{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# COSMOS master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on COSMOS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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",
      "2019-04-02 16:36:15.244738\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,
    "deletable": true,
    "editable": 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",
    "from collections import OrderedDict\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,
    "deletable": true,
    "editable": 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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## I - Reading the prepared pristine catalogues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#COSMOS was originally run with the official LAigle et al 2015 catalogue \n",
    "#so all those ids and photometry values must be preserved\n",
    "cosmos2015 = Table.read(\"{}/COSMOS2015_HELP.fits\".format(TMP_DIR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "candels = Table.read(\"{}/CANDELS.fits\".format(TMP_DIR))\n",
    "cfhtls = Table.read(\"{}/CFHTLS.fits\".format(TMP_DIR))\n",
    "decals = Table.read(\"{}/DECaLS.fits\".format(TMP_DIR))\n",
    "hsc_deep = Table.read(\"{}/HSC-DEEP.fits\".format(TMP_DIR))\n",
    "hsc_udeep = Table.read(\"{}/HSC-UDEEP.fits\".format(TMP_DIR))\n",
    "kids = Table.read(\"{}/KIDS.fits\".format(TMP_DIR))\n",
    "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))\n",
    "las = Table.read(\"{}/UKIDSS-LAS.fits\".format(TMP_DIR))\n",
    "wirds = Table.read(\"{}/CFHT-WIRDS.fits\".format(TMP_DIR))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## II - Merging tables\n",
    "\n",
    "We first merge the optical catalogues and then add the infrared ones: CANDELS, CFHTLS, DECaLS, HSC, KIDS, PanSTARRS, UKIDSS-LAS, and CFHT-WIRDS.\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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### COSMOS 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = cosmos2015\n",
    "master_catalogue['cosmos_ra'].name = 'ra'\n",
    "master_catalogue['cosmos_dec'].name = 'dec'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Add CANDELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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(candels['candels_ra'], candels['candels_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, candels, \"candels_ra\", \"candels_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Add CFHTLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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(cfhtls['cfhtls_ra'], cfhtls['cfhtls_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, cfhtls, \"cfhtls_ra\", \"cfhtls_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Add DECaLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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(decals['decals_ra'], decals['decals_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": 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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Add HSC-UDEEP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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(hsc_udeep['hsc-udeep_ra'], hsc_udeep['hsc-udeep_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, hsc_udeep, \"hsc-udeep_ra\", \"hsc-udeep_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Add HSC-DEEP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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(hsc_deep['hsc-deep_ra'], hsc_deep['hsc-deep_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, hsc_deep, \"hsc-deep_ra\", \"hsc-deep_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Add KIDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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(kids['kids_ra'], kids['kids_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, kids, \"kids_ra\", \"kids_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Add UKIDSS LAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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(las['las_ra'], las['las_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, las, \"las_ra\", \"las_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Add CFHT-WIRDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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(wirds['wirds_ra'], wirds['wirds_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, wirds, \"wirds_ra\", \"wirds_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Add PanSTARRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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(ps1['ps1_ra'], ps1['ps1_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, ps1, \"ps1_ra\", \"ps1_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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": 25,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    #print(col)\n",
    "    if (col.startswith(\"m_\") \n",
    "        or col.startswith(\"merr_\") \n",
    "        or col.startswith(\"f_\") \n",
    "        or col.startswith(\"ferr_\") \n",
    "        or \"stellarity\" in col):\n",
    "        master_catalogue[col] = master_catalogue[col].astype(float)\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": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table140433137161944-424563\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>help_id</th><th>cosmos_id</th><th>ra</th><th>dec</th><th>cosmos_stellarity</th><th>m_cosmos-vista_ks</th><th>merr_cosmos-vista_ks</th><th>f_ap_cosmos-vista_ks</th><th>ferr_ap_cosmos-vista_ks</th><th>m_cosmos-vista_y</th><th>merr_cosmos-vista_y</th><th>f_ap_cosmos-vista_y</th><th>ferr_ap_cosmos-vista_y</th><th>m_cosmos-vista_h</th><th>merr_cosmos-vista_h</th><th>f_ap_cosmos-vista_h</th><th>ferr_ap_cosmos-vista_h</th><th>m_cosmos-vista_j</th><th>merr_cosmos-vista_j</th><th>f_ap_cosmos-vista_j</th><th>ferr_ap_cosmos-vista_j</th><th>m_cosmos-megacam_u</th><th>merr_cosmos-megacam_u</th><th>f_ap_cosmos-megacam_u</th><th>ferr_ap_cosmos-megacam_u</th><th>m_cosmos-suprime_b</th><th>merr_cosmos-suprime_b</th><th>f_ap_cosmos-suprime_b</th><th>ferr_ap_cosmos-suprime_b</th><th>m_cosmos-suprime_v</th><th>merr_cosmos-suprime_v</th><th>f_ap_cosmos-suprime_v</th><th>ferr_ap_cosmos-suprime_v</th><th>m_cosmos-suprime_ip</th><th>merr_cosmos-suprime_ip</th><th>f_ap_cosmos-suprime_ip</th><th>ferr_ap_cosmos-suprime_ip</th><th>m_cosmos-suprime_rc</th><th>merr_cosmos-suprime_rc</th><th>f_ap_cosmos-suprime_rc</th><th>ferr_ap_cosmos-suprime_rc</th><th>m_cosmos-suprime_zp</th><th>merr_cosmos-suprime_zp</th><th>f_ap_cosmos-suprime_zp</th><th>ferr_ap_cosmos-suprime_zp</th><th>m_cosmos-suprime_zpp</th><th>merr_cosmos-suprime_zpp</th><th>f_ap_cosmos-suprime_zpp</th><th>ferr_ap_cosmos-suprime_zpp</th><th>m_cosmos-suprime_ia484</th><th>merr_cosmos-suprime_ia484</th><th>f_ap_cosmos-suprime_ia484</th><th>ferr_ap_cosmos-suprime_ia484</th><th>m_cosmos-suprime_ia527</th><th>merr_cosmos-suprime_ia527</th><th>f_ap_cosmos-suprime_ia527</th><th>ferr_ap_cosmos-suprime_ia527</th><th>m_cosmos-suprime_ia624</th><th>merr_cosmos-suprime_ia624</th><th>f_ap_cosmos-suprime_ia624</th><th>ferr_ap_cosmos-suprime_ia624</th><th>m_cosmos-suprime_ia679</th><th>merr_cosmos-suprime_ia679</th><th>f_ap_cosmos-suprime_ia679</th><th>ferr_ap_cosmos-suprime_ia679</th><th>m_cosmos-suprime_ia738</th><th>merr_cosmos-suprime_ia738</th><th>f_ap_cosmos-suprime_ia738</th><th>ferr_ap_cosmos-suprime_ia738</th><th>m_cosmos-suprime_ia767</th><th>merr_cosmos-suprime_ia767</th><th>f_ap_cosmos-suprime_ia767</th><th>ferr_ap_cosmos-suprime_ia767</th><th>m_cosmos-suprime_ib427</th><th>merr_cosmos-suprime_ib427</th><th>f_ap_cosmos-suprime_ib427</th><th>ferr_ap_cosmos-suprime_ib427</th><th>m_cosmos-suprime_ib464</th><th>merr_cosmos-suprime_ib464</th><th>f_ap_cosmos-suprime_ib464</th><th>ferr_ap_cosmos-suprime_ib464</th><th>m_cosmos-suprime_ib505</th><th>merr_cosmos-suprime_ib505</th><th>f_ap_cosmos-suprime_ib505</th><th>ferr_ap_cosmos-suprime_ib505</th><th>m_cosmos-suprime_ib574</th><th>merr_cosmos-suprime_ib574</th><th>f_ap_cosmos-suprime_ib574</th><th>ferr_ap_cosmos-suprime_ib574</th><th>m_cosmos-suprime_ib709</th><th>merr_cosmos-suprime_ib709</th><th>f_ap_cosmos-suprime_ib709</th><th>ferr_ap_cosmos-suprime_ib709</th><th>m_cosmos-suprime_ib827</th><th>merr_cosmos-suprime_ib827</th><th>f_ap_cosmos-suprime_ib827</th><th>ferr_ap_cosmos-suprime_ib827</th><th>m_cosmos-suprime_nb711</th><th>merr_cosmos-suprime_nb711</th><th>f_ap_cosmos-suprime_nb711</th><th>ferr_ap_cosmos-suprime_nb711</th><th>m_cosmos-suprime_nb816</th><th>merr_cosmos-suprime_nb816</th><th>f_ap_cosmos-suprime_nb816</th><th>ferr_ap_cosmos-suprime_nb816</th><th>m_cosmos-wircam_h</th><th>merr_cosmos-wircam_h</th><th>f_ap_cosmos-wircam_h</th><th>ferr_ap_cosmos-wircam_h</th><th>m_cosmos-wircam_ks</th><th>merr_cosmos-wircam_ks</th><th>f_ap_cosmos-wircam_ks</th><th>ferr_ap_cosmos-wircam_ks</th><th>m_cosmos-suprime_y</th><th>merr_cosmos-suprime_y</th><th>f_ap_cosmos-suprime_y</th><th>ferr_ap_cosmos-suprime_y</th><th>f_cosmos-irac_i1</th><th>ferr_cosmos-irac_i1</th><th>m_cosmos-irac_i1</th><th>merr_cosmos-irac_i1</th><th>f_cosmos-irac_i2</th><th>ferr_cosmos-irac_i2</th><th>m_cosmos-irac_i2</th><th>merr_cosmos-irac_i2</th><th>f_cosmos-irac_i3</th><th>ferr_cosmos-irac_i3</th><th>m_cosmos-irac_i3</th><th>merr_cosmos-irac_i3</th><th>f_cosmos-irac_i4</th><th>ferr_cosmos-irac_i4</th><th>m_cosmos-irac_i4</th><th>merr_cosmos-irac_i4</th><th>f_cosmos-vista_ks</th><th>ferr_cosmos-vista_ks</th><th>flag_cosmos-vista_ks</th><th>m_ap_cosmos-vista_ks</th><th>merr_ap_cosmos-vista_ks</th><th>f_cosmos-vista_y</th><th>ferr_cosmos-vista_y</th><th>flag_cosmos-vista_y</th><th>m_ap_cosmos-vista_y</th><th>merr_ap_cosmos-vista_y</th><th>f_cosmos-vista_h</th><th>ferr_cosmos-vista_h</th><th>flag_cosmos-vista_h</th><th>m_ap_cosmos-vista_h</th><th>merr_ap_cosmos-vista_h</th><th>f_cosmos-vista_j</th><th>ferr_cosmos-vista_j</th><th>flag_cosmos-vista_j</th><th>m_ap_cosmos-vista_j</th><th>merr_ap_cosmos-vista_j</th><th>f_cosmos-megacam_u</th><th>ferr_cosmos-megacam_u</th><th>flag_cosmos-megacam_u</th><th>m_ap_cosmos-megacam_u</th><th>merr_ap_cosmos-megacam_u</th><th>f_cosmos-suprime_b</th><th>ferr_cosmos-suprime_b</th><th>flag_cosmos-suprime_b</th><th>m_ap_cosmos-suprime_b</th><th>merr_ap_cosmos-suprime_b</th><th>f_cosmos-suprime_v</th><th>ferr_cosmos-suprime_v</th><th>flag_cosmos-suprime_v</th><th>m_ap_cosmos-suprime_v</th><th>merr_ap_cosmos-suprime_v</th><th>f_cosmos-suprime_ip</th><th>ferr_cosmos-suprime_ip</th><th>flag_cosmos-suprime_ip</th><th>m_ap_cosmos-suprime_ip</th><th>merr_ap_cosmos-suprime_ip</th><th>f_cosmos-suprime_rc</th><th>ferr_cosmos-suprime_rc</th><th>flag_cosmos-suprime_rc</th><th>m_ap_cosmos-suprime_rc</th><th>merr_ap_cosmos-suprime_rc</th><th>f_cosmos-suprime_zp</th><th>ferr_cosmos-suprime_zp</th><th>flag_cosmos-suprime_zp</th><th>m_ap_cosmos-suprime_zp</th><th>merr_ap_cosmos-suprime_zp</th><th>f_cosmos-suprime_zpp</th><th>ferr_cosmos-suprime_zpp</th><th>flag_cosmos-suprime_zpp</th><th>m_ap_cosmos-suprime_zpp</th><th>merr_ap_cosmos-suprime_zpp</th><th>f_cosmos-suprime_ia484</th><th>ferr_cosmos-suprime_ia484</th><th>flag_cosmos-suprime_ia484</th><th>m_ap_cosmos-suprime_ia484</th><th>merr_ap_cosmos-suprime_ia484</th><th>f_cosmos-suprime_ia527</th><th>ferr_cosmos-suprime_ia527</th><th>flag_cosmos-suprime_ia527</th><th>m_ap_cosmos-suprime_ia527</th><th>merr_ap_cosmos-suprime_ia527</th><th>f_cosmos-suprime_ia624</th><th>ferr_cosmos-suprime_ia624</th><th>flag_cosmos-suprime_ia624</th><th>m_ap_cosmos-suprime_ia624</th><th>merr_ap_cosmos-suprime_ia624</th><th>f_cosmos-suprime_ia679</th><th>ferr_cosmos-suprime_ia679</th><th>flag_cosmos-suprime_ia679</th><th>m_ap_cosmos-suprime_ia679</th><th>merr_ap_cosmos-suprime_ia679</th><th>f_cosmos-suprime_ia738</th><th>ferr_cosmos-suprime_ia738</th><th>flag_cosmos-suprime_ia738</th><th>m_ap_cosmos-suprime_ia738</th><th>merr_ap_cosmos-suprime_ia738</th><th>f_cosmos-suprime_ia767</th><th>ferr_cosmos-suprime_ia767</th><th>flag_cosmos-suprime_ia767</th><th>m_ap_cosmos-suprime_ia767</th><th>merr_ap_cosmos-suprime_ia767</th><th>f_cosmos-suprime_ib427</th><th>ferr_cosmos-suprime_ib427</th><th>flag_cosmos-suprime_ib427</th><th>m_ap_cosmos-suprime_ib427</th><th>merr_ap_cosmos-suprime_ib427</th><th>f_cosmos-suprime_ib464</th><th>ferr_cosmos-suprime_ib464</th><th>flag_cosmos-suprime_ib464</th><th>m_ap_cosmos-suprime_ib464</th><th>merr_ap_cosmos-suprime_ib464</th><th>f_cosmos-suprime_ib505</th><th>ferr_cosmos-suprime_ib505</th><th>flag_cosmos-suprime_ib505</th><th>m_ap_cosmos-suprime_ib505</th><th>merr_ap_cosmos-suprime_ib505</th><th>f_cosmos-suprime_ib574</th><th>ferr_cosmos-suprime_ib574</th><th>flag_cosmos-suprime_ib574</th><th>m_ap_cosmos-suprime_ib574</th><th>merr_ap_cosmos-suprime_ib574</th><th>f_cosmos-suprime_ib709</th><th>ferr_cosmos-suprime_ib709</th><th>flag_cosmos-suprime_ib709</th><th>m_ap_cosmos-suprime_ib709</th><th>merr_ap_cosmos-suprime_ib709</th><th>f_cosmos-suprime_ib827</th><th>ferr_cosmos-suprime_ib827</th><th>flag_cosmos-suprime_ib827</th><th>m_ap_cosmos-suprime_ib827</th><th>merr_ap_cosmos-suprime_ib827</th><th>f_cosmos-suprime_nb711</th><th>ferr_cosmos-suprime_nb711</th><th>flag_cosmos-suprime_nb711</th><th>m_ap_cosmos-suprime_nb711</th><th>merr_ap_cosmos-suprime_nb711</th><th>f_cosmos-suprime_nb816</th><th>ferr_cosmos-suprime_nb816</th><th>flag_cosmos-suprime_nb816</th><th>m_ap_cosmos-suprime_nb816</th><th>merr_ap_cosmos-suprime_nb816</th><th>f_cosmos-wircam_h</th><th>ferr_cosmos-wircam_h</th><th>flag_cosmos-wircam_h</th><th>m_ap_cosmos-wircam_h</th><th>merr_ap_cosmos-wircam_h</th><th>f_cosmos-wircam_ks</th><th>ferr_cosmos-wircam_ks</th><th>flag_cosmos-wircam_ks</th><th>m_ap_cosmos-wircam_ks</th><th>merr_ap_cosmos-wircam_ks</th><th>f_cosmos-suprime_y</th><th>ferr_cosmos-suprime_y</th><th>flag_cosmos-suprime_y</th><th>m_ap_cosmos-suprime_y</th><th>merr_ap_cosmos-suprime_y</th><th>flag_cosmos-irac_i1</th><th>flag_cosmos-irac_i2</th><th>flag_cosmos-irac_i3</th><th>flag_cosmos-irac_i4</th><th>ps1_flag_cleaned</th><th>cosmos_flag_gaia</th><th>flag_merged</th><th>candels_id</th><th>candels_stellarity</th><th>f_ap_candels_f140w</th><th>ferr_ap_candels_f140w</th><th>f_candels_f140w</th><th>ferr_candels_f140w</th><th>f_ap_candels_f160w</th><th>ferr_ap_candels_f160w</th><th>f_candels_f160w</th><th>ferr_candels_f160w</th><th>f_candels_f606w</th><th>ferr_candels_f606w</th><th>f_candels_f814w</th><th>ferr_candels_f814w</th><th>f_candels_f125w</th><th>ferr_candels_f125w</th><th>m_ap_candels_f140w</th><th>merr_ap_candels_f140w</th><th>m_candels_f140w</th><th>merr_candels_f140w</th><th>flag_candels_f140w</th><th>m_ap_candels_f160w</th><th>merr_ap_candels_f160w</th><th>m_candels_f160w</th><th>merr_candels_f160w</th><th>flag_candels_f160w</th><th>m_candels_f606w</th><th>merr_candels_f606w</th><th>flag_candels_f606w</th><th>m_candels_f814w</th><th>merr_candels_f814w</th><th>flag_candels_f814w</th><th>m_candels_f125w</th><th>merr_candels_f125w</th><th>flag_candels_f125w</th><th>candels_flag_cleaned</th><th>candels_flag_gaia</th><th>cfhtls_id</th><th>cfhtls_stellarity</th><th>m_megacam_u</th><th>merr_megacam_u</th><th>m_megacam_g</th><th>merr_megacam_g</th><th>m_megacam_r</th><th>merr_megacam_r</th><th>m_megacam_i</th><th>merr_megacam_i</th><th>m_megacam_z</th><th>merr_megacam_z</th><th>m_ap_megacam_u</th><th>merr_ap_megacam_u</th><th>m_ap_megacam_g</th><th>merr_ap_megacam_g</th><th>m_ap_megacam_r</th><th>merr_ap_megacam_r</th><th>m_ap_megacam_i</th><th>merr_ap_megacam_i</th><th>m_ap_megacam_z</th><th>merr_ap_megacam_z</th><th>f_megacam_u</th><th>ferr_megacam_u</th><th>flag_megacam_u</th><th>f_megacam_g</th><th>ferr_megacam_g</th><th>flag_megacam_g</th><th>f_megacam_r</th><th>ferr_megacam_r</th><th>flag_megacam_r</th><th>f_megacam_i</th><th>ferr_megacam_i</th><th>flag_megacam_i</th><th>f_megacam_z</th><th>ferr_megacam_z</th><th>flag_megacam_z</th><th>f_ap_megacam_u</th><th>ferr_ap_megacam_u</th><th>f_ap_megacam_g</th><th>ferr_ap_megacam_g</th><th>f_ap_megacam_r</th><th>ferr_ap_megacam_r</th><th>f_ap_megacam_i</th><th>ferr_ap_megacam_i</th><th>f_ap_megacam_z</th><th>ferr_ap_megacam_z</th><th>cfhtls_flag_cleaned</th><th>cfhtls_flag_gaia</th><th>decals_id</th><th>f_decam_g</th><th>f_decam_r</th><th>f_decam_z</th><th>ferr_decam_g</th><th>ferr_decam_r</th><th>ferr_decam_z</th><th>f_ap_decam_g</th><th>f_ap_decam_r</th><th>f_ap_decam_z</th><th>ferr_ap_decam_g</th><th>ferr_ap_decam_r</th><th>ferr_ap_decam_z</th><th>m_decam_g</th><th>merr_decam_g</th><th>flag_decam_g</th><th>m_decam_r</th><th>merr_decam_r</th><th>flag_decam_r</th><th>m_decam_z</th><th>merr_decam_z</th><th>flag_decam_z</th><th>m_ap_decam_g</th><th>merr_ap_decam_g</th><th>m_ap_decam_r</th><th>merr_ap_decam_r</th><th>m_ap_decam_z</th><th>merr_ap_decam_z</th><th>decals_stellarity</th><th>decals_flag_cleaned</th><th>decals_flag_gaia</th><th>hsc-udeep_id</th><th>m_ap_hsc-udeep_g</th><th>merr_ap_hsc-udeep_g</th><th>m_hsc-udeep_g</th><th>merr_hsc-udeep_g</th><th>m_ap_hsc-udeep_r</th><th>merr_ap_hsc-udeep_r</th><th>m_hsc-udeep_r</th><th>merr_hsc-udeep_r</th><th>m_ap_hsc-udeep_i</th><th>merr_ap_hsc-udeep_i</th><th>m_hsc-udeep_i</th><th>merr_hsc-udeep_i</th><th>m_ap_hsc-udeep_z</th><th>merr_ap_hsc-udeep_z</th><th>m_hsc-udeep_z</th><th>merr_hsc-udeep_z</th><th>m_ap_hsc-udeep_y</th><th>merr_ap_hsc-udeep_y</th><th>m_hsc-udeep_y</th><th>merr_hsc-udeep_y</th><th>m_ap_hsc-udeep_n921</th><th>merr_ap_hsc-udeep_n921</th><th>m_hsc-udeep_n921</th><th>merr_hsc-udeep_n921</th><th>hsc-udeep_stellarity</th><th>f_ap_hsc-udeep_g</th><th>ferr_ap_hsc-udeep_g</th><th>f_hsc-udeep_g</th><th>ferr_hsc-udeep_g</th><th>flag_hsc-udeep_g</th><th>f_ap_hsc-udeep_r</th><th>ferr_ap_hsc-udeep_r</th><th>f_hsc-udeep_r</th><th>ferr_hsc-udeep_r</th><th>flag_hsc-udeep_r</th><th>f_ap_hsc-udeep_i</th><th>ferr_ap_hsc-udeep_i</th><th>f_hsc-udeep_i</th><th>ferr_hsc-udeep_i</th><th>flag_hsc-udeep_i</th><th>f_ap_hsc-udeep_z</th><th>ferr_ap_hsc-udeep_z</th><th>f_hsc-udeep_z</th><th>ferr_hsc-udeep_z</th><th>flag_hsc-udeep_z</th><th>f_ap_hsc-udeep_y</th><th>ferr_ap_hsc-udeep_y</th><th>f_hsc-udeep_y</th><th>ferr_hsc-udeep_y</th><th>flag_hsc-udeep_y</th><th>f_ap_hsc-udeep_n921</th><th>ferr_ap_hsc-udeep_n921</th><th>f_hsc-udeep_n921</th><th>ferr_hsc-udeep_n921</th><th>flag_hsc-udeep_n921</th><th>hsc-udeep_flag_cleaned</th><th>hsc-udeep_flag_gaia</th><th>hsc-deep_id</th><th>m_ap_hsc-deep_g</th><th>merr_ap_hsc-deep_g</th><th>m_hsc-deep_g</th><th>merr_hsc-deep_g</th><th>m_ap_hsc-deep_r</th><th>merr_ap_hsc-deep_r</th><th>m_hsc-deep_r</th><th>merr_hsc-deep_r</th><th>m_ap_hsc-deep_i</th><th>merr_ap_hsc-deep_i</th><th>m_hsc-deep_i</th><th>merr_hsc-deep_i</th><th>m_ap_hsc-deep_z</th><th>merr_ap_hsc-deep_z</th><th>m_hsc-deep_z</th><th>merr_hsc-deep_z</th><th>m_ap_hsc-deep_y</th><th>merr_ap_hsc-deep_y</th><th>m_hsc-deep_y</th><th>merr_hsc-deep_y</th><th>m_ap_hsc-deep_n921</th><th>merr_ap_hsc-deep_n921</th><th>m_hsc-deep_n921</th><th>merr_hsc-deep_n921</th><th>hsc-deep_stellarity</th><th>f_ap_hsc-deep_g</th><th>ferr_ap_hsc-deep_g</th><th>f_hsc-deep_g</th><th>ferr_hsc-deep_g</th><th>flag_hsc-deep_g</th><th>f_ap_hsc-deep_r</th><th>ferr_ap_hsc-deep_r</th><th>f_hsc-deep_r</th><th>ferr_hsc-deep_r</th><th>flag_hsc-deep_r</th><th>f_ap_hsc-deep_i</th><th>ferr_ap_hsc-deep_i</th><th>f_hsc-deep_i</th><th>ferr_hsc-deep_i</th><th>flag_hsc-deep_i</th><th>f_ap_hsc-deep_z</th><th>ferr_ap_hsc-deep_z</th><th>f_hsc-deep_z</th><th>ferr_hsc-deep_z</th><th>flag_hsc-deep_z</th><th>f_ap_hsc-deep_y</th><th>ferr_ap_hsc-deep_y</th><th>f_hsc-deep_y</th><th>ferr_hsc-deep_y</th><th>flag_hsc-deep_y</th><th>f_ap_hsc-deep_n921</th><th>ferr_ap_hsc-deep_n921</th><th>f_hsc-deep_n921</th><th>ferr_hsc-deep_n921</th><th>flag_hsc-deep_n921</th><th>hsc-deep_flag_cleaned</th><th>hsc-deep_flag_gaia</th><th>kids_id</th><th>kids_stellarity</th><th>m_kids_u</th><th>merr_kids_u</th><th>m_kids_g</th><th>merr_kids_g</th><th>m_kids_r</th><th>merr_kids_r</th><th>m_kids_i</th><th>merr_kids_i</th><th>f_ap_kids_u</th><th>ferr_ap_kids_u</th><th>f_ap_kids_g</th><th>ferr_ap_kids_g</th><th>f_ap_kids_r</th><th>ferr_ap_kids_r</th><th>f_ap_kids_i</th><th>ferr_ap_kids_i</th><th>f_kids_u</th><th>ferr_kids_u</th><th>flag_kids_u</th><th>f_kids_g</th><th>ferr_kids_g</th><th>flag_kids_g</th><th>f_kids_r</th><th>ferr_kids_r</th><th>flag_kids_r</th><th>f_kids_i</th><th>ferr_kids_i</th><th>flag_kids_i</th><th>m_ap_kids_u</th><th>merr_ap_kids_u</th><th>m_ap_kids_g</th><th>merr_ap_kids_g</th><th>m_ap_kids_r</th><th>merr_ap_kids_r</th><th>m_ap_kids_i</th><th>merr_ap_kids_i</th><th>kids_flag_cleaned</th><th>kids_flag_gaia</th><th>las_id</th><th>m_ukidss_y</th><th>merr_ukidss_y</th><th>m_ap_ukidss_y</th><th>merr_ap_ukidss_y</th><th>m_ukidss_j</th><th>merr_ukidss_j</th><th>m_ap_ukidss_j</th><th>merr_ap_ukidss_j</th><th>m_ap_ukidss_h</th><th>merr_ap_ukidss_h</th><th>m_ukidss_h</th><th>merr_ukidss_h</th><th>m_ap_ukidss_k</th><th>merr_ap_ukidss_k</th><th>m_ukidss_k</th><th>merr_ukidss_k</th><th>las_stellarity</th><th>f_ukidss_y</th><th>ferr_ukidss_y</th><th>flag_ukidss_y</th><th>f_ap_ukidss_y</th><th>ferr_ap_ukidss_y</th><th>f_ukidss_j</th><th>ferr_ukidss_j</th><th>flag_ukidss_j</th><th>f_ap_ukidss_j</th><th>ferr_ap_ukidss_j</th><th>f_ap_ukidss_h</th><th>ferr_ap_ukidss_h</th><th>f_ukidss_h</th><th>ferr_ukidss_h</th><th>flag_ukidss_h</th><th>f_ap_ukidss_k</th><th>ferr_ap_ukidss_k</th><th>f_ukidss_k</th><th>ferr_ukidss_k</th><th>flag_ukidss_k</th><th>las_flag_cleaned</th><th>las_flag_gaia</th><th>wirds_id</th><th>m_wirds_u</th><th>merr_wirds_u</th><th>m_ap_wirds_u</th><th>merr_ap_wirds_u</th><th>m_wirds_g</th><th>merr_wirds_g</th><th>m_ap_wirds_g</th><th>merr_ap_wirds_g</th><th>m_wirds_r</th><th>merr_wirds_r</th><th>m_ap_wirds_r</th><th>merr_ap_wirds_r</th><th>m_wirds_i</th><th>merr_wirds_i</th><th>m_ap_wirds_i</th><th>merr_ap_wirds_i</th><th>m_wirds_z</th><th>merr_wirds_z</th><th>m_ap_wirds_z</th><th>merr_ap_wirds_z</th><th>m_wirds_j</th><th>merr_wirds_j</th><th>m_ap_wirds_j</th><th>merr_ap_wirds_j</th><th>m_wirds_h</th><th>merr_wirds_h</th><th>m_ap_wirds_h</th><th>merr_ap_wirds_h</th><th>m_wirds_k</th><th>merr_wirds_k</th><th>m_ap_wirds_k</th><th>merr_ap_wirds_k</th><th>f_wirds_u</th><th>ferr_wirds_u</th><th>flag_wirds_u</th><th>f_ap_wirds_u</th><th>ferr_ap_wirds_u</th><th>f_wirds_g</th><th>ferr_wirds_g</th><th>flag_wirds_g</th><th>f_ap_wirds_g</th><th>ferr_ap_wirds_g</th><th>f_wirds_r</th><th>ferr_wirds_r</th><th>flag_wirds_r</th><th>f_ap_wirds_r</th><th>ferr_ap_wirds_r</th><th>f_wirds_i</th><th>ferr_wirds_i</th><th>flag_wirds_i</th><th>f_ap_wirds_i</th><th>ferr_ap_wirds_i</th><th>f_wirds_z</th><th>ferr_wirds_z</th><th>flag_wirds_z</th><th>f_ap_wirds_z</th><th>ferr_ap_wirds_z</th><th>f_wirds_j</th><th>ferr_wirds_j</th><th>flag_wirds_j</th><th>f_ap_wirds_j</th><th>ferr_ap_wirds_j</th><th>f_wirds_h</th><th>ferr_wirds_h</th><th>flag_wirds_h</th><th>f_ap_wirds_h</th><th>ferr_ap_wirds_h</th><th>f_wirds_k</th><th>ferr_wirds_k</th><th>flag_wirds_k</th><th>f_ap_wirds_k</th><th>ferr_ap_wirds_k</th><th>wirds_flag_cleaned</th><th>cfht-wirds_flag_gaia</th><th>ps1_flag_cleaned_1</th><th>ps1_id</th><th>m_ap_gpc1_g</th><th>merr_ap_gpc1_g</th><th>m_gpc1_g</th><th>merr_gpc1_g</th><th>m_ap_gpc1_r</th><th>merr_ap_gpc1_r</th><th>m_gpc1_r</th><th>merr_gpc1_r</th><th>m_ap_gpc1_i</th><th>merr_ap_gpc1_i</th><th>m_gpc1_i</th><th>merr_gpc1_i</th><th>m_ap_gpc1_z</th><th>merr_ap_gpc1_z</th><th>m_gpc1_z</th><th>merr_gpc1_z</th><th>m_ap_gpc1_y</th><th>merr_ap_gpc1_y</th><th>m_gpc1_y</th><th>merr_gpc1_y</th><th>f_ap_gpc1_g</th><th>ferr_ap_gpc1_g</th><th>f_gpc1_g</th><th>ferr_gpc1_g</th><th>flag_gpc1_g</th><th>f_ap_gpc1_r</th><th>ferr_ap_gpc1_r</th><th>f_gpc1_r</th><th>ferr_gpc1_r</th><th>flag_gpc1_r</th><th>f_ap_gpc1_i</th><th>ferr_ap_gpc1_i</th><th>f_gpc1_i</th><th>ferr_gpc1_i</th><th>flag_gpc1_i</th><th>f_ap_gpc1_z</th><th>ferr_ap_gpc1_z</th><th>f_gpc1_z</th><th>ferr_gpc1_z</th><th>flag_gpc1_z</th><th>f_ap_gpc1_y</th><th>ferr_ap_gpc1_y</th><th>f_gpc1_y</th><th>ferr_gpc1_y</th><th>flag_gpc1_y</th><th>ps1_flag_cleaned_2</th><th>ps1_flag_gaia</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th>deg</th><th>deg</th><th></th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</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></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></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>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></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><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>HELP_J095914.43+021600.80</td><td>COSMOS2015_J095914.43+021600.80</td><td>149.810113605091</td><td>2.26688583985786</td><td>1.0</td><td>nan</td><td>nan</td><td>-2.224829950137064e-05</td><td>0.032126300036907196</td><td>nan</td><td>nan</td><td>0.03079020045697689</td><td>0.04474860057234764</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>26.325700759887695</td><td>0.3331409990787506</td><td>0.08171340078115463</td><td>0.06977520138025284</td><td>27.722900390625</td><td>0.3792949914932251</td><td>-0.0016348300268873572</td><td>0.016533099114894867</td><td>29.33679962158203</td><td>1.4130500555038452</td><td>0.00668556010350585</td><td>0.009602379985153675</td><td>27.513700485229492</td><td>0.4707289934158325</td><td>0.013516400009393692</td><td>0.018334299325942993</td><td>27.704099655151367</td><td>0.8654019832611084</td><td>0.01880040019750595</td><td>0.021642500534653664</td><td>27.51580047607422</td><td>0.45097699761390686</td><td>0.024797800928354263</td><td>0.014752100221812725</td><td>nan</td><td>nan</td><td>-0.01803950034081936</td><td>0.14826600253582</td><td>27.11319923400879</td><td>0.5287179946899414</td><td>0.021216999739408493</td><td>0.03574170172214508</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>31.638999938964844</td><td>38.73860168457031</td><td>-0.002777819987386465</td><td>0.03039270080626011</td><td>27.128999710083008</td><td>0.8493750095367432</td><td>0.042619798332452774</td><td>0.03905389830470085</td><td>25.626100540161133</td><td>0.2046239972114563</td><td>0.07468000054359436</td><td>0.05422620102763176</td><td>nan</td><td>nan</td><td>-0.04015880078077316</td><td>0.04943649843335152</td><td>nan</td><td>nan</td><td>-0.032903898507356644</td><td>0.06257860362529755</td><td>nan</td><td>nan</td><td>-0.017604200169444084</td><td>0.033940400928258896</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-0.03568980097770691</td><td>0.04103970155119896</td><td>32.57509994506836</td><td>129.8509979248047</td><td>0.032395098358392715</td><td>0.054776500910520554</td><td>nan</td><td>nan</td><td>-0.03633879870176315</td><td>0.04105839878320694</td><td>nan</td><td>nan</td><td>-0.023999100551009178</td><td>0.06253539770841599</td><td>nan</td><td>nan</td><td>0.03730649873614311</td><td>0.07784759998321533</td><td>nan</td><td>nan</td><td>-0.007673860061913729</td><td>0.06440850347280502</td><td>25.470399856567383</td><td>0.9133350253105164</td><td>0.09047289937734604</td><td>0.2874850034713745</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.030214399099349976</td><td>0.12773899734020233</td><td>0.18637099862098694</td><td>0.09500239789485931</td><td>25.72410011291504</td><td>0.5534539818763733</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.0</td><td>2.30679988861084</td><td>nan</td><td>nan</td><td>0.0</td><td>2.6868700981140137</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1567.791259765625</td><td>nan</td><td>nan</td><td>False</td><td>27.678970336914062</td><td>1.5779427289962769</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.10708270967006683</td><td>0.03285663574934006</td><td>False</td><td>26.61927032470703</td><td>0.9271118640899658</td><td>0.029569195583462715</td><td>0.010329809039831161</td><td>False</td><td>nan</td><td>-10.980091094970703</td><td>0.006687752436846495</td><td>0.008703891187906265</td><td>False</td><td>29.337158203125</td><td>1.5594282150268555</td><td>0.03585251420736313</td><td>0.015544123947620392</td><td>False</td><td>28.572845458984375</td><td>1.4727451801300049</td><td>0.03008570522069931</td><td>0.02398025244474411</td><td>False</td><td>28.214584350585938</td><td>1.249869465827942</td><td>0.03578322380781174</td><td>0.01486310362815857</td><td>False</td><td>27.913970947265625</td><td>0.6458995938301086</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-8.923625946044922</td><td>0.05184659734368324</td><td>0.025247598066926003</td><td>False</td><td>28.083290100097656</td><td>1.829007863998413</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.0008024165290407836</td><td>0.028629876673221588</td><td>False</td><td>nan</td><td>-11.879262924194336</td><td>0.05109751597046852</td><td>0.039973750710487366</td><td>False</td><td>27.325973510742188</td><td>0.9948950409889221</td><td>0.20396696031093597</td><td>0.0384407714009285</td><td>False</td><td>26.716995239257812</td><td>0.788368284702301</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.3365685939788818</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.064918041229248</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.093268871307373</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.248487949371338</td><td>0.0003388127079233527</td><td>0.0405210442841053</td><td>False</td><td>27.623802185058594</td><td>1.835858702659607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False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       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n</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>2</td><td>HELP_J095901.32+013751.84</td><td>COSMOS2015_J095901.32+013751.84</td><td>149.755485395101</td><td>1.6310639914812002</td><td>1.0</td><td>nan</td><td>nan</td><td>-6.758159724995494e-05</td><td>0.05155399814248085</td><td>24.692399978637695</td><td>0.1327420026063919</td><td>0.1425189971923828</td><td>0.060148000717163086</td><td>nan</td><td>nan</td><td>0.06490419805049896</td><td>0.13322700560092926</td><td>25.940799713134766</td><td>0.7053369879722595</td><td>0.18414899706840515</td><td>0.09476310014724731</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>27.590200424194336</td><td>0.5079659819602966</td><td>0.016207899898290634</td><td>0.00927828997373581</td><td>30.997800827026367</td><td>21.499399185180664</td><td>-0.012470300309360027</td><td>0.01731259934604168</td><td>nan</td><td>nan</td><td>0.04085579887032509</td><td>0.021752899512648582</td><td>nan</td><td>nan</td><td>-0.006400519981980324</td><td>0.013447700068354607</td><td>nan</td><td>nan</td><td>0.009490719996392727</td><td>0.1475369930267334</td><td>nan</td><td>nan</td><td>-0.03490870073437691</td><td>0.03556070104241371</td><td>26.94179916381836</td><td>0.9450860023498535</td><td>0.013661700300872326</td><td>0.03253659978508949</td><td>nan</td><td>nan</td><td>0.00808280985802412</td><td>0.02702159993350506</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.010754699818789959</td><td>0.05200650170445442</td><td>nan</td><td>nan</td><td>0.03724319860339165</td><td>0.044126201421022415</td><td>nan</td><td>nan</td><td>-0.02793310023844242</td><td>0.05926689878106117</td><td>nan</td><td>nan</td><td>0.011077700182795525</td><td>0.03365220129489899</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>26.010499954223633</td><td>0.48765799403190613</td><td>-0.024452200159430504</td><td>0.037741899490356445</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>26.359800338745117</td><td>0.6471790075302124</td><td>0.08777429908514023</td><td>0.03883260115981102</td><td>nan</td><td>nan</td><td>0.0013823099434375763</td><td>0.0646786019206047</td><td>25.993200302124023</td><td>1.1401100158691406</td><td>-0.03528809919953346</td><td>0.0687372013926506</td><td>nan</td><td>nan</td><td>-0.04942389950156212</td><td>0.06503500044345856</td><td>23.792200088500977</td><td>0.472476989030838</td><td>0.06117840111255646</td><td>0.35899001359939575</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>27.550100326538086</td><td>7.799170017242432</td><td>0.06922180205583572</td><td>0.1952970027923584</td><td>0.5253660082817078</td><td>0.06504180282354355</td><td>24.598800659179688</td><td>0.13441699743270874</td><td>0.7699800133705139</td><td>0.07242629677057266</td><td>24.183799743652344</td><td>0.10212700068950653</td><td>0.0</td><td>2.0552799701690674</td><td>nan</td><td>nan</td><td>0.0</td><td>3.1570699214935303</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-828.2437744140625</td><td>0.48199182748794556</td><td>0.05892827361822128</td><td>False</td><td>26.01531982421875</td><td>0.45821863412857056</td><td>nan</td><td>nan</td><td>False</td><td>26.86931610107422</td><td>2.2286598682403564</td><td>0.1526441127061844</td><td>0.09916362166404724</td><td>False</td><td>25.737075805664062</td><td>0.5587199926376343</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.03341331332921982</td><td>0.015632551163434982</td><td>False</td><td>28.375686645507812</td><td>0.6215348839759827</td><td>0.001448368770070374</td><td>0.02868013270199299</td><td>False</td><td>nan</td><td>-1.5073344707489014</td><td>nan</td><td>nan</td><td>False</td><td>27.371864318847656</td><td>0.5780797600746155</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.2811667919158936</td><td>nan</td><td>nan</td><td>False</td><td>28.956756591796875</td><td>16.878196716308594</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.1060147285461426</td><td>0.06071281433105469</td><td>0.05284785479307175</td><td>False</td><td>28.561241149902344</td><td>2.5857808589935303</td><td>nan</td><td>nan</td><td>False</td><td>29.13109588623047</td><td>3.629718780517578</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>28.821006774902344</td><td>5.2502946853637695</td><td>nan</td><td>nan</td><td>False</td><td>27.472381591796875</td><td>1.286393642425537</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.303654670715332</td><td>nan</td><td>nan</td><td>False</td><td>28.78887939453125</td><td>3.2982850074768066</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.14315280318260193</td><td>0.06429702788591385</td><td>False</td><td>nan</td><td>-1.6758304834365845</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.10377193242311478</td><td>0.0618557445704937</td><td>False</td><td>26.541587829589844</td><td>0.4803451597690582</td><td>nan</td><td>nan</td><td>False</td><td>31.048484802246094</td><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d>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>3</td><td>HELP_J095824.46+014508.83</td><td>COSMOS2015_J095824.46+014508.83</td><td>149.601900709348</td><td>1.75245014896677</td><td>1.0</td><td>27.62619972229004</td><td>4.17363977432251</td><td>-0.000207357996259816</td><td>0.09525839984416962</td><td>25.234100341796875</td><td>0.16923700273036957</td><td>0.18030600249767303</td><td>0.09816049784421921</td><td>25.900400161743164</td><td>0.5140020251274109</td><td>0.212567999958992</td><td>0.13914600014686584</td><td>28.43709945678711</td><td>3.981489896774292</td><td>-0.0126521997153759</td><td>0.043021101504564285</td><td>26.336200714111328</td><td>0.13953399658203125</td><td>0.08034829795360565</td><td>0.02400919981300831</td><td>26.257400512695312</td><td>0.09491019695997238</td><td>0.08275499939918518</td><td>0.012464799918234348</td><td>26.413700103759766</td><td>0.1781580001115799</td><td>0.07929970324039459</td><td>0.022014500573277473</td><td>27.014699935913086</td><td>0.5597950220108032</td><td>0.051746200770139694</td><td>0.02914400026202202</td><td>26.104000091552734</td><td>0.15081000328063965</td><td>0.10205800086259842</td><td>0.020201999694108963</td><td>27.897300720214844</td><td>2.3004400730133057</td><td>0.05719060078263283</td><td>0.14986099302768707</td><td>25.552900314331055</td><td>0.11762300133705139</td><td>0.18738600611686707</td><td>0.03684540092945099</td><td>26.739200592041016</td><td>0.3630940020084381</td><td>0.03693050146102905</td><td>0.03157680109143257</td><td>26.951200485229492</td><td>0.4254559874534607</td><td>0.05447009950876236</td><td>0.02731890045106411</td><td>26.305700302124023</td><td>0.32289400696754456</td><td>0.0883743017911911</td><td>0.034883901476860046</td><td>26.03619956970215</td><td>0.25768500566482544</td><td>0.11793199926614761</td><td>0.05172799900174141</td><td>26.24449920654297</td><td>0.3676219880580902</td><td>0.08679749816656113</td><td>0.04474179819226265</td><td>25.836299896240234</td><td>0.30692899227142334</td><td>0.1083969995379448</td><td>0.06508269906044006</td><td>26.631799697875977</td><td>0.34787601232528687</td><td>0.04856489971280098</td><td>0.03317689895629883</td><td>26.0664005279541</td><td>0.2245739996433258</td><td>0.10432899743318558</td><td>0.03778360038995743</td><td>26.467899322509766</td><td>0.3505220115184784</td><td>0.09926120191812515</td><td>0.03753510117530823</td><td>nan</td><td>nan</td><td>0.055400099605321884</td><td>0.04390709847211838</td><td>27.763700485229492</td><td>1.096909999847412</td><td>0.017430799081921577</td><td>0.037619899958372116</td><td>26.0846004486084</td><td>0.30439698696136475</td><td>0.15890200436115265</td><td>0.0658503994345665</td><td>27.036100387573242</td><td>1.379870057106018</td><td>-0.002285880036652088</td><td>0.06688810139894485</td><td>27.328699111938477</td><td>0.8559470176696777</td><td>-0.0024565299972891808</td><td>0.06475680321455002</td><td>26.96940040588379</td><td>3.401870012283325</td><td>-0.12172699719667435</td><td>0.29451099038124084</td><td>24.688499450683594</td><td>0.6014350056648254</td><td>0.20760700106620789</td><td>0.4273020029067993</td><td>nan</td><td>nan</td><td>-0.03176309913396835</td><td>0.1894250065088272</td><td>0.11259599775075912</td><td>0.015165000222623348</td><td>26.27120018005371</td><td>0.14623300731182098</td><td>0.14638100564479828</td><td>0.035701800137758255</td><td>25.986299514770508</td><td>0.2648079991340637</td><td>0.0</td><td>3.1667299270629883</td><td>nan</td><td>nan</td><td>0.0</td><td>3.084350109100342</td><td>nan</td><td>nan</td><td>0.032323624938726425</td><td>0.12425410002470016</td><td>False</td><td>nan</td><td>-498.7774658203125</td><td>0.2926574945449829</td><td>0.04561741277575493</td><td>False</td><td>25.759979248046875</td><td>0.5910862684249878</td><td>0.1584307998418808</td><td>0.0750032588839531</td><td>False</td><td>25.581260681152344</td><td>0.7107176780700684</td><td>0.015316521748900414</td><td>0.05616702511906624</td><td>False</td><td>nan</td><td>-3.6918139457702637</td><td>0.10605213046073914</td><td>0.013629348948597908</td><td>False</td><td>26.63756561279297</td><td>0.32443320751190186</td><td>0.11403541266918182</td><td>0.009968465194106102</td><td>False</td><td>26.60552215576172</td><td>0.16353674232959747</td><td>0.09874603897333145</td><td>0.016203196719288826</td><td>False</td><td>26.651824951171875</td><td>0.3014127016067505</td><td>0.05677012726664543</td><td>0.029270125553011894</td><td>False</td><td>27.11530303955078</td><td>0.6114979386329651</td><td>0.13134078681468964</td><td>0.01824338547885418</td><td>False</td><td>26.377883911132812</td><td>0.21491742134094238</td><td>0.02518138848245144</td><td>0.05335390940308571</td><td>False</td><td>27.006690979003906</td><td>2.845039129257202</td><td>0.21819235384464264</td><td>0.023637821897864342</td><td>False</td><td>25.718154907226562</td><td>0.21348650753498077</td><td>0.07316777110099792</td><td>0.024468906223773956</td><td>False</td><td>27.481536865234375</td><td>0.9283403158187866</td><td>0.060189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an</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       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an</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       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       "<tr><td>6</td><td>HELP_J100054.82+014728.67</td><td>COSMOS2015_J100054.82+014728.67</td><td>150.228391944512</td><td>1.7912940828254702</td><td>1.0</td><td>29.246599197387695</td><td>6.053830146789551</td><td>-0.00023756100563332438</td><td>0.04055729880928993</td><td>27.115999221801758</td><td>0.33643999695777893</td><td>0.05023150146007538</td><td>0.04507419839501381</td><td>nan</td><td>nan</td><td>-0.004802720155566931</td><td>0.03616439923644066</td><td>26.980899810791016</td><td>0.45163199305534363</td><td>0.054800599813461304</td><td>0.06388620287179947</td><td>29.459699630737305</td><td>1.3345600366592407</td><td>0.006226019933819771</td><td>0.016847100108861923</td><td>27.989900588989258</td><td>0.32492899894714355</td><td>0.023008599877357483</td><td>0.010755900293588638</td><td>27.910999298095703</td><td>0.5444920063018799</td><td>0.02508939988911152</td><td>0.02130099944770336</td><td>26.8572998046875</td><td>0.24190300703048706</td><td>0.06540630012750626</td><td>0.018897999078035355</td><td>28.412799835205078</td><td>0.7220950126647949</td><td>0.015615900047123432</td><td>0.014678600244224072</td><td>27.15760040283203</td><td>0.816769003868103</td><td>0.051327601075172424</td><td>0.13927200436592102</td><td>26.386600494384766</td><td>0.18057100474834442</td><td>0.10234300047159195</td><td>0.034286998212337494</td><td>nan</td><td>nan</td><td>-0.012409600429236889</td><td>0.03511530160903931</td><td>27.68549919128418</td><td>0.6387630105018616</td><td>0.029258999973535538</td><td>0.02723960019648075</td><td>27.41710090637207</td><td>0.6481980085372925</td><td>0.03956390172243118</td><td>0.03278509899973869</td><td>nan</td><td>nan</td><td>-0.03728500008583069</td><td>0.05100889876484871</td><td>27.379600524902344</td><td>0.8332549929618835</td><td>0.04123469814658165</td><td>0.046518899500370026</td><td>27.744400024414062</td><td>1.2617499828338623</td><td>0.030225099995732307</td><td>0.05993739888072014</td><td>29.22800064086914</td><td>2.8900699615478516</td><td>0.005414620041847229</td><td>0.03293519839644432</td><td>26.973499298095703</td><td>0.38068801164627075</td><td>0.06163389980792999</td><td>0.036066800355911255</td><td>nan</td><td>nan</td><td>-0.007592460140585899</td><td>0.03887160122394562</td><td>26.76919937133789</td><td>0.36511099338531494</td><td>0.06881099939346313</td><td>0.04646509885787964</td><td>27.302099227905273</td><td>0.5917850136756897</td><td>0.045516300946474075</td><td>0.040585801005363464</td><td>25.765100479125977</td><td>0.16414900124073029</td><td>0.17454500496387482</td><td>0.06101899966597557</td><td>25.995100021362305</td><td>0.4543190002441406</td><td>0.14447300136089325</td><td>0.07501079887151718</td><td>26.32699966430664</td><td>0.2595149874687195</td><td>0.10660199820995331</td><td>0.06433620303869247</td><td>nan</td><td>nan</td><td>-0.11388900130987167</td><td>0.35783401131629944</td><td>nan</td><td>nan</td><td>-0.18682099878787994</td><td>0.37527701258659363</td><td>25.861600875854492</td><td>0.36820200085639954</td><td>0.16353200376033783</td><td>0.12123099714517593</td><td>0.10062599927186966</td><td>0.023088200017809868</td><td>26.393199920654297</td><td>0.24911700189113617</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.0</td><td>2.324389934539795</td><td>nan</td><td>nan</td><td>0.0</td><td>2.1613199710845947</td><td>nan</td><td>nan</td><td>0.007267090026289225</td><td>0.04051971808075905</td><td>False</td><td>nan</td><td>-185.3609161376953</td><td>0.05171305313706398</td><td>0.016024461016058922</td><td>False</td><td>27.147560119628906</td><td>0.9742628931999207</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-8.17557430267334</td><td>0.05856526643037796</td><td>0.024361303076148033</td><td>False</td><td>27.05303955078125</td><td>1.2657445669174194</td><td>0.00597200496122241</td><td>0.007340640760958195</td><td>False</td><td>29.414474487304688</td><td>2.937913179397583</td><td>0.023122746497392654</td><td>0.006919959560036659</td><td>False</td><td>27.995277404785156</td><td>0.5075523853302002</td><td>0.024865679442882538</td><td>0.012470030225813389</td><td>False</td><td>27.901275634765625</td><td>0.9217942953109741</td><td>0.0656266137957573</td><td>0.014621670357882977</td><td>False</td><td>26.86095428466797</td><td>0.31370437145233154</td><td>0.015663182362914085</td><td>0.01041717641055584</td><td>False</td><td>28.41608428955078</td><td>1.0205680131912231</td><td>0.04976906627416611</td><td>0.03743987902998924</td><td>False</td><td>27.124122619628906</td><td>2.9460299015045166</td><td>0.10124175250530243</td><td>0.016837721690535545</td><td>False</td><td>26.374855041503906</td><td>0.3637438118457794</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-3.0722949504852295</td><td>0.030605554580688477</td><td>0.018005935475230217</td><td>False</td><td>27.734352111816406</td><td>1.0108007192611694</td><td>0.03918859362602234</td><td>0.023396076634526253</td><td>False</td><td>27.406753540039062</td><td>0.8997082114219666<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d>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>7</td><td>HELP_J100148.56+015305.15</td><td>COSMOS2015_J100148.56+015305.15</td><td>150.452331511501</td><td>1.88475989727483</td><td>1.0</td><td>23.819599151611328</td><td>0.09215229749679565</td><td>-0.000252103985985741</td><td>0.0323684997856617</td><td>23.358400344848633</td><td>0.02699040062725544</td><td>0.4151970148086548</td><td>0.04673109948635101</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>25.23870086669922</td><td>0.2368279993534088</td><td>0.06583669781684875</td><td>0.06857369840145111</td><td>26.0846004486084</td><td>0.17872600257396698</td><td>0.003673689905554056</td><td>0.01778149977326393</td><td>27.50309944152832</td><td>0.6248720288276672</td><td>0.0012436499819159508</td><td>0.011272500269114971</td><td>26.267200469970703</td><td>0.3021009862422943</td><td>-0.018131699413061142</td><td>0.019397500902414322</td><td>25.833599090576172</td><td>0.3169400095939636</td><td>-0.013504499569535255</td><td>0.022365599870681763</td><td>25.7278995513916</td><td>0.17303599417209625</td><td>-0.013602199964225292</td><td>0.014094100333750248</td><td>24.74690055847168</td><td>0.27194198966026306</td><td>0.05359189957380295</td><td>0.14892399311065674</td><td>26.983600616455078</td><td>0.8955249786376953</td><td>-0.021950900554656982</td><td>0.034578800201416016</td><td>26.147600173950195</td><td>0.4824160039424896</td><td>-0.014096999540925026</td><td>0.03341849893331528</td><td>26.159799575805664</td><td>0.47944799065589905</td><td>-0.0015686099650338292</td><td>0.0294272992759943</td><td>27.034700393676758</td><td>1.3430900573730469</td><td>0.025292400270700455</td><td>0.03430600091814995</td><td>25.508800506591797</td><td>0.35839399695396423</td><td>-0.030350299552083015</td><td>0.05374940112233162</td><td>26.48270034790039</td><td>1.1038299798965454</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>26.9158992767334</td><td>0.9699239730834961</td><td>0.009506010450422764</td><td>0.032875098288059235</td><td>26.40880012512207</td><td>0.6944720149040222</td><td>0.08561930060386658</td><td>0.03932179883122444</td><td>25.03499984741211</td><td>0.2413329929113388</td><td>0.0335065983235836</td><td>0.044328801333904266</td><td>24.700199127197266</td><td>0.16254200041294098</td><td>-0.024235300719738007</td><td>0.04897329956293106</td><td>25.981800079345703</td><td>0.5067870020866394</td><td>0.011954200454056263</td><td>0.041410401463508606</td><td>26.495399475097656</td><td>0.9835749864578247</td><td>nan</td><td>nan</td><td>24.369400024414062</td><td>0.2797969877719879</td><td>-0.015019799582660198</td><td>0.07301740348339081</td><td>25.304399490356445</td><td>0.28583699464797974</td><td>0.04646819829940796</td><td>0.06416960060596466</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>24.447099685668945</td><td>0.7317259907722473</td><td>0.18517500162124634</td><td>0.3070339858531952</td><td>24.71780014038086</td><td>0.3696199953556061</td><td>0.03629760071635246</td><td>0.12352599948644638</td><td>0.14323900640010834</td><td>0.06257960200309753</td><td>26.00979995727539</td><td>0.47434699535369873</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.0</td><td>0.34989601373672485</td><td>nan</td><td>nan</td><td>0.0</td><td>1.0869799852371216</td><td>nan</td><td>nan</td><td>1.0768625736236572</td><td>0.09139914065599442</td><td>False</td><td>nan</td><td>-139.40142822265625</td><td>1.6467949151992798</td><td>0.04093780368566513</td><td>False</td><td>24.8543701171875</td><td>0.12220136821269989</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.2914201021194458</td><td>0.06356649100780487</td><td>False</td><td>26.85382843017578</td><td>1.1308729648590088</td><td>0.13370870053768158</td><td>0.02201015315949917</td><td>False</td><td>29.98725128173828</td><td>5.255211353302002</td><td>0.036204300820827484</td><td>0.02083660289645195</td><td>False</td><td>31.163253784179688</td><td>9.841161727905273</td><td>0.11301072686910629</td><td>0.03144470229744911</td><td>False</td><td>nan</td><td>-1.1615331172943115</td><td>0.16848456859588623</td><td>0.04918275400996208</td><td>False</td><td>nan</td><td>-1.7981517314910889</td><td>0.18571193516254425</td><td>0.029597291722893715</td><td>False</td><td>nan</td><td>-1.1249998807907104</td><td>0.45839473605155945</td><td>0.11481313407421112</td><td>False</td><td>27.077255249023438</td><td>3.0171005725860596</td><td>0.058419737964868546</td><td>0.04818512499332428</td><td>False</td><td>nan</td><td>-1.7103378772735596</td><td>0.1261710673570633</td><td>0.05606052652001381</td><td>False</td><td>nan</td><td>-2.5738580226898193</td><td>0.12476128339767456</td><td>0.05509307608008385</td><td>False</td><td>nan</td><td>-20.368532180786133</td><td>0.05573390796780586</td><td>0.06894461065530777</td><td>False</td><td>27.89252471923828</td><td>1.4726662635803223</td><td>0.22723731398582458</td><td>0.07500945776700974</td><td>False</td><td>nan</td><td>-1.9228036403656006</td><td>0.0926658883690834</td><td>0.09421015530824661</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.062178485095500946</td><td>0.05554609373211861</td><td>False</td><td>28.955001831054688</td><td>3.754854440689087</td><td>0.09919276088476181</td><td>0.06344689428806305</td><td>False</td><td>26.568572998046875</td><td>0.49863874912261963</td><td>0.35156047344207764</td><td>0.07814342528581619</td><td>False</td><td>27.587173461914062</td><td>1.4364149570465088</td><td>0.47854217886924744</td><td>0.0716409757733345</td><td>False</td><td>nan</td><td>-2.193993091583252</td><td>0.14698727428913116</td><td>0.06860896944999695</td><td>False</td><td>28.706199645996094</td><td>3.7610857486724854</td><td>0.0915883332490921</td><td>0.08297042548656464</td><td>False</td><td>nan</td><td>nan</td><td>0.6489924788475037</td><td>0.167247012257576</td><td>False</td><td>nan</td><td>-5.2782087326049805</td><td>0.2743090093135834</td><td>0.07221612334251404</td><td>False</td><td>27.23210906982422</td><td>1.4993319511413574</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.6041710376739502</td><td>0.40717774629592896</td><td>False</td><td>25.731048583984375</td><td>1.800231695175171</td><td>0.47084689140319824</td><td>0.1602916270494461</td><td>False</td><td>27.50030517578125</td><td>3.6949174404144287</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</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>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>8</td><td>HELP_J095825.28+024722.91</td><td>COSMOS2015_J095825.28+024722.91</td><td>149.605323991903</td><td>2.7896954394515</td><td>1.0</td><td>24.891799926757812</td><td>0.4804820120334625</td><td>-0.0012573499698191881</td><td>0.14371000230312347</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>25.011600494384766</td><td>0.2879070043563843</td><td>0.2943649888038635</td><td>0.18665100634098053</td><td>nan</td><td>nan</td><td>0.010830000042915344</td><td>0.13997900485992432</td><td>nan</td><td>nan</td><td>-0.017782000824809074</td><td>0.02529890090227127</td><td>28.56529998779297</td><td>0.6193090081214905</td><td>0.013932500034570694</td><td>0.010006600059568882</td><td>31.792600631713867</td><td>20.278799057006836</td><td>0.0030724001117050648</td><td>0.01811249926686287</td><td>27.501800537109375</td><td>0.5967649817466736</td><td>0.033101100474596024</td><td>0.020975099876523018</td><td>27.675899505615234</td><td>0.4262180030345917</td><td>0.030699100345373154</td><td>0.014130700379610062</td><td>nan</td><td>nan</td><td>0.0064885299652814865</td><td>0.14663800597190857</td><td>25.809499740600586</td><td>0.14062899351119995</td><td>0.15000100433826447</td><td>0.03684629872441292</td><td>29.93160057067871</td><td>7.334469795227051</td><td>0.007880040444433689</td><td>0.035682499408721924</td><td>nan</td><td>nan</td><td>-0.012066599912941456</td><td>0.030326999723911285</td><td>nan</td><td>nan</td><td>-0.01838959939777851</td><td>0.03392979875206947</td><td>nan</td><td>nan</td><td>0.029273400083184242</td><td>0.05324539914727211</td><td>27.271400451660156</td><td>1.05308997631073</td><td>0.046681199222803116</td><td>0.05259409919381142</td><td>26.65060043334961</td><td>0.5370069742202759</td><td>0.03286759927868843</td><td>0.05656509846448898</td><td>nan</td><td>nan</td><td>-0.024102700874209404</td><td>0.037144798785448074</td><td>27.841699600219727</td><td>1.1036299467086792</td><td>0.005159109830856323</td><td>0.03797009959816933</td><td>26.821399688720703</td><td>0.5514510273933411</td><td>0.049783699214458466</td><td>0.044806499034166336</td><td>nan</td><td>nan</td><td>0.012224099598824978</td><td>0.04793189838528633</td><td>29.693300247192383</td><td>6.5020599365234375</td><td>0.009545629844069481</td><td>0.03977780044078827</td><td>26.11050033569336</td><td>0.28793200850486755</td><td>0.12089499831199646</td><td>0.06289739906787872</td><td>28.114700317382812</td><td>3.6974499225616455</td><td>0.02645459957420826</td><td>0.0695372000336647</td><td>27.31719970703125</td><td>0.8093860149383545</td><td>0.029327599331736565</td><td>0.06490059942007065</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>24.554399490356445</td><td>0.4204460084438324</td><td>0.452688992023468</td><td>0.3566330075263977</td><td>25.027000427246094</td><td>0.4429689943790436</td><td>0.2250099927186966</td><td>0.25366899371147156</td><td>0.0</td><td>0.1383489966392517</td><td>nan</td><td>nan</td><td>0.44479501247406006</td><td>0.10927899926900864</td><td>24.779600143432617</td><td>0.26674899458885193</td><td>-0.4560669958591461</td><td>0.7383729815483093</td><td>nan</td><td>nan</td><td>0.0</td><td>4.197509765625</td><td>nan</td><td>nan</td><td>0.40112531185150146</td><td>0.17751410603523254</td><td>False</td><td>nan</td><td>-124.09523010253906</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.3592194616794586</td><td>0.09525499492883682</td><td>False</td><td>25.227783203125</td><td>0.6884438395500183</td><td>nan</td><td>nan</td><td>False</td><td>28.813430786132812</td><td>14.033266067504883</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.5447041988372803</td><td>0.013610674999654293</td><td>0.007763592526316643</td><td>False</td><td>28.53992462158203</td><td>0.7797974348068237</td><td>0.0006965613574720919</td><td>0.013009999878704548</td><td>False</td><td>30.181312561035156</td><td>6.400662422180176</td><td>0.0362476147711277</td><td>0.019923171028494835</td><td>False</td><td>27.60039520263672</td><td>0.6879960298538208</td><td>0.03087736666202545</td><td>0.012121258303523064</td><td>False</td><td>27.68218994140625</td><td>0.4997609555721283</td><td>nan</td><td>nan</td><td>False</td><td>29.36963653564453</td><td>24.53717041015625</td><td>0.17226611077785492</td><td>0.02231261134147644</td><td>False</td><td>25.95977020263672</td><td>0.2667005956172943</td><td>0.003866868559271097</td><td>0.026121841743588448</td><td>False</td><td>29.15868377685547</td><td>4.916444301605225</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.7287821769714355</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.003241539001465</td><td>nan</td><td>nan</td><td>False</td><td>27.73381805419922</td><td>1.9748458862304688</td><td>0.04481668025255203</td><td>0.04346911981701851</td><td>False</td><td>27.227149963378906</td><td>1.2232615947723389</td><td>0.07938889414072037</td><td>0.03926588594913483</td><td>False</td><td>27.60808563232422</td><td>1.8685505390167236</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.6732336282730103</td><td>0.0265045128762722</td><td>0.02694132551550865</td><td>False</td><td>29.618568420410156</td><td>7.990818500518799</td><td>0.0678328350186348</td><td>0.0344526506960392</td><td>False</td><td>27.15728759765625</td><td>0.9771880507469177</td><td>nan</td><td>nan</td><td>False</td><td>28.681961059570312</td><td>4.257277965545654</td><td>0.004815929569303989</td><td>0.028840765357017517</td><td>False</td><td>28.95049285888672</td><td>4.524394512176514</td><td>0.13055692613124847</td><td>0.03462307155132294</td><td>False</td><td>26.19397735595703</td><td>0.5648701786994934</td><td>0.02061198465526104</td><td>0.07019364088773727</td><td>False</td><td>27.84375</td><td>2.853910207748413</td><td>0.042965516448020935</td><td>0.032029591500759125</td><td>False</td><td>27.73180389404297</td><td>2.4026830196380615</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.5473186373710632</td><td>0.21194645762443542</td><td>False</td><td>24.76050567626953</td><td>0.8553539514541626</td><td>0.3541600704193115</td><td>0.14449359476566315</td><td>False</td><td>25.519500732421875</td><td>1.2240238189697266</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</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>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>9</td><td>HELP_J100154.84+020628.04</td><td>COSMOS2015_J100154.84+020628.04</td><td>150.478458691686</td><td>2.1077854526048103</td><td>1.0</td><td>25.035999298095703</td><td>0.23545700311660767</td><td>-0.00030819998937658966</td><td>0.03476019948720932</td><td>nan</td><td>nan</td><td>0.08508999645709991</td><td>0.04862760007381439</td><td>26.249099731445312</td><td>0.763034999370575</td><td>0.06652569770812988</td><td>0.10621599853038788</td><td>nan</td><td>nan</td><td>0.019316399469971657</td><td>0.07231739908456802</td><td>nan</td><td>nan</td><td>-0.004923809785395861</td><td>0.016114600002765656</td><td>nan</td><td>nan</td><td>-0.0073474799282848835</td><td>0.00969650037586689</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-0.006511160172522068</td><td>0.021082300692796707</td><td>28.953399658203125</td><td>2.484339952468872</td><td>0.004510439932346344</td><td>0.0138349998742342</td><td>25.567399978637695</td><td>0.4667159914970398</td><td>0.03285469859838486</td><td>0.1550550013780594</td><td>nan</td><td>nan</td><td>0.03178200125694275</td><td>0.03601429983973503</td><td>26.41900062561035</td><td>0.4519430100917816</td><td>0.01083150040358305</td><td>0.031428299844264984</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.012414599768817425</td><td>0.03443190082907677</td><td>25.226299285888672</td><td>0.21817399561405182</td><td>0.10095000267028809</td><td>0.05498350039124489</td><td>nan</td><td>nan</td><td>0.005276429932564497</td><td>0.045866601169109344</td><td>nan</td><td>nan</td><td>-0.018124600872397423</td><td>0.05750240013003349</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.013475799933075905</td><td>0.03852749988436699</td><td>nan</td><td>nan</td><td>-0.040499698370695114</td><td>0.04198069870471954</td><td>30.243799209594727</td><td>19.240999221801758</td><td>-0.018841300159692764</td><td>0.045609500259160995</td><td>nan</td><td>nan</td><td>-0.02988320030272007</td><td>0.04068180173635483</td><td>nan</td><td>nan</td><td>-0.017823699861764908</td><td>0.0660225972533226</td><td>27.582199096679688</td><td>4.148960113525391</td><td>0.03416689857840538</td><td>0.07164239883422852</td><td>nan</td><td>nan</td><td>0.01638999953866005</td><td>0.06419520080089569</td><td>23.704599380493164</td><td>0.2511419951915741</td><td>0.09447640180587769</td><td>0.2620150148868561</td><td>23.725099563598633</td><td>0.2842470109462738</td><td>-0.08578349649906158</td><td>0.2917349934577942</td><td>25.2273006439209</td><td>0.4381519854068756</td><td>0.10212499648332596</td><td>0.11827000230550766</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.10468100011348724</td><td>0.043459899723529816</td><td>26.350299835205078</td><td>0.450762003660202</td><td>0.0</td><td>2.6039299964904785</td><td>nan</td><td>nan</td><td>0.0</td><td>2.4087400436401367</td><td>nan</td><td>nan</td><td>0.3512367308139801</td><td>0.07617057114839554</td><td>False</td><td>nan</td><td>-122.45426177978516</td><td>nan</td><td>nan</td><td>False</td><td>26.57530975341797</td><td>0.6204812526702881</td><td>0.1149105653166771</td><td>0.08075698465108871</td><td>False</td><td>26.84252166748047</td><td>1.7335036993026733</td><td>nan</td><td>nan</td><td>False</td><td>28.18518829345703</td><td>4.064816474914551</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-3.553387403488159</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.4328505992889404</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-3.515474319458008</td><td>0.009520057588815689</td><td>0.021783430129289627</td><td>False</td><td>29.76445770263672</td><td>3.3303091526031494</td><td>0.2152978926897049</td><td>0.09254823625087738</td><td>False</td><td>27.608505249023438</td><td>5.1240410804748535</td><td>nan</td><td>nan</td><td>False</td><td>27.644546508789062</td><td>1.230319857597351</td><td>0.09826519340276718</td><td>0.04090336710214615</td><td>False</td><td>28.813278198242188</td><td>3.150333881378174</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>28.66516876220703</td><td>3.0112898349761963</td><td>0.2947677969932556</td><td>0.05923231691122055</td><td>False</td><td>26.389732360839844</td><td>0.5913577675819397</td><td>nan</td><td>nan</td><td>False</td><td>29.594154357910156</td><td>9.43801498413086</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-3.4446239471435547</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>28.57611846923828</td><td>3.1041347980499268</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.1254395246505737</td><td>0.0029005513060837984</td><td>0.05140245705842972</td><td>False</td><td>nan</td><td>-2.628262519836426</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.4780781269073486</td><td>nan</td><td>nan</td><td>False</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><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></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(\"$('#table140433137161944-424563').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",
       "    $('#table140433137161944-424563').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",
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       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_catalogue[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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": 27,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": 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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Wirds was created with a merge so contains a flag to be merged with the merg flag produced here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# master_catalogue['flag_merged'] |= master_catalogue['wirds_flag_merged']\n",
    "# master_catalogue.remove_columns('wirds_flag_merged')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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": 29,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": 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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Each prisitine catalogue may contain one or several stellarity columns indicating the probability (0 to 1) of each source being a star.  We merge these columns taking the highest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cosmos_stellarity, candels_stellarity, cfhtls_stellarity, decals_stellarity, hsc-udeep_stellarity, hsc-deep_stellarity, kids_stellarity, las_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": 31,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# We create an masked array with all the stellarities and get the maximum value, as well as its\n",
    "# origin.  Some sources may not have an associated stellarity.\n",
    "stellarity_array = np.array([master_catalogue[column] for column in stellarity_columns])\n",
    "stellarity_array = np.ma.masked_array(stellarity_array, np.isnan(stellarity_array))\n",
    "\n",
    "max_stellarity = np.max(stellarity_array, axis=0)\n",
    "max_stellarity.fill_value = np.nan\n",
    "\n",
    "no_stellarity_mask = max_stellarity.mask\n",
    "\n",
    "master_catalogue.add_column(Column(data=max_stellarity.filled(), name=\"stellarity\"))\n",
    "\n",
    "stellarity_origin = np.full(len(master_catalogue), \"NO_INFORMATION\", dtype=\"S20\")\n",
    "stellarity_origin[~no_stellarity_mask] = np.array(stellarity_columns)[np.argmax(stellarity_array, axis=0)[~no_stellarity_mask]]\n",
    "\n",
    "master_catalogue.add_column(Column(data=stellarity_origin, name=\"stellarity_origin\"))\n",
    "\n",
    "master_catalogue.remove_columns(stellarity_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## IV - Adding E(B-V) column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    ebv(master_catalogue['ra'], master_catalogue['dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## V a - Adding HELP unique identifiers and field columns\n",
    "\n",
    "First we make a help_id column using the old values where we have them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#master_catalogue.add_column(Column(gen_help_id(master_catalogue['ra'], master_catalogue['dec']),\n",
    "#                                   name=\"help_id\"))\n",
    "#Use HELP ids from original cat to make sure they are identical\n",
    "\n",
    "master_catalogue['help_id'] = master_catalogue['help_id'].astype('S27')\n",
    "master_catalogue.add_column(Column(gen_help_id(master_catalogue['ra'], master_catalogue['dec']),\n",
    "                                   name=\"help_id_temp\"))\n",
    "mask = (master_catalogue['help_id'] == '-1') | (master_catalogue['help_id'] == '')\n",
    "master_catalogue['help_id'][mask] = master_catalogue['help_id_temp'][mask]\n",
    "master_catalogue.remove_column('help_id_temp')\n",
    "\n",
    "master_catalogue.add_column(Column(np.full(len(master_catalogue), \"COSMOS\", dtype='<U18'),\n",
    "                                   name=\"field\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The HELP IDs are not unique!!!\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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We now make a second help_id column using only the new naming convention but keeping the old column to permit joining in the old products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master_catalogue['help_id'].name = 'old_help_id'\n",
    "master_catalogue.add_column(Column(gen_help_id(master_catalogue['ra'], master_catalogue['dec']),\n",
    "                                   name=\"help_id\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK!\n"
     ]
    }
   ],
   "source": [
    "# Check that the HELP Ids are unique\n",
    "if len(master_catalogue) != len(np.unique(master_catalogue['help_id'])):\n",
    "    print(\"The HELP IDs are not unique!!!\")\n",
    "else:\n",
    "    print(\"OK!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## V b - Adding spec-z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "specz =  Table.read(\"../../dmu23/dmu23_COSMOS/data/COSMOS-specz-v2.5-public_helpcoverage_helpid_20160512.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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(specz['ra'] * u.deg, specz['dec'] * u.deg)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = specz_merge(master_catalogue, specz, radius=1. * u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## VI - Choosing between multiple values for the same filter\n",
    "\n",
    "### VI.a HSC-DEEP and HSC-UDEEP and COSMOS\n",
    "\n",
    "On COSMOS2015 we have early HSC y band photometry. To ensure values are the same as for the original run, we take fluxes in this order: COSMOS, HSC-DEEP, HSC-UDEEP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "suprime_origin = Table()\n",
    "suprime_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "suprime_stats = Table()\n",
    "suprime_stats.add_column(Column(data=['g','r','i','z','y', 'n921'], name=\"Band\"))\n",
    "for col in [\"HSC-UDEEP\",  \"HSC-DEEP\", \"COSMOS2015\"]:\n",
    "    suprime_stats.add_column(Column(data=np.full(6, 0), name=\"{}\".format(col)))\n",
    "    suprime_stats.add_column(Column(data=np.full(6, 0), name=\"use {}\".format(col)))\n",
    "    suprime_stats.add_column(Column(data=np.full(6, 0), name=\"{} ap\".format(col)))\n",
    "    suprime_stats.add_column(Column(data=np.full(6, 0), name=\"use {} ap\".format(col)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "suprime_bands = ['g','r','i','z','y', 'n921'] \n",
    "for band in suprime_bands:\n",
    "\n",
    "    # Suprime total flux \n",
    "    has_hsc_udeep = ~np.isnan(master_catalogue['f_hsc-udeep_' + band])\n",
    "    has_hsc_deep  = ~np.isnan(master_catalogue['f_hsc-deep_' + band])\n",
    "    if band == 'y':\n",
    "        has_cosmos = ~np.isnan(master_catalogue['f_cosmos-suprime_y'])\n",
    "    elif band != 'y':\n",
    "        has_cosmos = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    \n",
    "    use_cosmos = has_cosmos\n",
    "    use_hsc_udeep = has_hsc_udeep & ~has_cosmos\n",
    "    use_hsc_deep = has_hsc_deep & ~has_hsc_udeep & ~has_cosmos\n",
    "    \n",
    " \n",
    "    f_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        f_suprime[use_cosmos] = master_catalogue['f_cosmos-suprime_y'][use_cosmos]\n",
    "    f_suprime[use_hsc_udeep] = master_catalogue['f_hsc-udeep_' + band][use_hsc_udeep]\n",
    "    f_suprime[use_hsc_deep] = master_catalogue['f_hsc-deep_' + band][use_hsc_deep]\n",
    " \n",
    "\n",
    "    ferr_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        ferr_suprime[use_cosmos] = master_catalogue['ferr_cosmos-suprime_y'][use_cosmos]\n",
    "    ferr_suprime[use_hsc_udeep] = master_catalogue['ferr_hsc-udeep_' + band][use_hsc_udeep]\n",
    "    ferr_suprime[use_hsc_deep] = master_catalogue['ferr_hsc-deep_' + band][use_hsc_deep]\n",
    "\n",
    "    \n",
    "    m_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        m_suprime[use_cosmos] = master_catalogue['m_cosmos-suprime_y'][use_cosmos]\n",
    "    m_suprime[use_hsc_udeep] = master_catalogue['m_hsc-udeep_' + band][use_hsc_udeep]\n",
    "    m_suprime[use_hsc_deep] = master_catalogue['m_hsc-deep_' + band][use_hsc_deep]\n",
    "\n",
    "\n",
    "    merr_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        merr_suprime[use_cosmos] = master_catalogue['merr_cosmos-suprime_y'][use_cosmos]\n",
    "    merr_suprime[use_hsc_udeep] = master_catalogue['merr_hsc-udeep_' + band][use_hsc_udeep]\n",
    "    merr_suprime[use_hsc_deep] = master_catalogue['merr_hsc-deep_' + band][use_hsc_deep]\n",
    "\n",
    "\n",
    "    flag_suprime = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    if band == 'y':\n",
    "        flag_suprime[use_cosmos] = master_catalogue['flag_cosmos-suprime_y'][use_cosmos]\n",
    "    flag_suprime[use_hsc_udeep] = master_catalogue['flag_hsc-udeep_' + band][use_hsc_udeep]\n",
    "    flag_suprime[use_hsc_deep] = master_catalogue['flag_hsc-deep_' + band][use_hsc_deep]\n",
    "\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_suprime, name=\"f_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_suprime, name=\"ferr_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_suprime, name=\"m_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_suprime, name=\"merr_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=flag_suprime, name=\"flag_suprime_\" + band))\n",
    "\n",
    "    old_hsc_udeep_columns = ['f_hsc-udeep_' + band,\n",
    "                               'ferr_hsc-udeep_' + band,\n",
    "                               'm_hsc-udeep_' + band, \n",
    "                               'merr_hsc-udeep_' + band,\n",
    "                               'flag_hsc-udeep_' + band]\n",
    "    old_hsc_deep_columns = ['f_hsc-deep_' + band,\n",
    "                               'ferr_hsc-deep_' + band,\n",
    "                               'm_hsc-deep_' + band, \n",
    "                               'merr_hsc-deep_' + band,\n",
    "                               'flag_hsc-deep_' + band]\n",
    "    old_cosmos_columns = ['f_cosmos-suprime_' + band,\n",
    "                               'ferr_cosmos-suprime_' + band,\n",
    "                               'm_cosmos-suprime_' + band, \n",
    "                               'merr_cosmos-suprime_' + band,\n",
    "                               'flag_cosmos-suprime_' + band]\n",
    "    \n",
    "    old_columns = old_hsc_udeep_columns + old_hsc_deep_columns\n",
    "    if band == 'y':\n",
    "        old_columns += old_cosmos_columns\n",
    "    master_catalogue.remove_columns(old_columns)\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_hsc_udeep] = \"HSC-UDEEP\"\n",
    "    origin[use_hsc_deep] = \"HSC-DEEP\"\n",
    "    origin[use_cosmos] = \"COSMOS2015\"\n",
    " \n",
    "    \n",
    "    suprime_origin.add_column(Column(data=origin, name= 'f_suprime_' + band ))\n",
    "    \n",
    "  \n",
    "    # Suprime aperture flux \n",
    "    has_ap_hsc_udeep = ~np.isnan(master_catalogue['f_ap_hsc-udeep_' + band])\n",
    "    has_ap_hsc_deep  = ~np.isnan(master_catalogue['f_ap_hsc-deep_' + band])\n",
    "    if band == 'y':\n",
    "        has_ap_cosmos = ~np.isnan(master_catalogue['f_ap_cosmos-suprime_y'])\n",
    "    elif band != 'y':\n",
    "        has_ap_cosmos = np.full(len(master_catalogue), False, dtype=bool)\n",
    " \n",
    "    \n",
    "    use_ap_cosmos = has_ap_cosmos\n",
    "    use_ap_hsc_udeep = has_ap_hsc_udeep & ~has_cosmos\n",
    "    use_ap_hsc_deep = has_ap_hsc_deep & ~has_ap_hsc_udeep & ~has_cosmos\n",
    "  \n",
    "\n",
    "    f_ap_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        f_ap_suprime[use_ap_cosmos] = master_catalogue['f_ap_cosmos-suprime_y'][use_ap_cosmos]\n",
    "    f_ap_suprime[use_ap_hsc_udeep] = master_catalogue['f_ap_hsc-udeep_' + band][use_ap_hsc_udeep]\n",
    "    f_ap_suprime[use_ap_hsc_deep] = master_catalogue['f_ap_hsc-deep_' + band][use_ap_hsc_deep]\n",
    "\n",
    "\n",
    "    ferr_ap_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        ferr_ap_suprime[use_ap_cosmos] = master_catalogue['ferr_ap_cosmos-suprime_y'][use_ap_cosmos]\n",
    "    ferr_ap_suprime[use_ap_hsc_udeep] = master_catalogue['ferr_ap_hsc-udeep_' + band][use_ap_hsc_udeep]\n",
    "    ferr_ap_suprime[use_ap_hsc_deep] = master_catalogue['ferr_ap_hsc-deep_' + band][use_ap_hsc_deep]\n",
    "   \n",
    "    \n",
    "    m_ap_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        m_ap_suprime[use_ap_cosmos] = master_catalogue['m_ap_cosmos-suprime_y'][use_ap_cosmos]\n",
    "    m_ap_suprime[use_ap_hsc_udeep] = master_catalogue['m_ap_hsc-udeep_' + band][use_ap_hsc_udeep]\n",
    "    m_ap_suprime[use_ap_hsc_deep] = master_catalogue['m_ap_hsc-deep_' + band][use_ap_hsc_deep]\n",
    "  \n",
    "\n",
    "    merr_ap_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        merr_ap_suprime[use_ap_cosmos] = master_catalogue['merr_ap_cosmos-suprime_y'][use_ap_cosmos]\n",
    "    merr_ap_suprime[use_ap_hsc_udeep] = master_catalogue['merr_ap_hsc-udeep_' + band][use_ap_hsc_udeep]\n",
    "    merr_ap_suprime[use_ap_hsc_deep] = master_catalogue['merr_ap_hsc-deep_' + band][use_ap_hsc_deep]\n",
    "  \n",
    "\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_ap_suprime, name=\"f_ap_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_ap_suprime, name=\"ferr_ap_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_ap_suprime, name=\"m_ap_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_ap_suprime, name=\"merr_ap_suprime_\" + band))\n",
    "\n",
    "\n",
    "    old_ap_hsc_udeep_columns = ['f_ap_hsc-udeep_' + band,\n",
    "                               'ferr_ap_hsc-udeep_' + band,\n",
    "                               'm_ap_hsc-udeep_' + band, \n",
    "                               'merr_ap_hsc-udeep_' + band]\n",
    "    old_ap_hsc_deep_columns = ['f_ap_hsc-deep_' + band,\n",
    "                               'ferr_ap_hsc-deep_' + band,\n",
    "                               'm_ap_hsc-deep_' + band, \n",
    "                               'merr_ap_hsc-deep_' + band]\n",
    "    old_ap_cosmos_columns = ['f_ap_cosmos-suprime_' + band,\n",
    "                               'ferr_ap_cosmos-suprime_' + band,\n",
    "                               'm_ap_cosmos-suprime_' + band, \n",
    "                               'merr_ap_cosmos-suprime_' + band]\n",
    "\n",
    "    \n",
    "    old_ap_columns = old_ap_hsc_udeep_columns + old_ap_hsc_deep_columns \n",
    "    if band == 'y':\n",
    "        old_ap_columns += old_ap_cosmos_columns\n",
    "    master_catalogue.remove_columns(old_ap_columns)\n",
    "\n",
    "    origin_ap = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin_ap[use_ap_hsc_udeep] = \"HSC-UDEEP\"\n",
    "    origin_ap[use_ap_hsc_deep] = \"HSC-DEEP\"\n",
    "    origin_ap[use_ap_cosmos] = \"COSMOS2015\"\n",
    "\n",
    "    \n",
    "    suprime_origin.add_column(Column(data=origin_ap, name= 'f_ap_suprime_' + band ))\n",
    "\n",
    "   \n",
    "    suprime_stats['HSC-UDEEP'][suprime_stats['Band'] == band] = np.sum(has_hsc_udeep)\n",
    "    suprime_stats['HSC-DEEP'][suprime_stats['Band'] == band] = np.sum(has_hsc_deep)\n",
    "    suprime_stats['COSMOS2015'][suprime_stats['Band'] == band] = np.sum(has_cosmos)\n",
    "\n",
    "    suprime_stats['use HSC-UDEEP'][suprime_stats['Band'] == band] = np.sum(use_hsc_udeep)\n",
    "    suprime_stats['use HSC-DEEP'][suprime_stats['Band'] == band] = np.sum(use_hsc_deep)\n",
    "    suprime_stats['use COSMOS2015'][suprime_stats['Band'] == band] = np.sum(use_cosmos)\n",
    "\n",
    "    suprime_stats['HSC-UDEEP ap'][suprime_stats['Band'] == band] = np.sum(has_ap_hsc_udeep)\n",
    "    suprime_stats['HSC-DEEP ap'][suprime_stats['Band'] == band] = np.sum(has_ap_hsc_deep)\n",
    "    suprime_stats['COSMOS2015 ap'][suprime_stats['Band'] == band] = np.sum(has_ap_cosmos)\n",
    "    \n",
    "    suprime_stats['use HSC-UDEEP ap'][suprime_stats['Band'] == band] = np.sum(use_ap_hsc_udeep)\n",
    "    suprime_stats['use HSC-DEEP ap'][suprime_stats['Band'] == band] = np.sum(use_ap_hsc_deep)\n",
    "    suprime_stats['use COSMOS2015 ap'][suprime_stats['Band'] == band] = np.sum(use_ap_cosmos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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       "<table id=\"table140432966451888-856352\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>HSC-UDEEP</th><th>use HSC-UDEEP</th><th>HSC-UDEEP ap</th><th>use HSC-UDEEP ap</th><th>HSC-DEEP</th><th>use HSC-DEEP</th><th>HSC-DEEP ap</th><th>use HSC-DEEP ap</th><th>COSMOS2015</th><th>use COSMOS2015</th><th>COSMOS2015 ap</th><th>use COSMOS2015 ap</th></tr></thead>\n",
       "<tr><td>0</td><td>g</td><td>1146131</td><td>1146131</td><td>1257414</td><td>1257414</td><td>1643773</td><td>956277</td><td>1838222</td><td>1044705</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "<tr><td>1</td><td>r</td><td>1156722</td><td>1156722</td><td>1268883</td><td>1268883</td><td>1679055</td><td>989440</td><td>1903240</td><td>1102607</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "<tr><td>2</td><td>i</td><td>1160356</td><td>1160356</td><td>1291883</td><td>1291883</td><td>1714230</td><td>987298</td><td>1953737</td><td>1086535</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "<tr><td>3</td><td>z</td><td>1132842</td><td>1132842</td><td>1260729</td><td>1260729</td><td>1701530</td><td>984971</td><td>1933534</td><td>1081349</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "<tr><td>4</td><td>y</td><td>1035328</td><td>500551</td><td>1157878</td><td>574161</td><td>1467985</td><td>798995</td><td>1669840</td><td>893700</td><td>661669</td><td>661669</td><td>693726</td><td>693726</td></tr>\n",
       "<tr><td>5</td><td>n921</td><td>1088153</td><td>1088153</td><td>1207243</td><td>1207243</td><td>879166</td><td>494988</td><td>996296</td><td>546326</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
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       ".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(\"$('#table140432966451888-856352').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",
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       "\n",
       "    $('#table140432966451888-856352').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
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       "        columnDefs: [{targets: [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], type: \"optionalnum\"}]\n",
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       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suprime_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "suprime_origin.write(\"{}/cosmos_suprime_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## VII.b Megacam\n",
    "\n",
    "### COSMOS vs CFHT-WIRDS vs CFHTLS\n",
    "\n",
    "We take COSMOS over CFHTLS over CFHT-WIRDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "megacam_origin = Table()\n",
    "megacam_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "megacam_stats = Table()\n",
    "megacam_stats.add_column(Column(data=['u','g','r','i','z'], name=\"Band\"))\n",
    "for col in [\"COSMOS2015\", \"CFHTLS\",  \"CFHT-WIRDS\"]:\n",
    "    megacam_stats.add_column(Column(data=np.full(5, 0), name=\"{}\".format(col)))\n",
    "    megacam_stats.add_column(Column(data=np.full(5, 0), name=\"use {}\".format(col)))\n",
    "    megacam_stats.add_column(Column(data=np.full(5, 0), name=\"{} ap\".format(col)))\n",
    "    megacam_stats.add_column(Column(data=np.full(5, 0), name=\"use {} ap\".format(col)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "megacam_bands = ['u','g','r','i','z'] \n",
    "for band in megacam_bands:\n",
    "\n",
    "    # megacam total flux \n",
    "    has_cfhtls = ~np.isnan(master_catalogue['f_megacam_' + band])\n",
    "    has_wirds  = ~np.isnan(master_catalogue['f_wirds_' + band])\n",
    "    if band == 'u':\n",
    "        has_cosmos  = ~np.isnan(master_catalogue['f_cosmos-megacam_' + band])\n",
    "    elif band != 'u':\n",
    "        has_cosmos = np.full(len(master_catalogue), False, dtype=bool)\n",
    "        \n",
    "    \n",
    "    use_cosmos = has_cosmos\n",
    "    use_cfhtls = has_cfhtls & ~has_cosmos\n",
    "    use_wirds = has_wirds & ~has_cfhtls & ~has_cosmos\n",
    " \n",
    "    master_catalogue['f_megacam_' + band][use_wirds] = master_catalogue['f_wirds_' + band][use_wirds]\n",
    "    master_catalogue['ferr_megacam_' + band][use_wirds] = master_catalogue['ferr_wirds_' + band][use_wirds]\n",
    "    master_catalogue['m_megacam_' + band][use_wirds] = master_catalogue['m_wirds_' + band][use_wirds]\n",
    "    master_catalogue['merr_megacam_' + band][use_wirds] = master_catalogue['merr_wirds_' + band][use_wirds]\n",
    "    master_catalogue['flag_megacam_' + band][use_wirds] = master_catalogue['flag_wirds_' + band][use_wirds]\n",
    "\n",
    "\n",
    "    master_catalogue.remove_columns(['f_wirds_' + band,\n",
    "                               'ferr_wirds_' + band,\n",
    "                               'm_wirds_' + band, \n",
    "                               'merr_wirds_' + band,\n",
    "                               'flag_wirds_' + band])\n",
    " \n",
    "    if band == 'u':\n",
    "        master_catalogue['f_megacam_' + band][use_cosmos] = master_catalogue['f_cosmos-megacam_' + band][use_cosmos]\n",
    "        master_catalogue['ferr_megacam_' + band][use_cosmos] = master_catalogue['ferr_cosmos-megacam_' + band][use_cosmos]\n",
    "        master_catalogue['m_megacam_' + band][use_cosmos] = master_catalogue['m_cosmos-megacam_' + band][use_cosmos]\n",
    "        master_catalogue['merr_megacam_' + band][use_cosmos] = master_catalogue['merr_cosmos-megacam_' + band][use_cosmos]\n",
    "        master_catalogue['flag_megacam_' + band][use_cosmos] = master_catalogue['flag_cosmos-megacam_' + band][use_cosmos]\n",
    "        \n",
    "        master_catalogue.remove_columns(['f_cosmos-megacam_' + band,\n",
    "                               'ferr_cosmos-megacam_' + band,\n",
    "                               'm_cosmos-megacam_' + band, \n",
    "                               'merr_cosmos-megacam_' + band,\n",
    "                               'flag_cosmos-megacam_' + band])\n",
    "        \n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_cfhtls] = \"CFHTLS\"\n",
    "    origin[use_wirds] = \"CFHT-WIRDS\"\n",
    "    origin[use_cosmos] = \"COSMOS2015\"\n",
    " \n",
    "    \n",
    "    megacam_origin.add_column(Column(data=origin, name= 'f_megacam_' + band ))\n",
    "    \n",
    "  \n",
    "    # Megacam aperture flux \n",
    "    has_ap_cfhtls = ~np.isnan(master_catalogue['f_ap_megacam_' + band])\n",
    "    has_ap_wirds  = ~np.isnan(master_catalogue['f_ap_wirds_' + band])\n",
    "    if band == 'u':\n",
    "        has_ap_cosmos  = ~np.isnan(master_catalogue['f_ap_cosmos-megacam_' + band])\n",
    "    elif band != 'u':\n",
    "        has_ap_cosmos = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    \n",
    "    use_ap_cosmos =  has_ap_cosmos\n",
    "    use_ap_cfhtls = has_ap_cfhtls & ~has_ap_cosmos\n",
    "    use_ap_wirds = has_ap_wirds & ~has_ap_cfhtls & ~has_ap_cosmos\n",
    "  \n",
    "\n",
    "    master_catalogue['f_ap_megacam_' + band][use_ap_wirds] = master_catalogue['f_ap_wirds_' + band][use_ap_wirds]\n",
    "    master_catalogue['ferr_ap_megacam_' + band][use_ap_wirds] = master_catalogue['ferr_ap_wirds_' + band][use_ap_wirds]\n",
    "    master_catalogue['m_ap_megacam_' + band][use_ap_wirds] = master_catalogue['m_ap_wirds_' + band][use_ap_wirds]\n",
    "    master_catalogue['merr_ap_megacam_' + band][use_ap_wirds] = master_catalogue['merr_ap_wirds_' + band][use_ap_wirds]\n",
    "\n",
    "    master_catalogue.remove_columns(['f_ap_wirds_' + band,\n",
    "                               'ferr_ap_wirds_' + band,\n",
    "                               'm_ap_wirds_' + band, \n",
    "                               'merr_ap_wirds_' + band])\n",
    "    \n",
    "    if band == 'u':\n",
    "        master_catalogue['f_ap_megacam_' + band][use_ap_cosmos] = master_catalogue['f_ap_cosmos-megacam_' + band][use_ap_cosmos]\n",
    "        master_catalogue['ferr_ap_megacam_' + band][use_ap_cosmos] = master_catalogue['ferr_ap_cosmos-megacam_' + band][use_ap_cosmos]\n",
    "        master_catalogue['m_ap_megacam_' + band][use_ap_cosmos] = master_catalogue['m_ap_cosmos-megacam_' + band][use_ap_cosmos]\n",
    "        master_catalogue['merr_ap_megacam_' + band][use_ap_cosmos] = master_catalogue['merr_ap_cosmos-megacam_' + band][use_ap_cosmos]\n",
    "        \n",
    "        \n",
    "        master_catalogue.remove_columns(['f_ap_cosmos-megacam_' + band,\n",
    "                               'ferr_ap_cosmos-megacam_' + band,\n",
    "                               'm_ap_cosmos-megacam_' + band, \n",
    "                               'merr_ap_cosmos-megacam_' + band])\n",
    "\n",
    "    origin_ap = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin_ap[use_ap_cfhtls] = \"CFHTLS\"\n",
    "    origin_ap[use_ap_wirds] = \"CFHT-WIRDS\"\n",
    "    origin_ap[use_ap_cosmos] = \"COSMOS2015\"\n",
    "    \n",
    "    megacam_origin.add_column(Column(data=origin_ap, name= 'f_ap_megacam_' + band ))\n",
    "\n",
    "   \n",
    "    megacam_stats['CFHTLS'][megacam_stats['Band'] == band] = np.sum(has_cfhtls)\n",
    "    megacam_stats['CFHT-WIRDS'][megacam_stats['Band'] == band] = np.sum(has_wirds)\n",
    "    megacam_stats['COSMOS2015'][megacam_stats['Band'] == band] = np.sum(has_cosmos)\n",
    "\n",
    "    megacam_stats['use CFHTLS'][megacam_stats['Band'] == band] = np.sum(use_cfhtls)\n",
    "    megacam_stats['use CFHT-WIRDS'][megacam_stats['Band'] == band] = np.sum(use_wirds)\n",
    "    megacam_stats['use COSMOS2015'][megacam_stats['Band'] == band] = np.sum(use_cosmos)\n",
    "\n",
    "    megacam_stats['CFHTLS ap'][megacam_stats['Band'] == band] = np.sum(has_ap_cfhtls)\n",
    "    megacam_stats['CFHT-WIRDS ap'][megacam_stats['Band'] == band] = np.sum(has_ap_wirds)\n",
    "    megacam_stats['COSMOS2015 ap'][megacam_stats['Band'] == band] = np.sum(has_ap_cosmos)\n",
    "\n",
    "    megacam_stats['use CFHTLS ap'][megacam_stats['Band'] == band] = np.sum(use_ap_cfhtls)\n",
    "    megacam_stats['use CFHT-WIRDS ap'][megacam_stats['Band'] == band] = np.sum(use_ap_wirds)\n",
    "    megacam_stats['use COSMOS2015 ap'][megacam_stats['Band'] == band] = np.sum(use_ap_cosmos)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "<i>Table length=5</i>\n",
       "<table id=\"table140433884686824-730645\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>COSMOS2015</th><th>use COSMOS2015</th><th>COSMOS2015 ap</th><th>use COSMOS2015 ap</th><th>CFHTLS</th><th>use CFHTLS</th><th>CFHTLS ap</th><th>use CFHTLS ap</th><th>CFHT-WIRDS</th><th>use CFHT-WIRDS</th><th>CFHT-WIRDS ap</th><th>use CFHT-WIRDS ap</th></tr></thead>\n",
       "<tr><td>0</td><td>u</td><td>640825</td><td>640825</td><td>688187</td><td>688187</td><td>455496</td><td>183226</td><td>459328</td><td>182038</td><td>156347</td><td>17152</td><td>157331</td><td>16078</td></tr>\n",
       "<tr><td>1</td><td>g</td><td>0</td><td>0</td><td>0</td><td>0</td><td>516573</td><td>516573</td><td>518549</td><td>518549</td><td>164107</td><td>31365</td><td>163978</td><td>30985</td></tr>\n",
       "<tr><td>2</td><td>r</td><td>0</td><td>0</td><td>0</td><td>0</td><td>526336</td><td>526336</td><td>529160</td><td>529160</td><td>165217</td><td>31524</td><td>165010</td><td>31136</td></tr>\n",
       "<tr><td>3</td><td>i</td><td>0</td><td>0</td><td>0</td><td>0</td><td>520668</td><td>520668</td><td>523948</td><td>523948</td><td>165648</td><td>31737</td><td>165316</td><td>31298</td></tr>\n",
       "<tr><td>4</td><td>z</td><td>0</td><td>0</td><td>0</td><td>0</td><td>474766</td><td>474766</td><td>480581</td><td>480581</td><td>164234</td><td>30996</td><td>164359</td><td>30824</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(\"$('#table140433884686824-730645').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",
       "    $('#table140433884686824-730645').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "megacam_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "megacam_origin.write(\"{}/cosmos_megacam_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## WIRcam\n",
    "\n",
    "### COSMOS vs WIRDS\n",
    "\n",
    "We take COSMOS over WIRDS to ensure values are the same as for the original run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "wircam_origin = Table()\n",
    "wircam_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "wircam_stats = Table()\n",
    "wircam_stats.add_column(Column(data=['h','ks'], name=\"Band\"))\n",
    "for col in [\"CFHT-WIRDS\",   \"COSMOS2015\"]:\n",
    "    wircam_stats.add_column(Column(data=np.full(2, 0), name=\"{}\".format(col)))\n",
    "    wircam_stats.add_column(Column(data=np.full(2, 0), name=\"use {}\".format(col)))\n",
    "    wircam_stats.add_column(Column(data=np.full(2, 0), name=\"{} ap\".format(col)))\n",
    "    wircam_stats.add_column(Column(data=np.full(2, 0), name=\"use {} ap\".format(col)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "wircam_bands = ['h','ks'] \n",
    "for band in wircam_bands:\n",
    "\n",
    "    # wircam total flux \n",
    "    has_wirds = ~np.isnan(master_catalogue['f_wirds_' + band.rstrip('s')])\n",
    "\n",
    "    has_cosmos = ~np.isnan(master_catalogue['f_cosmos-wircam_' + band])\n",
    "\n",
    "    \n",
    "    use_cosmos = has_cosmos\n",
    "    use_wirds = has_wirds & ~has_cosmos\n",
    "\n",
    "    \n",
    " \n",
    "    f_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    f_wircam[use_cosmos] = master_catalogue['f_cosmos-wircam_' + band][use_cosmos]\n",
    "    f_wircam[use_wirds] = master_catalogue['f_wirds_' + band.rstrip('s').rstrip('s')][use_wirds]\n",
    "\n",
    " \n",
    "\n",
    "    ferr_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_wircam[use_cosmos] = master_catalogue['ferr_cosmos-wircam_' + band][use_cosmos]\n",
    "    ferr_wircam[use_wirds] = master_catalogue['ferr_wirds_' + band.rstrip('s')][use_wirds]\n",
    "\n",
    "\n",
    "    \n",
    "    m_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    m_wircam[use_cosmos] = master_catalogue['m_cosmos-wircam_' + band][use_cosmos]\n",
    "    m_wircam[use_wirds] = master_catalogue['m_wirds_' + band.rstrip('s')][use_wirds]\n",
    "\n",
    "\n",
    "    merr_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    merr_wircam[use_cosmos] = master_catalogue['merr_cosmos-wircam_' + band][use_cosmos]\n",
    "    merr_wircam[use_wirds] = master_catalogue['merr_wirds_' + band.rstrip('s')][use_wirds]\n",
    "\n",
    "\n",
    "\n",
    "    flag_wircam = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    flag_wircam[use_cosmos] = master_catalogue['flag_cosmos-wircam_' + band][use_cosmos]\n",
    "    flag_wircam[use_wirds] = master_catalogue['flag_wirds_' + band.rstrip('s')][use_wirds]\n",
    "\n",
    "\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_wircam, name=\"f_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_wircam, name=\"ferr_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_wircam, name=\"m_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_wircam, name=\"merr_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=flag_wircam, name=\"flag_wircam_\" + band))\n",
    "\n",
    "    old_wirds_columns = ['f_wirds_' + band.rstrip('s'),\n",
    "                               'ferr_wirds_' + band.rstrip('s'),\n",
    "                               'm_wirds_' + band.rstrip('s'), \n",
    "                               'merr_wirds_' + band.rstrip('s'),\n",
    "                               'flag_wirds_' + band.rstrip('s')]\n",
    "\n",
    "    old_cosmos_columns = ['f_cosmos-wircam_' + band,\n",
    "                               'ferr_cosmos-wircam_' + band,\n",
    "                               'm_cosmos-wircam_' + band, \n",
    "                               'merr_cosmos-wircam_' + band,\n",
    "                               'flag_cosmos-wircam_' + band]\n",
    "    \n",
    "    old_columns = old_wirds_columns + old_cosmos_columns\n",
    "    master_catalogue.remove_columns(old_columns)\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_wirds] = \"CFHT-WIRDS\"\n",
    "    origin[use_cosmos] = \"COSMOS2015\"\n",
    " \n",
    "    \n",
    "    wircam_origin.add_column(Column(data=origin, name= 'f_wircam_' + band ))\n",
    "    \n",
    "  \n",
    "    # wircam aperture flux \n",
    "    has_ap_wirds = ~np.isnan(master_catalogue['f_ap_wirds_' + band.rstrip('s')])\n",
    "    has_ap_cosmos = ~np.isnan(master_catalogue['f_ap_cosmos-wircam_' + band])\n",
    "    \n",
    " \n",
    "    \n",
    "    use_ap_cosmos = has_ap_cosmos\n",
    "    use_ap_wirds = has_ap_wirds & ~has_cosmos\n",
    "\n",
    "  \n",
    "\n",
    "    f_ap_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    f_ap_wircam[use_ap_cosmos] = master_catalogue['f_ap_cosmos-wircam_' + band][use_ap_cosmos]\n",
    "    f_ap_wircam[use_ap_wirds] = master_catalogue['f_ap_wirds_' + band.rstrip('s')][use_ap_wirds]\n",
    "\n",
    "\n",
    "\n",
    "    ferr_ap_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_ap_wircam[use_ap_cosmos] = master_catalogue['ferr_ap_cosmos-wircam_' + band][use_ap_cosmos]\n",
    "    ferr_ap_wircam[use_ap_wirds] = master_catalogue['ferr_ap_wirds_' + band.rstrip('s')][use_ap_wirds]\n",
    "\n",
    "   \n",
    "    \n",
    "    m_ap_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    m_ap_wircam[use_ap_cosmos] = master_catalogue['m_ap_cosmos-wircam_' + band][use_ap_cosmos]\n",
    "    m_ap_wircam[use_ap_wirds] = master_catalogue['m_ap_wirds_' + band.rstrip('s')][use_ap_wirds]\n",
    "\n",
    "  \n",
    "\n",
    "    merr_ap_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    merr_ap_wircam[use_ap_cosmos] = master_catalogue['merr_ap_cosmos-wircam_' + band][use_ap_cosmos]\n",
    "    merr_ap_wircam[use_ap_wirds] = master_catalogue['merr_ap_wirds_' + band.rstrip('s')][use_ap_wirds]\n",
    "\n",
    "  \n",
    "\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_ap_wircam, name=\"f_ap_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_ap_wircam, name=\"ferr_ap_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_ap_wircam, name=\"m_ap_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_ap_wircam, name=\"merr_ap_wircam_\" + band))\n",
    "\n",
    "\n",
    "    old_ap_wirds_columns = ['f_ap_wirds_' + band.rstrip('s'),\n",
    "                               'ferr_ap_wirds_' + band.rstrip('s'),\n",
    "                               'm_ap_wirds_' + band.rstrip('s'), \n",
    "                               'merr_ap_wirds_' + band.rstrip('s')]\n",
    "\n",
    "    old_ap_cosmos_columns = ['f_ap_cosmos-wircam_' + band,\n",
    "                               'ferr_ap_cosmos-wircam_' + band,\n",
    "                               'm_ap_cosmos-wircam_' + band, \n",
    "                               'merr_ap_cosmos-wircam_' + band]\n",
    "\n",
    "    \n",
    "    old_ap_columns = old_ap_wirds_columns + old_ap_cosmos_columns\n",
    "    master_catalogue.remove_columns(old_ap_columns)\n",
    "\n",
    "    origin_ap = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin_ap[use_ap_wirds] = \"CFHT-WIRDS\"\n",
    "    origin_ap[use_ap_cosmos] = \"COSMOS2015\"\n",
    "\n",
    "    \n",
    "    wircam_origin.add_column(Column(data=origin_ap, name= 'f_ap_wircam_' + band ))\n",
    "\n",
    "   \n",
    "    wircam_stats['CFHT-WIRDS'][wircam_stats['Band'] == band] = np.sum(has_wirds)\n",
    "    wircam_stats['COSMOS2015'][wircam_stats['Band'] == band] = np.sum(has_cosmos)\n",
    "\n",
    "    wircam_stats['use CFHT-WIRDS'][wircam_stats['Band'] == band] = np.sum(use_wirds)\n",
    "    wircam_stats['use COSMOS2015'][wircam_stats['Band'] == band] = np.sum(use_cosmos)\n",
    "\n",
    "    wircam_stats['CFHT-WIRDS ap'][wircam_stats['Band'] == band] = np.sum(has_ap_wirds)\n",
    "    wircam_stats['COSMOS2015 ap'][wircam_stats['Band'] == band] = np.sum(has_ap_cosmos)\n",
    "    \n",
    "    wircam_stats['use CFHT-WIRDS ap'][wircam_stats['Band'] == band] = np.sum(use_ap_wirds)\n",
    "    wircam_stats['use COSMOS2015 ap'][wircam_stats['Band'] == band] = np.sum(use_ap_cosmos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "<i>Table length=2</i>\n",
       "<table id=\"table140432964890240-429686\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>CFHT-WIRDS</th><th>use CFHT-WIRDS</th><th>CFHT-WIRDS ap</th><th>use CFHT-WIRDS ap</th><th>COSMOS2015</th><th>use COSMOS2015</th><th>COSMOS2015 ap</th><th>use COSMOS2015 ap</th></tr></thead>\n",
       "<tr><td>0</td><td>h</td><td>164175</td><td>32620</td><td>165228</td><td>33198</td><td>589027</td><td>589027</td><td>684053</td><td>684053</td></tr>\n",
       "<tr><td>1</td><td>ks</td><td>169103</td><td>35504</td><td>170077</td><td>36333</td><td>594551</td><td>594551</td><td>681987</td><td>681987</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(\"$('#table140432964890240-429686').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",
       "    $('#table140432964890240-429686').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 2, 3, 4, 5, 6, 7, 8, 9], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wircam_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "wircam_origin.write(\"{}/cosmos_wircam_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Final renaming\n",
    "\n",
    "We rename some columns in line with HELP filter naming standards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "renaming = OrderedDict({\n",
    "    '_wirds_j':'_wircam_j',\n",
    "    #'_wirds_h': '_wircam_h', #These two now merged with COSMOS\n",
    "    #'_wirds_k': '_wircam_ks',\n",
    "    '_kids_': '_omegacam_',\n",
    "    '_cosmos-suprime_': '_suprime_',\n",
    "    '_cosmos-vista_':'_vista_',\n",
    "    '_cosmos-irac_':'_irac_',\n",
    "    '_candels_f140w':'_wfc3_f140w',\n",
    "    '_candels_f160w':'_wfc3_f160w',\n",
    "    '_candels_f125w':'_wfc3_f125w',\n",
    "    '_candels_f606w': '_acs_f606w',\n",
    "    '_candels_f814w':'_acs_f814w',\n",
    "})\n",
    "\n",
    "\n",
    "for col in master_catalogue.colnames:\n",
    "    for rename_col in list(renaming):\n",
    "        if rename_col in col:\n",
    "            master_catalogue.rename_column(col, col.replace(rename_col, renaming[rename_col]))      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## VII.a Wavelength domain coverage\n",
    "\n",
    "We add a binary `flag_optnir_obs` indicating that a source was observed in a given wavelength domain:\n",
    "\n",
    "- 1 for observation in optical;\n",
    "- 2 for observation in near-infrared;\n",
    "- 4 for observation in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source observed both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: The observation flag is based on the creation of multi-order coverage maps from the catalogues, this may not be accurate, especially on the edges of the coverage.*\n",
    "\n",
    "*Note 2: Being on the observation coverage does not mean having fluxes in that wavelength domain. For sources observed in one domain but having no flux in it, one must take into consideration de different depths in the catalogue we are using.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "candels_moc = MOC(filename=\"../../dmu0/dmu0_CANDELS-3D-HST/data/CANDELS-3D-HST_COSMOS_MOC.fits\")\n",
    "cfhtls_moc = MOC(filename=\"../../dmu0/dmu0_CFHTLS/data/CFHTLS-DEEP_COSMOS_MOC.fits\")\n",
    "decals_moc = MOC(filename=\"../../dmu0/dmu0_DECaLS/data/DECaLS_COSMOS_MOC.fits\")\n",
    "hsc_udeep_moc = MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_deep_COSMOS_MOC.fits\")\n",
    "hsc_deep_moc = MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_uDeep_COSMOS_MOC.fits\")\n",
    "kids_moc = MOC(filename=\"../../dmu0/dmu0_KIDS/data/KIDS-DR3_COSMOS_MOC.fits\")\n",
    "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_COSMOS_MOC.fits\")\n",
    "las_moc = MOC(filename=\"../../dmu0/dmu0_UKIDSS-LAS/data/UKIDSS-LAS_COSMOS_MOC.fits\")\n",
    "wirds_moc = MOC(filename=\"../../dmu0/dmu0_CFHT-WIRDS/data/COSMOS_Ks-priors_MOC.fits\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    candels_moc + \n",
    "    cfhtls_moc + \n",
    "    decals_moc + \n",
    "    hsc_udeep_moc + \n",
    "    hsc_deep_moc + \n",
    "    kids_moc +\n",
    "    ps1_moc) \n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    las_moc + wirds_moc\n",
    ")\n",
    "\n",
    "was_observed_mir = np.zeros(len(master_catalogue), dtype=bool)\n",
    "\n",
    "#was_observed_mir = inMoc(\n",
    "#    master_catalogue['ra'], master_catalogue['dec'],   \n",
    "#)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": 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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## VII.b Wavelength domain detection\n",
    "\n",
    "We add a binary `flag_optnir_det` indicating that a source was detected in a given wavelength domain:\n",
    "\n",
    "- 1 for detection in optical;\n",
    "- 2 for detection in near-infrared;\n",
    "- 4 for detection in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source detected both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: We use the total flux columns to know if the source has flux, in some catalogues, we may have aperture flux and no total flux.*\n",
    "\n",
    "To get rid of artefacts (chip edges, star flares, etc.) we consider that a source is detected in one wavelength domain when it has a flux value in **at least two bands**. That means that good sources will be excluded from this flag when they are on the coverage of only one band."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "nb_optical_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_u']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_z']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_y']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_n921']) +\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",
    "    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_omegacam_u']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_omegacam_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_omegacam_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_omegacam_i']) \n",
    ")\n",
    "\n",
    "nb_nir_flux = (\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_vista_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_ks']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_wircam_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wircam_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wircam_ks']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_y']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_k'])\n",
    ")\n",
    "\n",
    "nb_mir_flux = np.zeros(len(master_catalogue), dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": 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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## VIII - Cross-identification table\n",
    "\n",
    "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogue. This can be used to easily get additional information from them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64 master list rows had multiple associations.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#\n",
    "# Addind SDSS ids\n",
    "#\n",
    "sdss = Table.read(\"../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_COSMOS.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": 62,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['old_help_id', 'cosmos_id', 'candels_id', 'cfhtls_id', 'decals_id', 'hsc-udeep_id', 'hsc-deep_id', 'kids_id', 'las_id', 'wirds_id', 'ps1_id', 'help_id', 'specz_id', 'sdss_id']\n"
     ]
    }
   ],
   "source": [
    "id_names = []\n",
    "for col in master_catalogue.colnames:\n",
    "    if '_id' in col:\n",
    "        id_names += [col]\n",
    "    if '_intid' in col:\n",
    "        id_names += [col]\n",
    "        \n",
    "print(id_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_cosmos{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)\n",
    "id_names.remove('help_id')\n",
    "id_names.remove('old_help_id')\n",
    "master_catalogue.remove_columns(id_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## IX - Adding HEALPix index\n",
    "\n",
    "We are adding a column with a HEALPix index at order 13 associated with each source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": 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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## IX - Saving the catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "columns = [\"help_id\", \"old_help_id\", \"field\", \"ra\", \"dec\", \"hp_idx\"]\n",
    "\n",
    "bands = [column[5:] for column in master_catalogue.colnames if 'f_ap' in column]\n",
    "for band in bands:\n",
    "    columns += [\"f_ap_{}\".format(band), \"ferr_ap_{}\".format(band),\n",
    "                \"m_ap_{}\".format(band), \"merr_ap_{}\".format(band),\n",
    "                \"f_{}\".format(band), \"ferr_{}\".format(band),\n",
    "                \"m_{}\".format(band), \"merr_{}\".format(band),\n",
    "                \"flag_{}\".format(band)]    \n",
    "    \n",
    "# columns += ['f_wfc3_f125w', 'ferr_wfc3_f125w', 'm_wfc3_f125w', 'merr_wfc3_f125w', 'flag_wfc3_f125w',\n",
    "#            'f_acs_f606w', 'ferr_acs_f606w', 'm_acs_f606w', 'merr_acs_f606w', 'flag_acs_f606w',\n",
    "#            'f_acs_f814w', 'ferr_acs_f814w', 'm_acs_f814w', 'merr_acs_f814w', 'flag_acs_f814w'\n",
    "#           ]\n",
    "\n",
    "for tot_band in ['wfc3_f125w', 'acs_f606w', 'acs_f814w', 'irac_i1', 'irac_i2', 'irac_i3', 'irac_i4']:\n",
    "    columns += ['f_' + tot_band,'ferr_' + tot_band,'m_' + tot_band,'merr_' + tot_band,'flag_' + tot_band,]\n",
    "    \n",
    "columns += [\"stellarity\", \"stellarity_origin\", \"flag_cleaned\", \n",
    "            \"flag_merged\", \"flag_gaia\", \"flag_optnir_obs\", \"flag_optnir_det\", \"ebv\",'zspec_association_flag', 'zspec_qual', 'zspec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing columns: set()\n"
     ]
    }
   ],
   "source": [
    "# We check for columns in the master catalogue that we will not save to disk.\n",
    "print(\"Missing columns: {}\".format(set(master_catalogue.colnames) - set(columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[columns].write(\"{}/master_catalogue_cosmos{}.fits\".format(OUT_DIR, SUFFIX), overwrite = True)"
   ]
  }
 ],
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