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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "El color de los objetos celestes: parte II"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Autor: [Eduardo Mart\u00edn Calleja](http://balbuceosastropy.blogspot.com.es/)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Tras mi anterior entrada en este blog, dedicada al tama del [color de los objetos celestes](http://balbuceosastropy.blogspot.com.es/2013/10/el-color-de-los-objetos-celestes.html), esta entrada constituye su continuaci\u00f3n, en la cual examinaremos los datos de un cat\u00e1logo de estrellas que contenga datos fotom\u00e9tricos y espectrogr\u00e1ficos y formaremos con ellos gr\u00e1ficos color-color en los que se tratar\u00e1 de diferenciar entre las diferentes clases estelares. Es decir, si el post anterior introduc\u00eda una serie de conceptos te\u00f3ricos, ahora vamos a aplicar estos conceptos a datos reales.\n",
      "\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Importaciones y referencias"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "El cat\u00e1logo que vamos a utilizar es el \"Bright Star Catalogue\", el cual incluye pr\u00e1cticamente todas las estrellas visibles a simple vista, hasta la magnitud 6.5 o m\u00e1s brillantes. Aqu\u00ed hay una primera [descripci\u00f3n](http://en.wikipedia.org/wiki/Bright_Star_Catalogue).\n",
      "\n",
      "Este cat\u00e1logo est\u00e1 disponible online [aqu\u00ed](http://tdc-www.harvard.edu/catalogs/bsc5.html). Para seguir los ejercicios de esta entrada habr\u00e1 que descargar de este sitio los tres ficheros de los que consta el cat\u00e1logo:\n",
      "\n",
      "* bsc5.dat        El cat\u00e1logo\n",
      "* bsc5.readme     Una descripci\u00f3n del cat\u00e1logo\n",
      "* bsc5.notes      Notas del cat\u00e1logo\n",
      "\n",
      "Tambi\u00e9n ser\u00e1 util este [art\u00edculo de la wikipedia](http://en.wikipedia.org/wiki/Stellar_classification) dedicado a la clasificaci\u00f3n de las estrellas por las caracter\u00edsticas de su espectro.\n",
      "\n",
      "Como de costumbre, esta entrada est\u00e1 escrita \u00edntegramente con el Notabook de IPython, y se utilizar\u00e1n una serie de librer\u00edas de Python que a continuaci\u00f3n importamos:"
     ]
    },
    {
     "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",
      "# This IPython magic generates a table with version information\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 44 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'>Sat Jan 24 12:07:22 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 44 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 44 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|}{Sat Jan 24 12:07:22 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 44 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",
        "Sat Jan 24 12:07:22 2015 CET"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Lectura de los datos del \"Bright Star Catalogue\" (BSC)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Una vez se han descargado los tres ficheros que componen el BSC, Vamos a leer una serie de campos importandolos en un dataframe de Pandas. Sobre esto unas observaciones:\n",
      "\n",
      "- La estructura de las entradas en el BSC viene detallada en el archivo bsc5.readme, junto con una somera descripci\u00f3n.\n",
      "- No vamos a leer todos los campos, sino tan solo aquellos que vamos a utilizar m\u00e1s adelante.\n",
      "- Al tratarse de registros con campos de ancho fijo, utilizaremos la funci\u00f3n read_fwf() de la librer\u00eda Pandas que permite proporcionar una descripci\u00f3n de la posici\u00f3n de comienzo y longitud de cada campo."
     ]
    },
    {
     "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": [
      "Veamos una muestra de los datos:"
     ]
    },
    {
     "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": [
      "Diagrama color-color de la secuencia principal"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "El prop\u00f3sito final de este post es hacer diagramas color-color de los diferentes tipos de estrellas, es decir, clases espectrales, para lo cual comenzaremos con las estrellas en la secuencia principal. Utilizaremos los \u00edndices de color del sistema UBV, al ser estos los recogidos en el BSC\n",
      "\n",
      "Se denominan as\u00ed las estrellas (las cuales constituyen aproxim\u00e1damente el 80% de la poblaci\u00f3n estelar) que utilizan hidr\u00f3geno como combustible en sus procesos de fusi\u00f3n nuclear.\n",
      "\n",
      "Para diferenciar estas estrellas del resto nos servremos del campo de tipo espectral del BSC (la columna del dataframe con el nombre SpType). El BSC utiliza el sistema de clasificaci\u00f3n de Morgan-Keenan (MK), el cual incluye la clasificaci\u00f3n espectral en el esquema de clasificaci\u00f3n de Harvard (O B A F G K M m\u00e1s un numeral 0-9), al cual se a\u00f1ade una clasificaci\u00f3n basada en la luminosidad, que utiliza numerales romanos I-VI, a los cuales se a\u00f1ade en ocasiones una letra min\u00fascula \"a\" o \"b\". Ejemplos de la clasificaci\u00f3n resultante son: \"B5III\", \"A5Ia\", \"K0V\", etc. En particular, la \"V\" significa \"main sequence\", que es lo que nos va a permitir reconocer las estrellas pertenecientes a la secuencia principal.\n",
      "\n",
      "Observaci\u00f3n importante: t\u00e9ngase en cuenta que el \"Bright Star Catalogue\" solo incluye las estrellas m\u00e1s brillantes, b\u00e1sicamente aquellas que son visibles a simple vista, por lo que el n\u00famero de estrellas de una determinada clase espectral en el cat\u00e1logo no es en modo alguno representativa de su abundancia en el universo. Las estrellas menos brillantes o m\u00e1s lejanas no estar\u00e1n representadas en el BSC."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u00bfCuantos tipos espectrales existen en el cat\u00e1logo?"
     ]
    },
    {
     "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": [
      "\u00bfHay algunos tipos espectrales sin datos (marcados como NaN en 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": [
      "Hay 14. Eliminemos del dataframe estas entradas"
     ]
    },
    {
     "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": [
      "En el resumen anterior comprobamos que el dataframe tiene ya 14 entradas menos, y el campo SpType carece de valores no nulos.\n",
      "\n",
      "A cpntinuaci\u00f3n vamos a seleccionar las estrellas en la secuencia principal, creando un nuevo dataframe al cual llamaremos main(). Estas son aquellas que en la clasificaci\u00f3n MK tienen una clase de luminosidad 'V':"
     ]
    },
    {
     "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",
      "# Clases de luminosidad de tipo \"V\", no \"IV\", ni \"VI\" ni \"VII\"\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": [
      "Hagamos una primera comprobaci\u00f3n visual:"
     ]
    },
    {
     "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": [
      "Ahora queremos comprobar que la primera letra de estas clases espectrales est\u00e1 en la secuencia 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": [
      "Preparamos ahora un diccionario de Python con los c\u00f3digos de colores que vamos a asignar a cada clase espectral, los cuales ir\u00e1n del azul (tipo O) al rojo (tipo 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": [
      "Y generamos el diagrama color-color con Python Matplotlib"
     ]
    },
    {
     "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(u'Color-color secuencia principal cat\u00e1logo BSC')\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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k9OhtdHZGaW6uJpGI4pxRUZGiqqqVIUPasMJnRwkcJXYiIiJS8mKxBCNG7Ch2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IceOsAPf/hjfvSjn+FwdHPhwgSRyG4AwuFdNDb20dn5KD5fFJ+v+W48prgBss6ntkn8apfErj7J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\nSWoIi50kSVJDWOwkSZIawmInSZLUEP8HfLZiXguZnBkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f704d0ada10>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Se ve que de la clase M hay muy poquitas estrellas, \u00a1pero esto es porque solo estamos considerando aquellas visibles a simple vista! y son poco luminosas. De hecho son las m\u00e1s abundantes con diferencia."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Diagrama color-color de las estrellas gigantes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Previamente, vamos a ver si en el BSC existen estrellas enanas, entendiendo por tal las correspondientes a las clases de luminosidad VI y 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": [
      "Vemos que no hay estrellas de estos tipos en el cat\u00e1logo BSC, lo que era de esperar al tratarse de un cat\u00e1logo de las estrellas m\u00e1s brillantes del cielo. Esto significa que en el campo SpType no hay valores \"VI\" ni VII\", lo que ser\u00e1 tenido en cuenta al hacer la selecci\u00f3n."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vamos pues a incorporar al gr\u00e1fico las estrellas pertenecientes a clases de luminosidad de tipos gigantes:\n",
      "\n",
      "* I  Supergigantes\n",
      "* II Gigantes brillantes\n",
      "* III Gigantes normales\n",
      "* IV Subgigantes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vamos a ver cuantas estrellas hay de estas clases en el 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": [
      "Y veamos a continuaci\u00f3n si entre estas aparecen nuevas clases espectrales adem\u00e1s de las habituales 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": [
      "Vemos que en efecto aparecen nuevas clases espectrales m\u00e1s ex\u00f3ticas: W (estrellas azules y brillantes que poseen sobre todo helio en lugar de hidr\u00f3geno en sus atm\u00f3sferas), y las estrellas de carbono de tipos C y S, gigantes y supergigantes rojas en fases finales de su evoluci\u00f3n, de modo que vamos a ampliar nuestro diccionario de colores para acomodar estos nuevos tipos"
     ]
    },
    {
     "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": [
      "A continuaci\u00f3n vamos a ver cual es la incidencia de los nuevos tipos W, S y C. En el gr\u00e1fico a continuaci\u00f3n veremos que su presencia en el BSC es muy escasa, y en el caso de las estrellas de carbono su posici\u00f3n en un esquema color-color es ocupando posiciones muy extremas (magnitudes muy elevadas, y por lo tanto baja temperatura y color rojo intenso)."
     ]
    },
    {
     "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(u'Color-color secuencia principal cat\u00e1logo BSC')\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 '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')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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M00RxBgAAkBCKM+RGj4I/MvdH5v7I3B+Zp4niDAAAICEUZ8iNHgV/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\nSX99UzX4+iYAAFAvGuHrmwAAAAYVijP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VIYRrix7PYBFCWGtmN0p6m6Q7Ch5OI3uHpDPM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\nckn2+AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f704cd95b10>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Dada la escasez de estrellas de las clases W, S y C en el cat\u00e1logo BSC, una vez hemos visto donde se ubican en los gr\u00e1ficos color-color,  vamos a omitir su representaci\u00f3n, ya que su inclusi\u00f3n alteraria mucho la escala de los ejes. A continuaci\u00f3n generaremos un gr\u00e1fico en el que vamos a superponer las estrellas de la secuencia principal y las gigantes, con excepci\u00f3n de las de tipo W, S y C. Con el fin de diferenciarlas, las estrellas de la secuencia principal se van a representar ahora con puntos peque\u00f1os de color negro, mientras que las estrellas gigantes (tipos I, II, III y IV) se van a mostrar con los colores convencionales, y discos semitransparentes.\n",
      "\n",
      "En el mismo gr\u00e1fico vamos a superponer la l\u00ednea recta correspondiente al diagrama color-color \"ideal\" que corresponder\u00eda a un cuerpo negro. para ello utilizaremos las funciones definidas en el [post anterior](http://balbuceosastropy.blogspot.com.es/2013/10/el-color-de-los-objetos-celestes.html)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def B(wl,T):\n",
      "    '''wl es un array de longitudes de onda con unidades de longitud\n",
      "    T es una temperatura expresada en Kelvin\n",
      "    el resultado es un array de valores de la radiancia espectral\n",
      "    con unidades 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",
      "# Definici\u00f3n de las constantes del sistema fotom\u00e9trico UBV\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",
      "# C\u00e1lculo de 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",
      "# C\u00e1lculo de 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",
      "# Funciones para calcular los \u00edndices de color del cuerpo negro\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": [
      "Y generamos el diagrama:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(figsize=(10, 10))\n",
      "ax.set_axis_bgcolor('0.6')\n",
      "\n",
      "#ax.set_xlim(-1, 6)\n",
      "#ax.set_ylim(-1, 2.5)\n",
      "ax.grid()\n",
      "ax.set_title(u'Color-color secuencia principal y gigantes cat\u00e1logo BSC')\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",
      "# Basta con dos puntos para determinar la recta\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": [
      {
       "ename": "NameError",
       "evalue": "name 'plt' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-1-fecb26930650>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_axis_bgcolor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'0.6'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#ax.set_xlim(-1, 6)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m#ax.set_ylim(-1, 2.5)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "La figura anterior presenta caracter\u00edsticas interesantes. La primera, que la radiaci\u00f3n emitida por las estrellas se aparta en cierta medida de la correspondiente a un cuerpo negro (recordemos que esta es una l\u00ednea recta). Las estrellas no son cuerpos negros ideales. La mejor aproximaci\u00f3n es la correspondiente a las estrellas m\u00e1s calientes (O y B en la figura).\n",
      "\n",
      "No obstante, la caracter\u00edstica m\u00e1s sobresaliente es la brusca desviaci\u00f3n de las estrellas de la clase A respecto de la l\u00ednea de emisi\u00f3n del cuerpo negro. Si nos fijamos en la secuencia principal, esta describe una curva en forma de U invertida que se separa y luego vuelve a aproximarse a la l\u00ednea del cuerpo negro. Esta caracter\u00edstica ilustra el fen\u00f3meno conocido como \"**Balmer jump**\" o \"salto de Balmer\".\n",
      "\n",
      "B\u00e1sicamente, cuando la temperatura superficial de la estrella alcanza un valor cr\u00edtico, lo que ocurre con las estrellas de clase espectral a partir de A0, se produce una intensa absorci\u00f3n de los fotones emitidos por el interior de la estrella en la regi\u00f3n del ultravioleta. Dichos fotones son absorbidos por los \u00e1tomos de hidr\u00f3geno en la superficie de la estrella excitando sus electrones, lo que se traduce en la aparici\u00f3n de las l\u00edneas de absorci\u00f3n de la serie de Balmer del hidr\u00f3geno en el espectro de estas estrellas. Esto implica que, como efecto de la absorci\u00f3n de estos fotones, se debilita la emisi\u00f3n de radiaci\u00f3n por la estrella en la regi\u00f3n del ultravioleta, y la magnitud U-B crece (recordemos que a menor flujo radiante recibido mayor magnitud), es decir, toma valores positivos mas deprisa de lo que le corresponder\u00eda a un cuerpo negro, separandose bruscamente de dicha l\u00ednea como se aprecia en la figura.\n",
      "\n",
      "A partir de la clase espectral A5 la temperatura superficial de la estrella disminuye y hay menos \u00e1tomos de hidr\u00f3geno en los niveles energ\u00e9ticos que les permiten capturar dichos fotones. Consecuentemente la radiaci\u00f3n en el ultravioleta se recupera y el \u00edndice de color U-B deja de crecer. La curva se aproxima de nuevo, tambi\u00e9n bruscamente, a la l\u00ednea correspondiente a la radiaci\u00f3n de un cuerpo negro.\n",
      "\n",
      "Este fen\u00f3meno est\u00e1 perfectamente explicado en el libro de  Keith Robinson: \n",
      "[Starlight: An Introduction to Stellar Physics for Amateurs](http://www.amazon.es/Starlight-Introduction-Practical-Astronomy-ebook/dp/B00623DCDG)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Diagramas color-color de las clases de luminosidad MK"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Para terminar este post, ya un tanto largo, realizaremos un conjunto de diagramas color-color para representar el comportamiento en estos diagramas de las distintas clases de luminosidad del esquema MK"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Creaci\u00f3n de un dataframe con solo estrellas clase IV\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": [
      "# Creaci\u00f3n de un dataframe con solo estrellas clase III\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": [
      "# Creaci\u00f3n de un dataframe con solo estrellas clase II\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": [
      "# Creaci\u00f3n de un dataframe con solo estrellas clase I\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('Secuencia principal (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('Subgigantes (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('Gigantes (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('Gigantes luminosas (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('Supergigantes (MK I)');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qga1bty7vEOoSSpwQTqyKc/FZajkspHMjlFgVZ3OFEmcjtCJCC/X29uYdQl1CiRPCiVVx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7xIm6n3lB+NymHKXyLtaSk5rG7Do+7+2hoc40juv9Nm9mngCuCsRlurzNckIunRqBym/CUilUrD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\ni0QrrcmaiIhI1vQtaXP3t7T6z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KRNREREJABK2kREREQCoKRNREREJABK2kREREQCoKRN\nREREJAD/H774f4fpU5ZEAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f704cefcd90>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Esto es todo por ahora: \u00a1hasta el pr\u00f3ximo post!"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}