{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EGS master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on EGS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "0246c5d (Thu Jan 25 17:01:47 2018 +0000) [with local modifications]\n",
      "This notebook was executed on: \n",
      "2018-05-01 13:22:26.602640\n"
     ]
    }
   ],
   "source": [
    "from herschelhelp_internal import git_version\n",
    "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))\n",
    "import datetime\n",
    "print(\"This notebook was executed on: \\n{}\".format(datetime.datetime.now()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "import os\n",
    "import time\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column, Table\n",
    "import numpy as np\n",
    "from pymoc import MOC\n",
    "\n",
    "from herschelhelp_internal.masterlist import merge_catalogues, nb_merge_dist_plot, specz_merge\n",
    "from herschelhelp_internal.utils import coords_to_hpidx, ebv, gen_help_id, inMoc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n",
    "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n",
    "SUFFIX = os.environ.get('SUFFIX', time.strftime(\"_%Y%m%d\"))\n",
    "\n",
    "try:\n",
    "    os.makedirs(OUT_DIR)\n",
    "except FileExistsError:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I - Reading the prepared pristine catalogues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aegis = Table.read(\"{}/AEGIS.fits\".format(TMP_DIR))\n",
    "candels = Table.read(\"{}/CANDELS-EGS.fits\".format(TMP_DIR))\n",
    "wirds = Table.read(\"{}/CFHT-WIRDS.fits\".format(TMP_DIR))\n",
    "cfhtls_wide = Table.read(\"{}/CFHTLS-WIDE.fits\".format(TMP_DIR))\n",
    "cfhtls_deep = Table.read(\"{}/CFHTLS-DEEP.fits\".format(TMP_DIR))\n",
    "cfhtlens = Table.read(\"{}/CFHTLENS.fits\".format(TMP_DIR))\n",
    "deep = Table.read(\"{}/DEEP2.fits\".format(TMP_DIR))\n",
    "irac = Table.read(\"{}/IRAC-EGS.fits\".format(TMP_DIR))\n",
    "hsc = Table.read(\"{}/HSC.fits\".format(TMP_DIR))\n",
    "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))\n",
    "legacy = Table.read(\"{}/LegacySurvey.fits\".format(TMP_DIR))\n",
    "uhs = Table.read(\"{}/UHS.fits\".format(TMP_DIR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Merging tables\n",
    "\n",
    "We first merge the optical catalogues and then add the infrared ones.\n",
    "\n",
    "At every step, we look at the distribution of the distances to the nearest source in the merged catalogue to determine the best crossmatching radius."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HSC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = hsc\n",
    "master_catalogue['hsc_ra'].name = 'ra'\n",
    "master_catalogue['hsc_dec'].name = 'dec'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add PanSTARRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Djd1aPkH9VaNG+qyy9AbBCgAAz40nWNVKKhpze2bovrHKJT1iZkckvVvSd8zs9lNfyDn3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b74f67cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(ps1['ps1_ra'], ps1['ps1_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, ps1, \"ps1_ra\", \"ps1_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AEGIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HGNdqKMxKJT8zVQ3sIiIiF6BgFePcnGE1yxjDipIsGhSsREREzkvBKsZNr1i5G6xgus/q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\nrCIiIiJ6JIlVRERERI8ksYqIiIjokX8BgWqPUPqpqZIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b6896a080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(aegis['aegis_ra'], aegis['aegis_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, aegis, \"aegis_ra\", \"aegis_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CANDELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1PCuJhUIBIMw451TZ0qcDNZ06dL5TR2o7L+7vGmFSdnKclmYlKS8Qp9yUOOUG4pQcN/0y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b74fe02e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(candels['candels-egs_ra'], candels['candels-egs_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, candels, \"candels-egs_ra\", \"candels-egs_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CFHT-WIRDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9AvgO0OVhfXFlaibEyOSM58EK0AJ2ERGRGLCYYFUGNM+73RK57wwzKwPeD3zZu9Lij5et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\n/AJsFGrWkOpocgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b64c31518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(wirds['wirds_ra'], wirds['wirds_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, wirds, \"wirds_ra\", \"wirds_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CFHTLS-WIDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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znn5fAADgHYKVx5aClYeL16Wzvaz6mA4EACBrEaw8NpymqcCGykSwYp0VAADZi2DlsdMT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\nAgAA8Mj/Dxu9aFV5UR6bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b1ac53128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(cfhtls_wide['cfhtls-wide_ra'], cfhtls_wide['cfhtls-wide_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, \n",
    "                                    cfhtls_wide, \n",
    "                                    \"cfhtls-wide_ra\", \n",
    "                                    \"cfhtls-wide_dec\", \n",
    "                                    radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CFHTLS-DEEP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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36AwTUzFqSwJ89rqF3La+gXVNpTz0ervbZUqGqC4OELWWgbEwVcH5e4OCgtUM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b38237d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(cfhtls_deep['cfhtls-deep_ra'], cfhtls_deep['cfhtls-deep_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, \n",
    "                                    cfhtls_deep, \n",
    "                                    \"cfhtls-deep_ra\", \n",
    "                                    \"cfhtls-deep_dec\", \n",
    "                                    radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CFHTLenS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D6jqSVGqKXlYAABQSgpVHppuDejRjNTlFLysAAAoNwcojPYPZcwI92GOVCWfsswIAoLAQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b413de160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(cfhtlens['cfhtlens_ra'], cfhtlens['cfhtlens_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, cfhtlens, \"cfhtlens_ra\", \"cfhtlens_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DEEP2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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28v3t3XSPTVNa6OUtK+u5bmkNVZruywntdSVY4MToFKubK9wuJyNlz10/ywTD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\nAUN0hi4H8Zd4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b38cfcba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(deep['deep2_ra'], deep['deep2_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, deep, \"deep2_ra\", \"deep2_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IRAC-EGS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cEMiZ2xk4PqNgdZr4edVPMmNzx9nkZcTHVCDA5rmu6691DitYiSShB3e3EwxF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\ngySxioiIiGiQJFYRERERDfI3xDglaFYMqdUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b689731d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(irac['irac-egs_ra'], irac['irac-egs_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, irac, \"irac-egs_ra\", \"irac-egs_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Legacy Survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JiIgkAwWrBGjtHQG8D1YrYsGqXvOsREREkoKCVQI0R4bJTAuSluLt252VnkJJdjoNHQpW\nIiIiyUDBKgFaIsPkebwicJpWBoqIiCQPBasEaIkMe9rDaqZVxQpWIiIiyULBKgFaIiOez6+atrIo\nTNfgGL1D2oxZRETEbwpWcdY3Ms7A6EQcbwVGN2Nu6NKolYiIiN8UrOIsXq0Wpq1UywUREZGkoWAV\nZy1xag46raogk4ChlYEiIiJJQMEqzpoj0R5WXm9nMy0tJUBlQaZ6WYmIiCQBBas4a40Mkxo0sjJS\n4nYNtVwQERFJDvH7aS9A9FZgaU4GAbOLfq1v7Tx1zufHJqaobRvgmztOcu811Rd9HREREbkwGrGK\ns5bICMvzQnG9RmFWOmOTU/SPTMT1OiIiInJ+ClZx1hwZpjzOwaooKzoxvnNgNK7XERERkfNTsIqj\nySnH6b4RludlxPU6RVnpAHQOjMX1OiIiInJ+ClZx1NE/yuSUi/utwNxQKikB04iViIiIzxSs4qg5\n1sNqeW58g1XAjMKsNAUrERERnylYxdF0c9B4j1hB9HagbgWKiIj4S8Eqjn4VrOI7xwqiwap7cJSJ\nyam4X0tERETOTcEqjloiw2RnpJCdEZ+u6zMVZaUx5aCpZzju1xIREZFzU7CKo5bekbi3Wpg2vTJQ\nHdhFRET8o2AVRy2RYcpy438bEKJNQgHtGSgiIuIjBas4aokMJ2TiOkA4LUhGaoCGzoGEXE9ERER+\nnYJVnAyNTdAzNJ6wYGVmFGWl61agiIiIjxSs4qQlMgKQsDlWEJ1n1dChYCUiIuIXBas4ae1NXA+r\naUVZabT0jjA8Npmwa4qIiMivKFjFyXQPq0RNXodfrQw80aVRKxERET8oWMVJc2QEM1jmQ7DSPCsR\nERF/KFjFSUtkmNLsDFKDiXuLC7PSAAUrERERvyhYxUlr73BCtrKZKT0lSGmOVgaKiIj4RcEqTloi\nIwmduD5tZVGY+g71shIREfHDnIKVmd1uZkfNrM7MPnOe8640swkz+03vSlx4nHM0R4YT2mph2uri\nLGrbB3DOJfzaIiIiS92swcrMgsADwB3AeuB9Zrb+dc77HPCU10UuNF2DY4xNTCV0ReC0mtJs+kcm\naO8fTfi1RURElrq5jFhdBdQ55+qdc2PAo8Dd5zjvd4HvAe0e1rcgTbda8ONW4NqSLACOtfUn/Noi\nIiJL3VyCVTnQOONxU+y5M8ysHHgn8KXzvZCZ3Wdmu81sd0dHx3xrXTCmu677EqxKswGobdM8KxER\nkUTzavL6PwCfds5Nne8k59xDzrltzrltxcXFHl06+fg5YlWUlUZeZiq17RqxEhERSbSUOZzTDFTO\neFwRe26mbcCjZgZQBNxpZhPOuX/3pMoFpjkyTCg1SH5masKvbWbUlGRrxEpERMQHcxmx2gWsNbOV\nZpYGvBf44cwTnHMrnXMrnHMrgP8LfGyphiqA5p5hyvNDxIJmwq0pzeJYW79WBoqIiCTYrMHKOTcB\nfAJ4EjgMPOacO2hm95vZ/fEucCFqjgz7chtwWk1JFn0jE3RoZaCIiEhCzeVWIM65J4Anznruwdc5\n97cvvqyFrSUyzMbyXN+uPz2B/VjbACU5iW/5ICIislSp87rHhscm6RocoyLfvxGrtaXRlguawC4i\nIpJYClYea46tCPSj6/q04qx0ckOpHNMEdhERkYRSsPKYn60WppkZNaVZ1GnESkREJKEUrDx2ZsTK\nx1uBEJ1ndaxNewaKiIgkkoKVx5p7hgkGjNLsdF/rWFuSRe/wOB0DWhkoIiKSKApWHmuJDLMsJ4OU\noL9vbY22thEREUk4BSuPNUWGfZ24Pm16M+ZabcYsIiKSMApWHpvuuu634uzYysB2jViJiIgkioKV\nhyYmpzjdN8LyPP+bcpoZa0uyqNOtQBERkYRRsPJQe/8ok1OO8rxMv0sBYisD27VnoIiISKIoWHko\nWVotTFtbkkVkaJzOgTG/SxEREVkSFKw81Nzjf9f1mX61MlAT2EVERBJBwcpDzWe6rvs/xwpm7hmo\neVYiIiKJoGDloebIMAXhNDLTUvwuBYCS7HRyMlI4phErERGRhFCw8lBzT3L0sJpmZqwtzdaIlYiI\nSIIoWHmoJTKcNLcBp9WUZlHbppWBIiIiiZAc96wWAecczZFhrltb7Hcpr7GmJJueoUa6BscoyvJ3\n/0IREVk4vrXz1Kzn3HN1VQIqWVg0YuWRyNA4Q2OTSdNqYVpNbAK75lmJiIjEn4KVR870sEq6W4HR\nlgt1mmclIiISd7oV6JFfBavk6Lo+rSQ7nWytDBQRkRnmcptPLoyClUfONAdNsluBZkZNaTa12jNQ\nRGRJUGjyl4KVR5ojw4RSg+Rnpvpdyq+pKc3iJ6+exjmHmfldjoiIeGhicormyDAnuobo6B/lQHMv\nGSkB0lODpKcECKenkJWuH/eJonfaI9OtFpIxuGwqz+PbLzVyqnuI6sKw3+WIiMgFcM7R1DPMA8/U\n0RwZ5nTvCF2DY0SGxpiapaPOJaXZ3HhJsX4GJICClUeaI8OU5yfX/KppWyvzAHilMaK/VCIiC0TX\nwCj7miK80tjLvsYIB5p76R4cAyAYMEqz0ynPC7G5IpfCcDqF4TRyQqmMTUwxOjHJyHj0c1vfKDsb\nuvjys/WsKMzkhpoSakqzknIgYDFQsPJIc88wG5bn+l3GOdWUZhFKDbL3VIS7t5b7XY6IiJxlYHSC\ng829HGjuZV9TL6809tDYHZ27G7DoCu83X1rC5oo8WiMjlOamkxKY+8L+G2qK2XWim+frOvnaiyco\ny83g3dsqKc1JrpXsi4GClQeGxybpGhxLulYL01KCATZX5LK3MeJ3KSIiS17v0DiHWvs42NLLwZY+\n9jdFqO8cZHqDjNxQKhX5IW7fkEtFQYjyvBDpKcEz338hi6TSUgJcu6aIq1cVsK+xl58ePM3XXjjB\nR29cTXZG8s0NXsgUrDzQ0pucKwJn2lqVxyPPNzAyPklGanD2bxARkYvinKOld4SDzb2xINXHoZa+\nM+15AEpz0tlUnsfbt5SzqSKH2raBuAadlECAK6rzKc1J51+eq+frL57kv163irQUtbX0ioKVB860\nWkiyHlYzXVaZz/hkPYda+7i8Kt/vckREFhXnHCe7htjf3MuBpkg0RLX2ERkaB8AMCsPplOVmsLE8\nl7LcDMpyM14Tok73jiZs9KgiP5P3XlnFN3ac5Du7TnHvNdUENOfKEwpWHpj+7SPZNmCe6bKq2AT2\nUxEFKxGRi9Q9OMaekz28fKqH/U0R9pzsYWR8CoCUgLEsN4O1JVmU5YZYnpvBstxQ0o0KXVqWw12b\ny3h8fys/PtDK2zYv97ukRUHBygMtkWGCAWNZEk8CLM3JYHluhuZZiYjM0zd3nKR7cIyGzkFOdg9x\nsmuIzoFRIDqxfFlOBpvK86jIC1GeH6I0J4NgYGGM/rxhdRHdg2NsP95FQWYa164p8rukBU/BygPN\nPcMsy8kgJZhcv42cbWtVHq809vhdhohI0mvqGeLF4128WN/FLw630zscvaUXSg1SXZjJ5VV5VBeG\nqcgPkZrk//bP5o5NZfQMjfPEgVZKstNZG9tjVi6MgpUHmiLDlOcl78T1aZdV5vPEgdN0DoxSlJXu\ndzkiIkmjb2ScF4938VxtB8/XdnKiawiAgnAalfkhbqgpZmVRmOLs9EU3Fylgxru3VfLPT9fyk1dP\ns7oka9H9NyaSgpUHWiLDbKtO/nlLW2fMs3rz+lKfqxER8c/klOOVxgjPHuvgB3ubaeoZYspBWjDA\nyqIwb91UxqriMKU5GUsiZKSlBLjl0hIe293EoZY+NpYnZ1/GhUDB6iJNTjlO944kdauFaRuX55IS\nMPY29ihYiciS09o7zLPHOnj2WCfP13XSOzyOGZTnhbi+ppi1JdlUFoTm1XhzMdlckcfTRzp4+kg7\n65fnLIlAGQ8KVheprW+EiSmX1K0WpoXSgqwry+YVTWAXkSVgZHySz/7kCLVt/dS2D9DeH51wnpOR\nwpqSbGpKs1hTnEWmNigGorcEb15XwmO7GznY0scmjVpdEP1pukgLodXCTJdV5vODvc1MTrkFs2pF\nRGQunHMcbu3n+boOnqvt5KWGbkYnpkgJGCuKwlxRnc/akmxKc9K1T97r2FyRyzNH2nn6SBsbNGp1\nQRSsLlJLLFhVLIBbgRDdkPnfdpzkeMcANVr5ISILXGvvMNvruni+toPn6zrpHIhuUry2JIt7r65m\nyjlWFIaTrodUspoetfqORq0umILVRWrqmR6xWhjBarpR6N5TPQpWIrLgdA+O8eLxLrYf7+TJV0/T\nNRgNUuH0FNYUh7mxpoTVJVnkhrT/3YXaVJHL00fb+cVhjVpdCAWri9TYPURhOI3MtOR4K7+189R5\njzvnyA2l8kpjhPdcWZWgqkRELkxH/ygvNXSzs6GLnfXdHG3rByArPYWK/BBXrypk9RJavZcIZ0at\ndjXyanMvmyvy/C5pQUmONLCA1bUPsLo4y+8y5szM2FqZx95TmsAuIsnFOUdj9zC7TnTz6K5TnOgc\noiPW4TwtGKC6MJPb1peyqjiL8ryQ5onG0abyXJ4+0s7TR9rZWJ6r0DoPClYXwTlHXccAd2ws87uU\nedlamcc/P13LwOgEWVoNIyI+GZ+c4nBrH7tP9LDnZA+7TnSfWbmXkRqguiA64XxlUZjlClIJFTDj\nlnUlPKpRq3nTT9WL0DU4RmRonLUlC2fECqLzrKYc7G+K8MbV2hdKRBKjd2icl0/1sPtkN08cOE1T\nzxDjkw6A3FAqKwozuWZVISsKw5TkLL4O5wvNxvJcig638WJ9l4LVPChYXYS69gEA1iywYLW1MtaB\nvVHBSkTiwznHia4hdp/oZs/J6IhUbezfzOlN67etKKC6IJOqgkzyMtN8rljOFjDjiqp8njzURtfA\nKIXaCm1OFKwuQu0CDVZ5mWmsKgprnpWIeGZsYopXW3rZc6KHH+xt5mTXIINjk8Cvbuvdur6UqoJM\nKvMz1f5ggdhalc9Th9rY2xjhzZdqx465ULC6CMfbBwinBSnLXRjNQWfaWpnHc3WdOOfUKE9E5q1/\nZJyXT0XYfaKblxq6eaUxwujEFBDduLimNJsVhWGqCjMX5cbFS0VuKJXVJVnsPdXDzetK9P9xDuYU\nrMzsduAfgSDwsHPus2cdvxf4NGBAP/BR59w+j2tNOnXtA6wpyVqQweSKFfl8f28zde0DrFU/KxGZ\nRWRojJcauvnaCydo6BqkNTKCAwIGZbkhtlXnU10Yprowk+wM9ZBaTC6vyuOx3U2c6BpkVdHCukPj\nh1mDlZkFgQeAW4EmYJeZ/dA5d2jGaQ3ADc65HjO7A3gIuDoeBSeTuvYB3rim0O8yLsgt60r5Y17l\nqUNtClYi8mt6h8bZ0dDFi8e72FHfxdG2fpyDlIBRWZDJjZeUsKIok6r8TNJTg36XK3G0viyX9JQW\n9p6MKFjNwVxGrK4C6pxz9QBm9ihwN3AmWDnnXphx/g6gwssik1HfyDin+0YW3PyqactyM9hSkctT\nh9r4+E1r/C5HRHw2MDrB3/z0CPUdg9R3DNDaGx2RSg0aVQWZ3LKuhJVFWVTkh0gNan7UUpKWEmBj\neS4Hmnt525blmh83i7kEq3KgccbjJs4/GvVh4CfnOmBm9wH3AVRVLeyu38enJ64voOagZ7ttwzI+\n/+RR2vpGKM1ZePPEROTCjU5MsvdUhBfqOtl+vIt9jREmYpuzVxVkcvOlJawqyqIyP0SKgtSSd3lV\nPntO9nCwpZfLqvL9LiepeTp53cxuIhqs3nSu4865h4jeJmTbtm3Oy2sn2kJttTDTbetL+fyTR/nZ\noTbef0213+WISBxNTTkOtfbxwvFOnq/rYldDN8PjkwQMNlXkcd/1qxgZn6K6MFMjUvJrqgszyc9M\n5eVTPQpWs5hLsGoGKmc8rog99xpmthl4GLjDOdflTXnJq659gLRggKqCTL9LuWBrSrJYWRTmKQUr\nkUXHOcep7iG210U3LH7mSDtDsfYHxdnpbKnMY01x9N+AUJrmSMn5Bcy4vCqfp4+0ExkaU9+x85hL\nsNoFrDWzlUQD1XuBe2aeYGZVwPeBDzjnjnleZRKqax9gZVF4QQ+Rmxm3rS/lke0N9I2Mk6OVPCIL\nWnvfCC/bof9dAAAaMUlEQVQc72J7XScvHO+iOTIMwLKcDNYty2Z1cRari7PICenvuszfZVX5/OJI\nO680RrjxkhK/y0laswYr59yEmX0CeJJou4VHnHMHzez+2PEHgT8FCoEvxloPTDjntsWvbP/VdQyw\ncXmu32VctFvXl/LlZ+v55dEO3r5lud/liMg89A6Ps6M+unJve13nmabFuaFU3rCqkPtvWMUb1xSx\nqijMt19qnOXVRM6vIJzGisIwL5/q4Yaa4gXZaigR5jTHyjn3BPDEWc89OOPrjwAf8ba05DUyPklj\n9xB3by33u5SLdllVPkVZaTx18LSClUiSGxmfZNeJbrbXdfHC8U5ebe5lykEoNUhFfojbNyxjVXF0\nw+LpRo4767vZWd/tc+WyWFxelcf39zbT2DO8oKfCxJM6r1+A+o5BphwLbvPlcwkGjDdfWsqP9rcy\nOjFJeormWogki8kpx6vNvXzhmTqOtw9wqnuIiSlHwDjTS2p1cRaVBSFSAgt3WoIsHBvLc3l8fwsv\nn+pRsHodClYXoK5j4a8InOm2DaU8uquRHfXd3FBT7Hc5IktaY/cQz9V28nxdB9vruugdHgei86Su\nWVXI6uIwK4rC+iVIfJGRGuTSshxebe7lbZt1l+NcFKwuQF37AAGDlUVhv0vxxBtXF5GZFuSpg6cV\nrEQS7CvbG6jvGKS2fYDatn66BscAyMlIYU1JNmtKslhdHNY2MZI0Npfnsb+pl+OxQQZ5LQWrC3C8\nfYDKgkwyFsk2DhmpQW6oKeZnh9r4y7s3EghoQqJIvExNOQ629PFsbQfPHutg94keJp0jNWisKsri\nDasLWVOcRXF2uiYHS1KqKc0iIzXA/qZev0tJSgpWF6C2vX9RzK+a6bYNpfzk1dPsa4qo+ZuIx1oi\nwzxf1xm9xVfbQc9Q9PbepWU5XLumkLWl2VQXZC7o9i2ydKQEA6wvy+FQa6/m5p6DgtU8TUxO0dA5\nyE3rFlcPj5svKSUYMJ461KZgJXKRptsgbK/r5Pm6Tuo7BgEoykrnpktKuK6miGvXFFGSncG3dp7y\nuVqR+dtckcfLpyI8e6yTW9eX+l1OUlGwmqdT3UOMT7oFvUfgueRmpnLNqgKeOniaT9++zu9yRBaU\nobEJdp3o4YXjnew43sWBWBuE1KCxsijMnRuXsboki2U5GZgZw2NT/PxQu99li1yw1cVZZKYF+dH+\nFgWrsyhYzdNi2CPw9dx6aSl//vghDrf2cWlZjt/liCStkfFJXj7Zw788V099xyBNPcNMOkfQjIqC\nEDfUlLCmRG0QZPEKBowNy3P42aE2hscmtS3SDApW81S7iIPV3VvL+Zsnj/Ll/zzOP7z3Mr/LEUka\nI+OTvHyqhx313ew43sUrjRHGJqcIGCzPC/GmtdHu5tWFYdJSFKRkadhckceuEz08c7SdOzeV+V1O\n0lCwmqfj7QMsy8lYlEuf88Np3HNVFV954QT//dZLqCpU8zdZmkbGJ/nbJ49S3zlIQ+cgjbHGnAaU\n54e4elXBmSC1WFYHi8zXyqIwRVnpPL6vRcFqBgWrearrGFiUo1XTPnLdKr7+4km+/Oxx/uqdm/wu\nRyQhRicm2Xsqcmbfvb2NEcYmpjCiI1LXrCpkVXGYFQpSImcEzHjrpmU8uquRgdEJstIVKUDBal6c\nc9S1D/DubZV+lxI3y3IzeNcV5Xx3TxOfvGUtJTkZfpck4rmR8UleaYyws76bHfVdvHyqh9GJKcxg\nfVkOH7ymmtGJKVYUhjV3ROQ87tqynK+9eJJfHG5bFPvnekHBah5aekcYGptc1CNWAL9z/Wq+s6uR\nf32+gT+681K/yxG5aMNjk+w91cPOhm52NnSx+0TPmVt7y3Iz2Fadz6riLAUpkXm6oiqfZTkZPL6v\nRcEqRsFqHhbzisCZVhSFuWvzcr6x4yQfu3ENuZmLbz6ZLG69Q+PsPtnNSye6eamhmwNNvWc2L16/\nPIdrVhWysiisICVykQIB467NZXztxRP0Do3r5wUKVvOyWILVXBoSfvTG1fxwXwtfe/EEv3fL2vgX\nJXKBnHM09Qyz+2Q3u0708PNDbbT3jwIQNKM8P8S1a4pYURimunDxbEUlkizu2rKch59v4MlDpxf1\nVJm5UrCah7r2fvIyUykMp/ldStxdWpbDLetK+Mr2Bj5y3Uoy0/RHRZLD6MQkB1v6ePlkD3tiH9NB\nKjs9hbK8DLZU5lFdkElFfqbaH4jE2ZaKXCoLQjy+r0XBCgWredl7KsL6spwlszHqx25aw7u+9ALf\nfqmRD79ppd/lyBLV3j/Cyycj7D3Vw09fPU1zZJiJKQdAfmYq1YVhrllVSHVhJqU5GQSWyN9PkWRh\nZrxjazkPPFNHa+8wZbkhv0vylYLVHLX3jXDkdD//4/ZL/C4lYa6ozufqlQX8y7P1fOCaav3mL3E3\nMTnF0bb+M6NRL5+KcKp7CIC0YIBluRlcs6qQqoJMqgozyVmE/eREFqLfuqKSf366ju/taeITNy/t\n6SMKVnP0fF0nANevLfa5ksT6+E1r+OAjL/HAM3V86tYav8uRRaZvZPw1t/R2n+hhbHIKgOyMFKoK\nMrlj4zKqCzJZnhciJahwL5KMqgozecOqQh7b3cTHblxDILB0R44VrObo+dpOCsJprF9ie+hdX1PM\nb1xezj89XcvVqwp44+oiv0uSBco5R3NkmN0neth1ops9J3s42taPc9F9x9Yty+by6nyqY6NReaHU\nJXPbXWQxePeVFXzqO/vY0dC1pH9WKFjNgXOOZ2s7edOaoiWZwv/y7o3sa4zwyUdf4SefvI6irHS/\nS5IFYHLKcfR0/5nVes8e66B3eByA9JQAVQWZ3LyuhOqCMJUFIdJTtFpPZCG7Y2MZf/ofB3lsV6OC\nlZzfkdP9dA6Mct3apfkHJZyewhfuuZx3PLCdT33nFb72oauWZMCU8xudmGR/Uy8vNXSz+0Q3u0/2\n0D8yAUBpTjpVBZmsKAqzQpPMRRaljNQgd29dznd3N/EXw+PkhpbmHEgFqzl4rrYDgOuW2PyqmS4t\ny+HP3raB//mDA3zpP4/z8ZvW+F2S+CwyNMaekz3sOtHDTw60vma1XnF2OuuW5bCiMJPqwjD5mbqt\nJ7IUvGdbFd/YcYof7mvhA9dU+12OLxSs5uC52k7WlmSxLHdp75v3vqsqebG+i7//2TGuWlnAlSsK\n/C5JEmRqylHfOcDLJyOx1Xo91MYa5qYGjWU5Gbwh1vKgujBMWJuxiixJG8tzuLQsh8d2NSpYybmN\njE/yUkM39169NP+AzGRm/PU7N3KgKcLvfmsvP/69N1Go+VaLUs/gGPuaIrzSGOGJA600dg8zPD4J\nQCg1SFVBJretL6W6MExFfohUrdYTEaI/J969rYK/ePwQh1r6WL98aS34AgWrWe060c3oxBTX1SzN\n+VVny85I5Qv3XM5vfOkFfuvBF/nX376SlUVhv8uSizA8Nsmh1l72NfayPxamTnRFe0eZQXFWOhuW\n51BdmEllQSZFWemaHyUir+sdW8v5308c4bHdjfz52zf4XU7CKVjN4rnaTtKCAa5eubRue822n+A3\nP3I19319N+/84na+/P4ruHpVYYIqk4sxOjHJ0dP97G/q5UBTL/uaIhxr6yc2NYqcjBQq8jN5y4Zl\nVOSHqMgLka699URkHvLDady2oZQf7G3mM3esW3L7cypYzeK52k6uqM7XXnlnuXJFAf/+8Wv50Fd3\n8f5/3clnf2Mz77qiwu+yZIbxySlq2wY40ByJBqnmXo609p9pwJmXmcrmijzKcjOoyM+kPD+kTuYi\n4on3XFnJj/a38rNDbbxty3K/y0kopYXzaO8f4XBr35LaxmY+qgvD/OCj1/LRb+7h97+7j4bOQf77\nrTVqxeCDickp6joGONDUy6vNvexv7uVQSx+jE9EQlZ4SoDw/xDWrCijPz6QiL0SeVuqJSJxcu7qI\n8rwQj+1uVLCSX9m+RLexmY/czFS+9l+u4k9+8CpfeKaOlxq6+cyd67i8Kt/v0hatsYkpjrX1c6il\nj1dboiNRh1v7GBmPhqhwWpCN5bl84Jpq+kcmKM8PURBO07woEUmYQMB475WV/N3PjnGgqZdNFbl+\nl5QwClbn8dyxpbmNzXylBgN89l2b2FqVx989dZTf+OILvGVDKX/4lnWsKcnyu7wFLTI0xpHT/Rxu\n7eNQSx8HW/qobe9nfDI6KSotJcDy3BBXVOVTnh+iPC+TwiyFKBHx329fu4JHtjfwuZ8e4Rsfudrv\nchJGwep1OOd4rq6Ta5foNjbzZWa876oq3r5lOQ8/18BDzx7nZ4f+k3dvq+TjN62hsiDT7xKT2tDY\nBMfbBznW1k9t+wBHT/dx5HQ/rb0jZ84pDKexfnkO19esYsPyHBo6BilQiBKRJJWdkcrHb1rD///j\nwzxf28mblsjuJQpWr+NoWz8d/Ut3G5vZnG/VYHF2Op98cw0tkWG+ufMkj+5qZEtlHndtKuPOzWWU\n54USWGnycM7R0T/K8Y5B6jsHqO8YpL5jgNr2AZp6hs+clxo0CsPpLMvNYEtFHstyMyjLzSArPeXM\nnKj+kQmKstVDTESS2/uvqeYr20/wN08e4do11y6JeZ0KVq/juWPR+VUKVhcmKz2FP3/7Bu67fhWP\n72vhxwda+asnDvNXTxzmsqo8bllXwpbKPDaX55GbuXhWoo1PTnG6d4TGniFOdQ1xsnuIk12DnOwa\n4mTXEAOjE2fOnQ5QJTnprFuWTUl2BiU56RSG0wlqlFREFoGM1CCfurWGP/juPp44cJq3bi7zu6S4\nU7B6Hc8cbWdNSRZluUtzdMUry/NC/M4Nq/mdG1ZzqmuIHx9o5Uf7W/jbp46dOWdFYSabKvJYX5ZD\nZUGIivxMKvJDFIbTkuq3m7GJKToHRmnrG6Gtb/rzCKd7R2jqGaY5Mkxr7/CZnlAAQTPyw6kUhNPY\nWJ5LcVYaRdnpFGelkxNK1W08EVn03nlZOf/ybD1/+9RRbttQuuh3alCwOocXj3fxwvEu/vAtarNw\nMc51uzA3lMq9V1czPDZJc2SYouw09jf2sudEN4/va3nNuRmp0YnZ+eE08jNTyctMoyCcRm4olcy0\nIJlpQTJSg2SmpRBKDZISNFICRjBgpAQCBGJ/d6emYMq52Ed0VGl0YorR8cno54kphscm6B+doH9k\ngoGRCQZGJ+gdHqdrcIye2Ef/jNGmaSkBozQng/L8EFevLKA8P0RzzzB5mWkUhtPIzVR4EpGlLRgw\n/vAtl/CRr+/mu7ubuOfqKr9LiisFq7NMTjn+vx8dojwvxIfftNLvchatUFrwzIrB62uKub6mmJHx\nSSJD4/QMjUU/BsfoHZkgPSVAc2SEgy199AyNnWkrEA8Biw5dp6cECKUGCaenUBBOoyI/RGZaClnp\nKeSEUsjJSCUnFvDODk4a5RQRea1bLi1hW3U+//DzY7zzsnJCaYu3G7uC1Vke293I4dY+vnDPZUuu\nDb/fMlKDLMsNsiw347znjU9OMT4xxdhk9GN8wjE2OfWrUakZI1RGdMWiGRjRz8GAkRowUoIBUmKf\n01ICpKdEHyfT7UcRkcXAzPj0Hev4rQdf5CsvNPCxG9f4XVLcKFjN0Dcyzt8+eZQrV+Tz1k2Lf4Ld\nQpUaDJAaDKAGDiIiC8eVKwq4ZV0JX/rlce7atJyqwsX5r/jinkE2Tw88XUf30Bh/etcGjVqIiIh4\n7E/uWk8wYHzwkZ10Doz6XU5cKFjFnOgc5JHtDfzm5RVLqvW+iIhIoqwsCvPIb1/J6b4RPvSVXa9p\nQbNYKFjF/PUTh0kLBrQSUEREJI4ur8rni/dezqHWPu7/tz2MTcRvQZIfFKyAF+o6eepQGx+7aQ0l\nOeefOC0iIiIX5+Z1pXzuXZt5vq6T3//uPqZmNgBc4Jb85PW69n7++N9fpSJf7RVEREQS5TevqKCj\nf5TP/fQIheE0/uxt6xfF/OYlG6wmpxyPPN/A5586SjgtyBfvvULtFURERBLo/htW0dE/yiPbG9h7\nqodP376ON65Z2FvJLclgdaJzkD/47j52n+zh1vWl/PU7N1GsDW1FREQSysz4k7deyrpl2fyfnx/j\nnod3ct3aIj59+zo2li/MhWRzClZmdjvwj0AQeNg599mzjlvs+J3AEPDbzrmXPa71okxNOeo7B/nF\n4Tb+4ee1pAaN//OeLbxja/miGHoUERFZiAIB491XVvL2rcv5txdP8sAv67jrn5/nrs1l3LmpjC2V\neSzPzVgwP6tnDVZmFgQeAG4FmoBdZvZD59yhGafdAayNfVwNfCn22Tf9I+O8eLyLfU0R9jX2sq8p\nQv9IdFnnDTXFfO5dm2ft8C0iIiKJkZEa5L9ev4p3X1nJQ88e5yvbT/Cj/a0AFGWls7Uyl80VeSzP\nC5EXSiU3MzX6OfZ1ekpyTOeZy4jVVUCdc64ewMweBe4GZgaru4GvO+ccsMPM8syszDnX6nnFc3Sy\na4j7/m0PKQFjXVk2b9+ynK2VeWytzGNNSdaCSb4iIiJLSW4olT98yzo+eUsNR0738UpjhFcaI+xr\njPDzw+3n/J73XVXJ//6NzQmu9NzmEqzKgcYZj5v49dGoc51TDrwmWJnZfcB9sYcDZnZ0XtVeoOPA\njxNxofMrAjr9LmKR0nsbX3p/40vvb3zp/Y2je/0uIOazsY84q57LSQmdvO6cewh4KJHXTBZmtts5\nt83vOhYjvbfxpfc3vvT+xpfeX0m0uTQIbQYqZzyuiD0333NEREREFrW5BKtdwFozW2lmacB7gR+e\ndc4PgQ9a1DVAr5/zq0RERET8MOutQOfchJl9AniSaLuFR5xzB83s/tjxB4EniLZaqCPabuFD8St5\nwVqSt0ATRO9tfOn9jS+9v/Gl91cSyqIL+URERETkYmkTZhERERGPKFiJiIiIeETBKs7M7HYzO2pm\ndWb2Gb/rWUzM7BEzazezV/2uZTEys0oze8bMDpnZQTP7pN81LSZmlmFmL5nZvtj7+xd+17TYmFnQ\nzPaa2Y/8rkWWDgWrOJqxHdAdwHrgfWa23t+qFpWvArf7XcQiNgH8vnNuPXAN8HH9+fXUKHCzc24L\nsBW4PbaqWrzzSeCw30XI0qJgFV9ntgNyzo0B09sBiQecc88C3X7XsVg551qnN1N3zvUT/QFV7m9V\ni4eLGog9TI19aDWRR8ysAngr8LDftcjSomAVX6+31Y/IgmJmK4DLgJ3+VrK4xG5VvQK0Az9zzun9\n9c4/AP8DmPK7EFlaFKxE5LzMLAv4HvDfnHN9ftezmDjnJp1zW4nuVnGVmW30u6bFwMzuAtqdc3v8\nrkWWHgWr+NJWP7KgmVkq0VD1Tefc9/2uZ7FyzkWAZ9CcQa9cC7zdzE4QnYJxs5l9w9+SZKlQsIqv\nuWwHJJKUzMyAfwUOO+f+3u96FhszKzazvNjXIeBW4Ii/VS0Ozrk/cs5VOOdWEP1392nn3Pt9LkuW\nCAWrOHLOTQDT2wEdBh5zzh30t6rFw8y+DbwIXGJmTWb2Yb9rWmSuBT5A9Lf9V2Ifd/pd1CJSBjxj\nZvuJ/hL2M+ec2gKILHDa0kZERETEIxqxEhEREfGIgpWIiIiIRxSsRERERDyiYCUiIiLiEQUrERER\nEY8oWImIiIh4RMFKRGZlZpOxPlYHzWyfmf2+mQVix7aZ2T+d53tXmNk9iav21649HNuPLymY2XvM\nrM7M1LNKZBFSsBKRuRh2zm11zm0g2iH8DuDPAJxzu51zv3ee710B+BKsYo7H9uObMzMLxqsY59x3\ngI/E6/VFxF8KViIyL865duA+4BMWdeP06IuZ3TCjS/teM8sGPgtcF3vuU7FRpOfM7OXYxxtj33uj\nmf3SzP6vmR0xs2/GttXBzK40sxdio2UvmVm2mQXN7PNmtsvM9pvZ78ylfjP7dzPbExt9u2/G8wNm\n9ndmtg94w+tcc0Ps61di11wb+973z3j+y9PBzMxuj/037jOzX3j4v0FEklSK3wWIyMLjnKuPhYeS\nsw79AfBx59x2M8sCRoDPAH/gnLsLwMwygVudcyOxYPJtYFvs+y8DNgAtwHbgWjN7CfgO8B7n3C4z\nywGGgQ8Dvc65K80sHdhuZk855xpmKf+/OOe6Y/vz7TKz7znnuoAwsNM59/uxvT2PnOOa9wP/6Jz7\nZuycoJldCrwHuNY5N25mXwTuNbOfAP8CXO+cazCzgnm/0SKy4ChYiYiXtgN/b2bfBL7vnGuKDTrN\nlAp8wcy2ApNAzYxjLznnmgBi86JWAL1Aq3NuF4Bzri92/DZgs5n9Zux7c4G1wGzB6vfM7J2xrytj\n39MVq+V7secveZ1rvgj8sZlVxP77as3sFuAKoiENIAS0A9cAz04HPedc9yx1icgioGAlIvNmZquI\nBpF24NLp551znzWzHwN3Eh1Bess5vv1TQBuwheh0hJEZx0ZnfD3J+f+NMuB3nXNPzqPuG4E3A29w\nzg2Z2S+BjNjhEefc5Pm+3zn3LTPbCbwVeCJ2+9GArznn/uisa71trnWJyOKhOVYiMi9mVgw8CHzB\nnbWLu5mtds4dcM59DtgFrAP6gewZp+USHQ2aAj4AzDZR/ChQZmZXxq6RbWYpwJPAR80sNfZ8jZmF\nZ3mtXKAnFqrWER1VmvM1Y4Gy3jn3T8B/AJuBXwC/aWYlsXMLzKwa2AFcb2Yrp5+fpTYRWQQ0YiUi\ncxGK3ZpLBSaAfwP+/hzn/TczuwmYAg4CP4l9PRmbFP5V4IvA98zsg8BPgcHzXdg5N2Zm7wH+OTYv\napjoqNPDRG8Vvhyb5N4BvGOW/46fAveb2WGi4WnHPK/5buADZjYOnAb+OjZf60+ApyzagmKc6Dyz\nHbHJ8d+PPd9OdEWliCxidtYvnCIii4aZrQB+5Jzb6HMprxG7JXlmQr+ILB66FSgii9kkkGtJ1iCU\n6Khdj9+1iIj3NGIlIiIi4hGNWImIiIh4RMFKRERExCMKViIiIiIeUbASERER8cj/AxsAGdVwzCva\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b38cc54e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(legacy['legacy_ra'], legacy['legacy_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, legacy, \"legacy_ra\", \"legacy_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UHS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H/YR7ACZnJBTWvmM92tbQqe0Nndre2KkDzb1yTjKL/NF1/XlztaYyV+uia+09\nvLHB77JnlfSUQORKwZcP6rldx/T+C0r9LmlWmEywKpU0/n9Do04/GvUJSc+eS1EzycZD7VpXlaeE\nGfyX2ZWLC/XwxsPaUt+hi5mYDphxwmGn2tZe7Wjs0o7GLm1v7NTupm4NjYYlSXnpyVpVlq0bzi/R\nmsocrSrPURZ9UTGhIi+oyxYW6JUDrVpVnq35BSzYPNU8bV43s6sVCVaXn+L5uyXdLUkVFTP/tNHx\n7kHVtfXrozN8ZvLLFuYrkGB6aX8LwQqIc845NXYMaHtjZyRENXRq55Eu9Q2HJEnJiQkqyUlVdWWu\nyvOCKssNKnfcYsUN7QNqaB/w80vASa5ZOke7mrr1k61H9OlrFnFmYYpNJlgdkVQ+7n5Z9LHfYWYr\nJd0v6XrnXNtb7cg5d58i/Veqrq52Z1xtnNkww/urxmSmJmltZa5e3Neiz1+31O9yAExg/GzkfUOj\nauzoV0PHgBo7+tXYMaD+aIhKTDAVZ6fqvNJsleUGVZqbpqLMFKY9iDPJgQS9b3WpHnj1kH69t1nv\nXDHX75JmtMkEq02SFpnZPEUC1S2Sbhu/gZlVSHpC0u3Ouf2eVxmnNh5qU0ZKQMuLs/wuZcpdtaRI\nX3lur453D2pOVqrf5QB4C6OhsPYe69FrtW063Nanho6BEzOZm6Q5WalaVpylstw0leUENSc7hSv1\nZoiFRRlaW5Grlw+06PyybBVnc5XgVJkwWDnnRs3sU5KeV2S6hQecc7vM7J7o8/dK+mtJ+ZK+Ex0O\nHnXOVU9d2fFhQ2271lbmKjALhl2vXFyorzy3Vy/ta9HN68onfgGAKdc7NKqt9R3aVNeuLfUd2tbQ\neWI0KjM1oPLcoNZV5ak8N02luWms+TnDXX/+XO093qMnth7RH121gJHHKTKpHivn3DOSnjnpsXvH\nfX6XpLu8LS2+tfUO6UBzr96/ZnZchbGsOFOlOWl6akcTwQrwSWf/sDYeao/c6tq1q6lbobBTYoJp\nWXGmPrS2TGsqc9XYMaCctKQZOWkxTi2YHNB7VhbrkU0NWn+wTZezFNmUYOb1KbKprkOSdNEM768a\nY2b6wJpSffPXNWrs6FdZbtDvkoAZ74HfHNKh1j7VRhcoPtY1KCcpkGAqyw3qikUFqipIV0Ve8MRo\nVN9QSLnBZH8Lh2/OL83WtoZO/XL3MS0vzlJeOt8LXiNYTZENh9qUmpSg80tz/C5l2nyoulzfeKFG\nj21p1GfDfhLCAAAZEklEQVTfsdjvcoAZZ3AkpM11HXr1YKvW17RqR2PXiSBVkRfUNcuKNK8gQ+W5\nabOiBQFnzsz03lUl+vp/H9DP3zyq22f4Vet+IFhNkY2H2rWmIndWrSxenhfUZQvz9V+bG/Xpty+a\n0XN3AdMhFHbaeaRLv6lp1as1rdpc36Hh0bACCaYLKnJ09dIizS9IV3lekEvoMWk5wWS9fWmRntt1\nTPuO9fhdzoxDsJoCXQMj2n20W5+5ZpHfpUy7m6vL9ZlHtunVg61626JCv8sB4opzTnVt/ZEgdaBV\n6w+2qntwVJK0rDhLd1xcqcsWFujCeXlKTwn8zrQJwJm4dGG+Nte36+kdTfrzG5Zy4YKHCFZTYEt9\nu5yTLpo3+ybLfNeKucpOS9KjmxoIVsAk/NtLB3WwpU8Hm3tV09J7Ym29nLQkLZ6TqQVFGVpQmKGM\nlMiP66Ndg/rZtiY/S8YMEEhI0HtWlujB9XW6/5VD+uTVC/0uacYgWE2BX+9tUWpSgi6omD39VWNS\nkxL1vtUlenhjgzr6hpVLYyTwO7oHR7Sxtj3aJ9Wmfccjp2LSkhI1vzBdVy0p1ILCDOWnJ3PVHqbU\nojmZWl6cpW+9UKP3X1CqkhzmtvICwcpjo6Gwnt15VNcsnaPUpNk5tHrzunL94LV6/XTbEX3ssnl+\nlwP4amA4pC31HVp/sFXrD7ZpR2Onwk5KTUpQdWWeqgrStbAwQ8U5qcwrhGl3w8pifeNXB/Tln+/R\ntz+yxu9yZgSClcc2HmpXa++wblxZ7HcpvllRkq3zS7P16KYG3XlpFX91Y1YZGg1p2+FOvVbbpvUH\n27TtcKeGQ2ElJphWl+foU1cv1CULCrSmMkcpgUT6pOCr3GCyPnn1Qn3tl/t1W02rLmNuq3NGsPLY\nUzuOKpicqKuWFPldiq9uXleuv/rpTr15pEsry2bfKVHMHiOhsHY0durel2pV29Krw+39Ggk5maSS\nnDRdND9P8wsyVJUfVEp0FPtQa58Otfb5WzgQdfcV8/XYlkZ94cldevYzb+MK03NEsPLQaCis53Ye\n1TuWzVFa8uw8DTjmvatK9LdP79ajmxoIVphRQmGn3U3dJ07tbaprP7FMzNysVK2rigSpeQXps/7n\nAOJDalKivvCe5frEDzbrwVcP6e4rFvhdUlwjWHlo/cE2dfSP6IZZfBpwTHZakt59frGe3Nakv7xh\nOb9gELecczrY0qtXa9r0ak2rXq9tOzEFwsKiDH1gTZkuXZCvxo4BpafwIxXx6Zplc/SOZUX6+n8f\n0I0rS2hkPwf8FPDQ0zualJES0JWLmWZAisxp9ZM3jujZnUf1e2vK/C4HmNBYv1PXwIgOtvTqYHOv\nDrb0nghSucHIFAjzCzM0vzBdWalJkqSO/hFCFeLeF96zQtf+y0v64lO79G+3V/tdTtziJ4FHhkfD\nen7XcV27fPZeDXiyi+fnaV5Bur73yiHdtLpUiczEjhjVNzSq12vb9NSOJtU096qlZ0iSFExO1ILC\nDC0szNCCogzWVcOMVp4X1KevWaR/fG6ffrXnuK5ZNsfvkuISwcojr9a0qmtgZEZcDXguVynddlHF\nic/NTJ9752J96qE39OPNDbr1worTvBKYPmNLxbxyoEUvH2jV1voOjYadAgmmeQXpqq7M1YLCDM3N\nZgoEzC53XT5fP9l6RF94cpcuXVBAG8dZIFh55OkdR5WZGtDli7hUdbwbzi/WD6vq9dXn9+mGlcUn\nTp0A062pc0C/OdCqlw606NWaVnX2R2Y4P680S3e9bb7etqhANc29XBGFWS05kKC/fd95+vB9r+ub\nLxzQ/75uqd8lxR2ClQeGRkP6xe5jeteKuay3dBIz01+/Z7ne863f6Fsv1OjP373M75Iww42NuA6N\nhnSotU8HmntVc7xXLb2R03uZqQEtKsrQwqJMLSz67VIx9W39hCpA0kXz8/WBNWX63iu1ev8FpVo0\nJ9PvkuIKwcoDr+xvVc/gKFcDnsJ5pdn60NoyPfjqId16YYXmFaT7XRJmoLH5pH6197gONvepob1f\nIeeUlBg5vbeuKlcL52RqTmYKk9YCE/jzdy/Vf+85rr/86U49cvfF/J85AwQrDzy9o0nZaUm6nBlr\nT+lP37VEz7x5TF/++R7d//tcbYJzFw477TnWrdcOtun12ja9Xtuu3qHRExNzXrYwXwuLMlWZH2Qk\nCjhD+Rkp+vx1S/XnP3lTj21p1Ieqy/0uKW4QrM7R4EhIv9x9XDeuLOGH92kUZabqk1cv1Fee26vf\nHGilFw1nLBx22ne8Rxtq2/RabZs2HGo/0SdVlR/Ue1eX6PKFBWrqGFCQqQ+Ac3bLunI9sbVRX3p6\nty5ZkK+y3KDfJcUFfvqcoxf3tahvOKQbV3EacCIfu6xKD22s15ee3qVnPv02BQiiOI2RUFi7m7q1\n8VC7Nhxq06a6DnUNRIJUWW6arl02R5csyNfF8/N/ZzJD1t4DvJGQYPrazat1/b++rM/9eLse+oOL\nmTZnEghW5+g/Xq9TQUayLpmf73cpMS81KVF/8e5luuc/t+qhjYd1xyVVfpeEGNIzOKI3Dndqc32H\nNte1a1Ndu0ZCTpKUn56sRUUZqipI17z8dOVG55MaHAnrxX0tfpYNzGgV+UH9zXtX6P9/bIe+90qt\n7rmS5W4mQrA6B6/WtOrVmjb91Y3LGX2ZpHetmKvLFubr757Zo+XFWaquyvO7JPjAOacjnQPaUt+h\nLfUd2lzXob3HuhV2UoJJy4qzVF2Zp8r8oKoK0pmmA/DRB9eW6YW9zfrnX+zT5QsLdF5ptt8lxTSC\n1Vlyzukfn9+nkuxUfeQiJr6cLDPTN265QB+69zV9/Pub9F/3XKolc7mUd6YaOy0XCjsd7RpQfVu/\n6tv7dbit78QyMcmBBFXkBnXVkiJV5gdVkRtUCqsXADHDzPR37z9fW+o79NlHt+npP76cFUZOg2B1\nln6x+7i2N3TqHz+wkm+wM5SfkaIffPxCffDe9brjgQ167J5LVZ5HU+RM0tk/rK2HO/T8rmOqb+vX\nkc7+E6f1coJJqipIV2V+uirzgpqTlUrfBhDjctOT9dUPrdIdD2zUPzy7V3/z3hV+lxSzCFZnIRR2\n+urz+zS/MF2/t6bU73LiUnleUD/8+EW6+d9e0x0PbNR/3XOJCjJS/C4LZ8E5p7q2fm2ua4+c1qvv\nUE1zr6TIab3i7DStq8pTZX66KvKCyk7jtB4Qj65YXKiPXValB1+t01VLCnXVkiK/S4pJBKuz8NM3\njuhAc6++fdsaeqvOwZK5mXrgzmp95P4NuvPBjXr4Dy5WJr00MW9oNKSdR7q1pb5dm+s6tPVwh1p7\nhyVJWakBra3M1ftWl2htZZ72HetRcoD/I8BM8fnrlmp9TZs+88g2PXbPJczK/hYIVmdoeDSsf/nv\n/TqvNEvXnzfX73Li3trKPH33I2t11w83684HN+nrH17NacEYc9/LtTrc1hftjerXkc4BjYYjp/Xy\n0pNVmRfU5QsLVZkfVGFmyolFiw+19hGqgBkmNSlR9/9+tX7vu+v1+w9s1BP/6zLNzU71u6yYQrA6\nQw9vPKzGjgF9+f3nK4G+EE9cvbRI/3rLan3+sR1619df1p++c4l+/9Iq+m58MDwa1u6j3XrjcIfe\nONypNxo61NA+IElKTDCV5qTp4vn5qsgLqjI/yAgjMAuV5wX14J3rdMt9r+vOBzfq0T+8hFP84xCs\nzkD/8Ki++UKNLpqXpyuYOdxTN64s0QUVufqLn7ypLz29W0/taNJXPrBSixlmnjKjobBqWnq1o7FL\nbzZ2aceRLu052q3h0bAkqTg7VavLc7SiOFuV+UGV5KSxugAASZE1YO/96Frd+eBG/eF/bNYPPn6h\nUgJcyCURrM7I914+pNbeIf3b7WtYkHIKlOak6cE71+ln25r0xad26YZvvKI/vGKBPnpxJUPN56h/\neFR7j/Vod1O39hzt1u6j3dp7tEcDIyFJUmZKQOeVZutjl1ZpdXmOVlfkqDg7Mps5M5kDeCuXLyrQ\nVz+0Sp99dJv+vx9v1zdvuYAzOSJYTdrTO5r09V/t140ri7W2kkktp4qZ6X0XlOptiwr0xad261u/\nrtG3X6zR5QsL9HtrSvWuFXMVTObb9lQGhkM62NKrmuZe7T/eo/3He1XT3KP69n65SFuUUpMSVJyd\npgsqclSak6ay3KDyM5JP9EZ19I/o13uZzRzAxN53QamOdw/q75/dq7xgsv7mvStmfRsHv6Em4ZUD\nLfqTR7epujJXX/3QKr/LmRXyM1L0jVsv0J9cu1g/2dqoJ944oj95dLuCyTv1zuVzVF2Vp1VlOVoy\nN3PWNUj3Do2qsaNfDe0Dqm/r06HW396Odg2e2C6QYJpXkK7lJVl63wWlau0ZVnFOqnLSkhhxBeCZ\nu6+Yr7a+Yd33cq3q2vr0zVsvUE4w2e+yfGNu7M/YaVZdXe02b97sy7HPxPaGTt36vddVkRf0pUFv\nNp2Gue00M9iHw06b6zv0kzca9dzOY+rojyzGm5yYoGXFmTq/LFuVeekqzU2LjsKkKS89Oe4CRN/Q\nqFp7h3Ssa1DHugd1rGtQR7siH490Dqiho1+d0a99THZakuYVpGt+QbrmFaRrXmG6Fs/JVFV++u+E\nztn0vQRg8k73s/dMPLLxsP76Z7s0JztF991erWXFWZ7sN1aY2RbnXPWE2xGsTq2muVcfune9MlID\nevyeS1WUNf19PrPpl+Fk/3M759TYMaDtjZ16s7FL2xs7taupWz3RJVLGpCYlqCAjRXnpycoNJp/4\nmJUWUEZKQJmpAWWkJCkjNaD05ESlJo3dEpSWlKjkQIKSEhMUSDAlJthpQ1o47DQcCms4FNbIaFhD\no2H1D4fUPzyqvqGQ+oZG1Tc8qq6BEXX1j6hzYERdAyPq7B9Wa++wWnuH1NY7fKLnabyMlIDmZKWo\nNDeowZGQcoPJyg0mKS89WXnBZAVTGHgGcPa8ClaS9MbhDt3zn1vUPTCqr3xwpd67qsSzffuNYHWO\nmjoH9MHvrtdwyOmxey5RVUG6L3XMpmB1LpxzGhwJq6N/WJ39I+ociHwcCzT9w5Fw0z8c0lD0qrcz\nFUiw3zZmjvtvE3buxLxOk5UcSFAwKVFpyYlKT4kEvfG3zLSAslOTlJWWxJJJAKaUl8FKkpp7BvXJ\nH23VproOfeyyKn32HYtnxHQMkw1W/Kl7knDY6afbjugfn9unvqFRPfKHF/sWqjB5Zqa05ESlJaep\nJCfttNuGndPQSFhDoyENjoY1NBLS8GhYIyGnkVD4xG007BQOO4Vc5DWhsFPkDxGLHvO3+xwb1frt\nxwQlBxKUEoh8HLsFkwNKTUpQIGF29YUBmD2KMlP1o7su1pd/vlsPvlqnx7c06u4r5uvOy+YpYxaM\nsM/8r/AMbDzUrr/9+W7taOzSyrJs/e371mpFSbbfZcFjCSdCGCNBADAVkgMJ+uJN5+nmdeX6l18e\n0Fd/sV///ptDuufKBbrjkqoZ/fN3UsHKzK6T9K+SEiXd75z7h5Oet+jz75bUL+lO59xWj2udMgdb\nevXV5/fp2Z3HVJydqn/58CrdtKqU+TgAADgHK0qydf/vV2tbQ6e+9sv9+vtn9+relw7qmmVz9I5l\nRbp8UeGMG8Wa8Ksxs0RJ35Z0raRGSZvM7Enn3O5xm10vaVH0dpGk70Y/xqT2vmGtP9iq9QfbtL6m\nVXVt/QomJ+pz1y7WXW+bP6OTNAAA0211eY5++PELtamuXT98rV7P7zqmx7Y0KjkxQRfNz9PVS4q0\nrDhLC4syVJARf1d0jzeZmHihpBrnXK0kmdkjkm6SND5Y3STphy7SgPK6meWYWbFz7qjnFU9S79Co\nth3u1NGuAR09ccn6gBo7BnSguVdS5Gqri+fn6Y5LqnTjymJfrvoDAGC2WFeVp3VVeRoJhbW5rkMv\n7D2uX+1p1pee/m2kyEoNaEFRhuYXZKgwM0W5wSTlBpOVE0xSbnqygsmJSgkkKiWQoJSkBKUEEk9c\nyR0LJhOsSiU1jLvfqP85GvVW25RK8i1Y1bX26aP/vuHE/fz0ZBXnpKoyP103rS7RpQsLtLI0WwHW\nPgMAYFolJSbokgX5umRBvv7ihuU62jWgmuZeHWzuVU1Lrw429+nVmla19Q1pJDTxVde3XVShv3v/\n+dNQ+cSm9cSmmd0t6e7o3V4z2zddx66frgN5r0BSq99FzAC8j97gffQG76M3eB898BG/C/DA30dv\nU6xyMhtNJlgdkVQ+7n5Z9LEz3UbOufsk3TeZwhBhZpsnM28GTo/30Ru8j97gffQG7yNi0WTOg22S\ntMjM5plZsqRbJD150jZPSrrDIi6W1OVnfxUAAIAfJhyxcs6NmtmnJD2vyHQLDzjndpnZPdHn75X0\njCJTLdQoMt3Cx6auZAAAgNg0qR4r59wzioSn8Y/dO+5zJ+mT3paGKE6deoP30Ru8j97gffQG7yNi\njm9rBQIAAMw0zDUAAADgEYJVjDKz68xsn5nVmNmf+V1PvDKzB8ys2cx2+l1LPDOzcjP7tZntNrNd\nZvYZv2uKR2aWamYbzWx79H38ot81xSszSzSzN8zsab9rAcYjWMWgccsIXS9puaRbzWy5v1XFre9L\nus7vImaAUUmfc84tl3SxpE/yPXlWhiS93Tm3StJqSddFr6TGmfuMpD1+FwGcjGAVm04sI+ScG5Y0\ntowQzpBz7mVJ7X7XEe+cc0fHFlZ3zvUo8gut1N+q4o+L6I3eTYreaHQ9Q2ZWJukGSff7XQtwMoJV\nbDrVEkGA78ysStIFkjacfku8legprG2SmiX90jnH+3jmvi7pf0sK+10IcDKCFYBJM7MMSY9L+qxz\nrtvveuKRcy7knFutyAoVF5rZeX7XFE/M7EZJzc65LX7XArwVglVsmtQSQcB0MrMkRULVj5xzT/hd\nT7xzznVK+rXoATxTl0l6r5nVKdIm8XYz+09/SwJ+i2AVmyazjBAwbczMJP27pD3Oua/5XU+8MrNC\nM8uJfp4m6VpJe/2tKr445/6Pc67MOVelyM/GF5xzH/W5LOAEglUMcs6NShpbRmiPpB8753b5W1V8\nMrOHJb0maYmZNZrZJ/yuKU5dJul2RUYHtkVv7/a7qDhULOnXZrZDkT+gfumcY7oAYAZh5nUAAACP\nMGIFAADgEYIVAACARwhWAAAAHiFYAQAAeIRgBQAA4BGCFQAAgEcIVgAmZGah6NxVu8xsu5l9zswS\nos9Vm9k3TvPaKjO7bfqq/R/HHoiuzRcTzOzDZlZjZsxfBcxABCsAkzHgnFvtnFuhyGzh10v6giQ5\n5zY75z59mtdWSfIlWEUdjK7NN2lmljhVxTjnHpV011TtH4C/CFYAzohzrlnS3ZI+ZRFXjY2+mNmV\n42Zmf8PMMiX9g6S3RR/7k+go0itmtjV6uzT62qvM7EUze8zM9prZj6JL6cjM1pnZ+uho2UYzyzSz\nRDP7JzPbZGY7zOwPJ1O/mf3UzLZER9/uHvd4r5n9s5ltl3TJKY65Ivr5tugxF0Vf+9Fxj//bWDAz\ns+uiX+N2M/uVh/8MAGJUwO8CAMQf51xtNDwUnfTUn0r6pHPuVTPLkDQo6c8k/alz7kZJMrOgpGud\nc4PRYPKwpOro6y+QtEJSk6RXJV1mZhslPSrpw865TWaWJWlA0ickdTnn1plZiqRXzewXzrlDE5T/\ncedce3Stvk1m9rhzrk1SuqQNzrnPRdfo3PsWx7xH0r86534U3SbRzJZJ+rCky5xzI2b2HUkfMbNn\nJX1P0hXOuUNmlnfGbzSAuEOwAuClVyV9zcx+JOkJ51xjdNBpvCRJ3zKz1ZJCkhaPe26jc65RkqJ9\nUVWSuiQddc5tkiTnXHf0+XdKWmlmH4y+NlvSIkkTBatPm9n7o5+XR1/TFq3l8ejjS05xzNck/YWZ\nlUW/vgNmdo2ktYqENElKk9Qs6WJJL48FPedc+wR1AZgBCFYAzpiZzVckiDRLWjb2uHPuH8zs55Le\nrcgI0rve4uV/Ium4pFWKtCMMjntuaNznIZ3+Z5RJ+mPn3PNnUPdVkt4h6RLnXL+ZvSgpNfr0oHMu\ndLrXO+ceMrMNkm6Q9Ez09KNJ+oFz7v+cdKz3TLYuADMHPVYAzoiZFUq6V9K33EmruJvZAufcm865\nr0jaJGmppB5JmeM2y1ZkNCgs6XZJEzWK75NUbGbrosfINLOApOcl/ZGZJUUfX2xm6RPsK1tSRzRU\nLVVkVGnSx4wGylrn3Dck/UzSSkm/kvRBMyuKbptnZpWSXpd0hZnNG3t8gtoAzACMWAGYjLToqbkk\nSaOS/kPS195iu8+a2dWSwpJ2SXo2+nko2hT+fUnfkfS4md0h6TlJfac7sHNu2Mw+LOmb0b6oAUVG\nne5X5FTh1miTe4uk903wdTwn6R4z26NIeHr9DI95s6TbzWxE0jFJfxft1/pLSb+wyBQUI4r0mb0e\nbY5/Ivp4syJXVAKYweykPzgBYMYwsypJTzvnzvO5lN8RPSV5oqEfwMzBqUAAM1lIUrbF2AShioza\ndfhdCwDvMWIFAADgEUasAAAAPEKwAgAA8AjBCgAAwCMEKwAAAI8QrAAAADzy/wBEopmrbK3xmAAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b19e12ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(uhs['uhs_ra'], uhs['uhs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, uhs, \"uhs_ra\", \"uhs_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": 28,
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table140304896744080-205713\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>hsc_id</th><th>ra</th><th>dec</th><th>m_ap_suprime_g</th><th>merr_ap_suprime_g</th><th>m_suprime_g</th><th>merr_suprime_g</th><th>m_ap_suprime_r</th><th>merr_ap_suprime_r</th><th>m_suprime_r</th><th>merr_suprime_r</th><th>m_ap_suprime_i</th><th>merr_ap_suprime_i</th><th>m_suprime_i</th><th>merr_suprime_i</th><th>m_ap_suprime_z</th><th>merr_ap_suprime_z</th><th>m_suprime_z</th><th>merr_suprime_z</th><th>m_ap_suprime_y</th><th>merr_ap_suprime_y</th><th>m_suprime_y</th><th>merr_suprime_y</th><th>hsc_stellarity</th><th>f_ap_suprime_g</th><th>ferr_ap_suprime_g</th><th>f_suprime_g</th><th>ferr_suprime_g</th><th>flag_suprime_g</th><th>f_ap_suprime_r</th><th>ferr_ap_suprime_r</th><th>f_suprime_r</th><th>ferr_suprime_r</th><th>flag_suprime_r</th><th>f_ap_suprime_i</th><th>ferr_ap_suprime_i</th><th>f_suprime_i</th><th>ferr_suprime_i</th><th>flag_suprime_i</th><th>f_ap_suprime_z</th><th>ferr_ap_suprime_z</th><th>f_suprime_z</th><th>ferr_suprime_z</th><th>flag_suprime_z</th><th>f_ap_suprime_y</th><th>ferr_ap_suprime_y</th><th>f_suprime_y</th><th>ferr_suprime_y</th><th>flag_suprime_y</th><th>hsc_flag_cleaned</th><th>hsc_flag_gaia</th><th>flag_merged</th><th>ps1_id</th><th>m_ap_gpc1_g</th><th>merr_ap_gpc1_g</th><th>m_gpc1_g</th><th>merr_gpc1_g</th><th>m_ap_gpc1_r</th><th>merr_ap_gpc1_r</th><th>m_gpc1_r</th><th>merr_gpc1_r</th><th>m_ap_gpc1_i</th><th>merr_ap_gpc1_i</th><th>m_gpc1_i</th><th>merr_gpc1_i</th><th>m_ap_gpc1_z</th><th>merr_ap_gpc1_z</th><th>m_gpc1_z</th><th>merr_gpc1_z</th><th>m_ap_gpc1_y</th><th>merr_ap_gpc1_y</th><th>m_gpc1_y</th><th>merr_gpc1_y</th><th>f_ap_gpc1_g</th><th>ferr_ap_gpc1_g</th><th>f_gpc1_g</th><th>ferr_gpc1_g</th><th>flag_gpc1_g</th><th>f_ap_gpc1_r</th><th>ferr_ap_gpc1_r</th><th>f_gpc1_r</th><th>ferr_gpc1_r</th><th>flag_gpc1_r</th><th>f_ap_gpc1_i</th><th>ferr_ap_gpc1_i</th><th>f_gpc1_i</th><th>ferr_gpc1_i</th><th>flag_gpc1_i</th><th>f_ap_gpc1_z</th><th>ferr_ap_gpc1_z</th><th>f_gpc1_z</th><th>ferr_gpc1_z</th><th>flag_gpc1_z</th><th>f_ap_gpc1_y</th><th>ferr_ap_gpc1_y</th><th>f_gpc1_y</th><th>ferr_gpc1_y</th><th>flag_gpc1_y</th><th>ps1_flag_cleaned</th><th>ps1_flag_gaia</th><th>aegis_id</th><th>m_aegis_k</th><th>merr_aegis_k</th><th>m_ap_aegis_k</th><th>merr_ap_aegis_k</th><th>m_ap_aegis_j</th><th>merr_ap_aegis_j</th><th>f_aegis_k</th><th>ferr_aegis_k</th><th>flag_aegis_k</th><th>f_ap_aegis_k</th><th>ferr_ap_aegis_k</th><th>f_ap_aegis_j</th><th>ferr_ap_aegis_j</th><th>m_aegis_j</th><th>merr_aegis_j</th><th>f_aegis_j</th><th>ferr_aegis_j</th><th>flag_aegis_j</th><th>aegis_flag_cleaned</th><th>aegis_flag_gaia</th><th>candels-egs_id</th><th>candels-egs_stellarity</th><th>f_ap_acs_f606w</th><th>ferr_ap_acs_f606w</th><th>f_acs_f606w</th><th>ferr_acs_f606w</th><th>f_ap_acs_f814w</th><th>ferr_ap_acs_f814w</th><th>f_acs_f814w</th><th>ferr_acs_f814w</th><th>f_ap_wfc3_f125w</th><th>ferr_ap_wfc3_f125w</th><th>f_wfc3_f125w</th><th>ferr_wfc3_f125w</th><th>f_ap_wfc3_f140w</th><th>ferr_ap_wfc3_f140w</th><th>f_wfc3_f140w</th><th>ferr_wfc3_f140w</th><th>f_ap_wfc3_f160w</th><th>ferr_ap_wfc3_f160w</th><th>f_wfc3_f160w</th><th>ferr_wfc3_f160w</th><th>f_candels-megacam_u</th><th>ferr_candels-megacam_u</th><th>f_candels-megacam_g</th><th>ferr_candels-megacam_g</th><th>f_candels-megacam_r</th><th>ferr_candels-megacam_r</th><th>f_candels-megacam_i</th><th>ferr_candels-megacam_i</th><th>f_candels-megacam_z</th><th>ferr_candels-megacam_z</th><th>f_candels-wircam_j</th><th>ferr_candels-wircam_j</th><th>f_candels-wircam_h</th><th>ferr_candels-wircam_h</th><th>f_candels-wircam_k</th><th>ferr_candels-wircam_k</th><th>f_candels-newfirm_j1</th><th>ferr_candels-newfirm_j1</th><th>f_candels-newfirm_j2</th><th>ferr_candels-newfirm_j2</th><th>f_candels-newfirm_j3</th><th>ferr_candels-newfirm_j3</th><th>f_candels-newfirm_h1</th><th>ferr_candels-newfirm_h1</th><th>f_candels-newfirm_h2</th><th>ferr_candels-newfirm_h2</th><th>f_candels-newfirm_k</th><th>ferr_candels-newfirm_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_ap_acs_f606w</th><th>merr_ap_acs_f606w</th><th>m_acs_f606w</th><th>merr_acs_f606w</th><th>flag_acs_f606w</th><th>m_ap_acs_f814w</th><th>merr_ap_acs_f814w</th><th>m_acs_f814w</th><th>merr_acs_f814w</th><th>flag_acs_f814w</th><th>m_ap_wfc3_f125w</th><th>merr_ap_wfc3_f125w</th><th>m_wfc3_f125w</th><th>merr_wfc3_f125w</th><th>flag_wfc3_f125w</th><th>m_ap_wfc3_f140w</th><th>merr_ap_wfc3_f140w</th><th>m_wfc3_f140w</th><th>merr_wfc3_f140w</th><th>flag_wfc3_f140w</th><th>m_ap_wfc3_f160w</th><th>merr_ap_wfc3_f160w</th><th>m_wfc3_f160w</th><th>merr_wfc3_f160w</th><th>flag_wfc3_f160w</th><th>m_candels-megacam_u</th><th>merr_candels-megacam_u</th><th>m_ap_candels-megacam_u</th><th>merr_ap_candels-megacam_u</th><th>f_ap_candels-megacam_u</th><th>ferr_ap_candels-megacam_u</th><th>flag_candels-megacam_u</th><th>m_candels-megacam_g</th><th>merr_candels-megacam_g</th><th>m_ap_candels-megacam_g</th><th>merr_ap_candels-megacam_g</th><th>f_ap_candels-megacam_g</th><th>ferr_ap_candels-megacam_g</th><th>flag_candels-megacam_g</th><th>m_candels-megacam_r</th><th>merr_candels-megacam_r</th><th>m_ap_candels-megacam_r</th><th>merr_ap_candels-megacam_r</th><th>f_ap_candels-megacam_r</th><th>ferr_ap_candels-megacam_r</th><th>flag_candels-megacam_r</th><th>m_candels-megacam_i</th><th>merr_candels-megacam_i</th><th>m_ap_candels-megacam_i</th><th>merr_ap_candels-megacam_i</th><th>f_ap_candels-megacam_i</th><th>ferr_ap_candels-megacam_i</th><th>flag_candels-megacam_i</th><th>m_candels-megacam_z</th><th>merr_candels-megacam_z</th><th>m_ap_candels-megacam_z</th><th>merr_ap_candels-megacam_z</th><th>f_ap_candels-megacam_z</th><th>ferr_ap_candels-megacam_z</th><th>flag_candels-megacam_z</th><th>m_candels-wircam_j</th><th>merr_candels-wircam_j</th><th>m_ap_candels-wircam_j</th><th>merr_ap_candels-wircam_j</th><th>f_ap_candels-wircam_j</th><th>ferr_ap_candels-wircam_j</th><th>flag_candels-wircam_j</th><th>m_candels-wircam_h</th><th>merr_candels-wircam_h</th><th>m_ap_candels-wircam_h</th><th>merr_ap_candels-wircam_h</th><th>f_ap_candels-wircam_h</th><th>ferr_ap_candels-wircam_h</th><th>flag_candels-wircam_h</th><th>m_candels-wircam_k</th><th>merr_candels-wircam_k</th><th>m_ap_candels-wircam_k</th><th>merr_ap_candels-wircam_k</th><th>f_ap_candels-wircam_k</th><th>ferr_ap_candels-wircam_k</th><th>flag_candels-wircam_k</th><th>m_candels-newfirm_j1</th><th>merr_candels-newfirm_j1</th><th>m_ap_candels-newfirm_j1</th><th>merr_ap_candels-newfirm_j1</th><th>f_ap_candels-newfirm_j1</th><th>ferr_ap_candels-newfirm_j1</th><th>flag_candels-newfirm_j1</th><th>m_candels-newfirm_j2</th><th>merr_candels-newfirm_j2</th><th>m_ap_candels-newfirm_j2</th><th>merr_ap_candels-newfirm_j2</th><th>f_ap_candels-newfirm_j2</th><th>ferr_ap_candels-newfirm_j2</th><th>flag_candels-newfirm_j2</th><th>m_candels-newfirm_j3</th><th>merr_candels-newfirm_j3</th><th>m_ap_candels-newfirm_j3</th><th>merr_ap_candels-newfirm_j3</th><th>f_ap_candels-newfirm_j3</th><th>ferr_ap_candels-newfirm_j3</th><th>flag_candels-newfirm_j3</th><th>m_candels-newfirm_h1</th><th>merr_candels-newfirm_h1</th><th>m_ap_candels-newfirm_h1</th><th>merr_ap_candels-newfirm_h1</th><th>f_ap_candels-newfirm_h1</th><th>ferr_ap_candels-newfirm_h1</th><th>flag_candels-newfirm_h1</th><th>m_candels-newfirm_h2</th><th>merr_candels-newfirm_h2</th><th>m_ap_candels-newfirm_h2</th><th>merr_ap_candels-newfirm_h2</th><th>f_ap_candels-newfirm_h2</th><th>ferr_ap_candels-newfirm_h2</th><th>flag_candels-newfirm_h2</th><th>m_candels-newfirm_k</th><th>merr_candels-newfirm_k</th><th>m_ap_candels-newfirm_k</th><th>merr_ap_candels-newfirm_k</th><th>f_ap_candels-newfirm_k</th><th>ferr_ap_candels-newfirm_k</th><th>flag_candels-newfirm_k</th><th>m_candels-irac_i1</th><th>merr_candels-irac_i1</th><th>m_ap_candels-irac_i1</th><th>merr_ap_candels-irac_i1</th><th>f_ap_candels-irac_i1</th><th>ferr_ap_candels-irac_i1</th><th>flag_candels-irac_i1</th><th>m_candels-irac_i2</th><th>merr_candels-irac_i2</th><th>m_ap_candels-irac_i2</th><th>merr_ap_candels-irac_i2</th><th>f_ap_candels-irac_i2</th><th>ferr_ap_candels-irac_i2</th><th>flag_candels-irac_i2</th><th>m_candels-irac_i3</th><th>merr_candels-irac_i3</th><th>m_ap_candels-irac_i3</th><th>merr_ap_candels-irac_i3</th><th>f_ap_candels-irac_i3</th><th>ferr_ap_candels-irac_i3</th><th>flag_candels-irac_i3</th><th>m_candels-irac_i4</th><th>merr_candels-irac_i4</th><th>m_ap_candels-irac_i4</th><th>merr_ap_candels-irac_i4</th><th>f_ap_candels-irac_i4</th><th>ferr_ap_candels-irac_i4</th><th>flag_candels-irac_i4</th><th>candels-egs_flag_cleaned</th><th>candels-egs_flag_gaia</th><th>wirds_id</th><th>m_wirds_u</th><th>merr_wirds_u</th><th>m_ap_wirds_u</th><th>merr_ap_wirds_u</th><th>m_wirds_g</th><th>merr_wirds_g</th><th>m_ap_wirds_g</th><th>merr_ap_wirds_g</th><th>m_wirds_r</th><th>merr_wirds_r</th><th>m_ap_wirds_r</th><th>merr_ap_wirds_r</th><th>m_wirds_i</th><th>merr_wirds_i</th><th>m_ap_wirds_i</th><th>merr_ap_wirds_i</th><th>m_wirds_z</th><th>merr_wirds_z</th><th>m_ap_wirds_z</th><th>merr_ap_wirds_z</th><th>m_wirds_j</th><th>merr_wirds_j</th><th>m_ap_wirds_j</th><th>merr_ap_wirds_j</th><th>m_wirds_h</th><th>merr_wirds_h</th><th>m_ap_wirds_h</th><th>merr_ap_wirds_h</th><th>m_wirds_k</th><th>merr_wirds_k</th><th>m_ap_wirds_k</th><th>merr_ap_wirds_k</th><th>f_wirds_u</th><th>ferr_wirds_u</th><th>flag_wirds_u</th><th>f_ap_wirds_u</th><th>ferr_ap_wirds_u</th><th>f_wirds_g</th><th>ferr_wirds_g</th><th>flag_wirds_g</th><th>f_ap_wirds_g</th><th>ferr_ap_wirds_g</th><th>f_wirds_r</th><th>ferr_wirds_r</th><th>flag_wirds_r</th><th>f_ap_wirds_r</th><th>ferr_ap_wirds_r</th><th>f_wirds_i</th><th>ferr_wirds_i</th><th>flag_wirds_i</th><th>f_ap_wirds_i</th><th>ferr_ap_wirds_i</th><th>f_wirds_z</th><th>ferr_wirds_z</th><th>flag_wirds_z</th><th>f_ap_wirds_z</th><th>ferr_ap_wirds_z</th><th>f_wirds_j</th><th>ferr_wirds_j</th><th>flag_wirds_j</th><th>f_ap_wirds_j</th><th>ferr_ap_wirds_j</th><th>f_wirds_h</th><th>ferr_wirds_h</th><th>flag_wirds_h</th><th>f_ap_wirds_h</th><th>ferr_ap_wirds_h</th><th>f_wirds_k</th><th>ferr_wirds_k</th><th>flag_wirds_k</th><th>f_ap_wirds_k</th><th>ferr_ap_wirds_k</th><th>wirds_flag_cleaned</th><th>cfht-wirds_flag_gaia</th><th>cfhtls-wide_id</th><th>cfhtls-wide_stellarity</th><th>m_cfhtls-wide_u</th><th>merr_cfhtls-wide_u</th><th>m_cfhtls-wide_g</th><th>merr_cfhtls-wide_g</th><th>m_cfhtls-wide_r</th><th>merr_cfhtls-wide_r</th><th>m_cfhtls-wide_i</th><th>merr_cfhtls-wide_i</th><th>m_cfhtls-wide_z</th><th>merr_cfhtls-wide_z</th><th>m_ap_cfhtls-wide_u</th><th>merr_ap_cfhtls-wide_u</th><th>m_ap_cfhtls-wide_g</th><th>merr_ap_cfhtls-wide_g</th><th>m_ap_cfhtls-wide_r</th><th>merr_ap_cfhtls-wide_r</th><th>m_ap_cfhtls-wide_i</th><th>merr_ap_cfhtls-wide_i</th><th>m_ap_cfhtls-wide_z</th><th>merr_ap_cfhtls-wide_z</th><th>f_cfhtls-wide_u</th><th>ferr_cfhtls-wide_u</th><th>flag_cfhtls-wide_u</th><th>f_cfhtls-wide_g</th><th>ferr_cfhtls-wide_g</th><th>flag_cfhtls-wide_g</th><th>f_cfhtls-wide_r</th><th>ferr_cfhtls-wide_r</th><th>flag_cfhtls-wide_r</th><th>f_cfhtls-wide_i</th><th>ferr_cfhtls-wide_i</th><th>flag_cfhtls-wide_i</th><th>f_cfhtls-wide_z</th><th>ferr_cfhtls-wide_z</th><th>flag_cfhtls-wide_z</th><th>f_ap_cfhtls-wide_u</th><th>ferr_ap_cfhtls-wide_u</th><th>f_ap_cfhtls-wide_g</th><th>ferr_ap_cfhtls-wide_g</th><th>f_ap_cfhtls-wide_r</th><th>ferr_ap_cfhtls-wide_r</th><th>f_ap_cfhtls-wide_i</th><th>ferr_ap_cfhtls-wide_i</th><th>f_ap_cfhtls-wide_z</th><th>ferr_ap_cfhtls-wide_z</th><th>cfhtls-wide_flag_cleaned</th><th>cfhtls-wide_flag_gaia</th><th>cfhtls-deep_id</th><th>cfhtls-deep_stellarity</th><th>m_cfhtls-deep_u</th><th>merr_cfhtls-deep_u</th><th>m_cfhtls-deep_g</th><th>merr_cfhtls-deep_g</th><th>m_cfhtls-deep_r</th><th>merr_cfhtls-deep_r</th><th>m_cfhtls-deep_i</th><th>merr_cfhtls-deep_i</th><th>m_cfhtls-deep_z</th><th>merr_cfhtls-deep_z</th><th>m_ap_cfhtls-deep_u</th><th>merr_ap_cfhtls-deep_u</th><th>m_ap_cfhtls-deep_g</th><th>merr_ap_cfhtls-deep_g</th><th>m_ap_cfhtls-deep_r</th><th>merr_ap_cfhtls-deep_r</th><th>m_ap_cfhtls-deep_i</th><th>merr_ap_cfhtls-deep_i</th><th>m_ap_cfhtls-deep_z</th><th>merr_ap_cfhtls-deep_z</th><th>f_cfhtls-deep_u</th><th>ferr_cfhtls-deep_u</th><th>flag_cfhtls-deep_u</th><th>f_cfhtls-deep_g</th><th>ferr_cfhtls-deep_g</th><th>flag_cfhtls-deep_g</th><th>f_cfhtls-deep_r</th><th>ferr_cfhtls-deep_r</th><th>flag_cfhtls-deep_r</th><th>f_cfhtls-deep_i</th><th>ferr_cfhtls-deep_i</th><th>flag_cfhtls-deep_i</th><th>f_cfhtls-deep_z</th><th>ferr_cfhtls-deep_z</th><th>flag_cfhtls-deep_z</th><th>f_ap_cfhtls-deep_u</th><th>ferr_ap_cfhtls-deep_u</th><th>f_ap_cfhtls-deep_g</th><th>ferr_ap_cfhtls-deep_g</th><th>f_ap_cfhtls-deep_r</th><th>ferr_ap_cfhtls-deep_r</th><th>f_ap_cfhtls-deep_i</th><th>ferr_ap_cfhtls-deep_i</th><th>f_ap_cfhtls-deep_z</th><th>ferr_ap_cfhtls-deep_z</th><th>cfhtls-deep_flag_cleaned</th><th>cfhtls-deep_flag_gaia</th><th>cfhtlens_id</th><th>cfhtlens_stellarity</th><th>m_cfhtlens_u</th><th>merr_cfhtlens_u</th><th>m_cfhtlens_g</th><th>merr_cfhtlens_g</th><th>m_cfhtlens_r</th><th>merr_cfhtlens_r</th><th>m_cfhtlens_i</th><th>merr_cfhtlens_i</th><th>m_cfhtlens_z</th><th>merr_cfhtlens_z</th><th>f_cfhtlens_u</th><th>ferr_cfhtlens_u</th><th>m_ap_cfhtlens_u</th><th>merr_ap_cfhtlens_u</th><th>f_ap_cfhtlens_u</th><th>ferr_ap_cfhtlens_u</th><th>flag_cfhtlens_u</th><th>f_cfhtlens_g</th><th>ferr_cfhtlens_g</th><th>m_ap_cfhtlens_g</th><th>merr_ap_cfhtlens_g</th><th>f_ap_cfhtlens_g</th><th>ferr_ap_cfhtlens_g</th><th>flag_cfhtlens_g</th><th>f_cfhtlens_r</th><th>ferr_cfhtlens_r</th><th>m_ap_cfhtlens_r</th><th>merr_ap_cfhtlens_r</th><th>f_ap_cfhtlens_r</th><th>ferr_ap_cfhtlens_r</th><th>flag_cfhtlens_r</th><th>f_cfhtlens_i</th><th>ferr_cfhtlens_i</th><th>m_ap_cfhtlens_i</th><th>merr_ap_cfhtlens_i</th><th>f_ap_cfhtlens_i</th><th>ferr_ap_cfhtlens_i</th><th>flag_cfhtlens_i</th><th>f_cfhtlens_z</th><th>ferr_cfhtlens_z</th><th>m_ap_cfhtlens_z</th><th>merr_ap_cfhtlens_z</th><th>f_ap_cfhtlens_z</th><th>ferr_ap_cfhtlens_z</th><th>flag_cfhtlens_z</th><th>cfhtlens_flag_cleaned</th><th>cfhtlens_flag_gaia</th><th>deep2_id</th><th>m_deep2_b</th><th>merr_deep2_b</th><th>m_deep2_r</th><th>merr_deep2_r</th><th>m_deep2_i</th><th>merr_deep2_i</th><th>f_deep2_b</th><th>ferr_deep2_b</th><th>f_ap_deep2_b</th><th>ferr_ap_deep2_b</th><th>m_ap_deep2_b</th><th>merr_ap_deep2_b</th><th>flag_deep2_b</th><th>f_deep2_r</th><th>ferr_deep2_r</th><th>f_ap_deep2_r</th><th>ferr_ap_deep2_r</th><th>m_ap_deep2_r</th><th>merr_ap_deep2_r</th><th>flag_deep2_r</th><th>f_deep2_i</th><th>ferr_deep2_i</th><th>f_ap_deep2_i</th><th>ferr_ap_deep2_i</th><th>m_ap_deep2_i</th><th>merr_ap_deep2_i</th><th>flag_deep2_i</th><th>deep_flag_cleaned</th><th>deep2_flag_gaia</th><th>irac-egs_id</th><th>f_irac-egs_i1</th><th>ferr_irac-egs_i1</th><th>f_irac-egs_i2</th><th>ferr_irac-egs_i2</th><th>f_irac-egs_i3</th><th>ferr_irac-egs_i3</th><th>f_irac-egs_i4</th><th>ferr_irac-egs_i4</th><th>f_suprime_rc</th><th>ferr_suprime_rc</th><th>f_irac-megacam_u</th><th>ferr_irac-megacam_u</th><th>f_irac-megacam_g</th><th>ferr_irac-megacam_g</th><th>f_irac-megacam_r</th><th>ferr_irac-megacam_r</th><th>f_irac-megacam_i</th><th>ferr_irac-megacam_i</th><th>f_irac-megacam_z</th><th>ferr_irac-megacam_z</th><th>f_cfht12k_b</th><th>ferr_cfht12k_b</th><th>f_cfht12k_r</th><th>ferr_cfht12k_r</th><th>f_cfht12k_i</th><th>ferr_cfht12k_i</th><th>f_irac-acs_f814w</th><th>ferr_irac-acs_f814w</th><th>f_irac-acs_f606w</th><th>ferr_irac-acs_f606w</th><th>f_nicmos_f110w</th><th>ferr_nicmos_f110w</th><th>f_nicmos_f160w</th><th>ferr_nicmos_f160w</th><th>f_moircs_ks</th><th>ferr_moircs_ks</th><th>f_irac-aegis_j</th><th>ferr_irac-aegis_j</th><th>f_irac-aegis_k</th><th>ferr_irac-aegis_k</th><th>f_omega2000_j</th><th>ferr_omega2000_j</th><th>f_omega2000_ks</th><th>ferr_omega2000_ks</th><th>f_mmt_u</th><th>ferr_mmt_u</th><th>f_mmt_g</th><th>ferr_mmt_g</th><th>f_mmt_i</th><th>ferr_mmt_i</th><th>f_mmt_z</th><th>ferr_mmt_z</th><th>m_irac-egs_i1</th><th>merr_irac-egs_i1</th><th>flag_irac-egs_i1</th><th>m_irac-egs_i2</th><th>merr_irac-egs_i2</th><th>flag_irac-egs_i2</th><th>m_irac-egs_i3</th><th>merr_irac-egs_i3</th><th>flag_irac-egs_i3</th><th>m_irac-egs_i4</th><th>merr_irac-egs_i4</th><th>flag_irac-egs_i4</th><th>m_suprime_rc</th><th>merr_suprime_rc</th><th>flag_suprime_rc</th><th>m_irac-megacam_u</th><th>merr_irac-megacam_u</th><th>flag_irac-megacam_u</th><th>m_irac-megacam_g</th><th>merr_irac-megacam_g</th><th>flag_irac-megacam_g</th><th>m_irac-megacam_r</th><th>merr_irac-megacam_r</th><th>flag_irac-megacam_r</th><th>m_irac-megacam_i</th><th>merr_irac-megacam_i</th><th>flag_irac-megacam_i</th><th>m_irac-megacam_z</th><th>merr_irac-megacam_z</th><th>flag_irac-megacam_z</th><th>m_cfht12k_b</th><th>merr_cfht12k_b</th><th>flag_cfht12k_b</th><th>m_cfht12k_r</th><th>merr_cfht12k_r</th><th>flag_cfht12k_r</th><th>m_cfht12k_i</th><th>merr_cfht12k_i</th><th>flag_cfht12k_i</th><th>m_irac-acs_f814w</th><th>merr_irac-acs_f814w</th><th>flag_irac-acs_f814w</th><th>m_irac-acs_f606w</th><th>merr_irac-acs_f606w</th><th>flag_irac-acs_f606w</th><th>m_nicmos_f110w</th><th>merr_nicmos_f110w</th><th>flag_nicmos_f110w</th><th>m_nicmos_f160w</th><th>merr_nicmos_f160w</th><th>flag_nicmos_f160w</th><th>m_moircs_ks</th><th>merr_moircs_ks</th><th>flag_moircs_ks</th><th>m_irac-aegis_j</th><th>merr_irac-aegis_j</th><th>flag_irac-aegis_j</th><th>m_irac-aegis_k</th><th>merr_irac-aegis_k</th><th>flag_irac-aegis_k</th><th>m_omega2000_j</th><th>merr_omega2000_j</th><th>flag_omega2000_j</th><th>m_omega2000_ks</th><th>merr_omega2000_ks</th><th>flag_omega2000_ks</th><th>m_mmt_u</th><th>merr_mmt_u</th><th>flag_mmt_u</th><th>m_mmt_g</th><th>merr_mmt_g</th><th>flag_mmt_g</th><th>m_mmt_i</th><th>merr_mmt_i</th><th>flag_mmt_i</th><th>m_mmt_z</th><th>merr_mmt_z</th><th>flag_mmt_z</th><th>wfc_flag_cleaned</th><th>irac-egs_flag_gaia</th><th>legacy_id</th><th>f_90prime_g</th><th>ferr_90prime_g</th><th>f_ap_90prime_g</th><th>ferr_ap_90prime_g</th><th>f_90prime_r</th><th>ferr_90prime_r</th><th>f_ap_90prime_r</th><th>ferr_ap_90prime_r</th><th>f_mosaic_z</th><th>ferr_mosaic_z</th><th>f_ap_mosaic_z</th><th>ferr_ap_mosaic_z</th><th>legacy_stellarity</th><th>m_90prime_g</th><th>merr_90prime_g</th><th>flag_90prime_g</th><th>m_ap_90prime_g</th><th>merr_ap_90prime_g</th><th>m_90prime_r</th><th>merr_90prime_r</th><th>flag_90prime_r</th><th>m_ap_90prime_r</th><th>merr_ap_90prime_r</th><th>m_mosaic_z</th><th>merr_mosaic_z</th><th>flag_mosaic_z</th><th>m_ap_mosaic_z</th><th>merr_ap_mosaic_z</th><th>legacy_flag_cleaned</th><th>legacy_flag_gaia</th><th>uhs_id</th><th>uhs_stellarity</th><th>m_uhs_j</th><th>merr_uhs_j</th><th>m_ap_uhs_j</th><th>merr_ap_uhs_j</th><th>f_uhs_j</th><th>ferr_uhs_j</th><th>flag_uhs_j</th><th>f_ap_uhs_j</th><th>ferr_ap_uhs_j</th><th>uhs_flag_cleaned</th><th>uhs_flag_gaia</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></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></tr></thead>\n",
       "<tr><td>0</td><td>74649014095605543</td><td>214.018853439</td><td>52.3873106204</td><td>21.7324</td><td>0.00281781</td><td>21.4124</td><td>0.00346162</td><td>20.3417</td><td>0.00166233</td><td>20.0309</td><td>0.00213693</td><td>19.7054</td><td>0.000949215</td><td>19.3417</td><td>0.00124168</td><td>19.3965</td><td>0.00163476</td><td>19.0623</td><td>0.0021455</td><td>19.1896</td><td>0.00287453</td><td>18.8219</td><td>0.00380818</td><td>0.0</td><td>7.36274</td><td>0.0191085</td><td>9.88617</td><td>0.0315197</td><td>False</td><td>26.5053</td><td>0.0405813</td><td>35.2906</td><td>0.0694584</td><td>False</td><td>47.6251</td><td>0.0416367</td><td>66.5745</td><td>0.0761364</td><td>False</td><td>63.2971</td><td>0.0953048</td><td>86.116</td><td>0.170172</td><td>False</td><td>76.5902</td><td>0.202776</td><td>107.46</td><td>0.376912</td><td>False</td><td>False</td><td>2</td><td>True</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>nan</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>1</td><td>73984930547265512</td><td>214.106791063</td><td>51.8816645222</td><td>21.374</td><td>0.00279864</td><td>21.5401</td><td>0.00268041</td><td>20.0547</td><td>0.00173781</td><td>20.1574</td><td>0.00164908</td><td>19.2057</td><td>0.000953079</td><td>19.2157</td><td>0.000884308</td><td>18.7132</td><td>0.00124381</td><td>18.8305</td><td>0.00118283</td><td>18.6136</td><td>0.00225828</td><td>18.734</td><td>0.00209718</td><td>1.0</td><td>10.2426</td><td>0.0264017</td><td>8.78964</td><td>0.0216994</td><td>False</td><td>34.5247</td><td>0.0552595</td><td>31.4075</td><td>0.0477036</td><td>False</td><td>75.4634</td><td>0.0662431</td><td>74.7693</td><td>0.0608979</td><td>False</td><td>118.769</td><td>0.13606</td><td>106.612</td><td>0.116146</td><td>False</td><td>130.184</td><td>0.270777</td><td>116.521</td><td>0.225068</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>nan</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>2</td><td>74648752102606669</td><td>214.797358318</td><td>53.0556994799</td><td>21.9691</td><td>0.00517305</td><td>21.0746</td><td>0.00600337</td><td>20.7914</td><td>0.00351091</td><td>19.8871</td><td>0.00405355</td><td>20.2688</td><td>0.00180964</td><td>19.3786</td><td>0.00235158</td><td>19.8816</td><td>0.00289854</td><td>19.0618</td><td>0.00393087</td><td>19.6516</td><td>0.00541094</td><td>18.8132</td><td>0.00767876</td><td>0.0</td><td>5.92075</td><td>0.0282098</td><td>13.494</td><td>0.0746127</td><td>False</td><td>17.5157</td><td>0.0566397</td><td>40.2871</td><td>0.15041</td><td>False</td><td>28.3463</td><td>0.047246</td><td>64.3499</td><td>0.139374</td><td>False</td><td>40.4917</td><td>0.108099</td><td>86.1532</td><td>0.311915</td><td>False</td><td>50.0457</td><td>0.249411</td><td>108.318</td><td>0.76607</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>nan</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>3</td><td>74649151534559278</td><td>213.854438446</td><td>52.4241173194</td><td>21.217</td><td>0.00248604</td><td>19.536</td><td>0.00247428</td><td>20.7845</td><td>0.00280202</td><td>18.9862</td><td>0.00293476</td><td>20.4015</td><td>0.0018715</td><td>18.6245</td><td>0.00217928</td><td>20.2957</td><td>0.00388259</td><td>18.455</td><td>0.00408036</td><td>20.0857</td><td>0.00638691</td><td>18.1963</td><td>0.00717455</td><td>0.0</td><td>11.8354</td><td>0.0271</td><td>55.6683</td><td>0.126862</td><td>False</td><td>17.6285</td><td>0.0454947</td><td>92.3655</td><td>0.249665</td><td>False</td><td>25.0838</td><td>0.0432373</td><td>128.879</td><td>0.258685</td><td>False</td><td>27.6506</td><td>0.0988782</td><td>150.654</td><td>0.566181</td><td>False</td><td>33.5531</td><td>0.197378</td><td>191.202</td><td>1.26346</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>nan</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>4</td><td>74648606073709035</td><td>214.929866865</td><td>52.5330508667</td><td>21.0958</td><td>0.00205324</td><td>20.714</td><td>0.00221118</td><td>20.602</td><td>0.00246907</td><td>20.0935</td><td>0.00257192</td><td>20.479</td><td>0.00200737</td><td>19.8383</td><td>0.0020236</td><td>20.2617</td><td>0.0035529</td><td>19.6526</td><td>0.00349328</td><td>20.1659</td><td>0.00841707</td><td>19.4989</td><td>0.00772799</td><td>0.0</td><td>13.2334</td><td>0.0250258</td><td>18.8112</td><td>0.0383102</td><td>False</td><td>20.854</td><td>0.047424</td><td>33.312</td><td>0.0789103</td><td>False</td><td>23.3552</td><td>0.0431805</td><td>42.1377</td><td>0.0785364</td><td>False</td><td>28.5314</td><td>0.0933646</td><td>49.9989</td><td>0.160868</td><td>False</td><td>31.1625</td><td>0.241584</td><td>57.6009</td><td>0.409988</td><td>False</td><td>False</td><td>2</td><td>True</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>nan</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>5</td><td>74648752102601952</td><td>214.703845693</td><td>52.9892052739</td><td>21.7395</td><td>0.00384379</td><td>20.8739</td><td>0.00530964</td><td>20.9294</td><td>0.00330692</td><td>20.0826</td><td>0.00509737</td><td>20.5199</td><td>0.00201802</td><td>19.7048</td><td>0.00343894</td><td>20.3121</td><td>0.00423611</td><td>19.4982</td><td>0.00644483</td><td>20.1838</td><td>0.00843314</td><td>19.3177</td><td>0.0118513</td><td>0.0</td><td>7.31464</td><td>0.0258958</td><td>16.2342</td><td>0.0793911</td><td>False</td><td>15.4249</td><td>0.046981</td><td>33.6495</td><td>0.157979</td><td>False</td><td>22.4916</td><td>0.0418044</td><td>47.6501</td><td>0.150926</td><td>False</td><td>27.2367</td><td>0.106267</td><td>57.637</td><td>0.342128</td><td>False</td><td>30.6523</td><td>0.238082</td><td>68.0664</td><td>0.742976</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>nan</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>6</td><td>74648464339789209</td><td>215.298740926</td><td>52.3458307485</td><td>22.0006</td><td>0.00544116</td><td>22.0676</td><td>0.00578405</td><td>20.798</td><td>0.00415282</td><td>20.8272</td><td>0.00441029</td><td>19.811</td><td>0.00224301</td><td>19.7963</td><td>0.00264097</td><td>19.2127</td><td>0.0018413</td><td>19.244</td><td>0.00194418</td><td>18.9853</td><td>0.00285285</td><td>19.0225</td><td>0.00293513</td><td>0.0</td><td>5.75109</td><td>0.0288215</td><td>5.40703</td><td>0.0288049</td><td>False</td><td>17.4107</td><td>0.0665941</td><td>16.9486</td><td>0.0688454</td><td>False</td><td>43.211</td><td>0.0892693</td><td>43.8004</td><td>0.106541</td><td>False</td><td>74.9725</td><td>0.127146</td><td>72.8425</td><td>0.130436</td><td>False</td><td>92.4408</td><td>0.242895</td><td>89.3319</td><td>0.241496</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>nan</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>7</td><td>74643928854322229</td><td>213.34298653</td><td>52.4084921321</td><td>21.6235</td><td>0.00369511</td><td>20.3512</td><td>0.00338246</td><td>20.9338</td><td>0.00383413</td><td>19.6742</td><td>0.00388684</td><td>20.4818</td><td>0.0023676</td><td>19.2494</td><td>0.00253565</td><td>20.2504</td><td>0.00425294</td><td>19.0407</td><td>0.00475823</td><td>20.0253</td><td>0.00686815</td><td>18.8397</td><td>0.00855373</td><td>0.0</td><td>8.13947</td><td>0.0277012</td><td>26.2726</td><td>0.0818486</td><td>False</td><td>15.3636</td><td>0.0542544</td><td>49.0143</td><td>0.175467</td><td>False</td><td>23.2968</td><td>0.0508021</td><td>72.4836</td><td>0.16928</td><td>False</td><td>28.8288</td><td>0.112925</td><td>87.8487</td><td>0.384996</td><td>False</td><td>35.4709</td><td>0.224382</td><td>105.71</td><td>0.832815</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>nan</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>8</td><td>74649018390570083</td><td>213.974891876</td><td>52.5329458218</td><td>23.3481</td><td>0.0120381</td><td>22.6526</td><td>0.0131219</td><td>21.6924</td><td>0.00551274</td><td>21.1273</td><td>0.00699708</td><td>20.7856</td><td>0.00244451</td><td>20.3113</td><td>0.00348642</td><td>20.3883</td><td>0.00417598</td><td>19.9547</td><td>0.00592467</td><td>20.2075</td><td>0.00800847</td><td>19.7446</td><td>0.0112592</td><td>0.0</td><td>1.66255</td><td>0.0184335</td><td>3.15473</td><td>0.0381273</td><td>False</td><td>7.63904</td><td>0.0387866</td><td>12.8551</td><td>0.0828455</td><td>False</td><td>17.6093</td><td>0.0396469</td><td>27.2577</td><td>0.0875277</td><td>False</td><td>25.3905</td><td>0.0976574</td><td>37.853</td><td>0.206557</td><td>False</td><td>29.9911</td><td>0.221217</td><td>45.9384</td><td>0.476386</td><td>False</td><td>False</td><td>0</td><td>True</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>nan</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "<tr><td>9</td><td>74648468634762458</td><td>215.352641628</td><td>52.6552939206</td><td>21.5383</td><td>0.00287522</td><td>20.8048</td><td>0.00342649</td><td>21.0368</td><td>0.00344222</td><td>20.2522</td><td>0.00437282</td><td>20.7831</td><td>0.00250012</td><td>19.9924</td><td>0.00340762</td><td>20.7145</td><td>0.00562539</td><td>19.9251</td><td>0.00741331</td><td>20.6657</td><td>0.0125949</td><td>19.8608</td><td>0.0160596</td><td>0.0</td><td>8.80435</td><td>0.0233155</td><td>17.3015</td><td>0.0546019</td><td>False</td><td>13.9731</td><td>0.0443003</td><td>28.7811</td><td>0.115916</td><td>False</td><td>17.6504</td><td>0.0406435</td><td>36.5618</td><td>0.11475</td><td>False</td><td>18.8015</td><td>0.0974137</td><td>38.9001</td><td>0.265606</td><td>False</td><td>19.6664</td><td>0.228136</td><td>41.2757</td><td>0.610527</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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>nan</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td></td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>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>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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\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(\"$('#table140304896744080-205713').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",
       "    $('#table140304896744080-205713').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, 28, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 51, 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, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 94, 95, 96, 97, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 185, 186, 187, 188, 190, 191, 192, 193, 195, 196, 197, 198, 200, 201, 202, 203, 205, 206, 207, 208, 209, 210, 212, 213, 214, 215, 216, 217, 219, 220, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 233, 234, 235, 236, 237, 238, 240, 241, 242, 243, 244, 245, 247, 248, 249, 250, 251, 252, 254, 255, 256, 257, 258, 259, 261, 262, 263, 264, 265, 266, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 282, 283, 284, 285, 286, 287, 289, 290, 291, 292, 293, 294, 296, 297, 298, 299, 300, 301, 303, 304, 305, 306, 307, 308, 310, 311, 312, 313, 314, 315, 317, 318, 319, 320, 321, 322, 324, 325, 326, 327, 328, 329, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 369, 370, 371, 372, 374, 375, 376, 377, 379, 380, 381, 382, 384, 385, 386, 387, 389, 390, 391, 392, 394, 395, 396, 397, 399, 400, 401, 402, 404, 405, 407, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437, 439, 440, 442, 443, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 456, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 482, 483, 485, 486, 488, 489, 491, 492, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 505, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 525, 526, 527, 528, 529, 530, 532, 533, 534, 535, 536, 537, 539, 540, 541, 542, 543, 544, 546, 547, 548, 549, 550, 551, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 569, 570, 571, 572, 573, 574, 576, 577, 578, 579, 580, 581, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 641, 642, 644, 645, 647, 648, 650, 651, 653, 654, 656, 657, 659, 660, 662, 663, 665, 666, 668, 669, 671, 672, 674, 675, 677, 678, 680, 681, 683, 684, 686, 687, 689, 690, 692, 693, 695, 696, 698, 699, 701, 702, 704, 705, 707, 708, 710, 711, 713, 714, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 735, 736, 737, 738, 740, 741, 742, 743, 745, 746, 748, 749, 750, 751, 752, 753, 754, 755, 756, 758, 759, 761], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "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": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_gaia_columns = [column for column in master_catalogue.colnames\n",
    "                     if 'flag_gaia' in column]\n",
    "\n",
    "master_catalogue.add_column(Column(\n",
    "    data=np.max([master_catalogue[column] for column in flag_gaia_columns], axis=0),\n",
    "    name=\"flag_gaia\"\n",
    "))\n",
    "master_catalogue.remove_columns(flag_gaia_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each prisitine catalogue may contain one or several stellarity columns indicating the probability (0 to 1) of each source being a star.  We merge these columns taking the highest value.  We keep trace of the origin of the stellarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hsc_stellarity, candels-egs_stellarity, cfhtls-wide_stellarity, cfhtls-deep_stellarity, cfhtlens_stellarity, legacy_stellarity, uhs_stellarity\n"
     ]
    }
   ],
   "source": [
    "stellarity_columns = [column for column in master_catalogue.colnames\n",
    "                      if 'stellarity' in column]\n",
    "\n",
    "print(\", \".join(stellarity_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We create an masked array with all the stellarities and get the maximum value, as well as its\n",
    "# origin.  Some sources may not have an associated stellarity.\n",
    "stellarity_array = np.array([master_catalogue[column] for column in stellarity_columns])\n",
    "stellarity_array = np.ma.masked_array(stellarity_array, np.isnan(stellarity_array))\n",
    "\n",
    "max_stellarity = np.max(stellarity_array, axis=0)\n",
    "max_stellarity.fill_value = np.nan\n",
    "\n",
    "no_stellarity_mask = max_stellarity.mask\n",
    "\n",
    "master_catalogue.add_column(Column(data=max_stellarity.filled(), name=\"stellarity\"))\n",
    "\n",
    "stellarity_origin = np.full(len(master_catalogue), \"NO_INFORMATION\", dtype=\"S20\")\n",
    "stellarity_origin[~no_stellarity_mask] = np.array(stellarity_columns)[np.argmax(stellarity_array, axis=0)[~no_stellarity_mask]]\n",
    "\n",
    "master_catalogue.add_column(Column(data=stellarity_origin, name=\"stellarity_origin\"))\n",
    "\n",
    "master_catalogue.remove_columns(stellarity_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV - Adding E(B-V) column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "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), \"EGS\", dtype='<U18'),\n",
    "                                   name=\"field\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK!\n"
     ]
    }
   ],
   "source": [
    "# Check that the HELP Ids are unique\n",
    "if len(master_catalogue) != len(np.unique(master_catalogue['help_id'])):\n",
    "    print(\"The HELP IDs are not unique!!!\")\n",
    "else:\n",
    "    print(\"OK!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VI - Cross-matching with spec-z catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz =  Table.read(\"../../dmu23/dmu23_EGS/data/EGS-specz-v2.1.csv\")\n",
    "for col in specz.colnames:\n",
    "    specz[col].name = specz[col].name.lower()\n",
    "\n",
    "specz['objid'].name = 'specz_id'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VMn9pxMplR1oGWJKVRIx3dv9TlBekEghZjrUOzup9REREFjIFK5dVtfazMt8/6/f5TQO7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\nRByiYCUiIiLiEAUrEREREYf8/2LaR1ixzGKZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b68f0a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(specz['ra'] * u.deg, specz['dec'] * u.deg)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "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",
    "There are many different bands to choose between here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CFHT Megacam\n",
    "\n",
    "CFHT-WIRDS is the only survey that has J, H and Ks so we take them directly. After that we need to select ugriz bands from between CFHTLS, CFHT-WIRDS and CFHTLenS. We take these in order of depth.\n",
    "\n",
    "|  Survey     | Bands         | Notes|\n",
    "|:------------|:--------------|:-----|\n",
    "| CFHTLS-DEEP | u, g, r, i, z |      |\n",
    "| CFHTLS-WIDE | u, g, r, i, z |      |\n",
    "| CFHT-WIRDS  | u, g, r, i, z | Ks selected so may have unique objects       |\n",
    "| CFHTLenS    | u, g, r, i, z | Reprocessing of CFHTLS-WIDE so not used      |\n",
    "| CANDELS-EGS | u, g, r, i, z | Priors from very deep data so may have unique objects     |\n",
    "| IRAC-EGS | u, g, r, i, z | Priors from IRAC so may have unique objects     |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "megacam_origin = Table()\n",
    "megacam_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(5, 0) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "megacam_stats = Table()\n",
    "megacam_stats.add_column(Column(data=['u','g','r','i','z'], name=\"Band\"))\n",
    "for col in [\"CFHTLS-DEEP\", \"CFHTLS-WIDE\", \"CFHT-WIRDS\", \"CFHTLenS\", \"CANDELS\", \"IRAC-EGS\"]:\n",
    "    megacam_stats.add_column(Column(data=np.full(5, 0), name=\"{}\".format(col)))\n",
    "    megacam_stats.add_column(Column(data=np.full(5, 0), name=\"use {}\".format(col)))\n",
    "    megacam_stats.add_column(Column(data=np.full(5, 0), name=\"{} ap\".format(col)))\n",
    "    megacam_stats.add_column(Column(data=np.full(5, 0), name=\"use {} ap\".format(col)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "megacam_bands = ['u','g','r','i','z'] \n",
    "for band in megacam_bands:\n",
    "\n",
    "    # Megacam total flux \n",
    "    has_cfhtls_deep = ~np.isnan(master_catalogue['f_cfhtls-deep_' + band])\n",
    "    has_cfhtls_wide = ~np.isnan(master_catalogue['f_cfhtls-wide_' + band])\n",
    "    has_wirds = ~np.isnan(master_catalogue['f_wirds_' + band])\n",
    "    has_cfhtlens = ~np.isnan(master_catalogue['f_cfhtlens_' + band])\n",
    "    has_candels = ~np.isnan(master_catalogue['f_candels-megacam_' + band])\n",
    "    has_irac = ~np.isnan(master_catalogue['f_irac-megacam_' + band])\n",
    "    \n",
    "\n",
    "    use_cfhtls_deep = has_cfhtls_deep \n",
    "    use_cfhtls_wide = has_cfhtls_wide & ~has_cfhtls_deep \n",
    "    use_wirds =       has_wirds       & ~has_cfhtls_deep & ~has_cfhtls_wide  \n",
    "    use_cfhtlens =    np.zeros(len(master_catalogue), dtype=bool) #We still merge CFHTLenS to keep ids in cross id\n",
    "    use_candels =     has_candels     & ~has_cfhtls_deep & ~has_cfhtls_wide & ~has_wirds & ~has_cfhtlens \n",
    "    use_irac = has_irac &~has_candels     & ~has_cfhtls_deep & ~has_cfhtls_wide & ~has_wirds & ~has_cfhtlens \n",
    "\n",
    "    f_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    f_megacam[use_cfhtls_deep] = master_catalogue['f_cfhtls-deep_' + band][use_cfhtls_deep]\n",
    "    f_megacam[use_cfhtls_wide] = master_catalogue['f_cfhtls-wide_' + band][use_cfhtls_wide]\n",
    "    f_megacam[use_wirds] = master_catalogue['f_wirds_' + band][use_wirds]\n",
    "    f_megacam[use_cfhtlens] = master_catalogue['f_cfhtlens_' + band][use_cfhtlens]\n",
    "    f_megacam[use_candels] = master_catalogue['f_candels-megacam_' + band][use_candels]\n",
    "    f_megacam[use_irac] = master_catalogue['f_irac-megacam_' + band][use_irac]\n",
    "\n",
    "    ferr_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_megacam[use_cfhtls_deep] = master_catalogue['ferr_cfhtls-deep_' + band][use_cfhtls_deep]\n",
    "    ferr_megacam[use_cfhtls_wide] = master_catalogue['ferr_cfhtls-wide_' + band][use_cfhtls_wide]\n",
    "    ferr_megacam[use_wirds] = master_catalogue['ferr_wirds_' + band][use_wirds]\n",
    "    ferr_megacam[use_cfhtlens] = master_catalogue['ferr_cfhtlens_' + band][use_cfhtlens]\n",
    "    ferr_megacam[use_candels] = master_catalogue['ferr_candels-megacam_' + band][use_candels]\n",
    "    ferr_megacam[use_irac] = master_catalogue['ferr_irac-megacam_' + band][use_irac]\n",
    "    \n",
    "    m_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    m_megacam[use_cfhtls_deep] = master_catalogue['m_cfhtls-deep_' + band][use_cfhtls_deep]\n",
    "    m_megacam[use_cfhtls_wide] = master_catalogue['m_cfhtls-wide_' + band][use_cfhtls_wide]\n",
    "    m_megacam[use_wirds] = master_catalogue['m_wirds_' + band][use_wirds]\n",
    "    m_megacam[use_cfhtlens] = master_catalogue['m_cfhtlens_' + band][use_cfhtlens]\n",
    "    m_megacam[use_candels] = master_catalogue['m_candels-megacam_' + band][use_candels]\n",
    "    m_megacam[use_irac] = master_catalogue['m_irac-megacam_' + band][use_irac]\n",
    "\n",
    "    merr_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    merr_megacam[use_cfhtls_deep] = master_catalogue['merr_cfhtls-deep_' + band][use_cfhtls_deep]\n",
    "    merr_megacam[use_cfhtls_wide] = master_catalogue['merr_cfhtls-wide_' + band][use_cfhtls_wide]\n",
    "    merr_megacam[use_wirds] = master_catalogue['merr_wirds_' + band][use_wirds]\n",
    "    merr_megacam[use_cfhtlens] = master_catalogue['merr_cfhtlens_' + band][use_cfhtlens]\n",
    "    merr_megacam[use_candels] = master_catalogue['merr_candels-megacam_' + band][use_candels]\n",
    "    merr_megacam[use_irac] = master_catalogue['merr_irac-megacam_' + band][use_irac]\n",
    "\n",
    "    flag_megacam = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    flag_megacam[use_cfhtls_deep] = master_catalogue['flag_cfhtls-deep_' + band][use_cfhtls_deep]\n",
    "    flag_megacam[use_cfhtls_wide] = master_catalogue['flag_cfhtls-wide_' + band][use_cfhtls_wide]\n",
    "    flag_megacam[use_wirds] = master_catalogue['flag_wirds_' + band][use_wirds]\n",
    "    flag_megacam[use_cfhtlens] = master_catalogue['flag_cfhtlens_' + band][use_cfhtlens]\n",
    "    flag_megacam[use_candels] = master_catalogue['flag_candels-megacam_' + band][use_candels]\n",
    "    flag_megacam[use_irac] = master_catalogue['flag_irac-megacam_' + band][use_irac]\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_megacam, name=\"f_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_megacam, name=\"ferr_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_megacam, name=\"m_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_megacam, name=\"merr_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=flag_megacam, name=\"flag_megacam_\" + band))\n",
    "\n",
    "    old_columns = ['f_cfhtls-deep_' + band,\n",
    "                    'ferr_cfhtls-deep_' + band,\n",
    "                    'm_cfhtls-deep_' + band, \n",
    "                    'merr_cfhtls-deep_' + band,\n",
    "                    'flag_cfhtls-deep_' + band,\n",
    "                   'f_cfhtls-wide_' + band,\n",
    "                    'ferr_cfhtls-wide_' + band,\n",
    "                    'm_cfhtls-wide_' + band, \n",
    "                    'merr_cfhtls-wide_' + band,\n",
    "                    'flag_cfhtls-wide_' + band,\n",
    "                  'f_wirds_' + band,\n",
    "                    'ferr_wirds_' + band,\n",
    "                    'm_wirds_' + band, \n",
    "                    'merr_wirds_' + band,\n",
    "                    'flag_wirds_' + band,\n",
    "                  'f_cfhtlens_' + band,\n",
    "                    'ferr_cfhtlens_' + band,\n",
    "                    'm_cfhtlens_' + band, \n",
    "                    'merr_cfhtlens_' + band,\n",
    "                    'flag_cfhtlens_' + band,\n",
    "                  'f_candels-megacam_' + band,\n",
    "                    'ferr_candels-megacam_' + band,\n",
    "                    'm_candels-megacam_' + band, \n",
    "                    'merr_candels-megacam_' + band,\n",
    "                    'flag_candels-megacam_' + band,\n",
    "                  'f_irac-megacam_' + band,\n",
    "                    'ferr_irac-megacam_' + band,\n",
    "                    'm_irac-megacam_' + band, \n",
    "                    'merr_irac-megacam_' + band,\n",
    "                    'flag_irac-megacam_' + band,]\n",
    "\n",
    "    \n",
    "    master_catalogue.remove_columns(old_columns)\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_cfhtls_deep] = \"CFHTLS-DEEP\"\n",
    "    origin[use_cfhtls_wide] = \"CFHTLS-WIDE\"\n",
    "    origin[use_wirds] = \"CFHT-WIRDS\"\n",
    "    origin[use_cfhtlens] = \"CFHTLenS\"\n",
    "    origin[use_candels] = \"CANDELS\"\n",
    "    origin[use_irac] = \"IRAC-EGS\"\n",
    "    \n",
    "    megacam_origin.add_column(Column(data=origin, name= 'f_megacam_' + band ))\n",
    "    \n",
    "    # Megacam aperture flux \n",
    "    has_ap_cfhtls_deep = ~np.isnan(master_catalogue['f_ap_cfhtls-deep_' + band])\n",
    "    has_ap_cfhtls_wide = ~np.isnan(master_catalogue['f_ap_cfhtls-wide_' + band])\n",
    "    has_ap_wirds = ~np.isnan(master_catalogue['f_ap_wirds_' + band])\n",
    "    has_ap_cfhtlens = ~np.isnan(master_catalogue['f_ap_cfhtlens_' + band])\n",
    "    has_ap_candels = ~np.isnan(master_catalogue['f_ap_candels-megacam_' + band])\n",
    "    \n",
    "    use_ap_cfhtls_deep = has_ap_cfhtls_deep\n",
    "    use_ap_cfhtls_wide = has_ap_cfhtls_wide & ~has_ap_cfhtls_deep\n",
    "    use_ap_wirds       = has_ap_wirds       & ~has_ap_cfhtls_deep & ~has_ap_cfhtls_wide\n",
    "    use_ap_cfhtlens    = has_ap_cfhtlens    & ~has_ap_cfhtls_deep & ~has_ap_cfhtls_wide & ~has_ap_wirds   \n",
    "    use_ap_candels     = has_ap_candels     & ~has_ap_cfhtls_deep & ~has_ap_cfhtls_wide & ~has_ap_wirds & ~has_ap_cfhtlens   \n",
    "\n",
    "    f_ap_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    f_ap_megacam[use_ap_cfhtls_deep] = master_catalogue['f_ap_cfhtls-deep_' + band][use_ap_cfhtls_deep]\n",
    "    f_ap_megacam[use_ap_cfhtls_wide] = master_catalogue['f_ap_cfhtls-wide_' + band][use_ap_cfhtls_wide]\n",
    "    f_ap_megacam[use_ap_wirds] = master_catalogue['f_ap_wirds_' + band][use_ap_wirds]\n",
    "    f_ap_megacam[use_ap_cfhtlens] = master_catalogue['f_ap_cfhtlens_' + band][use_ap_cfhtlens]\n",
    "    f_ap_megacam[use_ap_candels] = master_catalogue['f_ap_candels-megacam_' + band][use_ap_candels]\n",
    "\n",
    "    ferr_ap_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_ap_megacam[use_ap_cfhtls_deep] = master_catalogue['ferr_ap_cfhtls-deep_' + band][use_ap_cfhtls_deep]\n",
    "    ferr_ap_megacam[use_ap_cfhtls_wide] = master_catalogue['ferr_ap_cfhtls-wide_' + band][use_ap_cfhtls_wide]\n",
    "    ferr_ap_megacam[use_ap_wirds] = master_catalogue['ferr_ap_wirds_' + band][use_ap_wirds]\n",
    "    ferr_ap_megacam[use_ap_cfhtlens] = master_catalogue['ferr_ap_cfhtlens_' + band][use_ap_cfhtlens]\n",
    "    ferr_ap_megacam[use_ap_candels] = master_catalogue['ferr_ap_candels-megacam_' + band][use_ap_candels]\n",
    "    \n",
    "    m_ap_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    m_ap_megacam[use_ap_cfhtls_deep] = master_catalogue['m_ap_cfhtls-deep_' + band][use_ap_cfhtls_deep]\n",
    "    m_ap_megacam[use_ap_cfhtls_wide] = master_catalogue['m_ap_cfhtls-wide_' + band][use_ap_cfhtls_wide]\n",
    "    m_ap_megacam[use_ap_wirds] = master_catalogue['m_ap_wirds_' + band][use_ap_wirds]\n",
    "    m_ap_megacam[use_ap_cfhtlens] = master_catalogue['m_ap_cfhtlens_' + band][use_ap_cfhtlens]\n",
    "    m_ap_megacam[use_ap_candels] = master_catalogue['m_ap_candels-megacam_' + band][use_ap_candels]\n",
    "\n",
    "    merr_ap_megacam = np.full(len(master_catalogue), np.nan)\n",
    "    merr_ap_megacam[use_ap_cfhtls_deep] = master_catalogue['merr_ap_cfhtls-deep_' + band][use_ap_cfhtls_deep]\n",
    "    merr_ap_megacam[use_ap_cfhtls_wide] = master_catalogue['merr_ap_cfhtls-wide_' + band][use_ap_cfhtls_wide]\n",
    "    merr_ap_megacam[use_ap_wirds] = master_catalogue['merr_ap_wirds_' + band][use_ap_wirds]\n",
    "    merr_ap_megacam[use_ap_cfhtlens] = master_catalogue['merr_ap_cfhtlens_' + band][use_ap_cfhtlens]\n",
    "    merr_ap_megacam[use_ap_candels] = master_catalogue['merr_ap_candels-megacam_' + band][use_ap_candels]\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_ap_megacam, name=\"f_ap_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_ap_megacam, name=\"ferr_ap_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_ap_megacam, name=\"m_ap_megacam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_ap_megacam, name=\"merr_ap_megacam_\" + band))\n",
    "\n",
    "\n",
    "    old_ap_columns = ['f_ap_cfhtls-deep_' + band,\n",
    "                    'ferr_ap_cfhtls-deep_' + band,\n",
    "                    'm_ap_cfhtls-deep_' + band, \n",
    "                    'merr_ap_cfhtls-deep_' + band,\n",
    "                  'f_ap_cfhtls-wide_' + band,\n",
    "                    'ferr_ap_cfhtls-wide_' + band,\n",
    "                    'm_ap_cfhtls-wide_' + band, \n",
    "                    'merr_ap_cfhtls-wide_' + band,\n",
    "                  'f_ap_wirds_' + band,\n",
    "                    'ferr_ap_wirds_' + band,\n",
    "                    'm_ap_wirds_' + band, \n",
    "                    'merr_ap_wirds_' + band,\n",
    "                  'f_ap_cfhtlens_' + band,\n",
    "                    'ferr_ap_cfhtlens_' + band,\n",
    "                    'm_ap_cfhtlens_' + band, \n",
    "                    'merr_ap_cfhtlens_' + band,\n",
    "                  'f_ap_candels-megacam_' + band,\n",
    "                    'ferr_ap_candels-megacam_' + band,\n",
    "                    'm_ap_candels-megacam_' + band, \n",
    "                    'merr_ap_candels-megacam_' + band]\n",
    "\n",
    "    \n",
    "    master_catalogue.remove_columns(old_ap_columns)\n",
    "\n",
    "    origin_ap = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin_ap[use_ap_cfhtls_deep] = \"CFHTLS-DEEP\"\n",
    "    origin_ap[use_ap_cfhtls_wide] = \"CFHTLS-WIDE\"\n",
    "    origin_ap[use_ap_wirds] = \"CFHT-WIRDS\"\n",
    "    origin_ap[use_ap_cfhtlens] = \"CFHTLenS\"\n",
    "    origin_ap[use_ap_candels] = \"CANDELS\"\n",
    "    \n",
    "    megacam_origin.add_column(Column(data=origin_ap, name= 'f_ap_megacam_' + band ))\n",
    "    \n",
    "\n",
    "   \n",
    "    megacam_stats['CFHTLS-DEEP'][megacam_stats['Band'] == band] = np.sum(has_cfhtls_deep)\n",
    "    megacam_stats['CFHTLS-WIDE'][megacam_stats['Band'] == band] = np.sum(has_cfhtls_wide)\n",
    "    megacam_stats['CFHT-WIRDS'][megacam_stats['Band'] == band] = np.sum(has_wirds)\n",
    "    megacam_stats['CFHTLenS'][megacam_stats['Band'] == band] = np.sum(has_cfhtlens)\n",
    "    megacam_stats['CANDELS'][megacam_stats['Band'] == band] = np.sum(has_candels)\n",
    "    megacam_stats['use CFHTLS-DEEP'][megacam_stats['Band'] == band] = np.sum(use_cfhtls_deep)\n",
    "    megacam_stats['use CFHTLS-WIDE'][megacam_stats['Band'] == band] = np.sum(use_cfhtls_wide)\n",
    "    megacam_stats['use CFHT-WIRDS'][megacam_stats['Band'] == band] = np.sum(use_wirds)\n",
    "    megacam_stats['use CFHTLenS'][megacam_stats['Band'] == band] = np.sum(use_cfhtlens)\n",
    "    megacam_stats['use CANDELS'][megacam_stats['Band'] == band] = np.sum(use_candels)    \n",
    "    megacam_stats['CFHTLS-DEEP ap'][megacam_stats['Band'] == band] = np.sum(has_ap_cfhtls_deep)\n",
    "    megacam_stats['CFHTLS-WIDE ap'][megacam_stats['Band'] == band] = np.sum(has_ap_cfhtls_wide)\n",
    "    megacam_stats['CFHT-WIRDS ap'][megacam_stats['Band'] == band] = np.sum(has_ap_wirds)\n",
    "    megacam_stats['CFHTLenS ap'][megacam_stats['Band'] == band] = np.sum(has_ap_cfhtlens)\n",
    "    megacam_stats['CANDELS ap'][megacam_stats['Band'] == band] = np.sum(has_ap_candels)\n",
    "    megacam_stats['use CFHTLS-DEEP ap'][megacam_stats['Band'] == band] = np.sum(use_ap_cfhtls_deep)\n",
    "    megacam_stats['use CFHTLS-WIDE ap'][megacam_stats['Band'] == band] = np.sum(use_ap_cfhtls_wide)\n",
    "    megacam_stats['use CFHT-WIRDS ap'][megacam_stats['Band'] == band] = np.sum(use_ap_wirds)\n",
    "    megacam_stats['use CFHTLenS ap'][megacam_stats['Band'] == band] = np.sum(use_ap_cfhtlens)\n",
    "    megacam_stats['use CANDELS ap'][megacam_stats['Band'] == band] = np.sum(use_ap_candels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=5</i>\n",
       "<table id=\"table140304149863840-853667\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>CFHTLS-DEEP</th><th>use CFHTLS-DEEP</th><th>CFHTLS-DEEP ap</th><th>use CFHTLS-DEEP ap</th><th>CFHTLS-WIDE</th><th>use CFHTLS-WIDE</th><th>CFHTLS-WIDE ap</th><th>use CFHTLS-WIDE ap</th><th>CFHT-WIRDS</th><th>use CFHT-WIRDS</th><th>CFHT-WIRDS ap</th><th>use CFHT-WIRDS ap</th><th>CFHTLenS</th><th>use CFHTLenS</th><th>CFHTLenS ap</th><th>use CFHTLenS ap</th><th>CANDELS</th><th>use CANDELS</th><th>CANDELS ap</th><th>use CANDELS ap</th><th>IRAC-EGS</th><th>use IRAC-EGS</th><th>IRAC-EGS ap</th><th>use IRAC-EGS ap</th></tr></thead>\n",
       "<tr><td>0</td><td>u</td><td>462710.0</td><td>462710.0</td><td>466773.0</td><td>466773.0</td><td>672644.0</td><td>515912.0</td><td>686140.0</td><td>526685.0</td><td>85406.0</td><td>15360.0</td><td>85652.0</td><td>15000.0</td><td>321530.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>41449.0</td><td>14235.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>g</td><td>531957.0</td><td>531957.0</td><td>534321.0</td><td>534321.0</td><td>740867.0</td><td>568617.0</td><td>744387.0</td><td>571585.0</td><td>91050.0</td><td>16093.0</td><td>90419.0</td><td>15292.0</td><td>408054.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>41449.0</td><td>11609.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>2</td><td>r</td><td>543141.0</td><td>543141.0</td><td>546308.0</td><td>546308.0</td><td>735227.0</td><td>565264.0</td><td>742023.0</td><td>570390.0</td><td>91750.0</td><td>16283.0</td><td>91056.0</td><td>15446.0</td><td>402653.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>41449.0</td><td>11139.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>3</td><td>i</td><td>537151.0</td><td>537151.0</td><td>540837.0</td><td>540837.0</td><td>721821.0</td><td>553803.0</td><td>732290.0</td><td>562106.0</td><td>91903.0</td><td>16356.0</td><td>91175.0</td><td>15495.0</td><td>388011.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>41449.0</td><td>11235.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>4</td><td>z</td><td>475863.0</td><td>475863.0</td><td>482568.0</td><td>482568.0</td><td>650450.0</td><td>500464.0</td><td>673315.0</td><td>517921.0</td><td>89997.0</td><td>15702.0</td><td>89876.0</td><td>15082.0</td><td>350143.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>41449.0</td><td>13686.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140304149863840-853667').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",
       "    $('#table140304149863840-853667').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "megacam_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "megacam_origin.write(\"{}/egs_megacam_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CFHT WIRCAM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have WIRCAM J,H, and Ks from both CFHT-WIRDS (Ks prior and blind) and CANDELS-EGS. Since the CANDELS will have very deep priors the WIRCAM fluxes are worth keepting to constrain photo-z. We therefor take the CFHT-WIRDS fluxes if they are there but keep all the CANDELS fluxes for sources that only have those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wircam_origin = Table()\n",
    "wircam_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(3, 0) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "wircam_stats = Table()\n",
    "wircam_stats.add_column(Column(data=['j', 'h', 'k'], name=\"Band\"))\n",
    "for col in [\"CFHT-WIRDS\", \"CANDELS\"]:\n",
    "    wircam_stats.add_column(Column(data=np.full(3, 0), name=\"{}\".format(col)))\n",
    "    wircam_stats.add_column(Column(data=np.full(3, 0), name=\"use {}\".format(col)))\n",
    "    wircam_stats.add_column(Column(data=np.full(3, 0), name=\"{} ap\".format(col)))\n",
    "    wircam_stats.add_column(Column(data=np.full(3, 0), name=\"use {} ap\".format(col)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wircam_bands = ['j', 'h', 'k'] \n",
    "for band in wircam_bands:\n",
    "\n",
    "    # wircam total flux \n",
    "    has_wirds = ~np.isnan(master_catalogue['f_wirds_' + band])\n",
    "    has_candels = ~np.isnan(master_catalogue['f_candels-wircam_' + band])\n",
    "    \n",
    "    use_wirds =       has_wirds        \n",
    "    use_candels =     has_candels & ~has_wirds \n",
    "\n",
    "    f_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    f_wircam[use_wirds] = master_catalogue['f_wirds_' + band][use_wirds]\n",
    "    f_wircam[use_candels] = master_catalogue['f_candels-wircam_' + band][use_candels]\n",
    "\n",
    "    ferr_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_wircam[use_wirds] = master_catalogue['ferr_wirds_' + band][use_wirds]\n",
    "    ferr_wircam[use_candels] = master_catalogue['ferr_candels-wircam_' + band][use_candels]\n",
    "    \n",
    "    m_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    m_wircam[use_wirds] = master_catalogue['m_wirds_' + band][use_wirds]\n",
    "    m_wircam[use_candels] = master_catalogue['m_candels-wircam_' + band][use_candels]\n",
    "\n",
    "    merr_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    merr_wircam[use_wirds] = master_catalogue['merr_wirds_' + band][use_wirds]\n",
    "    merr_wircam[use_candels] = master_catalogue['merr_candels-wircam_' + band][use_candels]\n",
    "\n",
    "    flag_wircam = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    flag_wircam[use_wirds] = master_catalogue['flag_wirds_' + band][use_wirds]\n",
    "    flag_wircam[use_candels] = master_catalogue['flag_candels-wircam_' + band][use_candels]\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_wircam, name=\"f_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_wircam, name=\"ferr_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_wircam, name=\"m_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_wircam, name=\"merr_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=flag_wircam, name=\"flag_wircam_\" + band))\n",
    "\n",
    "    old_columns = ['f_wirds_' + band,\n",
    "                    'ferr_wirds_' + band,\n",
    "                    'm_wirds_' + band, \n",
    "                    'merr_wirds_' + band,\n",
    "                    'flag_wirds_' + band,\n",
    "                  'f_candels-wircam_' + band,\n",
    "                    'ferr_candels-wircam_' + band,\n",
    "                    'm_candels-wircam_' + band, \n",
    "                    'merr_candels-wircam_' + band,\n",
    "                    'flag_candels-wircam_' + band,]\n",
    "\n",
    "    \n",
    "    master_catalogue.remove_columns(old_columns)\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_wirds] = \"CFHT-WIRDS\"\n",
    "    origin[use_candels] = \"CANDELS\"\n",
    "    \n",
    "    wircam_origin.add_column(Column(data=origin, name= 'f_wircam_' + band ))\n",
    "    \n",
    "    # wircam aperture flux \n",
    "    has_ap_wirds = ~np.isnan(master_catalogue['f_ap_wirds_' + band])\n",
    "    has_ap_candels = ~np.isnan(master_catalogue['f_ap_candels-wircam_' + band])\n",
    "    \n",
    "    use_ap_wirds       = has_ap_wirds      \n",
    "    use_ap_candels     = has_ap_candels     & ~has_ap_wirds  \n",
    "\n",
    "    f_ap_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    f_ap_wircam[use_ap_wirds] = master_catalogue['f_ap_wirds_' + band][use_ap_wirds]\n",
    "    f_ap_wircam[use_ap_candels] = master_catalogue['f_ap_candels-wircam_' + band][use_ap_candels]\n",
    "\n",
    "    ferr_ap_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_ap_wircam[use_ap_wirds] = master_catalogue['ferr_ap_wirds_' + band][use_ap_wirds]\n",
    "    ferr_ap_wircam[use_ap_candels] = master_catalogue['ferr_ap_candels-wircam_' + band][use_ap_candels]\n",
    "    \n",
    "    m_ap_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    m_ap_wircam[use_ap_wirds] = master_catalogue['m_ap_wirds_' + band][use_ap_wirds]\n",
    "    m_ap_wircam[use_ap_candels] = master_catalogue['m_ap_candels-wircam_' + band][use_ap_candels]\n",
    "\n",
    "    merr_ap_wircam = np.full(len(master_catalogue), np.nan)\n",
    "    merr_ap_wircam[use_ap_wirds] = master_catalogue['merr_ap_wirds_' + band][use_ap_wirds]\n",
    "    merr_ap_wircam[use_ap_candels] = master_catalogue['merr_ap_candels-wircam_' + band][use_ap_candels]\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_ap_wircam, name=\"f_ap_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_ap_wircam, name=\"ferr_ap_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_ap_wircam, name=\"m_ap_wircam_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_ap_wircam, name=\"merr_ap_wircam_\" + band))\n",
    "\n",
    "\n",
    "    old_ap_columns = ['f_ap_wirds_' + band,\n",
    "                    'ferr_ap_wirds_' + band,\n",
    "                    'm_ap_wirds_' + band, \n",
    "                    'merr_ap_wirds_' + band,\n",
    "                  'f_ap_candels-wircam_' + band,\n",
    "                    'ferr_ap_candels-wircam_' + band,\n",
    "                    'm_ap_candels-wircam_' + band, \n",
    "                    'merr_ap_candels-wircam_' + band]\n",
    "\n",
    "    \n",
    "    master_catalogue.remove_columns(old_ap_columns)\n",
    "\n",
    "    origin_ap = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin_ap[use_ap_wirds] = \"CFHT-WIRDS\"\n",
    "    origin_ap[use_ap_candels] = \"CANDELS\"\n",
    "    \n",
    "    wircam_origin.add_column(Column(data=origin_ap, name= 'f_ap_wircam_' + band ))\n",
    "    \n",
    "\n",
    "   \n",
    "\n",
    "    wircam_stats['CFHT-WIRDS'][wircam_stats['Band'] == band] = np.sum(has_wirds)\n",
    "    wircam_stats['CANDELS'][wircam_stats['Band'] == band] = np.sum(has_candels)\n",
    "    wircam_stats['use CFHT-WIRDS'][wircam_stats['Band'] == band] = np.sum(use_wirds)\n",
    "    wircam_stats['use CANDELS'][wircam_stats['Band'] == band] = np.sum(use_candels)    \n",
    "    wircam_stats['CFHT-WIRDS ap'][wircam_stats['Band'] == band] = np.sum(has_ap_wirds)\n",
    "    wircam_stats['CANDELS ap'][wircam_stats['Band'] == band] = np.sum(has_ap_candels)\n",
    "    wircam_stats['use CFHT-WIRDS ap'][wircam_stats['Band'] == band] = np.sum(use_ap_wirds)\n",
    "    wircam_stats['use CANDELS ap'][wircam_stats['Band'] == band] = np.sum(use_ap_candels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=3</i>\n",
       "<table id=\"table140304149760936-758273\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>CFHT-WIRDS</th><th>use CFHT-WIRDS</th><th>CFHT-WIRDS ap</th><th>use CFHT-WIRDS ap</th><th>CANDELS</th><th>use CANDELS</th><th>CANDELS ap</th><th>use CANDELS ap</th></tr></thead>\n",
       "<tr><td>0</td><td>j</td><td>88730.0</td><td>88730.0</td><td>89464.0</td><td>89464.0</td><td>41449.0</td><td>31013.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>h</td><td>88563.0</td><td>88563.0</td><td>89226.0</td><td>89226.0</td><td>41449.0</td><td>30803.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>2</td><td>k</td><td>98224.0</td><td>98224.0</td><td>99097.0</td><td>99097.0</td><td>41449.0</td><td>30560.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140304149760936-758273').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",
       "    $('#table140304149760936-758273').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 2, 3, 4, 5, 6, 7, 8, 9], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wircam_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wircam_origin.write(\"{}/egs_wircam_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IRAC\n",
    "\n",
    "We have IRAC from the IRAC-EGS catalogue and from CANDELS. We take the CANDELS fluxes preferentially."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irac_origin = Table()\n",
    "irac_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(4, 0) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "irac_stats = Table()\n",
    "irac_stats.add_column(Column(data=['i1', 'i2', 'i3', 'i4'], name=\"Band\"))\n",
    "for col in [\"CANDELS\", \"IRAC-EGS\"]:\n",
    "    irac_stats.add_column(Column(data=np.full(4, 0), name=\"{}\".format(col)))\n",
    "    irac_stats.add_column(Column(data=np.full(4, 0), name=\"use {}\".format(col)))\n",
    "    irac_stats.add_column(Column(data=np.full(4, 0), name=\"{} ap\".format(col)))\n",
    "    irac_stats.add_column(Column(data=np.full(4, 0), name=\"use {} ap\".format(col)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irac_bands = ['i1', 'i2', 'i3', 'i4'] \n",
    "for band in irac_bands:\n",
    "\n",
    "    # IRAC total flux \n",
    "    has_candels = ~np.isnan(master_catalogue['f_candels-irac_' + band])\n",
    "    has_irac = ~np.isnan(master_catalogue['f_irac-egs_' + band])\n",
    "    \n",
    "    \n",
    "    use_candels =       has_candels       \n",
    "    use_irac =     has_irac & ~has_candels\n",
    "\n",
    "    f_irac = np.full(len(master_catalogue), np.nan)\n",
    "    f_irac[use_candels] = master_catalogue['f_candels-irac_' + band][use_candels]\n",
    "    f_irac[use_irac] = master_catalogue['f_irac-egs_' + band][use_irac]\n",
    "    \n",
    "    ferr_irac = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_irac[use_candels] = master_catalogue['ferr_candels-irac_' + band][use_candels]\n",
    "    ferr_irac[use_irac] = master_catalogue['ferr_irac-egs_' + band][use_irac]\n",
    "    \n",
    "    m_irac = np.full(len(master_catalogue), np.nan)\n",
    "    m_irac[use_candels] = master_catalogue['m_candels-irac_' + band][use_candels]\n",
    "    m_irac[use_irac] = master_catalogue['m_irac-egs_' + band][use_irac]\n",
    "    \n",
    "    merr_irac = np.full(len(master_catalogue), np.nan)\n",
    "    merr_irac[use_candels] = master_catalogue['merr_candels-irac_' + band][use_candels]\n",
    "    merr_irac[use_irac] = master_catalogue['merr_irac-egs_' + band][use_irac]\n",
    "    \n",
    "    flag_irac = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    flag_irac[use_candels] = master_catalogue['flag_candels-irac_' + band][use_candels]\n",
    "    flag_irac[use_irac] = master_catalogue['flag_irac-egs_' + band][use_irac]\n",
    "\n",
    "\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_irac, name=\"f_irac_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_irac, name=\"ferr_irac_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_irac, name=\"m_irac_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_irac, name=\"merr_irac_\" + band))\n",
    "    master_catalogue.add_column(Column(data=flag_irac, name=\"flag_irac_\" + band))\n",
    "\n",
    "    old_columns = ['f_candels-irac_' + band,\n",
    "                    'ferr_candels-irac_' + band,\n",
    "                    'm_candels-irac_' + band, \n",
    "                    'merr_candels-irac_' + band,\n",
    "                    'flag_candels-irac_' + band,\n",
    "                   'f_ap_candels-irac_' + band,\n",
    "                    'ferr_ap_candels-irac_' + band,\n",
    "                    'm_ap_candels-irac_' + band, \n",
    "                    'merr_ap_candels-irac_' + band,\n",
    "                  'f_irac-egs_' + band,\n",
    "                    'ferr_irac-egs_' + band,\n",
    "                    'm_irac-egs_' + band, \n",
    "                    'merr_irac-egs_' + band,\n",
    "                    'flag_irac-egs_' + band,]\n",
    "\n",
    "    \n",
    "    master_catalogue.remove_columns(old_columns)\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_candels] = \"CANDELS\"\n",
    "    origin[use_irac] = \"IRAC-EGS\"\n",
    "    \n",
    "    irac_origin.add_column(Column(data=origin, name= 'f_irac_' + band ))\n",
    "    \n",
    "  \n",
    " \n",
    "    \n",
    "\n",
    "   \n",
    "\n",
    "    irac_stats['CANDELS'][irac_stats['Band'] == band] = np.sum(has_candels)\n",
    "    irac_stats['IRAC-EGS'][irac_stats['Band'] == band] = np.sum(has_irac)\n",
    "    irac_stats['use CANDELS'][irac_stats['Band'] == band] = np.sum(use_candels)\n",
    "    irac_stats['use IRAC-EGS'][irac_stats['Band'] == band] = np.sum(use_irac)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=4</i>\n",
       "<table id=\"table140304149765760-37521\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>CANDELS</th><th>use CANDELS</th><th>CANDELS ap</th><th>use CANDELS ap</th><th>IRAC-EGS</th><th>use IRAC-EGS</th><th>IRAC-EGS ap</th><th>use IRAC-EGS ap</th></tr></thead>\n",
       "<tr><td>0</td><td>i1</td><td>41449.0</td><td>41449.0</td><td>0.0</td><td>0.0</td><td>117929.0</td><td>105191.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>i2</td><td>41449.0</td><td>41449.0</td><td>0.0</td><td>0.0</td><td>114170.0</td><td>101586.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>2</td><td>i3</td><td>41449.0</td><td>41449.0</td><td>0.0</td><td>0.0</td><td>88315.0</td><td>78388.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>3</td><td>i4</td><td>41449.0</td><td>41449.0</td><td>0.0</td><td>0.0</td><td>83319.0</td><td>73956.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140304149765760-37521').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",
       "    $('#table140304149765760-37521').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 2, 3, 4, 5, 6, 7, 8, 9], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irac_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "irac_origin.write(\"{}/egs_irac_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AEGIS\n",
    "We have AEGIS (WIRCS instrument on Palomar telescope) data from the AEGIS catalogue and from IRAC-EGS. We take the AEGIS fluxes preferentially "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aegis_origin = Table()\n",
    "aegis_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(2, 0) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "aegis_stats = Table()\n",
    "aegis_stats.add_column(Column(data=['j',  'k'], name=\"Band\"))\n",
    "for col in [\"AEGIS\", \"IRAC-EGS\"]:\n",
    "    aegis_stats.add_column(Column(data=np.full(2, 0), name=\"{}\".format(col)))\n",
    "    aegis_stats.add_column(Column(data=np.full(2, 0), name=\"use {}\".format(col)))\n",
    "    aegis_stats.add_column(Column(data=np.full(2, 0), name=\"{} ap\".format(col)))\n",
    "    aegis_stats.add_column(Column(data=np.full(2, 0), name=\"use {} ap\".format(col)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aegis_bands = ['j',  'k'] \n",
    "for band in aegis_bands:\n",
    "    \n",
    "\n",
    "    # wircs total flux \n",
    "    has_aegis = ~np.isnan(master_catalogue['f_aegis_' + band])\n",
    "    has_irac = ~np.isnan(master_catalogue['f_irac-aegis_' + band])\n",
    "    \n",
    "    use_aegis =       has_aegis        \n",
    "    use_irac =     has_irac & ~has_aegis\n",
    "\n",
    "    f_aegis = np.full(len(master_catalogue), np.nan)\n",
    "    f_aegis[use_aegis] = master_catalogue['f_aegis_' + band][use_aegis]\n",
    "    f_aegis[use_irac] = master_catalogue['f_irac-aegis_' + band][use_irac]\n",
    "\n",
    "    ferr_aegis = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_aegis[use_aegis] = master_catalogue['ferr_aegis_' + band][use_aegis]\n",
    "    ferr_aegis[use_irac] = master_catalogue['ferr_irac-aegis_' + band][use_irac]\n",
    "    \n",
    "    m_aegis = np.full(len(master_catalogue), np.nan)\n",
    "    m_aegis[use_aegis] = master_catalogue['m_aegis_' + band][use_aegis]\n",
    "    m_aegis[use_irac] = master_catalogue['m_irac-aegis_' + band][use_irac]\n",
    "    \n",
    "    merr_aegis = np.full(len(master_catalogue), np.nan)\n",
    "    merr_aegis[use_aegis] = master_catalogue['merr_aegis_' + band][use_aegis]\n",
    "    merr_aegis[use_irac] = master_catalogue['merr_irac-aegis_' + band][use_irac]\n",
    "    \n",
    "    flag_aegis = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    flag_aegis[use_aegis] = master_catalogue['flag_aegis_' + band][use_aegis]\n",
    "    flag_aegis[use_irac] = master_catalogue['flag_irac-aegis_' + band][use_irac]\n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_aegis, name=\"f_wircs_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_aegis, name=\"ferr_wircs_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_aegis, name=\"m_wircs_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_aegis, name=\"merr_wircs_\" + band))\n",
    "    master_catalogue.add_column(Column(data=flag_aegis, name=\"flag_wircs_\" + band))\n",
    "\n",
    "    old_columns = ['f_aegis_' + band,\n",
    "                    'ferr_aegis_' + band,\n",
    "                    'm_aegis_' + band, \n",
    "                    'merr_aegis_' + band,\n",
    "                    'flag_aegis_' + band,\n",
    "                  'f_irac-aegis_' + band,\n",
    "                    'ferr_irac-aegis_' + band,\n",
    "                    'm_irac-aegis_' + band, \n",
    "                    'merr_irac-aegis_' + band,\n",
    "                    'flag_irac-aegis_' + band,\n",
    "                  ]\n",
    "    \n",
    "\n",
    "\n",
    "    \n",
    "    master_catalogue.remove_columns(old_columns)\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_aegis] = \"AEGIS\"\n",
    "    origin[use_irac] = \"IRAC-EGS\"\n",
    "    \n",
    "    aegis_origin.add_column(Column(data=origin, name= 'f_wircs_' + band ))\n",
    "\n",
    "    aegis_stats['AEGIS'][aegis_stats['Band'] == band] = np.sum(has_aegis)\n",
    "    aegis_stats['IRAC-EGS'][aegis_stats['Band'] == band] = np.sum(has_irac)\n",
    "    aegis_stats['use AEGIS'][aegis_stats['Band'] == band] = np.sum(use_aegis)\n",
    "    aegis_stats['use IRAC-EGS'][aegis_stats['Band'] == band] = np.sum(use_irac)    \n",
    "    \n",
    "\n",
    "\n",
    "    # wircs aperture flux \n",
    "    has_ap_aegis = ~np.isnan(master_catalogue['f_ap_aegis_' + band])\n",
    "    has_ap_irac = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    \n",
    "    use_ap_aegis =       has_ap_aegis        \n",
    "    use_ap_irac =     has_ap_irac & ~has_ap_aegis\n",
    "\n",
    "    f_ap_aegis = np.full(len(master_catalogue), np.nan)\n",
    "    f_ap_aegis[use_ap_aegis] = master_catalogue['f_ap_aegis_' + band][use_ap_aegis]\n",
    "    #f_ap_aegis[use_ap_irac] = master_catalogue['f_ap_irac-aegis_' + band][use_ap_irac]\n",
    "\n",
    "    ferr_ap_aegis = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_ap_aegis[use_ap_aegis] = master_catalogue['ferr_ap_aegis_' + band][use_ap_aegis]\n",
    "    #ferr_ap_aegis[use_ap_irac] = master_catalogue['ferr_ap_irac-aegis_' + band][use_ap_irac]\n",
    "    \n",
    "    m_ap_aegis = np.full(len(master_catalogue), np.nan)\n",
    "    m_ap_aegis[use_ap_aegis] = master_catalogue['m_ap_aegis_' + band][use_ap_aegis]\n",
    "    #m_ap_aegis[use_ap_irac] = master_catalogue['m_ap_irac-aegis_' + band][use_irac]\n",
    "    \n",
    "    merr_ap_aegis = np.full(len(master_catalogue), np.nan)\n",
    "    merr_ap_aegis[use_ap_aegis] = master_catalogue['merr_ap_aegis_' + band][use_ap_aegis]\n",
    "    #merr_ap_aegis[use_ap_irac] = master_catalogue['merr_ap_irac-aegis_' + band][use_ap_irac]\n",
    "    \n",
    "\n",
    "    master_catalogue.add_column(Column(data=f_ap_aegis, name=\"f_ap_wircs_\" + band))\n",
    "    master_catalogue.add_column(Column(data=ferr_ap_aegis, name=\"ferr_ap_wircs_\" + band))\n",
    "    master_catalogue.add_column(Column(data=m_ap_aegis, name=\"m_ap_wircs_\" + band))\n",
    "    master_catalogue.add_column(Column(data=merr_ap_aegis, name=\"merr_ap_wircs_\" + band))\n",
    "  \n",
    "\n",
    "    old_ap_columns = ['f_ap_aegis_' + band,\n",
    "                    'ferr_ap_aegis_' + band,\n",
    "                    'm_ap_aegis_' + band, \n",
    "                    'merr_ap_aegis_' + band\n",
    "                    \n",
    "                 #   'f_irac-aegis_' + band,\n",
    "                 #   'ferr_irac-aegis_' + band,\n",
    "                 #   'm_irac-aegis_' + band, \n",
    "                 #   'merr_irac-aegis_' + band,\n",
    "                 #   'flag_irac-aegis_' + band,\n",
    "                  ]\n",
    "    \n",
    "\n",
    "\n",
    "    \n",
    "    master_catalogue.remove_columns(old_ap_columns)\n",
    "\n",
    "    origin_ap = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin_ap[use_ap_aegis] = \"AEGIS\"\n",
    "    origin_ap[use_ap_irac] = \"IRAC-EGS\"\n",
    "    \n",
    "    aegis_origin.add_column(Column(data=origin_ap, name= 'f_ap_wircs_' + band ))\n",
    "\n",
    "    aegis_stats['AEGIS ap'][aegis_stats['Band'] == band] = np.sum(has_ap_aegis)\n",
    "    aegis_stats['IRAC-EGS ap'][aegis_stats['Band'] == band] = np.sum(has_ap_irac)\n",
    "    aegis_stats['use AEGIS ap'][aegis_stats['Band'] == band] = np.sum(use_ap_aegis)\n",
    "    aegis_stats['use IRAC-EGS ap'][aegis_stats['Band'] == band] = np.sum(use_ap_irac)    \n",
    "    \n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=2</i>\n",
       "<table id=\"table140304150648704-597468\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>AEGIS</th><th>use AEGIS</th><th>AEGIS ap</th><th>use AEGIS ap</th><th>IRAC-EGS</th><th>use IRAC-EGS</th><th>IRAC-EGS ap</th><th>use IRAC-EGS ap</th></tr></thead>\n",
       "<tr><td>0</td><td>j</td><td>0.0</td><td>0.0</td><td>16285.0</td><td>16285.0</td><td>14575.0</td><td>14575.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>k</td><td>45065.0</td><td>45065.0</td><td>45065.0</td><td>45065.0</td><td>31115.0</td><td>2612.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140304150648704-597468').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",
       "    $('#table140304150648704-597468').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 2, 3, 4, 5, 6, 7, 8, 9], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aegis_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aegis_origin.write(\"{}/egs_aegis_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HST: CANDELS vs IRAC-EGS\n",
    "We take CANDELS over IRAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hst_origin = Table()\n",
    "hst_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(2, 0) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "hst_stats = Table()\n",
    "hst_stats.add_column(Column(data=['f814w',  'f606w'], name=\"Band\"))\n",
    "for col in [\"CANDELS\", \"IRAC-EGS\"]:\n",
    "    hst_stats.add_column(Column(data=np.full(2, 0), name=\"{}\".format(col)))\n",
    "    hst_stats.add_column(Column(data=np.full(2, 0), name=\"use {}\".format(col)))\n",
    "    hst_stats.add_column(Column(data=np.full(2, 0), name=\"{} ap\".format(col)))\n",
    "    hst_stats.add_column(Column(data=np.full(2, 0), name=\"use {} ap\".format(col)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hst_bands = ['f814w',  'f606w']\n",
    "\n",
    "for band in hst_bands:\n",
    "\n",
    "    # HST total flux \n",
    "    has_candels = ~np.isnan(master_catalogue['f_acs_' + band])\n",
    "    has_irac = ~np.isnan(master_catalogue['f_irac-acs_' + band])\n",
    "    \n",
    "    use_candels =       has_candels        \n",
    "    use_irac =     has_irac & ~has_candels\n",
    "\n",
    "    master_catalogue['f_acs_' + band][use_irac] = master_catalogue['f_irac-acs_' + band][use_irac]\n",
    "    master_catalogue['ferr_acs_' + band][use_irac] = master_catalogue['ferr_irac-acs_' + band][use_irac]\n",
    "    master_catalogue['m_acs_' + band][use_irac] = master_catalogue['m_irac-acs_' + band][use_irac]\n",
    "    master_catalogue['merr_acs_' + band][use_irac] = master_catalogue['merr_irac-acs_' + band][use_irac]\n",
    "    master_catalogue['flag_acs_' + band][use_irac] = master_catalogue['flag_irac-acs_' + band][use_irac]\n",
    "\n",
    "    old_columns = ['f_irac-acs_' + band,\n",
    "                    'ferr_irac-acs_' + band,\n",
    "                    'm_irac-acs_' + band, \n",
    "                    'merr_irac-acs_' + band,\n",
    "                    'flag_irac-acs_' + band,]\n",
    "\n",
    "    \n",
    "    master_catalogue.remove_columns(old_columns)\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_candels] = \"CANDELS\"\n",
    "    origin[use_irac] = \"IRAC-EGS\"\n",
    "    \n",
    "    hst_origin.add_column(Column(data=origin, name= 'f_cfht12k_' + band ))\n",
    "    \n",
    "\n",
    "        \n",
    "    \n",
    "\n",
    "   \n",
    "\n",
    "    hst_stats['CANDELS'][hst_stats['Band'] == band] = np.sum(has_candels)\n",
    "    hst_stats['IRAC-EGS'][hst_stats['Band'] == band] = np.sum(has_irac)\n",
    "    hst_stats['use CANDELS'][hst_stats['Band'] == band] = np.sum(use_candels)\n",
    "    hst_stats['use IRAC-EGS'][hst_stats['Band'] == band] = np.sum(use_irac)    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=2</i>\n",
       "<table id=\"table140304150569424-253705\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>CANDELS</th><th>use CANDELS</th><th>CANDELS ap</th><th>use CANDELS ap</th><th>IRAC-EGS</th><th>use IRAC-EGS</th><th>IRAC-EGS ap</th><th>use IRAC-EGS ap</th></tr></thead>\n",
       "<tr><td>0</td><td>f814w</td><td>41449.0</td><td>41449.0</td><td>0.0</td><td>0.0</td><td>42891.0</td><td>30174.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>f606w</td><td>41449.0</td><td>41449.0</td><td>0.0</td><td>0.0</td><td>42908.0</td><td>30190.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140304150569424-253705').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",
       "    $('#table140304150569424-253705').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 2, 3, 4, 5, 6, 7, 8, 9], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hst_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hst_origin.write(\"{}/egs_hst_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CFHT12k : DEEP2 vs IRAC-EGS\n",
    "We take DEEP2 preferentially"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cfht12k_origin = Table()\n",
    "cfht12k_origin.add_column(master_catalogue['help_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(3, 0) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "cfht12k_stats = Table()\n",
    "cfht12k_stats.add_column(Column(data=['b',  'r', 'i'], name=\"Band\"))\n",
    "for col in [\"DEEP2\", \"IRAC-EGS\"]:\n",
    "    cfht12k_stats.add_column(Column(data=np.full(3, 0), name=\"{}\".format(col)))\n",
    "    cfht12k_stats.add_column(Column(data=np.full(3, 0), name=\"use {}\".format(col)))\n",
    "    cfht12k_stats.add_column(Column(data=np.full(3, 0), name=\"{} ap\".format(col)))\n",
    "    cfht12k_stats.add_column(Column(data=np.full(3, 0), name=\"use {} ap\".format(col)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cfht12k_bands = ['b', 'r', 'i']\n",
    "\n",
    "for band in cfht12k_bands:\n",
    "\n",
    "    # cfht12k total flux \n",
    "    has_deep2 = ~np.isnan(master_catalogue['f_deep2_' + band])\n",
    "    has_irac = ~np.isnan(master_catalogue['f_cfht12k_' + band])\n",
    "    \n",
    "    use_deep2 =       has_deep2        \n",
    "    use_irac =     has_irac & ~has_deep2\n",
    "\n",
    "    master_catalogue['f_cfht12k_' + band][use_deep2] = master_catalogue['f_deep2_' + band][use_deep2]\n",
    "    master_catalogue['ferr_cfht12k_' + band][use_deep2] = master_catalogue['ferr_deep2_' + band][use_deep2]\n",
    "    master_catalogue['m_cfht12k_' + band][use_deep2] = master_catalogue['m_deep2_' + band][use_deep2]\n",
    "    master_catalogue['merr_cfht12k_' + band][use_deep2] = master_catalogue['merr_deep2_' + band][use_deep2]\n",
    "    master_catalogue['flag_cfht12k_' + band][use_deep2] = master_catalogue['flag_deep2_' + band][use_deep2]\n",
    "\n",
    "    old_columns = ['f_deep2_' + band,\n",
    "                    'ferr_deep2_' + band,\n",
    "                    'm_deep2_' + band, \n",
    "                    'merr_deep2_' + band,\n",
    "                    'flag_deep2_' + band,\n",
    "                   'f_ap_deep2_' + band,\n",
    "                    'ferr_ap_deep2_' + band,\n",
    "                    'm_ap_deep2_' + band, \n",
    "                    'merr_ap_deep2_' + band,]\n",
    "\n",
    "    \n",
    "    master_catalogue.remove_columns(old_columns)\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "    origin[use_deep2] = \"DEEP2\"\n",
    "    origin[use_irac] = \"IRAC-EGS\"\n",
    "    \n",
    "    cfht12k_origin.add_column(Column(data=origin, name= 'f_cfht12k_' + band ))\n",
    "    \n",
    "\n",
    "        \n",
    "    \n",
    "\n",
    "   \n",
    "\n",
    "    cfht12k_stats['DEEP2'][cfht12k_stats['Band'] == band] = np.sum(has_deep2)\n",
    "    cfht12k_stats['IRAC-EGS'][cfht12k_stats['Band'] == band] = np.sum(has_irac)\n",
    "    cfht12k_stats['use DEEP2'][cfht12k_stats['Band'] == band] = np.sum(use_deep2)\n",
    "    cfht12k_stats['use IRAC-EGS'][cfht12k_stats['Band'] == band] = np.sum(use_irac)    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "<thead><tr><th>idx</th><th>Band</th><th>DEEP2</th><th>use DEEP2</th><th>DEEP2 ap</th><th>use DEEP2 ap</th><th>IRAC-EGS</th><th>use IRAC-EGS</th><th>IRAC-EGS ap</th><th>use IRAC-EGS ap</th></tr></thead>\n",
       "<tr><td>0</td><td>b</td><td>198734.0</td><td>198734.0</td><td>0.0</td><td>0.0</td><td>117310.0</td><td>57891.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>r</td><td>204151.0</td><td>204151.0</td><td>0.0</td><td>0.0</td><td>117245.0</td><td>56599.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>2</td><td>i</td><td>203829.0</td><td>203829.0</td><td>0.0</td><td>0.0</td><td>116751.0</td><td>56193.0</td><td>0.0</td><td>0.0</td></tr>\n",
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       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
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       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
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       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
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       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
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     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfht12k_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cfht12k_origin.write(\"{}/egs_cfht12k_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": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aegis_moc =  MOC(filename=\"../../dmu0/dmu0_AEGIS/data/EGS_Palomar_20160804_MOC.fits\")\n",
    "candels_moc =  MOC(filename=\"../../dmu0/dmu0_CANDELS-EGS/data/hlsp_candels_hst_wfc3_egs-tot-multiband_f160w_v1_MOC.fits\")\n",
    "wirds_moc =  MOC(filename=\"../../dmu0/dmu0_CFHT-WIRDS/data/EGS_Ks-priors_MOC.fits\")\n",
    "cfhtls_deep_moc =  MOC(filename=\"../../dmu0/dmu0_CFHTLS/data/CFHTLS-DEEP_EGS_MOC.fits\")\n",
    "cfhtls_wide_moc =  MOC(filename=\"../../dmu0/dmu0_CFHTLS/data/CFHTLS-WIDE_EGS_MOC.fits\")\n",
    "cfhtlens_moc =  MOC(filename=\"../../dmu0/dmu0_CFHTLenS/data/CFHTLenS_EGS_MOC.fits\")\n",
    "deep_moc =  MOC(filename=\"../../dmu0/dmu0_DEEP2/data/DEEP2_EGS_MOC.fits\")\n",
    "irac_moc =  MOC(filename=\"../../dmu0/dmu0_IRAC-EGS/data/IRAC_EGS_MOC.fits\")\n",
    "hsc_moc =  MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_wide_EGS_MOC.fits\")\n",
    "ps1_moc =  MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_EGS_MOC.fits\")\n",
    "legacy_moc =  MOC(filename=\"../../dmu0/dmu0_LegacySurvey/data/LegacySurvey-dr4_EGS_MOC.fits\")\n",
    "uhs_moc =  MOC(filename=\"../../dmu0/dmu0_UHS/data/UHS-DR1_EGS_MOC.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    aegis_moc + candels_moc + cfhtls_deep_moc + cfhtls_wide_moc + cfhtlens_moc + deep_moc + hsc_moc + ps1_moc + legacy_moc) \n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    wirds_moc + uhs_moc \n",
    ")\n",
    "\n",
    "was_observed_mir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    irac_moc\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Rename UHS to UKIDSS\n",
    "replacements = [\n",
    "    ['_uhs_j', '_ukidss_j'],\n",
    "    ['_wircam_k', '_wircam_ks'],\n",
    "    ['_candels-newfirm_', '_newfirm_'],\n",
    "    #['_wircs_k', '_wircs_ks'],\n",
    "]\n",
    "for col in master_catalogue.colnames:\n",
    "    for rep in replacements:\n",
    "        if rep[0] in col:\n",
    "            master_catalogue.rename_column(col, col.replace(rep[0], rep[1]))\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "nb_optical_flux = (\n",
    "#HSC\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_suprime_y']) +\n",
    "#PS1\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_z']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_gpc1_y']) +\n",
    "#Legacy Survey\n",
    "    1 * ~np.isnan(master_catalogue['f_90prime_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_90prime_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_mosaic_z']) +\n",
    "#CFHT\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_u']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_g']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_r']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_i']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_megacam_z']) \n",
    "\n",
    ")\n",
    "\n",
    "nb_nir_flux = (\n",
    "    1 * ~np.isnan(master_catalogue['f_ukidss_j']) +\n",
    "    \n",
    "    1 * ~np.isnan(master_catalogue['f_wircs_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wircs_k']) +\n",
    "\n",
    "    1 * ~np.isnan(master_catalogue['f_wircam_j']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wircam_h']) +\n",
    "    1 * ~np.isnan(master_catalogue['f_wircam_ks']) \n",
    "    \n",
    ")\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": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "has_optical_flux = nb_optical_flux >= 2\n",
    "has_nir_flux = nb_nir_flux >= 2\n",
    "has_mir_flux = nb_mir_flux >= 2\n",
    "\n",
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * has_optical_flux + 2 * has_nir_flux + 4 * has_mir_flux,\n",
    "        name=\"flag_optnir_det\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IX - Cross-identification table\n",
    "\n",
    "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogues. This can be used to easily get additional information from them.\n",
    "\n",
    "For convenience, we also cross-match the master list with the SDSS catalogue and add the objID associated with each source, if any. **TODO: should we correct the astrometry with respect to Gaia positions?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 master list rows had multiple associations.\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Addind SDSS ids\n",
    "#\n",
    "sdss = Table.read(\"../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_EGS.fits\")['objID', 'ra', 'dec']\n",
    "sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)\n",
    "idx_ml, d2d, _ = sdss_coords.match_to_catalog_sky(SkyCoord(master_catalogue['ra'], master_catalogue['dec']))\n",
    "idx_sdss = np.arange(len(sdss))\n",
    "\n",
    "# Limit the cross-match to 1 arcsec\n",
    "mask = d2d <= 1. * u.arcsec\n",
    "idx_ml = idx_ml[mask]\n",
    "idx_sdss = idx_sdss[mask]\n",
    "d2d = d2d[mask]\n",
    "nb_orig_matches = len(idx_ml)\n",
    "\n",
    "# In case of multiple associations of one master list object to an SDSS object, we keep only the\n",
    "# association to the nearest one.\n",
    "sort_idx = np.argsort(d2d)\n",
    "idx_ml = idx_ml[sort_idx]\n",
    "idx_sdss = idx_sdss[sort_idx]\n",
    "_, unique_idx = np.unique(idx_ml, return_index=True)\n",
    "idx_ml = idx_ml[unique_idx]\n",
    "idx_sdss = idx_sdss[unique_idx]\n",
    "print(\"{} master list rows had multiple associations.\".format(nb_orig_matches - len(idx_ml)))\n",
    "\n",
    "# Adding the ObjID to the master list\n",
    "master_catalogue.add_column(Column(data=np.full(len(master_catalogue), -1, dtype='>i8'), name=\"sdss_id\"))\n",
    "master_catalogue['sdss_id'][idx_ml] = sdss['objID'][idx_sdss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['hsc_id', 'ps1_id', 'aegis_id', 'candels-egs_id', 'wirds_id', 'cfhtls-wide_id', 'cfhtls-deep_id', 'cfhtlens_id', 'deep2_id', 'irac-egs_id', 'legacy_id', 'uhs_id', 'help_id', 'specz_id', 'sdss_id']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_egs{}.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": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(\n",
    "    data=coords_to_hpidx(master_catalogue['ra'], master_catalogue['dec'], order=13),\n",
    "    name=\"hp_idx\"\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XI - Saving the catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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",
    "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",
    "bands_tot_only = (set([column[2:] for column in master_catalogue.colnames if (column.startswith('f_') & ~column.startswith('f_ap_'))]) \n",
    "                  - set(bands))\n",
    "\n",
    "for band in bands_tot_only:\n",
    "    columns += [\"f_{}\".format(band), \"ferr_{}\".format(band),\n",
    "                \"m_{}\".format(band), \"merr_{}\".format(band),\n",
    "                \"flag_{}\".format(band)] \n",
    "    \n",
    "columns += [\"stellarity\", \"stellarity_origin\", \"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \"flag_optnir_obs\", \n",
    "            \"flag_optnir_det\", \"zspec\", \"zspec_qual\", \"zspec_association_flag\", \"ebv\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing columns: set()\n"
     ]
    }
   ],
   "source": [
    "# We check for columns in the master catalogue that we will not save to disk.\n",
    "print(\"Missing columns: {}\".format(set(master_catalogue.colnames) - set(columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[columns].write(\"{}/master_catalogue_egs{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  }
 ],
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