{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lockman SWIRE master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on Lockman SWIRE."
   ]
  },
  {
   "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": [
    "wfc = Table.read(\"{}/INT-WFC.fits\".format(TMP_DIR))\n",
    "rcs = Table.read(\"{}/RCSLenS.fits\".format(TMP_DIR))\n",
    "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))\n",
    "sparcs = Table.read(\"{}/SpARCS.fits\".format(TMP_DIR))\n",
    "dxs = Table.read(\"{}/UKIDSS-DXS.fits\".format(TMP_DIR))\n",
    "swire= Table.read(\"{}/SWIRE.fits\".format(TMP_DIR))\n",
    "servs = Table.read(\"{}/SERVS.fits\".format(TMP_DIR))\n",
    "uhs = Table.read(\"{}/UHS.fits\".format(TMP_DIR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Merging tables\n",
    "\n",
    "We first merge the optical catalogues and then add the infrared ones: WFC, DXS, SpARCS, HSC, PS1, SERVS, SWIRE.\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": [
    "### WFC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = wfc\n",
    "master_catalogue['wfc_ra'].name = 'ra'\n",
    "master_catalogue['wfc_dec'].name = 'dec'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add RCSLenS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AANQqQlcAZnp0xSs70tWejGk8k1euMF3R1wUAAAtH6ArAeIW70Zd0cAcjAAA1i9AVgEpv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\nxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13a964cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(rcs['rcs_ra'], rcs['rcs_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, rcs, \"rcs_ra\", \"rcs_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add PanSTARRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jcvdbJN0iSZs3b/Yy1FUTxiZyGs/m1Vp/auu5vvTwvlcdW9iY0j3Pd2nVwkZJ0rsvWj2r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\ndAEAAISA0AUAABACQhcAAEAI/h/xearkIGzImQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f138c0ff358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(ps1['ps1_ra'], ps1['ps1_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, ps1, \"ps1_ra\", \"ps1_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add SpARCS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bH9r7isei4YB+s+uQuluLIev6Czcs6TUAAED1sGLlgVQmLydvh4OWtUfDmkwvq9OBAABo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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f138f9ce518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(sparcs['sparcs_ra'], sparcs['sparcs_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, sparcs, \"sparcs_ra\", \"sparcs_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add DXS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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baiMKldmu7lIQMNPvXtGlL/10t77zVK8+dOO5q3YEbynLGbG6StJe59w+STKz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\nLhuHbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1397859ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(dxs['dxs_ra'], dxs['dxs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, dxs, \"dxs_ra\", \"dxs_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add SERVS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nqqSA7StreTm5alWjVau3lD9JwEfDE8lglePH2ZxvQ1MFB3rGONI/zsbmSr/L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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1397859390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(servs['servs_ra'], servs['servs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, servs, \"servs_ra\", \"servs_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add SWIRE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4lS7K9QAAwIsRrAqoWNvZLGiujkqShlKzRbkeAAB4MYJVARWr6/qCmlhI4aBpmGAFAIAv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\nAOCR/x98+sh530XREQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f139782fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(swire['swire_ra'], swire['swire_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, swire, \"swire_ra\", \"swire_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add UHS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qiSjTrN5brfSyAgDAFwQrnxSi1UJOJBRQY3WYKwMBACgygpUPnHMFaQ46W2tdlBErAACK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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1372b43ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(uhs['uhs_ra'], uhs['uhs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, uhs, \"uhs_ra\", \"uhs_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning\n",
    "\n",
    "When we merge the catalogues, astropy masks the non-existent values (e.g. when a row comes only from a catalogue and has no counterparts in the other, the columns from the latest are masked for that row). We indicate to use NaN for masked values for floats columns, False for flag columns and -1 for ID columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    if \"m_\" in col or \"merr_\" in col or \"f_\" in col or \"ferr_\" in col or \"stellarity\" in col:\n",
    "        master_catalogue[col].fill_value = np.nan\n",
    "    elif \"flag\" in col:\n",
    "        master_catalogue[col].fill_value = 0\n",
    "    elif \"id\" in col:\n",
    "        master_catalogue[col].fill_value = -1\n",
    "        \n",
    "master_catalogue = master_catalogue.filled()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=10&gt;\n",
       "<table id=\"table139721740520192-277059\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>wfc_id</th><th>ra</th><th>dec</th><th>wfc_stellarity</th><th>m_ap_wfc_u</th><th>merr_ap_wfc_u</th><th>m_wfc_u</th><th>merr_wfc_u</th><th>m_ap_wfc_g</th><th>merr_ap_wfc_g</th><th>m_wfc_g</th><th>merr_wfc_g</th><th>m_ap_wfc_r</th><th>merr_ap_wfc_r</th><th>m_wfc_r</th><th>merr_wfc_r</th><th>m_ap_wfc_i</th><th>merr_ap_wfc_i</th><th>m_wfc_i</th><th>merr_wfc_i</th><th>m_ap_wfc_z</th><th>merr_ap_wfc_z</th><th>m_wfc_z</th><th>merr_wfc_z</th><th>f_ap_wfc_u</th><th>ferr_ap_wfc_u</th><th>f_wfc_u</th><th>ferr_wfc_u</th><th>flag_wfc_u</th><th>f_ap_wfc_g</th><th>ferr_ap_wfc_g</th><th>f_wfc_g</th><th>ferr_wfc_g</th><th>flag_wfc_g</th><th>f_ap_wfc_r</th><th>ferr_ap_wfc_r</th><th>f_wfc_r</th><th>ferr_wfc_r</th><th>flag_wfc_r</th><th>f_ap_wfc_i</th><th>ferr_ap_wfc_i</th><th>f_wfc_i</th><th>ferr_wfc_i</th><th>flag_wfc_i</th><th>f_ap_wfc_z</th><th>ferr_ap_wfc_z</th><th>f_wfc_z</th><th>ferr_wfc_z</th><th>flag_wfc_z</th><th>wfc_flag_cleaned</th><th>wfc_flag_gaia</th><th>flag_merged</th><th>rcs_id</th><th>rcs_stellarity</th><th>m_rcs_g</th><th>merr_rcs_g</th><th>m_rcs_r</th><th>merr_rcs_r</th><th>m_rcs_i</th><th>merr_rcs_i</th><th>m_rcs_z</th><th>merr_rcs_z</th><th>m_rcs_y</th><th>merr_rcs_y</th><th>f_rcs_g</th><th>ferr_rcs_g</th><th>flag_rcs_g</th><th>f_rcs_r</th><th>ferr_rcs_r</th><th>flag_rcs_r</th><th>f_rcs_i</th><th>ferr_rcs_i</th><th>flag_rcs_i</th><th>f_rcs_z</th><th>ferr_rcs_z</th><th>flag_rcs_z</th><th>f_rcs_y</th><th>ferr_rcs_y</th><th>flag_rcs_y</th><th>rcs_flag_cleaned</th><th>rcs_flag_gaia</th><th>ps1_id</th><th>m_ap_gpc1_g</th><th>merr_ap_gpc1_g</th><th>m_gpc1_g</th><th>merr_gpc1_g</th><th>m_ap_gpc1_r</th><th>merr_ap_gpc1_r</th><th>m_gpc1_r</th><th>merr_gpc1_r</th><th>m_ap_gpc1_i</th><th>merr_ap_gpc1_i</th><th>m_gpc1_i</th><th>merr_gpc1_i</th><th>m_ap_gpc1_z</th><th>merr_ap_gpc1_z</th><th>m_gpc1_z</th><th>merr_gpc1_z</th><th>m_ap_gpc1_y</th><th>merr_ap_gpc1_y</th><th>m_gpc1_y</th><th>merr_gpc1_y</th><th>f_ap_gpc1_g</th><th>ferr_ap_gpc1_g</th><th>f_gpc1_g</th><th>ferr_gpc1_g</th><th>flag_gpc1_g</th><th>f_ap_gpc1_r</th><th>ferr_ap_gpc1_r</th><th>f_gpc1_r</th><th>ferr_gpc1_r</th><th>flag_gpc1_r</th><th>f_ap_gpc1_i</th><th>ferr_ap_gpc1_i</th><th>f_gpc1_i</th><th>ferr_gpc1_i</th><th>flag_gpc1_i</th><th>f_ap_gpc1_z</th><th>ferr_ap_gpc1_z</th><th>f_gpc1_z</th><th>ferr_gpc1_z</th><th>flag_gpc1_z</th><th>f_ap_gpc1_y</th><th>ferr_ap_gpc1_y</th><th>f_gpc1_y</th><th>ferr_gpc1_y</th><th>flag_gpc1_y</th><th>ps1_flag_cleaned</th><th>ps1_flag_gaia</th><th>sparcs_intid</th><th>sparcs_stellarity</th><th>m_ap_cfht_megacam_u</th><th>merr_ap_cfht_megacam_u</th><th>f_ap_cfht_megacam_u</th><th>ferr_ap_cfht_megacam_u</th><th>m_cfht_megacam_u</th><th>merr_cfht_megacam_u</th><th>f_cfht_megacam_u</th><th>ferr_cfht_megacam_u</th><th>flag_cfht_megacam_u</th><th>m_ap_cfht_megacam_g</th><th>merr_ap_cfht_megacam_g</th><th>f_ap_cfht_megacam_g</th><th>ferr_ap_cfht_megacam_g</th><th>m_cfht_megacam_g</th><th>merr_cfht_megacam_g</th><th>f_cfht_megacam_g</th><th>ferr_cfht_megacam_g</th><th>flag_cfht_megacam_g</th><th>m_ap_cfht_megacam_r</th><th>merr_ap_cfht_megacam_r</th><th>f_ap_cfht_megacam_r</th><th>ferr_ap_cfht_megacam_r</th><th>m_cfht_megacam_r</th><th>merr_cfht_megacam_r</th><th>f_cfht_megacam_r</th><th>ferr_cfht_megacam_r</th><th>flag_cfht_megacam_r</th><th>m_ap_cfht_megacam_z</th><th>merr_ap_cfht_megacam_z</th><th>f_ap_cfht_megacam_z</th><th>ferr_ap_cfht_megacam_z</th><th>m_cfht_megacam_z</th><th>merr_cfht_megacam_z</th><th>f_cfht_megacam_z</th><th>ferr_cfht_megacam_z</th><th>flag_cfht_megacam_z</th><th>sparcs_flag_cleaned</th><th>sparcs_flag_gaia</th><th>dxs_id</th><th>m_ap_ukidss_j</th><th>merr_ap_ukidss_j</th><th>m_ukidss_j</th><th>merr_ukidss_j</th><th>m_ap_ukidss_k</th><th>merr_ap_ukidss_k</th><th>m_ukidss_k</th><th>merr_ukidss_k</th><th>dxs_stellarity</th><th>f_ap_ukidss_j</th><th>ferr_ap_ukidss_j</th><th>f_ukidss_j</th><th>ferr_ukidss_j</th><th>flag_ukidss_j</th><th>f_ap_ukidss_k</th><th>ferr_ap_ukidss_k</th><th>f_ukidss_k</th><th>ferr_ukidss_k</th><th>flag_ukidss_k</th><th>dxs_flag_cleaned</th><th>dxs_flag_gaia</th><th>servs_intid</th><th>f_ap_servs_irac1</th><th>ferr_ap_servs_irac1</th><th>f_servs_irac1</th><th>ferr_servs_irac1</th><th>servs_stellarity_irac1</th><th>f_ap_servs_irac2</th><th>ferr_ap_servs_irac2</th><th>f_servs_irac2</th><th>ferr_servs_irac2</th><th>servs_stellarity_irac2</th><th>m_ap_servs_irac1</th><th>merr_ap_servs_irac1</th><th>m_servs_irac1</th><th>merr_servs_irac1</th><th>flag_servs_irac1</th><th>m_ap_servs_irac2</th><th>merr_ap_servs_irac2</th><th>m_servs_irac2</th><th>merr_servs_irac2</th><th>flag_servs_irac2</th><th>servs_flag_cleaned</th><th>servs_flag_gaia</th><th>swire_intid</th><th>f_ap_swire_irac1</th><th>ferr_ap_swire_irac1</th><th>f_swire_irac1</th><th>ferr_swire_irac1</th><th>swire_stellarity_irac1</th><th>f_ap_swire_irac2</th><th>ferr_ap_swire_irac2</th><th>f_swire_irac2</th><th>ferr_swire_irac2</th><th>swire_stellarity_irac2</th><th>f_ap_irac_i3</th><th>ferr_ap_irac_i3</th><th>f_irac_i3</th><th>ferr_irac_i3</th><th>swire_stellarity_irac3</th><th>f_ap_irac_i4</th><th>ferr_ap_irac_i4</th><th>f_irac_i4</th><th>ferr_irac_i4</th><th>swire_stellarity_irac4</th><th>m_ap_swire_irac1</th><th>merr_ap_swire_irac1</th><th>m_swire_irac1</th><th>merr_swire_irac1</th><th>flag_swire_irac1</th><th>m_ap_swire_irac2</th><th>merr_ap_swire_irac2</th><th>m_swire_irac2</th><th>merr_swire_irac2</th><th>flag_swire_irac2</th><th>m_ap_irac_i3</th><th>merr_ap_irac_i3</th><th>m_irac_i3</th><th>merr_irac_i3</th><th>flag_irac_i3</th><th>m_ap_irac_i4</th><th>merr_ap_irac_i4</th><th>m_irac_i4</th><th>merr_irac_i4</th><th>flag_irac_i4</th><th>swire_flag_cleaned</th><th>swire_flag_gaia</th><th>uhs_id</th><th>uhs_stellarity</th><th>m_uhs_j</th><th>merr_uhs_j</th><th>m_ap_uhs_j</th><th>merr_ap_uhs_j</th><th>f_uhs_j</th><th>ferr_uhs_j</th><th>flag_uhs_j</th><th>f_ap_uhs_j</th><th>ferr_ap_uhs_j</th><th>uhs_flag_cleaned</th><th>uhs_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></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<tr><td>0</td><td>445739200939</td><td>161.119984028</td><td>57.5480451649</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>14.563</td><td>0.015</td><td>12.477</td><td>0.015</td><td>14.721</td><td>0.025</td><td>12.968</td><td>0.025</td><td>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>5430.0</td><td>75.0182</td><td>37085.1</td><td>512.35</td><td>False</td><td>4694.61</td><td>108.097</td><td>23593.9</td><td>543.27</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>1</td><td>True</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>1</td><td>445739200001</td><td>161.317870177</td><td>57.7697859649</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>13.968</td><td>0.015</td><td>10.556</td><td>0.015</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>9392.9</td><td>129.768</td><td>217570.0</td><td>3005.85</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>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>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>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>2</td><td>445733201324</td><td>162.372580983</td><td>57.7155488559</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>14.305</td><td>0.024</td><td>11.798</td><td>0.024</td><td>14.174</td><td>0.016</td><td>11.765</td><td>0.016</td><td>14.071</td><td>0.022</td><td>11.071</td><td>0.022</td><td>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>6886.52</td><td>152.225</td><td>69310.6</td><td>1532.1</td><td>False</td><td>7769.62</td><td>114.497</td><td>71449.6</td><td>1052.92</td><td>False</td><td>8542.79</td><td>173.1</td><td>135394.0</td><td>2743.46</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>2</td><td>True</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>3</td><td>445733300776</td><td>162.85355081</td><td>58.0652473519</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>14.498</td><td>0.024</td><td>13.113</td><td>0.024</td><td>14.469</td><td>0.016</td><td>12.926</td><td>0.016</td><td>14.371</td><td>0.022</td><td>12.783</td><td>0.022</td><td>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>5765.01</td><td>127.434</td><td>20644.3</td><td>456.338</td><td>False</td><td>5921.07</td><td>87.2561</td><td>24524.5</td><td>361.406</td><td>False</td><td>6480.37</td><td>131.31</td><td>27976.9</td><td>566.889</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>2</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>4</td><td>445733400548</td><td>162.951970116</td><td>57.8874447429</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>14.307</td><td>0.024</td><td>13.129</td><td>0.024</td><td>14.246</td><td>0.016</td><td>12.905</td><td>0.016</td><td>14.193</td><td>0.022</td><td>12.821</td><td>0.022</td><td>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>6873.84</td><td>151.945</td><td>20342.3</td><td>449.663</td><td>False</td><td>7271.09</td><td>107.151</td><td>25003.5</td><td>368.465</td><td>False</td><td>7634.84</td><td>154.703</td><td>27014.7</td><td>547.392</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>3</td><td>True</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>5</td><td>445733100796</td><td>162.801456204</td><td>57.6576678529</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>18.701</td><td>0.024</td><td>18.883</td><td>0.024</td><td>18.546</td><td>0.016</td><td>18.689</td><td>0.016</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>120.116</td><td>2.65514</td><td>101.578</td><td>2.24537</td><td>False</td><td>138.548</td><td>2.04172</td><td>121.451</td><td>1.78976</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>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>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>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>6</td><td>445733100980</td><td>162.74460315</td><td>57.7531915719</td><td>0.000171409</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>19.148</td><td>0.025</td><td>18.559</td><td>0.025</td><td>18.627</td><td>0.016</td><td>18.055</td><td>0.016</td><td>18.257</td><td>0.022</td><td>17.852</td><td>0.022</td><td>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>79.5792</td><td>1.83238</td><td>136.899</td><td>3.15221</td><td>False</td><td>128.588</td><td>1.89494</td><td>217.771</td><td>3.20919</td><td>False</td><td>180.801</td><td>3.66352</td><td>262.543</td><td>5.31984</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>True</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>7</td><td>445730400960</td><td>162.664609776</td><td>57.7583334499</td><td>0.00306748</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>19.242</td><td>0.026</td><td>18.407</td><td>0.026</td><td>18.677</td><td>0.016</td><td>17.702</td><td>0.017</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>72.9793</td><td>1.74763</td><td>157.471</td><td>3.77094</td><td>False</td><td>122.8</td><td>1.80965</td><td>301.439</td><td>4.71981</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>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>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>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>8</td><td>445739100452</td><td>161.950404962</td><td>57.5769644959</td><td>0.000171409</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>19.155</td><td>0.026</td><td>18.717</td><td>0.026</td><td>18.467</td><td>0.016</td><td>18.056</td><td>0.016</td><td>17.743</td><td>0.026</td><td>17.645</td><td>0.027</td><td>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>79.0678</td><td>1.89343</td><td>118.359</td><td>2.83432</td><td>False</td><td>149.005</td><td>2.19582</td><td>217.57</td><td>3.20624</td><td>False</td><td>290.268</td><td>6.95102</td><td>317.687</td><td>7.90022</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>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>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>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>9</td><td>445730100005</td><td>163.118808976</td><td>57.6290929629</td><td>0.0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>14.261</td><td>0.026</td><td>10.84</td><td>0.026</td><td>14.135</td><td>0.016</td><td>10.604</td><td>0.016</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>7171.33</td><td>171.731</td><td>167494.0</td><td>4010.96</td><td>False</td><td>8053.78</td><td>118.685</td><td>208161.0</td><td>3067.58</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>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>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>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></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",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table139721740520192-277059').dataTable()\");\n",
       "    $('#table139721740520192-277059').dataTable({\n",
       "        \"order\": [],\n",
       "        \"iDisplayLength\": 50,\n",
       "        \"aLengthMenu\": [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        \"pagingType\": \"full_numbers\"\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_catalogue[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III - Merging flags and stellarity\n",
    "\n",
    "Each pristine catalogue contains a flag indicating if the source was associated to a another nearby source that was removed during the cleaning process.  We merge these flags in a single one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_cleaned_columns = [column for column in master_catalogue.colnames\n",
    "                        if 'flag_cleaned' in column]\n",
    "\n",
    "flag_column = np.zeros(len(master_catalogue), dtype=bool)\n",
    "for column in flag_cleaned_columns:\n",
    "    flag_column |= master_catalogue[column]\n",
    "    \n",
    "master_catalogue.add_column(Column(data=flag_column, name=\"flag_cleaned\"))\n",
    "master_catalogue.remove_columns(flag_cleaned_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each pristine catalogue contains a flag indicating the probability of a source being a Gaia object (0: not a Gaia object, 1: possibly, 2: probably, 3: definitely).  We merge these flags taking the highest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_gaia_columns = [column for column in master_catalogue.colnames\n",
    "                     if 'flag_gaia' in column]\n",
    "\n",
    "master_catalogue.add_column(Column(\n",
    "    data=np.max([master_catalogue[column] for column in flag_gaia_columns], axis=0),\n",
    "    name=\"flag_gaia\"\n",
    "))\n",
    "master_catalogue.remove_columns(flag_gaia_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each prisitine catalogue may contain one or several stellarity columns indicating the probability (0 to 1) of each source being a star.  We merge these columns taking the highest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wfc_stellarity, rcs_stellarity, sparcs_stellarity, dxs_stellarity, servs_stellarity_irac1, servs_stellarity_irac2, swire_stellarity_irac1, swire_stellarity_irac2, swire_stellarity_irac3, swire_stellarity_irac4, uhs_stellarity\n"
     ]
    }
   ],
   "source": [
    "\n",
    "stellarity_columns = [column for column in master_catalogue.colnames\n",
    "                      if 'stellarity' in column]\n",
    "\n",
    "print(\", \".join(stellarity_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We create an masked array with all the stellarities and get the maximum value, as well as its\n",
    "# origin.  Some sources may not have an associated stellarity.\n",
    "stellarity_array = np.array([master_catalogue[column] for column in stellarity_columns])\n",
    "stellarity_array = np.ma.masked_array(stellarity_array, np.isnan(stellarity_array))\n",
    "\n",
    "max_stellarity = np.max(stellarity_array, axis=0)\n",
    "max_stellarity.fill_value = np.nan\n",
    "\n",
    "no_stellarity_mask = max_stellarity.mask\n",
    "\n",
    "master_catalogue.add_column(Column(data=max_stellarity.filled(), name=\"stellarity\"))\n",
    "\n",
    "stellarity_origin = np.full(len(master_catalogue), \"NO_INFORMATION\", dtype=\"S20\")\n",
    "stellarity_origin[~no_stellarity_mask] = np.array(stellarity_columns)[np.argmax(stellarity_array, axis=0)[~no_stellarity_mask]]\n",
    "\n",
    "master_catalogue.add_column(Column(data=stellarity_origin, name=\"stellarity_origin\"))\n",
    "\n",
    "master_catalogue.remove_columns(stellarity_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV - Adding E(B-V) column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    ebv(master_catalogue['ra'], master_catalogue['dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V - Adding HELP unique identifiers and field columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(gen_help_id(master_catalogue['ra'], master_catalogue['dec']),\n",
    "                                   name=\"help_id\"))\n",
    "master_catalogue.add_column(Column(np.full(len(master_catalogue), \"Lockman SWIRE\", dtype='<U18'),\n",
    "                                   name=\"field\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK!\n"
     ]
    }
   ],
   "source": [
    "# Check that the HELP Ids are unique\n",
    "if len(master_catalogue) != len(np.unique(master_catalogue['help_id'])):\n",
    "    print(\"The HELP IDs are not unique!!!\")\n",
    "else:\n",
    "    print(\"OK!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VI - Cross-matching with spec-z catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz =  Table.read(\"../../dmu23/dmu23_Lockman-SWIRE/data/Lockman_SWIRE-specz-v2.1.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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46TA/29pAz+AoF84riIhTl3LmptTE45zbAGw46b47J338XuC93pYWnzYeaKc4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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13736dd6d8>"
      ]
     },
     "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": 31,
   "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",
    "### VII.1 - SERVS vs SWIRE\n",
    "\n",
    "Both SERVS and SWIRE provide IRAC1 and IRAC2 fluxes. SERVS is deeper but tends to under-estimate flux of bright sources (Mattia said over 2000 µJy) as illustrated by this comparison of SWIRE, SERVS, and Spitzer-EIP fluxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: 'e/count' did not parse as fits unit: At col 0, Unit 'e' not supported by the FITS standard.  [astropy.units.core]\n",
      "WARNING: UnitsWarning: 'image' did not parse as fits unit: At col 0, Unit 'image' not supported by the FITS standard.  [astropy.units.core]\n"
     ]
    }
   ],
   "source": [
    "seip = Table.read(\"../../dmu0/dmu0_SEIP/data/SEIP_Lockman-SWIRE.fits\")\n",
    "seip_coords = SkyCoord(seip['ra'], seip['dec'])\n",
    "idx, d2d, _ = seip_coords.match_to_catalog_sky(SkyCoord(master_catalogue['ra'], master_catalogue['dec']))\n",
    "mask = d2d <= 2 * u.arcsec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q3cxdugm3B8qq3ZSUV5OeklD7+QprEaA2HMetxTsEWGvXGWPGhqMYEREJQv7q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1380d32a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(seip['i1_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_servs_irac1'], label=\"SERVS\", s=2.)\n",
    "ax.scatter(seip['i1_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_swire_irac1'], label=\"SWIRE\", s=2.)\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(\"SEIP flux [μJy]\")\n",
    "ax.set_ylabel(\"SERVS/SWIRE flux [μJy]\")\n",
    "ax.set_title(\"IRAC 1\")\n",
    "ax.legend()\n",
    "ax.axvline(2000, color=\"black\", linestyle=\"--\", linewidth=1.)\n",
    "ax.plot(seip['i1_f_ap1'][mask], seip['i1_f_ap1'][mask], linewidth=.1, color=\"black\", alpha=.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3Jl1NNCNtj1hr/9nQA9HMv4qISNfVf/UCUvBy8GiAX62rCbXwCLIW/EmpJF24\niPNzGg9soPMqRSKdiDCroe9rVQO7rbXb26owERHpgIInGwCB3EySbeNToSXuXnz92B2kDzmTZTlN\nj5hpZE26ukgjbZc28bwxxpjV1trvxrgmERHpoCr/+k1SS3cCcF8jLTwAzPir+STnIdJ1BqhI1CI1\n172lsceMMScBewGdOyoiXVf4eZmDp8S7mrjavi6Pga9cTwZV5L5VxVeGuoATw1px5mn0/f67AEwC\njZyJNEM0GxHurXeXG7jRWntSbQ83EZGuKXheJsANz8e3lngpWAvLb2BUxYE6pxlMG5ZUJ7CZS5dA\nzs30jWetIh1cNBsRjoZ97wHOAZ4FCNtJKiLS9UybW/drV5K/FP/f52F8lbjM8X5r4IyuFZYFnJ5r\ngKs2sIlI6zR5IsIJTzDGBaxN5J2jOhFBRJqkqc3o1P6cPv3CdMrWP8+BiXew5YP3ubPqV42fE2rB\ndV8ZdtlXu+4IpEgzxOxEhAb0Av7dgueJiCQOTW1GpXTF/WQWvk3m7rUMo5yTV8/hPFNTJ7DVX7f2\nmT8LKOuaI5AibSiaNW2bcUa4AQwwDDgcvN9aO77tygNjzBjgDiALWGmt/XVbvp+IdBFdeWozCsET\nDIptBudYyKQcC6SZmhOmQsEJbBUmlYzc/QwFeMBoBFMkxqIZabsk1m9qjHmq9nUPWmvHhd0/E1iC\ns9nhCWvtYmvtNuA/a6dllwEKbSLSeoOnaIQtgpq8RYyvzsfC8RMMGpgKhdqNBl9/kwyFNJE25Wrq\nAmvt3kh/Wvi+S4GZ4XcYY9zAY8CFwFhgtjFmbO1jlwGvAq+18P1ERLq09XtLuPHJNazfWxLV48dO\nucgJYzhhLXwq9ISea4NyNKom0g4aDW3GmA1NPTmaaxpirX0b+Lze3VOAXdbaPdbaGmA5cHnt9S9Z\nay8Erm/J+4mIdFoFa+HpWc5XGg9nS/J2ULFrNZ4/XcWnbzzGpkXT2b4uD3CmQqt+fzkVu1bzyNI/\nciy3H1O23Bdxo0ENHqeNx/DpMHPRCWUtWLCgrT6xSJcVaXp0jDFmU4THDZAZw1qygYKw2/uAqcaY\nacAsIJkII23GmNuB2wGGDBkSw7JERBJYvQ0V9Q9VD7pjxkg8B+9mfHU+Ze/vYJgtY1PeIpg8g+TX\nvsdZFDC52yY8AeuEtUbWrfkt3O55gKfmf9t5sJFWHrm5uTH/qCJdXaTQNjqK5/tjVUhjrLWrgFVR\nXPc48DitSEj5AAAgAElEQVQ4LT/atioRkQRRb0NF8Eiou8eXOyNwtS1NJg3tBV9bBKsW8/kXprNr\n3V9Z6r6aMx64hWsDBWCgm7ER1629Yr7Mk/3mMf+SsU2WVVRUxMCBA2P4QUUk0jFWLV2v1lKFwOCw\n24Nq7xMRkcbU21AROlT96Vl1W5oUrIUV86CmgmGly0j1H+FnNT8kCXvCRoP6Ya3Y9OIbNXeQfsqZ\nvBjlsVPZ2dk0tw+oiETWkj5tbWUdMKL2XNNC4FrguviWJCLSQU2bS2mVlyXll3LKms8Y9495jK8+\n3nS8HziLXGh83VrAws5Ln+Nov0k62F0kATS5e7QtGGP+DLwPjDLG7DPGzLHW+oBvA68D24BnrbUf\nxaM+EemA6i3IT2hN1NrUTs9oHTp4kEv2/y87XnuE5Mr9eK2L8MGvSLtCjQH3iOmMnjwjNHo3aWiv\nVtUjIq0TTXPdsdbarfXum1a71qxFrLWzG7n/NdTWQ0RaoiOdcBCh1u3r8vC8+kO+57e88up3mPTN\nW44fudX/NMh/CjIGwuX/W7fNRvCaMZfBB09D8Q5O8ZaBGyba3Rj38UsjrVuzwIv+s/hiHx/D1XhY\nJKFEMz36rDHmaeAhIKX2aw5wRlsWJiLSLB3phIMItdbkLWI8u8ANwz3PA7eEQl5g90pneqS6zLkv\nPPDVvwZnp5jLHm+OC41Phfox/Mh7K0XDr6mz61REEkc0oW0q8BNgNZABPAOc1ZZFiYg0W2MnHCTi\nwfARTmPoNmMeu964h/6ZKWTOnO/cOW0uZXvW0cOW1V7lckbUam1fl0fSZ4UMw0USgdD9boi4bg2c\nwPYkl1E89W6K9pfFbN2a+rSJxF40oc0LHAO644y0fWKtDUR+iohIgoj3tGm0obH2utHT5sI9znq2\nJW/s4I4ZJUwaOoWii37PwTfuYRiFJHnLYdtLTo+0/KUMf/X7ePCzN9CXIeZQkyNr4IS1EtL5es1/\nk37KmSy7aExMP7b6tInEXjShbR3wN2AyzqHtvzHGXGmt/Y82rUxEJBbiPW0abWgMXlf0AUxfQPrf\nH+N7NX7+8vx/MenO2xg9eQZMngEFa6n4210c+KwQ37o8Rv9jIR78BCz0pKLBwFY/rPmBx32X8MaA\n/6JHd0+b7ApVnzaR2DNN9dExxuRYa/Pr3XeDtfbpNq2sFXJycmx+fn7TF4qItLXmjLT96Ro49jk+\nT4YzmgZ87B6FnfkgNXmL6DFpFlk7nsUc2kaaqcYHWOtMiRpzfNdn4+vW4JGUb/F2xkVgDPMvGdtm\nO0KNMerTJhIlY8x6a21Ok9dFEdoaPBPKWvtZC2trcwptItJRbF+XR03eIrrNmMfo/j1g1WL2fbqL\nQX6nv7nPk8GnZHOKdztlpkfYurYTRZoK3eA/mSt9P+a5/zqzXVp3KLSJRC/a0BZNn7ZXgVdqv64E\n9gB/b115IiK1OlJ/tTZQk7eI8dX51OQtCm1Q8E39BhWkUoWHJG85Q3x7KCOVgmFXUkM3oLY9R+2f\nxvqtBZWbVG52L+bBr54GEJMecCLS/ppc02atPS38tjHmdOCbbVaRiHQt8d4o0NYiTY8WrOXkDB+7\nAqPpMWkWPD2L/akjyN78Ozz48VqXcyaoraEbNXj3vIMPSzfDCUdPwYmja1XuVAIBeLBmNhOH9+S6\nqUO48ck1DR4oLyKJr9nHWFlrNxhj9F+6iMRGvDcKtLXGQmnBWvxLLyfdX8lAkgnseBaKN9KPf+DG\nErDgMYHQCQbGwBfZFXp6Y2ENIADc7Z1D0ZBruGPGSIrCjqCq/1VEOo5oTkT4fthNF3A6UNRmFYlI\n1xKhZ1lH9Kc1n/Hw69u564LRXDd1SOOhdNVi3P5KAFKpZldpFacMnw57VoH1B9ur1dkNGmmTQXAj\nQo1JYv+Z91FUkMMdM0YeP0C+Vv3bbUV92kRiL5o1bRlhf5Jx1rZd3pZFiUgX04nWtT38+nZKKr08\n/Pp2547BU9g++pts+uM8tq/LO37htLkcwwldR203kibfSEnBVlwBf6Ov3dg5oX4/rPKP5z+TF7Hl\nll0MO/9bcT8rVH3aRGIvmjVtC9ujEBHpwqJd15aIpxvU2r4uj6Q37uGV7m6eDJzF13t/BAU9ARj4\n2i30sGXseuMeKtbcz4HSKnznPwAXP3N85+iae6Bmf+gEg2gb5H7sH8hM308B6OXx8NsEOdRdfdpE\nYq/R0GaMeRnn7OAGWWsva+wxEZFmiXZdWwJvWqjJW8Ro73bwwt3uT0gqrqTib3eRntmHHrYML24y\n3dWkF2/nFKDo1f+i0tObIal+Pn/9+/i8e0kydV8z0ro1vwW3AU+fwYwIpHGgrJq7LhjdDp80OtnZ\n2Wr5IRJjkUbaftpuVYhI1xbturZ4bFqIcnSv24x5HH3tRtLsUYz/GACFJccY9aXL8O3+Jx78pB3b\nRwBwGehnD5HkPQilkAqhETaIHNashc9sX5Z3u5IfDt3J8GlzeTPBRh1FpG1ECm33WmunG2N+Yq39\nYbtVJCLSmHhsWlgxDwrzoaoUblsZunv93hJeffVF7vA8T+bM+Yw+8g7YY4DBjcVrXbh8FRS/8wRZ\ntYe4pxov4AQvdyMTGRF3hVon8H0SGMD7PS+FG86O8YcVkUQWKbQNMMacCVxmjFlOnd8DndYfbVqZ\niEgCy3t6MXO9v6Ob8TsjcZ+8BbXhLGCS8OBjBIXYI4X4oM7UpzEnvl6ksAZQhYfF3MwNmZt4O3k2\n8y8+NcafSEQSXcSRNmA+MAj4GXVDmwXObcO6REQSw8xFx6dHgwrWcmdtYLMWPqlMIZ0s+vBvLAZj\nfVicvzSNAXeEpV1NTYV6cVOZPpSe1/6W3Npp0Htj+PFEpONoNLRZa/8K/NUYM99ae3871iQikjjC\np2QL1jrTpQe3hQKbMTB4/wo8+PncptPbVISeGny8oZE1aDywBSyU050Dgd6MchfSrf9JCbdbtinq\n0yYSe9G0/FBgE5GuK3wjwqrFzvq2Wn7AjTOiZi30oKJOo9vGRGrhUWpTuNU7lw12JNe6VjK/219J\nG9PxNuurT5tI7DX7GCsRkQ6piV2g29flHe+Z1r/H8Wv/9h0o3g5FGwgcO4Kxx0fOguvUkvCDqfsX\nanPWrVnrrIZzG9gYGMmHjMRt4La+W0grLYVtL0HOza3/GbQj9WkTiT2FNhHpmKJpxVF/lGz3SmcX\naErm8efVXpP0WSGjvdspe+0WGDjSGVEr+gBqnKOmOFbiHCHTyFRnJE2tW9vgP5kH/Dfy3aTnecQ3\ni9MG9eTFb50FBX1PXE/XQahPm0jsRWque6619h+1359krf0k7LFZ1trE6mwpIl1LNI12w68JBp+q\n0rrhraoUCvPpnzWBssM96GHLqC78gG4WzLHP8bs8uDneaby5ma2pwPZr3yU87L+OyycM5JfFk8EY\n5l8y1rmgk53L2hrr95awpPbg+3gezyUST0011z299vvnwr4H+BGgv0lEJH6iabQbfk0wAAVH32rD\n2zFS8JNK8cirGTZmMkefuoI0exRM8FxPH24T27AGcMx6uL7mHjbYkZwzIotfXjux6RdN4GO82tqS\nvB28vbMYoF0OvBdJRJFCm2nk+4Zui4i0r2hGoepfE9z9CTDxBsqKdtDDlgFQuGYpxasfpg9HgeM7\nPz3NnOJramTtmPWw3Q7hx74bGPLFaaQfreGOGSOje/EEPsarrQV/RlH/rEQ6oUihzTbyfUO3RUSi\n1x4jRg29R9juz8qKUlx9TmZvyedgLUP8n9Adb+jpwY0EjbXraEikjQbB1h9rA2O42euMAKYfrWne\nqFE8jvFKEJOG9tIIm3R5kULbycaYl3BG1YLfU3v7pDavTEQ6r7YaMWpo4wGw/pwnuf/ljxjlu4hc\n3qM7VaSU7sQFpODCYvDgB44HrOZoairUGKf32s5ANo/4ZjGiXzrFFdXMHDegeW/Ugda4qU+bSOyZ\nxnb3GGO+EumJ1tq32qSiGMjJybH5+flNXygi8dFGI20Vj36F9OKNVGRNIP1Lt8Cb86l2JRM4VoLb\nWlwEqDDp9KQcODGgNTewNRXWgnzWsH7cfJKm3MqSvB3sP3KMnYeOkpHsZumtU7WwXqSLM8ast9bm\nNHVdpBMRGg1lxpizWlqYiEhbjRgdKK3ilODXbS9BdRnJwQdrw1hmbWCDEwNatIGtxm958J1qoOGw\nVj/8bQqczGNlZ7OsdorvisfeA6C82s+SvB2dctpPfdpEYs/V2APGGLcxZrYx5r+NMeNq77vEGLMa\neLTdKhQRiZLv/AfY5RlN/8wUGHMZeNJCjwUnFVq7iyp3VVXEwBauwiZTkTWBjVmX8L+BHzsjjMD8\nS8YyYVAmEwb37LQL67Ozs+NdgkinE2l6dCkwGFgLTAWKgBxgrrX2xfYqsCU0PSrSSbRkGvXpWbB7\nJW8HxnOK6wADORiTUqKZCg2OsBUH0tliT2bjyd/ge7d8LVQTw6d3mDVprWWMUXNdkSi1enoUJ6CN\nt9YGjDEpwAFguLX2cKyKFBGJKNoNC8FwN+YyqCrlWMDDl80mApZQv7Xmbi4IinbdGhx/j5R+pzDt\n228xLVhbVSlk53TJXZ8iEjuRQluNtTYAYK2tMsbsUWATkXYVqcVFWFDzvpmLp7oE3+5/kkSAlNr2\nGq7glGgbBTZrnf5HLgM+67yRL30Q6Zc/fPyiYJuR4dO7XENcEYmtSKFttDFmU+33Bhhee9sA1lo7\nvs2rE5GuLdKGhdpROO++DXiqSwhYSDIBoGU91sJFO7pmDHzgH04ZafR0VTKBXRzNPImU8HDWhXur\niUhsRQptY9qtChGRKL2x4iXS/vVTkk69nKnDYcXBPsys+iseEyDAiburmjM12pypUHBaefzYdwOb\nzCguyviMWeXP8LZ3FveGXxQpeHbiY6nUp00k9iK1/NjbnoWIiERUG3BO3v0xp7CPD7cc5Ye9/ou5\n5bl4akfYGtoO39zAFims1Q+AhUmDCfSdzF8uPRU4gyV5E5u3GzSRjqWKcYDMzc1tfU0iUkejoc0Y\nU07Dx1UFp0d7tFlVIiL1lK64n8zCt8l2pUEAenGE+0v+h27G3y4bDayFAODGWb92wDOUYxf8nBcn\nnx26ptn91hJo6jT48y2t8pJ528utfj31aROJvUgjbRntWYiISKTRniXeWZzjP8LbfIkbzN8Zagtw\ntWJnaLOnQnGFRvR2uEdyUUUu52zKYNnk5r93SAIdSxX6+daf3m2h7OxstfwQibFII22/BN4D3rPW\nFrVfSSLSZUWYLrx68mCSXj3MHfZpulkfrnZo4RFUaT0sNxdxNW/i6pmN66xFnLMpo1M1xr344itY\nkje2U30mkc4mUnPdbwNn1v4BWF375z3gw2A7kESk5roiHVRjI20Fa/H+8Wo81SUADW44iEZzA1tt\nmzfIGg3fXtOCd+y61FxXJHqxOHv0UWqPqzLGDOR4gPse0A/QmjYRia2w6cL1e0u4/5WtjKrZysLy\nBaQEjoamQk0oTUWnOevWglOtFjDp/aHiACSnt+DDiIjEVqSWHxhjDHAaTlg7CxgL7AKebvvSRKTL\nKlhL5h+/w5PV++htKk7ou9ZWLTyMgQrbDWuSOHzmjxg2ZvLxkT8BnDC9JG8Hd8wYyaShveJdjkiX\nEmlN25s4o2kbgX8BD1prt7VXYSLS+X36xmP0fn8xn58xl6peo6jJW8SxUy5izNZfcoota9kcaK1o\nWnhA3dG1AIb05GSoKSfjX/dC7585I38Fa53zQzthP7XmWpK3g7d3FgORd8uqT5tI7EUaadsDjAdG\nAIeBYmPMIWttcbtUJiKdU/5SWLkQpi+g1/uL6GHLCaxexBZ7MmebD7Fb8jH1doW2ZYPc4Hv5TBKe\nrFOgeLvzQMAHr/0AvjA2sfqpxVlwo0JTGxbUp00k9hr9PdZa+w1r7RnAFcAqYBLwR2PMemPMH9qp\nPowxJxtjnjTG/LW93lNE2tCb8+HY5/DmfF5LOp8a62albzypgQoCtuEp0GgCW+6qqjqja9HuDPVZ\nuCPtJ2y6ZRdc/r/OGaFnfY+ASYKAj9IV9zsjbMOnRz1Nun1dHpsWTWf7uryorm/t89rTpKG9WDZn\napNTo0VFajogEmsR17TVqgYqgWO13w8CurXmTY0xTwGXAAettePC7p8JLMHpX/mEtXaxtXYPMEeh\nTaTjCq6DmjluAGd5MxlKGfsrDVe6XqSb8fNV93u4DFTZJJKtr1l911rSwsNa8ANJBnYHsnnp88Ec\nydvhTPfVjqT9eOcwztn/lNO3rJn91GryFjG+Op9NeYtg8ow2f14iUp82kdhrdKTNGPMLY8waYD+w\nEMgAfgOMstae1sr3XQrMrPd+buAx4EKcDQ+zjTFjW/k+IhIPwTVgBWtZv7eEr/9hHW/vLObuFzbz\n66rz+dymk+E6FjrNINgkN8W0fWADqMHNj7xzWOeeyFN9vs+EQZncPb48VDM4fcueGvZTLr74iugL\nqtVtxjw2JefQbca8hi8I+/k063ki0qVFGmn7BPgjsNFa64/lm1pr3zbGDKt39xRgV+3IGsaY5cDl\nwNZYvreIxN4JOwpr14C9u6uY2wPzGO3bzi88z/OIbxYz3evobSo4EOhJKjXOCzTzVIOWhDW/ddp4\nJBlINn5mutfxQO/FvPits5wLnp5VZ91acBqwJUZPnhF5pKyRNXJNPk9EurRIoe1vwJFgYDPG/D+c\n9W17gUettTUxriUbKAi7vQ+YaozpAzwATDTGzLPWLmroycaY24HbAYYMGRLj0kSkfjALvx3cUbi/\ntIoDpVVMNP+PW/2HWOHP4VfuBxngOcwoVyEDzGGO2hT2BvoyxBxq9vFTLQlrAQsFti93er+Fx+3i\nvuRn6JPejbeTb2X+xWGD+e15DmgCnTkqIh1HpBMR1gBftdYWGWMmAHnAIpwdpV5r7ddb9cbOSNsr\nwTVtxpirgJnB1zXG3ABMtdZ+u7mvrRMRRGLvxifX8PbOYs4ZkcWyOVM57+dvsfNgBakeNz+6ZCzP\nrvuMD/eVEv43ylLPYqa5N1Fmu9PDHMNnDUnGtui80GhbeIAz1QpQEejGA/4bmOlex2OBK/nqZbO4\nburxX+rUc6zt6EQEkehFeyJCpC5I3cPOHP0a8JS19mfALThTmbFWCAwOuz2o9j4RSQB3jy/npcyf\nO2u/gAOlxwCo9Pp5dt1nbDtQzkSzg6WexZxudgDwiG8WG/zD8Vo31kKScf4Rb+5UaHMCW/D1fbg4\nzbuUme51THNv4luu51ixZf/xiwrW4vnTVVTsWs2SvB3RFyRRUZ82kdiLND0a/tfqucA8AGttwDT3\nV+TorANGGGNOwglr1wLXtcUbiUjzjd7+K6jOh+2/gskzmHfRWBa+tIVqv2XjvlIAvut5nmnuTQDc\n7J3LDFc+E127mz2qBi3fZBBUHMigb0Y3/p56IyM9L/C26z/q9BYrXXE/46vz+VF6ADvj5uYXKBGp\nT5tI7EUKbf80xjyLs3u0F/APAGPMAKBV69mMMX8GpgFZxph9wAJr7ZO1h9S/jtPy4ylr7UeteR8R\niaGwdVjr95bw+3f3UO13Rs5ONzv4btLzrPBPpgdH6cFRrnWt5D+TXml2g9yWhrXwmThjYD9Z+PyW\nn9x5G3Ab99a7/sGjl3Oh/wh/73k9P9HUaMwVFRUxcODAeJch0qlEOjD+u8aYa4EBwNnWWm/tQ/2B\ne1rzptba2Y3c/xrwWmteW0RiK3zdF+c8yZI3dlB27CMyij9gqccJav/j+Qu9TQUAaaaKUa5Cvuja\njasFDXJD3zcjsAUsXFWTywY7ktPNDr7neYHHXVdx1wWjG/1Mr5UM4i/euUxIyoz6fSR66tMmEnuR\nzh59HVgB/N1aG1pbZq39oD0KE5H4W7+3hJufWkN5tZ89xUfZf+QYtYNrLK2dCh3v2kNvU8HnNp3D\nNoOvuJzpUXcbtvCoP2q3MTCcDdaZ+txgR/ItczfzLhrLii37GdU/o85u15njBvDw69spr/bTK9XD\n/EtPjb5QEZE4ijQ9ehNOA9xcY8xIYA1OiMuz1h5tj+JEJH6CTXHLq502jftKjtV5/BHfLABW+Cdz\ni3sF2a5ivup+r12a4wZZC5V041+BMSz1LHY2PtiRlFf7efj17ZRUOhMEy+ZMDbUl2VxYSkmll16p\nHp64abJ2jYpIhxFpevQAzskFS40xLmAqzmkF/2OMOQa8Ya19qF2qFJF2tyRvByWVXtyG0OhauA12\nJDd7nXVu/+P5C+mmus7jTa1hCwa2K0Z7mNDf3azagq8bANJMDdcmrQpNz97snUuvVA93XTCaFVv2\nn3DA+cxxA0L3xyywFax1GuZOmwuD22JzvYhIhD5tEZ9kTBZwgbX2mdiX1Hrq0ybScuv3lnD/K1s5\nWuUFYzhSWcOhish7j651rWSe50/YgKWHqwpL49OjLT0rFOqGQK918TvfRYx1fcYK/2QudK9jiW8W\nn3Q/9YQRtDbvxxY8TWH49GadUdqZqU+bSPSi7dMWaU3bbcAqa+1O4/T4eBK4EudEhJsSNbCJSHQa\nO+Fgf2kVOw9WNOu1dtjBHAj0ZrirCFcjI3OtnQo1BipsN8BQGMhinu82Zx2bHwb16s5/XDuf9Lwd\nPNFAMAtOjQKhExxiGuB0wsEJ1KdNJPYinYiwBZhorfUaY64DfgCcD0zEadHx5fYrs3k00ibStPon\nHFzx2HtsLDhCsttFtT/QrNcKnnzQkJaOrFmc6U+D4UX/mfQx5aE1a+F6dk/iyZunRAxg4QH11Vdf\n5Jz9T/H2gFu595u3RFWPiEhbavVIG+ALa/NxCbDMWnsYyDPGPByLIkUkfsLXeN345BqKy51w1VBg\nC/Zhayg0gbMp4WzX5hOOqGrprtDDgTRyvL+L6nMYY5ocMQs//P0Uz/Nkujcx0fM8zgEv0hbUp00k\n9iIdYxUwxgwwxqQA03HOHg1q/tyGiCSUSUN7cceMkSx8+SPe3lnMviNVjV773SSnvcd3k46v1zrd\n7OB5z3ye98wH4HHfxQRqQ1f9o6eaCmzWOlOq/trbma5jEa8PSk5yNdqLrTGZM+dTmn0OS7yzWL+3\npNHr1u8t4cYn10S8RhqXnZ0d7xJEOp1II233Avk4pxO8FDydwBjzFWBPO9QmIm1k/d4S5j63Keq1\na8H2HsGv4AS50927ne95ngHmMPe9VUXAgstEHlmzFqpxk4SfJOMEvXf849kaGMLXk/7OE74LI9bz\n4FdPq3Pwe7MMnsJ3XD/i7c+K2ZW3IzQCV1/4OrjGrhERaU+RQtvrwFAgw1ob/qtmPnBNm1YlIm0i\nuLZrT/HRE/quRRLe3iPoEd8senA09P3o1ffQzYAfN/O+kgo4qyusddamWSCpdtrUGPiX/1RW+Cdz\nt+dP7Le9Q1OvD/mbPnJ4xZb9XDd1SIt3hdZvA9LSa0RE2lOkjQgHgZeAPwH/tB1o77Y2IojUFQw3\nZVU+NhYcielrd3/3Eaa6ttGTcn55boCP/dnM893GoqTfMcJViMvA5zadr9f8Nz9Kepo0U8VRm8KP\nfTc0uD6uKeFNcetvppDEoZYfItGLxUaEMcBVwHxgmTHmOeDP1tp/xahGEWknwam+CYMy6ZXqCZ0U\n0BpH3nW6/kxzbWPpuaVs8A9nlT8tNGJ2gfdhrnWt5H88f+Eh7zVssCOZ5b2/xe+X7DYM6ZPG4ivH\nh0bVNBomIl1JpBMRDgO/BX5rjBkI/AfwC2NMP2C5tbZVh8aLSPuZOW4AH3xWwtEaP3ddMJq7X9jc\n4tfyfl7I0a2rAOh59vWUmcms8je8s3R5YDrLq6e3pnQA+mUkMzAzhfmXnlpnGjR8V2h7a/OGvR2c\n+rSJxF7UJyIYY9KBWcD3gQHW2i+0ZWGtoelRkdqTDV7+iKM1fg6UHgudIZqRnER5ta9FrxkcXQMn\nsLWH8HobmwZtrFFwU4GqNcFLU7MiEiuxmB6ltt3HpcBs4EycA+PnAm/GokgRaTv3v/wRG/eVhm5n\nJDvne7YksMUjrAX5AwFG9EsnrZu70WnQ+js9o935Gem6pgKdpmYjU582kdiLdIzVn4AZwFvAM8B1\n1trGGzmJSGIJO6gz1eNieL8Mrs4Z3Kyp0XiGNXDOL630BhiQmRIxfNUPUNEGqkjXNRX84jk12xFk\nZ2drI4JIjEXaPXoj8IK1trx9S2o9TY+K1D34vfBIFZVePyP6pbP7YAXRHFIVj8DmdkGy20VaShJV\nNX6unzqUrfvLGh3tast1ZVqz1jraPSoSvWinRxs9EcFauwyoNMZkhb1oN2PM7caYbTGqU0SaqalO\n/cHHAV781lkM6NmdSq+znu1A6bEmA9uRd58JBbaeZ1/f5oHN1P4B8AeckTWf31Je7Wfr/jKWzZna\naGgKjoYtydsR87qCI2kKbCKSKCJNj16Ls3v0qDFmJ/AA8BSwDmj/eRIRAZqetgs+vrmwlLsuGE3Z\nMa+zJiw5iR4pSaHn1hePkTWPy+ANHB+N6ZfRjYGZ3bl68hBWbNnf5PTmzHED2FxYysxxA9q6VBGR\nuIu0EeFHwCRr7S5jzOnA+8BV1tqX26c0EWnIHTNGUnbMS1mVj/V7S04YCbpjxkg2F5ZSUunl4de3\nU1LpJdXjwu1yUdHIJoTwkbX25K8NbC4DAQsDe6by4rfOAojqmKoVW/ZTUukNnZAgItKZRTowvsZa\nuwvAWrsB2KnAJhJ/k4b2okd3DxsLjjQ4LThpaC+euGkyEwZlkpWeTEaym0pvgPJqH/VXGNWfCm0r\nbgMj+qYxuFd3Uj0uPG5nQjS7V3fOGZHF8Kw058JmroG6Y8ZIzhmRpR2cCUh92kRiL9JIWz9jzPfD\nbvcMv22t/XnblSUikTS1O/LjA+VsLizFb2FEv3T8JZVUeo+vZmuvqVC3geQkNz+6ZCyj+mewJG8H\nfdK6sXFfKRnJbvqkdQt9huCi/+bQDs7ElZubG+8SRDqdSLtHI/6aZK1d2CYVxYB2j0pXN/G+N0JH\nVa1jkCoAABZeSURBVE0Y3JP5l4zlhifWUPTPZaFr2mMqNLzxbLAZ7YTBPemRkhQ6B1XNaTsn9WkT\niV6rm+smcigTkRMFW1TMHDeArPRkqr0Beqd3A2v5+EA5h956OnRtewQ2F9A7rRtXPPouR2v8YG0o\nQNY/tSC8frXY6BzUp00k9iLtHn3WWnt17fc/sdb+MOyxN6y157dHgSISneAJCPmffk6lNxAa0Xrp\n90v413NuvAFLz7OvJ9ntIoDF62+7f1CDGwte3FhU5/5zRmSFAtmkob24Y8bIUFCL9hQDEZGuKtKa\nthFh358H/DDsdt+2KUdEWmL93hJ2H6qoveUs8t/+6pOcM7IvQ3un8sN77uX3731CYUkl2b1SSevm\nrnPEldvl9EiLhV6pHq7JGcwT736CL2DJSHbTP7P7CcdQrd9bwtf/sC40jatjoUREIosU2iL9Gq4x\nb5EEsiRvB+XVfnqlerjrgtH85IH7mDqyL9+4cy5VeTsY1T8DcBrX7jxYwYh+6SS5wFcb1AyG4H/W\nLmi0AW9jjwWf3TejG7/5Wg6ThvbivFP7R5zuXJK3g5JKL71SPaFrNMImItK4SKEt1RgzEefv6e61\n3webl3dvj+JEJDrB0amUzc+z4++r+erpgxh54a1c89v38dX2QjtQeuz4E6xlXHZPjlZ5OVBWRXm1\nP/RQeChL9bgZ+YX00KhcAPjPc07mX3sOc7TGz55DFfitMx162qDj69Wg6Z2d4SNr9UOd1reJyP9v\n7/6Dq6rPPI5/HhICWCqlBt1wm2i7lbZit6DxJ9ayW60gFmc6rdPqTNdKx+oOu0xndFfrSK1OR93W\nOrq4bZlW07rrr7WdLQVGxO1QmIqFUKhglR9tIQk3CGlCDBATQp79I/fSS8ivG849555z368Zxtxz\nT859+E68+fD93u9zcLKhQts+Sd8b4OvsYwBF4pdPPaYPSdL7JhxvtTDz/pfV0+sqH2NadNU0bd/X\noQdX/qFvqXJc+fGdmw99/hPHlyknnzZW5WNMBw51S5Jqz5mszQ0n3i7r+fpGbV7c95HWZ37boMW/\n2KaeXj/eNy43qA0VvoYKdXy+Lf7o0wYEb9CWH3FGyw+UitxeWP37Yj3z2wZ9Z9VbuvOaj550t4Dc\nnaYvbWvW+99ToeWvN+vyvz1DmxsOatKEck2oKNd7KsqUbu/U/o6+EDdG0vwZU9V6uPt4ENu0p013\n/ex17Wvv1N3XnnfCa2XbfPRv6zHcTBozbQBKySnfMN7MLjKzv8l5/GUz+4WZPW5m7w+qUACjM1Rg\nk/puA7V58WcGvL1TdpbrpW3NWruzRctfb1ZPr2vtzhZ1dPXocPcxVU0ary1N7ersPnHp9Nc7Dpxw\nk/YLz56sqknj1dF1TC9ta5b015vWzzm/asA7Fgx2o/fcm91zs/Z4S6fTw58EIC9DLY/+UNJVkmRm\nV0p6SNI/S5ohaamkzxe8OgAnGS6s5SMbps6rOl3P1zfqU9Om6Nc7DujOaz56fPPCeVWn66lX/6zu\nHlfqfeP1T39/7kk3c++/83O45c3BdoqyLJoc9GkDgjfUHRF+7+6fyHz9hKQD7n5f5vEWd58RWpV5\nYnkUSTRcWCvUkmJ2iVPqu7uC3HW4+5jeU1Gmez87fcBNBA/88o0hzxkMy6LJYWaENmCETvmOCJLK\nzKzc3XskfVrSrSP8PgABy4a0oWbWRjtLlQ1ZMjth92fWnPOrtLmhTZNOq9COfe+ccA/T/hsPsse2\nNLVr8mljtfPI0QHPAQDkb6jw9aykX5tZi6ROSeskycw+LKl9iO8DEJB8lkJH25w2G7KyX/cPWC9t\na860BOk+HtiyrUAGeq3ssewmh0VXTRvxDBrLowAwuCF3j5rZpZKqJL3s7oczx6ZJmujuvwunxPyx\nPIq4GyqsBb2EmDvTdkNttV7Y2HB8afOGi2r0Qn3j8SXRnfsPHQ9s+Sx7DraLdKBaWB5NBpZHgZEL\nYnlU7v7aAMd2DHQugFO3YcMGrVy5UtLgM2tBz0ZdePZk/e/CKyT1havc21vtaX1LbUeO6spzK3Xv\nZ6frsVd26J3Oo9rS1J7XsudIZwG5K0Jy0KcNCB592oAiMdKl0ELORvXfRHDDRTXHlzizr8VsGAAE\na6QzbYQ2IGJBtvCQTm6cS7hCFNLptKZOnRp1GUAsBLI8CqBwgg5rWdnl061729V25KgkPtSP8NGn\nDQgeoQ2IwEhaeIzWQLs3g8YSKQCEj9AGhKhQs2u5cj/MP9AtrIIw3GYIQh0ABI/QBoQgjLAWpuF2\ng9JvDQCCV/Shzcw+JOkeSZPcnfudIlYOHTqk7373u5KSEdayhmvNMdpGvwCAwRU0tJnZk5Kuk7Tf\n3c/POT5H0mOSyiT9yN0fGuwa7v4nSQvM7MVC1goELRvSJk+erEWLFkVbTMjotwb6tAHBK/RMW52k\nJZJ+mj1gZmWSnpB0taQmSRvNbJn6AtyD/b7/FnffX+AagUAlbSkUGA1+9oHgFTS0uftaMzun3+GL\nJe3KzKDJzJ6TdL27P6i+WTkglh555BF1dHRI4hcWQJ82IHhRfKYtJakx53GTpEHXUczsDEnfljTT\nzO7OhLuBzrtV0q2SVFNTmB1zwGCyIW3x4sUaM2ZMtMUARYA+bUDwin4jgrv/RdJtIzhvqaSlUt8d\nEQpdFyCxFAoACE8UoW2vpOqcxx/IHANig7AWDPq5AcDIRRHaNko618w+qL6w9kVJN0ZQB5C33t5e\n3X///ZIIa0GgnxsAjFyhW348K2m2pEoza5L0TXf/sZktlLRKfTtGn3T3NwpZBxCEbEi75JJLNHfu\n3GiLSQj6uQHAyBV69+iXBjm+UtLKQr42EJSkL4VGuURJP7fkok8bELyi34gAROWPf/yjnn76aUnJ\nDGtZLFGiEJL8/wwQFUIbMIDsL5x7771XZWVl0RZTYCxRohDo0wYEz5LYR6e2ttbr6+ujLgMxlA1r\nEydO1B133BFtMUCMmRl92oARMrNN7l473HnMtAGSnnvuOb311luSWNYBABQnQhtKXjakEdYAAMWM\n0IaSlQ1pX/jCFzR9+vRoiwEAYBiENpScpLfwAAAkE6ENJaO1tVWPP/64JMIaUGj0aQOCR2hDSciG\ntHvuuUdjx46NthigBPAPIyB4hDYkWvYXR1VVlb72ta9FWwxQQujTBgSP0IZEWr9+vVatWiWJf/ED\nUUilUvRpAwJGaEPi0MIDAJBEhDYkBi08AABJRmhD7C1dulTpdFoSs2sAgOQitCG2Ojs79fDDD0si\nrAEAko/QhljKhrRvfOMbqqioiLYYACehTxsQPEIbYmXJkiVqaWnRZZddpmuuuSbqcgAMgtlvIHiE\nNsTC7t27VVdXJ4lfBkAc0KcNCB6hDUWPFh5A/NCnDQgeoQ1FK7sUumDBAlVXV0ddDgAAkSK0oehs\n3LhRK1as0PTp07Vw4cKoywEAoCgQ2lA0jh07pgceeEBmxlIoAAD9ENpQFB599FG1t7fTwgMAgEEQ\n2hCpX/3qV1q7dq3mz5+vCy64IOpyAASEPm1A8CyJu3tqa2u9vr4+6jIwhJaWFi1ZskRnn322vvKV\nr0RdDgAAkTGzTe5eO9x5zLQhdA8//LA6Ozv53BqQYPRpA4JHaENo1q9frw0bNujmm2/WWWedFXU5\nAAqIPm1A8AhtKLi9e/dq2bJluvTSS7Vo0aKoywEAIJYIbSgYd9fy5cvV29ur22+/PepyAACINUIb\nCmLTpk16++23NXfuXJWX82MGAMCp4rcpAtXY2KitW7dq5syZuvDCC6MuBwCAxCC0IRBdXV1avXq1\nqqurde2110ZdDoCI0acNCB6hDads3bp16urq0rx582RmUZcDoAjQ0gcIHqENo7Z9+3bt2rVLV1xx\nhSZNmhR1OQCKCH3agOCNiboAxE9bW5tWrFihMWPGaN68eQQ2ACdJpVJRlwAkDjNtGDF31+rVqzVx\n4kTNmzcv6nIAACgphDaMyObNm5VOp3X11VeroqIi6nIAACg5hDYMae/evdqyZYtmzJihmTNnRl0O\nAAAli9CGAXV3d+vll19WKpViKRQAgCJAaMNJXn31VR0+fJgWHgBGjT5tQPAIbThu586d2rFjhy6/\n/HJNnjw56nIAxBh92oDg0fIDam9v14oVK+TumjdvHoENwClLp9NRlwAkDjNtJczd9corr2jChAl8\nbg1AoFKplNw96jKARCn60GZmH5O0SFKlpP9z9+9HXFIi7Ny5U83Nzbryyis1bty4qMsBAADDKOjy\nqJk9aWb7zWxbv+NzzGy7me0ys7uGuoa7v+nut0m6QdKsQtZbClpaWvSb3/xGp59+OoENAIAYKfRM\nW52kJZJ+mj1gZmWSnpB0taQmSRvNbJmkMkkP9vv+W9x9v5nNl3S7pKcLXG9i9fT06LXXXlNlZaVm\nzSL7AgAQNwUNbe6+1szO6Xf4Ykm73P1PkmRmz0m63t0flHTdINdZJmmZma2Q9EzhKk6mbdu26fDh\nw5o1axYtPAAAiKkoPtOWktSY87hJ0iWDnWxmsyV9TtI4SSuHOO9WSbdKUk1NTRB1xl5TU5P27Nmj\n888/n5u6AwgVfdqA4BX9RgR3XyNpzQjOWyppqSTV1taW9Jal7u5u7d69WxUVFSyFAogEfdqA4EUR\n2vZKqs55/IHMMQQgnU7LzDRt2rSoSwFQwtLptKZOnRp1GUCiRBHaNko618w+qL6w9kVJN0ZQR6K0\ntrbq0KFDmjp1qsrLi34CFUDC0acNCF6hW348K2m9pI+YWZOZLXD3HkkLJa2S9KakF9z9jULWkWRH\njhxRQ0ODKioqVFNTQ2ADACChCr179EuDHF+pITYVYHjursbGRk2YMIGNFwAAlACmZWJo//796urq\nUnV1NS08AAAoEYS2GHnnnXd08OBBTZkyRWeeeWbU5QAAgBAR2mLg6NGjam5u1nvf+16WQgHEAn3a\ngOAR2opcOp2WRMNgAPFCnzYgeIS2ItXW1qaOjg5VVVVp7NixUZcDAHmhTxsQvIK2/ED+Ojs71dDQ\noPLyctXU1BDYAMRSKpWKugQgcZhpKxLZFh7jx49nKRQAAJyE0FYEDhw4oHfffZcWHgAAYFCEtgh1\ndHSora1NU6ZM0ZQpU6IuBwAAFDFCWwR6enqUTqc1ceJElkIBAMCIENpC1tzcrN7eXsIagESjTxsQ\nPEJbSGjhAaCU0KcNCB4tPwrs3XffVUNDg8rKymjhAaBkZBuDAwgOM20F4u5qampSRUUFS6EASk4q\nlZK7R10GkCiEtgJoaWlRZ2enUqmUxoxhMhMAAJw6QluADh06pNbWVlVWVqqysjLqcgAAQIIQ2gJw\n7Ngx7d27lxYeAACgYAhtp2jfvn06duwYYQ0AABQUoW2UDh48qI6ODp111lmqqKiIuhwAKCr0aQOC\nZ0nc3VNbW+v19fUFfY0jR47otNNOK+hrAACA5DOzTe5eO9x5bG0cJQIbAAyOPm1A8AhtAIDApVKp\nqEsAEofQBgAAEAOENgAAgBggtAEAAMQAoQ0AACAGCG0AgMDRpw0IHqENABC4++67L+oSgMQhtAEA\nAkefNiB4hDYAQODo0wYEj9AGAAAQA4Q2AACAGCC0AQAAxAChDQAAIAbM3aOuIXBmdkDSnqjrKDKV\nklqiLqIEMM7hYazDwTiHg3EOTzGO9dnuPmW4kxIZ2nAyM6t399qo60g6xjk8jHU4GOdwMM7hifNY\nszwKAAAQA4Q2AACAGCC0lY6lURdQIhjn8DDW4WCcw8E4hye2Y81n2gAAAGKAmTYAAIAYILQBAADE\nAKENAAAgBghtJc7MPmZmPzCzF83s9qjrSTIz+5CZ/djMXoy6lqRhbMPDe0Y4zGy2ma3LjPXsqOtJ\nKjP7ZGaMf2Rmr0Zdz3AIbTFmZk+a2X4z29bv+Bwz225mu8zsrqGu4e5vuvttkm6QNKuQ9cZZQGP9\nJ3dfUNhKkyOfMWdsT02eY817xijl+T7ikg5JGi+pKexa4yzPn+d1mZ/n5ZJ+EkW9+SC0xVudpDm5\nB8ysTNITkuZKOk/Sl8zsPDP7uJkt7/fnzMz3zJe0QtLKcMuPlToFMNbIS51GOObhl5Y4dcpjrHnP\nGLU6jXyc17n7XEn/JulbIdcZd3XK/73jRknPhFXgaBHaYszd10pq7Xf4Ykm7MjMP3ZKek3S9u291\n9+v6/dmfuc6yzJvDTeH+DeIjqLHGyOUz5qEXlzD5jjXvGaOT5/tIb+b5NknjQiwz9vL9eTazGknt\n7t4RbqX5I7QlT0pSY87jpsyxAWU+N/G4mf1Q/Ks5X/mO9Rlm9gNJM83s7kIXl1ADjjljWxCDjTXv\nGcEabJw/lxnjpyUtiaSyZBnq/XqBpKdCr2gUyqMuANFy9zWS1kRcRklw979Iui3qOpKIsQ0P7xnh\ncPefS/p51HWUAnf/ZtQ1jBQzbcmzV1J1zuMPZI4heIx1+Bjz8DDW4WCcw5GIcSa0Jc9GSeea2QfN\nrELSFyUti7impGKsw8eYh4exDgfjHI5EjDOhLcbM7FlJ6yV9xMyazGyBu/dIWihplaQ3Jb3g7m9E\nWWcSMNbhY8zDw1iHg3EOR5LHmRvGAwAAxAAzbQAAADFAaAMAAIgBQhsAAEAMENoAAABigNAGAAAQ\nA4Q2AACAGCC0ASh6ZnaPmb1hZq+b2RYzuyRzfI2Zbc8c22JmL2aO32dmd2S+rjOzP2ee/52ZXTbA\n9aeY2W/NbLOZfdLMdptZZQB1Z+ubP8x5s81s+TDnfN3MGsyM+1ACJYp7jwIoapmQdZ2kC9y9KxOm\nKnJOucnd64e5zJ3u/qKZfUbSDyX9Xb/nPy1pq7t/NfOaAVU/4vqG5e6PmlmbpNoAagIQQ8y0ASh2\nVZJa3L1Lkty9xd3To7zWWkkfzj1gZjMk/buk6zOzcRNynjvHzLblPL4jM4tXbmYbzWx25viDZvbt\n4V48M/NWm/m60sx293t+jJntNLMpOY93ZR8DKG2ENgDF7mVJ1Wa2w8z+08w+1e/5/85ZHv3OMNf6\nrKStuQfcfYukxZKed/cZ7t45XEGZW+LcLOn7ZnaVpDmSvjXCv89Q1+2V9F+SbsocukrS7939wKle\nG0D8EdoAFDV3PyTpQkm3Sjog6XkzuznnlJsyYWuGu985yGW+Y2ZbMtdYEFBdb0h6WtJySbe4e3cQ\n15X0pKQvZ76+RdJTAV0XQMzxmTYARc/dj0laI2mNmW2V9I+S6vK4xJ3u/uIoXrpHJ/7jdny/5z8u\n6aCkM/O4ZvYDc2MHetLdG83sbTP7B0kX66+zbgBKHDNtAIqamX3EzM7NOTRD0p6QXv5tSWea2Rlm\nNk59GyKydX1O0vslXSnpP8zsfSO85kWZ/86WVDbIOT9S3zLp/2QCKwAQ2gAUvYmSfmJmfzCz1yWd\nJ+m+nOdzP9P2SpAv7O5HJd0vaYOk1ZLekvo2EUh6SNJX3X2HpCWSHhvhZa8ys43q+7xaq5n9i/pW\nPbpyzlmmvr83S6MAjjN3j7oGAEgkM1sj6Y5sy4/+j3POWyQp5e7/mnlcK+lRd/9kv/NullTr7gsL\nXz2AYsNMGwAUTqukuqGa65rZjyXdKOmJzOO7JP1M0t39zvt65tg7BasWQFFjpg0AACAGmGkDAACI\nAUIbAABADBDaAAAAYoDQBgAAEAOENgAAgBggtAEAAMTA/wOUsM6PjVhMGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13826c74e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(seip['i2_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_servs_irac2'], label=\"SERVS\", s=2.)\n",
    "ax.scatter(seip['i2_f_ap1'][mask], master_catalogue[idx[mask]]['f_ap_swire_irac2'], label=\"SWIRE\", s=2.)\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(\"SEIP flux [μJy]\")\n",
    "ax.set_ylabel(\"SERVS/SWIRE flux [μJy]\")\n",
    "ax.set_title(\"IRAC 2\")\n",
    "ax.legend()\n",
    "ax.axvline(2000, color=\"black\", linestyle=\"--\", linewidth=1.)\n",
    "\n",
    "ax.plot(seip['i1_f_ap2'][mask], seip['i1_f_ap2'][mask], linewidth=.1, color=\"black\", alpha=.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When both SWIRE and SERVS fluxes are provided, we use the SERVS flux below 2000 μJy and the SWIRE flux over.\n",
    "\n",
    "We create a table indicating for each source the origin on the IRAC1 and IRAC2 fluxes that will be saved separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irac_origin = Table()\n",
    "irac_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "684599 sources with SERVS flux\n",
      "617006 sources with SWIRE flux\n",
      "280721 sources with SERVS and SWIRE flux\n",
      "683634 sources for which we use SERVS\n",
      "337250 sources for which we use SWIRE\n"
     ]
    }
   ],
   "source": [
    "# IRAC1 aperture flux and magnitudes\n",
    "has_servs = ~np.isnan(master_catalogue['f_ap_servs_irac1'])\n",
    "has_swire = ~np.isnan(master_catalogue['f_ap_swire_irac1'])\n",
    "has_both = has_servs & has_swire\n",
    "\n",
    "print(\"{} sources with SERVS flux\".format(np.sum(has_servs)))\n",
    "print(\"{} sources with SWIRE flux\".format(np.sum(has_swire)))\n",
    "print(\"{} sources with SERVS and SWIRE flux\".format(np.sum(has_both)))\n",
    "\n",
    "has_servs_above_limit = has_servs.copy()\n",
    "has_servs_above_limit[has_servs] = master_catalogue['f_ap_servs_irac1'][has_servs] > 2000\n",
    "\n",
    "use_swire = (has_swire & ~has_servs) | (has_both & has_servs_above_limit)\n",
    "use_servs = (has_servs & ~(has_both & has_servs_above_limit))\n",
    "\n",
    "print(\"{} sources for which we use SERVS\".format(np.sum(use_servs)))\n",
    "print(\"{} sources for which we use SWIRE\".format(np.sum(use_swire)))\n",
    "\n",
    "f_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac[use_servs] = master_catalogue['f_ap_servs_irac1'][use_servs]\n",
    "f_ap_irac[use_swire] = master_catalogue['f_ap_swire_irac1'][use_swire]\n",
    "\n",
    "ferr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac[use_servs] = master_catalogue['ferr_ap_servs_irac1'][use_servs]\n",
    "ferr_ap_irac[use_swire] = master_catalogue['ferr_ap_swire_irac1'][use_swire]\n",
    "\n",
    "m_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac[use_servs] = master_catalogue['m_ap_servs_irac1'][use_servs]\n",
    "m_ap_irac[use_swire] = master_catalogue['m_ap_swire_irac1'][use_swire]\n",
    "\n",
    "merr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac[use_servs] = master_catalogue['merr_ap_servs_irac1'][use_servs]\n",
    "merr_ap_irac[use_swire] = master_catalogue['merr_ap_swire_irac1'][use_swire]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac, name=\"f_ap_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac, name=\"ferr_ap_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac, name=\"m_ap_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac, name=\"merr_ap_irac_i1\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_ap_servs_irac1', 'f_ap_swire_irac1', 'ferr_ap_servs_irac1',\n",
    "                                     'ferr_ap_swire_irac1', 'm_ap_servs_irac1', 'm_ap_swire_irac1',\n",
    "                                     'merr_ap_servs_irac1', 'merr_ap_swire_irac1'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_servs] = \"SERVS\"\n",
    "origin[use_swire] = \"SWIRE\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC1_app\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "684599 sources with SERVS flux\n",
      "616836 sources with SWIRE flux\n",
      "280717 sources with SERVS and SWIRE flux\n",
      "683570 sources for which we use SERVS\n",
      "337148 sources for which we use SWIRE\n"
     ]
    }
   ],
   "source": [
    "# IRAC1 total flux and magnitudes\n",
    "has_servs = ~np.isnan(master_catalogue['f_servs_irac1'])\n",
    "has_swire = ~np.isnan(master_catalogue['f_swire_irac1'])\n",
    "has_both = has_servs & has_swire\n",
    "\n",
    "print(\"{} sources with SERVS flux\".format(np.sum(has_servs)))\n",
    "print(\"{} sources with SWIRE flux\".format(np.sum(has_swire)))\n",
    "print(\"{} sources with SERVS and SWIRE flux\".format(np.sum(has_both)))\n",
    "\n",
    "has_servs_above_limit = has_servs.copy()\n",
    "has_servs_above_limit[has_servs] = master_catalogue['f_servs_irac1'][has_servs] > 2000\n",
    "\n",
    "use_swire = (has_swire & ~has_servs) | (has_both & has_servs_above_limit)\n",
    "use_servs = (has_servs & ~(has_both & has_servs_above_limit))\n",
    "\n",
    "print(\"{} sources for which we use SERVS\".format(np.sum(use_servs)))\n",
    "print(\"{} sources for which we use SWIRE\".format(np.sum(use_swire)))\n",
    "\n",
    "f_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac[use_servs] = master_catalogue['f_servs_irac1'][use_servs]\n",
    "f_ap_irac[use_swire] = master_catalogue['f_swire_irac1'][use_swire]\n",
    "\n",
    "ferr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac[use_servs] = master_catalogue['ferr_servs_irac1'][use_servs]\n",
    "ferr_ap_irac[use_swire] = master_catalogue['ferr_swire_irac1'][use_swire]\n",
    "\n",
    "flag_irac = np.full(len(master_catalogue), False, dtype=bool)\n",
    "flag_irac[use_servs] = master_catalogue['flag_servs_irac1'][use_servs]\n",
    "flag_irac[use_swire] = master_catalogue['flag_swire_irac1'][use_swire]\n",
    "\n",
    "m_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac[use_servs] = master_catalogue['m_servs_irac1'][use_servs]\n",
    "m_ap_irac[use_swire] = master_catalogue['m_swire_irac1'][use_swire]\n",
    "\n",
    "merr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac[use_servs] = master_catalogue['merr_servs_irac1'][use_servs]\n",
    "merr_ap_irac[use_swire] = master_catalogue['merr_swire_irac1'][use_swire]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac, name=\"f_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac, name=\"ferr_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac, name=\"m_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac, name=\"merr_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=flag_irac, name=\"flag_irac_i1\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_servs_irac1', 'f_swire_irac1', 'ferr_servs_irac1',\n",
    "                                 'ferr_swire_irac1', 'm_servs_irac1', 'flag_servs_irac1', 'm_swire_irac1',\n",
    "                                 'merr_servs_irac1', 'merr_swire_irac1', 'flag_swire_irac1'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_servs] = \"SERVS\"\n",
    "origin[use_swire] = \"SWIRE\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC1_total\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "732936 sources with SERVS flux\n",
      "441887 sources with SWIRE flux\n",
      "197895 sources with SERVS and SWIRE flux\n",
      "732316 sources for which we use SERVS\n",
      "244612 sources for which we use SWIRE\n"
     ]
    }
   ],
   "source": [
    "# IRAC2 aperture flux and magnitudes\n",
    "has_servs = ~np.isnan(master_catalogue['f_ap_servs_irac2'])\n",
    "has_swire = ~np.isnan(master_catalogue['f_ap_swire_irac2'])\n",
    "has_both = has_servs & has_swire\n",
    "\n",
    "print(\"{} sources with SERVS flux\".format(np.sum(has_servs)))\n",
    "print(\"{} sources with SWIRE flux\".format(np.sum(has_swire)))\n",
    "print(\"{} sources with SERVS and SWIRE flux\".format(np.sum(has_both)))\n",
    "\n",
    "has_servs_above_limit = has_servs.copy()\n",
    "has_servs_above_limit[has_servs] = master_catalogue['f_ap_servs_irac2'][has_servs] > 2000\n",
    "\n",
    "use_swire = (has_swire & ~has_servs) | (has_both & has_servs_above_limit)\n",
    "use_servs = (has_servs & ~(has_both & has_servs_above_limit))\n",
    "\n",
    "print(\"{} sources for which we use SERVS\".format(np.sum(use_servs)))\n",
    "print(\"{} sources for which we use SWIRE\".format(np.sum(use_swire)))\n",
    "\n",
    "f_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_irac[use_servs] = master_catalogue['f_ap_servs_irac2'][use_servs]\n",
    "f_ap_irac[use_swire] = master_catalogue['f_ap_swire_irac2'][use_swire]\n",
    "\n",
    "ferr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_irac[use_servs] = master_catalogue['ferr_ap_servs_irac2'][use_servs]\n",
    "ferr_ap_irac[use_swire] = master_catalogue['ferr_ap_swire_irac2'][use_swire]\n",
    "\n",
    "m_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_irac[use_servs] = master_catalogue['m_ap_servs_irac2'][use_servs]\n",
    "m_ap_irac[use_swire] = master_catalogue['m_ap_swire_irac2'][use_swire]\n",
    "\n",
    "merr_ap_irac = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_irac[use_servs] = master_catalogue['merr_ap_servs_irac2'][use_servs]\n",
    "merr_ap_irac[use_swire] = master_catalogue['merr_ap_swire_irac2'][use_swire]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_irac, name=\"f_ap_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_irac, name=\"ferr_ap_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_irac, name=\"m_ap_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_irac, name=\"merr_ap_irac_i2\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_ap_servs_irac2', 'f_ap_swire_irac2', 'ferr_ap_servs_irac2',\n",
    "                                 'ferr_ap_swire_irac2', 'm_ap_servs_irac2', 'm_ap_swire_irac2',\n",
    "                                 'merr_ap_servs_irac2', 'merr_ap_swire_irac2'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_servs] = \"SERVS\"\n",
    "origin[use_swire] = \"SWIRE\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC2_app\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "732936 sources with SERVS flux\n",
      "441831 sources with SWIRE flux\n",
      "197895 sources with SERVS and SWIRE flux\n",
      "732273 sources for which we use SERVS\n",
      "244599 sources for which we use SWIRE\n"
     ]
    }
   ],
   "source": [
    "# IRAC2 total flux and magnitudes\n",
    "has_servs = ~np.isnan(master_catalogue['f_servs_irac2'])\n",
    "has_swire = ~np.isnan(master_catalogue['f_swire_irac2'])\n",
    "has_both = has_servs & has_swire\n",
    "\n",
    "print(\"{} sources with SERVS flux\".format(np.sum(has_servs)))\n",
    "print(\"{} sources with SWIRE flux\".format(np.sum(has_swire)))\n",
    "print(\"{} sources with SERVS and SWIRE flux\".format(np.sum(has_both)))\n",
    "\n",
    "has_servs_above_limit = has_servs.copy()\n",
    "has_servs_above_limit[has_servs] = master_catalogue['f_servs_irac2'][has_servs] > 2000\n",
    "\n",
    "use_swire = (has_swire & ~has_servs) | (has_both & has_servs_above_limit)\n",
    "use_servs = (has_servs & ~(has_both & has_servs_above_limit))\n",
    "\n",
    "print(\"{} sources for which we use SERVS\".format(np.sum(use_servs)))\n",
    "print(\"{} sources for which we use SWIRE\".format(np.sum(use_swire)))\n",
    "\n",
    "f_irac = np.full(len(master_catalogue), np.nan)\n",
    "f_irac[use_servs] = master_catalogue['f_servs_irac2'][use_servs]\n",
    "f_irac[use_swire] = master_catalogue['f_swire_irac2'][use_swire]\n",
    "\n",
    "ferr_irac = np.full(len(master_catalogue), np.nan)\n",
    "ferr_irac[use_servs] = master_catalogue['ferr_servs_irac2'][use_servs]\n",
    "ferr_irac[use_swire] = master_catalogue['ferr_swire_irac2'][use_swire]\n",
    "\n",
    "flag_irac = np.full(len(master_catalogue), False, dtype=bool)\n",
    "flag_irac[use_servs] = master_catalogue['flag_servs_irac2'][use_servs]\n",
    "flag_irac[use_swire] = master_catalogue['flag_swire_irac2'][use_swire]\n",
    "\n",
    "m_irac = np.full(len(master_catalogue), np.nan)\n",
    "m_irac[use_servs] = master_catalogue['m_servs_irac2'][use_servs]\n",
    "m_irac[use_swire] = master_catalogue['m_swire_irac2'][use_swire]\n",
    "\n",
    "merr_irac = np.full(len(master_catalogue), np.nan)\n",
    "merr_irac[use_servs] = master_catalogue['merr_servs_irac2'][use_servs]\n",
    "merr_irac[use_swire] = master_catalogue['merr_swire_irac2'][use_swire]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_irac, name=\"f_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=ferr_irac, name=\"ferr_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=m_irac, name=\"m_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=merr_irac, name=\"merr_irac_i2\"))\n",
    "master_catalogue.add_column(Column(data=flag_irac, name=\"flag_irac_i2\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_servs_irac2', 'f_swire_irac2', 'ferr_servs_irac2',\n",
    "                                 'ferr_swire_irac2', 'm_servs_irac2', 'flag_servs_irac2', 'm_swire_irac2',\n",
    "                                 'merr_servs_irac2', 'merr_swire_irac2', 'flag_swire_irac2'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_servs] = \"SERVS\"\n",
    "origin[use_swire] = \"SWIRE\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC2_total\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irac_origin.write(\"{}/lockman-swire_irac_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UHS WFCAM J vs DXS WFCAM J\n",
    "\n",
    "These both come from wfcam. If UHS is to be included (it currently is not) we will have to decide between the two. DXS is deeper so it would probably be a case of preferentially taking DXS J fluxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wfcam_origin = Table()\n",
    "wfcam_origin.add_column(master_catalogue['help_id'])\n",
    "\n",
    "#Total J flux\n",
    "use_dxs = ~np.isnan(master_catalogue['f_ukidss_j'])\n",
    "use_uhs = np.isnan(master_catalogue['f_ukidss_j']) & ~np.isnan(master_catalogue['f_uhs_j'])\n",
    "\n",
    "f_wfcam = np.full(len(master_catalogue), np.nan)\n",
    "f_wfcam[use_uhs] = master_catalogue['f_uhs_j'][use_uhs]\n",
    "f_wfcam[use_dxs] = master_catalogue['f_ukidss_j'][use_dxs]\n",
    "\n",
    "ferr_wfcam = np.full(len(master_catalogue), np.nan)\n",
    "ferr_wfcam[use_uhs] = master_catalogue['ferr_uhs_j'][use_uhs]\n",
    "ferr_wfcam[use_dxs] = master_catalogue['ferr_ukidss_j'][use_dxs]\n",
    "\n",
    "m_wfcam = np.full(len(master_catalogue), np.nan)\n",
    "m_wfcam[use_uhs] = master_catalogue['m_uhs_j'][use_uhs]\n",
    "m_wfcam[use_dxs] = master_catalogue['m_ukidss_j'][use_dxs]\n",
    "\n",
    "merr_wfcam = np.full(len(master_catalogue), np.nan)\n",
    "merr_wfcam[use_uhs] = master_catalogue['merr_uhs_j'][use_uhs]\n",
    "merr_wfcam[use_dxs] = master_catalogue['merr_ukidss_j'][use_dxs]\n",
    "\n",
    "flag_wfcam = np.full(len(master_catalogue), np.nan)\n",
    "flag_wfcam[use_uhs] = master_catalogue['flag_uhs_j'][use_uhs]\n",
    "flag_wfcam[use_dxs] = master_catalogue['flag_ukidss_j'][use_dxs]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_wfcam, name=\"f_wfcam_j\"))\n",
    "master_catalogue.add_column(Column(data=ferr_wfcam, name=\"ferr_wfcam_j\"))\n",
    "master_catalogue.add_column(Column(data=m_wfcam, name=\"m_wfcam_j\"))\n",
    "master_catalogue.add_column(Column(data=merr_wfcam, name=\"merr_wfcam_j\"))\n",
    "master_catalogue.add_column(Column(data=flag_wfcam, name=\"flag_wfcam_j\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_uhs_j', 'f_ukidss_j', \n",
    "                                 'ferr_uhs_j', 'ferr_ukidss_j', \n",
    "                                 'm_uhs_j', 'm_ukidss_j',\n",
    "                                 'merr_uhs_j', 'merr_ukidss_j', \n",
    "                                 'flag_uhs_j',  'flag_ukidss_j'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_uhs] = \"UHS\"\n",
    "origin[use_dxs] = \"DXS\"\n",
    "wfcam_origin.add_column(Column(data=origin, name=\"wfcam_j\"))\n",
    "\n",
    "#Aperture J flux\n",
    "use_ap_dxs = ~np.isnan(master_catalogue['f_ap_ukidss_j'])\n",
    "use_ap_uhs = np.isnan(master_catalogue['f_ap_ukidss_j']) & ~np.isnan(master_catalogue['f_ap_uhs_j'])\n",
    "\n",
    "f_ap_wfcam = np.full(len(master_catalogue), np.nan)\n",
    "f_ap_wfcam[use_ap_uhs] = master_catalogue['f_ap_uhs_j'][use_ap_uhs]\n",
    "f_ap_wfcam[use_ap_dxs] = master_catalogue['f_ap_ukidss_j'][use_ap_dxs]\n",
    "\n",
    "ferr_ap_wfcam = np.full(len(master_catalogue), np.nan)\n",
    "ferr_ap_wfcam[use_ap_uhs] = master_catalogue['ferr_ap_uhs_j'][use_ap_uhs]\n",
    "ferr_ap_wfcam[use_ap_dxs] = master_catalogue['ferr_ap_ukidss_j'][use_ap_dxs]\n",
    "\n",
    "m_ap_wfcam = np.full(len(master_catalogue), np.nan)\n",
    "m_ap_wfcam[use_ap_uhs] = master_catalogue['m_ap_uhs_j'][use_ap_uhs]\n",
    "m_ap_wfcam[use_ap_dxs] = master_catalogue['m_ap_ukidss_j'][use_ap_dxs]\n",
    "\n",
    "merr_ap_wfcam = np.full(len(master_catalogue), np.nan)\n",
    "merr_ap_wfcam[use_ap_uhs] = master_catalogue['merr_ap_uhs_j'][use_ap_uhs]\n",
    "merr_ap_wfcam[use_ap_dxs] = master_catalogue['merr_ap_ukidss_j'][use_ap_dxs]\n",
    "\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_ap_wfcam, name=\"f_ap_wfcam_j\"))\n",
    "master_catalogue.add_column(Column(data=ferr_ap_wfcam, name=\"ferr_ap_wfcam_j\"))\n",
    "master_catalogue.add_column(Column(data=m_ap_wfcam, name=\"m_ap_wfcam_j\"))\n",
    "master_catalogue.add_column(Column(data=merr_ap_wfcam, name=\"merr_ap_wfcam_j\"))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.remove_columns(['f_ap_uhs_j', 'f_ap_ukidss_j', \n",
    "                                 'ferr_ap_uhs_j', 'ferr_ap_ukidss_j', \n",
    "                                 'm_ap_uhs_j', 'm_ap_ukidss_j',\n",
    "                                 'merr_ap_uhs_j', 'merr_ap_ukidss_j'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_ap_uhs] = \"UHS\"\n",
    "origin[use_ap_dxs] = \"DXS\"\n",
    "wfcam_origin.add_column(Column(data=origin, name=\"wfcam_ap_j\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wfcam_origin.write(\"{}/lockman-swire_wfcam_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    if '_wfcam_' in col:\n",
    "        master_catalogue.rename_column(col, col.replace('_wfcam_', '_ukidss_'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CFHT Megacam SpARCS vs RCSLenS\n",
    "SpARCS apears to be significantly deeper and contains both total and aperture magnitudes so we take SpARCS over RCSLenS if both are available\n",
    "\n",
    "| Survey  | Bands     |\n",
    "|---------|-----------|\n",
    "| SpARCS  | ugrz      |\n",
    "| RCSLenS | grizy     |\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "megacam_origin = Table()\n",
    "megacam_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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(6, 0) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "megacam_stats = Table()\n",
    "megacam_stats.add_column(Column(data=['u','g','r','i','z','y'], name=\"Band\"))\n",
    "for col in [ \"SpARCS\", \"RCSLenS\"]:\n",
    "    megacam_stats.add_column(Column(data=np.full(6, 0), name=\"{}\".format(col)))\n",
    "    megacam_stats.add_column(Column(data=np.full(6, 0), name=\"use {}\".format(col)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "megacam_bands = ['u','g','r','i','z','y'] # Lowercase naming convention (k is Ks)\n",
    "for band in ['g', 'r', 'z']: #the bands in both catalogues\n",
    "\n",
    "    # Megacam total flux \n",
    "    has_sparcs = ~np.isnan(master_catalogue['f_cfht_megacam_' + band])\n",
    "    has_rcs = ~np.isnan(master_catalogue['f_rcs_' + band])   \n",
    "\n",
    "    use_sparcs = has_sparcs \n",
    "    use_rcs = has_rcs & ~has_sparcs\n",
    "\n",
    "    f_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    f_megacam[use_sparcs] = master_catalogue['f_cfht_megacam_' + band][use_sparcs]\n",
    "    f_megacam[use_rcs] = master_catalogue['f_rcs_' + band][use_rcs]\n",
    "\n",
    "    ferr_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_megacam[use_sparcs] = master_catalogue['ferr_cfht_megacam_' + band][use_sparcs]\n",
    "    ferr_megacam[use_rcs] = master_catalogue['ferr_rcs_' + band][use_rcs]\n",
    "\n",
    "    m_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    m_megacam[use_sparcs] = master_catalogue['m_cfht_megacam_' + band][use_sparcs]\n",
    "    m_megacam[use_rcs] = master_catalogue['m_rcs_' + band][use_rcs]\n",
    "    \n",
    "    merr_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    merr_megacam[use_sparcs] = master_catalogue['merr_cfht_megacam_' + band][use_sparcs]\n",
    "    merr_megacam[use_rcs] = master_catalogue['merr_rcs_' + band][use_rcs]\n",
    "    \n",
    "    flag_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    flag_megacam[use_sparcs] = master_catalogue['flag_cfht_megacam_' + band][use_sparcs]\n",
    "    flag_megacam[use_rcs] = master_catalogue['flag_rcs_' + band][use_rcs]\n",
    "\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_megacam, name=\"f_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_megacam, name=\"ferr_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_megacam, name=\"m_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_megacam, name=\"merr_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=flag_megacam, name=\"flag_megacam_\" + band))\n",
    "\n",
    "    old_columns = ['f_cfht_megacam_' + band,\n",
    "                               'ferr_cfht_megacam_' + band,\n",
    "                               'm_cfht_megacam_' + band, \n",
    "                               'merr_cfht_megacam_' + band,\n",
    "                               'flag_cfht_megacam_' + band,\n",
    "                               'f_rcs_' + band,\n",
    "                               'ferr_rcs_' + band,\n",
    "                               'm_rcs_' + band, \n",
    "                               'merr_rcs_' + band,\n",
    "                               'flag_rcs_' + band]\n",
    "    \n",
    "    master_catalogue.remove_columns(old_columns)\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_sparcs] = \"SpARCS\"\n",
    "    origin[use_rcs] = \"RCSLenS\"\n",
    "    \n",
    "    megacam_origin.add_column(Column(data=origin, name= 'f_megacam_' + band ))\n",
    "    \n",
    "\n",
    "    megacam_stats['RCSLenS'][megacam_stats['Band'] == band] = np.sum(has_rcs)\n",
    "    megacam_stats['SpARCS'][megacam_stats['Band'] == band] = np.sum(has_sparcs)\n",
    "    megacam_stats['use RCSLenS'][megacam_stats['Band'] == band] = np.sum(use_rcs)\n",
    "    megacam_stats['use SpARCS'][megacam_stats['Band'] == band] = np.sum(use_sparcs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#No aperture fluxes in rcs so just rename all the sparcs columns\n",
    "for col in master_catalogue.colnames:\n",
    "    if '_cfht_megacam_' in col:\n",
    "        master_catalogue[col].name = col.replace('_cfht_megacam_','_megacam_')\n",
    "        \n",
    "    if ('_rcs_' in col) and ('_ap_rcs_' not in col):\n",
    "        master_catalogue[col].name = col.replace('_rcs_','_megacam_')\n",
    "        \n",
    "    if '_ap_rcs_' in col:\n",
    "        master_catalogue.remove_columns(col)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=6&gt;\n",
       "<table id=\"table139721766972216-245656\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>SpARCS</th><th>use SpARCS</th><th>RCSLenS</th><th>use RCSLenS</th></tr></thead>\n",
       "<tr><td>0</td><td>u</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>g</td><td>2473322.0</td><td>2473322.0</td><td>1593724.0</td><td>681809.0</td></tr>\n",
       "<tr><td>2</td><td>r</td><td>2511363.0</td><td>2511363.0</td><td>2038335.0</td><td>928656.0</td></tr>\n",
       "<tr><td>3</td><td>i</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>4</td><td>z</td><td>2219251.0</td><td>2219251.0</td><td>1249636.0</td><td>547415.0</td></tr>\n",
       "<tr><td>5</td><td>y</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",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table139721766972216-245656').dataTable()\");\n",
       "    $('#table139721766972216-245656').dataTable({\n",
       "        \"order\": [],\n",
       "        \"iDisplayLength\": 50,\n",
       "        \"aLengthMenu\": [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        \"pagingType\": \"full_numbers\"\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "megacam_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "megacam_origin.write(\"{}/lockman-swire_megacam_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII.a Wavelenght domain coverage\n",
    "\n",
    "We add a binary `flag_optnir_obs` indicating that a source was observed in a given wavelenght domain:\n",
    "\n",
    "- 1 for observation in optical;\n",
    "- 2 for observation in near-infrared;\n",
    "- 4 for observation in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source observed both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: The observation flag is based on the creation of multi-order coverage maps from the catalogues, this may not be accurate, especially on the edges of the coverage.*\n",
    "\n",
    "*Note 2: Being on the observation coverage does not mean having fluxes in that wavelength domain. For sources observed in one domain but having no flux in it, one must take into consideration de different depths in the catalogue we are using.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wfc_moc = MOC(filename=\"../../dmu0/dmu0_INTWFC/data/lh_intwfc_v2.1_HELP_coverage_MOC.fits\")\n",
    "sparcs_moc = MOC(filename=\"../../dmu0/dmu0_SpARCS/data/SpARCS_HELP_Lockman-SWIRE_MOC.fits\")\n",
    "rcs_moc = MOC(filename=\"../../dmu0/dmu0_RCSLenS/data/RCSLenS_Lockman-SWIRE_MOC.fits\")\n",
    "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_Lockman-SWIRE_MOC.fits\")\n",
    "dxs_moc = MOC(filename=\"../../dmu0/dmu0_UKIDSS-DXS_DR10plus/data/UKIDSS-DR10plus_Lockman-SWIRE_MOC.fits\")\n",
    "servs_moc = MOC(filename=\"../../dmu0/dmu0_DataFusion-Spitzer/data/DF-SERVS_Lockman-SWIRE_MOC.fits\")\n",
    "swire_moc = MOC(filename=\"../../dmu0/dmu0_DataFusion-Spitzer/data/DF-SWIRE_Lockman-SWIRE_MOC.fits\")\n",
    "uhs_moc = MOC(filename=\"../../dmu0/dmu0_UHS/data/UHS-DR1_Lockman-SWIRE_MOC.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    wfc_moc + sparcs_moc + rcs_moc + ps1_moc) \n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    dxs_moc  + uhs_moc\n",
    ")\n",
    "\n",
    "was_observed_mir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    servs_moc + swire_moc\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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 Wavelenght domain detection\n",
    "\n",
    "We add a binary `flag_optnir_det` indicating that a source was detected in a given wavelenght 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": 54,
   "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",
    "    1 * ~np.isnan(master_catalogue['f_wfc_u']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wfc_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wfc_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wfc_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wfc_z']) +\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_megacam_u']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_y']) \n",
    ")\n",
    "\n",
    "nb_nir_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_k'])\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": 55,
   "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 catalogue. This can be used to easily get additional information from them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['wfc_id', 'rcs_id', 'ps1_id', 'sparcs_intid', 'dxs_id', 'servs_intid', 'swire_intid', 'uhs_id', 'help_id', 'specz_id']\n"
     ]
    }
   ],
   "source": [
    "id_names = []\n",
    "for col in master_catalogue.colnames:\n",
    "    if '_id' in col:\n",
    "        id_names += [col]\n",
    "    if '_intid' in col:\n",
    "        id_names += [col]\n",
    "        \n",
    "print(id_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_lockman-swire{}.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": 58,
   "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": 59,
   "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",
    "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)] #No flag_{}   \n",
    "\n",
    "columns += [ 'm_megacam_i', 'ferr_megacam_i', 'merr_megacam_i', 'flag_megacam_i', 'f_megacam_i',\n",
    "             'm_megacam_y', 'ferr_megacam_y', 'merr_megacam_y', 'flag_megacam_y', 'f_megacam_y']\n",
    "    \n",
    "columns += [\"stellarity\", \"stellarity_origin\", \"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \"flag_optnir_obs\", \"flag_optnir_det\", \n",
    "            \"zspec\", \"zspec_qual\", \"zspec_association_flag\", \"ebv\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing columns: {'flag_megacam_u', 'flag_gpc1_z', 'flag_irac_i3', 'flag_irac_i1', 'flag_gpc1_y', 'flag_wfc_g', 'flag_ukidss_k', 'flag_megacam_r', 'flag_wfc_u', 'flag_wfc_i', 'flag_megacam_z', 'flag_megacam_g', 'flag_gpc1_i', 'flag_irac_i2', 'flag_wfc_z', 'flag_gpc1_g', 'flag_gpc1_r', 'flag_ukidss_j', 'flag_wfc_r', 'flag_irac_i4'}\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": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[columns].write(\"{}/master_catalogue_lockman-swire{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  }
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