{
 "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",
      "708e28f (Tue May 8 18:05:21 2018 +0100)\n",
      "This notebook was executed on: \n",
      "2018-05-17 15:16:41.603581\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/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/seaborn/apionly.py:6: 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 to the nearest source in the merged catalogue to determine the best crossmatching 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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(legacy['legacy_ra'], legacy['legacy_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, legacy, \"legacy_ra\", \"legacy_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add IBIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(uhs['uhs_ra'], uhs['uhs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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=\"table139626801851360-285555\" 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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>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(\"$('#table139626801851360-285555').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",
       "    $('#table139626801851360-285555').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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": 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_newfirm_k', 'flag_mosaic_i', 'flag_mosaic_b', 'flag_irac_i4', 'flag_90prime_g', 'flag_newfirm_j', 'flag_gpc1_g', 'flag_irac_i1', 'flag_newfirm_h', 'flag_gpc1_r', 'flag_mosaic_z', 'flag_90prime_r', 'flag_irac_i3', 'flag_mosaic_r', 'flag_gpc1_z', 'flag_90prime_z', 'flag_irac_i2', 'flag_tifkam_ks', 'flag_gpc1_i', 'flag_gpc1_y', 'flag_ukidss_j'}\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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