{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# COSMOS master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on COSMOS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "017bb1e (Mon Jun 18 14:58:59 2018 +0100)\n",
      "This notebook was executed on: \n",
      "2018-06-19 17:18:58.720242\n"
     ]
    }
   ],
   "source": [
    "from herschelhelp_internal import git_version\n",
    "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))\n",
    "import datetime\n",
    "print(\"This notebook was executed on: \\n{}\".format(datetime.datetime.now()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/seaborn/apionly.py:6: UserWarning: As seaborn no longer sets a default style on import, the seaborn.apionly module is deprecated. It will be removed in a future version.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(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=\"table140173436327696-849110\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>help_id</th><th>cosmos_id</th><th>ra</th><th>dec</th><th>cosmos_stellarity</th><th>m_cosmos-vista_ks</th><th>merr_cosmos-vista_ks</th><th>f_ap_cosmos-vista_ks</th><th>ferr_ap_cosmos-vista_ks</th><th>m_cosmos-vista_y</th><th>merr_cosmos-vista_y</th><th>f_ap_cosmos-vista_y</th><th>ferr_ap_cosmos-vista_y</th><th>m_cosmos-vista_h</th><th>merr_cosmos-vista_h</th><th>f_ap_cosmos-vista_h</th><th>ferr_ap_cosmos-vista_h</th><th>m_cosmos-vista_j</th><th>merr_cosmos-vista_j</th><th>f_ap_cosmos-vista_j</th><th>ferr_ap_cosmos-vista_j</th><th>m_cosmos-megacam_u</th><th>merr_cosmos-megacam_u</th><th>f_ap_cosmos-megacam_u</th><th>ferr_ap_cosmos-megacam_u</th><th>m_cosmos-suprime_b</th><th>merr_cosmos-suprime_b</th><th>f_ap_cosmos-suprime_b</th><th>ferr_ap_cosmos-suprime_b</th><th>m_cosmos-suprime_v</th><th>merr_cosmos-suprime_v</th><th>f_ap_cosmos-suprime_v</th><th>ferr_ap_cosmos-suprime_v</th><th>m_cosmos-suprime_ip</th><th>merr_cosmos-suprime_ip</th><th>f_ap_cosmos-suprime_ip</th><th>ferr_ap_cosmos-suprime_ip</th><th>m_cosmos-suprime_rc</th><th>merr_cosmos-suprime_rc</th><th>f_ap_cosmos-suprime_rc</th><th>ferr_ap_cosmos-suprime_rc</th><th>m_cosmos-suprime_zp</th><th>merr_cosmos-suprime_zp</th><th>f_ap_cosmos-suprime_zp</th><th>ferr_ap_cosmos-suprime_zp</th><th>m_cosmos-suprime_zpp</th><th>merr_cosmos-suprime_zpp</th><th>f_ap_cosmos-suprime_zpp</th><th>ferr_ap_cosmos-suprime_zpp</th><th>m_cosmos-suprime_ia484</th><th>merr_cosmos-suprime_ia484</th><th>f_ap_cosmos-suprime_ia484</th><th>ferr_ap_cosmos-suprime_ia484</th><th>m_cosmos-suprime_ia527</th><th>merr_cosmos-suprime_ia527</th><th>f_ap_cosmos-suprime_ia527</th><th>ferr_ap_cosmos-suprime_ia527</th><th>m_cosmos-suprime_ia624</th><th>merr_cosmos-suprime_ia624</th><th>f_ap_cosmos-suprime_ia624</th><th>ferr_ap_cosmos-suprime_ia624</th><th>m_cosmos-suprime_ia679</th><th>merr_cosmos-suprime_ia679</th><th>f_ap_cosmos-suprime_ia679</th><th>ferr_ap_cosmos-suprime_ia679</th><th>m_cosmos-suprime_ia738</th><th>merr_cosmos-suprime_ia738</th><th>f_ap_cosmos-suprime_ia738</th><th>ferr_ap_cosmos-suprime_ia738</th><th>m_cosmos-suprime_ia767</th><th>merr_cosmos-suprime_ia767</th><th>f_ap_cosmos-suprime_ia767</th><th>ferr_ap_cosmos-suprime_ia767</th><th>m_cosmos-suprime_ib427</th><th>merr_cosmos-suprime_ib427</th><th>f_ap_cosmos-suprime_ib427</th><th>ferr_ap_cosmos-suprime_ib427</th><th>m_cosmos-suprime_ib464</th><th>merr_cosmos-suprime_ib464</th><th>f_ap_cosmos-suprime_ib464</th><th>ferr_ap_cosmos-suprime_ib464</th><th>m_cosmos-suprime_ib505</th><th>merr_cosmos-suprime_ib505</th><th>f_ap_cosmos-suprime_ib505</th><th>ferr_ap_cosmos-suprime_ib505</th><th>m_cosmos-suprime_ib574</th><th>merr_cosmos-suprime_ib574</th><th>f_ap_cosmos-suprime_ib574</th><th>ferr_ap_cosmos-suprime_ib574</th><th>m_cosmos-suprime_ib709</th><th>merr_cosmos-suprime_ib709</th><th>f_ap_cosmos-suprime_ib709</th><th>ferr_ap_cosmos-suprime_ib709</th><th>m_cosmos-suprime_ib827</th><th>merr_cosmos-suprime_ib827</th><th>f_ap_cosmos-suprime_ib827</th><th>ferr_ap_cosmos-suprime_ib827</th><th>m_cosmos-suprime_nb711</th><th>merr_cosmos-suprime_nb711</th><th>f_ap_cosmos-suprime_nb711</th><th>ferr_ap_cosmos-suprime_nb711</th><th>m_cosmos-suprime_nb816</th><th>merr_cosmos-suprime_nb816</th><th>f_ap_cosmos-suprime_nb816</th><th>ferr_ap_cosmos-suprime_nb816</th><th>m_cosmos-wircam_h</th><th>merr_cosmos-wircam_h</th><th>f_ap_cosmos-wircam_h</th><th>ferr_ap_cosmos-wircam_h</th><th>m_cosmos-wircam_ks</th><th>merr_cosmos-wircam_ks</th><th>f_ap_cosmos-wircam_ks</th><th>ferr_ap_cosmos-wircam_ks</th><th>m_cosmos-suprime_y</th><th>merr_cosmos-suprime_y</th><th>f_ap_cosmos-suprime_y</th><th>ferr_ap_cosmos-suprime_y</th><th>f_cosmos-irac_i1</th><th>ferr_cosmos-irac_i1</th><th>m_cosmos-irac_i1</th><th>merr_cosmos-irac_i1</th><th>f_cosmos-irac_i2</th><th>ferr_cosmos-irac_i2</th><th>m_cosmos-irac_i2</th><th>merr_cosmos-irac_i2</th><th>f_cosmos-irac_i3</th><th>ferr_cosmos-irac_i3</th><th>m_cosmos-irac_i3</th><th>merr_cosmos-irac_i3</th><th>f_cosmos-irac_i4</th><th>ferr_cosmos-irac_i4</th><th>m_cosmos-irac_i4</th><th>merr_cosmos-irac_i4</th><th>f_cosmos-vista_ks</th><th>ferr_cosmos-vista_ks</th><th>flag_cosmos-vista_ks</th><th>m_ap_cosmos-vista_ks</th><th>merr_ap_cosmos-vista_ks</th><th>f_cosmos-vista_y</th><th>ferr_cosmos-vista_y</th><th>flag_cosmos-vista_y</th><th>m_ap_cosmos-vista_y</th><th>merr_ap_cosmos-vista_y</th><th>f_cosmos-vista_h</th><th>ferr_cosmos-vista_h</th><th>flag_cosmos-vista_h</th><th>m_ap_cosmos-vista_h</th><th>merr_ap_cosmos-vista_h</th><th>f_cosmos-vista_j</th><th>ferr_cosmos-vista_j</th><th>flag_cosmos-vista_j</th><th>m_ap_cosmos-vista_j</th><th>merr_ap_cosmos-vista_j</th><th>f_cosmos-megacam_u</th><th>ferr_cosmos-megacam_u</th><th>flag_cosmos-megacam_u</th><th>m_ap_cosmos-megacam_u</th><th>merr_ap_cosmos-megacam_u</th><th>f_cosmos-suprime_b</th><th>ferr_cosmos-suprime_b</th><th>flag_cosmos-suprime_b</th><th>m_ap_cosmos-suprime_b</th><th>merr_ap_cosmos-suprime_b</th><th>f_cosmos-suprime_v</th><th>ferr_cosmos-suprime_v</th><th>flag_cosmos-suprime_v</th><th>m_ap_cosmos-suprime_v</th><th>merr_ap_cosmos-suprime_v</th><th>f_cosmos-suprime_ip</th><th>ferr_cosmos-suprime_ip</th><th>flag_cosmos-suprime_ip</th><th>m_ap_cosmos-suprime_ip</th><th>merr_ap_cosmos-suprime_ip</th><th>f_cosmos-suprime_rc</th><th>ferr_cosmos-suprime_rc</th><th>flag_cosmos-suprime_rc</th><th>m_ap_cosmos-suprime_rc</th><th>merr_ap_cosmos-suprime_rc</th><th>f_cosmos-suprime_zp</th><th>ferr_cosmos-suprime_zp</th><th>flag_cosmos-suprime_zp</th><th>m_ap_cosmos-suprime_zp</th><th>merr_ap_cosmos-suprime_zp</th><th>f_cosmos-suprime_zpp</th><th>ferr_cosmos-suprime_zpp</th><th>flag_cosmos-suprime_zpp</th><th>m_ap_cosmos-suprime_zpp</th><th>merr_ap_cosmos-suprime_zpp</th><th>f_cosmos-suprime_ia484</th><th>ferr_cosmos-suprime_ia484</th><th>flag_cosmos-suprime_ia484</th><th>m_ap_cosmos-suprime_ia484</th><th>merr_ap_cosmos-suprime_ia484</th><th>f_cosmos-suprime_ia527</th><th>ferr_cosmos-suprime_ia527</th><th>flag_cosmos-suprime_ia527</th><th>m_ap_cosmos-suprime_ia527</th><th>merr_ap_cosmos-suprime_ia527</th><th>f_cosmos-suprime_ia624</th><th>ferr_cosmos-suprime_ia624</th><th>flag_cosmos-suprime_ia624</th><th>m_ap_cosmos-suprime_ia624</th><th>merr_ap_cosmos-suprime_ia624</th><th>f_cosmos-suprime_ia679</th><th>ferr_cosmos-suprime_ia679</th><th>flag_cosmos-suprime_ia679</th><th>m_ap_cosmos-suprime_ia679</th><th>merr_ap_cosmos-suprime_ia679</th><th>f_cosmos-suprime_ia738</th><th>ferr_cosmos-suprime_ia738</th><th>flag_cosmos-suprime_ia738</th><th>m_ap_cosmos-suprime_ia738</th><th>merr_ap_cosmos-suprime_ia738</th><th>f_cosmos-suprime_ia767</th><th>ferr_cosmos-suprime_ia767</th><th>flag_cosmos-suprime_ia767</th><th>m_ap_cosmos-suprime_ia767</th><th>merr_ap_cosmos-suprime_ia767</th><th>f_cosmos-suprime_ib427</th><th>ferr_cosmos-suprime_ib427</th><th>flag_cosmos-suprime_ib427</th><th>m_ap_cosmos-suprime_ib427</th><th>merr_ap_cosmos-suprime_ib427</th><th>f_cosmos-suprime_ib464</th><th>ferr_cosmos-suprime_ib464</th><th>flag_cosmos-suprime_ib464</th><th>m_ap_cosmos-suprime_ib464</th><th>merr_ap_cosmos-suprime_ib464</th><th>f_cosmos-suprime_ib505</th><th>ferr_cosmos-suprime_ib505</th><th>flag_cosmos-suprime_ib505</th><th>m_ap_cosmos-suprime_ib505</th><th>merr_ap_cosmos-suprime_ib505</th><th>f_cosmos-suprime_ib574</th><th>ferr_cosmos-suprime_ib574</th><th>flag_cosmos-suprime_ib574</th><th>m_ap_cosmos-suprime_ib574</th><th>merr_ap_cosmos-suprime_ib574</th><th>f_cosmos-suprime_ib709</th><th>ferr_cosmos-suprime_ib709</th><th>flag_cosmos-suprime_ib709</th><th>m_ap_cosmos-suprime_ib709</th><th>merr_ap_cosmos-suprime_ib709</th><th>f_cosmos-suprime_ib827</th><th>ferr_cosmos-suprime_ib827</th><th>flag_cosmos-suprime_ib827</th><th>m_ap_cosmos-suprime_ib827</th><th>merr_ap_cosmos-suprime_ib827</th><th>f_cosmos-suprime_nb711</th><th>ferr_cosmos-suprime_nb711</th><th>flag_cosmos-suprime_nb711</th><th>m_ap_cosmos-suprime_nb711</th><th>merr_ap_cosmos-suprime_nb711</th><th>f_cosmos-suprime_nb816</th><th>ferr_cosmos-suprime_nb816</th><th>flag_cosmos-suprime_nb816</th><th>m_ap_cosmos-suprime_nb816</th><th>merr_ap_cosmos-suprime_nb816</th><th>f_cosmos-wircam_h</th><th>ferr_cosmos-wircam_h</th><th>flag_cosmos-wircam_h</th><th>m_ap_cosmos-wircam_h</th><th>merr_ap_cosmos-wircam_h</th><th>f_cosmos-wircam_ks</th><th>ferr_cosmos-wircam_ks</th><th>flag_cosmos-wircam_ks</th><th>m_ap_cosmos-wircam_ks</th><th>merr_ap_cosmos-wircam_ks</th><th>f_cosmos-suprime_y</th><th>ferr_cosmos-suprime_y</th><th>flag_cosmos-suprime_y</th><th>m_ap_cosmos-suprime_y</th><th>merr_ap_cosmos-suprime_y</th><th>flag_cosmos-irac_i1</th><th>flag_cosmos-irac_i2</th><th>flag_cosmos-irac_i3</th><th>flag_cosmos-irac_i4</th><th>ps1_flag_cleaned</th><th>cosmos_flag_gaia</th><th>flag_merged</th><th>candels_id</th><th>candels_stellarity</th><th>f_ap_candels_f140w</th><th>ferr_ap_candels_f140w</th><th>f_candels_f140w</th><th>ferr_candels_f140w</th><th>f_ap_candels_f160w</th><th>ferr_ap_candels_f160w</th><th>f_candels_f160w</th><th>ferr_candels_f160w</th><th>f_candels_f606w</th><th>ferr_candels_f606w</th><th>f_candels_f814w</th><th>ferr_candels_f814w</th><th>f_candels_f125w</th><th>ferr_candels_f125w</th><th>m_ap_candels_f140w</th><th>merr_ap_candels_f140w</th><th>m_candels_f140w</th><th>merr_candels_f140w</th><th>flag_candels_f140w</th><th>m_ap_candels_f160w</th><th>merr_ap_candels_f160w</th><th>m_candels_f160w</th><th>merr_candels_f160w</th><th>flag_candels_f160w</th><th>m_candels_f606w</th><th>merr_candels_f606w</th><th>flag_candels_f606w</th><th>m_candels_f814w</th><th>merr_candels_f814w</th><th>flag_candels_f814w</th><th>m_candels_f125w</th><th>merr_candels_f125w</th><th>flag_candels_f125w</th><th>candels_flag_cleaned</th><th>candels_flag_gaia</th><th>cfhtls_id</th><th>cfhtls_stellarity</th><th>m_megacam_u</th><th>merr_megacam_u</th><th>m_megacam_g</th><th>merr_megacam_g</th><th>m_megacam_r</th><th>merr_megacam_r</th><th>m_megacam_i</th><th>merr_megacam_i</th><th>m_megacam_z</th><th>merr_megacam_z</th><th>m_ap_megacam_u</th><th>merr_ap_megacam_u</th><th>m_ap_megacam_g</th><th>merr_ap_megacam_g</th><th>m_ap_megacam_r</th><th>merr_ap_megacam_r</th><th>m_ap_megacam_i</th><th>merr_ap_megacam_i</th><th>m_ap_megacam_z</th><th>merr_ap_megacam_z</th><th>f_megacam_u</th><th>ferr_megacam_u</th><th>flag_megacam_u</th><th>f_megacam_g</th><th>ferr_megacam_g</th><th>flag_megacam_g</th><th>f_megacam_r</th><th>ferr_megacam_r</th><th>flag_megacam_r</th><th>f_megacam_i</th><th>ferr_megacam_i</th><th>flag_megacam_i</th><th>f_megacam_z</th><th>ferr_megacam_z</th><th>flag_megacam_z</th><th>f_ap_megacam_u</th><th>ferr_ap_megacam_u</th><th>f_ap_megacam_g</th><th>ferr_ap_megacam_g</th><th>f_ap_megacam_r</th><th>ferr_ap_megacam_r</th><th>f_ap_megacam_i</th><th>ferr_ap_megacam_i</th><th>f_ap_megacam_z</th><th>ferr_ap_megacam_z</th><th>cfhtls_flag_cleaned</th><th>cfhtls_flag_gaia</th><th>decals_id</th><th>f_decam_g</th><th>f_decam_r</th><th>f_decam_z</th><th>ferr_decam_g</th><th>ferr_decam_r</th><th>ferr_decam_z</th><th>f_ap_decam_g</th><th>f_ap_decam_r</th><th>f_ap_decam_z</th><th>ferr_ap_decam_g</th><th>ferr_ap_decam_r</th><th>ferr_ap_decam_z</th><th>m_decam_g</th><th>merr_decam_g</th><th>flag_decam_g</th><th>m_decam_r</th><th>merr_decam_r</th><th>flag_decam_r</th><th>m_decam_z</th><th>merr_decam_z</th><th>flag_decam_z</th><th>m_ap_decam_g</th><th>merr_ap_decam_g</th><th>m_ap_decam_r</th><th>merr_ap_decam_r</th><th>m_ap_decam_z</th><th>merr_ap_decam_z</th><th>decals_stellarity</th><th>decals_flag_cleaned</th><th>decals_flag_gaia</th><th>hsc-udeep_id</th><th>m_ap_hsc-udeep_g</th><th>merr_ap_hsc-udeep_g</th><th>m_hsc-udeep_g</th><th>merr_hsc-udeep_g</th><th>m_ap_hsc-udeep_r</th><th>merr_ap_hsc-udeep_r</th><th>m_hsc-udeep_r</th><th>merr_hsc-udeep_r</th><th>m_ap_hsc-udeep_i</th><th>merr_ap_hsc-udeep_i</th><th>m_hsc-udeep_i</th><th>merr_hsc-udeep_i</th><th>m_ap_hsc-udeep_z</th><th>merr_ap_hsc-udeep_z</th><th>m_hsc-udeep_z</th><th>merr_hsc-udeep_z</th><th>m_ap_hsc-udeep_y</th><th>merr_ap_hsc-udeep_y</th><th>m_hsc-udeep_y</th><th>merr_hsc-udeep_y</th><th>m_ap_hsc-udeep_n921</th><th>merr_ap_hsc-udeep_n921</th><th>m_hsc-udeep_n921</th><th>merr_hsc-udeep_n921</th><th>hsc-udeep_stellarity</th><th>f_ap_hsc-udeep_g</th><th>ferr_ap_hsc-udeep_g</th><th>f_hsc-udeep_g</th><th>ferr_hsc-udeep_g</th><th>flag_hsc-udeep_g</th><th>f_ap_hsc-udeep_r</th><th>ferr_ap_hsc-udeep_r</th><th>f_hsc-udeep_r</th><th>ferr_hsc-udeep_r</th><th>flag_hsc-udeep_r</th><th>f_ap_hsc-udeep_i</th><th>ferr_ap_hsc-udeep_i</th><th>f_hsc-udeep_i</th><th>ferr_hsc-udeep_i</th><th>flag_hsc-udeep_i</th><th>f_ap_hsc-udeep_z</th><th>ferr_ap_hsc-udeep_z</th><th>f_hsc-udeep_z</th><th>ferr_hsc-udeep_z</th><th>flag_hsc-udeep_z</th><th>f_ap_hsc-udeep_y</th><th>ferr_ap_hsc-udeep_y</th><th>f_hsc-udeep_y</th><th>ferr_hsc-udeep_y</th><th>flag_hsc-udeep_y</th><th>f_ap_hsc-udeep_n921</th><th>ferr_ap_hsc-udeep_n921</th><th>f_hsc-udeep_n921</th><th>ferr_hsc-udeep_n921</th><th>flag_hsc-udeep_n921</th><th>hsc-udeep_flag_cleaned</th><th>hsc-udeep_flag_gaia</th><th>hsc-deep_id</th><th>m_ap_hsc-deep_g</th><th>merr_ap_hsc-deep_g</th><th>m_hsc-deep_g</th><th>merr_hsc-deep_g</th><th>m_ap_hsc-deep_r</th><th>merr_ap_hsc-deep_r</th><th>m_hsc-deep_r</th><th>merr_hsc-deep_r</th><th>m_ap_hsc-deep_i</th><th>merr_ap_hsc-deep_i</th><th>m_hsc-deep_i</th><th>merr_hsc-deep_i</th><th>m_ap_hsc-deep_z</th><th>merr_ap_hsc-deep_z</th><th>m_hsc-deep_z</th><th>merr_hsc-deep_z</th><th>m_ap_hsc-deep_y</th><th>merr_ap_hsc-deep_y</th><th>m_hsc-deep_y</th><th>merr_hsc-deep_y</th><th>m_ap_hsc-deep_n921</th><th>merr_ap_hsc-deep_n921</th><th>m_hsc-deep_n921</th><th>merr_hsc-deep_n921</th><th>hsc-deep_stellarity</th><th>f_ap_hsc-deep_g</th><th>ferr_ap_hsc-deep_g</th><th>f_hsc-deep_g</th><th>ferr_hsc-deep_g</th><th>flag_hsc-deep_g</th><th>f_ap_hsc-deep_r</th><th>ferr_ap_hsc-deep_r</th><th>f_hsc-deep_r</th><th>ferr_hsc-deep_r</th><th>flag_hsc-deep_r</th><th>f_ap_hsc-deep_i</th><th>ferr_ap_hsc-deep_i</th><th>f_hsc-deep_i</th><th>ferr_hsc-deep_i</th><th>flag_hsc-deep_i</th><th>f_ap_hsc-deep_z</th><th>ferr_ap_hsc-deep_z</th><th>f_hsc-deep_z</th><th>ferr_hsc-deep_z</th><th>flag_hsc-deep_z</th><th>f_ap_hsc-deep_y</th><th>ferr_ap_hsc-deep_y</th><th>f_hsc-deep_y</th><th>ferr_hsc-deep_y</th><th>flag_hsc-deep_y</th><th>f_ap_hsc-deep_n921</th><th>ferr_ap_hsc-deep_n921</th><th>f_hsc-deep_n921</th><th>ferr_hsc-deep_n921</th><th>flag_hsc-deep_n921</th><th>hsc-deep_flag_cleaned</th><th>hsc-deep_flag_gaia</th><th>kids_id</th><th>kids_stellarity</th><th>m_kids_u</th><th>merr_kids_u</th><th>m_kids_g</th><th>merr_kids_g</th><th>m_kids_r</th><th>merr_kids_r</th><th>m_kids_i</th><th>merr_kids_i</th><th>f_ap_kids_u</th><th>ferr_ap_kids_u</th><th>f_ap_kids_g</th><th>ferr_ap_kids_g</th><th>f_ap_kids_r</th><th>ferr_ap_kids_r</th><th>f_ap_kids_i</th><th>ferr_ap_kids_i</th><th>f_kids_u</th><th>ferr_kids_u</th><th>flag_kids_u</th><th>f_kids_g</th><th>ferr_kids_g</th><th>flag_kids_g</th><th>f_kids_r</th><th>ferr_kids_r</th><th>flag_kids_r</th><th>f_kids_i</th><th>ferr_kids_i</th><th>flag_kids_i</th><th>m_ap_kids_u</th><th>merr_ap_kids_u</th><th>m_ap_kids_g</th><th>merr_ap_kids_g</th><th>m_ap_kids_r</th><th>merr_ap_kids_r</th><th>m_ap_kids_i</th><th>merr_ap_kids_i</th><th>kids_flag_cleaned</th><th>kids_flag_gaia</th><th>las_id</th><th>m_ukidss_y</th><th>merr_ukidss_y</th><th>m_ap_ukidss_y</th><th>merr_ap_ukidss_y</th><th>m_ukidss_j</th><th>merr_ukidss_j</th><th>m_ap_ukidss_j</th><th>merr_ap_ukidss_j</th><th>m_ap_ukidss_h</th><th>merr_ap_ukidss_h</th><th>m_ukidss_h</th><th>merr_ukidss_h</th><th>m_ap_ukidss_k</th><th>merr_ap_ukidss_k</th><th>m_ukidss_k</th><th>merr_ukidss_k</th><th>las_stellarity</th><th>f_ukidss_y</th><th>ferr_ukidss_y</th><th>flag_ukidss_y</th><th>f_ap_ukidss_y</th><th>ferr_ap_ukidss_y</th><th>f_ukidss_j</th><th>ferr_ukidss_j</th><th>flag_ukidss_j</th><th>f_ap_ukidss_j</th><th>ferr_ap_ukidss_j</th><th>f_ap_ukidss_h</th><th>ferr_ap_ukidss_h</th><th>f_ukidss_h</th><th>ferr_ukidss_h</th><th>flag_ukidss_h</th><th>f_ap_ukidss_k</th><th>ferr_ap_ukidss_k</th><th>f_ukidss_k</th><th>ferr_ukidss_k</th><th>flag_ukidss_k</th><th>las_flag_cleaned</th><th>las_flag_gaia</th><th>wirds_id</th><th>m_wirds_u</th><th>merr_wirds_u</th><th>m_ap_wirds_u</th><th>merr_ap_wirds_u</th><th>m_wirds_g</th><th>merr_wirds_g</th><th>m_ap_wirds_g</th><th>merr_ap_wirds_g</th><th>m_wirds_r</th><th>merr_wirds_r</th><th>m_ap_wirds_r</th><th>merr_ap_wirds_r</th><th>m_wirds_i</th><th>merr_wirds_i</th><th>m_ap_wirds_i</th><th>merr_ap_wirds_i</th><th>m_wirds_z</th><th>merr_wirds_z</th><th>m_ap_wirds_z</th><th>merr_ap_wirds_z</th><th>m_wirds_j</th><th>merr_wirds_j</th><th>m_ap_wirds_j</th><th>merr_ap_wirds_j</th><th>m_wirds_h</th><th>merr_wirds_h</th><th>m_ap_wirds_h</th><th>merr_ap_wirds_h</th><th>m_wirds_k</th><th>merr_wirds_k</th><th>m_ap_wirds_k</th><th>merr_ap_wirds_k</th><th>f_wirds_u</th><th>ferr_wirds_u</th><th>flag_wirds_u</th><th>f_ap_wirds_u</th><th>ferr_ap_wirds_u</th><th>f_wirds_g</th><th>ferr_wirds_g</th><th>flag_wirds_g</th><th>f_ap_wirds_g</th><th>ferr_ap_wirds_g</th><th>f_wirds_r</th><th>ferr_wirds_r</th><th>flag_wirds_r</th><th>f_ap_wirds_r</th><th>ferr_ap_wirds_r</th><th>f_wirds_i</th><th>ferr_wirds_i</th><th>flag_wirds_i</th><th>f_ap_wirds_i</th><th>ferr_ap_wirds_i</th><th>f_wirds_z</th><th>ferr_wirds_z</th><th>flag_wirds_z</th><th>f_ap_wirds_z</th><th>ferr_ap_wirds_z</th><th>f_wirds_j</th><th>ferr_wirds_j</th><th>flag_wirds_j</th><th>f_ap_wirds_j</th><th>ferr_ap_wirds_j</th><th>f_wirds_h</th><th>ferr_wirds_h</th><th>flag_wirds_h</th><th>f_ap_wirds_h</th><th>ferr_ap_wirds_h</th><th>f_wirds_k</th><th>ferr_wirds_k</th><th>flag_wirds_k</th><th>f_ap_wirds_k</th><th>ferr_ap_wirds_k</th><th>wirds_flag_cleaned</th><th>cfht-wirds_flag_gaia</th><th>ps1_flag_cleaned_1</th><th>ps1_id</th><th>m_ap_gpc1_g</th><th>merr_ap_gpc1_g</th><th>m_gpc1_g</th><th>merr_gpc1_g</th><th>m_ap_gpc1_r</th><th>merr_ap_gpc1_r</th><th>m_gpc1_r</th><th>merr_gpc1_r</th><th>m_ap_gpc1_i</th><th>merr_ap_gpc1_i</th><th>m_gpc1_i</th><th>merr_gpc1_i</th><th>m_ap_gpc1_z</th><th>merr_ap_gpc1_z</th><th>m_gpc1_z</th><th>merr_gpc1_z</th><th>m_ap_gpc1_y</th><th>merr_ap_gpc1_y</th><th>m_gpc1_y</th><th>merr_gpc1_y</th><th>f_ap_gpc1_g</th><th>ferr_ap_gpc1_g</th><th>f_gpc1_g</th><th>ferr_gpc1_g</th><th>flag_gpc1_g</th><th>f_ap_gpc1_r</th><th>ferr_ap_gpc1_r</th><th>f_gpc1_r</th><th>ferr_gpc1_r</th><th>flag_gpc1_r</th><th>f_ap_gpc1_i</th><th>ferr_ap_gpc1_i</th><th>f_gpc1_i</th><th>ferr_gpc1_i</th><th>flag_gpc1_i</th><th>f_ap_gpc1_z</th><th>ferr_ap_gpc1_z</th><th>f_gpc1_z</th><th>ferr_gpc1_z</th><th>flag_gpc1_z</th><th>f_ap_gpc1_y</th><th>ferr_ap_gpc1_y</th><th>f_gpc1_y</th><th>ferr_gpc1_y</th><th>flag_gpc1_y</th><th>ps1_flag_cleaned_2</th><th>ps1_flag_gaia</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th>deg</th><th>deg</th><th></th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<tr><td>0</td><td>HELP_J095914.43+021600.80</td><td>COSMOS2015_J095914.43+021600.80</td><td>149.810113605091</td><td>2.26688583985786</td><td>1.0</td><td>nan</td><td>nan</td><td>-2.224829950137064e-05</td><td>0.032126300036907196</td><td>nan</td><td>nan</td><td>0.03079020045697689</td><td>0.04474860057234764</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>26.325700759887695</td><td>0.3331409990787506</td><td>0.08171340078115463</td><td>0.06977520138025284</td><td>27.722900390625</td><td>0.3792949914932251</td><td>-0.0016348300268873572</td><td>0.016533099114894867</td><td>29.33679962158203</td><td>1.4130500555038452</td><td>0.00668556010350585</td><td>0.009602379985153675</td><td>27.513700485229492</td><td>0.4707289934158325</td><td>0.013516400009393692</td><td>0.018334299325942993</td><td>27.704099655151367</td><td>0.8654019832611084</td><td>0.01880040019750595</td><td>0.021642500534653664</td><td>27.51580047607422</td><td>0.45097699761390686</td><td>0.024797800928354263</td><td>0.014752100221812725</td><td>nan</td><td>nan</td><td>-0.01803950034081936</td><td>0.14826600253582</td><td>27.11319923400879</td><td>0.5287179946899414</td><td>0.021216999739408493</td><td>0.03574170172214508</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>31.638999938964844</td><td>38.73860168457031</td><td>-0.002777819987386465</td><td>0.03039270080626011</td><td>27.128999710083008</td><td>0.8493750095367432</td><td>0.042619798332452774</td><td>0.03905389830470085</td><td>25.626100540161133</td><td>0.2046239972114563</td><td>0.07468000054359436</td><td>0.05422620102763176</td><td>nan</td><td>nan</td><td>-0.04015880078077316</td><td>0.04943649843335152</td><td>nan</td><td>nan</td><td>-0.032903898507356644</td><td>0.06257860362529755</td><td>nan</td><td>nan</td><td>-0.017604200169444084</td><td>0.033940400928258896</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-0.03568980097770691</td><td>0.04103970155119896</td><td>32.57509994506836</td><td>129.8509979248047</td><td>0.032395098358392715</td><td>0.054776500910520554</td><td>nan</td><td>nan</td><td>-0.03633879870176315</td><td>0.04105839878320694</td><td>nan</td><td>nan</td><td>-0.023999100551009178</td><td>0.06253539770841599</td><td>nan</td><td>nan</td><td>0.03730649873614311</td><td>0.07784759998321533</td><td>nan</td><td>nan</td><td>-0.007673860061913729</td><td>0.06440850347280502</td><td>25.470399856567383</td><td>0.9133350253105164</td><td>0.09047289937734604</td><td>0.2874850034713745</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.030214399099349976</td><td>0.12773899734020233</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>-1567.791259765625</td><td>nan</td><td>nan</td><td>False</td><td>27.678970336914062</td><td>1.5779427289962769</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.10708270967006683</td><td>0.03285663574934006</td><td>False</td><td>26.61927032470703</td><td>0.9271118640899658</td><td>0.029569195583462715</td><td>0.010329809039831161</td><td>False</td><td>nan</td><td>-10.980091094970703</td><td>0.006687752436846495</td><td>0.008703891187906265</td><td>False</td><td>29.337158203125</td><td>1.5594282150268555</td><td>0.03585251420736313</td><td>0.015544123947620392</td><td>False</td><td>28.572845458984375</td><td>1.4727451801300049</td><td>0.03008570522069931</td><td>0.02398025244474411</td><td>False</td><td>28.214584350585938</td><td>1.249869465827942</td><td>0.03578322380781174</td><td>0.01486310362815857</td><td>False</td><td>27.913970947265625</td><td>0.6458995938301086</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-8.923625946044922</td><td>0.05184659734368324</td><td>0.025247598066926003</td><td>False</td><td>28.083290100097656</td><td>1.829007863998413</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.0008024165290407836</td><td>0.028629876673221588</td><td>False</td><td>nan</td><td>-11.879262924194336</td><td>0.05109751597046852</td><td>0.039973750710487366</td><td>False</td><td>27.325973510742188</td><td>0.9948950409889221</td><td>0.20396696031093597</td><td>0.0384407714009285</td><td>False</td><td>26.716995239257812</td><td>0.788368284702301</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.3365685939788818</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.064918041229248</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.093268871307373</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.248487949371338</td><td>0.0003388127079233527</td><td>0.0405210442841053</td><td>False</td><td>27.623802185058594</td><td>1.835858702659607</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.2267491817474365</td><td>na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       "<tr><td>2</td><td>HELP_J095901.32+013751.84</td><td>COSMOS2015_J095901.32+013751.84</td><td>149.755485395101</td><td>1.6310639914812002</td><td>1.0</td><td>nan</td><td>nan</td><td>-6.758159724995494e-05</td><td>0.05155399814248085</td><td>24.692399978637695</td><td>0.1327420026063919</td><td>0.1425189971923828</td><td>0.060148000717163086</td><td>nan</td><td>nan</td><td>0.06490419805049896</td><td>0.13322700560092926</td><td>25.940799713134766</td><td>0.7053369879722595</td><td>0.18414899706840515</td><td>0.09476310014724731</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>27.590200424194336</td><td>0.5079659819602966</td><td>0.016207899898290634</td><td>0.00927828997373581</td><td>30.997800827026367</td><td>21.499399185180664</td><td>-0.012470300309360027</td><td>0.01731259934604168</td><td>nan</td><td>nan</td><td>0.04085579887032509</td><td>0.021752899512648582</td><td>nan</td><td>nan</td><td>-0.006400519981980324</td><td>0.013447700068354607</td><td>nan</td><td>nan</td><td>0.009490719996392727</td><td>0.1475369930267334</td><td>nan</td><td>nan</td><td>-0.03490870073437691</td><td>0.03556070104241371</td><td>26.94179916381836</td><td>0.9450860023498535</td><td>0.013661700300872326</td><td>0.03253659978508949</td><td>nan</td><td>nan</td><td>0.00808280985802412</td><td>0.02702159993350506</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.010754699818789959</td><td>0.05200650170445442</td><td>nan</td><td>nan</td><td>0.03724319860339165</td><td>0.044126201421022415</td><td>nan</td><td>nan</td><td>-0.02793310023844242</td><td>0.05926689878106117</td><td>nan</td><td>nan</td><td>0.011077700182795525</td><td>0.03365220129489899</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>26.010499954223633</td><td>0.48765799403190613</td><td>-0.024452200159430504</td><td>0.037741899490356445</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>26.359800338745117</td><td>0.6471790075302124</td><td>0.08777429908514023</td><td>0.03883260115981102</td><td>nan</td><td>nan</td><td>0.0013823099434375763</td><td>0.0646786019206047</td><td>25.993200302124023</td><td>1.1401100158691406</td><td>-0.03528809919953346</td><td>0.0687372013926506</td><td>nan</td><td>nan</td><td>-0.04942389950156212</td><td>0.06503500044345856</td><td>23.792200088500977</td><td>0.472476989030838</td><td>0.06117840111255646</td><td>0.35899001359939575</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>27.550100326538086</td><td>7.799170017242432</td><td>0.06922180205583572</td><td>0.1952970027923584</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>-828.2437744140625</td><td>0.48199182748794556</td><td>0.05892827361822128</td><td>False</td><td>26.01531982421875</td><td>0.45821863412857056</td><td>nan</td><td>nan</td><td>False</td><td>26.86931610107422</td><td>2.2286598682403564</td><td>0.1526441127061844</td><td>0.09916362166404724</td><td>False</td><td>25.737075805664062</td><td>0.5587199926376343</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.03341331332921982</td><td>0.015632551163434982</td><td>False</td><td>28.375686645507812</td><td>0.6215348839759827</td><td>0.001448368770070374</td><td>0.02868013270199299</td><td>False</td><td>nan</td><td>-1.5073344707489014</td><td>nan</td><td>nan</td><td>False</td><td>27.371864318847656</td><td>0.5780797600746155</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.2811667919158936</td><td>nan</td><td>nan</td><td>False</td><td>28.956756591796875</td><td>16.878196716308594</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.1060147285461426</td><td>0.06071281433105469</td><td>0.05284785479307175</td><td>False</td><td>28.561241149902344</td><td>2.5857808589935303</td><td>nan</td><td>nan</td><td>False</td><td>29.13109588623047</td><td>3.629718780517578</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>28.821006774902344</td><td>5.2502946853637695</td><td>nan</td><td>nan</td><td>False</td><td>27.472381591796875</td><td>1.286393642425537</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.303654670715332</td><td>nan</td><td>nan</td><td>False</td><td>28.78887939453125</td><td>3.2982850074768066</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.14315280318260193</td><td>0.06429702788591385</td><td>False</td><td>nan</td><td>-1.6758304834365845</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.10377193242311478</td><td>0.0618557445704937</td><td>False</td><td>26.541587829589844</td><td>0.4803451597690582</td><td>nan</td><td>nan</td><td>False</td><td>31.048484802246094</td><td>50.80184555053711</td><td>0.14545200765132904</td><td>0.15273626148700714</td><td>False</td><td>nan</td><td>-2.1148903369903564</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.428678274154663</td><td>1.1043834686279297</td><td>0.48059165477752686</td><td>False</td><td>26.933509826660156</td><td>6.371013641357422</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.03467048332095146</td><td>0.24904853105545044</td><td>False</td><td>26.799392700195312</td><td>3.06321120262146</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>3</td><td>HELP_J095824.46+014508.83</td><td>COSMOS2015_J095824.46+014508.83</td><td>149.601900709348</td><td>1.75245014896677</td><td>1.0</td><td>27.62619972229004</td><td>4.17363977432251</td><td>-0.000207357996259816</td><td>0.09525839984416962</td><td>25.234100341796875</td><td>0.16923700273036957</td><td>0.18030600249767303</td><td>0.09816049784421921</td><td>25.900400161743164</td><td>0.5140020251274109</td><td>0.212567999958992</td><td>0.13914600014686584</td><td>28.43709945678711</td><td>3.981489896774292</td><td>-0.0126521997153759</td><td>0.043021101504564285</td><td>26.336200714111328</td><td>0.13953399658203125</td><td>0.08034829795360565</td><td>0.02400919981300831</td><td>26.257400512695312</td><td>0.09491019695997238</td><td>0.08275499939918518</td><td>0.012464799918234348</td><td>26.413700103759766</td><td>0.1781580001115799</td><td>0.07929970324039459</td><td>0.022014500573277473</td><td>27.014699935913086</td><td>0.5597950220108032</td><td>0.051746200770139694</td><td>0.02914400026202202</td><td>26.104000091552734</td><td>0.15081000328063965</td><td>0.10205800086259842</td><td>0.020201999694108963</td><td>27.897300720214844</td><td>2.3004400730133057</td><td>0.05719060078263283</td><td>0.14986099302768707</td><td>25.552900314331055</td><td>0.11762300133705139</td><td>0.18738600611686707</td><td>0.03684540092945099</td><td>26.739200592041016</td><td>0.3630940020084381</td><td>0.03693050146102905</td><td>0.03157680109143257</td><td>26.951200485229492</td><td>0.4254559874534607</td><td>0.05447009950876236</td><td>0.02731890045106411</td><td>26.305700302124023</td><td>0.32289400696754456</td><td>0.0883743017911911</td><td>0.034883901476860046</td><td>26.03619956970215</td><td>0.25768500566482544</td><td>0.11793199926614761</td><td>0.05172799900174141</td><td>26.24449920654297</td><td>0.3676219880580902</td><td>0.08679749816656113</td><td>0.04474179819226265</td><td>25.836299896240234</td><td>0.30692899227142334</td><td>0.1083969995379448</td><td>0.06508269906044006</td><td>26.631799697875977</td><td>0.34787601232528687</td><td>0.04856489971280098</td><td>0.03317689895629883</td><td>26.0664005279541</td><td>0.2245739996433258</td><td>0.10432899743318558</td><td>0.03778360038995743</td><td>26.467899322509766</td><td>0.3505220115184784</td><td>0.09926120191812515</td><td>0.03753510117530823</td><td>nan</td><td>nan</td><td>0.055400099605321884</td><td>0.04390709847211838</td><td>27.763700485229492</td><td>1.096909999847412</td><td>0.017430799081921577</td><td>0.037619899958372116</td><td>26.0846004486084</td><td>0.30439698696136475</td><td>0.15890200436115265</td><td>0.0658503994345665</td><td>27.036100387573242</td><td>1.379870057106018</td><td>-0.002285880036652088</td><td>0.06688810139894485</td><td>27.328699111938477</td><td>0.8559470176696777</td><td>-0.0024565299972891808</td><td>0.06475680321455002</td><td>26.96940040588379</td><td>3.401870012283325</td><td>-0.12172699719667435</td><td>0.29451099038124084</td><td>24.688499450683594</td><td>0.6014350056648254</td><td>0.20760700106620789</td><td>0.4273020029067993</td><td>nan</td><td>nan</td><td>-0.03176309913396835</td><td>0.1894250065088272</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.032323624938726425</td><td>0.12425410002470016</td><td>False</td><td>nan</td><td>-498.7774658203125</td><td>0.2926574945449829</td><td>0.04561741277575493</td><td>False</td><td>25.759979248046875</td><td>0.5910862684249878</td><td>0.1584307998418808</td><td>0.0750032588839531</td><td>False</td><td>25.581260681152344</td><td>0.7107176780700684</td><td>0.015316521748900414</td><td>0.05616702511906624</td><td>False</td><td>nan</td><td>-3.6918139457702637</td><td>0.10605213046073914</td><td>0.013629348948597908</td><td>False</td><td>26.63756561279297</td><td>0.32443320751190186</td><td>0.11403541266918182</td><td>0.009968465194106102</td><td>False</td><td>26.60552215576172</td><td>0.16353674232959747</td><td>0.09874603897333145</td><td>0.016203196719288826</td><td>False</td><td>26.651824951171875</td><td>0.3014127016067505</td><td>0.05677012726664543</td><td>0.029270125553011894</td><td>False</td><td>27.11530303955078</td><td>0.6114979386329651</td><td>0.13134078681468964</td><td>0.01824338547885418</td><td>False</td><td>26.377883911132812</td><td>0.21491742134094238</td><td>0.02518138848245144</td><td>0.05335390940308571</td><td>False</td><td>27.006690979003906</td><td>2.845039129257202</td><td>0.21819235384464264</td><td>0.023637821897864342</td><td>False</td><td>25.718154907226562</td><td>0.21348650753498077</td><td>0.07316777110099792</td><td>0.024468906223773956</td><td>False</td><td>27.481536865234375</td><td>0.9283403158187866</td><td>0.06018933653831482</td><td>0.023585759103298187</td><td>False</td><td>27.059608459472656</td><td>0.5445394515991211</td><td>0.10907361656427383</td><td>0.03243809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d>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>4</td><td>HELP_J095836.75+014228.42</td><td>COSMOS2015_J095836.75+014228.42</td><td>149.653097393567</td><td>1.70789184178347</td><td>1.0</td><td>nan</td><td>nan</td><td>-0.0002465799916535616</td><td>0.10322000086307526</td><td>26.052000045776367</td><td>0.3110150098800659</td><td>0.08066640049219131</td><td>0.10296300053596497</td><td>25.23259925842285</td><td>0.2463500052690506</td><td>0.25881698727607727</td><td>0.14938899874687195</td><td>27.06839942932129</td><td>0.9899320006370544</td><td>0.03981989994645119</td><td>0.11974799633026123</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>28.220399856567383</td><td>0.39786800742149353</td><td>0.01988529972732067</td><td>0.010072899982333183</td><td>27.35409927368164</td><td>0.32145899534225464</td><td>0.03921059891581535</td><td>0.019547199830412865</td><td>27.728599548339844</td><td>0.6845070123672485</td><td>0.03402160108089447</td><td>0.022133899852633476</td><td>26.810699462890625</td><td>0.24874000251293182</td><td>0.06963930279016495</td><td>0.02018829993903637</td><td>26.164499282836914</td><td>0.38693198561668396</td><td>0.1237339973449707</td><td>0.15360000729560852</td><td>25.924800872802734</td><td>0.144228994846344</td><td>0.14012199640274048</td><td>0.03902279958128929</td><td>27.069400787353516</td><td>0.40570899844169617</td><td>0.04808440059423447</td><td>0.03156520053744316</td><td>28.766799926757812</td><td>1.9242199659347534</td><td>0.007695950102061033</td><td>0.028085799887776375</td><td>26.65679931640625</td><td>0.38825398683547974</td><td>0.06536559760570526</td><td>0.036590199917554855</td><td>26.641799926757812</td><td>0.40505000948905945</td><td>0.12998299300670624</td><td>0.056775398552417755</td><td>26.082599639892578</td><td>0.29546400904655457</td><td>0.1185780018568039</td><td>0.05023359879851341</td><td>27.043800354003906</td><td>0.7837049961090088</td><td>0.07536870241165161</td><td>0.06467989832162857</td><td>nan</td><td>nan</td><td>-0.0061323498375713825</td><td>0.03316750004887581</td><td>nan</td><td>nan</td><td>0.013743700459599495</td><td>0.04328709840774536</td><td>26.92460060119629</td><td>0.606145977973938</td><td>0.032586801797151566</td><td>0.051478300243616104</td><td>27.179899215698242</td><td>0.5667830109596252</td><td>0.04314570128917694</td><td>0.04569130018353462</td><td>26.41119956970215</td><td>0.2781530022621155</td><td>0.08915960043668747</td><td>0.039994701743125916</td><td>25.821800231933594</td><td>0.19441600143909454</td><td>0.14315399527549744</td><td>0.06366559863090515</td><td>26.006500244140625</td><td>0.45925599336624146</td><td>0.1566620022058487</td><td>0.06949210166931152</td><td>26.821699142456055</td><td>0.44283199310302734</td><td>0.0651032030582428</td><td>0.06431730091571808</td><td>25.309999465942383</td><td>0.5620149970054626</td><td>0.3318060040473938</td><td>0.2725889980792999</td><td>28.49850082397461</td><td>16.34869956970215</td><td>0.036659400910139084</td><td>0.4237940013408661</td><td>25.967500686645508</td><td>0.6327869892120361</td><td>0.12541800737380981</td><td>0.17618800699710846</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>-454.4963073730469</td><td>0.13778430223464966</td><td>0.03946905583143234</td><td>False</td><td>26.633270263671875</td><td>1.3858391046524048</td><td>0.293062299489975</td><td>0.06649487465620041</td><td>False</td><td>25.367523193359375</td><td>0.6266862154006958</td><td>0.054030656814575195</td><td>0.04926304891705513</td><td>False</td><td>27.399749755859375</td><td>3.2650692462921143</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.01869993656873703</td><td>0.006852590944617987</td><td>False</td><td>28.153671264648438</td><td>0.5499796867370605</td><td>0.0415298193693161</td><td>0.012295927852392197</td><td>False</td><td>27.41649627685547</td><td>0.5412593483924866</td><td>0.02941441535949707</td><td>0.01854444481432438</td><td>False</td><td>27.57061767578125</td><td>0.7063623070716858</td><td>0.06850465387105942</td><td>0.015694279223680496</td><td>False</td><td>26.792869567871094</td><td>0.31475281715393066</td><td>0.12422246485948563</td><td>0.044270094484090805</td><td>False</td><td>26.168777465820312</td><td>1.3478032350540161</td><td>0.15491008758544922</td><td>0.020578227937221527</td><td>False</td><td>26.033729553222656</td><td>0.3023684024810791</td><td>0.05398085340857506</td><td>0.020171120762825012</td><td>False</td><td>27.194992065429688</td><td>0.7127358913421631</td><td>0.011305241845548153</td><td>0.020035963505506516</td><td>False</td><td>29.184349060058594</td><td>3.9623141288757324</td><td>0.07893692702054977</td><td>0.028227463364601135</td><td>False</td><td>26.86162567138672</td><td>0.6077708601951599</td><td>0.08003496378660202</td><td>0.02985823154449463</td><td>False</td><td>26.11528778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</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>5</td><td>HELP_J095739.74+020106.13</td><td>COSMOS2015_J095739.74+020106.13</td><td>149.415581999546</td><td>2.0183670622076706</td><td>1.0</td><td>26.76289939880371</td><td>0.7536249756813049</td><td>-0.0001133800033130683</td><td>0.041202299296855927</td><td>25.63249969482422</td><td>0.11337800323963165</td><td>0.1713860034942627</td><td>0.05649179965257645</td><td>28.561800003051758</td><td>4.18720006942749</td><td>0.012659999541938305</td><td>0.12574300169944763</td><td>25.29949951171875</td><td>0.13934800028800964</td><td>0.2478870004415512</td><td>0.08941079676151276</td><td>27.481399536132812</td><td>0.3202170133590698</td><td>0.030972599983215332</td><td>0.020800799131393433</td><td>26.924800872802734</td><td>0.1463650017976761</td><td>0.04903170093894005</td><td>0.01099689956754446</td><td>26.789100646972656</td><td>0.22168399393558502</td><td>0.05283220112323761</td><td>0.02042119950056076</td><td>26.520200729370117</td><td>0.256989985704422</td><td>0.07533170282840729</td><td>0.02284570038318634</td><td>26.737699508666992</td><td>0.20089100301265717</td><td>0.06885749846696854</td><td>0.016304999589920044</td><td>25.49139976501465</td><td>0.25144898891448975</td><td>0.20130400359630585</td><td>0.1652349978685379</td><td>26.149499893188477</td><td>0.1857379972934723</td><td>0.08983220160007477</td><td>0.03625509887933731</td><td>31.036500930786133</td><td>18.67690086364746</td><td>0.022116100415587425</td><td>0.0336868017911911</td><td>26.68199920654297</td><td>0.32916000485420227</td><td>0.06547389924526215</td><td>0.029289299622178078</td><td>26.322799682617188</td><td>0.30408498644828796</td><td>0.08669430017471313</td><td>0.034937601536512375</td><td>25.998699188232422</td><td>0.25484201312065125</td><td>0.13686400651931763</td><td>0.05722710117697716</td><td>26.060300827026367</td><td>0.33415699005126953</td><td>0.12416400015354156</td><td>0.05224920064210892</td><td>26.6835994720459</td><td>0.5920159816741943</td><td>0.09773139655590057</td><td>0.062003400176763535</td><td>26.891799926757812</td><td>0.4304729998111725</td><td>0.06721659749746323</td><td>0.03499919921159744</td><td>nan</td><td>nan</td><td>-0.006792239844799042</td><td>0.041219498962163925</td><td>26.55069923400879</td><td>0.4201520085334778</td><td>0.07791110128164291</td><td>0.044979799538850784</td><td>nan</td><td>nan</td><td>0.003064770018681884</td><td>0.05306880176067352</td><td>26.497400283813477</td><td>0.37137800455093384</td><td>0.09716919809579849</td><td>0.04439700022339821</td><td>26.565000534057617</td><td>0.46879100799560547</td><td>0.036674901843070984</td><td>0.06912510097026825</td><td>27.02400016784668</td><td>1.5083600282669067</td><td>0.036743298172950745</td><td>0.0798444002866745</td><td>27.535999298095703</td><td>0.9614880084991455</td><td>0.04133069887757301</td><td>0.06483989953994751</td><td>nan</td><td>nan</td><td>-0.31312501430511475</td><td>0.36148300766944885</td><td>25.448999404907227</td><td>0.9694979786872864</td><td>0.1864289939403534</td><td>0.37154000997543335</td><td>nan</td><td>nan</td><td>0.03769740089774132</td><td>0.20017500221729279</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.07158800214529037</td><td>0.049690257757902145</td><td>False</td><td>nan</td><td>-394.55657958984375</td><td>0.20276834070682526</td><td>0.021174084395170212</td><td>False</td><td>25.8150634765625</td><td>0.3578774929046631</td><td>0.013654621317982674</td><td>0.0526597835123539</td><td>False</td><td>28.64391326904297</td><td>10.783864974975586</td><td>0.27554965019226074</td><td>0.03536521643400192</td><td>False</td><td>25.41436767578125</td><td>0.3916161060333252</td><td>0.03693516179919243</td><td>0.010893316008150578</td><td>False</td><td>27.67255401611328</td><td>0.7291663885116577</td><td>0.061670802533626556</td><td>0.00831366516649723</td><td>False</td><td>27.173805236816406</td><td>0.243510439991951</td><td>0.06988104432821274</td><td>0.014268208295106888</td><td>False</td><td>27.092758178710938</td><td>0.41966894268989563</td><td>0.08951989561319351</td><td>0.021189047023653984</td><td>False</td><td>26.707557678222656</td><td>0.32926908135414124</td><td>0.07326897978782654</td><td>0.013556771911680698</td><td>False</td><td>26.80512237548828</td><td>0.2570951581001282</td><td>0.2309085726737976</td><td>0.05347682535648346</td><td>False</td><td>25.64037322998047</td><td>0.8911974430084229</td><td>0.12595044076442719</td><td>0.021546470001339912</td><td>False</td><td>26.51641845703125</td><td>0.4381888806819916</td><td>0.0013976522022858262</td><td>0.02404250018298626</td><td>False</td><td>28.038230895996094</td><td>1.6537716388702393</td><td>0.0771259143948555</td><td>0.023382076993584633</td><td>False</td><td>26.859832763671875</td><td>0.48569661378860474</td><td>0.10736925899982452</td><td>0.030071191489696503</td><td>False</td><td>26.5550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an</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>150.228391944512</td><td>1.7912940828254702</td><td>1.0</td><td>29.246599197387695</td><td>6.053830146789551</td><td>-0.00023756100563332438</td><td>0.04055729880928993</td><td>27.115999221801758</td><td>0.33643999695777893</td><td>0.05023150146007538</td><td>0.04507419839501381</td><td>nan</td><td>nan</td><td>-0.004802720155566931</td><td>0.03616439923644066</td><td>26.980899810791016</td><td>0.45163199305534363</td><td>0.054800599813461304</td><td>0.06388620287179947</td><td>29.459699630737305</td><td>1.3345600366592407</td><td>0.006226019933819771</td><td>0.016847100108861923</td><td>27.989900588989258</td><td>0.32492899894714355</td><td>0.023008599877357483</td><td>0.010755900293588638</td><td>27.910999298095703</td><td>0.5444920063018799</td><td>0.02508939988911152</td><td>0.02130099944770336</td><td>26.8572998046875</td><td>0.24190300703048706</td><td>0.06540630012750626</td><td>0.018897999078035355</td><td>28.412799835205078</td><td>0.7220950126647949</td><td>0.015615900047123432</td><td>0.014678600244224072</td><td>27.15760040283203</td><td>0.816769003868103</td><td>0.051327601075172424</td><td>0.13927200436592102</td><td>26.386600494384766</td><td>0.18057100474834442</td><td>0.10234300047159195</td><td>0.034286998212337494</td><td>nan</td><td>nan</td><td>-0.012409600429236889</td><td>0.03511530160903931</td><td>27.68549919128418</td><td>0.6387630105018616</td><td>0.029258999973535538</td><td>0.02723960019648075</td><td>27.41710090637207</td><td>0.6481980085372925</td><td>0.03956390172243118</td><td>0.03278509899973869</td><td>nan</td><td>nan</td><td>-0.03728500008583069</td><td>0.05100889876484871</td><td>27.379600524902344</td><td>0.8332549929618835</td><td>0.04123469814658165</td><td>0.046518899500370026</td><td>27.744400024414062</td><td>1.2617499828338623</td><td>0.030225099995732307</td><td>0.05993739888072014</td><td>29.22800064086914</td><td>2.8900699615478516</td><td>0.005414620041847229</td><td>0.03293519839644432</td><td>26.973499298095703</td><td>0.38068801164627075</td><td>0.06163389980792999</td><td>0.036066800355911255</td><td>nan</td><td>nan</td><td>-0.007592460140585899</td><td>0.03887160122394562</td><td>26.76919937133789</td><td>0.36511099338531494</td><td>0.06881099939346313</td><td>0.04646509885787964</td><td>27.302099227905273</td><td>0.5917850136756897</td><td>0.045516300946474075</td><td>0.040585801005363464</td><td>25.765100479125977</td><td>0.16414900124073029</td><td>0.17454500496387482</td><td>0.06101899966597557</td><td>25.995100021362305</td><td>0.4543190002441406</td><td>0.14447300136089325</td><td>0.07501079887151718</td><td>26.32699966430664</td><td>0.2595149874687195</td><td>0.10660199820995331</td><td>0.06433620303869247</td><td>nan</td><td>nan</td><td>-0.11388900130987167</td><td>0.35783401131629944</td><td>nan</td><td>nan</td><td>-0.18682099878787994</td><td>0.37527701258659363</td><td>25.861600875854492</td><td>0.36820200085639954</td><td>0.16353200376033783</td><td>0.12123099714517593</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.007267090026289225</td><td>0.04051971808075905</td><td>False</td><td>nan</td><td>-185.3609161376953</td><td>0.05171305313706398</td><td>0.016024461016058922</td><td>False</td><td>27.147560119628906</td><td>0.9742628931999207</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-8.17557430267334</td><td>0.05856526643037796</td><td>0.024361303076148033</td><td>False</td><td>27.05303955078125</td><td>1.2657445669174194</td><td>0.00597200496122241</td><td>0.007340640760958195</td><td>False</td><td>29.414474487304688</td><td>2.937913179397583</td><td>0.023122746497392654</td><td>0.006919959560036659</td><td>False</td><td>27.995277404785156</td><td>0.5075523853302002</td><td>0.024865679442882538</td><td>0.012470030225813389</td><td>False</td><td>27.901275634765625</td><td>0.9217942953109741</td><td>0.0656266137957573</td><td>0.014621670357882977</td><td>False</td><td>26.86095428466797</td><td>0.31370437145233154</td><td>0.015663182362914085</td><td>0.01041717641055584</td><td>False</td><td>28.41608428955078</td><td>1.0205680131912231</td><td>0.04976906627416611</td><td>0.03743987902998924</td><td>False</td><td>27.124122619628906</td><td>2.9460299015045166</td><td>0.10124175250530243</td><td>0.016837721690535545</td><td>False</td><td>26.374855041503906</td><td>0.3637438118457794</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-3.0722949504852295</td><td>0.030605554580688477</td><td>0.018005935475230217</td><td>False</td><td>27.734352111816406</td><td>1.0108007192611694</td><td>0.03918859362602234</td><td>0.023396076634526253</td><td>False</td><td>27.406753540039062</td><td>0.8997082114219666</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.485374927520752</td><td>0.040565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an</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>7</td><td>HELP_J100148.56+015305.15</td><td>COSMOS2015_J100148.56+015305.15</td><td>150.452331511501</td><td>1.88475989727483</td><td>1.0</td><td>23.819599151611328</td><td>0.09215229749679565</td><td>-0.000252103985985741</td><td>0.0323684997856617</td><td>23.358400344848633</td><td>0.02699040062725544</td><td>0.4151970148086548</td><td>0.04673109948635101</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>25.23870086669922</td><td>0.2368279993534088</td><td>0.06583669781684875</td><td>0.06857369840145111</td><td>26.0846004486084</td><td>0.17872600257396698</td><td>0.003673689905554056</td><td>0.01778149977326393</td><td>27.50309944152832</td><td>0.6248720288276672</td><td>0.0012436499819159508</td><td>0.011272500269114971</td><td>26.267200469970703</td><td>0.3021009862422943</td><td>-0.018131699413061142</td><td>0.019397500902414322</td><td>25.833599090576172</td><td>0.3169400095939636</td><td>-0.013504499569535255</td><td>0.022365599870681763</td><td>25.7278995513916</td><td>0.17303599417209625</td><td>-0.013602199964225292</td><td>0.014094100333750248</td><td>24.74690055847168</td><td>0.27194198966026306</td><td>0.05359189957380295</td><td>0.14892399311065674</td><td>26.983600616455078</td><td>0.8955249786376953</td><td>-0.021950900554656982</td><td>0.034578800201416016</td><td>26.147600173950195</td><td>0.4824160039424896</td><td>-0.014096999540925026</td><td>0.03341849893331528</td><td>26.159799575805664</td><td>0.47944799065589905</td><td>-0.0015686099650338292</td><td>0.0294272992759943</td><td>27.034700393676758</td><td>1.3430900573730469</td><td>0.025292400270700455</td><td>0.03430600091814995</td><td>25.508800506591797</td><td>0.35839399695396423</td><td>-0.030350299552083015</td><td>0.05374940112233162</td><td>26.48270034790039</td><td>1.1038299798965454</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>26.9158992767334</td><td>0.9699239730834961</td><td>0.009506010450422764</td><td>0.032875098288059235</td><td>26.40880012512207</td><td>0.6944720149040222</td><td>0.08561930060386658</td><td>0.03932179883122444</td><td>25.03499984741211</td><td>0.2413329929113388</td><td>0.0335065983235836</td><td>0.044328801333904266</td><td>24.700199127197266</td><td>0.16254200041294098</td><td>-0.024235300719738007</td><td>0.04897329956293106</td><td>25.981800079345703</td><td>0.5067870020866394</td><td>0.011954200454056263</td><td>0.041410401463508606</td><td>26.495399475097656</td><td>0.9835749864578247</td><td>nan</td><td>nan</td><td>24.369400024414062</td><td>0.2797969877719879</td><td>-0.015019799582660198</td><td>0.07301740348339081</td><td>25.304399490356445</td><td>0.28583699464797974</td><td>0.04646819829940796</td><td>0.06416960060596466</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>24.447099685668945</td><td>0.7317259907722473</td><td>0.18517500162124634</td><td>0.3070339858531952</td><td>24.71780014038086</td><td>0.3696199953556061</td><td>0.03629760071635246</td><td>0.12352599948644638</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0768625736236572</td><td>0.09139914065599442</td><td>False</td><td>nan</td><td>-139.40142822265625</td><td>1.6467949151992798</td><td>0.04093780368566513</td><td>False</td><td>24.8543701171875</td><td>0.12220136821269989</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.2914201021194458</td><td>0.06356649100780487</td><td>False</td><td>26.85382843017578</td><td>1.1308729648590088</td><td>0.13370870053768158</td><td>0.02201015315949917</td><td>False</td><td>29.98725128173828</td><td>5.255211353302002</td><td>0.036204300820827484</td><td>0.02083660289645195</td><td>False</td><td>31.163253784179688</td><td>9.841161727905273</td><td>0.11301072686910629</td><td>0.03144470229744911</td><td>False</td><td>nan</td><td>-1.1615331172943115</td><td>0.16848456859588623</td><td>0.04918275400996208</td><td>False</td><td>nan</td><td>-1.7981517314910889</td><td>0.18571193516254425</td><td>0.029597291722893715</td><td>False</td><td>nan</td><td>-1.1249998807907104</td><td>0.45839473605155945</td><td>0.11481313407421112</td><td>False</td><td>27.077255249023438</td><td>3.0171005725860596</td><td>0.058419737964868546</td><td>0.04818512499332428</td><td>False</td><td>nan</td><td>-1.7103378772735596</td><td>0.1261710673570633</td><td>0.05606052652001381</td><td>False</td><td>nan</td><td>-2.5738580226898193</td><td>0.12476128339767456</td><td>0.05509307608008385</td><td>False</td><td>nan</td><td>-20.368532180786133</td><td>0.05573390796780586</td><td>0.06894461065530777</td><td>False</td><td>27.89252471923828</td><td>1.4726662635803223</td><td>0.22723731398582458</td><td>0.07500945776700974</td><td>False</td><td>nan</td><td>-1.9228036403656006</td><td>0.0926658883690834</td><td>0.09421015530824661</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.062178485095500946</td><td>0.05554609373211861</td><td>False</td><td>28.955001831054688</td><td>3.754854440689087</td><td>0.09919276088476181</td><td>0.06344689428806305</td><td>False</td><td>26.568572998046875</td><td>0.49863874912261963</td><td>0.35156047344207764</td><td>0.07814342528581619</td><td>False</td><td>27.587173461914062</td><td>1.4364149570465088</td><td>0.47854217886924744</td><td>0.0716409757733345</td><td>False</td><td>nan</td><td>-2.193993091583252</td><td>0.14698727428913116</td><td>0.06860896944999695</td><td>False</td><td>28.706199645996094</td><td>3.7610857486724854</td><td>0.0915883332490921</td><td>0.08297042548656464</td><td>False</td><td>nan</td><td>nan</td><td>0.6489924788475037</td><td>0.167247012257576</td><td>False</td><td>nan</td><td>-5.2782087326049805</td><td>0.2743090093135834</td><td>0.07221612334251404</td><td>False</td><td>27.23210906982422</td><td>1.4993319511413574</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.6041710376739502</td><td>0.40717774629592896</td><td>False</td><td>25.731048583984375</td><td>1.800231695175171</td><td>0.47084689140319824</td><td>0.1602916270494461</td><td>False</td><td>27.50030517578125</td><td>3.6949174404144287</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>8</td><td>HELP_J095825.28+024722.91</td><td>COSMOS2015_J095825.28+024722.91</td><td>149.605323991903</td><td>2.7896954394515</td><td>1.0</td><td>24.891799926757812</td><td>0.4804820120334625</td><td>-0.0012573499698191881</td><td>0.14371000230312347</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>25.011600494384766</td><td>0.2879070043563843</td><td>0.2943649888038635</td><td>0.18665100634098053</td><td>nan</td><td>nan</td><td>0.010830000042915344</td><td>0.13997900485992432</td><td>nan</td><td>nan</td><td>-0.017782000824809074</td><td>0.02529890090227127</td><td>28.56529998779297</td><td>0.6193090081214905</td><td>0.013932500034570694</td><td>0.010006600059568882</td><td>31.792600631713867</td><td>20.278799057006836</td><td>0.0030724001117050648</td><td>0.01811249926686287</td><td>27.501800537109375</td><td>0.5967649817466736</td><td>0.033101100474596024</td><td>0.020975099876523018</td><td>27.675899505615234</td><td>0.4262180030345917</td><td>0.030699100345373154</td><td>0.014130700379610062</td><td>nan</td><td>nan</td><td>0.0064885299652814865</td><td>0.14663800597190857</td><td>25.809499740600586</td><td>0.14062899351119995</td><td>0.15000100433826447</td><td>0.03684629872441292</td><td>29.93160057067871</td><td>7.334469795227051</td><td>0.007880040444433689</td><td>0.035682499408721924</td><td>nan</td><td>nan</td><td>-0.012066599912941456</td><td>0.030326999723911285</td><td>nan</td><td>nan</td><td>-0.01838959939777851</td><td>0.03392979875206947</td><td>nan</td><td>nan</td><td>0.029273400083184242</td><td>0.05324539914727211</td><td>27.271400451660156</td><td>1.05308997631073</td><td>0.046681199222803116</td><td>0.05259409919381142</td><td>26.65060043334961</td><td>0.5370069742202759</td><td>0.03286759927868843</td><td>0.05656509846448898</td><td>nan</td><td>nan</td><td>-0.024102700874209404</td><td>0.037144798785448074</td><td>27.841699600219727</td><td>1.1036299467086792</td><td>0.005159109830856323</td><td>0.03797009959816933</td><td>26.821399688720703</td><td>0.5514510273933411</td><td>0.049783699214458466</td><td>0.044806499034166336</td><td>nan</td><td>nan</td><td>0.012224099598824978</td><td>0.04793189838528633</td><td>29.693300247192383</td><td>6.5020599365234375</td><td>0.009545629844069481</td><td>0.03977780044078827</td><td>26.11050033569336</td><td>0.28793200850486755</td><td>0.12089499831199646</td><td>0.06289739906787872</td><td>28.114700317382812</td><td>3.6974499225616455</td><td>0.02645459957420826</td><td>0.0695372000336647</td><td>27.31719970703125</td><td>0.8093860149383545</td><td>0.029327599331736565</td><td>0.06490059942007065</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>24.554399490356445</td><td>0.4204460084438324</td><td>0.452688992023468</td><td>0.3566330075263977</td><td>25.027000427246094</td><td>0.4429689943790436</td><td>0.2250099927186966</td><td>0.25366899371147156</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.40112531185150146</td><td>0.17751410603523254</td><td>False</td><td>nan</td><td>-124.09523010253906</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.3592194616794586</td><td>0.09525499492883682</td><td>False</td><td>25.227783203125</td><td>0.6884438395500183</td><td>nan</td><td>nan</td><td>False</td><td>28.813430786132812</td><td>14.033266067504883</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.5447041988372803</td><td>0.013610674999654293</td><td>0.007763592526316643</td><td>False</td><td>28.53992462158203</td><td>0.7797974348068237</td><td>0.0006965613574720919</td><td>0.013009999878704548</td><td>False</td><td>30.181312561035156</td><td>6.400662422180176</td><td>0.0362476147711277</td><td>0.019923171028494835</td><td>False</td><td>27.60039520263672</td><td>0.6879960298538208</td><td>0.03087736666202545</td><td>0.012121258303523064</td><td>False</td><td>27.68218994140625</td><td>0.4997609555721283</td><td>nan</td><td>nan</td><td>False</td><td>29.36963653564453</td><td>24.53717041015625</td><td>0.17226611077785492</td><td>0.02231261134147644</td><td>False</td><td>25.95977020263672</td><td>0.2667005956172943</td><td>0.003866868559271097</td><td>0.026121841743588448</td><td>False</td><td>29.15868377685547</td><td>4.916444301605225</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.7287821769714355</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-2.003241539001465</td><td>nan</td><td>nan</td><td>False</td><td>27.73381805419922</td><td>1.9748458862304688</td><td>0.04481668025255203</td><td>0.04346911981701851</td><td>False</td><td>27.227149963378906</td><td>1.2232615947723389</td><td>0.07938889414072037</td><td>0.03926588594913483</td><td>False</td><td>27.60808563232422</td><td>1.8685505390167236</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.6732336282730103</td><td>0.0265045128762722</td><td>0.02694132551550865</td><td>False</td><td>29.618568420410156</td><td>7.990818500518799</td><td>0.0678328350186348</td><td>0.0344526506960392</td><td>False</td><td>27.15728759765625</td><td>0.9771880507469177</td><td>nan</td><td>nan</td><td>False</td><td>28.681961059570312</td><td>4.257277965545654</td><td>0.004815929569303989</td><td>0.028840765357017517</td><td>False</td><td>28.95049285888672</td><td>4.524394512176514</td><td>0.13055692613124847</td><td>0.03462307155132294</td><td>False</td><td>26.19397735595703</td><td>0.5648701786994934</td><td>0.02061198465526104</td><td>0.07019364088773727</td><td>False</td><td>27.84375</td><td>2.853910207748413</td><td>0.042965516448020935</td><td>0.032029591500759125</td><td>False</td><td>27.73180389404297</td><td>2.4026830196380615</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>0.5473186373710632</td><td>0.21194645762443542</td><td>False</td><td>24.76050567626953</td><td>0.8553539514541626</td><td>0.3541600704193115</td><td>0.14449359476566315</td><td>False</td><td>25.519500732421875</td><td>1.2240238189697266</td><td>False</td><td>False</td><td>False</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>9</td><td>HELP_J100154.84+020628.04</td><td>COSMOS2015_J100154.84+020628.04</td><td>150.478458691686</td><td>2.1077854526048103</td><td>1.0</td><td>25.035999298095703</td><td>0.23545700311660767</td><td>-0.00030819998937658966</td><td>0.03476019948720932</td><td>nan</td><td>nan</td><td>0.08508999645709991</td><td>0.04862760007381439</td><td>26.249099731445312</td><td>0.763034999370575</td><td>0.06652569770812988</td><td>0.10621599853038788</td><td>nan</td><td>nan</td><td>0.019316399469971657</td><td>0.07231739908456802</td><td>nan</td><td>nan</td><td>-0.004923809785395861</td><td>0.016114600002765656</td><td>nan</td><td>nan</td><td>-0.0073474799282848835</td><td>0.00969650037586689</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-0.006511160172522068</td><td>0.021082300692796707</td><td>28.953399658203125</td><td>2.484339952468872</td><td>0.004510439932346344</td><td>0.0138349998742342</td><td>25.567399978637695</td><td>0.4667159914970398</td><td>0.03285469859838486</td><td>0.1550550013780594</td><td>nan</td><td>nan</td><td>0.03178200125694275</td><td>0.03601429983973503</td><td>26.41900062561035</td><td>0.4519430100917816</td><td>0.01083150040358305</td><td>0.031428299844264984</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.012414599768817425</td><td>0.03443190082907677</td><td>25.226299285888672</td><td>0.21817399561405182</td><td>0.10095000267028809</td><td>0.05498350039124489</td><td>nan</td><td>nan</td><td>0.005276429932564497</td><td>0.045866601169109344</td><td>nan</td><td>nan</td><td>-0.018124600872397423</td><td>0.05750240013003349</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.013475799933075905</td><td>0.03852749988436699</td><td>nan</td><td>nan</td><td>-0.040499698370695114</td><td>0.04198069870471954</td><td>30.243799209594727</td><td>19.240999221801758</td><td>-0.018841300159692764</td><td>0.045609500259160995</td><td>nan</td><td>nan</td><td>-0.02988320030272007</td><td>0.04068180173635483</td><td>nan</td><td>nan</td><td>-0.017823699861764908</td><td>0.0660225972533226</td><td>27.582199096679688</td><td>4.148960113525391</td><td>0.03416689857840538</td><td>0.07164239883422852</td><td>nan</td><td>nan</td><td>0.01638999953866005</td><td>0.06419520080089569</td><td>23.704599380493164</td><td>0.2511419951915741</td><td>0.09447640180587769</td><td>0.2620150148868561</td><td>23.725099563598633</td><td>0.2842470109462738</td><td>-0.08578349649906158</td><td>0.2917349934577942</td><td>25.2273006439209</td><td>0.4381519854068756</td><td>0.10212499648332596</td><td>0.11827000230550766</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.3512367308139801</td><td>0.07617057114839554</td><td>False</td><td>nan</td><td>-122.45426177978516</td><td>nan</td><td>nan</td><td>False</td><td>26.57530975341797</td><td>0.6204812526702881</td><td>0.1149105653166771</td><td>0.08075698465108871</td><td>False</td><td>26.84252166748047</td><td>1.7335036993026733</td><td>nan</td><td>nan</td><td>False</td><td>28.18518829345703</td><td>4.064816474914551</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-3.553387403488159</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.4328505992889404</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-3.515474319458008</td><td>0.009520057588815689</td><td>0.021783430129289627</td><td>False</td><td>29.76445770263672</td><td>3.3303091526031494</td><td>0.2152978926897049</td><td>0.09254823625087738</td><td>False</td><td>27.608505249023438</td><td>5.1240410804748535</td><td>nan</td><td>nan</td><td>False</td><td>27.644546508789062</td><td>1.230319857597351</td><td>0.09826519340276718</td><td>0.04090336710214615</td><td>False</td><td>28.813278198242188</td><td>3.150333881378174</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>28.66516876220703</td><td>3.0112898349761963</td><td>0.2947677969932556</td><td>0.05923231691122055</td><td>False</td><td>26.389732360839844</td><td>0.5913577675819397</td><td>nan</td><td>nan</td><td>False</td><td>29.594154357910156</td><td>9.43801498413086</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-3.4446239471435547</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>28.57611846923828</td><td>3.1041347980499268</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.1254395246505737</td><td>0.0029005513060837984</td><td>0.05140245705842972</td><td>False</td><td>nan</td><td>-2.628262519836426</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-1.4780781269073486</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>-4.021786689758301</td><td>0.033660467714071274</td><td>0.128627881407737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      ],
      "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": 35,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master_catalogue['help_id'].name = 'old_help_id'\n",
    "master_catalogue.add_column(Column(gen_help_id(master_catalogue['ra'], master_catalogue['dec']),\n",
    "                                   name=\"help_id\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK!\n"
     ]
    }
   ],
   "source": [
    "# Check that the HELP Ids are unique\n",
    "if len(master_catalogue) != len(np.unique(master_catalogue['help_id'])):\n",
    "    print(\"The HELP IDs are not unique!!!\")\n",
    "else:\n",
    "    print(\"OK!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## V b - Adding spec-z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "specz =  Table.read(\"../../dmu23/dmu23_COSMOS/data/COSMOS-specz-v2.5-public_helpcoverage_helpid_20160512.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(specz['ra'] * u.deg, specz['dec'] * u.deg)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = specz_merge(master_catalogue, specz, radius=1. * u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## VI - Choosing between multiple values for the same filter\n",
    "\n",
    "### VI.a HSC-DEEP and HSC-UDEEP and COSMOS\n",
    "\n",
    "On COSMOS2015 we have early HSC y band photometry. To ensure values are the same as for the original run, we take fluxes in this order: COSMOS, HSC-DEEP, HSC-UDEEP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "suprime_origin = Table()\n",
    "suprime_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "suprime_stats = Table()\n",
    "suprime_stats.add_column(Column(data=['g','r','i','z','y', 'n921'], name=\"Band\"))\n",
    "for col in [\"HSC-UDEEP\",  \"HSC-DEEP\", \"COSMOS2015\"]:\n",
    "    suprime_stats.add_column(Column(data=np.full(6, 0), name=\"{}\".format(col)))\n",
    "    suprime_stats.add_column(Column(data=np.full(6, 0), name=\"use {}\".format(col)))\n",
    "    suprime_stats.add_column(Column(data=np.full(6, 0), name=\"{} ap\".format(col)))\n",
    "    suprime_stats.add_column(Column(data=np.full(6, 0), name=\"use {} ap\".format(col)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "suprime_bands = ['g','r','i','z','y', 'n921'] \n",
    "for band in suprime_bands:\n",
    "\n",
    "    # Suprime total flux \n",
    "    has_hsc_udeep = ~np.isnan(master_catalogue['f_hsc-udeep_' + band])\n",
    "    has_hsc_deep  = ~np.isnan(master_catalogue['f_hsc-deep_' + band])\n",
    "    if band == 'y':\n",
    "        has_cosmos = ~np.isnan(master_catalogue['f_cosmos-suprime_y'])\n",
    "    elif band != 'y':\n",
    "        has_cosmos = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    \n",
    "    use_cosmos = has_cosmos\n",
    "    use_hsc_udeep = has_hsc_udeep & ~has_cosmos\n",
    "    use_hsc_deep = has_hsc_deep & ~has_hsc_udeep & ~has_cosmos\n",
    "    \n",
    " \n",
    "    f_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        f_suprime[use_cosmos] = master_catalogue['f_cosmos-suprime_y'][use_cosmos]\n",
    "    f_suprime[use_hsc_udeep] = master_catalogue['f_hsc-udeep_' + band][use_hsc_udeep]\n",
    "    f_suprime[use_hsc_deep] = master_catalogue['f_hsc-deep_' + band][use_hsc_deep]\n",
    " \n",
    "\n",
    "    ferr_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        ferr_suprime[use_cosmos] = master_catalogue['ferr_cosmos-suprime_y'][use_cosmos]\n",
    "    ferr_suprime[use_hsc_udeep] = master_catalogue['ferr_hsc-udeep_' + band][use_hsc_udeep]\n",
    "    ferr_suprime[use_hsc_deep] = master_catalogue['ferr_hsc-deep_' + band][use_hsc_deep]\n",
    "\n",
    "    \n",
    "    m_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        m_suprime[use_cosmos] = master_catalogue['m_cosmos-suprime_y'][use_cosmos]\n",
    "    m_suprime[use_hsc_udeep] = master_catalogue['m_hsc-udeep_' + band][use_hsc_udeep]\n",
    "    m_suprime[use_hsc_deep] = master_catalogue['m_hsc-deep_' + band][use_hsc_deep]\n",
    "\n",
    "\n",
    "    merr_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        merr_suprime[use_cosmos] = master_catalogue['merr_cosmos-suprime_y'][use_cosmos]\n",
    "    merr_suprime[use_hsc_udeep] = master_catalogue['merr_hsc-udeep_' + band][use_hsc_udeep]\n",
    "    merr_suprime[use_hsc_deep] = master_catalogue['merr_hsc-deep_' + band][use_hsc_deep]\n",
    "\n",
    "\n",
    "    flag_suprime = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    if band == 'y':\n",
    "        flag_suprime[use_cosmos] = master_catalogue['flag_cosmos-suprime_y'][use_cosmos]\n",
    "    flag_suprime[use_hsc_udeep] = master_catalogue['flag_hsc-udeep_' + band][use_hsc_udeep]\n",
    "    flag_suprime[use_hsc_deep] = master_catalogue['flag_hsc-deep_' + band][use_hsc_deep]\n",
    "\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_suprime, name=\"f_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_suprime, name=\"ferr_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_suprime, name=\"m_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_suprime, name=\"merr_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=flag_suprime, name=\"flag_suprime_\" + band))\n",
    "\n",
    "    old_hsc_udeep_columns = ['f_hsc-udeep_' + band,\n",
    "                               'ferr_hsc-udeep_' + band,\n",
    "                               'm_hsc-udeep_' + band, \n",
    "                               'merr_hsc-udeep_' + band,\n",
    "                               'flag_hsc-udeep_' + band]\n",
    "    old_hsc_deep_columns = ['f_hsc-deep_' + band,\n",
    "                               'ferr_hsc-deep_' + band,\n",
    "                               'm_hsc-deep_' + band, \n",
    "                               'merr_hsc-deep_' + band,\n",
    "                               'flag_hsc-deep_' + band]\n",
    "    old_cosmos_columns = ['f_cosmos-suprime_' + band,\n",
    "                               'ferr_cosmos-suprime_' + band,\n",
    "                               'm_cosmos-suprime_' + band, \n",
    "                               'merr_cosmos-suprime_' + band,\n",
    "                               'flag_cosmos-suprime_' + band]\n",
    "    \n",
    "    old_columns = old_hsc_udeep_columns + old_hsc_deep_columns\n",
    "    if band == 'y':\n",
    "        old_columns += old_cosmos_columns\n",
    "    master_catalogue.remove_columns(old_columns)\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_hsc_udeep] = \"HSC-UDEEP\"\n",
    "    origin[use_hsc_deep] = \"HSC-DEEP\"\n",
    "    origin[use_cosmos] = \"COSMOS2015\"\n",
    " \n",
    "    \n",
    "    suprime_origin.add_column(Column(data=origin, name= 'f_suprime_' + band ))\n",
    "    \n",
    "  \n",
    "    # Suprime aperture flux \n",
    "    has_ap_hsc_udeep = ~np.isnan(master_catalogue['f_ap_hsc-udeep_' + band])\n",
    "    has_ap_hsc_deep  = ~np.isnan(master_catalogue['f_ap_hsc-deep_' + band])\n",
    "    if band == 'y':\n",
    "        has_ap_cosmos = ~np.isnan(master_catalogue['f_ap_cosmos-suprime_y'])\n",
    "    elif band != 'y':\n",
    "        has_ap_cosmos = np.full(len(master_catalogue), False, dtype=bool)\n",
    " \n",
    "    \n",
    "    use_ap_cosmos = has_ap_cosmos\n",
    "    use_ap_hsc_udeep = has_ap_hsc_udeep & ~has_cosmos\n",
    "    use_ap_hsc_deep = has_ap_hsc_deep & ~has_ap_hsc_udeep & ~has_cosmos\n",
    "  \n",
    "\n",
    "    f_ap_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        f_ap_suprime[use_ap_cosmos] = master_catalogue['f_ap_cosmos-suprime_y'][use_ap_cosmos]\n",
    "    f_ap_suprime[use_ap_hsc_udeep] = master_catalogue['f_ap_hsc-udeep_' + band][use_ap_hsc_udeep]\n",
    "    f_ap_suprime[use_ap_hsc_deep] = master_catalogue['f_ap_hsc-deep_' + band][use_ap_hsc_deep]\n",
    "\n",
    "\n",
    "    ferr_ap_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        ferr_ap_suprime[use_ap_cosmos] = master_catalogue['ferr_ap_cosmos-suprime_y'][use_ap_cosmos]\n",
    "    ferr_ap_suprime[use_ap_hsc_udeep] = master_catalogue['ferr_ap_hsc-udeep_' + band][use_ap_hsc_udeep]\n",
    "    ferr_ap_suprime[use_ap_hsc_deep] = master_catalogue['ferr_ap_hsc-deep_' + band][use_ap_hsc_deep]\n",
    "   \n",
    "    \n",
    "    m_ap_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        m_ap_suprime[use_ap_cosmos] = master_catalogue['m_ap_cosmos-suprime_y'][use_ap_cosmos]\n",
    "    m_ap_suprime[use_ap_hsc_udeep] = master_catalogue['m_ap_hsc-udeep_' + band][use_ap_hsc_udeep]\n",
    "    m_ap_suprime[use_ap_hsc_deep] = master_catalogue['m_ap_hsc-deep_' + band][use_ap_hsc_deep]\n",
    "  \n",
    "\n",
    "    merr_ap_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    if band == 'y':\n",
    "        merr_ap_suprime[use_ap_cosmos] = master_catalogue['merr_ap_cosmos-suprime_y'][use_ap_cosmos]\n",
    "    merr_ap_suprime[use_ap_hsc_udeep] = master_catalogue['merr_ap_hsc-udeep_' + band][use_ap_hsc_udeep]\n",
    "    merr_ap_suprime[use_ap_hsc_deep] = master_catalogue['merr_ap_hsc-deep_' + band][use_ap_hsc_deep]\n",
    "  \n",
    "\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_ap_suprime, name=\"f_ap_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_ap_suprime, name=\"ferr_ap_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_ap_suprime, name=\"m_ap_suprime_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_ap_suprime, name=\"merr_ap_suprime_\" + band))\n",
    "\n",
    "\n",
    "    old_ap_hsc_udeep_columns = ['f_ap_hsc-udeep_' + band,\n",
    "                               'ferr_ap_hsc-udeep_' + band,\n",
    "                               'm_ap_hsc-udeep_' + band, \n",
    "                               'merr_ap_hsc-udeep_' + band]\n",
    "    old_ap_hsc_deep_columns = ['f_ap_hsc-deep_' + band,\n",
    "                               'ferr_ap_hsc-deep_' + band,\n",
    "                               'm_ap_hsc-deep_' + band, \n",
    "                               'merr_ap_hsc-deep_' + band]\n",
    "    old_ap_cosmos_columns = ['f_ap_cosmos-suprime_' + band,\n",
    "                               'ferr_ap_cosmos-suprime_' + band,\n",
    "                               'm_ap_cosmos-suprime_' + band, \n",
    "                               'merr_ap_cosmos-suprime_' + band]\n",
    "\n",
    "    \n",
    "    old_ap_columns = old_ap_hsc_udeep_columns + old_ap_hsc_deep_columns \n",
    "    if band == 'y':\n",
    "        old_ap_columns += old_ap_cosmos_columns\n",
    "    master_catalogue.remove_columns(old_ap_columns)\n",
    "\n",
    "    origin_ap = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin_ap[use_ap_hsc_udeep] = \"HSC-UDEEP\"\n",
    "    origin_ap[use_ap_hsc_deep] = \"HSC-DEEP\"\n",
    "    origin_ap[use_ap_cosmos] = \"COSMOS2015\"\n",
    "\n",
    "    \n",
    "    suprime_origin.add_column(Column(data=origin_ap, name= 'f_ap_suprime_' + band ))\n",
    "\n",
    "   \n",
    "    suprime_stats['HSC-UDEEP'][suprime_stats['Band'] == band] = np.sum(has_hsc_udeep)\n",
    "    suprime_stats['HSC-DEEP'][suprime_stats['Band'] == band] = np.sum(has_hsc_deep)\n",
    "    suprime_stats['COSMOS2015'][suprime_stats['Band'] == band] = np.sum(has_cosmos)\n",
    "\n",
    "    suprime_stats['use HSC-UDEEP'][suprime_stats['Band'] == band] = np.sum(use_hsc_udeep)\n",
    "    suprime_stats['use HSC-DEEP'][suprime_stats['Band'] == band] = np.sum(use_hsc_deep)\n",
    "    suprime_stats['use COSMOS2015'][suprime_stats['Band'] == band] = np.sum(use_cosmos)\n",
    "\n",
    "    suprime_stats['HSC-UDEEP ap'][suprime_stats['Band'] == band] = np.sum(has_ap_hsc_udeep)\n",
    "    suprime_stats['HSC-DEEP ap'][suprime_stats['Band'] == band] = np.sum(has_ap_hsc_deep)\n",
    "    suprime_stats['COSMOS2015 ap'][suprime_stats['Band'] == band] = np.sum(has_ap_cosmos)\n",
    "    \n",
    "    suprime_stats['use HSC-UDEEP ap'][suprime_stats['Band'] == band] = np.sum(use_ap_hsc_udeep)\n",
    "    suprime_stats['use HSC-DEEP ap'][suprime_stats['Band'] == band] = np.sum(use_ap_hsc_deep)\n",
    "    suprime_stats['use COSMOS2015 ap'][suprime_stats['Band'] == band] = np.sum(use_ap_cosmos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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       "<table id=\"table140174353150472-194000\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>HSC-UDEEP</th><th>use HSC-UDEEP</th><th>HSC-UDEEP ap</th><th>use HSC-UDEEP ap</th><th>HSC-DEEP</th><th>use HSC-DEEP</th><th>HSC-DEEP ap</th><th>use HSC-DEEP ap</th><th>COSMOS2015</th><th>use COSMOS2015</th><th>COSMOS2015 ap</th><th>use COSMOS2015 ap</th></tr></thead>\n",
       "<tr><td>0</td><td>g</td><td>1146131</td><td>1146131</td><td>1257414</td><td>1257414</td><td>1643773</td><td>956277</td><td>1838222</td><td>1044705</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "<tr><td>1</td><td>r</td><td>1156722</td><td>1156722</td><td>1268883</td><td>1268883</td><td>1679055</td><td>989440</td><td>1903240</td><td>1102607</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "<tr><td>2</td><td>i</td><td>1160356</td><td>1160356</td><td>1291883</td><td>1291883</td><td>1714230</td><td>987298</td><td>1953737</td><td>1086535</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "<tr><td>3</td><td>z</td><td>1132842</td><td>1132842</td><td>1260729</td><td>1260729</td><td>1701530</td><td>984971</td><td>1933534</td><td>1081349</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "<tr><td>4</td><td>y</td><td>1035328</td><td>500551</td><td>1157878</td><td>574161</td><td>1467985</td><td>798995</td><td>1669840</td><td>893700</td><td>661669</td><td>661669</td><td>693726</td><td>693726</td></tr>\n",
       "<tr><td>5</td><td>n921</td><td>1088153</td><td>1088153</td><td>1207243</td><td>1207243</td><td>879166</td><td>494988</td><td>996296</td><td>546326</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
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       ".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(\"$('#table140174353150472-194000').dataTable()\");\n",
       "    \n",
       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
       "    \"optionalnum-asc\": astropy_sort_num,\n",
       "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
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       "\n",
       "    $('#table140174353150472-194000').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
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       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suprime_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "suprime_origin.write(\"{}/cosmos_suprime_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## VII.b Megacam\n",
    "\n",
    "### COSMOS vs CFHT-WIRDS vs CFHTLS\n",
    "\n",
    "We take COSMOS over CFHTLS over CFHT-WIRDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "megacam_origin = Table()\n",
    "megacam_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "megacam_stats = Table()\n",
    "megacam_stats.add_column(Column(data=['u','g','r','i','z'], name=\"Band\"))\n",
    "for col in [\"COSMOS2015\", \"CFHTLS\",  \"CFHT-WIRDS\"]:\n",
    "    megacam_stats.add_column(Column(data=np.full(5, 0), name=\"{}\".format(col)))\n",
    "    megacam_stats.add_column(Column(data=np.full(5, 0), name=\"use {}\".format(col)))\n",
    "    megacam_stats.add_column(Column(data=np.full(5, 0), name=\"{} ap\".format(col)))\n",
    "    megacam_stats.add_column(Column(data=np.full(5, 0), name=\"use {} ap\".format(col)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "megacam_bands = ['u','g','r','i','z'] \n",
    "for band in megacam_bands:\n",
    "\n",
    "    # megacam total flux \n",
    "    has_cfhtls = ~np.isnan(master_catalogue['f_megacam_' + band])\n",
    "    has_wirds  = ~np.isnan(master_catalogue['f_wirds_' + band])\n",
    "    if band == 'u':\n",
    "        has_cosmos  = ~np.isnan(master_catalogue['f_cosmos-megacam_' + band])\n",
    "    elif band != 'u':\n",
    "        has_cosmos = np.full(len(master_catalogue), False, dtype=bool)\n",
    "        \n",
    "    \n",
    "    use_cosmos = has_cosmos\n",
    "    use_cfhtls = has_cfhtls & ~has_cosmos\n",
    "    use_wirds = has_wirds & ~has_cfhtls & ~has_cosmos\n",
    " \n",
    "    master_catalogue['f_megacam_' + band][use_wirds] = master_catalogue['f_wirds_' + band][use_wirds]\n",
    "    master_catalogue['ferr_megacam_' + band][use_wirds] = master_catalogue['ferr_wirds_' + band][use_wirds]\n",
    "    master_catalogue['m_megacam_' + band][use_wirds] = master_catalogue['m_wirds_' + band][use_wirds]\n",
    "    master_catalogue['merr_megacam_' + band][use_wirds] = master_catalogue['merr_wirds_' + band][use_wirds]\n",
    "    master_catalogue['flag_megacam_' + band][use_wirds] = master_catalogue['flag_wirds_' + band][use_wirds]\n",
    "\n",
    "\n",
    "    master_catalogue.remove_columns(['f_wirds_' + band,\n",
    "                               'ferr_wirds_' + band,\n",
    "                               'm_wirds_' + band, \n",
    "                               'merr_wirds_' + band,\n",
    "                               'flag_wirds_' + band])\n",
    " \n",
    "    if band == 'u':\n",
    "        master_catalogue['f_megacam_' + band][use_cosmos] = master_catalogue['f_cosmos-megacam_' + band][use_cosmos]\n",
    "        master_catalogue['ferr_megacam_' + band][use_cosmos] = master_catalogue['ferr_cosmos-megacam_' + band][use_cosmos]\n",
    "        master_catalogue['m_megacam_' + band][use_cosmos] = master_catalogue['m_cosmos-megacam_' + band][use_cosmos]\n",
    "        master_catalogue['merr_megacam_' + band][use_cosmos] = master_catalogue['merr_cosmos-megacam_' + band][use_cosmos]\n",
    "        master_catalogue['flag_megacam_' + band][use_cosmos] = master_catalogue['flag_cosmos-megacam_' + band][use_cosmos]\n",
    "        \n",
    "        master_catalogue.remove_columns(['f_cosmos-megacam_' + band,\n",
    "                               'ferr_cosmos-megacam_' + band,\n",
    "                               'm_cosmos-megacam_' + band, \n",
    "                               'merr_cosmos-megacam_' + band,\n",
    "                               'flag_cosmos-megacam_' + band])\n",
    "        \n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_cfhtls] = \"CFHTLS\"\n",
    "    origin[use_wirds] = \"CFHT-WIRDS\"\n",
    "    origin[use_cosmos] = \"COSMOS2015\"\n",
    " \n",
    "    \n",
    "    megacam_origin.add_column(Column(data=origin, name= 'f_megacam_' + band ))\n",
    "    \n",
    "  \n",
    "    # Megacam aperture flux \n",
    "    has_ap_cfhtls = ~np.isnan(master_catalogue['f_ap_megacam_' + band])\n",
    "    has_ap_wirds  = ~np.isnan(master_catalogue['f_ap_wirds_' + band])\n",
    "    if band == 'u':\n",
    "        has_ap_cosmos  = ~np.isnan(master_catalogue['f_ap_cosmos-megacam_' + band])\n",
    "    elif band != 'u':\n",
    "        has_ap_cosmos = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    \n",
    "    use_ap_cosmos =  has_ap_cosmos\n",
    "    use_ap_cfhtls = has_ap_cfhtls & ~has_ap_cosmos\n",
    "    use_ap_wirds = has_ap_wirds & ~has_ap_cfhtls & ~has_ap_cosmos\n",
    "  \n",
    "\n",
    "    master_catalogue['f_ap_megacam_' + band][use_ap_wirds] = master_catalogue['f_ap_wirds_' + band][use_ap_wirds]\n",
    "    master_catalogue['ferr_ap_megacam_' + band][use_ap_wirds] = master_catalogue['ferr_ap_wirds_' + band][use_ap_wirds]\n",
    "    master_catalogue['m_ap_megacam_' + band][use_ap_wirds] = master_catalogue['m_ap_wirds_' + band][use_ap_wirds]\n",
    "    master_catalogue['merr_ap_megacam_' + band][use_ap_wirds] = master_catalogue['merr_ap_wirds_' + band][use_ap_wirds]\n",
    "\n",
    "    master_catalogue.remove_columns(['f_ap_wirds_' + band,\n",
    "                               'ferr_ap_wirds_' + band,\n",
    "                               'm_ap_wirds_' + band, \n",
    "                               'merr_ap_wirds_' + band])\n",
    "    \n",
    "    if band == 'u':\n",
    "        master_catalogue['f_ap_megacam_' + band][use_ap_cosmos] = master_catalogue['f_ap_cosmos-megacam_' + band][use_ap_cosmos]\n",
    "        master_catalogue['ferr_ap_megacam_' + band][use_ap_cosmos] = master_catalogue['ferr_ap_cosmos-megacam_' + band][use_ap_cosmos]\n",
    "        master_catalogue['m_ap_megacam_' + band][use_ap_cosmos] = master_catalogue['m_ap_cosmos-megacam_' + band][use_ap_cosmos]\n",
    "        master_catalogue['merr_ap_megacam_' + band][use_ap_cosmos] = master_catalogue['merr_ap_cosmos-megacam_' + band][use_ap_cosmos]\n",
    "        \n",
    "        \n",
    "        master_catalogue.remove_columns(['f_ap_cosmos-megacam_' + band,\n",
    "                               'ferr_ap_cosmos-megacam_' + band,\n",
    "                               'm_ap_cosmos-megacam_' + band, \n",
    "                               'merr_ap_cosmos-megacam_' + band])\n",
    "\n",
    "    origin_ap = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin_ap[use_ap_cfhtls] = \"CFHTLS\"\n",
    "    origin_ap[use_ap_wirds] = \"CFHT-WIRDS\"\n",
    "    origin_ap[use_ap_cosmos] = \"COSMOS2015\"\n",
    "    \n",
    "    megacam_origin.add_column(Column(data=origin_ap, name= 'f_ap_megacam_' + band ))\n",
    "\n",
    "   \n",
    "    megacam_stats['CFHTLS'][megacam_stats['Band'] == band] = np.sum(has_cfhtls)\n",
    "    megacam_stats['CFHT-WIRDS'][megacam_stats['Band'] == band] = np.sum(has_wirds)\n",
    "    megacam_stats['COSMOS2015'][megacam_stats['Band'] == band] = np.sum(has_cosmos)\n",
    "\n",
    "    megacam_stats['use CFHTLS'][megacam_stats['Band'] == band] = np.sum(use_cfhtls)\n",
    "    megacam_stats['use CFHT-WIRDS'][megacam_stats['Band'] == band] = np.sum(use_wirds)\n",
    "    megacam_stats['use COSMOS2015'][megacam_stats['Band'] == band] = np.sum(use_cosmos)\n",
    "\n",
    "    megacam_stats['CFHTLS ap'][megacam_stats['Band'] == band] = np.sum(has_ap_cfhtls)\n",
    "    megacam_stats['CFHT-WIRDS ap'][megacam_stats['Band'] == band] = np.sum(has_ap_wirds)\n",
    "    megacam_stats['COSMOS2015 ap'][megacam_stats['Band'] == band] = np.sum(has_ap_cosmos)\n",
    "\n",
    "    megacam_stats['use CFHTLS ap'][megacam_stats['Band'] == band] = np.sum(use_ap_cfhtls)\n",
    "    megacam_stats['use CFHT-WIRDS ap'][megacam_stats['Band'] == band] = np.sum(use_ap_wirds)\n",
    "    megacam_stats['use COSMOS2015 ap'][megacam_stats['Band'] == band] = np.sum(use_ap_cosmos)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=5</i>\n",
       "<table id=\"table140173435164824-902477\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>COSMOS2015</th><th>use COSMOS2015</th><th>COSMOS2015 ap</th><th>use COSMOS2015 ap</th><th>CFHTLS</th><th>use CFHTLS</th><th>CFHTLS ap</th><th>use CFHTLS ap</th><th>CFHT-WIRDS</th><th>use CFHT-WIRDS</th><th>CFHT-WIRDS ap</th><th>use CFHT-WIRDS ap</th></tr></thead>\n",
       "<tr><td>0</td><td>u</td><td>640825</td><td>640825</td><td>688187</td><td>688187</td><td>455496</td><td>183226</td><td>459328</td><td>182038</td><td>156347</td><td>17152</td><td>157331</td><td>16078</td></tr>\n",
       "<tr><td>1</td><td>g</td><td>0</td><td>0</td><td>0</td><td>0</td><td>516573</td><td>516573</td><td>518549</td><td>518549</td><td>164107</td><td>31365</td><td>163978</td><td>30985</td></tr>\n",
       "<tr><td>2</td><td>r</td><td>0</td><td>0</td><td>0</td><td>0</td><td>526336</td><td>526336</td><td>529160</td><td>529160</td><td>165217</td><td>31524</td><td>165010</td><td>31136</td></tr>\n",
       "<tr><td>3</td><td>i</td><td>0</td><td>0</td><td>0</td><td>0</td><td>520668</td><td>520668</td><td>523948</td><td>523948</td><td>165648</td><td>31737</td><td>165316</td><td>31298</td></tr>\n",
       "<tr><td>4</td><td>z</td><td>0</td><td>0</td><td>0</td><td>0</td><td>474766</td><td>474766</td><td>480581</td><td>480581</td><td>164234</td><td>30996</td><td>164359</td><td>30824</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140173435164824-902477').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",
       "    $('#table140173435164824-902477').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "megacam_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "megacam_origin.write(\"{}/cosmos_megacam_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## WIRcam\n",
    "\n",
    "### COSMOS vs WIRDS\n",
    "\n",
    "We take COSMOS over WIRDS to ensure values are the same as for the original run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "wircam_origin = Table()\n",
    "wircam_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "wircam_stats = Table()\n",
    "wircam_stats.add_column(Column(data=['h','ks'], name=\"Band\"))\n",
    "for col in [\"CFHT-WIRDS\",   \"COSMOS2015\"]:\n",
    "    wircam_stats.add_column(Column(data=np.full(2, 0), name=\"{}\".format(col)))\n",
    "    wircam_stats.add_column(Column(data=np.full(2, 0), name=\"use {}\".format(col)))\n",
    "    wircam_stats.add_column(Column(data=np.full(2, 0), name=\"{} ap\".format(col)))\n",
    "    wircam_stats.add_column(Column(data=np.full(2, 0), name=\"use {} ap\".format(col)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "wircam_bands = ['h','ks'] \n",
    "for band in wircam_bands:\n",
    "\n",
    "    # wircam total flux \n",
    "    has_wirds = ~np.isnan(master_catalogue['f_wirds_' + band.rstrip('s')])\n",
    "\n",
    "    has_cosmos = ~np.isnan(master_catalogue['f_cosmos-wircam_' + band])\n",
    "\n",
    "    \n",
    "    use_cosmos = has_cosmos\n",
    "    use_wirds = has_wirds & ~has_cosmos\n",
    "\n",
    "    \n",
    " \n",
    "    f_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    f_wircam[use_cosmos] = master_catalogue['f_cosmos-wircam_' + band][use_cosmos]\n",
    "    f_wircam[use_wirds] = master_catalogue['f_wirds_' + band.rstrip('s').rstrip('s')][use_wirds]\n",
    "\n",
    " \n",
    "\n",
    "    ferr_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_wircam[use_cosmos] = master_catalogue['ferr_cosmos-wircam_' + band][use_cosmos]\n",
    "    ferr_wircam[use_wirds] = master_catalogue['ferr_wirds_' + band.rstrip('s')][use_wirds]\n",
    "\n",
    "\n",
    "    \n",
    "    m_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    m_wircam[use_cosmos] = master_catalogue['m_cosmos-wircam_' + band][use_cosmos]\n",
    "    m_wircam[use_wirds] = master_catalogue['m_wirds_' + band.rstrip('s')][use_wirds]\n",
    "\n",
    "\n",
    "    merr_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    merr_wircam[use_cosmos] = master_catalogue['merr_cosmos-wircam_' + band][use_cosmos]\n",
    "    merr_wircam[use_wirds] = master_catalogue['merr_wirds_' + band.rstrip('s')][use_wirds]\n",
    "\n",
    "\n",
    "\n",
    "    flag_wircam = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    flag_wircam[use_cosmos] = master_catalogue['flag_cosmos-wircam_' + band][use_cosmos]\n",
    "    flag_wircam[use_wirds] = master_catalogue['flag_wirds_' + band.rstrip('s')][use_wirds]\n",
    "\n",
    "\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_wircam, name=\"f_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_wircam, name=\"ferr_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_wircam, name=\"m_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_wircam, name=\"merr_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=flag_wircam, name=\"flag_wircam_\" + band))\n",
    "\n",
    "    old_wirds_columns = ['f_wirds_' + band.rstrip('s'),\n",
    "                               'ferr_wirds_' + band.rstrip('s'),\n",
    "                               'm_wirds_' + band.rstrip('s'), \n",
    "                               'merr_wirds_' + band.rstrip('s'),\n",
    "                               'flag_wirds_' + band.rstrip('s')]\n",
    "\n",
    "    old_cosmos_columns = ['f_cosmos-wircam_' + band,\n",
    "                               'ferr_cosmos-wircam_' + band,\n",
    "                               'm_cosmos-wircam_' + band, \n",
    "                               'merr_cosmos-wircam_' + band,\n",
    "                               'flag_cosmos-wircam_' + band]\n",
    "    \n",
    "    old_columns = old_wirds_columns + old_cosmos_columns\n",
    "    master_catalogue.remove_columns(old_columns)\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_wirds] = \"CFHT-WIRDS\"\n",
    "    origin[use_cosmos] = \"COSMOS2015\"\n",
    " \n",
    "    \n",
    "    wircam_origin.add_column(Column(data=origin, name= 'f_wircam_' + band ))\n",
    "    \n",
    "  \n",
    "    # wircam aperture flux \n",
    "    has_ap_wirds = ~np.isnan(master_catalogue['f_ap_wirds_' + band.rstrip('s')])\n",
    "    has_ap_cosmos = ~np.isnan(master_catalogue['f_ap_cosmos-wircam_' + band])\n",
    "    \n",
    " \n",
    "    \n",
    "    use_ap_cosmos = has_ap_cosmos\n",
    "    use_ap_wirds = has_ap_wirds & ~has_cosmos\n",
    "\n",
    "  \n",
    "\n",
    "    f_ap_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    f_ap_wircam[use_ap_cosmos] = master_catalogue['f_ap_cosmos-wircam_' + band][use_ap_cosmos]\n",
    "    f_ap_wircam[use_ap_wirds] = master_catalogue['f_ap_wirds_' + band.rstrip('s')][use_ap_wirds]\n",
    "\n",
    "\n",
    "\n",
    "    ferr_ap_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_ap_wircam[use_ap_cosmos] = master_catalogue['ferr_ap_cosmos-wircam_' + band][use_ap_cosmos]\n",
    "    ferr_ap_wircam[use_ap_wirds] = master_catalogue['ferr_ap_wirds_' + band.rstrip('s')][use_ap_wirds]\n",
    "\n",
    "   \n",
    "    \n",
    "    m_ap_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    m_ap_wircam[use_ap_cosmos] = master_catalogue['m_ap_cosmos-wircam_' + band][use_ap_cosmos]\n",
    "    m_ap_wircam[use_ap_wirds] = master_catalogue['m_ap_wirds_' + band.rstrip('s')][use_ap_wirds]\n",
    "\n",
    "  \n",
    "\n",
    "    merr_ap_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    merr_ap_wircam[use_ap_cosmos] = master_catalogue['merr_ap_cosmos-wircam_' + band][use_ap_cosmos]\n",
    "    merr_ap_wircam[use_ap_wirds] = master_catalogue['merr_ap_wirds_' + band.rstrip('s')][use_ap_wirds]\n",
    "\n",
    "  \n",
    "\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_ap_wircam, name=\"f_ap_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_ap_wircam, name=\"ferr_ap_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_ap_wircam, name=\"m_ap_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_ap_wircam, name=\"merr_ap_wircam_\" + band))\n",
    "\n",
    "\n",
    "    old_ap_wirds_columns = ['f_ap_wirds_' + band.rstrip('s'),\n",
    "                               'ferr_ap_wirds_' + band.rstrip('s'),\n",
    "                               'm_ap_wirds_' + band.rstrip('s'), \n",
    "                               'merr_ap_wirds_' + band.rstrip('s')]\n",
    "\n",
    "    old_ap_cosmos_columns = ['f_ap_cosmos-wircam_' + band,\n",
    "                               'ferr_ap_cosmos-wircam_' + band,\n",
    "                               'm_ap_cosmos-wircam_' + band, \n",
    "                               'merr_ap_cosmos-wircam_' + band]\n",
    "\n",
    "    \n",
    "    old_ap_columns = old_ap_wirds_columns + old_ap_cosmos_columns\n",
    "    master_catalogue.remove_columns(old_ap_columns)\n",
    "\n",
    "    origin_ap = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin_ap[use_ap_wirds] = \"CFHT-WIRDS\"\n",
    "    origin_ap[use_ap_cosmos] = \"COSMOS2015\"\n",
    "\n",
    "    \n",
    "    wircam_origin.add_column(Column(data=origin_ap, name= 'f_ap_wircam_' + band ))\n",
    "\n",
    "   \n",
    "    wircam_stats['CFHT-WIRDS'][wircam_stats['Band'] == band] = np.sum(has_wirds)\n",
    "    wircam_stats['COSMOS2015'][wircam_stats['Band'] == band] = np.sum(has_cosmos)\n",
    "\n",
    "    wircam_stats['use CFHT-WIRDS'][wircam_stats['Band'] == band] = np.sum(use_wirds)\n",
    "    wircam_stats['use COSMOS2015'][wircam_stats['Band'] == band] = np.sum(use_cosmos)\n",
    "\n",
    "    wircam_stats['CFHT-WIRDS ap'][wircam_stats['Band'] == band] = np.sum(has_ap_wirds)\n",
    "    wircam_stats['COSMOS2015 ap'][wircam_stats['Band'] == band] = np.sum(has_ap_cosmos)\n",
    "    \n",
    "    wircam_stats['use CFHT-WIRDS ap'][wircam_stats['Band'] == band] = np.sum(use_ap_wirds)\n",
    "    wircam_stats['use COSMOS2015 ap'][wircam_stats['Band'] == band] = np.sum(use_ap_cosmos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "<i>Table length=2</i>\n",
       "<table id=\"table140172485750400-865771\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>CFHT-WIRDS</th><th>use CFHT-WIRDS</th><th>CFHT-WIRDS ap</th><th>use CFHT-WIRDS ap</th><th>COSMOS2015</th><th>use COSMOS2015</th><th>COSMOS2015 ap</th><th>use COSMOS2015 ap</th></tr></thead>\n",
       "<tr><td>0</td><td>h</td><td>164175</td><td>32620</td><td>165228</td><td>33198</td><td>589027</td><td>589027</td><td>684053</td><td>684053</td></tr>\n",
       "<tr><td>1</td><td>ks</td><td>169103</td><td>35504</td><td>170077</td><td>36333</td><td>594551</td><td>594551</td><td>681987</td><td>681987</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140172485750400-865771').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",
       "    $('#table140172485750400-865771').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 2, 3, 4, 5, 6, 7, 8, 9], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wircam_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "wircam_origin.write(\"{}/cosmos_wircam_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Final renaming\n",
    "\n",
    "We rename some columns in line with HELP filter naming standards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "renaming = OrderedDict({\n",
    "    '_wirds_j':'_wircam_j',\n",
    "    #'_wirds_h': '_wircam_h', #These two now merged with COSMOS\n",
    "    #'_wirds_k': '_wircam_ks',\n",
    "    '_kids_': '_omegacam_',\n",
    "    '_cosmos-suprime_': '_suprime_',\n",
    "    '_cosmos-vista_':'_vista_',\n",
    "    '_cosmos-irac_':'_irac_',\n",
    "    '_candels_f140w':'_wfc3_f140w',\n",
    "    '_candels_f160w':'_wfc3_f160w',\n",
    "    '_candels_f125w':'_wfc3_f125w',\n",
    "    '_candels_f606w': '_acs_f606w',\n",
    "    '_candels_f814w':'_acs_f814w',\n",
    "})\n",
    "\n",
    "\n",
    "for col in master_catalogue.colnames:\n",
    "    for rename_col in list(renaming):\n",
    "        if rename_col in col:\n",
    "            master_catalogue.rename_column(col, col.replace(rename_col, renaming[rename_col]))      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## VII.a Wavelength domain coverage\n",
    "\n",
    "We add a binary `flag_optnir_obs` indicating that a source was observed in a given wavelength domain:\n",
    "\n",
    "- 1 for observation in optical;\n",
    "- 2 for observation in near-infrared;\n",
    "- 4 for observation in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source observed both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: The observation flag is based on the creation of multi-order coverage maps from the catalogues, this may not be accurate, especially on the edges of the coverage.*\n",
    "\n",
    "*Note 2: Being on the observation coverage does not mean having fluxes in that wavelength domain. For sources observed in one domain but having no flux in it, one must take into consideration de different depths in the catalogue we are using.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "candels_moc = MOC(filename=\"../../dmu0/dmu0_CANDELS-3D-HST/data/CANDELS-3D-HST_COSMOS_MOC.fits\")\n",
    "cfhtls_moc = MOC(filename=\"../../dmu0/dmu0_CFHTLS/data/CFHTLS-DEEP_COSMOS_MOC.fits\")\n",
    "decals_moc = MOC(filename=\"../../dmu0/dmu0_DECaLS/data/DECaLS_COSMOS_MOC.fits\")\n",
    "hsc_udeep_moc = MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_deep_COSMOS_MOC.fits\")\n",
    "hsc_deep_moc = MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_uDeep_COSMOS_MOC.fits\")\n",
    "kids_moc = MOC(filename=\"../../dmu0/dmu0_KIDS/data/KIDS-DR3_COSMOS_MOC.fits\")\n",
    "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_COSMOS_MOC.fits\")\n",
    "las_moc = MOC(filename=\"../../dmu0/dmu0_UKIDSS-LAS/data/UKIDSS-LAS_COSMOS_MOC.fits\")\n",
    "wirds_moc = MOC(filename=\"../../dmu0/dmu0_CFHT-WIRDS/data/COSMOS_Ks-priors_MOC.fits\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    candels_moc + \n",
    "    cfhtls_moc + \n",
    "    decals_moc + \n",
    "    hsc_udeep_moc + \n",
    "    hsc_deep_moc + \n",
    "    kids_moc +\n",
    "    ps1_moc) \n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    las_moc + wirds_moc\n",
    ")\n",
    "\n",
    "was_observed_mir = np.zeros(len(master_catalogue), dtype=bool)\n",
    "\n",
    "#was_observed_mir = inMoc(\n",
    "#    master_catalogue['ra'], master_catalogue['dec'],   \n",
    "#)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * was_observed_optical + 2 * was_observed_nir + 4 * was_observed_mir,\n",
    "        name=\"flag_optnir_obs\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## VII.b Wavelength domain detection\n",
    "\n",
    "We add a binary `flag_optnir_det` indicating that a source was detected in a given wavelength domain:\n",
    "\n",
    "- 1 for detection in optical;\n",
    "- 2 for detection in near-infrared;\n",
    "- 4 for detection in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source detected both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: We use the total flux columns to know if the source has flux, in some catalogues, we may have aperture flux and no total flux.*\n",
    "\n",
    "To get rid of artefacts (chip edges, star flares, etc.) we consider that a source is detected in one wavelength domain when it has a flux value in **at least two bands**. That means that good sources will be excluded from this flag when they are on the coverage of only one band."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "nb_optical_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_u']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_z']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_y']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_n921']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_y']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_decam_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_decam_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_decam_z']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_omegacam_u']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_omegacam_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_omegacam_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_omegacam_i']) \n",
    ")\n",
    "\n",
    "nb_nir_flux = (\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_vista_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_ks']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_wircam_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wircam_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wircam_ks']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_y']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_k'])\n",
    ")\n",
    "\n",
    "nb_mir_flux = np.zeros(len(master_catalogue), dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "has_optical_flux = nb_optical_flux >= 2\n",
    "has_nir_flux = nb_nir_flux >= 2\n",
    "has_mir_flux = nb_mir_flux >= 2\n",
    "\n",
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * has_optical_flux + 2 * has_nir_flux + 4 * has_mir_flux,\n",
    "        name=\"flag_optnir_det\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## VIII - Cross-identification table\n",
    "\n",
    "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogue. This can be used to easily get additional information from them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64 master list rows had multiple associations.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#\n",
    "# Addind SDSS ids\n",
    "#\n",
    "sdss = Table.read(\"../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_COSMOS.fits\")['objID', 'ra', 'dec']\n",
    "sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)\n",
    "idx_ml, d2d, _ = sdss_coords.match_to_catalog_sky(SkyCoord(master_catalogue['ra'], master_catalogue['dec']))\n",
    "idx_sdss = np.arange(len(sdss))\n",
    "\n",
    "# Limit the cross-match to 1 arcsec\n",
    "mask = d2d <= 1. * u.arcsec\n",
    "idx_ml = idx_ml[mask]\n",
    "idx_sdss = idx_sdss[mask]\n",
    "d2d = d2d[mask]\n",
    "nb_orig_matches = len(idx_ml)\n",
    "\n",
    "# In case of multiple associations of one master list object to an SDSS object, we keep only the\n",
    "# association to the nearest one.\n",
    "sort_idx = np.argsort(d2d)\n",
    "idx_ml = idx_ml[sort_idx]\n",
    "idx_sdss = idx_sdss[sort_idx]\n",
    "_, unique_idx = np.unique(idx_ml, return_index=True)\n",
    "idx_ml = idx_ml[unique_idx]\n",
    "idx_sdss = idx_sdss[unique_idx]\n",
    "print(\"{} master list rows had multiple associations.\".format(nb_orig_matches - len(idx_ml)))\n",
    "\n",
    "# Adding the ObjID to the master list\n",
    "master_catalogue.add_column(Column(data=np.full(len(master_catalogue), -1, dtype='>i8'), name=\"sdss_id\"))\n",
    "master_catalogue['sdss_id'][idx_ml] = sdss['objID'][idx_sdss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['old_help_id', 'cosmos_id', 'candels_id', 'cfhtls_id', 'decals_id', 'hsc-udeep_id', 'hsc-deep_id', 'kids_id', 'las_id', 'wirds_id', 'ps1_id', 'help_id', 'specz_id', 'sdss_id']\n"
     ]
    }
   ],
   "source": [
    "id_names = []\n",
    "for col in master_catalogue.colnames:\n",
    "    if '_id' in col:\n",
    "        id_names += [col]\n",
    "    if '_intid' in col:\n",
    "        id_names += [col]\n",
    "        \n",
    "print(id_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_cosmos{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)\n",
    "id_names.remove('help_id')\n",
    "id_names.remove('old_help_id')\n",
    "master_catalogue.remove_columns(id_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## IX - Adding HEALPix index\n",
    "\n",
    "We are adding a column with a HEALPix index at order 13 associated with each source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(\n",
    "    data=coords_to_hpidx(master_catalogue['ra'], master_catalogue['dec'], order=13),\n",
    "    name=\"hp_idx\"\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## IX - Saving the catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "columns = [\"help_id\", \"old_help_id\", \"field\", \"ra\", \"dec\", \"hp_idx\"]\n",
    "\n",
    "bands = [column[5:] for column in master_catalogue.colnames if 'f_ap' in column]\n",
    "for band in bands:\n",
    "    columns += [\"f_ap_{}\".format(band), \"ferr_ap_{}\".format(band),\n",
    "                \"m_ap_{}\".format(band), \"merr_ap_{}\".format(band),\n",
    "                \"f_{}\".format(band), \"ferr_{}\".format(band),\n",
    "                \"m_{}\".format(band), \"merr_{}\".format(band),\n",
    "                \"flag_{}\".format(band)]    \n",
    "    \n",
    "# columns += ['f_wfc3_f125w', 'ferr_wfc3_f125w', 'm_wfc3_f125w', 'merr_wfc3_f125w', 'flag_wfc3_f125w',\n",
    "#            'f_acs_f606w', 'ferr_acs_f606w', 'm_acs_f606w', 'merr_acs_f606w', 'flag_acs_f606w',\n",
    "#            'f_acs_f814w', 'ferr_acs_f814w', 'm_acs_f814w', 'merr_acs_f814w', 'flag_acs_f814w'\n",
    "#           ]\n",
    "\n",
    "for tot_band in ['wfc3_f125w', 'acs_f606w', 'acs_f814w', 'irac_i1', 'irac_i2', 'irac_i3', 'irac_i4']:\n",
    "    columns += ['f_' + tot_band,'ferr_' + tot_band,'m_' + tot_band,'merr_' + tot_band,'flag_' + tot_band,]\n",
    "    \n",
    "columns += [\"stellarity\", \"stellarity_origin\", \"flag_cleaned\", \n",
    "            \"flag_merged\", \"flag_gaia\", \"flag_optnir_obs\", \"flag_optnir_det\", \"ebv\",'zspec_association_flag', 'zspec_qual', 'zspec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing columns: set()\n"
     ]
    }
   ],
   "source": [
    "# We check for columns in the master catalogue that we will not save to disk.\n",
    "print(\"Missing columns: {}\".format(set(master_catalogue.colnames) - set(columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[columns].write(\"{}/master_catalogue_cosmos{}.fits\".format(OUT_DIR, SUFFIX), overwrite = True)"
   ]
  }
 ],
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