{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bootes master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on ELAIS-N1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "017bb1e (Mon Jun 18 14:58:59 2018 +0100)\n",
      "This notebook was executed on: \n",
      "2019-01-31 17:42:48.232232\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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/pyenv/versions/3.7.2/lib/python3.7/site-packages/matplotlib/__init__.py:855: MatplotlibDeprecationWarning: \n",
      "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n",
      "  \"found relative to the 'datapath' directory.\".format(key))\n",
      "/opt/pyenv/versions/3.7.2/lib/python3.7/site-packages/matplotlib/__init__.py:846: MatplotlibDeprecationWarning: \n",
      "The text.latex.unicode rcparam was deprecated in Matplotlib 2.2 and will be removed in 3.1.\n",
      "  \"2.2\", name=key, obj_type=\"rcparam\", addendum=addendum)\n",
      "/opt/pyenv/versions/3.7.2/lib/python3.7/site-packages/seaborn/apionly.py:9: UserWarning: As seaborn no longer sets a default style on import, the seaborn.apionly module is deprecated. It will be removed in a future version.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "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",
    "datafusion = Table.read(\"{}/Datafusion.fits\".format(TMP_DIR))\n",
    "ibis = Table.read(\"{}/IBIS.fits\".format(TMP_DIR))\n",
    "legacy = Table.read(\"{}/LegacySurvey.fits\".format(TMP_DIR))\n",
    "ndwfs = Table.read(\"{}/NDWFS.fits\".format(TMP_DIR))\n",
    "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))\n",
    "#sdwfs = Table.read(\"{}/SDWFS.fits\".format(TMP_DIR)) #We use DataFusion instead\n",
    "uhs = Table.read(\"{}/UHS.fits\".format(TMP_DIR))\n",
    "zbootes = Table.read(\"{}/zBootes.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.\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": [
    "### PanSTARRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = ps1\n",
    "master_catalogue['ps1_ra'].name = 'ra'\n",
    "master_catalogue['ps1_dec'].name = 'dec'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add DECaLS\n",
    "\n",
    "DECaLS contains the LegacySurvey data so is not included again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# nb_merge_dist_plot(\n",
    "#     SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "#     SkyCoord(decals['decals_ra'], decals['decals_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, decals, \"decals_ra\", \"decals_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Legacy Survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(legacy['legacy_ra'], legacy['legacy_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, legacy, \"legacy_ra\", \"legacy_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add IBIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(ibis['ibis_ra'], ibis['ibis_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, ibis, \"ibis_ra\", \"ibis_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add NDWFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(ndwfs['ndwfs_ra'], ndwfs['ndwfs_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, ndwfs, \"ndwfs_ra\", \"ndwfs_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add UHS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(uhs['uhs_ra'], uhs['uhs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, uhs, \"uhs_ra\", \"uhs_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add zBootes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(zbootes['zbootes_ra'], zbootes['zbootes_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, zbootes, \"zbootes_ra\", \"zbootes_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add SDWFS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(sdwfs['sdwfs_ra'], sdwfs['sdwfs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, sdwfs, \"sdwfs_ra\", \"sdwfs_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Datafusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(datafusion['datafusion_ra'], datafusion['datafusion_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, datafusion, \"datafusion_ra\", \"datafusion_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": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table140261667971592-341855\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>ps1_id</th><th>ra</th><th>dec</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>flag_merged</th><th>legacy_id</th><th>f_90prime_g</th><th>ferr_90prime_g</th><th>f_ap_90prime_g</th><th>ferr_ap_90prime_g</th><th>f_90prime_r</th><th>ferr_90prime_r</th><th>f_ap_90prime_r</th><th>ferr_ap_90prime_r</th><th>f_mosaic_z</th><th>ferr_mosaic_z</th><th>f_ap_mosaic_z</th><th>ferr_ap_mosaic_z</th><th>legacy_stellarity</th><th>m_90prime_g</th><th>merr_90prime_g</th><th>flag_90prime_g</th><th>m_ap_90prime_g</th><th>merr_ap_90prime_g</th><th>m_90prime_r</th><th>merr_90prime_r</th><th>flag_90prime_r</th><th>m_ap_90prime_r</th><th>merr_ap_90prime_r</th><th>m_mosaic_z</th><th>merr_mosaic_z</th><th>flag_mosaic_z</th><th>m_ap_mosaic_z</th><th>merr_ap_mosaic_z</th><th>legacy_flag_cleaned</th><th>legacy_flag_gaia</th><th>ibis_id</th><th>ibis_stellarity</th><th>m_newfirm_j</th><th>merr_newfirm_j</th><th>m_ap_newfirm_j</th><th>merr_ap_newfirm_j</th><th>m_newfirm_h</th><th>merr_newfirm_h</th><th>m_ap_newfirm_h</th><th>merr_ap_newfirm_h</th><th>m_newfirm_k</th><th>merr_newfirm_k</th><th>m_ap_newfirm_k</th><th>merr_ap_newfirm_k</th><th>f_newfirm_j</th><th>ferr_newfirm_j</th><th>flag_newfirm_j</th><th>f_ap_newfirm_j</th><th>ferr_ap_newfirm_j</th><th>f_newfirm_h</th><th>ferr_newfirm_h</th><th>flag_newfirm_h</th><th>f_ap_newfirm_h</th><th>ferr_ap_newfirm_h</th><th>f_newfirm_k</th><th>ferr_newfirm_k</th><th>flag_newfirm_k</th><th>f_ap_newfirm_k</th><th>ferr_ap_newfirm_k</th><th>ibis_flag_cleaned</th><th>ibis_flag_gaia</th><th>ndwfs_id</th><th>ndwfs_stellarity</th><th>m_ap_mosaic_r</th><th>merr_ap_mosaic_r</th><th>m_mosaic_r</th><th>merr_mosaic_r</th><th>m_ap_mosaic_i</th><th>merr_ap_mosaic_i</th><th>m_mosaic_i</th><th>merr_mosaic_i</th><th>m_ap_mosaic_b</th><th>merr_ap_mosaic_b</th><th>m_mosaic_b</th><th>merr_mosaic_b</th><th>m_ap_tifkam_ks</th><th>merr_ap_tifkam_ks</th><th>m_tifkam_ks</th><th>merr_tifkam_ks</th><th>f_ap_mosaic_r</th><th>ferr_ap_mosaic_r</th><th>f_mosaic_r</th><th>ferr_mosaic_r</th><th>flag_mosaic_r</th><th>f_ap_mosaic_i</th><th>ferr_ap_mosaic_i</th><th>f_mosaic_i</th><th>ferr_mosaic_i</th><th>flag_mosaic_i</th><th>f_ap_mosaic_b</th><th>ferr_ap_mosaic_b</th><th>f_mosaic_b</th><th>ferr_mosaic_b</th><th>flag_mosaic_b</th><th>f_ap_tifkam_ks</th><th>ferr_ap_tifkam_ks</th><th>f_tifkam_ks</th><th>ferr_tifkam_ks</th><th>flag_tifkam_ks</th><th>ndwfs_flag_cleaned</th><th>ndwfs_flag_gaia</th><th>uhs_id</th><th>uhs_stellarity</th><th>m_ukidss_j</th><th>merr_ukidss_j</th><th>m_ap_ukidss_j</th><th>merr_ap_ukidss_j</th><th>f_ukidss_j</th><th>ferr_ukidss_j</th><th>flag_ukidss_j</th><th>f_ap_ukidss_j</th><th>ferr_ap_ukidss_j</th><th>uhs_flag_cleaned</th><th>uhs_flag_gaia</th><th>zbootes_id</th><th>zbootes_stellarity</th><th>m_ap_90prime_z</th><th>merr_ap_90prime_z</th><th>m_90prime_z</th><th>merr_90prime_z</th><th>f_ap_90prime_z</th><th>ferr_ap_90prime_z</th><th>f_90prime_z</th><th>ferr_90prime_z</th><th>flag_90prime_z</th><th>wfc_flag_cleaned</th><th>zbootes_flag_gaia</th><th>datafusion_intid</th><th>datafusion_stellarity</th><th>f_ap_irac_i1</th><th>ferr_ap_irac_i1</th><th>f_irac_i1</th><th>ferr_irac_i1</th><th>f_ap_irac_i2</th><th>ferr_ap_irac_i2</th><th>f_irac_i2</th><th>ferr_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>f_ap_irac_i4</th><th>ferr_ap_irac_i4</th><th>f_irac_i4</th><th>ferr_irac_i4</th><th>m_ap_irac_i1</th><th>merr_ap_irac_i1</th><th>m_irac_i1</th><th>merr_irac_i1</th><th>flag_irac_i1</th><th>m_ap_irac_i2</th><th>merr_ap_irac_i2</th><th>m_irac_i2</th><th>merr_irac_i2</th><th>flag_irac_i2</th><th>m_ap_irac_i3</th><th>merr_ap_irac_i3</th><th>m_irac_i3</th><th>merr_irac_i3</th><th>flag_irac_i3</th><th>m_ap_irac_i4</th><th>merr_ap_irac_i4</th><th>m_irac_i4</th><th>merr_irac_i4</th><th>flag_irac_i4</th><th>datafusion_flag_cleaned</th><th>datafusion_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>uJy</th><th>uJy</th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</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>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<tr><td>0</td><td>150292199054805142</td><td>219.905637565971</td><td>35.245638794068206</td><td>12.69909954071045</td><td>0.0010860000038519502</td><td>14.146100044250488</td><td>0.0012199999764561653</td><td>9.38068962097168</td><td>0.0</td><td>10.289299964904785</td><td>0.0</td><td>14.119199752807617</td><td>0.07498999685049057</td><td>14.607999801635742</td><td>0.10395699739456177</td><td>13.130000114440918</td><td>0.17569200694561005</td><td>14.145999908447266</td><td>0.13489900529384613</td><td>12.684800148010254</td><td>0.0010860000038519502</td><td>13.367400169372559</td><td>0.00203699991106987</td><td>30224.573672886934</td><td>30.231917275422322</td><td>7971.865858938918</td><td>8.957678778321467</td><td>False</td><td>642279.6345801096</td><td>0.0</td><td>278150.6082885889</td><td>0.0</td><td>False</td><td>8171.844592572415</td><td>564.415737058542</td><td>5209.548425071181</td><td>498.80349359134976</td><td>False</td><td>20323.567967177143</td><td>3288.7255935656217</td><td>7972.601125940405</td><td>990.5683874045147</td><td>False</td><td>30625.271026606602</td><td>30.632711985754344</td><td>16332.021096879142</td><td>30.64126016627149</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>1</td><td>147042185485050439</td><td>218.54838365597098</td><td>32.533216384068204</td><td>12.902999877929688</td><td>0.0010860000038519502</td><td>13.941800117492676</td><td>0.0012199999764561653</td><td>9.244199752807617</td><td>0.0</td><td>9.572549819946289</td><td>0.0</td><td>12.648200035095215</td><td>0.16843900084495544</td><td>12.443599700927734</td><td>0.12545999884605408</td><td>11.836400032043457</td><td>0.0010860000038519502</td><td>11.857799530029297</td><td>0.0010860000038519502</td><td>11.045299530029297</td><td>0.12740199267864227</td><td>10.928500175476074</td><td>0.12156300246715546</td><td>25049.556944069944</td><td>25.05564318342854</td><td>9622.323485022822</td><td>10.812234476234133</td><td>False</td><td>728316.4159413344</td><td>0.0</td><td>538245.0841076925</td><td>0.0</td><td>False</td><td>31675.245235987484</td><td>4914.035873250839</td><td>38243.71909889726</td><td>4419.173767061893</td><td>False</td><td>66902.13651958395</td><td>66.91839159416946</td><td>65596.42756509743</td><td>65.61236539426228</td><td>False</td><td>138637.33071127188</td><td>16267.922276938989</td><td>154383.16133116037</td><td>17285.304238305205</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>2</td><td>148432178563450118</td><td>217.856343425971</td><td>33.69128372406821</td><td>14.088899612426758</td><td>0.0015450000064447522</td><td>13.565699577331543</td><td>0.0010860000038519502</td><td>12.845499992370605</td><td>0.0</td><td>12.84529972076416</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>12.648900032043457</td><td>0.035895999521017075</td><td>12.46720027923584</td><td>0.03570599853992462</td><td>8403.112058024992</td><td>11.957608235691817</td><td>13605.677391936602</td><td>13.608983135404564</td><td>False</td><td>26411.92181831217</td><td>0.0</td><td>26416.794130634986</td><td>0.0</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>31654.830121453986</td><td>1046.554183141309</td><td>37421.38768877091</td><td>1230.6562204719578</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>3</td><td>148572188406089286</td><td>218.840600365971</td><td>33.81554305406821</td><td>14.753299713134766</td><td>0.0018119999440386891</td><td>15.154800415039062</td><td>0.028226999565958977</td><td>14.962400436401367</td><td>0.0</td><td>14.602700233459473</td><td>0.018834000453352928</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>14.061200141906738</td><td>0.010123999789357185</td><td>13.796600341796875</td><td>0.0147299999371171</td><td>6.3809099197387695</td><td>0.002744999947026372</td><td>6.751029968261719</td><td>0.018345000222325325</td><td>4557.011396999333</td><td>7.605258404536621</td><td>3148.3270006813136</td><td>81.85029152778925</td><td>False</td><td>3758.7187070863165</td><td>0.0</td><td>5235.038821378773</td><td>90.81093833205321</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>8620.2516285518</td><td>80.37995351836489</td><td>10999.168704601929</td><td>149.2238663656055</td><td>False</td><td>10177380.97781015</td><td>25730.845229739392</td><td>7237490.600299285</td><td>122287.3162833743</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>4</td><td>148612172599144113</td><td>217.26024942597098</td><td>33.844676164068204</td><td>13.475099563598633</td><td>0.0027379998937249184</td><td>13.024999618530273</td><td>0.03134699910879135</td><td>13.548299789428711</td><td>0.0</td><td>12.494999885559082</td><td>0.019236000254750252</td><td>13.95259952545166</td><td>0.0018820000113919377</td><td>14.664799690246582</td><td>0.0014909999445080757</td><td>nan</td><td>nan</td><td>11.398799896240234</td><td>0.046567998826503754</td><td>12.152000427246094</td><td>0.001133000012487173</td><td>12.543000221252441</td><td>0.001193000003695488</td><td>14789.727579836099</td><td>37.296603322565524</td><td>22387.219251355447</td><td>646.3560290659094</td><td>False</td><td>13825.475732122275</td><td>0.0</td><td>36475.3985372128</td><td>646.2350361709156</td><td>False</td><td>9527.088287003584</td><td>16.514122110045154</td><td>4944.0189181699225</td><td>6.789431816248952</td><td>False</td><td>nan</td><td>nan</td><td>100110.59475203436</td><td>4293.814683993039</td><td>False</td><td>50026.46655627219</td><td>52.2041974694693</td><td>34897.94963224664</td><td>38.34564405035187</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>5</td><td>149412164869384895</td><td>216.48684857597098</td><td>34.5118634140682</td><td>20.80470085144043</td><td>0.014188000001013279</td><td>20.511999130249023</td><td>0.022512000054121017</td><td>24.298099517822266</td><td>0.0</td><td>24.298500061035156</td><td>17.100799560546875</td><td>23.19499969482422</td><td>0.3605560064315796</td><td>23.93829917907715</td><td>1.3670899868011475</td><td>22.75119972229004</td><td>0.4972369968891144</td><td>22.221500396728516</td><td>1.4721299409866333</td><td>20.250600814819336</td><td>0.3530920147895813</td><td>20.14579963684082</td><td>0.27429699897766113</td><td>17.302930342720522</td><td>0.22610830756513312</td><td>22.656892699884665</td><td>0.4697752248205101</td><td>False</td><td>0.6930430183995168</td><td>0.0</td><td>0.692787392329157</td><td>10.91169133199724</td><td>False</td><td>1.914256463075016</td><td>0.6356946213891099</td><td>0.9653400584651993</td><td>1.2154948154094212</td><td>False</td><td>2.8808464514320007</td><td>1.3193475834471424</td><td>4.692452026739927</td><td>6.362410219843535</td><td>False</td><td>28.82436005513642</td><td>9.373963327635122</td><td>31.74535187327109</td><td>8.02004640921774</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>6</td><td>149912170289453574</td><td>217.02914164597098</td><td>34.927499044068206</td><td>15.240699768066406</td><td>0.015333999879658222</td><td>15.610199928283691</td><td>0.08968999981880188</td><td>13.514200210571289</td><td>0.0</td><td>14.414400100708008</td><td>0.5431780219078064</td><td>14.571999549865723</td><td>0.11269400268793106</td><td>15.306099891662598</td><td>0.138824000954628</td><td>13.715900421142578</td><td>0.0036140000447630882</td><td>13.31879997253418</td><td>0.003527000080794096</td><td>13.653300285339355</td><td>0.053954001516103745</td><td>13.079000473022461</td><td>0.09094999730587006</td><td>2908.841735589931</td><td>41.08196689767505</td><td>2069.760186496447</td><td>170.9778028385672</td><td>False</td><td>14266.581304917449</td><td>0.0</td><td>6226.442183013182</td><td>3114.998407228992</td><td>False</td><td>5385.179452610932</td><td>558.9547673255698</td><td>2738.7982603370833</td><td>350.1872099699104</td><td>False</td><td>11847.857758386344</td><td>39.43698136021318</td><td>17079.691065729112</td><td>55.483156502259696</td><td>False</td><td>12551.044976402462</td><td>623.7050000891887</td><td>21300.99109391225</td><td>1784.3423421946777</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>7</td><td>147492203624686868</td><td>220.362533475971</td><td>32.9138137640682</td><td>13.779899597167969</td><td>0.047460999339818954</td><td>13.823800086975098</td><td>0.29232901334762573</td><td>10.968899726867676</td><td>0.0</td><td>12.982099533081055</td><td>0.0</td><td>14.750900268554688</td><td>0.23555099964141846</td><td>13.952899932861328</td><td>0.07648999989032745</td><td>14.811800003051758</td><td>0.24015800654888153</td><td>15.470399856567383</td><td>0.10411400347948074</td><td>13.755599975585938</td><td>0.14483299851417542</td><td>13.419300079345703</td><td>0.05690399929881096</td><td>11169.665338436072</td><td>488.2617683101111</td><td>10727.041609107366</td><td>2888.20201075256</td><td>False</td><td>148744.22363831766</td><td>0.0</td><td>23289.50117755378</td><td>0.0</td><td>False</td><td>4567.093392413492</td><td>990.8331409823151</td><td>9524.452644938801</td><td>670.99667356961</td><td>False</td><td>4317.973759480901</td><td>955.1085851819093</td><td>2354.1821224825694</td><td>225.748505592188</td><td>False</td><td>11422.469570457988</td><td>1523.7131368340956</td><td>15569.690093895244</td><td>816.0155572808673</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>8</td><td>150742169177050441</td><td>216.91767834597098</td><td>35.616607664068205</td><td>22.07349967956543</td><td>0.09818600118160248</td><td>22.141199111938477</td><td>0.22458499670028687</td><td>21.465099334716797</td><td>0.0</td><td>20.88719940185547</td><td>1.6196999549865723</td><td>21.922300338745117</td><td>0.15705299377441406</td><td>22.11479949951172</td><td>0.4599840044975281</td><td>23.252099990844727</td><td>1.5700299739837646</td><td>22.101600646972656</td><td>1.1072200536727905</td><td>20.673799514770508</td><td>0.18573300540447235</td><td>20.21969985961914</td><td>0.21001100540161133</td><td>5.377744046773948</td><td>0.48632364014105056</td><td>5.052663260343535</td><td>1.0451455488717938</td><td>False</td><td>9.418034263684296</td><td>0.0</td><td>16.036893284604815</td><td>23.923817974721906</td><td>False</td><td>6.1813006059838465</td><td>0.8941322591389749</td><td>5.1770242607843135</td><td>2.1933028855631824</td><td>False</td><td>1.8161846619997986</td><td>2.626295729232387</td><td>5.240343327031079</td><td>5.3440358665314704</td><td>False</td><td>19.520017113872534</td><td>3.3392194422713746</td><td>29.656510993460056</td><td>5.736378379155134</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>9</td><td>149642198013901347</td><td>219.80142161597098</td><td>34.7006360840682</td><td>22.054100036621094</td><td>0.09893999993801117</td><td>22.264699935913086</td><td>0.5064250230789185</td><td>22.583099365234375</td><td>0.0</td><td>21.71030044555664</td><td>11.149499893188477</td><td>24.90719985961914</td><td>6.320859909057617</td><td>21.83679962158203</td><td>0.9590920209884644</td><td>22.495500564575195</td><td>0.7378649711608887</td><td>21.075199127197266</td><td>0.4934439957141876</td><td>20.82069969177246</td><td>0.338578999042511</td><td>20.94659996032715</td><td>1.3591300249099731</td><td>5.474695707032479</td><td>0.4988931846802429</td><td>4.5094131362856285</td><td>2.1033466891937764</td><td>False</td><td>3.36325854213706</td><td>0.0</td><td>7.5141493326687</td><td>77.1633172172383</td><td>False</td><td>0.39547593012966215</td><td>2.3023529479803284</td><td>6.687751868430504</td><td>5.907668388742072</td><td>False</td><td>3.6458582061516247</td><td>2.477720691591053</td><td>13.487155015372624</td><td>6.12962488718398</td><td>False</td><td>17.049832755684957</td><td>5.316867285961071</td><td>15.183084738069283</td><td>19.00626160187722</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\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(\"$('#table140261667971592-341855').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",
       "    $('#table140261667971592-341855').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, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 77, 79, 80, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 100, 101, 102, 103, 105, 106, 107, 108, 110, 111, 113, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 137, 138, 139, 140, 142, 143, 144, 145, 147, 148, 149, 150, 153, 154, 155, 156, 157, 158, 159, 160, 161, 163, 164, 166, 168, 169, 170, 171, 172, 173, 174, 175, 176, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 203, 204, 205, 206, 208, 209, 210, 211, 213, 214, 215, 216, 219], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "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": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_gaia_columns = [column for column in master_catalogue.colnames\n",
    "                     if 'flag_gaia' in column]\n",
    "\n",
    "master_catalogue.add_column(Column(\n",
    "    data=np.max([master_catalogue[column] for column in flag_gaia_columns], axis=0),\n",
    "    name=\"flag_gaia\"\n",
    "))\n",
    "master_catalogue.remove_columns(flag_gaia_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each prisitine catalogue may contain one or several stellarity columns indicating the probability (0 to 1) of each source being a star.  We merge these columns taking the highest value.  We keep trace of the origin of the stellarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "legacy_stellarity, ibis_stellarity, ndwfs_stellarity, uhs_stellarity, zbootes_stellarity, datafusion_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": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We create an masked array with all the stellarities and get the maximum value, as well as its\n",
    "# origin.  Some sources may not have an associated stellarity.\n",
    "stellarity_array = np.array([master_catalogue[column] for column in stellarity_columns])\n",
    "stellarity_array = np.ma.masked_array(stellarity_array, np.isnan(stellarity_array))\n",
    "\n",
    "max_stellarity = np.max(stellarity_array, axis=0)\n",
    "max_stellarity.fill_value = np.nan\n",
    "\n",
    "no_stellarity_mask = max_stellarity.mask\n",
    "\n",
    "master_catalogue.add_column(Column(data=max_stellarity.filled(), name=\"stellarity\"))\n",
    "\n",
    "stellarity_origin = np.full(len(master_catalogue), \"NO_INFORMATION\", dtype=\"S20\")\n",
    "stellarity_origin[~no_stellarity_mask] = np.array(stellarity_columns)[np.argmax(stellarity_array, axis=0)[~no_stellarity_mask]]\n",
    "\n",
    "master_catalogue.add_column(Column(data=stellarity_origin, name=\"stellarity_origin\"))\n",
    "\n",
    "master_catalogue.remove_columns(stellarity_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV - Adding E(B-V) column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "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), \"Bootes\", dtype='<U18'),\n",
    "                                   name=\"field\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz =  Table.read(\"../../dmu23/dmu23_Bootes/data/Bootes-specz-v2.4.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ISJRRt0yIqhu7KMhIIj4uej4Pq+ZnMjQyypunelg+LzPS5YjMmEH/CLtOtLP3ZCcjo6O8XtdJvC/w7TUvPXoaXNFE4R6ig03dUdPfPmb8NAQKd/GaQf8Iv9vXxMO769hxvI2B4T/9hrr5jSYqCtPZsCiX5cWZ+CI451O0UbiHoKNviMbOAVaVZEe6lD+yKD+NpHgfBxq6eO/aSFcjEh7ff/5Nth5tZfvxdnoH/eSmJbJmYQ4VBemU56WREG84B72DfnbVtrPjWBsPbKtlVUkW77u4lDifAh4U7iEZO2kZLSdTx8TH+VhRnMnrdRoOKbGvpXuA7z9/lJ+8cpyRUceyeRlctjiPJYXpE7bIs1MTefvyIq5eWsgLR07x1IFmBv2j3LV+IQlR1H0aKQr3EIwtjFEcZeEOcHFZDg9sPcGQf5TEeP1BS+xp6x3i356r4aevnmB4ZJRVJdlcs6yQ/BAHL8T5jGuWFZKSEMfjexv48SvH+fCGMpIS4ma48uimNAhBdWMX+emJZCQnRLqUP3FxWQ6D/lH262ImiTHdA8P8y9OHufLrW/jhS8d410XFPPPFq7l9XWnIwT7ehsV53L6uhBOtvTy2t2EGKo4tarmHoLopek9YrivLAWDXiXbWLMyJcDUikxsYHuGBrSe4b0sN7X3D3HTBPL5w/VIqg4u/v/pm67Rfe3VpDqe6B9ly6BQXlWSxLEr/3c4Ghfsk/COjHG7u4S8uK4t0KRMqzEymNDeFncfb+dgVka5G5Oz8I6P89a9e55mDLXT2D1NRmM5d6xdSkpPKjuPt7DjeHpbjXLOskP0NXfxmTwOffXsayXO0e0bdMpM4drqXIf8oK4qjtwWwriyXnSfataaqRKXRUccTrzfwjm+9wCOv1ZOZHM9HL1/EX25cRElOatiPFx/n47a1JXT1D/P7/U1hf/1YoXCfxNhImWjtlgFYW5bD6Z5Batv6Il2KyFucc2w52MLN33mJeza9Rnyc8cFLy/jEVUtYUpA+o8cuzU1lY0U+24+1cfR0z4weK1op3CdxsKmbhDijonBm/xjPx/h+d5FosO1oK7d/71U+8uMd9Az6+dYdq/jdZ6+kan7mrC0uf92KIrJTEvjD/uY5+a1Wfe6TqG7sYklBelQPM1xalEFGUjw7T7Tz3rUlkS5H5qhN22qp7+jnD/ubONLSQ0ZyPLeuns/FZTn0D43ynztOzmo9ifE+rlhawON7Gzh6unfGvy1EG4X7OTjneKO+kyuXFkS6lHOK8xlrynLYFaYTUiJTVdPSw6ZtJ3ijoYuUhDhuumAely7Ki3ijaF1ZDlsOtvD8oVNzLtxD+j9vZjea2SEzqzGzL0/w+BfM7ICZvW5mz5hZdA4tmaL6jn5O9wyxpjS6ph2YyLqyHA63dNPZr2X3ZPbUd/Tz17/ayzu+9TyHW3q4dnkh//2GZVxRWRDxYAdIiPNxeUU+Nad6ODnHzklN2nI3szjgPuB6oA7YYWaPOecOjNvtNWCdc67PzD4JfB24YyYKnk17TwYuDFoVA+F+cVkOzsHu2nauWVYY6XLE4073DHLflhp+vrUWDD6ycRFFmcmkR8H6wme6dFEuzx8+xXOHWvjQZeWRLmfWhPLRuh6occ4ddc4NAQ8Ct47fwTm3xTk39rG4FfBEx+/eug4S431RPVJmzOrSbOJ8pq4ZmVHdA8N886nDXPX1LfzkleO8Z818tnzpar5yc1VUBjtAUkIcb1uSR3VTN02dA5EuZ9aE8ttYAIw/E1IHXHqO/T8K/G6iB8zsbuBugIULF4ZYYuTsOdnByvmZUfH1cjJpSfGsKM5g+7G2SJciHrNpWy3DI6NsPdrK84dP0Tc0wsr5mVxfVURhRjLPHzoV6RInddmSPF6sOc2LR05x+7rSSJczK8L6UWtmHwTWAVdN9Lhz7n7gfoB169ZF9dgk/8go++o6ueOS2PlDuGppAd97/igdfUNkpyZGuhzxAP/IKDuOt/Fs8KrSysJ03lE1jwU50bW2wWRSE+NZXZrN7hPtvOuiYlITo/NbRjiF0iStB8YnXElw2x8xs+uA/wnc4pwbDE95kVNzqof+4RFWx0B/+5jrVhQxMup4LgZaUhLdRkcdj+1t4PpvvcCvg1eVfuzyRXxk46KYC/Yx68tz8Y86XqvtiHQpsyKUj68dQKWZLSIQ6ncCHxi/g5mtAb4P3Oicawl7lRGw92TgDyAWTqaOWVWSTX56Ek9VN/OeNQsiXY7EIOccz1S38M9PHaa6sYvl8zL40IYyls/LmLWLj2bK/OwUSnJS2H68jbctyYv59zOZSVvuzjk/cA/wJFANPOSc229m95rZLcHdvgGkA780sz1m9tiMVTxL9pzsIDM5nvK88M99MVN8PuO6FYU8f+gUQ34tmi1T80rNad77b6/wsZ/upH/Iz7fvXM3mz1zBiuLZu6p0pq0vz+VU9yDHW70/LDKkjifn3GZg8xnbvjru/nVhrivi9pzsZFVpdsz9UV+3oogHd5xk27FWrqiM7ouvJPI2baultrWXPxxo5ujpXrJSEvizNQtYuzCH3sERHpzlq0pn2kUl2fx2XyM7jrexKD8t0uXMKO+fVZiGviE/h5u7uW7FkkiXMmUbK/JJTvDx9IFmhbuc0766Tn7yynEONXeTlhTPzRcVc0l5rqeXqEuM97FmYTY7j7dz84XFkS5nRincJ7C/oYuRURd1C2KHIiUxjssrCni6uoWv3eJi7puHzLxDTd1866nD/H5/EykJcdxQVcRlS/JjYshvOKwvz2Pr0TZ213r7mhCF+wTGTqZeVJoV4Uqm5/qqQp6ubqa6sZuq+dF/AZbMjjdP9fAvTx/hidcbSE+M5/PXLSUjOX7OLWYxLyuZ0pwUdgTXQPBqA2hufFRP0Z6THSzITqEwI/oWxA7FtcuLMIOnq5sjXYpEgeOne/nCQ3u4/pvP80x1M5+8agkv/s01fPa6yjkX7GPWlQVOrO456d1hkWq5n8G5wDjYVTHaagcoyEhidWk2m/c18ulrKzzbMpFzu+/ZGrYcamF3bTs+M962JJ8rlxaQnhTP5n1zd4UigAtLsnhiXwO/2lXn2bWH1XI/Q21bH/Ud/WxYnBfpUs7LbWtLONjUrQU85qC69j7+9pF9/PNTh3jtZAeXLsrjSzcs450XFkft/C+zLTkhjpXzs3hsbwMDwyORLmdG6Dd9hpdrAiuvv21JfoQrOT/vXbuAr//+IP/x8nHWledGuhyZBXXtfXz3uTf55c6TGMYl5blcvayQrJSESJcWldYuzGHPyQ6e3N/Erau9d9Gfwv0ML795mqLMJJYUxPYY2NTEeO5av5AfvHSMho5+5mfH5iXjMrmTbX1897kafrWrDoD3ryvlU9dUaBqKSSwuSGNBdgq/2lWncPe60VHHKzWnuWZ5oSf6qT90WRn//uJRfrb1BH9z4/JIlyNhtGlbLS3dAzx/6BR76zowMy4pz+HKygKyUxMV7CHwmXHbxSV859kj1Hf0s8BjDSD1uY9T3dRFe98wG2O8S2ZMSU4q76iaxy+219I/5M1+xbno9boONm07wbefPsIbDZ1ctjiPL71jGbesWqDZQKfo9otLcA4eCX7r8RK13Md5JdjfvrHCG+EO8JGN5fx+fxO/2VPPXeujfw59mZhzjpdqTvO959/k5ZpWkhN8XLW0gLdV5Osk6XkozU3lssV5PLTrJJ+6pgKfL/a/sY/RX8U4L795msUFaczLis3x7RNZvyiXquJMfvDiUd53cYmnLy33oiH/KI/vbeDfXzzKwaZuCjOS+B/vXE68zzdnx6iH253rS/nsg3t45c1WLq/0TsNO4R405B9l+7E23nexJ1YIfIuZ8fnrl/Lxn+7k/heO8qlrKiJdkkxi07Zaegb9bD/WxrZjrXQP+CnKTOK2tQtYVZJNvD6gw+qGlfPITk3gFztqFe5etLeug76hkZgfAjmR66uKeNeFxXz76SPcsHIeFYXpkS5JzmJfXScP76pjb10H/lFHZWE6t63Np7Iw3RMn+aNRckIc711Tws+2Hqe1Z5C89KRIlxQWagIEvXTkND6Dy2L84qWz+dotK0lJjOPLD7/O6GhUr3A45/QPjQSG4933Mu/+15d4vb6DtWU5fO7tlXxk4yKWFsX+QhnR7q71pQyPOB7e7Z0Tq2q5B73y5mkuWJBFVmrsXPCxaVvtpPt84NLASdSCjCS+cnMVX/rlXh7YdoIPX1Y+w9XJZKobu3hwey2PvFZP94CfJQVpfO3dVYw61J8+yyqLMlhXlsOD20/y8SsWe+LDVOEOtPYMsru2g09ctTjSpcyo29Yu4NE99fzD5moqCzO4bIk3v6VEs9aeQR7d08DDu+vY39BFYryPmy6Yx52XLGTD4lzMLKQPbQm/O9cv5Eu/3Mu2Y20xP/0IKNwB+O2+RkZGHe9eNT/SpcwoM+Ob71/NB/59K3/54x386L9dooCfBb2Dfp460Myje+p5/vApRh0syE7h5ouKWV2aTWpiPMdO93LsdG+kS53T3nVhMf/78f38Ynutwt0rHt3TwPJ5GSyf5/25zwsyktj08Q0K+BnWO+jnmYMt/G5fI1sOtTAwPMr8rGQ2VuSzZmEO8zK9M9zWK1IS47htbQk/33aCL9+0nOKs2L5idc6fUD3Z1seuE+2enFvibAoykvjF3RsoyUnhL/5jO/dtqWF4RAtqn6+23iEe2nmSj/1kJ2v//ik+84vX2HmindsvLuWXn7iMl/7mWm66oFjBHsU+evkiRh38x8vHI13KeZvzLffH9jYA8O5V3l5P8Uz56Uk8ePcGvvLoG3zjyUM8vreBf7ztIlaXxt7SgpF0/HQvT1c384cDzew41oYDslMSWFuWwwXzsyjLS8VnxpHmHo4090S6XJlEaW4q77ywmE3barnn2goyk2NngMWZ5nS4O+f4zWv1rC/PpSQnNdLlzLq89CS+++cX84f9TXz10f28576X2ViRx4c2lHPdikJdLDMB/8gou2s7eOZgM89Ut1DTEgjs5fMyuHpZAVXFWczPTvbEaIu56q+uXMzjexvYtK2WT1y1JNLlTNucDvfqxm6OtPTw/7zngkiXMmNCHXnx1Beu5KevnuDnW0/wiQd2UZSZxPVVRVyzrJDLluSRmjh3/1Raewb5x98d5FBzN0eae+gfHiHOjEX5adx8UTHL52WSm6YJu7ziggVZXF6Rz49eOsZHNpaTFB+bw1Ln7r9Y4NE99cT7jHddOLe6ZCby+N5GclIT+eTVFRxq6mZXbTsP7ajjga21xPmMBdkpXF9VxOrSbFbOz6Q8L81TkyyNNzA8wu7adl46cpoXjpzijfouANKT4llRnMmyeRlUFqZrLLqH3X3lYj78o+08uqeB968rjXQ50zJnw90/Mspjexu4amkBOWp1vSXOZ1TNz6Rqfib+kVGOt/ZxpKWb2rY+Hth6gh++dAyAlIQ4ls3LYFlRBpVF6VQWZVBRmE5xZnLMhX7PoJ89tR3sPNHGtqNt7KptZ8g/SrzPWLswhy+9Yyl9QyPMz07Bp+6WOeGKynxWFGfyvefe5M/WLIjJCffmbLg/8lo9jZ0D3Hurd7tkzld8nI+KwvS35qIZGXU0dQ3Q2NFPY9cATZ0DPPF6A73j5opPSYhjcUEaiwvSWRL87+L8NBblp5EWBVPTdg8Mc6Slh/31neyr72RffReHmroYdWAGK+Zl8uENZVy2JI/1i3LJCJ5Q04VFc4uZ8YXghHs/ffUEH718UaRLmrLI/2uLgEH/CN9++girSrK4bkVhpMuJGWPdM2euWNMz6Kele4BT3YOc7h6kpXuQl46c4om9DYyfxSYzOZ7ctCTWL8qhLC+N0txUFmSnUJKTQkF6Ulha/M45ugb8NHb209DRz4nWPo6f7uVYax81zd00dA68tW9uWiIXLMhiXmYhZXmpLMxNfaurpblrkMf3Np53PRK7rltRyJVLC/iXpw5zy6r5FGTE1oRiczLcH9pxkvqOfv7f916oUQ1hkJ4UT3pSOovz/3i2yeGRUVp7hjjdM/jWrbVniGcPnuJ0zx9P0BTnM/LTEynISCI3LYmM5HgykxNIT4ojIc5HQpyPOJ/hH3WMjI4yPOLoHfTTM+inZ8BPa+8Qbb2BY/WdseqLEAxZAAAKGklEQVRUUryPvLRECjOTuWBBFkWZyRRnJZOVkqDfv5yVmfG/3l3FDd96ga///iDfuH1VpEuakjkX7v1DI3zn2RrWl+dyhYfmbo5GCXE+5mUlT7j4yaB/hPa+YTr6hujoG6arf5ieQT/dA36OnuohMc5H14CfnsFh/CMO/7iZLON8RkKcEWdGUkIcSfE+0pLiyU1LpDQnhcyUBLJSEshOSSA3PYm0xDiFuEzLkoJ0/vLyRdz/wlH+fENZTF0HMufC/YGtJ2jpHuQ7d63RP/gISoqPY15mXMhXazrnGHXgM/R7k1n16Wsr+PVr9XzlN2/w8CffRmJ8bJxcjY0qw+RU9yD/9vybXFGZz6UemBhoLjEz4nymYJdZl5GcwL23rGRffSdf+c0bOBcb6yHMmXAf9I/wiQd20Tfk53+8c0WkyxGRGHLThcV8+toK/nPnSX4UI/POzIlwd87xd79+g10n2vn/bl/FimLvz/4oIuH1+euWcsPKIv7Pbw+w5VBLpMuZ1JwI9x++dIxf7qrjM9dWcPNF3p6zXURmhs9nfOuO1Syfl8k9P9/N7/ZF91BZT4f78Mgo//yHQ/zD5mpuWFnE565bGumSRCSGpSbG8x8fuYSKogw++fPd/J/fHoja6bJDCnczu9HMDplZjZl9eYLHk8zsP4OPbzOz8nAXOlU1Ld2897uv8J1na3jv2hK+dcfqmLssXkSiT1FmMg/91QY+tKGMf3/xGHfdv5Xtx9qi7kTrpEMhzSwOuA+4HqgDdpjZY865A+N2+yjQ7pyrMLM7gX8C7piJgs/FPzLKizWn+fXuen6/v4n0pHi+98GLufGCebNdioh4WFJ8HH//ngtYW5bNvY8f4P3ff5VVJVn85eWLuHppIVmpkZ8HPpRx7uuBGufcUQAzexC4FRgf7rcCXwve/xXwr2ZmbgY+ygb9I7T1DtHeG7gAprl7gMPNPRxu6mbPyQ5ae4fITk3gjnWlfPrtFRRmaNUbEZkZf7amhBtXFvPw7jp++NIxPvvgHgCWFWVwcXkOi/LSKM5OpjgrhayUeNKSgrfEeOJmuCchlHBfAJwc93MdcOnZ9nHO+c2sE8gDToejyPF+8OIxvvHkoT/aFu8zlhSkc0VlPjddWMw1ywpj5kIDEYltKYlxfHBDGR9Yv5Adx9vYfqyN7cfbeHxPA92D/gmfc++tK/nwZeUzWtesXqFqZncDdwd/7DGzQ+fafyreBP4AfDtcLzixfGbgA2uW6T1EB72HKPDnETruX/wT/MX0n14Wyk6hhHs9MH62+pLgton2qTOzeCALaD3zhZxz9wP3h1JYNDKznc65dZGu43zoPUQHvQeZaaH0XewAKs1skZklAncCj52xz2P81wfR+4BnZ6K/XUREQjNpyz3Yh34P8CQQB/zIObffzO4FdjrnHgN+CPzMzGqANgIfACIiEiEh9bk75zYDm8/Y9tVx9weA28NbWlSK2S6lcfQeooPeg8woU++JiIj3aLygiIgHKdxDNNkUDNHOzH5kZi1m9kaka5kuMys1sy1mdsDM9pvZZyNd01SZWbKZbTezvcH38L8jXdN0mFmcmb1mZk9EuhaZmMI9BOOmYLgJqALuMrOqyFY1ZT8Gbox0EefJD3zROVcFbAA+FYO/h0HgWufcKmA1cKOZbYhwTdPxWaA60kXI2SncQ/PWFAzOuSFgbAqGmOGce4HASKaY5ZxrdM7tDt7vJhAuCyJb1dS4gJ7gjwnBW0yd+DKzEuBdwA8iXYucncI9NBNNwRBToeI1wZlH1wDbIlvJ1AW7NPYALcBTzrlYew//Avw1EJ1z3QqgcJcYZGbpwMPA55xzXZGuZ6qccyPOudUErvZeb2YXRLqmUJnZzUCLc25XpGuRc1O4hyaUKRhkFphZAoFg/7lz7pFI13M+nHMdwBZi61zIRuAWMztOoHvyWjN7ILIlyUQU7qEJZQoGmWFmZgSuhq52zn0z0vVMh5kVmFl28H4KgXUSDka2qtA55/7WOVfinCsn8O/gWefcByNclkxA4R4C55wfGJuCoRp4yDm3P7JVTY2Z/QJ4FVhmZnVm9tFI1zQNG4EPEWgt7gne3hnpoqaoGNhiZq8TaDQ85ZzTcEIJO12hKiLiQWq5i4h4kMJdRMSDFO4iIh6kcBcR8SCFu4iIByncRUQ8SOEus8bMRoJj0/cHp7z9opn5go+tM7P/e47nlpvZB2av2j85dn9wPpioYGZ3BKef1hh5mZDCXWZTv3NutXNuJYErM28C/heAc26nc+4z53huORCRcA96MzgfTMiCU0XPCOfcfwIfm6nXl9incJeIcM61AHcD91jA1WOtUDO7atwVqK+ZWQbwj8AVwW2fD7amXzSz3cHb24LPvdrMnjOzX5nZQTP7eXDaAszsEjN7JfitYbuZZQRnaPyGme0ws9fN7K9Cqd/MfmNmu4LfQu4et73HzP7ZzPYCl53lmCuD9/cEj1kZfO4Hx23//tiHQ3ChmN3B13gmjL8G8TLnnG66zcoN6JlgWwdQBFwNPBHc9jiwMXg/ncBC7m89HtyeCiQH71cCO4P3rwY6CUzu5iMw5cLlQCJwFLgkuF9m8HXvBv4uuC0J2AksOqPGcuCNM7blBv+bArwB5AV/dsD7g/fPdszvAH8+bp8UYEXwfScEt38X+DBQQGC66UXjjzvuvT4x0f9r3XSLn+JngchseBn4ppn9HHjEOVcXbHyPlwD8q5mtBkaApeMe2+6cqwMI9pOXEwj8RufcDgAXnCrYzN4BXGRm7ws+N4vAh8WxSWr8jJn9WfB+afA5rcFaHg5uX3aWY74K/M/gohePOOeOmNnbgYuBHcH3mkJgvvcNwAvOuWPB14jpBVdk9ijcJWLMbDGBMGwh0HIFwDn3j2b2W+CdwMtmdsMET/880AysItBCHxj32OC4+yOc++/cgE87556cQt1XA9cBlznn+szsOSA5+PCAc27kXM93zm0ys20EVjPaHOwKMuAnzrm/PeNY7w61LpHx1OcuEWFmBcD3gH91zrkzHlvinNvnnPsnAjMnLge6gYxxu2URaBWPEpgpcrKTl4eAYjO7JHiMDDOLJzDT5yeD88RjZkvNLG2S18oC2oPBvpxA6zrkYwY/1I465/4v8ChwEfAM8D4zKwzum2tmZcBW4EozWzS2fZLaRAC13GV2pQS7SRIILHb9M2Ciedk/Z2bXEFjGbT/wu+D9keCJyh8T6JN+2Mw+DPwe6D3XgZ1zQ2Z2B/Cd4Dzq/QRa3z8g0G2zO3ji9RTwnknex++BT5hZNYEA3zrFY74f+JCZDQNNwD8459rM7O+APwSHhw4Dn3LObQ2esH0kuL2FwEgjkXPSlL8ik7DAeq1POOeiajm8YPfQl5xzN0e6Fok+6pYRmdwIkBVtFzER+PbSHulaJDqp5S4i4kFquYuIeJDCXUTEgxTuIiIepHAXEfEghbuIiAf9/0eO+vH2BawZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 32,
   "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",
    "We take DataFusion Spitzer instead of SDWFS which is shallower."
   ]
  },
  {
   "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": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#decals_moc = MOC(filename=\"../../dmu0/dmu0_DECaLS/data/DECaLS_Bootes_MOC.fits\")\n",
    "datafusion_moc = MOC(filename=\"../../dmu0/dmu0_DataFusion-Spitzer/data/Datafusion-Bootes_MOC.fits\")\n",
    "ibis_moc = MOC(filename=\"../../dmu0/dmu0_IBIS/data/IBIS_MLselected_20160801_MOC.fits\")\n",
    "legacy_moc = MOC(filename=\"../../dmu0/dmu0_LegacySurvey/data/LegacySurvey-dr4_Bootes_MOC.fits\")\n",
    "ndwfs_moc = MOC(filename=\"../../dmu0/dmu0_NDWFS/data/NDWFS_MLselected_20160801_MOC.fits\")\n",
    "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_Bootes_v2_MOC.fits\")\n",
    "#sdwfs_moc = MOC(filename=\"../../dmu0/dmu0_SDWFS/data/SDWFS_MLselected_20160801_MOC.fits\")\n",
    "uhs_moc = MOC(filename=\"../../dmu0/dmu0_UHS/data/UHS-DR1_Bootes_MOC.fits\")\n",
    "zbootes_moc = MOC(filename=\"../../dmu0/dmu0_zBootes/data/zBootes_MLselected_20160801_MOC.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    #decals_moc + \n",
    "    ps1_moc + zbootes_moc + legacy_moc + ndwfs_moc) \n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    ibis_moc + uhs_moc \n",
    ")\n",
    "\n",
    "was_observed_mir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    datafusion_moc  # + sdwfs_moc\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#[column[5:] for column in master_catalogue.colnames if 'flag' in column]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
    "  \n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_90prime_z']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_mosaic_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_mosaic_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_mosaic_b']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_90prime_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_90prime_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_mosaic_z']) +\n",
    "    \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_z']) +\n",
    "\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_y'])\n",
    ")\n",
    "\n",
    "nb_nir_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_newfirm_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_newfirm_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_newfirm_k']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_tifkam_ks']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_j']) \n",
    "\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": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "has_optical_flux = nb_optical_flux >= 2\n",
    "has_nir_flux = nb_nir_flux >= 2\n",
    "has_mir_flux = nb_mir_flux >= 2\n",
    "\n",
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * has_optical_flux + 2 * has_nir_flux + 4 * has_mir_flux,\n",
    "        name=\"flag_optnir_det\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IX - Cross-identification table\n",
    "\n",
    "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogues. This can be used to easily get additional information from them.\n",
    "\n",
    "For convenience, we also cross-match the master list with the SDSS catalogue and add the objID associated with each source, if any. **TODO: should we correct the astrometry with respect to Gaia positions?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "242 master list rows had multiple associations.\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Addind SDSS ids\n",
    "#\n",
    "sdss = Table.read(\"../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_Bootes.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": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ps1_id', 'legacy_id', 'ibis_id', 'ndwfs_id', 'uhs_id', 'zbootes_id', 'datafusion_intid', 'help_id', 'specz_id', 'sdss_id']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_bootes_help{}.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": 42,
   "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": 43,
   "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",
    "    \n",
    "columns += [\"stellarity\",  \"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \"flag_optnir_obs\", \n",
    "            \"flag_optnir_det\", \"ebv\", 'zspec_association_flag', 'zspec_qual', 'zspec', \"stellarity_origin\"] \n",
    "#\"zspec\", \"zspec_qual\", \"zspec_association_flag\", \"stellarity_origin\","
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing columns: {'flag_90prime_g', 'flag_gpc1_r', 'flag_newfirm_h', 'flag_newfirm_j', 'flag_90prime_z', 'flag_irac_i4', 'flag_irac_i1', 'flag_gpc1_y', 'flag_mosaic_i', 'flag_gpc1_i', 'flag_gpc1_z', 'flag_irac_i3', 'flag_mosaic_b', 'flag_tifkam_ks', 'flag_ukidss_j', 'flag_mosaic_z', 'flag_90prime_r', 'flag_gpc1_g', 'flag_mosaic_r', 'flag_newfirm_k', 'flag_irac_i2'}\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": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[columns].write(\"{}/master_catalogue_bootes_help{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  }
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