{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "filename = 'gd1_candidates.hdf5'\n",
    "candidate_df = pd.read_hdf(filename, 'candidate_df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.table.table.Table"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astropy.table import Table\n",
    "\n",
    "candidate_table = Table.from_pandas(candidate_df)\n",
    "type(candidate_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = candidate_table[['source_id']]\n",
    "table.write('candidate_df.xml', format='votable', overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"SELECT *\n",
    "FROM gaiadr2.panstarrs1_best_neighbour as best\n",
    "JOIN tap_upload.candidate_df as candidate_df\n",
    "ON best.source_id = candidate_df.source_id\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created TAP+ (v1.2.1) - Connection:\n",
      "\tHost: gea.esac.esa.int\n",
      "\tUse HTTPS: True\n",
      "\tPort: 443\n",
      "\tSSL Port: 443\n",
      "Created TAP+ (v1.2.1) - Connection:\n",
      "\tHost: geadata.esac.esa.int\n",
      "\tUse HTTPS: True\n",
      "\tPort: 443\n",
      "\tSSL Port: 443\n",
      "INFO: Query finished. [astroquery.utils.tap.core]\n"
     ]
    }
   ],
   "source": [
    "from astroquery.gaia import Gaia\n",
    "\n",
    "job = Gaia.launch_job_async(query=query, \n",
    "                            upload_resource='candidate_df.xml', \n",
    "                            upload_table_name='candidate_df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=3724</i>\n",
       "<table id=\"table139938781808624\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>source_id</th><th>original_ext_source_id</th><th>angular_distance</th><th>number_of_neighbours</th><th>number_of_mates</th><th>best_neighbour_multiplicity</th><th>gaia_astrometric_params</th><th>source_id_2</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>arcsec</th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>float64</th><th>int32</th><th>int16</th><th>int16</th><th>int16</th><th>int64</th></tr></thead>\n",
       "<tr><td>635860218726658176</td><td>130911385187671349</td><td>0.053667035895467084</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635860218726658176</td></tr>\n",
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       "<tr><td>635598607974369792</td><td>130341392091279513</td><td>0.036524626853403054</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635598607974369792</td></tr>\n",
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       "<tr><td>635850945892748672</td><td>132011398654934147</td><td>0.021178742393378396</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635850945892748672</td></tr>\n",
       "<tr><td>635600532119713664</td><td>130421392285893623</td><td>0.04518820915043015</td><td>1</td><td>0</td><td>1</td><td>5</td><td>635600532119713664</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>612241781249124608</td><td>129751343755995561</td><td>0.04235715830001815</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612241781249124608</td></tr>\n",
       "<tr><td>612332147361443072</td><td>130141341458538777</td><td>0.02265249859012977</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612332147361443072</td></tr>\n",
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       "<tr><td>612282738058264960</td><td>129741340445933519</td><td>0.025293237353496898</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612282738058264960</td></tr>\n",
       "<tr><td>612386332668697600</td><td>130351354570219774</td><td>0.02010316001403086</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612386332668697600</td></tr>\n",
       "<tr><td>612296172717818624</td><td>129691338006168780</td><td>0.051264212025836205</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612296172717818624</td></tr>\n",
       "<tr><td>612250375480101760</td><td>129741346475897464</td><td>0.031783740347530905</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612250375480101760</td></tr>\n",
       "<tr><td>612394926899159168</td><td>130581355199751795</td><td>0.04019174830546698</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612394926899159168</td></tr>\n",
       "<tr><td>612256418500423168</td><td>129931349075297310</td><td>0.009242789669513156</td><td>1</td><td>0</td><td>1</td><td>5</td><td>612256418500423168</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3724>\n",
       "    source_id      original_ext_source_id ...    source_id_2    \n",
       "                                          ...                   \n",
       "      int64                int64          ...       int64       \n",
       "------------------ ---------------------- ... ------------------\n",
       "635860218726658176     130911385187671349 ... 635860218726658176\n",
       "635674126383965568     130831388428488720 ... 635674126383965568\n",
       "635535454774983040     130631378377657369 ... 635535454774983040\n",
       "635497276810313600     130811380445631930 ... 635497276810313600\n",
       "635614168640132864     130571395922140135 ... 635614168640132864\n",
       "635598607974369792     130341392091279513 ... 635598607974369792\n",
       "635737661835496576     131001399333502136 ... 635737661835496576\n",
       "635850945892748672     132011398654934147 ... 635850945892748672\n",
       "635600532119713664     130421392285893623 ... 635600532119713664\n",
       "               ...                    ... ...                ...\n",
       "612241781249124608     129751343755995561 ... 612241781249124608\n",
       "612332147361443072     130141341458538777 ... 612332147361443072\n",
       "612426744016802432     130521346852465656 ... 612426744016802432\n",
       "612331739340341760     130111341217793839 ... 612331739340341760\n",
       "612282738058264960     129741340445933519 ... 612282738058264960\n",
       "612386332668697600     130351354570219774 ... 612386332668697600\n",
       "612296172717818624     129691338006168780 ... 612296172717818624\n",
       "612250375480101760     129741346475897464 ... 612250375480101760\n",
       "612394926899159168     130581355199751795 ... 612394926899159168\n",
       "612256418500423168     129931349075297310 ... 612256418500423168"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = job.get_results()\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "table_ext = results[['source_id', 'original_ext_source_id']]\n",
    "table_ext.write('external.xml', format='votable', overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "query2 = \"\"\"SELECT\n",
    "external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag\n",
    "FROM gaiadr2.panstarrs1_original_valid as ps\n",
    "JOIN tap_upload.external as external\n",
    "ON ps.obj_id = external.original_ext_source_id\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Query finished. [astroquery.utils.tap.core]\n"
     ]
    }
   ],
   "source": [
    "job2 = Gaia.launch_job_async(query=query2, \n",
    "                       upload_resource='external.xml', \n",
    "                       upload_table_name='external')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=3724</i>\n",
       "<table id=\"table139938761143584\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>source_id</th><th>g_mean_psf_mag</th><th>i_mean_psf_mag</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>mag</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>635860218726658176</td><td>17.8978004455566</td><td>17.5174007415771</td></tr>\n",
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       "<tr><td>...</td><td>...</td><td>...</td></tr>\n",
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       "<tr><td>612332147361443072</td><td>21.3848991394043</td><td>20.3076000213623</td></tr>\n",
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       "<tr><td>612331739340341760</td><td>21.8656997680664</td><td>19.5223007202148</td></tr>\n",
       "<tr><td>612282738058264960</td><td>22.5151996612549</td><td>19.9743995666504</td></tr>\n",
       "<tr><td>612386332668697600</td><td>19.3792991638184</td><td>17.9923000335693</td></tr>\n",
       "<tr><td>612296172717818624</td><td>17.4944000244141</td><td>16.926700592041</td></tr>\n",
       "<tr><td>612250375480101760</td><td>15.3330001831055</td><td>14.6280002593994</td></tr>\n",
       "<tr><td>612394926899159168</td><td>16.4414005279541</td><td>15.8212003707886</td></tr>\n",
       "<tr><td>612256418500423168</td><td>20.8715991973877</td><td>19.9612007141113</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3724>\n",
       "    source_id       g_mean_psf_mag   i_mean_psf_mag \n",
       "                                          mag       \n",
       "      int64            float64          float64     \n",
       "------------------ ---------------- ----------------\n",
       "635860218726658176 17.8978004455566 17.5174007415771\n",
       "635674126383965568 19.2873001098633 17.6781005859375\n",
       "635535454774983040 16.9237995147705  16.478099822998\n",
       "635497276810313600 19.9242000579834 18.3339996337891\n",
       "635614168640132864 16.1515998840332 14.6662998199463\n",
       "635598607974369792 16.5223999023438 16.1375007629395\n",
       "635737661835496576 14.5032997131348 13.9849004745483\n",
       "635850945892748672 16.5174999237061 16.0450000762939\n",
       "635600532119713664 20.4505996704102 19.5177001953125\n",
       "               ...              ...              ...\n",
       "612241781249124608 20.2343997955322 18.6518001556396\n",
       "612332147361443072 21.3848991394043 20.3076000213623\n",
       "612426744016802432 17.8281002044678 17.4281005859375\n",
       "612331739340341760 21.8656997680664 19.5223007202148\n",
       "612282738058264960 22.5151996612549 19.9743995666504\n",
       "612386332668697600 19.3792991638184 17.9923000335693\n",
       "612296172717818624 17.4944000244141  16.926700592041\n",
       "612250375480101760 15.3330001831055 14.6280002593994\n",
       "612394926899159168 16.4414005279541 15.8212003707886\n",
       "612256418500423168 20.8715991973877 19.9612007141113"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results2 = job2.get_results()\n",
    "results2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'gd1_photo.fits'\n",
    "results2.write(filename, overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.table import Table\n",
    "\n",
    "photo_table = Table.read(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3724"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(photo_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=3724</i>\n",
       "<table id=\"table139938761143104\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>source_id</th><th>g_mean_psf_mag</th><th>i_mean_psf_mag</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>mag</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>635860218726658176</td><td>17.8978004455566</td><td>17.5174007415771</td></tr>\n",
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       "<tr><td>635600532119713664</td><td>20.4505996704102</td><td>19.5177001953125</td></tr>\n",
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       "<tr><td>612256418500423168</td><td>20.8715991973877</td><td>19.9612007141113</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3724>\n",
       "    source_id       g_mean_psf_mag   i_mean_psf_mag \n",
       "                                          mag       \n",
       "      int64            float64          float64     \n",
       "------------------ ---------------- ----------------\n",
       "635860218726658176 17.8978004455566 17.5174007415771\n",
       "635674126383965568 19.2873001098633 17.6781005859375\n",
       "635535454774983040 16.9237995147705  16.478099822998\n",
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       "635850945892748672 16.5174999237061 16.0450000762939\n",
       "635600532119713664 20.4505996704102 19.5177001953125\n",
       "               ...              ...              ...\n",
       "612241781249124608 20.2343997955322 18.6518001556396\n",
       "612332147361443072 21.3848991394043 20.3076000213623\n",
       "612426744016802432 17.8281002044678 17.4281005859375\n",
       "612331739340341760 21.8656997680664 19.5223007202148\n",
       "612282738058264960 22.5151996612549 19.9743995666504\n",
       "612386332668697600 19.3792991638184 17.9923000335693\n",
       "612296172717818624 17.4944000244141  16.926700592041\n",
       "612250375480101760 15.3330001831055 14.6280002593994\n",
       "612394926899159168 16.4414005279541 15.8212003707886\n",
       "612256418500423168 20.8715991973877 19.9612007141113"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "photo_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_cmd(photo_table):\n",
    "    y = photo_table['g_mean_psf_mag']\n",
    "    x = photo_table['g_mean_psf_mag'] - photo_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",
    "\n",
    "    plt.gca().invert_yaxis()\n",
    "\n",
    "    plt.ylabel(r'$g_0$')\n",
    "    plt.xlabel(r'$(g-i)_0$');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'module://ipykernel.pylab.backend_inline'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "\n",
    "mpl.get_backend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'module://ipykernel.pylab.backend_inline'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpl.rcParams['backend']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "import matplotlib as mpl\n",
    "\n",
    "# NOTE: Don't do this.  \n",
    "\n",
    "mpl.use('TkAgg')\n",
    "plot_cmd(photo_table)\n",
    "coords = plt.ginput(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.use('agg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords = [(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)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "xs, ys = np.transpose(coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.path import Path\n",
    "\n",
    "path = Path(coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = [(0.4, 20), (0.4, 16)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.contains_points(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=3724</i>\n",
       "<table id=\"table139938761143104\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>source_id</th><th>g_mean_psf_mag</th><th>i_mean_psf_mag</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>mag</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>635860218726658176</td><td>17.8978004455566</td><td>17.5174007415771</td></tr>\n",
       "<tr><td>635674126383965568</td><td>19.2873001098633</td><td>17.6781005859375</td></tr>\n",
       "<tr><td>635535454774983040</td><td>16.9237995147705</td><td>16.478099822998</td></tr>\n",
       "<tr><td>635497276810313600</td><td>19.9242000579834</td><td>18.3339996337891</td></tr>\n",
       "<tr><td>635614168640132864</td><td>16.1515998840332</td><td>14.6662998199463</td></tr>\n",
       "<tr><td>635598607974369792</td><td>16.5223999023438</td><td>16.1375007629395</td></tr>\n",
       "<tr><td>635737661835496576</td><td>14.5032997131348</td><td>13.9849004745483</td></tr>\n",
       "<tr><td>635850945892748672</td><td>16.5174999237061</td><td>16.0450000762939</td></tr>\n",
       "<tr><td>635600532119713664</td><td>20.4505996704102</td><td>19.5177001953125</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>612241781249124608</td><td>20.2343997955322</td><td>18.6518001556396</td></tr>\n",
       "<tr><td>612332147361443072</td><td>21.3848991394043</td><td>20.3076000213623</td></tr>\n",
       "<tr><td>612426744016802432</td><td>17.8281002044678</td><td>17.4281005859375</td></tr>\n",
       "<tr><td>612331739340341760</td><td>21.8656997680664</td><td>19.5223007202148</td></tr>\n",
       "<tr><td>612282738058264960</td><td>22.5151996612549</td><td>19.9743995666504</td></tr>\n",
       "<tr><td>612386332668697600</td><td>19.3792991638184</td><td>17.9923000335693</td></tr>\n",
       "<tr><td>612296172717818624</td><td>17.4944000244141</td><td>16.926700592041</td></tr>\n",
       "<tr><td>612250375480101760</td><td>15.3330001831055</td><td>14.6280002593994</td></tr>\n",
       "<tr><td>612394926899159168</td><td>16.4414005279541</td><td>15.8212003707886</td></tr>\n",
       "<tr><td>612256418500423168</td><td>20.8715991973877</td><td>19.9612007141113</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3724>\n",
       "    source_id       g_mean_psf_mag   i_mean_psf_mag \n",
       "                                          mag       \n",
       "      int64            float64          float64     \n",
       "------------------ ---------------- ----------------\n",
       "635860218726658176 17.8978004455566 17.5174007415771\n",
       "635674126383965568 19.2873001098633 17.6781005859375\n",
       "635535454774983040 16.9237995147705  16.478099822998\n",
       "635497276810313600 19.9242000579834 18.3339996337891\n",
       "635614168640132864 16.1515998840332 14.6662998199463\n",
       "635598607974369792 16.5223999023438 16.1375007629395\n",
       "635737661835496576 14.5032997131348 13.9849004745483\n",
       "635850945892748672 16.5174999237061 16.0450000762939\n",
       "635600532119713664 20.4505996704102 19.5177001953125\n",
       "               ...              ...              ...\n",
       "612241781249124608 20.2343997955322 18.6518001556396\n",
       "612332147361443072 21.3848991394043 20.3076000213623\n",
       "612426744016802432 17.8281002044678 17.4281005859375\n",
       "612331739340341760 21.8656997680664 19.5223007202148\n",
       "612282738058264960 22.5151996612549 19.9743995666504\n",
       "612386332668697600 19.3792991638184 17.9923000335693\n",
       "612296172717818624 17.4944000244141  16.926700592041\n",
       "612250375480101760 15.3330001831055 14.6280002593994\n",
       "612394926899159168 16.4414005279541 15.8212003707886\n",
       "612256418500423168 20.8715991973877 19.9612007141113"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "photo_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "candidate_df = pd.read_hdf('gd1_candidates.hdf5', 'candidate_df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7346, 3724)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(candidate_df), len(photo_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hint: that's a join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(candidate_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.table.table.Table"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(photo_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "photo_df = photo_table.to_pandas()\n",
    "type(photo_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "left: all the rows from the left table\n",
    "\n",
    "right: all the rows from the right table\n",
    "\n",
    "inner: intersection\n",
    " \n",
    "outer: union"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = pd.merge(candidate_df, photo_df,\n",
    "                  on='source_id',\n",
    "                  how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7346"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7346"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(candidate_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged['mag'] = merged['g_mean_psf_mag']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged['color'] = merged['g_mean_psf_mag'] - merged['i_mean_psf_mag']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['source_id', 'ra', 'dec', 'pmra', 'pmdec', 'parallax', 'parallax_error',\n",
       "       'radial_velocity', 'phi1', 'phi2', 'pm_phi1', 'pm_phi2',\n",
       "       'g_mean_psf_mag', 'i_mean_psf_mag', 'mag', 'color'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3724"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged['color'].notnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3622"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged['color'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Next step: select the rows where the photometry data falls in the polygon we identified**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib.path import Path\n",
    "\n",
    "path = Path(coords)\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = [(0.4, 20), (0.4, 16)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.contains_points(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points = merged[['color', 'mag']]    # select `color` and `mag`\n",
    "points.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "481"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_inside = path.contains_points(points)\n",
    "is_inside.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "      <th>mag</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>636170384085347968</td>\n",
       "      <td>136.762984</td>\n",
       "      <td>18.627660</td>\n",
       "      <td>-4.929947</td>\n",
       "      <td>-12.324957</td>\n",
       "      <td>0.080572</td>\n",
       "      <td>0.774262</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-60.526296</td>\n",
       "      <td>-0.857776</td>\n",
       "      <td>-7.890487</td>\n",
       "      <td>0.556206</td>\n",
       "      <td>20.595800</td>\n",
       "      <td>20.0585</td>\n",
       "      <td>20.595800</td>\n",
       "      <td>0.537300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>636199658582021632</td>\n",
       "      <td>136.690129</td>\n",
       "      <td>18.973757</td>\n",
       "      <td>-3.199581</td>\n",
       "      <td>-13.434429</td>\n",
       "      <td>0.062917</td>\n",
       "      <td>0.320584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-60.268864</td>\n",
       "      <td>-0.616365</td>\n",
       "      <td>-7.907829</td>\n",
       "      <td>-1.483160</td>\n",
       "      <td>19.345800</td>\n",
       "      <td>19.0042</td>\n",
       "      <td>19.345800</td>\n",
       "      <td>0.341600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>636363795052181888</td>\n",
       "      <td>136.586430</td>\n",
       "      <td>19.402934</td>\n",
       "      <td>-3.360965</td>\n",
       "      <td>-13.160503</td>\n",
       "      <td>0.156392</td>\n",
       "      <td>0.436150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.956071</td>\n",
       "      <td>-0.306603</td>\n",
       "      <td>-7.748759</td>\n",
       "      <td>-1.180119</td>\n",
       "      <td>19.855801</td>\n",
       "      <td>19.3862</td>\n",
       "      <td>19.855801</td>\n",
       "      <td>0.469601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>636456703784789248</td>\n",
       "      <td>136.061195</td>\n",
       "      <td>19.322230</td>\n",
       "      <td>-5.691713</td>\n",
       "      <td>-11.289811</td>\n",
       "      <td>-0.026094</td>\n",
       "      <td>0.377111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-60.285543</td>\n",
       "      <td>0.072223</td>\n",
       "      <td>-7.407813</td>\n",
       "      <td>1.842187</td>\n",
       "      <td>19.303400</td>\n",
       "      <td>18.8925</td>\n",
       "      <td>19.303400</td>\n",
       "      <td>0.410900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>636563700010029312</td>\n",
       "      <td>136.320112</td>\n",
       "      <td>19.709247</td>\n",
       "      <td>-5.901632</td>\n",
       "      <td>-12.474899</td>\n",
       "      <td>-0.654865</td>\n",
       "      <td>0.766358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.828026</td>\n",
       "      <td>0.068110</td>\n",
       "      <td>-8.503892</td>\n",
       "      <td>1.377714</td>\n",
       "      <td>20.515301</td>\n",
       "      <td>20.0103</td>\n",
       "      <td>20.515301</td>\n",
       "      <td>0.505001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             source_id          ra        dec      pmra      pmdec  parallax  \\\n",
       "52  636170384085347968  136.762984  18.627660 -4.929947 -12.324957  0.080572   \n",
       "58  636199658582021632  136.690129  18.973757 -3.199581 -13.434429  0.062917   \n",
       "78  636363795052181888  136.586430  19.402934 -3.360965 -13.160503  0.156392   \n",
       "84  636456703784789248  136.061195  19.322230 -5.691713 -11.289811 -0.026094   \n",
       "88  636563700010029312  136.320112  19.709247 -5.901632 -12.474899 -0.654865   \n",
       "\n",
       "    parallax_error  radial_velocity       phi1      phi2   pm_phi1   pm_phi2  \\\n",
       "52        0.774262              NaN -60.526296 -0.857776 -7.890487  0.556206   \n",
       "58        0.320584              NaN -60.268864 -0.616365 -7.907829 -1.483160   \n",
       "78        0.436150              NaN -59.956071 -0.306603 -7.748759 -1.180119   \n",
       "84        0.377111              NaN -60.285543  0.072223 -7.407813  1.842187   \n",
       "88        0.766358              NaN -59.828026  0.068110 -8.503892  1.377714   \n",
       "\n",
       "    g_mean_psf_mag  i_mean_psf_mag        mag     color  \n",
       "52       20.595800         20.0585  20.595800  0.537300  \n",
       "58       19.345800         19.0042  19.345800  0.341600  \n",
       "78       19.855801         19.3862  19.855801  0.469601  \n",
       "84       19.303400         18.8925  19.303400  0.410900  \n",
       "88       20.515301         20.0103  20.515301  0.505001  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected2 = merged[is_inside]\n",
    "selected2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.use('agg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4603e2c130>]"
      ]
     },
     "execution_count": 85,
     "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": [
    "plot_cmd(photo_table)\n",
    "plt.plot(xs, ys)\n",
    "\n",
    "#plt.plot(selected2['color'], selected2['mag'], 'gx');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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=0.6, alpha=0.6)\n",
    "\n",
    "plt.xlabel('phi1 (degree GD1)')\n",
    "plt.ylabel('phi2 (degree GD1)')\n",
    "\n",
    "plt.axis('equal');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm gd1_merged.hdf5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'gd1_merged.hdf5'\n",
    "\n",
    "merged.to_hdf(filename, 'merged')\n",
    "selected2.to_hdf(filename, 'selected', mode='a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-rw-r-- 1 downey downey 1.1M Nov 20 14:09 gd1_merged.hdf5\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lh gd1_merged.hdf5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7346"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "481"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(selected2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords_df = pd.DataFrame(coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'gd1_polygon.hdf5'\n",
    "\n",
    "coords_df.to_hdf(filename, 'coords_df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lesson_7 (1).ipynb\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from wget import download\n",
    "url = 'https://raw.githubusercontent.com/AllenDowney/AstronomicalData/main/class_notebooks/lesson_7.ipynb'\n",
    "print(download(url))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}