{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HDF-N master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce the HELP master catalogue on HDF-N."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "0246c5d (Thu Jan 25 17:01:47 2018 +0000) [with local modifications]\n"
     ]
    }
   ],
   "source": [
    "from herschelhelp_internal import git_version\n",
    "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "import os\n",
    "import time\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column, Table\n",
    "import numpy as np\n",
    "from pymoc import MOC\n",
    "\n",
    "from herschelhelp_internal.masterlist import merge_catalogues, nb_merge_dist_plot, specz_merge\n",
    "from herschelhelp_internal.utils import coords_to_hpidx, ebv, gen_help_id, inMoc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n",
    "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n",
    "SUFFIX = os.environ.get('SUFFIX', time.strftime(\"_%Y%m%d\"))\n",
    "\n",
    "try:\n",
    "    os.makedirs(OUT_DIR)\n",
    "except FileExistsError:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I - Reading the prepared pristine catalogues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#threed = Table.read(\"{}/CANDELS-3D-HST.fits\".format(TMP_DIR))       # 1.1\n",
    "#acs =   Table.read(\"{}/ACS.fits\".format(TMP_DIR))                   # 1.2 GOODS-ACS\n",
    "hawaii =  Table.read(\"{}/Hawaii.fits\".format(TMP_DIR))              # 1.3 Hawaii-HDFN\n",
    "ultra =  Table.read(\"{}/Ultradeep.fits\".format(TMP_DIR))            # 1.4 Ultradeep_Ks_GOODS-N\n",
    "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))                     # 1.5 PanSTARRS\n",
    "candels_gn =  Table.read(\"{}/CANDELS-GOODS-N.fits\".format(TMP_DIR)) # 1.6 CANDELS-GOODS-N"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Merging tables\n",
    "\n",
    "We first merge the optical catalogues and then add the infrared ones. We start with PanSTARRS because it coevrs the whole field.\n",
    "\n",
    "At every step, we look at the distribution of the distances separating the sources from one catalogue to the other (within a maximum radius) to determine the best cross-matching radius."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PanSTARRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = ps1\n",
    "master_catalogue['ps1_ra'].name = 'ra'\n",
    "master_catalogue['ps1_dec'].name = 'dec'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CANDELS-GOODS-N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kPbfkZDJ7aLIWqUu3GpEeQ5uF9/aWuB1FOknFys+88clxIkMCuXJokttRPpMx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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa61e73dcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(candels_gn['candels-gn_ra'], candels_gn['candels-gn_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, candels_gn, \"candels-gn_ra\", \"candels-gn_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ultradeep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I0I2dcbpYKwpTOdzcTd/QiAeViYiISKgUrMJE43jjesFFnrECWFmQQtBBVYMa2EVERKaT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6266306d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(ultra['ultradeep_ra'], ultra['ultradeep_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, ultra, \"ultradeep_ra\", \"ultradeep_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hawaii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gIYAtW7bosLrzmFkR6P1oFUBJ9mywGlKwEhER8cpCRqx2AzVmVm1mKcA9wLb5Fzjnqp1z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\nFKpEEszcTMSLGrV6SxqxipK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3gEdeSZxNn5UWzjI2Geah54/zoX/eQdCMr39qKzkZOg9Q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\niIgmSWIVERER0SRJrCIiIiKa5F9b+Bcl2hka9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa61909fba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(hawaii['hawaii_ra'], hawaii['hawaii_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, hawaii, \"hawaii_ra\", \"hawaii_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning\n",
    "\n",
    "When we merge the catalogues, astropy masks the non-existent values (e.g. when a row comes only from a catalogue and has no counterparts in the other, the columns from the latest are masked for that row). We indicate to use NaN for masked values for floats columns, False for flag columns and -1 for ID columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    if \"m_\" in col or \"merr_\" in col or \"f_\" in col or \"ferr_\" in col or \"stellarity\" in col:\n",
    "        master_catalogue[col] = 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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table140351170192608-21144\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>ps1_id</th><th>ra</th><th>dec</th><th>m_ap_gpc1_g</th><th>merr_ap_gpc1_g</th><th>m_gpc1_g</th><th>merr_gpc1_g</th><th>m_ap_gpc1_r</th><th>merr_ap_gpc1_r</th><th>m_gpc1_r</th><th>merr_gpc1_r</th><th>m_ap_gpc1_i</th><th>merr_ap_gpc1_i</th><th>m_gpc1_i</th><th>merr_gpc1_i</th><th>m_ap_gpc1_z</th><th>merr_ap_gpc1_z</th><th>m_gpc1_z</th><th>merr_gpc1_z</th><th>m_ap_gpc1_y</th><th>merr_ap_gpc1_y</th><th>m_gpc1_y</th><th>merr_gpc1_y</th><th>f_ap_gpc1_g</th><th>ferr_ap_gpc1_g</th><th>f_gpc1_g</th><th>ferr_gpc1_g</th><th>flag_gpc1_g</th><th>f_ap_gpc1_r</th><th>ferr_ap_gpc1_r</th><th>f_gpc1_r</th><th>ferr_gpc1_r</th><th>flag_gpc1_r</th><th>f_ap_gpc1_i</th><th>ferr_ap_gpc1_i</th><th>f_gpc1_i</th><th>ferr_gpc1_i</th><th>flag_gpc1_i</th><th>f_ap_gpc1_z</th><th>ferr_ap_gpc1_z</th><th>f_gpc1_z</th><th>ferr_gpc1_z</th><th>flag_gpc1_z</th><th>f_ap_gpc1_y</th><th>ferr_ap_gpc1_y</th><th>f_gpc1_y</th><th>ferr_gpc1_y</th><th>flag_gpc1_y</th><th>ps1_flag_cleaned</th><th>ps1_flag_gaia</th><th>flag_merged</th><th>candels-gn_id</th><th>candels-gn_stellarity</th><th>f_acs_f435w</th><th>ferr_acs_f435w</th><th>f_acs_f606w</th><th>ferr_acs_f606w</th><th>f_acs_f775w</th><th>ferr_acs_f775w</th><th>f_acs_f814w</th><th>ferr_acs_f814w</th><th>f_acs_f850lp</th><th>ferr_acs_f850lp</th><th>f_wfc3_f105w</th><th>ferr_wfc3_f105w</th><th>f_wfc3_f125w</th><th>ferr_wfc3_f125w</th><th>f_wfc3_f140w</th><th>ferr_wfc3_f140w</th><th>f_wfc3_f160w</th><th>ferr_wfc3_f160w</th><th>f_moircs_k</th><th>ferr_moircs_k</th><th>f_candels-wircam_k</th><th>ferr_candels-wircam_k</th><th>f_candels-irac_i1</th><th>ferr_candels-irac_i1</th><th>f_candels-irac_i2</th><th>ferr_candels-irac_i2</th><th>f_candels-irac_i3</th><th>ferr_candels-irac_i3</th><th>f_candels-irac_i4</th><th>ferr_candels-irac_i4</th><th>m_acs_f435w</th><th>merr_acs_f435w</th><th>flag_acs_f435w</th><th>m_acs_f606w</th><th>merr_acs_f606w</th><th>flag_acs_f606w</th><th>m_acs_f775w</th><th>merr_acs_f775w</th><th>flag_acs_f775w</th><th>m_acs_f814w</th><th>merr_acs_f814w</th><th>flag_acs_f814w</th><th>m_acs_f850lp</th><th>merr_acs_f850lp</th><th>flag_acs_f850lp</th><th>m_wfc3_f105w</th><th>merr_wfc3_f105w</th><th>flag_wfc3_f105w</th><th>m_wfc3_f125w</th><th>merr_wfc3_f125w</th><th>flag_wfc3_f125w</th><th>m_wfc3_f140w</th><th>merr_wfc3_f140w</th><th>flag_wfc3_f140w</th><th>m_wfc3_f160w</th><th>merr_wfc3_f160w</th><th>flag_wfc3_f160w</th><th>m_moircs_k</th><th>merr_moircs_k</th><th>flag_moircs_k</th><th>m_candels-wircam_k</th><th>merr_candels-wircam_k</th><th>flag_candels-wircam_k</th><th>m_candels-irac_i1</th><th>merr_candels-irac_i1</th><th>flag_candels-irac_i1</th><th>m_candels-irac_i2</th><th>merr_candels-irac_i2</th><th>flag_candels-irac_i2</th><th>m_candels-irac_i3</th><th>merr_candels-irac_i3</th><th>flag_candels-irac_i3</th><th>m_candels-irac_i4</th><th>merr_candels-irac_i4</th><th>flag_candels-irac_i4</th><th>candels-gn_flag_cleaned</th><th>candels-gn_flag_gaia</th><th>ultradeep_id</th><th>f_ultradeep-wircam_k</th><th>ferr_ultradeep-wircam_k</th><th>f_ultradeep-irac_i1</th><th>ferr_ultradeep-irac_i1</th><th>f_ultradeep-irac_i2</th><th>ferr_ultradeep-irac_i2</th><th>f_ultradeep-irac_i3</th><th>ferr_ultradeep-irac_i3</th><th>f_ultradeep-irac_i4</th><th>ferr_ultradeep-irac_i4</th><th>m_ultradeep-wircam_k</th><th>merr_ultradeep-wircam_k</th><th>flag_ultradeep-wircam_k</th><th>m_ultradeep-irac_i1</th><th>merr_ultradeep-irac_i1</th><th>flag_ultradeep-irac_i1</th><th>m_ultradeep-irac_i2</th><th>merr_ultradeep-irac_i2</th><th>flag_ultradeep-irac_i2</th><th>m_ultradeep-irac_i3</th><th>merr_ultradeep-irac_i3</th><th>flag_ultradeep-irac_i3</th><th>m_ultradeep-irac_i4</th><th>merr_ultradeep-irac_i4</th><th>flag_ultradeep-irac_i4</th><th>ultradeep_flag_cleaned</th><th>ultradeep_flag_gaia</th><th>hawaii_id</th><th>m_ap_mosaic_u</th><th>merr_ap_mosaic_u</th><th>m_mosaic_u</th><th>merr_mosaic_u</th><th>m_ap_suprime_b</th><th>merr_ap_suprime_b</th><th>m_suprime_b</th><th>merr_suprime_b</th><th>m_ap_suprime_v</th><th>merr_ap_suprime_v</th><th>m_suprime_v</th><th>merr_suprime_v</th><th>m_ap_suprime_r</th><th>merr_ap_suprime_r</th><th>m_suprime_r</th><th>merr_suprime_r</th><th>m_ap_suprime_ip</th><th>merr_ap_suprime_ip</th><th>m_suprime_ip</th><th>merr_suprime_ip</th><th>m_ap_suprime_zp</th><th>merr_ap_suprime_zp</th><th>m_suprime_zp</th><th>merr_suprime_zp</th><th>m_ap_quirc_hk</th><th>merr_ap_quirc_hk</th><th>m_quirc_hk</th><th>merr_quirc_hk</th><th>f_ap_mosaic_u</th><th>ferr_ap_mosaic_u</th><th>f_mosaic_u</th><th>ferr_mosaic_u</th><th>flag_mosaic_u</th><th>f_ap_suprime_b</th><th>ferr_ap_suprime_b</th><th>f_suprime_b</th><th>ferr_suprime_b</th><th>flag_suprime_b</th><th>f_ap_suprime_v</th><th>ferr_ap_suprime_v</th><th>f_suprime_v</th><th>ferr_suprime_v</th><th>flag_suprime_v</th><th>f_ap_suprime_r</th><th>ferr_ap_suprime_r</th><th>f_suprime_r</th><th>ferr_suprime_r</th><th>flag_suprime_r</th><th>f_ap_suprime_ip</th><th>ferr_ap_suprime_ip</th><th>f_suprime_ip</th><th>ferr_suprime_ip</th><th>flag_suprime_ip</th><th>f_ap_suprime_zp</th><th>ferr_ap_suprime_zp</th><th>f_suprime_zp</th><th>ferr_suprime_zp</th><th>flag_suprime_zp</th><th>f_ap_quirc_hk</th><th>ferr_ap_quirc_hk</th><th>f_quirc_hk</th><th>ferr_quirc_hk</th><th>flag_quirc_hk</th><th>hawaii_flag_cleaned</th><th>hawaii_flag_gaia</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>0 galaxy, 1 star</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><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>182231893921992736</td><td>189.392114087</td><td>61.8600144331</td><td>nan</td><td>nan</td><td>9.33771038055</td><td>0.391741007566</td><td>11.1943998337</td><td>0.0</td><td>12.9521999359</td><td>0.0</td><td>10.4635000229</td><td>0.0</td><td>12.4686002731</td><td>0.0</td><td>12.652299881</td><td>0.0665509998798</td><td>12.4560003281</td><td>0.130146995187</td><td>10.4020004272</td><td>0.262125998735</td><td>10.5015001297</td><td>0.254988998175</td><td>nan</td><td>nan</td><td>668214.426684</td><td>241096.310165</td><td>False</td><td>120848.166728</td><td>0.0</td><td>23939.7728666</td><td>0.0</td><td>False</td><td>236919.054533</td><td>0.0</td><td>37373.1660748</td><td>0.0</td><td>False</td><td>31555.8619615</td><td>1934.23978716</td><td>37809.4072439</td><td>4532.20655351</td><td>False</td><td>250726.263815</td><td>60532.0813874</td><td>228770.461412</td><td>53727.5541811</td><td>False</td><td>False</td><td>3</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>nan</td><td>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>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>0.0</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>-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>0.0</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>-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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>1</td><td>183141887446216712</td><td>188.744562567</td><td>62.6216493931</td><td>13.7244997025</td><td>0.000668999971822</td><td>13.7730998993</td><td>0.00151099998038</td><td>13.4221000671</td><td>0.00021199999901</td><td>13.469499588</td><td>0.00328200007789</td><td>13.330499649</td><td>0.00108600000385</td><td>13.3341999054</td><td>0.00108600000385</td><td>13.3352003098</td><td>0.00156700005755</td><td>13.4104003906</td><td>0.00339600001462</td><td>13.3154001236</td><td>0.00200799992308</td><td>13.3790998459</td><td>0.00289799994789</td><td>11754.3906199</td><td>7.24272337888</td><td>11239.8377024</td><td>15.6422844457</td><td>False</td><td>15529.589398</td><td>3.03229543473</td><td>14866.2066263</td><td>44.9380715976</td><td>False</td><td>16896.6318021</td><td>16.900737142</td><td>16839.1450475</td><td>16.84323642</td><td>False</td><td>16823.6364963</td><td>24.280888159</td><td>15697.8380308</td><td>49.1001939038</td><td>False</td><td>17133.2578356</td><td>31.6868685647</td><td>16156.9753221</td><td>43.1254971847</td><td>False</td><td>False</td><td>3</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>nan</td><td>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>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>0.0</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>-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>0.0</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>-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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>2</td><td>183141894117361384</td><td>189.411629347</td><td>62.6171794931</td><td>14.7763004303</td><td>0.00166599999648</td><td>14.8453998566</td><td>0.00209100008942</td><td>13.960100174</td><td>0.000735000008717</td><td>14.0101003647</td><td>0.00211500003934</td><td>13.648900032</td><td>0.00192599999718</td><td>13.7080001831</td><td>0.00239000003785</td><td>13.5349998474</td><td>0.00224300008267</td><td>13.5857000351</td><td>0.00244700000621</td><td>13.4406003952</td><td>0.00206599989906</td><td>13.5184001923</td><td>0.00348800001666</td><td>4461.48901366</td><td>6.8458992603</td><td>4186.39359589</td><td>8.06250113518</td><td>False</td><td>9461.49862129</td><td>6.4050563467</td><td>9035.65944866</td><td>17.6013473675</td><td>False</td><td>12602.014854</td><td>22.3548597412</td><td>11934.3817794</td><td>26.2708130939</td><td>False</td><td>13995.8751927</td><td>28.9137905475</td><td>13357.3401613</td><td>30.1043764723</td><td>False</td><td>15267.2157153</td><td>29.0513165062</td><td>14211.5001209</td><td>45.6553925725</td><td>False</td><td>False</td><td>3</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>nan</td><td>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>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>0.0</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>-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>0.0</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>-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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>183151896152897299</td><td>189.615264897</td><td>62.6304488331</td><td>15.1990995407</td><td>0.00311599997804</td><td>15.2462997437</td><td>0.00533499987796</td><td>14.6473999023</td><td>0.000748999998905</td><td>14.7025003433</td><td>0.0020230000373</td><td>14.4289999008</td><td>0.00190399994608</td><td>14.488699913</td><td>0.00122199999169</td><td>14.3299999237</td><td>0.00150599994231</td><td>14.3955001831</td><td>0.00341599993408</td><td>14.2683000565</td><td>0.00362999993376</td><td>14.3642997742</td><td>0.00190399994608</td><td>3022.45736729</td><td>8.67427746152</td><td>2893.87722932</td><td>14.2196922213</td><td>False</td><td>5023.88904303</td><td>3.46575242792</td><td>4775.29122657</td><td>8.89757041087</td><td>False</td><td>6143.27617389</td><td>10.7731486273</td><td>5814.60255503</td><td>6.54435602567</td><td>False</td><td>6729.76703574</td><td>9.33470646292</td><td>6335.77763821</td><td>19.9339543985</td><td>False</td><td>7123.27937024</td><td>23.8156409714</td><td>6520.48785496</td><td>11.4346454229</td><td>False</td><td>False</td><td>3</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>nan</td><td>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>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>0.0</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>-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>0.0</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>-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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>182771899391593788</td><td>189.939111597</td><td>62.3108263931</td><td>16.0858001709</td><td>0.00384999997914</td><td>16.141500473</td><td>0.00800499971956</td><td>15.5888004303</td><td>0.000845999980811</td><td>15.6396999359</td><td>0.000751000014134</td><td>15.4131002426</td><td>0.00142600003164</td><td>15.4758996964</td><td>0.00324800005183</td><td>15.342300415</td><td>0.00181399995927</td><td>15.4091997147</td><td>0.003202999942</td><td>15.2957000732</td><td>0.00341599993408</td><td>15.3903999329</td><td>0.00532099977136</td><td>1335.61082908</td><td>4.73605065538</td><td>1268.81940143</td><td>9.35484964773</td><td>False</td><td>2110.95914366</td><td>1.64484834088</td><td>2014.28085821</td><td>1.39327117062</td><td>False</td><td>2481.76069976</td><td>3.25953101764</td><td>2342.28707248</td><td>7.00699534516</td><td>False</td><td>2648.98704268</td><td>4.42581021658</td><td>2490.69250648</td><td>7.3477221436</td><td>False</td><td>2765.15812062</td><td>8.69988484896</td><td>2534.19498333</td><td>12.4196382765</td><td>False</td><td>False</td><td>3</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>nan</td><td>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>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>0.0</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>-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>0.0</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>-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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>183251894072041815</td><td>189.407112727</td><td>62.7091870231</td><td>15.5497999191</td><td>0.00547299999744</td><td>15.602399826</td><td>0.0012969999807</td><td>15.1979999542</td><td>0.000860999978613</td><td>15.2629003525</td><td>0.00123499997426</td><td>15.0812997818</td><td>0.00309200002812</td><td>15.1429004669</td><td>0.00235900003463</td><td>15.054400444</td><td>0.00186700001359</td><td>15.1072998047</td><td>0.00295299990103</td><td>15.0155000687</td><td>0.00427300017327</td><td>15.1017999649</td><td>0.00286299991421</td><td>2188.16482464</td><td>11.0301434429</td><td>2084.68321464</td><td>2.49032322704</td><td>False</td><td>3025.51993143</td><td>2.39926842712</td><td>2849.96718179</td><td>3.2417721549</td><td>False</td><td>3368.8376902</td><td>9.59390152707</td><td>3183.02326722</td><td>6.91581616667</td><td>False</td><td>3453.34384296</td><td>5.93826840578</td><td>3289.12272333</td><td>8.94580012519</td><td>False</td><td>3579.31542929</td><td>14.0866772081</td><td>3305.82617514</td><td>8.7172003792</td><td>False</td><td>False</td><td>3</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>nan</td><td>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>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>0.0</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>-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>0.0</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>-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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>183071896006252444</td><td>189.600564027</td><td>62.5597367731</td><td>14.0937004089</td><td>0.00176300003659</td><td>14.1364002228</td><td>0.0011040000245</td><td>13.5507001877</td><td>0.000933000002988</td><td>13.5909996033</td><td>0.00107400002889</td><td>13.3978996277</td><td>0.0032460000366</td><td>13.4413995743</td><td>0.00935299973935</td><td>13.3402004242</td><td>0.00363299995661</td><td>13.3892002106</td><td>0.00569400005043</td><td>13.2876996994</td><td>0.00177800003439</td><td>13.3584003448</td><td>0.00191800005268</td><td>8366.03806811</td><td>13.5846307377</td><td>8043.40452073</td><td>8.17870745123</td><td>False</td><td>13794.9434635</td><td>11.8543364735</td><td>13292.3007402</td><td>13.1486187128</td><td>False</td><td>15879.6215867</td><td>47.4749317798</td><td>15255.9820934</td><td>131.421606757</td><td>False</td><td>16746.3371429</td><td>56.0351969905</td><td>16007.3675039</td><td>83.9485235687</td><td>False</td><td>17576.003089</td><td>28.78243717</td><td>16467.9622227</td><td>29.091368854</td><td>False</td><td>False</td><td>3</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>nan</td><td>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>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>0.0</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>-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>0.0</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>-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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>182241891931461754</td><td>189.193256177</td><td>61.8675017031</td><td>14.8851003647</td><td>0.00237199990079</td><td>14.9246997833</td><td>0.000910000002477</td><td>14.3461999893</td><td>0.000975999981165</td><td>14.3998003006</td><td>0.00374100008048</td><td>14.163599968</td><td>0.00465000001714</td><td>14.2243003845</td><td>0.00617500022054</td><td>14.0869998932</td><td>0.00175299996044</td><td>14.1578998566</td><td>0.00438700011</td><td>14.0242996216</td><td>0.00298299989663</td><td>14.0852003098</td><td>0.00226600002497</td><td>4036.08081998</td><td>8.81759608145</td><td>3891.52734654</td><td>3.26164852887</td><td>False</td><td>6630.09886223</td><td>5.95998948576</td><td>6310.73407104</td><td>21.7441921566</td><td>False</td><td>7844.40509005</td><td>33.59608314</td><td>7417.88073069</td><td>42.1883464392</td><td>False</td><td>8417.82790473</td><td>13.5911945456</td><td>7885.69648515</td><td>31.8627592929</td><td>False</td><td>8918.26044954</td><td>24.5024250665</td><td>8431.79183515</td><td>17.5976820385</td><td>False</td><td>False</td><td>3</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>nan</td><td>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>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>0.0</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>-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>0.0</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>-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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>182711883352854935</td><td>188.335312907</td><td>62.2618624131</td><td>13.2623996735</td><td>0.00108600000385</td><td>13.3177995682</td><td>0.00108600000385</td><td>12.8603000641</td><td>0.00108600000385</td><td>13.3990001678</td><td>0.00108600000385</td><td>12.8718996048</td><td>0.00595800019801</td><td>12.9200000763</td><td>0.00352500006557</td><td>13.845700264</td><td>0.00152000004891</td><td>13.4448003769</td><td>0.0014009999577</td><td>12.7004995346</td><td>0.0019270000048</td><td>12.769200325</td><td>0.00305199995637</td><td>17990.3714596</td><td>17.9947425432</td><td>17095.435656</td><td>17.0995892988</td><td>False</td><td>26054.3338885</td><td>26.0606642564</td><td>15863.5336024</td><td>15.8673879326</td><td>False</td><td>25777.4621644</td><td>141.454364335</td><td>24660.3916395</td><td>80.063538238</td><td>False</td><td>10512.8361193</td><td>14.7176739114</td><td>15208.2712281</td><td>19.6242763701</td><td>False</td><td>30185.625953</td><td>53.5744328148</td><td>28334.7816312</td><td>79.6489533305</td><td>False</td><td>False</td><td>3</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>nan</td><td>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>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>0.0</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>-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>0.0</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>-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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>183161895594883740</td><td>189.559520157</td><td>62.6358052531</td><td>13.0670003891</td><td>0.00118899997324</td><td>13.1206998825</td><td>0.00215999991633</td><td>12.9576997757</td><td>0.00108600000385</td><td>12.995300293</td><td>0.00108600000385</td><td>12.685500145</td><td>0.00214300001971</td><td>12.7515001297</td><td>0.00541399978101</td><td>12.6693000793</td><td>0.00108600000385</td><td>12.7384004593</td><td>0.00108600000385</td><td>12.6830997467</td><td>0.00168600003235</td><td>12.741399765</td><td>0.00407900009304</td><td>21537.7258381</td><td>23.5861670016</td><td>20498.4039373</td><td>40.7802103268</td><td>False</td><td>23818.8116239</td><td>23.8245988316</td><td>23008.0537254</td><td>23.013643945</td><td>False</td><td>30605.5326343</td><td>60.4084645527</td><td>28800.494734</td><td>143.61303556</td><td>False</td><td>31065.6159001</td><td>31.0731638489</td><td>29150.0843831</td><td>29.1571669192</td><td>False</td><td>30673.2716829</td><td>47.6314014611</td><td>29069.6694862</td><td>109.211780927</td><td>False</td><td>False</td><td>3</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>nan</td><td>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>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>0.0</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>-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>0.0</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>-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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140351170192608-21144').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",
       "    $('#table140351170192608-21144').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 99, 100, 102, 103, 105, 106, 108, 109, 111, 112, 114, 115, 116, 117, 118, 120, 121, 123, 124, 126, 127, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 148, 149, 151, 152, 154, 155, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 193, 194, 195, 196, 198, 199, 200, 201, 203, 204, 205, 206, 208, 209, 210, 211, 213, 214, 215, 216, 218, 219, 220, 221, 224], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_catalogue[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III - Merging flags and stellarity\n",
    "\n",
    "Each pristine catalogue contains a flag indicating if the source was associated to a another nearby source that was removed during the cleaning process.  We merge these flags in a single one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_cleaned_columns = [column for column in master_catalogue.colnames\n",
    "                        if 'flag_cleaned' in column]\n",
    "\n",
    "flag_column = np.zeros(len(master_catalogue), dtype=bool)\n",
    "for column in flag_cleaned_columns:\n",
    "    flag_column |= master_catalogue[column]\n",
    "    \n",
    "master_catalogue.add_column(Column(data=flag_column, name=\"flag_cleaned\"))\n",
    "master_catalogue.remove_columns(flag_cleaned_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "combining the flag_merged column which contains information regarding multiple associations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue['flag_merged'].name = 'flag_merged_tmp'\n",
    "flag_merged_columns = [column for column in master_catalogue.colnames\n",
    "                        if 'flag_merged' in column]\n",
    "\n",
    "flag_merged_column = np.zeros(len(master_catalogue), dtype=bool)\n",
    "for column in flag_merged_columns:\n",
    "    flag_merged_column |= master_catalogue[column]\n",
    "    \n",
    "master_catalogue.add_column(Column(data=flag_merged_column, name=\"flag_merged\"))\n",
    "master_catalogue.remove_columns(flag_merged_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each pristine catalogue contains a flag indicating the probability of a source being a Gaia object (0: not a Gaia object, 1: possibly, 2: probably, 3: definitely).  We merge these flags taking the highest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_gaia_columns = [column for column in master_catalogue.colnames\n",
    "                     if 'flag_gaia' in column]\n",
    "\n",
    "master_catalogue.add_column(Column(\n",
    "    data=np.max([master_catalogue[column] for column in flag_gaia_columns], axis=0),\n",
    "    name=\"flag_gaia\"\n",
    "))\n",
    "master_catalogue.remove_columns(flag_gaia_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each prisitine catalogue may contain one or several stellarity columns indicating the probability (0 to 1) of each source being a star.  We merge these columns taking the highest value. We keep trace of the origin of the stellarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "candels-gn_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": 18,
   "metadata": {
    "collapsed": 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": {},
   "source": [
    "## IV - Adding E(B-V) column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    ebv(master_catalogue['ra'], master_catalogue['dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V - Adding HELP unique identifiers and field columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(gen_help_id(master_catalogue['ra'], master_catalogue['dec']),\n",
    "                                   name=\"help_id\"))\n",
    "master_catalogue.add_column(Column(np.full(len(master_catalogue), \"HDF-N\", dtype='<U18'),\n",
    "                                   name=\"field\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK!\n"
     ]
    }
   ],
   "source": [
    "# Check that the HELP Ids are unique\n",
    "if len(master_catalogue) != len(np.unique(master_catalogue['help_id'])):\n",
    "    print(\"The HELP IDs are not unique!!!\")\n",
    "else:\n",
    "    print(\"OK!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VI - Cross-matching with spec-z catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz =  Table.read(\"../../dmu23/dmu23_HDF-N/data/HDF-N-specz-v2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz.rename_column('RA', 'ra')\n",
    "specz.rename_column('DEC', 'dec')\n",
    "specz.rename_column('OBJID', 'specz_id')\n",
    "specz.rename_column('Z_SPEC', 'z_spec')\n",
    "specz.rename_column('Z_SOURCE', 'z_source')\n",
    "specz.rename_column('Z_QUAL', 'z_qual')\n",
    "specz.rename_column('REL', 'rel')\n",
    "specz.rename_column('AGN', 'agn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bxMzMOMKCYEmDEDNumJNFRWM7T+2oJicpRlcLBiEVqzGm3zn6nSMsJPB/iIiIXKi9VSd5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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa64dce8390>"
      ]
     },
     "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": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = specz_merge(master_catalogue, specz, radius=1. * u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VII - Choosing between multiple values for the same filter\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We have to choose between the various HST catalogues which may contains different objects depending on the prior catalogue. The CANDELS-GOODS-N catalogue is taken as a base and any missing wircam or IRAC fluxes are taken from the Ultradeep Ks selected catalogues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bands = [\n",
    "    ['candels-wircam_k', 'ultradeep-wircam_k', 'wircam_ks'],\n",
    "    ['candels-irac_i1',  'ultradeep-irac_i1',  'irac_i1'],\n",
    "    ['candels-irac_i2',  'ultradeep-irac_i2',  'irac_i2'],\n",
    "    ['candels-irac_i3',  'ultradeep-irac_i3',  'irac_i3'],\n",
    "    ['candels-irac_i4',  'ultradeep-irac_i4',  'irac_i4'],\n",
    "    \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ir_origin = Table()\n",
    "ir_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(5, 0) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "ir_stats = Table()\n",
    "ir_stats.add_column(Column(data=np.array(bands)[:,2], name=\"Band\"))\n",
    "for col in [\"CANDELS-GOODS-N\", \"Ultradeep\"]:\n",
    "    ir_stats.add_column(Column(data=np.full(5, 0), name=\"{}\".format(col), dtype=str))\n",
    "    ir_stats.add_column(Column(data=np.full(5, 0), name=\"use {}\".format(col), dtype=str))\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=5</i>\n",
       "<table id=\"table140351183422352-946665\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>CANDELS-GOODS-N</th><th>use CANDELS-GOODS-N</th><th>Ultradeep</th><th>use Ultradeep</th></tr></thead>\n",
       "<tr><td>0</td><td>wircam_ks</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>irac_i1</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>2</td><td>irac_i2</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>3</td><td>irac_i3</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>4</td><td>irac_i4</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140351183422352-946665').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",
       "    $('#table140351183422352-946665').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], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ir_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "for band in bands:\n",
    "\n",
    "    # total flux \n",
    "    has_candels = ~np.isnan(master_catalogue['f_' + band[0]])\n",
    "    has_ultradeep = ~np.isnan(master_catalogue['f_' + band[1]])\n",
    "    \n",
    "\n",
    "    use_candels = has_candels\n",
    "    use_ultradeep = has_ultradeep & ~has_candels\n",
    "\n",
    "    f_ir = np.full(len(master_catalogue), np.nan)\n",
    "    f_ir[use_candels] = master_catalogue['f_' + band[0]][use_candels]\n",
    "    f_ir[use_ultradeep] = master_catalogue['f_' + band[1]][use_ultradeep]\n",
    "\n",
    "    ferr_ir = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_ir[use_candels] = master_catalogue['ferr_' + band[0]][use_candels]\n",
    "    ferr_ir[use_ultradeep] = master_catalogue['ferr_' + band[1]][use_ultradeep]\n",
    "\n",
    "    m_ir = np.full(len(master_catalogue), np.nan)\n",
    "    m_ir[use_candels] = master_catalogue['m_' + band[0]][use_candels]\n",
    "    m_ir[use_ultradeep] = master_catalogue['m_' + band[1]][use_ultradeep]\n",
    "    \n",
    "    merr_ir = np.full(len(master_catalogue), np.nan)\n",
    "    merr_ir[use_candels] = master_catalogue['merr_' + band[0]][use_candels]\n",
    "    merr_ir[use_ultradeep] = master_catalogue['merr_' + band[1]][use_ultradeep]\n",
    "    \n",
    "    flag_ir = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    flag_ir[use_candels] = master_catalogue['flag_' + band[0]][use_candels]\n",
    "    flag_ir[use_ultradeep] = master_catalogue['flag_' + band[1]][use_ultradeep]\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_ir, name=\"f_\" + band[2]))\n",
    "    master_catalogue.add_column(Column(data=ferr_ir, name=\"ferr_\" + band[2]))\n",
    "    master_catalogue.add_column(Column(data=m_ir, name=\"m_\" + band[2]))\n",
    "    master_catalogue.add_column(Column(data=merr_ir, name=\"merr_\" + band[2]))\n",
    "    master_catalogue.add_column(Column(data=flag_ir, name=\"flag_\" + band[2]))\n",
    " \n",
    "    master_catalogue.remove_columns(['f_' + band[0], 'f_' + band[1],\n",
    "                                    'ferr_' + band[0], 'ferr_' + band[1],\n",
    "                                    'm_' + band[0], 'm_' + band[1],\n",
    "                                    'merr_' + band[0], 'merr_' + band[1],\n",
    "                                    'flag_' + band[0], 'flag_' + band[1],])\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_candels] = \"CANDELS-GOODS-N\"\n",
    "    origin[use_ultradeep] = \"Ultradeep\"\n",
    "   \n",
    "    \n",
    "    ir_origin.add_column(Column(data=origin, name= 'f_' + band[2] ))\n",
    "    \n",
    "    #Aperture fluxes\n",
    "\n",
    "    #has_ap_candels = ~np.isnan(master_catalogue['f_ap_' + band[0]])\n",
    "    #has_ap_ultradeep = ~np.isnan(master_catalogue['f_ap_' + band[1]])\n",
    "    \n",
    "\n",
    "    #use_ap_candels = has_ap_candels\n",
    "    #use_ap_ultradeep = has_ap_ultradeep & ~has_ap_candels\n",
    "\n",
    "    #f_ap_ir = np.full(len(master_catalogue), np.nan)\n",
    "    #f_ap_ir[use_ap_candels] = master_catalogue['f_ap_' + band[0]][use_ap_candels]\n",
    "    #f_ap_ir[use_ap_ultradeep] = master_catalogue['f_ap_' + band[1]][use_ap_ultradeep]\n",
    "\n",
    "    #ferr_ap_ir = np.full(len(master_catalogue), np.nan)\n",
    "    #ferr_ap_ir[use_ap_candels] = master_catalogue['ferr_ap_' + band[0]][use_ap_candels]\n",
    "    #ferr_ap_ir[use_ap_ultradeep] = master_catalogue['ferr_ap_' + band[1]][use_ap_ultradeep]\n",
    "\n",
    "    #m_ap_ir = np.full(len(master_catalogue), np.nan)\n",
    "    #m_ap_ir[use_ap_candels] = master_catalogue['m_ap_' + band[0]][use_ap_candels]\n",
    "    #m_ap_ir[use_ap_ultradeep] = master_catalogue['m_ap_' + band[1]][use_ap_ultradeep]\n",
    "    \n",
    "    #merr_ap_ir = np.full(len(master_catalogue), np.nan)\n",
    "    #merr_ap_ir[use_ap_candels] = master_catalogue['merr_ap_' + band[0]][use_ap_candels]\n",
    "    #merr_ap_ir[use_ap_ultradeep] = master_catalogue['merr_ap_' + band[1]][use_ap_ultradeep]\n",
    "    \n",
    "\n",
    "\n",
    "    #master_catalogue.add_column(Column(data=f_ap_ir, name=\"f_ap_\" + band[2]))\n",
    "    #master_catalogue.add_column(Column(data=ferr_ap_ir, name=\"ferr_ap_\" + band[2]))\n",
    "    #master_catalogue.add_column(Column(data=m_ap_ir, name=\"m_ap_\" + band[2]))\n",
    "    #master_catalogue.add_column(Column(data=merr_ap_ir, name=\"merr_ap_\" + band[2]))\n",
    "\n",
    " \n",
    "    #master_catalogue.remove_columns(['f_ap_' + band[0], 'f_ap_' + band[1],\n",
    "    #                                'ferr_ap_' + band[0], 'ferr_ap_' + band[1],\n",
    "    #                                'm_ap_' + band[0], 'm_ap_' + band[1],\n",
    "     #                               'merr_ap_' + band[0], 'merr_ap_' + band[1],\n",
    "     #                               'flag_ap_' + band[0], 'flag_ap_' + band[1],])\n",
    "\n",
    "    #origin_ap = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    #origin_ap[use_ap_candels] = \"CANDELS-GOODS-N\"\n",
    "    #origin_ap[use_ap_ultradeep] = \"Ultradeep\"\n",
    "   \n",
    "    \n",
    "    #ir_origin.add_column(Column(data=origin_ap, name= 'f_ap_' + band[2] ))\n",
    "    \n",
    "\n",
    "    \n",
    "\n",
    "   \n",
    "    ir_stats['CANDELS-GOODS-N'][ir_stats['Band'] == band[0]] = np.sum(has_candels)\n",
    "    ir_stats['Ultradeep'][ir_stats['Band'] == band[0]] = np.sum(has_ultradeep)\n",
    " \n",
    "    ir_stats['use CANDELS-GOODS-N'][ir_stats['Band'] == band[0]] = np.sum(use_candels)\n",
    "    ir_stats['use Ultradeep'][ir_stats['Band'] == band[0]] = np.sum(use_ultradeep)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 31,
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   ],
   "source": [
    "ir_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ir_origin.write(\"{}/hdf-n_wircam_irac_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII.a Wavelength domain coverage\n",
    "\n",
    "We add a binary `flag_optnir_obs` indicating that a source was observed in a given wavelength domain:\n",
    "\n",
    "- 1 for observation in optical;\n",
    "- 2 for observation in near-infrared;\n",
    "- 4 for observation in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source observed both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: The observation flag is based on the creation of multi-order coverage maps from the catalogues, this may not be accurate, especially on the edges of the coverage.*\n",
    "\n",
    "*Note 2: Being on the observation coverage does not mean having fluxes in that wavelength domain. For sources observed in one domain but having no flux in it, one must take into consideration the different depths in the catalogue we are using.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "candels_gn_moc =   MOC(filename=\"../../dmu0/dmu0_CANDELS-3D-HST/data/CANDELS-3D-HST_XMM-LSS_MOC.fits\")\n",
    "ultra_moc = MOC(filename=\"../../dmu0/dmu0_Ultradeep-Ks-GOODS-N/data/Ultradeep_Ks_GOODS-N_HELP-coverage_MOC.fits\")\n",
    "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_XMM-LSS_MOC.fits\")       \n",
    "hawaii_moc = MOC(filename=\"../../dmu0/dmu0_Hawaii-HDFN/data/R_MOC.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "      ps1_moc + hawaii_moc)\n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    candels_gn_moc + ultra_moc\n",
    ")\n",
    "\n",
    "was_observed_mir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    candels_gn_moc + ultra_moc\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * was_observed_optical + 2 * was_observed_nir + 4 * was_observed_mir,\n",
    "        name=\"flag_optnir_obs\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII.b Wavelength domain detection\n",
    "\n",
    "We add a binary `flag_optnir_det` indicating that a source was detected in a given wavelength domain:\n",
    "\n",
    "- 1 for detection in optical;\n",
    "- 2 for detection in near-infrared;\n",
    "- 4 for detection in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source detected both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: We use the total flux columns to know if the source has flux, in some catalogues, we may have aperture flux and no total flux.*\n",
    "\n",
    "To get rid of artefacts (chip edges, star flares, etc.) we consider that a source is detected in one wavelength domain when it has a flux value in **at least two bands**. That means that good sources will be excluded from this flag when they are on the coverage of only one band."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# SpARCS is a catalogue of sources detected in r (with fluxes measured at \n",
    "# this prior position in the other bands).  Thus, we are only using the r\n",
    "# CFHT band.\n",
    "# Check to use catalogue flags from HSC and PanSTARRS.\n",
    "nb_optical_flux = (\n",
    "    # PanSTARRS\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",
    "    1 * ~np.isnan(master_catalogue['f_suprime_r']) + \n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_ip']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_zp'])\n",
    ")\n",
    "\n",
    "nb_nir_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_wircam_ks'])\n",
    ")\n",
    "\n",
    "nb_mir_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_irac_i1']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_irac_i2']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_irac_i3']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_irac_i4'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "has_optical_flux = nb_optical_flux >= 2\n",
    "has_nir_flux = nb_nir_flux >= 2\n",
    "has_mir_flux = nb_mir_flux >= 2\n",
    "\n",
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * has_optical_flux + 2 * has_nir_flux + 4 * has_mir_flux,\n",
    "        name=\"flag_optnir_det\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IX - Cross-identification table\n",
    "\n",
    "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogues. This can be used to easily get additional information from them.\n",
    "\n",
    "For convenience, we also cross-match the master list with the SDSS catalogue and add the objID associated with each source, if any. **TODO: should we correct the astrometry with respect to Gaia positions?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 master list rows had multiple associations.\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Addind SDSS ids\n",
    "#\n",
    "sdss = Table.read(\"../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_HDF-N.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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ps1_id', 'candels-gn_id', 'ultradeep_id', 'hawaii_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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_hdf-n{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)\n",
    "id_names.remove('help_id')\n",
    "master_catalogue.remove_columns(id_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X - Adding HEALPix index\n",
    "\n",
    "We are adding a column with a HEALPix index at order 13 associated with each source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(\n",
    "    data=coords_to_hpidx(master_catalogue['ra'], master_catalogue['dec'], order=13),\n",
    "    name=\"hp_idx\"\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XI - Saving the catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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       "<i>Table length=10</i>\n",
       "<table id=\"table140351181210904-559971\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>ra</th><th>dec</th><th>m_ap_gpc1_g</th><th>merr_ap_gpc1_g</th><th>m_gpc1_g</th><th>merr_gpc1_g</th><th>m_ap_gpc1_r</th><th>merr_ap_gpc1_r</th><th>m_gpc1_r</th><th>merr_gpc1_r</th><th>m_ap_gpc1_i</th><th>merr_ap_gpc1_i</th><th>m_gpc1_i</th><th>merr_gpc1_i</th><th>m_ap_gpc1_z</th><th>merr_ap_gpc1_z</th><th>m_gpc1_z</th><th>merr_gpc1_z</th><th>m_ap_gpc1_y</th><th>merr_ap_gpc1_y</th><th>m_gpc1_y</th><th>merr_gpc1_y</th><th>f_ap_gpc1_g</th><th>ferr_ap_gpc1_g</th><th>f_gpc1_g</th><th>ferr_gpc1_g</th><th>flag_gpc1_g</th><th>f_ap_gpc1_r</th><th>ferr_ap_gpc1_r</th><th>f_gpc1_r</th><th>ferr_gpc1_r</th><th>flag_gpc1_r</th><th>f_ap_gpc1_i</th><th>ferr_ap_gpc1_i</th><th>f_gpc1_i</th><th>ferr_gpc1_i</th><th>flag_gpc1_i</th><th>f_ap_gpc1_z</th><th>ferr_ap_gpc1_z</th><th>f_gpc1_z</th><th>ferr_gpc1_z</th><th>flag_gpc1_z</th><th>f_ap_gpc1_y</th><th>ferr_ap_gpc1_y</th><th>f_gpc1_y</th><th>ferr_gpc1_y</th><th>flag_gpc1_y</th><th>f_acs_f435w</th><th>ferr_acs_f435w</th><th>f_acs_f606w</th><th>ferr_acs_f606w</th><th>f_acs_f775w</th><th>ferr_acs_f775w</th><th>f_acs_f814w</th><th>ferr_acs_f814w</th><th>f_acs_f850lp</th><th>ferr_acs_f850lp</th><th>f_wfc3_f105w</th><th>ferr_wfc3_f105w</th><th>f_wfc3_f125w</th><th>ferr_wfc3_f125w</th><th>f_wfc3_f140w</th><th>ferr_wfc3_f140w</th><th>f_wfc3_f160w</th><th>ferr_wfc3_f160w</th><th>f_moircs_k</th><th>ferr_moircs_k</th><th>m_acs_f435w</th><th>merr_acs_f435w</th><th>flag_acs_f435w</th><th>m_acs_f606w</th><th>merr_acs_f606w</th><th>flag_acs_f606w</th><th>m_acs_f775w</th><th>merr_acs_f775w</th><th>flag_acs_f775w</th><th>m_acs_f814w</th><th>merr_acs_f814w</th><th>flag_acs_f814w</th><th>m_acs_f850lp</th><th>merr_acs_f850lp</th><th>flag_acs_f850lp</th><th>m_wfc3_f105w</th><th>merr_wfc3_f105w</th><th>flag_wfc3_f105w</th><th>m_wfc3_f125w</th><th>merr_wfc3_f125w</th><th>flag_wfc3_f125w</th><th>m_wfc3_f140w</th><th>merr_wfc3_f140w</th><th>flag_wfc3_f140w</th><th>m_wfc3_f160w</th><th>merr_wfc3_f160w</th><th>flag_wfc3_f160w</th><th>m_moircs_k</th><th>merr_moircs_k</th><th>flag_moircs_k</th><th>m_ap_mosaic_u</th><th>merr_ap_mosaic_u</th><th>m_mosaic_u</th><th>merr_mosaic_u</th><th>m_ap_suprime_b</th><th>merr_ap_suprime_b</th><th>m_suprime_b</th><th>merr_suprime_b</th><th>m_ap_suprime_v</th><th>merr_ap_suprime_v</th><th>m_suprime_v</th><th>merr_suprime_v</th><th>m_ap_suprime_r</th><th>merr_ap_suprime_r</th><th>m_suprime_r</th><th>merr_suprime_r</th><th>m_ap_suprime_ip</th><th>merr_ap_suprime_ip</th><th>m_suprime_ip</th><th>merr_suprime_ip</th><th>m_ap_suprime_zp</th><th>merr_ap_suprime_zp</th><th>m_suprime_zp</th><th>merr_suprime_zp</th><th>m_ap_quirc_hk</th><th>merr_ap_quirc_hk</th><th>m_quirc_hk</th><th>merr_quirc_hk</th><th>f_ap_mosaic_u</th><th>ferr_ap_mosaic_u</th><th>f_mosaic_u</th><th>ferr_mosaic_u</th><th>flag_mosaic_u</th><th>f_ap_suprime_b</th><th>ferr_ap_suprime_b</th><th>f_suprime_b</th><th>ferr_suprime_b</th><th>flag_suprime_b</th><th>f_ap_suprime_v</th><th>ferr_ap_suprime_v</th><th>f_suprime_v</th><th>ferr_suprime_v</th><th>flag_suprime_v</th><th>f_ap_suprime_r</th><th>ferr_ap_suprime_r</th><th>f_suprime_r</th><th>ferr_suprime_r</th><th>flag_suprime_r</th><th>f_ap_suprime_ip</th><th>ferr_ap_suprime_ip</th><th>f_suprime_ip</th><th>ferr_suprime_ip</th><th>flag_suprime_ip</th><th>f_ap_suprime_zp</th><th>ferr_ap_suprime_zp</th><th>f_suprime_zp</th><th>ferr_suprime_zp</th><th>flag_suprime_zp</th><th>f_ap_quirc_hk</th><th>ferr_ap_quirc_hk</th><th>f_quirc_hk</th><th>ferr_quirc_hk</th><th>flag_quirc_hk</th><th>flag_cleaned</th><th>flag_merged</th><th>flag_gaia</th><th>stellarity</th><th>stellarity_origin</th><th>ebv</th><th>help_id</th><th>field</th><th>zspec</th><th>zspec_qual</th><th>zspec_association_flag</th><th>f_wircam_ks</th><th>ferr_wircam_ks</th><th>m_wircam_ks</th><th>merr_wircam_ks</th><th>flag_wircam_ks</th><th>f_irac_i1</th><th>ferr_irac_i1</th><th>m_irac_i1</th><th>merr_irac_i1</th><th>flag_irac_i1</th><th>f_irac_i2</th><th>ferr_irac_i2</th><th>m_irac_i2</th><th>merr_irac_i2</th><th>flag_irac_i2</th><th>f_irac_i3</th><th>ferr_irac_i3</th><th>m_irac_i3</th><th>merr_irac_i3</th><th>flag_irac_i3</th><th>f_irac_i4</th><th>ferr_irac_i4</th><th>m_irac_i4</th><th>merr_irac_i4</th><th>flag_irac_i4</th><th>flag_optnir_obs</th><th>flag_optnir_det</th><th>hp_idx</th></tr></thead>\n",
       "<thead><tr><th></th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><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>189.392114087</td><td>61.8600144331</td><td>nan</td><td>nan</td><td>9.33771038055</td><td>0.391741007566</td><td>11.1943998337</td><td>0.0</td><td>12.9521999359</td><td>0.0</td><td>10.4635000229</td><td>0.0</td><td>12.4686002731</td><td>0.0</td><td>12.652299881</td><td>0.0665509998798</td><td>12.4560003281</td><td>0.130146995187</td><td>10.4020004272</td><td>0.262125998735</td><td>10.5015001297</td><td>0.254988998175</td><td>nan</td><td>nan</td><td>668214.426684</td><td>241096.310165</td><td>False</td><td>120848.166728</td><td>0.0</td><td>23939.7728666</td><td>0.0</td><td>False</td><td>236919.054533</td><td>0.0</td><td>37373.1660748</td><td>0.0</td><td>False</td><td>31555.8619615</td><td>1934.23978716</td><td>37809.4072439</td><td>4532.20655351</td><td>False</td><td>250726.263815</td><td>60532.0813874</td><td>228770.461412</td><td>53727.5541811</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</td><td>3</td><td>nan</td><td>NO_INFORMATION</td><td>0.00892060480491</td><td>HELP_J123734.107+615136.052</td><td>HDF-N</td><td>nan</td><td>-99</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>1</td><td>184308828</td></tr>\n",
       "<tr><td>1</td><td>188.744562567</td><td>62.6216493931</td><td>13.7244997025</td><td>0.000668999971822</td><td>13.7730998993</td><td>0.00151099998038</td><td>13.4221000671</td><td>0.00021199999901</td><td>13.469499588</td><td>0.00328200007789</td><td>13.330499649</td><td>0.00108600000385</td><td>13.3341999054</td><td>0.00108600000385</td><td>13.3352003098</td><td>0.00156700005755</td><td>13.4104003906</td><td>0.00339600001462</td><td>13.3154001236</td><td>0.00200799992308</td><td>13.3790998459</td><td>0.00289799994789</td><td>11754.3906199</td><td>7.24272337888</td><td>11239.8377024</td><td>15.6422844457</td><td>False</td><td>15529.589398</td><td>3.03229543473</td><td>14866.2066263</td><td>44.9380715976</td><td>False</td><td>16896.6318021</td><td>16.900737142</td><td>16839.1450475</td><td>16.84323642</td><td>False</td><td>16823.6364963</td><td>24.280888159</td><td>15697.8380308</td><td>49.1001939038</td><td>False</td><td>17133.2578356</td><td>31.6868685647</td><td>16156.9753221</td><td>43.1254971847</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</td><td>3</td><td>nan</td><td>NO_INFORMATION</td><td>0.010016679481</td><td>HELP_J123458.695+623717.938</td><td>HDF-N</td><td>nan</td><td>-99</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>1</td><td>184359432</td></tr>\n",
       "<tr><td>2</td><td>189.411629347</td><td>62.6171794931</td><td>14.7763004303</td><td>0.00166599999648</td><td>14.8453998566</td><td>0.00209100008942</td><td>13.960100174</td><td>0.000735000008717</td><td>14.0101003647</td><td>0.00211500003934</td><td>13.648900032</td><td>0.00192599999718</td><td>13.7080001831</td><td>0.00239000003785</td><td>13.5349998474</td><td>0.00224300008267</td><td>13.5857000351</td><td>0.00244700000621</td><td>13.4406003952</td><td>0.00206599989906</td><td>13.5184001923</td><td>0.00348800001666</td><td>4461.48901366</td><td>6.8458992603</td><td>4186.39359589</td><td>8.06250113518</td><td>False</td><td>9461.49862129</td><td>6.4050563467</td><td>9035.65944866</td><td>17.6013473675</td><td>False</td><td>12602.014854</td><td>22.3548597412</td><td>11934.3817794</td><td>26.2708130939</td><td>False</td><td>13995.8751927</td><td>28.9137905475</td><td>13357.3401613</td><td>30.1043764723</td><td>False</td><td>15267.2157153</td><td>29.0513165062</td><td>14211.5001209</td><td>45.6553925725</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</td><td>3</td><td>nan</td><td>NO_INFORMATION</td><td>0.00939106127967</td><td>HELP_J123738.791+623701.846</td><td>HDF-N</td><td>nan</td><td>-99</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>1</td><td>184358063</td></tr>\n",
       "<tr><td>3</td><td>189.615264897</td><td>62.6304488331</td><td>15.1990995407</td><td>0.00311599997804</td><td>15.2462997437</td><td>0.00533499987796</td><td>14.6473999023</td><td>0.000748999998905</td><td>14.7025003433</td><td>0.0020230000373</td><td>14.4289999008</td><td>0.00190399994608</td><td>14.488699913</td><td>0.00122199999169</td><td>14.3299999237</td><td>0.00150599994231</td><td>14.3955001831</td><td>0.00341599993408</td><td>14.2683000565</td><td>0.00362999993376</td><td>14.3642997742</td><td>0.00190399994608</td><td>3022.45736729</td><td>8.67427746152</td><td>2893.87722932</td><td>14.2196922213</td><td>False</td><td>5023.88904303</td><td>3.46575242792</td><td>4775.29122657</td><td>8.89757041087</td><td>False</td><td>6143.27617389</td><td>10.7731486273</td><td>5814.60255503</td><td>6.54435602567</td><td>False</td><td>6729.76703574</td><td>9.33470646292</td><td>6335.77763821</td><td>19.9339543985</td><td>False</td><td>7123.27937024</td><td>23.8156409714</td><td>6520.48785496</td><td>11.4346454229</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</td><td>3</td><td>nan</td><td>NO_INFORMATION</td><td>0.00961707766829</td><td>HELP_J123827.664+623749.616</td><td>HDF-N</td><td>nan</td><td>-99</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>1</td><td>184358005</td></tr>\n",
       "<tr><td>4</td><td>189.939111597</td><td>62.3108263931</td><td>16.0858001709</td><td>0.00384999997914</td><td>16.141500473</td><td>0.00800499971956</td><td>15.5888004303</td><td>0.000845999980811</td><td>15.6396999359</td><td>0.000751000014134</td><td>15.4131002426</td><td>0.00142600003164</td><td>15.4758996964</td><td>0.00324800005183</td><td>15.342300415</td><td>0.00181399995927</td><td>15.4091997147</td><td>0.003202999942</td><td>15.2957000732</td><td>0.00341599993408</td><td>15.3903999329</td><td>0.00532099977136</td><td>1335.61082908</td><td>4.73605065538</td><td>1268.81940143</td><td>9.35484964773</td><td>False</td><td>2110.95914366</td><td>1.64484834088</td><td>2014.28085821</td><td>1.39327117062</td><td>False</td><td>2481.76069976</td><td>3.25953101764</td><td>2342.28707248</td><td>7.00699534516</td><td>False</td><td>2648.98704268</td><td>4.42581021658</td><td>2490.69250648</td><td>7.3477221436</td><td>False</td><td>2765.15812062</td><td>8.69988484896</td><td>2534.19498333</td><td>12.4196382765</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</td><td>3</td><td>nan</td><td>NO_INFORMATION</td><td>0.0109041692966</td><td>HELP_J123945.387+621838.975</td><td>HDF-N</td><td>nan</td><td>-99</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>1</td><td>184007074</td></tr>\n",
       "<tr><td>5</td><td>189.407112727</td><td>62.7091870231</td><td>15.5497999191</td><td>0.00547299999744</td><td>15.602399826</td><td>0.0012969999807</td><td>15.1979999542</td><td>0.000860999978613</td><td>15.2629003525</td><td>0.00123499997426</td><td>15.0812997818</td><td>0.00309200002812</td><td>15.1429004669</td><td>0.00235900003463</td><td>15.054400444</td><td>0.00186700001359</td><td>15.1072998047</td><td>0.00295299990103</td><td>15.0155000687</td><td>0.00427300017327</td><td>15.1017999649</td><td>0.00286299991421</td><td>2188.16482464</td><td>11.0301434429</td><td>2084.68321464</td><td>2.49032322704</td><td>False</td><td>3025.51993143</td><td>2.39926842712</td><td>2849.96718179</td><td>3.2417721549</td><td>False</td><td>3368.8376902</td><td>9.59390152707</td><td>3183.02326722</td><td>6.91581616667</td><td>False</td><td>3453.34384296</td><td>5.93826840578</td><td>3289.12272333</td><td>8.94580012519</td><td>False</td><td>3579.31542929</td><td>14.0866772081</td><td>3305.82617514</td><td>8.7172003792</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</td><td>3</td><td>nan</td><td>NO_INFORMATION</td><td>0.00963436382679</td><td>HELP_J123737.707+624233.073</td><td>HDF-N</td><td>nan</td><td>-99</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>1</td><td>184358658</td></tr>\n",
       "<tr><td>6</td><td>189.600564027</td><td>62.5597367731</td><td>14.0937004089</td><td>0.00176300003659</td><td>14.1364002228</td><td>0.0011040000245</td><td>13.5507001877</td><td>0.000933000002988</td><td>13.5909996033</td><td>0.00107400002889</td><td>13.3978996277</td><td>0.0032460000366</td><td>13.4413995743</td><td>0.00935299973935</td><td>13.3402004242</td><td>0.00363299995661</td><td>13.3892002106</td><td>0.00569400005043</td><td>13.2876996994</td><td>0.00177800003439</td><td>13.3584003448</td><td>0.00191800005268</td><td>8366.03806811</td><td>13.5846307377</td><td>8043.40452073</td><td>8.17870745123</td><td>False</td><td>13794.9434635</td><td>11.8543364735</td><td>13292.3007402</td><td>13.1486187128</td><td>False</td><td>15879.6215867</td><td>47.4749317798</td><td>15255.9820934</td><td>131.421606757</td><td>False</td><td>16746.3371429</td><td>56.0351969905</td><td>16007.3675039</td><td>83.9485235687</td><td>False</td><td>17576.003089</td><td>28.78243717</td><td>16467.9622227</td><td>29.091368854</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</td><td>3</td><td>nan</td><td>NO_INFORMATION</td><td>0.0101999541983</td><td>HELP_J123824.135+623335.052</td><td>HDF-N</td><td>nan</td><td>-99</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>1</td><td>184357936</td></tr>\n",
       "<tr><td>7</td><td>189.193256177</td><td>61.8675017031</td><td>14.8851003647</td><td>0.00237199990079</td><td>14.9246997833</td><td>0.000910000002477</td><td>14.3461999893</td><td>0.000975999981165</td><td>14.3998003006</td><td>0.00374100008048</td><td>14.163599968</td><td>0.00465000001714</td><td>14.2243003845</td><td>0.00617500022054</td><td>14.0869998932</td><td>0.00175299996044</td><td>14.1578998566</td><td>0.00438700011</td><td>14.0242996216</td><td>0.00298299989663</td><td>14.0852003098</td><td>0.00226600002497</td><td>4036.08081998</td><td>8.81759608145</td><td>3891.52734654</td><td>3.26164852887</td><td>False</td><td>6630.09886223</td><td>5.95998948576</td><td>6310.73407104</td><td>21.7441921566</td><td>False</td><td>7844.40509005</td><td>33.59608314</td><td>7417.88073069</td><td>42.1883464392</td><td>False</td><td>8417.82790473</td><td>13.5911945456</td><td>7885.69648515</td><td>31.8627592929</td><td>False</td><td>8918.26044954</td><td>24.5024250665</td><td>8431.79183515</td><td>17.5976820385</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</td><td>3</td><td>nan</td><td>NO_INFORMATION</td><td>0.00964287432676</td><td>HELP_J123646.381+615203.006</td><td>HDF-N</td><td>nan</td><td>-99</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>1</td><td>184308915</td></tr>\n",
       "<tr><td>8</td><td>188.335312907</td><td>62.2618624131</td><td>13.2623996735</td><td>0.00108600000385</td><td>13.3177995682</td><td>0.00108600000385</td><td>12.8603000641</td><td>0.00108600000385</td><td>13.3990001678</td><td>0.00108600000385</td><td>12.8718996048</td><td>0.00595800019801</td><td>12.9200000763</td><td>0.00352500006557</td><td>13.845700264</td><td>0.00152000004891</td><td>13.4448003769</td><td>0.0014009999577</td><td>12.7004995346</td><td>0.0019270000048</td><td>12.769200325</td><td>0.00305199995637</td><td>17990.3714596</td><td>17.9947425432</td><td>17095.435656</td><td>17.0995892988</td><td>False</td><td>26054.3338885</td><td>26.0606642564</td><td>15863.5336024</td><td>15.8673879326</td><td>False</td><td>25777.4621644</td><td>141.454364335</td><td>24660.3916395</td><td>80.063538238</td><td>False</td><td>10512.8361193</td><td>14.7176739114</td><td>15208.2712281</td><td>19.6242763701</td><td>False</td><td>30185.625953</td><td>53.5744328148</td><td>28334.7816312</td><td>79.6489533305</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</td><td>3</td><td>nan</td><td>NO_INFORMATION</td><td>0.0102366169901</td><td>HELP_J123320.475+621542.705</td><td>HDF-N</td><td>nan</td><td>-99</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>1</td><td>184317197</td></tr>\n",
       "<tr><td>9</td><td>189.559520157</td><td>62.6358052531</td><td>13.0670003891</td><td>0.00118899997324</td><td>13.1206998825</td><td>0.00215999991633</td><td>12.9576997757</td><td>0.00108600000385</td><td>12.995300293</td><td>0.00108600000385</td><td>12.685500145</td><td>0.00214300001971</td><td>12.7515001297</td><td>0.00541399978101</td><td>12.6693000793</td><td>0.00108600000385</td><td>12.7384004593</td><td>0.00108600000385</td><td>12.6830997467</td><td>0.00168600003235</td><td>12.741399765</td><td>0.00407900009304</td><td>21537.7258381</td><td>23.5861670016</td><td>20498.4039373</td><td>40.7802103268</td><td>False</td><td>23818.8116239</td><td>23.8245988316</td><td>23008.0537254</td><td>23.013643945</td><td>False</td><td>30605.5326343</td><td>60.4084645527</td><td>28800.494734</td><td>143.61303556</td><td>False</td><td>31065.6159001</td><td>31.0731638489</td><td>29150.0843831</td><td>29.1571669192</td><td>False</td><td>30673.2716829</td><td>47.6314014611</td><td>29069.6694862</td><td>109.211780927</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</td><td>3</td><td>nan</td><td>NO_INFORMATION</td><td>0.00954951214949</td><td>HELP_J123814.285+623808.899</td><td>HDF-N</td><td>nan</td><td>-99</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>1</td><td>184358011</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(\"$('#table140351181210904-559971').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",
       "    $('#table140351181210904-559971').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 71, 72, 74, 75, 77, 78, 80, 81, 83, 84, 86, 87, 89, 90, 92, 93, 95, 96, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 141, 142, 143, 144, 146, 147, 148, 149, 151, 152, 153, 154, 156, 157, 158, 159, 163, 164, 166, 169, 170, 172, 173, 174, 175, 177, 178, 179, 180, 182, 183, 184, 185, 187, 188, 189, 190, 192, 193, 194, 195, 197, 198, 199], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_catalogue[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns = [\"help_id\", \"field\", \"ra\", \"dec\", \"hp_idx\"]\n",
    "\n",
    "bands = [column[5:] for column in master_catalogue.colnames if 'f_ap' in column]\n",
    "\n",
    "\n",
    "bands_no_ap = (set([column[5:] for column in master_catalogue.colnames if 'flag' in column]) \n",
    "               - set(bands) \n",
    "               - set(['cleaned', 'gaia', 'merged', 'optnir_det', 'optnir_obs'])\n",
    "              )\n",
    "\n",
    "for band in bands:\n",
    "    columns += [\"f_ap_{}\".format(band), \"ferr_ap_{}\".format(band),\n",
    "                \"m_ap_{}\".format(band), \"merr_ap_{}\".format(band),\n",
    "                \"f_{}\".format(band), \"ferr_{}\".format(band),\n",
    "                \"m_{}\".format(band), \"merr_{}\".format(band),\n",
    "                #\"flag_{}\".format(band)\n",
    "               ]    \n",
    "\n",
    "for band in bands_no_ap:\n",
    "    columns += [\"f_{}\".format(band), \"ferr_{}\".format(band),\n",
    "                \"m_{}\".format(band), \"merr_{}\".format(band),\n",
    "                #\"flag_{}\".format(band)\n",
    "               ] \n",
    "\n",
    "columns += [\"stellarity\", \"stellarity_origin\", \"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \n",
    "            \"flag_optnir_obs\", \"flag_optnir_det\", \n",
    "            \"zspec\", \"zspec_qual\", \"zspec_association_flag\", \"ebv\"] # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing columns: {'flag_gpc1_y', 'flag_suprime_b', 'flag_quirc_hk', 'flag_irac_i2', 'flag_irac_i1', 'flag_suprime_v', 'flag_irac_i3', 'flag_moircs_k', 'flag_acs_f435w', 'flag_acs_f775w', 'flag_suprime_zp', 'flag_mosaic_u', 'flag_suprime_ip', 'flag_suprime_r', 'flag_wfc3_f140w', 'flag_wfc3_f105w', 'flag_irac_i4', 'flag_acs_f850lp', 'flag_wfc3_f125w', 'flag_wircam_ks', 'flag_gpc1_z', 'flag_gpc1_r', 'flag_acs_f814w', 'flag_acs_f606w', 'flag_gpc1_i', 'flag_gpc1_g', 'flag_wfc3_f160w'}\n"
     ]
    }
   ],
   "source": [
    "# We check for columns in the master catalogue that we will not save to disk.\n",
    "print(\"Missing columns: {}\".format(set(master_catalogue.colnames) - set(columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#master_catalogue[columns].write(\"{}/master_catalogue_hdf-n{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)\n",
    "master_catalogue.write(\"{}/master_catalogue_hdf-n{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (herschelhelp_internal)",
   "language": "python",
   "name": "helpint"
  },
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
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