{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ELAIS-N1 master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on ELAIS-N1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "0246c5d (Thu Jan 25 17:01:47 2018 +0000) [with local modifications]\n"
     ]
    }
   ],
   "source": [
    "from herschelhelp_internal import git_version\n",
    "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "import os\n",
    "import time\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column, Table\n",
    "import numpy as np\n",
    "from pymoc import MOC\n",
    "\n",
    "from herschelhelp_internal.masterlist import merge_catalogues, nb_merge_dist_plot, specz_merge\n",
    "from herschelhelp_internal.utils import coords_to_hpidx, ebv, gen_help_id, inMoc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n",
    "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n",
    "SUFFIX = os.environ.get('SUFFIX', time.strftime(\"_%Y%m%d\"))\n",
    "\n",
    "try:\n",
    "    os.makedirs(OUT_DIR)\n",
    "except FileExistsError:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I - Reading the prepared pristine catalogues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wfc = Table.read(\"{}/INT-WFC.fits\".format(TMP_DIR))\n",
    "dxs = Table.read(\"{}/UKIDSS-DXS.fits\".format(TMP_DIR))\n",
    "sparcs = Table.read(\"{}/SpARCS.fits\".format(TMP_DIR))\n",
    "hsc = Table.read(\"{}/HSC-SSP.fits\".format(TMP_DIR))\n",
    "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))\n",
    "servs = Table.read(\"{}/SERVS.fits\".format(TMP_DIR))\n",
    "swire= Table.read(\"{}/SWIRE.fits\".format(TMP_DIR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Merging tables\n",
    "\n",
    "We first merge the optical catalogues and then add the infrared ones: WFC, DXS, SpARCS, HSC, PS1, SERVS, SWIRE.\n",
    "\n",
    "At every step, we look at the distribution of the distances separating the sources from one catalogue to the other (within a maximum radius) to determine the best cross-matching radius."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WFC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = wfc\n",
    "master_catalogue['wfc_ra'].name = 'ra'\n",
    "master_catalogue['wfc_dec'].name = 'dec'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add SpARCS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8V+rLkk6W1CvpMUkfdc4t27mq4nY25Rixovs6AADVy6vF66+T9LCktZLOkPRlM2uZe5CZ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\nAAA88n8BnZbO/LRGQasAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6ad7b8d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(sparcs['sparcs_ra'], sparcs['sparcs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, sparcs, \"sparcs_ra\", \"sparcs_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add HSC-PSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SoJTZwD5KsAIAoKQQrNZgMVjlaymQYAUAQEkhWK3B4lJgHjpWLbVBxeLJxdcAAADFj2C1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\nAAAAHiFYAQAAeOT/B47BYQAHMLYnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff69b932b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(hsc['hsc_ra'], hsc['hsc_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, hsc, \"hsc_ra\", \"hsc_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add PanSTARRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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CYTEWM2J1taQW51yrJJnZ1yXdLelksHLOPTPv+B2SCnNV9yIdGRjXutpYQQ8p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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff69b87e198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(ps1['ps1_ra'], ps1['ps1_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, ps1, \"ps1_ra\", \"ps1_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add DXS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RIVh5IJcrVhLT1wEAKBYEqyVKppwmEsmc9VhJ0sA4Q0IBACgGBKslGo0n5OT9DCvp9WDV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\n2oaQdnbU6X+8cF6/csc6jhs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zs4OS3nyN17wx8/GBzGtuznztr856/G+ywczMHsj8Hg+a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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff69b99a668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(dxs['dxs_ra'], dxs['dxs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, dxs, \"dxs_ra\", \"dxs_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add SERVS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aKpwuR0SyXM9wmPt2dPC1Z08xMhml3O/lTetquaYlSHGBh32dw+zrHHa6TEmC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\niIgWSWAVERER0SIJrCIiIiJa5CE9yPVnRyezugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff699df3080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(servs['servs_ra'], servs['servs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, servs, \"servs_ra\", \"servs_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add SWIRE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ffy4RERG5PAUrh6UzWZLpbFFWrDoiuZELWrUSEREpDQpWDkukMgBFW7EC6FOwEhERKQkK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\ni4gs1ea1tdRU+tjbP8b7b17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x0eNfmg9mxph78n/Gl4wxTzj4f4OIlCif2wWISPmx1vbm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\nxCEKViIiIiIOUbASERERccj/D8uiTuUYRJbhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff699df3550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(swire['swire_ra'], swire['swire_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, swire, \"swire_ra\", \"swire_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning\n",
    "\n",
    "When we merge the catalogues, astropy masks the non-existent values (e.g. when a row comes only from a catalogue and has no counterparts in the other, the columns from the latest are masked for that row). We indicate to use NaN for masked values for floats columns, False for flag columns and -1 for ID columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    if \"m_\" in col or \"merr_\" in col or \"f_\" in col or \"ferr_\" in col or \"stellarity\" in col:\n",
    "        master_catalogue[col].fill_value = np.nan\n",
    "    elif \"flag\" in col:\n",
    "        master_catalogue[col].fill_value = 0\n",
    "    elif \"id\" in col:\n",
    "        master_catalogue[col].fill_value = -1\n",
    "        \n",
    "master_catalogue = master_catalogue.filled()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=10&gt;\n",
       "<table id=\"table140697149216136-421338\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>wfc_id</th><th>ra</th><th>dec</th><th>wfc_stellarity</th><th>m_ap_wfc_u</th><th>merr_ap_wfc_u</th><th>m_wfc_u</th><th>merr_wfc_u</th><th>m_ap_wfc_g</th><th>merr_ap_wfc_g</th><th>m_wfc_g</th><th>merr_wfc_g</th><th>m_ap_wfc_r</th><th>merr_ap_wfc_r</th><th>m_wfc_r</th><th>merr_wfc_r</th><th>m_ap_wfc_i</th><th>merr_ap_wfc_i</th><th>m_wfc_i</th><th>merr_wfc_i</th><th>m_ap_wfc_z</th><th>merr_ap_wfc_z</th><th>m_wfc_z</th><th>merr_wfc_z</th><th>f_ap_wfc_u</th><th>ferr_ap_wfc_u</th><th>f_wfc_u</th><th>ferr_wfc_u</th><th>flag_wfc_u</th><th>f_ap_wfc_g</th><th>ferr_ap_wfc_g</th><th>f_wfc_g</th><th>ferr_wfc_g</th><th>flag_wfc_g</th><th>f_ap_wfc_r</th><th>ferr_ap_wfc_r</th><th>f_wfc_r</th><th>ferr_wfc_r</th><th>flag_wfc_r</th><th>f_ap_wfc_i</th><th>ferr_ap_wfc_i</th><th>f_wfc_i</th><th>ferr_wfc_i</th><th>flag_wfc_i</th><th>f_ap_wfc_z</th><th>ferr_ap_wfc_z</th><th>f_wfc_z</th><th>ferr_wfc_z</th><th>flag_wfc_z</th><th>wfc_flag_cleaned</th><th>wfc_flag_gaia</th><th>flag_merged</th><th>sparcs_intid</th><th>sparcs_stellarity</th><th>m_ap_megacam_u</th><th>merr_ap_megacam_u</th><th>f_ap_megacam_u</th><th>ferr_ap_megacam_u</th><th>m_megacam_u</th><th>merr_megacam_u</th><th>f_megacam_u</th><th>ferr_megacam_u</th><th>flag_megacam_u</th><th>m_ap_megacam_g</th><th>merr_ap_megacam_g</th><th>f_ap_megacam_g</th><th>ferr_ap_megacam_g</th><th>m_megacam_g</th><th>merr_megacam_g</th><th>f_megacam_g</th><th>ferr_megacam_g</th><th>flag_megacam_g</th><th>m_ap_megacam_r</th><th>merr_ap_megacam_r</th><th>f_ap_megacam_r</th><th>ferr_ap_megacam_r</th><th>m_megacam_r</th><th>merr_megacam_r</th><th>f_megacam_r</th><th>ferr_megacam_r</th><th>flag_megacam_r</th><th>m_ap_megacam_z</th><th>merr_ap_megacam_z</th><th>f_ap_megacam_z</th><th>ferr_ap_megacam_z</th><th>m_megacam_z</th><th>merr_megacam_z</th><th>f_megacam_z</th><th>ferr_megacam_z</th><th>flag_megacam_z</th><th>sparcs_flag_cleaned</th><th>sparcs_flag_gaia</th><th>hsc_id</th><th>m_ap_suprime_g</th><th>merr_ap_suprime_g</th><th>m_suprime_g</th><th>merr_suprime_g</th><th>m_ap_suprime_r</th><th>merr_ap_suprime_r</th><th>m_suprime_r</th><th>merr_suprime_r</th><th>m_ap_suprime_i</th><th>merr_ap_suprime_i</th><th>m_suprime_i</th><th>merr_suprime_i</th><th>m_ap_suprime_z</th><th>merr_ap_suprime_z</th><th>m_suprime_z</th><th>merr_suprime_z</th><th>m_ap_suprime_y</th><th>merr_ap_suprime_y</th><th>m_suprime_y</th><th>merr_suprime_y</th><th>m_ap_suprime_n921</th><th>merr_ap_suprime_n921</th><th>m_suprime_n921</th><th>merr_suprime_n921</th><th>hsc_stellarity</th><th>f_ap_suprime_g</th><th>ferr_ap_suprime_g</th><th>f_suprime_g</th><th>ferr_suprime_g</th><th>flag_suprime_g</th><th>f_ap_suprime_r</th><th>ferr_ap_suprime_r</th><th>f_suprime_r</th><th>ferr_suprime_r</th><th>flag_suprime_r</th><th>f_ap_suprime_i</th><th>ferr_ap_suprime_i</th><th>f_suprime_i</th><th>ferr_suprime_i</th><th>flag_suprime_i</th><th>f_ap_suprime_z</th><th>ferr_ap_suprime_z</th><th>f_suprime_z</th><th>ferr_suprime_z</th><th>flag_suprime_z</th><th>f_ap_suprime_y</th><th>ferr_ap_suprime_y</th><th>f_suprime_y</th><th>ferr_suprime_y</th><th>flag_suprime_y</th><th>f_ap_suprime_n921</th><th>ferr_ap_suprime_n921</th><th>f_suprime_n921</th><th>ferr_suprime_n921</th><th>flag_suprime_n921</th><th>hsc_flag_cleaned</th><th>hsc_flag_gaia</th><th>ps1_id</th><th>m_ap_gpc1_g</th><th>merr_ap_gpc1_g</th><th>m_gpc1_g</th><th>merr_gpc1_g</th><th>m_ap_gpc1_r</th><th>merr_ap_gpc1_r</th><th>m_gpc1_r</th><th>merr_gpc1_r</th><th>m_ap_gpc1_i</th><th>merr_ap_gpc1_i</th><th>m_gpc1_i</th><th>merr_gpc1_i</th><th>m_ap_gpc1_z</th><th>merr_ap_gpc1_z</th><th>m_gpc1_z</th><th>merr_gpc1_z</th><th>m_ap_gpc1_y</th><th>merr_ap_gpc1_y</th><th>m_gpc1_y</th><th>merr_gpc1_y</th><th>f_ap_gpc1_g</th><th>ferr_ap_gpc1_g</th><th>f_gpc1_g</th><th>ferr_gpc1_g</th><th>flag_gpc1_g</th><th>f_ap_gpc1_r</th><th>ferr_ap_gpc1_r</th><th>f_gpc1_r</th><th>ferr_gpc1_r</th><th>flag_gpc1_r</th><th>f_ap_gpc1_i</th><th>ferr_ap_gpc1_i</th><th>f_gpc1_i</th><th>ferr_gpc1_i</th><th>flag_gpc1_i</th><th>f_ap_gpc1_z</th><th>ferr_ap_gpc1_z</th><th>f_gpc1_z</th><th>ferr_gpc1_z</th><th>flag_gpc1_z</th><th>f_ap_gpc1_y</th><th>ferr_ap_gpc1_y</th><th>f_gpc1_y</th><th>ferr_gpc1_y</th><th>flag_gpc1_y</th><th>ps1_flag_cleaned</th><th>ps1_flag_gaia</th><th>dxs_id</th><th>m_ap_ukidss_j</th><th>merr_ap_ukidss_j</th><th>m_ukidss_j</th><th>merr_ukidss_j</th><th>m_ap_ukidss_k</th><th>merr_ap_ukidss_k</th><th>m_ukidss_k</th><th>merr_ukidss_k</th><th>dxs_stellarity</th><th>f_ap_ukidss_j</th><th>ferr_ap_ukidss_j</th><th>f_ukidss_j</th><th>ferr_ukidss_j</th><th>flag_ukidss_j</th><th>f_ap_ukidss_k</th><th>ferr_ap_ukidss_k</th><th>f_ukidss_k</th><th>ferr_ukidss_k</th><th>flag_ukidss_k</th><th>dxs_flag_cleaned</th><th>dxs_flag_gaia</th><th>servs_intid</th><th>f_ap_servs_irac_i1</th><th>ferr_ap_servs_irac_i1</th><th>f_servs_irac_i1</th><th>ferr_servs_irac_i1</th><th>servs_stellarity_irac_i1</th><th>f_ap_servs_irac_i2</th><th>ferr_ap_servs_irac_i2</th><th>f_servs_irac_i2</th><th>ferr_servs_irac_i2</th><th>servs_stellarity_irac_i2</th><th>m_ap_servs_irac_i1</th><th>merr_ap_servs_irac_i1</th><th>m_servs_irac_i1</th><th>merr_servs_irac_i1</th><th>flag_servs_irac_i1</th><th>m_ap_servs_irac_i2</th><th>merr_ap_servs_irac_i2</th><th>m_servs_irac_i2</th><th>merr_servs_irac_i2</th><th>flag_servs_irac_i2</th><th>servs_flag_cleaned</th><th>servs_flag_gaia</th><th>swire_intid</th><th>f_ap_swire_irac_i1</th><th>ferr_ap_swire_irac_i1</th><th>f_swire_irac_i1</th><th>ferr_swire_irac_i1</th><th>swire_stellarity_irac_i1</th><th>f_ap_swire_irac_i2</th><th>ferr_ap_swire_irac_i2</th><th>f_swire_irac_i2</th><th>ferr_swire_irac_i2</th><th>swire_stellarity_irac_i2</th><th>f_ap_irac_i3</th><th>ferr_ap_irac_i3</th><th>f_irac_i3</th><th>ferr_irac_i3</th><th>swire_stellarity_irac_i3</th><th>f_ap_irac_i4</th><th>ferr_ap_irac_i4</th><th>f_irac_i4</th><th>ferr_irac_i4</th><th>swire_stellarity_irac_i4</th><th>m_ap_swire_irac_i1</th><th>merr_ap_swire_irac_i1</th><th>m_swire_irac_i1</th><th>merr_swire_irac_i1</th><th>flag_swire_irac_i1</th><th>m_ap_swire_irac_i2</th><th>merr_ap_swire_irac_i2</th><th>m_swire_irac_i2</th><th>merr_swire_irac_i2</th><th>flag_swire_irac_i2</th><th>m_ap_irac_i3</th><th>merr_ap_irac_i3</th><th>m_irac_i3</th><th>merr_irac_i3</th><th>flag_irac_i3</th><th>m_ap_irac_i4</th><th>merr_ap_irac_i4</th><th>m_irac_i4</th><th>merr_irac_i4</th><th>flag_irac_i4</th><th>swire_flag_cleaned</th><th>swire_flag_gaia</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<tr><td>0</td><td>189636202384</td><td>240.664902664</td><td>55.6405269985</td><td>0.0</td><td>14.517</td><td>0.034</td><td>14.485</td><td>0.034</td><td>14.668</td><td>0.029</td><td>12.438</td><td>0.029</td><td>14.559</td><td>0.017</td><td>12.197</td><td>0.017</td><td>14.319</td><td>0.028</td><td>12.713</td><td>0.028</td><td>13.087</td><td>0.047</td><td>11.399</td><td>0.047</td><td>5665.0</td><td>177.4</td><td>5834.45</td><td>182.707</td><td>False</td><td>4929.47</td><td>131.666</td><td>38441.5</td><td>1026.77</td><td>False</td><td>5450.04</td><td>85.3345</td><td>47995.4</td><td>751.492</td><td>False</td><td>6798.29</td><td>175.321</td><td>29840.1</td><td>769.544</td><td>False</td><td>21144.6</td><td>915.321</td><td>100092.0</td><td>4332.84653723</td><td>False</td><td>False</td><td>2</td><td>True</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>1</td><td>189636301738</td><td>241.083097973</td><td>56.0489784745</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>14.582</td><td>0.029</td><td>10.988</td><td>0.029</td><td>14.489</td><td>0.017</td><td>10.047</td><td>0.017</td><td>14.114</td><td>0.028</td><td>9.987</td><td>0.028</td><td>12.966</td><td>0.047</td><td>9.602</td><td>0.047</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>5335.81</td><td>142.519</td><td>146150.0</td><td>3903.67</td><td>False</td><td>5812.99</td><td>91.0174</td><td>347696.0</td><td>5444.08</td><td>False</td><td>8211.07</td><td>211.755</td><td>367451.0</td><td>9476.18</td><td>False</td><td>23637.4</td><td>1023.23</td><td>523841.0</td><td>22676.3578057</td><td>False</td><td>False</td><td>2</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>2</td><td>189636400422</td><td>241.338829551</td><td>55.8740765965</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>15.001</td><td>0.029</td><td>12.837</td><td>0.029</td><td>14.947</td><td>0.017</td><td>12.704</td><td>0.017</td><td>14.541</td><td>0.028</td><td>12.641</td><td>0.028</td><td>13.565</td><td>0.047</td><td>12.364</td><td>0.047</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>3627.44</td><td>96.8887</td><td>26619.5</td><td>711.006</td><td>False</td><td>3812.41</td><td>59.6932</td><td>30088.4</td><td>471.112</td><td>False</td><td>5541.15</td><td>142.9</td><td>31886.0</td><td>822.306</td><td>False</td><td>13614.4</td><td>589.35</td><td>41152.8</td><td>1781.44833818</td><td>False</td><td>False</td><td>2</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>3</td><td>189636202731</td><td>240.447994644</td><td>55.6084939005</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>14.658</td><td>0.029</td><td>12.099</td><td>0.029</td><td>14.625</td><td>0.017</td><td>12.293</td><td>0.017</td><td>14.388</td><td>0.028</td><td>12.41</td><td>0.028</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>4975.08</td><td>132.884</td><td>52529.1</td><td>1403.05</td><td>False</td><td>5128.61</td><td>80.3016</td><td>43933.9</td><td>687.899</td><td>False</td><td>6379.7</td><td>164.526</td><td>39445.7</td><td>1017.26</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>4</td><td>189636201865</td><td>240.478563706</td><td>55.7053889005</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>14.703</td><td>0.029</td><td>12.938</td><td>0.029</td><td>14.644</td><td>0.017</td><td>12.55</td><td>0.017</td><td>14.408</td><td>0.028</td><td>12.498</td><td>0.028</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>4773.09</td><td>127.489</td><td>24254.9</td><td>647.849</td><td>False</td><td>5039.64</td><td>78.9086</td><td>34673.7</td><td>542.906</td><td>False</td><td>6263.25</td><td>161.523</td><td>36374.7</td><td>938.066</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>5</td><td>189636401440</td><td>241.105681984</td><td>55.8248104955</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>21.926</td><td>0.043</td><td>21.832</td><td>0.049</td><td>17.62</td><td>0.017</td><td>17.68</td><td>0.017</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>19.622</td><td>0.054</td><td>19.625</td><td>0.056</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>6.16027</td><td>0.243974</td><td>6.71737</td><td>0.30316</td><td>False</td><td>325.087</td><td>5.09008</td><td>307.609</td><td>4.81642</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>51.428</td><td>2.55782</td><td>51.2861</td><td>2.64523192891</td><td>False</td><td>False</td><td>0</td><td>True</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>6</td><td>189636202743</td><td>240.447643458</td><td>55.6237090615</td><td>0.0030764</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>23.334</td><td>0.087</td><td>23.201</td><td>0.11</td><td>16.902</td><td>0.017</td><td>17.778</td><td>0.018</td><td>21.114</td><td>0.049</td><td>20.986</td><td>0.056</td><td>20.703</td><td>0.117</td><td>20.87</td><td>0.162</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>1.68422</td><td>0.134957</td><td>1.90371</td><td>0.192871</td><td>False</td><td>629.796</td><td>9.86107</td><td>281.061</td><td>4.65959</td><td>False</td><td>13.0137</td><td>0.587316</td><td>14.642</td><td>0.755203</td><td>False</td><td>19.002</td><td>2.04768</td><td>16.2929</td><td>2.43102894819</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>7</td><td>189636402325</td><td>240.973102084</td><td>55.7813213385</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>15.015</td><td>0.017</td><td>12.72</td><td>0.017</td><td>14.907</td><td>0.028</td><td>12.681</td><td>0.028</td><td>14.161</td><td>0.047</td><td>12.263</td><td>0.047</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>3580.96</td><td>56.0692</td><td>29648.3</td><td>464.221</td><td>False</td><td>3955.49</td><td>102.008</td><td>30732.6</td><td>792.563</td><td>False</td><td>7863.21</td><td>340.387</td><td>45164.8</td><td>1955.12069017</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>8</td><td>189636302235</td><td>240.948206428</td><td>56.0086996895</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>14.558</td><td>0.017</td><td>14.176</td><td>0.017</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>5455.07</td><td>85.4131</td><td>7755.33</td><td>121.43</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>9</td><td>189636202744</td><td>240.447729835</td><td>55.6230657115</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>16.323</td><td>0.017</td><td>16.486</td><td>0.017</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>1073.49</td><td>16.8083</td><td>923.846</td><td>14.4652</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140697149216136-421338').dataTable()\");\n",
       "    $('#table140697149216136-421338').dataTable({\n",
       "        \"order\": [],\n",
       "        \"iDisplayLength\": 50,\n",
       "        \"aLengthMenu\": [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        \"pagingType\": \"full_numbers\"\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_catalogue[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III - Merging flags and stellarity\n",
    "\n",
    "Each pristine catalogue contains a flag indicating if the source was associated to a another nearby source that was removed during the cleaning process.  We merge these flags in a single one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_cleaned_columns = [column for column in master_catalogue.colnames\n",
    "                        if 'flag_cleaned' in column]\n",
    "\n",
    "flag_column = np.zeros(len(master_catalogue), dtype=bool)\n",
    "for column in flag_cleaned_columns:\n",
    "    flag_column |= master_catalogue[column]\n",
    "    \n",
    "master_catalogue.add_column(Column(data=flag_column, name=\"flag_cleaned\"))\n",
    "master_catalogue.remove_columns(flag_cleaned_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each pristine catalogue contains a flag indicating the probability of a source being a Gaia object (0: not a Gaia object, 1: possibly, 2: probably, 3: definitely).  We merge these flags taking the highest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_gaia_columns = [column for column in master_catalogue.colnames\n",
    "                     if 'flag_gaia' in column]\n",
    "\n",
    "master_catalogue.add_column(Column(\n",
    "    data=np.max([master_catalogue[column] for column in flag_gaia_columns], axis=0),\n",
    "    name=\"flag_gaia\"\n",
    "))\n",
    "master_catalogue.remove_columns(flag_gaia_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each prisitine catalogue may contain one or several stellarity columns indicating the probability (0 to 1) of each source being a star.  We merge these columns taking the highest value.  We keep trace of the origin of the stellarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wfc_stellarity, sparcs_stellarity, hsc_stellarity, dxs_stellarity, servs_stellarity_irac_i1, servs_stellarity_irac_i2, swire_stellarity_irac_i1, swire_stellarity_irac_i2, swire_stellarity_irac_i3, swire_stellarity_irac_i4\n"
     ]
    }
   ],
   "source": [
    "stellarity_columns = [column for column in master_catalogue.colnames\n",
    "                      if 'stellarity' in column]\n",
    "\n",
    "print(\", \".join(stellarity_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We create an masked array with all the stellarities and get the maximum value, as well as its\n",
    "# origin.  Some sources may not have an associated stellarity.\n",
    "stellarity_array = np.array([master_catalogue[column] for column in stellarity_columns])\n",
    "stellarity_array = np.ma.masked_array(stellarity_array, np.isnan(stellarity_array))\n",
    "\n",
    "max_stellarity = np.max(stellarity_array, axis=0)\n",
    "max_stellarity.fill_value = np.nan\n",
    "\n",
    "no_stellarity_mask = max_stellarity.mask\n",
    "\n",
    "master_catalogue.add_column(Column(data=max_stellarity.filled(), name=\"stellarity\"))\n",
    "\n",
    "stellarity_origin = np.full(len(master_catalogue), \"NO_INFORMATION\", dtype=\"S20\")\n",
    "stellarity_origin[~no_stellarity_mask] = np.array(stellarity_columns)[np.argmax(stellarity_array, axis=0)[~no_stellarity_mask]]\n",
    "\n",
    "master_catalogue.add_column(Column(data=stellarity_origin, name=\"stellarity_origin\"))\n",
    "\n",
    "master_catalogue.remove_columns(stellarity_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV - Adding E(B-V) column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    ebv(master_catalogue['ra'], master_catalogue['dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V - Adding HELP unique identifiers and field columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(gen_help_id(master_catalogue['ra'], master_catalogue['dec']),\n",
    "                                   name=\"help_id\"))\n",
    "master_catalogue.add_column(Column(np.full(len(master_catalogue), \"ELAIS-N1\", dtype='<U18'),\n",
    "                                   name=\"field\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK!\n"
     ]
    }
   ],
   "source": [
    "# Check that the HELP Ids are unique\n",
    "if len(master_catalogue) != len(np.unique(master_catalogue['help_id'])):\n",
    "    print(\"The HELP IDs are not unique!!!\")\n",
    "else:\n",
    "    print(\"OK!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VI - Cross-matching with spec-z catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz =  Table.read(\"../../dmu23/dmu23_ELAIS-N1/data/ELAIS-N1-specz-v2.1.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uPMyjr5/AGPjE6hmkxUUSFe51O5qcJCsxmoKUGLYea+HimWk6i+jH9HEkAAwO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\nEQ2SxCoiIiKiQf4GybS520zhYSUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff699e0f198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(specz['ra'] * u.deg, specz['dec'] * u.deg)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = specz_merge(master_catalogue, specz, radius=1. * u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VII - Choosing between multiple values for the same filter\n",
    "\n",
    "Both SERVS and SWIRE provide IRAC1 and IRAC2 fluxes. SERVS is deeper but tends to under-estimate flux of bright sources (Mattia said over 2000 µJy) as illustrated by this comparison of SWIRE, SERVS, and Spitzer-EIP fluxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: 'e/count' did not parse as fits unit: At col 0, Unit 'e' not supported by the FITS standard.  [astropy.units.core]\n",
      "WARNING: UnitsWarning: 'image' did not parse as fits unit: At col 0, Unit 'image' not supported by the FITS standard.  [astropy.units.core]\n"
     ]
    }
   ],
   "source": [
    "seip = Table.read(\"../../dmu0/dmu0_SEIP/data/SEIP_ELAIS-N1.fits\")\n",
    "seip_coords = SkyCoord(seip['ra'], seip['dec'])\n",
    "idx, d2d, _ = seip_coords.match_to_catalog_sky(SkyCoord(master_catalogue['ra'], master_catalogue['dec']))\n",
    "mask = d2d <= 2 * u.arcsec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vnDVn97ZEISIisabd9Dn79BWvmemnr9T0Axs7vWafyU9f8f7y/VV/7/Ebd4Nz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff68cbf8400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(seip['i1_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_servs_irac_i1'], label=\"SERVS\", s=2.)\n",
    "ax.scatter(seip['i1_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_swire_irac_i1'], label=\"SWIRE\", s=2.)\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(\"SEIP flux [μJy]\")\n",
    "ax.set_ylabel(\"SERVS/SWIRE flux [μJy]\")\n",
    "ax.set_title(\"IRAC 1\")\n",
    "ax.legend()\n",
    "ax.axvline(2000, color=\"black\", linestyle=\"--\", linewidth=1.)\n",
    "ax.plot(seip['i1_f_ap1'][mask], seip['i1_f_ap1'][mask], linewidth=.1, color=\"black\", alpha=.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rjob8Og6cBzwHELKTVEREOorCdbB0mvMT2Ll+JVvnT2Tn+pV116xe4AS1YFjL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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff699bb1198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(seip['i2_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_servs_irac_i2'], label=\"SERVS\", s=2.)\n",
    "ax.scatter(seip['i2_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_swire_irac_i2'], label=\"SWIRE\", s=2.)\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(\"SEIP flux [μJy]\")\n",
    "ax.set_ylabel(\"SERVS/SWIRE flux [μJy]\")\n",
    "ax.set_title(\"IRAC 2\")\n",
    "ax.legend()\n",
    "ax.axvline(2000, color=\"black\", linestyle=\"--\", linewidth=1.)\n",
    "\n",
    "ax.plot(seip['i1_f_ap2'][mask], seip['i1_f_ap2'][mask], linewidth=.1, color=\"black\", alpha=.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When both SWIRE and SERVS fluxes are provided, we use the SERVS flux below 2000 μJy and the SWIRE flux over.\n",
    "\n",
    "We create a table indicating for each source the origin on the IRAC1 and IRAC2 fluxes that will be saved separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irac_origin = Table()\n",
    "irac_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293912 sources with SERVS flux\n",
      "535110 sources with SWIRE flux\n",
      "124465 sources with SERVS and SWIRE flux\n",
      "293347 sources for which we use SERVS\n",
      "411210 sources for which we use SWIRE\n"
     ]
    }
   ],
   "source": [
    "# IRAC1 aperture flux and magnitudes\n",
    "has_servs = ~np.isnan(master_catalogue['f_ap_servs_irac_i1'])\n",
    "has_swire = ~np.isnan(master_catalogue['f_ap_swire_irac_i1'])\n",
    "has_both = has_servs & has_swire\n",
    "\n",
    "print(\"{} sources with SERVS flux\".format(np.sum(has_servs)))\n",
    "print(\"{} sources with SWIRE flux\".format(np.sum(has_swire)))\n",
    "print(\"{} sources with SERVS and SWIRE flux\".format(np.sum(has_both)))\n",
    "\n",
    "has_servs_above_limit = has_servs.copy()\n",
    "has_servs_above_limit[has_servs] = master_catalogue['f_ap_servs_irac_i1'][has_servs] > 2000\n",
    "\n",
    "use_swire = (has_swire & ~has_servs) | (has_both & has_servs_above_limit)\n",
    "use_servs = (has_servs & ~(has_both & has_servs_above_limit))\n",
    "\n",
    "print(\"{} sources for which we use SERVS\".format(np.sum(use_servs)))\n",
    "print(\"{} sources for which we use SWIRE\".format(np.sum(use_swire)))\n",
    "\n",
    "f_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac_i[use_servs] = master_catalogue['f_ap_servs_irac_i1'][use_servs]\n",
    "f_ap_irac_i[use_swire] = master_catalogue['f_ap_swire_irac_i1'][use_swire]\n",
    "\n",
    "ferr_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac_i[use_servs] = master_catalogue['ferr_ap_servs_irac_i1'][use_servs]\n",
    "ferr_ap_irac_i[use_swire] = master_catalogue['ferr_ap_swire_irac_i1'][use_swire]\n",
    "\n",
    "m_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac_i[use_servs] = master_catalogue['m_ap_servs_irac_i1'][use_servs]\n",
    "m_ap_irac_i[use_swire] = master_catalogue['m_ap_swire_irac_i1'][use_swire]\n",
    "\n",
    "merr_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac_i[use_servs] = master_catalogue['merr_ap_servs_irac_i1'][use_servs]\n",
    "merr_ap_irac_i[use_swire] = master_catalogue['merr_ap_swire_irac_i1'][use_swire]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac_i, name=\"f_ap_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac_i, name=\"ferr_ap_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac_i, name=\"m_ap_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac_i, name=\"merr_ap_irac_i1\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_ap_servs_irac_i1', 'f_ap_swire_irac_i1', 'ferr_ap_servs_irac_i1',\n",
    "                                     'ferr_ap_swire_irac_i1', 'm_ap_servs_irac_i1', 'm_ap_swire_irac_i1',\n",
    "                                     'merr_ap_servs_irac_i1', 'merr_ap_swire_irac_i1'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_servs] = \"SERVS\"\n",
    "origin[use_swire] = \"SWIRE\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC1_app\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293912 sources with SERVS flux\n",
      "535030 sources with SWIRE flux\n",
      "124464 sources with SERVS and SWIRE flux\n",
      "293311 sources for which we use SERVS\n",
      "411167 sources for which we use SWIRE\n"
     ]
    }
   ],
   "source": [
    "# IRAC1 total flux and magnitudes\n",
    "has_servs = ~np.isnan(master_catalogue['f_servs_irac_i1'])\n",
    "has_swire = ~np.isnan(master_catalogue['f_swire_irac_i1'])\n",
    "has_both = has_servs & has_swire\n",
    "\n",
    "print(\"{} sources with SERVS flux\".format(np.sum(has_servs)))\n",
    "print(\"{} sources with SWIRE flux\".format(np.sum(has_swire)))\n",
    "print(\"{} sources with SERVS and SWIRE flux\".format(np.sum(has_both)))\n",
    "\n",
    "has_servs_above_limit = has_servs.copy()\n",
    "has_servs_above_limit[has_servs] = master_catalogue['f_servs_irac_i1'][has_servs] > 2000\n",
    "\n",
    "use_swire = (has_swire & ~has_servs) | (has_both & has_servs_above_limit)\n",
    "use_servs = (has_servs & ~(has_both & has_servs_above_limit))\n",
    "\n",
    "print(\"{} sources for which we use SERVS\".format(np.sum(use_servs)))\n",
    "print(\"{} sources for which we use SWIRE\".format(np.sum(use_swire)))\n",
    "\n",
    "f_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac_i[use_servs] = master_catalogue['f_servs_irac_i1'][use_servs]\n",
    "f_ap_irac_i[use_swire] = master_catalogue['f_swire_irac_i1'][use_swire]\n",
    "\n",
    "ferr_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac_i[use_servs] = master_catalogue['ferr_servs_irac_i1'][use_servs]\n",
    "ferr_ap_irac_i[use_swire] = master_catalogue['ferr_swire_irac_i1'][use_swire]\n",
    "\n",
    "flag_irac_i = np.full(len(master_catalogue), False, dtype=bool)\n",
    "flag_irac_i[use_servs] = master_catalogue['flag_servs_irac_i1'][use_servs]\n",
    "flag_irac_i[use_swire] = master_catalogue['flag_swire_irac_i1'][use_swire]\n",
    "\n",
    "m_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac_i[use_servs] = master_catalogue['m_servs_irac_i1'][use_servs]\n",
    "m_ap_irac_i[use_swire] = master_catalogue['m_swire_irac_i1'][use_swire]\n",
    "\n",
    "merr_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac_i[use_servs] = master_catalogue['merr_servs_irac_i1'][use_servs]\n",
    "merr_ap_irac_i[use_swire] = master_catalogue['merr_swire_irac_i1'][use_swire]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac_i, name=\"f_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac_i, name=\"ferr_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac_i, name=\"m_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac_i, name=\"merr_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=flag_irac_i, name=\"flag_irac_i1\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_servs_irac_i1', 'f_swire_irac_i1', 'ferr_servs_irac_i1',\n",
    "                                 'ferr_swire_irac_i1', 'm_servs_irac_i1', 'flag_servs_irac_i1', 'm_swire_irac_i1',\n",
    "                                 'merr_servs_irac_i1', 'merr_swire_irac_i1', 'flag_swire_irac_i1'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_servs] = \"SERVS\"\n",
    "origin[use_swire] = \"SWIRE\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC1_total\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300193 sources with SERVS flux\n",
      "390203 sources with SWIRE flux\n",
      "93525 sources with SERVS and SWIRE flux\n",
      "299810 sources for which we use SERVS\n",
      "297061 sources for which we use SWIRE\n"
     ]
    }
   ],
   "source": [
    "# IRAC2 aperture flux and magnitudes\n",
    "has_servs = ~np.isnan(master_catalogue['f_ap_servs_irac_i2'])\n",
    "has_swire = ~np.isnan(master_catalogue['f_ap_swire_irac_i2'])\n",
    "has_both = has_servs & has_swire\n",
    "\n",
    "print(\"{} sources with SERVS flux\".format(np.sum(has_servs)))\n",
    "print(\"{} sources with SWIRE flux\".format(np.sum(has_swire)))\n",
    "print(\"{} sources with SERVS and SWIRE flux\".format(np.sum(has_both)))\n",
    "\n",
    "has_servs_above_limit = has_servs.copy()\n",
    "has_servs_above_limit[has_servs] = master_catalogue['f_ap_servs_irac_i2'][has_servs] > 2000\n",
    "\n",
    "use_swire = (has_swire & ~has_servs) | (has_both & has_servs_above_limit)\n",
    "use_servs = (has_servs & ~(has_both & has_servs_above_limit))\n",
    "\n",
    "print(\"{} sources for which we use SERVS\".format(np.sum(use_servs)))\n",
    "print(\"{} sources for which we use SWIRE\".format(np.sum(use_swire)))\n",
    "\n",
    "f_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac_i[use_servs] = master_catalogue['f_ap_servs_irac_i2'][use_servs]\n",
    "f_ap_irac_i[use_swire] = master_catalogue['f_ap_swire_irac_i2'][use_swire]\n",
    "\n",
    "ferr_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac_i[use_servs] = master_catalogue['ferr_ap_servs_irac_i2'][use_servs]\n",
    "ferr_ap_irac_i[use_swire] = master_catalogue['ferr_ap_swire_irac_i2'][use_swire]\n",
    "\n",
    "m_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac_i[use_servs] = master_catalogue['m_ap_servs_irac_i2'][use_servs]\n",
    "m_ap_irac_i[use_swire] = master_catalogue['m_ap_swire_irac_i2'][use_swire]\n",
    "\n",
    "merr_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac_i[use_servs] = master_catalogue['merr_ap_servs_irac_i2'][use_servs]\n",
    "merr_ap_irac_i[use_swire] = master_catalogue['merr_ap_swire_irac_i2'][use_swire]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac_i, name=\"f_ap_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac_i, name=\"ferr_ap_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac_i, name=\"m_ap_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac_i, name=\"merr_ap_irac_i2\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_ap_servs_irac_i2', 'f_ap_swire_irac_i2', 'ferr_ap_servs_irac_i2',\n",
    "                                 'ferr_ap_swire_irac_i2', 'm_ap_servs_irac_i2', 'm_ap_swire_irac_i2',\n",
    "                                 'merr_ap_servs_irac_i2', 'merr_ap_swire_irac_i2'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_servs] = \"SERVS\"\n",
    "origin[use_swire] = \"SWIRE\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC2_app\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300193 sources with SERVS flux\n",
      "390193 sources with SWIRE flux\n",
      "93525 sources with SERVS and SWIRE flux\n",
      "299792 sources for which we use SERVS\n",
      "297069 sources for which we use SWIRE\n"
     ]
    }
   ],
   "source": [
    "# IRAC2 total flux and magnitudes\n",
    "has_servs = ~np.isnan(master_catalogue['f_servs_irac_i2'])\n",
    "has_swire = ~np.isnan(master_catalogue['f_swire_irac_i2'])\n",
    "has_both = has_servs & has_swire\n",
    "\n",
    "print(\"{} sources with SERVS flux\".format(np.sum(has_servs)))\n",
    "print(\"{} sources with SWIRE flux\".format(np.sum(has_swire)))\n",
    "print(\"{} sources with SERVS and SWIRE flux\".format(np.sum(has_both)))\n",
    "\n",
    "has_servs_above_limit = has_servs.copy()\n",
    "has_servs_above_limit[has_servs] = master_catalogue['f_servs_irac_i2'][has_servs] > 2000\n",
    "\n",
    "use_swire = (has_swire & ~has_servs) | (has_both & has_servs_above_limit)\n",
    "use_servs = (has_servs & ~(has_both & has_servs_above_limit))\n",
    "\n",
    "print(\"{} sources for which we use SERVS\".format(np.sum(use_servs)))\n",
    "print(\"{} sources for which we use SWIRE\".format(np.sum(use_swire)))\n",
    "\n",
    "f_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac_i[use_servs] = master_catalogue['f_servs_irac_i2'][use_servs]\n",
    "f_ap_irac_i[use_swire] = master_catalogue['f_swire_irac_i2'][use_swire]\n",
    "\n",
    "ferr_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac_i[use_servs] = master_catalogue['ferr_servs_irac_i2'][use_servs]\n",
    "ferr_ap_irac_i[use_swire] = master_catalogue['ferr_swire_irac_i2'][use_swire]\n",
    "\n",
    "flag_irac_i = np.full(len(master_catalogue), False, dtype=bool)\n",
    "flag_irac_i[use_servs] = master_catalogue['flag_servs_irac_i2'][use_servs]\n",
    "flag_irac_i[use_swire] = master_catalogue['flag_swire_irac_i2'][use_swire]\n",
    "\n",
    "m_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac_i[use_servs] = master_catalogue['m_servs_irac_i2'][use_servs]\n",
    "m_ap_irac_i[use_swire] = master_catalogue['m_swire_irac_i2'][use_swire]\n",
    "\n",
    "merr_ap_irac_i = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac_i[use_servs] = master_catalogue['merr_servs_irac_i2'][use_servs]\n",
    "merr_ap_irac_i[use_swire] = master_catalogue['merr_swire_irac_i2'][use_swire]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac_i, name=\"f_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac_i, name=\"ferr_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac_i, name=\"m_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac_i, name=\"merr_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=flag_irac_i, name=\"flag_irac_i2\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_servs_irac_i2', 'f_swire_irac_i2', 'ferr_servs_irac_i2',\n",
    "                                 'ferr_swire_irac_i2', 'm_servs_irac_i2', 'flag_servs_irac_i2', 'm_swire_irac_i2',\n",
    "                                 'merr_servs_irac_i2', 'merr_swire_irac_i2', 'flag_swire_irac_i2'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_servs] = \"SERVS\"\n",
    "origin[use_swire] = \"SWIRE\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC2_total\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irac_origin.write(\"{}/elais-n1_irac_i_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII.a Wavelength domain coverage\n",
    "\n",
    "We add a binary `flag_optnir_obs` indicating that a source was observed in a given wavelength domain:\n",
    "\n",
    "- 1 for observation in optical;\n",
    "- 2 for observation in near-infrared;\n",
    "- 4 for observation in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source observed both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: The observation flag is based on the creation of multi-order coverage maps from the catalogues, this may not be accurate, especially on the edges of the coverage.*\n",
    "\n",
    "*Note 2: Being on the observation coverage does not mean having fluxes in that wavelength domain. For sources observed in one domain but having no flux in it, one must take into consideration de different depths in the catalogue we are using.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wfc_moc = MOC(filename=\"../../dmu0/dmu0_INTWFC/data/en1_intwfc_v2.1_HELP_coverage_MOC.fits\")\n",
    "sparcs_moc = MOC(filename=\"../../dmu0/dmu0_SpARCS/data/SpARCS_HELP_ELAIS-N1_MOC.fits\")\n",
    "hsc_moc = MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_deep_ELAIS-N1_MOC.fits\")\n",
    "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_ELAIS-N1_MOC.fits\")\n",
    "dxs_moc = MOC(filename=\"../../dmu0/dmu0_UKIDSS-DXS_DR10plus/data/UKIDSS-DR10plus_ELAIS-N1_MOC.fits\")\n",
    "servs_moc = MOC(filename=\"../../dmu0/dmu0_DataFusion-Spitzer/data/DF-SERVS_ELAIS-N1_MOC.fits\")\n",
    "swire_moc = MOC(filename=\"../../dmu0/dmu0_DataFusion-Spitzer/data/DF-SWIRE_ELAIS-N1_MOC.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    wfc_moc + sparcs_moc + hsc_moc + ps1_moc) \n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    dxs_moc\n",
    ")\n",
    "\n",
    "was_observed_mir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    servs_moc + swire_moc\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * was_observed_optical + 2 * was_observed_nir + 4 * was_observed_mir,\n",
    "        name=\"flag_optnir_obs\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII.b Wavelength domain detection\n",
    "\n",
    "We add a binary `flag_optnir_det` indicating that a source was detected in a given wavelength domain:\n",
    "\n",
    "- 1 for detection in optical;\n",
    "- 2 for detection in near-infrared;\n",
    "- 4 for detection in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source detected both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: We use the total flux columns to know if the source has flux, in some catalogues, we may have aperture flux and no total flux.*\n",
    "\n",
    "To get rid of artefacts (chip edges, star flares, etc.) we consider that a source is detected in one wavelength domain when it has a flux value in **at least two bands**. That means that good sources will be excluded from this flag when they are on the coverage of only one band."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# SpARCS is a catalogue of sources detected in r (with fluxes measured at \n",
    "# this prior position in the other bands).  Thus, we are only using the r\n",
    "# CFHT band.\n",
    "# Check to use catalogue flags from HSC and PanSTARRS.\n",
    "nb_optical_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_wfc_u']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wfc_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wfc_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wfc_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wfc_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_y']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_n921']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_y'])\n",
    ")\n",
    "\n",
    "nb_nir_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_k'])\n",
    ")\n",
    "\n",
    "nb_mir_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_irac_i1']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_irac_i2']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_irac_i3']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_irac_i4'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "has_optical_flux = nb_optical_flux >= 2\n",
    "has_nir_flux = nb_nir_flux >= 2\n",
    "has_mir_flux = nb_mir_flux >= 2\n",
    "\n",
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * has_optical_flux + 2 * has_nir_flux + 4 * has_mir_flux,\n",
    "        name=\"flag_optnir_det\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IX - Cross-identification table\n",
    "\n",
    "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogues. This can be used to easily get additional information from them.\n",
    "\n",
    "For convenience, we also cross-match the master list with the SDSS catalogue and add the objID associated with each source, if any. **TODO: should we correct the astrometry with respect to Gaia positions?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126 master list rows had multiple associations.\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Addind SDSS ids\n",
    "#\n",
    "sdss = Table.read(\"../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_ELAIS-N1.fits\")['objID', 'ra', 'dec']\n",
    "sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)\n",
    "idx_ml, d2d, _ = sdss_coords.match_to_catalog_sky(SkyCoord(master_catalogue['ra'], master_catalogue['dec']))\n",
    "idx_sdss = np.arange(len(sdss))\n",
    "\n",
    "# Limit the cross-match to 1 arcsec\n",
    "mask = d2d <= 1. * u.arcsec\n",
    "idx_ml = idx_ml[mask]\n",
    "idx_sdss = idx_sdss[mask]\n",
    "d2d = d2d[mask]\n",
    "nb_orig_matches = len(idx_ml)\n",
    "\n",
    "# In case of multiple associations of one master list object to an SDSS object, we keep only the\n",
    "# association to the nearest one.\n",
    "sort_idx = np.argsort(d2d)\n",
    "idx_ml = idx_ml[sort_idx]\n",
    "idx_sdss = idx_sdss[sort_idx]\n",
    "_, unique_idx = np.unique(idx_ml, return_index=True)\n",
    "idx_ml = idx_ml[unique_idx]\n",
    "idx_sdss = idx_sdss[unique_idx]\n",
    "print(\"{} master list rows had multiple associations.\".format(nb_orig_matches - len(idx_ml)))\n",
    "\n",
    "# Adding the ObjID to the master list\n",
    "master_catalogue.add_column(Column(data=np.full(len(master_catalogue), -1, dtype='>i8'), name=\"sdss_id\"))\n",
    "master_catalogue['sdss_id'][idx_ml] = sdss['objID'][idx_sdss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['wfc_id', 'sparcs_intid', 'hsc_id', 'ps1_id', 'dxs_id', 'servs_intid', 'swire_intid', 'help_id', 'specz_id', 'sdss_id']\n"
     ]
    }
   ],
   "source": [
    "id_names = []\n",
    "for col in master_catalogue.colnames:\n",
    "    if '_id' in col:\n",
    "        id_names += [col]\n",
    "    if '_intid' in col:\n",
    "        id_names += [col]\n",
    "        \n",
    "print(id_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_elais-n1{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)\n",
    "id_names.remove('help_id')\n",
    "master_catalogue.remove_columns(id_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X - Adding HEALPix index\n",
    "\n",
    "We are adding a column with a HEALPix index at order 13 associated with each source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(\n",
    "    data=coords_to_hpidx(master_catalogue['ra'], master_catalogue['dec'], order=13),\n",
    "    name=\"hp_idx\"\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XI - Saving the catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns = [\"help_id\", \"field\", \"ra\", \"dec\", \"hp_idx\"]\n",
    "\n",
    "bands = [column[5:] for column in master_catalogue.colnames if 'f_ap' in column]\n",
    "for band in bands:\n",
    "    columns += [\"f_ap_{}\".format(band), \"ferr_ap_{}\".format(band),\n",
    "                \"m_ap_{}\".format(band), \"merr_ap_{}\".format(band),\n",
    "                \"f_{}\".format(band), \"ferr_{}\".format(band),\n",
    "                \"m_{}\".format(band), \"merr_{}\".format(band),\n",
    "                \"flag_{}\".format(band)]    \n",
    "    \n",
    "columns += [\"stellarity\", \"stellarity_origin\", \"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \"flag_optnir_obs\", \n",
    "            \"flag_optnir_det\", \"zspec\", \"zspec_qual\", \"zspec_association_flag\", \"ebv\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing columns: set()\n"
     ]
    }
   ],
   "source": [
    "# We check for columns in the master catalogue that we will not save to disk.\n",
    "print(\"Missing columns: {}\".format(set(master_catalogue.colnames) - set(columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[columns].write(\"{}/master_catalogue_elais-n1{}.fits\".format(OUT_DIR, SUFFIX))"
   ]
  }
 ],
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