{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ELAIS-S1 master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on ELAIS-S1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "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",
    "from collections import OrderedDict\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": [
    "video = Table.read(\"{}/VISTA-VIDEO.fits\".format(TMP_DIR))\n",
    "vhs = Table.read(\"{}/VISTA-VHS.fits\".format(TMP_DIR))\n",
    "voice = Table.read(\"{}/ESIS-VOICE.fits\".format(TMP_DIR))\n",
    "servs = Table.read(\"{}/SERVS.fits\".format(TMP_DIR))\n",
    "swire= Table.read(\"{}/SWIRE.fits\".format(TMP_DIR))\n",
    "des = Table.read(\"{}/DES.fits\".format(TMP_DIR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Merging tables\n",
    "\n",
    "We merge in order of increasing wavelength: VIDEO, VHS, VOICE, 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": [
    "### VIDEO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = video\n",
    "master_catalogue['video_ra'].name = 'ra'\n",
    "master_catalogue['video_dec'].name = 'dec'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add VHS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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RpPdJ+pJ3pS1svUPj6hwcm9cRq2V5yQrEGMEKAIA54lXz+r9I+hPn3Ns275jZI2a2x8z2\ntLe3e3Tq6FTbGbocV5GTMm/nTIgNqCInmSUXAACYI7MJVs2SSmbcLg7fN9MmSc+YWZ2kD0j6opm9\n9+wXcs496Zzb5JzblJube4klLwy1HaGlFuZzxEoKbW3DzEAAAObGbILVbkkrzKzCzOIlfUjS8zMP\ncM5VOOfKnXPlkr4n6b865/7T82oXkNr2QcWYVJoVnNfzrsxPVUPXkIbGJub1vAAALAYXDFbOuQlJ\nj0l6UdIxSd91zh0xs0fN7NG5LnChqukYVHFmcM43Xz7bqoJUOSedbB2Y1/MCALAYxM7mIOfcVklb\nz7rvifMc+zuXX9bCV9c5v0stTFtVEN7a5nS/1pdkzPv5AQBYyFh53QfOOdW2+xOsSrOCSoyLoYEd\nAIA5QLDyQXv/qAbHJud1DatpgRjTijwa2AEAmAsEKx9M7xHox4iVFLoceJy1rAAA8BzByge1fger\n/FS194+qa3DMl/MDALBQEax8UNsxqPjYGC1JT/Ll/CtnNLADAADvEKx8UNsxqPLsoGJizJfzrw4H\nK/qsAADwFsHKB7Ud/swInJaXmqD0pDhmBgIA4DGC1TybnHKq7xyc1z0Cz2ZmWlWQyqVAAAA8RrCa\nZ83dwxqfdFrq44iVFN4z8HS/nHO+1gEAwEJCsJpnNdObL/uwhtVMqwpS1T86oeaeYV/rAABgISFY\nzTO/l1qYtqYwTZJ0rIXLgQAAeIVgNc/qOgaVmhir7OR4X+tYXZAqM+lYS5+vdQAAsJAQrOZZTXhG\noJk/Sy1MS06IVVlWkGAFAICHCFbzzO+lFmZaU5hGsAIAwEMEq3k0Mj6p5p7hiApW9V1DGhyd8LsU\nAAAWBILVPGroGpJz/jeuT1tTmCbnxIbMAAB4hGA1j2raQzMCl/q4OOhMawpDW9twORAAAG8QrObR\n9FIL5TlBnysJKcpIUmpiLMEKAACPEKzmUV3HoHJTE5SaGOd3KZJCW9usKaCBHQAArxCs5lFtx6Aq\nsiOjv2ramsLQnoFTU2xtAwDA5SJYzaOaCFpqYdqawjQNjk2qsXvI71IAAIh6sX4XsFj0jYyrY2DU\n9z0Cz/arrW36VBZho2kAAO98e2fDRT/ngS2lc1DJwkawmid1EbJH4NlWFaQqxqSjLf26Z12h3+UA\nAGbpUoIS5h6XAufJr5ZaiKxglRgXUEVOMg3sAAB4gGA1T6raBhSIsYi83MbWNgAAeINgNU+q2gZU\nlhVUfGzkveVrCtPU1D2svpFxv0sBACCq0WM1T6rbB7QsLzJWXD/b2nAD+/GWfm2uyPK5GgBYnOiZ\nWhgIVvNgYnJKdZ2DeufafL9LOaeZMwMJVgDgD+ecOgfG1No/ou6hcXUPjal7cEw9Q+Man5xSWlKc\nMpLilJYUp/SkOOWnJao8Oygz87t0zECwmgf1XUMan3RanhuZI1b5aQnKDMbRZwUA82hyyulYS592\n1XZpd12XXj3ZocHRiTOPx8fGKCsYr8xgnGIDMeobHldt56D6hsc1vaZzaVZQ71yTr2W5yQSsCEGw\nmgdVbQOSpOUReinQzGhgB4B50NwzrFdPtOsXJ9v1y5Md6hsJBamijCStyEtRRXayCjMSlRWMV1J8\n4Jxhaco5DY5O6GhLn7ZXtutrO2pVnh0KWEsj9A/4xYRgNQ+mg1Wk9lhJocuB39pZr8kpp0AMf/UA\ngBeGxya1s7ZTr5xo16sn2lUdXnqnIC1R96wr0A3LcnRtRZaKMpJm3WMVY6bUxDhtqcjWxtJM7a7v\n1iuVbfrqL2u1PDdFH9pcomA8v979wjs/D6rbBlSQlqiUhMh9u9cUpmlkfEq1HYMRO7IGAJHOOaeT\nbQN69US7XjnRrp21XRqbmFJCbIw2V2Tpw5tLdfPKXK3IS/Hk0l1sIEbXL83WprJM7art0gtHTus/\nXqvTwzdVKCE24MF3hIsVub/pF5Cq9oGIDytrClMlhRrYI71WAIgkbf0j2lHVoV+c7NCOqg619o1K\nCrV/fOS6Mt28MldbKrKUGDd3QScuEKMbl+coIxinp3c16Btv1Otj15crLhB5S/wsdASrOeacU3Xb\ngH5rU4nfpbyt5Xkpio0xHWvp07vXL/G7HACIKDMv0w2PTaquc1DV7QOqaR/U6b4RSVIwPqBluSm6\nYVmOVuSlKCMYL0lq7h7Wc93N81LnFUvS9f4Nxfre3iY9s7tRD2wupb1jnhGs5tjpvhENjk1GdH+V\nJCXEBrQ8L4UGdgA4S//IuCpP96u2Y1A1HQNq7h6WkxQbYyrLDurutflanpeqwoxExUTAzLwNpZka\nHZ/UDw+26Nl9TfrAxuKIqGuxIFjNsTMzAqNgpsaawjS9Xt3pdxkA4KvOgVHtqe/Wrtou7art0pFT\nvZpyUsBMJVlJum11npbmJqs0M6jYCL3Udv2yHI1MTGnb0VYlxMboPeuXsBzDPCFYzbFIX2phpjWF\nqfr+m83qHhxTZnK83+UAwJybmnKqbh/Q3vpu7anv1r76btV0hGbuJcTG6JrSDD12+woNjk6oJDMy\ntyU7n1tX5mpkfFK/ONmhsuygri7J9LukRYFgNceq2gaUnhSnnJTIDyrrlqRLkg429+qWlbk+VwMA\n3mvvH9X+xh4daOwJfW7qUX94Lams5HhtKM3Ub20q0abyTF1VnH5mZl00bjdjZrr7igLVtA/qJ4dP\na01BmhLmsIEeIQSrOVbVNhA1K+JeVZKhGJP21ncTrABEve7BMR1q7tWh5l4dbOrR4eY+NfcMS5IC\nMabVBal69/olurokQ5vKMlWREx0/qy9GjJnes36JvvRKtV6ubNc96wr8LmnBI1jNser2Ad2+Os/v\nMmYlJSFWqwrS9GZDt9+lAMCsfXtng4ZGJ9TcM3zm41TPsLqHxs8ck50cr6LMJK0vTldJVlCF6Uln\nLutNTDq9UdOlN2q6/PoW5lRJVlAbSjO1o6pDm8oylZOa4HdJCxrBag71DI2pY2AsKvqrpm0sy9B/\nvnmKFdgBRKze4XEdbu7VwabQSNTrNZ3qmRGispLjVZQZ1OaKJBVnJmlJepKS4hf3JbC7r8jXkVO9\n+tGhU/rY9eULbmQukhCs5lB1e/Q0rk/bUJqpb77RoJNt/VpdkOZ3OQAWuZHxSR1t6dOBGX1RdZ1D\nZx4vzQqqODOo6yqStCQjSUUZhKhzSU2M0x2r87T18GlVnu7X6kJ+vs8VgtUc+tVSC6k+VzJ7G8tC\ns0b21ncTrADMK+ecHn+5Wg1dQ2roGlJj15BO945o0jlJUlpirIozg7prbb6KMkMhij3xZu/6ZTna\nXd+tHx1q0bK8FFZlnyP8HzmHqtoGlBAbo6LMJL9LmbXSrKCyk+O1r75HD24p87scAAvYyPikDjb1\nam99t/bWd2t/Y7c6BsYkSfGB0M/Om1bkqDgzScWZQaUnxflccXQLxJh+46pC/fuOOu2o6tCtq6Kj\n/zfaEKzmUFXbgCpykqOqV8nMtKEsU/toYAfgsba+Ee2p79aeum7tbejWkeZeTUyFRqOW5iTrlpV5\nmpiaUmlWUHmpiVH1szNarMhL1drCNL1c2aZrSjMJq3OAYDWHqtoHtL44w+8yLtqG0kxtO9qqrsEx\nZbFQKIBL4JxTdfugdtd1aXddl/bUdauhK9QblRgXo/XFGXrk5qXaWJapa0ozz/ysicb1oqLNfVcW\n6vPb+vTLk+1611XsDes1gtUcGRmfVFP3sH5zQ7HfpVy06T6rffXdeufafJ+rARANpqacTrT1a1dt\nl3bWdGlnbZc6BkYlSckJsSrLCuq+dQUqy07WkoykM6NRrX2jeuHwaT9LX3SykuN1ZVG69tR36441\n+Upk0VBPEazmSHX7gJyLrhmB064qTldsjGlfA8EKwK+cPZrUPTimqvYBVbUNqLp9QENjk5Kk9KQ4\nVeQk6x0rclSRnazslHim90eYG5fn6EBTr/bUd+um5Tl+l7OgEKzmSDTtEXi2xLiArliSpr319FkB\n+JXRiUnVtA+qsrVfVW0D6hoMNZqnJsZqVX6qluamqCInWZnBOIJUhCvODKo8O6jXqzt0/dJs+tk8\nRLCaI9Xtg4oxqTw72e9SLsk1pZn6zu5GjU9OMSUXWMSq2wf08vE2ba9s1+s1nZqccoqPjdHSnGTd\nsCxby3NTlJuaQJCKQjctz9E3dzboaEufrixK97ucBWNWwcrM7pH0r5ICkr7qnPvcWY8/KOlPJJmk\nfkm/55w74HGtUaW6bUAlWcGovXa9sSxTT71Wp+Mt/bqymH9wwGIxMTmlPfXd+tmxVv30WJtqOwYl\nSSvyUnT90mytKkhVWXZQsTH8wRXtVhemKSs5XjuqOghWHrpgsDKzgKTHJd0pqUnSbjN73jl3dMZh\ntZJucc51m9m9kp6UtGUuCo4WVW0DWp4bfZcBp22YbmBv6CZYAQvcwOiEXqls10+Pternx9vUOzyu\n+ECMrluWrYduLNftq/NUnBlkxt4CE2OmG5Zl60cHW9TYNaSSrKDfJS0Isxmx2iypyjlXI0lm9oyk\n+yWdCVbOuddmHP+GpOibCuehickp1XYM6tZVuX6XcsmWpCeqIC1Re+u79bEbyv0uB4DHTveO6G+3\nHtOxlj7VdAxqcsopGB/Q6oJUrS5I04q8FCWER9xfPdHhc7WYKxtLM/XTY636ZVWHPry51O9yFoTZ\nBKsiSY0zbjfp7UejPi7pJ5dTVLRr7B7W2OSUlkVQ4/ql/KW5oSyDBnZggXDO6XBzn356rFU/O96q\nw819kqTs5HhdvzRbawrTVJYdVAy9UotKQlxA15ZlaUd1h3qGxpQRZO3Cy+Vp87qZ3aZQsLrpPI8/\nIukRSSotXbjJOJpnBM60oTRTWw+dVmvfiPLTEv0uB8BFGh6b1GvVHfrZ8Tb9/FibTveNyCz0b/u/\n3bNKY+NTNJ5D1y/L1o7qDr1e3al7ryz0u5yoN5tg1SypZMbt4vB9b2FmV0n6qqR7nXOd53oh59yT\nCvVfadOmTe6iq40SJ1r7JS2AYDVjoVD+sQHRoal7SC8fb9PPjrfp9epOjU5MKTk+oJtX5uqONfm6\nbVWuslMSJLHKOUIygvG6Ykm6dtd36fbVeWcuAePSzCZY7Za0wswqFApUH5L0wMwDzKxU0nOSPuKc\nO+F5lVHmYFOPyrODSkuM7j2YrliSpvjYGO1rIFgBkWpickp767v188o2vXy8TSdaQyPm5dlBPbCl\nVHeszte1FZlKiOWXJc7vpuU5OtTcq30N3bp+GQuGXo4LBivn3ISZPSbpRYWWW/iac+6ImT0afvwJ\nSf9TUrakL4aHlCecc5vmruzIdqipVxvLs/wu47IlxAZ0ZVE6fVZAhOkeHNP2E2362bE2vXqiXX0j\nE6F183KSdd+6Aq0uSFNOamhUqqFr6MwefcD5lGQFVZSRpN113bpuaTaXhy/DrHqsnHNbJW09674n\nZnz9CUmf8La06NTeP6pTvSN6eIEsUbCxLFNP7ajT6MQkf/EC82jmZTrnnNoHRnW8pV/HT/epvnNI\nTqE9+Fblp2p1QaqW56VE7bp5iAybyjP1g/2n1NwzrOJMll64VKy87rGDTT2SpKuKM3yuxBsbSjP0\n5KtTOtzcq41l0T8KB0SLKefU2DWko6f6dLSlT53h7WMK0xN166pcrS5IU1FmErP44Jn1xRnaeqhF\ne+q6CVaXgWDlsQNNvYqxUH/SQrClIlsxJm2vbCdYAXNsZDw0i++lI6364cEWDY5OKGCmpbnJumF5\njlYXpCqT6fCYI4lxAa1bkq4DTT2678pCxceyuv6lIFh57FBTj5bnpSg5YWG8tZnJ8dpUnqVtR1v1\n2btW+V0OsOD0jYzr5eNteulIq7ZXtmlwbFIpCbFampOstUvStCo/lUt8mDebyrP0ZmOPDjf3npkZ\njouzMH77RwjnnA429eq21Xl+l+Kpu9bm669/fIwtDwCPtPQO66dHW/XS0Va9UdOp8Umn3NQE3X9N\nke6+okDXLc3Ss3t/bVUbYM6VZweVnRyv3fVdBKtLRLDyUHPPsDoHx7R+gTSuT3vnmlCw2na0VQ/f\nVOF3OUDUcc7p89tO6FhLn4619Ku5Z1hSaNXz6yqytXZJmkqyQqueN3cPE6rgGzPTpvIsvXjktNr7\nR/0uJyoNJkLRAAAaHklEQVQRrDx0sKlX0sJpXJ9WnpOsFXkpBCvgIvSPjGtHVae2V7Zpe2V7aNVz\nhaa13702X2sK01j1HBFpQ2mGth09rT31XX6XEpUIVh460NSjuIBpdWGq36V47s61+fryqzXsJQWc\nx8TklA419+q16k798mSHdtd1aWLKKTUhVjetyFFSXECrClKVGuULB2PhS02M06qCNO1r6NH45JTi\nAjSxXwyClYcONfVqdUHaglzv6c61+fri9mq9XNmm911T7Hc5gO+mppwqW/u1s6ZTO6o79UZNp/pH\nJiRJqwtS9fF3VOi2VXnaWJapuEAM28cgqlxblqljLX362bE23bOuwO9yogrByiNTU06Hmnr1nquX\n+F3KnFhfnKG81ARtO9pKsMKiNDYRGpHaXdelXbVd2lPXpb5wkCrJStK7rizUDctzdMOybOWE9+ID\notWK/FSlJcbqu3saCVYXiWDlkdrOQfWPTmj9AuuvmhYTY7pjTb6e39/MKuxYFNr6RrSvoVv7Gnq0\nt75bh5p7NTYxJUnKSUnQyvxUleckqzw7WVnJocvjAyMTeulIq59lA54IxJg2lGZqe2WbWnqHVZie\n5HdJUYNg5ZEzK66XLKwZgTPdtTZfT+9q0GvVnbpt1cJaUgKL29dfq9Op3hE1dg2psXtIjV1D6h4a\nlxT6BVOUkaTN5VkqzQqqPCdZKQtknTrg7Wwsy9T2E+363p4m/f4dK/wuJ2rw08EjB5t6lRQX0PLc\nFL9LmTPXL8tWMD6gnx5tJVghajnn1NwzrDcberSvoVtvNvToUHOvJqecJCkjKU4lWUFdvyyo0qyg\nlqQnKpbmXSxC2SkJumFZtr6zp1Gfum25YmKYwTobBCuPHGzq1RVL0hb0D+DEuIBuXpGrnx5r1V/d\nv45/ZIgKYxNTOnyqV3vrurWnvkv7GnrOrM+TGBejq4oydMOybJVmBVWSGVRaErP2gGkfvLZEn3lm\nv3ZUd+gdK3L9LicqEKw8MDE5pSOnevXA5jK/S5lzd67N1wtHTutQc6/WlyzMfjJEt/6Rce2p7z7T\nYH6g6Ve9UaVZQd24LFsbyjK1oTRTqwpSmbEHvI27ryhQRjBOz+xqJFjNEsHKAydaBzQyPqX1C7i/\natrtq/MUiDFtO9pKsEJE6B0e186aTu2sDc3WO9zcKycpxqQlGUm6tixTZdnJKs0OKm3GGlIHm3rP\nLOoL4NwS4wJ6/zXF+sYbdeocGFU2M14viGDlgUPN4cb1BTojcKbM5HhtKsvUtqOt+qO72ZQZ829o\nbEK767r1WnWHXq/u1OHmXk05KT42RteUZOi21Xkqz05WaVZQ8bEL99I8MF8+vLlEX9tRq2f3NemR\nm5f5XU7EI1h54EBTr1ITY1W2SDYovjO8KXND55BKsxfH9wz/fPONep3qGdbJtgGdbB1QY9eQJp1T\nwEwlWUm6dVWeluYmqzQzuKB7HAG/rMhP1cayTD2zu1G/+46lbMN0AQQrDxxs6tFVxemLppn7rrUF\n+usfH9PzB5r12O1MwYX3WnqH9eqJdr16okM/P96m4fFJSVJheqJuWJ6t5bkpKstOZkQKmCcfurZE\nf/y9g9pV26UtS7P9LieiEawu08j4pI639Ot3b17qdynzpjQ7qFtW5uqp1+r1iXcsVWIci4Xi8oxO\nTGpvXbdeOdGu7ZXtqmztlyTlpyVoTWGaVuSlaFleCutHAT5511WF+v9+eFTP7G4kWF0AP6Uu07GW\nPk1MOa0vXviN6zN98paleuArO/XsviY9uGXhz4aE9071DGt7ZbtermzTjqoODY1NKi5g2lyRpd/c\nuFq3rMzTyvwUPb2r0e9SgUUvGB+r+69Zov+3p0l/+e4rlB5kWZLzIVhdpkPNoVlFi6Fxfabrl2Zr\nfXG6vvJqjT50bakCi+QyKC7d+OSU/v6F4zpxul+Vrf1q7QutJZURjNOVRelamZ+qpbnJZ7ZL2lvf\nrb313X6WDGCGD11bqm++0aDvv9mk37mxwu9yIhbB6jLtrOlSflqCCtMT/S5lXpmZHr1lmX7vW/v0\nwuHTetdVhX6XhAjU1j+i7ZXt2l7Zpl+c6FD/6IQCZirLCeredZlalZ+q3NQEmmGBKLCuKF1XFqXr\nmd2N+tgN5fy7PQ+C1WUYGZ/U9so23X9N0aL8H+yuKwpUkZOsJ16p1n1XFizK9wBvNTnltL+xR9sr\n2/RyZZsON/dJCvVKveuqQgViTMtyU+jLA6LUhzaX6M+/f1j7G3t0TWmm3+VEJILVZXi9ulODY5O6\na22+36X4IhBjeuTmpfqz5w7ptepO3bg8x++SMM++vbNBA6MTOtnarxOt/TrROqDh8UmZQpMc7lqb\nr5X5qSpMTyR4AwvAe9Yv0V//6Jie3tVAsDoPgtVleOnoaaUkxOr6ZYt3hsT7rinSP710Qk+8Uk2w\nWiQmp5wONPVoe2W7ntvXpObuYTlJyfEBrS5I1aqCVC3PS1Ewnh8vwEKTmhin920o0vf2NumP716t\n3FRWYj8bP/ku0dSU07ajbbplVe6ZZtvFKDEuoIdvKtf/fqFSh5t7ta5occ2OXCxa+0b0yol2vXKi\nXb882aHe4XHFmFScGdQda/K0Mj9VSzKSFMOoFLDgfeKmCj29q0H/8VodO3CcA8HqEr3Z2KOOgdFF\nexlwpge3lOmLL1fry6/W6P98+Bq/y4EHRsYntbuuS7842aFXT7Tr+OnQulJ5qQm6a22+bl6Zq3es\nyNHWQ6d9rhTAfFuam6K71ubrG2/U6/duXaZk1pd7C96NS/TS0dOKC5huW53ndym+S0+K04NbSvWV\nX9Toj+9axTY3UWhqyumff3pCVW0DqmobUG3HoCamnAIxprKsoO65okAr8lNUkBbqleofmSBUAYvY\nJ29ZphePtOo7uxv18E0svTATweoSOOf00pFWXbc0W2mJLJImSQ/fVKF/31Gnv9l6VE/89kYalaPA\nqZ5h/bKqQ7882aHXqjvUMTAmKTQqtaUiS8vzUlWRw7YxAH7dhtJMXVueqf/7y1p95PoyxbFP5xkE\nq0tQ3R76i/7hG8v9LiVi5Kcl6rN3rdTf/eS4nnqtTg+xeFzE6RwY1Rs1XXqtukOvVXeqtmNQkpST\nkqCblucoNiZGy/JSlJ7EHwsALuyTNy/TJ76+R1sPtej+q4v8LidiEKwuwYtHWiVJ76S/6i1+9x1L\ntbuuS3+79ZiuKc3U1SWLazX6SNMzNKZdtV16o6ZLr9d06lhLaE2plIRYbanI0oNbSvWOFblamZ8i\nM9O3dzb4XDGAaHL76jwty03Wl1+p0XvWL+FKRRjB6hJsO9qq9cXpKkxP8ruUiBITY/rH31qvd33h\nl/rUt/bpx5++SRnBeL/LWjQ6B0a1u647HKZCQcpJio0xlWYHdefafC3LTVFRRtKZLYjYNgbApYoJ\nr2X4J88e0o6qTt20giV3JILVRWvtG9H+xh79MVNMzykjGK/HH9yg33riNX32uwf0lY9uUgz7CHrO\nOaeGriHtqevW7rou7arrUk176NJeQmyMNpZl6o41earISVFJZpJi6X8AMAfee02R/vGlE/ryq9UE\nqzCC1UXadjR0GZBlFs7v6pIM/fl9a/SXPzyqr/yiRp+8ZZnfJUW9obEJHWjs1ZuN3dpX36P9jd1n\nms3TEmO1qTxLH9hYrM3lWbqyOF0JsQEu7QGYcwmxAT10Y2gtwyOnenXFEtYyJFhdpJeOtqo8O6jl\neSl+lxLRPnZDuXbVdel/v1ip9SUZum7p4l2d/mKNjE/qaEufDjX16mBTrw419+hk64Bc+PGclASV\nZgV14/IclWYFlZ+WeGZhzhOtAzrROuBf8QAWnQe3lOnxn1fpy6/U6AusZUiwuhh9I+N6vbpDD91Y\nsSia9C52xOOBLaVnvjYzfe43r9Kxlh366Nd26S/evVYPbC5dFO/bbDnn1DEwpmMtfTra0hf6fKpP\nNR2DmpwKxaiclHhdVZyh4sygSjKTVJIZVJDF+ABEkPSkOH30hnJ9aXu1PnZDuTaWLe49BPkJfRFe\nqWzX+KTjMuAspSXG6XuPXq8//O4B/fn3D2tnTZf+9v1XKmWRBQPnnNr7R1XVPqCTrQM62RbarPhk\na7+6h8bPHLckPVFrl6TpnnUFWleUrquK088syMllPQCR7LHblusHbzbrv//nYf3wsRsXdV/n4voN\nd5l+sP+UspPj2dH7ImSnJOip37lWX9xepc9vO6HDzb16/MENWlOY5ndpnnLOqXtoXPWdg6rvHFJd\n56BqOwZV0x76PDA6cebY1MRYZQbjtTwvRXmpiSpIT1RheuJbNi3uHBjTy8fb/fhWAOCiJSfE6n++\n+wo9+s29euq1On3iHUv9Lsk3BKtZ2tfQrZ8ea9Vn7lhxZqo6ZicmxvTY7Su0qTxLn376Tb338R36\n7F0r9cFrS6NqMcqR8Uk19wyrsWtIjd3DauoaUmP3kBq6hlTVNqCR8akzx5qk9GCcclMSdGVRunJS\nE5STEq/81ESlJsZySRTAgnP3Ffm6bVWu/nnbCf3GVUtUkJ7od0m+IFjNgnNOf7f1mHJSEvTIzYs3\nhV+u65Zm68effoc++/8O6G+3Htfnt53QfVcW6oHNpdpYlul72OgbGdepnmE1dw/rVM+wmnqG1dQd\nut3UPayOgdG3HB8fiFFxZpKKs4K6uiRWWckJyk6OV1b4gy0eACwmZqb/9Z51uvOfX9Ff/eioHn9w\ng98l+YJgNQvbjrZqd123/uZ969jF+zLlpibo6w9v1qGmXj29u0HP7z+l5/Y1a0Veiu6/eonWFaVr\nbWGaclMTPAtazjn1jUyovX9ErX2jaukdUUvPsE71jqild1gtPSM61Tus/pGJtzwvYKaMYJwygnEq\nzw7q6pJ0ZQbjQx/J8UpNjD0zGw8AIJVmB/XYbcv1T9tO6L+caNctK3P9LmnekRIuYGJySp974biW\n5ibrg5tK/C5nwbiyOF1XFl+pP79vjX508JSe3tWof3zpxJnHs5LjtaYwVctzU5SWFKeUhFilJMYq\nJSFWwfhYTU5NaWzSaXxiSuOTUxqbnFLv0Lh6hsfVMzSunqExdQ+NqX1gVC09I5qYcr9WQzA+oIyk\nOKUnxemKJenKSIoLB6l4ZSTFKYXgBAAX7ZFblur7bzbrL35wWC/8wc1KjAv4XdK8IlhdwHf2NKqm\nfVBPfmTjop7lMBuXMnPtgS2l+uC1oY+eoTEdP92vY+GlB46f7tdzbzZrYHRC7tdz0TklxweUEYxX\nejgkbSjNVGfWmFITY5WWGBf6HA5TXKoDAO8lxAb0V+9dpwe/ulNPvFKtP3jnSr9LmlcEq7cxODqh\nf952UteWZ+pOlliYE+cKYwmxAV1dkqmrS0KzL51zGpuc0uj4lEYmJjU+4RQTIwViTAGz0OcYU1Jc\ngPALABHgxuU5es/6Jfq3n1dpfXGGblud53dJ84Zg9Ta+8osadQyM6isf3eh7Y/ViZmZKiA0oITag\nNEXPLEIAWMz+5n3rVNMxoEe/uVff+PgWba7I8rukecGf9+fR1j+iJ1+t0buuLGTdKgAALlJqYpz+\n46HNKspM0sef2q3Dzb1+lzQvCFbn4JzT535yXGMTU/rju1f5XQ4AAFEpOyVB3/z4FqUlxeljX9ul\n6vaFv5cpweosE5NT+pNnD+q5fc365C1LVZ6T7HdJAABErSUZSfrGxzdLkj7y1Z061TPsc0Vzi2A1\nw/DYpD75jb367p4mfeaOFfqjuxitAgDgci3NTdF/PLxZ/SMTevCrO3WgscfvkuYMwSqse3BMD371\nDf28sk1//d51+sM7V9KwDgCAR9YVpevfH7pWg6MTeu8Xd+h//Odh9Q6PX/iJUYZgJam5Z1gfeOI1\nHT7Vpy89uEG/fV2Z3yUBALDgbCrP0s8+e4s+dn25vrWzXnf80yv6wf5mudkuVhgFZhWszOweM6s0\nsyoz+9NzPG5m9oXw4wfNLCo2CDp6qk9/+fwR3fsvr6qtf1Rff3iz7llX6HdZAAAsWKmJcfrL91yh\n5x+7SUsyEvWZZ/brga/s1PMHTqlvJPpHsC64jpWZBSQ9LulOSU2SdpvZ8865ozMOu1fSivDHFklf\nCn+OOH0j43p+/yl9d0+jDjb1Kj4Qo7vXFejTty/XivxUv8sDAGBRWFeUru//1xv17Z31+tefndSn\nn35TcQHTdUuzddfafL1zbb4K05P8LvOizWaB0M2SqpxzNZJkZs9Iul/SzGB1v6Svu9BY3htmlmFm\nhc65Fs8rnqX+kXHtqetWbcegajsGVdcZ+nyqZ1hTTlpdkKq/ePdavffqImUmx/tVJgAAi1YgxvSR\n68v1wJYy7Wvo1rajrdp2tFX/4wdH9D9+cEQZwTiVZQVVmp0c+pwVVGZyvJITAqE9ZMMfaUlxEbMn\n4WyCVZGkxhm3m/Tro1HnOqZIkm/Bqr5zSA89tVuSlJoYq4qcZG0ozdT7NxTrjtV5uqo4neZ0AAAi\nQCDGdG15lq4tz9Kf3bta1e0D2l7ZrtqOQTV0DelAY4+2HmrR5NS5e7E+vLlUf/f+K+e56nOb1y1t\nzOwRSY+Ebw6YWeV8nfvwfJ3o0uRI6vC7iAWG99R7vKfe4z31Fu+nxx70u4BZ+lz4Y47NambbbIJV\ns6SSGbeLw/dd7DFyzj0p6cnZFLaYmNke59wmv+tYSHhPvcd76j3eU2/xfiISzGZW4G5JK8yswszi\nJX1I0vNnHfO8pI+GZwdeJ6nXz/4qAAAAP1xwxMo5N2Fmj0l6UVJA0tecc0fM7NHw409I2irpPklV\nkoYkPTR3JQMAAESmWfVYOee2KhSeZt73xIyvnaRPeVvaosLlUe/xnnqP99R7vKfe4v2E72whrXYK\nAADgJ7a0AQAA8AjBymcX2i4IF8fMvmZmbWYW4StsRAczKzGzl83sqJkdMbPP+F1TtDOzRDPbZWYH\nwu/p//K7poXAzAJm9qaZ/cjvWrC4Eax8NGO7oHslrZX0YTNb629VUe8pSff4XcQCMiHps865tZKu\nk/Qp/h+9bKOSbnfOrZd0taR7wrOpcXk+I+mY30UABCt/ndkuyDk3Jml6uyBcIufcq5K6/K5joXDO\ntTjn9oW/7lfoF1eRv1VFNxcyEL4ZF/6g2fUymFmxpHdJ+qrftQAEK3+dbysgIOKYWbmkayTt9LeS\n6Be+bLVfUpukbc453tPL8y+S/pukKb8LAQhWAC7IzFIkPSvpD5xzfX7XE+2cc5POuasV2qVis5mt\n87umaGVmvyGpzTm31+9aAIlg5bdZbQUE+MnM4hQKVd9yzj3ndz0LiXOuR9LLoi/wctwo6T1mVqdQ\nO8XtZvZNf0vCYkaw8tdstgsCfGNmJun/SjrmnPu83/UsBGaWa2YZ4a+TJN0p6bi/VUUv59yfOeeK\nnXPlCv0M/blz7rd9LguLGMHKR865CUnT2wUdk/Rd59wRf6uKbmb2tKTXJa0ysyYz+7jfNUW5GyV9\nRKFRgP3hj/v8LirKFUp62cwOKvTH1TbnHEsEAAsEK68DAAB4hBErAAAAjxCsAAAAPEKwAgAA8AjB\nCgAAwCMEKwAAAI8QrAAAADxCsAJwQWY2GV7D6oiZHTCzz5pZTPixTWb2hbd5brmZPTB/1f7auYfD\n+/JFBDP7oJlVmRlrVwELEMEKwGwMO+euds5dodBK4fdK+gtJcs7tcc59+m2eWy7Jl2AVVh3el2/W\nzCwwV8U4574j6RNz9foA/EWwAnBRnHNtkh6R9JiF3Do9+mJmt8xYof1NM0uV9DlJ7wjf94fhUaRf\nmNm+8McN4efeambbzex7ZnbczL4V3lJHZnatmb0WHi3bZWapZhYws38ws91mdtDMPjmb+s3sP81s\nb3j07ZEZ9w+Y2T+Z2QFJ15/nnFeEv94fPueK8HN/e8b9X54OZmZ2T/h7PGBmP/PwPwOACBXrdwEA\noo9zriYcHvLOeuiPJH3KObfDzFIkjUj6U0l/5Jz7DUkys6CkO51zI+Fg8rSkTeHnXyPpCkmnJO2Q\ndKOZ7ZL0HUkfdM7tNrM0ScOSPi6p1zl3rZklSNphZi8552ovUP7Dzrmu8D59u83sWedcp6RkSTud\nc58N7915/BznfFTSvzrnvhU+JmBmayR9UNKNzrlxM/uipAfN7CeSviLpZudcrZllXfQbDSDqEKwA\neGmHpM+b2bckPeecawoPOs0UJ+nfzOxqSZOSVs54bJdzrkmSwn1R5ZJ6JbU453ZLknOuL/z4XZKu\nMrMPhJ+bLmmFpAsFq0+b2fvCX5eEn9MZruXZ8P2rznPO1yX9uZkVh7+/k2Z2h6SNCoU0SUqS1Cbp\nOkmvTgc951zXBeoCsAAQrABcNDNbqlAQaZO0Zvp+59znzOzHku5TaATp7nM8/Q8ltUpar1A7wsiM\nx0ZnfD2pt/8ZZZJ+3zn34kXUfaukd0q63jk3ZGbbJSWGHx5xzk2+3fOdc982s52S3iVpa/jyo0n6\nD+fcn511rnfPti4ACwc9VgAuipnlSnpC0r+5s3ZxN7NlzrlDzrm/l7Rb0mpJ/ZJSZxyWrtBo0JSk\nj0i6UKN4paRCM7s2fI5UM4uV9KKk3zOzuPD9K80s+QKvlS6pOxyqVis0qjTrc4YDZY1z7guSfiDp\nKkk/k/QBM8sLH5tlZmWS3pB0s5lVTN9/gdoALACMWAGYjaTwpbk4SROSviHp8+c47g/M7DZJU5KO\nSPpJ+OvJcFP4U5K+KOlZM/uopBckDb7diZ1zY2b2QUn/J9wXNazQqNNXFbpUuC/c5N4u6b0X+D5e\nkPSomR1TKDy9cZHn/C+SPmJm45JOS/rbcL/Wf5f0koWWoBhXqM/sjXBz/HPh+9sUmlEJYAGzs/7g\nBIAFw8zKJf3IObfO51LeInxJ8kxDP4CFg0uBABaySUnpFmELhCo0atftdy0AvMeIFQAAgEcYsQIA\nAPAIwQoAAMAjBCsAAACPEKwAAAA8QrACAADwyP8PEdz9PIuJjAMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f49473e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(vhs['vhs_ra'], vhs['vhs_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, vhs, \"vhs_ra\", \"vhs_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add ESIS VOICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HbWZqioXVNcCIFQAAfiNYFVm+1YJ53HW9UHMsot2MWAEA4DuCVZENp9JFa7WQ1xyPMGIF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\n1LG14aDa66PqaI3rwo6W7Bq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f47c49f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(voice['voice_ra'], voice['voice_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, voice, \"voice_ra\", \"voice_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add SERVS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9OT8AAAsRwSqF0r2dzWwzVwbuOz7sy/kBAFiICFYpMhVLbmfj01RgqCCgovxc7T9BsAIA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\nADzy/wPc3tDrYu5rEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f1a4a0ac8>"
      ]
     },
     "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": 11,
   "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": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+GUkL5ndjetTCfBwOeqLLl2cu8/3+rzoJVgCKztD4lF49MqxXuof06pEh7eoe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\n8AjBCgAAwCMEKwAAAI8QrAAAADzyvwCxHdKHrcXhQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f53ca3d30>"
      ]
     },
     "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": 13,
   "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": [
    "### Add DES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DAAB4o5gRq4ckrTOz03IL0t8m6c5Zx3RLeo0kmdkSSesl7fGy0GrkdXPQQq3xiIYnpkry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\n6ZXOuSEzu1LSzZI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EdHZns87uata6jgZ1NNWpozGqtoaop03knHOayswolZ5Rcjqj1HT2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ee83ff4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(des['des_ra'], des['des_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, des, \"des_ra\", \"des_dec\", radius=0.8*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": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table140046439437872-385237\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>video_id</th><th>ra</th><th>dec</th><th>video_stellarity</th><th>m_ap_video_z</th><th>merr_ap_video_z</th><th>m_video_z</th><th>merr_video_z</th><th>f_ap_video_z</th><th>ferr_ap_video_z</th><th>f_video_z</th><th>ferr_video_z</th><th>m_ap_video_y</th><th>merr_ap_video_y</th><th>m_video_y</th><th>merr_video_y</th><th>f_ap_video_y</th><th>ferr_ap_video_y</th><th>f_video_y</th><th>ferr_video_y</th><th>m_ap_video_j</th><th>merr_ap_video_j</th><th>m_video_j</th><th>merr_video_j</th><th>f_ap_video_j</th><th>ferr_ap_video_j</th><th>f_video_j</th><th>ferr_video_j</th><th>m_ap_video_h</th><th>merr_ap_video_h</th><th>m_video_h</th><th>merr_video_h</th><th>f_ap_video_h</th><th>ferr_ap_video_h</th><th>f_video_h</th><th>ferr_video_h</th><th>m_ap_video_k</th><th>merr_ap_video_k</th><th>m_video_k</th><th>merr_video_k</th><th>f_ap_video_k</th><th>ferr_ap_video_k</th><th>f_video_k</th><th>ferr_video_k</th><th>flag_video_z</th><th>flag_video_y</th><th>flag_video_j</th><th>flag_video_h</th><th>flag_video_k</th><th>video_flag_cleaned</th><th>video_flag_gaia</th><th>flag_merged</th><th>vhs_id</th><th>vhs_stellarity</th><th>m_vhs_y</th><th>merr_vhs_y</th><th>m_ap_vhs_y</th><th>merr_ap_vhs_y</th><th>m_vhs_j</th><th>merr_vhs_j</th><th>m_ap_vhs_j</th><th>merr_ap_vhs_j</th><th>m_vhs_h</th><th>merr_vhs_h</th><th>m_ap_vhs_h</th><th>merr_ap_vhs_h</th><th>m_vhs_k</th><th>merr_vhs_k</th><th>m_ap_vhs_k</th><th>merr_ap_vhs_k</th><th>f_vhs_y</th><th>ferr_vhs_y</th><th>flag_vhs_y</th><th>f_ap_vhs_y</th><th>ferr_ap_vhs_y</th><th>f_vhs_j</th><th>ferr_vhs_j</th><th>flag_vhs_j</th><th>f_ap_vhs_j</th><th>ferr_ap_vhs_j</th><th>f_vhs_h</th><th>ferr_vhs_h</th><th>flag_vhs_h</th><th>f_ap_vhs_h</th><th>ferr_ap_vhs_h</th><th>f_vhs_k</th><th>ferr_vhs_k</th><th>flag_vhs_k</th><th>f_ap_vhs_k</th><th>ferr_ap_vhs_k</th><th>vhs_flag_cleaned</th><th>vhs_flag_gaia</th><th>voice_id</th><th>voice_stellarity</th><th>m_ap_voice_b99</th><th>merr_ap_voice_b99</th><th>m_voice_b99</th><th>merr_voice_b99</th><th>m_ap_voice_b123</th><th>merr_ap_voice_b123</th><th>m_voice_b123</th><th>merr_voice_b123</th><th>m_ap_voice_v</th><th>merr_ap_voice_v</th><th>m_voice_v</th><th>merr_voice_v</th><th>m_ap_voice_r</th><th>merr_ap_voice_r</th><th>m_voice_r</th><th>merr_voice_r</th><th>f_ap_voice_b99</th><th>ferr_ap_voice_b99</th><th>f_voice_b99</th><th>ferr_voice_b99</th><th>flag_voice_b99</th><th>f_ap_voice_b123</th><th>ferr_ap_voice_b123</th><th>f_voice_b123</th><th>ferr_voice_b123</th><th>flag_voice_b123</th><th>f_ap_voice_v</th><th>ferr_ap_voice_v</th><th>f_voice_v</th><th>ferr_voice_v</th><th>flag_voice_v</th><th>f_ap_voice_r</th><th>ferr_ap_voice_r</th><th>f_voice_r</th><th>ferr_voice_r</th><th>flag_voice_r</th><th>voice_flag_cleaned</th><th>voice_flag_gaia</th><th>servs_intid</th><th>f_ap_servs_irac1</th><th>ferr_ap_servs_irac1</th><th>f_servs_irac1</th><th>ferr_servs_irac1</th><th>servs_stellarity_irac1</th><th>f_ap_servs_irac2</th><th>ferr_ap_servs_irac2</th><th>f_servs_irac2</th><th>ferr_servs_irac2</th><th>servs_stellarity_irac2</th><th>m_ap_servs_irac1</th><th>merr_ap_servs_irac1</th><th>m_servs_irac1</th><th>merr_servs_irac1</th><th>flag_servs_irac1</th><th>m_ap_servs_irac2</th><th>merr_ap_servs_irac2</th><th>m_servs_irac2</th><th>merr_servs_irac2</th><th>flag_servs_irac2</th><th>servs_flag_cleaned</th><th>servs_flag_gaia</th><th>swire_intid</th><th>f_ap_swire_irac1</th><th>ferr_ap_swire_irac1</th><th>f_swire_irac1</th><th>ferr_swire_irac1</th><th>swire_stellarity_irac_i1</th><th>f_ap_swire_irac2</th><th>ferr_ap_swire_irac2</th><th>f_swire_irac2</th><th>ferr_swire_irac2</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_irac1</th><th>merr_ap_swire_irac1</th><th>m_swire_irac1</th><th>merr_swire_irac1</th><th>flag_swire_irac1</th><th>m_ap_swire_irac2</th><th>merr_ap_swire_irac2</th><th>m_swire_irac2</th><th>merr_swire_irac2</th><th>flag_swire_irac2</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><th>des_id</th><th>des_stellarity</th><th>m_decam_g</th><th>merr_decam_g</th><th>m_ap_decam_g</th><th>merr_ap_decam_g</th><th>m_decam_r</th><th>merr_decam_r</th><th>m_ap_decam_r</th><th>merr_ap_decam_r</th><th>m_decam_i</th><th>merr_decam_i</th><th>m_ap_decam_i</th><th>merr_ap_decam_i</th><th>m_decam_z</th><th>merr_decam_z</th><th>m_ap_decam_z</th><th>merr_ap_decam_z</th><th>m_decam_y</th><th>merr_decam_y</th><th>m_ap_decam_y</th><th>merr_ap_decam_y</th><th>f_decam_g</th><th>ferr_decam_g</th><th>flag_decam_g</th><th>f_ap_decam_g</th><th>ferr_ap_decam_g</th><th>f_decam_r</th><th>ferr_decam_r</th><th>flag_decam_r</th><th>f_ap_decam_r</th><th>ferr_ap_decam_r</th><th>f_decam_i</th><th>ferr_decam_i</th><th>flag_decam_i</th><th>f_ap_decam_i</th><th>ferr_ap_decam_i</th><th>f_decam_z</th><th>ferr_decam_z</th><th>flag_decam_z</th><th>f_ap_decam_z</th><th>ferr_ap_decam_z</th><th>f_decam_y</th><th>ferr_decam_y</th><th>flag_decam_y</th><th>f_ap_decam_y</th><th>ferr_ap_decam_y</th><th>des_flag_cleaned</th><th>des_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>mag</th><th>mag</th><th>mag</th><th></th><th>mag</th><th>mag</th><th>mag</th><th></th><th>mag</th><th>mag</th><th>mag</th><th></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></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><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><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>1188768</td><td>9.99730399722</td><td>-44.2539202904</td><td>0.845273</td><td>12.4298</td><td>8.29464e-06</td><td>11.0557</td><td>4.30405e-06</td><td>38731.7</td><td>0.295906</td><td>137311.0</td><td>0.544342</td><td>12.1202</td><td>7.29381e-06</td><td>10.573</td><td>3.67689e-06</td><td>51513.5</td><td>0.346071</td><td>214189.0</td><td>0.725383</td><td>12.7957</td><td>1.30126e-05</td><td>10.8361</td><td>5.53892e-06</td><td>27651.4</td><td>0.331415</td><td>168091.0</td><td>0.857548</td><td>12.5121</td><td>1.19357e-05</td><td>10.7426</td><td>6.1068e-06</td><td>35906.2</td><td>0.394737</td><td>183213.0</td><td>1.03053</td><td>12.2265</td><td>1.11855e-05</td><td>11.0334</td><td>9.13796e-06</td><td>46710.5</td><td>0.481237</td><td>140166.0</td><td>1.17973</td><td>False</td><td>False</td><td>False</td><td>False</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>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><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>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>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><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>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>False</td><td>0</td></tr>\n",
       "<tr><td>1</td><td>1245639</td><td>8.99597577631</td><td>-43.9901649654</td><td>0.999499</td><td>13.4314</td><td>1.53437e-05</td><td>13.2423</td><td>1.81819e-05</td><td>15396.7</td><td>0.217594</td><td>18327.0</td><td>0.306917</td><td>13.2494</td><td>1.32874e-05</td><td>13.0847</td><td>1.79655e-05</td><td>18206.5</td><td>0.222822</td><td>21189.1</td><td>0.350625</td><td>13.5742</td><td>2.07443e-05</td><td>13.384</td><td>2.9832e-05</td><td>13499.8</td><td>0.257938</td><td>16084.5</td><td>0.441957</td><td>13.612</td><td>2.93023e-05</td><td>13.4492</td><td>4.72072e-05</td><td>13037.6</td><td>0.351875</td><td>15146.5</td><td>0.658585</td><td>13.7361</td><td>4.16144e-05</td><td>13.616</td><td>6.95414e-05</td><td>11629.1</td><td>0.445738</td><td>12990.2</td><td>0.832048</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>3</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>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><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>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>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><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>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>False</td><td>0</td></tr>\n",
       "<tr><td>2</td><td>1470108</td><td>9.85018443955</td><td>-42.9183596534</td><td>0.999868</td><td>13.5124</td><td>1.77803e-05</td><td>13.5117</td><td>2.3502e-05</td><td>14290.3</td><td>0.23403</td><td>14299.3</td><td>0.309535</td><td>13.3027</td><td>1.65984e-05</td><td>13.3022</td><td>2.32838e-05</td><td>17335.0</td><td>0.265022</td><td>17342.8</td><td>0.371933</td><td>13.6897</td><td>2.55943e-05</td><td>13.6892</td><td>4.02832e-05</td><td>12137.0</td><td>0.286119</td><td>12143.3</td><td>0.450558</td><td>13.5537</td><td>2.94391e-05</td><td>13.5532</td><td>5.21257e-05</td><td>13756.9</td><td>0.373022</td><td>13763.7</td><td>0.66081</td><td>13.5124</td><td>3.57497e-05</td><td>13.512</td><td>6.31539e-05</td><td>14290.6</td><td>0.470558</td><td>14295.4</td><td>0.831549</td><td>False</td><td>False</td><td>False</td><td>False</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>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><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>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>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><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>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>False</td><td>0</td></tr>\n",
       "<tr><td>3</td><td>2077126</td><td>9.4958347577</td><td>-44.7706439444</td><td>0.999886</td><td>16.0742</td><td>9.25881e-05</td><td>16.0787</td><td>0.000188108</td><td>1349.9</td><td>0.115119</td><td>1344.41</td><td>0.232931</td><td>15.9624</td><td>0.000104337</td><td>15.9666</td><td>0.000216969</td><td>1496.31</td><td>0.143796</td><td>1490.62</td><td>0.29789</td><td>15.9559</td><td>0.000144136</td><td>15.9591</td><td>0.00029025</td><td>1505.35</td><td>0.199848</td><td>1500.94</td><td>0.401261</td><td>16.0119</td><td>0.000245034</td><td>16.0143</td><td>0.000482269</td><td>1429.72</td><td>0.322676</td><td>1426.5</td><td>0.633652</td><td>16.4166</td><td>0.000454808</td><td>16.4184</td><td>0.00088337</td><td>984.798</td><td>0.412539</td><td>983.211</td><td>0.799981</td><td>False</td><td>False</td><td>False</td><td>False</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>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><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>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>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><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>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>False</td><td>0</td></tr>\n",
       "<tr><td>4</td><td>1134045</td><td>8.9223737269</td><td>-44.486484523</td><td>0.999903</td><td>16.1065</td><td>0.000105574</td><td>16.0753</td><td>0.000192906</td><td>1310.33</td><td>0.127418</td><td>1348.63</td><td>0.239623</td><td>15.9766</td><td>0.000106767</td><td>15.9469</td><td>0.000213766</td><td>1476.93</td><td>0.145241</td><td>1517.9</td><td>0.298863</td><td>15.9926</td><td>0.000150457</td><td>15.9674</td><td>0.000293502</td><td>1455.31</td><td>0.201677</td><td>1489.44</td><td>0.402645</td><td>15.9623</td><td>0.000236628</td><td>15.9396</td><td>0.000451977</td><td>1496.57</td><td>0.326177</td><td>1528.11</td><td>0.636153</td><td>16.3181</td><td>0.000431909</td><td>16.3016</td><td>0.000803738</td><td>1078.31</td><td>0.42897</td><td>1094.87</td><td>0.810524</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>3</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>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><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>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>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><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>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>False</td><td>0</td></tr>\n",
       "<tr><td>5</td><td>1370873</td><td>10.4157526253</td><td>-43.3942658468</td><td>0.997012</td><td>16.7895</td><td>0.000172477</td><td>16.4412</td><td>0.000263048</td><td>698.532</td><td>0.110971</td><td>962.723</td><td>0.233252</td><td>16.6235</td><td>0.000194005</td><td>16.2801</td><td>0.000293795</td><td>813.969</td><td>0.145449</td><td>1116.8</td><td>0.30221</td><td>16.5175</td><td>0.000245278</td><td>16.1784</td><td>0.000361235</td><td>897.397</td><td>0.202737</td><td>1226.4</td><td>0.408051</td><td>16.3511</td><td>0.000343777</td><td>16.012</td><td>0.000491399</td><td>1046.11</td><td>0.331241</td><td>1429.53</td><td>0.647022</td><td>16.5133</td><td>0.000508362</td><td>16.1719</td><td>0.00071853</td><td>900.881</td><td>0.421823</td><td>1233.74</td><td>0.816507</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>3</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>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><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>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>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><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>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>False</td><td>0</td></tr>\n",
       "<tr><td>6</td><td>1308039</td><td>9.18326820378</td><td>-43.6902407583</td><td>0.999909</td><td>16.8273</td><td>0.000176143</td><td>16.83</td><td>0.000371383</td><td>674.627</td><td>0.109451</td><td>672.981</td><td>0.230205</td><td>16.5053</td><td>0.000167885</td><td>16.5084</td><td>0.000355406</td><td>907.547</td><td>0.140337</td><td>905.007</td><td>0.296256</td><td>16.2504</td><td>0.000185622</td><td>16.2534</td><td>0.000378954</td><td>1147.69</td><td>0.19622</td><td>1144.57</td><td>0.399501</td><td>16.3421</td><td>0.000331549</td><td>16.3442</td><td>0.000653201</td><td>1054.78</td><td>0.322108</td><td>1052.78</td><td>0.633398</td><td>16.5827</td><td>0.000535885</td><td>16.5838</td><td>0.00103184</td><td>845.106</td><td>0.417131</td><td>844.269</td><td>0.802383</td><td>False</td><td>False</td><td>False</td><td>False</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>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><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>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>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><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>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>False</td><td>0</td></tr>\n",
       "<tr><td>7</td><td>1096709</td><td>9.24495096212</td><td>-44.6535638872</td><td>0.997073</td><td>16.8904</td><td>0.0001944</td><td>16.4645</td><td>0.000365229</td><td>636.588</td><td>0.113984</td><td>942.293</td><td>0.316987</td><td>16.4991</td><td>0.000173648</td><td>16.0983</td><td>0.000331349</td><td>912.77</td><td>0.14599</td><td>1320.33</td><td>0.402957</td><td>16.2132</td><td>0.000186939</td><td>15.8849</td><td>0.000388969</td><td>1187.69</td><td>0.204501</td><td>1607.05</td><td>0.575752</td><td>16.0984</td><td>0.000270884</td><td>15.8028</td><td>0.000561395</td><td>1320.14</td><td>0.329377</td><td>1733.29</td><td>0.896251</td><td>16.2535</td><td>0.000401214</td><td>15.7893</td><td>0.000748446</td><td>1144.41</td><td>0.422909</td><td>1755.03</td><td>1.20986</td><td>False</td><td>False</td><td>False</td><td>False</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>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><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>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>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><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>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>False</td><td>0</td></tr>\n",
       "<tr><td>8</td><td>1015236</td><td>9.17269087916</td><td>-45.0469832099</td><td>0.999729</td><td>16.9253</td><td>0.000202438</td><td>16.855</td><td>0.000386193</td><td>616.397</td><td>0.114932</td><td>657.679</td><td>0.233942</td><td>16.816</td><td>0.000225258</td><td>16.7501</td><td>0.000445866</td><td>681.714</td><td>0.14144</td><td>724.38</td><td>0.297482</td><td>16.7469</td><td>0.000296307</td><td>16.6913</td><td>0.000570335</td><td>726.53</td><td>0.198283</td><td>764.653</td><td>0.401684</td><td>16.6312</td><td>0.000436006</td><td>16.5796</td><td>0.000815286</td><td>808.183</td><td>0.324558</td><td>847.538</td><td>0.636442</td><td>16.8811</td><td>0.000719061</td><td>16.8415</td><td>0.00132336</td><td>642.048</td><td>0.42523</td><td>665.877</td><td>0.811636</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>3</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>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><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>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>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><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>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>False</td><td>0</td></tr>\n",
       "<tr><td>9</td><td>1476954</td><td>10.0155864416</td><td>-42.8732945161</td><td>0.5368</td><td>16.9212</td><td>0.000208501</td><td>16.4593</td><td>0.000280934</td><td>618.746</td><td>0.118826</td><td>946.841</td><td>0.245003</td><td>16.6516</td><td>0.000211788</td><td>16.2535</td><td>0.000302866</td><td>793.141</td><td>0.154718</td><td>1144.46</td><td>0.319258</td><td>16.4468</td><td>0.000242386</td><td>16.1496</td><td>0.000372356</td><td>957.828</td><td>0.213838</td><td>1259.36</td><td>0.431916</td><td>16.2493</td><td>0.000329923</td><td>15.9811</td><td>0.0005121</td><td>1148.89</td><td>0.349123</td><td>1470.84</td><td>0.693761</td><td>16.3474</td><td>0.000463475</td><td>16.0972</td><td>0.000727516</td><td>1049.65</td><td>0.448086</td><td>1321.66</td><td>0.885631</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>3</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>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><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>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>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><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>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>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(\"$('#table140046439437872-385237').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",
       "    $('#table140046439437872-385237').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, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 75, 76, 77, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 119, 121, 122, 123, 124, 126, 127, 128, 129, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 152, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 182, 183, 184, 185, 187, 188, 189, 190, 192, 193, 194, 195, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 224, 225, 226, 227, 229, 230, 231, 232, 234, 235, 236, 237, 239, 240, 241, 242, 244, 245, 247], 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "video_stellarity, vhs_stellarity, voice_stellarity, servs_stellarity_irac1, servs_stellarity_irac2, swire_stellarity_irac_i1, swire_stellarity_irac_i2, swire_stellarity_irac_i3, swire_stellarity_irac_i4, des_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": [
    "\n",
    "# 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.a - 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), \"ELAIS-S1\", dtype='<U18'),\n",
    "                                   name=\"field\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "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": [
    "## V.b - Adding spec-z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz =  Table.read(\"../../dmu23/dmu23_ELAIS-S1/data/ELAIS-S1-specz-v2.2.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iIlMoWMWgjr5hX08DAiwpiQQr7RkoIiIyScEqxgyMBBkYHfetcX1CRkqA+QXpWrESERGZ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\nPNRg5X7rH+YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f1a1ed860>"
      ]
     },
     "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": [
    "## VI - Choosing between multiple values for the same filter\n",
    "\n",
    "### VI.a SERVS and SWIRE\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": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "seip = Table.read(\"../../dmu0/dmu0_SEIP/data/SEIP_ELAIS-S1.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": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NzCwbWA3Mds493dmfLyKJKWH6nAX3yjy8F0p3Nr/dUvbx8PW/HLmp+VFav+MA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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f47c55f98>"
      ]
     },
     "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_irac1'], label=\"SERVS\", s=2.)\n",
    "ax.scatter(seip['i1_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_swire_irac1'], 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": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ISCeXv4FDz32HzIo93Le28anQLb5TYM4/+b52goq0uUia674LBH8FM8Ag4EDw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\nIiKNaPWJCIGLpABTcZrrnotzYPw84B9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6q3zJOX2wv1bTxhVofnmpJp49/LSmv5PGjNBL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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f1a16f7f0>"
      ]
     },
     "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_irac2'], label=\"SERVS\", s=2.)\n",
    "ax.scatter(seip['i2_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_swire_irac2'], 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": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irac_origin = Table()\n",
    "irac_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "450197 sources with SERVS flux\n",
      "345740 sources with SWIRE flux\n",
      "152731 sources with SERVS and SWIRE flux\n",
      "449622 sources for which we use SERVS\n",
      "193584 sources for which we use SWIRE\n"
     ]
    }
   ],
   "source": [
    "# IRAC1 aperture flux and magnitudes\n",
    "has_servs = ~np.isnan(master_catalogue['f_ap_servs_irac1'])\n",
    "has_swire = ~np.isnan(master_catalogue['f_ap_swire_irac1'])\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_irac1'][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 = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac[use_servs] = master_catalogue['f_ap_servs_irac1'][use_servs]\n",
    "f_ap_irac[use_swire] = master_catalogue['f_ap_swire_irac1'][use_swire]\n",
    "\n",
    "ferr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac[use_servs] = master_catalogue['ferr_ap_servs_irac1'][use_servs]\n",
    "ferr_ap_irac[use_swire] = master_catalogue['ferr_ap_swire_irac1'][use_swire]\n",
    "\n",
    "m_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac[use_servs] = master_catalogue['m_ap_servs_irac1'][use_servs]\n",
    "m_ap_irac[use_swire] = master_catalogue['m_ap_swire_irac1'][use_swire]\n",
    "\n",
    "merr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac[use_servs] = master_catalogue['merr_ap_servs_irac1'][use_servs]\n",
    "merr_ap_irac[use_swire] = master_catalogue['merr_ap_swire_irac1'][use_swire]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac, name=\"f_ap_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac, name=\"ferr_ap_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac, name=\"m_ap_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac, name=\"merr_ap_irac_i1\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_ap_servs_irac1', 'f_ap_swire_irac1', 'ferr_ap_servs_irac1',\n",
    "                                     'ferr_ap_swire_irac1', 'm_ap_servs_irac1', 'm_ap_swire_irac1',\n",
    "                                     'merr_ap_servs_irac1', 'merr_ap_swire_irac1'])\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": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "450197 sources with SERVS flux\n",
      "345719 sources with SWIRE flux\n",
      "152731 sources with SERVS and SWIRE flux\n",
      "449600 sources for which we use SERVS\n",
      "193585 sources for which we use SWIRE\n"
     ]
    }
   ],
   "source": [
    "# IRAC1 total flux and magnitudes\n",
    "has_servs = ~np.isnan(master_catalogue['f_servs_irac1'])\n",
    "has_swire = ~np.isnan(master_catalogue['f_swire_irac1'])\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_irac1'][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 = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac[use_servs] = master_catalogue['f_servs_irac1'][use_servs]\n",
    "f_ap_irac[use_swire] = master_catalogue['f_swire_irac1'][use_swire]\n",
    "\n",
    "ferr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac[use_servs] = master_catalogue['ferr_servs_irac1'][use_servs]\n",
    "ferr_ap_irac[use_swire] = master_catalogue['ferr_swire_irac1'][use_swire]\n",
    "\n",
    "flag_irac = np.full(len(master_catalogue), False, dtype=bool)\n",
    "flag_irac[use_servs] = master_catalogue['flag_servs_irac1'][use_servs]\n",
    "flag_irac[use_swire] = master_catalogue['flag_swire_irac1'][use_swire]\n",
    "\n",
    "m_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac[use_servs] = master_catalogue['m_servs_irac1'][use_servs]\n",
    "m_ap_irac[use_swire] = master_catalogue['m_swire_irac1'][use_swire]\n",
    "\n",
    "merr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac[use_servs] = master_catalogue['merr_servs_irac1'][use_servs]\n",
    "merr_ap_irac[use_swire] = master_catalogue['merr_swire_irac1'][use_swire]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac, name=\"f_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac, name=\"ferr_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac, name=\"m_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac, name=\"merr_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=flag_irac, name=\"flag_irac_i1\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_servs_irac1', 'f_swire_irac1', 'ferr_servs_irac1',\n",
    "                                 'ferr_swire_irac1', 'm_servs_irac1', 'flag_servs_irac1', 'm_swire_irac1',\n",
    "                                 'merr_servs_irac1', 'merr_swire_irac1', 'flag_swire_irac1'])\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": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "457089 sources with SERVS flux\n",
      "244560 sources with SWIRE flux\n",
      "104641 sources with SERVS and SWIRE flux\n",
      "456721 sources for which we use SERVS\n",
      "140287 sources for which we use SWIRE\n"
     ]
    }
   ],
   "source": [
    "# IRAC2 aperture flux and magnitudes\n",
    "has_servs = ~np.isnan(master_catalogue['f_ap_servs_irac2'])\n",
    "has_swire = ~np.isnan(master_catalogue['f_ap_swire_irac2'])\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_irac2'][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 = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac[use_servs] = master_catalogue['f_ap_servs_irac2'][use_servs]\n",
    "f_ap_irac[use_swire] = master_catalogue['f_ap_swire_irac2'][use_swire]\n",
    "\n",
    "ferr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac[use_servs] = master_catalogue['ferr_ap_servs_irac2'][use_servs]\n",
    "ferr_ap_irac[use_swire] = master_catalogue['ferr_ap_swire_irac2'][use_swire]\n",
    "\n",
    "m_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac[use_servs] = master_catalogue['m_ap_servs_irac2'][use_servs]\n",
    "m_ap_irac[use_swire] = master_catalogue['m_ap_swire_irac2'][use_swire]\n",
    "\n",
    "merr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac[use_servs] = master_catalogue['merr_ap_servs_irac2'][use_servs]\n",
    "merr_ap_irac[use_swire] = master_catalogue['merr_ap_swire_irac2'][use_swire]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac, name=\"f_ap_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac, name=\"ferr_ap_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac, name=\"m_ap_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac, name=\"merr_ap_irac_i2\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_ap_servs_irac2', 'f_ap_swire_irac2', 'ferr_ap_servs_irac2',\n",
    "                                 'ferr_ap_swire_irac2', 'm_ap_servs_irac2', 'm_ap_swire_irac2',\n",
    "                                 'merr_ap_servs_irac2', 'merr_ap_swire_irac2'])\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": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "457089 sources with SERVS flux\n",
      "244544 sources with SWIRE flux\n",
      "104639 sources with SERVS and SWIRE flux\n",
      "456701 sources for which we use SERVS\n",
      "140293 sources for which we use SWIRE\n"
     ]
    }
   ],
   "source": [
    "# IRAC2 total flux and magnitudes\n",
    "has_servs = ~np.isnan(master_catalogue['f_servs_irac2'])\n",
    "has_swire = ~np.isnan(master_catalogue['f_swire_irac2'])\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_irac2'][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 = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac[use_servs] = master_catalogue['f_servs_irac2'][use_servs]\n",
    "f_ap_irac[use_swire] = master_catalogue['f_swire_irac2'][use_swire]\n",
    "\n",
    "ferr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac[use_servs] = master_catalogue['ferr_servs_irac2'][use_servs]\n",
    "ferr_ap_irac[use_swire] = master_catalogue['ferr_swire_irac2'][use_swire]\n",
    "\n",
    "flag_irac = np.full(len(master_catalogue), False, dtype=bool)\n",
    "flag_irac[use_servs] = master_catalogue['flag_servs_irac2'][use_servs]\n",
    "flag_irac[use_swire] = master_catalogue['flag_swire_irac2'][use_swire]\n",
    "\n",
    "m_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac[use_servs] = master_catalogue['m_servs_irac2'][use_servs]\n",
    "m_ap_irac[use_swire] = master_catalogue['m_swire_irac2'][use_swire]\n",
    "\n",
    "merr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac[use_servs] = master_catalogue['merr_servs_irac2'][use_servs]\n",
    "merr_ap_irac[use_swire] = master_catalogue['merr_swire_irac2'][use_swire]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac, name=\"f_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac, name=\"ferr_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac, name=\"m_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac, name=\"merr_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=flag_irac, name=\"flag_irac_i2\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_servs_irac2', 'f_swire_irac2', 'ferr_servs_irac2',\n",
    "                                 'ferr_swire_irac2', 'm_servs_irac2', 'flag_servs_irac2', 'm_swire_irac2',\n",
    "                                 'merr_servs_irac2', 'merr_swire_irac2', 'flag_swire_irac2'])\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": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irac_origin.write(\"{}/elais-s1_irac_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VI.b VIDEO and VHS\n",
    "According to Mattia Vacari VIDEO is deeper than VHS so we take the VIDEO flux if both are available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vista_origin = Table()\n",
    "vista_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vista_stats = Table()\n",
    "vista_stats.add_column(Column(data=['y','j','h','k','z'], name=\"Band\"))\n",
    "vista_stats.add_column(Column(data=np.full(5, 0, dtype=int), name=\"VIDEO\"))\n",
    "vista_stats.add_column(Column(data=np.full(5, 0, dtype=int), name=\"VHS\"))\n",
    "vista_stats.add_column(Column(data=np.full(5, 0, dtype=int), name=\"use VIDEO\"))\n",
    "vista_stats.add_column(Column(data=np.full(5, 0, dtype=int), name=\"use VHS\"))\n",
    "vista_stats.add_column(Column(data=np.full(5, 0, dtype=int), name=\"VIDEO ap\"))\n",
    "vista_stats.add_column(Column(data=np.full(5, 0, dtype=int), name=\"VHS ap\"))\n",
    "vista_stats.add_column(Column(data=np.full(5, 0, dtype=int), name=\"use VIDEO ap\"))\n",
    "vista_stats.add_column(Column(data=np.full(5, 0, dtype=int), name=\"use VHS ap\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vista_bands = ['y','j','h','k','z'] # Lowercase naming convention (k is Ks)\n",
    "for band in vista_bands:\n",
    "    #print('For VISTA band ' + band + ':')\n",
    "    # VISTA total flux \n",
    "    has_video = ~np.isnan(master_catalogue['f_video_' + band])\n",
    "\n",
    "    if band == 'z':\n",
    "        has_vhs = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    else:\n",
    "        has_vhs = ~np.isnan(master_catalogue['f_vhs_' + band])\n",
    "    \n",
    "\n",
    "    use_video = has_video \n",
    "    use_vhs = has_vhs & ~has_video \n",
    "\n",
    "    f_vista = np.full(len(master_catalogue), np.nan)\n",
    "    f_vista[use_video] = master_catalogue['f_video_' + band][use_video]\n",
    "    if not (band == 'z'):\n",
    "        f_vista[use_vhs] = master_catalogue['f_vhs_' + band][use_vhs]\n",
    "\n",
    "    ferr_vista = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_vista[use_video] = master_catalogue['ferr_video_' + band][use_video]\n",
    "    if not (band == 'z'):\n",
    "        ferr_vista[use_vhs] = master_catalogue['ferr_vhs_' + band][use_vhs]\n",
    "    \n",
    "    m_vista = np.full(len(master_catalogue), np.nan)\n",
    "    m_vista[use_video] = master_catalogue['m_video_' + band][use_video]\n",
    "    if not (band == 'z'):\n",
    "        m_vista[use_vhs] = master_catalogue['m_vhs_' + band][use_vhs]\n",
    "\n",
    "    merr_vista = np.full(len(master_catalogue), np.nan)\n",
    "    merr_vista[use_video] = master_catalogue['merr_video_' + band][use_video]\n",
    "    if not (band == 'z'):\n",
    "        merr_vista[use_vhs] = master_catalogue['merr_vhs_' + band][use_vhs]\n",
    "\n",
    "    flag_vista = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    flag_vista[use_video] = master_catalogue['flag_video_' + band][use_video]\n",
    "    if not (band == 'z'):\n",
    "        flag_vista[use_vhs] = master_catalogue['flag_vhs_' + band][use_vhs]\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_vista, name=\"f_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_vista, name=\"ferr_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_vista, name=\"m_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_vista, name=\"merr_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=flag_vista, name=\"flag_vista_\" + band))\n",
    "\n",
    "    old_video_columns = ['f_video_' + band, \n",
    "                                     'ferr_video_' + band,\n",
    "                                     'm_video_' + band, \n",
    "                                     'merr_video_' + band,\n",
    "                                     'flag_video_' + band]\n",
    "    old_vhs_columns = ['f_vhs_' + band, \n",
    "                       'ferr_vhs_' + band, \n",
    "                       'm_vhs_' + band, \n",
    "                       'merr_vhs_' + band,\n",
    "                       'flag_vhs_' + band]\n",
    "    \n",
    "    if not (band == 'z'):\n",
    "        old_columns = old_video_columns + old_vhs_columns\n",
    "    else:\n",
    "        old_columns = old_video_columns\n",
    "    master_catalogue.remove_columns(old_columns)\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_video] = \"VIDEO\"\n",
    "    origin[use_vhs] = \"VHS\"\n",
    "    \n",
    "    vista_origin.add_column(Column(data=origin, name= 'f_vista_' + band ))\n",
    " \n",
    "    # VISTA Aperture flux\n",
    "    has_ap_video = ~np.isnan(master_catalogue['f_ap_video_' + band])\n",
    "    if (band == 'z'):\n",
    "        has_ap_vhs = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    else:\n",
    "        has_ap_vhs = ~np.isnan(master_catalogue['f_ap_vhs_' + band])\n",
    "\n",
    "    use_ap_video = has_ap_video \n",
    "    use_ap_vhs = has_ap_vhs & ~has_ap_video \n",
    "\n",
    "    f_ap_vista = np.full(len(master_catalogue), np.nan)\n",
    "    f_ap_vista[use_ap_video] = master_catalogue['f_ap_video_' + band][use_ap_video]\n",
    "    if not (band == 'z'):\n",
    "        f_ap_vista[use_ap_vhs] = master_catalogue['f_ap_vhs_' + band][use_ap_vhs]\n",
    "\n",
    "    ferr_ap_vista = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_ap_vista[use_ap_video] = master_catalogue['ferr_ap_video_' + band][use_ap_video]\n",
    "    if not (band == 'z'):\n",
    "        ferr_ap_vista[use_ap_vhs] = master_catalogue['ferr_ap_vhs_' + band][use_ap_vhs]\n",
    "    \n",
    "    m_ap_vista = np.full(len(master_catalogue), np.nan)\n",
    "    m_ap_vista[use_ap_video] = master_catalogue['m_ap_video_' + band][use_ap_video]\n",
    "    if not (band == 'z'):\n",
    "        m_ap_vista[use_ap_vhs] = master_catalogue['m_ap_vhs_' + band][use_ap_vhs]\n",
    "\n",
    "    merr_ap_vista = np.full(len(master_catalogue), np.nan)\n",
    "    merr_ap_vista[use_ap_video] = master_catalogue['merr_ap_video_' + band][use_ap_video]\n",
    "    if not (band == 'z'):\n",
    "        merr_ap_vista[use_ap_vhs] = master_catalogue['merr_ap_vhs_' + band][use_ap_vhs]\n",
    "\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_ap_vista, name=\"f_ap_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_ap_vista, name=\"ferr_ap_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_ap_vista, name=\"m_ap_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_vista, name=\"merr_ap_vista_\" + band))\n",
    "\n",
    "\n",
    "    ap_old_video_columns = ['f_ap_video_' + band, \n",
    "                                     'ferr_ap_video_' + band,\n",
    "                                     'm_ap_video_' + band, \n",
    "                                     'merr_ap_video_' + band]\n",
    "    ap_old_vhs_columns = ['f_ap_vhs_' + band, \n",
    "                       'ferr_ap_vhs_' + band, \n",
    "                       'm_ap_vhs_' + band, \n",
    "                       'merr_ap_vhs_' + band]\n",
    "    \n",
    "    if not (band == 'z'):\n",
    "        ap_old_columns = ap_old_video_columns + ap_old_vhs_columns\n",
    "    else:\n",
    "        ap_old_columns = ap_old_video_columns\n",
    "    master_catalogue.remove_columns(ap_old_columns)\n",
    "\n",
    "    origin_ap = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin_ap[use_ap_video] = \"VIDEO\"\n",
    "    origin_ap[use_ap_vhs] = \"VHS\"\n",
    "    \n",
    "    vista_origin.add_column(Column(data=origin_ap, name= 'f_ap_vista_' + band ))\n",
    "    \n",
    "    vista_stats['VIDEO'][vista_stats['Band'] == band] = np.sum(has_video)\n",
    "    vista_stats['VHS'][vista_stats['Band'] == band] = np.sum(has_vhs)\n",
    "    vista_stats['use VIDEO'][vista_stats['Band'] == band] = np.sum(use_video)\n",
    "    vista_stats['use VHS'][vista_stats['Band'] == band] = np.sum(use_vhs)\n",
    "    vista_stats['VIDEO ap'][vista_stats['Band'] == band] = np.sum(has_ap_video)\n",
    "    vista_stats['VHS ap'][vista_stats['Band'] == band] = np.sum(has_ap_vhs)\n",
    "    vista_stats['use VIDEO ap'][vista_stats['Band'] == band] = np.sum(use_ap_video)\n",
    "    vista_stats['use VHS ap'][vista_stats['Band'] == band] = np.sum(use_ap_vhs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vista origin overview\n",
    "For each band show how many objects have fluxes from each survey for both total and aperture photometries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=5</i>\n",
       "<table id=\"table140045660463960-369606\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>VIDEO</th><th>VHS</th><th>use VIDEO</th><th>use VHS</th><th>VIDEO ap</th><th>VHS ap</th><th>use VIDEO ap</th><th>use VHS ap</th></tr></thead>\n",
       "<tr><td>0</td><td>y</td><td>809211</td><td>0</td><td>809211</td><td>0</td><td>806697</td><td>0</td><td>806697</td><td>0</td></tr>\n",
       "<tr><td>1</td><td>j</td><td>808244</td><td>200072</td><td>808244</td><td>166712</td><td>804199</td><td>199018</td><td>804199</td><td>165674</td></tr>\n",
       "<tr><td>2</td><td>h</td><td>796692</td><td>87456</td><td>796692</td><td>56691</td><td>786595</td><td>87400</td><td>786595</td><td>56648</td></tr>\n",
       "<tr><td>3</td><td>k</td><td>792072</td><td>98080</td><td>792072</td><td>63825</td><td>780762</td><td>97855</td><td>780762</td><td>63627</td></tr>\n",
       "<tr><td>4</td><td>z</td><td>812231</td><td>0</td><td>812231</td><td>0</td><td>810774</td><td>0</td><td>810774</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(\"$('#table140045660463960-369606').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",
       "    $('#table140045660463960-369606').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, 2, 3, 4, 5, 6, 7, 8, 9], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vista_stats.show_in_notebook()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vista_origin.write(\"{}/elais-s1_vista_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    if 'vista_k' in col:\n",
    "        master_catalogue[col].name = col.replace('vista_k', 'vista_ks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Column renaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "renaming = OrderedDict({\n",
    "    '_voice_b99':'_wfi_b',\n",
    "    '_voice_b123':'_wfi_b123',\n",
    "    '_voice_v': '_wfi_v',\n",
    "    '_voice_r':'_wfi_r',\n",
    "})\n",
    "\n",
    "\n",
    "for col in master_catalogue.colnames:\n",
    "    for rename_col in list(renaming):\n",
    "        if rename_col in col:\n",
    "            master_catalogue.rename_column(col, col.replace(rename_col, renaming[rename_col]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VII.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": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "video_moc = MOC(filename=\"../../dmu0/dmu0_VISTA-VIDEO-private/data/VIDEO-all_2017-02-12_fullcat_errfix_ELAIS-S1_MOC.fits\")\n",
    "vhs_moc = MOC(filename=\"../../dmu0/dmu0_VISTA-VHS/data/VHS_ELAIS-S1_MOC.fits\")\n",
    "voice_moc = MOC(filename=\"../../dmu0/dmu0_ESIS-VOICE/data/esis_b2vr_cat_03_HELP-coverage_MOC.fits\")\n",
    "servs_moc = MOC(filename=\"../../dmu0/dmu0_DataFusion-Spitzer/data/DF-SERVS_ELAIS-S1_MOC.fits\")\n",
    "swire_moc = MOC(filename=\"../../dmu0/dmu0_DataFusion-Spitzer/data/DF-SWIRE_ELAIS-S1_MOC.fits\")\n",
    "des_moc = MOC(filename=\"../../dmu0/dmu0_DES/data/DES-DR1_ELAIS-S1_MOC.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    voice_moc + des_moc) \n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    video_moc + vhs_moc + voice_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": 46,
   "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": [
    "## VII.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": 47,
   "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_wfi_r']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_decam_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_decam_r']) +    \n",
    "    1 * ~np.isnan(master_catalogue['f_decam_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_decam_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_decam_y']) \n",
    ")\n",
    "\n",
    "nb_nir_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_ks']) \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": 48,
   "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": [
    "## VIII - Cross-identification table\n",
    "\n",
    "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogue. This can be used to easily get additional information from them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['video_id', 'vhs_id', 'voice_id', 'servs_intid', 'swire_intid', 'des_id', 'help_id', 'specz_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": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_elais-s1{}.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": [
    "## IX - 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": 51,
   "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": [
    "## IX - Saving the catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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\", \n",
    "            \"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \"flag_optnir_obs\", \"flag_optnir_det\", \n",
    "            \"zspec\", \"zspec_qual\", \"zspec_association_flag\", \"ebv\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "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": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[columns].write(\"{}/master_catalogue_elais-s1{}.fits\".format(OUT_DIR, SUFFIX))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python (herschelhelp_internal)",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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