{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# xFLS master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on xFLS."
   ]
  },
  {
   "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",
      "This notebook was executed on: \n",
      "2018-05-01 13:44:22.041363\n"
     ]
    }
   ],
   "source": [
    "from herschelhelp_internal import git_version\n",
    "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))\n",
    "import datetime\n",
    "print(\"This notebook was executed on: \\n{}\".format(datetime.datetime.now()))"
   ]
  },
  {
   "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": [
    "spitzer = Table.read(\"{}/Spitzer.fits\".format(TMP_DIR))\n",
    "wfc = Table.read(\"{}/INT-WFC.fits\".format(TMP_DIR))\n",
    "kpno = Table.read(\"{}/KPNO.fits\".format(TMP_DIR))\n",
    "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))\n",
    "legacy = Table.read(\"{}/LegacySurvey.fits\".format(TMP_DIR))\n",
    "uhs = Table.read(\"{}/UHS.fits\".format(TMP_DIR))                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Merging tables\n",
    "\n",
    "At every step, we look at the distribution of the distances to the nearest source in the merged catalogue to determine the best crossmatching 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 PanSTARRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N5W7palA6k9O+3pGyXhcAAEytHKFrn6QlRa8XB9uOt/2Y3P02d1/n7uva2trKUNb01TNc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fccbb4dfc88>"
      ]
     },
     "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": 7,
   "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 Legacy Survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kcp5vjFqBkS5J6miq5fQiAAARInRVid6RCWVzXvZJ9AXtTTU6PDKhnHtFPh8AAJwYoatK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\nEVzeNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fccaf4eea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(legacy['legacy_ra'], legacy['legacy_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, legacy, \"legacy_ra\", \"legacy_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add KPNO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AC5gKrKB0NqdkJqeWhnBFztfeFNFMzikxnanI+QAAWOzmFazM7Aoz22Vmu83sU8d4vdXM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\nJvAxzYpTNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9c436e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(kpno['kpno_ra'], kpno['kpno_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, kpno, \"kpno_ra\", \"kpno_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add UHS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pz3+wikZMyxfU6EWCFQAAeUWwCsnBziElUk7L5tcU5HjXNtVqV3u/UmlXkOMBADAbEaxC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\n9LvOuXskycwqJL3JOTcaBJP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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fccaf498a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(uhs['uhs_ra'], uhs['uhs_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, uhs, \"uhs_ra\", \"uhs_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Spitzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D0ZjXJYmIyCyjBqFJpB5W02NyA9Hb1tbw0PZj3PHNF3nPuhrMjDs21ntUnYiI\nzCYKVknU2jvEJfXFXpcx66ytK6K9L8yT+9spy8vkmsXlXpckIpJSTrejxWT6D+mFmdJUoJndZGb7\nzazRzO4+zeMfMbPdZrbHzF4ws1m/x0gs5jgZimjEyiNvW1bJ6tpCfrXvBDuPdntdjoiIzBLnHLEy\nMz/wNeAGoBnYZmaPOOcm7oDbBFzrnOs2s5uBe4GNySh4pugaHGZ4NKYeVh7xmfH+S2oZiET58UvN\nvHPNHK7VyJWIyJRpVOvCTGUqcAPQ6Jw7BGBmDwK3AqeClXPuhQnnvwjUJrLImejEqR5W6rrulYDf\nx0c2zuWbzx7i89/fwYN3Xs7q2iKvyxIRSaqpBCJJnqlMBdYAxybcb44fO5PPAL+8mKLSwamu65oK\n9FRW0M8nr2ygNC+DT31nG4c7BrwuSURE0lhCF6+b2e8wFqyuPsPjdwJ3AtTXp/fw4Qk1B00Z+VlB\nvvupDbz/ns18/L6t/Nfnr6AiX38uIjLz9QwO89qJPvaf6OO1E3209AzR2R8hM+AnM+gjM+AjLzPA\n6toi8jJ1vdp0mMq73ALUTbhfGz/2Bma2GvgWcLNzrvN038g5dy9j669Yv359Wu+Y2xoKE/AZZXmZ\nXpciwPzyPO775GV8+N4X+fi3t/LgnZdTlKPGrSIys3T0R/j7X77G/pN9HO4YoDccPfVYdtBPSW4G\nI6MxItEYkegokZEYDnhs3wmumF/K1YvKFbCSbCrv7jZgkZnNYyxQfQi4Y+IJZlYP/AT4mHPu9YRX\nOQOdCIWpLMjC51Nz0FSxtq6Ib358PZ/+7jY+9u2t3P+7GynICnpdlojIGTnneLW1j1+/coIn97ez\nu7kH5yAvM8CC8lyqC7OpKsyiqiCL/KzAmxpSO+do74vw1OvtPHugg82HOhWwkuyc76pzLmpmXwAe\nA/zAfc65fWZ2V/zxe4C/AEqBr8f/UKPOufXJKzv1tYbCmgZMQVcvKuOej17C5/5zB5/6zja+9+kN\n5OqHi4ikiPGF5x19EV5u6WH3sRDt/REMqC3O5m1LK1hSWUB1URa+KezqYWZUFGTxwfV1XLeknKf2\n/zZg3bFhLkuq8pP8O5p9pvSJ4pzbBGyadOyeCbc/C3w2saXNbCd6w6yYU+B1GRI3+SqZD1xax4Pb\njvLOf3uOT17ZQNDv02XDIuKptr4wzx1oZ1dzD8d7whjQUJbLlQtLWTGn8KJHmCryfxuwfrjtGA9u\nO8pd1y47tBQuAAAgAElEQVSgUm2BEkr/VU8C5xytoSGuX1bhdSlyBitrCnl/rI4fbT/G9188wscu\nn+t1SSIyC4VHRvnNKyf5yUvNPHOgg9GYo6Yom1tWVrGqtojC7MQvV6jIz+Jjl8/l608d5D9fPMLv\nXbuAHI3cJ4zeySQIDY0QHomph1WKW1tXRHQ0xk92tvDA1qN88LI6soJ+r8sSkTQXizm2H+nmpztb\n+MXu4/SFo1QVZHHnNfPJ9PuomIYRpKKcDD66sZ5vPtfEA9uO8qkr5+HXmuCEULBKAvWwmjnWN5Qw\n6hw/23Wc3/3edu792HqyMxSuRCTxGtv6eXhnCw/vaqG5e4jsoJ+bVlbxvktquWJBKX6fTWtzz/rS\nXN6zrob/2tHML3Yf59a1Z2tRKVOlYJUEv+26rmA1E2ycV0rQ5+MnO5v5xHe2ct8nL9PVMiKSEEc7\nB3l0Tyub9rSypyWEAQsr8vjApbUsn1NAZsDP0a5BjnYNelLfJfXFnAyFebaxg6rCLDbOK/WkjnSi\nT48k0IjVzHPJ3GKuXVLOH/9wFx/91ha+++kNSVnbICLpr6ljgE3xMLXveC8Aa2oLuWVVNatrC1Ou\nzcvbV1bR1hfh5y8fp6ogi7mluV6XNKMpWCXBka4Bgn5Td+8Z5l1r5pAZ8PGFB3Zyxzdf5D8/s5GS\nXDURFZGzGxmNsf1wN0/ub+PxV09ysH1s66x19UX8+S3LuHlVFbXFOSm7h5/PjNsvq+NfHj/Ar/ae\n4M5r5r+pH5ZMnYJVEhzuGGBuaa4WAs5AN66o4t6PX8rn/nMHH/zGZv7jU5dRW5zjdVkikmJO9oZ5\n5vV2no5/9YWjZPh9bJxfwkcvn8uNK6qoKZo5FzBlBf1cs6iMn+9upalzgPlleV6XNGMpWCVBU8cA\n88o0lDpTXbekgu99egO/+73tvOfrL/CdT17GyppCr8sSEY88sOUoI6MxDncOcOBkPwfa+jjZGwHG\nOqAvqcpnaVU+C8vzyIxfWfz0/nYvS74g6xtKeHJ/O0/tb1ewuggKVgkWizkOdw5y3RL1sJppJg/T\nf+qqeXz3hcO89+svcMfGehZX5quJqMgs4Zxj/8k+nn29g4e2H6OpY4BozOH3GXNLc7hpRTGLKvOo\nKshKm2mzoN/H1QvL+NW+ExzrGqSuRKP1F0LBKsGOh4YYjsY0YpUGKguyuOvaBXx382G+t/kwt62t\nUbASSWMd/RGeb+zg6dfbee5AB219Y6NS5fmZbJhXwsKKPOaX5ZER8HlcafJsnFfC06+389T+Nj52\nRYPX5cxIClYJ1tQxtmhRwSo9FGQHufMt83lg61F+srOFmuJsvnj9Ym2uLZIGhqMxdhzp5pkD7Tx7\noJ29LWNX8BXnBLlqYRnXLC7nLYvKePK1mTetd6Eyg36uXFjK46+2cSIUVtugC6BglWAKVuknM+jn\n41c08PCuFv7tiUZebe3jK7evSblLpkXk3I50DsQXnXew+WAHA8Oj+AzqS3K5YXkliyrymFOUjc+M\n6KibVaFq3JXzy3juQAdPvd7Ghy7TKP35UrBKsEPtA+Rk+KnIz/S6FEkgv89477oa3rW6mr959FVu\n++rzfONjl7KoUjvDi6Sy7zzXxKGOAV4/2ceBtn66BoaBsVGpFTWFLK7IZ355rrazmiA7w8/GeaU8\ne6Cd65dGKNPn2XlRsEqww51jVwSmy2JG+S0z45NXzWNZdQG//8BL3Pa15/nHD67hppXVXpcmInGj\nMce+4yGePdDBM6+3s+1wFzEHGX4f88tzuXJBKYsr8ynNzdDP6bO4elEZmw+NrTd736W1XpczoyhY\nJVhTxwCrdGl+Wts4v5Sf/8HVfP77L3HX91/i89ct4Es3LCbgT98FrSKprKVniOcOtPPMgQ5eaOyg\ne3AEgOXVBVy9sJxFlXnMLcnRv9HzkJcZYH1DCVsOdfLWZRUU56hZ8lQpWCXQcDTGsa5B3r1mjtel\nSJJVF2bzw89dzv9+ZB///tRBNh/s5J9vX0uD1taJJF1ocITNhzr4zvOHaWzrpzM+vVeQFWBhRR43\nVOSzsCJPe35epGsWlbP1UBfPN3bwztX6XJsq/a1LoKNdg8ScFq6ns8m9rlbVFOEug4d3tXDjPz3D\nO1dX848fXKMpBpEEGhoeZfuRLp5v7GTzwQ72tIROTe/NK8tl4/xSFlXkUZGfqX97CVSYHWTZnAJe\nPtbDzSurtZvIFClYJdBhXRE4K62uLaK+JIcf7WjmJztbGBwe5W/fu4pi7TMockEi0VH+4bHXOdTe\nz8H2AY51DTLqHD6DupIcfmdJBQsr8qgtztGHfZKtqytib0uIxrY+llQVeF3OjKBglUBqtTB7FeVk\n8Jmr5/HcgQ4ef+0kb//nbv761pXctLLK69JEUt7IaIzdzSFePNTJ5oOdbD/SRXgkhgFzirK5cmEp\nC8rzmFuaQ2ZAV+9Np0WVeWQH/ew61qNgNUUKVgl0qGOA4pwgRVrkNyv5zLhmcTlfeOtC/uRHL3PX\n93dw/bJK/urWFTNqM1aRZBuNOV453svmQx28cLCTbU1dDAyPArC0Kp8PXVZPdDTGvLI8sjMUpLwU\n8PlYVVvIzqPdREZGT+2FKGemYJVATR39Gq0SVtYU8vM/uJrvPN/EP/3mADd85Wn+xw2L+eSVDboq\nSWYl5xxHOgd5trGD5w60s/lgJ73hKADleZmsrClkfnke88pyteA8Ba2rK2JrUxevtPayrr7Y63JS\nnv4GJ9DhjkGuWljmdRmSAoJ+H3des4CbV1bzFz/by//76Kv8dGcLf/muFWyYV+J1eSJJ9cCWo4RH\nRmls6+dAWx+Nbf2nWiAUZQdZVJnPgvJc5pflUZCtHQxSXX1JDsU5QXYd61GwmgIFqwQZiEQ50Rtm\nfrlGrGa7yVcOXr+skqrCbB7dfZwPfmMz1y+r5O6bl7CwQl3bJb00dQzwxGttPLDlCIc7xhacZwZ8\nzC/P4+pF5Swqz6M0T405ZxozY21dEU/tb6c3PKLtvM5BwSpBDneOLVxvKFWwkjcyM1bVFLKkMp8X\nDo6tKbnxn57h9svq+eL1i6go0CanMjM559h3vJdf7T3BL/e2crB97OdgRX4mVy4sZUlVPnNLcnXl\nXhpYU1fEk/vb2d0c4mrNzJyVglWC6IpAOZeMgI/rllTwt+9dxb890cj3XzzCwztb+Ojl9Xz2LfOp\nVMCSGeD+F49wrHuIvS0h9h0P0T04ggHzynN55+pqllYVUKJWI2mnIj+LmqJsXj7Wo2B1DgpWCTLe\nw6qhLMfjSiTVleZl8r/fvYJPXtnAP/3363z7uSa++8IR3ndpDZ+7ZoG6t0vKGR+Z+vnu4/xw2zF6\nBkfwm7GgIpffWVLBsuoCcrXoPO2trSvi0T2ttPWGNdJ+FvqXkCCHOgaoLswiJ0NvqZzdxDVYG+eV\nsrA8j2cPdPDQ9mYe3HqMlTWF/M1tK1lTW6i1KOIZ5xz7T/bx6O5WfrG7laaOAQI+Y355LtcvrWRZ\ndYFaIcwyq2sL2bSnlV3NPdy4XD36zkQpIEGaOga0vkouSGleJretq+Gtyyp4obGDLU1d3Pa151kx\np4CPbJzLu9fO0SXoMi3uf/EIJ3sj7GkJsaclREd/5NQ033vW1rBiTgE5+rs4a+VnBVlYkcfLx3q4\nflklPv3H77T0LyRBmjoGuGVVtddlyAxWkBXkppXVXLekgmDAx/0vHuF//nQP/+fRV7htXQ23X1bH\nqhqNYkliRUdjvHS0h8dfO8lPdrTQPh6mynK5ckEpK+YUkK+rwCRubV0RP9rRzNHOQS1bOAMFqwTo\nHhimZ3CE+fpLJgmQFfRzx8Z6Prqxnp3Herj/xaP8145m7t9ylIbSHN69Zg7vXjtH7RrkgrX1hdl8\nsJMnX2vjqdfb6RkcIeAz6ktzuEJhSs5i+ZwCgruMXcd6FKzOQMEqAZrUakESbOI6rEvnFrO8uoB9\nx0O83NzDvz3RyL8+0cjy6gLesbqa65dVsrgyTyNZclrOOVp6hth2uIsth7rY2tTFofjFNiW5Gbx1\naQXXL6vk6kVl/OLlVo+rlVSXGfCzrLqAvcdDvGvNHK/LSUkKVgnQFO/dMk/NQSVJsjP8rG8oYX1D\nCb3hEfY0h2jpGeLLj+3ny4/tp7Y4m7ctreBtyyrZOL9EG9XOUtHRGIc7B9l3PMQrrb28cnzsq3Ng\nGID8rAAbGkq4/bI6Ns4vZVVNoXpMyXlbOaeQ3c2hU22G5I0UrBKgqWMAv8+oK1arBUm+gqwgVy0s\n446N9ZzsDfPEa208/upJfrj9GN/dfITsoJ/1DcVcsaCUK+IfntqjMH3EYo4TvWGOdg1ypHOAQ+0D\nHGwfoKmjn6Ndg4yMOgCCfmNxZT5vXVrByppCOvojVBZknVpwPB66RM7Xkqp8Mvw+9raEvC4lJU0p\nWJnZTcC/AH7gW865v5v0+FLgO8AlwJ875/4h0YWmsqbOAeqKs8kI6MNLps/E6cK3Lq3kLYvKOdjW\nz4G2fl4/2cezBzoAyMsMsL6hmLV1RaytK2JNbRHFauCYspxz9AyOcKx7kObuIY51DXKse5BjXWO3\nm7uHGB6NnTo/w++joSyHzICfKxeUUZ6XSXVRFuX5mQR8v/2ZVF2Y7cVvR9JQ0O9jSVU++46HiI7G\n9B+3Sc4ZrMzMD3wNuAFoBraZ2SPOuVcmnNYF/CFwW1KqTHFN7QNaxCeeC/p9LK0uYGl1AQD9kShz\nirLYfLCTrU1dPP16O25sMIP6khxW145ts7OoMp/FlXnMLdXWI9PBOUdH/zDHe4Zo6Rni5y8fp3tw\nhJ7BYboHh+keHGE4GnvDc7KDfopzgpTkZnD5/BKKczMozc2kJDeDopygLnuXabeqppA9LSG2NnVx\npTqxv8FURqw2AI3OuUMAZvYgcCtwKlg559qANjN7R1KqTGHOOZo6Btg4v8TrUkTeIC8zQO9QlBVz\nClkxp5DIyCgtPUNjoyDdgzzf2MEvdv92sXJGwMeC8jwaSnOoKxn7qi/Joa44mzlF2WQFtW7rXJxz\n9IajtIaGaO0Jczw0xIlQmOM9YVpDQxzvGeJ4KPym4JQZ8FGSm0FJbibzy/MozsmgJCdIcW4GxTkZ\neu8l5SyuzCfoNx7d06pgNclUglUNcGzC/WZgY3LKmXlO9kYYGhlVqwVJeZlBP/PL85hfnnfqWCQ6\nSntfhJO9Edp6w7T1Rdh+pJvfvHKSaMy94flFOUGqCrKoLMiiujCLivxMygvGfq3Iz6SiIIuyvIy0\nXjg/NDzK8XhAau0J09IzdvtEb3jsWCjM4PDoG55jQEF2kML418aSHIpyMiiK3y/OyVAHc5lxMgI+\nllYV8Ni+E/z1rSs12j3BtC5eN7M7gTsB6uvrp/Olk+a3my/nneNMkdSTGfBTW5xD7aQLL2LO0R+O\n0jUwTNfgML1DI4SGRugdGqGxrZ8dR7oZiERxp/mehdlByvIyKM/PpCwvk/L8TEpzMyjNe+OvhdlB\nCrKDKfEDORZzdA0O094XOfX12L4ThOK/79DQCD2DIwyNvDk05WcFTv1e1tUVnbpdlB2kMCeDvMxA\nSvweRRJtZXw6cEtTJ1cu0KjVuKkEqxagbsL92vix8+acuxe4F2D9+vWn+5k84zRp82VJQz4zCuIB\noYHTj8aOxhwDkSh9kSh94RH6wlH6wlH6I1H6wyO0hsK8frKfgUiUyKSpr4nyswIU5QQpyAqSmxkg\nN8NPzvivGQEyAj4y/D6Cfh8ZAR9Bv+Ezw2fg8xkWvx1zYwEp5hyj8V9HRh2RkVHC0djYryMx+oej\n9A6N0BuO0jc0Qm94hO7BEUZjb/6RlB30nxppqivJOTXKND7ilJ8deMMCcZHZZEllPtlBP5v2tCpY\nTTCVYLUNWGRm8xgLVB8C7khqVTPIwfZ+MgI+5uiKG5ll/L7fhi84+9//kdEYA5EoA5FR+iNRBoaj\nDA2PMjQy+oZfDejoH2aga5DByCgDw1FGRmOMjLrTBp+pMCDgNwI+HwG/xUfpssfCUnE2BdlBinOC\nVOSPXUlXnp9JeV4mT+1v15W+ImeREfDx1qUV/GrvSf7q3ZoOHHfOYOWci5rZF4DHGGu3cJ9zbp+Z\n3RV//B4zqwK2AwVAzMz+GFjunEv7Jik7jnSzuqYQn/5CiZxR0O8bG+W5iIHd8ZGo0ZjDubGF4jHG\nfnUOzBgbvWLsVzMI+Ax/fFRrojs2nnspQsbBzgsvVmSWuGVVNY/uaWVrUxdXLCj1upyUMKU1Vs65\nTcCmScfumXD7BGNThLPK4HCUvS0hfvea+V6XIpL2fGb4/EYiLpCb2ANMRC7c7ywtJyvoY9OeVgWr\nOI1zX4RdR3uIxhwbGtRqQUREZp+cjABvXVrBL/eeuODp+nSjYHURtjR1YQaXNhR7XYqIiIgnbllV\nTUd/hG2Hu7wuJSUoWF2EbYe7WFZVQEFW0OtSREREPPHWpRWnpgNFweqCDUdjvHS0mw3zNA0oIiKz\n1/h04KY9rYyMnrm1ymyhYHWB9h4PER6JKViJiMis9951tXT0D/P0/navS/GcgtUF2tY0Npd8mRau\ni4jILHftknLK8jL40Y5j5z45zSlYXaCtTV3ML8ulPD/T61JEREQ8FfT7eM+6Gh5/tY3O/ojX5XhK\nweoCxGKO7Ue6NVolIiIS9/5L64jGHD/bddzrUjylYHUBXm/rIzQ0wmVaXyUiIgLAkqp8VtcW8qMd\nzV6X4ikFqwuwNb6+aqOClYiIyCnvv7SWV1t72Xc85HUpnlGwugBbm7qoKsiitlgbL4uIiIx795o5\nZPh9/Gj77B21UrA6T845th3uYsO8kjdt7CoiIjKbFeVkcMPySn62q4Xh6OzsaaVgdZ6Odg1ysjei\n9VUiIiKn8f71tXQPjvDEaye9LsUTClbnaXx9lTZeFhERebNrFpVTWZA5a6cDFazO07bDXRTlBFlU\nked1KSIiIinH7zPes66Wp15vp60v7HU5007B6jxtbepi/dwSfD6trxIRETmd919ay2jM8fDOFq9L\nmXYKVuehrTfM4c5BNswr9roUERGRlLWwIo919UU8tL0Z55zX5UwrBavzsPVwfH3VvFKPKxEREUlt\nH904l8a2fn6194TXpUwrBavzsLWpi+ygnxVzCrwuRUREJKXdtq6GhRV5/MOv9xMdnT2tFxSspmhw\nOMojLx/nLYvKCPr1tomIiJyN32d86YbFHGwf4KezaK2VEsIU/WDrMXoGR/jctQu8LkVERGRGuGll\nFatqCvnn/z5AJDrqdTnTQsFqCoajMb717CE2zCvh0rlauC4iIjIVZsafvn0JLT1D/GDLUa/LmRYK\nVlPws10ttIbCfP46jVaJiIicj7csKuPy+SV89clGBoejXpeTdApW5xCLOe55+iDLqgu4bnG51+WI\niIjMKGOjVkvp6B/mO88f9rqcpFOwOodfv3KSg+0DfP66Bdp0WURE5AJcOreY65dVcM/TBwkNjnhd\nTlIpWJ2Fc45/f/og9SU53LKyyutyREREZqwv3biE/kiUbzxz0OtSkkrB6iw2H+zk5WM9fO7a+QTU\nYkFEROSCLasu4N1r5nDf8028dqLX63KSRmnhLP796YOU52fyvktqvS5FRERkxvuzm5dRlJ3BJ+7b\nSkvPkNflJIWC1RnsaQ7x7IEOPnP1PLKCfq/LERERmfGqCrP4j09fxuDwKJ+4bys9g8Nel5RwClan\nMTIa4/977DXyswJ8ZGO91+WIiIikjaVVBdz7sfUc7Rzks9/dTngkvRqHKlhNMhCJ8pnvbufZAx38\nyY1LyM8Kel2SiIhIWrliQSn/dPtadhzt5g9/sJPRmPO6pIRRsJqgoz/Ch7/5Is8daOfv3ruKT1zZ\n4HVJIiIiaekdq6v5i3cu59evnOQvH9lLLE3CVcDrAlLFkc4BPnHfVk70hrn3Y+u5fnml1yWJiIik\ntU9dNY8ToTDfeOYQu471cPdNy7h6UZnXZV2UKY1YmdlNZrbfzBrN7O7TPG5m9q/xx3eb2SWJLzV5\ndjf38L5/f4GeoRHu/+zlClUiIiLT5O6bl/KVD66he2CEj357Cx/91hb2NIe8LuuCnXPEysz8wNeA\nG4BmYJuZPeKce2XCaTcDi+JfG4F/j/+akpxzHGzv57F9J/n1vhO83ByipiibBz+9gYUVeV6XJyIi\nMmuYGe+9pJZ3rK7m+y8e5atPHOBdX32Od66u5pZV1aypK2JOYdaM2f1kKlOBG4BG59whADN7ELgV\nmBisbgW+55xzwItmVmRm1c651oRXPEWxmKNrcJj2vggd/WNf7X0RjveEeeb1dg51DACwpq6IP337\nEm6/rI6yvEyvyhUREZnVMgN+PnP1PD6wvpZvPnOIbz/XxC92j8WI8vxM1tQWsaa2kDlF2RRmBynK\nCVKYHf/KCZIZSI3WSFMJVjXAsQn3m3nzaNTpzqkBPAtWe1pC3Pq15990PDPg47KGEj51VQPXL6+k\nujDbg+pERETkdAqygnzpxiX8wVsX8WprLy8397DrWA8vH+vhv189edrnfHhDHX/73tXTXOnpTevi\ndTO7E7gzfrffzPZP5+uPex24f/pftgzomP6XnRX03iaX3t/k0vubXHp/k+gjXhcQ93fxrySbO5WT\nphKsWoC6Cfdr48fO9xycc/cC906lsHRjZtudc+u9riMd6b1NLr2/yaX3N7n0/sp0m8pVgduARWY2\nz8wygA8Bj0w65xHg4/GrAy8HQl6urxIRERHxwjlHrJxzUTP7AvAY4Afuc87tM7O74o/fA2wCbgEa\ngUHgU8krWURERCQ1TWmNlXNuE2PhaeKxeybcdsDvJ7a0tDMrp0Cnid7b5NL7m1x6f5NL769MKxvL\nRCIiIiJysbRXoIiIiEiCKFgl2bm2A5ILZ2b3mVmbme31upZ0ZGZ1Zvakmb1iZvvM7I+8rimdmFmW\nmW01s5fj7+9feV1TujEzv5ntNLNfeF2LzB4KVkk0YTugm4HlwIfNbLm3VaWV/wBu8rqINBYFvuSc\nWw5cDvy+/v4mVAR4q3NuDbAWuCl+VbUkzh8Br3pdhMwuClbJdWo7IOfcMDC+HZAkgHPuGaDL6zrS\nlXOu1Tn3Uvx2H2MfUDXeVpU+3Jj++N1g/EuLXhPEzGqBdwDf8roWmV0UrJLrTFv9iMwoZtYArAO2\neFtJeolPVe0C2oDfOOf0/ibOPwP/FxDzuhCZXRSsROSszCwP+DHwx865Xq/rSSfOuVHn3FrGdqvY\nYGYrva4pHZjZO4E259wOr2uR2UfBKrmmtNWPSKoysyBjoep+59xPvK4nXTnneoAn0ZrBRLkKeLeZ\nHWZsCcZbzez73pYks4WCVXJNZTsgkZRkZgZ8G3jVOfcVr+tJN2ZWbmZF8dvZwA3Aa95WlR6cc3/m\nnKt1zjUw9nP3CefcRz0uS2YJBaskcs5FgfHtgF4FHnLO7fO2qvRhZj8ANgNLzKzZzD7jdU1p5irg\nY4z9b39X/OsWr4tKI9XAk2a2m7H/hP3GOae2ACIznDqvi4iIiCSIRqxEREREEkTBSkRERCRBFKxE\nREREEkTBSkRERCRBFKxEREREEkTBSkRERCRBFKxE5JzMbDTex2qfmb1sZl8yM1/8sfVm9q9neW6D\nmd0xfdW+6bWH4vvxpQQzu93MGs1MPatE0pCClYhMxZBzbq1zbgVjHcJvBv4SwDm33Tn3h2d5bgPg\nSbCKOxjfj2/KzMyfrGKccz8EPpus7y8i3lKwEpHz4pxrA+4EvmBjrhsffTGzayd0ad9pZvnA3wFv\niR/7YnwU6Vkzeyn+dWX8udeZ2VNm9l9m9pqZ3R/fVgczu8zMXoiPlm01s3wz85vZl81sm5ntNrPP\nTaV+M3vYzHbER9/unHC838z+0cxeBq44w2uuiN/eFX/NRfHnfnTC8W+MBzMzuyn+e3zZzB5P4B+D\niKSogNcFiMjM45w7FA8PFZMe+hPg951zz5tZHhAG7gb+xDn3TgAzywFucM6F48HkB8D6+PPXASuA\n48DzwFVmthX4IXC7c26bmRUAQ8BngJBz7jIzywSeN7NfO+eazlH+p51zXfH9+baZ2Y+dc51ALrDF\nOfel+N6er53mNe8C/sU5d3/8HL+ZLQNuB65yzo2Y2deBj5jZL4FvAtc455rMrOS832gRmXEUrEQk\nkZ4HvmJm9wM/cc41xwedJgoCXzWztcAosHjCY1udc80A8XVRDUAIaHXObQNwzvXGH78RWG1m748/\ntxBYBJwrWP2hmb0nfrsu/pzOeC0/jh9fcobX3Az8uZnVxn9/B8zsbcCljIU0gGygDbgceGY86Dnn\nus5Rl4ikAQUrETlvZjafsSDSBiwbP+6c+zszexS4hbERpLef5ulfBE4CaxhbjhCe8Fhkwu1Rzv4z\nyoA/cM49dh51XwdcD1zhnBs0s6eArPjDYefc6Nme75x7wMy2AO8ANsWnHw34rnPuzya91rumWpeI\npA+tsRKR82Jm5cA9wFfdpF3czWyBc26Pc+7vgW3AUqAPyJ9wWiFjo0Ex+P/buWOWOoIoDMPvB95C\nRIQUgpXahBRiZyv4Dywkqa510vkDUqdMEcHKImAdQRA0AftbpQshjXUwhVUQNDoWOwoGcQ0sGJb3\nqZZh2DPbnT3n7DIE2gbFfwAzSZZqjMkkY8Bn4E2SQV1/nmSi5V5TwGlNql7QVJUeHbMmlMellA/A\nHrAIHAFrSabr3mdJZoERsJxk/ma95WySesCKlaTHGK+tuQHwB9gB3t+zbyPJCnAFfAMO6vVlHQr/\nCGwBn5KsA4fA74cCl1LOk7wCNutc1BlN1WmbplX4tQ65/wJWW57jEHid5DtN8jT6x5gvgWGSC+An\n8K7Oa70FvqT5BcUFzZzZqA7H79b1E5ovKiX1WP564ZSk3kgyB+yXUhae+Ch31Jbk7UC/pP6wFSip\nzy6BqfxnPwilqdqdPvVZJHXPipUkSVJHrFhJkiR1xMRKkiSpIyZWkiRJHTGxkiRJ6oiJlSRJUkeu\nAb1E4tkAAAAESURBVLAgUjaIK0UyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fccaf4a0358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(spitzer['spitzer_ra'], spitzer['spitzer_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, spitzer, \"spitzer_ra\", \"spitzer_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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table140516750081160-609000\" 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>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>legacy_id</th><th>f_90prime_g</th><th>ferr_90prime_g</th><th>f_ap_90prime_g</th><th>ferr_ap_90prime_g</th><th>f_90prime_r</th><th>ferr_90prime_r</th><th>f_ap_90prime_r</th><th>ferr_ap_90prime_r</th><th>f_mosaic_z</th><th>ferr_mosaic_z</th><th>f_ap_mosaic_z</th><th>ferr_ap_mosaic_z</th><th>legacy_stellarity</th><th>m_90prime_g</th><th>merr_90prime_g</th><th>flag_90prime_g</th><th>m_ap_90prime_g</th><th>merr_ap_90prime_g</th><th>m_90prime_r</th><th>merr_90prime_r</th><th>flag_90prime_r</th><th>m_ap_90prime_r</th><th>merr_ap_90prime_r</th><th>m_mosaic_z</th><th>merr_mosaic_z</th><th>flag_mosaic_z</th><th>m_ap_mosaic_z</th><th>merr_ap_mosaic_z</th><th>legacy_flag_cleaned</th><th>legacy_flag_gaia</th><th>kpno_intid</th><th>kpno_stellarity</th><th>m_ap_mosaic_r</th><th>merr_ap_mosaic_r</th><th>m_mosaic_r</th><th>merr_mosaic_r</th><th>f_ap_mosaic_r</th><th>ferr_ap_mosaic_r</th><th>f_mosaic_r</th><th>ferr_mosaic_r</th><th>flag_mosaic_r</th><th>kpno_flag_cleaned</th><th>kpno_flag_gaia</th><th>uhs_id</th><th>uhs_stellarity</th><th>m_ukidss_j</th><th>merr_ukidss_j</th><th>m_ap_ukidss_j</th><th>merr_ap_ukidss_j</th><th>f_ukidss_j</th><th>ferr_ukidss_j</th><th>flag_ukidss_j</th><th>f_ap_ukidss_j</th><th>ferr_ap_ukidss_j</th><th>uhs_flag_cleaned</th><th>uhs_flag_gaia</th><th>spitzer_intid</th><th>spitzer_stellarity</th><th>f_irac_i1</th><th>ferr_irac_i1</th><th>f_ap_irac_i1</th><th>ferr_ap_irac_i1</th><th>f_irac_i2</th><th>ferr_irac_i2</th><th>f_ap_irac_i2</th><th>ferr_ap_irac_i2</th><th>f_irac_i3</th><th>ferr_irac_i3</th><th>f_ap_irac_i3</th><th>ferr_ap_irac_i3</th><th>f_irac_i4</th><th>ferr_irac_i4</th><th>f_ap_irac_i4</th><th>ferr_ap_irac_i4</th><th>m_irac_i1</th><th>merr_irac_i1</th><th>flag_irac_i1</th><th>m_ap_irac_i1</th><th>merr_ap_irac_i1</th><th>m_irac_i2</th><th>merr_irac_i2</th><th>flag_irac_i2</th><th>m_ap_irac_i2</th><th>merr_ap_irac_i2</th><th>m_irac_i3</th><th>merr_irac_i3</th><th>flag_irac_i3</th><th>m_ap_irac_i3</th><th>merr_ap_irac_i3</th><th>m_irac_i4</th><th>merr_irac_i4</th><th>flag_irac_i4</th><th>m_ap_irac_i4</th><th>merr_ap_irac_i4</th><th>spitzer_flag_cleaned</th><th>spitzer_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></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></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></th><th></th><th>uJy</th><th>uJy</th><th></th><th></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>mag</th><th>mag</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>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</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></tr></thead>\n",
       "<tr><td>0</td><td>465698102781</td><td>258.815386176</td><td>59.5803267457</td><td>0.0</td><td>13.397</td><td>0.035</td><td>13.447</td><td>0.035</td><td>15.012</td><td>0.018</td><td>12.037</td><td>0.018</td><td>14.731</td><td>0.016</td><td>11.857</td><td>0.016</td><td>14.443</td><td>0.022</td><td>11.621</td><td>0.022</td><td>12.424</td><td>0.055</td><td>11.481</td><td>0.055</td><td>15892.8</td><td>512.323</td><td>15177.5</td><td>489.265</td><td>False</td><td>3590.87</td><td>59.5317</td><td>55616.0</td><td>922.036</td><td>False</td><td>4651.57</td><td>68.5481</td><td>65644.7</td><td>967.376112938</td><td>False</td><td>6064.57</td><td>122.885</td><td>81583.0</td><td>1653.09621394</td><td>False</td><td>38940.4</td><td>1972.6</td><td>92811.1</td><td>4701.52</td><td>False</td><td>False</td><td>3</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>False</td><td>nan</td><td>nan</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>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>False</td><td>0</td></tr>\n",
       "<tr><td>1</td><td>465698400822</td><td>259.205396297</td><td>59.8006091747</td><td>0.0</td><td>16.314</td><td>0.035</td><td>16.315</td><td>0.035</td><td>16.802</td><td>0.018</td><td>16.696</td><td>0.018</td><td>15.298</td><td>0.016</td><td>14.634</td><td>0.016</td><td>15.227</td><td>0.022</td><td>14.599</td><td>0.022</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1082.43</td><td>34.8935</td><td>1081.43</td><td>34.8613</td><td>False</td><td>690.557</td><td>11.4485</td><td>761.378</td><td>12.6226</td><td>False</td><td>2759.31</td><td>40.6626</td><td>5086.28</td><td>74.9541297555</td><td>False</td><td>2945.78</td><td>59.6895</td><td>5252.91</td><td>106.438377872</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>False</td><td>nan</td><td>nan</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>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>False</td><td>0</td></tr>\n",
       "<tr><td>2</td><td>465698403144</td><td>258.710224095</td><td>59.6732362657</td><td>0.0</td><td>14.72</td><td>0.035</td><td>14.651</td><td>0.035</td><td>14.997</td><td>0.018</td><td>14.097</td><td>0.018</td><td>14.809</td><td>0.016</td><td>13.772</td><td>0.016</td><td>14.605</td><td>0.022</td><td>13.795</td><td>0.022</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>4698.94</td><td>151.476</td><td>5007.26</td><td>161.415</td><td>False</td><td>3640.83</td><td>60.3598</td><td>8340.65</td><td>138.276</td><td>False</td><td>4329.12</td><td>63.7963</td><td>11251.2</td><td>165.804132819</td><td>False</td><td>5223.96</td><td>105.852</td><td>11015.4</td><td>223.202072084</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>False</td><td>nan</td><td>nan</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>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>False</td><td>0</td></tr>\n",
       "<tr><td>3</td><td>465698103597</td><td>258.654800922</td><td>59.5928175867</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>15.002</td><td>0.018</td><td>13.898</td><td>0.018</td><td>14.742</td><td>0.016</td><td>13.335</td><td>0.016</td><td>14.48</td><td>0.022</td><td>13.283</td><td>0.022</td><td>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>3624.1</td><td>60.0825</td><td>10018.4</td><td>166.092</td><td>False</td><td>4604.68</td><td>67.8571</td><td>16826.7</td><td>247.967913747</td><td>False</td><td>5861.38</td><td>118.768</td><td>17652.2</td><td>357.682913542</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>1</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>False</td><td>nan</td><td>nan</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>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>False</td><td>0</td></tr>\n",
       "<tr><td>4</td><td>465698102599</td><td>258.869895906</td><td>59.5954132377</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>15.003</td><td>0.018</td><td>12.786</td><td>0.018</td><td>14.722</td><td>0.016</td><td>12.48</td><td>0.016</td><td>14.475</td><td>0.022</td><td>12.436</td><td>0.022</td><td>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>3620.76</td><td>60.0272</td><td>27899.7</td><td>462.539</td><td>False</td><td>4690.29</td><td>69.1187</td><td>36982.8</td><td>544.999063015</td><td>False</td><td>5888.43</td><td>119.316</td><td>38512.3</td><td>780.365847051</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>False</td><td>nan</td><td>nan</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>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>False</td><td>0</td></tr>\n",
       "<tr><td>5</td><td>465698402740</td><td>258.781877973</td><td>59.6159953617</td><td>0.0524781</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>16.775</td><td>0.018</td><td>16.823</td><td>0.018</td><td>17.514</td><td>0.016</td><td>17.473</td><td>0.016</td><td>17.4</td><td>0.022</td><td>17.366</td><td>0.022</td><td>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>707.946</td><td>11.7368</td><td>677.329</td><td>11.2292</td><td>False</td><td>358.426</td><td>5.28197</td><td>372.22</td><td>5.4852408357</td><td>False</td><td>398.107</td><td>8.06674</td><td>410.771</td><td>8.3233587211</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>False</td><td>nan</td><td>nan</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>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>False</td><td>0</td></tr>\n",
       "<tr><td>6</td><td>465698302968</td><td>258.768483709</td><td>59.9384138877</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>15.031</td><td>0.018</td><td>12.152</td><td>0.018</td><td>14.963</td><td>0.016</td><td>12.367</td><td>0.016</td><td>14.679</td><td>0.022</td><td>12.288</td><td>0.022</td><td>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>3528.58</td><td>58.4989</td><td>50026.4</td><td>829.369</td><td>False</td><td>3756.64</td><td>55.3599</td><td>41039.3</td><td>604.77745533</td><td>False</td><td>4879.77</td><td>98.8777</td><td>44136.7</td><td>894.330792129</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>False</td><td>nan</td><td>nan</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>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>False</td><td>0</td></tr>\n",
       "<tr><td>7</td><td>465698301564</td><td>259.053415086</td><td>59.8252238897</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>15.125</td><td>0.018</td><td>13.504</td><td>0.018</td><td>14.95</td><td>0.016</td><td>13.138</td><td>0.016</td><td>14.666</td><td>0.022</td><td>12.887</td><td>0.022</td><td>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>3235.93</td><td>53.6473</td><td>14401.2</td><td>238.753</td><td>False</td><td>3801.89</td><td>56.0268</td><td>20174.4</td><td>297.300547361</td><td>False</td><td>4938.55</td><td>100.069</td><td>25421.4</td><td>515.107814223</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>1</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>False</td><td>nan</td><td>nan</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>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>False</td><td>0</td></tr>\n",
       "<tr><td>8</td><td>465698300805</td><td>259.21094373</td><td>59.8208436017</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>17.114</td><td>0.03</td><td>16.781</td><td>0.03</td><td>14.976</td><td>0.016</td><td>13.536</td><td>0.016</td><td>14.628</td><td>0.024</td><td>12.719</td><td>0.024</td><td>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>518.084</td><td>14.3152</td><td>704.044</td><td>19.4535</td><td>False</td><td>3711.93</td><td>54.701</td><td>13983.0</td><td>206.060796976</td><td>False</td><td>5114.46</td><td>113.054</td><td>29675.6</td><td>655.974403024</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>1</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>False</td><td>nan</td><td>nan</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>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>False</td><td>0</td></tr>\n",
       "<tr><td>9</td><td>465698303358</td><td>258.69449952</td><td>59.8970663117</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>17.333</td><td>0.03</td><td>17.091</td><td>0.03</td><td>15.045</td><td>0.016</td><td>13.887</td><td>0.016</td><td>14.844</td><td>0.022</td><td>13.734</td><td>0.022</td><td>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>423.448</td><td>11.7003</td><td>529.176</td><td>14.6217</td><td>False</td><td>3483.37</td><td>51.3329</td><td>10120.4</td><td>149.140462279</td><td>False</td><td>4191.79</td><td>84.9372</td><td>11652.0</td><td>236.101187766</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>False</td><td>nan</td><td>nan</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>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>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",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\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(\"$('#table140516750081160-609000').dataTable()\");\n",
       "    \n",
       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
       "    \"optionalnum-asc\": astropy_sort_num,\n",
       "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
       "});\n",
       "\n",
       "    $('#table140516750081160-609000').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 94, 95, 96, 97, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 118, 119, 120, 121, 123, 124, 125, 126, 128, 129, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 179, 180, 181, 182, 184, 185, 186, 187, 189, 190, 191, 192, 194, 195, 197], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "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": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wfc_stellarity, legacy_stellarity, kpno_stellarity, uhs_stellarity, spitzer_stellarity\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": 21,
   "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": 22,
   "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": 23,
   "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), \"xFLS\", dtype='<U18'),\n",
    "                                   name=\"field\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz =  Table.read(\"../../dmu23/dmu23_xFLS/data/xFLS-specz-v2.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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auSV+lyJJykpjOBAAkPwIVklqU02zhkacrvZ5ftVo6xaXq6lrQFsZDgQAJCmCVZJ6cV+T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\nAMAjBCsAAACP/P+UeX+28c0MoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcce44413c8>"
      ]
     },
     "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": 27,
   "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",
    "No overlapping filters on xFLS."
   ]
  },
  {
   "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": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spitzer_moc = MOC(filename=\"../../dmu0/dmu0_DataFusion-Spitzer/data/Datafusion-xFLS_MOC.fits\")\n",
    "wfc_moc = MOC(filename=\"../../dmu0/dmu0_INTWFC/data/fls_intwfc_v2.1_HELP_coverage_MOC.fits\")\n",
    "kpno_moc = MOC(filename=\"../../dmu0/dmu0_KPNO-FLS/data/KPNO-FLS_xFLS_MOC.fits\")\n",
    "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_xFLS_v2_MOC.fits\")\n",
    "legacy_moc = MOC(filename=\"../../dmu0/dmu0_LegacySurvey/data/LegacySurvey-dr4_xFLS_MOC.fits\")\n",
    "uhs_moc = MOC(filename=\"../../dmu0/dmu0_UHS/data/UHS-DR1_xFLS_MOC.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    wfc_moc + legacy_moc + kpno_moc + ps1_moc) \n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    uhs_moc\n",
    ")\n",
    "\n",
    "was_observed_mir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    spitzer_moc\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 31,
   "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",
    "    #Legacy Survey\n",
    "    1 * ~np.isnan(master_catalogue['f_90prime_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_90prime_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_mosaic_z']) +\n",
    "    #KPNO-FLS\n",
    "    1 * ~np.isnan(master_catalogue['f_mosaic_r']) +\n",
    "    #PanSTARRS\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",
    ")\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": 32,
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91 master list rows had multiple associations.\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Addind SDSS ids\n",
    "#\n",
    "sdss = Table.read(\"../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_xFLS.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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['wfc_id', 'ps1_id', 'legacy_id', 'kpno_intid', 'uhs_id', 'spitzer_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)\n",
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_xfls{}.fits\".format(OUT_DIR, SUFFIX))\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": 35,
   "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": 36,
   "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": 37,
   "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": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[columns].write(\"{}/master_catalogue_xfls{}.fits\".format(OUT_DIR, SUFFIX))"
   ]
  }
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