{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GAMA-12 master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on GAMA-12."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "0246c5d (Thu Jan 25 17:01:47 2018 +0000)\n"
     ]
    }
   ],
   "source": [
    "from herschelhelp_internal import git_version\n",
    "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))\n",
    "import datetime\n",
    "print(\"This notebook was executed on: \\n{}\".format(datetime.datetime.now()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "import os\n",
    "import time\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column, Table\n",
    "import numpy as np\n",
    "from pymoc import MOC\n",
    "\n",
    "from herschelhelp_internal.masterlist import merge_catalogues, nb_merge_dist_plot, specz_merge\n",
    "from herschelhelp_internal.utils import coords_to_hpidx, ebv, gen_help_id, inMoc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n",
    "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n",
    "SUFFIX = os.environ.get('SUFFIX', time.strftime(\"_%Y%m%d\"))\n",
    "\n",
    "try:\n",
    "    os.makedirs(OUT_DIR)\n",
    "except FileExistsError:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I - Reading the prepared pristine catalogues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "decals = Table.read(\"{}/DECaLS.fits\".format(TMP_DIR))\n",
    "hsc = Table.read(\"{}/HSC-SSP.fits\".format(TMP_DIR))\n",
    "kids = Table.read(\"{}/KIDS.fits\".format(TMP_DIR))\n",
    "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))\n",
    "las = Table.read(\"{}/UKIDSS-LAS.fits\".format(TMP_DIR))\n",
    "viking = Table.read(\"{}/VISTA-VIKING.fits\".format(TMP_DIR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Merging tables\n",
    "\n",
    "We first merge the optical catalogues and then add the infrared ones: DECaLS, HSC, KIDS, PanSTARRS, UKIDSS-LAS, and VISTA-VIKING.\n",
    "\n",
    "At every step, we look at the distribution of the distances to the nearest source in the merged catalogue to determine the best crossmatching radius."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DECaLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = decals\n",
    "master_catalogue['decals_ra'].name = 'ra'\n",
    "master_catalogue['decals_dec'].name = 'dec'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add HSC-PSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SenyM6UAAAMpZPsMtRyR15jzuyDyXq0/SsHMuJilmZg9IukTS855UWYKGYwk11gSXtE9g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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3abaeba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(hsc['hsc_ra'], hsc['hsc_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, hsc, \"hsc_ra\", \"hsc_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add KIDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9XAqMhkPqjEeYvg4AQAsgWC1ArlDyZdRCBdPXAQBoDQSrBcj6eFeg5E1fp2MFAEDTI1gt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\nKGWXVahow18AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe38f42c588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(kids['kids_ra'], kids['kids_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, kids, \"kids_ra\", \"kids_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add PanSTARRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WcxV1JelKDwBAoyN0hSSVzqq9ZTEjXYQuAAAaGaErBO6uVObEpxe7WuOaTGc1m6NBKgAA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\nB2e8NodoToLMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe38c862320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(ps1['ps1_ra'], ps1['ps1_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, ps1, \"ps1_ra\", \"ps1_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add UKIDSS LAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2xGqEESsAAMoSwcpjk6nsaFJdEdZYSVJDLKLk7JxS6UxR3h8AAJw6gpXHJnLBqmhTgbkr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\n4BGCFQAAgEcIVgAAAB75/wEOZ+xQZYpJvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe347777b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(las['las_ra'], las['las_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, las, \"las_ra\", \"las_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add VIKING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V0fFZHZsoBahSeDoyPquR6bnTro8EA+psC6knHtElF3Sotxya4mH1xiNqiwQr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\nwQoAAMAnBCsAAACfEKwAAAB88v8DDCXgp8Csz9wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe38f487c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(viking['viking_ra'], viking['viking_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, viking, \"viking_ra\", \"viking_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning\n",
    "\n",
    "When we merge the catalogues, astropy masks the non-existent values (e.g. when a row comes only from a catalogue and has no counterparts in the other, the columns from the latest are masked for that row). We indicate to use NaN for masked values for floats columns, False for flag columns and -1 for ID columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    if \"m_\" in col or \"merr_\" in col or \"f_\" in col or \"ferr_\" in col or \"stellarity\" in col:\n",
    "        master_catalogue[col].fill_value = np.nan\n",
    "    elif \"flag\" in col:\n",
    "        master_catalogue[col].fill_value = 0\n",
    "    elif \"id\" in col:\n",
    "        master_catalogue[col].fill_value = -1\n",
    "        \n",
    "master_catalogue = master_catalogue.filled()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=10&gt;\n",
       "<table id=\"table140614133209576-123878\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>decals_id</th><th>ra</th><th>dec</th><th>f_decam_g</th><th>f_decam_r</th><th>f_decam_z</th><th>ferr_decam_g</th><th>ferr_decam_r</th><th>ferr_decam_z</th><th>f_ap_decam_g</th><th>f_ap_decam_r</th><th>f_ap_decam_z</th><th>ferr_ap_decam_g</th><th>ferr_ap_decam_r</th><th>ferr_ap_decam_z</th><th>m_decam_g</th><th>merr_decam_g</th><th>flag_decam_g</th><th>m_decam_r</th><th>merr_decam_r</th><th>flag_decam_r</th><th>m_decam_z</th><th>merr_decam_z</th><th>flag_decam_z</th><th>m_ap_decam_g</th><th>merr_ap_decam_g</th><th>m_ap_decam_r</th><th>merr_ap_decam_r</th><th>m_ap_decam_z</th><th>merr_ap_decam_z</th><th>decals_stellarity</th><th>decals_flag_cleaned</th><th>decals_flag_gaia</th><th>flag_merged</th><th>hsc_id</th><th>m_ap_suprime_g</th><th>merr_ap_suprime_g</th><th>m_suprime_g</th><th>merr_suprime_g</th><th>m_ap_suprime_r</th><th>merr_ap_suprime_r</th><th>m_suprime_r</th><th>merr_suprime_r</th><th>m_ap_suprime_i</th><th>merr_ap_suprime_i</th><th>m_suprime_i</th><th>merr_suprime_i</th><th>m_ap_suprime_z</th><th>merr_ap_suprime_z</th><th>m_suprime_z</th><th>merr_suprime_z</th><th>m_ap_suprime_y</th><th>merr_ap_suprime_y</th><th>m_suprime_y</th><th>merr_suprime_y</th><th>hsc_stellarity</th><th>f_ap_suprime_g</th><th>ferr_ap_suprime_g</th><th>f_suprime_g</th><th>ferr_suprime_g</th><th>flag_suprime_g</th><th>f_ap_suprime_r</th><th>ferr_ap_suprime_r</th><th>f_suprime_r</th><th>ferr_suprime_r</th><th>flag_suprime_r</th><th>f_ap_suprime_i</th><th>ferr_ap_suprime_i</th><th>f_suprime_i</th><th>ferr_suprime_i</th><th>flag_suprime_i</th><th>f_ap_suprime_z</th><th>ferr_ap_suprime_z</th><th>f_suprime_z</th><th>ferr_suprime_z</th><th>flag_suprime_z</th><th>f_ap_suprime_y</th><th>ferr_ap_suprime_y</th><th>f_suprime_y</th><th>ferr_suprime_y</th><th>flag_suprime_y</th><th>hsc_flag_cleaned</th><th>hsc_flag_gaia</th><th>kids_id</th><th>kids_stellarity</th><th>m_kids_u</th><th>merr_kids_u</th><th>m_kids_g</th><th>merr_kids_g</th><th>m_kids_r</th><th>merr_kids_r</th><th>m_kids_i</th><th>merr_kids_i</th><th>f_ap_kids_u</th><th>ferr_ap_kids_u</th><th>f_ap_kids_g</th><th>ferr_ap_kids_g</th><th>f_ap_kids_r</th><th>ferr_ap_kids_r</th><th>f_ap_kids_i</th><th>ferr_ap_kids_i</th><th>f_kids_u</th><th>ferr_kids_u</th><th>flag_kids_u</th><th>f_kids_g</th><th>ferr_kids_g</th><th>flag_kids_g</th><th>f_kids_r</th><th>ferr_kids_r</th><th>flag_kids_r</th><th>f_kids_i</th><th>ferr_kids_i</th><th>flag_kids_i</th><th>m_ap_kids_u</th><th>merr_ap_kids_u</th><th>m_ap_kids_g</th><th>merr_ap_kids_g</th><th>m_ap_kids_r</th><th>merr_ap_kids_r</th><th>m_ap_kids_i</th><th>merr_ap_kids_i</th><th>kids_flag_cleaned</th><th>kids_flag_gaia</th><th>ps1_id</th><th>m_ap_gpc1_g</th><th>merr_ap_gpc1_g</th><th>m_gpc1_g</th><th>merr_gpc1_g</th><th>m_ap_gpc1_r</th><th>merr_ap_gpc1_r</th><th>m_gpc1_r</th><th>merr_gpc1_r</th><th>m_ap_gpc1_i</th><th>merr_ap_gpc1_i</th><th>m_gpc1_i</th><th>merr_gpc1_i</th><th>m_ap_gpc1_z</th><th>merr_ap_gpc1_z</th><th>m_gpc1_z</th><th>merr_gpc1_z</th><th>m_ap_gpc1_y</th><th>merr_ap_gpc1_y</th><th>m_gpc1_y</th><th>merr_gpc1_y</th><th>f_ap_gpc1_g</th><th>ferr_ap_gpc1_g</th><th>f_gpc1_g</th><th>ferr_gpc1_g</th><th>flag_gpc1_g</th><th>f_ap_gpc1_r</th><th>ferr_ap_gpc1_r</th><th>f_gpc1_r</th><th>ferr_gpc1_r</th><th>flag_gpc1_r</th><th>f_ap_gpc1_i</th><th>ferr_ap_gpc1_i</th><th>f_gpc1_i</th><th>ferr_gpc1_i</th><th>flag_gpc1_i</th><th>f_ap_gpc1_z</th><th>ferr_ap_gpc1_z</th><th>f_gpc1_z</th><th>ferr_gpc1_z</th><th>flag_gpc1_z</th><th>f_ap_gpc1_y</th><th>ferr_ap_gpc1_y</th><th>f_gpc1_y</th><th>ferr_gpc1_y</th><th>flag_gpc1_y</th><th>ps1_flag_cleaned</th><th>ps1_flag_gaia</th><th>las_id</th><th>m_ukidss_y</th><th>merr_ukidss_y</th><th>m_ap_ukidss_y</th><th>merr_ap_ukidss_y</th><th>m_ukidss_j</th><th>merr_ukidss_j</th><th>m_ap_ukidss_j</th><th>merr_ap_ukidss_j</th><th>m_ap_ukidss_h</th><th>merr_ap_ukidss_h</th><th>m_ukidss_h</th><th>merr_ukidss_h</th><th>m_ap_ukidss_k</th><th>merr_ap_ukidss_k</th><th>m_ukidss_k</th><th>merr_ukidss_k</th><th>las_stellarity</th><th>f_ukidss_y</th><th>ferr_ukidss_y</th><th>flag_ukidss_y</th><th>f_ap_ukidss_y</th><th>ferr_ap_ukidss_y</th><th>f_ukidss_j</th><th>ferr_ukidss_j</th><th>flag_ukidss_j</th><th>f_ap_ukidss_j</th><th>ferr_ap_ukidss_j</th><th>f_ap_ukidss_h</th><th>ferr_ap_ukidss_h</th><th>f_ukidss_h</th><th>ferr_ukidss_h</th><th>flag_ukidss_h</th><th>f_ap_ukidss_k</th><th>ferr_ap_ukidss_k</th><th>f_ukidss_k</th><th>ferr_ukidss_k</th><th>flag_ukidss_k</th><th>las_flag_cleaned</th><th>las_flag_gaia</th><th>viking_id</th><th>viking_stellarity</th><th>m_viking_z</th><th>merr_viking_z</th><th>m_ap_viking_z</th><th>merr_ap_viking_z</th><th>m_viking_y</th><th>merr_viking_y</th><th>m_ap_viking_y</th><th>merr_ap_viking_y</th><th>m_viking_j</th><th>merr_viking_j</th><th>m_ap_viking_j</th><th>merr_ap_viking_j</th><th>m_viking_h</th><th>merr_viking_h</th><th>m_ap_viking_h</th><th>merr_ap_viking_h</th><th>m_viking_k</th><th>merr_viking_k</th><th>m_ap_viking_k</th><th>merr_ap_viking_k</th><th>f_viking_z</th><th>ferr_viking_z</th><th>flag_viking_z</th><th>f_ap_viking_z</th><th>ferr_ap_viking_z</th><th>f_viking_y</th><th>ferr_viking_y</th><th>flag_viking_y</th><th>f_ap_viking_y</th><th>ferr_ap_viking_y</th><th>f_viking_j</th><th>ferr_viking_j</th><th>flag_viking_j</th><th>f_ap_viking_j</th><th>ferr_ap_viking_j</th><th>f_viking_h</th><th>ferr_viking_h</th><th>flag_viking_h</th><th>f_ap_viking_h</th><th>ferr_ap_viking_h</th><th>f_viking_k</th><th>ferr_viking_k</th><th>flag_viking_k</th><th>f_ap_viking_k</th><th>ferr_ap_viking_k</th><th>viking_flag_cleaned</th><th>viking_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>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><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>33973207980</td><td>181.091767342</td><td>1.62159672883</td><td>0.0731542</td><td>0.51553</td><td>0.597203</td><td>0.120171</td><td>0.108346</td><td>0.184399</td><td>nan</td><td>3.5601e-07</td><td>3.10886e-07</td><td>nan</td><td>1.73201e-07</td><td>2.8408e-07</td><td>26.7394</td><td>1.78355</td><td>False</td><td>24.6194</td><td>0.228183</td><td>False</td><td>24.4597</td><td>0.335243</td><td>False</td><td>nan</td><td>-24659.6</td><td>25.0213</td><td>0.528218</td><td>25.1685</td><td>0.992117</td><td>0.05</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>32679101322</td><td>185.966441881</td><td>-0.776502444662</td><td>-0.157565</td><td>-0.377468</td><td>4.19406</td><td>0.143498</td><td>0.218585</td><td>0.28461</td><td>nan</td><td>nan</td><td>3.46974e-06</td><td>nan</td><td>nan</td><td>5.16332e-07</td><td>nan</td><td>-0.988805</td><td>False</td><td>nan</td><td>-0.628731</td><td>False</td><td>22.3434</td><td>0.0736784</td><td>False</td><td>nan</td><td>-20294.8</td><td>nan</td><td>-1.35018</td><td>22.5493</td><td>0.161568</td><td>0.9</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>33541300300</td><td>181.436111304</td><td>0.631731406702</td><td>-0.152992</td><td>-0.0530225</td><td>2.14033</td><td>0.164385</td><td>0.265116</td><td>0.264013</td><td>nan</td><td>nan</td><td>3.42751e-06</td><td>nan</td><td>nan</td><td>4.79124e-07</td><td>nan</td><td>-1.16659</td><td>False</td><td>nan</td><td>-5.42875</td><td>False</td><td>23.0738</td><td>0.133927</td><td>False</td><td>nan</td><td>-20234.4</td><td>nan</td><td>-0.945665</td><td>22.5626</td><td>0.151773</td><td>0.9</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>33682701811</td><td>174.763279123</td><td>0.979492542854</td><td>0.431807</td><td>0.670884</td><td>1.25302</td><td>0.18941</td><td>0.306688</td><td>0.233986</td><td>nan</td><td>1.54345e-06</td><td>5.17447e-07</td><td>nan</td><td>5.81353e-07</td><td>4.53326e-07</td><td>24.8118</td><td>0.476252</td><td>False</td><td>24.3334</td><td>0.496333</td><td>False</td><td>23.6551</td><td>0.202747</td><td>False</td><td>nan</td><td>-10894.0</td><td>23.4288</td><td>0.408951</td><td>24.6153</td><td>0.951193</td><td>0.9</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>33974702525</td><td>184.957306423</td><td>1.53357603044</td><td>0.395624</td><td>0.673061</td><td>1.76791</td><td>0.194392</td><td>0.290789</td><td>0.31497</td><td>nan</td><td>1.08798e-06</td><td>1.40156e-06</td><td>nan</td><td>5.15912e-07</td><td>6.13968e-07</td><td>24.9068</td><td>0.533483</td><td>False</td><td>24.3299</td><td>0.46908</td><td>False</td><td>23.2813</td><td>0.193434</td><td>False</td><td>nan</td><td>-9175.95</td><td>23.8084</td><td>0.514847</td><td>23.5335</td><td>0.475618</td><td>0.9</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>32245500796</td><td>181.931287884</td><td>-1.5854225463</td><td>0.00600954</td><td>0.0635288</td><td>2.27738</td><td>0.1484</td><td>0.247808</td><td>0.259275</td><td>nan</td><td>nan</td><td>3.53832e-06</td><td>nan</td><td>nan</td><td>5.10661e-07</td><td>29.4529</td><td>26.8112</td><td>False</td><td>26.8926</td><td>4.23515</td><td>False</td><td>23.0064</td><td>0.123609</td><td>False</td><td>nan</td><td>-6351.39</td><td>nan</td><td>-13.7244</td><td>22.528</td><td>0.156697</td><td>0.9</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>33538703499</td><td>174.84501691</td><td>0.808821923246</td><td>0.0836933</td><td>0.700444</td><td>1.89785</td><td>0.185532</td><td>0.295052</td><td>0.322191</td><td>nan</td><td>8.5073e-07</td><td>1.47194e-06</td><td>nan</td><td>4.2024e-07</td><td>4.72775e-07</td><td>26.5933</td><td>2.40686</td><td>False</td><td>24.2866</td><td>0.457352</td><td>False</td><td>23.2043</td><td>0.184322</td><td>False</td><td>nan</td><td>-6342.91</td><td>24.0755</td><td>0.536327</td><td>23.4803</td><td>0.348728</td><td>0.05</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>32535203202</td><td>186.215967608</td><td>-0.883551244903</td><td>-0.25869</td><td>1.80063</td><td>-0.0255894</td><td>0.147623</td><td>0.293028</td><td>0.241092</td><td>nan</td><td>2.04879e-06</td><td>5.0936e-07</td><td>nan</td><td>4.77172e-07</td><td>4.97237e-07</td><td>nan</td><td>-0.619581</td><td>False</td><td>23.2614</td><td>0.176689</td><td>False</td><td>nan</td><td>-10.2293</td><td>False</td><td>nan</td><td>-5624.53</td><td>23.1213</td><td>0.252873</td><td>24.6324</td><td>1.0599</td><td>0.9</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>31237101261</td><td>180.618639506</td><td>-3.30599937137</td><td>0.0506985</td><td>0.508543</td><td>1.48878</td><td>0.149518</td><td>0.241467</td><td>0.269623</td><td>nan</td><td>8.83092e-07</td><td>9.96323e-07</td><td>nan</td><td>4.93801e-07</td><td>5.16414e-07</td><td>27.1375</td><td>3.202</td><td>False</td><td>24.6342</td><td>0.515531</td><td>False</td><td>23.4679</td><td>0.196631</td><td>False</td><td>nan</td><td>-5610.26</td><td>24.035</td><td>0.607115</td><td>23.904</td><td>0.562759</td><td>0.9</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>33538101646</td><td>173.262408365</td><td>0.718977072901</td><td>0.259009</td><td>0.160905</td><td>1.64267</td><td>0.223435</td><td>0.333117</td><td>0.276513</td><td>nan</td><td>4.46499e-07</td><td>1.71557e-06</td><td>nan</td><td>6.30789e-07</td><td>5.49959e-07</td><td>25.3667</td><td>0.936611</td><td>False</td><td>25.8836</td><td>2.24777</td><td>False</td><td>23.3611</td><td>0.182763</td><td>False</td><td>nan</td><td>-4976.62</td><td>24.7754</td><td>1.53387</td><td>23.314</td><td>0.348054</td><td>0.9</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140614133209576-123878').dataTable()\");\n",
       "    $('#table140614133209576-123878').dataTable({\n",
       "        \"order\": [],\n",
       "        \"iDisplayLength\": 50,\n",
       "        \"aLengthMenu\": [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        \"pagingType\": \"full_numbers\"\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 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": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:343: RuntimeWarning: All-NaN slice encountered\n",
      "  warnings.warn(\"All-NaN slice encountered\", RuntimeWarning)\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": null,
   "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": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    ebv(master_catalogue['ra'], master_catalogue['dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V - Adding HELP unique identifiers and field columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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), \"GAMA-12\", dtype='<U18'),\n",
    "                                   name=\"field\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK!\n"
     ]
    }
   ],
   "source": [
    "# Check that the HELP Ids are unique\n",
    "if len(master_catalogue) != len(np.unique(master_catalogue['help_id'])):\n",
    "    print(\"The HELP IDs are not unique!!!\")\n",
    "else:\n",
    "    print(\"OK!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VI - Cross-matching with spec-z catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz =  Table.read(\"../../dmu23/dmu23_GAMA-12/data/GAMA12-specz-v2-PRIVATE.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz.rename_column('RA', 'ra')\n",
    "specz.rename_column('DEC', 'dec')\n",
    "specz.rename_column('OBJID', 'specz_id')\n",
    "specz.rename_column('Z_SPEC', 'z_spec')\n",
    "specz.rename_column('Z_SOURCE', 'z_source')\n",
    "specz.rename_column('Z_QUAL', 'z_qual')\n",
    "specz.rename_column('REL', 'rel')\n",
    "specz.rename_column('AGN', 'agn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=10&gt;\n",
       "<table id=\"table140614133196000-514767\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>ra</th><th>dec</th><th>z_spec</th><th>z_source</th><th>z_qual</th><th>rel</th><th>specz_id</th><th>agn</th></tr></thead>\n",
       "<tr><td>0</td><td>174.006</td><td>0.72093</td><td>0.05054</td><td>1</td><td>3</td><td>0.987</td><td>GAMAJ113601.43+004315.3</td><td>0</td></tr>\n",
       "<tr><td>1</td><td>174.02279</td><td>0.70594</td><td>0.33119</td><td>1</td><td>3</td><td>0.987</td><td>GAMAJ113605.47+004221.3</td><td>0</td></tr>\n",
       "<tr><td>2</td><td>174.10071</td><td>0.65891</td><td>0.22979</td><td>1</td><td>4</td><td>1.0</td><td>GAMAJ113624.17+003932.0</td><td>0</td></tr>\n",
       "<tr><td>3</td><td>174.15312</td><td>0.81543</td><td>0.00375</td><td>1</td><td>3</td><td>0.987</td><td>GAMAJ113636.75+004855.5</td><td>0</td></tr>\n",
       "<tr><td>4</td><td>174.2805</td><td>0.70608</td><td>0.11397</td><td>1</td><td>3</td><td>0.987</td><td>GAMAJ113707.32+004221.8</td><td>0</td></tr>\n",
       "<tr><td>5</td><td>174.303583333</td><td>0.789916666667</td><td>0.0744</td><td>17</td><td>4</td><td>0.991</td><td>TGN376Z215</td><td>0</td></tr>\n",
       "<tr><td>6</td><td>174.30554</td><td>0.79034</td><td>0.07453</td><td>1</td><td>3</td><td>0.987</td><td>GAMAJ113713.33+004725.2</td><td>0</td></tr>\n",
       "<tr><td>7</td><td>174.335166667</td><td>0.836361111111</td><td>0.1067</td><td>16401</td><td>4</td><td>0.991</td><td>TGN445Z190</td><td>0</td></tr>\n",
       "<tr><td>8</td><td>174.34688</td><td>0.69645</td><td>0.19309</td><td>1</td><td>3</td><td>0.986</td><td>GAMAJ113723.25+004147.2</td><td>0</td></tr>\n",
       "<tr><td>9</td><td>174.39962</td><td>0.67831</td><td>0.38525</td><td>1</td><td>4</td><td>1.0</td><td>GAMAJ113735.90+004041.9</td><td>0</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140614133196000-514767').dataTable()\");\n",
       "    $('#table140614133196000-514767').dataTable({\n",
       "        \"order\": [],\n",
       "        \"iDisplayLength\": 50,\n",
       "        \"aLengthMenu\": [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        \"pagingType\": \"full_numbers\"\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specz[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y+5zQPzSiYyf7z2t+roly41FVFuWosZ2eLgAAwjaVnq71kva6+353H5T0XUk3v0r7WyXd\nn4riIO082qkRd9VMczzXmNqyfDWc6JUzSSoAAKGaSuiqltQw7n1jsOwVzCxf0o2SHhi32CX9zMye\nM7MN51voXLWtMXnrnlSFrpryPPUOjuhE71BK9gcAAKYmluL9vV3SkxNOLV7j7k1mNl/SY2b2krs/\nPnHDIJBtkKS6uroUl5W9tjZ2qCgnppK8eEr2VxvM9cW4LgAAwjWVnq4mSbXj3tcEy87kFk04teju\nTcFzi6SHlDxd+Qrufpe717t7fWVl5RTKmhu2NnSouixPNs1B9GOqinMVj5oama8LAIBQTSV0bZK0\n3MyWmllCyWD18MRGZlYi6fWSfjhuWYGZFY29lvRmSS+movC5oLN/SPvbelJ2alGSohHTopI8NTBt\nBAAAoZr09KK7D5vZ7ZIelRSVdLe7bzezjwbr7wya/q6kn7p7z7jNqyQ9FPTSxCTd5+4/SeUHmM1e\nbDwp9+RM8qlUW56vZ/Yf19DIqOJRpmoDACAMUxrT5e6PSHpkwrI7J7y/R9I9E5btl7R2WhXOYVvH\nBtGXpq6nS0oOyh8ede061qU11SUp3TcAADgzujky2LbGDtWV5ys/J7XXO4wNpn++oSOl+wUAAGdH\n6MpgWxs6tLa2NOX7Lc2PqyAR1VZCFwAAoSF0ZajWrgEdOdmvtTWpP/1nZqotz9cWQhcAAKEhdGWo\nbY3JQDQTPV1SclzXvtZudfYzSSoAAGEgdGWorQ0dipi0elHxjOy/tixf7tILwWB9AAAwswhdGWpr\n40mtqCpSfiLVNw1IGpuGglOMAACEg9CVoXYc7ZzR6RzyElEtqyhgMD0AACEhdGWg9p5BtXYN6KIF\nRTN6nLW1pdrS0CF3n9HjAAAAQldGeulYpyRpRdXMhq51taVq6RrQsc7+GT0OAAAgdGWk3ce6JCmU\nni5JnGIEACAEhK4MtKu5S2X5cVUW5czocS5aUKRoxPRiU+eMHgcAABC6MtKuY11aUVWk4EbhMyY3\nHtXy+YXafoRpIwAAmGmErgzj7trd3D3jpxbHrFpUrBeP0NMFAMBMI3RlmKaOPnUPDGtFSKFrzaIS\ntXYNqIXB9AAAzChCV4bZFdIg+jFjM95vp7cLAIAZRejKMLuak6Fr+QxPFzFm1anQxbguAABmEqEr\nw+w61qXq0jwV58ZDOV5RblxL5uVzBSMAADOM0JVhklcuFoZ6zNXVJdp+lJ4uAABmEqErgwyNjGpf\na7dWLigO9birFxWrob1PJ3uHQj0uAABzCaErgxxs69HQiGvlgnB7utYsSt5Ym94uAABmDqErg7wU\nXLm4sir8ni5J2s64LgAAZgyhK4PsOtalaMR0wfyCUI87rzBHC0tyuYIRAIAZROjKILuau7S0okA5\nsWjox17NzPQAAMwoQlcG2XWsSytDmp9rotWLSrSvtVu9g8NpOT4AALMdoStD9A4O63B7r1aGNBP9\nRKsXFctd2nm0Ky3HBwBgtiN0ZYjdzd2SpBVp6ulaUx1cwci4LgAAZgShK0PsDvmeixMtLMlVWX6c\nKxgBAJghhK4M8dKxLuXGI6otz0/L8c1Ma6pL9CI9XQAAzAhCV4bY3dylFVVFikYsbTWsWlSs3c1d\nGhweTVsNAADMVoSuDPFSGq9cHLNmUYmGRly7mxlMDwBAqhG6MsDx7gG1dQ+k7crFMWMz0+9gvi4A\nAFJuSqHLzG40s11mttfM7jjD+uvN7KSZbQken53qtkhOiiop7aFrybwCFSSijOsCAGAGxCZrYGZR\nSV+R9CZJjZI2mdnD7r5jQtNfu/tvn+e2c9ruU/dcTG/oikRMqxYVazs9XQAApNxUerrWS9rr7vvd\nfVDSdyXdPMX9T2fbOWNXc5fK8uOqLMpJdylavahEO450amTU010KAACzylRCV7WkhnHvG4NlE73O\nzLaZ2Y/NbPU5bjunvXQseeWiWfquXByzelGx+oZGdKCtJ92lAAAwq6RqIP1vJNW5+6WS/q+kH5zr\nDsxsg5ltNrPNra2tKSor842OuvY0d6d9PNeYsZnpX2xiXBcAAKk0ldDVJKl23PuaYNkp7t7p7t3B\n60ckxc2sYirbjtvHXe5e7+71lZWV5/ARslvDiV51Dwzr4oXF6S5FknTh/ELlxiPa2tiR7lIAAJhV\nphK6NklabmZLzSwh6RZJD49vYGYLLDg3Zmbrg/0en8q2c93Oo8lB66syJHTFoxFdUl2iLQ2ELgAA\nUmnSqxfdfdjMbpf0qKSopLvdfbuZfTRYf6ekd0r6mJkNS+qTdIu7u6QzbjtDnyUr7TjapYilf7qI\n8dbVlupbTx/S4PCoEjGmcgMAIBUmDV3SqVOGj0xYdue411+W9OWpbouX7TzaqaUVBcqNR9Ndyinr\nasv0L78+oJ1HO7W2tjTd5QAAMCvQjZFmO450Zsx4rjHr6pJBi1OMAACkDqErjU72Dampo0+rFmVW\n6FpUkqvKohxCFwAAKUToSqOXgkH0mdbTZWZaV1tK6AIAIIUIXWmUaVcujndZXakOtPXoRM9guksB\nAGBWIHSl0Y6jnSovSGh+Btz+Z6J1wQD6LczXBQBAShC60mjn0S6tWlicEbf/mejSmlKZSVsOE7oA\nAEgFQleaDI+Maldzly5emDnzc41XmBPTivlFjOsCACBFCF1pcqCtR4PDoxk3iH68dbWl2trYoeQ8\ntwAAYDoIXWmyI0OvXBxvXV2pOnqHdPB4b7pLAQAg6xG60mTH0U4lohFdUFmY7lLO6tRg+oYTaa4E\nAIDsR+hKk51Hu3Th/MKMvrfhiqoi5SeiDKYHACAFMvc3/iy382jm3f5nomjEdGlNiZ5nMD0AANNG\n6EqD1q4BtXYNZOyVi+Otqy3TzqOd6h8aSXcpAABkNUJXGpyaiT7D7rl4JutqSzU04tp+pDPdpQAA\nkNUIXWmQybf/meiyurHB9JxiBABgOghdabDzaKcWluSqND+R7lImVVWcq4UluYQuAACmidCVBjuy\nYBD9eJfVlTJtBAAA00ToCln/0Ij2tfZkxanFMetqS9XQ3qfj3QPpLgUAgKxF6ArZ3pZujYx6VvV0\nrastk8S4LgAApoPQFbKXb/+T+dNFjLmkukTRiOk3hznFCADA+SJ0hWzHkU7lxaNaPK8g3aVMWV4i\nqrU1JXpiT1u6SwEAIGsRukK282inLlpYpGjE0l3KObluRaW2NZ1Ue89guksBACArEbpC5O5Zcfuf\nM7luRaXcpSf20tsFAMD5IHSF6HB7rzr7h7U6C2ain2htTalK8uJ6fHdruksBACArEbpCtOlgciB6\n/eLyNFdy7qIR0zUXVujXe1rl7ukuBwCArEPoCtGmA+0qyYtr+fzCdJdyXq5bUaHmzgHtau5KdykA\nAGQdQleINh1qV/3iMkWybBD9mOtWVEoSpxgBADgPhK6QtHUPaH9rj65Ymn2nFscsLMnT8vmFenw3\ng+kBADhXhK6QbA7Gc12xpCzNlUzPdSsqtfFgu/oGR9JdCgAAWYXQFZJNB9uViEW0prok3aVMy+tX\nVGpweFTPHDie7lIAAMgqhK6QbD7YrnW1pcqJRdNdyrSsX1qunFiEcV0AAJyj2FQamdmNkr4oKSrp\nG+7+jxPW3ybprySZpC5JH3P3rcG6g8GyEUnD7l6fsuqzRM/AsF480qmPvn5ZukuZttx4VFcum0fo\nAoBZ5L5nD0+57XuurJvBSma3SXu6zCwq6SuSbpK0StKtZrZqQrMDkl7v7pdI+jtJd01Yf4O7r5uL\ngUuStjR0aGTUdcWS7B1EP951yyu0r7VHTR196S4FAICsMZXTi+sl7XX3/e4+KOm7km4e38Ddn3L3\nE8HbZyTVpLbM7LbxQLvMpNcszu5B9GNez9QRAACcs6mcXqyW1DDufaOkK1+l/Yck/Xjce5f0MzMb\nkfR1d5/YCzbrbT7UrosXFKs4N57uUlLiwvmFWliSq8d3t+rW9XQzA8D5cnf1DY2oo3co+egb1Mne\nIXX1D6tvaCT5GBxR//CIXmg8qaiZopGXH7FoRKV5cZUVJFSen1BeIrvHDc92UxrTNVVmdoOSoeua\ncYuvcfcmM5sv6TEze8ndHz/DthskbZCkurrZ84t8aGRUzx/u0B9cPns6/8xM1y2v1CMvHtXwyKhi\nUa7HAADp5bFRY2HqZN+QOvuG1dk3pJP9Q+rqH1J3/7C6B5KPvqER9Q+NTrrfnFjy5+you0ZGXaNn\nuRtbXjyq8oKELqgs0MULi1Vbnq+IZeeE3LPRVEJXk6Tace9rgmWnMbNLJX1D0k3ufmo+AXdvCp5b\nzOwhJU9XviJ0BT1gd0lSfX39rLm5344jneodHFF9Bo7nOpeBk9LpgyevW1Gp721u0NbGDl2ehfeS\nBIDp6uwf0uHjvTp0vFeNJ3rVeKJPGw+060TvoE70Dmpo5PRfZSYpPxFVUW5chTkxzSvMUWFOTAU5\nMeUnosqLR5PPwet4NKJ4NKJY1F4RnEbdNequoWHXid5BtfcMnnpu6RrQE3vb9PieNhUkorpoQbEu\nXlikFQuKFIvwj+R0mkro2iRpuZktVTJs3SLpPeMbmFmdpAclvc/dd49bXiAp4u5dwes3S/rbVBWf\nDTYdbJekWTOIfsw1F1YoYtKvdrcRugDMWt0DwzrY1qP9bT060NqjA23dOni8V4fbe9XeM3ha25K8\nuAoSUVUU5mj5/EKV5CdUnBtTSV5cxXlxFeXGUhZ6IpYMYrGElJfI06LSvNPW9w+NaFdzl3Ye7dT2\noyf13OETKsuP682rF+iS6hJ6v9Jk0tDl7sNmdrukR5WcMuJud99uZh8N1t8p6bOS5kn6qiX/IMem\nhqiS9FCwLCbpPnf/yYx8kgy16WC7asvztKAkN92lpFRJflz1i8v1o21H9GdvXC7jLzCALDUy6mo6\n0ad9bd3a39qjfa3d2t+afN3SNXCqnSkZrOYVJnRBZaHWL0movCCheYUJleUnlBvPnPFUufGo1taU\nam1NqYZHR7WnuVs/29ms721q0BN72nTjmgW6oLIw3WXOOVMa0+Xuj0h6ZMKyO8e9/rCkD59hu/2S\n1k6zxqzl7tp88IRev7Iy3aXMiHddUatPfX+rnj3QrquWzUt3OQDwqjp6B7W/rUf7W3v0n1uPqK17\nQK1dA2rvGdTwuEFSefGoKotyVFOWr8tqSzWvMEcVRTmaV5BQPAvHsMYiEV28sFgrFxRpa0OHHtvR\nrG8+cUArqgr1tksWqbIoJ90lzhkpHUiP0+1v69HxnsFZd2pxzG9fulB/+5/bdd+zhwldADJCZ/+Q\nDrX16uDxHh063qMDbb060NatA209OtE7dKpdxKTygoQqCnO0sqpIFUU5qgzCVUEiOit77yNmuqyu\nTGuqS/TM/uP6xa4WfeWXe/Wuy2u1alFxusubEwhdM2jzqfFcs2N+roly41H93mtqdN+zh9XeM6jy\ngkS6SwIwy7m7OnqHglCVDFeHj/eeen98wjirquIcLaso1E2XLNSyigItmVegpZUFemrvcUUjsy9Y\nTUU8GtG1yyt1aU2pvv3MIX372UN6w8XzdcPK+Yz1mmGErhm08UBy4OJsPm/+nivrdM9TB/XAc436\nyHXZf5sjAJmhq39IX/vlPrV2Deh4z6COdyef27oHTptiwSQV58U1ryChpRUFumJJueYVJjSvIEfl\nBQklYqefDmzpGlBL18CcDVzjleTFteG6ZfrB8036+c4WHe3o1x9cXqOcDBqbNtsQumbQ5kPtql9S\nPiu7qcesqCpS/eIy3b/xsD587dJZ/VkBpJa761hnv/a2dGtPc7f2tnZrX0u39rf1qHXCAPbS/Ljm\nFeRobU1yjNW8goTmFSRUlqXjrDJFPBrROy+v0aLSPP34xaP62q/26X1XLda8QsZ5zQRC1wxp7uzX\noeO9eu+Vi9Ndyox7z5V1+vN/36qn9x/X6y6oSHc5ADKMu6ula0B3/mqfmjsH1NzZr5bOfrV0DWhg\n+OVeq7EB7HVl+XpNXZkqCpNjruYVJJiEeQaZma6+sEJVxbm6f+Nh3fX4fn3k2mWqYIB9yhG6ZshP\ntx+TJF2zfPaHkLdeslB/8587dP/GBkIXMMd19Q9pd3OXdh7t0q5jwaO5Syf7Xh7EXpATU1VRji6r\nK9P8opyEhEdcAAAVw0lEQVTkozh31g5gzxYXzi/UhuuW6Ru/3q9vPLFfG667gLG6KUbomiE/2HJE\nK6oKddGConSXMuOSA+qr9e1nDul49yq6pYE5YHB4VPvburXrWJdeOtal3cFzU0ffqTaFOTGtqCrU\nWy9ZqIsWFKmhvVfzi3NVmMOvnkxVVZyrD16zVN/49QF984lkj1dpPsErVfg/fwY0tPfquUMn9Jdv\nWTln/tV225V1+tcnD+qB3zRqw3UXpLscACkyOupqONGrXce6tLs5CFjNXdrf2nNqbquISZVFOaoq\nztXqRcVaUJyrqpJclebFT/sZuGwWX1Q0mywsydMHr16qbzyxX9984oA+cu0yFefF013WrEDomgEP\nbz0iSfqdtYvSXEl4LpxfpPVLynX/xgZ95NplcyZsArOFu+vIyX7tbk72Wu1u7tbu5i7taek67WrB\n2vI8rawq0hsvrtLKBUXa19KjiqIE9/SbZarL8vSB1y3R3U8dTAav65bRQ5kCfIMp5u76wfNNql9c\nptry/HSXE6pbr6zVn31vq57ed1yvu5CxXUAmGhvUPtZzlXx0a29Lt7oHhk+1K86NaX5xri6vK1NV\nca7mF+eqqijntOkEegZGZt0tzvCyunkF+sPXLtE9Tx3Qvz6Z7PHKpFsdZSNCV4rtPNqlPS3d+rt3\nrEl3KaG7aU1yQP29Tx8idAEZoKN3ULuOdek7zx7Wsc7+4KrBAfUNjZxqU5CIqqo4V2uqS1RVnKP5\nRblaUJyrvAS/XCEtrSjQbVcu1r1PH9T9Gw/r/a9dku6SshqhK8V+uKVJsYjpbZcsTHcpocuNR/X+\n1y7Rl36+R0/ubdPVBC8gFP1DI9rb0q2XjnVp17HOU+OumjtfnusqNx5RVVGuLhkLV8W5qmJQO6Zg\nRVWR3rGuWg8+36QfbmnSe6+qYwjJeeJvWwqNjroe3npE162onLOX2f636y/Qf249os889IIe/eR1\ndEUDKTQ2qH3n0S59/7kGNZ/s17HOAR3vHtDY7ZpjEdP8ohwtKsnTZbVlWlCSDFfFuTF+UeK81S8p\nV3vvoH65q1Vf/eU+ffyGC9NdUlYidKXQxoPtOnqyX3fcdFG6S0mb3HhU//C7l+jWf3lGX/z5Hv3V\njXP3uwCm43j3wKnpGHYd69JLzV3a09yl3sGXTw2WFyS0oDjZe5UMVzmaV5DDLW4wI950cZU6eof0\nT4/uUk1Znm5eV53ukrIOoSuFfrjliPITUb1pVVW6S0mr114wT++qr9Fdj+/X2y9dxN3rgVfRPTB8\n6orBXcHA9l3HutXW/fKpwbL8uFYuKNK76mt10YIirVxQpC0NHcqJ0ZOM8JiZfu+yauXGI/rL729T\nVXGurlo2L91lZRVCV4oMDo/qkReO6s2rqpSf4Gv9zFsv1v97qUWffnCbHvxvV/Mvb8x5Xf1Dp+4x\nuKclecXgnuYuHTnZf6pNIhrR/OIcLS7P1/olZaoqSQ5qL8x5+dTgqCcv2CFwIR1i0Yi+/t56/f6d\nT2nDvZv1Hx97nVZUzf5JwFOFdJAiv9rdqpN9Q7r5MrpbJak0P6HPvn21PnH/8/rWUwf1wWuWprsk\nIBTtPYPa25KcguFH246opWtArV0Dp90GJxaxU5OJJq8aTI67Ks2PK8K4K2S4kvy47vnAFfq9rz6l\nP7x7ox742Ou0qDQv3WVlBUJXivxgS5PKCxK6hiv2Tnn7pQv14G8a9f/9dJfesmaBqvlLiVlidNR1\n5GSf9rZ0a19rT/K5pVt7W7vV3jN4ql08appflKtlFQWaX5SjyqJczS/OUXlBgnCFrFZTlq97PrBe\n7/760/qjf92o7//x61SSz6z1kyF0pUBX/5B+tqNZ776iVvEoszKPMTP9/TvW6E1feFx3PLBN3/zD\nK5SI8f0gewwOj+rQ8Z5TPVd7W7u16WC7WrsGNDTip9rlxaOaX5yjCyoLdNWyeUHAylFJHj1XmL1W\nLSrW199/uf7o7k36yL2bde+H1nPF+iQIXSnwnWcPa2B4lCs5zqCmLF+fffsqffrBF7Th3zbra7dd\nzqSLyDgn+4a0v3Vcr1VrsufqUHuvRkZfDlfVpXkqSMS0dEmBKotyVRmEK+a6wlz1ugsq9Pl3rdWf\n3P+8PvndLfrKba9hDO+r4CfFNDV19OmLP9ujN15cpcsXl6W7nIx06/o6maTPPPSC3vvNZ3X3H15B\nNzRCNzQyqob2Xh1o69GBtp5ksGrt0Y4jnafd/iZqpnmFCVUW5eja5RWnTgtWFCYYvA6cwdvXLlJr\n14D+9kc79LmHX9Tf3byGOeHOgtA1TX/98Pbk8++sSnMlM+++Zw9Pue17rqw77f0t6+tUkhfXn353\ni95919O690PrNb+Ie7YhtQaHR9V4oleH2nt1qK1Hh9p7dTAIWQ0n+k7rtSovSGhZRYFWLihSZWHO\nqV6rsvwE/1IHztEHr1mq5q5+ff1X+xUx01+/fbUi/D16BULXNDy2o1mP7WjWHTddpJqyuXVz6/Nx\n0yULVZQb14Z/26w/uPNpfftDV865m4JjekZHXW3dA2rs6FNDe2/w6NPh9l41nOjVkY4+jctVSkQj\nmleY0LzCHF27vEAVBTmqCN4XcEoQSKk7brxI7tJdj+9X7+CI/vH3LlGMcc6n4afOeeodHNZfP7xd\nK6oK9SGmQ5iya5ZX6DsfvlIfuGeT3vGVJ/WJNyzXLetrOW0DScmeqmMn+9XU0aemjj4dCR6bD57Q\nid5Bnewb0vD4VCWpKCemsoKE5hUktHx+UTJkFSRUXpA4bX4rADPLzPTpmy5SYU5MX3hst3oHh/XP\n776MC6jGIXSdpy/+fI+aOvr073/8Wq5YPEeX1ZXpPz76Wn3moRf1uYe36+u/2qc/ecNyvfPyGr7L\nWax/aETHTvbrWGe/mjv7dexkv46e7NfRk33Bc7/augfkp2cqVRQmlBuPamFpnlYtKlZpfkJleXGV\nFSRUlp/gBzqQQcxMn3jDcuUnovr7/9qpvsHN+tp7L+eqxgCh6zzsOtalb/76gN5VX6P1S8vTXU5W\nunB+kb634So9ufe4Pv/YLn36wRf0tV/u0+03XKi3rF7AQPssMTrq6ugbUlv3QPAYVGvXgFq6+tXa\nOaCW4HVL14A6eodesX1OLKKSvLhK8uJaXJ6vS6tLVJofV0leQmX5cRXnxQniQBb68LXLVJAT02ce\nekF/9K8b9bXbLldZQSLdZaUdoescjY66/ucPXlBhbkx33HRxusvJamama5ZX6OoL5+kXu1r0+Z/u\n1n9/YJvueHCbLqkp1bUXVujqCyt0+eIyejNC0D80os6+IXX2D+lk35A6eoNH35A6egd1ondQJ3qG\ndLxnQCd6htTeO6j2nsHTBqePiUZMRTkxFeXGVJQb18qqIhXnxVWcmwxYxbkxFefF+dcvMIvdur5O\n+YmoPvX9rbrxi4/rC+9ap6vn+ATihK5zMDrq+vxju7Tp4An9n9+/VOWk9pQwM/3WRVW6YeV8PXfo\nhH69p01P7G3T1361T1/+xV7lxiNaMq9ANWX5qivPV115nmrL81Wan1Bx8Eu9MDemgkR0zozfGR4Z\n1cBw8tE/NKLewRH1DY6ob2hEvYPD6h8aUfdA8nX3wLB6B0bUPZB83d0/rK6BoeB5WJ19w+rsH9Lg\n8OhZj2eS8hJRFSRiys9JPi8uz9eqhcUqzImpMDeWfM6JqSgnprw59GcB4OxuXletCyoL9afffV63\nfeNZfeTapfrUW1bO2XG85hMHUGSA+vp637x5c7rLOE33wLD+/Htb9NMdzXrn5TX6P79/aWiXw57L\nVA3ZauIUE5LU2T+kp/cd18YD7Tp0vCd5hVp7n/qGRs64D7Pk1WrxaESxqCkWMcUikWlf/m+WfETM\nZAqeg/envY6MrTNFxrUfyx4mS6YXSXLJ5XKXXNKou0ZHXSPuGhlNBvzh0VENjbiGRl5+Hhwe1eDI\n6Bl7l171M0hKxCLKjUeVEzznxiPKiUWVG48qLz62LPnITyQfefGo8hMx5cQjzKwOQNKZf15Ppm9w\nRP/rkR369jOHdfHCYn3plnVaPotulG1mz7l7/aTtCF2TO3S8Rx+5d7P2tnTrf7xtlT549ZJQ/xU/\nF0LXVLm7ugeG1dE7pN7BEQ0Mj6h/aDR4HtHw6MvhZXRUGnEfNzDbx+3n5TD0chI64xFPBSMpGY7G\n9ufu8mBfo8FCHxemRoP1Y4ee+DfNgv8kg1kyqJmCZzNFIsngGDVTNPLyIxkoI4pHTbFoRPGIKR6L\nnAqciWjyfU4sqkQsopxYRLGI0fMEICXOJ3SN+dmOZv3VA9vUPTCs9121WB+6dqkWlmT/fXmnGro4\nvTiJX+9p1e33PS9JuveDV+qa5XP7fHS6mZmKcuMqymWgPQBkmzeuqtKPa6/VP/zXTv3rUwf1racP\n6h3rqvXHr1+mC+fPnp6vs5nS6GQzu9HMdpnZXjO74wzrzcy+FKzfZmavmeq2mcjdtflguz7z0Av6\nw7s3qqo4Rw/ffjWBCwCAaZpflKt/vuUy/fJT1+s96+v0n9uO6I1feFwfuXezHnnhqDp6B9Nd4oyZ\ntKfLzKKSviLpTZIaJW0ys4fdfce4ZjdJWh48rpT0NUlXTnHbjLG7uUs/3NKkH245osYTfcqNR/TO\ny2v02bev5oa2AACkUG15vv7m5jX6xBuW61tPH9K9Tx/UYzuaZSZdWl2iqy+s0DXLK3RJdcmsObsx\nlSSxXtJed98vSWb2XUk3SxofnG6WdK8nB4g9Y2alZrZQ0pIpbBu6rQ0dOtDWo0PHe3W4vVeH25Ov\nW7oGFI2Yrr6wQn/+phV68+oFhC0AAGbQvMIc/fmbVuhPfutCbWvsSF7BvqdNX398v776y32SkvdK\nrS3P1+LyfC2el6/5RTmnpqEpCqagyU9ET41tjceS417jkUhG3QNyKomiWlLDuPeNSvZmTdameorb\nhu6vHtiml451SZIWFOeqbl6+rltRqUuqS/TWSxaqsignzRUCADC3xKMRXb64XJcvLtcn37hCXf1D\n2nigXXtauoNOkh4933BCP9p2RFO9gDsRi2j33980s4Wfg4zpxjGzDZI2BG+7zWxXGMc9JOnZMA40\nPRWS2tJdxCzC95lafJ+px3eaWnyfKXRbugs4R/a/QjnM4qk0mkroapJUO+59TbBsKm3iU9hWkuTu\nd0m6awr1zDlmtnkql6Jiavg+U4vvM/X4TlOL7xOZYipXL26StNzMlppZQtItkh6e0OZhSe8PrmK8\nStJJdz86xW0BAABmvUl7utx92Mxul/SopKiku919u5l9NFh/p6RHJL1V0l5JvZI+8GrbzsgnAQAA\nyGBTGtPl7o8oGazGL7tz3GuX9PGpbotzxmnX1OL7TC2+z9TjO00tvk9khIy8DRAAAMBsM6UZ6QEA\nADA9hK4Mlo23UMpkZna3mbWY2YvprmU2MLNaM/uFme0ws+1m9qfprimbmVmumW00s63B9/k36a5p\nNjCzqJk9b2Y/SnctAKErQ427hdJNklZJutXMVqW3qqx3j6Qb013ELDIs6S/cfZWkqyR9nP9Hp2VA\n0m+5+1pJ6yTdGFwNjun5U0k7010EIBG6Mtmp2y+5+6CksVso4Ty5++OS2tNdx2zh7kfd/TfB6y4l\nf7FVp7eq7OVJ3cHbePBg0O00mFmNpLdJ+ka6awEkQlcmO9utlYCMY2ZLJF2mrLjBQ+YKToVtkdQi\n6TF35/ucnn+W9N8ljaa7EEAidAGYJjMrlPSApE+6e2e668lm7j7i7uuUvHvHejNbk+6aspWZ/bak\nFnd/Lt21AGMIXZlrKrdfAtLKzOJKBq7vuPuD6a5ntnD3Dkm/EGMQp+NqSb9jZgeVHJ7xW2b27fSW\nhLmO0JW5uIUSMpqZmaRvStrp7l9Idz3Zzswqzaw0eJ0n6U2SXkpvVdnL3T/t7jXuvkTJn5//z93f\nm+ayMMcRujKUuw9LGruF0k5J/84tlKbHzO6X9LSklWbWaGYfSndNWe5qSe9TsgdhS/B4a7qLymIL\nJf3CzLYp+Y+ux9ydaQ6AWYQZ6QEAAEJATxcAAEAICF0AAAAhIHQBAACEgNAFAAAQAkIXAABACAhd\nAAAAISB0AZgWMxsJ5ujabmZbzewvzCwSrKs3sy+9yrZLzOw94VX7imP3Bfc6zAhm9m4z22tmzM8F\nzEKELgDT1efu69x9tZKzqN8k6XOS5O6b3f0Tr7LtEklpCV2BfcG9DqfMzKIzVYy7f0/Sh2dq/wDS\ni9AFIGXcvUXSBkm3W9L1Y702Zvb6cTPXP29mRZL+UdK1wbI/C3qffm1mvwkerwu2vd7Mfmlm/2Fm\nL5nZd4LbEMnMrjCzp4Jeto1mVmRmUTP7JzPbZGbbzOyPp1K/mf3AzJ4Leu02jFvebWafN7Otkl57\nlmOuDl5vCY65PNj2veOWf30stJnZjcFn3GpmP0/hHwOADBVLdwEAZhd33x8Ei/kTVn1K0sfd/Ukz\nK5TUL+kOSZ9y99+WJDPLl/Qmd+8PQsv9kuqD7S+TtFrSEUlPSrrazDZK+p6kd7v7JjMrltQn6UOS\nTrr7FWaWI+lJM/upux+YpPwPunt7cO/DTWb2gLsfl1Qg6Vl3/4vgXqgvneGYH5X0RXf/TtAmamYX\nS3q3pKvdfcjMvirpNjP7saR/kXSdux8ws/Jz/qIBZB1CF4CwPCnpC2b2HUkPuntj0Fk1XlzSl81s\nnaQRSSvGrdvo7o2SFIzDWiLppKSj7r5Jkty9M1j/ZkmXmtk7g21LJC2XNFno+oSZ/W7wujbY5nhQ\nywPB8pVnOebTkv6HmdUEn2+Pmb1B0uVKBjhJypPUIukqSY+PhUB3b5+kLgCzAKELQEqZ2TIlQ0qL\npIvHlrv7P5rZf0l6q5I9T285w+Z/JqlZ0lolhz/0j1s3MO71iF7955dJ+hN3f/Qc6r5e0hslvdbd\ne83sl5Jyg9X97j7yatu7+31m9qykt0l6JDilaZK+5e6fnnCst0+1LgCzB2O6AKSMmVVKulPSl93d\nJ6y7wN1fcPf/LWmTpIskdUkqGtesRMlepFFJ75M02aD1XZIWmtkVwTGKzCwm6VFJHzOzeLB8hZkV\nTLKvEkkngsB1kZK9UVM+ZhA297v7lyT9UNKlkn4u6Z1mNj9oW25miyU9I+k6M1s6tnyS2gDMAvR0\nAZiuvOB0X1zSsKR/k/SFM7T7pJndIGlU0nZJPw5ejwQD1O+R9FVJD5jZ+yX9RFLPqx3Y3QfN7N2S\n/m8wDqtPyd6qbyh5+vE3wYD7VknvmORz/ETSR81sp5LB6plzPOa7JL3PzIYkHZP0D8H4sP8p6aeW\nnEZjSMlxbc8EA/UfDJa3KHnlJ4BZzCb8YxQA5gQzWyLpR+6+Js2lnCY4zXnq4gIAswenFwHMVSOS\nSizDJkdVsrfvRLprAZB69HQBAACEgJ4uAACAEBC6AAAAQkDoAgAACAGhCwAAIASELgAAgBD8/xuf\nWBB0aN0hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3abb15518>"
      ]
     },
     "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": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = specz_merge(master_catalogue, specz, radius=1. * u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VII - Choosing between multiple values for the same filter\n",
    "\n",
    "In GAMA-12 we don't have any pairs of surveys from the same instruments. All we need to do is rename some columns to the name of the camera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Rename columns for UKIDSS-LAS and VISTA-VIKING\n",
    "\n",
    "for band in ['u', 'g', 'r', 'i']:\n",
    "    master_catalogue['f_ap_kids_' + band].name = 'f_ap_omegacam_' + band\n",
    "    master_catalogue['ferr_ap_kids_' + band].name = 'ferr_ap_omegacam_' + band\n",
    "    master_catalogue['m_ap_kids_' + band].name = 'm_ap_omegacam_' + band\n",
    "    master_catalogue['merr_ap_kids_' + band].name = 'merr_ap_omegacam_' + band\n",
    "    master_catalogue['f_kids_' + band].name = 'f_omegacam_' + band\n",
    "    master_catalogue['ferr_kids_' + band].name = 'ferr_omegacam_' + band\n",
    "    master_catalogue['m_kids_' + band].name = 'm_omegacam_' + band\n",
    "    master_catalogue['merr_kids_' + band].name = 'merr_omegacam_' + band\n",
    "    master_catalogue['flag_kids_' + band].name = 'flag_omegacam_' + band    \n",
    "\n",
    "for band in ['z','y','j','h','ks']:\n",
    "    master_catalogue['f_ap_viking_' + band.strip('s')].name = 'f_ap_vista_' + band\n",
    "    master_catalogue['ferr_ap_viking_' + band.strip('s')].name = 'ferr_ap_vista_' + band\n",
    "    master_catalogue['m_ap_viking_' + band.strip('s')].name = 'm_ap_vista_' + band\n",
    "    master_catalogue['merr_ap_viking_' + band.strip('s')].name = 'merr_ap_vista_' + band\n",
    "    master_catalogue['f_viking_' + band.strip('s')].name = 'f_vista_' + band\n",
    "    master_catalogue['ferr_viking_' + band.strip('s')].name = 'ferr_vista_' + band\n",
    "    master_catalogue['m_viking_' + band.strip('s')].name = 'm_vista_' + band\n",
    "    master_catalogue['merr_viking_' + band.strip('s')].name = 'merr_vista_' + band\n",
    "    master_catalogue['flag_viking_' + band.strip('s')].name = 'flag_vista_' + band"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII.a Wavelength domain coverage\n",
    "\n",
    "We add a binary `flag_optnir_obs` indicating that a source was observed in a given wavelength domain:\n",
    "\n",
    "- 1 for observation in optical;\n",
    "- 2 for observation in near-infrared;\n",
    "- 4 for observation in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source observed both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: The observation flag is based on the creation of multi-order coverage maps from the catalogues, this may not be accurate, especially on the edges of the coverage.*\n",
    "\n",
    "*Note 2: Being on the observation coverage does not mean having fluxes in that wavelength domain. For sources observed in one domain but having no flux in it, one must take into consideration de different depths in the catalogue we are using.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "decals_moc = MOC(filename=\"../../dmu0/dmu0_DECaLS/data/DECaLS_GAMA-12_MOC.fits\")\n",
    "hsc_moc = MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_wide_GAMA-12_MOC.fits\")\n",
    "kids_moc = MOC(filename=\"../../dmu0/dmu0_KIDS/data/KIDS-DR3_GAMA-12_MOC.fits\")\n",
    "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_GAMA-12_MOC.fits\")\n",
    "las_moc = MOC(filename=\"../../dmu0/dmu0_UKIDSS-LAS/data/UKIDSS-LAS_GAMA-12_MOC.fits\")\n",
    "viking_moc = MOC(filename=\"../../dmu0/dmu0_VISTA-VIKING/data/VIKING_GAMA-12_MOC.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    decals_moc + hsc_moc + ps1_moc) \n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    las_moc + viking_moc\n",
    ")\n",
    "\n",
    "was_observed_mir = np.zeros(len(master_catalogue), dtype=bool)\n",
    "\n",
    "#was_observed_mir = inMoc(\n",
    "#    master_catalogue['ra'], master_catalogue['dec'],   \n",
    "#)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * was_observed_optical + 2 * was_observed_nir + 4 * was_observed_mir,\n",
    "        name=\"flag_optnir_obs\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII.b Wavelength domain detection\n",
    "\n",
    "We add a binary `flag_optnir_det` indicating that a source was detected in a given wavelength domain:\n",
    "\n",
    "- 1 for detection in optical;\n",
    "- 2 for detection in near-infrared;\n",
    "- 4 for detection in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source detected both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: We use the total flux columns to know if the source has flux, in some catalogues, we may have aperture flux and no total flux.*\n",
    "\n",
    "To get rid of artefacts (chip edges, star flares, etc.) we consider that a source is detected in one wavelength domain when it has a flux value in **at least two bands**. That means that good sources will be excluded from this flag when they are on the coverage of only one band."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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_suprime_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_y']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_y']) +\n",
    "    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_omegacam_u']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_omegacam_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_omegacam_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_omegacam_i']) \n",
    ")\n",
    "\n",
    "nb_nir_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_y']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_k']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_y']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_ks'])\n",
    ")\n",
    "\n",
    "nb_mir_flux = np.zeros(len(master_catalogue), dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "has_optical_flux = nb_optical_flux >= 2\n",
    "has_nir_flux = nb_nir_flux >= 2\n",
    "has_mir_flux = nb_mir_flux >= 2\n",
    "\n",
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * has_optical_flux + 2 * has_nir_flux + 4 * has_mir_flux,\n",
    "        name=\"flag_optnir_det\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IX - Cross-identification table\n",
    "\n",
    "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogue. This can be used to easily get additional information from them.\n",
    "\n",
    "For convenience, we also cross-match the master list with the SDSS catalogue and add the objID associated with each source, if any. **TODO: should we correct the astrometry with respect to Gaia positions?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "921 master list rows had multiple associations.\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Addind SDSS ids\n",
    "#\n",
    "sdss = Table.read(\"../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_GAMA-12.fits\")['objID', 'ra', 'dec']\n",
    "sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)\n",
    "idx_ml, d2d, _ = sdss_coords.match_to_catalog_sky(SkyCoord(master_catalogue['ra'], master_catalogue['dec']))\n",
    "idx_sdss = np.arange(len(sdss))\n",
    "\n",
    "# Limit the cross-match to 1 arcsec\n",
    "mask = d2d <= 1. * u.arcsec\n",
    "idx_ml = idx_ml[mask]\n",
    "idx_sdss = idx_sdss[mask]\n",
    "d2d = d2d[mask]\n",
    "nb_orig_matches = len(idx_ml)\n",
    "\n",
    "# In case of multiple associations of one master list object to an SDSS object, we keep only the\n",
    "# association to the nearest one.\n",
    "sort_idx = np.argsort(d2d)\n",
    "idx_ml = idx_ml[sort_idx]\n",
    "idx_sdss = idx_sdss[sort_idx]\n",
    "_, unique_idx = np.unique(idx_ml, return_index=True)\n",
    "idx_ml = idx_ml[unique_idx]\n",
    "idx_sdss = idx_sdss[unique_idx]\n",
    "print(\"{} master list rows had multiple associations.\".format(nb_orig_matches - len(idx_ml)))\n",
    "\n",
    "# Adding the ObjID to the master list\n",
    "master_catalogue.add_column(Column(data=np.full(len(master_catalogue), -1, dtype='>i8'), name=\"sdss_id\"))\n",
    "master_catalogue['sdss_id'][idx_ml] = sdss['objID'][idx_sdss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_gama-12{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)\n",
    "id_names.remove('help_id')\n",
    "master_catalogue.remove_columns(id_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X - Adding HEALPix index\n",
    "\n",
    "We are adding a column with a HEALPix index at order 13 associated with each source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(\n",
    "    data=coords_to_hpidx(master_catalogue['ra'], master_catalogue['dec'], order=13),\n",
    "    name=\"hp_idx\"\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XI - Saving the catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns = [\"help_id\", \"field\", \"ra\", \"dec\", \"hp_idx\"]\n",
    "\n",
    "bands = [column[5:] for column in master_catalogue.colnames if 'f_ap' in column]\n",
    "for band in bands:\n",
    "    columns += [\"f_ap_{}\".format(band), \"ferr_ap_{}\".format(band),\n",
    "                \"m_ap_{}\".format(band), \"merr_ap_{}\".format(band),\n",
    "                \"f_{}\".format(band), \"ferr_{}\".format(band),\n",
    "                \"m_{}\".format(band), \"merr_{}\".format(band),\n",
    "                \"flag_{}\".format(band)]    \n",
    "    \n",
    "columns += [\"stellarity\", \"stellarity_origin\",\"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \n",
    "            \"flag_optnir_obs\", \"flag_optnir_det\", \"ebv\",\n",
    "            \"zspec_association_flag\", \"zspec_qual\", \"zspec\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing columns: set()\n"
     ]
    }
   ],
   "source": [
    "# We check for columns in the master catalogue that we will not save to disk.\n",
    "print(\"Missing columns: {}\".format(set(master_catalogue.colnames) - set(columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[columns].write(\"{}/master_catalogue_gama-12{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  }
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