plot-catalog.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "astropy-tutorials": {
      "author": "Adrian M. Price-Whelan <adrn@astro.columbia.edu> and Kelle Cruz <kellecruz@gmail.com>",
      "date": "July 2013",
      "description": "Demonstrates use of astropy.io.ascii for reading and writing ASCII data, astropy.coordinates and astropy.units for converting RA (as a sexagesimal angle) to decimal degrees, and matplotlib for making a color-magnitude diagram and on-sky locations in a mollweide projection.",
      "link_name": "Read and plot catalog information from a text file",
      "name": "",
      "published": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    },
    "colab": {
      "name": "plot-catalog.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98R7Jg289aC_"
      },
      "source": [
        "# Ler a informação de um catálogo a partir de um arquivo texto e fazer gráficos de alguns parâmetros\n",
        "\n",
        "## Autores\n",
        "Adrian Price-Whelan, Kelle Cruz, Stephanie T. Douglas\n",
        "\n",
        "## Tradução\n",
        "Ricardo Ogando, Micaele Vitória\n",
        "\n",
        "## Objetivos do aprendizado\n",
        "* Ler um arquivo ASCII usando o `astropy.io`\n",
        "* Converter entre representações de componentes de coordenadas usando o  `astropy.coordinates` (de horas para graus)\n",
        "* Fazer um gráfico do céu com projeção esférica usando o `matplotlib`\n",
        "\n",
        "## Palavras-chave\n",
        "arquivo entrada/saída, coordenadas, tabelas, unidades, gráficos de dispersão, matplotlib\n",
        "\n",
        "## Sumário\n",
        "\n",
        "Esse tutorial demonstra o uso do `astropy.io.ascii` para ler dados em ASCII, `astropy.coordinates` e `astropy.units` para converter Ascenção Reta (AR) (como um ângulo sexagesimal) para graus decimais, e `matplotlib` para fazer um diagrama cor-magnitude e localizações no céu em uma projeção Mollweide."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9WO1PwxD3kb",
        "outputId": "4f815128-9727-46ac-8218-453244649e62"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/astropy/astropy-tutorials/main/tutorials/notebooks/plot-catalog/Young-Objects-Compilation.csv"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-09-13 21:58:14--  https://raw.githubusercontent.com/astropy/astropy-tutorials/main/tutorials/notebooks/plot-catalog/Young-Objects-Compilation.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 13153 (13K) [text/plain]\n",
            "Saving to: ‘Young-Objects-Compilation.csv.1’\n",
            "\n",
            "\r          Young-Obj   0%[                    ]       0  --.-KB/s               \rYoung-Objects-Compi 100%[===================>]  12.84K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-09-13 21:58:14 (94.7 MB/s) - ‘Young-Objects-Compilation.csv.1’ saved [13153/13153]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gT0L1Km0-G76",
        "outputId": "4d6aa639-f78f-49f2-86cd-f5232dea8021"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/astropy/astropy-tutorials/main/tutorials/notebooks/plot-catalog/simple_table.csv"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-09-13 21:58:28--  https://raw.githubusercontent.com/astropy/astropy-tutorials/main/tutorials/notebooks/plot-catalog/simple_table.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 126 [text/plain]\n",
            "Saving to: ‘simple_table.csv’\n",
            "\n",
            "simple_table.csv    100%[===================>]     126  --.-KB/s    in 0s      \n",
            "\n",
            "2021-09-13 21:58:28 (4.67 MB/s) - ‘simple_table.csv’ saved [126/126]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMrRnQOWLLkl",
        "outputId": "b3ceccbe-aa3d-494a-f6e6-8a471f93bf04",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pwd"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvNDid5lK9w4"
      },
      "source": [
        "!rm Young-Objects-Compilation.csv.1"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fTqA5Z7F2P5",
        "outputId": "c48b4296-2d72-4b57-880e-c35beec9c588"
      },
      "source": [
        "!cat simple_table.csv"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# name,ra,dec\n",
            "BLG100,17:51:00.0,-29:59:48\n",
            "BLG101,17:53:40.2,-29:49:52\n",
            "BLG102,17:56:20.2,-29:30:51\n",
            "BLG103,17:56:20.2,-30:06:22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9OlGYUF9aDC"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "# Set up matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwuCAO6W9aDD"
      },
      "source": [
        "Astropy provê funcionalidade para ler e manipular dados tabulares através do subpacote `astropy.table`. Um conjunto adicional de ferramentas para ler e escrever dados no formato ASCII, são dadas no subpacote `astropy.io.ascii`, mas usa fundamentalmente as classes e métodos implementadas no `astropy.table`.\n",
        "\n",
        "Vamos começar importando o subpacote `ascii`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3nIu1ox9aDD"
      },
      "source": [
        "from astropy.io import ascii"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9AMlsUr89aDE"
      },
      "source": [
        "Em muitos casos, é suficiente usar a função `ascii.read('filename')` \n",
        "como uma caixa preta para ler dados de arquivos com texto em formato de tabela. \n",
        "Por padrão, essa função vai tentar descobrir como o seu dado está formatado/delimitado (por default, `guess=True`). Por exemplo, se o seu dado se parece com o a seguir:\n",
        "\n",
        "    # name,ra,dec\n",
        "    BLG100,17:51:00.0,-29:59:48\n",
        "    BLG101,17:53:40.2,-29:49:52\n",
        "    BLG102,17:56:20.2,-29:30:51\n",
        "    BLG103,17:56:20.2,-30:06:22\n",
        "    ...\n",
        "\n",
        "(veja o _simple_table.csv_)\n",
        "\n",
        "`ascii.read()` vai retornar um objeto `Table`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lg4uLRwfPu1R",
        "outputId": "87fc5490-a86f-4134-8d02-224558fa9449",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        }
      },
      "source": [
        "ascii.read(\"simple_table.csv\")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div><i>Table length=4</i>\n",
              "<table id=\"table140459969236752\" class=\"table-striped table-bordered table-condensed\">\n",
              "<thead><tr><th>name</th><th>ra</th><th>dec</th></tr></thead>\n",
              "<thead><tr><th>str6</th><th>str10</th><th>str9</th></tr></thead>\n",
              "<tr><td>BLG100</td><td>17:51:00.0</td><td>-29:59:48</td></tr>\n",
              "<tr><td>BLG101</td><td>17:53:40.2</td><td>-29:49:52</td></tr>\n",
              "<tr><td>BLG102</td><td>17:56:20.2</td><td>-29:30:51</td></tr>\n",
              "<tr><td>BLG103</td><td>17:56:20.2</td><td>-30:06:22</td></tr>\n",
              "</table></div>"
            ],
            "text/plain": [
              "<Table length=4>\n",
              " name      ra        dec   \n",
              " str6    str10       str9  \n",
              "------ ---------- ---------\n",
              "BLG100 17:51:00.0 -29:59:48\n",
              "BLG101 17:53:40.2 -29:49:52\n",
              "BLG102 17:56:20.2 -29:30:51\n",
              "BLG103 17:56:20.2 -30:06:22"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8bqzpxXy9aDF",
        "outputId": "fbdec877-b430-4387-8908-43066bcb545a"
      },
      "source": [
        "tbl = ascii.read(\"simple_table.csv\")\n",
        "print(tbl)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " name      ra        dec   \n",
            "------ ---------- ---------\n",
            "BLG100 17:51:00.0 -29:59:48\n",
            "BLG101 17:53:40.2 -29:49:52\n",
            "BLG102 17:56:20.2 -29:30:51\n",
            "BLG103 17:56:20.2 -30:06:22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OKd9LmGr9aDF"
      },
      "source": [
        "Os nomes no cabeçalho são extraídos do topo do arquivo, e o delimitador é inferido a partir do resto do arquivo -- incrível!!!!\n",
        "\n",
        "Podemos acessar as colunas diretamente usando seus nomes como 'chaves' da tabela:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "eArZKqRe9aDG",
        "outputId": "9f0a5fb0-a81e-490c-b76d-7192ca906c1a"
      },
      "source": [
        "tbl[\"ra\"]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "&lt;Column name=&apos;ra&apos; dtype=&apos;str10&apos; length=4&gt;\n",
              "<table>\n",
              "<tr><td>17:51:00.0</td></tr>\n",
              "<tr><td>17:53:40.2</td></tr>\n",
              "<tr><td>17:56:20.2</td></tr>\n",
              "<tr><td>17:56:20.2</td></tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<Column name='ra' dtype='str10' length=4>\n",
              "17:51:00.0\n",
              "17:53:40.2\n",
              "17:56:20.2\n",
              "17:56:20.2"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 97
        },
        "id": "6usjgojkHhF8",
        "outputId": "b12175ae-3f68-4c06-c1f9-7de8e8bff70d"
      },
      "source": [
        "tbl[-1]"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<i>Row index=3</i>\n",
              "<table id=\"table140459969271952\">\n",
              "<thead><tr><th>name</th><th>ra</th><th>dec</th></tr></thead>\n",
              "<thead><tr><th>str6</th><th>str10</th><th>str9</th></tr></thead>\n",
              "<tr><td>BLG103</td><td>17:56:20.2</td><td>-30:06:22</td></tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<Row index=3>\n",
              " name      ra        dec   \n",
              " str6    str10       str9  \n",
              "------ ---------- ---------\n",
              "BLG103 17:56:20.2 -30:06:22"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCa6Mbx39aDH"
      },
      "source": [
        "If we want to then convert the first RA (as a sexagesimal angle) to\n",
        "decimal degrees, for example, we can pluck out the first (0th) item in\n",
        "the column and use the `coordinates` subpackage to parse the string:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ox-ypoa9aDI",
        "outputId": "e6e2c7ae-9ea6-458b-ed90-f3599bb0d667"
      },
      "source": [
        "import astropy.coordinates as coord\n",
        "import astropy.units as u\n",
        "\n",
        "first_row = tbl[0] # get the first (0th) row\n",
        "ra = coord.Angle(first_row[\"ra\"], unit=u.hour) # create an Angle object\n",
        "ra.degree # convert to degrees"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "267.75"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wln3t7VIIajG"
      },
      "source": [
        "Qual é a conta que o astropy está fazendo por você"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHbzp4GiH9gi",
        "outputId": "6facec40-393b-4f9f-c3ad-578415a83a10"
      },
      "source": [
        "17*15+(51/60)*15"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "267.75"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q2jKq3BE9aDJ"
      },
      "source": [
        "Now let's look at a case where this breaks, and we have to specify some\n",
        "more options to the `read()` function. Our data may look a bit messier::\n",
        "\n",
        "    ,,,,2MASS Photometry,,,,,,WISE Photometry,,,,,,,,Spectra,,,,Astrometry,,,,,,,,,,,\n",
        "    Name,Designation,RA,Dec,Jmag,J_unc,Hmag,H_unc,Kmag,K_unc,W1,W1_unc,W2,W2_unc,W3,W3_unc,W4,W4_unc,Spectral Type,Spectra (FITS),Opt Spec Refs,NIR Spec Refs,pm_ra (mas),pm_ra_unc,pm_dec (mas),pm_dec_unc,pi (mas),pi_unc,radial velocity (km/s),rv_unc,Astrometry Refs,Discovery Refs,Group/Age,Note\n",
        "    ,00 04 02.84 -64 10 35.6,1.01201,-64.18,15.79,0.07,14.83,0.07,14.01,0.05,13.37,0.03,12.94,0.03,12.18,0.24,9.16,null,L1γ,,Kirkpatrick et al. 2010,,,,,,,,,,,Kirkpatrick et al. 2010,,\n",
        "    PC 0025+04,00 27 41.97 +05 03 41.7,6.92489,5.06,16.19,0.09,15.29,0.10,14.96,0.12,14.62,0.04,14.14,0.05,12.24,null,8.89,null,M9.5β,,Mould et al. 1994,,0.0105,0.0004,-0.0008,0.0003,,,,,Faherty et al. 2009,Schneider et al. 1991,,,00 32 55.84 -44 05 05.8,8.23267,-44.08,14.78,0.04,13.86,0.03,13.27,0.04,12.82,0.03,12.49,0.03,11.73,0.19,9.29,null,L0γ,,Cruz et al. 2009,,0.1178,0.0043,-0.0916,0.0043,38.4,4.8,,,Faherty et al. 2012,Reid et al. 2008,,\n",
        "    ...\n",
        "\n",
        "(see _Young-Objects-Compilation.csv_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpLQ7pz59aDK"
      },
      "source": [
        "If we try to just use `ascii.read()` on this data, it fails to parse the names out and the column names become `col` followed by the number of the column:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-VulMmOz9aDK",
        "outputId": "66900701-ebf3-4b11-c83c-9dfcc9c982e4"
      },
      "source": [
        "tbl = ascii.read(\"Young-Objects-Compilation.csv\")\n",
        "tbl.colnames"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['col1',\n",
              " 'col2',\n",
              " 'col3',\n",
              " 'col4',\n",
              " 'col5',\n",
              " 'col6',\n",
              " 'col7',\n",
              " 'col8',\n",
              " 'col9',\n",
              " 'col10',\n",
              " 'col11',\n",
              " 'col12',\n",
              " 'col13',\n",
              " 'col14',\n",
              " 'col15',\n",
              " 'col16',\n",
              " 'col17',\n",
              " 'col18',\n",
              " 'col19',\n",
              " 'col20',\n",
              " 'col21',\n",
              " 'col22',\n",
              " 'col23',\n",
              " 'col24',\n",
              " 'col25',\n",
              " 'col26',\n",
              " 'col27',\n",
              " 'col28',\n",
              " 'col29',\n",
              " 'col30',\n",
              " 'col31',\n",
              " 'col32',\n",
              " 'col33',\n",
              " 'col34']"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PxVvI1d2I3Fu",
        "outputId": "0b7894b8-9e7b-440b-d9c6-001da7b88128"
      },
      "source": [
        "tbl"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div><i>Table length=66</i>\n",
              "<table id=\"table140461668784016\" class=\"table-striped table-bordered table-condensed\">\n",
              "<thead><tr><th>col1</th><th>col2</th><th>col3</th><th>col4</th><th>col5</th><th>col6</th><th>col7</th><th>col8</th><th>col9</th><th>col10</th><th>col11</th><th>col12</th><th>col13</th><th>col14</th><th>col15</th><th>col16</th><th>col17</th><th>col18</th><th>col19</th><th>col20</th><th>col21</th><th>col22</th><th>col23</th><th>col24</th><th>col25</th><th>col26</th><th>col27</th><th>col28</th><th>col29</th><th>col30</th><th>col31</th><th>col32</th><th>col33</th><th>col34</th></tr></thead>\n",
              "<thead><tr><th>str24</th><th>str25</th><th>str9</th><th>str6</th><th>str16</th><th>str5</th><th>str5</th><th>str5</th><th>str5</th><th>str5</th><th>str15</th><th>str6</th><th>str5</th><th>str6</th><th>str5</th><th>str6</th><th>str4</th><th>str6</th><th>str13</th><th>str14</th><th>str26</th><th>str23</th><th>str11</th><th>str9</th><th>str12</th><th>str10</th><th>str8</th><th>str6</th><th>str22</th><th>str6</th><th>str19</th><th>str23</th><th>str9</th><th>str38</th></tr></thead>\n",
              "<tr><td>--</td><td>--</td><td>--</td><td>--</td><td>2MASS Photometry</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>WISE Photometry</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Spectra</td><td>--</td><td>--</td><td>--</td><td>Astrometry</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>Name</td><td>Designation</td><td>RA</td><td>Dec</td><td>Jmag</td><td>J_unc</td><td>Hmag</td><td>H_unc</td><td>Kmag</td><td>K_unc</td><td>W1</td><td>W1_unc</td><td>W2</td><td>W2_unc</td><td>W3</td><td>W3_unc</td><td>W4</td><td>W4_unc</td><td>Spectral Type</td><td>Spectra (FITS)</td><td>Opt Spec Refs</td><td>NIR Spec Refs</td><td>pm_ra (mas)</td><td>pm_ra_unc</td><td>pm_dec (mas)</td><td>pm_dec_unc</td><td>pi (mas)</td><td>pi_unc</td><td>radial velocity (km/s)</td><td>rv_unc</td><td>Astrometry Refs</td><td>Discovery Refs</td><td>Group/Age</td><td>Note</td></tr>\n",
              "<tr><td>--</td><td>00 04 02.84 -64 10 35.6</td><td>1.01201</td><td>-64.18</td><td>15.79</td><td>0.07</td><td>14.83</td><td>0.07</td><td>14.01</td><td>0.05</td><td>13.37</td><td>0.03</td><td>12.94</td><td>0.03</td><td>12.18</td><td>0.24</td><td>9.16</td><td>null</td><td>L1γ</td><td>--</td><td>Kirkpatrick et al. 2010</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Kirkpatrick et al. 2010</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>PC 0025+04</td><td>00 27 41.97 +05 03 41.7</td><td>6.92489</td><td>5.06</td><td>16.19</td><td>0.09</td><td>15.29</td><td>0.10</td><td>14.96</td><td>0.12</td><td>14.62</td><td>0.04</td><td>14.14</td><td>0.05</td><td>12.24</td><td>null</td><td>8.89</td><td>null</td><td>M9.5β</td><td>--</td><td>Mould et al. 1994</td><td>--</td><td>0.0105</td><td>0.0004</td><td>-0.0008</td><td>0.0003</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Faherty et al. 2009</td><td>Schneider et al. 1991</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>--</td><td>00 32 55.84 -44 05 05.8</td><td>8.23267</td><td>-44.08</td><td>14.78</td><td>0.04</td><td>13.86</td><td>0.03</td><td>13.27</td><td>0.04</td><td>12.82</td><td>0.03</td><td>12.49</td><td>0.03</td><td>11.73</td><td>0.19</td><td>9.29</td><td>null</td><td>L0γ</td><td>--</td><td>Cruz et al. 2009</td><td>--</td><td>0.1178</td><td>0.0043</td><td>-0.0916</td><td>0.0043</td><td>38.4</td><td>4.8</td><td>--</td><td>--</td><td>Faherty et al. 2012</td><td>Reid et al. 2008</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>--</td><td>00 37 43.06 -58 46 22.9</td><td>9.42942</td><td>-58.77</td><td>15.37</td><td>0.05</td><td>14.26</td><td>0.05</td><td>13.59</td><td>0.05</td><td>13.13</td><td>0.03</td><td>12.74</td><td>0.03</td><td>12.56</td><td>0.38</td><td>9.32</td><td>null</td><td>L0γ</td><td>--</td><td>Cruz et al. 2009</td><td>--</td><td>0.049</td><td>0.01</td><td>-0.051</td><td>0.02</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Faherty et al. 2009</td><td>Reid et al. 2008</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>--</td><td>00 45 21.43 +16 34 44.6</td><td>11.33929</td><td>16.58</td><td>13.06</td><td>0.02</td><td>12.06</td><td>0.04</td><td>11.37</td><td>0.02</td><td>10.77</td><td>0.02</td><td>10.39</td><td>0.02</td><td>9.74</td><td>0.04</td><td>8.42</td><td>0.26</td><td>L2β</td><td>--</td><td>Cruz et al. 2009</td><td>Wilson et al. 2003</td><td>0.385</td><td>0.017</td><td>-0.026</td><td>0.012</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Faherty et al. 2009</td><td>Wilson et al. 2003</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>--</td><td>Empire</td><td>--</td><td>--</td><td>13.8</td><td>--</td><td>13.1</td><td>--</td><td>12.5</td><td>--</td><td>12.07</td><td>0.03</td><td>11.64</td><td>0.02</td><td>11.14</td><td>0.18</td><td>9.02</td><td>null</td><td>M9β</td><td>--</td><td>Reid et al. 2008</td><td>Wilson et al. 2003</td><td>0.098</td><td>0.022</td><td>-0.051</td><td>0.01</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Faherty et al. 2009</td><td>Wilson et al. 2003</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>--</td><td>Flatiron</td><td>--</td><td>--</td><td>16.4</td><td>0.1</td><td>15.2</td><td>--</td><td>14.4</td><td>--</td><td>13.68</td><td>0.03</td><td>13.20</td><td>0.03</td><td>11.99</td><td>null</td><td>8.48</td><td>null</td><td>L2γ</td><td>--</td><td>Cruz et al. in prep</td><td>Cruz et al. in prep</td><td>0.044</td><td>0.024</td><td>-0.082</td><td>0.024</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Faherty et al. 2009</td><td>Cruz et al. in prep</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>--</td><td>Liberty</td><td>--</td><td>--</td><td>15.1</td><td>--</td><td>14.2</td><td>--</td><td>13.4</td><td>--</td><td>13.03</td><td>0.03</td><td>12.62</td><td>0.03</td><td>11.80</td><td>0.19</td><td>9.22</td><td>null</td><td>L1β</td><td>--</td><td>Cruz et al. in prep</td><td>Cruz et al. in prep</td><td>0.08</td><td>0.017</td><td>-0.062</td><td>0.02</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Faherty et al. 2009</td><td>Cruz et al. 2003</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
              "<tr><td>--</td><td>Rockefeller</td><td>--</td><td>--</td><td>15.3</td><td>--</td><td>14.2</td><td>--</td><td>13.4</td><td>--</td><td>12.5</td><td>0.0</td><td>12.1</td><td>0.03</td><td>11.44</td><td>0.21</td><td>8.82</td><td>null</td><td>L5β</td><td>--</td><td>Cruz et al. in prep</td><td>Cruz et al. in prep</td><td>-0.098</td><td>0.013</td><td>-0.105</td><td>0.014</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Faherty et al. 2009</td><td>Cruz et al. 2007</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>--</td><td>20 13 51.52 -28 06 02.0</td><td>303.46467</td><td>-28.10</td><td>14.24</td><td>0.03</td><td>13.46</td><td>0.03</td><td>12.94</td><td>0.03</td><td>12.53</td><td>0.03</td><td>12.16</td><td>0.03</td><td>11.88</td><td>0.28</td><td>8.66</td><td>null</td><td>M9γ</td><td>--</td><td>Cruz et al. in prep</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Reid et al. 2008</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>--</td><td>21 26 50.40 -81 40 29.3</td><td>321.71000</td><td>-81.67</td><td>15.54</td><td>0.06</td><td>14.41</td><td>0.05</td><td>13.55</td><td>0.04</td><td>12.91</td><td>0.02</td><td>12.47</td><td>0.02</td><td>11.89</td><td>0.16</td><td>9.36</td><td>null</td><td>L3γ</td><td>--</td><td>Cruz et al. 2009</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Reid et al. 2008</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>--</td><td>Tribeca</td><td>--</td><td>--</td><td>16.4</td><td>0.1</td><td>15.0</td><td>--</td><td>14.1</td><td>--</td><td>13.37</td><td>0.03</td><td>12.92</td><td>0.03</td><td>12.05</td><td>0.32</td><td>8.43</td><td>null</td><td>L4β</td><td>--</td><td>Cruz et al. in prep</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Cruz et al. in prep</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>--</td><td>Harlem</td><td>--</td><td>--</td><td>15.5</td><td>--</td><td>14.4</td><td>--</td><td>13.6</td><td>--</td><td>12.82</td><td>0.02</td><td>12.38</td><td>0.03</td><td>11.89</td><td>0.22</td><td>9.26</td><td>null</td><td>L4γ</td><td>--</td><td>Cruz et al. in prep</td><td>--</td><td>0.111</td><td>0.013</td><td>-0.182</td><td>0.018</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Faherty et al. 2009</td><td>K00</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>--</td><td>22 08 13.63 +29 21 21.5</td><td>332.05679</td><td>29.36</td><td>15.80</td><td>0.09</td><td>14.79</td><td>0.07</td><td>14.15</td><td>0.07</td><td>13.35</td><td>0.03</td><td>12.89</td><td>0.03</td><td>12.58</td><td>0.39</td><td>9.30</td><td>null</td><td>L3γ</td><td>--</td><td>Cruz et al. 2009</td><td>--</td><td>0.111</td><td>0.014</td><td>-0.011</td><td>0.014</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Faherty et al. 2009</td><td>Kirkpatrick et al. 2000</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>--</td><td>22 13 44.9 -21 36 07</td><td>333.43715</td><td>-21.60</td><td>15.38</td><td>0.04</td><td>14.40</td><td>0.06</td><td>13.76</td><td>0.04</td><td>13.23</td><td>0.03</td><td>12.83</td><td>0.03</td><td>11.55</td><td>0.20</td><td>9.07</td><td>null</td><td>L0γ</td><td>--</td><td>Cruz et al. 2009</td><td>--</td><td>0.06</td><td>0.011</td><td>-0.063</td><td>0.017</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Faherty et al. 2009</td><td>Cruz et al. 2007</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>SDSS 2249+0044</td><td>22 49 53.45 +00 44 04.6</td><td>342.47273</td><td>0.73</td><td>16.59</td><td>0.12</td><td>15.42</td><td>0.11</td><td>14.36</td><td>0.07</td><td>13.58</td><td>0.03</td><td>13.14</td><td>0.05</td><td>11.28</td><td>null</td><td>7.69</td><td>null</td><td>L4γ</td><td>--</td><td>Cruz et al. in prep</td><td>Geballe et al. 2002</td><td>0.075</td><td>0.018</td><td>0.026</td><td>0.018</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Faherty et al. 2009</td><td>Geballe et al. 2002</td><td>--</td><td>Binary: Allers et al. 2010</td></tr>\n",
              "<tr><td>--</td><td>Chinatown</td><td>--</td><td>--</td><td>15.8</td><td>--</td><td>14.7</td><td>--</td><td>14.0</td><td>--</td><td>13.55</td><td>0.03</td><td>13.10</td><td>0.03</td><td>11.67</td><td>0.23</td><td>8.59</td><td>null</td><td>L0γ</td><td>--</td><td>Cruz et al. in prep</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Cruz et al. in prep</td><td>--</td><td>--</td></tr>\n",
              "<tr><td>--</td><td>23 22 52.99 -61 51 27.5</td><td>350.72079</td><td>-61.86</td><td>15.55</td><td>0.06</td><td>14.54</td><td>0.06</td><td>13.86</td><td>0.04</td><td>13.24</td><td>0.03</td><td>12.84</td><td>0.03</td><td>12.68</td><td>0.39</td><td>9.38</td><td>null</td><td>L2γ</td><td>--</td><td>Cruz et al. 2009</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Reid et al. 2008</td><td>--</td><td>--</td></tr>\n",
              "</table></div>"
            ],
            "text/plain": [
              "<Table length=66>\n",
              "     col1                col2          ...   col33             col34           \n",
              "    str24               str25          ...    str9             str38           \n",
              "-------------- ----------------------- ... --------- --------------------------\n",
              "            --                      -- ...        --                         --\n",
              "          Name             Designation ... Group/Age                       Note\n",
              "            -- 00 04 02.84 -64 10 35.6 ...        --                         --\n",
              "    PC 0025+04 00 27 41.97 +05 03 41.7 ...        --                         --\n",
              "            -- 00 32 55.84 -44 05 05.8 ...        --                         --\n",
              "            -- 00 37 43.06 -58 46 22.9 ...        --                         --\n",
              "            -- 00 45 21.43 +16 34 44.6 ...        --                         --\n",
              "            --                  Empire ...        --                         --\n",
              "            --                Flatiron ...        --                         --\n",
              "            --                 Liberty ...        --                         --\n",
              "           ...                     ... ...       ...                        ...\n",
              "            --             Rockefeller ...        --                         --\n",
              "            -- 20 13 51.52 -28 06 02.0 ...        --                         --\n",
              "            -- 21 26 50.40 -81 40 29.3 ...        --                         --\n",
              "            --                 Tribeca ...        --                         --\n",
              "            --                  Harlem ...        --                         --\n",
              "            -- 22 08 13.63 +29 21 21.5 ...        --                         --\n",
              "            --    22 13 44.9 -21 36 07 ...        --                         --\n",
              "SDSS 2249+0044 22 49 53.45 +00 44 04.6 ...        -- Binary: Allers et al. 2010\n",
              "            --               Chinatown ...        --                         --\n",
              "            -- 23 22 52.99 -61 51 27.5 ...        --                         --"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TBlRkhIL9aDL"
      },
      "source": [
        "What happened? The column names are just `col1`, `col2`, etc., the\n",
        "default names if `ascii.read()` is unable to parse out column\n",
        "names. We know it failed to read the column names, but also notice\n",
        "that the first row of data are strings -- something else went wrong!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "id": "iBqWS_eW9aDL",
        "outputId": "75d7f9d3-690c-4d0e-b649-650356727a0e"
      },
      "source": [
        "tbl[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<i>Row index=0</i>\n",
              "<table id=\"table140461668784016\">\n",
              "<thead><tr><th>col1</th><th>col2</th><th>col3</th><th>col4</th><th>col5</th><th>col6</th><th>col7</th><th>col8</th><th>col9</th><th>col10</th><th>col11</th><th>col12</th><th>col13</th><th>col14</th><th>col15</th><th>col16</th><th>col17</th><th>col18</th><th>col19</th><th>col20</th><th>col21</th><th>col22</th><th>col23</th><th>col24</th><th>col25</th><th>col26</th><th>col27</th><th>col28</th><th>col29</th><th>col30</th><th>col31</th><th>col32</th><th>col33</th><th>col34</th></tr></thead>\n",
              "<thead><tr><th>str24</th><th>str25</th><th>str9</th><th>str6</th><th>str16</th><th>str5</th><th>str5</th><th>str5</th><th>str5</th><th>str5</th><th>str15</th><th>str6</th><th>str5</th><th>str6</th><th>str5</th><th>str6</th><th>str4</th><th>str6</th><th>str13</th><th>str14</th><th>str26</th><th>str23</th><th>str11</th><th>str9</th><th>str12</th><th>str10</th><th>str8</th><th>str6</th><th>str22</th><th>str6</th><th>str19</th><th>str23</th><th>str9</th><th>str38</th></tr></thead>\n",
              "<tr><td>--</td><td>--</td><td>--</td><td>--</td><td>2MASS Photometry</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>WISE Photometry</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>Spectra</td><td>--</td><td>--</td><td>--</td><td>Astrometry</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td></tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<Row index=0>\n",
              " col1  col2 col3 col4       col5       col6 col7 col8 col9 col10      col11      col12 col13 col14 col15 col16 col17 col18  col19  col20 col21 col22   col23    col24 col25 col26 col27 col28 col29 col30 col31 col32 col33 col34\n",
              "str24 str25 str9 str6      str16       str5 str5 str5 str5  str5      str15       str6  str5  str6  str5  str6  str4  str6  str13  str14 str26 str23   str11     str9 str12 str10  str8  str6 str22  str6 str19 str23  str9 str38\n",
              "----- ----- ---- ---- ---------------- ---- ---- ---- ---- ----- --------------- ----- ----- ----- ----- ----- ----- ----- ------- ----- ----- ----- ---------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- -----\n",
              "   --    --   --   -- 2MASS Photometry   --   --   --   --    -- WISE Photometry    --    --    --    --    --    --    -- Spectra    --    --    -- Astrometry    --    --    --    --    --    --    --    --    --    --    --"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dhrnh9_v9aDM"
      },
      "source": [
        "A few things are causing problems here. First, there are two header \n",
        "lines in the file and the header lines are not denoted by comment \n",
        "characters. The first line is actually some meta data that we don't\n",
        "care about, so we want to skip it. We can get around this problem by \n",
        "specifying the `header_start` keyword to the `ascii.read()` function. \n",
        "This keyword argument specifies the index of the row in the text file \n",
        "to read the column names from:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ciLGvg3R9aDM",
        "outputId": "ab759269-ad75-4554-fe04-525639a0894c"
      },
      "source": [
        "tbl = ascii.read(\"Young-Objects-Compilation.csv\", header_start=1)\n",
        "tbl.colnames"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Name',\n",
              " 'Designation',\n",
              " 'RA',\n",
              " 'Dec',\n",
              " 'Jmag',\n",
              " 'J_unc',\n",
              " 'Hmag',\n",
              " 'H_unc',\n",
              " 'Kmag',\n",
              " 'K_unc',\n",
              " 'W1',\n",
              " 'W1_unc',\n",
              " 'W2',\n",
              " 'W2_unc',\n",
              " 'W3',\n",
              " 'W3_unc',\n",
              " 'W4',\n",
              " 'W4_unc',\n",
              " 'Spectral Type',\n",
              " 'Spectra (FITS)',\n",
              " 'Opt Spec Refs',\n",
              " 'NIR Spec Refs',\n",
              " 'pm_ra (mas)',\n",
              " 'pm_ra_unc',\n",
              " 'pm_dec (mas)',\n",
              " 'pm_dec_unc',\n",
              " 'pi (mas)',\n",
              " 'pi_unc',\n",
              " 'radial velocity (km/s)',\n",
              " 'rv_unc',\n",
              " 'Astrometry Refs',\n",
              " 'Discovery Refs',\n",
              " 'Group/Age',\n",
              " 'Note']"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Tfx3t_Y9aDN"
      },
      "source": [
        "Great! Now the columns have the correct names, but there is still a\n",
        "problem: all of the columns have string data types, and the column\n",
        "names are still included as a row in the table. This is because by\n",
        "default the data are assumed to start on the second row (index=1). \n",
        "We can specify `data_start=2` to tell the reader that the data in\n",
        "this file actually start on the 3rd (index=2) row:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2HQ8yV_9aDN"
      },
      "source": [
        "tbl = ascii.read(\"Young-Objects-Compilation.csv\", header_start=1, data_start=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3UsayRv9aDO"
      },
      "source": [
        "Some of the columns have missing data, for example, some of the `RA` values are missing (denoted by -- when printed):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pbhkuK4c9aDO",
        "outputId": "77429765-3e2e-4bad-e793-f36279665a78"
      },
      "source": [
        "print(tbl['RA'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    RA   \n",
            "---------\n",
            "  1.01201\n",
            "  6.92489\n",
            "  8.23267\n",
            "  9.42942\n",
            " 11.33929\n",
            "       --\n",
            "       --\n",
            "       --\n",
            " 21.19163\n",
            "  21.5275\n",
            "      ...\n",
            "300.20171\n",
            "       --\n",
            "303.46467\n",
            "   321.71\n",
            "       --\n",
            "       --\n",
            "332.05679\n",
            "333.43715\n",
            "342.47273\n",
            "       --\n",
            "350.72079\n",
            "Length = 64 rows\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWbbSwUI9aDO"
      },
      "source": [
        "This is called a __Masked column__ because some missing values are \n",
        "masked out upon display. If we want to use this numeric data, we have\n",
        "to tell `astropy` what to fill the missing values with. We can do this\n",
        "with the `.filled()` method. For example, to fill all of the missing\n",
        "values with `NaN`'s:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "N4jlUMB59aDO",
        "outputId": "c76ab307-f4cd-4be6-d13d-a0849e2a1d4f"
      },
      "source": [
        "tbl['RA'].filled(np.nan)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "&lt;Column name=&apos;RA&apos; dtype=&apos;float64&apos; length=64&gt;\n",
              "<table>\n",
              "<tr><td>1.01201</td></tr>\n",
              "<tr><td>6.92489</td></tr>\n",
              "<tr><td>8.23267</td></tr>\n",
              "<tr><td>9.42942</td></tr>\n",
              "<tr><td>11.33929</td></tr>\n",
              "<tr><td>nan</td></tr>\n",
              "<tr><td>nan</td></tr>\n",
              "<tr><td>nan</td></tr>\n",
              "<tr><td>21.19163</td></tr>\n",
              "<tr><td>21.5275</td></tr>\n",
              "<tr><td>25.49263</td></tr>\n",
              "<tr><td>nan</td></tr>\n",
              "<tr><td>...</td></tr>\n",
              "<tr><td>299.19583</td></tr>\n",
              "<tr><td>300.20171</td></tr>\n",
              "<tr><td>nan</td></tr>\n",
              "<tr><td>303.46467</td></tr>\n",
              "<tr><td>321.71</td></tr>\n",
              "<tr><td>nan</td></tr>\n",
              "<tr><td>nan</td></tr>\n",
              "<tr><td>332.05679</td></tr>\n",
              "<tr><td>333.43715</td></tr>\n",
              "<tr><td>342.47273</td></tr>\n",
              "<tr><td>nan</td></tr>\n",
              "<tr><td>350.72079</td></tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<Column name='RA' dtype='float64' length=64>\n",
              "  1.01201\n",
              "  6.92489\n",
              "  8.23267\n",
              "  9.42942\n",
              " 11.33929\n",
              "      nan\n",
              "      nan\n",
              "      nan\n",
              " 21.19163\n",
              "  21.5275\n",
              " 25.49263\n",
              "      nan\n",
              "      ...\n",
              "299.19583\n",
              "300.20171\n",
              "      nan\n",
              "303.46467\n",
              "   321.71\n",
              "      nan\n",
              "      nan\n",
              "332.05679\n",
              "333.43715\n",
              "342.47273\n",
              "      nan\n",
              "350.72079"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHqTlquT9aDP"
      },
      "source": [
        "Let's recap what we've done so far, then make some plots with the\n",
        "data. Our data file has an extra line above the column names, so we\n",
        "use the `header_start` keyword to tell it to start from line 1 instead\n",
        "of line 0 (remember Python is 0-indexed!). We then used had to specify\n",
        "that the data starts on line 2 using the `data_start`\n",
        "keyword. Finally, we note some columns have missing values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTS2PReD9aDP"
      },
      "source": [
        "data = ascii.read(\"Young-Objects-Compilation.csv\", header_start=1, data_start=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kG2g3KXK9aDP"
      },
      "source": [
        "Now that we have our data loaded, let's plot a color-magnitude diagram."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4mp1FbU99aDP"
      },
      "source": [
        "Here we simply make a scatter plot of the J-K color on the x-axis\n",
        "against the J magnitude on the y-axis. We use a trick to flip the\n",
        "y-axis `plt.ylim(reversed(plt.ylim()))`. Called with no arguments,\n",
        "`plt.ylim()` will return a tuple with the axis bounds, \n",
        "e.g. (0,10). Calling the function _with_ arguments will set the limits \n",
        "of the axis, so we simply set the limits to be the reverse of whatever they\n",
        "were before. Using this `pylab`-style plotting is convenient for\n",
        "making quick plots and interactive use, but is not great if you need\n",
        "more control over your figures."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "E1HiJkVn9aDQ",
        "outputId": "c97a4997-870d-4649-d68f-bedb405684af"
      },
      "source": [
        "plt.scatter(data[\"Jmag\"] - data[\"Kmag\"], data[\"Jmag\"]) # plot J-K vs. J\n",
        "plt.ylim(reversed(plt.ylim())) # flip the y-axis\n",
        "plt.xlabel(\"$J-K_s$\", fontsize=20)\n",
        "plt.ylabel(\"$J$\", fontsize=20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '$J$')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U07UKk8X9aDR"
      },
      "source": [
        "As a final example, we will plot the angular positions from the\n",
        "catalog on a 2D projection of the sky. Instead of using `pylab`-style\n",
        "plotting, we'll take a more object-oriented approach. We'll start by\n",
        "creating a `Figure` object and adding a single subplot to the\n",
        "figure. We can specify a projection with the `projection` keyword; in\n",
        "this example we will use a Mollweide projection. Unfortunately, it is \n",
        "highly non-trivial to make the matplotlib projection defined this way \n",
        "follow the celestial convention of longitude/RA increasing to the left. \n",
        "\n",
        "The axis object, `ax`, knows to expect angular coordinate\n",
        "values. An important fact is that it expects the values to be in\n",
        "_radians_, and it expects the azimuthal angle values to be between\n",
        "(-180º,180º). This is (currently) not customizable, so we have to\n",
        "coerce our RA data to conform to these rules! `astropy` provides a\n",
        "coordinate class for handling angular values, `astropy.coordinates.Angle`. \n",
        "We can convert our column of RA values to radians, and wrap the \n",
        "angle bounds using this class."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTj4Gvy29aDS"
      },
      "source": [
        "ra = coord.Angle(data['RA'].filled(np.nan)*u.degree)\n",
        "ra = ra.wrap_at(180*u.degree)\n",
        "dec = coord.Angle(data['Dec'].filled(np.nan)*u.degree)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "deAPpSHZ9aDS",
        "outputId": "2ec9774c-a946-46c9-cda4-3a02efdd2d2c"
      },
      "source": [
        "fig = plt.figure(figsize=(8,6))\n",
        "ax = fig.add_subplot(111, projection=\"mollweide\")\n",
        "ax.scatter(ra.radian, dec.radian)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbfc81ef810>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wuqzQGnb9aDS"
      },
      "source": [
        "By default, matplotlib will add degree tick labels, so let's change the\n",
        "horizontal (x) tick labels to be in units of hours, and display a grid:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SCl11pQF9aDT"
      },
      "source": [
        "fig = plt.figure(figsize=(8,6))\n",
        "ax = fig.add_subplot(111, projection=\"mollweide\")\n",
        "ax.scatter(ra.radian, dec.radian)\n",
        "ax.set_xticklabels(['14h','16h','18h','20h','22h','0h','2h','4h','6h','8h','10h'])\n",
        "ax.grid(True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75l-Q7Gx9aDT"
      },
      "source": [
        "We can save this figure as a PDF using the `savefig` function:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKVwB-V59aDT"
      },
      "source": [
        "fig.savefig(\"map.pdf\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S4Um_odM9aDU"
      },
      "source": [
        "## Exercises"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MX1b1MGD9aDU"
      },
      "source": [
        "Make the map figures as just above, but color the points by the `'Kmag'` column of the table."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1jNS9z89aDU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DDsrFyqQ9aDU"
      },
      "source": [
        "Try making the maps again, but with each of the following projections: `aitoff`, `hammer`, `lambert`, and `None` (which is the same as not giving any projection).  Do any of them make the data seem easier to understand?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dV__ULSZ9aDV"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}