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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "The color of celestial objects: part II"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Author: [Eduardo Mart\u00edn Calleja](http://balbuceosastropy.blogspot.com.es/)\n",
      "\n",
      "After my [previous post](http://balbuceosastropy.blogspot.com.es/2013/11/the-color-of-celestial-objects-part-i.html) on this blog, dedicated to the topic of the color of celestial bodies , this entry is its sequel, in which we will examine data from a star catalog containing photometric and spectroscopic data , and show them in color-color diagrams which will attempt to differentiate between the various star classes . That is, if the previous post introduced a number of theoretical concepts , we will now apply these concepts to real data."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Imports and references"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The catalog that we will use is the **\"Bright Star Catalogue (BSC)\"**, which includes virtually all stars visible to the naked eye, to magnitude 6.5 or brighter. Here's a first [description](http://en.wikipedia.org/wiki/Bright_Star_Catalogue)\n",
      "\n",
      "This catalog is available online [here](http://tdc-www.harvard.edu/catalogs/bsc5.html). To follow the exercises from this post we must download the three files that comprise the catalog:\n",
      "\n",
      "* bsc5.dat:        The catalog\n",
      "* bsc5.readme:     A catalog description\n",
      "* bsc5.notes:      Catalog notes\n",
      "\n",
      "It will also be useful this [wikipedia article](http://en.wikipedia.org/wiki/Stellar_classification) dedicated to the classification of the stars by the characteristics of its spectrum.\n",
      "\n",
      "This post will be written as usual with the Ipython Notebook, toghether with some Python libraries which we are going to import:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "from __future__ import division\n",
      "\n",
      "import quantities as pq\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas as pd\n",
      "\n",
      "# Generar un cuadro con versiones de las librer\u00edas utilizadas en este notebook\n",
      "#https://github.com/jrjohansson/version_information\n",
      "%load_ext version_information\n",
      "%version_information numpy, matplotlib, quantities, pandas"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>2.7.9 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]</td></tr><tr><td>IPython</td><td>2.3.1</td></tr><tr><td>OS</td><td>Linux 3.13.0 45 generic x86_64 with debian jessie sid</td></tr><tr><td>numpy</td><td>1.9.1</td></tr><tr><td>matplotlib</td><td>1.4.2</td></tr><tr><td>quantities</td><td>0.10.1</td></tr><tr><td>pandas</td><td>0.15.2</td></tr><tr><td colspan='2'>Mon Feb 23 19:01:56 2015 CET</td></tr></table>"
       ],
       "json": [
        "{\"Software versions\": [{\"version\": \"2.7.9 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\", \"module\": \"Python\"}, {\"version\": \"2.3.1\", \"module\": \"IPython\"}, {\"version\": \"Linux 3.13.0 45 generic x86_64 with debian jessie sid\", \"module\": \"OS\"}, {\"version\": \"1.9.1\", \"module\": \"numpy\"}, {\"version\": \"1.4.2\", \"module\": \"matplotlib\"}, {\"version\": \"0.10.1\", \"module\": \"quantities\"}, {\"version\": \"0.15.2\", \"module\": \"pandas\"}]}"
       ],
       "latex": [
        "\\begin{tabular}{|l|l|}\\hline\n",
        "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
        "Python & 2.7.9 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] \\\\ \\hline\n",
        "IPython & 2.3.1 \\\\ \\hline\n",
        "OS & Linux 3.13.0 45 generic x86\\_64 with debian jessie sid \\\\ \\hline\n",
        "numpy & 1.9.1 \\\\ \\hline\n",
        "matplotlib & 1.4.2 \\\\ \\hline\n",
        "quantities & 0.10.1 \\\\ \\hline\n",
        "pandas & 0.15.2 \\\\ \\hline\n",
        "\\hline \\multicolumn{2}{|l|}{Mon Feb 23 19:01:56 2015 CET} \\\\ \\hline\n",
        "\\end{tabular}\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "Software versions\n",
        "Python 2.7.9 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
        "IPython 2.3.1\n",
        "OS Linux 3.13.0 45 generic x86_64 with debian jessie sid\n",
        "numpy 1.9.1\n",
        "matplotlib 1.4.2\n",
        "quantities 0.10.1\n",
        "pandas 0.15.2\n",
        "Mon Feb 23 19:01:56 2015 CET"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Reading data from the \"Bright Star Catalogue\" (BSC)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Once downloaded the three files which make up the BSC, We will read a number of fields and we'll import them into a  Pandas dataframe. Just some remarks about this:\n",
      "\n",
      "- The structure of the entries in the BSC is detailed in bsc5.readme file, along with a brief description.\n",
      "- We are not going to import all the fields, but only those that we will use later.\n",
      "- As they are records made of fixed-width fields, we'll use the read_fwf() function from the Pandas library, which allows to provide a description of the starting position and length of each field."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colspecs = [(0,4), (4,14), (25,31), (31,37), (41,42), (43,44), (51,60), \n",
      "            (90,96), (96,102), (102,107), (109,114), (115,120), (127,147)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "labels =['HR', 'Name', 'HD', 'SAO', 'IRflag', 'Multiple', 'VarID', \n",
      "         'GLON', 'GLAT', 'Vmag', 'B-V', 'U-B', 'SpType']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bsc = pd.read_fwf('../datos/bsc5.dat', header= None, colspecs=colspecs,\n",
      "                    names=labels, index_col=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's see a sample of the data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bsc.head()"
     ],
     "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>Name</th>\n",
        "      <th>HD</th>\n",
        "      <th>SAO</th>\n",
        "      <th>IRflag</th>\n",
        "      <th>Multiple</th>\n",
        "      <th>VarID</th>\n",
        "      <th>GLON</th>\n",
        "      <th>GLAT</th>\n",
        "      <th>Vmag</th>\n",
        "      <th>B-V</th>\n",
        "      <th>U-B</th>\n",
        "      <th>SpType</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HR</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>       NaN</td>\n",
        "      <td>   3</td>\n",
        "      <td>  36042</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 114.44</td>\n",
        "      <td>-16.88</td>\n",
        "      <td> 6.70</td>\n",
        "      <td> 0.07</td>\n",
        "      <td> 0.08</td>\n",
        "      <td>         A1Vn</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>       NaN</td>\n",
        "      <td>   6</td>\n",
        "      <td> 128569</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  98.33</td>\n",
        "      <td>-61.14</td>\n",
        "      <td> 6.29</td>\n",
        "      <td> 1.10</td>\n",
        "      <td> 1.02</td>\n",
        "      <td>          gG9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 33    Psc</td>\n",
        "      <td>  28</td>\n",
        "      <td> 128572</td>\n",
        "      <td>   I</td>\n",
        "      <td> NaN</td>\n",
        "      <td>     Var?</td>\n",
        "      <td>  93.75</td>\n",
        "      <td>-65.93</td>\n",
        "      <td> 4.61</td>\n",
        "      <td> 1.04</td>\n",
        "      <td> 0.89</td>\n",
        "      <td> K0IIIbCN-0.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 86    Peg</td>\n",
        "      <td>  87</td>\n",
        "      <td>  91701</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 106.19</td>\n",
        "      <td>-47.98</td>\n",
        "      <td> 5.51</td>\n",
        "      <td> 0.90</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>        G5III</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>       NaN</td>\n",
        "      <td> 123</td>\n",
        "      <td>  21085</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> V640 Cas</td>\n",
        "      <td> 117.03</td>\n",
        "      <td> -3.92</td>\n",
        "      <td> 5.96</td>\n",
        "      <td> 0.67</td>\n",
        "      <td> 0.20</td>\n",
        "      <td>          G5V</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "         Name   HD     SAO IRflag Multiple     VarID    GLON   GLAT  Vmag  \\\n",
        "HR                                                                          \n",
        "1         NaN    3   36042    NaN      NaN       NaN  114.44 -16.88  6.70   \n",
        "2         NaN    6  128569    NaN      NaN       NaN   98.33 -61.14  6.29   \n",
        "3   33    Psc   28  128572      I      NaN      Var?   93.75 -65.93  4.61   \n",
        "4   86    Peg   87   91701    NaN      NaN       NaN  106.19 -47.98  5.51   \n",
        "5         NaN  123   21085    NaN      NaN  V640 Cas  117.03  -3.92  5.96   \n",
        "\n",
        "     B-V   U-B        SpType  \n",
        "HR                            \n",
        "1   0.07  0.08          A1Vn  \n",
        "2   1.10  1.02           gG9  \n",
        "3   1.04  0.89  K0IIIbCN-0.5  \n",
        "4   0.90   NaN         G5III  \n",
        "5   0.67  0.20           G5V  "
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bsc.tail()"
     ],
     "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>Name</th>\n",
        "      <th>HD</th>\n",
        "      <th>SAO</th>\n",
        "      <th>IRflag</th>\n",
        "      <th>Multiple</th>\n",
        "      <th>VarID</th>\n",
        "      <th>GLON</th>\n",
        "      <th>GLAT</th>\n",
        "      <th>Vmag</th>\n",
        "      <th>B-V</th>\n",
        "      <th>U-B</th>\n",
        "      <th>SpType</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HR</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>9106</th>\n",
        "      <td> NaN</td>\n",
        "      <td> 225233</td>\n",
        "      <td> 255629</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 307.68</td>\n",
        "      <td>-43.80</td>\n",
        "      <td> 7.31</td>\n",
        "      <td> 0.44</td>\n",
        "      <td> 0.01</td>\n",
        "      <td>       F2V</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9107</th>\n",
        "      <td> NaN</td>\n",
        "      <td> 225239</td>\n",
        "      <td>  53622</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 112.17</td>\n",
        "      <td>-27.24</td>\n",
        "      <td> 6.12</td>\n",
        "      <td> 0.62</td>\n",
        "      <td> 0.09</td>\n",
        "      <td>       G2V</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9108</th>\n",
        "      <td> NaN</td>\n",
        "      <td> 225253</td>\n",
        "      <td> 255631</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 308.18</td>\n",
        "      <td>-45.21</td>\n",
        "      <td> 5.59</td>\n",
        "      <td>-0.12</td>\n",
        "      <td>-0.42</td>\n",
        "      <td>    B8IV-V</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9109</th>\n",
        "      <td> NaN</td>\n",
        "      <td> 225276</td>\n",
        "      <td>  73731</td>\n",
        "      <td>   I</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 110.22</td>\n",
        "      <td>-35.07</td>\n",
        "      <td> 6.25</td>\n",
        "      <td> 1.40</td>\n",
        "      <td> 1.59</td>\n",
        "      <td>    K4IIIb</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9110</th>\n",
        "      <td> NaN</td>\n",
        "      <td> 225289</td>\n",
        "      <td>  10962</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> V567 Cas</td>\n",
        "      <td> 117.40</td>\n",
        "      <td> -1.06</td>\n",
        "      <td> 5.80</td>\n",
        "      <td>-0.09</td>\n",
        "      <td>-0.32</td>\n",
        "      <td> B8IVpHgMn</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "     Name      HD     SAO IRflag Multiple     VarID    GLON   GLAT  Vmag  \\\n",
        "HR                                                                         \n",
        "9106  NaN  225233  255629    NaN      NaN       NaN  307.68 -43.80  7.31   \n",
        "9107  NaN  225239   53622    NaN      NaN       NaN  112.17 -27.24  6.12   \n",
        "9108  NaN  225253  255631    NaN      NaN       NaN  308.18 -45.21  5.59   \n",
        "9109  NaN  225276   73731      I      NaN       NaN  110.22 -35.07  6.25   \n",
        "9110  NaN  225289   10962    NaN      NaN  V567 Cas  117.40  -1.06  5.80   \n",
        "\n",
        "       B-V   U-B     SpType  \n",
        "HR                           \n",
        "9106  0.44  0.01        F2V  \n",
        "9107  0.62  0.09        G2V  \n",
        "9108 -0.12 -0.42     B8IV-V  \n",
        "9109  1.40  1.59     K4IIIb  \n",
        "9110 -0.09 -0.32  B8IVpHgMn  "
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Color-color diagram of the main sequence"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The ultimate purpose of this post is to make color-color diagrams of different types of stars, ie spectral classes, for which we will start with the stars on the main sequence. We will use the color indices from the UBV system, as these are those contained in the BSC.\n",
      "\n",
      "Main sequence stars are the stars (which constitute approximately 80% of the stellar population) using hydrogen as fuel in their nuclear fusion processes.\n",
      "\n",
      "To distinguish these stars from the rest we will use the BSC spectral type (the column of the dataframe with the name SpType). The BSC uses the Morgan-Keenan (MK) classification system, which includes a spectral class in the Harvard classification scheme (O B A F G K M, plus a numeral 0-9), to which it is added a classification based on the luminosity, which uses Roman numerals I-VI, plus occasionally a lower case \"a\" or \"b\"\n",
      "\n",
      "Examples of the resulting classification are: \"B5III\", \"A5Ia\", \"K0V\" etc. In particular, the \"V\" means \"main sequence\", which is what will allow us to recognize the stars belonging to the main sequence.\n",
      "\n",
      "Important remark: please note that the \"Bright Star Catalogue\" includes only the brightest stars, basically those that are visible to the naked eye, so that the number of stars of a given spectral class in the catalog is not in any way representative of its abundance in the universe. The fainter or more distant stars will not be represented in the BSC."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How many spectral types exist in the catalog?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bsc['SpType'].nunique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "1975"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Are there any missing spectral types (marked as NaN in Pandas)?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.isnull(bsc['SpType']).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "14"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are 14. Let's get rid of these entries in the dataframe"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = pd.notnull(bsc['SpType'])\n",
      "bsc = bsc[b]\n",
      "bsc.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 9096 entries, 1 to 9110\n",
        "Data columns (total 12 columns):\n",
        "Name        3143 non-null object\n",
        "HD          9096 non-null float64\n",
        "SAO         9071 non-null float64\n",
        "IRflag      1743 non-null object\n",
        "Multiple    1577 non-null object\n",
        "VarID       2172 non-null object\n",
        "GLON        9096 non-null float64\n",
        "GLAT        9096 non-null float64\n",
        "Vmag        9096 non-null float64\n",
        "B-V         8786 non-null float64\n",
        "U-B         7206 non-null float64\n",
        "SpType      9096 non-null object\n",
        "dtypes: float64(7), object(5)\n",
        "memory usage: 923.8+ KB\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the above summary we see that the data frame has 14 records less, and the SpType field lacks nonzero values.\n",
      "\n",
      "Then we will select the stars belonging to the main sequence, creating a new dataframe which we will call main. These are those that have a luminosity class 'V' in the MK classification."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b1 = bsc['SpType'].str.contains('V')\n",
      "b2 = bsc['SpType'].str.contains('IV')\n",
      "b3 = bsc['SpType'].str.contains('VI')\n",
      "\n",
      "# Luminosity class \"V\", not (\"IV\" or \"VI\" or \"VII\"9\n",
      "b = np.logical_and(b1,np.logical_and(np.logical_not(b2),np.logical_not(b3)))\n",
      "main = bsc[b]\n",
      "main.shape\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(3025, 12)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's make a first check:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "main[0:10]"
     ],
     "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>Name</th>\n",
        "      <th>HD</th>\n",
        "      <th>SAO</th>\n",
        "      <th>IRflag</th>\n",
        "      <th>Multiple</th>\n",
        "      <th>VarID</th>\n",
        "      <th>GLON</th>\n",
        "      <th>GLAT</th>\n",
        "      <th>Vmag</th>\n",
        "      <th>B-V</th>\n",
        "      <th>U-B</th>\n",
        "      <th>SpType</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HR</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>       NaN</td>\n",
        "      <td>   3</td>\n",
        "      <td>  36042</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 114.44</td>\n",
        "      <td>-16.88</td>\n",
        "      <td> 6.70</td>\n",
        "      <td> 0.07</td>\n",
        "      <td> 0.08</td>\n",
        "      <td>    A1Vn</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>       NaN</td>\n",
        "      <td> 123</td>\n",
        "      <td>  21085</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> V640 Cas</td>\n",
        "      <td> 117.03</td>\n",
        "      <td> -3.92</td>\n",
        "      <td> 5.96</td>\n",
        "      <td> 0.67</td>\n",
        "      <td> 0.20</td>\n",
        "      <td>     G5V</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>       NaN</td>\n",
        "      <td> 166</td>\n",
        "      <td>  73743</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>       33</td>\n",
        "      <td> 111.26</td>\n",
        "      <td>-32.83</td>\n",
        "      <td> 6.13</td>\n",
        "      <td> 0.75</td>\n",
        "      <td> 0.33</td>\n",
        "      <td>     K0V</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>       NaN</td>\n",
        "      <td> 203</td>\n",
        "      <td> 166053</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  52.21</td>\n",
        "      <td>-79.14</td>\n",
        "      <td> 6.18</td>\n",
        "      <td> 0.38</td>\n",
        "      <td> 0.05</td>\n",
        "      <td>     A7V</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>       NaN</td>\n",
        "      <td> 256</td>\n",
        "      <td> 147090</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  74.36</td>\n",
        "      <td>-75.90</td>\n",
        "      <td> 6.19</td>\n",
        "      <td> 0.14</td>\n",
        "      <td> 0.10</td>\n",
        "      <td>    A6Vn</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>       NaN</td>\n",
        "      <td> 319</td>\n",
        "      <td> 166066</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>       46</td>\n",
        "      <td>  55.56</td>\n",
        "      <td>-79.07</td>\n",
        "      <td> 5.94</td>\n",
        "      <td> 0.14</td>\n",
        "      <td> 0.06</td>\n",
        "      <td>   A2Vp:</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>   Kap1Scl</td>\n",
        "      <td> 493</td>\n",
        "      <td> 166083</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  25.24</td>\n",
        "      <td>-80.63</td>\n",
        "      <td> 5.42</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 0.08</td>\n",
        "      <td>     F2V</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td> 34    Psc</td>\n",
        "      <td> 560</td>\n",
        "      <td>  91750</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 106.87</td>\n",
        "      <td>-50.43</td>\n",
        "      <td> 5.51</td>\n",
        "      <td>-0.07</td>\n",
        "      <td>-0.24</td>\n",
        "      <td>    B9Vn</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>       NaN</td>\n",
        "      <td> 661</td>\n",
        "      <td> 255642</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   W</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 306.98</td>\n",
        "      <td>-43.58</td>\n",
        "      <td> 6.64</td>\n",
        "      <td> 0.37</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> F2V+F6V</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>  6    Cet</td>\n",
        "      <td> 693</td>\n",
        "      <td> 147133</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  82.24</td>\n",
        "      <td>-75.06</td>\n",
        "      <td> 4.89</td>\n",
        "      <td> 0.49</td>\n",
        "      <td>-0.03</td>\n",
        "      <td>     F7V</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "         Name   HD     SAO IRflag Multiple     VarID    GLON   GLAT  Vmag  \\\n",
        "HR                                                                          \n",
        "1         NaN    3   36042    NaN      NaN       NaN  114.44 -16.88  6.70   \n",
        "5         NaN  123   21085    NaN      NaN  V640 Cas  117.03  -3.92  5.96   \n",
        "8         NaN  166   73743    NaN      NaN        33  111.26 -32.83  6.13   \n",
        "9         NaN  203  166053    NaN      NaN       NaN   52.21 -79.14  6.18   \n",
        "10        NaN  256  147090    NaN      NaN       NaN   74.36 -75.90  6.19   \n",
        "12        NaN  319  166066    NaN      NaN        46   55.56 -79.07  5.94   \n",
        "24    Kap1Scl  493  166083    NaN      NaN       NaN   25.24 -80.63  5.42   \n",
        "26  34    Psc  560   91750    NaN      NaN       NaN  106.87 -50.43  5.51   \n",
        "32        NaN  661  255642    NaN        W       NaN  306.98 -43.58  6.64   \n",
        "33   6    Cet  693  147133    NaN      NaN       NaN   82.24 -75.06  4.89   \n",
        "\n",
        "     B-V   U-B   SpType  \n",
        "HR                       \n",
        "1   0.07  0.08     A1Vn  \n",
        "5   0.67  0.20      G5V  \n",
        "8   0.75  0.33      K0V  \n",
        "9   0.38  0.05      A7V  \n",
        "10  0.14  0.10     A6Vn  \n",
        "12  0.14  0.06    A2Vp:  \n",
        "24  0.42  0.08      F2V  \n",
        "26 -0.07 -0.24     B9Vn  \n",
        "32  0.37  0.06  F2V+F6V  \n",
        "33  0.49 -0.03      F7V  "
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we want to check that the first letter of these spectral classes is in the sequence O, B, A, F, G, K, M"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "main['SpType'].map(lambda s:s[0]).unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "array(['A', 'G', 'K', 'F', 'B', 'M', 'O'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We prepare now a Python dictionary with the color codes that we will assign to each spectral class, which will go from blue (type O) to red (type M):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = {'O':'#0000FF', 'B':'#CCCCFF', 'A':'#FFFFFF', 'F':'#FFFFB2', 'G':'#FFFF00', 'K':'#FFA500', 'M':'#FF0000'}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And we generate a color-color diagram with Python Matplotlib"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(figsize=(10, 10))\n",
      "ax.set_axis_bgcolor('0.6')\n",
      "\n",
      "ax.grid()\n",
      "ax.set_title('Color-color plot: main sequence BSC catalog')\n",
      "\n",
      "ax.title.set_fontsize(20)\n",
      "ax.set_xlabel('B-V')\n",
      "ax.xaxis.label.set_fontsize(20)\n",
      "ax.set_ylabel('U-B')\n",
      "ax.yaxis.label.set_fontsize(20)\n",
      "\n",
      "for cls in 'OBAFGKM':\n",
      "    f = lambda s: s[0] == cls\n",
      "    b = main['SpType'].astype('string').map(f)\n",
      "    x = main[b]['B-V']\n",
      "    y = main[b]['U-B']\n",
      "    c = colors[cls]\n",
      "    ax.scatter(x, y, c = c, s=6, edgecolors='none', label = cls)\n",
      "\n",
      "legend = ax.legend(scatterpoints=1,markerscale = 6, shadow=True)\n",
      "frame = legend.get_frame()\n",
      "frame.set_facecolor('0.90')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Fm+IXXopdadIaOxERESl6FRUVVFVVEY16qU8ymaS1tZXm5uadvxZRDDRiJ4Gn\ndSLhpviFm+IXXoqdJxaLMWLECIYPH86gQYOIRqNEo1HKy8upqalhzz33ZPDgwYXuZt4osRMREZGi\nVF5eTl1d3c5RunTMjCFDhlBTU5PXts877zwmTpxIe3v319DrD0rsJPC0TiTcFL9wU/zCq9RjZ2bU\n1tZiGV6krrq6msrKyry0vXbtWl5++WXq6ur4y1/+kpc6M6XETkRERIpOVVUVkUh2aU51dXVe2r7n\nnns48cQTOf/887n77rvzUmemlNhJ4GmdSLgpfuGm+IVXqceuqqoq631isRixWKzPbd97773MmDGD\nc845h/nz57Np06Y+15kpJXYiIiJSVCKRSI/r6npSXl7ep7YXLFjA+++/z2mnncZ+++3H+PHjuf/+\n+/tUZzaU2Englfo6kbBT/MJN8QuvUo5dpuvq0sl2+jbVPffcw0knnbTzTNtzzjlnQKdjdR07ERER\nKSp9uS5dMpnMed+WlhYefPBBkskkkydPBqC9vZ1t27axdOlSJk6cmHPdmdKInQReqa8TCTvFL9wU\nv/Aq5dglk0k6Ojpy2retrS3ndh999FGi0Sjz58/nr3/9K3/961+ZP38+06ZN45577sm53mwosRMR\nEZGi09zcnPU+7e3txOPxnNu89957ufDCCxk1ahT19fXU19czYsQILr30UubOndun0cBMaSpWAq+U\n14kUA8Uv3BS/8Cr12LW0tDB48GDKysoy3qexsbFPbf7ud79Lu/2cc87hnHPO6VPdmdKInYiIiBQd\n5xwNDQ0Zj5Lt2LGjT9OwQaHETgKvlNeJFAPFL9wUv/BS7CAej7Np06Yef9YrkUiwbdu2Po/WBYWm\nYkVERKRoJRIJNm/eTDQapaqqimg0ipmRTCZpaWmhtbW10F3MKyV2Enilvk4k7BS/cFP8wkux21U8\nHmf79u2F7ka/01SsiIiIFNRQ+4BzK7/H1PI/FroroRfoxM7MxprZPDN73cyWmNkV3ZT7kZmtMLNX\nzOyIge6n9C+tEwk3xS/cFL/wClPsxkRfZ4+yNRwce6bQXQm9oE/FdgBfd84tNrPBwEtm9rhzblln\nATM7CzjAOTfezKYB/wscU6D+ioiISJaWdxxHlW3n/cT+he5K6AU6sXPOvQ+87/+/0cyWAaOAZV2K\nfRz4rV9mgZkNM7M9nXMfDHiHpV9onUi4KX7hpviFV5hiF2cQL7af248tOCoqtlFVtYlotAVwJJNR\nWluH09wLMEBoAAAgAElEQVRcj3OBToeyEppHYmbjgCOABSl3jQbWdrm9DhgDKLETEREpcbFYI8OG\nrSIaTb1GXTvl5c0MGbKexsY9aWwcBVghuphXgV5j18mfhr0X+KpzLt2FZlIjkfuv/0rghGmdiOxO\n8Qs3xS+8FDsoL99BXd2baZK6D5klGTLkPWpq3ulze1OnTmW//fZj/PjxjB8/ngkTJrBhw4Y+15uN\nwI/YmVkMuA+40zk3N02Rd4GxXW6P8bftZvbs2dTX1wNQWVnJ2LFjdw5Vdx4Auq3buq3buq3bxXC7\nU1D6k6/bmTKLU1v7FmaZ/fJEdfVGOjqqaWmpz6qdXds0br/9dk444YSs9tu8eTPt7e08+OCDbNq0\nKef2Acy54A5umZnhrZ/b7Jz7ejdlzgIud86dZWbHALc653Y7ecLM3KxZs/q3wyIiItKvZs6cyfr1\n63stV139PjU167Kqu6Ojkk2bJuXaNaZNm8Ytt9ySVWI3atQorrjiCpqampg6derO7TNnzsQ5l/Xc\ncNBH7I4HPgO8amYv+9u+CewN4Jyb5Zx7xMzOMrO3gCbg0sJ0VURERILBUVW1Meu9YrEWYrEddHQM\nyb3lAg+YBTqxc879jQzWATrnLh+A7kiBLF++PFRnd8muFL9wU/zCK+PYJZPE2tvpGDSo/zs1QCKR\neI/r6npSXt6Yc2LnnOPzn/880aiXXh133HH86le/yqmuXAU6sRMREZH+dcZPf8qYpUt59PLLWTcp\n92nIIDFL5LxvJJL7vmbG7Nmzs15jl0+hOCtWSptGC8JN8Qs3xS+8Mo1dzcaNRJJJBm/e3M89GjjO\nleW8bzKZ+75BoBE7ERGREvbw177G8HffZe0hhxS6K2lF29rYc+VK1k+YgCvLLOlKJmN0dFQSi7Vk\n3V5b29Cs9wkSjdhJ4OlaTOGm+IWb4hdemcauafhw1h56KFgwL8570h13cPattzLtvvuy2q+5eY+s\n22pvryYer8p6vyDRiJ2IiIgEVssQ70SG1iHZndDQ0jKcwYPXU1bWkfE+jY17ZdVGqgULUn8ca+Ap\nsZPA0xqfcFP8wk3xC69iid3zF1zAq6edRlNtbVb7OVdGQ8N46uqWZ3RCxI4do2hry66NINJUrIiI\niASXWdZJXad4vIpNmw6ivb262zKJRJRt2/bxfys2/JTYSeBpjU+4KX7hpviFl2LnSSQq2bz5YDZu\nnEhT0wja2gbT3l5Na+swtmzZjw0bDqO5eUShu5k3mooVERGRohePV7F9+z6F7ka/04idBF6xrBMp\nVYpfuCl+4aXYlSYldiIiIiJFQomdBJ7WiYSb4hduil94KXalSWvsREREpMglqaiYR1XVnUSjK4B2\nksl6Wltn0Nx8Ic4NK3QH80aJnQSe1omEm+IXbopfeCl2nlhsEcOGfYVodE3KPesoL1/MkCE309h4\nGY2NVwHB/PWNbGgqVkRERIpSefnz1NVdkCap+5BZK0OG/JCammvy1u7cuXM5++yzOeCAAzjssMP4\n2Mc+xm9/+9u81d8TJXYSeFonEm6KX7gpfuFV6rEz20pt7Rcwa82ofHX17VRWzulzuz//+c+5/vrr\n+fKXv8yrr77Kq6++yk033cSLL75Ie3t7n+vvjRI7ERERKTpVVXOIRLZmtU919S/71Ob27dv5wQ9+\nwE033cRZZ51FVVUVAIcccgg/+clPKC8v71P9mVBiJ4GndSLhpviFm+IXXqUdO0dV1R1Z7xWLLSUW\nW5Bzqy+99BLt7e2cfvrpOdfRV0rsREREpKhEIhuJRlfmtG95+cKc221oaGD48OFEIh+mV+eccw4H\nH3ww++23HwsW5J40ZkqJnQReqa8TCTvFL9wUv/Aq5diZNee8byTSlPO+tbW1NDQ0kEwmd2578MEH\nWbZsGbW1tTjncq47U0rsREREpKg4NzjnfZPJ3PedMmUK5eXlPProoznX0VdK7CTwSnudSPgpfuGm\n+IVXKccumayno2NiTvu2tZ2cc7tDhw7lyiuv5JprruHhhx+msbGRZDLJkiVLaGlpybnebOgCxSIi\nIlJ0mpsvYejQb2S1T3v7FOLxQ/vU7mWXXcZee+3Fz372M6644gqqqqrYZ599uPbaa5kyZUqf6s6E\nEjsJvOXLl5f0N8+wU/zCTfELr1KPXUvLeQwefCtlZe9lvE9j42V5afuTn/wkn/zkJ/NSV7Y0FSsi\nIiJFx7kqGhpuJ5mszaj8jh1X09Z2Zj/3qv8psZPAK+VvnMVA8Qs3xS/4xpQt4ZLqr3FM+T27bFfs\nIB6fxKZNc2lvP6rbMonECLZtu5nGxisHsGf9R1OxIiIiIbZn2UoqrIVRZaV7eZOeJBLj2bz5AaLR\nJVRV3Uk0ugKzDpLJOlpaZtDaejYQK3Q380aJnQReqa8TCTvFL9wUv+B7pf10WpI1vJs4aJftit2u\n4vFD2L79pkJ3o98psRMREQmxOBUsjU8vdDckILTGTgJP3zjDTfELN8UvvBS70qTETkREJARGly1l\nz8hbhe6GBJwSOwm8Uv69w2Kg+IWb4hcMdZG1nF15G+dU3kK1bcloH8WuC5ekYvMT1L52KSNeOIER\nz0+lbtFZVL8zC+vYWuje5ZXW2ImIiARcs6uhKTmUOBW0ucpCdydUYtsWMWzpV4i2rtn1jtZ1lO9Y\nzJBVN9O492U0jrsKzArTyTzSiJ0EntaJhJviF26KXzC0uKH8rvkm5jTfSJxBGe2j2EH5luepW3zB\n7kldF5ZsZcjqH1Lz5jV9bm/q1Kk888wzO2/PnTuXiRMnsmDBgj7XnSkldiIiIqEQAcI/ojRQrGMr\ntUu+gCVbMypfvf52Kt+b07c2zTB/1O/uu+/m2muv5Y477mDatGl9qjcbSuwk8LROJNwUv3BT/MKr\n1GNX9d4cIvHs1s9Vr/1ln9t1znHHHXdw4403ctdddzFlypQ+15kNrbETERGR4uIcVevvyHq3WNNS\nYlsX0DEs9xG23/72t7z44ovcc889HHzwwTnXkyuN2EngaZ1IuCl+4ab4hVcpxy7SvpFoy8qc9i3f\ntjDndp1zPPPMM0yZMoWDDjqo9x36gRI7ERERKSqWbM5530iiKfd2zbjpppt4++23ueqqq3Kupy+U\n2Englfo6kbBT/MJN8QuvUo6dKxuc877JPuwLMGLECO6++24WLlzINdf0/UzbbCmxExERkaKSLK+n\nY/DEnPZtG35yn9vfc889mTNnDvPmzeOGG27oc33ZUGIngVfK60SKgeIXbopfeJV67JpHXZL1Pu01\nU4gPOTQv7Y8ePZp77rmHhx56iJtuuikvdWZCZ8WKiIhI0WnZ6zwGr7mVsrb3Mt6nce/L+tRm6oWI\nx44dy6JFi/pUZ7Y0YieBV8rrRIqB4hduil94lXrsXFkVDYfdTjJam1H5HeOupm3Emf3cq/6nxE5E\nRESKUnzwJDYdOZf2mqO6LZMoH8G2CTfTuO+VA9iz/qOpWAm8Ul8nEnaKX7gpfuGl2HkS1ePZPOUB\nojuWULX+TqLNK7BkB8nyOlr2mEHriLMhEit0N/NGiZ2IiIgUvfiQQ9h+4MCdxFAomoqVwCv1dSJh\np/iFm+IXXopdaVJiJyIiIlIklNhJ4GmdSLgpfuGm+IWXYleatMZOREREilsyScW8eVTdeSfRFSug\nvZ1kfT2tM2bQfOGFuGHDCt3DvNGInQSe1omEm+IXbopfeCl2ntiiRYw4/niGf/azDHrsMaIrVxJd\nt47yxYup+fa32fPIIxn8gx+Ac4Xual4osRMREZGiVP7889RdcAHRNWu6LWOtrQz54Q+pueaaPrc3\ndepUxo0bR0NDwy7b/+Ef/oHRo0ezbt26PrfRGyV2EnhaJxJuil+4KX7hVeqxs61bqf3CF7DW1ozK\nV99+O5Vz5vStTTP23ntv5s6du3PbsmXLaG1txcz6VHemlNiJiIhI0amaM4fI1q1Z7VP9y1/2ud3z\nzjuPe++9d+ftu+++m0996lO4AZrqVWIngad1IuGm+IWb4hdeJR0756i6446sd4stXUpswYI+NX3k\nkUeyY8cOVqxYQSKR4IEHHuCTn/xkn+rMhhI7ERERKSqRjRuJrlyZ077lCxf2uf3zzz+fe++9l6ef\nfpoJEyYwcuTIPteZKV3uRAKv1NeJhJ3iF26KX3iVcuysuTnnfSNNTX1r24zzzjuPT3ziE7zzzjsD\nOg0LGrETERGRIuMGD85532Qf9u00ZswY9tlnH+bNm8eZZ57Z5/qyocROAq+k14kUAcUv3BS/8Crl\n2CXr6+mYODGnfdtOPjkvfbjlllu4++67qayszEt9mVJiJyIiIkWn+ZJLst6nfcoU4ocempf299ln\nHw477LCdt3W5E5+Z/drMPjCz17q5f7qZbTOzl/2/6wa6j9K/SnmdSDFQ/MJN8QuvUo9dy3nnkcjy\npIXGyy7rU5sLFizghBNO2G17NBpl3bp1jBkzpk/1ZyLwiR0wGzijlzLznXNH+H/fGYhOiYiISHC5\nqioabr+dZG1tRuV3XH01bQO8Hq4/BD6xc849A2zppdjAjG9KQZTyOpFioPiFm+IXXoodxCdNYtPc\nubQfdVS3ZRIjRrDt5ptpvPLKAexZ/ymGy5044DgzewV4F7jaObe0wH0SERGRAEiMH8/mBx4gumQJ\nVXfeSXTFCqyjg2RdHS0zZtB69tkQixW6m3ljA3ltlVyZ2TjgQefcbisazWwIkHDONZvZmcBtzrkJ\nacq5Y445hvr6egAqKysZO3bszjUInd9sdFu3dVu3dVu3dTu4t2fOnMn69espFqNGjeLiiy+mvb2d\nlpYWNm3aBMALL7yAcy7rGcnQJ3Zpyq4CpjjnGlK2u1mzZvVPB0VERGRAFGNid8UVV9DU1MTUqVN3\nbp85c2ZOiV3g19j1xsz2NP8cYjObipesNvSym4SI1omEm+IXbopfeCl2pSnwa+zM7C7gZKDezNYC\n1wMxAOfcLOB84EtmFgeagU8Xqq8iIiLS/0aNGlXoLgRWKKZi80FTsSIiEgQROhhb9jrvJSbQTlWh\nuxNaCxcupLq6utDdyJt8TcUGfsRORESkmEwtv5/Dyp9gVXwyj7d+qdDdCa3y8nKampoK3Y28KS8v\nz0s9Suwk8JYvX17yV1APM8Uv3BS//NvhvKsz7EjW9Ws7xR67yZMnF7oLgaTETkREZAC93vFRVnRM\no53imUaU4Aj9WbFS/Ir5G2cpUPzCTfHrHwOR1Cl2pUmJnYiIiEiRUGIngadrMYWb4hduil94KXal\nSYmdiIiISJFQYieBp3Ui4ab4hZviF16KXWlSYiciIiJSJJTYSeBpnUi4KX7hpviFl2JXmpTYiYiI\niBQJJXYSeFonEm6KX7gpfuGl2JUmJXYiIiIiRUKJnQSe1omEm+IXbopfeCl2pUmJnYiIiEiRUGIn\ngad1IuGm+IWb4hc8Z952G5+96ipq16/vsZxiV5qU2ImIiIRFMsleb79NZWMjte++W+jehFq0tZWp\nf/wj+yxeXOiu5JUSOwk8rRMJN8Uv3BS/gIlEeOjrX2feP/0TK6dM6bGoYtez8QsWMPmxx/jor39d\n6K7kVbTQHRAREZHMbdx3Xzbuu2+huxF66yZO5P399uP98eML3ZW8MudcofswIMzMzZo1q9DdEBER\nEenVzJkzcc5ZtvtpKlZERESkSCixk8DTOpFwU/zCTfELL8WuNCmxExERESkSSuwk8HQtpnBT/MJN\n8Qsvxa40KbETERERKRJK7CTwtE4k3BS/cFP8wkuxK01K7ERERESKhBI7CTytEwk3xS/cFL/g2Kds\nMaPLlmZcXrErTUrsREREAq4u8g6nV/4vZw76EdXWUOjuSIApsZPA0zqRcFP8wk3xC4amZC3bk/Vs\nSY6izVVntI9iV5r0W7EiIiIB18oQ/tD83UJ3Q0JAI3YSeFonEm6KX7gpfn1XbVsYau/3WGawbaLG\nNuS1XcWuNCmxExER6ScxWjm/6kY+VXUDdZF30paptG1cUHUDn6r6NkNs4wD3UIqNEjsJPK0TCTfF\nL9wUv75xGAkXxVFGkrL0ZVyEBFGSPZTJhWJXmrTGTkREpJ/EqeDu5huIWgfNbljaMq0M4Q9N3yFi\nSVpczQD3UIqNEjsJPK0TCTfFL9wUv75rp5p213OZNgZDL2WypdiVJk3FioiIiBQJJXYSeFonEm6K\nX7gpfuGl2JUmJXYiIlIUqm1Lt2eeBkE5zewZeavQ3ZAip8ROAk/rRMJN8Qu3sMTPSPDJqu9wXtV3\nGVkWzJGqMyp/woyq/2ZS7MkBaS8ssZP8UmInIiJFwOhwg0i4MuKuotCdSavNVQHQ7v8r0h90VqwE\n3vLly/XNM8QUv3ALS/wcEe5t/hYxa6XFDS10d9L6S+uXqLJtNLnhA9JeWGIn+aUROxERKQpxKgKb\n1AE4ygYsqUunnOaCtS0DR4mdBJ6+cYab4hduil94dY3dxOhT/NPgr3Ns+ZwC9kgGghI7ERGRIjck\nsgmAmoh+i7bYKbGTwNO1mMJN8Qs3xS+8usbuxfYZPNbyJZ5qvbSAPZKBoJMnREREilySGGsSkwvd\nDRkAGrGTwNMan3BT/MJN8Qsvxa40KbETEZHQidLK4bFHGRFZXeiuSA5GrFrF4Y8+SrStrdBdKTpK\n7CTwtMYn3BS/cAtq/A6JPcm0ivv56KD/K3RXAiuosQM49Ze/ZNr99zPpqacK3ZWio8RORERCZ33i\nQHYk61gdP6LQXZEcrJo8me11dayfMKHQXSk65pwrdB8GhJm5WbNmFbobIiIiIr2aOXMmzjnLdj+N\n2ImIiIgUCSV2EnhBXicivVP8wk3xCy/FrjTpOnYiIpIXlbaNswfdSrMbyp9bv4KjrNBdEik5GrGT\nwNO1mMJN8Qu3bOJXG3mP4WXrGVX2BhUZ/OD8/tGFTIzN23m72hqYUv4ANfZBTn2VXenYK00asRMR\nkbxYnziIp1s/Q4uroZUhPZatti2cMuhXADQkxvB+cjxTy+9nfGwh9ZF3eKz18l3KR+igwlpocTX9\n1n+RYqAROwk8rRMJN8Uv3LKN3xvxE1mTOLzXcs2uhnfih/BBYl+2JEcBsDo+me3J+rSXMJlR+V9c\nXPUNRpUty6o/pUzHXmnSiJ2IiAw4RxmPtn5ll22rElNY1TwlbflB1kTEkhlN8YqUMl3HTkREAq/S\ntlFjm/gguX+huxJKZXQwtuw13ktMoI3Bhe6OZKBor2NnZr82sw/M7LUeyvzIzFaY2StmpsuQi4gU\nmRY3VEldHxxV/idOq5zF9EG/KXRXpJ8FPrEDZgNndHenmZ0FHOCcGw/8C/C/A9UxGRhaJxJuil+4\nZRO/StvGBVXf4mOVt2Ak0papsQ1cVPVNTh/0k3x1UbrRNXbbkyP8f/coVHdkgAR+jZ1z7hkzG9dD\nkY8Dv/XLLjCzYWa2p3NO58uLiAygYZH3GRb5gBq3gXJa0k751UXWMSSymSrbRoQ4yeB/DAXW8LVr\nOeDFF1nykY/QXFvbY9ll8ZN5q3EaHQwaoN5JoRTDETUaWNvl9jpgDKDErkjoWkzhpviFWzbxey9x\nIH9rvZBmN7TbdVyrEpN5uvUzbHN7KKnro+P/8AdGvvUW5S0t/O3ii3e7PzV2SupKQ7EcVamLC0vj\njBARkQAZZu9xTMV9tLlq1jUfQpyKNKUivBE/ccD7VozemjaNQY2NrDpCS8vlQ8WQ2L0LjO1ye4y/\nbTezZ8+mvr4egMrKSsaOHbvzG03nWgTdDt7trutEgtAf3Vb8Sul2NvE76eAIZcR5YdkOlrcuZ/8D\nD+uhvOPzh/6dCmtm1qvHkiQaiMcbptucdBLLTjrJu718+e73s2sMC91f3e4lnsCbb77Jpk2b6ItQ\nXO7EX2P3oHPu0DT3nQVc7pw7y8yOAW51zh2TppwudxJSy7u8YUn4KH7hlk38xpQt4azKHwNwR9N/\n0eKGdlt2sG3mH6u/CcCDLVfyXkKvkXzTsRduuV7uJPAjdmZ2F3AyUG9ma4HrgRiAc26Wc+4RMzvL\nzN4CmoBLC9db6Q96Ywo3xS/csonf+sRBvNp+Cs1uWI9JHUCjq2Nh27lUWBPvJw7oazclDR17pSnw\niZ1z7qIMylzeWxkREelfSaK80H5BxuUXd5zZj70RKU1huI6dlLiu6w8kfBS/cFP8BsakefM49PHH\n81qnYleaAj9iJyIiUsyGvv8+x//hDwC8P348G8eNK2yHJNSU2EngaZ1IuCl+4ab49b8d9fWsmjyZ\nsnicLSNH5q1exa40KbETEZFeldHB9IrfkCDK/LbP4SgrdJeKRjIa5fEvfanQ3ZAioTV2EnhaJxJu\nil+4dcZveORd9o8tYkLsBYaaftgnDHTslSaN2ImISK82JvdhUdvHSBJlqxtV6O6ISDeU2EngaZ1I\nuCl+4fZh/Iy/d5xT0L5IdnTslSZNxYqISODtW/YS08rvJUZrobsiEmhK7CTwtE4k3BS/cEsXv3Ka\nGWrvD2g/pg/6DYeXP84B0QUD2m6Y6dgrTUrsREQkK5+o+j4XVl/PmLIlA9bmax2n8F7iANYlJg1Y\nmyJhpDV2EnhaJxJuil+4HX1QLScP+m/ejR9U0DV2i9rPLVjbYaVjrzQpsZOSt99++9HY2MiGDRsK\n3RWRwNk7+iojy96iNrJ+Z2J3f/M1VNp2trm9Ctw7EUmlqVgJvP5cJzJmzBhOPfVUPvaxjxGN6ntO\nf9A6nzBL8qcle/Bq+ynMb71k59Z2qpTUhYCOvdKkTzIpadu2baOlpYVt27aRSCQK3R2RwDgk9gTH\nlt/D/0aP54X2zxa6OyKSISV2Enj9uU5kx44d3HHHHf1Wv2idT1jVRdZi5jh9YitPtBW6N5ILHXul\nSYmdiIjs5oX282isquX1pukMj6ylMTmcdqoL3S0R6YXW2EngaZ1IuCl+4XTkMY8w5VOPMKjtJ5xf\n9R0+XvmDQndJsqRjrzQpsRMRkd0kk96ETtKVAZDoMsFzTPndzKi8iSG2qSB9E5HuaSpWAk/rRMJN\n8QunBQs+wfLlx7K9Yi/uatpMi6vZed/E2HyiFmdU2Rssj59QwF5KT3TslSYldiIikkaErVtHAbDD\njdjlnidbv0h92Rreik+ljA4SxHJqwSwJOJw/Ktgf+tI/kTDSVKwEntaJhJviF27p4rc6cQSL2s/l\nyPJH+MLgyzk4Oj/resvK2rngguv5zGf+jerqhnx0dTf7Rxfy+eqvcHzF7/ul/qDTsVealNiJiAhH\nxB5mavl9GMmM96mNrAdguP9vNmKxVgYP3kxlZSOVlduz3j8TtZH1mLmd/RQpBeacK3QfBoSZuVmz\nZhW6GyIigTPENnJR9XUAPNB8Ne8nx2e0XwWNjIkuZU38cOJUZN3uiBGricVaWb/+oKz3zUSEDsZF\nF/NeYgItbmi/tCHSX2bOnIlzzrLdT2vsRERK3A5Xx9KOkyinhY3JfTLer43BvB2fmnO7GzeOy3nf\nTCSJsTJ+dL+2IRI0moqVwNM6kXBT/MIgwt/aLubJti9yRPkjnFd5I8MjawHFL8wUu9KkxE5ERHaa\nEH2eurJ3GVu2tNBdkQAaYps4NPY4FTQWuivSDU3FSuDpWkzhpvgFW1lZO8lkFOe87/nz2j7PqLLl\nLO04CfDiF6WNODE0FhAu/XHsnVDxO8ZGlzIs8gHPtH0m7/VL3+koFREpUfX1a7jkkis599zv79z2\nXuJAXmr/OB1UAjCq7A0uqf46Zw36UaG6KQHybuJgWl017yb0hS2olNhJ4GmdSLgpfsFVXb2VaLSD\nmppN0M1lTt5a/jpllqAmsmFgOyd91h/H3qsdp3F70w91UkqAaSpWRKRErVlzOA8//FUaG4eT+j0/\nEulg5Mi3eGrFgfy5ZQJbkiML00kRyYoSOwk8rdEKN8Uv2N59d2La7cceew+TJs3ngAOOY/78Swa4\nV5IPOvZKk6ZiRUSKWpJTB83iE5XfpdK2ZbxX569B9NevQohI/1BiJ4GnNVrhpvgVVow2xpW9zIiy\ndxgRWZPxfs6VAfDccy391TXpZzr2SpOmYkVEilgHlfy19V8YEmngncQhvZY3SxKLtbBgwSfYunVP\nXn99T8aOHYCOikheKLGTwNM6kXBT/ApvdeJISGRW9owzfszo0cv4y18u46WXPq6kLsR07JUmJXYi\nIkXuiCMepqZmE88++2ni8QqGD1/HlCkP8sYbJ7B27aGAY2r5/cSslerKBiIRR1XV1kJ3W0RyoDV2\nEnhaJxJuil9hlZc3cfSBD3Dg8OcYPXoZAJMmzWPffRdz5JEPA97PRE0uf4xJsfksenQGDz/8Vd54\n40RA8Qszxa40acRORKSIJdujJB6JUpaME2+LAfD669OprNzBsmVe8rbD1fNK+2mUWwvvtB1Gslkf\nDSJhpaNXAk/rRMJN8SusJGU0xodTbVtoTtQC0NAwlr/85bIupYwF7eel3V/xCy/FrjQpsRMRCYky\n2jk09gTvJw7g/eT4jPZJEuXe5m8RIb7z9187GQkOiT3JluRI1mVwxqyIBJ/W2EngaZ1IuCl++XNw\n7GmmVszl1EG/yGq/BLHdkjqA/aIvcWzFvZw+6GdYN78Vq/iFl2JXmjRiJyISEu8lDmRrcg/WxvMz\nurYhsS8NiVFsTo7B6Xu+SFEw51yh+zAgzMzNmjWr0N0QEelXQ+19jqm4j7fjU3grfkyhu8N+0UWM\nj77AwvZPsiU5qtDdEQmNmTNn4pyzbPfTVzQRkSJyYOw59om+ypHlj+SpRsfYsUsYPHhzTnsfEfsz\n+0Rf46DoM3nqj4j0RImdBJ7WiYSb4jewlnWcyNsdR/Fi27l5qa+5+X7OPPPHfOxjP8xp/0XtH+ft\njiks7Ziel/5I5nTslSatsRMRCYkRkdWcNuh/eSdxKM+0fQaAU0+dxYgRa/jzn7/C1q0j2eFG8ETb\nP/dYz7hxL3Piib/j1VdP5ZVXzuixbEvLYJLJCNu31+fU5zWJw1mTODynfUUke0rsJPB0LaZwU/z6\nrgZgr+wAACAASURBVKZmAwce+CzJNyJUu63szWtMjM0j7srZe+/XiEY7qK9fw9atIzOqb9SoN6is\n3MHYsa/3mtjV1Z3Gb397IvF4RT4eigwgHXulSYmdiEjATZv2R/bd92VWDZnMc89cQJur5CODfgvA\nXx/6AoPqm3n77aMzru+ll86hsbGO1aszG0nr6Nj9Uin9ZYhtYoerA7JeMy4iaI2dhIDWiYSb4td3\nK1dOYdu2PVi55iiWdJzC6vgRrE9M4J34IazecARLl07HubKM62trG8yrr57G9u179lp2IOM3pfxB\nLqq+lmPL7x6wNouZjr3SpBE7EZGAe/vto3cZkeugkodargLgiNjDjChbwzNtn6HF1RSqi3lRTrP3\nr7UUuCci4aXETgJP60TCTfHrT44p5Q8RsSRr4oezPH48RoJxZa/wQXJfml1tn1sYyPi90H4+q+NH\nsCG574C1Wcx07JWmPiV2ZjYNmIo3pTvfObc4L70SEZEMGH9ru4gRZe+wMn4kAIfH/sLUirlsSIxj\nbss1Be5fdhxlvJecUOhuiIRar2vszOwMM5tnZq+Z2Wwz28s8dwLPA7cB/wO8ZGa39XeHpfRonUi4\nKX59d3B0Pv9U/VWmV/yKf66eyYVV1+28b+/oEvYue5XBkS0AbEvuSdIZW5N75aVtxS+8FLvS1OOI\nnT8i90CXcpOAA4C7gH8EVgMvA7XAicDlZvaMc+7e/uqwiEipGVW2nHJrZVTZm5jBEDb59zhGly0j\nZu0Mj6xjS3IUqxJHMrvpRyQoL2ifRaQwepuKvRoo8/99HDgV+G9gb+Ae4GLnXBzAzI4GngW+CCix\nk7zROpFwU/yyV0EjEUvuPBniufYL+SC5P2vih3JU+QOsiR/mlzQe67iM+vJ3WBk/auf++UzqFL/w\nUuxKU2+J3bHAX51znb8l85qZnQ6cBpzVmdQBOOdeNLMH8EbuREQkBxU08unq/yBCnHuar6fR1dPi\nhrKk4xQA5rV9cZfyJ5z7e2pqNrLx4XG8954+yEVKXW9r7PYAXknZ9pr/74o05d8C6vraKZGutE4k\n3BS/7EQsSYQ4ERJESAIwpmwJ51T+N2PKluxWvqpqO5GIo6pqa7/0R/ELL8WuNPU2YhcFGlO2NQE4\n59rTlG9FFz0WEclZi6vhnuYbiJDAahz71b3IAetfZGTZW7TGnmFd4pAupR3RqPdWHI3G01coIiVF\n17GTwNM6kXBT/LLX6LyJj0+f+U1qajbz9yfOoG1dFUs6PppS0nj66c9SX7+GlSun9EtfFL/wUuxK\nUyaJ3TAz29v/vwFDAbpsI/U+ERHpu8gLCdw2aI4OZVHbJ9KWefPN43jzzeMGuGciElSZTJt+DVjl\n/60EvupvX5Xy13mfy383pZRpnUi4KX7ZOyT2BIfHHqVsQxzrgCHbGjiu/C5GlK9k2rR7GTv2td4r\nyRPFL7wUu9LU24jdOznUmdfEzszOAG7Fu+zK/znnbk65fzrwJ7zEEuA+59x38tkHEZGBUmMfcFzF\n3QC0JQcB3skTdWXvMbLqTeoOX8+ECS9wxx0/KGQ3RSSgekzsnHPjBqgfaZlZGfATvOvnvQu8aGYP\nOOeWpRSd75z7+IB3UAaE1omEm+KXnR2unrc7phC1djYnxjA6+gYrOqZxEM+yrPUEJnzwAmvXHtJ7\nRXmi+IWXYleagn7yxFTgLefcagAz+wMwA0hN7GyA+yUikldVtoWPVMxmY3IcT7T9y87tizq8f5fG\nPwItsOxP0wvTQREJhZwvTWJmXzWzlb2X7JPRwNout9f527pywHFm9oqZPWJmE/u5TzLAtE4k3Iot\nfoPYwYHRv1HhXfkpY+PG/Z099tj9LTNGKwdG/8Z+ZYsYHV3OYbHH6W5Fi1mC8eNfYNiw9xg7dgkj\nR/b/c1ts8Sslil1p6suIXS0wLk/96E4m6/X+Dox1zjWb2ZnAXGBC/3ZLRErV8RV3sX/sJUZ2rOCp\ntkt7KJmk87vz6NFLOe20WcTjUe644xY6OgbtLHV0+f0cUv4U6+IH80r7qWxK7kN3kxAHH/wMJ5xw\nF01NQ6mu3kYyadx11/doahqeUtJ1W4eIFLegT8W+C4ztcnss3qjdTs65HV3+/2cz+5mZDXfONaRW\nNnv2bOrr6wGorKxk7NixO9cgdH6z0e3g3T7wwAMD1R/dLu34bUyOY83rf+f1eCUV+5O2fH399dTW\nvs8ee5zEs89ezAsv7CAaHcQRR+xFPF6+W31Pvm6sTZRx0aF/Y2NiLY+8PjRt+3vtNZqOjnIeeKCO\nYcMSHHtsNW1tVbvUt/ferxCL/Zx16ybR1na54qfbuh2S2wBvvvkmmzZtoi/MudxOYjWzG4BvOef6\n7ZcmzCwKLAdOAdYDC4GLup48YWZ7Ahucc87MpgJ3pzvpw8zcrFmz+qurIlJSeh4R+9znvs6gQc1s\n3DiW+++/Lm2Z0WVLGRddzMvtZ9HshrJfdBGnDvo/2t0gftN0K2CMG/cyo0a9wUsvfZy2tuqM2j7i\niIc5+ugH2LRpLH/8Y/q2RST4Zs6ciXMu66H3QI/YOefiZnY58Bje5U5+5ZxbZmYz/ftnAecDXzKz\nONAMfLpgHZZ+sXz58p3fbCR8ijN+Pb/XPvzwV5k0aT4LF57bbZnjK/7AsMgH/5+9946u67rvfD/7\nnHN7Ry8ECIIkwN4kVpEUKcoqlm1ZlpS4yH6yVzxeyTjJzJvxvFmx56WtyWSSN2syeUnmOcW24qpi\nybJkFUoUSYmU2HsDCYDoIPoFbi/nnPfHAS4BEqwCCUDYn7W4Ls49++zzO3vjgt+796+QNl0cTD9B\nY/Ze3k3CoFGCxxNGCJ1Nm36CyxUlGs3nxImHburex48/RDSax6VL8271ocblkzl/MwM5dzOTjyPs\nfgU0TZAd18Q0zTeBN6947/ujfv574O/vtB0SiURys/T1VfH++1XXbXMq/QDzbAdozN47/I6gMbsa\np3OILz79PVRV59SprRQWNtHUtPym720YNi5cWH/7xkskkmnNbQs70zSPAccm0BaJZFzkN87pzSdt\n/ubMOczKlW9y+PBnaW6+ecFlYXL//c/h8QywY8c3OZPYMk4bAQhMU3DmzBYikQKq1KM86PonjqQf\no0lfed07FBQ0sWnTT6mvX8vJkw/eon1X80mbv5mEnLuZyXWFnRDiItePTDWAMHAc+JFpmh9MoG0S\niURyV7HbY8ybd5CmpuXE46Fx29TUfERBQSvz5u2/ZWHncMSorf0IgIXBXYTCXexN/TZpvLk2yaSP\n55//c4TQczbMt+2jQG1lvm3fDYXdnDlHKSxsQdPSEyLsJBLJ9OJGK3azb7KfVcCzQoj/bprmH31M\nmySSMUg/kenNdJq/tWtfYeHCD6isPMlbb/3+uG0OHHiCSKSA06e3jHteCB3TVMc9l0p52b37a3g8\n/ayKvoVmy+AWg/wm+X+OaZdI+MccH0w9TswMciZ943ueOrUNVc3cxmri+Eyn+ZOMRc7dzORGwq76\nBucVoABYD3wH+L+EEO+bpvnWRBgnkUgkd5NLl+Yyf/4+OjvnX7PNwEA5H344fozW44//JaFQB7/+\n9Xfo768Yt01d3X0AzHEdJU9pp12/8X+8s5edZMnaXUT35RE+UTrm3Gc/+9cUFLTy61//R/r6Kkkk\n/Ozb91s37FMikXwyuVGt2Kab6KMROCCE+CVwBvhdQAo7yYQhv3FOb6bT/F24sP62Aw+EMAiFOrHb\nU/j9vWOEnUqG1fZXiJgFiFoDr3eAVw78EYZxc27OeXntAOTnt19xxiQvrwObLYXf30NfX+Vt2X49\nptP8ScYi525mMmHpTkzTbBNCvIqVc04ikUhmFKap8Oqr/wm/v4e2tkUUFzfQ1TUHUJilnmaZfYfV\n8F7ADu3tC2htXXpTfe/d+0VaWxfR0rLsijOCX//6O/j93RO29SoBn+hBYDJkFk22KRLJLTPRyYWb\nsbZmJZIJY3RWbsn0YybN38BAOc3NK3jggR/w+ON/xapVvwGgU59PU3Y5J9LbOFn3ABcvrrilPHPp\ntJv6+nWk0+5x7llGc/MK7lQJsZk0fwAeMcDT7j/lafef4hUfrwLAZDPT5k5iMdEJiv1AYoL7lEgk\nkmlFJuMAIJu1XtN42J78PevkR5NlleRmMFDR0RCYGIwfBCORTGVuu6TYuJ0JcQaImqa5ZsI6nSBk\nSTGJZGbi93dTWXmSuroNZDKuu3JPIXR8vl6GhoqvOleunsEtBrmQlUmEpyoOoghMkvgm2xTJDGZS\nS4oJIfKAvwYWAN+diD4lEolkIrj//ucoLa3H6+27a9GipqmOK+rsxHjU+bcowiSeCNKuL7wr9khu\njdSovIKS6YOWSpF1OCbbjEnnRgmKd3L9BMUKkA/UADasqNi/nTDrJBJkLqbpzmTPX0fHAvLyOujs\nrJk0G0bQ0axtPjOLbk7pUt05Jnv+JLfPTJq7TT/5CQv27GHns89Sv27dZJszqdzoL8v9N9lPGngO\n+A+macY+nkkSiUQycRw+/FkOH/7spNzbJ3q51/5rLhorKF9zjlg4iGgCIUzMOxTsMNHYiXO/4zk6\n9FouZKfXf5h+0c099teoz66lVV8y2eZI7iCBri6EaRLo7p5sUyadGwm7B25w3gAGgXOmaaYmxiSJ\nZCwz5RvnJ5WJnL9A4BIAg4MlE9bnnWSRbRfzbfspVc/jXTwAwKv1/xE1m6XLuPmo2Mnkc0v6qLV9\nSJV2bNoJu8W2ncy3HSCkdNKamHnCbib97dzxzW9SUl9P87IrUwLNPG6UoHjXXbJDIpFIrovX28dT\nT/05YPL8839GNDr1Myudz24gT2mnMbOKWQ1nGRoqpCt17aoWU5HG7CrK1HO06wsm25Rbpi67gaBy\nifPZDZNtiuQOk/D7ubhq1WSbMSWY6Dx2EsmEI3MxTW8mav503UY2ayebdbBq1es89dSfEgx2Tkjf\nd4oBo4w3k39Ij1JF5dmTzO/aj7XRMX04cq6Ht5Pf5lTmwck25ZbpNyp4M/mHNGRXT7Ypk4L82zkz\nmR7euxKJZFpTXl6O3+/n7Nmzt91HIuHn5z//r4DJl7/8R9jtSUpLzxMOl97w2rtNQHRRpR2jgXuo\nXngE/1AXtt40mkjjtoWJZ/Im20SJRPIJRQo7yZRnJvmJfBJZtGgRjz76KIqiEI/HaW5uvu2+Riov\nvPPOtygqusj581MzF9wm548pUy9QHTpM4apmBmN5cAlMN2Teuzu59CYK+fmbvsi5m5lIYSeRSO4o\nuq7T2tpKMBikr69vQvpsb19Ee/uiCenrTtCaXUJQXCISC1HwUjNG0EZ4QzFDQwW5qhQSiURyJ5DC\nTjLlmUm5mD6J3Kyfzz33eHE6FT76aAjjNt3QFCXD+vUvkkp5OHTo8dvrZAwGa9e+jKLo7Nv3NKZ5\n2S3ZIwZYa/8lbcZCCte0kE47OXjw84DgeOYRjmceYUXmDaodx0j2+Xjthe9MgD13H/n5m77IuZuZ\nSGEnkUgmHY9H4Z57rPJNHR3dpFIX6ehYwK0Wti8paWDx4t0AtLQsQgiFrq65t21XKNTJ8uXvAFBf\nv4aenjm5c/O1fcyzHaRCnMKx2CqRfe7cRiKRwlyb45lH6Ddm0W1U3bYNEolEcitIYSeZ8shvnNOb\nm5m/WMzg+PEoTqfCypX/g4KCFvbs+RJnzmy5pXt1dc3l3Ln7SKVcPPLIP+B0xvnNb/7wtrdtw+FS\nTp3aiqLo9PZWjjlnzgY0yAbsNJ69h0zGSSQyNgWLiUKLPr3zasnP3/RFzt3MRAo7iUQyJdi/PwLA\nI4/4MU1BIuG/5T503cb7738NMKisPIXNliaVco/bVgidhx/+B9zuQd544w9IJv1UVR1hw4YXOHbs\nEc6c2YKiZMnLa0dRsmhamkzmcuBDOFOKuVUw0F7GB298dUzfS5e+y5IlO9iz58u0ti695eeQSCSS\n20XmsZNMeSYiF9OsWbNYsmQJQkyPMk6fJG51/o4c+TRHjjxKW9vHCY5QePnl7/GTn/wVvb1V47Zw\nOGJUVJymoKCVvLx2ACorT+H1DlBVdQwAr7efsrLzlJQ04vf3AODz9bJy5W/o7a3kxz/+a9588/ev\n6ruq6ig+Xz+VlSc/xjNMDWQutOmLnLuZiVyxk3ziEULw8MMPo6oq8XicxsbGyTZJArhcQyQSXq78\nfvnAAz/A7+9F120cO/bp2+5f123ouu2a55NJP++99w3c7sFhfz44ePBxotE8GhqshLaDgyW8997X\nUVWdvj5rK3b9+heoqjpOKNTJe+/9DgA2EpgoZLEiXj/44CvMmXOUs2c33bb9kpmFlkwigIzTOdmm\nSKY5UthJpjwf10/ENE2amprIy8ujp6dngqyS3Czjzd+CBe+zefNPqatbz+7dz44519i4iurqwzmx\ndadQ1TRr1ryC0xmjuXkZQ0PFJBIBjhz5zJh29fVj66Mqij78mgXAK/p4yv1nZEwHL8T/lAwuwuEy\njh4tu6P23y2kn9adxxGL8fQf/zGqrvPiH/8x8WBwQvqVczczkcJOMiPYsWPHZJswrVi7di12u529\ne/di3G7ukevgdg+OeR3NgQNPcuDAk7ljTXNRUbGBaPQSPT2nJ8wGVc3idMbQtDR2uxXVWljYxKpV\nr3PhwlrmzDlKV1c1p05ZpbQWLtxNefm5XB66ZNKK4nUoUWwiiaZYlSUy5mU/vEBAZe1aP/X1CRob\nkxNmu+SThZpO40gkEIaBLZWabHMk0xwp7CRTHpmLaWIJBAK4XC4uXbo07nmfz8fy5csBqK+vp7Pz\n49VjHW/+jh59jO7uarq751zjqsuEQnPIy5tLMDj7Ywk7IXRmzTpDd/ccUikv6bSbl1/+LnZ7glgs\nREXFSebN28/s2ScJBDoJBnupqjpGZ2ctQhisWfMKDkeC/fuf4Pz5DXR0WM9kK0whNoJQDbQ30hC5\nfM8FC9xUVTnx+9VpK+zk5+/OEw+FePm730XRdQaLiyesXzl3MxMp7CSSGYSqqjzxxBPY7XZee+21\ncUVbJBLh6NGj2O12urq67ogdpqnQ1rb4ptoODDTi8RQTjY4vRG+WFSveYvXqX9PRUcPrr/8HAAYH\nrf9EP//5v6CoqJmenorh1pbfn65rPPHEXwAmhw9/jlCog/Pn15NIBHL9dnXN5XTn/aTTrqvSnZw9\nG8fnU6mvn56iTnL3GCj7ZGzdSyYfKewkUx75jXPiMAyDeDyOEIJk8tpi4+DBgxN2z487f9lskqam\nnbd83aec/5s8pYM3En9AxCwkGs0DIBLJv6ptNJpPYWEL/f3lFBa2Eo/7CQS6UdUsyaQXRdE5d+6+\nMYJuBNNU2bv3y+PaMDSk8+674Vu2fSohP3/TFzl3MxMp7CSSGYRpmrz00kuoqkogEOCee+7h1KlT\npG7Sr0dVYdkyL11daTo60mPOBQKXmD9/P2fPbiYWC90J828ahSyztRMoGBSqTUSyhVy4sJ7m5mWk\n05fz2q1f/wu83gHeeeeb2O0pbLY0yaQXn68XIaxAiUOHPoOi6IRCnSxYsIcTJz6FrttvyR4hDJYs\n2UE0ms/Fi6sm+nElEokkhxR2kimP9BOZWAzDwDAMNm/eTEFBAaqqcuDAgZu6trbWzerVPpJJg3/9\n17HbtBs2PE9FxRk8noExka53e/4cjihgwhYTokCzCRetczZbmkzGiWmqBIMdLFmyEyFg6dIdnDz5\nMOvXv0Rt7Ye0tdXS3LyE/v4yNm/+GQCplBOHI0k67eb06a23ZFNFxSnWr38JwxA899z/HJPoeKoj\nP3/TFzl3MxMp7CSSGUpDQwOaptHa2nrT13R2punvz9Denr7qXGPjPQQC3TQ1rZhIM28Jn6+Hp576\nMzIZJxc7VxHI66b7kFUrdiTFSn39at5773dIp61VN9MkV52iqWk5paXn6eurYOnS9/D5+tB1BSHg\n4sUVFBU109FRc8t29fTMpqenkqGhQjIZmadMIpHcOaSwk0x55DfOO8Px48c5fvz4LV0zMJDlpZd6\nxz1XV7eRurqNV71/N+fPZkuhqhkA9uz5MqmUN3fO6YwNv0aH37mcGHlka7W5eQXNzSuYPfsYy5e/\ni8MRQwgTIUzi8SBDQwnSaWu1zefrZcOGX9DSspSzZ++/rl2JRIBXXvnuRD3mXUV+/qYvcu5mJlLY\nSSSSTwz9/bN4+eXvkck4xog6gGPHHqarq5re3krKy88SjwfYvfureL39NDSsHdO2uXkFH330FPG4\nj23bfgjAkiU7sdtTdHTUcOrUg8yde5DZs09SWNh8Q2EnkUgkdwsp7CRTHuknMr252/PX3z/rGmcU\nOjtrKS8/y2OP/Q3ptB1FMdC0LG1tS+jurs61rK4+yPr1L5FMOjFN672zZ+/D6UxQX2+JwLq6DQSD\nl2htvbm0LdMV+fmbvsi5m5lIYSeRSCaU0lI7Dz4YpKmpk9LSP2HHDo2Bge9iGJPz52bVqtdYsuQ9\ndu/+P2huXkE8HiCddhCNhgiF+jBNgd0uANiw4ecsXryLS5fmks1qRCIFOBxtADQ23ktPz9xcv4lE\ngF27vj4pzySRSCTXQrlxE4lkcpHfOKcXxcU2XC6VWbP6CAa7eeKJTuz2+B29p90uWL3aR2np1WlI\nZs8+htMZZ+7cfTz++F9SWNjI8eOf4uzZbYCOECbBoLUsV15+FiEgGOziuef+Jzt2/A5CgBCg6447\n+gxTFfn5m77IuZuZyBU7ieQm8Hg8pFIpstnsZJsy5Tl5MkYmY9LRUUZ+/tdJJr0kk/47es9Fi9ys\nXBlj3jw/P/+5FbFrsyWx2RIoyohoO4/LFaOgoAVV1TFNwbFjv4PHk+DUKWsb9u23f491617m2LGH\n0HU7Q0OlvP3276Io+nW2eCWfNBxYATYpvDdoKZFMPaSwk0x5JttPpLS0lMcee4zBwUFefPHFSbNj\nuqDrcPq0tUI3MLBueP7u7D1N833gHzHNxcAfIITBk0/+OV5vP4ODRcNtRtoKurrmMDRUQG3t89jt\ncY4dm0c4XMrQUAnbt//emL6bmycvfctUYLI/f3cblxjit9z/NwKTF+J/Qtyc3GTbH4eZNncSC7kV\nK5kWlJSU8JnPfIbZs2ff9Xt7PB4URcHtdt+wraqqPPDAA2zatAkhxC3fq6jIxmc+k8ecOTLX2a2Q\nTCaGX2PD75hoWgpFMUilPACk09ZrJuPk1Vf/M3v2fAVVzaAoOqp645XYZcve5tFH/xdeb98deQbJ\n1EBBRyWLShYFfbLNkUhuGbliJ5ny1NbWsnDhQsrKyjAMg+bm5tvuy+VyUVZWRlNTE7o+/h9tj8dD\ncXExra2tVFZW4nRaIstms12zXyEEVVVVAMybNw+w8sQNDQ3l2iiKwpw5c7h06RKhUJZEQh+2SaWt\nzSrptWCBm7IyB4oiuHhx6hSOt9lszJ49m9bW1psuPzbC7a4Y2GyCykoHbW0pUinzum3r6jbS318+\nanVO5Ze//C+4XEMoSobly7fzwQdfprr6GM3NywDIZFy8996f4XanSKUUqqsPcvHiKkxTHfceK1e+\nicORoKrqKKdOPYgQBlVVR+ntrSQSKbytZ5wOzLQVn5gZ4sX4HyMwiJoFk23Ox2KmzZ3EQgo7ybTg\n2LFjmKbJ2bNnP1Y/W7duZdasWRw/fpz9+/eP2+bhhx+moKCAS5cuUVJSQktLC6dPn6anp+ea/S5Y\nsIBNmzYRi1krRqZpXiUEly9fzurVqxkYqCcUOks2a4kVTRO89lofnZ1pjh+PIgTU1SU+1nNONOvX\nr2fBggU0NDSwY8eOu3LPtWt9LFrk4eLFBO+8E75h+56eOWOOE4kAiUSAr33t3+N0xvF6w7z66n/O\nnXc4BA89VIOqCgYH/y2BQCv79n2BEyceHrf/PXu+TFnZeS5cWAfAwoW72bjxF4TDRbzwwp9/jCeV\nTDUi5idXqEs++UhhJ5nyjPiJ7Nq1a9zz27Zto7y8nDfffPO64gtgYGCA8vJy+vv7r9mmv7+fvLy8\nXKCEpmns3bt3TJsvfvGL+Hw+3n//ferq6hgcHETXdQYHu3G7GwGNVMryM3vssccoKyujpaUFwzDo\n74/jdhvEYtaKncej5n4eHNTZvXvwZoblrjIyXtcbt2txu34+AwPZ4XtevU36jW/8CFU9xaFD/4Gj\nR0spLbXz8MMhmpouUVLyXVIpD+m0i1Cok0TCh8MRp69vbPBDNmsyNKTjcin095fj87Xjdg/y9a//\nPkeOPMbx44+Mad/QsIaGhjW548HBYnRdZWCg/JafbToh/bSmL3LuZiZS2EmmPaWlpTidTvLz868p\n7ObOnUsoFOLAgQPs378fwzCualNbW4vb7aauro5UKoXHY/lkKYrC6tWr6e3txe12o6oqPp8PIQSz\nZs2irq6O3t5eTp8+zdBQK2VlBqYJpmltWRYV2RHiNMFgPj/4wQ8wTYOVK71EozrnzycQ4rJjv9er\nsnSphwsXEvT2ZiZsjISAFSu8xOP6ba0Gnjp1ijNnzow7bneK06fjnD0bR1GsVCbd3Wmam60xVdXT\nCBGhoqKDo0dLKSlJYbf/iFmzQrjdfRhGH4ahoWlZ9uz5Mm1tC8lmrS11RcmycuUbDA4W8eKL64bH\n/+soyle5775fYLOlKS29cJWwu5L29kX88Id/O2n5+SQSiWQ85F8kyZTnym+cPp+PZDJJJmMJnzff\nfJOCggLOnz8/7vVCCB544AGEEITDYXp7ewmHL2/teb1ehBDcf79VFioajeL1enOrUy6Xi5UrV6Lr\nOqpq+V9lMhlsNhs9PT2EQiEqKipYtmwZyWQNMIAQKoqyA4gAp4dfBzAMg8pKB/fe6wOgszOFYUAy\nKfB6vSxbBgsXuiko0GhoSNDdnSEa1VAUhXQ6jdvtHuO3d7OUlztYvdq6Z3NzimTy1gXa7Yq6j7Ni\nYBhQW+ti5UovmYzBr35VD0A2+8fYbE3U198DpBDiTeBVhLCeUVFg586v4nJFaGtbQFlZHS0tw8t1\nIgAAIABJREFUywGorDzBPff8BtOEzs55gEIslodhaOzf/wR9feU3HQk7E0SdXPGZvsi5m5l88v8q\nST5RzJo1i0cffZRwOJxLPdLX10df37UjFc3h5TDTNLnnnnsIBALs27ePEydOUFBQwOc//3kSiQTZ\nbBZVVWlqaqK4uBhNsz4eLpcL0zQRQuT6unjxIsFgkLy8PNatW0d7e/uo+5UA4Pf7iUQixOMFBAKC\ncNiKqs1kvJhmPrpu4/HHdZxOha6uGkpLF3DixHv09obRNMF99wUASKcfRlVVwuEwBQUFvPPOO1y8\nePGWxq2nJ017e4pYTL8tUTeZtLWl6OpKEw538dRT3wMglfp7bLZPoeuWQG9qWkZl5TFaWxdTXHyR\nZNLDmjWv4vEMsGLF27jdEZqalrF9+78lm7VjmpDN2vnCF/4bdnuCl1/+HgMDZaTTHs6c2TqZjyuR\nSCQfCynsJFOe0X4iiqIghEBRFLZu3YqiKAwMDFBaWsr7779PJBIhEAiwadMmmpubOXnyZG6VDchF\nuBYVFfG5z32Ojo6OXL8j7YqLi/H7/cTjl6slXJm6pKysDLfbjaJYGYPsdqviQTabxeFwjLmmt9dF\nIHA/PT2HAMhkBKa5jkwmjhDW/T0eS/S5XB6y2QHs9n6E+AdMcy422yMIoebsG7nneNx7770UFRWx\na9euMfanUia/+c2t+8dNBLfq57NkiZuqKid79gwRDmeHRZiJYaSBkeTDfwl0MmvWw9TWHuXw4c/x\nq1/9Ua4PIQyeffYPEcJKewLgclkrnem0CxBks3aEsCpPCDG9xO7dRPppTV/k3M1MpLCTTCtaWlp4\n8cUXsdvtPP7448DlbdHq6moSiQR+v5+ysjKCwSCapuWCIEYEIUAgECA/Px+3250Td7NmWc71eXl5\nqKrK4OAg4XAYXdfJz8/P9QFWShQhBH6/VVEhGo0yODhIQ0MD5eXlpFIp8vLyWLRoEWfOHCEQGKK+\n/hzz58+ns7OTvXv3EolEEGIAr1dlyRLf8L1j5OfbCYePA8eAEyiKBgg+/PBDEonEuAEM5eXlGIbB\n8uXLUVWViooK6urqxrSprHSQTBp0d0+c797toqpQXe2ioyNFLDZWVC1f7sXjUVm0yEV+vo3u7hTl\n5fspKlJyAkxRLgBQUfEudvsgCxfu5P77f0h9/Wo6OxeQzdrQtDRCQDLpxW7vp7e3EoDu7rm88MKf\nkE67ARObLcnQUPFdfX6JRCK5U4iRraVPOkII8/vf//5kmyGZQFasWJHzmystLcXr9VJVVUVPTw+F\nhYUkk0mcTiemaWKaJoqiMDQ0hN/vz/nLXbnFKoTAMAwURcm1GX08smI4cl0sFqOlpQWPx0NlZWWu\nrWEYCCGG236EEL1kMrOw2VYyMDBAKBQim00A76Bpgn37BH5/NefPH6O2VsVmK2HevFcxzeXs31+L\nzWbj8OHD445DKBTi6aefxjAM9u3bh8/n49ChQzkfRICSEjuf+1w+um7y4x93kU5P7ud+zRofK1Z4\n6ehI8frrY4Xql75UiM+nkc0aaJqCru9AVf8X6bQbm81ahTQMFVXVOXnyATQtzdy5B7Db05imFShi\nGAr79j2J1ztAa+tiqquPcPToI0Sj0zsvmUQimTl861vfwjTNW850L1fsJNOC2tpaNm3axOHDhzl6\n9Chg5bYb4eLFizzyyCO5/HGmaaJp2hjBZpomXq/3KjE3+svNeOdG/o1uM/Iai8X44IMPePDBB8e8\nP7KyZ70XwDT7ME1LUCYSfQSDB1BVF+DHNFNEoylOnNjD/Pkuamq8dHZGMc2HME0vtbUd2GwKp08r\n4/rHJRIJotEouq5TV1c3RtCNEI3qJBI6sZiRy583mfT2ZjAMk0gky9e+Vkw4nMVmE8P+hmlcLpVI\nRCcYFGSzZSiKE5ttPpFIO2Di8YQxTUinHXz00W+Tn99CYWELAIYhMAyFOXMOEgp1Y7M9TE3NA7S3\nh4lGp07SZ4lEIrkTSGEnmfLU1dWxadMmFEWhuLiYdevW0d3dzYYNGxBC8OMf/xiw/NuEEGialtt2\nvXI17srX8VbsRguz0a8j27ij29jtdr7xjW8QjUav6lfXW9C0GIbhRFHcmKaTf/mXf2Hz5gWUlaUx\nzQzwEELYWLr0IzZutNPZGUVVBS5XCiFsZLN9BAIaiiKYO9eJ36/R3Z1m5Uovx45FKSy00dGR5mc/\n+9l1xzAa1fnxj7tveswXL3bjcCgcOWIVQw8GNZYscXP2bJy+Pmtre8UKD6YJx4/HxlzrdA6xatUb\ntLYupahoLR9+eBqHY2wpuMbGJI2Nl5g920FtrYeCAhuKAooiuHRpH/H4MY4ceYxk0sdnP1tNaekv\nME1BOPwzwMTnewEhYP78w6xc+Q7nzj1Ad/dshoZK2LDhRcCgqKgFVTUoLu5AVWspKxuitPSXNDbe\nQ2dnzU2PxUxH+mlNX+TczUyksJNMaTRNw+fzsX//fuLxONlslrVr15LNZnNRqxs3buTUqVPs3buX\ngYGBXEmvK4XW9RivruuIkBsdrHBlu0AggBCCQCBwRX+gqicAK2WHokAyeYilS2cTDPqAQYQYsUlQ\nVNSHEFBU5KKxMcahQ0PMm+emuztNJmNVp7j//gBut0o67cJuV9i4MYDdrjB/vsEvf9kzHGSg4XK5\nGBy8dpJjp1PBbhcMDY1fUs3rVXMRud3dOrGYk1WrTObOdeH1qrz11gD5+Rpr1lj+hS0tqVwyYYDF\ni3ezZMlOqqtP4XY/SCTiobVVIZEwUFVLJI6Iw+bmFHv3DhIOZ9E0gculsHLlv+Lz9ZFI+GhpeZzG\nxiSlpd3o+hCVlc8DkM2qaJqOz9eNELBw4XaEgGg0yL59nyeVcrN5syV2z5yJoOth8vNfZcmSXRQX\nN/Dyy98b88yalsLv76G/f2wSY4lEIpluSGEnmdI88sgjlJWV0dzczOzZs2loaKCjo4O+vj4WL14M\nWAmKFy1aRGtrKxUVFaTT6dz1422hjvx8LcEXj8dxuVzE43G8Xi/ZbDZXHuxGAnG04LxwQcfjyeLz\nqfj9Gk6nk/Xr0xjG0HBCYoWBgTAulw9VVbDbDez2LNXVLmbPturFmib88z9fAuDUqRgVFU5sNigo\nsBOJZEmnobs7zW//dhGGYZJK3YfPV8Abb7xBW1vbVfYpCjz1VAFOp8Krr/bR03P1tm0spnP+fByH\nQ+Hee7dRVFTGsWPb6e6OcO6cldw4HM7S2JjANGFwcGxliMbGVZSXn6G5+R5KS5OUls7l/HlLaG7b\nFqKqyslHHw1x8mSM4mIb69f7h0WfwGYTnDy5jZKSw3i99/Hkk4X0938I/CWKUoJpKoDJuXObyMtr\np7i4EVXV0XUrGbGi6Kxd+ysAzp1bh88X5uLFGhKJBIWF91JY2EBd3YarnvmRR/6OsrLz7N79Verq\nNl53jmcacsVn+iLnbmYihZ1kShMKhQByK2JOp5PXX38dRVFykalVVVWAJfCAMelNbsR4K3XpdBq7\n3U4qlcLr9aLrek6s3YjRq3v5+Zvwer1kMvuxVui8QD+gAALQ8HgOo2kmPT0pSkpsGMbI9ZaAGW3e\nsWMxjh2LsW6dj4ICO319Ar9/A0I0AdY2q91uxzTNXMqVEex2wbZtIRKJ8VfpxhsXqyKDJWT7+zUO\nHLicK1DX4d13x9Zv3bDBR22tm3PnfAjxVyjK1f5sBQWWQJ4718m993rp7c0gBLhcSi6go7DwafLy\nvkw8nhq2pWX4tY9s1gaYLFr0AULopFIeVDVGLBYkEOglmfTickWsEVRAUS77JPb0zBlTK3Y0t+Gf\nLJFIJFMSKewkk46macybN4/29naKioqIRCJ0d1tCxeFwsGvXLtassWp0KorC/PnzyWazVFdXA5fF\nx3h53kYHMYznb3flz2CJSEVRcmLSZrNd5XuXSqVwOBxkMhnsdjuGYaCqas6vDyAvT0GIHmy29UBi\nWLS1E4m4OXjwKELE2LbNEjrptI1Dh4bw+5dTU+MkFovj9zdjGCYbN5YO+7t1UVho48CBCOfPJygv\nX0BNTQmhUIgzZ94lmzVYvjyLEG1o2lhxW1Rkp6LCEnvPP9+NYUAkMr7I83hU5s93AfCb37xHLOYY\nU6njWsyb58ZmU6ipsfzzfD4Vt1tl166TuFxFJBIGTqcyPDYamqZQXGwfHi+T06ejOJ0qCxf2oyhn\ncTqX0dz8NopyBkvfZ/jgg68DsHXrD4e3u3XeeeebtLfP47HH/l/q6tbT01ONYah84Qv/DSFMysrO\n09Cw+rq2v/XWt/H7exkYKLvhc840pJ/WxOHv6qL0wgXq165FH94FuJPIuZuZSGEnmXRWrlzJypWj\n04Bkee6559B1nYMHDzI4OEh3dzdVVVW4XC62bt1KOp0eI7RGc+X7422fjhZ6Vwq+lpYWvF4vp06d\nYunSpZw9e5b58+fT1dVFLBZDUZRcipWioiJKSkro6ekhEAjQ1tbGrFnl6HoWl2svqmpiGMtRlErg\nBNCMaUJj4yXcbgXTLEYIyMvTqaz009s7BCwhkeigsfE0oLF8uRMhDMrLC3A6re3KM2filA1rECEi\nLFtm1afV9b3Dz3e57JgQ0N6e4vDhCLGYzuDg9VftolGdvXsHsdsV2tsTwI1rywoBe/cOsmKFl8OH\nI5SWOmhtTVJUZOfEiRhOp4EQ1jZvIKCRzZpo2mX/Q9OEVat8w3PxHaCNwsJ52Gz1pNMrgHlks1U8\n8MAPAYjH83E6B9i+/fdob6/lqaf+hLy8TgoKXuSf/slKa7Rz57OEQh00Nd24PJiu26Wok9xxPvWP\n/0h+WxvuoSGOfvrTk22O5BOKFHaSSaenp4dsNsulS5dwOp2Ew2GeeOIJXC4XL7/8MrFYLCfkDMPI\nvcLY1CPXSkky+r2R45E8c6P950baaJpGXl4eBQUF5OXl4Xa7efXVV7HZbDz55JMoikJ/fz9lZWWc\nPXuWwsJCenp6KCiwhJfD8QGmqZDNelGUJENDJqGQSVdXkvJyk7Y2KxdbMmmQyRjYbALDGEmrog8/\nn+DAgQiBgJPly93DUbaWeCsrs7Nhg58zZ3pIp9N0dg5QUeHENAW6HkNVBbpu+b1VVjp46KEQTU1J\nysocxOM6588nuFHZ19On49c973AInnyyENM0GRrSKSqy8frr/fzyl71UVDhYtMiNx6NQWmqnuvoe\ndN3E79e4eDGBx6Ny6VKa2bOdpFJWrkAhrO1dRTEBy3ZdTw+Lvwim2YaiuAiHiwETpzOGEAZebxdQ\nS2fnPEKhTjIZ58hMsnjxLkKhTurr1zIwUH79B5ZcE7niM3F0VVfj7+6mp7LyrtxPzt3MRAo7yaTT\n1NTED37wAwA++OADHA4HzzzzDKqqMnfuXAKBAHa7tWVniQAxRozBzacyGfGXy2QyaJqWE3gjYlFV\nVQoKClAUhdLSUoQQFBQUsGHDBiKRCF6vF0VRUBQFTdPQNI1IJILNZkPTNPLzvShKGtMUCLEWIfx0\ndZ1FUQY5ebKbt9++lLPZMOBHP+oC4Ld+q2DYdmPYzhTr1/uJRhVM81EURcE0f40QglBIG7YrzYUL\nL9PRkaa3V0PXTZYv9w5H6Wps3uwknTZQFEFeng2nU8FmEyxevACPJ0h7+zGqqhwcORK5qvrDlYRC\nGkuXejhzJk5vbwanU8HjGSmnpmCzKVRUOFi40E0yaQVCFBTYcLlUHA4FXTex2RQuXkzx/vtD+Hwq\nyaRJYaGGx2PlG/zZzy6RTMI3vzk4nGQ4ixCQyeg4nSkUpYVotAQhIBCwomFLS5uoq9vM3r3PsHfv\nM6N+Hwzy8jqw2VLMnn2cpUt3cPToo0QihahqhtWrf8XgYBFnz97/MX5zJZJbY89XvsKer3xlss2Q\nfMKRwk4y5UilUrz22ms4HA42btzI4cOHWbZsGQAul+X7NdqX7WZSmYy0icVi9Pf34/P5cLlcuRqv\nQgi2b98OwEMPPQRYEa47d+4cjmZdj67rvP7666iqSjQaJT8/nw0bNuB2u7HZbOzYsYOenh7mzPGS\nTusMDGynoMDLvfc+gN1u57777uPFF18cY9vItYpiPculS5doaTlMOt3A+vUeDMNkz57XhrdFw8ya\n5WPpUktQhUI2iosdzJ9vpT8BeP/9MIGASlmZg7IyB3V1cQ4eHOLixRRz5jhIJGDz5oeADHPmtODz\nmWQyBv39WZqaEowEFHs8Cqp6OSXKqlVe5s514XYrvPXWADabyI3/rl0DKIpg8WI3ZWUO6uvj7Ngx\ngKLA1q0h3n//FPF4Gaoq6OnJUFPjoqDAxoIFbnp60vT3Z+jtTaOqCoGAYGTrV1WT7Nz5LC7XPaxb\nd5hsNsWsWT8HIJP5NDZbjJ6eZxmpHzsa01R5++3fJRTqYP78AxQVNaHrGnv3fpnKypMsW/YuABcu\nrCWbdV51veQy0k9r+iLnbmYihZ1kSjISPDEiyEYE3eiI1ysF3ZV+c+O10TSNOXPm0NvbC0AyaUVu\nxuNxWltbAauKRUVFBceOHaO5uRm3282sWbPo7u7m0qXLK26Dg4MEg0GWLVvG6dOnaWhoAOD4ccu/\n7ctfLsLrzRCNtmO3z2FoaGi0KaiqylNPPTUsLt8EDObNK8Tluof+fgdwESEUNm0yAJPBwfUEg/MJ\nh1/H7ba2ZkFB0wQNDQmyWZONGwMoiiASsbYzg0GN2lo38+ZlCAatj3s2uwNVNbh4MU5BgUpxsZ1l\ny7zEYj5++tNuHA7B008XommCl17qJRzOcu5cHLdb5cwZa4vWZhstlnV6e7Ns2GDltcvLs/Hee4Os\nW+fNbX8nkwa9vVmeeaYIt1slm7VWCPPzbQhhrQhWVbnQNIFhVKGqLXR2fpYLF9azYcMZ4Eeoah4j\n06mq+4B+gsHlwOKrfn+E0HnggR/gckU4evRhdF3j/Pn1AHR01NDcvJTBwSIp6iQSyScOKewkk05x\ncTEbN26koaGByspKwuEw5eXluZQjW7ZsQdetlSNrS3L8YIjxGF0ndrw2yWSSVCrF0NAQW7duxePx\n8M4775BKpXJtFEXBbrfnVvdGc+TIEY4cOTLuvXXdxDBMBgYG8XoZIwovt9GH7VIAg2zW+khmMgqm\nCYahoSjpXFuAcFghGNSJxw1cLivwYMcOK2p13jwXpmkSjxv4fOTKhzkclyOFU6kMLpeCaRo4HDYy\nGUtkGYbJ5z6Xz+BgFsOwtooXLnRRWupg164wr712Od2JYVweyyVLPASD2rBYU9F1ky98oYB02rJ3\ny5aluSCJ4UfICbTLYtwaL0UR9PX9PxQX22lsHACSwz52AMlR93cghCCRuFZqG4Gu2zBNQWvrMg4d\nemLU83t5++1vX+M6yZXIFZ/pi5y7mcmUF3ZCiEeAvwFU4J9N0/zv47T5W+BRIA48a5rm0btrpeTj\nUFNTQ35+Pk6nE4/HQ3FxcW7F7VrVH65clbtW+pKR61599VUcDgcrV67E4/HkzttsNvLy8igpKcld\nV1hYOCa5b3l5OcXFxRQUFPDRRx/d9HO9/HIvDodCLNaF3193VTUIXdd58cUXsdlsZDIJZs/2c+FC\nJ4FAJ0NDQ9xzT5BIJML99/sA+PDD/USjR9E0HVUtpr8/QX4+udUvgO3b+ykpsdPSkmTVKh+RSJby\ncgeKcnlcDh+OoGmCRYvcBIM26ut7icffoqNjKbW11RQV2Th2LIrTqQxvv6pUVjro77+ciDibvdxf\ndbW10rZ//yBer4ZpGixZ4iOVUkfNozlcls1SdJFIhGx2B4cP15BMFhOLGei6iaoK3O5WKisbaG/f\nwOLFfpLJ1HBwhY6m/R0Ab7yhAmGGhgSLF79Hff0aUilvzj7TVHjppf+C3Z4gFsu76TmTSCSS6c6U\nFnZCCBX4O+BBoB04KIT4tWmaZ0e1+TQwzzTN+UKItcD/BtZNisGSW2YkWAGs5MMjjIg1TdPYtWsX\n999/e07u+/fvJ5VK0dXVhaIoxONx5s6di91up6CggGw2y8GDB4lEIiiKgtvtpr29fUwf9fX1eL3e\n3PbtjbHETCZjkslYS1TXKvGVTqdzlTIuXLBW3aLRKNXVDlatcpLJ2HNjkcnoRCIRvvSlWny+IYqK\ngkBqOFDD6m/bthB2u8LixR7sdiWXkFjXR6KBTe67L4CqXt6unTPnZVT1dWpqlgB/gWnCypVWEEYq\ndVk02u2QToOqwsKF7stPO3zvefPc5OfbSKX04fbWiZ07T7B8+ULcbgVNU1AU8PlewmZ7lW3bFvCv\n//rvMQwr4AFMPvOZ7xMMdjF7tklh4Rfp7f001spdLXv3BhACLl2KA3k89NA/UFV1nIKCFnbvfnbM\n2GYyLjIZ103OmeRaSD+t6Yucu5nJlBZ2wBqg3jTNJgAhxC+Ax4Gzo9p8DngOwDTN/UKIoBCi2DTN\nrrttrOTWuO+++1i0aBHNzc0UFBSQTCZxuy8LhitTmVzpNzdeKpMry4QtW7aMTCbDwoULCQaDvPrq\nq+zfv5+Kigqqq6tpbm7m6NHrL/Dqus7hw4dv6pk0zcWiRU9iGFnOnPklhnF1ya7rsWDBAjZv3sy5\nc4eIx9vo7c3gcqnYbCKXUDiViuPzQTKZxTQN2ttTfOUrRei6iaaJ4a1YHZvt8ni5XOpw5C/DW9NW\nKTK7XSGbXYDbvQNdX0IqpTM4mKW01BKU2ayB3S6YN8/FmjV+enrS5OXZGBzM5MY5mzVQFCXnwzfa\n/27k9c03e4nF4JlnilBVBSF0TBNstgxf+1oxQ0NxbLZ/h8MRI5NZiWnGgBoAFOUipvlrstlaTp+e\nPWa8OjpqKCs7R2fn/FsaZ4lEIvmkMtWFXTnQOuq4DVh7E21mAVLYTXGCwSBCCAYGBti+fTtbtmyh\npqZmTHoS0zTZunXrGLE2Xo3XK98bOR6JfHU6ndhsNrxeL/39/bS2tvLcc89d175QKMSKFSs4e/bs\nuP5x42G3u6iqcqPrBnV1tlsWdsFgEAC3u4if/GR8371MJg0oJBIZnn++i2BQ4+mnCzBNa/VMCEF/\nf5aengyzZztylR0ub4VaEa2GYeXSGxhYTVXV8wwMpHnllW5cLoVnnikaXjG12nq9li9bIKChqgKP\n57Jvm6qKMdusVjmyy9vlW7YsJRz+FS5XJ4ryLEK4URTncMkyJ3b7PxIM5iFEP6qaRdc/ixD/CZtt\nF/BNNG0hQmSAltw9N278MaWlF3jrrW9z6tSDtzTGkltDrvhMX+TczUymurC7fh6Ly1xZfuBmr5NM\nIu+99x6lpaU0NTVRUlJCe3s7NTU141aTGI9rRb4ahpHzrXvttdfIZrNks1m8Xm8u8vVmWLZsGfPn\nz8fj8fD666/f1DU+n42SEgAFl0sjc2u6joMHD9Ld3U1HR8c12/j9ASCC3++nsLCfoaEsO3eG0XWT\nbdtCw5UsbASDWi4wYoSRtCpg+capqshtmZom5OdreL2XfeNG0qiMuDjqusmpUzE0DRYssHwVb1Sa\nV4gUodAPh++xFNiEEF8EqtH1ITTt/0PT4IMPvoXDkWLxYmulzuV6DujDbo/y9tu/Szhcmutz4cI9\nCAFr1rzCjh3fur4BEolEMoOY6sKuHagYdVyBtSJ3vTazht+7ih/+8Ic5fy6Xy0VFRUXuG01dXR2A\nPL7Lx4lEghUrVhCPx4lErOLtpmmya9cuALZs2cKuXbtyqz8jvnajz8diSQ4c2EdnZydPPvkF6uvr\naW9vp7e3l1gsdtv2dXd38+yzz3L69Ombvl5VVS5cuMDevXs5deoUNTU1t3z/xsbG654/cSJCJNJA\nT0+cr3xlKYODWQ4fPoOiwIkTdkIhjSNHzhAK2Vi1agE2m8KOHSdQVcHmzUsRAnbvPkk6bfLww8vp\n6Umzf/9ZBgYy/Jt/s5ZkUmfnzhPD47sUsNqrqsLmzUtYssTDjh0naG012bx5KaoKe/acQtcNtm1b\njq6b7NlzCsMw2bp1Kbt2nSeV2ojN1sf99y9D0+Cdd85itwdYvXoxmnaMHTsUFOUHbNyoc+5cLSdP\nJgmHl/O1r+3j9OktbN/uAsLU1hYD8MILVQQCXcTjD9/R3095XJv7earYI49v/njkvalijzy+8Xyd\nP3/+Fvy5x0fcKLnrZCKE0IA6YBvQARwAvjRO8MS3TdP8tBBiHfA3pmleFTwhhDC///3v3yXLJbfC\nunXrWLZsGYlEYkwAxQi7d+9m8+bN10xZ8uKLLxIOh5k3bx6rVq3iwIEDNDU1oap25s59CF1P09Dw\nDh93IVcIlblzP4UQKg0N2295m7W42MbmzQHOn09w/Hjspq5RFPjUp0LYbIK33x4gkzGprnZy770+\nmpuTLFvmobc3QyCgYRjQ3JwkP9+GYZgUFdnp7U1TUGAnGs3i8aiYJsTjOi6XSiSSJRi0/OX8fo1E\nwsDpVIjF9NzWq/XcAl03UFWFbNaqKjGSvgRgYCBLMKhhGCaaZrXRNAVdt6pe7Np1kvb2YuJxgy98\nIZ+CAjsdHSnKyhxcutROcfH/GK6ZewDIsn37H9HcfHdKLklujHTAn77IuZvefOtb38I0zZvbwhrF\nlF6xM00zK4T4NvA2VrqTfzFN86wQ4lvD579vmuYbQohPCyHqgRjw9Uk0WXITlJWV4ff76ezspLKy\nErvdAZArG3ZloMSWLVvG1IYdEXgjbfLy8giHw9TU1BAMBpk/fz5NTU04nSF8Pquwu83mJpO5OTF1\nLex2L4GAJTiczgDx+K19q6qsdBIK2Zg/n5sWdh6PyuzZltitqnJitwvKyuwEgxrhsMahQxE6OtKE\nw1lME555phhNEwwMjIhOa4w0baRSh8mRIxE0TWHFCmsr1eezSpS5XArRqE4kksXns/40XCmkEwkd\nn882XH7NUnY9PWnq6xPce6+VlmVku1dRLF+7rVuXcfx4BJdLzZ3r78+ya9cgS5acoqSkHmhACOte\n6XTimuMxZ85hhDBpbLz3psZP8vGRwmD6IuduZjKlhR2AaZpvYqXlH/3e9684ltlGpwmNzSFSAAAg\nAElEQVSqqvLoo4+iqioDAwOEQiEGB61qBqO/mFwrN93Iq2EYNDQ0oCgKjY2N494rFuuitfVDstnU\nxxZ1AKnUIE1Nu1EU7ZZFHcCJE1FM06SlJTXueSuf3dhVwEhEZ/fuMJomWL3ah9ercvhwhMHBKLpu\nsnq1n1hM56c/tSp1jPi7DQxkaWlJoWlQUGDLVakA2LgxiKKMrqF7+X4+n+VjNzL+I5G2I+Pu9Vpi\nEkzuvdeqNFFd7cJmU3JJi0f319ycJJHQWbbMO5xQWM+1iUZ1urtXAV/FMAIoyt8N14i9nLPQZkvk\nUpYEg5186lP/CMALL5QRDpfdwuhLJBLJzEC5cROJZOLQdZ22tjaGhoZyq3AjYkQI86oUJgA7d+7E\nMAxM0ySTuZxmY9euXbz33nu59k1NTSSTSZqamnLvdXefor//woTZ39dXR0/P6du6NpUyOXQoSnf3\n1Vu4W7du5dlnn6W6uvqqc3V1CU6fjtPSkiQW02lpSXHgQITm5hSxmE5r62WhaBjWGDqdCsuXezEM\nQTyu09GRxjRNdN0cjka9nI5kRJDpujmcNobcuba2FPG4TjptzVU6bXLkSDTXn2maXLqUJhrVcznv\nMhlz+F5QUmKnoeECyaSBYZi5FTu/35r0nh6ToaEnaGvbwKVL8xgYKGVw0PKjW7r0Hb7+9X/H6tWv\nABCLhejpqaS3t4JYLHRbcyC5dUb7/0imF3LuZiZTfsVO8smjo6ODRCJBVZUlYuz2y7+Go9OWjN6W\n1XWTnp4Y2WyUWbNKUBSFNWvWIITgwIEDmKZJX18f7e3tDAwMTMpzfRz8fj9CCPx+/zXb7NkzxJ49\nl+vN9vZmcit1I6TTJi6XVT8WrJxyI1u1VkoSq91ov7lMRkdVFZJJg7a2NImEzooVVhWHSEQnk0lT\nWKjhdKqk0wZbtgRIJC5H2x45EqWrK8ODDwaprnbR12flwYtGLd87l0vhhRd6SKVMvv51S7SNlDiL\nRHR+8Yue4Z6+c8WY9Ix5zWScvPLKd29lWCUSiWTGIYWd5K6iqirr11vF2EcEnKbZgKu3W0ewfOxM\nSkq8xGKWP56u66xYsQKAhoYGent7WbVqFRUVFQghePfdd+/K80wU27dvp6ioiJaWlhs3vg6/+lUf\nfr9KT0+GsjI7RUV2VqzwEotZdW5HpzvJZKzVUk2zRJbHo7JggXtMf/Pnu3A61dyqnterUlPjJpMx\ncvPkdjtZuDCPDz/sob8/w8mTMRYv9pBMGmzeHOSBB5bxk590ASaRiE5enkJX140DT/bte4r29oW0\nty+46pzLNYjf30NX17wb9mOz2SgsLKSzs/OaNYUl10b6aU1f5NzNTKSwk9xVdF3n0KFDhEIhZs2q\nxOGwDVeccA0Xp3dgGCZXaLvhcloZzp8/R2lpkP7+flauXIkQIleSq6tL4PdDd/ctBxFNOolEgubm\n5o/dTySi5ypUNDWlGBjIEgppNDcnKS21k0waLFniQQhBb28aXYdwOMOSJV7a2pJks1Zgg8MhcDgU\nysrsOJ3ktlENw6rnqqoit227bVseipJmyZISQiGT0lIH5eUOenrSnD8fJ5EwiMetFb7Dh6PU1Lg4\ndy5+w2fRdTtNTSvHPff443+F39/Lu+/+Do2Nq6/bz7Zt26isrOTw/8/enQfJcV8Hnv/+MuuurqOr\nq+8LfQDdAAgSIMVLIiWK1GlJlCiPPdaMPZrw2qGIiZ2Yf2ZiJnY3Yv8d78Y6Zjb2D2tsD2WZEmXK\ntmQdpkRKIkhJFCmCIIi7G2j0fVYfdd+ZuX9kdQHNbgCNq9GFep8IRXdVZWVm9aPhV7/jvXff3XYH\nESGEqFWyxk7suHA4TDgcJp22pxUdDns0ye12VqZeNx5/9OjRStFdD93d3czOhonHA9U+q0eOHOH3\nfu/3MAydixchlTI/eMm65fHYrb5CIQfhsINg8PJuV79fp7vbjdNplziZnS3y85/HOXUqzd69Xvr6\nPNVj02k7WczlzOpavWLR/h/Y7cE8Hns0zOu1/1nJZk2OHk3wzW8e45lnwnz5y1GWlor89Kdr+Hwa\nf/AHzdx/v/+mPlc+34BpahQKDdc9NpfLbfgpboys06pdErv6JCN24o5yOBwcPHiQ2dlZotEoxWKR\n/v5+NE0jm81VjrETgStbUa0/Xv9d1zU0TdHWFmFpScPl8vPii98C4Ctf+Qoej4dQaAW7HrFMt63r\n6/MQCjkYGrKnVC3L4rvfXcTh0Hj22WY0TbFnjwefT6e/34tlXe4tC3atOqUUMzMFZmeLvP9+ptK3\ntlydiv3DP2zB6dTQdXvnrcOhOH48xeRknuFhL8mkk74+D5qmaG52kcnk6elxEww66O/3YBgWiUSZ\nmZnitj/XD37wH3G5cuTzV1+TuO6NN97gnXfeIZu9/iihEELUul1doPh2kgLFd8fhw4d55JFHSKfT\nNDQ0YFkW4+MWTqdGZ+fl6b318hsfTOxGR0dpbW3l2LHjHDnyMO+++y6lkp9kcolk0m5C0t7eTmtr\nK6Ojk4RC/Swvn78t5U3uBV/4QoT2djflssWxY0kyGZOxsTwAAwMehod9vP12kt5eD8WixeOPBzEM\ni3PnMng8OgMDHpRSFAombrfGxESeV17ZuDmls9NFNOpkdbXEAw80sLZW5uBBP+m0US2d8tprCbxe\njdOnM7hcdgJ/4IC9Vu/xx0OYpsXzzy9gGHfjrySEELvPPVmgWNS++fl5UqkUk5OTtLW1kc/nWVlp\nQSknHR12Q3p7hG1j39dEwsDl0jh+/DTJ5FE+/elPE4k08PDDDxAMBonFYvzTP81UrzE/Pw9ANitr\nqK40NpantdXF2lqJhx4KkMuZTEzkMQz7tfUkb3k5TSCgs3+/j9XVEm++abd36+xsqXajcLu16q7a\nK83OFpmdtUfbpqdXaW110tvrYW6uwN69XgwDJiZylMvwsY+FGBry8frrcY4fT9PQoDM8XCYeL0lS\nJ4QQt4EkduKOWlxc5MUXX6w+Vkrj8OF/u2nn6+XX7RG7fF6nVFIcOvRRLl58n1DIrlvm8XjQNI1w\nOExX1+MopZie/g07Nf3a1fUYSulMT7+5Y9e8Effd56OlxcWvf52gULA4ezbL2bNZWlqcfOlLdp9k\nTbPLxzz6aIDBQS+vvx5nZqZIKmXw0kuxDef727+1y6l88pNhIhEnDofimWfCzM0VOXdu66nNxcUS\n3/72EtGok337fLz++kk0rQWw1/UB1Z/ptMF3vxvb8jxid5C2VLVLYlefJLETd1ww2EU2u0K5nAMU\nwaADr/fKIzYmeUop2trs38PhJmIxnURCcfHiMQzD4NFHH8XhcNDaajeoX1kZvalOEDfK4wnT2no/\nAKurF8hklq7zjp332GNBNE0xN1fg/PnLmwWWlkp8//vLFAompZKdkB486K92tJiZWdlwnsZGBw6H\nIp02iEQu/zPh9Wp0dLjp7nZXEzu/XyMYdDA/v3GN3PJyie99b5kTJxJ0djYD8POfr9HS4mJmZuvu\nG0IIIW6NJHbijmpuPkhPz0fIZpc5d+4fcTqd7Ntnr6WrNJ6oNpQvl2F8fJRUKsWRIw9WRpbKPPXU\nU5w8OcLx48dpbR1idRUSiTKLi2dRSpHNrlz7Jm6TfD7O4uJJlNLJZHbnKNNbbyVpbnYxPp7f9NoH\nO16cOZNhcNDLO++kNjzv8Wg891wUXYdk0iAUcrC8bL83lzNZXc0xN3c5ifvSl6L4/Tqvvrq26bqx\nWInOzsu15goFa0OnDLH7yYhP7ZLY1SdJ7MQdpWkOLMvC7fbwh3/4p5TL9vN2Kys7oVvvR1os2qVN\ndF1n3759uN1uUqkCbreDeDwBQCaTYGzMIJdL4Pc3AwpNc2Ca1y94ezvMzLy1I9e5WadPZ4Ht7f58\n++0Ub7+doqfHzb/+1y2cOpXh5MkMhmGRz5s4nZfr1WWzBuAkmTTo6HBVypzYMhkDj0erdqNobXXy\nzDONXLyY47e/TV3l6kIIIe4EqWMn7ihNc6CUoqHBg8+nCATs55VSlMvrPUrXn4NDhw7R1dXFSy+9\nxLe+9S3icY2XX36dfN7uTpFOL3Dy5N8yPn6UhoY2GhpacbsDd+Oj3TO6utz4/Tq9vXbdulLJ4u/+\nbolvf3upulN5bq7IN7+5SCxWIhh00NfnYWjIy7599ojfe++lWVqyR/E6Otw0NOjs2WOfT2pp1TaJ\nX+2S2NUnGbETOyKZLDE15aBcthM6jwei9lp+TNOuf6ZpJo8//jimafKtb70IKPr7vczOwt69LUxM\n2McbRhGn03eVK4kb9e67KbJZg4mJy1OkhmGPpP7sZ3Ha212cOZPBMGBkJIvDocjnDT7+cXtDS6lk\nVoscj4zkOHUqg2lazM7KlKsQQuw0SezEHZVITBGJDGIYy3R29mMYcPo0JJN2hwldh/XNE5pmsby8\nTCqV4vd//ys4HIq1tTWefPJJfvWrX204b6GQIpmcRSmNQiG58x/sHlIoWJw4sXXdv1isRCx2eZrb\nNOHUqQwOh2J4uIBpQipVprXVxcKCPWJXLlu8//7l88k6n9om8atdErv6JImduCOCwS5aWu4jHp9E\n1504nd5KF4P1/ykWF2FpCfbts9+jFCQSCdLpLD09Ck2Ds2cXKJVSzMzYdeocDi89PU+QTi9w4cKP\n7+InrG/lssUPf7h6t29DCCHEB8gaO3FbuVwBgsEuWlvvJxTqob39IaJRH01NbSil0DTF9PRRxseP\nVt+TzdojdoahGBgY4IEHDvGjH/2EH//45zgc3Vy8OE4ksheAcHgPjY19dHY+jM8XxedrvhsfU9wA\nWedT2yR+tUtiV59kxE7cVkNDz+Jy+Zmbe5dyuUBXVwODgz7ylSoYlmWRy2VIJObYs+ejgMKy7DV2\nhUKBVMpHLldmaWkWyzLJ5SCVWmS5UqZube0Sfn8L+Xyc4eEvAXDmzEsyHSuEEEIgI3biNisUkpim\nga67CAY7cTrdG3rAWhaUSnaWZxj22q21tTQAS0sLjI+XmJpKVluL+f2tPPHEc9Wdr06nj2CwE7+/\nmVIpR7mcxzC23zxe7DxZ51PbJH61S2JXn2TETtxWo6M/QtN09u79HA6HB6dTq7QPs6o/m5p6CQZ7\nuHDhn2lq2sf8/HHeeqtMuWzwxBOfYXV1lfV2XeHwHpxOH4FAJ+n0An5/Cy5XA7ru4v33v4k94idN\nRoUQQgiQxE7cdhamWcbjCQGwtGShaXanCL/fSbmsaG5+CKU0mpoSeDwhLMtkaekUBw4cZP/+Liyr\nkwsXTmGaBkopjh07SmdnELDbh2maTja7UpnCFbud9KusbRK/2iWxq0+S2Ik7olTK4nB4KJcNlpbA\nNBX5/Hq3Anta1un0AxAMdtPaeohsdppkskQ6nWN4+Dl03UUsdo5iMUk8vlZ5r0UsdvaufCYhhBBi\nt5PETtwRpVIOrxc8Hujvh7U1jeVlezRP05wopSpTs6Drzsp7LL7znefRdRef//y/wem0N1YcOnSE\nEydG6ex8hKWlUyQSU3fzo4kbJCMGtU3iV7skdvVJEjtxR2iaDkAkogiF7ATv3XffoFwuMjj4qQ3H\nlst5Ll36GbmcXRetoSFAa6u9r+fQoSG8Xg/Foo94vAl77Z0in49TKCR28iOJu8ait/d94vF2EonW\nu30zQgixq0liJ+4It9teE7e8DH4/rKwU2bPnKUzTqO6SNYwSmqaTy62SySxW35tIrHD27BRer5eL\nF08wPz+P19tIKDRMoZBgcPDTlEpZTp584W59PHEDbnWdz+Dg2zz99PNks0FeeOH/vo13JrZD1mnV\nLoldfZLETtwR+Xwcp9NHqQQTExbgwjDylMt5XK4GQKdcLuBwuCmXc4CdDO7b93ny+Ti/+tU/V8+1\n/o/T2to8fn8LbW2Hyefjd+eDiR2XSjVTLjtYW2u/27cihBC7niR24o5YWxvH4wmjlKNSww4WFk5S\nLufweiN4vRHc7hBKqeomCo+nEZerAYfDg1J6tYzJld84s9kVFhdPkcks3ZXPJW7crY4YLC4O8I1v\n/DdMU/65uhtkxKd2Sezqk/xLKe6Irq7H0DQdwyhSLGZIJufp6noEYEPBYoBAoAOARGKSqak3yefj\nV61N19S0l/b2IxhGiRMnnt/2/WiaE113USpt3exe7G6m6bzbtyCEEDVBOk+IO+LKxMzl8hMO91Q6\nT1gUCgkMo1jtPLG+acLna6a7+7Fqq7F1V/Y7TCZnyWSWWF4+d0P3c+DA73Lo0Ffw+2Xx/U6TfpW1\nTeJXuyR29UlG7MRttXfv7+DzRSmVCui6q9JejGppE4BQaI5gsIGJCXA4ekgmZwBwODyAQtfdG0b0\nrlQspjh//vs3eFcKpXRAVXfrXks0up9IZICpqV+Tz69d93ghhBBit5DETtwWDQ32wvZAoBOlFJlM\njGIxxfj467S3HyKXi9Pb+wRKwX33HQDg0qVfMzu7xNLSacCuZ6eUqtS1u5wI3vo6EYtz5/4Bh8O7\nrUSttfV+PJ4QkcgAc3PHbvHaQtb51DaJX+2S2NUnSezELXO7Q+zb93kAVlYu4PM14fU24XL5OHDg\nizidPgqFNACmafHmm8doaHCjaf10drajaTpzc+8Qj0+yuHiSfD5x2/u/lsv2jtztmJ5+k3B4j3S4\nEEIIUXNkjZ24ZeVynlIpQ7GYJhTqwuuNoOuOylSqVllbR3VqVdfvp1g8hK57sSwLTbO/X1iWwczM\nW6yuXuDIkT/mwQf/BK+3iZGREZqahjhy5H+hre3wpuv393+Cw4f/LT5f8235PMnkNFNTv6RUyt6W\n89U7WedT2yR+tUtiV58ksRO3zDAKnDr1bU6ffhGHw1tpF6ZXfmoopXA4nJV1dha67qq8tl4KZePo\nnNPpr7xfw+9vAcDvb0HT9C03PzQ0tKHrLrzeyE58XCGEEGLXkqlYcVsEAp2APeqmlAPLMgE7OQN7\n/dzY2CuUywXc7iBebxOx2GkikUEWF09uOFehkGB6+je4XD6Wl88xNDTE7OzbZLPLxOMTm6594cI/\n4/U2sbp68U5/THETZJ1PbZP41S6JXX2SxE7csmCwm8HBzwCwunqxWmTY622kUEjgdgdIpearSVk6\nPQ9Af/8naWzswzBKLC2d2nDOWOz0hseGUbxqiZNcbrVaMkUIIYSoZzIVK26DyztYV1ZGGR39YWXE\nzl5/d+LENxgb++nV331FKZStjIyMEAh0Mjz8JRob+7d9V5rmYGDg0+zZ89SGexQ7S9b51DaJX+2S\n2NUnGbETtyyZnGJ29hhgkUrNAnZRYrD7v64LhXoxjALp9AIA4+O/YH4+TC63ctVzu91BgsFuotFh\n/P4WmpsPsrZ2aVv35fNFCYd7AZibO0axmL6ZjyeEEELUDEnsxC1zu0N0dT2MZVnE45coFJIUCikc\nDg+5nF03zu9vYXDw01iWycmTL1Au57Es45pJHcDAwKe4774I2ey1j9tKOr3A3NwxyuWCJHV3kazz\nqW0Sv9olsatPktiJW6Zpjmopk56ej+L3N5PLrWJZFk6njwcf/FOKxQyWZWJZJqZpT9MODz+Hzxdl\naek0MzO/2fLcmcwSbneQZHIWr7ex2qXiWgKBDgYHP0M8PsH4+C9u3wcVQgghdjlZYydumcPhrf7u\n9zej6040zS534nR6Kj+9ldInGppm/2fndgdRSlVLmmxlcvINvvOd/8zs7FscP/5XLC2dpqvrcRob\nBzYd6/U20dPzJIFAJ5rmwOeL3v4PK26YrPOpbRK/2iWxq08yYidumcPh2rQBYmXlEplMjHI5T3v7\nEUyzXEn2tGoJlIsXX6a19QEmJl7f9rUikUFaWw9hmmXW1sY2vNbZ+TChUA+rq2OMj/+CTGZp0/uV\n0vF6G8lml2/ikwohhBC7myR24pZlsyvVqdhY7Dw+X4SWlmHc7mC1jdf6dK09FVsGoK3tMOHwHjKZ\nJRYX37/q+a9cJ2KaRuVnedNxsdg5dN3F8vL56iaOD+rr+ziNjf3Mzr7DwsJ7N/eBxQ2RdT61TeJX\nuyR29UkSO3HT3O4ge/Z8nGx2rfqcrrvQNEd1elapy+3C1kfquroex+0OYhhFAJxOH/v3f5nl5fPX\n7c9aLmexLItSKbfpNU3T0TR7Gvhq1suw3Gov2o6ODxEMdjE+/hqFQuKWziWEEELcLrLGTty0YLCL\nhoZWmpoGKi3CFE1NgzQ0tOJ26zQ3g8Nhsbh4ipmZ36KUQtN0mpr2EQi0s7p6kTNnXgLs0iRNTVt/\nu7xynUgyOcPp09/h/PnvV5/zeptoahoiEtmHz9dEJLL3qvc8MXGUM2de2tTt4toUTU37NvSibW4+\ngN/fQijUfQPnqU+yzqe2Sfxql8SuPsmInbghSmlYlolSGi0t9wP2NOu69VZi7e05Wlr8aFoWyzpE\nPh9ndva3lMt5DKOEx9NIPD4OUJmGtbZdn65YTG14vHfvZ3E6fczNHaNQSBCLnbnqey3LpFBIbvtz\nAkQiA+zZ8xSGUeTEiW8AMD7+GoFAO8vL8g+nEEKI3UMSO7FtfX3P0NjYx/LyeZqbD5DPJ6tr69Z/\n2gmRRTarYxiQTpsYRpF8PklHx8MAZLPLeDwh3O4AkcggExOvMTPz1lWve711IqnUPMFgJ4nENNls\n7JrHdnU9TkvLfUxMHGV19cKWx+zZ8xSRyCBjY6+SSEySy61SKuU2bMZIJqdJJqeveS1hk3U+tU3i\nV7skdvVJEjuxbV5vBKU0vN4IAErZ7cDWkzr7OQ2lFLGYg5UVKBScnD79DdraHiIc7sGyLDyeMLru\nrJxP4fE0Vt6r09X1KPl8nGCwG6fTy/nzPwDMa97X+PjPAfD5mtmz5ykWF09etXes19uIUqr6Ga71\nOT2ecDWxO3nyb2/kTyWEEELcFZLYiW27ePEn+HxRkslZQqFukskZ2toewLJM2tsfBGB29l10XSca\nHa5M0Zr4/S3kcnZ5EaUUY2OvoGk6mUyMQKCDeHwCgHC4l5aW+zZcs7X1EG+88dKW3zxdrgBKqerU\nakfHQ4RCPSilV5O9D7KnUC9fc+vP+VMaGlpZWxu/wb+Q2MrIyIiMHNQwiV/tktjVJ0nsxLYVi6nq\n+rb1GnKzs78lEOigre0IYG9uyGYXiUaHAdA0F8PDX6qWRLEsk2w2Vt0Re2UtumRylnh8ojpip+tu\nVla2XsPmdPo5ePD3AMWZMy9RLKZYWjqDpjmuubO2XM5tqn/3QaVSZtvr/YQQQojdRBI7sW3t7Q8S\nDvexvDxCc/N+EolJWlruo1C4vJmhufkgXu+HKZfzOBweyuUcTqcH0yxhWQaGUdwwdXslwygwNvYK\nYCeM67b6xmnXwzMqU8F26RJZ97Y7yYhBbZP41S6JXX2SxE5sW1PTEG53gGh0H15vY7VmnccTrnae\niET2oGkOZmZ+i8vlZ2npLP39zxCPTzA29iqWZWCaJcCugxcK9bC8PFJ97voU0egwudwKp09/B7AT\nQiGEEEJIHTtxAyYmjrKw8D7j40eJxU4xNvYqqdQ8qdRc9Zj10bhIZICWloMMDz+Lzxehs/NRyuVc\ndQoW7N2n3d0frq7Pu5orazE1Ne2lt/dJBgc/i2EUdjypu7IlmtgeqaVV2yR+tUtiV59kxE5sW0vL\nfYTDvXR0uDhyZD/nz4dIp9sq06x2QpdKLeDzRaojcKVSFl13VVuLXSmZnMXjCZNOL2z7HrLZZYrF\nzA2953bRdRcHDvwLlNI5e/bvKZc3d78QQggh7iZJ7MS2eTwhlNIIh+3yJOFwI5mMQtOcgJ3YpdNz\nlMtZfL4mAIrFNGfPfnfL883Pv8v8/LtbvtbWdgSn08vMzFsb1okUixmSyZlrJnZOp4/OzkdIJKav\nu1HiRmiaE6fTBygcDvdtSexaWx/A5WpgZuatW25ztlvJOp/aJvGrXRK7+iSJndi2CxdexueLksnM\ncvFiNw7HXoLBAAAjIz/AsiyGhr6ApjkwDHvEzuMJ09DQRi63Vp02dTr9OJ1estnlLa9jJ2Z2MeN4\nfJJUarb6WmNjP9HoEJHIQHXHrMPhxe0OksksAvZaQLttWWc1sbOv6btuAWMAXXfj9TZuSh5LpQzn\nz/8TSmnk8/Ft/92udZ2urkcBezdxIjF5y+cUQghR32SxkNi2UilDIjFJuVxmfHycmZl3KZWyxOOT\nZDJLZLMxZmffIR6fIBY7Q6mUI51eYGjoWfbu/WzlLIoDB36X/fu/TCDQcZXrZFlcPMnKygXS6YUN\n60Ti8QnW1saZmztWfW5o6FmGh79IY+MAAKurF4nHJ5mfP1695v79X2b//ucIBDqv+zkHBz/N0NCz\nNDcf3PRaNhurJpC3yjAKLCycYHX14oZ1ivcaWedT2yR+tUtiV59kxE5ch2Jg4FM4HG7S6SXC4R4y\nmRiRyADx+ATlcp5yOcvevZ8D7CLGS0unaGoa2rDTVdfdl89YaTvW2DhAT8+TTE39asOoHHDVFmPl\nco5Ll179wHN5LCtQHRF0ONx4PCFyuYbKERaGUUDXXdvafVsu2+e5UxszHA4vg4OfplhMc+nSz+7I\nNYQQQtQnSezENTmdPsLhXsAuT+J0+nA4PCilEQx2oesuXK4GdN1VPSafXyMSGcTtDlanZNfLoSil\noWkOlFIEAh14PCFCoZ5qYheN7qdQSG5I9K63TmR09Ifo+uU1b4FAJx5PmEhkkLm5dwA4e/Yf0HXn\nlps4Pmhs7BWcTi+lUvZG/lTb5vNF8ftb8Pma0XXXhp3C9yJZ51PbJH61S2JXnySxE9dhYVkWSinm\n5t7F6fTS0NBGMNhFLhcnlZpD05y0ttrTln5/K/n8WjWRKxSSxOPjJJOzaJoDyzIZG3sVjydEMjlL\nOLyHWOwMuu4mEGint/dJLMvkvfee3/ZmAssyN2xkiMXOomk6yeTsFccYlMvb3Zxg3bGkDuxCytPT\nv6FYTN/zSZ0QQoidJWvsxDWVy3nS6Xmy2RXW1i4xP38ch8MDgNsdoK3tAaLRvb4/GJAAACAASURB\nVJV2YVa1J6zXa++K9fubmZ8/TqmU5f77/5CDB/8lqdQsi4snyeVWmJ9/l1Coh8OHv0pj4wD5fJxE\nYmpDUnej60RMs8T8/PHbthbuTlhaOkU8Xh+9aGWdT22T+NUuiV192rUjdkqpCPB3QC8wAfy+ZVmb\ntiIqpSaAJGAAJcuyHtnB27zneDxhOjo+xOrqGKFQD/n8GsHgAm63m3C4h2Cwp7pebn1KVdMu/2cU\nDHbT0nKo+tz6FK3D4UbXXZUCvzpQrr7H5WqoHOPhzJmXbvreGxraaGk5xMLCiW3tfq1XbW2HcbuD\nTE+/iWmWr/8GIYQQNWPXJnbAfwFetSzr/1JK/efK4/+yxXEW8JRlWas7enf3qObmgzQ29uP1NuHx\nhHC54NCh9Vf7yee9ZLP2nzqdXkbTFOVynsbGPYDdT1bT9MraOp1yuUhr6/0kElOcP/99DKNU3ZSg\n6y78/hbm50+QycTIZJa2vKftrhNpaztMKNSDZZmMj//8Fv4Kd57P14xhFCgUkjt6XU1z0tlpf/dJ\nJKaIxyfu+DVlnU9tk/jVLoldfdrNid2zwMcqv/8NcJStEzsAtRM3VA9isbO4XP7qiF2hkCQaPYLD\noUilTJxOcLl8AAQCzZV1c1a184Rplivr26ZpaGijVMrQ1fUY7e0PceLE8xuu1df3DKFQN3Nzx64o\nTXLzFhdPYlkWS0unbvlcd5Lf38rw8BcxjCInT76wo6Nmpllibu4YbndwwxpEIYQQ94bdvMau1bKs\n9UVSi0DrVY6zgJ8ppY4ppf50Z27t3pXPrzE29gpra2NMTLzG0tJpZmZMJictLMvuBVsq2TtLDaNc\nTejWWZaBZVnE45OcPPkC6fQClmVhmmX27/8yg4OfZT0PX9+gcL2NCttdJ5JKzTE29tOrjvztFoZR\nwDTLlTIt5o5ff37+OBMTR7dV+uV2kHU+tU3iV7skdvXpro7YKaVeBdq2eOl/v/KBZVmWUsra4jiA\nj1iWNa+UagZeVUqdtyzrl1sd+PzzzxONRgHwer10d3dXh6rX/w9AHq8/HqWxsY/h4UE0zcGxY0cx\njCKPPvopCoUUp069SzYb46mn/gDDKPHee78G4MiRj6CUYmTkPEppOJ2nWF4+z4ULl+jufpyHHvpY\nZS3dCUZGRjh48AEsyyIWM8lkFtmzp33HP29z80Hee+/XZDKLO3K9fD7OSy/9H1iWwb59e+9SfOWx\nPJbH9/rjdbvlfuTx9eM1OjrK8vLWXZm2S31wxGW3UEqdx147t6CUagdesyxr+Drv+T+BtGVZ/88W\nr1lf//rX79Dd3nsaG/vp7/8ElmWilD2wu1725Mqfs7Nv43D4aWuzF+ItLp6kVMoSCHQSCnWzsjLK\nzMxblMsFotFhyuX8pt2gXV2P0dp6P5nMEufPf39HP2co1MPg4GewLJMTJ/5m0yiWw+G9LT1hhRBC\niBvxta99Dcuybnip2W6eiv0B8NXK718FNv1/fKWUTykVqPzuBz4F7O4FVjUik1kil1slkZjZsIbO\nnla1p1vL5RyLiydJJCarx8TjUywunqzWsXO5Atx//x8xMPAplpfPbVniI5GYplBIsba28+U/stkV\nstkV4vHJTUldS8shHnjgj+ju/vCO35cQQghxM3ZzYvdfgU8qpUaBpyuPUUp1KKV+XDmmDfilUuoE\n8DbwI8uyXrkrd3uPMYwiudwqhUKq+pw9eqeIx8c5fvwvOXnyhcrz1oZjgGqHB7u8icLrjVz1WqnU\nLKdPv8ji4vvV5zo7H6G392MopW+aVrgdPJ4wAwOfpqGhlXPn/mFTmzKwu25c+VPcnDsRP7FzJH61\nS2JXn3btrthK+ZJPbPH8HPC5yu+XgMM7fGt1IRTqIRIZBGBi4g0cDg+lUpaWlgNMTLxWPa6t7TDF\nYqY6QmeadieF6enfkM/HAY2GhlaU0vH7WzGMIpZl4nT6SKfnt7y20+mjrc0O6+rqReDsbf980egw\n4XAvbneAtbVLWx4zO/tbUqlZ0undW+hYCCGEuNKuTezE3RWPT7K8PEIut8rKynkAHnzwT1FKMTz8\nHOfPf4/u7o/Q0nIQyzKrpUZyObvGXXf3h4lEBqp12hwOD8PDX8Q0y1iWia67GB398YaesOtKpWy1\nfVk6PX9HajHFYmdxOv2VxPFqLJLJmdt+7XpzJ+Indo7Er3ZJ7OqTJHZig1Coh97ejxKPTxKJDGIY\nBbq6HmW9RMmV6+0Mo1R9HI3uB+zNE+VyDofDXTnGLoliGHlMU6+U+jBwu7XqpoRgsJs9ez5GLHa2\nWs9ufv7dO/o5C4Xkri9iLIQQQtwoSezEBoFAJ06nj2CwB03TUcoLsGE3rFIaQ0PPksmsVJ/XdScA\nHR0P43I1VJO/XG6FZHKGeHychoZWisUsa2tjaJqO2x2io+ND6Lq7cs3uamLX2DiA0+llaek0IyMj\n8s2zhkn8apvEr3ZJ7OqTJHaiQuF0+pifP06plCES2Qf47VfUxt3WXm8jmqZXN0SYpsnq6vnKyN0w\nSinW1sZZWjqNYZRpbz9MJDKAy2Wf7/33ZymXc/T2Ponf30IsdpaZmbdIJKYAu8RIf/8zAORyazgc\nkzf8aZxO344XAL4b1xRCCCGuJImdAKCv72kikQEmJ99gcfEkPl8rXm8jsHHXK9hTsHY/2CKa5sSy\nDJqahirHGoCO39+Cy+WvbKAAsLAsE8syqy20VlfH0HUXa2uXSKXmque3a91N4HB4aWt7gN///c9x\n6dLPWVsb29ZnCYf7GBj4JMnkDBcu/POt/WG2KRTqZWDgU6TT84yO/mhHrlkrZMSgtkn8apfErj5J\nYicAqmvidN3+aZrF6jTrZRagKsmbneC5XKpawBigVMrjdjdUR/mUclSOLeJ0+qolUwCWlk5dpa+r\nxdiYXbXm4ME/ACAQ6CAaHWJx8RTJ5PQ1P4uuuyqfybP9P8AtcjjcKKWqfz8hhBDibpDETgAwNvYq\nPl+UTGaRcLiPdHqRaHRowzTs5Rxv49SsUhoXLrwM2Gvq3O4AuVycpqZ9xGJn8fubyefjhEK9lMt5\nDKO47ftSSnHs2FEOHDiEz9cEcN3EbmVlhHw+fsVo4Z23sjJKPp/Y0WvWClnnU9skfrVLYlefJLET\nAJhmiXR6no6Oh2lvP0KpZBcYXh+xU0phGCUcDhfJ5DSaprO8PEok0k86vUAqdbksyPpu11jsNACZ\nzCI+XzN9fU9hmgYnT76AYRS2dV8zM2+RTC4wM5MhGh0iFtteTbtMZudrz92NawohhBBXksRObFAo\nJAC7ltz69Ky9Pg6UWm8pVmZy8nW83iZCoR40zcHKyigAfX3PEAi0c+HCy+RyK9Xz2mVRTNbX2m3F\n5QowNPQF8vl4dW1cPD5OKGR3p7iy5p2mORka+gJKaYyM/OCGRgHFzpIRg9om8atdErv6tJtbiokd\n5Pe30Nn5CInENMeP/zXz88cra+xMVlYukEhMoZReaQ/WxPDwl4hGhyplSrqq5wmFenA6fTQ0tG04\nv106RUMpDU2zv0/4fM10dj6Kw2GXVPF6I7hcDTQ0tKOUfs37dTp9+HxRvN4ITqf/up8vHO6jvf3B\nDesBhRBCiHuNjNgJAHp6nsDni6KUxszMW1dsftCIRu1vfcvLo7hcftzuEG53Ay5XgMnJX5LNLlc3\nKqwnbX5/K7HYmer5M5lFLl36OYZRqE7V9vR8BL+/pXLN35BITDIxcZRCIVXdoAFbrxMpFBJcvPhT\nlNLI59eu8+kU/f3PVI5NbHt3rbg9ZJ1PbZP41S6JXX2SxE5UXO4sAXZitv77+s/Gxj503Uk6vYTT\n6WFt7RLLy+dwOn3cd5+9ezWXW8XtDlbbkF1pc0KlPvCT6pTudiQS261vZ7G8PILXGyGdXtj2+YUQ\nQohaI4mdAKBUygBNKKWzb9/nUcqxqdzJ+jSmYRTIZJbIZGIMDn6WXG4NTbM7T1y69DMKhQR79jzD\n3r2/w9LSWWZm3txwrc7OR/B4GimX7Q0a5XIWsEut7NnzFPl8HE1z4HR6mZg4elu+cU5N/fKWzyFu\njowY1DaJX+2S2NUnSewEYCdkXm+ESGSIQKADwygB9m7YxcWzOBweYrFTNDYO4nR6CIUGcDi8eL2N\nNDS0Vaduvd4IhUKCxsY91Wnc9cQuEOjENMu0tR0GYHz8F8zPv0smswRAMNhJONwL9FbvKxY7t2HT\nxN3m97eilEY6PX+3b0UIIYTYRBI7AYBplslklujufqLyzOWp2ZaW/SilaGhowe1uYGHhBMvLI6yu\nXqCxsR/Lgubm/YC9SQKgUEji9TaSzdo7Y32+Zvbt+xymaTAz8xZOp5+1tfENa+ni8UkWF0+Rz8fR\ndScOh5dUam7XrBNxuRoYGvoCoDh79u+3sbZPgKzzqXUSv9olsatPktjVuX37Po/HE0bTnGiaA8Mo\nVaZfrUppk1Jlh6pGLreG0+kFNCKRAcrlAo2N/dXkDezermCvp/N6P0Q2G+Pw4X9LPD5JqZTDMAos\nLZ3ZkNCtsyyDmZnf3PBn6Ot7hmCwkwsXXiabjd3kX+L6yuUCxWIapfTqBhAhhBBiN5HEri4p2toO\nUy7n8ftb0DQHlmVVWmLZa+su/8/B9PSbOBwuFhZOAIre3o+iaQ4CgQ4cDg8+X5Tjx/9ywxXm548z\nP/8era33o+su/P5mTp58Abst2Y253jfOQKC9ch9NdzSxM80Sp09/B3s088Y/h9379jDx+ERdTeXK\niEFtk/jVLoldfZLErg4FAh10dj4MwMTE6zidfqLRoUorsDV8viYMo4Sm2Uleb689PZvNrmJZJjMz\nb1EqZTCMEn5/9Bq14SyWlk5hGIXKbtQbT4a248KFl/H5oje0o/bW3NznaG09RGvrIYLBTs6e/fvb\nfE9CCCGEJHZ1yTRLlU4QBr29TwIK07SnRi3LIpOJUSxmCYd7AFhZuYjD4aa//2l03UUmE8Pvb2Zl\n5UKlndjVR58sy2R5eXPpkxtxvXUiudzKhi4Xu9Xa2jiBQCerqxfu9q3sKFnnU9skfrVLYlefJLGr\nQ6ZZBuzWYLq+3jbMbvNVLKa5dOkVmpvvqyR2dgJ45Zq49RE60ywzMvIDlNLo63sGgImJ167aMmzd\n8PCXcbn8jI7+kHw+frs/3q6VzcY4f/57d/s2hBBC3MMksatDudwqU1O/plTKMzj4SQBWVi4RDncx\nNWWXJonFTpPPx7Eso7ITFObmjuH3t3Lx4iv4fJd3vHo8jUQiAwAsLLxHLreKw+EhFOplbe0Splmq\nXlvTXPh8TSiliEb3b2uzhHzjrG0Sv9om8atdErv6JIldHWpoaKO390lM02Bq6k1cLh+trQ+glOLg\nwd/l/ff/BoBUagaA6ek30XU3bW1H0DSd/v5nuHTpler5crkVZmd/W/l9FYCenidpbOzD72/ZUBzY\nNIusrIzi8TQyN/fuTn3ku86yQKnrHyeEEELcCkns6pBpGtWSJisr5zHNMq2tD1R2xjp48ME/IR6f\nIBzuwzBKlEppdN1dmZLVqp0irmTvmL0sm10mHO7FNMscOfLHrK6OMTn5OkD15we53SGGh79IPh9n\nZOQH1edrfZ3I97//IX7608N89auv89hj9bW+Dmo/fvVO4le7JHb16WrbGcU9TNP0SqcIValRZ1NK\noWkaSmnVbhK67sTjCeNy+SmXCyilKJeL9PQ8gdfbVH1vW9thWlsfqD5eWHiP48f/ilIpg6Y58Ptb\naG9/iObmg9VjGhra6O7+CE6nHwC3O1gtn3Llfd1Ofn8rPT1P4HI13JHzb2ViogXT1JicjO7YNYUQ\nQtQnGbGrQ+n0AhMTb1AqZav13wqFDB5PA7ncKj5fE6VSltXVi2SzyxSLaXTdRT6fwO9vIRTqJhIZ\nxOVqYGbmbXTdTWfnI9VzG0ahuiliaek0pZJdzLev7+MAxOPjlEpZuroex+9vBiymp98kmZxmbOxV\nisX0hs0at/MbZ1fXYzQ0tGJZFtPTv75t572Wr371KGfOdPOhD13akevtNjJiUNskfrVLYlefJLGr\nQ15vhN7eJzCMYnVX7MrKKOVyiOXl80Sj+1lePrdlXbhCIUGplMXlCpDNrnDw4O+Ry62xujoGwJ49\nH8PjCXPx4k9IJKawLJPV1Qv4fM2VEitmtQ9tLHYWyxpmdfVi9fzx+Pgd/eyx2FmADde80xobszzx\nxMiOXU8IIUT9kqnYOqSUA1BomoMri+0qpVXWt/3TNYv9plKzjIz8E5nMYuUZi/HxnzM+/vNqoqjr\nng3vsWvnmZhmia6ux9i37/MkElOV8yxd835HRm5fUrS6euED9y7utNsZP7HzJH61S2JXn2TEro4o\npdPUtBfTNKvtwuLxSTyeMIFAB253gObmAzQ3HyQWO1NNuAKBDnTdRTw+seF8icQUp0//HaXS5c0U\ndrJor+O7Uj4fr7TjgkOH/hVKKQKBdtbW7t3pyYmJZhYWwjzyyAU0+QolhBBiB0hid8+73Ne0tfUQ\nnZ2PUCgkAbsrRCjUg1KqsksWGhsH0DSdUKiH99//GxwOL3v3/g5KaZw//30ymRhXjvLZ57r8eGLi\nNfz+1i27K9gJoMXExGt4POEPJIpX779ai+tETBP+/M8/T6HgRNcNHn743k1gr6cW4ycuk/jVLold\nfZLE7h62d+/n8PtbGB39EdlsjEwmhmGUyOcTuFwB7GSKansx0KslTS53lzCqnSTa2x8kEOhkbOyn\nJJMz+P2t7Nv3OVKpeS5efBmAeHxi08gegNPpZ//+L1Mu5zl37h83bI5obBygr+/jxGJnmZ5+847+\nTXaKpsHAwAKTk810dKzd7dsRQghRJ2SC6B7m8zWh607c7iBgr407ceJ54vGJSrkTsCyjUsLELmVi\nGEZlBM9O5uzSKHai5/E0omk6Hk8YAI8nhKY58Pmatr6BKzidPpxOL253EF13bnjN642glLahfMqV\nanWdyH/4Dy/z53/+TTo76zuxq9X4CZvEr3ZJ7OqTjNjdw0ZGfliZ8ty403R5+RweT5hCIU0+v4rH\nE6Kr6zHAni6Nxy+xvHwej6eRcjnP2Nir6LqbfD5OU9MgS0tnAHsnrWGUqt0mriWbjXHhwj9jGEXK\n5Txgr8fzeiPMzx8nn18jlZrb9D5Nc1YTyavRdTcuVwO53Mqm19zuEKZZplTKXPcehRBCiFonid09\nLJ9fI5/fPFrk9TbR0nIQw7B3qjocHhKJSRoa2llcPMHa2hgNDe0MDX2BUimLpjnQNAemaaDrTjTN\nWe0ecSPlSZLJmQ2P+/qeIRzuZXb2t5s6V6zbu/ezHDnSxuTkGywvn9/ymKGhZ/F6Gxkbe3XD/Xg8\nYQ4c+BeYZplTp17EMArbvldx+8g6n9om8atdErv6JIldHfH5ovT2fpR0ehG73IlOuVwCLObnj5PN\nLlePtSyzUp6kjN2hQqtusFivQ+d2h+jre5pUao7Z2bdv+H7sc1/+ee1jjKseY68LtDadZ/0z2NPK\nGzdmXLzYyre//QQf/vAIn/jE6Ru+dyGEEGI3ksSujoRCvfh8URwOT7XcycjID7EsE01z0Nx8kJWV\nUUyzRCazWBnlKlbX2Jmmgc/XRD6/RkvLfZVWYc14PKGbSuzGx3/B3NwxCoXEVY+5ePGnjI1N0N/f\nfdVjRkZ+gMPhoVhMb3i+UEhy6tSLlaLIxQ2vnTixh9nZJn7zm32S2N1h0q+ytkn8apfErj5JYldH\nlpZOo2k6xWKGnp6PVLtAmGaRoaFn8ftbcDp9zM29A7DlurR0ep7e3o8RjQ4Rj08yP3+8MgJ4M6xr\nJnVgj8atr8m7GtMsb0rq1pXLuS2f/9SnTqIUHD58ZztdCCGEEDtJrU+v3euUUtbXv/71u30bu4LT\n6WNo6NnK+jknbneA1dUxGhv7mZh4jURi6prvb2raR3f3h5mbO8bS0p0d7erre4bGxj7Gxl657n0J\nIYQQ94qvfe1rWJalbvR9Uu6kDpXLeeLxSRKJaTyeRnTdRamUI5GYJJe7fmmOlZVRTpz4BisrF+jt\n/SiRyN7qa01N++jpebLaWszp9NHb+zHC4b5N5wkEOtmz5+PVcixb8XjCKKXhdodu4pMKIYQQ9UWm\nYutQINBBa+uhDc81N+/H6fRSLheIx8fJZpevuakBIBIZJBodprFxoNppoqfnCTTNQS63Qix2lmh0\nmGh0iECgY9MO2s7OR/D7mzGMwlULE1+8+BMmJubo7Lx2yROxe8k6n9om8atdErv6JCN2dahcLlZ2\nkRqk04sUi2kWFt4jmZxBKY2hoWfp63vmuudZL2J8ZReJubl3iccnq90nLk/1b57yX1w8STI5w/Ly\n1YtolkoZ0umFLd8vhBBCiI1kxK4OqeqMvYXT6UXX3aRS8ywtnaa5+SDApl2kWykWU1iWSaGQqj63\nuPg+i4vvVx+v96XN5zdvklhbG6vWzDt48PeJxc6xtHRq03HyjbO2Sfxqm8Svdkns6pMkdnVE0xxE\no/sxzVK13InLFUApRWNjf2X69AyJxCTFYpZodD+FQmLLjhBgFxw+derblMtXL/y7tjbGyZMLV92d\nChAO9+LxhIlEBqqJXSjUg8PhYWVl9NY+tBBCCFFHZCq2jrS0HKK7+3FaWg4xPf0bJiaOVl/LZGLV\n34vFNOFwL729TzI4+FmUuvp/JqVSdsNU7NbHZCq18lxo2ubvEgsLJ5ifP87U1K/QNAcOh4+BgU+x\nZ89TNDS031C/w0zGte1jxc6QfpW1TeJXuyR29UlG7OpIOr1AqZQlkZiqjow1NvbjdofI5ZY3HJvL\nrVAoJMnlVqtr6W6F39/G0NAXAIsTJ76JaV6e6i2X88zNHcPrjXD//X9EsZginV7A4fCSz8e3fY3v\nf/9DvPzygzz33Nt85jPvX/8NQgghxD1GErs6kk7Pc/LkCyil09PzBKVSlosXf7LlsaVSjlRqjlxu\njfb2h3A4PMzM/GbbSV5DQzstLQdZWDhBNruM292AUgrLotIlYvMaPofDi647cbkaOHfuH6vXunKd\nSDrt5rvffZz+/kU+9rFzAPzqV0OMjnZQLOoAzM5G+Ku/eppDh6Z49NGLN/Q3ErefrPOpbRK/2iWx\nq0+S2NUBu/VXK6nUHIFAO7ruprn5AACx2Lkt17+Fw71Eo8MbnovHx6+63u6D2tuPEAx2YVkmy8vn\nSSZnmJz8JaVSlmIxueV7UqlZRkZ+WJne3TqBfPfdft56ax/HjvVXE7vvfvdx8nkXzz33No89doHp\n6SZ+9KMPMTraLomdEEKIuiKJXR3o7f0YkcgAyeQswWAnmcwSKyujlErZq25qSCSmWF0dI5dbRddd\nOByeG2odtrDwPpZlUixm2Lfv82QyMc6f/95135dOz2967spaTEeOTHD+fCcDA5fv5YtffIfR0Q4e\ne+wC4XCWrq4Vpqej3H//5LbvV9w5Ukurtkn8apfErj5JYlcH1pO39Y0LdqcHha676e//BG53iIsX\nf7KhN6xhFBkf//mG83R2PkI43Mf4+C/IZmNcSyo1Syo1Szjch2VZ19wVeyOam1389//eSDpdYKrS\nYezpp8/w9NNnqsdEo2n+3b975bZcTwghhKglktjdgyKRvViWydraGADT02+ysHCCvr6nAdB1F35/\nc6V4sIVSGj5flEQic42zrm+0CBIIdFw3sXO5AkQig6ysjHDy5AuUy/nqa8FgF253iFjsLNcrPDwx\nEWV8/AF6e8/i8ZRoaGjD623E5fLz3e/OY5qKxx+/cP0/irhrZMSgtkn8apfErj5JYneP8Xqb6Ov7\nOACnTy9TKNiFgS3LJJNZIhDooFTKsbDwHqVSDrBwu0MkElObzqXrLkyzXF3vNj7+CwKBDmKxM5uO\n/aCeno8QCvXg8YQ2lFVRSmdw8DMopVEqZTe1Gfugv/7rp1laCmMYGp/73Husrl6obOTI8j//p52o\ndnev0NW1up0/jxBCCHFPkzp295hCIUk6vUgqNU+xmAagq+txHnjg36CUg0IhxcrKKLHYWeLxCTo7\nH6Gj4yG83sYN5/H5otx//x+yf/+Xq89lMkssLJy4bg9ZgHh8kmIxvSlhtCyDeHyCXG7tuqN+YK+p\nCwb/meHhucr7TRYX38c0xxkcnKevb5FoNHWds4i7SWpp1TaJX+2S2NUnGbG7x5hmmWx2GcsyaW7e\nj9/fckVRYJPTp19EKZ3e3o9SKuVxOLxomgNdd284j8PhQdMcOJ0+QHGjvVqXl8+xvHxuy9cuXfrZ\ndd//ve89TCrl5Stf+TUHD55lYGDjlILbXeY//acf3tA9CSGEEPc6SezuMT5flJYWu9+raZpomsbU\n1JvEYudIpWYJBrvRdVe1lMn5898HFPl8nGCwm2RyBrBIJmeYmvo12ewKHk8YTXNsa4TtWi5dasHj\nKdHRsXbN49bWfPzkJ0cA+NCHxjhw4NqdLcTuJut8apvEr3ZJ7OqTJHb3mGx2maWl05imQbGYxu9v\nYXX1AoZRqLYUy2SWiMXOUSplyWSWANi37/MEAh3Mzr7DwsJ7BINd9PR8hHK5gKbpKKVz7tw/ksut\n3NR9TU018Wd/9iWczjL/9b9+i4aGq/eXbWzM8ju/c5xk0svevQs3dT0hhBCiHklid4+xixG3YFkm\nhYKfhoYWWlsPEY0Ok0zOYVkWuu5iauqXG963vh5vveRJuZzHNMuUShl03YWuuzCMqydj1+P1FvF4\nivh8BZxOewSure0wzc0HmZx8g2RyesPxX/ziservUouptkn8apvEr3ZJ7OqTJHb3GLc7iN/fAoDX\nW0LXnYTDe3A6fXg8oUpbL4vm5oOUyznW1i4BMDFxlOnp31STt2x2mfff/1ssywAUSqltbZo4d66T\niYlmPvGJU9UEDqC5OcWf/dkL6LrF2bNdLC6G+Pf/vheXy08w2LkpsRNCCCHEjZPErkYppaHr7k2F\nf3O5FSYn38A0TYrFFD5fE/H4FJFIP6urYzQ29mEYJXp7n8SyLFKpuWqNSSFtzgAAD0dJREFUuQ+O\nyJlmqfq7tc29E3/xF58kn3fh9+f56EfPb3jN4ylTKun8xV98EtPUGBh4kw9/eILl5fNXOZtNvnHW\nNolfbZP41S6JXX2SxK5GDQ5+lkCgg7GxV0gkLrfOcjp9dHY+ClicPv131RZdCwsnAFhcPInD4SEa\nHaq0FLv56dWtPProBUZH29m3b3NrMPv+DB566BLz82EaG8dZXLx2UWQhhBBCbJ8kdjXK4fCglMLh\n8Gx43i5d4sSyTDTNseW6uHI5X9kNe/v9q3/16+se8yd/8osbOqesE6ltEr/aJvGrXRK7+iSJXY26\ncOHHeDxh0umNu0YLhSRnz/4DlmVu6P0qhBBCiHvfru08oZT6PaXUGaWUoZR68BrHfUYpdV4pdUEp\n9Z938h7vpnI5vympW5fPr1Vbid0L5BtnbZP41TaJX+2S2NWnXZvYAaeA54A3rnaAUkoH/j/gM8AB\n4CtKqf07c3vidgkGu3nggX9De/tDd/tWhBBCiJq2axM7y7LOW5Y1ep3DHgEuWpY1YVlWCfgO8MU7\nf3f3jnC4j7a2w9htw27e0aMHeP31m8upA4EOHA4PwWDXlq9Lv8PaJvGrbRK/2iWxq0+1vsauE7iy\nANoM8Ohdupeao2kO+vufQSmNfD5OPD5xU+eZmYnw4otPADA4uEBn57Vbhn3Q/PxxSqUsicTUTV1f\nCCGEELa7mtgppV4F2rZ46X+zLGs7Hd5vrDO92MA0y6yuXsTtDpFOL970eVpaEhw4YOfXzc3Jm7iP\nEktLp676uqwTqW0Sv9om8atdErv6dFcTO8uyPnmLp5gFuq943I09arel559/nmg0CoDX66W7u7v6\nH/76kHWtPn7nnSl+9rNDPPpoL3/8xxFOnHiLUinHY499hqmpX3L69Htbvn/drVzf5TL4zGf+GwAu\n1+74e8hjeSyP5bE8lse19BhgdHSU5eVlboWytttS4C5RSr0G/EfLst7d4jUHMAI8A8wBvwW+YlnW\nuS2Otb7+9a/f6dvdcZrmJBTq4Qc/CPDCC4/w4INF/sf/cAFgmiaapjE5+cZ1uzvsJI+nEZerYdtt\nxEZGpBZTLZP41TaJX+2S2NW2r33ta1iWdcML4HftGjul1HPA/wtEgR8rpd6zLOuzSqkO4C8ty/qc\nZVllpdT/CvwU0IG/3iqpu5d1d3+YaHSIr3zlEhMT5xgammN+PoJl2S3F/P6Waj/Y3UApjeHhL6Hr\nTi5ceFl6xAohhBC30a5N7CzL+h7wvS2enwM+d8Xjl4GXd/DWdhVddwLgduv80R/9El130dj4EKZp\nMDLyA1ZWrrexeGdZlkk+H8fjCVEsprf1HvnGWdskfrVN4le7JHb1adcmdmJ7TNMAwLLsn06nH48n\njGVZOBweisVS9dh02s1PfnKY/7+9+4+1uq7jOP56gYIKiToLf13HDF0ZqLCJTtrEWU1sK7UyK82a\nmnM5W2utZm7OrTX/qNac04kzZdW0VunMHxNSM5cmGqgkojLFoESg9CZcWALv/jhf8HA959xzudx7\nvu/v9/nY2Dn3nM89fi4vP+N1z/fz/Z4ZM9bo1VenasqUAc2d+1LL1x1NK1feLXucInaM+X8bAIAq\no9glt3btk9q8ef2uw61bt76lVase2nUottkjj8zQ4sUnasmS6ervnyRJmjnzHzrwwC1jPu/hlDr2\nieRGfrmRX15kV08Uu+S2bduqDRte2O2x/v7XW4496aTVWr78aJ1wwut65ZXDNWXKgCZPHvtSBwAA\nRkfpz4rdWzKfFdvfv7/uuGOe+vr+rfPOW9Lr6QAAgFG2p2fFlvYjxfCeFSuO0ooVfVq06ETt2NHI\neOLEA3XwwR/WwMBEPfHEcdq0aWKPZwkAAHqNYpfA7Nmv6cwzl+uiix7TuHGNd1inT5+vY445U+vW\nnaWFC+ft+kivKmq+eCPyIb/cyC8vsqsn9tglMHHiNp1//pO7PTYwsFETJkzS9u0bZH9IfX0ju1I1\nAADIjz12FbBjhzSO914BAKgM9tjVGKUOAABIFDskwD6R3MgvN/LLi+zqiWIHAABQERQ7lB5XTs+N\n/HIjv7zIrp4odgAAABVBsUPpsU8kN/LLjfzyIrt6otgBAABUBMUOpcc+kdzILzfyy4vs6oliBwAA\nUBEUO5Qe+0RyI7/cyC8vsqsnih0AAEBFUOxQeuwTyY38ciO/vMiunih2AAAAFUGxQ+mxTyQ38suN\n/PIiu3qi2AEAAFQExQ6lxz6R3MgvN/LLi+zqiWIHAABQERQ7lB77RHIjv9zILy+yqyeKHQAAQEVQ\n7FB67BPJjfxyI7+8yK6eKHYAAAAVQbFD6bFPJDfyy4388iK7eqLYAQAAVATFDqXHPpHcyC838suL\n7OqJYgcAAFARFDuUHvtEciO/3MgvL7KrJ4odAABARVDsUHrsE8mN/HIjv7zIrp4odgAAABVBsUPp\nsU8kN/LLjfzyIrt6otgBAABUBMUOpcc+kdzILzfyy4vs6oliBwAAUBEUO5Qe+0RyI7/cyC8vsqsn\nih0AAEBFUOxQeuwTyY38ciO/vMiunih2AAAAFUGxQ+mxTyQ38suN/PIiu3qi2AEAAFQExQ6lxz6R\n3MgvN/LLi+zqiWIHAABQERQ7lB77RHIjv9zILy+yqyeKHQAAQEVQ7FB67BPJjfxyI7+8yK6eKHYA\nAAAVQbFD6bFPJDfyy4388iK7eqLYAQAAVATFDqXHPpHcyC838suL7OqJYgcAAFARFDuUHvtEciO/\n3MgvL7KrJ4odAABARVDsUHrsE8mN/HIjv7zIrp4odgAAABVR2mJn+wu2X7C93fbsDuNW237e9jLb\nS8Zyjhgb7BPJjfxyI7+8yK6eSlvsJC2XdK6kPw8xLiTNi4hZETFn9KeFsbZmzZpeTwEjQH65kV9e\nZFdP+/R6Au1ExEpJst3N8K4GIactW7b0egoYAfLLjfzyIrt6KvM7dt0KSX+0/Yzty3o9GQAAgF7p\n6Tt2thdLOqzFU1dHxB+6fJm5EfGG7Q9KWmx7ZUQ8vvdmiV7buHFjr6eAESC/3MgvL7KrJ0dEr+fQ\nke1HJX0nIpZ2MfZaSZsi4ictniv3DwoAANAkIoa91ay0e+wGafmD2T5A0viIeMf2JEmfknRdq7F7\n8pcDAACQSWn32Nk+1/YaSadKut/2g8XjR9i+vxh2mKTHbT8r6SlJ90XEot7MGAAAoLdKfygWAAAA\n3SntO3YjZfsQ24ttv2x7ke2D2ozjAsclYfss2yttv2L7e23G3FA8/5ztWWM9R7Q3VH6259nuL9ba\nMtvX9GKe2J3tn9t+0/byDmNYdyU1VH6su/Ky3Wf70eLDGP5u+6o244a1/ipb7CR9X9LiiDhO0sPF\n161wgeMSsD1e0o2SzpJ0vKQv2f7ooDFnS5oeEcdK+oakm8d8omipm/wKjxVrbVZE/HBMJ4l2blcj\nt5ZYd6XXMb8C666c3pX07Yj4mBrbzr65N/7dq3Kx+4ykhcX9hZLO6TCWEyt6b46kVRGxOiLelXSX\npM8OGrMr04h4StJBtqeO7TTRRjf5Say10ikuD/VWhyGsuxLrIj+JdVdKEbEuIp4t7m+S9KKkIwYN\nG/b6q3KxmxoRbxb335TU7i+CCxyXw5GSmj//Zm3x2FBjjhrleaE73eQXkk4rDic8YPv4MZsdRoJ1\nlxvrLgHb0yTNUuNE0GbDXn9ZLnfSUocLHP+g+YuIiA7XseMCx+XQ7Vk8g3/z5Oyfcugmh6WS+iJi\nwPZ8SfdIOm50p4W9hHWXF+uu5GxPlvRbSd8q3rl735BBX3dcf6mLXUR8st1zxWbSwyJine3DJa1v\n8xpvFLcbbN+txiElit3Y+6ekvqav+9T4zaTTmKOKx9B7Q+YXEe803X/Q9k22D4mI/4zRHLFnWHeJ\nse7Kzfa+kn4n6ZcRcU+LIcNef1U+FHuvpIuL+xer8VvKbmwfYPsDxf2dFzhue2YYRtUzko61Pc32\nBElfVCPDZvdK+qok2T5V0ttNh9vRW0PmZ3uqbRf356hxuSX+cSk/1l1irLvyKnK5TdKKiPhZm2HD\nXn+p37EbwvWSfmP7EkmrJZ0vNS5wLOnWiPi0Godxf1/8P7+PpF9xgePeiIhttq+U9JCk8ZJui4gX\nbV9ePH9LRDxg+2zbqyRtlvT1Hk4ZTbrJT9LnJV1he5ukAUkX9GzC2MX2nZJOl3RocVH4ayXtK7Hu\nMhgqP7HuymyupAslPW97WfHY1ZKOlvZ8/XGBYgAAgIqo8qFYAACAWqHYAQAAVATFDgAAoCIodgAA\nABVBsQMAAKgIih0AAEBFUOwAAAAqgmIHoNZs72jxZ6vt12zfYfsjXb7OX4rvnd/F2JeKsSeO/CcA\ngPdwgWIAtWZ7hxofqn1d08NTJJ0i6TQ1rvb+8Yh4bojXuVjS7ZLujojPdRh3uqRHJT0dEaeMcPoA\nsBuKHYBa21nsImJ8i+dukHSlpIUR0fGjfGzvJ2mdpP0kHR0R69uM+4Wkr0i6LCJuG+n8AaAZh2IB\noL3Fxe2hQw2MiK2SfiFpgqSvtRpj+yA1PrvzHUl37p0pAsB7KHYA0N4nittnuhx/a3F7SZvnL5Q0\nUdKdETEwkokBQCscigVQa8WhWKmxx87F/QMlnazGHrv7JH05IjZ3+Xp/lTRH0hkR8dig556VNFPS\nyRGxdC9MHwB2s0+vJwAAJXFti8dWSLqr21JXWKBGsbtU0q5iZ/tkSSdIWkqpAzBaeMcOQK21OnnC\n9v6SZki6XtIZkn4UEdcUz03T+/fQRURcVzx/gBonUYyXdGREvF08vkCNsndFRNwyij8SgBqj2AGo\ntSHOip0iaa0a++KOiYi1tudJemTQ0MHF8GZJl0u6KiJutD1J0hvF00dExKZR+FEAgJMnAKCdiOiX\n9LIa21ZmF4/9KSLGDfozuBQuKG4vLW4vkDRZ0q8pdQBGE8UOADo7uLh1x1FNImKZpL9JmlnsrdtZ\n8Ba0/y4AGDmKHQC0YfscSdMk/U/SE8P89lvVKIM/VuNTLJ6LiKf36gQBYBD22AGotTaXO5kk6XhJ\n89X4uLHvRsRPh/m6kyX9S41DsJJ0ZUTcNPIZA0B7FDsAtdb0WbHNh1q3S1ovaYmkGyPi4T187Z1n\nwg6ocdLEf0c4XQDoiGIHAABQEeyxAwAAqAiKHQAAQEVQ7AAAACqCYgcAAFARFDsAAICKoNgBAABU\nBMUOAACgIih2AAAAFUGxAwAAqAiKHQAAQEX8H9lLIKwpOr9pAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe20b33c910>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We see that there are very few stars of class M. But this is because we are only considering those visible to the naked eye and they are not very bright!. In fact they are the most abundant by far."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Color-color diagram of the giant stars"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Previously, we'll see if in the BSC catalog there are dwarf stars, meaning the ones corresponding to luminosity classes VI and VII"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b =  bsc['SpType'].str.contains('VI')\n",
      "bsc[b].shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "(0, 12)"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We see that there are no stars of these types in the BSC catalog, which was to be expected in a catalog of just the brightest stars in the sky. This means that in the SpType field there are no \"VI\" and VII\" values. This fact will be taken into account when making the selection."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's then include in the graph the stars belonging to luminosity classes of the giant types:\n",
      "\n",
      "* I  Supergiants\n",
      "* II Bright giants\n",
      "* III Normal giants\n",
      "* IV Subgiants"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's see how many stars there are in these classes in the BSC:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b =  bsc['SpType'].str.contains('I')\n",
      "giants = bsc[b]\n",
      "giants.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "(5161, 12)"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And then see if among these are new classes in addition to the usual spectral types O, B, A, F, G, K, M"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "giants['SpType'].map(lambda s:s[0]).unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array(['K', 'G', 'B', 'F', 'M', 'A', 'O', 'S', 'C', 'W'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Indeed we see that there appear more exotic new spectral classes: W (bright blue stars that have mostly helium instead of hydrogen in their atmospheres), and carbon stars of types C and S: red giants and supergiants in the final stages of its evolution."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will expand our dictionary of colors to accommodate these new types"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = {'O':'#0000FF', 'B':'#CCCCFF', 'A':'#FFFFFF', 'F':'#FFFFB2', 'G':'#FFFF00', 'K':'#FFA500', 'M':'#FF0000',\n",
      "          'W':'#000099' ,'S':'#B80000', 'C':'#780000'}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then let's see what is the impact of the new types W, S and C. In the chart below we see that its presence in the BSC is very scarce, and in the case of carbon stars its position in a color-color diagram is taking very extreme positions (very high magnitudes, and therefore low temperature and deep red color)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots()\n",
      "ax.set_axis_bgcolor('0.6')\n",
      "ax.grid()\n",
      "ax.set_title('Color-color plot: exotic classes BSC catalog')\n",
      "\n",
      "ax.title.set_fontsize(15)\n",
      "ax.set_xlabel('B-V')\n",
      "ax.xaxis.label.set_fontsize(12)\n",
      "ax.set_ylabel('U-B')\n",
      "ax.yaxis.label.set_fontsize(12)\n",
      "\n",
      "for cls in 'WSC':\n",
      "    f = lambda s: s[0] == cls\n",
      "    b = giants['SpType'].astype('string').map(f)\n",
      "    x = giants[b]['B-V']\n",
      "    y = giants[b]['U-B']\n",
      "    c = colors[cls]\n",
      "    ax.scatter(x, y, c = c, s=30,\n",
      "               edgecolors='None', label = cls)\n",
      "  \n",
      "legend = ax.legend(scatterpoints=1,markerscale = 2, shadow=True)\n",
      "frame = legend.get_frame()\n",
      "frame.set_facecolor('0.90')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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T46xrKCZc7Idt4nJukPn8aletYuvChSBCr2OPpWjffTM6/bay9ec+KwTGxEhN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\nk+P+P/WPaYrdhjNCAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe20b233c90>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Given the shortage of stars of classes W, S and C in the BSC catalog, once we saw where they stand in the color-color graphics, we will omit its representation, as their inclusion would alter much the scale of the axes. Then we generate a graph in which we'll overlay the main sequence stars and giants, with the exception of type W, S and C. To differentiate, the main sequence stars will be represented now with black colored small dots, while giant stars (types I, II, III and IV) are displayed with conventional colors, and semitransparent disks.\n",
      "\n",
      "In the same graph we are going to superimpose the straight line corresponding to the \"ideal\" color-color diagram, which would correspond to a black body. For this we will use the functions defined in the [previous post](http://balbuceosastropy.blogspot.com.es/2013/11/the-color-of-celestial-objects-part-i.html)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def B(wl,T):\n",
      "    '''wl is an array of wavelengths with units of length\n",
      "    T is a temperature in Kelvin\n",
      "    the result is an array of spectral radiances\n",
      "    with units W/(m**2 * nm * sr)\n",
      "    '''\n",
      "    I = 2 * pq.constants.h * (pq.c)**2 / wl**5 *  \\\n",
      "        1 / (np.exp((pq.constants.h*pq.c \\\n",
      "        / (wl*pq.constants.k*T)).simplified)-1)\n",
      "    return I.rescale(pq.watt/(pq.m**2 * pq.nm *pq.sr))\n",
      "\n",
      "#  Defining the UBV photometric system constants\n",
      "lambda_u = 365 * pq.nm\n",
      "delta_u = 68 * pq.nm\n",
      "lambda_b = 440 * pq.nm\n",
      "delta_b = 98 * pq.nm\n",
      "lambda_v = 550 * pq.nm\n",
      "delta_v = 89 * pq.nm\n",
      "\n",
      "# Function to calculate Cu-b\n",
      "T = 42000*pq.kelvin\n",
      "F = B(lambda_u, T) * delta_u/(B(lambda_b, T)* delta_b)\n",
      "Cub = -1.19 + 2.5 * np.log10(F)\n",
      "\n",
      "# Function to calculate Cb-v\n",
      "F = B(lambda_b, T) * delta_b/(B(lambda_v, T)* delta_v)\n",
      "Cbv = -0.33 + 2.5 * np.log10(F)\n",
      "\n",
      "# Functions to calculate blackbody color indices\n",
      "def get_UB(T):\n",
      "    F = B(lambda_u, T) * delta_u/(B(lambda_b, T)* delta_b)\n",
      "    return -2.5 * np.log10(F) + Cub\n",
      "\n",
      "def get_BV(T):\n",
      "    F = B(lambda_b, T) * delta_b/(B(lambda_v, T)* delta_v)\n",
      "    return -2.5 * np.log10(F) + Cbv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now we generate the diagram:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(figsize=(10, 10))\n",
      "ax.set_axis_bgcolor('0.6')\n",
      "ax.grid()\n",
      "ax.set_title('Color-color main sequence and giants BSC catalog')\n",
      "\n",
      "ax.title.set_fontsize(20)\n",
      "ax.set_xlabel('B-V')\n",
      "ax.xaxis.label.set_fontsize(20)\n",
      "ax.set_ylabel('U-B')\n",
      "ax.yaxis.label.set_fontsize(20)\n",
      "\n",
      "\n",
      "for cls in 'OBAFGKM':\n",
      "    f = lambda s: s[0] == cls\n",
      "    b = giants['SpType'].astype('string').map(f)\n",
      "    x = giants[b]['B-V']\n",
      "    y = giants[b]['U-B']\n",
      "    c = colors[cls]\n",
      "    ax.scatter(x, y, c = c, s=50, alpha = 0.5,\n",
      "               edgecolors='None', label = cls)\n",
      "\n",
      "x = main['B-V']\n",
      "y = main['U-B']\n",
      "ax.scatter(x, y, c = 'black', s=6, edgecolors='none',\n",
      "           label='Main')    \n",
      "    \n",
      "legend = ax.legend(scatterpoints=1, markerscale = 2,shadow=True)\n",
      "frame = legend.get_frame()\n",
      "frame.set_facecolor('0.90')\n",
      "\n",
      "T1 = 2500 * pq.kelvin\n",
      "T2 = 25000 * pq.kelvin\n",
      "\n",
      "BminusV_1 = get_BV(T1)\n",
      "UminusB_1 = get_UB(T1)\n",
      "BminusV_2 = get_BV(T2)\n",
      "UminusB_2 = get_UB(T2)\n",
      "\n",
      "# Just two points are sufficient to determine the line\n",
      "ax.plot([BminusV_1, BminusV_2], [UminusB_1, UminusB_2], lw = 3, c='k') \n",
      "ax.text(0.9,0.4,'blackbody', fontsize=20, rotation = 35);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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R09NDKBQiHA5z5plncs0119DZ2Vl5prOIdsUqiqIoilI1ZLPOhEZdPum0SzIZ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\nMJ2PH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       "text": [
        "<matplotlib.figure.Figure at 0x7fe20b1aaed0>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The figure above presents interesting features. The first, that the radiation emitted by stars deviates somewhat from that corresponding to a black body. The stars are not perfect black bodies. The best fit is the one corresponding to the hottest stars (O and B in the figure).\n",
      "\n",
      "However, the most striking feature is the sharp deviation of the class A stars from the line of the black body emission. If you look at the main sequence, this describes an inverted U-shaped curve  that separates from and then approach again the black body line. This characteristic shows the phenomenon known as **\"Balmer jump\"**\n",
      "\n",
      "Basically, when the surface temperature of the star reaches a critical value, which occurs with stars with spectral class from A0, there is a strong absorption of the photons emitted from the interior of the star in the ultraviolet region.\n",
      "\n",
      "These photons are absorbed by the hydrogen atoms on the surface of the star exciting their electrons, resulting in the appearance of the absorption lines of the hydrogen Balmer series in the spectrum of these stars.\n",
      "\n",
      "This implies that, as a result of the absorption of these photons, it weakens the emission of radiation from the star in the ultraviolet region, and the magnitude U-B grows (remember that the lower the radiant flux received, the greater the magnitude), ie takes values positive more quickly than would correspond to a black body, separating sharply from that line as shown in the figure.\n",
      "\n",
      "From the A5 spectral class onwards, the surface temperature of the star decreases and there are fewer hydrogen atoms in energy levels that allow them to capture these photons. Consequently the ultraviolet radiation is recovered and the color index UB stops growing. The curve approaches again, abruptly too, the line corresponding to the black body radiation.\n",
      "\n",
      "This phenomenon is very well explained in the book of Keith Robinson: \n",
      "[Starlight: An Introduction to Stellar Physics for Amateurs](http://www.amazon.com/dp/1441907076/ref=rdr_ext_tmb)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Color-color diagrams of the MK luminosity classes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To end this post, already a little long, we will make a set of color-color diagrams to represent the behavior in these diagrams of different luminosity classes of the MK scheme"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# dataframe with only class IV stars\n",
      "bIV = giants['SpType'].str.contains('IV')\n",
      "giantsIV = giants[bIV]\n",
      "giantsIV.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "(1274, 12)"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# dataframe with only class III stars\n",
      "bIII = giants['SpType'].str.contains('III')\n",
      "giantsIII = giants[bIII]\n",
      "giantsIII.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "(3525, 12)"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# dataframe with only class II stars\n",
      "bII = giants['SpType'].str.contains('II')\n",
      "b = np.logical_and(bII,np.logical_not(bIII))\n",
      "giantsII = giants[b]\n",
      "giantsII.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "(287, 12)"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# dataframe with only class I stars\n",
      "bI = giants['SpType'].str.contains('I')\n",
      "b = np.logical_and(bI,np.logical_not(bII))\n",
      "giantsI = giants[b]\n",
      "giantsI.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "(1349, 12)"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(10,15))\n",
      "ax1 = fig.add_subplot(321)\n",
      "x = main['B-V']\n",
      "y = main['U-B']\n",
      "ax1.scatter(x, y, c = 'black', s=0.1)\n",
      "ax1.set_xlim(-0.5, 2.5)\n",
      "ax1.set_ylim(-1.5, 3)\n",
      "ax1.set_xlabel('B-V')\n",
      "ax1.set_ylabel('U-B')\n",
      "ax1.grid()\n",
      "ax1.set_title('main sequence (MK V)');\n",
      "\n",
      "ax2 = fig.add_subplot(322)\n",
      "x = giantsIV['B-V']\n",
      "y = giantsIV['U-B']\n",
      "ax2.scatter(x, y, c = 'black', s=0.1)\n",
      "ax2.set_xlim(-0.5, 2.5)\n",
      "ax2.set_ylim(-1.5, 3)\n",
      "ax2.set_xlabel('B-V')\n",
      "ax2.set_ylabel('U-B')\n",
      "ax2.grid()\n",
      "ax2.set_title('Subgiants (MK IV)');\n",
      "\n",
      "ax3 = fig.add_subplot(323)\n",
      "x = giantsIII['B-V']\n",
      "y = giantsIII['U-B']\n",
      "ax3.scatter(x, y, c = 'black', s=0.1)\n",
      "ax3.set_xlim(-0.5, 2.5)\n",
      "ax3.set_ylim(-1.5, 3)\n",
      "ax3.set_xlabel('B-V')\n",
      "ax3.set_ylabel('U-B')\n",
      "ax3.grid()\n",
      "ax3.set_title('Giants (MK III)');\n",
      "\n",
      "ax4 = fig.add_subplot(324)\n",
      "x = giantsII['B-V']\n",
      "y = giantsII['U-B']\n",
      "ax4.scatter(x, y, c = 'black', s=0.1)\n",
      "ax4.set_xlim(-0.5, 2.5)\n",
      "ax4.set_ylim(-1.5, 3)\n",
      "ax4.set_xlabel('B-V')\n",
      "ax4.set_ylabel('U-B')\n",
      "ax4.grid()\n",
      "ax4.set_title('Bright giants (MK II)');\n",
      "\n",
      "ax5 = fig.add_subplot(325)\n",
      "x = giantsI['B-V']\n",
      "y = giantsI['U-B']\n",
      "ax5.scatter(x, y, c = 'black', s=0.1)\n",
      "ax5.set_xlim(-0.5, 2.5)\n",
      "ax5.set_ylim(-1.5, 3)\n",
      "ax5.set_xlabel('B-V')\n",
      "ax5.set_ylabel('U-B')\n",
      "ax5.grid()\n",
      "ax5.set_title('Supergiants (MK I)');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7ds3Yrxy1fDWtT5u7P2nWEus0Sw6NjIxQKpWSPmtpKoxJIyiHyVJLT00UN9kV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F8OOMfdnzCz1wG3AOc3NqxlSfte1ipT99PMng38HXBF8VvgaaeUHadyXxeJM1P3NKL81XCtlL8g\n/ftZi0zdz1DyFySbwzLb0+bur3H3Cyr89zngUTN7PhRWHAceq3KN0sKWQLSwZaNNAefEjs+h0Mpf\n6Jyzi48106JxuvsJd3+i+PMXgJVmdlbzQqxJFu5lTbJ0P81sJfBp4BPufkuFUzJxXxeLM0v3NE75\nq+FaJX9BNu7norJ0P0PJX5B8Dstso20RnwMuLf58KYWW6TyW3sKWXwf6zGyDma0Chovxxn0OeHsx\ntouAo7HhkmZZNE4ze55ZYUdsM7uQwhIxP2hynIvJwr2sSVbuZzGGvwK+7e7XVjkt9ftaS5xZuadL\npPxVv1bJX5CN+7morNzPUPJX8b0Tz2GZHR5dxJ8AnzKzdwKHgF+FwsKWwEfd/RIKQxM3F+/FM4C/\n9SYsbOnuPzaz9wB/T2GG01+5+31m9q7i8ze4+34z22pmk8As8I5Gx7WcOIFfAX7NzH4MPAG8udlx\nmtkngS3Ac6ywmOkfUJgtlpl7WWusZOB+Fv0c8Fbgm2b2jeJj7wN+EjJ1XxeNk+zc06VQ/mpCnGTk\nsxFKDlP+aojEc5gW1xUREREJQKjDoyIiIiJtRY02ERERkQCo0SYiIiISADXaRERERAKgRpuIiIhI\nANRoExEREQmAGm2SaWZ2ysy+YWZ5M7vbzH62wjkPmNn5ZY9da2a/07xIRUTmU/6SpGmdNsk0Mzvh\n7tHK8L8AvM/dB8vO+WPgR+7+R8XjDuBfgVe4+8OIiKRA+UuSpp42CUkXUGl7j09S2MYm8irgX5Xw\nRCRDlL+kbqFuYyXt4yeK2390AuuAV5ef4O7fMrOnzOwl7v5NCtuA/O8mxykiUk75SxKl4VHJtLLh\nhYuAve7+0xXOex/wbOC/A48AL3H36aYGKyISo/wlSVNPmwTD3b9qZs8xsx7gN4GthYd9ABilsHn0\nAeCbSngikiXKX5IE9bRJppV9U30RcAfwXK/wwTWzrwLPBK519xubG6mIyHzKX5I09bRJ1kU1IQAG\nvL1Swiv6JHA1cHNTIhMRWZjylyRKPW0iIiIiAdCSHyIiIiIBUKNNREREJABqtImIiIgEQI02ERER\nkQCo0SYiIiISADXaRERERAKgRptkipn9JzP7+7TjADCzq83siia/551m9lPNfE8REQmDGm1twsxe\naWZfMbOjZjZjZl82s59JO65y7v637v4fkriWmR0ys9M2aK7xtT3A24CPFI8Hi5s631x23qbi41+K\nPfaUmZ0bO/5tMztsZi+u8D7bzeyO2EP/E/ij5cQsIiKtTY22NmBma4D/A1wHnAn0An8I/KjJcZiZ\nWRPf0imsQr4c24Fb3T1+j6aBi8zsrNhjlwLfLb7Xaczs94DfAF7l7vfV8L6fB37ezJ63rKhFRKRl\nqdHWHs6nsDHxqBfMuftt7n4vgJm938w+Hp1sZhuKvUUdxePx4lDhnWZ2zMxuMbMzY+dfVOzFe9zM\n8ma2JfbcuJl90Mz+GZgFXmBmv2Bm3yn2+v2FmR0ws3cWz5/X82Rm15nZQ8X3/bqZvTL23PvN7FNm\ndqOZHTezb5nZy4rPfRz4SeDzZnai2Nv1TDP7hJl9vxjrXWb23Cr37LUUNm+OOwncAry5+B4rgF8F\n/pbTG4dmZh8E/jOFBtvkwr+iAnefA+4GEultFBGR1qFGW3v4DnDKzP7GzF4bb3AV1bKX2duAdwDr\ngB8D1wOYWS+FXrw/cvczgd8GPm1m3bHXvhW4DHg2cALYB7wXOKsY288uEMNdwCYKPYT/G9hnZqti\nz/8ShT37uoDPAX8O4O5vAx4CftHdz3D3/0mh92wNcHbxvd8F/FuV972gGFu5jwNvL/78H4BvAYcr\nnPenFBp0r3L3Q1Xeo5r7KPybRUREStRoawPufgJ4JYWG0UeBx8zss7FepsWGEB34mLt/292fAP47\n8KvFnri3Avvd/YvF9/pH4OvAJbHX/o273+fuTwGvA77l7re4+1Pufj3wvQVi/1t3f7x47m7gmcAL\nY6fc4e5fLG7C/AkWbuycBLqBvmKP4zeK96aStRQamOXx/AtwlpmdT6HxdmOV1/974Ivu/sgC8VRz\novj+IiIiJWq0tQl3v9/d3+Hu5wA/DawHrl3CJR6O/fwQsBJ4DvDvgG3F4cbHzexx4OeA51d57Xqg\nvCFTtWFTHNb8dnEo9XEKPWrPiZ3yaOznJ4DOaFi3go8Dfw/cZGZTZvanZvaMKuc+DpyxwHV+HRgE\nPkPlRu+bgV8xs/dXucZC1hTfX0REpESNtjbk7t+h0EP008WHZoFnxU55/mkvKtSHxX9+kkJh/kPA\nx939zNh/Z7j7h+JvGfv5MIXhSaBQ+BU/jjOzi4FdwDZ3X1scfj1G7ZML5g25uvuP3f2P3P3/AV4B\n/CJPD3WW+ybze/TiPgH8GoWJCnNVzvkuhd62y83svTXGG3kxcM8SXyMiIi1OjbY2YGYvNLOdxfoz\nzOwc4C3AvxRPyQOvMrNzzKwLuKr8EsBbzezFZvYsCktS7IsNSf5ScXLBCjPrLC6P0Vv2+sitwAVm\n9oZiL9d/pXIjEQo9XT8Gvm9mq8zs9yn0QtXqUeC82H0YNLMLihMITlBoeJ6q8tr9wJZKT7j7g8Cr\ngN9d6M3d/dsUGm67rMb13sysExgAbqvlfBERaR9qtLWHE8Bm4E4z+yGFxto3gSsB3P02YLT42Nco\nLDsR76VyCkOCfwMcAVZRWMaCYs3WG4D3AY9R6Hm7kvkNtdK13H0G2AZ8CPg+hV6lr/P08iMeO/+L\nxf++CxyiMGngobLrlk9giB9fDfxecdj2SgqNw30Ueuu+DYwX/12VfAzYWmxEVfp3fMXdvxd7vPx+\nRed9k8KEhT8wsx0V3qf8tb8EfCl2bREREQCs0FmSwhsX/s/wAIXC8lXAZ929vIcHM7ueQvH6E8B2\nd/9GUwMVigvHftzd/7oB1+6gUPP2H929fImNVJnZHwOPuft1TXzPrwL/udhLJyIiUlKtCLvh3H3O\nzH7e3Z8oDpN92cxe6e5fjs4xs63ARnfvM7PNwIeBi9KKuc0ltiiumf0ChaU8/o1CzRrAV5O6flLc\nfcHhzwa9pz7fIiJSUarDo8XlI6DQ07YC+EHZKa+nuKSCu98JrNVK8alJskv2Z4FJChMZLgF+uWzn\nARERESmTWk8blIbGchSKxT9cYUiol/nLRTxCYabho0jTuPvPJ3y9P6SwjZaIiIjUKO2etqfcvZ9C\nQ+xVZjZY4bTyYbl0ivBEREREUpRqT1vE3Y+Z2a3Az1CY0ReZAs6JHZ9dfGweM1NDTqTNuHtidZYi\nIiFIrafNzJ5jZmuLP/8E8BqgfGbo5ygufmpmFwFH3b3i0Ki7Z/6/Sy+9NPUYWinOkGJVnMn+JyLS\njtLsaVsH3Fisa+ugsKTEmJm9C8Ddb3D3/Wa21cwmKaza/44U4xURERFJTZpLftxLYeX38sdvKDt+\nT9OCarANGzakHUJNQokTwolVcYqISL20I0ITDQ4Oph1CTUKJE8KJVXGKiEi91GgTERERCYAabSIi\nIiIBSG3v0SSZmbfCv0NEamNmuJb8EJE2o542ERERkQCo0dZE4+PjaYdQk1DihHBiVZwiIv9/e/cb\nJNd51Xn8ezRxInDwzEYWdjQWzNrqBLIEdVqFbJICDXRC2XIRoIphkiqKiN1CBuOCRF7hENgK7JIK\nYUqKE5IFGydganeTQeBkk1guYqYyWvMiIlG7nQA29DgMWDOKPWtn5EQurx357Ivp27pzp7unZ/rP\nvc/c36dK5b7dz9w+uk55Ts5znueRbilpExEREQmAetpEJDjqaRORPFKlTURERCQAStoGKJR+oVDi\nhHBiVZwiItItJW0iIiIiAVBPm4gERz1tIpJHqrSJiIiIBEBJ2wCF0i8USpwQTqyKU0REuqWkTURE\nRCQA6mkTkeCop01E8kiVNhEREZEAKGkboFD6hUKJE8KJVXGKiEi3UkvazGy3mX3BzP7BzP7ezH6t\nyZhxMztvZg/X//x2GrGKiIiIpC21njYzuxq42t2rZvZK4Azw0+7+aGzMOHDE3d+6zr3U0yaSI+pp\nE5E8Sq3S5u5fd/dq/fW3gEeBXU2G6j/MIiIiknuZ6GkzszHgDcDpxEcOvNHMHjGzk2b2ukHH1kuh\n9AuFEieEE6viFBGRbr0s7QDqU6N/Cfx6veIWVwF2u/tzZnYT8GngNYOOUURERCRtqe7TZmaXAZ8D\nHnD3OzsY/y/APnd/JvG+v+Md72BsbAyAkZERisUi4+PjwKXqga51revBXV+8eBGAoaGhru9XrVZZ\nXl4GYH5+nnvvvVc9bSKSO2kuRDDgXuBpd39XizFXAU+5u5vZfuAv3H2syTgtRBDJmJmZGQDK5XLP\n762FCCKSR2n2tL0J+Hngx2JbetxkZreY2S31MT8LfNXMqsCdwNvSCrYXQukXCiVOCCfWPMZZLpf7\nkrCJiORVaj1t7v63rJM0uvtHgY8OJiIRSUM/K3IiIluJzh4VkTUGmUht5rs0PSoieZT66lERyTdV\n2EREOpOJfdryIo99Tf0WSqyhxal+NBGR7FHSJiIiIhIA9bSJSHDU0yYieaRKm4iIiEgAlLQNUGh9\nTSEIJdYsxzkzM9NYwZnlOEVE8k5Jm4iIiEgA1NMmIsFRT5uI5JEqbSIiIiIBUNI2QKH0C4USJ4QT\na8hxxnveREQkPUraRKStSqVCpVJJOwwRkdxTT5uItJXFA93V0yYieaSkTUSCo6RNRPJI06MDFHJf\nU1aFEqviFBGRbilpExEREQmApkdFJDiaHhWRPFKlTSSntJWHiEhYlLQNUCj9QqHECeHEqjhFRKRb\nL0vri81sN/DnwHcDDtzt7h9uMu7DwE3Ac8Ahd394oIGKBKiTbTqytIWHiIisL7WeNjO7Grja3atm\n9krgDPDT7v5obMxB4DZ3P2hm1wMfcvcbmtxLPW0iMVncW62X1NMmInmUWqXN3b8OfL3++ltm9iiw\nC3g0NuytwL31MafNbMTMrnL3JwcesEjGzczMUKlUKJVKWzZZExHJs0z0tJnZGPAG4HTio1Hgidj1\nWeCawUTVe6H0C4USJ4QTa6/j7NciglCep4hIHqVWaYvUp0b/Evh1d/9WsyGJ66bzoIcOHWJsbAyA\nkZERisUi4+PjwKVfRGlfR7IST6vrarWaqXi2wnW1Wu3J/WZmZhr/forF4qrPy+Uy5XKZ2dlZZmdn\nM/X37/a6Wq2yvLwMwPz8PCIieZTqPm1mdhnwOeABd7+zyed/DMy6+yfr148BB5LTo+ppk1BttPds\nq/eqdUo9bSKSR2muHjXgY8A/NkvY6j4D3AZ80sxuAJbVzyZb2XpJWfL9qakpAI4ePdr1vUVEJNvS\n7Gl7E/DzwI+Z2cP1PzeZ2S1mdguAu58EvmZmc8BdwK0pxtu1UPqFQokTwom1VZzRlGYzaWx+G8rz\nFBHJozRXj/4tHSSN7n7bAMIRCVKpVOp4rCpsIiJhy8Tq0byIGquzLpQ4IZxYexHnICpvoTxPEZE8\nSn31qIh03m9WqVRWjVP1TEQkP1RpG6BQ+oVCiRPCibWTOCuVyqpKWrLfrVwub2g6dDNCeZ4iInmk\nSptIBnRaMVNlTUQkv1Ldp61XtE+bbDXanqM97dMmInmk6VERERGRAChpG6BQ+oVCiRPCiXWjcbbb\nv62fQnmeIiJ5pKRNRPomjQ2CRUS2KvW0iUjf9Ks3Tz1tIpJHStpEBqSfiwvytnBBSZuI5JGmRwco\nlH6hUOKEcGJtFmcWpw5DeZ4iInmkfdpEtoC8VNhERPJM06MiA5K3Kcx+0vSoiOSRkjaRlCmZ2zgl\nbSKSR+ppG6BQ+oVCiROyG2uyXy2rcSaFEqeISB4paRMREREJgKZHRVKm6dGN0/SoiOSRkjYRCY6S\nNhHJo1SnR83s42b2pJl9tcXn42Z23swerv/57UHH2Euh9AuFEieEE6viFBGRbqW9T9ufAn8I/Hmb\nMafc/a0DikdEREQkk1KfHjWzMeCz7v76Jp+NA7e7+0+ucw9Nj4rkiKZHRSSPss9jZZoAACAASURB\nVL561IE3mtkjZnbSzF6XdkAiIiIiach60lYBdrv7XlamUT+dcjxdCaVfKJQ4IZxYFaeIiHQr7Z62\nttz9m7HXD5jZfzezV7n7M8mxhw4dYmxsDICRkRGKxSLj4+PApV9EaV9HshJPq+tqtZqpeLbCdbVa\nzVQ8oV1Xq1WWl5cBmJ+fR0Qkj7Le03YV8JS7u5ntB/7C3ceajFNPm6RCe6ylQz1tIpJHqVbazOwT\nwAHgSjN7AngvcBmAu98F/CzwK2b2beA54G1pxSoiIiKSplR72tz97e6+y91f7u673f3j7n5XPWHD\n3T/q7j/g7kV3f6O7fzHNeLsVSr9QKHFCurFupMoWyjMNJU4RkTzK+kIEkYFLHvYuIiKSBan3tPWC\netqkl9Snln3qaRORPFLSJiLBUdImInmk6dEBCqVfKJQ4IZxYBxlnN9O7vYhT08siIv2hpE2kS0pS\nRERkEDQ9KpKw0Z429cANnqZHRSSPMn0igkgIlKyJiMggaHp0gNR/1Xu9jDWa5iyXy20Tsc1Mh4by\nTEOJU0Qkj5S0iYiIiARAPW0iXepVT5t64zqnnjYRySNV2kS6MDMzQ6VSSTsMERHJASVtAxRKv1Ao\ncUI2Yi2VSutWxzqJc71eukHIwvMUEZHmtHpUpAtpJ1m9oGlZEZEwqKdNcqGTxGS9MVs1uQnx76We\nNhHJI1XaJBeivrN4YtKLRK5TWU6MshiTiIispZ62AQqlXyiUOKHzWEulEqVSqe2Y9XrKuuk5q1ar\nm/q5QQvp372ISN6o0ia50CzZ6iQB61UVat++fYyPj/fkXiIikk/qaZMtK8tTktId9bSJSB6lWmkz\ns48DNwNPufvrW4z5MHAT8BxwyN0fHmCIEqCNHjElIiISgrR72v4UuLHVh2Z2ENjj7gXgMPBHgwqs\nH0LpFwolTmgda7OFB+1s5jzRjQjlmYYSp4hIHqVaaXP3h8xsrM2QtwL31seeNrMRM7vK3Z8cRHwS\npvUSteS0afJUg35Mp545c4aLFy9qqlZERDYt6wsRRoEnYtdngWuAIJO2UBrRQ4kT1sa62T629VaW\nttLp9xWLxU3df9BC+ncvIpI3WU/aAJLNxlpxID3VSYLX7aIGVdhERKRbWU/aFoDdsetr6u+tcejQ\nIcbGxgAYGRmhWCw2qgZRn07a19F7WYmn1fWdd96ZyefX7Dr5bMvlMrOzs8zOzrb8+eSeaUNDQ5w5\nc4Zisdj4+eT3VavVRrUs/nkn3xf9/Dvf+c7Un9d618nnmXY80XW1WmV5eRmA+fl5RETyKPUtP+o9\nbZ9ttnq0vhDhNnc/aGY3AHe6+w1NxgWx5Uf8F3uWhRInXIq100pYfFyr1/2MM+tCiVNbfohIHqW9\n5ccngAPAlWb2BPBe4DIAd7/L3U+a2UEzmwMuAL+YXrTdC+GXIYQTJ2w81viCAxjctGUozzSUOEVE\n8ijt1aNv72DMbYOIRcITr451eqboZhcciIiIpC3tfdpyJd4vlGWhxAkbP9MzSvCSiV4354p2IpRn\nGkqcIiJ5pKRNglar1RqVtZmZGaampoC1VbZ+b54rIiLSb1lfPbqlhNIvFEqc09PTVKtVrrvuuqZJ\nWpaE8kxDiVNEJI9aJm1m9h3AJPAM8DngKPCjwBzw39z9/w4kQpEmoqRs586djffiU5xRxS3qYWuV\n1A1i5aiIiEgvtJse/XPgLcB/BL4AfA/wEeBbwJ/1PbItKJR+oVDiLBQK7N69u+niglKpRKlU6nuv\nWqdCeaahxCkikkftpke/391/wMxeBpx19wP19x8ws0cGEJvIKskpz1qtxuLiYtNqWfxc0ei61WrT\nLCR1IiIi62lXaXsRwN2/DZxLfPZS3yLawkLpFwolzoWFBdptqpw8CD5NoTzTUOIUEcmjlicimNkS\n8AlWzv6cBD7JpXNAJ939uwcSYQdCORFBeuvw4cMA3H333Y33okQtPmWa9Uqaeuo2TiciiEgetau0\nHQXOAF8GfiP2+kz9M9mgUPqFshxnfOuOyclJXvva1zYdF1XYspIIZfmZxoUSp4hIHrXsaXP3P0u+\nZ2a3uPtdfY1IpIWoilar1ZienqZQKLBt27bGZ3CpXy1rW360k5XEUkREsm1DB8ab2cPu/oY+xrMp\nmh7Nh2gbD1hZhFAoFBorRJNJmhKhrU3ToyKSR0raJPM2kpCpPywflLSJSB617Gkzs9sTf44AHzSz\nfz/A+LaUUPqFshRnfAVochsPWIk13ufWal+2tI+xytIzbSeUOEVE8qjdPm3fBSTLV98L/JaZ/Y67\nf6J/YYmsaLWgIErAhoaGBh6TiIhIGjY0PQpgZq8CZrI0Tarp0a2r1RYeMzMzjcUIR49qMXPeaHpU\nRPKo3ZYfTbn7M/0IRCQ+hRm9jips09PTqzbKLZfLFAqFVOIUERFJw4aTNjP7MeAbfYhlywulX6jf\ncXbaX1apVDh8+HAjnmilaOTo0aP80A/9UL/C7Cn9uxcRkW617Gkzs682efvfsXKk1S/0LSLJpSiJ\nq1QqVCoVjh49SqVSYWFhAVg5HL5ZX1u1WtXRSyIikgvtFiL8ZOLagafd/Vt9jGdLCyW56HecnW7H\nUSqVVvWyNVMsFnsRUt/p372IiHRrwwsRevrlZjcCdwJDwD3u/oHE5+PA/wa+Vn/rr9z995rcRwsR\nAhVfaBBP5lq9LwJaiCAi+dSu0tZXZjYEfAR4M7AAfMnMPuPujyaGnnL3tw48wD6YnZ0NopKx2Tg7\n2dg2PiZaAbqwsECtVgNWpkdnZ2cZHR0FaLzf6p5b/ZkOWihxiojkUWpJG7AfmHP3eQAz+yTwU0Ay\nadP/mw5EJ4e0x8dEr5NJwtzcHEtLS0xMTPQnUBERkQClmbSNAk/Ers8C1yfGOPBGM3uElWrcf3b3\nfxxQfD0XSgWjn3FGPWpRxW1ycnLN53v27GFpaamjqVE9094KJU4RkTxKM2nrpAmtAux29+fM7Cbg\n08Brmg08dOgQY2NjAIyMjFAsFhu/gKJtDHTdn+tjx44R1258uVzm2LFj1Gq1RiXtgx/8INdccw3X\nXXcdALt372b37t2NhC3tv5+u07+uVqssLy8DMD8/j4hIHqW2EMHMbgB+x91vrF//JvBScjFC4mf+\nBdiX3OA3lIUIofQLbTTOjRzoHh8f9bU1O/GgX7GmRXH2lhYiiEgepVlp+zJQMLMxYBGYBN4eH2Bm\nVwFPubub2X5WkkydyJBR7ZKtqakpYGVD3Pi4Vge8i4iIyGppb/lxE5e2/PiYu7/fzG4BcPe7zOxX\ngV8Bvg08Bxxx9y82uU8QlbatJFkti163Gjs9PQ2s9LA1+5n1rkXiVGkTkTxKs9KGuz8APJB4767Y\n648CHx10XNJeckqzk0RrcnKycdpBfOWokjIREZHObPjsUdm8qME66zqJM76ys9VZotH7UbIWnXAQ\njU2uJN3MtOlWeqab0ek5rp0K5XmKiOSRkjZZVzIxaJVQJStuzT6Pxqy3nUevkxEREZHQpdrT1ivq\naeuNVtObGzlSKr7goNl1t7GIgHraRCSfUu1pkzA0S5xaJWPJA96jY6iS1kvKlKyJiIispqRtgLK+\nB1Z8M9sozo1WvJLjkice9HrKM+vPNKI4RUSkW0raZFPWm+5cb0pVlTQREZGNUU+b9EW0N1uhUGgk\neFu1T22r/r2yTD1tIpJHqrRtYRs9Xir5c+ut7lxvTKFQWNPjJiIiIpujLT8GKI09sKI90jaiWq1S\nqVS67j9LTo3248iqLOwr1snfKwtxdiKUOEVE8kiVtsBsZCoumTB1ep99+/Zx8eLFDcUwNTVFrVaj\nUCgAazff7TRmERERaU49bQFp19zfLjFq9lk3iVQUB1za0mNhYYGlpSV27tzJ0tISxWKxcXQVrL+Z\nrshGqKdNRPJIlbaMatWPViqVGlOerVZwJg9zjxYEdJs0xWOKJ2sAo6OjjI6OUigUVlXcopiVsImI\niHRHSdsANdsDq13FK16lSh7Svt7Pxm10QUAne3U1u+f09DQAd999d+O9fidroewrpjhFRKRbStpS\nFE/EkglY9M9KpcKtt97KhQsXOHjwYOPQ9cj09PSaZK5dL9t673ciarxPxrzRBQ8iIiLSOfW0pSg+\n3RhPeKIp0MgHPvABAK6//npGR0dZWFhgdHSUu+++m8OHD1OtVpmYmGgkbsmEarNbfzSLEy5V1KLT\nDjT1KYOmnjYRySNV2lIUT8w+9KEP8fzzz3Pttddy4sQJFhcX2bt3LwA7duxgeHiYubk55ubmePrp\np9m+fXvj/M/FxUXuuece9uzZw/j4+JrKV5S4JStySc0Svfi9op+Nksbkz0Xfd/ToUa0YFRER6TEl\nbQMU9QvFK1cnTpwA4Omnn+bixYucP3+eCxcucPnllzM6OsqpU6cAOH/+POfOneOKK67ghRde4Nln\nn+XEiRMUi0V27doFwNzc3KpkKim5IKBVYjU7O9uopsUdP34coPEd09PTa84WHbRQerAUp4iIdEtJ\nWwqmp6epVqucP3++UTXbsWNH4/Oownbq1CnOnTsHwKtf/erG5y9/+ct5/vnnOX/+PAATExPUajVO\nnTrFyZMnee1rX9uoukU9b1H1a2pqqpG8xStxSdEq0Ch5KxQKjWQtvkK0Vf+c9mgTERHprVSTNjO7\nEbgTGALucfcPNBnzYeAm4DngkLs/PNgoeyeqslWrVb72ta8BcO211zb2Ntu5c2djXK1WY2lpiXPn\nzvHiiy+yZ88e9uzZA6xU1Hbs2MGBAwcaqzhnZ2f5t3/7N55//nm2b9/eGHvy5MnG95dKJWq1Gh/6\n0IfYtWtXI9mLEqtbb72V4eFh3v/+9wMryVlUCZycnGxs8xEthshCIpZmVWgjCWko1atQ4hQRyaPU\nkjYzGwI+ArwZWAC+ZGafcfdHY2MOAnvcvWBm1wN/BNyQSsA9EPWVLS4uAnDHHXdQKpWYnp5etccZ\nXNo24+abbwbgyJEjwErf2OjoKJOTk6t6z8bHxzl9+jQAr3/96xu/fPfu3cvc3By1Wq2xr1s05Zrs\nbxseHgZWKoELCwuMj483Ekm4tCfber1xcVlI7ERERLaC1FaPmtkPA+919xvr1+8GcPffj435Y+AL\n7j5dv34MOODuTybulfnVo4cPH+YrX/kK73vf+xp9YHfccUdjmjFKglolRK2mG+OLAO655x4A9uzZ\nw9zcHBcuXGDXrl0Ui8VVx0vB6sQsWq1aq9VYWFjAzDhw4EAjoQTW9K5lJRkLpQdLcfaWVo+KSB6l\nOT06CjwRuz4LXN/BmGuAJwnQlVdeSaVSYfv27Y3FA4VCYdXJBuslZ8lkKblSNKqWRRYXFxvVsoWF\nBWq1Gvfddx/PPvssQ0NDnD59mgsXLgA0+uqKxeKa2LOSpImIiORVmklbp6Wx5P+bznZJrYmbb76Z\npaUlJiYmmJ2dZe/evaumO2dmZlomY+uJkrnkkVZRBe348eONrUKGh4c5ffo0Tz/9NNu2bQPgG9/4\nBi+99BLbtm1r9MMdOHCAWq22pgqYRSFUhUBxiohI99JM2haA3bHr3axU0tqNuab+3hqHDh1ibGwM\ngJGREYrFYuMX0OzsLMDAry9evAisbOdx7tw5arUao6OjbNu2jaGhocbn1WqVoaGhtveLf37s2DEA\nbr/99sbPnzhxgieeeILR0VEWFxe55pprKJVKLC0t8cwzz/DCCy80KmoAr3jFK9i9ezfnzp1jeHiY\n0dFRzp5defzbtm1jcXGRp556ikKhwNDQ0Kpps9nZWc6cOUOxWKRcLq/5+w4NDTXijxZe7Nu3L/V/\nH7oO97parbK8vAzA/Pw8IiJ5lGZP28uAfwLKwCLwd8DbmyxEuM3dD5rZDcCd7r5mIUJWe9riU5pT\nU1M8/vjjXHfddcClythmtsRotglu1JOW7F07fvw4S0tLnD9/nuHhYXbu3MlDDz3EFVdcwcGDBxsb\n5U5OTjbGTk5O8tJLL1Gr1RoLHtrFsNH3eimUHizF2VvqaRORPEqt0ubu3zaz24C/ZmXLj4+5+6Nm\ndkv987vc/aSZHTSzOeAC8ItpxbsZzRKVjU41Nkt6mt03OT0aFz94Plp9GiV3k5OTVCoVjh8/zujo\nKEeOHFlV1WulWQydviciIiIbp7NH+yh5tmirQ907uUe3lbjkUVbJatj09PSaRREiWaVKm4jkkU5E\n6IPoTNBoz7VCodDo00lW2tZLyqJkq9lihY2IErZW39Gr7xEREZH+UNLWI/Hkq1arNY6pim/B8fjj\nj2+qktXuuKlmklOprSpsrb4nvkii2eHxWRJKD5biFBGRbilp64NoI9ro5IFCocDk5GRjVWVcJ4lQ\nJ31w6yVW8YSt1dgsb+0hIiKSd+pp67HkilFov0igH9/by7EiWaSeNhHJIyVtPdYsUVOSJNJbStpE\nJI+2pR3AVlMqlZpOM05PT/PLv/zLG75ftDig1XU392olWjQRglBiVZwiItIt9bT1WLTJbVy5XKZS\nqfD444+nFJWIiIiETtOjPRTtdwaXNq2FlalSTZGK9I6mR0Ukj1Rp66Hjx48DcP/99zfeiw6EX88g\nk7p236XkUkREJJvU09YjMzMzLC0tNV4nE7VyucyJEyeYmppq/OnFd262v62darXa83v2Syg9WIpT\nRES6pUpbj1QqFXbu3Mn4+HjTkweaJVfxqla/K1udfle5XG66n5yIiIikSz1tPRL1sy0sLAAwPj6+\nZhVpMlnq1VRkJ/fRtKdsJeppE5E8UqWtR+IJWzRNmjyUPV5tG0R1La5f36VkUEREZDDU09YjUcJ2\n5MgRJiYmKJVKa3rX4r1im9l/rdWY5IHvmxH/2ZD6mkKJVXGKiEi3VGnrgSjZGR0dXVVBi7b/iD7f\nt28f4+Pjqw5wb3e/ZKWu3c+kRRU2ERGRwVBPWw9E/WyFQqHp8VWR9Q5sT/5cMmlr9zMieaKeNhHJ\nI1XaupRM2OLJVXxz3Whs9Fm7+zUbk1ZPmpJFERGRbFDStklRMlOpVDh16lSjXy15hFWtVmNmZoZy\nuUy1WqVYLA481o2anZ1lfHw87TA6EkqsilNERLqVStJmZq8CpoHvBeaBn3P35Sbj5oFngYvAi+6+\nf4Bhdmx4eBhY+YU3OjoK0LL/LKq+tapcrfd5p6IFEPHp2shG9odThU1ERCQb0qq0vRt40N3/wMzu\nqF+/u8k4B8bd/ZmBRteBZDITVdgKhQJAI2ErlUqNlZ0vvfTShr+n2/62ZmPbJYbrbQKcNaFUhRSn\niIh0K62k7a3Agfrre4FZmidtAJltNj58+DALCwssLS1x/vx5Dhw4QK1Wo1AoUC6XmZqaWjVdGiVw\n7TSrjCV1Uo2L+uuaVfw2sgI1q6tWRURE8iatfdqucvcn66+fBK5qMc6BvzGzL5vZLw0mtI1ZWlpi\ncXGRCxcuACuVtlYJzmbO9Gw2hVkqlTpOopoliu2mRZsdY9VJspmWUPYVU5wiItKtvlXazOxB4Oom\nH/1W/MLd3cxa7dfxJnc/Z2Y7gQfN7DF3f6jXsW5UVH2anJxsvLdz505gdYITX03azXfB5qYme5Fo\nZTVZExERyZu+JW3u/pZWn5nZk2Z2tbt/3cxeDTzV4h7n6v9cMrNPAfuBpknboUOHGBsbA2BkZIRi\nsdjoz4mqB724npmZ4cSJE5w9e7bx3efOnePZZ59laWmJarVKqVTibW97GxcvXqRSqbBt2zb27dvH\n7bff3rjfmTNnKBaLlMtljh07BrDq8/j3V6tVhoaGVl3DpYSql3+/SHwVYa/v38vr8fHxTMXT7jqS\nlXhCep7VapXl5ZW1SvPz84iI5FEqm+ua2R8AT7v7B8zs3cCIu787MeY7gSF3/6aZXQ58Hvhdd/98\nk/sNbHPdqPIVnTUa/yUHK9OlxWJx1YKEZpvqJl9HfWPrHSrfbmFCJHn/zfz9VGGTLNPmuiKSR2n1\ntP0+8BYz+2fgx+vXmNkuM7u/PuZq4CEzqwKngc81S9gGLdo0N1qAUKvVOHLkCOPj44yOjjIxMUGh\nUKBWq61JwuI9bfG+snK5vOFG/27OGW13z0qlsqneu7SE0oOlOEVEpFuprB6tb+Hx5ibvLwI3119/\nDcj0TrRRH1u0mjO+CCH6Z7SxLlw6ezTSyX5pzRYRJFd0NvvZzVbKSqXSmoUIIiIikj6dPboJ8aOr\nYGWPtsnJyaZTm62mPaPPof9TmJrylK1G06Mikkc6xmqDZmZmuPXWWxkeHmZycnJV8jY1NbVqM11o\nvyfaZpMoJV8iIiL5o0rbBh0+fJj77rsPgGuvvZbz58+zZ8+exn5tBw8eXLUVSDzBmp2d5eLFi2ve\n34huDnhfr/IXjzOUnfFDiVVx9pYqbSKSR2ktRAjS1NQUJ0+eBGD79u3ApXNHAS6//PJG1S1Kiqam\npjpaMNDJwoIo6Wr3c5VKZc0YERERCZ+mRzdgdnaWp556ihdffJHt27eza9cuJiYmGlOgySpWpVJp\nrCKF3pzrmKySJRO09aZjO6nwhVBpiYQSq+IUEZFuaXp0A/bv38+XvvQlAHbs2MH111/f+OzIkSON\n153sqdZuzEZMTU01XrdaECGy1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       "text": [
        "<matplotlib.figure.Figure at 0x7fe20b110d10>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That's all for now. Until next post!"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}