{
 "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",
      "0246c5d (Thu Jan 25 17:01:47 2018 +0000) [with local modifications]\n",
      "This notebook was executed on: \n",
      "2018-03-13 20:14:56.180271\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": [],
   "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 to the nearest source in the merged catalogue to determine the best crossmatching 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": {
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AACaHYOWj2saQzKQrS7M8e8/y3IAkaVdDp2fvCQAAJodg5aPaYJcW5qcpIyXRs/csykxR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\nBQAA4JH/D0aLJbMKwCVmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5601ca6908>"
      ]
     },
     "metadata": {},
     "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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OK1iJyBUbHJ/m5RMBXjqeXJXqTd0Us6SqjFvXNuIyRvOkZN7aasqxJAe+Lq33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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55bf165dd8>"
      ]
     },
     "metadata": {},
     "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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RRKFgFUd9I5NMTIXjNmJVXZRNU+8ozrm4fH0RERG5OApWcdTaH58eVjHVhdmMTEzTN6Je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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55f5c21c50>"
      ]
     },
     "metadata": {},
     "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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lU7BahJlUxpGu63l1YT8jClYiIiIVT8FqERLJtCPNQfPqIzq8LiIiUg0UrBYhkco40hw0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\nRMQhClYiIiIiDvlfs78Vc8mSgWsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5600e902e8>"
      ]
     },
     "metadata": {},
     "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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vD6A8OefUPxbT4b4Xxx081z+hqdmkJKkxGtQNW+p1780dGpmeU2tdeN1dMo+V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\nj/z/wSDtmF+/JzwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55bf1b14a8>"
      ]
     },
     "metadata": {},
     "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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VClhrmQxGHVyxKmJiNsJ0KOrI5xMREZHUULBKgdlwjGjcXvBw0HmN/rk7A/t1Z6CIiEhG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\nRLRIAquIiIiIFklgFREREdEi/wHRKgtvAXbJvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55be4a0e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(kids['kids_ra'], kids['kids_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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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zaISy3FQSYuh8qpXFGRxqG/C7DBGJYVPTM+xs6OGJ/e08Vd9B38gkmckJvOWq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\nS/4FpfwB2OCnurkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5584eb2f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(las['las_ra'], las['las_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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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i2Y9YgXpZiYiI5BoFKw8lm4MGPRixAh1rIyIikmsUrDw0fuY4G2+CVW2ZpgJFRERyiYKV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\nHZevQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55be516dd8>"
      ]
     },
     "metadata": {},
     "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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5awMAgMtHsIqS052DqshLi8prVxemq294XO39I1F5fQAAcHkIVlHgnFN956CW5kcpWJ3d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\nRwhWAAAAHiFYAQAAeIRgBQAA4JH/H12N32spRjWJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55bf1d0c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(ps1['ps1_ra'], ps1['ps1_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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=\"table140006255205680-130307\" 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>149.810113605</td><td>2.26688583986</td><td>1.0</td><td>nan</td><td>nan</td><td>-2.22482995014e-05</td><td>0.0321263000369</td><td>nan</td><td>nan</td><td>0.030790200457</td><td>0.0447486005723</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>26.3257007599</td><td>0.333140999079</td><td>0.0817134007812</td><td>0.0697752013803</td><td>27.7229003906</td><td>0.379294991493</td><td>-0.00163483002689</td><td>0.0165330991149</td><td>29.3367996216</td><td>1.4130500555</td><td>0.00668556010351</td><td>0.00960237998515</td><td>27.5137004852</td><td>0.470728993416</td><td>0.0135164000094</td><td>0.0183342993259</td><td>27.7040996552</td><td>0.865401983261</td><td>0.0188004001975</td><td>0.0216425005347</td><td>27.5158004761</td><td>0.450976997614</td><td>0.0247978009284</td><td>0.0147521002218</td><td>nan</td><td>nan</td><td>-0.0180395003408</td><td>0.148266002536</td><td>27.113199234</td><td>0.52871799469</td><td>0.0212169997394</td><td>0.0357417017221</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>31.638999939</td><td>38.7386016846</td><td>-0.00277781998739</td><td>0.0303927008063</td><td>27.1289997101</td><td>0.849375009537</td><td>0.0426197983325</td><td>0.0390538983047</td><td>25.6261005402</td><td>0.204623997211</td><td>0.0746800005436</td><td>0.0542262010276</td><td>nan</td><td>nan</td><td>-0.0401588007808</td><td>0.0494364984334</td><td>nan</td><td>nan</td><td>-0.0329038985074</td><td>0.0625786036253</td><td>nan</td><td>nan</td><td>-0.0176042001694</td><td>0.0339404009283</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-0.0356898009777</td><td>0.0410397015512</td><td>32.5750999451</td><td>129.850997925</td><td>0.0323950983584</td><td>0.0547765009105</td><td>nan</td><td>nan</td><td>-0.0363387987018</td><td>0.0410583987832</td><td>nan</td><td>nan</td><td>-0.023999100551</td><td>0.0625353977084</td><td>nan</td><td>nan</td><td>0.0373064987361</td><td>0.0778475999832</td><td>nan</td><td>nan</td><td>-0.00767386006191</td><td>0.0644085034728</td><td>25.4703998566</td><td>0.913335025311</td><td>0.0904728993773</td><td>0.287485003471</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.0302143990993</td><td>0.12773899734</td><td>0.186370998621</td><td>0.0950023978949</td><td>25.7241001129</td><td>0.553453981876</td><td>-99.9000015259</td><td>-99.9000015259</td><td>-99.9000015259</td><td>-99.9000015259</td><td>0.0</td><td>2.30679988861</td><td>-99.9000015259</td><td>-99.9000015259</td><td>0.0</td><td>2.68687009811</td><td>-99.9000015259</td><td>-99.9000015259</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1567.79125977</td><td>nan</td><td>nan</td><td>False</td><td>27.6789703369</td><td>1.577942729</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.10708270967</td><td>0.0328566357493</td><td>False</td><td>26.6192703247</td><td>0.92711186409</td><td>0.0295691955835</td><td>0.0103298090398</td><td>False</td><td>nan</td><td>-10.980091095</td><td>0.00668775243685</td><td>0.00870389118791</td><td>False</td><td>29.3371582031</td><td>1.55942821503</td><td>0.0358525142074</td><td>0.0155441239476</td><td>False</td><td>28.572845459</td><td>1.47274518013</td><td>0.0300857052207</td><td>0.0239802524447</td><td>False</td><td>28.2145843506</td><td>1.24986946583</td><td>0.0357832238078</td><td>0.0148631036282</td><td>False</td><td>27.9139709473</td><td>0.64589959383</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-8.92362594604</td><td>0.0518465973437</td><td>0.0252475980669</td><td>False</td><td>28.0832901001</td><td>1.829007864</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.000802416529041</td><td>0.0286298766732</td><td>False</td><td>nan</td><td>-11.8792629242</td><td>0.0510975159705</td><td>0.0399737507105</td><td>False</td><td>27.3259735107</td><td>0.994895040989</td><td>0.203966960311</td><td>0.0384407714009</td><td>False</td><td>26.7169952393</td><td>0.788368284702</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.33656859398</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.06491804123</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.09326887131</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.24848794937</td><td>0.000338812707923</td><td>0.0405210442841</td><td>False</td><td>27.6238021851</td><td>1.83585870266</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.22674918175</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.8291451931</td><td>nan</td><td>nan</td><td>False</td><td>27.4705429077</td><td>2.26560950279</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-9.11283779144</td><td>0.235418185592</td><td>0.198036745191</td><td>False</td><td>26.5087051392</td><td>3.45001459122</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td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</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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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><t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nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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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n</td><td>nan</td><td>nan</td><td>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>5</td><td>HELP_J095739.74+020106.13</td><td>COSMOS2015_J095739.74+020106.13</td><td>149.415582</td><td>2.01836706221</td><td>1.0</td><td>26.7628993988</td><td>0.753624975681</td><td>-0.000113380003313</td><td>0.0412022992969</td><td>25.6324996948</td><td>0.11337800324</td><td>0.171386003494</td><td>0.0564917996526</td><td>28.5618000031</td><td>4.18720006943</td><td>0.0126599995419</td><td>0.125743001699</td><td>25.2994995117</td><td>0.139348000288</td><td>0.247887000442</td><td>0.0894107967615</td><td>27.4813995361</td><td>0.320217013359</td><td>0.0309725999832</td><td>0.0208007991314</td><td>26.9248008728</td><td>0.146365001798</td><td>0.0490317009389</td><td>0.0109968995675</td><td>26.789100647</td><td>0.221683993936</td><td>0.0528322011232</td><td>0.0204211995006</td><td>26.5202007294</td><td>0.256989985704</td><td>0.0753317028284</td><td>0.0228457003832</td><td>26.7376995087</td><td>0.200891003013</td><td>0.068857498467</td><td>0.0163049995899</td><td>25.491399765</td><td>0.251448988914</td><td>0.201304003596</td><td>0.165234997869</td><td>26.1494998932</td><td>0.185737997293</td><td>0.0898322016001</td><td>0.0362550988793</td><td>31.0365009308</td><td>18.6769008636</td><td>0.0221161004156</td><td>0.0336868017912</td><td>26.6819992065</td><td>0.329160004854</td><td>0.0654738992453</td><td>0.0292892996222</td><td>26.3227996826</td><td>0.304084986448</td><td>0.0866943001747</td><td>0.0349376015365</td><td>25.9986991882</td><td>0.254842013121</td><td>0.136864006519</td><td>0.057227101177</td><td>26.060300827</td><td>0.334156990051</td><td>0.124164000154</td><td>0.0522492006421</td><td>26.683599472</td><td>0.592015981674</td><td>0.0977313965559</td><td>0.0620034001768</td><td>26.8917999268</td><td>0.430472999811</td><td>0.0672165974975</td><td>0.0349991992116</td><td>nan</td><td>nan</td><td>-0.0067922398448</td><td>0.0412194989622</td><td>26.550699234</td><td>0.420152008533</td><td>0.0779111012816</td><td>0.0449797995389</td><td>nan</td><td>nan</td><td>0.00306477001868</td><td>0.0530688017607</td><td>26.4974002838</td><td>0.371378004551</td><td>0.0971691980958</td><td>0.0443970002234</td><td>26.5650005341</td><td>0.468791007996</td><td>0.0366749018431</td><td>0.0691251009703</td><td>27.0240001678</td><td>1.50836002827</td><td>0.036743298173</td><td>0.0798444002867</td><td>27.5359992981</td><td>0.961488008499</td><td>0.0413306988776</td><td>0.0648398995399</td><td>nan</td><td>nan</td><td>-0.313125014305</td><td>0.361483007669</td><td>25.4489994049</td><td>0.969497978687</td><td>0.18642899394</td><td>0.371540009975</td><td>nan</td><td>nan</td><td>0.0376974008977</td><td>0.200175002217</td><td>-0.0186358001083</td><td>0.0352833010256</td><td>-99.9000015259</td><td>-99.9000015259</td><td>-99.9000015259</td><td>-99.9000015259</td><td>-99.9000015259</td><td>-99.9000015259</td><td>0.0</td><td>2.16131997108</td><td>-99.9000015259</td><td>-99.9000015259</td><td>0.0</td><td>3.47568011284</td><td>-99.9000015259</td><td>-99.9000015259</td><td>0.0715880021453</td><td>0.0496902577579</td><td>False</td><td>nan</td><td>-394.55657959</td><td>0.202768340707</td><td>0.0211740843952</td><td>False</td><td>25.8150634766</td><td>0.357877492905</td><td>0.013654621318</td><td>0.0526597835124</td><td>False</td><td>28.643913269</td><td>10.783864975</td><td>0.275549650192</td><td>0.035365216434</td><td>False</td><td>25.4143676758</td><td>0.391616106033</td><td>0.0369351617992</td><td>0.0108933160082</td><td>False</td><td>27.6725540161</td><td>0.729166388512</td><td>0.0616708025336</td><td>0.0083136651665</td><td>False</td><td>27.1738052368</td><td>0.243510439992</td><td>0.0698810443282</td><td>0.0142682082951</td><td>False</td><td>27.0927581787</td><td>0.41966894269</td><td>0.0895198956132</td><td>0.0211890470237</td><td>False</td><td>26.7075576782</td><td>0.329269081354</td><td>0.0732689797878</td><td>0.0135567719117</td><td>False</td><td>26.8051223755</td><td>0.2570951581</td><td>0.230908572674</td><td>0.0534768253565</td><td>False</td><td>25.64037323</td><td>0.891197443008</td><td>0.125950440764</td><td>0.0215464700013</td><td>False</td><td>26.516418457</td><td>0.438188880682</td><td>0.00139765220229</td><td>0.024042500183</td><td>False</td><td>28.038230896</td><td>1.65377163887</td><td>0.0771259143949</td><td>0.0233820769936</td><td>False</td><td>26.8598327637</td><td>0.485696613789</td><td>0.107369259</td><td>0.0300711914897</td><td>False</td><td>26.5550231934</td><td>0.437549173832</td><td>0.144717171788</td><td>0.0339677520096</td><td>False</td><td>26.0592803955</td><td>0.453980088234</td><td>0.136734932661</td><td>0.0420829057693</td><td>False</td><td>26.1650085449</td><td>0.456886380911</td><td>0.077012270689</td><td>0.0419922396541</td><td>False</td><td>26.4249191284</td><td>0.688819944859</td><td>0.0635740458965</td><td>0.0252058561891</td><td>False</td><td>26.8313064575</td><td>0.565335035324</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-6.58891630173</td><td>0.0870402529836</td><td>0.0336823426187</td><td>False</td><td>26.671005249</td><td>0.626819431782</td><td>nan</td><td>nan</td><td>False</td><td>30.1840133667</td><td>18.8003387451</td><td>0.0914196521044</td><td>0.0312702544034</td><td>False</td><td>26.4311752319</td><td>0.496077239513</td><td>0.085901260376</td><td>0.0370898023248</td><td>False</td><td>27.4890823364</td><td>2.04640293121</td><td>0.0562858991325</td><td>0.0781952440739</td><td>False</td><td>27.4870529175</td><td>2.35934042931</td><td>0.0351236723363</td><td>0.0311042312533</td><td>False</td><td>27.3593215942</td><td>1.70331084728</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.25341367722</td><td>0.240104436874</td><td>0.214399009943</td><td>False</td><td>25.7237167358</td><td>2.16379666328</td><td>nan</td><td>nan</td><td>False</td><td>27.4592208862</td><td>5.76531076431</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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>6</td><td>HELP_J100054.82+014728.67</td><td>COSMOS2015_J100054.82+014728.67</td><td>150.228391945</td><td>1.79129408283</td><td>1.0</td><td>29.2465991974</td><td>6.05383014679</td><td>-0.000237561005633</td><td>0.0405572988093</td><td>27.1159992218</td><td>0.336439996958</td><td>0.0502315014601</td><td>0.045074198395</td><td>nan</td><td>nan</td><td>-0.00480272015557</td><td>0.0361643992364</td><td>26.9808998108</td><td>0.451631993055</td><td>0.0548005998135</td><td>0.0638862028718</td><td>29.4596996307</td><td>1.33456003666</td><td>0.00622601993382</td><td>0.0168471001089</td><td>27.989900589</td><td>0.324928998947</td><td>0.0230085998774</td><td>0.0107559002936</td><td>27.9109992981</td><td>0.544492006302</td><td>0.0250893998891</td><td>0.0213009994477</td><td>26.8572998047</td><td>0.24190300703</td><td>0.0654063001275</td><td>0.018897999078</td><td>28.4127998352</td><td>0.722095012665</td><td>0.0156159000471</td><td>0.0146786002442</td><td>27.1576004028</td><td>0.816769003868</td><td>0.0513276010752</td><td>0.139272004366</td><td>26.3866004944</td><td>0.180571004748</td><td>0.102343000472</td><td>0.0342869982123</td><td>nan</td><td>nan</td><td>-0.0124096004292</td><td>0.035115301609</td><td>27.6854991913</td><td>0.638763010502</td><td>0.0292589999735</td><td>0.0272396001965</td><td>27.4171009064</td><td>0.648198008537</td><td>0.0395639017224</td><td>0.0327850989997</td><td>nan</td><td>nan</td><td>-0.0372850000858</td><td>0.0510088987648</td><td>27.3796005249</td><td>0.833254992962</td><td>0.0412346981466</td><td>0.0465188995004</td><td>27.7444000244</td><td>1.26174998283</td><td>0.0302250999957</td><td>0.0599373988807</td><td>29.2280006409</td><td>2.89006996155</td><td>0.00541462004185</td><td>0.0329351983964</td><td>26.9734992981</td><td>0.380688011646</td><td>0.0616338998079</td><td>0.0360668003559</td><td>nan</td><td>nan</td><td>-0.00759246014059</td><td>0.0388716012239</td><td>26.7691993713</td><td>0.365110993385</td><td>0.0688109993935</td><td>0.0464650988579</td><td>27.3020992279</td><td>0.591785013676</td><td>0.0455163009465</td><td>0.0405858010054</td><td>25.7651004791</td><td>0.164149001241</td><td>0.174545004964</td><td>0.061018999666</td><td>25.9951000214</td><td>0.454319000244</td><td>0.144473001361</td><td>0.0750107988715</td><td>26.3269996643</td><td>0.259514987469</td><td>0.10660199821</td><td>0.0643362030387</td><td>nan</td><td>nan</td><td>-0.11388900131</td><td>0.357834011316</td><td>nan</td><td>nan</td><td>-0.186820998788</td><td>0.375277012587</td><td>25.8616008759</td><td>0.368202000856</td><td>0.16353200376</td><td>0.121230997145</td><td>0.100625999272</td><td>0.0230882000178</td><td>26.3931999207</td><td>0.249117001891</td><td>-99.9000015259</td><td>-99.9000015259</td><td>-99.9000015259</td><td>-99.9000015259</td><td>0.0</td><td>2.32438993454</td><td>-99.9000015259</td><td>-99.9000015259</td><td>0.0</td><td>2.16131997108</td><td>-99.9000015259</td><td>-99.9000015259</td><td>0.00726709002629</td><td>0.0405197180808</td><td>False</td><td>nan</td><td>-185.360916138</td><td>0.0517130531371</td><td>0.0160244610161</td><td>False</td><td>27.1475601196</td><td>0.9742628932</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-8.17557430267</td><td>0.0585652664304</td><td>0.0243613030761</td><td>False</td><td>27.0530395508</td><td>1.26574456692</td><td>0.00597200496122</td><td>0.00734064076096</td><td>False</td><td>29.4144744873</td><td>2.9379131794</td><td>0.0231227464974</td><td>0.00691995956004</td><td>False</td><td>27.9952774048</td><td>0.50755238533</td><td>0.0248656794429</td><td>0.0124700302258</td><td>False</td><td>27.9012756348</td><td>0.921794295311</td><td>0.0656266137958</td><td>0.0146216703579</td><td>False</td><td>26.8609542847</td><td>0.313704371452</td><td>0.0156631823629</td><td>0.0104171764106</td><td>False</td><td>28.4160842896</td><td>1.02056801319</td><td>0.0497690662742</td><td>0.03743987903</td><td>False</td><td>27.1241226196</td><td>2.9460299015</td><td>0.101241752505</td><td>0.0168377216905</td><td>False</td><td>26.3748550415</td><td>0.363743811846</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-3.07229495049</td><td>0.0306055545807</td><td>0.0180059354752</td><td>False</td><td>27.7343521118</td><td>1.01080071926</td><td>0.039188593626</td><td>0.0233960766345</td><td>False</td><td>27.40675354</td><td>0.899708211422</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.48537492752</td><td>0.040565751493</td><td>0.0311324372888</td><td>False</td><td>27.3618469238</td><td>1.2248724699</td><td>0.0289894342422</td><td>0.0336890481412</td><td>False</td><td>27.6990814209</td><td>2.15305161476</td><td>0.00739263417199</td><td>0.0196781046689</td><td>False</td><td>29.566078186</td><td>6.60414457321</td><td>0.0589658170938</td><td>0.0206749849021</td><td>False</td><td>26.925453186</td><td>0.635348916054</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-5.55871248245</td><td>0.0711738094687</td><td>0.0239343028516</td><td>False</td><td>26.8058547974</td><td>0.733150839806</td><td>0.0435672514141</td><td>0.0237465109676</td><td>False</td><td>27.2545852661</td><td>0.968124985695</td><td>0.179456695914</td><td>0.0271314866841</td><td>False</td><td>25.7952346802</td><td>0.379561334848</td><td>0.145197674632</td><td>0.0607569888234</td><td>False</td><td>26.0005340576</td><td>0.56371730566</td><td>0.106954753399</td><td>0.0255645513535</td><td>False</td><td>26.3305892944</td><td>0.655261099339</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-3.41133308411</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.18097448349</td><td>0.164194911718</td><td>0.0556828565896</td><td>False</td><td>25.8659973145</td><td>0.804887533188</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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>7</td><td>HELP_J100148.56+015305.15</td><td>COSMOS2015_J100148.56+015305.15</td><td>150.452331512</td><td>1.88475989727</td><td>1.0</td><td>23.8195991516</td><td>0.0921522974968</td><td>-0.000252103985986</td><td>0.0323684997857</td><td>23.3584003448</td><td>0.0269904006273</td><td>0.415197014809</td><td>0.0467310994864</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>25.2387008667</td><td>0.236827999353</td><td>0.0658366978168</td><td>0.0685736984015</td><td>26.0846004486</td><td>0.178726002574</td><td>0.00367368990555</td><td>0.0177814997733</td><td>27.5030994415</td><td>0.624872028828</td><td>0.00124364998192</td><td>0.0112725002691</td><td>26.26720047</td><td>0.302100986242</td><td>-0.0181316994131</td><td>0.0193975009024</td><td>25.8335990906</td><td>0.316940009594</td><td>-0.0135044995695</td><td>0.0223655998707</td><td>25.7278995514</td><td>0.173035994172</td><td>-0.0136021999642</td><td>0.0140941003338</td><td>24.7469005585</td><td>0.27194198966</td><td>0.0535918995738</td><td>0.148923993111</td><td>26.9836006165</td><td>0.895524978638</td><td>-0.0219509005547</td><td>0.0345788002014</td><td>26.147600174</td><td>0.482416003942</td><td>-0.0140969995409</td><td>0.0334184989333</td><td>26.1597995758</td><td>0.479447990656</td><td>-0.00156860996503</td><td>0.029427299276</td><td>27.0347003937</td><td>1.34309005737</td><td>0.0252924002707</td><td>0.0343060009181</td><td>25.5088005066</td><td>0.358393996954</td><td>-0.0303502995521</td><td>0.0537494011223</td><td>26.4827003479</td><td>1.1038299799</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>26.9158992767</td><td>0.969923973083</td><td>0.00950601045042</td><td>0.0328750982881</td><td>26.4088001251</td><td>0.694472014904</td><td>0.0856193006039</td><td>0.0393217988312</td><td>25.0349998474</td><td>0.241332992911</td><td>0.0335065983236</td><td>0.0443288013339</td><td>24.7001991272</td><td>0.162542000413</td><td>-0.0242353007197</td><td>0.0489732995629</td><td>25.9818000793</td><td>0.506787002087</td><td>0.0119542004541</td><td>0.0414104014635</td><td>26.4953994751</td><td>0.983574986458</td><td>nan</td><td>nan</td><td>24.3694000244</td><td>0.279796987772</td><td>-0.0150197995827</td><td>0.0730174034834</td><td>25.3043994904</td><td>0.285836994648</td><td>0.0464681982994</td><td>0.064169600606</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>24.4470996857</td><td>0.731725990772</td><td>0.185175001621</td><td>0.307033985853</td><td>24.7178001404</td><td>0.369619995356</td><td>0.0362976007164</td><td>0.123525999486</td><td>0.1432390064</td><td>0.0625796020031</td><td>26.0097999573</td><td>0.474346995354</td><td>-99.9000015259</td><td>-99.9000015259</td><td>-99.9000015259</td><td>-99.9000015259</td><td>0.0</td><td>0.349896013737</td><td>-99.9000015259</td><td>-99.9000015259</td><td>0.0</td><td>1.08697998524</td><td>-99.9000015259</td><td>-99.9000015259</td><td>1.07686257362</td><td>0.091399140656</td><td>False</td><td>nan</td><td>-139.401428223</td><td>1.6467949152</td><td>0.0409378036857</td><td>False</td><td>24.8543701172</td><td>0.122201368213</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.291420102119</td><td>0.0635664910078</td><td>False</td><td>26.8538284302</td><td>1.13087296486</td><td>0.133708700538</td><td>0.0220101531595</td><td>False</td><td>29.9872512817</td><td>5.2552113533</td><td>0.0362043008208</td><td>0.0208366028965</td><td>False</td><td>31.1632537842</td><td>9.84116172791</td><td>0.113010726869</td><td>0.0314447022974</td><td>False</td><td>nan</td><td>-1.16153311729</td><td>0.168484568596</td><td>0.04918275401</td><td>False</td><td>nan</td><td>-1.79815173149</td><td>0.185711935163</td><td>0.0295972917229</td><td>False</td><td>nan</td><td>-1.12499988079</td><td>0.458394736052</td><td>0.114813134074</td><td>False</td><td>27.077255249</td><td>3.01710057259</td><td>0.0584197379649</td><td>0.0481851249933</td><td>False</td><td>nan</td><td>-1.71033787727</td><td>0.126171067357</td><td>0.05606052652</td><td>False</td><td>nan</td><td>-2.57385802269</td><td>0.124761283398</td><td>0.0550930760801</td><td>False</td><td>nan</td><td>-20.3685321808</td><td>0.0557339079678</td><td>0.0689446106553</td><td>False</td><td>27.8925247192</td><td>1.47266626358</td><td>0.227237313986</td><td>0.075009457767</td><td>False</td><td>nan</td><td>-1.92280364037</td><td>0.0926658883691</td><td>0.0942101553082</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.0621784850955</td><td>0.0555460937321</td><td>False</td><td>28.9550018311</td><td>3.75485444069</td><td>0.0991927608848</td><td>0.0634468942881</td><td>False</td><td>26.568572998</td><td>0.498638749123</td><td>0.351560473442</td><td>0.0781434252858</td><td>False</td><td>27.5871734619</td><td>1.43641495705</td><td>0.478542178869</td><td>0.0716409757733</td><td>False</td><td>nan</td><td>-2.19399309158</td><td>0.146987274289</td><td>0.06860896945</td><td>False</td><td>28.70619964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n</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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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/td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>150.478458692</td><td>2.1077854526</td><td>1.0</td><td>25.0359992981</td><td>0.235457003117</td><td>-0.000308199989377</td><td>0.0347601994872</td><td>nan</td><td>nan</td><td>0.0850899964571</td><td>0.0486276000738</td><td>26.2490997314</td><td>0.763034999371</td><td>0.0665256977081</td><td>0.10621599853</td><td>nan</td><td>nan</td><td>0.01931639947</td><td>0.0723173990846</td><td>nan</td><td>nan</td><td>-0.0049238097854</td><td>0.0161146000028</td><td>nan</td><td>nan</td><td>-0.00734747992828</td><td>0.00969650037587</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-0.00651116017252</td><td>0.0210823006928</td><td>28.9533996582</td><td>2.48433995247</td><td>0.00451043993235</td><td>0.0138349998742</td><td>25.5673999786</td><td>0.466715991497</td><td>0.0328546985984</td><td>0.155055001378</td><td>nan</td><td>nan</td><td>0.0317820012569</td><td>0.0360142998397</td><td>26.4190006256</td><td>0.451943010092</td><td>0.0108315004036</td><td>0.0314282998443</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.0124145997688</td><td>0.0344319008291</td><td>25.2262992859</td><td>0.218173995614</td><td>0.10095000267</td><td>0.0549835003912</td><td>nan</td><td>nan</td><td>0.00527642993256</td><td>0.0458666011691</td><td>nan</td><td>nan</td><td>-0.0181246008724</td><td>0.05750240013</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.0134757999331</td><td>0.0385274998844</td><td>nan</td><td>nan</td><td>-0.0404996983707</td><td>0.0419806987047</td><td>30.2437992096</td><td>19.2409992218</td><td>-0.0188413001597</td><td>0.0456095002592</td><td>nan</td><td>nan</td><td>-0.0298832003027</td><td>0.0406818017364</td><td>nan</td><td>nan</td><td>-0.0178236998618</td><td>0.0660225972533</td><td>27.5821990967</td><td>4.14896011353</td><td>0.0341668985784</td><td>0.0716423988342</td><td>nan</td><td>nan</td><td>0.0163899995387</td><td>0.0641952008009</td><td>23.7045993805</td><td>0.251141995192</td><td>0.0944764018059</td><td>0.262015014887</td><td>23.7250995636</td><td>0.284247010946</td><td>-0.0857834964991</td><td>0.291734993458</td><td>25.2273006439</td><td>0.438151985407</td><td>0.102124996483</td><td>0.118270002306</td><td>-99.9000015259</td><td>-99.9000015259</td><td>-99.9000015259</td><td>-99.9000015259</td><td>0.104681000113</td><td>0.0434598997235</td><td>26.3502998352</td><td>0.45076200366</td><td>0.0</td><td>2.60392999649</td><td>-99.9000015259</td><td>-99.9000015259</td><td>0.0</td><td>2.40874004364</td><td>-99.9000015259</td><td>-99.9000015259</td><td>0.351236730814</td><td>0.0761705711484</td><td>False</td><td>nan</td><td>-122.45426178</td><td>nan</td><td>nan</td><td>False</td><td>26.5753097534</td><td>0.62048125267</td><td>0.114910565317</td><td>0.0807569846511</td><td>False</td><td>26.8425216675</td><td>1.7335036993</td><td>nan</td><td>nan</td><td>False</td><td>28.1851882935</td><td>4.06481647491</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-3.55338740349</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.43285059929</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-3.51547431946</td><td>0.00952005758882</td><td>0.0217834301293</td><td>False</td><td>29.7644577026</td><td>3.3303091526</td><td>0.21529789269</td><td>0.0925482362509</td><td>False</td><td>27.608505249</td><td>5.12404108047</td><td>nan</td><td>nan</td><td>False</td><td>27.6445465088</td><td>1.2303198576</td><td>0.0982651934028</td><td>0.0409033671021</td><td>False</td><td>28.8132781982</td><td>3.15033388138</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>28.6651687622</td><td>3.01128983498</td><td>0.294767796993</td><td>0.0592323169112</td><td>False</td><td>26.3897323608</td><td>0.591357767582</td><td>nan</td><td>nan</td><td>False</td><td>29.5941543579</td><td>9.43801498413</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-3.44462394714</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>28.5761184692</td><td>3.10413479805</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.12543952465</td><td>0.00290055130608</td><td>0.0514024570584</td><td>False</td><td>nan</td><td>-2.62826251984</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.47807812691</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-4.02178668976</td><td>0.0336604677141</td><td>0.128627881408</td><td>False</td><td>27.5659866333</td><td>2.27661108971</td><td>nan</td><td>nan</td><td>False</td><td>28.3635559082</td><td>4.25253534317</td><td>1.19718229771</td><td>0.276920646429</td><td>False</td><td>26.4616928101</td><td>3.01111364365</td><td>1.17478966713</td><td>0.307561308146</td><td>False</td><td>nan</td><td>-3.69240307808</td><td>0.294496059418</td><td>0.118844740093</td><td>False</td><td>26.3771743774</td><td>1.25738096237</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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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",
       "</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(\"$('#table140006255205680-130307').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",
       "    $('#table140006255205680-130307').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, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 137, 138, 139, 140, 142, 143, 144, 145, 147, 148, 149, 150, 152, 153, 154, 155, 157, 158, 159, 160, 162, 163, 164, 165, 167, 168, 169, 170, 172, 173, 174, 175, 177, 178, 179, 180, 182, 183, 184, 185, 187, 188, 189, 190, 192, 193, 194, 195, 197, 198, 199, 200, 202, 203, 204, 205, 207, 208, 209, 210, 212, 213, 214, 215, 217, 218, 219, 220, 222, 223, 224, 225, 227, 228, 229, 230, 232, 233, 234, 235, 237, 238, 239, 240, 242, 243, 244, 245, 247, 248, 249, 250, 252, 253, 254, 255, 257, 258, 259, 260, 262, 263, 264, 265, 267, 268, 269, 270, 272, 273, 279, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 302, 303, 304, 305, 307, 308, 310, 311, 313, 314, 317, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 343, 344, 346, 347, 349, 350, 352, 353, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 383, 384, 386, 387, 389, 390, 391, 392, 393, 394, 395, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 429, 430, 431, 432, 434, 435, 436, 437, 439, 440, 441, 442, 444, 445, 446, 447, 449, 450, 451, 452, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 487, 488, 489, 490, 492, 493, 494, 495, 497, 498, 499, 500, 502, 503, 504, 505, 507, 508, 509, 510, 513, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 535, 536, 538, 539, 541, 542, 544, 545, 546, 547, 548, 549, 550, 551, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 575, 576, 577, 578, 580, 581, 582, 583, 584, 585, 587, 588, 589, 590, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 630, 631, 632, 633, 635, 636, 637, 638, 640, 641, 642, 643, 645, 646, 647, 648, 650, 651, 652, 653, 655, 656, 657, 658, 660, 661, 662, 663, 665, 666, 668, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 696, 697, 698, 699, 701, 702, 703, 704, 706, 707, 708, 709, 711, 712, 713, 714, 717], type: \"optionalnum\"}]\n",
       "    });\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": [
      "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": [
    "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": null,
   "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": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "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": 35,
   "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": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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CZGh8moxEZ0arAIwxLMpLoV4jViIiImFDi9dDZHB8mvQkZ9/uipwUaloHHX1OEREJX2db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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55bf1c9630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(specz['ra'] * u.deg, specz['dec'] * u.deg)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 38,
   "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": 39,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(6, 0) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "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": 40,
   "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": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "<i>Table length=6</i>\n",
       "<table id=\"table140006254811120-643995\" 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>1146131.0</td><td>1146131.0</td><td>1257414.0</td><td>1257414.0</td><td>1643773.0</td><td>956277.0</td><td>1838222.0</td><td>1044705.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>r</td><td>1156722.0</td><td>1156722.0</td><td>1268883.0</td><td>1268883.0</td><td>1679055.0</td><td>989440.0</td><td>1903240.0</td><td>1102607.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>2</td><td>i</td><td>1160356.0</td><td>1160356.0</td><td>1291883.0</td><td>1291883.0</td><td>1714230.0</td><td>987298.0</td><td>1953737.0</td><td>1086535.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>3</td><td>z</td><td>1132842.0</td><td>1132842.0</td><td>1260729.0</td><td>1260729.0</td><td>1701530.0</td><td>984971.0</td><td>1933534.0</td><td>1081349.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>4</td><td>y</td><td>1035328.0</td><td>500551.0</td><td>1157878.0</td><td>574161.0</td><td>1467985.0</td><td>798995.0</td><td>1669840.0</td><td>893700.0</td><td>661669.0</td><td>661669.0</td><td>693726.0</td><td>693726.0</td></tr>\n",
       "<tr><td>5</td><td>n921</td><td>1088153.0</td><td>1088153.0</td><td>1207243.0</td><td>1207243.0</td><td>879166.0</td><td>494988.0</td><td>996296.0</td><td>546326.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</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(\"$('#table140006254811120-643995').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",
       "    $('#table140006254811120-643995').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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suprime_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 43,
   "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": 44,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(5, 0) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "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": 45,
   "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": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "<i>Table length=5</i>\n",
       "<table id=\"table140006255075848-461183\" 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>640825.0</td><td>640825.0</td><td>688187.0</td><td>688187.0</td><td>455496.0</td><td>183226.0</td><td>459328.0</td><td>182038.0</td><td>156347.0</td><td>17152.0</td><td>157331.0</td><td>16078.0</td></tr>\n",
       "<tr><td>1</td><td>g</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>516573.0</td><td>516573.0</td><td>518549.0</td><td>518549.0</td><td>164107.0</td><td>31365.0</td><td>163978.0</td><td>30985.0</td></tr>\n",
       "<tr><td>2</td><td>r</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>526336.0</td><td>526336.0</td><td>529160.0</td><td>529160.0</td><td>165217.0</td><td>31524.0</td><td>165010.0</td><td>31136.0</td></tr>\n",
       "<tr><td>3</td><td>i</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>520668.0</td><td>520668.0</td><td>523948.0</td><td>523948.0</td><td>165648.0</td><td>31737.0</td><td>165316.0</td><td>31298.0</td></tr>\n",
       "<tr><td>4</td><td>z</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>474766.0</td><td>474766.0</td><td>480581.0</td><td>480581.0</td><td>164234.0</td><td>30996.0</td><td>164359.0</td><td>30824.0</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(\"$('#table140006255075848-461183').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",
       "    $('#table140006255075848-461183').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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "megacam_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 48,
   "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": 49,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(2, 0) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "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": 50,
   "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": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [
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       "<table id=\"table140007173182408-171754\" 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>164175.0</td><td>32620.0</td><td>165228.0</td><td>33198.0</td><td>589027.0</td><td>589027.0</td><td>684053.0</td><td>684053.0</td></tr>\n",
       "<tr><td>1</td><td>ks</td><td>169103.0</td><td>35504.0</td><td>170077.0</td><td>36333.0</td><td>594551.0</td><td>594551.0</td><td>681987.0</td><td>681987.0</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(\"$('#table140007173182408-171754').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",
       "    $('#table140007173182408-171754').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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wircam_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 53,
   "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": 54,
   "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": 55,
   "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": 56,
   "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": 57,
   "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": 58,
   "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": 59,
   "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": 60,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['help_id', 'cosmos_id', 'candels_id', 'cfhtls_id', 'decals_id', 'hsc-udeep_id', 'hsc-deep_id', 'kids_id', 'las_id', 'wirds_id', 'ps1_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": 61,
   "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": 62,
   "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": 63,
   "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": 64,
   "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": 65,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[columns].write(\"{}/master_catalogue_cosmos{}.fits\".format(OUT_DIR, SUFFIX), overwrite = True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (herschelhelp_internal)",
   "language": "python",
   "name": "helpint"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}