AstronomicalData/06_photo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 6\n",
    "\n",
    "This is the sixth in a series of notebooks related to astronomy data.\n",
    "\n",
    "As a continuing example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n",
    "\n",
    "In the previous lesson we downloaded photometry data from Pan-STARRS, which is available from the same server we've been using to get Gaia data. \n",
    "\n",
    "The next step in the analysis is to select candidate stars based on the photometry data.  The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:\n",
    "\n",
    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png\">\n",
    "\n",
    "In red is a theoretical isochrone, showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster. \n",
    "\n",
    "By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from younger background stars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outline\n",
    "\n",
    "Here are the steps in this notebook:\n",
    "\n",
    "1. We'll reload the data from the previous notebook and make a color-magnitude diagram.\n",
    "\n",
    "2. Then we'll specify a polygon in the diagram that contains stars with the photometry we expect.\n",
    "\n",
    "3. Then we'll merge the photometry data with the list of candidate stars, storing the result in a Pandas `DataFrame`.\n",
    "\n",
    "After completing this lesson, you should be able to\n",
    "\n",
    "* Use Matplotlib to specify a `Polygon` and determine which points fall inside it.\n",
    "\n",
    "* Use Pandas to merge data from multiple `DataFrames`, much like a database `JOIN` operation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "## Installing libraries\n",
    "\n",
    "If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we'll use.\n",
    "\n",
    "If you are running this notebook on your own computer, you might have to install these libraries yourself.  See the instructions in the preface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# If we're running on Colab, install libraries\n",
    "\n",
    "import sys\n",
    "IN_COLAB = 'google.colab' in sys.modules\n",
    "\n",
    "if IN_COLAB:\n",
    "    !pip install astroquery astro-gala python-wget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reload the data\n",
    "\n",
    "The following cell downloads the photometry data we created in the previous notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from wget import download\n",
    "\n",
    "filename = 'gd1_photo.fits'\n",
    "filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
    "\n",
    "if not os.path.exists(filename):\n",
    "    print(download(filepath+filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can read the data back into an Astropy `Table`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.table import Table\n",
    "\n",
    "photo_table = Table.read(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting photometry data\n",
    "\n",
    "Now that we have photometry data from Pan-STARRS, we can replicate the [color-magnitude diagram](https://en.wikipedia.org/wiki/Galaxy_color%E2%80%93magnitude_diagram) from the original paper:\n",
    "\n",
    "<img width=\"300\" src=\"https://github.com/datacarpentry/astronomy-python/raw/gh-pages/fig/gd1-3.png\">\n",
    "\n",
    "The y-axis shows the apparent magnitude of each source with the [g filter](https://en.wikipedia.org/wiki/Photometric_system).\n",
    "\n",
    "The x-axis shows the difference in apparent magnitude between the g and i filters, which indicates color.\n",
    "\n",
    "Stars with lower values of (g-i) are brighter in g-band than in i-band, compared to other stars, which means they are bluer.\n",
    "\n",
    "Stars in the lower-left quadrant of this diagram are less bright and less metallic than the others, which means they are [likely to be older](http://spiff.rit.edu/classes/ladder/lectures/ordinary_stars/ordinary.html).\n",
    "\n",
    "Since we expect the stars in GD-1 to be older than the background stars, the stars in the lower-left are more likely to be in GD-1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_cmd(table):\n",
    "    \"\"\"Plot a color magnitude diagram.\n",
    "    \n",
    "    table: Table or DataFrame with photometry data\n",
    "    \"\"\"\n",
    "    y = table['g_mean_psf_mag']\n",
    "    x = table['g_mean_psf_mag'] - table['i_mean_psf_mag']\n",
    "\n",
    "    plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n",
    "\n",
    "    plt.xlim([0, 1.5])\n",
    "    plt.ylim([14, 22])\n",
    "    plt.gca().invert_yaxis()\n",
    "\n",
    "    plt.ylabel('$g_0$')\n",
    "    plt.xlabel('$(g-i)_0$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plot_cmd` uses a new function, `invert_yaxis`, to invert the `y` axis, which is conventional when plotting magnitudes, since lower magnitude indicates higher brightness.\n",
    "\n",
    "`invert_yaxis` is a little different from the other functions we've used.  You can't call it like this:\n",
    "\n",
    "```\n",
    "plt.invert_yaxis()          # doesn't work\n",
    "```\n",
    "\n",
    "You have to call it like this:\n",
    "\n",
    "```\n",
    "plt.gca().invert_yaxis()          # works\n",
    "```\n",
    "\n",
    "`gca` stands for \"get current axis\".  It returns an object that represents the axes of the current figure, and that object provides `invert_yaxis`.\n",
    "\n",
    "**In case anyone asks:** The most likely reason for this inconsistency in the interface is that `invert_yaxis` is a lesser-used function, so it's not made available at the top level of the interface."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the results look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cmd(photo_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our figure does not look exactly like the one in the paper because we are working with a smaller region of the sky, so we don't have as many stars.  But we can see an overdense region in the lower left that contains stars with the photometry we expect for GD-1.\n",
    "\n",
    "The authors of the original paper derive a detailed polygon that defines a boundary between stars that are likely to be in GD-1 or not.\n",
    "\n",
    "As a simplification, we'll choose a boundary by eye that seems to contain the overdense region."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Isochrone\n",
    "\n",
    "http://waps.cfa.harvard.edu/MIST/interp_isos.html\n",
    "    \n",
    "MIST Version 1.2\n",
    "\n",
    "Rotation initial v/v_crit = 0.4\n",
    "\n",
    "Single age, log10 scale = 10.079\n",
    "\n",
    "Composition [Fe/H] = -1.35\n",
    "\n",
    "Synthetic Photometry, PanStarrs\n",
    "\n",
    "Extinction av = 0\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.079181246047625"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "log_age = np.log10(12e9)\n",
    "log_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.176091259055681"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "log_age = np.log10(15e9)\n",
    "log_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from wget import download\n",
    "\n",
    "filename = 'read_mist_models.py'\n",
    "filepath = 'https://github.com/jieunchoi/MIST_codes/raw/master/scripts/'\n",
    "\n",
    "if not os.path.exists(filename):\n",
    "    print(download(filepath+filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading in: mist_iso_12.0_-1.35.cmd\n"
     ]
    }
   ],
   "source": [
    "import read_mist_models\n",
    "\n",
    "filename = 'mist_iso_12.0_-1.35.cmd'\n",
    "iso = read_mist_models.ISOCMD(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "read_mist_models.ISOCMD"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iso.isocmds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(iso.isocmds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iso.isocmds[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype([('EEP', '<i4'), ('log10_isochrone_age_yr', '<f8'), ('initial_mass', '<f8'), ('star_mass', '<f8'), ('log_Teff', '<f8'), ('log_g', '<f8'), ('log_L', '<f8'), ('[Fe/H]_init', '<f8'), ('[Fe/H]', '<f8'), ('PS_g', '<f8'), ('PS_r', '<f8'), ('PS_i', '<f8'), ('PS_z', '<f8'), ('PS_y', '<f8'), ('PS_w', '<f8'), ('PS_open', '<f8'), ('phase', '<f8')])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso.isocmds[0].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=5</i>\n",
       "<table id=\"table139627634205072\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>EEP</th><th>log10_isochrone_age_yr</th><th>initial_mass</th><th>star_mass</th><th>log_Teff</th><th>log_g</th><th>log_L</th><th>[Fe/H]_init</th><th>[Fe/H]</th><th>PS_g</th><th>PS_r</th><th>PS_i</th><th>PS_z</th><th>PS_y</th><th>PS_w</th><th>PS_open</th><th>phase</th></tr></thead>\n",
       "<thead><tr><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>251</td><td>10.079</td><td>0.10587085010820896</td><td>0.10586970558936482</td><td>3.5403606829263956</td><td>5.321292252841703</td><td>-2.7463861921790302</td><td>-1.35</td><td>-1.309024</td><td>13.833278</td><td>12.395283</td><td>11.638535</td><td>11.286309</td><td>11.099877</td><td>12.304645</td><td>11.970072</td><td>0.0</td></tr>\n",
       "<tr><td>252</td><td>10.079</td><td>0.10880997479817517</td><td>0.10880875821722344</td><td>3.5425829062520973</td><td>5.312318300317242</td><td>-2.7181724188486394</td><td>-1.35</td><td>-1.308887</td><td>13.728065</td><td>12.302358</td><td>11.562305</td><td>11.216564</td><td>11.030572</td><td>12.220695</td><td>11.891695</td><td>0.0</td></tr>\n",
       "<tr><td>253</td><td>10.079</td><td>0.11265246844845123</td><td>0.11265115316268977</td><td>3.5455190149596207</td><td>5.300571015130943</td><td>-2.6811483213239216</td><td>-1.35</td><td>-1.3087</td><td>13.590113</td><td>12.180699</td><td>11.461113</td><td>11.124659</td><td>10.939051</td><td>12.110069</td><td>11.788712</td><td>0.0</td></tr>\n",
       "<tr><td>254</td><td>10.079</td><td>0.11642732871189566</td><td>0.11642590928591964</td><td>3.5484038428414566</td><td>5.288998772212527</td><td>-2.644742589781073</td><td>-1.35</td><td>-1.308527</td><td>13.454544</td><td>12.061398</td><td>11.361128</td><td>11.033194</td><td>10.848769</td><td>12.000729</td><td>11.686994</td><td>0.0</td></tr>\n",
       "<tr><td>255</td><td>10.079</td><td>0.12022239788961175</td><td>0.12022086648010438</td><td>3.551307877859248</td><td>5.277331450307816</td><td>-2.608093791390564</td><td>-1.35</td><td>-1.308285</td><td>13.31837</td><td>11.94182</td><td>11.260087</td><td>10.940106</td><td>10.757708</td><td>11.89031</td><td>11.584202</td><td>0.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=5>\n",
       " EEP  log10_isochrone_age_yr     initial_mass    ...    PS_w    PS_open   phase \n",
       "int32        float64               float64       ...  float64   float64  float64\n",
       "----- ---------------------- ------------------- ... --------- --------- -------\n",
       "  251                 10.079 0.10587085010820896 ... 12.304645 11.970072     0.0\n",
       "  252                 10.079 0.10880997479817517 ... 12.220695 11.891695     0.0\n",
       "  253                 10.079 0.11265246844845123 ... 12.110069 11.788712     0.0\n",
       "  254                 10.079 0.11642732871189566 ... 12.000729 11.686994     0.0\n",
       "  255                 10.079 0.12022239788961175 ...  11.89031 11.584202     0.0"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astropy.table import Table \n",
    "\n",
    "iso_table = Table(iso.isocmds[0])\n",
    "iso_table[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['EEP',\n",
       " 'log10_isochrone_age_yr',\n",
       " 'initial_mass',\n",
       " 'star_mass',\n",
       " 'log_Teff',\n",
       " 'log_g',\n",
       " 'log_L',\n",
       " '[Fe/H]_init',\n",
       " '[Fe/H]',\n",
       " 'PS_g',\n",
       " 'PS_r',\n",
       " 'PS_i',\n",
       " 'PS_z',\n",
       " 'PS_y',\n",
       " 'PS_w',\n",
       " 'PS_open',\n",
       " 'phase']"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso_table.colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.4604730134524"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import astropy.coordinates as coord\n",
    "import astropy.units as u\n",
    "\n",
    "distance = 7.8 * u.kpc\n",
    "dm = coord.Distance(distance).distmod.value\n",
    "dm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = iso_table['PS_g'] + dm\n",
    "gi = iso_table['PS_g'] - iso_table['PS_i']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efd97389fd0>]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plot_cmd(photo_table)\n",
    "plt.plot(gi, g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_and_clean_cmd(filename, distance):\n",
    "    iso = read_mist_models.ISOCMD(filename)\n",
    "    iso_table = Table(iso.isocmds[0])\n",
    "\n",
    "    phase_mask = (iso_table['phase'] >= 0) & (iso_table['phase'] < 3)\n",
    "    table = iso_table[phase_mask]\n",
    "    \n",
    "    dm = coord.Distance(distance).distmod.value\n",
    "    g = iso_table['PS_g'] + dm\n",
    "    gi = iso_table['PS_g'] - iso_table['PS_i']\n",
    "    \n",
    "    return gi, g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading in: mist_iso_12.0_-1.35.cmd\n"
     ]
    }
   ],
   "source": [
    "filename = 'mist_iso_12.0_-1.35.cmd'\n",
    "\n",
    "gi1, g1 = read_and_clean_cmd(filename, distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading in: mist_iso_15.0_-1.35.cmd\n"
     ]
    }
   ],
   "source": [
    "filename = 'mist_iso_15.0_-1.35.cmd'\n",
    "\n",
    "gi2, g2 = read_and_clean_cmd(filename, distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efd9703c550>]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plot_cmd(photo_table)\n",
    "plt.plot(gi1, g1)\n",
    "plt.plot(gi2, g2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_gi = gi - 0.5*(g/28)**5\n",
    "right_gi = gi + 0.55*(g/28)**5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efd97a51b80>]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plot_cmd(photo_table)\n",
    "plt.plot(gi1, g1)\n",
    "plt.plot(gi2, g2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO\n",
    "# ind = (poly[:,1]<21.) & (poly[:,1]>17.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drawing a polygon\n",
    "\n",
    "Matplotlib provides a function called `ginput` that lets us click on the figure and make a list of coordinates.\n",
    "\n",
    "It's a little tricky to use `ginput` in a Jupyter notebook.  \n",
    "Before calling `plt.ginput` we have to tell Matplotlib to use `TkAgg` to draw the figure in a new window.\n",
    "\n",
    "When you run the following cell, a figure should appear in a new window.  Click on it 10 times to draw a polygon around the overdense area.  A red cross should appear where you click."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "\n",
    "coords = None\n",
    "\n",
    "if not IN_COLAB:\n",
    "    mpl.use('TkAgg')\n",
    "    plot_cmd(photo_table)\n",
    "    coords = plt.ginput(10)\n",
    "    mpl.use('agg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The argument to `ginput` is the number of times the user has to click on the figure.\n",
    "\n",
    "The result from `ginput` is a list of coordinate pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.2643369175627239, 17.84253127299485),\n",
       " (0.3539426523297491, 18.799116997792495),\n",
       " (0.47491039426523296, 19.682119205298015),\n",
       " (0.6317204301075269, 20.454746136865342),\n",
       " (0.7661290322580645, 20.785871964679913),\n",
       " (0.8064516129032258, 21.41133186166299),\n",
       " (0.5869175627240143, 21.300956585724798),\n",
       " (0.39426523297491034, 20.565121412803535),\n",
       " (0.22401433691756267, 19.240618101545255),\n",
       " (0.19713261648745517, 18.02649006622517)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If `ginput` doesn't work for you, you could use the following coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if coords is None:\n",
    "    coords = [(0.2, 17.5), \n",
    "              (0.2, 19.5), \n",
    "              (0.65, 22),\n",
    "              (0.75, 21),\n",
    "              (0.4, 19),\n",
    "              (0.4, 17.5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to convert the coordinates to a format we can use to plot them, which is a sequence of `x` coordinates and a sequence of `y` coordinates.  The NumPy function `transpose` does what we want. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.26433692, 0.35394265, 0.47491039, 0.63172043, 0.76612903,\n",
       "        0.80645161, 0.58691756, 0.39426523, 0.22401434, 0.19713262]),\n",
       " array([17.84253127, 18.799117  , 19.68211921, 20.45474614, 20.78587196,\n",
       "        21.41133186, 21.30095659, 20.56512141, 19.2406181 , 18.02649007]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "xs, ys = np.transpose(coords)\n",
    "xs, ys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display the polygon, we'll draw the figure again and use `plt.plot` to draw the polygon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cmd(photo_table)\n",
    "plt.plot(xs, ys);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If it looks like your polygon does a good job surrounding the overdense area, go on to the next section.  Otherwise you can try again.\n",
    "\n",
    "If you want a polygon with more points (or fewer), you can change the argument to `ginput`.\n",
    "\n",
    "The polygon does not have to be \"closed\".  When we use this polygon in the next section, the last and first points will be connected by a straight line.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which points are in the polygon?\n",
    "\n",
    "Matplotlib provides a `Path` object that we can use to check which points fall in the polygon we selected.\n",
    "\n",
    "Here's how we make a `Path` using a list of coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path(array([[ 0.26433692, 17.84253127],\n",
       "       [ 0.35394265, 18.799117  ],\n",
       "       [ 0.47491039, 19.68211921],\n",
       "       [ 0.63172043, 20.45474614],\n",
       "       [ 0.76612903, 20.78587196],\n",
       "       [ 0.80645161, 21.41133186],\n",
       "       [ 0.58691756, 21.30095659],\n",
       "       [ 0.39426523, 20.56512141],\n",
       "       [ 0.22401434, 19.2406181 ],\n",
       "       [ 0.19713262, 18.02649007]]), None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib.path import Path\n",
    "\n",
    "path = Path(coords)\n",
    "path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Path` provides `contains_points`, which figures out which points are inside the polygon.\n",
    "\n",
    "To test it, we'll create a list with two points, one inside the polygon and one outside."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = [(0.4, 20), \n",
    "          (0.4, 30)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can make sure `contains_points` does what we expect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inside = path.contains_points(points)\n",
    "inside"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is an array of Boolean values.\n",
    "\n",
    "We are almost ready to select stars whose photometry data falls in this polygon.  But first we need to do some data cleaning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reloading the data\n",
    "\n",
    "Now we need to combine the photometry data with the list of candidate stars we identified in a previous notebook.  The following cell downloads it:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from wget import download\n",
    "\n",
    "filename = 'gd1_candidates.hdf5'\n",
    "filepath = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'\n",
    "\n",
    "if not os.path.exists(filename):\n",
    "    print(download(filepath+filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "candidate_df = pd.read_hdf(filename, 'candidate_df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`candidate_df` is the Pandas DataFrame that contains the results from Notebook XX, which selects stars likely to be in GD-1 based on proper motion.  It also includes position and proper motion transformed to the ICRS frame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging photometry data\n",
    "\n",
    "Before we select stars based on photometry data, we have to solve two problems:\n",
    "\n",
    "1. We only have Pan-STARRS data for some stars in `candidate_df`.\n",
    "\n",
    "2. Even for the stars where we have Pan-STARRS data in `photo_table`, some photometry data is missing.\n",
    "\n",
    "We will solve these problems in two step:\n",
    "\n",
    "1. We'll merge the data from `candidate_df` and `photo_table` into a single Pandas `DataFrame`.\n",
    "\n",
    "2. We'll use Pandas functions to deal with missing data.\n",
    "\n",
    "`candidate_df` is already a `DataFrame`, but `results` is an Astropy `Table`.  Let's convert it to Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source_id\n",
      "g_mean_psf_mag\n",
      "i_mean_psf_mag\n"
     ]
    }
   ],
   "source": [
    "photo_df = photo_table.to_pandas()\n",
    "\n",
    "for colname in photo_df.columns:\n",
    "    print(colname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to combine `candidate_df` and `photo_df` into a single table, using `source_id` to match up the rows.\n",
    "\n",
    "You might recognize this task; it's the same as the JOIN operation in ADQL/SQL.\n",
    "\n",
    "Pandas provides a function called `merge` that does what we want.  Here's how we use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_id</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>pmra</th>\n",
       "      <th>pmdec</th>\n",
       "      <th>parallax</th>\n",
       "      <th>parallax_error</th>\n",
       "      <th>radial_velocity</th>\n",
       "      <th>phi1</th>\n",
       "      <th>phi2</th>\n",
       "      <th>pm_phi1</th>\n",
       "      <th>pm_phi2</th>\n",
       "      <th>g_mean_psf_mag</th>\n",
       "      <th>i_mean_psf_mag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>635559124339440000</td>\n",
       "      <td>137.586717</td>\n",
       "      <td>19.196544</td>\n",
       "      <td>-3.770522</td>\n",
       "      <td>-12.490482</td>\n",
       "      <td>0.791393</td>\n",
       "      <td>0.271754</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.630489</td>\n",
       "      <td>-1.216485</td>\n",
       "      <td>-7.361363</td>\n",
       "      <td>-0.592633</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>635860218726658176</td>\n",
       "      <td>138.518707</td>\n",
       "      <td>19.092339</td>\n",
       "      <td>-5.941679</td>\n",
       "      <td>-11.346409</td>\n",
       "      <td>0.307456</td>\n",
       "      <td>0.199466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.247330</td>\n",
       "      <td>-2.016078</td>\n",
       "      <td>-7.527126</td>\n",
       "      <td>1.748779</td>\n",
       "      <td>17.8978</td>\n",
       "      <td>17.517401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>635674126383965568</td>\n",
       "      <td>138.842874</td>\n",
       "      <td>19.031798</td>\n",
       "      <td>-3.897001</td>\n",
       "      <td>-12.702780</td>\n",
       "      <td>0.779463</td>\n",
       "      <td>0.223692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.133391</td>\n",
       "      <td>-2.306901</td>\n",
       "      <td>-7.560608</td>\n",
       "      <td>-0.741800</td>\n",
       "      <td>19.2873</td>\n",
       "      <td>17.678101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>635535454774983040</td>\n",
       "      <td>137.837752</td>\n",
       "      <td>18.864007</td>\n",
       "      <td>-4.335041</td>\n",
       "      <td>-14.492309</td>\n",
       "      <td>0.314514</td>\n",
       "      <td>0.102775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.785300</td>\n",
       "      <td>-1.594569</td>\n",
       "      <td>-9.357536</td>\n",
       "      <td>-1.218492</td>\n",
       "      <td>16.9238</td>\n",
       "      <td>16.478100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>635497276810313600</td>\n",
       "      <td>138.044516</td>\n",
       "      <td>19.009471</td>\n",
       "      <td>-7.172931</td>\n",
       "      <td>-12.291499</td>\n",
       "      <td>0.425404</td>\n",
       "      <td>0.337689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.557744</td>\n",
       "      <td>-1.682147</td>\n",
       "      <td>-9.000831</td>\n",
       "      <td>2.334407</td>\n",
       "      <td>19.9242</td>\n",
       "      <td>18.334000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            source_id          ra        dec      pmra      pmdec  parallax  \\\n",
       "0  635559124339440000  137.586717  19.196544 -3.770522 -12.490482  0.791393   \n",
       "1  635860218726658176  138.518707  19.092339 -5.941679 -11.346409  0.307456   \n",
       "2  635674126383965568  138.842874  19.031798 -3.897001 -12.702780  0.779463   \n",
       "3  635535454774983040  137.837752  18.864007 -4.335041 -14.492309  0.314514   \n",
       "4  635497276810313600  138.044516  19.009471 -7.172931 -12.291499  0.425404   \n",
       "\n",
       "   parallax_error  radial_velocity       phi1      phi2   pm_phi1   pm_phi2  \\\n",
       "0        0.271754              NaN -59.630489 -1.216485 -7.361363 -0.592633   \n",
       "1        0.199466              NaN -59.247330 -2.016078 -7.527126  1.748779   \n",
       "2        0.223692              NaN -59.133391 -2.306901 -7.560608 -0.741800   \n",
       "3        0.102775              NaN -59.785300 -1.594569 -9.357536 -1.218492   \n",
       "4        0.337689              NaN -59.557744 -1.682147 -9.000831  2.334407   \n",
       "\n",
       "   g_mean_psf_mag  i_mean_psf_mag  \n",
       "0             NaN             NaN  \n",
       "1         17.8978       17.517401  \n",
       "2         19.2873       17.678101  \n",
       "3         16.9238       16.478100  \n",
       "4         19.9242       18.334000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = pd.merge(candidate_df, \n",
    "                  photo_df, \n",
    "                  on='source_id', \n",
    "                  how='left')\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first argument is the \"left\" table, the second argument is the \"right\" table, and the keyword argument `on='source_id'` specifies a column to use to match up the rows.\n",
    "\n",
    "The argument `how='left'` means that the result should have all rows from the left table, even if some of them don't match up with a row in the right table.\n",
    "\n",
    "If you are interested in the other options for `how`, you can [read the documentation of `merge`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html).\n",
    "\n",
    "You can also do different types of join in ADQL/SQL; [you can read about that here](https://www.w3schools.com/sql/sql_join.asp).\n",
    "\n",
    "The result is a `DataFrame` that contains the same number of rows as `candidate_df`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7346, 3724, 7346)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(candidate_df), len(photo_df), len(merged)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And all columns from both tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source_id\n",
      "ra\n",
      "dec\n",
      "pmra\n",
      "pmdec\n",
      "parallax\n",
      "parallax_error\n",
      "radial_velocity\n",
      "phi1\n",
      "phi2\n",
      "pm_phi1\n",
      "pm_phi2\n",
      "g_mean_psf_mag\n",
      "i_mean_psf_mag\n"
     ]
    }
   ],
   "source": [
    "for colname in merged.columns:\n",
    "    print(colname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Detail** You might notice that Pandas also provides a function called `join`; it does almost the same thing, but the interface is slightly different.  We think `merge` is a little easier to use, so that's what we chose.  It's also more consistent with JOIN in SQL, so if you learn how to use `pd.merge`, you are also learning how to use SQL JOIN.\n",
    "\n",
    "Also, someone might ask why we have to use Pandas to do this join; why didn't we do it in ADQL.  The answer is that we could have done that, but since we already have the data we need, we should probably do the computation locally rather than make another round trip to the Gaia server."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing data\n",
    "\n",
    "Let's add columns to the merged table for magnitude and color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged['mag'] = merged['g_mean_psf_mag']\n",
    "merged['color'] = merged['g_mean_psf_mag'] - merged['i_mean_psf_mag']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These columns contain the special value `NaN` where we are missing data.\n",
    "\n",
    "We can use `notnull` to see which rows contain value data, that is, not null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1        True\n",
       "2        True\n",
       "3        True\n",
       "4        True\n",
       "        ...  \n",
       "7341     True\n",
       "7342    False\n",
       "7343    False\n",
       "7344     True\n",
       "7345    False\n",
       "Name: color, Length: 7346, dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged['color'].notnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And `sum` to count the number of valid values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3724"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged['color'].notnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For scientific purposes, it's not obvious what we should do with candidate stars if we don't have photometry data.  Should we give them the benefit of the doubt or leave them out?\n",
    "\n",
    "In part the answer depends on the goal: are we trying to identify more stars that might be in GD-1, or a smaller set of stars that have higher probability?\n",
    "\n",
    "In the next section, we'll leave them out, but you can experiment with the alternative."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting based on photometry\n",
    "\n",
    "Now let's see how many of these points are inside the polygon we chose.\n",
    "\n",
    "We can use a list of column names to select `color` and `mag`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>mag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3804</td>\n",
       "      <td>17.8978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.6092</td>\n",
       "      <td>19.2873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4457</td>\n",
       "      <td>16.9238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.5902</td>\n",
       "      <td>19.9242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    color      mag\n",
       "0     NaN      NaN\n",
       "1  0.3804  17.8978\n",
       "2  1.6092  19.2873\n",
       "3  0.4457  16.9238\n",
       "4  1.5902  19.9242"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points = merged[['color', 'mag']]\n",
    "points.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is a `DataFrame` that can be treated as a sequence of coordinates, so we can pass it to `contains_points`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inside = path.contains_points(points)\n",
    "inside"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is a Boolean array.  We can use `sum` to see how many stars fall in the polygon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "481"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inside.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use `inside` as a mask to select stars that fall inside the polygon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected2 = merged[inside]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a color-magnitude plot one more time, highlighting the selected stars with green `x` marks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cmd(photo_table)\n",
    "plt.plot(xs, ys)\n",
    "\n",
    "plt.plot(selected2['color'], selected2['mag'], 'gx');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the selected stars are, in fact, inside the polygon, which means they have photometry data consistent with GD-1.\n",
    "\n",
    "Finally, we can plot the coordinates of the selected stars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,2.5))\n",
    "\n",
    "x = selected2['phi1']\n",
    "y = selected2['phi2']\n",
    "\n",
    "plt.plot(x, y, 'ko', markersize=0.7, alpha=0.9)\n",
    "\n",
    "plt.xlabel('ra (degree GD1)')\n",
    "plt.ylabel('dec (degree GD1)')\n",
    "\n",
    "plt.axis('equal');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example includes two new Matplotlib commands:\n",
    "\n",
    "* `figure` creates the figure.  In previous examples, we didn't have to use this function; the figure was created automatically.  But when we call it explicitly, we can provide arguments like `figsize`, which sets the size of the figure.\n",
    "\n",
    "* `axis` with the parameter `equal` sets up the axes so a unit is the same size along the `x` and `y` axes.\n",
    "\n",
    "In an example like this, where `x` and `y` represent coordinates in space, equal axes ensures that the distance between points is represented accurately.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write the data\n",
    "\n",
    "Let's write the merged DataFrame to a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'gd1_merged.hdf5'\n",
    "\n",
    "merged.to_hdf(filename, 'merged')\n",
    "selected2.to_hdf(filename, 'selected2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-rw-r-- 1 downey downey 2.0M Nov 18 19:28 gd1_merged.hdf5\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lh gd1_merged.hdf5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are using Windows, `ls` might not work; in that case, try:\n",
    "\n",
    "```\n",
    "!dir gd1_merged.hdf5\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the polygon\n",
    "\n",
    "[Reproducibile research](https://en.wikipedia.org/wiki/Reproducibility#Reproducible_research) is \"the idea that ... the full computational environment used to produce the results in the paper such as the code, data, etc. can be used to reproduce the results and create new work based on the research.\"\n",
    "\n",
    "This Jupyter notebook is an example of reproducible research because it contains all of the code needed to reproduce the results, including the database queries that download the data and and analysis.\n",
    "\n",
    "However, when we used `ginput` to define a polygon by hand, we introduced a non-reproducible element to the analysis.  If someone running this notebook chooses a different polygon, they will get different results.  So it is important to record the polygon we chose as part of the data analysis pipeline.\n",
    "\n",
    "Since `coords` is a NumPy array, we can't use `to_hdf` to save it in a file.  But we can convert it to a Pandas `DataFrame` and save that.\n",
    "\n",
    "As an alternative, we could use [PyTables](http://www.pytables.org/index.html), which is the library Pandas uses to read and write files.  It is a powerful library, but not easy to use directly.  So let's take advantage of Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords_df = pd.DataFrame(coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'gd1_polygon.hdf5'\n",
    "coords_df.to_hdf(filename, 'coords_df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can read it back like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords2_df = pd.read_hdf(filename, 'coords_df')\n",
    "coords2 = coords2_df.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And verify that the data we read back is the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(coords2 == coords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this notebook, we worked with two datasets: the list of candidate stars from Gaia and the photometry data from Pan-STARRS.\n",
    "\n",
    "We drew a color-magnitude diagram and used it to identify stars we think are likely to be in GD-1.\n",
    "\n",
    "Then we used a Pandas `merge` operation to combine the data into a single `DataFrame`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best practices\n",
    "\n",
    "* If you want to perform something like a database `JOIN` operation with data that is in a Pandas `DataFrame`, you can use the `join` or `merge` function.  In many cases, `merge` is easier to use because the arguments are more like SQL.\n",
    "\n",
    "* Use Matplotlib options to control the size and aspect ratio of figures to make them easier to interpret.  In this example, we scaled the axes so the size of a degree is equal along both axes.\n",
    "\n",
    "* Matplotlib also provides operations for working with points, polygons, and other geometric entities, so it's not just for making figures.\n",
    "\n",
    "* Be sure to record every element of the data analysis pipeline that would be needed to replicate the results."
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