{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CDFS SWIRE master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce HELP master catalogue on CDFS SWIRE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "708e28f (Tue May 8 18:05:21 2018 +0100)\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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/seaborn/apionly.py:6: UserWarning: As seaborn no longer sets a default style on import, the seaborn.apionly module is deprecated. It will be removed in a future version.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "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, specz_merge, nb_merge_dist_plot\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": [
    "atlas = Table.read(\"{}/ATLAS.fits\".format(TMP_DIR))\n",
    "combo = Table.read(\"{}/COMBO.fits\".format(TMP_DIR))\n",
    "#fireworks = Table.read(\"{}/Fireworks.fits\".format(TMP_DIR))\n",
    "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))\n",
    "servs = Table.read(\"{}/SERVS.fits\".format(TMP_DIR))\n",
    "swire= Table.read(\"{}/SWIRE.fits\".format(TMP_DIR))\n",
    "video= Table.read(\"{}/VISTA-VIDEO.fits\".format(TMP_DIR))\n",
    "vhs= Table.read(\"{}/VISTA-VHS.fits\".format(TMP_DIR))\n",
    "des= Table.read(\"{}/DES.fits\".format(TMP_DIR))\n",
    "candels= Table.read(\"{}/CANDELS.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: PS1, COMBO, ATLAS, VIDEO, VHS, SERVS, SWIRE. Fireworks is no longer included.\n",
    "\n",
    "At every step, we look at the distribution of the distances separating the sources from one catalogue to the other (within a maximum radius) to determine the best cross-matching radius."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PanSTARRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = ps1\n",
    "master_catalogue['ps1_ra'].name = 'ra'\n",
    "master_catalogue['ps1_dec'].name = 'dec'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Fireworks\n",
    "\n",
    "We are no longer including Fireworks under Mattia's advice. I leave the code in the notebook commented out in case the user wishes to include it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#nb_merge_dist_plot(\n",
    "#    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "#    SkyCoord(fireworks['fireworks_ra'], fireworks['fireworks_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, fireworks, \"fireworks_ra\", \"fireworks_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add COMBO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(combo['combo_ra'], combo['combo_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, combo, \"combo_ra\", \"combo_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add ATLAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(atlas['atlas_ra'], atlas['atlas_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, atlas, \"atlas_ra\", \"atlas_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add VIDEO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(video['video_ra'], video['video_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, video, \"video_ra\", \"video_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add VHS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(vhs['vhs_ra'], vhs['vhs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, vhs, \"vhs_ra\", \"vhs_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add SERVS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(servs['servs_ra'], servs['servs_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, servs, \"servs_ra\", \"servs_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add SWIRE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(swire['swire_ra'], swire['swire_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, swire, \"swire_ra\", \"swire_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add DES\n",
    "\n",
    "DES and CANDELS are added at the end because they were not in teh original masterlist. By adding them at the end we ensure that the original HELP ids are maintained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(des['des_ra'], des['des_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, des, \"des_ra\", \"des_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add CANDELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(candels['candels_ra'], candels['candels_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, candels, \"candels_ra\", \"candels_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 24,
   "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": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table139734984761072-433139\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>ps1_id</th><th>ra</th><th>dec</th><th>m_ap_ps1_g</th><th>merr_ap_ps1_g</th><th>m_ps1_g</th><th>merr_ps1_g</th><th>m_ap_ps1_r</th><th>merr_ap_ps1_r</th><th>m_ps1_r</th><th>merr_ps1_r</th><th>m_ap_ps1_i</th><th>merr_ap_ps1_i</th><th>m_ps1_i</th><th>merr_ps1_i</th><th>m_ap_ps1_z</th><th>merr_ap_ps1_z</th><th>m_ps1_z</th><th>merr_ps1_z</th><th>m_ap_ps1_y</th><th>merr_ap_ps1_y</th><th>m_ps1_y</th><th>merr_ps1_y</th><th>f_ap_ps1_g</th><th>ferr_ap_ps1_g</th><th>f_ps1_g</th><th>ferr_ps1_g</th><th>flag_ps1_g</th><th>f_ap_ps1_r</th><th>ferr_ap_ps1_r</th><th>f_ps1_r</th><th>ferr_ps1_r</th><th>flag_ps1_r</th><th>f_ap_ps1_i</th><th>ferr_ap_ps1_i</th><th>f_ps1_i</th><th>ferr_ps1_i</th><th>flag_ps1_i</th><th>f_ap_ps1_z</th><th>ferr_ap_ps1_z</th><th>f_ps1_z</th><th>ferr_ps1_z</th><th>flag_ps1_z</th><th>f_ap_ps1_y</th><th>ferr_ap_ps1_y</th><th>f_ps1_y</th><th>ferr_ps1_y</th><th>flag_ps1_y</th><th>ps1_flag_cleaned</th><th>ps1_flag_gaia</th><th>flag_merged</th><th>combo_id</th><th>combo_stellarity</th><th>m_combo_r</th><th>merr_combo_r</th><th>f_ap_combo_420</th><th>ferr_ap_combo_420</th><th>f_ap_combo_462</th><th>ferr_ap_combo_462</th><th>f_ap_combo_485</th><th>ferr_ap_combo_485</th><th>f_ap_combo_518</th><th>ferr_ap_combo_518</th><th>f_ap_combo_571</th><th>ferr_ap_combo_571</th><th>f_ap_combo_604</th><th>ferr_ap_combo_604</th><th>f_ap_combo_646</th><th>ferr_ap_combo_646</th><th>f_ap_combo_696</th><th>ferr_ap_combo_696</th><th>f_ap_combo_753</th><th>ferr_ap_combo_753</th><th>f_ap_combo_815</th><th>ferr_ap_combo_815</th><th>f_ap_combo_856</th><th>ferr_ap_combo_856</th><th>f_ap_combo_914</th><th>ferr_ap_combo_914</th><th>f_ap_combo_u</th><th>ferr_ap_combo_u</th><th>f_ap_combo_b</th><th>ferr_ap_combo_b</th><th>f_ap_combo_v</th><th>ferr_ap_combo_v</th><th>f_ap_combo_r</th><th>ferr_ap_combo_r</th><th>f_ap_combo_i</th><th>ferr_ap_combo_i</th><th>m_ap_combo_420</th><th>merr_ap_combo_420</th><th>m_combo_420</th><th>merr_combo_420</th><th>f_combo_420</th><th>ferr_combo_420</th><th>flag_combo_420</th><th>m_ap_combo_462</th><th>merr_ap_combo_462</th><th>m_combo_462</th><th>merr_combo_462</th><th>f_combo_462</th><th>ferr_combo_462</th><th>flag_combo_462</th><th>m_ap_combo_485</th><th>merr_ap_combo_485</th><th>m_combo_485</th><th>merr_combo_485</th><th>f_combo_485</th><th>ferr_combo_485</th><th>flag_combo_485</th><th>m_ap_combo_518</th><th>merr_ap_combo_518</th><th>m_combo_518</th><th>merr_combo_518</th><th>f_combo_518</th><th>ferr_combo_518</th><th>flag_combo_518</th><th>m_ap_combo_571</th><th>merr_ap_combo_571</th><th>m_combo_571</th><th>merr_combo_571</th><th>f_combo_571</th><th>ferr_combo_571</th><th>flag_combo_571</th><th>m_ap_combo_604</th><th>merr_ap_combo_604</th><th>m_combo_604</th><th>merr_combo_604</th><th>f_combo_604</th><th>ferr_combo_604</th><th>flag_combo_604</th><th>m_ap_combo_646</th><th>merr_ap_combo_646</th><th>m_combo_646</th><th>merr_combo_646</th><th>f_combo_646</th><th>ferr_combo_646</th><th>flag_combo_646</th><th>m_ap_combo_696</th><th>merr_ap_combo_696</th><th>m_combo_696</th><th>merr_combo_696</th><th>f_combo_696</th><th>ferr_combo_696</th><th>flag_combo_696</th><th>m_ap_combo_753</th><th>merr_ap_combo_753</th><th>m_combo_753</th><th>merr_combo_753</th><th>f_combo_753</th><th>ferr_combo_753</th><th>flag_combo_753</th><th>m_ap_combo_815</th><th>merr_ap_combo_815</th><th>m_combo_815</th><th>merr_combo_815</th><th>f_combo_815</th><th>ferr_combo_815</th><th>flag_combo_815</th><th>m_ap_combo_856</th><th>merr_ap_combo_856</th><th>m_combo_856</th><th>merr_combo_856</th><th>f_combo_856</th><th>ferr_combo_856</th><th>flag_combo_856</th><th>m_ap_combo_914</th><th>merr_ap_combo_914</th><th>m_combo_914</th><th>merr_combo_914</th><th>f_combo_914</th><th>ferr_combo_914</th><th>flag_combo_914</th><th>m_ap_combo_u</th><th>merr_ap_combo_u</th><th>m_combo_u</th><th>merr_combo_u</th><th>f_combo_u</th><th>ferr_combo_u</th><th>flag_combo_u</th><th>m_ap_combo_b</th><th>merr_ap_combo_b</th><th>m_combo_b</th><th>merr_combo_b</th><th>f_combo_b</th><th>ferr_combo_b</th><th>flag_combo_b</th><th>m_ap_combo_v</th><th>merr_ap_combo_v</th><th>m_combo_v</th><th>merr_combo_v</th><th>f_combo_v</th><th>ferr_combo_v</th><th>flag_combo_v</th><th>m_ap_combo_r</th><th>merr_ap_combo_r</th><th>flag_combo_r</th><th>m_ap_combo_i</th><th>merr_ap_combo_i</th><th>m_combo_i</th><th>merr_combo_i</th><th>f_combo_i</th><th>ferr_combo_i</th><th>flag_combo_i</th><th>f_combo_r</th><th>ferr_combo_r</th><th>combo_flag_cleaned</th><th>combo_flag_gaia</th><th>atlas_id</th><th>atlas_stellarity</th><th>m_ap_atlas_u</th><th>merr_ap_atlas_u</th><th>m_atlas_u</th><th>merr_atlas_u</th><th>m_ap_atlas_ul</th><th>merr_ap_atlas_ul</th><th>m_atlas_ul</th><th>merr_atlas_ul</th><th>m_ap_atlas_g</th><th>merr_ap_atlas_g</th><th>m_atlas_g</th><th>merr_atlas_g</th><th>m_ap_atlas_r</th><th>merr_ap_atlas_r</th><th>m_atlas_r</th><th>merr_atlas_r</th><th>m_ap_atlas_i</th><th>merr_ap_atlas_i</th><th>m_atlas_i</th><th>merr_atlas_i</th><th>m_ap_atlas_z</th><th>merr_ap_atlas_z</th><th>m_atlas_z</th><th>merr_atlas_z</th><th>f_ap_atlas_u</th><th>ferr_ap_atlas_u</th><th>f_atlas_u</th><th>ferr_atlas_u</th><th>flag_atlas_u</th><th>f_ap_atlas_ul</th><th>ferr_ap_atlas_ul</th><th>f_atlas_ul</th><th>ferr_atlas_ul</th><th>flag_atlas_ul</th><th>f_ap_atlas_g</th><th>ferr_ap_atlas_g</th><th>f_atlas_g</th><th>ferr_atlas_g</th><th>flag_atlas_g</th><th>f_ap_atlas_r</th><th>ferr_ap_atlas_r</th><th>f_atlas_r</th><th>ferr_atlas_r</th><th>flag_atlas_r</th><th>f_ap_atlas_i</th><th>ferr_ap_atlas_i</th><th>f_atlas_i</th><th>ferr_atlas_i</th><th>flag_atlas_i</th><th>f_ap_atlas_z</th><th>ferr_ap_atlas_z</th><th>f_atlas_z</th><th>ferr_atlas_z</th><th>flag_atlas_z</th><th>atlas_flag_cleaned</th><th>atlas_flag_gaia</th><th>video_id</th><th>video_stellarity</th><th>m_ap_video_z</th><th>merr_ap_video_z</th><th>m_video_z</th><th>merr_video_z</th><th>m_ap_video_y</th><th>merr_ap_video_y</th><th>m_video_y</th><th>merr_video_y</th><th>m_ap_video_j</th><th>merr_ap_video_j</th><th>m_video_j</th><th>merr_video_j</th><th>m_ap_video_h</th><th>merr_ap_video_h</th><th>m_video_h</th><th>merr_video_h</th><th>m_ap_video_k</th><th>merr_ap_video_k</th><th>m_video_k</th><th>merr_video_k</th><th>f_ap_video_z</th><th>ferr_ap_video_z</th><th>f_video_z</th><th>ferr_video_z</th><th>f_ap_video_y</th><th>ferr_ap_video_y</th><th>f_video_y</th><th>ferr_video_y</th><th>f_ap_video_j</th><th>ferr_ap_video_j</th><th>f_video_j</th><th>ferr_video_j</th><th>f_ap_video_h</th><th>ferr_ap_video_h</th><th>f_video_h</th><th>ferr_video_h</th><th>f_ap_video_k</th><th>ferr_ap_video_k</th><th>f_video_k</th><th>ferr_video_k</th><th>flag_video_z</th><th>flag_video_y</th><th>flag_video_j</th><th>flag_video_h</th><th>flag_video_k</th><th>video_flag_cleaned</th><th>video_flag_gaia</th><th>vhs_id</th><th>vhs_stellarity</th><th>m_vhs_y</th><th>merr_vhs_y</th><th>m_ap_vhs_y</th><th>merr_ap_vhs_y</th><th>m_vhs_j</th><th>merr_vhs_j</th><th>m_ap_vhs_j</th><th>merr_ap_vhs_j</th><th>m_vhs_h</th><th>merr_vhs_h</th><th>m_ap_vhs_h</th><th>merr_ap_vhs_h</th><th>m_vhs_k</th><th>merr_vhs_k</th><th>m_ap_vhs_k</th><th>merr_ap_vhs_k</th><th>f_vhs_y</th><th>ferr_vhs_y</th><th>flag_vhs_y</th><th>f_ap_vhs_y</th><th>ferr_ap_vhs_y</th><th>f_vhs_j</th><th>ferr_vhs_j</th><th>flag_vhs_j</th><th>f_ap_vhs_j</th><th>ferr_ap_vhs_j</th><th>f_vhs_h</th><th>ferr_vhs_h</th><th>flag_vhs_h</th><th>f_ap_vhs_h</th><th>ferr_ap_vhs_h</th><th>f_vhs_k</th><th>ferr_vhs_k</th><th>flag_vhs_k</th><th>f_ap_vhs_k</th><th>ferr_ap_vhs_k</th><th>vhs_flag_cleaned</th><th>vhs_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_irac3</th><th>ferr_ap_irac3</th><th>f_irac3</th><th>ferr_irac3</th><th>swire_stellarity_irac3</th><th>f_ap_irac4</th><th>ferr_ap_irac4</th><th>f_irac4</th><th>ferr_irac4</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_irac3</th><th>merr_ap_irac3</th><th>m_irac3</th><th>merr_irac3</th><th>flag_irac3</th><th>m_ap_irac4</th><th>merr_ap_irac4</th><th>m_irac4</th><th>merr_irac4</th><th>flag_irac4</th><th>swire_flag_cleaned</th><th>swire_flag_gaia</th><th>des_id</th><th>des_stellarity</th><th>m_decam_g</th><th>merr_decam_g</th><th>m_ap_decam_g</th><th>merr_ap_decam_g</th><th>m_decam_r</th><th>merr_decam_r</th><th>m_ap_decam_r</th><th>merr_ap_decam_r</th><th>m_decam_i</th><th>merr_decam_i</th><th>m_ap_decam_i</th><th>merr_ap_decam_i</th><th>m_decam_z</th><th>merr_decam_z</th><th>m_ap_decam_z</th><th>merr_ap_decam_z</th><th>m_decam_y</th><th>merr_decam_y</th><th>m_ap_decam_y</th><th>merr_ap_decam_y</th><th>f_decam_g</th><th>ferr_decam_g</th><th>flag_decam_g</th><th>f_ap_decam_g</th><th>ferr_ap_decam_g</th><th>f_decam_r</th><th>ferr_decam_r</th><th>flag_decam_r</th><th>f_ap_decam_r</th><th>ferr_ap_decam_r</th><th>f_decam_i</th><th>ferr_decam_i</th><th>flag_decam_i</th><th>f_ap_decam_i</th><th>ferr_ap_decam_i</th><th>f_decam_z</th><th>ferr_decam_z</th><th>flag_decam_z</th><th>f_ap_decam_z</th><th>ferr_ap_decam_z</th><th>f_decam_y</th><th>ferr_decam_y</th><th>flag_decam_y</th><th>f_ap_decam_y</th><th>ferr_ap_decam_y</th><th>des_flag_cleaned</th><th>des_flag_gaia</th><th>candels_id</th><th>candels_stellarity</th><th>f_acs_f435w</th><th>ferr_acs_f435w</th><th>f_acs_f606w</th><th>ferr_acs_f606w</th><th>f_acs_f775w</th><th>ferr_acs_f775w</th><th>f_acs_f814w</th><th>ferr_acs_f814w</th><th>f_acs_f850lp</th><th>ferr_acs_f850lp</th><th>f_wfc3_f098m</th><th>ferr_wfc3_f098m</th><th>f_wfc3_f105w</th><th>ferr_wfc3_f105w</th><th>f_wfc3_f125w</th><th>ferr_wfc3_f125w</th><th>f_wfc3_f160w</th><th>ferr_wfc3_f160w</th><th>f_isaac_k</th><th>ferr_isaac_k</th><th>f_hawki_k</th><th>ferr_hawki_k</th><th>f_candels-irac_i1</th><th>ferr_candels-irac_i1</th><th>f_candels-irac_i2</th><th>ferr_candels-irac_i2</th><th>f_candels-irac_i3</th><th>ferr_candels-irac_i3</th><th>f_candels-irac_i4</th><th>ferr_candels-irac_i4</th><th>m_acs_f435w</th><th>merr_acs_f435w</th><th>flag_acs_f435w</th><th>m_acs_f606w</th><th>merr_acs_f606w</th><th>flag_acs_f606w</th><th>m_acs_f775w</th><th>merr_acs_f775w</th><th>flag_acs_f775w</th><th>m_acs_f814w</th><th>merr_acs_f814w</th><th>flag_acs_f814w</th><th>m_acs_f850lp</th><th>merr_acs_f850lp</th><th>flag_acs_f850lp</th><th>m_wfc3_f098m</th><th>merr_wfc3_f098m</th><th>flag_wfc3_f098m</th><th>m_wfc3_f105w</th><th>merr_wfc3_f105w</th><th>flag_wfc3_f105w</th><th>m_wfc3_f125w</th><th>merr_wfc3_f125w</th><th>flag_wfc3_f125w</th><th>m_wfc3_f160w</th><th>merr_wfc3_f160w</th><th>flag_wfc3_f160w</th><th>m_isaac_k</th><th>merr_isaac_k</th><th>flag_isaac_k</th><th>m_hawki_k</th><th>merr_hawki_k</th><th>flag_hawki_k</th><th>m_candels-irac_i1</th><th>merr_candels-irac_i1</th><th>flag_candels-irac_i1</th><th>m_candels-irac_i2</th><th>merr_candels-irac_i2</th><th>flag_candels-irac_i2</th><th>m_candels-irac_i3</th><th>merr_candels-irac_i3</th><th>flag_candels-irac_i3</th><th>m_candels-irac_i4</th><th>merr_candels-irac_i4</th><th>flag_candels-irac_i4</th><th>candels_flag_cleaned</th><th>candels_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></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></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><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><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>1042650000004878</td><td>51.49149351002285</td><td>-27.2731577059918</td><td>nan</td><td>3.000000106112566e-06</td><td>nan</td><td>nan</td><td>16.46579933166504</td><td>0.0007820000173524022</td><td>nan</td><td>nan</td><td>13.743499755859375</td><td>0.0001449999981559813</td><td>nan</td><td>nan</td><td>12.928099632263184</td><td>0.00015700000221841037</td><td>12.680299758911133</td><td>0.0005840000230818987</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>941.1964216025909</td><td>0.6778954361102915</td><td>nan</td><td>nan</td><td>False</td><td>11550.481734090625</td><td>1.5425660697360069</td><td>nan</td><td>nan</td><td>False</td><td>24477.110424067683</td><td>3.5394475877637244</td><td>30752.476580572642</td><td>16.541262006530232</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>True</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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></tr>\n",
       "<tr><td>1</td><td>1044470000006525</td><td>52.39969538002285</td><td>-28.126664395991803</td><td>nan</td><td>7.999999979801942e-06</td><td>nan</td><td>nan</td><td>15.864800453186035</td><td>0.0005469999741762877</td><td>nan</td><td>nan</td><td>16.011899948120117</td><td>0.0004020000051241368</td><td>nan</td><td>nan</td><td>17.401599884033203</td><td>0.0010949999559670687</td><td>nan</td><td>nan</td><td>12.250200271606445</td><td>0.00010099999781232327</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>1637.117378613091</td><td>0.8247888943008994</td><td>nan</td><td>nan</td><td>False</td><td>1429.6838810333659</td><td>0.5293485885269366</td><td>nan</td><td>nan</td><td>False</td><td>397.5209726691479</td><td>0.40091271310757504</td><td>nan</td><td>nan</td><td>False</td><td>45700.38842897149</td><td>4.251252846796886</td><td>nan</td><td>nan</td><td>False</td><td>True</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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></tr>\n",
       "<tr><td>2</td><td>1044470000006529</td><td>52.39971068002285</td><td>-28.1259474559918</td><td>nan</td><td>7.999999979801942e-06</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>15.638899803161621</td><td>0.0003389999910723418</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>12.136500358581543</td><td>9.600000339560211e-05</td><td>nan</td><td>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>2015.7658285837279</td><td>0.6293836337953239</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>50745.771148469204</td><td>4.486904075977407</td><td>nan</td><td>nan</td><td>False</td><td>True</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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></tr>\n",
       "<tr><td>3</td><td>1047131000002831</td><td>54.824128630022855</td><td>-27.930928285991797</td><td>nan</td><td>9.000000318337698e-06</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>15.61520004272461</td><td>0.00036100001307204366</td><td>nan</td><td>nan</td><td>15.06190013885498</td><td>0.00036299999919719994</td><td>nan</td><td>nan</td><td>13.1628999710083</td><td>0.00016599999798927456</td><td>nan</td><td>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>2060.2502850984765</td><td>0.685019415022563</td><td>nan</td><td>nan</td><td>False</td><td>3429.57215513007</td><td>1.1466272231718477</td><td>nan</td><td>nan</td><td>False</td><td>19716.962552141227</td><td>3.0145589045169388</td><td>nan</td><td>nan</td><td>False</td><td>True</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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></tr>\n",
       "<tr><td>4</td><td>1042815000001762</td><td>51.86128719002285</td><td>-29.069251605991802</td><td>nan</td><td>1.1000000085914508e-05</td><td>nan</td><td>nan</td><td>18.594100952148438</td><td>0.0020290000829845667</td><td>nan</td><td>nan</td><td>13.321700096130371</td><td>0.05000000074505806</td><td>13.784899711608887</td><td>0.0007360000163316727</td><td>nan</td><td>0.0015770000172778964</td><td>nan</td><td>nan</td><td>12.415499687194824</td><td>0.00012700000661425292</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>132.54386152242276</td><td>0.24769507072668803</td><td>nan</td><td>nan</td><td>False</td><td>17034.130164908853</td><td>784.4506954860127</td><td>11118.344215965039</td><td>7.5369150339371656</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>39246.42704240539</td><td>4.590706722432302</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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></tr>\n",
       "<tr><td>5</td><td>1045685000001531</td><td>53.947721780022846</td><td>-28.8894944559918</td><td>nan</td><td>1.1000000085914508e-05</td><td>nan</td><td>nan</td><td>nan</td><td>0.0042779999785125256</td><td>nan</td><td>nan</td><td>17.66119956970215</td><td>0.0009410000056959689</td><td>nan</td><td>nan</td><td>12.374600410461426</td><td>0.00012099999730708078</td><td>12.964300155639648</td><td>0.0006900000153109431</td><td>nan</td><td>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>312.98258424034634</td><td>0.2712598256024028</td><td>nan</td><td>nan</td><td>False</td><td>40753.023582845584</td><td>4.541725441382167</td><td>23674.4521473852</td><td>15.045433939280883</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>True</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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></tr>\n",
       "<tr><td>6</td><td>1042566000001475</td><td>51.48726830002285</td><td>-27.9988690759918</td><td>nan</td><td>2.2000000171829015e-05</td><td>13.205599784851074</td><td>0.0006970000104047358</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>18956.58427990145</td><td>12.169382749166939</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>True</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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></tr>\n",
       "<tr><td>7</td><td>1042346000004332</td><td>51.53970074002285</td><td>-26.5479743059918</td><td>nan</td><td>2.4000000848900527e-05</td><td>nan</td><td>nan</td><td>18.043100357055664</td><td>0.0014270000392571092</td><td>nan</td><td>nan</td><td>nan</td><td>0.014178000390529633</td><td>nan</td><td>nan</td><td>17.71179962158203</td><td>0.001307999948039651</td><td>13.21780014038086</td><td>0.0011180000146850944</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>220.17086931593096</td><td>0.28937400980111594</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>298.7309028004855</td><td>0.3598848446137232</td><td>18744.762571463416</td><td>19.301783194043196</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>True</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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></tr>\n",
       "<tr><td>8</td><td>1045878000001352</td><td>53.150471800022856</td><td>-28.405121385991798</td><td>nan</td><td>2.499999936844688e-05</td><td>nan</td><td>nan</td><td>17.379899978637695</td><td>0.0013150000013411045</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>15.71310043334961</td><td>0.0005699999746866524</td><td>nan</td><td>nan</td><td>15.629199981689453</td><td>0.0006079999729990959</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>405.54589386404245</td><td>0.4911808675422199</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>1882.6076826997946</td><td>0.988349036148088</td><td>nan</td><td>nan</td><td>False</td><td>2033.855093056133</td><td>1.1389358080194787</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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></tr>\n",
       "<tr><td>9</td><td>1047777000004790</td><td>54.50581861002286</td><td>-29.571993815991796</td><td>nan</td><td>7.000000186963007e-05</td><td>nan</td><td>nan</td><td>24.038299560546875</td><td>0.031881000846624374</td><td>nan</td><td>nan</td><td>21.73259925842285</td><td>0.006546999793499708</td><td>21.76759910583496</td><td>0.15689699351787567</td><td>22.424299240112305</td><td>0.015533000230789185</td><td>nan</td><td>nan</td><td>23.61199951171875</td><td>0.028243999928236008</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0.880400287659025</td><td>0.025851622330746966</td><td>nan</td><td>nan</td><td>False</td><td>7.361397962341626</td><td>0.04438930076026733</td><td>7.127879639804525</td><td>1.0300318629346463</td><td>False</td><td>3.892963250045723</td><td>0.055694374744448036</td><td>nan</td><td>nan</td><td>False</td><td>1.3037676334732222</td><td>0.03391580089609092</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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>False</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table139734984761072-433139').dataTable()\");\n",
       "    \n",
       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
       "    \"optionalnum-asc\": astropy_sort_num,\n",
       "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
       "});\n",
       "\n",
       "    $('#table139734984761072-433139').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 121, 122, 123, 125, 126, 127, 128, 129, 130, 132, 133, 134, 135, 136, 137, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149, 150, 151, 153, 154, 155, 156, 157, 158, 160, 161, 162, 163, 164, 165, 167, 168, 169, 170, 171, 172, 174, 175, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 188, 189, 190, 191, 192, 193, 195, 196, 198, 199, 200, 201, 202, 203, 205, 206, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 240, 241, 242, 243, 245, 246, 247, 248, 250, 251, 252, 253, 255, 256, 257, 258, 260, 261, 262, 263, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 337, 338, 339, 340, 342, 343, 344, 345, 347, 348, 349, 350, 352, 353, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 372, 373, 374, 375, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 405, 406, 407, 408, 410, 411, 412, 413, 415, 416, 417, 418, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 447, 448, 449, 450, 452, 453, 454, 455, 457, 458, 459, 460, 462, 463, 464, 465, 467, 468, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 506, 507, 509, 510, 512, 513, 515, 516, 518, 519, 521, 522, 524, 525, 527, 528, 530, 531, 533, 534, 536, 537, 539, 540, 542, 543, 545, 546, 549], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "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": 27,
   "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": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "combo_stellarity, atlas_stellarity, video_stellarity, vhs_stellarity, servs_stellarity_irac1, servs_stellarity_irac2, swire_stellarity_irac1, swire_stellarity_irac2, swire_stellarity_irac3, swire_stellarity_irac4, des_stellarity, candels_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": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# We create an masked array with all the stellarities and get the maximum value, as well as its\n",
    "# origin.  Some sources may not have an associated stellarity.\n",
    "stellarity_array = np.array([master_catalogue[column] for column in stellarity_columns])\n",
    "stellarity_array = np.ma.masked_array(stellarity_array, np.isnan(stellarity_array))\n",
    "\n",
    "max_stellarity = np.max(stellarity_array, axis=0)\n",
    "max_stellarity.fill_value = np.nan\n",
    "\n",
    "no_stellarity_mask = max_stellarity.mask\n",
    "\n",
    "master_catalogue.add_column(Column(data=max_stellarity.filled(), name=\"stellarity\"))\n",
    "\n",
    "stellarity_origin = np.full(len(master_catalogue), \"NO_INFORMATION\", dtype=\"S20\")\n",
    "stellarity_origin[~no_stellarity_mask] = np.array(stellarity_columns)[np.argmax(stellarity_array, axis=0)[~no_stellarity_mask]]\n",
    "\n",
    "master_catalogue.add_column(Column(data=stellarity_origin, name=\"stellarity_origin\"))\n",
    "\n",
    "master_catalogue.remove_columns(stellarity_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV - Adding E(B-V) column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 31,
   "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), \"CDFS-SWIRE\", dtype='<U18'),\n",
    "                                   name=\"field\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK!\n"
     ]
    }
   ],
   "source": [
    "# Check that the HELP Ids are unique\n",
    "if len(master_catalogue) != len(np.unique(master_catalogue['help_id'])):\n",
    "    print(\"The HELP IDs are not unique!!!\")\n",
    "else:\n",
    "    print(\"OK!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VI - Cross-matching with the spec-z catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz =  Table.read(\"../../dmu23/dmu23_CDFS-SWIRE/data/CDFS_SWIRE-specz-v2.3.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(specz['ra'] * u.deg, specz['dec'] * u.deg)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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.a SERVS vs SWIRE vs CANDELS\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. On a small section there are also CANDELS forced irac fluxes which since thay are very deep we always take preferentially."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "seip = Table.read(\"../../dmu0/dmu0_SEIP/data/SEIP_CDFS-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": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irac_origin = Table()\n",
    "irac_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "620841 sources with SERVS flux\n",
      "433129 sources with SWIRE flux\n",
      "247077 sources with SERVS and SWIRE flux\n",
      "619863 sources for which we use SERVS\n",
      "187030 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",
    "\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",
    "\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",
    "\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",
    "\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",
    "\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",
    "\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",
    "\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",
    "\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC1_ap\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "620841 sources with SERVS flux\n",
      "433097 sources with SWIRE flux\n",
      "247077 sources with SERVS and SWIRE flux\n",
      "34926 sources with CANDELS flux\n",
      "614662 sources for which we use SERVS\n",
      "186876 sources for which we use SWIRE\n",
      "34926 sources for which we use CANDELS\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",
    "has_candels = ~np.isnan(master_catalogue['f_candels-irac_i1'])\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",
    "print(\"{} sources with CANDELS flux\".format(np.sum(has_candels)))\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_candels = has_candels\n",
    "use_swire = ~has_candels & ((has_swire & ~has_servs) | (has_both & has_servs_above_limit))\n",
    "use_servs = ~has_candels & ((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",
    "print(\"{} sources for which we use CANDELS\".format(np.sum(use_candels)))\n",
    "\n",
    "f_irac = np.full(len(master_catalogue), np.nan)\n",
    "f_irac[use_servs] = master_catalogue['f_servs_irac1'][use_servs]\n",
    "f_irac[use_swire] = master_catalogue['f_swire_irac1'][use_swire]\n",
    "f_irac[use_candels] = master_catalogue['f_candels-irac_i1'][use_candels]\n",
    "\n",
    "ferr_irac = np.full(len(master_catalogue), np.nan)\n",
    "ferr_irac[use_servs] = master_catalogue['ferr_servs_irac1'][use_servs]\n",
    "ferr_irac[use_swire] = master_catalogue['ferr_swire_irac1'][use_swire]\n",
    "ferr_irac[use_candels] = master_catalogue['ferr_candels-irac_i1'][use_candels]\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",
    "flag_irac[use_candels] = master_catalogue['flag_candels-irac_i1'][use_candels]\n",
    "\n",
    "m_irac = np.full(len(master_catalogue), np.nan)\n",
    "m_irac[use_servs] = master_catalogue['m_servs_irac1'][use_servs]\n",
    "m_irac[use_swire] = master_catalogue['m_swire_irac1'][use_swire]\n",
    "m_irac[use_candels] = master_catalogue['m_candels-irac_i1'][use_candels]\n",
    "\n",
    "merr_irac = np.full(len(master_catalogue), np.nan)\n",
    "merr_irac[use_servs] = master_catalogue['merr_servs_irac1'][use_servs]\n",
    "merr_irac[use_swire] = master_catalogue['merr_swire_irac1'][use_swire]\n",
    "merr_irac[use_candels] = master_catalogue['merr_candels-irac_i1'][use_candels]\n",
    "\n",
    "master_catalogue.add_column(Column(data=f_irac, name=\"f_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=ferr_irac, name=\"ferr_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=m_irac, name=\"m_irac_i1\"))\n",
    "master_catalogue.add_column(Column(data=merr_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",
    "                                'f_candels-irac_i1',\n",
    "                                 'ferr_candels-irac_i1',\n",
    "                                 'm_candels-irac_i1',\n",
    "                                 'merr_candels-irac_i1',\n",
    "                                'flag_candels-irac_i1'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_servs] = \"SERVS\"\n",
    "origin[use_swire] = \"SWIRE\"\n",
    "origin[use_candels] = \"CANDELS\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC1_total\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "634320 sources with SERVS flux\n",
      "318895 sources with SWIRE flux\n",
      "186454 sources with SERVS and SWIRE flux\n",
      "633670 sources for which we use SERVS\n",
      "133091 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_ap\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "634320 sources with SERVS flux\n",
      "318886 sources with SWIRE flux\n",
      "186454 sources with SERVS and SWIRE flux\n",
      "34926 sources with CANDELS flux\n",
      "628496 sources for which we use SERVS\n",
      "133020 sources for which we use SWIRE\n",
      "34926 sources for which we use CANDELS\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",
    "has_candels = ~np.isnan(master_catalogue['f_candels-irac_i2'])\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",
    "print(\"{} sources with CANDELS flux\".format(np.sum(has_candels)))\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_candels = has_candels\n",
    "use_swire = ~has_candels & ((has_swire & ~has_servs) | (has_both & has_servs_above_limit))\n",
    "use_servs = ~has_candels & ((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",
    "print(\"{} sources for which we use CANDELS\".format(np.sum(use_candels)))\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",
    "f_irac[use_candels] = master_catalogue['f_candels-irac_i2'][use_candels]\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",
    "ferr_irac[use_candels] = master_catalogue['ferr_candels-irac_i2'][use_candels]\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",
    "flag_irac[use_candels] = master_catalogue['flag_candels-irac_i2'][use_candels]\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",
    "m_irac[use_candels] = master_catalogue['m_candels-irac_i2'][use_candels]\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",
    "merr_irac[use_candels] = master_catalogue['merr_candels-irac_i2'][use_candels]\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",
    "                                'f_candels-irac_i2',\n",
    "                                'ferr_candels-irac_i2',\n",
    "                                'm_candels-irac_i2',\n",
    "                                'merr_candels-irac_i2',\n",
    "                                'flag_candels-irac_i2'])\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_servs] = \"SERVS\"\n",
    "origin[use_swire] = \"SWIRE\"\n",
    "origin[use_candels] = \"CANDELS\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC2_total\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "has_candels_irac_i3 = ~np.isnan(master_catalogue['f_candels-irac_i3'])\n",
    "has_candels_irac_i4 = ~np.isnan(master_catalogue['f_candels-irac_i4'])\n",
    "has_swire_irac_i3 = ~np.isnan(master_catalogue['f_irac3'])\n",
    "has_swire_irac_i4 = ~np.isnan(master_catalogue['f_irac4'])\n",
    "\n",
    "use_candels_irac_i3 = has_candels_irac_i3 & ~has_swire_irac_i3\n",
    "use_candels_irac_i4 = has_candels_irac_i4 & ~has_swire_irac_i4\n",
    "\n",
    "for col in master_catalogue.colnames:\n",
    "    if '_irac3' in col:\n",
    "        master_catalogue[col].name = col.replace('_irac3', '_irac_i3')\n",
    "    if '_irac4' in col:\n",
    "        master_catalogue[col].name = col.replace('_irac4', '_irac_i4')\n",
    "\n",
    "master_catalogue['f_irac_i3'][use_candels_irac_i3] = master_catalogue['f_candels-irac_i3'][use_candels_irac_i3]\n",
    "master_catalogue['ferr_irac_i3'][use_candels_irac_i3] = master_catalogue['ferr_candels-irac_i3'][use_candels_irac_i3]\n",
    "master_catalogue['m_irac_i3'][use_candels_irac_i3] = master_catalogue['m_candels-irac_i3'][use_candels_irac_i3]\n",
    "master_catalogue['merr_irac_i3'][use_candels_irac_i3] = master_catalogue['merr_candels-irac_i3'][use_candels_irac_i3]\n",
    "master_catalogue['flag_irac_i3'][use_candels_irac_i3] = master_catalogue['flag_candels-irac_i3'][use_candels_irac_i3]\n",
    "\n",
    "master_catalogue['f_irac_i4'][use_candels_irac_i3] = master_catalogue['f_candels-irac_i4'][use_candels_irac_i3]\n",
    "master_catalogue['ferr_irac_i4'][use_candels_irac_i3] = master_catalogue['ferr_candels-irac_i4'][use_candels_irac_i3]\n",
    "master_catalogue['m_irac_i4'][use_candels_irac_i3] = master_catalogue['m_candels-irac_i4'][use_candels_irac_i3]\n",
    "master_catalogue['merr_irac_i4'][use_candels_irac_i3] = master_catalogue['merr_candels-irac_i4'][use_candels_irac_i3]\n",
    "master_catalogue['flag_irac_i4'][use_candels_irac_i3] = master_catalogue['flag_candels-irac_i4'][use_candels_irac_i3]\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[has_swire_irac_i3] = \"SWIRE\"\n",
    "origin[use_candels_irac_i3] = \"CANDELS\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC3_total\"))\n",
    "\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[has_swire_irac_i4] = \"SWIRE\"\n",
    "origin[use_candels_irac_i4] = \"CANDELS\"\n",
    "irac_origin.add_column(Column(data=origin, name=\"IRAC4_total\"))\n",
    "\n",
    "master_catalogue.remove_columns(['f_candels-irac_i3',    'f_candels-irac_i4',\n",
    "                                 'ferr_candels-irac_i3', 'ferr_candels-irac_i4',\n",
    "                                 'm_candels-irac_i3',    'm_candels-irac_i4',\n",
    "                                 'merr_candels-irac_i3', 'merr_candels-irac_i4',\n",
    "                                 'flag_candels-irac_i3', 'flag_candels-irac_i4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irac_origin.write(\"{}/cdfs-swire_irac_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VII.b VIDEO vs VHS\n",
    "VIDEO is deeper than VHS so we take VIDEO flux for any source that has both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vista_origin = Table()\n",
    "vista_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For VISTA band y:\n",
      "1063464 sources with VIDEO flux\n",
      "14179 sources with VHS flux\n",
      "10792 sources with VIDEO and VHS flux\n",
      "1063464 sources for which we use VIDEO\n",
      "3387 sources for which we use VHS\n",
      "1061411 sources with VIDEO aperture flux\n",
      "14179 sources with VHS aperture flux\n",
      "10791 sources with VIDEO and VHS aperture flux\n",
      "1061411 sources for which we use VIDEO aperture fluxes\n",
      "3388 sources for which we use VHS aperture fluxes\n",
      "For VISTA band j:\n",
      "1061794 sources with VIDEO flux\n",
      "105677 sources with VHS flux\n",
      "31304 sources with VIDEO and VHS flux\n",
      "1061794 sources for which we use VIDEO\n",
      "74373 sources for which we use VHS\n",
      "1058115 sources with VIDEO aperture flux\n",
      "105674 sources with VHS aperture flux\n",
      "31305 sources with VIDEO and VHS aperture flux\n",
      "1058115 sources for which we use VIDEO aperture fluxes\n",
      "74369 sources for which we use VHS aperture fluxes\n",
      "For VISTA band h:\n",
      "1051715 sources with VIDEO flux\n",
      "82037 sources with VHS flux\n",
      "25260 sources with VIDEO and VHS flux\n",
      "1051715 sources for which we use VIDEO\n",
      "56777 sources for which we use VHS\n",
      "1039701 sources with VIDEO aperture flux\n",
      "82025 sources with VHS aperture flux\n",
      "25256 sources with VIDEO and VHS aperture flux\n",
      "1039701 sources for which we use VIDEO aperture fluxes\n",
      "56769 sources for which we use VHS aperture fluxes\n",
      "For VISTA band k:\n",
      "1040547 sources with VIDEO flux\n",
      "73765 sources with VHS flux\n",
      "24516 sources with VIDEO and VHS flux\n",
      "1040547 sources for which we use VIDEO\n",
      "49249 sources for which we use VHS\n",
      "1024472 sources with VIDEO aperture flux\n",
      "73760 sources with VHS aperture flux\n",
      "24518 sources with VIDEO and VHS aperture flux\n",
      "1024472 sources for which we use VIDEO aperture fluxes\n",
      "49242 sources for which we use VHS aperture fluxes\n"
     ]
    }
   ],
   "source": [
    "vista_bands = ['y','j','h','k'] # Lowercase naming convention (k is Ks)\n",
    "for band in vista_bands:\n",
    "    print('For VISTA band ' + band + ':')\n",
    "    # VISTA total flux \n",
    "    has_video = ~np.isnan(master_catalogue['f_video_' + band])\n",
    "    has_vhs = ~np.isnan(master_catalogue['f_vhs_' + band])\n",
    "    has_both = has_video & has_vhs\n",
    "\n",
    "    print(\"{} sources with VIDEO flux\".format(np.sum(has_video)))\n",
    "    print(\"{} sources with VHS flux\".format(np.sum(has_vhs)))\n",
    "    print(\"{} sources with VIDEO and VHS flux\".format(np.sum(has_both)))\n",
    "\n",
    "\n",
    "    use_video = has_video \n",
    "    use_vhs = has_vhs & ~has_both\n",
    "\n",
    "    print(\"{} sources for which we use VIDEO\".format(np.sum(use_video)))\n",
    "    print(\"{} sources for which we use VHS\".format(np.sum(use_vhs)))\n",
    "\n",
    "    f_vista = np.full(len(master_catalogue), np.nan)\n",
    "    f_vista[use_video] = master_catalogue['f_video_' + band][use_video]\n",
    "    f_vista[use_vhs] = master_catalogue['f_vhs_' + band][use_vhs]\n",
    "\n",
    "    ferr_vista = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_vista[use_video] = master_catalogue['ferr_video_' + band][use_video]\n",
    "    ferr_vista[use_vhs] = master_catalogue['ferr_vhs_' + band][use_vhs]\n",
    "    \n",
    "    m_vista = np.full(len(master_catalogue), np.nan)\n",
    "    m_vista[use_video] = master_catalogue['m_video_' + band][use_video]\n",
    "    m_vista[use_vhs] = master_catalogue['m_vhs_' + band][use_vhs]\n",
    "\n",
    "    merr_vista = np.full(len(master_catalogue), np.nan)\n",
    "    merr_vista[use_video] = master_catalogue['merr_video_' + band][use_video]\n",
    "    merr_vista[use_vhs] = master_catalogue['merr_vhs_' + band][use_vhs]\n",
    "\n",
    "    flag_vista = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    flag_vista[use_video] = master_catalogue['flag_video_' + band][use_video]\n",
    "    flag_vista[use_vhs] = master_catalogue['flag_vhs_' + band][use_vhs]\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_vista, name=\"f_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_vista, name=\"ferr_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_vista, name=\"m_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_vista, name=\"merr_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=flag_vista, name=\"flag_vista_\" + band))\n",
    "\n",
    "    master_catalogue.remove_columns(['f_video_' + band, \n",
    "                                     'f_vhs_' + band, \n",
    "                                     'ferr_video_' + band,\n",
    "                                     'ferr_vhs_' + band, \n",
    "                                     'm_video_' + band, \n",
    "                                     'm_vhs_' + band, \n",
    "                                     'merr_video_' + band,\n",
    "                                     'merr_vhs_' + band,\n",
    "                                     'flag_video_' + band, \n",
    "                                     'flag_vhs_' + band])\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_video] = \"VIDEO\"\n",
    "    origin[use_vhs] = \"VHS\"\n",
    "    \n",
    "    vista_origin.add_column(Column(data=origin, name= 'f_vista_' + band ))\n",
    "    \n",
    "    \n",
    "    \n",
    "    # VISTA Aperture flux\n",
    "    has_ap_video = ~np.isnan(master_catalogue['f_ap_video_' + band])\n",
    "    has_ap_vhs = ~np.isnan(master_catalogue['f_ap_vhs_' + band])\n",
    "    has_ap_both = has_ap_video & has_ap_vhs\n",
    "\n",
    "    print(\"{} sources with VIDEO aperture flux\".format(np.sum(has_ap_video)))\n",
    "    print(\"{} sources with VHS aperture flux\".format(np.sum(has_ap_vhs)))\n",
    "    print(\"{} sources with VIDEO and VHS aperture flux\".format(np.sum(has_ap_both)))\n",
    "\n",
    "\n",
    "    use_ap_video = has_ap_video \n",
    "    use_ap_vhs = has_ap_vhs & ~has_ap_both\n",
    "\n",
    "    print(\"{} sources for which we use VIDEO aperture fluxes\".format(np.sum(use_ap_video)))\n",
    "    print(\"{} sources for which we use VHS aperture fluxes\".format(np.sum(use_ap_vhs)))\n",
    "\n",
    "    f_ap_vista = np.full(len(master_catalogue), np.nan)\n",
    "    f_ap_vista[use_ap_video] = master_catalogue['f_ap_video_' + band][use_ap_video]\n",
    "    f_ap_vista[use_ap_vhs] = master_catalogue['f_ap_vhs_' + band][use_ap_vhs]\n",
    "\n",
    "    ferr_ap_vista = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_ap_vista[use_ap_video] = master_catalogue['ferr_ap_video_' + band][use_ap_video]\n",
    "    ferr_ap_vista[use_ap_vhs] = master_catalogue['ferr_ap_vhs_' + band][use_ap_vhs]\n",
    "    \n",
    "    m_ap_vista = np.full(len(master_catalogue), np.nan)\n",
    "    m_ap_vista[use_ap_video] = master_catalogue['m_ap_video_' + band][use_ap_video]\n",
    "    m_ap_vista[use_ap_vhs] = master_catalogue['m_ap_vhs_' + band][use_ap_vhs]\n",
    "\n",
    "    merr_ap_vista = np.full(len(master_catalogue), np.nan)\n",
    "    merr_ap_vista[use_ap_video] = master_catalogue['merr_ap_video_' + band][use_ap_video]\n",
    "    merr_ap_vista[use_ap_vhs] = master_catalogue['merr_ap_vhs_' + band][use_ap_vhs]\n",
    "\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_ap_vista, name=\"f_ap_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_ap_vista, name=\"ferr_ap_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_ap_vista, name=\"m_ap_vista_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_vista, name=\"merr_ap_vista_\" + band))\n",
    "\n",
    "\n",
    "    master_catalogue.remove_columns(['f_ap_video_' + band, \n",
    "                                     'f_ap_vhs_' + band, \n",
    "                                     'ferr_ap_video_' + band,\n",
    "                                     'ferr_ap_vhs_' + band,\n",
    "                                     'm_ap_video_' + band, \n",
    "                                     'm_ap_vhs_' + band, \n",
    "                                     'merr_ap_video_' + band,\n",
    "                                     'merr_ap_vhs_' + band])\n",
    "\n",
    "    origin_ap = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin_ap[use_ap_video] = \"VIDEO\"\n",
    "    origin_ap[use_ap_vhs] = \"VHS\"\n",
    "    \n",
    "    vista_origin.add_column(Column(data=origin_ap, name= 'f_ap_vista_' + band ))\n",
    "                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "      \n",
    "#Z band only in VIDEO\n",
    "               \n",
    "master_catalogue['f_ap_video_z'].name = 'f_ap_vista_z'\n",
    "master_catalogue['ferr_ap_video_z'].name = 'ferr_ap_vista_z'\n",
    "master_catalogue['f_video_z'].name = 'f_vista_z'\n",
    "master_catalogue['ferr_video_z'].name = 'ferr_vista_z'\n",
    "master_catalogue['m_ap_video_z'].name = 'm_ap_vista_z'\n",
    "master_catalogue['merr_ap_video_z'].name = 'merr_ap_vista_z'\n",
    "master_catalogue['m_video_z'].name = 'm_vista_z'\n",
    "master_catalogue['merr_video_z'].name = 'merr_vista_z'\n",
    "master_catalogue['flag_video_z'].name = 'flag_vista_z'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vista_origin.write(\"{}/cdfs-swire_vista_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII.a Wavelength domain coverage\n",
    "\n",
    "We add a binary `flag_optnir_obs` indicating that a source was observed in a given wavelength domain:\n",
    "\n",
    "- 1 for observation in optical;\n",
    "- 2 for observation in near-infrared;\n",
    "- 4 for observation in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source observed both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: The observation flag is based on the creation of multi-order coverage maps from the catalogues, this may not be accurate, especially on the edges of the coverage.*\n",
    "\n",
    "*Note 2: Being on the observation coverage does not mean having fluxes in that wavelength domain. For sources observed in one domain but having no flux in it, one must take into consideration de different depths in the catalogue we are using.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "atlas_moc = MOC(filename=\"../../dmu0/dmu0_ATLAS/data/ATLAS_CDFS-SWIRE_MOC.fits\")\n",
    "combo_moc = MOC(filename=\"../../dmu0/dmu0_COMBO-17/data/table3_MOC.fits\")\n",
    "#fireworks_moc = MOC(filename=\"../../dmu0/dmu0_Fireworks/data/Fireworks_MOC.fits\")\n",
    "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_CDFS-SWIRE_MOC.fits\")\n",
    "servs_moc = MOC(filename=\"../../dmu0/dmu0_DataFusion-Spitzer/data/DF-SERVS_CDFS-SWIRE_MOC.fits\")\n",
    "swire_moc = MOC(filename=\"../../dmu0/dmu0_DataFusion-Spitzer/data/DF-SWIRE_CDFS-SWIRE_MOC.fits\")\n",
    "video_moc= MOC(filename=\"../../dmu0/dmu0_VISTA-VIDEO-private/data/VIDEO-all_2017-02-12_fullcat_errfix_CDFS-SWIRE_MOC.fits\")\n",
    "vhs_moc= MOC(filename=\"../../dmu0/dmu0_VISTA-VHS/data/VHS_CDFS-SWIRE_MOC.fits\")\n",
    "des_moc =MOC(filename=\"../../dmu0/dmu0_DES/data/DES-DR1_CDFS-SWIRE_MOC.fits\")\n",
    "candels_moc =MOC(filename=\"../../dmu0/dmu0_CANDELS-GOODS-S/data/hlsp_candels_hst_wfc3_goodss-tot-multiband_f160w_v1_MOC.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    ps1_moc + atlas_moc + combo_moc + des_moc + candels_moc ) #+ fireworks_moc\n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    vhs_moc + video_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": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * was_observed_optical + 2 * was_observed_nir + 4 * was_observed_mir,\n",
    "        name=\"flag_optnir_obs\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII.b Wavelength domain detection\n",
    "\n",
    "We add a binary `flag_optnir_det` indicating that a source was detected in a given wavelength domain:\n",
    "\n",
    "- 1 for detection in optical;\n",
    "- 2 for detection in near-infrared;\n",
    "- 4 for detection in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source detected both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: We use the total flux columns to know if the source has flux, in some catalogues, we may have aperture flux and no total flux.*\n",
    "\n",
    "To get rid of artefacts (chip edges, star flares, etc.) we consider that a source is detected in one wavelength domain when it has a flux value in **at least two bands**. That means that good sources will be excluded from this flag when they are on the coverage of only one band."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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_ps1_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ps1_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ps1_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ps1_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_ps1_y']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_atlas_u']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_atlas_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_atlas_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_atlas_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_atlas_z']) +\n",
    "    #DES\n",
    "    1 * ~np.isnan(master_catalogue['f_decam_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_decam_r']) +    \n",
    "    1 * ~np.isnan(master_catalogue['f_decam_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_decam_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_decam_y']) \n",
    "\n",
    ")\n",
    "\n",
    "nb_nir_flux = (\n",
    "\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_y']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_vista_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": 54,
   "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",
    "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogues. This can be used to easily get additional information from them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ps1_id', 'combo_id', 'atlas_id', 'video_id', 'vhs_id', 'servs_intid', 'swire_intid', 'des_id', 'candels_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": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_cdfs-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": 57,
   "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 - Renaming columns\n",
    "\n",
    "We rename some columns to follow the intrument_filter standard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# PanSTARRS: The column name must use the instrument name gpc1\n",
    "for col in master_catalogue.colnames:\n",
    "        if 'ps1' in col:\n",
    "            master_catalogue[col].name = col.replace(\"ps1\", \"gpc1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# COMBO-17: The instrument name is wfi\n",
    "new_name = {\n",
    "    'combo_420': \"wfi_416nm\",\n",
    "    'combo_462': \"wfi_461nm\",\n",
    "    'combo_485': \"wfi_485nm\",\n",
    "    'combo_518': \"wfi_518nm\",\n",
    "    'combo_571': \"wfi_571nm\",\n",
    "    'combo_604': \"wfi_604nm\",\n",
    "    'combo_646': \"wfi_646nm\",\n",
    "    'combo_696': \"wfi_696nm\",\n",
    "    'combo_753': \"wfi_753nm\",\n",
    "    'combo_815': \"wfi_815nm\",\n",
    "    'combo_856': \"wfi_856nm\",\n",
    "    'combo_914': \"wfi_914nm\",\n",
    "    'combo_b': \"wfi_b\",\n",
    "    'combo_i': \"wfi_i\",\n",
    "    'combo_r': \"wfi_r\",\n",
    "    'combo_u': \"wfi_u\",\n",
    "    'combo_v': \"wfi_v\"\n",
    "}\n",
    "\n",
    "for col in master_catalogue.colnames:\n",
    "    if 'combo' in col:\n",
    "        for old_name in new_name:\n",
    "            if old_name in col:\n",
    "                master_catalogue[col].name = col.replace(old_name, new_name[old_name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ATLAS: The instrument is omegacam\n",
    "# The catalogue contains a \"UL\" band that is not described anywhere except with\n",
    "# “Bandpass UL comes from CASU created list driven measurement”. We are removing it.\n",
    "master_catalogue.remove_columns(['m_ap_atlas_ul', 'merr_ap_atlas_ul', 'm_atlas_ul', 'merr_atlas_ul',\n",
    "                                 'f_ap_atlas_ul', 'ferr_ap_atlas_ul', 'f_atlas_ul', 'ferr_atlas_ul',\n",
    "                                 'flag_atlas_ul'])\n",
    "for col in master_catalogue.colnames:\n",
    "        if 'atlas' in col:\n",
    "            master_catalogue[col].name = col.replace(\"atlas\", \"omegacam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# VISTA: The instrument is vircam but vista is used in the filter names.\n",
    "# The K band is in fact a Ks one.\n",
    "for col in master_catalogue.colnames:\n",
    "        if 'vista_k' in col:\n",
    "            master_catalogue[col].name = col.replace(\"vista_k\", \"vista_ks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XII - Saving the catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns = [\"help_id\", \"field\", \"ra\", \"dec\", \"hp_idx\"]\n",
    "\n",
    "bands = [column[5:] for column in master_catalogue.colnames if 'f_ap' in column]\n",
    "\n",
    "\n",
    "bands_no_ap = (set([column[5:] for column in master_catalogue.colnames if 'flag' in column]) \n",
    "               - set(bands) \n",
    "               - set(['cleaned', 'gaia', 'merged', 'optnir_det', 'optnir_obs', '_association_flag'])\n",
    "              )\n",
    "\n",
    "\n",
    "for band in bands:\n",
    "    columns += [\"f_ap_{}\".format(band), \"ferr_ap_{}\".format(band),\n",
    "                \"m_ap_{}\".format(band), \"merr_ap_{}\".format(band),\n",
    "                \"f_{}\".format(band), \"ferr_{}\".format(band),\n",
    "                \"m_{}\".format(band), \"merr_{}\".format(band),\n",
    "                #\"flag_{}\".format(band)\n",
    "               ]    \n",
    "\n",
    "for band in bands_no_ap:\n",
    "    columns += [\"f_{}\".format(band), \"ferr_{}\".format(band),\n",
    "                \"m_{}\".format(band), \"merr_{}\".format(band),\n",
    "                #\"flag_{}\".format(band)\n",
    "               ]   \n",
    "    \n",
    "columns += [\"stellarity\", \"stellarity_origin\", \"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \"flag_optnir_obs\", \"flag_optnir_det\",\n",
    "            \"zspec\", \"zspec_qual\", \"zspec_association_flag\", \"ebv\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing columns: {'flag_decam_i', 'flag_omegacam_r', 'flag_irac_i3', 'flag_wfc3_f125w', 'flag_wfi_856nm', 'flag_acs_f850lp', 'flag_irac_i1', 'flag_irac_i2', 'flag_vista_y', 'flag_decam_y', 'flag_wfi_485nm', 'flag_wfi_u', 'flag_wfc3_f160w', 'flag_irac_i4', 'flag_gpc1_y', 'flag_wfi_i', 'flag_omegacam_g', 'flag_vista_h', 'flag_wfi_696nm', 'flag_wfi_416nm', 'flag_gpc1_i', 'flag_omegacam_z', 'flag_wfi_604nm', 'flag_gpc1_r', 'flag_wfi_753nm', 'flag_wfi_914nm', 'flag_vista_ks', 'flag_wfi_v', 'flag_acs_f606w', 'flag_omegacam_i', 'flag_wfi_518nm', 'flag_wfi_461nm', 'flag_wfc3_f105w', 'flag_wfi_r', 'flag_wfi_571nm', 'flag_wfc3_f098m', 'flag_acs_f435w', 'flag_gpc1_g', 'flag_hawki_k', 'flag_decam_r', 'flag_wfi_815nm', 'flag_vista_z', 'flag_acs_f814w', 'flag_wfi_646nm', 'flag_acs_f775w', 'flag_isaac_k', 'flag_decam_z', 'flag_gpc1_z', 'flag_wfi_b', 'flag_decam_g', 'flag_omegacam_u', 'flag_vista_j'}\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": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[columns].write(\"{}/master_catalogue_cdfs-swire{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  }
 ],
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