStXV\nVK1fj+x2EzJyJLoIpeXFtmcPGddci1QRwArWRKLnzm1UvAE4M9KRJalJi2e6iHDaz6svmVX244/k\nPvSwd6OBYBMsFiJmNJ/A3vRDqmq7y+EV9x6XRM7BUlx2NzGdw/j5vZ2q/ZsDk1WPxy0heWREUWDC\nlfXJqbsPaU9YtAWPy8Oy/+ymuqze2ixLcputtKAzBP+3dN4td7fgTDQ0vAQScI0lrCluzoloaJzM\nPPaHsth8Q8w6M7cOupWDpQdVS2b9Y8g/FG1ljjIWbFAvCTUlaQpD44Ye3WSPE57SUlIvnYG7pAQB\nby60rl99hbFTx7o+7qIi0i+fhexoPquMedQo2t+l/DyPxF1Sgm37DnXxZjBg6NgRV3pavQVJr8fc\nty+d334LfZTX+V6WZXIffsQ3vYgoou/Ykcjp04mZe9OxXk4dW5dlqLYX51RTnFPJV89vxu1o3uVR\nBQKMvDCJgWd14cCmwzhtbjr1iiamUyjgLZ7+zUt/UZJXjSCAziBijTBiq3Ch0wucc30fImItLTvH\no2TIeRey7481uBq5F2P7D6H78FHHaVYapzKBBFwhkAWohXDJQHKLzEhD4zhwPK1vsixT4ijxuz/G\nHMPDox5mfKfxXP795XV+cbXEWmJVLWnz1sxT9ZWLMEbw+JjHj33iLUzBwjdxHT4MLpdXA9lsHH7m\nGTq/9SbVf/9N1Z9/Url6dbOKt9DzzqPTv19qtM6ou6SEg+edj1SqTMwqhISQ9NVXmJKTKHzvPQpf\nex3Z4yFk5Ag6vvoautCQur6y06mYv2AyEXvzzURe0nypXfIOldblT1MgwxdPb+QogiiDJrF/NAMn\ndqZ9UgQmi/drpc/YBEW/v5dlUJRTVVcfVXBKdO4dxaQ5/TCYdK1e/zUQsYlJXP7ki2z5v2+wV1cR\n3bEzcUndSB4ynNK8XCoKC4nrfhqhkW0jclbj5CeQgHsNOBNYB3wBrOWo8mtraJzaCIJAt8huHCxV\n96EpcZTw0a6P2F+8n8zKTJ99ekHPa2e+phizKW8Tv+f8rjwXAgvPXnhClMxyZWRAwzqgkoQrO5uC\nNxZS+NZbQaf/CJaImTNIePLJoPoW//czVfGGyUTCggWYkr1VMGLmzKHdjTeC2+1NDXIEosmEMSnJ\nG3BRu3wqy1gGDjjq61AjPz3w8nJLirfuw9oz6cZ+3vPIMttWZrJ/fR5Gi55R07oRlxRe17c4t8qn\nuL0syZQerq7zkWvrtO+azHm336va3r6rZtPQOL4ECpG5CxgE/A+4CtgKvAgo6/doaJxgHO9aqJMS\nJyH6+XPzyB72FO1hecZyhfUtITSBfjH9fNpkWebxP9QtbFOSpzAwVpnLrLmR3W4qf/+d8mXLcBcV\nNT5ABevoUQiW+uUywWRCn9CBwjfeCEq86ePj6bZmNSFBWFMtw4bR4Ykn/O53FxZy+OWXSTl3EnuH\nDqNo4ULVfnH330f4JF/ndEEQVMVbLV3eexdjchKIIoLFQofnnsXUvXujc24KLSnQ/GG06LjgzgF1\n4g1gy8/prP/mIPnpFWTtLeHbl/+iKKcSl8NDYVYl0Qkh6I31fweiXiCua7ja4U9Y2kKdZY1Tg8Ze\neyRgBfAXMAt4EjgAvNvC89LQOGn4v4P/57cgfS1uya2w0OkFPX3aKdM/LN6/mMyKTEW7VW/l8dEt\nv3QqOZ2kX3U1jpSUujQJif/9FHOvXk06TvQ11+DYv5+ypd7ardaRI3Hs3tPouLCpUwk7/XTCp5yP\np7ycqrVrAw8QRSKmX+x3ea74v59x+LnnvEl1GzlO2NlNz6ZvSEig2//9H5LTiWAwtMgy4YHNh5v9\nmIHoPa4DZ16lLMW0c1WWT6Jet1Pi718ySN1egCyD5JaIaG+lNL8aQRCIirdy+qyeiuNoaGg0TiAB\nFwpMAy4DYoGvgaGAuqeskp54l15rSQYeA6KAG4GCmvaHgJ9qfp4PXI/X7+5O4Nea9qHAR4AZ+BFo\n3ANZQyMAx9MH7tuUb7F7/NfIBFTFXZgxTFHztNxZzr83/1v1GM+Nf+64LJ2WfrkYx/79yHZ7nU9F\nzvz5JH9THz1b6XBz31fbWHuggDCzgWcu6sdZveN8jiOIIgkLFhD/+OPg8WDfu4/0K64IeO7YeQ8Q\nc523pqS7uJjMO//RuLXOZMIYH69odhcVkXnLrdi3+08u25Do667DoHKcYBGNTauI0BSqy5W+kMeC\nqAPJz8fasVekqngDbxoQ3wY4sOUwkrve+6a80MaU2wYQ2d5KWJRZOeYEp7UrvGicOgRaQj0M3A/8\nCfwLOAQMAy4BgvG+3QcMrvk3FKjGKwJl4N8N9tWKtz54xWIfYDLwJvWpr98CbgBOq/k3OZiL09Bo\nC4QaQxVtQhCJKaafNl1RgP6jnR9R7Vbm9eoa3pWJXSaqHqfMUcYfOX+wvWC7aoRrU3FmZymKtrvz\nfC1Ad3+5lRV78qlyeMgrs3Pb53+xO0c9X5toMiFYLGTfHSD1gigSPn16nXiTnU5SL52BffPmRucb\nMnw41tGjfa8hL4+Uc88NWryFTZpE3P33BdW3NejQLQKh4dP8GDWRP/HWpV8UF92lnvQYYMikxPol\nUgEMRh2y5Os6LQgCtnIX4e0sJ51409A4ngSywH1V83+Pmn9H8nUTznM2kAJk4n20qP3VTgMWAS4g\nrab/SCAdCANqCw1+AlwE/NyE82to+HA888DdPPBm/sj5A7vbjoyMXtDjlgMv15l0JqZ1n+bTVlBd\nwEc7P1Ltrxbo8NOhn/j+0PdsyN2AXvSe0yga6RLWhVm9Z3FR94uO6nqsg4dQ+uVib1UBAFHEmOzr\nwL1mfwFOT71Y9Egy61IK6JOg7u+U988ncB9WLgPq2rcncsYMQkeNxDrcW4XCmZlJ1l13487JaXyy\nBgOhEyf4LFt6SktJu/hi5Cr1BLdHIosi8Y8HTgPTmsiSTGl+tW9BiBYINwuNMnHB7YMD9uk/oRMm\nq579Gw9jMOnod0ZHfnhzGy57g8AFWSYq3tr8E2wjtIU6yxqnBoEE3OxmPM/leMUZeB8tdwDXAJuB\ne4FSIAFY32BMFtARr6DLatCeXdOuoXFCoBN0JIQkkFWRhVlvpsypzJbfEBGR58c/T1KEb7zQa3+/\nhktW1pw8s/OZir7v73ifd7a9U7d0W5tuxOlxsrt4N0/++STL05dzcfeLObPLmU3yywo79xyitm+n\n+MOaWqOyjH3nTkq/+47ICy8EwGLU4XDXf2nrRYEws7qjf8F771P65Zeq++IefoiISZMA8JSXU/L5\nIgrefBOcwS0ZCjodgt73MZf/8it4yoKr3iCGh5N/xx3ooxtLi9l6FOVUUZxT1SyiLTzWRHmBetqW\ni+/1b3lrSI8R8dgqXfyxJIXUbQWYrAZkyY2oE/C4ZQae3Zn2iSdX4IKGRmsQaAl1FLANqMK7jHq0\nVYWNwAXUW/TewhvJOgjIBV46yuNqaBw1x+sNucRewrU/X8uhskM4JEej4k1AYFLSJM7scqZPe1ZF\nFt8f/F51zDPjnlG0vbv93YB+dy7Jxeqs1cxfN59nNijHB5yjIBA6biyCucbfTpaRHQ7yHnkUuSZV\nxhMX9MVsEBEFMOtF4iMsTBukzAtWuX4DhS+pPwIMiYn14q2sjEMXTqPg9deDFm+AIvBAdrupXLky\nqBJWuqgoevy+jvFXXxX8+VoBt8vjk5rjaLn0oSF+xVv/iR0JjwkuwW7eoTLWf3sQySPjccvYKpyE\nRZuZettAZj02klEXdjvmubZlNOubxvEikAVuIXAf3vxvFwAvA5OO4hznAVuoD1rIb7DvfaD2Wykb\n6NxgXye8lrfsmp8btmernWj27Nl07doVgMjISAYNGlT3x1Qb2q1ta9vHc9ud5MblcamWzjoSAYE5\nA+Zw26DbWL16tc/x5v80H4+sdEy6rMdlhBpDfc6/q3AXDndwyW9tbhvfHPiG/pX9idBHMGHCBGxu\nG0tWLCFMDGPa2dNUr2/HmrWESZLPG6DkdiNV29CFhhBRdoB7hxipDutMpNVIXHUqG/9Y5/v5VFUR\n9+B81XnJQO51s6lNtrHl+ecJzc9H8CO8aj9d4Yi26Mtmoo+Kqpt/7y1/NZ72RBCQ9XqKLptJj5r0\nIG3lflLbztxb7Lt8ehTE9oNvX/1LdV9otAkpLpdVq3KDmk9+ejked8MlUyjJq2Zv5t9M7D6xyden\nbWvbJ+v2sRJo3eRvvEEG/raD5Qu8gQof12x3wGt5A7gbGA5cgdfC9zkwAu8S6XKgO97n8Aa8Uakb\ngR/wJhk+0gdOlo/1KaZxyrDqOPiplDvKmb50OodtgVM89IjqwcTOEzk/6XySI5MV+1PLUpn27TSF\nCDQIBtbNWofVUO9PtDx9OfPXzm806rUhVr2VRVMWkRyZzJ6iPcz5dQ5u2Ss8r+t3HbcPvl0xxpGa\nSurF032DGXQ6Ej/6T52vWiBkl4sDZ52NJz9fdX/UdbOJnzevbjv/pZcoeu/9oK8JAL2exE8+xjqk\nfulv3+gxSCX+q2LUjkta/CXmPt5Fh+NxrxwtBZkVfPfqVuyVyqX1YBEEOPu6Piz7UFlTF+DaZ8cQ\nGhV8dPOhrQUs/89uXI76Fw5LmIHrXxx/1HM8kWjL94tG20I4xpxCgSxwEXijTQWVbZngghhC8AYw\nzGnQ9jze5VMZSAXm1rTvBhbX/O8GbqX+xfpWvGlELHjTiGgBDBptmoLqAi5aehHlTv9WhZR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c+xWkl97FGizzwjcD/27CHv+edxHMhEr1aCSy8vZ8+kSShWK0nT7iPh2mtr7msD0DX/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\nJEmSJEmSJEmStO34Pxni1gAG2ixXAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xafb2898>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Histograma del pedal del acelerador\n",
      "\n",
      "Mediante un histograma podemos analizar el uso que hace el piloto del pedal del acelerador."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig2, ax2 = plt.subplots(figsize=(10,6))\n",
      "\n",
      "data['Throttle Pos'].hist(ax=ax2)\n",
      "ax2.set_xlabel('Throttle position (%)')\n",
      "ax2.set_xlim(0,100)\n",
      "ax2.set_ylabel('Time spent (@ 60 Hz)')\n",
      "ax2.set_axisbelow(True)\n",
      "# Quitamos los m\u00e1rgenes derecho y superior\n",
      "ax2.spines[\"top\"].set_visible(False)  \n",
      "ax2.spines[\"right\"].set_visible(False)\n",
      "# Dejamos s\u00f3lo los ticks abajo y a la izquierda\n",
      "ax2.get_xaxis().tick_bottom()  \n",
      "ax2.get_yaxis().tick_left()  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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09tq7n8c2PVL2MArzxy2be/ZyGl5KQ73K4k3SmHgtLUnqLHveVLpe77mZ3L8P\nfX19PfuQJHWWM29SwZyZkiSNJ2feVDp73iRJap/FmyRJUiAumwZx1LOfw5bNj5U9jELsOWkv/tCj\nP5skSePN4i2ILZsf69m+KXumJElqn8umkiRJgTjzJkmt7DLBS6JI6ioWb5LUytYne7ZlAWxbkCJy\n2VSSJCkQizdJkqRALN4kSZICidTzdirwKWACcDlwcbnD0bixIVySpLZFKd4mAJcCs4ANwI+B64Bf\nlDkojRMbwiVJ1TMILB3NF0ZZNj0WWAusAx4HvgLMKXNAkiRJYzA42i+MUrxNAx6o216f90mSJFVK\nlGXToXY+9J/XLyh6HKX446MPlz0ESZLUJaJ0iR8PfIh00gLAfGAr2560sBY4uLPDkiRJGpVFwNyy\nB1GkicCvgAFgN+BnwJFlDkiSJEmt/Vfgl6QZtvklj0WSJEmSJEnqfacCq4E1wPklj0U7dgBwG3AP\n8HNgXt6/L3ALcC9wM9BfyujUjgnACmBJ3ja7OPqBr5OukbkKOA7zi2Q+6Xfn3cDVwO6YX7daCGwk\nZVXTKqv5pDpmNTC7Q2MszQTSMuoAsCv2wkUwBZiZX08iLYUfCVwCvDfvPx+4qPNDU5veCVxFulA2\nmF0ki4A35tcTgb0xvygGgH8nFWwAXwXOxvy61QnA0WxbvDXLagapftmVlPNa4lzKbVT+DrixbvuC\n/FAc3yLdOWM1sF/eNyVvq/tMB24FTmR45s3sYtib9D//kcwvhn1Jf+zuQyq8lwAnY37dbIBti7dm\nWc1n25XDG0lX2WgqemXnxXtjGyD9ZXIH6V/ojXn/Rob/BVd3+STwHtKlemrMLoZnAr8Dvgj8FPgC\n8FTML4qHgY8DvwZ+A2wiLcGZXxzNsppKql9qdljLRC/e2rp4r7rSJOAbwHnA5hHvDWG23eg04Lek\nfrdm14g0u+41EXgucFl+/gPbr1SYX/c6GHg76Y/eqaTfoa8f8Rnzi2NHWbXMMXrxtoHUAF9zANtW\nr+pOu5IKty+Rlk0h/RUyJb/en1QkqLv8PXAGcB+wGDiJlKHZxbA+P36ct79OKuIexPwieD5wO/AQ\n8ARwLal1yPziaPa7cmQtMz3vayp68fYT4FCGL957FsNN1OpOfcC/kM50+1Td/utIzbfk52+hbvM+\n0i+YZwKvBv4VeANmF8WDpDaTw/L2LNKZi0swvwhWk/qgnkL6PTqL9HvU/OJo9rvyOtLv1N1Iv18P\nBZZ3fHRxcDcOAAAEMklEQVQd5sV7Y3kBqV/qZ6TltxWky73sS2qE93T3GF7E8B9KZhfHc0gzbytJ\nMzd7Y36RvJfhS4UsIq1imF93WkzqTfwL6Y+mc2id1ftIdcxq4JSOjlSSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEXyNIav4/cfpCv7rwAeIV2naizeTrpQac37Rry/ZYzffzROZ/jG0mcCR9a992Hg\nxeN0nKOAhfn1K4CfAz8gXT8K0i2UvlL3+d3z+9EvyC5JkjroQuCd+fWBpAuM7siEFu/dRyoOa0be\nG3fkdqddQSqsinAlcEx+fRuwB/A64G1539WkAq7eR4CXFzQeSSXwrzFJndBX9zwB+Dxp1ugmUgEC\nsBT4JOkOAOeRZqt+CtxFuqXabsA80k25byPdnuujpFm4FaT7rI70HtJtZlYCH2oyti3AJ/J4bgX+\nJu+fCSxj+G4EtauhzyPNHq4kFUsAc4HPku41eTrwsTz2g9i2mGv0MwGsy+O7M793eINx7k66PVLt\n3qRbSf/snkq6ivsJpFnOX434uuuA1zT52SVJkrZzIfCu/HoAeBx4dt7+KmnmCFJBdml+vQfwa+CQ\nvL2IVNBBmnmrLRNC85m32cD/za93Id0D8oQG49vKcHHzP0hFGKQiqvb5D5MKS0g3jN41v56cn8+u\n+7ovsu1MV217Rz/TP+XXbwW+0GCcx+efoWYW6f7O387juInGt0banR3c5FpSLM68Seq0+0iFEaSZ\npoG6976anw/Pn1ubtxcBL9zJ48zOjxX5OIczXDjV21p33C+T7r87mXTfzx82OP5dpBm31wFPNjl2\nX4PtHf1M1+bnn7LtP5OaA0kzazW3As8H5pD67L4DHAF8jTSzWesL/DPpd/0eSOoJE8segKTK+XPd\n6yfZtqj4Q5Ov6QOGRnGsj5IKmXY1O059MfZSUtF1OvB+0kkEI4u1Rt9j5L6Rx6r9c3mSxr+bhxoc\nB2BP0szfKcD1wMuAV5KKy8ubHEtSYM68SSpbX4PXvyTNPtWa798AfD+/3szwciWkZdhGxc5NwBtJ\nPWEA04CnN/jcLqRiB+C1pNm2x0hnxr6g7vhL8/j+Nr++gDQ7N2nE9xs5PkiFU6ufqR33A1Ma7H8P\n8GngCYZn24ZIRR2kZdMn2bZolhSYM2+SOmGoyetm7/0JOIe0BDiRdNLB5/J7nwduJPVxvThv30Va\nGn1D3fe4hXTJjh/l7c3A64HfjTj+H4BjgQ8AG4Gz8v6z8zH3JJ0EcE4ey5dIRVsfqWh6NB+zdtyv\nkHrW/pnhohBS8dTsZxr5z6DRLNlKtj+RYSrp7NMP5+3Pkk5oeIS0lApwdN0/A0mSpPDKvrTIzrgC\nOG4nv2YBaSlVkiSpJzxW9gB2wn8hnb3artpFehv1ykmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\npOL8f6mtW48iOOQRAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb207518>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Gr\u00e1ficas con `plotly`\n",
      "\n",
      "`plotly` ofrece un [API](https://plot.ly/api/) excelente para crear gr\u00e1ficas interactivas que pueden ser incluidas en webs y blogs.\n",
      "El paquete no viene incluido en los repositorios de Continuum Analytics (`conda`) pero se puede instalar de manera muy sencilla con `pip`:\n",
      "\n",
      "    pip install plotly"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import plotly.plotly as py\n",
      "from plotly.graph_objs import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Para poder utilizar `plotly` necesitamos una credenciales. Registrarse es gratuito, y ofrecen almacenamiento ilimitado para gr\u00e1ficos p\u00fablicos. Sin embargo, el n\u00famero de archivos privados que podemos alojar es limitado en la versi\u00f3n gratuita.\n",
      "\n",
      "Para mayor comodidad podemos guardar nuestras credenciales en el sistema de modo siguiente,\n",
      "\n",
      "    >>> import plotly.tools as tls\n",
      "    >>> tls.set_credentials_file(username=\"your_username\", \n",
      "                                 api_key=\"your_api_key\")\n",
      "                                 \n",
      "que rellenaremos con nuestros datos, que encontraremos en la secci\u00f3n de `Configuration` de nuestra cuenta.\n",
      "Nuestras credenciales las podremos recuperar, a partir de entonces, de la siguente manera."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_creds = py.get_credentials()\n",
      "    \n",
      "username = my_creds['username']\n",
      "api_key = my_creds['api_key']\n",
      "\n",
      "py.sign_in(username, api_key)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Podemos utilizar `plotly` de dos maneras.\n",
      "La primera de ellas es generar las gr\u00e1ficas directamente con la API.\n",
      "La segunda, y la que resultar\u00e1 m\u00e1s c\u00f3moda para la mayor\u00eda, es convertir directamente las figuras generadas con `matplotlib`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Publicar una figura de matplotlib en la web\n",
      "unique_url = py.iplot_mpl(fig, filename='pybonacci/velocidades', strip_style=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "C:\\Miniconda3\\lib\\site-packages\\plotly\\matplotlylib\\renderer.py:306: UserWarning:\n",
        "\n",
        "Bummer! Plotly can currently only draw Line2D objects from matplotlib that are in 'data' coordinates!\n",
        "\n",
        "C:\\Miniconda3\\lib\\site-packages\\plotly\\matplotlylib\\renderer.py:427: UserWarning:\n",
        "\n",
        "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
        "\n"
       ]
      },
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~pfsq/138\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0xb3422e8>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unique_url1 = py.iplot_mpl(fig1, filename='pybonacci/rpm', strip_style=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "C:\\Miniconda3\\lib\\site-packages\\plotly\\matplotlylib\\renderer.py:384: UserWarning:\n",
        "\n",
        "Dang! That path collection is out of this world. I totally don't know what to do with it yet! Plotly can only import path collections linked to 'data' coordinates\n",
        "\n"
       ]
      },
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~pfsq/139\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0xb833a90>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unique_url2 = py.iplot_mpl(fig2, filename='pybonacci/acelerador', strip_style=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~pfsq/140\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0xba77a90>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Retocar `plotly`\n",
      "\n",
      "Como podemos ver en las gr\u00e1ficas superiores, la conversi\u00f3n a `plotly` todav\u00eda no es perfecta. El conversor interpreta las leyendas de `matplotlib` como anotaciones de texto. Por suerte, eso lo podemos subsanar f\u00e1cilmente.\n",
      "\n",
      "Necesitaremos importar una serie de paquetes adicionales, disponibles en las versiones >1 de `plotly`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Herramientas Python/Plotly\n",
      "import plotly.tools as tls   \n",
      "\n",
      "# Objetos para componer Leyendas\n",
      "from plotly.graph_objs import Legend"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Convertimos la figura de `matplotlib` a una figura de `plotly`,"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py_fig = tls.mpl_to_plotly(fig, strip_style=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "y eliminamos todas las anotaciones de texto y a\u00f1adimos una leyenda en la esquina superior derecha."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Borrar anotaciones\n",
      "py_fig['layout'].pop('annotations',None)\n",
      "# A\u00f1adir leyenda\n",
      "py_fig['layout'].update(dict(showlegend=True))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ya podemos publicar la nueva gr\u00e1fica, \u00e9sta vez s\u00f3lo con `py.plot()` o `py.iplot()` pues ya hemos hecho la conversi\u00f3n a `plotly` unos pasos atr\u00e1s."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unique_url3 = py.iplot(py_fig, filename='pybonacci/velocidades_edit')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~pfsq/141\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0xb4d7160>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Repetimos el procedimiento para la segunda gr\u00e1fica, pero en esta ocasi\u00f3n colocaremos la leyenda arriba a la izquierda y cambiamos el `hovermode` a `closest`.\n",
      "\n",
      "Con `hovermode` controlamos c\u00f3mo se comporta `plotly` al pasar el rat\u00f3n por encima de la gr\u00e1fica. En modo `closest` nos resaltar\u00e1 el valor m\u00e1s cercano al rat\u00f3n, y en modo `compare`, para cada *x* nos mostrar\u00e1 los diferentes valores que toma la variable."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py_fig1 = tls.mpl_to_plotly(fig1, strip_style=True)\n",
      "# Borrar anotaciones\n",
      "py_fig1['layout'].pop('annotations',None)\n",
      "# A\u00f1adir leyenda\n",
      "py_fig1['layout'].update(dict(showlegend=True,\n",
      "                              legend=Legend(x=0,y=1),\n",
      "                              hovermode='closest'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pero esta vez tenemos que editar los nombres de los puntos que se muestran en la leyenda."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Editar leyenda\n",
      "n=0\n",
      "for key, grp in data.groupby(['Gear']):\n",
      "    py_fig1['data'][n].update(dict(name='Gear {}'.format(key)))\n",
      "    n += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unique_url4 = py.iplot(py_fig1, filename='pybonacci/rpm_edit')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~pfsq/142\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0xb833eb8>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Por \u00faltimo nos falta retocar el histograma de posiciones del pedal del acelerador. En \u00e9sta gr\u00e1fica no quedan anotaciones, pero tampoco necesitamos mostrar la leyenda. Aprovechamos tambi\u00e9n para hacer las barras m\u00e1s anchar reduciendo el `bargap`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py_fig2 = tls.mpl_to_plotly(fig2, strip_style=True)\n",
      "# Borrar leyenda\n",
      "py_fig2['layout'].update(dict(showlegend=False,\n",
      "                              bargap=0.01))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unique_url5 = py.iplot(py_fig2, filename='pybonacci/acelerador_edit')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~pfsq/143\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0xba58d68>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Introducci\u00f3n a `plotly`\n",
      "\n",
      "En un pr\u00f3ximo Notebook introduciremos el uso de `plotly` para crear gr\u00e1ficas sin partir de `matplotlib`."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}